444 lines
16 KiB
Python
444 lines
16 KiB
Python
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import re
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import numpy as np
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import pytest
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from pandas.errors import PerformanceWarning
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import pandas as pd
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from pandas import DataFrame, Index, MultiIndex, Series, Timestamp
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import pandas._testing as tm
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@pytest.mark.parametrize(
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"msg,labels,level",
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[
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(r"labels \[4\] not found in level", 4, "a"),
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(r"labels \[7\] not found in level", 7, "b"),
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],
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)
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def test_drop_raise_exception_if_labels_not_in_level(msg, labels, level):
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# GH 8594
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mi = MultiIndex.from_arrays([[1, 2, 3], [4, 5, 6]], names=["a", "b"])
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s = pd.Series([10, 20, 30], index=mi)
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df = DataFrame([10, 20, 30], index=mi)
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with pytest.raises(KeyError, match=msg):
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s.drop(labels, level=level)
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with pytest.raises(KeyError, match=msg):
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df.drop(labels, level=level)
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@pytest.mark.parametrize("labels,level", [(4, "a"), (7, "b")])
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def test_drop_errors_ignore(labels, level):
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# GH 8594
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mi = MultiIndex.from_arrays([[1, 2, 3], [4, 5, 6]], names=["a", "b"])
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s = pd.Series([10, 20, 30], index=mi)
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df = DataFrame([10, 20, 30], index=mi)
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expected_s = s.drop(labels, level=level, errors="ignore")
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tm.assert_series_equal(s, expected_s)
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expected_df = df.drop(labels, level=level, errors="ignore")
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tm.assert_frame_equal(df, expected_df)
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def test_drop_with_non_unique_datetime_index_and_invalid_keys():
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# GH 30399
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# define dataframe with unique datetime index
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df = DataFrame(
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np.random.randn(5, 3),
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columns=["a", "b", "c"],
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index=pd.date_range("2012", freq="H", periods=5),
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)
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# create dataframe with non-unique datetime index
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df = df.iloc[[0, 2, 2, 3]].copy()
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with pytest.raises(KeyError, match="not found in axis"):
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df.drop(["a", "b"]) # Dropping with labels not exist in the index
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class TestDataFrameDrop:
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def test_drop_names(self):
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df = DataFrame(
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[[1, 2, 3], [3, 4, 5], [5, 6, 7]],
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index=["a", "b", "c"],
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columns=["d", "e", "f"],
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)
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df.index.name, df.columns.name = "first", "second"
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df_dropped_b = df.drop("b")
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df_dropped_e = df.drop("e", axis=1)
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df_inplace_b, df_inplace_e = df.copy(), df.copy()
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return_value = df_inplace_b.drop("b", inplace=True)
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assert return_value is None
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return_value = df_inplace_e.drop("e", axis=1, inplace=True)
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assert return_value is None
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for obj in (df_dropped_b, df_dropped_e, df_inplace_b, df_inplace_e):
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assert obj.index.name == "first"
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assert obj.columns.name == "second"
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assert list(df.columns) == ["d", "e", "f"]
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msg = r"\['g'\] not found in axis"
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with pytest.raises(KeyError, match=msg):
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df.drop(["g"])
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with pytest.raises(KeyError, match=msg):
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df.drop(["g"], 1)
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# errors = 'ignore'
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dropped = df.drop(["g"], errors="ignore")
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expected = Index(["a", "b", "c"], name="first")
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tm.assert_index_equal(dropped.index, expected)
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dropped = df.drop(["b", "g"], errors="ignore")
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expected = Index(["a", "c"], name="first")
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tm.assert_index_equal(dropped.index, expected)
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dropped = df.drop(["g"], axis=1, errors="ignore")
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expected = Index(["d", "e", "f"], name="second")
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tm.assert_index_equal(dropped.columns, expected)
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dropped = df.drop(["d", "g"], axis=1, errors="ignore")
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expected = Index(["e", "f"], name="second")
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tm.assert_index_equal(dropped.columns, expected)
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# GH 16398
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dropped = df.drop([], errors="ignore")
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expected = Index(["a", "b", "c"], name="first")
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tm.assert_index_equal(dropped.index, expected)
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def test_drop(self):
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simple = DataFrame({"A": [1, 2, 3, 4], "B": [0, 1, 2, 3]})
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tm.assert_frame_equal(simple.drop("A", axis=1), simple[["B"]])
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tm.assert_frame_equal(simple.drop(["A", "B"], axis="columns"), simple[[]])
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tm.assert_frame_equal(simple.drop([0, 1, 3], axis=0), simple.loc[[2], :])
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tm.assert_frame_equal(simple.drop([0, 3], axis="index"), simple.loc[[1, 2], :])
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with pytest.raises(KeyError, match=r"\[5\] not found in axis"):
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simple.drop(5)
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with pytest.raises(KeyError, match=r"\['C'\] not found in axis"):
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simple.drop("C", 1)
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with pytest.raises(KeyError, match=r"\[5\] not found in axis"):
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simple.drop([1, 5])
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with pytest.raises(KeyError, match=r"\['C'\] not found in axis"):
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simple.drop(["A", "C"], 1)
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# errors = 'ignore'
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tm.assert_frame_equal(simple.drop(5, errors="ignore"), simple)
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tm.assert_frame_equal(
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simple.drop([0, 5], errors="ignore"), simple.loc[[1, 2, 3], :]
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)
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tm.assert_frame_equal(simple.drop("C", axis=1, errors="ignore"), simple)
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tm.assert_frame_equal(
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simple.drop(["A", "C"], axis=1, errors="ignore"), simple[["B"]]
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)
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# non-unique - wheee!
