<jupyter_start><jupyter_text>Is soccer a highly talented sport, or a higly atletic/skilled sports?Soccer is old as 2000 years. One begin to wonder if the actual founders created & predicted the game as a higly talented sport, or a highly atletic sports. I will put together different important characteristics of soccer players against one another, not forgetting the dribbling skill. There was a study done by University of Queensland that said accurate passing and ball control is more important than speed, strength, or fitnesssoccer is based more on athleticism or technique or game knowledge<jupyter_code># Standard imports 
import pandas as pd
import numpy as np

# Plotting
import seaborn as sns
import matplotlib.pyplot as plt 
%matplotlib inline
footballers = pd.read_csv('data.csv')






footballers.head()
footballers.tail()
print(footballers.shape)
#dropping the less important columns 
df_footballers = footballers.drop(['Unnamed: 0', 'ID', 'Photo', 
                                   'Nationality',	'Flag', 'Club', 'Club Logo',	
                                   'Value',	'Wage',	'Special', 
                                   'International Reputation',	'Real Face',	
                                   'Position',	'Jersey Number',	'Joined',	
                                   'Loaned From',	'Contract Valid Until',	'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',	
                                   'GKDiving',	'GKHandling',	'GKKicking',	
                                   'GKPositioning',	'GKReflexes',	
                                   'Release Clause', 'Preferred Foot', 
                                   'Work Rate', 'Weight', 'Body Type'], axis=1)


df_footballers.head()
df_footballers.isnull().sum() #To find out how many missing values my dataset has
df_footballers.isnull().sum().value_counts()
df_footballers.dropna(inplace=True) #To remove the rows that has the missing values
print(df_footballers.shape)
df_footballers.isnull().sum()
df_footballers.head()
soccer = df_footballers.sort_values(by = ['Finishing'],  ascending=False)
soccer.head()
ronaldo = df_footballers.sort_values(by = ['Overall'],  ascending=False)
ronaldo.head()
top_five = soccer[soccer.Finishing >= 93] 
top_five.shape
 finishing_list = list(top_five['Finishing']) 
 finishing_list
#@title Default title text
import matplotlib.pyplot as plt
import numpy as np


# Fixing random state for reproducibility
np.random.seed(19680801)


plt.rcdefaults()
fig, ax = plt.subplots()

# Example data
Names = ('<NAME>', '<NAME>', '<NAME>', '<NAME>', '<NAME>')
ronaldo = np.arange(len(Names))
performance = 3 + 10 * np.random.rand(len(Names))
error = np.random.rand(len(Names))

ax.barh(y_pos, performance, xerr=error, align='center')
ax.set_yticks(ronaldo)
ax.set_yticklabels(Names)
ax.invert_yaxis()  # labels read top-to-bottom
ax.set_xlabel('Performance')
ax.set_title('L. Messi leads')

plt.show()



import seaborn as sns
from numpy import mean

sns.barplot(x = top_five['Name'], y = top_five['Finishing'],estimator= mean)
plt.ylim(90,100)
plt.title('Ball Control/Finishing')
soccer.mean()
soccer.corr()
sns.scatterplot(x = 'Age', y = 'Potential', data = soccer)
sns.barplot(x='Dribbling', y ='Overall')
f, ax = plt.subplots(figsize=(12,12))
sns.heatmap(soccer.corr(),annot=True, linewidths = .5, fmt= '.1f', ax=ax)
plt.show()<jupyter_output><empty_output>
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