CODEWITHSUNDEEP
pythonpandasnumpydataframe
Booley
Booley

Reputation: 859

How to add numpy matrix as new columns for pandas dataframe?

I have a NxM dataframe and a NxL numpy matrix. I'd like to add the matrix to the dataframe to create L new columns by simply appending the columns and rows the same order they appear. I tried merge() and join(), but I end up with errors:

assign() keywords must be strings

and

columns overlap but no suffix specified

respectively.

Is there a way I can add a numpy matrix as dataframe columns?

Upvotes: 24

Views: 35714

Answers (2)

user3483203
user3483203

Reputation: 51165

Setup

df = pd.DataFrame({'a': [5,0,1,0,6], 'b': [5,7,0,4,4]})
mat = np.random.rand(5,3)

Using join:

df.join(pd.DataFrame(mat))


   a  b         0         1         2
0  5  5  0.884061  0.803747  0.727161
1  0  7  0.464009  0.447346  0.171881
2  1  0  0.353604  0.912781  0.199477
3  0  4  0.466095  0.136218  0.405766
4  6  4  0.764678  0.874614  0.310778

If there is the chance of overlapping column names, simply supply a suffix:

df = pd.DataFrame({0: [5,0,1,0,6], 1: [5,7,0,4,4]})
mat = np.random.rand(5,3)


df.join(pd.DataFrame(mat), rsuffix='_')


   0  1        0_        1_         2
0  5  5  0.783722  0.976951  0.563798
1  0  7  0.946070  0.391593  0.273339
2  1  0  0.710195  0.827352  0.839212
3  0  4  0.528824  0.625430  0.465386
4  6  4  0.848423  0.467256  0.962953

Upvotes: 5

sacuL
sacuL

Reputation: 51335

You can turn the matrix into a datframe and use concat with axis=1:

For example, given a dataframe df and a numpy array mat:

>>> df
   a  b
0  5  5
1  0  7
2  1  0
3  0  4
4  6  4

>>> mat
array([[0.44926098, 0.29567859, 0.60728561],
       [0.32180566, 0.32499134, 0.94950085],
       [0.64958125, 0.00566706, 0.56473627],
       [0.17357589, 0.71053224, 0.17854188],
       [0.38348102, 0.12440952, 0.90359566]])

You can do:

>>> pd.concat([df, pd.DataFrame(mat)], axis=1)
   a  b         0         1         2
0  5  5  0.449261  0.295679  0.607286
1  0  7  0.321806  0.324991  0.949501
2  1  0  0.649581  0.005667  0.564736
3  0  4  0.173576  0.710532  0.178542
4  6  4  0.383481  0.124410  0.903596

Upvotes: 41

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