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# https://blog.streamlit.io/auto-generate-a-dataframe-filtering-ui-in-streamlit-with-filter_dataframe/
from pandas.api.types import (
is_categorical_dtype,
is_datetime64_any_dtype,
is_numeric_dtype,
is_object_dtype,
)
import pandas as pd
import streamlit as st
def filter_dataframe(df: pd.DataFrame, force_on: bool = False, force_on_columns: list[str] = []) -> pd.DataFrame:
"""
Adds a UI on top of a dataframe to let viewers filter columns
Args:
df (pd.DataFrame): Original dataframe
Returns:
pd.DataFrame: Filtered dataframe
"""
if force_on:
modify = True
else:
modify = st.checkbox("Add more filters")
if not modify:
return df
df = df.copy()
# Try to convert datetimes into a standard format (datetime, no timezone)
for col in df.columns:
if is_object_dtype(df[col]):
try:
df[col] = pd.to_datetime(df[col])
except Exception:
pass
if is_datetime64_any_dtype(df[col]):
df[col] = df[col].dt.tz_localize(None)
modification_container = st.container()
with modification_container:
to_filter_columns = st.multiselect("Filter dataframe on", df.columns) + force_on_columns
for column in to_filter_columns:
left, right = st.columns((1, 20))
# Treat columns with < 17 unique values as categorical
if is_categorical_dtype(df[column]) or df[column].nunique() < 50:
user_cat_input = right.multiselect(
f"Values for {column}",
df[column].unique(),
default=list(df[column].unique()),
)
df = df[df[column].isin(user_cat_input)]
elif is_numeric_dtype(df[column]):
_min = float(df[column].min())
_max = float(df[column].max())
step = (_max - _min) / 100
user_num_input = right.slider(
f"Values for {column}",
min_value=_min,
max_value=_max,
value=(_min, _max),
step=step,
)
df = df[df[column].between(*user_num_input)]
elif is_datetime64_any_dtype(df[column]):
user_date_input = right.date_input(
f"Values for {column}",
value=(
df[column].min(),
df[column].max(),
),
)
if isinstance(user_date_input, tuple):
if len(user_date_input) == 2:
user_date_input_dt = tuple(map(pd.to_datetime, user_date_input))
start_date, end_date = user_date_input_dt
df = df.loc[df[column].between(start_date, end_date)]
else:
user_text_input = right.text_input(
f"Substring or regex in {column}",
)
if user_text_input:
df = df[df[column].astype(str).str.contains(user_text_input)]
return df
def get_multiselect_for_df_column(df: pd.DataFrame, column_name: str) -> list:
options_list = sorted(df[column_name].unique().tolist())
if len(options_list) > 1:
selected = (
st.multiselect(column_name.title(), options_list, placeholder=f"Select a {column_name} to filter")
or options_list
)
else:
selected = options_list
return selected
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