python_for_analytics
/
3.2.1_LEC_pandas_apply_examples.ipynb
2214 строк · 85.6 Кб
1{2"cells": [3{4"cell_type": "markdown",5"metadata": {},6"source": [7"# Создание столбцов на ходу\n",8"- Лена - разработчик\n",9"- Ваня - дизайнер\n",10"- Настя - редактор"11]12},13{14"cell_type": "code",15"execution_count": 1,16"metadata": {},17"outputs": [],18"source": [19"import pandas as pd"20]21},22{23"cell_type": "code",24"execution_count": 2,25"metadata": {},26"outputs": [],27"source": [28"def departments(row):\n",29" # row['разработка'] = 1 # создается новый столбец\n",30" info = row['dicts']\n",31" # print(info)\n",32" \n",33" if 'Лена' in info:\n",34" row['разработка'] = info['Лена']\n",35" \n",36" if 'Настя' in info:\n",37" row['редакторы'] = info['Настя']\n",38" \n",39" return row"40]41},42{43"cell_type": "code",44"execution_count": 3,45"metadata": {},46"outputs": [47{48"ename": "NameError",49"evalue": "name 'df' is not defined",50"output_type": "error",51"traceback": [52"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",53"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",54"Cell \u001b[1;32mIn[3], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43mdf\u001b[49m\u001b[38;5;241m.\u001b[39mapply(departments, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\u001b[38;5;241m.\u001b[39mfillna(\u001b[38;5;241m0\u001b[39m)\n\u001b[0;32m 2\u001b[0m df\n",55"\u001b[1;31mNameError\u001b[0m: name 'df' is not defined"56]57}58],59"source": [60"df = df.apply(departments, axis=1).fillna(0)\n",61"df"62]63},64{65"cell_type": "code",66"execution_count": 4,67"metadata": {},68"outputs": [69{70"ename": "NameError",71"evalue": "name 'df' is not defined",72"output_type": "error",73"traceback": [74"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",75"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",76"Cell \u001b[1;32mIn[4], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mdf\u001b[49m\u001b[38;5;241m.\u001b[39mastype({\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mразработка\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mint32\u001b[39m\u001b[38;5;124m'\u001b[39m})\n",77"\u001b[1;31mNameError\u001b[0m: name 'df' is not defined"78]79}80],81"source": [82"df.astype({'разработка': 'int32'})"83]84},85{86"cell_type": "code",87"execution_count": 5,88"metadata": {},89"outputs": [90{91"data": {92"text/html": [93"<div>\n",94"<style scoped>\n",95" .dataframe tbody tr th:only-of-type {\n",96" vertical-align: middle;\n",97" }\n",98"\n",99" .dataframe tbody tr th {\n",100" vertical-align: top;\n",101" }\n",102"\n",103" .dataframe thead th {\n",104" text-align: right;\n",105" }\n",106"</style>\n",107"<table border=\"1\" class=\"dataframe\">\n",108" <thead>\n",109" <tr style=\"text-align: right;\">\n",110" <th></th>\n",111" <th>dicts</th>\n",112" </tr>\n",113" </thead>\n",114" <tbody>\n",115" <tr>\n",116" <th>0</th>\n",117" <td>{'Лена': 1, 'Ваня': 2}</td>\n",118" </tr>\n",119" <tr>\n",120" <th>1</th>\n",121" <td>{'Ваня': 3, 'Леша': 5}</td>\n",122" </tr>\n",123" <tr>\n",124" <th>2</th>\n",125" <td>{'Настя': 3}</td>\n",126" </tr>\n",127" </tbody>\n",128"</table>\n",129"</div>"130],131"text/plain": [132" dicts\n",133"0 {'Лена': 1, 'Ваня': 2}\n",134"1 {'Ваня': 3, 'Леша': 5}\n",135"2 {'Настя': 3}"136]137},138"execution_count": 5,139"metadata": {},140"output_type": "execute_result"141}142],143"source": [144"df = pd.DataFrame({'dicts': [{'Лена': 1, 'Ваня': 2}, {'Ваня': 3, 'Леша': 5}, {'Настя': 3}]})\n",145"df"146]147},148{149"cell_type": "code",150"execution_count": null,151"metadata": {},152"outputs": [],153"source": []154},155{156"cell_type": "code",157"execution_count": null,158"metadata": {},159"outputs": [],160"source": []161},162{163"cell_type": "code",164"execution_count": null,165"metadata": {},166"outputs": [],167"source": []168},169{170"cell_type": "code",171"execution_count": null,172"metadata": {},173"outputs": [],174"source": []175},176{177"cell_type": "markdown",178"metadata": {},179"source": [180"# Группировки и оконные функции"181]182},183{184"cell_type": "code",185"execution_count": 17,186"metadata": {187"scrolled": true188},189"outputs": [190{191"data": {192"text/html": [193"<div>\n",194"<style scoped>\n",195" .dataframe tbody tr th:only-of-type {\n",196" vertical-align: middle;\n",197" }\n",198"\n",199" .dataframe tbody tr th {\n",200" vertical-align: top;\n",201" }\n",202"\n",203" .dataframe thead th {\n",204" text-align: right;\n",205" }\n",206"</style>\n",207"<table border=\"1\" class=\"dataframe\">\n",208" <thead>\n",209" <tr style=\"text-align: right;\">\n",210" <th></th>\n",211" <th>region</th>\n",212" <th>city</th>\n",213" <th>sales</th>\n",214" </tr>\n",215" </thead>\n",216" <tbody>\n",217" <tr>\n",218" <th>0</th>\n",219" <td>СФО</td>\n",220" <td>Новосибирск</td>\n",221" <td>140</td>\n",222" </tr>\n",223" <tr>\n",224" <th>1</th>\n",225" <td>СФО</td>\n",226" <td>Томск</td>\n",227" <td>135</td>\n",228" </tr>\n",229" <tr>\n",230" <th>2</th>\n",231" <td>ДВФО</td>\n",232" <td>Владивосток</td>\n",233" <td>290</td>\n",234" </tr>\n",235" <tr>\n",236" <th>3</th>\n",237" <td>СФО</td>\n",238" <td>Новосибирск</td>\n",239" <td>95</td>\n",240" </tr>\n",241" <tr>\n",242" <th>4</th>\n",243" <td>ДВФО</td>\n",244" <td>Владивосток</td>\n",245" <td>100</td>\n",246" </tr>\n",247" <tr>\n",248" <th>5</th>\n",249" <td>ДВФО</td>\n",250" <td>Владивосток</td>\n",251" <td>50</td>\n",252" </tr>\n",253" <tr>\n",254" <th>6</th>\n",255" <td>ДВФО</td>\n",256" <td>Владивосток</td>\n",257" <td>40</td>\n",258" </tr>\n",259" <tr>\n",260" <th>7</th>\n",261" <td>ДВФО</td>\n",262" <td>Владивосток</td>\n",263" <td>120</td>\n",264" </tr>\n",265" <tr>\n",266" <th>8</th>\n",267" <td>СФО</td>\n",268" <td>Томск</td>\n",269" <td>190</td>\n",270" </tr>\n",271" <tr>\n",272" <th>9</th>\n",273" <td>ДВФО</td>\n",274" <td>Владивосток</td>\n",275" <td>200</td>\n",276" </tr>\n",277" </tbody>\n",278"</table>\n",279"</div>"280],281"text/plain": [282" region city sales\n",283"0 СФО