withdraw_functions.py

import datetime
from typing import List

import gspread
import pandas as pd
import streamlit as st
from gspread_dataframe import get_as_dataframe


def load_withdraws():
    sheet_credentials = st.secrets["sheet_credentials"]
    gc = gspread.service_account_from_dict(sheet_credentials)

    pepper_workbook = gc.open_by_key(st.secrets["sheet_key"])
    deposits_sheet = pepper_workbook.worksheet("Withdraws")
    withdraw_df = get_as_dataframe(deposits_sheet, parse_dates=True)

    withdraw_df.drop(columns=["Timestamp"], inplace=True)
    withdraw_df['Date'] = pd.to_datetime(withdraw_df['Date'], format='%d/%m/%y')

    result = withdraw_df.dropna(how='all')

    return result


def format_date(input_date):
    # Convert the input string to a datetime object
    # date_object = datetime.strptime(input_date, '%d/%m/%y')

    # Format the datetime object as required (8/Oct/2023)
    formatted_date = input_date.strftime('%d/%b/%Y')

    return formatted_date


def process_withdraw_month(withdraw_df, month):
    # Ensure the Date column is in datetime format
    withdraw_df['Date'] = pd.to_datetime(withdraw_df['Date'], format='%d/%b/%Y')

    withdraw_df['Month'] = withdraw_df['Date'].dt.month_name()

    # Filter the DataFrame for the given month
    filtered_df = withdraw_df[withdraw_df['Month'] == month]

    # Sort the filtered DataFrame by Date in descending order
    filtered_df = filtered_df.sort_values(by='Date', ascending=False)

    # Reset the index and drop the old index
    filtered_df = filtered_df.reset_index(drop=True)
    filtered_df.index += 1

    # Drop the temporary 'Month' column
    filtered_df = filtered_df.drop(columns=['Month'])
    filtered_df["Date"] = filtered_df["Date"].apply(format_date)

    return filtered_df


def display_expander(month, month_df):
    ttl_withdraw = month_df["Amount"].sum()
    formatted_ttl_withdraw = "{:,.0f}".format(ttl_withdraw)

    with st.expander(f'{month} - {formatted_ttl_withdraw} ugx'):
        st.dataframe(month_df, use_container_width=True)


def filter_data(data: pd.DataFrame, filter_name: str, values: List[str]) -> pd.DataFrame:
    if not values:
        return data

    if filter_name == "years":
        data = data[data['Date'].dt.year.isin(values)]

    if filter_name == "months":
        data = data[data['Date'].dt.month.isin(values)]

    if filter_name == "start_date":
        date_string = str(values)
        formatted_start_date = datetime.datetime.strptime(date_string, "%Y-%m-%d").strftime("%d/%m/%Y")
        data = data[data['Date'] >= formatted_start_date]

    if filter_name == "end_date":
        date_string = str(values)
        formatted_start_date = datetime.datetime.strptime(date_string, "%Y-%m-%d").strftime("%d/%m/%Y")
        data = data[data['Date'] <= formatted_start_date]

    return data