Loading, please wait...

Become An Author
A to Z Full Forms and Acronyms

Important Libraries in Python for Data Science

Jun 24, 2021 Data Science, Functions, Libraries, 3122 Views
List of important libraries and functions for data science in python

Important functions and libraries for Data Science in Python

 
There are various libraries and packages that help us in execution of the programme in python during data analysis or performing and machine learning operation, and practising these in day to day life one can even master them and do various jobs too.
Today is these article at Tutorials link platform we are gonna discuss the main and important python packages and libraries, so let's get started:
 

So the major libraries in python are as follows:

  1. NUMPY
  2. MATPLOTLIB
  3. PANDAS
  4. SCIPY
  5. SCIKIT-LEARN

WHAT IS NUMPY?

 

Well, NumPy is one of the most important libraries you will or have come across in Python, helps to manage multi-dimensional arrays very effective. Without Numpy you won't be able to apply Scipy, or matplotlib or Scikit-Learn. It plays an important part in data analysis and data manipulation in python.

What is MatplotLib?

Data Visualization is an important task in Data Science or Machine Learning m matlotlib take sit to another level, Matplotlib has features which add a python a positive to make data analysis to a whole new essence.

Why Pandas?

In python Panda is used to handle two-dimensional array, well to be precise as to use and handle data from excel or any format, so to have this advantage in python pandas is used.

WHY SCIPY?

Scipy is behind the math operation as optimisation, integration interpolation in python data analysis and hence requires much practise to explore the field, well it helps the calculation efficient and requires very much low profile memory.

Why Scikit-Learn?

This is the library behind the machine learning models and algorithm to be implemented in Data analysis in python platform, it helps you with regression methods, classification methods, and clustering, as well as model validation and model selection with optimisation methods.

CONCLUSION

There are plenty of library and methods to be implemented in python to help you understand what is your need and moreover, you have to check every documentation to make sure your algorithm is optimised and results are accurate.
So keep learning and keep growing.
A to Z Full Forms and Acronyms

Related Article

Five Data Science Algorithms
Function Application in Pandas | pipe() | apply() | applymap()
Introduction to Numpy | Pandas | Matplotlib
What are the basic Functionality of Pandas Data Structure| Pandas tutorial
Data structure -DataFrame |Pandas tutorial
Data Structure-SERIES |Pandas tutorial
Introduction to Pandas | Pandas Tutorial
EDA using Pandas Profiling | EDA part-2

Advertisement

JOIN TUTORIALS LINK

Our Newsletter Will Let You Know When Any New
Articles, Tutorials and Video Are Released.

CONTRIBUTE

MOBILE APP

Google Play

Follow us

Copyright © 2024. All Rights Reserved.