Новосибирск 140\n",284"1 СФО Томск 135\n",285"2 ДВФО Владивосток 290\n",286"3 СФО Новосибирск 95\n",287"4 ДВФО Владивосток 100\n",288"5 ДВФО Владивосток 50\n",289"6 ДВФО Владивосток 40\n",290"7 ДВФО Владивосток 120\n",291"8 СФО Томск 190\n",292"9 ДВФО Владивосток 200"293]294},295"execution_count": 17,296"metadata": {},297"output_type": "execute_result"298}299],300"source": [301"df = pd.DataFrame(\n",302" {\n",303" 'region': ['СФО', 'СФО', 'ДВФО', 'СФО', 'ДВФО', 'ДВФО', 'ДВФО', 'ДВФО', 'СФО', 'ДВФО'],\n",304" 'city': ['Новосибирск', 'Томск', 'Владивосток', 'Новосибирск', 'Владивосток', 'Владивосток', 'Владивосток', 'Владивосток', 'Томск', 'Владивосток'],\n",305" 'sales': [140, 135, 290, 95, 100, 50, 40, 120, 190, 200],\n",306" }\n",307")\n",308"df"309]310},311{312"cell_type": "code",313"execution_count": 19,314"metadata": {},315"outputs": [316{317"data": {318"text/plain": [319"array(['СФО', 'ДВФО'], dtype=object)"320]321},322"execution_count": 19,323"metadata": {},324"output_type": "execute_result"325}326],327"source": [328"df['region'].unique()"329]330},331{332"cell_type": "code",333"execution_count": 20,334"metadata": {},335"outputs": [336{337"data": {338"text/html": [339"<div>\n",340"<style scoped>\n",341" .dataframe tbody tr th:only-of-type {\n",342" vertical-align: middle;\n",343" }\n",344"\n",345" .dataframe tbody tr th {\n",346" vertical-align: top;\n",347" }\n",348"\n",349" .dataframe thead th {\n",350" text-align: right;\n",351" }\n",352"</style>\n",353"<table border=\"1\" class=\"dataframe\">\n",354" <thead>\n",355" <tr style=\"text-align: right;\">\n",356" <th></th>\n",357" <th>region</th>\n",358" <th>city</th>\n",359" <th>sales</th>\n",360" </tr>\n",361" </thead>\n",362" <tbody>\n",363" <tr>\n",364" <th>0</th>\n",365" <td>СФО</td>\n",366" <td>Новосибирск</td>\n",367" <td>140</td>\n",368" </tr>\n",369" <tr>\n",370" <th>1</th>\n",371" <td>СФО</td>\n",372" <td>Томск</td>\n",373" <td>135</td>\n",374" </tr>\n",375" <tr>\n",376" <th>3</th>\n",377" <td>СФО</td>\n",378" <td>Новосибирск</td>\n",379" <td>95</td>\n",380" </tr>\n",381" <tr>\n",382" <th>8</th>\n",383" <td>СФО</td>\n",384" <td>Томск</td>\n",385" <td>190</td>\n",386" </tr>\n",387" </tbody>\n",388"</table>\n",389"</div>"390],391"text/plain": [392" region city sales\n",393"0 СФО Новосибирск 140\n",394"1 СФО Томск 135\n",395"3 СФО Новосибирск 95\n",396"8 СФО Томск 190"397]398},399"execution_count": 20,400"metadata": {},401"output_type": "execute_result"402}403],404"source": [405"df_sfo = df[df['region'] == 'СФО']\n",406"df_sfo"407]408},409{410"cell_type": "code",411"execution_count": 22,412"metadata": {},413"outputs": [414{415"data": {416"text/plain": [417"region 4\n",418"city 4\n",419"sales 4\n",420"dtype: int64"421]422},423"execution_count": 22,424"metadata": {},425"output_type": "execute_result"426}427],428"source": [429"df_sfo.count() # количество строк с ненулевыми значениями"430]431},432{433"cell_type": "code",434"execution_count": 18,435"metadata": {},436"outputs": [437{438"data": {439"text/html": [440"<div>\n",441"<style scoped>\n",442" .dataframe tbody tr th:only-of-type {\n",443" vertical-align: middle;\n",444" }\n",445"\n",446" .dataframe tbody tr th {\n",447" vertical-align: top;\n",448" }\n",449"\n",450" .dataframe thead th {\n",451" text-align: right;\n",452" }\n",453"</style>\n",454"<table border=\"1\" class=\"dataframe\">\n",455" <thead>\n",456" <tr style=\"text-align: right;\">\n",457" <th></th>\n",458" <th>city</th>\n",459" <th>sales</th>\n",460" </tr>\n",461" <tr>\n",462" <th>region</th>\n",463" <th></th>\n",464" <th></th>\n",465" </tr>\n",466" </thead>\n",467" <tbody>\n",468" <tr>\n",469" <th>ДВФО</th>\n",470" <td>6</td>\n",471" <td>6</td>\n",472" </tr>\n",473" <tr>\n",474" <th>СФО</th>\n",475" <td>4</td>\n",476" <td>4</td>\n",477" </tr>\n",478" </tbody>\n",479"</table>\n",480"</div>"481],482"text/plain": [483" city sales\n",484"region \n",485"ДВФО 6 6\n",486"СФО 4 4"487]488},489"execution_count": 18,490"metadata": {},491"output_type": "execute_result"492}493],494"source": [495"df.groupby('region').count()"496]497},498{499"cell_type": "code",500"execution_count": null,501"metadata": {},502"outputs": [],503"source": []504},505{506"cell_type": "markdown",507"metadata": {},508"source": [509"Посчитать размах значений"510]511},512{513"cell_type": "code",514"execution_count": 23,515"metadata": {},516"outputs": [517{518"data": {519"text/html": [520"<div>\n",521"<style scoped>\n",522" .dataframe tbody tr th:only-of-type {\n",523" vertical-align: middle;\n",524" }\n",525"\n",526" .dataframe tbody tr th {\n",527" vertical-align: top;\n",528" }\n",529"\n",530" .dataframe thead th {\n",531" text-align: right;\n",532" }\n",533"</style>\n",534"<table border=\"1\" class=\"dataframe\">\n",535" <thead>\n",536" <tr style=\"text-align: right;\">\n",537" <th></th>\n",538" <th>region</th>\n",539" <th>city</th>\n",540" <th>sales</th>\n",541" </tr>\n",542" </thead>\n",543" <tbody>\n",544" <tr>\n",545" <th>0</th>\n",546" <td>СФО</td>\n",547" <td>Новосибирск</td>\n",548" <td>140</td>\n",549" </tr>\n",550" <tr>\n",551" <th>1</th>\n",552" <td>СФО</td>\n",553" <td>Томск</td>\n",554" <td>135</td>\n",555" </tr>\n",556" <tr>\n",557" <th>2</th>\n",558" <td>ДВФО</td>\n",559" <td>Владивосток</td>\n",560" <td>290</td>\n",561" </tr>\n",562" <tr>\n",563" <th>3</th>\n",564" <td>СФО</td>\n",565" <td>Новосибирск</td>\n",566" <td>95</td>\n",567" </tr>\n",568" <tr>\n",569" <th>4</th>\n",570" <td>ДВФО</td>\n",571" <td>Владивосток</td>\n",572" <td>100</td>\n",573" </tr>\n",574" <tr>\n",575" <th>5</th>\n",576" <td>ДВФО</td>\n",577" <td>Владивосток</td>\n",578" <td>50</td>\n",579" </tr>\n",580" <tr>\n",581" <th>6</th>\n",582" <td>ДВФО</td>\n",583" <td>Владивосток</td>\n",584" <td>40</td>\n",585" </tr>\n",586" <tr>\n",587" <th>7</th>\n",588" <td>ДВФО</td>\n",589" <td>Владивосток</td>\n",590" <td>120</td>\n",591" </tr>\n",592" <tr>\n",593" <th>8</th>\n",594" <td>СФО</td>\n",595" <td>Томск</td>\n",596" <td>190</td>\n",597" </tr>\n",598" <tr>\n",599" <th>9</th>\n",600" <td>ДВФО</td>\n",601" <td>Владивосток</td>\n",602" <td>200</td>\n",603" </tr>\n",604" </tbody>\n",605"</table>\n",606"</div>"607],608"text/plain": [609" region city sales\n",610"0 СФО Новосибирск 140\n",611"1 СФО Томск 135\n",612"2 ДВФО Владивосток 290\n",613"3 СФО Новосибирск 95\n",614"4 ДВФО Владивосток 100\n",615"5 ДВФО Владивосток 50\n",616"6 ДВФО Владивосток 40\n",617"7 ДВФО Владивосток 120\n",618"8 СФО Томск 190\n",619"9 ДВФО Владивосток 200"620]621},622"execution_count": 23,623"metadata": {},624"output_type": "execute_result"625}626],627"source": [628"df"629]630},631{632"cell_type": "code",633"execution_count": 24,634"metadata": {},635"outputs": [636{637"data": {638"text/html": [639"<div>\n",640"<style scoped>\n",641" .dataframe tbody tr th:only-of-type {\n",642" vertical-align: middle;\n",643" }\n",644"\n",645" .dataframe tbody tr th {\n",646" vertical-align: top;\n",647" }\n",648"\n",649" .dataframe thead th {\n",650" text-align: right;\n",651" }\n",652"</style>\n",653"<table border=\"1\" class=\"dataframe\">\n",654" <thead>\n",655" <tr style=\"text-align: right;\">\n",656" <th></th>\n",657" <th>region</th>\n",658" <th>city</th>\n",659" <th>sales</th>\n",660" </tr>\n",661" </thead>\n",662" <tbody>\n",663" <tr>\n",664" <th>0</th>\n",665" <td>СФО</td>\n",666" <td>Новосибирск</td>\n",667" <td>140</td>\n",668" </tr>\n",669" <tr>\n",670" <th>1</th>\n",671" <td>СФО</td>\n",672" <td>Томск</td>\n",673" <td>135</td>\n",674" </tr>\n",675" <tr>\n",676" <th>3</th>\n",677" <td>СФО</td>\n",678" <td>Новосибирск</td>\n",679" <td>95</td>\n",680" </tr>\n",681" <tr>\n",682" <th>8</th>\n",683" <td>СФО</td>\n",684" <td>Томск</td>\n",685" <td>190</td>\n",686" </tr>\n",687" </tbody>\n",688"</table>\n",689"</div>"690],691"text/plain": [692" region city sales\n",693"0 СФО Новосибирск 140\n",694"1 СФО Томск 135\n",695"3 СФО Новосибирск 95\n",696"8 СФО Томск 190"697]698},699"execution_count": 24,700"metadata": {},701"output_type": "execute_result"702}703],704"source": [705"df_sfo"706]707},708{709"cell_type": "code",710"execution_count": 25,711"metadata": {},712"outputs": [713{714"data": {715"text/plain": [716"95"717]718},719"execution_count": 25,720"metadata": {},721"output_type": "execute_result"722}723],724"source": [725"min_sales = df_sfo['sales'].min()\n",726"min_sales"727]728},729{730"cell_type": "code",731"execution_count": 26,732"metadata": {},733"outputs": [734{735"data": {736"text/plain": [737"190"738]739},740"execution_count": 26,741"metadata": {},742"output_type": "execute_result"743}744],745"source": [746"max_sales = df_sfo['sales'].max()\n",747"max_sales"748]749},750{751"cell_type": "code",752"execution_count": 27,753"metadata": {},754"outputs": [755{756"data": {757"text/plain": [758"95"759]760},761"execution_count": 27,762"metadata": {},763"output_type": "execute_result"764}765],766"source": [767"max_sales - min_sales"768]769},770{771"cell_type": "code",772"execution_count": 28,773"metadata": {},774"outputs": [],775"source": [776"def min_max(df):\n",777" min_sales = df['sales'].min()\n",778" max_sales = df['sales'].max()\n",779" \n",780" return max_sales - min_sales"781]782},783{784"cell_type": "code",785"execution_count": 30,786"metadata": {},787"outputs": [788{789"data": {790"text/plain": [791"<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7fc77860ac40>"792]793},794"execution_count": 30,795"metadata": {},796"output_type": "execute_result"797}798],799"source": [800"df.groupby('region')"801]802},803{804"cell_type": "code",805"execution_count": 29,806"metadata": {},807"outputs": [808{809"data": {810"text/plain": [811"region\n",812"ДВФО 250\n",813"СФО 95\n",814"dtype: int64"815]816},817"execution_count": 29,818"metadata": {},819"output_type": "execute_result"820}821],822"source": [823"df.groupby('region').apply(min_max) # на выход функции подается датафрейм вроде df_sfo"824]825},826{827"cell_type": "code",828"execution_count": 37,829"metadata": {},830"outputs": [831{832"data": {833"text/plain": [834"region city \n",835"ДВФО Владивосток 250.0\n",836"СФО Новосибирск 0.0\n",837" Томск 190.0\n",838"dtype: float64"839]840},841"execution_count": 37,842"metadata": {},843"output_type": "execute_result"844}845],846"source": [847"df.groupby(['region', 'city']).apply(min_max)"848]849},850{851"cell_type": "markdown",852"metadata": {},853"source": [854"# Группировки с пустыми значениями"855]856},857{858"cell_type": "code",859"execution_count": 34,860"metadata": {},861"outputs": [862{863"data": {864"text/html": [865"<div>\n",866"<style scoped>\n",867" .dataframe tbody tr th:only-of-type {\n",868" vertical-align: middle;\n",869" }\n",870"\n",871" .dataframe tbody tr th {\n",872" vertical-align: top;\n",873" }\n",874"\n",875" .dataframe thead th {\n",876" text-align: right;\n",877" }\n",878"</style>\n",879"<table border=\"1\" class=\"dataframe\">\n",880" <thead>\n",881" <tr style=\"text-align: right;\">\n",882" <th></th>\n",883" <th>region</th>\n",884" <th>city</th>\n",885" <th>sales</th>\n",886" </tr>\n",887" </thead>\n",888" <tbody>\n",889" <tr>\n",890" <th>0</th>\n",891" <td>СФО</td>\n",892" <td>Новосибирск</td>\n",893" <td>NaN</td>\n",894" </tr>\n",895" <tr>\n",896" <th>1</th>\n",897" <td>СФО</td>\n",898" <td>Томск</td>\n",899" <td>0.0</td>\n",900" </tr>\n",901" <tr>\n",902" <th>2</th>\n",903" <td>ДВФО</td>\n",904" <td>Владивосток</td>\n",905" <td>290.0</td>\n",906" </tr>\n",907" <tr>\n",908" <th>3</th>\n",909" <td>СФО</td>\n",910" <td>Новосибирск</td>\n",911" <td>95.0</td>\n",912" </tr>\n",913" <tr>\n",914" <th>4</th>\n",915" <td>ДВФО</td>\n",916" <td>Владивосток</td>\n",917" <td>100.0</td>\n",918" </tr>\n",919" <tr>\n",920" <th>5</th>\n",921" <td>ДВФО</td>\n",922" <td>Владивосток</td>\n",923" <td>50.0</td>\n",924" </tr>\n",925" <tr>\n",926" <th>6</th>\n",927" <td>ДВФО</td>\n",928" <td>Владивосток</td>\n",929" <td>40.0</td>\n",930" </tr>\n",931" <tr>\n",932" <th>7</th>\n",933" <td>ДВФО</td>\n",934" <td>Владивосток</td>\n",935" <td>120.0</td>\n",936" </tr>\n",937" <tr>\n",938" <th>8</th>\n",939" <td>СФО</td>\n",940" <td>Томск</td>\n",941" <td>190.0</td>\n",942" </tr>\n",943" <tr>\n",944" <th>9</th>\n",945" <td>None</td>\n",946" <td>Владивосток</td>\n",947" <td>200.0</td>\n",948" </tr>\n",949" </tbody>\n",950"</table>\n",951"</div>"952],953"text/plain": [954" region