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"""
SQL-style merge routines
"""
import copy
import warnings
import string
import numpy as np
from pandas.compat import range, lzip, zip, map, filter
import pandas.compat as compat
from pandas import (Categorical, Series, DataFrame,
Index, MultiIndex, Timedelta)
from pandas.core.frame import _merge_doc
from pandas.core.dtypes.common import (
is_datetime64tz_dtype,
is_datetime64_dtype,
needs_i8_conversion,
is_int64_dtype,
is_categorical_dtype,
is_integer_dtype,
is_float_dtype,
is_numeric_dtype,
is_integer,
is_int_or_datetime_dtype,
is_dtype_equal,
is_bool,
is_list_like,
_ensure_int64,
_ensure_float64,
_ensure_object,
_get_dtype)
from pandas.core.dtypes.missing import na_value_for_dtype
from pandas.core.internals import (items_overlap_with_suffix,
concatenate_block_managers)
from pandas.util._decorators import Appender, Substitution
from pandas.core.sorting import is_int64_overflow_possible
import pandas.core.algorithms as algos
import pandas.core.sorting as sorting
import pandas.core.common as com
from pandas._libs import hashtable as libhashtable, join as libjoin, lib
from pandas.errors import MergeError
@Substitution('\nleft : DataFrame')
@Appender(_merge_doc, indents=0)
def merge(left, right, how='inner', on=None, left_on=None, right_on=None,
left_index=False, right_index=False, sort=False,
suffixes=('_x', '_y'), copy=True, indicator=False,
validate=None):
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, on, left, right, _merge_pieces,
check_duplicates=True):
"""
groupby & merge; we are always performing a left-by type operation
Parameters
----------
by: field to group
on: duplicates field
left: left frame
right: right frame
_merge_pieces: function for merging
check_duplicates: boolean, default True
should we check & clean duplicates
"""
pieces = []
if not isinstance(by, (list, tuple)):
by = [by]
lby = left.groupby(by, sort=False)
# if we can groupby the rhs
# then we can get vastly better perf
try:
# we will check & remove duplicates if indicated
if check_duplicates:
if on is None:
on = []
elif not isinstance(on, (list, tuple)):
on = [on]
if right.duplicated(by + on).any():
right = right.drop_duplicates(by + on, keep='last')
rby = right.groupby(by, sort=False)
except KeyError:
rby = None
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?
for k in by:
try:
if k in merged:
merged[k] = key
except KeyError:
pass
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 ordered_merge(left, right, on=None,
left_on=None, right_on=None,
left_by=None, right_by=None,
fill_method=None, suffixes=('_x', '_y')):
warnings.warn("ordered_merge is deprecated and replaced by merge_ordered",
FutureWarning, stacklevel=2)
return merge_ordered(left, right, on=on,
left_on=left_on, right_on=right_on,
left_by=left_by, right_by=right_by,
fill_method=fill_method, suffixes=suffixes)
def merge_ordered(left, right, on=None,
left_on=None, right_on=None,
left_by=None, right_by=None,
fill_method=None, suffixes=('_x', '_y'),
how='outer'):
"""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 : 2-length sequence (tuple, list, ...)
Suffix to apply to overlapping column names in the left and right
side, respectively
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)
.. versionadded:: 0.19.0
Examples
--------
>>> A >>> B
key lvalue group key rvalue
0 a 1 a 0 b 1
1 c 2 a 1 c 2
2 e 3 a 2 d 3
3 a 1 b
4 c 2 b
5 e 3 b
>>> ordered_merge(A, B, fill_method='ffill', left_by='group')
key lvalue group rvalue
0 a 1 a NaN
1 b 1 a 1
2 c 2 a 2
3 d 2 a 3
4 e 3 a 3
5 f 3 a 4
6 a 1 b NaN
7 b 1 b 1
8 c 2 b 2
9 d 2 b 3
10 e 3 b 3
11 f 3 b 4
Returns
-------
merged : DataFrame
The output type will the be same as 'left', if it is a subclass
of DataFrame.
