Explore this free video tutorial for Complete Python Pandas Data Science Tutorial! (2025 Updated Edition) instructor Keith Galli. The total lesson runtime is 34:11 minutes providing step-by-step visual instructions. Access this full online lesson on any desktop PC, Mac, tablet, or smartphone.
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Course Description & Lesson Notes
Official Video Description:
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/complete-pandas-tutorial
Pandas 2.0 Blog Post: https://datapythonista.me/blog/pandas-20-and-the-arrow-revolution-part-i
Datetime percentage format cheat sheet: https://strftime.org/
Kaggle NOC dataset: https://www.kaggle.com/datasets/heesoo37/120-years-of-olympic-history-athletes-and-results
Videos Mentioned!
Olympic Data Cleaning Tutorial: https://www.youtube.com/live/oad9tVEsfI0?si=QeIp8AGcGqrYgzBK
Regex Tutorial: https://youtu.be/vsa9GGzMFXQ?si=tH6vaaKJm5o4Ud93
100 Pandas Problems: https://www.youtube.com/watch?v=i7v2m-ebXB4&t=47s
Practice!
StrataScratch: https://www.stratascratch.com/?via=keith
Analyst Builder: https://www.analystbuilder.com/?via=keith
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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
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…
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π Lesson Overview & Learning Outcomes:
Welcome to the step-by-step video guide for Complete Python Pandas Data Science Tutorial! (2025 Updated Edition) taught by Keith Galli. This tutorial provides a comprehensive walkthrough designed to take you from foundational principles to practical implementation.
π‘ Key Topics Covered in This Course:
- Core Fundamentals & Setup: Understanding the workspace, essential tools, and initial setup for Complete Python Pandas Data Science Tutorial! (2025 Updated Edition).
- Step-by-Step Practical Demonstration: Hands-on implementation guided by Keith Galli with real-world examples.
- Best Practices & Key Shortcuts: Time-saving workflows, keyboard shortcuts, and industry-standard recommendations.
- Troubleshooting & Common Pitfalls: How to avoid common beginner errors and optimize your workflow for peak efficiency.
π Recommended Prerequisites & Study Notes:
No prior advanced experience is required. Follow along with the video player above on any desktop computer, tablet, or mobile device. Pause and rewind at key steps to practice along with the instructor.
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