Fix incomplete docstring for `BuilderConfig`

Show benchmarks

PyArrow==8.0.0

Show updated benchmarks!

### Benchmark: benchmark_array_xd.json

| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.007658 / 0.011353 (-0.003695) | 0.005497 / 0.011008 (-0.005511) | 0.097142 / 0.038508 (0.058633) | 0.034602 / 0.023109 (0.011493) | 0.304191 / 0.275898 (0.028293) | 0.329103 / 0.323480 (0.005624) | 0.005936 / 0.007986 (-0.002049) | 0.004324 / 0.004328 (-0.000004) | 0.073387 / 0.004250 (0.069137) | 0.049657 / 0.037052 (0.012604) | 0.301352 / 0.258489 (0.042863) | 0.343095 / 0.293841 (0.049254) | 0.036767 / 0.128546 (-0.091779) | 0.012438 / 0.075646 (-0.063208) | 0.333804 / 0.419271 (-0.085468) | 0.064557 / 0.043533 (0.021024) | 0.302397 / 0.255139 (0.047258) | 0.319739 / 0.283200 (0.036540) | 0.119264 / 0.141683 (-0.022418) | 1.465309 / 1.452155 (0.013155) | 1.578194 / 1.492716 (0.085478) |

### 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) | 0.256552 / 0.018006 (0.238545) | 0.555344 / 0.000490 (0.554854) | 0.004845 / 0.000200 (0.004645) | 0.000082 / 0.000054 (0.000027) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.027215 / 0.037411 (-0.010197) | 0.107071 / 0.014526 (0.092545) | 0.116343 / 0.176557 (-0.060213) | 0.172646 / 0.737135 (-0.564490) | 0.123366 / 0.296338 (-0.172973) |

### 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) | 0.411421 / 0.215209 (0.196212) | 4.126028 / 2.077655 (2.048373) | 1.975826 / 1.504120 (0.471706) | 1.784404 / 1.541195 (0.243210) | 1.848697 / 1.468490 (0.380207) | 0.686400 / 4.584777 (-3.898377) | 3.677649 / 3.745712 (-0.068063) | 2.077787 / 5.269862 (-3.192075) | 1.310912 / 4.565676 (-3.254764) | 0.083980 / 0.424275 (-0.340295) | 0.012183 / 0.007607 (0.004575) | 0.506969 / 0.226044 (0.280924) | 5.094730 / 2.268929 (2.825802) | 2.419790 / 55.444624 (-53.024834) | 2.106592 / 6.876477 (-4.769884) | 2.244309 / 2.142072 (0.102237) | 0.814312 / 4.805227 (-3.990915) | 0.167872 / 6.500664 (-6.332792) | 0.065339 / 0.075469 (-0.010130) |

### 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) | 1.193314 / 1.841788 (-0.648474) | 14.980621 / 8.074308 (6.906313) | 14.352452 / 10.191392 (4.161060) | 0.164531 / 0.680424 (-0.515893) | 0.017432 / 0.534201 (-0.516769) | 0.422193 / 0.579283 (-0.157090) | 0.410047 / 0.434364 (-0.024317) | 0.497011 / 0.540337 (-0.043326) | 0.581395 / 1.386936 (-0.805541) |

PyArrow==latest

Show updated benchmarks!

### Benchmark: benchmark_array_xd.json

| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.007214 / 0.011353 (-0.004139) | 0.005449 / 0.011008 (-0.005559) | 0.074320 / 0.038508 (0.035812) | 0.034261 / 0.023109 (0.011152) | 0.378265 / 0.275898 (0.102367) | 0.414419 / 0.323480 (0.090939) | 0.005804 / 0.007986 (-0.002182) | 0.004205 / 0.004328 (-0.000124) | 0.073266 / 0.004250 (0.069015) | 0.050444 / 0.037052 (0.013392) | 0.372999 / 0.258489 (0.114510) | 0.436032 / 0.293841 (0.142191) | 0.035432 / 0.128546 (-0.093114) | 0.012581 / 0.075646 (-0.063065) | 0.085777 / 0.419271 (-0.333495) | 0.046902 / 0.043533 (0.003369) | 0.378732 / 0.255139 (0.123593) | 0.401746 / 0.283200 (0.118547) | 0.113398 / 0.141683 (-0.028285) | 1.463851 / 1.452155 (0.011696) | 1.566387 / 1.492716 (0.073670) |

### 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) | 0.261246 / 0.018006 (0.243240) | 0.546730 / 0.000490 (0.546241) | 0.005245 / 0.000200 (0.005045) | 0.000103 / 0.000054 (0.000048) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.029441 / 0.037411 (-0.007970) | 0.111834 / 0.014526 (0.097308) | 0.122411 / 0.176557 (-0.054145) | 0.171288 / 0.737135 (-0.565847) | 0.130338 / 0.296338 (-0.166001) |

### 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) | 0.433405 / 0.215209 (0.218196) | 4.315790 / 2.077655 (2.238135) | 2.121934 / 1.504120 (0.617814) | 1.924123 / 1.541195 (0.382928) | 2.029077 / 1.468490 (0.560587) | 0.710245 / 4.584777 (-3.874532) | 3.844393 / 3.745712 (0.098681) | 3.576580 / 5.269862 (-1.693281) | 1.930985 / 4.565676 (-2.634691) | 0.092186 / 0.424275 (-0.332090) | 0.012307 / 0.007607 (0.004700) | 0.533722 / 0.226044 (0.307677) | 5.324447 / 2.268929 (3.055519) | 2.615451 / 55.444624 (-52.829174) | 2.282310 / 6.876477 (-4.594167) | 2.319847 / 2.142072 (0.177774) | 0.849364 / 4.805227 (-3.955864) | 0.172722 / 6.500664 (-6.327942) | 0.064721 / 0.075469 (-0.010748) |

### 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) | 1.289942 / 1.841788 (-0.551846) | 15.875062 / 8.074308 (7.800754) | 14.784682 / 10.191392 (4.593290) | 0.144432 / 0.680424 (-0.535991) | 0.017703 / 0.534201 (-0.516498) | 0.424357 / 0.579283 (-0.154926) | 0.419078 / 0.434364 (-0.015286) | 0.489331 / 0.540337 (-0.051006) | 0.585284 / 1.386936 (-0.801652) |

![](https://cml.dev/watermark.png#e3f4f124a1b118a5bfff5bae76b25a68aedbebbc "CML watermark")

Fixes #5820
Also fixed a couple of typos I spotted


[2.12.0] DatasetDict.save_to_disk not saving to S3

Hi ! Can you try adding the `s3://` prefix ?
```python
f"s3://{s3_bucket}/{s3_dir}/{dataset_name}"
```

Hi @thejamesmarq, by any chance, did you use multiprocessing `num_proc > 1` when saving your dataset on the s3 bucket ? I'm struggling making it work in a multiprocessing setting while everything works fine with one processor.

### Describe the bug

When trying to save a `DatasetDict` to a private S3 bucket using `save_to_disk`, the artifacts are instead saved locally, and not in the S3 bucket.

I have tried using the deprecated `fs` as well as the `storage_options` arguments and I get the same results.

### Steps to reproduce the bug

1. Create a DatsetDict `dataset`
2. Create a S3FileSystem object
`s3 = datasets.filesystems.S3FileSystem(key=aws_access_key_id, secret=aws_secret_access_key)`
3. Save using `dataset_dict.save_to_disk(f"{s3_bucket}/{s3_dir}/{dataset_name}", storage_options=s3.storage_options)` or `dataset_dict.save_to_disk(f"{s3_bucket}/{s3_dir}/{dataset_name}", fs=s3)`
4. Check the corresponding S3 bucket and verify nothing has been uploaded
5. Check the path at f"{s3_bucket}/{s3_dir}/{dataset_name}" and verify that files have been saved there

### Expected behavior

Artifacts are uploaded at the f"{s3_bucket}/{s3_dir}/{dataset_name}" S3 location.

### Environment info

- `datasets` version: 2.12.0
- Platform: macOS-13.3.1-x86_64-i386-64bit
- Python version: 3.11.2
- Huggingface_hub version: 0.14.1
- PyArrow version: 12.0.0
- Pandas version: 2.0.1


Audio Dataset with_format torch problem

Hi ! Can you try with a more recent version of `datasets` ?

