619 lines
23 KiB
Python
619 lines
23 KiB
Python
import numpy as np
|
|
import pytest
|
|
|
|
from pandas.core.dtypes.common import is_integer_dtype
|
|
|
|
import pandas as pd
|
|
from pandas import Categorical, CategoricalIndex, DataFrame, Series, get_dummies
|
|
import pandas._testing as tm
|
|
from pandas.core.arrays.sparse import SparseArray, SparseDtype
|
|
|
|
|
|
class TestGetDummies:
|
|
@pytest.fixture
|
|
def df(self):
|
|
return DataFrame({"A": ["a", "b", "a"], "B": ["b", "b", "c"], "C": [1, 2, 3]})
|
|
|
|
@pytest.fixture(params=["uint8", "i8", np.float64, bool, None])
|
|
def dtype(self, request):
|
|
return np.dtype(request.param)
|
|
|
|
@pytest.fixture(params=["dense", "sparse"])
|
|
def sparse(self, request):
|
|
# params are strings to simplify reading test results,
|
|
# e.g. TestGetDummies::test_basic[uint8-sparse] instead of [uint8-True]
|
|
return request.param == "sparse"
|
|
|
|
def effective_dtype(self, dtype):
|
|
if dtype is None:
|
|
return np.uint8
|
|
return dtype
|
|
|
|
def test_get_dummies_raises_on_dtype_object(self, df):
|
|
with pytest.raises(ValueError):
|
|
get_dummies(df, dtype="object")
|
|
|
|
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"))
|
|
|
|
expected = DataFrame(
|
|
{"a": [1, 0, 0], "b": [0, 1, 0], "c": [0, 0, 1]},
|
|
dtype=self.effective_dtype(dtype),
|
|
)
|
|
if sparse:
|
|
expected = expected.apply(SparseArray, fill_value=0.0)
|
|
result = get_dummies(s_list, sparse=sparse, dtype=dtype)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
result = get_dummies(s_series, sparse=sparse, dtype=dtype)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
expected.index = list("ABC")
|
|
result = get_dummies(s_series_index, sparse=sparse, dtype=dtype)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_get_dummies_basic_types(self, sparse, dtype):
|
|
# GH 10531
|
|
s_list = list("abc")
|
|
s_series = Series(s_list)
|
|
s_df = DataFrame(
|
|
{"a": [0, 1, 0, 1, 2], "b": ["A", "A", "B", "C", "C"], "c": [2, 3, 3, 3, 2]}
|
|
)
|
|
|
|
expected = DataFrame(
|
|
{"a": [1, 0, 0], "b": [0, 1, 0], "c": [0, 0, 1]},
|
|
dtype=self.effective_dtype(dtype),
|
|
columns=list("abc"),
|
|
)
|
|
if sparse:
|
|
if is_integer_dtype(dtype):
|
|
fill_value = 0
|
|
elif dtype == bool:
|
|
fill_value = False
|
|
else:
|
|
fill_value = 0.0
|
|
|
|
expected = expected.apply(SparseArray, fill_value=fill_value)
|
|
result = get_dummies(s_list, sparse=sparse, dtype=dtype)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
result = get_dummies(s_series, sparse=sparse, dtype=dtype)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
result = get_dummies(s_df, columns=s_df.columns, sparse=sparse, dtype=dtype)
|
|
if sparse:
|
|
dtype_name = f"Sparse[{self.effective_dtype(dtype).name}, {fill_value}]"
|
|
else:
|
|
dtype_name = self.effective_dtype(dtype).name
|
|
|
|
expected = Series({dtype_name: 8})
|
|
result = result.dtypes.value_counts()
|
|
result.index = [str(i) for i in result.index]
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
result = get_dummies(s_df, columns=["a"], sparse=sparse, dtype=dtype)
|
|
|
|
