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Inzynierka/Lib/site-packages/pandas/core/reshape/merge.py

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2023-06-02 12:51:02 +02:00
"""
SQL-style merge routines
"""
from __future__ import annotations
import copy as cp
import datetime
from functools import partial
import string
from typing import (
TYPE_CHECKING,
Hashable,
Literal,
Sequence,
cast,
)
import uuid
import warnings
import numpy as np
from pandas._libs import (
Timedelta,
hashtable as libhashtable,
join as libjoin,
lib,
)
from pandas._libs.lib import is_range_indexer
from pandas._typing import (
AnyArrayLike,
ArrayLike,
AxisInt,
DtypeObj,
IndexLabel,
JoinHow,
MergeHow,
Shape,
Suffixes,
npt,
)
from pandas.errors import MergeError
from pandas.util._decorators import (
Appender,
Substitution,
cache_readonly,
)
from pandas.util._exceptions import find_stack_level
from pandas.core.dtypes.base import ExtensionDtype
from pandas.core.dtypes.cast import find_common_type
from pandas.core.dtypes.common import (
ensure_float64,
ensure_int64,
ensure_object,
is_array_like,
is_bool,
is_bool_dtype,
is_categorical_dtype,
is_dtype_equal,
is_extension_array_dtype,
is_float_dtype,
is_integer,
is_integer_dtype,
is_list_like,
is_number,
is_numeric_dtype,
is_object_dtype,
needs_i8_conversion,
)
from pandas.core.dtypes.dtypes import DatetimeTZDtype
from pandas.core.dtypes.generic import (
ABCDataFrame,
ABCSeries,
)
from pandas.core.dtypes.missing import (
isna,
na_value_for_dtype,
)
from pandas import (
ArrowDtype,
Categorical,
Index,
MultiIndex,
Series,
)
import pandas.core.algorithms as algos
from pandas.core.arrays import (
ArrowExtensionArray,
BaseMaskedArray,
ExtensionArray,
)
from pandas.core.arrays._mixins import NDArrayBackedExtensionArray
import pandas.core.common as com
from pandas.core.construction import (
ensure_wrapped_if_datetimelike,
extract_array,
)
from pandas.core.frame import _merge_doc
from pandas.core.indexes.api import default_index
from pandas.core.sorting import is_int64_overflow_possible
if TYPE_CHECKING:
from pandas import DataFrame
from pandas.core import groupby
from pandas.core.arrays import DatetimeArray
_factorizers = {
np.int64: libhashtable.Int64Factorizer,
np.longlong: libhashtable.Int64Factorizer,
np.int32: libhashtable.Int32Factorizer,
np.int16: libhashtable.Int16Factorizer,
np.int8: libhashtable.Int8Factorizer,
np.uint64: libhashtable.UInt64Factorizer,
np.uint32: libhashtable.UInt32Factorizer,
np.uint16: libhashtable.UInt16Factorizer,
np.uint8: libhashtable.UInt8Factorizer,
np.bool_: libhashtable.UInt8Factorizer,
np.float64: libhashtable.Float64Factorizer,
np.float32: libhashtable.Float32Factorizer,
np.complex64: libhashtable.Complex64Factorizer,
np.complex128: libhashtable.Complex128Factorizer,
np.object_: libhashtable.ObjectFactorizer,
}
# See https://github.com/pandas-dev/pandas/issues/52451
if np.intc is not np.int32:
_factorizers[np.intc] = libhashtable.Int64Factorizer
@Substitution("\nleft : DataFrame or named Series")
@Appender(_merge_doc, indents=0)
def merge(
left: DataFrame | Series,
right: DataFrame | Series,
how: MergeHow = "inner",
on: IndexLabel | None = None,
left_on: IndexLabel | None = None,
right_on: IndexLabel | None = None,
left_index: bool = False,
right_index: bool = False,
sort: bool = False,
suffixes: Suffixes = ("_x", "_y"),
copy: bool | None = None,
indicator: str | bool = False,
validate: str | None = None,
) -> DataFrame:
op = _MergeOperation(
left,
right,
how=how,
on=on,
left_on=left_on,
right_on=right_on,
left_index=left_index,
right_index=right_index,
sort=sort,
suffixes=suffixes,
indicator=indicator,
validate=validate,
)
return op.get_result(copy=copy)
def _groupby_and_merge(by, left: DataFrame, right: DataFrame, merge_pieces):
"""
groupby & merge; we are always performing a left-by type operation
Parameters
----------
by: field to group
left: DataFrame
right: DataFrame
merge_pieces: function for merging
"""
pieces = []
if not isinstance(by, (list, tuple)):
by = [by]
lby = left.groupby(by, sort=False)
rby: groupby.DataFrameGroupBy | None = None
# if we can groupby the rhs
# then we can get vastly better perf
if all(item in right.columns for item in by):
rby = right.groupby(by, sort=False)
for key, lhs in lby.grouper.get_iterator(lby._selected_obj, axis=lby.axis):
if rby is None:
rhs = right
else:
try:
rhs = right.take(rby.indices[key])
except KeyError:
# key doesn't exist in left
lcols = lhs.columns.tolist()
cols = lcols + [r for r in right.columns if r not in set(lcols)]
merged = lhs.reindex(columns=cols)
merged.index = range(len(merged))
pieces.append(merged)
continue
merged = merge_pieces(lhs, rhs)
# make sure join keys are in the merged
# TODO, should merge_pieces do this?
merged[by] = key
pieces.append(merged)
# preserve the original order
# if we have a missing piece this can be reset
from pandas.core.reshape.concat import concat
result = concat(pieces, ignore_index=True)
result = result.reindex(columns=pieces[0].columns, copy=False)
return result, lby
def merge_ordered(
left: DataFrame,
right: DataFrame,
on: IndexLabel | None = None,
left_on: IndexLabel | None = None,
right_on: IndexLabel | None = None,
left_by=None,
right_by=None,
fill_method: str | None = None,
suffixes: Suffixes = ("_x", "_y"),
how: JoinHow = "outer",
) -> DataFrame:
"""
Perform a merge for ordered data with optional filling/interpolation.
Designed for ordered data like time series data. Optionally
perform group-wise merge (see examples).
Parameters
----------
left : DataFrame or named Series
right : DataFrame or named Series
on : label or list
Field names to join on. Must be found in both DataFrames.
left_on : label or list, or array-like
Field names to join on in left DataFrame. Can be a vector or list of
vectors of the length of the DataFrame to use a particular vector as
the join key instead of columns.
right_on : label or list, or array-like
Field names to join on in right DataFrame or vector/list of vectors per
left_on docs.
left_by : column name or list of column names
Group left DataFrame by group columns and merge piece by piece with
right DataFrame. Must be None if either left or right are a Series.
right_by : column name or list of column names
Group right DataFrame by group columns and merge piece by piece with
left DataFrame. Must be None if either left or right are a Series.
fill_method : {'ffill', None}, default None
Interpolation method for data.
suffixes : list-like, default is ("_x", "_y")
A length-2 sequence where each element is optionally a string
indicating the suffix to add to overlapping column names in
`left` and `right` respectively. Pass a value of `None` instead
of a string to indicate that the column name from `left` or
`right` should be left as-is, with no suffix. At least one of the
values must not be None.
how : {'left', 'right', 'outer', 'inner'}, default 'outer'
* left: use only keys from left frame (SQL: left outer join)
* right: use only keys from right frame (SQL: right outer join)
* outer: use union of keys from both frames (SQL: full outer join)
* inner: use intersection of keys from both frames (SQL: inner join).
Returns
-------
DataFrame
The merged DataFrame output type will be the same as
'left', if it is a subclass of DataFrame.
See Also
--------
merge : Merge with a database-style join.
merge_asof : Merge on nearest keys.
Examples
--------
>>> from pandas import merge_ordered
>>> df1 = pd.DataFrame(
... {
... "key": ["a", "c", "e", "a", "c", "e"],
... "lvalue": [1, 2, 3, 1, 2, 3],
... "group": ["a", "a", "a", "b", "b", "b"]
... }
... )
>>> df1
key lvalue group
0 a 1 a
1 c 2 a
2 e 3 a
3 a 1 b
4 c 2 b
5 e 3 b
>>> df2 = pd.DataFrame({"key": ["b", "c", "d"], "rvalue": [1, 2, 3]})
>>> df2
key rvalue
0 b 1
1 c 2
2 d 3
>>> merge_ordered(df1, df2, fill_method="ffill", left_by="group")
key lvalue group rvalue
0 a 1 a NaN
1 b 1 a 1.0
2 c 2 a 2.0
3 d 2 a 3.0
4 e 3 a 3.0
5 a 1 b NaN
6 b 1 b 1.0
7 c 2 b 2.0
8 d 2 b 3.0
9 e 3 b 3.0
"""
def _merger(x, y) -> DataFrame:
# perform the ordered merge operation
op = _OrderedMerge(
x,
y,
on=on,
left_on=left_on,
right_on=right_on,
suffixes=suffixes,
fill_method=fill_method,
how=how,
)
return op.get_result()
if left_by is not None and right_by is not None:
raise ValueError("Can only group either left or right frames")
if left_by is not None:
if isinstance(left_by, str):
left_by = [left_by]
check = set(left_by).difference(left.columns)
if len(check) != 0:
raise KeyError(f"{check} not found in left columns")
result, _ = _groupby_and_merge(left_by, left, right, lambda x, y: _merger(x, y))
elif right_by is not None:
if isinstance(right_by, str):
right_by = [right_by]
check = set(right_by).difference(right.columns)
if len(check) != 0:
raise KeyError(f"{check} not found in right columns")
result, _ = _groupby_and_merge(
right_by, right, left, lambda x, y: _merger(y, x)
)
else:
result = _merger(left, right)
return result
def merge_asof(
left: DataFrame | Series,
right: DataFrame | Series,
on: IndexLabel | None = None,
left_on: IndexLabel | None = None,
right_on: IndexLabel | None = None,
left_index: bool = False,
right_index: bool = False,
by=None,
left_by=None,
right_by=None,
suffixes: Suffixes = ("_x", "_y"),
tolerance=None,
allow_exact_matches: bool = True,
direction: str = "backward",
) -> DataFrame:
"""
Perform a merge by key distance.
This is similar to a left-join except that we match on nearest
key rather than equal keys. Both DataFrames must be sorted by the key.
For each row in the left DataFrame:
- A "backward" search selects the last row in the right DataFrame whose
'on' key is less than or equal to the left's key.
- A "forward" search selects the first row in the right DataFrame whose
'on' key is greater than or equal to the left's key.
- A "nearest" search selects the row in the right DataFrame whose 'on'
key is closest in absolute distance to the left's key.
The default is "backward" and is compatible in versions below 0.20.0.
The direction parameter was added in version 0.20.0 and introduces
"forward" and "nearest".
Optionally match on equivalent keys with 'by' before searching with 'on'.
Parameters
----------
left : DataFrame or named Series
right : DataFrame or named Series
on : label
Field name to join on. Must be found in both DataFrames.
The data MUST be ordered. Furthermore this must be a numeric column,
such as datetimelike, integer, or float. On or left_on/right_on
must be given.
left_on : label
Field name to join on in left DataFrame.
right_on : label
Field name to join on in right DataFrame.
left_index : bool
Use the index of the left DataFrame as the join key.
right_index : bool
Use the index of the right DataFrame as the join key.
by : column name or list of column names
Match on these columns before performing merge operation.
left_by : column name
Field names to match on in the left DataFrame.
right_by : column name
Field names to match on in the right DataFrame.
suffixes : 2-length sequence (tuple, list, ...)
Suffix to apply to overlapping column names in the left and right
side, respectively.
tolerance : int or Timedelta, optional, default None
Select asof tolerance within this range; must be compatible
with the merge index.
allow_exact_matches : bool, default True
- If True, allow matching with the same 'on' value
(i.e. less-than-or-equal-to / greater-than-or-equal-to)
- If False, don't match the same 'on' value
(i.e., strictly less-than / strictly greater-than).
direction : 'backward' (default), 'forward', or 'nearest'
Whether to search for prior, subsequent, or closest matches.
Returns
-------
DataFrame
See Also
--------
merge : Merge with a database-style join.
merge_ordered : Merge with optional filling/interpolation.
Examples
--------
>>> left = pd.DataFrame({"a": [1, 5, 10], "left_val": ["a", "b", "c"]})
>>> left
a left_val
0 1 a
1 5 b
2 10 c
>>> right = pd.DataFrame({"a": [1, 2, 3, 6, 7], "right_val": [1, 2, 3, 6, 7]})
>>> right
a right_val
0 1 1
1 2 2
2 3 3
3 6 6
4 7 7
>>> pd.merge_asof(left, right, on="a")
a left_val right_val
0 1 a 1
1 5 b 3
2 10 c 7
>>> pd.merge_asof(left, right, on="a", allow_exact_matches=False)
a left_val right_val
0 1 a NaN
1 5 b 3.0
2 10 c 7.0
>>> pd.merge_asof(left, right, on="a", direction="forward")
a left_val right_val
0 1 a 1.0
1 5 b 6.0
2 10 c NaN
>>> pd.merge_asof(left, right, on="a", direction="nearest")
a left_val right_val
0 1 a 1
1 5 b 6
2 10 c 7
We can use indexed DataFrames as well.
>>> left = pd.DataFrame({"left_val": ["a", "b", "c"]}, index=[1, 5, 10])
>>> left
left_val
1 a
5 b
10 c
>>> right = pd.DataFrame({"right_val": [1, 2, 3, 6, 7]}, index=[1, 2, 3, 6, 7])
>>> right
right_val
1 1
2 2
3 3
6 6
7 7
>>> pd.merge_asof(left, right, left_index=True, right_index=True)
left_val right_val
1 a 1
5 b 3
10 c 7
Here is a real-world times-series example
>>> quotes = pd.DataFrame(
... {
... "time": [
... pd.Timestamp("2016-05-25 13:30:00.023"),
... pd.Timestamp("2016-05-25 13:30:00.023"),
... pd.Timestamp("2016-05-25 13:30:00.030"),
... pd.Timestamp("2016-05-25 13:30:00.041"),
... pd.Timestamp("2016-05-25 13:30:00.048"),
... pd.Timestamp("2016-05-25 13:30:00.049"),
... pd.Timestamp("2016-05-25 13:30:00.072"),
... pd.Timestamp("2016-05-25 13:30:00.075")
... ],
... "ticker": [
... "GOOG",
... "MSFT",
... "MSFT",
... "MSFT",
... "GOOG",
... "AAPL",
... "GOOG",
... "MSFT"
... ],
... "bid": [720.50, 51.95, 51.97, 51.99, 720.50, 97.99, 720.50, 52.01],
... "ask": [720.93, 51.96, 51.98, 52.00, 720.93, 98.01, 720.88, 52.03]
... }
... )
>>> quotes
time ticker bid ask
0 2016-05-25 13:30:00.023 GOOG 720.50 720.93
1 2016-05-25 13:30:00.023 MSFT 51.95 51.96
2 2016-05-25 13:30:00.030 MSFT 51.97 51.98
3 2016-05-25 13:30:00.041 MSFT 51.99 52.00
4 2016-05-25 13:30:00.048 GOOG 720.50 720.93
5 2016-05-25 13:30:00.049 AAPL 97.99 98.01
6 2016-05-25 13:30:00.072 GOOG 720.50 720.88
7 2016-05-25 13:30:00.075 MSFT 52.01 52.03
>>> trades = pd.DataFrame(
... {
... "time": [
... pd.Timestamp("2016-05-25 13:30:00.023"),
... pd.Timestamp("2016-05-25 13:30:00.038"),
... pd.Timestamp("2016-05-25 13:30:00.048"),
... pd.Timestamp("2016-05-25 13:30:00.048"),
... pd.Timestamp("2016-05-25 13:30:00.048")
... ],
... "ticker": ["MSFT", "MSFT", "GOOG", "GOOG", "AAPL"],
... "price": [51.95, 51.95, 720.77, 720.92, 98.0],
... "quantity": [75, 155, 100, 100, 100]
... }
... )
>>> trades
time ticker price quantity
0 2016-05-25 13:30:00.023 MSFT 51.95 75
1 2016-05-25 13:30:00.038 MSFT 51.95 155
2 2016-05-25 13:30:00.048 GOOG 720.77 100
3 2016-05-25 13:30:00.048 GOOG 720.92 100
4 2016-05-25 13:30:00.048 AAPL 98.00 100
By default we are taking the asof of the quotes
>>> pd.merge_asof(trades, quotes, on="time", by="ticker")
time ticker price quantity bid ask
0 2016-05-25 13:30:00.023 MSFT 51.95 75 51.95 51.96
1 2016-05-25 13:30:00.038 MSFT 51.95 155 51.97 51.98
2 2016-05-25 13:30:00.048 GOOG 720.77 100 720.50 720.93
3 2016-05-25 13:30:00.048 GOOG 720.92 100 720.50 720.93
4 2016-05-25 13:30:00.048 AAPL 98.00 100 NaN NaN
We only asof within 2ms between the quote time and the trade time
>>> pd.merge_asof(
... trades, quotes, on="time", by="ticker", tolerance=pd.Timedelta("2ms")
... )
time ticker price quantity bid ask
0 2016-05-25 13:30:00.023 MSFT 51.95 75 51.95 51.96
1 2016-05-25 13:30:00.038 MSFT 51.95 155 NaN NaN
2 2016-05-25 13:30:00.048 GOOG 720.77 100 720.50 720.93
3 2016-05-25 13:30:00.048 GOOG 720.92 100 720.50 720.93
4 2016-05-25 13:30:00.048 AAPL 98.00 100 NaN NaN
We only asof within 10ms between the quote time and the trade time
and we exclude exact matches on time. However *prior* data will
propagate forward
>>> pd.merge_asof(
... trades,
... quotes,
... on="time",
... by="ticker",
... tolerance=pd.Timedelta("10ms"),
... allow_exact_matches=False
... )
time ticker price quantity bid ask
0 2016-05-25 13:30:00.023 MSFT 51.95 75 NaN NaN
1 2016-05-25 13:30:00.038 MSFT 51.95 155 51.97 51.98
2 2016-05-25 13:30:00.048 GOOG 720.77 100 NaN NaN
3 2016-05-25 13:30:00.048 GOOG 720.92 100 NaN NaN
4 2016-05-25 13:30:00.048 AAPL 98.00 100 NaN NaN
"""
op = _AsOfMerge(
left,
right,
on=on,
left_on=left_on,
right_on=right_on,
left_index=left_index,
right_index=right_index,
by=by,
left_by=left_by,
right_by=right_by,
suffixes=suffixes,
how="asof",
tolerance=tolerance,
allow_exact_matches=allow_exact_matches,
direction=direction,
)
return op.get_result()
