# Transform Your Dataframe into a Powerful Numpy Array with Pandas Dataframe.to_numpy() - GeeksforGeeks

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Pandas DataFrame presents a potentially heterogeneous tabular data structure that is mutable in size and has labeled axes for both rows and columns. By using the DataFrame.to_numpy() functionality, it is viable to transform this data structure into a NumPy ndarray. This article outlines the steps needed to facilitate the conversion of dataframe to numpy array.

## Syntax of Pandas DataFrame.to_numpy()

The Syntax is as Follows:  DataFrame.to_numpy(dtype = None, copy = False)

Parameters:

dtype: Defines the data type, e.g. str. copy: [bool, default False] Ensures that there is no dependence on another array and that the returned value is a new one.Returns: numpy.ndarray

## Converting DataFrame to Numpy Array

In this section, we will concentrate on how to transform a DataFrame to a Numpy array.

import pandas as pd

df = pd.DataFrame(

[[1, 2, 3],

[4, 5, 6],

[7, 8, 9],

[10, 11, 12]],

columns=['a', 'b', 'c'])

arr = df.to_numpy()

print('\nNumpy Array\n----------\n', arr)

print(type(arr))

Result:

Numpy Array:

• ----------
• The following is a Numpy Array:
• [[1 2 3]
• [4 5 6]
• [7 8 9]
• [10 11 12]]

Our goal is to transform a specified column into a Numpy Array. To accomplish this, we need to utilize the Python library Pandas. First, we import this library using the following code:

import pandas as pd

After importing Pandas, we create a DataFrame with rows and columns of data. Here is an example of a DataFrame:

• df = pd.DataFrame([[1, 2, 3],
• [4, 5, 6],
• [7, 8, 9],
• [10, 11, 12]],
• columns=['a', 'b', 'c'])

Next, we create a Numpy Array from the DataFrame. We choose to select only columns "a" and "c" from the DataFrame to convert into a Numpy Array. We do this using the code:

arr = df[['a', 'c']].to_numpy()

Our Output will be:

• Numpy Array:
• ----------
• [[ 1 3]
• [ 4 6]
• [ 7 9]
• [10 12]]
• It is useful to note that this specific operation converts a DataFrame with different data types. To be able to perform this procedure, we also import Numpy into our Python script using the following code:
• import numpy as np

As can be seen from the results, the Numpy Array provides us with a cleaner and more organized display of the specified columns from our Pandas DataFrame. The Numpy Array can be used to further process the data in machine learning or data science applications.

## Transforming Data from CSV File to Numpy Array

In this code snippet, we convert data from a CSV file to a Numpy array utilizing pandas. We use the DataFrame.to_numpy() method to convert the data from the CSV file (nba.csv) to a Numpy array.

First, let's take a look at the data that we will convert:

abc
123
456.5
78.59
101112

### Converting CSV Data to Numpy Array

Here's the code snippet for converting data from a CSV file to a Numpy array:

``import pandas as pd# Reading data from CSV filedata = pd.read_csv("nba.csv")# Dropping null valuesdata.dropna(inplace=True)# Creating a dataframe and selecting the "Weight" column from the CSV filedf = pd.DataFrame(data['Weight'].head())# Converting the selected column to a Numpy arrayarr = df.to_numpy()print("Numpy Array:", arr)``print("Numpy Array Datatype:", arr.dtype)``

The resulting Numpy array is:

Numpy Array:

 180 235

Numpy Array Datatype: float64

To access the CSV file, click on the following link: nba.csv

## Reformatted Code Examples

### Illustration 2:

The following code is an illustration where we simply enter the parameters in the same code to present the numpy data type dtype.

`import pandas as pd`

`data = pd.read_csv("nba.csv")`

`data.dropna(inplace=True)`

`df = pd.DataFrame(data['Weight'].head())`

`print(df.to_numpy(dtype='float32'))`

Output:

`[[180.]`

`[235.]`

`[185.]`

`[235.]`

`[238.]]`

### Example 3:

The code below validates the type of the array following conversion.

`import pandas as pd`

`data = pd.read_csv("nba.csv")`

`data.dropna(inplace=True)`

`df = pd.DataFrame(data['Weight'].head())`

`print(type(df.to_numpy()))`

Output:

Last Updated :

• 05 Jan, 2023
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