Update csv.py
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_The documentation is not available anymore as the PR was closed or merged._
2023-02-22T11:08:43Z
https://github.com/huggingface/datasets/pull/5562#issuecomment-1439832750
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CONTRIBUTOR
Removed it :)
2023-02-22T13:14:03Z
https://github.com/huggingface/datasets/pull/5562#issuecomment-1439997539
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CONTRIBUTOR
Changed it :)
2023-02-22T17:26:11Z
https://github.com/huggingface/datasets/pull/5562#issuecomment-1440467536
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<details>
<summary>Show benchmarks</summary>

PyArrow==6.0.0

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

### Benchmark: benchmark_array_xd.json

| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.008358 / 0.011353 (-0.002995) | 0.004555 / 0.011008 (-0.006453) | 0.100935 / 0.038508 (0.062427) | 0.029473 / 0.023109 (0.006364) | 0.336165 / 0.275898 (0.060266) | 0.420397 / 0.323480 (0.096917) | 0.006609 / 0.007986 (-0.001376) | 0.003338 / 0.004328 (-0.000991) | 0.078639 / 0.004250 (0.074388) | 0.034051 / 0.037052 (-0.003001) | 0.342820 / 0.258489 (0.084331) | 0.399392 / 0.293841 (0.105551) | 0.033935 / 0.128546 (-0.094611) | 0.011555 / 0.075646 (-0.064092) | 0.323467 / 0.419271 (-0.095804) | 0.040675 / 0.043533 (-0.002858) | 0.321247 / 0.255139 (0.066108) | 0.370967 / 0.283200 (0.087767) | 0.085766 / 0.141683 (-0.055917) | 1.461158 / 1.452155 (0.009003) | 1.504641 / 1.492716 (0.011925) |

### 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) | 0.180060 / 0.018006 (0.162053) | 0.403623 / 0.000490 (0.403134) | 0.002253 / 0.000200 (0.002053) | 0.000072 / 0.000054 (0.000017) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.022793 / 0.037411 (-0.014618) | 0.098869 / 0.014526 (0.084343) | 0.104512 / 0.176557 (-0.072045) | 0.167721 / 0.737135 (-0.569414) | 0.107969 / 0.296338 (-0.188370) |

### 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) | 0.411179 / 0.215209 (0.195969) | 4.095345 / 2.077655 (2.017690) | 1.825992 / 1.504120 (0.321872) | 1.624386 / 1.541195 (0.083192) | 1.654903 / 1.468490 (0.186413) | 0.695041 / 4.584777 (-3.889736) | 3.319087 / 3.745712 (-0.426625) | 1.881945 / 5.269862 (-3.387917) | 1.250360 / 4.565676 (-3.315316) | 0.082405 / 0.424275 (-0.341870) | 0.012499 / 0.007607 (0.004892) | 0.522846 / 0.226044 (0.296801) | 5.241103 / 2.268929 (2.972175) | 2.293100 / 55.444624 (-53.151524) | 1.942937 / 6.876477 (-4.933540) | 1.957434 / 2.142072 (-0.184638) | 0.809782 / 4.805227 (-3.995445) | 0.148290 / 6.500664 (-6.352374) | 0.064157 / 0.075469 (-0.011312) |

### 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) | 1.185616 / 1.841788 (-0.656172) | 13.616791 / 8.074308 (5.542483) | 13.741806 / 10.191392 (3.550414) | 0.137396 / 0.680424 (-0.543028) | 0.028751 / 0.534201 (-0.505450) | 0.397636 / 0.579283 (-0.181647) | 0.403594 / 0.434364 (-0.030770) | 0.484039 / 0.540337 (-0.056299) | 0.568398 / 1.386936 (-0.818538) |

