442 lines
16 KiB
Python
442 lines
16 KiB
Python
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from datetime import datetime
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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 DataFrame, Index, MultiIndex, Series
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import pandas._testing as tm
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class TestDataFrameCombineFirst:
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def test_combine_first_mixed(self):
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a = Series(["a", "b"], index=range(2))
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b = Series(range(2), index=range(2))
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f = DataFrame({"A": a, "B": b})
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a = Series(["a", "b"], index=range(5, 7))
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b = Series(range(2), index=range(5, 7))
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g = DataFrame({"A": a, "B": b})
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exp = DataFrame(
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{"A": list("abab"), "B": [0.0, 1.0, 0.0, 1.0]}, index=[0, 1, 5, 6]
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)
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combined = f.combine_first(g)
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tm.assert_frame_equal(combined, exp)
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def test_combine_first(self, float_frame):
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# disjoint
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head, tail = float_frame[:5], float_frame[5:]
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combined = head.combine_first(tail)
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reordered_frame = float_frame.reindex(combined.index)
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tm.assert_frame_equal(combined, reordered_frame)
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assert tm.equalContents(combined.columns, float_frame.columns)
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tm.assert_series_equal(combined["A"], reordered_frame["A"])
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# same index
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fcopy = float_frame.copy()
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fcopy["A"] = 1
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del fcopy["C"]
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fcopy2 = float_frame.copy()
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fcopy2["B"] = 0
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del fcopy2["D"]
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combined = fcopy.combine_first(fcopy2)
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assert (combined["A"] == 1).all()
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tm.assert_series_equal(combined["B"], fcopy["B"])
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tm.assert_series_equal(combined["C"], fcopy2["C"])
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tm.assert_series_equal(combined["D"], fcopy["D"])
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# overlap
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head, tail = reordered_frame[:10].copy(), reordered_frame
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head["A"] = 1
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combined = head.combine_first(tail)
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assert (combined["A"][:10] == 1).all()
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# reverse overlap
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tail["A"][:10] = 0
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combined = tail.combine_first(head)
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assert (combined["A"][:10] == 0).all()
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# no overlap
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f = float_frame[:10]
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g = float_frame[10:]
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combined = f.combine_first(g)
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tm.assert_series_equal(combined["A"].reindex(f.index), f["A"])
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tm.assert_series_equal(combined["A"].reindex(g.index), g["A"])
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# corner cases
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comb = float_frame.combine_first(DataFrame())
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tm.assert_frame_equal(comb, float_frame)
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comb = DataFrame().combine_first(float_frame)
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tm.assert_frame_equal(comb, float_frame)
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comb = float_frame.combine_first(DataFrame(index=["faz", "boo"]))
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assert "faz" in comb.index
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# #2525
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df = DataFrame({"a": [1]}, index=[datetime(2012, 1, 1)])
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df2 = DataFrame(columns=["b"])
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result = df.combine_first(df2)
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assert "b" in result
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def test_combine_first_mixed_bug(self):