expected_counts = {"int64": 1, "object": 1}
|
|
expected_counts[dtype_name] = 3 + expected_counts.get(dtype_name, 0)
|
|
|
|
expected = Series(expected_counts).sort_index()
|
|
result = result.dtypes.value_counts()
|
|
result.index = [str(i) for i in result.index]
|
|
result = result.sort_index()
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
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"])
|
|
|
|
res_list = get_dummies(just_na_list, sparse=sparse)
|
|
res_series = get_dummies(just_na_series, sparse=sparse)
|
|
res_series_index = get_dummies(just_na_series_index, sparse=sparse)
|
|
|
|
assert res_list.empty
|
|
assert res_series.empty
|
|
assert res_series_index.empty
|
|
|
|
assert res_list.index.tolist() == [0]
|
|
assert res_series.index.tolist() == [0]
|
|
assert res_series_index.index.tolist() == ["A"]
|
|
|
|
def test_get_dummies_include_na(self, sparse, dtype):
|
|
s = ["a", "b", np.nan]
|
|
res = get_dummies(s, sparse=sparse, dtype=dtype)
|
|
exp = DataFrame(
|
|
{"a": [1, 0, 0], "b": [0, 1, 0]}, dtype=self.effective_dtype(dtype)
|
|
)
|
|
if sparse:
|
|
exp = exp.apply(SparseArray, fill_value=0.0)
|
|
tm.assert_frame_equal(res, exp)
|
|
|
|
# Sparse dataframes do not allow nan labelled columns, see #GH8822
|
|
res_na = get_dummies(s, dummy_na=True, sparse=sparse, dtype=dtype)
|
|
exp_na = DataFrame(
|
|
{np.nan: [0, 0, 1], "a": [1, 0, 0], "b": [0, 1, 0]},
|
|
dtype=self.effective_dtype(dtype),
|
|
)
|
|
exp_na = exp_na.reindex(["a", "b", np.nan], axis=1)
|
|
# hack (NaN handling in assert_index_equal)
|
|
exp_na.columns = res_na.columns
|
|
if sparse:
|
|
exp_na = exp_na.apply(SparseArray, fill_value=0.0)
|
|
tm.assert_frame_equal(res_na, exp_na)
|
|
|
|
res_just_na = get_dummies([np.nan], dummy_na=True, sparse=sparse, dtype=dtype)
|
|
exp_just_na = DataFrame(
|
|
Series(1, index=[0]), columns=[np.nan], dtype=self.effective_dtype(dtype)
|
|
)
|
|
tm.assert_numpy_array_equal(res_just_na.values, exp_just_na.values)
|
|
|
|
def test_get_dummies_unicode(self, sparse):
|
|
# See GH 6885 - get_dummies chokes on unicode values
|
|
import unicodedata
|
|
|
|
e = "e"
|
|
eacute = unicodedata.lookup("LATIN SMALL LETTER E WITH ACUTE")
|
|
s = [e, eacute, eacute]
|
|
res = get_dummies(s, prefix="letter", sparse=sparse)
|
|
exp = DataFrame(
|
|
{"letter_e": [1, 0, 0], f"letter_{eacute}": [0, 1, 1]}, dtype=np.uint8
|
|
)
|
|
if sparse:
|
|
exp = exp.apply(SparseArray, fill_value=0)
|
|
tm.assert_frame_equal(res, exp)
|
|
|
|
def test_dataframe_dummies_all_obj(self, df, sparse):
|
|
df = df[["A", "B"]]
|
|
result = get_dummies(df, sparse=sparse)
|
|
expected = DataFrame(
|
|
{"A_a": [1, 0, 1], "A_b": [0, 1, 0], "B_b": [1, 1, 0], "B_c": [0, 0, 1]},
|
|
dtype=np.uint8,
|
|
)
|
|
if sparse:
|
|
expected = DataFrame(
|
|
{
|
|
"A_a": SparseArray([1, 0, 1], dtype="uint8"),
|
|
"A_b": SparseArray([0, 1, 0], dtype="uint8"),
|
|
"B_b": SparseArray([1, 1, 0], dtype="uint8"),
|
|
"B_c": SparseArray([0, 0, 1], dtype="uint8"),
|
|
}
|
|
)
|
|
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_dataframe_dummies_mix_default(self, df, sparse, dtype):
|
|
result = get_dummies(df, sparse=sparse, dtype=dtype)
