app_explore_df.py

import pandas as pd
import streamlit as st
from pandas.api.types import (
    is_categorical_dtype,
    is_datetime64_any_dtype,
    is_numeric_dtype,
    is_object_dtype,
)
import glob
import polars as pl


from utils import describe_numeric

st.set_page_config(layout="wide", page_title='Explore Table')


def filter_dataframe(df: pd.DataFrame) -> 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
    """
    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
            if is_categorical_dtype(df[column]) or df[column].nunique() < 10:
                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,
                    _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


tab0, tab1, tab2 = st.tabs(['File', 'Table', 'Summary'])


with tab0:
    file_path_txt = st.text_input("File path:")

df = None

with tab1:
    if file_path_txt:

        if file_path_txt.endswith('.csv'):
            df = pd.read_csv(file_path_txt)
        else:
            files_ = glob.glob(f'{file_path_txt}/*.csv')
            df = pl.concat([pl.read_csv(f) for f in files_]).to_pandas()

        if 'smape_avg_val' in df.columns and 'smape_avg_test' in df.columns:
            df['diff_smape_val_test'] = df['smape_avg_val'] - df['smape_avg_test']

        st.dataframe(filter_dataframe(df))

with tab2:
    if df is not None:
        st.table(describe_numeric(df))


# streamlit run app_explore_df.py --server.port 8001