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TST: astyp via loc to int64 #36942

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TST: astyp via loc to int64 #36942

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a63365b
TST: astype cast via loc with nan values (#31861)
hardikpnsp Oct 3, 2020
52b7c21
TST: added NA to the astype via loc test (#31861)
hardikpnsp Oct 7, 2020
129b1b5
TST: updated to proper comment and variable name
hardikpnsp Oct 9, 2020
5a993b8
TST: added working conditions for astype via loc (#31861)
hardikpnsp Oct 11, 2020
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29 changes: 29 additions & 0 deletions pandas/tests/frame/methods/test_astype.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,6 +4,7 @@
import pytest

from pandas import (
NA,
Categorical,
CategoricalDtype,
DataFrame,
Expand Down Expand Up @@ -136,6 +137,34 @@ def test_astype_cast_nan_inf_int(self, val, dtype):
with pytest.raises(ValueError, match=msg):
df.astype(dtype)

@pytest.mark.parametrize("dtype", ["Int64", "Int32", "Int16"])
@pytest.mark.parametrize("val", [np.nan, NA])
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use nulls_fixture instead and any_nullable_int_dtype fixture

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@hardikpnsp hardikpnsp Oct 11, 2020 •
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@jreback , here in nulls_fixture, the test case fails for pd.NaT because it is inferred as datetime64. I think it is expected behavior but would like your opinion on it.

code snippet for reference

>>> df = pd.DataFrame({"a": ["foo"], "b": [pd.NaT]})
>>> df.dtypes
a            object
b    datetime64[ns]
dtype: object
>>> df.loc[:, "b"] = df.loc[:, "b"].astype('Int64')
TypeError: datetime64[ns] cannot be converted to an IntegerDtype

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@arw2019 @jreback , Any updates on this one? would appreciate some help and direction.

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Could use pytest.skipif for the datetime case
or test for the TypeError somehow but I'm not sure that's necessary

def test_astype_cast_via_loc_nan_int(self, val, dtype):
# GH31861
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can you add the working conditions as well (e.g. nan + a value) from the OP

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sure! let me try.

expected = DataFrame({"a": ["foo"], "b": integer_array([val], dtype=dtype)})
df = DataFrame({"a": ["foo"], "b": [val]})
df.loc[:, "b"] = df.loc[:, "b"].astype(dtype)
tm.assert_frame_equal(df, expected)

expected2 = DataFrame(
{"a": ["foo", "foo"], "b": integer_array([val, 1.0], dtype=dtype)}
)
df2 = DataFrame({"a": ["foo", "foo"], "b": [val, 1.0]})
df2.loc[:, "b"] = df2.loc[:, "b"].astype(dtype)
tm.assert_frame_equal(df2, expected2)

expected3 = DataFrame(
{"a": ["foo", "foo"], "b": integer_array([val, val], dtype=dtype)}
)
df3 = DataFrame({"a": ["foo", "foo"], "b": [val, val]})
df3.loc[:, "b"] = df3.loc[:, "b"].astype(dtype)
tm.assert_frame_equal(df3, expected3)

expected4 = DataFrame({"b": integer_array([val], dtype=dtype)})
df4 = DataFrame({"b": [val]})
df4.loc[:, "b"] = df4.loc[:, "b"].astype(dtype)
tm.assert_frame_equal(df4, expected4)

def test_astype_str(self):
# see GH#9757
a = Series(date_range("2010-01-04", periods=5))
Expand Down