fix: avoid seaborn warnings

README.md

@@ -22,6 +22,9 @@ If I were using Windows, it might look like this:
 Set these aside for now and we'll pick them up in chapter 2.
 
 ## Changelog
+### v0.11.0 (2024-06-18)
+Update visualization code to get rid of a few seaborn warnings.
+
 ### v0.10.10 (2024-02-07)
 Fix Pandas `mean` example. More: Pandas changed their defaults to throw an
 error if you try to call this on string columns. Fixed example to explicitely

code/06_summary.py

@@ -55,7 +55,7 @@
 df[['pos', 'std', 'ppr', 'half_ppr']].head()  # have this
 
 def score_type_df(_df, scoring):
-    _df = _df[['pos', scoring]]
+    _df = _df[['pos', scoring]].copy()
     _df.columns = ['pos', 'pts']
     _df['scoring'] = scoring
     return _df
@@ -84,18 +84,18 @@ def score_type_df(_df, scoring):
 g = sns.displot(x='caught_airyards', y='raw_yac', data=df, kind='kde')
 plt.show()
 
-g.fig.subplots_adjust(top=0.9)
-g.fig.suptitle('Airyards vs YAC')
+g.figure.subplots_adjust(top=0.9)
+g.figure.suptitle('Airyards vs YAC')
 
 # airyards vs yac
 g = sns.relplot(x='caught_airyards', y='raw_yac', data=df)
-g.fig.subplots_adjust(top=0.9)
-g.fig.suptitle('Airyards vs YAC')
+g.figure.subplots_adjust(top=0.9)
+g.figure.suptitle('Airyards vs YAC')
 
 # airyards vs yac - colored yb position
 g = sns.relplot(x='caught_airyards', y='raw_yac', hue='pos', data=df)
-g.fig.subplots_adjust(top=0.9)
-g.fig.suptitle('Airyards vs YAC - by Position')
+g.figure.subplots_adjust(top=0.9)
+g.figure.suptitle('Airyards vs YAC - by Position')
 
 # correlation
 df.loc[df['pos'] == 'WR',
@@ -123,15 +123,15 @@ def score_type_df(_df, scoring):
 
 g = sns.relplot(x='week', y='std', kind='line', hue='pos', style='pos',
                 data=max_pts_by_pos_week, height=4, aspect=1.2)
-g.fig.subplots_adjust(top=0.9)
-g.fig.suptitle("Max Points by Week and Position")
+g.figure.subplots_adjust(top=0.9)
+g.figure.suptitle("Max Points by Week and Position")
 
 # points by player and week
 g = sns.relplot(x='week', y='std', kind='line', hue='player_name',
                 col='player_name', height=2, aspect=1.2, col_wrap=3,
                 legend=False, data=df.query("pos == 'QB'"))
-g.fig.subplots_adjust(top=0.9)
-g.fig.suptitle("Points by Week = QBs")
+g.figure.subplots_adjust(top=0.9)
+g.figure.suptitle("Points by Week = QBs")
 
 ##############
 # plot options
@@ -146,8 +146,8 @@ def score_type_df(_df, scoring):
                 fill=True, col_wrap=2)
 
 # adding a title
-g.fig.subplots_adjust(top=0.9) # adding a title
-g.fig.suptitle('Fantasy Points by Position, Scoring System')
+g.figure.subplots_adjust(top=0.9) # adding a title
+g.figure.suptitle('Fantasy Points by Position, Scoring System')
 
 # modifying axis
 g.set(xlim=(-10, 40), ylim=(0, 0.014))

solutions-to-exercises/06_plotting_answers.py

@@ -20,44 +20,44 @@
 # 6.1a
 ###############################################################################
 g = sns.displot(pbp, x='yards_gained', kind='kde', fill=True)
-g.fig.subplots_adjust(top=0.9)
-g.fig.suptitle('Distribution of Yards Gained Per Play, LTCWFF Sample')
+g.figure.subplots_adjust(top=0.9)
+g.figure.suptitle('Distribution of Yards Gained Per Play, LTCWFF Sample')
 g.savefig('./solutions-to-exercises/6-1a.png')
 
 ###############################################################################
 # 6.1b
 ###############################################################################
 g = sns.displot(pbp.query("down <= 3"), x='yards_gained', kind='kde',
                 hue='down', fill=True)
-g.fig.subplots_adjust(top=0.9)
-g.fig.suptitle('Distribution of Yards Gained Per Play by Down, LTCWFF Sample')
+g.figure.subplots_adjust(top=0.9)
+g.figure.suptitle('Distribution of Yards Gained Per Play by Down, LTCWFF Sample')
 g.savefig('./solutions-to-exercises/6-1b.png')
 
 ###############################################################################
 # 6.1c
 ###############################################################################
 g = sns.displot(pbp.query("down <= 3"), x='yards_gained', kind='kde',
                 col='down', fill=True)
-g.fig.subplots_adjust(top=0.8)
-g.fig.suptitle('Distribution of Yards Gained Per Play by Down, LTCWFF Sample')
+g.figure.subplots_adjust(top=0.8)
+g.figure.suptitle('Distribution of Yards Gained Per Play by Down, LTCWFF Sample')
 g.savefig('./solutions-to-exercises/6-1c.png')
 
 ###############################################################################
 # 6.1d
 ###############################################################################
 g = sns.displot(pbp.query("down <= 3"), x='yards_gained', kind='kde',
                 row='posteam', col='down', fill=True)
-g.fig.subplots_adjust(top=0.9)
-g.fig.suptitle('Distribution of Yards Gained Per Play by Down, Team, LTCWFF Sample')
+g.figure.subplots_adjust(top=0.9)
+g.figure.suptitle('Distribution of Yards Gained Per Play by Down, Team, LTCWFF Sample')
 g.savefig('./solutions-to-exercises/6-1d.png')
 
 ###############################################################################
 # 6.1e
 ###############################################################################
 g = sns.displot(pbp.query("down <= 3"), x='yards_gained', col='down',
                 row='posteam', hue='posteam', fill=True)
-g.fig.subplots_adjust(top=0.9)
-g.fig.suptitle('Distribution of Yards Gained Per Play by Down, Team, LTCWFF Sample')
+g.figure.subplots_adjust(top=0.9)
+g.figure.suptitle('Distribution of Yards Gained Per Play by Down, Team, LTCWFF Sample')
 g.savefig('./solutions-to-exercises/6-1e.png')
 
 ###############################################################################
@@ -66,8 +66,8 @@
 # relationships
 pg = pd.read_csv(path.join(DATA_DIR, 'player_game_2017_sample.csv'))
 g = sns.relplot(x='carries', y='rush_yards', hue='pos', data=pg)
-g.fig.subplots_adjust(top=0.9)
-g.fig.suptitle('Carries vs Rush Yards by Position, LTCWFF Sample')
+g.figure.subplots_adjust(top=0.9)
+g.figure.suptitle('Carries vs Rush Yards by Position, LTCWFF Sample')
 g.savefig('./answers-to-exercises/6-2.png')
 
 pg['ypc'] = pg['rush_yards']/pg['carries']