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If you’re looking to make a career in data, you probably know that Python is the go-to language for data science. Besides being simple to learn, Python also has a super rich suite of Python libraries that let you do any data science task with just a few lines of code.
So whether you’re just starting out as a data scientist or looking to switch to a career in data, learning to work with these libraries will be helpful. In this article, we’ll look at some must-know Python libraries for data science.
We specifically focus on Python libraries for data analysis and visualization, web scraping, working with APIs, machine learning, and more. Let’s get started.
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1. Pandas
Pandas is one of the first libraries you’ll be introduced to, if you’re into data analysis. Series and dataframes, the key pandas data structures, simplify the process of working with structured data.
You can use pandas for data cleaning, transformation, merging, and joining, so it’s helpful for both data preprocessing and analysis.
Let’s go over the key features of pandas:
- Pandas provides two primary data structures: Series (one-dimensional) and DataFrame (two-dimensional), which allow for easy manipulation of structured data
- Functions and methods to handle missing data, filter data, and perform various operations to clean and preprocess your datasets
- Functions to merge, join, and concatenate datasets in a flexible and efficient manner
- Specialized functions for handling time series data, making it easier to work with temporal data
This short course on Pandas from Kaggle will help you get started with analyzing data using pandas.
2. Matplotlib
You have to go beyond analysis and visualize data as well to understand it. Matplotlib is the data visualization first library you’ll dabble with before moving to other libraries Seaborn, Plotly, and the like.
It is customizable (though it requires some effort) and is suitable for a range of plotting tasks, from simple line graphs to more complex visualizations. Some features include:
- Simple visualizations such as line graphs, bar charts, histograms, scatter plots, and more.
- Customizable plots with rather granular control over every aspect of the figure, such as colors, labels, and scales.
- Works well with other Python libraries like Pandas and NumPy, making it easier to visualize data stored in DataFrames and arrays.
The Matplotlib tutorials should help you get started with plotting.
3. Seaborn
Seaborn is built on top of Matplotlib (it’s the easier Matplotlib) and is designed specifically for statistical and easier data visualization. It simplifies the process of creating complex visualizations with its high-level interface and integrates well with pandas dataframes.
Seaborn has:
- Built-in themes and color palettes to improve plots without much effort
- Functions for creating helpful visualizations such as violin plots, pair plots, and heatmaps
The Data Visualization micro-course on Kaggle will help you get up and running with Seaborn.
4. Plotly
After you’re comfortable working with Seaborn, you can learn to use Plotly, a Python library for creating interactive data visualizations.
Besides the various chart types, with Plotly, you can:
- Create interactive plots
- Build web apps and data dashboards with Plotly Dash
- Export plots to static images, HTML files, or embed them in web applications
The guide Plotly Python Open Source Graphing Library Fundamentals will help you become familiar with graphing with Plotly.
5. Requests
You’ll often have to fetch data from APIs by sending HTTP requests, and for this you can use the Requests library.
It’s simple to use and makes fetching data from APIs or web pages a breeze with out-of-the-box support for session management, authentication, and more. With Requests, you can:
- Send HTTP requests, including GET and POST requests, to interact with web services
- Manage and persist settings across requests, such as cookies and headers
- Use various authentication methods, including basic and OAuth
- Handling of timeouts, retries, and errors to ensure reliable web interactions
You can refer to the Requests documentation for simple and advanced usage examples.
6. Beautiful Soup
Web scraping is a must-have skill for data scientists and Beautiful Soup is the go-to library for all things web scraping. Once you have fetched the data using the Requests library, you can use Beautiful Soup for navigating and searching the parse tree, making it easy to locate and extract the desired information.
Beautiful Soup is, therefore, often used in conjunction with the Requests library to fetch and parse web pages. You can:
- Parse HTML documents to find specific information
- Navigate and search through the parse tree using Pythonic idioms to extract specific data
- Find and modify tags and attributes within the document
Mastering Web Scraping with BeautifulSoup is a comprehensive guide to learn about Beautiful Soup.
7. Scikit-Learn
Scikit-Learn is a machine learning library that provides ready-to-use implementations of algorithms for classification, regression, clustering, and dimensionality reduction. It also includes modules for model selection, preprocessing, and evaluation, making it a nifty tool for building and evaluating machine learning models.
The Scikit-Learn library also has dedicated modules for:
- Preprocessing data, such as scaling, normalization, and encoding categorical features
- Model selection and hyperparameters tuning
- Model evaluation
Machine Learning with Python and Scikit-Learn – Full Course is a good resource to learn to build machine learning models with Scikit-Learn.
8. Statsmodels
Statsmodels is a library dedicated to statistical modeling. It offers a range of tools for estimating statistical models, performing hypothesis tests, and data exploration. Statsmodels is particularly useful if you’re looking to explore econometrics and other fields that require rigorous statistical analysis.
You can use statsmodels for estimation, statistical tests, and more. Statsmodels provides the following:
- Functions for summarizing and exploring datasets to gain insights before modeling
- Different types of statistical models, including linear regression, generalized linear models, and time series analysis
- A range of statistical tests, including t-tests, chi-squared tests, and non-parametric tests
- Tools for diagnosing and validating models, including residual analysis and goodness-of-fit tests
The Getting started with statsmodels guide should help you learn the basics of this library.
9. XGBoost
XGBoost is an optimized gradient boosting library designed for high performance and efficiency. It is widely used both in machine learning competitions and in practice. XGBoost is suitable for various tasks, including classification, regression, and ranking, and includes features for regularization and cross-platform integration.
Some features of XGBoost include:
- Implementations of state-of-the-art boosting algorithms that can be used for classification, regression, and ranking problems
- Built-in regularization to prevent overfitting and improve model generalization.
XGBoost tutorial on Kaggle is a good place to become familiar.
10. FastAPI
So far we’ve looked at Python libraries. Let’s wrap up with a framework for building APIs—FastAPI.
FastAPI is a web framework for building APIs with Python. It is ideal for creating APIs to serve machine learning models, providing a robust and efficient way to deploy data science applications.
- FastAPI is easy to use and learn, allowing for quick development of APIs
- Provides full support for asynchronous programming, making it suitable for handling many simultaneous connections
FastAPI Tutorial: Build APIs with Python in Minutes is a comprehensive tutorial to learn the basics of building APIs with FastAPI.
Wrapping Up
I hope you found this round-up of data science libraries helpful. If there’s one takeaway, it should be that these Python libraries are useful additions to your data science toolbox.
We’ve looked at Python libraries that cover a range of functionalities—from data manipulation and visualization to machine learning, web scraping, and API development. If you’re interested in Python libraries for data engineering, you may find 7 Python Libraries Every Data Engineer Should Know helpful.
Bala Priya C is a developer and technical writer from India. She likes working at the intersection of math, programming, data science, and content creation. Her areas of interest and expertise include DevOps, data science, and natural language processing. She enjoys reading, writing, coding, and coffee! Currently, she’s working on learning and sharing her knowledge with the developer community by authoring tutorials, how-to guides, opinion pieces, and more. Bala also creates engaging resource overviews and coding tutorials.
