This is the NBA Stats 23-24 Analysis Program. My Python application allows you to explore detailed statistics from the 2023-2024 NBA season. The program offers several options to interact with the data.

You can start with the Overview option, which provides a comprehensive summary of key player statistics such as points, rebounds, and assists.

The basic statistics option calculates essential measures like mean, median, mode, and standard deviation across various player attributes. Visual learners can utilize Simple Visualizations to view histograms, scatter plots, and line graphs to visualize trends and distributions within the dataset.

Finally, the program includes an exit option to exit the program seamlessly. Enjoy exploring NBA player statistics.

General Solution

This program uses organized functions to analyze NBA player statistics based on position. The main function guides the program's flow and provides a simple interface for analysis. It starts by loading NBA stats from a CSV file into a pandas DataFrame using the read_as_dataframe() function. The visually_inspect() function gives a quick overview of the dataset’s shape, column names, and sample rows. The clean_dataframe() function removes duplicates and rows with missing values in key columns like player position, points (PTS), and rebounds (TRB). The get_subset() function filters the data to include only guards, focusing the analysis on specific player roles. Aggregated statistics are calculated using groupby_sum_table() and piv_sum_table(), which show average points and rebounds by position. Visualizations include scatter_plot() to show the relationship between points and rebounds for guards, line_chart() and bar_chart() for average points and rebounds by position, and summary_chart() for a combined view of both. This structured setup helps highlight key patterns in NBA player performance and makes it easy to compare positions. 

Expected Output