See also
--------
merge
merge_asof
"""
def _merger(x, y):
# 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:
result, _ = _groupby_and_merge(left_by, on, left, right,
lambda x, y: _merger(x, y),
check_duplicates=False)
elif right_by is not None:
result, _ = _groupby_and_merge(right_by, on, right, left,
lambda x, y: _merger(y, x),
check_duplicates=False)
else:
result = _merger(left, right)
return result
ordered_merge.__doc__ = merge_ordered.__doc__
def merge_asof(left, right, on=None,
left_on=None, right_on=None,
left_index=False, right_index=False,
by=None, left_by=None, right_by=None,
suffixes=('_x', '_y'),
tolerance=None,
allow_exact_matches=True,
direction='backward'):
"""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'.
.. versionadded:: 0.19.0
Parameters
----------
left : DataFrame
right : DataFrame
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 : boolean
Use the index of the left DataFrame as the join key.
.. versionadded:: 0.19.2
right_index : boolean
Use the index of the right DataFrame as the join key.
.. versionadded:: 0.19.2
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.
.. versionadded:: 0.19.2
right_by : column name
Field names to match on in the right DataFrame.
.. versionadded:: 0.19.2
suffixes : 2-length sequence (tuple, list, ...)
Suffix to apply to overlapping column names in the left and right
side, respectively.
tolerance : integer or Timedelta, optional, default None
Select asof tolerance within this range; must be compatible
with the merge index.
allow_exact_matches : boolean, 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., stricly less-than / strictly greater-than)
direction : 'backward' (default), 'forward', or 'nearest'
Whether to search for prior, subsequent, or closest matches.
.. versionadded:: 0.20.0
Returns
-------
merged : DataFrame
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
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
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 720.77 100 720.50 720.93
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
See also
--------
merge
merge_ordered
"""
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(object):
"""
Perform a database (SQL) merge operation between two DataFrame objects
using either columns as keys or their row indexes
"""
_merge_type = 'merge'
def __init__(self, left, right, how='inner', on=None,
left_on=None, right_on=None, axis=1,
left_index=False, right_index=False, sort=True,
suffixes=('_x', '_y'), copy=True, indicator=False,
validate=None):
self.left = self.orig_left = left
self.right = self.orig_right = right
self.how = how
self.axis = axis
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
if isinstance(self.indicator, compat.string_types):
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 isinstance(left, DataFrame):
raise ValueError('can not merge DataFrame with instance of '
'type {left}'.format(left=type(left)))
if not isinstance(right, DataFrame):
raise ValueError('can not merge DataFrame with instance of '
'type {right}'.format(right=type(right)))
if not is_bool(left_index):
raise ValueError(
'left_index parameter must be of type bool, not '
'{left_index}'.format(left_index=type(left_index)))
if not is_bool(right_index):
raise ValueError(
'right_index parameter must be of type bool, not '
'{right_index}'.format(right_index=type(right_index)))
# warn user when merging between different levels
if left.columns.nlevels != right.columns.nlevels:
msg = ('merging between different levels can give an unintended '
'result ({left} levels on the left, {right} on the right)'
).format(left=left.columns.nlevels,
right=right.columns.nlevels)
warnings.warn(msg, UserWarning)
self._validate_specification()
# 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 incompat 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):
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()
ldata, rdata = self.left._data, self.right._data
lsuf, rsuf = self.suffixes
llabels, rlabels = items_overlap_with_suffix(ldata.items, lsuf,
rdata.items, rsuf)
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_block_managers(
[(ldata, lindexers), (rdata, 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)
return result
def _indicator_pre_merge(self, left, right):
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 "
"data contains a column named {name}"
.format(name=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):
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_add_join_keys(self, result, left_indexer, right_indexer):
