Python 如何在Groupby中仅显示具有值的列
你好,数据科学家和熊猫专家 我需要一些帮助,因为我无法正确组织我的数据。 这是我的数据框:Python 如何在Groupby中仅显示具有值的列,python,pandas,pandas-groupby,pivot-table,Python,Pandas,Pandas Groupby,Pivot Table,你好,数据科学家和熊猫专家 我需要一些帮助,因为我无法正确组织我的数据。 这是我的数据框: 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':
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'}]
我想按如下方式组织我的输出:
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
因此,我试着按表达式分组如下:
df_group = dict_df.groupby([pd.Grouper(key='Date', freq='W-MON'), 'Store', 'employee'])\
['duties'].count().unstack(level=1).unstack(level=1).reset_index()
但是,它显示的是所有员工,而不是该特定商店中的员工工作示例:
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
那么我怎样才能得到我想要的结果呢。基本上我想过滤掉那些不在那家商店工作的员工
最好是使用GROMPBY来满足这个需求,还是应该考虑其他方法?< /P>
提前感谢您的帮助和考虑。尝试取消堆叠多个级别
[1,2]
:
df_out = (df.groupby([pd.Grouper(key='Date', freq='W-MON'), 'Store', 'employee'])['duties']
.count()
.unstack(level=[1, 2])
)
print(df_out)
印刷品:
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
可以同时取消堆叠两个标高:
(df.groupby([pd.Grouper(key='Date', freq='W-MON'), 'Store','employee'])
.size().unstack(['Store','employee'])
)
输出:
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
很好的解决方案,我不知道你可以按名称堆叠/取消堆叠:)谢谢你,Quang,这很有效。非常感谢您的及时回复。事实上,我学到了一个新的东西,我们可以使用列名称来使用unstack。嗨,Andrej,谢谢你们的及时回复。我也试过了,效果不错。但我喜欢指定列名,因为代码看起来更可读。非常感谢您的及时回复。