A collection of geospatial bug fixes #4444
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mistercrunch
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| p = Point(s) | ||
| return (p.latitude, p.longitude) | ||
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| df[key] = df[spatial.get('lonlatCol')].apply(tupleify) |
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We should definitely benchmark this with our current data before deploying, since it's using regular expressions to do the job: https://github.com/geopy/geopy/blob/master/geopy/point.py#L310-L339
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In [2]: from geopy.point import Point
In [3]: Point('234,239')
Out[3]: Point(54.0, -121.0, 0.0)
In [4]: %timeit Point('234,239')
The slowest run took 4.29 times longer than the fastest. This could mean that an intermediate result is being cached.
100000 loops, best of 3: 11.7 µs per loop
In [5]: %timeit (float(v) for v in '234,239'.split(','))
The slowest run took 6.84 times longer than the fastest. This could mean that an intermediate result is being cached.
1000000 loops, best of 3: 593 ns per loopSo roughly double the time. On 1M points, that means 0.5 second which to me is fine as almost negligible compare to the network time it takes to bring that over. Note that there's probably a numpy way of doing this that would be much faster.
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Wait, you mean 20x the time. Calling Point on 1M points would take ~12 seconds (11.7e-6 s * 1e6).
I looked at the code and I'm not sure how to optimize this with Numpy.
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Oh snap. 12 seconds is long.
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Let's optimize |
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Also using
geopyto parse single-column spatial info, supports many different types of lat/lng styles