import numpy as np
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from pandas.core.dtypes.common import is_float_dtype
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from pandas import (
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DataFrame,
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isna,
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)
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class TestSetValue:
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def test_set_value(self, float_frame):
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for idx in float_frame.index:
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for col in float_frame.columns:
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float_frame._set_value(idx, col, 1)
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assert float_frame[col][idx] == 1
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def test_set_value_resize(self, float_frame):
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res = float_frame._set_value("foobar", "B", 0)
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assert res is None
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assert float_frame.index[-1] == "foobar"
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assert float_frame._get_value("foobar", "B") == 0
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float_frame.loc["foobar", "qux"] = 0
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assert float_frame._get_value("foobar", "qux") == 0
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res = float_frame.copy()
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res._set_value("foobar", "baz", "sam")
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assert res["baz"].dtype == np.object_
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res = float_frame.copy()
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res._set_value("foobar", "baz", True)
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assert res["baz"].dtype == np.object_
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res = float_frame.copy()
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res._set_value("foobar", "baz", 5)
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assert is_float_dtype(res["baz"])
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assert isna(res["baz"].drop(["foobar"])).all()
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res._set_value("foobar", "baz", "sam")
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assert res.loc["foobar", "baz"] == "sam"
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def test_set_value_with_index_dtype_change(self):
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df_orig = DataFrame(np.random.randn(3, 3), index=range(3), columns=list("ABC"))
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# this is actually ambiguous as the 2 is interpreted as a positional
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# so column is not created
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df = df_orig.copy()
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df._set_value("C", 2, 1.0)
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assert list(df.index) == list(df_orig.index) + ["C"]
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# assert list(df.columns) == list(df_orig.columns) + [2]
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df = df_orig.copy()
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df.loc["C", 2] = 1.0
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assert list(df.index) == list(df_orig.index) + ["C"]
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# assert list(df.columns) == list(df_orig.columns) + [2]
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# create both new
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df = df_orig.copy()
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df._set_value("C", "D", 1.0)
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assert list(df.index) == list(df_orig.index) + ["C"]
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assert list(df.columns) == list(df_orig.columns) + ["D"]
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df = df_orig.copy()
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df.loc["C", "D"] = 1.0
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assert list(df.index) == list(df_orig.index) + ["C"]
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assert list(df.columns) == list(df_orig.columns) + ["D"]
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