Ok, yes it worked with the most recent version. Thanks

### Describe the bug

Common Voice v10 Delta (German) Dataset from here https://commonvoice.mozilla.org/de/datasets

```
audio_dataset = \
   (Dataset
    .from_dict({"audio": ('/tmp/cv-corpus-10.0-delta-2022-07-04/de/clips/' + df.path).to_list()})
    .cast_column("audio", Audio(sampling_rate=16_000))
    .with_format('numpy'))
audio_dataset[0]["audio"]
```

works, but

```
audio_dataset = \
   (Dataset
    .from_dict({"audio": ('/tmp/cv-corpus-10.0-delta-2022-07-04/de/clips/' + df.path).to_list()})
    .cast_column("audio", Audio(sampling_rate=16_000))
    .with_format('torch'))
audio_dataset[0]["audio"]
```

does not instead I get

```
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Cell In[54], line 1
----> 1 audio_dataset[0]["audio"]

File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/datasets/arrow_dataset.py:2154, in Dataset.__getitem__(self, key)
   2152 def __getitem__(self, key):  # noqa: F811
   2153     """Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools)."""
-> 2154     return self._getitem(
   2155         key,
   2156     )

File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/datasets/arrow_dataset.py:2139, in Dataset._getitem(self, key, decoded, **kwargs)
   2137 formatter = get_formatter(format_type, features=self.features, decoded=decoded, **format_kwargs)
   2138 pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None)
-> 2139 formatted_output = format_table(
   2140     pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns
   2141 )
   2142 return formatted_output

File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/datasets/formatting/formatting.py:532, in format_table(table, key, formatter, format_columns, output_all_columns)
    530 python_formatter = PythonFormatter(features=None)
    531 if format_columns is None:
--> 532     return formatter(pa_table, query_type=query_type)
    533 elif query_type == "column":
    534     if key in format_columns:

File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/datasets/formatting/formatting.py:281, in Formatter.__call__(self, pa_table, query_type)
    279 def __call__(self, pa_table: pa.Table, query_type: str) -> Union[RowFormat, ColumnFormat, BatchFormat]:
    280     if query_type == "row":
--> 281         return self.format_row(pa_table)
    282     elif query_type == "column":
    283         return self.format_column(pa_table)

File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/datasets/formatting/torch_formatter.py:58, in TorchFormatter.format_row(self, pa_table)
     56 def format_row(self, pa_table: pa.Table) -> dict:
     57     row = self.numpy_arrow_extractor().extract_row(pa_table)
---> 58     return self.recursive_tensorize(row)

File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/datasets/formatting/torch_formatter.py:54, in TorchFormatter.recursive_tensorize(self, data_struct)
     53 def recursive_tensorize(self, data_struct: dict):
---> 54     return map_nested(self._recursive_tensorize, data_struct, map_list=False)

File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/datasets/utils/py_utils.py:356, in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, types, disable_tqdm, desc)
    354     num_proc = 1
    355 if num_proc <= 1 or len(iterable) <= num_proc:
--> 356     mapped = [
    357         _single_map_nested((function, obj, types, None, True, None))
    358         for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc)
    359     ]
    360 else:
    361     split_kwds = []  # We organize the splits ourselve (contiguous splits)

File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/datasets/utils/py_utils.py:357, in <listcomp>(.0)
    354     num_proc = 1
    355 if num_proc <= 1 or len(iterable) <= num_proc:
    356     mapped = [
--> 357         _single_map_nested((function, obj, types, None, True, None))
    358         for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc)
    359     ]
    360 else:
    361     split_kwds = []  # We organize the splits ourselve (contiguous splits)

File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/datasets/utils/py_utils.py:309, in _single_map_nested(args)
    306 pbar = logging.tqdm(pbar_iterable, disable=disable_tqdm, position=rank, unit="obj", desc=pbar_desc)
    308 if isinstance(data_struct, dict):
--> 309     return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar}
    310 else:
    311     mapped = [_single_map_nested((function, v, types, None, True, None)) for v in pbar]

File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/datasets/utils/py_utils.py:309, in <dictcomp>(.0)
    306 pbar = logging.tqdm(pbar_iterable, disable=disable_tqdm, position=rank, unit="obj", desc=pbar_desc)
    308 if isinstance(data_struct, dict):
--> 309     return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar}
    310 else:
    311     mapped = [_single_map_nested((function, v, types, None, True, None)) for v in pbar]

File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/datasets/utils/py_utils.py:293, in _single_map_nested(args)
    291 # Singleton first to spare some computation
    292 if not isinstance(data_struct, dict) and not isinstance(data_struct, types):
--> 293     return function(data_struct)
    295 # Reduce logging to keep things readable in multiprocessing with tqdm
    296 if rank is not None and logging.get_verbosity() < logging.WARNING:

File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/datasets/formatting/torch_formatter.py:51, in TorchFormatter._recursive_tensorize(self, data_struct)
     49     if data_struct.dtype == np.object:  # pytorch tensors cannot be instantied from an array of objects
     50         return [self.recursive_tensorize(substruct) for substruct in data_struct]
---> 51 return self._tensorize(data_struct)

File /anaconda/envs/azureml_py38/lib/python3.8/site-packages/datasets/formatting/torch_formatter.py:38, in TorchFormatter._tensorize(self, value)
     35 import torch
     37 default_dtype = {}
---> 38 if np.issubdtype(value.dtype, np.integer):
     39     default_dtype = {"dtype": torch.int64}
     40 elif np.issubdtype(value.dtype, np.floating):

AttributeError: 'NoneType' object has no attribute 'dtype'
```

### Steps to reproduce the bug

1. Download some audio dataset in this case I used Common Voice v10 Delta (German) Dataset from here https://commonvoice.mozilla.org/de/datasets
2. Try the Code from above

### Expected behavior

It should work for torch

### Environment info

pytorch: 2.0.0
datasets: 2.3.2
numpy: 1.21.6

Python: 3.8
Linux


IterableDataset Arrow formatting

Show benchmarks

PyArrow==8.0.0

Show updated benchmarks!

### Benchmark: benchmark_array_xd.json

| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.007593 / 0.011353 (-0.003760) | 0.005554 / 0.011008 (-0.005454) | 0.097663 / 0.038508 (0.059155) | 0.034915 / 0.023109 (0.011806) | 0.303116 / 0.275898 (0.027218) | 0.342376 / 0.323480 (0.018897) | 0.006044 / 0.007986 (-0.001942) | 0.004239 / 0.004328 (-0.000090) | 0.074561 / 0.004250 (0.070310) | 0.049109 / 0.037052 (0.012057) | 0.311302 / 0.258489 (0.052813) | 0.360717 / 0.293841 (0.066876) | 0.035119 / 0.128546 (-0.093428) | 0.012465 / 0.075646 (-0.063181) | 0.333648 / 0.419271 (-0.085624) | 0.051294 / 0.043533 (0.007762) | 0.297298 / 0.255139 (0.042159) | 0.321957 / 0.283200 (0.038757) | 0.108206 / 0.141683 (-0.033477) | 1.425023 / 1.452155 (-0.027132) | 1.526395 / 1.492716 (0.033678) |

### 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) | 0.300694 / 0.018006 (0.282688) | 0.515141 / 0.000490 (0.514651) | 0.003965 / 0.000200 (0.003765) | 0.000260 / 0.000054 (0.000206) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.029428 / 0.037411 (-0.007983) | 0.107634 / 0.014526 (0.093108) | 0.123662 / 0.176557 (-0.052895) | 0.182886 / 0.737135 (-0.554249) | 0.128361 / 0.296338 (-0.167977) |

### 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) | 0.398809 / 0.215209 (0.183600) | 3.984428 / 2.077655 (1.906773) | 1.795337 / 1.504120 (0.291217) | 1.609235 / 1.541195 (0.068040) | 1.724825 / 1.468490 (0.256335) | 0.698413 / 4.584777 (-3.886364) | 3.857479 / 3.745712 (0.111767) | 2.135203 / 5.269862 (-3.134659) | 1.348458 / 4.565676 (-3.217218) | 0.086445 / 0.424275 (-0.337830) | 0.012717 / 0.007607 (0.005110) | 0.498713 / 0.226044 (0.272668) | 4.988685 / 2.268929 (2.719757) | 2.284764 / 55.444624 (-53.159860) | 1.961162 / 6.876477 (-4.915315) | 2.147514 / 2.142072 (0.005441) | 0.850334 / 4.805227 (-3.954894) | 0.171664 / 6.500664 (-6.329000) | 0.065526 / 0.075469 (-0.009943) |

### 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) | 1.204398 / 1.841788 (-0.637390) | 15.625790 / 8.074308 (7.551482) | 14.614980 / 10.191392 (4.423588) | 0.167135 / 0.680424 (-0.513289) | 0.017631 / 0.534201 (-0.516570) | 0.427337 / 0.579283 (-0.151946) | 0.439203 / 0.434364 (0.004839) | 0.499670 / 0.540337 (-0.040668) | 0.587577 / 1.386936 (-0.799359) |

PyArrow==latest

Show updated benchmarks!

