The result is a tuple of arrays (one for each axis) containing the indices where value 19 exists in the array. Let’s create a 2D numpy array. When we use Numpy argmax, the function identifies the maximum value in the array. When True, yield x, otherwise yield y.. x, y: array_like, optional. Returns the indices of the maximum values along an axis. pos = np.where(elem == c) numpy.insert - This function inserts values in the input array along the given axis and before the given index. If you want to find the index of the value in Python numpy array, then numpy.where(). If the type of values is converted to be inserted, it is differ Indexing can be done in numpy by using an array as an index. By default, the index is into the flattened array, otherwise along the specified axis. Like in our case, it’s a two-dimension array, so numpy.where() will return the tuple of two arrays. Save my name, email, and website in this browser for the next time I comment. numpy.argmax ¶ numpy.argmax(a, ... Indices of the maximum values along an axis. numpy.where() accepts a condition and 2 optional arrays i.e. You can access an array element by referring to its index number. NumPy in python is a general-purpose array-processing package. start, end : [int, optional] Range to search in. The length of both the arrays will be the same. We covered how it is used with its syntax and values returned by this function along … print(pos), elem = np.array([[‘one’, ‘two’, ‘three’]]) The boolean index in Python Numpy ndarray object is an important part to notice. unravel_index Convert a flat index into an index tuple. NumPy insert() helps us by allowing us to insert values in a given axis before the given index number. import numpy as np a = np.arange(10) s = slice(2,7,2) print a[s] Its output is as follows − [2 4 6] In the above example, an ndarray object is prepared by arange() function. t=’one’ Index.to_numpy(dtype=None, copy=False, na_value=