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nu_df = DataFrame(
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list(zip(range(3), range(-3, 1), list("abc"))), columns=["a", "a", "b"]
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)
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tm.assert_frame_equal(nu_df.drop("a", axis=1), nu_df[["b"]])
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tm.assert_frame_equal(nu_df.drop("b", axis="columns"), nu_df["a"])
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tm.assert_frame_equal(nu_df.drop([]), nu_df) # GH 16398
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nu_df = nu_df.set_index(Index(["X", "Y", "X"]))
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nu_df.columns = list("abc")
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tm.assert_frame_equal(nu_df.drop("X", axis="rows"), nu_df.loc[["Y"], :])
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tm.assert_frame_equal(nu_df.drop(["X", "Y"], axis=0), nu_df.loc[[], :])
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# inplace cache issue
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# GH#5628
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df = DataFrame(np.random.randn(10, 3), columns=list("abc"))
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expected = df[~(df.b > 0)]
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return_value = df.drop(labels=df[df.b > 0].index, inplace=True)
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assert return_value is None
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tm.assert_frame_equal(df, expected)
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def test_drop_multiindex_not_lexsorted(self):
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# GH#11640
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# define the lexsorted version
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lexsorted_mi = MultiIndex.from_tuples(
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[("a", ""), ("b1", "c1"), ("b2", "c2")], names=["b", "c"]
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)
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lexsorted_df = DataFrame([[1, 3, 4]], columns=lexsorted_mi)
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assert lexsorted_df.columns.is_lexsorted()
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# define the non-lexsorted version
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not_lexsorted_df = DataFrame(
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columns=["a", "b", "c", "d"], data=[[1, "b1", "c1", 3], [1, "b2", "c2", 4]]
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)
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not_lexsorted_df = not_lexsorted_df.pivot_table(
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index="a", columns=["b", "c"], values="d"
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)
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not_lexsorted_df = not_lexsorted_df.reset_index()
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assert not not_lexsorted_df.columns.is_lexsorted()
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# compare the results
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tm.assert_frame_equal(lexsorted_df, not_lexsorted_df)
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expected = lexsorted_df.drop("a", axis=1)
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with tm.assert_produces_warning(PerformanceWarning):
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result = not_lexsorted_df.drop("a", axis=1)
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tm.assert_frame_equal(result, expected)
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def test_drop_api_equivalence(self):
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# equivalence of the labels/axis and index/columns API's (GH#12392)
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df = DataFrame(
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[[1, 2, 3], [3, 4, 5], [5, 6, 7]],
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index=["a", "b", "c"],
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columns=["d", "e", "f"],
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)
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res1 = df.drop("a")
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res2 = df.drop(index="a")
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tm.assert_frame_equal(res1, res2)
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res1 = df.drop("d", 1)
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res2 = df.drop(columns="d")
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tm.assert_frame_equal(res1, res2)
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res1 = df.drop(labels="e", axis=1)
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res2 = df.drop(columns="e")
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tm.assert_frame_equal(res1, res2)
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res1 = df.drop(["a"], axis=0)
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res2 = df.drop(index=["a"])
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tm.assert_frame_equal(res1, res2)
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res1 = df.drop(["a"], axis=0).drop(["d"], axis=1)
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res2 = df.drop(index=["a"], columns=["d"])
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tm.assert_frame_equal(res1, res2)
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msg = "Cannot specify both 'labels' and 'index'/'columns'"
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with pytest.raises(ValueError, match=msg):
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df.drop(labels="a", index="b")
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with pytest.raises(ValueError, match=msg):
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df.drop(labels="a", columns="b")
