Title: Copy features
State: closed

Some users (even internally at HF) are doing
```python
dset_features = dset.features
dset_features.pop(col_to_remove)
dset = dset.map(..., features=dset_features)
```

Right now this causes issues because it modifies the features dict in place before the map.

In this PR I modified `dset.features` to return a copy of the features, so that users can modify it if they want.

Comments:
_The documentation is not available anymore as the PR was closed or merged._

<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) | 0.007455 / 0.011353 (-0.003898) | 0.005278 / 0.011008 (-0.005731) | 0.098981 / 0.038508 (0.060473) | 0.029208 / 0.023109 (0.006099) | 0.304132 / 0.275898 (0.028234) | 0.340010 / 0.323480 (0.016530) | 0.005514 / 0.007986 (-0.002472) | 0.003636 / 0.004328 (-0.000692) | 0.076737 / 0.004250 (0.072486) | 0.041985 / 0.037052 (0.004933) | 0.314941 / 0.258489 (0.056452) | 0.346686 / 0.293841 (0.052845) | 0.032528 / 0.128546 (-0.096018) | 0.011795 / 0.075646 (-0.063851) | 0.322122 / 0.419271 (-0.097150) | 0.051548 / 0.043533 (0.008015) | 0.310561 / 0.255139 (0.055422) | 0.329443 / 0.283200 (0.046243) | 0.092820 / 0.141683 (-0.048863) | 1.495764 / 1.452155 (0.043609) | 1.586734 / 1.492716 (0.094018) |

### 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) | 0.195830 / 0.018006 (0.177824) | 0.422075 / 0.000490 (0.421586) | 0.005483 / 0.000200 (0.005283) | 0.000133 / 0.000054 (0.000078) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.023468 / 0.037411 (-0.013943) | 0.097713 / 0.014526 (0.083187) | 0.105331 / 0.176557 (-0.071225) | 0.166237 / 0.737135 (-0.570898) | 0.108924 / 0.296338 (-0.187415) |

### 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) | 0.671901 / 0.215209 (0.456692) | 6.745691 / 2.077655 (4.668036) | 2.132508 / 1.504120 (0.628388) | 1.693808 / 1.541195 (0.152614) | 1.715282 / 1.468490 (0.246792) | 0.955354 / 4.584777 (-3.629422) | 3.810296 / 3.745712 (0.064584) | 2.214891 / 5.269862 (-3.054970) | 1.461513 / 4.565676 (-3.104164) | 0.109846 / 0.424275 (-0.314430) | 0.013546 / 0.007607 (0.005939) | 0.780046 / 0.226044 (0.554001) | 7.789020 / 2.268929 (5.520091) | 2.602411 / 55.444624 (-52.842213) | 1.995096 / 6.876477 (-4.881380) | 2.009022 / 2.142072 (-0.133051) | 1.069215 / 4.805227 (-3.736012) | 0.179812 / 6.500664 (-6.320852) | 0.068125 / 0.075469 (-0.007344) |

### 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) | 1.201866 / 1.841788 (-0.639921) | 13.878814 / 8.074308 (5.804506) | 14.179264 / 10.191392 (3.987872) | 0.128908 / 0.680424 (-0.551515) | 0.017257 / 0.534201 (-0.516944) | 0.379500 / 0.579283 (-0.199783) | 0.393308 / 0.434364 (-0.041056) | 0.444700 / 0.540337 (-0.095638) | 0.531043 / 1.386936 (-0.855893) |

</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) | 0.007413 / 0.011353 (-0.003940) | 0.005431 / 0.011008 (-0.005577) | 0.078158 / 0.038508 (0.039650) | 0.028837 / 0.023109 (0.005728) | 0.343635 / 0.275898 (0.067737) | 0.383041 / 0.323480 (0.059561) | 0.005283 / 0.007986 (-0.002703) | 0.003673 / 0.004328 (-0.000655) | 0.076461 / 0.004250 (0.072211) | 0.038625 / 0.037052 (0.001573) | 0.341109 / 0.258489 (0.082620) | 0.387027 / 0.293841 (0.093186) | 0.032512 / 0.128546 (-0.096034) | 0.011903 / 0.075646 (-0.063744) | 0.086340 / 0.419271 (-0.332931) | 0.043211 / 0.043533 (-0.000321) | 0.339994 / 0.255139 (0.084855) | 0.370868 / 0.283200 (0.087668) | 0.091679 / 0.141683 (-0.050004) | 1.547188 / 1.452155 (0.095033) | 1.578545 / 1.492716 (0.085829) |

### 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) | 0.216981 / 0.018006 (0.198975) | 0.412206 / 0.000490 (0.411716) | 0.004243 / 0.000200 (0.004043) | 0.000130 / 0.000054 (0.000075) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.025392 / 0.037411 (-0.012020) | 0.102577 / 0.014526 (0.088051) | 0.107672 / 0.176557 (-0.068884) | 0.160657 / 0.737135 (-0.576478) | 0.111646 / 0.296338 (-0.184692) |

