python：pandas dataframe中的列行

Question

我有以下dataFrame：

data.head()

OUT：

     metric_name            metric_date     warehouse   value          week  year   day
0   Crossdock Transfer Out  2022-05-10      WR1        1.370313e+06     19  2022    2
21  New Vendor Freight      2022-04-19      WR1        1.583337e+06     16  2022    2
59  Crossdock Transfer Out  2021-12-26      WR1        3.805000e+03     51  2021    0
80  New Vendor Freight      2021-12-30      WR1        2.832327e+06     52  2021    4
90  Crossdock Transfer In   2022-05-22      WR1        0.000000e+00     20  2022    0
...     ...     ...     ...     ...     ...     ...     ...
127699  LF Forecasted New Crossdock Transfer Out    2021-11-01  WR1     2.595843e+06    44  2021    1

“ Metric_name”列有不同的指标。要拥有一个更好的数据集用于时间序列预测，我想转换我的数据框架。我希望“ metric_name”中的每个度量作为数据集中的新列。

请参阅“ metric_name”中的所有不同指标：

data.metric_name.value_counts()

out：

LF Forecasted End Vendor Freight + End Transfer In Backlog    364
LF Forecasted New Crossdock Transfer Out                      364
LF Forecasted New Vendor Freight                              364
LF Forecasted New Crossdock Transfer In                       364
Forecasted New Crossdock Transfer Out                         359
Forecasted End Vendor Freight + End Transfer In Backlog       359
Forecasted New Crossdock Transfer In                          359
Forecasted New Vendor Freight                                 359
Crossdock Transfer Out                                        345
New Vendor Freight                                            345
Crossdock Transfer In                                         345
End Vendor Freight + End Transfer In Backlog                  345
Name: metric_name, dtype: int64

我该怎么办？

我尝试了这个，但这并没有给我价值观，只是计数：

bhx4.groupby('metric_date')['metric_name'].value_counts().unstack()

metric_name     Crossdock Transfer In   Crossdock Transfer Out  End Vendor Freight + End Transfer In Backlog    Forecasted End Vendor Freight + End Transfer In Backlog     Forecasted New Crossdock Transfer In    Forecasted New Crossdock Transfer Out   Forecasted New Vendor Freight   LF Forecasted End Vendor Freight + End Transfer In Backlog  LF Forecasted New Crossdock Transfer In     LF Forecasted New Crossdock Transfer Out    LF Forecasted New Vendor Freight    New Vendor Freight
 metric_date                                                
 2021-07-25     1.0     1.0     1.0     1.0     1.0     1.0     1.0         1.0     1.0     1.0     1.0     1.0
 2021-07-26     1.0     1.0     1.0     1.0     1.0     1.0     1.0     1.0     1.0     1.0     1.0     1.0
 2021-07-27     1.0     1.0     1.0     1.0     1.0     1.0     1.0     1.0     1.0     1.0     1.0     1.0
 2021-07-28     1.0     1.0     1.0     1.0     1.0     1.0     1.0     1.0     1.0     1.0     1.0     1.0
 2021-07-29     1.0     1.0     1.0     1.0     1.0     1.0     1.0     1.0     1.0     1.0     1.0     1.0

我希望DF看起来像这样： 谢谢！

Answer 1

如果我把你说对了：

data.groupby(['metric_date', 'metric_name'])['value'].sum().unstack().fillna(0)