Data Manipulation in Python: A Pandas Crash Course (Udemy.com)
Learn how to use Python and Pandas for data analysis and data manipulation. Transform, clean and merge data with Python.
Created by: Samuel Hinton
Last updated February 2025
What you will learn
- Visualise data using methods from histograms to dimensionality reduction.
- Create, save and serialise data frames in and out of multiple formats.
- Clean and format data easily.
- Detect and intelligently fill missing values.
- Group, aggregate and summarise your data.
- Merge data sources into a beautiful whole.
- Pivot and cross-tabulate data like a pro.
- Intersplice, summarise and investigate time series data.
- Seamlessly work with data from different time zones.
- Learn the common pitfalls and traps that ensnare beginners and how to avoid them.
How it compares
Against the other 371 rated Udemy courses in Data Analysis on CourseDuck:
- Rating4.6 out of 5higher than 73% of them
- Reviews1,890#102 of 372 by number of reviews
- Length8.8 hourslonger than 72% of them
- Last updatedFebruary 2025earlier than 57% of them
Course Description
In the real-world, data is anything but clean, which is why Python libraries like Pandas are so valuable.
If data manipulation is setting your data analysis workflow behind then this course is the key to taking your power back.
Own your data, don’t let your data own you!
When data manipulation and preparation accounts for up to 80% of your work as a data scientist, learning data munging techniques that take raw data to a final product for analysis as efficiently as possible is essential for success.
Data analysis with Python library Pandas makes it easier for you to achieve better results, increase your productivity, spend more time problem-solving and less time data-wrangling, and communicate your insights more effectively.
This course prepares you to do just that!
With Pandas DataFrame, prepare to learn advanced data manipulation, preparation, sorting, blending, and data cleaning approaches to turn chaotic bits of data into a final pre-analysis product. This is exactly why Pandas is the most popular Python library in data science and why data scientists at Google, Facebook, JP Morgan, and nearly every other major company that analyzes data use Pandas.
If you want to learn how to efficiently utilize Pandas to manipulate, transform, pivot, stack, merge and aggregate your data for preparation of visualization, statistical analysis, or machine learning, then this course is for you.
Here’s what you can expect when you enrolled with your instructor, Ph.D. Samuel Hinton:
Learn common and advanced Pandas data manipulation techniques to take raw data to a final product for analysis as efficiently as possible.
Achieve better results by spending more time problem-solving and less time data-wrangling.
Learn how to shape and manipulate data to make statistical analysis and machine learning as simple as possible.
Utilize the latest version of Python and the industry-standard Pandas library.
Performing data analysis with Python’s Pandas library can help you do a lot, but it does have its downsides. And this course helps you beat them head-on:
1. Pandas has a steep learning curve: As you dive deeper into the Pandas library, the learning slope becomes steeper and steeper. This course guides beginners and intermediate users smoothly into every aspect of Pandas.
2. Inadequate documentation: Without proper documentation, it’s difficult to learn a new library. When it comes to advanced functions, Pandas documentation is rarely helpful. This course helps you grasp advanced Pandas techniques easily and saves you time in searching for help.
After this course, you will feel comfortable delving into complex and heterogeneous datasets knowing with absolute confidence that you can produce a useful result for the next stage of data analysis.
Here’s a closer look at the curriculum:
Loading and creating Pandas DataFrames
Displaying your data with basic plots, and 1D, 2D and multidimensional visualizations.
Performing basic DataFrame manipulations: indexing, labeling, ordering slicing, filtering and more.
Performing advanced Pandas DataFrame manipulations: multiIndexing, stacking, hierarchical indexing, pivoting, melting and more.
Carrying out DataFrame grouping: aggregation, imputation, and more.
Mastering time series manipulations: reindexing, resampling, rolling functions, method chaining and filtering, and more.
Merging Pandas DataFrames
Lastly, this course is packed with a cheatsheet and practical exercises that are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice with Pandas too.
Instructor Details
- 4.6 Rating
1,890 Reviews
Samuel Hinton
Hi, I'm Sam and I'm an astrophysicist, data scientist, robotics and software engineer, astronomer and public presenter.
My work right now is all about renewable energy. Battery assets, optimising their utilisation and trading energy in markets to cut out as many fossil fuel generators as humanly possible.
