NumPy tricks for beginners :

👉 Reshaping arrays: NumPy provides the np.reshape() function, which allows you to change the shape of an array while preserving its data. This can be useful for converting between different data formats, such as converting a one-dimensional array into a two-dimensional matrix. For example, the following code reshapes a one-dimensional array into a two-dimensional matrix with two rows and three columns:

import numpy as np

# Create a one-dimensional NumPy array
x = np.array([1, 2, 3, 4, 5, 6])

# Reshape the array into a two-dimensional matrix with 2 rows and 3 columns
x_matrix = np.reshape(x, (2, 3))

# Print the resulting matrix
print(x_matrix)

output:
[[1 2 3]
[4 5 6]]

👉Stacking arrays: NumPy provides the np.vstack() and np.hstack() functions, which allow you to stack arrays vertically or horizontally. This can be useful for combining multiple arrays into a single array, or for splitting a single array into multiple arrays. For example, the following code stacks two one-dimensional arrays vertically to create a two-dimensional matrix:

import numpy as np

# Create two one-dimensional NumPy arrays
x = np.array([1, 2, 3])
y = np.array([4, 5, 6])

# Stack the arrays vertically to create a two-dimensional matrix
z = np.vstack((x, y))

# Print the resulting matrix
print(z)

output:
[[1 2 3]
[4 5 6]]

👉Broadcasting: NumPy allows you to perform mathematical operations on arrays with different shapes, using a technique called broadcasting. This allows you to perform operations on arrays of different sizes, as long as their shapes are compatible. For example, the following code adds a scalar value to each element of a two-dimensional array:

import numpy as np

# Create a two-dimensional NumPy array
x = np.array([[1, 2, 3],
              [4, 5, 6]])

# Add a scalar value to each element of the array
y = x + 10

# Print the resulting array
print(y)

output:
[[11 12 13]
[14 15 16]]

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