mgaldi/Python-Exploratory-Data-Analysis-and-Pandas

Assignment 1

fifa19 players dataset

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
df = pd.read_csv("data.csv", index_col=0)

pd.set_option('display.max_columns', None)

df

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	ID	Name	Age	Photo	Nationality	Flag	Overall	Potential	Club	Club Logo	Value	Wage	Special	Preferred Foot	International Reputation	Weak Foot	Skill Moves	Work Rate	Body Type	Real Face	Position	Jersey Number	Joined	Loaned From	Contract Valid Until	Height	Weight	LS	ST	RS	LW	LF	CF	RF	RW	LAM	CAM	RAM	LM	LCM	CM	RCM	RM	LWB	LDM	CDM	RDM	RWB	LB	LCB	CB	RCB	RB	Crossing	Finishing	HeadingAccuracy	ShortPassing	Volleys	Dribbling	Curve	FKAccuracy	LongPassing	BallControl	Acceleration	SprintSpeed	Agility	Reactions	Balance	ShotPower	Jumping	Stamina	Strength	LongShots	Aggression	Interceptions	Positioning	Vision	Penalties	Composure	Marking	StandingTackle	SlidingTackle	GKDiving	GKHandling	GKKicking	GKPositioning	GKReflexes	Release Clause
0	158023	L. Messi	31	https://cdn.sofifa.org/players/4/19/158023.png	Argentina	https://cdn.sofifa.org/flags/52.png	94	94	FC Barcelona	https://cdn.sofifa.org/teams/2/light/241.png	€110.5M	€565K	2202	Left	5.0	4.0	4.0	Medium/ Medium	Messi	Yes	RF	10.0	Jul 1, 2004	NaN	2021	5'7	159lbs	88+2	88+2	88+2	92+2	93+2	93+2	93+2	92+2	93+2	93+2	93+2	91+2	84+2	84+2	84+2	91+2	64+2	61+2	61+2	61+2	64+2	59+2	47+2	47+2	47+2	59+2	84.0	95.0	70.0	90.0	86.0	97.0	93.0	94.0	87.0	96.0	91.0	86.0	91.0	95.0	95.0	85.0	68.0	72.0	59.0	94.0	48.0	22.0	94.0	94.0	75.0	96.0	33.0	28.0	26.0	6.0	11.0	15.0	14.0	8.0	€226.5M
1	20801	Cristiano Ronaldo	33	https://cdn.sofifa.org/players/4/19/20801.png	Portugal	https://cdn.sofifa.org/flags/38.png	94	94	Juventus	https://cdn.sofifa.org/teams/2/light/45.png	€77M	€405K	2228	Right	5.0	4.0	5.0	High/ Low	C. Ronaldo	Yes	ST	7.0	Jul 10, 2018	NaN	2022	6'2	183lbs	91+3	91+3	91+3	89+3	90+3	90+3	90+3	89+3	88+3	88+3	88+3	88+3	81+3	81+3	81+3	88+3	65+3	61+3	61+3	61+3	65+3	61+3	53+3	53+3	53+3	61+3	84.0	94.0	89.0	81.0	87.0	88.0	81.0	76.0	77.0	94.0	89.0	91.0	87.0	96.0	70.0	95.0	95.0	88.0	79.0	93.0	63.0	29.0	95.0	82.0	85.0	95.0	28.0	31.0	23.0	7.0	11.0	15.0	14.0	11.0	€127.1M
2	190871	Neymar Jr	26	https://cdn.sofifa.org/players/4/19/190871.png	Brazil	https://cdn.sofifa.org/flags/54.png	92	93	Paris Saint-Germain	https://cdn.sofifa.org/teams/2/light/73.png	€118.5M	€290K	2143	Right	5.0	5.0	5.0	High/ Medium	Neymar	Yes	LW	10.0	Aug 3, 2017	NaN	2022	5'9	150lbs	84+3	84+3	84+3	89+3	89+3	89+3	89+3	89+3	89+3	89+3	89+3	88+3	81+3	81+3	81+3	88+3	65+3	60+3	60+3	60+3	65+3	60+3	47+3	47+3	47+3	60+3	79.0	87.0	62.0	84.0	84.0	96.0	88.0	87.0	78.0	95.0	94.0	90.0	96.0	94.0	84.0	80.0	61.0	81.0	49.0	82.0	56.0	36.0	89.0	87.0	81.0	94.0	27.0	24.0	33.0	9.0	9.0	15.0	15.0	11.0	€228.1M
3	193080	De Gea	27	https://cdn.sofifa.org/players/4/19/193080.png	Spain	https://cdn.sofifa.org/flags/45.png	91	93	Manchester United	https://cdn.sofifa.org/teams/2/light/11.png	€72M	€260K	1471	Right	4.0	3.0	1.0	Medium/ Medium	Lean	Yes	GK	1.0	Jul 1, 2011	NaN	2020	6'4	168lbs	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	17.0	13.0	21.0	50.0	13.0	18.0	21.0	19.0	51.0	42.0	57.0	58.0	60.0	90.0	43.0	31.0	67.0	43.0	64.0	12.0	38.0	30.0	12.0	68.0	40.0	68.0	15.0	21.0	13.0	90.0	85.0	87.0	88.0	94.0	€138.6M
4	192985	K. De Bruyne	27	https://cdn.sofifa.org/players/4/19/192985.png	Belgium	https://cdn.sofifa.org/flags/7.png	91	92	Manchester City	https://cdn.sofifa.org/teams/2/light/10.png	€102M	€355K	2281	Right	4.0	5.0	4.0	High/ High	Normal	Yes	RCM	7.0	Aug 30, 2015	NaN	2023	5'11	154lbs	82+3	82+3	82+3	87+3	87+3	87+3	87+3	87+3	88+3	88+3	88+3	88+3	87+3	87+3	87+3	88+3	77+3	77+3	77+3	77+3	77+3	73+3	66+3	66+3	66+3	73+3	93.0	82.0	55.0	92.0	82.0	86.0	85.0	83.0	91.0	91.0	78.0	76.0	79.0	91.0	77.0	91.0	63.0	90.0	75.0	91.0	76.0	61.0	87.0	94.0	79.0	88.0	68.0	58.0	51.0	15.0	13.0	5.0	10.0	13.0	€196.4M
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
18202	238813	J. Lundstram	19	https://cdn.sofifa.org/players/4/19/238813.png	England	https://cdn.sofifa.org/flags/14.png	47	65	Crewe Alexandra	https://cdn.sofifa.org/teams/2/light/121.png	€60K	€1K	1307	Right	1.0	2.0	2.0	Medium/ Medium	Lean	No	CM	22.0	May 3, 2017	NaN	2019	5'9	134lbs	42+2	42+2	42+2	44+2	44+2	44+2	44+2	44+2	45+2	45+2	45+2	44+2	45+2	45+2	45+2	44+2	44+2	45+2	45+2	45+2	44+2	45+2	45+2	45+2	45+2	45+2	34.0	38.0	40.0	49.0	25.0	42.0	30.0	34.0	45.0	43.0	54.0	57.0	60.0	49.0	76.0	43.0	55.0	40.0	47.0	38.0	46.0	46.0	39.0	52.0	43.0	45.0	40.0	48.0	47.0	10.0	13.0	7.0	8.0	9.0	€143K
18203	243165	N. Christoffersson	19	https://cdn.sofifa.org/players/4/19/243165.png	Sweden	https://cdn.sofifa.org/flags/46.png	47	63	Trelleborgs FF	https://cdn.sofifa.org/teams/2/light/703.png	€60K	€1K	1098	Right	1.0	2.0	2.0	Medium/ Medium	Normal	No	ST	21.0	Mar 19, 2018	NaN	2020	6'3	170lbs	45+2	45+2	45+2	39+2	42+2	42+2	42+2	39+2	40+2	40+2	40+2	38+2	35+2	35+2	35+2	38+2	30+2	31+2	31+2	31+2	30+2	29+2	32+2	32+2	32+2	29+2	23.0	52.0	52.0	43.0	36.0	39.0	32.0	20.0	25.0	40.0	41.0	39.0	38.0	40.0	52.0	41.0	47.0	43.0	67.0	42.0	47.0	16.0	46.0	33.0	43.0	42.0	22.0	15.0	19.0	10.0	9.0	9.0	5.0	12.0	€113K
18204	241638	B. Worman	16	https://cdn.sofifa.org/players/4/19/241638.png	England	https://cdn.sofifa.org/flags/14.png	47	67	Cambridge United	https://cdn.sofifa.org/teams/2/light/1944.png	€60K	€1K	1189	Right	1.0	3.0	2.0	Medium/ Medium	Normal	No	ST	33.0	Jul 1, 2017	NaN	2021	5'8	148lbs	45+2	45+2	45+2	45+2	46+2	46+2	46+2	45+2	44+2	44+2	44+2	44+2	38+2	38+2	38+2	44+2	34+2	30+2	30+2	30+2	34+2	33+2	28+2	28+2	28+2	33+2	25.0	40.0	46.0	38.0	38.0	45.0	38.0	27.0	28.0	44.0	70.0	69.0	50.0	47.0	58.0	45.0	60.0	55.0	32.0	45.0	32.0	15.0	48.0	43.0	55.0	41.0	32.0	13.0	11.0	6.0	5.0	10.0	6.0	13.0	€165K
18205	246268	D. Walker-Rice	17	https://cdn.sofifa.org/players/4/19/246268.png	England	https://cdn.sofifa.org/flags/14.png	47	66	Tranmere Rovers	https://cdn.sofifa.org/teams/2/light/15048.png	€60K	€1K	1228	Right	1.0	3.0	2.0	Medium/ Medium	Lean	No	RW	34.0	Apr 24, 2018	NaN	2019	5'10	154lbs	47+2	47+2	47+2	47+2	46+2	46+2	46+2	47+2	45+2	45+2	45+2	46+2	39+2	39+2	39+2	46+2	36+2	32+2	32+2	32+2	36+2	35+2	31+2	31+2	31+2	35+2	44.0	50.0	39.0	42.0	40.0	51.0	34.0	32.0	32.0	52.0	61.0	60.0	52.0	21.0	71.0	64.0	42.0	40.0	48.0	34.0	33.0	22.0	44.0	47.0	50.0	46.0	20.0	25.0	27.0	14.0	6.0	14.0	8.0	9.0	€143K
18206	246269	G. Nugent	16	https://cdn.sofifa.org/players/4/19/246269.png	England	https://cdn.sofifa.org/flags/14.png	46	66	Tranmere Rovers	https://cdn.sofifa.org/teams/2/light/15048.png	€60K	€1K	1321	Right	1.0	3.0	2.0	Medium/ Medium	Lean	No	CM	33.0	Oct 30, 2018	NaN	2019	5'10	176lbs	43+2	43+2	43+2	45+2	44+2	44+2	44+2	45+2	45+2	45+2	45+2	46+2	45+2	45+2	45+2	46+2	46+2	46+2	46+2	46+2	46+2	46+2	47+2	47+2	47+2	46+2	41.0	34.0	46.0	48.0	30.0	43.0	40.0	34.0	44.0	51.0	57.0	55.0	55.0	51.0	63.0	43.0	62.0	47.0	60.0	32.0	56.0	42.0	34.0	49.0	33.0	43.0	40.0	43.0	50.0	10.0	15.0	9.0	12.0	9.0	€165K

18207 rows × 88 columns

Cleanup

The dataset has some information that either cannot be analyzed or needs to be changed

Removed

• Photo
• Flag
• Club Logo

Changed

• Value
• Wage
• Release Clause
• Preferred Foot
• Height
• Weight

Changes

Value, Wage, Release Clause

Each value of column Value, Wage, Release Clause, x = df.loc[:,['Value|Wage|Release Clause']] has format: €x[M,K].
The character € is removed and depending on M or K the value is changed.
Ex: €110.5M -> 110500000 | €260K -> 260000

## Applying the changes for Value, Wage and Release Clause

arr = ['Value','Wage', 'Release Clause']
for x in arr:
    df[x] = df[x].str.strip('€')
    df[x] = df[x].str.translate(str.maketrans({'.':'','K':'000','M':'000000'}))
    df[x] = df[x].fillna("0")
    df[x] = pd.to_numeric(df[x])

Preferred Foot

The values Right and left, and null are changed to 0, 1, and 2
Right -> 0
Left -> 1
null -> 2

## Right -> 0, Left -> 1
df['Preferred Foot'] = df['Preferred Foot'].str.replace("Right","0")
df['Preferred Foot'] = df['Preferred Foot'].str.replace("Left","1")
## Changing null values to 2
df['Preferred Foot'] = df['Preferred Foot'].fillna("2")
df['Preferred Foot'] = pd.to_numeric(df['Preferred Foot'])

Height

The height is transformed to a readable numerical value 5'11 -> 5.11
Null value changed with the average value ~5.8

df['Height'] = df['Height'].str.translate(str.maketrans({"'":"."}))
df['Height'] = pd.to_numeric(df['Height'])
df['Height'] = df['Height'].fillna(df.Height.mean())

df['Height'].describe()

count    18207.000000
mean         5.797367
std          0.447641
min          5.100000
25%          5.110000
50%          5.900000
75%          6.100000
max          6.900000
Name: Height, dtype: float64

Weight

The characters lbs are removed 158lbs -> 158

df['Weight'] = ((df['Weight'].str.strip('lbs')))
df['Weight'] = pd.to_numeric(df['Weight'])
df['Weight']

0        159.0
1        183.0
2        150.0
3        168.0
4        154.0
         ...  
18202    134.0
18203    170.0
18204    148.0
18205    154.0
18206    176.0
Name: Weight, Length: 18207, dtype: float64

Photo, Flag, Club Logo

df = df.drop(columns=['Photo', 'Flag', 'Club Logo'])

Average age of the players

The method describe() can be used in this case. More specifically, invoking mean() on the the dataframe column Age returns the average age of the players: 25.122206

df.describe()

