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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()) | |
| if _min == _max: | |
| _max += 1 | |
| step = 1 | |
| else: | |
| _min = float(df[column].min()) | |
| _max = float(df[column].max()) | |
| if _min == _max: | |
| _max += 0.1 | |
| 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 | |