# Convert pandas DataFrame to NumPy Array in Python (3 Examples)

In this Python tutorial you’ll learn how to **transform a pandas DataFrame to a NumPy Array**.

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## Example Data & Software Libraries

We first have to import the pandas library, in order to use the corresponding functions:

import pandas as pd # Import pandas library in Python |

import pandas as pd # Import pandas library in Python

Furthermore, we’ll use the following data as basement for this Python tutorial:

data = pd.DataFrame({'x1':range(101, 106), # Create example DataFrame 'x2':['x', 'y', 'z', 'x', 'y'], 'x3':range(16, 11, - 1), 'x4':range(5, 10)}) print(data) # Print example DataFrame |

data = pd.DataFrame({'x1':range(101, 106), # Create example DataFrame 'x2':['x', 'y', 'z', 'x', 'y'], 'x3':range(16, 11, - 1), 'x4':range(5, 10)}) print(data) # Print example DataFrame

Have a look at the previous table. It shows that the example data is made of five rows and four columns called “x1”, “x2”, “x3”, and “x4”.

## Example 1: Transform pandas DataFrame to NumPy Array Using to_numpy() Function

The following syntax shows how to convert a pandas DataFrame to a NumPy array using the to_numpy function.

In order to use the functions of the NumPy package, we first have to load the numpy library to Python:

import numpy as np # Import NumPy library in Python |

import numpy as np # Import NumPy library in Python

In the next step, we can apply the to_numpy function as shown below:

data_array1 = data.to_numpy() # Apply to_numpy function to entire DataFrame print(data_array1) # Print array # [[101 'x' 16 5] # [102 'y' 15 6] # [103 'z' 14 7] # [104 'x' 13 8] # [105 'y' 12 9]] |

data_array1 = data.to_numpy() # Apply to_numpy function to entire DataFrame print(data_array1) # Print array # [[101 'x' 16 5] # [102 'y' 15 6] # [103 'z' 14 7] # [104 'x' 13 8] # [105 'y' 12 9]]

Have a look at the previous output: It shows that we have created a new array object called data_array1 that contains the values of our pandas DataFrame.

## Example 2: Transform Specific Columns of pandas DataFrame to NumPy Array

In this example, I’ll show how to convert only a subset of a pandas DataFrame to a NumPy array.

For this, we can use the following Python syntax:

data_array2 = data[['x2', 'x4']].to_numpy() # Apply to_numpy to DataFrame subset print(data_array2) # Print array # [['x' 5] # ['y' 6] # ['z' 7] # ['x' 8] # ['y' 9]] |

data_array2 = data[['x2', 'x4']].to_numpy() # Apply to_numpy to DataFrame subset print(data_array2) # Print array # [['x' 5] # ['y' 6] # ['z' 7] # ['x' 8] # ['y' 9]]

As you can see based on the previous console output, we have created a NumPy array containing the values of the variables x2 and x4 of our pandas DataFrame.

## Example 3: Transform pandas DataFrame to NumPy Array Using values Attribute

So far, we have used the to_numpy function to change from the pandas DataFrame class to the NumPy array class.

However, it is also possible to extract the values of a pandas DataFrame to create a NumPy array using the values attribute of our DataFrame.

Have a look at the following Python code:

data_array3 = data.values # Extract values of DataFrame print(data_array3) # Print array # [[101 'x' 16 5] # [102 'y' 15 6] # [103 'z' 14 7] # [104 'x' 13 8] # [105 'y' 12 9]] |

data_array3 = data.values # Extract values of DataFrame print(data_array3) # Print array # [[101 'x' 16 5] # [102 'y' 15 6] # [103 'z' 14 7] # [104 'x' 13 8] # [105 'y' 12 9]]

The previously shown output is exactly the same as in Example 1. However, this time we have used the values attribute instead of the to_numpy command.

## Video & Further Resources

Do you need more information on NumPy arrays in Python? Then I recommend watching the following video on the YouTube channel of Joe James. In the video, he explains how to handle numerical arrays in Python:

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In addition, you may read the other articles on Statistics Globe:

To summarize: In this tutorial, I have explained how to **convert a pandas DataFrame to a NumPy Array** in the Python programming language. Please let me know in the comments below, in case you have further questions.

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