Pandas is a great tool for doing analysis on spreadsheets. It's easy to say that but let's actually learn why by doing something real.

We're going to be analyzing NBA data to help understand why Pandas should be a tool in your data science toolkit.

Launch your project with: [ Ссылка ]

But more importantly, doing something practical will help you better understand the need for a tool like Pandas.

To help us work with Pandas in a practical way, we've teamed up with Deepnote. Deepnote is a service that makes it easy to run interactive notebooks (also known as Juptyer Notebooks). These notebooks allow us to run Python & Pandas in a highly visual and highly interactive manner.

What's better, notebooks, especially on Deepnote, allow non-technical team members to participate in a code-heavy document (as we'll see how).

Resources:
- Code: [ Ссылка ]
- Deepnote Sign up (includes bonus resources): [ Ссылка ]
- Setup Jupyter Notebooks on Mac, Windows or Linux: [ Ссылка ]

00:00:00 Welcome to Try Pandas
00:01:13 Overview & Requirements
00:02:12 Setup Project on Deepnote
00:06:05 Creating your First DataFrame
00:11:06 Importing & Exporting in Pandas
00:20:17 Rename Columns in Pandas
00:28:04 Clean Rows
00:45:13 Basic Analysis
00:52:32 Group Data & Plots
01:04:20 Clean & Analyze Real Data Part 1
01:12:03 Clean & Analyze Real Data Part 2
01:18:06 Merge DataFrames Part 1
01:25:02 Merge DataFrames Part 2
01:37:11 Experimenting with a REST API
01:58:52 Flatten Dictionary Columns in DataFrames
02:22:09 Stats DataFrame Convenience Method
02:30:17 Final Player Analysis
02:45:49 The Appendix
02:47:17 Thank you for watching try pandas

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