mirror of
https://github.com/PiBrewing/craftbeerpi4.git
synced 2024-12-30 17:31:44 +01:00
706 lines
23 KiB
Python
706 lines
23 KiB
Python
import numpy as np
|
|
import pytest
|
|
|
|
import pandas.util._test_decorators as td
|
|
|
|
import pandas as pd
|
|
from pandas import DataFrame, Index, MultiIndex, Series
|
|
import pandas._testing as tm
|
|
|
|
|
|
class TestDataFrameSubclassing:
|
|
def test_frame_subclassing_and_slicing(self):
|
|
# Subclass frame and ensure it returns the right class on slicing it
|
|
# In reference to PR 9632
|
|
|
|
class CustomSeries(Series):
|
|
@property
|
|
def _constructor(self):
|
|
return CustomSeries
|
|
|
|
def custom_series_function(self):
|
|
return "OK"
|
|
|
|
class CustomDataFrame(DataFrame):
|
|
"""
|
|
Subclasses pandas DF, fills DF with simulation results, adds some
|
|
custom plotting functions.
|
|
"""
|
|
|
|
def __init__(self, *args, **kw):
|
|
super().__init__(*args, **kw)
|
|
|
|
@property
|
|
def _constructor(self):
|
|
return CustomDataFrame
|
|
|
|
_constructor_sliced = CustomSeries
|
|
|
|
def custom_frame_function(self):
|
|
return "OK"
|
|
|
|
data = {"col1": range(10), "col2": range(10)}
|
|
cdf = CustomDataFrame(data)
|
|
|
|
# Did we get back our own DF class?
|
|
assert isinstance(cdf, CustomDataFrame)
|
|
|
|
# Do we get back our own Series class after selecting a column?
|
|
cdf_series = cdf.col1
|
|
assert isinstance(cdf_series, CustomSeries)
|
|
assert cdf_series.custom_series_function() == "OK"
|
|
|
|
# Do we get back our own DF class after slicing row-wise?
|
|
cdf_rows = cdf[1:5]
|
|
assert isinstance(cdf_rows, CustomDataFrame)
|
|
assert cdf_rows.custom_frame_function() == "OK"
|
|
|
|
# Make sure sliced part of multi-index frame is custom class
|
|
mcol = pd.MultiIndex.from_tuples([("A", "A"), ("A", "B")])
|
|
cdf_multi = CustomDataFrame([[0, 1], [2, 3]], columns=mcol)
|
|
assert isinstance(cdf_multi["A"], CustomDataFrame)
|
|
|
|
mcol = pd.MultiIndex.from_tuples([("A", ""), ("B", "")])
|
|
cdf_multi2 = CustomDataFrame([[0, 1], [2, 3]], columns=mcol)
|
|
assert isinstance(cdf_multi2["A"], CustomSeries)
|
|
|
|
def test_dataframe_metadata(self):
|
|
df = tm.SubclassedDataFrame(
|
|
{"X": [1, 2, 3], "Y": [1, 2, 3]}, index=["a", "b", "c"]
|
|
)
|
|
df.testattr = "XXX"
|
|
|
|
assert df.testattr == "XXX"
|
|
assert df[["X"]].testattr == "XXX"
|
|
assert df.loc[["a", "b"], :].testattr == "XXX"
|
|
assert df.iloc[[0, 1], :].testattr == "XXX"
|
|
|
|
# see gh-9776
|
|
assert df.iloc[0:1, :].testattr == "XXX"
|
|
|
|
# see gh-10553
|
|
unpickled = tm.round_trip_pickle(df)
|
|
tm.assert_frame_equal(df, unpickled)
|
|
