Datasets load error for saved github issues
State: open

### Describe the bug

Loading a previously downloaded & saved dataset as described in the HuggingFace course:

issues_dataset = load_dataset("json", data_files="issues/datasets-issues.jsonl", split="train")

Gives this error:
datasets.builder.DatasetGenerationError: An error occurred while generating the dataset

A work-around I found was to use streaming.

### Steps to reproduce the bug

Reproduce by executing the code provided:
https://huggingface.co/course/chapter5/5?fw=pt
From the heading:
'let’s create a function that can download all the issues from a GitHub repository'

### Expected behavior

No error

### Environment info

Datasets version 2.8.0. Note that version 2.6.1 gives the same error (related to null timestamp).

**[EDIT]**

This is the complete error trace confirming the issue is related to the timestamp (`Couldn't cast array of type timestamp[s] to null`)

```
Using custom data configuration default-950028611d2860c8
Downloading and preparing dataset json/default to [...]/.cache/huggingface/datasets/json/default-950028611d2860c8/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51...
Downloading data files: 100%|██████████| 1/1 [00:00<?, ?it/s]
Extracting data files: 100%|██████████| 1/1 [00:00<00:00, 500.63it/s]
Generating train split: 2619 examples [00:00, 7155.72 examples/s]Traceback (most recent call last):
  File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\builder.py", line 1831, in _prepare_split_single
    writer.write_table(table)
  File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\arrow_writer.py", line 567, in write_table
    pa_table = table_cast(pa_table, self._schema)
  File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\table.py", line 2282, in table_cast
    return cast_table_to_schema(table, schema)
  File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\table.py", line 2241, in cast_table_to_schema
    arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
  File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\table.py", line 2241, in <listcomp>
    arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
  File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\table.py", line 1807, in wrapper
    return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
  File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\table.py", line 1807, in <listcomp>
    return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
  File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\table.py", line 2035, in cast_array_to_feature
    arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]
  File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\table.py", line 2035, in <listcomp>
    arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]
  File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\table.py", line 1809, in wrapper
    return func(array, *args, **kwargs)
  File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\table.py", line 2101, in cast_array_to_feature
    return array_cast(array, feature(), allow_number_to_str=allow_number_to_str)
  File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\table.py", line 1809, in wrapper
    return func(array, *args, **kwargs)
  File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\table.py", line 1990, in array_cast
    raise TypeError(f"Couldn't cast array of type {array.type} to {pa_type}")
TypeError: Couldn't cast array of type timestamp[s] to null
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
  File "C:\Program Files\JetBrains\PyCharm 2022.1.3\plugins\python\helpers\pydev\pydevconsole.py", line 364, in runcode
    coro = func()
  File "<input>", line 1, in <module>
  File "C:\Program Files\JetBrains\PyCharm 2022.1.3\plugins\python\helpers\pydev\_pydev_bundle\pydev_umd.py", line 198, in runfile
    pydev_imports.execfile(filename, global_vars, local_vars)  # execute the script
  File "C:\Program Files\JetBrains\PyCharm 2022.1.3\plugins\python\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile
    exec(compile(contents+"\n", file, 'exec'), glob, loc)
  File "[...]\PycharmProjects\TransformersTesting\dataset_issues.py", line 20, in <module>
    issues_dataset = load_dataset("json", data_files="issues/datasets-issues.jsonl", split="train")
  File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\load.py", line 1757, in load_dataset
    builder_instance.download_and_prepare(
  File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\builder.py", line 860, in download_and_prepare
    self._download_and_prepare(
  File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\builder.py", line 953, in _download_and_prepare
    self._prepare_split(split_generator, **prepare_split_kwargs)
  File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\builder.py", line 1706, in _prepare_split
    for job_id, done, content in self._prepare_split_single(
  File "[...]\miniconda3\envs\HuggingFace\lib\site-packages\datasets\builder.py", line 1849, in _prepare_split_single
    raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.builder.DatasetGenerationError: An error occurred while generating the dataset
Generating train split: 2619 examples [00:19, 7155.72 examples/s]
```

Comments:
I can confirm that the error exists!
I'm trying to read 3 parquet files locally:
```python
from datasets import load_dataset, Features, Value, ClassLabel

review_dataset = load_dataset(
    "parquet",
    data_files={
        "train": os.path.join(sentiment_analysis_data_path, "train.parquet"),
        "validation": os.path.join(sentiment_analysis_data_path, "validation.parquet"),
        "test": os.path.join(sentiment_analysis_data_path, "test.parquet"),
    },
)
```

But you can fix it, by specifying `features` for `load_dataset()` function like this:
```python
from datasets import load_dataset, Features, Value, ClassLabel

features = Features(
    {
        "label": ClassLabel(
            num_classes=3,
            names=["negative", "neutral", "positive"],
        ),
        "text": Value(dtype="string"),
    }
)

review_dataset = load_dataset(
    "parquet",
    data_files={
        "train": os.path.join(sentiment_analysis_data_path, "train.parquet"),
        "validation": os.path.join(sentiment_analysis_data_path, "validation.parquet"),
        "test": os.path.join(sentiment_analysis_data_path, "test.parquet"),
    },
    features=features,
)

print(review_dataset)
```

@Extremesarova I think this is a different issue, but understand using features could be a work-around.
It seems the field `closed_at` is `null` in many cases.

I've not found a way to specify only a single feature without (succesfully) specifiying the full and quite detailed set of expected features. Using this features set gives an error the column names don't match.
`features = Features({'closed_at': Value(dtype='timestamp[s]', id=None)})`



Found this when searching for the same error, looks like based on #3965 it's just an issue with the data. I found that changing `df = pd.DataFrame.from_records(all_issues)` to `df = pd.DataFrame.from_records(all_issues).dropna(axis=1, how='all').drop(['milestone'], axis=1)` from the fetch_issues function fixed the issue. 
The "milestone" column seemed to be problematic (only ~50 non null rows) and dropped any columns that were all null as well just in case.

I have this same issue.  I saved a dataset to disk and now I can't load it.

Ok the solution was to use load_from_disk instead of load_dataset.

Hi @folterj , I faced same issue while creating `issues_dataset` (https://huggingface.co/learn/nlp-course/chapter5/5?fw=pt). The fix which worked for me was loading the *.jsonl file as pd.read_json and then converting it into a Dataset using datasets API.
```
import pandas as pd
df=pd.read_json("datasets-issues.jsonl", lines=True)
df.head()

from datasets import Dataset
issues_dataset = Dataset.from_pandas(df)
issues_dataset
sample = issues_dataset.shuffle(seed=666).select(range(3))
sample[0]
```

I understand some work-around suggestions would be to not use load_dataset(), and instead using a different API function. Another alternative would be using the same function using streaming, as I had already suggested in my original post.
However, the fact remains that there is an issue in this function as reported.