Use more efficient and idiomatic way to construct list.

[ttsugriy]

Using * is ~2X faster according to benchmark with just 4 patterns. This doesn't matter much since this tiny difference is not going to be noticeable, but why not?

[lhoestq]

Thanks, as you said: why not :p

[HuggingFaceDocBuilderDev]

The documentation is not available anymore as the PR was closed or merged.

[github-actions]

Show benchmarks

PyArrow==8.0.0

Show updated benchmarks!

Benchmark: benchmark_array_xd.json

metric	read_batch_formatted_as_numpy after write_array2d	read_batch_formatted_as_numpy after write_flattened_sequence	read_batch_formatted_as_numpy after write_nested_sequence	read_batch_unformated after write_array2d	read_batch_unformated after write_flattened_sequence	read_batch_unformated after write_nested_sequence	read_col_formatted_as_numpy after write_array2d	read_col_formatted_as_numpy after write_flattened_sequence	read_col_formatted_as_numpy after write_nested_sequence	read_col_unformated after write_array2d	read_col_unformated after write_flattened_sequence	read_col_unformated after write_nested_sequence	read_formatted_as_numpy after write_array2d	read_formatted_as_numpy after write_flattened_sequence	read_formatted_as_numpy after write_nested_sequence	read_unformated after write_array2d	read_unformated after write_flattened_sequence	read_unformated after write_nested_sequence	write_array2d	write_flattened_sequence	write_nested_sequence
new / old (diff)	0.008156 / 0.011353 (-0.003197)	0.005563 / 0.011008 (-0.005445)	0.118319 / 0.038508 (0.079810)	0.044305 / 0.023109 (0.021195)	0.366221 / 0.275898 (0.090323)	0.407585 / 0.323480 (0.084105)	0.006961 / 0.007986 (-0.001024)	0.004841 / 0.004328 (0.000513)	0.089949 / 0.004250 (0.085698)	0.062197 / 0.037052 (0.025144)	0.360721 / 0.258489 (0.102232)	0.415332 / 0.293841 (0.121491)	0.035709 / 0.128546 (-0.092837)	0.010617 / 0.075646 (-0.065030)	0.397454 / 0.419271 (-0.021817)	0.063490 / 0.043533 (0.019958)	0.374289 / 0.255139 (0.119150)	0.382827 / 0.283200 (0.099628)	0.121014 / 0.141683 (-0.020669)	1.729933 / 1.452155 (0.277779)	1.896222 / 1.492716 (0.403506)

Benchmark: benchmark_getitem_100B.json

metric	get_batch_of_1024_random_rows	get_batch_of_1024_rows	get_first_row	get_last_row
new / old (diff)	0.254030 / 0.018006 (0.236023)	0.491225 / 0.000490 (0.490736)	0.018933 / 0.000200 (0.018734)	0.000413 / 0.000054 (0.000358)

Benchmark: benchmark_indices_mapping.json

metric	select	shard	shuffle	sort	train_test_split
new / old (diff)	0.033085 / 0.037411 (-0.004327)	0.132837 / 0.014526 (0.118311)	0.143275 / 0.176557 (-0.033282)	0.215800 / 0.737135 (-0.521335)	0.149802 / 0.296338 (-0.146536)

Benchmark: benchmark_iterating.json

metric	read 5000	read 50000	read_batch 50000 10	read_batch 50000 100	read_batch 50000 1000	read_formatted numpy 5000	read_formatted pandas 5000	read_formatted tensorflow 5000	read_formatted torch 5000	read_formatted_batch numpy 5000 10	read_formatted_batch numpy 5000 1000	shuffled read 5000	shuffled read 50000	shuffled read_batch 50000 10	shuffled read_batch 50000 100	shuffled read_batch 50000 1000	shuffled read_formatted numpy 5000	shuffled read_formatted_batch numpy 5000 10	shuffled read_formatted_batch numpy 5000 1000
new / old (diff)	0.474688 / 0.215209 (0.259479)	4.743223 / 2.077655 (2.665569)	2.163107 / 1.504120 (0.658988)	1.946396 / 1.541195 (0.405201)	2.057538 / 1.468490 (0.589047)	0.618836 / 4.584777 (-3.965941)	4.605934 / 3.745712 (0.860222)	2.201537 / 5.269862 (-3.068324)	1.275758 / 4.565676 (-3.289919)	0.077782 / 0.424275 (-0.346493)	0.014830 / 0.007607 (0.007223)	0.593372 / 0.226044 (0.367328)	5.927000 / 2.268929 (3.658072)	2.687293 / 55.444624 (-52.757331)	2.301797 / 6.876477 (-4.574679)	2.489928 / 2.142072 (0.347856)	0.756779 / 4.805227 (-4.048449)	0.168065 / 6.500664 (-6.332600)	0.077276 / 0.075469 (0.001807)

Benchmark: benchmark_map_filter.json

metric	filter	map fast-tokenizer batched	map identity	map identity batched	map no-op batched	map no-op batched numpy	map no-op batched pandas	map no-op batched pytorch	map no-op batched tensorflow
new / old (diff)	1.608169 / 1.841788 (-0.233619)	19.048790 / 8.074308 (10.974482)	16.100228 / 10.191392 (5.908836)	0.215346 / 0.680424 (-0.465077)	0.022293 / 0.534201 (-0.511907)	0.535899 / 0.579283 (-0.043384)	0.533729 / 0.434364 (0.099365)	0.562697 / 0.540337 (0.022360)	0.764082 / 1.386936 (-0.622854)

PyArrow==latest

Show updated benchmarks!

Benchmark: benchmark_array_xd.json

metric	read_batch_formatted_as_numpy after write_array2d	read_batch_formatted_as_numpy after write_flattened_sequence	read_batch_formatted_as_numpy after write_nested_sequence	read_batch_unformated after write_array2d	read_batch_unformated after write_flattened_sequence	read_batch_unformated after write_nested_sequence	read_col_formatted_as_numpy after write_array2d	read_col_formatted_as_numpy after write_flattened_sequence	read_col_formatted_as_numpy after write_nested_sequence	read_col_unformated after write_array2d	read_col_unformated after write_flattened_sequence	read_col_unformated after write_nested_sequence	read_formatted_as_numpy after write_array2d	read_formatted_as_numpy after write_flattened_sequence	read_formatted_as_numpy after write_nested_sequence	read_unformated after write_array2d	read_unformated after write_flattened_sequence	read_unformated after write_nested_sequence	write_array2d	write_flattened_sequence	write_nested_sequence
new / old (diff)	0.010087 / 0.011353 (-0.001266)	0.005357 / 0.011008 (-0.005651)	0.092678 / 0.038508 (0.054170)	0.041207 / 0.023109 (0.018098)	0.437464 / 0.275898 (0.161566)	0.527867 / 0.323480 (0.204387)	0.006861 / 0.007986 (-0.001125)	0.006131 / 0.004328 (0.001802)	0.093741 / 0.004250 (0.089490)	0.064142 / 0.037052 (0.027090)	0.433577 / 0.258489 (0.175088)	0.537148 / 0.293841 (0.243307)	0.035339 / 0.128546 (-0.093207)	0.010432 / 0.075646 (-0.065214)	0.102838 / 0.419271 (-0.316434)	0.057905 / 0.043533 (0.014372)	0.437956 / 0.255139 (0.182817)	0.509562 / 0.283200 (0.226362)	0.120620 / 0.141683 (-0.021063)	1.798686 / 1.452155 (0.346531)	2.013290 / 1.492716 (0.520574)

Benchmark: benchmark_getitem_100B.json

metric	get_batch_of_1024_random_rows	get_batch_of_1024_rows	get_first_row	get_last_row
new / old (diff)	0.249067 / 0.018006 (0.231061)	0.462219 / 0.000490 (0.461729)	0.000476 / 0.000200 (0.000276)	0.000068 / 0.000054 (0.000013)

Benchmark: benchmark_indices_mapping.json

metric	select	shard	shuffle	sort	train_test_split
new / old (diff)	0.033988 / 0.037411 (-0.003424)	0.135863 / 0.014526 (0.121337)	0.144082 / 0.176557 (-0.032474)	0.201715 / 0.737135 (-0.535421)	0.152079 / 0.296338 (-0.144259)

Benchmark: benchmark_iterating.json

metric	read 5000	read 50000	read_batch 50000 10	read_batch 50000 100	read_batch 50000 1000	read_formatted numpy 5000	read_formatted pandas 5000	read_formatted tensorflow 5000	read_formatted torch 5000	read_formatted_batch numpy 5000 10	read_formatted_batch numpy 5000 1000	shuffled read 5000	shuffled read 50000	shuffled read_batch 50000 10	shuffled read_batch 50000 100	shuffled read_batch 50000 1000	shuffled read_formatted numpy 5000	shuffled read_formatted_batch numpy 5000 10	shuffled read_formatted_batch numpy 5000 1000
new / old (diff)	0.522820 / 0.215209 (0.307611)	5.216723 / 2.077655 (3.139068)	2.582355 / 1.504120 (1.078235)	2.352799 / 1.541195 (0.811604)	2.451943 / 1.468490 (0.983453)	0.620381 / 4.584777 (-3.964396)	4.537841 / 3.745712 (0.792129)	2.206431 / 5.269862 (-3.063431)	1.269865 / 4.565676 (-3.295811)	0.078744 / 0.424275 (-0.345531)	0.014375 / 0.007607 (0.006768)	0.648215 / 0.226044 (0.422171)	6.482809 / 2.268929 (4.213881)	3.210670 / 55.444624 (-52.233954)	2.847485 / 6.876477 (-4.028992)	2.820946 / 2.142072 (0.678873)	0.762711 / 4.805227 (-4.042516)	0.171235 / 6.500664 (-6.329429)	0.080230 / 0.075469 (0.004761)

Benchmark: benchmark_map_filter.json

metric	filter	map fast-tokenizer batched	map identity	map identity batched	map no-op batched	map no-op batched numpy	map no-op batched pandas	map no-op batched pytorch	map no-op batched tensorflow
new / old (diff)	1.646840 / 1.841788 (-0.194948)	19.400451 / 8.074308 (11.326142)	16.758845 / 10.191392 (6.567453)	0.171377 / 0.680424 (-0.509046)	0.020400 / 0.534201 (-0.513801)	0.467675 / 0.579283 (-0.111608)	0.529745 / 0.434364 (0.095381)	0.605989 / 0.540337 (0.065652)	0.694659 / 1.386936 (-0.692277)

[mariosasko]

It's faster because all the items are the same object, but this also means modifying one of them will alter each unless these items are immutable, and they are in this case (tuples). So we should be careful when using this idiom.