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query_compiler.py
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query_compiler.py
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# Licensed to Modin Development Team under one or more contributor license agreements.
# See the NOTICE file distributed with this work for additional information regarding
# copyright ownership. The Modin Development Team licenses this file to you under the
# Apache License, Version 2.0 (the "License"); you may not use this file except in
# compliance with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software distributed under
# the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
# ANY KIND, either express or implied. See the License for the specific language
# governing permissions and limitations under the License.
"""
Module contains class ``BaseQueryCompiler``.
``BaseQueryCompiler`` is a parent abstract class for any other query compiler class.
"""
import abc
from modin.data_management.functions.default_methods import (
DataFrameDefault,
SeriesDefault,
DateTimeDefault,
StrDefault,
BinaryDefault,
ResampleDefault,
RollingDefault,
CatDefault,
GroupByDefault,
)
from modin.error_message import ErrorMessage
import modin.backends.base.doc_utils as doc_utils
from pandas.core.dtypes.common import is_scalar
import pandas.core.resample
import pandas
import numpy as np
from typing import List, Hashable
def _get_axis(axis):
"""
Build index labels getter of the specified axis.
Parameters
----------
axis : {0, 1}
Axis to get labels from.
Returns
-------
callable(BaseQueryCompiler) -> pandas.Index
"""
def axis_getter(self):
ErrorMessage.default_to_pandas(f"DataFrame.get_axis({axis})")
return self.to_pandas().axes[axis]
return axis_getter
def _set_axis(axis):
"""
Build index labels setter of the specified axis.
Parameters
----------
axis : {0, 1}
Axis to set labels on.
Returns
-------
callable(BaseQueryCompiler)
"""
def axis_setter(self, labels):
new_qc = DataFrameDefault.register(pandas.DataFrame.set_axis)(
self, axis=axis, labels=labels
)
self.__dict__.update(new_qc.__dict__)
return axis_setter
# FIXME: many of the BaseQueryCompiler methods are hiding actual arguments
# by using *args and **kwargs. They should be spread into actual parameters.
# Currently actual arguments are placed in the methods docstrings, but since they're
# not presented in the function's signature it makes linter to raise `PR02: unknown parameters`
# warning. For now, they're silenced by using `noqa` (Modin issue #3108).
class BaseQueryCompiler(abc.ABC):
"""
Abstract class that handles the queries to Modin dataframes.
This class defines common query compilers API, most of the methods
are already implemented and defaulting to pandas.
Attributes
----------
lazy_execution : bool
Whether underlying execution engine is designed to be executed in a lazy mode only.
If True, such QueryCompiler will be handled differently at the front-end in order
to reduce execution triggering as much as possible.
Notes
-----
See the Abstract Methods and Fields section immediately below this
for a list of requirements for subclassing this object.
"""
@abc.abstractmethod
def default_to_pandas(self, pandas_op, *args, **kwargs):
"""
Do fallback to pandas for the passed function.
Parameters
----------
pandas_op : callable(pandas.DataFrame) -> object
Function to apply to the casted to pandas frame.
*args : iterable
Positional arguments to pass to `pandas_op`.
**kwargs : dict
Key-value arguments to pass to `pandas_op`.
Returns
-------
BaseQueryCompiler
The result of the `pandas_op`, converted back to ``BaseQueryCompiler``.
"""
pass
# Abstract Methods and Fields: Must implement in children classes
# In some cases, there you may be able to use the same implementation for
# some of these abstract methods, but for the sake of generality they are
# treated differently.
lazy_execution = False
# Metadata modification abstract methods
def add_prefix(self, prefix, axis=1):
"""
Add string prefix to the index labels along specified axis.
Parameters
----------
prefix : str
The string to add before each label.
axis : {0, 1}, default: 1
Axis to add prefix along. 0 is for index and 1 is for columns.
Returns
-------
BaseQueryCompiler
New query compiler with updated labels.
"""
if axis:
return DataFrameDefault.register(pandas.DataFrame.add_prefix)(
self, prefix=prefix
)
else:
return SeriesDefault.register(pandas.Series.add_prefix)(self, prefix=prefix)
def add_suffix(self, suffix, axis=1):
"""
Add string suffix to the index labels along specified axis.
