Creating Graphs For The Data – Data Management and Visualization

Background of the Dataset CSV file Used:

The background to the Dataset CSV file used has been explained extensively in the week 2’s assignment. Not to bore assessors and readers by repeating everything here again, please simply check the background information from my previous assignment which can be assessed at this link: http://adabadata.tumblr.com/ OR in tumblr, it can be seen as one of the past posts specifically the one with the title

(PYTHON PROGRAM For The Research
Topic Association Of The Literacy Rate And Life Expectancy & Association Of
The Literacy Rate And Income Per Person: The Case of Ghana)

For easy access though, I will post the link to the actual dataset csv which has been used for this project here again.

The gapminder_ghana_updated.csv dataset csv for this project can be view and dowloaded here:

https://drive.google.com/file/d/0B2KfPRxy4ootbzl5N0g1dUtIVzA/view?pref=2&pli=1

see screenshot here
for guide (http://prntscr.com/9gctxn)

Creating Graphs For My Data

As instructed in the assignment, I will be continuing with the program I have successfully run.
That will be the program written in week 2.

MY PYTHON PROGRAM
CODE:

# -*- coding: utf-8 -*-

"""

Created on Sat Jan  2
12:33:55 2016



@author: Bernard

"""



#import statements

import pandas

import numpy

import seaborn

import matplotlib.pyplot as plt









#load the gapminder_ghana_updated dataset csv into the
program

data = pandas.read_csv('gapminder_ghana_updated.csv',
low_memory = False)









#print number of observations(rows) which is the number of
years this data

#has been looked at; print length

print("number of observations(rows) which is the number
of years this data has been looked at: ")

print(len(data))



#print number of variables (columns)

print("number of variables (columns) available in the
dataset: ")

print(len(data.columns))



print("data index: ")

print(len(data.index))











#Converting datat to numeric

data["incomeperperson"] =
data["incomeperperson"].convert_objects(convert_numeric=True)

data["lifeexpectancy"] =
data["lifeexpectancy"].convert_objects(convert_numeric=True)

data["literacyrate"] =
data["literacyrate"].convert_objects(convert_numeric= True)











#displaying rows or observation in Dataframe.

#inc_pp_count is the name that will hold the result from
incomeperperson count

# sort = false ; i use value false so that the data will be
sorted according

#to the original format and sequence  of the loaded data



print("counts for incomeperperson - 2010 Gross Domestic
Product per capita in constant 2000 US$ of Ghana. ")

inc_pp_count =
data["incomeperperson"].value_counts(sort = False)

#print the count of inc_pp_count ; incomeperperson

print(inc_pp_count)



print("percentages for incomeperperson - 2010 Gross
Domestic Product per capita in constant 2000 US$ of Ghana. ")

inc_pp_percent =
data["incomeperperson"].value_counts(sort=False, normalize =True)

#print the percentage of incomeperperson

print(inc_pp_percent)











print("counts for lifeexpectancy- 2011 life expectancy
at birth (years) of Ghana")

life_exp_count =
data["lifeexpectancy"].value_counts(sort = False)

#print the count of life_exp_count ; lifeexpectancy

print(life_exp_count)



print("percentages for lifeexpectancy- 2011 life
expectancy at birth (years) of Ghana ")

life_exp_percent =
data["lifeexpectancy"].value_counts(sort =False, normalize = True)

#print the percentage of life_exp_count ; lifeexpectancy

print(life_exp_percent)











print("counts for literacyrate - 2010, Literacy rate,
adult total (% of people ages 15 and above) of Ghana")

lit_rate_count =
data["literacyrate"].value_counts(sort = False ,dropna=False) #dropna
displays missen values

#print the count of lit_rate_count ; literacyrate

print(lit_rate_count)











print("percentages literacyrate - 2010, Literacy rate,
adult total (% of people ages 15 and above) of Ghana ")

lit_rate_percent =
data["literacyrate"].value_counts(sort =False, normalize = True)

#print the percentage of lit_rate_count ; literacyrate

print(lit_rate_percent)











#univariate bar graph for quantitative variable -
incomeperperson

seaborn.distplot(data["incomeperperson"].dropna(),
kde=False);

plt.xlabel("Incomeperperson - 2010 Gross Domestic
Product per capita in constant 2000 US$")

plt.title("Incomeperperson - 2010 Gross Domestic
Product per capita in constant 2000 US$ of Ghana. ")











#univariate bar graph for quantitative variable -
lifeexpectancy

seaborn.distplot(data["lifeexpectancy"].dropna(),
kde=False);

plt.xlabel("Lifeexpectancy- 2011 life expectancy at
birth (years)")

plt.title("Lifeexpectancy- 2011 life expectancy at
birth (years) of Ghana ")











#univariate bar graph for quantitative variable -
literacyrate

seaborn.distplot(data["literacyrate"].dropna(),
kde=False);

plt.xlabel("literacyrate - 2010, Literacy rate, adult
total (% of people ages 15 and above)")

plt.title("Literacyrate - 2010, Literacy rate, adult
total (% of people ages 15 and above) of Ghana ")











#Standard deviation and other descriptive statistics for the
quantitative variables

print("describe Incomeperperson - 2010 Gross Domestic
Product per capita in constant 2000 US$ of Ghana.")

desc1 = data["incomeperperson"].describe()

print(desc1)











print("describe Lifeexpectancy- 2011 life expectancy at
birth (years) of Ghana")

desc2 = data["lifeexpectancy"].describe()

print(desc2)











print("describe Literacyrate - 2010, Literacy rate,
adult total (% of people ages 15 and above) of Ghana")

desc3 = data["literacyrate"].describe()

print(desc3)











#Scatterplot for the relationship between Literacy Rate and
Life Expectancy of Ghana

scat1 = seaborn.regplot(x="literacyrate",
y="lifeexpectancy", fit_reg=False, data=data)

plt.xlabel("LITERACYRATE")

plt.ylabel("LIFEEXPECTANCY")

plt.title("Scatterplot for the Association between
Literacy Rate and Life Expectancy of Ghana")











#Showing The Line of Best Fit by dropping
"fit_reg" in the seaborn.regplot function to show Scatterplot for the

#relationship between Literacy Rate and Life Expectancy of
Ghana

scat2 = seaborn.regplot(x="literacyrate",
y="lifeexpectancy", data=data)

plt.xlabel("LITERACYRATE")

plt.ylabel("LIFEEXPECTANCY")

plt.title("Scatterplot for the Association between
Literacy Rate and Life Expectancy of Ghana")











#Scatterplot for the relationship between Literacy Rate and
Income Per Person of Ghana

scat3 = seaborn.regplot(x="literacyrate",
y="incomeperperson", fit_reg=False, data=data)

plt.xlabel("LITERACYRATE")

plt.ylabel("INCOMEPERPERSON")

plt.title("Scatterplot for the Association between
Literacy Rate and Income Per Person of Ghana")











#Showing The Line of Best Fit by dropping
"fit_reg" in the seaborn.regplot function to show Scatterplot for the

#relationship between Literacy Rate and Income Per Person of
Ghana

scat4 = seaborn.regplot(x="literacyrate",
y="incomeperperson", data=data)

plt.xlabel("LITERACYRATE")

plt.ylabel("INCOMEPERPERSON")

plt.title("Scatterplot for the Association between
Literacy Rate and Income Per Person of Ghana")

The Univariate graph for quantitative variable – incomeperperson