numpy.repeat() in Python Last Updated : 28 Mar, 2022 Summarize Comments Improve Suggest changes Share Like Article Like Report The numpy.repeat() function repeats elements of the array - arr. Syntax : numpy.repeat(arr, repetitions, axis = None) Parameters : array : [array_like]Input array. repetitions : No. of repetitions of each array elements along the given axis. axis : Axis along which we want to repeat values. By default, it returns a flat output array. Return : An array with repetitions of array - arr elements as per repetitions, number of times we want to repeat arr Code 1 : Python # Python Program illustrating # numpy.repeat() import numpy as geek #Working on 1D arr = geek.arange(5) print("arr : \n", arr) repetitions = 2 a = geek.repeat(arr, repetitions) print("\nRepeating arr 2 times : \n", a) print("Shape : ", a.shape) repetitions = 3 a = geek.repeat(arr, repetitions) print("\nRepeating arr 3 times : \n", a) # [0 0 0 ..., 4 4 4] means [0 0 0 1 1 1 2 2 2 3 3 3 4 4 4] # since it was long output, so it uses [ ... ] print("Shape : ", a.shape) Output : arr : [0 1 2 3 4] Repeating arr 2 times : [0 0 1 1 2 2 3 3 4 4] Shape : (10,) Repeating arr 3 times : [0 0 0 ..., 4 4 4] Shape : (15,) Code 2 : Python # Python Program illustrating # numpy.repeat() import numpy as geek arr = geek.arange(6).reshape(2, 3) print("arr : \n", arr) repetitions = 2 print("\nRepeating arr : \n", geek.repeat(arr, repetitions, 1)) print("arr Shape : \n", geek.repeat(arr, repetitions).shape) repetitions = 2 print("\nRepeating arr : \n", geek.repeat(arr, repetitions, 0)) print("arr Shape : \n", geek.repeat(arr, repetitions).shape) repetitions = 3 print("\nRepeating arr : \n", geek.repeat(arr, repetitions, 1)) print("arr Shape : \n", geek.repeat(arr, repetitions).shape) Output : arr : [[0 1 2] [3 4 5]] Repeating arr : [[0 0 1 1 2 2] [3 3 4 4 5 5]] arr Shape : (12,) Repeating arr : [[0 1 2] [0 1 2] [3 4 5] [3 4 5]] arr Shape : (12,) Repeating arr : [[0 0 0 ..., 2 2 2] [3 3 3 ..., 5 5 5]] arr Shape : (18,) References : https://numpy.org/doc/stable/reference/generated/numpy.repeat.html Note : These codes won’t run on online IDE's. Please run them on your systems to explore the working . Comment More infoAdvertise with us Next Article Numpy recarray.repeat() function | Python M Mohit Gupta Improve Article Tags : Python Python-numpy Python numpy-arrayManipulation Practice Tags : python Similar Reads numpy.tile() in Python The numpy.tile() function constructs a new array by repeating array - 'arr', the number of times we want to repeat as per repetitions. The resulted array will have dimensions max(arr.ndim, repetitions) where, repetitions is the length of repetitions. If arr.ndim > repetitions, reps is promoted to 3 min read Python | Pandas Index.repeat() Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier. Pandas Index.repeat() function repeat elements of an Index. The function returns a new 2 min read Numpy recarray.repeat() function | Python In numpy, arrays may have a data-types containing fields, analogous to columns in a spreadsheet. An example is [(a, int), (b, float)], where each entry in the array is a pair of (int, float). Normally, these attributes are accessed using dictionary lookups such as arr['a'] and arr['b']. Record array 4 min read Python - itertools.repeat() Python’s Itertool is a module that provides various functions that work on iterators to produce complex iterators. This module works as a fast, memory-efficient tool that is used either by themselves or in combination to form iterator algebra. Note: For more information, refer to Python Itertools re 2 min read numpy.multiply() in Python The numpy.multiply() is a numpy function in Python which is used to find element-wise multiplication of two arrays or scalar (single value). It returns the product of two input array element by element.Syntax:numpy.multiply(arr1, arr2, out=None, where=True, casting='same_kind', order='K', dtype=None 3 min read Numpy np.unique() method-Python numpy.unique() finds the unique elements of an array. It is often used in data analysis to eliminate duplicate values and return only the distinct values in sorted order. Example:Pythonimport numpy as np a = np.array([1, 2, 2, 3, 4, 4, 4]) res = np.unique(a) print(res)Output[1 2 3 4] Explanation: nu 2 min read Like