Parameters
----------
suffix : str
The string to add after each label.
axis : {0, 1}, default: 1
Axis to add suffix along. 0 is for index and 1 is for columns.
Returns
-------
BaseQueryCompiler
New query compiler with updated labels.
"""
if axis:
return DataFrameDefault.register(pandas.DataFrame.add_suffix)(
self, suffix=suffix
)
else:
return SeriesDefault.register(pandas.Series.add_suffix)(self, suffix=suffix)
# END Metadata modification abstract methods
# Abstract copy
def copy(self):
"""
Make a copy of this object.
Returns
-------
BaseQueryCompiler
Copy of self.
Notes
-----
For copy, we don't want a situation where we modify the metadata of the
copies if we end up modifying something here. We copy all of the metadata
to prevent that.
"""
return DataFrameDefault.register(pandas.DataFrame.copy)(self)
# END Abstract copy
# Abstract join and append helper functions
def concat(self, axis, other, **kwargs): # noqa: PR02
"""
Concatenate `self` with passed query compilers along specified axis.
Parameters
----------
axis : {0, 1}
Axis to concatenate along. 0 is for index and 1 is for columns.
other : BaseQueryCompiler or list of such
Objects to concatenate with `self`.
join : {'outer', 'inner', 'right', 'left'}, default: 'outer'
Type of join that will be used if indices on the other axis are different.
(note: if specified, has to be passed as ``join=value``).
ignore_index : bool, default: False
If True, do not use the index values along the concatenation axis.
The resulting axis will be labeled 0, …, n - 1.
(note: if specified, has to be passed as ``ignore_index=value``).
sort : bool, default: False
Whether or not to sort non-concatenation axis.
(note: if specified, has to be passed as ``sort=value``).
**kwargs : dict
Serves the compatibility purpose. Does not affect the result.
Returns
-------
BaseQueryCompiler
Concatenated objects.
"""
concat_join = ["inner", "outer"]
def concat(df, axis, other, **kwargs):
kwargs.pop("join_axes", None)
ignore_index = kwargs.get("ignore_index", False)
if kwargs.get("join", "outer") in concat_join:
if not isinstance(other, list):
other = [other]
other = [df] + other
result = pandas.concat(other, axis=axis, **kwargs)
else:
if isinstance(other, (list, np.ndarray)) and len(other) == 1:
other = other[0]
ignore_index = kwargs.pop("ignore_index", None)
kwargs["how"] = kwargs.pop("join", None)
result = df.join(other, rsuffix="r_", **kwargs)
if ignore_index:
if axis == 0:
result = result.reset_index(drop=True)
else:
result.columns = pandas.RangeIndex(len(result.columns))
return result
return DataFrameDefault.register(concat)(self, axis=axis, other=other, **kwargs)
# END Abstract join and append helper functions
# Data Management Methods
@abc.abstractmethod
def free(self):
"""Trigger a cleanup of this object."""
pass
@abc.abstractmethod
def finalize(self):
"""Finalize constructing the dataframe calling all deferred functions which were used to build it."""
pass
# END Data Management Methods
# To/From Pandas
@abc.abstractmethod
def to_pandas(self):
"""
Convert underlying query compilers data to ``pandas.DataFrame``.
Returns
-------
pandas.DataFrame
The QueryCompiler converted to pandas.
"""
pass
@classmethod
@abc.abstractmethod
def from_pandas(cls, df, data_cls):
"""
Build QueryCompiler from pandas DataFrame.
Parameters
----------
df : pandas.DataFrame
The pandas DataFrame to convert from.
data_cls : type
:py:class:`~modin.engines.base.frame.data.BasePandasFrame` class
(or its descendant) to convert to.
Returns
-------
BaseQueryCompiler
QueryCompiler containing data from the pandas DataFrame.
"""
pass
# END To/From Pandas
# From Arrow
@classmethod
@abc.abstractmethod
def from_arrow(cls, at, data_cls):
"""
Build QueryCompiler from Arrow Table.
Parameters
----------
at : Arrow Table
The Arrow Table to convert from.
data_cls : type
:py:class:`~modin.engines.base.frame.data.BasePandasFrame` class
(or its descendant) to convert to.
