Divide Numpy array by Scalar where Array-Element is below a certain value
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I have a numpy array like
[[1, 2, 3], [4, 5, 6], [7, 8, 9]]
Now I want divide every Element which is less than 5 by 2. The Result should be
[[0.5, 1, 1.5], [2, 5, 6], [7, 8, 9]]
How can I do that?
np.where(arr<5, arr/2, arr)
array([[0.5, 1. , 1.5], [2. , 5. , 6. ], [7. , 8. , 9. ]])
numpy.divide — NumPy v1.19 Manual, Returns a true division of the inputs, element-wise. Instead of the Elsewhere, the out array will retain its original value. outndarray or scalar. NumPy Array Object Exercises, Practice and Solution: Write a NumPy program to divide each row by a vector element.
You can do this by logical indexing:
>>> import numpy as np >>> x = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype = float) >>> x[x<5] = x[x<5]/2 >>> x array([[0.5, 1. , 1.5], [2. , 5. , 6. ], [7. , 8. , 9. ]])
numpy.divide() in Python, numpy.divide(arr1, arr2, out = None, where = True, casting Array element from first array is divided by elements from second where : [array_like, optional]True value means to calculate the anything incorrect by clicking on the "Improve Article" button below. @geeksforgeeks, Some rights reserved. Iterating over list of tuples. As is typical, you can do this a number of ways. This function returns an array of unique elements in the input array. So I have created a NumPy array: import numpy as np. If I have two arrays as shown below: a = numpy. When an array is no longer needed in the program, it can be … Online Read. array([[1,2,3.
Here's what you could do
x[x<5] = x[x<5]/2
numpy.true_divide() in Python, Array element from first array is divided by the elements from second that position, False value means to leave the value in the output alone. Return : If inputs are scalar then scalar; otherwise array with arr1 / arr2(element- wise) i.e. find anything incorrect by clicking on the "Improve Article" button below. Equivalent to x1 / x2 in terms of array-broadcasting. Behavior on division by zero can be changed using seterr. In Python 2, when both x1 and x2 are of an integer type, divide will behave like floor_divide. In Python 3, it behaves like true_divide. Examples
For example, if your array is named
numpy.where(np_array >=5, np_array, np_array/2)
See the documentation here
numpy.divide — NumPy v1.13 Manual, This is documentation for an old release of NumPy (version 1.13.0). Divide arguments element-wise. If not provided or None, a freshly-allocated array is returned. A tuple Values of True indicate to calculate the ufunc at that position, values of False indicate to leave the value in the output alone. y : ndarray or scalar. Fill the array with a scalar value. flatten ([order]) Return a copy of the array collapsed into one dimension. getfield (dtype[, offset]) Returns a field of the given array as a certain type. item (*args) Copy an element of an array to a standard Python scalar and return it. itemset (*args) Insert scalar into an array (scalar is cast to array
Python Numpy – Divide all the elements of array by a constant, Pass array and constant as operands to the division operator as shown below. b = a / c. where a is input array and c is a constant. b is the� RuntimeWarning: invalid value encountered in true_divide On the other hand, 1 / my_array. If 1 divided by 0, you will also get a warning: [ inf 1. 0.5 0.33333333 0.25 0.2 0.16666667 0.14285714 0.125 0.11111111 0.1 ] RuntimeWarning: divide by zero encountered in true_divide In this case, it will show infinity instead of nan.
NumPy: Divide each row by a vector element, NumPy Array Object Exercises, Practice and Solution: Write a NumPy program to divide each row by a vector element. Python: Use Get() to Retrieve a Value in a Dictionary >>> number_dict This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. (An array scalar is an instance of the types/classes float32, float64, etc., whereas a 0-dimensional array is an ndarray instance containing precisely one array scalar.) If axis is an integer, then the operation is done over the given axis (for each 1-D subarray that can be created along the given axis).
NumPy Cheat Sheet — Python for Data Science – Dataquest, Includes importing, exporting, filtering, sorting, scalar and vector maths and more. np.arange(0,10,3) | Array of values from 0 to less than 10 with step 3 (eg [0,3,6, 9] ) np.full((2 arr.sort(axis=0) | Sorts specific axis of arr np.divide(arr,4) | Divide each array element by 4 (returns np.nan for division by zero) (An array scalar is an instance of the types/classes float32, float64, etc., whereas a 0-dimensional array is an ndarray instance containing precisely one array scalar.) If axis is an integer, then the operation is done over the given axis (for each 1-D subarray that can be created along the given axis).