Bump pyarrow to 8.0.0
_The documentation is not available anymore as the PR was closed or merged._ <details>
<summary>Show benchmarks</summary>

PyArrow==6.0.0

<details>
<summary>Show updated benchmarks!</summary>

### 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) | 0.009873 / 0.011353 (-0.001480) | 0.005180 / 0.011008 (-0.005828) | 0.099587 / 0.038508 (0.061079) | 0.035674 / 0.023109 (0.012565) | 0.299156 / 0.275898 (0.023258) | 0.361253 / 0.323480 (0.037773) | 0.008159 / 0.007986 (0.000173) | 0.004245 / 0.004328 (-0.000084) | 0.076809 / 0.004250 (0.072559) | 0.045251 / 0.037052 (0.008199) | 0.306002 / 0.258489 (0.047513) | 0.345758 / 0.293841 (0.051917) | 0.037826 / 0.128546 (-0.090721) | 0.011887 / 0.075646 (-0.063759) | 0.333804 / 0.419271 (-0.085467) | 0.047859 / 0.043533 (0.004326) | 0.291866 / 0.255139 (0.036727) | 0.319356 / 0.283200 (0.036157) | 0.104241 / 0.141683 (-0.037442) | 1.443816 / 1.452155 (-0.008338) | 1.514654 / 1.492716 (0.021938) |

### 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) | 0.009846 / 0.018006 (-0.008160) | 0.439488 / 0.000490 (0.438999) | 0.003227 / 0.000200 (0.003028) | 0.000092 / 0.000054 (0.000037) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.027553 / 0.037411 (-0.009858) | 0.105337 / 0.014526 (0.090811) | 0.116203 / 0.176557 (-0.060354) | 0.161140 / 0.737135 (-0.575995) | 0.123002 / 0.296338 (-0.173336) |

### 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) | 0.400102 / 0.215209 (0.184893) | 3.976748 / 2.077655 (1.899094) | 1.794763 / 1.504120 (0.290643) | 1.602477 / 1.541195 (0.061282) | 1.703689 / 1.468490 (0.235199) | 0.696751 / 4.584777 (-3.888026) | 3.713832 / 3.745712 (-0.031880) | 2.124536 / 5.269862 (-3.145326) | 1.313005 / 4.565676 (-3.252671) | 0.086130 / 0.424275 (-0.338146) | 0.012085 / 0.007607 (0.004477) | 0.512976 / 0.226044 (0.286932) | 5.135313 / 2.268929 (2.866384) | 2.318173 / 55.444624 (-53.126451) | 1.996360 / 6.876477 (-4.880117) | 2.060150 / 2.142072 (-0.081922) | 0.853534 / 4.805227 (-3.951693) | 0.165586 / 6.500664 (-6.335078) | 0.062365 / 0.075469 (-0.013104) |

### 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) | 1.178843 / 1.841788 (-0.662945) | 14.541639 / 8.074308 (6.467331) | 14.090782 / 10.191392 (3.899390) | 0.158717 / 0.680424 (-0.521707) | 0.028825 / 0.534201 (-0.505376) | 0.441427 / 0.579283 (-0.137856) | 0.439856 / 0.434364 (0.005492) | 0.530610 / 0.540337 (-0.009727) | 0.634044 / 1.386936 (-0.752892) |

</details>
PyArrow==latest

<details>
<summary>Show updated benchmarks!</summary>

### 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) | 0.007502 / 0.011353 (-0.003851) | 0.005208 / 0.011008 (-0.005801) | 0.075020 / 0.038508 (0.036512) | 0.033297 / 0.023109 (0.010188) | 0.342218 / 0.275898 (0.066320) | 0.376716 / 0.323480 (0.053236) | 0.005906 / 0.007986 (-0.002080) | 0.005320 / 0.004328 (0.000992) | 0.073531 / 0.004250 (0.069281) | 0.049091 / 0.037052 (0.012039) | 0.344202 / 0.258489 (0.085713) | 0.380556 / 0.293841 (0.086715) | 0.037500 / 0.128546 (-0.091047) | 0.012404 / 0.075646 (-0.063242) | 0.087254 / 0.419271 (-0.332017) | 0.055145 / 0.043533 (0.011612) | 0.344112 / 0.255139 (0.088973) | 0.359052 / 0.283200 (0.075852) | 0.108337 / 0.141683 (-0.033345) | 1.450332 / 1.452155 (-0.001822) | 1.553607 / 1.492716 (0.060891) |

