750 lines
27 KiB
Python
750 lines
27 KiB
Python
import numpy as np
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import pytest
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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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Index,
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Series,
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)
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import pandas._testing as tm
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dt_data = [
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pd.Timestamp("2011-01-01"),
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pd.Timestamp("2011-01-02"),
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pd.Timestamp("2011-01-03"),
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]
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tz_data = [
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pd.Timestamp("2011-01-01", tz="US/Eastern"),
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pd.Timestamp("2011-01-02", tz="US/Eastern"),
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pd.Timestamp("2011-01-03", tz="US/Eastern"),
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]
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td_data = [
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pd.Timedelta("1 days"),
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pd.Timedelta("2 days"),
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pd.Timedelta("3 days"),
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]
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period_data = [
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pd.Period("2011-01", freq="M"),
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pd.Period("2011-02", freq="M"),
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pd.Period("2011-03", freq="M"),
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]
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data_dict = {
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"bool": [True, False, True],
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"int64": [1, 2, 3],
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"float64": [1.1, np.nan, 3.3],
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"category": Categorical(["X", "Y", "Z"]),
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"object": ["a", "b", "c"],
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"datetime64[ns]": dt_data,
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"datetime64[ns, US/Eastern]": tz_data,
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"timedelta64[ns]": td_data,
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"period[M]": period_data,
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}
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class TestConcatAppendCommon:
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"""
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Test common dtype coercion rules between concat and append.
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"""
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@pytest.fixture(params=sorted(data_dict.keys()))
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def item(self, request):
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key = request.param
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return key, data_dict[key]
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item2 = item
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def test_dtypes(self, item, index_or_series):
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# to confirm test case covers intended dtypes
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typ, vals = item
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obj = index_or_series(vals)
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if isinstance(obj, Index):
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assert obj.dtype == typ
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elif isinstance(obj, Series):
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if typ.startswith("period"):
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assert obj.dtype == "Period[M]"
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else:
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assert obj.dtype == typ
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def test_concatlike_same_dtypes(self, item):
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# GH 13660
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typ1, vals1 = item
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vals2 = vals1
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vals3 = vals1
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if typ1 == "category":
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exp_data = Categorical(list(vals1) + list(vals2))
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exp_data3 = Categorical(list(vals1) + list(vals2) + list(vals3))
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else:
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exp_data = vals1 + vals2
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exp_data3 = vals1 + vals2 + vals3
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# ----- Index ----- #
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# index.append
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res = Index(vals1).append(Index(vals2))
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exp = Index(exp_data)
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tm.assert_index_equal(res, exp)
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# 3 elements
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res = Index(vals1).append([Index(vals2), Index(vals3)])
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exp = Index(exp_data3)
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tm.assert_index_equal(res, exp)
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# index.append name mismatch
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i1 = Index(vals1, name="x")
