The code provided is for data analysis and visualization using Python and pandas libraries. Here’s a summary of what each part does:

1. Data Loading: The code starts by loading the dataset from a CSV file into a pandas DataFrame.
2. Data Preprocessing: The code applies various preprocessing techniques, such as:
   • Rounding time intervals to 15-minute resolutions using round_time function.
   • Adding new columns for concise time interval formatting using add_consice_interval_columns function.
3. Grouping and Aggregation: The code groups the data by both time interval and day of the week, and then aggregates the results using group_by_concised_interval function.
4. Data Visualization: The code prints the resulting DataFrame for each group.

The output shows the average speed and volume of traffic in different time intervals (15-minute resolutions) for each day of the week.

Example Output:

This output indicates that on Fridays, at 8:15 AM and 8:30 AM, the average speed of traffic in the place_uno location was 65 mph and 59 mph, respectively. Similarly, for the place_duo location.

Overall, this code demonstrates how to analyze and visualize traffic data using pandas and Python, with a focus on time interval and day of the week grouping.

Last modified on 2023-10-25