Complete Python Pandas Data Science Tutorial! (2025 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 GitHub Repo (code): https://github.com/KeithGalli/complet... Pandas 2.0 Blog Post: https://datapythonista.me/blog/pandas... Datetime percentage format cheat sheet: https://strftime.org/ Kaggle NOC dataset: https://www.kaggle.com/datasets/heeso... Videos Mentioned! Olympic Data Cleaning Tutorial: https://www.youtube.com/live/oad9tVEs... Regex Tutorial:    • Complete Regular Expressions Tutorial! (wi...   100 Pandas Problems:    • Solving 100 Python Pandas Problems! (from ...   Practice! StrataScratch: https://www.stratascratch.com/?via=keith Analyst Builder: https://www.analystbuilder.com/?via=k... ------------------------- 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 23:01 - Iterating over a DataFrame (df) with a For Loop | df.iterrows() 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 41:40 - Adding / Removing Columns | String Operations, Datetime (pd.to_datetime) Operations 46:38 - Saving our Updated DataFrame (df.to_csv, df.to_excel, df.to_parquet, etc) 47:14 - Adding / Removing Columns | Using Lambda & Custom Functions w/ .apply() 50:42 - Merging & Concatenating Data | pd.merge(), pd.concat(), types of joins 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… ------------------------- Follow me on social media! Instagram |   / keithgalli   Twitter |   / keithgalli   TikTok |   / keithgalli   ------------------------- Learn data skills with hands-on exercises & tutorials at Datacamp! https://datacamp.pxf.io/c/3588040/101... Practice your Python Pandas data science skills with problems on StrataScratch! https://stratascratch.com/?via=keith Join the Python Army to get access to perks! YouTube -    / @keithgalli   Patreon -   / keithgalli   *I use affiliate links on the products that I recommend. I may earn a purchase commission or a referral bonus from the usage of these links.