How to Create NumPy Arrays from Lists

Speed: Operations are 10-100x faster
Memory efficiency: Contiguous memory layout
Broadcasting: Operate on entire arrays at once
Rich functionality: Built-in mathematical operations

#python #programming

From Lists to Numerical Powerhouse

Python lists are great, but when you need serious number crunching, NumPy arrays are your secret weapon. The conversion is simple, but knowing the nuances makes all the difference.

Array Creation Magic

import numpy as np

# Basic conversion
python_list = [1, 2, 3, 4, 5]
array = np.array(python_list)
print(array)  # [1 2 3 4 5]

# 2D arrays from nested lists
matrix_list = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
matrix = np.array(matrix_list)
print(matrix)
# [[1 2 3]
#  [4 5 6]
#  [7 8 9]]

# Specify data type explicitly
float_array = np.array([1, 2, 3], dtype=np.float32)
print(float_array)  # [1. 2. 3.]

# From list of tuples
coordinates = [(1, 2), (3, 4), (5, 6)]
coord_array = np.array(coordinates)
print(coord_array.shape)  # (3, 2)

# Mixed types get promoted
mixed = [1, 2.5, 3]
mixed_array = np.array(mixed)
print(mixed_array.dtype)  # float64

# Creating arrays of objects
objects = ['hello', 42, [1, 2, 3]]
obj_array = np.array(objects, dtype=object)