image_output_1.png
from subprocess import check_output

from subprocess import check_output

import matplotlib.pyplot as plt

import matplotlib.pyplot as plt

import matplotlib.pyplot as plt

import numpy as np # linear algebra

import numpy as np # linear algebra

import os

import os

import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

import seaborn as sns

import seaborn as sns

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
from subprocess import check_output

data2015 = pd.read_csv('../input/2015.csv')
data2016 = pd.read_csv('../input/2016.csv')
data2017 = pd.read_csv('../input/2017.csv')

data2015.corr()

f,ax = plt.subplots(figsize=(18, 18))
sns.heatmap(data2015.corr(), annot=True, linewidths=.5, fmt= '.1f',ax=ax)
plt.show()

import matplotlib.pyplot as plt

fig, axes = plt.subplots(nrows=2, ncols=2)

df1.plot(ax=axes[0,0])
df2.plot(ax=axes[0,1])
...

#try to set index to dataframe
fig, axes = plt.subplots(figsize=(10, 10),nrows=2, ncols=2)

data_updated=data2015.rename( index=str ,columns={"Happiness Rank":"Happiness_Rank","Standard Error":"Standard_Error"})
data_2015U=data_updated.rename( index=str ,columns={"Happiness Score":"Happiness_Score"})
data_2015U=data_2015U.rename( index=str,columns={"Economy (GDP per Capita)":"Economy","Dystopia Residual":"Dystopia_Residual","Health (Life Expectancy)":"Health","Trust (Government Corruption)":"Trust"})

plt.legend(loc='upper right') 

data_2015U=data_2015U.set_index('Happiness_Rank')
data_2015U.Happiness_Score.plot(ax=axes[0,0],kind = 'line', color = 'red',title = 'Happiness Score',linewidth=1,grid = True,linestyle = ':')
data_2015U.Family.plot( ax=axes[0,1],kind='line' ,color='green' ,title='Family' ,linewidth=1 , grid=True ,linestyle=':' )
data_2015U.Economy.plot( ax=axes[1,0],kind='line' ,color='yellow', title='Economy',linewidth=1,grid=True ,linestyle=':' )
data_2015U.Health.plot( ax=axes[1,1],kind='line' ,color='blue', title='Health',linewidth=1,grid=True ,linestyle=':' )

    # legend = puts label into plot
              # label = name of label

          # title = title of plot

rng = np.random.RandomState(0)
x = rng.randn(100)
y = rng.randn(100)
colors = rng.rand(100)
sizes = 1000 * rng.rand(50)
plt.colorbar()

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns  
# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory

import os
print(os.listdir("../input"))

from subprocess import check_output
print(check_output(["ls", "../input"]).decode("utf8"))

# Any results you write to the current directory are saved as output.

data2015=pd.read_csv('../input/2015.csv')

#fig, axes = plt.subplots(figsize=(10, 10),nrows=2, ncols=2)

data_updated=data2015.rename( index=str ,columns={"Happiness Rank":"Happiness_Rank"})
data_2015U=data_updated.rename( index=str ,columns={"Happiness Score":"Happiness_Score"})
data_2015U=data_2015U.rename( index=str,columns={"Economy (GDP per Capita)":"Economy","Health (Life Expectancy)":"Health","Trust (Government Corruption)":"Trust"})

f,ax = plt.subplots(figsize=(20, 12))

Western_Europe=data_2015U[ data_2015U.Region=='Western Europe']
North_America=data_2015U[ data_2015U.Region=='North America']
Australian_New_Zealand=data_2015U[ data_2015U.Region=='Australia and New Zealand']
Middle_East_and_Northern_Africa=data_2015U[ data_2015U.Region=='Middle East and Northern Africa']
Latin_America_and_Caribbean=data_2015U[ data_2015U.Region=='Latin America and Caribbean']
Southeastern_Asia=data_2015U[ data_2015U.Region=='Southeastern Asia']
Central_and_Eastern_Europe=data_2015U[ data_2015U.Region=='Central and Eastern Europe']
Eastern_Asia=data_2015U[ data_2015U.Region=='Eastern_Asia']
#Sub_Saharan_Africa=data_2015U[ data_2015U.Region=='Sub Saharan Africa']
Southern_Asia=data_2015U[ data_2015U.Region=='Southern Asia']

for each in range(0,len(Western_Europe.Country)):
    x = Western_Europe.Happiness_Score[each]
    y = Western_Europe.Freedom[each]    
    plt.scatter( Western_Europe.Happiness_Score,Western_Europe.Freedom,color='red',linewidth=1)
    plt.text(x, y, Western_Europe.Country[each], fontsize=12)
    
for each in range(0,len(North_America.Country)):
    x = North_America.Happiness_Score[each]
    y = North_America.Freedom[each]    
    plt.scatter( North_America.Happiness_Score,North_America.Freedom,color='blue',linewidth=1)
    plt.text(x, y, North_America.Country[each], fontsize=12)

    
for each in range(0,len( Middle_East_and_Northern_Africa.Country)):
    x =Middle_East_and_Northern_Africa.Happiness_Score[each]
    y =Middle_East_and_Northern_Africa.Freedom[each]    
    plt.scatter(  Middle_East_and_Northern_Africa.Happiness_Score, Middle_East_and_Northern_Africa.Freedom,color='purple',linewidth=1)
    plt.text(x, y,  Middle_East_and_Northern_Africa.Country[each], fontsize=12)

plt.title("Happiness Score-Freedom Scatter Plot")
plt.xlabel("Happiness Score")
plt.ylabel("Freedom")

melted = pd.melt(frame=data_2015U, id_vars='Country', value_vars=['Generosity', 'Dystopia_Residual'])
melted.loc[:10]

data_2015U1 = data_2015U.head()
data_2015U2 = data_2015U.tail()
concat_data_row = pd.concat([data_2015U1, data_2015U2], axis=0, ignore_index=True)
concat_data_row