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Time Series Analysis and Visualization Using Python and Plotly

In this project, we’ll explore a century-long correlation between a country’s GDP per capita and life expectancy through visualizations and animations using the Plotly library in Python.

Time Series Analysis and Visualization Using Python and Plotly

You will learn to:

Unpivot data from wide to long format.

Merge datasets into one using a similar column.

Apply feature engineering to generate new columns.

Analyze data to find trends and insights.

Visualize data using Plotly.

Animate a visual to show changes over time.


Data Visualization

Data Manipulation

Data Science


Basic knowledge of Python

Understanding of the Plotly library

Basic knowledge of cleaning data using pandas







Project Description

In 2006, Hans Rosling gave a lecture at a TED conference titled “The Best Stats You’ve Ever Seen,” where he used statistics to show a decrease in worldwide fertility and that the era of fast population growth would end by mid century. He also stated that the distinction between developed and developing countries has blurred, global health is improving, and extreme poverty in the world is decreasing. This has been considered one of the best implementations of data analysis, visualization, and storytelling. 

In this project, we’ll endeavor to replicate that animated visualization using the Plotly library in Python. This analysis will revolve around how a country’s GDP per capita correlates with the life expectancy of its citizens over a period of 100+ years.

We’ll perform extensive cleaning and data manipulation to get the data ready for visualization, and also perform some feature engineering to derive new columns from existing columns that would aid our analysis. By the end of the project, we’ll be able to unpivot a dataset from the wide format to the long format, merge multiple datasets together using primary and foreign keys, animate a visual, and export it as a GIF file.

Project Tasks


Introduction to Project

Task 0: Get Started

Task 1: Import the Libraries


Data Import

Task 2: Read in Data


Data Assessment

Task 3: Perform Visual and Programmatic Assessment


Data Cleaning

Task 4: Unpivot the Data

Task 5: Standardize Units k, M and B

Task 6: Correct Data Types of Columns

Task 7: Create a Data Cleaning Function

Task 8: Merge the Three Datasets

Task 9: Drop Null Rows

Task 10: Generate Continent Column


Data Visualization

Task 11: Filter Data by Years

Task 12: Plot the Data

Task 13: Save Chart as GIF

Task 14: Congratulations!