Overview
- Know what the 13 main data science libraries in python
- Find suitable resources to learn about these Python libraries for data science
- This list is by no means exhaustive.. Feel free to add more in the comments..
Introduction
Python has quickly become the reference language in the data science space and is one of the first things recruiters look for in a data scientist's skill set., No doubt about it. It has consistently ranked # 1 in global data science polls and its widespread popularity only keeps increasing!!
But, What makes python so special to data scientists?
Like our human body it consists of multiple organs for multiple tasks and a heart to keep them running., similarly, Python core provides us with the high-level language, easy to code, object-oriented and high-level (the heart). We have different libraries for each type of work like math, data mining, data exploration and visualization (the organs).
It is of utmost importance that we master each and every one of the libraries, these are the main libraries and will not be changed overnight. the AI and ML BlackBelt program + help you master these 13 libraries along with many more.
Thats not all, you will get personalized tutoring sessions where your expert mentor will customize the learning path according to your professional needs.
Let's learn about 13 Top Python Libraries for Data Science You Must Master!
Before starting, I have an additional resource for you! Python is a diverse language and it is difficult to remember each and every line of syntax, so here is the link to python cheat sheet to help you.
Table of Contents
- NumPy
- Science
- Beautiful soup
- Deshilvanado
- Pandas
- Matplotlib
- Plotly
- Seaborn
- Learn Scikit
- PyCaret
- TensorFlow
- Hard
- PyTorch
Math
NumPy
NumPy is one of the most essential Python libraries for scientific computing and is widely used for machine learning and deep learning applications. NumPy stands for NUMerical PYthon. Machine learning algorithms are computationally complex and require multidimensional array operations. NumPy provides support for large multidimensional array objects and various tools for working with them.
Various other libraries that we are going to discuss further such as Pandas, Matplotlib and Scikit-learn are built on top of this amazing library! I have the right resource to get you started with NumPy:
Science
SciPy (Scientific Python) is the reference library when it comes to scientific computing that is widely used in the fields of mathematics, science and engineering. It is equivalent to using Matlab, what is a payment tool.
SciPy, as the documentation says, “provides many efficient and easy-to-use number routines, as routines for numerical integration and optimization”. It is built on top of the NumPy library.
Data processing
Beautiful soup
Beautiful soup is an amazing Python parsing library that enables web scraping from HTML and XML documents.
Beautiful soup automatically detects encodings and elegantly handles HTML documents even with special characters. We can navigate through a parsed document and find what we need, which makes extracting data from web pages quick and easy. In this article, we will learn how to build web scrapers using Beautiful Soup in detail.
Scrapy
Scrapy is a Piton framework for large scale web scraping. Provides you with all the tools you need to extract website data, process as you wish and save them in your preferred place. structure and format.
You can learn all about web scraping and data mining in this article.:
Data exploration and visualization
Pandas
From data exploration to visualization and analysis: Pandas is the almighty library you must master!!
Pandas is an open source package. Helps you perform data analysis and data manipulation in Python language. What's more, provides us with fast and flexible data structures that make it easy to work with relational and structured data.
If you are new to Pandas, you should definitely check out this free course:
Matplotlib
Matplotlib is the most popular library for data exploration and visualization in the Python ecosystem. All other libraries are based on this library.
Matplotlib offers endless graphics and customizations, from histograms to scatterplots, matplotlib sets a variety of colors, topics, palettes and other options to customize and personalize our diagrams. matplotlib is useful whether you are doing data exploration for a machine learning project or creating a report for stakeholders, It is surely the most practical library!
If you are just starting, I have some resources to help you get started:
Plotly
Plotly is a free and open source data visualization library. Personally, i love this library because of its interactive graphics, ready for publication and of high quality. Box plots, Heatmaps and bubble charts are some examples of the types of charts available.
It is one of the best data visualization tools available, built on top of the D3.js display library, HTML y CSS. It is created using Python and the Django framework. Then, if you are looking to explore data or just want to impress your stakeholders, Plotly is the way to go!
This is a great resource for getting started:
Seaborn
Seaborn is a free and open source data visualization library based on Matplotlib. Many data scientists prefer seaborn over matplotlib due to its high-level interface for drawing attractive and informative statistical graphs..
Seaborn provides easy features to help you focus on the plot and now how to draw it. Seaborn is an essential library that you must master. Here's a great resource to pay for:
Machine learning
Learn Scikit
Sklearn is the Swiss Army Knife of Data Science Libraries. It's an indispensable tool in your data science arsenal that will blast your way through seemingly impregnable obstacles.. In simple words, used to make machine learning models.
Scikit-learn is probably the most useful library for machine learning in Python. The sklearn library contains many efficient tools for machine learning and statistical modeling, that include classification, regression, grouping and dimensionality reduction.
Sklearn is a mandatory Python library that you must master. DataPeaker offers a free course on this topic. You can check the resources here:
PyCaret
Tired of writing endless lines of code to build your machine learning model? PyCaret is the way to go!
PyCaret is an open source machine learning library in Python that helps you from data preparation to model implementation. Helps you save tons of time by being a low-code library.
It's an easy-to-use machine learning library that will help you run end-to-end machine learning experiments., either imputing missing values, encoding categorical data, feature engineering, hyperparameter tuning or building set models. This is an excellent resource for you to learn PyCaret from scratch:
TensorFlow
Over the years, TensorFlow, developed by the Google Brain team, has gained traction and has become the state-of-the-art library when it comes to machine learning and deep learning. TensorFlow had its first public release in 2015. At that moment, the evolving deep learning landscape for developers and researchers was occupied by Caffe and Theano. Soon, TensorFlow emerged as the most popular library for deep learning.
TensorFlow is an end-to-end machine learning library that includes tools, Libraries and resources for the research community to advance the state of the art in deep learning and industry developers to create applications with ML and DL technology.
To be a data scientist ready for the future, here are some resources for learning TensorFlow:
Hard
Keras is a deep learning API written in Python, running on machine learning platform. TensorFlow. It was developed with a focus on allowing rapid experimentation. According to Keras – “Being able to go from idea to result as quickly as possible is key to doing good research..“
Many prefer Keras to TensorFlow, because of his “user experience” better, Keras was developed in Python and, Thus, ease of understanding by Python developers. It is easy to use and, but nevertheless, a very powerful library.
Some resources to consult:
PyTorch
Many data science enthusiasts praise Pytorch as the best deep learning framework (that's a debate for later). It has helped accelerate the investigation of deep learning models by making them computationally faster and less expensive.
PyTorch is a Python-based library that provides maximum flexibility and speed. Some of the features of Pytorch are as follows:
- Ready for production
- Distributed training
- Robust ecosystem
- Cloud support
Excited? You can learn more about PyTorch here:
Final notes
Python is a powerful yet simple language for all your machine learning tasks.
In this article, we analyze 13 libraries to help you achieve your data science goals, like math, data mining, data exploration and visualization, machine learning.
From a data science perspective, you can master all of these libraries and many more as part of AI and ML Blackbelt Program + de DataPeaker. You will get a personalized tutoring session where your learning path will be customized according to your professional needs.
Do you have any other favorite libraries that we should know about?? Let me know in the comments!!
