final.html - master - mayaoaoa/HSE_python

final.html
10 569 строк2 MB
Maya Ovsiannikova
upload final project
16 мар 2026, 03:55
16 мар 2026, 03:555422e74
<!DOCTYPE html>

<html lang="en">
<head><meta charset="utf-8"/>
<meta content="width=device-width, initial-scale=1.0" name="viewport"/>
<title>final (1)</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
<style type="text/css">
    pre { line-height: 125%; }
td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
.highlight .hll { background-color: var(--jp-cell-editor-active-background) }
.highlight { background: var(--jp-cell-editor-background); color: var(--jp-mirror-editor-variable-color) }
.highlight .c { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment */
.highlight .err { color: var(--jp-mirror-editor-error-color) } /* Error */
.highlight .k { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword */
.highlight .o { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator */
.highlight .p { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation */
.highlight .ch { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Hashbang */
.highlight .cm { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Multiline */
.highlight .cp { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Preproc */
.highlight .cpf { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.PreprocFile */
.highlight .c1 { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Single */
.highlight .cs { color: var(--jp-mirror-editor-comment-color); font-style: italic } /* Comment.Special */
.highlight .kc { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Constant */
.highlight .kd { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Declaration */
.highlight .kn { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Namespace */
.highlight .kp { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Pseudo */
.highlight .kr { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Reserved */
.highlight .kt { color: var(--jp-mirror-editor-keyword-color); font-weight: bold } /* Keyword.Type */
.highlight .m { color: var(--jp-mirror-editor-number-color) } /* Literal.Number */
.highlight .s { color: var(--jp-mirror-editor-string-color) } /* Literal.String */
.highlight .ow { color: var(--jp-mirror-editor-operator-color); font-weight: bold } /* Operator.Word */
.highlight .pm { color: var(--jp-mirror-editor-punctuation-color) } /* Punctuation.Marker */
.highlight .w { color: var(--jp-mirror-editor-variable-color) } /* Text.Whitespace */
.highlight .mb { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Bin */
.highlight .mf { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Float */
.highlight .mh { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Hex */
.highlight .mi { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer */
.highlight .mo { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Oct */
.highlight .sa { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Affix */
.highlight .sb { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Backtick */
.highlight .sc { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Char */
.highlight .dl { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Delimiter */
.highlight .sd { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Doc */
.highlight .s2 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Double */
.highlight .se { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Escape */
.highlight .sh { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Heredoc */
.highlight .si { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Interpol */
.highlight .sx { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Other */
.highlight .sr { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Regex */
.highlight .s1 { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Single */
.highlight .ss { color: var(--jp-mirror-editor-string-color) } /* Literal.String.Symbol */
.highlight .il { color: var(--jp-mirror-editor-number-color) } /* Literal.Number.Integer.Long */
  </style>
<style type="text/css">
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

/*
 * Mozilla scrollbar styling
 */

/* use standard opaque scrollbars for most nodes */
[data-jp-theme-scrollbars='true'] {
  scrollbar-color: rgb(var(--jp-scrollbar-thumb-color))
    var(--jp-scrollbar-background-color);
}

/* for code nodes, use a transparent style of scrollbar. These selectors
 * will match lower in the tree, and so will override the above */
[data-jp-theme-scrollbars='true'] .CodeMirror-hscrollbar,
[data-jp-theme-scrollbars='true'] .CodeMirror-vscrollbar {
  scrollbar-color: rgba(var(--jp-scrollbar-thumb-color), 0.5) transparent;
}

/* tiny scrollbar */

.jp-scrollbar-tiny {
  scrollbar-color: rgba(var(--jp-scrollbar-thumb-color), 0.5) transparent;
  scrollbar-width: thin;
}

/* tiny scrollbar */

.jp-scrollbar-tiny::-webkit-scrollbar,
.jp-scrollbar-tiny::-webkit-scrollbar-corner {
  background-color: transparent;
  height: 4px;
  width: 4px;
}

.jp-scrollbar-tiny::-webkit-scrollbar-thumb {
  background: rgba(var(--jp-scrollbar-thumb-color), 0.5);
}

.jp-scrollbar-tiny::-webkit-scrollbar-track:horizontal {
  border-left: 0 solid transparent;
  border-right: 0 solid transparent;
}

.jp-scrollbar-tiny::-webkit-scrollbar-track:vertical {
  border-top: 0 solid transparent;
  border-bottom: 0 solid transparent;
}

/*
 * Lumino
 */

.lm-ScrollBar[data-orientation='horizontal'] {
  min-height: 16px;
  max-height: 16px;
  min-width: 45px;
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}

.lm-ScrollBar[data-orientation='vertical'] {
  min-width: 16px;
  max-width: 16px;
  min-height: 45px;
  border-left: 1px solid #a0a0a0;
}

.lm-ScrollBar-button {
  background-color: #f0f0f0;
  background-position: center center;
  min-height: 15px;
  max-height: 15px;
  min-width: 15px;
  max-width: 15px;
}

.lm-ScrollBar-button:hover {
  background-color: #dadada;
}

.lm-ScrollBar-button.lm-mod-active {
  background-color: #cdcdcd;
}

.lm-ScrollBar-track {
  background: #f0f0f0;
}

.lm-ScrollBar-thumb {
  background: #cdcdcd;
}

.lm-ScrollBar-thumb:hover {
  background: #bababa;
}

.lm-ScrollBar-thumb.lm-mod-active {
  background: #a0a0a0;
}

.lm-ScrollBar[data-orientation='horizontal'] .lm-ScrollBar-thumb {
  height: 100%;
  min-width: 15px;
  border-left: 1px solid #a0a0a0;
  border-right: 1px solid #a0a0a0;
}

.lm-ScrollBar[data-orientation='vertical'] .lm-ScrollBar-thumb {
  width: 100%;
  min-height: 15px;
  border-top: 1px solid #a0a0a0;
  border-bottom: 1px solid #a0a0a0;
}

.lm-ScrollBar[data-orientation='horizontal']
  .lm-ScrollBar-button[data-action='decrement'] {
  background-image: var(--jp-icon-caret-left);
  background-size: 17px;
}

.lm-ScrollBar[data-orientation='horizontal']
  .lm-ScrollBar-button[data-action='increment'] {
  background-image: var(--jp-icon-caret-right);
  background-size: 17px;
}

.lm-ScrollBar[data-orientation='vertical']
  .lm-ScrollBar-button[data-action='decrement'] {
  background-image: var(--jp-icon-caret-up);
  background-size: 17px;
}

.lm-ScrollBar[data-orientation='vertical']
  .lm-ScrollBar-button[data-action='increment'] {
  background-image: var(--jp-icon-caret-down);
  background-size: 17px;
}

/*
 * Copyright (c) Jupyter Development Team.
 * Distributed under the terms of the Modified BSD License.
 */

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Copyright (c) 2014-2017, PhosphorJS Contributors
|
| Distributed under the terms of the BSD 3-Clause License.
|
| The full license is in the file LICENSE, distributed with this software.
|----------------------------------------------------------------------------*/

.lm-Widget {
  box-sizing: border-box;
  position: relative;
  overflow: hidden;
}

.lm-Widget.lm-mod-hidden {
  display: none !important;
}

/*
 * Copyright (c) Jupyter Development Team.
 * Distributed under the terms of the Modified BSD License.
 */

.lm-AccordionPanel[data-orientation='horizontal'] > .lm-AccordionPanel-title {
  /* Title is rotated for horizontal accordion panel using CSS */
  display: block;
  transform-origin: top left;
  transform: rotate(-90deg) translate(-100%);
}

/*
 * Copyright (c) Jupyter Development Team.
 * Distributed under the terms of the Modified BSD License.
 */

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Copyright (c) 2014-2017, PhosphorJS Contributors
|
| Distributed under the terms of the BSD 3-Clause License.
|
| The full license is in the file LICENSE, distributed with this software.
|----------------------------------------------------------------------------*/

.lm-CommandPalette {
  display: flex;
  flex-direction: column;
  -webkit-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
}

.lm-CommandPalette-search {
  flex: 0 0 auto;
}

.lm-CommandPalette-content {
  flex: 1 1 auto;
  margin: 0;
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  min-height: 0;
  overflow: auto;
  list-style-type: none;
}

.lm-CommandPalette-header {
  overflow: hidden;
  white-space: nowrap;
  text-overflow: ellipsis;
}

.lm-CommandPalette-item {
  display: flex;
  flex-direction: row;
}

.lm-CommandPalette-itemIcon {
  flex: 0 0 auto;
}

.lm-CommandPalette-itemContent {
  flex: 1 1 auto;
  overflow: hidden;
}

.lm-CommandPalette-itemShortcut {
  flex: 0 0 auto;
}

.lm-CommandPalette-itemLabel {
  overflow: hidden;
  white-space: nowrap;
  text-overflow: ellipsis;
}

.lm-close-icon {
  border: 1px solid transparent;
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  position: absolute;
  z-index: 1;
  right: 3%;
  top: 0;
  bottom: 0;
  margin: auto;
  padding: 7px 0;
  display: none;
  vertical-align: middle;
  outline: 0;
  cursor: pointer;
}
.lm-close-icon:after {
  content: 'X';
  display: block;
  width: 15px;
  height: 15px;
  text-align: center;
  color: #000;
  font-weight: normal;
  font-size: 12px;
  cursor: pointer;
}

/*
 * Copyright (c) Jupyter Development Team.
 * Distributed under the terms of the Modified BSD License.
 */

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Copyright (c) 2014-2017, PhosphorJS Contributors
|
| Distributed under the terms of the BSD 3-Clause License.
|
| The full license is in the file LICENSE, distributed with this software.
|----------------------------------------------------------------------------*/

.lm-DockPanel {
  z-index: 0;
}

.lm-DockPanel-widget {
  z-index: 0;
}

.lm-DockPanel-tabBar {
  z-index: 1;
}

.lm-DockPanel-handle {
  z-index: 2;
}

.lm-DockPanel-handle.lm-mod-hidden {
  display: none !important;
}

.lm-DockPanel-handle:after {
  position: absolute;
  top: 0;
  left: 0;
  width: 100%;
  height: 100%;
  content: '';
}

.lm-DockPanel-handle[data-orientation='horizontal'] {
  cursor: ew-resize;
}

.lm-DockPanel-handle[data-orientation='vertical'] {
  cursor: ns-resize;
}

.lm-DockPanel-handle[data-orientation='horizontal']:after {
  left: 50%;
  min-width: 8px;
  transform: translateX(-50%);
}

.lm-DockPanel-handle[data-orientation='vertical']:after {
  top: 50%;
  min-height: 8px;
  transform: translateY(-50%);
}

.lm-DockPanel-overlay {
  z-index: 3;
  box-sizing: border-box;
  pointer-events: none;
}

.lm-DockPanel-overlay.lm-mod-hidden {
  display: none !important;
}

/*
 * Copyright (c) Jupyter Development Team.
 * Distributed under the terms of the Modified BSD License.
 */

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Copyright (c) 2014-2017, PhosphorJS Contributors
|
| Distributed under the terms of the BSD 3-Clause License.
|
| The full license is in the file LICENSE, distributed with this software.
|----------------------------------------------------------------------------*/

.lm-Menu {
  z-index: 10000;
  position: absolute;
  white-space: nowrap;
  overflow-x: hidden;
  overflow-y: auto;
  outline: none;
  -webkit-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
}

.lm-Menu-content {
  margin: 0;
  padding: 0;
  display: table;
  list-style-type: none;
}

.lm-Menu-item {
  display: table-row;
}

.lm-Menu-item.lm-mod-hidden,
.lm-Menu-item.lm-mod-collapsed {
  display: none !important;
}

.lm-Menu-itemIcon,
.lm-Menu-itemSubmenuIcon {
  display: table-cell;
  text-align: center;
}

.lm-Menu-itemLabel {
  display: table-cell;
  text-align: left;
}

.lm-Menu-itemShortcut {
  display: table-cell;
  text-align: right;
}

/*
 * Copyright (c) Jupyter Development Team.
 * Distributed under the terms of the Modified BSD License.
 */

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Copyright (c) 2014-2017, PhosphorJS Contributors
|
| Distributed under the terms of the BSD 3-Clause License.
|
| The full license is in the file LICENSE, distributed with this software.
|----------------------------------------------------------------------------*/

.lm-MenuBar {
  outline: none;
  -webkit-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
}

.lm-MenuBar-content {
  margin: 0;
  padding: 0;
  display: flex;
  flex-direction: row;
  list-style-type: none;
}

.lm-MenuBar-item {
  box-sizing: border-box;
}

.lm-MenuBar-itemIcon,
.lm-MenuBar-itemLabel {
  display: inline-block;
}

/*
 * Copyright (c) Jupyter Development Team.
 * Distributed under the terms of the Modified BSD License.
 */

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Copyright (c) 2014-2017, PhosphorJS Contributors
|
| Distributed under the terms of the BSD 3-Clause License.
|
| The full license is in the file LICENSE, distributed with this software.
|----------------------------------------------------------------------------*/

.lm-ScrollBar {
  display: flex;
  -webkit-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
}

.lm-ScrollBar[data-orientation='horizontal'] {
  flex-direction: row;
}

.lm-ScrollBar[data-orientation='vertical'] {
  flex-direction: column;
}

.lm-ScrollBar-button {
  box-sizing: border-box;
  flex: 0 0 auto;
}

.lm-ScrollBar-track {
  box-sizing: border-box;
  position: relative;
  overflow: hidden;
  flex: 1 1 auto;
}

.lm-ScrollBar-thumb {
  box-sizing: border-box;
  position: absolute;
}

/*
 * Copyright (c) Jupyter Development Team.
 * Distributed under the terms of the Modified BSD License.
 */

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Copyright (c) 2014-2017, PhosphorJS Contributors
|
| Distributed under the terms of the BSD 3-Clause License.
|
| The full license is in the file LICENSE, distributed with this software.
|----------------------------------------------------------------------------*/

.lm-SplitPanel-child {
  z-index: 0;
}

.lm-SplitPanel-handle {
  z-index: 1;
}

.lm-SplitPanel-handle.lm-mod-hidden {
  display: none !important;
}

.lm-SplitPanel-handle:after {
  position: absolute;
  top: 0;
  left: 0;
  width: 100%;
  height: 100%;
  content: '';
}

.lm-SplitPanel[data-orientation='horizontal'] > .lm-SplitPanel-handle {
  cursor: ew-resize;
}

.lm-SplitPanel[data-orientation='vertical'] > .lm-SplitPanel-handle {
  cursor: ns-resize;
}

.lm-SplitPanel[data-orientation='horizontal'] > .lm-SplitPanel-handle:after {
  left: 50%;
  min-width: 8px;
  transform: translateX(-50%);
}

.lm-SplitPanel[data-orientation='vertical'] > .lm-SplitPanel-handle:after {
  top: 50%;
  min-height: 8px;
  transform: translateY(-50%);
}

/*
 * Copyright (c) Jupyter Development Team.
 * Distributed under the terms of the Modified BSD License.
 */

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Copyright (c) 2014-2017, PhosphorJS Contributors
|
| Distributed under the terms of the BSD 3-Clause License.
|
| The full license is in the file LICENSE, distributed with this software.
|----------------------------------------------------------------------------*/

.lm-TabBar {
  display: flex;
  -webkit-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
}

.lm-TabBar[data-orientation='horizontal'] {
  flex-direction: row;
  align-items: flex-end;
}

.lm-TabBar[data-orientation='vertical'] {
  flex-direction: column;
  align-items: flex-end;
}

.lm-TabBar-content {
  margin: 0;
  padding: 0;
  display: flex;
  flex: 1 1 auto;
  list-style-type: none;
}

.lm-TabBar[data-orientation='horizontal'] > .lm-TabBar-content {
  flex-direction: row;
}

.lm-TabBar[data-orientation='vertical'] > .lm-TabBar-content {
  flex-direction: column;
}

.lm-TabBar-tab {
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  flex-direction: row;
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  touch-action: none; /* Disable native Drag/Drop */
}

.lm-TabBar-tabIcon,
.lm-TabBar-tabCloseIcon {
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}

.lm-TabBar-tabLabel {
  flex: 1 1 auto;
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  white-space: nowrap;
}

.lm-TabBar-tabInput {
  user-select: all;
  width: 100%;
  box-sizing: border-box;
}

.lm-TabBar-tab.lm-mod-hidden {
  display: none !important;
}

.lm-TabBar-addButton.lm-mod-hidden {
  display: none !important;
}

.lm-TabBar.lm-mod-dragging .lm-TabBar-tab {
  position: relative;
}

.lm-TabBar.lm-mod-dragging[data-orientation='horizontal'] .lm-TabBar-tab {
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  transition: left 150ms ease;
}

.lm-TabBar.lm-mod-dragging[data-orientation='vertical'] .lm-TabBar-tab {
  top: 0;
  transition: top 150ms ease;
}

.lm-TabBar.lm-mod-dragging .lm-TabBar-tab.lm-mod-dragging {
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}

.lm-TabBar-tabLabel .lm-TabBar-tabInput {
  user-select: all;
  width: 100%;
  box-sizing: border-box;
  background: inherit;
}

/*
 * Copyright (c) Jupyter Development Team.
 * Distributed under the terms of the Modified BSD License.
 */

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Copyright (c) 2014-2017, PhosphorJS Contributors
|
| Distributed under the terms of the BSD 3-Clause License.
|
| The full license is in the file LICENSE, distributed with this software.
|----------------------------------------------------------------------------*/

.lm-TabPanel-tabBar {
  z-index: 1;
}

.lm-TabPanel-stackedPanel {
  z-index: 0;
}

/*
 * Copyright (c) Jupyter Development Team.
 * Distributed under the terms of the Modified BSD License.
 */

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Copyright (c) 2014-2017, PhosphorJS Contributors
|
| Distributed under the terms of the BSD 3-Clause License.
|
| The full license is in the file LICENSE, distributed with this software.
|----------------------------------------------------------------------------*/

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

.jp-Collapse {
  display: flex;
  flex-direction: column;
  align-items: stretch;
}

.jp-Collapse-header {
  padding: 1px 12px;
  background-color: var(--jp-layout-color1);
  border-bottom: solid var(--jp-border-width) var(--jp-border-color2);
  color: var(--jp-ui-font-color1);
  cursor: pointer;
  display: flex;
  align-items: center;
  font-size: var(--jp-ui-font-size0);
  font-weight: 600;
  text-transform: uppercase;
  user-select: none;
}

.jp-Collapser-icon {
  height: 16px;
}

.jp-Collapse-header-collapsed .jp-Collapser-icon {
  transform: rotate(-90deg);
  margin: auto 0;
}

.jp-Collapser-title {
  line-height: 25px;
}

.jp-Collapse-contents {
  padding: 0 12px;
  background-color: var(--jp-layout-color1);
  color: var(--jp-ui-font-color1);
  overflow: auto;
}

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

/* This file was auto-generated by ensureUiComponents() in @jupyterlab/buildutils */

/**
 * (DEPRECATED) Support for consuming icons as CSS background images
 */

/* Icons urls */

:root {
  --jp-icon-add-above: url(data:image/svg+xml;base64,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);
  --jp-icon-add-below: url(data:image/svg+xml;base64,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);
  --jp-icon-add: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDI0IDI0Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTE5IDEzaC02djZoLTJ2LTZINXYtMmg2VjVoMnY2aDZ2MnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
  --jp-icon-bell: url(data:image/svg+xml;base64,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);
  --jp-icon-bug-dot: url(data:image/svg+xml;base64,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);
  --jp-icon-bug: url(data:image/svg+xml;base64,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);
  --jp-icon-build: url(data:image/svg+xml;base64,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);
  --jp-icon-caret-down-empty-thin: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIwIDIwIj4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSIgc2hhcGUtcmVuZGVyaW5nPSJnZW9tZXRyaWNQcmVjaXNpb24iPgoJCTxwb2x5Z29uIGNsYXNzPSJzdDEiIHBvaW50cz0iOS45LDEzLjYgMy42LDcuNCA0LjQsNi42IDkuOSwxMi4yIDE1LjQsNi43IDE2LjEsNy40ICIvPgoJPC9nPgo8L3N2Zz4K);
  --jp-icon-caret-down-empty: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiIHNoYXBlLXJlbmRlcmluZz0iZ2VvbWV0cmljUHJlY2lzaW9uIj4KICAgIDxwYXRoIGQ9Ik01LjIsNS45TDksOS43bDMuOC0zLjhsMS4yLDEuMmwtNC45LDVsLTQuOS01TDUuMiw1Ljl6Ii8+CiAgPC9nPgo8L3N2Zz4K);
  --jp-icon-caret-down: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiIHNoYXBlLXJlbmRlcmluZz0iZ2VvbWV0cmljUHJlY2lzaW9uIj4KICAgIDxwYXRoIGQ9Ik01LjIsNy41TDksMTEuMmwzLjgtMy44SDUuMnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
  --jp-icon-caret-left: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KCTxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSIgc2hhcGUtcmVuZGVyaW5nPSJnZW9tZXRyaWNQcmVjaXNpb24iPgoJCTxwYXRoIGQ9Ik0xMC44LDEyLjhMNy4xLDlsMy44LTMuOGwwLDcuNkgxMC44eiIvPgogIDwvZz4KPC9zdmc+Cg==);
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  --jp-icon-python: url(data:image/svg+xml;base64,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);
  --jp-icon-r-kernel: url(data:image/svg+xml;base64,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);
  --jp-icon-react: url(data:image/svg+xml;base64,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);
  --jp-icon-redo: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIGhlaWdodD0iMjQiIHZpZXdCb3g9IjAgMCAyNCAyNCIgd2lkdGg9IjE2Ij4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgICA8cGF0aCBkPSJNMCAwaDI0djI0SDB6IiBmaWxsPSJub25lIi8+PHBhdGggZD0iTTE4LjQgMTAuNkMxNi41NSA4Ljk5IDE0LjE1IDggMTEuNSA4Yy00LjY1IDAtOC41OCAzLjAzLTkuOTYgNy4yMkwzLjkgMTZjMS4wNS0zLjE5IDQuMDUtNS41IDcuNi01LjUgMS45NSAwIDMuNzMuNzIgNS4xMiAxLjg4TDEzIDE2aDlWN2wtMy42IDMuNnoiLz4KICA8L2c+Cjwvc3ZnPgo=);
  --jp-icon-refresh: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDE4IDE4Ij4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTkgMTMuNWMtMi40OSAwLTQuNS0yLjAxLTQuNS00LjVTNi41MSA0LjUgOSA0LjVjMS4yNCAwIDIuMzYuNTIgMy4xNyAxLjMzTDEwIDhoNVYzbC0xLjc2IDEuNzZDMTIuMTUgMy42OCAxMC42NiAzIDkgMyA1LjY5IDMgMy4wMSA1LjY5IDMuMDEgOVM1LjY5IDE1IDkgMTVjMi45NyAwIDUuNDMtMi4xNiA1LjktNWgtMS41MmMtLjQ2IDItMi4yNCAzLjUtNC4zOCAzLjV6Ii8+CiAgICA8L2c+Cjwvc3ZnPgo=);
  --jp-icon-regex: url(data:image/svg+xml;base64,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);
  --jp-icon-run: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTggNXYxNGwxMS03eiIvPgogICAgPC9nPgo8L3N2Zz4K);
  --jp-icon-running: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDUxMiA1MTIiPgogIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICA8cGF0aCBkPSJNMjU2IDhDMTE5IDggOCAxMTkgOCAyNTZzMTExIDI0OCAyNDggMjQ4IDI0OC0xMTEgMjQ4LTI0OFMzOTMgOCAyNTYgOHptOTYgMzI4YzAgOC44LTcuMiAxNi0xNiAxNkgxNzZjLTguOCAwLTE2LTcuMi0xNi0xNlYxNzZjMC04LjggNy4yLTE2IDE2LTE2aDE2MGM4LjggMCAxNiA3LjIgMTYgMTZ2MTYweiIvPgogIDwvZz4KPC9zdmc+Cg==);
  --jp-icon-save: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTE3IDNINWMtMS4xMSAwLTIgLjktMiAydjE0YzAgMS4xLjg5IDIgMiAyaDE0YzEuMSAwIDItLjkgMi0yVjdsLTQtNHptLTUgMTZjLTEuNjYgMC0zLTEuMzQtMy0zczEuMzQtMyAzLTMgMyAxLjM0IDMgMy0xLjM0IDMtMyAzem0zLTEwSDVWNWgxMHY0eiIvPgogICAgPC9nPgo8L3N2Zz4K);
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  --jp-icon-toc: url(data:image/svg+xml;base64,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);
  --jp-icon-tree-view: url(data:image/svg+xml;base64,PHN2ZyBoZWlnaHQ9IjI0IiB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICAgIDxnIGNsYXNzPSJqcC1pY29uMyIgZmlsbD0iIzYxNjE2MSI+CiAgICAgICAgPHBhdGggZD0iTTAgMGgyNHYyNEgweiIgZmlsbD0ibm9uZSIvPgogICAgICAgIDxwYXRoIGQ9Ik0yMiAxMVYzaC03djNIOVYzSDJ2OGg3VjhoMnYxMGg0djNoN3YtOGgtN3YzaC0yVjhoMnYzeiIvPgogICAgPC9nPgo8L3N2Zz4K);
  --jp-icon-trusted: url(data:image/svg+xml;base64,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);
  --jp-icon-undo: url(data:image/svg+xml;base64,PHN2ZyB2aWV3Qm94PSIwIDAgMjQgMjQiIHdpZHRoPSIxNiIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTEyLjUgOGMtMi42NSAwLTUuMDUuOTktNi45IDIuNkwyIDd2OWg5bC0zLjYyLTMuNjJjMS4zOS0xLjE2IDMuMTYtMS44OCA1LjEyLTEuODggMy41NCAwIDYuNTUgMi4zMSA3LjYgNS41bDIuMzctLjc4QzIxLjA4IDExLjAzIDE3LjE1IDggMTIuNSA4eiIvPgogIDwvZz4KPC9zdmc+Cg==);
  --jp-icon-user: url(data:image/svg+xml;base64,PHN2ZyB3aWR0aD0iMTYiIHZpZXdCb3g9IjAgMCAyNCAyNCIgeG1sbnM9Imh0dHA6Ly93d3cudzMub3JnLzIwMDAvc3ZnIj4KICA8ZyBjbGFzcz0ianAtaWNvbjMiIGZpbGw9IiM2MTYxNjEiPgogICAgPHBhdGggZD0iTTE2IDdhNCA0IDAgMTEtOCAwIDQgNCAwIDAxOCAwek0xMiAxNGE3IDcgMCAwMC03IDdoMTRhNyA3IDAgMDAtNy03eiIvPgogIDwvZz4KPC9zdmc+Cg==);
  --jp-icon-users: url(data:image/svg+xml;base64,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);
  --jp-icon-vega: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgdmlld0JveD0iMCAwIDIyIDIyIj4KICA8ZyBjbGFzcz0ianAtaWNvbjEganAtaWNvbi1zZWxlY3RhYmxlIiBmaWxsPSIjMjEyMTIxIj4KICAgIDxwYXRoIGQ9Ik0xMC42IDUuNGwyLjItMy4ySDIuMnY3LjNsNC02LjZ6Ii8+CiAgICA8cGF0aCBkPSJNMTUuOCAyLjJsLTQuNCA2LjZMNyA2LjNsLTQuOCA4djUuNWgxNy42VjIuMmgtNHptLTcgMTUuNEg1LjV2LTQuNGgzLjN2NC40em00LjQgMEg5LjhWOS44aDMuNHY3Ljh6bTQuNCAwaC0zLjRWNi41aDMuNHYxMS4xeiIvPgogIDwvZz4KPC9zdmc+Cg==);
  --jp-icon-word: url(data:image/svg+xml;base64,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);
  --jp-icon-yaml: url(data:image/svg+xml;base64,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);
}

/* Icon CSS class declarations */

.jp-AddAboveIcon {
  background-image: var(--jp-icon-add-above);
}

.jp-AddBelowIcon {
  background-image: var(--jp-icon-add-below);
}

.jp-AddIcon {
  background-image: var(--jp-icon-add);
}

.jp-BellIcon {
  background-image: var(--jp-icon-bell);
}

.jp-BugDotIcon {
  background-image: var(--jp-icon-bug-dot);
}

.jp-BugIcon {
  background-image: var(--jp-icon-bug);
}

.jp-BuildIcon {
  background-image: var(--jp-icon-build);
}

.jp-CaretDownEmptyIcon {
  background-image: var(--jp-icon-caret-down-empty);
}

.jp-CaretDownEmptyThinIcon {
  background-image: var(--jp-icon-caret-down-empty-thin);
}

.jp-CaretDownIcon {
  background-image: var(--jp-icon-caret-down);
}

.jp-CaretLeftIcon {
  background-image: var(--jp-icon-caret-left);
}

.jp-CaretRightIcon {
  background-image: var(--jp-icon-caret-right);
}

.jp-CaretUpEmptyThinIcon {
  background-image: var(--jp-icon-caret-up-empty-thin);
}

.jp-CaretUpIcon {
  background-image: var(--jp-icon-caret-up);
}

.jp-CaseSensitiveIcon {
  background-image: var(--jp-icon-case-sensitive);
}

.jp-CheckIcon {
  background-image: var(--jp-icon-check);
}

.jp-CircleEmptyIcon {
  background-image: var(--jp-icon-circle-empty);
}

.jp-CircleIcon {
  background-image: var(--jp-icon-circle);
}

.jp-ClearIcon {
  background-image: var(--jp-icon-clear);
}

.jp-CloseIcon {
  background-image: var(--jp-icon-close);
}

.jp-CodeCheckIcon {
  background-image: var(--jp-icon-code-check);
}

.jp-CodeIcon {
  background-image: var(--jp-icon-code);
}

.jp-CollapseAllIcon {
  background-image: var(--jp-icon-collapse-all);
}

.jp-ConsoleIcon {
  background-image: var(--jp-icon-console);
}

.jp-CopyIcon {
  background-image: var(--jp-icon-copy);
}

.jp-CopyrightIcon {
  background-image: var(--jp-icon-copyright);
}

.jp-CutIcon {
  background-image: var(--jp-icon-cut);
}

.jp-DeleteIcon {
  background-image: var(--jp-icon-delete);
}

.jp-DownloadIcon {
  background-image: var(--jp-icon-download);
}

.jp-DuplicateIcon {
  background-image: var(--jp-icon-duplicate);
}

.jp-EditIcon {
  background-image: var(--jp-icon-edit);
}

.jp-EllipsesIcon {
  background-image: var(--jp-icon-ellipses);
}

.jp-ErrorIcon {
  background-image: var(--jp-icon-error);
}

.jp-ExpandAllIcon {
  background-image: var(--jp-icon-expand-all);
}

.jp-ExtensionIcon {
  background-image: var(--jp-icon-extension);
}

.jp-FastForwardIcon {
  background-image: var(--jp-icon-fast-forward);
}

.jp-FileIcon {
  background-image: var(--jp-icon-file);
}

.jp-FileUploadIcon {
  background-image: var(--jp-icon-file-upload);
}

.jp-FilterDotIcon {
  background-image: var(--jp-icon-filter-dot);
}

.jp-FilterIcon {
  background-image: var(--jp-icon-filter);
}

.jp-FilterListIcon {
  background-image: var(--jp-icon-filter-list);
}

.jp-FolderFavoriteIcon {
  background-image: var(--jp-icon-folder-favorite);
}

.jp-FolderIcon {
  background-image: var(--jp-icon-folder);
}

.jp-HomeIcon {
  background-image: var(--jp-icon-home);
}

.jp-Html5Icon {
  background-image: var(--jp-icon-html5);
}

.jp-ImageIcon {
  background-image: var(--jp-icon-image);
}

.jp-InfoIcon {
  background-image: var(--jp-icon-info);
}

.jp-InspectorIcon {
  background-image: var(--jp-icon-inspector);
}

.jp-JsonIcon {
  background-image: var(--jp-icon-json);
}

.jp-JuliaIcon {
  background-image: var(--jp-icon-julia);
}

.jp-JupyterFaviconIcon {
  background-image: var(--jp-icon-jupyter-favicon);
}

.jp-JupyterIcon {
  background-image: var(--jp-icon-jupyter);
}

.jp-JupyterlabWordmarkIcon {
  background-image: var(--jp-icon-jupyterlab-wordmark);
}

.jp-KernelIcon {
  background-image: var(--jp-icon-kernel);
}

.jp-KeyboardIcon {
  background-image: var(--jp-icon-keyboard);
}

.jp-LaunchIcon {
  background-image: var(--jp-icon-launch);
}

.jp-LauncherIcon {
  background-image: var(--jp-icon-launcher);
}

.jp-LineFormIcon {
  background-image: var(--jp-icon-line-form);
}

.jp-LinkIcon {
  background-image: var(--jp-icon-link);
}

.jp-ListIcon {
  background-image: var(--jp-icon-list);
}

.jp-MarkdownIcon {
  background-image: var(--jp-icon-markdown);
}

.jp-MoveDownIcon {
  background-image: var(--jp-icon-move-down);
}

.jp-MoveUpIcon {
  background-image: var(--jp-icon-move-up);
}

.jp-NewFolderIcon {
  background-image: var(--jp-icon-new-folder);
}

.jp-NotTrustedIcon {
  background-image: var(--jp-icon-not-trusted);
}

.jp-NotebookIcon {
  background-image: var(--jp-icon-notebook);
}

.jp-NumberingIcon {
  background-image: var(--jp-icon-numbering);
}

.jp-OfflineBoltIcon {
  background-image: var(--jp-icon-offline-bolt);
}

.jp-PaletteIcon {
  background-image: var(--jp-icon-palette);
}

.jp-PasteIcon {
  background-image: var(--jp-icon-paste);
}

.jp-PdfIcon {
  background-image: var(--jp-icon-pdf);
}

.jp-PythonIcon {
  background-image: var(--jp-icon-python);
}

.jp-RKernelIcon {
  background-image: var(--jp-icon-r-kernel);
}

.jp-ReactIcon {
  background-image: var(--jp-icon-react);
}

.jp-RedoIcon {
  background-image: var(--jp-icon-redo);
}

.jp-RefreshIcon {
  background-image: var(--jp-icon-refresh);
}

.jp-RegexIcon {
  background-image: var(--jp-icon-regex);
}

.jp-RunIcon {
  background-image: var(--jp-icon-run);
}

.jp-RunningIcon {
  background-image: var(--jp-icon-running);
}

.jp-SaveIcon {
  background-image: var(--jp-icon-save);
}

.jp-SearchIcon {
  background-image: var(--jp-icon-search);
}

.jp-SettingsIcon {
  background-image: var(--jp-icon-settings);
}

.jp-ShareIcon {
  background-image: var(--jp-icon-share);
}

.jp-SpreadsheetIcon {
  background-image: var(--jp-icon-spreadsheet);
}

.jp-StopIcon {
  background-image: var(--jp-icon-stop);
}

.jp-TabIcon {
  background-image: var(--jp-icon-tab);
}

.jp-TableRowsIcon {
  background-image: var(--jp-icon-table-rows);
}

.jp-TagIcon {
  background-image: var(--jp-icon-tag);
}

.jp-TerminalIcon {
  background-image: var(--jp-icon-terminal);
}

.jp-TextEditorIcon {
  background-image: var(--jp-icon-text-editor);
}

.jp-TocIcon {
  background-image: var(--jp-icon-toc);
}

.jp-TreeViewIcon {
  background-image: var(--jp-icon-tree-view);
}

.jp-TrustedIcon {
  background-image: var(--jp-icon-trusted);
}

.jp-UndoIcon {
  background-image: var(--jp-icon-undo);
}

.jp-UserIcon {
  background-image: var(--jp-icon-user);
}

.jp-UsersIcon {
  background-image: var(--jp-icon-users);
}

.jp-VegaIcon {
  background-image: var(--jp-icon-vega);
}

.jp-WordIcon {
  background-image: var(--jp-icon-word);
}

.jp-YamlIcon {
  background-image: var(--jp-icon-yaml);
}

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

/**
 * (DEPRECATED) Support for consuming icons as CSS background images
 */

.jp-Icon,
.jp-MaterialIcon {
  background-position: center;
  background-repeat: no-repeat;
  background-size: 16px;
  min-width: 16px;
  min-height: 16px;
}

.jp-Icon-cover {
  background-position: center;
  background-repeat: no-repeat;
  background-size: cover;
}

/**
 * (DEPRECATED) Support for specific CSS icon sizes
 */

.jp-Icon-16 {
  background-size: 16px;
  min-width: 16px;
  min-height: 16px;
}

.jp-Icon-18 {
  background-size: 18px;
  min-width: 18px;
  min-height: 18px;
}

.jp-Icon-20 {
  background-size: 20px;
  min-width: 20px;
  min-height: 20px;
}

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

.lm-TabBar .lm-TabBar-addButton {
  align-items: center;
  display: flex;
  padding: 4px;
  padding-bottom: 5px;
  margin-right: 1px;
  background-color: var(--jp-layout-color2);
}

.lm-TabBar .lm-TabBar-addButton:hover {
  background-color: var(--jp-layout-color1);
}

.lm-DockPanel-tabBar .lm-TabBar-tab {
  width: var(--jp-private-horizontal-tab-width);
}

.lm-DockPanel-tabBar .lm-TabBar-content {
  flex: unset;
}

.lm-DockPanel-tabBar[data-orientation='horizontal'] {
  flex: 1 1 auto;
}

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

/**
 * Support for icons as inline SVG HTMLElements
 */

/* recolor the primary elements of an icon */
.jp-icon0[fill] {
  fill: var(--jp-inverse-layout-color0);
}

.jp-icon1[fill] {
  fill: var(--jp-inverse-layout-color1);
}

.jp-icon2[fill] {
  fill: var(--jp-inverse-layout-color2);
}

.jp-icon3[fill] {
  fill: var(--jp-inverse-layout-color3);
}

.jp-icon4[fill] {
  fill: var(--jp-inverse-layout-color4);
}

.jp-icon0[stroke] {
  stroke: var(--jp-inverse-layout-color0);
}

.jp-icon1[stroke] {
  stroke: var(--jp-inverse-layout-color1);
}

.jp-icon2[stroke] {
  stroke: var(--jp-inverse-layout-color2);
}

.jp-icon3[stroke] {
  stroke: var(--jp-inverse-layout-color3);
}

.jp-icon4[stroke] {
  stroke: var(--jp-inverse-layout-color4);
}

/* recolor the accent elements of an icon */
.jp-icon-accent0[fill] {
  fill: var(--jp-layout-color0);
}

