from typing import List

import pandas as pd
import streamlit as st


def by_gender(
    df: pd.DataFrame,
    artist: str,
    territory: List[str],
    gender: List[str],
    possible_genders: List[str],
) -> None:
    """
    Function to show filtered gender group basket data
    :param df: Dataframe containing data
    :param artist: used for filtering specific artist data
    :param territory: used for filtering specific territories
    :param gender: List of gender values to filter on
    :param possible_genders: List of all available values
    :return: None
    """
    if not gender:
        gender = possible_genders
    gender_input = df[
        (df["ARTIST"] == artist) & (df["TERRITORY"].isin(territory)) & (df["GENDER_C"].isin(gender))
    ]
    st.dataframe(
        gender_input.groupby(["PRODUCT_NAME", "GENDER_C"])["TOTAL_PRODUCTS"]
        .sum()
        .reset_index()
        .pivot(index="PRODUCT_NAME", columns="GENDER_C", values="TOTAL_PRODUCTS")
    )


def by_age_group(
    df: pd.DataFrame,
    artist: str,
    territory: List[str],
    age_group: List[str],
    possible_ages: List[str],
) -> None:
    """
    Function to show filtered age group basket data
    :param df: Dataframe containing data
    :param artist: used for filtering specific artist data
    :param territory: used for filtering specific territories
    :param age_group: List of age group values to filter on
    :param possible_ages: List of all available values
    :return: None
    """
    if not age_group:
        age_group = possible_ages
    age_input = df[
        (df["ARTIST"] == artist)
        & (df["TERRITORY"].isin(territory))
        & (df["AGE_RANGE_C"].isin(age_group))
    ]
    st.dataframe(
        age_input.groupby(["PRODUCT_NAME", "AGE_RANGE_C"])["TOTAL_PRODUCTS"]
        .sum()
        .reset_index()
        .pivot(index="PRODUCT_NAME", columns="AGE_RANGE_C", values="TOTAL_PRODUCTS")
    )