Filter pandas DataFrame by multiple thresholds defined in a dictionary

I want to filter a DataFrame against multiple thresholds, based on the ID's prefix.

Ideally I'd configure these thresholds with a dictionary e.g.

minimum_thresholds = {
    'alpha': 3,
    'beta' : 5,
    'gamma': 7,
    'default': 4
}

For example:

data = {
    'id': [
         'alpha-164232e7-75c9-4e2e-9bb2-b6ba2449beba', 'alpha-205acbf0-64ba-40ad-a026-cc1c6fc06a6f',
         'beta-76ece555-e336-42d8-9f8d-ee92dd90ef19', 'beta-6c91c1cc-1025-4714-a2b2-c30b2717e3c4',
         'gamma-f650fd43-03d3-440c-8e14-da18cdeb78d4', 'gamma-a8cb84b5-e94c-46f7-b2c5-135b59dcd1e3',
         'pi-8189aff9-ea1c-4e22-bcf4-584821c9dfd6'
        ],
    'freq': [4, 2, 1, 4, 7, 9, 8]
}

                                           id  freq
0  alpha-164232e7-75c9-4e2e-9bb2-b6ba2449beba     4
1  alpha-205acbf0-64ba-40ad-a026-cc1c6fc06a6f     2
2   beta-76ece555-e336-42d8-9f8d-ee92dd90ef19     1
3   beta-6c91c1cc-1025-4714-a2b2-c30b2717e3c4     4
4  gamma-f650fd43-03d3-440c-8e14-da18cdeb78d4     7
5  gamma-a8cb84b5-e94c-46f7-b2c5-135b59dcd1e3     9
6     pi-8189aff9-ea1c-4e22-bcf4-584821c9dfd6     8

I would then get an output like:

                                           id  freq
0  alpha-164232e7-75c9-4e2e-9bb2-b6ba2449beba     4
1  gamma-f650fd43-03d3-440c-8e14-da18cdeb78d4     7
2  gamma-a8cb84b5-e94c-46f7-b2c5-135b59dcd1e3     9
3     pi-8189aff9-ea1c-4e22-bcf4-584821c9dfd6     8

I could do this bluntly by looping through each threshold, but it feels like there must be a more Pythonic way?

Solution

Another possible solution, whose steps are:

First, the id column is split at each hyphen using the str.split method, extracting the first part of each split with str[0].
Then, the resulting first parts are mapped to their corresponding threshold values using the map function, referencing the thresholds dictionary. If a value is not found in thresholds, the default threshold is used.
The freq column is then compared to these threshold values using the ge method, which checks if freq is greater than or equal to the threshold.
Finally, the dataframe is filtered to include only rows where this condition is met.

df[df['freq']
   .ge(df['id'].str.split('-').str[0]
       .map(lambda x: thresholds.get(x, thresholds['default'])))]

Output:

                                           id  freq
0  alpha-164232e7-75c9-4e2e-9bb2-b6ba2449beba     4
4  gamma-f650fd43-03d3-440c-8e14-da18cdeb78d4     7
5  gamma-a8cb84b5-e94c-46f7-b2c5-135b59dcd1e3     9
6     pi-8189aff9-ea1c-4e22-bcf4-584821c9dfd6     8