numpy.atleast_1d() in Python

numpy.atleast_2d() in Python

Last Updated : 28 Nov, 2018

numpy.atleast_2d() function is used when we want to Convert inputs to arrays with at least two dimension. Scalar and 1-dimensional inputs are converted to 2-dimensional arrays, whilst higher-dimensional inputs are preserved.

Syntax : numpy.atleast_2d(*arrays) Parameters : arrays1, arrays2, ... : [array_like] One or more array-like sequences. Non-array inputs are converted to arrays. Arrays that already have two or more dimensions are preserved. Return : An array, or list of arrays, each with a.ndim >= 2. Copies are avoided where possible, and views with two or more dimensions are returned.

Code #1 : Working

Python

# Python program explaining
# numpy.atleast_2d() function

import numpy as geek
in_num = 10

print ("Input  number : ", in_num)
 
  
out_arr = geek.atleast_2d(in_num)
print ("output 2d array from input number : ", out_arr)

Output :

Input  number :  10
output 2d array from input number :  [[10]]

Code #2 : Working

Python

# Python program explaining
# numpy.atleast_2d() function

import numpy as geek

my_list = [2, 6, 10], 
 
print ("Input  list : ", my_list)
  
out_arr = geek.atleast_2d(my_list) 
print ("output 2d  array : ", out_arr)

Output :

Input  list :  ([2, 6, 10], )
output 2d  array :  [[ 2  6 10]]

Code #3 : Working

Python

# Python program explaining
# numpy.atleast_2d() function
# when inputs are in high dimension

import numpy as geek

in_arr = geek.arange(9).reshape(3, 3)
print ("Input  array :\n ", in_arr)

out_arr = geek.atleast_2d(in_arr)
print ("output  array :\n ", out_arr)
print(in_arr is out_arr)

Output :

Input  array :
  [[0 1 2]
 [3 4 5]
 [6 7 8]]
output  array :
  [[0 1 2]
 [3 4 5]
 [6 7 8]]
True

numpy.atleast_1d() in Python

jana_sayantan

Improve

Article Tags :

Practice Tags :

python

Similar Reads

numpy.atleast_1d() in Python

numpy.atleast_1d()function is used when we want to Convert inputs to arrays with at least one dimension. Scalar inputs are converted to 1-dimensional arrays, whilst higher-dimensional inputs are preserved. Syntax : numpy.atleast_1d(*arrays) Parameters : arrays1, arrays2, ... : [array_like] One or mo

numpy.atleast_3d() in Python

numpy.atleast_3d() function is used when we want to Convert inputs to arrays with at least three dimension. Scalar, 1 and 2 dimensional inputs are converted to 3-dimensional arrays, whilst higher-dimensional inputs are preserved. Input includes scalar, lists, lists of tuples, tuples, tuples of tuple

numpy.diagflat() in Python

numpy.diagflat (a, k = 0): Create a two-dimensional array with the array_like input as a diagonal to the new output array. Parameters : a : array_like input data with diagonal elements strong>k : [int, optional, 0 by default] Diagonal we require; k>0 means diagonal above main diagonal or vice

numpy.alen() in Python

numpy.alen() function is used to return the length of the first dimension of the input array. Syntax : numpy.alen(arr) Parameters : arr : [array_like] Input array. Return : [int]Length of the first dimension of arr. Code #1 : Python3 # Python program explaining # alen() function import numpy as geek

numpy.all() in Python

The numpy.all() function tests whether all array elements along the mentioned axis evaluate to True. Syntax: numpy.all(array, axis = None, out = None, keepdims = class numpy._globals._NoValue at 0x40ba726c) Parameters :Â array :[array_like]Input array or object whose elements, we need to test. axis

numpy.amax() in Python

The numpy.amax() method returns the maximum of an array or maximum along the axis(if mentioned). Syntax: numpy.amax(arr, axis = None, out = None, keepdims = <class numpy._globals._NoValue>) Parameters -Â arr : [array_like] input dataaxis : [int or tuples of int] axis along which we want the ma

numpy.greater() in Python

The numpy.greater() checks whether x1 is greater than x2 or not. Syntax : numpy.greater(x1, x2[, out]) Parameters : x1, x2 : [array_like]Input arrays. If x1.shape != x2.shape, they must be broadcastable to a common shape out : [ndarray, boolean]Array of bools, or a single bool if x1 and x2 are scala

NumPy Array in Python

NumPy (Numerical Python) is a powerful library for numerical computations in Python. It is commonly referred to multidimensional container that holds the same data type. It is the core data structure of the NumPy library and is optimized for numerical and scientific computation in Python. Table of C

numpy.empty() in Python

numpy.empty(shape, dtype = float, order = 'C') : Return a new array of given shape and type, with random values. Parameters : -> shape : Number of rows -> order : C_contiguous or F_contiguous -> dtype : [optional, float(by Default)] Data type of returned array. Python # Python Programming i

numpy.any() in Python

The numpy.any() function tests whether any array elements along the mentioned axis evaluate to True. Syntax :Â numpy.any(a, axis = None, out = None, keepdims = class numpy._globals._NoValue at 0x40ba726c) Parameters :Â array :[array_like]Input array or object whose elements, we need to test. axis :