Source code for pandas.core.reshape.merge

"""
SQL-style merge routines
"""
from __future__ import annotations

import copy
import datetime
from functools import partial
import hashlib
import string
from typing import (
    TYPE_CHECKING,
    Hashable,
    cast,
)
import warnings

import numpy as np

from pandas._libs import (
    Timedelta,
    hashtable as libhashtable,
    join as libjoin,
    lib,
)
from pandas._typing import (
    ArrayLike,
    DtypeObj,
    FrameOrSeries,
    IndexLabel,
    Suffixes,
)
from pandas.errors import MergeError
from pandas.util._decorators import (
    Appender,
    Substitution,
)

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_datetime64tz_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.generic import (
    ABCDataFrame,
    ABCSeries,
)
from pandas.core.dtypes.missing import (
    isna,
    na_value_for_dtype,
)

from pandas import (
    Categorical,
    Index,
    MultiIndex,
    Series,
)
from pandas.core import groupby
import pandas.core.algorithms as algos
from pandas.core.arrays import ExtensionArray
import pandas.core.common as com
from pandas.core.construction import extract_array
from pandas.core.frame import _merge_doc
from pandas.core.internals import concatenate_managers
from pandas.core.sorting import is_int64_overflow_possible

if TYPE_CHECKING:
    from pandas import DataFrame
    from pandas.core.arrays import DatetimeArray