### Benchmark: benchmark_array_xd.json

| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.007866 / 0.011353 (-0.003486) | 0.005798 / 0.011008 (-0.005210) | 0.075803 / 0.038508 (0.037295) | 0.035773 / 0.023109 (0.012664) | 0.361965 / 0.275898 (0.086067) | 0.402780 / 0.323480 (0.079300) | 0.006521 / 0.007986 (-0.001465) | 0.004613 / 0.004328 (0.000284) | 0.075196 / 0.004250 (0.070946) | 0.055324 / 0.037052 (0.018272) | 0.363468 / 0.258489 (0.104979) | 0.410344 / 0.293841 (0.116503) | 0.036324 / 0.128546 (-0.092222) | 0.012891 / 0.075646 (-0.062755) | 0.086991 / 0.419271 (-0.332280) | 0.048082 / 0.043533 (0.004549) | 0.357238 / 0.255139 (0.102099) | 0.377065 / 0.283200 (0.093865) | 0.118586 / 0.141683 (-0.023097) | 1.463161 / 1.452155 (0.011007) | 1.582686 / 1.492716 (0.089969) |

### 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) | 0.267916 / 0.018006 (0.249909) | 0.540862 / 0.000490 (0.540373) | 0.003148 / 0.000200 (0.002948) | 0.000101 / 0.000054 (0.000047) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.032290 / 0.037411 (-0.005122) | 0.115468 / 0.014526 (0.100943) | 0.125743 / 0.176557 (-0.050814) | 0.177469 / 0.737135 (-0.559667) | 0.133579 / 0.296338 (-0.162759) |

### 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) | 0.446727 / 0.215209 (0.231518) | 4.467938 / 2.077655 (2.390284) | 2.330171 / 1.504120 (0.826052) | 2.165624 / 1.541195 (0.624429) | 2.298063 / 1.468490 (0.829573) | 0.702241 / 4.584777 (-3.882536) | 3.845302 / 3.745712 (0.099590) | 2.169278 / 5.269862 (-3.100584) | 1.401392 / 4.565676 (-3.164285) | 0.086672 / 0.424275 (-0.337603) | 0.012355 / 0.007607 (0.004748) | 0.543639 / 0.226044 (0.317595) | 5.425876 / 2.268929 (3.156947) | 2.781794 / 55.444624 (-52.662831) | 2.503724 / 6.876477 (-4.372752) | 2.622580 / 2.142072 (0.480507) | 0.847143 / 4.805227 (-3.958084) | 0.171721 / 6.500664 (-6.328943) | 0.067894 / 0.075469 (-0.007575) |

### 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) | 1.292194 / 1.841788 (-0.549594) | 15.497311 / 8.074308 (7.423003) | 15.002463 / 10.191392 (4.811071) | 0.152244 / 0.680424 (-0.528180) | 0.018085 / 0.534201 (-0.516116) | 0.445787 / 0.579283 (-0.133496) | 0.448960 / 0.434364 (0.014596) | 0.515319 / 0.540337 (-0.025019) | 0.623840 / 1.386936 (-0.763096) |

![](https://cml.dev/watermark.png#f8417a41547ce0c939bd342398be621f5ce3e340 "CML watermark")

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

Show benchmarks

PyArrow==8.0.0

Show updated benchmarks!

### Benchmark: benchmark_array_xd.json

| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.006938 / 0.011353 (-0.004415) | 0.005100 / 0.011008 (-0.005909) | 0.096525 / 0.038508 (0.058017) | 0.033764 / 0.023109 (0.010655) | 0.301107 / 0.275898 (0.025209) | 0.333140 / 0.323480 (0.009660) | 0.005719 / 0.007986 (-0.002266) | 0.005192 / 0.004328 (0.000864) | 0.073685 / 0.004250 (0.069434) | 0.048149 / 0.037052 (0.011096) | 0.299244 / 0.258489 (0.040754) | 0.347518 / 0.293841 (0.053677) | 0.034810 / 0.128546 (-0.093736) | 0.012284 / 0.075646 (-0.063363) | 0.333600 / 0.419271 (-0.085672) | 0.050750 / 0.043533 (0.007217) | 0.299782 / 0.255139 (0.044643) | 0.322712 / 0.283200 (0.039512) | 0.105659 / 0.141683 (-0.036024) | 1.457536 / 1.452155 (0.005381) | 1.571604 / 1.492716 (0.078887) |

### 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) | 0.207190 / 0.018006 (0.189184) | 0.439230 / 0.000490 (0.438740) | 0.006403 / 0.000200 (0.006203) | 0.000282 / 0.000054 (0.000228) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.027424 / 0.037411 (-0.009987) | 0.107180 / 0.014526 (0.092655) | 0.118356 / 0.176557 (-0.058201) | 0.175557 / 0.737135 (-0.561579) | 0.125671 / 0.296338 (-0.170668) |

### 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) | 0.411249 / 0.215209 (0.196039) | 4.094494 / 2.077655 (2.016839) | 1.946843 / 1.504120 (0.442723) | 1.766503 / 1.541195 (0.225308) | 1.831406 / 1.468490 (0.362916) | 0.704637 / 4.584777 (-3.880140) | 3.819204 / 3.745712 (0.073492) | 3.412598 / 5.269862 (-1.857263) | 1.796385 / 4.565676 (-2.769291) | 0.084591 / 0.424275 (-0.339684) | 0.012568 / 0.007607 (0.004961) | 0.506372 / 0.226044 (0.280327) | 5.049461 / 2.268929 (2.780532) | 2.409860 / 55.444624 (-53.034765) | 2.064514 / 6.876477 (-4.811963) | 2.192808 / 2.142072 (0.050735) | 0.833773 / 4.805227 (-3.971455) | 0.167948 / 6.500664 (-6.332716) | 0.064617 / 0.075469 (-0.010852) |

### 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) | 1.174739 / 1.841788 (-0.667048) | 14.605634 / 8.074308 (6.531326) | 14.321043 / 10.191392 (4.129651) | 0.145892 / 0.680424 (-0.534532) | 0.017413 / 0.534201 (-0.516788) | 0.444940 / 0.579283 (-0.134343) | 0.430792 / 0.434364 (-0.003572) | 0.539699 / 0.540337 (-0.000638) | 0.640279 / 1.386936 (-0.746657) |

PyArrow==latest

Show updated benchmarks!

### Benchmark: benchmark_array_xd.json

| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.007159 / 0.011353 (-0.004194) | 0.005313 / 0.011008 (-0.005695) | 0.073630 / 0.038508 (0.035122) | 0.033459 / 0.023109 (0.010350) | 0.356959 / 0.275898 (0.081061) | 0.385918 / 0.323480 (0.062438) | 0.005714 / 0.007986 (-0.002272) | 0.004074 / 0.004328 (-0.000254) | 0.073278 / 0.004250 (0.069028) | 0.047193 / 0.037052 (0.010140) | 0.360300 / 0.258489 (0.101811) | 0.398052 / 0.293841 (0.104212) | 0.035670 / 0.128546 (-0.092876) | 0.012499 / 0.075646 (-0.063147) | 0.086677 / 0.419271 (-0.332595) | 0.046534 / 0.043533 (0.003001) | 0.370029 / 0.255139 (0.114890) | 0.376040 / 0.283200 (0.092841) | 0.105184 / 0.141683 (-0.036499) | 1.419779 / 1.452155 (-0.032375) | 1.538925 / 1.492716 (0.046209) |

### 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) | 0.220465 / 0.018006 (0.202459) | 0.438836 / 0.000490 (0.438346) | 0.000428 / 0.000200 (0.000228) | 0.000060 / 0.000054 (0.000006) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.029114 / 0.037411 (-0.008298) | 0.111871 / 0.014526 (0.097345) | 0.124367 / 0.176557 (-0.052189) | 0.173737 / 0.737135 (-0.563398) | 0.128435 / 0.296338 (-0.167904) |

