问题
Hello Data Scientist and Pandas Experts,
I need some help as I can’t get my data organized properly. Here is my data frame:
df_dict = [ {'Date': Timestamp('2014-01-03 00:00:00'), 'Store': 'store1', 'employee': 'emp1', 'duties': 'opening'}, \
{'Date': Timestamp('2014-01-03 00:00:00'), 'Store': 'store1', 'employee': 'emp2', 'duties': 'deli'}, \
{'Date': Timestamp('2014-01-03 00:00:00'), 'Store': 'store1', 'employee': 'emp3', 'duties': 'cashier'},\
{'Date': Timestamp('2014-01-03 00:00:00'), 'Store': 'store1', 'employee': 'emp2', 'duties': 'closing'},\
{'Date': Timestamp('2014-01-03 00:00:00'), 'Store': 'store2', 'employee': 'emp1', 'duties': 'closing'},\
{'Date': Timestamp('2014-01-03 00:00:00'), 'Store': 'store2', 'employee': 'emp4', 'duties': 'opening'},\
{'Date': Timestamp('2014-01-03 00:00:00'), 'Store': 'store2', 'employee': 'emp4', 'duties': 'cashier'},\
{'Date': Timestamp('2014-01-03 00:00:00'), 'Store': 'store2', 'employee': 'emp5', 'duties': 'deli'},\
{'Date': Timestamp('2014-01-03 00:00:00'), 'Store': 'store3', 'employee': 'emp2', 'duties': 'closing'},\
{'Date': Timestamp('2014-01-03 00:00:00'), 'Store': 'store3', 'employee': 'emp6', 'duties': 'opening'},\
{'Date': Timestamp('2014-01-03 00:00:00'), 'Store': 'store3', 'employee': 'emp7', 'duties': 'cashier'},\
{'Date': Timestamp('2014-01-03 00:00:00'), 'Store': 'store3', 'employee': 'emp6', 'duties': 'deli'},\
{'Date': Timestamp('2014-01-04 00:00:00'), 'Store': 'store1', 'employee': 'emp1', 'duties': 'opening'},\
{'Date': Timestamp('2014-01-04 00:00:00'), 'Store': 'store1', 'employee': 'emp2', 'duties': 'deli'},\
{'Date': Timestamp('2014-01-04 00:00:00'), 'Store': 'store1', 'employee': 'emp3', 'duties': 'cashier'},\
{'Date': Timestamp('2014-01-04 00:00:00'), 'Store': 'store1', 'employee': 'emp2', 'duties': 'closing'},\
{'Date': Timestamp('2014-01-04 00:00:00'), 'Store': 'store2', 'employee': 'emp1', 'duties': 'closing'},\
{'Date': Timestamp('2014-01-04 00:00:00'), 'Store': 'store2', 'employee': 'emp4', 'duties': 'opening'},\
{'Date': Timestamp('2014-01-04 00:00:00'), 'Store': 'store2', 'employee': 'emp4', 'duties': 'cashier'},\
{'Date': Timestamp('2014-01-04 00:00:00'), 'Store': 'store2', 'employee': 'emp5', 'duties': 'deli'},\
{'Date': Timestamp('2014-01-04 00:00:00'), 'Store': 'store3', 'employee': 'emp2', 'duties': 'closing'},\
{'Date': Timestamp('2014-01-04 00:00:00'), 'Store': 'store3', 'employee': 'emp6', 'duties': 'opening'},\
{'Date': Timestamp('2014-01-04 00:00:00'), 'Store': 'store3', 'employee': 'emp7', 'duties': 'cashier'},\
{'Date': Timestamp('2014-01-04 00:00:00'), 'Store': 'store3', 'employee': 'emp6', 'duties': 'deli'},\
{'Date': Timestamp('2014-01-10 00:00:00'), 'Store': 'store1', 'employee': 'emp1', 'duties': 'opening'},\
{'Date': Timestamp('2014-01-10 00:00:00'), 'Store': 'store1', 'employee': 'emp2', 'duties': 'deli'},\
{'Date': Timestamp('2014-01-10 00:00:00'), 'Store': 'store1', 'employee': 'emp3', 'duties': 'cashier'},\
{'Date': Timestamp('2014-01-10 00:00:00'), 'Store': 'store1', 'employee': 'emp2', 'duties': 'closing'},\
{'Date': Timestamp('2014-01-10 00:00:00'), 'Store': 'store2', 'employee': 'emp1', 'duties': 'closing'},\
{'Date': Timestamp('2014-01-10 00:00:00'), 'Store': 'store2', 'employee': 'emp4', 'duties': 'opening'},\
{'Date': Timestamp('2014-01-10 00:00:00'), 'Store': 'store2', 'employee': 'emp4', 'duties': 'cashier'},\
{'Date': Timestamp('2014-01-10 00:00:00'), 'Store': 'store2', 'employee': 'emp5', 'duties': 'deli'},\
{'Date': Timestamp('2014-01-10 00:00:00'), 'Store': 'store3', 'employee': 'emp2', 'duties': 'closing'},\
{'Date': Timestamp('2014-01-10 00:00:00'), 'Store': 'store3', 'employee': 'emp6', 'duties': 'opening'},\
{'Date': Timestamp('2014-01-10 00:00:00'), 'Store': 'store3', 'employee': 'emp7', 'duties': 'cashier'},\
{'Date': Timestamp('2014-01-10 00:00:00'), 'Store': 'store3', 'employee': 'emp6', 'duties': 'deli'}]
I want to organize my output as follow:
Store 1 Store 2 store3
Week emp1 emp2 emp3 emp1 emp4 emp5 emp2 emp6 emp7
2013-12-30 2 4 2 2 4 2 2 4 2
2014-01-06 1 1 1 1 1 1 2 1 1
So I have tried following Group by expression:
df_group = dict_df.groupby([pd.Grouper(key='Date', freq='W-MON'), 'Store', 'employee'])\
['duties'].count().unstack(level=1).unstack(level=1).reset_index()
However it shows all employee instead of showing employees work in that particular store example:
Store 1
Week emp1 emp2 emp3 emp4 emp5 emp6 emp7
2013-12-30 2 4 2 NaN NaN NaN NaN
2014-01-06 1 1 1 NaN NaN NaN NaN
So how can I get my desire outcome. Basically I want to filter out the employees who are not working in that store.
Is it better to use Groupby for this need or should I consider some other method?
Thank you in advance for your help and consideration.
回答1:
Try to unstack multiple levels [1, 2]:
df_out = (df.groupby([pd.Grouper(key='Date', freq='W-MON'), 'Store', 'employee'])['duties']
.count()
.unstack(level=[1, 2])
)
print(df_out)
Prints:
Store store1 store2 store3
employee emp1 emp2 emp3 emp1 emp4 emp5 emp2 emp6 emp7
Date
2014-01-06 2 4 2 2 4 2 2 4 2
2014-01-13 1 2 1 1 2 1 1 2 1
回答2:
You can unstack both levels at once:
(df.groupby([pd.Grouper(key='Date', freq='W-MON'), 'Store','employee'])
.size().unstack(['Store','employee'])
)
Output:
Store store1 store2 store3
employee emp1 emp2 emp3 emp1 emp4 emp5 emp2 emp6 emp7
Date
2014-01-06 2 4 2 2 4 2 2 4 2
2014-01-13 1 2 1 1 2 1 1 2 1
来源：https://stackoverflow.com/questions/64487049/how-to-show-only-column-with-values-in-pandas-groupby