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	ID	Age	Overall	Potential	Value	Wage	Special	Preferred Foot	International Reputation	Weak Foot	Skill Moves	Jersey Number	Height	Weight	Crossing	Finishing	HeadingAccuracy	ShortPassing	Volleys	Dribbling	Curve	FKAccuracy	LongPassing	BallControl	Acceleration	SprintSpeed	Agility	Reactions	Balance	ShotPower	Jumping	Stamina	Strength	LongShots	Aggression	Interceptions	Positioning	Vision	Penalties	Composure	Marking	StandingTackle	SlidingTackle	GKDiving	GKHandling	GKKicking	GKPositioning	GKReflexes	Release Clause
count	18207.000000	18207.000000	18207.000000	18207.000000	1.820700e+04	18207.000000	18207.000000	18207.000000	18159.000000	18159.000000	18159.000000	18147.000000	18207.000000	18159.000000	18159.000000	18159.000000	18159.000000	18159.000000	18159.000000	18159.000000	18159.000000	18159.000000	18159.000000	18159.000000	18159.000000	18159.000000	18159.000000	18159.000000	18159.000000	18159.000000	18159.000000	18159.000000	18159.000000	18159.000000	18159.000000	18159.000000	18159.000000	18159.000000	18159.000000	18159.000000	18159.000000	18159.000000	18159.000000	18159.000000	18159.000000	18159.000000	18159.000000	18159.000000	1.820700e+04
mean	214298.338606	25.122206	66.238699	71.307299	1.362983e+07	9731.312133	1597.809908	0.236557	1.113222	2.947299	2.361308	19.546096	5.797367	165.979129	49.734181	45.550911	52.298144	58.686712	42.909026	55.371001	47.170824	42.863153	52.711933	58.369459	64.614076	64.726967	63.503607	61.836610	63.966573	55.460047	65.089432	63.219946	65.311967	47.109973	55.868991	46.698276	49.958478	53.400903	48.548598	58.648274	47.281623	47.697836	45.661435	16.616223	16.391596	16.232061	16.388898	16.710887	3.620049e+07
std	29965.244204	4.669943	6.908930	6.136496	3.987587e+07	21999.290406	272.586016	0.431139	0.394031	0.660456	0.756164	15.947765	0.447641	15.593344	18.364524	19.525820	17.379909	14.699495	17.694408	18.910371	18.395264	17.478763	15.327870	16.686595	14.927780	14.649953	14.766049	9.010464	14.136166	17.237958	11.820044	15.894741	12.557000	19.260524	17.367967	20.696909	19.529036	14.146881	15.704053	11.436133	19.904397	21.664004	21.289135	17.695349	16.906900	16.502864	17.034669	17.955119	1.033686e+08
min	16.000000	16.000000	46.000000	48.000000	0.000000e+00	0.000000	731.000000	0.000000	1.000000	1.000000	1.000000	1.000000	5.100000	110.000000	5.000000	2.000000	4.000000	7.000000	4.000000	4.000000	6.000000	3.000000	9.000000	5.000000	12.000000	12.000000	14.000000	21.000000	16.000000	2.000000	15.000000	12.000000	17.000000	3.000000	11.000000	3.000000	2.000000	10.000000	5.000000	3.000000	3.000000	2.000000	3.000000	1.000000	1.000000	1.000000	1.000000	1.000000	0.000000e+00
25%	200315.500000	21.000000	62.000000	67.000000	3.000000e+05	1000.000000	1457.000000	0.000000	1.000000	3.000000	2.000000	8.000000	5.110000	154.000000	38.000000	30.000000	44.000000	54.000000	30.000000	49.000000	34.000000	31.000000	43.000000	54.000000	57.000000	57.000000	55.000000	56.000000	56.000000	45.000000	58.000000	56.000000	58.000000	33.000000	44.000000	26.000000	38.000000	44.000000	39.000000	51.000000	30.000000	27.000000	24.000000	8.000000	8.000000	8.000000	8.000000	8.000000	3.920000e+05
50%	221759.000000	25.000000	66.000000	71.000000	6.750000e+05	3000.000000	1635.000000	0.000000	1.000000	3.000000	2.000000	17.000000	5.900000	165.000000	54.000000	49.000000	56.000000	62.000000	44.000000	61.000000	48.000000	41.000000	56.000000	63.000000	67.000000	67.000000	66.000000	62.000000	66.000000	59.000000	66.000000	66.000000	67.000000	51.000000	59.000000	52.000000	55.000000	55.000000	49.000000	60.000000	53.000000	55.000000	52.000000	11.000000	11.000000	11.000000	11.000000	11.000000	1.000000e+06
75%	236529.500000	28.000000	71.000000	75.000000	1.300000e+07	9000.000000	1787.000000	0.000000	1.000000	3.000000	3.000000	26.000000	6.100000	176.000000	64.000000	62.000000	64.000000	68.000000	57.000000	68.000000	62.000000	57.000000	64.000000	69.000000	75.000000	75.000000	74.000000	68.000000	74.000000	68.000000	73.000000	74.000000	74.000000	62.000000	69.000000	64.000000	64.000000	64.000000	60.000000	67.000000	64.000000	66.000000	64.000000	14.000000	14.000000	14.000000	14.000000	14.000000	2.400000e+07
max	246620.000000	45.000000	94.000000	95.000000	1.185000e+09	565000.000000	2346.000000	2.000000	5.000000	5.000000	5.000000	99.000000	6.900000	243.000000	93.000000	95.000000	94.000000	93.000000	90.000000	97.000000	94.000000	94.000000	93.000000	96.000000	97.000000	96.000000	96.000000	96.000000	96.000000	95.000000	95.000000	96.000000	97.000000	94.000000	95.000000	92.000000	95.000000	94.000000	92.000000	96.000000	94.000000	93.000000	91.000000	90.000000	92.000000	91.000000	90.000000	94.000000	2.281000e+09

df.Age.mean()

25.122205745043114

Name of the oldest player

The name of the oldest player can be found with the method loc() by leveraging the output from of max() with a conditional statement. The Name is return with by specifying the Name column: O. Pérez

## Finding the name of the oldest player with conditional statement
## The max value for df.Age.max() is 45
(df.loc[df['Age'] == df.Age.max()])['Name']

4741    O. Pérez
Name: Name, dtype: object

The highest salary

The Wage column holds the data needed for finding the player's salary. Analyzing the data type of the different columns with the method info(), shows that Wage is interpreted as an object.
The method head() shows that the characters € and K prevent the column data from being interpreted as a numerical value.
The method strip() is used to remove the characters € and K, and the method astype() is used to cast the output to integer.
The methods describe() and max() are ultimately used to find the highest salary: €565K

