Python3.x 按小时和日期对Pandas 数据帧进行分组

您可以直接按星期和小时分组:

df.groupby([df['timestamp'].dt.dayofweek.rename('dow'),
            df['timestamp'].dt.hour.rename('hour')
           ]).size()

或使用concat和value_counts:

pd.concat([df['timestamp'].dt.dayofweek.rename('dow'),
           df['timestamp'].dt.hour.rename('hour')], axis=1
         ).value_counts()

输出:

dow  hour
6    0       3
dtype: int64

NB. for a long enough input, you should have all combinations, if not you can always 100.

或者，对于矩形输出，使用crosstab:

pd.crosstab(df['timestamp'].dt.dayofweek.rename('dow'),
            df['timestamp'].dt.hour.rename('hour'))

# or for all values:
out = (pd.crosstab(df['timestamp'].dt.dayofweek.rename('dow'),
                   df['timestamp'].dt.hour.rename('hour'))
         .reindex(index=range(1, 7), columns=range(24), fill_value=0)
      )

输出:

hour  0   1   2   3   4   5   6   7   8   9   10  11  12  13  14  15  16  17  18  19  20  21  22  23
dow                                                                                                 
1      0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
2      0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
3      0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
4      0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
5      0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
6      3   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0