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keshe/.venv/Lib/site-packages/pandas/tests/strings/test_extract.py

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from datetime import datetime
import re
import numpy as np
import pytest
from pandas.core.dtypes.dtypes import ArrowDtype
from pandas import (
DataFrame,
Index,
MultiIndex,
Series,
_testing as tm,
)
def test_extract_expand_kwarg_wrong_type_raises(any_string_dtype):
# TODO: should this raise TypeError
values = Series(["fooBAD__barBAD", np.nan, "foo"], dtype=any_string_dtype)
with pytest.raises(ValueError, match="expand must be True or False"):
values.str.extract(".*(BAD[_]+).*(BAD)", expand=None)
def test_extract_expand_kwarg(any_string_dtype):
s = Series(["fooBAD__barBAD", np.nan, "foo"], dtype=any_string_dtype)
expected = DataFrame(["BAD__", np.nan, np.nan], dtype=any_string_dtype)
result = s.str.extract(".*(BAD[_]+).*")
tm.assert_frame_equal(result, expected)
result = s.str.extract(".*(BAD[_]+).*", expand=True)
tm.assert_frame_equal(result, expected)
expected = DataFrame(
[["BAD__", "BAD"], [np.nan, np.nan], [np.nan, np.nan]], dtype=any_string_dtype
)
result = s.str.extract(".*(BAD[_]+).*(BAD)", expand=False)
tm.assert_frame_equal(result, expected)
def test_extract_expand_False_mixed_object():
ser = Series(
["aBAD_BAD", np.nan, "BAD_b_BAD", True, datetime.today(), "foo", None, 1, 2.0]
)
# two groups
result = ser.str.extract(".*(BAD[_]+).*(BAD)", expand=False)
er = [np.nan, np.nan] # empty row
expected = DataFrame(
[["BAD_", "BAD"], er, ["BAD_", "BAD"], er, er, er, er, er, er], dtype=object
)
tm.assert_frame_equal(result, expected)
# single group
result = ser.str.extract(".*(BAD[_]+).*BAD", expand=False)
expected = Series(
["BAD_", np.nan, "BAD_", np.nan, np.nan, np.nan, None, np.nan, np.nan],
dtype=object,
)
tm.assert_series_equal(result, expected)
def test_extract_expand_index_raises():
# GH9980
# Index only works with one regex group since
# multi-group would expand to a frame
idx = Index(["A1", "A2", "A3", "A4", "B5"])
msg = "only one regex group is supported with Index"
with pytest.raises(ValueError, match=msg):
idx.str.extract("([AB])([123])", expand=False)
def test_extract_expand_no_capture_groups_raises(index_or_series, any_string_dtype):
s_or_idx = index_or_series(["A1", "B2", "C3"], dtype=any_string_dtype)
msg = "pattern contains no capture groups"
# no groups
with pytest.raises(ValueError, match=msg):
s_or_idx.str.extract("[ABC][123]", expand=False)
# only non-capturing groups
with pytest.raises(ValueError, match=msg):
s_or_idx.str.extract("(?:[AB]).*", expand=False)
def test_extract_expand_single_capture_group(index_or_series, any_string_dtype):
# single group renames series/index properly
s_or_idx = index_or_series(["A1", "A2"], dtype=any_string_dtype)
result = s_or_idx.str.extract(r"(?P<uno>A)\d", expand=False)
expected = index_or_series(["A", "A"], name="uno", dtype=any_string_dtype)
if index_or_series == Series:
tm.assert_series_equal(result, expected)
else:
tm.assert_index_equal(result, expected)
def test_extract_expand_capture_groups(any_string_dtype):
s = Series(["A1", "B2", "C3"], dtype=any_string_dtype)
# one group, no matches
result = s.str.extract("(_)", expand=False)
expected = Series([np.nan, np.nan, np.nan], dtype=any_string_dtype)
tm.assert_series_equal(result, expected)
# two groups, no matches
result = s.str.extract("(_)(_)", expand=False)
expected = DataFrame(
[[np.nan, np.nan], [np.nan, np.nan], [np.nan, np.nan]], dtype=any_string_dtype
)
tm.assert_frame_equal(result, expected)
# one group, some matches
result = s.str.extract("([AB])[123]", expand=False)
expected = Series(["A", "B", np.nan], dtype=any_string_dtype)
tm.assert_series_equal(result, expected)
# two groups, some matches
result = s.str.extract("([AB])([123])", expand=False)
expected = DataFrame(
[["A", "1"], ["B", "2"], [np.nan, np.nan]], dtype=any_string_dtype
)
tm.assert_frame_equal(result, expected)
# one named group
result = s.str.extract("(?P<letter>[AB])", expand=False)
expected = Series(["A", "B", np.nan], name="letter", dtype=any_string_dtype)
tm.assert_series_equal(result, expected)
# two named groups
result = s.str.extract("(?P<letter>[AB])(?P<number>[123])", expand=False)
expected = DataFrame(
[["A", "1"], ["B", "2"], [np.nan, np.nan]],
columns=["letter", "number"],
dtype=any_string_dtype,
)
tm.assert_frame_equal(result, expected)
# mix named and unnamed groups
result = s.str.extract("([AB])(?P<number>[123])", expand=False)
expected = DataFrame(
[["A", "1"], ["B", "2"], [np.nan, np.nan]],
columns=[0, "number"],
dtype=any_string_dtype,
)
tm.assert_frame_equal(result, expected)
# one normal group, one non-capturing group
result = s.str.extract("([AB])(?:[123])", expand=False)
expected = Series(["A", "B", np.nan], dtype=any_string_dtype)
tm.assert_series_equal(result, expected)
# two normal groups, one non-capturing group
s = Series(["A11", "B22", "C33"], dtype=any_string_dtype)
result = s.str.extract("([AB])([123])(?:[123])", expand=False)
expected = DataFrame(
[["A", "1"], ["B", "2"], [np.nan, np.nan]], dtype=any_string_dtype
)
tm.assert_frame_equal(result, expected)
# one optional group followed by one normal group
s = Series(["A1", "B2", "3"], dtype=any_string_dtype)
result = s.str.extract("(?P<letter>[AB])?(?P<number>[123])", expand=False)
expected = DataFrame(
[["A", "1"], ["B", "2"], [np.nan, "3"]],
columns=["letter", "number"],
dtype=any_string_dtype,
)
tm.assert_frame_equal(result, expected)
# one normal group followed by one optional group
s = Series(["A1", "B2", "C"], dtype=any_string_dtype)
result = s.str.extract("(?P<letter>[ABC])(?P<number>[123])?", expand=False)
expected = DataFrame(
[["A", "1"], ["B", "2"], ["C", np.nan]],
columns=["letter", "number"],
dtype=any_string_dtype,
)
tm.assert_frame_equal(result, expected)
def test_extract_expand_capture_groups_index(index, any_string_dtype):
# https://github.com/pandas-dev/pandas/issues/6348
# not passing index to the extractor
data = ["A1", "B2", "C"]
if len(index) == 0:
pytest.skip("Test requires len(index) > 0")
while len(index) < len(data):
index = index.repeat(2)
index = index[: len(data)]
ser = Series(data, index=index, dtype=any_string_dtype)
result = ser.str.extract(r"(\d)", expand=False)
expected = Series(["1", "2", np.nan], index=index, dtype=any_string_dtype)
tm.assert_series_equal(result, expected)
result = ser.str.extract(r"(?P<letter>\D)(?P<number>\d)?", expand=False)
expected = DataFrame(
[["A", "1"], ["B", "2"], ["C", np.nan]],
columns=["letter", "number"],
index=index,
dtype=any_string_dtype,
)
tm.assert_frame_equal(result, expected)
def test_extract_single_series_name_is_preserved(any_string_dtype):
s = Series(["a3", "b3", "c2"], name="bob", dtype=any_string_dtype)
result = s.str.extract(r"(?P<sue>[a-z])", expand=False)
expected = Series(["a", "b", "c"], name="sue", dtype=any_string_dtype)
tm.assert_series_equal(result, expected)
def test_extract_expand_True(any_string_dtype):