### 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) | 0.216335 / 0.018006 (0.198329) | 0.436813 / 0.000490 (0.436323) | 0.005055 / 0.000200 (0.004855) | 0.000088 / 0.000054 (0.000033) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.030037 / 0.037411 (-0.007374) | 0.110854 / 0.014526 (0.096329) | 0.121967 / 0.176557 (-0.054589) | 0.174029 / 0.737135 (-0.563107) | 0.128340 / 0.296338 (-0.167998) |

### 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) | 0.424463 / 0.215209 (0.209254) | 4.201822 / 2.077655 (2.124167) | 2.043075 / 1.504120 (0.538956) | 1.851841 / 1.541195 (0.310647) | 1.947790 / 1.468490 (0.479300) | 0.684110 / 4.584777 (-3.900667) | 3.763536 / 3.745712 (0.017824) | 3.106988 / 5.269862 (-2.162873) | 1.498305 / 4.565676 (-3.067372) | 0.085079 / 0.424275 (-0.339196) | 0.012241 / 0.007607 (0.004634) | 0.520877 / 0.226044 (0.294832) | 5.181455 / 2.268929 (2.912527) | 2.443038 / 55.444624 (-53.001586) | 2.130823 / 6.876477 (-4.745654) | 2.217901 / 2.142072 (0.075829) | 0.837116 / 4.805227 (-3.968111) | 0.166581 / 6.500664 (-6.334083) | 0.065510 / 0.075469 (-0.009959) |

### 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) | 1.289317 / 1.841788 (-0.552471) | 15.122019 / 8.074308 (7.047710) | 13.919670 / 10.191392 (3.728278) | 0.150047 / 0.680424 (-0.530377) | 0.017612 / 0.534201 (-0.516589) | 0.426239 / 0.579283 (-0.153044) | 0.425686 / 0.434364 (-0.008678) | 0.521436 / 0.540337 (-0.018901) | 0.618217 / 1.386936 (-0.768719) |

</details>
</details>

![](https://cml.dev/watermark.png#879fc6d5186ce593fe819f1e9e67897a1873766b "CML watermark")
 We haven't updated the minimal version requirement for PyArrow in a while, so it's ok to make a bigger leap IMO, e.g., PyArrow 8.0 (Colab installs 9.0). With this change, we should also remove the PyArrow version check in `folder_based_builder.py`, and the ones in `table.py`/`arrow_dataset.py` regarding the `to_reader` API if we decide to bump PyArrow to version 8.0. I think it's a good opportunity to bump the version to 8.0 which offers higher performance anyway, I wouldn't bother trying to support 6.0.1 anymore. Only 1% of users based on 6.0.1 use the latest `datasets` version 2.10.1

Bumping to 8.0 if it sounds good to you Sure, it is OK for those other reasons. I would just not stress that the increase of the minimum version is to support pandas 2.0 though... If requiring min 8.0, do you know the percentage of people using 7.0 and latest datasets version? Around 10% of users have 7.0.0, and 25% among them use the latest datasets version <details>
<summary>Show benchmarks</summary>

PyArrow==6.0.0

<details>
<summary>Show updated benchmarks!</summary>

### 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) | 0.006744 / 0.011353 (-0.004609) | 0.004585 / 0.011008 (-0.006423) | 0.097828 / 0.038508 (0.059320) | 0.028230 / 0.023109 (0.005121) | 0.302190 / 0.275898 (0.026292) | 0.335022 / 0.323480 (0.011542) | 0.005107 / 0.007986 (-0.002878) | 0.004648 / 0.004328 (0.000320) | 0.076842 / 0.004250 (0.072592) | 0.038291 / 0.037052 (0.001239) | 0.313286 / 0.258489 (0.054797) | 0.342534 / 0.293841 (0.048693) | 0.031325 / 0.128546 (-0.097221) | 0.011632 / 0.075646 (-0.064014) | 0.321879 / 0.419271 (-0.097392) | 0.042204 / 0.043533 (-0.001329) | 0.304442 / 0.255139 (0.049303) | 0.330912 / 0.283200 (0.047712) | 0.085446 / 0.141683 (-0.056237) | 1.469990 / 1.452155 (0.017835) | 1.551147 / 1.492716 (0.058431) |