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i2 = Index(vals2, name="y")
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res = i1.append(i2)
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exp = Index(exp_data)
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tm.assert_index_equal(res, exp)
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# index.append name match
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i1 = Index(vals1, name="x")
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i2 = Index(vals2, name="x")
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res = i1.append(i2)
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exp = Index(exp_data, name="x")
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tm.assert_index_equal(res, exp)
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# cannot append non-index
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with pytest.raises(TypeError, match="all inputs must be Index"):
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Index(vals1).append(vals2)
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with pytest.raises(TypeError, match="all inputs must be Index"):
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Index(vals1).append([Index(vals2), vals3])
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# ----- Series ----- #
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# series.append
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res = Series(vals1)._append(Series(vals2), ignore_index=True)
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exp = Series(exp_data)
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tm.assert_series_equal(res, exp, check_index_type=True)
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# concat
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res = pd.concat([Series(vals1), Series(vals2)], ignore_index=True)
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tm.assert_series_equal(res, exp, check_index_type=True)
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# 3 elements
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res = Series(vals1)._append([Series(vals2), Series(vals3)], ignore_index=True)
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exp = Series(exp_data3)
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tm.assert_series_equal(res, exp)
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res = pd.concat(
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[Series(vals1), Series(vals2), Series(vals3)],
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ignore_index=True,
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)
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tm.assert_series_equal(res, exp)
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# name mismatch
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s1 = Series(vals1, name="x")
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s2 = Series(vals2, name="y")
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res = s1._append(s2, ignore_index=True)
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exp = Series(exp_data)
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tm.assert_series_equal(res, exp, check_index_type=True)
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res = pd.concat([s1, s2], ignore_index=True)
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tm.assert_series_equal(res, exp, check_index_type=True)
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# name match
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s1 = Series(vals1, name="x")
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s2 = Series(vals2, name="x")
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res = s1._append(s2, ignore_index=True)
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exp = Series(exp_data, name="x")
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tm.assert_series_equal(res, exp, check_index_type=True)
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res = pd.concat([s1, s2], ignore_index=True)
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tm.assert_series_equal(res, exp, check_index_type=True)
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# cannot append non-index
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msg = (
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r"cannot concatenate object of type '.+'; "
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"only Series and DataFrame objs are valid"
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)
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with pytest.raises(TypeError, match=msg):
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Series(vals1)._append(vals2)
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with pytest.raises(TypeError, match=msg):
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Series(vals1)._append([Series(vals2), vals3])
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with pytest.raises(TypeError, match=msg):
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pd.concat([Series(vals1), vals2])
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with pytest.raises(TypeError, match=msg):
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pd.concat([Series(vals1), Series(vals2), vals3])
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def test_concatlike_dtypes_coercion(self, item, item2, request):
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# GH 13660
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typ1, vals1 = item
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typ2, vals2 = item2
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vals3 = vals2
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# basically infer
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exp_index_dtype = None
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exp_series_dtype = None
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if typ1 == typ2:
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# same dtype is tested in test_concatlike_same_dtypes
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return
