NestedExtensionArray

class NestedExtensionArray(values: Array | ChunkedArray, *, validate: bool = True)

Pandas extension array for nested dataframes

Parameters:

• values (pyarrow.Array or pyarrow.ChunkedArray) – The array to be wrapped, must be a struct array with all fields being list arrays of the same lengths.

• validate (bool, default True) – Whether to validate the input array.

Raises:

ValueError – If the input array is not a struct array or if any of the fields is not a list array or if the list arrays have different lengths.

__init__(values: Array | ChunkedArray, *, validate: bool = True) → None

Methods

__init__(values, *[, validate])
argmax([skipna])	Return the index of maximum value.
argmin([skipna])	Return the index of minimum value.
argsort(*[, ascending, kind, na_position])	Return the indices that would sort this array.
astype(dtype[, copy])	Cast to a NumPy array or ExtensionArray with 'dtype'.
copy()	Return a copy of the extension array.
delete(loc)
dropna()	Return a new ExtensionArray with missing values removed.
duplicated([keep])	Return boolean ndarray denoting duplicate values.
equals(other)	Check equality with another NestedExtensionArray.
factorize([use_na_sentinel])	Encode the extension array as an enumerated type.
fill_field_lists(field, value, *[, keep_dtype])	Fill list-arrays with values from the input array
fillna([value, method, limit, copy])	Fill NA/NaN values using the specified method.
from_arrow_ext_array(array)	Create a NestedExtensionArray from pandas' ArrowExtensionArray
from_sequence(scalars, *[, dtype])	Construct a NestedExtensionArray from a sequence of items
get_list_index()	Keys mapping values to lists
insert(loc, item)	Insert an item at the given position.
interpolate(*, method, axis, index, limit, ...)	Interpolate missing values, not implemented yet.
is_input_pa_type_supported(pa_type)	Check whether a pyarrow data type is supported by the constructor.
isin(values)	Pointwise comparison for set containment in the given values.
isna()	Boolean NumPy array indicating if each value is missing.
iter_field_lists(field)	Iterate over single field nested lists, as numpy arrays
map(mapper[, na_action])	Map values using an input mapping or function.
pop_fields(fields)	Delete fields from the struct array
ravel([order])	Return a flattened view on this array.
repeat(repeats[, axis])	Repeat elements of a ExtensionArray.
searchsorted(value[, side, sorter])	Find indices where elements should be inserted to maintain order.
set_flat_field(field, value, *[, keep_dtype])	Set the field from flat-array of values
set_list_field(field, value, *[, keep_dtype])	Set the field from list-array
shift([periods, fill_value])	Shift values by desired number.
take(indices, *[, allow_fill, fill_value])	Take elements from an array.
to_arrow_ext_array([list_struct, large_list])	Convert the extension array to pandas' ArrowExtensionArray
to_numpy(dtype, ...)	Convert the extension array to a numpy array.
to_pyarrow_scalar([list_struct, large_list])	Convert to a pyarrow scalar of a list type
tolist()	Return a list of the values.
transpose(*axes)	Return a transposed view on this array.
unique()	Compute the ExtensionArray of unique values.
view([dtype])	Return a view on the array.
view_fields(fields)	Get a view of the extension array with the specified fields only

Attributes

T
dtype	ExtensionArray dtype
field_names	Names of the nested columns
flat_length	Length of the flat arrays
list_array	Pyarrow chunked list-struct array representation
list_lengths	Lengths of the list arrays
list_offsets	The list offsets of the field arrays.
nbytes	Number of bytes consumed by the data in memory.
ndim	Extension Arrays are only allowed to be 1-dimensional.
num_chunks	Number of chunk_lens in underlying pyarrow.ChunkedArray
pa_table	Pyarrow table representation of the extension array.
shape	Return a tuple of the array dimensions.
size	The number of elements in the array.
struct_array	Pyarrow chunked struct-list array representation