In this full series, we’re going to master Pandas — one of the most essential Python libraries for data analysis — in Moroccan Darija.
We’ll start from the basics and gradually move toward real-life data transformations, cleaning, aggregations, and even plotting. All practical, all hands-on, and all explained clearly in your own language.
Hey everyone,
After releasing my Python and NumPy courses, there was one obvious next step: Pandas.
Why? Because if you want to work with data — whether in machine learning, AI, analytics, or backend systems — Pandas is your daily tool. But most of the resources online are full of jargon and in English or French.
So I created 3 complete courses that walk you through:
- What Pandas is and why it’s powerful
- How to load, clean, transform, and analyze data
- How to deal with real-world messy data and fix it
- How to write clean code and export results
And finally, how to work with grouped data, dates, files, and basic plots
Everything is explained clearly in Darija, for free, on YouTube.
What You’ll Learn
Pandas Full Course
- Series & DataFrames
- Reading data (CSV, JSON)
- Cleaning missing and wrongly formatted data
- Fixing duplicates and analyzing correlations
Pandas 2 Course
- Retrieving, sorting, and aggregating data
- Grouping by values
- Merging multiple sources
- Date-based operations
- Exporting and writing files
- Quick plotting basics
Pandas Applications Course
- Full walkthroughs of real-world operations
- Data filtering, sorting, merging
- Grouping and applying functions
- Generating stats from your data
Why I Made This
I’ve worked with teams that had zero background in data, including warehouse staff, field engineers, and even managers — and I saw the same pattern:
Everyone can understand Pandas if it’s taught the right way.
That’s why I made this series. To make it feel more like a conversation than a classroom.
If you’ve never touched Pandas before — or you want to solidify your understanding — this series is for you.
No assumptions. No fluff. Just clean explanations and real use cases.
