9 Python Libraries for Data Science

Key takeaways

Data scientists and other data professionals often use Python libraries because this popular programming language is easy to use, flexible, and offers many resources and tools for organizing, manipulating, and visualizing data.

• Instead of writing code from scratch, you can use Python libraries to add pre-written code so you can accomplish tasks more efficiently.

• Python has hundreds of thousands of libraries to choose from.

• Some of the most popular libraries for data science include Pandas, NumPy, Matplotlib, Seaborn, SciPy, Scikit-learn, Statsmodels, Plotly, and Requests.

Learn more about the most popular Python libraries for data science, including what each is best for. Afterward, develop your ability to apply exploratory data analysis techniques with the Google Data Analysis with Python Specialization. In as little as one month, you'll build a strong foundation in analyzing and cleaning real-world datasets using NumPy and Pandas.

9 Python libraries for data science

Python has many libraries you can use for data science because its popularity and rapid growth have fostered a vast network of resources, documentation, and online support. 

1. Pandas

Pandas, short for Python data analysis, is an open-source tool for data manipulation. Pandas is flexible, easy to use, and has higher-level tools like data structures and operations. 

• Best used for: Data cleaning, data visualizations, and analysis 

• How to start: You can start learning Pandas by looking at the documentation available online or with an online course like Data Analysis with Pandas and Python Specialization [1]. 

2. NumPy

Numerical Python, typically shortened to NumPy, is a basic library for numerical computing. This library can support high-level functions, and once you have experience with NumPy, you may find it easier to learn other, more advanced Python libraries. 

• Best used for: Numerical computing, data manipulation, data analysis

• How to start: You can start learning NumPy with the online user guide and documentation or with a course like Data Analysis with Python from IBM [2,3]. 

3. Matplotlib

Matplotlib is a data visualization library that can help you create a wide range of data visualizations, including static, animated, and interactive ones. 

• Best used for: Static data visualizations, animated data visualizations

• How to start: You can start learning Matplotlib by following the user guide and documentation available online or taking a course like Data Visualization with Python from IBM [4,5].

4. Seaborn

Seaborn is another Python data visualization library that builds on Matplotlib and offers high-level functions to make complex data visualizations more digestible. 

• Best used for: Statistical graphics, data visualizations for complex data sets

• How to start: You can start learning Seaborn with the user guide and tutorial available online or with a Guided Project like Python for Data Visualization: Matplotlib & Seaborn (Enhanced) [6]. 

5. SciPy

SciPy, an abbreviation of Scientific Python, is a library for high-level statistical computations for manipulating data in ways you can apply to many situations. 

• Best used for: Scientific programming like linear algebra, numerical integration, and optimization

• How to start: You can start learning SciPy by following the user guide and documentation available online or taking a course like Data Analysis with Python from IBM [7,8]. 

6. Scikit-learn

Scikit-learn is a Python library for machine learning, including classification, regression, clustering, model selection, and more. 

• Best used for: Statistical modeling, supervised and unsupervised learning

• How to start: You can start learning Scikit-learn with the user guide available online or with a Guided Project like Scikit-Learn For Machine Learning Classification Problems [9]. 

7. Statsmodels

Statsmodels is a Python library for statistical modeling, such as regression or time series analysis, hypothesis testing, and model diagnostics.

• Best used for: Regression and linear models, time series analysis, and other statistical modeling

• How to start: You can start learning Statsmodels with the user guide and documentation available online or with a program like Statistics with Python Specialization [10,11]. 

8. Plotly

Plotly is another Python library for data visualizations. It can create a wide variety of static and interactive charts and graphs with statistical, financial, or scientific applications. 

• Best used for: Statistical visualizations, financial visualizations, and scientific visualizations

• How to start: You can start learning Plotly with the Getting Started guide or the documentation available online or with a Guided Project like Data Visualization with Plotly Express [12,13]. 

9. Requests

Requests is an HTTP library in Python that works with APIs and retrieves data from other sources. It improves on the standard Python module with simple syntax and parsing. 

• Best used for: Integrating APIs and retrieving data

• How to start: You can start learning Requests with the Quickstart guide or the documentation available online or with a course like Python for Data Science, AI & Development [14,15]. 

Is Python or SQL better for data science?

SQL is another popular programming language for data science. Both SQL and Python will enable you to work with data. However, SQL is designed to transform and query data, while Python provides more of the power that you will need to perform complex data analysis tasks.

How are Python libraries for data science used?

Data scientists and other professionals use Python libraries for many different reasons. Let's review a few of the most common areas that Python can support.

Machine learning

Python and its various data science libraries provide a framework for building machine learning models. Python's features allow for easy data validation, cleansing, processing, and analysis. Since Python libraries for data science come with important code already in place, you have to worry less about the technical aspects of coding, where costly errors may occur.

Automated machine learning (AutoML)

AutoML builds upon the ideas of traditional machine learning and aims to “automate” the repeated and lengthy steps involved with training and building a model. Auto-PyTorch and Auto-Sklearn are two Python libraries for data science specifically geared towards facilitating AutoML.

Auto-PyTorch offers full automation in critical areas and the ability to work with neural networks. Auto-Sklearn leverages meta-learning and a few other techniques to pinpoint the exact algorithm you need to train your model based on the characteristics of your input data. 

Deep learning

Deep learning aims to train models with mass quantities of data to optimize prediction-making capabilities. Python libraries, such as TensorFlow and Keras, enable you to conduct deep learning. Keras, in particular, combines other popular Python libraries to create a user-friendly environment for handling neural networks.

Natural language processing

Natural language processing aims to accurately decipher human language through various algorithms and models. Many Python libraries for data science exist to explore natural language processing, such as NLTK, TextBlob, and spaCy. These libraries allow you to create applications capable of classification, sentiment analysis, tokenization, and more fairly easily.

Industries that use Python libraries for data science

Thanks to Python's versatility and significant volume of libraries, many different disciplines and industries leverage this pre-set code:

• Web development

• Computer vision

• Game development

• Biology

• Psychology

• Medicine

• Robotics

• Autonomous vehicles

Pros and cons of using Python libraries for data science

As with any programming language, Python has different benefits and considerations.

Pros

The pros of using Python libraries for data science include:

• Popularity and versatility

• Ease of use

• Only a small learning curve

• Open source

• Quick development abilities

• Relevancy for a wide range of jobs

• A large community of users

• Robust standard libraries

• Ease of reproducibility

Cons

The cons of using Python libraries for data science include:

• Inability to efficiently handle large data sets

• Slow computation

• Runtime errors

• Memory inefficiencies

• Struggles in working with databases

• Fewer data science libraries

• Less informative visualizations

Build your data skills on Coursera

Whether you want to develop a new skill, get comfortable with an in-demand technology, or advance your abilities, keep growing with a Coursera Plus subscription. You’ll get access to over 10,000 flexible courses from over 350 top universities and companies.

Build job-ready skills with Coursera Plus

Start 7-day free trial

• 
• 
• 
• 

Start 7-day free trial