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If you want to make a career in data science, you probably know that Python is the go-to language for data science. In addition to being basic to learn, Python also has a super affluent set of Python libraries that let you accomplish any data science task with just a few lines of code.
Whether you’re just starting out as a data scientist or looking to transition into a data-related career, learning how to work with these libraries will be helpful. In this article, we’ll take a look at some imperative Python libraries for data science.
We focus specifically 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 get to know if you’re interested in data analysis. Series and data frames, the key data structures of pandas, simplify the process of working with structured data.
You can exploit pandas to tidy, transform, merge, and combine data, so it is useful for both preprocessing and data analysis.
Let’s take a look at the most critical features of pandas:
- Pandas provides two basic data structures: Series (one-dimensional) and DataFrame (two-dimensional), which allow basic manipulation of structured data.
- Functions and methods for handling missing data, filtering data, and performing various operations to tidy and preprocess data sets
- Features that enable you to connect, combine, and combine data sets in a pliant and capable way
- Specialized time series data handling functions to make working with time data easier
This a short course on pandas from Kaggle will support you get started with data analysis with pandas.
2. matplotlibli library
To understand data, you need to go beyond analysis and visualize it. Matplotlib Library is the first data visualization library you will explore before moving on to other libraries like Seaborn, Plotly, and the like.
It’s configurable (although it does require some effort) and is suitable for a range of charting tasks, from elementary line graphs to more elaborate visualizations. Some of the features include:
- Basic visualizations such as line charts, bar charts, histograms, scatter plots, and more.
- Ability to personalize graphs with quite detailed control over every aspect of the drawing, such as colors, labels, and scales.
- It plays well with other Python libraries like Pandas and NumPy, making it easier to visualize data stored in DataFrames and arrays.
This Matplotlib Tutorials should support you get started on your planning adventure.
3. Born in the Sea
Seaborn is built on top of Matplotlib (it’s a simpler Matplotlib) and is designed specifically for statistical and easier data visualization. It simplifies the process of creating elaborate visualizations with its high-level interface and integrates well with pandas data frames.
Seaborn has:
- Built-in themes and color palettes to enhance your story without much effort
- Features for creating helpful visualizations such as violin plots, pair charts, and heat maps
This Data Visualization Micro-Course on Kaggle will support you get started with Seaborn.
4. Plots
Once you are familiar with working with Seaborn, you can learn how to exploit it Plotlya Python library for creating interactive data visualizations.
In addition to different types of charts, with Plotly you can:
- Create interactive charts
- Build web apps and data dashboards with Plotly Dash
- Export charts to inert images, HTML files, or embed them in web apps
Guide Plotly Python Open Source Graphics Library Basics will support you get started with creating graphs with Plotly.
5. Requests
Often you will need to retrieve data from APIs by sending HTTP requests. To do this you can exploit Requests library.
It’s elementary to exploit and makes fetching data from APIs or websites a piece of cake 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 maintain settings across requests, such as cookies and headers
- Apply different authentication methods, including basic and OAuth
- Handling timeouts, retries, and errors for reliable network interaction
You can check out Documentation requests for elementary and advanced exploit cases.
6. Pretty soup
Web scraping is an imperative skill for data scientists and Beautiful soup is a library for everything related to web scraping. Once you have retrieved your data using the Requests library, you can exploit Pretty Soup to navigate and search the parse tree, making it basic to locate and extract the desired information.
Pretty Soup is therefore often used in conjunction with the Requests library to fetch and parse web pages. You can:
- Analyze HTML documents to find specific information
- Navigate and search the syntax tree using Python idioms to extract specific data
- Find and modify tags and attributes in a document
Mastering Web Scraping with BeautifulSoup is a comprehensive guide to learning the basics of Pretty Soup.
7. Scikit-Learn
Scikit-Science 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 dedicated modules for:
- Data preprocessing such as scaling, normalization, and categorical feature encoding
- Model Selection and Hyperparameter Tuning
- Model evaluation
Machine Learning with Python and Scikit-Learn – Full Course is a good resource for learning how to create machine learning models with Scikit-Learn.
8. State Models
State models is a library dedicated to statistical modeling. It offers a range of tools for estimating statistical models, performing hypothesis tests, and data mining. Statsmodels is particularly useful if you want to delve into econometrics and other fields that require strict statistical analysis.
You can exploit statsmodels for estimation, statistical testing, and other purposes. Statsmodels provides the following features:
- Features that summarize and explore data sets to gain insight before modeling
- Various types of statistical models, including linear regression, generalized linear models, and time series analysis
- A wide range of statistical tests, including t-tests, chi-square tests, and nonparametric tests
- Model diagnostics and validation tools, including residual analysis and goodness of fit tests
This Getting Started with Statistics This guide will support you learn the basics of this library.
9. Download
Download 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 a variety of tasks, including classification, regression, and ranking, and includes features for regularization and cross-platform integration.
Some of the 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.
Download A good place to get started with Kaggle is the tutorial.
10.FastAPI
So far we’ve looked at Python libraries. Let’s finish with a framework for building APIs, FastAPI.
FastAPI is a web framework for building APIs with Python. It is ideal for building APIs to serve machine learning models, providing a stalwart and capable way to deploy data science applications.
- FastAPI is basic to exploit and learn, allowing you to quickly create APIs
- Provides full support for asynchronous programming, making it suitable for handling multiple simultaneous connections
FastAPI Tutorial: Creating Python APIs in Minutes is a comprehensive tutorial that lets you learn the basics of creating APIs with FastAPI.
Summary
I hope this roundup of data science libraries has been helpful. If there’s one takeaway, it’s that Python libraries are a useful addition to your data science toolbox.
We’ve 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 these 7 Python libraries every data engineer should know helpful.
Bala Priya C is a software 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 enjoys reading, writing, coding, and drinking coffee! She is currently working on learning and sharing her knowledge with the developer community by creating tutorials, how-to guides, opinion pieces, and more. Bala also creates engaging resource reviews and coding tutorials.