Batch

Overview: Batch

class Batch(items: Iterator[Tuple[Hashable, Union[static_frame.core.frame.Frame, static_frame.core.series.Series]]], *, name: Optional[Hashable] = None, config: Union[static_frame.core.store.StoreConfig, Mapping[Any, static_frame.core.store.StoreConfig], None, static_frame.core.store.StoreConfigMap] = None, max_workers: Optional[int] = None, chunksize: int = 1, use_threads: bool = False)[source]

A lazy, sequentially evaluated container of Frame that broadcasts operations on contained Frame by return new Batch instances. Full evaluation of operations only occurs when iterating or calling an exporter.

Batch: Constructor

Overview: Batch: Constructor

Batch.__init__(items: Iterator[Tuple[Hashable, Union[static_frame.core.frame.Frame, static_frame.core.series.Series]]], *, name: Optional[Hashable] = None, config: Union[static_frame.core.store.StoreConfig, Mapping[Any, static_frame.core.store.StoreConfig], None, static_frame.core.store.StoreConfigMap] = None, max_workers: Optional[int] = None, chunksize: int = 1, use_threads: bool = False)[source]

Default constructor of a Batch.

Parameters
  • name – A hashable object to label the container.

  • config – A StoreConfig, or a mapping of label ot StoreConfig

  • 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.

classmethod Batch.from_frames(frames: Iterable[static_frame.core.frame.Frame], *, name: Optional[Hashable] = None, config: Union[static_frame.core.store.StoreConfig, Mapping[Any, static_frame.core.store.StoreConfig], None, static_frame.core.store.StoreConfigMap] = None, max_workers: Optional[int] = None, chunksize: int = 1, use_threads: bool = False)static_frame.core.batch.Batch[source]

Return a Batch from an iterable of Frame; labels will be drawn from Frame.name.

classmethod Batch.from_hdf5(fp: Union[str, os.PathLike], *, config: Union[static_frame.core.store.StoreConfig, Mapping[Any, static_frame.core.store.StoreConfig], None, static_frame.core.store.StoreConfigMap] = None, max_workers: Optional[int] = None, chunksize: int = 1, use_threads: bool = False)static_frame.core.batch.Batch[source]

Given a file path to a HDF5 Batch store, return a Batch instance.

Parameters
  • fp – A string file path or Path instance.

  • config – A StoreConfig, or a mapping of label ot StoreConfig

  • 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.

classmethod Batch.from_sqlite(fp: Union[str, os.PathLike], *, config: Union[static_frame.core.store.StoreConfig, Mapping[Any, static_frame.core.store.StoreConfig], None, static_frame.core.store.StoreConfigMap] = None, max_workers: Optional[int] = None, chunksize: int = 1, use_threads: bool = False)static_frame.core.batch.Batch[source]

Given a file path to an SQLite Batch store, return a Batch instance.

Parameters
  • fp – A string file path or Path instance.

  • config – A StoreConfig, or a mapping of label ot StoreConfig

  • 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.

classmethod Batch.from_xlsx(fp: Union[str, os.PathLike], *, config: Union[static_frame.core.store.StoreConfig, Mapping[Any, static_frame.core.store.StoreConfig], None, static_frame.core.store.StoreConfigMap] = None, max_workers: Optional[int] = None, chunksize: int = 1, use_threads: bool = False)static_frame.core.batch.Batch[source]

Given a file path to an XLSX Batch store, return a Batch instance.

Parameters
  • fp – A string file path or Path instance.

  • config – A StoreConfig, or a mapping of label ot StoreConfig

  • 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.

classmethod Batch.from_zip_csv(fp: Union[str, os.PathLike], *, config: Union[static_frame.core.store.StoreConfig, Mapping[Any, static_frame.core.store.StoreConfig], None, static_frame.core.store.StoreConfigMap] = None, max_workers: Optional[int] = None, chunksize: int = 1, use_threads: bool = False)static_frame.core.batch.Batch[source]

Given a file path to zipped CSV Batch store, return a Batch instance.

Parameters
  • fp – A string file path or Path instance.

  • config – A StoreConfig, or a mapping of label ot StoreConfig

  • 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.

classmethod Batch.from_zip_parquet(fp: Union[str, os.PathLike], *, config: Union[static_frame.core.store.StoreConfig, Mapping[Any, static_frame.core.store.StoreConfig], None, static_frame.core.store.StoreConfigMap] = None, max_workers: Optional[int] = None, chunksize: int = 1, use_threads: bool = False)static_frame.core.batch.Batch[source]

Given a file path to zipped parquet Batch store, return a Batch instance.

Parameters
  • fp – A string file path or Path instance.

  • config – A StoreConfig, or a mapping of label ot StoreConfig

  • 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.

classmethod Batch.from_zip_pickle(fp: Union[str, os.PathLike], *, config: Union[static_frame.core.store.StoreConfig, Mapping[Any, static_frame.core.store.StoreConfig], None, static_frame.core.store.StoreConfigMap] = None, max_workers: Optional[int] = None, chunksize: int = 1, use_threads: bool = False)static_frame.core.batch.Batch[source]

Given a file path to zipped pickle Batch store, return a Batch instance.

Parameters
  • fp – A string file path or Path instance.

  • config – A StoreConfig, or a mapping of label ot StoreConfig

  • 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.

classmethod Batch.from_zip_tsv(fp: Union[str, os.PathLike], *, config: Union[static_frame.core.store.StoreConfig, Mapping[Any, static_frame.core.store.StoreConfig], None, static_frame.core.store.StoreConfigMap] = None, max_workers: Optional[int] = None, chunksize: int = 1, use_threads: bool = False)static_frame.core.batch.Batch[source]

Given a file path to zipped TSV Batch store, return a Batch instance.

Parameters
  • fp – A string file path or Path instance.

  • config – A StoreConfig, or a mapping of label ot StoreConfig

  • 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.

Batch: Constructor | Exporter | Attribute | Method | Dictionary-Like | Display | Selector | Operator Binary | Operator Unary

Batch: Exporter

Overview: Batch: Exporter

Batch.to_bus()static_frame.core.bus.Bus[source]

Realize the Batch as an Bus. Note that, as a Bus must have all labels (even if Frame are loaded lazily), this Batch will be exhausted.

Batch.to_frame(*, axis: int = 0, union: bool = True, index: Optional[Union[IndexBase, Iterable[Hashable], Iterable[Sequence[Hashable]], Type[static_frame.core.index_auto.IndexAutoFactory]]] = None, columns: Optional[Union[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[source]

Consolidate stored Frame into a new Frame using the stored labels as the index on the provided axis using Frame.from_concat. This assumes that that the contained Frame have been reduced to single dimension along the provided axis.

Batch.to_hdf5(fp: Union[str, os.PathLike], *, config: Union[static_frame.core.store.StoreConfig, Mapping[Any, static_frame.core.store.StoreConfig], None, static_frame.core.store.StoreConfigMap] = None)None

Write the complete Bus as an HDF5 table.

Parameters
Batch.to_sqlite(fp: Union[str, os.PathLike], *, config: Union[static_frame.core.store.StoreConfig, Mapping[Any, static_frame.core.store.StoreConfig], None, static_frame.core.store.StoreConfigMap] = None)None

Write the complete Bus as an SQLite database file.

Parameters
Batch.to_xlsx(fp: Union[str, os.PathLike], *, config: Union[static_frame.core.store.StoreConfig, Mapping[Any, static_frame.core.store.StoreConfig], None, static_frame.core.store.StoreConfigMap] = None)None

Write the complete Bus as a XLSX workbook.

Parameters
Batch.to_zip_csv(fp: Union[str, os.PathLike], *, config: Union[static_frame.core.store.StoreConfig, Mapping[Any, static_frame.core.store.StoreConfig], None, static_frame.core.store.StoreConfigMap] = None)None

Write the complete Bus as a zipped archive of CSV files.

Parameters
Batch.to_zip_parquet(fp: Union[str, os.PathLike], *, config: Union[static_frame.core.store.StoreConfig, Mapping[Any, static_frame.core.store.StoreConfig], None, static_frame.core.store.StoreConfigMap] = None)None

Write the complete Bus as a zipped archive of parquet files.

Parameters
Batch.to_zip_pickle(fp: Union[str, os.PathLike], *, config: Union[static_frame.core.store.StoreConfig, Mapping[Any, static_frame.core.store.StoreConfig], None, static_frame.core.store.StoreConfigMap] = None)None

Write the complete Bus as a zipped archive of pickles.

Parameters
Batch.to_zip_tsv(fp: Union[str, os.PathLike], *, config: Union[static_frame.core.store.StoreConfig, Mapping[Any, static_frame.core.store.StoreConfig], None, static_frame.core.store.StoreConfigMap] = None)None

Write the complete Bus as a zipped archive of TSV files.

Parameters

Batch: Constructor | Exporter | Attribute | Method | Dictionary-Like | Display | Selector | Operator Binary | Operator Unary

Batch: Attribute

Overview: Batch: Attribute

Batch.STATIC: bool = True
Batch.T

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

Batch.name

A hashable label attached to this container.

