10 Best Pandas Courses in 2026: Free and Paid Picks, Checked Live

Last updated: 28 October 2026. Written by Josh Hutcheson, OnlineCourseing editor. Every course below was opened on its platform on 28 October 2026 to confirm it is live and to read its rating, enrollment and last-updated date; version facts come from PyPI and the pandas release notes. See our review methodology.

Quick verdict

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pandas is the Python library for working with tables of data: loading CSV and Excel files, cleaning messy values, filtering, grouping, joining and reshaping, and getting data ready for charts or machine learning. Its DataFrame is the workhorse of the Python data ecosystem, which is why pandas is one of the first serious skills on almost every data-analyst, data-scientist and machine-learning path.

The library itself changed in 2026. pandas 3.0, released on 21 January 2026, made a dedicated string data type and Copy-on-Write the defaults and requires Python 3.11 or later; the current release is 3.1.0, published on 22 October 2026. Most courses were recorded on pandas 2, which still teaches you nearly everything, but we flag below which picks are freshly updated. This guide was rebuilt in October 2026: we checked each course live and expanded the list from three picks to ten.

The best pandas courses at a glance

Ratings, enrollment and update dates as shown on each course page on 28 October 2026.

1. Data Analysis with Pandas and Python (Udemy): best overall

Boris Paskhaver’s course is one of the most complete treatments of pandas anywhere, and it is the one we recommend to most people: 4.7 stars from 26,816 ratings, 229,992 students, about 17.5 hours of video, and an update in October 2026. It covers Series and DataFrames, importing and exporting data, filtering and sorting, groupby, merging and joining, multi-indexes, dates, text and pivot tables, taught against real datasets that come with the course.

It is long, and that is the point: pandas has a lot of surface area, and Paskhaver covers it methodically without assuming more than basic Python. If you want to know pandas well rather than just get by, this is the course.

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2. Python Data Analysis: NumPy & Pandas Masterclass (Udemy): best for business analysts

Maven Analytics, with Chris Bruehl teaching, built this course for analysts who work with business data. It teaches NumPy and pandas together for data analysis, data science and business intelligence. It holds 4.7 stars from 2,884 ratings with 24,395 students and was updated in June 2026.

Choose it over pick 1 if you come from Excel and want the material framed around business questions rather than a complete tour of the library.

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3. Master Pandas and Python for Data Handling (Udemy): highest rated

Henrik Johansson’s course has the highest rating of the pandas courses we checked, 4.9 from 1,146 ratings, and it was updated in October 2026. It covers Python, pandas and advanced data handling for data analysis, data science and machine learning across about 32.5 hours of video.

It has fewer students (6,172) than the big names, so the rating rests on a smaller base, but it is a strong pick if you want depth and a recently updated course.

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4. The Complete Pandas Bootcamp (Udemy): best for data science projects

Alexander Hagmann’s bootcamp is long, at about 37 hours, and goes beyond data manipulation into Seaborn charts, machine learning and using ChatGPT with pandas, with online exercises along the way. It holds 4.7 stars from 3,900 ratings with 30,622 students and was last updated in December 2025.

Take it if you want one course that carries you from pandas into data science. If you only need the data-wrangling core, pick 1 or pick 5 is a more direct route.

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5. Data Manipulation in Python: A Pandas Crash Course (Udemy): fastest start

Samuel Hinton’s crash course is for people who want to be productive quickly: transforming, cleaning and merging data with pandas without a long introduction. It holds 4.6 stars from 1,890 ratings with 38,924 students and was updated in February 2025. A common complaint about pandas courses is that they spend too long on importing pandas and creating a DataFrame; as a crash course, this one is short by design.

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6. Introduction to Data Science in Python (Coursera): best university course

Christopher Brooks of the University of Michigan teaches this course, the first in Coursera’s Applied Data Science with Python specialization. Three of its four modules are about pandas: basic and advanced data processing, then answering questions with messy data. It is rated 4.5 from 27,303 reviews with 869,936 enrolled, and Coursera estimates about three weeks at 10 hours a week.

Coursera marks it intermediate, and some reviews call it challenging; that rigor is the reason to take it. You can enroll for free to see the material, and the graded assignments and certificate come with a paid enrollment or Coursera Plus.

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7. Python for Data Analysis: Pandas & NumPy (Coursera): two-hour hands-on intro

Dr. Ryan Ahmed’s guided project takes about two hours in a browser workspace. You learn Python fundamentals for data analysis, NumPy arrays and the basics of pandas, working along with the instructor in a split screen. It is rated 4.5 from 393 reviews with 34,026 enrolled. It is a short introduction, not a full course; use it to see whether pandas clicks before committing to one of the longer picks.

