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Basic Numpy Array Operations.
Basic Numpy Array Operations. Table Of Contents: addition, subtraction, multiplication, division sum( ) (1) Numpy Addition You can use ‘+’ operator for addition operation. Example: data = np.array([1, 2]) data array([1, 2]) ones = np.ones(2, dtype=int) ones array([1, 1]) data + ones Output: array([2, 3]) (2) Numpy Substraction You can use ‘-‘ operator for subtraction operation. Example: data = np.array([1, 2]) data array([1, 2]) ones = np.ones(2, dtype=int) ones array([1, 1]) data – ones Output: array([0, 1]) (3) Numpy Multiplication You can use ‘*’ operator for multiplication operation. Example: data = np.array([1, 2]) data array([1, 2]) ones = np.ones(2, dtype=int) ones
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How To Create An Array From Existing Data ?
How To Create An Array From Existing Data ? Table Of Contents: Slicing and Indexing np.vstack( ) np.hstack( ) np.hsplit( ) .view( ) copy( ) (1) Slicing and Indexing You can easily create a new array from a section of an existing array. You need to mention the start and end index position of the array. Example: import numpy as np a = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]) arr1 = a[3:8] arr1 Output: array([4, 5, 6, 7, 8]) Note: Here I have created a new array from an existing array by mentioning the start and
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Indexing And Slicing In Numpy Array.
Indexing And Slicing In Numpy Array. Table Of Contents: Numpy Indexing Numpy Slicing (1) Numpy Indexing Syntax: array[start:end:steps] Parameters: Start: Starting position of the element End: Ending Position Of The Element Steps: Number Of Steps To Jump While Travelling. Example-1: data = np.array([1, 2, 3]) data array([1, 2, 3]) data[0] 1 Note: It will select the value at index position ‘0’ which is ‘1’. Example-2: data[0:2] array([1, 2]) Note: It will select the value from ‘0’ to (end_index -1) = (2 – 1) = 1. Example-3: data[1:] array([2, 3]) Note: Here start index is ‘1’ and we did not mention
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How To Convert A 1D Array Into A 2D Array?
How To Convert A 1D Array Into A 2D Array? Table Of Contents: np.newaxis np.expand_dims (1) np.newaxis Using np.newaxis will increase the dimensions of your array by one dimension when used once. This means that a 1D array will become a 2D array, a 2D array will become a 3D array, and so on. You can explicitly convert a 1D array with either a row vector or a column vector using np.newaxis. For example, you can convert a 1D array to a row vector by inserting an axis along the first dimension. Example-1: a = np.array([1, 2, 3, 4, 5, 6]) a array([1, 2, 3, 4, 5, 6]) a.shape (6,) Adding
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How To Reshape A Numpy Array ?
How To Reshape A Numpy Array ? Table Of Contents: numpy.reshape( ) Examples Of numpy.reshape( ) (1) numpy.reshape( ) Syntax: numpy.reshape(a, newshape, order=’C’) Parameters: a: array_like – Array to be reshaped. newshape: int or tuple of ints – The new shape should be compatible with the original shape. If an integer, then the result will be a 1-D array of that length. One shape dimension can be -1. In this case, the value is inferred from the length of the array and the remaining dimensions. order: {‘C’, ‘F’, ‘A’}, optional – Read the elements of a using this index order, and place
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How Do You Know The Shape and Size Of a Numpy Array ?
How Do You Know The Shape and Size Of a Numpy Array ? Table Of Contents: ndarray.ndim ndarray.size ndarray.shape (1) ndarray.ndim ndarray.ndim will tell you the number of axes, or dimensions, of the array. Example-1: a = np.array([1, 2, 3]) a array([1, 2, 3]) a.ndim Output: 1 Example-2: b = np.array([[1,4],[3,2]]) b array([[1, 4], [3, 2]]) b.ndim Output: 2 Example-3: c = np.array([[[1,4],[3,2]]]) c array([[[1, 4], [3, 2]]]) c.ndim Output: 3 Example-4: y = np.zeros((2, 3, 4)) y array([[[0., 0., 0., 0.], [0., 0., 0., 0.], [0., 0., 0., 0.]], [[0., 0., 0., 0.], [0., 0., 0., 0.], [0., 0., 0.,
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How To Remove Elements From Numpy Array?
How To Remove Elements From Numpy Array? Table Of Contents: np.delete( ) Examples Of np.delete() Method. (1) np.delete( ) A copy of arr with the elements specified by obj removed. Note that delete does not occur in-place. If axis is None, out is a flattened array. Syntax: numpy.delete(arr, obj, axis=None) Parameters: arr: array_like – Input array. obj: slice, int or array of ints – Indicate indices of sub-arrays to remove along the specified axis. axis: int, optional – The axis along which to delete the subarray defined by obj. If axis is None, obj is applied to the flattened array. Returns: out: ndarray – A copy of arr with the elements specified by obj removed. Note that delete does not occur
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How To Sort A Numpy Array ?
How To Sort A Numpy Array? Table Of Contents: np.sort( ) Examples Of Sorting Numpy Array. (1) np.sort( ) Return a sorted copy of an array. Syntax: numpy.sort(a, axis=-1, kind=None, order=None) Parameters: a: array_like – Array to be sorted. axis: int or None, optional – Axis along which to sort. If None, the array is flattened before sorting. The default is -1, which sorts along the last axis. kind: {‘quicksort’, ‘mergesort’, ‘heapsort’, ‘stable’}, optional – Sorting algorithm. The default is ‘quicksort’. order: str or list of str, optional – When a is an array with fields defined, this argument specifies which fields to
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How To Add Numpy Arrays ?
How To Add Numpy Arrays ? Table Of Contents: ‘+’ Operator np.concatenate() (1) ‘+’ Operator The ‘+’ operator will ‘sum’ the elements of numpy array. Example-1 a = np.array([1,2,3,4,5]) b = np.array([6,7,8,9,10]) a + b Output: array([ 7, 9, 11, 13, 15]) Example-2 a = np.array([1,2,3,4,5]) b = np.array([6,7,8,9,10]) c = np.array([11,12,13,14,15]) a + b + c Output: array([18, 21, 24, 27, 30]) (2) np.concatenate() Join a sequence of arrays along an existing axis. Syntax: numpy.concatenate((a1, a2, …), axis=0, out=None, dtype=None, casting="same_kind") Parameters: a1, a2, …sequence of array_like – The arrays must have the same shape, except in the dimension
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How To Create Numpy Array ?
How To Create Numpy Array ? Table Of Contents: np.array( ) np.zeros( ) np.ones( ) np.empty( ) np.arange( ) np.linspace( ) (1) np.array( ) To create a NumPy array, you can use the function np.array(). All you need to do to create a simple array is, pass a list to it. you can also specify the type of data in your list. Example-1 import numpy as np lst = [1,2,3,4,5,6] a = np.array(lst) a Output: array([1, 2, 3, 4, 5, 6]) (2) np.zeros( ) You can also create an array which will contain only zeros. np.zeros( ) will take an integer
