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# https://blog.streamlit.io/auto-generate-a-dataframe-filtering-ui-in-streamlit-with-filter_dataframe/
import streamlit.components.v1 as components
import pandas as pd
import streamlit as st
from pandas.api.types import (
is_categorical_dtype,
is_datetime64_any_dtype,
is_numeric_dtype,
is_integer_dtype,
is_object_dtype,
)
def filter_dataframe(df: pd.DataFrame, numeric_as_categorical: bool = True) -> pd.DataFrame:
"""
Adds a UI on top of a dataframe to let viewers filter columns
Args:
df (pd.DataFrame): Original dataframe
numeric_as_categorical (bool, optional): Whether to treat numeric columns with low number of unique values as categorical. Defaults to True.
Returns:
pd.DataFrame: Filtered dataframe
"""
modify = st.checkbox("Add 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)
for column in to_filter_columns:
left, right = st.columns((1, 20))
left.write("↳")
# Treat columns with < 10 unique values as categorical
low_nunique = df[column].nunique() < 10
is_categorical = is_categorical_dtype(df[column])
is_numeric = is_numeric_dtype(df[column])
treat_as_categorical = False
if is_categorical:
treat_as_categorical = True
elif low_nunique:
if is_numeric:
treat_as_categorical = numeric_as_categorical
else:
treat_as_categorical = True
if treat_as_categorical:
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:
if is_integer_dtype(df[column]):
_min = int(df[column].min())
_max = int(df[column].max())
step = 1
else:
_min = float(df[column].min())
_max = float(df[column].max())
step = (_max - _min) / 100
user_num_input = right.slider(
f"Values for {column}",
_min,
_max,
(_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 len(user_date_input) == 2:
user_date_input = tuple(map(pd.to_datetime, user_date_input))
start_date, end_date = user_date_input
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].str.contains(user_text_input)]
return df
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