Pandas Dataframe.to_numpy() – Convert dataframe to Numpy array

Pandas DataFrame is a two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns). This data structure can be converted to NumPy ndarray with the help of the DataFrame.to_numpy() method. In this article we will see how to convert dataframe to numpy array.

Syntax of Pandas DataFrame.to_numpy()

Syntax: Dataframe.to_numpy(dtype = None, copy = False)
Parameters:
dtype: Data type which we are passing like str.
copy: [bool, default False] Ensures that the returned value is a not a view on another array.
Returns: numpy.ndarray

Convert DataFrame to Numpy Array

Here, we will see how to convert DataFrame to a Numpy array.

Python3

import pandas as pd
 
# initialize a dataframe
df = pd.DataFrame(
    [[1, 2, 3],
     [4, 5, 6],
     [7, 8, 9],
     [10, 11, 12]],
    columns=['a', 'b', 'c'])
 
# convert dataframe to numpy array
arr = df.to_numpy()
 
print('\nNumpy Array\n----------\n', arr)
print(type(arr))

Output:

Numpy Array
----------
 [[ 1  2  3]
 [ 4  5  6]
 [ 7  8  9]
 [10 11 12]]
<class 'numpy.ndarray'>

Here we want to convert a particular column into numpy array.

Python3

import pandas as pd
 
# initialize a dataframe
df = pd.DataFrame(
    [[1, 2, 3],
     [4, 5, 6],
     [7, 8, 9],
     [10, 11, 12]],
    columns=['a', 'b', 'c'])
 
# convert dataframe to numpy array
arr = df[['a', 'c']].to_numpy()
 
print('\nNumpy Array\n----------\n', arr)
print(type(arr))

Output:

Numpy Array
----------
[[ 1  3]
[ 4  6]
[ 7  9]
[10 12]]
<class 'numpy.ndarray'>

Here we are converting a dataframe with different datatypes.

Python3

import pandas as pd
import numpy as np
 
#initialize a dataframe
df = pd.DataFrame(
     [[1, 2, 3],
     [4, 5, 6.5],
     [7, 8.5, 9],
     [10, 11, 12]],
    columns=['a', 'b', 'c'])
arr = df.to_numpy()
 
print('Numpy Array', arr)
print('Numpy Array Datatype :', arr.dtype)

Output:

Numpy Array [[ 1.   2.   3. ]
[ 4.   5.   6.5]
[ 7.   8.5  9. ]
[10.  11.  12. ]]
Numpy Array Datatype : float64

To get the link to the CSV file, click on nba.csv

Example 1:

Here, we are using a CSV file for changing the Dataframe into a Numpy array by using the method DataFrame.to_numpy(). After that, we are printing the first five values of the Weight column by using the df.head() method.

Python3

# importing pandas
import pandas as pd
 
# reading the csv
data = pd.read_csv("nba.csv")
 
data.dropna(inplace=True)
 
# creating DataFrame from weight column
df = pd.DataFrame(data['Weight'].head())
 
# using to_numpy() function
print(df.to_numpy())

Output:

[[180.]
 [235.]
 [185.]
 [235.]
 [238.]]

Example 2:

In this example, we are just providing the parameters in the same code to provide the dtype here.

Python3

# importing pandas
import pandas as pd
 
# read csv file
data = pd.read_csv("nba.csv")
 
data.dropna(inplace=True)
 
# creating DataFrame from weight column
df = pd.DataFrame(data['Weight'].head())
 
# providing dtype
print(df.to_numpy(dtype='float32'))

Output:

[[180.]
 [235.]
 [185.]
 [235.]
 [238.]]

Example 3:

Validating the type of the array after conversion.

Python3

# importing pandas
import pandas as pd
 
# reading csv
data = pd.read_csv("nba.csv")
 
data.dropna(inplace=True)
 
# creating DataFrame from weight column
df = pd.DataFrame(data['Weight'].head())
 
# using to_numpy()
print(type(df.to_numpy()))