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idx = Index(["a", "b", "c", "e"])
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ser1 = Series([5.0, -9.0, 4.0, 100.0], index=idx)
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ser2 = Series(["a", "b", "c", "e"], index=idx)
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ser3 = Series([12, 4, 5, 97], index=idx)
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frame1 = DataFrame({"col0": ser1, "col2": ser2, "col3": ser3})
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idx = Index(["a", "b", "c", "f"])
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ser1 = Series([5.0, -9.0, 4.0, 100.0], index=idx)
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ser2 = Series(["a", "b", "c", "f"], index=idx)
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ser3 = Series([12, 4, 5, 97], index=idx)
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frame2 = DataFrame({"col1": ser1, "col2": ser2, "col5": ser3})
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combined = frame1.combine_first(frame2)
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assert len(combined.columns) == 5
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def test_combine_first_same_as_in_update(self):
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# gh 3016 (same as in update)
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df = DataFrame(
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[[1.0, 2.0, False, True], [4.0, 5.0, True, False]],
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columns=["A", "B", "bool1", "bool2"],
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)
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other = DataFrame([[45, 45]], index=[0], columns=["A", "B"])
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result = df.combine_first(other)
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tm.assert_frame_equal(result, df)
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df.loc[0, "A"] = np.nan
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result = df.combine_first(other)
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df.loc[0, "A"] = 45
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tm.assert_frame_equal(result, df)
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def test_combine_first_doc_example(self):
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# doc example
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df1 = DataFrame(
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{"A": [1.0, np.nan, 3.0, 5.0, np.nan], "B": [np.nan, 2.0, 3.0, np.nan, 6.0]}
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)
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df2 = DataFrame(
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{
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"A": [5.0, 2.0, 4.0, np.nan, 3.0, 7.0],
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"B": [np.nan, np.nan, 3.0, 4.0, 6.0, 8.0],
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}
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)
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result = df1.combine_first(df2)
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expected = DataFrame({"A": [1, 2, 3, 5, 3, 7.0], "B": [np.nan, 2, 3, 4, 6, 8]})
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tm.assert_frame_equal(result, expected)
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def test_combine_first_return_obj_type_with_bools(self):
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# GH3552
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df1 = DataFrame(
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[[np.nan, 3.0, True], [-4.6, np.nan, True], [np.nan, 7.0, False]]
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)
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df2 = DataFrame([[-42.6, np.nan, True], [-5.0, 1.6, False]], index=[1, 2])
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expected = Series([True, True, False], name=2, dtype=object)
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result_12 = df1.combine_first(df2)[2]
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tm.assert_series_equal(result_12, expected)
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result_21 = df2.combine_first(df1)[2]
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tm.assert_series_equal(result_21, expected)
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@pytest.mark.parametrize(
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"data1, data2, data_expected",
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(
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(
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[datetime(2000, 1, 1), datetime(2000, 1, 2), datetime(2000, 1, 3)],
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[None, None, None],
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[datetime(2000, 1, 1), datetime(2000, 1, 2), datetime(2000, 1, 3)],
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),
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(
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[None, None, None],
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[datetime(2000, 1, 1), datetime(2000, 1, 2), datetime(2000, 1, 3)],
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[datetime(2000, 1, 1), datetime(2000, 1, 2), datetime(2000, 1, 3)],
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),
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(
