In [81]:

    

import pandas as pd
import numpy as np
import matplotlib as plt
%matplotlib inline


    

In [82]:

    

WHO_df = pd.read_csv("xmart.csv")


    

In [83]:

    

print WHO_df.head()


    

    




                                Indicator; Age Group  2013; Female  \
0  nMx - age-specific death rate between ages x a...      0.005325   
1  nMx - age-specific death rate between ages x a...      0.000226   
2  nMx - age-specific death rate between ages x a...      0.000107   
3  nMx - age-specific death rate between ages x a...      0.000123   
4  nMx - age-specific death rate between ages x a...      0.000313   

   2013; Male  2012; Female  2012; Male  2000; Female  2000; Male  \
0    0.006437      0.005527    0.006639      0.006437    0.007754   
1    0.000302      0.000226    0.000301      0.000302    0.000403   
2    0.000128      0.000109    0.000131      0.000140    0.000176   
3    0.000175      0.000125    0.000177      0.000161    0.000243   
4    0.000767      0.000318    0.000782      0.000393    0.000933   

   1990; Female  1990; Male  
0      0.008363    0.010498  
1      0.000429    0.000531  
2      0.000185    0.000256  
3      0.000202    0.000316  
4      0.000464    0.001273  

In [84]:

    

Age=[]
measure = []
for instance in WHO_df['Indicator; Age Group']:
    Age.append(instance.split(';')[-1])
    measure.append(instance.split('-')[0])
WHO_df['Age Group'] = Age
WHO_df['Indicator'] = measure


    

In [85]:

    

WHO_df.drop(['Indicator; Age Group'],axis=1,inplace=True)


    

In [86]:

    

WHO_df.head()


    

    
Out[86]:






  

    

      

      
2013; Female
      
2013; Male
      
2012; Female
      
2012; Male
      
2000; Female
      
2000; Male
      
1990; Female
      
1990; Male
      
Age Group
      
Indicator
    
  
  

    

      
0
      
0.005325
      
0.006437
      
0.005527
      
0.006639
      
0.006437
      
0.007754
      
0.008363
      
0.010498
      
1 year
      
nMx
    
    

      
1
      
0.000226
      
0.000302
      
0.000226
      
0.000301
      
0.000302
      
0.000403
      
0.000429
      
0.000531
      
1-4 years
      
nMx
    
    

      
2
      
0.000107
      
0.000128
      
0.000109
      
0.000131
      
0.000140
      
0.000176
      
0.000185
      
0.000256
      
5-9 years
      
nMx
    
    

      
3
      
0.000123
      
0.000175
      
0.000125
      
0.000177
      
0.000161
      
0.000243
      
0.000202
      
0.000316
      
10-14 years
      
nMx
    
    

      
4
      
0.000313
      
0.000767
      
0.000318
      
0.000782
      
0.000393
      
0.000933
      
0.000464
      
0.001273
      
15-19  years
      
nMx
    
  

In [87]:

    

WHO_df.to_csv('WHO_data.csv')


    

In [88]:

    

print "Getting Number of people lived above a age threshold"
req_df=WHO_df.where(WHO_df['Indicator']=='Tx ').dropna()


    

    




Getting Number of people lived above a age threshold

In [89]:

    

req_df.to_csv('Life Expectation.csv')


    

In [90]:

    

req_df.keys()


    

    
Out[90]:





Index([u'2013; Female', u'2013; Male', u'2012; Female', u'2012; Male',
       u'2000; Female', u'2000; Male', u'1990; Female', u'1990; Male',
       u'Age Group', u'Indicator'],
      dtype='object')

In [91]:

    

print "Getting Number of people lived above a age threshold"
req_df=WHO_df.where(WHO_df['Indicator']=='ex ').dropna()


    

    




Getting Number of people lived above a age threshold

In [92]:

    

req_df.to_csv('Exp Life.csv')


    

In [93]:

    

print "Getting Death Rate above a age threshold"
req_df=WHO_df.where(WHO_df['Indicator']=='nMx ').dropna()
req_df.to_csv('Death Rate.csv')


    

    




Getting Death Rate above a age threshold

In [ ]: