Remove deprecated verbose parameter from CSV builder
State: closed

Remove deprecated `verbose` parameter from CSV builder.

Note that the `verbose` parameter is deprecated since pandas 2.2.0. See:
- https://github.com/pandas-dev/pandas/pull/56556
- https://github.com/pandas-dev/pandas/pull/57450 

Fix #6671.

Comments:
The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6672). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

I am merging this PR (so that it is included in the next patch release) to remove the deprecation warning raised by the CSV builder from pandas 2.2.0.

<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) | 0.005374 / 0.011353 (-0.005979) | 0.003833 / 0.011008 (-0.007175) | 0.063465 / 0.038508 (0.024957) | 0.029564 / 0.023109 (0.006455) | 0.252759 / 0.275898 (-0.023139) | 0.274726 / 0.323480 (-0.048754) | 0.004014 / 0.007986 (-0.003971) | 0.002754 / 0.004328 (-0.001574) | 0.049351 / 0.004250 (0.045101) | 0.041858 / 0.037052 (0.004806) | 0.269023 / 0.258489 (0.010534) | 0.290670 / 0.293841 (-0.003171) | 0.028435 / 0.128546 (-0.100111) | 0.010988 / 0.075646 (-0.064658) | 0.207447 / 0.419271 (-0.211824) | 0.035945 / 0.043533 (-0.007588) | 0.257336 / 0.255139 (0.002197) | 0.267310 / 0.283200 (-0.015890) | 0.018575 / 0.141683 (-0.123108) | 1.144515 / 1.452155 (-0.307640) | 1.214614 / 1.492716 (-0.278102) |

### 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) | 0.103527 / 0.018006 (0.085521) | 0.310607 / 0.000490 (0.310117) | 0.000216 / 0.000200 (0.000016) | 0.000044 / 0.000054 (-0.000010) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.018597 / 0.037411 (-0.018814) | 0.063176 / 0.014526 (0.048650) | 0.073553 / 0.176557 (-0.103003) | 0.120648 / 0.737135 (-0.616487) | 0.075625 / 0.296338 (-0.220713) |

### 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) | 0.289148 / 0.215209 (0.073939) | 2.798351 / 2.077655 (0.720696) | 1.487909 / 1.504120 (-0.016211) | 1.369945 / 1.541195 (-0.171250) | 1.378889 / 1.468490 (-0.089602) | 0.569825 / 4.584777 (-4.014952) | 2.413309 / 3.745712 (-1.332403) | 2.795668 / 5.269862 (-2.474193) | 1.757748 / 4.565676 (-2.807929) | 0.064686 / 0.424275 (-0.359589) | 0.005027 / 0.007607 (-0.002580) | 0.341835 / 0.226044 (0.115791) | 3.349915 / 2.268929 (1.080987) | 1.864253 / 55.444624 (-53.580371) | 1.595788 / 6.876477 (-5.280688) | 1.666127 / 2.142072 (-0.475945) | 0.665239 / 4.805227 (-4.139989) | 0.120563 / 6.500664 (-6.380101) | 0.043649 / 0.075469 (-0.031820) |

### 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) | 0.988543 / 1.841788 (-0.853244) | 11.973275 / 8.074308 (3.898967) | 9.685401 / 10.191392 (-0.505991) | 0.141416 / 0.680424 (-0.539008) | 0.014328 / 0.534201 (-0.519873) | 0.287063 / 0.579283 (-0.292220) | 0.266284 / 0.434364 (-0.168080) | 0.324643 / 0.540337 (-0.215694) | 0.423845 / 1.386936 (-0.963091) |

</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) | 0.005430 / 0.011353 (-0.005923) | 0.003770 / 0.011008 (-0.007239) | 0.050879 / 0.038508 (0.012371) | 0.031929 / 0.023109 (0.008819) | 0.297739 / 0.275898 (0.021841) | 0.319380 / 0.323480 (-0.004100) | 0.004348 / 0.007986 (-0.003637) | 0.002783 / 0.004328 (-0.001545) | 0.050024 / 0.004250 (0.045774) | 0.045209 / 0.037052 (0.008157) | 0.307608 / 0.258489 (0.049119) | 0.338168 / 0.293841 (0.044327) | 0.051712 / 0.128546 (-0.076834) | 0.011092 / 0.075646 (-0.064554) | 0.059830 / 0.419271 (-0.359441) | 0.033894 / 0.043533 (-0.009638) | 0.295278 / 0.255139 (0.040139) | 0.310749 / 0.283200 (0.027550) | 0.018676 / 0.141683 (-0.123007) | 1.201086 / 1.452155 (-0.251069) | 1.258214 / 1.492716 (-0.234502) |

### 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) | 0.094079 / 0.018006 (0.076073) | 0.304657 / 0.000490 (0.304168) | 0.000225 / 0.000200 (0.000026) | 0.000057 / 0.000054 (0.000002) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.021969 / 0.037411 (-0.015442) | 0.075749 / 0.014526 (0.061223) | 0.087878 / 0.176557 (-0.088679) | 0.126022 / 0.737135 (-0.611114) | 0.089466 / 0.296338 (-0.206873) |

### 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) | 0.286415 / 0.215209 (0.071206) | 2.831867 / 2.077655 (0.754212) | 1.584119 / 1.504120 (0.079999) | 1.468454 / 1.541195 (-0.072740) | 1.495831 / 1.468490 (0.027341) | 0.579569 / 4.584777 (-4.005208) | 2.477248 / 3.745712 (-1.268464) | 2.830536 / 5.269862 (-2.439325) | 1.820188 / 4.565676 (-2.745488) | 0.064408 / 0.424275 (-0.359867) | 0.005156 / 0.007607 (-0.002451) | 0.342391 / 0.226044 (0.116347) | 3.424380 / 2.268929 (1.155452) | 1.993110 / 55.444624 (-53.451514) | 1.702971 / 6.876477 (-5.173506) | 1.844281 / 2.142072 (-0.297792) | 0.668208 / 4.805227 (-4.137020) | 0.120306 / 6.500664 (-6.380358) | 0.042127 / 0.075469 (-0.033342) |

### 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) | 1.019118 / 1.841788 (-0.822670) | 12.418330 / 8.074308 (4.344022) | 10.474226 / 10.191392 (0.282834) | 0.148510 / 0.680424 (-0.531914) | 0.015107 / 0.534201 (-0.519094) | 0.289488 / 0.579283 (-0.289795) | 0.278149 / 0.434364 (-0.156215) | 0.334655 / 0.540337 (-0.205682) | 0.419127 / 1.386936 (-0.967809) |

</details>
</details>

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