Best Python Libraries For Data Science
by Mahmut on November 24, 2020
In this article, I am going to talk about the most important libraries of Python used in the field of data science.
Libraries are collection of functions and methods that enable you to perform a wide variety of actions without writing the code yourself.
First of all, there are over 137.000 libraries in Python. In this article we are going to learn :
- Scientific Computing Libraries in Python
- Visualization Libraries in Python
- High-Level Machine Learning and Deep Learning Libraries in Python
- Deep Learning Libraries in Python
- Python Libraries for NLP ( Natural Language Processing )
Before we start explaining all of them, Let’s learn what data science is and why we use python programming language in this field.
What is Data Science
Everybody can define data science their own way, but if we want to explain it as simple as possible :
Data Science is the process of deriving useful insights from huge amounts of data to solve real-world problems.
Data science involves data and some science. The definition or the name came up in the 80s and 90s when some professors were looking into the statistics curriculum, and they thought it would be better to call it data science. I would see data science as one’s attempt to work with data to find answers to questions that they are exploring. In a nutshell, it’s more about data than it is about science. If you have data, and if you have curiosity, and you are working with data and you are manipulating it, you are exploring it, the very exercises of going through analyzing data, trying to get some answers from it is data science.
Data science is relevant today because we have tons of data available. We used to worry about lack of data. Now we have data deluge. In the past, we didn’t have algorithms, now we have algorithms. In the past, software was expensive, now it’s open source and free. In the past, we couldn’t store large amounts of data, now for a fraction of the cost, we can have gazillions of datasets for a very low cost. So, the tools to work with data, the very availability of data, and the ability to store and analyze data, it’s all cheap, it’s all available, it’s all ubiquitous, it’s here.
Why Use Python
Python is a powerhouse language. It is by far the most popular programming language for data science.
According to the 2019 Kaggle Data Science and Machine Learning Survey, %75 of the over 10.000 respondents from around the world reported that they use Python on a regular basis.
Glassdoor reported that in 2019 more than %75 of data science positions listed included Python in their job descriptions.
When asked which language an inspiring data scientist should learn first, most data scientists say Python.
If you already know how to program, then Python is great for you because it uses clear, readable syntax. You can do many of the things you are used to doing in other programming languages but with Python, you can do it with less code. If you want to learn to program, it’s also a great starter language because of the huge global community and wealth of documentation.
Python is useful for many situations, including data science, AI and Machine Learning, web development, and IoT devices like Raspberry Pi.
Large organizations that use Python heavily include IBM, Wikipedia, Google, Yahoo!, CERN, NASA, Facebook, Amazon, Instagram, Spotify, and Reddit.
If you want to learn Python, I created a free Python Tutorial for those who want to learn Python. Click here to go to my Python Tutorial.
Scientific Computing Libraries in Python
Pandas offers data structures and tools for effective data cleaning, manipulation, and analysis. It provides tools to work with different types of data.
The primary instrument of Pandas is a two-dimensional table consisting of columns and rows. This table is called a “DataFrame” and is designed to provide easy indexing so you can work with your data.
If you want to learn more about Pandas, then check out my “ Best Way to Learn Pandas “ article.
Numpy libraries are based on arrays, enabling you to apply mathematical functions to these arrays.
Pandas is actually built on top of Numpy.
It is a Python library that provides a multidimensional array object for fast operations on arrays, including mathematical, logical, shape manipulation, sorting, selecting, I/O, discrete Fourier transforms, basic linear algebra, basic statistical operations, and much more.
If you want to learn more about Numpy, then check out my “ Best Way to Learn Numpy “ article.
Visualization Libraries in Python
Data visualization methods are a great way to communicate with others and show the meaningful results of analysis. These libraries enable you to create graphs, charts, and maps.
The matplotlib package is the most well-known library for data visualization, and it’s excellent for making graphs and plots. The graphs are also highly customizable.
It was created by John Hunter, who was a neurobiologist and was part of a research team that was working on analyzing Electrocorticography signals, ECoG for short. The team was using a proprietary software for the analysis. However, they had only one license and were taking turns in using it. So in order to overcome this limitation, John set out to replace the proprietary software with a MATLAB based version that could be utilized by him and his teammates, and that could be extended by multiple investigators. As a result, Matplotlib was originally developed as an ECoG visualization tool, and just like MATLAB, Matplotlib was equipped with a scripting interface for quick and easy generation of graphics, represented by pyplot.
Another high-level visualization library, Seaborn, is based on matplotlib.
Seaborn makes it easy to generate plots like heat maps, time series, and violin plots.
It builds on top of matplotlib and integrates closely with pandas data structures. Seaborn helps you explore your data. Its plotting functions operate on dataframes and arrays containing whole datasets and internally perform the necessary semantic mapping and statistical aggregation to produce informative plots.
Its dataset-oriented, declarative API lets you focus on what the different elements of your plots mean, rather than on the details of how to draw them.
High-Level Machine Learning and Deep Learning Libraries in Python
For machine learning, the scikit-learn library contains tools for statistical modeling, including regression, classification, clustering, and others.
It is built on Numpy, Scipy, and Matplotlib, and it’s relatively simple to get started.
For this high-level approach, you define the model and specify the parameter types you would like to use.
For deep learning, Keras enables you to build the standard deep learning model.
Like scikit-learn, the high-level interface enables you to build models quickly and simply.
It can function using graphics processing unit ( GPU ), but for many deep learning cases a lower-level environment is required.
Deep Learning Libraries in Python
TensorFlow is a low-level framework used in large scale production of deep learning models.
It’s designed for production but can be unwieldy for experimentation.
TensorFlow is cross-platform. It runs on nearly everything: GPUs and CPUs—including mobile and embedded platforms—and even tensor processing units (TPUs), which are specialized hardware to do tensor math on.
Pytorch is used for experimentation, making it simple for researchers to test their ideas.
PyTorch supports dynamic computation graphs that allow you to change how the network behaves on the fly, unlike static graphs that are used in frameworks such as Tensorflow.
PyTorch was developed by Facebook’s AI Research and is adapted by several industries like Uber, Twitter, Salesforce, and NVIDIA.
Python Libraries for NLP ( Natural Language Processing )
The technology behind Alexa, Siri, and other Chatbots is Natural Language Processing. NLP has played a huge role in designing AI-based systems that help in describing the interaction between human language and computers.
NLTK stands for Natural Language Toolkit.
The Natural Language Toolkit (NLTK) is a platform used for building Python programs that work with human language data for applying in statistical natural language processing (NLP).
This toolkit is one of the most powerful NLP libraries which contains packages to make machines understand human language and reply to it with an appropriate response.
NLTK includes more than 50 corpora and lexical sources such as the Penn Treebank Corpus, Open Multilingual Wordnet, Problem Report Corpus, and Lin’s Dependency Thesaurus.
SpaCy is one of the oldest and well-known network packet library for Python.
Spacy is a Python interpreter that enables you to create, forge, or decode packets on the network, to capture packets and analyze them, to dissect the packets, etc.
Spacy can easily handle most classical tasks like scanning, tracerouting, probing, unit tests, attacks or network discovery. It can replace hping, arpspoof, arp-sk, arping, p0f and even some parts of Nmap, tcpdump, and tshark