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[datetime(2000, 1, 2), None, None],
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[datetime(2000, 1, 1), datetime(2000, 1, 2), datetime(2000, 1, 3)],
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[datetime(2000, 1, 2), datetime(2000, 1, 2), datetime(2000, 1, 3)],
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),
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(
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[datetime(2000, 1, 1), datetime(2000, 1, 2), datetime(2000, 1, 3)],
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[datetime(2000, 1, 2), None, None],
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[datetime(2000, 1, 1), datetime(2000, 1, 2), datetime(2000, 1, 3)],
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),
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),
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)
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def test_combine_first_convert_datatime_correctly(
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self, data1, data2, data_expected
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):
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# GH 3593
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df1, df2 = DataFrame({"a": data1}), DataFrame({"a": data2})
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result = df1.combine_first(df2)
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expected = DataFrame({"a": data_expected})
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tm.assert_frame_equal(result, expected)
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def test_combine_first_align_nan(self):
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# GH 7509 (not fixed)
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dfa = DataFrame([[pd.Timestamp("2011-01-01"), 2]], columns=["a", "b"])
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dfb = DataFrame([[4], [5]], columns=["b"])
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assert dfa["a"].dtype == "datetime64[ns]"
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assert dfa["b"].dtype == "int64"
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res = dfa.combine_first(dfb)
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exp = DataFrame(
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{"a": [pd.Timestamp("2011-01-01"), pd.NaT], "b": [2.0, 5.0]},
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columns=["a", "b"],
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)
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tm.assert_frame_equal(res, exp)
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assert res["a"].dtype == "datetime64[ns]"
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# ToDo: this must be int64
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assert res["b"].dtype == "float64"
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res = dfa.iloc[:0].combine_first(dfb)
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exp = DataFrame({"a": [np.nan, np.nan], "b": [4, 5]}, columns=["a", "b"])
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tm.assert_frame_equal(res, exp)
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# ToDo: this must be datetime64
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assert res["a"].dtype == "float64"
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# ToDo: this must be int64
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assert res["b"].dtype == "int64"
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def test_combine_first_timezone(self):
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# see gh-7630
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data1 = pd.to_datetime("20100101 01:01").tz_localize("UTC")
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df1 = DataFrame(
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columns=["UTCdatetime", "abc"],
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data=data1,
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index=pd.date_range("20140627", periods=1),
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dtype="object",
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)
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data2 = pd.to_datetime("20121212 12:12").tz_localize("UTC")
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df2 = DataFrame(
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columns=["UTCdatetime", "xyz"],
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data=data2,
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index=pd.date_range("20140628", periods=1),
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dtype="object",
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)
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res = df2[["UTCdatetime"]].combine_first(df1)
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exp = DataFrame(
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{
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"UTCdatetime": [
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pd.Timestamp("2010-01-01 01:01", tz="UTC"),
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pd.Timestamp("2012-12-12 12:12", tz="UTC"),
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],
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"abc": [pd.Timestamp("2010-01-01 01:01:00", tz="UTC"), pd.NaT],
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},
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columns=["UTCdatetime", "abc"],
