Daddy Awesome's Writings

All the things I am writing good or bad

Project maintained by daddyawesome Hosted on GitHub Pages —

Making Data Management Decisions

Data Management and Visualization - Week 3

Back

Questions:

How employment rate is associated with a high suicide rate?
How Income per Person is associated with a high suicide rate?
What is a number of breast cancer cases associated with a high suicide rate?

To answer this questions we are going to use gapminder data

Start with import

load gapminder dataset

data = pd.read_csv('gapminder.csv',low_memory=False)

I will be using url to get the data online (data is store in github)

Converting selected columns to numeric dtypes:

Summary Statistics about the Data

Install sidetable

sidetable is a powerful tool for making summary table.

Statistics for Suicide Rates

Our new dataframe sub_copy contains all data with high suicide rate which we going to compare with employment rate, income per person and breast canrcer rate.

Employment Rate Versus Suicide Rate

From the frequency distribution above we are able to determine that group 2 which belongs to 51 to 59 employment rate has the highest number of suicide rate.

Income per Person Versus Suicide Rate

From the frequency table above we found out that there is no significant difference between quartiles when it come to high suicide rate.

BREAST CANCER RATE versus High Suicide Rate

For the breast cancer rate, I grouped the data into 4 groups by number of breast cancer cases (1-23, 24-46, 47-69, 70-92) using pandas.cut function.
People with lower breast cancer rate experience a high suicide rate.