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 isinstance(self.left_join_keys[i], np.ndarray):
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:
lfill = na_value_for_dtype(take_left.dtype)
lvals = algos.take_1d(take_left, left_indexer,
fill_value=lfill)
if take_right is None:
rvals = result[name]._values
else:
rfill = na_value_for_dtype(take_right.dtype)
rvals = algos.take_1d(take_right, right_indexer,
fill_value=rfill)
# if we have an all missing left_indexer
# make sure to just use the right values
mask = left_indexer == -1
if mask.all():
key_col = rvals
else:
key_col = Index(lvals).where(~mask, rvals)
if name in result:
result[name] = key_col
else:
result.insert(i, name or 'key_{i}'.format(i=i), key_col)
def _get_join_indexers(self):
""" 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):
left_ax = self.left._data.axes[self.axis]
right_ax = self.right._data.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.left.index.take(left_indexer)
else:
join_index = self.right.index.take(right_indexer)
left_indexer = np.array([-1] * len(join_index))
elif self.left_index:
if len(self.right) > 0:
join_index = self.right.index.take(right_indexer)
else:
join_index = self.left.index.take(left_indexer)
right_indexer = np.array([-1] * len(join_index))
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 _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 = []
join_names = []
right_drop = []
left_drop = []
left, right = self.left, self.right
is_lkey = lambda x: isinstance(
x, (np.ndarray, Series)) and len(x) == len(left)
is_rkey = lambda x: isinstance(
x, (np.ndarray, Series)) 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[rk]._values)
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[rk]._values)
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[lk]._values)
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[k]._values)
join_names.append(k)
if isinstance(self.right.index, MultiIndex):
right_keys = [lev._values.take(lab)
for lev, lab in zip(self.right.index.levels,
self.right.index.labels)]
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[k]._values)
join_names.append(k)
if isinstance(self.left.index, MultiIndex):
left_keys = [lev._values.take(lab)
for lev, lab in zip(self.left.index.levels,
self.left.index.labels)]
else:
left_keys = [self.left.index.values]
if left_drop:
self.left = self.left.drop(left_drop, axis=1)
if right_drop:
self.right = self.right.drop(right_drop, axis=1)
return left_keys, right_keys, join_names
def _maybe_coerce_merge_keys(self):
# we have valid mergees 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)
rk_is_cat = is_categorical_dtype(rk)
# 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.is_dtype_equal(rk):
continue
elif lk_is_cat or rk_is_cat:
pass
elif is_dtype_equal(lk.dtype, rk.dtype):
continue
# if we are numeric, then allow differing
# kinds to proceed, eg. int64 and int8
# further if we are object, but we infer to
# the same, then proceed
if is_numeric_dtype(lk) and is_numeric_dtype(rk):
if lk.dtype.kind == rk.dtype.kind:
continue
# let's infer and see if we are ok
if lib.infer_dtype(lk) == lib.infer_dtype(rk):
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 _validate_specification(self):
# Hm, any way to make this logic less complicated??
if 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:
if self.right_on is None:
raise MergeError('Must pass right_on or right_index=True')
elif self.right_index:
if self.left_on is None:
raise MergeError('Must pass left_on or left_index=True')
else:
# use the common columns
common_cols = self.left.columns.intersection(
self.right.columns)
if len(common_cols) == 0:
raise MergeError('No common columns to perform merge on')
if not common_cols.is_unique:
raise MergeError("Data columns not unique: {common!r}"
.format(common=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.')
self.left_on = self.right_on = self.on
elif self.left_on is not None:
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:
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 len(self.right_on) != len(self.left_on):
raise ValueError("len(right_on) must equal len(left_on)")
def _validate(self, validate):
# 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=False, how='inner',
**kwargs):
"""
Parameters
----------
left_keys: ndarray, Index, Series
right_keys: ndarray, Index, Series
sort: boolean, default False
how: string {'inner', 'outer', 'left', 'right'}, default 'inner'
