fix

Author: rok

docs/source/python/data.rst

@@ -714,7 +714,7 @@ A record batch can be created from this list of arrays using
    4
    >>> batch.schema
    f0: int64
-   f1: ...string
+   f1: string
    f2: bool
    >>>
    >>> batch[1]
@@ -761,7 +761,7 @@ one or more copies of the batch using ``Table.from_batches``:
    >>> table
    pyarrow.Table
    f0: int64
-   f1: ...string
+   f1: string
    f2: bool
    ----
    f0: [[1,2,3,4],[1,2,3,4],...,[1,2,3,4],[1,2,3,4]]

docs/source/python/ipc.rst

@@ -94,7 +94,7 @@ convenience function ``pyarrow.ipc.open_stream``:
    ...     batches = [b for b in reader]
    >>> schema
    f0: int64
-   f1: ...string
+   f1: string
    f2: bool
    >>> len(batches)
    5

docs/source/python/pandas.rst

@@ -224,7 +224,7 @@ use the ``datetime64[ns]`` type in Pandas and are converted to an Arrow
 
    >>> df = pd.DataFrame({"datetime": pd.date_range("2020-01-01T00:00:00Z", freq="h", periods=3)})
    >>> df.dtypes
-   datetime    datetime64[ns, UTC]
+   datetime    datetime64[..., UTC]
    dtype: object
    >>> df
                       datetime
@@ -234,9 +234,9 @@ use the ``datetime64[ns]`` type in Pandas and are converted to an Arrow
    >>> table = pa.Table.from_pandas(df)
    >>> table
    pyarrow.Table
-   datetime: timestamp[ns, tz=UTC]
+   datetime: timestamp[..., tz=UTC]
    ----
-   datetime: [[2020-01-01 00:00:00.000000000Z,...,2020-01-01 02:00:00.000000000Z]]
+   datetime: [[2020-01-01 00:00:00...]]
 
 In this example the Pandas Timestamp is time zone aware
 (``UTC`` on this case), and this information is used to create the Arrow

docs/source/python/parquet.rst

@@ -350,7 +350,7 @@ and improved performance for columns with many repeated string values.
    >>> pq.read_table('example.parquet', read_dictionary=['two'])
    pyarrow.Table
    one: double
-   two: dictionary<values=...string, indices=int32, ordered=0>
+   two: dictionary<values=string, indices=int32, ordered=0>
    three: bool
    __index_level_0__: ...string
    ----