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Hi so I have the following dataframe:

 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

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]

I have tried doing this:

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

But this results in a DataError: No numeric types to aggregate.

I have also tried doing this:

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

Resulting in:

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

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

Use:

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]

Or:

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]

f = lambda x: pd.Series(x).dropna().astype(int).tolist()
c = pd.Series.cumsum
df.assign(metric_cum=df.metric.apply(f).groupby(df.Fruit).apply(c))

 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]