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TST: Adding to_dict numeric consistency test (#22620) #29338

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Merged
merged 4 commits into from
Nov 5, 2019
Merged

TST: Adding to_dict numeric consistency test (#22620) #29338

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TST: Adding DataFrame.to_dict(orient=records) numeric consistency tes…
sofiane87 Nov 2, 2019
2f26ed2
Pandas black correction
sofiane87 Nov 2, 2019
61f9cd7
simplifying test
sofiane87 Nov 2, 2019
23d886e
Test: testing all indices
sofiane87 Nov 2, 2019
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16 changes: 16 additions & 0 deletions pandas/tests/frame/test_convert_to.py
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Original file line number Diff line number Diff line change
Expand Up @@ -612,3 +612,19 @@ def test_to_dict_wide(self):
result = df.to_dict("records")[0]
expected = {"A_{:d}".format(i): i for i in range(256)}
assert result == expected

def test_to_dict_orient_dtype(self):
# https://github.com/pandas-dev/pandas/issues/22620
# Input Data
input_data = {"a": [1, 2, 3], "b": [1.0, 2.0, 3.0], "c": ["X", "Y", "Z"]}
df = DataFrame(input_data)
# Expected Dtypes
expected = {"a": int, "b": float, "c": str}
# Extracting dtypes out of to_dict operation
for df_dict in df.to_dict("records"):
result = {
"a": type(df_dict["a"]),
"b": type(df_dict["b"]),
"c": type(df_dict["c"]),
}
assert result == expected
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I would check that the result of df.dtypes is correct. That would be simpler than this for-loop.

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the point is to check that the type returned from to_dict are the same as the ones within the dataset. I am not sure how to do that using df.dtypes. @simonjayhawkins recommended that I make the tests as explicit as possible.

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actually this is ok

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yeah this is enough here