df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 18207 entries, 0 to 18206
Data columns (total 85 columns):
 #   Column                    Non-Null Count  Dtype  
---  ------                    --------------  -----  
 0   ID                        18207 non-null  int64  
 1   Name                      18207 non-null  object 
 2   Age                       18207 non-null  int64  
 3   Nationality               18207 non-null  object 
 4   Overall                   18207 non-null  int64  
 5   Potential                 18207 non-null  int64  
 6   Club                      17966 non-null  object 
 7   Value                     18207 non-null  int64  
 8   Wage                      18207 non-null  int64  
 9   Special                   18207 non-null  int64  
 10  Preferred Foot            18207 non-null  int64  
 11  International Reputation  18159 non-null  float64
 12  Weak Foot                 18159 non-null  float64
 13  Skill Moves               18159 non-null  float64
 14  Work Rate                 18159 non-null  object 
 15  Body Type                 18159 non-null  object 
 16  Real Face                 18159 non-null  object 
 17  Position                  18147 non-null  object 
 18  Jersey Number             18147 non-null  float64
 19  Joined                    16654 non-null  object 
 20  Loaned From               1264 non-null   object 
 21  Contract Valid Until      17918 non-null  object 
 22  Height                    18207 non-null  float64
 23  Weight                    18159 non-null  float64
 24  LS                        16122 non-null  object 
 25  ST                        16122 non-null  object 
 26  RS                        16122 non-null  object 
 27  LW                        16122 non-null  object 
 28  LF                        16122 non-null  object 
 29  CF                        16122 non-null  object 
 30  RF                        16122 non-null  object 
 31  RW                        16122 non-null  object 
 32  LAM                       16122 non-null  object 
 33  CAM                       16122 non-null  object 
 34  RAM                       16122 non-null  object 
 35  LM                        16122 non-null  object 
 36  LCM                       16122 non-null  object 
 37  CM                        16122 non-null  object 
 38  RCM                       16122 non-null  object 
 39  RM                        16122 non-null  object 
 40  LWB                       16122 non-null  object 
 41  LDM                       16122 non-null  object 
 42  CDM                       16122 non-null  object 
 43  RDM                       16122 non-null  object 
 44  RWB                       16122 non-null  object 
 45  LB                        16122 non-null  object 
 46  LCB                       16122 non-null  object 
 47  CB                        16122 non-null  object 
 48  RCB                       16122 non-null  object 
 49  RB                        16122 non-null  object 
 50  Crossing                  18159 non-null  float64
 51  Finishing                 18159 non-null  float64
 52  HeadingAccuracy           18159 non-null  float64
 53  ShortPassing              18159 non-null  float64
 54  Volleys                   18159 non-null  float64
 55  Dribbling                 18159 non-null  float64
 56  Curve                     18159 non-null  float64
 57  FKAccuracy                18159 non-null  float64
 58  LongPassing               18159 non-null  float64
 59  BallControl               18159 non-null  float64
 60  Acceleration              18159 non-null  float64
 61  SprintSpeed               18159 non-null  float64
 62  Agility                   18159 non-null  float64
 63  Reactions                 18159 non-null  float64
 64  Balance                   18159 non-null  float64
 65  ShotPower                 18159 non-null  float64
 66  Jumping                   18159 non-null  float64
 67  Stamina                   18159 non-null  float64
 68  Strength                  18159 non-null  float64
 69  LongShots                 18159 non-null  float64
 70  Aggression                18159 non-null  float64
 71  Interceptions             18159 non-null  float64
 72  Positioning               18159 non-null  float64
 73  Vision                    18159 non-null  float64
 74  Penalties                 18159 non-null  float64
 75  Composure                 18159 non-null  float64
 76  Marking                   18159 non-null  float64
 77  StandingTackle            18159 non-null  float64
 78  SlidingTackle             18159 non-null  float64
 79  GKDiving                  18159 non-null  float64
 80  GKHandling                18159 non-null  float64
 81  GKKicking                 18159 non-null  float64
 82  GKPositioning             18159 non-null  float64
 83  GKReflexes                18159 non-null  float64
 84  Release Clause            18207 non-null  int64  
dtypes: float64(40), int64(9), object(36)
memory usage: 11.9+ MB

## Removing the characters with strip() and casting the output to int with astype()
## The highest salary is found with describe()
df['Wage'].describe()

count     18207.000000
mean       9731.312133
std       21999.290406
min           0.000000
25%        1000.000000
50%        3000.000000
75%        9000.000000
max      565000.000000
Name: Wage, dtype: float64

## The max value can also be found with max()
df['Wage'].max()

565000

Histogram

Few things can be noticed from the histograms.

df.hist(figsize=(35,25));

Age

The Age histogram is skewed to the left, indicating most of the players are between 20 and 30 years old

df.hist(column=["Age"]);

Preferred Foot

Most of the players prefer the Right foot

df.hist(column=["Preferred Foot"]);

Acceleration, SprintSpeed, Agility

These three histograms present a similar distribution. Normally these three skills are related.

df.hist(column=["Acceleration", "SprintSpeed", "Agility"], figsize=(25,15));

Ball Control and Dribbling

The distribution and the outliers are very similar. The similar outliers highlights how ball control and dribbling skills are related.

df.hist(column=["BallControl", "Dribbling"], figsize=(25,15));

