Python Pandas sum of dataframe with one column

问题:

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

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

For the first row:

• A: 0.04176 + 0.04176 = 0.08352
• B: 0.04176 + 0.60439 = 0.64615
• etc

Requirements: I cannot refer to the first column using its column name. eg.: df.A is not acceptable; df.iloc[:,0] is acceptable.

Attempt: I tried this using:

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

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:\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

Is it possible to take the sum of all columns of a DataFrame with the first column?

回答1:

That's what you need to do:

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
>>> 

回答2:

I would try something like this:

firstol = df.columns[0]
df2 = df.add(df[firstcol], axis=0)

回答3:

I used a combination of the above two posts to answer this question.

Since I cannot refer to a specific column by its name, I cannot use df.add(df.A, axis=0). But this is along the correct lines. Since df += df[firstcol] produced a dataframe of NaNs, I could not use this approach, but the way that this solution obtains a list of columns from the dataframe was the trick I needed.

Here is how I did it:

col_0 = df.columns.tolist()[0]
print(df.add(df[col_0], axis=0))

回答4:

You can use numpy and broadcasting for this:

df = pd.DataFrame(df.values + df['A'].values[:, None],
                  columns=df.columns)

I expect this to be more efficient than series-based methods.