5 Numpy functions you didn't know you needed in scientific computation

NumPy is the fundamental package for scientific computing in Python. It is a Python library that provides a multidimensional array object, various derived objects (such as masked arrays and matrices), and an assortment of routines for fast operations on arrays, including mathematical, logical, shape manipulation, sorting, selecting, I/O, discrete Fourier transforms, basic linear algebra, basic statistical operations, random simulation and much more.
Want to learn more about this visit https://numpy.org/doc/stable/user/whatisnumpy.html

5 useful Numpy Functions

1. np.char.add() This function concatenates element-wise the strings of two arrays of matrices.
1. np.linalg.det() This function is used to calculate the determinant of a square matrix
1. np.trim_zeros() This function will trim the leading and trailing zeros
1. np.sort() This function is used to sort the values in an array
1. np.unique() This function help us to find the unquie values in an array

!pip install jovian --upgrade -q

import jovian

jovian.commit(project='numpy-array-operations')

[jovian] Updating notebook "anilpiparaiya/numpy-array-operations" on https://jovian.ai
[jovian] Committed successfully! https://jovian.ai/anilpiparaiya/numpy-array-operations

'https://jovian.ai/anilpiparaiya/numpy-array-operations'

Let's begin by importing Numpy and listing out the functions covered in this notebook.