Data Analysis and Manipulation#

Analyse NBA player data and manipulate it.

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

Import data from CSV

For this dataset, we use the Player Name (first Column) as index

nba_data = pd.read_csv('../input/nba_logreg.csv', index_col=0)

nba_data.rename(columns={'GP': 'GamesPlayed',
'MIN':'MinutesPlayed',
'PTS':'PointsPerGame',
'FGM':'FieldGoalsMade',
'FGA':'FieldGoalAttempts',
'FG%':'FieldGoalPercent',
'3P Made':'3PointMade',
'3PA':'3PointAttempts',
'3P%':'3PointAttemptsPercent',
'FTM':'FreeThrowMade',
'FTA':'FreeThrowAttempts',
'FT%':'FreeThrowPercent',
'OREB':'OffensiveRebounds',
'DREB':'DefensiveRebounds',
'REB':'Rebounds',
'AST':'Assists',
'STL':'Steals',
'BLK':'Blocks',
'TOV':'Turnovers',
'TARGET_5Yrs':'CareerLongerThan5Years'
},inplace=True)
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

Now, we should take a look at the data to better understand it.

First, we will display a description of the colums.

Columns

Next, we should look at the data.

nba_data.head()

	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

nba_data.sample(5, random_state=13)

	GamesPlayed	MinutesPlayed	PointsPerGame	FieldGoalsMade	FieldGoalAttempts	FieldGoalPercent	3PointMade	3PointAttempts	3PointAttemptsPercent	FreeThrowMade	FreeThrowAttempts	FreeThrowPercent	OffensiveRebounds	DefensiveRebounds	Rebounds	Assists	Steals	Blocks	Turnovers	CareerLongerThan5Years
Name
Tyler Johnson	32	18.8	5.9	2.2	5.2	41.9	0.6	1.5	37.5	1.0	1.5	68.1	0.4	2.1	2.5	1.3	1.0	0.3	0.9	0.0
Eric Washington	66	23.3	7.7	3.0	7.5	40.4	0.7	2.1	32.1	1.0	1.3	78.3	0.7	1.2	1.9	1.2	0.8	0.4	1.1	0.0
Sam Williams	59	18.2	6.1	2.6	4.7	55.6	0.0	0.0	NaN	0.8	1.5	55.1	1.5	3.7	5.2	0.6	0.8	1.3	1.1	0.0
John Williams	78	22.7	9.2	3.6	8.0	45.4	0.1	0.5	22.2	1.8	2.9	64.6	1.7	3.0	4.7	2.4	1.6	0.4	1.6	1.0
Jim Brogan	63	16.3	6.3	2.6	5.8	45.3	0.1	0.5	28.1	1.0	1.3	72.6	1.0	0.9	1.9	2.5	0.8	0.2	1.3	0.0

nba_data.info()

<class 'pandas.core.frame.DataFrame'>
Index: 1340 entries, Brandon Ingram to Jon Barry
Data columns (total 20 columns):
 #   Column                  Non-Null Count  Dtype  
---  ------                  --------------  -----  
 GamesPlayed             1340 non-null   int64  
 MinutesPlayed           1340 non-null   float64
 PointsPerGame           1340 non-null   float64
 FieldGoalsMade          1340 non-null   float64
 FieldGoalAttempts       1340 non-null   float64
 FieldGoalPercent        1340 non-null   float64
 3PointMade              1340 non-null   float64
 3PointAttempts          1340 non-null   float64
 3PointAttemptsPercent   1329 non-null   float64
 FreeThrowMade           1340 non-null   float64
FreeThrowAttempts       1340 non-null   float64
FreeThrowPercent        1340 non-null   float64
OffensiveRebounds       1340 non-null   float64
DefensiveRebounds       1340 non-null   float64
Rebounds                1340 non-null   float64
Assists                 1340 non-null   float64
Steals                  1340 non-null   float64
Blocks                  1340 non-null   float64
Turnovers               1340 non-null   float64
CareerLongerThan5Years  1340 non-null   float64
dtypes: float64(19), int64(1)
memory usage: 219.8+ KB

By taking a look at the info, we can see that the 3PointAttemptsPercent column has 11 null values. Those

nba_data.describe()