city sales\n",955"0 СФО Новосибирск NaN\n",956"1 СФО Томск 0.0\n",957"2 ДВФО Владивосток 290.0\n",958"3 СФО Новосибирск 95.0\n",959"4 ДВФО Владивосток 100.0\n",960"5 ДВФО Владивосток 50.0\n",961"6 ДВФО Владивосток 40.0\n",962"7 ДВФО Владивосток 120.0\n",963"8 СФО Томск 190.0\n",964"9 None Владивосток 200.0"965]966},967"execution_count": 34,968"metadata": {},969"output_type": "execute_result"970}971],972"source": [973"df = pd.DataFrame(\n",974" {\n",975" 'region': ['СФО', 'СФО', 'ДВФО', 'СФО', 'ДВФО', 'ДВФО', 'ДВФО', 'ДВФО', 'СФО', None],\n",976" 'city': ['Новосибирск', 'Томск', 'Владивосток', 'Новосибирск', 'Владивосток', 'Владивосток', 'Владивосток', 'Владивосток', 'Томск', 'Владивосток'],\n",977" 'sales': [None, 0, 290, 95, 100, 50, 40, 120, 190, 200],\n",978" }\n",979")\n",980"df"981]982},983{984"cell_type": "code",985"execution_count": 35,986"metadata": {},987"outputs": [988{989"data": {990"text/plain": [991"ДВФО 5\n",992"СФО 4\n",993"Name: region, dtype: int64"994]995},996"execution_count": 35,997"metadata": {},998"output_type": "execute_result"999}1000],1001"source": [1002"df['region'].value_counts() # группировка НЕ учитывает строки с пустым ключом"1003]1004},1005{1006"cell_type": "code",1007"execution_count": 47,1008"metadata": {},1009"outputs": [1010{1011"data": {1012"text/plain": [1013"Index(['ДВФО', 'СФО'], dtype='object')"1014]1015},1016"execution_count": 47,1017"metadata": {},1018"output_type": "execute_result"1019}1020],1021"source": [1022"df['region'].value_counts().index"1023]1024},1025{1026"cell_type": "code",1027"execution_count": 48,1028"metadata": {},1029"outputs": [1030{1031"data": {1032"text/plain": [1033"array([5, 4])"1034]1035},1036"execution_count": 48,1037"metadata": {},1038"output_type": "execute_result"1039}1040],1041"source": [1042"df['region'].value_counts().values"1043]1044},1045{1046"cell_type": "code",1047"execution_count": 36,1048"metadata": {},1049"outputs": [1050{1051"data": {1052"text/html": [1053"<div>\n",1054"<style scoped>\n",1055" .dataframe tbody tr th:only-of-type {\n",1056" vertical-align: middle;\n",1057" }\n",1058"\n",1059" .dataframe tbody tr th {\n",1060" vertical-align: top;\n",1061" }\n",1062"\n",1063" .dataframe thead th {\n",1064" text-align: right;\n",1065" }\n",1066"</style>\n",1067"<table border=\"1\" class=\"dataframe\">\n",1068" <thead>\n",1069" <tr style=\"text-align: right;\">\n",1070" <th></th>\n",1071" <th>city</th>\n",1072" <th>sales</th>\n",1073" </tr>\n",1074" <tr>\n",1075" <th>region</th>\n",1076" <th></th>\n",1077" <th></th>\n",1078" </tr>\n",1079" </thead>\n",1080" <tbody>\n",1081" <tr>\n",1082" <th>ДВФО</th>\n",1083" <td>5</td>\n",1084" <td>5</td>\n",1085" </tr>\n",1086" <tr>\n",1087" <th>СФО</th>\n",1088" <td>4</td>\n",1089" <td>3</td>\n",1090" </tr>\n",1091" </tbody>\n",1092"</table>\n",1093"</div>"1094],1095"text/plain": [1096" city sales\n",1097"region \n",1098"ДВФО 5 5\n",1099"СФО 4 3"1100]1101},1102"execution_count": 36,1103"metadata": {},1104"output_type": "execute_result"1105}1106],1107"source": [1108"df.groupby('region').count() # метод count возвращает количетство НЕПУСТЫХ строк"1109]1110},1111{1112"cell_type": "markdown",1113"metadata": {},1114"source": [1115"### Метод agg"1116]1117},1118{1119"cell_type": "code",1120"execution_count": 38,1121"metadata": {},1122"outputs": [1123{1124"data": {1125"text/html": [1126"<div>\n",1127"<style scoped>\n",1128" .dataframe tbody tr th:only-of-type {\n",1129" vertical-align: middle;\n",1130" }\n",1131"\n",1132" .dataframe tbody tr th {\n",1133" vertical-align: top;\n",1134" }\n",1135"\n",1136" .dataframe thead th {\n",1137" text-align: right;\n",1138" }\n",1139"</style>\n",1140"<table border=\"1\" class=\"dataframe\">\n",1141" <thead>\n",1142" <tr style=\"text-align: right;\">\n",1143" <th></th>\n",1144" <th>region</th>\n",1145" <th>city</th>\n",1146" <th>sales</th>\n",1147" </tr>\n",1148" </thead>\n",1149" <tbody>\n",1150" <tr>\n",1151" <th>0</th>\n",1152" <td>СФО</td>\n",1153" <td>Новосибирск</td>\n",1154" <td>NaN</td>\n",1155" </tr>\n",1156" <tr>\n",1157" <th>1</th>\n",1158" <td>СФО</td>\n",1159" <td>Томск</td>\n",1160" <td>0.0</td>\n",1161" </tr>\n",1162" <tr>\n",1163" <th>2</th>\n",1164" <td>ДВФО</td>\n",1165" <td>Владивосток</td>\n",1166" <td>290.0</td>\n",1167" </tr>\n",1168" <tr>\n",1169" <th>3</th>\n",1170" <td>СФО</td>\n",1171" <td>Новосибирск</td>\n",1172" <td>95.0</td>\n",1173" </tr>\n",1174" <tr>\n",1175" <th>4</th>\n",1176" <td>ДВФО</td>\n",1177" <td>Владивосток</td>\n",1178" <td>100.0</td>\n",1179" </tr>\n",1180" <tr>\n",1181" <th>5</th>\n",1182" <td>ДВФО</td>\n",1183" <td>Владивосток</td>\n",1184" <td>50.0</td>\n",1185" </tr>\n",1186" <tr>\n",1187" <th>6</th>\n",1188" <td>ДВФО</td>\n",1189" <td>Владивосток</td>\n",1190" <td>40.0</td>\n",1191" </tr>\n",1192" <tr>\n",1193" <th>7</th>\n",1194" <td>ДВФО</td>\n",1195" <td>Владивосток</td>\n",1196" <td>120.0</td>\n",1197" </tr>\n",1198" <tr>\n",1199" <th>8</th>\n",1200" <td>СФО</td>\n",1201" <td>Томск</td>\n",1202" <td>190.0</td>\n",1203" </tr>\n",1204" <tr>\n",1205" <th>9</th>\n",1206" <td>None</td>\n",1207" <td>Владивосток</td>\n",1208" <td>200.0</td>\n",1209" </tr>\n",1210" </tbody>\n",1211"</table>\n",1212"</div>"1213],1214"text/plain": [1215" region city sales\n",1216"0 СФО Новосибирск NaN\n",1217"1 СФО Томск 0.0\n",1218"2 ДВФО Владивосток 290.0\n",1219"3 СФО Новосибирск 95.0\n",1220"4 ДВФО Владивосток 100.0\n",1221"5 ДВФО Владивосток 50.0\n",1222"6 ДВФО Владивосток 40.0\n",1223"7 ДВФО Владивосток 120.0\n",1224"8 СФО Томск 190.0\n",1225"9 None Владивосток 200.0"1226]1227},1228"execution_count": 