Combine two numpy arrays and covert them into a dataframe

Question

I have two Dataframes (X & y) sliced off the main dataframe df as below :

 X = df.ix[:,df.columns!='Class']
 y = df.ix[:,df.columns=='Class']

 from imblearn.over_sampling  import SMOTE
 sm = SMOTE()

 X_resampled , y_resampled = sm.fit_sample(X,y.values.ravel())

The last line returns a numpy 2-d array for X_resampled and y_resampled. So I would want to know how to convert X_resampled and y_resampled back into a dataframe.

Example Data :

 X_resampled :Dimensions(2,30) : 2 rows,30 columns

    array([[ 0.        , -1.35980713, -0.07278117,  2.53634674,  1.37815522,
            -0.33832077,  0.46238778,  0.23959855,  0.0986979 ,  0.36378697,
             0.09079417, -0.55159953, -0.61780086, -0.99138985, -0.31116935,
             1.46817697, -0.47040053,  0.20797124,  0.02579058,  0.40399296,
             0.2514121 , -0.01830678,  0.27783758, -0.11047391,  0.06692807,
             0.12853936, -0.18911484,  0.13355838, -0.02105305,  0.24496426],
           [ 0.        ,  1.19185711,  0.26615071,  0.16648011,  0.44815408,
             0.06001765, -0.08236081, -0.07880298,  0.08510165, -0.25542513,
            -0.16697441,  1.61272666,  1.06523531,  0.48909502, -0.1437723 ,
             0.63555809,  0.46391704, -0.11480466, -0.18336127, -0.14578304,
            -0.06908314, -0.22577525, -0.63867195,  0.10128802, -0.33984648,
             0.1671704 ,  0.12589453, -0.0089831 ,  0.01472417, -0.34247454]])


        y_resampled :Dimensions (2,) - Coressponding to the two rows of X_resampled.

        array([0, 0], dtype=int64)

Can you give us some example data. It doesn't have to be large. 2-3 rows will suffice. This will not only help us test solutions, but will make your question more clear to visitors. — jpp
– jpp, Commented Feb 22, 2018 at 9:17
@jpp - I have edited my question and added some sample data . Hope it's clear now — user1411837
– user1411837, Commented Feb 22, 2018 at 9:23
What's your expected output given the data you have provided? You don't need to give us an entire array, (2, 5) is easy enough. — jpp
– jpp, Commented Feb 22, 2018 at 9:26

jezrael · Accepted Answer · 2018-02-22 09:35:31Z

I believe you need numpy.hstack:

a = np. array([[ 0.        , -1.35980713, -0.07278117,  2.53634674,  1.37815522,
            -0.33832077,  0.46238778,  0.23959855,  0.0986979 ,  0.36378697,
             0.09079417, -0.55159953, -0.61780086, -0.99138985, -0.31116935,
             1.46817697, -0.47040053,  0.20797124,  0.02579058,  0.40399296,
             0.2514121 , -0.01830678,  0.27783758, -0.11047391,  0.06692807,
             0.12853936, -0.18911484,  0.13355838, -0.02105305,  0.24496426],
           [ 0.        ,  1.19185711,  0.26615071,  0.16648011,  0.44815408,
             0.06001765, -0.08236081, -0.07880298,  0.08510165, -0.25542513,
            -0.16697441,  1.61272666,  1.06523531,  0.48909502, -0.1437723 ,
             0.63555809,  0.46391704, -0.11480466, -0.18336127, -0.14578304,
            -0.06908314, -0.22577525, -0.63867195,  0.10128802, -0.33984648,
             0.1671704 ,  0.12589453, -0.0089831 ,  0.01472417, -0.34247454]])
b = np.array([0, 100])
c = pd.DataFrame(np.hstack((a,b[:, None])))
print (c)
    0         1         2         3         4         5         6         7   \
0  0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   
1  0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   

         8         9   ...          21        22        23        24  \
0  0.098698  0.363787  ...   -0.018307  0.277838 -0.110474  0.066928   
1  0.085102 -0.255425  ...   -0.225775 -0.638672  0.101288 -0.339846   

         25        26        27        28        29     30  
0  0.128539 -0.189115  0.133558 -0.021053  0.244964    0.0  
1  0.167170  0.125895 -0.008983  0.014724 -0.342475  100.0  

[2 rows x 31 columns]

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