	GamesPlayed	MinutesPlayed	PointsPerGame	FieldGoalsMade	FieldGoalAttempts	FieldGoalPercent	3PointMade	3PointAttempts	3PointAttemptsPercent	FreeThrowMade	FreeThrowAttempts	FreeThrowPercent	OffensiveRebounds	DefensiveRebounds	Rebounds	Assists	Steals	Blocks	Turnovers	CareerLongerThan5Years
count	1340.000000	1340.000000	1340.000000	1340.000000	1340.000000	1340.000000	1340.000000	1340.000000	1329.000000	1340.000000	1340.000000	1340.000000	1340.000000	1340.000000	1340.000000	1340.000000	1340.000000	1340.000000	1340.000000	1340.000000
mean	60.414179	17.624627	6.801493	2.629104	5.885299	44.169403	0.247612	0.779179	19.308126	1.297687	1.821940	70.300299	1.009403	2.025746	3.034478	1.550522	0.618507	0.368582	1.193582	0.620149
std	17.433992	8.307964	4.357545	1.683555	3.593488	6.137679	0.383688	1.061847	16.022916	0.987246	1.322984	10.578479	0.777119	1.360008	2.057774	1.471169	0.409759	0.429049	0.722541	0.485531
min	11.000000	3.100000	0.700000	0.300000	0.800000	23.800000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.200000	0.300000	0.000000	0.000000	0.000000	0.100000	0.000000
25%	47.000000	10.875000	3.700000	1.400000	3.300000	40.200000	0.000000	0.000000	0.000000	0.600000	0.900000	64.700000	0.400000	1.000000	1.500000	0.600000	0.300000	0.100000	0.700000	0.000000
50%	63.000000	16.100000	5.550000	2.100000	4.800000	44.100000	0.100000	0.300000	22.400000	1.000000	1.500000	71.250000	0.800000	1.700000	2.500000	1.100000	0.500000	0.200000	1.000000	1.000000
75%	77.000000	22.900000	8.800000	3.400000	7.500000	47.900000	0.400000	1.200000	32.500000	1.600000	2.300000	77.600000	1.400000	2.600000	4.000000	2.000000	0.800000	0.500000	1.500000	1.000000
max	82.000000	40.900000	28.200000	10.200000	19.800000	73.700000	2.300000	6.500000	100.000000	7.700000	10.200000	100.000000	5.300000	9.600000	13.900000	10.600000	2.500000	3.900000	4.400000	1.000000

nba_data.dtypes

GamesPlayed                 int64
MinutesPlayed             float64
PointsPerGame             float64
FieldGoalsMade            float64
FieldGoalAttempts         float64
FieldGoalPercent          float64
3PointMade                float64
3PointAttempts            float64
3PointAttemptsPercent     float64
FreeThrowMade             float64
FreeThrowAttempts         float64
FreeThrowPercent          float64
OffensiveRebounds         float64
DefensiveRebounds         float64
Rebounds                  float64
Assists                   float64
Steals                    float64
Blocks                    float64
Turnovers                 float64
CareerLongerThan5Years    float64
dtype: object

nba_data.isnull().sum()

GamesPlayed                0
MinutesPlayed              0
PointsPerGame              0
FieldGoalsMade             0
FieldGoalAttempts          0
FieldGoalPercent           0
3PointMade                 0
3PointAttempts             0
3PointAttemptsPercent     11
FreeThrowMade              0
FreeThrowAttempts          0
FreeThrowPercent           0
OffensiveRebounds          0
DefensiveRebounds          0
Rebounds                   0
Assists                    0
Steals                     0
Blocks                     0
Turnovers                  0
CareerLongerThan5Years     0
dtype: int64

It looks like there are 11 rows which have null 3PointAttemtsPercent

nba_data.loc[nba_data["3PointAttemptsPercent"].isnull()]

