revert the added okwarning to user guide

doc/source/user_guide/10min.rst

@@ -763,14 +763,12 @@ Parquet
 Writing to a Parquet file:
 
 .. ipython:: python
-   :okwarning:
 
    df.to_parquet("foo.parquet")
 
 Reading from a Parquet file Store using :func:`read_parquet`:
 
 .. ipython:: python
-   :okwarning:
 
    pd.read_parquet("foo.parquet")
 

doc/source/user_guide/io.rst

@@ -2247,7 +2247,6 @@ For line-delimited json files, pandas can also return an iterator which reads in
 Line-limited json can also be read using the pyarrow reader by specifying ``engine="pyarrow"``.
 
 .. ipython:: python
-   :okwarning:
 
    from io import BytesIO
    df = pd.read_json(BytesIO(jsonl.encode()), lines=True, engine="pyarrow")
@@ -5372,15 +5371,13 @@ See the documentation for `pyarrow <https://arrow.apache.org/docs/python/>`__ an
 Write to a parquet file.
 
 .. ipython:: python
-   :okwarning:
 
    df.to_parquet("example_pa.parquet", engine="pyarrow")
    df.to_parquet("example_fp.parquet", engine="fastparquet")
 
 Read from a parquet file.
 
 .. ipython:: python
-   :okwarning:
 
    result = pd.read_parquet("example_fp.parquet", engine="fastparquet")
    result = pd.read_parquet("example_pa.parquet", engine="pyarrow")
@@ -5390,7 +5387,6 @@ Read from a parquet file.
 By setting the ``dtype_backend`` argument you can control the default dtypes used for the resulting DataFrame.
 
 .. ipython:: python
-   :okwarning:
 
    result = pd.read_parquet("example_pa.parquet", engine="pyarrow", dtype_backend="pyarrow")
 
@@ -5404,7 +5400,6 @@ By setting the ``dtype_backend`` argument you can control the default dtypes use
 Read only certain columns of a parquet file.
 
 .. ipython:: python
-   :okwarning:
 
    result = pd.read_parquet(
        "example_fp.parquet",
@@ -5433,7 +5428,6 @@ Serializing a ``DataFrame`` to parquet may include the implicit index as one or
 more columns in the output file. Thus, this code:
 
 .. ipython:: python
-   :okwarning:
 
     df = pd.DataFrame({"a": [1, 2], "b": [3, 4]})
     df.to_parquet("test.parquet", engine="pyarrow")
@@ -5450,7 +5444,6 @@ If you want to omit a dataframe's indexes when writing, pass ``index=False`` to
 :func:`~pandas.DataFrame.to_parquet`:
 
 .. ipython:: python
-   :okwarning:
 
     df.to_parquet("test.parquet", index=False)
 
@@ -5473,7 +5466,6 @@ Partitioning Parquet files
 Parquet supports partitioning of data based on the values of one or more columns.
 
 .. ipython:: python
-   :okwarning:
 
     df = pd.DataFrame({"a": [0, 0, 1, 1], "b": [0, 1, 0, 1]})
     df.to_parquet(path="test", engine="pyarrow", partition_cols=["a"], compression=None)
@@ -5539,14 +5531,12 @@ ORC format, :func:`~pandas.read_orc` and :func:`~pandas.DataFrame.to_orc`. This
 Write to an orc file.
 
 .. ipython:: python
-   :okwarning:
 
    df.to_orc("example_pa.orc", engine="pyarrow")
 
 Read from an orc file.
 
 .. ipython:: python
-   :okwarning:
 
    result = pd.read_orc("example_pa.orc")
 
@@ -5555,7 +5545,6 @@ Read from an orc file.
 Read only certain columns of an orc file.
 
 .. ipython:: python
-   :okwarning:
 
    result = pd.read_orc(
        "example_pa.orc",

doc/source/user_guide/pyarrow.rst

@@ -104,7 +104,6 @@ To convert a :external+pyarrow:py:class:`pyarrow.Table` to a :class:`DataFrame`,
 :external+pyarrow:py:meth:`pyarrow.Table.to_pandas` method with ``types_mapper=pd.ArrowDtype``.
 
 .. ipython:: python
-   :okwarning:
 
    table = pa.table([pa.array([1, 2, 3], type=pa.int64())], names=["a"])
 
@@ -165,7 +164,6 @@ functions provide an ``engine`` keyword that can dispatch to PyArrow to accelera
 * :func:`read_feather`
 
 .. ipython:: python
-   :okwarning:
 
    import io
    data = io.StringIO("""a,b,c
@@ -180,7 +178,6 @@ PyArrow-backed data by specifying the parameter ``dtype_backend="pyarrow"``. A r
 ``engine="pyarrow"`` to necessarily return PyArrow-backed data.
 
 .. ipython:: python
-   :okwarning:
 
     import io
     data = io.StringIO("""a,b,c,d,e,f,g,h,i

doc/source/user_guide/scale.rst

@@ -51,7 +51,6 @@ To load the columns we want, we have two options.
 Option 1 loads in all the data and then filters to what we need.
 
 .. ipython:: python
-   :okwarning:
 
    columns = ["id_0", "name_0", "x_0", "y_0"]
 
@@ -60,7 +59,6 @@ Option 1 loads in all the data and then filters to what we need.
 Option 2 only loads the columns we request.
 
 .. ipython:: python
-   :okwarning:
 
    pd.read_parquet("timeseries_wide.parquet", columns=columns)
 
@@ -202,7 +200,6 @@ counts up to this point. As long as each individual file fits in memory, this wi
 work for arbitrary-sized datasets.
 
 .. ipython:: python
-   :okwarning:
 
    %%time
    files = pathlib.Path("data/timeseries/").glob("ts*.parquet")

doc/source/whatsnew/v2.0.0.rst

@@ -152,7 +152,6 @@ When this keyword is set to ``"pyarrow"``, then these functions will return pyar
 * :meth:`Series.convert_dtypes`
 
 .. ipython:: python
-    :okwarning:
 
     import io
     data = io.StringIO("""a,b,c,d,e,f,g,h,i