numpy.in1d() function in Python Last Updated : 17 May, 2020 Comments Improve Suggest changes Like Article Like Report numpy.in1d() function test whether each element of a 1-D array is also present in a second array and return a boolean array the same length as arr1 that is True where an element of arr1 is in arr2 and False otherwise. Syntax : numpy.in1d(arr1, arr2, assume_unique = False, invert = False) Parameters : arr1 : [array_like] Input array. arr2 : [array_like] The values against which to test each value of arr1. assume_unique : [bool, optional] If True, the input arrays are both assumed to be unique, which can speed up the calculation. Default is False. invert : [bool, optional] If True, the values in the returned array are inverted. Default is False. Return : [ndarray, bool] The values arr1[in1d] are in arr2. Code #1 : Python3 # Python program explaining # numpy.in1d() function # importing numpy as geek import numpy as geek arr1 = geek.array([0, 1, 2, 3, 0, 4, 5]) arr2 = [0, 2, 5] gfg = geek.in1d(arr1, arr2) print (gfg) Output : [ True False True False True False True] Code #2 : Python3 # Python program explaining # numpy.in1d() function # importing numpy as geek import numpy as geek arr1 = geek.array([0, 1, 2, 3, 0, 4, 5]) arr2 = [0, 2, 5] gfg = geek.in1d(arr1, arr2, invert = True) print (gfg) Output : [False True False True False True False] Comment More infoAdvertise with us Next Article numpy.in1d() function in Python S sanjoy_62 Follow Improve Article Tags : Python Python-numpy Python numpy-arrayManipulation Practice Tags : python Similar Reads numpy.iinfo() function – Python numpy.iinfo() function shows machine limits for integer types. Syntax : numpy.iinfo(dtype) Parameters : dtype : [integer type, dtype, or instance] The kind of integer data type to get information about. Return : Machine limits for integer types. Code #1 : Python3 # Python program explaining # numpy. 1 min read numpy.intersect1d() function in Python numpy.intersect1d() function find the intersection of two arrays and return the sorted, unique values that are in both of the input arrays. Syntax: numpy.intersect1d(arr1, arr2, assume_unique = False, return_indices = False) Parameters : arr1, arr2 : [array_like] Input arrays. assume_unique : [bool] 2 min read numpy.i0() function | Python numpy.i0() function is the modified Bessel function of the first kind, order 0. it's usually denoted by I0. Syntax : numpy.i0(x) Parameters : x : [array_like, dtype float or complex] Argument of the Bessel function. Return : [ndarray, shape = x.shape, dtype = x.dtype] The modified Bessel function ev 1 min read numpy.interp() function - Python numpy.interp() function returns the one-dimensional piecewise linear interpolant to a function with given discrete data points (xp, fp), evaluated at x. Syntax : numpy.interp(x, xp, fp, left = None, right = None, period = None) Parameters : x : [array_like] The x-coordinates at which to evaluate the 2 min read numpy.mask_indices() function | Python numpy.mask_indices() function return the indices to access (n, n) arrays, given a masking function. Syntax : numpy.mask_indices(n, mask_func, k = 0) Parameters : n : [int] The returned indices will be valid to access arrays of shape (n, n). mask_func : [callable] A function whose call signature is s 1 min read Like