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CLN avoid some upcasting when its not the purpose of the test #50493

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merged 3 commits into from
Jan 2, 2023
Merged

CLN avoid some upcasting when its not the purpose of the test #50493

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96e4dc9
avoid some upcasting when its not the purpose of the test
Dec 29, 2022
d4ecbc8
add comments
Dec 30, 2022
37a6053
add one more explicit upcast
Dec 30, 2022
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avoid some upcasting when its not the purpose of the test
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MarcoGorelli committed Dec 30, 2022
commit 96e4dc90ae7234075c6b9716b2b7e313db3b13d0
4 changes: 2 additions & 2 deletions pandas/tests/frame/test_query_eval.py
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Expand Up @@ -448,7 +448,7 @@ def test_date_index_query(self):
def test_date_index_query_with_NaT(self):
engine, parser = self.engine, self.parser
n = 10
df = DataFrame(np.random.randn(n, 3))
df = DataFrame(np.random.randn(n, 3)).astype({0: object})
df["dates1"] = date_range("1/1/2012", periods=n)
df["dates3"] = date_range("1/1/2014", periods=n)
df.iloc[0, 0] = pd.NaT
Comment on lines +452 to 455
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@MarcoGorelli MarcoGorelli Dec 29, 2022

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here, df[0] is first created of int dtype, and then df.iloc[0, 0] = pd.NaT upcasts it to object. Might as well create it of dtype object in the first place, as the purpose of this test comes in the lines below (df.query(...)
Expand Down Expand Up @@ -808,7 +808,7 @@ def test_date_index_query(self):
def test_date_index_query_with_NaT(self):
engine, parser = self.engine, self.parser
n = 10
df = DataFrame(np.random.randn(n, 3))
df = DataFrame(np.random.randn(n, 3)).astype({0: object})
df["dates1"] = date_range("1/1/2012", periods=n)
df["dates3"] = date_range("1/1/2014", periods=n)
df.iloc[0, 0] = pd.NaT
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@MarcoGorelli MarcoGorelli Dec 29, 2022

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same as above
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8 changes: 6 additions & 2 deletions pandas/tests/frame/test_reductions.py
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Expand Up @@ -448,11 +448,15 @@ def test_var_std(self, datetime_frame):
@pytest.mark.parametrize("meth", ["sem", "var", "std"])
def test_numeric_only_flag(self, meth):
# GH 9201
df1 = DataFrame(np.random.randn(5, 3), columns=["foo", "bar", "baz"])
df1 = DataFrame(np.random.randn(5, 3), columns=["foo", "bar", "baz"]).astype(
{"foo": object}
)
# set one entry to a number in str format
df1.loc[0, "foo"] = "100"

df2 = DataFrame(np.random.randn(5, 3), columns=["foo", "bar", "baz"])
df2 = DataFrame(np.random.randn(5, 3), columns=["foo", "bar", "baz"]).astype(
{"foo": object}
)
# set one entry to a non-number str
df2.loc[0, "foo"] = "a"
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@MarcoGorelli MarcoGorelli Dec 29, 2022

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in both df1 and df2, column 'foo' is initially created to be of dtype 'int', and then by setting an element to a string it is upcasting to object. This is done on purpose to check the numeric_only flag below

Might as well declare column 'foo' to be of dtype object from the start then

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2 changes: 1 addition & 1 deletion pandas/tests/groupby/test_timegrouper.py
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Expand Up @@ -102,7 +102,7 @@ def test_groupby_with_timegrouper(self):
index=date_range(
"20130901", "20131205", freq="5D", name="Date", inclusive="left"
),
)
).astype({"Buyer": object})
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@MarcoGorelli MarcoGorelli Dec 29, 2022

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column 'Buyer' is originally set to 0, and then some strings are set (thus upcasting to object). Might as well construct it as object from the start - furthermore, this is the expected dataframe, so it doesn't change what's being tested
expected.iloc[0, 0] = "CarlCarlCarl"
expected.iloc[6, 0] = "CarlCarl"
expected.iloc[18, 0] = "Joe"
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2 changes: 1 addition & 1 deletion pandas/tests/series/methods/test_replace.py
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Expand Up @@ -16,7 +16,7 @@ def test_replace_explicit_none(self):
expected = pd.Series([0, 0, None], dtype=object)
tm.assert_series_equal(result, expected)

df = pd.DataFrame(np.zeros((3, 3)))
df = pd.DataFrame(np.zeros((3, 3))).astype({2: object})
df.iloc[2, 2] = ""
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@MarcoGorelli MarcoGorelli Dec 29, 2022

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the third column df[2] is initially made up of ints, and then an element is set to ' ' (thus upcasting to object)

might as well set to object from the start, as the purpose of the test is on the line below df.replace(...
result = df.replace("", None)
expected = pd.DataFrame(
Expand Down