# Contains tests like those in test_match and some others.
s = Series(["fooBAD__barBAD", np.nan, "foo"], dtype=any_string_dtype)
result = s.str.extract(".*(BAD[_]+).*(BAD)", expand=True)
expected = DataFrame(
[["BAD__", "BAD"], [np.nan, np.nan], [np.nan, np.nan]], dtype=any_string_dtype
)
tm.assert_frame_equal(result, expected)
def test_extract_expand_True_mixed_object():
er = [np.nan, np.nan] # empty row
mixed = Series(
[
"aBAD_BAD",
np.nan,
"BAD_b_BAD",
True,
datetime.today(),
"foo",
None,
1,
2.0,
]
)
result = mixed.str.extract(".*(BAD[_]+).*(BAD)", expand=True)
expected = DataFrame(
[["BAD_", "BAD"], er, ["BAD_", "BAD"], er, er, er, er, er, er], dtype=object
)
tm.assert_frame_equal(result, expected)
def test_extract_expand_True_single_capture_group_raises(
index_or_series, any_string_dtype
):
# these should work for both Series and Index
# no groups
s_or_idx = index_or_series(["A1", "B2", "C3"], dtype=any_string_dtype)
msg = "pattern contains no capture groups"
with pytest.raises(ValueError, match=msg):
s_or_idx.str.extract("[ABC][123]", expand=True)
# only non-capturing groups
with pytest.raises(ValueError, match=msg):
s_or_idx.str.extract("(?:[AB]).*", expand=True)
def test_extract_expand_True_single_capture_group(index_or_series, any_string_dtype):
# single group renames series/index properly
s_or_idx = index_or_series(["A1", "A2"], dtype=any_string_dtype)
result = s_or_idx.str.extract(r"(?P<uno>A)\d", expand=True)
expected = DataFrame({"uno": ["A", "A"]}, dtype=any_string_dtype)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("name", [None, "series_name"])
def test_extract_series(name, any_string_dtype):
# extract should give the same result whether or not the series has a name.
s = Series(["A1", "B2", "C3"], name=name, dtype=any_string_dtype)
# one group, no matches
result = s.str.extract("(_)", expand=True)
expected = DataFrame([np.nan, np.nan, np.nan], dtype=any_string_dtype)
tm.assert_frame_equal(result, expected)
# two groups, no matches
result = s.str.extract("(_)(_)", expand=True)
expected = DataFrame(
[[np.nan, np.nan], [np.nan, np.nan], [np.nan, np.nan]], dtype=any_string_dtype
)
tm.assert_frame_equal(result, expected)
# one group, some matches
result = s.str.extract("([AB])[123]", expand=True)
expected = DataFrame(["A", "B", np.nan], dtype=any_string_dtype)
tm.assert_frame_equal(result, expected)
# two groups, some matches
result = s.str.extract("([AB])([123])", expand=True)
expected = DataFrame(
[["A", "1"], ["B", "2"], [np.nan, np.nan]], dtype=any_string_dtype
)
tm.assert_frame_equal(result, expected)
# one named group
result = s.str.extract("(?P<letter>[AB])", expand=True)
expected = DataFrame({"letter": ["A", "B", np.nan]}, dtype=any_string_dtype)
tm.assert_frame_equal(result, expected)
# two named groups
result = s.str.extract("(?P<letter>[AB])(?P<number>[123])", expand=True)
expected = DataFrame(
[["A", "1"], ["B", "2"], [np.nan, np.nan]],
columns=["letter", "number"],
dtype=any_string_dtype,
)
tm.assert_frame_equal(result, expected)
# mix named and unnamed groups
result = s.str.extract("([AB])(?P<number>[123])", expand=True)
