[Docs] Add description of select_columns to guide

[unifyh]

Closes #6116

[HuggingFaceDocBuilderDev]

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

[stevhliu]

Thanks for adding! 🤗

Pinging @mariosasko @lhoestq for a final look

[lhoestq]

LGTM thanks !

[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)	0.007755 / 0.011353 (-0.003598)	0.004618 / 0.011008 (-0.006391)	0.098132 / 0.038508 (0.059624)	0.086759 / 0.023109 (0.063650)	0.374668 / 0.275898 (0.098770)	0.417131 / 0.323480 (0.093651)	0.004604 / 0.007986 (-0.003382)	0.005461 / 0.004328 (0.001132)	0.077249 / 0.004250 (0.072999)	0.063247 / 0.037052 (0.026195)	0.391801 / 0.258489 (0.133312)	0.432139 / 0.293841 (0.138298)	0.036755 / 0.128546 (-0.091791)	0.010011 / 0.075646 (-0.065636)	0.346175 / 0.419271 (-0.073097)	0.061503 / 0.043533 (0.017971)	0.374063 / 0.255139 (0.118924)	0.435873 / 0.283200 (0.152673)	0.029476 / 0.141683 (-0.112207)	1.786945 / 1.452155 (0.334790)	1.857190 / 1.492716 (0.364474)

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)	0.253939 / 0.018006 (0.235933)	0.506847 / 0.000490 (0.506358)	0.007278 / 0.000200 (0.007079)	0.000451 / 0.000054 (0.000397)

Benchmark: benchmark_indices_mapping.json

metric	select	shard	shuffle	sort	train_test_split
new / old (diff)	0.032938 / 0.037411 (-0.004474)	0.097493 / 0.014526 (0.082967)	0.112090 / 0.176557 (-0.064467)	0.177986 / 0.737135 (-0.559149)	0.112060 / 0.296338 (-0.184278)

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)	0.481858 / 0.215209 (0.266649)	4.814894 / 2.077655 (2.737239)	2.496428 / 1.504120 (0.992308)	2.309965 / 1.541195 (0.768770)	2.393819 / 1.468490 (0.925329)	0.564670 / 4.584777 (-4.020107)	4.151222 / 3.745712 (0.405510)	3.676115 / 5.269862 (-1.593747)	2.346165 / 4.565676 (-2.219512)	0.066344 / 0.424275 (-0.357931)	0.009006 / 0.007607 (0.001399)	0.567699 / 0.226044 (0.341654)	5.686799 / 2.268929 (3.417871)	3.031044 / 55.444624 (-52.413580)	2.606259 / 6.876477 (-4.270217)	2.864876 / 2.142072 (0.722804)	0.681730 / 4.805227 (-4.123498)	0.155405 / 6.500664 (-6.345259)	0.071492 / 0.075469 (-0.003977)

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)	1.514446 / 1.841788 (-0.327341)	22.624912 / 8.074308 (14.550604)	16.754145 / 10.191392 (6.562753)	0.193113 / 0.680424 (-0.487311)	0.021808 / 0.534201 (-0.512393)	0.468241 / 0.579283 (-0.111042)	0.499647 / 0.434364 (0.065283)	0.539571 / 0.540337 (-0.000766)	0.771268 / 1.386936 (-0.615668)

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)	0.007562 / 0.011353 (-0.003791)	0.004548 / 0.011008 (-0.006460)	0.075998 / 0.038508 (0.037490)	0.081648 / 0.023109 (0.058539)	0.462876 / 0.275898 (0.186978)	0.499366 / 0.323480 (0.175886)	0.005839 / 0.007986 (-0.002147)	0.003753 / 0.004328 (-0.000576)	0.075918 / 0.004250 (0.071668)	0.063233 / 0.037052 (0.026181)	0.459024 / 0.258489 (0.200535)	0.506388 / 0.293841 (0.212547)	0.036179 / 0.128546 (-0.092367)	0.009961 / 0.075646 (-0.065685)	0.082061 / 0.419271 (-0.337211)	0.056469 / 0.043533 (0.012936)	0.459567 / 0.255139 (0.204428)	0.482578 / 0.283200 (0.199378)	0.026363 / 0.141683 (-0.115320)	1.742247 / 1.452155 (0.290092)	1.807166 / 1.492716 (0.314450)

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)	0.330526 / 0.018006 (0.312520)	0.511674 / 0.000490 (0.511184)	0.040969 / 0.000200 (0.040769)	0.000176 / 0.000054 (0.000121)

Benchmark: benchmark_indices_mapping.json

metric	select	shard	shuffle	sort	train_test_split
new / old (diff)	0.035492 / 0.037411 (-0.001920)	0.104338 / 0.014526 (0.089813)	0.116973 / 0.176557 (-0.059583)	0.180218 / 0.737135 (-0.556917)	0.118801 / 0.296338 (-0.177538)

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)	0.492196 / 0.215209 (0.276987)	4.910271 / 2.077655 (2.832616)	2.542562 / 1.504120 (1.038442)	2.333516 / 1.541195 (0.792321)	2.439682 / 1.468490 (0.971192)	0.571966 / 4.584777 (-4.012811)	4.089801 / 3.745712 (0.344089)	3.732129 / 5.269862 (-1.537733)	2.375887 / 4.565676 (-2.189789)	0.067376 / 0.424275 (-0.356900)	0.008350 / 0.007607 (0.000743)	0.583942 / 0.226044 (0.357897)	5.840002 / 2.268929 (3.571074)	3.062520 / 55.444624 (-52.382104)	2.722512 / 6.876477 (-4.153965)	2.938307 / 2.142072 (0.796234)	0.689459 / 4.805227 (-4.115769)	0.155632 / 6.500664 (-6.345032)	0.072387 / 0.075469 (-0.003082)

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)	1.595587 / 1.841788 (-0.246201)	23.035478 / 8.074308 (14.961170)	16.457675 / 10.191392 (6.266283)	0.170819 / 0.680424 (-0.509605)	0.022042 / 0.534201 (-0.512159)	0.466824 / 0.579283 (-0.112459)	0.486350 / 0.434364 (0.051986)	0.574330 / 0.540337 (0.033993)	0.764913 / 1.386936 (-0.622023)