Python Libraries for Data Analysis: Essential Tools for Data Scientists

Data scientists and other professionals use Python libraries for data analysis 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 that adds functionality to your program and allows you to manipulate data quickly. 

While Python has more than 137,000 libraries to choose from, these cover a wide range of functions. Some of the most popular libraries that add functionality for data analytics include Pandas, NumPy, Matplotlib, Seaborn, SciPy, Scikit-learn, Statsmodels, Plotly, and Requests. Explore these libraries and how you can use them for data analysis. 

How is Python used for data analysis?

Python is one of the most popular programming languages, and it offers many different libraries and resources to work with data. Data analysis is crucial for companies and organizations across various industries as data enables organizations to make better, more effective, fact-based decisions. Using Python libraries, you can work with large and complex data sets, create high-level visualizations, and manipulate data in advanced ways. 

Python is a good choice for data analysis due to its ease of use, intuitive syntax, and vast popularity. Its widespread adoption means you can find many resources, tutorials, and support available from a large and active community of Python users online. 

What Python libraries are used for data analysis?

Python has many libraries you can use for data analysis due to its inherent advantages as a programming language. Its popularity and rapid growth have fostered a vast network of resources, documentation, and online support. Explore some of the most popular Python libraries for data analysis, including Pandas, NumPy, Matplotlib, Seaborn, SciPy, Scikit-learn, Statsmodels, Plotly, and Requests. 

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. 

NumPy

NumPy, short for Numerical Python, 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 user guide and documentation available online or with a course like Data Analysis with Python. 

Matplotlib

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

• 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.

Seaborn

Seaborn is another Python data visualization library that builds off of 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. 

SciPy

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

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. 

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. 

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. 

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. 

Who uses Python libraries for data analysis?

If you want to explore career options where you can work with Python libraries for data analysis, you can choose from a wide range of occupations within data science. Three potential careers to consider are data scientist, business analyst, and financial analyst. 

Data scientists

Average annual salary in the US (Glassdoor): $118,399 [1]

Job outlook (projected growth from 2023 to 2033): 36 percent [2]

As a data scientist, you help companies or organizations make data-driven decisions by collecting, sorting, storing, and analyzing data. You present your findings to senior stakeholders and create visualizations to demonstrate the data in a format that non-technical colleagues can understand. You work with algorithms and computer programming to manipulate and interact with data. 

Business analysts

Average annual salary in the US (Glassdoor): $93,819 [3]

Job outlook (projected growth from 2023 to 2033): 11 percent [4]

As a business analyst, you apply data science principles to solve business-related problems. You may work with an organization, or you may offer your services as a consultant. In either case, you determine your client’s company goals, decide which data you will collect and how, then collect and review the data. Using your analysis, you make recommendations about what actions the company could take to make more progress on its goals. 

Financial analysts

Average annual salary in the US (Glassdoor): $78,675 [5]

Job outlook (projected growth from 2023 to 2033): 9 percent [6]

As a financial analyst, you use data to determine investment products' potential risks and rewards. You usually work to help companies and organizations determine how they should invest their money. Alternatively, you could collaborate with companies that sell financial products like stocks or bonds. You consult historical financial data as well as industry trends, financial statements, and the management team to determine whether investments will fit into the company’s overall investment strategy. 

Learn more about Python data analysis with Coursera.

Python offers many powerful tools for data analysis that help you manipulate, sort, clean, and visualize data. If you want to learn more about using Python libraries for data analysis, you can choose from many different types of courses, Specializations, and Guided Projects on Coursera. For example, the IBM Data Analyst Professional Certificate can help you learn job-ready skills to start a career as a data analyst.