Correlation

df_corr = df.corr()
df_corr

<style scoped> .dataframe tbody tr th:only-of-type { vertical-align: middle; }

.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}

</style>

	ID	Age	Overall	Potential	Value	Wage	Special	Preferred Foot	International Reputation	Weak Foot	Skill Moves	Jersey Number	Height	Weight	Crossing	Finishing	HeadingAccuracy	ShortPassing	Volleys	Dribbling	Curve	FKAccuracy	LongPassing	BallControl	Acceleration	SprintSpeed	Agility	Reactions	Balance	ShotPower	Jumping	Stamina	Strength	LongShots	Aggression	Interceptions	Positioning	Vision	Penalties	Composure	Marking	StandingTackle	SlidingTackle	GKDiving	GKHandling	GKKicking	GKPositioning	GKReflexes	Release Clause
ID	1.000000	-0.739208	-0.417025	0.047074	-0.106880	-0.204610	-0.231352	0.003771	-0.356191	-0.075784	-0.056914	0.182074	-0.054103	-0.191425	-0.131994	-0.082323	-0.106815	-0.136279	-0.159915	-0.030340	-0.169511	-0.199549	-0.186764	-0.100184	0.133236	0.132437	-0.019897	-0.408617	0.048463	-0.166133	-0.169369	-0.053895	-0.259756	-0.161549	-0.228329	-0.160602	-0.088330	-0.215170	-0.140657	-0.384473	-0.110198	-0.085929	-0.068409	-0.105594	-0.111149	-0.106652	-0.118250	-0.105778	-0.114893
Age	-0.739208	1.000000	0.452350	-0.253312	0.058848	0.141145	0.236695	-0.002846	0.253765	0.059867	0.027649	-0.241156	0.053174	0.230213	0.130545	0.068660	0.147183	0.132894	0.142472	0.010166	0.143276	0.193467	0.181310	0.084969	-0.158667	-0.151682	-0.019395	0.453124	-0.089877	0.156947	0.177167	0.097793	0.332798	0.155096	0.265190	0.197845	0.082443	0.187422	0.139535	0.391023	0.142817	0.119745	0.103089	0.101277	0.106419	0.104964	0.116402	0.103313	0.057294
Overall	-0.417025	0.452350	1.000000	0.660939	0.499790	0.571926	0.606960	0.036196	0.499491	0.212015	0.414463	-0.218931	0.040774	0.154634	0.394972	0.332515	0.340776	0.502550	0.391338	0.372426	0.419491	0.396892	0.483909	0.460197	0.196869	0.210647	0.264952	0.850045	0.103160	0.441118	0.264435	0.365656	0.349326	0.420795	0.395470	0.321326	0.356493	0.498894	0.341429	0.727655	0.286505	0.252629	0.222811	-0.025937	-0.025062	-0.029372	-0.017674	-0.023276	0.562588
Potential	0.047074	-0.253312	0.660939	1.000000	0.457905	0.486413	0.383727	0.028867	0.372993	0.162346	0.354290	-0.010474	0.013914	-0.006947	0.246319	0.243355	0.200988	0.369189	0.254906	0.315019	0.279944	0.230544	0.321437	0.354396	0.234608	0.236771	0.222310	0.513425	0.138025	0.288318	0.109151	0.202563	0.075769	0.266740	0.171174	0.154908	0.245616	0.348141	0.224281	0.440008	0.162801	0.143564	0.128980	-0.053446	-0.054672	-0.059061	-0.052589	-0.053341	0.525832
Value	-0.106880	0.058848	0.499790	0.457905	1.000000	0.623611	0.310190	0.009475	0.454649	0.134772	0.264022	-0.075694	0.008694	0.033088	0.211863	0.209650	0.152528	0.271076	0.235255	0.228060	0.241345	0.221563	0.252284	0.257700	0.142778	0.143827	0.161905	0.431399	0.095574	0.234275	0.083713	0.175257	0.098425	0.235310	0.149122	0.118884	0.214564	0.291482	0.194791	0.366372	0.113887	0.094844	0.077349	-0.033054	-0.031571	-0.031964	-0.031807	-0.033406	0.694382
Wage	-0.204610	0.141145	0.571926	0.486413	0.623611	1.000000	0.347835	0.010576	0.668635	0.140849	0.263205	-0.086561	0.023286	0.064776	0.232834	0.217439	0.187967	0.296691	0.257357	0.237150	0.259550	0.236385	0.276762	0.277615	0.124985	0.130315	0.156287	0.495560	0.088873	0.258351	0.129691	0.177562	0.139360	0.249084	0.194581	0.157415	0.226775	0.315395	0.222440	0.419597	0.145594	0.126291	0.111025	-0.025595	-0.025177	-0.028325	-0.025489	-0.025992	0.787085
Special	-0.231352	0.236695	0.606960	0.383727	0.310190	0.347835	1.000000	0.122518	0.292208	0.341855	0.763412	-0.133716	-0.295836	-0.267830	0.866417	0.724244	0.644421	0.906729	0.773974	0.874274	0.851900	0.806414	0.846302	0.912107	0.654337	0.645963	0.699673	0.597169	0.586788	0.835277	0.321846	0.792762	0.192990	0.840049	0.666236	0.561676	0.824307	0.761992	0.734533	0.752331	0.561866	0.538802	0.506968	-0.674637	-0.673625	-0.670254	-0.668272	-0.673238	0.332148
Preferred Foot	0.003771	-0.002846	0.036196	0.028867	0.009475	0.010576	0.122518	1.000000	-0.001914	-0.072325	0.109496	-0.018032	-0.043386	-0.073749	0.207002	0.041634	0.043197	0.102362	0.052706	0.132130	0.160309	0.150728	0.108396	0.116102	0.119571	0.118210	0.109280	0.026705	0.098055	0.074045	-0.020562	0.092773	-0.039058	0.080111	0.057480	0.101602	0.093108	0.062553	0.060223	0.056024	0.102301	0.110979	0.120486	-0.102638	-0.103829	-0.104356	-0.104633	-0.103949	0.008330
International Reputation	-0.356191	0.253765	0.499491	0.372993	0.454649	0.668635	0.292208	-0.001914	1.000000	0.128317	0.208153	-0.077298	0.028510	0.088340	0.191770	0.178373	0.157483	0.242803	0.243089	0.179041	0.233681	0.223564	0.239525	0.217946	0.044319	0.044070	0.100869	0.445614	0.050076	0.227772	0.120931	0.094780	0.131280	0.213960	0.173327	0.129586	0.183003	0.284600	0.218620	0.392787	0.115208	0.092846	0.079176	0.004526	0.003942	0.000651	0.006904	0.003444	0.579212
Weak Foot	-0.075784	0.059867	0.212015	0.162346	0.134772	0.140849	0.341855	-0.072325	0.128317	1.000000	0.340721	-0.035410	-0.122047	-0.130724	0.307925	0.357416	0.183238	0.322133	0.357340	0.352658	0.345468	0.330472	0.277174	0.356383	0.261435	0.248822	0.302062	0.201341	0.254022	0.332855	0.069752	0.232094	-0.008470	0.355967	0.131524	0.053097	0.346896	0.337897	0.330252	0.278132	0.065673	0.042646	0.026105	-0.231905	-0.233098	-0.229395	-0.231298	-0.232574	0.142225
Skill Moves	-0.056914	0.027649	0.414463	0.354290	0.264022	0.263205	0.763412	0.109496	0.208153	0.340721	1.000000	-0.035194	-0.301805	-0.351209	0.741035	0.743439	0.443005	0.730363	0.745077	0.839757	0.771052	0.701068	0.622342	0.818051	0.652356	0.624098	0.681765	0.377044	0.578459	0.718237	0.107553	0.570226	-0.041475	0.752980	0.347795	0.209604	0.781248	0.674057	0.690434	0.586836	0.241428	0.210517	0.178607	-0.621675	-0.619755	-0.616990	-0.618853	-0.621925	0.274604
Jersey Number	0.182074	-0.241156	-0.218931	-0.010474	-0.075694	-0.086561	-0.133716	-0.018032	-0.077298	-0.035410	-0.035194	1.000000	-0.023435	-0.087319	-0.076585	-0.006639	-0.091688	-0.100241	-0.026731	-0.028021	-0.055428	-0.068843	-0.117424	-0.073210	-0.004395	-0.015069	-0.034158	-0.192622	0.008009	-0.053860	-0.104179	-0.127822	-0.158411	-0.046174	-0.146907	-0.158526	-0.025422	-0.078050	-0.028023	-0.167523	-0.142474	-0.133285	-0.124610	0.004807	0.001543	0.001162	-0.002736	0.003255	-0.087608
Height	-0.054103	0.053174	0.040774	0.013914	0.008694	0.023286	-0.295836	-0.043386	0.028510	-0.122047	-0.301805	-0.023435	1.000000	0.451535	-0.368309	-0.279658	-0.049551	-0.275337	-0.265432	-0.361843	-0.326768	-0.299573	-0.247482	-0.311275	-0.380082	-0.326244	-0.408780	-0.016399	-0.494316	-0.227042	-0.057926	-0.236818	0.288265	-0.290521	-0.068753	-0.072856	-0.335176	-0.271443	-0.259990	-0.098100	-0.083355	-0.076717	-0.081207	0.283029	0.283273	0.278904	0.282880	0.284345	0.009536
Weight	-0.191425	0.230213	0.154634	-0.006947	0.033088	0.064776	-0.267830	-0.073749	0.088340	-0.130724	-0.351209	-0.087319	0.451535	1.000000	-0.393323	-0.292407	0.035956	-0.290366	-0.262884	-0.414228	-0.345941	-0.305175	-0.260863	-0.337702	-0.477853	-0.410936	-0.534264	0.086364	-0.663905	-0.191950	0.010857	-0.223317	0.615798	-0.278069	0.032396	-0.025339	-0.350330	-0.284113	-0.253387	-0.034444	-0.049356	-0.046835	-0.056164	0.340034	0.339024	0.337717	0.342178	0.341135	0.034893
Crossing	-0.131994	0.130545	0.394972	0.246319	0.211863	0.232834	0.866417	0.207002	0.191770	0.307925	0.741035	-0.076585	-0.368309	-0.393323	1.000000	0.655300	0.469507	0.809660	0.690339	0.856647	0.833105	0.761107	0.756527	0.840916	0.668365	0.645578	0.698320	0.389574	0.618280	0.705503	0.135486	0.672633	-0.029403	0.742065	0.473570	0.427739	0.783185	0.684948	0.645805	0.575446	0.443101	0.428963	0.409961	-0.663053	-0.660193	-0.659767	-0.660160	-0.662539	0.217514
