Analyze NBA data in Python (Clean, Analyze, and Visualize Data with the Python Pandas)

Make yourself ready to transition from being a non-coder to becoming a data analyst pro in only three hours! Using Python Pandas, understand how NBA numbers can be cleaned, analyzed, and visualized. Moreover, once you’re through the course "Analyze NBA data in Python," you can use these abilities on any dataset that interests you. Therefore, join us NOW.

Analyze NBA data in Python Table of Contents:

1. Introduction: 00:19
2. If you need a refresher on python basics, take my 1 hour intro course!: 01:19
3. Why Aren't We Learning Excel or SQL?: 00:35
4. Installing and Importing Pandas: 03:18
5. Creating a Dataframe from "Player Game Data.csv": 05:38
6. Understanding Dataframe Contents: 04:55
7. Understanding Dataframe Contents Continued: 08:43
8. Copying Dataframes: 02:55
9. Adding Columns to Dataframes: 02:32
10. Saving Dataframes to CSVs: 06:01
11. Finding the Top Scorer on Each Team by Average Points per Game: 03:41
12. Using 'Groupby' to Calculate the Avg_PPG for Each Player: 02:09
13. Using 'Transform' to Save the Avg_PPG to the Dataframe: 04:30
14. Ranking Players by Avg_PPG on Each Team: 02:41
15. Deduplicating the Dataframe to Only Include 1 Row per Player: 07:07
16. Filtering the Dataframe to Only Include the Top Players on Each Team: 05:20
17. Sorting the Dataframe by Avg_PPG: 05:47
18. Specifying Columns We Want to Keep in the Dataframe: 02:39
19. Finding the Top Scorer on Each Team That Played Half Their Team's Games: 06:58
20. Merging team_games_played_df with player_game_data_df: 02:02
21. Calculating the Number of Games Each Player Played: 10:14
22. Determining If the Player Played Half of the Team's Games: 02:31
23. Creating an Algorithm to Find the 2019 MVP: 05:14
24. Calculating Each Player's Share of Statistics for the Season: 05:11
25. Updating the Code by Implementing a List + for Loop: 11:42
26. Lambda Functions: 13:05
27. Calculating the Win Bonus for Each Player Using a Lambda Function: 08:46
28. Calculating the Win Bonus for Each Player Using NumPy: 08:50
29. Calculating the MVP Scores for Each Player: 05:28
30. Formatting and Saving the Dataframe: 04:11
31. Visualizing Our Data: 09:04
32. Installing and Importing Matplotlib: 01:45
33. Creating a Bar Chart Showing the Top 10 MVP Candidates: 02:14
34. Scatter Plot of Season_avg_PTS and Season_MVP_Score: 06:46
35. Histogram of the Game_MVP_Score for the MVP: 05:37
36. Getting the PLAYER_ID for the MVP Dynamically So We Don't Have to Hard-Code It: 07:22
37. That's a Wrap!: 07:25

Who is this course for?

1. Intermediate Python learners who would like to gain knowledge in data analysis skills.

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