8000 ENH: Add return_inverse to cython-unique; unify unique/factorize-code by h-vetinari · Pull Request #23400 · pandas-dev/pandas · GitHub
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ENH: Add return_inverse to cython-unique; unify unique/factorize-code #23400

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Merged
merged 28 commits into from
Nov 29, 2018
Merged

ENH: Add return_inverse to cython-unique; unify unique/factorize-code #23400

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858f54e
Unify unique/factorize, remove kwargs (perf); enable inverse for unique
h-vetinari Oct 15, 2018
4ed354a
Template over {return_inverse, ignore_na} for perf
h-vetinari Oct 26, 2018
906cd50
Re-add kwargs to method signature
h-vetinari Oct 26, 2018
0d6dad0
Merge remote-tracking branch 'upstream/master' into unique_inverse_cy…
h-vetinari Oct 28, 2018
19c7c1f
Fix small oversight
h-vetinari Oct 28, 2018
a8f079f
Simplify an if-condition
h-vetinari Oct 29, 2018
1c5b97a
Reword comment 8000
h-vetinari Oct 29, 2018
d71d68c
Merge remote-tracking branch 'upstream/master' into unique_inverse_cy…
h-vetinari Oct 29, 2018
c7327fd
Revert templating over {return_inverse, ignore_na}
h-vetinari Oct 31, 2018
e09f6fe
Merge branch 'master' into unique_inverse_cython
h-vetinari Nov 2, 2018
f93a912
Merge branch 'master' into unique_inverse_cython
h-vetinari Nov 3, 2018
a06494e
Add new kwargs at the end (review jreback)
h-vetinari Nov 3, 2018
799fdfb
Merge branch 'master' into unique_inverse_cython
h-vetinari Nov 6, 2018
0ad2272
Merge remote-tracking branch 'upstream/master' into unique_inverse_cy…
h-vetinari Nov 11, 2018
906a2b9
Retrigger CircleCI
h-vetinari Nov 11, 2018
29aecdd
Retrigger CI after flaky hypothesis test
h-vetinari Nov 11, 2018
746c0e3
Retrigger CircleCI
h-vetinari Nov 11, 2018
8da33f4
Retrigger CI after timeout
h-vetinari Nov 11, 2018
ba9d8b8
Retrigger CircleCI
h-vetinari Nov 11, 2018
2423a05
Merge remote-tracking branch 'upstream/master' into unique_inverse_cy…
h-vetinari Nov 12, 2018
6079c26
Merge remote-tracking branch 'upstream/master' into unique_inverse_cy…
h-vetinari Nov 18, 2018
30de418
Always calculate inverse
h-vetinari Nov 18, 2018
feb32d6
Merge remote-tracking branch 'upstream/master' into unique_inverse_cy…
h-vetinari Nov 19, 2018
0b85759
Revert "Always calculate inverse"
h-vetinari Nov 19, 2018
aad4b91
Merge remote-tracking branch 'upstream/master' into unique_inverse_cy…
h-vetinari Nov 21, 2018
4451854
Add comments to ignore_na branches (review jreback)
h-vetinari Nov 21, 2018
e6b71b5
Merge remote-tracking branch 'upstream/master' into unique_inverse_cy…
h-vetinari Nov 27, 2018
00a304d
Switch signature of hashtable.factorize (review jreback)
h-vetinari Nov 27, 2018
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Switch signature of hashtable.factorize (review jreback)
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@h-vetinari
h-vetinari committed Nov 27, 2018
commit 00a304d467483135ad2c035d218a562fbdbeecd4
39 changes: 15 additions & 24 deletions pandas/_libs/hashtable_class_helper.pxi.in
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Expand Up @@ -502,18 +502,15 @@ cdef class {{name}}HashTable(HashTable):

Returns
-------
labels : ndarray[int64]
The labels from values to uniques
uniques : ndarray[{{dtype}}]
Unique values of input, not sorted
labels : ndarray[int64]
The labels from values to uniques
"""
uniques_vector = {{name}}Vector()
uniques, labels = self._unique(values, uniques_vector,
na_sentinel=na_sentinel,
na_value=na_value, ignore_na=True,
return_inverse=True)
# factorize has reversed outputs compared to _unique
return labels, uniques
return self._unique(values, uniques_vector, na_sentinel=na_sentinel,
na_value=na_value, ignore_na=True,
return_inverse=True)

def get_labels(self, const {{dtype}}_t[:] values, {{name}}Vector uniques,
Py_ssize_t count_prior=0, Py_ssize_t na_sentinel=-1,
Expand Down Expand Up @@ -858,18 +855,15 @@ cdef class StringHashTable(HashTable):

Returns
-------
labels : ndarray[int64]
The labels from values to uniques
uniques : ndarray[object]
Unique values of input, not sorted
labels : ndarray[int64]
The labels from values to uniques
"""
uniques_vector = ObjectVector()
uniques, labels = self._unique(values, uniques_vector,
na_sentinel=na_sentinel,
na_value=na_value, ignore_na=True,
return_inverse=True)
# factorize has reversed outputs compared to _unique
return labels, uniques
return self._unique(values, uniques_vector, na_sentinel=na_sentinel,
na_value=na_value, ignore_na=True,
return_inverse=True)

def get_labels(self, ndarray[object] values, ObjectVector uniques,
Py_ssize_t count_prior=0, Py_ssize_t na_sentinel=-1,
Expand Down Expand Up @@ -1092,18 +1086,15 @@ cdef class PyObjectHashTable(HashTable):

Returns
-------
labels : ndarray[int64]
The labels from values to uniques
uniques : ndarray[object]
Unique values of input, not sorted
labels : ndarray[int64]
The labels from values to uniques
"""
uniques_vector = ObjectVector()
uniques, labels = self._unique(values, uniques_vector,
na_sentinel=na_sentinel,
na_value=na_value, ignore_na=True,
return_inverse=True)
# factorize has reversed outputs compared to _unique
return labels, uniques
return self._unique(values, uniques_vector, na_sentinel=na_sentinel,
na_value=na_value, ignore_na=True,
return_inverse=True)

def get_labels(self, ndarray[object] values, ObjectVector uniques,
Py_ssize_t count_prior=0, Py_ssize_t na_sentinel=-1,
Expand Down
2 changes: 1 addition & 1 deletion pandas/core/algorithms.py
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Expand Up @@ -460,7 +460,7 @@ def _factorize_array(values, na_sentinel=-1, size_hint=None,
(hash_klass, _), values = _get_data_algo(values, _hashtables)

table = hash_klass(size_hint or len(values))
labels, uniques = table.factorize(values, na_sentinel=na_sentinel,
uniques, labels = table.factorize(values, na_sentinel=na_sentinel,
na_value=na_value)

labels = ensure_platform_int(labels)
Expand Down
2 changes: 1 addition & 1 deletion pandas/tests/test_algos.py
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Expand Up @@ -1391,7 +1391,7 @@ def test_hashtable_factorize(self, htable, tm_dtype, writable):
s_duplicated.values.setflags(write=writable)
na_mask = s_duplicated.isna().values

result_inverse, result_unique = htable().factorize(s_duplicated.values)
result_unique, result_inverse = htable().factorize(s_duplicated.values)

# drop_duplicates has own cython code (hash_table_func_helper.pxi)
# and is tested separately; keeps first occurrence like ht.factorize()
Expand Down
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