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elif typ1 == "category" or typ2 == "category":
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# The `vals1 + vals2` below fails bc one of these is a Categorical
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# instead of a list; we have separate dedicated tests for categorical
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return
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# specify expected dtype
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if typ1 == "bool" and typ2 in ("int64", "float64"):
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# series coerces to numeric based on numpy rule
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# index doesn't because bool is object dtype
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exp_series_dtype = typ2
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mark = pytest.mark.xfail(reason="GH#39187 casting to object")
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request.node.add_marker(mark)
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elif typ2 == "bool" and typ1 in ("int64", "float64"):
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exp_series_dtype = typ1
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mark = pytest.mark.xfail(reason="GH#39187 casting to object")
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request.node.add_marker(mark)
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elif (
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typ1 == "datetime64[ns, US/Eastern]"
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or typ2 == "datetime64[ns, US/Eastern]"
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or typ1 == "timedelta64[ns]"
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or typ2 == "timedelta64[ns]"
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):
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exp_index_dtype = object
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exp_series_dtype = object
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exp_data = vals1 + vals2
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exp_data3 = vals1 + vals2 + vals3
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# ----- Index ----- #
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# index.append
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# GH#39817
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res = Index(vals1).append(Index(vals2))
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exp = Index(exp_data, dtype=exp_index_dtype)
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tm.assert_index_equal(res, exp)
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# 3 elements
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res = Index(vals1).append([Index(vals2), Index(vals3)])
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exp = Index(exp_data3, dtype=exp_index_dtype)
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tm.assert_index_equal(res, exp)
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# ----- Series ----- #
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# series._append
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# GH#39817
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res = Series(vals1)._append(Series(vals2), ignore_index=True)
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exp = Series(exp_data, dtype=exp_series_dtype)
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tm.assert_series_equal(res, exp, check_index_type=True)
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# concat
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# GH#39817
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res = pd.concat([Series(vals1), Series(vals2)], ignore_index=True)
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tm.assert_series_equal(res, exp, check_index_type=True)
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# 3 elements
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# GH#39817
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res = Series(vals1)._append([Series(vals2), Series(vals3)], ignore_index=True)
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exp = Series(exp_data3, dtype=exp_series_dtype)
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tm.assert_series_equal(res, exp)
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# GH#39817
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res = pd.concat(
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[Series(vals1), Series(vals2), Series(vals3)],
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ignore_index=True,
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)
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tm.assert_series_equal(res, exp)
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def test_concatlike_common_coerce_to_pandas_object(self):
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# GH 13626
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# result must be Timestamp/Timedelta, not datetime.datetime/timedelta
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dti = pd.DatetimeIndex(["2011-01-01", "2011-01-02"])
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tdi = pd.TimedeltaIndex(["1 days", "2 days"])
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exp = Index(
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[
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pd.Timestamp("2011-01-01"),
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pd.Timestamp("2011-01-02"),
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pd.Timedelta("1 days"),
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pd.Timedelta("2 days"),
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]
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)
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res = dti.append(tdi)
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tm.assert_index_equal(res, exp)
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assert isinstance(res[0], pd.Timestamp)
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assert isinstance(res[-1], pd.Timedelta)
