23 lines
1.0 KiB
Python
23 lines
1.0 KiB
Python
import pandas
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import pandas as pd
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def analysCustomersDataFrame(df: pandas.DataFrame):
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df['Size'] = df['Store_Area'].apply(lambda x: 'Small' if x <= 1100 else ('Average' if 1100 < x <= 1800 else 'Big'))
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table_one = df.query("Size == 'Small'").groupby('Size')
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table_two = df.query("Size == 'Average'").groupby('Size')
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table_three = df.query("Size == 'Big'").groupby('Size')
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minMaxMean_one = table_one.agg({'Daily_Customer_Count': ['min', 'max', 'mean']}).round(2).reset_index()
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minMaxMean_two = table_two.agg({'Daily_Customer_Count': ['min', 'max', 'mean']}).round(2).reset_index()
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minMaxMean_three = table_three.agg({'Daily_Customer_Count': ['min', 'max', 'mean']}).round(2).reset_index()
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totalTable = pd.merge(minMaxMean_one, minMaxMean_two, left_index=True, right_index=True)
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totalTable = pd.merge(totalTable, minMaxMean_three, left_index=True, right_index=True)
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# for data in roundedListShops.items():
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# roundedListShops.loc[data[0], 'Store_Area'] = (data[1].astype("Int64")/100)*100
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return totalTable |