مشروع: تقرير السعادة العالمية مع علم البيانات

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

df=pd.read_csv('2019.csv')
df.head()
def highlight_max(s):    
    is_max = s == s.max()
    return ['background-color: limegreen' if v else '' for v in is_max]
 
df.style.apply(highlight_max)

df.shape

import seaborn as sns
sns.pairplot(df)

fig, axes = plt.subplots(nrows=2, ncols=2,constrained_layout=True,figsize=(12,8))

sns.barplot(x='GDP per capita',y='Country or region',data=df.nlargest(10,'GDP per capita'),ax=axes[0,0],palette="Blues_d")

sns.barplot(x='Social support' ,y='Country or region',data=df.nlargest(10,'Social support'),ax=axes[0,1],palette="YlGn")

sns.barplot(x='Healthy life expectancy' ,y='Country or region',data=df.nlargest(10,'Healthy life expectancy'),ax=axes[1,0],palette='OrRd')

sns.barplot(x='Freedom to make life choices' ,y='Country or region',data=df.nlargest(10,'Freedom to make life choices'),ax=axes[1,1],palette='YlOrBr')

fig, axes = plt.subplots(nrows=1, ncols=2,constrained_layout=True,figsize=(10,4))

sns.barplot(x='Generosity' ,y='Country or region',data=df.nlargest(10,'Generosity'),ax=axes[0],palette='Spectral')
sns.barplot(x='Perceptions of corruption' ,y='Country or region',data=df.nlargest(10,'Perceptions of corruption'),ax=axes[1],palette='RdYlGn')

print('max:',df['Score'].max())
print('min:',df['Score'].min())
add=df['Score'].max()-df['Score'].min()
grp=round(add/3,3)
print('range difference:',(grp))

low=df['Score'].min()+grp
mid=low+grp

print('upper bound of Low grp',low)
print('upper bound of Mid grp',mid)
print('upper bound of High grp','max:',df['Score'].max())

cat=[]
for i in df.Score:
    if(i>0 and i<low):
        cat.append('Low')
        
        
    elif(i>low and i<mid):
         cat.append('Mid')
    else:
         cat.append('High')

df['Category']=cat  

color = (df.Category == 'High' ).map({True: 'background-color: limegreen',False:'background-color: red'})
df.style.apply(lambda s: color)

df.loc[df['Country or region']=='India']

data={
    'Country or region':['Canada','US','UK','India'],
    'Score':[7.278,6.892,7.054,4.015],
    'GDP per capita':[1.365,1.433,1.333,0.755],
    'Social support':[1.505,1.457,1.538,0.765],
    'Healthy life expectancy':[1.039,0.874,0.996,0.588],
    'Freedom to make life choices':[0.584,0.454,0.45,0.498],
    'Generosity':[0.285,0.28,0.348,0.2],
    'Perceptions of corruption':[0.308,0.128,0.278,0.085]
}
d=pd.DataFrame(data)
d

ax = d.plot(y="Social support", x="Country or region", kind="bar",color='C3')
d.plot(y="GDP per capita", x="Country or region", kind="bar", ax=ax, color="C1")
d.plot(y="Healthy life expectancy", x="Country or region", kind="bar", ax=ax, color="C2")

plt.show()

ax = d.plot(y="Freedom to make life choices", x="Country or region", kind="bar",color='C3')
d.plot(y="Generosity", x="Country or region", kind="bar", ax=ax, color="C1",)
d.plot(y="Perceptions of corruption", x="Country or region", kind="bar", ax=ax, color="C2",)

plt.show()

import plotly.graph_objs as go
from plotly.offline import iplot

data = dict(type = 'choropleth', 
           locations = df['Country or region'],
           locationmode = 'country names',
           colorscale='RdYlGn',
           z = df['Score'], 
           text = df['Country or region'],
           colorbar = {'title':'Happiness Score'})

layout = dict(title = 'Geographical Visualization of Happiness Score', 
              geo = dict(showframe = True, projection = {'type': 'azimuthal equal area'}))

choromap3 = go.Figure(data = [data], layout=layout)
iplot(choromap3)