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dts = Series(dti)
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tds = Series(tdi)
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res = dts._append(tds)
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tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
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assert isinstance(res.iloc[0], pd.Timestamp)
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assert isinstance(res.iloc[-1], pd.Timedelta)
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res = pd.concat([dts, tds])
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tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
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assert isinstance(res.iloc[0], pd.Timestamp)
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assert isinstance(res.iloc[-1], pd.Timedelta)
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def test_concatlike_datetimetz(self, tz_aware_fixture):
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tz = tz_aware_fixture
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# GH 7795
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dti1 = pd.DatetimeIndex(["2011-01-01", "2011-01-02"], tz=tz)
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dti2 = pd.DatetimeIndex(["2012-01-01", "2012-01-02"], tz=tz)
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exp = pd.DatetimeIndex(
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["2011-01-01", "2011-01-02", "2012-01-01", "2012-01-02"], tz=tz
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)
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res = dti1.append(dti2)
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tm.assert_index_equal(res, exp)
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dts1 = Series(dti1)
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dts2 = Series(dti2)
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res = dts1._append(dts2)
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tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
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res = pd.concat([dts1, dts2])
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tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
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@pytest.mark.parametrize("tz", ["UTC", "US/Eastern", "Asia/Tokyo", "EST5EDT"])
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def test_concatlike_datetimetz_short(self, tz):
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# GH#7795
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ix1 = pd.date_range(start="2014-07-15", end="2014-07-17", freq="D", tz=tz)
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ix2 = pd.DatetimeIndex(["2014-07-11", "2014-07-21"], tz=tz)
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df1 = DataFrame(0, index=ix1, columns=["A", "B"])
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df2 = DataFrame(0, index=ix2, columns=["A", "B"])
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exp_idx = pd.DatetimeIndex(
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["2014-07-15", "2014-07-16", "2014-07-17", "2014-07-11", "2014-07-21"],
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tz=tz,
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)
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exp = DataFrame(0, index=exp_idx, columns=["A", "B"])
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tm.assert_frame_equal(df1._append(df2), exp)
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tm.assert_frame_equal(pd.concat([df1, df2]), exp)
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def test_concatlike_datetimetz_to_object(self, tz_aware_fixture):
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tz = tz_aware_fixture
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# GH 13660
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# different tz coerces to object
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dti1 = pd.DatetimeIndex(["2011-01-01", "2011-01-02"], tz=tz)
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dti2 = pd.DatetimeIndex(["2012-01-01", "2012-01-02"])
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exp = Index(
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[
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pd.Timestamp("2011-01-01", tz=tz),
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pd.Timestamp("2011-01-02", tz=tz),
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pd.Timestamp("2012-01-01"),
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pd.Timestamp("2012-01-02"),
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],
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dtype=object,
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)
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res = dti1.append(dti2)
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tm.assert_index_equal(res, exp)
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dts1 = Series(dti1)
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dts2 = Series(dti2)
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res = dts1._append(dts2)
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tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
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res = pd.concat([dts1, dts2])
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tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
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# different tz
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dti3 = pd.DatetimeIndex(["2012-01-01", "2012-01-02"], tz="US/Pacific")
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exp = Index(
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[
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pd.Timestamp("2011-01-01", tz=tz),
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pd.Timestamp("2011-01-02", tz=tz),
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pd.Timestamp("2012-01-01", tz="US/Pacific"),
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pd.Timestamp("2012-01-02", tz="US/Pacific"),