### 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) | 0.185961 / 0.018006 (0.167955) | 0.404675 / 0.000490 (0.404186) | 0.003212 / 0.000200 (0.003012) | 0.000074 / 0.000054 (0.000019) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.023876 / 0.037411 (-0.013535) | 0.097820 / 0.014526 (0.083295) | 0.107382 / 0.176557 (-0.069174) | 0.167598 / 0.737135 (-0.569537) | 0.108789 / 0.296338 (-0.187550) |

### 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) | 0.455004 / 0.215209 (0.239795) | 4.529104 / 2.077655 (2.451449) | 2.180068 / 1.504120 (0.675948) | 1.982109 / 1.541195 (0.440914) | 2.041856 / 1.468490 (0.573366) | 0.702029 / 4.584777 (-3.882747) | 3.368613 / 3.745712 (-0.377099) | 1.932303 / 5.269862 (-3.337559) | 1.278340 / 4.565676 (-3.287336) | 0.082836 / 0.424275 (-0.341439) | 0.012349 / 0.007607 (0.004742) | 0.548197 / 0.226044 (0.322153) | 5.509982 / 2.268929 (3.241053) | 2.612889 / 55.444624 (-52.831736) | 2.278157 / 6.876477 (-4.598320) | 2.386923 / 2.142072 (0.244851) | 0.803332 / 4.805227 (-4.001896) | 0.151222 / 6.500664 (-6.349442) | 0.066673 / 0.075469 (-0.008796) |

### 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) | 1.209453 / 1.841788 (-0.632335) | 13.649733 / 8.074308 (5.575424) | 14.065917 / 10.191392 (3.874525) | 0.128872 / 0.680424 (-0.551551) | 0.016773 / 0.534201 (-0.517428) | 0.385475 / 0.579283 (-0.193809) | 0.386208 / 0.434364 (-0.048156) | 0.475144 / 0.540337 (-0.065194) | 0.564183 / 1.386936 (-0.822753) |

</details>
PyArrow==latest

<details>
<summary>Show updated benchmarks!</summary>

### 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) | 0.006629 / 0.011353 (-0.004724) | 0.004433 / 0.011008 (-0.006575) | 0.076008 / 0.038508 (0.037500) | 0.027471 / 0.023109 (0.004362) | 0.339837 / 0.275898 (0.063939) | 0.376857 / 0.323480 (0.053377) | 0.004930 / 0.007986 (-0.003055) | 0.003312 / 0.004328 (-0.001016) | 0.075070 / 0.004250 (0.070820) | 0.035897 / 0.037052 (-0.001156) | 0.342398 / 0.258489 (0.083909) | 0.380202 / 0.293841 (0.086361) | 0.031781 / 0.128546 (-0.096766) | 0.011697 / 0.075646 (-0.063950) | 0.085926 / 0.419271 (-0.333345) | 0.041599 / 0.043533 (-0.001934) | 0.343098 / 0.255139 (0.087959) | 0.371275 / 0.283200 (0.088076) | 0.090489 / 0.141683 (-0.051194) | 1.483738 / 1.452155 (0.031584) | 1.554973 / 1.492716 (0.062256) |

### 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) | 0.183703 / 0.018006 (0.165697) | 0.395105 / 0.000490 (0.394616) | 0.002162 / 0.000200 (0.001963) | 0.000074 / 0.000054 (0.000020) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.025432 / 0.037411 (-0.011979) | 0.101322 / 0.014526 (0.086796) | 0.107839 / 0.176557 (-0.068718) | 0.160328 / 0.737135 (-0.576807) | 0.109899 / 0.296338 (-0.186440) |

### 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) | 0.448001 / 0.215209 (0.232792) | 4.485321 / 2.077655 (2.407666) | 2.157064 / 1.504120 (0.652944) | 1.966141 / 1.541195 (0.424947) | 2.032808 / 1.468490 (0.564318) | 0.705684 / 4.584777 (-3.879093) | 3.359802 / 3.745712 (-0.385910) | 2.694952 / 5.269862 (-2.574910) | 1.471309 / 4.565676 (-3.094368) | 0.084185 / 0.424275 (-0.340090) | 0.012330 / 0.007607 (0.004723) | 0.554083 / 0.226044 (0.328038) | 5.569137 / 2.268929 (3.300208) | 2.586009 / 55.444624 (-52.858615) | 2.234920 / 6.876477 (-4.641557) | 2.285128 / 2.142072 (0.143056) | 0.818825 / 4.805227 (-3.986402) | 0.152604 / 6.500664 (-6.348060) | 0.067722 / 0.075469 (-0.007747) |