.jp-icon-accent1[fill] {
  fill: var(--jp-layout-color1);
}

.jp-icon-accent2[fill] {
  fill: var(--jp-layout-color2);
}

.jp-icon-accent3[fill] {
  fill: var(--jp-layout-color3);
}

.jp-icon-accent4[fill] {
  fill: var(--jp-layout-color4);
}

.jp-icon-accent0[stroke] {
  stroke: var(--jp-layout-color0);
}

.jp-icon-accent1[stroke] {
  stroke: var(--jp-layout-color1);
}

.jp-icon-accent2[stroke] {
  stroke: var(--jp-layout-color2);
}

.jp-icon-accent3[stroke] {
  stroke: var(--jp-layout-color3);
}

.jp-icon-accent4[stroke] {
  stroke: var(--jp-layout-color4);
}

/* set the color of an icon to transparent */
.jp-icon-none[fill] {
  fill: none;
}

.jp-icon-none[stroke] {
  stroke: none;
}

/* brand icon colors. Same for light and dark */
.jp-icon-brand0[fill] {
  fill: var(--jp-brand-color0);
}

.jp-icon-brand1[fill] {
  fill: var(--jp-brand-color1);
}

.jp-icon-brand2[fill] {
  fill: var(--jp-brand-color2);
}

.jp-icon-brand3[fill] {
  fill: var(--jp-brand-color3);
}

.jp-icon-brand4[fill] {
  fill: var(--jp-brand-color4);
}

.jp-icon-brand0[stroke] {
  stroke: var(--jp-brand-color0);
}

.jp-icon-brand1[stroke] {
  stroke: var(--jp-brand-color1);
}

.jp-icon-brand2[stroke] {
  stroke: var(--jp-brand-color2);
}

.jp-icon-brand3[stroke] {
  stroke: var(--jp-brand-color3);
}

.jp-icon-brand4[stroke] {
  stroke: var(--jp-brand-color4);
}

/* warn icon colors. Same for light and dark */
.jp-icon-warn0[fill] {
  fill: var(--jp-warn-color0);
}

.jp-icon-warn1[fill] {
  fill: var(--jp-warn-color1);
}

.jp-icon-warn2[fill] {
  fill: var(--jp-warn-color2);
}

.jp-icon-warn3[fill] {
  fill: var(--jp-warn-color3);
}

.jp-icon-warn0[stroke] {
  stroke: var(--jp-warn-color0);
}

.jp-icon-warn1[stroke] {
  stroke: var(--jp-warn-color1);
}

.jp-icon-warn2[stroke] {
  stroke: var(--jp-warn-color2);
}

.jp-icon-warn3[stroke] {
  stroke: var(--jp-warn-color3);
}

/* icon colors that contrast well with each other and most backgrounds */
.jp-icon-contrast0[fill] {
  fill: var(--jp-icon-contrast-color0);
}

.jp-icon-contrast1[fill] {
  fill: var(--jp-icon-contrast-color1);
}

.jp-icon-contrast2[fill] {
  fill: var(--jp-icon-contrast-color2);
}

.jp-icon-contrast3[fill] {
  fill: var(--jp-icon-contrast-color3);
}

.jp-icon-contrast0[stroke] {
  stroke: var(--jp-icon-contrast-color0);
}

.jp-icon-contrast1[stroke] {
  stroke: var(--jp-icon-contrast-color1);
}

.jp-icon-contrast2[stroke] {
  stroke: var(--jp-icon-contrast-color2);
}

.jp-icon-contrast3[stroke] {
  stroke: var(--jp-icon-contrast-color3);
}

.jp-icon-dot[fill] {
  fill: var(--jp-warn-color0);
}

.jp-jupyter-icon-color[fill] {
  fill: var(--jp-jupyter-icon-color, var(--jp-warn-color0));
}

.jp-notebook-icon-color[fill] {
  fill: var(--jp-notebook-icon-color, var(--jp-warn-color0));
}

.jp-json-icon-color[fill] {
  fill: var(--jp-json-icon-color, var(--jp-warn-color1));
}

.jp-console-icon-color[fill] {
  fill: var(--jp-console-icon-color, white);
}

.jp-console-icon-background-color[fill] {
  fill: var(--jp-console-icon-background-color, var(--jp-brand-color1));
}

.jp-terminal-icon-color[fill] {
  fill: var(--jp-terminal-icon-color, var(--jp-layout-color2));
}

.jp-terminal-icon-background-color[fill] {
  fill: var(
    --jp-terminal-icon-background-color,
    var(--jp-inverse-layout-color2)
  );
}

.jp-text-editor-icon-color[fill] {
  fill: var(--jp-text-editor-icon-color, var(--jp-inverse-layout-color3));
}

.jp-inspector-icon-color[fill] {
  fill: var(--jp-inspector-icon-color, var(--jp-inverse-layout-color3));
}

/* CSS for icons in selected filebrowser listing items */
.jp-DirListing-item.jp-mod-selected .jp-icon-selectable[fill] {
  fill: #fff;
}

.jp-DirListing-item.jp-mod-selected .jp-icon-selectable-inverse[fill] {
  fill: var(--jp-brand-color1);
}

/* stylelint-disable selector-max-class, selector-max-compound-selectors */

/**
* TODO: come up with non css-hack solution for showing the busy icon on top
*  of the close icon
* CSS for complex behavior of close icon of tabs in the main area tabbar
*/
.lm-DockPanel-tabBar
  .lm-TabBar-tab.lm-mod-closable.jp-mod-dirty
  > .lm-TabBar-tabCloseIcon
  > :not(:hover)
  > .jp-icon3[fill] {
  fill: none;
}

.lm-DockPanel-tabBar
  .lm-TabBar-tab.lm-mod-closable.jp-mod-dirty
  > .lm-TabBar-tabCloseIcon
  > :not(:hover)
  > .jp-icon-busy[fill] {
  fill: var(--jp-inverse-layout-color3);
}

/* stylelint-enable selector-max-class, selector-max-compound-selectors */

/* CSS for icons in status bar */
#jp-main-statusbar .jp-mod-selected .jp-icon-selectable[fill] {
  fill: #fff;
}

#jp-main-statusbar .jp-mod-selected .jp-icon-selectable-inverse[fill] {
  fill: var(--jp-brand-color1);
}

/* special handling for splash icon CSS. While the theme CSS reloads during
   splash, the splash icon can loose theming. To prevent that, we set a
   default for its color variable */
:root {
  --jp-warn-color0: var(--md-orange-700);
}

/* not sure what to do with this one, used in filebrowser listing */
.jp-DragIcon {
  margin-right: 4px;
}

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

/**
 * Support for alt colors for icons as inline SVG HTMLElements
 */

/* alt recolor the primary elements of an icon */
.jp-icon-alt .jp-icon0[fill] {
  fill: var(--jp-layout-color0);
}

.jp-icon-alt .jp-icon1[fill] {
  fill: var(--jp-layout-color1);
}

.jp-icon-alt .jp-icon2[fill] {
  fill: var(--jp-layout-color2);
}

.jp-icon-alt .jp-icon3[fill] {
  fill: var(--jp-layout-color3);
}

.jp-icon-alt .jp-icon4[fill] {
  fill: var(--jp-layout-color4);
}

.jp-icon-alt .jp-icon0[stroke] {
  stroke: var(--jp-layout-color0);
}

.jp-icon-alt .jp-icon1[stroke] {
  stroke: var(--jp-layout-color1);
}

.jp-icon-alt .jp-icon2[stroke] {
  stroke: var(--jp-layout-color2);
}

.jp-icon-alt .jp-icon3[stroke] {
  stroke: var(--jp-layout-color3);
}

.jp-icon-alt .jp-icon4[stroke] {
  stroke: var(--jp-layout-color4);
}

/* alt recolor the accent elements of an icon */
.jp-icon-alt .jp-icon-accent0[fill] {
  fill: var(--jp-inverse-layout-color0);
}

.jp-icon-alt .jp-icon-accent1[fill] {
  fill: var(--jp-inverse-layout-color1);
}

.jp-icon-alt .jp-icon-accent2[fill] {
  fill: var(--jp-inverse-layout-color2);
}

.jp-icon-alt .jp-icon-accent3[fill] {
  fill: var(--jp-inverse-layout-color3);
}

.jp-icon-alt .jp-icon-accent4[fill] {
  fill: var(--jp-inverse-layout-color4);
}

.jp-icon-alt .jp-icon-accent0[stroke] {
  stroke: var(--jp-inverse-layout-color0);
}

.jp-icon-alt .jp-icon-accent1[stroke] {
  stroke: var(--jp-inverse-layout-color1);
}

.jp-icon-alt .jp-icon-accent2[stroke] {
  stroke: var(--jp-inverse-layout-color2);
}

.jp-icon-alt .jp-icon-accent3[stroke] {
  stroke: var(--jp-inverse-layout-color3);
}

.jp-icon-alt .jp-icon-accent4[stroke] {
  stroke: var(--jp-inverse-layout-color4);
}

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

.jp-icon-hoverShow:not(:hover) .jp-icon-hoverShow-content {
  display: none !important;
}

/**
 * Support for hover colors for icons as inline SVG HTMLElements
 */

/**
 * regular colors
 */

/* recolor the primary elements of an icon */
.jp-icon-hover :hover .jp-icon0-hover[fill] {
  fill: var(--jp-inverse-layout-color0);
}

.jp-icon-hover :hover .jp-icon1-hover[fill] {
  fill: var(--jp-inverse-layout-color1);
}

.jp-icon-hover :hover .jp-icon2-hover[fill] {
  fill: var(--jp-inverse-layout-color2);
}

.jp-icon-hover :hover .jp-icon3-hover[fill] {
  fill: var(--jp-inverse-layout-color3);
}

.jp-icon-hover :hover .jp-icon4-hover[fill] {
  fill: var(--jp-inverse-layout-color4);
}

.jp-icon-hover :hover .jp-icon0-hover[stroke] {
  stroke: var(--jp-inverse-layout-color0);
}

.jp-icon-hover :hover .jp-icon1-hover[stroke] {
  stroke: var(--jp-inverse-layout-color1);
}

.jp-icon-hover :hover .jp-icon2-hover[stroke] {
  stroke: var(--jp-inverse-layout-color2);
}

.jp-icon-hover :hover .jp-icon3-hover[stroke] {
  stroke: var(--jp-inverse-layout-color3);
}

.jp-icon-hover :hover .jp-icon4-hover[stroke] {
  stroke: var(--jp-inverse-layout-color4);
}

/* recolor the accent elements of an icon */
.jp-icon-hover :hover .jp-icon-accent0-hover[fill] {
  fill: var(--jp-layout-color0);
}

.jp-icon-hover :hover .jp-icon-accent1-hover[fill] {
  fill: var(--jp-layout-color1);
}

.jp-icon-hover :hover .jp-icon-accent2-hover[fill] {
  fill: var(--jp-layout-color2);
}

.jp-icon-hover :hover .jp-icon-accent3-hover[fill] {
  fill: var(--jp-layout-color3);
}

.jp-icon-hover :hover .jp-icon-accent4-hover[fill] {
  fill: var(--jp-layout-color4);
}

.jp-icon-hover :hover .jp-icon-accent0-hover[stroke] {
  stroke: var(--jp-layout-color0);
}

.jp-icon-hover :hover .jp-icon-accent1-hover[stroke] {
  stroke: var(--jp-layout-color1);
}

.jp-icon-hover :hover .jp-icon-accent2-hover[stroke] {
  stroke: var(--jp-layout-color2);
}

.jp-icon-hover :hover .jp-icon-accent3-hover[stroke] {
  stroke: var(--jp-layout-color3);
}

.jp-icon-hover :hover .jp-icon-accent4-hover[stroke] {
  stroke: var(--jp-layout-color4);
}

/* set the color of an icon to transparent */
.jp-icon-hover :hover .jp-icon-none-hover[fill] {
  fill: none;
}

.jp-icon-hover :hover .jp-icon-none-hover[stroke] {
  stroke: none;
}

/**
 * inverse colors
 */

/* inverse recolor the primary elements of an icon */
.jp-icon-hover.jp-icon-alt :hover .jp-icon0-hover[fill] {
  fill: var(--jp-layout-color0);
}

.jp-icon-hover.jp-icon-alt :hover .jp-icon1-hover[fill] {
  fill: var(--jp-layout-color1);
}

.jp-icon-hover.jp-icon-alt :hover .jp-icon2-hover[fill] {
  fill: var(--jp-layout-color2);
}

.jp-icon-hover.jp-icon-alt :hover .jp-icon3-hover[fill] {
  fill: var(--jp-layout-color3);
}

.jp-icon-hover.jp-icon-alt :hover .jp-icon4-hover[fill] {
  fill: var(--jp-layout-color4);
}

.jp-icon-hover.jp-icon-alt :hover .jp-icon0-hover[stroke] {
  stroke: var(--jp-layout-color0);
}

.jp-icon-hover.jp-icon-alt :hover .jp-icon1-hover[stroke] {
  stroke: var(--jp-layout-color1);
}

.jp-icon-hover.jp-icon-alt :hover .jp-icon2-hover[stroke] {
  stroke: var(--jp-layout-color2);
}

.jp-icon-hover.jp-icon-alt :hover .jp-icon3-hover[stroke] {
  stroke: var(--jp-layout-color3);
}

.jp-icon-hover.jp-icon-alt :hover .jp-icon4-hover[stroke] {
  stroke: var(--jp-layout-color4);
}

/* inverse recolor the accent elements of an icon */
.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent0-hover[fill] {
  fill: var(--jp-inverse-layout-color0);
}

.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent1-hover[fill] {
  fill: var(--jp-inverse-layout-color1);
}

.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent2-hover[fill] {
  fill: var(--jp-inverse-layout-color2);
}

.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent3-hover[fill] {
  fill: var(--jp-inverse-layout-color3);
}

.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent4-hover[fill] {
  fill: var(--jp-inverse-layout-color4);
}

.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent0-hover[stroke] {
  stroke: var(--jp-inverse-layout-color0);
}

.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent1-hover[stroke] {
  stroke: var(--jp-inverse-layout-color1);
}

.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent2-hover[stroke] {
  stroke: var(--jp-inverse-layout-color2);
}

.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent3-hover[stroke] {
  stroke: var(--jp-inverse-layout-color3);
}

.jp-icon-hover.jp-icon-alt :hover .jp-icon-accent4-hover[stroke] {
  stroke: var(--jp-inverse-layout-color4);
}

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

.jp-IFrame {
  width: 100%;
  height: 100%;
}

.jp-IFrame > iframe {
  border: none;
}

/*
When drag events occur, `lm-mod-override-cursor` is added to the body.
Because iframes steal all cursor events, the following two rules are necessary
to suppress pointer events while resize drags are occurring. There may be a
better solution to this problem.
*/
body.lm-mod-override-cursor .jp-IFrame {
  position: relative;
}

body.lm-mod-override-cursor .jp-IFrame::before {
  content: '';
  position: absolute;
  top: 0;
  left: 0;
  right: 0;
  bottom: 0;
  background: transparent;
}

/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2016, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

.jp-HoverBox {
  position: fixed;
}

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

.jp-FormGroup-content fieldset {
  border: none;
  padding: 0;
  min-width: 0;
  width: 100%;
}

/* stylelint-disable selector-max-type */

.jp-FormGroup-content fieldset .jp-inputFieldWrapper input,
.jp-FormGroup-content fieldset .jp-inputFieldWrapper select,
.jp-FormGroup-content fieldset .jp-inputFieldWrapper textarea {
  font-size: var(--jp-content-font-size2);
  border-color: var(--jp-input-border-color);
  border-style: solid;
  border-radius: var(--jp-border-radius);
  border-width: 1px;
  padding: 6px 8px;
  background: none;
  color: var(--jp-ui-font-color0);
  height: inherit;
}

.jp-FormGroup-content fieldset input[type='checkbox'] {
  position: relative;
  top: 2px;
  margin-left: 0;
}

.jp-FormGroup-content button.jp-mod-styled {
  cursor: pointer;
}

.jp-FormGroup-content .checkbox label {
  cursor: pointer;
  font-size: var(--jp-content-font-size1);
}

.jp-FormGroup-content .jp-root > fieldset > legend {
  display: none;
}

.jp-FormGroup-content .jp-root > fieldset > p {
  display: none;
}

/** copy of `input.jp-mod-styled:focus` style */
.jp-FormGroup-content fieldset input:focus,
.jp-FormGroup-content fieldset select:focus {
  -moz-outline-radius: unset;
  outline: var(--jp-border-width) solid var(--md-blue-500);
  outline-offset: -1px;
  box-shadow: inset 0 0 4px var(--md-blue-300);
}

.jp-FormGroup-content fieldset input:hover:not(:focus),
.jp-FormGroup-content fieldset select:hover:not(:focus) {
  background-color: var(--jp-border-color2);
}

/* stylelint-enable selector-max-type */

.jp-FormGroup-content .checkbox .field-description {
  /* Disable default description field for checkbox:
   because other widgets do not have description fields,
   we add descriptions to each widget on the field level.
  */
  display: none;
}

.jp-FormGroup-content #root__description {
  display: none;
}

.jp-FormGroup-content .jp-modifiedIndicator {
  width: 5px;
  background-color: var(--jp-brand-color2);
  margin-top: 0;
  margin-left: calc(var(--jp-private-settingeditor-modifier-indent) * -1);
  flex-shrink: 0;
}

.jp-FormGroup-content .jp-modifiedIndicator.jp-errorIndicator {
  background-color: var(--jp-error-color0);
  margin-right: 0.5em;
}

/* RJSF ARRAY style */

.jp-arrayFieldWrapper legend {
  font-size: var(--jp-content-font-size2);
  color: var(--jp-ui-font-color0);
  flex-basis: 100%;
  padding: 4px 0;
  font-weight: var(--jp-content-heading-font-weight);
  border-bottom: 1px solid var(--jp-border-color2);
}

.jp-arrayFieldWrapper .field-description {
  padding: 4px 0;
  white-space: pre-wrap;
}

.jp-arrayFieldWrapper .array-item {
  width: 100%;
  border: 1px solid var(--jp-border-color2);
  border-radius: 4px;
  margin: 4px;
}

.jp-ArrayOperations {
  display: flex;
  margin-left: 8px;
}

.jp-ArrayOperationsButton {
  margin: 2px;
}

.jp-ArrayOperationsButton .jp-icon3[fill] {
  fill: var(--jp-ui-font-color0);
}

button.jp-ArrayOperationsButton.jp-mod-styled:disabled {
  cursor: not-allowed;
  opacity: 0.5;
}

/* RJSF form validation error */

.jp-FormGroup-content .validationErrors {
  color: var(--jp-error-color0);
}

/* Hide panel level error as duplicated the field level error */
.jp-FormGroup-content .panel.errors {
  display: none;
}

/* RJSF normal content (settings-editor) */

.jp-FormGroup-contentNormal {
  display: flex;
  align-items: center;
  flex-wrap: wrap;
}

.jp-FormGroup-contentNormal .jp-FormGroup-contentItem {
  margin-left: 7px;
  color: var(--jp-ui-font-color0);
}

.jp-FormGroup-contentNormal .jp-FormGroup-description {
  flex-basis: 100%;
  padding: 4px 7px;
}

.jp-FormGroup-contentNormal .jp-FormGroup-default {
  flex-basis: 100%;
  padding: 4px 7px;
}

.jp-FormGroup-contentNormal .jp-FormGroup-fieldLabel {
  font-size: var(--jp-content-font-size1);
  font-weight: normal;
  min-width: 120px;
}

.jp-FormGroup-contentNormal fieldset:not(:first-child) {
  margin-left: 7px;
}

.jp-FormGroup-contentNormal .field-array-of-string .array-item {
  /* Display `jp-ArrayOperations` buttons side-by-side with content except
    for small screens where flex-wrap will place them one below the other.
  */
  display: flex;
  align-items: center;
  flex-wrap: wrap;
}

.jp-FormGroup-contentNormal .jp-objectFieldWrapper .form-group {
  padding: 2px 8px 2px var(--jp-private-settingeditor-modifier-indent);
  margin-top: 2px;
}

/* RJSF compact content (metadata-form) */

.jp-FormGroup-content.jp-FormGroup-contentCompact {
  width: 100%;
}

.jp-FormGroup-contentCompact .form-group {
  display: flex;
  padding: 0.5em 0.2em 0.5em 0;
}

.jp-FormGroup-contentCompact
  .jp-FormGroup-compactTitle
  .jp-FormGroup-description {
  font-size: var(--jp-ui-font-size1);
  color: var(--jp-ui-font-color2);
}

.jp-FormGroup-contentCompact .jp-FormGroup-fieldLabel {
  padding-bottom: 0.3em;
}

.jp-FormGroup-contentCompact .jp-inputFieldWrapper .form-control {
  width: 100%;
  box-sizing: border-box;
}

.jp-FormGroup-contentCompact .jp-arrayFieldWrapper .jp-FormGroup-compactTitle {
  padding-bottom: 7px;
}

.jp-FormGroup-contentCompact
  .jp-objectFieldWrapper
  .jp-objectFieldWrapper
  .form-group {
  padding: 2px 8px 2px var(--jp-private-settingeditor-modifier-indent);
  margin-top: 2px;
}

.jp-FormGroup-contentCompact ul.error-detail {
  margin-block-start: 0.5em;
  margin-block-end: 0.5em;
  padding-inline-start: 1em;
}

/*
 * Copyright (c) Jupyter Development Team.
 * Distributed under the terms of the Modified BSD License.
 */

.jp-SidePanel {
  display: flex;
  flex-direction: column;
  min-width: var(--jp-sidebar-min-width);
  overflow-y: auto;
  color: var(--jp-ui-font-color1);
  background: var(--jp-layout-color1);
  font-size: var(--jp-ui-font-size1);
}

.jp-SidePanel-header {
  flex: 0 0 auto;
  display: flex;
  border-bottom: var(--jp-border-width) solid var(--jp-border-color2);
  font-size: var(--jp-ui-font-size0);
  font-weight: 600;
  letter-spacing: 1px;
  margin: 0;
  padding: 2px;
  text-transform: uppercase;
}

.jp-SidePanel-toolbar {
  flex: 0 0 auto;
}

.jp-SidePanel-content {
  flex: 1 1 auto;
}

.jp-SidePanel-toolbar,
.jp-AccordionPanel-toolbar {
  height: var(--jp-private-toolbar-height);
}

.jp-SidePanel-toolbar.jp-Toolbar-micro {
  display: none;
}

.lm-AccordionPanel .jp-AccordionPanel-title {
  box-sizing: border-box;
  line-height: 25px;
  margin: 0;
  display: flex;
  align-items: center;
  background: var(--jp-layout-color1);
  color: var(--jp-ui-font-color1);
  border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color);
  box-shadow: var(--jp-toolbar-box-shadow);
  font-size: var(--jp-ui-font-size0);
}

.jp-AccordionPanel-title {
  cursor: pointer;
  user-select: none;
  -moz-user-select: none;
  -webkit-user-select: none;
  text-transform: uppercase;
}

.lm-AccordionPanel[data-orientation='horizontal'] > .jp-AccordionPanel-title {
  /* Title is rotated for horizontal accordion panel using CSS */
  display: block;
  transform-origin: top left;
  transform: rotate(-90deg) translate(-100%);
}

.jp-AccordionPanel-title .lm-AccordionPanel-titleLabel {
  user-select: none;
  text-overflow: ellipsis;
  white-space: nowrap;
  overflow: hidden;
}

.jp-AccordionPanel-title .lm-AccordionPanel-titleCollapser {
  transform: rotate(-90deg);
  margin: auto 0;
  height: 16px;
}

.jp-AccordionPanel-title.lm-mod-expanded .lm-AccordionPanel-titleCollapser {
  transform: rotate(0deg);
}

.lm-AccordionPanel .jp-AccordionPanel-toolbar {
  background: none;
  box-shadow: none;
  border: none;
  margin-left: auto;
}

.lm-AccordionPanel .lm-SplitPanel-handle:hover {
  background: var(--jp-layout-color3);
}

.jp-text-truncated {
  overflow: hidden;
  text-overflow: ellipsis;
  white-space: nowrap;
}

/*-----------------------------------------------------------------------------
| Copyright (c) 2017, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

.jp-Spinner {
  position: absolute;
  display: flex;
  justify-content: center;
  align-items: center;
  z-index: 10;
  left: 0;
  top: 0;
  width: 100%;
  height: 100%;
  background: var(--jp-layout-color0);
  outline: none;
}

.jp-SpinnerContent {
  font-size: 10px;
  margin: 50px auto;
  text-indent: -9999em;
  width: 3em;
  height: 3em;
  border-radius: 50%;
  background: var(--jp-brand-color3);
  background: linear-gradient(
    to right,
    #f37626 10%,
    rgba(255, 255, 255, 0) 42%
  );
  position: relative;
  animation: load3 1s infinite linear, fadeIn 1s;
}

.jp-SpinnerContent::before {
  width: 50%;
  height: 50%;
  background: #f37626;
  border-radius: 100% 0 0;
  position: absolute;
  top: 0;
  left: 0;
  content: '';
}

.jp-SpinnerContent::after {
  background: var(--jp-layout-color0);
  width: 75%;
  height: 75%;
  border-radius: 50%;
  content: '';
  margin: auto;
  position: absolute;
  top: 0;
  left: 0;
  bottom: 0;
  right: 0;
}

@keyframes fadeIn {
  0% {
    opacity: 0;
  }

  100% {
    opacity: 1;
  }
}

@keyframes load3 {
  0% {
    transform: rotate(0deg);
  }

  100% {
    transform: rotate(360deg);
  }
}

/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2017, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

button.jp-mod-styled {
  font-size: var(--jp-ui-font-size1);
  color: var(--jp-ui-font-color0);
  border: none;
  box-sizing: border-box;
  text-align: center;
  line-height: 32px;
  height: 32px;
  padding: 0 12px;
  letter-spacing: 0.8px;
  outline: none;
  appearance: none;
  -webkit-appearance: none;
  -moz-appearance: none;
}

input.jp-mod-styled {
  background: var(--jp-input-background);
  height: 28px;
  box-sizing: border-box;
  border: var(--jp-border-width) solid var(--jp-border-color1);
  padding-left: 7px;
  padding-right: 7px;
  font-size: var(--jp-ui-font-size2);
  color: var(--jp-ui-font-color0);
  outline: none;
  appearance: none;
  -webkit-appearance: none;
  -moz-appearance: none;
}

input[type='checkbox'].jp-mod-styled {
  appearance: checkbox;
  -webkit-appearance: checkbox;
  -moz-appearance: checkbox;
  height: auto;
}

input.jp-mod-styled:focus {
  border: var(--jp-border-width) solid var(--md-blue-500);
  box-shadow: inset 0 0 4px var(--md-blue-300);
}

.jp-select-wrapper {
  display: flex;
  position: relative;
  flex-direction: column;
  padding: 1px;
  background-color: var(--jp-layout-color1);
  box-sizing: border-box;
  margin-bottom: 12px;
}

.jp-select-wrapper:not(.multiple) {
  height: 28px;
}

.jp-select-wrapper.jp-mod-focused select.jp-mod-styled {
  border: var(--jp-border-width) solid var(--jp-input-active-border-color);
  box-shadow: var(--jp-input-box-shadow);
  background-color: var(--jp-input-active-background);
}

select.jp-mod-styled:hover {
  cursor: pointer;
  color: var(--jp-ui-font-color0);
  background-color: var(--jp-input-hover-background);
  box-shadow: inset 0 0 1px rgba(0, 0, 0, 0.5);
}

select.jp-mod-styled {
  flex: 1 1 auto;
  width: 100%;
  font-size: var(--jp-ui-font-size2);
  background: var(--jp-input-background);
  color: var(--jp-ui-font-color0);
  padding: 0 25px 0 8px;
  border: var(--jp-border-width) solid var(--jp-input-border-color);
  border-radius: 0;
  outline: none;
  appearance: none;
  -webkit-appearance: none;
  -moz-appearance: none;
}

select.jp-mod-styled:not([multiple]) {
  height: 32px;
}

select.jp-mod-styled[multiple] {
  max-height: 200px;
  overflow-y: auto;
}

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

.jp-switch {
  display: flex;
  align-items: center;
  padding-left: 4px;
  padding-right: 4px;
  font-size: var(--jp-ui-font-size1);
  background-color: transparent;
  color: var(--jp-ui-font-color1);
  border: none;
  height: 20px;
}

.jp-switch:hover {
  background-color: var(--jp-layout-color2);
}

.jp-switch-label {
  margin-right: 5px;
  font-family: var(--jp-ui-font-family);
}

.jp-switch-track {
  cursor: pointer;
  background-color: var(--jp-switch-color, var(--jp-border-color1));
  -webkit-transition: 0.4s;
  transition: 0.4s;
  border-radius: 34px;
  height: 16px;
  width: 35px;
  position: relative;
}

.jp-switch-track::before {
  content: '';
  position: absolute;
  height: 10px;
  width: 10px;
  margin: 3px;
  left: 0;
  background-color: var(--jp-ui-inverse-font-color1);
  -webkit-transition: 0.4s;
  transition: 0.4s;
  border-radius: 50%;
}

.jp-switch[aria-checked='true'] .jp-switch-track {
  background-color: var(--jp-switch-true-position-color, var(--jp-warn-color0));
}

.jp-switch[aria-checked='true'] .jp-switch-track::before {
  /* track width (35) - margins (3 + 3) - thumb width (10) */
  left: 19px;
}

/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2016, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

:root {
  --jp-private-toolbar-height: calc(
    28px + var(--jp-border-width)
  ); /* leave 28px for content */
}

.jp-Toolbar {
  color: var(--jp-ui-font-color1);
  flex: 0 0 auto;
  display: flex;
  flex-direction: row;
  border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color);
  box-shadow: var(--jp-toolbar-box-shadow);
  background: var(--jp-toolbar-background);
  min-height: var(--jp-toolbar-micro-height);
  padding: 2px;
  z-index: 8;
  overflow-x: hidden;
}

/* Toolbar items */

.jp-Toolbar > .jp-Toolbar-item.jp-Toolbar-spacer {
  flex-grow: 1;
  flex-shrink: 1;
}

.jp-Toolbar-item.jp-Toolbar-kernelStatus {
  display: inline-block;
  width: 32px;
  background-repeat: no-repeat;
  background-position: center;
  background-size: 16px;
}

.jp-Toolbar > .jp-Toolbar-item {
  flex: 0 0 auto;
  display: flex;
  padding-left: 1px;
  padding-right: 1px;
  font-size: var(--jp-ui-font-size1);
  line-height: var(--jp-private-toolbar-height);
  height: 100%;
}

/* Toolbar buttons */

/* This is the div we use to wrap the react component into a Widget */
div.jp-ToolbarButton {
  color: transparent;
  border: none;
  box-sizing: border-box;
  outline: none;
  appearance: none;
  -webkit-appearance: none;
  -moz-appearance: none;
  padding: 0;
  margin: 0;
}

button.jp-ToolbarButtonComponent {
  background: var(--jp-layout-color1);
  border: none;
  box-sizing: border-box;
  outline: none;
  appearance: none;
  -webkit-appearance: none;
  -moz-appearance: none;
  padding: 0 6px;
  margin: 0;
  height: 24px;
  border-radius: var(--jp-border-radius);
  display: flex;
  align-items: center;
  text-align: center;
  font-size: 14px;
  min-width: unset;
  min-height: unset;
}

button.jp-ToolbarButtonComponent:disabled {
  opacity: 0.4;
}

button.jp-ToolbarButtonComponent > span {
  padding: 0;
  flex: 0 0 auto;
}

button.jp-ToolbarButtonComponent .jp-ToolbarButtonComponent-label {
  font-size: var(--jp-ui-font-size1);
  line-height: 100%;
  padding-left: 2px;
  color: var(--jp-ui-font-color1);
  font-family: var(--jp-ui-font-family);
}

#jp-main-dock-panel[data-mode='single-document']
  .jp-MainAreaWidget
  > .jp-Toolbar.jp-Toolbar-micro {
  padding: 0;
  min-height: 0;
}

#jp-main-dock-panel[data-mode='single-document']
  .jp-MainAreaWidget
  > .jp-Toolbar {
  border: none;
  box-shadow: none;
}

/*
 * Copyright (c) Jupyter Development Team.
 * Distributed under the terms of the Modified BSD License.
 */

.jp-WindowedPanel-outer {
  position: relative;
  overflow-y: auto;
}

.jp-WindowedPanel-inner {
  position: relative;
}

.jp-WindowedPanel-window {
  position: absolute;
  left: 0;
  right: 0;
  overflow: visible;
}

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

/* Sibling imports */

body {
  color: var(--jp-ui-font-color1);
  font-size: var(--jp-ui-font-size1);
}

/* Disable native link decoration styles everywhere outside of dialog boxes */
a {
  text-decoration: unset;
  color: unset;
}

a:hover {
  text-decoration: unset;
  color: unset;
}

/* Accessibility for links inside dialog box text */
.jp-Dialog-content a {
  text-decoration: revert;
  color: var(--jp-content-link-color);
}

.jp-Dialog-content a:hover {
  text-decoration: revert;
}

/* Styles for ui-components */
.jp-Button {
  color: var(--jp-ui-font-color2);
  border-radius: var(--jp-border-radius);
  padding: 0 12px;
  font-size: var(--jp-ui-font-size1);

  /* Copy from blueprint 3 */
  display: inline-flex;
  flex-direction: row;
  border: none;
  cursor: pointer;
  align-items: center;
  justify-content: center;
  text-align: left;
  vertical-align: middle;
  min-height: 30px;
  min-width: 30px;
}

.jp-Button:disabled {
  cursor: not-allowed;
}

.jp-Button:empty {
  padding: 0 !important;
}

.jp-Button.jp-mod-small {
  min-height: 24px;
  min-width: 24px;
  font-size: 12px;
  padding: 0 7px;
}

/* Use our own theme for hover styles */
.jp-Button.jp-mod-minimal:hover {
  background-color: var(--jp-layout-color2);
}

.jp-Button.jp-mod-minimal {
  background: none;
}

.jp-InputGroup {
  display: block;
  position: relative;
}

.jp-InputGroup input {
  box-sizing: border-box;
  border: none;
  border-radius: 0;
  background-color: transparent;
  color: var(--jp-ui-font-color0);
  box-shadow: inset 0 0 0 var(--jp-border-width) var(--jp-input-border-color);
  padding-bottom: 0;
  padding-top: 0;
  padding-left: 10px;
  padding-right: 28px;
  position: relative;
  width: 100%;
  -webkit-appearance: none;
  -moz-appearance: none;
  appearance: none;
  font-size: 14px;
  font-weight: 400;
  height: 30px;
  line-height: 30px;
  outline: none;
  vertical-align: middle;
}

.jp-InputGroup input:focus {
  box-shadow: inset 0 0 0 var(--jp-border-width)
      var(--jp-input-active-box-shadow-color),
    inset 0 0 0 3px var(--jp-input-active-box-shadow-color);
}

.jp-InputGroup input:disabled {
  cursor: not-allowed;
  resize: block;
  background-color: var(--jp-layout-color2);
  color: var(--jp-ui-font-color2);
}

.jp-InputGroup input:disabled ~ span {
  cursor: not-allowed;
  color: var(--jp-ui-font-color2);
}

.jp-InputGroup input::placeholder,
input::placeholder {
  color: var(--jp-ui-font-color2);
}

.jp-InputGroupAction {
  position: absolute;
  bottom: 1px;
  right: 0;
  padding: 6px;
}

.jp-HTMLSelect.jp-DefaultStyle select {
  background-color: initial;
  border: none;
  border-radius: 0;
  box-shadow: none;
  color: var(--jp-ui-font-color0);
  display: block;
  font-size: var(--jp-ui-font-size1);
  font-family: var(--jp-ui-font-family);
  height: 24px;
  line-height: 14px;
  padding: 0 25px 0 10px;
  text-align: left;
  -moz-appearance: none;
  -webkit-appearance: none;
}

.jp-HTMLSelect.jp-DefaultStyle select:disabled {
  background-color: var(--jp-layout-color2);
  color: var(--jp-ui-font-color2);
  cursor: not-allowed;
  resize: block;
}

.jp-HTMLSelect.jp-DefaultStyle select:disabled ~ span {
  cursor: not-allowed;
}

/* Use our own theme for hover and option styles */
/* stylelint-disable-next-line selector-max-type */
.jp-HTMLSelect.jp-DefaultStyle select:hover,
.jp-HTMLSelect.jp-DefaultStyle select > option {
  background-color: var(--jp-layout-color2);
  color: var(--jp-ui-font-color0);
}

select {
  box-sizing: border-box;
}

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

/*-----------------------------------------------------------------------------
| Styles
|----------------------------------------------------------------------------*/

.jp-StatusBar-Widget {
  display: flex;
  align-items: center;
  background: var(--jp-layout-color2);
  min-height: var(--jp-statusbar-height);
  justify-content: space-between;
  padding: 0 10px;
}

.jp-StatusBar-Left {
  display: flex;
  align-items: center;
  flex-direction: row;
}

.jp-StatusBar-Middle {
  display: flex;
  align-items: center;
}

.jp-StatusBar-Right {
  display: flex;
  align-items: center;
  flex-direction: row-reverse;
}

.jp-StatusBar-Item {
  max-height: var(--jp-statusbar-height);
  margin: 0 2px;
  height: var(--jp-statusbar-height);
  white-space: nowrap;
  text-overflow: ellipsis;
  color: var(--jp-ui-font-color1);
  padding: 0 6px;
}

.jp-mod-highlighted:hover {
  background-color: var(--jp-layout-color3);
}

.jp-mod-clicked {
  background-color: var(--jp-brand-color1);
}

.jp-mod-clicked:hover {
  background-color: var(--jp-brand-color0);
}

.jp-mod-clicked .jp-StatusBar-TextItem {
  color: var(--jp-ui-inverse-font-color1);
}

.jp-StatusBar-HoverItem {
  box-shadow: '0px 4px 4px rgba(0, 0, 0, 0.25)';
}

.jp-StatusBar-TextItem {
  font-size: var(--jp-ui-font-size1);
  font-family: var(--jp-ui-font-family);
  line-height: 24px;
  color: var(--jp-ui-font-color1);
}

