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209 lines
7.2 KiB
209 lines
7.2 KiB
from copy import deepcopy
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from operator import methodcaller
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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 (
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DataFrame,
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MultiIndex,
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Series,
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date_range,
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)
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import pandas._testing as tm
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class TestDataFrame:
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@pytest.mark.parametrize("func", ["_set_axis_name", "rename_axis"])
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def test_set_axis_name(self, func):
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df = DataFrame([[1, 2], [3, 4]])
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result = methodcaller(func, "foo")(df)
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assert df.index.name is None
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assert result.index.name == "foo"
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result = methodcaller(func, "cols", axis=1)(df)
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assert df.columns.name is None
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assert result.columns.name == "cols"
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@pytest.mark.parametrize("func", ["_set_axis_name", "rename_axis"])
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def test_set_axis_name_mi(self, func):
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df = DataFrame(
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np.empty((3, 3)),
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index=MultiIndex.from_tuples([("A", x) for x in list("aBc")]),
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columns=MultiIndex.from_tuples([("C", x) for x in list("xyz")]),
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)
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level_names = ["L1", "L2"]
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result = methodcaller(func, level_names)(df)
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assert result.index.names == level_names
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assert result.columns.names == [None, None]
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result = methodcaller(func, level_names, axis=1)(df)
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assert result.columns.names == ["L1", "L2"]
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assert result.index.names == [None, None]
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def test_nonzero_single_element(self):
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# allow single item via bool method
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msg_warn = (
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"DataFrame.bool is now deprecated and will be removed "
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"in future version of pandas"
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)
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df = DataFrame([[True]])
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df1 = DataFrame([[False]])
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with tm.assert_produces_warning(FutureWarning, match=msg_warn):
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assert df.bool()
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with tm.assert_produces_warning(FutureWarning, match=msg_warn):
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assert not df1.bool()
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df = DataFrame([[False, False]])
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msg_err = "The truth value of a DataFrame is ambiguous"
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with pytest.raises(ValueError, match=msg_err):
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bool(df)
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with tm.assert_produces_warning(FutureWarning, match=msg_warn):
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with pytest.raises(ValueError, match=msg_err):
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df.bool()
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def test_metadata_propagation_indiv_groupby(self):
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# groupby
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df = DataFrame(
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{
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"A": ["foo", "bar", "foo", "bar", "foo", "bar", "foo", "foo"],
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"B": ["one", "one", "two", "three", "two", "two", "one", "three"],
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"C": np.random.default_rng(2).standard_normal(8),
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"D": np.random.default_rng(2).standard_normal(8),
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}
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)
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result = df.groupby("A").sum()
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tm.assert_metadata_equivalent(df, result)
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def test_metadata_propagation_indiv_resample(self):
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# resample
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df = DataFrame(
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np.random.default_rng(2).standard_normal((1000, 2)),
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index=date_range("20130101", periods=1000, freq="s"),
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)
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result = df.resample("1T")
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tm.assert_metadata_equivalent(df, result)
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def test_metadata_propagation_indiv(self, monkeypatch):
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# merging with override
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# GH 6923
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def finalize(self, other, method=None, **kwargs):
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for name in self._metadata:
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if method == "merge":
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left, right = other.left, other.right
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value = getattr(left, name, "") + "|" + getattr(right, name, "")
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object.__setattr__(self, name, value)
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elif method == "concat":
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value = "+".join(
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[getattr(o, name) for o in other.objs if getattr(o, name, None)]
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)
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object.__setattr__(self, name, value)
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else:
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object.__setattr__(self, name, getattr(other, name, ""))
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return self
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with monkeypatch.context() as m:
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m.setattr(DataFrame, "_metadata", ["filename"])
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m.setattr(DataFrame, "__finalize__", finalize)
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df1 = DataFrame(
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np.random.default_rng(2).integers(0, 4, (3, 2)), columns=["a", "b"]
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)
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df2 = DataFrame(
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np.random.default_rng(2).integers(0, 4, (3, 2)), columns=["c", "d"]
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)
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DataFrame._metadata = ["filename"]
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df1.filename = "fname1.csv"
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df2.filename = "fname2.csv"
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result = df1.merge(df2, left_on=["a"], right_on=["c"], how="inner")
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assert result.filename == "fname1.csv|fname2.csv"
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# concat
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# GH#6927
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df1 = DataFrame(
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np.random.default_rng(2).integers(0, 4, (3, 2)), columns=list("ab")
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)
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df1.filename = "foo"
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result = pd.concat([df1, df1])
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assert result.filename == "foo+foo"
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def test_set_attribute(self):
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# Test for consistent setattr behavior when an attribute and a column
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# have the same name (Issue #8994)
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df = DataFrame({"x": [1, 2, 3]})
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df.y = 2
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df["y"] = [2, 4, 6]
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df.y = 5
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assert df.y == 5
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tm.assert_series_equal(df["y"], Series([2, 4, 6], name="y"))
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def test_deepcopy_empty(self):
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# This test covers empty frame copying with non-empty column sets
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# as reported in issue GH15370
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empty_frame = DataFrame(data=[], index=[], columns=["A"])
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empty_frame_copy = deepcopy(empty_frame)
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tm.assert_frame_equal(empty_frame_copy, empty_frame)
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# formerly in Generic but only test DataFrame
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class TestDataFrame2:
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@pytest.mark.parametrize("value", [1, "True", [1, 2, 3], 5.0])
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def test_validate_bool_args(self, value):
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df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
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msg = 'For argument "inplace" expected type bool, received type'
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with pytest.raises(ValueError, match=msg):
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df.copy().rename_axis(mapper={"a": "x", "b": "y"}, axis=1, inplace=value)
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with pytest.raises(ValueError, match=msg):
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df.copy().drop("a", axis=1, inplace=value)
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with pytest.raises(ValueError, match=msg):
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df.copy().fillna(value=0, inplace=value)
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with pytest.raises(ValueError, match=msg):
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df.copy().replace(to_replace=1, value=7, inplace=value)
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with pytest.raises(ValueError, match=msg):
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df.copy().interpolate(inplace=value)
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with pytest.raises(ValueError, match=msg):
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df.copy()._where(cond=df.a > 2, inplace=value)
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with pytest.raises(ValueError, match=msg):
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df.copy().mask(cond=df.a > 2, inplace=value)
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def test_unexpected_keyword(self):
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# GH8597
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df = DataFrame(
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np.random.default_rng(2).standard_normal((5, 2)), columns=["jim", "joe"]
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)
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ca = pd.Categorical([0, 0, 2, 2, 3, np.nan])
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ts = df["joe"].copy()
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ts[2] = np.nan
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msg = "unexpected keyword"
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with pytest.raises(TypeError, match=msg):
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df.drop("joe", axis=1, in_place=True)
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with pytest.raises(TypeError, match=msg):
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df.reindex([1, 0], inplace=True)
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with pytest.raises(TypeError, match=msg):
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ca.fillna(0, inplace=True)
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with pytest.raises(TypeError, match=msg):
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ts.fillna(0, in_place=True)
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