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 selected slices of an array along mentioned axis in Numpy

In NumPy, you can select slices of an array along specific axes using standard slicing techniques combined with the take() function or using basic slice notation.

Let's walk through some examples:

1. Basic Setup:

Start by importing NumPy:

import numpy as np

2. Slicing using basic notation:

For a 2D array:

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

# Get the first row (axis 0)
print(arr[0, :])  # Output: [1 2 3]

# Get the first column (axis 1)
print(arr[:, 0])  # Output: [1 4 7]

3. Using the take() function:

The take() function allows you to specify indices and an axis:

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

# Get the first and third row (along axis 0)
rows = np.take(arr, indices=[0, 2], axis=0)
print(rows)
# Output:
# [[1 2 3]
#  [7 8 9]]

# Get the first and third column (along axis 1)
cols = np.take(arr, indices=[0, 2], axis=1)
print(cols)
# Output:
# [[1 3]
#  [4 6]
#  [7 9]]

4. Slicing in 3D arrays:

For a 3D array:

arr_3d = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])

# Get the first matrix (along axis 0)
print(arr_3d[0, :, :])
# Output:
# [[1 2]
#  [3 4]]

# Get the first row of each matrix (along axis 1)
print(arr_3d[:, 0, :])
# Output:
# [[1 2]
#  [5 6]]

# Get the first column of each matrix (along axis 2)
print(arr_3d[:, :, 0])
# Output:
# [[1 3]
#  [5 7]]

5. Conclusion:

Slicing arrays in NumPy is a fundamental skill that allows you to access and manipulate specific portions of your data efficiently. Both basic slice notation and the take() function offer flexible ways to get slices along the desired axes.

1. Selecting slices along a specific axis in NumPy:

Description: NumPy allows you to select specific slices along a particular axis in a multidimensional array.

Code:

import numpy as np

# Create a 2D NumPy array
array_2d = np.array([[1, 2, 3],
                     [4, 5, 6],
                     [7, 8, 9]])

# Select slices along the second axis
slices_along_axis = array_2d[:, 1]

print("Original Array:")
print(array_2d)
print("Selected Slices along Axis 1:")
print(slices_along_axis)

2. How to extract specific slices from an array in NumPy:

Description: You can extract specific slices from a NumPy array using slicing notation.

Code:

import numpy as np

# Create a 1D NumPy array
array_1d = np.array([1, 2, 3, 4, 5])

# Extract specific slices
selected_slices = array_1d[1:4]

print("Original Array:")
print(array_1d)
print("Selected Slices:")
print(selected_slices)

3. Indexing and slicing along an axis in NumPy:

Description: NumPy supports indexing and slicing along specific axes, allowing you to access and manipulate array elements.

Code:

import numpy as np

# Create a 2D NumPy array
array_2d = np.array([[1, 2, 3],
                     [4, 5, 6],
                     [7, 8, 9]])

# Indexing and slicing along the second axis
selected_elements = array_2d[:, 1:3]

print("Original Array:")
print(array_2d)
print("Selected Elements along Axis 1:")
print(selected_elements)

4. Python NumPy advanced array slicing:

Description: Advanced array slicing in NumPy allows you to extract non-contiguous elements or sections from an array.

Code:

import numpy as np

# Create a 1D NumPy array
array_1d = np.array([1, 2, 3, 4, 5])

# Advanced slicing to extract non-contiguous elements
selected_elements = array_1d[[1, 3, 4]]

print("Original Array:")
print(array_1d)
print("Selected Elements using Advanced Slicing:")
print(selected_elements)

5. Slicing along the third axis of a 3D array in NumPy:

Description: In a 3D array, you can slice along the third axis to extract specific sections.

Code:

import numpy as np

# Create a 3D NumPy array
array_3d = np.array([[[1, 2, 3],
                      [4, 5, 6]],
                     
                     [[7, 8, 9],
                      [10, 11, 12]]])

# Slicing along the third axis
selected_sections = array_3d[:, :, 1]

print("Original Array:")
print(array_3d)
print("Selected Sections along Axis 2:")
print(selected_sections)

6. Extracting specific sections from a multi-dimensional array in NumPy:

Description: You can extract specific sections from a multi-dimensional array using slicing notation.

Code:

import numpy as np

# Create a 2D NumPy array
array_2d = np.array([[1, 2, 3],
                     [4, 5, 6],
                     [7, 8, 9]])

# Extract specific sections using slicing
selected_sections = array_2d[1:3, 0:2]

print("Original Array:")
print(array_2d)
print("Selected Sections:")
print(selected_sections)

7. NumPy array slice notation along a particular axis:

Description: NumPy array slicing notation along a specific axis allows you to efficiently extract sections.

Code:

import numpy as np

# Create a 2D NumPy array
array_2d = np.array([[1, 2, 3],
                     [4, 5, 6],
                     [7, 8, 9]])

# Slice notation along the second axis
selected_slices = array_2d[:, 1:3]

print("Original Array:")
print(array_2d)
print("Selected Slices along Axis 1:")
print(selected_slices)

8. Indexing and retrieving slices along an axis in NumPy:

Description: Indexing and retrieving slices along a specific axis in NumPy allows you to access elements or sections efficiently.

Code:

import numpy as np

# Create a 2D NumPy array
array_2d = np.array([[1, 2, 3],
                     [4, 5, 6],
                     [7, 8, 9]])

# Indexing and retrieving slices along the second axis
selected_slices = array_2d[:, 1:3]

print("Original Array:")
print(array_2d)
print("Selected Slices along Axis 1:")
print(selected_slices)

9. Getting specific slices from a NumPy array along a given axis:

Description: You can get specific slices from a NumPy array along a given axis using slicing notation.

Code:

import numpy as np

# Create a 2D NumPy array
array_2d = np.array([[1, 2, 3],
                     [4, 5, 6],
                     [7, 8, 9]])

# Get specific slices along the first axis
selected_slices = array_2d[1:3, :]

print("Original Array:")
print(array_2d)
print("Selected Slices along Axis 0:")
print(selected_slices)
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