expected = DataFrame(
[["A", "1"], ["B", "2"], [np.nan, np.nan]],
columns=[0, "number"],
dtype=any_string_dtype,
)
tm.assert_frame_equal(result, expected)
# one normal group, one non-capturing group
result = s.str.extract("([AB])(?:[123])", expand=True)
expected = DataFrame(["A", "B", np.nan], dtype=any_string_dtype)
tm.assert_frame_equal(result, expected)
def test_extract_optional_groups(any_string_dtype):
# two normal groups, one non-capturing group
s = Series(["A11", "B22", "C33"], dtype=any_string_dtype)
result = s.str.extract("([AB])([123])(?:[123])", expand=True)
expected = DataFrame(
[["A", "1"], ["B", "2"], [np.nan, np.nan]], dtype=any_string_dtype
)
tm.assert_frame_equal(result, expected)
# one optional group followed by one normal group
s = Series(["A1", "B2", "3"], dtype=any_string_dtype)
result = s.str.extract("(?P<letter>[AB])?(?P<number>[123])", expand=True)
expected = DataFrame(
[["A", "1"], ["B", "2"], [np.nan, "3"]],
columns=["letter", "number"],
dtype=any_string_dtype,
)
tm.assert_frame_equal(result, expected)
# one normal group followed by one optional group
s = Series(["A1", "B2", "C"], dtype=any_string_dtype)
result = s.str.extract("(?P<letter>[ABC])(?P<number>[123])?", expand=True)
expected = DataFrame(
[["A", "1"], ["B", "2"], ["C", np.nan]],
columns=["letter", "number"],
dtype=any_string_dtype,
)
tm.assert_frame_equal(result, expected)
def test_extract_dataframe_capture_groups_index(index, any_string_dtype):
# GH6348
# not passing index to the extractor
data = ["A1", "B2", "C"]
if len(index) < len(data):
pytest.skip(f"Index needs more than {len(data)} values")
index = index[: len(data)]
s = Series(data, index=index, dtype=any_string_dtype)
result = s.str.extract(r"(\d)", expand=True)
expected = DataFrame(["1", "2", np.nan], index=index, dtype=any_string_dtype)
tm.assert_frame_equal(result, expected)
result = s.str.extract(r"(?P<letter>\D)(?P<number>\d)?", expand=True)
expected = DataFrame(
[["A", "1"], ["B", "2"], ["C", np.nan]],
columns=["letter", "number"],
index=index,
dtype=any_string_dtype,
)
tm.assert_frame_equal(result, expected)
def test_extract_single_group_returns_frame(any_string_dtype):
# GH11386 extract should always return DataFrame, even when
# there is only one group. Prior to v0.18.0, extract returned
# Series when there was only one group in the regex.
s = Series(["a3", "b3", "c2"], name="series_name", dtype=any_string_dtype)
result = s.str.extract(r"(?P<letter>[a-z])", expand=True)
expected = DataFrame({"letter": ["a", "b", "c"]}, dtype=any_string_dtype)
tm.assert_frame_equal(result, expected)
def test_extractall(any_string_dtype):
data = [
"dave@google.com",
"tdhock5@gmail.com",
"maudelaperriere@gmail.com",
"rob@gmail.com some text steve@gmail.com",
"a@b.com some text c@d.com and e@f.com",
np.nan,
"",
]
expected_tuples = [
("dave", "google", "com"),
("tdhock5", "gmail", "com"),
("maudelaperriere", "gmail", "com"),
("rob", "gmail", "com"),
("steve", "gmail", "com"),
("a", "b", "com"),
("c", "d", "com"),
("e", "f", "com"),
]
pat = r"""
(?P<user>[a-z0-9]+)
@
(?P<domain>[a-z]+)
\.
