Course Intro
Introduction to Pandas
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2.1 - Installing and importing Pandas (5:38)
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2.2 - Creating a dataframe from 'Player Game Data.csv' (4:55)
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2.3 - Understanding dataframe contents (8:43)
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2.4 - Understanding dataframe contents continued (2:55)
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2.5 - Copying dataframes (2:32)
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2.6 - Adding columns to dataframes (6:01)
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2.7 - Saving dataframes to CSV's (3:41)
Finding the top scorer on each team by average points per game
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3.1 - Finding the top scorer on each team by average points per game (2:09)
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3.2 - Using 'groupby' to calculate the avg_ppg for each player (4:30)
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3.3 - Using 'transform' to save the avg_ppg to the dataframe (2:41)
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3.4 - Ranking players by avg_ppg on each team (7:07)
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3.5 - Deduplicating the dataframe to only include 1 row per player and running our ranking again (5:20)
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3.6 - Filter the dataframe to only include the top players on each team (5:47)
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3.7 - Sorting the dataframe by avg_ppg (2:39)
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3.8 - Specifying columns we want to keep in the dataframe (6:58)
Finding the top scorer on each team that played half their team's games
Creating an algorithm to find the 2019 MVP
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5.1 - Creating an algorithm to find the 2019 MVP (5:11)
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5.2 - Calculate each player's share of points, assists, and rebounds for the season (11:42)
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5.3 - Update the code by implementing a list + for loop (13:05)
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5.4 - Lambda Functions (8:46)
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5.5 - Calculate the win bonus for each player using a lambda function (8:50)
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5.6 - Calculate the win bonus for each player using NumPy (5:28)
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5.7 - Calculate the mvp_scores for each player for each game and the whole regular season (4:11)
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5.8 - Format and save the dataframe (9:04)
Visualizing our data
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6.1 - Visualizing our data (1:45)
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6.2 - Install and import Matplot (2:14)
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6.3 - Create a bar chart showing the season_mvp_score for the top 10 mvp candidates (6:46)
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6.4 - Scatter plot of season_avg_PTS and season_mvp_score (5:37)
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6.5 - Histogram of the game_mvp_score for the MVP (7:22)
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6.6 - Get the PLAYER_ID for the MVP dynamically so we don't have to hard-code it in (7:25)
Course Wrap
