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TST: ro_replace changing dtype of series #50664

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Merged
merged 9 commits into from
Jan 12, 2023
Merged

TST: ro_replace changing dtype of series #50664

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81c9858
added test based on issue #25797 to check if changing dtype of series…
pooja-subramaniam Jan 10, 2023
6e50525
Merge remote-tracking branch 'upstream/main'
pooja-subramaniam Jan 10, 2023
d21d88e
Merge branch 'main' of https://github.com/pandas-dev/pandas
pooja-subramaniam Jan 11, 2023
b2621b1
Merge branch 'pandas-dev:main' into main
pooja-subramaniam Jan 11, 2023
83d9801
changed single quotes to double and added new line at the end
pooja-subramaniam Jan 11, 2023
e269bb7
Merge branch 'main' of https://github.com/pooja-subramaniam/pandas
pooja-subramaniam Jan 11, 2023
fe3e454
remove print statements and unnecessary assert, and use expected resu…
pooja-subramaniam Jan 11, 2023
ca92c77
change assert statements to compare dataframes rather than only dtype
pooja-subramaniam Jan 11, 2023
250c044
remove redundant expected assignment statements
pooja-subramaniam Jan 12, 2023
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23 changes: 23 additions & 0 deletions pandas/tests/series/methods/test_replace.py
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Original file line number Diff line number Diff line change
Expand Up @@ -675,3 +675,26 @@ def test_replace_value_none_dtype_numeric(self, val):
result = ser.replace(val, None)
expected = pd.Series([1, None], dtype=object)
tm.assert_series_equal(result, expected)

def test_replace_change_dtype_series(self):
# GH#25797
df = pd.DataFrame.from_dict({"Test": ["0.5", True, "0.6"]})
print(df["Test"].dtype)
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Some pointers: Please remove the print statements.

Generally, you can remove your first assert statements per block, but we want to compare the whole df with the second assert statement, e.g. define your expected object and then call assert_frame_equal

expected = ...
tm.assert_frame_equal(df, expected)

if expected is the same as your initial df, you can define expected through

expected = df.copy()

in the beginning

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Thanks! Made the changes.

Correct me if I am wrong, but here the assert needs to check if the series dtype remains 'object' and not if the dataframes are equal. I've made changes accordingly.

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tm.assert_frame_equal raises if your dtypes are different. So this is covered as well. In general, we want to check that the DataFrame is as expected (values, columns, index, dtype, ...). That is why we are using assert_frame_equal

The functions could modify the values unexpectedly and the test would not fail, if you only check the dtypes

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Ah okay! Thanks for explaining :)

assert df["Test"].dtype == "object"
df["Test"] = df["Test"].replace([True], [np.nan])
print(df["Test"].dtype)
assert df["Test"].dtype == "object"

df = pd.DataFrame.from_dict({"Test": ["0.5", None, "0.6"]})
print(df["Test"].dtype)
assert df["Test"].dtype == "object"
df["Test"] = df["Test"].replace([None], [np.nan])
print(df["Test"].dtype)
assert df["Test"].dtype == "object"

df = pd.DataFrame.from_dict({"Test": ["0.5", None, "0.6"]})
print(df["Test"].dtype)
assert df["Test"].dtype == "object"
df["Test"] = df["Test"].fillna(np.nan)
print(df["Test"].dtype)
assert df["Test"].dtype == "object"