|
|
if sparse:
|
|
arr = SparseArray
|
|
typ = SparseDtype(dtype, 0)
|
|
else:
|
|
arr = np.array
|
|
typ = dtype
|
|
expected = DataFrame(
|
|
{
|
|
"C": [1, 2, 3],
|
|
"A_a": arr([1, 0, 1], dtype=typ),
|
|
"A_b": arr([0, 1, 0], dtype=typ),
|
|
"B_b": arr([1, 1, 0], dtype=typ),
|
|
"B_c": arr([0, 0, 1], dtype=typ),
|
|
}
|
|
)
|
|
expected = expected[["C", "A_a", "A_b", "B_b", "B_c"]]
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_dataframe_dummies_prefix_list(self, df, sparse):
|
|
prefixes = ["from_A", "from_B"]
|
|
result = get_dummies(df, prefix=prefixes, sparse=sparse)
|
|
expected = DataFrame(
|
|
{
|
|
"C": [1, 2, 3],
|
|
"from_A_a": [1, 0, 1],
|
|
"from_A_b": [0, 1, 0],
|
|
"from_B_b": [1, 1, 0],
|
|
"from_B_c": [0, 0, 1],
|
|
},
|
|
dtype=np.uint8,
|
|
)
|
|
expected[["C"]] = df[["C"]]
|
|
cols = ["from_A_a", "from_A_b", "from_B_b", "from_B_c"]
|
|
expected = expected[["C"] + cols]
|
|
|
|
typ = SparseArray if sparse else Series
|
|
expected[cols] = expected[cols].apply(lambda x: typ(x))
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_dataframe_dummies_prefix_str(self, df, sparse):
|
|
# not that you should do this...
|
|
result = get_dummies(df, prefix="bad", sparse=sparse)
|
|
bad_columns = ["bad_a", "bad_b", "bad_b", "bad_c"]
|
|
expected = DataFrame(
|
|
[[1, 1, 0, 1, 0], [2, 0, 1, 1, 0], [3, 1, 0, 0, 1]],
|
|
columns=["C"] + bad_columns,
|
|
dtype=np.uint8,
|
|
)
|
|
expected = expected.astype({"C": np.int64})
|
|
if sparse:
|
|
# work around astyping & assigning with duplicate columns
|
|
# https://github.com/pandas-dev/pandas/issues/14427
|
|
expected = pd.concat(
|
|
[
|
|
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,
|
|
)
|
|
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_dataframe_dummies_subset(self, df, sparse):
|
|
result = get_dummies(df, prefix=["from_A"], columns=["A"], sparse=sparse)
|
|
expected = DataFrame(
|
|
{
|
|
"B": ["b", "b", "c"],
|
|
"C": [1, 2, 3],
|
|
"from_A_a": [1, 0, 1],
|
|
"from_A_b": [0, 1, 0],
|
|
},
|
|
dtype=np.uint8,
|
|
)
|
|
expected[["C"]] = df[["C"]]
|
|
if sparse:
|
|
cols = ["from_A_a", "from_A_b"]
|
|
expected[cols] = expected[cols].astype(SparseDtype("uint8", 0))
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_dataframe_dummies_prefix_sep(self, df, sparse):
|
|
result = get_dummies(df, prefix_sep="..", sparse=sparse)
|
|
expected = DataFrame(
|
|
{
|
|
"C": [1, 2, 3],
|
|
"A..a": [1, 0, 1],
|
|
"A..b": [0, 1, 0],
|
|
"B..b": [1, 1, 0],
|
|
"B..c": [0, 0, 1],
|
|
},
|
|
dtype=np.uint8,
|
|
)
|
|
expected[["C"]] = df[["C"]]
|
|
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(SparseDtype("uint8", 0))
|
|
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
result = get_dummies(df, prefix_sep=["..", "__"], sparse=sparse)
|
|
expected = expected.rename(columns={"B..b": "B__b", "B..c": "B__c"})
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
result = get_dummies(df, prefix_sep={"A": "..", "B": "__"}, sparse=sparse)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_dataframe_dummies_prefix_bad_length(self, df, sparse):
|
|
with pytest.raises(ValueError):
