Understanding NumPy Array Shape in Python

Understanding the shape of a NumPy array in Python is crucial for effectively managing and manipulating data. The shape of a NumPy array gives you information about the array’s structure, including its dimensions and the size along each dimension.

What is the Shape of a NumPy Array?

The shape of a NumPy array is a tuple that indicates the size of the array along each dimension. For example, a 2D array representing a matrix with 3 rows and 4 columns will have a shape of (3, 4).

Accessing the Shape

You can access the shape of a NumPy array using the .shape attribute.

Example:

Here’s a simple example to illustrate:

import numpy as np

# Creating a 2D array (3x4)
array = np.array([[1, 2, 3, 4],
                  [5, 6, 7, 8],
                  [9, 10, 11, 12]])

# Getting the shape of the array
shape = array.shape

print(shape)  # Output: (3, 4)

Understanding Different Shapes:

1D Array: A one-dimensional array, similar to a list.

arr = np.array([1, 2, 3])
print(arr.shape)  # Output: (3,)

arr = np.array([[1, 2, 3], [4, 5, 6]])
print(arr.shape)  # Output: (2, 3)

arr = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
print(arr.shape)  # Output: (2, 2, 2)

arr = np.array([1, 2, 3, 4, 5, 6])
reshaped_arr = arr.reshape(2, 3)
print(reshaped_arr)
# Output:
# [[1 2 3]
#  [4 5 6]]