Numpy - Reshaping and Flattening

Lecture 10:- Numpy - Reshaping and Flattening

In NumPy, you can extract specific elements from an array using various indexing techniques. You can access individual elements, select specific rows or columns, and filter elements based on certain conditions. Here are some common methods to extract specific elements from a NumPy array:

Accessing Individual Elements: You can access specific elements in a NumPy array using integer indexing. The indexing starts from 0, similar to Python lists.

Selecting Specific Rows and Columns: For 2D arrays, you can use integer indexing to access specific rows and columns.

Filtering Elements with Conditions: You can use boolean indexing to filter elements in the array based on specific conditions.

Indexing with Boolean Arrays: You can use boolean arrays to extract elements based on certain conditions or logical operations.

These are some of the common ways to extract specific elements from a NumPy array. NumPy offers powerful indexing capabilities, allowing you to efficiently access and manipulate data in arrays.

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import numpy as np # Create a 1D array arr = np.array([10, 20, 30, 40, 50]) # Create a boolean array for indexing bool_array = np.array([True, False, True, False, True]) # Use the boolean array for indexing selected_elements = arr[bool_array]  # Result: [10, 30, 50]

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import numpy as np # Create a 1D array arr = np.array([10, 20, 30, 40, 50]) # Filter elements greater than 30 filtered_elements = arr[arr > 30]  # Result: [40, 50]

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import numpy as np # Create a 2D array arr_2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) # Accessing specific row row_1 = arr_2d[1]  # Access the second row, which is [4, 5, 6] # Accessing specific column col_2 = arr_2d[:, 1]  # Access the second column, which is [2, 5, 8]

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import numpy as np # Create a 1D array arr = np.array([10, 20, 30, 40, 50]) # Accessing individual elements element_1 = arr[0]  # Access the first element, which is 10 element_3 = arr[2]  # Access the third element, which is 30