Pandas same previous year quarter difference

I am able to get the results I need but am curious to see if this can be done more efficiently (as in fewer steps) or by some built-in function.

As mockup data, I have 3 years of daily widget sales. The goal is to compare totals for previous-year same-quarter sales. So Q1 2019 sales compared to Q1 2020; Q1 2020 to Q1 2021 sales; Q2 2019 to Q2 2020 etc..

My strategy was:

1. Resample/downsample sales data into quarters
2. Create a conditional that creates a new column that names each quarter values like Q1, Q2, Q3 and Q4.
3. Create a column that only has the years.
4. create a new df by a groupby that results in a multiindex df that groups each quarter by years…so all Q1s are together and listed by years.
5. Now we do another groupby that finds the difference for like quarters year by year.

Here is the code with resampled/downsampled data:

sample_data = [{'date': Timestamp('2019-03-31 00:00:00'), '#_widgets': 4502},
 {'date': Timestamp('2019-06-30 00:00:00'), '#_widgets': 4388},
 {'date': Timestamp('2019-09-30 00:00:00'), '#_widgets': 4703},
 {'date': Timestamp('2019-12-31 00:00:00'), '#_widgets': 4344},
 {'date': Timestamp('2020-03-31 00:00:00'), '#_widgets': 4206},
 {'date': Timestamp('2020-06-30 00:00:00'), '#_widgets': 4281},
 {'date': Timestamp('2020-09-30 00:00:00'), '#_widgets': 4879},
 {'date': Timestamp('2020-12-31 00:00:00'), '#_widgets': 4115},
 {'date': Timestamp('2021-03-31 00:00:00'), '#_widgets': 4313},
 {'date': Timestamp('2021-06-30 00:00:00'), '#_widgets': 4578},
 {'date': Timestamp('2021-09-30 00:00:00'), '#_widgets': 4467},
 {'date': Timestamp('2021-12-31 00:00:00'), '#_widgets': 4871}]

Import libraries

import pandas as pd
from datetime import datetime 
import numpy as np

Create conditions to add Q1, Q2, etc using the idea that Q1 will have the month 3, Q2 month 6 etc.

abbrv = dfq["date"].dt.month
conditions = {
    "Q1": abbrv == 3,
    "Q2": abbrv == 6,
     "Q3": abbrv == 9,
    "Q4": abbrv == 12 
}

Then i passed the conditions:

## use numpy to run conditions 
dfq['quarter'] = np.select(conditions.values(), conditions.keys(), default="Not Available") 

Add a year column so i can grouby:

## add years to it
dfq["year"] = dfq["date"].dt.year

Create new df using groupby:

## group by to get sort by quarters in each year
dff = dfq.groupby(["quarter", "year", ])["#_widgets"].sum().to_frame()

In my step final, I groupby again but get the difference for same-period quarters:

## find the difference for previous year quarter comparisons
dff['diff'] = dff.groupby(['quarter']).diff()

This works fine but it seems like there has to be a way with fewer steps without adding too much complexity.