### 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) | 0.698815 / 0.215209 (0.483606) | 6.958931 / 2.077655 (4.881276) | 2.344216 / 1.504120 (0.840096) | 1.907752 / 1.541195 (0.366557) | 1.964251 / 1.468490 (0.495761) | 0.950754 / 4.584777 (-3.634023) | 3.829700 / 3.745712 (0.083988) | 3.055565 / 5.269862 (-2.214297) | 1.575851 / 4.565676 (-2.989825) | 0.109227 / 0.424275 (-0.315048) | 0.013163 / 0.007607 (0.005556) | 0.804613 / 0.226044 (0.578569) | 8.015035 / 2.268929 (5.746107) | 2.796358 / 55.444624 (-52.648266) | 2.212561 / 6.876477 (-4.663916) | 2.229918 / 2.142072 (0.087845) | 1.062041 / 4.805227 (-3.743186) | 0.181384 / 6.500664 (-6.319280) | 0.068564 / 0.075469 (-0.006905) |

### 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) | 1.287904 / 1.841788 (-0.553884) | 14.539222 / 8.074308 (6.464914) | 14.232097 / 10.191392 (4.040705) | 0.130870 / 0.680424 (-0.549554) | 0.016710 / 0.534201 (-0.517491) | 0.384454 / 0.579283 (-0.194829) | 0.391750 / 0.434364 (-0.042614) | 0.443995 / 0.540337 (-0.096343) | 0.526255 / 1.386936 (-0.860681) |

</details>
</details>

![](https://cml.dev/watermark.png#bd46874a580b888bdc82b53daace79884f04bc62 "CML watermark")


Arf I need to fix some tests first - sorry

<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) | 0.008393 / 0.011353 (-0.002959) | 0.005635 / 0.011008 (-0.005373) | 0.114840 / 0.038508 (0.076332) | 0.039272 / 0.023109 (0.016163) | 0.352116 / 0.275898 (0.076218) | 0.386614 / 0.323480 (0.063134) | 0.006348 / 0.007986 (-0.001638) | 0.005872 / 0.004328 (0.001544) | 0.086437 / 0.004250 (0.082187) | 0.054003 / 0.037052 (0.016951) | 0.350302 / 0.258489 (0.091813) | 0.400148 / 0.293841 (0.106308) | 0.042436 / 0.128546 (-0.086111) | 0.013987 / 0.075646 (-0.061660) | 0.399434 / 0.419271 (-0.019837) | 0.059223 / 0.043533 (0.015690) | 0.354511 / 0.255139 (0.099372) | 0.377764 / 0.283200 (0.094564) | 0.112297 / 0.141683 (-0.029386) | 1.677483 / 1.452155 (0.225328) | 1.784942 / 1.492716 (0.292226) |

### 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) | 0.233334 / 0.018006 (0.215328) | 0.450575 / 0.000490 (0.450085) | 0.000376 / 0.000200 (0.000176) | 0.000068 / 0.000054 (0.000014) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.031995 / 0.037411 (-0.005416) | 0.126798 / 0.014526 (0.112272) | 0.138453 / 0.176557 (-0.038104) | 0.207360 / 0.737135 (-0.529775) | 0.147744 / 0.296338 (-0.148594) |

### 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) | 0.481496 / 0.215209 (0.266287) | 4.810495 / 2.077655 (2.732840) | 2.457917 / 1.504120 (0.953797) | 2.300073 / 1.541195 (0.758879) | 2.065595 / 1.468490 (0.597105) | 0.814589 / 4.584777 (-3.770188) | 4.566496 / 3.745712 (0.820784) | 2.386947 / 5.269862 (-2.882914) | 1.531639 / 4.565676 (-3.034037) | 0.099569 / 0.424275 (-0.324706) | 0.014971 / 0.007607 (0.007364) | 0.590359 / 0.226044 (0.364314) | 5.885250 / 2.268929 (3.616322) | 2.706799 / 55.444624 (-52.737826) | 2.324485 / 6.876477 (-4.551992) | 2.452751 / 2.142072 (0.310678) | 0.966955 / 4.805227 (-3.838272) | 0.198165 / 6.500664 (-6.302499) | 0.076877 / 0.075469 (0.001408) |

### 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) | 1.499085 / 1.841788 (-0.342702) | 17.705516 / 8.074308 (9.631208) | 16.481174 / 10.191392 (6.289782) | 0.191832 / 0.680424 (-0.488592) | 0.021417 / 0.534201 (-0.512784) | 0.519647 / 0.579283 (-0.059636) | 0.498432 / 0.434364 (0.064068) | 0.598206 / 0.540337 (0.057868) | 0.700990 / 1.386936 (-0.685946) |

