从数据框中的转置数据中选择列

易卜拉欣·马哈茂德·埃马拉

大家好，我有带有列名称日期的日期框架，问题是我想在每一列中获得 4 个弱形式，所以我尝试转置日期，然后当列变成行时，它会在一列中出现，我可以选择 4它很弱，但是当我转置它时，列变为行但不添加到数据框索引数据中，我无法再选择它我附上图片以获得更清晰的视图任何帮助，问候

在转置之前首先显示图像数据帧

转置后的第二个图像

里奇V

我仍然不清楚您想在第 5 周做什么，但是这将为您提供当月一周的数字。

# this will only work if the first date belongs to the first week of that month
# and if there is only one date per week
wom = ( # week of month
    df.index.to_series()
    .groupby([df.index.year, df.index.month])
    .cumcount() + 1 # create the 1, 2, 3, 4, 5 tags for week of month
)

# you can keep it as a separate indexer
sales_month_over = df.loc[wom < 5, :]


# or you can create a MultiIndex
df.index = pd.MultiIndex.from_arrays([df.index, wom], names=['date', 'wom'])
sales_month_over = df.loc[df.index.get_level_values('wom') < 5]
fifth_weeks = df.loc[~df.index.isin(sales_month_over.index)]

>>> print(sales_month_over)
                0    1    2    3    4    5    6    7    ...  107  108  109  110  111  112  113  114
date       wom                                          ...
2019-01-05 1     78  135   66   68   64   69  109   70  ...   58  166  122   81  162  193   74  196
2019-01-12 2    138  191  130   80  177   60  139  114  ...  147  188   59  149  126  131  133  178
2019-01-19 3    198  111  181  145   91   60  128  184  ...   80   54  110  152  114  165   86   68
2019-01-26 4    154  169  134   90  173  122  140  182  ...  186  140  150   65   68   92  128  169
2019-02-02 1    105   55   82   74  125  163   91   95  ...  199   67  116  155  128  162  133  110
...             ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...
2020-05-23 4    163  176   92   78   88   64   55   79  ...  142  156  134  158   63  157   77   75
2020-06-06 1    133  167  117   91  180  106  169  154  ...   58  170  115  101  108   89   57   56
2020-06-13 2     78   86   93  192   53  143  182  184  ...  193  139   68  179   55   61  131  167
2020-06-20 3    119  123   91  145   71  193   97  182  ...  146  163   52  120  195   56  153  126
2020-06-27 4     50  191   72   89   76  151  166   89  ...  132   95  111  134   83   64  188  150

[72 rows x 115 columns]

>>> print(fifth_weeks)
                0    1    2    3    4    5    6    7    ...  107  108  109  110  111  112  113  114
date       wom                                          ...
2019-03-30 5    199  120  147   81   61   85  132  174  ...   99  162  177  104  118  168  117   92
2019-06-29 5    113   72   92   64  192  188   51  164  ...  143  137  126  117  162  157   53  102
2019-08-31 5    129  192   60  156  153  137  183  117  ...  155  115   57   92  124   99  143  119
2019-11-30 5    133  190  156  179   79  107  158  118  ...  165  180   91  139  176  159   61  103
2020-02-29 5    123  195  182  170  155  145  189   84  ...  152  115   74  128  190   72   53  104
2020-05-30 5    176  121  132  155   60   57  120  182  ...   57  136   52  190  152  168   65  164

[6 rows x 115 columns]

现在您可以获取与上个月相比的百分比变化

sales_month_over = sales_month_over.groupby(level='wom').pct_change()

>>> print(sales_month_over)
                     0         1         2         3    ...       111       112       113       114
date       wom                                          ...
2019-01-05 1         NaN       NaN       NaN       NaN  ...       NaN       NaN       NaN       NaN
2019-01-12 2         NaN       NaN       NaN       NaN  ...       NaN       NaN       NaN       NaN
2019-01-19 3         NaN       NaN       NaN       NaN  ...       NaN       NaN       NaN       NaN
2019-01-26 4         NaN       NaN       NaN       NaN  ...       NaN       NaN       NaN       NaN
2019-02-02 1   -0.063953 -0.259259 -0.259067 -0.401961  ...  0.084416  0.452055 -0.481250  0.012579
...                  ...       ...       ...       ...  ...       ...       ...       ...       ...
2020-05-23 4   -0.554878 -0.191860  0.285714  0.265734  ... -0.658824  0.943820  0.444444  0.950000
2020-06-06 1   -0.598540  1.763889 -0.155844 -0.338983  ... -0.248000 -0.006757 -0.512821 -0.043243
2020-06-13 2    0.130435  0.390244  0.423358 -0.460177  ... -0.013158 -0.167702  0.015385  0.305785
2020-06-20 3   -0.437500 -0.100000  1.650000  0.175439  ...  0.666667 -0.088235  0.155556  0.246753
2020-06-27 4    0.452055 -0.474820 -0.374269 -0.414365  ...  1.172414 -0.710983 -0.525641 -0.521368

[72 rows x 115 columns]

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