### 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) | 0.440706 / 0.215209 (0.225497) | 4.414826 / 2.077655 (2.337171) | 2.128899 / 1.504120 (0.624780) | 1.929551 / 1.541195 (0.388357) | 2.013130 / 1.468490 (0.544640) | 0.708566 / 4.584777 (-3.876211) | 3.846459 / 3.745712 (0.100747) | 2.158829 / 5.269862 (-3.111032) | 1.339454 / 4.565676 (-3.226223) | 0.086345 / 0.424275 (-0.337930) | 0.012085 / 0.007607 (0.004478) | 0.546360 / 0.226044 (0.320316) | 5.461612 / 2.268929 (3.192683) | 2.657388 / 55.444624 (-52.787237) | 2.298403 / 6.876477 (-4.578074) | 2.344572 / 2.142072 (0.202499) | 0.844276 / 4.805227 (-3.960951) | 0.170225 / 6.500664 (-6.330439) | 0.064684 / 0.075469 (-0.010785) |

### 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) | 1.265114 / 1.841788 (-0.576674) | 15.058156 / 8.074308 (6.983848) | 14.485182 / 10.191392 (4.293790) | 0.165960 / 0.680424 (-0.514464) | 0.017481 / 0.534201 (-0.516719) | 0.425141 / 0.579283 (-0.154142) | 0.434883 / 0.434364 (0.000519) | 0.506701 / 0.540337 (-0.033637) | 0.613240 / 1.386936 (-0.773697) |

![](https://cml.dev/watermark.png#8f019dffffb214b44b30dd9ac56fdea12259e148 "CML watermark")

Show benchmarks

PyArrow==8.0.0

Show updated benchmarks!

### Benchmark: benchmark_array_xd.json

| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.007651 / 0.011353 (-0.003702) | 0.005503 / 0.011008 (-0.005505) | 0.098751 / 0.038508 (0.060243) | 0.036822 / 0.023109 (0.013713) | 0.340754 / 0.275898 (0.064856) | 0.387247 / 0.323480 (0.063767) | 0.006513 / 0.007986 (-0.001473) | 0.006135 / 0.004328 (0.001807) | 0.073656 / 0.004250 (0.069406) | 0.055508 / 0.037052 (0.018456) | 0.352493 / 0.258489 (0.094004) | 0.408003 / 0.293841 (0.114162) | 0.036346 / 0.128546 (-0.092201) | 0.012562 / 0.075646 (-0.063085) | 0.335111 / 0.419271 (-0.084160) | 0.051928 / 0.043533 (0.008395) | 0.339405 / 0.255139 (0.084266) | 0.366840 / 0.283200 (0.083640) | 0.114353 / 0.141683 (-0.027330) | 1.449062 / 1.452155 (-0.003092) | 1.567310 / 1.492716 (0.074594) |

### 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) | 0.262975 / 0.018006 (0.244968) | 0.570302 / 0.000490 (0.569813) | 0.003419 / 0.000200 (0.003219) | 0.000100 / 0.000054 (0.000046) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.027363 / 0.037411 (-0.010049) | 0.109033 / 0.014526 (0.094507) | 0.119048 / 0.176557 (-0.057509) | 0.175891 / 0.737135 (-0.561244) | 0.124577 / 0.296338 (-0.171762) |

### 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) | 0.397988 / 0.215209 (0.182779) | 3.993210 / 2.077655 (1.915555) | 1.809275 / 1.504120 (0.305155) | 1.614664 / 1.541195 (0.073469) | 1.723650 / 1.468490 (0.255159) | 0.698484 / 4.584777 (-3.886293) | 3.914135 / 3.745712 (0.168423) | 2.142622 / 5.269862 (-3.127239) | 1.360215 / 4.565676 (-3.205461) | 0.086340 / 0.424275 (-0.337935) | 0.012836 / 0.007607 (0.005229) | 0.500728 / 0.226044 (0.274684) | 5.006744 / 2.268929 (2.737815) | 2.350668 / 55.444624 (-53.093956) | 1.979816 / 6.876477 (-4.896660) | 2.190159 / 2.142072 (0.048087) | 0.854063 / 4.805227 (-3.951164) | 0.170203 / 6.500664 (-6.330461) | 0.066903 / 0.075469 (-0.008566) |

### 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) | 1.184012 / 1.841788 (-0.657775) | 15.407350 / 8.074308 (7.333042) | 14.758180 / 10.191392 (4.566788) | 0.169280 / 0.680424 (-0.511144) | 0.017419 / 0.534201 (-0.516781) | 0.434359 / 0.579283 (-0.144925) | 0.442515 / 0.434364 (0.008151) | 0.503132 / 0.540337 (-0.037205) | 0.602589 / 1.386936 (-0.784347) |

PyArrow==latest

Show updated benchmarks!

### Benchmark: benchmark_array_xd.json

| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.008022 / 0.011353 (-0.003331) | 0.005473 / 0.011008 (-0.005535) | 0.076106 / 0.038508 (0.037598) | 0.037065 / 0.023109 (0.013956) | 0.380039 / 0.275898 (0.104141) | 0.394205 / 0.323480 (0.070725) | 0.006447 / 0.007986 (-0.001539) | 0.006011 / 0.004328 (0.001682) | 0.075236 / 0.004250 (0.070985) | 0.054425 / 0.037052 (0.017372) | 0.381707 / 0.258489 (0.123218) | 0.411237 / 0.293841 (0.117396) | 0.037222 / 0.128546 (-0.091324) | 0.012627 / 0.075646 (-0.063020) | 0.086733 / 0.419271 (-0.332538) | 0.053857 / 0.043533 (0.010324) | 0.373374 / 0.255139 (0.118235) | 0.381680 / 0.283200 (0.098480) | 0.121962 / 0.141683 (-0.019721) | 1.430804 / 1.452155 (-0.021351) | 1.562517 / 1.492716 (0.069801) |

### 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) | 0.262034 / 0.018006 (0.244028) | 0.563497 / 0.000490 (0.563007) | 0.002726 / 0.000200 (0.002526) | 0.000099 / 0.000054 (0.000044) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.031071 / 0.037411 (-0.006341) | 0.111983 / 0.014526 (0.097457) | 0.126634 / 0.176557 (-0.049923) | 0.177511 / 0.737135 (-0.559625) | 0.132599 / 0.296338 (-0.163739) |

### 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) | 0.436148 / 0.215209 (0.220939) | 4.344850 / 2.077655 (2.267195) | 2.105877 / 1.504120 (0.601757) | 1.920934 / 1.541195 (0.379739) | 2.072930 / 1.468490 (0.604440) | 0.701793 / 4.584777 (-3.882984) | 3.841621 / 3.745712 (0.095909) | 3.602550 / 5.269862 (-1.667311) | 1.775999 / 4.565676 (-2.789677) | 0.086024 / 0.424275 (-0.338251) | 0.012275 / 0.007607 (0.004668) | 0.532815 / 0.226044 (0.306770) | 5.336273 / 2.268929 (3.067344) | 2.638842 / 55.444624 (-52.805782) | 2.301842 / 6.876477 (-4.574635) | 2.407448 / 2.142072 (0.265376) | 0.855836 / 4.805227 (-3.949392) | 0.170348 / 6.500664 (-6.330317) | 0.066926 / 0.075469 (-0.008543) |

### 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) | 1.291515 / 1.841788 (-0.550272) | 15.869825 / 8.074308 (7.795517) | 15.068227 / 10.191392 (4.876835) | 0.156953 / 0.680424 (-0.523471) | 0.017761 / 0.534201 (-0.516440) | 0.429515 / 0.579283 (-0.149768) | 0.432758 / 0.434364 (-0.001605) | 0.500080 / 0.540337 (-0.040258) | 0.601451 / 1.386936 (-0.785485) |

![](https://cml.dev/watermark.png#00b148b09da2074fcaba0538a23c7f46d28d387c "CML watermark")

Will need to take https://github.com/huggingface/datasets/pull/5810 into account if it gets merged before this one

Show benchmarks

PyArrow==8.0.0

Show updated benchmarks!

