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utils.py
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utils.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.
"""Implement utils for pandas component."""
from typing import Iterator, Optional, Tuple
import numpy as np
import pandas
from pandas._typing import AggFuncType, AggFuncTypeBase, AggFuncTypeDict, IndexLabel
from pandas.util._decorators import doc
from modin.utils import func_from_deprecated_location, hashable
_doc_binary_operation = """
Return {operation} of {left} and `{right}` (binary operator `{bin_op}`).
Parameters
----------
{right} : {right_type}
The second operand to perform computation.
Returns
-------
{returns}
"""
SET_DATAFRAME_ATTRIBUTE_WARNING = (
"Modin doesn't allow columns to be created via a new attribute name - see "
+ "https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access"
)
from_pandas = func_from_deprecated_location(
"from_pandas",
"modin.pandas.io",
"Importing ``from_pandas`` from ``modin.pandas.utils`` is deprecated and will be removed in a future version. "
+ "This function was moved to ``modin.pandas.io``, please import it from there instead.",
)
from_arrow = func_from_deprecated_location(
"from_arrow",
"modin.pandas.io",
"Importing ``from_arrow`` from ``modin.pandas.utils`` is deprecated and will be removed in a future version. "
+ "This function was moved to ``modin.pandas.io``, please import it from there instead.",
)
from_dataframe = func_from_deprecated_location(
"from_dataframe",
"modin.pandas.io",
"Importing ``from_dataframe`` from ``modin.pandas.utils`` is deprecated and will be removed in a future version. "
+ "This function was moved to ``modin.pandas.io``, please import it from there instead.",
)
from_non_pandas = func_from_deprecated_location(
"from_non_pandas",
"modin.pandas.io",
"Importing ``from_non_pandas`` from ``modin.pandas.utils`` is deprecated and will be removed in a future version. "
+ "This function was moved to ``modin.pandas.io``, please import it from there instead.",
)
def cast_function_modin2pandas(func):
"""
Replace Modin functions with pandas functions if `func` is callable.
Parameters
----------
func : object
Returns
-------
object
"""
if callable(func):
if func.__module__ == "modin.pandas.series":
func = getattr(pandas.Series, func.__name__)
elif func.__module__ in ("modin.pandas.dataframe", "modin.pandas.base"):
# FIXME: when the method is defined in `modin.pandas.base` file, then the
# type cannot be determined, in general there may be an error, but at the
# moment it is better.
func = getattr(pandas.DataFrame, func.__name__)
return func
def is_scalar(obj):
"""
Return True if given object is scalar.
This method works the same as is_scalar method from pandas but
it is optimized for Modin frames. For BasePandasDataset objects
pandas version of is_scalar tries to access missing attribute
causing index scan. This triggers execution for lazy frames and
we avoid it by handling BasePandasDataset objects separately.
Parameters
----------
obj : object
Object to check.
Returns
-------
bool
True if given object is scalar and False otherwise.
"""
from pandas.api.types import is_scalar as pandas_is_scalar
from .base import BasePandasDataset
return not isinstance(obj, BasePandasDataset) and pandas_is_scalar(obj)
def is_full_grab_slice(slc, sequence_len=None):
"""
Check that the passed slice grabs the whole sequence.
Parameters
----------
slc : slice
Slice object to check.
sequence_len : int, optional
Length of the sequence to index with the passed `slc`.
If not specified the function won't be able to check whether
``slc.stop`` is equal or greater than the sequence length to
consider `slc` to be a full-grab, and so, only slices with
``.stop is None`` are considered to be a full-grab.
Returns
-------
bool
"""
assert isinstance(slc, slice), "slice object required"
return (
slc.start in (None, 0)
and slc.step in (None, 1)
and (
slc.stop is None or (sequence_len is not None and slc.stop >= sequence_len)
)
)
def from_modin_frame_to_mi(df, sortorder=None, names=None):
"""
Make a pandas.MultiIndex from a DataFrame.
