code: import pandas as pd
  import matplotlib.pyplot as plt
  from frames import games, info, events
  
  plays = games.query("type == 'play' & event != 'NP'")
  plays.columns = ['type', 'inning', 'team', 'player', 'count', 'pitches', 'event', 'game_id', 'year']
  
  pa = plays.loc[plays['player'].shift() != plays['player'], ['year', 'game_id', 'inning', 'team', 'player']]
  pa = pa.groupby(['year', 'game_id', 'team']).size().reset_index(name='PA')
  
  events = events.set_index(['year', 'game_id', 'team', 'event_type'])
  events = events.unstack().fillna(0).reset_index()
  events.columns = events.columns.droplevel()
  events.columns = ['year', 'game_id', 'team', 'BB', 'E', 'H', 'HBP', 'HR', 'ROE', 'SO']
  events = events.rename_axis(None, axis='columns')
  events_plus_pa = pd.merge(events, pa, how='outer', left_on=['year', 'game_id', 'team'], right_on=['year', 'game_id', 'team'])
  defense = pd.merge(events_plus_pa, info)
  defense.loc[:, 'DER'] = 1 - ((defense['H'] + defense['ROE']) / (defense['PA'] - defense['BB'] -defense['SO'] - defense['HBP'] - defense['HR']))
  defense.loc[:, 'year'] = pd.to_numeric(defense['year'])
  der = defense.loc[defense['year'] >= 1978, ['year', 'defense', 'DER']]
  
  der = der.pivot(index='year', columns='defense', values='DER')
  der.plot(x_compat=True, xticks=range(1978, 2018, 4), rot=45)
  
  plt.show()
apis: pandas.to_numeric matplotlib.pyplot.show pandas.merge