241 lines
8.1 KiB
Python
241 lines
8.1 KiB
Python
import numpy as np
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from pandas.core.dtypes.dtypes import CategoricalDtype
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import pandas as pd
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from pandas import (
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Categorical,
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DataFrame,
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Series,
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)
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import pandas._testing as tm
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class TestCategoricalConcat:
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def test_categorical_concat(self, sort):
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# See GH 10177
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df1 = DataFrame(
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np.arange(18, dtype="int64").reshape(6, 3), columns=["a", "b", "c"]
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)
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df2 = DataFrame(np.arange(14, dtype="int64").reshape(7, 2), columns=["a", "c"])
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cat_values = ["one", "one", "two", "one", "two", "two", "one"]
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df2["h"] = Series(Categorical(cat_values))
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res = pd.concat((df1, df2), axis=0, ignore_index=True, sort=sort)
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exp = DataFrame(
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{
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"a": [0, 3, 6, 9, 12, 15, 0, 2, 4, 6, 8, 10, 12],
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"b": [
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1,
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4,
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7,
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10,
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13,
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16,
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np.nan,
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np.nan,
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np.nan,
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np.nan,
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np.nan,
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np.nan,
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np.nan,
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],
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"c": [2, 5, 8, 11, 14, 17, 1, 3, 5, 7, 9, 11, 13],
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"h": [None] * 6 + cat_values,
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}
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)
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exp["h"] = exp["h"].astype(df2["h"].dtype)
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tm.assert_frame_equal(res, exp)
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def test_categorical_concat_dtypes(self):
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# GH8143
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index = ["cat", "obj", "num"]
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cat = Categorical(["a", "b", "c"])
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obj = Series(["a", "b", "c"])
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num = Series([1, 2, 3])
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df = pd.concat([Series(cat), obj, num], axis=1, keys=index)
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result = df.dtypes == "object"
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expected = Series([False, True, False], index=index)
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tm.assert_series_equal(result, expected)
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result = df.dtypes == "int64"
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expected = Series([False, False, True], index=index)
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tm.assert_series_equal(result, expected)
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result = df.dtypes == "category"
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expected = Series([True, False, False], index=index)
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tm.assert_series_equal(result, expected)
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def test_concat_categoricalindex(self):
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# GH 16111, categories that aren't lexsorted
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categories = [9, 0, 1, 2, 3]
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a = Series(1, index=pd.CategoricalIndex([9, 0], categories=categories))
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b = Series(2, index=pd.CategoricalIndex([0, 1], categories=categories))
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c = Series(3, index=pd.CategoricalIndex([1, 2], categories=categories))
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result = pd.concat([a, b, c], axis=1)
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exp_idx = pd.CategoricalIndex([9, 0, 1, 2], categories=categories)
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exp = DataFrame(
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{
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0: [1, 1, np.nan, np.nan],
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1: [np.nan, 2, 2, np.nan],
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2: [np.nan, np.nan, 3, 3],
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},
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columns=[0, 1, 2],
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index=exp_idx,
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)
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tm.assert_frame_equal(result, exp)
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def test_categorical_concat_preserve(self):
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# GH 8641 series concat not preserving category dtype
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# GH 13524 can concat different categories
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s = Series(list("abc"), dtype="category")
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s2 = Series(list("abd"), dtype="category")
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exp = Series(list("abcabd"))
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res = pd.concat([s, s2], ignore_index=True)
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tm.assert_series_equal(res, exp)
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exp = Series(list("abcabc"), dtype="category")
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res = pd.concat([s, s], ignore_index=True)
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tm.assert_series_equal(res, exp)
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exp = Series(list("abcabc"), index=[0, 1, 2, 0, 1, 2], dtype="category")
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res = pd.concat([s, s])
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tm.assert_series_equal(res, exp)
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a = Series(np.arange(6, dtype="int64"))
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b = Series(list("aabbca"))
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df2 = DataFrame({"A": a, "B": b.astype(CategoricalDtype(list("cab")))})
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res = pd.concat([df2, df2])
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exp = DataFrame(
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{
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"A": pd.concat([a, a]),
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"B": pd.concat([b, b]).astype(CategoricalDtype(list("cab"))),
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}
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)
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tm.assert_frame_equal(res, exp)
