01 / The brief
The question
Use Python to analyze demographic data and answer clear questions about income, education, work patterns, and population groups.
This GitHub project analyzes demographic census data using Python and Pandas. The analysis answers practical questions about age, education, race, salary groups, working hours, occupation, and country-level income distribution. The project is relevant for the portfolio because it shows core data analysis skills: reading structured data, filtering records, grouping data, calculating percentages, and producing accurate summary insights from a real-world demographic dataset.
Results & observations
- Delivered a clean Python analysis that explains important demographic patterns.
- The project demonstrates strong Pandas fundamentals, data filtering, aggregation, and analytical thinking.
Challenges
- Raw census records needed cleaning and aggregation
- Many categorical fields had to be grouped correctly
- Percentages needed accurate rounding and filtering
Approach
- Loaded and explored the demographic dataset with Pandas.
- Grouped and filtered data by race, education, salary, country, occupation, and working hours.
- Calculated percentages and summary statistics.
- Structured the analysis so each business-style question returns a clear result.
Implementation details
Data Loading
Loaded the census demographic data into Pandas.
Data Exploration
Reviewed columns such as age, education, salary, occupation, race, sex, and country.
Grouped Analysis
Used grouping and filtering to answer demographic questions.
Percentage Calculations
Calculated education, salary, and country-level percentages.
Result Validation
Checked outputs against expected analytical results.
Explore the report
Select a screenshot for a larger view.