This project analyzes the Gapminder dataset, which contains information about various countries around the world, including their population, income, life expectancy, and more. Specifically, this project focuses on the relationship between fertility rate and life expectancy over time.
The data for this project was collected from the Gapminder project, which provides access to a wide range of datasets related to global development. The dataset used in this project contains information about fertility rate and life expectancy for various countries from 1960 to 2015.
To wrangle and explore the data, the Python libraries Pandas and Seaborn were used. The data was cleaned and preprocessed to remove any missing or inconsistent values. Various visualizations were created using Seaborn to explore the data and identify any trends or patterns.
To analyze the relationship between fertility rate and life expectancy over time, an interactive data visualization was created using IPython.display and ipywidgets. This allows users to explore the relationship between these two variables for different countries and time periods.
Additionally, a gif was generated using Seaborn and ImageIO to show how the relationship between these two variables has changed over time for various countries.
The following Python libraries were used in this project:
- Pandas
- Seaborn
- Matplotlib.pyplot
- Numpy
- ImageIO
- IPython.display
- ipywidgets
This project provides insights into the relationship between fertility rate and life expectancy over time for various countries. The interactive data visualization created using IPython.display and ipywidgets allows for a deeper understanding of this relationship and highlights the importance of these two variables in determining a country's overall development. The gif generated using Seaborn and ImageIO provides a visual representation of how this relationship has changed over time.