Create function to convert to parquet
Opened by albertvillanova (closed)

Analogously with `delete_from_hub`, this PR:
- creates the Python function `convert_to_parquet`
- makes the corresponding CLI command use that function.

This way, the functionality can be used both from a terminal and from a Python console.

This PR also implements a test for convert_to_parquet function.

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6878). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

<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) | 0.005519 / 0.011353 (-0.005834) | 0.003877 / 0.011008 (-0.007131) | 0.063989 / 0.038508 (0.025480) | 0.032348 / 0.023109 (0.009239) | 0.238288 / 0.275898 (-0.037611) | 0.265337 / 0.323480 (-0.058143) | 0.004363 / 0.007986 (-0.003623) | 0.002755 / 0.004328 (-0.001574) | 0.049836 / 0.004250 (0.045585) | 0.048456 / 0.037052 (0.011403) | 0.246526 / 0.258489 (-0.011963) | 0.280753 / 0.293841 (-0.013088) | 0.027721 / 0.128546 (-0.100825) | 0.011031 / 0.075646 (-0.064615) | 0.204168 / 0.419271 (-0.215104) | 0.036203 / 0.043533 (-0.007330) | 0.238282 / 0.255139 (-0.016857) | 0.259608 / 0.283200 (-0.023591) | 0.017781 / 0.141683 (-0.123902) | 1.147821 / 1.452155 (-0.304334) | 1.194855 / 1.492716 (-0.297861) |

### 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) | 0.102837 / 0.018006 (0.084831) | 0.312300 / 0.000490 (0.311811) | 0.000224 / 0.000200 (0.000024) | 0.000047 / 0.000054 (-0.000008) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.019410 / 0.037411 (-0.018001) | 0.065114 / 0.014526 (0.050588) | 0.076828 / 0.176557 (-0.099728) | 0.121741 / 0.737135 (-0.615394) | 0.079864 / 0.296338 (-0.216474) |

### 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) | 0.287773 / 0.215209 (0.072564) | 2.848936 / 2.077655 (0.771281) | 1.543819 / 1.504120 (0.039700) | 1.412708 / 1.541195 (-0.128487) | 1.454685 / 1.468490 (-0.013805) | 0.580155 / 4.584777 (-4.004622) | 2.372783 / 3.745712 (-1.372929) | 2.910514 / 5.269862 (-2.359347) | 1.813542 / 4.565676 (-2.752134) | 0.064569 / 0.424275 (-0.359706) | 0.005434 / 0.007607 (-0.002173) | 0.339309 / 0.226044 (0.113265) | 3.329972 / 2.268929 (1.061043) | 1.827597 / 55.444624 (-53.617028) | 1.592324 / 6.876477 (-5.284152) | 1.619743 / 2.142072 (-0.522329) | 0.659358 / 4.805227 (-4.145869) | 0.119887 / 6.500664 (-6.380777) | 0.043649 / 0.075469 (-0.031821) |

### 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) | 0.984563 / 1.841788 (-0.857225) | 12.395302 / 8.074308 (4.320994) | 9.904944 / 10.191392 (-0.286448) | 0.136141 / 0.680424 (-0.544282) | 0.014779 / 0.534201 (-0.519422) | 0.286146 / 0.579283 (-0.293137) | 0.265392 / 0.434364 (-0.168972) | 0.329484 / 0.540337 (-0.210854) | 0.425530 / 1.386936 (-0.961406) |

</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) | 0.005920 / 0.011353 (-0.005433) | 0.004068 / 0.011008 (-0.006940) | 0.052281 / 0.038508 (0.013773) | 0.034907 / 0.023109 (0.011798) | 0.269551 / 0.275898 (-0.006347) | 0.292390 / 0.323480 (-0.031090) | 0.004340 / 0.007986 (-0.003646) | 0.002864 / 0.004328 (-0.001464) | 0.051466 / 0.004250 (0.047216) | 0.046410 / 0.037052 (0.009358) | 0.280103 / 0.258489 (0.021614) | 0.310616 / 0.293841 (0.016775) | 0.031044 / 0.128546 (-0.097502) | 0.011004 / 0.075646 (-0.064643) | 0.059955 / 0.419271 (-0.359316) | 0.034156 / 0.043533 (-0.009377) | 0.268113 / 0.255139 (0.012974) | 0.283569 / 0.283200 (0.000369) | 0.019758 / 0.141683 (-0.121925) | 1.155583 / 1.452155 (-0.296572) | 1.225611 / 1.492716 (-0.267106) |

### 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) | 0.104302 / 0.018006 (0.086295) | 0.307324 / 0.000490 (0.306834) | 0.000219 / 0.000200 (0.000019) | 0.000045 / 0.000054 (-0.000009) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.023672 / 0.037411 (-0.013739) | 0.081110 / 0.014526 (0.066584) | 0.091783 / 0.176557 (-0.084773) | 0.131738 / 0.737135 (-0.605397) | 0.092391 / 0.296338 (-0.203948) |

### 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) | 0.289341 / 0.215209 (0.074132) | 2.849894 / 2.077655 (0.772239) | 1.539679 / 1.504120 (0.035559) | 1.417975 / 1.541195 (-0.123220) | 1.473631 / 1.468490 (0.005141) | 0.583013 / 4.584777 (-4.001764) | 0.960106 / 3.745712 (-2.785606) | 2.962785 / 5.269862 (-2.307077) | 1.827539 / 4.565676 (-2.738138) | 0.063875 / 0.424275 (-0.360400) | 0.005251 / 0.007607 (-0.002356) | 0.347127 / 0.226044 (0.121082) | 3.417364 / 2.268929 (1.148435) | 1.965901 / 55.444624 (-53.478723) | 1.632337 / 6.876477 (-5.244140) | 1.683100 / 2.142072 (-0.458972) | 0.664951 / 4.805227 (-4.140277) | 0.119046 / 6.500664 (-6.381618) | 0.042828 / 0.075469 (-0.032641) |

### 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) | 0.999569 / 1.841788 (-0.842218) | 13.366482 / 8.074308 (5.292174) | 10.635396 / 10.191392 (0.444004) | 0.133840 / 0.680424 (-0.546584) | 0.016232 / 0.534201 (-0.517969) | 0.292764 / 0.579283 (-0.286519) | 0.128558 / 0.434364 (-0.305806) | 0.405596 / 0.540337 (-0.134741) | 0.429633 / 1.386936 (-0.957303) |

</details>
</details>

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