38,1229"metadata": {},1230"output_type": "execute_result"1231}1232],1233"source": [1234"df"1235]1236},1237{1238"cell_type": "code",1239"execution_count": 41,1240"metadata": {},1241"outputs": [1242{1243"data": {1244"text/html": [1245"<div>\n",1246"<style scoped>\n",1247" .dataframe tbody tr th:only-of-type {\n",1248" vertical-align: middle;\n",1249" }\n",1250"\n",1251" .dataframe tbody tr th {\n",1252" vertical-align: top;\n",1253" }\n",1254"\n",1255" .dataframe thead th {\n",1256" text-align: right;\n",1257" }\n",1258"</style>\n",1259"<table border=\"1\" class=\"dataframe\">\n",1260" <thead>\n",1261" <tr style=\"text-align: right;\">\n",1262" <th></th>\n",1263" <th>city</th>\n",1264" <th>sales</th>\n",1265" </tr>\n",1266" <tr>\n",1267" <th>region</th>\n",1268" <th></th>\n",1269" <th></th>\n",1270" </tr>\n",1271" </thead>\n",1272" <tbody>\n",1273" <tr>\n",1274" <th>ДВФО</th>\n",1275" <td>5</td>\n",1276" <td>5</td>\n",1277" </tr>\n",1278" <tr>\n",1279" <th>СФО</th>\n",1280" <td>4</td>\n",1281" <td>3</td>\n",1282" </tr>\n",1283" </tbody>\n",1284"</table>\n",1285"</div>"1286],1287"text/plain": [1288" city sales\n",1289"region \n",1290"ДВФО 5 5\n",1291"СФО 4 3"1292]1293},1294"execution_count": 41,1295"metadata": {},1296"output_type": "execute_result"1297}1298],1299"source": [1300"df.groupby('region').count()"1301]1302},1303{1304"cell_type": "code",1305"execution_count": 45,1306"metadata": {},1307"outputs": [1308{1309"data": {1310"text/html": [1311"<div>\n",1312"<style scoped>\n",1313" .dataframe tbody tr th:only-of-type {\n",1314" vertical-align: middle;\n",1315" }\n",1316"\n",1317" .dataframe tbody tr th {\n",1318" vertical-align: top;\n",1319" }\n",1320"\n",1321" .dataframe thead tr th {\n",1322" text-align: left;\n",1323" }\n",1324"\n",1325" .dataframe thead tr:last-of-type th {\n",1326" text-align: right;\n",1327" }\n",1328"</style>\n",1329"<table border=\"1\" class=\"dataframe\">\n",1330" <thead>\n",1331" <tr>\n",1332" <th></th>\n",1333" <th>sales</th>\n",1334" <th colspan=\"2\" halign=\"left\">city</th>\n",1335" </tr>\n",1336" <tr>\n",1337" <th></th>\n",1338" <th>count</th>\n",1339" <th>count</th>\n",1340" <th>min</th>\n",1341" </tr>\n",1342" <tr>\n",1343" <th>region</th>\n",1344" <th></th>\n",1345" <th></th>\n",1346" <th></th>\n",1347" </tr>\n",1348" </thead>\n",1349" <tbody>\n",1350" <tr>\n",1351" <th>ДВФО</th>\n",1352" <td>5</td>\n",1353" <td>5</td>\n",1354" <td>Владивосток</td>\n",1355" </tr>\n",1356" <tr>\n",1357" <th>СФО</th>\n",1358" <td>3</td>\n",1359" <td>4</td>\n",1360" <td>Новосибирск</td>\n",1361" </tr>\n",1362" </tbody>\n",1363"</table>\n",1364"</div>"1365],1366"text/plain": [1367" sales city \n",1368" count count min\n",1369"region \n",1370"ДВФО 5 5 Владивосток\n",1371"СФО 3 4 Новосибирск"1372]1373},1374"execution_count": 45,1375"metadata": {},1376"output_type": "execute_result"1377}1378],1379"source": [1380"df.groupby('region').agg({'sales': 'count', 'city': ['count', 'min']})"1381]1382},1383{1384"cell_type": "code",1385"execution_count": 49,1386"metadata": {1387"scrolled": true1388},1389"outputs": [1390{1391"ename": "KeyError",1392"evalue": "'sales'",1393"output_type": "error",1394"traceback": [1395"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",1396"\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",1397"\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 3620\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3621\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3622\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",1398"\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",1399"\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",1400"\u001b[0;32mpandas/_libs/index_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.index.Int64Engine._check_type\u001b[0;34m()\u001b[0m\n",1401"\u001b[0;31mKeyError\u001b[0m: 'sales'",1402"\nThe above exception was the direct cause of the following exception:\n",1403"\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",1404"\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/pandas/core/groupby/generic.py\u001b[0m in \u001b[0;36maggregate\u001b[0;34m(self, func, engine, engine_kwargs, *args, **kwargs)\u001b[0m\n\u001b[1;32m 286\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 287\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_python_agg_general\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 288\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",1405"\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/pandas/core/groupby/groupby.py\u001b[0m in \u001b[0;36m_python_agg_general\u001b[0;34m(self, func, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1480\u001b[0m \u001b[0;31m# if this function is invalid for this dtype, we will ignore it.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1481\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrouper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0magg_series\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1482\u001b[0m \u001b[0;32mexcept\u001b[0m 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\u001b[0minitialized\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",1432"\u001b[0;32m<ipython-input-28-fed892ac6ed2>\u001b[0m in \u001b[0;36mmin_max\u001b[0;34m(df)\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmin_max\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmin_sales\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'sales'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mmax_sales\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'sales'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mmax_sales\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mmin_sales\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",1433"\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 956\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 957\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mkey_is_scalar\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 