Convert Pandas DataFrame to Numpy array

For performing some high-level mathematical functions, we can convert Pandas DataFrame to numpy arrays. It uses the DataFrame.to_numpy() function.

The DataFrame.to_numpy() function is applied on the DataFrame that returns the numpy ndarray.

Syntax

Parameters

dtype: It is an optional parameter that pass the dtype to numpy.asarray().
copy: It returns the boolean value that has the default value False.

It ensures that the returned value is not a view on another array.

Returns

It returns the numpy.ndarray as an output.

Example1

import pandas as pd
pd.DataFrame({"P": [2, 3], "Q": [4, 5]}).to_numpy()
info = pd.DataFrame({"P": [2, 3], "Q": [4.0, 5.8]})
info.to_numpy()
info['R'] = pd.date_range('2000', periods=2)
info.to_numpy()

Output

array([[2, 4.0, Timestamp('2000-01-01 00:00:00')],
       [3, 5.8, Timestamp('2000-01-02 00:00:00')]], dtype=object)

Example2

import pandas as pd
#initializing the dataframe
info = pd.DataFrame([[17, 62, 35],[25, 36, 54],[42, 20, 15],[48, 62, 76]],
columns=['x', 'y', 'z'])
print('DataFrame\n----------\n', info)
#convert the dataframe to a numpy array
arr = info.to_numpy()
print('\nNumpy Array\n----------\n', arr)

Output

DataFrame
----------
   x    y   z
0  17  62  35
1  25  36  54
2  42  20  15
3  48  62  76

Numpy Array
----------
 [[17 62 35]
 [25 36 54]
 [42 20 15]
 [48 62 76]]

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