Returns
-------
BaseQueryCompiler
QueryCompiler containing data from the pandas DataFrame.
"""
pass
# END From Arrow
# To NumPy
def to_numpy(self, **kwargs): # noqa: PR02
"""
Convert underlying query compilers data to NumPy array.
Parameters
----------
dtype : dtype
The dtype of the resulted array.
copy : bool
Whether to ensure that the returned value is not a view on another array.
na_value : object
The value to replace missing values with.
**kwargs : dict
Serves the compatibility purpose. Does not affect the result.
Returns
-------
np.ndarray
The QueryCompiler converted to NumPy array.
"""
return DataFrameDefault.register(pandas.DataFrame.to_numpy)(self, **kwargs)
# END To NumPy
# Abstract inter-data operations (e.g. add, sub)
# These operations require two DataFrames and will change the shape of the
# data if the index objects don't match. An outer join + op is performed,
# such that columns/rows that don't have an index on the other DataFrame
# result in NaN values.
@doc_utils.doc_binary_method(operation="addition", sign="+")
def add(self, other, **kwargs): # noqa: PR02
return BinaryDefault.register(pandas.DataFrame.add)(self, other=other, **kwargs)
@doc_utils.add_refer_to("DataFrame.combine")
def combine(self, other, **kwargs): # noqa: PR02
"""
Perform column-wise combine with another QueryCompiler with passed `func`.
If axes are not equal, perform frames alignment first.
Parameters
----------
other : BaseQueryCompiler
Left operand of the binary operation.
func : callable(pandas.Series, pandas.Series) -> pandas.Series
Function that takes two ``pandas.Series`` with aligned axes
and returns one ``pandas.Series`` as resulting combination.
fill_value : float or None
Value to fill missing values with after frame alignment occurred.
overwrite : bool
If True, columns in `self` that do not exist in `other`
will be overwritten with NaNs.
**kwargs : dict
Serves the compatibility purpose. Does not affect the result.
Returns
-------
BaseQueryCompiler
Result of combine.
"""
return BinaryDefault.register(pandas.DataFrame.combine)(
self, other=other, **kwargs
)
@doc_utils.add_refer_to("DataFrame.combine_first")
def combine_first(self, other, **kwargs): # noqa: PR02
"""
Fill null elements of `self` with value in the same location in `other`.
If axes are not equal, perform frames alignment first.
Parameters
----------
other : BaseQueryCompiler
Provided frame to use to fill null values from.
**kwargs : dict
Serves the compatibility purpose. Does not affect the result.
Returns
-------
BaseQueryCompiler
"""
return BinaryDefault.register(pandas.DataFrame.combine_first)(
self, other=other, **kwargs
)
@doc_utils.doc_binary_method(operation="equality comparison", sign="==")
def eq(self, other, **kwargs): # noqa: PR02
return BinaryDefault.register(pandas.DataFrame.eq)(self, other=other, **kwargs)
@doc_utils.doc_binary_method(operation="integer division", sign="//")
def floordiv(self, other, **kwargs): # noqa: PR02
return BinaryDefault.register(pandas.DataFrame.floordiv)(
self, other=other, **kwargs
)
@doc_utils.doc_binary_method(
operation="greater than or equal comparison", sign=">=", op_type="comparison"
)
def ge(self, other, **kwargs): # noqa: PR02
return BinaryDefault.register(pandas.DataFrame.ge)(self, other=other, **kwargs)
@doc_utils.doc_binary_method(
operation="greater than comparison", sign=">", op_type="comparison"
)
def gt(self, other, **kwargs): # noqa: PR02
return BinaryDefault.register(pandas.DataFrame.gt)(self, other=other, **kwargs)
@doc_utils.doc_binary_method(
operation="less than or equal comparison", sign="<=", op_type="comparison"
)
def le(self, other, **kwargs): # noqa: PR02
return BinaryDefault.register(pandas.DataFrame.le)(self, other=other, **kwargs)
@doc_utils.doc_binary_method(
operation="less than comparison", sign="<", op_type="comparison"
)
def lt(self, other, **kwargs): # noqa: PR02
return BinaryDefault.register(pandas.DataFrame.lt)(self, other=other, **kwargs)
@doc_utils.doc_binary_method(operation="modulo", sign="%")
def mod(self, other, **kwargs): # noqa: PR02
return BinaryDefault.register(pandas.DataFrame.mod)(self, other=other, **kwargs)
@doc_utils.doc_binary_method(operation="multiplication", sign="*")
def mul(self, other, **kwargs): # noqa: PR02
return BinaryDefault.register(pandas.DataFrame.mul)(self, other=other, **kwargs)
@doc_utils.add_refer_to("DataFrame.corr")
def corr(self, **kwargs): # noqa: PR02
"""
Compute pairwise correlation of columns, excluding NA/null values.
