How to Combine Pandas Dataframe and Numpy Matrix

You can combine Pandas dataframes and Numpy Matrices by using the pd.concat() function in Pandas.

pd.concat([df,pd.DataFrame(Matrix)],axis=1)

The following are the steps to combine Pandas dataframe and Numpy matrix.

Step 1: Generate a dataframe

The following is to generate a dataframe and a matrix first. 

# Generate a dataframe
car_data = {'Brand': ['Tesla', 'Tesla','Tesla','Ford'], 
     'Location': ['CA', 'CA','NY','MA'],
    'Year':[2019,2018,2020,2019]}
car_data=pd.DataFrame(data=car_data)

# print out the dataframe
print('Dataframe: \n',car_data)

The following is the print out of the dataframe.

Dataframe: 
    Brand Location  Year
0  Tesla       CA  2019
1  Tesla       CA  2018
2  Tesla       NY  2020
3   Ford       MA  2019

Step 2: Generate a matrix

# Generate a matrix
mt_1=np.matrix([[88,33,44,55],[4,2,3,5]])
mt_1_T=mt_1.transpose()

# print out the matrix
print('Generated Matrix:\n',mt_1_T)

The following is the print out of the generated matrix. 

Generated Matrix:
 [[88  4]
 [33  2]
 [44  3]
 [55  5]]

Step 3: Combine Pandas dataframe and Numpy Matrix

The following is the Python code combining a dataframe and a matrix. 

# Combine the dataframe and the matrix
df_combined=pd.concat([car_data,pd.DataFrame(mt_1_T)],axis=1)
print(df_combined)

The following is the combined dataframe-a combinatino of the original dataframe and the matrix. 

   Brand Location  Year   0  1
0  Tesla       CA  2019  88  4
1  Tesla       CA  2018  33  2
2  Tesla       NY  2020  44  3
3   Ford       MA  2019  55  5

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