Question

我想在pandas或pyspark数据帧中的连续列中执行两个不同的计算。

列为周，指标显示为行。我想计算各列的实际值和百分比差异。

输入/输出表包括。 Excel中使用的计算显示在下图中。我想在pandas或pyspark数据帧上复制这些计算。

附加原始数据：

Metrics         Week20  Week21  Week22  Week23  Week24  Week25  Week26  Week27
Sales           20301   21132   20059   23062   19610   22734   22140   20699
TRXs            739     729     690     779     701     736     762     655
Attachment Rate 4.47    4.44    4.28    4.56    4.41    4.58    4.55    4.96
AOV             27.47   28.99   29.07   29.6    27.97   30.89   29.06   31.6
Profit          5177    5389    5115    5881    5001    5797    5646    5278
Profit per TRX  7.01    7.39    7.41    7.55    7.13    7.88    7.41    8.06

Answer 1

在pandas中你可以使用pct_change(axis=1)和diff(axis=1)方法：

df = df.set_index('Metrics')

# list of metrics with "actual diff"
actual = ['AOV', 'Attachment Rate']

rep = (df[~df.index.isin(actual)].pct_change(axis=1).round(2)*100).fillna(0).astype(str).add('%')
rep = pd.concat([rep,
                 df[df.index.isin(actual)].diff(axis=1).fillna(0)
                ])


In [131]: rep
Out[131]:
                Week20 Week21 Week22 Week23  Week24 Week25 Week26  Week27
Metrics
Sales             0.0%   4.0%  -5.0%  15.0%  -15.0%  16.0%  -3.0%   -7.0%
TRXs              0.0%  -1.0%  -5.0%  13.0%  -10.0%   5.0%   4.0%  -14.0%
Profit            0.0%   4.0%  -5.0%  15.0%  -15.0%  16.0%  -3.0%   -7.0%
Profit per TRX    0.0%   5.0%   0.0%   2.0%   -6.0%  11.0%  -6.0%    9.0%
Attachment Rate      0  -0.03  -0.16   0.28   -0.15   0.17  -0.03    0.41
AOV                  0   1.52   0.08   0.53   -1.63   2.92  -1.83    2.54

Pandas或Pyspark Dataframe中连续列的实际和百分比差异

1 个答案: