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Apr 1, 2020
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REF/CLN: test_get_dummies #33184

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f86028c
CLN: get_dummies tests
jbrockmendel Mar 31, 2020
d826ea0
rename
jbrockmendel Mar 31, 2020
6d8d2b7
Merge branch 'master' of https://github.com/pandas-dev/pandas into ts…
jbrockmendel Mar 31, 2020
99b4714
fixup typo
jbrockmendel Mar 31, 2020
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130 changes: 54 additions & 76 deletions pandas/tests/reshape/test_reshape.py → pandas/tests/reshape/test_get_dummies.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,7 +6,7 @@
from pandas.core.dtypes.common import is_integer_dtype

import pandas as pd
from pandas import Categorical, DataFrame, Index, Series, get_dummies
from pandas import Categorical, CategoricalIndex, DataFrame, Series, get_dummies
import pandas._testing as tm
from pandas.core.arrays.sparse import SparseArray, SparseDtype

Expand All @@ -31,11 +31,11 @@ def effective_dtype(self, dtype):
return np.uint8
return dtype

def test_raises_on_dtype_object(self, df):
def test_get_dummies_raises_on_dtype_object(self, df):
with pytest.raises(ValueError):
get_dummies(df, dtype="object")

def test_basic(self, sparse, dtype):
def test_get_dummies_basic(self, sparse, dtype):
s_list = list("abc")
s_series = Series(s_list)
s_series_index = Series(s_list, list("ABC"))
Expand All @@ -56,7 +56,7 @@ def test_basic(self, sparse, dtype):
result = get_dummies(s_series_index, sparse=sparse, dtype=dtype)
tm.assert_frame_equal(result, expected)

def test_basic_types(self, sparse, dtype):
def test_get_dummies_basic_types(self, sparse, dtype):
# GH 10531
s_list = list("abc")
s_series = Series(s_list)
Expand Down Expand Up @@ -106,7 +106,7 @@ def test_basic_types(self, sparse, dtype):
result = result.sort_index()
tm.assert_series_equal(result, expected)

def test_just_na(self, sparse):
def test_get_dummies_just_na(self, sparse):
just_na_list = [np.nan]
just_na_series = Series(just_na_list)
just_na_series_index = Series(just_na_list, index=["A"])
Expand All @@ -123,7 +123,7 @@ def test_just_na(self, sparse):
assert res_series.index.tolist() == [0]
assert res_series_index.index.tolist() == ["A"]

def test_include_na(self, sparse, dtype):
def test_get_dummies_include_na(self, sparse, dtype):
s = ["a", "b", np.nan]
res = get_dummies(s, sparse=sparse, dtype=dtype)
exp = DataFrame(
Expand Down Expand Up @@ -152,7 +152,7 @@ def test_include_na(self, sparse, dtype):
)
tm.assert_numpy_array_equal(res_just_na.values, exp_just_na.values)

def test_unicode(self, sparse):
def test_get_dummies_unicode(self, sparse):
# See GH 6885 - get_dummies chokes on unicode values
import unicodedata

Expand All @@ -175,7 +175,7 @@ def test_dataframe_dummies_all_obj(self, df, sparse):
dtype=np.uint8,
)
if sparse:
expected = pd.DataFrame(
expected = DataFrame(
{
"A_a": SparseArray([1, 0, 1], dtype="uint8"),
"A_b": SparseArray([0, 1, 0], dtype="uint8"),
Expand Down Expand Up @@ -223,7 +223,7 @@ def test_dataframe_dummies_prefix_list(self, df, sparse):
cols = ["from_A_a", "from_A_b", "from_B_b", "from_B_c"]
expected = expected[["C"] + cols]

typ = SparseArray if sparse else pd.Series
typ = SparseArray if sparse else Series
expected[cols] = expected[cols].apply(lambda x: typ(x))
tm.assert_frame_equal(result, expected)

