Question

我有一个问题，nunique是由一个空组来调用它以错误结束

>df
Empty DataFrame
Columns: [A, B]
Index: []
>df.groupby(['A'])['B'].nunique()
IndexError: index 0 is out of bounds for axis 0 with size 0

我想添加一个简单的检查，如果groupby为空，则返回一个空系列。

我在python portable中更改了nunique的def并添加了一个有效的检查：

def nunique(self, dropna=True):
    """ Returns number of unique elements in the group """
    ids, _, _ = self.grouper.group_info
    val = self.obj.get_values()

    try:
        sorter = np.lexsort((val, ids))
    except TypeError:  # catches object dtypes
        assert val.dtype == object, \
            'val.dtype must be object, got %s' % val.dtype
        val, _ = algos.factorize(val, sort=False)
        sorter = np.lexsort((val, ids))
        isnull = lambda a: a == -1
    else:
        isnull = com.isnull

    ids, val = ids[sorter], val[sorter]

    if ids.size == 0: ######Thats what I've added
        return Series(ids,index=self.grouper.result_index,name=self.name)

    # group boundaries are where group ids change
    # unique observations are where sorted values change
    idx = np.r_[0, 1 + np.nonzero(ids[1:] != ids[:-1])[0]]
    inc = np.r_[1, val[1:] != val[:-1]]

    # 1st item of each group is a new unique observation
    mask = isnull(val)
    if dropna:
        inc[idx] = 1
        inc[mask] = 0
    else:
        inc[mask & np.r_[False, mask[:-1]]] = 0
        inc[idx] = 1

    out = np.add.reduceat(inc, idx).astype('int64', copy=False)
    res = out if ids[0] != -1 else out[1:]
    ri = self.grouper.result_index

    # we might have duplications among the bins
    if len(res) != len(ri):
        res, out = np.zeros(len(ri), dtype=out.dtype), res
        res[ids] = out

    return Series(res,
                  index=ri,
                  name=self.name)

问题是我无法自行更改便携式，我需要以某种方式覆盖nunique或添加一个包装函数，该函数将在调用groupby（...）。nunique（）时调用。我在网上看了，但找不到（也没理解）任何东西。对不起，如果它可能很简单Q，但我是一个新手程序员，所以对我很轻松:)）

谢谢，

Answer 1

使用apply函数添加条件来检查组的长度怎么样？

df.groupby(['A'])['B'].apply(lambda x: x.nunique() if len(x)>0 else 0)

覆盖nunique（）或为其添加包装器

1 个答案: