import pandas
import pandas as pd

def analysCustomersDataFrame(df: pandas.DataFrame):

    df['Size'] = df['Store_Area'].apply(lambda x: 'Small' if x <= 1100 else ('Average' if 1100 < x <= 1800 else 'Big'))

    table_one = df.query("Size == 'Small'").groupby('Size')
    table_two = df.query("Size == 'Average'").groupby('Size')
    table_three = df.query("Size == 'Big'").groupby('Size')

    minMaxMean_one = table_one.agg({'Daily_Customer_Count': ['min', 'max', 'mean']}).round(2).reset_index()
    minMaxMean_two = table_two.agg({'Daily_Customer_Count': ['min', 'max', 'mean']}).round(2).reset_index()
    minMaxMean_three = table_three.agg({'Daily_Customer_Count': ['min', 'max', 'mean']}).round(2).reset_index()

    totalTable = pd.merge(minMaxMean_one, minMaxMean_two, left_index=True, right_index=True)

    totalTable = pd.merge(totalTable, minMaxMean_three, left_index=True, right_index=True)

    # for data in roundedListShops.items():
    #     roundedListShops.loc[data[0], 'Store_Area'] = (data[1].astype("Int64")/100)*100

    return totalTable