	GamesPlayed	MinutesPlayed	PointsPerGame	FieldGoalsMade	FieldGoalAttempts	FieldGoalPercent	3PointMade	3PointAttempts	3PointAttemptsPercent	FreeThrowMade	FreeThrowAttempts	FreeThrowPercent	OffensiveRebounds	DefensiveRebounds	Rebounds	Assists	Steals	Blocks	Turnovers	CareerLongerThan5Years
Name
Ken Johnson	64	12.7	4.1	1.8	3.3	52.8	0.0	0.0	NaN	0.6	1.3	43.5	1.4	2.4	3.8	0.3	0.2	0.3	0.9	0.0
Ken Johnson	64	12.7	4.1	1.8	3.3	52.8	0.0	0.0	NaN	0.6	1.3	43.5	1.4	2.4	3.8	0.3	0.2	0.3	0.9	0.0
Pete Williams	53	10.8	2.8	1.3	2.1	60.4	0.0	0.0	NaN	0.3	0.8	42.5	0.9	1.9	2.8	0.3	0.4	0.4	0.4	0.0
Melvin Turpin	79	24.7	10.6	4.6	9.0	51.1	0.0	0.0	NaN	1.4	1.8	78.4	2.0	3.8	5.7	0.5	0.5	1.1	1.5	1.0
Jim Petersen	60	11.9	3.2	1.2	2.4	48.6	0.0	0.0	NaN	0.8	1.1	75.8	0.7	1.7	2.5	0.5	0.2	0.5	1.2	1.0
Tom Scheffler	39	6.9	1.3	0.5	1.3	41.2	0.0	0.0	NaN	0.3	0.5	50.0	0.5	1.5	1.9	0.3	0.2	0.3	0.4	0.0
Sam Williams	59	18.2	6.1	2.6	4.7	55.6	0.0	0.0	NaN	0.8	1.5	55.1	1.5	3.7	5.2	0.6	0.8	1.3	1.1	0.0
Kurt Nimphius	63	17.2	5.3	2.2	4.7	46.1	0.0	0.0	NaN	1.0	1.7	58.3	1.5	3.2	4.7	1.0	0.3	1.3	0.9	1.0
Pete Verhoeven	71	17.0	4.9	2.1	4.2	50.3	0.0	0.0	NaN	0.7	1.0	70.8	1.5	2.1	3.6	0.7	0.6	0.3	0.8	1.0
Jim Smith	72	11.9	2.9	1.2	2.3	50.9	0.0	0.0	NaN	0.5	1.2	45.9	1.0	1.5	2.5	0.6	0.3	0.7	0.7	0.0
Jeff Wilkins	56	18.9	4.7	2.1	4.6	45.0	0.0	0.0	NaN	0.5	0.7	67.5	1.1	3.8	4.9	0.7	0.6	0.8	1.1	1.0

print("Rows with null 3PointAttemptsPercent: " + str(len(nba_data.loc[nba_data["3PointAttemptsPercent"].isnull()])))

print("Rows with null 3PointAttemptsPercent and a 3PointAttempts > 0: " + str(len(nba_data.loc[nba_data["3PointAttemptsPercent"].isnull() & nba_data["3PointAttempts"] > 0])))

Rows with null 3PointAttemptsPercent: 11
Rows with null 3PointAttemptsPercent and a 3PointAttempts > 0: 0

Due to 3PointsAttemptsPercent being null only when the 3PointAttempts are 0, 3PointsAttemptPercent can be set to 0 where it is nan.

nba_data["3PointAttemptsPercent"].fillna(0, inplace=True)

nba_data.info()

<class 'pandas.core.frame.DataFrame'>
Index: 1340 entries, Brandon Ingram to Jon Barry
Data columns (total 20 columns):
 #   Column                  Non-Null Count  Dtype  
---  ------                  --------------  -----  
 GamesPlayed             1340 non-null   int64  
 MinutesPlayed           1340 non-null   float64
 PointsPerGame           1340 non-null   float64
 FieldGoalsMade          1340 non-null   float64
 FieldGoalAttempts       1340 non-null   float64
 FieldGoalPercent        1340 non-null   float64
 3PointMade              1340 non-null   float64
 3PointAttempts          1340 non-null   float64
 3PointAttemptsPercent   1340 non-null   float64
 FreeThrowMade           1340 non-null   float64
FreeThrowAttempts       1340 non-null   float64
FreeThrowPercent        1340 non-null   float64
OffensiveRebounds       1340 non-null   float64
DefensiveRebounds       1340 non-null   float64
Rebounds                1340 non-null   float64
Assists                 1340 non-null   float64
Steals                  1340 non-null   float64
Blocks                  1340 non-null   float64
Turnovers               1340 non-null   float64
CareerLongerThan5Years  1340 non-null   float64
dtypes: float64(19), int64(1)
memory usage: 219.8+ KB

nba_data["CareerLongerThan5Years"].value_counts()

1.0    831
0.0    509
Name: CareerLongerThan5Years, dtype: int64

A lot more players have a career longer than 5 years (about 300 more).