Parameters
----------
method : {'pearson', 'kendall', 'spearman'} or callable(pandas.Series, pandas.Series) -> pandas.Series
Correlation method.
min_periods : int
Minimum number of observations required per pair of columns
to have a valid result. If fewer than `min_periods` non-NA values
are present the result will be NA.
**kwargs : dict
Serves the compatibility purpose. Does not affect the result.
Returns
-------
BaseQueryCompiler
Correlation matrix.
"""
return DataFrameDefault.register(pandas.DataFrame.corr)(self, **kwargs)
@doc_utils.add_refer_to("DataFrame.cov")
def cov(self, **kwargs): # noqa: PR02
"""
Compute pairwise covariance of columns, excluding NA/null values.
Parameters
----------
min_periods : int
**kwargs : dict
Serves the compatibility purpose. Does not affect the result.
Returns
-------
BaseQueryCompiler
Covariance matrix.
"""
return DataFrameDefault.register(pandas.DataFrame.cov)(self, **kwargs)
def dot(self, other, **kwargs): # noqa: PR02
"""
Compute the matrix multiplication of `self` and `other`.
Parameters
----------
other : BaseQueryCompiler or NumPy array
The other query compiler or NumPy array to matrix multiply with `self`.
squeeze_self : boolean
If `self` is a one-column query compiler, indicates whether it represents Series object.
squeeze_other : boolean
If `other` is a one-column query compiler, indicates whether it represents Series object.
**kwargs : dict
Serves the compatibility purpose. Does not affect the result.
Returns
-------
BaseQueryCompiler
A new query compiler that contains result of the matrix multiply.
"""
if kwargs.get("squeeze_self", False):
applyier = pandas.Series.dot
else:
applyier = pandas.DataFrame.dot
return BinaryDefault.register(applyier)(self, other=other, **kwargs)
@doc_utils.doc_binary_method(
operation="not equal comparison", sign="!=", op_type="comparison"
)
def ne(self, other, **kwargs): # noqa: PR02
return BinaryDefault.register(pandas.DataFrame.ne)(self, other=other, **kwargs)
@doc_utils.doc_binary_method(operation="exponential power", sign="**")
def pow(self, other, **kwargs): # noqa: PR02
return BinaryDefault.register(pandas.DataFrame.pow)(self, other=other, **kwargs)
@doc_utils.doc_binary_method(
operation="integer division", sign="//", self_on_right=True
)
def rfloordiv(self, other, **kwargs): # noqa: PR02
return BinaryDefault.register(pandas.DataFrame.rfloordiv)(
self, other=other, **kwargs
)
@doc_utils.doc_binary_method(operation="modulo", sign="%", self_on_right=True)
def rmod(self, other, **kwargs): # noqa: PR02
return BinaryDefault.register(pandas.DataFrame.rmod)(
self, other=other, **kwargs
)
@doc_utils.doc_binary_method(
operation="exponential power", sign="**", self_on_right=True
)
def rpow(self, other, **kwargs): # noqa: PR02
return BinaryDefault.register(pandas.DataFrame.rpow)(
self, other=other, **kwargs
)
@doc_utils.doc_binary_method(operation="substraction", sign="-", self_on_right=True)
def rsub(self, other, **kwargs): # noqa: PR02
return BinaryDefault.register(pandas.DataFrame.rsub)(
self, other=other, **kwargs
)
@doc_utils.doc_binary_method(operation="division", sign="/", self_on_right=True)
def rtruediv(self, other, **kwargs): # noqa: PR02
return BinaryDefault.register(pandas.DataFrame.rtruediv)(
self, other=other, **kwargs
)
@doc_utils.doc_binary_method(operation="substraction", sign="-")
def sub(self, other, **kwargs): # noqa: PR02
return BinaryDefault.register(pandas.DataFrame.sub)(self, other=other, **kwargs)
@doc_utils.doc_binary_method(operation="division", sign="/")