Returns
-------
tuple of (left_indexer, right_indexer)
indexers into the left_keys, right_keys
"""
from functools import partial
assert len(left_keys) == len(right_keys), \
'left_key and right_keys must be the same length'
# bind `sort` arg. of _factorize_keys
fkeys = partial(_factorize_keys, sort=sort)
# get left & right join labels and num. of levels at each location
llab, rlab, shape = map(list, zip(* map(fkeys, left_keys, right_keys)))
# 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 = fkeys(lkey, rkey)
# preserve left frame order if how == 'left' and sort == False
kwargs = copy.copy(kwargs)
if how == 'left':
kwargs['sort'] = sort
join_func = _join_functions[how]
return join_func(lkey, rkey, count, **kwargs)
class _OrderedMerge(_MergeOperation):
_merge_type = 'ordered_merge'
def __init__(self, left, right, on=None, left_on=None, right_on=None,
left_index=False, right_index=False, axis=1,
suffixes=('_x', '_y'), copy=True,
fill_method=None, how='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):
join_index, left_indexer, right_indexer = self._get_join_info()
# this is a bit kludgy
ldata, rdata = self.left._data, self.right._data
lsuf, rsuf = self.suffixes
llabels, rlabels = items_overlap_with_suffix(ldata.items, lsuf,
rdata.items, rsuf)
if self.fill_method == 'ffill':
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
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_block_managers(
[(ldata, lindexers), (rdata, 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)
self._maybe_add_join_keys(result, left_indexer, right_indexer)
return result
def _asof_function(direction, on_type):
name = 'asof_join_{dir}_{on}'.format(dir=direction, on=on_type)
return getattr(libjoin, name, None)
def _asof_by_function(direction, on_type, by_type):
name = 'asof_join_{dir}_{on}_by_{by}'.format(
dir=direction, on=on_type, by=by_type)
return getattr(libjoin, name, None)
_type_casters = {
'int64_t': _ensure_int64,
'double': _ensure_float64,
'object': _ensure_object,
}
_cython_types = {
'uint8': 'uint8_t',
'uint32': 'uint32_t',
'uint16': 'uint16_t',
'uint64': 'uint64_t',
'int8': 'int8_t',
'int32': 'int32_t',
'int16': 'int16_t',
'int64': 'int64_t',
'float16': 'error',
'float32': 'float',
'float64': 'double',
}
def _get_cython_type(dtype):
""" Given a dtype, return a C name like 'int64_t' or 'double' """
type_name = _get_dtype(dtype).name
ctype = _cython_types.get(type_name, 'object')
if ctype == 'error':
raise MergeError('unsupported type: {type}'.format(type=type_name))
return ctype
def _get_cython_type_upcast(dtype):
""" 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, right, on=None, left_on=None, right_on=None,
left_index=False, right_index=False,
by=None, left_by=None, right_by=None,
axis=1, suffixes=('_x', '_y'), copy=True,
fill_method=None,
how='asof', tolerance=None,
allow_exact_matches=True,
direction='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):
super(_AsOfMerge, self)._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')
# 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('direction invalid: {direction}'
.format(direction=self.direction))
@property
def _asof_key(self):
""" This is our asof key, the 'on' """
return self.left_on[-1]
def _get_merge_keys(self):
# note this function has side effects
(left_join_keys,
right_join_keys,
join_names) = super(_AsOfMerge, self)._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):
raise MergeError("incompatible merge keys [{i}] {lkdtype} and "
"{rkdtype}, must be the same type"
.format(i=i, lkdtype=lk.dtype,
rkdtype=rk.dtype))
# validate tolerance; must be a 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 = ("incompatible tolerance {tolerance}, must be compat "
"with type {lkdtype}".format(
tolerance=type(self.tolerance),
lkdtype=lt.dtype))
if is_datetime64_dtype(lt) or is_datetime64tz_dtype(lt):
if not isinstance(self.tolerance, Timedelta):
raise MergeError(msg)
if self.tolerance < Timedelta(0):
raise MergeError("tolerance must be positive")
elif is_int64_dtype(lt):
if not is_integer(self.tolerance):
raise MergeError(msg)
if self.tolerance < 0:
raise MergeError("tolerance must be positive")
else:
raise MergeError("key must be integer or timestamp")
# validate allow_exact_matches
if not is_bool(self.allow_exact_matches):
msg = "allow_exact_matches must be boolean, passed {passed}"
raise MergeError(msg.format(passed=self.allow_exact_matches))
return left_join_keys, right_join_keys, join_names
def _get_join_indexers(self):
