You are ready to deepen your skills by analyzing rainfall data on a monthly basis. Imagine you have a pandas DataFrame containing daily rainfall amounts for a region, with each row representing a specific date and the corresponding rainfall in millimeters. Your objective is to group this data by month, calculate the total rainfall for each month, and then identify which month experienced the highest and lowest rainfall. Presenting your findings both in a table and as a plot will help you visualize seasonal rainfall patterns and understand environmental trends more clearly.

To begin, consider a DataFrame where the date column contains daily dates for one year, and the rainfall_mm column holds the measured rainfall for each day. You will use pandas to group the data by month, sum the rainfall totals, and then find the months with the highest and lowest rainfall. Finally, you will display the results in both tabular and graphical formats.

As you can see, the code groups the daily rainfall data by month, calculates the monthly totals, and then identifies which month had the most and least rainfall. The results are printed in a table and visualized as a bar chart, making it easy to compare rainfall across months. This approach helps you quickly spot wet and dry periods, which can be crucial for environmental planning and analysis.