[docs]@Substitution("\nleft : DataFrame or named Series") @Appender(_merge_doc, indents=0) def merge( left: DataFrame | Series, right: DataFrame | Series, how: str = "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 = True, indicator: 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, copy=copy, indicator=indicator, validate=validate, ) return op.get_result()
if __debug__: merge.__doc__ = _merge_doc % "\nleft : DataFrame" 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: 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: str = "outer", ) -> DataFrame: """ Perform merge with optional filling/interpolation. Designed for ordered data like time series data. Optionally perform group-wise merge (see examples). Parameters ---------- left : DataFrame right : DataFrame 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. right_by : column name or list of column names Group right DataFrame by group columns and merge piece by piece with left DataFrame. 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. .. versionchanged:: 0.25.0 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 the be 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 -------- >>> 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") elif 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
[docs]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 an asof merge. 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 ------- merged : 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" def __init__( self, left: DataFrame | Series, right: DataFrame | Series, how: str = "inner", on: IndexLabel | None = None, left_on: IndexLabel | None = None, right_on: IndexLabel | None = None, axis: int = 1, left_index: bool = False, right_index: bool = False, sort: bool = True, suffixes: Suffixes = ("_x", "_y"), copy: bool = True, indicator: bool = False, validate: str | 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.left_on = com.maybe_make_list(left_on) self.right_on = com.maybe_make_list(right_on) self.copy = copy self.suffixes = suffixes self.sort = sort self.left_index = left_index self.right_index = right_index self.indicator = indicator self.indicator_name: str | None if isinstance(self.indicator, str): self.indicator_name = self.indicator elif isinstance(self.indicator, bool): self.indicator_name = "_merge" if self.indicator else None else: raise ValueError( "indicator option can only accept boolean or string arguments" ) 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)}" ) # warn user when merging between different levels if _left.columns.nlevels != _right.columns.nlevels: msg = ( "merging between different levels is deprecated and will be removed " f"in a future version. ({left.columns.nlevels} levels on the left," f"{right.columns.nlevels} on the right)" ) # stacklevel chosen to be correct when this is reached via pd.merge # (and not DataFrame.join) warnings.warn(msg, FutureWarning, stacklevel=3) self._validate_specification() 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 get_result(self) -> 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() llabels, rlabels = _items_overlap_with_suffix( self.left._info_axis, self.right._info_axis, self.suffixes ) lindexers = {1: left_indexer} if left_indexer is not None else {} rindexers = {1: right_indexer} if right_indexer is not None else {} result_data = concatenate_managers( [(self.left._mgr, lindexers), (self.right._mgr, rindexers)], axes=[llabels.append(rlabels), join_index], concat_axis=0, copy=self.copy, ) typ = self.left._constructor result = typ(result_data).__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: result.drop(columns=cross_col, inplace=True) 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 ( self.orig_left._is_level_reference(left_key) and self.orig_right._is_level_reference(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 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 and is_array_like(self.left_join_keys[i]): 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? take_right = extract_array(take_right, extract_numpy=True) rfill = na_value_for_dtype(take_right.dtype) rvals = algos.take_nd(take_right, 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 mask_right = right_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 # error: Item "bool" of "Union[Any, bool]" has no attribute "all" elif ( right_indexer is not None and mask_right.all() # type: ignore[union-attr] ): 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 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[np.ndarray, np.ndarray]: """return the join indexers""" # Both returned ndarrays are np.intp 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, np.ndarray | None, np.ndarray | None]: # Both returned ndarrays are np.intp (if not None) 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) left_indexer = np.array([-1] * len(join_index), dtype=np.intp) 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) right_indexer = np.array([-1] * len(join_index), dtype=np.intp) else: join_index = Index(np.arange(len(left_indexer))) if len(join_index) == 0: join_index = join_index.astype(object) return join_index, left_indexer, right_indexer def _create_join_index( self, index: Index, other_index: Index, indexer: np.ndarray, how: str = "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): """ Note: has side effects (copy/delete key columns) Parameters ---------- left right on Returns ------- left_keys, right_keys """ left_keys = [] right_keys = [] # error: Need type annotation for 'join_names' (hint: "join_names: List[<type>] # = ...") join_names = [] # type: ignore[var-annotated] right_drop = [] left_drop = [] 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_specification() 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): left_keys.append(lk) if is_rkey(rk): right_keys.append(rk) join_names.append(None) # what to do? else: 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): 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: # avoid key upcast in corner case (length-0) if len(left) > 0: right_drop.append(rk) else: left_drop.append(lk) else: right_keys.append(rk) if lk is not None: 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): left_keys.append(k) join_names.append(None) else: 