Parameters
----------
df : DataFrame
DataFrame to be converted to pandas.MultiIndex.
sortorder : int, default: None
Level of sortedness (must be lexicographically sorted by that
level).
names : list-like, optional
If no names are provided, use the column names, or tuple of column
names if the columns is a MultiIndex. If a sequence, overwrite
names with the given sequence.
Returns
-------
pandas.MultiIndex
The pandas.MultiIndex representation of the given DataFrame.
"""
from .dataframe import DataFrame
if isinstance(df, DataFrame):
from modin.error_message import ErrorMessage
ErrorMessage.default_to_pandas("`MultiIndex.from_frame`")
df = df._to_pandas()
return _original_pandas_MultiIndex_from_frame(df, sortorder, names)
def is_label(obj, label, axis=0):
"""
Check whether or not 'obj' contain column or index level with name 'label'.
Parameters
----------
obj : modin.pandas.DataFrame, modin.pandas.Series or modin.core.storage_formats.base.BaseQueryCompiler
Object to check.
label : object
Label name to check.
axis : {0, 1}, default: 0
Axis to search for `label` along.
Returns
-------
bool
True if check is successful, False otherwise.
"""
qc = getattr(obj, "_query_compiler", obj)
return hashable(label) and (
label in qc.get_axis(axis ^ 1) or label in qc.get_index_names(axis)
)
def check_both_not_none(option1, option2):
"""
Check that both `option1` and `option2` are not None.
Parameters
----------
option1 : Any
First object to check if not None.
option2 : Any
Second object to check if not None.
Returns
-------
bool
True if both option1 and option2 are not None, False otherwise.
"""
return not (option1 is None or option2 is None)
def broadcast_item(
obj,
row_lookup,
col_lookup,
item,
need_columns_reindex=True,
):
"""
Use NumPy to broadcast or reshape item with reindexing.
Parameters
----------
obj : DataFrame or Series or query compiler
The object containing the necessary information about the axes.
row_lookup : slice or scalar
The global row index to locate inside of `item`.
col_lookup : range, array, list, slice or scalar
The global col index to locate inside of `item`.
item : DataFrame, Series, or query_compiler
Value that should be broadcast to a new shape of `to_shape`.
need_columns_reindex : bool, default: True
In the case of assigning columns to a dataframe (broadcasting is
part of the flow), reindexing is not needed.
Returns
-------
(np.ndarray, Optional[Series])
* np.ndarray - `item` after it was broadcasted to `to_shape`.
* Series - item's dtypes.
Raises
------
ValueError
1) If `row_lookup` or `col_lookup` contains values missing in
DataFrame/Series index or columns correspondingly.
2) If `item` cannot be broadcast from its own shape to `to_shape`.
Notes
-----
NumPy is memory efficient, there shouldn't be performance issue.
"""
# It is valid to pass a DataFrame or Series to __setitem__ that is larger than
# the target the user is trying to overwrite.
from .dataframe import DataFrame
from .series import Series
new_row_len = (
len(obj.index[row_lookup]) if isinstance(row_lookup, slice) else len(row_lookup)
)
new_col_len = (
len(obj.columns[col_lookup])
if isinstance(col_lookup, slice)
else len(col_lookup)
)
to_shape = new_row_len, new_col_len
dtypes = None
if isinstance(item, (pandas.Series, pandas.DataFrame, Series, DataFrame)):
# convert indices in lookups to names, as pandas reindex expects them to be so
axes_to_reindex = {}
index_values = obj.index[row_lookup]
if not index_values.equals(item.index):
axes_to_reindex["index"] = index_values
if need_columns_reindex and isinstance(item, (pandas.DataFrame, DataFrame)):
column_values = obj.columns[col_lookup]
if not column_values.equals(item.columns):
axes_to_reindex["columns"] = column_values
# New value for columns/index make that reindex add NaN values
if axes_to_reindex:
item = item.reindex(**axes_to_reindex)
dtypes = item.dtypes
if not isinstance(dtypes, pandas.Series):
dtypes = pandas.Series([dtypes])
try:
# Cast to numpy drop information about heterogeneous types (cast to common)
# TODO: we shouldn't do that, maybe there should be the if branch
item = np.array(item)
if dtypes is None:
dtypes = pandas.Series([item.dtype] * len(col_lookup))
if np.prod(to_shape) == np.prod(item.shape):
return item.reshape(to_shape), dtypes
else:
return np.broadcast_to(item, to_shape), dtypes
except ValueError:
from_shape = np.array(item).shape
raise ValueError(
f"could not broadcast input array from shape {from_shape} into shape "
+ f"{to_shape}"
)
def _walk_aggregation_func(
key: IndexLabel, value: AggFuncType, depth: int = 0
) -> Iterator[Tuple[IndexLabel, AggFuncTypeBase, Optional[str], bool]]:
"""
Walk over a function from a dictionary-specified aggregation.