958\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 959\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 960\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_hashable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",1434"\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m_get_value\u001b[0;34m(self, label, takeable)\u001b[0m\n\u001b[1;32m 1067\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1068\u001b[0m \u001b[0;31m# Similar to Index.get_value, but we do not fall back to positional\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1069\u001b[0;31m \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1070\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_values_for_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1071\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",1435"\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 3621\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3622\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3623\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3624\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3625\u001b[0m \u001b[0;31m# If we have a listlike key, _check_indexing_error will raise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",1436"\u001b[0;31mKeyError\u001b[0m: 'sales'"1437]1438}1439],1440"source": [1441"df.groupby('region').agg({'sales': ['count', min_max], 'city': ['count', 'min']})"1442]1443},1444{1445"cell_type": "code",1446"execution_count": null,1447"metadata": {},1448"outputs": [],1449"source": []1450},1451{1452"cell_type": "markdown",1453"metadata": {},1454"source": [1455"Функция min_max_agg для метода agg"1456]1457},1458{1459"cell_type": "code",1460"execution_count": 50,1461"metadata": {},1462"outputs": [],1463"source": [1464"def min_max_agg(df_column): # на вход подается конкретный столбец\n",1465" min_sales = df_column.min()\n",1466" max_sales = df_column.max()\n",1467" \n",1468" return max_sales - min_sales"1469]1470},1471{1472"cell_type": "code",1473"execution_count": 52,1474"metadata": {},1475"outputs": [1476{1477"data": {1478"text/html": [1479"<div>\n",1480"<style scoped>\n",1481" .dataframe tbody tr th:only-of-type {\n",1482" vertical-align: middle;\n",1483" }\n",1484"\n",1485" .dataframe tbody tr th {\n",1486" vertical-align: top;\n",1487" }\n",1488"\n",1489" .dataframe thead tr th {\n",1490" text-align: left;\n",1491" }\n",1492"\n",1493" .dataframe thead tr:last-of-type th {\n",1494" text-align: right;\n",1495" }\n",1496"</style>\n",1497"<table border=\"1\" class=\"dataframe\">\n",1498" <thead>\n",1499" <tr>\n",1500" <th></th>\n",1501" <th colspan=\"2\" halign=\"left\">sales</th>\n",1502" <th colspan=\"2\" halign=\"left\">city</th>\n",1503" </tr>\n",1504" <tr>\n",1505" <th></th>\n",1506" <th>count</th>\n",1507" <th>min_max_agg</th>\n",1508" <th>count</th>\n",1509" <th>min</th>\n",1510" </tr>\n",1511" <tr>\n",1512" <th>region</th>\n",1513" <th></th>\n",1514" <th></th>\n",1515" <th></th>\n",1516" <th></th>\n",1517" </tr>\n",1518" </thead>\n",1519" <tbody>\n",1520" <tr>\n",1521" <th>ДВФО</th>\n",1522" <td>5</td>\n",1523" <td>250.0</td>\n",1524" <td>5</td>\n",1525" <td>Владивосток</td>\n",1526" </tr>\n",1527" <tr>\n",1528" <th>СФО</th>\n",1529" <td>3</td>\n",1530" <td>190.0</td>\n",1531" <td>4</td>\n",1532" <td>Новосибирск</td>\n",1533" </tr>\n",1534" </tbody>\n",1535"</table>\n",1536"</div>"1537],1538"text/plain": [1539" sales city \n",1540" count min_max_agg count min\n",1541"region \n",1542"ДВФО 5 250.0 5 Владивосток\n",1543"СФО 3 190.0 4 Новосибирск"1544]1545},1546"execution_count": 52,1547"metadata": {},1548"output_type": "execute_result"1549}1550],1551"source": [1552"df.groupby('region').agg({'sales': ['count', min_max_agg], 'city': ['count', min]})"1553]1554},1555{1556"cell_type": "code",1557"execution_count": null,1558"metadata": {},1559"outputs": [],1560"source": []1561},1562{1563"cell_type": "markdown",1564"metadata": {},1565"source": [1566"### Посчитать процент строки от ее региона"1567]1568},1569{1570"cell_type": "code",1571"execution_count": 55,1572"metadata": {},1573"outputs": [1574{1575"data": {1576"text/html": [1577"<div>\n",1578"<style scoped>\n",1579" .dataframe tbody tr th:only-of-type {\n",1580" vertical-align: middle;\n",1581" }\n",1582"\n",1583" .dataframe tbody tr th {\n",1584" vertical-align: top;\n",1585" }\n",1586"\n",1587" .dataframe thead th {\n",1588" text-align: right;\n",1589" }\n",1590"</style>\n",1591"<table border=\"1\" class=\"dataframe\">\n",1592" <thead>\n",1593" <tr style=\"text-align: right;\">\n",1594" <th></th>\n",1595" <th>region</th>\n",1596" <th>city</th>\n",1597" <th>sales</th>\n",1598" </tr>\n",1599" </thead>\n",1600" <tbody>\n",1601" <tr>\n",1602" <th>2</th>\n",1603" <td>ДВФО</td>\n",1604" <td>Владивосток</td>\n",1605" <td>290</td>\n",1606" </tr>\n",1607" <tr>\n",1608" <th>4</th>\n",1609" <td>ДВФО</td>\n",1610" <td>Владивосток</td>\n",1611" <td>100</td>\n",1612" </tr>\n",1613" <tr>\n",1614" <th>5</th>\n",1615" <td>ДВФО</td>\n",1616" <td>Владивосток</td>\n",1617" <td>50</td>\n",1618" </tr>\n",1619" <tr>\n",1620" <th>6</th>\n",1621" <td>ДВФО</td>\n",1622" <td>Владивосток</td>\n",1623" <td>40</td>\n",1624" </tr>\n",1625" <tr>\n",1626" <th>7</th>\n",1627" <td>ДВФО</td>\n",1628" <td>Владивосток</td>\n",1629" <td>120</td>\n",1630" </tr>\n",1631" <tr>\n",1632" <th>9</th>\n",1633" <td>ДВФО</td>\n",1634" <td>Владивосток</td>\n",1635" <td>200</td>\n",1636" </tr>\n",1637" <tr>\n",1638" <th>0</th>\n",1639" <td>СФО</td>\n",1640" <td>Новосибирск</td>\n",1641" <td>140</td>\n",1642" </tr>\n",1643" <tr>\n",1644" <th>3</th>\n",1645" <td>СФО</td>\n",1646" <td>Новосибирск</td>\n",1647" <td>95</td>\n",1648" </tr>\n",1649" <tr>\n",1650" <th>1</th>\n",1651" <td>СФО</td>\n",1652" <td>Томск</td>\n",1653" <td>135</td>\n",1654" </tr>\n",1655" <tr>\n",1656" <th>8</th>\n",1657" <td>СФО</td>\n",1658" <td>Томск</td>\n",1659" <td>190</td>\n",1660" </tr>\n",1661" </tbody>\n",1662"</table>\n",1663"</div>"1664],1665"text/plain": [1666" region