Finishing	-0.082323	0.068660	0.332515	0.243355	0.209650	0.217439	0.724244	0.041634	0.178373	0.357416	0.743439	-0.006639	-0.279658	-0.292407	0.655300	1.000000	0.473427	0.661830	0.882675	0.824337	0.759229	0.697550	0.512806	0.788376	0.606378	0.593864	0.644273	0.331376	0.523787	0.815472	0.097464	0.510891	-0.009744	0.877834	0.242825	-0.020703	0.888790	0.697290	0.837827	0.533414	0.024218	-0.033023	-0.071811	-0.588752	-0.587145	-0.583268	-0.584852	-0.586913	0.217351
HeadingAccuracy	-0.106815	0.147183	0.340776	0.200988	0.152528	0.187967	0.644421	0.043197	0.157483	0.183238	0.443005	-0.091688	-0.049551	0.035956	0.469507	0.473427	1.000000	0.640091	0.505639	0.550750	0.440846	0.407772	0.510779	0.658175	0.329647	0.379453	0.260514	0.325867	0.168834	0.611736	0.380041	0.634589	0.486903	0.506814	0.692847	0.548689	0.533818	0.275673	0.551978	0.507208	0.583123	0.561063	0.533643	-0.750417	-0.749888	-0.746444	-0.744443	-0.748895	0.159469
ShortPassing	-0.136279	0.132894	0.502550	0.369189	0.271076	0.296691	0.906729	0.102362	0.242803	0.322133	0.730363	-0.100241	-0.275337	-0.290366	0.809660	0.661830	0.640091	1.000000	0.698309	0.843722	0.775398	0.736659	0.895722	0.911451	0.565752	0.554681	0.612899	0.483028	0.533126	0.771845	0.197535	0.716659	0.133831	0.761750	0.611570	0.543350	0.757776	0.713524	0.676063	0.685137	0.559576	0.541131	0.508644	-0.729785	-0.728024	-0.724381	-0.723782	-0.728721	0.290176
Volleys	-0.159915	0.142472	0.391338	0.254906	0.235255	0.257357	0.773974	0.052706	0.243089	0.357340	0.745077	-0.026731	-0.265432	-0.262884	0.690339	0.882675	0.505639	0.698309	1.000000	0.809639	0.807285	0.749637	0.571050	0.794935	0.572064	0.556955	0.624995	0.393713	0.513682	0.832479	0.126228	0.527395	0.029505	0.868253	0.330116	0.088385	0.848333	0.699471	0.829257	0.595281	0.120919	0.072788	0.035457	-0.590808	-0.588668	-0.584954	-0.586131	-0.588670	0.240956
Dribbling	-0.030340	0.010166	0.372426	0.315019	0.228060	0.237150	0.874274	0.132130	0.179041	0.352658	0.839757	-0.028021	-0.361843	-0.414228	0.856647	0.824337	0.550750	0.843722	0.809639	1.000000	0.842652	0.753600	0.722465	0.938942	0.748292	0.726835	0.765153	0.369265	0.663086	0.804732	0.143079	0.686511	-0.033550	0.843619	0.441075	0.296020	0.896932	0.730150	0.769594	0.597498	0.336072	0.301251	0.273963	-0.754625	-0.753181	-0.749816	-0.751348	-0.754341	0.235479
Curve	-0.169511	0.143276	0.419491	0.279944	0.241345	0.259550	0.851900	0.160309	0.233681	0.345468	0.771052	-0.055428	-0.326768	-0.345941	0.833105	0.759229	0.440846	0.775398	0.807285	0.842652	1.000000	0.861277	0.710807	0.829568	0.607239	0.578628	0.682104	0.413413	0.586969	0.792143	0.111934	0.590381	-0.035587	0.835309	0.399332	0.273756	0.811082	0.744774	0.751833	0.616532	0.289529	0.261481	0.232869	-0.606286	-0.603141	-0.600266	-0.603540	-0.604960	0.248018
FKAccuracy	-0.199549	0.193467	0.396892	0.230544	0.221563	0.236385	0.806414	0.150728	0.223564	0.330472	0.701068	-0.068843	-0.299573	-0.305175	0.761107	0.697550	0.407772	0.736659	0.749637	0.753600	0.861277	1.000000	0.703544	0.759548	0.498215	0.466686	0.590159	0.398242	0.521513	0.754413	0.082026	0.537477	-0.018669	0.802667	0.396068	0.295357	0.729506	0.717173	0.734440	0.585120	0.297976	0.279153	0.247903	-0.556605	-0.553644	-0.549911	-0.552641	-0.554920	0.227925
LongPassing	-0.186764	0.181310	0.483909	0.321437	0.252284	0.276762	0.846302	0.108396	0.239525	0.277174	0.622342	-0.117424	-0.247482	-0.260863	0.756527	0.512806	0.510779	0.895722	0.571050	0.722465	0.710807	0.703544	1.000000	0.788650	0.442566	0.426586	0.523426	0.461527	0.462300	0.671426	0.154740	0.635627	0.114448	0.667847	0.590522	0.596821	0.614498	0.698199	0.542247	0.645797	0.587106	0.587430	0.562230	-0.596820	-0.594999	-0.591453	-0.591561	-0.595887	0.269962
BallControl	-0.100184	0.084969	0.460197	0.354396	0.257700	0.277615	0.912107	0.116102	0.217946	0.356383	0.818051	-0.073210	-0.311275	-0.337702	0.840916	0.788376	0.658175	0.911451	0.794935	0.938942	0.829568	0.759548	0.788650	1.000000	0.675737	0.663990	0.704604	0.443750	0.600908	0.831287	0.195235	0.728604	0.087841	0.836047	0.549840	0.418584	0.863915	0.718411	0.769791	0.674881	0.452705	0.417566	0.384802	-0.788444	-0.786797	-0.783423	-0.783607	-0.787939	0.269669
Acceleration	0.133236	-0.158667	0.196869	0.234608	0.142778	0.124985	0.654337	0.119571	0.044319	0.261435	0.652356	-0.004395	-0.380082	-0.477853	0.668365	0.606378	0.329647	0.565752	0.572064	0.748292	0.607239	0.498215	0.442566	0.675737	1.000000	0.921928	0.810832	0.188685	0.711466	0.539515	0.215221	0.607240	-0.166507	0.579948	0.250186	0.152146	0.682309	0.461552	0.532908	0.347427	0.195369	0.163000	0.157565	-0.593008	-0.594866	-0.592127	-0.592143	-0.593201	0.150723
SprintSpeed	0.132437	-0.151682	0.210647	0.236771	0.143827	0.130315	0.645963	0.118210	0.044070	0.248822	0.624098	-0.015069	-0.326244	-0.410936	0.645578	0.593864	0.379453	0.554681	0.556955	0.726835	0.578628	0.466686	0.426586	0.663990	0.921928	1.000000	0.763623	0.192402	0.643505	0.544640	0.232372	0.619919	-0.083206	0.561240	0.278364	0.164031	0.665239	0.429554	0.521071	0.351607	0.212575	0.178214	0.171980	-0.597677	-0.599694	-0.597320	-0.596498	-0.597837	0.154334
Agility	-0.019897	-0.019395	0.264952	0.222310	0.161905	0.156287	0.699673	0.109280	0.100869	0.302062	0.681765	-0.034158	-0.408780	-0.534264	0.698320	0.644273	0.260514	0.612899	0.624995	0.765153	0.682104	0.590159	0.523426	0.704604	0.810832	0.763623	1.000000	0.275893	0.770506	0.574020	0.214917	0.568706	-0.234199	0.645085	0.240699	0.138893	0.708151	0.597327	0.566175	0.432511	0.167122	0.129204	0.116686	-0.527756	-0.528482	-0.527164	-0.526983	-0.528899	0.169361
Reactions	-0.408617	0.453124	0.850045	0.513425	0.431399	0.495560	0.597169	0.026705	0.445614	0.201341	0.377044	-0.192622	-0.016399	0.086364	0.389574	0.331376	0.325867	0.483028	0.393713	0.369265	0.413413	0.398242	0.461527	0.443750	0.188685	0.192402	0.275893	1.000000	0.149670	0.418361	0.254131	0.369347	0.285813	0.421649	0.402974	0.338155	0.386476	0.502536	0.346143	0.685558	0.283607	0.255399	0.228355	-0.062967	-0.061940	-0.065927	-0.055031	-0.059961	0.486208
Balance	0.048463	-0.089877	0.103160	0.138025	0.095574	0.088873	0.586788	0.098055	0.050076	0.254022	0.578459	0.008009	-0.494316	-0.663905	0.618280	0.523787	0.168834	0.533126	0.513682	0.663086	0.586969	0.521513	0.462300	0.600908	0.711466	0.643505	0.770506	0.149670	1.000000	0.458608	0.188489	0.474932	-0.390841	0.533349	0.184140	0.150289	0.596091	0.491626	0.482794	0.310763	0.178695	0.154045	0.152470	-0.504727	-0.506102	-0.503970	-0.503652	-0.505974	0.102419
ShotPower	-0.166133	0.156947	0.441118	0.288318	0.234275	0.258351	0.835277	0.074045	0.227772	0.332855	0.718237	-0.053860	-0.227042	-0.191950	0.705503	0.815472	0.611736	0.771845	0.832479	0.804732	0.792143	0.754413	0.671426	0.831287	0.539515	0.544640	0.574020	0.418361	0.458608	1.000000	0.185823	0.616385	0.169515	0.889254	0.491386	0.265125	0.809068	0.680335	0.795220	0.634495	0.296944	0.256403	0.220237	-0.654117	-0.654099	-0.649403	-0.651409	-0.653475	0.240140
Jumping	-0.169369	0.177167	0.264435	0.109151	0.083713	0.129691	0.321846	-0.020562	0.120931	0.069752	0.107553	-0.104179	-0.057926	0.010857	0.135486	0.097464	0.380041	0.197535	0.126228	0.143079	0.111934	0.082026	0.154740	0.195235	0.215221	0.232372	0.214917	0.254131	0.188489	0.185823	1.000000	0.345968	0.284021	0.135932	0.373281	0.289043	0.143678	0.059931	0.133294	0.252353	0.279196	0.260645	0.260261	-0.192700	-0.193692	-0.195282	-0.189079	-0.192050	0.108904
Stamina	-0.053895	0.097793	0.365656	0.202563	0.175257	0.177562	0.792762	0.092773	0.094780	0.232094	0.570226	-0.127822	-0.236818	-0.223317	0.672633	0.510891	0.634589	0.716659	0.527395	0.686511	0.590381	0.537477	0.635627	0.728604	0.607240	0.619919	0.568706	0.369347	0.474932	0.616385	0.345968	1.000000	0.262694	0.596110	0.645687	0.576353	0.640982	0.472335	0.516426	0.523112	0.587782	0.570055	0.544702	-0.701467	-0.698556	-0.696729	-0.696073	-0.699670	0.183974