.jp-StatusBar-GroupItem {
  display: flex;
  align-items: center;
  flex-direction: row;
}

.jp-Statusbar-ProgressCircle svg {
  display: block;
  margin: 0 auto;
  width: 16px;
  height: 24px;
  align-self: normal;
}

.jp-Statusbar-ProgressCircle path {
  fill: var(--jp-inverse-layout-color3);
}

.jp-Statusbar-ProgressBar-progress-bar {
  height: 10px;
  width: 100px;
  border: solid 0.25px var(--jp-brand-color2);
  border-radius: 3px;
  overflow: hidden;
  align-self: center;
}

.jp-Statusbar-ProgressBar-progress-bar > div {
  background-color: var(--jp-brand-color2);
  background-image: linear-gradient(
    -45deg,
    rgba(255, 255, 255, 0.2) 25%,
    transparent 25%,
    transparent 50%,
    rgba(255, 255, 255, 0.2) 50%,
    rgba(255, 255, 255, 0.2) 75%,
    transparent 75%,
    transparent
  );
  background-size: 40px 40px;
  float: left;
  width: 0%;
  height: 100%;
  font-size: 12px;
  line-height: 14px;
  color: #fff;
  text-align: center;
  animation: jp-Statusbar-ExecutionTime-progress-bar 2s linear infinite;
}

.jp-Statusbar-ProgressBar-progress-bar p {
  color: var(--jp-ui-font-color1);
  font-family: var(--jp-ui-font-family);
  font-size: var(--jp-ui-font-size1);
  line-height: 10px;
  width: 100px;
}

@keyframes jp-Statusbar-ExecutionTime-progress-bar {
  0% {
    background-position: 0 0;
  }

  100% {
    background-position: 40px 40px;
  }
}

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

/*-----------------------------------------------------------------------------
| Variables
|----------------------------------------------------------------------------*/

:root {
  --jp-private-commandpalette-search-height: 28px;
}

/*-----------------------------------------------------------------------------
| Overall styles
|----------------------------------------------------------------------------*/

.lm-CommandPalette {
  padding-bottom: 0;
  color: var(--jp-ui-font-color1);
  background: var(--jp-layout-color1);

  /* This is needed so that all font sizing of children done in ems is
   * relative to this base size */
  font-size: var(--jp-ui-font-size1);
}

/*-----------------------------------------------------------------------------
| Modal variant
|----------------------------------------------------------------------------*/

.jp-ModalCommandPalette {
  position: absolute;
  z-index: 10000;
  top: 38px;
  left: 30%;
  margin: 0;
  padding: 4px;
  width: 40%;
  box-shadow: var(--jp-elevation-z4);
  border-radius: 4px;
  background: var(--jp-layout-color0);
}

.jp-ModalCommandPalette .lm-CommandPalette {
  max-height: 40vh;
}

.jp-ModalCommandPalette .lm-CommandPalette .lm-close-icon::after {
  display: none;
}

.jp-ModalCommandPalette .lm-CommandPalette .lm-CommandPalette-header {
  display: none;
}

.jp-ModalCommandPalette .lm-CommandPalette .lm-CommandPalette-item {
  margin-left: 4px;
  margin-right: 4px;
}

.jp-ModalCommandPalette
  .lm-CommandPalette
  .lm-CommandPalette-item.lm-mod-disabled {
  display: none;
}

/*-----------------------------------------------------------------------------
| Search
|----------------------------------------------------------------------------*/

.lm-CommandPalette-search {
  padding: 4px;
  background-color: var(--jp-layout-color1);
  z-index: 2;
}

.lm-CommandPalette-wrapper {
  overflow: overlay;
  padding: 0 9px;
  background-color: var(--jp-input-active-background);
  height: 30px;
  box-shadow: inset 0 0 0 var(--jp-border-width) var(--jp-input-border-color);
}

.lm-CommandPalette.lm-mod-focused .lm-CommandPalette-wrapper {
  box-shadow: inset 0 0 0 1px var(--jp-input-active-box-shadow-color),
    inset 0 0 0 3px var(--jp-input-active-box-shadow-color);
}

.jp-SearchIconGroup {
  color: white;
  background-color: var(--jp-brand-color1);
  position: absolute;
  top: 4px;
  right: 4px;
  padding: 5px 5px 1px;
}

.jp-SearchIconGroup svg {
  height: 20px;
  width: 20px;
}

.jp-SearchIconGroup .jp-icon3[fill] {
  fill: var(--jp-layout-color0);
}

.lm-CommandPalette-input {
  background: transparent;
  width: calc(100% - 18px);
  float: left;
  border: none;
  outline: none;
  font-size: var(--jp-ui-font-size1);
  color: var(--jp-ui-font-color0);
  line-height: var(--jp-private-commandpalette-search-height);
}

.lm-CommandPalette-input::-webkit-input-placeholder,
.lm-CommandPalette-input::-moz-placeholder,
.lm-CommandPalette-input:-ms-input-placeholder {
  color: var(--jp-ui-font-color2);
  font-size: var(--jp-ui-font-size1);
}

/*-----------------------------------------------------------------------------
| Results
|----------------------------------------------------------------------------*/

.lm-CommandPalette-header:first-child {
  margin-top: 0;
}

.lm-CommandPalette-header {
  border-bottom: solid var(--jp-border-width) var(--jp-border-color2);
  color: var(--jp-ui-font-color1);
  cursor: pointer;
  display: flex;
  font-size: var(--jp-ui-font-size0);
  font-weight: 600;
  letter-spacing: 1px;
  margin-top: 8px;
  padding: 8px 0 8px 12px;
  text-transform: uppercase;
}

.lm-CommandPalette-header.lm-mod-active {
  background: var(--jp-layout-color2);
}

.lm-CommandPalette-header > mark {
  background-color: transparent;
  font-weight: bold;
  color: var(--jp-ui-font-color1);
}

.lm-CommandPalette-item {
  padding: 4px 12px 4px 4px;
  color: var(--jp-ui-font-color1);
  font-size: var(--jp-ui-font-size1);
  font-weight: 400;
  display: flex;
}

.lm-CommandPalette-item.lm-mod-disabled {
  color: var(--jp-ui-font-color2);
}

.lm-CommandPalette-item.lm-mod-active {
  color: var(--jp-ui-inverse-font-color1);
  background: var(--jp-brand-color1);
}

.lm-CommandPalette-item.lm-mod-active .lm-CommandPalette-itemLabel > mark {
  color: var(--jp-ui-inverse-font-color0);
}

.lm-CommandPalette-item.lm-mod-active .jp-icon-selectable[fill] {
  fill: var(--jp-layout-color0);
}

.lm-CommandPalette-item.lm-mod-active:hover:not(.lm-mod-disabled) {
  color: var(--jp-ui-inverse-font-color1);
  background: var(--jp-brand-color1);
}

.lm-CommandPalette-item:hover:not(.lm-mod-active):not(.lm-mod-disabled) {
  background: var(--jp-layout-color2);
}

.lm-CommandPalette-itemContent {
  overflow: hidden;
}

.lm-CommandPalette-itemLabel > mark {
  color: var(--jp-ui-font-color0);
  background-color: transparent;
  font-weight: bold;
}

.lm-CommandPalette-item.lm-mod-disabled mark {
  color: var(--jp-ui-font-color2);
}

.lm-CommandPalette-item .lm-CommandPalette-itemIcon {
  margin: 0 4px 0 0;
  position: relative;
  width: 16px;
  top: 2px;
  flex: 0 0 auto;
}

.lm-CommandPalette-item.lm-mod-disabled .lm-CommandPalette-itemIcon {
  opacity: 0.6;
}

.lm-CommandPalette-item .lm-CommandPalette-itemShortcut {
  flex: 0 0 auto;
}

.lm-CommandPalette-itemCaption {
  display: none;
}

.lm-CommandPalette-content {
  background-color: var(--jp-layout-color1);
}

.lm-CommandPalette-content:empty::after {
  content: 'No results';
  margin: auto;
  margin-top: 20px;
  width: 100px;
  display: block;
  font-size: var(--jp-ui-font-size2);
  font-family: var(--jp-ui-font-family);
  font-weight: lighter;
}

.lm-CommandPalette-emptyMessage {
  text-align: center;
  margin-top: 24px;
  line-height: 1.32;
  padding: 0 8px;
  color: var(--jp-content-font-color3);
}

/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2017, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

.jp-Dialog {
  position: absolute;
  z-index: 10000;
  display: flex;
  flex-direction: column;
  align-items: center;
  justify-content: center;
  top: 0;
  left: 0;
  margin: 0;
  padding: 0;
  width: 100%;
  height: 100%;
  background: var(--jp-dialog-background);
}

.jp-Dialog-content {
  display: flex;
  flex-direction: column;
  margin-left: auto;
  margin-right: auto;
  background: var(--jp-layout-color1);
  padding: 24px 24px 12px;
  min-width: 300px;
  min-height: 150px;
  max-width: 1000px;
  max-height: 500px;
  box-sizing: border-box;
  box-shadow: var(--jp-elevation-z20);
  word-wrap: break-word;
  border-radius: var(--jp-border-radius);

  /* This is needed so that all font sizing of children done in ems is
   * relative to this base size */
  font-size: var(--jp-ui-font-size1);
  color: var(--jp-ui-font-color1);
  resize: both;
}

.jp-Dialog-content.jp-Dialog-content-small {
  max-width: 500px;
}

.jp-Dialog-button {
  overflow: visible;
}

button.jp-Dialog-button:focus {
  outline: 1px solid var(--jp-brand-color1);
  outline-offset: 4px;
  -moz-outline-radius: 0;
}

button.jp-Dialog-button:focus::-moz-focus-inner {
  border: 0;
}

button.jp-Dialog-button.jp-mod-styled.jp-mod-accept:focus,
button.jp-Dialog-button.jp-mod-styled.jp-mod-warn:focus,
button.jp-Dialog-button.jp-mod-styled.jp-mod-reject:focus {
  outline-offset: 4px;
  -moz-outline-radius: 0;
}

button.jp-Dialog-button.jp-mod-styled.jp-mod-accept:focus {
  outline: 1px solid var(--jp-accept-color-normal, var(--jp-brand-color1));
}

button.jp-Dialog-button.jp-mod-styled.jp-mod-warn:focus {
  outline: 1px solid var(--jp-warn-color-normal, var(--jp-error-color1));
}

button.jp-Dialog-button.jp-mod-styled.jp-mod-reject:focus {
  outline: 1px solid var(--jp-reject-color-normal, var(--md-grey-600));
}

button.jp-Dialog-close-button {
  padding: 0;
  height: 100%;
  min-width: unset;
  min-height: unset;
}

.jp-Dialog-header {
  display: flex;
  justify-content: space-between;
  flex: 0 0 auto;
  padding-bottom: 12px;
  font-size: var(--jp-ui-font-size3);
  font-weight: 400;
  color: var(--jp-ui-font-color1);
}

.jp-Dialog-body {
  display: flex;
  flex-direction: column;
  flex: 1 1 auto;
  font-size: var(--jp-ui-font-size1);
  background: var(--jp-layout-color1);
  color: var(--jp-ui-font-color1);
  overflow: auto;
}

.jp-Dialog-footer {
  display: flex;
  flex-direction: row;
  justify-content: flex-end;
  align-items: center;
  flex: 0 0 auto;
  margin-left: -12px;
  margin-right: -12px;
  padding: 12px;
}

.jp-Dialog-checkbox {
  padding-right: 5px;
}

.jp-Dialog-checkbox > input:focus-visible {
  outline: 1px solid var(--jp-input-active-border-color);
  outline-offset: 1px;
}

.jp-Dialog-spacer {
  flex: 1 1 auto;
}

.jp-Dialog-title {
  overflow: hidden;
  white-space: nowrap;
  text-overflow: ellipsis;
}

.jp-Dialog-body > .jp-select-wrapper {
  width: 100%;
}

.jp-Dialog-body > button {
  padding: 0 16px;
}

.jp-Dialog-body > label {
  line-height: 1.4;
  color: var(--jp-ui-font-color0);
}

.jp-Dialog-button.jp-mod-styled:not(:last-child) {
  margin-right: 12px;
}

/*
 * Copyright (c) Jupyter Development Team.
 * Distributed under the terms of the Modified BSD License.
 */

.jp-Input-Boolean-Dialog {
  flex-direction: row-reverse;
  align-items: end;
  width: 100%;
}

.jp-Input-Boolean-Dialog > label {
  flex: 1 1 auto;
}

/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2016, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

.jp-MainAreaWidget > :focus {
  outline: none;
}

.jp-MainAreaWidget .jp-MainAreaWidget-error {
  padding: 6px;
}

.jp-MainAreaWidget .jp-MainAreaWidget-error > pre {
  width: auto;
  padding: 10px;
  background: var(--jp-error-color3);
  border: var(--jp-border-width) solid var(--jp-error-color1);
  border-radius: var(--jp-border-radius);
  color: var(--jp-ui-font-color1);
  font-size: var(--jp-ui-font-size1);
  white-space: pre-wrap;
  word-wrap: break-word;
}

/*
 * Copyright (c) Jupyter Development Team.
 * Distributed under the terms of the Modified BSD License.
 */

/**
 * google-material-color v1.2.6
 * https://github.com/danlevan/google-material-color
 */
:root {
  --md-red-50: #ffebee;
  --md-red-100: #ffcdd2;
  --md-red-200: #ef9a9a;
  --md-red-300: #e57373;
  --md-red-400: #ef5350;
  --md-red-500: #f44336;
  --md-red-600: #e53935;
  --md-red-700: #d32f2f;
  --md-red-800: #c62828;
  --md-red-900: #b71c1c;
  --md-red-A100: #ff8a80;
  --md-red-A200: #ff5252;
  --md-red-A400: #ff1744;
  --md-red-A700: #d50000;
  --md-pink-50: #fce4ec;
  --md-pink-100: #f8bbd0;
  --md-pink-200: #f48fb1;
  --md-pink-300: #f06292;
  --md-pink-400: #ec407a;
  --md-pink-500: #e91e63;
  --md-pink-600: #d81b60;
  --md-pink-700: #c2185b;
  --md-pink-800: #ad1457;
  --md-pink-900: #880e4f;
  --md-pink-A100: #ff80ab;
  --md-pink-A200: #ff4081;
  --md-pink-A400: #f50057;
  --md-pink-A700: #c51162;
  --md-purple-50: #f3e5f5;
  --md-purple-100: #e1bee7;
  --md-purple-200: #ce93d8;
  --md-purple-300: #ba68c8;
  --md-purple-400: #ab47bc;
  --md-purple-500: #9c27b0;
  --md-purple-600: #8e24aa;
  --md-purple-700: #7b1fa2;
  --md-purple-800: #6a1b9a;
  --md-purple-900: #4a148c;
  --md-purple-A100: #ea80fc;
  --md-purple-A200: #e040fb;
  --md-purple-A400: #d500f9;
  --md-purple-A700: #a0f;
  --md-deep-purple-50: #ede7f6;
  --md-deep-purple-100: #d1c4e9;
  --md-deep-purple-200: #b39ddb;
  --md-deep-purple-300: #9575cd;
  --md-deep-purple-400: #7e57c2;
  --md-deep-purple-500: #673ab7;
  --md-deep-purple-600: #5e35b1;
  --md-deep-purple-700: #512da8;
  --md-deep-purple-800: #4527a0;
  --md-deep-purple-900: #311b92;
  --md-deep-purple-A100: #b388ff;
  --md-deep-purple-A200: #7c4dff;
  --md-deep-purple-A400: #651fff;
  --md-deep-purple-A700: #6200ea;
  --md-indigo-50: #e8eaf6;
  --md-indigo-100: #c5cae9;
  --md-indigo-200: #9fa8da;
  --md-indigo-300: #7986cb;
  --md-indigo-400: #5c6bc0;
  --md-indigo-500: #3f51b5;
  --md-indigo-600: #3949ab;
  --md-indigo-700: #303f9f;
  --md-indigo-800: #283593;
  --md-indigo-900: #1a237e;
  --md-indigo-A100: #8c9eff;
  --md-indigo-A200: #536dfe;
  --md-indigo-A400: #3d5afe;
  --md-indigo-A700: #304ffe;
  --md-blue-50: #e3f2fd;
  --md-blue-100: #bbdefb;
  --md-blue-200: #90caf9;
  --md-blue-300: #64b5f6;
  --md-blue-400: #42a5f5;
  --md-blue-500: #2196f3;
  --md-blue-600: #1e88e5;
  --md-blue-700: #1976d2;
  --md-blue-800: #1565c0;
  --md-blue-900: #0d47a1;
  --md-blue-A100: #82b1ff;
  --md-blue-A200: #448aff;
  --md-blue-A400: #2979ff;
  --md-blue-A700: #2962ff;
  --md-light-blue-50: #e1f5fe;
  --md-light-blue-100: #b3e5fc;
  --md-light-blue-200: #81d4fa;
  --md-light-blue-300: #4fc3f7;
  --md-light-blue-400: #29b6f6;
  --md-light-blue-500: #03a9f4;
  --md-light-blue-600: #039be5;
  --md-light-blue-700: #0288d1;
  --md-light-blue-800: #0277bd;
  --md-light-blue-900: #01579b;
  --md-light-blue-A100: #80d8ff;
  --md-light-blue-A200: #40c4ff;
  --md-light-blue-A400: #00b0ff;
  --md-light-blue-A700: #0091ea;
  --md-cyan-50: #e0f7fa;
  --md-cyan-100: #b2ebf2;
  --md-cyan-200: #80deea;
  --md-cyan-300: #4dd0e1;
  --md-cyan-400: #26c6da;
  --md-cyan-500: #00bcd4;
  --md-cyan-600: #00acc1;
  --md-cyan-700: #0097a7;
  --md-cyan-800: #00838f;
  --md-cyan-900: #006064;
  --md-cyan-A100: #84ffff;
  --md-cyan-A200: #18ffff;
  --md-cyan-A400: #00e5ff;
  --md-cyan-A700: #00b8d4;
  --md-teal-50: #e0f2f1;
  --md-teal-100: #b2dfdb;
  --md-teal-200: #80cbc4;
  --md-teal-300: #4db6ac;
  --md-teal-400: #26a69a;
  --md-teal-500: #009688;
  --md-teal-600: #00897b;
  --md-teal-700: #00796b;
  --md-teal-800: #00695c;
  --md-teal-900: #004d40;
  --md-teal-A100: #a7ffeb;
  --md-teal-A200: #64ffda;
  --md-teal-A400: #1de9b6;
  --md-teal-A700: #00bfa5;
  --md-green-50: #e8f5e9;
  --md-green-100: #c8e6c9;
  --md-green-200: #a5d6a7;
  --md-green-300: #81c784;
  --md-green-400: #66bb6a;
  --md-green-500: #4caf50;
  --md-green-600: #43a047;
  --md-green-700: #388e3c;
  --md-green-800: #2e7d32;
  --md-green-900: #1b5e20;
  --md-green-A100: #b9f6ca;
  --md-green-A200: #69f0ae;
  --md-green-A400: #00e676;
  --md-green-A700: #00c853;
  --md-light-green-50: #f1f8e9;
  --md-light-green-100: #dcedc8;
  --md-light-green-200: #c5e1a5;
  --md-light-green-300: #aed581;
  --md-light-green-400: #9ccc65;
  --md-light-green-500: #8bc34a;
  --md-light-green-600: #7cb342;
  --md-light-green-700: #689f38;
  --md-light-green-800: #558b2f;
  --md-light-green-900: #33691e;
  --md-light-green-A100: #ccff90;
  --md-light-green-A200: #b2ff59;
  --md-light-green-A400: #76ff03;
  --md-light-green-A700: #64dd17;
  --md-lime-50: #f9fbe7;
  --md-lime-100: #f0f4c3;
  --md-lime-200: #e6ee9c;
  --md-lime-300: #dce775;
  --md-lime-400: #d4e157;
  --md-lime-500: #cddc39;
  --md-lime-600: #c0ca33;
  --md-lime-700: #afb42b;
  --md-lime-800: #9e9d24;
  --md-lime-900: #827717;
  --md-lime-A100: #f4ff81;
  --md-lime-A200: #eeff41;
  --md-lime-A400: #c6ff00;
  --md-lime-A700: #aeea00;
  --md-yellow-50: #fffde7;
  --md-yellow-100: #fff9c4;
  --md-yellow-200: #fff59d;
  --md-yellow-300: #fff176;
  --md-yellow-400: #ffee58;
  --md-yellow-500: #ffeb3b;
  --md-yellow-600: #fdd835;
  --md-yellow-700: #fbc02d;
  --md-yellow-800: #f9a825;
  --md-yellow-900: #f57f17;
  --md-yellow-A100: #ffff8d;
  --md-yellow-A200: #ff0;
  --md-yellow-A400: #ffea00;
  --md-yellow-A700: #ffd600;
  --md-amber-50: #fff8e1;
  --md-amber-100: #ffecb3;
  --md-amber-200: #ffe082;
  --md-amber-300: #ffd54f;
  --md-amber-400: #ffca28;
  --md-amber-500: #ffc107;
  --md-amber-600: #ffb300;
  --md-amber-700: #ffa000;
  --md-amber-800: #ff8f00;
  --md-amber-900: #ff6f00;
  --md-amber-A100: #ffe57f;
  --md-amber-A200: #ffd740;
  --md-amber-A400: #ffc400;
  --md-amber-A700: #ffab00;
  --md-orange-50: #fff3e0;
  --md-orange-100: #ffe0b2;
  --md-orange-200: #ffcc80;
  --md-orange-300: #ffb74d;
  --md-orange-400: #ffa726;
  --md-orange-500: #ff9800;
  --md-orange-600: #fb8c00;
  --md-orange-700: #f57c00;
  --md-orange-800: #ef6c00;
  --md-orange-900: #e65100;
  --md-orange-A100: #ffd180;
  --md-orange-A200: #ffab40;
  --md-orange-A400: #ff9100;
  --md-orange-A700: #ff6d00;
  --md-deep-orange-50: #fbe9e7;
  --md-deep-orange-100: #ffccbc;
  --md-deep-orange-200: #ffab91;
  --md-deep-orange-300: #ff8a65;
  --md-deep-orange-400: #ff7043;
  --md-deep-orange-500: #ff5722;
  --md-deep-orange-600: #f4511e;
  --md-deep-orange-700: #e64a19;
  --md-deep-orange-800: #d84315;
  --md-deep-orange-900: #bf360c;
  --md-deep-orange-A100: #ff9e80;
  --md-deep-orange-A200: #ff6e40;
  --md-deep-orange-A400: #ff3d00;
  --md-deep-orange-A700: #dd2c00;
  --md-brown-50: #efebe9;
  --md-brown-100: #d7ccc8;
  --md-brown-200: #bcaaa4;
  --md-brown-300: #a1887f;
  --md-brown-400: #8d6e63;
  --md-brown-500: #795548;
  --md-brown-600: #6d4c41;
  --md-brown-700: #5d4037;
  --md-brown-800: #4e342e;
  --md-brown-900: #3e2723;
  --md-grey-50: #fafafa;
  --md-grey-100: #f5f5f5;
  --md-grey-200: #eee;
  --md-grey-300: #e0e0e0;
  --md-grey-400: #bdbdbd;
  --md-grey-500: #9e9e9e;
  --md-grey-600: #757575;
  --md-grey-700: #616161;
  --md-grey-800: #424242;
  --md-grey-900: #212121;
  --md-blue-grey-50: #eceff1;
  --md-blue-grey-100: #cfd8dc;
  --md-blue-grey-200: #b0bec5;
  --md-blue-grey-300: #90a4ae;
  --md-blue-grey-400: #78909c;
  --md-blue-grey-500: #607d8b;
  --md-blue-grey-600: #546e7a;
  --md-blue-grey-700: #455a64;
  --md-blue-grey-800: #37474f;
  --md-blue-grey-900: #263238;
}

/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2017, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

/*-----------------------------------------------------------------------------
| RenderedText
|----------------------------------------------------------------------------*/

:root {
  /* This is the padding value to fill the gaps between lines containing spans with background color. */
  --jp-private-code-span-padding: calc(
    (var(--jp-code-line-height) - 1) * var(--jp-code-font-size) / 2
  );
}

.jp-RenderedText {
  text-align: left;
  padding-left: var(--jp-code-padding);
  line-height: var(--jp-code-line-height);
  font-family: var(--jp-code-font-family);
}

.jp-RenderedText pre,
.jp-RenderedJavaScript pre,
.jp-RenderedHTMLCommon pre {
  color: var(--jp-content-font-color1);
  font-size: var(--jp-code-font-size);
  border: none;
  margin: 0;
  padding: 0;
}

.jp-RenderedText pre a:link {
  text-decoration: none;
  color: var(--jp-content-link-color);
}

.jp-RenderedText pre a:hover {
  text-decoration: underline;
  color: var(--jp-content-link-color);
}

.jp-RenderedText pre a:visited {
  text-decoration: none;
  color: var(--jp-content-link-color);
}

/* console foregrounds and backgrounds */
.jp-RenderedText pre .ansi-black-fg {
  color: #3e424d;
}

.jp-RenderedText pre .ansi-red-fg {
  color: #e75c58;
}

.jp-RenderedText pre .ansi-green-fg {
  color: #00a250;
}

.jp-RenderedText pre .ansi-yellow-fg {
  color: #ddb62b;
}

.jp-RenderedText pre .ansi-blue-fg {
  color: #208ffb;
}

.jp-RenderedText pre .ansi-magenta-fg {
  color: #d160c4;
}

.jp-RenderedText pre .ansi-cyan-fg {
  color: #60c6c8;
}

.jp-RenderedText pre .ansi-white-fg {
  color: #c5c1b4;
}

.jp-RenderedText pre .ansi-black-bg {
  background-color: #3e424d;
  padding: var(--jp-private-code-span-padding) 0;
}

.jp-RenderedText pre .ansi-red-bg {
  background-color: #e75c58;
  padding: var(--jp-private-code-span-padding) 0;
}

.jp-RenderedText pre .ansi-green-bg {
  background-color: #00a250;
  padding: var(--jp-private-code-span-padding) 0;
}

.jp-RenderedText pre .ansi-yellow-bg {
  background-color: #ddb62b;
  padding: var(--jp-private-code-span-padding) 0;
}

.jp-RenderedText pre .ansi-blue-bg {
  background-color: #208ffb;
  padding: var(--jp-private-code-span-padding) 0;
}

.jp-RenderedText pre .ansi-magenta-bg {
  background-color: #d160c4;
  padding: var(--jp-private-code-span-padding) 0;
}

.jp-RenderedText pre .ansi-cyan-bg {
  background-color: #60c6c8;
  padding: var(--jp-private-code-span-padding) 0;
}

.jp-RenderedText pre .ansi-white-bg {
  background-color: #c5c1b4;
  padding: var(--jp-private-code-span-padding) 0;
}

.jp-RenderedText pre .ansi-black-intense-fg {
  color: #282c36;
}

.jp-RenderedText pre .ansi-red-intense-fg {
  color: #b22b31;
}

.jp-RenderedText pre .ansi-green-intense-fg {
  color: #007427;
}

.jp-RenderedText pre .ansi-yellow-intense-fg {
  color: #b27d12;
}

.jp-RenderedText pre .ansi-blue-intense-fg {
  color: #0065ca;
}

.jp-RenderedText pre .ansi-magenta-intense-fg {
  color: #a03196;
}

.jp-RenderedText pre .ansi-cyan-intense-fg {
  color: #258f8f;
}

.jp-RenderedText pre .ansi-white-intense-fg {
  color: #a1a6b2;
}

.jp-RenderedText pre .ansi-black-intense-bg {
  background-color: #282c36;
  padding: var(--jp-private-code-span-padding) 0;
}

.jp-RenderedText pre .ansi-red-intense-bg {
  background-color: #b22b31;
  padding: var(--jp-private-code-span-padding) 0;
}

.jp-RenderedText pre .ansi-green-intense-bg {
  background-color: #007427;
  padding: var(--jp-private-code-span-padding) 0;
}

.jp-RenderedText pre .ansi-yellow-intense-bg {
  background-color: #b27d12;
  padding: var(--jp-private-code-span-padding) 0;
}

.jp-RenderedText pre .ansi-blue-intense-bg {
  background-color: #0065ca;
  padding: var(--jp-private-code-span-padding) 0;
}

.jp-RenderedText pre .ansi-magenta-intense-bg {
  background-color: #a03196;
  padding: var(--jp-private-code-span-padding) 0;
}

.jp-RenderedText pre .ansi-cyan-intense-bg {
  background-color: #258f8f;
  padding: var(--jp-private-code-span-padding) 0;
}

.jp-RenderedText pre .ansi-white-intense-bg {
  background-color: #a1a6b2;
  padding: var(--jp-private-code-span-padding) 0;
}

.jp-RenderedText pre .ansi-default-inverse-fg {
  color: var(--jp-ui-inverse-font-color0);
}

.jp-RenderedText pre .ansi-default-inverse-bg {
  background-color: var(--jp-inverse-layout-color0);
  padding: var(--jp-private-code-span-padding) 0;
}

.jp-RenderedText pre .ansi-bold {
  font-weight: bold;
}

.jp-RenderedText pre .ansi-underline {
  text-decoration: underline;
}

.jp-RenderedText[data-mime-type='application/vnd.jupyter.stderr'] {
  background: var(--jp-rendermime-error-background);
  padding-top: var(--jp-code-padding);
}

/*-----------------------------------------------------------------------------
| RenderedLatex
|----------------------------------------------------------------------------*/

.jp-RenderedLatex {
  color: var(--jp-content-font-color1);
  font-size: var(--jp-content-font-size1);
  line-height: var(--jp-content-line-height);
}

/* Left-justify outputs.*/
.jp-OutputArea-output.jp-RenderedLatex {
  padding: var(--jp-code-padding);
  text-align: left;
}

/*-----------------------------------------------------------------------------
| RenderedHTML
|----------------------------------------------------------------------------*/

.jp-RenderedHTMLCommon {
  color: var(--jp-content-font-color1);
  font-family: var(--jp-content-font-family);
  font-size: var(--jp-content-font-size1);
  line-height: var(--jp-content-line-height);

  /* Give a bit more R padding on Markdown text to keep line lengths reasonable */
  padding-right: 20px;
}

.jp-RenderedHTMLCommon em {
  font-style: italic;
}

.jp-RenderedHTMLCommon strong {
  font-weight: bold;
}

.jp-RenderedHTMLCommon u {
  text-decoration: underline;
}

.jp-RenderedHTMLCommon a:link {
  text-decoration: none;
  color: var(--jp-content-link-color);
}

.jp-RenderedHTMLCommon a:hover {
  text-decoration: underline;
  color: var(--jp-content-link-color);
}

.jp-RenderedHTMLCommon a:visited {
  text-decoration: none;
  color: var(--jp-content-link-color);
}

/* Headings */

.jp-RenderedHTMLCommon h1,
.jp-RenderedHTMLCommon h2,
.jp-RenderedHTMLCommon h3,
.jp-RenderedHTMLCommon h4,
.jp-RenderedHTMLCommon h5,
.jp-RenderedHTMLCommon h6 {
  line-height: var(--jp-content-heading-line-height);
  font-weight: var(--jp-content-heading-font-weight);
  font-style: normal;
  margin: var(--jp-content-heading-margin-top) 0
    var(--jp-content-heading-margin-bottom) 0;
}

.jp-RenderedHTMLCommon h1:first-child,
.jp-RenderedHTMLCommon h2:first-child,
.jp-RenderedHTMLCommon h3:first-child,
.jp-RenderedHTMLCommon h4:first-child,
.jp-RenderedHTMLCommon h5:first-child,
.jp-RenderedHTMLCommon h6:first-child {
  margin-top: calc(0.5 * var(--jp-content-heading-margin-top));
}

.jp-RenderedHTMLCommon h1:last-child,
.jp-RenderedHTMLCommon h2:last-child,
.jp-RenderedHTMLCommon h3:last-child,
.jp-RenderedHTMLCommon h4:last-child,
.jp-RenderedHTMLCommon h5:last-child,
.jp-RenderedHTMLCommon h6:last-child {
  margin-bottom: calc(0.5 * var(--jp-content-heading-margin-bottom));
}

.jp-RenderedHTMLCommon h1 {
  font-size: var(--jp-content-font-size5);
}

.jp-RenderedHTMLCommon h2 {
  font-size: var(--jp-content-font-size4);
}

.jp-RenderedHTMLCommon h3 {
  font-size: var(--jp-content-font-size3);
}

.jp-RenderedHTMLCommon h4 {
  font-size: var(--jp-content-font-size2);
}

.jp-RenderedHTMLCommon h5 {
  font-size: var(--jp-content-font-size1);
}

.jp-RenderedHTMLCommon h6 {
  font-size: var(--jp-content-font-size0);
}

/* Lists */

/* stylelint-disable selector-max-type, selector-max-compound-selectors */

.jp-RenderedHTMLCommon ul:not(.list-inline),
.jp-RenderedHTMLCommon ol:not(.list-inline) {
  padding-left: 2em;
}

.jp-RenderedHTMLCommon ul {
  list-style: disc;
}

.jp-RenderedHTMLCommon ul ul {
  list-style: square;
}

.jp-RenderedHTMLCommon ul ul ul {
  list-style: circle;
}

.jp-RenderedHTMLCommon ol {
  list-style: decimal;
}

.jp-RenderedHTMLCommon ol ol {
  list-style: upper-alpha;
}

.jp-RenderedHTMLCommon ol ol ol {
  list-style: lower-alpha;
}

.jp-RenderedHTMLCommon ol ol ol ol {
  list-style: lower-roman;
}

.jp-RenderedHTMLCommon ol ol ol ol ol {
  list-style: decimal;
}

.jp-RenderedHTMLCommon ol,
.jp-RenderedHTMLCommon ul {
  margin-bottom: 1em;
}

.jp-RenderedHTMLCommon ul ul,
.jp-RenderedHTMLCommon ul ol,
.jp-RenderedHTMLCommon ol ul,
.jp-RenderedHTMLCommon ol ol {
  margin-bottom: 0;
}

/* stylelint-enable selector-max-type, selector-max-compound-selectors */

.jp-RenderedHTMLCommon hr {
  color: var(--jp-border-color2);
  background-color: var(--jp-border-color1);
  margin-top: 1em;
  margin-bottom: 1em;
}

.jp-RenderedHTMLCommon > pre {
  margin: 1.5em 2em;
}

.jp-RenderedHTMLCommon pre,
.jp-RenderedHTMLCommon code {
  border: 0;
  background-color: var(--jp-layout-color0);
  color: var(--jp-content-font-color1);
  font-family: var(--jp-code-font-family);
  font-size: inherit;
  line-height: var(--jp-code-line-height);
  padding: 0;
  white-space: pre-wrap;
}

.jp-RenderedHTMLCommon :not(pre) > code {
  background-color: var(--jp-layout-color2);
  padding: 1px 5px;
}

/* Tables */

.jp-RenderedHTMLCommon table {
  border-collapse: collapse;
  border-spacing: 0;
  border: none;
  color: var(--jp-ui-font-color1);
  font-size: var(--jp-ui-font-size1);
  table-layout: fixed;
  margin-left: auto;
  margin-bottom: 1em;
  margin-right: auto;
}

.jp-RenderedHTMLCommon thead {
  border-bottom: var(--jp-border-width) solid var(--jp-border-color1);
  vertical-align: bottom;
}

.jp-RenderedHTMLCommon td,
.jp-RenderedHTMLCommon th,
.jp-RenderedHTMLCommon tr {
  vertical-align: middle;
  padding: 0.5em;
  line-height: normal;
  white-space: normal;
  max-width: none;
  border: none;
}

.jp-RenderedMarkdown.jp-RenderedHTMLCommon td,
.jp-RenderedMarkdown.jp-RenderedHTMLCommon th {
  max-width: none;
}

:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon td,
:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon th,
:not(.jp-RenderedMarkdown).jp-RenderedHTMLCommon tr {
  text-align: right;
}

.jp-RenderedHTMLCommon th {
  font-weight: bold;
}

.jp-RenderedHTMLCommon tbody tr:nth-child(odd) {
  background: var(--jp-layout-color0);
}

.jp-RenderedHTMLCommon tbody tr:nth-child(even) {
  background: var(--jp-rendermime-table-row-background);
}

.jp-RenderedHTMLCommon tbody tr:hover {
  background: var(--jp-rendermime-table-row-hover-background);
}

.jp-RenderedHTMLCommon p {
  text-align: left;
  margin: 0;
  margin-bottom: 1em;
}

.jp-RenderedHTMLCommon img {
  -moz-force-broken-image-icon: 1;
}

/* Restrict to direct children as other images could be nested in other content. */
.jp-RenderedHTMLCommon > img {
  display: block;
  margin-left: 0;
  margin-right: 0;
  margin-bottom: 1em;
}

/* Change color behind transparent images if they need it... */
[data-jp-theme-light='false'] .jp-RenderedImage img.jp-needs-light-background {
  background-color: var(--jp-inverse-layout-color1);
}

[data-jp-theme-light='true'] .jp-RenderedImage img.jp-needs-dark-background {
  background-color: var(--jp-inverse-layout-color1);
}

.jp-RenderedHTMLCommon img,
.jp-RenderedImage img,
.jp-RenderedHTMLCommon svg,
.jp-RenderedSVG svg {
  max-width: 100%;
  height: auto;
}

.jp-RenderedHTMLCommon img.jp-mod-unconfined,
.jp-RenderedImage img.jp-mod-unconfined,
.jp-RenderedHTMLCommon svg.jp-mod-unconfined,
.jp-RenderedSVG svg.jp-mod-unconfined {
  max-width: none;
}

.jp-RenderedHTMLCommon .alert {
  padding: var(--jp-notebook-padding);
  border: var(--jp-border-width) solid transparent;
  border-radius: var(--jp-border-radius);
  margin-bottom: 1em;
}

.jp-RenderedHTMLCommon .alert-info {
  color: var(--jp-info-color0);
  background-color: var(--jp-info-color3);
  border-color: var(--jp-info-color2);
}

.jp-RenderedHTMLCommon .alert-info hr {
  border-color: var(--jp-info-color3);
}

.jp-RenderedHTMLCommon .alert-info > p:last-child,
.jp-RenderedHTMLCommon .alert-info > ul:last-child {
  margin-bottom: 0;
}

