pandas.array

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

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'.