### 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) | 1.305571 / 1.841788 (-0.536217) | 13.687471 / 8.074308 (5.613163) | 13.305401 / 10.191392 (3.114009) | 0.140477 / 0.680424 (-0.539947) | 0.018138 / 0.534201 (-0.516063) | 0.377255 / 0.579283 (-0.202028) | 0.379522 / 0.434364 (-0.054842) | 0.458489 / 0.540337 (-0.081849) | 0.543767 / 1.386936 (-0.843169) |

</details>
</details>

![](https://cml.dev/watermark.png#02570894db6ecc46bf25b7fa1cb1bcdc1dede853 "CML watermark")
 <details>
<summary>Show benchmarks</summary>

PyArrow==8.0.0

<details>
<summary>Show updated benchmarks!</summary>

### 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) | 0.009606 / 0.011353 (-0.001747) | 0.006795 / 0.011008 (-0.004213) | 0.133738 / 0.038508 (0.095230) | 0.043379 / 0.023109 (0.020270) | 0.412917 / 0.275898 (0.137019) | 0.418790 / 0.323480 (0.095310) | 0.007290 / 0.007986 (-0.000696) | 0.004960 / 0.004328 (0.000632) | 0.095496 / 0.004250 (0.091246) | 0.057607 / 0.037052 (0.020555) | 0.402638 / 0.258489 (0.144149) | 0.436206 / 0.293841 (0.142365) | 0.056023 / 0.128546 (-0.072523) | 0.019909 / 0.075646 (-0.055737) | 0.463958 / 0.419271 (0.044687) | 0.064073 / 0.043533 (0.020541) | 0.398337 / 0.255139 (0.143198) | 0.421786 / 0.283200 (0.138586) | 0.131563 / 0.141683 (-0.010120) | 1.840217 / 1.452155 (0.388063) | 1.912013 / 1.492716 (0.419296) |

### 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) | 0.230519 / 0.018006 (0.212513) | 0.550506 / 0.000490 (0.550017) | 0.003649 / 0.000200 (0.003449) | 0.000107 / 0.000054 (0.000053) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.029713 / 0.037411 (-0.007698) | 0.129913 / 0.014526 (0.115387) | 0.131543 / 0.176557 (-0.045013) | 0.203571 / 0.737135 (-0.533565) | 0.141483 / 0.296338 (-0.154856) |

### 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) | 0.626383 / 0.215209 (0.411174) | 6.193043 / 2.077655 (4.115388) | 2.442728 / 1.504120 (0.938608) | 2.079049 / 1.541195 (0.537855) | 2.117761 / 1.468490 (0.649271) | 1.315296 / 4.584777 (-3.269481) | 5.643709 / 3.745712 (1.897997) | 5.245789 / 5.269862 (-0.024073) | 2.757442 / 4.565676 (-1.808235) | 0.151655 / 0.424275 (-0.272620) | 0.014686 / 0.007607 (0.007079) | 0.779937 / 0.226044 (0.553893) | 7.796685 / 2.268929 (5.527756) | 3.349580 / 55.444624 (-52.095045) | 2.493750 / 6.876477 (-4.382727) | 2.506200 / 2.142072 (0.364128) | 1.534964 / 4.805227 (-3.270263) | 0.260001 / 6.500664 (-6.240663) | 0.080543 / 0.075469 (0.005074) |

### 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) | 1.541940 / 1.841788 (-0.299848) | 17.851935 / 8.074308 (9.777627) | 22.418859 / 10.191392 (12.227467) | 0.258602 / 0.680424 (-0.421822) | 0.027679 / 0.534201 (-0.506522) | 0.548379 / 0.579283 (-0.030904) | 0.625505 / 0.434364 (0.191141) | 0.664074 / 0.540337 (0.123737) | 0.797418 / 1.386936 (-0.589518) |

