Data Visualisation#

Visualize NBA player data to find out which features can be used for classification.

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import math 

nba_data = pd.read_csv('../output/new_nba_data.csv', index_col=0)
nba_data
GamesPlayed MinutesPlayed PointsPerGame FieldGoalsMade FieldGoalAttempts FieldGoalPercent 3PointMade 3PointAttempts 3PointAttemptsPercent FreeThrowMade FreeThrowAttempts FreeThrowPercent OffensiveRebounds DefensiveRebounds Rebounds Assists Steals Blocks Turnovers CareerLongerThan5Years
Name
Brandon Ingram 36 27.4 7.4 2.6 7.6 34.7 0.5 2.1 25.0 1.6 2.3 69.9 0.7 3.4 4.1 1.9 0.4 0.4 1.3 0.0
Andrew Harrison 35 26.9 7.2 2.0 6.7 29.6 0.7 2.8 23.5 2.6 3.4 76.5 0.5 2.0 2.4 3.7 1.1 0.5 1.6 0.0
JaKarr Sampson 74 15.3 5.2 2.0 4.7 42.2 0.4 1.7 24.4 0.9 1.3 67.0 0.5 1.7 2.2 1.0 0.5 0.3 1.0 0.0
Malik Sealy 58 11.6 5.7 2.3 5.5 42.6 0.1 0.5 22.6 0.9 1.3 68.9 1.0 0.9 1.9 0.8 0.6 0.1 1.0 1.0
Matt Geiger 48 11.5 4.5 1.6 3.0 52.4 0.0 0.1 0.0 1.3 1.9 67.4 1.0 1.5 2.5 0.3 0.3 0.4 0.8 1.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
Chris Smith 80 15.8 4.3 1.6 3.6 43.3 0.0 0.2 14.3 1.2 1.5 79.2 0.4 0.8 1.2 2.5 0.6 0.2 0.8 0.0
Brent Price 68 12.6 3.9 1.5 4.1 35.8 0.1 0.7 16.7 0.8 1.0 79.4 0.4 1.1 1.5 2.3 0.8 0.0 1.3 1.0
Marlon Maxey 43 12.1 5.4 2.2 3.9 55.0 0.0 0.0 0.0 1.0 1.6 64.3 1.5 2.3 3.8 0.3 0.3 0.4 0.9 0.0
Litterial Green 52 12.0 4.5 1.7 3.8 43.9 0.0 0.2 10.0 1.2 1.8 62.5 0.2 0.4 0.7 2.2 0.4 0.1 0.8 1.0
Jon Barry 47 11.7 4.4 1.6 4.4 36.9 0.4 1.3 33.3 0.7 1.0 67.3 0.2 0.7 0.9 1.4 0.7 0.1 0.9 1.0

1340 rows × 20 columns

colors = sns.xkcd_palette(['dark blue','light red'])
sns.palplot(colors)
../_images/DataVisualisation_3_0.png 
sns.pairplot(data=nba_data.iloc[:,:5].join(nba_data.iloc[:,-1:]), hue='CareerLongerThan5Years',palette=colors)

<seaborn.axisgrid.PairGrid at 0x7ff8a3231ee0>
../_images/DataVisualisation_4_1.png 
sns.pairplot(data=nba_data.iloc[:,5:10].join(nba_data.iloc[:,-1:]), hue='CareerLongerThan5Years',palette=colors)

<seaborn.axisgrid.PairGrid at 0x7ff86728a610>
../_images/DataVisualisation_5_1.png 
sns.pairplot(data=nba_data.iloc[:,10:15].join(nba_data.iloc[:,-1:]), hue='CareerLongerThan5Years',palette=colors)

<seaborn.axisgrid.PairGrid at 0x7ff864f25310>
../_images/DataVisualisation_6_1.png 
sns.pairplot(data=nba_data.iloc[:,15:], hue='CareerLongerThan5Years',palette=colors)

<seaborn.axisgrid.PairGrid at 0x7ff85d376df0>
../_images/DataVisualisation_7_1.png

The column “Gamesplayed” is one of the most decisive columns because it provides the clearest data which does not overlap a lot between players that have a career longer than five years and those who have not played for 5 years.

#sns.violinplot(data=nba_data, x='CareerLongerThan5Years', y='GamesPlayed' ,color='lightgrey')
#sns.stripplot(data=nba_data, x='CareerLongerThan5Years', y='GamesPlayed',palette=colors)


fig, axes = plt.subplots(int(len(nba_data.axes[1])/4), 4, figsize=(18,25))
for i in range(int(len(nba_data.columns)/4)):
    for j in range(4):
        sns.violinplot(ax=axes[i, j], data=nba_data, x='CareerLongerThan5Years', y=nba_data.columns[i*4+j],color='lightgrey')
        sns.stripplot(ax=axes[i, j], data=nba_data, x='CareerLongerThan5Years', y=nba_data.columns[i*4+j],palette=colors,hue='CareerLongerThan5Years')
        plt.figure()

/tmp/ipykernel_1839/2495864547.py:10: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). Consider using `matplotlib.pyplot.close()`.
  plt.figure()
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fig, axes = plt.subplots(int(len(nba_data.axes[1])/4), 4, figsize=(18,25))
sns.boxplot(ax=axes[0, 0], data=nba_data, x='CareerLongerThan5Years', y='GamesPlayed',palette=colors)
plt.figure()
#sns.FacetGrid(data=nba_data, hue="CareerLongerThan5Years", col="GamesPlayed", height=4, palette=colors).map(sns.kdeplot, "GamesPlayed", shade=True, cut=0).add_legend()
for i in range(int(len(nba_data.columns)/4)):
    for j in range(4):
        sns.boxplot(ax=axes[i, j], data=nba_data, x='CareerLongerThan5Years', y=nba_data.columns[i*4+j],palette=colors)
        plt.figure()

/tmp/ipykernel_1839/1619567479.py:8: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). Consider using `matplotlib.pyplot.close()`.
  plt.figure()
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We can see that those players who play more games have a higher chance of having a career longer than 5 years. But it overlaps a lot, so we can’t say that it is a good predictor.It requires other fields to get a better overview.

gamesPlayed_values = ['GamesPlayed','MinutesPlayed','PointsPerGame','FieldGoalsMade']

fig, axes = plt.subplots(4, 4, figsize=(18,20))
plt.figure()
for i in range(int(len(gamesPlayed_values))):
    for j in range(int(len(gamesPlayed_values))):
        if i != j:
            sns.kdeplot(ax=axes[i, j], data=nba_data, hue='CareerLongerThan5Years', x=gamesPlayed_values[i],y=gamesPlayed_values[j],palette=colors)
            plt.figure()
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fig, axes = plt.subplots(int(len(nba_data.axes[1])/4), 4, figsize=(18,25))
plt.figure()
for i in range(int(len(nba_data.columns)/4)):
    for j in range(4):
        sns.histplot(ax=axes[i, j], data=nba_data, hue='CareerLongerThan5Years', x=nba_data.columns[i*4+j],palette=colors)
        plt.figure()

/tmp/ipykernel_1839/1784320525.py:6: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). Consider using `matplotlib.pyplot.close()`.
  plt.figure()
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