Strength	-0.259756	0.332798	0.349326	0.075769	0.098425	0.139360	0.192990	-0.039058	0.131280	-0.008470	-0.041475	-0.158411	0.288265	0.615798	-0.029403	-0.009744	0.486903	0.133831	0.029505	-0.033550	-0.035587	-0.018669	0.114448	0.087841	-0.166507	-0.083206	-0.234199	0.285813	-0.390841	0.169515	0.284021	0.262694	1.000000	0.050173	0.474120	0.356533	0.006923	-0.046929	0.054491	0.280522	0.333334	0.332159	0.304849	-0.111012	-0.109660	-0.110253	-0.103878	-0.107497	0.108956
LongShots	-0.161549	0.155096	0.420795	0.266740	0.235310	0.249084	0.840049	0.080111	0.213960	0.355967	0.752980	-0.046174	-0.290521	-0.278069	0.742065	0.877834	0.506814	0.761750	0.868253	0.843619	0.835309	0.802667	0.667847	0.836047	0.579948	0.561240	0.645085	0.421649	0.533349	0.889254	0.135932	0.596110	0.050173	1.000000	0.392495	0.193814	0.861080	0.753701	0.812446	0.616102	0.215510	0.172331	0.133603	-0.612381	-0.610739	-0.605952	-0.607200	-0.610087	0.237264
Aggression	-0.228329	0.265190	0.395470	0.171174	0.149122	0.194581	0.666236	0.057480	0.173327	0.131524	0.347795	-0.146907	-0.068753	0.032396	0.473570	0.242825	0.692847	0.611570	0.330116	0.441075	0.399332	0.396068	0.590522	0.549840	0.250186	0.278364	0.240699	0.402974	0.184140	0.491386	0.373281	0.645687	0.474120	0.392495	1.000000	0.751897	0.381700	0.300083	0.336089	0.515776	0.723961	0.744216	0.721384	-0.575843	-0.576114	-0.573607	-0.571201	-0.575142	0.163909
Interceptions	-0.160602	0.197845	0.321326	0.154908	0.118884	0.157415	0.561676	0.101602	0.129586	0.053097	0.209604	-0.158526	-0.072856	-0.025339	0.427739	-0.020703	0.548689	0.543350	0.088385	0.296020	0.273756	0.295357	0.596821	0.418584	0.152146	0.164031	0.138893	0.338155	0.150289	0.265125	0.289043	0.576353	0.356533	0.193814	0.751897	1.000000	0.170970	0.183096	0.110834	0.397450	0.888349	0.941471	0.928282	-0.485585	-0.486324	-0.485394	-0.481279	-0.486036	0.133022
Positioning	-0.088330	0.082443	0.356493	0.245616	0.214564	0.226775	0.824307	0.093108	0.183003	0.346896	0.781248	-0.025422	-0.335176	-0.350330	0.783185	0.888790	0.533818	0.757776	0.848333	0.896932	0.811082	0.729506	0.614498	0.863915	0.682309	0.665239	0.708151	0.386476	0.596091	0.809068	0.143678	0.640982	0.006923	0.861080	0.381700	0.170970	1.000000	0.734367	0.801268	0.580498	0.202597	0.158060	0.124228	-0.679480	-0.677699	-0.674393	-0.675569	-0.678582	0.219500
Vision	-0.215170	0.187422	0.498894	0.348141	0.291482	0.315395	0.761992	0.062553	0.284600	0.337897	0.674057	-0.078050	-0.271443	-0.284113	0.684948	0.697290	0.275673	0.713524	0.699471	0.730150	0.744774	0.717173	0.698199	0.718411	0.461552	0.429554	0.597327	0.502536	0.491626	0.680335	0.059931	0.472335	-0.046929	0.753701	0.300083	0.183096	0.734367	1.000000	0.632927	0.636280	0.176760	0.146460	0.113228	-0.381899	-0.377807	-0.374737	-0.375775	-0.381158	0.313117
Penalties	-0.140657	0.139535	0.341429	0.224281	0.194791	0.222440	0.734533	0.060223	0.218620	0.330252	0.690434	-0.028023	-0.259990	-0.253387	0.645805	0.837827	0.551978	0.676063	0.829257	0.769594	0.751833	0.734440	0.542247	0.769791	0.532908	0.521071	0.566175	0.346143	0.482794	0.795220	0.133294	0.516426	0.054491	0.812446	0.336089	0.110834	0.801268	0.632927	1.000000	0.551801	0.152296	0.101920	0.066693	-0.620069	-0.618968	-0.614006	-0.617074	-0.619099	0.201017
Composure	-0.384473	0.391023	0.727655	0.440008	0.366372	0.419597	0.752331	0.056024	0.392787	0.278132	0.586836	-0.167523	-0.098100	-0.034444	0.575446	0.533414	0.507208	0.685137	0.595281	0.597498	0.616532	0.585120	0.645797	0.674881	0.347427	0.351607	0.432511	0.685558	0.310763	0.634495	0.252353	0.523112	0.280522	0.616102	0.515776	0.397450	0.580498	0.636280	0.551801	1.000000	0.384081	0.351726	0.317492	-0.378750	-0.375720	-0.374897	-0.370234	-0.377626	0.399369
Marking	-0.110198	0.142817	0.286505	0.162801	0.113887	0.145594	0.561866	0.102301	0.115208	0.065673	0.241428	-0.142474	-0.083355	-0.049356	0.443101	0.024218	0.583123	0.559576	0.120919	0.336072	0.289529	0.297976	0.587106	0.452705	0.195369	0.212575	0.167122	0.283607	0.178695	0.296944	0.279196	0.587782	0.333334	0.215510	0.723961	0.888349	0.202597	0.176760	0.152296	0.384081	1.000000	0.906541	0.895908	-0.550978	-0.552263	-0.549498	-0.546670	-0.551290	0.124521
StandingTackle	-0.085929	0.119745	0.252629	0.143564	0.094844	0.126291	0.538802	0.110979	0.092846	0.042646	0.210517	-0.133285	-0.076717	-0.046835	0.428963	-0.033023	0.561063	0.541131	0.072788	0.301251	0.261481	0.279153	0.587430	0.417566	0.163000	0.178214	0.129204	0.255399	0.154045	0.256403	0.260645	0.570055	0.332159	0.172331	0.744216	0.941471	0.158060	0.146460	0.101920	0.351726	0.906541	1.000000	0.974659	-0.530989	-0.532160	-0.531092	-0.527792	-0.531474	0.106268
SlidingTackle	-0.068409	0.103089	0.222811	0.128980	0.077349	0.111025	0.506968	0.120486	0.079176	0.026105	0.178607	-0.124610	-0.081207	-0.056164	0.409961	-0.071811	0.533643	0.508644	0.035457	0.273963	0.232869	0.247903	0.562230	0.384802	0.157565	0.171980	0.116686	0.228355	0.152470	0.220237	0.260261	0.544702	0.304849	0.133603	0.721384	0.928282	0.124228	0.113228	0.066693	0.317492	0.895908	0.974659	1.000000	-0.509337	-0.510591	-0.509378	-0.505792	-0.509425	0.089027
GKDiving	-0.105594	0.101277	-0.025937	-0.053446	-0.033054	-0.025595	-0.674637	-0.102638	0.004526	-0.231905	-0.621675	0.004807	0.283029	0.340034	-0.663053	-0.588752	-0.750417	-0.729785	-0.590808	-0.754625	-0.606286	-0.556605	-0.596820	-0.788444	-0.593008	-0.597677	-0.527756	-0.062967	-0.504727	-0.654117	-0.192700	-0.701467	-0.111012	-0.612381	-0.575843	-0.485585	-0.679480	-0.381899	-0.620069	-0.378750	-0.550978	-0.530989	-0.509337	1.000000	0.970280	0.965685	0.969864	0.973320	-0.021037
GKHandling	-0.111149	0.106419	-0.025062	-0.054672	-0.031571	-0.025177	-0.673625	-0.103829	0.003942	-0.233098	-0.619755	0.001543	0.283273	0.339024	-0.660193	-0.587145	-0.749888	-0.728024	-0.588668	-0.753181	-0.603141	-0.553644	-0.594999	-0.786797	-0.594866	-0.599694	-0.528482	-0.061940	-0.506102	-0.654099	-0.193692	-0.698556	-0.109660	-0.610739	-0.576114	-0.486324	-0.677699	-0.377807	-0.618968	-0.375720	-0.552263	-0.532160	-0.510591	0.970280	1.000000	0.965239	0.969408	0.970264	-0.021648
GKKicking	-0.106652	0.104964	-0.029372	-0.059061	-0.031964	-0.028325	-0.670254	-0.104356	0.000651	-0.229395	-0.616990	0.001162	0.278904	0.337717	-0.659767	-0.583268	-0.746444	-0.724381	-0.584954	-0.749816	-0.600266	-0.549911	-0.591453	-0.783423	-0.592127	-0.597320	-0.527164	-0.065927	-0.503970	-0.649403	-0.195282	-0.696729	-0.110253	-0.605952	-0.573607	-0.485394	-0.674393	-0.374737	-0.614006	-0.374897	-0.549498	-0.531092	-0.509378	0.965685	0.965239	1.000000	0.964336	0.966337	-0.022945
GKPositioning	-0.118250	0.116402	-0.017674	-0.052589	-0.031807	-0.025489	-0.668272	-0.104633	0.006904	-0.231298	-0.618853	-0.002736	0.282880	0.342178	-0.660160	-0.584852	-0.744443	-0.723782	-0.586131	-0.751348	-0.603540	-0.552641	-0.591561	-0.783607	-0.592143	-0.596498	-0.526983	-0.055031	-0.503652	-0.651409	-0.189079	-0.696073	-0.103878	-0.607200	-0.571201	-0.481279	-0.675569	-0.375775	-0.617074	-0.370234	-0.546670	-0.527792	-0.505792	0.969864	0.969408	0.964336	1.000000	0.970130	-0.019560
GKReflexes	-0.105778	0.103313	-0.023276	-0.053341	-0.033406	-0.025992	-0.673238	-0.103949	0.003444	-0.232574	-0.621925	0.003255	0.284345	0.341135	-0.662539	-0.586913	-0.748895	-0.728721	-0.588670	-0.754341	-0.604960	-0.554920	-0.595887	-0.787939	-0.593201	-0.597837	-0.528899	-0.059961	-0.505974	-0.653475	-0.192050	-0.699670	-0.107497	-0.610087	-0.575142	-0.486036	-0.678582	-0.381158	-0.619099	-0.377626	-0.551290	-0.531474	-0.509425	0.973320	0.970264	0.966337	0.970130	1.000000	-0.021371
Release Clause	-0.114893	0.057294	0.562588	0.525832	0.694382	0.787085	0.332148	0.008330	0.579212	0.142225	0.274604	-0.087608	0.009536	0.034893	0.217514	0.217351	0.159469	0.290176	0.240956	0.235479	0.248018	0.227925	0.269962	0.269669	0.150723	0.154334	0.169361	0.486208	0.102419	0.240140	0.108904	0.183974	0.108956	0.237264	0.163909	0.133022	0.219500	0.313117	0.201017	0.399369	0.124521	0.106268	0.089027	-0.021037	-0.021648	-0.022945	-0.019560	-0.021371	1.000000