</details>
PyArrow==latest

<details>
<summary>Show updated benchmarks!</summary>

### 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) | 0.009800 / 0.011353 (-0.001553) | 0.006178 / 0.011008 (-0.004830) | 0.105667 / 0.038508 (0.067159) | 0.039380 / 0.023109 (0.016271) | 0.419528 / 0.275898 (0.143630) | 0.469857 / 0.323480 (0.146377) | 0.006672 / 0.007986 (-0.001314) | 0.004745 / 0.004328 (0.000417) | 0.101647 / 0.004250 (0.097397) | 0.048531 / 0.037052 (0.011478) | 0.433364 / 0.258489 (0.174875) | 0.459719 / 0.293841 (0.165878) | 0.054291 / 0.128546 (-0.074256) | 0.020406 / 0.075646 (-0.055240) | 0.122321 / 0.419271 (-0.296951) | 0.059719 / 0.043533 (0.016186) | 0.416083 / 0.255139 (0.160944) | 0.455277 / 0.283200 (0.172077) | 0.119342 / 0.141683 (-0.022341) | 1.862544 / 1.452155 (0.410390) | 2.001428 / 1.492716 (0.508712) |

### 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) | 0.240951 / 0.018006 (0.222945) | 0.516958 / 0.000490 (0.516468) | 0.000449 / 0.000200 (0.000249) | 0.000092 / 0.000054 (0.000037) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.032725 / 0.037411 (-0.004686) | 0.130291 / 0.014526 (0.115765) | 0.139834 / 0.176557 (-0.036723) | 0.214995 / 0.737135 (-0.522140) | 0.150925 / 0.296338 (-0.145414) |

### 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) | 0.652062 / 0.215209 (0.436853) | 6.584447 / 2.077655 (4.506793) | 2.654838 / 1.504120 (1.150718) | 2.297209 / 1.541195 (0.756015) | 2.420394 / 1.468490 (0.951904) | 1.299285 / 4.584777 (-3.285492) | 5.605849 / 3.745712 (1.860137) | 3.166103 / 5.269862 (-2.103759) | 2.138123 / 4.565676 (-2.427554) | 0.152562 / 0.424275 (-0.271713) | 0.015499 / 0.007607 (0.007892) | 0.816300 / 0.226044 (0.590256) | 8.308746 / 2.268929 (6.039817) | 3.482982 / 55.444624 (-51.961642) | 2.689247 / 6.876477 (-4.187229) | 2.792728 / 2.142072 (0.650656) | 1.566320 / 4.805227 (-3.238907) | 0.264110 / 6.500664 (-6.236554) | 0.083652 / 0.075469 (0.008183) |

### 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) | 1.643027 / 1.841788 (-0.198760) | 18.612349 / 8.074308 (10.538041) | 19.460644 / 10.191392 (9.269252) | 0.260795 / 0.680424 (-0.419629) | 0.026050 / 0.534201 (-0.508151) | 0.539750 / 0.579283 (-0.039533) | 0.620791 / 0.434364 (0.186428) | 0.645023 / 0.540337 (0.104686) | 0.765604 / 1.386936 (-0.621332) |

</details>
</details>

![](https://cml.dev/watermark.png#e6dcf4c50e14ee6dbc6d763ed1b7ce3501460863 "CML watermark")
 ready for re-review :) <details>
<summary>Show benchmarks</summary>

PyArrow==8.0.0

<details>
<summary>Show updated benchmarks!</summary>

### 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) | 0.006388 / 0.011353 (-0.004965) | 0.004469 / 0.011008 (-0.006540) | 0.097082 / 0.038508 (0.058573) | 0.028005 / 0.023109 (0.004895) | 0.364797 / 0.275898 (0.088899) | 0.399671 / 0.323480 (0.076191) | 0.005062 / 0.007986 (-0.002923) | 0.004580 / 0.004328 (0.000252) | 0.075670 / 0.004250 (0.071420) | 0.038328 / 0.037052 (0.001276) | 0.365948 / 0.258489 (0.107459) | 0.402631 / 0.293841 (0.108790) | 0.031378 / 0.128546 (-0.097168) | 0.011443 / 0.075646 (-0.064203) | 0.321590 / 0.419271 (-0.097682) | 0.042263 / 0.043533 (-0.001270) | 0.368238 / 0.255139 (0.113099) | 0.389928 / 0.283200 (0.106728) | 0.085203 / 0.141683 (-0.056480) | 1.462820 / 1.452155 (0.010665) | 1.529207 / 1.492716 (0.036490) |

### 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) | 0.197194 / 0.018006 (0.179188) | 0.410897 / 0.000490 (0.410407) | 0.003394 / 0.000200 (0.003194) | 0.000075 / 0.000054 (0.000021) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.022911 / 0.037411 (-0.014500) | 0.097012 / 0.014526 (0.082486) | 0.102247 / 0.176557 (-0.074309) | 0.163363 / 0.737135 (-0.573772) | 0.106897 / 0.296338 (-0.189441) |

### 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) | 0.416303 / 0.215209 (0.201094) | 4.159325 / 2.077655 (2.081671) | 1.844893 / 1.504120 (0.340773) | 1.646131 / 1.541195 (0.104936) | 1.706763 / 1.468490 (0.238273) | 0.699607 / 4.584777 (-3.885170) | 3.462048 / 3.745712 (-0.283664) | 1.939076 / 5.269862 (-3.330786) | 1.324744 / 4.565676 (-3.240932) | 0.082949 / 0.424275 (-0.341326) | 0.012327 / 0.007607 (0.004720) | 0.513812 / 0.226044 (0.287768) | 5.171021 / 2.268929 (2.902093) | 2.288039 / 55.444624 (-53.156585) | 1.957403 / 6.876477 (-4.919074) | 1.990060 / 2.142072 (-0.152013) | 0.805571 / 4.805227 (-3.999656) | 0.152641 / 6.500664 (-6.348023) | 0.068169 / 0.075469 (-0.007300) |

### 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) | 1.200624 / 1.841788 (-0.641164) | 13.836334 / 8.074308 (5.762026) | 14.065340 / 10.191392 (3.873948) | 0.143406 / 0.680424 (-0.537018) | 0.016709 / 0.534201 (-0.517492) | 0.380080 / 0.579283 (-0.199204) | 0.398414 / 0.434364 (-0.035950) | 0.479192 / 0.540337 (-0.061145) | 0.572508 / 1.386936 (-0.814428) |

</details>
PyArrow==latest

<details>
<summary>Show updated benchmarks!</summary>

### 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) | 0.006622 / 0.011353 (-0.004731) | 0.004511 / 0.011008 (-0.006497) | 0.076454 / 0.038508 (0.037946) | 0.027431 / 0.023109 (0.004322) | 0.339041 / 0.275898 (0.063143) | 0.375691 / 0.323480 (0.052211) | 0.004854 / 0.007986 (-0.003131) | 0.004654 / 0.004328 (0.000325) | 0.075300 / 0.004250 (0.071049) | 0.036469 / 0.037052 (-0.000583) | 0.341357 / 0.258489 (0.082868) | 0.381561 / 0.293841 (0.087720) | 0.031754 / 0.128546 (-0.096792) | 0.011544 / 0.075646 (-0.064102) | 0.085956 / 0.419271 (-0.333315) | 0.041704 / 0.043533 (-0.001828) | 0.340088 / 0.255139 (0.084950) | 0.364037 / 0.283200 (0.080838) | 0.091016 / 0.141683 (-0.050667) | 1.483515 / 1.452155 (0.031360) | 1.562878 / 1.492716 (0.070162) |

### 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) | 0.228019 / 0.018006 (0.210013) | 0.404809 / 0.000490 (0.404320) | 0.000384 / 0.000200 (0.000184) | 0.000060 / 0.000054 (0.000006) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.025230 / 0.037411 (-0.012181) | 0.099790 / 0.014526 (0.085264) | 0.107923 / 0.176557 (-0.068634) | 0.157651 / 0.737135 (-0.579484) | 0.112525 / 0.296338 (-0.183813) |

### 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) | 0.440360 / 0.215209 (0.225151) | 4.387749 / 2.077655 (2.310094) | 2.077592 / 1.504120 (0.573472) | 1.872532 / 1.541195 (0.331337) | 1.941607 / 1.468490 (0.473117) | 0.699394 / 4.584777 (-3.885383) | 3.411210 / 3.745712 (-0.334502) | 1.901816 / 5.269862 (-3.368046) | 1.177042 / 4.565676 (-3.388634) | 0.083536 / 0.424275 (-0.340739) | 0.012418 / 0.007607 (0.004811) | 0.548463 / 0.226044 (0.322419) | 5.487107 / 2.268929 (3.218178) | 2.548076 / 55.444624 (-52.896548) | 2.215012 / 6.876477 (-4.661465) | 2.253472 / 2.142072 (0.111400) | 0.812925 / 4.805227 (-3.992302) | 0.152935 / 6.500664 (-6.347729) | 0.068144 / 0.075469 (-0.007325) |

