def test_subclassed_apply(self):
# GH 19822
def check_row_subclass(row):
assert isinstance(row, tm.SubclassedSeries)
def strech(row):
if row["variable"] == "height":
row["value"] += 0.5
return row
df = tm.SubclassedDataFrame(
[
["John", "Doe", "height", 5.5],
["Mary", "Bo", "height", 6.0],
["John", "Doe", "weight", 130],
["Mary", "Bo", "weight", 150],
],
columns=["first", "last", "variable", "value"],
)
df.apply(lambda x: check_row_subclass(x))
df.apply(lambda x: check_row_subclass(x), axis=1)
expected = tm.SubclassedDataFrame(
[
["John", "Doe", "height", 6.0],
["Mary", "Bo", "height", 6.5],
["John", "Doe", "weight", 130],
["Mary", "Bo", "weight", 150],
],
columns=["first", "last", "variable", "value"],
)
result = df.apply(lambda x: strech(x), axis=1)
assert isinstance(result, tm.SubclassedDataFrame)
tm.assert_frame_equal(result, expected)
expected = tm.SubclassedDataFrame([[1, 2, 3], [1, 2, 3], [1, 2, 3], [1, 2, 3]])
result = df.apply(lambda x: tm.SubclassedSeries([1, 2, 3]), axis=1)
assert isinstance(result, tm.SubclassedDataFrame)
tm.assert_frame_equal(result, expected)
result = df.apply(lambda x: [1, 2, 3], axis=1, result_type="expand")
assert isinstance(result, tm.SubclassedDataFrame)
tm.assert_frame_equal(result, expected)
expected = tm.SubclassedSeries([[1, 2, 3], [1, 2, 3], [1, 2, 3], [1, 2, 3]])
result = df.apply(lambda x: [1, 2, 3], axis=1)
assert not isinstance(result, tm.SubclassedDataFrame)
tm.assert_series_equal(result, expected)
def test_indexing_sliced(self):
s = tm.SubclassedSeries([1, 2, 3, 4], index=list("abcd"))
res = s.loc[["a", "b"]]
exp = tm.SubclassedSeries([1, 2], index=list("ab"))
tm.assert_series_equal(res, exp)
res = s.iloc[[2, 3]]
exp = tm.SubclassedSeries([3, 4], index=list("cd"))
tm.assert_series_equal(res, exp)
res = s.loc[["a", "b"]]
exp = tm.SubclassedSeries([1, 2], index=list("ab"))
tm.assert_series_equal(res, exp)
def test_asof(self):
N = 3
rng = pd.date_range("1/1/1990", periods=N, freq="53s")
s = tm.SubclassedSeries({"A": [np.nan, np.nan, np.nan]}, index=rng)
result = s.asof(rng[-2:])
assert isinstance(result, tm.SubclassedSeries)
def test_subclass_align_combinations(self):
# GH 12983
df = tm.SubclassedDataFrame({
"a": [1, 3, 5],
"b": [1, 3, 5]
},
index=list("ACE"))
s = tm.SubclassedSeries([1, 2, 4], index=list("ABD"), name="x")
# frame + series
res1, res2 = df.align(s, axis=0)
exp1 = pd.DataFrame(
{
"a": [1, np.nan, 3, np.nan, 5],
"b": [1, np.nan, 3, np.nan, 5]
},
index=list("ABCDE"),
)
# name is lost when
exp2 = pd.Series([1, 2, np.nan, 4, np.nan],
index=list("ABCDE"),
name="x")
assert isinstance(res1, tm.SubclassedDataFrame)
tm.assert_frame_equal(res1, exp1)
assert isinstance(res2, tm.SubclassedSeries)
tm.assert_series_equal(res2, exp2)
# series + frame
res1, res2 = s.align(df)
assert isinstance(res1, tm.SubclassedSeries)
tm.assert_series_equal(res1, exp2)
assert isinstance(res2, tm.SubclassedDataFrame)
tm.assert_frame_equal(res2, exp1)
def test_equals(self):
# https://github.com/pandas-dev/pandas/pull/34402
# allow subclass in both directions
s1 = pd.Series([1, 2, 3])
s2 = tm.SubclassedSeries([1, 2, 3])
assert s1.equals(s2)
assert s2.equals(s1)
def test_subclass_unstack(self):
# GH 15564
s = tm.SubclassedSeries([1, 2, 3, 4], index=[list("aabb"), list("xyxy")])
res = s.unstack()
exp = tm.SubclassedDataFrame({"x": [1, 3], "y": [2, 4]}, index=["a", "b"])
tm.assert_frame_equal(res, exp)
def test_dot(self):
df = tm.SubclassedDataFrame([[0, 1, -2, -1], [1, 1, 1, 1]])
s = tm.SubclassedSeries([1, 1, 2, 1])
result = df.dot(s)
assert isinstance(result, tm.SubclassedSeries)
df = tm.SubclassedDataFrame([[0, 1, -2, -1], [1, 1, 1, 1]])
s = tm.SubclassedDataFrame([1, 1, 2, 1])
result = df.dot(s)
assert isinstance(result, tm.SubclassedDataFrame)
def test_indexing_sliced(self):
# GH 11559
df = tm.SubclassedDataFrame(
