Converting Pandas Dataframe To Numpy Array

Converting Pandas DataFrame to NumPy Array

Converting a pandas DataFrame to a NumPy array is a common operation in data analysis, especially when preparing tabular data for numerical libraries, machine-learning models, or mathematical calculations. In practice, the main method is DataFrame. In real terms, to_numpy(), which returns the DataFrame’s values as a NumPy ndarray while preserving its row and column dimensions. Understanding how this conversion works helps avoid common problems involving mixed data types, missing values, unwanted copies, and lost DataFrame labels.

What Is a Pandas DataFrame?

A pandas DataFrame is a two-dimensional, labeled table. It consists of rows and columns, with each column having a name and a data type. For example:

import pandas as pd

df = pd.DataFrame({
    "name": ["Ana", "Ben", "Carla"],
    "age": [23, 31, 27],
    "score": [88.5, 92.0, 79.

print(df)

The output is:

   name  age  score
0   Ana   23   88.5
1   Ben   31   92.0
2 Carla   27   79.5

A NumPy array, by comparison, contains values without DataFrame-style row or column labels:

array = df.to_numpy()
print(array)

This produces an array containing the same three rows and three columns:

[['Ana' 23 88.5]
 ['Ben' 31 92.0]
 ['Carla' 27 79.5]]

The DataFrame’s index and column names are not included in the resulting NumPy array. If those labels are needed later, they should be stored separately or the data should be converted using a structured format.

The Recommended Method: `to_numpy()`

The clearest and most explicit way of converting a DataFrame into a NumPy array is to call to_numpy():

import pandas as pd
import numpy as np

df = pd.Still, dataFrame({
    "product": ["Notebook", "Pen", "Desk"],
    "price": [4. On top of that, 50, 2. 25, 120.

array = df.to_numpy()

print(type(array))
print(array.shape)
print(array.dtype)

The resulting object is a NumPy ndarray with the shape (3, 3). In practice, to_numpy() is generally the preferred conversion method because it communicates the intended operation and provides clear options for controlling data types and missing values Less friction, more output..

You can also create a new array with a specific dtype:

array = df.to_numpy(dtype=np.float32)

print(array.dtype)

This converts all values to 32-bit floating-point numbers. This is useful when the DataFrame will be passed to a numerical library that expects a particular precision.

Converting Only Selected Columns

You do not have to convert the entire DataFrame. Selecting the required columns before conversion can save memory and avoid unwanted text or categorical columns:

df = pd.DataFrame({
    "name": ["Ana", "Ben", "Carla"],
    "age": [23, 31, 27],
    "score": [88.5, 92.0, 79.5]
})

numeric_array = df[["age", "score"]].to_numpy()

print(numeric_array)
print(numeric_array.dtype)

The output is:

[[23.  88.5]
 [31.  92. ]
 [27.  79.5]]

Because both

Converting Pandas Dataframe To Numpy Array