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): right_keys.append(k) join_names.append(None) else: 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_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: 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 elif is_integer_dtype(rk.dtype) and is_float_dtype(lk.dtype): if not (lk == lk.astype(rk.dtype))[~np.isnan(lk)].all(): warnings.warn( "You are merging on int and float " "columns where the float values " "are not equal to their int representation", UserWarning, ) continue elif is_float_dtype(rk.dtype) and is_integer_dtype(lk.dtype): if not (rk == rk.astype(lk.dtype))[~np.isnan(rk)].all(): warnings.warn( "You are merging on int and float " "columns where the float values " "are not equal to their int representation", UserWarning, ) continue # let's infer and see if we are ok elif 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 is_datetime64tz_dtype(lk.dtype) and not is_datetime64tz_dtype( rk.dtype ): raise ValueError(msg) elif not is_datetime64tz_dtype(lk.dtype) and is_datetime64tz_dtype( rk.dtype ): raise ValueError(msg) 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 = lk.categories.dtype if lk_is_cat else object self.left = self.left.assign(**{name: self.left[name].astype(typ)}) if name in self.right.columns: typ = rk.categories.dtype if rk_is_cat else object self.right = self.right.assign(**{name: self.right[name].astype(typ)}) def _create_cross_configuration( self, left: DataFrame, right: DataFrame ) -> tuple[DataFrame, DataFrame, str, 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_{hashlib.md5().hexdigest()}" how = "inner" return ( left.assign(**{cross_col: 1}), right.assign(**{cross_col: 1}), how, cross_col, ) def _validate_specification(self) -> None: if self.how == "cross": if ( self.left_index or self.right_index or self.right_on is not None or self.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" ) return # Hm, any way to make this logic less complicated?? elif self.on is None and self.left_on is None and self.right_on is None: if self.left_index and self.right_index: self.left_on, self.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={self.left_on}, " f"right_on={self.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)}") self.left_on = self.right_on = common_cols elif self.on is not None: if self.left_on is not None or self.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.' ) self.left_on = self.right_on = self.on elif self.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 self.right_on is None: raise MergeError('Must pass "right_on" OR "right_index".') n = len(self.left_on) if self.right_index: if len(self.left_on) != self.right.index.nlevels: raise ValueError( "len(left_on) must equal the number " 'of levels in the index of "right"' ) self.right_on = [None] * n elif self.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 self.left_on is None: raise MergeError('Must pass "left_on" OR "left_index".') n = len(self.right_on) if self.left_index: if len(self.right_on) != self.left.index.nlevels: raise ValueError( "len(right_on) must equal the number " 'of levels in the index of "left"' ) self.left_on = [None] * n if self.how != "cross" and len(self.right_on) != len(self.left_on): raise ValueError("len(right_on) must equal len(left_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" ) elif not left_unique: raise MergeError( "Merge keys are not unique in left dataset; not a one-to-one merge" ) elif 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("Not a valid argument for validate") def get_join_indexers( left_keys, right_keys, sort: bool = False, how: str = "inner", **kwargs ) -> tuple[np.ndarray, np.ndarray]: """ 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" # 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, 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 = copy.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: np.ndarray, rindexer: np.ndarray, ) -> tuple[list[Index], np.ndarray, 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 lidx, 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 : intp array 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 # lindexer and rindexer hold the indexes where the join occurred # for left and right respectively. If left/right is None then # the join occurred on all indices of left/right if lindexer is None: lindexer = range(left.size) if rindexer is None: rindexer = range(right.size) # 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] restore_codes = algos.take_nd(codes, indexer, fill_value=-1) join_levels = join_levels + [restore_levels] 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: int = 1, suffixes: Suffixes = ("_x", "_y"), copy: bool = True, fill_method: str | None = None, how: str = "outer", ): 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) -> 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": # error: Argument 1 to "ffill_indexer" has incompatible type # "Optional[ndarray]"; expected "ndarray" left_join_indexer = libjoin.ffill_indexer( left_indexer # type: ignore[arg-type] ) # error: Argument 1 to "ffill_indexer" has incompatible type # "Optional[ndarray]"; expected "ndarray" right_join_indexer = libjoin.ffill_indexer( right_indexer # type: ignore[arg-type] ) else: left_join_indexer = left_indexer right_join_indexer = right_indexer lindexers = {1: left_join_indexer} if left_join_indexer is not None else {} rindexers = {1: right_join_indexer} if right_join_indexer is not None else {} result_data = concatenate_managers( [(self.left._mgr, lindexers), (self.right._mgr, rindexers)], axes=[llabels.append(rlabels), join_index], concat_axis=0, copy=self.copy, ) typ = self.left._constructor result = typ(result_data) self._maybe_add_join_keys(result, left_indexer, right_indexer) return result def _asof_function(direction: str): name = f"asof_join_{direction}" return getattr(libjoin, name, None) 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: int = 1, suffixes: Suffixes = ("_x", "_y"), copy: bool = True, fill_method: str | None = None, how: str = "asof", tolerance=None, allow_exact_matches: bool = True, direction: str = "backward", ): 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_specification(self) -> None: super()._validate_specification() # we only allow on to be a single item for on if len(self.left_on) != 1 and not self.left_index: raise MergeError("can only asof on a key for left") if len(self.