Numpy Tutorial

Creating NumPy Array

NumPy Array Manipulation

Matrix in NumPy

Operations on NumPy Array

Reshaping NumPy Array

Indexing NumPy Array

Arithmetic operations on NumPy Array

Linear Algebra in NumPy Array

NumPy and Random Data

Sorting and Searching in NumPy Array

Universal Functions

Working With Images

Projects and Applications with NumPy

Get the Shape of NumPy Array

If you want to understand how to get the shape of a NumPy array, let's go through a simple tutorial.

1. Introduction:

The shape of an array is a tuple that gives you information about the number of dimensions and the size of each dimension. In NumPy, you can use the shape attribute of an array to get its shape.

2. Basic Use:

Installation:

If you haven't installed NumPy, do it with:

pip install numpy

Importing:

You need to import NumPy to start working with it:

import numpy as np

Create an Array and Get its Shape:

# Creating a 1D array
arr_1d = np.array([1, 2, 3, 4, 5])
print(arr_1d.shape)  # Output: (5,) indicating there's 1 dimension with 5 elements.

# Creating a 2D array
arr_2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
print(arr_2d.shape)  # Output: (3, 3) indicating a 2x3 matrix.

# Creating a 3D array
arr_3d = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
print(arr_3d.shape)  # Output: (2, 2, 2) indicating it has two 2x2 matrices.

3. Reshaping Arrays:

You can reshape an array to change its shape:

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

4. Dimensions:

The number of dimensions can be found using ndim:

print(arr_1d.ndim)  # Output: 1
print(arr_2d.ndim)  # Output: 2
print(arr_3d.ndim)  # Output: 3

5. Modifying Shape:

You can modify the shape of an array in-place using the shape attribute:

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

Conclusion:

The shape attribute in NumPy allows you to get and set the shape of an array. It's an essential tool when working with data in NumPy as understanding the shape of your data is crucial for many operations.

1. How to get the shape of an array in NumPy:

Description: In NumPy, the shape of an array refers to the dimensions (size along each axis) of the array.

Code:

import numpy as np

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

# Get the shape of the array
array_shape = array.shape

print("Array:")
print(array)
print("Array Shape:")
print(array_shape)

2. Python NumPy array dimensions:

Description: The dimensions of a NumPy array represent the number of axes or ranks in the array.

Code:

import numpy as np

# Create a NumPy array
array = np.array([1, 2, 3])

# Get the number of dimensions
array_dimensions = array.ndim

print("Array:")
print(array)
print("Number of Dimensions:")
print(array_dimensions)

3. Get array shape in NumPy using shape attribute:

Description: The shape attribute of a NumPy array provides a tuple representing the dimensions of the array.

Code:

import numpy as np

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

# Get the shape using the shape attribute
array_shape = array.shape

print("Array:")
print(array)
print("Array Shape:")
print(array_shape)

4. Checking array size and shape with NumPy:

Description: The size of an array in NumPy is the total number of elements in the array, while the shape represents its dimensions.

Code:

import numpy as np

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

# Get the size and shape
array_size = array.size
array_shape = array.shape

print("Array:")
print(array)
print("Array Size:")
print(array_size)
print("Array Shape:")
print(array_shape)

5. Accessing dimensions of arrays in NumPy:

Description: The dimensions of a NumPy array can be accessed using various attributes, such as shape and ndim.

Code:

import numpy as np

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

# Accessing dimensions using shape and ndim
array_shape = array.shape
array_dimensions = array.ndim

print("Array:")
print(array)
print("Array Shape:")
print(array_shape)
print("Number of Dimensions:")
print(array_dimensions)

6. NumPy array shape vs size:

Description: The shape of an array describes its dimensions, while the size represents the total number of elements in the array.

Code:

import numpy as np

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

# Get the shape and size
array_shape = array.shape
array_size = array.size

print("Array:")
print(array)
print("Array Shape:")
print(array_shape)
print("Array Size:")
print(array_size)

7. Python NumPy array shape manipulation:

Description: NumPy provides functions to manipulate the shape of an array, such as reshape and flatten.

Code:

import numpy as np

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

# Reshape the array
reshaped_array = array.reshape(3, 2)

# Flatten the array
flattened_array = array.flatten()

print("Original Array:")
print(array)
print("Reshaped Array:")
print(reshaped_array)
print("Flattened Array:")
print(flattened_array)

8. Getting the number of dimensions in a NumPy array:

Description: The ndim attribute of a NumPy array provides the number of dimensions or axes in the array.

Code:

import numpy as np

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

# Get the number of dimensions using ndim
array_dimensions = array.ndim

print("Array:")
print(array)
print("Number of Dimensions:")
print(array_dimensions)