Pandas is a powerful, open-source Python library designed for data manipulation and analysis, enabling you to seamlessly clean, transform, and visualize data with just a few lines of code.
It is the most important data science library after NumPy, with more than half of Python developers using it for data science tasks.
With Pandas, you can easily read, write, and manipulate data in various formats such as CSV, Excel, SQL databases, and more. You can also perform various operations such as filtering, grouping, merging, and reshaping data with ease. It’s a must-have tool for any data scientist or analyst!
In this Best Courses Guides (BCG), we’ve gathered top-notch pandas courses for all levels of Python programmers — from beginners just starting out to advanced programmers with experience to show — to teach you how to handle data like a pro.
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Here are our top picks
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What is pandas?
Pandas is an open-source Python library for handling and manipulating huge, complex real-world datasets. Contrary to what you may have believed, pandas is not named after the animal but a much more boring term, panel data (don’t ask me where the letter S in pandas comes from). It’s like the Swiss Army knife for data science — it has a lot of ready-made tools (from data preprocessing to analysis), it’s very versatile (you can make your own functions), and it’s a must-have for anyone working with structured data (really!).
Originally designed out of need for intensive quantitative analysis on financial data, pandas has since expanded in scope and excels in handling mixed data types, missing values, and data alignment thanks to two handy data structures:
- DataFrame: basically a very fancy spreadsheet, being two-dimensional, one-data-type-only data structure in table form with rows and columns
- Series: the opposite of a table — it is a one-dimensional array-like structure or list that can hold any data type and serves as the building block for DataFrames.
These data structures combined form a formidable team.
With half of Python developers using pandas for data science (according to the 2023 State of Python survey), pandas has established itself as one of the most widely used Python libraries for data manipulation and analysis. As a result, Pandas has become a key component in the Python data science ecosystem, alongside other prominent libraries such as SciPy, NumPy, TensorFlow, scikit-learn, and Matplotlib.
Additionally, pandas offers a plethora of built-in functions for data cleaning, aggregation, transformation, filtering, and time series analysis, making it an indispensable tool for data scientists and analysts alike.
Courses Overview
- Only one course is suitable for complete beginners, two are for intermediates, one for advanced, and the rest for beginners
- All of the courses except for two are free or free-to-audit
- Most of the courses are video-based
- Two of the courses are produced by universities, two by institutions, and the rest by independents.
Do you have no experience with Python or programming? Then the free-to-audit course, Python and Pandas for Data Engineering, is for you! This course will teach you Python & pandas programming skills, as well as other tools needed to manage code and build scalable projects as a data or machine learning engineer. With plenty of exercises and labs for practice, this course is perfect for beginners in programming who want to transform and manipulate data as a data engineer.
If you enjoy this course, you can continue learning with the Python, Bash, and SQL Essentials for Data Engineering specialization, which covers topics like Bash scripting, SQL, web scraping, web development, and more.
What you’ll learn:
- Basics of Python programming: statements and data structures like sequences, dictionaries, and generators
- Data manipulation with pandas DataFrames: loading, filtering, and applying functions
- Overview of alternative data manipulation: comparing pandas to NumPy and Dask
- Introduction to development environments and version control: Streaming code editing with Vim, Coding with Visual Studio Code, and version control with Git
Verified learners will have access to labs where they can complete projects and hands-on tasks. Passing all graded assignments is necessary to receive the course certificate.
Python Pandas For Your Grandpa is a free course that covers important aspects of pandas such as reading and writing data to a file, creating data, merging data, grouping data, and more. You’ll learn through animations and practical problems and examples to help you learn intuitively.
To take this course, you should have a basic understanding of Python (data types, lists, sets, and lambda functions) and optionally, NumPy. Developed with Pandas version 1.2.0, this course is the successor to Python NumPy For Your Grandma.
What you’ll learn:
- Overview of pandas library: purpose and benefits in data manipulation and analysis
- Series: fundamental one-dimensional data structure, operations like vectorizing, filtering, applying functions, and handling missing values
- DataFrame: two-dimensional tabular structure, operations including merging, summarizing, and grouping data
- Advanced data types and structures: handling strings, dates, times, and categorical data, using multiindex, and transforming DataFrames with pivoting and stacking.
| Channel | GormAnalysis |
| Provider | YouTube |
| Instructor | Ben Gorman |
| Level | Beginners |
| Workload | 2–3 hours |
| Views | 16K |
| Exercises | 20 in-video challenges and Google Colab exercises |
| Certificate | None |
Kaggle is an online platform for data science competitions and collaboration, and what better way to learn pandas than from a data science website?
In this free micro-course with a free certificate, you’ll learn how to manipulate data and extract insights with pandas through Jupyter notebook tutorials and hands-on challenges. These exercises will have you struggle against the quirkiness of real-world data to develop your data-wrangling skills.
No experience with pandas is required to take Kaggle’s pandas course.
You’ll learn:
- Creating and structuring data with DataFrames and Series, including reading external data like CSV files
- Selecting and modifying specific values using indexes or labels
- Using built-in aggregate functions and creating custom ones to extract insights from data
- Cleaning messy data: fixing incorrect data types, and handling missing or malformed data
- Renaming columns and combining data from multiple DataFrames or Series for coherent data packaging.
Once you finish all tutorials and exercises, you’ll earn a certificate of completion that you can show off to employers!
There’s also an active discussion board for the course that learners can use to share and discuss ideas.
| Institution | Kaggle |
| Instructor | Aleksey Bilogur |
| Level | Beginners |
| Workload | 4 hours |
| Exercises | Hands-on challenges |
| Certificate | Free |
Fabio revised the research and the latest version of this article.
