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BUG: Fixing DataFrame.Update crashes when NaT present #49395

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Merged
merged 11 commits into from
Nov 15, 2022
Merged

BUG: Fixing DataFrame.Update crashes when NaT present #49395

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2d126dd
Fixes DataFrame.update crashes when NaT present. GH16713
wxing11 Oct 30, 2022
b4c792a
Formatting
wxing11 Oct 30, 2022
c63d1cc
No longer reindexing columns, skipping non-matching columns
wxing11 Nov 3, 2022
7bea232
switching to using set intersection to find shared columns
wxing11 Nov 3, 2022
9ee4329
switching to using index intersection
wxing11 Nov 3, 2022
f191629
Removing unnecessary variable creation
wxing11 Nov 8, 2022
35fa0a8
Formatting and removing test
wxing11 Nov 8, 2022
ead244a
Adding to whatsnew
wxing11 Nov 8, 2022
bd07c5a
updating whatsnew message
wxing11 Nov 12, 2022
8a1e0f7
Merge branch 'main' into fixing-dataframe-update-crashes
MarcoGorelli Nov 14, 2022
d8167ea
Update doc/source/whatsnew/v2.0.0.rst
MarcoGorelli Nov 15, 2022
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8 changes: 6 additions & 2 deletions pandas/core/frame.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8190,11 +8190,15 @@ def update(
if not isinstance(other, DataFrame):
other = DataFrame(other)

other = other.reindex_like(self)
# reindex rows, non-matching columns get skipped
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not sure we need this comment

other = other.reindex(self.index)

for col in self.columns:
shared_cols = self.columns.intersection(other.columns)
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@MarcoGorelli I know this solution kinda side-steps the original issue of null-matching (and reindex introducing an entire NA-column that doesn't need updating I think), but happy to have your thoughts on this solution.

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I think this is fine

Something that comes to mind is that it'll still error if other contains a column all of nan, e.g.:

In [1]: df1 = pd.DataFrame({'a': [NaT]})

In [2]: df2 = pd.DataFrame({'a': [np.nan]})

In [3]: df1.update(df2, overwrite=False)
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In [3], line 1
----> 1 df1.update(df2, overwrite=False)

File ~/pandas-dev/pandas/core/frame.py:8096, in DataFrame.update(self, other, join, overwrite, filter_func, errors)
   8093 if mask.all():
   8094     continue
-> 8096 self.loc[:, col] = expressions.where(mask, this, that)

File ~/pandas-dev/pandas/core/computation/expressions.py:258, in where(cond, a, b, use_numexpr)
    246 """
    247 Evaluate the where condition cond on a and b.
    248 
   (...)
    255     Whether to try to use numexpr.
    256 """
    257 assert _where is not None
--> 258 return _where(cond, a, b) if use_numexpr else _where_standard(cond, a, b)

File ~/pandas-dev/pandas/core/computation/expressions.py:188, in _where_numexpr(cond, a, b)
    181     result = ne.evaluate(
    182         "where(cond_value, a_value, b_value)",
    183         local_dict={"cond_value": cond, "a_value": a, "b_value": b},
    184         casting="safe",
    185     )
    187 if result is None:
--> 188     result = _where_standard(cond, a, b)
    190 return result

File ~/pandas-dev/pandas/core/computation/expressions.py:172, in _where_standard(cond, a, b)
    170 def _where_standard(cond, a, b):
    171     # Caller is responsible for extracting ndarray if necessary
--> 172     return np.where(cond, a, b)

File <__array_function__ internals>:180, in where(*args, **kwargs)

TypeError: The DType <class 'numpy.dtype[datetime64]'> could not be promoted by <class 'numpy.dtype[float64]'>. This means that no common DType exists for the given inputs. For example they cannot be stored in a single array unless the dtype is `object`. The full list of DTypes is: (<class 'numpy.dtype[datetime64]'>, <class 'numpy.dtype[float64]'>)

but arguably that's desirable behaviour - you wouldn't want to update with a column of an incompatible dtype, regardless of whether its values were all missing or not.

And value-dependent behaviour wouldn't be great, so I like this solution more than the originally-suggested "if all nan then skip"


for col in shared_cols:
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let's keep it on one line

        for col in self.columns.intersection(other.columns):

this = self[col]._values
that = other[col]._values

if filter_func is not None:
with np.errstate(all="ignore"):
mask = ~filter_func(this) | isna(that)
Expand Down
34 changes: 34 additions & 0 deletions pandas/tests/frame/methods/test_update.py
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Expand Up @@ -166,3 +166,37 @@ def test_update_modify_view(self, using_copy_on_write):
tm.assert_frame_equal(result_view, df2_orig)
else:
tm.assert_frame_equal(result_view, expected)

def test_update_dt_column_with_NaT_create_column(self):
df = DataFrame(
{
"A": [1, None],
"B": [
pd.NaT,
pd.to_datetime("2016-01-01"),
],
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can we keep this on a single line?

}
)
df2 = DataFrame({"A": [2, 3]})

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let's remove all these newlines in the tests

df.update(df2, overwrite=False)

expected = DataFrame(
{"A": [1.0, 3.0], "B": [pd.NaT, pd.to_datetime("2016-01-01")]}
)

tm.assert_frame_equal(df, expected)

def test_update_dt_column_with_NaT_create_row(self):
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not really sure what this test adds, I'd suggest to either:

  • parametrize over the first test
  • just remove this one


df = DataFrame({"A": [1, None], "B": [pd.to_datetime("2017-1-1"), pd.NaT]})

df2 = DataFrame({"A": [2], "B": [pd.to_datetime("2016-01-01")]})

df.update(df2, overwrite=False)

expected = DataFrame(
{"A": [1, None], "B": [pd.to_datetime("2017-1-1"), pd.NaT]}
)

tm.assert_frame_equal(df, expected)