Generate graphs from NBA data

point-diff.py

import pandas as pd
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import glob
import os.path

font = {'family' : 'sans-serif',
        'size'   : 8}

matplotlib.rc('font', **font)

files = glob.glob('./out/point-diff-*.csv')

for name in files:
  sname = os.path.splitext(os.path.basename(name))[0]

  data = pd.read_csv(name)

  # Point spread barchart
  ps_data = data[['Abbr', 'Above 500', 'Below 500', 'Point spread']].sort(columns='Point spread', ascending=False)
  ps_data = ps_data[['Abbr', 'Point spread']]

  ps_data.plot(kind='bar', x='Abbr', color=['green'], edgecolor='darkgreen', linewidth=0.5)
  plt.xlabel('Team')
  plt.ylabel('Points better vs below .500 than above .500')
  plt.text(17.6, -1.8,'Authored 02/08/2014 by Justin Palmer (dealloc.me).  Source: stats.nba.com', fontsize=6, color='gray')
  plt.tight_layout()
  plt.savefig(sname + '-spread.png', dpi=150)
  plt.close()

  # Above/Below 500 grouped bar
  ab_data = data[['Abbr', 'Above 500', 'Below 500']].sort(columns='Above 500', ascending=False)

  ab_data.plot(kind='bar', x='Abbr', color=['green', 'lightgrey'], edgecolor='black', linewidth=0.5)
  plt.xlabel('Team')
  plt.ylabel('Point differential')
  plt.text(17.5, -23.8,'Authored 02/08/2014 by Justin Palmer (dealloc.me).  Source: stats.nba.com', fontsize=6, color='gray')
  plt.tight_layout()
  plt.savefig(sname + '-ab.png', dpi=150)
  plt.close()