# TODO: transformations??
# TODO: only copy DataFrames when modification necessary
class _MergeOperation:
"""
Perform a database (SQL) merge operation between two DataFrame or Series
objects using either columns as keys or their row indexes
"""
_merge_type = "merge"
how: MergeHow | Literal["asof"]
on: IndexLabel | None
# left_on/right_on may be None when passed, but in validate_specification
# get replaced with non-None.
left_on: Sequence[Hashable | AnyArrayLike]
right_on: Sequence[Hashable | AnyArrayLike]
left_index: bool
right_index: bool
axis: AxisInt
bm_axis: AxisInt
sort: bool
suffixes: Suffixes
copy: bool
indicator: str | bool
validate: str | None
join_names: list[Hashable]
right_join_keys: list[AnyArrayLike]
left_join_keys: list[AnyArrayLike]
def __init__(
self,
left: DataFrame | Series,
right: DataFrame | Series,
how: MergeHow | Literal["asof"] = "inner",
on: IndexLabel | None = None,
left_on: IndexLabel | None = None,
right_on: IndexLabel | None = None,
axis: AxisInt = 1,
left_index: bool = False,
right_index: bool = False,
sort: bool = True,
suffixes: Suffixes = ("_x", "_y"),
indicator: str | bool = False,
validate: str | None = None,
) -> None:
_left = _validate_operand(left)
_right = _validate_operand(right)
self.left = self.orig_left = _left
self.right = self.orig_right = _right
self.how = how
# bm_axis -> the axis on the BlockManager
self.bm_axis = axis
# axis --> the axis on the Series/DataFrame
self.axis = 1 - axis if self.left.ndim == 2 else 0
self.on = com.maybe_make_list(on)
self.suffixes = suffixes
self.sort = sort
self.left_index = left_index
self.right_index = right_index
self.indicator = indicator
if not is_bool(left_index):
raise ValueError(
f"left_index parameter must be of type bool, not {type(left_index)}"
)
if not is_bool(right_index):
raise ValueError(
f"right_index parameter must be of type bool, not {type(right_index)}"
)
# GH 40993: raise when merging between different levels; enforced in 2.0
if _left.columns.nlevels != _right.columns.nlevels:
msg = (
"Not allowed to merge between different levels. "
f"({_left.columns.nlevels} levels on the left, "
f"{_right.columns.nlevels} on the right)"
)
raise MergeError(msg)
self.left_on, self.right_on = self._validate_left_right_on(left_on, right_on)
cross_col = None
if self.how == "cross":
(
self.left,
self.right,
self.how,
cross_col,
) = self._create_cross_configuration(self.left, self.right)
self.left_on = self.right_on = [cross_col]
self._cross = cross_col
# note this function has side effects
(
self.left_join_keys,
self.right_join_keys,
self.join_names,
) = self._get_merge_keys()
# validate the merge keys dtypes. We may need to coerce
# to avoid incompatible dtypes
self._maybe_coerce_merge_keys()
# If argument passed to validate,
# check if columns specified as unique
# are in fact unique.
if validate is not None:
self._validate(validate)
def _reindex_and_concat(
self,
join_index: Index,
left_indexer: npt.NDArray[np.intp] | None,
right_indexer: npt.NDArray[np.intp] | None,
copy: bool | None,
) -> DataFrame:
"""
reindex along index and concat along columns.
"""
# Take views so we do not alter the originals
left = self.left[:]
right = self.right[:]
llabels, rlabels = _items_overlap_with_suffix(
self.left._info_axis, self.right._info_axis, self.suffixes
)
if left_indexer is not None and not is_range_indexer(left_indexer, len(left)):
# Pinning the index here (and in the right code just below) is not
# necessary, but makes the `.take` more performant if we have e.g.
# a MultiIndex for left.index.
lmgr = left._mgr.reindex_indexer(
join_index,
left_indexer,
axis=1,
copy=False,
only_slice=True,
allow_dups=True,
use_na_proxy=True,
)
left = left._constructor(lmgr)
left.index = join_index
if right_indexer is not None and not is_range_indexer(
right_indexer, len(right)
):
rmgr = right._mgr.reindex_indexer(
join_index,
right_indexer,
axis=1,
copy=False,
only_slice=True,
allow_dups=True,
use_na_proxy=True,
)
right = right._constructor(rmgr)
right.index = join_index
from pandas import concat
left.columns = llabels
right.columns = rlabels
result = concat([left, right], axis=1, copy=copy)
return result
def get_result(self, copy: bool | None = True) -> DataFrame:
if self.indicator:
self.left, self.right = self._indicator_pre_merge(self.left, self.right)
join_index, left_indexer, right_indexer = self._get_join_info()
result = self._reindex_and_concat(
join_index, left_indexer, right_indexer, copy=copy
)
result = result.__finalize__(self, method=self._merge_type)
if self.indicator:
result = self._indicator_post_merge(result)
self._maybe_add_join_keys(result, left_indexer, right_indexer)
self._maybe_restore_index_levels(result)
self._maybe_drop_cross_column(result, self._cross)
return result.__finalize__(self, method="merge")
def _maybe_drop_cross_column(
self, result: DataFrame, cross_col: str | None
) -> None:
if cross_col is not None:
del result[cross_col]
@cache_readonly
def _indicator_name(self) -> str | None:
if isinstance(self.indicator, str):
return self.indicator
elif isinstance(self.indicator, bool):
return "_merge" if self.indicator else None
else:
raise ValueError(
"indicator option can only accept boolean or string arguments"
)
def _indicator_pre_merge(
self, left: DataFrame, right: DataFrame
) -> tuple[DataFrame, DataFrame]:
columns = left.columns.union(right.columns)
for i in ["_left_indicator", "_right_indicator"]:
if i in columns:
raise ValueError(
"Cannot use `indicator=True` option when "
f"data contains a column named {i}"
)
if self._indicator_name in columns:
raise ValueError(
"Cannot use name of an existing column for indicator column"
)
left = left.copy()
right = right.copy()
left["_left_indicator"] = 1
left["_left_indicator"] = left["_left_indicator"].astype("int8")
right["_right_indicator"] = 2
right["_right_indicator"] = right["_right_indicator"].astype("int8")
return left, right
def _indicator_post_merge(self, result: DataFrame) -> DataFrame:
result["_left_indicator"] = result["_left_indicator"].fillna(0)
result["_right_indicator"] = result["_right_indicator"].fillna(0)
result[self._indicator_name] = Categorical(
(result["_left_indicator"] + result["_right_indicator"]),
categories=[1, 2, 3],
)
result[self._indicator_name] = result[
self._indicator_name
].cat.rename_categories(["left_only", "right_only", "both"])
result = result.drop(labels=["_left_indicator", "_right_indicator"], axis=1)
return result
def _maybe_restore_index_levels(self, result: DataFrame) -> None:
"""
Restore index levels specified as `on` parameters
Here we check for cases where `self.left_on` and `self.right_on` pairs
each reference an index level in their respective DataFrames. The
joined columns corresponding to these pairs are then restored to the
index of `result`.
**Note:** This method has side effects. It modifies `result` in-place
Parameters
----------
result: DataFrame
merge result
Returns
-------
None
"""
names_to_restore = []
for name, left_key, right_key in zip(
self.join_names, self.left_on, self.right_on
):
if (
# Argument 1 to "_is_level_reference" of "NDFrame" has incompatible
# type "Union[Hashable, ExtensionArray, Index, Series]"; expected
# "Hashable"
self.orig_left._is_level_reference(left_key) # type: ignore[arg-type]
# Argument 1 to "_is_level_reference" of "NDFrame" has incompatible
# type "Union[Hashable, ExtensionArray, Index, Series]"; expected
# "Hashable"
and self.orig_right._is_level_reference(
right_key # type: ignore[arg-type]
)
and left_key == right_key
and name not in result.index.names
):
names_to_restore.append(name)
if names_to_restore:
result.set_index(names_to_restore, inplace=True)
def _maybe_add_join_keys(
self,
result: DataFrame,
left_indexer: np.ndarray | None,
right_indexer: np.ndarray | None,
) -> None:
left_has_missing = None
right_has_missing = None
assert all(is_array_like(x) for x in self.left_join_keys)
keys = zip(self.join_names, self.left_on, self.right_on)
for i, (name, lname, rname) in enumerate(keys):
if not _should_fill(lname, rname):
continue
take_left, take_right = None, None
if name in result:
if left_indexer is not None and right_indexer is not None:
if name in self.left:
if left_has_missing is None:
left_has_missing = (left_indexer == -1).any()
if left_has_missing:
take_right = self.right_join_keys[i]
if not is_dtype_equal(
result[name].dtype, self.left[name].dtype
):
take_left = self.left[name]._values
elif name in self.right:
if right_has_missing is None:
right_has_missing = (right_indexer == -1).any()
if right_has_missing:
take_left = self.left_join_keys[i]
if not is_dtype_equal(
result[name].dtype, self.right[name].dtype
):
take_right = self.right[name]._values
elif left_indexer is not None:
take_left = self.left_join_keys[i]
take_right = self.right_join_keys[i]
if take_left is not None or take_right is not None:
if take_left is None:
lvals = result[name]._values
else:
# TODO: can we pin down take_left's type earlier?
take_left = extract_array(take_left, extract_numpy=True)
lfill = na_value_for_dtype(take_left.dtype)
lvals = algos.take_nd(take_left, left_indexer, fill_value=lfill)
if take_right is None:
rvals = result[name]._values
else:
