Python Pandas 一列数据框总和

Question

我有一个 Python Pandas DataFrame:

df = pd.DataFrame(np.random.rand(5,3),columns=list('ABC'))
print df
              A           B           C
0   0.041761178 0.60439116  0.349372206
1   0.820455992 0.245314299 0.635568504
2   0.517482167 0.7257227   0.982969949
3   0.208934899 0.594973111 0.671030326
4   0.651299752 0.617672419 0.948121305

问题:我想将第一列添加到整个数据框中。我想得到这个:

              A           B           C
0   0.083522356 0.646152338 0.391133384
1   1.640911984 1.065770291 1.456024496
2   1.034964334 1.243204867 1.500452116
3   0.417869798 0.80390801  0.879965225
4   1.302599505 1.268972171 1.599421057

对于第一行:

• 答:0.04176 + 0.04176 = 0.08352
• 乙:0.04176 + 0.60439 = 0.64615
• 等等

要求:我无法使用其列名来引用第一列。例如:df.A 是 Not Acceptable ； df.iloc[:,0] 是可以接受的。

尝试:我尝试使用:

print df.add(df.iloc[:,0], fill_value=0)

但它不起作用。它返回错误信息:

Traceback (most recent call last):
  File "C:test.py", line 20, in <module>
    print df.add(df.iloc[:,0], fill_value=0)
  File "C:\python27\lib\site-packages\pandas\core\ops.py", line 771, in f
    return self._combine_series(other, na_op, fill_value, axis, level)
  File "C:\python27\lib\site-packages\pandas\core\frame.py", line 2939, in _combine_series
    return self._combine_match_columns(other, func, level=level, fill_value=fill_value)
  File "C:\python27\lib\site-packages\pandas\core\frame.py", line 2975, in _combine_match_columns
    fill_value)
NotImplementedError: fill_value 0 not supported

是否可以将 DataFrame 的所有列与第一列相加？

Answer 1

这就是你需要做的:

df.add(df.A, axis=0)


Example:
>>> df = pd.DataFrame(np.random.rand(5,3),columns=['A','B','C'])
>>> col_0 = df.columns.tolist()[0]

>>> print df
          A         B         C
0  0.502962  0.093555  0.854267
1  0.165805  0.263960  0.353374
2  0.386777  0.143079  0.063389
3  0.639575  0.269359  0.681811
4  0.874487  0.992425  0.660696
>>> df = df.add(df.col_0, axis=0)
>>> print df
          A         B         C
0  1.005925  0.596517  1.357229
1  0.331611  0.429766  0.519179
2  0.773553  0.529855  0.450165
3  1.279151  0.908934  1.321386
4  1.748975  1.866912  1.535183
>>>