.jp-RenderedHTMLCommon .alert-warning {
  color: var(--jp-warn-color0);
  background-color: var(--jp-warn-color3);
  border-color: var(--jp-warn-color2);
}

.jp-RenderedHTMLCommon .alert-warning hr {
  border-color: var(--jp-warn-color3);
}

.jp-RenderedHTMLCommon .alert-warning > p:last-child,
.jp-RenderedHTMLCommon .alert-warning > ul:last-child {
  margin-bottom: 0;
}

.jp-RenderedHTMLCommon .alert-success {
  color: var(--jp-success-color0);
  background-color: var(--jp-success-color3);
  border-color: var(--jp-success-color2);
}

.jp-RenderedHTMLCommon .alert-success hr {
  border-color: var(--jp-success-color3);
}

.jp-RenderedHTMLCommon .alert-success > p:last-child,
.jp-RenderedHTMLCommon .alert-success > ul:last-child {
  margin-bottom: 0;
}

.jp-RenderedHTMLCommon .alert-danger {
  color: var(--jp-error-color0);
  background-color: var(--jp-error-color3);
  border-color: var(--jp-error-color2);
}

.jp-RenderedHTMLCommon .alert-danger hr {
  border-color: var(--jp-error-color3);
}

.jp-RenderedHTMLCommon .alert-danger > p:last-child,
.jp-RenderedHTMLCommon .alert-danger > ul:last-child {
  margin-bottom: 0;
}

.jp-RenderedHTMLCommon blockquote {
  margin: 1em 2em;
  padding: 0 1em;
  border-left: 5px solid var(--jp-border-color2);
}

a.jp-InternalAnchorLink {
  visibility: hidden;
  margin-left: 8px;
  color: var(--md-blue-800);
}

h1:hover .jp-InternalAnchorLink,
h2:hover .jp-InternalAnchorLink,
h3:hover .jp-InternalAnchorLink,
h4:hover .jp-InternalAnchorLink,
h5:hover .jp-InternalAnchorLink,
h6:hover .jp-InternalAnchorLink {
  visibility: visible;
}

.jp-RenderedHTMLCommon kbd {
  background-color: var(--jp-rendermime-table-row-background);
  border: 1px solid var(--jp-border-color0);
  border-bottom-color: var(--jp-border-color2);
  border-radius: 3px;
  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
  display: inline-block;
  font-size: var(--jp-ui-font-size0);
  line-height: 1em;
  padding: 0.2em 0.5em;
}

/* Most direct children of .jp-RenderedHTMLCommon have a margin-bottom of 1.0.
 * At the bottom of cells this is a bit too much as there is also spacing
 * between cells. Going all the way to 0 gets too tight between markdown and
 * code cells.
 */
.jp-RenderedHTMLCommon > *:last-child {
  margin-bottom: 0.5em;
}

/*
 * Copyright (c) Jupyter Development Team.
 * Distributed under the terms of the Modified BSD License.
 */

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Copyright (c) 2014-2017, PhosphorJS Contributors
|
| Distributed under the terms of the BSD 3-Clause License.
|
| The full license is in the file LICENSE, distributed with this software.
|----------------------------------------------------------------------------*/

.lm-cursor-backdrop {
  position: fixed;
  width: 200px;
  height: 200px;
  margin-top: -100px;
  margin-left: -100px;
  will-change: transform;
  z-index: 100;
}

.lm-mod-drag-image {
  will-change: transform;
}

/*
 * Copyright (c) Jupyter Development Team.
 * Distributed under the terms of the Modified BSD License.
 */

.jp-lineFormSearch {
  padding: 4px 12px;
  background-color: var(--jp-layout-color2);
  box-shadow: var(--jp-toolbar-box-shadow);
  z-index: 2;
  font-size: var(--jp-ui-font-size1);
}

.jp-lineFormCaption {
  font-size: var(--jp-ui-font-size0);
  line-height: var(--jp-ui-font-size1);
  margin-top: 4px;
  color: var(--jp-ui-font-color0);
}

.jp-baseLineForm {
  border: none;
  border-radius: 0;
  position: absolute;
  background-size: 16px;
  background-repeat: no-repeat;
  background-position: center;
  outline: none;
}

.jp-lineFormButtonContainer {
  top: 4px;
  right: 8px;
  height: 24px;
  padding: 0 12px;
  width: 12px;
}

.jp-lineFormButtonIcon {
  top: 0;
  right: 0;
  background-color: var(--jp-brand-color1);
  height: 100%;
  width: 100%;
  box-sizing: border-box;
  padding: 4px 6px;
}

.jp-lineFormButton {
  top: 0;
  right: 0;
  background-color: transparent;
  height: 100%;
  width: 100%;
  box-sizing: border-box;
}

.jp-lineFormWrapper {
  overflow: hidden;
  padding: 0 8px;
  border: 1px solid var(--jp-border-color0);
  background-color: var(--jp-input-active-background);
  height: 22px;
}

.jp-lineFormWrapperFocusWithin {
  border: var(--jp-border-width) solid var(--md-blue-500);
  box-shadow: inset 0 0 4px var(--md-blue-300);
}

.jp-lineFormInput {
  background: transparent;
  width: 200px;
  height: 100%;
  border: none;
  outline: none;
  color: var(--jp-ui-font-color0);
  line-height: 28px;
}

/*-----------------------------------------------------------------------------
| Copyright (c) 2014-2016, Jupyter Development Team.
|
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

.jp-JSONEditor {
  display: flex;
  flex-direction: column;
  width: 100%;
}

.jp-JSONEditor-host {
  flex: 1 1 auto;
  border: var(--jp-border-width) solid var(--jp-input-border-color);
  border-radius: 0;
  background: var(--jp-layout-color0);
  min-height: 50px;
  padding: 1px;
}

.jp-JSONEditor.jp-mod-error .jp-JSONEditor-host {
  border-color: red;
  outline-color: red;
}

.jp-JSONEditor-header {
  display: flex;
  flex: 1 0 auto;
  padding: 0 0 0 12px;
}

.jp-JSONEditor-header label {
  flex: 0 0 auto;
}

.jp-JSONEditor-commitButton {
  height: 16px;
  width: 16px;
  background-size: 18px;
  background-repeat: no-repeat;
  background-position: center;
}

.jp-JSONEditor-host.jp-mod-focused {
  background-color: var(--jp-input-active-background);
  border: 1px solid var(--jp-input-active-border-color);
  box-shadow: var(--jp-input-box-shadow);
}

.jp-Editor.jp-mod-dropTarget {
  border: var(--jp-border-width) solid var(--jp-input-active-border-color);
  box-shadow: var(--jp-input-box-shadow);
}

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/
.jp-DocumentSearch-input {
  border: none;
  outline: none;
  color: var(--jp-ui-font-color0);
  font-size: var(--jp-ui-font-size1);
  background-color: var(--jp-layout-color0);
  font-family: var(--jp-ui-font-family);
  padding: 2px 1px;
  resize: none;
}

.jp-DocumentSearch-overlay {
  position: absolute;
  background-color: var(--jp-toolbar-background);
  border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color);
  border-left: var(--jp-border-width) solid var(--jp-toolbar-border-color);
  top: 0;
  right: 0;
  z-index: 7;
  min-width: 405px;
  padding: 2px;
  font-size: var(--jp-ui-font-size1);

  --jp-private-document-search-button-height: 20px;
}

.jp-DocumentSearch-overlay button {
  background-color: var(--jp-toolbar-background);
  outline: 0;
}

.jp-DocumentSearch-overlay button:hover {
  background-color: var(--jp-layout-color2);
}

.jp-DocumentSearch-overlay button:active {
  background-color: var(--jp-layout-color3);
}

.jp-DocumentSearch-overlay-row {
  display: flex;
  align-items: center;
  margin-bottom: 2px;
}

.jp-DocumentSearch-button-content {
  display: inline-block;
  cursor: pointer;
  box-sizing: border-box;
  width: 100%;
  height: 100%;
}

.jp-DocumentSearch-button-content svg {
  width: 100%;
  height: 100%;
}

.jp-DocumentSearch-input-wrapper {
  border: var(--jp-border-width) solid var(--jp-border-color0);
  display: flex;
  background-color: var(--jp-layout-color0);
  margin: 2px;
}

.jp-DocumentSearch-input-wrapper:focus-within {
  border-color: var(--jp-cell-editor-active-border-color);
}

.jp-DocumentSearch-toggle-wrapper,
.jp-DocumentSearch-button-wrapper {
  all: initial;
  overflow: hidden;
  display: inline-block;
  border: none;
  box-sizing: border-box;
}

.jp-DocumentSearch-toggle-wrapper {
  width: 14px;
  height: 14px;
}

.jp-DocumentSearch-button-wrapper {
  width: var(--jp-private-document-search-button-height);
  height: var(--jp-private-document-search-button-height);
}

.jp-DocumentSearch-toggle-wrapper:focus,
.jp-DocumentSearch-button-wrapper:focus {
  outline: var(--jp-border-width) solid
    var(--jp-cell-editor-active-border-color);
  outline-offset: -1px;
}

.jp-DocumentSearch-toggle-wrapper,
.jp-DocumentSearch-button-wrapper,
.jp-DocumentSearch-button-content:focus {
  outline: none;
}

.jp-DocumentSearch-toggle-placeholder {
  width: 5px;
}

.jp-DocumentSearch-input-button::before {
  display: block;
  padding-top: 100%;
}

.jp-DocumentSearch-input-button-off {
  opacity: var(--jp-search-toggle-off-opacity);
}

.jp-DocumentSearch-input-button-off:hover {
  opacity: var(--jp-search-toggle-hover-opacity);
}

.jp-DocumentSearch-input-button-on {
  opacity: var(--jp-search-toggle-on-opacity);
}

.jp-DocumentSearch-index-counter {
  padding-left: 10px;
  padding-right: 10px;
  user-select: none;
  min-width: 35px;
  display: inline-block;
}

.jp-DocumentSearch-up-down-wrapper {
  display: inline-block;
  padding-right: 2px;
  margin-left: auto;
  white-space: nowrap;
}

.jp-DocumentSearch-spacer {
  margin-left: auto;
}

.jp-DocumentSearch-up-down-wrapper button {
  outline: 0;
  border: none;
  width: var(--jp-private-document-search-button-height);
  height: var(--jp-private-document-search-button-height);
  vertical-align: middle;
  margin: 1px 5px 2px;
}

.jp-DocumentSearch-up-down-button:hover {
  background-color: var(--jp-layout-color2);
}

.jp-DocumentSearch-up-down-button:active {
  background-color: var(--jp-layout-color3);
}

.jp-DocumentSearch-filter-button {
  border-radius: var(--jp-border-radius);
}

.jp-DocumentSearch-filter-button:hover {
  background-color: var(--jp-layout-color2);
}

.jp-DocumentSearch-filter-button-enabled {
  background-color: var(--jp-layout-color2);
}

.jp-DocumentSearch-filter-button-enabled:hover {
  background-color: var(--jp-layout-color3);
}

.jp-DocumentSearch-search-options {
  padding: 0 8px;
  margin-left: 3px;
  width: 100%;
  display: grid;
  justify-content: start;
  grid-template-columns: 1fr 1fr;
  align-items: center;
  justify-items: stretch;
}

.jp-DocumentSearch-search-filter-disabled {
  color: var(--jp-ui-font-color2);
}

.jp-DocumentSearch-search-filter {
  display: flex;
  align-items: center;
  user-select: none;
}

.jp-DocumentSearch-regex-error {
  color: var(--jp-error-color0);
}

.jp-DocumentSearch-replace-button-wrapper {
  overflow: hidden;
  display: inline-block;
  box-sizing: border-box;
  border: var(--jp-border-width) solid var(--jp-border-color0);
  margin: auto 2px;
  padding: 1px 4px;
  height: calc(var(--jp-private-document-search-button-height) + 2px);
}

.jp-DocumentSearch-replace-button-wrapper:focus {
  border: var(--jp-border-width) solid var(--jp-cell-editor-active-border-color);
}

.jp-DocumentSearch-replace-button {
  display: inline-block;
  text-align: center;
  cursor: pointer;
  box-sizing: border-box;
  color: var(--jp-ui-font-color1);

  /* height - 2 * (padding of wrapper) */
  line-height: calc(var(--jp-private-document-search-button-height) - 2px);
  width: 100%;
  height: 100%;
}

.jp-DocumentSearch-replace-button:focus {
  outline: none;
}

.jp-DocumentSearch-replace-wrapper-class {
  margin-left: 14px;
  display: flex;
}

.jp-DocumentSearch-replace-toggle {
  border: none;
  background-color: var(--jp-toolbar-background);
  border-radius: var(--jp-border-radius);
}

.jp-DocumentSearch-replace-toggle:hover {
  background-color: var(--jp-layout-color2);
}

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

.cm-editor {
  line-height: var(--jp-code-line-height);
  font-size: var(--jp-code-font-size);
  font-family: var(--jp-code-font-family);
  border: 0;
  border-radius: 0;
  height: auto;

  /* Changed to auto to autogrow */
}

.cm-editor pre {
  padding: 0 var(--jp-code-padding);
}

.jp-CodeMirrorEditor[data-type='inline'] .cm-dialog {
  background-color: var(--jp-layout-color0);
  color: var(--jp-content-font-color1);
}

.jp-CodeMirrorEditor {
  cursor: text;
}

/* When zoomed out 67% and 33% on a screen of 1440 width x 900 height */
@media screen and (min-width: 2138px) and (max-width: 4319px) {
  .jp-CodeMirrorEditor[data-type='inline'] .cm-cursor {
    border-left: var(--jp-code-cursor-width1) solid
      var(--jp-editor-cursor-color);
  }
}

/* When zoomed out less than 33% */
@media screen and (min-width: 4320px) {
  .jp-CodeMirrorEditor[data-type='inline'] .cm-cursor {
    border-left: var(--jp-code-cursor-width2) solid
      var(--jp-editor-cursor-color);
  }
}

.cm-editor.jp-mod-readOnly .cm-cursor {
  display: none;
}

.jp-CollaboratorCursor {
  border-left: 5px solid transparent;
  border-right: 5px solid transparent;
  border-top: none;
  border-bottom: 3px solid;
  background-clip: content-box;
  margin-left: -5px;
  margin-right: -5px;
}

.cm-searching,
.cm-searching span {
  /* `.cm-searching span`: we need to override syntax highlighting */
  background-color: var(--jp-search-unselected-match-background-color);
  color: var(--jp-search-unselected-match-color);
}

.cm-searching::selection,
.cm-searching span::selection {
  background-color: var(--jp-search-unselected-match-background-color);
  color: var(--jp-search-unselected-match-color);
}

.jp-current-match > .cm-searching,
.jp-current-match > .cm-searching span,
.cm-searching > .jp-current-match,
.cm-searching > .jp-current-match span {
  background-color: var(--jp-search-selected-match-background-color);
  color: var(--jp-search-selected-match-color);
}

.jp-current-match > .cm-searching::selection,
.cm-searching > .jp-current-match::selection,
.jp-current-match > .cm-searching span::selection {
  background-color: var(--jp-search-selected-match-background-color);
  color: var(--jp-search-selected-match-color);
}

.cm-trailingspace {
  background-image: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAgAAAAFCAYAAAB4ka1VAAAAsElEQVQIHQGlAFr/AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA7+r3zKmT0/+pk9P/7+r3zAAAAAAAAAAABAAAAAAAAAAA6OPzM+/q9wAAAAAA6OPzMwAAAAAAAAAAAgAAAAAAAAAAGR8NiRQaCgAZIA0AGR8NiQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAQyoYJ/SY80UAAAAASUVORK5CYII=);
  background-position: center left;
  background-repeat: repeat-x;
}

.jp-CollaboratorCursor-hover {
  position: absolute;
  z-index: 1;
  transform: translateX(-50%);
  color: white;
  border-radius: 3px;
  padding-left: 4px;
  padding-right: 4px;
  padding-top: 1px;
  padding-bottom: 1px;
  text-align: center;
  font-size: var(--jp-ui-font-size1);
  white-space: nowrap;
}

.jp-CodeMirror-ruler {
  border-left: 1px dashed var(--jp-border-color2);
}

/* Styles for shared cursors (remote cursor locations and selected ranges) */
.jp-CodeMirrorEditor .cm-ySelectionCaret {
  position: relative;
  border-left: 1px solid black;
  margin-left: -1px;
  margin-right: -1px;
  box-sizing: border-box;
}

.jp-CodeMirrorEditor .cm-ySelectionCaret > .cm-ySelectionInfo {
  white-space: nowrap;
  position: absolute;
  top: -1.15em;
  padding-bottom: 0.05em;
  left: -1px;
  font-size: 0.95em;
  font-family: var(--jp-ui-font-family);
  font-weight: bold;
  line-height: normal;
  user-select: none;
  color: white;
  padding-left: 2px;
  padding-right: 2px;
  z-index: 101;
  transition: opacity 0.3s ease-in-out;
}

.jp-CodeMirrorEditor .cm-ySelectionInfo {
  transition-delay: 0.7s;
  opacity: 0;
}

.jp-CodeMirrorEditor .cm-ySelectionCaret:hover > .cm-ySelectionInfo {
  opacity: 1;
  transition-delay: 0s;
}

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

.jp-MimeDocument {
  outline: none;
}

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

/*-----------------------------------------------------------------------------
| Variables
|----------------------------------------------------------------------------*/

:root {
  --jp-private-filebrowser-button-height: 28px;
  --jp-private-filebrowser-button-width: 48px;
}

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

.jp-FileBrowser .jp-SidePanel-content {
  display: flex;
  flex-direction: column;
}

.jp-FileBrowser-toolbar.jp-Toolbar {
  flex-wrap: wrap;
  row-gap: 12px;
  border-bottom: none;
  height: auto;
  margin: 8px 12px 0;
  box-shadow: none;
  padding: 0;
  justify-content: flex-start;
}

.jp-FileBrowser-Panel {
  flex: 1 1 auto;
  display: flex;
  flex-direction: column;
}

.jp-BreadCrumbs {
  flex: 0 0 auto;
  margin: 8px 12px;
}

.jp-BreadCrumbs-item {
  margin: 0 2px;
  padding: 0 2px;
  border-radius: var(--jp-border-radius);
  cursor: pointer;
}

.jp-BreadCrumbs-item:hover {
  background-color: var(--jp-layout-color2);
}

.jp-BreadCrumbs-item:first-child {
  margin-left: 0;
}

.jp-BreadCrumbs-item.jp-mod-dropTarget {
  background-color: var(--jp-brand-color2);
  opacity: 0.7;
}

/*-----------------------------------------------------------------------------
| Buttons
|----------------------------------------------------------------------------*/

.jp-FileBrowser-toolbar > .jp-Toolbar-item {
  flex: 0 0 auto;
  padding-left: 0;
  padding-right: 2px;
  align-items: center;
  height: unset;
}

.jp-FileBrowser-toolbar > .jp-Toolbar-item .jp-ToolbarButtonComponent {
  width: 40px;
}

/*-----------------------------------------------------------------------------
| Other styles
|----------------------------------------------------------------------------*/

.jp-FileDialog.jp-mod-conflict input {
  color: var(--jp-error-color1);
}

.jp-FileDialog .jp-new-name-title {
  margin-top: 12px;
}

.jp-LastModified-hidden {
  display: none;
}

.jp-FileSize-hidden {
  display: none;
}

.jp-FileBrowser .lm-AccordionPanel > h3:first-child {
  display: none;
}

/*-----------------------------------------------------------------------------
| DirListing
|----------------------------------------------------------------------------*/

.jp-DirListing {
  flex: 1 1 auto;
  display: flex;
  flex-direction: column;
  outline: 0;
}

.jp-DirListing-header {
  flex: 0 0 auto;
  display: flex;
  flex-direction: row;
  align-items: center;
  overflow: hidden;
  border-top: var(--jp-border-width) solid var(--jp-border-color2);
  border-bottom: var(--jp-border-width) solid var(--jp-border-color1);
  box-shadow: var(--jp-toolbar-box-shadow);
  z-index: 2;
}

.jp-DirListing-headerItem {
  padding: 4px 12px 2px;
  font-weight: 500;
}

.jp-DirListing-headerItem:hover {
  background: var(--jp-layout-color2);
}

.jp-DirListing-headerItem.jp-id-name {
  flex: 1 0 84px;
}

.jp-DirListing-headerItem.jp-id-modified {
  flex: 0 0 112px;
  border-left: var(--jp-border-width) solid var(--jp-border-color2);
  text-align: right;
}

.jp-DirListing-headerItem.jp-id-filesize {
  flex: 0 0 75px;
  border-left: var(--jp-border-width) solid var(--jp-border-color2);
  text-align: right;
}

.jp-id-narrow {
  display: none;
  flex: 0 0 5px;
  padding: 4px;
  border-left: var(--jp-border-width) solid var(--jp-border-color2);
  text-align: right;
  color: var(--jp-border-color2);
}

.jp-DirListing-narrow .jp-id-narrow {
  display: block;
}

.jp-DirListing-narrow .jp-id-modified,
.jp-DirListing-narrow .jp-DirListing-itemModified {
  display: none;
}

.jp-DirListing-headerItem.jp-mod-selected {
  font-weight: 600;
}

/* increase specificity to override bundled default */
.jp-DirListing-content {
  flex: 1 1 auto;
  margin: 0;
  padding: 0;
  list-style-type: none;
  overflow: auto;
  background-color: var(--jp-layout-color1);
}

.jp-DirListing-content mark {
  color: var(--jp-ui-font-color0);
  background-color: transparent;
  font-weight: bold;
}

.jp-DirListing-content .jp-DirListing-item.jp-mod-selected mark {
  color: var(--jp-ui-inverse-font-color0);
}

/* Style the directory listing content when a user drops a file to upload */
.jp-DirListing.jp-mod-native-drop .jp-DirListing-content {
  outline: 5px dashed rgba(128, 128, 128, 0.5);
  outline-offset: -10px;
  cursor: copy;
}

.jp-DirListing-item {
  display: flex;
  flex-direction: row;
  align-items: center;
  padding: 4px 12px;
  -webkit-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
}

.jp-DirListing-checkboxWrapper {
  /* Increases hit area of checkbox. */
  padding: 4px;
}

.jp-DirListing-header
  .jp-DirListing-checkboxWrapper
  + .jp-DirListing-headerItem {
  padding-left: 4px;
}

.jp-DirListing-content .jp-DirListing-checkboxWrapper {
  position: relative;
  left: -4px;
  margin: -4px 0 -4px -8px;
}

.jp-DirListing-checkboxWrapper.jp-mod-visible {
  visibility: visible;
}

/* For devices that support hovering, hide checkboxes until hovered, selected...
*/
@media (hover: hover) {
  .jp-DirListing-checkboxWrapper {
    visibility: hidden;
  }

  .jp-DirListing-item:hover .jp-DirListing-checkboxWrapper,
  .jp-DirListing-item.jp-mod-selected .jp-DirListing-checkboxWrapper {
    visibility: visible;
  }
}

.jp-DirListing-item[data-is-dot] {
  opacity: 75%;
}

.jp-DirListing-item.jp-mod-selected {
  color: var(--jp-ui-inverse-font-color1);
  background: var(--jp-brand-color1);
}

.jp-DirListing-item.jp-mod-dropTarget {
  background: var(--jp-brand-color3);
}

.jp-DirListing-item:hover:not(.jp-mod-selected) {
  background: var(--jp-layout-color2);
}

.jp-DirListing-itemIcon {
  flex: 0 0 20px;
  margin-right: 4px;
}

.jp-DirListing-itemText {
  flex: 1 0 64px;
  white-space: nowrap;
  overflow: hidden;
  text-overflow: ellipsis;
  user-select: none;
}

.jp-DirListing-itemText:focus {
  outline-width: 2px;
  outline-color: var(--jp-inverse-layout-color1);
  outline-style: solid;
  outline-offset: 1px;
}

.jp-DirListing-item.jp-mod-selected .jp-DirListing-itemText:focus {
  outline-color: var(--jp-layout-color1);
}

.jp-DirListing-itemModified {
  flex: 0 0 125px;
  text-align: right;
}

.jp-DirListing-itemFileSize {
  flex: 0 0 90px;
  text-align: right;
}

.jp-DirListing-editor {
  flex: 1 0 64px;
  outline: none;
  border: none;
  color: var(--jp-ui-font-color1);
  background-color: var(--jp-layout-color1);
}

.jp-DirListing-item.jp-mod-running .jp-DirListing-itemIcon::before {
  color: var(--jp-success-color1);
  content: '\25CF';
  font-size: 8px;
  position: absolute;
  left: -8px;
}

.jp-DirListing-item.jp-mod-running.jp-mod-selected
  .jp-DirListing-itemIcon::before {
  color: var(--jp-ui-inverse-font-color1);
}

.jp-DirListing-item.lm-mod-drag-image,
.jp-DirListing-item.jp-mod-selected.lm-mod-drag-image {
  font-size: var(--jp-ui-font-size1);
  padding-left: 4px;
  margin-left: 4px;
  width: 160px;
  background-color: var(--jp-ui-inverse-font-color2);
  box-shadow: var(--jp-elevation-z2);
  border-radius: 0;
  color: var(--jp-ui-font-color1);
  transform: translateX(-40%) translateY(-58%);
}

.jp-Document {
  min-width: 120px;
  min-height: 120px;
  outline: none;
}

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

/*-----------------------------------------------------------------------------
| Main OutputArea
| OutputArea has a list of Outputs
|----------------------------------------------------------------------------*/

.jp-OutputArea {
  overflow-y: auto;
}

.jp-OutputArea-child {
  display: table;
  table-layout: fixed;
  width: 100%;
  overflow: hidden;
}

.jp-OutputPrompt {
  width: var(--jp-cell-prompt-width);
  color: var(--jp-cell-outprompt-font-color);
  font-family: var(--jp-cell-prompt-font-family);
  padding: var(--jp-code-padding);
  letter-spacing: var(--jp-cell-prompt-letter-spacing);
  line-height: var(--jp-code-line-height);
  font-size: var(--jp-code-font-size);
  border: var(--jp-border-width) solid transparent;
  opacity: var(--jp-cell-prompt-opacity);

  /* Right align prompt text, don't wrap to handle large prompt numbers */
  text-align: right;
  white-space: nowrap;
  overflow: hidden;
  text-overflow: ellipsis;

  /* Disable text selection */
  -webkit-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
}

.jp-OutputArea-prompt {
  display: table-cell;
  vertical-align: top;
}

.jp-OutputArea-output {
  display: table-cell;
  width: 100%;
  height: auto;
  overflow: auto;
  user-select: text;
  -moz-user-select: text;
  -webkit-user-select: text;
  -ms-user-select: text;
}

.jp-OutputArea .jp-RenderedText {
  padding-left: 1ch;
}

/**
 * Prompt overlay.
 */

.jp-OutputArea-promptOverlay {
  position: absolute;
  top: 0;
  width: var(--jp-cell-prompt-width);
  height: 100%;
  opacity: 0.5;
}

.jp-OutputArea-promptOverlay:hover {
  background: var(--jp-layout-color2);
  box-shadow: inset 0 0 1px var(--jp-inverse-layout-color0);
  cursor: zoom-out;
}

.jp-mod-outputsScrolled .jp-OutputArea-promptOverlay:hover {
  cursor: zoom-in;
}

/**
 * Isolated output.
 */
.jp-OutputArea-output.jp-mod-isolated {
  width: 100%;
  display: block;
}

/*
When drag events occur, `lm-mod-override-cursor` is added to the body.
Because iframes steal all cursor events, the following two rules are necessary
to suppress pointer events while resize drags are occurring. There may be a
better solution to this problem.
*/
body.lm-mod-override-cursor .jp-OutputArea-output.jp-mod-isolated {
  position: relative;
}

body.lm-mod-override-cursor .jp-OutputArea-output.jp-mod-isolated::before {
  content: '';
  position: absolute;
  top: 0;
  left: 0;
  right: 0;
  bottom: 0;
  background: transparent;
}

/* pre */

.jp-OutputArea-output pre {
  border: none;
  margin: 0;
  padding: 0;
  overflow-x: auto;
  overflow-y: auto;
  word-break: break-all;
  word-wrap: break-word;
  white-space: pre-wrap;
}

/* tables */

.jp-OutputArea-output.jp-RenderedHTMLCommon table {
  margin-left: 0;
  margin-right: 0;
}

/* description lists */

.jp-OutputArea-output dl,
.jp-OutputArea-output dt,
.jp-OutputArea-output dd {
  display: block;
}

.jp-OutputArea-output dl {
  width: 100%;
  overflow: hidden;
  padding: 0;
  margin: 0;
}

.jp-OutputArea-output dt {
  font-weight: bold;
  float: left;
  width: 20%;
  padding: 0;
  margin: 0;
}

.jp-OutputArea-output dd {
  float: left;
  width: 80%;
  padding: 0;
  margin: 0;
}

.jp-TrimmedOutputs pre {
  background: var(--jp-layout-color3);
  font-size: calc(var(--jp-code-font-size) * 1.4);
  text-align: center;
  text-transform: uppercase;
}

/* Hide the gutter in case of
 *  - nested output areas (e.g. in the case of output widgets)
 *  - mirrored output areas
 */
.jp-OutputArea .jp-OutputArea .jp-OutputArea-prompt {
  display: none;
}

/* Hide empty lines in the output area, for instance due to cleared widgets */
.jp-OutputArea-prompt:empty {
  padding: 0;
  border: 0;
}

/*-----------------------------------------------------------------------------
| executeResult is added to any Output-result for the display of the object
| returned by a cell
|----------------------------------------------------------------------------*/

.jp-OutputArea-output.jp-OutputArea-executeResult {
  margin-left: 0;
  width: 100%;
}

/* Text output with the Out[] prompt needs a top padding to match the
 * alignment of the Out[] prompt itself.
 */
.jp-OutputArea-executeResult .jp-RenderedText.jp-OutputArea-output {
  padding-top: var(--jp-code-padding);
  border-top: var(--jp-border-width) solid transparent;
}

/*-----------------------------------------------------------------------------
| The Stdin output
|----------------------------------------------------------------------------*/

.jp-Stdin-prompt {
  color: var(--jp-content-font-color0);
  padding-right: var(--jp-code-padding);
  vertical-align: baseline;
  flex: 0 0 auto;
}

.jp-Stdin-input {
  font-family: var(--jp-code-font-family);
  font-size: inherit;
  color: inherit;
  background-color: inherit;
  width: 42%;
  min-width: 200px;

  /* make sure input baseline aligns with prompt */
  vertical-align: baseline;

  /* padding + margin = 0.5em between prompt and cursor */
  padding: 0 0.25em;
  margin: 0 0.25em;
  flex: 0 0 70%;
}

.jp-Stdin-input::placeholder {
  opacity: 0;
}

.jp-Stdin-input:focus {
  box-shadow: none;
}

.jp-Stdin-input:focus::placeholder {
  opacity: 1;
}

/*-----------------------------------------------------------------------------
| Output Area View
|----------------------------------------------------------------------------*/

.jp-LinkedOutputView .jp-OutputArea {
  height: 100%;
  display: block;
}

.jp-LinkedOutputView .jp-OutputArea-output:only-child {
  height: 100%;
}

/*-----------------------------------------------------------------------------
| Printing
|----------------------------------------------------------------------------*/

@media print {
  .jp-OutputArea-child {
    break-inside: avoid-page;
  }
}

/*-----------------------------------------------------------------------------
| Mobile
|----------------------------------------------------------------------------*/
@media only screen and (max-width: 760px) {
  .jp-OutputPrompt {
    display: table-row;
    text-align: left;
  }

  .jp-OutputArea-child .jp-OutputArea-output {
    display: table-row;
    margin-left: var(--jp-notebook-padding);
  }
}

/* Trimmed outputs warning */
.jp-TrimmedOutputs > a {
  margin: 10px;
  text-decoration: none;
  cursor: pointer;
}

.jp-TrimmedOutputs > a:hover {
  text-decoration: none;
}

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

/*-----------------------------------------------------------------------------
| Table of Contents
|----------------------------------------------------------------------------*/

:root {
  --jp-private-toc-active-width: 4px;
}

.jp-TableOfContents {
  display: flex;
  flex-direction: column;
  background: var(--jp-layout-color1);
  color: var(--jp-ui-font-color1);
  font-size: var(--jp-ui-font-size1);
  height: 100%;
}

.jp-TableOfContents-placeholder {
  text-align: center;
}

.jp-TableOfContents-placeholderContent {
  color: var(--jp-content-font-color2);
  padding: 8px;
}

.jp-TableOfContents-placeholderContent > h3 {
  margin-bottom: var(--jp-content-heading-margin-bottom);
}

.jp-TableOfContents .jp-SidePanel-content {
  overflow-y: auto;
}

.jp-TableOfContents-tree {
  margin: 4px;
}

.jp-TableOfContents ol {
  list-style-type: none;
}

/* stylelint-disable-next-line selector-max-type */
.jp-TableOfContents li > ol {
  /* Align left border with triangle icon center */
  padding-left: 11px;
}

.jp-TableOfContents-content {
  /* left margin for the active heading indicator */
  margin: 0 0 0 var(--jp-private-toc-active-width);
  padding: 0;
  background-color: var(--jp-layout-color1);
}

.jp-tocItem {
  -webkit-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
}

.jp-tocItem-heading {
  display: flex;
  cursor: pointer;
}

.jp-tocItem-heading:hover {
  background-color: var(--jp-layout-color2);
}

.jp-tocItem-content {
  display: block;
  padding: 4px 0;
  white-space: nowrap;
  text-overflow: ellipsis;
  overflow-x: hidden;
}

.jp-tocItem-collapser {
  height: 20px;
  margin: 2px 2px 0;
  padding: 0;
  background: none;
  border: none;
  cursor: pointer;
}

.jp-tocItem-collapser:hover {
  background-color: var(--jp-layout-color3);
}

/* Active heading indicator */

.jp-tocItem-heading::before {
  content: ' ';
  background: transparent;
  width: var(--jp-private-toc-active-width);
  height: 24px;
  position: absolute;
  left: 0;
  border-radius: var(--jp-border-radius);
}

.jp-tocItem-heading.jp-tocItem-active::before {
  background-color: var(--jp-brand-color1);
}

.jp-tocItem-heading:hover.jp-tocItem-active::before {
  background: var(--jp-brand-color0);
  opacity: 1;
}

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

.jp-Collapser {
  flex: 0 0 var(--jp-cell-collapser-width);
  padding: 0;
  margin: 0;
  border: none;
  outline: none;
  background: transparent;
  border-radius: var(--jp-border-radius);
  opacity: 1;
}

.jp-Collapser-child {
  display: block;
  width: 100%;
  box-sizing: border-box;

  /* height: 100% doesn't work because the height of its parent is computed from content */
  position: absolute;
  top: 0;
  bottom: 0;
}

/*-----------------------------------------------------------------------------
| Printing
|----------------------------------------------------------------------------*/

/*
Hiding collapsers in print mode.

