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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 easy to learn, Python also has a super rich suite of Python libraries that allow you to tackle 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 start a career in data, learning how to work with these libraries is useful. In this article, we’ll look at some of the must-have 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 will encounter if you are into data analysis. Series and DataFrames, the core Pandas data structures, simplify the process of working with structured data.

Pandas can be used to clean, transform, merge and join data, so it is useful for both preprocessing and analyzing data.

Let’s take a look at the main features of pandas:

• Pandas provides two primary data structures: Series (one-dimensional) and DataFrame (two-dimensional), which allow 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, link and concatenate datasets in a flexible and efficient way
• Specialized functions for processing time series data, making working with temporal data easier

This short course on Pandas from Kaggle will help you get started with analyzing data using Pandas.

2. Matplotlib

You need to go beyond analysis and data visualization to understand it. Matplotlib is the data visualization library to get you started before moving on to other libraries like Seaborn, Plotly, and the like.

It is customizable (although it takes 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 charts, bar charts, histograms, scatter plots and more.
• Customizable plots with precise control over every aspect of the figure, such as colors, labels, and scales.
• Works well with other Python libraries such as Pandas and NumPy, making it easier to visualize data stored in DataFrames and arrays.

The Matplotlib tutorials will help you get started with creating graphs.

3. Seaborn

Seaborn is built on Matplotlib (it’s the simpler Matplotlib) and is specifically designed for statistical and simple 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
• Features for creating useful visualizations such as violin plots, pair plots and heat maps

Get started with Seaborn with the Data Visualization microcourse on Kaggle.

4. Plots

Once you are familiar with Seaborn, you can learn how to use Plotly, a Python library for creating interactive data visualizations.

In addition to the different chart types, Plotly lets you:

• Creating interactive plots
• Build web apps and data dashboards with Plotly Dash
• Export graphs to static images, HTML files or integrate them into web applications

The Plotly Python Open Source Graphing Library Fundamentals guide will help you get familiar with creating graphs with Plotly.

5. Requests

Often you need to retrieve data from APIs by sending HTTP requests. For this you can use the Requests library.

It’s easy 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 communicate with web services
• Manage and maintain settings for all requests, such as cookies and headers
• Use different authentication methods, including basic and OAuth
• Handling timeouts, replays, and errors to ensure reliable web interactions

Please see the Applications documentation for simple and advanced usage examples.

6. Nice soup

Web scraping is a must-have skill for data scientists, and Beautiful Soup is the go-to library for all things web scraping. After you’ve fetched your data using the Requests library, you can use Beautiful Soup to navigate and search the parse tree, making it easy to find and extract the information you’re looking for.

Beautiful Soup is therefore often used in conjunction with the Requests library to fetch and parse web pages. You can:

• Parsing HTML documents to find specific information
• Navigate and search the parse tree using Pythonic idioms to extract specific data
• Find and change tags and attributes within the document

Mastering Web Scraping with BeautifulSoup is a comprehensive guide to learning more 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 useful tool for building and evaluating machine learning models.

The Scikit-Learn library also has special modules for:

• Data preprocessing, such as scaling, normalization, and coding of categorical features
• Model selection and hyperparameter tuning
• Model evaluation

Machine Learning with Python and Scikit-Learn – Complete Course is a great resource to learn how to build machine learning models with Scikit-Learn.

8. Statistics models

Statsmodels is a library dedicated to statistical modeling. It provides a range of tools for estimating statistical models, performing hypothesis testing, and data exploration. Statsmodels is especially useful if you want to explore econometrics and other fields that require rigorous statistical analysis.

You can use statsmodels for estimation, statistical testing and more. Statsmodels offers the following:

• Features for summarizing and exploring datasets to gain insight before modeling
• Different types of statistical models, including linear regression, generalized linear models, and time series analysis
• A series of statistical tests including t-tests, chi-square tests, and nonparametric tests
• Tools for diagnosing and validating models, including residual analysis and goodness-of-fit testing

The Getting Started with StatsModels guide will help you master the basics of this library.

9. XGBoost

XGBoost is an optimized gradient boosting library designed for high performance and efficiency. It is widely used in both machine learning competitions and in practice. XGBoost is suitable for various tasks including classification, regression, and ranking, and provides regularization features and cross-platform integration.

Some of the features of XGBoost are:

• 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.

The XGBoost tutorial on Kaggle is a good place to get familiar.

10. Fast API

So far we’ve looked at Python libraries. Let’s close 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, and provides a robust and efficient way to implement data science applications.

• FastAPI is easy to use and learn, enabling rapid API development
• Provides full support for asynchronous programming, making it suitable for handling many concurrent connections

FastAPI Tutorial: Build APIs with Python in Minutes is a comprehensive tutorial that teaches you the basics of building APIs with FastAPI.

Complete

I hope you found this summary of data science libraries useful. If there’s one thing to take away, it’s that these Python libraries are useful additions to your data science toolbox.

We looked at Python libraries that cover a range of functionality, from data manipulation and visualization to machine learning, web scraping, and API development. If you’re interested in Python libraries for data engineering, you might find 7 Python Libraries Every Data Engineer Should Know useful.

Bala Priya C is a developer and technical writer from India. She enjoys working at the intersection of mathematics, programming, data science, and content creation. Her areas of interest and expertise include DevOps, data science, and natural language processing. She loves reading, writing, coding, and coffee! She is currently working on learning and sharing her knowledge with the developer community by writing tutorials, how-to guides, opinion pieces, and more. Bala also creates engaging resource lists and coding tutorials.