</details>
PyArrow==latest

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

### Benchmark: benchmark_array_xd.json

| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |
|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| new / old (diff) | 0.006712 / 0.011353 (-0.004640) | 0.004511 / 0.011008 (-0.006497) | 0.076946 / 0.038508 (0.038438) | 0.027219 / 0.023109 (0.004110) | 0.350769 / 0.275898 (0.074871) | 0.408539 / 0.323480 (0.085059) | 0.005014 / 0.007986 (-0.002971) | 0.003361 / 0.004328 (-0.000968) | 0.077106 / 0.004250 (0.072856) | 0.040105 / 0.037052 (0.003053) | 0.342041 / 0.258489 (0.083552) | 0.426355 / 0.293841 (0.132514) | 0.031684 / 0.128546 (-0.096863) | 0.011575 / 0.075646 (-0.064072) | 0.085797 / 0.419271 (-0.333474) | 0.041575 / 0.043533 (-0.001958) | 0.340837 / 0.255139 (0.085698) | 0.390461 / 0.283200 (0.107262) | 0.089531 / 0.141683 (-0.052152) | 1.504600 / 1.452155 (0.052445) | 1.538712 / 1.492716 (0.045996) |

### 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) | 0.236679 / 0.018006 (0.218673) | 0.396258 / 0.000490 (0.395768) | 0.006479 / 0.000200 (0.006279) | 0.000081 / 0.000054 (0.000026) |

### Benchmark: benchmark_indices_mapping.json

| metric | select | shard | shuffle | sort | train_test_split |
|--------|---|---|---|---|---|
| new / old (diff) | 0.024682 / 0.037411 (-0.012729) | 0.100167 / 0.014526 (0.085641) | 0.106627 / 0.176557 (-0.069929) | 0.174592 / 0.737135 (-0.562543) | 0.109499 / 0.296338 (-0.186839) |

### 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) | 0.444702 / 0.215209 (0.229493) | 4.462779 / 2.077655 (2.385125) | 2.087711 / 1.504120 (0.583591) | 1.874900 / 1.541195 (0.333705) | 1.918609 / 1.468490 (0.450119) | 0.705867 / 4.584777 (-3.878910) | 3.355483 / 3.745712 (-0.390229) | 2.808348 / 5.269862 (-2.461514) | 1.253319 / 4.565676 (-3.312358) | 0.083747 / 0.424275 (-0.340528) | 0.012491 / 0.007607 (0.004884) | 0.542885 / 0.226044 (0.316841) | 5.453921 / 2.268929 (3.184993) | 2.545688 / 55.444624 (-52.898937) | 2.185022 / 6.876477 (-4.691455) | 2.215351 / 2.142072 (0.073279) | 0.808201 / 4.805227 (-3.997027) | 0.151754 / 6.500664 (-6.348910) | 0.066886 / 0.075469 (-0.008583) |

### 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) | 1.298583 / 1.841788 (-0.543205) | 14.014276 / 8.074308 (5.939968) | 13.505338 / 10.191392 (3.313946) | 0.142033 / 0.680424 (-0.538391) | 0.016863 / 0.534201 (-0.517338) | 0.381195 / 0.579283 (-0.198088) | 0.384455 / 0.434364 (-0.049909) | 0.465765 / 0.540337 (-0.074572) | 0.552571 / 1.386936 (-0.834366) |

</details>
</details>

![](https://cml.dev/watermark.png#a29cca79ce64a5c64ad7047e57845b22154d7b8d "CML watermark")

2023-02-23T11:07:49Z
https://github.com/huggingface/datasets/pull/5562#issuecomment-1441570052
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Removed mangle_dup_cols=True from BuilderConfig.

It triggered following deprecation warning:
/usr/local/lib/python3.8/dist-packages/datasets/download/streaming_download_manager.py:776: FutureWarning: the 'mangle_dupe_cols' keyword is deprecated and will be removed in a future version. Please take steps to stop the use of 'mangle_dupe_cols'
  return pd.read_csv(xopen(filepath_or_buffer, "rb", use_auth_token=use_auth_token), **kwargs)

Further documentation of pandas: https://pandas.pydata.org/docs/whatsnew/v1.4.0.html#mangle-dupe-cols-in-read-csv-no-longer-renames-unique-columns-conflicting-with-target-names

At first sight it seems like this flag is resolved internally, it might need some more research.