# TODO: can we pin down take_right's type earlier?
taker = extract_array(take_right, extract_numpy=True)
rfill = na_value_for_dtype(taker.dtype)
rvals = algos.take_nd(taker, right_indexer, fill_value=rfill)
# if we have an all missing left_indexer
# make sure to just use the right values or vice-versa
mask_left = left_indexer == -1
# error: Item "bool" of "Union[Any, bool]" has no attribute "all"
if mask_left.all(): # type: ignore[union-attr]
key_col = Index(rvals)
result_dtype = rvals.dtype
elif right_indexer is not None and (right_indexer == -1).all():
key_col = Index(lvals)
result_dtype = lvals.dtype
else:
key_col = Index(lvals).where(~mask_left, rvals)
result_dtype = find_common_type([lvals.dtype, rvals.dtype])
if (
lvals.dtype.kind == "M"
and rvals.dtype.kind == "M"
and result_dtype.kind == "O"
):
# TODO(non-nano) Workaround for common_type not dealing
# with different resolutions
result_dtype = key_col.dtype
if result._is_label_reference(name):
result[name] = Series(
key_col, dtype=result_dtype, index=result.index
)
elif result._is_level_reference(name):
if isinstance(result.index, MultiIndex):
key_col.name = name
idx_list = [
result.index.get_level_values(level_name)
if level_name != name
else key_col
for level_name in result.index.names
]
result.set_index(idx_list, inplace=True)
else:
result.index = Index(key_col, name=name)
else:
result.insert(i, name or f"key_{i}", key_col)
def _get_join_indexers(self) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]:
"""return the join indexers"""
return get_join_indexers(
self.left_join_keys, self.right_join_keys, sort=self.sort, how=self.how
)
def _get_join_info(
self,
) -> tuple[Index, npt.NDArray[np.intp] | None, npt.NDArray[np.intp] | None]:
# make mypy happy
assert self.how != "cross"
left_ax = self.left.axes[self.axis]
right_ax = self.right.axes[self.axis]
if self.left_index and self.right_index and self.how != "asof":
join_index, left_indexer, right_indexer = left_ax.join(
right_ax, how=self.how, return_indexers=True, sort=self.sort
)
elif self.right_index and self.how == "left":
join_index, left_indexer, right_indexer = _left_join_on_index(
left_ax, right_ax, self.left_join_keys, sort=self.sort
)
elif self.left_index and self.how == "right":
join_index, right_indexer, left_indexer = _left_join_on_index(
right_ax, left_ax, self.right_join_keys, sort=self.sort
)
else:
(left_indexer, right_indexer) = self._get_join_indexers()
if self.right_index:
if len(self.left) > 0:
join_index = self._create_join_index(
self.left.index,
self.right.index,
left_indexer,
how="right",
)
else:
join_index = self.right.index.take(right_indexer)
elif self.left_index:
if self.how == "asof":
# GH#33463 asof should always behave like a left merge
join_index = self._create_join_index(
self.left.index,
self.right.index,
left_indexer,
how="left",
)
elif len(self.right) > 0:
join_index = self._create_join_index(
self.right.index,
self.left.index,
right_indexer,
how="left",
)
else:
join_index = self.left.index.take(left_indexer)
else:
join_index = default_index(len(left_indexer))
if len(join_index) == 0 and not isinstance(join_index, MultiIndex):
join_index = default_index(0).set_names(join_index.name)
return join_index, left_indexer, right_indexer
def _create_join_index(
self,
index: Index,
other_index: Index,
indexer: npt.NDArray[np.intp],
how: JoinHow = "left",
) -> Index:
"""
Create a join index by rearranging one index to match another
Parameters
----------
index : Index being rearranged
other_index : Index used to supply values not found in index
indexer : np.ndarray[np.intp] how to rearrange index
how : str
Replacement is only necessary if indexer based on other_index.
Returns
-------
Index
"""
if self.how in (how, "outer") and not isinstance(other_index, MultiIndex):
# if final index requires values in other_index but not target
# index, indexer may hold missing (-1) values, causing Index.take
# to take the final value in target index. So, we set the last
# element to be the desired fill value. We do not use allow_fill
# and fill_value because it throws a ValueError on integer indices
mask = indexer == -1
if np.any(mask):
fill_value = na_value_for_dtype(index.dtype, compat=False)
index = index.append(Index([fill_value]))
return index.take(indexer)
def _get_merge_keys(
self,
) -> tuple[list[AnyArrayLike], list[AnyArrayLike], list[Hashable]]:
"""
Note: has side effects (copy/delete key columns)
Parameters
----------
left
right
on
Returns
-------
left_keys, right_keys, join_names
"""
# left_keys, right_keys entries can actually be anything listlike
# with a 'dtype' attr
left_keys: list[AnyArrayLike] = []
right_keys: list[AnyArrayLike] = []
join_names: list[Hashable] = []
right_drop: list[Hashable] = []
left_drop: list[Hashable] = []
left, right = self.left, self.right
is_lkey = lambda x: is_array_like(x) and len(x) == len(left)
is_rkey = lambda x: is_array_like(x) and len(x) == len(right)
# Note that pd.merge_asof() has separate 'on' and 'by' parameters. A
# user could, for example, request 'left_index' and 'left_by'. In a
# regular pd.merge(), users cannot specify both 'left_index' and
# 'left_on'. (Instead, users have a MultiIndex). That means the
# self.left_on in this function is always empty in a pd.merge(), but
# a pd.merge_asof(left_index=True, left_by=...) will result in a
# self.left_on array with a None in the middle of it. This requires
# a work-around as designated in the code below.
# See _validate_left_right_on() for where this happens.
# ugh, spaghetti re #733
if _any(self.left_on) and _any(self.right_on):
for lk, rk in zip(self.left_on, self.right_on):
if is_lkey(lk):
lk = cast(AnyArrayLike, lk)
left_keys.append(lk)
if is_rkey(rk):
rk = cast(AnyArrayLike, rk)
right_keys.append(rk)
join_names.append(None) # what to do?
else:
# Then we're either Hashable or a wrong-length arraylike,
# the latter of which will raise
rk = cast(Hashable, rk)
if rk is not None:
right_keys.append(right._get_label_or_level_values(rk))
join_names.append(rk)
else:
# work-around for merge_asof(right_index=True)
right_keys.append(right.index)
join_names.append(right.index.name)
else:
if not is_rkey(rk):
# Then we're either Hashable or a wrong-length arraylike,
# the latter of which will raise
rk = cast(Hashable, rk)
if rk is not None:
right_keys.append(right._get_label_or_level_values(rk))
else:
# work-around for merge_asof(right_index=True)
right_keys.append(right.index)
if lk is not None and lk == rk: # FIXME: what about other NAs?
# avoid key upcast in corner case (length-0)
lk = cast(Hashable, lk)
if len(left) > 0:
right_drop.append(rk)
else:
left_drop.append(lk)
else:
rk = cast(AnyArrayLike, rk)
right_keys.append(rk)
if lk is not None:
# Then we're either Hashable or a wrong-length arraylike,
# the latter of which will raise
lk = cast(Hashable, lk)
left_keys.append(left._get_label_or_level_values(lk))
join_names.append(lk)
else:
# work-around for merge_asof(left_index=True)
left_keys.append(left.index)
join_names.append(left.index.name)
elif _any(self.left_on):
for k in self.left_on:
if is_lkey(k):
k = cast(AnyArrayLike, k)
left_keys.append(k)
join_names.append(None)
else:
# Then we're either Hashable or a wrong-length arraylike,
# the latter of which will raise
k = cast(Hashable, k)
left_keys.append(left._get_label_or_level_values(k))
join_names.append(k)
if isinstance(self.right.index, MultiIndex):
right_keys = [
lev._values.take(lev_codes)
for lev, lev_codes in zip(
self.right.index.levels, self.right.index.codes
)
]
else:
right_keys = [self.right.index._values]
elif _any(self.right_on):
for k in self.right_on:
if is_rkey(k):
k = cast(AnyArrayLike, k)
right_keys.append(k)
join_names.append(None)
else:
# Then we're either Hashable or a wrong-length arraylike,
# the latter of which will raise
k = cast(Hashable, k)
right_keys.append(right._get_label_or_level_values(k))
join_names.append(k)
if isinstance(self.left.index, MultiIndex):
left_keys = [
lev._values.take(lev_codes)
for lev, lev_codes in zip(
self.left.index.levels, self.left.index.codes
)
]
else:
left_keys = [self.left.index._values]
if left_drop:
self.left = self.left._drop_labels_or_levels(left_drop)
if right_drop:
self.right = self.right._drop_labels_or_levels(right_drop)
return left_keys, right_keys, join_names
def _maybe_coerce_merge_keys(self) -> None:
# we have valid merges but we may have to further
# coerce these if they are originally incompatible types
#
# for example if these are categorical, but are not dtype_equal
# or if we have object and integer dtypes
for lk, rk, name in zip(
self.left_join_keys, self.right_join_keys, self.join_names
):
if (len(lk) and not len(rk)) or (not len(lk) and len(rk)):
continue
lk = extract_array(lk, extract_numpy=True)
rk = extract_array(rk, extract_numpy=True)
lk_is_cat = is_categorical_dtype(lk.dtype)
rk_is_cat = is_categorical_dtype(rk.dtype)
lk_is_object = is_object_dtype(lk.dtype)
rk_is_object = is_object_dtype(rk.dtype)
# if either left or right is a categorical
# then the must match exactly in categories & ordered
if lk_is_cat and rk_is_cat:
lk = cast(Categorical, lk)
rk = cast(Categorical, rk)
if lk._categories_match_up_to_permutation(rk):
continue
elif lk_is_cat or rk_is_cat:
pass
elif is_dtype_equal(lk.dtype, rk.dtype):
continue
msg = (
f"You are trying to merge on {lk.dtype} and "
f"{rk.dtype} columns. If you wish to proceed you should use pd.concat"
)
# if we are numeric, then allow differing
# kinds to proceed, eg. int64 and int8, int and float
# further if we are object, but we infer to
# the same, then proceed
if is_numeric_dtype(lk.dtype) and is_numeric_dtype(rk.dtype):
if lk.dtype.kind == rk.dtype.kind:
continue
# check whether ints and floats
if is_integer_dtype(rk.dtype) and is_float_dtype(lk.dtype):
# GH 47391 numpy > 1.24 will raise a RuntimeError for nan -> int
with np.errstate(invalid="ignore"):
# error: Argument 1 to "astype" of "ndarray" has incompatible
# type "Union[ExtensionDtype, Any, dtype[Any]]"; expected
# "Union[dtype[Any], Type[Any], _SupportsDType[dtype[Any]]]"
casted = lk.astype(rk.dtype) # type: ignore[arg-type]
mask = ~np.isnan(lk)
match = lk == casted
if not match[mask].all():
warnings.warn(
"You are merging on int and float "
"columns where the float values "
"are not equal to their int representation.",
UserWarning,
stacklevel=find_stack_level(),
)
continue
if is_float_dtype(rk.dtype) and is_integer_dtype(lk.dtype):
# GH 47391 numpy > 1.24 will raise a RuntimeError for nan -> int
with np.errstate(invalid="ignore"):
# error: Argument 1 to "astype" of "ndarray" has incompatible
# type "Union[ExtensionDtype, Any, dtype[Any]]"; expected
# "Union[dtype[Any], Type[Any], _SupportsDType[dtype[Any]]]"
casted = rk.astype(lk.dtype) # type: ignore[arg-type]
mask = ~np.isnan(rk)
match = rk == casted
if not match[mask].all():
warnings.warn(
"You are merging on int and float "
"columns where the float values "
"are not equal to their int representation.",
UserWarning,
stacklevel=find_stack_level(),
)
continue
# let's infer and see if we are ok
if lib.infer_dtype(lk, skipna=False) == lib.infer_dtype(
rk, skipna=False
):
continue
# Check if we are trying to merge on obviously
# incompatible dtypes GH 9780, GH 15800
# bool values are coerced to object
elif (lk_is_object and is_bool_dtype(rk.dtype)) or (
is_bool_dtype(lk.dtype) and rk_is_object
):
pass
# object values are allowed to be merged
elif (lk_is_object and is_numeric_dtype(rk.dtype)) or (
is_numeric_dtype(lk.dtype) and rk_is_object
):
inferred_left = lib.infer_dtype(lk, skipna=False)
inferred_right = lib.infer_dtype(rk, skipna=False)
bool_types = ["integer", "mixed-integer", "boolean", "empty"]
string_types = ["string", "unicode", "mixed", "bytes", "empty"]
# inferred bool
if inferred_left in bool_types and inferred_right in bool_types:
pass
# unless we are merging non-string-like with string-like
elif (
inferred_left in string_types and inferred_right not in string_types
) or (
inferred_right in string_types and inferred_left not in string_types
):
raise ValueError(msg)
# datetimelikes must match exactly
elif needs_i8_conversion(lk.dtype) and not needs_i8_conversion(rk.dtype):
raise ValueError(msg)
elif not needs_i8_conversion(lk.dtype) and needs_i8_conversion(rk.dtype):
raise ValueError(msg)
elif isinstance(lk.dtype, DatetimeTZDtype) and not isinstance(
rk.dtype, DatetimeTZDtype
):
raise ValueError(msg)
elif not isinstance(lk.dtype, DatetimeTZDtype) and isinstance(
rk.dtype, DatetimeTZDtype
):
raise ValueError(msg)
elif (
isinstance(lk.dtype, DatetimeTZDtype)
and isinstance(rk.dtype, DatetimeTZDtype)
) or (lk.dtype.kind == "M" and rk.dtype.kind == "M"):
# allows datetime with different resolutions
continue
elif lk_is_object and rk_is_object:
continue
# Houston, we have a problem!
# let's coerce to object if the dtypes aren't
# categorical, otherwise coerce to the category
# dtype. If we coerced categories to object,
# then we would lose type information on some
# columns, and end up trying to merge
# incompatible dtypes. See GH 16900.
if name in self.left.columns:
typ = cast(Categorical, lk).categories.dtype if lk_is_cat else object
self.left = self.left.copy()
self.left[name] = self.left[name].astype(typ)
if name in self.right.columns:
typ = cast(Categorical, rk).categories.dtype if rk_is_cat else object
self.right = self.right.copy()
self.right[name] = self.right[name].astype(typ)
def _create_cross_configuration(
self, left: DataFrame, right: DataFrame
) -> tuple[DataFrame, DataFrame, JoinHow, str]:
"""
Creates the configuration to dispatch the cross operation to inner join,
e.g. adding a join column and resetting parameters. Join column is added
to a new object, no inplace modification
Parameters
----------
left : DataFrame
right : DataFrame
Returns
-------
a tuple (left, right, how, cross_col) representing the adjusted
DataFrames with cross_col, the merge operation set to inner and the column
to join over.
"""
cross_col = f"_cross_{uuid.uuid4()}"
how: JoinHow = "inner"
return (
left.assign(**{cross_col: 1}),
right.assign(**{cross_col: 1}),
how,
cross_col,
)
def _validate_left_right_on(self, left_on, right_on):
left_on = com.maybe_make_list(left_on)
right_on = com.maybe_make_list(right_on)
if self.how == "cross":
if (
self.left_index
or self.right_index
or right_on is not None
or left_on is not None
or self.on is not None
):
raise MergeError(
"Can not pass on, right_on, left_on or set right_index=True or "
"left_index=True"
)
# Hm, any way to make this logic less complicated??
elif self.on is None and left_on is None and right_on is None:
if self.left_index and self.right_index:
left_on, right_on = (), ()
elif self.left_index:
raise MergeError("Must pass right_on or right_index=True")
elif self.right_index:
raise MergeError("Must pass left_on or left_index=True")
else:
# use the common columns
left_cols = self.left.columns
right_cols = self.right.columns
common_cols = left_cols.intersection(right_cols)
if len(common_cols) == 0:
raise MergeError(
"No common columns to perform merge on. "
f"Merge options: left_on={left_on}, "
f"right_on={right_on}, "
f"left_index={self.left_index}, "
f"right_index={self.right_index}"
)
if (
not left_cols.join(common_cols, how="inner").is_unique
or not right_cols.join(common_cols, how="inner").is_unique
):
raise MergeError(f"Data columns not unique: {repr(common_cols)}")
left_on = right_on = common_cols
elif self.on is not None:
if left_on is not None or right_on is not None:
raise MergeError(
'Can only pass argument "on" OR "left_on" '
'and "right_on", not a combination of both.'
)
if self.left_index or self.right_index:
raise MergeError(
'Can only pass argument "on" OR "left_index" '
'and "right_index", not a combination of both.'
)
left_on = right_on = self.on
elif left_on is not None:
if self.left_index:
raise MergeError(
'Can only pass argument "left_on" OR "left_index" not both.'
)
if not self.right_index and right_on is None:
raise MergeError('Must pass "right_on" OR "right_index".')
n = len(left_on)
if self.right_index:
if len(left_on) != self.right.index.nlevels:
raise ValueError(
"len(left_on) must equal the number "
'of levels in the index of "right"'
)
right_on = [None] * n
elif right_on is not None:
if self.right_index:
raise MergeError(
'Can only pass argument "right_on" OR "right_index" not both.'
)
if not self.left_index and left_on is None:
raise MergeError('Must pass "left_on" OR "left_index".')
n = len(right_on)
if self.left_index:
if len(right_on) != self.left.index.nlevels:
raise ValueError(
"len(right_on) must equal the number "
'of levels in the index of "left"'
)
left_on = [None] * n
if self.how != "cross" and len(right_on) != len(left_on):
raise ValueError("len(right_on) must equal len(left_on)")
return left_on, right_on
def _validate(self, validate: str) -> None:
# Check uniqueness of each
if self.left_index:
left_unique = self.orig_left.index.is_unique
else:
left_unique = MultiIndex.from_arrays(self.left_join_keys).is_unique
if self.right_index:
right_unique = self.orig_right.index.is_unique
else:
right_unique = MultiIndex.from_arrays(self.right_join_keys).is_unique
# Check data integrity
if validate in ["one_to_one", "1:1"]:
if not left_unique and not right_unique:
raise MergeError(
"Merge keys are not unique in either left "
"or right dataset; not a one-to-one merge"
)
if not left_unique:
raise MergeError(
"Merge keys are not unique in left dataset; not a one-to-one merge"
)
if not right_unique:
raise MergeError(
"Merge keys are not unique in right dataset; not a one-to-one merge"
)
elif validate in ["one_to_many", "1:m"]:
if not left_unique:
raise MergeError(
"Merge keys are not unique in left dataset; not a one-to-many merge"
)
elif validate in ["many_to_one", "m:1"]:
if not right_unique:
raise MergeError(
"Merge keys are not unique in right dataset; "
"not a many-to-one merge"
)
elif validate in ["many_to_many", "m:m"]:
pass
else:
raise ValueError(
f'"{validate}" is not a valid argument. '
"Valid arguments are:\n"
'- "1:1"\n'
'- "1:m"\n'
'- "m:1"\n'
'- "m:m"\n'
'- "one_to_one"\n'
'- "one_to_many"\n'
'- "many_to_one"\n'
'- "many_to_many"'
)
def get_join_indexers(
left_keys,
right_keys,
sort: bool = False,
how: MergeHow | Literal["asof"] = "inner",
**kwargs,
) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]:
"""
Parameters
----------
left_keys : ndarray, Index, Series
right_keys : ndarray, Index, Series
sort : bool, default False
how : {'inner', 'outer', 'left', 'right'}, default 'inner'
Returns
-------
np.ndarray[np.intp]
Indexer into the left_keys.
np.ndarray[np.intp]
Indexer into the right_keys.
