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 arr.ndim by pre-pending 1’s to it. If arr.ndim < repetitions, reps is promoted to arr.ndim by pre-pending new axis. Syntax :
numpy.tile(arr, repetitions)
Parameters :
array : [array_like]Input array. repetitions : No. of repetitions of arr along each axis.
Return :
An array with repetitions of array - arr as per d, number of times we want to repeat arr
Code 1 :
Python # Python Program illustrating # numpy.tile() import numpy as geek #Working on 1D arr = geek.arange(5) print("arr : \n", arr) repetitions = 2 print("Repeating arr 2 times : \n", geek.tile(arr, repetitions)) repetitions = 3 print("\nRepeating arr 3 times : \n", geek.tile(arr, repetitions)) # [0 1 2 ..., 2 3 4] means [0 1 2 3 4 0 1 2 3 4 0 1 2 3 4] # since it was long output, so it uses [ ... ] Output :
arr : [0 1 2 3 4] Repeating arr 2 times : [0 1 2 3 4 0 1 2 3 4] Repeating arr 3 times : [0 1 2 ..., 2 3 4]
Code 2 :
Python # Python Program illustrating # numpy.tile() import numpy as geek arr = geek.arange(3) print("arr : \n", arr) a = 2 b = 2 repetitions = (a, b) print("\nRepeating arr : \n", geek.tile(arr, repetitions)) print("arr Shape : \n", geek.tile(arr, repetitions).shape) a = 3 b = 2 repetitions = (a, b) print("\nRepeating arr : \n", geek.tile(arr, repetitions)) print("arr Shape : \n", geek.tile(arr, repetitions).shape) a = 2 b = 3 repetitions = (a, b) print("\nRepeating arr : \n", geek.tile(arr, repetitions)) print("arr Shape : \n", geek.tile(arr, repetitions).shape) Output :
arr : [0 1 2] Repeating arr : [[0 1 2 0 1 2] [0 1 2 0 1 2]] arr Shape : (2, 6) Repeating arr : [[0 1 2 0 1 2] [0 1 2 0 1 2] [0 1 2 0 1 2]] arr Shape : (3, 6) Repeating arr : [[0 1 2 ..., 0 1 2] [0 1 2 ..., 0 1 2]] arr Shape : (2, 9)
Code 3 : (repetitions == arr.ndim) == 0
Python # Python Program illustrating # numpy.tile() import numpy as geek arr = geek.arange(4).reshape(2, 2) print("arr : \n", arr) a = 2 b = 1 repetitions = (a, b) print("\nRepeating arr : \n", geek.tile(arr, repetitions)) print("arr Shape : \n", geek.tile(arr, repetitions).shape) a = 3 b = 2 repetitions = (a, b) print("\nRepeating arr : \n", geek.tile(arr, repetitions)) print("arr Shape : \n", geek.tile(arr, repetitions).shape) a = 2 b = 3 repetitions = (a, b) print("\nRepeating arr : \n", geek.tile(arr, repetitions)) print("arr Shape : \n", geek.tile(arr, repetitions).shape) Output :
arr : [[0 1] [2 3]] Repeating arr : [[0 1] [2 3] [0 1] [2 3]] arr Shape : (4, 2) Repeating arr : [[0 1 0 1] [2 3 2 3] [0 1 0 1] [2 3 2 3] [0 1 0 1] [2 3 2 3]] arr Shape : (6, 4) Repeating arr : [[0 1 0 1 0 1] [2 3 2 3 2 3] [0 1 0 1 0 1] [2 3 2 3 2 3]] arr Shape : (4, 6)
References : https://docs.scipy.org/doc/numpy/reference/generated/numpy.tile.html Note : These codes won’t run on online IDE's. Please run them on your systems to explore the working .
Similar Reads
numpy.take() in Python The numpy.take() function returns elements from array along the mentioned axis and indices. Syntax: numpy.take(array, indices, axis = None, out = None, mode ='raise') Parameters : array : array_like, input array indices : index of the values to be fetched axis : [int, optional] axis over which we ne
2 min read
numpy.roll() in Python The numpy.roll() function rolls array elements along the specified axis. Basically what happens is that elements of the input array are being shifted. If an element is being rolled first to the last position, it is rolled back to the first position. Syntax : numpy.roll(array, shift, axis = None) Par
2 min read
numpy.zeros() in Python numpy.zeros() function creates a new array of specified shapes and types, filled with zeros. It is beneficial when you need a placeholder array to initialize variables or store intermediate results. We can create 1D array using numpy.zeros().Let's understand with the help of an example:Pythonimport
2 min read
numpy.repeat() in Python The numpy.repeat() function repeats elements of the array - arr. Syntax :Â numpy.repeat(arr, repetitions, axis = None) Parameters :Â 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 def
2 min read
Numpy size() function | Python numpy.size() function in Python is used to count the number of elements in a NumPy array. You can use it to get the total count of all elements, or to count elements along a specific axis, such as rows or columns in a multidimensional array. This makes it useful when quickly trying to understand the
2 min read