Note: input and output wrappers have "display: block" propery in print mode.
*/

@media print {
  .jp-Collapser {
    display: none;
  }
}

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

/*-----------------------------------------------------------------------------
| Header/Footer
|----------------------------------------------------------------------------*/

/* Hidden by zero height by default */
.jp-CellHeader,
.jp-CellFooter {
  height: 0;
  width: 100%;
  padding: 0;
  margin: 0;
  border: none;
  outline: none;
  background: transparent;
}

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

/*-----------------------------------------------------------------------------
| Input
|----------------------------------------------------------------------------*/

/* All input areas */
.jp-InputArea {
  display: table;
  table-layout: fixed;
  width: 100%;
  overflow: hidden;
}

.jp-InputArea-editor {
  display: table-cell;
  overflow: hidden;
  vertical-align: top;

  /* This is the non-active, default styling */
  border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
  border-radius: 0;
  background: var(--jp-cell-editor-background);
}

.jp-InputPrompt {
  display: table-cell;
  vertical-align: top;
  width: var(--jp-cell-prompt-width);
  color: var(--jp-cell-inprompt-font-color);
  font-family: var(--jp-cell-prompt-font-family);
  padding: var(--jp-code-padding);
  letter-spacing: var(--jp-cell-prompt-letter-spacing);
  opacity: var(--jp-cell-prompt-opacity);
  line-height: var(--jp-code-line-height);
  font-size: var(--jp-code-font-size);
  border: var(--jp-border-width) solid transparent;

  /* Right align prompt text, don't wrap to handle large prompt numbers */
  text-align: right;
  white-space: nowrap;
  overflow: hidden;
  text-overflow: ellipsis;

  /* Disable text selection */
  -webkit-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
}

/*-----------------------------------------------------------------------------
| Mobile
|----------------------------------------------------------------------------*/
@media only screen and (max-width: 760px) {
  .jp-InputArea-editor {
    display: table-row;
    margin-left: var(--jp-notebook-padding);
  }

  .jp-InputPrompt {
    display: table-row;
    text-align: left;
  }
}

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

/*-----------------------------------------------------------------------------
| Placeholder
|----------------------------------------------------------------------------*/

.jp-Placeholder {
  display: table;
  table-layout: fixed;
  width: 100%;
}

.jp-Placeholder-prompt {
  display: table-cell;
  box-sizing: border-box;
}

.jp-Placeholder-content {
  display: table-cell;
  padding: 4px 6px;
  border: 1px solid transparent;
  border-radius: 0;
  background: none;
  box-sizing: border-box;
  cursor: pointer;
}

.jp-Placeholder-contentContainer {
  display: flex;
}

.jp-Placeholder-content:hover,
.jp-InputPlaceholder > .jp-Placeholder-content:hover {
  border-color: var(--jp-layout-color3);
}

.jp-Placeholder-content .jp-MoreHorizIcon {
  width: 32px;
  height: 16px;
  border: 1px solid transparent;
  border-radius: var(--jp-border-radius);
}

.jp-Placeholder-content .jp-MoreHorizIcon:hover {
  border: 1px solid var(--jp-border-color1);
  box-shadow: 0 0 2px 0 rgba(0, 0, 0, 0.25);
  background-color: var(--jp-layout-color0);
}

.jp-PlaceholderText {
  white-space: nowrap;
  overflow-x: hidden;
  color: var(--jp-inverse-layout-color3);
  font-family: var(--jp-code-font-family);
}

.jp-InputPlaceholder > .jp-Placeholder-content {
  border-color: var(--jp-cell-editor-border-color);
  background: var(--jp-cell-editor-background);
}

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

/*-----------------------------------------------------------------------------
| Private CSS variables
|----------------------------------------------------------------------------*/

:root {
  --jp-private-cell-scrolling-output-offset: 5px;
}

/*-----------------------------------------------------------------------------
| Cell
|----------------------------------------------------------------------------*/

.jp-Cell {
  padding: var(--jp-cell-padding);
  margin: 0;
  border: none;
  outline: none;
  background: transparent;
}

/*-----------------------------------------------------------------------------
| Common input/output
|----------------------------------------------------------------------------*/

.jp-Cell-inputWrapper,
.jp-Cell-outputWrapper {
  display: flex;
  flex-direction: row;
  padding: 0;
  margin: 0;

  /* Added to reveal the box-shadow on the input and output collapsers. */
  overflow: visible;
}

/* Only input/output areas inside cells */
.jp-Cell-inputArea,
.jp-Cell-outputArea {
  flex: 1 1 auto;
}

/*-----------------------------------------------------------------------------
| Collapser
|----------------------------------------------------------------------------*/

/* Make the output collapser disappear when there is not output, but do so
 * in a manner that leaves it in the layout and preserves its width.
 */
.jp-Cell.jp-mod-noOutputs .jp-Cell-outputCollapser {
  border: none !important;
  background: transparent !important;
}

.jp-Cell:not(.jp-mod-noOutputs) .jp-Cell-outputCollapser {
  min-height: var(--jp-cell-collapser-min-height);
}

/*-----------------------------------------------------------------------------
| Output
|----------------------------------------------------------------------------*/

/* Put a space between input and output when there IS output */
.jp-Cell:not(.jp-mod-noOutputs) .jp-Cell-outputWrapper {
  margin-top: 5px;
}

.jp-CodeCell.jp-mod-outputsScrolled .jp-Cell-outputArea {
  overflow-y: auto;
  max-height: 24em;
  margin-left: var(--jp-private-cell-scrolling-output-offset);
  resize: vertical;
}

.jp-CodeCell.jp-mod-outputsScrolled .jp-Cell-outputArea[style*='height'] {
  max-height: unset;
}

.jp-CodeCell.jp-mod-outputsScrolled .jp-Cell-outputArea::after {
  content: ' ';
  box-shadow: inset 0 0 6px 2px rgb(0 0 0 / 30%);
  width: 100%;
  height: 100%;
  position: sticky;
  bottom: 0;
  top: 0;
  margin-top: -50%;
  float: left;
  display: block;
  pointer-events: none;
}

.jp-CodeCell.jp-mod-outputsScrolled .jp-OutputArea-child {
  padding-top: 6px;
}

.jp-CodeCell.jp-mod-outputsScrolled .jp-OutputArea-prompt {
  width: calc(
    var(--jp-cell-prompt-width) - var(--jp-private-cell-scrolling-output-offset)
  );
}

.jp-CodeCell.jp-mod-outputsScrolled .jp-OutputArea-promptOverlay {
  left: calc(-1 * var(--jp-private-cell-scrolling-output-offset));
}

/*-----------------------------------------------------------------------------
| CodeCell
|----------------------------------------------------------------------------*/

/*-----------------------------------------------------------------------------
| MarkdownCell
|----------------------------------------------------------------------------*/

.jp-MarkdownOutput {
  display: table-cell;
  width: 100%;
  margin-top: 0;
  margin-bottom: 0;
  padding-left: var(--jp-code-padding);
}

.jp-MarkdownOutput.jp-RenderedHTMLCommon {
  overflow: auto;
}

/* collapseHeadingButton (show always if hiddenCellsButton is _not_ shown) */
.jp-collapseHeadingButton {
  display: flex;
  min-height: var(--jp-cell-collapser-min-height);
  font-size: var(--jp-code-font-size);
  position: absolute;
  background-color: transparent;
  background-size: 25px;
  background-repeat: no-repeat;
  background-position-x: center;
  background-position-y: top;
  background-image: var(--jp-icon-caret-down);
  right: 0;
  top: 0;
  bottom: 0;
}

.jp-collapseHeadingButton.jp-mod-collapsed {
  background-image: var(--jp-icon-caret-right);
}

/*
 set the container font size to match that of content
 so that the nested collapse buttons have the right size
*/
.jp-MarkdownCell .jp-InputPrompt {
  font-size: var(--jp-content-font-size1);
}

/*
  Align collapseHeadingButton with cell top header
  The font sizes are identical to the ones in packages/rendermime/style/base.css
*/
.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='1'] {
  font-size: var(--jp-content-font-size5);
  background-position-y: calc(0.3 * var(--jp-content-font-size5));
}

.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='2'] {
  font-size: var(--jp-content-font-size4);
  background-position-y: calc(0.3 * var(--jp-content-font-size4));
}

.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='3'] {
  font-size: var(--jp-content-font-size3);
  background-position-y: calc(0.3 * var(--jp-content-font-size3));
}

.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='4'] {
  font-size: var(--jp-content-font-size2);
  background-position-y: calc(0.3 * var(--jp-content-font-size2));
}

.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='5'] {
  font-size: var(--jp-content-font-size1);
  background-position-y: top;
}

.jp-mod-rendered .jp-collapseHeadingButton[data-heading-level='6'] {
  font-size: var(--jp-content-font-size0);
  background-position-y: top;
}

/* collapseHeadingButton (show only on (hover,active) if hiddenCellsButton is shown) */
.jp-Notebook.jp-mod-showHiddenCellsButton .jp-collapseHeadingButton {
  display: none;
}

.jp-Notebook.jp-mod-showHiddenCellsButton
  :is(.jp-MarkdownCell:hover, .jp-mod-active)
  .jp-collapseHeadingButton {
  display: flex;
}

/* showHiddenCellsButton (only show if jp-mod-showHiddenCellsButton is set, which
is a consequence of the showHiddenCellsButton option in Notebook Settings)*/
.jp-Notebook.jp-mod-showHiddenCellsButton .jp-showHiddenCellsButton {
  margin-left: calc(var(--jp-cell-prompt-width) + 2 * var(--jp-code-padding));
  margin-top: var(--jp-code-padding);
  border: 1px solid var(--jp-border-color2);
  background-color: var(--jp-border-color3) !important;
  color: var(--jp-content-font-color0) !important;
  display: flex;
}

.jp-Notebook.jp-mod-showHiddenCellsButton .jp-showHiddenCellsButton:hover {
  background-color: var(--jp-border-color2) !important;
}

.jp-showHiddenCellsButton {
  display: none;
}

/*-----------------------------------------------------------------------------
| Printing
|----------------------------------------------------------------------------*/

/*
Using block instead of flex to allow the use of the break-inside CSS property for
cell outputs.
*/

@media print {
  .jp-Cell-inputWrapper,
  .jp-Cell-outputWrapper {
    display: block;
  }
}

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

/*-----------------------------------------------------------------------------
| Variables
|----------------------------------------------------------------------------*/

:root {
  --jp-notebook-toolbar-padding: 2px 5px 2px 2px;
}

/*-----------------------------------------------------------------------------

/*-----------------------------------------------------------------------------
| Styles
|----------------------------------------------------------------------------*/

.jp-NotebookPanel-toolbar {
  padding: var(--jp-notebook-toolbar-padding);

  /* disable paint containment from lumino 2.0 default strict CSS containment */
  contain: style size !important;
}

.jp-Toolbar-item.jp-Notebook-toolbarCellType .jp-select-wrapper.jp-mod-focused {
  border: none;
  box-shadow: none;
}

.jp-Notebook-toolbarCellTypeDropdown select {
  height: 24px;
  font-size: var(--jp-ui-font-size1);
  line-height: 14px;
  border-radius: 0;
  display: block;
}

.jp-Notebook-toolbarCellTypeDropdown span {
  top: 5px !important;
}

.jp-Toolbar-responsive-popup {
  position: absolute;
  height: fit-content;
  display: flex;
  flex-direction: row;
  flex-wrap: wrap;
  justify-content: flex-end;
  border-bottom: var(--jp-border-width) solid var(--jp-toolbar-border-color);
  box-shadow: var(--jp-toolbar-box-shadow);
  background: var(--jp-toolbar-background);
  min-height: var(--jp-toolbar-micro-height);
  padding: var(--jp-notebook-toolbar-padding);
  z-index: 1;
  right: 0;
  top: 0;
}

.jp-Toolbar > .jp-Toolbar-responsive-opener {
  margin-left: auto;
}

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

/*-----------------------------------------------------------------------------
| Variables
|----------------------------------------------------------------------------*/

/*-----------------------------------------------------------------------------

/*-----------------------------------------------------------------------------
| Styles
|----------------------------------------------------------------------------*/

.jp-Notebook-ExecutionIndicator {
  position: relative;
  display: inline-block;
  height: 100%;
  z-index: 9997;
}

.jp-Notebook-ExecutionIndicator-tooltip {
  visibility: hidden;
  height: auto;
  width: max-content;
  width: -moz-max-content;
  background-color: var(--jp-layout-color2);
  color: var(--jp-ui-font-color1);
  text-align: justify;
  border-radius: 6px;
  padding: 0 5px;
  position: fixed;
  display: table;
}

.jp-Notebook-ExecutionIndicator-tooltip.up {
  transform: translateX(-50%) translateY(-100%) translateY(-32px);
}

.jp-Notebook-ExecutionIndicator-tooltip.down {
  transform: translateX(calc(-100% + 16px)) translateY(5px);
}

.jp-Notebook-ExecutionIndicator-tooltip.hidden {
  display: none;
}

.jp-Notebook-ExecutionIndicator:hover .jp-Notebook-ExecutionIndicator-tooltip {
  visibility: visible;
}

.jp-Notebook-ExecutionIndicator span {
  font-size: var(--jp-ui-font-size1);
  font-family: var(--jp-ui-font-family);
  color: var(--jp-ui-font-color1);
  line-height: 24px;
  display: block;
}

.jp-Notebook-ExecutionIndicator-progress-bar {
  display: flex;
  justify-content: center;
  height: 100%;
}

/*
 * Copyright (c) Jupyter Development Team.
 * Distributed under the terms of the Modified BSD License.
 */

/*
 * Execution indicator
 */
.jp-tocItem-content::after {
  content: '';

  /* Must be identical to form a circle */
  width: 12px;
  height: 12px;
  background: none;
  border: none;
  position: absolute;
  right: 0;
}

.jp-tocItem-content[data-running='0']::after {
  border-radius: 50%;
  border: var(--jp-border-width) solid var(--jp-inverse-layout-color3);
  background: none;
}

.jp-tocItem-content[data-running='1']::after {
  border-radius: 50%;
  border: var(--jp-border-width) solid var(--jp-inverse-layout-color3);
  background-color: var(--jp-inverse-layout-color3);
}

.jp-tocItem-content[data-running='0'],
.jp-tocItem-content[data-running='1'] {
  margin-right: 12px;
}

/*
 * Copyright (c) Jupyter Development Team.
 * Distributed under the terms of the Modified BSD License.
 */

.jp-Notebook-footer {
  height: 27px;
  margin-left: calc(
    var(--jp-cell-prompt-width) + var(--jp-cell-collapser-width) +
      var(--jp-cell-padding)
  );
  width: calc(
    100% -
      (
        var(--jp-cell-prompt-width) + var(--jp-cell-collapser-width) +
          var(--jp-cell-padding) + var(--jp-cell-padding)
      )
  );
  border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
  color: var(--jp-ui-font-color3);
  margin-top: 6px;
  background: none;
  cursor: pointer;
}

.jp-Notebook-footer:focus {
  border-color: var(--jp-cell-editor-active-border-color);
}

/* For devices that support hovering, hide footer until hover */
@media (hover: hover) {
  .jp-Notebook-footer {
    opacity: 0;
  }

  .jp-Notebook-footer:focus,
  .jp-Notebook-footer:hover {
    opacity: 1;
  }
}

/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

/*-----------------------------------------------------------------------------
| Imports
|----------------------------------------------------------------------------*/

/*-----------------------------------------------------------------------------
| CSS variables
|----------------------------------------------------------------------------*/

:root {
  --jp-side-by-side-output-size: 1fr;
  --jp-side-by-side-resized-cell: var(--jp-side-by-side-output-size);
  --jp-private-notebook-dragImage-width: 304px;
  --jp-private-notebook-dragImage-height: 36px;
  --jp-private-notebook-selected-color: var(--md-blue-400);
  --jp-private-notebook-active-color: var(--md-green-400);
}

/*-----------------------------------------------------------------------------
| Notebook
|----------------------------------------------------------------------------*/

/* stylelint-disable selector-max-class */

.jp-NotebookPanel {
  display: block;
  height: 100%;
}

.jp-NotebookPanel.jp-Document {
  min-width: 240px;
  min-height: 120px;
}

.jp-Notebook {
  padding: var(--jp-notebook-padding);
  outline: none;
  overflow: auto;
  background: var(--jp-layout-color0);
}

.jp-Notebook.jp-mod-scrollPastEnd::after {
  display: block;
  content: '';
  min-height: var(--jp-notebook-scroll-padding);
}

.jp-MainAreaWidget-ContainStrict .jp-Notebook * {
  contain: strict;
}

.jp-Notebook .jp-Cell {
  overflow: visible;
}

.jp-Notebook .jp-Cell .jp-InputPrompt {
  cursor: move;
}

/*-----------------------------------------------------------------------------
| Notebook state related styling
|
| The notebook and cells each have states, here are the possibilities:
|
| - Notebook
|   - Command
|   - Edit
| - Cell
|   - None
|   - Active (only one can be active)
|   - Selected (the cells actions are applied to)
|   - Multiselected (when multiple selected, the cursor)
|   - No outputs
|----------------------------------------------------------------------------*/

/* Command or edit modes */

.jp-Notebook .jp-Cell:not(.jp-mod-active) .jp-InputPrompt {
  opacity: var(--jp-cell-prompt-not-active-opacity);
  color: var(--jp-cell-prompt-not-active-font-color);
}

.jp-Notebook .jp-Cell:not(.jp-mod-active) .jp-OutputPrompt {
  opacity: var(--jp-cell-prompt-not-active-opacity);
  color: var(--jp-cell-prompt-not-active-font-color);
}

/* cell is active */
.jp-Notebook .jp-Cell.jp-mod-active .jp-Collapser {
  background: var(--jp-brand-color1);
}

/* cell is dirty */
.jp-Notebook .jp-Cell.jp-mod-dirty .jp-InputPrompt {
  color: var(--jp-warn-color1);
}

.jp-Notebook .jp-Cell.jp-mod-dirty .jp-InputPrompt::before {
  color: var(--jp-warn-color1);
  content: '•';
}

.jp-Notebook .jp-Cell.jp-mod-active.jp-mod-dirty .jp-Collapser {
  background: var(--jp-warn-color1);
}

/* collapser is hovered */
.jp-Notebook .jp-Cell .jp-Collapser:hover {
  box-shadow: var(--jp-elevation-z2);
  background: var(--jp-brand-color1);
  opacity: var(--jp-cell-collapser-not-active-hover-opacity);
}

/* cell is active and collapser is hovered */
.jp-Notebook .jp-Cell.jp-mod-active .jp-Collapser:hover {
  background: var(--jp-brand-color0);
  opacity: 1;
}

/* Command mode */

.jp-Notebook.jp-mod-commandMode .jp-Cell.jp-mod-selected {
  background: var(--jp-notebook-multiselected-color);
}

.jp-Notebook.jp-mod-commandMode
  .jp-Cell.jp-mod-active.jp-mod-selected:not(.jp-mod-multiSelected) {
  background: transparent;
}

/* Edit mode */

.jp-Notebook.jp-mod-editMode .jp-Cell.jp-mod-active .jp-InputArea-editor {
  border: var(--jp-border-width) solid var(--jp-cell-editor-active-border-color);
  box-shadow: var(--jp-input-box-shadow);
  background-color: var(--jp-cell-editor-active-background);
}

/*-----------------------------------------------------------------------------
| Notebook drag and drop
|----------------------------------------------------------------------------*/

.jp-Notebook-cell.jp-mod-dropSource {
  opacity: 0.5;
}

.jp-Notebook-cell.jp-mod-dropTarget,
.jp-Notebook.jp-mod-commandMode
  .jp-Notebook-cell.jp-mod-active.jp-mod-selected.jp-mod-dropTarget {
  border-top-color: var(--jp-private-notebook-selected-color);
  border-top-style: solid;
  border-top-width: 2px;
}

.jp-dragImage {
  display: block;
  flex-direction: row;
  width: var(--jp-private-notebook-dragImage-width);
  height: var(--jp-private-notebook-dragImage-height);
  border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
  background: var(--jp-cell-editor-background);
  overflow: visible;
}

.jp-dragImage-singlePrompt {
  box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.12);
}

.jp-dragImage .jp-dragImage-content {
  flex: 1 1 auto;
  z-index: 2;
  font-size: var(--jp-code-font-size);
  font-family: var(--jp-code-font-family);
  line-height: var(--jp-code-line-height);
  padding: var(--jp-code-padding);
  border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
  background: var(--jp-cell-editor-background-color);
  color: var(--jp-content-font-color3);
  text-align: left;
  margin: 4px 4px 4px 0;
}

.jp-dragImage .jp-dragImage-prompt {
  flex: 0 0 auto;
  min-width: 36px;
  color: var(--jp-cell-inprompt-font-color);
  padding: var(--jp-code-padding);
  padding-left: 12px;
  font-family: var(--jp-cell-prompt-font-family);
  letter-spacing: var(--jp-cell-prompt-letter-spacing);
  line-height: 1.9;
  font-size: var(--jp-code-font-size);
  border: var(--jp-border-width) solid transparent;
}

.jp-dragImage-multipleBack {
  z-index: -1;
  position: absolute;
  height: 32px;
  width: 300px;
  top: 8px;
  left: 8px;
  background: var(--jp-layout-color2);
  border: var(--jp-border-width) solid var(--jp-input-border-color);
  box-shadow: 2px 2px 4px 0 rgba(0, 0, 0, 0.12);
}

/*-----------------------------------------------------------------------------
| Cell toolbar
|----------------------------------------------------------------------------*/

.jp-NotebookTools {
  display: block;
  min-width: var(--jp-sidebar-min-width);
  color: var(--jp-ui-font-color1);
  background: var(--jp-layout-color1);

  /* This is needed so that all font sizing of children done in ems is
    * relative to this base size */
  font-size: var(--jp-ui-font-size1);
  overflow: auto;
}

.jp-ActiveCellTool {
  padding: 12px 0;
  display: flex;
}

.jp-ActiveCellTool-Content {
  flex: 1 1 auto;
}

.jp-ActiveCellTool .jp-ActiveCellTool-CellContent {
  background: var(--jp-cell-editor-background);
  border: var(--jp-border-width) solid var(--jp-cell-editor-border-color);
  border-radius: 0;
  min-height: 29px;
}

.jp-ActiveCellTool .jp-InputPrompt {
  min-width: calc(var(--jp-cell-prompt-width) * 0.75);
}

.jp-ActiveCellTool-CellContent > pre {
  padding: 5px 4px;
  margin: 0;
  white-space: normal;
}

.jp-MetadataEditorTool {
  flex-direction: column;
  padding: 12px 0;
}

.jp-RankedPanel > :not(:first-child) {
  margin-top: 12px;
}

.jp-KeySelector select.jp-mod-styled {
  font-size: var(--jp-ui-font-size1);
  color: var(--jp-ui-font-color0);
  border: var(--jp-border-width) solid var(--jp-border-color1);
}

.jp-KeySelector label,
.jp-MetadataEditorTool label,
.jp-NumberSetter label {
  line-height: 1.4;
}

.jp-NotebookTools .jp-select-wrapper {
  margin-top: 4px;
  margin-bottom: 0;
}

.jp-NumberSetter input {
  width: 100%;
  margin-top: 4px;
}

.jp-NotebookTools .jp-Collapse {
  margin-top: 16px;
}

/*-----------------------------------------------------------------------------
| Presentation Mode (.jp-mod-presentationMode)
|----------------------------------------------------------------------------*/

.jp-mod-presentationMode .jp-Notebook {
  --jp-content-font-size1: var(--jp-content-presentation-font-size1);
  --jp-code-font-size: var(--jp-code-presentation-font-size);
}

.jp-mod-presentationMode .jp-Notebook .jp-Cell .jp-InputPrompt,
.jp-mod-presentationMode .jp-Notebook .jp-Cell .jp-OutputPrompt {
  flex: 0 0 110px;
}

/*-----------------------------------------------------------------------------
| Side-by-side Mode (.jp-mod-sideBySide)
|----------------------------------------------------------------------------*/
.jp-mod-sideBySide.jp-Notebook .jp-Notebook-cell {
  margin-top: 3em;
  margin-bottom: 3em;
  margin-left: 5%;
  margin-right: 5%;
}

.jp-mod-sideBySide.jp-Notebook .jp-CodeCell {
  display: grid;
  grid-template-columns: minmax(0, 1fr) min-content minmax(
      0,
      var(--jp-side-by-side-output-size)
    );
  grid-template-rows: auto minmax(0, 1fr) auto;
  grid-template-areas:
    'header header header'
    'input handle output'
    'footer footer footer';
}

.jp-mod-sideBySide.jp-Notebook .jp-CodeCell.jp-mod-resizedCell {
  grid-template-columns: minmax(0, 1fr) min-content minmax(
      0,
      var(--jp-side-by-side-resized-cell)
    );
}

.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellHeader {
  grid-area: header;
}

.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-Cell-inputWrapper {
  grid-area: input;
}

.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-Cell-outputWrapper {
  /* overwrite the default margin (no vertical separation needed in side by side move */
  margin-top: 0;
  grid-area: output;
}

.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellFooter {
  grid-area: footer;
}

.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellResizeHandle {
  grid-area: handle;
  user-select: none;
  display: block;
  height: 100%;
  cursor: ew-resize;
  padding: 0 var(--jp-cell-padding);
}

.jp-mod-sideBySide.jp-Notebook .jp-CodeCell .jp-CellResizeHandle::after {
  content: '';
  display: block;
  background: var(--jp-border-color2);
  height: 100%;
  width: 5px;
}

.jp-mod-sideBySide.jp-Notebook
  .jp-CodeCell.jp-mod-resizedCell
  .jp-CellResizeHandle::after {
  background: var(--jp-border-color0);
}

.jp-CellResizeHandle {
  display: none;
}

/*-----------------------------------------------------------------------------
| Placeholder
|----------------------------------------------------------------------------*/

.jp-Cell-Placeholder {
  padding-left: 55px;
}

.jp-Cell-Placeholder-wrapper {
  background: #fff;
  border: 1px solid;
  border-color: #e5e6e9 #dfe0e4 #d0d1d5;
  border-radius: 4px;
  -webkit-border-radius: 4px;
  margin: 10px 15px;
}

.jp-Cell-Placeholder-wrapper-inner {
  padding: 15px;
  position: relative;
}

.jp-Cell-Placeholder-wrapper-body {
  background-repeat: repeat;
  background-size: 50% auto;
}

.jp-Cell-Placeholder-wrapper-body div {
  background: #f6f7f8;
  background-image: -webkit-linear-gradient(
    left,
    #f6f7f8 0%,
    #edeef1 20%,
    #f6f7f8 40%,
    #f6f7f8 100%
  );
  background-repeat: no-repeat;
  background-size: 800px 104px;
  height: 104px;
  position: absolute;
  right: 15px;
  left: 15px;
  top: 15px;
}

div.jp-Cell-Placeholder-h1 {
  top: 20px;
  height: 20px;
  left: 15px;
  width: 150px;
}

div.jp-Cell-Placeholder-h2 {
  left: 15px;
  top: 50px;
  height: 10px;
  width: 100px;
}

div.jp-Cell-Placeholder-content-1,
div.jp-Cell-Placeholder-content-2,
div.jp-Cell-Placeholder-content-3 {
  left: 15px;
  right: 15px;
  height: 10px;
}

div.jp-Cell-Placeholder-content-1 {
  top: 100px;
}

div.jp-Cell-Placeholder-content-2 {
  top: 120px;
}

div.jp-Cell-Placeholder-content-3 {
  top: 140px;
}

</style>
<style type="text/css">
/*-----------------------------------------------------------------------------
| Copyright (c) Jupyter Development Team.
| Distributed under the terms of the Modified BSD License.
|----------------------------------------------------------------------------*/

/*
The following CSS variables define the main, public API for styling JupyterLab.
These variables should be used by all plugins wherever possible. In other
words, plugins should not define custom colors, sizes, etc unless absolutely
necessary. This enables users to change the visual theme of JupyterLab
by changing these variables.

Many variables appear in an ordered sequence (0,1,2,3). These sequences
are designed to work well together, so for example, `--jp-border-color1` should
be used with `--jp-layout-color1`. The numbers have the following meanings:

* 0: super-primary, reserved for special emphasis
* 1: primary, most important under normal situations
* 2: secondary, next most important under normal situations
* 3: tertiary, next most important under normal situations

Throughout JupyterLab, we are mostly following principles from Google's
Material Design when selecting colors. We are not, however, following
all of MD as it is not optimized for dense, information rich UIs.
*/

:root {
  /* Elevation
   *
   * We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
   *
   * https://github.com/material-components/material-components-web
   * https://material-components-web.appspot.com/elevation.html
   */

  --jp-shadow-base-lightness: 0;
  --jp-shadow-umbra-color: rgba(
    var(--jp-shadow-base-lightness),
    var(--jp-shadow-base-lightness),
    var(--jp-shadow-base-lightness),
    0.2
  );
  --jp-shadow-penumbra-color: rgba(
    var(--jp-shadow-base-lightness),
    var(--jp-shadow-base-lightness),
    var(--jp-shadow-base-lightness),
    0.14
  );
  --jp-shadow-ambient-color: rgba(
    var(--jp-shadow-base-lightness),
    var(--jp-shadow-base-lightness),
    var(--jp-shadow-base-lightness),
    0.12
  );
  --jp-elevation-z0: none;
  --jp-elevation-z1: 0 2px 1px -1px var(--jp-shadow-umbra-color),
    0 1px 1px 0 var(--jp-shadow-penumbra-color),
    0 1px 3px 0 var(--jp-shadow-ambient-color);
  --jp-elevation-z2: 0 3px 1px -2px var(--jp-shadow-umbra-color),
    0 2px 2px 0 var(--jp-shadow-penumbra-color),
    0 1px 5px 0 var(--jp-shadow-ambient-color);
  --jp-elevation-z4: 0 2px 4px -1px var(--jp-shadow-umbra-color),
    0 4px 5px 0 var(--jp-shadow-penumbra-color),
    0 1px 10px 0 var(--jp-shadow-ambient-color);
  --jp-elevation-z6: 0 3px 5px -1px var(--jp-shadow-umbra-color),
    0 6px 10px 0 var(--jp-shadow-penumbra-color),
    0 1px 18px 0 var(--jp-shadow-ambient-color);
  --jp-elevation-z8: 0 5px 5px -3px var(--jp-shadow-umbra-color),
    0 8px 10px 1px var(--jp-shadow-penumbra-color),
    0 3px 14px 2px var(--jp-shadow-ambient-color);
  --jp-elevation-z12: 0 7px 8px -4px var(--jp-shadow-umbra-color),
    0 12px 17px 2px var(--jp-shadow-penumbra-color),
    0 5px 22px 4px var(--jp-shadow-ambient-color);
  --jp-elevation-z16: 0 8px 10px -5px var(--jp-shadow-umbra-color),
    0 16px 24px 2px var(--jp-shadow-penumbra-color),
    0 6px 30px 5px var(--jp-shadow-ambient-color);
  --jp-elevation-z20: 0 10px 13px -6px var(--jp-shadow-umbra-color),
    0 20px 31px 3px var(--jp-shadow-penumbra-color),
    0 8px 38px 7px var(--jp-shadow-ambient-color);
  --jp-elevation-z24: 0 11px 15px -7px var(--jp-shadow-umbra-color),
    0 24px 38px 3px var(--jp-shadow-penumbra-color),
    0 9px 46px 8px var(--jp-shadow-ambient-color);

  /* Borders
   *
   * The following variables, specify the visual styling of borders in JupyterLab.
   */

  --jp-border-width: 1px;
  --jp-border-color0: var(--md-grey-400);
  --jp-border-color1: var(--md-grey-400);
  --jp-border-color2: var(--md-grey-300);
  --jp-border-color3: var(--md-grey-200);
  --jp-inverse-border-color: var(--md-grey-600);
  --jp-border-radius: 2px;

  /* UI Fonts
   *
   * The UI font CSS variables are used for the typography all of the JupyterLab
   * user interface elements that are not directly user generated content.
   *
   * The font sizing here is done assuming that the body font size of --jp-ui-font-size1
   * is applied to a parent element. When children elements, such as headings, are sized
   * in em all things will be computed relative to that body size.
   */

  --jp-ui-font-scale-factor: 1.2;
  --jp-ui-font-size0: 0.83333em;
  --jp-ui-font-size1: 13px; /* Base font size */
  --jp-ui-font-size2: 1.2em;
  --jp-ui-font-size3: 1.44em;
  --jp-ui-font-family: system-ui, -apple-system, blinkmacsystemfont, 'Segoe UI',
    helvetica, arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji',
    'Segoe UI Symbol';

  /*
   * Use these font colors against the corresponding main layout colors.
   * In a light theme, these go from dark to light.
   */

  /* Defaults use Material Design specification */
  --jp-ui-font-color0: rgba(0, 0, 0, 1);
  --jp-ui-font-color1: rgba(0, 0, 0, 0.87);
  --jp-ui-font-color2: rgba(0, 0, 0, 0.54);
  --jp-ui-font-color3: rgba(0, 0, 0, 0.38);

  /*
   * Use these against the brand/accent/warn/error colors.
   * These will typically go from light to darker, in both a dark and light theme.
   */

  --jp-ui-inverse-font-color0: rgba(255, 255, 255, 1);
  --jp-ui-inverse-font-color1: rgba(255, 255, 255, 1);
  --jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7);
  --jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5);

  /* Content Fonts
   *
   * Content font variables are used for typography of user generated content.
   *
   * The font sizing here is done assuming that the body font size of --jp-content-font-size1
   * is applied to a parent element. When children elements, such as headings, are sized
   * in em all things will be computed relative to that body size.
   */

  --jp-content-line-height: 1.6;
  --jp-content-font-scale-factor: 1.2;
  --jp-content-font-size0: 0.83333em;
  --jp-content-font-size1: 14px; /* Base font size */
  --jp-content-font-size2: 1.2em;
  --jp-content-font-size3: 1.44em;
  --jp-content-font-size4: 1.728em;
  --jp-content-font-size5: 2.0736em;

  /* This gives a magnification of about 125% in presentation mode over normal. */
  --jp-content-presentation-font-size1: 17px;
  --jp-content-heading-line-height: 1;
  --jp-content-heading-margin-top: 1.2em;
  --jp-content-heading-margin-bottom: 0.8em;
  --jp-content-heading-font-weight: 500;

  /* Defaults use Material Design specification */
  --jp-content-font-color0: rgba(0, 0, 0, 1);
  --jp-content-font-color1: rgba(0, 0, 0, 0.87);
  --jp-content-font-color2: rgba(0, 0, 0, 0.54);
  --jp-content-font-color3: rgba(0, 0, 0, 0.38);
  --jp-content-link-color: var(--md-blue-900);
  --jp-content-font-family: system-ui, -apple-system, blinkmacsystemfont,
    'Segoe UI', helvetica, arial, sans-serif, 'Apple Color Emoji',
    'Segoe UI Emoji', 'Segoe UI Symbol';

  /*
   * Code Fonts
   *
   * Code font variables are used for typography of code and other monospaces content.
   */

  --jp-code-font-size: 13px;
  --jp-code-line-height: 1.3077; /* 17px for 13px base */
  --jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
  --jp-code-font-family-default: menlo, consolas, 'DejaVu Sans Mono', monospace;
  --jp-code-font-family: var(--jp-code-font-family-default);

  /* This gives a magnification of about 125% in presentation mode over normal. */
  --jp-code-presentation-font-size: 16px;

  /* may need to tweak cursor width if you change font size */
  --jp-code-cursor-width0: 1.4px;
  --jp-code-cursor-width1: 2px;
  --jp-code-cursor-width2: 4px;

  /* Layout
   *
   * The following are the main layout colors use in JupyterLab. In a light
   * theme these would go from light to dark.
   */

  --jp-layout-color0: white;
  --jp-layout-color1: white;
  --jp-layout-color2: var(--md-grey-200);
  --jp-layout-color3: var(--md-grey-400);
  --jp-layout-color4: var(--md-grey-600);

  /* Inverse Layout
   *
   * The following are the inverse layout colors use in JupyterLab. In a light
   * theme these would go from dark to light.
   */

  --jp-inverse-layout-color0: #111;
  --jp-inverse-layout-color1: var(--md-grey-900);
  --jp-inverse-layout-color2: var(--md-grey-800);
  --jp-inverse-layout-color3: var(--md-grey-700);
  --jp-inverse-layout-color4: var(--md-grey-600);

  /* Brand/accent */

  --jp-brand-color0: var(--md-blue-900);
  --jp-brand-color1: var(--md-blue-700);
  --jp-brand-color2: var(--md-blue-300);
  --jp-brand-color3: var(--md-blue-100);
  --jp-brand-color4: var(--md-blue-50);
  --jp-accent-color0: var(--md-green-900);
  --jp-accent-color1: var(--md-green-700);
  --jp-accent-color2: var(--md-green-300);
  --jp-accent-color3: var(--md-green-100);

  /* State colors (warn, error, success, info) */

  --jp-warn-color0: var(--md-orange-900);
  --jp-warn-color1: var(--md-orange-700);
  --jp-warn-color2: var(--md-orange-300);
  --jp-warn-color3: var(--md-orange-100);
  --jp-error-color0: var(--md-red-900);
  --jp-error-color1: var(--md-red-700);
  --jp-error-color2: var(--md-red-300);
  --jp-error-color3: var(--md-red-100);
  --jp-success-color0: var(--md-green-900);
  --jp-success-color1: var(--md-green-700);
  --jp-success-color2: var(--md-green-300);
  --jp-success-color3: var(--md-green-100);
  --jp-info-color0: var(--md-cyan-900);
  --jp-info-color1: var(--md-cyan-700);
  --jp-info-color2: var(--md-cyan-300);
  --jp-info-color3: var(--md-cyan-100);

  /* Cell specific styles */

  --jp-cell-padding: 5px;
  --jp-cell-collapser-width: 8px;
  --jp-cell-collapser-min-height: 20px;
  --jp-cell-collapser-not-active-hover-opacity: 0.6;
  --jp-cell-editor-background: var(--md-grey-100);
  --jp-cell-editor-border-color: var(--md-grey-300);
  --jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
  --jp-cell-editor-active-background: var(--jp-layout-color0);
  --jp-cell-editor-active-border-color: var(--jp-brand-color1);
  --jp-cell-prompt-width: 64px;
  --jp-cell-prompt-font-family: var(--jp-code-font-family-default);
  --jp-cell-prompt-letter-spacing: 0;
  --jp-cell-prompt-opacity: 1;
  --jp-cell-prompt-not-active-opacity: 0.5;
  --jp-cell-prompt-not-active-font-color: var(--md-grey-700);

  /* A custom blend of MD grey and blue 600
   * See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
  --jp-cell-inprompt-font-color: #307fc1;

  /* A custom blend of MD grey and orange 600
   * https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
  --jp-cell-outprompt-font-color: #bf5b3d;

  /* Notebook specific styles */

  --jp-notebook-padding: 10px;
  --jp-notebook-select-background: var(--jp-layout-color1);
  --jp-notebook-multiselected-color: var(--md-blue-50);

  /* The scroll padding is calculated to fill enough space at the bottom of the
  notebook to show one single-line cell (with appropriate padding) at the top
  when the notebook is scrolled all the way to the bottom. We also subtract one
  pixel so that no scrollbar appears if we have just one single-line cell in the
  notebook. This padding is to enable a 'scroll past end' feature in a notebook.
  */
  --jp-notebook-scroll-padding: calc(
    100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
      var(--jp-code-padding) - var(--jp-cell-padding) - 1px
  );

  /* Rendermime styles */

  --jp-rendermime-error-background: #fdd;
  --jp-rendermime-table-row-background: var(--md-grey-100);
  --jp-rendermime-table-row-hover-background: var(--md-light-blue-50);

  /* Dialog specific styles */

  --jp-dialog-background: rgba(0, 0, 0, 0.25);

  /* Console specific styles */

  --jp-console-padding: 10px;

  /* Toolbar specific styles */

  --jp-toolbar-border-color: var(--jp-border-color1);
  --jp-toolbar-micro-height: 8px;
  --jp-toolbar-background: var(--jp-layout-color1);
  --jp-toolbar-box-shadow: 0 0 2px 0 rgba(0, 0, 0, 0.24);
  --jp-toolbar-header-margin: 4px 4px 0 4px;
  --jp-toolbar-active-background: var(--md-grey-300);

  /* Statusbar specific styles */

  --jp-statusbar-height: 24px;

  /* Input field styles */

  --jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
  --jp-input-active-background: var(--jp-layout-color1);
  --jp-input-hover-background: var(--jp-layout-color1);
  --jp-input-background: var(--md-grey-100);
  --jp-input-border-color: var(--jp-inverse-border-color);
  --jp-input-active-border-color: var(--jp-brand-color1);
  --jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);

  /* General editor styles */

  --jp-editor-selected-background: #d9d9d9;
  --jp-editor-selected-focused-background: #d7d4f0;
  --jp-editor-cursor-color: var(--jp-ui-font-color0);

  /* Code mirror specific styles */

  --jp-mirror-editor-keyword-color: #008000;
  --jp-mirror-editor-atom-color: #88f;
  --jp-mirror-editor-number-color: #080;
  --jp-mirror-editor-def-color: #00f;
  --jp-mirror-editor-variable-color: var(--md-grey-900);
  --jp-mirror-editor-variable-2-color: rgb(0, 54, 109);
  --jp-mirror-editor-variable-3-color: #085;
  --jp-mirror-editor-punctuation-color: #05a;
  --jp-mirror-editor-property-color: #05a;
  --jp-mirror-editor-operator-color: #a2f;
  --jp-mirror-editor-comment-color: #408080;
  --jp-mirror-editor-string-color: #ba2121;
  --jp-mirror-editor-string-2-color: #708;
  --jp-mirror-editor-meta-color: #a2f;
  --jp-mirror-editor-qualifier-color: #555;
  --jp-mirror-editor-builtin-color: #008000;
  --jp-mirror-editor-bracket-color: #997;
  --jp-mirror-editor-tag-color: #170;
  --jp-mirror-editor-attribute-color: #00c;
  --jp-mirror-editor-header-color: blue;
  --jp-mirror-editor-quote-color: #090;
  --jp-mirror-editor-link-color: #00c;
  --jp-mirror-editor-error-color: #f00;
  --jp-mirror-editor-hr-color: #999;

