How to bucket transactions by months and then calculate the difference per month in pandas

This is what the dataset looks like:

Trans ID| Trans Amount | Trans Date |
| -------- | --------- |
1| 50 | 2023-03-31 |
1| 600 | 2023-04-30 |
1| 40 | 2023-05-31 |
2| 500 | 2023-03-31 |
2| 500 | 2023-04-30 |
2| 10 | 2023-05-31 |
3| 980 | 2023-03-31 |
3| 1800 | 2023-04-30 |
3| 35 | 2023-05-31 |

I want to bucket the transactions by months and then calculate what the difference was from the one month to the other month.

I would like the data to be grouped as such:

Bucket By Month: March

Trans ID| Trans Amount | Trans_Date |
| -------- | -------- |
1| 50 | 2023-03-31 |
2| 500 | 2023-03-31 |
3| 980 | 2023-03-31 |

April

Trans_ID| Trans_Amount | Trans_Date |
| -------- | -------- |
1| 600 | 2023-04-30 |
2| 500 | 2023-04-30 |
3| 1800 | 2023-04-30 |

May

Trans_ID| Trans_Amount | Trans_Date |
| -------- | -------- |
1| 40 | 2023-05-31 |
2| 10 | 2023-05-31 |
3| 35 | 2023-05-31 |

From here I would like to then calculate the difference from March to April to May.

I tried to use group by as such, but I'm not sure whether it does what I need it to do and I am not sure what to do next for calculating the difference from one month to the next:

d = {x : y for x, y in df.groupby(pd.to_date(df.Trans_Date).dt.strftime('%y-%m'))}

Here you just need to use month date accessor with groupby:

import pandas as pd
df = pd.DataFrame(
    {
        'a': [1, 2, 3, 4, 5, 6],
        'date': ['2020-1-1', '2020-1-5', '2020-2-7', '2020-2-9', '2020-2-20', '2020-3-1']
    }
)
df['date'] = pd.to_datetime(df.date)
months = {1: 'Jan', 2 : 'Feb', 3: 'Mar', 4: 'Apr'}
for group, val in df.groupby(df.date.dt.month):
    print(f'{months.get(group)}\n{val}\n\n')

And this is the output:

Jan
   a       date
0  1 2020-01-01
1  2 2020-01-05
Feb
   a       date
2  3 2020-02-07
3  4 2020-02-09
4  5 2020-02-20
Mar
   a       date
5  6 2020-03-01

EDIT:
This is how you calculate the difference between average transaction amounts compared to the previous month:

month_average = df.groupby(df.date.dt.month).mean().reset_index()
month_average['date'] = [months.get(m) for m in month_average.date]
month_average['diff_to_previous'] = month_average.a.diff()

And the output:

  date    a  diff_to_previous
0  Jan  1.5               NaN
1  Feb  4.0               2.5
2  Mar  6.0               2.0