This method is used to create an array from a sequence in desired data type.
Syntax : pandas.array(data: Sequence[object], dtype: Union[str, numpy.dtype, pandas.core.dtypes.base.ExtensionDtype, NoneType] = None, copy: bool = True)
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 : tr, 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.
- 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“.
Below is the implementation of the above method with some examples :
Example 1 :
Python3
# importing packages import pandas # create Pandas array with dtype string pd_arr = pandas.array(data=[1,2,3,4,5],dtype=str) # print the formed array print(pd_arr) |
Output :
<PandasArray> ['1', '2', '3', '4', '5'] Length: 5, dtype: str32
Example 2 :
Python3
# importing packages import pandas import numpy # create Pandas array with dtype from numpy pd_arr = pandas.array(data=['1', '2', '3', '4', '5'], dtype=numpy.int8) # print the formed array print(pd_arr) |
Output :
<PandasArray> [1, 2, 3, 4, 5] Length: 5, dtype: int8
