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jbrockmendeljbrockmendel
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Add indexing, assign, arithmetic methods (#94)
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‎spec/API_specification/dataframe_api/column_object.py

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class Column:
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pass

‎spec/API_specification/dataframe_api/dataframe_object.py

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__all__ = ["DataFrame"]
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from typing import Sequence, TYPE_CHECKING
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if TYPE_CHECKING:
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from .column_object import Column
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class DataFrame:
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pass
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def get_column_by_name(self, name: str, /) -> Column:
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"""
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Select a column by name.
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Parameters
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----------
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name : str
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Returns
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-------
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Column
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Raises
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------
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KeyError
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If the key is not present.
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"""
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...
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def get_columns_by_name(self, names: Sequence[str], /) -> "DataFrame":
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"""
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Select multiple columns by name.
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Parameters
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----------
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names : Sequence[str]
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Returns
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-------
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DataFrame
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Raises
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------
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KeyError
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If the any requested key is not present.
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"""
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...
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def get_rows(self, indices: Sequence[int]) -> "DataFrame":
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"""
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Select a subset of rows, similar to `ndarray.take`.
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Parameters
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----------
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indices : Sequence[int]
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Positions of rows to select.
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Returns
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-------
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DataFrame
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Notes
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-----
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Some discussion participants prefer a stricter type Column[int] for
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indices in order to make it easier to implement in a performant manner
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on GPUs.
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"""
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...
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def slice_rows(
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self, start: int | None, stop: int | None, step: int | None
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) -> "DataFrame":
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"""
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Select a subset of rows corresponding to a slice.
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Parameters
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----------
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start : int or None
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stop : int or None
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step : int or None
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Returns
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-------
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DataFrame
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"""
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...
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def get_rows_by_mask(self, mask: Column[bool]) -> "DataFrame":
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"""
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Select a subset of rows corresponding to a mask.
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Parameters
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----------
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mask : Column[bool]
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Returns
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-------
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DataFrame
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Notes
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-----
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Some participants preferred a weaker type Arraylike[bool] for mask,
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where 'Arraylike' denotes an object adhering to the Array API standard.
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"""
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...
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def insert(self, loc: int, label: str, value: Column) -> "DataFrame":
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"""
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Insert column into DataFrame at specified location.
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Parameters
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----------
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loc : int
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Insertion index. Must verify 0 <= loc <= len(columns).
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label : str
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Label of the inserted column.
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value : Column
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"""
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...
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def drop_column(self, label: str) -> "DataFrame":
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"""
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Drop the specified column.
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Parameters
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----------
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label : str
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Returns
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-------
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DataFrame
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Raises
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------
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KeyError
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If the label is not present.
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"""
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...
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def set_column(self, label: str, value: Column) -> "DataFrame":
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"""
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Add or replace a column.
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Parameters
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----------
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label : str
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value : Column
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Returns
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-------
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DataFrame
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"""
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...
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def __eq__(self, other: DataFrame | "Scalar") -> "DataFrame":
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"""
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Parameters
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----------
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other : DataFrame or Scalar
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If DataFrame, must have same length and matching columns.
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"Scalar" here is defined implicitly by what scalar types are allowed
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for the operation by the underling dtypes.
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Returns
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-------
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DataFrame
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"""
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...
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def __ne__(self, other: DataFrame | "Scalar") -> "DataFrame":
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"""
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Parameters
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----------
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other : DataFrame or Scalar
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If DataFrame, must have same length and matching columns.
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"Scalar" here is defined implicitly by what scalar types are allowed
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for the operation by the underling dtypes.
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Returns
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-------
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DataFrame
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"""
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...
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def __ge__(self, other: DataFrame | "Scalar") -> "DataFrame":
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"""
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Parameters
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----------
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other : DataFrame or Scalar
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If DataFrame, must have same length and matching columns.