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index=pd.date_range("20140627", periods=2, freq="D"),
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dtype="object",
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)
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assert res["UTCdatetime"].dtype == "datetime64[ns, UTC]"
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assert res["abc"].dtype == "datetime64[ns, UTC]"
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# Need to cast all to "obejct" because combine_first does not retain dtypes:
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# GH Issue 7509
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res = res.astype("object")
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tm.assert_frame_equal(res, exp)
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# see gh-10567
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dts1 = pd.date_range("2015-01-01", "2015-01-05", tz="UTC")
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df1 = DataFrame({"DATE": dts1})
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dts2 = pd.date_range("2015-01-03", "2015-01-05", tz="UTC")
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df2 = DataFrame({"DATE": dts2})
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res = df1.combine_first(df2)
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tm.assert_frame_equal(res, df1)
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assert res["DATE"].dtype == "datetime64[ns, UTC]"
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dts1 = pd.DatetimeIndex(
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["2011-01-01", "NaT", "2011-01-03", "2011-01-04"], tz="US/Eastern"
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)
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df1 = DataFrame({"DATE": dts1}, index=[1, 3, 5, 7])
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dts2 = pd.DatetimeIndex(
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["2012-01-01", "2012-01-02", "2012-01-03"], tz="US/Eastern"
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)
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df2 = DataFrame({"DATE": dts2}, index=[2, 4, 5])
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res = df1.combine_first(df2)
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exp_dts = pd.DatetimeIndex(
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[
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"2011-01-01",
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"2012-01-01",
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"NaT",
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"2012-01-02",
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"2011-01-03",
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"2011-01-04",
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],
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tz="US/Eastern",
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)
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exp = DataFrame({"DATE": exp_dts}, index=[1, 2, 3, 4, 5, 7])
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tm.assert_frame_equal(res, exp)
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# different tz
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dts1 = pd.date_range("2015-01-01", "2015-01-05", tz="US/Eastern")
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df1 = DataFrame({"DATE": dts1})
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dts2 = pd.date_range("2015-01-03", "2015-01-05")
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df2 = DataFrame({"DATE": dts2})
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# if df1 doesn't have NaN, keep its dtype
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res = df1.combine_first(df2)
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tm.assert_frame_equal(res, df1)
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assert res["DATE"].dtype == "datetime64[ns, US/Eastern]"
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dts1 = pd.date_range("2015-01-01", "2015-01-02", tz="US/Eastern")
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df1 = DataFrame({"DATE": dts1})
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dts2 = pd.date_range("2015-01-01", "2015-01-03")
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df2 = DataFrame({"DATE": dts2})
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res = df1.combine_first(df2)
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exp_dts = [
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pd.Timestamp("2015-01-01", tz="US/Eastern"),
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pd.Timestamp("2015-01-02", tz="US/Eastern"),
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pd.Timestamp("2015-01-03"),
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]
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exp = DataFrame({"DATE": exp_dts})
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tm.assert_frame_equal(res, exp)
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assert res["DATE"].dtype == "object"
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def test_combine_first_timedelta(self):
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data1 = pd.TimedeltaIndex(["1 day", "NaT", "3 day", "4day"])
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df1 = DataFrame({"TD": data1}, index=[1, 3, 5, 7])
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data2 = pd.TimedeltaIndex(["10 day", "11 day", "12 day"])