</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) | 0.008746 / 0.011353 (-0.002607) | 0.006052 / 0.011008 (-0.004956) | 0.092938 / 0.038508 (0.054430) | 0.038932 / 0.023109 (0.015823) | 0.406919 / 0.275898 (0.131021) | 0.444325 / 0.323480 (0.120845) | 0.006735 / 0.007986 (-0.001251) | 0.005972 / 0.004328 (0.001643) | 0.088152 / 0.004250 (0.083902) | 0.051009 / 0.037052 (0.013957) | 0.407415 / 0.258489 (0.148926) | 0.481048 / 0.293841 (0.187207) | 0.043268 / 0.128546 (-0.085278) | 0.014574 / 0.075646 (-0.061072) | 0.103555 / 0.419271 (-0.315716) | 0.058251 / 0.043533 (0.014719) | 0.406294 / 0.255139 (0.151155) | 0.429229 / 0.283200 (0.146029) | 0.116977 / 0.141683 (-0.024705) | 1.765885 / 1.452155 (0.313730) | 1.885557 / 1.492716 (0.392841) |

### 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) | 0.284014 / 0.018006 (0.266008) | 0.458066 / 0.000490 (0.457576) | 0.022286 / 0.000200 (0.022086) | 0.000158 / 0.000054 (0.000104) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.033971 / 0.037411 (-0.003440) | 0.132030 / 0.014526 (0.117504) | 0.141725 / 0.176557 (-0.034831) | 0.199818 / 0.737135 (-0.537318) | 0.149176 / 0.296338 (-0.147162) |

### 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) | 0.511463 / 0.215209 (0.296254) | 4.917921 / 2.077655 (2.840267) | 2.382377 / 1.504120 (0.878257) | 2.154599 / 1.541195 (0.613404) | 2.247858 / 1.468490 (0.779368) | 0.834524 / 4.584777 (-3.750253) | 4.560010 / 3.745712 (0.814297) | 2.403055 / 5.269862 (-2.866806) | 1.780784 / 4.565676 (-2.784893) | 0.101409 / 0.424275 (-0.322866) | 0.014657 / 0.007607 (0.007050) | 0.610137 / 0.226044 (0.384093) | 6.051011 / 2.268929 (3.782083) | 2.887357 / 55.444624 (-52.557267) | 2.518225 / 6.876477 (-4.358252) | 2.559654 / 2.142072 (0.417582) | 0.981226 / 4.805227 (-3.824001) | 0.197323 / 6.500664 (-6.303341) | 0.076851 / 0.075469 (0.001382) |

### 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) | 1.554662 / 1.841788 (-0.287126) | 18.038993 / 8.074308 (9.964685) | 16.719948 / 10.191392 (6.528556) | 0.195641 / 0.680424 (-0.484783) | 0.020699 / 0.534201 (-0.513502) | 0.498949 / 0.579283 (-0.080334) | 0.487775 / 0.434364 (0.053411) | 0.591413 / 0.540337 (0.051075) | 0.708520 / 1.386936 (-0.678416) |

</details>
</details>

![](https://cml.dev/watermark.png#39de0d78224c070be33d0820ec9203018fb721d1 "CML watermark")


Ready for review @mariosasko :)

Yea it does behave as expected, but modifying a dataset's features dict is not recommended and can lead to unpredictable behaviors. By copying the features, we make sure users don't modify the dataset's features dict.

Since the attribute is public, users expect to be able to do whatever they want with it, without checking if they have to copy it or not

<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) | 0.008069 / 0.011353 (-0.003284) | 0.005051 / 0.011008 (-0.005958) | 0.096587 / 0.038508 (0.058079) | 0.032954 / 0.023109 (0.009844) | 0.317877 / 0.275898 (0.041979) | 0.328677 / 0.323480 (0.005197) | 0.005524 / 0.007986 (-0.002462) | 0.003958 / 0.004328 (-0.000370) | 0.072692 / 0.004250 (0.068441) | 0.044554 / 0.037052 (0.007502) | 0.311121 / 0.258489 (0.052632) | 0.355912 / 0.293841 (0.062071) | 0.035934 / 0.128546 (-0.092612) | 0.012056 / 0.075646 (-0.063590) | 0.332575 / 0.419271 (-0.086696) | 0.049788 / 0.043533 (0.006255) | 0.307918 / 0.255139 (0.052779) | 0.326757 / 0.283200 (0.043557) | 0.098671 / 0.141683 (-0.043012) | 1.424625 / 1.452155 (-0.027530) | 1.507944 / 1.492716 (0.015228) |

