Numpy Replace All Values With Another

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If we need to replace all the greater values than a certain threshold in a NumPy array we can use the numpy clip function We can specify the upper and the lower limits of an array using the numpy clip function The numpy clip function returns an array where the elements less than the specified limit are replaced with the lowest limit . .

Use the numpy where function to replace all instances of the specified value with another value new arr np where arr 2 0 arr 4 The resulting new arr is a Numpy array with all occurrences of the value 2 replaced by the value 0 Understanding the numpy where Function Here is some code to do that: M=30 N=40 X = np.zeros ( (M,N)) # random values, but 0s work too Y = np.where (np.random.rand (M,N) > .5, True, False) A=np.array ( [ 7, 8, 10, 13]), # in my setting, it's (1,4), not (4,) for i in A [0]: X [i] [Y [A] [i]==True]=-1 However, what I actually want is only replace some of the entries.

Numpy Replace All Values With AnotherReplaces specified elements of an array with given values. The indexing works on the flattened target array. put is roughly equivalent to: a.flat[ind] = v Parameters: andarray Target array. indarray_like Target indices, interpreted as integers. varray_like Values to place in a at target indices. 8 Answers Sorted by 460 I think both the fastest and most concise way to do this is to use NumPy s built in Fancy indexing If you have an ndarray named arr you can replace all elements 255 with a value x as follows arr arr 255 x