Complete Python Pandas Data Science Tutorial! (2024 Updated Edition)

Hey, what's up everyone? Welcome back to another video! I'm super excited for this one. We're doing another complete Python Pandas tutorial walkthrough. Five years have passed since the last iteration, and both the library and my knowledge have evolved. We'll cover all the basics and advanced techniques to analyze and manipulate tabular data with Pandas. Whether you're a beginner or an experienced user looking to level up, there's something here for everyone. Let's dive in!

What We’ll Cover:
- Setting up your environment
- Introduction to DataFrames
- Loading data from CSV, Excel, Parquet, and more
- Accessing and manipulating data
- Filtering, adding, and removing columns
- Handling missing values
- Aggregating data with GroupBy and Pivot Tables
- Advanced functionalities like shift, rank, and rolling functions
- Exploring the new features in Pandas 2.0
- Using AI tools like GitHub Copilot and ChatGPT to enhance your workflow

Links Mentioned

Videos Mentioned!

Practice!

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Video Timeline!
0:00 - Video Overview
1:11 - Getting Started with Python Pandas | Google Colab
1:21 - Getting Started with Python Pandas | Local Environment Setup (Cloning code, using virtual environment, VS Code)
3:58 - Intro to Dataframes | Creating DataFrames, Index/Columns, Basic Functionality
8:25 - Loading in DataFrames from Files (CSV, Excel, Parquet, etc.)
13:42 - Accessing Data | .head() .tail() .sample()
15:28 - Accessing Data | .loc() .iloc()
19:20 - Setting DataFrame Values w/ loc() & iloc()
20:20 - Accessing Single Values | .at() .iat()
21:11 - Accessing Data | Grab Columns, Sort Values, Ascending/Descending
24:12 - Filtering Data | Syntax Options, Numeric Values, Multiple Conditions
27:58 - Filtering Data | String Operations, Regular Expressions (Regex)
33:09 - Filtering Data | Query Functions
34:20 - Adding / Removing Columns | Basics, Conditional Values, Math Operations, Renaming Columns
47:14 - Adding / Removing Columns | Using Lambda & Custom Functions w/ .apply()
58:33 - Handling Null Values (NaNs) | .fillna() .interpolate() .dropna() .isna() .notna()
1:04:05 - Aggregating Data | value_counts()
1:05:47 - Aggregating Data | Using Groupby - groupby() .sum() .mean() .agg()
1:08:24 - Aggregating Data | Pivot Tables
1:10:28 - Groupby combined with Datetime Operations
1:14:38 - Advanced Functionality | .shift() .rank() .cumsum() .rolling()
1:22:10 - New Functionality | Pandas 1.0 vs Pandas 2.0 - pyarrow
1:25:29 - New Functionality | GitHub Copilot & OpenAI ChatGPT
1:32:05 - What Next?? | Continuing your Python Pandas Learning…

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[Kevin-cydr]

Back when the first iteration was released i was in college having no idea about what a dataframe is now I'm a developer and still watching your videos. Thanks Keith for being a part of my learning journey❤

[dabunnisher]

Your last pandas tutorial helped save me hours and hours of work. Don't ever forget that you are AWESOME!!!!

[pierresorel]

People wait for new episodes on Netflix but legends wait for Keith's new tutorials 😎

[RealBenBizman]

No way- I just watched your other video on this the other day! Crazy!

[skyeshwin]

Hey Keith! Big fan of your work! Keep it going brother!

[udaynj]

Awesome video, right speed and comprehensive. My thanks to you for taking the time to do this - am sure it was hours and hours of work and I truly appreciate your effort

[CesarSantosLopezYolo]

Hey I love these vids... Keep them coming! Love from Mexico buddy

[ahillsavio]

Good stuff man! Keep up the good work!

[gaumeuvlog]

Thanks for uploading new video about Pandas. I learn a lot from you. Can't wait to watch your next videos 🤩

[meeFaizul]

Keith, your tutorial is a game-changer!

Your content is top-notch. Can't wait for more!

❤️ from 🇵🇰

[benjoanc]

I always love your content because of the ease of understanding ❤

I've been hearing alot of the polars library but there's limited content on it. Please if possible do something on it

[rimpan]

Great tutorial. You keep teaching new things all the time with practicao examples and speak just the exact amount not to make it boring. Good job. I wait for sklearn, np, matplotlib, sns, streamlit tutorials 😂

[aflah]

Strongly resonating with another comment here
I recall watching your tutorials in my first year of college. I just graduated recently and became research software engineer. Your videos have been pivotal for all the stuff I've done :)

[omsingh]

Hey, Thanks for the amazing tutorial.

[bouallaguiali]

Well done Keith . Please do more videos about Data Analysis .

[mikhailbandurist]

It's an honour to me to be among the first viewers of this excellent tutorial!

[ben_tyler]

Did anyone notice how our keith has been sneaking a quick peek to the right at the beginning in the last few videos? 😂 Seriously though, loving the content!"

[rodrigokk]

Absolutely amazing! A hint: make a Python Pandas Advanced Tutorial more focused on graphics.

[JJGhostHunters]

This is great content! Please make a similar tutorial or recommend one that relates to using vectorization via Numpy arrays. I have applications that do what I need them to do, but involve nested loops that iterate over millions of rows of data. I really need to move away from these loops to improve execution time.

[FIBONACCIVEGA]

good its the update of the old video . Excellent!!!