See the guided project on Coursera

8. Data Manipulation with pandas (DataCamp): best interactive practice

DataCamp’s pandas course runs entirely in the browser: short videos followed by coding exercises, so you write pandas from the first lesson without installing anything. It is part of a DataCamp subscription, which on 28 October 2026 was $336 a year for Premium.

The honest caveat, which learners raise often, is that many exercises have you fill in missing code rather than write it from scratch. It builds familiarity quickly; pair it with a project of your own so the skills stick. Our DataCamp review covers the subscription in detail.

See Data Manipulation with pandas on DataCamp

9. Kaggle Learn: Pandas (free): best free course with a certificate

Kaggle’s pandas course by Aleksey Bilogur is free, runs in the browser and takes about four hours. It is built from short tutorials and hands-on exercises: creating, reading and writing data, indexing and selecting, summary functions, grouping and sorting, data types and missing values, and renaming and combining. Kaggle issues a free certificate when you finish. Each exercise checks your answer and offers a hint and a solution you can reveal when stuck, so you are never blocked for long. It is the fastest free way to become productive.

10. freeCodeCamp Data Analysis with Python (free): best free certification

freeCodeCamp’s Data Analysis with Python certification teaches pandas, NumPy and data visualization for free, and you earn the certification by completing five projects, including a demographic data analyzer and a medical data visualizer. The projects are the useful part: they make you write pandas against a specification rather than follow along. Learners moving on from CS50 Python often take it alongside Kaggle’s course.

Free ways to learn pandas

You can get remarkably far with pandas for free:

• Kaggle Learn: Pandas (pick 9): four hours, in the browser, with a certificate.
• freeCodeCamp Data Analysis with Python (pick 10): five projects and a free certification.
• The official “10 minutes to pandas” quick start and user guide, always current for pandas 3.1.
• Coursera’s free enrollment on the University of Michigan course (pick 6), to read and watch without the graded work.

A motivated learner can reach working competence on these alone. Many people start free and buy pick 1 when they want systematic depth.

Is there a pandas certification?

There is no official pandas certification from the library’s maintainers. When people search for one, they usually mean a certificate of completion from Kaggle, freeCodeCamp, Coursera, DataCamp or Udemy, or a broader data-analysis credential that includes pandas, such as Google’s Data Analytics or IBM’s Data Analyst certificates. Those broader certificates carry more weight because they signal end-to-end analysis skills.

Our advice: do not chase a pandas-only certificate. Earn a respected data-analysis certificate that includes pandas, or, better, publish a notebook that shows you cleaning and analyzing a real dataset. See our guide to the best data analysis courses for those programs.

What changed in pandas 3.0, and does it matter for courses?

From the pandas 3.0.0 release notes (21 January 2026). The current release is 3.1.0 (22 October 2026).

None of this makes a pandas 2 course worthless: DataFrames, groupby, joins and reshaping work the same way. It does mean that when a lesson’s output looks different from yours, the version is the first thing to check. Picks 1 and 3 were updated in October 2026.

What a good pandas course covers

Use this as a checklist. The foundations are the Series and DataFrame, importing and exporting data (CSV, Excel, SQL), and selection and filtering with loc and iloc. A complete course then covers groupby and aggregation, merging and joining, reshaping with pivot tables and melt, handling missing data, and working with dates, times and text. Stronger courses add multi-indexes, vectorized operations instead of loops, and plotting.

A course that only reads a CSV and filters rows is a starter. Real pandas fluency lives in groupby, joins and reshaping. For where pandas fits in the bigger picture, see our best data science courses guide, and for Python itself our best Udemy Python courses.

How to choose a pandas course

Start from what you will use pandas for, not from the ratings:

Two checks apply to every course. First, look at the last-updated date: the October 2026 updates on picks 1 and 3 cover the pandas 3 era, while older courses will show some outdated output. Second, make sure the course uses real, messy datasets; a course that only works on tidy toy tables will not prepare you for the cleaning work that takes most of an analyst’s time.