Returns

Hashable

Batch.shapes

A Series describing the shape of each iterated Frame.

Returns

tp.Tuple[int]

Batch: Constructor | Exporter | Attribute | Method | Dictionary-Like | Display | Selector | Operator Binary | Operator Unary

Batch: Method

Overview: Batch: Method

Batch.__bool__()bool

Raises ValueError to prohibit ambiguous use of truethy evaluation.

Batch.__round__(decimals: int = 0)static_frame.core.batch.Batch[source]

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

Parameters

decimals – number of decimals to round to.

Batch.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.

Batch.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.

Batch.apply(func: Callable[[], Any])static_frame.core.batch.Batch[source]

Apply a function to each Frame contained in this Frame, where a function is given the Frame as an argument.

Batch.apply_except(func: Callable[[], Any], exception: Type[Exception])static_frame.core.batch.Batch[source]

Apply a function to each Frame contained in this Frame, where a function is given the Frame as an argument. Exceptions raised that matching the except argument will be silenced.

Batch.apply_items(func: Callable[[], Any])static_frame.core.batch.Batch[source]

Apply a function to each Frame contained in this Frame, where a function is given the pair of label, Frame as an argument.

Batch.apply_items_except(func: Callable[[], Any], exception: Type[Exception])static_frame.core.batch.Batch[source]

Apply a function to each Frame contained in this Frame, where a function is given the pair of label, Frame as an argument. Exceptions raised that matching the except argument will be silenced.

Batch.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.batch.Batch[source]

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

Parameters
Batch.count(*, skipna: bool = True, axis: int = 0)static_frame.core.batch.Batch[source]

Apply count on contained Frames.

Batch.cov(*, axis: int = 1, ddof: int = 1)static_frame.core.batch.Batch[source]

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.

Batch.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.

Batch.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.

Batch.drop_duplicated(*, axis: int = 0, exclude_first: bool = False, exclude_last: bool = False)static_frame.core.batch.Batch[source]

Return a Batch with contained 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.

Batch.duplicated(*, axis: int = 0, exclude_first: bool = False, exclude_last: bool = False)static_frame.core.batch.Batch[source]

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.

Batch.equals(other: Any, *, compare_name: bool = False, compare_dtype: bool = False, compare_class: bool = False, skipna: bool = True)bool
Batch.head(count: int = 5)static_frame.core.batch.Batch[source]

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

Parameters

count – Number of elements to be returned from the top of the Batch

Batch.iloc_max(*, skipna: bool = True, axis: int = 0)static_frame.core.batch.Batch[source]

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).

Batch.iloc_min(*, skipna: bool = True, axis: int = 0)static_frame.core.batch.Batch[source]

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).

Batch.isin(other: Any)static_frame.core.batch.Batch[source]

Return a new Batch with contained Frame as a same-sized Boolean Frame that shows if the same-positioned element is in the passed iterable.

Batch.loc_max(*, skipna: bool = True, axis: int = 0)static_frame.core.batch.Batch[source]

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).

Batch.loc_min(*, skipna: bool = True, axis: int = 0)static_frame.core.batch.Batch[source]

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).

Batch.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.

Batch.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.

Batch.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.

Batch.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.

Batch.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.

Batch.rename(name: Optional[Hashable])static_frame.core.batch.Batch[source]

Return a new Batch with an updated name attribute.

Batch.roll(index: int = 0, columns: int = 0, include_index: bool = False, include_columns: bool = False)static_frame.core.batch.Batch[source]

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.

Batch.sample(index: Optional[int] = None, columns: Optional[int] = None, *, seed: Optional[int] = None)static_frame.core.batch.Batch[source]

Apply sample on contained Frames.

Parameters
  • of labels to select from the index. (Number) –

  • of labels to select from the columns. (Number) –

  • state of random selection. (Initial) –

Batch.shift(index: int = 0, columns: int = 0, fill_value: Any = nan)static_frame.core.batch.Batch[source]

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.

Batch.sort_columns(*, ascending: bool = True, kind: str = 'mergesort')static_frame.core.batch.Batch[source]

Return a new Batch with contained Frame ordered by the sorted columns.

Batch.sort_index(*, ascending: bool = True, kind: str = 'mergesort')static_frame.core.batch.Batch[source]

Return a new Batch with contained :obj;`Frame` ordered by the sorted index.

Batch.sort_values(label: Union[Hashable, Iterable[Hashable]], *, ascending: bool = True, axis: int = 1, kind: str = 'mergesort')static_frame.core.batch.Batch[source]

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

Parameters

label – a label or iterable of keys.

