Python is a multipurpose language. It is arguably one of the friendliest languages to learn. It has seen a lot of popularity in recent years. There are many of them. Nonetheless, one of the main ones is web development. It’s not difficult to write code, and there are a lot of libraries. In this blog post we will explain 10 most famous Python libraries and their uses.
Here we are going to analyze 10 most famous Python libraries.
1. NumPy: Numerical Python
NumPy is a collection of the most basic data structures of NumPy arrays. It includes a large collection of high-speed numerical operations. These operations perform various mathematical computations on large multi-dimensional data structures like matrices. Numpy also use to provides links to C and Fortran code and makes simple numerical operations easy to do.
Installation: To install NumPy with NumPy pip package manager, use the next command.
pip install numpy
2. Pandas: for Data Analysis
Pandas is one of 10 most famous Python libraries. It is most efficient tools to work on the data analysis and operation on the data. i.e. DataFrames and Series, have been pointed out. It is often used by data scientists or analysts, and you can especially use it for importing datasets. And you can also use for cleaning or transforming them.
Installation: Install Pandas using pip:
pip install pandas
3. Matplotlib: Data Visualization
Matplotlib is a library which its users use to plot static, animated and interactive graphics. The popularity of this tool among various scientists in their everyday work for more complex data visualization system. These are the reasons behind its use.
Installation: Install Matplotlib using pip:
pip install matplotlib
4. SciPy: Scientific Computing:
SciPy is a package of routines for numerical computing and theillaume. Jun 23 ’11 at 19:56 Larry’s comment on SciPy 0 was the first time that I saw it. Check out this answer for information about the integration of SciPy in Anaconda: SciPy in Anaconda. : It is applied for optimization, linear algebra, integration and interpolation. And also for signal and image processing and statistics and is based on NumPy.
Installation: Install SciPy using pip:
pip install scipy
5. Seaborn:
Statistical Data Visualization is converting large collections of Related Statistical Data on to graphical displays and graphs. Seaborn is a Matplotlib interface to provide statistical data visualization in a general way along with an attractive default aesthetic. Parts of data visualization that it covers is several plots such as scatter plots. line plots, violin plots, box plots and heat plots.
Installation: Install Seaborn using pip:
pip install seaborn

6. TensorFlow:
This paper will discuss Artificial Intelligence and its subfield; Machine Learning and Deep Learning.
The Google Brain team’s open-source machine learning platform is TensorFlow. It offers all necessary toolkits, libraries and forums for implementing and training Deep learning models.
Installation: Install TensorFlow using pip:
pip install tensorflow
7. Keras: High Level Neural Networks API
Keras is a high level neural networks API. Which written in Python and capable of running on top of TensorFlow, CNTK, or Theano. Keras has been designed to help us build deep learning models and run experiments fast.
Installation: Install Keras using pip:
pip install keras
8. Flask: Library for web framework
IT is a micro web framework for Python. FLASK is used to build small web application that do not need a large framework around them. It is easy to use with a simple layout and offers developers lots of tools to create web applications.
Installation: Install Flask using pip:
pip install flask
9. Django: High-level Web Framework:
10 most famous Python libraries Django is one of them.
Django is a Python framework that provides a lightweight web development framework. And encouraging fast pliable web development, with great emphasis on reuse of components. Simply, this is a framework that adheres to Model-View-Controller architectural design pattern and Object Relational Mapping (ORM). Making it suitable for web application development.
Installation: Install Django using pip:
pip install django
10. Requests: HTTP Requests:
Requests is a Python HttpRequest object that can be used to make HTTP/1.1 requests and manage responses. Making web request is usually a tedious task which it makes an important library for web scraping, automation and API.
Installation: Install Requests using pip:
pip install requests
Conclusion:
Among these, this article has mentioned the ten most mentioned Python libraries. Which used for data analysis, manipulation, visualization, machine learning, web and making HTTP requests. Each library is quite different and can be used in various situations to help developers. That unleash Python as a tool for solving many problems and creating high level applications.