  /*
    RTC user specific colors.
    These colors are used for the cursor, username in the editor,
    and the icon of the user.
  */

  --jp-collaborator-color1: #ffad8e;
  --jp-collaborator-color2: #dac83d;
  --jp-collaborator-color3: #72dd76;
  --jp-collaborator-color4: #00e4d0;
  --jp-collaborator-color5: #45d4ff;
  --jp-collaborator-color6: #e2b1ff;
  --jp-collaborator-color7: #ff9de6;

  /* Vega extension styles */

  --jp-vega-background: white;

  /* Sidebar-related styles */

  --jp-sidebar-min-width: 250px;

  /* Search-related styles */

  --jp-search-toggle-off-opacity: 0.5;
  --jp-search-toggle-hover-opacity: 0.8;
  --jp-search-toggle-on-opacity: 1;
  --jp-search-selected-match-background-color: rgb(245, 200, 0);
  --jp-search-selected-match-color: black;
  --jp-search-unselected-match-background-color: var(
    --jp-inverse-layout-color0
  );
  --jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);

  /* Icon colors that work well with light or dark backgrounds */
  --jp-icon-contrast-color0: var(--md-purple-600);
  --jp-icon-contrast-color1: var(--md-green-600);
  --jp-icon-contrast-color2: var(--md-pink-600);
  --jp-icon-contrast-color3: var(--md-blue-600);

  /* Button colors */
  --jp-accept-color-normal: var(--md-blue-700);
  --jp-accept-color-hover: var(--md-blue-800);
  --jp-accept-color-active: var(--md-blue-900);
  --jp-warn-color-normal: var(--md-red-700);
  --jp-warn-color-hover: var(--md-red-800);
  --jp-warn-color-active: var(--md-red-900);
  --jp-reject-color-normal: var(--md-grey-600);
  --jp-reject-color-hover: var(--md-grey-700);
  --jp-reject-color-active: var(--md-grey-800);

  /* File or activity icons and switch semantic variables */
  --jp-jupyter-icon-color: #f37626;
  --jp-notebook-icon-color: #f37626;
  --jp-json-icon-color: var(--md-orange-700);
  --jp-console-icon-background-color: var(--md-blue-700);
  --jp-console-icon-color: white;
  --jp-terminal-icon-background-color: var(--md-grey-800);
  --jp-terminal-icon-color: var(--md-grey-200);
  --jp-text-editor-icon-color: var(--md-grey-700);
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<!-- End of mermaid configuration --></head>
<body class="jp-Notebook" data-jp-theme-light="true" data-jp-theme-name="JupyterLab Light">
<main>
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=d5cbb4e0">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
<h3 id="%D0%9E%D0%B2%D1%81%D1%8F%D0%BD%D0%BD%D0%B8%D0%BA%D0%BE%D0%B2%D0%B0-%D0%9C%D0%B0%D0%B9%D1%8F-%D0%91%D0%91%D0%98241%D0%A1"><strong>Овсянникова Майя ББИ241С</strong><a class="anchor-link" href="#%D0%9E%D0%B2%D1%81%D1%8F%D0%BD%D0%BD%D0%B8%D0%BA%D0%BE%D0%B2%D0%B0-%D0%9C%D0%B0%D0%B9%D1%8F-%D0%91%D0%91%D0%98241%D0%A1">¶</a></h3>
</div>
</div>
</div>
</div>
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=d00b4931">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
<h2 id="%D0%94%D0%BE%D0%BA%D1%83%D0%BC%D0%B5%D0%BD%D1%82%D0%B0%D1%86%D0%B8%D1%8F-%D0%B8-%D1%82%D0%B5%D0%BE%D1%80%D0%B8%D1%8F"><strong>Документация и теория</strong><a class="anchor-link" href="#%D0%94%D0%BE%D0%BA%D1%83%D0%BC%D0%B5%D0%BD%D1%82%D0%B0%D1%86%D0%B8%D1%8F-%D0%B8-%D1%82%D0%B5%D0%BE%D1%80%D0%B8%D1%8F">¶</a></h2>
</div>
</div>
</div>
</div>
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=425a6120">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
<p>Ниже перечислены основные используемые библиотеки работы с данными в данном проекте</p>
</div>
</div>
</div>
</div>
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=3dd9308b">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
<p><strong>Загрузка и разведочный анализ данных</strong></p>
<p><strong><code>pandas</code></strong> – основная библиотека для работы с табличными данными</p>
<ul>
<li><code>pd.read_csv()</code> – загружает данные из csv-файла в объект <code>DataFrame</code></li>
<li><code>df.head()</code>, <code>df.info()</code>, <code>df.describe()</code> – получение первичной информацией о данных</li>
<li><code>df.isnull().sum()</code>, <code>df.duplicated().sum()</code> – проверка наличия пропусков и дубликатов</li>
<li><code>df.select_dtypes()</code> – выбор колонок по типу данных</li>
</ul>
<p><strong><code>matplotlib.pyplot</code> и <code>seaborn</code></strong> – основные библиотеки для визуализации данных</p>
<ul>
<li><code>.histplot()</code> – построение гистограмм для анализа распределения числовых признаков</li>
<li><code>.boxplot()</code> – построение ящиков с усами для выявления выбросов и анализа медианы и квартилей</li>
<li><code>.heatmap()</code> – построение тепловой карты для визуализации корреляционной матрицы</li>
<li><code>.kdeplot()</code> – построение графика плотности распределения</li>
<li><code>.pie()</code> – создание круговых диаграмм для отображения долей категориальных переменных</li>
</ul>
<p><strong><code>scipy.stats</code></strong> – библиотека для проведеня статистических тестов</p>
<ul>
<li><code>.shapiro()</code> – тест Шапиро-Уилка для проверки нормальности распределения выборки</li>
<li><code>.chi2_contingency()</code> – критерий хи-квадрат для проверки независимости двух категориальных переменных</li>
<li><code>.ttest_ind()</code> – двухвыборочный t-тест для сравнения средних двух независимых групп</li>
<li><code>.f_oneway()</code> – однофакторный дисперсионный анализ ANOVA для сравнения средних трех и более групп</li>
<li><code>sklearn.utils.resample()</code> – метод для случайной передискретизации (bootstrap-выборки) массивов</li>
</ul>
<p><strong>Построение и оценка моделей</strong></p>
<ul>
<li><code>LabelEncoder()</code> – кодирование категориальных признаков в числовой формат</li>
<li><code>StandardScaler()</code> – стандартизация данных (приведение к среднему 0 и дисперсии 1)</li>
<li><code>sklearn.model_selection.train_test_split()</code> – разделение исходного набора данных на обучающую и тестовую выборки</li>
<li><code>sklearn.linear_model.LinearRegression</code> – линейная регрессия: модель для предсказания непрерывной целевой переменной</li>
<li><code>mean_squared_error()</code> (MSE) – среднеквадратичная ошибка</li>
<li><code>mean_absolute_error()</code> (MAE) – средняя абсолютная ошибка</li>
<li><code>r2_score()</code> (R2) – коэффициент детерминации, показывающий долю объясненной дисперсии модели</li>
<li><code>sklearn.cluster.KMeans</code> – кластеризация: модель для разделения данных на кластеры</li>
<li>Метод локтя – анализ графика зависимости инерции (суммы квадратов расстояний до центров кластеров) от количества кластеров</li>
<li>Коэффициент силуэта (<code>sklearn.metrics.silhouette_score</code>) – метрика, оценивающая, насколько объекты в своем кластере близки и насколько далеки от объектов в других кластерах</li>
<li><code>sklearn.decomposition.PCA</code> – метод главных компонент для уменьшения размерности данных (с 5 до 2 признаков) для их визуализации на плоскости</li>
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<p>Выбранный датасет: <strong>Lifestyle Factors and Their Impact on Students</strong></p>
<p><a href="https://www.kaggle.com/datasets/charlottebennett1234/lifestyle-factors-and-their-impact-on-students/data">https://www.kaggle.com/datasets/charlottebennett1234/lifestyle-factors-and-their-impact-on-students/data</a></p>
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<h2 id="EDA"><strong>EDA</strong><a class="anchor-link" href="#EDA">¶</a></h2>
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<h4 id="%D0%97%D0%B0%D0%B3%D1%80%D1%83%D0%B7%D0%BA%D0%B0-%D0%B4%D0%B0%D0%BD%D0%BD%D1%8B%D1%85-%D0%B8-%D0%B8%D0%BC%D0%BF%D0%BE%D1%80%D1%82-%D0%B1%D0%B8%D0%B1%D0%BB%D0%B8%D0%BE%D1%82%D0%B5%D0%BA"><strong>Загрузка данных и импорт библиотек</strong><a class="anchor-link" href="#%D0%97%D0%B0%D0%B3%D1%80%D1%83%D0%B7%D0%BA%D0%B0-%D0%B4%D0%B0%D0%BD%D0%BD%D1%8B%D1%85-%D0%B8-%D0%B8%D0%BC%D0%BF%D0%BE%D1%80%D1%82-%D0%B1%D0%B8%D0%B1%D0%BB%D0%B8%D0%BE%D1%82%D0%B5%D0%BA">¶</a></h4>
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<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">pandas</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">pd</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">scipy.stats</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">stats</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.pyplot</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">plt</span>
<span class="kn">import</span><span class="w"> </span><span class="nn">seaborn</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">sns</span>

<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">'student_lifestyle_dataset.csv'</span><span class="p">)</span>

<span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
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<style scoped="">
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    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
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<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>Student_ID</th>
<th>Study_Hours_Per_Day</th>
<th>Extracurricular_Hours_Per_Day</th>
<th>Sleep_Hours_Per_Day</th>
<th>Social_Hours_Per_Day</th>
<th>Physical_Activity_Hours_Per_Day</th>
<th>Stress_Level</th>
<th>Gender</th>
<th>Grades</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>1</td>
<td>6.9</td>
<td>3.8</td>
<td>8.7</td>
<td>2.8</td>
<td>1.8</td>
<td>Moderate</td>
<td>Male</td>
<td>7.48</td>
</tr>
<tr>
<th>1</th>
<td>2</td>
<td>5.3</td>
<td>3.5</td>
<td>8.0</td>
<td>4.2</td>
<td>3.0</td>
<td>Low</td>
<td>Female</td>
<td>6.88</td>
</tr>
<tr>
<th>2</th>
<td>3</td>
<td>5.1</td>
<td>3.9</td>
<td>9.2</td>
<td>1.2</td>
<td>4.6</td>
<td>Low</td>
<td>Male</td>
<td>6.68</td>
</tr>
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<th>3</th>
<td>4</td>
<td>6.5</td>
<td>2.1</td>
<td>7.2</td>
<td>1.7</td>
<td>6.5</td>
<td>Moderate</td>
<td>Male</td>
<td>7.20</td>
</tr>
<tr>
<th>4</th>
<td>5</td>
<td>8.1</td>
<td>0.6</td>
<td>6.5</td>
<td>2.2</td>
<td>6.6</td>
<td>High</td>
<td>Male</td>
<td>8.78</td>
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<h4 id="%D0%9F%D1%80%D0%BE%D0%B2%D0%B5%D1%80%D0%BA%D0%B0-%D0%BD%D0%B0-%D0%BF%D1%80%D0%BE%D0%BF%D1%83%D1%81%D0%BA%D0%B8-%D0%B8-%D0%B4%D1%83%D0%B1%D0%BB%D0%B8%D0%BA%D0%B0%D1%82%D1%8B"><strong>Проверка на пропуски и дубликаты</strong><a class="anchor-link" href="#%D0%9F%D1%80%D0%BE%D0%B2%D0%B5%D1%80%D0%BA%D0%B0-%D0%BD%D0%B0-%D0%BF%D1%80%D0%BE%D0%BF%D1%83%D1%81%D0%BA%D0%B8-%D0%B8-%D0%B4%D1%83%D0%B1%D0%BB%D0%B8%D0%BA%D0%B0%D1%82%D1%8B">¶</a></h4>
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<div class="highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Количество пропусков:</span><span class="se">\n</span><span class="si">{</span><span class="n">df</span><span class="o">.</span><span class="n">isnull</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="si">}</span><span class="se">\n</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Количество дубликатов: </span><span class="si">{</span><span class="n">df</span><span class="o">.</span><span class="n">duplicated</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<pre>Количество пропусков:
Student_ID                         0
Study_Hours_Per_Day                0
Extracurricular_Hours_Per_Day      0
Sleep_Hours_Per_Day                0
Social_Hours_Per_Day               0
Physical_Activity_Hours_Per_Day    0
Stress_Level                       0
Gender                             0
Grades                             0
dtype: int64

Количество дубликатов: 0
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">metric_columns</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">select_dtypes</span><span class="p">(</span><span class="n">include</span><span class="o">=</span><span class="p">[</span><span class="s1">'int64'</span><span class="p">,</span> <span class="s1">'float64'</span><span class="p">])</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
<span class="n">metric_columns</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="s1">'Student_ID'</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"metric_columns: </span><span class="si">{</span><span class="n">metric_columns</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>

<span class="n">categorical_columns</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">select_dtypes</span><span class="p">(</span><span class="n">include</span><span class="o">=</span><span class="p">[</span><span class="s1">'object'</span><span class="p">,</span> <span class="s1">'category'</span><span class="p">])</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"categorical_columns: </span><span class="si">{</span><span class="n">categorical_columns</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<pre>metric_columns: ['Study_Hours_Per_Day', 'Extracurricular_Hours_Per_Day', 'Sleep_Hours_Per_Day', 'Social_Hours_Per_Day', 'Physical_Activity_Hours_Per_Day', 'Grades']
categorical_columns: ['Stress_Level', 'Gender']
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<p>Датасет оказался хорошим, без пропущенных значений и дубликатов, как-либо удалять или заменять их не требуется</p>
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<h4 id="%D0%9C%D0%B5%D1%82%D1%80%D0%B8%D1%87%D0%B5%D1%81%D0%BA%D0%B8%D0%B5-%D0%BF%D0%B5%D1%80%D0%B5%D0%BC%D0%B5%D0%BD%D0%BD%D1%8B%D0%B5"><strong>Метрические переменные</strong><a class="anchor-link" href="#%D0%9C%D0%B5%D1%82%D1%80%D0%B8%D1%87%D0%B5%D1%81%D0%BA%D0%B8%D0%B5-%D0%BF%D0%B5%D1%80%D0%B5%D0%BC%D0%B5%D0%BD%D0%BD%D1%8B%D0%B5">¶</a></h4>
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<p>Посмотрим на распределения самой важной переменной датасета – оценки (<code>Grades</code>). Что интересно, распределение близко к нормальному. Все значения лежат в диапазоне 5-10, среднее, медиана и мода ≈7.8</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>

<span class="n">mean_val</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'Grades'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
<span class="n">median_val</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'Grades'</span><span class="p">]</span><span class="o">.</span><span class="n">median</span><span class="p">()</span>
<span class="n">mode_val</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'Grades'</span><span class="p">]</span><span class="o">.</span><span class="n">mode</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">q1</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'Grades'</span><span class="p">]</span><span class="o">.</span><span class="n">quantile</span><span class="p">(</span><span class="mf">0.25</span><span class="p">)</span>
<span class="n">q3</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'Grades'</span><span class="p">]</span><span class="o">.</span><span class="n">quantile</span><span class="p">(</span><span class="mf">0.75</span><span class="p">)</span>

<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'Grades'</span><span class="p">],</span> <span class="n">bins</span><span class="o">=</span><span class="mi">30</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'plum'</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">'m'</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.7</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">mean_val</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'orange'</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">'-'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s1">'mean: </span><span class="si">{</span><span class="n">mean_val</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">median_val</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'m'</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">'--'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s1">'median: </span><span class="si">{</span><span class="n">median_val</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">mode_val</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'cornflowerblue'</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">':'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s1">'mode: </span><span class="si">{</span><span class="n">mode_val</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Гистограмма распределения Grades'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'Grades'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">11</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'Количество студентов'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">11</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s1">'upper right'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">9</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'lightgray'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.7</span><span class="p">)</span>

<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">boxplot</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'Grades'</span><span class="p">],</span> <span class="n">patch_artist</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">boxprops</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">facecolor</span><span class="o">=</span><span class="s1">'plum'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'m'</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.7</span><span class="p">),</span> <span class="n">medianprops</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="s1">'m'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">),</span> <span class="n">flierprops</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">markerfacecolor</span><span class="o">=</span><span class="s1">'magenta'</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">5</span><span class="p">))</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Box plot Grades'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'Распределение оценок'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">11</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'Grades'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">11</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'lightgray'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.7</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">axes</span><span class="o">.</span><span class="n">get_xaxis</span><span class="p">()</span><span class="o">.</span><span class="n">set_visible</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>

<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="mf">1.15</span><span class="p">,</span> <span class="n">median_val</span><span class="p">,</span> <span class="sa">f</span><span class="s1">' median: </span><span class="si">{</span><span class="n">median_val</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1">'</span><span class="p">,</span> <span class="n">verticalalignment</span><span class="o">=</span><span class="s1">'center'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">9</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'k'</span><span class="p">,</span> <span class="n">fontweight</span><span class="o">=</span><span class="s1">'bold'</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="mf">1.15</span><span class="p">,</span> <span class="n">q3</span><span class="p">,</span> <span class="sa">f</span><span class="s1">' Q3 (75%): </span><span class="si">{</span><span class="n">q3</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1">'</span><span class="p">,</span> <span class="n">verticalalignment</span><span class="o">=</span><span class="s1">'center'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">9</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'k'</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="mf">1.15</span><span class="p">,</span> <span class="n">q1</span><span class="p">,</span> <span class="sa">f</span><span class="s1">' Q1 (25%): </span><span class="si">{</span><span class="n">q1</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1">'</span><span class="p">,</span> <span class="n">verticalalignment</span><span class="o">=</span><span class="s1">'center'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">9</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'k'</span><span class="p">)</span>

<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [308]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="p">[</span><span class="n">metric_columns</span><span class="p">]</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
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<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[308]:</div>
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<style scoped="">
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>Study_Hours_Per_Day</th>
<th>Extracurricular_Hours_Per_Day</th>
<th>Sleep_Hours_Per_Day</th>
<th>Social_Hours_Per_Day</th>
<th>Physical_Activity_Hours_Per_Day</th>
<th>Grades</th>
</tr>
</thead>
<tbody>
<tr>
<th>count</th>
<td>2000.00</td>
<td>2000.00</td>
<td>2000.00</td>
<td>2000.00</td>
<td>2000.00</td>
<td>2000.00</td>
</tr>
<tr>
<th>mean</th>
<td>7.48</td>
<td>1.99</td>
<td>7.50</td>
<td>2.70</td>
<td>4.33</td>
<td>7.79</td>
</tr>
<tr>
<th>std</th>
<td>1.42</td>
<td>1.16</td>
<td>1.46</td>
<td>1.69</td>
<td>2.51</td>
<td>0.75</td>
</tr>
<tr>
<th>min</th>
<td>5.00</td>
<td>0.00</td>
<td>5.00</td>
<td>0.00</td>
<td>0.00</td>
<td>5.60</td>
</tr>
<tr>
<th>25%</th>
<td>6.30</td>
<td>1.00</td>
<td>6.20</td>
<td>1.20</td>
<td>2.40</td>
<td>7.25</td>
</tr>
<tr>
<th>50%</th>
<td>7.40</td>
<td>2.00</td>
<td>7.50</td>
<td>2.60</td>
<td>4.10</td>
<td>7.78</td>
</tr>
<tr>
<th>75%</th>
<td>8.70</td>
<td>3.00</td>
<td>8.80</td>
<td>4.10</td>
<td>6.10</td>
<td>8.32</td>
</tr>
<tr>
<th>max</th>
<td>10.00</td>
<td>4.00</td>
<td>10.00</td>
<td>6.00</td>
<td>13.00</td>
<td>10.00</td>
</tr>
</tbody>
</table>
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<p>Все значения переменных лежат в адекватных для их смысла диапазонах, неотрицательных значений нет. Каких-либо ошибочных значений не выявлено</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [309]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">7</span><span class="p">))</span>
<span class="n">axes</span> <span class="o">=</span> <span class="n">axes</span><span class="o">.</span><span class="n">ravel</span><span class="p">()</span>

<span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">col</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">([</span><span class="s1">'Study_Hours_Per_Day'</span><span class="p">,</span> <span class="s1">'Extracurricular_Hours_Per_Day'</span><span class="p">,</span> <span class="s1">'Sleep_Hours_Per_Day'</span><span class="p">,</span> <span class="s1">'Social_Hours_Per_Day'</span><span class="p">,</span> <span class="s1">'Physical_Activity_Hours_Per_Day'</span><span class="p">,</span> <span class="s1">'Grades'</span><span class="p">]):</span>
    <span class="n">median_val</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">median</span><span class="p">()</span>
    <span class="n">min_val</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">()</span>
    <span class="n">max_val</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span>
    
    <span class="n">axes</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span><span class="o">.</span><span class="n">boxplot</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">col</span><span class="p">],</span> <span class="n">patch_artist</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">boxprops</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">facecolor</span><span class="o">=</span><span class="s1">'plum'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'m'</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.7</span><span class="p">),</span> <span class="n">medianprops</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="s1">'m'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">),</span><span class="n">flierprops</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">markerfacecolor</span><span class="o">=</span><span class="s1">'magenta'</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">5</span><span class="p">))</span>
    <span class="n">axes</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">col</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">)</span>
    <span class="n">axes</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span><span class="o">.</span><span class="n">axes</span><span class="o">.</span><span class="n">get_xaxis</span><span class="p">()</span><span class="o">.</span><span class="n">set_visible</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
    <span class="n">axes</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'lightgray'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.7</span><span class="p">)</span>
    <span class="n">axes</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'Значения'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
    
    <span class="n">axes</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="mf">1.15</span><span class="p">,</span> <span class="n">median_val</span><span class="p">,</span> <span class="sa">f</span><span class="s1">' median: </span><span class="si">{</span><span class="n">median_val</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1">'</span><span class="p">,</span> <span class="n">verticalalignment</span><span class="o">=</span><span class="s1">'center'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'k'</span><span class="p">,</span> <span class="n">fontweight</span><span class="o">=</span><span class="s1">'bold'</span><span class="p">)</span>
    <span class="n">axes</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="mf">1.15</span><span class="p">,</span> <span class="n">min_val</span> <span class="o">+</span> <span class="mf">0.1</span><span class="p">,</span> <span class="sa">f</span><span class="s1">' min: </span><span class="si">{</span><span class="n">min_val</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1">'</span><span class="p">,</span> <span class="n">verticalalignment</span><span class="o">=</span><span class="s1">'center'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'k'</span><span class="p">)</span>
    <span class="n">axes</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="mf">1.15</span><span class="p">,</span> <span class="n">max_val</span> <span class="o">-</span> <span class="mf">0.1</span><span class="p">,</span> <span class="sa">f</span><span class="s1">' max: </span><span class="si">{</span><span class="n">max_val</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1">'</span><span class="p">,</span> <span class="n">verticalalignment</span><span class="o">=</span><span class="s1">'center'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'k'</span><span class="p">)</span>

<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<p>Выбросы есть только в двух переменных – <code>Physical_Activity_Hours_Per_Day</code> и <code>Grades</code></p>
<p>Выведем их с помощью функции межквартильного размаха</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [310]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="k">def</span><span class="w"> </span><span class="nf">outliers</span><span class="p">(</span><span class="n">series</span><span class="p">):</span>
    <span class="n">Q1</span> <span class="o">=</span> <span class="n">series</span><span class="o">.</span><span class="n">quantile</span><span class="p">(</span><span class="mf">0.25</span><span class="p">)</span>
    <span class="n">Q3</span> <span class="o">=</span> <span class="n">series</span><span class="o">.</span><span class="n">quantile</span><span class="p">(</span><span class="mf">0.75</span><span class="p">)</span>
    <span class="n">IQR</span> <span class="o">=</span> <span class="n">Q3</span> <span class="o">-</span> <span class="n">Q1</span>
    <span class="n">lower_bound</span> <span class="o">=</span> <span class="n">Q1</span> <span class="o">-</span> <span class="mf">1.5</span> <span class="o">*</span> <span class="n">IQR</span>
    <span class="n">upper_bound</span> <span class="o">=</span> <span class="n">Q3</span> <span class="o">+</span> <span class="mf">1.5</span> <span class="o">*</span> <span class="n">IQR</span>
    <span class="n">outliers_series</span> <span class="o">=</span> <span class="n">series</span><span class="p">[(</span><span class="n">series</span> <span class="o">&lt;</span> <span class="n">lower_bound</span><span class="p">)</span> <span class="o">|</span> <span class="p">(</span><span class="n">series</span> <span class="o">&gt;</span> <span class="n">upper_bound</span><span class="p">)]</span>
    <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="n">outliers_series</span><span class="p">),</span> <span class="n">lower_bound</span><span class="p">,</span> <span class="n">upper_bound</span><span class="p">,</span> <span class="n">outliers_series</span>

<span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="n">metric_columns</span><span class="p">:</span>
    <span class="n">count</span><span class="p">,</span> <span class="n">lower</span><span class="p">,</span> <span class="n">upper</span><span class="p">,</span> <span class="n">outliers_data</span> <span class="o">=</span> <span class="n">outliers</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">col</span><span class="p">])</span>
    
    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="se">\n</span><span class="si">{</span><span class="n">col</span><span class="si">}</span><span class="s2"> – </span><span class="si">{</span><span class="n">count</span><span class="si">}</span><span class="s2"> выбросов"</span><span class="p">,</span> <span class="n">end</span><span class="o">=</span><span class="s2">""</span><span class="p">)</span>
    <span class="k">if</span> <span class="n">count</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>            
        <span class="nb">print</span><span class="p">(</span><span class="s2">":"</span><span class="p">,</span> <span class="n">end</span><span class="o">=</span><span class="s2">" "</span><span class="p">)</span>
        <span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">val</span> <span class="ow">in</span> <span class="n">outliers_data</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
                <span class="nb">print</span><span class="p">(</span><span class="n">val</span><span class="p">,</span> <span class="n">end</span><span class="o">=</span><span class="s2">" "</span><span class="p">)</span>
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<pre>
Study_Hours_Per_Day – 0 выбросов
Extracurricular_Hours_Per_Day – 0 выбросов
Sleep_Hours_Per_Day – 0 выбросов
Social_Hours_Per_Day – 0 выбросов
Physical_Activity_Hours_Per_Day – 5 выбросов: 13.0 12.4 12.1 12.3 11.7 
Grades – 4 выбросов: 10.0 5.62 5.6 5.62 </pre>
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<p>Полученные выбросы, как мне кажется, не являются погрешностью измерений или ошибкой в данных, они не являются некорректным значеним оценки, поэтому их предлагается оставить, как реальные данные, чтобы не исказить реальную картину при дальнейшем анализе</p>
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<h4 id="%D0%9A%D0%B0%D1%82%D0%B5%D0%B3%D0%BE%D1%80%D0%B8%D0%B0%D0%BB%D1%8C%D0%BD%D1%8B%D0%B5-%D0%BF%D0%B5%D1%80%D0%B5%D0%BC%D0%B5%D0%BD%D0%BD%D1%8B%D0%B5"><strong>Категориальные переменные</strong><a class="anchor-link" href="#%D0%9A%D0%B0%D1%82%D0%B5%D0%B3%D0%BE%D1%80%D0%B8%D0%B0%D0%BB%D1%8C%D0%BD%D1%8B%D0%B5-%D0%BF%D0%B5%D1%80%D0%B5%D0%BC%D0%B5%D0%BD%D0%BD%D1%8B%D0%B5">¶</a></h4>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [311]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="p">[</span><span class="n">categorical_columns</span><span class="p">]</span><span class="o">.</span><span class="n">describe</span><span class="p">()</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
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<style scoped="">
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>Stress_Level</th>
<th>Gender</th>
</tr>
</thead>
<tbody>
<tr>
<th>count</th>
<td>2000</td>
<td>2000</td>
</tr>
<tr>
<th>unique</th>
<td>3</td>
<td>2</td>
</tr>
</tbody>
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<p>Все категориальные атрибуты прнимают 3 для <code>Stress_Level</code> и 2 для <code>Gender</code> уникальных значения, каких-то некорректных случайных значений нет</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [312]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>

<span class="n">stress_counts</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'Stress_Level'</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span>
<span class="n">colors_stress</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'darkorange'</span><span class="p">,</span> <span class="s1">'m'</span><span class="p">,</span> <span class="s1">'cornflowerblue'</span><span class="p">]</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">pie</span><span class="p">(</span><span class="n">stress_counts</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">stress_counts</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">autopct</span><span class="o">=</span><span class="s1">'</span><span class="si">%1.1f%%</span><span class="s1">'</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="n">colors_stress</span><span class="p">,</span> <span class="n">startangle</span><span class="o">=</span><span class="mi">90</span><span class="p">,</span> <span class="n">wedgeprops</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">edgecolor</span><span class="o">=</span><span class="s1">'m'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">1.5</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.4</span><span class="p">))</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Stress_Level'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>

<span class="n">legend_labels</span> <span class="o">=</span> <span class="p">[</span><span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">idx</span><span class="si">}</span><span class="s1">: </span><span class="si">{</span><span class="n">count</span><span class="si">}</span><span class="s1">'</span> <span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">count</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">stress_counts</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">stress_counts</span><span class="o">.</span><span class="n">values</span><span class="p">)]</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">legend_labels</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">'center left'</span><span class="p">,</span> <span class="n">bbox_to_anchor</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mf">0.8</span><span class="p">),</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">9</span><span class="p">,</span> <span class="n">frameon</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">facecolor</span><span class="o">=</span><span class="s1">'white'</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">'lightgray'</span><span class="p">,</span><span class="n">framealpha</span><span class="o">=</span><span class="mf">0.9</span><span class="p">)</span>


<span class="n">gender_counts</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'Gender'</span><span class="p">]</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span>
<span class="n">colors_gender</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'cornflowerblue'</span><span class="p">,</span> <span class="s1">'m'</span><span class="p">]</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">pie</span><span class="p">(</span><span class="n">gender_counts</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">gender_counts</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">autopct</span><span class="o">=</span><span class="s1">'</span><span class="si">%1.1f%%</span><span class="s1">'</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="n">colors_gender</span><span class="p">,</span> <span class="n">startangle</span><span class="o">=</span><span class="mi">90</span><span class="p">,</span> <span class="n">wedgeprops</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">edgecolor</span><span class="o">=</span><span class="s1">'m'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">1.5</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.4</span><span class="p">))</span> 
<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Gender'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>

<span class="n">legend_labels</span> <span class="o">=</span> <span class="p">[</span><span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">idx</span><span class="si">}</span><span class="s1">: </span><span class="si">{</span><span class="n">count</span><span class="si">}</span><span class="s1">'</span> <span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">count</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">gender_counts</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">gender_counts</span><span class="o">.</span><span class="n">values</span><span class="p">)]</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">legend_labels</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="s1">'center left'</span><span class="p">,</span> <span class="n">bbox_to_anchor</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mf">0.8</span><span class="p">),</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">9</span><span class="p">,</span> <span class="n">frameon</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">facecolor</span><span class="o">=</span><span class="s1">'white'</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">'lightgray'</span><span class="p">,</span> <span class="n">framealpha</span><span class="o">=</span><span class="mf">0.9</span><span class="p">)</span>

<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<h2 id="%D0%9F%D1%80%D0%BE%D0%B2%D0%B5%D1%80%D0%BA%D0%B0-%D1%81%D1%82%D0%B0%D1%82%D0%B8%D1%81%D1%82%D0%B8%D1%87%D0%B5%D1%81%D0%BA%D0%BE%D0%B9-%D0%B3%D0%B8%D0%BF%D0%BE%D1%82%D0%B5%D0%B7%D1%8B"><strong>Проверка статистической гипотезы</strong><a class="anchor-link" href="#%D0%9F%D1%80%D0%BE%D0%B2%D0%B5%D1%80%D0%BA%D0%B0-%D1%81%D1%82%D0%B0%D1%82%D0%B8%D1%81%D1%82%D0%B8%D1%87%D0%B5%D1%81%D0%BA%D0%BE%D0%B9-%D0%B3%D0%B8%D0%BF%D0%BE%D1%82%D0%B5%D0%B7%D1%8B">¶</a></h2>
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<p>На двух выборках проведем сравнение среднего количества часов сна у студентов мужского и женского пола с помощью статистических тестов, проверив соответствие выборок условиям применимости</p>
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<h4 id="%D0%A4%D0%BE%D1%80%D0%BC%D1%83%D0%BB%D0%B8%D1%80%D0%BE%D0%B2%D0%BA%D0%B0-%D0%B3%D0%B8%D0%BF%D0%BE%D1%82%D0%B5%D0%B7"><strong>Формулировка гипотез</strong><a class="anchor-link" href="#%D0%A4%D0%BE%D1%80%D0%BC%D1%83%D0%BB%D0%B8%D1%80%D0%BE%D0%B2%D0%BA%D0%B0-%D0%B3%D0%B8%D0%BF%D0%BE%D1%82%D0%B5%D0%B7">¶</a></h4><p><strong>H₀ (нулевая гипотеза):</strong> Среднее количество часов сна в сутки у студентов мужского и женского пола одинаково</p>
<p><strong>H₁ (альтернативная гипотеза):</strong> Среднее количество часов сна в сутки у студентов мужского и женского пола отличается</p>
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<h4 id="%D0%92%D1%8B%D0%B1%D0%BE%D1%80-%D1%81%D1%82%D0%B0%D1%82%D0%B8%D1%81%D1%82%D0%B8%D1%87%D0%B5%D1%81%D0%BA%D0%BE%D0%B3%D0%BE-%D1%82%D0%B5%D1%81%D1%82%D0%B0"><strong>Выбор статистического теста</strong><a class="anchor-link" href="#%D0%92%D1%8B%D0%B1%D0%BE%D1%80-%D1%81%D1%82%D0%B0%D1%82%D0%B8%D1%81%D1%82%D0%B8%D1%87%D0%B5%D1%81%D0%BA%D0%BE%D0%B3%D0%BE-%D1%82%D0%B5%D1%81%D1%82%D0%B0">¶</a></h4><p>Предполагаем, что при соблюдении условий применимости, будем использовать <strong>независимый (двухвыборочный) t-тест</strong>, поскольку</p>
<ul>
<li>проводится сравнение 2 выборок</li>
<li>выборки независимы (разделение по полу <code>Male</code> / <code>Female</code>)</li>
<li>проверяем гипотезы о равнестве средних (количественные данные)</li>
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<p><strong>Критерии применимости t-теста</strong></p>
<ul>
<li>независимость данных (соблюдается)</li>
<li>нормальность распределения в каждой выборке (проверим)</li>
<li>равенство дисперсий (проверим)</li>
</ul>
<p>Уровень значимости стандартно задаем $\alpha = 0.05$</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">male_sleep</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="s1">'Gender'</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'Male'</span><span class="p">][</span><span class="s1">'Sleep_Hours_Per_Day'</span><span class="p">]</span>
<span class="n">female_sleep</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="s1">'Gender'</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'Female'</span><span class="p">][</span><span class="s1">'Sleep_Hours_Per_Day'</span><span class="p">]</span>

<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Male: n = </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">male_sleep</span><span class="p">)</span><span class="si">}</span><span class="s2">, mean = </span><span class="si">{</span><span class="n">male_sleep</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">, var = </span><span class="si">{</span><span class="n">male_sleep</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="n">ddof</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Female: n = </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">female_sleep</span><span class="p">)</span><span class="si">}</span><span class="s2">, mean = </span><span class="si">{</span><span class="n">female_sleep</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">, var = </span><span class="si">{</span><span class="n">female_sleep</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="n">ddof</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<pre>Male: n = 1016, mean = 7.53, var = 2.15
Female: n = 984, mean = 7.48, var = 2.12
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<h4 id="%D0%9F%D1%80%D0%BE%D0%B2%D0%B5%D1%80%D0%BA%D0%B0-%D0%BD%D0%BE%D1%80%D0%BC%D0%B0%D0%BB%D1%8C%D0%BD%D0%BE%D1%81%D1%82%D0%B8-%D1%80%D0%B0%D1%81%D0%BF%D1%80%D0%B5%D0%B4%D0%B5%D0%BB%D0%B5%D0%BD%D0%B8%D0%B9"><strong>Проверка нормальности распределений</strong><a class="anchor-link" href="#%D0%9F%D1%80%D0%BE%D0%B2%D0%B5%D1%80%D0%BA%D0%B0-%D0%BD%D0%BE%D1%80%D0%BC%D0%B0%D0%BB%D1%8C%D0%BD%D0%BE%D1%81%D1%82%D0%B8-%D1%80%D0%B0%D1%81%D0%BF%D1%80%D0%B5%D0%B4%D0%B5%D0%BB%D0%B5%D0%BD%D0%B8%D0%B9">¶</a></h4><p>Для проверки нормальности распределений используем тест Шапиро-Уилка</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">shapiro_male</span> <span class="o">=</span> <span class="n">stats</span><span class="o">.</span><span class="n">shapiro</span><span class="p">(</span><span class="n">male_sleep</span><span class="p">)</span>
<span class="n">shapiro_female</span> <span class="o">=</span> <span class="n">stats</span><span class="o">.</span><span class="n">shapiro</span><span class="p">(</span><span class="n">female_sleep</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Male: тест Шапиро–Уилка p_value = </span><span class="si">{</span><span class="n">shapiro_male</span><span class="o">.</span><span class="n">pvalue</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Female: тест Шапиро–Уилка p_value = </span><span class="si">{</span><span class="n">shapiro_female</span><span class="o">.</span><span class="n">pvalue</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<pre>Male: тест Шапиро–Уилка p_value = 0.00
Female: тест Шапиро–Уилка p_value = 0.00
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<p>По результатам теста в обоих выборках <code>p_value = 0.00 &lt; alpha = 0.05</code></p>
<p>То есть $p_{value}$ меньше уровня статистической значимости, а значим мы <strong>отвергаем</strong> нулевую гипотезу теста Шапиро-Уилка от том, что данные в выборках распределены нормально</p>
<p>Распределение данных в выборках нельзя назвать нормальным и наша идея применить t-тест <strong>не</strong> может быть реализована</p>
<p>Убедимся в том, что распределение не нормальное, на графиках:</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [315]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>

