Python NumPy for Data Science
ganesh kavhar
Python | PySpark | Databricks | ETL | SQL | Unix | Big Data | Data warehouse
NumPy Introduction
NumPy stands for ‘Numerical Python.’ It is a package in Python to work with arrays. It is a basic scientific library. Its most important feature is the n-dimensional array object. It has uses in statistical functions, linear algebra, arithmetic operations, bitwise operations, etc.
The NumPy array’s most important element is its n-dimensional array object. We perform all the operations on the array elements. We can initialize these arrays in several ways.
Prerequisite to Learn NumPy
The two basic prerequisites for NumPy are Python and Mathematics. We need to know the python basics to work with the NumPy module. The functions available in NumPy are built on python language. We can hence combine the knowledge of python arrays and list for array initialisation and operations.
NumPy Installation
We can install Python NumPy by going to the command prompt and typing a simple command pip install NumPy. Then go to the IDE and use the import command import NumPy as np. We can now access all the functionalities of the NumPy module.
Uses of NumPy
NumPy is one of the most useful external libraries available in Python. It has a wide variety of functions to work with arrays and a powerful multi-dimensional array object. It has operations that are applicable to a vast range of platforms.
Numpy can be put to use for storing, manipulation, and deletion of array elements. We can use it for sorting, indexing, and stacking of the array elements. It has modules regarding various operations:
- Arithmetic operations
- Statistical Operations
- Bitwise Operators
- Linear Algebra
- Copying and viewing arrays
- Stacking
- Searching, Sorting, and counting, etc.
- Mathematical Operations
- Broadcasting
- Matplotlib for graphical representations
- Matrix Operations, etc.