In academia, my primary work involves investigating the nature of dark energy, however I also spend a lot of time advocating of open-source development and proper coding practices.
With years of experience from the financial software industry to machine learning pipelines classifying objects in the night sky, and teaching experience in statistics, software engineering, data manipulation, computational physics, and much more, I'm dedicated to increasing the level of coding proficiency in the scientific fields, and bringing basic coding knowledge to any eager student.
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Reviews
By Jim Brodeur on 5/22/2025
Sam is a ton of fun and super smart. This course hits you with a lot. I don't have a lot of Python experience, so I found myself rewatching things often to understand. I do question the desire to talk really fast so your videos are shorter. 20 mins worth of content in a 10 min video is still 20 mins worth of content, so maybe a normal speed could've had me rewatching less?
By Safrizal on 10/8/2024
The tutor is very expert in pandas and try to deliver everything so quick. Though this is good, but it is quite hard for me to follow every step because he missed to explain in detail of what he was doing and left me with lots of questions. I need to pause and do some research on google, then came back to start again. But overall the content is superb
By Chrysovalantis Chatzimagkas on 12/27/2022
This course covered properly the necessary concepts and features of Pandas. The course very organized and concise. It started from point A and guided to point B in a pretty straightforward manner. I would also like to add that the exercises along with their solutions in every chapter was a good opportunity to practice and study what one is lacking in order to solve them. Finally, the lecturer is very knowledgeable seems to be someone who has used pandas for hands-on, real-world use cases which help a lot because it helps you understand the importance of the concepts taught in this course and their applicability.
By Anonymized User on 6/29/2022
This course covers a vast amount of very useful information for data analysis with Pandas. That being said, the one slight negative is that the instructor flies through it all super fast. I know he does this to cover as many things as possible in a reasonable amount of time. Some people may love that about this course, and others may find they need to pause and/or rewind to listen again to explanations that were given very quickly. Still, it was a very good course and I learned a lot!
By Ira McDaniel on 5/25/2022
Excellent topic coverage, although subjects are covered quickly, so may require watching more than once. That said the instructor chose many different interesting data sets and keeps the attention using humor. Overall, I highly recommend for learning data manipulation, cleaning and creating sharp plots to show insights with very efficient, clean code examples.
By Abhishek Pandey on 5/3/2022
Yes the initial part was extremely good and would be very useful for anyone in general. Later on the content was more suitable to the people who are more into working with a list data and excel data sheets. But yes I learned some very nice visualization techniques and so much more about the Pandas library and how to present the data in a more beautiful way.
By Josh DeKeersgieter on 5/21/2021
Overall, a really good course. Teaches you a lot about using Pandas as expected. I would've appreciated if the instructor went a bit slower, both typing out his code and explaining syntax as he went through; I had to pause fairly frequently to catch up & study exactly what was happening to ensure I understood how to apply it later. The supplemental exercises at the end of each chapter have vague questions sometimes but still really good for practicing the content so it's retained
By Alex Wang on 1/4/2021
Amazing course, feel the wisdom everywhere. Samuel is either super smart with a 200+ IQ or he really spent a lot of time did perfect preparation for every single lecture. Just purchased the other course by Samuel(Python for Statistical Analysis) as well, couldn't go wrong with anything taught by him. And I'm glad I supported an Aussie tutor as I'm in Australia too.
By Sebastian Strand on 11/22/2020
Stressful to follow at times
By Frances Ho on 11/15/2020
Too fast, not enough info about matplotlib settings.
By Jeff Jozwiak on 11/11/2020
I was expecting more of a data preparation course for machine learning so that's on me. I think the instructor was very knowledgeable. Two things that are pet-peeves to me. 1) the scrolling back and forth is hard to track and only gives eye strain, headaches and the sense of being lost in the topic. 2) the attempts to add humor to typos and such. It is already an annoyance while trying to follow and then it's compounded by joking about it. If you are expecting a course a Pandas then this is a good one.
By Ivan Fedorovich Zaporozhtsev on 11/8/2020
Great course with no chatter and many examples to illistrate concepts being either easy or special attention required!
Quality Score
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Overall Score : 92 / 100