<span class="n">male_mean</span> <span class="o">=</span> <span class="n">male_sleep</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
<span class="n">male_median</span> <span class="o">=</span> <span class="n">male_sleep</span><span class="o">.</span><span class="n">median</span><span class="p">()</span>
<span class="n">male_mode</span> <span class="o">=</span> <span class="n">male_sleep</span><span class="o">.</span><span class="n">mode</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span>

<span class="n">female_mean</span> <span class="o">=</span> <span class="n">female_sleep</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
<span class="n">female_median</span> <span class="o">=</span> <span class="n">female_sleep</span><span class="o">.</span><span class="n">median</span><span class="p">()</span>
<span class="n">female_mode</span> <span class="o">=</span> <span class="n">female_sleep</span><span class="o">.</span><span class="n">mode</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span>

<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">male_sleep</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="mi">30</span><span class="p">,</span> <span class="n">density</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'royalblue'</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">'midnightblue'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Гистограмма'</span><span class="p">)</span>
<span class="n">male_kde</span> <span class="o">=</span> <span class="n">male_sleep</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">kde</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">'royalblue'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">2.5</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Плотность распределения'</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">male_mean</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'orange'</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">'-'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s1">'mean: </span><span class="si">{</span><span class="n">male_mean</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">male_median</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'midnightblue'</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">'--'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s1">'median: </span><span class="si">{</span><span class="n">male_median</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">male_mode</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'cornflowerblue'</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">':'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s1">'mode: </span><span class="si">{</span><span class="n">male_mode</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'male_sleep'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'Sleep_Hours_Per_Day'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">11</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'Плотность'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">11</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s1">'upper right'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">9</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'lightgray'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.7</span><span class="p">)</span>


<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">female_sleep</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="mi">30</span><span class="p">,</span> <span class="n">density</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'magenta'</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">'purple'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Гистограмма'</span><span class="p">)</span>
<span class="n">female_kde</span> <span class="o">=</span> <span class="n">female_sleep</span><span class="o">.</span><span class="n">plot</span><span class="o">.</span><span class="n">kde</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">'magenta'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">2.5</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Плотность распределения'</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">female_mean</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'orange'</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">'-'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s1">'mean: </span><span class="si">{</span><span class="n">female_mean</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">female_median</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'purple'</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">'--'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s1">'median: </span><span class="si">{</span><span class="n">female_median</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">female_mode</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'orange'</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">':'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s1">'mode: </span><span class="si">{</span><span class="n">female_mode</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'female_sleep'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'Sleep_Hours_Per_Day'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">11</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'Плотность'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">11</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s1">'upper right'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">9</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'lightgray'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.7</span><span class="p">)</span>


<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<p>Для дальнейшего анализа выберу <strong>Bootstrap-тест</strong>. В отличии от U-критерия Манна-Уитни, который также не требует нормальности распределений, bootstrap позволяет сравнивать именно средние*</p>
<p>* H₀ и H₁ соответственно сохраняются в прежней формулировке</p>
<p><strong>Критерии применимости bootstrap-теста</strong></p>
<ul>
<li>независимость данных (соблюдается)</li>
<li>достаточный размер выборки (соблюдается, $n_{male} = 1016$, $n_{female} = 984$)</li>
<li>выборка отражает генеральную совокупность (соблюдается)</li>
<li>отсутствие существенных выбросов (визуально соблюдается, но лучше проверить)</li>
</ul>
<p>Bootstrap может быть чувствителен к сильно разным дисперсиям и крайней асимметрии, но в данном случае, исходя из преданализа выше (дисперсии, графики и очистка выбросов) у нас классический bootstrap должен работать корректно</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.utils</span><span class="w"> </span><span class="kn">import</span> <span class="n">resample</span>

<span class="k">def</span><span class="w"> </span><span class="nf">bootstrap_mean_test</span><span class="p">(</span><span class="n">group1</span><span class="p">,</span> <span class="n">group2</span><span class="p">,</span> <span class="n">n</span><span class="o">=</span><span class="mi">10000</span><span class="p">,</span> <span class="n">ci_level</span><span class="o">=</span><span class="mf">0.95</span><span class="p">):</span>
    <span class="n">observed_diff</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">group1</span><span class="p">)</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">group2</span><span class="p">)</span>
    <span class="n">conc</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">group1</span><span class="p">,</span> <span class="n">group2</span><span class="p">])</span>
    
    <span class="n">bootstrap_diffs</span> <span class="o">=</span> <span class="p">[]</span>
    
    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
        <span class="n">sample1</span> <span class="o">=</span> <span class="n">resample</span><span class="p">(</span><span class="n">conc</span><span class="p">,</span> <span class="n">n_samples</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">group1</span><span class="p">),</span> <span class="n">replace</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="n">i</span><span class="p">)</span>
        <span class="n">sample2</span> <span class="o">=</span> <span class="n">resample</span><span class="p">(</span><span class="n">conc</span><span class="p">,</span> <span class="n">n_samples</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">group2</span><span class="p">),</span> <span class="n">replace</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="n">i</span> <span class="o">+</span> <span class="mi">10000</span><span class="p">)</span>

        <span class="n">bootstrap_diff</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">sample1</span><span class="p">)</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">sample2</span><span class="p">)</span>
        <span class="n">bootstrap_diffs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">bootstrap_diff</span><span class="p">)</span>
    
    <span class="n">bootstrap_diffs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">bootstrap_diffs</span><span class="p">)</span>
    <span class="n">p_value</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">bootstrap_diffs</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">observed_diff</span><span class="p">))</span>

    <span class="n">alpha</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">ci_level</span>
    <span class="n">ci_lower</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">bootstrap_diffs</span><span class="p">,</span> <span class="mi">100</span> <span class="o">*</span> <span class="n">alpha</span><span class="o">/</span><span class="mi">2</span><span class="p">)</span>
    <span class="n">ci_upper</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">bootstrap_diffs</span><span class="p">,</span> <span class="mi">100</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">alpha</span><span class="o">/</span><span class="mi">2</span><span class="p">))</span>
    
    <span class="k">return</span> <span class="n">observed_diff</span><span class="p">,</span> <span class="n">p_value</span><span class="p">,</span> <span class="n">bootstrap_diffs</span><span class="p">,</span> <span class="n">ci_lower</span><span class="p">,</span> <span class="n">ci_upper</span>

<span class="n">observed_diff</span><span class="p">,</span> <span class="n">p_value</span><span class="p">,</span> <span class="n">bootstrap_diffs</span><span class="p">,</span> <span class="n">ci_lower</span><span class="p">,</span> <span class="n">ci_upper</span> <span class="o">=</span> <span class="n">bootstrap_mean_test</span><span class="p">(</span><span class="n">male_sleep</span><span class="p">,</span> <span class="n">female_sleep</span><span class="p">,</span> <span class="n">n</span><span class="o">=</span><span class="mi">10000</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Наблюдаемая разница средних Male – Female: </span><span class="si">{</span><span class="n">observed_diff</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Bootstrap p-value: </span><span class="si">{</span><span class="n">p_value</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<pre>Наблюдаемая разница средних Male – Female: 0.05
Bootstrap p-value: 0.45
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<p>По результатам теста <code>p_value = 0.42 &gt; alpha = 0.05</code> – значительно больше уровня статистической значимости $\alpha$, а значим мы <strong>не отвергаем</strong> нулевую гипотезу</p>
<p>То есть действительно <strong>Среднее количество часов сна в сутки у студентов мужского и женского пола одинаково</strong></p>
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<h2 id="%D0%90%D0%BD%D0%B0%D0%BB%D0%B8%D0%B7-%D0%B2%D0%B7%D0%B0%D0%B8%D0%BC%D0%BE%D1%81%D0%B2%D1%8F%D0%B7%D0%B8-%D0%BF%D0%B5%D1%80%D0%B5%D0%BC%D0%B5%D0%BD%D0%BD%D1%8B%D1%85"><strong>Анализ взаимосвязи переменных</strong><a class="anchor-link" href="#%D0%90%D0%BD%D0%B0%D0%BB%D0%B8%D0%B7-%D0%B2%D0%B7%D0%B0%D0%B8%D0%BC%D0%BE%D1%81%D0%B2%D1%8F%D0%B7%D0%B8-%D0%BF%D0%B5%D1%80%D0%B5%D0%BC%D0%B5%D0%BD%D0%BD%D1%8B%D1%85">¶</a></h2>
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<h4 id="%D0%9E%D1%86%D0%B5%D0%BD%D0%BA%D0%B0-%D0%B2%D0%B7%D0%B0%D0%B8%D0%BC%D0%BE%D1%81%D0%B2%D1%8F%D0%B7%D0%B5%D0%B9-%D0%BC%D0%B5%D1%82%D1%80%D0%B8%D1%87%D0%B5%D1%81%D0%BA%D0%B8%D1%85-%D0%BF%D0%B0%D1%80%D0%B0%D0%BC%D0%B5%D1%82%D1%80%D0%BE%D0%B2-%E2%80%94-%D0%BC%D0%B0%D1%82%D1%80%D0%B8%D1%86%D0%B0-%D0%BA%D0%BE%D1%80%D1%80%D0%B5%D0%BB%D1%8F%D1%86%D0%B8%D0%B9"><strong>Оценка взаимосвязей метрических параметров — матрица корреляций</strong><a class="anchor-link" href="#%D0%9E%D1%86%D0%B5%D0%BD%D0%BA%D0%B0-%D0%B2%D0%B7%D0%B0%D0%B8%D0%BC%D0%BE%D1%81%D0%B2%D1%8F%D0%B7%D0%B5%D0%B9-%D0%BC%D0%B5%D1%82%D1%80%D0%B8%D1%87%D0%B5%D1%81%D0%BA%D0%B8%D1%85-%D0%BF%D0%B0%D1%80%D0%B0%D0%BC%D0%B5%D1%82%D1%80%D0%BE%D0%B2-%E2%80%94-%D0%BC%D0%B0%D1%82%D1%80%D0%B8%D1%86%D0%B0-%D0%BA%D0%BE%D1%80%D1%80%D0%B5%D0%BB%D1%8F%D1%86%D0%B8%D0%B9">¶</a></h4>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">corr_matrix</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">metric_columns</span><span class="p">]</span><span class="o">.</span><span class="n">corr</span><span class="p">()</span>

<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">9</span><span class="p">,</span> <span class="mi">8</span><span class="p">))</span>
<span class="n">sns</span><span class="o">.</span><span class="n">heatmap</span><span class="p">(</span><span class="n">corr_matrix</span><span class="p">,</span> <span class="n">annot</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">'magma'</span><span class="p">,</span> <span class="n">center</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">'.2f'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Матрица корреляций метрических признаков'</span><span class="p">)</span>

<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<p>Можно заметить, что наибольшую корреляцию имеют атрибуты <code>Grades</code> и <code>Study_Hours_Per_Day</code>. Это довольно логично – чем больше студент тратит времени в день на учебу, тем лучше должны быть его оценки</p>
<p>Что интересно, корреляция между <code>Grades</code> и <code>Sleep_Hours_Per_Day</code> ≈ 0.00</p>
<p>В моем предположении между этими атрибутами должна бы быть неслабая прямая корреляция – ведь, чем больше спит и соответственно лучше высыпается студент, тем лучше должны быть его оценки и успеваемость (без ухода в крайности и слишком большое количества сна в день, которое мешает учебе). Но, исходя из корреляции, никакой прямой линейной зависимости не наблюдается. Возможно тут более сложная зависимость – квадратичная функция? (Парабола ветками вниз – при большом дефиците и слишком большом количестве сна в день оценки плохие, а в середине самые лучшие). Для проверки предположения построим линейный график:</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="p">[</span><span class="s1">'Sleep_Hours_Rounded'</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'Sleep_Hours_Per_Day'</span><span class="p">]</span><span class="o">.</span><span class="n">round</span><span class="p">()</span>
<span class="n">sleep_grades</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">'Sleep_Hours_Rounded'</span><span class="p">)[</span><span class="s1">'Grades'</span><span class="p">]</span><span class="o">.</span><span class="n">agg</span><span class="p">([</span><span class="s1">'mean'</span><span class="p">,</span> <span class="s1">'std'</span><span class="p">,</span> <span class="s1">'count'</span><span class="p">])</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span>

<span class="n">display</span><span class="p">(</span><span class="n">sleep_grades</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="mi">2</span><span class="p">))</span>
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    .dataframe thead th {
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<th>Sleep_Hours_Rounded</th>
<th>mean</th>
<th>std</th>
<th>count</th>
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<th>0</th>
<td>5.0</td>
<td>7.86</td>
<td>0.73</td>
<td>192</td>
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<th>1</th>
<td>6.0</td>
<td>7.73</td>
<td>0.69</td>
<td>439</td>
</tr>
<tr>
<th>2</th>
<td>7.0</td>
<td>7.83</td>
<td>0.74</td>
<td>333</td>
</tr>
<tr>
<th>3</th>
<td>8.0</td>
<td>7.85</td>
<td>0.74</td>
<td>467</td>
</tr>
<tr>
<th>4</th>
<td>9.0</td>
<td>7.74</td>
<td>0.78</td>
<td>335</td>
</tr>
<tr>
<th>5</th>
<td>10.0</td>
<td>7.75</td>
<td>0.82</td>
<td>234</td>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>

<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">sleep_grades</span><span class="p">[</span><span class="s1">'Sleep_Hours_Rounded'</span><span class="p">],</span> <span class="n">sleep_grades</span><span class="p">[</span><span class="s1">'mean'</span><span class="p">],</span> <span class="s1">'o-'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'m'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">markerfacecolor</span><span class="o">=</span><span class="s1">'plum'</span><span class="p">,</span> <span class="n">markeredgecolor</span><span class="o">=</span><span class="s1">'m'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Средняя оценка'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">fill_between</span><span class="p">(</span><span class="n">sleep_grades</span><span class="p">[</span><span class="s1">'Sleep_Hours_Rounded'</span><span class="p">],</span> <span class="n">sleep_grades</span><span class="p">[</span><span class="s1">'mean'</span><span class="p">]</span> <span class="o">-</span> <span class="n">sleep_grades</span><span class="p">[</span><span class="s1">'std'</span><span class="p">],</span> <span class="n">sleep_grades</span><span class="p">[</span><span class="s1">'mean'</span><span class="p">]</span> <span class="o">+</span> <span class="n">sleep_grades</span><span class="p">[</span><span class="s1">'std'</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">'plum'</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Стандартное отклонение'</span><span class="p">)</span>

<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">sleep_grades</span><span class="o">.</span><span class="n">iterrows</span><span class="p">():</span>
    <span class="n">ax</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="sa">f</span><span class="s1">'n=</span><span class="si">{</span><span class="nb">int</span><span class="p">(</span><span class="n">row</span><span class="p">[</span><span class="s2">"count"</span><span class="p">])</span><span class="si">}</span><span class="s1">'</span><span class="p">,</span> <span class="p">(</span><span class="n">row</span><span class="p">[</span><span class="s1">'Sleep_Hours_Rounded'</span><span class="p">],</span> <span class="n">row</span><span class="p">[</span><span class="s1">'mean'</span><span class="p">]),</span> <span class="n">textcoords</span><span class="o">=</span><span class="s2">"offset points"</span><span class="p">,</span> <span class="n">xytext</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">10</span><span class="p">),</span> <span class="n">ha</span><span class="o">=</span><span class="s1">'center'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">9</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'m'</span><span class="p">)</span>

<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'Sleep_Hours_Rounded'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'Grades'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Зависимость оценок от количества часов сна'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">,</span> <span class="n">fontweight</span><span class="o">=</span><span class="s1">'bold'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s1">'best'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'lightgray'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.7</span><span class="p">)</span>

<span class="n">mean_grade</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'Grades'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
<span class="n">ax</span><span class="o">.</span><span class="n">axhline</span><span class="p">(</span><span class="n">y</span><span class="o">=</span><span class="n">mean_grade</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'orange'</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">'--'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s1">'Средняя оценка: </span><span class="si">{</span><span class="n">mean_grade</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>

<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<p>Но нет, никакой квадратичной зависимости на графике не наблюдается. Подробнее о средней оценке для каждой категории количества сна можно также посмотреть благодаря функции группировки и агрегации выше</p>
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<h4 id="%D0%9E%D1%86%D0%B5%D0%BD%D0%BA%D0%B0-%D0%B2%D0%B7%D0%B0%D0%B8%D0%BC%D0%BE%D1%81%D0%B2%D1%8F%D0%B7%D0%B5%D0%B9-%D0%BD%D0%B5%D0%BC%D0%B5%D1%82%D1%80%D0%B8%D1%87%D0%B5%D1%81%D0%BA%D0%B8%D1%85-%D0%BF%D0%B0%D1%80%D0%B0%D0%BC%D0%B5%D1%82%D1%80%D0%BE%D0%B2-%E2%80%94-$%5Cchi%5E2$"><strong>Оценка взаимосвязей неметрических параметров — $\chi^2$</strong><a class="anchor-link" href="#%D0%9E%D1%86%D0%B5%D0%BD%D0%BA%D0%B0-%D0%B2%D0%B7%D0%B0%D0%B8%D0%BC%D0%BE%D1%81%D0%B2%D1%8F%D0%B7%D0%B5%D0%B9-%D0%BD%D0%B5%D0%BC%D0%B5%D1%82%D1%80%D0%B8%D1%87%D0%B5%D1%81%D0%BA%D0%B8%D1%85-%D0%BF%D0%B0%D1%80%D0%B0%D0%BC%D0%B5%D1%82%D1%80%D0%BE%D0%B2-%E2%80%94-$%5Cchi%5E2$">¶</a></h4>
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<div class="highlight hl-ipython3"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">scipy.stats</span><span class="w"> </span><span class="kn">import</span> <span class="n">chi2_contingency</span>

<span class="n">chi2</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">dof</span><span class="p">,</span> <span class="n">expected</span> <span class="o">=</span> <span class="n">chi2_contingency</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">crosstab</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'Stress_Level'</span><span class="p">],</span> <span class="n">df</span><span class="p">[</span><span class="s1">'Gender'</span><span class="p">]))</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Хи-квадрат p-value: </span><span class="si">{</span><span class="n">p</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<pre>Хи-квадрат p-value: 0.70
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<p>У нас всего 2 неметрических параметра – <code>Stress_Level</code> и <code>Gender</code>, для оценки связи между ними используем критерий Хи-квадрат</p>
<p><code>p-value = 0.74 &gt; alpha = 0.05</code> говорит о том, что у нас нет оснований отвергнуть нулевую гипотезу <strong>H₀</strong> критерия $\chi^2$ о том, что переменные независимы</p>
<p>То есть нет статистически значимой связи между уровнем стресса и полом студента</p>
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<h4 id="%D0%9E%D1%86%D0%B5%D0%BD%D0%BA%D0%B0-%D1%81%D0%B2%D1%8F%D0%B7%D0%B8-%D0%BC%D0%B5%D0%B6%D0%B4%D1%83-%D0%BA%D0%B0%D1%82%D0%B5%D0%B3%D0%BE%D1%80%D0%B8%D0%B0%D0%BB%D1%8C%D0%BD%D1%8B%D0%BC%D0%B8-%D0%BF%D0%B5%D1%80%D0%B5%D0%BC%D0%B5%D0%BD%D0%BD%D1%8B%D0%BC%D0%B8-%D0%B8-%D0%BC%D0%B5%D1%82%D1%80%D0%B8%D1%87%D0%B5%D1%81%D0%BA%D0%B8%D0%BC%D0%B8-%E2%80%94-t-%D1%82%D0%B5%D1%81%D1%82-%D0%B8-ANOVA"><strong>Оценка связи между категориальными переменными и метрическими — t-тест и ANOVA</strong><a class="anchor-link" href="#%D0%9E%D1%86%D0%B5%D0%BD%D0%BA%D0%B0-%D1%81%D0%B2%D1%8F%D0%B7%D0%B8-%D0%BC%D0%B5%D0%B6%D0%B4%D1%83-%D0%BA%D0%B0%D1%82%D0%B5%D0%B3%D0%BE%D1%80%D0%B8%D0%B0%D0%BB%D1%8C%D0%BD%D1%8B%D0%BC%D0%B8-%D0%BF%D0%B5%D1%80%D0%B5%D0%BC%D0%B5%D0%BD%D0%BD%D1%8B%D0%BC%D0%B8-%D0%B8-%D0%BC%D0%B5%D1%82%D1%80%D0%B8%D1%87%D0%B5%D1%81%D0%BA%D0%B8%D0%BC%D0%B8-%E2%80%94-t-%D1%82%D0%B5%D1%81%D1%82-%D0%B8-ANOVA">¶</a></h4>
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<p>Используем функции <code>ttest_ind</code> и <code>f_oneway</code> из библиотеки <code>SciPy.stats</code> для проведение t-теста для 2х групп и ANOVA теста для 3х групп</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">scipy.stats</span><span class="w"> </span><span class="kn">import</span> <span class="n">ttest_ind</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">scipy.stats</span><span class="w"> </span><span class="kn">import</span> <span class="n">f_oneway</span>

<span class="k">for</span> <span class="n">cat</span> <span class="ow">in</span> <span class="n">categorical_columns</span><span class="p">:</span>
    <span class="k">for</span> <span class="n">met</span> <span class="ow">in</span> <span class="n">metric_columns</span><span class="p">:</span>
            <span class="k">if</span> <span class="n">df</span><span class="p">[</span><span class="n">cat</span><span class="p">]</span><span class="o">.</span><span class="n">nunique</span><span class="p">()</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span> <span class="c1"># t-test для 2 групп</span>
                <span class="n">groups</span> <span class="o">=</span> <span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="n">cat</span><span class="p">]</span> <span class="o">==</span> <span class="n">val</span><span class="p">][</span><span class="n">met</span><span class="p">]</span> <span class="k">for</span> <span class="n">val</span> <span class="ow">in</span> <span class="n">df</span><span class="p">[</span><span class="n">cat</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">()]</span>
                <span class="n">t_stat</span><span class="p">,</span> <span class="n">p_val</span> <span class="o">=</span> <span class="n">ttest_ind</span><span class="p">(</span><span class="n">groups</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">groups</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">nan_policy</span><span class="o">=</span><span class="s1">'omit'</span><span class="p">)</span>
                <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">cat</span><span class="si">}</span><span class="s2"> — </span><span class="si">{</span><span class="n">met</span><span class="si">}</span><span class="s2">: p-value = </span><span class="si">{</span><span class="n">p_val</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>

            <span class="k">elif</span> <span class="n">df</span><span class="p">[</span><span class="n">cat</span><span class="p">]</span><span class="o">.</span><span class="n">nunique</span><span class="p">()</span> <span class="o">&gt;</span> <span class="mi">2</span><span class="p">:</span> <span class="c1"># ANOVA для &gt; 2 групп</span>
                <span class="n">groups</span> <span class="o">=</span> <span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="n">cat</span><span class="p">]</span> <span class="o">==</span> <span class="n">val</span><span class="p">][</span><span class="n">met</span><span class="p">]</span> <span class="k">for</span> <span class="n">val</span> <span class="ow">in</span> <span class="n">df</span><span class="p">[</span><span class="n">cat</span><span class="p">]</span><span class="o">.</span><span class="n">unique</span><span class="p">()]</span>
                <span class="n">f_stat</span><span class="p">,</span> <span class="n">p_val</span> <span class="o">=</span> <span class="n">f_oneway</span><span class="p">(</span><span class="o">*</span><span class="n">groups</span><span class="p">)</span>
                <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">cat</span><span class="si">}</span><span class="s2"> — </span><span class="si">{</span><span class="n">met</span><span class="si">}</span><span class="s2">: p-value = </span><span class="si">{</span><span class="n">p_val</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>   
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<pre>Stress_Level — Study_Hours_Per_Day: p-value = 0.00
Stress_Level — Extracurricular_Hours_Per_Day: p-value = 0.90
Stress_Level — Sleep_Hours_Per_Day: p-value = 0.00
Stress_Level — Social_Hours_Per_Day: p-value = 0.05
Stress_Level — Physical_Activity_Hours_Per_Day: p-value = 0.00
Stress_Level — Grades: p-value = 0.00
Gender — Study_Hours_Per_Day: p-value = 0.12
Gender — Extracurricular_Hours_Per_Day: p-value = 0.64
Gender — Sleep_Hours_Per_Day: p-value = 0.45
Gender — Social_Hours_Per_Day: p-value = 0.65
Gender — Physical_Activity_Hours_Per_Day: p-value = 0.16
Gender — Grades: p-value = 0.54
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<p>Есть статистически значимая сильная связь между <code>Stress_Level</code> и <code>Study_Hours_Per_Day</code>, <code>Sleep_Hours_Per_Day</code>, <code>Physical_Activity_Hours_Per_Day</code> и <code>Grades</code> (<code>p-value</code> значительно меньше 0.05, близок к 0.00)</p>
<p>Каждая эта связь логично интерпретируется</p>
<p>Для <code>Gender</code> нет статистически значимой связи между полом студента и метрическими переменными</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.model_selection</span><span class="w"> </span><span class="kn">import</span> <span class="n">train_test_split</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.linear_model</span><span class="w"> </span><span class="kn">import</span> <span class="n">LinearRegression</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.metrics</span><span class="w"> </span><span class="kn">import</span> <span class="n">mean_squared_error</span><span class="p">,</span> <span class="n">mean_absolute_error</span><span class="p">,</span> <span class="n">r2_score</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.preprocessing</span><span class="w"> </span><span class="kn">import</span> <span class="n">StandardScaler</span><span class="p">,</span> <span class="n">LabelEncoder</span>
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<h4 id="%D0%9E%D1%87%D0%B8%D1%81%D1%82%D0%BA%D0%B0-%D0%BE%D1%82-%D0%B2%D1%8B%D0%B1%D1%80%D0%BE%D1%81%D0%BE%D0%B2,-%D0%BF%D1%80%D0%BE%D0%B2%D0%B5%D1%80%D0%BA%D0%B0-%D0%BD%D0%B0-%D0%BC%D1%83%D0%BB%D1%8C%D1%82%D0%B8%D0%BA%D0%BE%D0%BB%D0%BB%D0%B8%D0%BD%D0%B5%D0%B0%D1%80%D0%BD%D0%BE%D1%81%D1%82%D1%8C"><strong>Очистка от выбросов, проверка на мультиколлинеарность</strong><a class="anchor-link" href="#%D0%9E%D1%87%D0%B8%D1%81%D1%82%D0%BA%D0%B0-%D0%BE%D1%82-%D0%B2%D1%8B%D0%B1%D1%80%D0%BE%D1%81%D0%BE%D0%B2,-%D0%BF%D1%80%D0%BE%D0%B2%D0%B5%D1%80%D0%BA%D0%B0-%D0%BD%D0%B0-%D0%BC%D1%83%D0%BB%D1%8C%D1%82%D0%B8%D0%BA%D0%BE%D0%BB%D0%BB%D0%B8%D0%BD%D0%B5%D0%B0%D1%80%D0%BD%D0%BE%D1%81%D1%82%D1%8C">¶</a></h4>
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<p>Для построения модели линейной регрессии важно провести очистку данных от выбросов, потому что линейная регрессия минимизирует сумму квадратов ошибок <strong>MSE</strong>, которая к ним сильно чувствительна. Выбросы мы уже удалили ранее, также, как и проверили данные на отсутствие пустых значений и дубликатов</p>
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<p>Чуть выше мы также уже строили матрицу корреляций, которая показала, что мультиколлинеарности не выявлено</p>
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<h4 id="%D0%9A%D0%BE%D0%B4%D0%B8%D1%80%D0%BE%D0%B2%D0%B0%D0%BD%D0%B8%D0%B5-%D0%BA%D0%B0%D1%82%D0%B5%D0%B3%D0%BE%D1%80%D0%B8%D0%B0%D0%BB%D1%8C%D0%BD%D1%8B%D1%85-%D0%BF%D0%B5%D1%80%D0%B5%D0%BC%D0%B5%D0%BD%D0%BD%D1%8B%D1%85"><strong>Кодирование категориальных переменных</strong><a class="anchor-link" href="#%D0%9A%D0%BE%D0%B4%D0%B8%D1%80%D0%BE%D0%B2%D0%B0%D0%BD%D0%B8%D0%B5-%D0%BA%D0%B0%D1%82%D0%B5%D0%B3%D0%BE%D1%80%D0%B8%D0%B0%D0%BB%D1%8C%D0%BD%D1%8B%D1%85-%D0%BF%D0%B5%D1%80%D0%B5%D0%BC%D0%B5%D0%BD%D0%BD%D1%8B%D1%85">¶</a></h4><p>Кодируем категориальные переменные (для <code>Stress_Level_Encoded</code> удобнеее использовать маппинг, поскольку иначе присвоение "не по возрастанию")</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">label_encoder_gender</span> <span class="o">=</span> <span class="n">LabelEncoder</span><span class="p">()</span>
<span class="n">df</span><span class="p">[</span><span class="s1">'Gender_Encoded'</span><span class="p">]</span> <span class="o">=</span> <span class="n">label_encoder_gender</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'Gender'</span><span class="p">])</span>

<span class="n">stress_mapping</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'Low'</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span> <span class="s1">'Moderate'</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span> <span class="s1">'High'</span><span class="p">:</span> <span class="mi">2</span><span class="p">}</span>
<span class="n">df</span><span class="p">[</span><span class="s1">'Stress_Level_Encoded'</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'Stress_Level'</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">stress_mapping</span><span class="p">)</span>

<span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
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<style scoped="">
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<th></th>
<th>Student_ID</th>
<th>Study_Hours_Per_Day</th>
<th>Extracurricular_Hours_Per_Day</th>
<th>Sleep_Hours_Per_Day</th>
<th>Social_Hours_Per_Day</th>
<th>Physical_Activity_Hours_Per_Day</th>
<th>Stress_Level</th>
<th>Gender</th>
<th>Grades</th>
<th>Sleep_Hours_Rounded</th>
<th>Gender_Encoded</th>
<th>Stress_Level_Encoded</th>
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<th>0</th>
<td>1</td>
<td>6.9</td>
<td>3.8</td>
<td>8.7</td>
<td>2.8</td>
<td>1.8</td>
<td>Moderate</td>
<td>Male</td>
<td>7.48</td>
<td>9.0</td>
<td>1</td>
<td>1</td>
</tr>
<tr>
<th>1</th>
<td>2</td>
<td>5.3</td>
<td>3.5</td>
<td>8.0</td>
<td>4.2</td>
<td>3.0</td>
<td>Low</td>
<td>Female</td>
<td>6.88</td>
<td>8.0</td>
<td>0</td>
<td>0</td>
</tr>
<tr>
<th>2</th>
<td>3</td>
<td>5.1</td>
<td>3.9</td>
<td>9.2</td>
<td>1.2</td>
<td>4.6</td>
<td>Low</td>
<td>Male</td>
<td>6.68</td>
<td>9.0</td>
<td>1</td>
<td>0</td>
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<th>3</th>
<td>4</td>
<td>6.5</td>
<td>2.1</td>
<td>7.2</td>
<td>1.7</td>
<td>6.5</td>
<td>Moderate</td>
<td>Male</td>
<td>7.20</td>
<td>7.0</td>
<td>1</td>
<td>1</td>
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<th>4</th>
<td>5</td>
<td>8.1</td>
<td>0.6</td>
<td>6.5</td>
<td>2.2</td>
<td>6.6</td>
<td>High</td>
<td>Male</td>
<td>8.78</td>
<td>6.0</td>
<td>1</td>
<td>2</td>
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<h4 id="%D0%92%D1%8B%D0%B1%D0%BE%D1%80-%D0%BF%D1%80%D0%B8%D0%B7%D0%BD%D0%B0%D0%BA%D0%BE%D0%B2-%D0%B4%D0%BB%D1%8F-%D0%BC%D0%BE%D0%B4%D0%B5%D0%BB%D0%B8"><strong>Выбор признаков для модели</strong><a class="anchor-link" href="#%D0%92%D1%8B%D0%B1%D0%BE%D1%80-%D0%BF%D1%80%D0%B8%D0%B7%D0%BD%D0%B0%D0%BA%D0%BE%D0%B2-%D0%B4%D0%BB%D1%8F-%D0%BC%D0%BE%D0%B4%D0%B5%D0%BB%D0%B8">¶</a></h4><p>Выбираем числовые признаки (исключаем <code>Student_ID</code>, исходные категориальные и саму целевую переменную <code>Grades</code>)</p>
<p>Мы выбрали все метрические переменные (кроме целевой и айдишника) и закодированные категориальные — итого 7 признаков, так как это было первым предположением, которое попадает в идеальное количество параметров для модели:</p>
<blockquote>
<p>Для линейной регрессии оптимально:</p>
<ul>
<li>минимум: 1-2 ключевых признака</li>
<li>хорошо: 3-7 признаков</li>
<li>максимум: не более $n$/(10 ... 20), где $n$ — число наблюдений</li>
</ul>
</blockquote>
<p>В отдельном ноутбуке, посвященном только модели линейной регрессии, была также построена модель из 5 признаков (исключив наименее значимые коэффициенты), но метрики модели практически не изменились. Данные признаки показали неплохой результат, поэтому были выбраны оптимальными:</p>
<p>Признаки <strong>X</strong>: <code>Study_Hours_Per_Day</code>, <code>Extracurricular_Hours_Per_Day</code>, <code>Sleep_Hours_Per_Day</code>, <code>Social_Hours_Per_Day</code>, <code>Physical_Activity_Hours_Per_Day</code>, <code>Gender_Encoded</code>, <code>Stress_Level_Encoded</code></p>
<p>Целевая переменная <strong>y</strong>: <code>Grades</code></p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">features_for_linear</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'Study_Hours_Per_Day'</span><span class="p">,</span>
                       <span class="s1">'Extracurricular_Hours_Per_Day'</span><span class="p">,</span> 
                       <span class="s1">'Sleep_Hours_Per_Day'</span><span class="p">,</span>
                       <span class="s1">'Social_Hours_Per_Day'</span><span class="p">,</span>
                       <span class="s1">'Physical_Activity_Hours_Per_Day'</span><span class="p">,</span>
                       <span class="s1">'Gender_Encoded'</span><span class="p">,</span>
                       <span class="s1">'Stress_Level_Encoded'</span><span class="p">]</span>

<span class="n">X</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">features_for_linear</span><span class="p">]</span>
<span class="n">y</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'Grades'</span><span class="p">]</span> 
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<p>Вспомним распределение и характеристики целевой переменной <code>Grades</code></p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>

<span class="n">mean_val</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'Grades'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
<span class="n">median_val</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'Grades'</span><span class="p">]</span><span class="o">.</span><span class="n">median</span><span class="p">()</span>
<span class="n">mode_val</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'Grades'</span><span class="p">]</span><span class="o">.</span><span class="n">mode</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span>

<span class="n">plt</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="s1">'Grades'</span><span class="p">],</span> <span class="n">bins</span><span class="o">=</span><span class="mi">30</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'plum'</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">'m'</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.7</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">mean_val</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'orange'</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">'-'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s1">'mean: </span><span class="si">{</span><span class="n">mean_val</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">median_val</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'m'</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">'--'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s1">'median: </span><span class="si">{</span><span class="n">median_val</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">mode_val</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'cornflowerblue'</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">':'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s1">'mode: </span><span class="si">{</span><span class="n">mode_val</span><span class="si">:</span><span class="s1">.2f</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">'Гистограмма распределения Grades'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">'Grades'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">11</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">'Количество студентов'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">11</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s1">'upper right'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">9</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'lightgray'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.7</span><span class="p">)</span>


<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<div class="highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Grades mean: </span><span class="si">{</span><span class="n">df</span><span class="p">[</span><span class="s1">'Grades'</span><span class="p">]</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Grades median: </span><span class="si">{</span><span class="n">df</span><span class="p">[</span><span class="s1">'Grades'</span><span class="p">]</span><span class="o">.</span><span class="n">median</span><span class="p">()</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Grades std: </span><span class="si">{</span><span class="n">df</span><span class="p">[</span><span class="s1">'Grades'</span><span class="p">]</span><span class="o">.</span><span class="n">std</span><span class="p">()</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Grades min: </span><span class="si">{</span><span class="n">df</span><span class="p">[</span><span class="s1">'Grades'</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">()</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Grades max: </span><span class="si">{</span><span class="n">df</span><span class="p">[</span><span class="s1">'Grades'</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<pre>Grades mean: 7.79
Grades median: 7.78
Grades std: 0.75
Grades min: 5.60
Grades max: 10.00
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<h4 id="%D0%9E%D0%B1%D1%83%D1%87%D0%B5%D0%BD%D0%B8%D0%B5-%D0%BC%D0%BE%D0%B4%D0%B5%D0%BB%D0%B8"><strong>Обучение модели</strong><a class="anchor-link" href="#%D0%9E%D0%B1%D1%83%D1%87%D0%B5%D0%BD%D0%B8%D0%B5-%D0%BC%D0%BE%D0%B4%D0%B5%D0%BB%D0%B8">¶</a></h4>
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<h5 id="%D0%A0%D0%B0%D0%B7%D0%B4%D0%B5%D0%BB%D0%B5%D0%BD%D0%B8%D0%B5-%D0%BD%D0%B0-%D0%BE%D0%B1%D1%83%D1%87%D0%B0%D1%8E%D1%89%D1%83%D1%8E-%D0%B8-%D1%82%D0%B5%D1%81%D1%82%D0%BE%D0%B2%D1%83%D1%8E-%D0%B2%D1%8B%D0%B1%D0%BE%D1%80%D0%BA%D0%B8">Разделение на обучающую и тестовую выборки<a class="anchor-link" href="#%D0%A0%D0%B0%D0%B7%D0%B4%D0%B5%D0%BB%D0%B5%D0%BD%D0%B8%D0%B5-%D0%BD%D0%B0-%D0%BE%D0%B1%D1%83%D1%87%D0%B0%D1%8E%D1%89%D1%83%D1%8E-%D0%B8-%D1%82%D0%B5%D1%81%D1%82%D0%BE%D0%B2%D1%83%D1%8E-%D0%B2%D1%8B%D0%B1%D0%BE%D1%80%D0%BA%D0%B8">¶</a></h5>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [327]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="se">\n</span><span class="s2">Размер обучающей выборки: </span><span class="si">{</span><span class="n">X_train</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Размер тестовой выборки: </span><span class="si">{</span><span class="n">X_test</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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Размер обучающей выборки: 1600
Размер тестовой выборки: 400
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<h5 id="%D0%9C%D0%B0%D1%81%D1%88%D1%82%D0%B0%D0%B1%D0%B8%D1%80%D0%BE%D0%B2%D0%B0%D0%BD%D0%B8%D0%B5-%D0%BF%D1%80%D0%B8%D0%B7%D0%BD%D0%B0%D0%BA%D0%BE%D0%B2">Масштабирование признаков<a class="anchor-link" href="#%D0%9C%D0%B0%D1%81%D1%88%D1%82%D0%B0%D0%B1%D0%B8%D1%80%D0%BE%D0%B2%D0%B0%D0%BD%D0%B8%D0%B5-%D0%BF%D1%80%D0%B8%D0%B7%D0%BD%D0%B0%D0%BA%D0%BE%D0%B2">¶</a></h5><p>Линейная регрессия чувствительна к масштабу признаков. Стандартизация ставит все признаки в один "вес", чтобы модель могла корректно оценить их важность (коэффициенты)</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [328]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">scaler</span> <span class="o">=</span> <span class="n">StandardScaler</span><span class="p">()</span>
<span class="n">X_train_scaled</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span>
<span class="n">X_test_scaled</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">LinearRegression</span><span class="p">()</span>
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train_scaled</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>