|
|
get_dummies(df, prefix=["too few"], sparse=sparse)
|
|
|
|
def test_dataframe_dummies_prefix_sep_bad_length(self, df, sparse):
|
|
with pytest.raises(ValueError):
|
|
get_dummies(df, prefix_sep=["bad"], sparse=sparse)
|
|
|
|
def test_dataframe_dummies_prefix_dict(self, sparse):
|
|
prefixes = {"A": "from_A", "B": "from_B"}
|
|
df = DataFrame({"C": [1, 2, 3], "A": ["a", "b", "a"], "B": ["b", "b", "c"]})
|
|
result = get_dummies(df, prefix=prefixes, sparse=sparse)
|
|
|
|
expected = DataFrame(
|
|
{
|
|
"C": [1, 2, 3],
|
|
"from_A_a": [1, 0, 1],
|
|
"from_A_b": [0, 1, 0],
|
|
"from_B_b": [1, 1, 0],
|
|
"from_B_c": [0, 0, 1],
|
|
}
|
|
)
|
|
|
|
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(SparseDtype("uint8", 0))
|
|
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_dataframe_dummies_with_na(self, df, sparse, dtype):
|
|
df.loc[3, :] = [np.nan, np.nan, np.nan]
|
|
result = get_dummies(df, dummy_na=True, sparse=sparse, dtype=dtype).sort_index(
|
|
axis=1
|
|
)
|
|
|
|
if sparse:
|
|
arr = SparseArray
|
|
typ = SparseDtype(dtype, 0)
|
|
else:
|
|
arr = np.array
|
|
typ = dtype
|
|
|
|
expected = DataFrame(
|
|
{
|
|
"C": [1, 2, 3, np.nan],
|
|
"A_a": arr([1, 0, 1, 0], dtype=typ),
|
|
"A_b": arr([0, 1, 0, 0], dtype=typ),
|
|
"A_nan": arr([0, 0, 0, 1], dtype=typ),
|
|
"B_b": arr([1, 1, 0, 0], dtype=typ),
|
|
"B_c": arr([0, 0, 1, 0], dtype=typ),
|
|
"B_nan": arr([0, 0, 0, 1], dtype=typ),
|
|
}
|
|
).sort_index(axis=1)
|
|
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
result = get_dummies(df, dummy_na=False, sparse=sparse, dtype=dtype)
|
|
expected = expected[["C", "A_a", "A_b", "B_b", "B_c"]]
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_dataframe_dummies_with_categorical(self, df, sparse, dtype):
|
|
df["cat"] = Categorical(["x", "y", "y"])
|
|
result = get_dummies(df, sparse=sparse, dtype=dtype).sort_index(axis=1)
|
|
if sparse:
|
|
arr = SparseArray
|
|
typ = SparseDtype(dtype, 0)
|
|
else:
|
|
arr = np.array
|
|
typ = dtype
|
|
|
|
expected = DataFrame(
|
|
{
|
|
"C": [1, 2, 3],
|
|
"A_a": arr([1, 0, 1], dtype=typ),
|
|
"A_b": arr([0, 1, 0], dtype=typ),
|
|
"B_b": arr([1, 1, 0], dtype=typ),
|
|
"B_c": arr([0, 0, 1], dtype=typ),
|
|
"cat_x": arr([1, 0, 0], dtype=typ),
|
|
"cat_y": arr([0, 1, 1], dtype=typ),
|
|
}
|
|
).sort_index(axis=1)
|
|
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize(
|
|
"get_dummies_kwargs,expected",
|
|
[
|
|
(
|
|
{"data": DataFrame({"ä": ["a"]})},
|
|
DataFrame({"ä_a": [1]}, dtype=np.uint8),
|
|
),
|
|
(
|
|
{"data": DataFrame({"x": ["ä"]})},
|
|
DataFrame({"x_ä": [1]}, dtype=np.uint8),
|
|
),
|
|
(
|
|
{"data": DataFrame({"x": ["a"]}), "prefix": "ä"},
|
|
DataFrame({"ä_a": [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 get_dummies incorrectly encodes unicode characters
|
|
# in dataframe column names
|
|
result = get_dummies(**get_dummies_kwargs)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
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")
|
|
s_series = Series(s_list)
|
|
s_series_index = Series(s_list, list("ABC"))
|
|
|
|
expected = DataFrame({"b": [0, 1, 0], "c": [0, 0, 1]}, dtype=np.uint8)
