NumPy – Handling NdArray In Python

To clearly understand and analyze data - cleaning, transformation, enhancement, analysis and visualization is required.

In real business scenarios we generally deal with multidimensional data. Python libraries such as NumPy support handling and streamlining the data and at the same time help in eliminating unwanted and invalid data set (e.g NumPy.ma module for masked array)

This article will help in understanding 1) Basics of NumPy 2) Python NumPy operations and 3)How to mask unwanted or invalid data.

Why NumPy: NumPy is one of the most powerful Python libraries that can help in multi dimensional array computing. With NumPy we can perform a number of mathematical operations on multi-dimensional arrays such as trigonometric, statistical, and algebra.

List vs NumPy : The core of NumPy is its n-dimensional array data structure called ‘ndarray’.The main difference from other built in list data structure is that all elements should be homogeneous which allows element level operations.

Known for high-performance and provides efficient storage and data operations as arrays grow in size.

With anaconda we can simply install NumPy form terminal

pip install numpy

Some basic operations :

Note : Covid 19 data from 2020 is used for some examples

Create an array

Basic Operations on n dimension array:

Reshape/Transpose/Sum

Inverse/ndimension:

Linear Algebra : NumPy.Linalg

Rank/Determinant/Eigen value

Generate random numbers

Parsing Array

Example :working with Matplotlib and NumPy

Equation like y=3x+7 and x is a range of value

Masked Array in NumPy:

Handling unwanted missing or invalid data filtering is required in almost all large datasets. NumPy provides the concept of masking which can be very useful in eliminating the invalid data.

When an element of the mask is False, the corresponding element of the associated array is valid and is said to be unmasked. When an element of the mask is True, the corresponding element of the associated array is said to be masked (invalid).

 Explore More :

·      Mathematical Functions

https://numpy.org/doc/stable/reference/routines.math.html

·      Binary, string or datetype operations

https://numpy.org/doc/stable/reference/routines.bitwise.html

·      Cheat Sheet

Happy learning and let me know your feedback/suggestions.