NumPy Review

We might use numpy occasionally in this course. Here is a quick review of some common numpy functions and operations.

import numpy as np

# Creating an array and checking its shape
arr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
print(arr)

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

# Shape of the array:
print(arr.shape)

(3, 3)

# Zeros Array:
zeros = np.zeros((2, 3))
print(zeros)

[[0. 0. 0.]
 [0. 0. 0.]]

# Ones Array:
ones = np.ones((3, 2))
print(ones)

[[1. 1.]
 [1. 1.]
 [1. 1.]]

# Identity Matrix:
identity = np.eye(3)
print(identity)

[[1. 0. 0.]
 [0. 1. 0.]
 [0. 0. 1.]]

# Random Integer Array (0-9):
randint_arr = np.random.randint(0, 10, size=(2, 3))
print(randint_arr)

[[5 6 6]
 [6 6 2]]

# Random Float Array (0-1):
rand_arr = np.random.rand(2, 3)
print(rand_arr)

[[0.09298605 0.79698855 0.49523985]
 [0.70020571 0.73783847 0.27804818]]

# Indexing and Slicing
arr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])

# Element at row 0, column 1:
print(arr[0, 1])

2

# First row:
print(arr[0, :])

[1 2 3]

# First column:
print(arr[:, 0])

[1 4 7]

# Element-wise Operations
arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])

print(arr1 + arr2)
print(arr1 - arr2)
print(arr1 * arr2)
print(arr1 / arr2)

[5 7 9]
[-3 -3 -3]
[ 4 10 18]
[0.25 0.4  0.5 ]

# operations between array and scalar
scalar = 5
print(arr + scalar)

[[ 6  7  8]
 [ 9 10 11]
 [12 13 14]]

# Common Functions
# Mean:
print(np.mean(arr))
print(np.sum(arr))
print(np.max(arr))

5.0
45
9

# You can also specify the axis along which to perform the operation.
# Mean along rows (axis=1):
print(np.mean(arr, axis=1))

[2. 5. 8.]

# Mean along columns (axis=0):
print(np.mean(arr, axis=0))

[4. 5. 6.]

# Reshaping Arrays
reshaped_arr = arr.reshape(1, 9)
print(reshaped_arr)

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

# Boolean Indexing
# Array > 5 gives:
print(arr > 5)

[[False False False]
 [False False  True]
 [ True  True  True]]

# collect all elements greater than 5
print(arr[arr > 5])

[6 7 8 9]

# Sum of True values (treating True as 1, False as 0):
print(np.sum(arr > 5))

4