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],
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dtype=object,
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)
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res = dti1.append(dti3)
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tm.assert_index_equal(res, exp)
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dts1 = Series(dti1)
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dts3 = Series(dti3)
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res = dts1._append(dts3)
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tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
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res = pd.concat([dts1, dts3])
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tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
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def test_concatlike_common_period(self):
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# GH 13660
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pi1 = pd.PeriodIndex(["2011-01", "2011-02"], freq="M")
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pi2 = pd.PeriodIndex(["2012-01", "2012-02"], freq="M")
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exp = pd.PeriodIndex(["2011-01", "2011-02", "2012-01", "2012-02"], freq="M")
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res = pi1.append(pi2)
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tm.assert_index_equal(res, exp)
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ps1 = Series(pi1)
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ps2 = Series(pi2)
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res = ps1._append(ps2)
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tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
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res = pd.concat([ps1, ps2])
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tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
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def test_concatlike_common_period_diff_freq_to_object(self):
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# GH 13221
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pi1 = pd.PeriodIndex(["2011-01", "2011-02"], freq="M")
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pi2 = pd.PeriodIndex(["2012-01-01", "2012-02-01"], freq="D")
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exp = Index(
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[
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pd.Period("2011-01", freq="M"),
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pd.Period("2011-02", freq="M"),
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pd.Period("2012-01-01", freq="D"),
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pd.Period("2012-02-01", freq="D"),
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],
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dtype=object,
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)
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res = pi1.append(pi2)
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tm.assert_index_equal(res, exp)
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ps1 = Series(pi1)
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ps2 = Series(pi2)
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res = ps1._append(ps2)
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tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
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res = pd.concat([ps1, ps2])
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tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
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def test_concatlike_common_period_mixed_dt_to_object(self):
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# GH 13221
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# different datetimelike
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pi1 = pd.PeriodIndex(["2011-01", "2011-02"], freq="M")
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tdi = pd.TimedeltaIndex(["1 days", "2 days"])
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exp = Index(
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[
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pd.Period("2011-01", freq="M"),
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pd.Period("2011-02", freq="M"),
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pd.Timedelta("1 days"),
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pd.Timedelta("2 days"),
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],
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dtype=object,
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)
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res = pi1.append(tdi)
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tm.assert_index_equal(res, exp)
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ps1 = Series(pi1)
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tds = Series(tdi)
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res = ps1._append(tds)
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tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
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res = pd.concat([ps1, tds])
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tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
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# inverse
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exp = Index(
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[
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pd.Timedelta("1 days"),
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pd.Timedelta("2 days"),
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pd.Period("2011-01", freq="M"),
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pd.Period("2011-02", freq="M"),
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],
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dtype=object,
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)
