Regression in DataFrame.sum with mixed datetime, numeric and missing values

[TomAugspurger]

On 0.25.3

In [3]: df = pd.DataFrame({"A": pd.date_range("2000", periods=4), "B": [1, 2, 3, 4]}).reindex([2, 3, 4])

In [4]: df.sum()

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
TypeError: unsupported operand type(s) for +: 'Timestamp' and 'Timestamp'

The above exception was the direct cause of the following exception:

SystemError                               Traceback (most recent call last)
<ipython-input-4-7e5fdb616c56> in <module>
----> 1 df.sum()

~/sandbox/pandas/pandas/core/generic.py in stat_func(self, axis, skipna, level, numeric_only, min_count, **kwargs)
  11035             skipna=skipna,
  11036             numeric_only=numeric_only,
> 11037             min_count=min_count,
  11038         )
  11039

~/sandbox/pandas/pandas/core/frame.py in _reduce(self, op, name, axis, skipna, numeric_only, filter_type, **kwds)
   7885             values = self.values
   7886             try:
-> 7887                 result = f(values)
   7888
   7889                 if filter_type == "bool" and is_object_dtype(values) and axis is None:

~/sandbox/pandas/pandas/core/frame.py in f(x)
   7843
   7844         def f(x):
-> 7845             return op(x, axis=axis, skipna=skipna, **kwds)
   7846
   7847         def _get_data(axis_matters):

~/sandbox/pandas/pandas/core/nanops.py in _f(*args, **kwargs)
     67             try:
     68                 with np.errstate(invalid="ignore"):
---> 69                     return f(*args, **kwargs)
     70             except ValueError as e:
     71                 # we want to transform an object array

~/sandbox/pandas/pandas/core/nanops.py in nansum(values, axis, skipna, min_count, mask)
    491     elif is_timedelta64_dtype(dtype):
    492         dtype_sum = np.float64
--> 493     the_sum = values.sum(axis, dtype=dtype_sum)
    494     the_sum = _maybe_null_out(the_sum, axis, mask, values.shape, min_count=min_count)
    495

~/Envs/pandas-dev/lib/python3.7/site-packages/numpy/core/_methods.py in _sum(a, axis, dtype, out, keepdims, initial, where)
     36 def _sum(a, axis=None, dtype=None, out=None, keepdims=False,
     37          initial=_NoValue, where=True):
---> 38     return umr_sum(a, axis, dtype, out, keepdims, initial, where)
     39
     40 def _prod(a, axis=None, dtype=None, out=None, keepdims=False,

~/sandbox/pandas/pandas/_libs/tslibs/c_timestamp.pyx in pandas._libs.tslibs.c_timestamp._Timestamp.__add__()

~/sandbox/pandas/pandas/_libs/tslibs/c_timestamp.pyx in pandas._libs.tslibs.c_timestamp.integer_op_not_supported()

SystemError: <class 'TypeError'> returned a result with an error set

On 0.25.x

In [1]: import pandas as pd
df
In [2]: df = pd.DataFrame({"A": pd.date_range("2000", periods=4), "B": [1, 2, 3, 4]}).reindex([2, 3, 4])

In [3]: df.sum()
Out[3]:
B    7.0
dtype: float64

cc @jbrockmendel

[jbrockmendel]

huh, the fact that the reindex is necessary is weird. I'll take a look a this.

[TomAugspurger]

I assume it's the introduction of the missing values, but haven't looked closely.

[jbrockmendel]

Tentatively looks like moving from fstring back to .format in integer_op_not_supported fixes this

[scoder]

Could you paste the C code that is generated by Cython for the f-string and the return TypeError(…) ?

[jbrockmendel]

Could you paste the C code that is generated by Cython for the f-string and the return TypeError(…) ?

fstring version:

  /* "pandas/_libs/tslibs/c_timestamp.pyx":61
 * 
 *     int_addsub_msg = (
 *         f"Addition/subtraction of integers and integer-arrays with {cls} is "             # <<<<<<<<<<<<<<
 *         "no longer supported.  Instead of adding/subtracting `n`, "
 *         "use `n * obj.freq`"
 */
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  /* "pandas/_libs/tslibs/c_timestamp.pyx":65
 *         "use `n * obj.freq`"
 *     )
 *     return TypeError(int_addsub_msg)             # <<<<<<<<<<<<<<
 * 
 * 
 */
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.format version

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  /* "pandas/_libs/tslibs/c_timestamp.pyx":65
 *         "no longer supported.  Instead of adding/subtracting `n`, "
 *         "use `n * obj.freq`"
 *     ).format(cls=cls)             # <<<<<<<<<<<<<<
 *     return TypeError(int_addsub_msg)
 * 
 */
  __pyx_t_1 = __Pyx_PyObject_GetAttrStr(__pyx_kp_u_Addition_subtraction_of_integers, __pyx_n_s_format); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 65, __pyx_L1_error)
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  /* "pandas/_libs/tslibs/c_timestamp.pyx":66
 *         "use `n * obj.freq`"
 *     ).format(cls=cls)
 *     return TypeError(int_addsub_msg)             # <<<<<<<<<<<<<<
 * 
 * 
 */
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[scoder]

Thanks. Both look completely correct to me. Even the call to TypeError is exactly the same, I can see no reason why one would raise a SystemError and the other wouldn't.

@TomAugspurger, are both tests using the exact same CPython installation and version? Can you confirm that the only difference is the Pandas version?

@jbrockmendel, can you confirm that the C code snippets you pasted are those that allowed you to reproduce the differing behaviour?

[jbrockmendel]

can you confirm that the C code snippets you pasted are those that allowed you to reproduce the differing behaviour?

Just double checked: the pasted code matches what I have locally for master and #30905, respectively, which reproduce the differing behavior.

[scoder]

the fact that the reindex is necessary is weird

I agree. May suggest that the problem is somewhere else entirely. From what I gather, the creation of the TypeError isn't the place where things go wrong. It's only the call that happens to detect that an exception was set earlier without being reported and now reports the inconsistent system state.

It could be that the .format() accidentally clears the error state along the way without looking at it, so that the TypeError creation doesn't find it any more and succeeds normally. My guess is that the f-string version is correct in reporting that there is an issue, and that going back to the .format() just covers it up without solving it.

Could someone run both versions in a CPython debug build to see if that's more informative?