Expand All @@ -242,11 +242,11 @@ def test_dataframe_dummies_prefix_str(self, df, sparse):
# https://github.com/pandas-dev/pandas/issues/14427
expected = pd.concat(
[
pd.Series([1, 2, 3], name="C"),
pd.Series([1, 0, 1], name="bad_a", dtype="Sparse[uint8]"),
pd.Series([0, 1, 0], name="bad_b", dtype="Sparse[uint8]"),
pd.Series([1, 1, 0], name="bad_b", dtype="Sparse[uint8]"),
pd.Series([0, 0, 1], name="bad_c", dtype="Sparse[uint8]"),
Series([1, 2, 3], name="C"),
Series([1, 0, 1], name="bad_a", dtype="Sparse[uint8]"),
Series([0, 1, 0], name="bad_b", dtype="Sparse[uint8]"),
Series([1, 1, 0], name="bad_b", dtype="Sparse[uint8]"),
Series([0, 0, 1], name="bad_c", dtype="Sparse[uint8]"),
],
axis=1,
)
Expand All @@ -267,7 +267,7 @@ def test_dataframe_dummies_subset(self, df, sparse):
expected[["C"]] = df[["C"]]
if sparse:
cols = ["from_A_a", "from_A_b"]
expected[cols] = expected[cols].astype(pd.SparseDtype("uint8", 0))
expected[cols] = expected[cols].astype(SparseDtype("uint8", 0))
tm.assert_frame_equal(result, expected)

def test_dataframe_dummies_prefix_sep(self, df, sparse):
Expand All @@ -286,7 +286,7 @@ def test_dataframe_dummies_prefix_sep(self, df, sparse):
expected = expected[["C", "A..a", "A..b", "B..b", "B..c"]]
if sparse:
cols = ["A..a", "A..b", "B..b", "B..c"]
expected[cols] = expected[cols].astype(pd.SparseDtype("uint8", 0))
expected[cols] = expected[cols].astype(SparseDtype("uint8", 0))

tm.assert_frame_equal(result, expected)

Expand Down Expand Up @@ -323,7 +323,7 @@ def test_dataframe_dummies_prefix_dict(self, sparse):
columns = ["from_A_a", "from_A_b", "from_B_b", "from_B_c"]
expected[columns] = expected[columns].astype(np.uint8)
if sparse:
expected[columns] = expected[columns].astype(pd.SparseDtype("uint8", 0))
expected[columns] = expected[columns].astype(SparseDtype("uint8", 0))

tm.assert_frame_equal(result, expected)

Expand Down Expand Up @@ -359,7 +359,7 @@ def test_dataframe_dummies_with_na(self, df, sparse, dtype):
tm.assert_frame_equal(result, expected)

def test_dataframe_dummies_with_categorical(self, df, sparse, dtype):
df["cat"] = pd.Categorical(["x", "y", "y"])
df["cat"] = Categorical(["x", "y", "y"])
result = get_dummies(df, sparse=sparse, dtype=dtype).sort_index(axis=1)
if sparse:
arr = SparseArray
Expand All @@ -386,30 +386,30 @@ def test_dataframe_dummies_with_categorical(self, df, sparse, dtype):
"get_dummies_kwargs,expected",
[
(
{"data": pd.DataFrame(({"ä": ["a"]}))},
pd.DataFrame({"ä_a": [1]}, dtype=np.uint8),
{"data": DataFrame(({"ä": ["a"]}))},
DataFrame({"ä_a": [1]}, dtype=np.uint8),
),
(
{"data": pd.DataFrame({"x": ["ä"]})},
pd.DataFrame({"x_ä": [1]}, dtype=np.uint8),
{"data": DataFrame({"x": ["ä"]})},
DataFrame({"x_ä": [1]}, dtype=np.uint8),
),
(
{"data": pd.DataFrame({"x": ["a"]}), "prefix": "ä"},
pd.DataFrame({"ä_a": [1]}, dtype=np.uint8),
{"data": DataFrame({"x": ["a"]}), "prefix": "ä"},
DataFrame({"ä_a": [1]}, dtype=np.uint8),
),
(
{"data": pd.DataFrame({"x": ["a"]}), "prefix_sep": "ä"},
pd.DataFrame({"xäa": [1]}, dtype=np.uint8),
{"data": DataFrame({"x": ["a"]}), "prefix_sep": "ä"},
DataFrame({"xäa": [1]}, dtype=np.uint8),
),
],
)
def test_dataframe_dummies_unicode(self, get_dummies_kwargs, expected):
# GH22084 pd.get_dummies incorrectly encodes unicode characters
# GH22084 get_dummies incorrectly encodes unicode characters
# in dataframe column names
result = get_dummies(**get_dummies_kwargs)
tm.assert_frame_equal(result, expected)