def truediv(self, other, **kwargs): # noqa: PR02
return BinaryDefault.register(pandas.DataFrame.truediv)(
self, other=other, **kwargs
)
@doc_utils.doc_binary_method(operation="conjunction", sign="&", op_type="logical")
def __and__(self, other, **kwargs): # noqa: PR02
return BinaryDefault.register(pandas.DataFrame.__and__)(
self, other=other, **kwargs
)
@doc_utils.doc_binary_method(operation="disjunction", sign="|", op_type="logical")
def __or__(self, other, **kwargs): # noqa: PR02
return BinaryDefault.register(pandas.DataFrame.__or__)(
self, other=other, **kwargs
)
@doc_utils.doc_binary_method(
operation="conjunction", sign="&", op_type="logical", self_on_right=True
)
def __rand__(self, other, **kwargs): # noqa: PR02
return BinaryDefault.register(pandas.DataFrame.__rand__)(
self, other=other, **kwargs
)
@doc_utils.doc_binary_method(
operation="disjunction", sign="|", op_type="logical", self_on_right=True
)
def __ror__(self, other, **kwargs): # noqa: PR02
return BinaryDefault.register(pandas.DataFrame.__ror__)(
self, other=other, **kwargs
)
@doc_utils.doc_binary_method(
operation="exclusive or", sign="^", op_type="logical", self_on_right=True
)
def __rxor__(self, other, **kwargs): # noqa: PR02
return BinaryDefault.register(pandas.DataFrame.__rxor__)(
self, other=other, **kwargs
)
@doc_utils.doc_binary_method(operation="exclusive or", sign="^", op_type="logical")
def __xor__(self, other, **kwargs): # noqa: PR02
return BinaryDefault.register(pandas.DataFrame.__xor__)(
self, other=other, **kwargs
)
# FIXME: query compiler shoudln't care about differences between Frame and Series.
# We should combine `df_update` and `series_update` into one method (Modin issue #3101).
@doc_utils.add_refer_to("DataFrame.update")
def df_update(self, other, **kwargs): # noqa: PR02
"""
Update values of `self` using non-NA values of `other` at the corresponding positions.
If axes are not equal, perform frames alignment first.
Parameters
----------
other : BaseQueryCompiler
Frame to grab replacement values from.
join : {"left"}
Specify type of join to align frames if axes are not equal
(note: currently only one type of join is implemented).
overwrite : bool
Whether to overwrite every corresponding value of self, or only if it's NAN.
filter_func : callable(pandas.Series, pandas.Series) -> numpy.ndarray<bool>
Function that takes column of the self and return bool mask for values, that
should be overwriten in the self frame.
errors : {"raise", "ignore"}
If "raise", will raise a ``ValueError`` if `self` and `other` both contain
non-NA data in the same place.
**kwargs : dict
Serves the compatibility purpose. Does not affect the result.
Returns
-------
BaseQueryCompiler
New QueryCompiler with updated values.
"""
return BinaryDefault.register(pandas.DataFrame.update, inplace=True)(
self, other=other, **kwargs
)
@doc_utils.add_refer_to("Series.update")
def series_update(self, other, **kwargs): # noqa: PR02
"""
Update values of `self` using values of `other` at the corresponding indices.
Parameters
----------
other : BaseQueryCompiler
One-column query compiler with updated values.
**kwargs : dict
Serves the compatibility purpose. Does not affect the result.
Returns
-------
BaseQueryCompiler
New QueryCompiler with updated values.
"""
return BinaryDefault.register(pandas.Series.update, inplace=True)(
self,
other=other,
squeeze_self=True,
squeeze_other=True,
**kwargs,
)
@doc_utils.add_refer_to("DataFrame.clip")
def clip(self, lower, upper, **kwargs): # noqa: PR02
"""
Trim values at input threshold.