assert df._metadata == unpickled._metadata
|
|
assert df.testattr == unpickled.testattr
|
|
|
|
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_attr_err_propagation(self):
|
|
# GH 11808
|
|
class A(DataFrame):
|
|
@property
|
|
def bar(self):
|
|
return self.i_dont_exist
|
|
|
|
with pytest.raises(AttributeError, match=".*i_dont_exist.*"):
|
|
A().bar
|
|
|
|
def test_subclass_align(self):
|
|
# GH 12983
|
|
df1 = tm.SubclassedDataFrame(
|
|
{"a": [1, 3, 5], "b": [1, 3, 5]}, index=list("ACE")
|
|
)
|
|
df2 = tm.SubclassedDataFrame(
|
|
{"c": [1, 2, 4], "d": [1, 2, 4]}, index=list("ABD")
|
|
)
|
|
|
|
res1, res2 = df1.align(df2, axis=0)
|
|
exp1 = tm.SubclassedDataFrame(
|
|
{"a": [1, np.nan, 3, np.nan, 5], "b": [1, np.nan, 3, np.nan, 5]},
|
|
index=list("ABCDE"),
|
|
)
|
|
exp2 = tm.SubclassedDataFrame(
|
|
{"c": [1, 2, np.nan, 4, np.nan], "d": [1, 2, np.nan, 4, np.nan]},
|
|
index=list("ABCDE"),
|
|
)
|
|
assert isinstance(res1, tm.SubclassedDataFrame)
|
|
tm.assert_frame_equal(res1, exp1)
|
|
assert isinstance(res2, tm.SubclassedDataFrame)
|
|
tm.assert_frame_equal(res2, exp2)
|
|
|
|
res1, res2 = df1.a.align(df2.c)
|
|
assert isinstance(res1, tm.SubclassedSeries)
|
|
tm.assert_series_equal(res1, exp1.a)
|
|
assert isinstance(res2, tm.SubclassedSeries)
|
|
tm.assert_series_equal(res2, exp2.c)
|
|
|
|
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 = tm.SubclassedDataFrame(
|
|
{"a": [1, np.nan, 3, np.nan, 5], "b": [1, np.nan, 3, np.nan, 5]},
|
|
index=list("ABCDE"),
|
|
)
|
|
# name is lost when
|
|
exp2 = tm.SubclassedSeries(
|
|
[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_subclass_iterrows(self):
|
|
# GH 13977
|
|
df = tm.SubclassedDataFrame({"a": [1]})
|
|
for i, row in df.iterrows():
|
|
assert isinstance(row, tm.SubclassedSeries)
|
|
tm.assert_series_equal(row, df.loc[i])
|
|
|
|
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_stack_multi(self):
|
|
# GH 15564
|
|
df = tm.SubclassedDataFrame(
|
|
[[10, 11, 12, 13], [20, 21, 22, 23], [30, 31, 32, 33], [40, 41, 42, 43]],
|
|
index=MultiIndex.from_tuples(
|
|
list(zip(list("AABB"), list("cdcd"))), names=["aaa", "ccc"]
|
|
),
|
|
columns=MultiIndex.from_tuples(
|
|
list(zip(list("WWXX"), list("yzyz"))), names=["www", "yyy"]
|
|
),
|
|
)
|
|
|
|
exp = tm.SubclassedDataFrame(
|
|
[
|
|
[10, 12],
|
|
[11, 13],
|
|
[20, 22],
|
|
[21, 23],
|
|
[30, 32],
|
|
[31, 33],
|
|
[40, 42],
|
|
[41, 43],
|
|
],
|
|
index=MultiIndex.from_tuples(
|
|
list(zip(list("AAAABBBB"), list("ccddccdd"), list("yzyzyzyz"))),
|
|
names=["aaa", "ccc", "yyy"],
|
|
),
|
|
columns=Index(["W", "X"], name="www"),
|
|
)
|
|
|
|
