10 Python Libraries Every Data Scientist Should Know

If you're looking to make a career in the data field, you probably know that Python is the go-to language for data science. Aside from being easy to learn, Python also has a very comprehensive set of Python libraries that allow you to accomplish any data science task with just a few lines of code.

So, whether you are just starting out as a data scientist or are looking to switch to a career in data, learning how to work with these libraries will come in handy. In this article, we will discuss some Python libraries that you should know 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.

Python Data Science Libraries | Image by the author

1. Pandas

Pandas Pandas is one of the first libraries you'll encounter if you're interested in data analysis. Series and dataframes, Pandas' key data structures, simplify the process of working with structured data.

You can use pandas to clean, transform, merge, and join data, making it useful for both data preprocessing and analysis.

Let's review the key features of pandas:

• Pandas provides two main 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 data sets.
• Functions to merge, join and concatenate data sets in a flexible and efficient way
• Specialized functions for handling time series data, making it easier to work with temporal data.

This Short course on pandas Kaggle will help you get started analyzing data using pandas.

2. Matplotlib

We need to go beyond analysis and also visualize the data to understand it. Matplotlib It is the first data visualization library you will experiment with before moving on to other libraries like Seaborn, Plotly and the like.

It is customizable (although it does require some effort) and is suitable for a variety of plotting tasks, from simple line graphs to more complex visualizations. Some features include:

• Simple visualizations like line graphs, bar graphs, histograms, scatter plots, and more.
• Customizable charts with quite granular control over every aspect of the figure, such as colors, labels, and scales.
• It works well with other Python libraries like Pandas and NumPy, making it easy to visualize data stored in DataFrames and arrays.

He Matplotlib Tutorials It should help you get started with graphing.

3. Born at sea

born in the sea It is built on top of Matplotlib (it is the simpler version of Matplotlib) and is specifically designed for simple statistical data visualization. It simplifies the process of creating complex visualizations with its high-level interface and integrates well with Pandas data frames.

Seaborn has:

• Built-in themes and color palettes to enhance your graphics without much effort.
• Functions to create useful visualizations, such as violin plots, pair charts, and heat maps.

He Data visualization microcourse on Kaggle will help you get started with Seaborn.

4. Argumentatively

Once you feel comfortable working with Seaborn, you can learn how to use With argumenta Python library for creating interactive data visualizations.

In addition to the different types of graphs, with Plotly you can:

• Create interactive charts
• Create web apps and data dashboards with Plotly Dash
• Export graphics to static images, HTML files, or embed them in web applications

The guide Plotly Python Open Source Charting Library Basics It will help you get familiar with creating graphs with Plotly.

5. Requests

You will often need to get data from APIs by sending HTTP requests, and for this you can use the Requests library.

It's easy to use and makes it easy to get data from APIs or web pages, 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
• Use multiple authentication methods, including basic and OAuth
• Handling timeouts, retries, and errors to ensure reliable web interactions

You can check the Request 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 everything related to web scraping. Once you have obtained data using the Requests library, you can use Beautiful Soup to navigate and search through the parse tree, making it easy to locate and extract the desired information.

Therefore, Beautiful Soup is 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 complete guide to learning Beautiful Soup.

7. Scikit-Learn

Scikit-Learn is a machine learning library that provides ready-to-use implementations of classification, regression, clustering, and dimensionality reduction algorithms. 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 encoding of categorical features
• Model selection and hyperparameter tuning
• Model evaluation

Machine Learning with Python and Scikit-Learn: Complete Course is a good resource to learn how to build 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 exploring data. Statsmodels is particularly useful if you want to explore econometrics and other fields that require rigorous statistical analysis.

You can use statsmodels to perform estimates, statistical tests, and more. Statsmodels offers the following:

• Functions to summarize and explore data sets 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-square tests, and nonparametric tests
• Tools to diagnose and validate models, including residual analysis and goodness-of-fit tests

He Introduction to statsmodels The guide should help you learn the basics of this library.

9. XGBoost

XGBoost XGBoost is an optimized gradient boosting library designed for high performance and efficiency. It is widely used in both machine learning competitions and practice. XGBoost is suitable for various tasks including classification, regression, and sorting, 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 avoid overfitting and improve model generalization.

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

10. Fast API

So far we have discussed Python libraries. Let's finish with a framework for creating APIs: FastAPI.

Fast API is a web framework for building APIs with Python. It is ideal for building APIs that serve machine learning models, providing a robust and efficient way to deploy data science applications.

• FastAPI is easy to use and learn, enabling rapid API development.
• It 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.

Ending up

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

We've reviewed 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 might find 7 Python Libraries Every Data Engineer Should Know useful.

twitter.com/balawc27″ rel=”noopener”>Bala Priya C. Bala is a technical developer and 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, programming, and drinking coffee! Currently, she is working on learning and sharing her knowledge with the developer community by creating tutorials, how-to guides, opinion pieces, and more. Bala also creates interesting resource overviews and coding tutorials.