This 7-day journey aims to enhance Python and Pandas skills through a hands-on project focused on library loan data analysis. Tasks include importing, cleaning, exploring, and visualizing data, addressing questions related to loan trends, library usage patterns, and more.
- Day 1-3: Data Preparation Import and organize loan and inventory data from the UFRN library. Merge datasets, clean null values, and eliminate duplicates. Create a new column based on the CDU classification system. Format and enhance column readability.
- Day 4-6: Data Analysis and Visualization Analyze total loan quantities over the years with a line graph. Explore monthly loan distribution for optimal staffing schedules. Identify peak loan hours throughout the day using bar charts. Investigate loan distribution based on user type, collection, library, and CDU classification. Generate frequency tables with percentages for in-depth analysis.
- Day 7: Comparative Analysis and HTML Output Calculate percentage differences in loans for 2017-2018, 2018-2019, and 2019-2022 for each course. Create an HTML table with custom styling for front-end integration. Optionally, provide pre-styled CSS to assist the front-end team.
Celebrate the successful completion of the challenge, gaining practical experience in working with library data. Share your insights, analyses, and visualizations. Consider extending the project to explore additional metrics and optimizations for effective library information management.