e2e_playwright/st_area_chart.py - develop - vshmidt/streamlit

e2e_playwright/st_area_chart.py
101 строка3 KB
Ken McGrady
Update copyright header to 2026 (#13491)
02 янв 2026, 16:21
02 янв 2026, 16:21374540a
# Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022-2026)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from datetime import date

import numpy as np
import pandas as pd
from vega_datasets import data as vega_data

import streamlit as st

np.random.seed(0)


data = np.random.randn(20, 3)
df = pd.DataFrame(data, columns=["a", "b", "c"])

# st.area/bar/line_chart all use Altair/Vega-Lite under the hood.
# By default, Vega-Lite displays time values in the browser's local
# time zone, but data is sent down to the browser as UTC. This means
# Times need to be set correctly to the users timezone.
utc_df = pd.DataFrame(
    {
        "index": [
            date(2019, 8, 9),
            date(2019, 8, 10),
            date(2019, 8, 11),
            date(2019, 8, 12),
        ],
        "numbers": [10, 50, 30, 40],
    }
)

utc_df.set_index("index", inplace=True)

# Dataframe to test the color parameter support:
N = 100

color_df = pd.DataFrame(
    {
        # Using a negative range so certain kinds of bugs are more visible.
        "a": -np.arange(N),
        "b": np.random.rand(N) * 10,
        "c": np.random.rand(N) * 10,
        "d": np.random.randn(N) * 30,
        "e": ["bird" if x % 2 else "airplane" for x in range(N)],
    }
)

st.header("Area Chart")

st.area_chart()
st.area_chart(df)
st.area_chart(df, x="a")
st.area_chart(df, y="a")
st.area_chart(df, y=["a", "b"])
st.area_chart(df, x="a", y="b", height=500, width=300)
st.area_chart(df, x="b", y="a")
st.area_chart(df, x="a", y=["b", "c"])
st.area_chart(utc_df)
st.area_chart(color_df, x="a", y="b", color="e")
st.area_chart(df, x_label="X Axis Label", y_label="Y Axis Label")

# Additional tests for stacking options
np.random.seed(5)
df = pd.DataFrame(np.random.randn(20, 3), columns=["a", "b", "c"])
st.area_chart(df, color=["#ffaa00", "#3399ff", "#009900"], stack=False)
source = vega_data.unemployment_across_industries()
st.area_chart(source, x="date", y="count", color="series", stack=True)
st.area_chart(source, x="date", y="count", color="series", stack="normalize")
st.area_chart(source, x="date", y="count", color="series", stack="center")

# Test that add_rows maintains original styling params:
# color, width, height, horizontal, stack
area_data = pd.DataFrame({"Area 1": [], "Area 2": []})

empty_area = st.area_chart(
    area_data,
    y=["Area 1", "Area 2"],
    color=["#800080", "#0000FF"],  # Purple and Blue
    width=600,
    height=300,
    stack="center",
)

if st.button("Add data to Area Chart"):
    new_data = pd.DataFrame(
        {"Area 1": np.abs(np.random.randn(10)), "Area 2": np.abs(np.random.randn(10))}
    )

    empty_area.add_rows(new_data)