Introduction
In this chapter, we will explore how to create arrays using NumPy. Arrays are the central data structure in NumPy, and understanding how to create and manipulate them is essential for effective numerical computing.
Importing NumPy
Before you start creating arrays, you need to import the NumPy library. It is common practice to import NumPy with the alias np:
import numpy as np
Creating Arrays from Lists
You can create a NumPy array from a Python list using the np.array function.
Example: Creating a 1D Array
import numpy as np
# Creating a 1D array from a list
arr = np.array([1, 2, 3, 4, 5])
print(arr)
Output:
[1 2 3 4 5]
Example: Creating a 2D Array
import numpy as np
# Creating a 2D array from a list of lists
arr = np.array([[1, 2, 3], [4, 5, 6]])
print(arr)
Output:
[[1 2 3]
[4 5 6]]
Creating Arrays with Initial Values
NumPy provides several functions to create arrays with initial placeholder values.
Example: Creating an Array of Zeros
import numpy as np
# Creating an array of zeros
zeros = np.zeros((3, 3))
print(zeros)
Output:
[[0. 0. 0.]
[0. 0. 0.]
[0. 0. 0.]]
Example: Creating an Array of Ones
import numpy as np
# Creating an array of ones
ones = np.ones((2, 4))
print(ones)
Output:
[[1. 1. 1. 1.]
[1. 1. 1. 1.]]
Example: Creating an Array with a Constant Value
import numpy as np
# Creating an array with a constant value
full = np.full((3, 3), 7)
print(full)
Output:
[[7 7 7]
[7 7 7]
[7 7 7]]
Creating Arrays with a Range of Values
NumPy provides functions to create arrays with a range of values.
Example: Creating an Array with arange
import numpy as np
# Creating an array with a range of values
arr = np.arange(0, 10, 2) # Start at 0, end before 10, step by 2
print(arr)
Output:
[0 2 4 6 8]
Example: Creating an Array with linspace
import numpy as np
# Creating an array with linearly spaced values
arr = np.linspace(0, 1, 5) # 5 values from 0 to 1
print(arr)
Output:
[0. 0.25 0.5 0.75 1. ]
Creating Arrays with Random Values
NumPy provides functions to create arrays with random values.
Example: Creating an Array with Random Values
import numpy as np
# Creating an array with random values
random_arr = np.random.rand(3, 3)
print(random_arr)
Output:
[[0.5488135 0.71518937 0.60276338]
[0.54488318 0.4236548 0.64589411]
[0.43758721 0.891773 0.96366276]]
Example: Creating an Array with Random Integers
import numpy as np
# Creating an array with random integers
random_ints = np.random.randint(0, 10, (3, 3)) # Random integers from 0 to 9
print(random_ints)
Output:
[[5 0 3]
[3 7 9]
[3 5 2]]
Creating Identity Matrices
An identity matrix is a square matrix with ones on the diagonal and zeros elsewhere.
Example: Creating an Identity Matrix
import numpy as np
# Creating an identity matrix
identity = np.eye(4)
print(identity)
Output:
[[1. 0. 0. 0.]
[0. 1. 0. 0.]
[0. 0. 1. 0.]
[0. 0. 0. 1.]]
Reshaping Arrays
You can change the shape of an array using the reshape function.
Example: Reshaping a 1D Array to a 2D Array
import numpy as np
arr = np.array([1, 2, 3, 4, 5, 6])
# Reshaping to a 2D array
reshaped_arr = arr.reshape((2, 3))
print(reshaped_arr)
Output:
[[1 2 3]
[4 5 6]]
Conclusion
Creating arrays is a fundamental skill in NumPy, as arrays are the primary data structure used for numerical computations. This chapter covered various methods to create arrays, including from lists, with initial values, with ranges of values, with random values, and reshaping arrays.