Note: this function is not supposed to be called directly and
is used by ``walk_aggregation_dict``.
Parameters
----------
key : IndexLabel
A key in a dictionary-specified aggregation for the passed `value`.
This means an index label to apply the `value` functions against.
value : AggFuncType
An aggregation function matching the `key`.
depth : int, default: 0
Specifies a nesting level for the `value` where ``depth=0`` is when
you call the function on a raw dictionary value.
Yields
------
(col: IndexLabel, func: AggFuncTypeBase, func_name: Optional[str], col_renaming_required: bool)
Yield an aggregation function with its metadata:
- `col`: column name to apply the function.
- `func`: aggregation function to apply to the column.
- `func_name`: custom function name that was specified in the dict.
- `col_renaming_required`: whether it's required to rename the
`col` into ``(col, func_name)``.
"""
col_renaming_required = bool(depth)
if isinstance(value, (list, tuple)):
if depth == 0:
for val in value:
yield from _walk_aggregation_func(key, val, depth + 1)
elif depth == 1:
if len(value) != 2:
raise ValueError(
f"Incorrect rename format. Renamer must consist of exactly two elements, got: {len(value)}."
)
func_name, func = value
yield key, func, func_name, col_renaming_required
else:
# pandas doesn't support this as well
raise NotImplementedError("Nested renaming is not supported.")
else:
yield key, value, None, col_renaming_required
def walk_aggregation_dict(
agg_dict: AggFuncTypeDict,
) -> Iterator[Tuple[IndexLabel, AggFuncTypeBase, Optional[str], bool]]:
"""
Walk over an aggregation dictionary.
Parameters
----------
agg_dict : AggFuncTypeDict
Yields
------
(col: IndexLabel, func: AggFuncTypeBase, func_name: Optional[str], col_renaming_required: bool)
Yield an aggregation function with its metadata:
- `col`: column name to apply the function.
- `func`: aggregation function to apply to the column.
- `func_name`: custom function name that was specified in the dict.
- `col_renaming_required`: whether it's required to rename the
`col` into ``(col, func_name)``.
"""
for key, value in agg_dict.items():
yield from _walk_aggregation_func(key, value)
def _doc_binary_op(operation, bin_op, left="Series", right="right", returns="Series"):
"""
Return callable documenting `Series` or `DataFrame` binary operator.
Parameters
----------
operation : str
Operation name.
bin_op : str
Binary operation name.
left : str, default: 'Series'
The left object to document.
right : str, default: 'right'
The right operand name.
returns : str, default: 'Series'
Type of returns.
Returns
-------
callable
"""
if left == "Series":
right_type = "Series or scalar value"
elif left == "DataFrame":
right_type = "DataFrame, Series or scalar value"
elif left == "BasePandasDataset":
right_type = "BasePandasDataset or scalar value"
else:
raise NotImplementedError(
f"Only 'BasePandasDataset', `DataFrame` and 'Series' `left` are allowed, actually passed: {left}"
)
doc_op = doc(
_doc_binary_operation,
operation=operation,
right=right,
right_type=right_type,
bin_op=bin_op,
returns=returns,
left=left,
)
return doc_op
_original_pandas_MultiIndex_from_frame = pandas.MultiIndex.from_frame
pandas.MultiIndex.from_frame = from_modin_frame_to_mi