def test_concat_duplicate_columns():
rnd = numpy.random.RandomState(14)
numeric_df = pandas.DataFrame.from_dict(
OrderedDict([
("col_N", rnd.randn(100)),
("col_B", rnd.randn(100)),
("col_A",
pandas.Series(pandas.Categorical.from_codes(
rnd.binomial(4, 0.2, 100),
["C1", "C2", "C3", "C4", "C5"]),
name="col_A")),
]))
cat_df = pandas.DataFrame.from_dict(
OrderedDict([
("col_A",
pandas.Series(pandas.Categorical.from_codes(
rnd.binomial(4, 0.6, 100),
["C1", "C2", "C3", "C4", "C5"]),
name="col_A")),
("col_C",
pandas.Series(pandas.Categorical.from_codes(
rnd.binomial(1, 0.6, 100), ["Yes", "No"]),
name="col_C")),
]))
with pytest.raises(ValueError, match="duplicate columns col_A"):
safe_concat((numeric_df, cat_df), axis=1)
def test_concat_categorical_mismatch():
rnd = numpy.random.RandomState(14)
a = pandas.DataFrame.from_dict(OrderedDict([
("col_A", pandas.Series(pandas.Categorical.from_codes(
rnd.binomial(2, 0.6, 100), ["C1", "C2", "C3"]), name="col_A")),
("col_B", rnd.randn(100))]))
b = pandas.DataFrame.from_dict(OrderedDict([
("col_A", pandas.Series(pandas.Categorical.from_codes(
rnd.binomial(3, 0.6, 100), ["C1", "C2", "C3", "C4"]), name="col_A")),
("col_B", rnd.randn(100))]))
with pytest.raises(ValueError, match="categories for column col_A do not match"):
safe_concat((a, b), axis=0)
def test_concat_categorical():
rnd = numpy.random.RandomState(14)
a = pandas.DataFrame.from_dict(
OrderedDict([("col_A",
pandas.Series(pandas.Categorical.from_codes(
rnd.binomial(2, 0.6, 100), ["C1", "C2", "C3"]),
name="col_A")),
("col_B", rnd.randn(100))]))
b = pandas.DataFrame.from_dict(
OrderedDict([("col_A",
pandas.Series(pandas.Categorical.from_codes(
rnd.binomial(2, 0.2, 100), ["C1", "C2", "C3"]),
name="col_A")),
("col_B", rnd.randn(100))]))
expected_series = pandas.DataFrame.from_dict(
OrderedDict([
("col_A",
pandas.Series(
pandas.Categorical.from_codes(
numpy.concatenate((a.col_A.cat.codes.values,
b.col_A.cat.codes.values)),
["C1", "C2", "C3"]))),
("col_B", numpy.concatenate((a.col_B.values, b.col_B.values)))
]))
expected_series.index = pandas.Index(a.index.tolist() +
b.index.tolist())
actual_series = safe_concat((a, b), axis=0)
tm.assert_frame_equal(actual_series, expected_series)
def test_concat_numeric():
rnd = numpy.random.RandomState(14)
a = pandas.Series(rnd.randn(100), name="col_A")
b = pandas.Series(rnd.randn(100), name="col_B")
expected_df = pandas.DataFrame.from_items([(a.name, a), (b.name, b)])
actual_df = safe_concat((a, b), axis=1)
tm.assert_frame_equal(actual_df, expected_df)
def test_concat_numeric_categorical():
rnd = numpy.random.RandomState(14)
a = pandas.Series(rnd.randn(100), name="col_A")
b = pandas.Series(pandas.Categorical.from_codes(
rnd.binomial(4, 0.6, 100), ["C1", "C2", "C3", "C4", "C5"]),
name="col_B")
expected_df = pandas.DataFrame.from_items([(a.name, a), (b.name, b)])
actual_df = safe_concat((a, b), axis=1)
tm.assert_frame_equal(actual_df, expected_df)
def test_concat_dataframe_numeric_categorical(self):
rnd = numpy.random.RandomState(14)
numeric_df = pandas.DataFrame.from_items(
[("col_A", rnd.randn(100)), ("col_B", rnd.randn(100))]
)
cat_series = pandas.Series(pandas.Categorical.from_codes(
rnd.binomial(4, 0.6, 100), ["C1", "C2", "C3", "C4", "C5"]), name="col_C")
expected_df = numeric_df.copy()
expected_df["col_C"] = cat_series
actual_df = safe_concat((numeric_df, cat_series), axis=1)
tm.assert_frame_equal(actual_df, expected_df)