Positive Correlations

Ball Control has different positive correlations. Some of the highest are Dribbling, ShortPassing, and Crossing which makes sense as these three skills are heavily based on ball control.
Volleys has positive correlations with Finishing, Positioning, and Longshots. These correlations make sense as mostly volleys in football occur when trying score; they require good positions as the ball might come fast; in order to make good longshots for scoring it's better to take advantage of the momentum of the ball.

Negative Correlations

Balance has negative correlations with Weight and Strength. The correlation with Weight doesn't really make sense as is commonly associated with balance. Strength does make sense: a player can have a lot of strength but still no balance or a lot of strength and a lot of balance. It doesn't matter.

Inputs and Target

I would use LongShots, Positioning, Dribbling, Volley as inputs and Finishing as target. These inputs and target are all highly correlated. The sets of these ability as can help determine the ability of scoring.

Heatmap

plt.figure(figsize=(50, 25))
sns.heatmap(df_corr, annot=True, annot_kws={'size':13})

<AxesSubplot:>

Visualizations

SprintSpeed and Acceleration

These two columns present a strong linear relationship as SprintSpeed and Acceleration usually are correlated.

plt.scatter(x=df.SprintSpeed, y=df.Acceleration)
plt.xlabel('SprintSpeed')
plt.ylabel('Acceleration')
plt.title('SprintSpeed vs Acceleration')
plt.show()

Volleys and LongShots

These two columns present a strong linear relationship as the ability of executing longshots and volleys is correlated.

plt.scatter(x=df.Volleys, y=df.LongShots)
plt.xlabel('Volleys')
plt.ylabel('Longshots')
plt.title('Volleys vs LongShots')
plt.show()