Investigating Child Mortality Levels Around The World

import csv

from json import loads, dumps

import matplotlib.pyplot as plt

import numpy as np

w, h = 5, 5240;

matinil = [[0 for x in range(w)] for y in range(h)]

variable1990,variableco1,variableco2,variableco3,myred1,myred2,myred3=[],[],[],[],[],[],[]

variable2015,val1,val2,val3=[],0,0,0

j,k=0,0 

# Start CSV Read

with open(‘WHOSIS_000003.csv’) as inputcsv:

    datastore={}

    WHOSIS1=[]

    WHOSIS2=[]

    WHOSIS3=[]

    WHOSIS4=[]

    WHOSIS5=[]

    reader = csv.DictReader(inputcsv)   

    # DO the processing

    for whorow in reader:

        datastore=loads(dumps(whorow))

        WHOSIS1.append(datastore[‘Country’])

        WHOSIS2.append(datastore[‘Year’])

        spl1=datastore[‘Infant mortality rate (probability of dying between birth and age 1 per 1000 live births)’]

        spl2=spl1.split(‘[‘)

        WHOSIS3.append(spl2[0])

        spl1=datastore[‘Neonatal mortality rate (per 1000 live births)’]

        spl2=spl1.split(‘[‘)

        WHOSIS4.append(spl2[0])

        spl1=datastore[‘Under-five mortality rate (probability of dying by age 5 per 1000 live births)’]

        spl2=spl1.split(‘[‘)

        WHOSIS5.append(spl2[0])                            

 w, h = 10, 4442;

Matrix = [[0 for x in range(w)] for y in range(h)]

with open(‘COUNTRY.csv’) as csvfile:

    myvals={}    COUNTRYcomb1,COUNTRYcomb2,COUNTRYcomb3,COUNTRYcomb4,COUNTRYcomb5,COUNTRYcomb6,COUNTRYcomb7,COUNTRYcomb8,COUNTRYcomb9,COUNTRYcomb10=[],[],[],[],[],[],[],[],[],[]

    reader = csv.DictReader(csvfile)

    for row in reader:

        myvals=loads(dumps(row))

        COUNTRYcomb1.append(myvals[‘attribute__label’])

        COUNTRYcomb2.append(myvals[‘attribute__display’])

        COUNTRYcomb3.append(myvals[‘dimension__code__display’])

        COUNTRYcomb4.append(myvals[‘dimension__code__attr__category’])

        COUNTRYcomb5.append(myvals[‘dimension__code__attr__value’]) 

regionmat1,regionmat2,regionmat3,regionmat4,regionmat5=[],[],[],[],[]

incomemat1,incomemat2,incomemat3,incomemat4,incomemat5=[],[],[],[],[] 

for i in range(len(COUNTRYcomb4)):       

    if ‘WORLD_BANK_INCOME_GROUP’ in COUNTRYcomb4[i]:

        incomemat2.append(COUNTRYcomb2[i])

        incomemat3.append(COUNTRYcomb3[i])

        incomemat4.append(COUNTRYcomb4[i])

        incomemat5.append(COUNTRYcomb5[i])       

    if ‘WORLD_BANK_INCOME_GROUP_RELEASE_DATE’ in COUNTRYcomb4[i]:

        incomemat1.append(COUNTRYcomb5[i])      

    if ‘WHO_REGION_CODE’ in COUNTRYcomb4[i]:

        regionmat1.append(COUNTRYcomb5[i])       

    if ‘WHO_REGION’ in COUNTRYcomb4[i]:

        regionmat2.append(COUNTRYcomb2[i])

        regionmat3.append(COUNTRYcomb3[i])

        regionmat4.append(COUNTRYcomb4[i])

        regionmat5.append(COUNTRYcomb5[i])

   fig = plt.figure(figsize=(20,10))

plt.plot(incomemat5,marker=’o’,markerfacecolor=’red’)

plt.xlabel(” dimension__code__attr__category”)

plt.ylabel(“dimension__code__attr__value”)

plt.title(“WORLD_BANK_INCOME_GROUP value”)

plt.grid(b=None, which=’major’, axis=’both’)

fig = plt.figure(figsize=(20,10))

plt.plot(incomemat1,marker=’o’,markerfacecolor=’red’)

plt.xlabel(” dimension__code__attr__category”)

plt.ylabel(“dimension__code__attr__value”)

plt.title(“WORLD_BANK_INCOME_GROUP_RELEASE_DATE value”)

plt.grid(b=None, which=’major’, axis=’both’)    

fig = plt.figure(figsize=(20,10))

plt.plot(regionmat1,marker=’o’,markerfacecolor=’red’)

plt.xlabel(” dimension__code__attr__category”)

plt.ylabel(“dimension__code__attr__value”)

plt.title(“WHO_REGION_CODE Value”)

plt.grid(b=None, which=’major’, axis=’both’) 

fig = plt.figure(figsize=(20,10))

plt.plot(incomemat5,marker=’o’,markerfacecolor=’red’)

plt.xlabel(” dimension__code__attr__category”)

plt.ylabel(“dimension__code__attr__value”)

plt.title(“WORLD_BANK_INCOME_GROUP value”)

plt.grid(b=None, which=’major’, axis=’both’) 

plt.show()