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Python Pandas For Your Grandpa - 4.10 Challenge: OB-GYM

2021-02-01 496 words 3 minutes

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Setup

You own a gym for pregnant women called “OB-GYM” and you recently opened a second location. You’d like to analyze its performance, but your reporting software has given you the sales data in an awkward format. Reshape it into a DataFrame like this

#               sales_1  sales_2  members_1  members_2
# date
# 2020-01-01    737.54    629.00         22         20
# 2020-01-02    750.75    699.01         23         22
# 2020-01-03    750.60    640.20         20         24
# 2020-01-04    752.65    695.64         21         22
# 2020-01-05    747.02    632.40         20         22

import numpy as np
import pandas as pd

generator = np.random.default_rng(314)

sales = pd.DataFrame({
    'date':pd.date_range(start = '2020-01-01', periods=5).repeat(2),
    'store_id':np.tile([1,2], 5),
    'sales1':np.round(generator.normal(loc=750, scale=20, size=10), 2),
    'sales2':np.round(generator.normal(loc=650, scale=40, size=10), 2),
    'members':generator.integers(low=20, high=25, size=10)
})
sales.loc[sales.store_id == 2, 'sales1'] = np.nan
sales.loc[sales.store_id == 1, 'sales2'] = np.nan
print(sales)
##         date  store_id  sales1  sales2  members
## 0 2020-01-01         1  737.54     NaN       22
## 1 2020-01-01         2     NaN  629.00       20
## 2 2020-01-02         1  750.75     NaN       23
## 3 2020-01-02         2     NaN  699.01       22
## 4 2020-01-03         1  750.60     NaN       20
## 5 2020-01-03         2     NaN  640.20       24
## 6 2020-01-04         1  752.65     NaN       21
## 7 2020-01-04         2     NaN  695.64       22
## 8 2020-01-05         1  747.02     NaN       20
## 9 2020-01-05         2     NaN  632.40       22

Solution 1

sol1 = sales.pivot(index='date', columns='store_id').dropna(axis=1)
sol1.columns = [f'{x[:-1]}_{y}' if x[-1] in ('1', '2') else f'{x}_{y}' for x,y in sol1.columns]
print(sol1)
##             sales_1  sales_2  members_1  members_2
## date                                              
## 2020-01-01   737.54   629.00         22         20
## 2020-01-02   750.75   699.01         23         22
## 2020-01-03   750.60   640.20         20         24
## 2020-01-04   752.65   695.64         21         22
## 2020-01-05   747.02   632.40         20         22

Solution 2

sol2 = sales.set_index(['date','store_id']).unstack().dropna(axis=1)
sol2.columns = [f'{x[:-1]}_{y}' if x[-1] in ('1', '2') else f'{x}_{y}' for x,y in sol2.columns]
print(sol2)
##             sales_1  sales_2  members_1  members_2
## date                                              
## 2020-01-01   737.54   629.00         22         20
## 2020-01-02   750.75   699.01         23         22
## 2020-01-03   750.60   640.20         20         24
## 2020-01-04   752.65   695.64         21         22
## 2020-01-05   747.02   632.40         20         22

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