### Benchmark: benchmark_array_xd.json

| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.006914 / 0.011353 (-0.004439) | 0.004727 / 0.011008 (-0.006281) | 0.098880 / 0.038508 (0.060372) | 0.036663 / 0.023109 (0.013554) | 0.317575 / 0.275898 (0.041677) | 0.360301 / 0.323480 (0.036821) | 0.006084 / 0.007986 (-0.001901) | 0.004118 / 0.004328 (-0.000210) | 0.074330 / 0.004250 (0.070079) | 0.042422 / 0.037052 (0.005369) | 0.335625 / 0.258489 (0.077136) | 0.366616 / 0.293841 (0.072775) | 0.028523 / 0.128546 (-0.100023) | 0.008883 / 0.075646 (-0.066763) | 0.332475 / 0.419271 (-0.086797) | 0.051746 / 0.043533 (0.008214) | 0.324952 / 0.255139 (0.069813) | 0.339660 / 0.283200 (0.056460) | 0.103714 / 0.141683 (-0.037969) | 1.472130 / 1.452155 (0.019976) | 1.516548 / 1.492716 (0.023831) |

### 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) | 0.229538 / 0.018006 (0.211532) | 0.449077 / 0.000490 (0.448588) | 0.003707 / 0.000200 (0.003507) | 0.000086 / 0.000054 (0.000032) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.027897 / 0.037411 (-0.009514) | 0.115452 / 0.014526 (0.100926) | 0.118830 / 0.176557 (-0.057726) | 0.176228 / 0.737135 (-0.560907) | 0.125966 / 0.296338 (-0.170372) |

### 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) | 0.436947 / 0.215209 (0.221738) | 4.355687 / 2.077655 (2.278033) | 2.195857 / 1.504120 (0.691737) | 2.028133 / 1.541195 (0.486938) | 2.119872 / 1.468490 (0.651382) | 0.524256 / 4.584777 (-4.060521) | 3.864064 / 3.745712 (0.118352) | 3.446181 / 5.269862 (-1.823680) | 1.610307 / 4.565676 (-2.955370) | 0.065981 / 0.424275 (-0.358294) | 0.012172 / 0.007607 (0.004565) | 0.545341 / 0.226044 (0.319297) | 5.451728 / 2.268929 (3.182800) | 2.690734 / 55.444624 (-52.753890) | 2.368203 / 6.876477 (-4.508274) | 2.549533 / 2.142072 (0.407460) | 0.651296 / 4.805227 (-4.153931) | 0.143697 / 6.500664 (-6.356968) | 0.065170 / 0.075469 (-0.010299) |

### 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) | 1.198898 / 1.841788 (-0.642890) | 15.349348 / 8.074308 (7.275040) | 15.314467 / 10.191392 (5.123075) | 0.177219 / 0.680424 (-0.503205) | 0.018223 / 0.534201 (-0.515978) | 0.396209 / 0.579283 (-0.183074) | 0.427810 / 0.434364 (-0.006554) | 0.475107 / 0.540337 (-0.065230) | 0.561224 / 1.386936 (-0.825712) |

PyArrow==latest

Show updated benchmarks!

### Benchmark: benchmark_array_xd.json

| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.007024 / 0.011353 (-0.004329) | 0.004851 / 0.011008 (-0.006157) | 0.075031 / 0.038508 (0.036523) | 0.036411 / 0.023109 (0.013302) | 0.375999 / 0.275898 (0.100101) | 0.433033 / 0.323480 (0.109553) | 0.006089 / 0.007986 (-0.001897) | 0.005638 / 0.004328 (0.001309) | 0.072599 / 0.004250 (0.068348) | 0.048489 / 0.037052 (0.011436) | 0.381807 / 0.258489 (0.123318) | 0.441531 / 0.293841 (0.147691) | 0.029044 / 0.128546 (-0.099503) | 0.009052 / 0.075646 (-0.066595) | 0.080086 / 0.419271 (-0.339186) | 0.046919 / 0.043533 (0.003386) | 0.360399 / 0.255139 (0.105260) | 0.405445 / 0.283200 (0.122245) | 0.108815 / 0.141683 (-0.032868) | 1.415168 / 1.452155 (-0.036987) | 1.511756 / 1.492716 (0.019040) |

### 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) | 0.210287 / 0.018006 (0.192281) | 0.445139 / 0.000490 (0.444650) | 0.000386 / 0.000200 (0.000186) | 0.000056 / 0.000054 (0.000002) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.030457 / 0.037411 (-0.006954) | 0.117225 / 0.014526 (0.102699) | 0.122833 / 0.176557 (-0.053724) | 0.170441 / 0.737135 (-0.566694) | 0.131589 / 0.296338 (-0.164750) |

### 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) | 0.446541 / 0.215209 (0.231332) | 4.471214 / 2.077655 (2.393560) | 2.145894 / 1.504120 (0.641774) | 1.958113 / 1.541195 (0.416919) | 2.069623 / 1.468490 (0.601132) | 0.527562 / 4.584777 (-4.057215) | 3.838285 / 3.745712 (0.092573) | 1.884780 / 5.269862 (-3.385081) | 1.088124 / 4.565676 (-3.477553) | 0.066099 / 0.424275 (-0.358176) | 0.011973 / 0.007607 (0.004366) | 0.540369 / 0.226044 (0.314325) | 5.403554 / 2.268929 (3.134626) | 2.749920 / 55.444624 (-52.694704) | 2.543169 / 6.876477 (-4.333308) | 2.403116 / 2.142072 (0.261043) | 0.638723 / 4.805227 (-4.166505) | 0.142232 / 6.500664 (-6.358432) | 0.065551 / 0.075469 (-0.009918) |

### 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) | 1.298307 / 1.841788 (-0.543481) | 15.986177 / 8.074308 (7.911869) | 15.530453 / 10.191392 (5.339061) | 0.160138 / 0.680424 (-0.520286) | 0.017988 / 0.534201 (-0.516213) | 0.397857 / 0.579283 (-0.181427) | 0.435071 / 0.434364 (0.000707) | 0.480096 / 0.540337 (-0.060241) | 0.589139 / 1.386936 (-0.797797) |

![](https://cml.dev/watermark.png#5bd9c974e08e059ce36dc0843256747016e843c5 "CML watermark")

Show benchmarks

PyArrow==8.0.0

Show updated benchmarks!

### Benchmark: benchmark_array_xd.json

| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.006976 / 0.011353 (-0.004377) | 0.005068 / 0.011008 (-0.005940) | 0.098178 / 0.038508 (0.059670) | 0.035167 / 0.023109 (0.012057) | 0.324093 / 0.275898 (0.048195) | 0.350749 / 0.323480 (0.027269) | 0.006128 / 0.007986 (-0.001858) | 0.004361 / 0.004328 (0.000033) | 0.075412 / 0.004250 (0.071161) | 0.052083 / 0.037052 (0.015031) | 0.326726 / 0.258489 (0.068237) | 0.371450 / 0.293841 (0.077609) | 0.028522 / 0.128546 (-0.100025) | 0.009210 / 0.075646 (-0.066436) | 0.329296 / 0.419271 (-0.089976) | 0.051182 / 0.043533 (0.007649) | 0.319863 / 0.255139 (0.064724) | 0.329140 / 0.283200 (0.045941) | 0.111653 / 0.141683 (-0.030030) | 1.464205 / 1.452155 (0.012050) | 1.555779 / 1.492716 (0.063062) |

### 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) | 0.282372 / 0.018006 (0.264366) | 0.569227 / 0.000490 (0.568737) | 0.005289 / 0.000200 (0.005089) | 0.000095 / 0.000054 (0.000041) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.029875 / 0.037411 (-0.007537) | 0.111889 / 0.014526 (0.097364) | 0.125678 / 0.176557 (-0.050878) | 0.184695 / 0.737135 (-0.552441) | 0.129737 / 0.296338 (-0.166602) |

### 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) | 0.417404 / 0.215209 (0.202195) | 4.172367 / 2.077655 (2.094712) | 2.008088 / 1.504120 (0.503968) | 1.813182 / 1.541195 (0.271988) | 1.882727 / 1.468490 (0.414237) | 0.525764 / 4.584777 (-4.059013) | 3.815202 / 3.745712 (0.069490) | 1.884197 / 5.269862 (-3.385664) | 1.073779 / 4.565676 (-3.491897) | 0.066125 / 0.424275 (-0.358150) | 0.012473 / 0.007607 (0.004866) | 0.522197 / 0.226044 (0.296153) | 5.218486 / 2.268929 (2.949557) | 2.413846 / 55.444624 (-53.030779) | 2.093298 / 6.876477 (-4.783179) | 2.320583 / 2.142072 (0.178511) | 0.648832 / 4.805227 (-4.156395) | 0.146168 / 6.500664 (-6.354496) | 0.065869 / 0.075469 (-0.009600) |

### 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) | 1.181859 / 1.841788 (-0.659929) | 15.369517 / 8.074308 (7.295209) | 14.896270 / 10.191392 (4.704878) | 0.146793 / 0.680424 (-0.533630) | 0.017960 / 0.534201 (-0.516241) | 0.421801 / 0.579283 (-0.157482) | 0.438357 / 0.434364 (0.003993) | 0.524554 / 0.540337 (-0.015783) | 0.621041 / 1.386936 (-0.765895) |

PyArrow==latest

Show updated benchmarks!

