A strip plot is a scatter plot where one of the variables is categorical. They can be combined with other plots to provide additional information. For example, a boxplot with an overlaid strip plot becomes more similar to a violin plot because some additional information about how the underlying data is distributed becomes visible. Seaborn's swarmplot is virtually identical except that it prevents datapoints from overlapping.
dataset: Kaggle: NBA shot logs
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%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
plt.rcParams['figure.figsize'] = (20.0, 10.0)
plt.rcParams['font.family'] = "serif"
This is a cool dataset that contains information about shot attempts made by professional basketball players.
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df = pd.read_csv('../stripplot/shot_logs.csv',usecols=['player_name','SHOT_DIST','PTS_TYPE','SHOT_RESULT'])
players_to_use = ['kyrie irving', 'lebron james', 'stephen curry', 'jj redick']
df = df.loc[df.player_name.isin(players_to_use)]
df.head()
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Basic plot
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p = sns.stripplot(data=df, x='player_name', y='SHOT_DIST')
Change the color to represent whether the shot was made or missed
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p = sns.stripplot(data=df,
x='player_name',
y='SHOT_DIST',
hue='SHOT_RESULT')
Change the order in which the names are displayed
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p = sns.stripplot(data=df,
x='player_name',
y='SHOT_DIST',
hue='SHOT_RESULT',
order=sorted(players_to_use))
jitter can be used to randomly provide displacements along the horizontal axis, which is useful when there are large clusters of datapoints
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p = sns.stripplot(data=df,
x='player_name',
y='SHOT_DIST',
hue='SHOT_RESULT',
order=sorted(players_to_use),
jitter=0.25)
We see the default behavior is to stack the different hues on top of each other. This can be avoided with dodge (formerly called split)
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p = sns.stripplot(data=df,
x='player_name',
y='SHOT_DIST',
hue='SHOT_RESULT',
order=sorted(players_to_use),
jitter=0.25,
dodge=True)
Flipping x and y inputs and setting orient to 'h' can be used to make a horizontal plot
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p = sns.stripplot(data=df,
y='player_name',
x='SHOT_DIST',
hue='SHOT_RESULT',
order=sorted(players_to_use),
jitter=0.25,
dodge=False,
orient='h')
For coloring, you can either provide a single color to color...
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p = sns.stripplot(data=df,
y='player_name',
x='SHOT_DIST',
hue='SHOT_RESULT',
order=sorted(players_to_use),
jitter=0.25,
dodge=True,
orient='h',
color=(.25,.5,.75))
...or you can use one of the many variations of the palette parameter
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p = sns.stripplot(data=df,
x='player_name',
y='SHOT_DIST',
hue='SHOT_RESULT',
order=sorted(players_to_use),
jitter=0.25,
dodge=True,
palette=sns.husl_palette(2, l=0.5, s=.95))
Adjust the marker size
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p = sns.stripplot(data=df,
x='player_name',
y='SHOT_DIST',
hue='SHOT_RESULT',
order=sorted(players_to_use),
jitter=0.25,
dodge=True,
palette=sns.husl_palette(2, l=0.5, s=.95),
size=8)
Adjust the linewidth of the edges of the circles
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p = sns.stripplot(data=df,
x='player_name',
y='SHOT_DIST',
hue='SHOT_RESULT',
order=sorted(players_to_use),
jitter=0.25,
dodge=True,
palette=sns.husl_palette(2, l=0.5, s=.95),
size=8,
linewidth=3)
Change the color of these lines with edgecolor
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p = sns.stripplot(data=df,
x='player_name',
y='SHOT_DIST',
hue='SHOT_RESULT',
order=sorted(players_to_use),
jitter=0.25,
dodge=True,
palette=sns.husl_palette(2, l=0.5, s=.95),
size=8,
linewidth=3,
edgecolor='blue')
Swarmplots look good when overlaid on top of another categorical plot, like boxplot
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params = dict(data=df,
x='player_name',
y='SHOT_DIST',
hue='SHOT_RESULT',
#jitter=0.25,
order=sorted(players_to_use),
dodge=True)
p = sns.stripplot(size=8,
jitter=0.35,
palette=['#91bfdb','#fc8d59'],
edgecolor='black',
linewidth=1,
**params)
p_box = sns.boxplot(palette=['#BBBBBB','#DDDDDD'],linewidth=6,**params)
Finalize
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plt.rcParams['font.size'] = 30
params = dict(data=df,
x='player_name',
y='SHOT_DIST',
hue='SHOT_RESULT',
#jitter=0.25,
order=sorted(players_to_use),
dodge=True)
p = sns.stripplot(size=8,
jitter=0.35,
palette=['#91bfdb','#fc8d59'],
edgecolor='black',
linewidth=1,
**params)
p_box = sns.boxplot(palette=['#BBBBBB','#DDDDDD'],linewidth=6,**params)
handles,labels = p.get_legend_handles_labels()
#for h in handles:
# h.set_height(3)
#handles[2].set_linewidth(33)
plt.legend(handles[2:],
labels[2:],
bbox_to_anchor = (.3,.95),
fontsize = 40,
markerscale = 5,
frameon=False,
labelspacing=0.2)
plt.text(1.85,35, "Strip Plot", fontsize = 95, color='Black', fontstyle='italic')
plt.xlabel('')
plt.ylabel('Shot Distance (ft)')
plt.gca().set_xlim(-0.5,3.5)
xlabs = p.get_xticklabels()
xlabs[0].set_text('JJ Redick')
for l in xlabs[1:]:
l.set_text(" ".join(i.capitalize() for i in l.get_text().split() ))
p.set_xticklabels(xlabs)
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p.get_figure().savefig('../../figures/stripplot.png')
A fair bit of information is conveyed with a plot like this. JJ Redick is a shooting guard, and you see most of his shots are from a significant distances, whereas Lebron James has unsurprisingly a lot more attempts at close range. The median for Lebron's made shots is significantly lower than that for his misses, which is likely a result of him having many points from high percentage close shots/layups. There are a few outlying shots from very high distances, essentially all misses, that most likely are right before a buzzer.
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