def test_basic_drop_first(self, sparse):
def test_get_dummies_basic_drop_first(self, sparse):
# GH12402 Add a new parameter `drop_first` to avoid collinearity
# Basic case
s_list = list("abc")
Expand All @@ -430,7 +430,7 @@ def test_basic_drop_first(self, sparse):
result = get_dummies(s_series_index, drop_first=True, sparse=sparse)
tm.assert_frame_equal(result, expected)

def test_basic_drop_first_one_level(self, sparse):
def test_get_dummies_basic_drop_first_one_level(self, sparse):
# Test the case that categorical variable only has one level.
s_list = list("aaa")
s_series = Series(s_list)
Expand All @@ -448,7 +448,7 @@ def test_basic_drop_first_one_level(self, sparse):
result = get_dummies(s_series_index, drop_first=True, sparse=sparse)
tm.assert_frame_equal(result, expected)

def test_basic_drop_first_NA(self, sparse):
def test_get_dummies_basic_drop_first_NA(self, sparse):
# Test NA handling together with drop_first
s_NA = ["a", "b", np.nan]
res = get_dummies(s_NA, drop_first=True, sparse=sparse)
Expand Down Expand Up @@ -481,7 +481,7 @@ def test_dataframe_dummies_drop_first(self, df, sparse):
tm.assert_frame_equal(result, expected)

def test_dataframe_dummies_drop_first_with_categorical(self, df, sparse, dtype):
df["cat"] = pd.Categorical(["x", "y", "y"])
df["cat"] = Categorical(["x", "y", "y"])
result = get_dummies(df, drop_first=True, sparse=sparse)
expected = DataFrame(
{"C": [1, 2, 3], "A_b": [0, 1, 0], "B_c": [0, 0, 1], "cat_y": [0, 1, 1]}
Expand Down Expand Up @@ -521,24 +521,24 @@ def test_dataframe_dummies_drop_first_with_na(self, df, sparse):
expected = expected[["C", "A_b", "B_c"]]
tm.assert_frame_equal(result, expected)

def test_int_int(self):
def test_get_dummies_int_int(self):
data = Series([1, 2, 1])
result = pd.get_dummies(data)
result = get_dummies(data)
expected = DataFrame([[1, 0], [0, 1], [1, 0]], columns=[1, 2], dtype=np.uint8)
tm.assert_frame_equal(result, expected)

data = Series(pd.Categorical(["a", "b", "a"]))
result = pd.get_dummies(data)
data = Series(Categorical(["a", "b", "a"]))
result = get_dummies(data)
expected = DataFrame(
[[1, 0], [0, 1], [1, 0]], columns=pd.Categorical(["a", "b"]), dtype=np.uint8
[[1, 0], [0, 1], [1, 0]], columns=Categorical(["a", "b"]), dtype=np.uint8
)
tm.assert_frame_equal(result, expected)

def test_int_df(self, dtype):
def test_get_dummies_int_df(self, dtype):
data = DataFrame(
{
"A": [1, 2, 1],
"B": pd.Categorical(["a", "b", "a"]),
"B": Categorical(["a", "b", "a"]),
"C": [1, 2, 1],
"D": [1.0, 2.0, 1.0],
}
Expand All @@ -549,22 +549,22 @@ def test_int_df(self, dtype):
columns=columns,
)
expected[columns[2:]] = expected[columns[2:]].astype(dtype)
result = pd.get_dummies(data, columns=["A", "B"], dtype=dtype)
result = get_dummies(data, columns=["A", "B"], dtype=dtype)
tm.assert_frame_equal(result, expected)

