Remove dead code for pyarrow < 15.0.0

[albertvillanova]

Remove dead code for pyarrow < 15.0.0.

Code is dead since the merge of:

• Add support for categorical/dictionary types #6892

Fix #7022.

[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)	0.005669 / 0.011353 (-0.005684)	0.004233 / 0.011008 (-0.006775)	0.063550 / 0.038508 (0.025041)	0.031269 / 0.023109 (0.008160)	0.234280 / 0.275898 (-0.041618)	0.264517 / 0.323480 (-0.058963)	0.003310 / 0.007986 (-0.004676)	0.003640 / 0.004328 (-0.000688)	0.050139 / 0.004250 (0.045889)	0.046909 / 0.037052 (0.009856)	0.253101 / 0.258489 (-0.005388)	0.280281 / 0.293841 (-0.013560)	0.029558 / 0.128546 (-0.098989)	0.012537 / 0.075646 (-0.063110)	0.209624 / 0.419271 (-0.209648)	0.036857 / 0.043533 (-0.006676)	0.236957 / 0.255139 (-0.018182)	0.260510 / 0.283200 (-0.022689)	0.019802 / 0.141683 (-0.121881)	1.141747 / 1.452155 (-0.310407)	1.172617 / 1.492716 (-0.320099)

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)	0.107381 / 0.018006 (0.089375)	0.308401 / 0.000490 (0.307911)	0.000227 / 0.000200 (0.000027)	0.000056 / 0.000054 (0.000001)

Benchmark: benchmark_indices_mapping.json

metric	select	shard	shuffle	sort	train_test_split
new / old (diff)	0.019504 / 0.037411 (-0.017907)	0.063920 / 0.014526 (0.049394)	0.075375 / 0.176557 (-0.101181)	0.122707 / 0.737135 (-0.614428)	0.080015 / 0.296338 (-0.216324)

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)	0.288716 / 0.215209 (0.073507)	2.862022 / 2.077655 (0.784368)	1.472510 / 1.504120 (-0.031610)	1.332989 / 1.541195 (-0.208206)	1.395140 / 1.468490 (-0.073350)	0.728042 / 4.584777 (-3.856735)	2.409914 / 3.745712 (-1.335799)	2.912514 / 5.269862 (-2.357347)	1.986980 / 4.565676 (-2.578697)	0.078587 / 0.424275 (-0.345688)	0.005601 / 0.007607 (-0.002006)	0.342510 / 0.226044 (0.116466)	3.354621 / 2.268929 (1.085692)	1.852472 / 55.444624 (-53.592153)	1.542567 / 6.876477 (-5.333910)	1.726756 / 2.142072 (-0.415317)	0.794567 / 4.805227 (-4.010660)	0.135279 / 6.500664 (-6.365386)	0.042591 / 0.075469 (-0.032878)

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)	0.968336 / 1.841788 (-0.873452)	12.334614 / 8.074308 (4.260305)	9.638775 / 10.191392 (-0.552617)	0.143625 / 0.680424 (-0.536799)	0.015475 / 0.534201 (-0.518726)	0.313357 / 0.579283 (-0.265926)	0.271257 / 0.434364 (-0.163107)	0.362074 / 0.540337 (-0.178263)	0.468595 / 1.386936 (-0.918341)

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)	0.006243 / 0.011353 (-0.005110)	0.004496 / 0.011008 (-0.006512)	0.051271 / 0.038508 (0.012763)	0.035718 / 0.023109 (0.012609)	0.272623 / 0.275898 (-0.003275)	0.297060 / 0.323480 (-0.026420)	0.004801 / 0.007986 (-0.003185)	0.003060 / 0.004328 (-0.001269)	0.049990 / 0.004250 (0.045740)	0.042413 / 0.037052 (0.005360)	0.281268 / 0.258489 (0.022779)	0.327224 / 0.293841 (0.033383)	0.033745 / 0.128546 (-0.094801)	0.012777 / 0.075646 (-0.062869)	0.061808 / 0.419271 (-0.357464)	0.034428 / 0.043533 (-0.009105)	0.272211 / 0.255139 (0.017072)	0.327260 / 0.283200 (0.044061)	0.019756 / 0.141683 (-0.121927)	1.137768 / 1.452155 (-0.314387)	1.220347 / 1.492716 (-0.272369)

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)	0.099737 / 0.018006 (0.081731)	0.304627 / 0.000490 (0.304137)	0.000210 / 0.000200 (0.000011)	0.000052 / 0.000054 (-0.000002)

Benchmark: benchmark_indices_mapping.json

metric	select	shard	shuffle	sort	train_test_split
new / old (diff)	0.023177 / 0.037411 (-0.014234)	0.077505 / 0.014526 (0.062979)	0.088957 / 0.176557 (-0.087599)	0.129187 / 0.737135 (-0.607948)	0.090386 / 0.296338 (-0.205953)

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)	0.291558 / 0.215209 (0.076349)	2.874297 / 2.077655 (0.796642)	1.562316 / 1.504120 (0.058196)	1.439950 / 1.541195 (-0.101244)	1.492316 / 1.468490 (0.023826)	0.729885 / 4.584777 (-3.854892)	0.985075 / 3.745712 (-2.760637)	3.108313 / 5.269862 (-2.161549)	1.998072 / 4.565676 (-2.567604)	0.079367 / 0.424275 (-0.344908)	0.005210 / 0.007607 (-0.002398)	0.347335 / 0.226044 (0.121290)	3.519375 / 2.268929 (1.250446)	1.949395 / 55.444624 (-53.495229)	1.650379 / 6.876477 (-5.226097)	1.691606 / 2.142072 (-0.450466)	0.816023 / 4.805227 (-3.989204)	0.135318 / 6.500664 (-6.365346)	0.041390 / 0.075469 (-0.034079)

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)	1.018964 / 1.841788 (-0.822823)	13.120135 / 8.074308 (5.045827)	10.618095 / 10.191392 (0.426703)	0.134507 / 0.680424 (-0.545917)	0.015895 / 0.534201 (-0.518306)	0.302864 / 0.579283 (-0.276420)	0.131117 / 0.434364 (-0.303247)	0.342374 / 0.540337 (-0.197964)	0.441640 / 1.386936 (-0.945296)