Python Library For Data Science Cheat Sheet
Updated: Jul 4, 2021
Python has rapidly become the go-to language in the data science space and is among the first things recruiters search for in a data scientist’s skill set, there’s no doubt about it. It has consistently ranked top in global data science surveys and its widespread popularity only keeps on increasing!
But what makes Python so special for data scientists?
Just like our human body consists of multiple organs for multiple tasks and a heart to keep them running, similarly, the core Python provides us with the easy easy-to-code, object-oriented, high-level language (the heart). We have different libraries for each type of job like Math, Data Mining, Data Exploration, and visualization(the organs).
All Libraries For Data Science
NumPy is one of the most essential Python Libraries for scientific computing and it is used heavily for the applications of Machine Learning and Deep Learning. 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 to work with them.
The Python Data Analysis Library. It’s a powerful tool to load the data into so called data frames, which is basically a table that you can then easily analyze, modify, and also visualize.From Data Exploration to visualization to analysis – Pandas is the almighty library you must master!
Matplotlib is the most popular library for exploration and data visualization in the Python ecosystem. Every other library is built upon this library.
Matplotlib offers endless charts and customizations from histograms to scatterplots, matplotlib lays down an array of colors, themes, palettes, and other options to customize and personalize our plots. matplotlib is useful whether you’re performing data exploration for a machine learning project or building a report for stakeholders, it is surely the handiest library!
The base Machine Learning library in Python is scikit-learn. It offers almost all the „classical“ Machine Learning models you need, so it offers models for Regression, Classification, Clustering, and Dimensionality Reduction. Additionally, there are algorithms to preprocess data, e.g., for feature extraction or feature normalization.
Seaborn is another data visualization library based on matplotlib. It provides additional visualization methods and seaborn plots often look a little bit more beautiful than plain matplotlib plots.Seaborn provides easy functions that help you focus on the plot and now how to draw it. Seaborn is an essential library you must master.
BeautifulSoup is a Data Mining python library. BeautifulSoup is an amazing parsing library in Python that enables web scraping from HTML and XML documents.
BeautifulSoup automatically detects encodings and gracefully handles HTML documents even with special characters. We can navigate a parsed document and find what we need which makes it quick and painless to extract the data from the webpages.
Scrapy is also a data mining python library. It is a Python framework for large scale web scraping. It gives you all the tools you need to efficiently extract data from websites, process them as you want, and store them in your preferred structure and format.
Plotly is a free and open-source data visualization library. I personally love this library because of its high quality, publication-ready and interactive charts. Boxplot, heatmaps, bubble charts are a few examples of the types of available charts. It is one of the finest data visualization tools available built on top of visualization library D3.js, HTML, and CSS. It is created using Python and the Django framework.
This is a super helpful library when you have to deal with imbalanced data, e.g., if you have a lot of samples from the negative class but not from the positive class. You should address this problem in your preprocessing steps and imbalanced-learn offers a lot of different algorithms to do this, for example different under- and oversampling methods.
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 push the state of the art in deep learning and developers in the industry to build ML & DL powered applications. It will build the base for Computer Vision and NLP tasks.
Many data science enthusiasts hail Pytorch as the best deep learning framework (that’s a debate for later on). It has helped accelerate the research that goes into 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 –
1. Production Ready
2. Distributed Training
3. Robust Ecosystem
4. Cloud support
Keras is a deep learning API written in Python, which runs on top of the machine learning platform TensorFlow. Keras is preferred over TensorFlow by many, due to its much better “user experience”, Keras was developed in Python and hence the ease of understanding by Python developers. It is simple to use and yet a very powerful library.
It offers powerful algorithms for real time image and video processing. Some techniques could be used for preprocessing or labelling the data and then combine it with TensorFlow or PyTorch, but it also has algorithms for full pipelines, e.g., for object detection, object segmentation, and face recognition algorithms .
HuggingFace is one of the most important library for NLP. HuggingFace Transformers library, which offers many pretrained State-of-the-art Natural Language Processing models and algorithms that can be combined directly with both PyTorch and TensorFlow. It’s one of the most popular NLP frameworks in Python right now.
NLTK, the Natural Language Toolkit. This is another essential library when working with language data. It offers algorithms for text classification, tokenization, stemming, tagging, and many more text processing techniques.
Flask is an API of Python that allows us to build up web-applications. Flask’s framework is more explicit than Django’s framework and is also easier to learn because it has less base code to implement a simple web-Application. Flask is based on WSGI(Web Server Gateway Interface) toolkit and Jinja2 template engine.
Streamlit makes it super simple to build beautiful web apps without having to worry about implementing the UI. You get beautiful widgets just out of the box and can add for example buttons, sliders, and plots with just one line of code. I use this a lot when I quickly need an app with a nice UI to demonstrate my machine learning models.
I hope this attempt to summarize all the python libraries for data science would be very helpful and handy to you guys.
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