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msg = "Need to specify at least one of 'labels', 'index' or 'columns'"
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with pytest.raises(ValueError, match=msg):
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df.drop(axis=1)
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data = [[1, 2, 3], [1, 2, 3]]
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@pytest.mark.parametrize(
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"actual",
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[
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DataFrame(data=data, index=["a", "a"]),
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DataFrame(data=data, index=["a", "b"]),
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DataFrame(data=data, index=["a", "b"]).set_index([0, 1]),
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DataFrame(data=data, index=["a", "a"]).set_index([0, 1]),
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],
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)
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def test_raise_on_drop_duplicate_index(self, actual):
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# GH#19186
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level = 0 if isinstance(actual.index, MultiIndex) else None
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msg = re.escape("\"['c'] not found in axis\"")
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with pytest.raises(KeyError, match=msg):
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actual.drop("c", level=level, axis=0)
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with pytest.raises(KeyError, match=msg):
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actual.T.drop("c", level=level, axis=1)
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expected_no_err = actual.drop("c", axis=0, level=level, errors="ignore")
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tm.assert_frame_equal(expected_no_err, actual)
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expected_no_err = actual.T.drop("c", axis=1, level=level, errors="ignore")
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tm.assert_frame_equal(expected_no_err.T, actual)
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@pytest.mark.parametrize("index", [[1, 2, 3], [1, 1, 2]])
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@pytest.mark.parametrize("drop_labels", [[], [1], [2]])
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def test_drop_empty_list(self, index, drop_labels):
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# GH#21494
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expected_index = [i for i in index if i not in drop_labels]
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frame = DataFrame(index=index).drop(drop_labels)
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tm.assert_frame_equal(frame, DataFrame(index=expected_index))
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@pytest.mark.parametrize("index", [[1, 2, 3], [1, 2, 2]])
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@pytest.mark.parametrize("drop_labels", [[1, 4], [4, 5]])
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def test_drop_non_empty_list(self, index, drop_labels):
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# GH# 21494
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with pytest.raises(KeyError, match="not found in axis"):
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DataFrame(index=index).drop(drop_labels)
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def test_mixed_depth_drop(self):
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arrays = [
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["a", "top", "top", "routine1", "routine1", "routine2"],
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["", "OD", "OD", "result1", "result2", "result1"],
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["", "wx", "wy", "", "", ""],
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]
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tuples = sorted(zip(*arrays))
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index = MultiIndex.from_tuples(tuples)
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df = DataFrame(np.random.randn(4, 6), columns=index)
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result = df.drop("a", axis=1)
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expected = df.drop([("a", "", "")], axis=1)
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tm.assert_frame_equal(expected, result)
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result = df.drop(["top"], axis=1)
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expected = df.drop([("top", "OD", "wx")], axis=1)
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expected = expected.drop([("top", "OD", "wy")], axis=1)
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tm.assert_frame_equal(expected, result)
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result = df.drop(("top", "OD", "wx"), axis=1)
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expected = df.drop([("top", "OD", "wx")], axis=1)
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tm.assert_frame_equal(expected, result)
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expected = df.drop([("top", "OD", "wy")], axis=1)
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expected = df.drop("top", axis=1)
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result = df.drop("result1", level=1, axis=1)
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expected = df.drop(
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[("routine1", "result1", ""), ("routine2", "result1", "")], axis=1
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)
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tm.assert_frame_equal(expected, result)
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def test_drop_multiindex_other_level_nan(self):
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# GH#12754
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df = (