Batch.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.

Batch.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.

Batch.tail(count: int = 5)static_frame.core.batch.Batch[source]

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

Parameters

count – Number of elements to be returned from the bottom of the Batch

Batch.transpose()static_frame.core.batch.Batch[source]

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

Batch.unique(*, axis: Optional[int] = None)static_frame.core.batch.Batch[source]

Return a NumPy array of unqiue values. If the axis argument is provied, uniqueness is determined by columns or row.

Batch.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.

Batch: Constructor | Exporter | Attribute | Method | Dictionary-Like | Display | Selector | Operator Binary | Operator Unary

Batch: Dictionary-Like

Overview: Batch: Dictionary-Like

Batch.__iter__()Iterator[Hashable][source]

Iterator of Frame labels, same as Batch.keys.

Batch.items()Iterator[Tuple[Hashable, Union[static_frame.core.frame.Frame, static_frame.core.series.Series]]][source]

Iterator of labels, Frame.

Batch.keys()Iterator[Hashable][source]

Iterator of Frame labels.

Batch: Constructor | Exporter | Attribute | Method | Dictionary-Like | Display | Selector | Operator Binary | Operator Unary

Batch: Display

Overview: Batch: Display

Batch.interface

A Frame documenting the interface of this class.

Batch.__repr__()str[source]

Provide a display of the Batch that does not exhaust the generator.

Batch.__str__()

Return str(self).

Batch.display(config: Optional[static_frame.core.display_config.DisplayConfig] = None)static_frame.core.display.Display[source]

Provide a Series-style display of the Batch. Note that if the held iterator is a generator, this display will exhaust the generator.

Batch.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.

Batch.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.

Batch: Constructor | Exporter | Attribute | Method | Dictionary-Like | Display | Selector | Operator Binary | Operator Unary

Batch: Selector

Overview: Batch: Selector

Batch.bloc[key]
Batch.bloc
Batch.drop[key]
Batch.drop
InterfaceSelectTrio.__getitem__(key: Union[int, numpy.integer, slice, List[Any], None, Index, Series, numpy.ndarray])Any[source]

Label-based selection.

Batch.drop.iloc[key]
Batch.drop
InterfaceSelectTrio.iloc

Integer-position based selection.

Batch.drop.loc[key]
Batch.drop
InterfaceSelectTrio.loc

Label-based selection.

Batch[key]
Batch.__getitem__ = <function Batch.__getitem__>[source]
Batch.iloc[key]
Batch.iloc
Batch.loc[key]
Batch.loc

Batch: Constructor | Exporter | Attribute | Method | Dictionary-Like | Display | Selector | Operator Binary | Operator Unary

Batch: Operator Binary

Overview: Batch: Operator Binary

Batch.__add__(other: Any)Any
Batch.__and__(other: Any)Any
Batch.__eq__(other: Any)Any

Return self==value.

Batch.__floordiv__(other: Any)Any
Batch.__ge__(other: Any)Any

Return self>=value.

Batch.__gt__(other: Any)Any

Return self>value.

Batch.__le__(other: Any)Any

Return self<=value.

Batch.__lt__(other: Any)Any

Return self<value.

Batch.__matmul__(other: Any)Any
Batch.__mod__(other: Any)Any
Batch.__mul__(other: Any)Any
Batch.__ne__(other: Any)Any

Return self!=value.

Batch.__or__(other: Any)Any
Batch.__pow__(other: Any)Any
Batch.__radd__(other: Any)Any
Batch.__rfloordiv__(other: Any)Any
Batch.__rmatmul__(other: Any)Any
Batch.__rmul__(other: Any)Any
Batch.__rshift__(other: Any)Any
Batch.__rsub__(other: Any)Any
Batch.__rtruediv__(other: Any)Any
Batch.__sub__(other: Any)Any
Batch.__truediv__(other: Any)Any
Batch.__xor__(other: Any)Any

Batch: Constructor | Exporter | Attribute | Method | Dictionary-Like | Display | Selector | Operator Binary | Operator Unary

Batch: Operator Unary

Overview: Batch: Operator Unary

Batch.__abs__()static_frame.core.container.ContainerOperand
Batch.__invert__()static_frame.core.container.ContainerOperand
Batch.__neg__()static_frame.core.container.ContainerOperand
Batch.__pos__()static_frame.core.container.ContainerOperand

Batch: Constructor | Exporter | Attribute | Method | Dictionary-Like | Display | Selector | Operator Binary | Operator Unary