Question

我正在学习Python并特别尝试在Pandas中进行矢量化操作。但是，当我尝试使用Vectorized操作规范化Pandas数据帧时，我收到错误消息。

此可重现的示例使用了可在此链接中找到的surveys.csv数据集：http://www.datacarpentry.org/python-ecology-lesson/setup/

   surveys_df = pd.read_csv("surveys.csv")

    surveys_df_normalized = (surveys_df["weight"] - 
surveys_df["weight"].mean())/surveys_df["weight"].std() # Returns NaNs

        surveys_df_normalized = (surveys_df - 
surveys_df.mean())/surveys_df.std() # Returns error

您的建议将不胜感激。

错误消息如下：

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
~\lib\site-packages\pandas\core\ops.py in na_op(x, y)
   1175             result = expressions.evaluate(op, str_rep, x, y,
-> 1176                                           raise_on_error=True, **eval_kwargs)
   1177         except TypeError:

~\lib\site-packages\pandas\core\computation\expressions.py in evaluate(op, op_str, a, b, raise_on_error, use_numexpr, **eval_kwargs)
    210         return _evaluate(op, op_str, a, b, raise_on_error=raise_on_error,
--> 211                          **eval_kwargs)
    212     return _evaluate_standard(op, op_str, a, b, raise_on_error=raise_on_error)

~\lib\site-packages\pandas\core\computation\expressions.py in _evaluate_numexpr(op, op_str, a, b, raise_on_error, truediv, reversed, **eval_kwargs)
    121     if result is None:
--> 122         result = _evaluate_standard(op, op_str, a, b, raise_on_error)
    123 

~\lib\site-packages\pandas\core\computation\expressions.py in _evaluate_standard(op, op_str, a, b, raise_on_error, **eval_kwargs)
     63     with np.errstate(all='ignore'):
---> 64         return op(a, b)
     65 

TypeError: unsupported operand type(s) for -: 'str' and 'float'

During handling of the above exception, another exception occurred:

ValueError                                Traceback (most recent call last)
<ipython-input-88-43990c071f94> in <module>()
----> 1 surveys_df_normalized = (surveys_df - surveys_df.mean())/surveys_df.std()

~\lib\site-packages\pandas\core\ops.py in f(self, other, axis, level, fill_value)
   1234             return self._combine_frame(other, na_op, fill_value, level)
   1235         elif isinstance(other, ABCSeries):
-> 1236             return self._combine_series(other, na_op, fill_value, axis, level)
   1237         else:
   1238             if fill_value is not None:

~\lib\site-packages\pandas\core\frame.py in _combine_series(self, other, func, fill_value, axis, level)
   3504                                                    fill_value=fill_value)
   3505         return self._combine_series_infer(other, func, level=level,
-> 3506                                           fill_value=fill_value)
   3507 
   3508     def _combine_series_infer(self, other, func, level=None, fill_value=None):

~\lib\site-packages\pandas\core\frame.py in _combine_series_infer(self, other, func, level, fill_value)
   3516 
   3517         return self._combine_match_columns(other, func, level=level,
-> 3518                                            fill_value=fill_value)
   3519 
   3520     def _combine_match_index(self, other, func, level=None, fill_value=None):

~\lib\site-packages\pandas\core\frame.py in _combine_match_columns(self, other, func, level, fill_value)
   3536 
   3537         new_data = left._data.eval(func=func, other=right,
-> 3538                                    axes=[left.columns, self.index])
   3539         return self._constructor(new_data)
   3540 

~\lib\site-packages\pandas\core\internals.py in eval(self, **kwargs)
   3195 
   3196     def eval(self, **kwargs):
-> 3197         return self.apply('eval', **kwargs)
   3198 
   3199     def quantile(self, **kwargs):

~\lib\site-packages\pandas\core\internals.py in apply(self, f, axes, filter, do_integrity_check, consolidate, **kwargs)
   3089 
   3090             kwargs['mgr'] = self
-> 3091             applied = getattr(b, f)(**kwargs)
   3092             result_blocks = _extend_blocks(applied, result_blocks)
   3093 

~\lib\site-packages\pandas\core\internals.py in eval(self, func, other, raise_on_error, try_cast, mgr)
   1182         try:
   1183             with np.errstate(all='ignore'):
-> 1184                 result = get_result(other)
   1185 
   1186         # if we have an invalid shape/broadcast error

~\lib\site-packages\pandas\core\internals.py in get_result(other)
   1151 
   1152             else:
-> 1153                 result = func(values, other)
   1154 
   1155             # mask if needed

~\lib\site-packages\pandas\core\ops.py in na_op(x, y)
   1181                 result = np.empty(x.size, dtype=dtype)
   1182                 yrav = y.ravel()
-> 1183                 mask = notnull(xrav) & notnull(yrav)
   1184                 xrav = xrav[mask]
   1185 

ValueError: operands could not be broadcast together with shapes (71098,) (2,)

Answer 1

这是因为您需要了解数据集。权重列中有null个值。您需要删除具有空值的列以规范权重操作。将数据片段设为test_data并执行操作。

surveys_df = pd.read_csv("surveys.csv")
test_data = surveys_df.dropna()

虽然放弃所有null值不是一个好习惯，但现在你可以试验。现在检查test_data是否有任何空值。

test_data.isnull().any()

如果一切都是假的，那么执行标准化。

surveys_df_normalized = (test_data["weight"] - test_data["weight"].mean())/test_data["weight"].std()

现在，请注意，由于您尝试使用整个数据框{{1}在one维度['weight上进行计算，因此它无法执行您的最后一行代码。 }。我希望这有帮助

Pandas中的矢量化操作 - Python

1 个答案: