Notice: Constant automattic\jetpack\extensions\social_previews\FEATURE_NAME already defined in /home/beaczwhx/ on line 14
5 Best Python Frameworks For Data Science To Learn In 2022 - Dggul AI Tutorial

5 Best Python Frameworks For Data Science To Learn In 2022

Notice: Undefined index: limited_lang in /home/beaczwhx/ on line 297

Hi everyone, I have shared lots of stuffs such as tutorials, books etc., related to data science. Now in this post I am going to share a list of some best python frameworks for data science.

Data Science industry is booming day by day. Many peoples are learning data science and making a bright career in this field. To scale our expertise in data science, learning about various frameworks used in data science is very important. There are many frameworks for data science which you can learn and improve your data science knowledge. These frameworks are developed with both R and Python programming language. In this post I am going to discuss some useful python frameworks which you should learn for data science.

So let’s get started !!!

Stop here for a while !! do you know what is framework and why we use them? No, then first of all let’s discuss little bit about frameworks.

What is framework ? 

Guess a scenario where you have to write lines of code from scratch for each and every new project. Do you not feel it is very difficult and frustrating task. So will you like it when you don’t have to write codes from beginning for every projects you work on. In this case Programming frameworks plays an important role.

So programming frameworks are prepackaged set of components or solutions that is used to solve the common development problems. Frameworks are essentially starting blocks for creating codes, these code blocks have been built, tested, and optimized by a community.

The main benefit of using frameworks is saving time but there are also lots of other benefits that framework gives.

Why We Use Programming Frameworks ? 

  • Frameworks create better code
  • They are Pre-Tested and Pre-Optimized
  • Faster Implementation
  • Saves development time and cost

So are you ready to know best python frameworks for data science? Let’s explore them in detail.

5 Best Python Frameworks For Data Science

1. TensorFlow

TensorFlow is an open source framework for machine learning which was firstly created by Google Brain Team. Intel, Coca Cola, Airbnb, Nvidia, Uber, and lots of other prominent companies are using TensorFlow.

Main benefits of using TensorFlow are – easy model building, Powerful experimentation for research and Robust ML production anywhere.

Its formulation is convenient for data integration such as inputting graphs, SQL tables, and images together.

TensorFlow is a very flexible and easy to use framework that’s why many small and medium size businesses are using this tool and getting great benefits in their business.

TensorFlow has always provided a direct path to production. Whether it’s on servers, edge devices, or the web, TensorFlow lets you train and deploy your model easily, no matter what language or platform you use.

This framework provides high-level APIs and building blocks (similar to Matlab) for creating Deep Learning models.

So the final word about TensorFlow is that it is one of the best python framework for data science.

2. Pandas

Pandas is one of the most popular and useful framework for data science. Pandas is an open-source Python library which is built on top of NumPy. Pandas library is mainly used in data analysis and manipulation.

The main features of pandas is that it provides High-level abstraction and contains high-level data structures and manipulation tools. It mainly deals with two types of data structure series and dataframes.

Many industries such as engineering, finance, statistics, and web analytics to neuroscience, marketing, and many more are using pandas. Target, Tesla Motors, NVIDIA etc. are some famous companies which are using Pandas library.

It was created in 2008. The main features of pandas are following –

  • integrated handling of missing data
  • intelligent data alignment
  • ability to quickly merge and join data sets
  • flexible reshaping and pivoting of data sets
  • data aggregation and transformation

So if you want to learn pandas framework then you can learn from here.

3. Numpy

Numpy is yet another very important library for data science. NumPy was created in 2006. It is mainly used for numeric computation. It is an open source library which is used to work with matrices and multi-dimensional arrays. It is an array processing package.

NumPy’s speed-optimized C code provides array objects that are 50x faster than Python lists, making them ideal for Data Science purposes.

It also forms the base of other libraries, such as SciPy and SciKit-Learn.  It also supports linear algebra, random number generation and other operations.

Some of the features of Numpy are following –

  • Provides fast, precompiled functions for numerical routines
  • Supports an object-oriented approach

Check some best NumPy Tutorial to learn Numpy library in easy way.

4. Matplotlib

Matplotlib is an open source library used to create data visualization. It is one of the popular data science library that helps to create data visualization by plotting histogram, bar charts, scatterplots, 3Dplots etc.

It is also very widely used python visualization library by which we can create static, animated, and interactive visualizations very easily by just writing some lines of codes.

The main feature of this library is that it supports dozens of backends and output types, which means you can use it regardless of which operating system you’re using or which output format you wish to use.

It is very useful library to visualize the distribution of data to gain instant insights.

5. PyTorch

PyTorch is a very popular open source python library for data science. It is first released publicly in 2017. It is widely used library to build and train neural networks models.

The main benefit of using PyTorch is that it provides more flexibility and speed than Torch. It provides high-level APIs and building blocks for creating deep learning models.

Key features of PyTorch

  • Production ready
  • Distributed training
  • Robust ecosystem
  • Cloud support

Amazon advertising, Sales force, Stanford University etc. some popular companies and universities which are using PyTorch.

So guys I am wrapping up this 5 best python frameworks for data science. I hope you have found this article very helpful. The list of python framework for data science is not only limited to 5, there are huge amount of python libraries are available for data science. Here I have just discussed 5 popular python frameworks for data science. Tell me in comment section which library you are learning currently and also give your valuable feedback about this post. THANKS

Leave a Comment