{"X": [1, 2, 3], "Y": [4, 5, 6], "Z": [7, 8, 9]}, index=["a", "b", "c"]
)
res = df.loc[:, "X"]
exp = tm.SubclassedSeries([1, 2, 3], index=list("abc"), name="X")
tm.assert_series_equal(res, exp)
assert isinstance(res, tm.SubclassedSeries)
res = df.iloc[:, 1]
exp = tm.SubclassedSeries([4, 5, 6], index=list("abc"), name="Y")
tm.assert_series_equal(res, exp)
assert isinstance(res, tm.SubclassedSeries)
res = df.loc[:, "Z"]
exp = tm.SubclassedSeries([7, 8, 9], index=list("abc"), name="Z")
tm.assert_series_equal(res, exp)
assert isinstance(res, tm.SubclassedSeries)
res = df.loc["a", :]
exp = tm.SubclassedSeries([1, 4, 7], index=list("XYZ"), name="a")
tm.assert_series_equal(res, exp)
assert isinstance(res, tm.SubclassedSeries)
res = df.iloc[1, :]
exp = tm.SubclassedSeries([2, 5, 8], index=list("XYZ"), name="b")
tm.assert_series_equal(res, exp)
assert isinstance(res, tm.SubclassedSeries)
res = df.loc["c", :]
exp = tm.SubclassedSeries([3, 6, 9], index=list("XYZ"), name="c")
tm.assert_series_equal(res, exp)
assert isinstance(res, tm.SubclassedSeries)
def test_subclass_stack(self):
# GH 15564
df = tm.SubclassedDataFrame(
[[1, 2, 3], [4, 5, 6], [7, 8, 9]],
index=["a", "b", "c"],
columns=["X", "Y", "Z"],
)
res = df.stack()
exp = tm.SubclassedSeries(
[1, 2, 3, 4, 5, 6, 7, 8, 9], index=[list("aaabbbccc"), list("XYZXYZXYZ")]
)
tm.assert_series_equal(res, exp)
def test_subclass_unstack(self):
# GH 15564
df = tm.SubclassedDataFrame(
[[1, 2, 3], [4, 5, 6], [7, 8, 9]],
index=["a", "b", "c"],
columns=["X", "Y", "Z"],
)
res = df.unstack()
exp = tm.SubclassedSeries(
[1, 4, 7, 2, 5, 8, 3, 6, 9], index=[list("XXXYYYZZZ"), list("abcabcabc")]
)
tm.assert_series_equal(res, exp)
def test_supported_for_subclass_series(self):
data = [1, 2, 3]
sser = tm.SubclassedSeries(data, dtype=np.intp)
expected = Series(data, dtype=np.intp)
with ensure_clean_path("temp.h5") as path:
sser.to_hdf(path, "ser")
result = read_hdf(path, "ser")
tm.assert_series_equal(result, expected)
with ensure_clean_path("temp.h5") as path:
with HDFStore(path) as store:
store.put("ser", sser)
result = read_hdf(path, "ser")
tm.assert_series_equal(result, expected)
def test_indexing_sliced(self, idx_method, indexer, exp_data, exp_idx):
s = tm.SubclassedSeries([1, 2, 3, 4], index=list("abcd"))
res = getattr(s, idx_method)[indexer]
exp = tm.SubclassedSeries(exp_data, index=list(exp_idx))
tm.assert_series_equal(res, exp)
def test_to_frame(self):
s = tm.SubclassedSeries([1, 2, 3, 4], index=list("abcd"), name="xxx")
res = s.to_frame()
exp = tm.SubclassedDataFrame({"xxx": [1, 2, 3, 4]}, index=list("abcd"))
tm.assert_frame_equal(res, exp)
from datetime import datetime
import numpy as np
import pytest
from pandas import DataFrame, Series
import pandas._testing as tm
@pytest.mark.parametrize(
"obj",
[
tm.SubclassedDataFrame({"A": np.arange(0, 10)}),
tm.SubclassedSeries(np.arange(0, 10), name="A"),
],
)
@pytest.mark.filterwarnings("ignore:tshift is deprecated:FutureWarning")
def test_groupby_preserves_subclass(obj, groupby_func):
# GH28330 -- preserve subclass through groupby operations
if isinstance(obj, Series) and groupby_func in {"corrwith"}:
pytest.skip("Not applicable")
grouped = obj.groupby(np.arange(0, 10))
# Groups should preserve subclass type
assert isinstance(grouped.get_group(0), type(obj))
args = []
if groupby_func in {"fillna", "nth"}:
args.append(0)
def test_subclass_empty_repr(self):
with tm.assert_produces_warning(DeprecationWarning, check_stacklevel=False):
sub_series = tm.SubclassedSeries()
assert "SubclassedSeries" in repr(sub_series)
def test_explode(self):
s = tm.SubclassedSeries([[1, 2, 3], "foo", [], [3, 4]])
result = s.explode()
assert isinstance(result, tm.SubclassedSeries)
def test_subclass_empty_repr(self):
with tm.assert_produces_warning(FutureWarning):
sub_series = tm.SubclassedSeries()
assert "SubclassedSeries" in repr(sub_series)