""" return the join indexers """
def flip(xs):
""" unlike np.transpose, this returns an array of tuples """
labels = list(string.ascii_lowercase[:len(xs)])
dtypes = [x.dtype for x in xs]
labeled_dtypes = list(zip(labels, dtypes))
return np.array(lzip(*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 required sortedness in the join keys
msg = "{side} keys must be sorted"
if not Index(left_values).is_monotonic:
raise ValueError(msg.format(side='left'))
if not Index(right_values).is_monotonic:
raise ValueError(msg.format(side='right'))
# 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 = 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]
left_by_values = by_type_caster(left_by_values)
right_by_values = by_type_caster(right_by_values)
# choose appropriate function by type
on_type = _get_cython_type(left_values.dtype)
func = _asof_by_function(self.direction, on_type, by_type)
return func(left_values,
right_values,
left_by_values,
right_by_values,
self.allow_exact_matches,
tolerance)
else:
# choose appropriate function by type
on_type = _get_cython_type(left_values.dtype)
func = _asof_function(self.direction, on_type)
return func(left_values,
right_values,
self.allow_exact_matches,
tolerance)
def _get_multiindex_indexer(join_keys, index, sort):
from functools import partial
# bind `sort` argument
fkeys = partial(_factorize_keys, sort=sort)
# left & right join labels and num. of levels at each location
rlab, llab, shape = map(list, zip(* map(fkeys, index.levels, join_keys)))
if sort:
rlab = list(map(np.take, rlab, index.labels))
else:
i8copy = lambda a: a.astype('i8', subok=False, copy=True)
rlab = list(map(i8copy, index.labels))
# fix right labels if there were any nulls
for i in range(len(join_keys)):
mask = index.labels[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][llab[i] == shape[i] - 1]
if a.size == 0 or not a[0] != a[0]:
shape[i] += 1
rlab[i][mask] = shape[i] - 1
# get flat i8 join keys
lkey, rkey = _get_join_keys(llab, rlab, shape, sort)
# factorize keys to a dense i8 space
lkey, rkey, count = fkeys(lkey, rkey)
return libjoin.left_outer_join(lkey, rkey, count, sort=sort)
def _get_single_indexer(join_key, index, sort=False):
left_key, right_key, count = _factorize_keys(join_key, index, sort=sort)
left_indexer, right_indexer = libjoin.left_outer_join(
_ensure_int64(left_key),
_ensure_int64(right_key),
count, sort=sort)
return left_indexer, right_indexer
def _left_join_on_index(left_ax, right_ax, join_keys, sort=False):
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 _right_outer_join(x, y, max_groups):
right_indexer, left_indexer = libjoin.left_outer_join(y, x, max_groups)
return left_indexer, right_indexer
_join_functions = {
'inner': libjoin.inner_join,
'left': libjoin.left_outer_join,
'right': _right_outer_join,
'outer': libjoin.full_outer_join,
}
def _factorize_keys(lk, rk, sort=True):
if is_datetime64tz_dtype(lk) and is_datetime64tz_dtype(rk):
lk = lk.values
rk = rk.values
# if we exactly match in categories, allow us to factorize on codes
if (is_categorical_dtype(lk) and
is_categorical_dtype(rk) and
lk.is_dtype_equal(rk)):
klass = libhashtable.Int64Factorizer
lk = _ensure_int64(lk.codes)
rk = _ensure_int64(rk.codes)
elif is_int_or_datetime_dtype(lk) and is_int_or_datetime_dtype(rk):
klass = libhashtable.Int64Factorizer
lk = _ensure_int64(com._values_from_object(lk))
rk = _ensure_int64(com._values_from_object(rk))
else:
klass = libhashtable.Factorizer
lk = _ensure_object(lk)
rk = _ensure_object(rk)
rizer = klass(max(len(lk), len(rk)))
llab = rizer.factorize(lk)
rlab = rizer.factorize(rk)
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
return llab, rlab, count
def _sort_labels(uniques, left, right):
if not isinstance(uniques, np.ndarray):
# tuplesafe
uniques = Index(uniques).values
llength = len(left)
labels = np.concatenate([left, right])
_, new_labels = sorting.safe_sort(uniques, labels, na_sentinel=-1)
new_labels = _ensure_int64(new_labels)
new_left, new_right = new_labels[:llength], new_labels[llength:]
return new_left, new_right
def _get_join_keys(llab, rlab, shape, sort):
# how many levels can be done without overflow
pred = lambda i: not is_int64_overflow_possible(shape[:i])
nlev = next(filter(pred, range(len(shape), 0, -1)))
# 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):
if (not isinstance(lname, compat.string_types) or
not isinstance(rname, compat.string_types)):
return True
return lname == rname
def _any(x):
return x is not None and com._any_not_none(*x)