Add test to check numeric precision GH33234 #51753
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liang3zy22
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phofl
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Mar 5, 2023
| else 2147483647 | ||
| ], | ||
| } | ||
| ).astype({"key3": dtype}) |
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Can you define all those objects explicitly? e.g. no astype
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Ok. Could I drop the astype part and keep just DataFrame creation expression?
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I think I defined all those objects explicitly in the updated commit.
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mroeschke
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| "Float32", | ||
| ], | ||
| ) | ||
| def test_groupby_agg_precision(dtype): |
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Could you use the any_real_numeric_dtype fixture here?
mroeschke
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| "key1": ["a"], | ||
| "key2": ["b"], | ||
| "key3": [ | ||
| pd.array([1583715738627261039], dtype=dtype)[0] |
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Could you specify a max_value variable above this df assignment and then write this as
df = pd.DataFrame({..., "key3": pd.array([max_value], dtype=dtype)})
mroeschke
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Mar 10, 2023
| } | ||
| ) | ||
| expected = df[["key3"]] | ||
| result = df.groupby(["key1", "key2"]).agg(lambda x: x).reset_index()[["key3"]] |
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Could you construct expected such that result is just df.groupby(["key1", "key2"]).agg(lambda x: x)?
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Signed-off-by: Liang Yan <ckgppl_yan@sina.cn>
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mroeschke
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|
Thanks @liang3zy22 |
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This a new PR to fix #33234 since old PR closed.