### 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) | 1.267914 / 1.841788 (-0.573873) | 14.015185 / 8.074308 (5.940877) | 13.153967 / 10.191392 (2.962575) | 0.140666 / 0.680424 (-0.539758) | 0.016718 / 0.534201 (-0.517483) | 0.383411 / 0.579283 (-0.195872) | 0.395424 / 0.434364 (-0.038940) | 0.466069 / 0.540337 (-0.074269) | 0.553825 / 1.386936 (-0.833111) |

</details>
</details>

![](https://cml.dev/watermark.png#14568bf072b38e3b295f29774c874c8e78b9fe37 "CML watermark")
 <details>
<summary>Show benchmarks</summary>

PyArrow==8.0.0

<details>
<summary>Show updated benchmarks!</summary>

### 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) | 0.007463 / 0.011353 (-0.003890) | 0.005017 / 0.011008 (-0.005991) | 0.098777 / 0.038508 (0.060269) | 0.033859 / 0.023109 (0.010750) | 0.298569 / 0.275898 (0.022670) | 0.343717 / 0.323480 (0.020237) | 0.005806 / 0.007986 (-0.002180) | 0.005403 / 0.004328 (0.001074) | 0.075840 / 0.004250 (0.071590) | 0.046539 / 0.037052 (0.009487) | 0.300058 / 0.258489 (0.041569) | 0.345036 / 0.293841 (0.051195) | 0.036258 / 0.128546 (-0.092288) | 0.011992 / 0.075646 (-0.063654) | 0.334986 / 0.419271 (-0.084286) | 0.050427 / 0.043533 (0.006894) | 0.295319 / 0.255139 (0.040180) | 0.318980 / 0.283200 (0.035780) | 0.098407 / 0.141683 (-0.043276) | 1.437626 / 1.452155 (-0.014529) | 1.562548 / 1.492716 (0.069832) |

### 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) | 0.231502 / 0.018006 (0.213496) | 0.441550 / 0.000490 (0.441060) | 0.005863 / 0.000200 (0.005663) | 0.000724 / 0.000054 (0.000670) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.027501 / 0.037411 (-0.009911) | 0.111490 / 0.014526 (0.096964) | 0.117503 / 0.176557 (-0.059054) | 0.173849 / 0.737135 (-0.563286) | 0.124521 / 0.296338 (-0.171818) |

### 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) | 0.419266 / 0.215209 (0.204057) | 4.170337 / 2.077655 (2.092683) | 2.015883 / 1.504120 (0.511763) | 1.832683 / 1.541195 (0.291488) | 1.950195 / 1.468490 (0.481705) | 0.698150 / 4.584777 (-3.886627) | 3.775601 / 3.745712 (0.029889) | 2.094581 / 5.269862 (-3.175281) | 1.325437 / 4.565676 (-3.240240) | 0.085382 / 0.424275 (-0.338894) | 0.012151 / 0.007607 (0.004544) | 0.526441 / 0.226044 (0.300397) | 5.256124 / 2.268929 (2.987196) | 2.488408 / 55.444624 (-52.956216) | 2.157228 / 6.876477 (-4.719249) | 2.228991 / 2.142072 (0.086919) | 0.837002 / 4.805227 (-3.968225) | 0.167520 / 6.500664 (-6.333144) | 0.066435 / 0.075469 (-0.009035) |

### 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) | 1.174544 / 1.841788 (-0.667243) | 14.684207 / 8.074308 (6.609899) | 14.494676 / 10.191392 (4.303284) | 0.143423 / 0.680424 (-0.537001) | 0.017289 / 0.534201 (-0.516912) | 0.424727 / 0.579283 (-0.154556) | 0.417077 / 0.434364 (-0.017287) | 0.498955 / 0.540337 (-0.041383) | 0.584838 / 1.386936 (-0.802098) |

