pandas.array(data, dtype=None, copy=True)[source]

Create an array.

New in version 0.24.0.

dataSequence 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.

dtypestr, 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

















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

Changed in version 1.0.0: Pandas infers nullable-integer dtype for integer data, string dtype for string data, and nullable-boolean dtype for boolean data.

Changed in version 1.2.0: Pandas now also infers nullable-floating dtype for float-like input data

copybool, 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.


The newly created array.


When data is not 1-dimensional.

See also


Construct a NumPy array.


Construct a pandas Series.


Construct a pandas Index.


ExtensionArray wrapping a NumPy array.


Extract the array stored within a Series.


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

Finally, Pandas has arrays that mostly overlap with NumPy

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]')
['2015-01-01 00:00:00', '2016-01-01 00:00:00']
Length: 2, dtype: datetime64[ns]
>>> pd.array(["1H", "2H"], dtype='timedelta64[ns]')
['0 days 01:00:00', '0 days 02:00:00']
Length: 2, dtype: timedelta64[ns]


If a dtype is not specified, pandas will infer the best dtype from the values. See the description of dtype for the types pandas infers for.

>>> pd.array([1, 2])
[1, 2]
Length: 2, dtype: Int64
>>> pd.array([1, 2, np.nan])
[1, 2, <NA>]
Length: 3, dtype: Int64
>>> pd.array([1.1, 2.2])
[1.1, 2.2]
Length: 2, dtype: Float64
>>> pd.array(["a", None, "c"])
['a', <NA>, 'c']
Length: 3, dtype: string
>>> pd.array([pd.Period('2000', freq="D"), pd.Period("2000", freq="D")])
['2000-01-01', '2000-01-01']
Length: 2, dtype: period[D]

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']

If pandas does not infer a dedicated extension type a arrays.PandasArray is returned.

>>> pd.array([1 + 1j, 3 + 2j])
[(1+1j), (3+2j)]
Length: 2, dtype: complex128

As mentioned in the “Notes” section, new extension types may be added in the future (by pandas or 3rd party libraries), causing the return value to no longer be a arrays.PandasArray. Specify the dtype as a NumPy dtype if you need to ensure there’s no future change in behavior.

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

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'.