shuaib-8 / dand-gapminder-eda Goto Github PK

Created the project file on: 27/06/19
created the README file on: 17/07/19

Investigate a dataset: Gapminder

I used python's data analysis libraries (pandas; numpy; matplotlib) to analyse the Gapminder data set which contains economic and development indicators for which I communicated my findings around. Going through the data analysis process gave me insight into the data wrangling, cleaning and visualisation stages. I utilised my analysis entirely around descriptive rather than inferential statistics.

I chose datasets that expressed the following indicators (latest up to 2017):

• GDP per capita (income_pc) - dependent variable
• Life expectancy (life_exp) - independent variable
• Exports (% of GDP) - independent variable
• Investment (% of GDP) - independent variable

Before any cleaning, they arrived in this format:

The years studied in this Exploratory Data Analysis (EDA) were 1960-2017.

After cleaning - the final outcome made this dataset functional for EDA and viewing:

I choose to find trends for countries categorised under the BRIC - regarded among the fastest-growing emerging economies in recent times:

• Brazil
• China
• India

Questions:

Q1 Have certain regions of the world been growing in terms of income per capita better than others? - cases of Brazil, China and India

Mainly capturing the countries that grew the fastest in the sample, also uncovering the distribution of income (percentiles) in each case, utilising line plots, bar charts, box plots and histograms

Q2 Are there trends that can be observed between the selected metrics? - cases of Brazil, China and India

Finding associated trends between the selected variables for each country using correlation heatmaps and scatterplots.

If you want to see the original files from Gapminder, they can be accessed under List of indicators in Gapminder Tools (✅data currently used):

Gapminder - {.csv or .xlsx format}

Beyond my Udacity mentor, peers and lectures, I consulted a number of resources including:

• DataCamp - Cleaning Data in Python
• Youtube - Data School
• McKinney (2017) - chapters 7 (Data Cleaning and Preparation) and 8 (Data Wrangling: Join, Combine, Reshape)
• stackoverflow