res = df.stack()
|
|
tm.assert_frame_equal(res, exp)
|
|
|
|
res = df.stack("yyy")
|
|
tm.assert_frame_equal(res, exp)
|
|
|
|
exp = tm.SubclassedDataFrame(
|
|
[
|
|
[10, 11],
|
|
[12, 13],
|
|
[20, 21],
|
|
[22, 23],
|
|
[30, 31],
|
|
[32, 33],
|
|
[40, 41],
|
|
[42, 43],
|
|
],
|
|
index=MultiIndex.from_tuples(
|
|
list(zip(list("AAAABBBB"), list("ccddccdd"), list("WXWXWXWX"))),
|
|
names=["aaa", "ccc", "www"],
|
|
),
|
|
columns=Index(["y", "z"], name="yyy"),
|
|
)
|
|
|
|
res = df.stack("www")
|
|
tm.assert_frame_equal(res, exp)
|
|
|
|
def test_subclass_stack_multi_mixed(self):
|
|
# GH 15564
|
|
df = tm.SubclassedDataFrame(
|
|
[
|
|
[10, 11, 12.0, 13.0],
|
|
[20, 21, 22.0, 23.0],
|
|
[30, 31, 32.0, 33.0],
|
|
[40, 41, 42.0, 43.0],
|
|
],
|
|
index=MultiIndex.from_tuples(
|
|
list(zip(list("AABB"), list("cdcd"))), names=["aaa", "ccc"]
|
|
),
|
|
columns=MultiIndex.from_tuples(
|
|
list(zip(list("WWXX"), list("yzyz"))), names=["www", "yyy"]
|
|
),
|
|
)
|
|
|
|
exp = tm.SubclassedDataFrame(
|
|
[
|
|
[10, 12.0],
|
|
[11, 13.0],
|
|
[20, 22.0],
|
|
[21, 23.0],
|
|
[30, 32.0],
|
|
[31, 33.0],
|
|
[40, 42.0],
|
|
[41, 43.0],
|
|
],
|
|
index=MultiIndex.from_tuples(
|
|
list(zip(list("AAAABBBB"), list("ccddccdd"), list("yzyzyzyz"))),
|
|
names=["aaa", "ccc", "yyy"],
|
|
),
|
|
columns=Index(["W", "X"], name="www"),
|
|
)
|
|
|
|
res = df.stack()
|
|
tm.assert_frame_equal(res, exp)
|
|
|
|
res = df.stack("yyy")
|
|
tm.assert_frame_equal(res, exp)
|
|
|
|
exp = tm.SubclassedDataFrame(
|
|
[
|
|
[10.0, 11.0],
|
|
[12.0, 13.0],
|
|
[20.0, 21.0],
|
|
[22.0, 23.0],
|
|
[30.0, 31.0],
|
|
[32.0, 33.0],
|
|
[40.0, 41.0],
|
|
[42.0, 43.0],
|
|
],
|
|
index=MultiIndex.from_tuples(
|
|
list(zip(list("AAAABBBB"), list("ccddccdd"), list("WXWXWXWX"))),
|
|
names=["aaa", "ccc", "www"],
|
|
),
|
|
columns=Index(["y", "z"], name="yyy"),
|
|
)
|
|
|
|
res = df.stack("www")
|
|
tm.assert_frame_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_subclass_unstack_multi(self):
|
|
# GH 15564
|
|
df = tm.SubclassedDataFrame(
|
|
[[10, 11, 12, 13], [20, 21, 22, 23], [30, 31, 32, 33], [40, 41, 42, 43]],
|
|
index=MultiIndex.from_tuples(
|
|
list(zip(list("AABB"), list("cdcd"))), names=["aaa", "ccc"]
|
|
),
|
|
columns=MultiIndex.from_tuples(
|
|
list(zip(list("WWXX"), list("yzyz"))), names=["www", "yyy"]
|
|
),
|
|
)
|
|
|
|
exp = tm.SubclassedDataFrame(
|
|
[[10, 20, 11, 21, 12, 22, 13, 23], [30, 40, 31, 41, 32, 42, 33, 43]],
|
|
index=Index(["A", "B"], name="aaa"),
|
|
columns=MultiIndex.from_tuples(
|
|
list(zip(list("WWWWXXXX"), list("yyzzyyzz"), list("cdcdcdcd"))),
|
|
names=["www", "yyy", "ccc"],
|
|
),