"""
assert len(left_keys) == len(
right_keys
), "left_key and right_keys must be the same length"
# fast-path for empty left/right
left_n = len(left_keys[0])
right_n = len(right_keys[0])
if left_n == 0:
if how in ["left", "inner", "cross"]:
return _get_empty_indexer()
elif not sort and how in ["right", "outer"]:
return _get_no_sort_one_missing_indexer(right_n, True)
elif right_n == 0:
if how in ["right", "inner", "cross"]:
return _get_empty_indexer()
elif not sort and how in ["left", "outer"]:
return _get_no_sort_one_missing_indexer(left_n, False)
# get left & right join labels and num. of levels at each location
mapped = (
_factorize_keys(left_keys[n], right_keys[n], sort=sort, how=how)
for n in range(len(left_keys))
)
zipped = zip(*mapped)
llab, rlab, shape = (list(x) for x in zipped)
# get flat i8 keys from label lists
lkey, rkey = _get_join_keys(llab, rlab, tuple(shape), sort)
# factorize keys to a dense i8 space
# `count` is the num. of unique keys
# set(lkey) | set(rkey) == range(count)
lkey, rkey, count = _factorize_keys(lkey, rkey, sort=sort, how=how)
# preserve left frame order if how == 'left' and sort == False
kwargs = cp.copy(kwargs)
if how in ("left", "right"):
kwargs["sort"] = sort
join_func = {
"inner": libjoin.inner_join,
"left": libjoin.left_outer_join,
"right": lambda x, y, count, **kwargs: libjoin.left_outer_join(
y, x, count, **kwargs
)[::-1],
"outer": libjoin.full_outer_join,
}[how]
# error: Cannot call function of unknown type
return join_func(lkey, rkey, count, **kwargs) # type: ignore[operator]
def restore_dropped_levels_multijoin(
left: MultiIndex,
right: MultiIndex,
dropped_level_names,
join_index: Index,
lindexer: npt.NDArray[np.intp],
rindexer: npt.NDArray[np.intp],
) -> tuple[list[Index], npt.NDArray[np.intp], list[Hashable]]:
"""
*this is an internal non-public method*
Returns the levels, labels and names of a multi-index to multi-index join.
Depending on the type of join, this method restores the appropriate
dropped levels of the joined multi-index.
The method relies on lindexer, rindexer which hold the index positions of
left and right, where a join was feasible
Parameters
----------
left : MultiIndex
left index
right : MultiIndex
right index
dropped_level_names : str array
list of non-common level names
join_index : Index
the index of the join between the
common levels of left and right
lindexer : np.ndarray[np.intp]
left indexer
rindexer : np.ndarray[np.intp]
right indexer
Returns
-------
levels : list of Index
levels of combined multiindexes
labels : np.ndarray[np.intp]
labels of combined multiindexes
names : List[Hashable]
names of combined multiindex levels
"""
def _convert_to_multiindex(index: Index) -> MultiIndex:
if isinstance(index, MultiIndex):
return index
else:
return MultiIndex.from_arrays([index._values], names=[index.name])
# For multi-multi joins with one overlapping level,
# the returned index if of type Index
# Assure that join_index is of type MultiIndex
# so that dropped levels can be appended
join_index = _convert_to_multiindex(join_index)
join_levels = join_index.levels
join_codes = join_index.codes
join_names = join_index.names
# Iterate through the levels that must be restored
for dropped_level_name in dropped_level_names:
if dropped_level_name in left.names:
idx = left
indexer = lindexer
else:
idx = right
indexer = rindexer
# The index of the level name to be restored
name_idx = idx.names.index(dropped_level_name)
restore_levels = idx.levels[name_idx]
# Inject -1 in the codes list where a join was not possible
# IOW indexer[i]=-1
codes = idx.codes[name_idx]
if indexer is None:
restore_codes = codes
else:
restore_codes = algos.take_nd(codes, indexer, fill_value=-1)
# error: Cannot determine type of "__add__"
join_levels = join_levels + [restore_levels] # type: ignore[has-type]
join_codes = join_codes + [restore_codes]
join_names = join_names + [dropped_level_name]
return join_levels, join_codes, join_names
class _OrderedMerge(_MergeOperation):
_merge_type = "ordered_merge"
def __init__(
self,
left: DataFrame | Series,
right: DataFrame | Series,
on: IndexLabel | None = None,
left_on: IndexLabel | None = None,
right_on: IndexLabel | None = None,
left_index: bool = False,
right_index: bool = False,
axis: AxisInt = 1,
suffixes: Suffixes = ("_x", "_y"),
fill_method: str | None = None,
how: JoinHow | Literal["asof"] = "outer",
) -> None:
self.fill_method = fill_method
_MergeOperation.__init__(
self,
left,
right,
on=on,
left_on=left_on,
left_index=left_index,
right_index=right_index,
right_on=right_on,
axis=axis,
how=how,
suffixes=suffixes,
sort=True, # factorize sorts
)
def get_result(self, copy: bool | None = True) -> DataFrame:
join_index, left_indexer, right_indexer = self._get_join_info()
llabels, rlabels = _items_overlap_with_suffix(
self.left._info_axis, self.right._info_axis, self.suffixes
)
left_join_indexer: np.ndarray | None
right_join_indexer: np.ndarray | None
if self.fill_method == "ffill":
if left_indexer is None:
raise TypeError("left_indexer cannot be None")
left_indexer, right_indexer = cast(np.ndarray, left_indexer), cast(
np.ndarray, right_indexer
)
left_join_indexer = libjoin.ffill_indexer(left_indexer)
right_join_indexer = libjoin.ffill_indexer(right_indexer)
else:
left_join_indexer = left_indexer
right_join_indexer = right_indexer
result = self._reindex_and_concat(
join_index, left_join_indexer, right_join_indexer, copy=copy
)
self._maybe_add_join_keys(result, left_indexer, right_indexer)
return result
def _asof_by_function(direction: str):
name = f"asof_join_{direction}_on_X_by_Y"
return getattr(libjoin, name, None)
_type_casters = {
"int64_t": ensure_int64,
"double": ensure_float64,
"object": ensure_object,
}
def _get_cython_type_upcast(dtype: DtypeObj) -> str:
"""Upcast a dtype to 'int64_t', 'double', or 'object'"""
if is_integer_dtype(dtype):
return "int64_t"
elif is_float_dtype(dtype):
return "double"
else:
return "object"
class _AsOfMerge(_OrderedMerge):
_merge_type = "asof_merge"
def __init__(
self,
left: DataFrame | Series,
right: DataFrame | Series,
on: IndexLabel | None = None,
left_on: IndexLabel | None = None,
right_on: IndexLabel | None = None,
left_index: bool = False,
right_index: bool = False,
by=None,
left_by=None,
right_by=None,
axis: AxisInt = 1,
suffixes: Suffixes = ("_x", "_y"),
copy: bool = True,
fill_method: str | None = None,
how: Literal["asof"] = "asof",
tolerance=None,
allow_exact_matches: bool = True,
direction: str = "backward",
) -> None:
self.by = by
self.left_by = left_by
self.right_by = right_by
self.tolerance = tolerance
self.allow_exact_matches = allow_exact_matches
self.direction = direction
_OrderedMerge.__init__(
self,
left,
right,
on=on,
left_on=left_on,
right_on=right_on,
left_index=left_index,
right_index=right_index,
axis=axis,
how=how,
suffixes=suffixes,
fill_method=fill_method,
)
def _validate_left_right_on(self, left_on, right_on):
left_on, right_on = super()._validate_left_right_on(left_on, right_on)
# we only allow on to be a single item for on
if len(left_on) != 1 and not self.left_index:
raise MergeError("can only asof on a key for left")
if len(right_on) != 1 and not self.right_index:
raise MergeError("can only asof on a key for right")
if self.left_index and isinstance(self.left.index, MultiIndex):
raise MergeError("left can only have one index")
if self.right_index and isinstance(self.right.index, MultiIndex):
raise MergeError("right can only have one index")
# set 'by' columns
if self.by is not None:
if self.left_by is not None or self.right_by is not None:
raise MergeError("Can only pass by OR left_by and right_by")
self.left_by = self.right_by = self.by
if self.left_by is None and self.right_by is not None:
raise MergeError("missing left_by")
if self.left_by is not None and self.right_by is None:
raise MergeError("missing right_by")
# GH#29130 Check that merge keys do not have dtype object
if not self.left_index:
left_on_0 = left_on[0]
if is_array_like(left_on_0):
lo_dtype = left_on_0.dtype
else:
lo_dtype = (
self.left._get_label_or_level_values(left_on_0).dtype
if left_on_0 in self.left.columns
else self.left.index.get_level_values(left_on_0)
)
else:
lo_dtype = self.left.index.dtype
if not self.right_index:
right_on_0 = right_on[0]
if is_array_like(right_on_0):
ro_dtype = right_on_0.dtype
else:
ro_dtype = (
self.right._get_label_or_level_values(right_on_0).dtype
if right_on_0 in self.right.columns
else self.right.index.get_level_values(right_on_0)
)
else:
ro_dtype = self.right.index.dtype
if is_object_dtype(lo_dtype) or is_object_dtype(ro_dtype):
raise MergeError(
f"Incompatible merge dtype, {repr(ro_dtype)} and "
f"{repr(lo_dtype)}, both sides must have numeric dtype"
)
# add 'by' to our key-list so we can have it in the
# output as a key
if self.left_by is not None:
if not is_list_like(self.left_by):
self.left_by = [self.left_by]
if not is_list_like(self.right_by):
self.right_by = [self.right_by]
if len(self.left_by) != len(self.right_by):
raise MergeError("left_by and right_by must be same length")
left_on = self.left_by + list(left_on)
right_on = self.right_by + list(right_on)
# check 'direction' is valid
if self.direction not in ["backward", "forward", "nearest"]:
raise MergeError(f"direction invalid: {self.direction}")
return left_on, right_on
def _get_merge_keys(
self,
) -> tuple[list[AnyArrayLike], list[AnyArrayLike], list[Hashable]]:
# note this function has side effects
(left_join_keys, right_join_keys, join_names) = super()._get_merge_keys()
# validate index types are the same
for i, (lk, rk) in enumerate(zip(left_join_keys, right_join_keys)):
if not is_dtype_equal(lk.dtype, rk.dtype):
if is_categorical_dtype(lk.dtype) and is_categorical_dtype(rk.dtype):