### Benchmark: benchmark_array_xd.json

| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.007104 / 0.011353 (-0.004249) | 0.004895 / 0.011008 (-0.006113) | 0.075641 / 0.038508 (0.037133) | 0.034821 / 0.023109 (0.011712) | 0.363875 / 0.275898 (0.087977) | 0.403042 / 0.323480 (0.079562) | 0.006747 / 0.007986 (-0.001238) | 0.005793 / 0.004328 (0.001465) | 0.074709 / 0.004250 (0.070458) | 0.058801 / 0.037052 (0.021749) | 0.366900 / 0.258489 (0.108411) | 0.414442 / 0.293841 (0.120601) | 0.029099 / 0.128546 (-0.099448) | 0.009394 / 0.075646 (-0.066253) | 0.082612 / 0.419271 (-0.336659) | 0.049076 / 0.043533 (0.005543) | 0.358828 / 0.255139 (0.103689) | 0.378261 / 0.283200 (0.095061) | 0.122147 / 0.141683 (-0.019535) | 1.454155 / 1.452155 (0.002000) | 1.572437 / 1.492716 (0.079720) |

### 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) | 0.293133 / 0.018006 (0.275127) | 0.536785 / 0.000490 (0.536295) | 0.000457 / 0.000200 (0.000257) | 0.000058 / 0.000054 (0.000003) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.031046 / 0.037411 (-0.006366) | 0.113929 / 0.014526 (0.099403) | 0.126222 / 0.176557 (-0.050335) | 0.173992 / 0.737135 (-0.563143) | 0.129635 / 0.296338 (-0.166704) |

### 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) | 0.441984 / 0.215209 (0.226775) | 4.406002 / 2.077655 (2.328348) | 2.173912 / 1.504120 (0.669792) | 2.000507 / 1.541195 (0.459312) | 2.172766 / 1.468490 (0.704276) | 0.524530 / 4.584777 (-4.060247) | 3.758827 / 3.745712 (0.013115) | 1.886701 / 5.269862 (-3.383160) | 1.073601 / 4.565676 (-3.492075) | 0.066137 / 0.424275 (-0.358139) | 0.011926 / 0.007607 (0.004319) | 0.541103 / 0.226044 (0.315059) | 5.404162 / 2.268929 (3.135233) | 2.634271 / 55.444624 (-52.810354) | 2.366156 / 6.876477 (-4.510321) | 2.566877 / 2.142072 (0.424804) | 0.639088 / 4.805227 (-4.166139) | 0.141810 / 6.500664 (-6.358854) | 0.065446 / 0.075469 (-0.010023) |

### 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) | 1.288173 / 1.841788 (-0.553614) | 15.897051 / 8.074308 (7.822743) | 15.243404 / 10.191392 (5.052012) | 0.162380 / 0.680424 (-0.518043) | 0.017716 / 0.534201 (-0.516485) | 0.396400 / 0.579283 (-0.182883) | 0.420479 / 0.434364 (-0.013885) | 0.476238 / 0.540337 (-0.064099) | 0.583039 / 1.386936 (-0.803897) |

![](https://cml.dev/watermark.png#bd373f69f12e926f4e2a489c14df36c38ce07bcc "CML watermark")

Show benchmarks

PyArrow==8.0.0

Show updated benchmarks!

### Benchmark: benchmark_array_xd.json

| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.006310 / 0.011353 (-0.005043) | 0.004297 / 0.011008 (-0.006711) | 0.098288 / 0.038508 (0.059780) | 0.029295 / 0.023109 (0.006185) | 0.386804 / 0.275898 (0.110906) | 0.425717 / 0.323480 (0.102237) | 0.005516 / 0.007986 (-0.002470) | 0.005058 / 0.004328 (0.000730) | 0.074318 / 0.004250 (0.070068) | 0.040609 / 0.037052 (0.003557) | 0.388159 / 0.258489 (0.129670) | 0.428683 / 0.293841 (0.134842) | 0.026207 / 0.128546 (-0.102340) | 0.008655 / 0.075646 (-0.066991) | 0.321601 / 0.419271 (-0.097671) | 0.055329 / 0.043533 (0.011796) | 0.390452 / 0.255139 (0.135313) | 0.409084 / 0.283200 (0.125884) | 0.099555 / 0.141683 (-0.042128) | 1.484289 / 1.452155 (0.032134) | 1.549892 / 1.492716 (0.057176) |

### 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) | 0.219466 / 0.018006 (0.201460) | 0.437288 / 0.000490 (0.436798) | 0.003556 / 0.000200 (0.003356) | 0.000080 / 0.000054 (0.000025) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.023876 / 0.037411 (-0.013535) | 0.100205 / 0.014526 (0.085679) | 0.106365 / 0.176557 (-0.070191) | 0.164353 / 0.737135 (-0.572782) | 0.109987 / 0.296338 (-0.186352) |

### 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) | 0.418819 / 0.215209 (0.203610) | 4.168558 / 2.077655 (2.090903) | 1.862883 / 1.504120 (0.358764) | 1.673308 / 1.541195 (0.132114) | 1.742338 / 1.468490 (0.273848) | 0.550113 / 4.584777 (-4.034664) | 3.492085 / 3.745712 (-0.253627) | 1.734579 / 5.269862 (-3.535283) | 1.006876 / 4.565676 (-3.558801) | 0.068014 / 0.424275 (-0.356261) | 0.012242 / 0.007607 (0.004634) | 0.520633 / 0.226044 (0.294588) | 5.214095 / 2.268929 (2.945167) | 2.319282 / 55.444624 (-53.125343) | 1.979521 / 6.876477 (-4.896956) | 2.099595 / 2.142072 (-0.042477) | 0.659306 / 4.805227 (-4.145921) | 0.135282 / 6.500664 (-6.365382) | 0.067417 / 0.075469 (-0.008052) |

### 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) | 1.232099 / 1.841788 (-0.609689) | 13.967219 / 8.074308 (5.892910) | 14.347105 / 10.191392 (4.155713) | 0.146360 / 0.680424 (-0.534063) | 0.017021 / 0.534201 (-0.517180) | 0.363254 / 0.579283 (-0.216030) | 0.404391 / 0.434364 (-0.029973) | 0.428670 / 0.540337 (-0.111668) | 0.514942 / 1.386936 (-0.871994) |

PyArrow==latest

Show updated benchmarks!

### Benchmark: benchmark_array_xd.json

| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.006360 / 0.011353 (-0.004993) | 0.004160 / 0.011008 (-0.006848) | 0.074856 / 0.038508 (0.036347) | 0.028624 / 0.023109 (0.005515) | 0.355624 / 0.275898 (0.079726) | 0.403678 / 0.323480 (0.080198) | 0.005253 / 0.007986 (-0.002732) | 0.004808 / 0.004328 (0.000480) | 0.074215 / 0.004250 (0.069964) | 0.040641 / 0.037052 (0.003588) | 0.358473 / 0.258489 (0.099984) | 0.414442 / 0.293841 (0.120601) | 0.025595 / 0.128546 (-0.102951) | 0.008506 / 0.075646 (-0.067140) | 0.081547 / 0.419271 (-0.337725) | 0.039719 / 0.043533 (-0.003814) | 0.355420 / 0.255139 (0.100281) | 0.380953 / 0.283200 (0.097753) | 0.100064 / 0.141683 (-0.041618) | 1.459639 / 1.452155 (0.007484) | 1.557288 / 1.492716 (0.064572) |

### 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) | 0.232837 / 0.018006 (0.214831) | 0.424788 / 0.000490 (0.424298) | 0.000397 / 0.000200 (0.000197) | 0.000059 / 0.000054 (0.000004) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.026156 / 0.037411 (-0.011256) | 0.103633 / 0.014526 (0.089107) | 0.109633 / 0.176557 (-0.066923) | 0.159407 / 0.737135 (-0.577728) | 0.113874 / 0.296338 (-0.182465) |