|
|
)
|
|
|
|
res = df.unstack()
|
|
tm.assert_frame_equal(res, exp)
|
|
|
|
res = df.unstack("ccc")
|
|
tm.assert_frame_equal(res, exp)
|
|
|
|
exp = tm.SubclassedDataFrame(
|
|
[[10, 30, 11, 31, 12, 32, 13, 33], [20, 40, 21, 41, 22, 42, 23, 43]],
|
|
index=Index(["c", "d"], name="ccc"),
|
|
columns=MultiIndex.from_tuples(
|
|
list(zip(list("WWWWXXXX"), list("yyzzyyzz"), list("ABABABAB"))),
|
|
names=["www", "yyy", "aaa"],
|
|
),
|
|
)
|
|
|
|
res = df.unstack("aaa")
|
|
tm.assert_frame_equal(res, exp)
|
|
|
|
def test_subclass_unstack_multi_mixed(self):
|
|
# GH 15564
|
|
df = tm.SubclassedDataFrame(
|
|
[
|
|
[10, 11, 12.0, 13.0],
|
|
[20, 21, 22.0, 23.0],
|
|
[30, 31, 32.0, 33.0],
|
|
[40, 41, 42.0, 43.0],
|
|
],
|
|
index=MultiIndex.from_tuples(
|
|
list(zip(list("AABB"), list("cdcd"))), names=["aaa", "ccc"]
|
|
),
|
|
columns=MultiIndex.from_tuples(
|
|
list(zip(list("WWXX"), list("yzyz"))), names=["www", "yyy"]
|
|
),
|
|
)
|
|
|
|
exp = tm.SubclassedDataFrame(
|
|
[
|
|
[10, 20, 11, 21, 12.0, 22.0, 13.0, 23.0],
|
|
[30, 40, 31, 41, 32.0, 42.0, 33.0, 43.0],
|
|
],
|
|
index=Index(["A", "B"], name="aaa"),
|
|
columns=MultiIndex.from_tuples(
|
|
list(zip(list("WWWWXXXX"), list("yyzzyyzz"), list("cdcdcdcd"))),
|
|
names=["www", "yyy", "ccc"],
|
|
),
|
|
)
|
|
|
|
res = df.unstack()
|
|
tm.assert_frame_equal(res, exp)
|
|
|
|
res = df.unstack("ccc")
|
|
tm.assert_frame_equal(res, exp)
|
|
|
|
exp = tm.SubclassedDataFrame(
|
|
[
|
|
[10, 30, 11, 31, 12.0, 32.0, 13.0, 33.0],
|
|
[20, 40, 21, 41, 22.0, 42.0, 23.0, 43.0],
|
|
],
|
|
index=Index(["c", "d"], name="ccc"),
|
|
columns=MultiIndex.from_tuples(
|
|
list(zip(list("WWWWXXXX"), list("yyzzyyzz"), list("ABABABAB"))),
|
|
names=["www", "yyy", "aaa"],
|
|
),
|
|
)
|
|
|
|
res = df.unstack("aaa")
|
|
tm.assert_frame_equal(res, exp)
|
|
|
|
def test_subclass_pivot(self):
|
|
# GH 15564
|
|
df = tm.SubclassedDataFrame(
|
|
{
|
|
"index": ["A", "B", "C", "C", "B", "A"],
|
|
"columns": ["One", "One", "One", "Two", "Two", "Two"],
|
|
"values": [1.0, 2.0, 3.0, 3.0, 2.0, 1.0],
|
|
}
|
|
)
|
|
|
|
pivoted = df.pivot(index="index", columns="columns", values="values")
|
|
|
|
expected = tm.SubclassedDataFrame(
|
|
{
|
|
"One": {"A": 1.0, "B": 2.0, "C": 3.0},
|
|
"Two": {"A": 1.0, "B": 2.0, "C": 3.0},
|
|
}
|
|
)
|
|
|
|
expected.index.name, expected.columns.name = "index", "columns"
|
|
|
|
tm.assert_frame_equal(pivoted, expected)
|
|
|
|
def test_subclassed_melt(self):
|
|
# GH 15564
|
|
cheese = tm.SubclassedDataFrame(
|
|
{
|
|
"first": ["John", "Mary"],
|
|
"last": ["Doe", "Bo"],
|
|
"height": [5.5, 6.0],
|
|
"weight": [130, 150],
|
|
}
