Question

我有一个具有多个行和列的pandas数据框，其中每个单元格都包含一个值列表。我想分别计算每一行的值平均值（在列表中）。

数据框如下所示：

l1 = [[1,2,4,3],[1,2,4,3], [1,2,4,3]]
l2 = [[8,2,6,4],[1,2,4,3],[1,2,4,3]]
l3 = [[1,2,4,9],[1,2,4,3],[1,2,4,3]]

df = pd.DataFrame([l1, l2, l3], columns=list('xyz'))

df：

      x          y       z ...
x [1,2,4,3] [1,2,4,3] [1,2,4,3]

y [8,2,6,4] [1,2,4,3] [1,2,4,3]

z [1,2,4,9] [1,2,4,3] [1,2,4,3]

我想要这样的结果：

      x          y       z         MEAN
x [1,2,4,3] [1,2,4,3] [1,2,4,3]   2.50000

y [8,2,6,4] [1,2,4,3] [1,2,4,3]   3.33333

z [1,2,4,9] [1,2,4,3] [1,2,4,3]   3.00000

有什么建议吗？

Answer 1

您可以将numpy.concatenate的每行值展平为numpy数组，然后调用mean：

df['MEAN'] = [np.concatenate(x).mean() for x in df.to_numpy()]
#for oldier pandas versions
#df['MEAN'] = [np.concatenate(x).mean() for x in df.values]
print (df)
              x             y             z      MEAN
x  [1, 2, 4, 3]  [1, 2, 4, 3]  [1, 2, 4, 3]  2.500000
y  [8, 2, 6, 4]  [1, 2, 4, 3]  [1, 2, 4, 3]  3.333333
z  [1, 2, 4, 9]  [1, 2, 4, 3]  [1, 2, 4, 3]  3.000000

计算包含值列表的每一行的平均值

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