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 = self.left_on[0] lo_dtype = ( self.left[left_on].dtype if left_on in self.left.columns else self.left.index.get_level_values(left_on) ) else: lo_dtype = self.left.index.dtype if not self.right_index: right_on = self.right_on[0] ro_dtype = ( self.right[right_on].dtype if right_on in self.right.columns else self.right.index.get_level_values(right_on) ) 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") self.left_on = self.left_by + list(self.left_on) self.right_on = self.right_by + list(self.right_on) # check 'direction' is valid if self.direction not in ["backward", "forward", "nearest"]: raise MergeError(f"direction invalid: {self.direction}") def _get_merge_keys(self): # 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: lt = 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[np.ndarray, np.ndarray]: # Both returned ndarrays are np.intp """return the join indexers""" def flip(xs) -> np.ndarray: """unlike np.transpose, this returns an array of tuples""" # error: Item "ndarray" of "Union[Any, Union[ExtensionArray, ndarray]]" has # no attribute "_values_for_argsort" xs = [ x if not is_extension_array_dtype(x) else extract_array(x)._values_for_argsort() # type: ignore[union-attr] 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: side = "left" if isna(left_values).any(): raise ValueError(f"Merge keys contain null values on {side} side") else: raise ValueError(f"{side} keys must be sorted") if not Index(right_values).is_monotonic: side = "right" if isna(right_values).any(): raise ValueError(f"Merge keys contain null values on {side} side") else: raise ValueError(f"{side} keys must be sorted") # initial type conversion as needed if needs_i8_conversion(left_values): left_values = left_values.view("i8") right_values = right_values.view("i8") if tolerance is not None: tolerance = Timedelta(tolerance) tolerance = tolerance.value # 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: left_by_values = left_by_values[0] right_by_values = right_by_values[0] else: left_by_values = flip(left_by_values) right_by_values = flip(right_by_values) # upcast 'by' parameter because HashTable is limited by_type = _get_cython_type_upcast(left_by_values.dtype) by_type_caster = _type_casters[by_type] # error: Cannot call function of unknown type left_by_values = by_type_caster(left_by_values) # type: ignore[operator] # error: Cannot call function of unknown type right_by_values = by_type_caster(right_by_values) # type: ignore[operator] # 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_function(self.direction) return func(left_values, right_values, self.allow_exact_matches, tolerance) def _get_multiindex_indexer( join_keys, index: MultiIndex, sort: bool ) -> tuple[np.ndarray, np.ndarray]: # Both returned ndarrays are 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 in range(len(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_keys[i][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, 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[np.ndarray, np.ndarray]: # Both returned ndarrays are 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 _left_join_on_index( left_ax: Index, right_ax: Index, join_keys, sort: bool = False ) -> tuple[Index, np.ndarray | None, np.ndarray]: # Both returned ndarrays are np.intp (if not None) 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: str = "inner" ) -> tuple[np.ndarray, np.ndarray, 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 is_datetime64tz_dtype(lk.dtype) and is_datetime64tz_dtype(rk.dtype): # Extract the ndarray (UTC-localized) values # Note: we dont need the dtypes to match, as these can still be compared 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): # error: Incompatible types in assignment (expression has type "ndarray", # variable has type "ExtensionArray") lk, _ = lk._values_for_factorize() # error: Incompatible types in assignment (expression has type # "ndarray", variable has type "ExtensionArray") # error: Item "ndarray" of "Union[Any, ndarray]" has no attribute # "_values_for_factorize" rk, _ = rk._values_for_factorize() # type: ignore[union-attr,assignment] klass: type[libhashtable.Factorizer] | type[libhashtable.Int64Factorizer] if is_integer_dtype(lk.dtype) and is_integer_dtype(rk.dtype): # GH#23917 TODO: needs tests for case where lk is integer-dtype # and rk is datetime-dtype klass = libhashtable.Int64Factorizer lk = ensure_int64(np.asarray(lk)) rk = ensure_int64(np.asarray(rk)) elif needs_i8_conversion(lk.dtype) and is_dtype_equal(lk.dtype, rk.dtype): # GH#23917 TODO: Needs tests for non-matching dtypes klass = libhashtable.Int64Factorizer lk = ensure_int64(np.asarray(lk, dtype=np.int64)) rk = ensure_int64(np.asarray(rk, dtype=np.int64)) else: klass = libhashtable.ObjectFactorizer lk = ensure_object(lk) rk = ensure_object(rk) rizer = klass(max(len(lk), len(rk))) llab = rizer.factorize(lk) rlab = rizer.factorize(rk) assert llab.dtype == np.intp, llab.dtype assert rlab.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 _sort_labels( uniques: np.ndarray, left: np.ndarray, right: np.ndarray ) -> tuple[np.ndarray, np.ndarray]: # Both returned ndarrays are np.intp llength = len(left) labels = np.concatenate([left, right]) _, new_labels = algos.safe_sort(uniques, labels, na_sentinel=-1) assert new_labels.dtype == np.intp new_left, new_right = new_labels[:llength], new_labels[llength:] return new_left, new_right def _get_join_keys(llab, rlab, shape, sort: bool): # 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: FrameOrSeries) -> 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") else: 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): warnings.warn( f"Passing 'suffixes' as a {type(suffixes)}, is not supported and may give " "unexpected results. Provide 'suffixes' as a tuple instead. In the " "future a 'TypeError' will be raised.", FutureWarning, stacklevel=4, ) 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: warnings.warn( f"Passing 'suffixes' which cause duplicate columns {set(dups)} in the " f"result is deprecated and will raise a MergeError in a future version.", FutureWarning, stacklevel=4, ) return llabels, rlabels