Coursera Data Analysis First Assignment

submission.py

#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Thu Jan 26 21:44:11 2017

@author: mllearner
"""

import pandas
import numpy # Ignore the warning. going to be used later on.

data = pandas.read_csv('gapminder.csv')
print len(data)         # Number of observations
print len(data.columns) # Number of variables

# IMPORTANT NOTE: Since I know all my variables are lower case, I skipped the part where
# we manually change every char upper case.
pandas.set_option('display.float_format', lambda x: '%f' %x)

datavariable1 = "femaleemployrate"
datavariable2 = "internetuserate"
datavariable3 = "urbanrate"

data[datavariable1] = data[datavariable1].convert_objects(convert_numeric=True)
data[datavariable2] = data[datavariable2].convert_objects(convert_numeric=True)
data[datavariable3] = data[datavariable3].convert_objects(convert_numeric=True)

print "counts for Female Employ Rate - usual frequency how females are employed"
c1 = data[datavariable1].value_counts(sort=False)

print "percentages for Female Employ Rate - usual frequency how females are employed"
p1 = data[datavariable1].value_counts(sort=False, normalize=True)

print "counts for Internet Use Rate - usual frequency how many people are using the internet"
c2 = data[datavariable2].value_counts(sort=False)

print "percnetages for Internet Use Rate - usual frequency how many people are using the internet"
p2 = data[datavariable2].value_counts(sort=False, normalize=True)

print "counts for Urban Rate - usual frequency how many people are living in the urbans"
c3 = data[datavariable3].value_counts(sort=False)

print "percnetages for Urban Rate - usual frequency how many people are living in the urbans"
p4 = data[datavariable3].value_counts(sort=False, normalize=True)

# Since there are lots of rows (213), I mean countries, the percentages are really low.

# Subset of observations with no empty data. You can aswell add more complicated logic
# statements to narrow our results down.
sub1 = data[(data['femaleemployrate'] >= 0) & (data['internetuserate'] >= 0)]

sub2 = sub1.copy()

print "counts for femaleemployrate"
c11 = sub2[datavariable1].value_counts(sort=False)
print c11

print "percentages for femaleemployrate"
p12 = sub2[datavariable1].value_counts(sort=False, normalize=True)
print p12

print "counts for internetuserate"
c21 = sub2[datavariable2].value_counts(sort=False)
print c21

print "percentages for internetuserate"
p22 = sub2[datavariable2].value_counts(sort=False, normalize=True)
print p22

print "counts for urbanrate"
c31 = sub2[datavariable3].value_counts(sort=False)
print c31

print "percentages for urbanrate"
p32 = sub2[datavariable3].value_counts(sort=False, normalize=True)
print p32