remove_columns/rename_columns doc fixes

[mariosasko]

Use more consistent wording in remove_columns to explain why it's faster than map and update remove_columns/rename_columns docstrings to fix in-place calls.

Reported in #6700

[HuggingFaceDocBuilderDev]

The docs for this PR live here. All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

[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)	0.005728 / 0.011353 (-0.005624)	0.003809 / 0.011008 (-0.007199)	0.062930 / 0.038508 (0.024422)	0.032320 / 0.023109 (0.009211)	0.251072 / 0.275898 (-0.024826)	0.275397 / 0.323480 (-0.048083)	0.003314 / 0.007986 (-0.004671)	0.002869 / 0.004328 (-0.001460)	0.049070 / 0.004250 (0.044819)	0.049282 / 0.037052 (0.012229)	0.263546 / 0.258489 (0.005057)	0.291471 / 0.293841 (-0.002370)	0.028462 / 0.128546 (-0.100084)	0.010528 / 0.075646 (-0.065119)	0.211249 / 0.419271 (-0.208023)	0.036840 / 0.043533 (-0.006693)	0.250038 / 0.255139 (-0.005101)	0.268883 / 0.283200 (-0.014317)	0.021417 / 0.141683 (-0.120266)	1.139754 / 1.452155 (-0.312400)	1.197319 / 1.492716 (-0.295397)

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)	0.094191 / 0.018006 (0.076185)	0.302413 / 0.000490 (0.301923)	0.000220 / 0.000200 (0.000020)	0.000048 / 0.000054 (-0.000006)

Benchmark: benchmark_indices_mapping.json

metric	select	shard	shuffle	sort	train_test_split
new / old (diff)	0.018490 / 0.037411 (-0.018922)	0.063361 / 0.014526 (0.048835)	0.075854 / 0.176557 (-0.100702)	0.121499 / 0.737135 (-0.615637)	0.075982 / 0.296338 (-0.220356)

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)	0.286030 / 0.215209 (0.070821)	2.778487 / 2.077655 (0.700832)	1.440963 / 1.504120 (-0.063157)	1.326217 / 1.541195 (-0.214977)	1.359228 / 1.468490 (-0.109262)	0.566999 / 4.584777 (-4.017778)	2.453344 / 3.745712 (-1.292368)	2.841448 / 5.269862 (-2.428413)	1.825197 / 4.565676 (-2.740479)	0.062301 / 0.424275 (-0.361974)	0.004948 / 0.007607 (-0.002659)	0.334578 / 0.226044 (0.108534)	3.302327 / 2.268929 (1.033399)	1.799808 / 55.444624 (-53.644817)	1.529693 / 6.876477 (-5.346783)	1.564684 / 2.142072 (-0.577389)	0.632891 / 4.805227 (-4.172336)	0.116594 / 6.500664 (-6.384070)	0.042695 / 0.075469 (-0.032774)

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)	0.999994 / 1.841788 (-0.841794)	12.767365 / 8.074308 (4.693057)	10.550439 / 10.191392 (0.359047)	0.133437 / 0.680424 (-0.546986)	0.015252 / 0.534201 (-0.518949)	0.293285 / 0.579283 (-0.285998)	0.274773 / 0.434364 (-0.159590)	0.328718 / 0.540337 (-0.211619)	0.428021 / 1.386936 (-0.958915)

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)	0.005538 / 0.011353 (-0.005815)	0.003738 / 0.011008 (-0.007271)	0.050179 / 0.038508 (0.011671)	0.032441 / 0.023109 (0.009332)	0.294721 / 0.275898 (0.018823)	0.322616 / 0.323480 (-0.000864)	0.004255 / 0.007986 (-0.003731)	0.002913 / 0.004328 (-0.001416)	0.049044 / 0.004250 (0.044794)	0.042361 / 0.037052 (0.005309)	0.304162 / 0.258489 (0.045673)	0.332757 / 0.293841 (0.038916)	0.029355 / 0.128546 (-0.099191)	0.010546 / 0.075646 (-0.065100)	0.058213 / 0.419271 (-0.361058)	0.032648 / 0.043533 (-0.010885)	0.298241 / 0.255139 (0.043102)	0.313710 / 0.283200 (0.030510)	0.017836 / 0.141683 (-0.123847)	1.135050 / 1.452155 (-0.317104)	1.178277 / 1.492716 (-0.314439)

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)	0.094387 / 0.018006 (0.076381)	0.301955 / 0.000490 (0.301466)	0.000220 / 0.000200 (0.000020)	0.000052 / 0.000054 (-0.000002)

Benchmark: benchmark_indices_mapping.json

metric	select	shard	shuffle	sort	train_test_split
new / old (diff)	0.023135 / 0.037411 (-0.014276)	0.078109 / 0.014526 (0.063583)	0.087519 / 0.176557 (-0.089037)	0.127815 / 0.737135 (-0.609320)	0.090107 / 0.296338 (-0.206231)

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)	0.289149 / 0.215209 (0.073940)	2.832354 / 2.077655 (0.754699)	1.574003 / 1.504120 (0.069883)	1.449190 / 1.541195 (-0.092005)	1.465798 / 1.468490 (-0.002692)	0.561953 / 4.584777 (-4.022824)	2.445788 / 3.745712 (-1.299924)	2.882453 / 5.269862 (-2.387409)	1.813267 / 4.565676 (-2.752409)	0.063163 / 0.424275 (-0.361112)	0.005785 / 0.007607 (-0.001822)	0.340125 / 0.226044 (0.114081)	3.355370 / 2.268929 (1.086442)	1.924226 / 55.444624 (-53.520398)	1.643242 / 6.876477 (-5.233234)	1.650149 / 2.142072 (-0.491924)	0.654818 / 4.805227 (-4.150409)	0.114968 / 6.500664 (-6.385696)	0.042044 / 0.075469 (-0.033425)

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)	1.024867 / 1.841788 (-0.816921)	12.656140 / 8.074308 (4.581832)	10.927014 / 10.191392 (0.735622)	0.155929 / 0.680424 (-0.524495)	0.015356 / 0.534201 (-0.518845)	0.289834 / 0.579283 (-0.289449)	0.280889 / 0.434364 (-0.153475)	0.331490 / 0.540337 (-0.208847)	0.418037 / 1.386936 (-0.968899)