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df2 = DataFrame({"TD": data2}, index=[2, 4, 5])
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res = df1.combine_first(df2)
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exp_dts = pd.TimedeltaIndex(
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["1 day", "10 day", "NaT", "11 day", "3 day", "4 day"]
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)
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exp = DataFrame({"TD": exp_dts}, index=[1, 2, 3, 4, 5, 7])
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tm.assert_frame_equal(res, exp)
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assert res["TD"].dtype == "timedelta64[ns]"
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def test_combine_first_period(self):
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data1 = pd.PeriodIndex(["2011-01", "NaT", "2011-03", "2011-04"], freq="M")
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df1 = DataFrame({"P": data1}, index=[1, 3, 5, 7])
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data2 = pd.PeriodIndex(["2012-01-01", "2012-02", "2012-03"], freq="M")
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df2 = DataFrame({"P": data2}, index=[2, 4, 5])
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res = df1.combine_first(df2)
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||
|
|
exp_dts = pd.PeriodIndex(
|
||
|
|
["2011-01", "2012-01", "NaT", "2012-02", "2011-03", "2011-04"], freq="M"
|
||
|
|
)
|
||
|
|
exp = DataFrame({"P": exp_dts}, index=[1, 2, 3, 4, 5, 7])
|
||
|
|
tm.assert_frame_equal(res, exp)
|
||
|
|
assert res["P"].dtype == data1.dtype
|
||
|
|
|
||
|
|
# different freq
|
||
|
|
dts2 = pd.PeriodIndex(["2012-01-01", "2012-01-02", "2012-01-03"], freq="D")
|
||
|
|
df2 = DataFrame({"P": dts2}, index=[2, 4, 5])
|
||
|
|
|
||
|
|
res = df1.combine_first(df2)
|
||
|
|
exp_dts = [
|
||
|
|
pd.Period("2011-01", freq="M"),
|
||
|
|
pd.Period("2012-01-01", freq="D"),
|
||
|
|
pd.NaT,
|
||
|
|
pd.Period("2012-01-02", freq="D"),
|
||
|
|
pd.Period("2011-03", freq="M"),
|
||
|
|
pd.Period("2011-04", freq="M"),
|
||
|
|
]
|
||
|
|
exp = DataFrame({"P": exp_dts}, index=[1, 2, 3, 4, 5, 7])
|
||
|
|
tm.assert_frame_equal(res, exp)
|
||
|
|
assert res["P"].dtype == "object"
|
||
|
|
|
||
|
|
def test_combine_first_int(self):
|
||
|
|
# GH14687 - integer series that do no align exactly
|
||
|
|
|
||
|
|
df1 = DataFrame({"a": [0, 1, 3, 5]}, dtype="int64")
|
||
|
|
df2 = DataFrame({"a": [1, 4]}, dtype="int64")
|
||
|
|
|
||
|
|
result_12 = df1.combine_first(df2)
|
||
|
|
expected_12 = DataFrame({"a": [0, 1, 3, 5]}, dtype="float64")
|
||
|
|
tm.assert_frame_equal(result_12, expected_12)
|
||
|
|
|
||
|
|
result_21 = df2.combine_first(df1)
|
||
|
|
expected_21 = DataFrame({"a": [1, 4, 3, 5]}, dtype="float64")
|
||
|
|
|
||
|
|
tm.assert_frame_equal(result_21, expected_21)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("val", [1, 1.0])
|
||
|
|
def test_combine_first_with_asymmetric_other(self, val):
|
||
|
|
# see gh-20699
|
||
|
|
df1 = DataFrame({"isNum": [val]})
|
||
|
|
df2 = DataFrame({"isBool": [True]})
|
||
|
|
|
||
|
|
res = df1.combine_first(df2)
|
||
|
|
exp = DataFrame({"isBool": [True], "isNum": [val]})
|
||
|
|
|
||
|
|
tm.assert_frame_equal(res, exp)
|
||
|
|
|
||
|
|
def test_combine_first_string_dtype_only_na(self):
|
||
|
|
# GH: 37519
|
||
|
|
df = DataFrame({"a": ["962", "85"], "b": [pd.NA] * 2}, dtype="string")
|
||
|
|
df2 = DataFrame({"a": ["85"], "b": [pd.NA]}, dtype="string")
|
||
|
|
df.set_index(["a", "b"], inplace=True)
|
||
|
|
df2.set_index(["a", "b"], inplace=True)
|
||
|
|
result = df.combine_first(df2)
|
||
|
|
expected = DataFrame(
|
||
|
|
{"a": ["962", "85"], "b": [pd.NA] * 2}, dtype="string"
|
||
|
|
).set_index(["a", "b"])
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"scalar1, scalar2",
|
||
|
|
[
|
||
|
|
(datetime(2020, 1, 1), datetime(2020, 1, 2)),
|
||
|
|
(pd.Period("2020-01-01", "D"), pd.Period("2020-01-02", "D")),
|
||
|
|
(pd.Timedelta("89 days"), pd.Timedelta("60 min")),
|
||
|
|
(pd.Interval(left=0, right=1), pd.Interval(left=2, right=3, closed="left")),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_combine_first_timestamp_bug(scalar1, scalar2, nulls_fixture):
|
||
|
|
# GH28481
|
||
|
|
na_value = nulls_fixture
|
||
|
|
frame = DataFrame([[na_value, na_value]], columns=["a", "b"])
|
||
|
|
other = DataFrame([[scalar1, scalar2]], columns=["b", "c"])
|
||
|
|
|
||
|
|
result = frame.combine_first(other)
|
||
|
|
expected = DataFrame([[na_value, scalar1, scalar2]], columns=["a", "b", "c"])
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
def test_combine_first_with_nan_multiindex():
|
||
|
|
# gh-36562
|
||
|
|
|
||
|
|
mi1 = MultiIndex.from_arrays(
|
||
|
|
[["b", "b", "c", "a", "b", np.nan], [1, 2, 3, 4, 5, 6]], names=["a", "b"]
|
||
|
|
)
|
||
|
|
df = DataFrame({"c": [1, 1, 1, 1, 1, 1]}, index=mi1)
|
||
|
|
mi2 = MultiIndex.from_arrays(
|
||
|
|
[["a", "b", "c", "a", "b", "d"], [1, 1, 1, 1, 1, 1]], names=["a", "b"]
|
||
|
|
)
|
||
|
|
s = Series([1, 2, 3, 4, 5, 6], index=mi2)
|
||
|
|
res = df.combine_first(DataFrame({"d": s}))
|
||
|
|
mi_expected = MultiIndex.from_arrays(
|
||
|
|
[
|
||
|
|
["a", "a", "a", "b", "b", "b", "b", "c", "c", "d", np.nan],
|
||
|
|
[1, 1, 4, 1, 1, 2, 5, 1, 3, 1, 6],
|
||
|
|
],
|
||
|
|
names=["a", "b"],
|
||
|
|
)
|
||
|
|
expected = DataFrame(
|
||
|
|
{
|
||
|
|
"c": [np.nan, np.nan, 1, 1, 1, 1, 1, np.nan, 1, np.nan, 1],
|
||
|
|
"d": [1.0, 4.0, np.nan, 2.0, 5.0, np.nan, np.nan, 3.0, np.nan, 6.0, np.nan],
|
||
|
|
},
|
||
|
|
index=mi_expected,
|
||
|
|
)
|
||
|
|
tm.assert_frame_equal(res, expected)
|