NumPy: Calculate mean across dimension, in Syntax of Python numpy.where() This function accepts a numpy-like array (ex. to Count Occurrences of Elements in NumPy numpy.mean ( a, axis =None, dtype = None, out = None, keepdims =) The syntax entered by the user is sent in terms of float * 64 intermediate and there by returns the value for the associated integers corresponding for the mean value. x and y need to have the same shape as condition. Returns: out: ndarray … This method is call boolean mask slicing. NumPy Let’s first define the conditions and associated values. The numPy.where () function is used to deliver back to the user the specific indices of certain elements which are present in the array which has been entered by the user where certain predefined conditions with respect to the function parameters get satisfied. condition: a NumPy array of elements that evaluate to True or False; x: an optional array-like result for elements that evaluate to True; y: an optional array-like result for elements that evaluate to False; The elements of condition don’t actually need to have a boolean type as long as they can be coerced to a boolean (e.g. However, np.count_nonzero () is faster than np.sum (). count_nonzero (x < 6) Method 3: Count Occurrences of Values that Meet One of Several Conditions where x and y are optional and should be array like. Syntax: numpy.where(condition[, x, y]) Parameters: condition : When True, yield x, otherwise yield y. x, y : Values from which to choose. Learn NumPy functions like np.where, np.select, np.piecewise, and more! count_nonzero (x == 2) Method 2: Count Occurrences of Values that Meet One Condition. ; First, we have to create a dataframe with random numbers 0 and 100. NumPy: Calculate mean across dimension, in a 2D numpy array