|
|
)
|
|
|
|
melted = pd.melt(cheese, id_vars=["first", "last"])
|
|
|
|
expected = 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"],
|
|
)
|
|
|
|
tm.assert_frame_equal(melted, expected)
|
|
|
|
def test_subclassed_wide_to_long(self):
|
|
# GH 9762
|
|
|
|
np.random.seed(123)
|
|
x = np.random.randn(3)
|
|
df = tm.SubclassedDataFrame(
|
|
{
|
|
"A1970": {0: "a", 1: "b", 2: "c"},
|
|
"A1980": {0: "d", 1: "e", 2: "f"},
|
|
"B1970": {0: 2.5, 1: 1.2, 2: 0.7},
|
|
"B1980": {0: 3.2, 1: 1.3, 2: 0.1},
|
|
"X": dict(zip(range(3), x)),
|
|
}
|
|
)
|
|
|
|
df["id"] = df.index
|
|
exp_data = {
|
|
"X": x.tolist() + x.tolist(),
|
|
"A": ["a", "b", "c", "d", "e", "f"],
|
|
"B": [2.5, 1.2, 0.7, 3.2, 1.3, 0.1],
|
|
"year": [1970, 1970, 1970, 1980, 1980, 1980],
|
|
"id": [0, 1, 2, 0, 1, 2],
|
|
}
|
|
expected = tm.SubclassedDataFrame(exp_data)
|
|
expected = expected.set_index(["id", "year"])[["X", "A", "B"]]
|
|
long_frame = pd.wide_to_long(df, ["A", "B"], i="id", j="year")
|
|
|
|
tm.assert_frame_equal(long_frame, expected)
|
|
|
|
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_subclassed_reductions(self, all_reductions):
|
|
# GH 25596
|
|
|
|
df = tm.SubclassedDataFrame({"A": [1, 2, 3], "B": [4, 5, 6], "C": [7, 8, 9]})
|
|
result = getattr(df, all_reductions)()
|
|
assert isinstance(result, tm.SubclassedSeries)
|
|
|
|
def test_subclassed_count(self):
|
|
|
|
df = tm.SubclassedDataFrame(
|
|
{
|
|
"Person": ["John", "Myla", "Lewis", "John", "Myla"],
|
|
"Age": [24.0, np.nan, 21.0, 33, 26],
|
|
"Single": [False, True, True, True, False],
|
|
}
|
|
)
|
|
result = df.count()
|
|
assert isinstance(result, tm.SubclassedSeries)
|
|
|
|
df = tm.SubclassedDataFrame({"A": [1, 0, 3], "B": [0, 5, 6], "C": [7, 8, 0]})
|
|
result = df.count()
|
|
assert isinstance(result, tm.SubclassedSeries)
|
|
|
|
df = tm.SubclassedDataFrame(
|
|
[[10, 11, 12, 13], [20, 21, 22, 23], [30, 31, 32, 33], [40, 41, 42, 43]],
|
|
index=MultiIndex.from_tuples(
|
|
list(zip(list("AABB"), list("cdcd"))), names=["aaa", "ccc"]
|
|
),
|
|
columns=MultiIndex.from_tuples(
|
|
list(zip(list("WWXX"), list("yzyz"))), names=["www", "yyy"]
|
|
),
|
|
)
|
|
result = df.count(level=1)
|
|
assert isinstance(result, tm.SubclassedDataFrame)
|
|
|
|
df = tm.SubclassedDataFrame()
|
|
result = df.count()
|
|
assert isinstance(result, tm.SubclassedSeries)
|
|
|
|
def test_isin(self):
|
|
|
|
df = tm.SubclassedDataFrame(
|
|
{"num_legs": [2, 4], "num_wings": [2, 0]}, index=["falcon", "dog"]
|
|
)
|
|
result = df.isin([0, 2])
|
|
assert isinstance(result, tm.SubclassedDataFrame)
|
|
|
|
def test_duplicated(self):
|
|
|
|
