FrameGO¶

class FrameGO(data: Union[Iterable[Iterable[Any]], numpy.ndarray] = <object object>, *, index: Optional[Union[static_frame.core.index_base.IndexBase, Iterable[Hashable], Iterable[Sequence[Hashable]], Type[static_frame.core.index_auto.IndexAutoFactory]]] = None, columns: Optional[Union[static_frame.core.index_base.IndexBase, Iterable[Hashable], Iterable[Sequence[Hashable]], Type[static_frame.core.index_auto.IndexAutoFactory]]] = None, name: Optional[Hashable] = <object object>, index_constructor: Optional[Callable[[...], static_frame.core.index_base.IndexBase]] = None, columns_constructor: Optional[Callable[[...], static_frame.core.index_base.IndexBase]] = None, own_data: bool = False, own_index: bool = False, own_columns: bool = False)[source]¶: A grow-only Frame, providing a two-dimensional, ordered, labelled container, immutable with grow-only columns.

FrameGO: Constructor¶

FrameGO.__init__(data: Union[Iterable[Iterable[Any]], numpy.ndarray] = <object object>, *, index: Optional[Union[static_frame.core.index_base.IndexBase, Iterable[Hashable], Iterable[Sequence[Hashable]], Type[static_frame.core.index_auto.IndexAutoFactory]]] = None, columns: Optional[Union[static_frame.core.index_base.IndexBase, Iterable[Hashable], Iterable[Sequence[Hashable]], Type[static_frame.core.index_auto.IndexAutoFactory]]] = None, name: Optional[Hashable] = <object object>, index_constructor: Optional[Callable[[...], static_frame.core.index_base.IndexBase]] = None, columns_constructor: Optional[Callable[[...], static_frame.core.index_base.IndexBase]] = None, own_data: bool = False, own_index: bool = False, own_columns: bool = False) → None

Initializer.

Parameters

data – Default Frame initialization requires typed data such as a NumPy array. All other initialization should use specialized constructors.
index – Optional index initializer. If provided in addition to data values, lengths must be compatible.
columns – Optional column initializer. If provided in addition to data values, lengths must be compatible.
own_data – Flag the data values as ownable by this static_frame.Frame. Primarily used by internal clients.
own_index – Flag the passed index as ownable by this static_frame.Frame. Primarily used by internal clients.
own_columns – Flag the passed columns as ownable by this static_frame.Frame. Primarily used by internal clients.

>>> f = sf.FrameGO(np.array([[76.1, 0.967], [3.3, 0.847]]), columns=('Period', 'Eccentricity'), index=('Halley', 'Encke'), name='Orbits')
>>> f
<FrameGO: Orbits>
<IndexGO>         Period    Eccentricity <<U12>
<Index>
Halley            76.1      0.967
Encke             3.3       0.847
<<U6>             <float64> <float64>
>>> f['Inclination'] = (162.2, 11.8)
>>> f
<FrameGO: Orbits>
<IndexGO>         Period    Eccentricity Inclination <<U12>
<Index>
Halley            76.1      0.967        162.2
Encke             3.3       0.847        11.8
<<U6>             <float64> <float64>    <float64>

classmethod FrameGO.from_arrow(value: pyarrow.Table, *, index_depth: int = 0, index_name_depth_level: Optional[Union[int, Iterable[int]]] = None, columns_depth: int = 1, columns_name_depth_level: Optional[Union[int, Iterable[int]]] = None, dtypes: Union[str, numpy.dtype, type, None, Iterable[Optional[Union[str, numpy.dtype, type]]], Dict[Hashable, Optional[Union[str, numpy.dtype, type]]]] = None, name: Hashable = None, consolidate_blocks: bool = False) → Frame

Realize a Frame from an Arrow Table.

Parameters

value – A pyarrow.Table instance.
index_depth – integer specification of how many columns to use in forming the index. A value of 0 will select none; a value greater than 1 will create an IndexHierarchy.
columns_depth – integer specification of how many rows to use in forming the columns. A value of 0 will select none; a value greater than 1 will create an IndexHierarchy.
dtypes – Optionally provide an iterable of dtypes, equal in length to the length of each row, or a mapping by column name. If a dtype is given as None, NumPy’s default type determination will be used.
name – A hashable object to label the container.
consolidate_blocks – Optionally consolidate adjacent same-typed columns into contiguous arrays.

classmethod FrameGO.from_clipboard(*, delimiter: str = '\t', index_depth: int = 0, index_column_first: Optional[Union[int, str]] = None, index_name_depth_level: Optional[Union[int, Iterable[int]]] = None, index_continuation_token: Optional[Hashable] = <object object>, columns_depth: int = 1, columns_name_depth_level: Optional[Union[int, Iterable[int]]] = None, columns_continuation_token: Optional[Hashable] = <object object>, skip_header: int = 0, skip_footer: int = 0, quote_char: str = '"', encoding: Optional[str] = None, dtypes: Union[str, numpy.dtype, type, None, Iterable[Optional[Union[str, numpy.dtype, type]]], Dict[Hashable, Optional[Union[str, numpy.dtype, type]]]] = None, name: Optional[Hashable] = None, consolidate_blocks: bool = False, store_filter: Optional[static_frame.core.store_filter.StoreFilter] = <static_frame.core.store_filter.StoreFilter object>) → static_frame.core.frame.Frame

Create a Frame from the contents of the clipboard (assuming a table is stored as delimited file).

Returns: static_frame.Frame

classmethod FrameGO.from_concat(frames: Iterable[Union[static_frame.core.frame.Frame, static_frame.core.series.Series]], *, axis: int = 0, union: bool = True, index: Optional[Union[static_frame.core.index_base.IndexBase, Iterable[Hashable], Iterable[Sequence[Hashable]], Type[static_frame.core.index_auto.IndexAutoFactory]]] = None, columns: Optional[Union[static_frame.core.index_base.IndexBase, Iterable[Hashable], Iterable[Sequence[Hashable]], Type[static_frame.core.index_auto.IndexAutoFactory]]] = None, name: Optional[Hashable] = None, fill_value: object = nan, consolidate_blocks: bool = False) → static_frame.core.frame.Frame

Concatenate multiple Frames into a new Frame. If index or columns are provided and appropriately sized, the resulting Frame will use those indices. If the axis along concatenation (index for axis 0, columns for axis 1) is unique after concatenation, it will be preserved; otherwise, a new index or an IndexAutoFactory must be supplied.

Parameters

frames – Iterable of Frames.
axis – Integer specifying 0 to concatenate supplied Frames vertically (aligning on columns), 1 to concatenate horizontally (aligning on rows).
union – If True, the union of the aligned indices is used; if False, the intersection is used.
index – Optionally specify a new index.
columns – Optionally specify new columns.
name – A hashable object to label the container.f
consolidate_blocks – Optionally consolidate adjacent same-typed columns into contiguous arrays.

Returns

static_frame.Frame

classmethod FrameGO.from_concat_items(items: Iterable[Tuple[Hashable, Union[static_frame.core.frame.Frame, static_frame.core.series.Series]]], *, axis: int = 0, union: bool = True, name: Optional[Hashable] = None, fill_value: object = nan, index_constructor: Optional[Callable[[…], static_frame.core.index_base.IndexBase]] = None, columns_constructor: Optional[Callable[[…], static_frame.core.index_base.IndexBase]] = None, consolidate_blocks: bool = False) → static_frame.core.frame.Frame

Produce a Frame with a hierarchical index from an iterable of pairs of labels, Frame. The IndexHierarchy is formed from the provided labels and the Index if each Frame.

Parameters

items – Iterable of pairs of label, Frame
axis –
union –
name –
fill_value –
index_constructor –
columns_constructor –
consolidate_blocks –

classmethod FrameGO.from_csv(fp: Union[str, os.PathLike, TextIO, Iterator[str]], *, index_depth: int = 0, index_column_first: Optional[Union[int, str]] = None, index_name_depth_level: Optional[Union[int, Iterable[int]]] = None, index_continuation_token: Optional[Hashable] = <object object>, columns_depth: int = 1, columns_name_depth_level: Optional[Union[int, Iterable[int]]] = None, columns_continuation_token: Optional[Hashable] = <object object>, skip_header: int = 0, skip_footer: int = 0, quote_char: str = '"', encoding: Optional[str] = None, dtypes: Union[str, numpy.dtype, type, None, Iterable[Optional[Union[str, numpy.dtype, type]]], Dict[Hashable, Optional[Union[str, numpy.dtype, type]]]] = None, name: Optional[Hashable] = None, consolidate_blocks: bool = False, store_filter: Optional[static_frame.core.store_filter.StoreFilter] = <static_frame.core.store_filter.StoreFilter object>) → static_frame.core.frame.Frame

Specialized version of Frame.from_delimited for CSV files.

Returns: Frame

classmethod FrameGO.from_delimited(fp: Union[str, os.PathLike, TextIO, Iterator[str]], *, delimiter: str, index_depth: int = 0, index_column_first: Optional[Union[int, str]] = None, index_name_depth_level: Optional[Union[int, Iterable[int]]] = None, index_continuation_token: Optional[Hashable] = <object object>, columns_depth: int = 1, columns_name_depth_level: Optional[Union[int, Iterable[int]]] = None, columns_continuation_token: Optional[Hashable] = <object object>, skip_header: int = 0, skip_footer: int = 0, quote_char: str = '"', encoding: Optional[str] = None, dtypes: Union[str, numpy.dtype, type, None, Iterable[Optional[Union[str, numpy.dtype, type]]], Dict[Hashable, Optional[Union[str, numpy.dtype, type]]]] = None, name: Optional[Hashable] = None, consolidate_blocks: bool = False, store_filter: Optional[static_frame.core.store_filter.StoreFilter] = <static_frame.core.store_filter.StoreFilter object>) → static_frame.core.frame.Frame

Create a Frame from a file path or a file-like object defining a delimited (CSV, TSV) data file.

Parameters

fp – A file path or a file-like object.
delimiter – The character used to seperate row elements.
index_depth – Specify the number of columns used to create the index labels; a value greater than 1 will attempt to create a hierarchical index.
index_column_first – Optionally specify a column, by position or name, to become the start of the index if index_depth is greater than 0. If not set and index_depth is greater than 0, the first column will be used.
index_name_depth_level – If columns_depth is greater than 0, interpret values over index as the index name.
columns_depth – Specify the number of rows after the skip_header used to create the column labels. A value of 0 will be no header; a value greater than 1 will attempt to create a hierarchical index.
columns_name_depth_level – If index_depth is greater than 0, interpret values over index as the columns name.
skip_header – Number of leading lines to skip.
skip_footer – Number of trailing lines to skip.
store_filter – A StoreFilter instance, defining translation between unrepresentable types. Presently nly the to_nan attributes is used.
dtypes – Optionally provide an iterable of dtypes, equal in length to the length of each row, or a mapping by column name. If a dtype is given as None, NumPy’s default type determination will be used.
name – A hashable object to label the container.
consolidate_blocks – Optionally consolidate adjacent same-typed columns into contiguous arrays.

Returns

static_frame.Frame

classmethod FrameGO.from_dict(mapping: Dict[Hashable, Iterable[Any]], *, index: Optional[Union[static_frame.core.index_base.IndexBase, Iterable[Hashable], Iterable[Sequence[Hashable]]]] = None, fill_value: object = nan, dtypes: Union[str, numpy.dtype, type, None, Iterable[Optional[Union[str, numpy.dtype, type]]], Dict[Hashable, Optional[Union[str, numpy.dtype, type]]]] = None, name: Optional[Hashable] = None, index_constructor: Optional[Callable[[…], static_frame.core.index_base.IndexBase]] = None, columns_constructor: Optional[Callable[[…], static_frame.core.index_base.IndexBase]] = None, consolidate_blocks: bool = False) → static_frame.core.frame.Frame

Create a Frame from a dictionary (or any object that has an items() method) where keys are column labels and values are columns values (either sequence types or Series).

Parameters

mapping – a dictionary or similar mapping interface.
index –
fill_value –
dtypes – Optionally provide an iterable of dtypes, equal in length to the length of each row, or a mapping by column name. If a dtype is given as None, NumPy’s default type determination will be used.
name – A hashable object to label the container.
index_constructor –
columns_constructor –
consolidate_blocks – Optionally consolidate adjacent same-typed columns into contiguous arrays.

>>> f = sf.FrameGO.from_dict(dict(diameter=(12756, 142984, 120536), mass=(5.97, 1898, 568)), index=('Earth', 'Jupiter', 'Saturn'), dtypes=dict(diameter=np.int64))
>>> f['radius'] = f['diameter'] * 0.5
>>> f
<FrameGO>
<IndexGO> diameter mass      radius    <<U8>
<Index>
Earth     12756    5.97      6378.0
Jupiter   142984   1898.0    71492.0
Saturn    120536   568.0     60268.0
<<U7>     <int64>  <float64> <float64>

classmethod FrameGO.from_dict_records(records: Iterable[Dict[Hashable, Any]], *, index: Optional[Union[static_frame.core.index_base.IndexBase, Iterable[Hashable], Iterable[Sequence[Hashable]]]] = None, dtypes: Union[str, numpy.dtype, type, None, Iterable[Optional[Union[str, numpy.dtype, type]]], Dict[Hashable, Optional[Union[str, numpy.dtype, type]]]] = None, name: Optional[Hashable] = None, fill_value: object = nan, consolidate_blocks: bool = False, index_constructor: Optional[Callable[[…], static_frame.core.index_base.IndexBase]] = None, columns_constructor: Optional[Callable[[…], static_frame.core.index_base.IndexBase]] = None, own_index: bool = False) → static_frame.core.frame.Frame

Frame constructor from an iterable of dictionaries, where each dictionary represents a row; column names will be derived from the union of all row dictionary keys.

Parameters

records – Iterable of row values, where row values are dictionaries.
index – Optionally provide an iterable of index labels, equal in length to the number of records. If a generator, this value will not be evaluated until after records are loaded.
dtypes – Optionally provide an iterable of dtypes, equal in length to the length of each row, or a mapping by column name. If a dtype is given as None, NumPy’s default type determination will be used.
name – A hashable object to label the container.
consolidate_blocks – Optionally consolidate adjacent same-typed columns into contiguous arrays.

Returns

static_frame.Frame

classmethod FrameGO.from_dict_records_items(items: Iterator[Tuple[Hashable, Iterable[Any]]], *, dtypes: Union[str, numpy.dtype, type, None, Iterable[Optional[Union[str, numpy.dtype, type]]], Dict[Hashable, Optional[Union[str, numpy.dtype, type]]]] = None, name: Optional[Hashable] = None, consolidate_blocks: bool = False) → static_frame.core.frame.Frame

Frame constructor from iterable of pairs of index label, row, where row is a dictionary. Column names will be derived from the union of all row dictionary keys.

Parameters

items – Iterable of pairs of index label, row values, where row values are arrays, tuples, lists, dictionaries, or namedtuples.
dtypes – Optionally provide an iterable of dtypes, equal in length to the length of each row, or a mapping by column name. If a dtype is given as None, NumPy’s default type determination will be used.
name – A hashable object to label the container.
consolidate_blocks – Optionally consolidate adjacent same-typed columns into contiguous arrays.

Returns

static_frame.Frame

classmethod FrameGO.from_element(element: Any, *, index: Union[static_frame.core.index_base.IndexBase, Iterable[Hashable], Iterable[Sequence[Hashable]]], columns: Union[static_frame.core.index_base.IndexBase, Iterable[Hashable], Iterable[Sequence[Hashable]]], dtype: Optional[Union[str, numpy.dtype, type]] = None, name: Optional[Hashable] = None, index_constructor: Optional[Callable[[…], static_frame.core.index_base.IndexBase]] = None, columns_constructor: Optional[Callable[[…], static_frame.core.index_base.IndexBase]] = None, own_index: bool = False, own_columns: bool = False) → static_frame.core.frame.Frame: Create a Frame from an element, i.e., a single value stored in a single cell. Both index and columns are required, and cannot be specified with IndexAutoFactory.

classmethod FrameGO.from_element_items(items: Iterable[Tuple[Tuple[Hashable, Hashable], Any]], *, index: Union[static_frame.core.index_base.IndexBase, Iterable[Hashable], Iterable[Sequence[Hashable]]], columns: Union[static_frame.core.index_base.IndexBase, Iterable[Hashable], Iterable[Sequence[Hashable]]], dtype: Union[str, numpy.dtype, type, None, Iterable[Optional[Union[str, numpy.dtype, type]]], Dict[Hashable, Optional[Union[str, numpy.dtype, type]]]] = None, axis: Optional[int] = None, name: Optional[Hashable] = None, fill_value: object = <object object>, index_constructor: Optional[Callable[[...], static_frame.core.index_base.IndexBase]] = None, columns_constructor: Optional[Callable[[...], static_frame.core.index_base.IndexBase]] = None, own_index: bool = False, own_columns: bool = False) → static_frame.core.frame.Frame

Create a Frame from an iterable of key, value, where key is a pair of row, column labels.

This function is partialed (setting the index and columns) and used by IterNodeDelegate as the apply constructor for doing application on element iteration.

Parameters

items – an iterable of pairs of 2-tuples of row, column loc labels and values.
axis – when None, items can be in an order; when 0, items must be well-formed and ordered row major; when 1, items must be well-formed and ordered columns major.

Returns

static_frame.Frame

classmethod FrameGO.from_elements(elements: Iterable[Any], *, index: Optional[Union[static_frame.core.index_base.IndexBase, Iterable[Hashable], Iterable[Sequence[Hashable]], Type[static_frame.core.index_auto.IndexAutoFactory]]] = None, columns: Optional[Union[static_frame.core.index_base.IndexBase, Iterable[Hashable], Iterable[Sequence[Hashable]], Type[static_frame.core.index_auto.IndexAutoFactory]]] = None, dtype: Optional[Union[str, numpy.dtype, type]] = None, name: Optional[Hashable] = None, index_constructor: Optional[Callable[[…], static_frame.core.index_base.IndexBase]] = None, columns_constructor: Optional[Callable[[…], static_frame.core.index_base.IndexBase]] = None, own_index: bool = False, own_columns: bool = False) → static_frame.core.frame.Frame: Create a Frame from an iterable of elements, to be formed into a Frame with a single column.

classmethod FrameGO.from_fields(fields: Iterable[Iterable[Any]], *, index: Optional[Union[static_frame.core.index_base.IndexBase, Iterable[Hashable], Iterable[Sequence[Hashable]]]] = None, columns: Optional[Union[static_frame.core.index_base.IndexBase, Iterable[Hashable], Iterable[Sequence[Hashable]]]] = None, fill_value: object = nan, dtypes: Union[str, numpy.dtype, type, None, Iterable[Optional[Union[str, numpy.dtype, type]]], Dict[Hashable, Optional[Union[str, numpy.dtype, type]]]] = None, name: Optional[Hashable] = None, index_constructor: Optional[Callable[[…], static_frame.core.index_base.IndexBase]] = None, columns_constructor: Optional[Callable[[…], static_frame.core.index_base.IndexBase]] = None, own_index: bool = False, own_columns: bool = False, consolidate_blocks: bool = False) → static_frame.core.frame.Frame

Frame constructor from an iterator of columns, where columns are iterables. Series can be provided as values if an index argument is supplied.

Parameters

fields – Iterable of column values.
index – Iterable of values to create an Index.
fill_value – If pairs include Series, they will be reindexed with the provided index; reindexing will use this fill value.
dtypes – Optionally provide an iterable of dtypes, equal in length to the length of each row, or a mapping by column name. If a dtype is given as None, NumPy’s default type determination will be used.
name – A hashable object to label the container.
consolidate_blocks – Optionally consolidate adjacent same-typed columns into contiguous arrays.

Returns

static_frame.Frame

classmethod FrameGO.from_hdf5(fp: Union[str, os.PathLike], *, label: Hashable = <object object>, index_depth: int = 0, columns_depth: int = 1, consolidate_blocks: bool = False) → static_frame.core.frame.Frame: Load Frame from the contents of a table in an HDF5 file.

classmethod FrameGO.from_items(pairs: Iterable[Tuple[Hashable, Iterable[Any]]], *, index: Optional[Union[static_frame.core.index_base.IndexBase, Iterable[Hashable], Iterable[Sequence[Hashable]]]] = None, fill_value: object = nan, dtypes: Union[str, numpy.dtype, type, None, Iterable[Optional[Union[str, numpy.dtype, type]]], Dict[Hashable, Optional[Union[str, numpy.dtype, type]]]] = None, name: Optional[Hashable] = None, index_constructor: Optional[Callable[[…], static_frame.core.index_base.IndexBase]] = None, columns_constructor: Optional[Callable[[…], static_frame.core.index_base.IndexBase]] = None, consolidate_blocks: bool = False) → static_frame.core.frame.Frame

Frame constructor from an iterator of pairs, where the first value is the column label and the second value is an iterable of column values. Series can be provided as values if an index argument is supplied.

Parameters

pairs – Iterable of pairs of column name, column values.
index – Iterable of values to create an Index.
fill_value – If pairs include Series, they will be reindexed with the provided index; reindexing will use this fill value.
dtypes – Optionally provide an iterable of dtypes, equal in length to the length of each row, or a mapping by column name. If a dtype is given as None, NumPy’s default type determination will be used.
name – A hashable object to label the container.
index_constructor –
columns_constructor –
consolidate_blocks – Optionally consolidate adjacent same-typed columns into contiguous arrays.

Returns

Frame

classmethod FrameGO.from_json(json_data: str, *, dtypes: Union[str, numpy.dtype, type, None, Iterable[Optional[Union[str, numpy.dtype, type]]], Dict[Hashable, Optional[Union[str, numpy.dtype, type]]]] = None, name: Optional[Hashable] = None, consolidate_blocks: bool = False) → static_frame.core.frame.Frame

Frame constructor from an in-memory JSON document.

Parameters

json_data – a string of JSON, encoding a table as an array of JSON objects.
dtypes – Optionally provide an iterable of dtypes, equal in length to the length of each row, or a mapping by column name. If a dtype is given as None, NumPy’s default type determination will be used.
name – A hashable object to label the container.
consolidate_blocks – Optionally consolidate adjacent same-typed columns into contiguous arrays.

Returns

static_frame.Frame

classmethod FrameGO.from_json_url(url: str, *, dtypes: Union[str, numpy.dtype, type, None, Iterable[Optional[Union[str, numpy.dtype, type]]], Dict[Hashable, Optional[Union[str, numpy.dtype, type]]]] = None, name: Optional[Hashable] = None, consolidate_blocks: bool = False) → static_frame.core.frame.Frame

Frame constructor from a JSON documenst provided via a URL.

Parameters

url – URL to the JSON resource.
dtypes – Optionally provide an iterable of dtypes, equal in length to the length of each row, or a mapping by column name. If a dtype is given as None, NumPy’s default type determination will be used.
name – A hashable object to label the container.
consolidate_blocks – Optionally consolidate adjacent same-typed columns into contiguous arrays.

Returns

static_frame.Frame

static FrameGO.from_msgpack(msgpack_data: bin) → static_frame.core.frame.Frame

Frame constructor from an in-memory binary object formatted as a msgpack.

Parameters: msgpack_data – A binary msgpack object, encoding a Frame as produced from to_msgpack()

classmethod FrameGO.from_overlay(containers: Iterable[static_frame.core.frame.Frame], *, index: Optional[Union[static_frame.core.index_base.IndexBase, Iterable[Hashable], Iterable[Sequence[Hashable]]]] = None, columns: Optional[Union[static_frame.core.index_base.IndexBase, Iterable[Hashable], Iterable[Sequence[Hashable]]]] = None, union: bool = True, name: Optional[Hashable] = None) → static_frame.core.frame.Frame

Return a new Frame made by overlaying containers, filling in missing values (None or NaN) with aligned values from subsequent containers.

Parameters

containers – Iterable of Frame.
index – An optional Index, IndexHierarchy, or index initializer, to be used as the index upon which all containers are aligned. IndexAutoFactory is not supported.
columns – An optional Index, IndexHierarchy, or columns initializer, to be used as the columns upon which all containers are aligned. IndexAutoFactory is not supported.
union – If True, and no index or columns argument is supplied, a union index or columns from containers will be used; if False, the intersection index or columns will be used.

classmethod FrameGO.from_pandas(value: pandas.DataFrame, *, index_constructor: Callable[[...], IndexBase] = None, columns_constructor: Callable[[...], IndexBase] = None, name: Optional[Hashable] = <object object>, consolidate_blocks: bool = False, own_data: bool = False) → Frame

Given a Pandas DataFrame, return a Frame.

Parameters

value – Pandas DataFrame.
index_constructor – Optional class or constructor function to create the Index applied to the rows.
columns_constructor – Optional class or constructor function to create the Index applied to the columns.
consolidate_blocks – Optionally consolidate adjacent same-typed columns into contiguous arrays.
own_data – If True, the underlying NumPy data array will be made immutable and used without a copy.

Returns

Frame

classmethod FrameGO.from_parquet(fp: Union[str, os.PathLike], *, index_depth: int = 0, index_name_depth_level: Optional[Union[int, Iterable[int]]] = None, columns_depth: int = 1, columns_name_depth_level: Optional[Union[int, Iterable[int]]] = None, columns_select: Optional[Iterable[str]] = None, dtypes: Union[str, numpy.dtype, type, None, Iterable[Optional[Union[str, numpy.dtype, type]]], Dict[Hashable, Optional[Union[str, numpy.dtype, type]]]] = None, name: Optional[Hashable] = None, consolidate_blocks: bool = False) → static_frame.core.frame.Frame

Realize a Frame from a Parquet file.

Parameters

fp – A string file path or Path instance.
index_depth – integer specification of how many columns to use in forming the index. A value of 0 will select none; a value greater than 1 will create an IndexHierarchy.
columns_depth – integer specification of how many rows to use in forming the columns. A value of 0 will select none; a value greater than 1 will create an IndexHierarchy.
columns_select – An optional iterable of column names to load.
dtypes – Optionally provide an iterable of dtypes, equal in length to the length of each row, or a mapping by column name. If a dtype is given as None, NumPy’s default type determination will be used.
name – A hashable object to label the container.
consolidate_blocks – Optionally consolidate adjacent same-typed columns into contiguous arrays.

classmethod FrameGO.from_records(records: Iterable[Any], *, index: Optional[Union[static_frame.core.index_base.IndexBase, Iterable[Hashable], Iterable[Sequence[Hashable]]]] = None, columns: Optional[Union[static_frame.core.index_base.IndexBase, Iterable[Hashable], Iterable[Sequence[Hashable]]]] = None, dtypes: Union[str, numpy.dtype, type, None, Iterable[Optional[Union[str, numpy.dtype, type]]], Dict[Hashable, Optional[Union[str, numpy.dtype, type]]]] = None, name: Optional[Hashable] = None, consolidate_blocks: bool = False, index_constructor: Optional[Callable[[…], static_frame.core.index_base.IndexBase]] = None, columns_constructor: Optional[Callable[[…], static_frame.core.index_base.IndexBase]] = None, own_index: bool = False, own_columns: bool = False) → static_frame.core.frame.Frame

Construct a Frame from an iterable of rows, where rows are defined as iterables, including tuples, lists, and arrays. If each row is a NamedTuple, and columns is not provided, column names will be derived from the NamedTuple fields.

Supplying dtypes will significantly improve performance, as otherwise columnar array types must be derived by element-wise examination.

For records defined as Series, use Frame.from_concat; for records defined as dictionary, use Frame.from_dict_records; for creating a Frame from a single dictionary, where keys are column labels and values are columns, use Frame.from_dict.

Parameters

records – Iterable of row values, where row values are arrays, tuples, lists, or namedtuples. For dictionary records, use Frame.from_dict_records.
index – Optionally provide an iterable of index labels, equal in length to the number of records. If a generator, this value will not be evaluated until after records are loaded.
columns – Optionally provide an iterable of column labels, equal in length to the number of elements in a row.
dtypes – Optionally provide an iterable of dtypes, equal in length to the length of each row, or a mapping by column name. If a dtype is given as None, NumPy’s default type determination will be used.
name – A hashable object to label the container.
consolidate_blocks – Optionally consolidate adjacent same-typed columns into contiguous arrays.

Returns

Frame

classmethod FrameGO.from_records_items(items: Iterator[Tuple[Hashable, Iterable[Any]]], *, columns: Optional[Union[static_frame.core.index_base.IndexBase, Iterable[Hashable], Iterable[Sequence[Hashable]]]] = None, dtypes: Union[str, numpy.dtype, type, None, Iterable[Optional[Union[str, numpy.dtype, type]]], Dict[Hashable, Optional[Union[str, numpy.dtype, type]]]] = None, name: Optional[Hashable] = None, consolidate_blocks: bool = False, index_constructor: Optional[Callable[[…], static_frame.core.index_base.IndexBase]] = None, columns_constructor: Optional[Callable[[…], static_frame.core.index_base.IndexBase]] = None, own_columns: bool = False) → static_frame.core.frame.Frame

Frame constructor from iterable of pairs of index value, row (where row is an iterable).

Parameters

items – Iterable of pairs of index label, row values, where row values are arrays, tuples, lists, dictionaries, or namedtuples.
columns – Optionally provide an iterable of column labels, equal in length to the length of each row.
dtypes – Optionally provide an iterable of dtypes, equal in length to the length of each row, or a mapping by column name. If a dtype is given as None, NumPy’s default type determination will be used.
name – A hashable object to label the container.
consolidate_blocks – Optionally consolidate adjacent same-typed columns into contiguous arrays.

Returns

static_frame.Frame

classmethod FrameGO.from_series(series: static_frame.core.series.Series, *, name: Optional[Hashable] = None, columns_constructor: Optional[Callable[[…], static_frame.core.index_base.IndexBase]] = None) → static_frame.core.frame.Frame

Frame constructor from a Series:

Parameters: series – A Series instance, to be realized as single column, with the column label taken from the name attribute.

classmethod FrameGO.from_sql(query: str, *, connection: sqlite3.Connection, index_depth: int = 0, columns_depth: int = 1, columns_select: Optional[Iterable[str]] = None, dtypes: Union[str, numpy.dtype, type, None, Iterable[Optional[Union[str, numpy.dtype, type]]], Dict[Hashable, Optional[Union[str, numpy.dtype, type]]]] = None, name: Optional[Hashable] = None, consolidate_blocks: bool = False, parameters: Iterable[Any] = ()) → static_frame.core.frame.Frame

Frame constructor from an SQL query and a database connection object.

Parameters

query – A query string.
connection – A DBAPI2 (PEP 249) Connection object, such as those returned from SQLite (via the sqlite3 module) or PyODBC.
dtypes – Optionally provide an iterable of dtypes, equal in length to the length of each row, or a mapping by column name. If a dtype is given as None, NumPy’s default type determination will be used.
columns_select – An optional iterable of field names to extract from the results of the query.
name – A hashable object to label the container.
consolidate_blocks – Optionally consolidate adjacent same-typed columns into contiguous arrays.
parameters – Provide a list of values for an SQL query expecting parameter substitution.

classmethod FrameGO.from_sqlite(fp: Union[str, os.PathLike], *, label: Hashable = <object object>, index_depth: int = 0, columns_depth: int = 1, dtypes: Union[str, numpy.dtype, type, None, Iterable[Optional[Union[str, numpy.dtype, type]]], Dict[Hashable, Optional[Union[str, numpy.dtype, type]]]] = None, consolidate_blocks: bool = False) → static_frame.core.frame.Frame: Load Frame from the contents of a table in an SQLite database file.

classmethod FrameGO.from_structured_array(array: numpy.ndarray, *, index_depth: int = 0, index_column_first: Optional[Union[int, Hashable]] = None, columns_depth: int = 1, dtypes: Union[str, numpy.dtype, type, None, Iterable[Optional[Union[str, numpy.dtype, type]]], Dict[Hashable, Optional[Union[str, numpy.dtype, type]]]] = None, name: Optional[Hashable] = None, consolidate_blocks: bool = False, store_filter: Optional[static_frame.core.store_filter.StoreFilter] = <static_frame.core.store_filter.StoreFilter object>) → static_frame.core.frame.Frame

Convert a NumPy structed array into a Frame.

Parameters

array – Structured NumPy array.
index_depth – Depth if index levels, where (for example) 0 is no index, 1 is a single column index, and 2 is a two-columns IndexHierarchy.
index_column_first – Optionally provide the name or position offset of the column to use as the index.
dtypes – Optionally provide an iterable of dtypes, equal in length to the length of each row, or a mapping by column name. If a dtype is given as None, NumPy’s default type determination will be used.
name – A hashable object to label the container.
consolidate_blocks – Optionally consolidate adjacent same-typed columns into contiguous arrays.

Returns

static_frame.Frame

classmethod FrameGO.from_tsv(fp: Union[str, os.PathLike, TextIO, Iterator[str]], *, index_depth: int = 0, index_column_first: Optional[Union[int, str]] = None, index_name_depth_level: Optional[Union[int, Iterable[int]]] = None, index_continuation_token: Optional[Hashable] = <object object>, columns_depth: int = 1, columns_name_depth_level: Optional[Union[int, Iterable[int]]] = None, columns_continuation_token: Optional[Hashable] = <object object>, skip_header: int = 0, skip_footer: int = 0, quote_char: str = '"', encoding: Optional[str] = None, dtypes: Union[str, numpy.dtype, type, None, Iterable[Optional[Union[str, numpy.dtype, type]]], Dict[Hashable, Optional[Union[str, numpy.dtype, type]]]] = None, name: Optional[Hashable] = None, consolidate_blocks: bool = False, store_filter: Optional[static_frame.core.store_filter.StoreFilter] = <static_frame.core.store_filter.StoreFilter object>) → static_frame.core.frame.Frame

Specialized version of Frame.from_delimited for TSV files.

Returns: static_frame.Frame

classmethod FrameGO.from_xlsx(fp: Union[str, os.PathLike], *, label: Hashable = <object object>, index_depth: int = 0, index_name_depth_level: Optional[Union[int, Iterable[int]]] = None, columns_depth: int = 1, columns_name_depth_level: Optional[Union[int, Iterable[int]]] = None, dtypes: Union[str, numpy.dtype, type, None, Iterable[Optional[Union[str, numpy.dtype, type]]], Dict[Hashable, Optional[Union[str, numpy.dtype, type]]]] = None, consolidate_blocks: bool = False, skip_header: int = 0, skip_footer: int = 0, trim_nadir: bool = False, store_filter: Optional[static_frame.core.store_filter.StoreFilter] = <static_frame.core.store_filter.StoreFilter object>) → static_frame.core.frame.Frame

Load Frame from the contents of a sheet in an XLSX workbook.

Parameters: label – Optionally provide the sheet name from with to read. If not provided, the first sheet will be used.

FrameGO: Exporter¶

Overview: FrameGO: Exporter

FrameGO.to_arrow(*, include_index: bool = True, include_index_name: bool = True, include_columns: bool = True, include_columns_name: bool = False) → pyarrow.Table: Return a pyarrow.Table from this Frame.

FrameGO.to_clipboard(*, delimiter: str = '\t', include_index: bool = True, include_index_name: bool = True, include_columns: bool = True, include_columns_name: bool = False, encoding: Optional[str] = None, line_terminator: str = '\n', quote_char: str = '"', quote_double: bool = True, escape_char: Optional[str] = None, quoting: int = 0, store_filter: Optional[static_frame.core.store_filter.StoreFilter] = <static_frame.core.store_filter.StoreFilter object>) → None

Given a file path or file-like object, write the Frame as delimited text. The delimiter defaults to a tab.

Parameters

file path, PathLib instance, or file-like object. (A) –
* –
delimiter – Character to be used for delimiterarating elements.
include_index – If True, the index will be written.
include_index_name – If including columns, populate the row above the index with the index name. Cannot be True if include_columns_name is True.
include_columns – If True, the columns will be written.
include_columns_name – If including index, populate the column to the left of the columns with the columns name. Cannot be True if include_index_name is True.
encoding – Encoding type to be used when opening the file.
line_terminator – The string used to terminate lines.
quote_char – A one-character string used to quote fields containing special characters, such as the delimiter or quote_char, or which contain new-line characters.
quote_double – Controls how instances of quote_char appearing inside a field should themselves be quoted. When True, the character is doubled. When False, the escape_char is used as a prefix to the quote_char. It defaults to True.
escape_char – A one-character string used by the writer to escape the delimiter if quoting is set to QUOTE_NONE and the quotechar if quote_double is False.
quoting – Controls when quotes should be generated. It can take on any of the QUOTE_* constants from the standard library csv module.
store_filter – A StoreFilter instance.

FrameGO.to_csv(fp: Union[str, os.PathLike, TextIO], *, include_index: bool = True, include_index_name: bool = True, include_columns: bool = True, include_columns_name: bool = False, encoding: Optional[str] = None, line_terminator: str = '\n', quote_char: str = '"', quote_double: bool = True, escape_char: Optional[str] = None, quoting: int = 0, store_filter: Optional[static_frame.core.store_filter.StoreFilter] = <static_frame.core.store_filter.StoreFilter object>) → None

Given a file path or file-like object, write the Frame as delimited text. The delimiter is set to a comma.

Parameters

file path, PathLib instance, or file-like object. (A) –
* –
include_index – If True, the index will be written.
include_index_name – If including columns, populate the row above the index with the index name. Cannot be True if include_columns_name is True.
include_columns – If True, the columns will be written.
include_columns_name – If including index, populate the column to the left of the columns with the columns name. Cannot be True if include_index_name is True.
encoding – Encoding type to be used when opening the file.
line_terminator – The string used to terminate lines.
quote_char – A one-character string used to quote fields containing special characters, such as the delimiter or quote_char, or which contain new-line characters.
quote_double – Controls how instances of quote_char appearing inside a field should themselves be quoted. When True, the character is doubled. When False, the escape_char is used as a prefix to the quote_char. It defaults to True.
escape_char – A one-character string used by the writer to escape the delimiter if quoting is set to QUOTE_NONE and the quotechar if quote_double is False.
quoting – Controls when quotes should be generated. It can take on any of the QUOTE_* constants from the standard library csv module.
store_filter – A StoreFilter instance.

FrameGO.to_delimited(fp: Union[str, os.PathLike, TextIO], *, delimiter: str, include_index: bool = True, include_index_name: bool = True, include_columns: bool = True, include_columns_name: bool = False, encoding: Optional[str] = None, line_terminator: str = '\n', quote_char: str = '"', quote_double: bool = True, escape_char: Optional[str] = None, quoting: int = 0, store_filter: Optional[static_frame.core.store_filter.StoreFilter] = <static_frame.core.store_filter.StoreFilter object>) → None

Given a file path or file-like object, write the Frame as delimited text. A delimiter character must be specified.

Parameters

file path, PathLib instance, or file-like object. (A) –
* –
delimiter – Character to be used for delimiterarating elements.
include_index – If True, the index will be written.
include_index_name – If including columns, populate the row above the index with the index name. Cannot be True if include_columns_name is True.
include_columns – If True, the columns will be written.
include_columns_name – If including index, populate the column to the left of the columns with the columns name. Cannot be True if include_index_name is True.
encoding – Encoding type to be used when opening the file.
line_terminator – The string used to terminate lines.
quote_char – A one-character string used to quote fields containing special characters, such as the delimiter or quote_char, or which contain new-line characters.
quote_double – Controls how instances of quote_char appearing inside a field should themselves be quoted. When True, the character is doubled. When False, the escape_char is used as a prefix to the quote_char. It defaults to True.
escape_char – A one-character string used by the writer to escape the delimiter if quoting is set to QUOTE_NONE and the quotechar if quote_double is False.
quoting – Controls when quotes should be generated. It can take on any of the QUOTE_* constants from the standard library csv module.
store_filter – A StoreFilter instance.

FrameGO.to_frame() → static_frame.core.frame.Frame[source]: Return Frame version of this FrameGO.

FrameGO.to_frame_go() → static_frame.core.frame.FrameGO[source]: Return a FrameGO version of this FrameGO.

FrameGO.to_frame_he() → static_frame.core.frame.FrameHE[source]: Return a FrameGO version of this FrameGO.

FrameGO.to_hdf5(fp: Union[str, os.PathLike], *, label: Hashable = <object object>, include_index: bool = True, include_columns: bool = True) → None: Write the Frame as single-table SQLite file.

FrameGO.to_html(config: Optional[static_frame.core.display_config.DisplayConfig] = None, style_config: Optional[static_frame.core.style_config.StyleConfig] = <static_frame.core.style_config.StyleConfig object>) → str

Return an HTML table representation of this Frame using standard TABLE, TR, and TD tags. This is not a complete HTML page.

Parameters: config – Optional DisplayConfig instance.
Returns: str

FrameGO.to_html_datatables(fp: Optional[Union[str, os.PathLike, TextIO]] = None, show: bool = True, config: Optional[static_frame.core.display_config.DisplayConfig] = None) → Optional[str]

Return a complete HTML representation of this Frame using the DataTables JS library for table naviagation and search. The page links to CDNs for JS resources, and thus will not fully render without an internet connection.

Parameters

fp – optional file path to write; if not provided, a temporary file will be created. Note: the caller is responsible for deleting this file.
show – if True, the file will be opened with a webbrowser.
config – Optional DisplayConfig instance.

Returns

str, absolute file path to the file written.

FrameGO.to_latex(config: Optional[static_frame.core.display_config.DisplayConfig] = None) → str: Display the Frame as a LaTeX formatted table.

FrameGO.to_markdown(config: Optional[static_frame.core.display_config.DisplayConfig] = None) → str: Display the Frame as a Markdown formatted table.

FrameGO.to_msgpack() → bin: Return a msgpack.

FrameGO.to_pairs(axis: int = 0) → Iterable[Tuple[Hashable, Iterable[Tuple[Hashable, Any]]]]: Return a tuple of major axis key, minor axis key vlaue pairs, where major axis is determined by the axis argument.

FrameGO.to_pandas() → pandas.DataFrame: Return a Pandas DataFrame.

FrameGO.to_parquet(fp: Union[str, os.PathLike, _io.BytesIO], *, include_index: bool = True, include_index_name: bool = True, include_columns: bool = True, include_columns_name: bool = False) → None: Write an Arrow Parquet binary file.

FrameGO.to_rst(config: Optional[static_frame.core.display_config.DisplayConfig] = None) → str: Display the Frame as an RST formatted table.

FrameGO.to_series(*, index_constructor: Callable[[...], static_frame.core.index_base.IndexBase] = <class 'static_frame.core.index.Index'>) → static_frame.core.series.Series

Return a Series representation of this Frame, where the index is extended with columns to from tuple labels for each element in the Frame.

Parameters: index_constructor – Index constructor of the tuples produced by combining index and columns into one label. Providing IndexHierarchy.from_labels will produce a hierarchical index.

FrameGO.to_sqlite(fp: Union[str, os.PathLike], *, label: Hashable = <object object>, include_index: bool = True, include_columns: bool = True) → None: Write the Frame as single-table SQLite file.

FrameGO.to_tsv(fp: Union[str, os.PathLike, TextIO], *, include_index: bool = True, include_index_name: bool = True, include_columns: bool = True, include_columns_name: bool = False, encoding: Optional[str] = None, line_terminator: str = '\n', quote_char: str = '"', quote_double: bool = True, escape_char: Optional[str] = None, quoting: int = 0, store_filter: Optional[static_frame.core.store_filter.StoreFilter] = <static_frame.core.store_filter.StoreFilter object>) → None

Given a file path or file-like object, write the Frame as delimited text. The delimiter is set to a tab.

Parameters

file path, PathLib instance, or file-like object. (A) –
* –
include_index – If True, the index will be written.
include_index_name – If including columns, populate the row above the index with the index name. Cannot be True if include_columns_name is True.
include_columns – If True, the columns will be written.
include_columns_name – If including index, populate the column to the left of the columns with the columns name. Cannot be True if include_index_name is True.
encoding – Encoding type to be used when opening the file.
line_terminator – The string used to terminate lines.
quote_char – A one-character string used to quote fields containing special characters, such as the delimiter or quote_char, or which contain new-line characters.
quote_double – Controls how instances of quote_char appearing inside a field should themselves be quoted. When True, the character is doubled. When False, the escape_char is used as a prefix to the quote_char. It defaults to True.
escape_char – A one-character string used by the writer to escape the delimiter if quoting is set to QUOTE_NONE and the quotechar if quote_double is False.
quoting – Controls when quotes should be generated. It can take on any of the QUOTE_* constants from the standard library csv module.
store_filter – A StoreFilter instance.

FrameGO.to_visidata() → None: Open an interactive VisiData session.

FrameGO.to_xarray() → Dataset

Return an xarray Dataset.

In order to preserve columnar types, and following the precedent of Pandas, the Frame, with a 1D index, is translated as a Dataset of 1D arrays, where each DataArray is a 1D array. If the index is an IndexHierarhcy, each column is mapped into an ND array of shape equal to the unique values found at each depth of the index.

FrameGO.to_xlsx(fp: Union[str, os.PathLike], *, label: Hashable = <object object>, include_index: bool = True, include_index_name: bool = True, include_columns: bool = True, include_columns_name: bool = False, merge_hierarchical_labels: bool = True, store_filter: Optional[static_frame.core.store_filter.StoreFilter] = <static_frame.core.store_filter.StoreFilter object>) → None: Write the Frame as single-sheet XLSX file.

FrameGO: Attribute¶

Overview: FrameGO: Attribute

FrameGO.STATIC: bool = False

FrameGO.T: Transpose. Return a Frame with index as columns and vice versa.

FrameGO.columns: The IndexBase instance assigned for column labels.

FrameGO.dtypes

Return a Series of dytpes for each realizable column.

Returns: static_frame.Series

FrameGO.index: The IndexBase instance assigned for row labels.

FrameGO.mloc: The memory locations, represented as an array of integers, of the underlying NumPy arrays.

FrameGO.name

A hashable label attached to this container.

Returns: Hashable

FrameGO.nbytes

Return the total bytes of the underlying NumPy array.

Returns: int

FrameGO.ndim

Return the number of dimensions, which for a Frame is always 2.

Returns: int

FrameGO.shape

Return a tuple describing the shape of the underlying NumPy array.

Returns: tp.Tuple[int]

FrameGO.size

Return the size of the underlying NumPy array.

Returns: int

FrameGO: Method¶

Overview: FrameGO: Method

FrameGO.__bool__() → bool: Raises ValueError to prohibit ambiguous use of truethy evaluation.

FrameGO.__deepcopy__(memo: Dict[int, Any]) → static_frame.core.frame.Frame

FrameGO.__len__() → int: Length of rows in values.

FrameGO.__round__(decimals: int = 0) → static_frame.core.frame.Frame

Return a Frame rounded to the given decimals. Negative decimals round to the left of the decimal point.

Parameters: decimals – number of decimals to round to.
Returns: Frame

FrameGO.all(axis: int = 0, skipna: bool = True, out: Optional[numpy.ndarray] = None) → Any

Logical and over values along the specified axis.

Parameters

axis – Axis, defaulting to axis 0.
skipna – Skip missing (NaN) values, defaulting to True.

FrameGO.any(axis: int = 0, skipna: bool = True, out: Optional[numpy.ndarray] = None) → Any

Logical or over values along the specified axis.

Parameters

axis – Axis, defaulting to axis 0.
skipna – Skip missing (NaN) values, defaulting to True.

FrameGO.astype[key](dtypes, *, consolidate_blocks)

FrameGO.astype

Retype one or more columns. When used as a function, can provide retype the entire Frame; Alternatively, when used as a __getitem__ interface, loc-style column selection can be used to type one or more coloumns.

Parameters: dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.

InterfaceAsType.__getitem__(key: Union[int, numpy.integer, slice, List[Any], None, Index, Series, numpy.ndarray]) → FrameAsType[source]

Selector of columns by label.

Parameters: key – A loc selector, either a label, a list of labels, a slice of labels, or a Boolean array.

FrameGO.astype(dtype)

astype

Retype one or more columns. When used as a function, can provide retype the entire Frame; Alternatively, when used as a __getitem__ interface, loc-style column selection can be used to type one or more coloumns.

Parameters: dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.

InterfaceAsType.__call__(dtype: numpy.dtype) → Frame[source]: Apply a single dtype to all columns.

FrameGO.clip(*, lower: Optional[Union[float, static_frame.core.series.Series, static_frame.core.frame.Frame]] = None, upper: Optional[Union[float, static_frame.core.series.Series, static_frame.core.frame.Frame]] = None, axis: Optional[int] = None) → static_frame.core.frame.Frame

Apply a clip operation to this Frame. Note that clip operations can be applied to object types, but cannot be applied to non-numerical objects (e.g., strings, None)

Parameters

lower – value, Series, Frame
upper – value, Series, Frame
axis – required if lower or upper are given as a Series.

FrameGO.count(*, skipna: bool = True, skipfalsy: bool = False, unique: bool = False, axis: int = 0) → static_frame.core.series.Series

Return the count of non-NA values along the provided axis, where 0 provides counts per column, 1 provides counts per row.

Parameters: axis –

FrameGO.cov(*, axis: int = 1, ddof: int = 1) → static_frame.core.frame.Frame

Compute a covariance matrix.

Parameters

axis – if 0, each row represents a variable, with observations as columns; if 1, each column represents a variable, with observations as rows. Defaults to 1.
ddof – Delta degrees of freedom, defaults to 1.

FrameGO.cumprod(axis: int = 0, skipna: bool = True) → Any

Return the cumulative product over the specified axis.

Parameters

axis – Axis, defaulting to axis 0.
skipna – Skip missing (NaN) values, defaulting to True.

FrameGO.cumsum(axis: int = 0, skipna: bool = True) → Any

Return the cumulative sum over the specified axis.

Parameters

axis – Axis, defaulting to axis 0.
skipna – Skip missing (NaN) values, defaulting to True.

FrameGO.drop_duplicated(*, axis: int = 0, exclude_first: bool = False, exclude_last: bool = False) → static_frame.core.frame.Frame

Return a Frame with duplicated rows (axis 0) or columns (axis 1) removed. All values in the row or column are compared to determine duplication.

Parameters

axis – Integer specifying axis, where 0 is rows and 1 is columns. Axis 0 is set by default.
exclude_first – Boolean to select if the first duplicated value is excluded.
exclude_last – Boolean to select if the last duplicated value is excluded.

FrameGO.dropfalsy(axis: int = 0, condition: Callable[[numpy.ndarray], bool] = <function all>) → static_frame.core.frame.Frame

Return a new Frame after removing rows (axis 0) or columns (axis 1) where any or all values are falsy. The condition is determined by a NumPy ufunc that process the Boolean array returned by isfalsy(); the default is np.all.

Parameters

axis –
condition –

FrameGO.dropna(axis: int = 0, condition: Callable[[numpy.ndarray], bool] = <function all>) → static_frame.core.frame.Frame

Return a new Frame after removing rows (axis 0) or columns (axis 1) where any or all values are NA (NaN or None). The condition is determined by a NumPy ufunc that process the Boolean array returned by isna(); the default is np.all.

Parameters

axis –
condition –

FrameGO.duplicated(*, axis: int = 0, exclude_first: bool = False, exclude_last: bool = False) → static_frame.core.series.Series

Return an axis-sized Boolean Series that shows True for all rows (axis 0) or columns (axis 1) duplicated.

Parameters

axis – Integer specifying axis, where 0 is rows and 1 is columns. Axis 0 is set by default.
exclude_first – Boolean to select if the first duplicated value is excluded.
exclude_last – Boolean to select if the last duplicated value is excluded.

FrameGO.equals(other: Any, *, compare_name: bool = False, compare_dtype: bool = False, compare_class: bool = False, skipna: bool = True) → bool

Return a bool from comparison to any other object.

Parameters

compare_name – Include equality of the container’s name (and all composed containers) in the comparison.
compare_dtype – Include equality of the container’s dtype (and all composed containers) in the comparison.
compare_class – Include equality of the container’s class (and all composed containers) in the comparison.
skipna – If True, comparisons between missing values are equal.

FrameGO.extend(container: Union[static_frame.core.frame.Frame, static_frame.core.series.Series], fill_value: Any = nan) → None[source]

Extend this FrameGO (in-place) with another Frame’s blocks or Series array; as blocks are immutable, this is a no-copy operation when indices align. If indices do not align, the passed-in Frame or Series will be reindexed (as happens when adding a column to a FrameGO).

If a Series is passed in, the column name will be taken from the Series name attribute.

This method differs from FrameGO.extend_items() by permitting contiguous underlying blocks to be extended from another Frame into this Frame.

FrameGO.extend_items(pairs: Iterable[Tuple[Hashable, static_frame.core.series.Series]], fill_value: Any = nan) → None[source]: Given an iterable of pairs of column name, column value, extend this FrameGO. Columns values can be any iterable suitable for usage in __setitem__.

FrameGO.fillfalsy(value: Any) → static_frame.core.frame.Frame

Return a new Frame after replacing null (NaN or None) values with the supplied value.

Parameters: value – Value to be used to replace missing values (NaN or None).

FrameGO.fillna(value: Any) → static_frame.core.frame.Frame

Return a new Frame after replacing null (NaN or None) values with the supplied value.

Parameters: value – Value to be used to replace missing values (NaN or None).

FrameGO.fillna_backward(limit: int = 0, *, axis: int = 0) → static_frame.core.frame.Frame

Return a new Frame after filling backward null (NaN or None) with the first observed value.

Parameters

limit – Set the maximum count of missing values (NaN or None) to be filled per contiguous region of missing vlaues. A value of 0 is equivalent to no limit.
axis – Axis upon which to evaluate contiguous missing values, where 0 is vertically (between row values) and 1 is horizontally (between column values).

FrameGO.fillna_forward(limit: int = 0, *, axis: int = 0) → static_frame.core.frame.Frame

Return a new Frame after filling forward null (NaN or None) with the last observed value.

Parameters

limit – Set the maximum count of missing values (NaN or None) to be filled per contiguous region of missing vlaues. A value of 0 is equivalent to no limit.
axis – Axis upon which to evaluate contiguous missing values, where 0 is vertically (between row values) and 1 is horizontally (between column values).

FrameGO.fillna_leading(value: Any, *, axis: int = 0) → static_frame.core.frame.Frame

Return a new Frame after filling leading (and only leading) null (NaN or None) with the first observed value.

Parameters

value – Value to be used to replace missing values (NaN or None).
axis – Axis upon which to evaluate contiguous missing values, where 0 is vertically (between row values) and 1 is horizontally (between column values).

FrameGO.fillna_trailing(value: Any, *, axis: int = 0) → static_frame.core.frame.Frame

Return a new Frame after filling trailing (and only trailing) null (NaN or None) with the last observed value.

Parameters

value – Value to be used to replace missing values (NaN or None).
axis – Axis upon which to evaluate contiguous missing values, where 0 is vertically (between row values) and 1 is horizontally (between column values).

FrameGO.head(count: int = 5) → static_frame.core.frame.Frame

Return a Frame consisting only of the top elements as specified by count.

Parameters: count – Number of elements to be returned from the top of the Frame

FrameGO.iloc_max(*, skipna: bool = True, axis: int = 0) → static_frame.core.series.Series

Return the integer indices corresponding to the maximum values found.

Parameters

skipna – if True, NaN or None values will be ignored; if False, a found NaN will propagate.
axis – Axis upon which to evaluate contiguous missing values, where 0 is vertically (between row values) and 1 is horizontally (between column values).

FrameGO.iloc_min(*, skipna: bool = True, axis: int = 0) → static_frame.core.series.Series

Return the integer indices corresponding to the minimum values found.

Parameters

skipna – if True, NaN or None values will be ignored; if False, a found NaN will propagate.
axis – Axis upon which to evaluate contiguous missing values, where 0 is vertically (between row values) and 1 is horizontally (between column values).

FrameGO.insert_after(key: Hashable, container: Union[static_frame.core.frame.Frame, static_frame.core.series.Series], *, fill_value: Any = nan) → static_frame.core.frame.Frame

Create a new Frame by inserting a named Series or Frame at the position after the label specified by key.

Parameters

key – Label after which the new container will be inserted.
container – Container to be inserted.
fill_value – A value to be used to fill space after reindexing the new container.

Returns

Frame

FrameGO.insert_before(key: Hashable, container: Union[static_frame.core.frame.Frame, static_frame.core.series.Series], *, fill_value: Any = nan) → static_frame.core.frame.Frame

Create a new Frame by inserting a named Series or Frame at the position before the label specified by key.

Parameters

key – Label before which the new container will be inserted.
container – Container to be inserted.
fill_value – A value to be used to fill space after reindexing the new container.

Returns

Frame

FrameGO.isfalsy() → static_frame.core.frame.Frame: Return a same-indexed, Boolean Frame indicating True which values are falsy.

FrameGO.isin(other: Any) → static_frame.core.frame.Frame: Return a same-sized Boolean Frame that shows if the same-positioned element is in the passed iterable.

FrameGO.isna() → static_frame.core.frame.Frame: Return a same-indexed, Boolean Frame indicating True which values are NaN or None.

FrameGO.join_inner(other: static_frame.core.frame.Frame, *, left_depth_level: Optional[Union[int, Iterable[int]]] = None, left_columns: Union[int, numpy.integer, slice, List[Any], None, static_frame.core.index.Index, static_frame.core.series.Series, numpy.ndarray] = None, right_depth_level: Optional[Union[int, Iterable[int]]] = None, right_columns: Union[int, numpy.integer, slice, List[Any], None, static_frame.core.index.Index, static_frame.core.series.Series, numpy.ndarray] = None, left_template: str = '{}', right_template: str = '{}', fill_value: Any = nan, composite_index: bool = True, composite_index_fill_value: Optional[Hashable] = None) → static_frame.core.frame.Frame

Perform an inner join.

Parameters

left_depth_level – Specify one or more left index depths to include in the join predicate.
left_columns – Specify one or more left columns to include in the join predicate.
right_depth_level – Specify one or more right index depths to include in the join predicate.
right_columns – Specify one or more right columns to include in the join predicate.
left_template – Provide a format string for naming left columns in the joined result.
right_template – Provide a format string for naming right columns in the joined result.
fill_value – A value to be used to fill space created in the join.
composite_index – If True, an index of tuples will be returned, formed from the left index label and the right index label; if False, an index of matching labels, if unique, will be returned.
composite_index_fill_value – Value to be used when forming a composite index when a label is missing.

Returns

Frame

FrameGO.join_left(other: static_frame.core.frame.Frame, *, left_depth_level: Optional[Union[int, Iterable[int]]] = None, left_columns: Union[int, numpy.integer, slice, List[Any], None, static_frame.core.index.Index, static_frame.core.series.Series, numpy.ndarray] = None, right_depth_level: Optional[Union[int, Iterable[int]]] = None, right_columns: Union[int, numpy.integer, slice, List[Any], None, static_frame.core.index.Index, static_frame.core.series.Series, numpy.ndarray] = None, left_template: str = '{}', right_template: str = '{}', fill_value: Any = nan, composite_index: bool = True, composite_index_fill_value: Optional[Hashable] = None) → static_frame.core.frame.Frame

Perform a left outer join.

Parameters

left_depth_level – Specify one or more left index depths to include in the join predicate.
left_columns – Specify one or more left columns to include in the join predicate.
right_depth_level – Specify one or more right index depths to include in the join predicate.
right_columns – Specify one or more right columns to include in the join predicate.
left_template – Provide a format string for naming left columns in the joined result.
right_template – Provide a format string for naming right columns in the joined result.
fill_value – A value to be used to fill space created in the join.
composite_index – If True, an index of tuples will be returned, formed from the left index label and the right index label; if False, an index of matching labels, if unique, will be returned.
composite_index_fill_value – Value to be used when forming a composite index when a label is missing.

Returns

Frame

FrameGO.join_outer(other: static_frame.core.frame.Frame, *, left_depth_level: Optional[Union[int, Iterable[int]]] = None, left_columns: Union[int, numpy.integer, slice, List[Any], None, static_frame.core.index.Index, static_frame.core.series.Series, numpy.ndarray] = None, right_depth_level: Optional[Union[int, Iterable[int]]] = None, right_columns: Union[int, numpy.integer, slice, List[Any], None, static_frame.core.index.Index, static_frame.core.series.Series, numpy.ndarray] = None, left_template: str = '{}', right_template: str = '{}', fill_value: Any = nan, composite_index: bool = True, composite_index_fill_value: Optional[Hashable] = None) → static_frame.core.frame.Frame

Perform an outer join.

Parameters

left_depth_level – Specify one or more left index depths to include in the join predicate.
left_columns – Specify one or more left columns to include in the join predicate.
right_depth_level – Specify one or more right index depths to include in the join predicate.
right_columns – Specify one or more right columns to include in the join predicate.
left_template – Provide a format string for naming left columns in the joined result.
right_template – Provide a format string for naming right columns in the joined result.
fill_value – A value to be used to fill space created in the join.
composite_index – If True, an index of tuples will be returned, formed from the left index label and the right index label; if False, an index of matching labels, if unique, will be returned.
composite_index_fill_value – Value to be used when forming a composite index when a label is missing.

Returns

Frame

FrameGO.join_right(other: static_frame.core.frame.Frame, *, left_depth_level: Optional[Union[int, Iterable[int]]] = None, left_columns: Union[int, numpy.integer, slice, List[Any], None, static_frame.core.index.Index, static_frame.core.series.Series, numpy.ndarray] = None, right_depth_level: Optional[Union[int, Iterable[int]]] = None, right_columns: Union[int, numpy.integer, slice, List[Any], None, static_frame.core.index.Index, static_frame.core.series.Series, numpy.ndarray] = None, left_template: str = '{}', right_template: str = '{}', fill_value: Any = nan, composite_index: bool = True, composite_index_fill_value: Optional[Hashable] = None) → static_frame.core.frame.Frame

Perform a right outer join.

Parameters

left_depth_level – Specify one or more left index depths to include in the join predicate.
left_columns – Specify one or more left columns to include in the join predicate.
right_depth_level – Specify one or more right index depths to include in the join predicate.
right_columns – Specify one or more right columns to include in the join predicate.
left_template – Provide a format string for naming left columns in the joined result.
right_template – Provide a format string for naming right columns in the joined result.
fill_value – A value to be used to fill space created in the join.
composite_index – If True, an index of tuples will be returned, formed from the left index label and the right index label; if False, an index of matching labels, if unique, will be returned.
composite_index_fill_value – Value to be used when forming a composite index when a label is missing.

Returns

Frame

FrameGO.loc_max(*, skipna: bool = True, axis: int = 0) → static_frame.core.series.Series

Return the labels corresponding to the maximum values found.

Parameters

skipna – if True, NaN or None values will be ignored; if False, a found NaN will propagate.
axis – Axis upon which to evaluate contiguous missing values, where 0 is vertically (between row values) and 1 is horizontally (between column values).

FrameGO.loc_min(*, skipna: bool = True, axis: int = 0) → static_frame.core.series.Series

Return the labels corresponding to the minimum value found.

Parameters

skipna – if True, NaN or None values will be ignored; if False, a found NaN will propagate.
axis – Axis upon which to evaluate contiguous missing values, where 0 is vertically (between row values) and 1 is horizontally (between column values).

FrameGO.max(axis: int = 0, skipna: bool = True) → Any

Return the maximum along the specified axis.

Parameters

axis – Axis, defaulting to axis 0.
skipna – Skip missing (NaN) values, defaulting to True.

FrameGO.mean(axis: int = 0, skipna: bool = True, out: Optional[numpy.ndarray] = None) → Any

Return the mean along the specified axis.

Parameters

axis – Axis, defaulting to axis 0.
skipna – Skip missing (NaN) values, defaulting to True.

FrameGO.median(axis: int = 0, skipna: bool = True, out: Optional[numpy.ndarray] = None) → Any

Return the median along the specified axis.

Parameters

axis – Axis, defaulting to axis 0.
skipna – Skip missing (NaN) values, defaulting to True.

FrameGO.min(axis: int = 0, skipna: bool = True, out: Optional[numpy.ndarray] = None) → Any

Return the minimum along the specified axis.

Parameters

axis – Axis, defaulting to axis 0.
skipna – Skip missing (NaN) values, defaulting to True.

FrameGO.notfalsy() → static_frame.core.frame.Frame: Return a same-indexed, Boolean Frame indicating True which values are not falsy.

FrameGO.notna() → static_frame.core.frame.Frame: Return a same-indexed, Boolean Frame indicating True which values are not NaN or None.

FrameGO.pivot(index_fields: Union[Hashable, Iterable[Hashable]], columns_fields: Union[Hashable, Iterable[Hashable]] = (), data_fields: Union[Hashable, Iterable[Hashable]] = (), *, func: Optional[Union[Callable[[…], Any], Mapping[Hashable, Callable[[…], Any]]]] = None, fill_value: object = nan) → static_frame.core.frame.Frame

Produce a pivot table, where one or more columns is selected for each of index_fields, columns_fields, and data_fields. Unique values from the provided index_fields will be used to create a new index; unique values from the provided columns_fields will be used to create a new columns; if one data_fields value is selected, that is the value that will be displayed; if more than one values is given, those values will be presented with a hierarchical index on the columns; if data_fields is not provided, all unused fields will be displayed.

Parameters

index_fields –
columns_fields –
data_fields –
fill_value – If the index expansion produces coordinates that have no existing data value, fill that position with this value.
func – function to apply to data_fields, or a dictionary of labelled functions to apply to data fields, producing an additional hierarchical level.

FrameGO.pivot_stack(depth_level: Union[int, Iterable[int]] = - 1, *, fill_value: object = nan) → static_frame.core.frame.Frame

Move labels from the columns to the index, creating or extending an IndexHierarchy on the index.

Parameters: depth_level – selection of columns depth or depth to move onto the index.

FrameGO.pivot_unstack(depth_level: Union[int, Iterable[int]] = - 1, *, fill_value: object = nan) → static_frame.core.frame.Frame

Move labels from the index to the columns, creating or extending an IndexHierarchy on the columns.

Parameters: depth_level – selection of index depth or depth to move onto the columns.

FrameGO.prod(axis: int = 0, skipna: bool = True, out: Optional[numpy.ndarray] = None) → Any

Return the product along the specified axis.

Parameters

axis – Axis, defaulting to axis 0.
skipna – Skip missing (NaN) values, defaulting to True.

FrameGO.rank_dense(*, axis: int = 0, skipna: bool = True, ascending: Union[bool, Iterable[bool]] = True, start: int = 0, fill_value: Any = nan) → static_frame.core.frame.Frame

Rank values as compactly as possible, where ties get the same value, and ranks are contiguous (potentially non-unique) integers.

Parameters

axis – Integer specifying axis of ranking, where 0 ranks vertically (within each column) and 1 ranks horizontally (within each row)
skipna – If True, exclude NA values (NaN or None) from ranking, replacing those values with fill_value.
ascending – Boolean, or iterable of Booleans; if True, the lowest ranks correspond to the lowest values; if an iterable, apply per column or row. The default is True.
start – The reference value for the lowest rank. Some ranking methodologies (mean, max) may not return this value given some inputs. The default is 0; for ranks that start from 1, provide a value of 1.
fill_value – A value to be used to fill NA values ignored in ranking when skipna is True. The default is np.nan but can be set to any value to force NA values to the “bottom” or “top” of a rank as needed.

Returns

Frame

FrameGO.rank_max(*, axis: int = 0, skipna: bool = True, ascending: Union[bool, Iterable[bool]] = True, start: int = 0, fill_value: Any = nan) → static_frame.core.frame.Frame

Rank values where tied values are assigned the maximum ordinal rank; ranks are potentially non-contiguous and non-unique integers.

Parameters

axis – Integer specifying axis of ranking, where 0 ranks vertically (within each column) and 1 ranks horizontally (within each row)
skipna – If True, exclude NA values (NaN or None) from ranking, replacing those values with fill_value.
ascending – Boolean, or iterable of Booleans; if True, the lowest ranks correspond to the lowest values; if an iterable, apply per column or row. The default is True.
start – The reference value for the lowest rank. Some ranking methodologies (mean, max) may not return this value given some inputs. The default is 0; for ranks that start from 1, provide a value of 1.
fill_value – A value to be used to fill NA values ignored in ranking when skipna is True. The default is np.nan but can be set to any value to force NA values to the “bottom” or “top” of a rank as needed.

Returns

Frame

FrameGO.rank_mean(*, axis: int = 0, skipna: bool = True, ascending: Union[bool, Iterable[bool]] = True, start: int = 0, fill_value: Any = nan) → static_frame.core.frame.Frame

Rank values where tied values are assigned the mean of the ordinal ranks; ranks are potentially non-contiguous and non-unique floats.

Parameters

axis – Integer specifying axis of ranking, where 0 ranks vertically (within each column) and 1 ranks horizontally (within each row)
skipna – If True, exclude NA values (NaN or None) from ranking, replacing those values with fill_value.
ascending – Boolean, or iterable of Booleans; if True, the lowest ranks correspond to the lowest values; if an iterable, apply per column or row. The default is True.
start – The reference value for the lowest rank. Some ranking methodologies (mean, max) may not return this value given some inputs. The default is 0; for ranks that start from 1, provide a value of 1.
fill_value – A value to be used to fill NA values ignored in ranking when skipna is True. The default is np.nan but can be set to any value to force NA values to the “bottom” or “top” of a rank as needed.

Returns

Frame

FrameGO.rank_min(*, axis: int = 0, skipna: bool = True, ascending: Union[bool, Iterable[bool]] = True, start: int = 0, fill_value: Any = nan) → static_frame.core.frame.Frame

Rank values where tied values are assigned the minimum ordinal rank; ranks are potentially non-contiguous and non-unique integers.

Parameters

axis – Integer specifying axis of ranking, where 0 ranks vertically (within each column) and 1 ranks horizontally (within each row)
skipna – If True, exclude NA values (NaN or None) from ranking, replacing those values with fill_value.
ascending – Boolean, or iterable of Booleans; if True, the lowest ranks correspond to the lowest values; if an iterable, apply per column or row. The default is True.
start – The reference value for the lowest rank. Some ranking methodologies (mean, max) may not return this value given some inputs. The default is 0; for ranks that start from 1, provide a value of 1.
fill_value – A value to be used to fill NA values ignored in ranking when skipna is True. The default is np.nan but can be set to any value to force NA values to the “bottom” or “top” of a rank as needed.

Returns

Frame

FrameGO.rank_ordinal(*, axis: int = 0, skipna: bool = True, ascending: Union[bool, Iterable[bool]] = True, start: int = 0, fill_value: Any = nan) → static_frame.core.frame.Frame

Rank values distinctly, where ties get distinct values that maintain their ordering, and ranks are contiguous unique integers.

Parameters

axis – Integer specifying axis of ranking, where 0 ranks vertically (within each column) and 1 ranks horizontally (within each row)
skipna – If True, exclude NA values (NaN or None) from ranking, replacing those values with fill_value.
ascending – Boolean, or iterable of Booleans; if True, the lowest ranks correspond to the lowest values; if an iterable, apply per column or row. The default is True.
start – The reference value for the lowest rank. Some ranking methodologies (mean, max) may not return this value given some inputs. The default is 0; for ranks that start from 1, provide a value of 1.
fill_value – A value to be used to fill NA values ignored in ranking when skipna is True. The default is np.nan but can be set to any value to force NA values to the “bottom” or “top” of a rank as needed.

Returns

Series

FrameGO.rehierarch(index: Optional[Iterable[int]] = None, columns: Optional[Iterable[int]] = None) → static_frame.core.frame.Frame: Produce a new Frame with index and/or columns constructed with a transformed hierarchy.

FrameGO.reindex(index: Optional[Union[static_frame.core.index_base.IndexBase, Iterable[Hashable], Iterable[Sequence[Hashable]]]] = None, columns: Optional[Union[static_frame.core.index_base.IndexBase, Iterable[Hashable], Iterable[Sequence[Hashable]]]] = None, *, fill_value: object = nan, own_index: bool = False, own_columns: bool = False, check_equals: bool = True) → static_frame.core.frame.Frame

Return a new Frame with labels defined by the provided index. The size and ordering of the data is determined by the newly provided index, where data will continue to be aligned under labels found in both the new and the old index. Labels found only in the new index will be filled with fill_value.

Parameters

index – An iterable of unique, hashable values, or another Index or IndexHierarchy, to be used as the labels of the index.
columns – An iterable of unique, hashable values, or another Index or IndexHierarchy, to be used as the labels of the index.
fill_value – A value to be used to fill space created by a new index that has values not found in the previous index.
own_index – Flag the passed index as ownable by this static_frame.Frame. Primarily used by internal clients.
own_columns – Flag the passed columns as ownable by this static_frame.Frame. Primarily used by internal clients.

FrameGO.relabel(index: Optional[Union[Callable[[…], Any], Mapping[Hashable, Any], static_frame.core.series.Series, Type[static_frame.core.index_auto.IndexAutoFactory], static_frame.core.index_base.IndexBase, Iterable[Hashable], Iterable[Sequence[Hashable]]]] = None, columns: Optional[Union[Callable[[…], Any], Mapping[Hashable, Any], static_frame.core.series.Series, Type[static_frame.core.index_auto.IndexAutoFactory], static_frame.core.index_base.IndexBase, Iterable[Hashable], Iterable[Sequence[Hashable]]]] = None) → static_frame.core.frame.Frame

Return a new Frame with transformed labels on the index. The size and ordering of the data is never changed in a relabeling operation. The resulting index must be unique.

Parameters

index – One of the following types, used to create a new Index with the same size as the previous index. (a) A mapping (as a dictionary or Series), used to lookup and transform the labels in the previous index. Previous labels not found in the mapping will be reused. (b) A function, returning a hashable, that is applied to each label in the previous index. (c) The IndexAutoFactory type, to apply an auto-incremented integer index. (d) An index initializer, i.e., either an iterable of hashables or an Index instance.
columns – One of the following types, used to create a new Index with the same size as the previous index. (a) A mapping (as a dictionary or Series), used to lookup and transform the labels in the previous index. Previous labels not found in the mapping will be reused. (b) A function, returning a hashable, that is applied to each label in the previous index. (c) The IndexAutoFactory type, to apply an auto-incremented integer index. (d) An index initializer, i.e., either an iterable of hashables or an Index instance.

FrameGO.relabel_flat(index: bool = False, columns: bool = False) → static_frame.core.frame.Frame

Return a new Frame, where an IndexHierarchy (if defined) is replaced with a flat, one-dimension index of tuples.

Parameters

index – Boolean to flag flatening on the index.
columns – Boolean to flag flatening on the columns.

FrameGO.relabel_level_add(index: Optional[Hashable] = None, columns: Optional[Hashable] = None) → static_frame.core.frame.Frame

Return a new Frame, adding a new root level to an existing IndexHierarchy, or creating an IndexHierarchy if one is not yet defined.

Parameters

index – A hashable value to be used as a new root level, extending or creating an IndexHierarchy
columns – A hashable value to be used as a new root level, extending or creating an IndexHierarchy

FrameGO.relabel_level_drop(index: int = 0, columns: int = 0) → static_frame.core.frame.Frame

Return a new Frame, dropping one or more levels from a either the root or the leaves of an IndexHierarchy. The resulting index must be unique.

Parameters

index – A positive integer drops that many outer-most (root) levels; a negative integer drops that many inner-most (leaf)levels. Default is zero.
columns – A positive integer drops that many outer-most (root) levels; a negative integer drops that many inner-most (leaf)levels. Default is zero.

FrameGO.relabel_shift_in(key: Union[int, numpy.integer, slice, List[Any], None, static_frame.core.index.Index, static_frame.core.series.Series, numpy.ndarray], *, axis: int = 0) → static_frame.core.frame.Frame

Create, or augment, an IndexHierarchy by providing one or more selections via axis-appropriate loc selections.

Parameters

key – a loc-style selection on the opposite axis.
axis – 0 modifies the index by selecting columns with key; 1 modifies the columns by selecting rows with key.

FrameGO.relabel_shift_out(depth_level: Union[int, Iterable[int]], *, axis: int = 0) → static_frame.core.frame.Frame

Shift values from an index on an axis to the Frame by providing one or more depth level selections.

Parameters

key – an iloc-style selection on the axis.
axis – 0 modifies the index by selecting columns with key; 1 modifies the columns by selecting rows with key.

FrameGO.rename(name: Optional[Hashable] = <object object>, *, index: Optional[Hashable] = <object object>, columns: Optional[Hashable] = <object object>) → static_frame.core.frame.Frame: Return a new Frame with an updated name attribute. Optionally update the name attribute of index and columns.

FrameGO.roll(index: int = 0, columns: int = 0, *, include_index: bool = False, include_columns: bool = False) → static_frame.core.frame.Frame

Roll columns and/or rows by positive or negative integer counts, where columns and/or rows roll around the axis.

Parameters

include_index – Determine if index is included in index-wise rotation.
include_columns – Determine if column index is included in index-wise rotation.

FrameGO.sample(index: Optional[int] = None, columns: Optional[int] = None, *, seed: Optional[int] = None) → static_frame.core.frame.Frame

Randomly (optionally made deterministic with a fixed seed) extract items from the container to return a subset of the container.

Parameters

of labels to select from the index. (Number) –
of labels to select from the columns. (Number) –
state of random selection. (Initial) –

FrameGO.set_index(column: Hashable, *, drop: bool = False, index_constructor: Callable[[...], static_frame.core.index_base.IndexBase] = <class 'static_frame.core.index.Index'>) → static_frame.core.frame.Frame: Return a new frame produced by setting the given column as the index, optionally removing that column from the new Frame.

FrameGO.set_index_hierarchy(columns: Union[int, numpy.integer, slice, List[Any], None, static_frame.core.index.Index, static_frame.core.series.Series, numpy.ndarray], *, drop: bool = False, index_constructors: Optional[Sequence[Callable[[…], static_frame.core.index_base.IndexBase]]] = None, reorder_for_hierarchy: bool = False) → static_frame.core.frame.Frame

Given an iterable of column labels, return a new Frame with those columns as an IndexHierarchy on the index.

Parameters

columns – Iterable of column labels.
drop – Boolean to determine if selected columns should be removed from the data.
index_constructors – Optionally provide a sequence of Index constructors, of length equal to depth, to be used in converting columns Index components in the IndexHierarchy.
reorder_for_hierarchy – reorder the rows to produce a hierarchible Index from the selected columns, assuming hierarchability is possible.

Returns

Frame

FrameGO.shift(index: int = 0, columns: int = 0, *, fill_value: Any = nan) → static_frame.core.frame.Frame: Shift columns and/or rows by positive or negative integer counts, where columns and/or rows fall of the axis and introduce missing values, filled by fill_value.

FrameGO.sort_columns(*, ascending: Union[bool, Iterable[bool]] = True, kind: str = 'mergesort', key: Optional[Callable[[static_frame.core.index_base.IndexBase], Union[numpy.ndarray, static_frame.core.index_base.IndexBase]]] = None) → static_frame.core.frame.Frame

Return a new Frame ordered by the sorted columns.

Parameters

ascending – Boolean, or iterable of Booleans; if True, the lowest ranks correspond to the lowest values; if an iterable, apply per column or row. The default is True.
kind – Name of the sort algorithm as passed to NumPy.
key – A function that is used to pre-process the selected columns or rows and derive new values to sort by.

FrameGO.sort_index(*, ascending: Union[bool, Iterable[bool]] = True, kind: str = 'mergesort', key: Optional[Callable[[static_frame.core.index_base.IndexBase], Union[numpy.ndarray, static_frame.core.index_base.IndexBase]]] = None) → static_frame.core.frame.Frame

Return a new Frame ordered by the sorted Index.

Parameters

ascending – Boolean, or iterable of Booleans; if True, the lowest ranks correspond to the lowest values; if an iterable, apply per column or row. The default is True.
kind – Name of the sort algorithm as passed to NumPy.
key – A function that is used to pre-process the selected columns or rows and derive new values to sort by.

FrameGO.sort_values(label: Union[Hashable, Iterable[Hashable]], *, ascending: Union[bool, Iterable[bool]] = True, axis: int = 1, kind: str = 'mergesort', key: Optional[Callable[[Union[static_frame.core.frame.Frame, static_frame.core.series.Series]], Union[numpy.ndarray, static_frame.core.series.Series, static_frame.core.frame.Frame]]] = None) → static_frame.core.frame.Frame

Return a new Frame ordered by the sorted values, where values are given by single column or iterable of columns.

Parameters

label – A label or iterable of labels to select the columns (for axis 1) or rows (for axis 0) to sort.
* –
ascending – Boolean, or iterable of Booleans; if True, the lowest ranks correspond to the lowest values; if an iterable, apply per column or row. The default is True.
axis – Axis upon which to sort; 0 orders columns based on one or more rows; 1 orders rows based on one or more columns.
kind – Name of the sort algorithm as passed to NumPy.
key – A function that is used to pre-process the selected columns or rows and derive new values to sort by.

FrameGO.std(axis: int = 0, skipna: bool = True, ddof: int = 0, out: Optional[numpy.ndarray] = None) → Any

Return the standard deviaton along the specified axis.

Parameters

axis – Axis, defaulting to axis 0.
skipna – Skip missing (NaN) values, defaulting to True.

FrameGO.sum(axis: int = 0, skipna: bool = True, out: Optional[numpy.ndarray] = None) → Any

Sum values along the specified axis.

Parameters

axis – Axis, defaulting to axis 0.
skipna – Skip missing (NaN) values, defaulting to True.

FrameGO.tail(count: int = 5) → static_frame.core.frame.Frame

Return a Frame consisting only of the bottom elements as specified by count.

Parameters: count – Number of elements to be returned from the bottom of the Frame

FrameGO.transpose() → static_frame.core.frame.Frame: Transpose. Return a Frame with index as columns and vice versa.

FrameGO.unique(*, axis: Optional[int] = None) → numpy.ndarray: Return a NumPy array of unqiue values. If the axis argument is provied, uniqueness is determined by columns or row.

FrameGO.unset_index(*, names: Iterable[Hashable] = (), consolidate_blocks: bool = False) → static_frame.core.frame.Frame

Return a new Frame where the index is added to the front of the data, and an :obj:`IndexAutoFactory is used to populate a new index. If the Index has a name, that name will be used for the column name, otherwise a suitable default will be used. As underlying NumPy arrays are immutable, data is not copied.

Parameters: names – An iterable of hashables to be used to name the unset index. If an Index, a single hashable should be provided; if an IndexHierarchy, as many hashables as the depth must be provided.

FrameGO.var(axis: int = 0, skipna: bool = True, ddof: int = 0, out: Optional[numpy.ndarray] = None) → Any

Return the variance along the specified axis.

Parameters

axis – Axis, defaulting to axis 0.
skipna – Skip missing (NaN) values, defaulting to True.

FrameGO: Dictionary-Like¶

Overview: FrameGO: Dictionary-Like

FrameGO.__contains__(value: Hashable) → bool: Inclusion of value in column labels.

FrameGO.__iter__() → Iterable[Hashable]: Iterator of column labels, same as Frame.keys().

FrameGO.__reversed__() → Iterator[Hashable]: Returns a reverse iterator on the frame’s columns.

FrameGO.get(key: Hashable, default: Optional[static_frame.core.series.Series] = None) → static_frame.core.series.Series: Return the value found at the columns key, else the default if the key is not found. This method is implemented to complete the dictionary-like interface.

FrameGO.items() → Iterator[Tuple[Hashable, static_frame.core.series.Series]]: Iterator of pairs of column label and corresponding column Series.

FrameGO.keys() → Iterable[Hashable]: Iterator of column labels.

FrameGO.values: np.ndarray: A 2D NumPy array of all values in the Frame. As this is a single array, heterogenous columnar types might be coerced to a compatible type.

FrameGO: Display¶

Overview: FrameGO: Display

FrameGO.interface¶

A Frame documenting the interface of this class.

>>> sf.FrameGO.interface.loc[sf.FrameGO.interface.index.via_str.startswith('drop')]
<Frame: FrameGO>
<Index>                              cls_name group    doc                  <<U18>
<Index: signature>
drop_duplicated(*, axis, exclude_... FrameGO  Method   Return a Frame wi...
dropfalsy(axis, condition)           FrameGO  Method   Return a new Fram...
dropna(axis, condition)              FrameGO  Method   Return a new Fram...
drop[key]                            FrameGO  Selector Label-based selec...
drop.iloc[key]                       FrameGO  Selector
drop.loc[key]                        FrameGO  Selector
<<U94>                               <<U7>    <<U27>   <<U83>

FrameGO.__repr__() → str: Return repr(self).

FrameGO.__str__(): Return str(self).

FrameGO.display(config: Optional[static_frame.core.display_config.DisplayConfig] = None, *, style_config: Optional[static_frame.core.style_config.StyleConfig] = None) → static_frame.core.display.Display

Return a static_frame.Display, capable of providing a string representation.

Parameters: config – A static_frame.DisplayConfig instance. If not provided, the static_frame.DisplayActive will be used.

FrameGO.display_tall(config: Optional[static_frame.core.display_config.DisplayConfig] = None) → static_frame.core.display.Display

Maximize vertical presentation. Return a static_frame.Display, capable of providing a string representation.

Parameters: config – A static_frame.DisplayConfig instance. If not provided, the static_frame.DisplayActive will be used.

FrameGO.display_wide(config: Optional[static_frame.core.display_config.DisplayConfig] = None) → static_frame.core.display.Display

Maximize horizontal presentation. Return a static_frame.Display, capable of providing a string representation.

Parameters: config – A static_frame.DisplayConfig instance. If not provided, the static_frame.DisplayActive will be used.

FrameGO: Assignment¶

Overview: FrameGO: Assignment

FrameGO.assign[key](value, *, fill_value)

FrameGO.assign

FrameAssign.__call__(value: Any, *, fill_value: Any = nan) → static_frame.core.frame.Frame[source]

Assign the value in the position specified by the selector. The name attribute is propagated to the returned container.

Parameters

value – Value to assign, which can be a Series, Frame, np.ndarray, or element.
*. –
fill_value – If the value parameter has to be reindexed, this element will be used to fill newly created elements.

FrameGO.assign[key].apply(func, *, fill_value)

FrameGO.assign

FrameAssign.apply(func: Callable[[…], Any], *, fill_value: Any = nan) → static_frame.core.frame.Frame[source]

Provide a function to apply to the assignment target, and use that as the assignment value.

Parameters

func – A function to apply to the assignment target.
*. –
fill_value – If the function does not produce a container with a matching index, the element will be used to fill newly created elements.

FrameGO.assign.iloc[key](value, *, fill_value)

FrameGO.assign

FrameAssign.__call__(value: Any, *, fill_value: Any = nan) → static_frame.core.frame.Frame[source]

Assign the value in the position specified by the selector. The name attribute is propagated to the returned container.

Parameters

value – Value to assign, which can be a Series, Frame, np.ndarray, or element.
*. –
fill_value – If the value parameter has to be reindexed, this element will be used to fill newly created elements.

FrameGO.assign.iloc[key].apply(func, *, fill_value)

FrameGO.assign

FrameAssign.apply(func: Callable[[…], Any], *, fill_value: Any = nan) → static_frame.core.frame.Frame[source]

Provide a function to apply to the assignment target, and use that as the assignment value.

Parameters

func – A function to apply to the assignment target.
*. –
fill_value – If the function does not produce a container with a matching index, the element will be used to fill newly created elements.

FrameGO.assign.loc[key](value, *, fill_value)

FrameGO.assign

FrameAssign.__call__(value: Any, *, fill_value: Any = nan) → static_frame.core.frame.Frame[source]

Assign the value in the position specified by the selector. The name attribute is propagated to the returned container.

Parameters

value – Value to assign, which can be a Series, Frame, np.ndarray, or element.
*. –
fill_value – If the value parameter has to be reindexed, this element will be used to fill newly created elements.

FrameGO.assign.loc[key].apply(func, *, fill_value)

FrameGO.assign

FrameAssign.apply(func: Callable[[…], Any], *, fill_value: Any = nan) → static_frame.core.frame.Frame[source]

Provide a function to apply to the assignment target, and use that as the assignment value.

Parameters

func – A function to apply to the assignment target.
*. –
fill_value – If the function does not produce a container with a matching index, the element will be used to fill newly created elements.

FrameGO.assign.bloc[key](value, *, fill_value)

FrameGO.assign

FrameAssign.__call__(value: Any, *, fill_value: Any = nan) → static_frame.core.frame.Frame[source]

Assign the value in the position specified by the selector. The name attribute is propagated to the returned container.

Parameters

value – Value to assign, which can be a Series, Frame, np.ndarray, or element.
*. –
fill_value – If the value parameter has to be reindexed, this element will be used to fill newly created elements.

FrameGO.assign.bloc[key].apply(func, *, fill_value)

FrameGO.assign

FrameAssign.apply(func: Callable[[…], Any], *, fill_value: Any = nan) → static_frame.core.frame.Frame[source]

Provide a function to apply to the assignment target, and use that as the assignment value.

Parameters

func – A function to apply to the assignment target.
*. –
fill_value – If the function does not produce a container with a matching index, the element will be used to fill newly created elements.

FrameGO: Selector¶

Overview: FrameGO: Selector

FrameGO.bloc[key]

FrameGO.bloc

FrameGO.drop[key]

FrameGO.drop

InterfaceSelectTrio.__getitem__(key: Union[int, numpy.integer, slice, List[Any], None, Index, Series, numpy.ndarray]) → Any[source]: Label-based selection.

FrameGO.drop.iloc[key]

FrameGO.drop

InterfaceSelectTrio.iloc: Integer-position based selection.

FrameGO.drop.loc[key]

FrameGO.drop

InterfaceSelectTrio.loc: Label-based selection.

FrameGO.mask[key]

FrameGO.mask

InterfaceSelectTrio.__getitem__(key: Union[int, numpy.integer, slice, List[Any], None, Index, Series, numpy.ndarray]) → Any[source]: Label-based selection.

FrameGO.mask.iloc[key]

FrameGO.mask

InterfaceSelectTrio.iloc: Integer-position based selection.

FrameGO.mask.loc[key]

FrameGO.mask

InterfaceSelectTrio.loc: Label-based selection.

FrameGO.masked_array[key]

FrameGO.masked_array

InterfaceSelectTrio.__getitem__(key: Union[int, numpy.integer, slice, List[Any], None, Index, Series, numpy.ndarray]) → Any[source]: Label-based selection.

FrameGO.masked_array.iloc[key]

FrameGO.masked_array

InterfaceSelectTrio.iloc: Integer-position based selection.

FrameGO.masked_array.loc[key]

FrameGO.masked_array

InterfaceSelectTrio.loc: Label-based selection.

FrameGO[key]

FrameGO.__getitem__ = <function Frame.__getitem__>

FrameGO.iloc[key]

FrameGO.iloc

FrameGO.loc[key]

FrameGO.loc

FrameGO: Iterator¶

Overview: FrameGO: Iterator

FrameGO.iter_array(*, axis)

iter_array: Iterator of np.array, where arrays are drawn from columns (axis=0) or rows (axis=1)

FrameGO.iter_array(*, axis).apply(func, *, dtype, name)

iter_array: Iterator of np.array, where arrays are drawn from columns (axis=0) or rows (axis=1)

IterNodeDelegate.apply(func: Callable[[…], Any], *, dtype: Optional[Union[str, numpy.dtype, type]] = None, name: Optional[Hashable] = None) → FrameOrSeries[source]

Apply a function to each value. Returns a new container.

Parameters

func – A function that takes a value.
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.

FrameGO.iter_array(*, axis).apply_iter(func)

iter_array: Iterator of np.array, where arrays are drawn from columns (axis=0) or rows (axis=1)

IterNodeDelegate.apply_iter(func: Callable[[…], Any]) → Iterator[Any][source]

Apply a function to each value. A generator of resulting values.

Parameters: func – A function that takes a value.

FrameGO.iter_array(*, axis).apply_iter_items(func)

iter_array: Iterator of np.array, where arrays are drawn from columns (axis=0) or rows (axis=1)

IterNodeDelegate.apply_iter_items(func: Callable[[…], Any]) → Iterator[Tuple[Any, Any]][source]

Apply a function to each value. A generator of resulting key, value pairs.

Parameters: func – A function that takes a value.

FrameGO.iter_array(*, axis).apply_pool(func, *, dtype, name, max_workers, chunksize, use_threads)

iter_array: Iterator of np.array, where arrays are drawn from columns (axis=0) or rows (axis=1)

IterNodeDelegate.apply_pool(func: Callable[[…], Any], *, dtype: Optional[Union[str, numpy.dtype, type]] = None, name: Optional[Hashable] = None, max_workers: Optional[int] = None, chunksize: int = 1, use_threads: bool = False) → FrameOrSeries[source]

Apply a function to each value. Employ parallel processing with either the ProcessPoolExecutor or ThreadPoolExecutor.

Parameters

func – A function that takes a value.
* –
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.
name – A hashable object to label the container.
max_workers – Number of parallel executors, as passed to the Thread- or ProcessPoolExecutor; None defaults to the max number of machine processes.
chunksize – Units of work per executor, as passed to the Thread- or ProcessPoolExecutor.
use_threads – Use the ThreadPoolExecutor instead of the ProcessPoolExecutor.

FrameGO.iter_array(*, axis).map_all(mapping, *, dtype, name)

iter_array: Iterator of np.array, where arrays are drawn from columns (axis=0) or rows (axis=1)

IterNodeDelegate.map_all(mapping: Union[Mapping[Hashable, Any], Series], *, dtype: Optional[Union[str, numpy.dtype, type]] = None, name: Optional[Hashable] = None) → FrameOrSeries[source]

Apply a mapping; for values not in the mapping, an Exception is raised. Returns a new container.

Parameters

mapping – A mapping type, such as a dictionary or Series.
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.

FrameGO.iter_array(*, axis).map_all_iter(mapping)

iter_array: Iterator of np.array, where arrays are drawn from columns (axis=0) or rows (axis=1)

IterNodeDelegate.map_all_iter(mapping: Union[Mapping[Hashable, Any], Series]) → Iterator[Any][source]

Apply a mapping; for values not in the mapping, an Exception is raised. A generator of resulting values.

Parameters: mapping – A mapping type, such as a dictionary or Series.

FrameGO.iter_array(*, axis).map_all_iter_items(mapping)

iter_array: Iterator of np.array, where arrays are drawn from columns (axis=0) or rows (axis=1)

IterNodeDelegate.map_all_iter_items(mapping: Union[Mapping[Hashable, Any], Series]) → Iterator[Tuple[Any, Any]][source]

Apply a mapping; for values not in the mapping, an Exception is raised. A generator of resulting key, value pairs.

Parameters: mapping – A mapping type, such as a dictionary or Series.

FrameGO.iter_array(*, axis).map_any(mapping, *, dtype, name)

iter_array: Iterator of np.array, where arrays are drawn from columns (axis=0) or rows (axis=1)

IterNodeDelegate.map_any(mapping: Union[Mapping[Hashable, Any], Series], *, dtype: Optional[Union[str, numpy.dtype, type]] = None, name: Optional[Hashable] = None) → FrameOrSeries[source]

Apply a mapping; for values not in the mapping, the value is returned. Returns a new container.

Parameters

mapping – A mapping type, such as a dictionary or Series.
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.

FrameGO.iter_array(*, axis).map_any_iter(mapping)

iter_array: Iterator of np.array, where arrays are drawn from columns (axis=0) or rows (axis=1)

IterNodeDelegate.map_any_iter(mapping: Union[Mapping[Hashable, Any], Series]) → Iterator[Any][source]

Apply a mapping; for values not in the mapping, the value is returned. A generator of resulting values.

Parameters: mapping – A mapping type, such as a dictionary or Series.

FrameGO.iter_array(*, axis).map_any_iter_items(mapping)

iter_array: Iterator of np.array, where arrays are drawn from columns (axis=0) or rows (axis=1)

IterNodeDelegate.map_any_iter_items(mapping: Union[Mapping[Hashable, Any], Series]) → Iterator[Tuple[Any, Any]][source]

Apply a mapping; for values not in the mapping, the value is returned. A generator of resulting key, value pairs.

Parameters: mapping – A mapping type, such as a dictionary or Series.

FrameGO.iter_array(*, axis).map_fill(mapping, *, fill_value, dtype, name)

iter_array: Iterator of np.array, where arrays are drawn from columns (axis=0) or rows (axis=1)

IterNodeDelegate.map_fill(mapping: Union[Mapping[Hashable, Any], Series], *, fill_value: Any = nan, dtype: Optional[Union[str, numpy.dtype, type]] = None, name: Optional[Hashable] = None) → FrameOrSeries[source]

Apply a mapping; for values not in the mapping, the fill_value is returned. Returns a new container.

Parameters

mapping – A mapping type, such as a dictionary or Series.
fill_value – Value to be returned if the values is not a key in the mapping.
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.

FrameGO.iter_array(*, axis).map_fill_iter(mapping, *, fill_value)

iter_array: Iterator of np.array, where arrays are drawn from columns (axis=0) or rows (axis=1)

IterNodeDelegate.map_fill_iter(mapping: Union[Mapping[Hashable, Any], Series], *, fill_value: Any = nan) → Iterator[Any][source]

Apply a mapping; for values not in the mapping, the fill_value is returned. A generator of resulting values.

Parameters

mapping – A mapping type, such as a dictionary or Series.
fill_value – Value to be returned if the values is not a key in the mapping.

FrameGO.iter_array(*, axis).map_fill_iter_items(mapping, *, fill_value)

iter_array: Iterator of np.array, where arrays are drawn from columns (axis=0) or rows (axis=1)

IterNodeDelegate.map_fill_iter_items(mapping: Union[Mapping[Hashable, Any], Series], *, fill_value: Any = nan) → Iterator[Tuple[Any, Any]][source]

Apply a mapping; for values not in the mapping, the fill_value is returned. A generator of resulting key, value pairs.

Parameters

mapping – A mapping type, such as a dictionary or Series.
fill_value – Value to be returned if the values is not a key in the mapping.

FrameGO.iter_array_items(*, axis)

iter_array_items: Iterator of pairs of label, np.array, where arrays are drawn from columns (axis=0) or rows (axis=1)

FrameGO.iter_array_items(*, axis).apply(func, *, dtype, name)

iter_array_items: Iterator of pairs of label, np.array, where arrays are drawn from columns (axis=0) or rows (axis=1)

IterNodeDelegate.apply(func: Callable[[…], Any], *, dtype: Optional[Union[str, numpy.dtype, type]] = None, name: Optional[Hashable] = None) → FrameOrSeries[source]

Apply a function to each value. Returns a new container.

Parameters

func – A function that takes a value.
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.

FrameGO.iter_array_items(*, axis).apply_iter(func)

iter_array_items: Iterator of pairs of label, np.array, where arrays are drawn from columns (axis=0) or rows (axis=1)

IterNodeDelegate.apply_iter(func: Callable[[…], Any]) → Iterator[Any][source]

Apply a function to each value. A generator of resulting values.

Parameters: func – A function that takes a value.

FrameGO.iter_array_items(*, axis).apply_iter_items(func)

iter_array_items: Iterator of pairs of label, np.array, where arrays are drawn from columns (axis=0) or rows (axis=1)

IterNodeDelegate.apply_iter_items(func: Callable[[…], Any]) → Iterator[Tuple[Any, Any]][source]

Apply a function to each value. A generator of resulting key, value pairs.

Parameters: func – A function that takes a value.

FrameGO.iter_array_items(*, axis).apply_pool(func, *, dtype, name, max_workers, chunksize, use_threads)

iter_array_items: Iterator of pairs of label, np.array, where arrays are drawn from columns (axis=0) or rows (axis=1)

IterNodeDelegate.apply_pool(func: Callable[[…], Any], *, dtype: Optional[Union[str, numpy.dtype, type]] = None, name: Optional[Hashable] = None, max_workers: Optional[int] = None, chunksize: int = 1, use_threads: bool = False) → FrameOrSeries[source]

Apply a function to each value. Employ parallel processing with either the ProcessPoolExecutor or ThreadPoolExecutor.

Parameters

func – A function that takes a value.
* –
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.
name – A hashable object to label the container.
max_workers – Number of parallel executors, as passed to the Thread- or ProcessPoolExecutor; None defaults to the max number of machine processes.
chunksize – Units of work per executor, as passed to the Thread- or ProcessPoolExecutor.
use_threads – Use the ThreadPoolExecutor instead of the ProcessPoolExecutor.

FrameGO.iter_array_items(*, axis).map_all(mapping, *, dtype, name)

iter_array_items: Iterator of pairs of label, np.array, where arrays are drawn from columns (axis=0) or rows (axis=1)

IterNodeDelegate.map_all(mapping: Union[Mapping[Hashable, Any], Series], *, dtype: Optional[Union[str, numpy.dtype, type]] = None, name: Optional[Hashable] = None) → FrameOrSeries[source]

Apply a mapping; for values not in the mapping, an Exception is raised. Returns a new container.

Parameters

mapping – A mapping type, such as a dictionary or Series.
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.

FrameGO.iter_array_items(*, axis).map_all_iter(mapping)

iter_array_items: Iterator of pairs of label, np.array, where arrays are drawn from columns (axis=0) or rows (axis=1)

IterNodeDelegate.map_all_iter(mapping: Union[Mapping[Hashable, Any], Series]) → Iterator[Any][source]

Apply a mapping; for values not in the mapping, an Exception is raised. A generator of resulting values.

Parameters: mapping – A mapping type, such as a dictionary or Series.

FrameGO.iter_array_items(*, axis).map_all_iter_items(mapping)

iter_array_items: Iterator of pairs of label, np.array, where arrays are drawn from columns (axis=0) or rows (axis=1)

IterNodeDelegate.map_all_iter_items(mapping: Union[Mapping[Hashable, Any], Series]) → Iterator[Tuple[Any, Any]][source]

Apply a mapping; for values not in the mapping, an Exception is raised. A generator of resulting key, value pairs.

Parameters: mapping – A mapping type, such as a dictionary or Series.

FrameGO.iter_array_items(*, axis).map_any(mapping, *, dtype, name)

iter_array_items: Iterator of pairs of label, np.array, where arrays are drawn from columns (axis=0) or rows (axis=1)

IterNodeDelegate.map_any(mapping: Union[Mapping[Hashable, Any], Series], *, dtype: Optional[Union[str, numpy.dtype, type]] = None, name: Optional[Hashable] = None) → FrameOrSeries[source]

Apply a mapping; for values not in the mapping, the value is returned. Returns a new container.

Parameters

mapping – A mapping type, such as a dictionary or Series.
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.

FrameGO.iter_array_items(*, axis).map_any_iter(mapping)

iter_array_items: Iterator of pairs of label, np.array, where arrays are drawn from columns (axis=0) or rows (axis=1)

IterNodeDelegate.map_any_iter(mapping: Union[Mapping[Hashable, Any], Series]) → Iterator[Any][source]

Apply a mapping; for values not in the mapping, the value is returned. A generator of resulting values.

Parameters: mapping – A mapping type, such as a dictionary or Series.

FrameGO.iter_array_items(*, axis).map_any_iter_items(mapping)

iter_array_items: Iterator of pairs of label, np.array, where arrays are drawn from columns (axis=0) or rows (axis=1)

IterNodeDelegate.map_any_iter_items(mapping: Union[Mapping[Hashable, Any], Series]) → Iterator[Tuple[Any, Any]][source]

Apply a mapping; for values not in the mapping, the value is returned. A generator of resulting key, value pairs.

Parameters: mapping – A mapping type, such as a dictionary or Series.

FrameGO.iter_array_items(*, axis).map_fill(mapping, *, fill_value, dtype, name)

iter_array_items: Iterator of pairs of label, np.array, where arrays are drawn from columns (axis=0) or rows (axis=1)

IterNodeDelegate.map_fill(mapping: Union[Mapping[Hashable, Any], Series], *, fill_value: Any = nan, dtype: Optional[Union[str, numpy.dtype, type]] = None, name: Optional[Hashable] = None) → FrameOrSeries[source]

Apply a mapping; for values not in the mapping, the fill_value is returned. Returns a new container.

Parameters

mapping – A mapping type, such as a dictionary or Series.
fill_value – Value to be returned if the values is not a key in the mapping.
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.

FrameGO.iter_array_items(*, axis).map_fill_iter(mapping, *, fill_value)

iter_array_items: Iterator of pairs of label, np.array, where arrays are drawn from columns (axis=0) or rows (axis=1)

IterNodeDelegate.map_fill_iter(mapping: Union[Mapping[Hashable, Any], Series], *, fill_value: Any = nan) → Iterator[Any][source]

Apply a mapping; for values not in the mapping, the fill_value is returned. A generator of resulting values.

Parameters

mapping – A mapping type, such as a dictionary or Series.
fill_value – Value to be returned if the values is not a key in the mapping.

FrameGO.iter_array_items(*, axis).map_fill_iter_items(mapping, *, fill_value)

iter_array_items: Iterator of pairs of label, np.array, where arrays are drawn from columns (axis=0) or rows (axis=1)

IterNodeDelegate.map_fill_iter_items(mapping: Union[Mapping[Hashable, Any], Series], *, fill_value: Any = nan) → Iterator[Tuple[Any, Any]][source]

Apply a mapping; for values not in the mapping, the fill_value is returned. A generator of resulting key, value pairs.

Parameters

mapping – A mapping type, such as a dictionary or Series.
fill_value – Value to be returned if the values is not a key in the mapping.

FrameGO.iter_element(*, axis)

iter_element: Iterator of elements, ordered by row then column.

FrameGO.iter_element(*, axis).apply(func, *, dtype, name)

iter_element: Iterator of elements, ordered by row then column.

IterNodeDelegate.apply(func: Callable[[…], Any], *, dtype: Optional[Union[str, numpy.dtype, type]] = None, name: Optional[Hashable] = None) → FrameOrSeries[source]

Apply a function to each value. Returns a new container.

Parameters

func – A function that takes a value.
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.

FrameGO.iter_element(*, axis).apply_iter(func)

iter_element: Iterator of elements, ordered by row then column.

IterNodeDelegate.apply_iter(func: Callable[[…], Any]) → Iterator[Any][source]

Apply a function to each value. A generator of resulting values.

Parameters: func – A function that takes a value.

FrameGO.iter_element(*, axis).apply_iter_items(func)

iter_element: Iterator of elements, ordered by row then column.

IterNodeDelegate.apply_iter_items(func: Callable[[…], Any]) → Iterator[Tuple[Any, Any]][source]

Apply a function to each value. A generator of resulting key, value pairs.

Parameters: func – A function that takes a value.

FrameGO.iter_element(*, axis).apply_pool(func, *, dtype, name, max_workers, chunksize, use_threads)

iter_element: Iterator of elements, ordered by row then column.

IterNodeDelegate.apply_pool(func: Callable[[…], Any], *, dtype: Optional[Union[str, numpy.dtype, type]] = None, name: Optional[Hashable] = None, max_workers: Optional[int] = None, chunksize: int = 1, use_threads: bool = False) → FrameOrSeries[source]

Apply a function to each value. Employ parallel processing with either the ProcessPoolExecutor or ThreadPoolExecutor.

Parameters

func – A function that takes a value.
* –
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.
name – A hashable object to label the container.
max_workers – Number of parallel executors, as passed to the Thread- or ProcessPoolExecutor; None defaults to the max number of machine processes.
chunksize – Units of work per executor, as passed to the Thread- or ProcessPoolExecutor.
use_threads – Use the ThreadPoolExecutor instead of the ProcessPoolExecutor.

FrameGO.iter_element(*, axis).map_all(mapping, *, dtype, name)

iter_element: Iterator of elements, ordered by row then column.

IterNodeDelegate.map_all(mapping: Union[Mapping[Hashable, Any], Series], *, dtype: Optional[Union[str, numpy.dtype, type]] = None, name: Optional[Hashable] = None) → FrameOrSeries[source]

Apply a mapping; for values not in the mapping, an Exception is raised. Returns a new container.

Parameters

mapping – A mapping type, such as a dictionary or Series.
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.

FrameGO.iter_element(*, axis).map_all_iter(mapping)

iter_element: Iterator of elements, ordered by row then column.

IterNodeDelegate.map_all_iter(mapping: Union[Mapping[Hashable, Any], Series]) → Iterator[Any][source]

Apply a mapping; for values not in the mapping, an Exception is raised. A generator of resulting values.

Parameters: mapping – A mapping type, such as a dictionary or Series.

FrameGO.iter_element(*, axis).map_all_iter_items(mapping)

iter_element: Iterator of elements, ordered by row then column.

IterNodeDelegate.map_all_iter_items(mapping: Union[Mapping[Hashable, Any], Series]) → Iterator[Tuple[Any, Any]][source]

Apply a mapping; for values not in the mapping, an Exception is raised. A generator of resulting key, value pairs.

Parameters: mapping – A mapping type, such as a dictionary or Series.

FrameGO.iter_element(*, axis).map_any(mapping, *, dtype, name)

iter_element: Iterator of elements, ordered by row then column.

IterNodeDelegate.map_any(mapping: Union[Mapping[Hashable, Any], Series], *, dtype: Optional[Union[str, numpy.dtype, type]] = None, name: Optional[Hashable] = None) → FrameOrSeries[source]

Apply a mapping; for values not in the mapping, the value is returned. Returns a new container.

Parameters

mapping – A mapping type, such as a dictionary or Series.
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.

FrameGO.iter_element(*, axis).map_any_iter(mapping)

iter_element: Iterator of elements, ordered by row then column.

IterNodeDelegate.map_any_iter(mapping: Union[Mapping[Hashable, Any], Series]) → Iterator[Any][source]

Apply a mapping; for values not in the mapping, the value is returned. A generator of resulting values.

Parameters: mapping – A mapping type, such as a dictionary or Series.

FrameGO.iter_element(*, axis).map_any_iter_items(mapping)

iter_element: Iterator of elements, ordered by row then column.

IterNodeDelegate.map_any_iter_items(mapping: Union[Mapping[Hashable, Any], Series]) → Iterator[Tuple[Any, Any]][source]

Apply a mapping; for values not in the mapping, the value is returned. A generator of resulting key, value pairs.

Parameters: mapping – A mapping type, such as a dictionary or Series.

FrameGO.iter_element(*, axis).map_fill(mapping, *, fill_value, dtype, name)

iter_element: Iterator of elements, ordered by row then column.

IterNodeDelegate.map_fill(mapping: Union[Mapping[Hashable, Any], Series], *, fill_value: Any = nan, dtype: Optional[Union[str, numpy.dtype, type]] = None, name: Optional[Hashable] = None) → FrameOrSeries[source]

Apply a mapping; for values not in the mapping, the fill_value is returned. Returns a new container.

Parameters

mapping – A mapping type, such as a dictionary or Series.
fill_value – Value to be returned if the values is not a key in the mapping.
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.

FrameGO.iter_element(*, axis).map_fill_iter(mapping, *, fill_value)

iter_element: Iterator of elements, ordered by row then column.

IterNodeDelegate.map_fill_iter(mapping: Union[Mapping[Hashable, Any], Series], *, fill_value: Any = nan) → Iterator[Any][source]

Apply a mapping; for values not in the mapping, the fill_value is returned. A generator of resulting values.

Parameters

mapping – A mapping type, such as a dictionary or Series.
fill_value – Value to be returned if the values is not a key in the mapping.

FrameGO.iter_element(*, axis).map_fill_iter_items(mapping, *, fill_value)

iter_element: Iterator of elements, ordered by row then column.

IterNodeDelegate.map_fill_iter_items(mapping: Union[Mapping[Hashable, Any], Series], *, fill_value: Any = nan) → Iterator[Tuple[Any, Any]][source]

Apply a mapping; for values not in the mapping, the fill_value is returned. A generator of resulting key, value pairs.

Parameters

mapping – A mapping type, such as a dictionary or Series.
fill_value – Value to be returned if the values is not a key in the mapping.

FrameGO.iter_element_items(*, axis)

iter_element_items: Iterator of pairs of label, element, where labels are pairs of index, columns labels, ordered by row then column.

FrameGO.iter_element_items(*, axis).apply(func, *, dtype, name)

iter_element_items: Iterator of pairs of label, element, where labels are pairs of index, columns labels, ordered by row then column.

IterNodeDelegate.apply(func: Callable[[…], Any], *, dtype: Optional[Union[str, numpy.dtype, type]] = None, name: Optional[Hashable] = None) → FrameOrSeries[source]

Apply a function to each value. Returns a new container.

Parameters

func – A function that takes a value.
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.

FrameGO.iter_element_items(*, axis).apply_iter(func)

iter_element_items: Iterator of pairs of label, element, where labels are pairs of index, columns labels, ordered by row then column.

IterNodeDelegate.apply_iter(func: Callable[[…], Any]) → Iterator[Any][source]

Apply a function to each value. A generator of resulting values.

Parameters: func – A function that takes a value.

FrameGO.iter_element_items(*, axis).apply_iter_items(func)

iter_element_items: Iterator of pairs of label, element, where labels are pairs of index, columns labels, ordered by row then column.

IterNodeDelegate.apply_iter_items(func: Callable[[…], Any]) → Iterator[Tuple[Any, Any]][source]

Apply a function to each value. A generator of resulting key, value pairs.

Parameters: func – A function that takes a value.

FrameGO.iter_element_items(*, axis).apply_pool(func, *, dtype, name, max_workers, chunksize, use_threads)

iter_element_items: Iterator of pairs of label, element, where labels are pairs of index, columns labels, ordered by row then column.

IterNodeDelegate.apply_pool(func: Callable[[…], Any], *, dtype: Optional[Union[str, numpy.dtype, type]] = None, name: Optional[Hashable] = None, max_workers: Optional[int] = None, chunksize: int = 1, use_threads: bool = False) → FrameOrSeries[source]

Apply a function to each value. Employ parallel processing with either the ProcessPoolExecutor or ThreadPoolExecutor.

Parameters

func – A function that takes a value.
* –
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.
name – A hashable object to label the container.
max_workers – Number of parallel executors, as passed to the Thread- or ProcessPoolExecutor; None defaults to the max number of machine processes.
chunksize – Units of work per executor, as passed to the Thread- or ProcessPoolExecutor.
use_threads – Use the ThreadPoolExecutor instead of the ProcessPoolExecutor.

FrameGO.iter_element_items(*, axis).map_all(mapping, *, dtype, name)

iter_element_items: Iterator of pairs of label, element, where labels are pairs of index, columns labels, ordered by row then column.

IterNodeDelegate.map_all(mapping: Union[Mapping[Hashable, Any], Series], *, dtype: Optional[Union[str, numpy.dtype, type]] = None, name: Optional[Hashable] = None) → FrameOrSeries[source]

Apply a mapping; for values not in the mapping, an Exception is raised. Returns a new container.

Parameters

mapping – A mapping type, such as a dictionary or Series.
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.

FrameGO.iter_element_items(*, axis).map_all_iter(mapping)

iter_element_items: Iterator of pairs of label, element, where labels are pairs of index, columns labels, ordered by row then column.

IterNodeDelegate.map_all_iter(mapping: Union[Mapping[Hashable, Any], Series]) → Iterator[Any][source]

Apply a mapping; for values not in the mapping, an Exception is raised. A generator of resulting values.

Parameters: mapping – A mapping type, such as a dictionary or Series.

FrameGO.iter_element_items(*, axis).map_all_iter_items(mapping)

iter_element_items: Iterator of pairs of label, element, where labels are pairs of index, columns labels, ordered by row then column.

IterNodeDelegate.map_all_iter_items(mapping: Union[Mapping[Hashable, Any], Series]) → Iterator[Tuple[Any, Any]][source]

Apply a mapping; for values not in the mapping, an Exception is raised. A generator of resulting key, value pairs.

Parameters: mapping – A mapping type, such as a dictionary or Series.

FrameGO.iter_element_items(*, axis).map_any(mapping, *, dtype, name)

iter_element_items: Iterator of pairs of label, element, where labels are pairs of index, columns labels, ordered by row then column.

IterNodeDelegate.map_any(mapping: Union[Mapping[Hashable, Any], Series], *, dtype: Optional[Union[str, numpy.dtype, type]] = None, name: Optional[Hashable] = None) → FrameOrSeries[source]

Apply a mapping; for values not in the mapping, the value is returned. Returns a new container.

Parameters

mapping – A mapping type, such as a dictionary or Series.
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.

FrameGO.iter_element_items(*, axis).map_any_iter(mapping)

iter_element_items: Iterator of pairs of label, element, where labels are pairs of index, columns labels, ordered by row then column.

IterNodeDelegate.map_any_iter(mapping: Union[Mapping[Hashable, Any], Series]) → Iterator[Any][source]

Apply a mapping; for values not in the mapping, the value is returned. A generator of resulting values.

Parameters: mapping – A mapping type, such as a dictionary or Series.

FrameGO.iter_element_items(*, axis).map_any_iter_items(mapping)

iter_element_items: Iterator of pairs of label, element, where labels are pairs of index, columns labels, ordered by row then column.

IterNodeDelegate.map_any_iter_items(mapping: Union[Mapping[Hashable, Any], Series]) → Iterator[Tuple[Any, Any]][source]

Apply a mapping; for values not in the mapping, the value is returned. A generator of resulting key, value pairs.

Parameters: mapping – A mapping type, such as a dictionary or Series.

FrameGO.iter_element_items(*, axis).map_fill(mapping, *, fill_value, dtype, name)

iter_element_items: Iterator of pairs of label, element, where labels are pairs of index, columns labels, ordered by row then column.

IterNodeDelegate.map_fill(mapping: Union[Mapping[Hashable, Any], Series], *, fill_value: Any = nan, dtype: Optional[Union[str, numpy.dtype, type]] = None, name: Optional[Hashable] = None) → FrameOrSeries[source]

Apply a mapping; for values not in the mapping, the fill_value is returned. Returns a new container.

Parameters

mapping – A mapping type, such as a dictionary or Series.
fill_value – Value to be returned if the values is not a key in the mapping.
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.

FrameGO.iter_element_items(*, axis).map_fill_iter(mapping, *, fill_value)

iter_element_items: Iterator of pairs of label, element, where labels are pairs of index, columns labels, ordered by row then column.

IterNodeDelegate.map_fill_iter(mapping: Union[Mapping[Hashable, Any], Series], *, fill_value: Any = nan) → Iterator[Any][source]

Apply a mapping; for values not in the mapping, the fill_value is returned. A generator of resulting values.

Parameters

mapping – A mapping type, such as a dictionary or Series.
fill_value – Value to be returned if the values is not a key in the mapping.

FrameGO.iter_element_items(*, axis).map_fill_iter_items(mapping, *, fill_value)

iter_element_items: Iterator of pairs of label, element, where labels are pairs of index, columns labels, ordered by row then column.

IterNodeDelegate.map_fill_iter_items(mapping: Union[Mapping[Hashable, Any], Series], *, fill_value: Any = nan) → Iterator[Tuple[Any, Any]][source]

Apply a mapping; for values not in the mapping, the fill_value is returned. A generator of resulting key, value pairs.

Parameters

mapping – A mapping type, such as a dictionary or Series.
fill_value – Value to be returned if the values is not a key in the mapping.

FrameGO.iter_group(key, *, axis)

iter_group: Iterator of Frame grouped by unique values found in one or more columns (axis=0) or rows (axis=1).

FrameGO.iter_group(key, *, axis).apply(func, *, dtype, name)

iter_group: Iterator of Frame grouped by unique values found in one or more columns (axis=0) or rows (axis=1).

IterNodeDelegate.apply(func: Callable[[…], Any], *, dtype: Optional[Union[str, numpy.dtype, type]] = None, name: Optional[Hashable] = None) → FrameOrSeries[source]

Apply a function to each value. Returns a new container.

Parameters

func – A function that takes a value.
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.

FrameGO.iter_group(key, *, axis).apply_iter(func)

iter_group: Iterator of Frame grouped by unique values found in one or more columns (axis=0) or rows (axis=1).

IterNodeDelegate.apply_iter(func: Callable[[…], Any]) → Iterator[Any][source]

Apply a function to each value. A generator of resulting values.

Parameters: func – A function that takes a value.

FrameGO.iter_group(key, *, axis).apply_iter_items(func)

iter_group: Iterator of Frame grouped by unique values found in one or more columns (axis=0) or rows (axis=1).

IterNodeDelegate.apply_iter_items(func: Callable[[…], Any]) → Iterator[Tuple[Any, Any]][source]

Apply a function to each value. A generator of resulting key, value pairs.

Parameters: func – A function that takes a value.

FrameGO.iter_group(key, *, axis).apply_pool(func, *, dtype, name, max_workers, chunksize, use_threads)

iter_group: Iterator of Frame grouped by unique values found in one or more columns (axis=0) or rows (axis=1).

IterNodeDelegate.apply_pool(func: Callable[[…], Any], *, dtype: Optional[Union[str, numpy.dtype, type]] = None, name: Optional[Hashable] = None, max_workers: Optional[int] = None, chunksize: int = 1, use_threads: bool = False) → FrameOrSeries[source]

Apply a function to each value. Employ parallel processing with either the ProcessPoolExecutor or ThreadPoolExecutor.

Parameters

func – A function that takes a value.
* –
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.
name – A hashable object to label the container.
max_workers – Number of parallel executors, as passed to the Thread- or ProcessPoolExecutor; None defaults to the max number of machine processes.
chunksize – Units of work per executor, as passed to the Thread- or ProcessPoolExecutor.
use_threads – Use the ThreadPoolExecutor instead of the ProcessPoolExecutor.

FrameGO.iter_group(key, *, axis).map_all(mapping, *, dtype, name)

iter_group: Iterator of Frame grouped by unique values found in one or more columns (axis=0) or rows (axis=1).

IterNodeDelegate.map_all(mapping: Union[Mapping[Hashable, Any], Series], *, dtype: Optional[Union[str, numpy.dtype, type]] = None, name: Optional[Hashable] = None) → FrameOrSeries[source]

Apply a mapping; for values not in the mapping, an Exception is raised. Returns a new container.

Parameters

mapping – A mapping type, such as a dictionary or Series.
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.

FrameGO.iter_group(key, *, axis).map_all_iter(mapping)

iter_group: Iterator of Frame grouped by unique values found in one or more columns (axis=0) or rows (axis=1).

IterNodeDelegate.map_all_iter(mapping: Union[Mapping[Hashable, Any], Series]) → Iterator[Any][source]

Apply a mapping; for values not in the mapping, an Exception is raised. A generator of resulting values.

Parameters: mapping – A mapping type, such as a dictionary or Series.

FrameGO.iter_group(key, *, axis).map_all_iter_items(mapping)

iter_group: Iterator of Frame grouped by unique values found in one or more columns (axis=0) or rows (axis=1).

IterNodeDelegate.map_all_iter_items(mapping: Union[Mapping[Hashable, Any], Series]) → Iterator[Tuple[Any, Any]][source]

Apply a mapping; for values not in the mapping, an Exception is raised. A generator of resulting key, value pairs.

Parameters: mapping – A mapping type, such as a dictionary or Series.

FrameGO.iter_group(key, *, axis).map_any(mapping, *, dtype, name)

iter_group: Iterator of Frame grouped by unique values found in one or more columns (axis=0) or rows (axis=1).

IterNodeDelegate.map_any(mapping: Union[Mapping[Hashable, Any], Series], *, dtype: Optional[Union[str, numpy.dtype, type]] = None, name: Optional[Hashable] = None) → FrameOrSeries[source]

Apply a mapping; for values not in the mapping, the value is returned. Returns a new container.

Parameters

mapping – A mapping type, such as a dictionary or Series.
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.

FrameGO.iter_group(key, *, axis).map_any_iter(mapping)

iter_group: Iterator of Frame grouped by unique values found in one or more columns (axis=0) or rows (axis=1).

IterNodeDelegate.map_any_iter(mapping: Union[Mapping[Hashable, Any], Series]) → Iterator[Any][source]

Apply a mapping; for values not in the mapping, the value is returned. A generator of resulting values.

Parameters: mapping – A mapping type, such as a dictionary or Series.

FrameGO.iter_group(key, *, axis).map_any_iter_items(mapping)

iter_group: Iterator of Frame grouped by unique values found in one or more columns (axis=0) or rows (axis=1).

IterNodeDelegate.map_any_iter_items(mapping: Union[Mapping[Hashable, Any], Series]) → Iterator[Tuple[Any, Any]][source]

Apply a mapping; for values not in the mapping, the value is returned. A generator of resulting key, value pairs.

Parameters: mapping – A mapping type, such as a dictionary or Series.

FrameGO.iter_group(key, *, axis).map_fill(mapping, *, fill_value, dtype, name)

iter_group: Iterator of Frame grouped by unique values found in one or more columns (axis=0) or rows (axis=1).

IterNodeDelegate.map_fill(mapping: Union[Mapping[Hashable, Any], Series], *, fill_value: Any = nan, dtype: Optional[Union[str, numpy.dtype, type]] = None, name: Optional[Hashable] = None) → FrameOrSeries[source]

Apply a mapping; for values not in the mapping, the fill_value is returned. Returns a new container.

Parameters

mapping – A mapping type, such as a dictionary or Series.
fill_value – Value to be returned if the values is not a key in the mapping.
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.

FrameGO.iter_group(key, *, axis).map_fill_iter(mapping, *, fill_value)

iter_group: Iterator of Frame grouped by unique values found in one or more columns (axis=0) or rows (axis=1).

IterNodeDelegate.map_fill_iter(mapping: Union[Mapping[Hashable, Any], Series], *, fill_value: Any = nan) → Iterator[Any][source]

Apply a mapping; for values not in the mapping, the fill_value is returned. A generator of resulting values.

Parameters

mapping – A mapping type, such as a dictionary or Series.
fill_value – Value to be returned if the values is not a key in the mapping.

FrameGO.iter_group(key, *, axis).map_fill_iter_items(mapping, *, fill_value)

iter_group: Iterator of Frame grouped by unique values found in one or more columns (axis=0) or rows (axis=1).

IterNodeDelegate.map_fill_iter_items(mapping: Union[Mapping[Hashable, Any], Series], *, fill_value: Any = nan) → Iterator[Tuple[Any, Any]][source]

Apply a mapping; for values not in the mapping, the fill_value is returned. A generator of resulting key, value pairs.

Parameters

mapping – A mapping type, such as a dictionary or Series.
fill_value – Value to be returned if the values is not a key in the mapping.

FrameGO.iter_group_items(key, *, axis)

iter_group_items: Iterator of pairs of label, Frame grouped by unique values found in one or more columns (axis=0) or rows (axis=1).

FrameGO.iter_group_items(key, *, axis).apply(func, *, dtype, name)

iter_group_items: Iterator of pairs of label, Frame grouped by unique values found in one or more columns (axis=0) or rows (axis=1).

IterNodeDelegate.apply(func: Callable[[…], Any], *, dtype: Optional[Union[str, numpy.dtype, type]] = None, name: Optional[Hashable] = None) → FrameOrSeries[source]

Apply a function to each value. Returns a new container.

Parameters

func – A function that takes a value.
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.

FrameGO.iter_group_items(key, *, axis).apply_iter(func)

iter_group_items: Iterator of pairs of label, Frame grouped by unique values found in one or more columns (axis=0) or rows (axis=1).

IterNodeDelegate.apply_iter(func: Callable[[…], Any]) → Iterator[Any][source]

Apply a function to each value. A generator of resulting values.

Parameters: func – A function that takes a value.

FrameGO.iter_group_items(key, *, axis).apply_iter_items(func)

iter_group_items: Iterator of pairs of label, Frame grouped by unique values found in one or more columns (axis=0) or rows (axis=1).

IterNodeDelegate.apply_iter_items(func: Callable[[…], Any]) → Iterator[Tuple[Any, Any]][source]

Apply a function to each value. A generator of resulting key, value pairs.

Parameters: func – A function that takes a value.

FrameGO.iter_group_items(key, *, axis).apply_pool(func, *, dtype, name, max_workers, chunksize, use_threads)

iter_group_items: Iterator of pairs of label, Frame grouped by unique values found in one or more columns (axis=0) or rows (axis=1).

IterNodeDelegate.apply_pool(func: Callable[[…], Any], *, dtype: Optional[Union[str, numpy.dtype, type]] = None, name: Optional[Hashable] = None, max_workers: Optional[int] = None, chunksize: int = 1, use_threads: bool = False) → FrameOrSeries[source]

Apply a function to each value. Employ parallel processing with either the ProcessPoolExecutor or ThreadPoolExecutor.

Parameters

func – A function that takes a value.
* –
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.
name – A hashable object to label the container.
max_workers – Number of parallel executors, as passed to the Thread- or ProcessPoolExecutor; None defaults to the max number of machine processes.
chunksize – Units of work per executor, as passed to the Thread- or ProcessPoolExecutor.
use_threads – Use the ThreadPoolExecutor instead of the ProcessPoolExecutor.

FrameGO.iter_group_items(key, *, axis).map_all(mapping, *, dtype, name)

iter_group_items: Iterator of pairs of label, Frame grouped by unique values found in one or more columns (axis=0) or rows (axis=1).

IterNodeDelegate.map_all(mapping: Union[Mapping[Hashable, Any], Series], *, dtype: Optional[Union[str, numpy.dtype, type]] = None, name: Optional[Hashable] = None) → FrameOrSeries[source]

Apply a mapping; for values not in the mapping, an Exception is raised. Returns a new container.

Parameters

mapping – A mapping type, such as a dictionary or Series.
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.

FrameGO.iter_group_items(key, *, axis).map_all_iter(mapping)

iter_group_items: Iterator of pairs of label, Frame grouped by unique values found in one or more columns (axis=0) or rows (axis=1).

IterNodeDelegate.map_all_iter(mapping: Union[Mapping[Hashable, Any], Series]) → Iterator[Any][source]

Apply a mapping; for values not in the mapping, an Exception is raised. A generator of resulting values.

Parameters: mapping – A mapping type, such as a dictionary or Series.

FrameGO.iter_group_items(key, *, axis).map_all_iter_items(mapping)

iter_group_items: Iterator of pairs of label, Frame grouped by unique values found in one or more columns (axis=0) or rows (axis=1).

IterNodeDelegate.map_all_iter_items(mapping: Union[Mapping[Hashable, Any], Series]) → Iterator[Tuple[Any, Any]][source]

Apply a mapping; for values not in the mapping, an Exception is raised. A generator of resulting key, value pairs.

Parameters: mapping – A mapping type, such as a dictionary or Series.

FrameGO.iter_group_items(key, *, axis).map_any(mapping, *, dtype, name)

iter_group_items: Iterator of pairs of label, Frame grouped by unique values found in one or more columns (axis=0) or rows (axis=1).

IterNodeDelegate.map_any(mapping: Union[Mapping[Hashable, Any], Series], *, dtype: Optional[Union[str, numpy.dtype, type]] = None, name: Optional[Hashable] = None) → FrameOrSeries[source]

Apply a mapping; for values not in the mapping, the value is returned. Returns a new container.

Parameters

mapping – A mapping type, such as a dictionary or Series.
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.

FrameGO.iter_group_items(key, *, axis).map_any_iter(mapping)

iter_group_items: Iterator of pairs of label, Frame grouped by unique values found in one or more columns (axis=0) or rows (axis=1).

IterNodeDelegate.map_any_iter(mapping: Union[Mapping[Hashable, Any], Series]) → Iterator[Any][source]

Apply a mapping; for values not in the mapping, the value is returned. A generator of resulting values.

Parameters: mapping – A mapping type, such as a dictionary or Series.

FrameGO.iter_group_items(key, *, axis).map_any_iter_items(mapping)

iter_group_items: Iterator of pairs of label, Frame grouped by unique values found in one or more columns (axis=0) or rows (axis=1).

IterNodeDelegate.map_any_iter_items(mapping: Union[Mapping[Hashable, Any], Series]) → Iterator[Tuple[Any, Any]][source]

Apply a mapping; for values not in the mapping, the value is returned. A generator of resulting key, value pairs.

Parameters: mapping – A mapping type, such as a dictionary or Series.

FrameGO.iter_group_items(key, *, axis).map_fill(mapping, *, fill_value, dtype, name)

iter_group_items: Iterator of pairs of label, Frame grouped by unique values found in one or more columns (axis=0) or rows (axis=1).

IterNodeDelegate.map_fill(mapping: Union[Mapping[Hashable, Any], Series], *, fill_value: Any = nan, dtype: Optional[Union[str, numpy.dtype, type]] = None, name: Optional[Hashable] = None) → FrameOrSeries[source]

Apply a mapping; for values not in the mapping, the fill_value is returned. Returns a new container.

Parameters

mapping – A mapping type, such as a dictionary or Series.
fill_value – Value to be returned if the values is not a key in the mapping.
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.

FrameGO.iter_group_items(key, *, axis).map_fill_iter(mapping, *, fill_value)

iter_group_items: Iterator of pairs of label, Frame grouped by unique values found in one or more columns (axis=0) or rows (axis=1).

IterNodeDelegate.map_fill_iter(mapping: Union[Mapping[Hashable, Any], Series], *, fill_value: Any = nan) → Iterator[Any][source]

Apply a mapping; for values not in the mapping, the fill_value is returned. A generator of resulting values.

Parameters

mapping – A mapping type, such as a dictionary or Series.
fill_value – Value to be returned if the values is not a key in the mapping.

FrameGO.iter_group_items(key, *, axis).map_fill_iter_items(mapping, *, fill_value)

iter_group_items: Iterator of pairs of label, Frame grouped by unique values found in one or more columns (axis=0) or rows (axis=1).

IterNodeDelegate.map_fill_iter_items(mapping: Union[Mapping[Hashable, Any], Series], *, fill_value: Any = nan) → Iterator[Tuple[Any, Any]][source]

Apply a mapping; for values not in the mapping, the fill_value is returned. A generator of resulting key, value pairs.

Parameters

mapping – A mapping type, such as a dictionary or Series.
fill_value – Value to be returned if the values is not a key in the mapping.

FrameGO.iter_group_labels(depth_level, *, axis)

iter_group_labels: Iterator of Frame grouped by unique labels found in one or more index depths (axis=0) or columns depths (axis=1).

FrameGO.iter_group_labels(depth_level, *, axis).apply(func, *, dtype, name)

iter_group_labels: Iterator of Frame grouped by unique labels found in one or more index depths (axis=0) or columns depths (axis=1).

IterNodeDelegate.apply(func: Callable[[…], Any], *, dtype: Optional[Union[str, numpy.dtype, type]] = None, name: Optional[Hashable] = None) → FrameOrSeries[source]

Apply a function to each value. Returns a new container.

Parameters

func – A function that takes a value.
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.

FrameGO.iter_group_labels(depth_level, *, axis).apply_iter(func)

iter_group_labels: Iterator of Frame grouped by unique labels found in one or more index depths (axis=0) or columns depths (axis=1).

IterNodeDelegate.apply_iter(func: Callable[[…], Any]) → Iterator[Any][source]

Apply a function to each value. A generator of resulting values.

Parameters: func – A function that takes a value.

FrameGO.iter_group_labels(depth_level, *, axis).apply_iter_items(func)

iter_group_labels: Iterator of Frame grouped by unique labels found in one or more index depths (axis=0) or columns depths (axis=1).

IterNodeDelegate.apply_iter_items(func: Callable[[…], Any]) → Iterator[Tuple[Any, Any]][source]

Apply a function to each value. A generator of resulting key, value pairs.

Parameters: func – A function that takes a value.

FrameGO.iter_group_labels(depth_level, *, axis).apply_pool(func, *, dtype, name, max_workers, chunksize, use_threads)

iter_group_labels: Iterator of Frame grouped by unique labels found in one or more index depths (axis=0) or columns depths (axis=1).

IterNodeDelegate.apply_pool(func: Callable[[…], Any], *, dtype: Optional[Union[str, numpy.dtype, type]] = None, name: Optional[Hashable] = None, max_workers: Optional[int] = None, chunksize: int = 1, use_threads: bool = False) → FrameOrSeries[source]

Apply a function to each value. Employ parallel processing with either the ProcessPoolExecutor or ThreadPoolExecutor.

Parameters

func – A function that takes a value.
* –
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.
name – A hashable object to label the container.
max_workers – Number of parallel executors, as passed to the Thread- or ProcessPoolExecutor; None defaults to the max number of machine processes.
chunksize – Units of work per executor, as passed to the Thread- or ProcessPoolExecutor.
use_threads – Use the ThreadPoolExecutor instead of the ProcessPoolExecutor.

FrameGO.iter_group_labels(depth_level, *, axis).map_all(mapping, *, dtype, name)

iter_group_labels: Iterator of Frame grouped by unique labels found in one or more index depths (axis=0) or columns depths (axis=1).

IterNodeDelegate.map_all(mapping: Union[Mapping[Hashable, Any], Series], *, dtype: Optional[Union[str, numpy.dtype, type]] = None, name: Optional[Hashable] = None) → FrameOrSeries[source]

Apply a mapping; for values not in the mapping, an Exception is raised. Returns a new container.

Parameters

mapping – A mapping type, such as a dictionary or Series.
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.

FrameGO.iter_group_labels(depth_level, *, axis).map_all_iter(mapping)

iter_group_labels: Iterator of Frame grouped by unique labels found in one or more index depths (axis=0) or columns depths (axis=1).

IterNodeDelegate.map_all_iter(mapping: Union[Mapping[Hashable, Any], Series]) → Iterator[Any][source]

Apply a mapping; for values not in the mapping, an Exception is raised. A generator of resulting values.

Parameters: mapping – A mapping type, such as a dictionary or Series.

FrameGO.iter_group_labels(depth_level, *, axis).map_all_iter_items(mapping)

iter_group_labels: Iterator of Frame grouped by unique labels found in one or more index depths (axis=0) or columns depths (axis=1).

IterNodeDelegate.map_all_iter_items(mapping: Union[Mapping[Hashable, Any], Series]) → Iterator[Tuple[Any, Any]][source]

Apply a mapping; for values not in the mapping, an Exception is raised. A generator of resulting key, value pairs.

Parameters: mapping – A mapping type, such as a dictionary or Series.

FrameGO.iter_group_labels(depth_level, *, axis).map_any(mapping, *, dtype, name)

iter_group_labels: Iterator of Frame grouped by unique labels found in one or more index depths (axis=0) or columns depths (axis=1).

IterNodeDelegate.map_any(mapping: Union[Mapping[Hashable, Any], Series], *, dtype: Optional[Union[str, numpy.dtype, type]] = None, name: Optional[Hashable] = None) → FrameOrSeries[source]

Apply a mapping; for values not in the mapping, the value is returned. Returns a new container.

Parameters

mapping – A mapping type, such as a dictionary or Series.
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.

FrameGO.iter_group_labels(depth_level, *, axis).map_any_iter(mapping)

iter_group_labels: Iterator of Frame grouped by unique labels found in one or more index depths (axis=0) or columns depths (axis=1).

IterNodeDelegate.map_any_iter(mapping: Union[Mapping[Hashable, Any], Series]) → Iterator[Any][source]

Apply a mapping; for values not in the mapping, the value is returned. A generator of resulting values.

Parameters: mapping – A mapping type, such as a dictionary or Series.

FrameGO.iter_group_labels(depth_level, *, axis).map_any_iter_items(mapping)

iter_group_labels: Iterator of Frame grouped by unique labels found in one or more index depths (axis=0) or columns depths (axis=1).

IterNodeDelegate.map_any_iter_items(mapping: Union[Mapping[Hashable, Any], Series]) → Iterator[Tuple[Any, Any]][source]

Apply a mapping; for values not in the mapping, the value is returned. A generator of resulting key, value pairs.

Parameters: mapping – A mapping type, such as a dictionary or Series.

FrameGO.iter_group_labels(depth_level, *, axis).map_fill(mapping, *, fill_value, dtype, name)

iter_group_labels: Iterator of Frame grouped by unique labels found in one or more index depths (axis=0) or columns depths (axis=1).

IterNodeDelegate.map_fill(mapping: Union[Mapping[Hashable, Any], Series], *, fill_value: Any = nan, dtype: Optional[Union[str, numpy.dtype, type]] = None, name: Optional[Hashable] = None) → FrameOrSeries[source]

Apply a mapping; for values not in the mapping, the fill_value is returned. Returns a new container.

Parameters

mapping – A mapping type, such as a dictionary or Series.
fill_value – Value to be returned if the values is not a key in the mapping.
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.

FrameGO.iter_group_labels(depth_level, *, axis).map_fill_iter(mapping, *, fill_value)

iter_group_labels: Iterator of Frame grouped by unique labels found in one or more index depths (axis=0) or columns depths (axis=1).

IterNodeDelegate.map_fill_iter(mapping: Union[Mapping[Hashable, Any], Series], *, fill_value: Any = nan) → Iterator[Any][source]

Apply a mapping; for values not in the mapping, the fill_value is returned. A generator of resulting values.

Parameters

mapping – A mapping type, such as a dictionary or Series.
fill_value – Value to be returned if the values is not a key in the mapping.

FrameGO.iter_group_labels(depth_level, *, axis).map_fill_iter_items(mapping, *, fill_value)

iter_group_labels: Iterator of Frame grouped by unique labels found in one or more index depths (axis=0) or columns depths (axis=1).

IterNodeDelegate.map_fill_iter_items(mapping: Union[Mapping[Hashable, Any], Series], *, fill_value: Any = nan) → Iterator[Tuple[Any, Any]][source]

Apply a mapping; for values not in the mapping, the fill_value is returned. A generator of resulting key, value pairs.

Parameters

mapping – A mapping type, such as a dictionary or Series.
fill_value – Value to be returned if the values is not a key in the mapping.

FrameGO.iter_group_labels_items(depth_level, *, axis)

iter_group_labels_items: Iterator of pairs of label, Frame grouped by unique labels found in one or more index depths (axis=0) or columns depths (axis=1).

FrameGO.iter_group_labels_items(depth_level, *, axis).apply(func, *, dtype, name)

iter_group_labels_items: Iterator of pairs of label, Frame grouped by unique labels found in one or more index depths (axis=0) or columns depths (axis=1).

IterNodeDelegate.apply(func: Callable[[…], Any], *, dtype: Optional[Union[str, numpy.dtype, type]] = None, name: Optional[Hashable] = None) → FrameOrSeries[source]

Apply a function to each value. Returns a new container.

Parameters

func – A function that takes a value.
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.

FrameGO.iter_group_labels_items(depth_level, *, axis).apply_iter(func)

iter_group_labels_items: Iterator of pairs of label, Frame grouped by unique labels found in one or more index depths (axis=0) or columns depths (axis=1).

IterNodeDelegate.apply_iter(func: Callable[[…], Any]) → Iterator[Any][source]

Apply a function to each value. A generator of resulting values.

Parameters: func – A function that takes a value.

FrameGO.iter_group_labels_items(depth_level, *, axis).apply_iter_items(func)

iter_group_labels_items: Iterator of pairs of label, Frame grouped by unique labels found in one or more index depths (axis=0) or columns depths (axis=1).

IterNodeDelegate.apply_iter_items(func: Callable[[…], Any]) → Iterator[Tuple[Any, Any]][source]

Apply a function to each value. A generator of resulting key, value pairs.

Parameters: func – A function that takes a value.

FrameGO.iter_group_labels_items(depth_level, *, axis).apply_pool(func, *, dtype, name, max_workers, chunksize, use_threads)

iter_group_labels_items: Iterator of pairs of label, Frame grouped by unique labels found in one or more index depths (axis=0) or columns depths (axis=1).

IterNodeDelegate.apply_pool(func: Callable[[…], Any], *, dtype: Optional[Union[str, numpy.dtype, type]] = None, name: Optional[Hashable] = None, max_workers: Optional[int] = None, chunksize: int = 1, use_threads: bool = False) → FrameOrSeries[source]

Apply a function to each value. Employ parallel processing with either the ProcessPoolExecutor or ThreadPoolExecutor.

Parameters

func – A function that takes a value.
* –
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.
name – A hashable object to label the container.
max_workers – Number of parallel executors, as passed to the Thread- or ProcessPoolExecutor; None defaults to the max number of machine processes.
chunksize – Units of work per executor, as passed to the Thread- or ProcessPoolExecutor.
use_threads – Use the ThreadPoolExecutor instead of the ProcessPoolExecutor.

FrameGO.iter_group_labels_items(depth_level, *, axis).map_all(mapping, *, dtype, name)

iter_group_labels_items: Iterator of pairs of label, Frame grouped by unique labels found in one or more index depths (axis=0) or columns depths (axis=1).

IterNodeDelegate.map_all(mapping: Union[Mapping[Hashable, Any], Series], *, dtype: Optional[Union[str, numpy.dtype, type]] = None, name: Optional[Hashable] = None) → FrameOrSeries[source]

Apply a mapping; for values not in the mapping, an Exception is raised. Returns a new container.

Parameters

mapping – A mapping type, such as a dictionary or Series.
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.

FrameGO.iter_group_labels_items(depth_level, *, axis).map_all_iter(mapping)

iter_group_labels_items: Iterator of pairs of label, Frame grouped by unique labels found in one or more index depths (axis=0) or columns depths (axis=1).

IterNodeDelegate.map_all_iter(mapping: Union[Mapping[Hashable, Any], Series]) → Iterator[Any][source]

Apply a mapping; for values not in the mapping, an Exception is raised. A generator of resulting values.

Parameters: mapping – A mapping type, such as a dictionary or Series.

FrameGO.iter_group_labels_items(depth_level, *, axis).map_all_iter_items(mapping)

iter_group_labels_items: Iterator of pairs of label, Frame grouped by unique labels found in one or more index depths (axis=0) or columns depths (axis=1).

IterNodeDelegate.map_all_iter_items(mapping: Union[Mapping[Hashable, Any], Series]) → Iterator[Tuple[Any, Any]][source]

Apply a mapping; for values not in the mapping, an Exception is raised. A generator of resulting key, value pairs.

Parameters: mapping – A mapping type, such as a dictionary or Series.

FrameGO.iter_group_labels_items(depth_level, *, axis).map_any(mapping, *, dtype, name)

iter_group_labels_items: Iterator of pairs of label, Frame grouped by unique labels found in one or more index depths (axis=0) or columns depths (axis=1).

IterNodeDelegate.map_any(mapping: Union[Mapping[Hashable, Any], Series], *, dtype: Optional[Union[str, numpy.dtype, type]] = None, name: Optional[Hashable] = None) → FrameOrSeries[source]

Apply a mapping; for values not in the mapping, the value is returned. Returns a new container.

Parameters

mapping – A mapping type, such as a dictionary or Series.
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.

FrameGO.iter_group_labels_items(depth_level, *, axis).map_any_iter(mapping)

iter_group_labels_items: Iterator of pairs of label, Frame grouped by unique labels found in one or more index depths (axis=0) or columns depths (axis=1).

IterNodeDelegate.map_any_iter(mapping: Union[Mapping[Hashable, Any], Series]) → Iterator[Any][source]

Apply a mapping; for values not in the mapping, the value is returned. A generator of resulting values.

Parameters: mapping – A mapping type, such as a dictionary or Series.

FrameGO.iter_group_labels_items(depth_level, *, axis).map_any_iter_items(mapping)

iter_group_labels_items: Iterator of pairs of label, Frame grouped by unique labels found in one or more index depths (axis=0) or columns depths (axis=1).

IterNodeDelegate.map_any_iter_items(mapping: Union[Mapping[Hashable, Any], Series]) → Iterator[Tuple[Any, Any]][source]

Apply a mapping; for values not in the mapping, the value is returned. A generator of resulting key, value pairs.

Parameters: mapping – A mapping type, such as a dictionary or Series.

FrameGO.iter_group_labels_items(depth_level, *, axis).map_fill(mapping, *, fill_value, dtype, name)

iter_group_labels_items: Iterator of pairs of label, Frame grouped by unique labels found in one or more index depths (axis=0) or columns depths (axis=1).

IterNodeDelegate.map_fill(mapping: Union[Mapping[Hashable, Any], Series], *, fill_value: Any = nan, dtype: Optional[Union[str, numpy.dtype, type]] = None, name: Optional[Hashable] = None) → FrameOrSeries[source]

Apply a mapping; for values not in the mapping, the fill_value is returned. Returns a new container.

Parameters

mapping – A mapping type, such as a dictionary or Series.
fill_value – Value to be returned if the values is not a key in the mapping.
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.

FrameGO.iter_group_labels_items(depth_level, *, axis).map_fill_iter(mapping, *, fill_value)

iter_group_labels_items: Iterator of pairs of label, Frame grouped by unique labels found in one or more index depths (axis=0) or columns depths (axis=1).

IterNodeDelegate.map_fill_iter(mapping: Union[Mapping[Hashable, Any], Series], *, fill_value: Any = nan) → Iterator[Any][source]

Apply a mapping; for values not in the mapping, the fill_value is returned. A generator of resulting values.

Parameters

mapping – A mapping type, such as a dictionary or Series.
fill_value – Value to be returned if the values is not a key in the mapping.

FrameGO.iter_group_labels_items(depth_level, *, axis).map_fill_iter_items(mapping, *, fill_value)

iter_group_labels_items: Iterator of pairs of label, Frame grouped by unique labels found in one or more index depths (axis=0) or columns depths (axis=1).

IterNodeDelegate.map_fill_iter_items(mapping: Union[Mapping[Hashable, Any], Series], *, fill_value: Any = nan) → Iterator[Tuple[Any, Any]][source]

Apply a mapping; for values not in the mapping, the fill_value is returned. A generator of resulting key, value pairs.

Parameters

mapping – A mapping type, such as a dictionary or Series.
fill_value – Value to be returned if the values is not a key in the mapping.

FrameGO.iter_series(*, axis)

iter_series: Iterator of Series, where Series are drawn from columns (axis=0) or rows (axis=1)

FrameGO.iter_series(*, axis).apply(func, *, dtype, name)

iter_series: Iterator of Series, where Series are drawn from columns (axis=0) or rows (axis=1)

IterNodeDelegate.apply(func: Callable[[…], Any], *, dtype: Optional[Union[str, numpy.dtype, type]] = None, name: Optional[Hashable] = None) → FrameOrSeries[source]

Apply a function to each value. Returns a new container.

Parameters

func – A function that takes a value.
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.

FrameGO.iter_series(*, axis).apply_iter(func)

iter_series: Iterator of Series, where Series are drawn from columns (axis=0) or rows (axis=1)

IterNodeDelegate.apply_iter(func: Callable[[…], Any]) → Iterator[Any][source]

Apply a function to each value. A generator of resulting values.

Parameters: func – A function that takes a value.

FrameGO.iter_series(*, axis).apply_iter_items(func)

iter_series: Iterator of Series, where Series are drawn from columns (axis=0) or rows (axis=1)

IterNodeDelegate.apply_iter_items(func: Callable[[…], Any]) → Iterator[Tuple[Any, Any]][source]

Apply a function to each value. A generator of resulting key, value pairs.

Parameters: func – A function that takes a value.

FrameGO.iter_series(*, axis).apply_pool(func, *, dtype, name, max_workers, chunksize, use_threads)

iter_series: Iterator of Series, where Series are drawn from columns (axis=0) or rows (axis=1)

IterNodeDelegate.apply_pool(func: Callable[[…], Any], *, dtype: Optional[Union[str, numpy.dtype, type]] = None, name: Optional[Hashable] = None, max_workers: Optional[int] = None, chunksize: int = 1, use_threads: bool = False) → FrameOrSeries[source]

Apply a function to each value. Employ parallel processing with either the ProcessPoolExecutor or ThreadPoolExecutor.

Parameters

func – A function that takes a value.
* –
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.
name – A hashable object to label the container.
max_workers – Number of parallel executors, as passed to the Thread- or ProcessPoolExecutor; None defaults to the max number of machine processes.
chunksize – Units of work per executor, as passed to the Thread- or ProcessPoolExecutor.
use_threads – Use the ThreadPoolExecutor instead of the ProcessPoolExecutor.

FrameGO.iter_series(*, axis).map_all(mapping, *, dtype, name)

iter_series: Iterator of Series, where Series are drawn from columns (axis=0) or rows (axis=1)

IterNodeDelegate.map_all(mapping: Union[Mapping[Hashable, Any], Series], *, dtype: Optional[Union[str, numpy.dtype, type]] = None, name: Optional[Hashable] = None) → FrameOrSeries[source]

Apply a mapping; for values not in the mapping, an Exception is raised. Returns a new container.

Parameters

mapping – A mapping type, such as a dictionary or Series.
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.

FrameGO.iter_series(*, axis).map_all_iter(mapping)

iter_series: Iterator of Series, where Series are drawn from columns (axis=0) or rows (axis=1)

IterNodeDelegate.map_all_iter(mapping: Union[Mapping[Hashable, Any], Series]) → Iterator[Any][source]

Apply a mapping; for values not in the mapping, an Exception is raised. A generator of resulting values.

Parameters: mapping – A mapping type, such as a dictionary or Series.

FrameGO.iter_series(*, axis).map_all_iter_items(mapping)

iter_series: Iterator of Series, where Series are drawn from columns (axis=0) or rows (axis=1)

IterNodeDelegate.map_all_iter_items(mapping: Union[Mapping[Hashable, Any], Series]) → Iterator[Tuple[Any, Any]][source]

Apply a mapping; for values not in the mapping, an Exception is raised. A generator of resulting key, value pairs.

Parameters: mapping – A mapping type, such as a dictionary or Series.

FrameGO.iter_series(*, axis).map_any(mapping, *, dtype, name)

iter_series: Iterator of Series, where Series are drawn from columns (axis=0) or rows (axis=1)

IterNodeDelegate.map_any(mapping: Union[Mapping[Hashable, Any], Series], *, dtype: Optional[Union[str, numpy.dtype, type]] = None, name: Optional[Hashable] = None) → FrameOrSeries[source]

Apply a mapping; for values not in the mapping, the value is returned. Returns a new container.

Parameters

mapping – A mapping type, such as a dictionary or Series.
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.

FrameGO.iter_series(*, axis).map_any_iter(mapping)

iter_series: Iterator of Series, where Series are drawn from columns (axis=0) or rows (axis=1)

IterNodeDelegate.map_any_iter(mapping: Union[Mapping[Hashable, Any], Series]) → Iterator[Any][source]

Apply a mapping; for values not in the mapping, the value is returned. A generator of resulting values.

Parameters: mapping – A mapping type, such as a dictionary or Series.

FrameGO.iter_series(*, axis).map_any_iter_items(mapping)

iter_series: Iterator of Series, where Series are drawn from columns (axis=0) or rows (axis=1)

IterNodeDelegate.map_any_iter_items(mapping: Union[Mapping[Hashable, Any], Series]) → Iterator[Tuple[Any, Any]][source]

Apply a mapping; for values not in the mapping, the value is returned. A generator of resulting key, value pairs.

Parameters: mapping – A mapping type, such as a dictionary or Series.

FrameGO.iter_series(*, axis).map_fill(mapping, *, fill_value, dtype, name)

iter_series: Iterator of Series, where Series are drawn from columns (axis=0) or rows (axis=1)

IterNodeDelegate.map_fill(mapping: Union[Mapping[Hashable, Any], Series], *, fill_value: Any = nan, dtype: Optional[Union[str, numpy.dtype, type]] = None, name: Optional[Hashable] = None) → FrameOrSeries[source]

Apply a mapping; for values not in the mapping, the fill_value is returned. Returns a new container.

Parameters

mapping – A mapping type, such as a dictionary or Series.
fill_value – Value to be returned if the values is not a key in the mapping.
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.

FrameGO.iter_series(*, axis).map_fill_iter(mapping, *, fill_value)

iter_series: Iterator of Series, where Series are drawn from columns (axis=0) or rows (axis=1)

IterNodeDelegate.map_fill_iter(mapping: Union[Mapping[Hashable, Any], Series], *, fill_value: Any = nan) → Iterator[Any][source]

Apply a mapping; for values not in the mapping, the fill_value is returned. A generator of resulting values.

Parameters

mapping – A mapping type, such as a dictionary or Series.
fill_value – Value to be returned if the values is not a key in the mapping.

FrameGO.iter_series(*, axis).map_fill_iter_items(mapping, *, fill_value)

iter_series: Iterator of Series, where Series are drawn from columns (axis=0) or rows (axis=1)

IterNodeDelegate.map_fill_iter_items(mapping: Union[Mapping[Hashable, Any], Series], *, fill_value: Any = nan) → Iterator[Tuple[Any, Any]][source]

Apply a mapping; for values not in the mapping, the fill_value is returned. A generator of resulting key, value pairs.

Parameters

mapping – A mapping type, such as a dictionary or Series.
fill_value – Value to be returned if the values is not a key in the mapping.

FrameGO.iter_series_items(*, axis)

iter_series_items: Iterator of pairs of label, Series, where Series are drawn from columns (axis=0) or rows (axis=1)

FrameGO.iter_series_items(*, axis).apply(func, *, dtype, name)

iter_series_items: Iterator of pairs of label, Series, where Series are drawn from columns (axis=0) or rows (axis=1)

IterNodeDelegate.apply(func: Callable[[…], Any], *, dtype: Optional[Union[str, numpy.dtype, type]] = None, name: Optional[Hashable] = None) → FrameOrSeries[source]

Apply a function to each value. Returns a new container.

Parameters

func – A function that takes a value.
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.

FrameGO.iter_series_items(*, axis).apply_iter(func)

iter_series_items: Iterator of pairs of label, Series, where Series are drawn from columns (axis=0) or rows (axis=1)

IterNodeDelegate.apply_iter(func: Callable[[…], Any]) → Iterator[Any][source]

Apply a function to each value. A generator of resulting values.

Parameters: func – A function that takes a value.

FrameGO.iter_series_items(*, axis).apply_iter_items(func)

iter_series_items: Iterator of pairs of label, Series, where Series are drawn from columns (axis=0) or rows (axis=1)

IterNodeDelegate.apply_iter_items(func: Callable[[…], Any]) → Iterator[Tuple[Any, Any]][source]

Apply a function to each value. A generator of resulting key, value pairs.

Parameters: func – A function that takes a value.

FrameGO.iter_series_items(*, axis).apply_pool(func, *, dtype, name, max_workers, chunksize, use_threads)

iter_series_items: Iterator of pairs of label, Series, where Series are drawn from columns (axis=0) or rows (axis=1)

IterNodeDelegate.apply_pool(func: Callable[[…], Any], *, dtype: Optional[Union[str, numpy.dtype, type]] = None, name: Optional[Hashable] = None, max_workers: Optional[int] = None, chunksize: int = 1, use_threads: bool = False) → FrameOrSeries[source]

Apply a function to each value. Employ parallel processing with either the ProcessPoolExecutor or ThreadPoolExecutor.

Parameters

func – A function that takes a value.
* –
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.
name – A hashable object to label the container.
max_workers – Number of parallel executors, as passed to the Thread- or ProcessPoolExecutor; None defaults to the max number of machine processes.
chunksize – Units of work per executor, as passed to the Thread- or ProcessPoolExecutor.
use_threads – Use the ThreadPoolExecutor instead of the ProcessPoolExecutor.

FrameGO.iter_series_items(*, axis).map_all(mapping, *, dtype, name)

iter_series_items: Iterator of pairs of label, Series, where Series are drawn from columns (axis=0) or rows (axis=1)

IterNodeDelegate.map_all(mapping: Union[Mapping[Hashable, Any], Series], *, dtype: Optional[Union[str, numpy.dtype, type]] = None, name: Optional[Hashable] = None) → FrameOrSeries[source]

Apply a mapping; for values not in the mapping, an Exception is raised. Returns a new container.

Parameters

mapping – A mapping type, such as a dictionary or Series.
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.

FrameGO.iter_series_items(*, axis).map_all_iter(mapping)

iter_series_items: Iterator of pairs of label, Series, where Series are drawn from columns (axis=0) or rows (axis=1)

IterNodeDelegate.map_all_iter(mapping: Union[Mapping[Hashable, Any], Series]) → Iterator[Any][source]

Apply a mapping; for values not in the mapping, an Exception is raised. A generator of resulting values.

Parameters: mapping – A mapping type, such as a dictionary or Series.

FrameGO.iter_series_items(*, axis).map_all_iter_items(mapping)

iter_series_items: Iterator of pairs of label, Series, where Series are drawn from columns (axis=0) or rows (axis=1)

IterNodeDelegate.map_all_iter_items(mapping: Union[Mapping[Hashable, Any], Series]) → Iterator[Tuple[Any, Any]][source]

Apply a mapping; for values not in the mapping, an Exception is raised. A generator of resulting key, value pairs.

Parameters: mapping – A mapping type, such as a dictionary or Series.

FrameGO.iter_series_items(*, axis).map_any(mapping, *, dtype, name)

iter_series_items: Iterator of pairs of label, Series, where Series are drawn from columns (axis=0) or rows (axis=1)

IterNodeDelegate.map_any(mapping: Union[Mapping[Hashable, Any], Series], *, dtype: Optional[Union[str, numpy.dtype, type]] = None, name: Optional[Hashable] = None) → FrameOrSeries[source]

Apply a mapping; for values not in the mapping, the value is returned. Returns a new container.

Parameters

mapping – A mapping type, such as a dictionary or Series.
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.

FrameGO.iter_series_items(*, axis).map_any_iter(mapping)

iter_series_items: Iterator of pairs of label, Series, where Series are drawn from columns (axis=0) or rows (axis=1)

IterNodeDelegate.map_any_iter(mapping: Union[Mapping[Hashable, Any], Series]) → Iterator[Any][source]

Apply a mapping; for values not in the mapping, the value is returned. A generator of resulting values.

Parameters: mapping – A mapping type, such as a dictionary or Series.

FrameGO.iter_series_items(*, axis).map_any_iter_items(mapping)

iter_series_items: Iterator of pairs of label, Series, where Series are drawn from columns (axis=0) or rows (axis=1)

IterNodeDelegate.map_any_iter_items(mapping: Union[Mapping[Hashable, Any], Series]) → Iterator[Tuple[Any, Any]][source]

Apply a mapping; for values not in the mapping, the value is returned. A generator of resulting key, value pairs.

Parameters: mapping – A mapping type, such as a dictionary or Series.

FrameGO.iter_series_items(*, axis).map_fill(mapping, *, fill_value, dtype, name)

iter_series_items: Iterator of pairs of label, Series, where Series are drawn from columns (axis=0) or rows (axis=1)

IterNodeDelegate.map_fill(mapping: Union[Mapping[Hashable, Any], Series], *, fill_value: Any = nan, dtype: Optional[Union[str, numpy.dtype, type]] = None, name: Optional[Hashable] = None) → FrameOrSeries[source]

Apply a mapping; for values not in the mapping, the fill_value is returned. Returns a new container.

Parameters

mapping – A mapping type, such as a dictionary or Series.
fill_value – Value to be returned if the values is not a key in the mapping.
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.

FrameGO.iter_series_items(*, axis).map_fill_iter(mapping, *, fill_value)

iter_series_items: Iterator of pairs of label, Series, where Series are drawn from columns (axis=0) or rows (axis=1)

IterNodeDelegate.map_fill_iter(mapping: Union[Mapping[Hashable, Any], Series], *, fill_value: Any = nan) → Iterator[Any][source]

Apply a mapping; for values not in the mapping, the fill_value is returned. A generator of resulting values.

Parameters

mapping – A mapping type, such as a dictionary or Series.
fill_value – Value to be returned if the values is not a key in the mapping.

FrameGO.iter_series_items(*, axis).map_fill_iter_items(mapping, *, fill_value)

iter_series_items: Iterator of pairs of label, Series, where Series are drawn from columns (axis=0) or rows (axis=1)

IterNodeDelegate.map_fill_iter_items(mapping: Union[Mapping[Hashable, Any], Series], *, fill_value: Any = nan) → Iterator[Tuple[Any, Any]][source]

Apply a mapping; for values not in the mapping, the fill_value is returned. A generator of resulting key, value pairs.

Parameters

mapping – A mapping type, such as a dictionary or Series.
fill_value – Value to be returned if the values is not a key in the mapping.

FrameGO.iter_tuple(*, axis, constructor)

iter_tuple: Iterator of NamedTuple, where tuples are drawn from columns (axis=0) or rows (axis=1). An optional constructor callable can be used to provide a NamedTuple class (or any other constructor called with a single iterable) to be used to create each yielded axis value.

FrameGO.iter_tuple(*, axis, constructor).apply(func, *, dtype, name)

iter_tuple: Iterator of NamedTuple, where tuples are drawn from columns (axis=0) or rows (axis=1). An optional constructor callable can be used to provide a NamedTuple class (or any other constructor called with a single iterable) to be used to create each yielded axis value.

IterNodeDelegate.apply(func: Callable[[…], Any], *, dtype: Optional[Union[str, numpy.dtype, type]] = None, name: Optional[Hashable] = None) → FrameOrSeries[source]

Apply a function to each value. Returns a new container.

Parameters

func – A function that takes a value.
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.

FrameGO.iter_tuple(*, axis, constructor).apply_iter(func)

iter_tuple: Iterator of NamedTuple, where tuples are drawn from columns (axis=0) or rows (axis=1). An optional constructor callable can be used to provide a NamedTuple class (or any other constructor called with a single iterable) to be used to create each yielded axis value.

IterNodeDelegate.apply_iter(func: Callable[[…], Any]) → Iterator[Any][source]

Apply a function to each value. A generator of resulting values.

Parameters: func – A function that takes a value.

FrameGO.iter_tuple(*, axis, constructor).apply_iter_items(func)

iter_tuple: Iterator of NamedTuple, where tuples are drawn from columns (axis=0) or rows (axis=1). An optional constructor callable can be used to provide a NamedTuple class (or any other constructor called with a single iterable) to be used to create each yielded axis value.

IterNodeDelegate.apply_iter_items(func: Callable[[…], Any]) → Iterator[Tuple[Any, Any]][source]

Apply a function to each value. A generator of resulting key, value pairs.

Parameters: func – A function that takes a value.

FrameGO.iter_tuple(*, axis, constructor).apply_pool(func, *, dtype, name, max_workers, chunksize, use_threads)

iter_tuple: Iterator of NamedTuple, where tuples are drawn from columns (axis=0) or rows (axis=1). An optional constructor callable can be used to provide a NamedTuple class (or any other constructor called with a single iterable) to be used to create each yielded axis value.

IterNodeDelegate.apply_pool(func: Callable[[…], Any], *, dtype: Optional[Union[str, numpy.dtype, type]] = None, name: Optional[Hashable] = None, max_workers: Optional[int] = None, chunksize: int = 1, use_threads: bool = False) → FrameOrSeries[source]

Apply a function to each value. Employ parallel processing with either the ProcessPoolExecutor or ThreadPoolExecutor.

Parameters

func – A function that takes a value.
* –
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.
name – A hashable object to label the container.
max_workers – Number of parallel executors, as passed to the Thread- or ProcessPoolExecutor; None defaults to the max number of machine processes.
chunksize – Units of work per executor, as passed to the Thread- or ProcessPoolExecutor.
use_threads – Use the ThreadPoolExecutor instead of the ProcessPoolExecutor.

FrameGO.iter_tuple(*, axis, constructor).map_all(mapping, *, dtype, name)

iter_tuple: Iterator of NamedTuple, where tuples are drawn from columns (axis=0) or rows (axis=1). An optional constructor callable can be used to provide a NamedTuple class (or any other constructor called with a single iterable) to be used to create each yielded axis value.

IterNodeDelegate.map_all(mapping: Union[Mapping[Hashable, Any], Series], *, dtype: Optional[Union[str, numpy.dtype, type]] = None, name: Optional[Hashable] = None) → FrameOrSeries[source]

Apply a mapping; for values not in the mapping, an Exception is raised. Returns a new container.

Parameters

mapping – A mapping type, such as a dictionary or Series.
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.

FrameGO.iter_tuple(*, axis, constructor).map_all_iter(mapping)

iter_tuple: Iterator of NamedTuple, where tuples are drawn from columns (axis=0) or rows (axis=1). An optional constructor callable can be used to provide a NamedTuple class (or any other constructor called with a single iterable) to be used to create each yielded axis value.

IterNodeDelegate.map_all_iter(mapping: Union[Mapping[Hashable, Any], Series]) → Iterator[Any][source]

Apply a mapping; for values not in the mapping, an Exception is raised. A generator of resulting values.

Parameters: mapping – A mapping type, such as a dictionary or Series.

FrameGO.iter_tuple(*, axis, constructor).map_all_iter_items(mapping)

iter_tuple: Iterator of NamedTuple, where tuples are drawn from columns (axis=0) or rows (axis=1). An optional constructor callable can be used to provide a NamedTuple class (or any other constructor called with a single iterable) to be used to create each yielded axis value.

IterNodeDelegate.map_all_iter_items(mapping: Union[Mapping[Hashable, Any], Series]) → Iterator[Tuple[Any, Any]][source]

Apply a mapping; for values not in the mapping, an Exception is raised. A generator of resulting key, value pairs.

Parameters: mapping – A mapping type, such as a dictionary or Series.

FrameGO.iter_tuple(*, axis, constructor).map_any(mapping, *, dtype, name)

iter_tuple: Iterator of NamedTuple, where tuples are drawn from columns (axis=0) or rows (axis=1). An optional constructor callable can be used to provide a NamedTuple class (or any other constructor called with a single iterable) to be used to create each yielded axis value.

IterNodeDelegate.map_any(mapping: Union[Mapping[Hashable, Any], Series], *, dtype: Optional[Union[str, numpy.dtype, type]] = None, name: Optional[Hashable] = None) → FrameOrSeries[source]

Apply a mapping; for values not in the mapping, the value is returned. Returns a new container.

Parameters

mapping – A mapping type, such as a dictionary or Series.
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.

FrameGO.iter_tuple(*, axis, constructor).map_any_iter(mapping)

iter_tuple: Iterator of NamedTuple, where tuples are drawn from columns (axis=0) or rows (axis=1). An optional constructor callable can be used to provide a NamedTuple class (or any other constructor called with a single iterable) to be used to create each yielded axis value.

IterNodeDelegate.map_any_iter(mapping: Union[Mapping[Hashable, Any], Series]) → Iterator[Any][source]

Apply a mapping; for values not in the mapping, the value is returned. A generator of resulting values.

Parameters: mapping – A mapping type, such as a dictionary or Series.

FrameGO.iter_tuple(*, axis, constructor).map_any_iter_items(mapping)

iter_tuple: Iterator of NamedTuple, where tuples are drawn from columns (axis=0) or rows (axis=1). An optional constructor callable can be used to provide a NamedTuple class (or any other constructor called with a single iterable) to be used to create each yielded axis value.

IterNodeDelegate.map_any_iter_items(mapping: Union[Mapping[Hashable, Any], Series]) → Iterator[Tuple[Any, Any]][source]

Apply a mapping; for values not in the mapping, the value is returned. A generator of resulting key, value pairs.

Parameters: mapping – A mapping type, such as a dictionary or Series.

FrameGO.iter_tuple(*, axis, constructor).map_fill(mapping, *, fill_value, dtype, name)

iter_tuple: Iterator of NamedTuple, where tuples are drawn from columns (axis=0) or rows (axis=1). An optional constructor callable can be used to provide a NamedTuple class (or any other constructor called with a single iterable) to be used to create each yielded axis value.

IterNodeDelegate.map_fill(mapping: Union[Mapping[Hashable, Any], Series], *, fill_value: Any = nan, dtype: Optional[Union[str, numpy.dtype, type]] = None, name: Optional[Hashable] = None) → FrameOrSeries[source]

Apply a mapping; for values not in the mapping, the fill_value is returned. Returns a new container.

Parameters

mapping – A mapping type, such as a dictionary or Series.
fill_value – Value to be returned if the values is not a key in the mapping.
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.

FrameGO.iter_tuple(*, axis, constructor).map_fill_iter(mapping, *, fill_value)

iter_tuple: Iterator of NamedTuple, where tuples are drawn from columns (axis=0) or rows (axis=1). An optional constructor callable can be used to provide a NamedTuple class (or any other constructor called with a single iterable) to be used to create each yielded axis value.

IterNodeDelegate.map_fill_iter(mapping: Union[Mapping[Hashable, Any], Series], *, fill_value: Any = nan) → Iterator[Any][source]

Apply a mapping; for values not in the mapping, the fill_value is returned. A generator of resulting values.

Parameters

mapping – A mapping type, such as a dictionary or Series.
fill_value – Value to be returned if the values is not a key in the mapping.

FrameGO.iter_tuple(*, axis, constructor).map_fill_iter_items(mapping, *, fill_value)

iter_tuple: Iterator of NamedTuple, where tuples are drawn from columns (axis=0) or rows (axis=1). An optional constructor callable can be used to provide a NamedTuple class (or any other constructor called with a single iterable) to be used to create each yielded axis value.

IterNodeDelegate.map_fill_iter_items(mapping: Union[Mapping[Hashable, Any], Series], *, fill_value: Any = nan) → Iterator[Tuple[Any, Any]][source]

Apply a mapping; for values not in the mapping, the fill_value is returned. A generator of resulting key, value pairs.

Parameters

mapping – A mapping type, such as a dictionary or Series.
fill_value – Value to be returned if the values is not a key in the mapping.

FrameGO.iter_tuple_items(*, axis, constructor)

iter_tuple_items: Iterator of pairs of label, NamedTuple, where tuples are drawn from columns (axis=0) or rows (axis=1)

FrameGO.iter_tuple_items(*, axis, constructor).apply(func, *, dtype, name)

iter_tuple_items: Iterator of pairs of label, NamedTuple, where tuples are drawn from columns (axis=0) or rows (axis=1)

IterNodeDelegate.apply(func: Callable[[…], Any], *, dtype: Optional[Union[str, numpy.dtype, type]] = None, name: Optional[Hashable] = None) → FrameOrSeries[source]

Apply a function to each value. Returns a new container.

Parameters

func – A function that takes a value.
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.

FrameGO.iter_tuple_items(*, axis, constructor).apply_iter(func)

iter_tuple_items: Iterator of pairs of label, NamedTuple, where tuples are drawn from columns (axis=0) or rows (axis=1)

IterNodeDelegate.apply_iter(func: Callable[[…], Any]) → Iterator[Any][source]

Apply a function to each value. A generator of resulting values.

Parameters: func – A function that takes a value.

FrameGO.iter_tuple_items(*, axis, constructor).apply_iter_items(func)

iter_tuple_items: Iterator of pairs of label, NamedTuple, where tuples are drawn from columns (axis=0) or rows (axis=1)

IterNodeDelegate.apply_iter_items(func: Callable[[…], Any]) → Iterator[Tuple[Any, Any]][source]

Apply a function to each value. A generator of resulting key, value pairs.

Parameters: func – A function that takes a value.

FrameGO.iter_tuple_items(*, axis, constructor).apply_pool(func, *, dtype, name, max_workers, chunksize, use_threads)

iter_tuple_items: Iterator of pairs of label, NamedTuple, where tuples are drawn from columns (axis=0) or rows (axis=1)

IterNodeDelegate.apply_pool(func: Callable[[…], Any], *, dtype: Optional[Union[str, numpy.dtype, type]] = None, name: Optional[Hashable] = None, max_workers: Optional[int] = None, chunksize: int = 1, use_threads: bool = False) → FrameOrSeries[source]

Apply a function to each value. Employ parallel processing with either the ProcessPoolExecutor or ThreadPoolExecutor.

Parameters

func – A function that takes a value.
* –
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.
name – A hashable object to label the container.
max_workers – Number of parallel executors, as passed to the Thread- or ProcessPoolExecutor; None defaults to the max number of machine processes.
chunksize – Units of work per executor, as passed to the Thread- or ProcessPoolExecutor.
use_threads – Use the ThreadPoolExecutor instead of the ProcessPoolExecutor.

FrameGO.iter_tuple_items(*, axis, constructor).map_all(mapping, *, dtype, name)

iter_tuple_items: Iterator of pairs of label, NamedTuple, where tuples are drawn from columns (axis=0) or rows (axis=1)

IterNodeDelegate.map_all(mapping: Union[Mapping[Hashable, Any], Series], *, dtype: Optional[Union[str, numpy.dtype, type]] = None, name: Optional[Hashable] = None) → FrameOrSeries[source]

Apply a mapping; for values not in the mapping, an Exception is raised. Returns a new container.

Parameters

mapping – A mapping type, such as a dictionary or Series.
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.

FrameGO.iter_tuple_items(*, axis, constructor).map_all_iter(mapping)

iter_tuple_items: Iterator of pairs of label, NamedTuple, where tuples are drawn from columns (axis=0) or rows (axis=1)

IterNodeDelegate.map_all_iter(mapping: Union[Mapping[Hashable, Any], Series]) → Iterator[Any][source]

Apply a mapping; for values not in the mapping, an Exception is raised. A generator of resulting values.

Parameters: mapping – A mapping type, such as a dictionary or Series.

FrameGO.iter_tuple_items(*, axis, constructor).map_all_iter_items(mapping)

iter_tuple_items: Iterator of pairs of label, NamedTuple, where tuples are drawn from columns (axis=0) or rows (axis=1)

IterNodeDelegate.map_all_iter_items(mapping: Union[Mapping[Hashable, Any], Series]) → Iterator[Tuple[Any, Any]][source]

Apply a mapping; for values not in the mapping, an Exception is raised. A generator of resulting key, value pairs.

Parameters: mapping – A mapping type, such as a dictionary or Series.

FrameGO.iter_tuple_items(*, axis, constructor).map_any(mapping, *, dtype, name)

iter_tuple_items: Iterator of pairs of label, NamedTuple, where tuples are drawn from columns (axis=0) or rows (axis=1)

IterNodeDelegate.map_any(mapping: Union[Mapping[Hashable, Any], Series], *, dtype: Optional[Union[str, numpy.dtype, type]] = None, name: Optional[Hashable] = None) → FrameOrSeries[source]

Apply a mapping; for values not in the mapping, the value is returned. Returns a new container.

Parameters

mapping – A mapping type, such as a dictionary or Series.
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.

FrameGO.iter_tuple_items(*, axis, constructor).map_any_iter(mapping)

iter_tuple_items: Iterator of pairs of label, NamedTuple, where tuples are drawn from columns (axis=0) or rows (axis=1)

IterNodeDelegate.map_any_iter(mapping: Union[Mapping[Hashable, Any], Series]) → Iterator[Any][source]

Apply a mapping; for values not in the mapping, the value is returned. A generator of resulting values.

Parameters: mapping – A mapping type, such as a dictionary or Series.

FrameGO.iter_tuple_items(*, axis, constructor).map_any_iter_items(mapping)

iter_tuple_items: Iterator of pairs of label, NamedTuple, where tuples are drawn from columns (axis=0) or rows (axis=1)

IterNodeDelegate.map_any_iter_items(mapping: Union[Mapping[Hashable, Any], Series]) → Iterator[Tuple[Any, Any]][source]

Apply a mapping; for values not in the mapping, the value is returned. A generator of resulting key, value pairs.

Parameters: mapping – A mapping type, such as a dictionary or Series.

FrameGO.iter_tuple_items(*, axis, constructor).map_fill(mapping, *, fill_value, dtype, name)

iter_tuple_items: Iterator of pairs of label, NamedTuple, where tuples are drawn from columns (axis=0) or rows (axis=1)

IterNodeDelegate.map_fill(mapping: Union[Mapping[Hashable, Any], Series], *, fill_value: Any = nan, dtype: Optional[Union[str, numpy.dtype, type]] = None, name: Optional[Hashable] = None) → FrameOrSeries[source]

Apply a mapping; for values not in the mapping, the fill_value is returned. Returns a new container.

Parameters

mapping – A mapping type, such as a dictionary or Series.
fill_value – Value to be returned if the values is not a key in the mapping.
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.

FrameGO.iter_tuple_items(*, axis, constructor).map_fill_iter(mapping, *, fill_value)

iter_tuple_items: Iterator of pairs of label, NamedTuple, where tuples are drawn from columns (axis=0) or rows (axis=1)

IterNodeDelegate.map_fill_iter(mapping: Union[Mapping[Hashable, Any], Series], *, fill_value: Any = nan) → Iterator[Any][source]

Apply a mapping; for values not in the mapping, the fill_value is returned. A generator of resulting values.

Parameters

mapping – A mapping type, such as a dictionary or Series.
fill_value – Value to be returned if the values is not a key in the mapping.

FrameGO.iter_tuple_items(*, axis, constructor).map_fill_iter_items(mapping, *, fill_value)

iter_tuple_items: Iterator of pairs of label, NamedTuple, where tuples are drawn from columns (axis=0) or rows (axis=1)

IterNodeDelegate.map_fill_iter_items(mapping: Union[Mapping[Hashable, Any], Series], *, fill_value: Any = nan) → Iterator[Tuple[Any, Any]][source]

Apply a mapping; for values not in the mapping, the fill_value is returned. A generator of resulting key, value pairs.

Parameters

mapping – A mapping type, such as a dictionary or Series.
fill_value – Value to be returned if the values is not a key in the mapping.

FrameGO.iter_window(*, size, axis, step, window_sized, window_func, window_valid, label_shift, start_shift, size_increment)

iter_window

Iterator of windowed values, where values are given as a Frame.

Parameters

size – Elements per window, given as an integer greater than 0.
axis – Integer specifying axis, where 0 is rows and 1 is columns. Axis 0 is set by default.
step – Element shift per window, given as an integer greater than 0. Determines the step size between windows. A step of 1 shifts each window 1 element; a step equal to the size will result in non-overlapping windows.
window_sized – if True, windows with fewer elements than size are skipped.
window_func – Array processor of window values, executed before function application (if used): can be used for applying a weighting function to each window.
window_valid – Function that, given an array window, returns True if the window is valid; invalid windows are skipped.
label_shift – A shift, relative to the right-most element contained in the window, to derive the label to be paired with the window. For example, to label each window with the label found at the start of the window, label_shift can be set to one less than size.
start_shift – A shift to determine the first element where window collection begins.
size_increment – A value to be added to size with each window after the first, so as to, in combination with setting step to 0, permit iterating over expanding windows.

FrameGO.iter_window(*, size, axis, step, window_sized, window_func, window_valid, label_shift, start_shift, size_increment).apply(func, *, dtype, name)

iter_window

Iterator of windowed values, where values are given as a Frame.

Parameters

size – Elements per window, given as an integer greater than 0.
axis – Integer specifying axis, where 0 is rows and 1 is columns. Axis 0 is set by default.
step – Element shift per window, given as an integer greater than 0. Determines the step size between windows. A step of 1 shifts each window 1 element; a step equal to the size will result in non-overlapping windows.
window_sized – if True, windows with fewer elements than size are skipped.
window_func – Array processor of window values, executed before function application (if used): can be used for applying a weighting function to each window.
window_valid – Function that, given an array window, returns True if the window is valid; invalid windows are skipped.
label_shift – A shift, relative to the right-most element contained in the window, to derive the label to be paired with the window. For example, to label each window with the label found at the start of the window, label_shift can be set to one less than size.
start_shift – A shift to determine the first element where window collection begins.
size_increment – A value to be added to size with each window after the first, so as to, in combination with setting step to 0, permit iterating over expanding windows.

IterNodeDelegate.apply(func: Callable[[…], Any], *, dtype: Optional[Union[str, numpy.dtype, type]] = None, name: Optional[Hashable] = None) → FrameOrSeries[source]

Apply a function to each value. Returns a new container.

Parameters

func – A function that takes a value.
dtype – A value suitable for specyfying a NumPy dtype, such as a Python type (float), NumPy array protocol strings (‘f8’), or a dtype instance.

FrameGO.iter_window(*, size, axis, step, window_sized, window_func, window_valid, label_shift, start_shift, size_increment).apply_iter(func)

iter_window

Iterator of windowed values, where values are given as a Frame.

Parameters

size – Elements per window, given as an integer greater than 0.
axis – Integer specifying axis, where 0 is rows and 1 is columns. Axis 0 is set by default.
step – Element shift per window, given as an integer greater than 0. Determines the step size between windows. A step of 1 shifts each window 1 element; a step equal to the size will result in non-overlapping windows.
window_sized – if True, windows with fewer elements than size are skipped.
window_func – Array processor of window values, executed before function application (if used): can be used for applying a weighting function to each window.
window_valid – Function that, given an array window, returns True if the window is valid; invalid windows are skipped.
label_shift – A shift, relative to the right-most element contained in the window, to derive the label to be paired with the window. For example, to label each window with the label found at the start of the window, label_shift can be set to one less than size.
start_shift – A shift to determine the first element where window collection begins.
size_increment – A value to be added to size with each window after the first, so as to, in combination with setting step to 0, permit iterating over expanding windows.

IterNodeDelegate.