为什么在pandas DataFrame中应用有时不比for-loop快？

Question

在大多数情况下，apply似乎可以加速对dataframe的操作过程。但是当我使用apply时，我没有发现加速效果。下面是我的示例，我有一个包含两列的数据表

>>>df
index col1 col2
1 10 20
2 20 30
3 30 40

我要做的是通过在col1上实现函数r(x)来计算dataframe中每一行的值，结果将被col2中的值除以。例如，第一行的结果应该是r(10)/20。下面是我的函数，它将在apply中调用

def _f(input):
  return R(input['col1'])/input['col2']

然后在apply中调用_f:df.apply(_f,axis=1)

for i in list(df.index)
  new_df.loc[i] = R(df.loc[i,'col1'])/df.loc[i,'col2']

Answer 1

根据我的理解，.apply通常不比轴上的迭代快。我相信在下面，它只是一个轴上的循环，只是在这种情况下，您每次都要承受函数调用的开销。

如果我们查看源代码，我们可以看到本质上我们在指示的轴上迭代并应用函数，将单个结果作为系列构建到字典中，最后在字典上调用dataframe构造函数，返回一个新的dataframe：

    if axis == 0:
        series_gen = (self._ixs(i, axis=1)
                      for i in range(len(self.columns)))
        res_index = self.columns
        res_columns = self.index
    elif axis == 1:
        res_index = self.index
        res_columns = self.columns
        values = self.values
        series_gen = (Series.from_array(arr, index=res_columns, name=name,
                                        dtype=dtype)
                      for i, (arr, name) in enumerate(zip(values,
                                                          res_index)))
    else:  # pragma : no cover
        raise AssertionError('Axis must be 0 or 1, got %s' % str(axis))

    i = None
    keys = []
    results = {}
    if ignore_failures:
        successes = []
        for i, v in enumerate(series_gen):
            try:
                results[i] = func(v)
                keys.append(v.name)
                successes.append(i)
            except Exception:
                pass
        # so will work with MultiIndex
        if len(successes) < len(res_index):
            res_index = res_index.take(successes)
    else:
        try:
            for i, v in enumerate(series_gen):
                results[i] = func(v)
                keys.append(v.name)
        except Exception as e:
            if hasattr(e, 'args'):
                # make sure i is defined
                if i is not None:
                    k = res_index[i]
                    e.args = e.args + ('occurred at index %s' %
                                       pprint_thing(k), )
            raise

    if len(results) > 0 and is_sequence(results[0]):
        if not isinstance(results[0], Series):
            index = res_columns
        else:
            index = None

        result = self._constructor(data=results, index=index)
        result.columns = res_index

        if axis == 1:
            result = result.T
        result = result._convert(datetime=True, timedelta=True, copy=False)

    else:

        result = Series(results)
        result.index = res_index

    return result

具体来说：

for i, v in enumerate(series_gen):
                results[i] = func(v)
                keys.append(v.name)

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