(?P<tld>[a-z]{2,4})
"""
expected_columns = ["user", "domain", "tld"]
s = Series(data, dtype=any_string_dtype)
# extractall should return a DataFrame with one row for each match, indexed by the
# subject from which the match came.
expected_index = MultiIndex.from_tuples(
[(0, 0), (1, 0), (2, 0), (3, 0), (3, 1), (4, 0), (4, 1), (4, 2)],
names=(None, "match"),
)
expected = DataFrame(
expected_tuples, expected_index, expected_columns, dtype=any_string_dtype
)
result = s.str.extractall(pat, flags=re.VERBOSE)
tm.assert_frame_equal(result, expected)
# The index of the input Series should be used to construct the index of the output
# DataFrame:
mi = MultiIndex.from_tuples(
[
("single", "Dave"),
("single", "Toby"),
("single", "Maude"),
("multiple", "robAndSteve"),
("multiple", "abcdef"),
("none", "missing"),
("none", "empty"),
]
)
s = Series(data, index=mi, dtype=any_string_dtype)
expected_index = MultiIndex.from_tuples(
[
("single", "Dave", 0),
("single", "Toby", 0),
("single", "Maude", 0),
("multiple", "robAndSteve", 0),
("multiple", "robAndSteve", 1),
("multiple", "abcdef", 0),
("multiple", "abcdef", 1),
("multiple", "abcdef", 2),
],
names=(None, None, "match"),
)
expected = DataFrame(
expected_tuples, expected_index, expected_columns, dtype=any_string_dtype
)
result = s.str.extractall(pat, flags=re.VERBOSE)
tm.assert_frame_equal(result, expected)
# MultiIndexed subject with names.
s = Series(data, index=mi, dtype=any_string_dtype)
s.index.names = ("matches", "description")
expected_index.names = ("matches", "description", "match")
expected = DataFrame(
expected_tuples, expected_index, expected_columns, dtype=any_string_dtype
)
result = s.str.extractall(pat, flags=re.VERBOSE)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"pat,expected_names",
[
# optional groups.
("(?P<letter>[AB])?(?P<number>[123])", ["letter", "number"]),
# only one of two groups has a name.
("([AB])?(?P<number>[123])", [0, "number"]),
],
)
def test_extractall_column_names(pat, expected_names, any_string_dtype):
s = Series(["", "A1", "32"], dtype=any_string_dtype)
result = s.str.extractall(pat)
expected = DataFrame(
[("A", "1"), (np.nan, "3"), (np.nan, "2")],
index=MultiIndex.from_tuples([(1, 0), (2, 0), (2, 1)], names=(None, "match")),
columns=expected_names,
dtype=any_string_dtype,
)
tm.assert_frame_equal(result, expected)
def test_extractall_single_group(any_string_dtype):
s = Series(["a3", "b3", "d4c2"], name="series_name", dtype=any_string_dtype)
expected_index = MultiIndex.from_tuples(
[(0, 0), (1, 0), (2, 0), (2, 1)], names=(None, "match")
)
# extractall(one named group) returns DataFrame with one named column.
result = s.str.extractall(r"(?P<letter>[a-z])")
expected = DataFrame(
{"letter": ["a", "b", "d", "c"]}, index=expected_index, dtype=any_string_dtype
)
tm.assert_frame_equal(result, expected)
# extractall(one un-named group) returns DataFrame with one un-named column.
result = s.str.extractall(r"([a-z])")
expected = DataFrame(
["a", "b", "d", "c"], index=expected_index, dtype=any_string_dtype
)
tm.assert_frame_equal(result, expected)
def test_extractall_single_group_with_quantifier(any_string_dtype):
