streamlit

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# Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022-2024)
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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#     http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import time
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import altair as alt
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import numpy as np
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import pandas as pd
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import streamlit as st
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df = pd.DataFrame({"a": [1, 2], "b": [3, 4], "c": [5, 6]})
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table_element = st.table(df)
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dataframe_element = st.dataframe(df)
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chart_element_1 = st.line_chart()
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chart_element_2 = st.line_chart(df)
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# 4 identical charts, built in different ways.
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vega_element_1 = st.vega_lite_chart(
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    df,
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    {
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        "mark": {"type": "line", "point": True},
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        "encoding": {
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            "x": {"field": "a", "type": "quantitative"},
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            "y": {"field": "b", "type": "quantitative"},
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        },
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    },
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    use_container_width=True,
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)
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vega_element_2 = st.vega_lite_chart(
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    {
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        "datasets": {"foo": df},
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        "data": {"name": "foo"},
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        "mark": {"type": "line", "point": True},
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        "encoding": {
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            "x": {"field": "a", "type": "quantitative"},
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            "y": {"field": "b", "type": "quantitative"},
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        },
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    },
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    use_container_width=True,
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)
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vega_element_3 = st.vega_lite_chart(
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    {
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        "datasets": {"foo": df},
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        "data": {"name": "foo"},
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        "mark": {"type": "line", "point": True},
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        "encoding": {
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            "x": {"field": "a", "type": "quantitative"},
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            "y": {"field": "b", "type": "quantitative"},
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        },
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    },
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    use_container_width=True,
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)
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altair_element = st.altair_chart(
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    alt.Chart(df).mark_line(point=True).encode(x="a", y="b").interactive(),
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    use_container_width=True,
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)
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table_element.add_rows(df)
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dataframe_element.add_rows(df)
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chart_element_1.add_rows(df)
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chart_element_2.add_rows(df)
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vega_element_1.add_rows(df)
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vega_element_2.add_rows(df)
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vega_element_3.add_rows(foo=df)
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altair_element.add_rows(df)
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# The following example was failing due to an issue (#3653) in st.add_rows.
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# In the previous implementation of Quiver, we were mutating the Quiver element
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# in the addRows function, which prevented re-rendering of the line chart.
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# This example reproduces the issue, so that we don't repeat the same mistake
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# in the future.
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current_time = pd.to_datetime("08:00:00 2021-01-01", utc=True)
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simulation_step = pd.Timedelta(seconds=10)
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df1 = pd.DataFrame(data=[[current_time, 1]], columns=["t", "y"]).set_index("t")
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line_chart = st.line_chart(df1, use_container_width=True)
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for count in range(5):
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    current_time += simulation_step
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    df2 = pd.DataFrame(data=[[current_time, count]], columns=["t", "y"]).set_index("t")
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    line_chart.add_rows(df2)
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    time.sleep(0.25)
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# Test that `add_rows` errors out when the dataframe dimensions don't match.
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# This should show an error!
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dataframe_element = st.dataframe(df)
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dataframe_element.add_rows(np.abs(np.random.randn(1, 6)))
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