Converting to and from Python lists in NumPy

Introduction

We convert between NumPy arrays and Python lists to use the best features of both. Lists are easy to use and flexible, while arrays are fast for math.

When you want to use Python functions that only accept lists.

When you want to prepare data for visualization libraries that need lists.

When you want to convert a list of data into a NumPy array for fast calculations.

When you want to save or share data in a simple Python list format.

When you want to switch between flexible lists and fast arrays during analysis.

Syntax

NumPy

import numpy as np

# Convert Python list to NumPy array
python_list = [1, 2, 3]
numpy_array = np.array(python_list)

# Convert NumPy array to Python list
converted_list = numpy_array.tolist()

Use np.array() to convert a Python list to a NumPy array.

Use array.tolist() to convert a NumPy array back to a Python list.

Examples

This shows converting an empty list to an array and an array with one element back to a list.

NumPy

import numpy as np

# Empty list to array
empty_list = []
empty_array = np.array(empty_list)
print(empty_array)

# Array to list when array has one element
single_element_array = np.array([42])
single_element_list = single_element_array.tolist()
print(single_element_list)

This example converts a nested list (2D) to a NumPy 2D array and back to a nested list.

NumPy

import numpy as np

# List with nested lists to 2D array
nested_list = [[1, 2], [3, 4]]
array_2d = np.array(nested_list)
print(array_2d)

# Convert 2D array back to nested list
list_2d = array_2d.tolist()
print(list_2d)

Shows that NumPy arrays convert mixed types to a common type (usually strings) and converting back to list keeps that type.

NumPy

import numpy as np

# List with mixed types
mixed_list = [1, 2.5, '3']
mixed_array = np.array(mixed_list)
print(mixed_array)

# Convert back to list
back_to_list = mixed_array.tolist()
print(back_to_list)

Sample Program

This program shows how to convert a Python list to a NumPy array, do a math operation, and convert back to a list.

NumPy

import numpy as np

# Create a Python list
original_list = [10, 20, 30, 40]
print('Original Python list:', original_list)

# Convert list to NumPy array
array_from_list = np.array(original_list)
print('Converted to NumPy array:', array_from_list)

# Perform a simple operation on the array
array_from_list = array_from_list * 2
print('Array after multiplication by 2:', array_from_list)

# Convert the array back to a Python list
list_from_array = array_from_list.tolist()
print('Converted back to Python list:', list_from_array)

Important Notes

Converting a list to a NumPy array is fast and allows vectorized math operations.

Converting back to a list is useful when you need Python-native data structures.

Be careful with mixed data types in lists; NumPy will convert them to a common type, often strings.

Time complexity for conversion is O(n), where n is the number of elements.

Space complexity is O(n) because a new object is created during conversion.

Use arrays when doing math, lists when you need flexibility or compatibility with Python functions.

Summary

Use np.array() to convert Python lists to NumPy arrays for fast math.

Use array.tolist() to convert NumPy arrays back to Python lists for flexibility.

Conversions are simple but watch out for data types and empty or nested lists.