# The generic error message is confusing for categoricals.
#
# In this function, the join keys include both the original
# ones of the merge_asof() call, and also the keys passed
# to its by= argument. Unordered but equal categories
# are not supported for the former, but will fail
# later with a ValueError, so we don't *need* to check
# for them here.
msg = (
f"incompatible merge keys [{i}] {repr(lk.dtype)} and "
f"{repr(rk.dtype)}, both sides category, but not equal ones"
)
else:
msg = (
f"incompatible merge keys [{i}] {repr(lk.dtype)} and "
f"{repr(rk.dtype)}, must be the same type"
)
raise MergeError(msg)
# validate tolerance; datetime.timedelta or Timedelta if we have a DTI
if self.tolerance is not None:
if self.left_index:
# Actually more specifically an Index
lt = cast(AnyArrayLike, self.left.index)
else:
lt = left_join_keys[-1]
msg = (
f"incompatible tolerance {self.tolerance}, must be compat "
f"with type {repr(lt.dtype)}"
)
if needs_i8_conversion(lt):
if not isinstance(self.tolerance, datetime.timedelta):
raise MergeError(msg)
if self.tolerance < Timedelta(0):
raise MergeError("tolerance must be positive")
elif is_integer_dtype(lt):
if not is_integer(self.tolerance):
raise MergeError(msg)
if self.tolerance < 0:
raise MergeError("tolerance must be positive")
elif is_float_dtype(lt):
if not is_number(self.tolerance):
raise MergeError(msg)
if self.tolerance < 0:
raise MergeError("tolerance must be positive")
else:
raise MergeError("key must be integer, timestamp or float")
# validate allow_exact_matches
if not is_bool(self.allow_exact_matches):
msg = (
"allow_exact_matches must be boolean, "
f"passed {self.allow_exact_matches}"
)
raise MergeError(msg)
return left_join_keys, right_join_keys, join_names
def _get_join_indexers(self) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]:
"""return the join indexers"""
def flip(xs) -> np.ndarray:
"""unlike np.transpose, this returns an array of tuples"""
def injection(obj):
if not is_extension_array_dtype(obj):
# ndarray
return obj
obj = extract_array(obj)
if isinstance(obj, NDArrayBackedExtensionArray):
# fastpath for e.g. dt64tz, categorical
return obj._ndarray
# FIXME: returning obj._values_for_argsort() here doesn't
# break in any existing test cases, but i (@jbrockmendel)
# am pretty sure it should!
# e.g.
# arr = pd.array([0, pd.NA, 255], dtype="UInt8")
# will have values_for_argsort (before GH#45434)
# np.array([0, 255, 255], dtype=np.uint8)
# and the non-injectivity should make a difference somehow
# shouldn't it?
return np.asarray(obj)
xs = [injection(x) for x in xs]
labels = list(string.ascii_lowercase[: len(xs)])
dtypes = [x.dtype for x in xs]
labeled_dtypes = list(zip(labels, dtypes))
return np.array(list(zip(*xs)), labeled_dtypes)
# values to compare
left_values = (
self.left.index._values if self.left_index else self.left_join_keys[-1]
)
right_values = (
self.right.index._values if self.right_index else self.right_join_keys[-1]
)
tolerance = self.tolerance
# we require sortedness and non-null values in the join keys
if not Index(left_values).is_monotonic_increasing:
side = "left"
if isna(left_values).any():
raise ValueError(f"Merge keys contain null values on {side} side")
raise ValueError(f"{side} keys must be sorted")
if not Index(right_values).is_monotonic_increasing:
side = "right"
if isna(right_values).any():
raise ValueError(f"Merge keys contain null values on {side} side")
raise ValueError(f"{side} keys must be sorted")
# initial type conversion as needed
if needs_i8_conversion(left_values):
if tolerance is not None:
tolerance = Timedelta(tolerance)
# TODO: we have no test cases with PeriodDtype here; probably
# need to adjust tolerance for that case.
if left_values.dtype.kind in ["m", "M"]:
# Make sure the i8 representation for tolerance
# matches that for left_values/right_values.
lvs = ensure_wrapped_if_datetimelike(left_values)
tolerance = tolerance.as_unit(lvs.unit)
tolerance = tolerance._value
# TODO: require left_values.dtype == right_values.dtype, or at least
# comparable for e.g. dt64tz
left_values = left_values.view("i8")
right_values = right_values.view("i8")
# a "by" parameter requires special handling
if self.left_by is not None:
# remove 'on' parameter from values if one existed
if self.left_index and self.right_index:
left_by_values = self.left_join_keys
right_by_values = self.right_join_keys
else:
left_by_values = self.left_join_keys[0:-1]
right_by_values = self.right_join_keys[0:-1]
# get tuple representation of values if more than one
if len(left_by_values) == 1:
lbv = left_by_values[0]
rbv = right_by_values[0]
else:
# We get here with non-ndarrays in test_merge_by_col_tz_aware
# and test_merge_groupby_multiple_column_with_categorical_column
lbv = flip(left_by_values)
rbv = flip(right_by_values)
# upcast 'by' parameter because HashTable is limited
by_type = _get_cython_type_upcast(lbv.dtype)
by_type_caster = _type_casters[by_type]
# error: Incompatible types in assignment (expression has type
# "ndarray[Any, dtype[generic]]", variable has type
# "List[Union[Union[ExtensionArray, ndarray[Any, Any]], Index, Series]]")
left_by_values = by_type_caster(lbv) # type: ignore[assignment]
# error: Incompatible types in assignment (expression has type
# "ndarray[Any, dtype[generic]]", variable has type
# "List[Union[Union[ExtensionArray, ndarray[Any, Any]], Index, Series]]")
right_by_values = by_type_caster(rbv) # type: ignore[assignment]
# choose appropriate function by type
func = _asof_by_function(self.direction)
return func(
left_values,
right_values,
left_by_values,
right_by_values,
self.allow_exact_matches,
tolerance,
)
else:
# choose appropriate function by type
func = _asof_by_function(self.direction)
# TODO(cython3):
# Bug in beta1 preventing Cython from choosing
# right specialization when one fused memview is None
# Doesn't matter what type we choose
# (nothing happens anyways since it is None)
# GH 51640
return func[f"{left_values.dtype}_t", object](
left_values,
right_values,
None,
None,
self.allow_exact_matches,
tolerance,
False,
)
def _get_multiindex_indexer(
join_keys, index: MultiIndex, sort: bool
) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]:
# left & right join labels and num. of levels at each location
mapped = (
_factorize_keys(index.levels[n], join_keys[n], sort=sort)
for n in range(index.nlevels)
)
zipped = zip(*mapped)
rcodes, lcodes, shape = (list(x) for x in zipped)
if sort:
rcodes = list(map(np.take, rcodes, index.codes))
else:
i8copy = lambda a: a.astype("i8", subok=False, copy=True)
rcodes = list(map(i8copy, index.codes))
# fix right labels if there were any nulls
for i, join_key in enumerate(join_keys):
mask = index.codes[i] == -1
if mask.any():
# check if there already was any nulls at this location
# if there was, it is factorized to `shape[i] - 1`
a = join_key[lcodes[i] == shape[i] - 1]
if a.size == 0 or not a[0] != a[0]:
shape[i] += 1
rcodes[i][mask] = shape[i] - 1
# get flat i8 join keys
lkey, rkey = _get_join_keys(lcodes, rcodes, tuple(shape), sort)
# factorize keys to a dense i8 space
lkey, rkey, count = _factorize_keys(lkey, rkey, sort=sort)
return libjoin.left_outer_join(lkey, rkey, count, sort=sort)
def _get_single_indexer(
join_key, index: Index, sort: bool = False
) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]:
left_key, right_key, count = _factorize_keys(join_key, index._values, sort=sort)
return libjoin.left_outer_join(left_key, right_key, count, sort=sort)
def _get_empty_indexer() -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]:
"""Return empty join indexers."""
return (
np.array([], dtype=np.intp),
np.array([], dtype=np.intp),
)
def _get_no_sort_one_missing_indexer(
n: int, left_missing: bool
) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]:
"""
Return join indexers where all of one side is selected without sorting
and none of the other side is selected.
Parameters
----------
n : int
Length of indexers to create.
left_missing : bool
If True, the left indexer will contain only -1's.
If False, the right indexer will contain only -1's.
Returns
-------
np.ndarray[np.intp]
Left indexer
np.ndarray[np.intp]
Right indexer
"""
idx = np.arange(n, dtype=np.intp)
idx_missing = np.full(shape=n, fill_value=-1, dtype=np.intp)
if left_missing:
return idx_missing, idx
return idx, idx_missing
def _left_join_on_index(
left_ax: Index, right_ax: Index, join_keys, sort: bool = False
) -> tuple[Index, npt.NDArray[np.intp] | None, npt.NDArray[np.intp]]:
if len(join_keys) > 1:
if not (
isinstance(right_ax, MultiIndex) and len(join_keys) == right_ax.nlevels
):
raise AssertionError(
"If more than one join key is given then "
"'right_ax' must be a MultiIndex and the "
"number of join keys must be the number of levels in right_ax"
)
left_indexer, right_indexer = _get_multiindex_indexer(
join_keys, right_ax, sort=sort
)
else:
jkey = join_keys[0]
left_indexer, right_indexer = _get_single_indexer(jkey, right_ax, sort=sort)
if sort or len(left_ax) != len(left_indexer):
# if asked to sort or there are 1-to-many matches
join_index = left_ax.take(left_indexer)
return join_index, left_indexer, right_indexer
# left frame preserves order & length of its index
return left_ax, None, right_indexer
def _factorize_keys(
lk: ArrayLike,
rk: ArrayLike,
sort: bool = True,
how: MergeHow | Literal["asof"] = "inner",
) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp], int]:
"""
Encode left and right keys as enumerated types.
This is used to get the join indexers to be used when merging DataFrames.
Parameters
----------
lk : array-like
Left key.
rk : array-like
Right key.
sort : bool, defaults to True
If True, the encoding is done such that the unique elements in the
keys are sorted.
how : {‘left’, ‘right’, ‘outer’, ‘inner’}, default ‘inner’
Type of merge.
Returns
-------
np.ndarray[np.intp]
Left (resp. right if called with `key='right'`) labels, as enumerated type.
np.ndarray[np.intp]
Right (resp. left if called with `key='right'`) labels, as enumerated type.
int
Number of unique elements in union of left and right labels.
