def _reduce(self, op, name, axis=0, skipna=True, numeric_only=None,
filter_type=None, **kwds):
axis = self._get_axis_number(axis)
def f(x):
return op(x, axis=axis, skipna=skipna, **kwds)
labels = self._get_agg_axis(axis)
# exclude timedelta/datetime unless we are uniform types
if axis == 1 and self._is_mixed_type and self._is_datelike_mixed_type:
numeric_only = True
if numeric_only is None:
try:
values = self.values
result = f(values)
except Exception as e:
# try by-column first
if filter_type is None and axis == 0:
try:
# this can end up with a non-reduction
# but not always. if the types are mixed
# with datelike then need to make sure a series
result = self.apply(f, reduce=False)
if result.ndim == self.ndim:
result = result.iloc[0]
return result
except:
pass
if filter_type is None or filter_type == 'numeric':
data = self._get_numeric_data()
elif filter_type == 'bool':
data = self._get_bool_data()
else: # pragma: no cover
e = NotImplementedError("Handling exception with filter_"
"type %s not implemented." %
filter_type)
raise_with_traceback(e)
result = f(data.values)
labels = data._get_agg_axis(axis)
else:
if numeric_only:
if filter_type is None or filter_type == 'numeric':
data = self._get_numeric_data()
elif filter_type == 'bool':
data = self._get_bool_data()
else: # pragma: no cover
msg = ("Generating numeric_only data with filter_type %s"
"not supported." % filter_type)
raise NotImplementedError(msg)
values = data.values
labels = data._get_agg_axis(axis)
else:
values = self.values
result = f(values)
if hasattr(result, 'dtype') and is_object_dtype(result.dtype):
try:
if filter_type is None or filter_type == 'numeric':
result = result.astype(np.float64)
elif filter_type == 'bool' and notnull(result).all():
result = result.astype(np.bool_)
except (ValueError, TypeError):
# try to coerce to the original dtypes item by item if we can
if axis == 0:
result = com._coerce_to_dtypes(result, self.dtypes)
return Series(result, index=labels)
天哪,说说失控的功能。有人需要重构!让我们放大故障线路:
if hasattr(result, 'dtype') and is_object_dtype(result.dtype):
try:
if filter_type is None or filter_type == 'numeric':
result = result.astype(np.float64)
你最好相信最后一行会被执行。以下是一些pdb跟踪:
> c:\users\matthew\anaconda2\lib\site-packages\pandas\core\frame.py(4801)_reduce()
-> result = result.astype(np.float64)
(Pdb) l
4796 result = f(values)
4797
4798 if hasattr(result, 'dtype') and is_object_dtype(result.dtype):
4799 try:
4800 if filter_type is None or filter_type == 'numeric':
4801 -> result = result.astype(np.float64)
4802 elif filter_type == 'bool' and notnull(result).all():
4803 result = result.astype(np.bool_)
4804 except (ValueError, TypeError):
4805
4806 # try to coerce to the original dtypes item by item if we can
与旧的堆栈跟踪相匹配。通过Pycharm也学到了一些关于pdb的知识。结果如下:
(一)
让我们看看
_make_stat_function
(二)
^{pr2}$最后一行是关键。这有点有趣,因为在
pandas.core
内大约有7个不同的_reduces
。pdb说它是pandas.core.frame
中的一个。让我们看看。在(三)
天哪,说说失控的功能。有人需要重构!让我们放大故障线路:
你最好相信最后一行会被执行。以下是一些pdb跟踪:
如果你是个不信教的人,敞开心扉熊猫.核心.框架.py并在第4801行的正上方放置一个
print "OI"
。它应该弹出到控制台:)。注意我在水蟒2号,窗户上。在我要用“虫子”来回答你的问题。在
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