[ENH] Improve conditional_join with cython

[samukweku]

PR Description

Please describe the changes proposed in the pull request:

• add cythonised code for improved performance
• improve perf significantly with cython
• take advantage of cython 3.0 which supports writing .py files in pure python mode, while still getting the benefits of a lower level language - in this case C.
• the Cython code is mainly for loops.
• precursor to adding support for aggregations within conditional join
• fairly large PR; tests remain the same - mostly refactoring and the cython functions

This PR resolves #1490 .

Example (as always with benchmarks/tests, take with a pinch of salt YMMV):

url = "https://raw.githubusercontent.com/samukweku/data-wrangling-blog/master/notebooks/Data_files/flights.csv"
flights = pd.read_csv(url, sep = '\t', names=['orig','dest','orig_time', 'dest_time'], parse_dates  = ['orig_time', 'dest_time'])
flights = flights.factorize_columns(['orig','dest']).iloc[:, 2:]
flights.columns = [ent.split("_")[0] if ent.endswith('enc') else ent for ent in flights]
flights.columns = ['takeoff','landing','orig','dest']
flights = flights.assign(start=flights.landing+pd.Timedelta(minutes=45), end=flights.landing+pd.Timedelta(hours=3))
flights.head()

                takeoff	                 landing	orig	dest	start	                  end
0	2021-11-27 07:15:00	2021-11-27 08:55:00	0	0	2021-11-27 09:40:00	2021-11-27 11:55:00
1	2021-11-27 20:05:00	2021-11-28 00:50:00	1	1	2021-11-28 01:35:00	2021-11-28 03:50:00
2	2021-11-27 21:00:00	2021-11-27 21:35:00	2	2	2021-11-27 22:20:00	2021-11-28 00:35:00
3	2021-11-27 21:15:00	2021-11-27 22:25:00	0	2	2021-11-27 23:10:00	2021-11-28 01:25:00
4	2021-11-26 11:40:00	2021-11-26 14:45:00	3	3	2021-11-26 15:30:00	2021-11-26 17:45:00

# classic Pandas merge and filter
%%timeit 
outt = (flights
        .merge(flights, left_on='dest', right_on='orig')
        .loc[lambda f: f.takeoff_y.between(f.start_x, f.end_x) & (f.orig_x != f.orig_y), 
             ['start_x', 'takeoff_y', 'start_y', 'dest_x', 'orig_y']
             ]
        )
9.85 s ± 69.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

# dev, with use_numba=False
%%timeit
outer = (flights
 .conditional_join(
     flights, 
     ('end', 'takeoff' ,'>='), 
     ('start', 'takeoff', '<='), 
     ('orig','orig','!='), 
    ('dest', 'orig', '=='), 
    df_columns = ['start', 'end', 'dest'],
     right_columns = ['takeoff', 'orig'],
     use_numba=False)
)
2.03 s ± 19.2 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)



# this PR, with use_numba=False
%%timeit
outerr = (flights
 .conditional_join(
     flights, 
     ('end', 'takeoff' ,'>='), 
     ('start', 'takeoff', '<='), 
     ('orig','orig','!='), 
    ('dest', 'orig', '=='), 
    df_columns = ['start', 'end', 'dest'],
     right_columns = ['takeoff', 'orig'],
     use_numba=False)
)
29.9 ms ± 470 μs per loop (mean ± std. dev. of 7 runs, 10 loops each)





# dev, with force=True
%%timeit
out = (flights
 .conditional_join(
     flights, 
     ('end', 'takeoff' ,'>='), 
    ('start', 'takeoff', '<='), 
     ('orig','orig','!='), 
     ('dest', 'orig', '=='), 
     df_columns = ['start', 'end', 'dest'],
     right_columns = ['takeoff', 'orig'],
     force=True,
     use_numba=False)
)
255 ms ± 3.16 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

# this PR, with force=True
%%timeit
outt = (flights
 .conditional_join(
     flights, 
     ('end', 'takeoff' ,'>='), 
    ('start', 'takeoff', '<='), 
     ('orig','orig','!='), 
     ('dest', 'orig', '=='), 
     df_columns = ['start', 'end', 'dest'],
     right_columns = ['takeoff', 'orig'],
     force=True,
     use_numba=False)
)
83.7 ms ± 552 μs per loop (mean ± std. dev. of 7 runs, 10 loops each)

PR Checklist

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Automatic checks

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• Building a preview of the docs on Netlify
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• Making sure the code is documented
• Making sure that all tests are passed
• Making sure that code coverage doesn't go down.

Relevant Reviewers

Please tag maintainers to review.

• @ericmjl