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DataFrame(
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{
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"A": ["one", "one", "two", "two"],
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"B": [np.nan, 0.0, 1.0, 2.0],
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"C": ["a", "b", "c", "c"],
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"D": [1, 2, 3, 4],
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}
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)
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.set_index(["A", "B", "C"])
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.sort_index()
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)
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result = df.drop("c", level="C")
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expected = DataFrame(
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[2, 1],
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columns=["D"],
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index=MultiIndex.from_tuples(
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[("one", 0.0, "b"), ("one", np.nan, "a")], names=["A", "B", "C"]
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),
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)
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tm.assert_frame_equal(result, expected)
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def test_drop_nonunique(self):
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df = DataFrame(
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[
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["x-a", "x", "a", 1.5],
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["x-a", "x", "a", 1.2],
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||
|
|
["z-c", "z", "c", 3.1],
|
||
|
|
["x-a", "x", "a", 4.1],
|
||
|
|
["x-b", "x", "b", 5.1],
|
||
|
|
["x-b", "x", "b", 4.1],
|
||
|
|
["x-b", "x", "b", 2.2],
|
||
|
|
["y-a", "y", "a", 1.2],
|
||
|
|
["z-b", "z", "b", 2.1],
|
||
|
|
],
|
||
|
|
columns=["var1", "var2", "var3", "var4"],
|
||
|
|
)
|
||
|
|
|
||
|
|
grp_size = df.groupby("var1").size()
|
||
|
|
drop_idx = grp_size.loc[grp_size == 1]
|
||
|
|
|
||
|
|
idf = df.set_index(["var1", "var2", "var3"])
|
||
|
|
|
||
|
|
# it works! GH#2101
|
||
|
|
result = idf.drop(drop_idx.index, level=0).reset_index()
|
||
|
|
expected = df[-df.var1.isin(drop_idx.index)]
|
||
|
|
|
||
|
|
result.index = expected.index
|
||
|
|
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_drop_level(self):
|
||
|
|
index = MultiIndex(
|
||
|
|
levels=[["foo", "bar", "baz", "qux"], ["one", "two", "three"]],
|
||
|
|
codes=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3], [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
|
||
|
|
names=["first", "second"],
|
||
|
|
)
|
||
|
|
frame = DataFrame(
|
||
|
|
np.random.randn(10, 3),
|
||
|
|
index=index,
|
||
|
|
columns=Index(["A", "B", "C"], name="exp"),
|
||
|
|
)
|
||
|
|
|
||
|
|
result = frame.drop(["bar", "qux"], level="first")
|
||
|
|
expected = frame.iloc[[0, 1, 2, 5, 6]]
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
result = frame.drop(["two"], level="second")
|
||
|
|
expected = frame.iloc[[0, 2, 3, 6, 7, 9]]
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
result = frame.T.drop(["bar", "qux"], axis=1, level="first")
|
||
|
|
expected = frame.iloc[[0, 1, 2, 5, 6]].T
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
result = frame.T.drop(["two"], axis=1, level="second")
|
||
|
|
expected = frame.iloc[[0, 2, 3, 6, 7, 9]].T
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_drop_level_nonunique_datetime(self):
|
||
|
|
# GH#12701
|
||
|
|
idx = Index([2, 3, 4, 4, 5], name="id")
|
||
|
|
idxdt = pd.to_datetime(
|
||
|
|
[
|
||
|
|
"201603231400",
|
||
|
|
"201603231500",
|
||
|
|
"201603231600",
|
||
|
|
"201603231600",
|
||
|
|
"201603231700",
|
||
|
|
]
|
||
|
|
)
|
||
|
|
df = DataFrame(np.arange(10).reshape(5, 2), columns=list("ab"), index=idx)
|
||
|
|
df["tstamp"] = idxdt
|
||
|
|
df = df.set_index("tstamp", append=True)
|
||
|
|
ts = Timestamp("201603231600")
|
||
|
|
assert df.index.is_unique is False
|
||
|
|
|
||
|
|
result = df.drop(ts, level="tstamp")
|
||
|
|
expected = df.loc[idx != 4]
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("box", [Series, DataFrame])
|
||
|
|
def test_drop_tz_aware_timestamp_across_dst(self, box):
|
||
|
|
# GH#21761
|
||
|
|
start = Timestamp("2017-10-29", tz="Europe/Berlin")
|
||
|
|
end = Timestamp("2017-10-29 04:00:00", tz="Europe/Berlin")
|
||
|
|
index = pd.date_range(start, end, freq="15min")
|
||
|
|
data = box(data=[1] * len(index), index=index)
|
||
|
|
result = data.drop(start)
|
||
|
|
expected_start = Timestamp("2017-10-29 00:15:00", tz="Europe/Berlin")
|
||
|
|
expected_idx = pd.date_range(expected_start, end, freq="15min")
|
||
|
|
expected = box(data=[1] * len(expected_idx), index=expected_idx)
|
||
|
|
tm.assert_equal(result, expected)
|
||
|
|
|
||
|
|
def test_drop_preserve_names(self):
|
||
|
|
index = MultiIndex.from_arrays(
|
||
|
|
[[0, 0, 0, 1, 1, 1], [1, 2, 3, 1, 2, 3]], names=["one", "two"]
|
||
|
|
)
|
||
|
|
|
||
|
|
df = DataFrame(np.random.randn(6, 3), index=index)
|
||
|
|
|
||
|
|
result = df.drop([(0, 2)])
|
||
|
|
assert result.index.names == ("one", "two")
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"operation", ["__iadd__", "__isub__", "__imul__", "__ipow__"]
|
||
|
|
)
|
||
|
|
@pytest.mark.parametrize("inplace", [False, True])
|
||
|
|
def test_inplace_drop_and_operation(self, operation, inplace):
|
||
|
|
# GH#30484
|
||
|
|
df = DataFrame({"x": range(5)})
|
||
|
|
expected = df.copy()
|
||
|
|
df["y"] = range(5)
|
||
|
|
y = df["y"]
|
||
|
|
|
||
|
|
with tm.assert_produces_warning(None):
|
||
|
|
if inplace:
|
||
|
|
df.drop("y", axis=1, inplace=inplace)
|
||
|
|
else:
|
||
|
|
df = df.drop("y", axis=1, inplace=inplace)
|
||
|
|
|
||
|
|
# Perform operation and check result
|
||
|
|
getattr(y, operation)(1)
|
||
|
|
tm.assert_frame_equal(df, expected)
|