### 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) | 0.471980 / 0.215209 (0.256771) | 4.724424 / 2.077655 (2.646769) | 2.459950 / 1.504120 (0.955830) | 2.280926 / 1.541195 (0.739731) | 2.368478 / 1.468490 (0.899987) | 0.552809 / 4.584777 (-4.031968) | 3.461985 / 3.745712 (-0.283728) | 1.757060 / 5.269862 (-3.512802) | 1.009599 / 4.565676 (-3.556077) | 0.068407 / 0.424275 (-0.355868) | 0.012341 / 0.007607 (0.004734) | 0.576287 / 0.226044 (0.350242) | 5.767331 / 2.268929 (3.498402) | 2.965743 / 55.444624 (-52.478882) | 2.644935 / 6.876477 (-4.231542) | 2.699663 / 2.142072 (0.557591) | 0.656005 / 4.805227 (-4.149222) | 0.136315 / 6.500664 (-6.364349) | 0.068355 / 0.075469 (-0.007114) |

### 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) | 1.308301 / 1.841788 (-0.533486) | 14.587268 / 8.074308 (6.512960) | 14.385670 / 10.191392 (4.194278) | 0.148154 / 0.680424 (-0.532270) | 0.016798 / 0.534201 (-0.517402) | 0.360761 / 0.579283 (-0.218523) | 0.392566 / 0.434364 (-0.041798) | 0.431604 / 0.540337 (-0.108734) | 0.528463 / 1.386936 (-0.858473) |

![](https://cml.dev/watermark.png#f2778e1ab255545cb2171379fd2276c85768a2ad "CML watermark")

let me know if it sounds good for you now @albertvillanova :)

Show benchmarks

PyArrow==8.0.0

Show updated benchmarks!

### Benchmark: benchmark_array_xd.json

| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.008414 / 0.011353 (-0.002939) | 0.005320 / 0.011008 (-0.005688) | 0.115585 / 0.038508 (0.077077) | 0.040815 / 0.023109 (0.017706) | 0.363453 / 0.275898 (0.087555) | 0.385954 / 0.323480 (0.062474) | 0.006463 / 0.007986 (-0.001523) | 0.005571 / 0.004328 (0.001242) | 0.084831 / 0.004250 (0.080581) | 0.050294 / 0.037052 (0.013242) | 0.375684 / 0.258489 (0.117195) | 0.394672 / 0.293841 (0.100831) | 0.033618 / 0.128546 (-0.094928) | 0.010451 / 0.075646 (-0.065195) | 0.388937 / 0.419271 (-0.030334) | 0.059974 / 0.043533 (0.016441) | 0.360437 / 0.255139 (0.105298) | 0.375149 / 0.283200 (0.091950) | 0.118397 / 0.141683 (-0.023286) | 1.726759 / 1.452155 (0.274604) | 1.811928 / 1.492716 (0.319212) |

### 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) | 0.239186 / 0.018006 (0.221180) | 0.483728 / 0.000490 (0.483238) | 0.003285 / 0.000200 (0.003085) | 0.000097 / 0.000054 (0.000043) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.030514 / 0.037411 (-0.006898) | 0.127111 / 0.014526 (0.112585) | 0.136185 / 0.176557 (-0.040371) | 0.204541 / 0.737135 (-0.532594) | 0.143228 / 0.296338 (-0.153111) |

### 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) | 0.465840 / 0.215209 (0.250631) | 4.611160 / 2.077655 (2.533506) | 2.119307 / 1.504120 (0.615187) | 1.882463 / 1.541195 (0.341268) | 1.946067 / 1.468490 (0.477577) | 0.602352 / 4.584777 (-3.982425) | 4.576313 / 3.745712 (0.830601) | 2.112860 / 5.269862 (-3.157001) | 1.224388 / 4.565676 (-3.341289) | 0.073808 / 0.424275 (-0.350467) | 0.013157 / 0.007607 (0.005550) | 0.592208 / 0.226044 (0.366163) | 5.948971 / 2.268929 (3.680042) | 2.690144 / 55.444624 (-52.754480) | 2.236489 / 6.876477 (-4.639987) | 2.423617 / 2.142072 (0.281545) | 0.752053 / 4.805227 (-4.053175) | 0.168185 / 6.500664 (-6.332480) | 0.075454 / 0.075469 (-0.000015) |

### 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) | 1.407432 / 1.841788 (-0.434356) | 17.054545 / 8.074308 (8.980236) | 15.661362 / 10.191392 (5.469970) | 0.175027 / 0.680424 (-0.505397) | 0.020262 / 0.534201 (-0.513939) | 0.479052 / 0.579283 (-0.100231) | 0.509829 / 0.434364 (0.075465) | 0.601935 / 0.540337 (0.061598) | 0.726754 / 1.386936 (-0.660182) |

PyArrow==latest

Show updated benchmarks!

### Benchmark: benchmark_array_xd.json

| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.007698 / 0.011353 (-0.003655) | 0.005267 / 0.011008 (-0.005741) | 0.085832 / 0.038508 (0.047324) | 0.041974 / 0.023109 (0.018865) | 0.418966 / 0.275898 (0.143068) | 0.466314 / 0.323480 (0.142834) | 0.006580 / 0.007986 (-0.001406) | 0.007063 / 0.004328 (0.002735) | 0.087120 / 0.004250 (0.082870) | 0.054908 / 0.037052 (0.017856) | 0.423813 / 0.258489 (0.165323) | 0.489878 / 0.293841 (0.196037) | 0.032823 / 0.128546 (-0.095723) | 0.010471 / 0.075646 (-0.065175) | 0.095839 / 0.419271 (-0.323432) | 0.056421 / 0.043533 (0.012888) | 0.420526 / 0.255139 (0.165387) | 0.447975 / 0.283200 (0.164775) | 0.126604 / 0.141683 (-0.015079) | 1.723097 / 1.452155 (0.270942) | 1.819539 / 1.492716 (0.326822) |

### 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) | 0.279604 / 0.018006 (0.261598) | 0.496129 / 0.000490 (0.495639) | 0.005419 / 0.000200 (0.005219) | 0.000096 / 0.000054 (0.000041) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.035069 / 0.037411 (-0.002343) | 0.133064 / 0.014526 (0.118538) | 0.145404 / 0.176557 (-0.031152) | 0.205237 / 0.737135 (-0.531898) | 0.150684 / 0.296338 (-0.145654) |

### 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) | 0.513596 / 0.215209 (0.298387) | 5.104861 / 2.077655 (3.027206) | 2.487908 / 1.504120 (0.983788) | 2.271383 / 1.541195 (0.730188) | 2.421043 / 1.468490 (0.952553) | 0.625204 / 4.584777 (-3.959573) | 4.555389 / 3.745712 (0.809677) | 4.181518 / 5.269862 (-1.088344) | 1.676059 / 4.565676 (-2.889617) | 0.078786 / 0.424275 (-0.345489) | 0.014186 / 0.007607 (0.006579) | 0.638360 / 0.226044 (0.412315) | 6.367915 / 2.268929 (4.098986) | 3.095175 / 55.444624 (-52.349449) | 2.706707 / 6.876477 (-4.169769) | 2.735907 / 2.142072 (0.593835) | 0.756323 / 4.805227 (-4.048905) | 0.164783 / 6.500664 (-6.335881) | 0.076291 / 0.075469 (0.000822) |

### 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) | 1.667058 / 1.841788 (-0.174730) | 18.687459 / 8.074308 (10.613151) | 17.111596 / 10.191392 (6.920204) | 0.167218 / 0.680424 (-0.513206) | 0.020995 / 0.534201 (-0.513206) | 0.463985 / 0.579283 (-0.115298) | 0.502705 / 0.434364 (0.068341) | 0.562877 / 0.540337 (0.022540) | 0.682249 / 1.386936 (-0.704687) |

![](https://cml.dev/watermark.png#028822a5d657f6c1251f61b56a701c4d7d2ab0a7 "CML watermark")

> Maybe we should fix all the tests in test_iterable_dataset.py that contain .with_format("torch")?

they're updated in https://github.com/huggingface/datasets/pull/5852

Show benchmarks

PyArrow==8.0.0

Show updated benchmarks!

### Benchmark: benchmark_array_xd.json

| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.005931 / 0.011353 (-0.005421) | 0.004004 / 0.011008 (-0.007004) | 0.098632 / 0.038508 (0.060124) | 0.027820 / 0.023109 (0.004711) | 0.302944 / 0.275898 (0.027046) | 0.332684 / 0.323480 (0.009204) | 0.005529 / 0.007986 (-0.002457) | 0.004814 / 0.004328 (0.000485) | 0.074477 / 0.004250 (0.070227) | 0.034875 / 0.037052 (-0.002178) | 0.304542 / 0.258489 (0.046053) | 0.342853 / 0.293841 (0.049012) | 0.025263 / 0.128546 (-0.103283) | 0.008558 / 0.075646 (-0.067089) | 0.322522 / 0.419271 (-0.096750) | 0.043980 / 0.043533 (0.000447) | 0.306618 / 0.255139 (0.051479) | 0.331692 / 0.283200 (0.048492) | 0.087434 / 0.141683 (-0.054248) | 1.464686 / 1.452155 (0.012531) | 1.575038 / 1.492716 (0.082322) |

### 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) | 0.221920 / 0.018006 (0.203914) | 0.417108 / 0.000490 (0.416619) | 0.004625 / 0.000200 (0.004425) | 0.000079 / 0.000054 (0.000024) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.023493 / 0.037411 (-0.013918) | 0.096684 / 0.014526 (0.082158) | 0.102035 / 0.176557 (-0.074522) | 0.166609 / 0.737135 (-0.570526) | 0.107456 / 0.296338 (-0.188883) |

### 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) | 0.418713 / 0.215209 (0.203504) | 4.156913 / 2.077655 (2.079258) | 1.869064 / 1.504120 (0.364944) | 1.666219 / 1.541195 (0.125024) | 1.676491 / 1.468490 (0.208001) | 0.553843 / 4.584777 (-4.030934) | 3.380471 / 3.745712 (-0.365241) | 2.970370 / 5.269862 (-2.299491) | 1.421597 / 4.565676 (-3.144080) | 0.068019 / 0.424275 (-0.356256) | 0.012995 / 0.007607 (0.005387) | 0.519410 / 0.226044 (0.293365) | 5.198251 / 2.268929 (2.929323) | 2.352969 / 55.444624 (-53.091655) | 2.008981 / 6.876477 (-4.867496) | 2.066519 / 2.142072 (-0.075553) | 0.658982 / 4.805227 (-4.146245) | 0.134341 / 6.500664 (-6.366323) | 0.065893 / 0.075469 (-0.009576) |

### 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) | 1.207509 / 1.841788 (-0.634279) | 13.863838 / 8.074308 (5.789530) | 13.363359 / 10.191392 (3.171967) | 0.129076 / 0.680424 (-0.551348) | 0.016818 / 0.534201 (-0.517383) | 0.357956 / 0.579283 (-0.221327) | 0.386174 / 0.434364 (-0.048189) | 0.418663 / 0.540337 (-0.121674) | 0.498708 / 1.386936 (-0.888228) |

PyArrow==latest

Show updated benchmarks!

### Benchmark: benchmark_array_xd.json

| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.006132 / 0.011353 (-0.005220) | 0.004335 / 0.011008 (-0.006673) | 0.078517 / 0.038508 (0.040009) | 0.027685 / 0.023109 (0.004576) | 0.357956 / 0.275898 (0.082058) | 0.392397 / 0.323480 (0.068918) | 0.005364 / 0.007986 (-0.002622) | 0.004922 / 0.004328 (0.000593) | 0.078061 / 0.004250 (0.073810) | 0.038889 / 0.037052 (0.001837) | 0.360952 / 0.258489 (0.102463) | 0.402790 / 0.293841 (0.108949) | 0.025542 / 0.128546 (-0.103004) | 0.008718 / 0.075646 (-0.066929) | 0.085799 / 0.419271 (-0.333472) | 0.044256 / 0.043533 (0.000723) | 0.358366 / 0.255139 (0.103227) | 0.393500 / 0.283200 (0.110300) | 0.096382 / 0.141683 (-0.045301) | 1.530889 / 1.452155 (0.078735) | 1.621007 / 1.492716 (0.128291) |

### 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) | 0.180572 / 0.018006 (0.162566) | 0.429478 / 0.000490 (0.428988) | 0.002966 / 0.000200 (0.002766) | 0.000074 / 0.000054 (0.000020) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.024530 / 0.037411 (-0.012881) | 0.101401 / 0.014526 (0.086875) | 0.108208 / 0.176557 (-0.068349) | 0.159582 / 0.737135 (-0.577554) | 0.111170 / 0.296338 (-0.185168) |

### 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) | 0.465768 / 0.215209 (0.250559) | 4.706311 / 2.077655 (2.628656) | 2.437756 / 1.504120 (0.933636) | 2.245694 / 1.541195 (0.704499) | 2.282637 / 1.468490 (0.814147) | 0.552752 / 4.584777 (-4.032025) | 3.432992 / 3.745712 (-0.312720) | 1.800054 / 5.269862 (-3.469808) | 1.037852 / 4.565676 (-3.527824) | 0.068240 / 0.424275 (-0.356035) | 0.012433 / 0.007607 (0.004826) | 0.574867 / 0.226044 (0.348822) | 5.707623 / 2.268929 (3.438695) | 2.909746 / 55.444624 (-52.534878) | 2.585423 / 6.876477 (-4.291054) | 2.636801 / 2.142072 (0.494729) | 0.686593 / 4.805227 (-4.118634) | 0.136633 / 6.500664 (-6.364031) | 0.068598 / 0.075469 (-0.006871) |

### 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) | 1.286628 / 1.841788 (-0.555159) | 14.333258 / 8.074308 (6.258949) | 14.355793 / 10.191392 (4.164401) | 0.133459 / 0.680424 (-0.546965) | 0.017090 / 0.534201 (-0.517111) | 0.358852 / 0.579283 (-0.220431) | 0.399929 / 0.434364 (-0.034435) | 0.422838 / 0.540337 (-0.117500) | 0.515199 / 1.386936 (-0.871737) |

![](https://cml.dev/watermark.png#7437d0f676da8634b5655a227cb8c3508c7372a2 "CML watermark")

Adding an optional `.iter_arrow` to examples iterable. This allows to use Arrow formatting in map/filter.

This will also be useful for torch formatting, since we can reuse the TorchFormatter that converts Arrow data to torch tensors

Related to https://github.com/huggingface/datasets/issues/5793 and https://github.com/huggingface/datasets/issues/3444

Required for https://github.com/huggingface/datasets/pull/5852

### Example:

Speed x10 in map

```python
from datasets import Dataset
import pyarrow.compute as pc
import time

ds = Dataset.from_dict({"a": range(100_000)})

ids = ds.to_iterable_dataset()
ids = ids.map(lambda x: {"a": [a + 10 for a in x["a"]]}, batched=True)

_start = time.time()
print(f"Python ({sum(1 for _ in ids)} items):\t{(time.time() - _start) * 1000:.1f}ms")
# Python (100000 items):  695.7ms

ids = ds.to_iterable_dataset().with_format("arrow")
ids = ids.map(lambda t: t.set_column(0, "a", pc.add(t[0], 10)), batched=True)
ids = ids.with_format(None)

_start = time.time()
print(f"Arrow ({sum(1 for _ in ids)} items):\t{(time.time() - _start) * 1000:.1f}ms)")
# Arrow (100000 items):   81.0ms)
```

### Implementation details

I added an optional `iter_arrow` method to examples iterable. If an example iterable has this method, then it can be used to iterate on the examples by batch of arrow tables.


Incomplete docstring for `BuilderConfig`

good first issue

Good for newcomers

Thanks for reporting! You are more than welcome to improve `BuilderConfig`'s docstring.

This class serves an identical purpose as `tensorflow_datasets`'s `BuilderConfig`, and its docstring is [here](https://github.com/tensorflow/datasets/blob/a95e38b5bb018312c3d3720619c2a8ef83ebf57f/tensorflow_datasets/core/dataset_builder.py#L81), so feel free to re-use parts of it.

Hi guys !
I stumbled upon this docstring while working on a project.
Some of the attributes have missing descriptions.

https://github.com/huggingface/datasets/blob/bc5fef5b6d91f009e4101684adcb374df2c170f6/src/datasets/builder.py#L104-L117