Parameters
----------
lower : float or list-like
upper : float or list-like
axis : {0, 1}
inplace : {False}
This parameter serves the compatibility purpose. Always has to be False.
**kwargs : dict
Serves the compatibility purpose. Does not affect the result.
Returns
-------
BaseQueryCompiler
QueryCompiler with values limited by the specified thresholds.
"""
return DataFrameDefault.register(pandas.DataFrame.clip)(
self, lower=lower, upper=upper, **kwargs
)
@doc_utils.add_refer_to("DataFrame.where")
def where(self, cond, other, **kwargs): # noqa: PR02
"""
Update values of `self` using values from `other` at positions where `cond` is False.
Parameters
----------
cond : BaseQueryCompiler
Boolean mask. True - keep the self value, False - replace by `other` value.
other : BaseQueryCompiler or pandas.Series
Object to grab replacement values from.
axis : {0, 1}
Axis to align frames along if axes of self, `cond` and `other` are not equal.
0 is for index, when 1 is for columns.
level : int or label, optional
Level of MultiIndex to align frames along if axes of self, `cond`
and `other` are not equal. Currently `level` parameter is not implemented,
so only None value is acceptable.
**kwargs : dict
Serves the compatibility purpose. Does not affect the result.
Returns
-------
BaseQueryCompiler
QueryCompiler with updated data.
"""
return DataFrameDefault.register(pandas.DataFrame.where)(
self, cond=cond, other=other, **kwargs
)
@doc_utils.add_refer_to("DataFrame.merge")
def merge(self, right, **kwargs): # noqa: PR02
"""
Merge QueryCompiler objects using a database-style join.
Parameters
----------
right : BaseQueryCompiler
QueryCompiler of the right frame to merge with.
how : {"left", "right", "outer", "inner", "cross"}
on : label or list of such
left_on : label or list of such
right_on : label or list of such
left_index : bool
right_index : bool
sort : bool
suffixes : list-like
copy : bool
indicator : bool or str
validate : str
**kwargs : dict
Serves the compatibility purpose. Does not affect the result.
Returns
-------
BaseQueryCompiler
QueryCompiler that contains result of the merge.
"""
return DataFrameDefault.register(pandas.DataFrame.merge)(
self, right=right, **kwargs
)
@doc_utils.add_refer_to("DataFrame.join")
def join(self, right, **kwargs): # noqa: PR02
"""
Join columns of another QueryCompiler.
Parameters
----------
right : BaseQueryCompiler
QueryCompiler of the right frame to join with.
on : label or list of such
how : {"left", "right", "outer", "inner"}
lsuffix : str
rsuffix : str
sort : bool
**kwargs : dict
Serves the compatibility purpose. Does not affect the result.
Returns
-------
BaseQueryCompiler
QueryCompiler that contains result of the join.
"""
return DataFrameDefault.register(pandas.DataFrame.join)(self, right, **kwargs)
# END Abstract inter-data operations
# Abstract Transpose
def transpose(self, *args, **kwargs): # noqa: PR02
"""
Transpose this QueryCompiler.
Parameters
----------
copy : bool
Whether to copy the data after transposing.
*args : iterable
Serves the compatibility purpose. Does not affect the result.
**kwargs : dict
Serves the compatibility purpose. Does not affect the result.
Returns
-------
BaseQueryCompiler
Transposed new QueryCompiler.
"""
return DataFrameDefault.register(pandas.DataFrame.transpose)(
self, *args, **kwargs
)
def columnarize(self):
"""
Transpose this QueryCompiler if it has a single row but multiple columns.
This method should be called for QueryCompilers representing a Series object,
i.e. ``self.is_series_like()`` should be True.
Returns
-------
BaseQueryCompiler
Transposed new QueryCompiler or self.
"""
if len(self.columns) != 1 or (
len(self.index) == 1 and self.index[0] == "__reduced__"
):
return self.transpose()
return self
def is_series_like(self):
"""
Check whether this QueryCompiler can represent ``modin.pandas.Series`` object.
Returns
-------
bool
Return True if QueryCompiler has a single column or row, False otherwise.