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def test_categorical_index_preserver(self):
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a = Series(np.arange(6, dtype="int64"))
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b = Series(list("aabbca"))
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df2 = DataFrame(
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{"A": a, "B": b.astype(CategoricalDtype(list("cab")))}
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).set_index("B")
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result = pd.concat([df2, df2])
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expected = DataFrame(
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{
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"A": pd.concat([a, a]),
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"B": pd.concat([b, b]).astype(CategoricalDtype(list("cab"))),
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}
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).set_index("B")
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tm.assert_frame_equal(result, expected)
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# wrong categories -> uses concat_compat, which casts to object
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df3 = DataFrame(
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{"A": a, "B": Categorical(b, categories=list("abe"))}
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).set_index("B")
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result = pd.concat([df2, df3])
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expected = pd.concat(
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[
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df2.set_axis(df2.index.astype(object), axis=0),
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df3.set_axis(df3.index.astype(object), axis=0),
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]
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)
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tm.assert_frame_equal(result, expected)
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def test_concat_categorical_tz(self):
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# GH-23816
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a = Series(pd.date_range("2017-01-01", periods=2, tz="US/Pacific"))
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b = Series(["a", "b"], dtype="category")
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result = pd.concat([a, b], ignore_index=True)
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expected = Series(
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[
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pd.Timestamp("2017-01-01", tz="US/Pacific"),
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pd.Timestamp("2017-01-02", tz="US/Pacific"),
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"a",
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"b",
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]
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)
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tm.assert_series_equal(result, expected)
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def test_concat_categorical_unchanged(self):
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# GH-12007
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# test fix for when concat on categorical and float
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# coerces dtype categorical -> float
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df = DataFrame(Series(["a", "b", "c"], dtype="category", name="A"))
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ser = Series([0, 1, 2], index=[0, 1, 3], name="B")
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result = pd.concat([df, ser], axis=1)
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expected = DataFrame(
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{
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"A": Series(["a", "b", "c", np.nan], dtype="category"),
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"B": Series([0, 1, np.nan, 2], dtype="float"),
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}
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)
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tm.assert_equal(result, expected)
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def test_categorical_concat_gh7864(self):
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# GH 7864
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# make sure ordering is preserved
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df = DataFrame({"id": [1, 2, 3, 4, 5, 6], "raw_grade": list("abbaae")})
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df["grade"] = Categorical(df["raw_grade"])
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df["grade"].cat.set_categories(["e", "a", "b"])
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df1 = df[0:3]
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df2 = df[3:]
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tm.assert_index_equal(df["grade"].cat.categories, df1["grade"].cat.categories)
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tm.assert_index_equal(df["grade"].cat.categories, df2["grade"].cat.categories)
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dfx = pd.concat([df1, df2])
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tm.assert_index_equal(df["grade"].cat.categories, dfx["grade"].cat.categories)
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dfa = df1._append(df2)
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tm.assert_index_equal(df["grade"].cat.categories, dfa["grade"].cat.categories)
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def test_categorical_index_upcast(self):
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# GH 17629
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# test upcasting to object when concatinating on categorical indexes
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# with non-identical categories
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a = DataFrame({"foo": [1, 2]}, index=Categorical(["foo", "bar"]))
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b = DataFrame({"foo": [4, 3]}, index=Categorical(["baz", "bar"]))
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res = pd.concat([a, b])
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exp = DataFrame({"foo": [1, 2, 4, 3]}, index=["foo", "bar", "baz", "bar"])
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tm.assert_equal(res, exp)
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a = Series([1, 2], index=Categorical(["foo", "bar"]))
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b = Series([4, 3], index=Categorical(["baz", "bar"]))
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res = pd.concat([a, b])
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exp = Series([1, 2, 4, 3], index=["foo", "bar", "baz", "bar"])
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tm.assert_equal(res, exp)
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def test_categorical_missing_from_one_frame(self):
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# GH 25412
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df1 = DataFrame({"f1": [1, 2, 3]})
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df2 = DataFrame({"f1": [2, 3, 1], "f2": Series([4, 4, 4]).astype("category")})
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result = pd.concat([df1, df2], sort=True)
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dtype = CategoricalDtype([4])
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expected = DataFrame(
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{
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"f1": [1, 2, 3, 2, 3, 1],
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"f2": Categorical.from_codes([-1, -1, -1, 0, 0, 0], dtype=dtype),
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},
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index=[0, 1, 2, 0, 1, 2],
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)
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tm.assert_frame_equal(result, expected)
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