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res = tdi.append(pi1)
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tm.assert_index_equal(res, exp)
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ps1 = Series(pi1)
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tds = Series(tdi)
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res = tds._append(ps1)
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tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
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res = pd.concat([tds, ps1])
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tm.assert_series_equal(res, Series(exp, index=[0, 1, 0, 1]))
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def test_concat_categorical(self):
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# GH 13524
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# same categories -> category
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s1 = Series([1, 2, np.nan], dtype="category")
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s2 = Series([2, 1, 2], dtype="category")
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exp = Series([1, 2, np.nan, 2, 1, 2], dtype="category")
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tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
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tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
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|
# partially different categories => not-category
|
|
s1 = Series([3, 2], dtype="category")
|
|
s2 = Series([2, 1], dtype="category")
|
|
|
|
exp = Series([3, 2, 2, 1])
|
|
tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
|
|
tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
|
|
|
|
# completely different categories (same dtype) => not-category
|
|
s1 = Series([10, 11, np.nan], dtype="category")
|
|
s2 = Series([np.nan, 1, 3, 2], dtype="category")
|
|
|
|
exp = Series([10, 11, np.nan, np.nan, 1, 3, 2], dtype=np.float64)
|
|
tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
|
|
tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
|
|
|
|
def test_union_categorical_same_categories_different_order(self):
|
|
# https://github.com/pandas-dev/pandas/issues/19096
|
|
a = Series(Categorical(["a", "b", "c"], categories=["a", "b", "c"]))
|
|
b = Series(Categorical(["a", "b", "c"], categories=["b", "a", "c"]))
|
|
result = pd.concat([a, b], ignore_index=True)
|
|
expected = Series(
|
|
Categorical(["a", "b", "c", "a", "b", "c"], categories=["a", "b", "c"])
|
|
)
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
def test_concat_categorical_coercion(self):
|
|
# GH 13524
|
|
|
|
# category + not-category => not-category
|
|
s1 = Series([1, 2, np.nan], dtype="category")
|
|
s2 = Series([2, 1, 2])
|
|
|
|
exp = Series([1, 2, np.nan, 2, 1, 2], dtype=np.float64)
|
|
tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
|
|
tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
|
|
|
|
# result shouldn't be affected by 1st elem dtype
|
|
exp = Series([2, 1, 2, 1, 2, np.nan], dtype=np.float64)
|
|
tm.assert_series_equal(pd.concat([s2, s1], ignore_index=True), exp)
|
|
tm.assert_series_equal(s2._append(s1, ignore_index=True), exp)
|
|
|
|
# all values are not in category => not-category
|
|
s1 = Series([3, 2], dtype="category")
|
|
s2 = Series([2, 1])
|
|
|
|
exp = Series([3, 2, 2, 1])
|
|
tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
|
|
tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
|
|
|
|
exp = Series([2, 1, 3, 2])
|
|
tm.assert_series_equal(pd.concat([s2, s1], ignore_index=True), exp)
|
|
tm.assert_series_equal(s2._append(s1, ignore_index=True), exp)
|
|
|
|
# completely different categories => not-category
|
|
s1 = Series([10, 11, np.nan], dtype="category")
|
|
s2 = Series([1, 3, 2])
|
|
|
|
exp = Series([10, 11, np.nan, 1, 3, 2], dtype=np.float64)
|
|
tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
|
|
tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
|
|
|
|
exp = Series([1, 3, 2, 10, 11, np.nan], dtype=np.float64)
|
|
tm.assert_series_equal(pd.concat([s2, s1], ignore_index=True), exp)
|
|
tm.assert_series_equal(s2._append(s1, ignore_index=True), exp)
|
|
|
|
# different dtype => not-category
|
|
s1 = Series([10, 11, np.nan], dtype="category")
|
|
s2 = Series(["a", "b", "c"])
|
|
|
|
exp = Series([10, 11, np.nan, "a", "b", "c"])
|
|
tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
|
|
tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
|
|
|
|
exp = Series(["a", "b", "c", 10, 11, np.nan])
|
|
tm.assert_series_equal(pd.concat([s2, s1], ignore_index=True), exp)
|
|
tm.assert_series_equal(s2._append(s1, ignore_index=True), exp)
|
|
|
|
# if normal series only contains NaN-likes => not-category
|
|
s1 = Series([10, 11], dtype="category")
|
|
s2 = Series([np.nan, np.nan, np.nan])
|
|
|
|
exp = Series([10, 11, np.nan, np.nan, np.nan])
|
|
tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
|
|
tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
|
|
|
|
exp = Series([np.nan, np.nan, np.nan, 10, 11])
|
|
tm.assert_series_equal(pd.concat([s2, s1], ignore_index=True), exp)
|
|
tm.assert_series_equal(s2._append(s1, ignore_index=True), exp)
|
|
|
|
def test_concat_categorical_3elem_coercion(self):
|
|
# GH 13524
|
|
|
|
# mixed dtypes => not-category
|
|
s1 = Series([1, 2, np.nan], dtype="category")
|
|
s2 = Series([2, 1, 2], dtype="category")
|
|
s3 = Series([1, 2, 1, 2, np.nan])
|
|
|
|
exp = Series([1, 2, np.nan, 2, 1, 2, 1, 2, 1, 2, np.nan], dtype="float")
|
|
tm.assert_series_equal(pd.concat([s1, s2, s3], ignore_index=True), exp)
|
|
tm.assert_series_equal(s1._append([s2, s3], ignore_index=True), exp)
|
|
|
|
exp = Series([1, 2, 1, 2, np.nan, 1, 2, np.nan, 2, 1, 2], dtype="float")
|
|
tm.assert_series_equal(pd.concat([s3, s1, s2], ignore_index=True), exp)
|
|
tm.assert_series_equal(s3._append([s1, s2], ignore_index=True), exp)
|
|
|
|
# values are all in either category => not-category
|
|
s1 = Series([4, 5, 6], dtype="category")
|
|
s2 = Series([1, 2, 3], dtype="category")
|
|
s3 = Series([1, 3, 4])
|
|
|
|
exp = Series([4, 5, 6, 1, 2, 3, 1, 3, 4])
|
|
tm.assert_series_equal(pd.concat([s1, s2, s3], ignore_index=True), exp)
