partial-solution.py

import pandas as pd
import os
import matplotlib.pyplot as plt
from datetime import datetime, date
from os.path import join
from os import listdir

def createdf() -> pd.DataFrame:
    firstdf = None
    for f in listdir("./data"):
        if f.endswith(".csv"):
            if firstdf is None:
                firstdf = pd.read_csv(join("./data", f))
            else:
                first_df = pd.concat([first_df, pd.read_csv(join("./data", f))])

    return first_df


def average_organic_price(df: pd.DataFrame) -> float:
    df_filtered = df[df.Type == 'Organic']
    return df_filtered['High Price'].mean()


def best_seller(df: pd.DataFrame, day: datetime, city: str) -> pd.Series:
    df_filtered = df[(df['City Name'] == city)]
    df_filtered['DateConverted'] = pd.to_datetime(df_filtered['Date'])
    df_dated = df_filtered[ df_filtered['DateConverted'] == day]
    result = df_dated[['Variety','High Price']].groupby('Variety').max()
    result_index = result['High Price']

    return result_index



def plot_prices(df: pd.DataFrame, city: str, variety: str) -> None:
    df_filtered = df[(df['City Name'] == city) & (df['Variety'] == variety)]
    df_grouped = df_filtered[['Low Price', 'Date']].groupby('Date').min()
    plot = df_grouped.plot(title=f'{city} LOW PRICES FOR {variety}', color='blue')
    plt.xticks(rotation=60)
    fig = plot.get_figure()
    fig.savefig('prices_plot.png', bbox_inches='tight')


if __name == "__main":
    df = create_df()
    print("Average organic price: ")
    print(average_organic_price(df))
    print("Best seller: ")
    dd = datetime(2016, 9, 24)
    best_seller_df = best_seller(df, dd, 'ATLANTA')
    print(best_seller_df)
    print(type(best_seller_df))
    print(best_seller_df.index)
    print("Plotting prices: ")
    plot_prices(df, 'ATLANTA', 'HOWDEN TYPE')