# GH#13382
# extractall(one un-named group with quantifier) returns DataFrame with one un-named
# column.
s = Series(["ab3", "abc3", "d4cd2"], name="series_name", dtype=any_string_dtype)
result = s.str.extractall(r"([a-z]+)")
expected = DataFrame(
["ab", "abc", "d", "cd"],
index=MultiIndex.from_tuples(
[(0, 0), (1, 0), (2, 0), (2, 1)], names=(None, "match")
),
dtype=any_string_dtype,
)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"data, names",
[
([], (None,)),
([], ("i1",)),
([], (None, "i2")),
([], ("i1", "i2")),
(["a3", "b3", "d4c2"], (None,)),
(["a3", "b3", "d4c2"], ("i1", "i2")),
(["a3", "b3", "d4c2"], (None, "i2")),
(["a3", "b3", "d4c2"], ("i1", "i2")),
],
)
def test_extractall_no_matches(data, names, any_string_dtype):
# GH19075 extractall with no matches should return a valid MultiIndex
n = len(data)
if len(names) == 1:
index = Index(range(n), name=names[0])
else:
tuples = (tuple([i] * (n - 1)) for i in range(n))
index = MultiIndex.from_tuples(tuples, names=names)
s = Series(data, name="series_name", index=index, dtype=any_string_dtype)
expected_index = MultiIndex.from_tuples([], names=(names + ("match",)))
# one un-named group.
result = s.str.extractall("(z)")
expected = DataFrame(columns=[0], index=expected_index, dtype=any_string_dtype)
tm.assert_frame_equal(result, expected)
# two un-named groups.
result = s.str.extractall("(z)(z)")
expected = DataFrame(columns=[0, 1], index=expected_index, dtype=any_string_dtype)
tm.assert_frame_equal(result, expected)
# one named group.
result = s.str.extractall("(?P<first>z)")
expected = DataFrame(
columns=["first"], index=expected_index, dtype=any_string_dtype
)
tm.assert_frame_equal(result, expected)
# two named groups.
result = s.str.extractall("(?P<first>z)(?P<second>z)")
expected = DataFrame(
columns=["first", "second"], index=expected_index, dtype=any_string_dtype
)
tm.assert_frame_equal(result, expected)
# one named, one un-named.
result = s.str.extractall("(z)(?P<second>z)")
expected = DataFrame(
columns=[0, "second"], index=expected_index, dtype=any_string_dtype
)
tm.assert_frame_equal(result, expected)
def test_extractall_stringindex(any_string_dtype):
s = Series(["a1a2", "b1", "c1"], name="xxx", dtype=any_string_dtype)
result = s.str.extractall(r"[ab](?P<digit>\d)")
expected = DataFrame(
{"digit": ["1", "2", "1"]},
index=MultiIndex.from_tuples([(0, 0), (0, 1), (1, 0)], names=[None, "match"]),
dtype=any_string_dtype,
)
tm.assert_frame_equal(result, expected)
# index should return the same result as the default index without name thus
# index.name doesn't affect to the result
if any_string_dtype == "object":
for idx in [
Index(["a1a2", "b1", "c1"], dtype=object),
Index(["a1a2", "b1", "c1"], name="xxx", dtype=object),
]:
result = idx.str.extractall(r"[ab](?P<digit>\d)")
tm.assert_frame_equal(result, expected)
s = Series(
["a1a2", "b1", "c1"],
name="s_name",
index=Index(["XX", "yy", "zz"], name="idx_name"),
dtype=any_string_dtype,
)
result = s.str.extractall(r"[ab](?P<digit>\d)")
expected = DataFrame(
{"digit": ["1", "2", "1"]},
index=MultiIndex.from_tuples(
[("XX", 0), ("XX", 1), ("yy", 0)], names=["idx_name", "match"]
),
dtype=any_string_dtype,
)
tm.assert_frame_equal(result, expected)
def test_extractall_no_capture_groups_raises(any_string_dtype):