city sales\n",1667"2 ДВФО Владивосток 290\n",1668"4 ДВФО Владивосток 100\n",1669"5 ДВФО Владивосток 50\n",1670"6 ДВФО Владивосток 40\n",1671"7 ДВФО Владивосток 120\n",1672"9 ДВФО Владивосток 200\n",1673"0 СФО Новосибирск 140\n",1674"3 СФО Новосибирск 95\n",1675"1 СФО Томск 135\n",1676"8 СФО Томск 190"1677]1678},1679"execution_count": 55,1680"metadata": {},1681"output_type": "execute_result"1682}1683],1684"source": [1685"df = pd.DataFrame(\n",1686" {\n",1687" 'region': ['СФО', 'СФО', 'ДВФО', 'СФО', 'ДВФО', 'ДВФО', 'ДВФО', 'ДВФО', 'СФО', 'ДВФО'],\n",1688" 'city': ['Новосибирск', 'Томск', 'Владивосток', 'Новосибирск', 'Владивосток', 'Владивосток', 'Владивосток', 'Владивосток', 'Томск', 'Владивосток'],\n",1689" 'sales': [140, 135, 290, 95, 100, 50, 40, 120, 190, 200],\n",1690" }\n",1691")\n",1692"df.sort_values(['region', 'city'])"1693]1694},1695{1696"cell_type": "code",1697"execution_count": null,1698"metadata": {},1699"outputs": [],1700"source": [1701"a = [1, 2, 3]\n",1702"b = a.copy()"1703]1704},1705{1706"cell_type": "code",1707"execution_count": 59,1708"metadata": {},1709"outputs": [1710{1711"data": {1712"text/html": [1713"<div>\n",1714"<style scoped>\n",1715" .dataframe tbody tr th:only-of-type {\n",1716" vertical-align: middle;\n",1717" }\n",1718"\n",1719" .dataframe tbody tr th {\n",1720" vertical-align: top;\n",1721" }\n",1722"\n",1723" .dataframe thead th {\n",1724" text-align: right;\n",1725" }\n",1726"</style>\n",1727"<table border=\"1\" class=\"dataframe\">\n",1728" <thead>\n",1729" <tr style=\"text-align: right;\">\n",1730" <th></th>\n",1731" <th>region</th>\n",1732" <th>city</th>\n",1733" <th>sales</th>\n",1734" </tr>\n",1735" </thead>\n",1736" <tbody>\n",1737" <tr>\n",1738" <th>2</th>\n",1739" <td>ДВФО</td>\n",1740" <td>Владивосток</td>\n",1741" <td>290</td>\n",1742" </tr>\n",1743" <tr>\n",1744" <th>4</th>\n",1745" <td>ДВФО</td>\n",1746" <td>Владивосток</td>\n",1747" <td>100</td>\n",1748" </tr>\n",1749" <tr>\n",1750" <th>5</th>\n",1751" <td>ДВФО</td>\n",1752" <td>Владивосток</td>\n",1753" <td>50</td>\n",1754" </tr>\n",1755" <tr>\n",1756" <th>6</th>\n",1757" <td>ДВФО</td>\n",1758" <td>Владивосток</td>\n",1759" <td>40</td>\n",1760" </tr>\n",1761" <tr>\n",1762" <th>7</th>\n",1763" <td>ДВФО</td>\n",1764" <td>Владивосток</td>\n",1765" <td>120</td>\n",1766" </tr>\n",1767" <tr>\n",1768" <th>9</th>\n",1769" <td>ДВФО</td>\n",1770" <td>Владивосток</td>\n",1771" <td>200</td>\n",1772" </tr>\n",1773" </tbody>\n",1774"</table>\n",1775"</div>"1776],1777"text/plain": [1778" region city sales\n",1779"2 ДВФО Владивосток 290\n",1780"4 ДВФО Владивосток 100\n",1781"5 ДВФО Владивосток 50\n",1782"6 ДВФО Владивосток 40\n",1783"7 ДВФО Владивосток 120\n",1784"9 ДВФО Владивосток 200"1785]1786},1787"execution_count": 59,1788"metadata": {},1789"output_type": "execute_result"1790}1791],1792"source": [1793"df_dv = df.copy()\n",1794"\n",1795"df_dv = df_dv[df_dv['region'] == 'ДВФО']\n",1796"df_dv"1797]1798},1799{1800"cell_type": "code",1801"execution_count": 60,1802"metadata": {},1803"outputs": [1804{1805"data": {1806"text/plain": [1807"800"1808]1809},1810"execution_count": 60,1811"metadata": {},1812"output_type": "execute_result"1813}1814],1815"source": [1816"sum_sales = df_dv['sales'].sum()\n",1817"sum_sales"1818]1819},1820{1821"cell_type": "code",1822"execution_count": 61,1823"metadata": {},1824"outputs": [1825{1826"data": {1827"text/html": [1828"<div>\n",1829"<style scoped>\n",1830" .dataframe tbody tr th:only-of-type {\n",1831" vertical-align: middle;\n",1832" }\n",1833"\n",1834" .dataframe tbody tr th {\n",1835" vertical-align: top;\n",1836" }\n",1837"\n",1838" .dataframe thead th {\n",1839" text-align: right;\n",1840" }\n",1841"</style>\n",1842"<table border=\"1\" class=\"dataframe\">\n",1843" <thead>\n",1844" <tr style=\"text-align: right;\">\n",1845" <th></th>\n",1846" <th>region</th>\n",1847" <th>city</th>\n",1848" <th>sales</th>\n",1849" <th>share</th>\n",1850" </tr>\n",1851" </thead>\n",1852" <tbody>\n",1853" <tr>\n",1854" <th>2</th>\n",1855" <td>ДВФО</td>\n",1856" <td>Владивосток</td>\n",1857" <td>290</td>\n",1858" <td>0.3625</td>\n",1859" </tr>\n",1860" <tr>\n",1861" <th>4</th>\n",1862" <td>ДВФО</td>\n",1863" <td>Владивосток</td>\n",1864" <td>100</td>\n",1865" <td>0.1250</td>\n",1866" </tr>\n",1867" <tr>\n",1868" <th>5</th>\n",1869" <td>ДВФО</td>\n",1870" <td>Владивосток</td>\n",1871" <td>50</td>\n",1872" <td>0.0625</td>\n",1873" </tr>\n",1874" <tr>\n",1875" <th>6</th>\n",1876" <td>ДВФО</td>\n",1877" <td>Владивосток</td>\n",1878" <td>40</td>\n",1879" <td>0.0500</td>\n",1880" </tr>\n",1881" <tr>\n",1882" <th>7</th>\n",1883" <td>ДВФО</td>\n",1884" <td>Владивосток</td>\n",1885" <td>120</td>\n",1886" <td>0.1500</td>\n",1887" </tr>\n",1888" <tr>\n",1889" <th>9</th>\n",1890" <td>ДВФО</td>\n",1891" <td>Владивосток</td>\n",1892" <td>200</td>\n",1893" <td>0.2500</td>\n",1894" </tr>\n",1895" </tbody>\n",1896"</table>\n",1897"</div>"1898],1899"text/plain": [1900" region city sales share\n",1901"2 ДВФО Владивосток 290 0.3625\n",1902"4 ДВФО Владивосток 100 0.1250\n",1903"5 ДВФО Владивосток 50 0.0625\n",1904"6 ДВФО Владивосток 40 0.0500\n",1905"7 ДВФО Владивосток 120 0.1500\n",1906"9 ДВФО Владивосток 200 0.2500"1907]1908},1909"execution_count": 61,1910"metadata": {},1911"output_type": "execute_result"1912}1913],1914"source": [1915"df_dv['share'] = df_dv['sales'] / sum_sales\n",1916"df_dv"1917]1918},1919{1920"cell_type": "code",1921"execution_count": 62,1922"metadata": {},1923"outputs": [],1924"source": [1925"def share(df):\n",1926" sum_sales = df['sales'].sum()\n",1927" df['share'] = df['sales'] / sum_sales\n",1928" \n",1929" return df"1930]1931},1932{1933"cell_type": "code",1934"execution_count": 63,1935"metadata": {},1936"outputs": [1937{1938"data": {1939"text/html": [1940"<div>\n",1941"<style scoped>\n",1942" .dataframe tbody tr th:only-of-type {\n",1943" vertical-align: middle;\n",1944" }\n",1945"\n",1946" .dataframe