Pandas - 按列分组并将数据转换为 numpy 数组

Pandas - group by column and transform the data to numpy array(Pandas - 按列分组并将数据转换为 numpy 数组)

问题描述

Having the following data frame, group A have 4 samples, B 3 samples and C 1 sample:

  group   data_1   data_2
0     A        1        4
1     A        2        5
2     A        3        6
3     A        4        7
4     B        1        4
5     B        2        5
6     B        3        6
7     C        1        4

I would like to transform the data into numpy array, where each row is a group with all its samples and zero padding for groups that have fewer samples.

Resulting in an array like so:

[
   [[1,4],[2,5],[3,6],[4,7]], # this is A group 4 samples
   [[1,4],[2,5],[3,6],[0,0]], # this is B group 3 samples
   [[1,4],[0,0],[0,0],[0,0]], # this is C group 1 sample
]

First is necessary add missing values - first solution with unstack and stack, counter Series is created by cumcount.

Second solution use reindex by MultiIndex.

Last use lambda function with groupby, convert to numpy array by values and last to lists:

g = df.groupby('group').cumcount()
L = (df.set_index(['group',g])
       .unstack(fill_value=0)
       .stack().groupby(level=0)
       .apply(lambda x: x.values.tolist())
       .tolist())
print (L)

[[[1, 4], [2, 5], [3, 6], [4, 7]], 
 [[1, 4], [2, 5], [3, 6], [0, 0]], 
 [[1, 4], [0, 0], [0, 0], [0, 0]]]

Another solution:

g = df.groupby('group').cumcount()
mux = pd.MultiIndex.from_product([df['group'].unique(), g.unique()])
L = (df.set_index(['group',g])
       .reindex(mux, fill_value=0)
       .groupby(level=0)['data_1','data_2']
       .apply(lambda x: x.values.tolist())
       .tolist()
)

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