NumPy Copy and View of ArrayWhile working with NumPy, you may notice that some operations return a copy, while others return a view. A copy creates a new, independent array with its own memory, while a view shares the same memory as the original array. As a result, changes made to a view also affect the original and vice versa
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How to swap columns of a given NumPy array?Swapping columns of a NumPy array means exchanging the positions of two specified columns across all rows. For example, if you have a 3x3 array with values like [[0, 1, 2], [3, 4, 5], [6, 7, 9]] and you swap column 0 with column 2, the array becomes [[2, 1, 0], [5, 4, 3], [9, 7, 6]]. Let’s explore d
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Joining NumPy ArrayJoining NumPy arrays means combining multiple arrays into one larger array. For example, joining two arrays [1, 2] and [3, 4] results in a combined array [1, 2, 3, 4]. Let’s explore some common ways to join arrays using NumPy.1. Using numpy.concatenate()numpy.concatenate() joins two or more arrays a
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Combining a one and a two-dimensional NumPy ArraySometimes we need to combine 1-D and 2-D arrays and display their elements. Numpy has a function named as numpy.nditer(), which provides this facility. Syntax: numpy.nditer(op, flags=None, op_flags=None, op_dtypes=None, order='K', casting='safe', op_axes=None, itershape=None, buffersize=0) Example 1
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Numpy dstack() method-Pythonnumpy.dstack() stacks arrays depth-wise along the third axis (axis=2). For 1D arrays, it promotes them to (1, N, 1) before stacking. For 2D arrays, it stacks them along axis=2 to form a 3D array. Example: Pythonimport numpy as np a = np.array([1, 2, 3]) b = np.array([4, 5, 6]) res = np.dstack((a, b)
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Find the union of two NumPy arraysFinding the union of two NumPy arrays means combining both arrays into one while removing any duplicate values. For example, if you have two arrays [10, 20, 30] and [20, 30, 40], the union would be [10, 20, 30, 40]. Let's explore different ways to do this efficiently. Using np.union1d()If you want t
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Find unique rows in a NumPy arrayFinding unique rows means removing duplicate rows from a 2D array so that each row appears only once. For example, given [[1, 2], [3, 4], [1, 2], [5, 6]], the unique rows would be [[1, 2], [3, 4], [5, 6]]. Let’s explore efficient ways to achieve this using NumPy.Using np.unique(axis=0)np.unique(axis
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Numpy np.unique() method-Pythonnumpy.unique() finds the unique elements of an array. It is often used in data analysis to eliminate duplicate values and return only the distinct values in sorted order. Example:Pythonimport numpy as np a = np.array([1, 2, 2, 3, 4, 4, 4]) res = np.unique(a) print(res)Output[1 2 3 4] Explanation: nu
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numpy.trim_zeros() in Pythonnumpy.trim_zeros() removes the leading and trailing zeros from a 1-D array. It is often used to clean up data by trimming unnecessary zeros from the beginning or end of the array. Example:Pythonimport numpy as np a = np.array([0, 0, 3, 4, 0, 5, 0, 0]) res = np.trim_zeros(a) print(res)Output[3 4 0 5]
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