st_table.py

# Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022-2024)
#
# 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.

import random

import numpy as np
import pandas as pd

import streamlit as st
from shared.data_mocks import (
    BASE_TYPES_DF,
    DATETIME_TYPES_DF,
    INTERVAL_TYPES_DF,
    LIST_TYPES_DF,
    NUMBER_TYPES_DF,
    PERIOD_TYPES_DF,
    SPECIAL_TYPES_DF,
    UNSUPPORTED_TYPES_DF,
)

np.random.seed(0)
random.seed(0)

st.set_page_config(layout="wide")

st.header("Empty tables")
st.table()
st.table([])
st.table(np.array(0))
st.table(pd.DataFrame([]))
st.table(np.array([]))
st.table(pd.DataFrame({"lat": [], "lon": []}))

st.header("Column types")

st.subheader("Base types")
st.table(BASE_TYPES_DF)

st.subheader("Number types")
st.table(NUMBER_TYPES_DF)

st.subheader("Date, time and datetime types")
st.table(DATETIME_TYPES_DF)

st.subheader("List types")
st.table(LIST_TYPES_DF)

st.subheader("Interval dtypes in pd.DataFrame")
st.table(INTERVAL_TYPES_DF)

st.subheader("Period dtypes in pd.DataFrame")
st.table(PERIOD_TYPES_DF)

st.subheader("Special types")
st.table(SPECIAL_TYPES_DF)

st.subheader("Unsupported types (by Arrow)")
st.table(UNSUPPORTED_TYPES_DF)

st.header("Index types")

st.subheader("String Index (pd.Index)")
st.table(BASE_TYPES_DF.set_index("string"))

st.subheader("Float64 Index (pd.Float64Index)")
st.table(NUMBER_TYPES_DF.set_index("float64"))

st.subheader("Int64 Index (pd.Int64Index)")
st.table(NUMBER_TYPES_DF.set_index("int64"))

st.subheader("Uint64 Index (pd.UInt64Index)")
st.table(NUMBER_TYPES_DF.set_index("uint64"))

st.subheader("Datetime Index (pd.DatetimeIndex)")
st.table(DATETIME_TYPES_DF.set_index("datetime"))

st.subheader("Date Index (pd.Index)")
st.table(DATETIME_TYPES_DF.set_index("date"))

st.subheader("Time Index (pd.Index)")
st.table(DATETIME_TYPES_DF.set_index("time"))

st.subheader("Interval Index (pd.IntervalIndex)")
st.table(INTERVAL_TYPES_DF.set_index("int64_both"))

st.subheader("List Index (pd.Index)")
st.table(LIST_TYPES_DF.set_index("string_list"))

st.subheader("Multi Index (pd.MultiIndex)")
st.table(BASE_TYPES_DF.set_index(["string", "int64"]))

st.subheader("Categorical Index (pd.CategoricalIndex)")
st.table(SPECIAL_TYPES_DF.set_index("categorical"))

st.subheader("Period Index (pd.PeriodIndex)")
st.table(PERIOD_TYPES_DF.set_index("D"))

st.subheader("Timedelta Index (pd.TimedeltaIndex)")
st.table(SPECIAL_TYPES_DF.set_index("timedelta"))

st.header("Pandas Styler Support")

st.subheader("Pandas Styler: Value formatting")
df = pd.DataFrame({"test": [3.1423424, 3.1]})
st.table(df.style.format({"test": "{:.2f}"}))

st.subheader("Pandas Styler: Background color")


def highlight_first(value):
    return "background-color: yellow" if value == 0 else ""


df = pd.DataFrame(np.arange(0, 100, 1).reshape(10, 10))
st.table(df.style.map(highlight_first))

st.subheader("Pandas Styler: Background and font styling")

df = pd.DataFrame(np.random.randn(10, 4), columns=["A", "B", "C", "D"])


def style_negative(v, props=""):
    return props if v < 0 else None


def highlight_max(s, props=""):
    return np.where(s == np.nanmax(s.values), props, "")


# Passing style values w/ all color formats to test css-style-string parsing robustness.
styled_df = df.style.map(style_negative, props="color:#FF0000;").map(
    lambda v: "opacity: 20%;" if (v < 0.3) and (v > -0.3) else None
)

styled_df.apply(
    highlight_max, props="color:white;background-color:rgb(255, 0, 0)", axis=0
)

styled_df.apply(
    highlight_max, props="color:white;background-color:hsl(273, 98%, 60%);", axis=1
).apply(highlight_max, props="color:white;background-color:purple", axis=None)

st.table(styled_df)

st.subheader("Pandas Styler: Gradient Styling + Caption")

weather_df = pd.DataFrame(
    np.random.rand(10, 2) * 5,
    index=pd.date_range(start="2021-01-01", periods=10),
    columns=["Tokyo", "Beijing"],
)


def rain_condition(v):
    if v < 1.75:
        return "Dry"
    elif v < 2.75:
        return "Rain"
    return "Heavy Rain"


def make_pretty(styler):
    styler.set_caption("Weather Conditions")
    styler.format(rain_condition)
    styler.background_gradient(axis=None, vmin=1, vmax=5, cmap="YlGnBu")
    return styler


styled_df = weather_df.style.pipe(make_pretty)

st.table(styled_df)