Repository: pandas-dev/pandas
File: pandas/core/construction.py (Python)

Code before:
    2. the returned array type doesn't change as new extension types
       are added by pandas and third-party libraries

    Additionally, if the underlying memory representation of the returned
    array matters, we recommend specifying the `dtype` as a concrete object
    rather than a string alias or allowing it to be inferred. For example,
    a future version of pandas or a 3rd-party library may include a
    dedicated ExtensionArray for string data. In this event, the following
    would no longer return a :class:`arrays.NumpyExtensionArray` backed by a
    NumPy array.

    >>> pd.array(["a", "b"], dtype=str)
    <ArrowStringArray>
    ['a', 'b']
    Length: 2, dtype: str

    This would instead return the new ExtensionArray dedicated for string
    data. If you really need the new array to be backed by a  NumPy array,
    specify that in the dtype.

    >>> pd.array(["a", "b"], dtype=np.dtype("<U1"))
    <NumpyExtensionArray>
    ['a', 'b']
    Length: 2, dtype: str32

    Pandas converts entries of a multidimensional sequence(excluding NumPy arrays)
    to its string representation if the ``dtype`` is ``str`` or any extension string
    data type.

    >>> pd.array([[1], [2], [3]], dtype="str")
    <ArrowStringArray>
    ['[1]', '[2]', '[3]']
    Length: 3, dtype: str

    Finally, Pandas has arrays that mostly overlap with NumPy

      * :class:`arrays.DatetimeArray`
      * :class:`arrays.TimedeltaArray`

    When data with a ``datetime64[ns]`` or ``timedelta64[ns]`` dtype is
    passed, pandas will always return a ``DatetimeArray`` or ``TimedeltaArray``
    rather than a ``NumpyExtensionArray``. This is for symmetry with the case of
    timezone-aware data, which NumPy does not natively support.

    >>> pd.array(["2015", "2016"], dtype="datetime64[ns]")
    <DatetimeArray>
    ['2015-01-01 00:00:00', '2016-01-01 00:00:00']
    Length: 2, dtype: datetime64[ns]

    >>> pd.array(["1h", "2h"], dtype="timedelta64[ns]")
    <TimedeltaArray>

Review comment (suggestion):
```suggestion
    pandas converts entries of a multidimensional sequence (excluding NumPy arrays)
```

Code after:
    2. the returned array type doesn't change as new extension types
       are added by pandas and third-party libraries

    Additionally, if the underlying memory representation of the returned
    array matters, we recommend specifying the `dtype` as a concrete object
    rather than a string alias or allowing it to be inferred. For example,
    a future version of pandas or a 3rd-party library may include a
    dedicated ExtensionArray for string data. In this event, the following
    would no longer return a :class:`arrays.NumpyExtensionArray` backed by a
    NumPy array.

    >>> pd.array(["a", "b"], dtype=str)
    <ArrowStringArray>
    ['a', 'b']
    Length: 2, dtype: str

    This would instead return the new ExtensionArray dedicated for string
    data. If you really need the new array to be backed by a  NumPy array,
    specify that in the dtype.

    >>> pd.array(["a", "b"], dtype=np.dtype("<U1"))
    <NumpyExtensionArray>
    ['a', 'b']
    Length: 2, dtype: str32

    pandas converts entries of a multidimensional sequence (excluding NumPy arrays)
    to its string representation if the ``dtype`` is a string data type.

    >>> pd.array([[1], [2], [3]], dtype="str")
    <ArrowStringArray>
    ['[1]', '[2]', '[3]']
    Length: 3, dtype: str

    Finally, Pandas has arrays that mostly overlap with NumPy

      * :class:`arrays.DatetimeArray`
      * :class:`arrays.TimedeltaArray`

    When data with a ``datetime64[ns]`` or ``timedelta64[ns]`` dtype is
    passed, pandas will always return a ``DatetimeArray`` or ``TimedeltaArray``
    rather than a ``NumpyExtensionArray``. This is for symmetry with the case of
    timezone-aware data, which NumPy does not natively support.

    >>> pd.array(["2015", "2016"], dtype="datetime64[ns]")
    <DatetimeArray>
    ['2015-01-01 00:00:00', '2016-01-01 00:00:00']
    Length: 2, dtype: datetime64[ns]

    >>> pd.array(["1h", "2h"], dtype="timedelta64[ns]")
    <TimedeltaArray>
    ['0 days 01:00:00', '0 days 02:00:00']