<span class="n">y_train_pred</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_train_scaled</span><span class="p">)</span>
<span class="n">y_test_pred</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test_scaled</span><span class="p">)</span>
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<h4 id="%D0%9E%D1%86%D0%B5%D0%BD%D0%BA%D0%B0-%D0%BC%D0%BE%D0%B4%D0%B5%D0%BB%D0%B8"><strong>Оценка модели</strong><a class="anchor-link" href="#%D0%9E%D1%86%D0%B5%D0%BD%D0%BA%D0%B0-%D0%BC%D0%BE%D0%B4%D0%B5%D0%BB%D0%B8">¶</a></h4><ul>
<li><strong>MSE</strong> – среднеквадратичная ошибка</li>
<li><strong>RMSE</strong> – корень из среднеквадратичнрй ошибки</li>
<li><strong>MAE</strong> – средняя абсолютная ошибка</li>
<li><strong>R2</strong> – коэффициент детерминации (показывает, какую долю дисперсии целевой переменной объясняет модель; чем ближе к 1, тем лучше)</li>
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<h5 id="%D0%9C%D0%B5%D1%82%D1%80%D0%B8%D0%BA%D0%B8-%D0%B4%D0%BB%D1%8F-%D0%BE%D0%B1%D1%83%D1%87%D0%B0%D1%8E%D1%89%D0%B5%D0%B9-%D0%B2%D1%8B%D0%B1%D0%BE%D1%80%D0%BA%D0%B8">Метрики для обучающей выборки<a class="anchor-link" href="#%D0%9C%D0%B5%D1%82%D1%80%D0%B8%D0%BA%D0%B8-%D0%B4%D0%BB%D1%8F-%D0%BE%D0%B1%D1%83%D1%87%D0%B0%D1%8E%D1%89%D0%B5%D0%B9-%D0%B2%D1%8B%D0%B1%D0%BE%D1%80%D0%BA%D0%B8">¶</a></h5>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">train_mse</span> <span class="o">=</span> <span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_train</span><span class="p">,</span> <span class="n">y_train_pred</span><span class="p">)</span>
<span class="n">train_rmse</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">train_mse</span><span class="p">)</span>
<span class="n">train_mae</span> <span class="o">=</span> <span class="n">mean_absolute_error</span><span class="p">(</span><span class="n">y_train</span><span class="p">,</span> <span class="n">y_train_pred</span><span class="p">)</span>
<span class="n">train_r2</span> <span class="o">=</span> <span class="n">r2_score</span><span class="p">(</span><span class="n">y_train</span><span class="p">,</span> <span class="n">y_train_pred</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">"Обучающая выборка"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"MSE: </span><span class="si">{</span><span class="n">train_mse</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"RMSE: </span><span class="si">{</span><span class="n">train_rmse</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"MAE: </span><span class="si">{</span><span class="n">train_mae</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"R2: </span><span class="si">{</span><span class="n">train_r2</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<pre>Обучающая выборка
MSE: 0.2540
RMSE: 0.5040
MAE: 0.3982
R2: 0.5388
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<h5 id="%D0%9C%D0%B5%D1%82%D1%80%D0%B8%D0%BA%D0%B8-%D0%B4%D0%BB%D1%8F-%D1%82%D0%B5%D1%81%D1%82%D0%BE%D0%B2%D0%BE%D0%B9-%D0%B2%D1%8B%D0%B1%D0%BE%D1%80%D0%BA%D0%B8">Метрики для тестовой выборки<a class="anchor-link" href="#%D0%9C%D0%B5%D1%82%D1%80%D0%B8%D0%BA%D0%B8-%D0%B4%D0%BB%D1%8F-%D1%82%D0%B5%D1%81%D1%82%D0%BE%D0%B2%D0%BE%D0%B9-%D0%B2%D1%8B%D0%B1%D0%BE%D1%80%D0%BA%D0%B8">¶</a></h5>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [331]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">test_mse</span> <span class="o">=</span> <span class="n">mean_squared_error</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_test_pred</span><span class="p">)</span>
<span class="n">test_rmse</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">test_mse</span><span class="p">)</span>
<span class="n">test_mae</span> <span class="o">=</span> <span class="n">mean_absolute_error</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_test_pred</span><span class="p">)</span>
<span class="n">test_r2</span> <span class="o">=</span> <span class="n">r2_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_test_pred</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">"Тестовая выборка"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"MSE: </span><span class="si">{</span><span class="n">test_mse</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"RMSE: </span><span class="si">{</span><span class="n">test_rmse</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"MAE: </span><span class="si">{</span><span class="n">test_mae</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"R2: </span><span class="si">{</span><span class="n">test_r2</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<pre>Тестовая выборка
MSE: 0.2640
RMSE: 0.5138
MAE: 0.4109
R2: 0.5478
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<h5 id="%D0%9A%D0%BE%D1%8D%D1%84%D1%84%D0%B8%D1%86%D0%B8%D0%B5%D0%BD%D1%82%D1%8B-%D0%BC%D0%BE%D0%B4%D0%B5%D0%BB%D0%B8">Коэффициенты модели<a class="anchor-link" href="#%D0%9A%D0%BE%D1%8D%D1%84%D1%84%D0%B8%D1%86%D0%B8%D0%B5%D0%BD%D1%82%D1%8B-%D0%BC%D0%BE%D0%B4%D0%B5%D0%BB%D0%B8">¶</a></h5>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [332]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">coefficients</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">({</span><span class="s1">'Признак'</span><span class="p">:</span> <span class="n">features_for_linear</span><span class="p">,</span><span class="s1">'Коэффициент'</span><span class="p">:</span> <span class="n">model</span><span class="o">.</span><span class="n">coef_</span><span class="p">})</span>
<span class="n">coefficients</span> <span class="o">=</span> <span class="n">coefficients</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="s1">'Коэффициент'</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="nb">abs</span><span class="p">,</span> <span class="n">ascending</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">coefficients</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="mi">4</span><span class="p">))</span>

<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="se">\n</span><span class="s2">Свободный член: </span><span class="si">{</span><span class="n">model</span><span class="o">.</span><span class="n">intercept_</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<pre>                           Признак  Коэффициент
0              Study_Hours_Per_Day       0.4723
4  Physical_Activity_Hours_Per_Day      -0.1239
1    Extracurricular_Hours_Per_Day      -0.0860
2              Sleep_Hours_Per_Day      -0.0820
3             Social_Hours_Per_Day      -0.0807
5                   Gender_Encoded      -0.0166
6             Stress_Level_Encoded       0.0049

Свободный член: 7.7924
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<h4 id="%D0%92%D0%B8%D0%B7%D1%83%D0%B0%D0%BB%D0%B8%D0%B7%D0%B0%D1%86%D0%B8%D1%8F-%D1%80%D0%B5%D0%B7%D1%83%D0%BB%D1%8C%D1%82%D0%B0%D1%82%D0%BE%D0%B2"><strong>Визуализация результатов</strong><a class="anchor-link" href="#%D0%92%D0%B8%D0%B7%D1%83%D0%B0%D0%BB%D0%B8%D0%B7%D0%B0%D1%86%D0%B8%D1%8F-%D1%80%D0%B5%D0%B7%D1%83%D0%BB%D1%8C%D1%82%D0%B0%D1%82%D0%BE%D0%B2">¶</a></h4>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [333]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">8</span><span class="p">))</span>

<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">y_train</span><span class="p">,</span> <span class="n">y_train_pred</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'plum'</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="n">y_train</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">y_train</span><span class="o">.</span><span class="n">max</span><span class="p">()],</span> <span class="p">[</span><span class="n">y_train</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">y_train</span><span class="o">.</span><span class="n">max</span><span class="p">()],</span> <span class="s1">'--'</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'m'</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'Фактические оценки'</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'Предсказанные оценки'</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'train: Фактические - Предсказанные'</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span>

<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">y_test_pred</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.6</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'orange'</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="n">y_test</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">y_test</span><span class="o">.</span><span class="n">max</span><span class="p">()],</span> <span class="p">[</span><span class="n">y_test</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">y_test</span><span class="o">.</span><span class="n">max</span><span class="p">()],</span> <span class="s1">'--'</span><span class="p">,</span> <span class="n">lw</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'m'</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'Фактические оценки'</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'Предсказанные оценки'</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'test: Фактические - Предсказанные'</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span>

<span class="n">residuals_train</span> <span class="o">=</span> <span class="n">y_train</span> <span class="o">-</span> <span class="n">y_train_pred</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">y_train_pred</span><span class="p">,</span> <span class="n">residuals_train</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.6</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'cornflowerblue'</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">axhline</span><span class="p">(</span><span class="n">y</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'m'</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'Предсказанные значения'</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'Остатки'</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'train: График остатков'</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span>

<span class="n">residuals_test</span> <span class="o">=</span> <span class="n">y_test</span> <span class="o">-</span> <span class="n">y_test_pred</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">y_test_pred</span><span class="p">,</span> <span class="n">residuals_test</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.6</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'royalblue'</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">axhline</span><span class="p">(</span><span class="n">y</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'m'</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'Предсказанные значения'</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'Остатки'</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'test: График остатков'</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span>

<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<h4 id="%D0%92%D1%8B%D0%B2%D0%BE%D0%B4%D1%8B"><strong>Выводы</strong><a class="anchor-link" href="#%D0%92%D1%8B%D0%B2%D0%BE%D0%B4%D1%8B">¶</a></h4>
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<ul>
<li><p><strong>RMSE</strong> <code>(0.51)</code> и <strong>MAE</strong> <code>(0.41)</code> показывают, что в среднем модель ошибается при предсказании оценки студента примерно на 0,41-0,51 балла. Так как диапазон <code>Grades</code> в датасете составляет примерно от 5.5 до 9 баллов, ошибка в 0.5 балла является вполне приемлемой, модель может довольно точно предсказать оценку. То, что <strong>RMSE</strong> выше, чем <strong>MAE</strong>, говорит о наличии небольших выбросов (<strong>RMSE</strong> через квадрат сильнее реагирует на них)</p>
</li>
<li><p><strong>R2</strong> <code>(0.5487)</code> означает, что модель объясняет около 55% вариации (дисперсии) оценок студентов. То есть чуть более половины того, почему один студент учится лучше другого, можно объяснить теми факторами, которые мы включили в модель
Это довольно логично, например, остальныим могли быть такие факторы: мотивация, как-либо измеренные способности, первоначальный уровень подготовки и тд</p>
</li>
<li><p>Наибольший положительный эффект на оценку оказывает количество часов учебы в день <code>Study_Hours_Per_Day</code>, мы также это получили матрицей корреляций</p>
</li>
</ul>
<p>Итого, модель имеет достаточно хороший уровень точности предсказаний</p>
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<h2 id="%D0%9F%D0%BE%D1%81%D1%82%D1%80%D0%BE%D0%B5%D0%BD%D0%B8%D0%B5-%D0%BC%D0%BE%D0%B4%D0%B5%D0%BB%D0%B8-%D0%BA%D0%BB%D0%B0%D1%81%D1%82%D0%B5%D1%80%D0%B8%D0%B7%D0%B0%D1%86%D0%B8%D0%B8"><strong>Построение модели кластеризации</strong><a class="anchor-link" href="#%D0%9F%D0%BE%D1%81%D1%82%D1%80%D0%BE%D0%B5%D0%BD%D0%B8%D0%B5-%D0%BC%D0%BE%D0%B4%D0%B5%D0%BB%D0%B8-%D0%BA%D0%BB%D0%B0%D1%81%D1%82%D0%B5%D1%80%D0%B8%D0%B7%D0%B0%D1%86%D0%B8%D0%B8">¶</a></h2>
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<h4 id="%D0%98%D0%BC%D0%BF%D0%BE%D1%80%D1%82-%D0%BD%D0%B5%D0%BE%D0%B1%D1%85%D0%BE%D0%B4%D0%B8%D0%BC%D1%8B%D1%85-%D0%B1%D0%B8%D0%B1%D0%BB%D0%B8%D0%BE%D1%82%D0%B5%D0%BA"><strong>Импорт необходимых библиотек</strong><a class="anchor-link" href="#%D0%98%D0%BC%D0%BF%D0%BE%D1%80%D1%82-%D0%BD%D0%B5%D0%BE%D0%B1%D1%85%D0%BE%D0%B4%D0%B8%D0%BC%D1%8B%D1%85-%D0%B1%D0%B8%D0%B1%D0%BB%D0%B8%D0%BE%D1%82%D0%B5%D0%BA">¶</a></h4>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [334]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.preprocessing</span><span class="w"> </span><span class="kn">import</span> <span class="n">StandardScaler</span><span class="p">,</span> <span class="n">LabelEncoder</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.cluster</span><span class="w"> </span><span class="kn">import</span> <span class="n">KMeans</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.metrics</span><span class="w"> </span><span class="kn">import</span> <span class="n">silhouette_score</span><span class="p">,</span> <span class="n">silhouette_samples</span>
<span class="kn">from</span><span class="w"> </span><span class="nn">sklearn.decomposition</span><span class="w"> </span><span class="kn">import</span> <span class="n">PCA</span>
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<h4 id="%D0%92%D1%8B%D0%B1%D0%BE%D1%80-%D0%BF%D1%80%D0%B8%D0%B7%D0%BD%D0%B0%D0%BA%D0%BE%D0%B2-%D0%B4%D0%BB%D1%8F-%D0%BC%D0%BE%D0%B4%D0%B5%D0%BB%D0%B8"><strong>Выбор признаков для модели</strong><a class="anchor-link" href="#%D0%92%D1%8B%D0%B1%D0%BE%D1%80-%D0%BF%D1%80%D0%B8%D0%B7%D0%BD%D0%B0%D0%BA%D0%BE%D0%B2-%D0%B4%D0%BB%D1%8F-%D0%BC%D0%BE%D0%B4%D0%B5%D0%BB%D0%B8">¶</a></h4><p>Для кластеризации образа жизни выберем все колонки, связанные с распределением времени:</p>
<ul>
<li><code>Study_Hours_Per_Day</code></li>
<li><code>Extracurricular_Hours_Per_Day</code></li>
<li><code>Sleep_Hours_Per_Day</code></li>
<li><code>Social_Hours_Per_Day</code></li>
<li><code>Physical_Activity_Hours_Per_Day</code></li>
</ul>
<p>Сразу же мастшабируем их</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [335]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">features_for_clustering</span> <span class="o">=</span> <span class="p">[</span>
    <span class="s1">'Study_Hours_Per_Day'</span><span class="p">,</span>
    <span class="s1">'Extracurricular_Hours_Per_Day'</span><span class="p">,</span>
    <span class="s1">'Sleep_Hours_Per_Day'</span><span class="p">,</span>
    <span class="s1">'Social_Hours_Per_Day'</span><span class="p">,</span>
    <span class="s1">'Physical_Activity_Hours_Per_Day'</span>
<span class="p">]</span>

<span class="n">X</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">features_for_clustering</span><span class="p">]</span>

<span class="n">scaler</span> <span class="o">=</span> <span class="n">StandardScaler</span><span class="p">()</span>
<span class="n">X_scaled</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
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<h4 id="%D0%92%D1%8B%D0%B1%D0%BE%D1%80-%D0%BC%D0%B5%D1%82%D1%80%D0%B8%D0%BA%D0%B8-%D1%80%D0%B0%D1%81%D1%81%D1%82%D0%BE%D1%8F%D0%BD%D0%B8%D1%8F"><strong>Выбор метрики расстояния</strong><a class="anchor-link" href="#%D0%92%D1%8B%D0%B1%D0%BE%D1%80-%D0%BC%D0%B5%D1%82%D1%80%D0%B8%D0%BA%D0%B8-%D1%80%D0%B0%D1%81%D1%81%D1%82%D0%BE%D1%8F%D0%BD%D0%B8%D1%8F">¶</a></h4><p>Метрику выберем стандартно – <strong>Евклидово расстояние</strong></p>
<p>Это стандартная и наиболее распространенная метрика для K-means, так как алгоритм минимизирует сумму квадратов евклидовых расстояний (инерцию). Наши признаки после масштабирования становятся сравнимыми и имеют нормальное распределение (примерно), что делает евклидово расстояние оптимальной мерой</p>
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<h4 id="%D0%92%D1%8B%D0%B1%D0%BE%D1%80-%D0%BE%D0%BF%D1%82%D0%B8%D0%BC%D0%B0%D0%BB%D1%8C%D0%BD%D0%BE%D0%B3%D0%BE-%D0%BA%D0%BE%D0%BB%D0%B8%D1%87%D0%B5%D1%81%D1%82%D0%B2%D0%B0-%D0%BA%D0%BB%D0%B0%D1%81%D1%82%D0%B5%D1%80%D0%BE%D0%B2"><strong>Выбор оптимального количества кластеров</strong><a class="anchor-link" href="#%D0%92%D1%8B%D0%B1%D0%BE%D1%80-%D0%BE%D0%BF%D1%82%D0%B8%D0%BC%D0%B0%D0%BB%D1%8C%D0%BD%D0%BE%D0%B3%D0%BE-%D0%BA%D0%BE%D0%BB%D0%B8%D1%87%D0%B5%D1%81%D1%82%D0%B2%D0%B0-%D0%BA%D0%BB%D0%B0%D1%81%D1%82%D0%B5%D1%80%D0%BE%D0%B2">¶</a></h4><p>Используем два метода:</p>
<ol>
<li><strong>Метод локтя (Elbow method)</strong> – график зависимости инерции WCSS от k</li>
<li><strong>Метод силуэта (Silhouette analysis)</strong> – коэффициент силуэта (чем выше, тем лучше)</li>
</ol>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [336]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">inertia</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">silhouette_scores</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">K_range</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">11</span><span class="p">)</span>

<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">K_range</span><span class="p">:</span>
    <span class="n">kmeans</span> <span class="o">=</span> <span class="n">KMeans</span><span class="p">(</span><span class="n">n_clusters</span><span class="o">=</span><span class="n">k</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">,</span> <span class="n">n_init</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
    <span class="n">kmeans</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_scaled</span><span class="p">)</span>
    <span class="n">inertia</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">kmeans</span><span class="o">.</span><span class="n">inertia_</span><span class="p">)</span>
    <span class="n">silhouette_scores</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">silhouette_score</span><span class="p">(</span><span class="n">X_scaled</span><span class="p">,</span> <span class="n">kmeans</span><span class="o">.</span><span class="n">labels_</span><span class="p">))</span>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>

<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">K_range</span><span class="p">,</span> <span class="n">inertia</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">'o'</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">'-'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'cornflowerblue'</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'Количество кластеров (k)'</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'Инерция (WCSS)'</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Метод локтя для выбора k'</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'lightgray'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.7</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="s1">'Возможный "локоть"'</span><span class="p">,</span> <span class="n">xy</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="n">inertia</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="n">xytext</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="n">inertia</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">+</span><span class="mi">50</span><span class="p">),</span> <span class="n">arrowprops</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">arrowstyle</span><span class="o">=</span><span class="s1">'-&gt;'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'m'</span><span class="p">),</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">)</span>

<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">K_range</span><span class="p">,</span> <span class="n">silhouette_scores</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="s1">'o'</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">'-'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'orange'</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'Количество кластеров (k)'</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'Средний коэффициент силуэта'</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Метод силуэта для выбора k'</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'lightgray'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.7</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="mi">9</span><span class="p">,</span><span class="n">silhouette_scores</span><span class="p">[</span><span class="mi">7</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">'m'</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">zorder</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s1">'Максимум: k = 9'</span><span class="p">)</span>
<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>

<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<p>Метод локтя показывает замедление падения инерции после <code>k = 3</code>, метод силуэта  показывает максимум при <code>k = 9</code></p>
<p>При выборе k равным 9 получится разбиение на 9 "групп" студентов, что сложно содержательно различить и описать. Оптимальным k было выбрано значение 3</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [338]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">kmeans_final</span> <span class="o">=</span> <span class="n">KMeans</span><span class="p">(</span><span class="n">n_clusters</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">,</span> <span class="n">n_init</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
<span class="n">kmeans_final</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_scaled</span><span class="p">)</span>

<span class="n">df</span><span class="p">[</span><span class="s1">'Cluster'</span><span class="p">]</span> <span class="o">=</span> <span class="n">kmeans_final</span><span class="o">.</span><span class="n">labels_</span>
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<h4 id="%D0%92%D0%B8%D0%B7%D1%83%D0%B0%D0%BB%D0%B8%D0%B7%D0%B0%D1%86%D0%B8%D1%8F-%D1%80%D0%B5%D0%B7%D1%83%D0%BB%D1%8C%D1%82%D0%B0%D1%82%D0%BE%D0%B2"><strong>Визуализация результатов</strong><a class="anchor-link" href="#%D0%92%D0%B8%D0%B7%D1%83%D0%B0%D0%BB%D0%B8%D0%B7%D0%B0%D1%86%D0%B8%D1%8F-%D1%80%D0%B5%D0%B7%D1%83%D0%BB%D1%8C%D1%82%D0%B0%D1%82%D0%BE%D0%B2">¶</a></h4>
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<h5 id="PCA-%D0%BF%D1%80%D0%BE%D0%B5%D0%BA%D1%86%D0%B8%D1%8F">PCA проекция<a class="anchor-link" href="#PCA-%D0%BF%D1%80%D0%BE%D0%B5%D0%BA%D1%86%D0%B8%D1%8F">¶</a></h5><p>Так как у нас многомерные данные (5 признаков), нам нужно уменьшить размерность для визуализации на плоскости. Используем <strong>PCA (Principal Component Analysis)</strong> для проекции на 2 главные компоненты</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [339]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">pca</span> <span class="o">=</span> <span class="n">PCA</span><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">42</span><span class="p">)</span>
<span class="n">X_pca</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">X_scaled</span><span class="p">)</span>

<span class="n">viz_df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">X_pca</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">'PC1'</span><span class="p">,</span> <span class="s1">'PC2'</span><span class="p">])</span>
<span class="n">viz_df</span><span class="p">[</span><span class="s1">'Cluster'</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'Cluster'</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
<span class="n">viz_df</span><span class="p">[</span><span class="s1">'Gender'</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'Gender'</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
<span class="n">viz_df</span><span class="p">[</span><span class="s1">'Stress_Level'</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'Stress_Level'</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
<span class="n">viz_df</span><span class="p">[</span><span class="s1">'Grades'</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'Grades'</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>

<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">8</span><span class="p">))</span>

<span class="n">cluster_colors</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'plum'</span><span class="p">,</span> <span class="s1">'cornflowerblue'</span><span class="p">,</span> <span class="s1">'orange'</span><span class="p">]</span>
<span class="n">sns</span><span class="o">.</span><span class="n">scatterplot</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">viz_df</span><span class="p">,</span> <span class="n">x</span><span class="o">=</span><span class="s1">'PC1'</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span><span class="s1">'PC2'</span><span class="p">,</span> <span class="n">hue</span><span class="o">=</span><span class="s1">'Cluster'</span><span class="p">,</span> <span class="n">palette</span><span class="o">=</span><span class="n">cluster_colors</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.8</span><span class="p">)</span>

<span class="n">centroids_pca</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">kmeans_final</span><span class="o">.</span><span class="n">cluster_centers_</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">centroids_pca</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">centroids_pca</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">marker</span><span class="o">=</span><span class="s1">'o'</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">150</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s1">'m'</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">'purple'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Центроиды'</span><span class="p">)</span>

<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s1">'Визуализация кластеров (PCA проекция, k = </span><span class="si">{</span><span class="n">optimal_k</span><span class="si">}</span><span class="s1">)'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="sa">f</span><span class="s1">'PC1 (</span><span class="si">{</span><span class="n">pca</span><span class="o">.</span><span class="n">explained_variance_ratio_</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">:</span><span class="s1">.1%</span><span class="si">}</span><span class="s1"> дисперсии)'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="sa">f</span><span class="s1">'PC2 (</span><span class="si">{</span><span class="n">pca</span><span class="o">.</span><span class="n">explained_variance_ratio_</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="si">:</span><span class="s1">.1%</span><span class="si">}</span><span class="s1"> дисперсии)'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>

<span class="n">plt</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'lightgray'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.7</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<h4 id="%D0%92%D0%BA%D0%BB%D0%B0%D0%B4-%D0%BF%D1%80%D0%B8%D0%B7%D0%BD%D0%B0%D0%BA%D0%BE%D0%B2-%D0%B2-%D0%B3%D0%BB%D0%B0%D0%B2%D0%BD%D1%8B%D0%B5-%D0%BA%D0%BE%D0%BC%D0%BF%D0%BE%D0%BD%D0%B5%D0%BD%D1%82%D1%8B">Вклад признаков в главные компоненты<a class="anchor-link" href="#%D0%92%D0%BA%D0%BB%D0%B0%D0%B4-%D0%BF%D1%80%D0%B8%D0%B7%D0%BD%D0%B0%D0%BA%D0%BE%D0%B2-%D0%B2-%D0%B3%D0%BB%D0%B0%D0%B2%D0%BD%D1%8B%D0%B5-%D0%BA%D0%BE%D0%BC%D0%BF%D0%BE%D0%BD%D0%B5%D0%BD%D1%82%D1%8B">¶</a></h4>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">loadings</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">pca</span><span class="o">.</span><span class="n">components_</span><span class="o">.</span><span class="n">T</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">'PC1'</span><span class="p">,</span> <span class="s1">'PC2'</span><span class="p">],</span> <span class="n">index</span><span class="o">=</span><span class="n">features_for_clustering</span><span class="p">)</span>

<span class="n">display</span><span class="p">(</span><span class="n">loadings</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="mi">2</span><span class="p">))</span>
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<th>PC1</th>
<th>PC2</th>
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<th>Study_Hours_Per_Day</th>
<td>-0.43</td>
<td>-0.14</td>
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<th>Extracurricular_Hours_Per_Day</th>
<td>-0.31</td>
<td>-0.22</td>
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<th>Sleep_Hours_Per_Day</th>
<td>-0.41</td>
<td>-0.36</td>
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<th>Social_Hours_Per_Day</th>
<td>-0.15</td>
<td>0.87</td>
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<th>Physical_Activity_Hours_Per_Day</th>
<td>0.73</td>
<td>-0.20</td>
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<h5 id="%D0%A1%D0%B2%D0%BE%D0%B4%D0%BA%D0%B0-%D0%BF%D0%BE-%D0%BA%D0%BB%D0%B0%D1%81%D1%82%D0%B5%D1%80%D0%B0%D0%BC">Сводка по кластерам<a class="anchor-link" href="#%D0%A1%D0%B2%D0%BE%D0%B4%D0%BA%D0%B0-%D0%BF%D0%BE-%D0%BA%D0%BB%D0%B0%D1%81%D1%82%D0%B5%D1%80%D0%B0%D0%BC">¶</a></h5>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [341]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">cluster_summary</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">'Cluster'</span><span class="p">)</span><span class="o">.</span><span class="n">agg</span><span class="p">({</span><span class="o">**</span><span class="p">{</span><span class="n">feat</span><span class="p">:</span> <span class="s1">'mean'</span> <span class="k">for</span> <span class="n">feat</span> <span class="ow">in</span> <span class="n">features_for_clustering</span><span class="p">},</span> <span class="s1">'Grades'</span><span class="p">:</span> <span class="s1">'mean'</span><span class="p">,</span> <span class="s1">'Student_ID'</span><span class="p">:</span> <span class="s1">'count'</span><span class="p">})</span><span class="o">.</span><span class="n">rename</span><span class="p">(</span><span class="n">columns</span><span class="o">=</span><span class="p">{</span><span class="s1">'Student_ID'</span><span class="p">:</span> <span class="s1">'Count'</span><span class="p">})</span>

<span class="n">display</span><span class="p">(</span><span class="n">cluster_summary</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="mi">2</span><span class="p">))</span>
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<th>Study_Hours_Per_Day</th>
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<th>Sleep_Hours_Per_Day</th>
<th>Social_Hours_Per_Day</th>
<th>Physical_Activity_Hours_Per_Day</th>
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<th>0</th>
<td>8.40</td>
<td>2.51</td>
<td>8.60</td>
<td>1.61</td>
<td>2.87</td>
<td>8.11</td>
<td>638</td>
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<th>1</th>
<td>7.36</td>
<td>1.92</td>
<td>7.21</td>
<td>4.49</td>
<td>3.03</td>
<td>7.76</td>
<td>724</td>
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<th>2</th>
<td>6.68</td>
<td>1.56</td>
<td>6.73</td>
<td>1.77</td>
<td>7.26</td>
<td>7.50</td>
<td>638</td>
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<h5 id="%D0%92%D0%B8%D0%B7%D1%83%D0%B0%D0%BB%D0%B8%D0%B7%D0%B8%D1%80%D1%83%D0%B5%D0%BC-%D0%BF%D1%80%D0%BE%D1%84%D0%B8%D0%BB%D0%B8-%D0%BA%D0%BB%D0%B0%D1%81%D1%82%D0%B5%D1%80%D0%BE%D0%B2">Визуализируем профили кластеров<a class="anchor-link" href="#%D0%92%D0%B8%D0%B7%D1%83%D0%B0%D0%BB%D0%B8%D0%B7%D0%B8%D1%80%D1%83%D0%B5%D0%BC-%D0%BF%D1%80%D0%BE%D1%84%D0%B8%D0%BB%D0%B8-%D0%BA%D0%BB%D0%B0%D1%81%D1%82%D0%B5%D1%80%D0%BE%D0%B2">¶</a></h5>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">cluster_centers_scaled</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">kmeans_final</span><span class="o">.</span><span class="n">cluster_centers_</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="n">features_for_clustering</span><span class="p">)</span><span class="o">.</span><span class="n">T</span>
<span class="n">colors</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'plum'</span><span class="p">,</span> <span class="s1">'cornflowerblue'</span><span class="p">,</span> <span class="s1">'orange'</span><span class="p">]</span>

<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">7</span><span class="p">))</span>

<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">cluster_centers_scaled</span><span class="o">.</span><span class="n">index</span><span class="p">))</span>
<span class="n">width</span> <span class="o">=</span> <span class="mf">0.25</span>

<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">color</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">colors</span><span class="p">):</span>
    <span class="n">ax</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">x</span> <span class="o">+</span> <span class="n">i</span><span class="o">*</span><span class="n">width</span><span class="p">,</span> <span class="n">cluster_centers_scaled</span><span class="o">.</span><span class="n">iloc</span><span class="p">[:,</span> <span class="n">i</span><span class="p">],</span> <span class="n">width</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">f</span><span class="s1">'Кластер </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1">'</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.8</span><span class="p">)</span>

<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'Признаки'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'Среднее значение (стандартизованное)'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">'Профили кластеров (стандартизованные признаки)'</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">,</span> <span class="n">fontweight</span><span class="o">=</span><span class="s1">'bold'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(</span><span class="n">x</span> <span class="o">+</span> <span class="n">width</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xticklabels</span><span class="p">(</span><span class="n">cluster_centers_scaled</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">rotation</span><span class="o">=</span><span class="mi">45</span><span class="p">,</span> <span class="n">ha</span><span class="o">=</span><span class="s1">'right'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">ax</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="kc">True</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'lightgray'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.7</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">axhline</span><span class="p">(</span><span class="n">y</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'black'</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">'-'</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>

<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<h4 id="%D0%92%D1%8B%D0%B2%D0%BE%D0%B4%D1%8B"><strong>Выводы</strong><a class="anchor-link" href="#%D0%92%D1%8B%D0%B2%D0%BE%D0%B4%D1%8B">¶</a></h4><p>Выделяются 3 характерные группы:</p>
<ul>
<li><strong>Отличники</strong> – студенты, кторые много времени тратят на учебу, внеклассные дополнительные занятия, хорошо спят, меньше всех общаются и занимаются спортом</li>
<li><strong>Социально активные студенты</strong> – лидеры в количестве часов общения в сутки и "среднячки" в остальном</li>
<li><strong>Спортсмены</strong> – студенты, которые большинство времени уделяют спорту, а также имеют наименьшие остальные хаарктеристики</li>
</ul>
<p>Такая классификация студентов очень логично может быть "в реальности" и мне кажется модель классификации получила отличный результат, четко выделив кластеры</p>
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<h2 id="%D0%92%D1%8B%D0%B2%D0%BE%D0%B4"><strong>Вывод</strong><a class="anchor-link" href="#%D0%92%D1%8B%D0%B2%D0%BE%D0%B4">¶</a></h2>
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<h5 id="%D0%9A%D0%BB%D1%8E%D1%87%D0%B5%D0%B2%D0%BE%D0%B9-%D1%84%D0%B0%D0%BA%D1%82%D0%BE%D1%80-%D1%83%D1%81%D0%BF%D0%B5%D0%B2%D0%B0%D0%B5%D0%BC%D0%BE%D1%81%D1%82%D0%B8"><strong>Ключевой фактор успеваемости</strong><a class="anchor-link" href="#%D0%9A%D0%BB%D1%8E%D1%87%D0%B5%D0%B2%D0%BE%D0%B9-%D1%84%D0%B0%D0%BA%D1%82%D0%BE%D1%80-%D1%83%D1%81%D0%BF%D0%B5%D0%B2%D0%B0%D0%B5%D0%BC%D0%BE%D1%81%D1%82%D0%B8">¶</a></h5><p>При помощи статистических методов, а также при получении коэффициентов модели линейной регрессии подтверждена единственная сильная прямая взаимосвязь между количеством часов учебы в день и средним баллом. Проанализированы и визуализированы взаимосвязи других атрибутов</p>
<h5 id="%D0%9F%D1%80%D0%BE%D0%B2%D0%B5%D1%80%D0%BA%D0%B0-%D0%B3%D0%B8%D0%BF%D0%BE%D1%82%D0%B5%D0%B7"><strong>Проверка гипотез</strong><a class="anchor-link" href="#%D0%9F%D1%80%D0%BE%D0%B2%D0%B5%D1%80%D0%BA%D0%B0-%D0%B3%D0%B8%D0%BF%D0%BE%D1%82%D0%B5%D0%B7">¶</a></h5><p>Статистически значимой связи между полом студента и количеством сна не обнаружено. Гипотеза о влиянии сна на оценки также не подтвердилась – линейной или квадратичной зависимости не выявлено</p>
<h5 id="%D0%9C%D0%BE%D0%B4%D0%B5%D0%BB%D1%8C-%D0%BB%D0%B8%D0%BD%D0%B5%D0%B9%D0%BD%D0%BE%D0%B9-%D1%80%D0%B5%D0%B3%D1%80%D0%B5%D1%81%D1%81%D0%B8%D0%B8"><strong>Модель линейной регрессии</strong><a class="anchor-link" href="#%D0%9C%D0%BE%D0%B4%D0%B5%D0%BB%D1%8C-%D0%BB%D0%B8%D0%BD%D0%B5%D0%B9%D0%BD%D0%BE%D0%B9-%D1%80%D0%B5%D0%B3%D1%80%D0%B5%D1%81%D1%81%D0%B8%D0%B8">¶</a></h5><p>Построенная модель линейной регрессии позволяет прогнозировать средний балл с приемлемой точностью (ошибка MAE ~0.41 балл) и объясняет 55% вариации оценок</p>
<h5 id="%D0%9C%D0%BE%D0%B4%D0%B5%D0%BB%D1%8C-%D0%BA%D0%BB%D0%B0%D1%81%D1%82%D0%B5%D1%80%D0%B8%D0%B7%D0%B0%D1%86%D0%B8%D0%B8"><strong>Модель кластеризации</strong><a class="anchor-link" href="#%D0%9C%D0%BE%D0%B4%D0%B5%D0%BB%D1%8C-%D0%BA%D0%BB%D0%B0%D1%81%D1%82%D0%B5%D1%80%D0%B8%D0%B7%D0%B0%D1%86%D0%B8%D0%B8">¶</a></h5><p>Кластерный анализ выделил три характерных типа студентов: «Отличники», «Социально активные» и «Спортсмены», которые логично интерпретируются "в реальность"</p>
<h4 id="%D0%9F%D0%BE%D0%B4%D0%B2%D0%BE%D0%B4%D1%8F-%D0%B8%D1%82%D0%BE%D0%B3,"><strong>Подводя итог,</strong><a class="anchor-link" href="#%D0%9F%D0%BE%D0%B4%D0%B2%D0%BE%D0%B4%D1%8F-%D0%B8%D1%82%D0%BE%D0%B3,">¶</a></h4><p>В работе продемонстрировано использование различных методов для анализа данных в Python, построены множество разных визуализаций, использованы статистические инструменты, выполнена агрегация и группировка. Проведена предварительная подготовка данных, разведочный анализ и построены две ML модели, что позволило достичь поставленной цели — проанализировать ключевые взаимосвязи между образом жизни и успеваемостью студентов</p>
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