A four-week pandas learning plan

1. Week 1: the basics. Take Kaggle’s course (pick 9). Learn to read a CSV, select columns and rows with loc and iloc, and summarize with describe and value_counts.
2. Week 2: cleaning and grouping. Work through the groupby, missing-data and data-type sections of your main course. Practice on a dataset you care about: your bank exports, a sports league table or a public dataset from Kaggle.
3. Week 3: combining and reshaping. Merge two tables, pivot a long table into a wide one and back with melt, and handle dates. This is where most learners stall, so slow down.
4. Week 4: one finished analysis. Answer three questions about your dataset, add two charts, and publish the notebook on GitHub or Kaggle. Then attempt one of freeCodeCamp’s projects (pick 10) to test yourself against a specification.

An hour a day for four weeks is enough to reach working competence. The learners who stall are usually the ones who watch lessons without writing their own code, so keep a notebook open alongside every course.

Practice ideas and datasets

Every course on this page gets more useful when you apply it to data you chose yourself. Good first datasets:

• Your own exports: a bank or credit card statement as CSV, a fitness app export, or a spreadsheet from work (with anything sensitive removed).
• Public datasets on Kaggle: thousands of free datasets, each with notebooks by other users you can compare your work against.
• Government open data: census, transport or weather data, which is realistically messy and well documented.
• The freeCodeCamp projects (pick 10), which come with tests that tell you whether your pandas code is right.

Exercises that build fluency fast: convert a wide table into a long one, compute a new column from two others, find the top 10 of something by group, fill or drop missing values and explain the choice, then plot the result. When you can do those without looking anything up, you know pandas.

Is pandas hard to learn?

No, if you already know basic Python. You can load a file, filter it and group it within a day or two. What takes longer is fluency: knowing the idiomatic way to reshape data, avoiding slow row-by-row loops, and handling dates and missing values cleanly. That usually takes a few weeks of practice on real data. Learners who make the fastest progress pick a dataset they care about and keep answering questions with it: turn wide data into long, summarize it, create new columns, filter, then plot the results.

pandas vs. Polars: does it still matter?

Polars is faster on large datasets and has a clean API, and it is worth knowing it exists. But pandas remains the library most tutorials, Stack Overflow answers, other libraries and job listings assume. Learn pandas first; the concepts (DataFrames, groupby, joins) transfer directly to Polars and to SQL. Pick up Polars later if your data outgrows what pandas handles comfortably. If you work with streaming data, our Apache Kafka courses guide covers the other end of the pipeline.

pandas courses: frequently asked questions

What is the best pandas course?

For most people, Data Analysis with Pandas and Python by Boris Paskhaver on Udemy: 4.7 stars from 26,816 ratings, 229,992 students and updated in October 2026. For a university course with a certificate, the University of Michigan’s Introduction to Data Science in Python on Coursera is the strongest option.

Can I learn pandas for free?

Yes. Kaggle’s free pandas course takes about four hours and issues a certificate, freeCodeCamp’s Data Analysis with Python certification is free and project-based, and the official “10 minutes to pandas” guide is always current. Together with practice on a real dataset, they are a complete free path.

Is pandas part of Python?

No. pandas is a separate, free library that you install with pip install pandas or get bundled with distributions such as Anaconda. It is not part of the Python standard library, but it is installed alongside Python in most data science setups.

Is pandas easy to learn?

The basics are, if you know Python. Most people can load, filter and group data within a few days. Fluency with joins, reshaping and dates takes a few weeks of regular practice on real data.

Do I need to know Python before learning pandas?

Yes. pandas is a Python library, so be comfortable with variables, functions, lists and dictionaries first. You do not need advanced Python; with the fundamentals in place, pandas is approachable and immediately useful.

How long does it take to learn pandas?

You can be productive with basic loading and filtering in a few days. Kaggle’s course takes about four hours; Paskhaver’s course runs about 17.5 hours of video. Real fluency with groupby, joins and reshaping takes a few weeks of practice.

Is there an official pandas certification?

No. The pandas maintainers do not offer one. Kaggle, freeCodeCamp, Coursera, DataCamp and Udemy issue certificates of completion, and broader credentials such as Google’s Data Analytics certificate include pandas.

Is there a free pandas course with a certificate?

Yes. Kaggle Learn’s pandas course is free and issues a certificate on completion, and freeCodeCamp’s Data Analysis with Python certification is free once you complete its five projects.

Should I learn pandas or Polars?

pandas first. It is still the library most tutorials, libraries and employers expect, and its concepts transfer to Polars. Learn Polars later if you need more speed on large datasets.

Related guides

Best Data Science Courses · Best Data Analysis Courses · Best Udemy Python Courses · Best Free Python Courses · Best Apache Kafka Courses

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