An exploratory data analysis pipeline in Python tracking global demographic trends, statistical correlations, and historical population growth across continents from 1970 to 2022. The analytics workflow focuses heavily on identifying population trends, isolating geographic density spikes, mapping linear statistical associations via multi-variable correlation indices, and visualizing dynamic growth metrics grouped by global continents.
- Target Applications: Demographic Research, Trend Analysis, Macro-Demographic Modeling.
- Core Technology Stack: Python 3, Pandas, Seaborn, Matplotlib, Jupyter Notebook.
The script executes sequential exploratory data stages to map mathematical variables:
- Statistical Correlation Mapping: Generates automated Seaborn heatmaps to evaluate spatial-temporal growth associations.
- Aggregated Continental Tracking: Implements transposed aggregation matrices (
groupby) to compare historical means from 1970 through 2022. - Linear Trend Visualization: Renders structured multi-line charts tracking localized continental population velocity profiles.
The repository is structured following industry-standard analytical coding practices:
01_world_population_EDA.ipynb- Documented Python notebook containing data ingestion, analysis steps, and inline code plots.02_world_population.csv- Target historical demographic source dataset file.requirements.txt- Complete environment configuration file containing package dependencies.
Ensure your local development environment is configured by installing the core library dependencies directly from the project's layout configuration file:
pip install -r requirements.txt-
Clone this repository to your machine:
git clone https://github.com/usmanali9999/World_Population_EDA.git cd World_Population_EDA -
Start the Jupyter workspace environment:
jupyter notebook
-
Open and run all cell groups inside
01_world_population_EDA.ipynbto regenerate the live analytical data models and plots.