### 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) | 0.207976 / 0.018006 (0.189970) | 0.439604 / 0.000490 (0.439114) | 0.000435 / 0.000200 (0.000235) | 0.000057 / 0.000054 (0.000003) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.026961 / 0.037411 (-0.010451) | 0.106627 / 0.014526 (0.092101) | 0.115292 / 0.176557 (-0.061264) | 0.171901 / 0.737135 (-0.565234) | 0.123276 / 0.296338 (-0.173062) |

### 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) | 0.407679 / 0.215209 (0.192469) | 4.071958 / 2.077655 (1.994303) | 1.854270 / 1.504120 (0.350151) | 1.678406 / 1.541195 (0.137211) | 1.715890 / 1.468490 (0.247400) | 0.705536 / 4.584777 (-3.879241) | 3.774198 / 3.745712 (0.028486) | 2.096429 / 5.269862 (-3.173432) | 1.431810 / 4.565676 (-3.133866) | 0.085557 / 0.424275 (-0.338718) | 0.012191 / 0.007607 (0.004584) | 0.502937 / 0.226044 (0.276893) | 5.034391 / 2.268929 (2.765463) | 2.393826 / 55.444624 (-53.050799) | 2.037383 / 6.876477 (-4.839094) | 2.192037 / 2.142072 (0.049964) | 0.829298 / 4.805227 (-3.975929) | 0.167781 / 6.500664 (-6.332883) | 0.063405 / 0.075469 (-0.012064) |

### 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) | 1.179189 / 1.841788 (-0.662599) | 14.464132 / 8.074308 (6.389824) | 14.869024 / 10.191392 (4.677632) | 0.172864 / 0.680424 (-0.507560) | 0.017817 / 0.534201 (-0.516384) | 0.427849 / 0.579283 (-0.151434) | 0.434447 / 0.434364 (0.000083) | 0.502077 / 0.540337 (-0.038260) | 0.599587 / 1.386936 (-0.787349) |

</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) | 0.007366 / 0.011353 (-0.003987) | 0.004939 / 0.011008 (-0.006069) | 0.074982 / 0.038508 (0.036474) | 0.032611 / 0.023109 (0.009501) | 0.340670 / 0.275898 (0.064772) | 0.372471 / 0.323480 (0.048991) | 0.005567 / 0.007986 (-0.002418) | 0.003956 / 0.004328 (-0.000372) | 0.074550 / 0.004250 (0.070300) | 0.047097 / 0.037052 (0.010045) | 0.337049 / 0.258489 (0.078560) | 0.391512 / 0.293841 (0.097671) | 0.035712 / 0.128546 (-0.092835) | 0.012040 / 0.075646 (-0.063606) | 0.087126 / 0.419271 (-0.332146) | 0.048290 / 0.043533 (0.004757) | 0.335069 / 0.255139 (0.079930) | 0.362080 / 0.283200 (0.078881) | 0.098606 / 0.141683 (-0.043077) | 1.456802 / 1.452155 (0.004647) | 1.554652 / 1.492716 (0.061936) |

### 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) | 0.200015 / 0.018006 (0.182009) | 0.442772 / 0.000490 (0.442283) | 0.004594 / 0.000200 (0.004394) | 0.000089 / 0.000054 (0.000035) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.028510 / 0.037411 (-0.008901) | 0.109654 / 0.014526 (0.095128) | 0.119921 / 0.176557 (-0.056636) | 0.170289 / 0.737135 (-0.566846) | 0.125288 / 0.296338 (-0.171051) |

### 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) | 0.430919 / 0.215209 (0.215710) | 4.274132 / 2.077655 (2.196478) | 2.014385 / 1.504120 (0.510265) | 1.822094 / 1.541195 (0.280899) | 1.938188 / 1.468490 (0.469698) | 0.707812 / 4.584777 (-3.876965) | 3.925730 / 3.745712 (0.180018) | 2.117481 / 5.269862 (-3.152381) | 1.369521 / 4.565676 (-3.196155) | 0.088414 / 0.424275 (-0.335861) | 0.013101 / 0.007607 (0.005494) | 0.538468 / 0.226044 (0.312424) | 5.384614 / 2.268929 (3.115685) | 2.487709 / 55.444624 (-52.956915) | 2.152060 / 6.876477 (-4.724417) | 2.225777 / 2.142072 (0.083705) | 0.856749 / 4.805227 (-3.948479) | 0.173299 / 6.500664 (-6.327366) | 0.068872 / 0.075469 (-0.006597) |

### 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) | 1.268009 / 1.841788 (-0.573778) | 15.102648 / 8.074308 (7.028340) | 14.216810 / 10.191392 (4.025418) | 0.163661 / 0.680424 (-0.516763) | 0.017394 / 0.534201 (-0.516807) | 0.418030 / 0.579283 (-0.161253) | 0.413717 / 0.434364 (-0.020647) | 0.487526 / 0.540337 (-0.052811) | 0.581499 / 1.386936 (-0.805437) |

</details>
</details>

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