# Does not make sense to use extractall with a regex that has no capture groups.
# (it returns DataFrame with one column for each capture group)
s = Series(["a3", "b3", "d4c2"], name="series_name", dtype=any_string_dtype)
with pytest.raises(ValueError, match="no capture groups"):
s.str.extractall(r"[a-z]")
def test_extract_index_one_two_groups():
s = Series(["a3", "b3", "d4c2"], index=["A3", "B3", "D4"], name="series_name")
r = s.index.str.extract(r"([A-Z])", expand=True)
e = DataFrame(["A", "B", "D"])
tm.assert_frame_equal(r, e)
# Prior to v0.18.0, index.str.extract(regex with one group)
# returned Index. With more than one group, extract raised an
# error (GH9980). Now extract always returns DataFrame.
r = s.index.str.extract(r"(?P<letter>[A-Z])(?P<digit>[0-9])", expand=True)
e_list = [("A", "3"), ("B", "3"), ("D", "4")]
e = DataFrame(e_list, columns=["letter", "digit"])
tm.assert_frame_equal(r, e)
def test_extractall_same_as_extract(any_string_dtype):
s = Series(["a3", "b3", "c2"], name="series_name", dtype=any_string_dtype)
pattern_two_noname = r"([a-z])([0-9])"
extract_two_noname = s.str.extract(pattern_two_noname, expand=True)
has_multi_index = s.str.extractall(pattern_two_noname)
no_multi_index = has_multi_index.xs(0, level="match")
tm.assert_frame_equal(extract_two_noname, no_multi_index)
pattern_two_named = r"(?P<letter>[a-z])(?P<digit>[0-9])"
extract_two_named = s.str.extract(pattern_two_named, expand=True)
has_multi_index = s.str.extractall(pattern_two_named)
no_multi_index = has_multi_index.xs(0, level="match")
tm.assert_frame_equal(extract_two_named, no_multi_index)
pattern_one_named = r"(?P<group_name>[a-z])"
extract_one_named = s.str.extract(pattern_one_named, expand=True)
has_multi_index = s.str.extractall(pattern_one_named)
no_multi_index = has_multi_index.xs(0, level="match")
tm.assert_frame_equal(extract_one_named, no_multi_index)
pattern_one_noname = r"([a-z])"
extract_one_noname = s.str.extract(pattern_one_noname, expand=True)
has_multi_index = s.str.extractall(pattern_one_noname)
no_multi_index = has_multi_index.xs(0, level="match")
tm.assert_frame_equal(extract_one_noname, no_multi_index)
def test_extractall_same_as_extract_subject_index(any_string_dtype):
# same as above tests, but s has an MultiIndex.
mi = MultiIndex.from_tuples(
[("A", "first"), ("B", "second"), ("C", "third")],
names=("capital", "ordinal"),
)
s = Series(["a3", "b3", "c2"], index=mi, name="series_name", dtype=any_string_dtype)
pattern_two_noname = r"([a-z])([0-9])"
extract_two_noname = s.str.extract(pattern_two_noname, expand=True)
has_match_index = s.str.extractall(pattern_two_noname)
no_match_index = has_match_index.xs(0, level="match")
tm.assert_frame_equal(extract_two_noname, no_match_index)
pattern_two_named = r"(?P<letter>[a-z])(?P<digit>[0-9])"
extract_two_named = s.str.extract(pattern_two_named, expand=True)
has_match_index = s.str.extractall(pattern_two_named)
no_match_index = has_match_index.xs(0, level="match")
tm.assert_frame_equal(extract_two_named, no_match_index)
pattern_one_named = r"(?P<group_name>[a-z])"
extract_one_named = s.str.extract(pattern_one_named, expand=True)
has_match_index = s.str.extractall(pattern_one_named)
no_match_index = has_match_index.xs(0, level="match")
tm.assert_frame_equal(extract_one_named, no_match_index)
pattern_one_noname = r"([a-z])"
extract_one_noname = s.str.extract(pattern_one_noname, expand=True)
has_match_index = s.str.extractall(pattern_one_noname)
no_match_index = has_match_index.xs(0, level="match")
tm.assert_frame_equal(extract_one_noname, no_match_index)
def test_extractall_preserves_dtype():
# Ensure that when extractall is called on a series with specific dtypes set, that
# the dtype is preserved in the resulting DataFrame's column.
pa = pytest.importorskip("pyarrow")
result = Series(["abc", "ab"], dtype=ArrowDtype(pa.string())).str.extractall("(ab)")
assert result.dtypes[0] == "string[pyarrow]"
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