def test_dataframe_dummies_preserve_categorical_dtype(self, dtype):
@pytest.mark.parametrize("ordered", [True, False])
def test_dataframe_dummies_preserve_categorical_dtype(self, dtype, ordered):
# GH13854
for ordered in [False, True]:
cat = pd.Categorical(list("xy"), categories=list("xyz"), ordered=ordered)
result = get_dummies(cat, dtype=dtype)
cat = Categorical(list("xy"), categories=list("xyz"), ordered=ordered)
result = get_dummies(cat, dtype=dtype)

data = np.array([[1, 0, 0], [0, 1, 0]], dtype=self.effective_dtype(dtype))
cols = pd.CategoricalIndex(
cat.categories, categories=cat.categories, ordered=ordered
)
expected = DataFrame(data, columns=cols, dtype=self.effective_dtype(dtype))
data = np.array([[1, 0, 0], [0, 1, 0]], dtype=self.effective_dtype(dtype))
cols = CategoricalIndex(
cat.categories, categories=cat.categories, ordered=ordered
)
expected = DataFrame(data, columns=cols, dtype=self.effective_dtype(dtype))

tm.assert_frame_equal(result, expected)
tm.assert_frame_equal(result, expected)

@pytest.mark.parametrize("sparse", [True, False])
def test_get_dummies_dont_sparsify_all_columns(self, sparse):
Expand Down Expand Up @@ -593,10 +593,10 @@ def test_get_dummies_duplicate_columns(self, df):
tm.assert_frame_equal(result, expected)

def test_get_dummies_all_sparse(self):
df = pd.DataFrame({"A": [1, 2]})
result = pd.get_dummies(df, columns=["A"], sparse=True)
df = DataFrame({"A": [1, 2]})
result = get_dummies(df, columns=["A"], sparse=True)
dtype = SparseDtype("uint8", 0)
expected = pd.DataFrame(
expected = DataFrame(
{
"A_1": SparseArray([1, 0], dtype=dtype),
"A_2": SparseArray([0, 1], dtype=dtype),
Expand All @@ -607,7 +607,7 @@ def test_get_dummies_all_sparse(self):
@pytest.mark.parametrize("values", ["baz"])
def test_get_dummies_with_string_values(self, values):
# issue #28383
df = pd.DataFrame(
df = DataFrame(
{
"bar": [1, 2, 3, 4, 5, 6],
"foo": ["one", "one", "one", "two", "two", "two"],
Expand All @@ -619,26 +619,4 @@ def test_get_dummies_with_string_values(self, values):
msg = "Input must be a list-like for parameter `columns`"

with pytest.raises(TypeError, match=msg):
pd.get_dummies(df, columns=values)


class TestCategoricalReshape:
def test_reshaping_multi_index_categorical(self):

cols = ["ItemA", "ItemB", "ItemC"]
data = {c: tm.makeTimeDataFrame() for c in cols}
df = pd.concat({c: data[c].stack() for c in data}, axis="columns")
df.index.names = ["major", "minor"]
df["str"] = "foo"

df["category"] = df["str"].astype("category")
result = df["category"].unstack()

dti = df.index.levels[0]
c = Categorical(["foo"] * len(dti))
expected = DataFrame(
{"A": c.copy(), "B": c.copy(), "C": c.copy(), "D": c.copy()},
columns=Index(list("ABCD"), name="minor"),
index=dti.rename("major"),
)
tm.assert_frame_equal(result, expected)
get_dummies(df, columns=values)
17 changes: 17 additions & 0 deletions pandas/tests/series/methods/test_unstack.py
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Original file line number Diff line number Diff line change
Expand Up @@ -118,3 +118,20 @@ def test_unstack_mixed_type_name_in_multiindex(
expected_values, columns=expected_columns, index=expected_index,
)
tm.assert_frame_equal(result, expected)


def test_unstack_multi_index_categorical_values():

mi = tm.makeTimeDataFrame().stack().index.rename(["major", "minor"])
ser = pd.Series(["foo"] * len(mi), index=mi, name="category", dtype="category")

result = ser.unstack()

dti = ser.index.levels[0]
c = pd.Categorical(["foo"] * len(dti))
expected = DataFrame(
{"A": c.copy(), "B": c.copy(), "C": c.copy(), "D": c.copy()},
columns=pd.Index(list("ABCD"), name="minor"),
index=dti.rename("major"),
)
tm.assert_frame_equal(result, expected)