pandas groupby删除列 [英] pandas groupby dropping columns

问题描述

我正在按操作进行简单分组，试图比较分组均值.如下所示，我从一个较大的数据框中选择了特定的列，从中删除了所有缺失的值.

I'm doing a simple group by operation, trying to compare group means. As you can see below, I have selected specific columns from a larger dataframe, from which all missing values have been removed.

但是当我分组时，我丢失了几列:

But when I group by, I am losing a couple of columns:

我从来没有遇到过熊猫，而且在堆栈溢出中我找不到其他类似的东西.有人有见识吗?

I have never encountered this with pandas, and I'm not finding anything else on stack overflow that is all that similar. Does anybody have any insight?

推荐答案

我认为它是Automatic exclusion of 'nuisance' columns，它描述了

I think it is Automatic exclusion of 'nuisance' columns, what described here.

示例:

df = pd.DataFrame({'C': {0: -0.91985400000000006, 1: -0.042379, 2: 1.2476419999999999, 3: -0.00992, 4: 0.290213, 5: 0.49576700000000001, 6: 0.36294899999999997, 7: 1.548106}, 'A': {0: 'foo', 1: 'bar', 2: 'foo', 3: 'bar', 4: 'foo', 5: 'bar', 6: 'foo', 7: 'foo'}, 'B': {0: 'one', 1: 'one', 2: 'two', 3: 'three', 4: 'two', 5: 'two', 6: 'one', 7: 'three'}, 'D': {0: -1.131345, 1: -0.089328999999999992, 2: 0.33786300000000002, 3: -0.94586700000000001, 4: -0.93213199999999996, 5: 1.9560299999999999, 6: 0.017587000000000002, 7: -0.016691999999999999}})

print (df)
     A      B         C         D
0  foo    one -0.919854 -1.131345
1  bar    one -0.042379 -0.089329
2  foo    two  1.247642  0.337863
3  bar  three -0.009920 -0.945867
4  foo    two  0.290213 -0.932132
5  bar    two  0.495767  1.956030
6  foo    one  0.362949  0.017587
7  foo  three  1.548106 -0.016692

print( df.groupby('A').mean())
            C         D
A                      
bar  0.147823  0.306945
foo  0.505811 -0.344944

我认为您可以检查 DataFrame.dtypes .

I think you can check DataFrame.dtypes.

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