Introduction to NumPy atleast_1d Python Function
Guide to NumPy atleast_1d Python Function – When we wish to convert inputs to arrays with at least one dimension. We utilize the numpy.atleast_1d() function. Higher-dimensional inputs are maintained while scalar inputs are transformed into 1-dimensional arrays.
- arrays1, arrays2, … : [array_like] One or more input arrays.
Return: [ndarray] An array, or list of arrays, each with a.ndim >= 1. Copies are made only if necessary.
Code Examples of NumPy atleast_1d Python Function
# welcome to softhunt.net # Python program explaining # numpy.atleast_1d() function import numpy as np num = 15 print ("Input number : ", num) out_arr = np.atleast_1d(num) print ("output 1d array from input number : ", out_arr)
Input number : 15 output 1d array from input number : 
# welcome to softhunt.net # Python program explaining # numpy.atleast_1d() function import numpy as np my_list = [[1, 2, 3], [4, 5, 6]] print ("Input list : \n", my_list) out_arr = np.atleast_1d(my_list) print ("output array : \n", out_arr)
Input list : [[1, 2, 3], [4, 5, 6]] output array : [[1 2 3] [4 5 6]]
# welcome to softhunt.net # Python program explaining # numpy.atleast_1d() function # when inputs are in high dimension import numpy as np in_arr = np.arange(9).reshape(3, 3) print ("Input array :\n ", in_arr) out_arr = np.atleast_1d(in_arr) print ("output array :\n ", out_arr) print(in_arr is out_arr)
Input array : [[0 1 2] [3 4 5] [6 7 8]] output array : [[0 1 2] [3 4 5] [6 7 8]] True
What is Python NumPy package?
NumPy is the most important Python module for scientific computing. It’s a Python library that includes a multidimensional array object, derived objects (such as masked arrays and matrices), and a variety of routines for performing fast array operations. Such as mathematical, logical, shape manipulation, sorting, selecting, I/O, discrete Fourier transforms basic linear algebra, basic statistical operations, random simulation, and more.
Why NumPy is so fast?
NumPy is a Python core module for manipulating and operating on high-level mathematical functions, multi-dimensional arrays, linear algebra, Fourier transformations, and random number capabilities, among other things. It offers Python with tools for integrating C, C++, and Fortran programs. NumPy is mostly used for scientific computing in Python.
NumPy Arrays are faster than Python Lists because of the following reasons:
- A collection of homogenous data types store in contiguous memory spaces is refer to as an array. A list, on the other hand, is a collection of disparate data types store in non-contiguous memory regions in Python.
- The NumPy package divides a job into many pieces, which are subsequently process in parallel.
- NumPy is a Python library that combines C, C++, and Fortran code. In comparison to Python, these programming languages have an extremely short execution time.