</details>
PyArrow==latest

<details>
<summary>Show updated benchmarks!</summary>

### 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) | 0.007666 / 0.011353 (-0.003687) | 0.005269 / 0.011008 (-0.005739) | 0.073548 / 0.038508 (0.035040) | 0.033683 / 0.023109 (0.010573) | 0.342646 / 0.275898 (0.066747) | 0.380948 / 0.323480 (0.057468) | 0.005737 / 0.007986 (-0.002248) | 0.005366 / 0.004328 (0.001038) | 0.073228 / 0.004250 (0.068978) | 0.050065 / 0.037052 (0.013013) | 0.348593 / 0.258489 (0.090104) | 0.393930 / 0.293841 (0.100089) | 0.037411 / 0.128546 (-0.091135) | 0.012476 / 0.075646 (-0.063170) | 0.084884 / 0.419271 (-0.334387) | 0.049368 / 0.043533 (0.005835) | 0.343142 / 0.255139 (0.088003) | 0.362828 / 0.283200 (0.079628) | 0.102962 / 0.141683 (-0.038721) | 1.505703 / 1.452155 (0.053549) | 1.580695 / 1.492716 (0.087979) |

### 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) | 0.207621 / 0.018006 (0.189615) | 0.437678 / 0.000490 (0.437188) | 0.003931 / 0.000200 (0.003731) | 0.000093 / 0.000054 (0.000038) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.029079 / 0.037411 (-0.008332) | 0.108600 / 0.014526 (0.094074) | 0.124787 / 0.176557 (-0.051770) | 0.173354 / 0.737135 (-0.563781) | 0.126124 / 0.296338 (-0.170214) |

### 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) | 0.427911 / 0.215209 (0.212702) | 4.254227 / 2.077655 (2.176572) | 2.052142 / 1.504120 (0.548022) | 1.857042 / 1.541195 (0.315848) | 1.965244 / 1.468490 (0.496754) | 0.707994 / 4.584777 (-3.876783) | 3.807593 / 3.745712 (0.061880) | 3.387588 / 5.269862 (-1.882274) | 1.844853 / 4.565676 (-2.720824) | 0.088548 / 0.424275 (-0.335727) | 0.012398 / 0.007607 (0.004791) | 0.565896 / 0.226044 (0.339851) | 5.228024 / 2.268929 (2.959095) | 2.467220 / 55.444624 (-52.977405) | 2.144413 / 6.876477 (-4.732064) | 2.214049 / 2.142072 (0.071977) | 0.869381 / 4.805227 (-3.935846) | 0.170991 / 6.500664 (-6.329673) | 0.064932 / 0.075469 (-0.010537) |

### 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) | 1.246661 / 1.841788 (-0.595127) | 14.902743 / 8.074308 (6.828435) | 13.264294 / 10.191392 (3.072902) | 0.165328 / 0.680424 (-0.515095) | 0.017567 / 0.534201 (-0.516634) | 0.425491 / 0.579283 (-0.153792) | 0.427327 / 0.434364 (-0.007037) | 0.526475 / 0.540337 (-0.013862) | 0.627309 / 1.386936 (-0.759627) |

</details>
</details>

![](https://cml.dev/watermark.png#dd31bce76b554447bccb2b1447440e1f8ddba035 "CML watermark")

Fix those for Pandas 2.0 (tested [here](https://github.com/huggingface/datasets/actions/runs/4346221280/jobs/7592010397) with pandas==2.0.0.rc0):

```python
=========================== short test summary info ============================
FAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_to_parquet_in_memory - ImportError: Unable to find a usable engine; tried using: 'pyarrow', 'fastparquet'.
A suitable version of pyarrow or fastparquet is required for parquet support.
Trying to import the above resulted in these errors:
 - Pandas requires version '7.0.0' or newer of 'pyarrow' (version '6.0.1' currently installed).
 - Missing optional dependency 'fastparquet'. fastparquet is required for parquet support. Use pip or conda to install fastparquet.
FAILED tests/test_arrow_dataset.py::BaseDatasetTest::test_to_parquet_on_disk - ImportError: Unable to find a usable engine; tried using: 'pyarrow', 'fastparquet'.
A suitable version of pyarrow or fastparquet is required for parquet support.
Trying to import the above resulted in these errors:
 - Pandas requires version '7.0.0' or newer of 'pyarrow' (version '6.0.1' currently installed).
 - Missing optional dependency 'fastparquet'. fastparquet is required for parquet support. Use pip or conda to install fastparquet.
===== 2 failed, 2137 passed, 18 skipped, 32 warnings in 212.76s (0:03:32) ======
```

EDIT: also for performance - with 8.0 we can use `.to_reader()`