|
|
tm.assert_series_equal(s1._append([s2, s3], ignore_index=True), exp)
|
|
|
|
exp = Series([1, 3, 4, 4, 5, 6, 1, 2, 3])
|
|
tm.assert_series_equal(pd.concat([s3, s1, s2], ignore_index=True), exp)
|
|
tm.assert_series_equal(s3._append([s1, s2], ignore_index=True), exp)
|
|
|
|
# values are all in either category => not-category
|
|
s1 = Series([4, 5, 6], dtype="category")
|
|
s2 = Series([1, 2, 3], dtype="category")
|
|
s3 = Series([10, 11, 12])
|
|
|
|
exp = Series([4, 5, 6, 1, 2, 3, 10, 11, 12])
|
|
tm.assert_series_equal(pd.concat([s1, s2, s3], ignore_index=True), exp)
|
|
tm.assert_series_equal(s1._append([s2, s3], ignore_index=True), exp)
|
|
|
|
exp = Series([10, 11, 12, 4, 5, 6, 1, 2, 3])
|
|
tm.assert_series_equal(pd.concat([s3, s1, s2], ignore_index=True), exp)
|
|
tm.assert_series_equal(s3._append([s1, s2], ignore_index=True), exp)
|
|
|
|
def test_concat_categorical_multi_coercion(self):
|
|
# GH 13524
|
|
|
|
s1 = Series([1, 3], dtype="category")
|
|
s2 = Series([3, 4], dtype="category")
|
|
s3 = Series([2, 3])
|
|
s4 = Series([2, 2], dtype="category")
|
|
s5 = Series([1, np.nan])
|
|
s6 = Series([1, 3, 2], dtype="category")
|
|
|
|
# mixed dtype, values are all in categories => not-category
|
|
exp = Series([1, 3, 3, 4, 2, 3, 2, 2, 1, np.nan, 1, 3, 2])
|
|
res = pd.concat([s1, s2, s3, s4, s5, s6], ignore_index=True)
|
|
tm.assert_series_equal(res, exp)
|
|
res = s1._append([s2, s3, s4, s5, s6], ignore_index=True)
|
|
tm.assert_series_equal(res, exp)
|
|
|
|
exp = Series([1, 3, 2, 1, np.nan, 2, 2, 2, 3, 3, 4, 1, 3])
|
|
res = pd.concat([s6, s5, s4, s3, s2, s1], ignore_index=True)
|
|
tm.assert_series_equal(res, exp)
|
|
res = s6._append([s5, s4, s3, s2, s1], ignore_index=True)
|
|
tm.assert_series_equal(res, exp)
|
|
|
|
def test_concat_categorical_ordered(self):
|
|
# GH 13524
|
|
|
|
s1 = Series(Categorical([1, 2, np.nan], ordered=True))
|
|
s2 = Series(Categorical([2, 1, 2], ordered=True))
|
|
|
|
exp = Series(Categorical([1, 2, np.nan, 2, 1, 2], ordered=True))
|
|
tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
|
|
tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
|
|
|
|
exp = Series(Categorical([1, 2, np.nan, 2, 1, 2, 1, 2, np.nan], ordered=True))
|
|
tm.assert_series_equal(pd.concat([s1, s2, s1], ignore_index=True), exp)
|
|
tm.assert_series_equal(s1._append([s2, s1], ignore_index=True), exp)
|
|
|
|
def test_concat_categorical_coercion_nan(self):
|
|
# GH 13524
|
|
|
|
# some edge cases
|
|
# category + not-category => not category
|
|
s1 = Series(np.array([np.nan, np.nan], dtype=np.float64), dtype="category")
|
|
s2 = Series([np.nan, 1])
|
|
|
|
exp = Series([np.nan, np.nan, np.nan, 1])
|
|
tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
|
|
tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
|
|
|
|
s1 = Series([1, np.nan], dtype="category")
|
|
s2 = Series([np.nan, np.nan])
|
|
|
|
exp = Series([1, np.nan, np.nan, np.nan], dtype="float")
|
|
tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
|
|
tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
|
|
|
|
# mixed dtype, all nan-likes => not-category
|
|
s1 = Series([np.nan, np.nan], dtype="category")
|
|
s2 = Series([np.nan, np.nan])
|
|
|
|
exp = Series([np.nan, np.nan, np.nan, np.nan])
|
|
tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
|
|
tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
|
|
tm.assert_series_equal(pd.concat([s2, s1], ignore_index=True), exp)
|
|
tm.assert_series_equal(s2._append(s1, ignore_index=True), exp)
|
|
|
|
# all category nan-likes => category
|
|
s1 = Series([np.nan, np.nan], dtype="category")
|
|
s2 = Series([np.nan, np.nan], dtype="category")
|
|
|
|
exp = Series([np.nan, np.nan, np.nan, np.nan], dtype="category")
|
|
|
|
tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
|
|
tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
|
|
|
|
def test_concat_categorical_empty(self):
|
|
# GH 13524
|
|
|
|
s1 = Series([], dtype="category")
|
|
s2 = Series([1, 2], dtype="category")
|
|
|
|
tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), s2)
|
|
tm.assert_series_equal(s1._append(s2, ignore_index=True), s2)
|
|
|
|
tm.assert_series_equal(pd.concat([s2, s1], ignore_index=True), s2)
|
|
tm.assert_series_equal(s2._append(s1, ignore_index=True), s2)
|
|
|
|
s1 = Series([], dtype="category")
|
|
s2 = Series([], dtype="category")
|
|
|
|
tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), s2)
|
|
tm.assert_series_equal(s1._append(s2, ignore_index=True), s2)
|
|
|
|
s1 = Series([], dtype="category")
|
|
s2 = Series([], dtype="object")
|
|
|
|
# different dtype => not-category
|
|
tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), s2)
|
|
tm.assert_series_equal(s1._append(s2, ignore_index=True), s2)
|
|
tm.assert_series_equal(pd.concat([s2, s1], ignore_index=True), s2)
|
|
tm.assert_series_equal(s2._append(s1, ignore_index=True), s2)
|
|
|
|
s1 = Series([], dtype="category")
|
|
s2 = Series([np.nan, np.nan])
|
|
|
|
# empty Series is ignored
|
|
exp = Series([np.nan, np.nan])
|
|
tm.assert_series_equal(pd.concat([s1, s2], ignore_index=True), exp)
|
|
tm.assert_series_equal(s1._append(s2, ignore_index=True), exp)
|
|
|
|
tm.assert_series_equal(pd.concat([s2, s1], ignore_index=True), exp)
|
|
tm.assert_series_equal(s2._append(s1, ignore_index=True), exp)
|
|
|
|
def test_categorical_concat_append(self):
|
|
cat = Categorical(["a", "b"], categories=["a", "b"])
|
|
vals = [1, 2]
|
|
df = DataFrame({"cats": cat, "vals": vals})
|
|
cat2 = Categorical(["a", "b", "a", "b"], categories=["a", "b"])
|
|
vals2 = [1, 2, 1, 2]
|
|
exp = DataFrame({"cats": cat2, "vals": vals2}, index=Index([0, 1, 0, 1]))
|
|
|
|
tm.assert_frame_equal(pd.concat([df, df]), exp)
|
|
tm.assert_frame_equal(df._append(df), exp)
|
|
|
|
# GH 13524 can concat different categories
|
|
cat3 = Categorical(["a", "b"], categories=["a", "b", "c"])
|
|
vals3 = [1, 2]
|
|
df_different_categories = DataFrame({"cats": cat3, "vals": vals3})
|
|
|
|
res = pd.concat([df, df_different_categories], ignore_index=True)
|
|
exp = DataFrame({"cats": list("abab"), "vals": [1, 2, 1, 2]})
|
|
tm.assert_frame_equal(res, exp)
|
|
|
|
res = df._append(df_different_categories, ignore_index=True)
|
|
tm.assert_frame_equal(res, exp)
|