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"Scalar" here is defined implicitly by what scalar types are allowed
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for the operation by the underling dtypes.
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Returns
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-------
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DataFrame
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"""
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...
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def __gt__(self, other: DataFrame | "Scalar") -> "DataFrame":
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"""
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Parameters
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----------
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other : DataFrame or Scalar
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If DataFrame, must have same length and matching columns.
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"Scalar" here is defined implicitly by what scalar types are allowed
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for the operation by the underling dtypes.
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Returns
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-------
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DataFrame
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"""
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...
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def __le__(self, other: DataFrame | "Scalar") -> "DataFrame":
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"""
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Parameters
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----------
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other : DataFrame or Scalar
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If DataFrame, must have same length and matching columns.
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"Scalar" here is defined implicitly by what scalar types are allowed
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for the operation by the underling dtypes.
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Returns
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-------
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DataFrame
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"""
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...
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def __lt__(self, other: DataFrame | "Scalar") -> "DataFrame":
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"""
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Parameters
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----------
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other : DataFrame or Scalar
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If DataFrame, must have same length and matching columns.
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"Scalar" here is defined implicitly by what scalar types are allowed
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for the operation by the underling dtypes.
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Returns
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-------
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DataFrame
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"""
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...
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def __add__(self, other: DataFrame | "Scalar") -> "DataFrame":
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"""
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Parameters
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----------
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other : DataFrame or Scalar
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If DataFrame, must have same length and matching columns.
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"Scalar" here is defined implicitly by what scalar types are allowed
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for the operation by the underling dtypes.
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Returns
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-------
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DataFrame
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"""
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...
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def __sub__(self, other: DataFrame | "Scalar") -> "DataFrame":
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"""
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Parameters
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----------
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other : DataFrame or Scalar
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If DataFrame, must have same length and matching columns.
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"Scalar" here is defined implicitly by what scalar types are allowed
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for the operation by the underling dtypes.
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Returns
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-------
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DataFrame
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"""
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...
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def __mul__(self, other: DataFrame | "Scalar") -> "DataFrame":
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"""
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Parameters
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----------
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other : DataFrame or Scalar
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If DataFrame, must have same length and matching columns.
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"Scalar" here is defined implicitly by what scalar types are allowed
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for the operation by the underling dtypes.
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Returns
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-------
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DataFrame
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"""
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...
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def __truediv__(self, other: DataFrame | "Scalar") -> "DataFrame":
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"""
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Parameters
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----------
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other : DataFrame or Scalar
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If DataFrame, must have same length and matching columns.
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"Scalar" here is defined implicitly by what scalar types are allowed
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for the operation by the underling dtypes.
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Returns
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-------
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DataFrame
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"""
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...
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def __floordiv__(self, other: DataFrame | "Scalar") -> "DataFrame":
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"""
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Parameters
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----------
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other : DataFrame or Scalar
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If DataFrame, must have same length and matching columns.
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"Scalar" here is defined implicitly by what scalar types are allowed
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for the operation by the underling dtypes.
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Returns
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-------
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DataFrame
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"""
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...
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def __pow__(self, other: DataFrame | "Scalar") -> "DataFrame":
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"""
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Parameters
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----------
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other : DataFrame or Scalar
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If DataFrame, must have same length and matching columns.
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"Scalar" here is defined implicitly by what scalar types are allowed
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for the operation by the underling dtypes.
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Returns
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-------
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DataFrame
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"""
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...
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def __mod__(self, other: DataFrame | "Scalar") -> "DataFrame":
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"""
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Parameters
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----------
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other : DataFrame or Scalar
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If DataFrame, must have same length and matching columns.
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"Scalar" here is defined implicitly by what scalar types are allowed
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for the operation by the underling dtypes.
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Returns
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-------
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DataFrame
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"""
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...
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def __divmod__(self, other: DataFrame | "Scalar") -> tuple["DataFrame", "DataFrame"]:
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"""
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Parameters
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----------
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other : DataFrame or Scalar
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If DataFrame, must have same length and matching columns.
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"Scalar" here is defined implicitly by what scalar types are allowed
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for the operation by the underling dtypes.
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Returns
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-------
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DataFrame
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DataFrame
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"""
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...

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