PERF: MultiIndex._engine use smaller dtypes #58411
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GianlucaFicarelli
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mroeschke
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GianlucaFicarelli
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| # Check the total number of bits needed for our representation: | ||
| if lev_bits[0] > 64: | ||
| # The levels would overflow a 64 bit uint - use Python integers: | ||
| return MultiIndexPyIntEngine(self.levels, self.codes, offsets) | ||
| return MultiIndexUIntEngine(self.levels, self.codes, offsets) | ||
| if lev_bits[0] > 32: |
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Is it possible for the existing engine to get resized where it would exceed e.g. int32 max? Or would a new engine just be created?
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Could you clarify in what cases the existing engine would exceed int32 max?
In cases where MultiIndex._set_levels() is called internally, this would also call _reset_cache() so the engine is recreated.
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I don't exactly remember what path this goes through, but a snippet like
n = np.iinfo(np.uint8).max
ser = pd.Series(range(n), index=pd.MultiIndex.from_arrays([range(n]))
ser.iloc[n+1] = n+1
ser.index.some_method_that_uses_the_engine
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I tried the snippet above with some slight adjustment:
In [34]: n = np.iinfo(np.uint8).max - 1
In [35]: ser = pd.Series(range(n), index=pd.MultiIndex.from_arrays([range(n)]))
In [36]: ser.index._cache
Out[36]: {}
In [37]: ser.index._engine
Out[37]: <pandas.core.indexes.multi.MultiIndexUInt8Engine at 0x118e0fed0>
In [38]: ser.index._engine.values
Out[38]:
array([ 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,
15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27,
28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,
41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66,
67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79,
80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92,
93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105,
106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118,
119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131,
132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144,
145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157,
158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170,
171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183,
184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196,
197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209,
210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222,
223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235,
236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248,
249, 250, 251, 252, 253, 254, 255], dtype=uint8)
In [39]: n
Out[39]: 254
In [40]: ser.index._cache
Out[40]:
{'levels': FrozenList([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, ...]]),
'_engine': <pandas.core.indexes.multi.MultiIndexUInt8Engine at 0x118e0fed0>}
In [41]: ser.loc[n+1] = n+1
In [42]: ser.index._cache
Out[42]: {'levels': FrozenList([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, ...]])}
In [43]: ser.index._engine
Out[43]: <pandas.core.indexes.multi.MultiIndexUInt16Engine at 0x107288270>
In [44]: ser.index._cache
Out[44]:
{'levels': FrozenList([[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, ...]]),
'_engine': <pandas.core.indexes.multi.MultiIndexUInt16Engine at 0x107288270>}
So it seems that after calling ser.loc, the engine is automatically deleted from the cache (or the index is a copy, since id returns a different number)
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I executed the asv benchmarks for All of them improved, except Side note, I also tried to move the class |
mroeschke
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Apr 30, 2024
|
Thanks @GianlucaFicarelli |
pmhatre1
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May 7, 2024
* PERF: MultiIndex._engine use smaller dtypes * Move offsets downcasting to MultiIndex._engine * Remove unused import uint64_t
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Use smaller dtypes in
MultiIndex._engineif possible.Reduce both loading time and memory (peak and used).
The improvement should be more relevant when working with big indices.
Below there are 2 examples.
doc/source/whatsnew/vX.X.X.rstfile if fixing a bug or adding a new feature.Improvement when
engine.valuesusesuint32instead ofuint64:idx._engine: from 8.53 s to 2.45 s_engine.values.nbytes: from 800000000 to 400000000_engine:Time, Pandas 2.2.2:
Time, PR branch:
Memory, Pandas 2.2.2:
Memory, PR branch:
Improvement when
engine.valuesstill needs to useuint64:idx._engine: from 17.6 s to 4.56 s_engine.values.nbytes: from 390625000 to 390625000 (unchanged since the dtype is the same)_engine:Time, Pandas 2.2.2:
Time, PR branch:
Memory, Pandas 2.2.2:
Memory, PR branch: