使用groupby的列的累积列表

问题描述

我有以下数据框:

   Fruit  metric
0  Apple     NaN
1  Apple   100.0
2  Apple     NaN
3  Peach    70.0
4   Pear   120.0
5   Pear   100.0
6   Pear     NaN

我的目标是对水果进行分组，然后依次将每个不为空的metric值添加到具有自己单独列的累积列表中，如下所示:

My objective is to groupby fruit and in order, add each value of metric that is not null to a cumulative list with its own separate column like so:

   Fruit  metric  metric_cum
0  Apple     NaN          []
1  Apple   100.0       [100]
2  Apple     NaN       [100]
3  Peach    70.0        [70]
4   Pear   120.0       [120]
5   Pear   100.0  [120, 100]
6   Pear     NaN  [120, 100]

我尝试这样做:

df['metric1'] = df['metric'].astype(str)
df.groupby('Fruit')['metric1'].cumsum()

但这会导致DataError: No numeric types to aggregate.

我也尝试过这样做:

df.groupby('Fruit')['metric'].apply(list)

结果:

Fruit
Apple      [nan, 100.0, nan]
Peach                 [70.0]
Pear     [120.0, 100.0, nan]
Name: metric, dtype: object

但这不是累积性的，因此无法列成一列.谢谢您的帮助

But this is not cumulative and isn't able to made into a column.Thanks for your help

推荐答案

使用:

df['metric'] = df['metric'].apply(lambda x: [] if pd.isnull(x) else [int(x)])
df['metric_cum'] = df.groupby('Fruit')['metric'].apply(lambda x: x.cumsum())
print (df)
   Fruit metric  metric_cum
0  Apple     []          []
1  Apple  [100]       [100]
2  Apple     []       [100]
3  Peach   [70]        [70]
4   Pear  [120]       [120]
5   Pear  [100]  [120, 100]
6   Pear     []  [120, 100]

或者:

a = df['metric'].apply(lambda x: [] if pd.isnull(x) else [int(x)])
df['metric_cum'] = a.groupby(df['Fruit']).apply(lambda x: x.cumsum())
print (df)
   Fruit  metric  metric_cum
0  Apple     NaN          []
1  Apple   100.0       [100]
2  Apple     NaN       [100]
3  Peach    70.0        [70]
4   Pear   120.0       [120]
5   Pear   100.0  [120, 100]
6   Pear     NaN  [120, 100]

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