问题描述
我有一个 Python Pandas 数据框:
I have a 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:
Question: I would like to add the first column to the whole dataframe. I would like to get this:
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
- B:0.04176 + 0.60439 = 0.64615
- 等
要求:我无法使用其列名引用第一列.例如:df.A 不可接受;df.iloc[:,0] 是可以接受的.
Requirements: I cannot refer to the first column using its column name. eg.: df.A is not acceptable; df.iloc[:,0] is acceptable.
尝试:我试过这个:
print df.add(df.iloc[:,0], fill_value=0)
但它不起作用.它返回错误消息:
but it is not working. It returns the error message:
Traceback (most recent call last): File "C:test.py", line 20, in <module> print df.add(df.iloc[:,0], fill_value=0) File "C:python27libsite-packagespandascoreops.py", line 771, in f return self._combine_series(other, na_op, fill_value, axis, level) File "C:python27libsite-packagespandascoreframe.py", line 2939, in _combine_series return self._combine_match_columns(other, func, level=level, fill_value=fill_value) File "C:python27libsite-packagespandascoreframe.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?
Is it possible to take the sum of all columns of a DataFrame with the first column?
推荐答案
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 >>>