src/ecr.py

import os
import pandas as pd
import streamlit as st

from streamlit_filter import filter_dataframe


def load_adp() -> pd.DataFrame:
    df = pd.read_csv(r"https://raw.githubusercontent.com/dynastyprocess/data/master/files/db_fpecr_latest.csv")
    df["ranking_type"] = df["fp_page"].apply(lambda x: os.path.split(x)[-1].replace(".php", ""))
    return df


@st.cache_data(ttl=60 * 60 * 24)
def load_adp_data():
    # Merge ADP
    data = load_adp()
    ranking_type_list = sorted(list(data.ranking_type.unique()))
    return data, ranking_type_list


def filtered_ecr_dataframe(data: pd.DataFrame, ranking_type_list: list[str]):
    default_ix = ranking_type_list.index("ppr-superflex-cheatsheets")
    ranking_type_selected = st.selectbox("ECR Format:", ranking_type_list, index=default_ix)
    ranking_type_filter = data["ranking_type"] == ranking_type_selected

    is_advanced = st.checkbox("Show Advanced View")

    id_cols = [
        # "player_square_image_url",
        "player",
        "pos",
        "team",
    ]

    id_cols_advanced = [
        "bye",
        "player_owned_yahoo",
    ]

    adp_cols: list[str] = [
        "ecr",
    ]

    adp_cols_advanced = ["sd", "best", "worst"]

    if is_advanced:
        show_columns = id_cols + id_cols_advanced + adp_cols + adp_cols_advanced
    else:
        show_columns = id_cols + adp_cols

    data_filtered_by_ranking_type = data.loc[ranking_type_filter]
    latest_scrape_date = data_filtered_by_ranking_type.scrape_date.max()
    st.write(f"Scraped data as of: {latest_scrape_date}")

    filtered_data = filter_dataframe(data.loc[ranking_type_filter, show_columns])
    st.dataframe(
        filtered_data,
        hide_index=True,
        height=35 * (len(filtered_data) + 1) + 12,
        use_container_width=True,
        column_config={
            # "player_square_image_url": st.column_config.ImageColumn(label="", help="Player image"),
        },
    )

    st.write("Source: https://github.com/dynastyprocess/data")