tbody tr th {\n",1947" vertical-align: top;\n",1948" }\n",1949"\n",1950" .dataframe thead th {\n",1951" text-align: right;\n",1952" }\n",1953"</style>\n",1954"<table border=\"1\" class=\"dataframe\">\n",1955" <thead>\n",1956" <tr style=\"text-align: right;\">\n",1957" <th></th>\n",1958" <th>region</th>\n",1959" <th>city</th>\n",1960" <th>sales</th>\n",1961" <th>share</th>\n",1962" </tr>\n",1963" </thead>\n",1964" <tbody>\n",1965" <tr>\n",1966" <th>2</th>\n",1967" <td>ДВФО</td>\n",1968" <td>Владивосток</td>\n",1969" <td>290</td>\n",1970" <td>0.362500</td>\n",1971" </tr>\n",1972" <tr>\n",1973" <th>4</th>\n",1974" <td>ДВФО</td>\n",1975" <td>Владивосток</td>\n",1976" <td>100</td>\n",1977" <td>0.125000</td>\n",1978" </tr>\n",1979" <tr>\n",1980" <th>5</th>\n",1981" <td>ДВФО</td>\n",1982" <td>Владивосток</td>\n",1983" <td>50</td>\n",1984" <td>0.062500</td>\n",1985" </tr>\n",1986" <tr>\n",1987" <th>6</th>\n",1988" <td>ДВФО</td>\n",1989" <td>Владивосток</td>\n",1990" <td>40</td>\n",1991" <td>0.050000</td>\n",1992" </tr>\n",1993" <tr>\n",1994" <th>7</th>\n",1995" <td>ДВФО</td>\n",1996" <td>Владивосток</td>\n",1997" <td>120</td>\n",1998" <td>0.150000</td>\n",1999" </tr>\n",2000" <tr>\n",2001" <th>9</th>\n",2002" <td>ДВФО</td>\n",2003" <td>Владивосток</td>\n",2004" <td>200</td>\n",2005" <td>0.250000</td>\n",2006" </tr>\n",2007" <tr>\n",2008" <th>0</th>\n",2009" <td>СФО</td>\n",2010" <td>Новосибирск</td>\n",2011" <td>140</td>\n",2012" <td>0.250000</td>\n",2013" </tr>\n",2014" <tr>\n",2015" <th>3</th>\n",2016" <td>СФО</td>\n",2017" <td>Новосибирск</td>\n",2018" <td>95</td>\n",2019" <td>0.169643</td>\n",2020" </tr>\n",2021" <tr>\n",2022" <th>1</th>\n",2023" <td>СФО</td>\n",2024" <td>Томск</td>\n",2025" <td>135</td>\n",2026" <td>0.241071</td>\n",2027" </tr>\n",2028" <tr>\n",2029" <th>8</th>\n",2030" <td>СФО</td>\n",2031" <td>Томск</td>\n",2032" <td>190</td>\n",2033" <td>0.339286</td>\n",2034" </tr>\n",2035" </tbody>\n",2036"</table>\n",2037"</div>"2038],2039"text/plain": [2040" region city sales share\n",2041"2 ДВФО Владивосток 290 0.362500\n",2042"4 ДВФО Владивосток 100 0.125000\n",2043"5 ДВФО Владивосток 50 0.062500\n",2044"6 ДВФО Владивосток 40 0.050000\n",2045"7 ДВФО Владивосток 120 0.150000\n",2046"9 ДВФО Владивосток 200 0.250000\n",2047"0 СФО Новосибирск 140 0.250000\n",2048"3 СФО Новосибирск 95 0.169643\n",2049"1 СФО Томск 135 0.241071\n",2050"8 СФО Томск 190 0.339286"2051]2052},2053"execution_count": 63,2054"metadata": {},2055"output_type": "execute_result"2056}2057],2058"source": [2059"df.groupby('region').apply(share).sort_values(['region', 'city'])"2060]2061},2062{2063"cell_type": "code",2064"execution_count": 64,2065"metadata": {},2066"outputs": [2067{2068"data": {2069"text/html": [2070"<div>\n",2071"<style scoped>\n",2072" .dataframe tbody tr th:only-of-type {\n",2073" vertical-align: middle;\n",2074" }\n",2075"\n",2076" .dataframe tbody tr th {\n",2077" vertical-align: top;\n",2078" }\n",2079"\n",2080" .dataframe thead th {\n",2081" text-align: right;\n",2082" }\n",2083"</style>\n",2084"<table border=\"1\" class=\"dataframe\">\n",2085" <thead>\n",2086" <tr style=\"text-align: right;\">\n",2087" <th></th>\n",2088" <th>region</th>\n",2089" <th>city</th>\n",2090" <th>sales</th>\n",2091" <th>share</th>\n",2092" </tr>\n",2093" </thead>\n",2094" <tbody>\n",2095" <tr>\n",2096" <th>2</th>\n",2097" <td>ДВФО</td>\n",2098" <td>Владивосток</td>\n",2099" <td>290</td>\n",2100" <td>0.362500</td>\n",2101" </tr>\n",2102" <tr>\n",2103" <th>4</th>\n",2104" <td>ДВФО</td>\n",2105" <td>Владивосток</td>\n",2106" <td>100</td>\n",2107" <td>0.125000</td>\n",2108" </tr>\n",2109" <tr>\n",2110" <th>5</th>\n",2111" <td>ДВФО</td>\n",2112" <td>Владивосток</td>\n",2113" <td>50</td>\n",2114" <td>0.062500</td>\n",2115" </tr>\n",2116" <tr>\n",2117" <th>6</th>\n",2118" <td>ДВФО</td>\n",2119" <td>Владивосток</td>\n",2120" <td>40</td>\n",2121" <td>0.050000</td>\n",2122" </tr>\n",2123" <tr>\n",2124" <th>7</th>\n",2125" <td>ДВФО</td>\n",2126" <td>Владивосток</td>\n",2127" <td>120</td>\n",2128" <td>0.150000</td>\n",2129" </tr>\n",2130" <tr>\n",2131" <th>9</th>\n",2132" <td>ДВФО</td>\n",2133" <td>Владивосток</td>\n",2134" <td>200</td>\n",2135" <td>0.250000</td>\n",2136" </tr>\n",2137" <tr>\n",2138" <th>0</th>\n",2139" <td>СФО</td>\n",2140" <td>Новосибирск</td>\n",2141" <td>140</td>\n",2142" <td>0.595745</td>\n",2143" </tr>\n",2144" <tr>\n",2145" <th>3</th>\n",2146" <td>СФО</td>\n",2147" <td>Новосибирск</td>\n",2148" <td>95</td>\n",2149" <td>0.404255</td>\n",2150" </tr>\n",2151" <tr>\n",2152" <th>1</th>\n",2153" <td>СФО</td>\n",2154" <td>Томск</td>\n",2155" <td>135</td>\n",2156" <td>0.415385</td>\n",2157" </tr>\n",2158" <tr>\n",2159" <th>8</th>\n",2160" <td>СФО</td>\n",2161" <td>Томск</td>\n",2162" <td>190</td>\n",2163" <td>0.584615</td>\n",2164" </tr>\n",2165" </tbody>\n",2166"</table>\n",2167"</div>"2168],2169"text/plain": [2170" region city sales share\n",2171"2 ДВФО Владивосток 290 0.362500\n",2172"4 ДВФО Владивосток 100 0.125000\n",2173"5 ДВФО Владивосток 50 0.062500\n",2174"6 ДВФО Владивосток 40 0.050000\n",2175"7 ДВФО Владивосток 120 0.150000\n",2176"9 ДВФО Владивосток 200 0.250000\n",2177"0 СФО Новосибирск 140 0.595745\n",2178"3 СФО Новосибирск 95 0.404255\n",2179"1 СФО Томск 135 0.415385\n",2180"8 СФО Томск 190 0.584615"2181]2182},2183"execution_count": 64,2184"metadata": {},2185"output_type": "execute_result"2186}2187],2188"source": [2189"df.groupby(['region', 'city']).apply(share).sort_values(['region', 'city'])"2190]2191}2192],2193"metadata": {2194"kernelspec": {2195"display_name": "Python 3 (ipykernel)",2196"language": "python",2197"name": "python3"2198},2199"language_info": {2200"codemirror_mode": {2201"name": "ipython",2202"version": 32203},2204"file_extension": ".py",2205"mimetype": "text/x-python",2206"name": "python",2207"nbconvert_exporter": "python",2208"pygments_lexer": "ipython3",2209"version": "3.9.13"2210}2211},2212"nbformat": 4,2213"nbformat_minor": 42214}2215