nba_data.iloc[:,:5].join(nba_data.iloc[:,-1:]).groupby('CareerLongerThan5Years').agg(['min', 'mean', 'max'])

	GamesPlayed			MinutesPlayed			PointsPerGame			FieldGoalsMade			FieldGoalAttempts
	min	mean	max	min	mean	max	min	mean	max	min	mean	max	min	mean	max
CareerLongerThan5Years
0.0	11	52.645270	82	3.1	14.617061	37.2	0.7	5.258953	21.9	0.3	2.021959	8.0	0.8	4.672804	17.6
1.0	16	64.932548	82	4.4	19.353747	40.9	1.0	7.777623	28.2	0.4	3.003961	10.2	0.8	6.609315	19.8

nba_data.iloc[:,5:10].join(nba_data.iloc[:,-1:]).groupby('CareerLongerThan5Years').agg(['min', 'mean', 'max'])

	FieldGoalPercent			3PointMade			3PointAttempts			3PointAttemptsPercent			FreeThrowMade
	min	mean	max	min	mean	max	min	mean	max	min	mean	max	min	mean	max
CareerLongerThan5Years
0.0	23.8	42.602196	65.9	0.0	0.222635	1.6	0.0	0.738851	4.8	0.0	18.394088	100.0	0.0	0.992905	5.0
1.0	29.2	45.131585	73.7	0.0	0.247002	2.3	0.0	0.769058	6.5	0.0	18.557602	100.0	0.1	1.521949	7.7

nba_data.iloc[:,10:15].join(nba_data.iloc[:,-1:]).groupby('CareerLongerThan5Years').agg(['min', 'mean', 'max'])

	FreeThrowAttempts			FreeThrowPercent			OffensiveRebounds			DefensiveRebounds			Rebounds
	min	mean	max	min	mean	max	min	mean	max	min	mean	max	min	mean	max
CareerLongerThan5Years
0.0	0.0	1.427027	6.2	0.0	68.449155	100.0	0.0	0.754392	3.9	0.2	1.585304	7.7	0.3	2.338851	11.0
1.0	0.1	2.126874	10.2	28.6	70.717666	100.0	0.1	1.173662	5.3	0.3	2.289615	9.6	0.4	3.465203	13.9

nba_data.iloc[:,15:].groupby('CareerLongerThan5Years').agg(['min', 'mean', 'max'])

	Assists			Steals			Blocks			Turnovers
	min	mean	max	min	mean	max	min	mean	max	min	mean	max
CareerLongerThan5Years
0.0	0.0	1.221022	8.7	0.0	0.498232	2.1	0.0	0.253438	2.1	0.1	0.942240	3.9
1.0	0.0	1.752347	10.6	0.0	0.692178	2.5	0.0	0.439110	3.9	0.1	1.347533	4.4

The average player who has a career longer than 5 years plays aprox. 10 games more than a player whose career doesnt last that long.

He also plays on average more minutes per game.

Surprisingly, the difference between the avg. Three Points made aren’t that big, allthough the players with longer careers succeed more in free throws and are better at defence.

They also have on average a higher amount of points made per game.

corr = nba_data.corr()[['CareerLongerThan5Years']].sort_values("CareerLongerThan5Years", ascending=False, key=lambda col: abs(col))
corr.iloc[1:] # Ignore CareerLongerThan5Years row

	CareerLongerThan5Years
GamesPlayed	0.396833
MinutesPlayed	0.317805
FieldGoalsMade	0.317594
PointsPerGame	0.315981
Rebounds	0.299406
FreeThrowMade	0.296841
FreeThrowAttempts	0.296089
OffensiveRebounds	0.293307
FieldGoalAttempts	0.292660
DefensiveRebounds	0.284677
Turnovers	0.272348
Steals	0.229811
FieldGoalPercent	0.227134
Blocks	0.210114
Assists	0.175353
FreeThrowPercent	0.106706
3PointMade	0.036619
3PointAttempts	0.018110
3PointAttemptsPercent	-0.000004

By taking a look at the correlation matrix, the 4 most decisive columns for the prediction are GamesPlayed,MinutesPlayed, FieldGoalsMade and PointsPerGame.

nba_data.to_csv('../output/new_nba_data.csv')

NBA Player Classification

Data Analysis and Manipulation

Data Analysis and Manipulation#