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test_categorical.py
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import numpy as np
from pandas.core.dtypes.dtypes import CategoricalDtype
import pandas as pd
from pandas import (
Categorical,
DataFrame,
Series,
)
import pandas._testing as tm
class TestCategoricalConcat:
def test_categorical_concat(self, sort):
# See GH 10177
df1 = DataFrame(
np.arange(18, dtype="int64").reshape(6, 3), columns=["a", "b", "c"]
)
df2 = DataFrame(np.arange(14, dtype="int64").reshape(7, 2), columns=["a", "c"])
cat_values = ["one", "one", "two", "one", "two", "two", "one"]
df2["h"] = Series(Categorical(cat_values))
res = pd.concat((df1, df2), axis=0, ignore_index=True, sort=sort)
exp = DataFrame(
{
"a": [0, 3, 6, 9, 12, 15, 0, 2, 4, 6, 8, 10, 12],
"b": [
1,
4,
7,
10,
13,
16,
np.nan,
np.nan,
np.nan,
np.nan,
np.nan,
np.nan,
np.nan,
],
"c": [2, 5, 8, 11, 14, 17, 1, 3, 5, 7, 9, 11, 13],
"h": [None] * 6 + cat_values,
}
)
exp["h"] = exp["h"].astype(df2["h"].dtype)
tm.assert_frame_equal(res, exp)
def test_categorical_concat_dtypes(self):
# GH8143
index = ["cat", "obj", "num"]
cat = Categorical(["a", "b", "c"])
obj = Series(["a", "b", "c"])
num = Series([1, 2, 3])
df = pd.concat([Series(cat), obj, num], axis=1, keys=index)
result = df.dtypes == "object"
expected = Series([False, True, False], index=index)
tm.assert_series_equal(result, expected)
result = df.dtypes == "int64"
expected = Series([False, False, True], index=index)
tm.assert_series_equal(result, expected)
result = df.dtypes == "category"
expected = Series([True, False, False], index=index)
tm.assert_series_equal(result, expected)
def test_concat_categoricalindex(self):
# GH 16111, categories that aren't lexsorted
categories = [9, 0, 1, 2, 3]
a = Series(1, index=pd.CategoricalIndex([9, 0], categories=categories))
b = Series(2, index=pd.CategoricalIndex([0, 1], categories=categories))
c = Series(3, index=pd.CategoricalIndex([1, 2], categories=categories))
result = pd.concat([a, b, c], axis=1)
exp_idx = pd.CategoricalIndex([9, 0, 1, 2], categories=categories)
exp = DataFrame(
{
0: [1, 1, np.nan, np.nan],
1: [np.nan, 2, 2, np.nan],
2: [np.nan, np.nan, 3, 3],
},
columns=[0, 1, 2],
index=exp_idx,
)
tm.assert_frame_equal(result, exp)
def test_categorical_concat_preserve(self):
# GH 8641 series concat not preserving category dtype
# GH 13524 can concat different categories
s = Series(list("abc"), dtype="category")
s2 = Series(list("abd"), dtype="category")
exp = Series(list("abcabd"))
res = pd.concat([s, s2], ignore_index=True)
tm.assert_series_equal(res, exp)
exp = Series(list("abcabc"), dtype="category")
res = pd.concat([s, s], ignore_index=True)
tm.assert_series_equal(res, exp)
exp = Series(list("abcabc"), index=[0, 1, 2, 0, 1, 2], dtype="category")
res = pd.concat([s, s])
tm.assert_series_equal(res, exp)
a = Series(np.arange(6, dtype="int64"))
b = Series(list("aabbca"))
df2 = DataFrame({"A": a, "B": b.astype(CategoricalDtype(list("cab")))})
res = pd.concat([df2, df2])
exp = DataFrame(
{
"A": pd.concat([a, a]),
"B": pd.concat([b, b]).astype(CategoricalDtype(list("cab"))),
}
)
tm.assert_frame_equal(res, exp)
def test_categorical_index_preserver(self):
a = Series(np.arange(6, dtype="int64"))
b = Series(list("aabbca"))
df2 = DataFrame(
{"A": a, "B": b.astype(CategoricalDtype(list("cab")))}
).set_index("B")
result = pd.concat([df2, df2])
expected = DataFrame(
{
"A": pd.concat([a, a]),
"B": pd.concat([b, b]).astype(CategoricalDtype(list("cab"))),
}
).set_index("B")
tm.assert_frame_equal(result, expected)
# wrong categories -> uses concat_compat, which casts to object
df3 = DataFrame(
{"A": a, "B": Categorical(b, categories=list("abe"))}
).set_index("B")
result = pd.concat([df2, df3])
expected = pd.concat(
[
df2.set_axis(df2.index.astype(object), axis=0),
df3.set_axis(df3.index.astype(object), axis=0),
]
)
tm.assert_frame_equal(result, expected)
def test_concat_categorical_tz(self):
# GH-23816
a = Series(pd.date_range("2017-01-01", periods=2, tz="US/Pacific"))
b = Series(["a", "b"], dtype="category")
result = pd.concat([a, b], ignore_index=True)
expected = Series(
[
pd.Timestamp("2017-01-01", tz="US/Pacific"),
pd.Timestamp("2017-01-02", tz="US/Pacific"),
"a",
"b",
]
)
tm.assert_series_equal(result, expected)
def test_concat_categorical_unchanged(self):
# GH-12007
# test fix for when concat on categorical and float
# coerces dtype categorical -> float
df = DataFrame(Series(["a", "b", "c"], dtype="category", name="A"))
ser = Series([0, 1, 2], index=[0, 1, 3], name="B")
result = pd.concat([df, ser], axis=1)
expected = DataFrame(
{
"A": Series(["a", "b", "c", np.nan], dtype="category"),
"B": Series([0, 1, np.nan, 2], dtype="float"),
}
)
tm.assert_equal(result, expected)
def test_categorical_concat_gh7864(self):
# GH 7864
# make sure ordering is preserved
df = DataFrame({"id": [1, 2, 3, 4, 5, 6], "raw_grade": list("abbaae")})
df["grade"] = Categorical(df["raw_grade"])
df["grade"].cat.set_categories(["e", "a", "b"])
df1 = df[0:3]
df2 = df[3:]
tm.assert_index_equal(df["grade"].cat.categories, df1["grade"].cat.categories)
tm.assert_index_equal(df["grade"].cat.categories, df2["grade"].cat.categories)
dfx = pd.concat([df1, df2])
tm.assert_index_equal(df["grade"].cat.categories, dfx["grade"].cat.categories)
dfa = df1.append(df2)
tm.assert_index_equal(df["grade"].cat.categories, dfa["grade"].cat.categories)
def test_categorical_index_upcast(self):
# GH 17629
# test upcasting to object when concatinating on categorical indexes
# with non-identical categories
a = DataFrame({"foo": [1, 2]}, index=Categorical(["foo", "bar"]))
b = DataFrame({"foo": [4, 3]}, index=Categorical(["baz", "bar"]))
res = pd.concat([a, b])
exp = DataFrame({"foo": [1, 2, 4, 3]}, index=["foo", "bar", "baz", "bar"])
tm.assert_equal(res, exp)
a = Series([1, 2], index=Categorical(["foo", "bar"]))
b = Series([4, 3], index=Categorical(["baz", "bar"]))
res = pd.concat([a, b])
exp = Series([1, 2, 4, 3], index=["foo", "bar", "baz", "bar"])
tm.assert_equal(res, exp)