# pandas.array

`pandas.``array`(data: Sequence[object], dtype: Union[str, numpy.dtype, pandas.core.dtypes.base.ExtensionDtype, None] = None, copy: bool = True) → pandas.core.dtypes.generic.ABCExtensionArray

  Create an array.

  New in version 0.24.0.

  Parameters:

  data : Sequence of objects

  > The scalars inside data should be instances of the scalar type for dtype. It’s expected that data represents a 1-dimensional array of data.

  > When data is an Index or Series, the underlying array will be extracted from data.

  dtype : str, np.dtype, or ExtensionDtype, optional

  > The dtype to use for the array. This may be a NumPy dtype or an extension type registered with pandas using `pandas.api.extensions.register_extension_dtype()`.

  > If not specified, there are two possibilities:

  > 1. When data is a `Series`, `Index`, or `ExtensionArray`, the dtype will be taken from the data.
  > 2. Otherwise, pandas will attempt to infer the dtype from the data.

  > Note that when data is a NumPy array, `data.dtype` is not used for inferring the array type. This is because NumPy cannot represent all the types of data that can be held in extension arrays.

  > Currently, pandas will infer an extension dtype for sequences of

  > Scalar Type | Array Type
  > --- | ---
  > `pandas.Interval` | `pandas.arrays.IntervalArray`
  > `pandas.Period` | `pandas.arrays.PeriodArray`
  > `datetime.datetime` | `pandas.arrays.DatetimeArray`
  > `datetime.timedelta` | `pandas.arrays.TimedeltaArray`

  > For all other cases, NumPy’s usual inference rules will be used.

  copy : bool, default True

  > Whether to copy the data, even if not necessary. Depending on the type of data, creating the new array may require copying data, even if `copy=False`.

  Returns:

  ExtensionArray

  > The newly created array.

  Raises:

  ValueError

  > When data is not 1-dimensional.

  See also

  `numpy.array`
    Construct a NumPy array.
  `Series`
    Construct a pandas Series.
  `Index`
    Construct a pandas Index.
  `arrays.PandasArray`
    ExtensionArray wrapping a NumPy array.
  `Series.array`
    Extract the array stored within a Series.

  Notes

  Omitting the dtype argument means pandas will attempt to infer the best array type from the values in the data. As new array types are added by pandas and 3rd party libraries, the “best” array type may change. We recommend specifying dtype to ensure that

  1. the correct array type for the data is returned
  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 `arrays.PandasArray` backed by a NumPy array.

      >>> pd.array(['a', 'b'], dtype=str)
      <PandasArray>
      ['a', 'b']
      Length: 2, dtype: str32

  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"))
      <PandasArray>
      ['a', 'b']
      Length: 2, dtype: str32

  Or use the dedicated constructor for the array you’re expecting, and wrap that in a PandasArray

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

  Finally, Pandas has arrays that mostly overlap with NumPy

  > - `arrays.DatetimeArray`
  > - `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 `PandasArray`. 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>
      ['01:00:00', '02:00:00']
      Length: 2, dtype: timedelta64[ns]

  Examples

  If a dtype is not specified, data is passed through to `numpy.array()`, and a `arrays.PandasArray` is returned.

      >>> pd.array([1, 2])
      <PandasArray>
      [1, 2]
      Length: 2, dtype: int64

  Or the NumPy dtype can be specified

      >>> pd.array([1, 2], dtype=np.dtype("int32"))
      <PandasArray>
      [1, 2]
      Length: 2, dtype: int32

  You can use the string alias for dtype

      >>> pd.array(['a', 'b', 'a'], dtype='category')
      [a, b, a]
      Categories (2, object): [a, b]

  Or specify the actual dtype

      >>> pd.array(['a', 'b', 'a'],
      ...          dtype=pd.CategoricalDtype(['a', 'b', 'c'], ordered=True))
      [a, b, a]
      Categories (3, object): [a < b < c]

  Because omitting the dtype passes the data through to NumPy, a mixture of valid integers and NA will return a floating-point NumPy array.

      >>> pd.array([1, 2, np.nan])
      <PandasArray>
      [1.0,  2.0, nan]
      Length: 3, dtype: float64

  To use pandas’ nullable `pandas.arrays.IntegerArray`, specify the dtype:

      >>> pd.array([1, 2, np.nan], dtype='Int64')
      <IntegerArray>
      [1, 2, NaN]
      Length: 3, dtype: Int64

  Pandas will infer an ExtensionArray for some types of data:

      >>> pd.array([pd.Period('2000', freq="D"), pd.Period("2000", freq="D")])
      <PeriodArray>
      ['2000-01-01', '2000-01-01']
      Length: 2, dtype: period[D]

  data must be 1-dimensional. A ValueError is raised when the input has the wrong dimensionality.

      >>> pd.array(1)
      Traceback (most recent call last):
        ...
      ValueError: Cannot pass scalar '1' to 'pandas.array'.

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