Analyze NBA data in Python

Course Curriculum

Chapter 1: Course Intro

Lecture 1: 1.1 – Intro

Lecture 2: 1.2 – If you need a refresher on Python basics, take my 1 hour intro course!

Lecture 3: 1.3 – …why aren't we just learning Excel or SQL?

Chapter 2: Introduction to Pandas

Lecture 1: 2.1 – Installing and importing Pandas

Lecture 2: 2.2 Creating a dataframe from "Player Game Data.csv"

Lecture 3: 2.3 – Understanding dataframe contents

Lecture 4: 2.4 – Understanding dataframe contents continued

Lecture 5: 2.5 – Copying dataframes

Lecture 6: 2.6 – Adding columns to dataframes

Lecture 7: 2.7 – Saving dataframes to CSV's

Chapter 3: Finding the top scorer on each team by average points per game

Lecture 1: 3.1 – Finding the top scorer on each team by average points per game

Lecture 2: 3.2 – Using 'groupby' to calculate the avg_ppg for each player

Lecture 3: 3.3 – Using 'transform' to save the avg_ppg to the dataframe

Lecture 4: 3.4 – Ranking players by avg_ppg on each team

Lecture 5: 3.5 – Deduplicating the dataframe to only include 1 row per player

Lecture 6: 3.6 – Filter the dataframe to only include the top players on each team

Lecture 7: 3.7 – Sorting the dataframe by avg_ppg

Lecture 8: 3.8 – Specifying columns we want to keep in the dataframe

Chapter 4: Finding the top scorer on each team that played half their team's games

Lecture 1: 4.1 – Finding the top scorer on each team that played half their team's games

Lecture 2: 4.2 – Merging team_games_played_df with player_game_data_df

Lecture 3: 4.3 – Calculating the number of games each player played

Lecture 4: 4.4 – Determine if the player played half of the team's games

Chapter 5: Creating an algorithm to find the 2019 MVP

Lecture 1: 5.1 – Creating an algorithm to find the 2019 MVP

Lecture 2: 5.2 – Calculate each player's share of statistics for the season

Lecture 3: 5.3 – Update the code by implementing a list + for loop

Lecture 4: 5.4 – Lambda Functions

Lecture 5: 5.5 – Calculate the win bonus for each player using a lambda function

Lecture 6: 5.6 – Calculate the win bonus for each player using NumPy

Lecture 7: 5.7 – Calculate the mvp_scores for each player

Lecture 8: 5.8 – Format and save the dataframe

Chapter 6: Visualizing our data

Lecture 1: 6.1 – Visualizing our data

Lecture 2: 6.2 – Install and import Matplot

Lecture 3: 6.3 – Create a bar chart showing the top 10 mvp candidates

Lecture 4: 6.4 – Scatter plot of season_avg_PTS and season_mvp_score

Lecture 5: 6.5 – Histogram of the game_mvp_score for the MVP

Lecture 6: 6.6 – Get the PLAYER_ID for the MVP dynamically so we don't have to hard-code it

Chapter 7: Course Wrap

Lecture 1: 7.1 – That's a wrap!