See Also
--------
merge : Merge DataFrame or named Series objects
with a database-style join.
algorithms.factorize : Encode the object as an enumerated type
or categorical variable.
Examples
--------
>>> lk = np.array(["a", "c", "b"])
>>> rk = np.array(["a", "c"])
Here, the unique values are `'a', 'b', 'c'`. With the default
`sort=True`, the encoding will be `{0: 'a', 1: 'b', 2: 'c'}`:
>>> pd.core.reshape.merge._factorize_keys(lk, rk)
(array([0, 2, 1]), array([0, 2]), 3)
With the `sort=False`, the encoding will correspond to the order
in which the unique elements first appear: `{0: 'a', 1: 'c', 2: 'b'}`:
>>> pd.core.reshape.merge._factorize_keys(lk, rk, sort=False)
(array([0, 1, 2]), array([0, 1]), 3)
"""
# Some pre-processing for non-ndarray lk / rk
lk = extract_array(lk, extract_numpy=True, extract_range=True)
rk = extract_array(rk, extract_numpy=True, extract_range=True)
# TODO: if either is a RangeIndex, we can likely factorize more efficiently?
if isinstance(lk.dtype, DatetimeTZDtype) and isinstance(rk.dtype, DatetimeTZDtype):
# Extract the ndarray (UTC-localized) values
# Note: we dont need the dtypes to match, as these can still be compared
lk, rk = cast("DatetimeArray", lk)._ensure_matching_resos(rk)
lk = cast("DatetimeArray", lk)._ndarray
rk = cast("DatetimeArray", rk)._ndarray
elif (
is_categorical_dtype(lk.dtype)
and is_categorical_dtype(rk.dtype)
and is_dtype_equal(lk.dtype, rk.dtype)
):
assert isinstance(lk, Categorical)
assert isinstance(rk, Categorical)
# Cast rk to encoding so we can compare codes with lk
rk = lk._encode_with_my_categories(rk)
lk = ensure_int64(lk.codes)
rk = ensure_int64(rk.codes)
elif isinstance(lk, ExtensionArray) and is_dtype_equal(lk.dtype, rk.dtype):
if not isinstance(lk, BaseMaskedArray) and not (
# exclude arrow dtypes that would get cast to object
isinstance(lk.dtype, ArrowDtype)
and is_numeric_dtype(lk.dtype.numpy_dtype)
):
lk, _ = lk._values_for_factorize()
# error: Item "ndarray" of "Union[Any, ndarray]" has no attribute
# "_values_for_factorize"
rk, _ = rk._values_for_factorize() # type: ignore[union-attr]
klass, lk, rk = _convert_arrays_and_get_rizer_klass(lk, rk)
rizer = klass(max(len(lk), len(rk)))
if isinstance(lk, BaseMaskedArray):
assert isinstance(rk, BaseMaskedArray)
llab = rizer.factorize(lk._data, mask=lk._mask)
rlab = rizer.factorize(rk._data, mask=rk._mask)
elif isinstance(lk, ArrowExtensionArray):
assert isinstance(rk, ArrowExtensionArray)
# we can only get here with numeric dtypes
# TODO: Remove when we have a Factorizer for Arrow
llab = rizer.factorize(
lk.to_numpy(na_value=1, dtype=lk.dtype.numpy_dtype), mask=lk.isna()
)
rlab = rizer.factorize(
rk.to_numpy(na_value=1, dtype=lk.dtype.numpy_dtype), mask=rk.isna()
)
else:
# Argument 1 to "factorize" of "ObjectFactorizer" has incompatible type
# "Union[ndarray[Any, dtype[signedinteger[_64Bit]]],
# ndarray[Any, dtype[object_]]]"; expected "ndarray[Any, dtype[object_]]"
llab = rizer.factorize(lk) # type: ignore[arg-type]
rlab = rizer.factorize(rk) # type: ignore[arg-type]
assert llab.dtype == np.dtype(np.intp), llab.dtype
assert rlab.dtype == np.dtype(np.intp), rlab.dtype
count = rizer.get_count()
if sort:
uniques = rizer.uniques.to_array()
llab, rlab = _sort_labels(uniques, llab, rlab)
# NA group
lmask = llab == -1
lany = lmask.any()
rmask = rlab == -1
rany = rmask.any()
if lany or rany:
if lany:
np.putmask(llab, lmask, count)
if rany:
np.putmask(rlab, rmask, count)
count += 1
if how == "right":
return rlab, llab, count
return llab, rlab, count
def _convert_arrays_and_get_rizer_klass(
lk: ArrayLike, rk: ArrayLike
) -> tuple[type[libhashtable.Factorizer], ArrayLike, ArrayLike]:
klass: type[libhashtable.Factorizer]
if is_numeric_dtype(lk.dtype):
if not is_dtype_equal(lk, rk):
dtype = find_common_type([lk.dtype, rk.dtype])
if isinstance(dtype, ExtensionDtype):
cls = dtype.construct_array_type()
if not isinstance(lk, ExtensionArray):
lk = cls._from_sequence(lk, dtype=dtype, copy=False)
else:
lk = lk.astype(dtype)
if not isinstance(rk, ExtensionArray):
rk = cls._from_sequence(rk, dtype=dtype, copy=False)
else:
rk = rk.astype(dtype)
else:
lk = lk.astype(dtype)
rk = rk.astype(dtype)
if isinstance(lk, BaseMaskedArray):
# Invalid index type "type" for "Dict[Type[object], Type[Factorizer]]";
# expected type "Type[object]"
klass = _factorizers[lk.dtype.type] # type: ignore[index]
elif isinstance(lk.dtype, ArrowDtype):
klass = _factorizers[lk.dtype.numpy_dtype.type]
else:
klass = _factorizers[lk.dtype.type]
else:
klass = libhashtable.ObjectFactorizer
lk = ensure_object(lk)
rk = ensure_object(rk)
return klass, lk, rk
def _sort_labels(
uniques: np.ndarray, left: npt.NDArray[np.intp], right: npt.NDArray[np.intp]
) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]:
llength = len(left)
labels = np.concatenate([left, right])
_, new_labels = algos.safe_sort(uniques, labels, use_na_sentinel=True)
new_left, new_right = new_labels[:llength], new_labels[llength:]
return new_left, new_right
def _get_join_keys(
llab: list[npt.NDArray[np.int64 | np.intp]],
rlab: list[npt.NDArray[np.int64 | np.intp]],
shape: Shape,
sort: bool,
) -> tuple[npt.NDArray[np.int64], npt.NDArray[np.int64]]:
# how many levels can be done without overflow
nlev = next(
lev
for lev in range(len(shape), 0, -1)
if not is_int64_overflow_possible(shape[:lev])
)
# get keys for the first `nlev` levels
stride = np.prod(shape[1:nlev], dtype="i8")
lkey = stride * llab[0].astype("i8", subok=False, copy=False)
rkey = stride * rlab[0].astype("i8", subok=False, copy=False)
for i in range(1, nlev):
with np.errstate(divide="ignore"):
stride //= shape[i]
lkey += llab[i] * stride
rkey += rlab[i] * stride
if nlev == len(shape): # all done!
return lkey, rkey
# densify current keys to avoid overflow
lkey, rkey, count = _factorize_keys(lkey, rkey, sort=sort)
llab = [lkey] + llab[nlev:]
rlab = [rkey] + rlab[nlev:]
shape = (count,) + shape[nlev:]
return _get_join_keys(llab, rlab, shape, sort)
def _should_fill(lname, rname) -> bool:
if not isinstance(lname, str) or not isinstance(rname, str):
return True
return lname == rname
def _any(x) -> bool:
return x is not None and com.any_not_none(*x)
def _validate_operand(obj: DataFrame | Series) -> DataFrame:
if isinstance(obj, ABCDataFrame):
return obj
elif isinstance(obj, ABCSeries):
if obj.name is None:
raise ValueError("Cannot merge a Series without a name")
return obj.to_frame()
else:
raise TypeError(
f"Can only merge Series or DataFrame objects, a {type(obj)} was passed"
)
def _items_overlap_with_suffix(
left: Index, right: Index, suffixes: Suffixes
) -> tuple[Index, Index]:
"""
Suffixes type validation.
If two indices overlap, add suffixes to overlapping entries.
If corresponding suffix is empty, the entry is simply converted to string.
"""
if not is_list_like(suffixes, allow_sets=False) or isinstance(suffixes, dict):
raise TypeError(
f"Passing 'suffixes' as a {type(suffixes)}, is not supported. "
"Provide 'suffixes' as a tuple instead."
)
to_rename = left.intersection(right)
if len(to_rename) == 0:
return left, right
lsuffix, rsuffix = suffixes
if not lsuffix and not rsuffix:
raise ValueError(f"columns overlap but no suffix specified: {to_rename}")
def renamer(x, suffix):
"""
Rename the left and right indices.
If there is overlap, and suffix is not None, add
suffix, otherwise, leave it as-is.
Parameters
----------
x : original column name
suffix : str or None
Returns
-------
x : renamed column name
"""
if x in to_rename and suffix is not None:
return f"{x}{suffix}"
return x
lrenamer = partial(renamer, suffix=lsuffix)
rrenamer = partial(renamer, suffix=rsuffix)
llabels = left._transform_index(lrenamer)
rlabels = right._transform_index(rrenamer)
dups = []
if not llabels.is_unique:
# Only warn when duplicates are caused because of suffixes, already duplicated
# columns in origin should not warn
dups = llabels[(llabels.duplicated()) & (~left.duplicated())].tolist()
if not rlabels.is_unique:
dups.extend(rlabels[(rlabels.duplicated()) & (~right.duplicated())].tolist())
if dups:
raise MergeError(
f"Passing 'suffixes' which cause duplicate columns {set(dups)} is "
f"not allowed.",
)
return llabels, rlabels
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