This project dives deep into the intriguing relationship between life expectancy and Gross Domestic Product (GDP) across different countries from 2000 to 2015. By harnessing the power of data visualization, we uncover fascinating patterns and trends hidden within this dataset.
- pandas
- numpy
- seaborn
- matplotlib.pyplot
- Data manipulation:
- list
- dictionary
- value_counts()
- groupby()
- min()
- mean()
- median()
- max()
- zip()
- Data visualization:
- line plot
- scatter plot (sns.lmplot)
- histogram plot (plt.hist)
- side-by-side plot
- bar plot
- pie chart
- marker
- edgecolor
- bins
- linestyle
- label
- alpha
- plt.grid
- plt.axvline
- plt.ylim
- line plot
- sns.lmplot
- plt.hist (histogram plot)
- side by side plot
- bar plot
- pie chart
- Explore global trends: Delve into the evolution of life expectancy and GDP for each country over the analyzed period.
- Uncover disparities: Identify countries with the highest and lowest life expectancy alongside their economic performance.
- Visualize correlations: Discover the intriguing connection between national wealth and lifespan through captivating plots and charts.
- Interactive notebooks: Dive deeper and experiment with the analysis using the provided Jupyter notebooks.
- Data wrangling with powerful pandas functions like groupby, value_counts, and sorting.
- Creating stunning visualizations using line plots, scatter plots, histograms, and many more.
- Analyzing trends and correlations to unlock valuable insights into the data.
- Clone this repository.
- Install the required libraries:
pip install pandas numpy seaborn matplotlib. - Run the main Jupyter notebook:
life_expectancy_gdp.ipynb.
- Explore the generated visualizations to gain insights into the data.
- Modify the code to experiment with different visualizations and analyses.
Feel free to submit issues or pull requests for improvements or additions.