"""
return len(self.columns) == 1 or len(self.index) == 1
# END Abstract Transpose
# Abstract reindex/reset_index (may shuffle data)
@doc_utils.add_refer_to("DataFrame.reindex")
def reindex(self, axis, labels, **kwargs): # noqa: PR02
"""
Align QueryCompiler data with a new index along specified axis.
Parameters
----------
axis : {0, 1}
Axis to align labels along. 0 is for index, 1 is for columns.
labels : list-like
Index-labels to align with.
method : {None, "backfill"/"bfill", "pad"/"ffill", "nearest"}
Method to use for filling holes in reindexed frame.
fill_value : scalar
Value to use for missing values in the resulted frame.
limit : int
tolerance : int
**kwargs : dict
Serves the compatibility purpose. Does not affect the result.
Returns
-------
BaseQueryCompiler
QueryCompiler with aligned axis.
"""
return DataFrameDefault.register(pandas.DataFrame.reindex)(
self, axis=axis, labels=labels, **kwargs
)
@doc_utils.add_refer_to("DataFrame.reset_index")
def reset_index(self, **kwargs): # noqa: PR02
"""
Reset the index, or a level of it.
Parameters
----------
drop : bool
Whether to drop the reset index or insert it at the beginning of the frame.
level : int or label, optional
Level to remove from index. Removes all levels by default.
col_level : int or label
If the columns have multiple levels, determines which level the labels
are inserted into.
col_fill : label
If the columns have multiple levels, determines how the other levels
are named.
**kwargs : dict
Serves the compatibility purpose. Does not affect the result.
Returns
-------
BaseQueryCompiler
QueryCompiler with reset index.
"""
return DataFrameDefault.register(pandas.DataFrame.reset_index)(self, **kwargs)
def set_index_from_columns(
self, keys: List[Hashable], drop: bool = True, append: bool = False
):
"""
Create new row labels from a list of columns.
Parameters
----------
keys : list of hashable
The list of column names that will become the new index.
drop : bool, default: True
Whether or not to drop the columns provided in the `keys` argument.
append : bool, default: True
Whether or not to add the columns in `keys` as new levels appended to the
existing index.
Returns
-------
BaseQueryCompiler
A new QueryCompiler with updated index.
"""
return DataFrameDefault.register(pandas.DataFrame.set_index)(
self, keys=keys, drop=drop, append=append
)
# END Abstract reindex/reset_index
# Full Reduce operations
#
# These operations result in a reduced dimensionality of data.
# Currently, this means a Pandas Series will be returned, but in the future
# we will implement a Distributed Series, and this will be returned
# instead.
def is_monotonic_increasing(self):
"""
Return boolean if values in the object are monotonicly increasing.
Returns
-------
bool
"""
return SeriesDefault.register(pandas.Series.is_monotonic_increasing)(self)
def is_monotonic_decreasing(self):
"""
Return boolean if values in the object are monotonicly decreasing.
Returns
-------
bool
"""
return SeriesDefault.register(pandas.Series.is_monotonic_decreasing)(self)
@doc_utils.doc_reduce_agg(
method="number of non-NaN values", refer_to="count", extra_params=["**kwargs"]
)
def count(self, **kwargs): # noqa: PR02
return DataFrameDefault.register(pandas.DataFrame.count)(self, **kwargs)
@doc_utils.doc_reduce_agg(
method="maximum value", refer_to="max", extra_params=["skipna", "**kwargs"]
)
def max(self, **kwargs): # noqa: PR02
return DataFrameDefault.register(pandas.DataFrame.max)(self, **kwargs)
@doc_utils.doc_reduce_agg(
method="mean value", refer_to="mean", extra_params=["skipna", "**kwargs"]
)
def mean(self, **kwargs): # noqa: PR02
return DataFrameDefault.register(pandas.DataFrame.mean)(self, **kwargs)
@doc_utils.doc_reduce_agg(
method="minimum value", refer_to="min", extra_params=["skipna", "**kwargs"]
)
def min(self, **kwargs): # noqa: PR02
return DataFrameDefault.register(pandas.DataFrame.min)(self, **kwargs)
@doc_utils.doc_reduce_agg(
method="production",
refer_to="prod",
extra_params=["**kwargs"],
params="axis : {0, 1}",