|
|
|
|
result = get_dummies(s_list, drop_first=True, sparse=sparse)
|
|
if sparse:
|
|
expected = expected.apply(SparseArray, fill_value=0)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
result = get_dummies(s_series, drop_first=True, sparse=sparse)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
expected.index = list("ABC")
|
|
result = get_dummies(s_series_index, drop_first=True, sparse=sparse)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
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)
|
|
s_series_index = Series(s_list, list("ABC"))
|
|
|
|
expected = DataFrame(index=np.arange(3))
|
|
|
|
result = get_dummies(s_list, drop_first=True, sparse=sparse)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
result = get_dummies(s_series, drop_first=True, sparse=sparse)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
expected = DataFrame(index=list("ABC"))
|
|
result = get_dummies(s_series_index, drop_first=True, sparse=sparse)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
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)
|
|
exp = DataFrame({"b": [0, 1, 0]}, dtype=np.uint8)
|
|
if sparse:
|
|
exp = exp.apply(SparseArray, fill_value=0)
|
|
|
|
tm.assert_frame_equal(res, exp)
|
|
|
|
res_na = get_dummies(s_NA, dummy_na=True, drop_first=True, sparse=sparse)
|
|
exp_na = DataFrame({"b": [0, 1, 0], np.nan: [0, 0, 1]}, dtype=np.uint8).reindex(
|
|
["b", np.nan], axis=1
|
|
)
|
|
if sparse:
|
|
exp_na = exp_na.apply(SparseArray, fill_value=0)
|
|
tm.assert_frame_equal(res_na, exp_na)
|
|
|
|
res_just_na = get_dummies(
|
|
[np.nan], dummy_na=True, drop_first=True, sparse=sparse
|
|
)
|
|
exp_just_na = DataFrame(index=np.arange(1))
|
|
tm.assert_frame_equal(res_just_na, exp_just_na)
|
|
|
|
def test_dataframe_dummies_drop_first(self, df, sparse):
|
|
df = df[["A", "B"]]
|
|
result = get_dummies(df, drop_first=True, sparse=sparse)
|
|
expected = DataFrame({"A_b": [0, 1, 0], "B_c": [0, 0, 1]}, dtype=np.uint8)
|
|
if sparse:
|
|
expected = expected.apply(SparseArray, fill_value=0)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_dataframe_dummies_drop_first_with_categorical(self, df, sparse, dtype):
|
|
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]}
|
|
)
|
|
cols = ["A_b", "B_c", "cat_y"]
|
|
expected[cols] = expected[cols].astype(np.uint8)
|
|
expected = expected[["C", "A_b", "B_c", "cat_y"]]
|
|
if sparse:
|
|
for col in cols:
|
|
expected[col] = SparseArray(expected[col])
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_dataframe_dummies_drop_first_with_na(self, df, sparse):
|
|
df.loc[3, :] = [np.nan, np.nan, np.nan]
|
|
result = get_dummies(
|
|
df, dummy_na=True, drop_first=True, sparse=sparse
|
|
).sort_index(axis=1)
|
|
expected = DataFrame(
|
|
{
|
|
"C": [1, 2, 3, np.nan],
|
|
"A_b": [0, 1, 0, 0],
|
|
"A_nan": [0, 0, 0, 1],
|
|
"B_c": [0, 0, 1, 0],
|
|
"B_nan": [0, 0, 0, 1],
|
|
}
|
|
)
|
|
cols = ["A_b", "A_nan", "B_c", "B_nan"]
|
|
expected[cols] = expected[cols].astype(np.uint8)
|
|
expected = expected.sort_index(axis=1)
|
|
if sparse:
|
|
for col in cols:
|
|
expected[col] = SparseArray(expected[col])