df = tm.SubclassedDataFrame({"A": [1, 2, 3], "B": [4, 5, 6], "C": [7, 8, 9]})
|
|
result = df.duplicated()
|
|
assert isinstance(result, tm.SubclassedSeries)
|
|
|
|
df = tm.SubclassedDataFrame()
|
|
result = df.duplicated()
|
|
assert isinstance(result, tm.SubclassedSeries)
|
|
|
|
@pytest.mark.parametrize("idx_method", ["idxmax", "idxmin"])
|
|
def test_idx(self, idx_method):
|
|
|
|
df = tm.SubclassedDataFrame({"A": [1, 2, 3], "B": [4, 5, 6], "C": [7, 8, 9]})
|
|
result = getattr(df, idx_method)()
|
|
assert isinstance(result, tm.SubclassedSeries)
|
|
|
|
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_memory_usage(self):
|
|
|
|
df = tm.SubclassedDataFrame({"A": [1, 2, 3], "B": [4, 5, 6], "C": [7, 8, 9]})
|
|
result = df.memory_usage()
|
|
assert isinstance(result, tm.SubclassedSeries)
|
|
|
|
result = df.memory_usage(index=False)
|
|
assert isinstance(result, tm.SubclassedSeries)
|
|
|
|
@td.skip_if_no_scipy
|
|
def test_corrwith(self):
|
|
index = ["a", "b", "c", "d", "e"]
|
|
columns = ["one", "two", "three", "four"]
|
|
df1 = tm.SubclassedDataFrame(
|
|
np.random.randn(5, 4), index=index, columns=columns
|
|
)
|
|
df2 = tm.SubclassedDataFrame(
|
|
np.random.randn(4, 4), index=index[:4], columns=columns
|
|
)
|
|
correls = df1.corrwith(df2, axis=1, drop=True, method="kendall")
|
|
|
|
assert isinstance(correls, (tm.SubclassedSeries))
|
|
|
|
def test_asof(self):
|
|
|
|
N = 3
|
|
rng = pd.date_range("1/1/1990", periods=N, freq="53s")
|
|
df = tm.SubclassedDataFrame(
|
|
{
|
|
"A": [np.nan, np.nan, np.nan],
|
|
"B": [np.nan, np.nan, np.nan],
|
|
"C": [np.nan, np.nan, np.nan],
|
|
},
|
|
index=rng,
|
|
)
|
|
|
|
result = df.asof(rng[-2:])
|
|
assert isinstance(result, tm.SubclassedDataFrame)
|
|
|
|
result = df.asof(rng[-2])
|
|
assert isinstance(result, tm.SubclassedSeries)
|
|
|
|
result = df.asof("1989-12-31")
|
|
assert isinstance(result, tm.SubclassedSeries)
|
|
|
|
def test_idxmin_preserves_subclass(self):
|
|
# GH 28330
|
|
|
|
df = tm.SubclassedDataFrame({"A": [1, 2, 3], "B": [4, 5, 6], "C": [7, 8, 9]})
|
|
result = df.idxmin()
|
|
assert isinstance(result, tm.SubclassedSeries)
|
|
|
|
def test_idxmax_preserves_subclass(self):
|
|
# GH 28330
|
|
|
|
df = tm.SubclassedDataFrame({"A": [1, 2, 3], "B": [4, 5, 6], "C": [7, 8, 9]})
|
|
result = df.idxmax()
|
|
assert isinstance(result, tm.SubclassedSeries)
|
|
|
|
def test_equals_subclass(self):
|
|
# https://github.com/pandas-dev/pandas/pull/34402
|
|
# allow subclass in both directions
|
|
df1 = pd.DataFrame({"a": [1, 2, 3]})
|
|
df2 = tm.SubclassedDataFrame({"a": [1, 2, 3]})
|
|
assert df1.equals(df2)
|
|
assert df2.equals(df1)
|