Convert Python List to NumPy Arrays

What is a NumPy Array?

A NumPy array (numpy.ndarray) is a data structure that stores elements of the same data type in a contiguous memory block. Unlike Python lists, NumPy arrays enable fast mathematical operations, less memory usage, and efficient computation.

Advantages of Using NumPy Arrays Over Python Lists

Feature	NumPy Array (`ndarray`)	Python List
Performance	Fast operations (vectorized)	Slow (loops required)
Memory Efficiency	Uses less memory	Uses more memory
Data Type	Homogeneous (same type)	Heterogeneous (mixed types)
Mathematical Operations	Supported directly	Requires loops or `map()`
Convenience	Rich set of built-in functions	Limited operations

Step 1: Import NumPy

Before using NumPy, install it using:

pip install numpy

Now, import it in Python:

import numpy as np

Step 2: Convert a Python List to a NumPy Array

The numpy.array() function is used to convert a Python list into a NumPy array.

Example 1: Converting a Simple List

import numpy as np

# Define a Python list
my_list = [1, 2, 3, 4, 5]

# Convert list to NumPy array
np_array = np.array(my_list)

print(np_array)
print(type(np_array))  # Checking the type

Output:

[1 2 3 4 5]
<class 'numpy.ndarray'>

Here, the Python list [1, 2, 3, 4, 5] is successfully converted into a NumPy array.

Step 3: Converting a Nested List (2D List)

A nested list (list of lists) can be converted into a 2D NumPy array.

nested_list = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
np_2d_array = np.array(nested_list)

print(np_2d_array)

Output:

[[1 2 3]
 [4 5 6]
 [7 8 9]]

Here, the Python list is converted into a matrix-like NumPy array.

Step 4: Specifying Data Type (`dtype`)

NumPy automatically detects data types, but you can explicitly specify a data type using the dtype parameter.

Example 1: Default Data Type

arr = np.array([1, 2, 3])
print(arr.dtype)  # NumPy automatically chooses int type

Output:

int64  # (or int32 on some systems)

Example 2: Converting List Elements to Float

arr_float = np.array([1, 2, 3], dtype=float)
print(arr_float)
print(arr_float.dtype)

Output:

[1. 2. 3.]
float64

All elements are converted to floats.

Example 3: Converting List Elements to Strings

arr_str = np.array([1, 2, 3], dtype=str)
print(arr_str)
print(arr_str.dtype)

Output:

['1' '2' '3']
<U1  # Unicode string type

All elements are converted to strings.

Step 5: Converting Different Types of Lists

Example 1: List with Mixed Data Types

mixed_list = [1, 2.5, 3, 4.8]
np_array = np.array(mixed_list)

print(np_array)
print(np_array.dtype)

Output:

[1.  2.5 3.  4.8]
float64

Since the list contains both integers and floats, NumPy converts all elements to float64 (upcasting).

Example 2: Converting a List of Strings to Integers

str_list = ['1', '2', '3']
np_array = np.array(str_list, dtype=int)  # Convert to integers
print(np_array)

Output:

[1 2 3]

The string elements are converted to integers.

Step 6: Memory and Performance Comparison

NumPy is faster and more memory-efficient than Python lists.

Memory Usage Comparison

import sys

py_list = list(range(1000))
np_array = np.array(py_list)

print("Python List Size:", sys.getsizeof(py_list))
print("NumPy Array Size:", sys.getsizeof(np_array))

Output (approximate values):

Python List Size: 8056 bytes
NumPy Array Size: 4096 bytes

NumPy arrays take less memory than Python lists.

Performance Comparison

import time

# Python List Multiplication
py_list = list(range(1000000))
start = time.time()
py_list = [x * 2 for x in py_list]
end = time.time()
print("Python List Time:", end - start)

# NumPy Array Multiplication
np_array = np.array(range(1000000))
start = time.time()
np_array = np_array * 2  # Vectorized operation
end = time.time()
print("NumPy Array Time:", end - start)

Output (approximate):

Python List Time: 0.25 sec
NumPy Array Time: 0.005 sec

NumPy operations are 50x faster due to vectorized computation.

Step 7: Converting a NumPy Array Back to a Python List

To convert a NumPy array back to a list, use .tolist().

np_array = np.array([1, 2, 3, 4, 5])
py_list = np_array.tolist()
print(py_list)

Output:

[1, 2, 3, 4, 5]

Final Summary

Conversion	Code Example
List → NumPy Array	`np.array([1, 2, 3])`
Nested List → 2D NumPy Array	`np.array([[1, 2], [3, 4]])`
Specify Data Type	`np.array([1, 2, 3], dtype=float)`
Convert Back to List	`np_array.tolist()`

Conclusion

NumPy arrays are faster and more memory-efficient than Python lists.
Use numpy.array() to convert a list to a NumPy array.
Specify dtype for better control over data types.
NumPy supports vectorized operations, making calculations much faster.