Cursarium logoCursarium
beginnerCertificateFree

Pandas

by Aleksey Bilogur · Kaggle

4.6
(12,000 reviews)
600K+ enrolled4 hoursUpdated 2024-03
Go to Course

Our Verdict

Worth taking

Kaggle Learn's Pandas course (taught by Aleksey Bilogur) is a free, in-browser micro-course that is genuinely one of the best fast on-ramps to Pandas for people who already know basic Python. It is six short tutorial-plus-exercise lessons covering exactly the operations you use daily: reading/writing data, indexing and selection, summary functions and maps, grouping and sorting, data types and missing values, and renaming/combining DataFrames. Its strengths are concision, hands-on notebooks that run with zero setup, and hints plus full solutions for every exercise; reviewers on Class Central and the Björnström course-review blog consistently call it well-structured and beginner-friendly. The honest caveat is the difficulty curve: the exercises step up sharply around the 'Grouping and Sorting' lesson, to the point that Kaggle has a public 'Difficulty in Pandas course' feedback thread, and the syllabus is deliberately narrow (no visualization, no real end-to-end project, no NumPy depth). Treat it as a focused 4-hour skill primer, not a comprehensive data-analysis bootcamp.

It is free, fast (~4 hours), entirely hands-on with solutions provided, and teaches the exact Pandas operations needed for data science work. The only real risks are the mid-course difficulty spike and the narrow scope, neither of which outweighs the value for the right learner.

Best for: Learners who already know basic Python and want to get productive with Pandas quickly: aspiring data analysts/scientists, students prepping for Kaggle competitions, and engineers who need to read, filter, group, and clean tabular data. The in-browser, exercise-driven format suits people who learn by doing in short sittings.

Skip if: Complete programming beginners (you need to be comfortable with Python first), anyone wanting a broad data-analysis curriculum with visualization or a full capstone project, and learners who prefer long-form video lectures or deep conceptual theory over terse text plus coding drills. Those needing hand-holding may find the exercise difficulty jump frustrating.

About This Course

Master data manipulation with Pandas covering DataFrames, indexing, grouping, merging, and handling missing data.

What You'll Learn

Create Series and DataFrame objects by hand and by reading data files (CSV/Excel), and write data back out
Index, select, and assign data using loc/iloc and conditional filtering
Apply summary functions and transform columns with map() and apply()
Group rows with groupby(), aggregate, and sort results to extract insight from larger datasets
Inspect and convert data types and detect/handle missing values
Rename columns/indexes and combine multiple DataFrames (concat/join/merge)

Curriculum

Creating, Reading and Writing

Build Series and DataFrames by hand and by loading data files; the foundational 'you can't analyze what you can't read in' lesson.

Indexing, Selecting & Assigning

Select and assign data with loc/iloc and boolean filtering, the operations a working data scientist uses dozens of times a day.

Summary Functions and Maps

Use built-in summary functions and reshape/transform column values with map() and apply().

Grouping and Sorting

Group with groupby(), aggregate, and sort to scale up insight on complex datasets; widely reported as the hardest lesson and the difficulty spike of the course.

Data Types and Missing Values

Understand and convert dtypes and detect, fill, or drop missing (NaN) values.

Renaming and Combining

Rename indexes/columns and combine multiple DataFrames using concat, join, and merge.

Prerequisites

  • Comfort with basic Python (Kaggle's own Python micro-course or equivalent is the assumed starting point)
  • A free Kaggle account to run the interactive notebooks and earn the certificate
  • No local setup, installs, or prior Pandas experience required

Instructor

Aleksey Bilogur

Instructor · Kaggle

Pros & Cons

Pros

  • Completely free with no paywall, plus a free certificate on completion
  • Fully interactive: notebooks run in-browser with zero setup, easy to pause and resume in short sessions
  • Every exercise ships with hints and full solutions, so you are never stuck
  • Tightly scoped to the high-value Pandas operations used in real data work, finishable in ~4 hours
  • Consistently positive public sentiment (Class Central reviews, KDnuggets, Björnström course-review blog) for being concise and beginner-friendly

Cons

  • Noticeable difficulty jump in the exercises, especially the 'Grouping and Sorting' lesson; Kaggle hosts a public 'Difficulty in Pandas course' feedback thread about this
  • Narrow scope: no data visualization, no NumPy depth, and no end-to-end capstone project
  • Terse text-plus-code format with little conceptual depth; not ideal for learners who want lectures or theory
  • Assumes prior Python knowledge, so it is not a true zero-to-one starting point

Alternatives To Consider

Intro to Machine Learning

Kaggle

View course

Machine Learning Scientist with Python

DataCamp

View course

Machine Learning A-Z: AI, Python & R

Udemy

View course

Frequently Asked Questions

Is Pandas free?

Yes — Pandas is free to access. Free. All Kaggle Learn micro-courses, including this one, are 100% free with no trial or upsell, and a shareable completion certificate is included at no cost.

Who is Pandas for?

Learners who already know basic Python and want to get productive with Pandas quickly: aspiring data analysts/scientists, students prepping for Kaggle competitions, and engineers who need to read, filter, group, and clean tabular data. The in-browser, exercise-driven format suits people who learn by doing in short sittings.

What will you learn in Pandas?

Create Series and DataFrame objects by hand and by reading data files (CSV/Excel), and write data back out; Index, select, and assign data using loc/iloc and conditional filtering; Apply summary functions and transform columns with map() and apply(); Group rows with groupby(), aggregate, and sort results to extract insight from larger datasets.

What are the prerequisites for Pandas?

Comfort with basic Python (Kaggle's own Python micro-course or equivalent is the assumed starting point); A free Kaggle account to run the interactive notebooks and earn the certificate; No local setup, installs, or prior Pandas experience required.

Is Pandas worth it?

It is free, fast (~4 hours), entirely hands-on with solutions provided, and teaches the exact Pandas operations needed for data science work. The only real risks are the mid-course difficulty spike and the narrow scope, neither of which outweighs the value for the right learner.

How we reviewed this course

This is an independent editorial assessment by Cursarium, based on Kaggle's published course materials and aggregated public learner feedback (last reviewed 2026-06). We have not independently completed the course. Links to providers are standard references, not paid placements.

Sources

Free
Go to Course