|
|
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
result = get_dummies(df, dummy_na=False, drop_first=True, sparse=sparse)
|
|
expected = expected[["C", "A_b", "B_c"]]
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_get_dummies_int_int(self):
|
|
data = Series([1, 2, 1])
|
|
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(Categorical(["a", "b", "a"]))
|
|
result = get_dummies(data)
|
|
expected = DataFrame(
|
|
[[1, 0], [0, 1], [1, 0]], columns=Categorical(["a", "b"]), dtype=np.uint8
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_get_dummies_int_df(self, dtype):
|
|
data = DataFrame(
|
|
{
|
|
"A": [1, 2, 1],
|
|
"B": Categorical(["a", "b", "a"]),
|
|
"C": [1, 2, 1],
|
|
"D": [1.0, 2.0, 1.0],
|
|
}
|
|
)
|
|
columns = ["C", "D", "A_1", "A_2", "B_a", "B_b"]
|
|
expected = DataFrame(
|
|
[[1, 1.0, 1, 0, 1, 0], [2, 2.0, 0, 1, 0, 1], [1, 1.0, 1, 0, 1, 0]],
|
|
columns=columns,
|
|
)
|
|
expected[columns[2:]] = expected[columns[2:]].astype(dtype)
|
|
result = get_dummies(data, columns=["A", "B"], dtype=dtype)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize("ordered", [True, False])
|
|
def test_dataframe_dummies_preserve_categorical_dtype(self, dtype, ordered):
|
|
# GH13854
|
|
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 = CategoricalIndex(
|
|
cat.categories, categories=cat.categories, ordered=ordered
|
|
)
|
|
expected = DataFrame(data, columns=cols, dtype=self.effective_dtype(dtype))
|
|
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize("sparse", [True, False])
|
|
def test_get_dummies_dont_sparsify_all_columns(self, sparse):
|
|
# GH18914
|
|
df = DataFrame.from_dict({"GDP": [1, 2], "Nation": ["AB", "CD"]})
|
|
df = get_dummies(df, columns=["Nation"], sparse=sparse)
|
|
df2 = df.reindex(columns=["GDP"])
|
|
|
|
tm.assert_frame_equal(df[["GDP"]], df2)
|
|
|
|
def test_get_dummies_duplicate_columns(self, df):
|
|
# GH20839
|
|
df.columns = ["A", "A", "A"]
|
|
result = get_dummies(df).sort_index(axis=1)
|
|
|
|
expected = DataFrame(
|
|
[[1, 1, 0, 1, 0], [2, 0, 1, 1, 0], [3, 1, 0, 0, 1]],
|
|
columns=["A", "A_a", "A_b", "A_b", "A_c"],
|
|
dtype=np.uint8,
|
|
).sort_index(axis=1)
|
|
|
|
expected = expected.astype({"A": np.int64})
|
|
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
def test_get_dummies_all_sparse(self):
|
|
df = DataFrame({"A": [1, 2]})
|
|
result = get_dummies(df, columns=["A"], sparse=True)
|
|
dtype = SparseDtype("uint8", 0)
|
|
expected = DataFrame(
|
|
{
|
|
"A_1": SparseArray([1, 0], dtype=dtype),
|
|
"A_2": SparseArray([0, 1], dtype=dtype),
|
|
}
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
@pytest.mark.parametrize("values", ["baz"])
|
|
def test_get_dummies_with_string_values(self, values):
|
|
# issue #28383
|
|
df = DataFrame(
|
|
{
|
|
"bar": [1, 2, 3, 4, 5, 6],
|
|
"foo": ["one", "one", "one", "two", "two", "two"],
|
|
"baz": ["A", "B", "C", "A", "B", "C"],
|
|
"zoo": ["x", "y", "z", "q", "w", "t"],
|
|
}
|
|
)
|
|
|
|
msg = "Input must be a list-like for parameter `columns`"
|
|
|
|
with pytest.raises(TypeError, match=msg):
|
|
get_dummies(df, columns=values)
|