如何在不添加额外索引的情况下使用 Pandas groupby apply() [英] How to use Pandas groupby apply() without adding an extra index
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问题描述
我经常想通过组合分组数据帧的多列来创建一个新的数据帧.apply() 函数允许我这样做，但它要求我创建一个不需要的索引:
I very often want to create a new DataFrame by combining multiple columns of a grouped DataFrame. The apply() function allows me to do that, but it requires that I create an unneeded index:
In [359]: df = pandas.DataFrame({'x': 3 * ['a'] + 2 * ['b'], 'y': np.random.normal(size=5), 'z': np.random.normal(size=5)})
In [360]: df
Out[360]:
x y z
0 a 0.201980 -0.470388
1 a 0.190846 -2.089032
2 a -1.131010 0.227859
3 b -0.263865 -1.906575
4 b -1.335956 -0.722087
In [361]: df.groupby('x').apply(lambda x: pandas.DataFrame({'r': (x.y + x.z).sum() / x.z.sum(), 's': (x.y + x.z ** 2).sum() / x.z.sum()}))
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
/home/emarkley/work/src/partner_analysis2/main.py in <module>()
----> 1 df.groupby('x').apply(lambda x: pandas.DataFrame({'r': (x.y + x.z).sum() / x.z.sum(), 's': (x.y + x.z ** 2).sum() / x.z.sum()}))
/usr/local/lib/python3.2/site-packages/pandas-0.8.2.dev-py3.2-linux-x86_64.egg/pandas/core/groupby.py in apply(self, func, *args, **kwargs)
267 applied : type depending on grouped object and function
268 """
--> 269 return self._python_apply_general(func, *args, **kwargs)
270
271 def aggregate(self, func, *args, **kwargs):
/usr/local/lib/python3.2/site-packages/pandas-0.8.2.dev-py3.2-linux-x86_64.egg/pandas/core/groupby.py in _python_apply_general(self, func, *args, **kwargs)
417 group_axes = _get_axes(group)
418
--> 419 res = func(group, *args, **kwargs)
420
421 if not _is_indexed_like(res, group_axes):
/home/emarkley/work/src/partner_analysis2/main.py in <lambda>(x)
----> 1 df.groupby('x').apply(lambda x: pandas.DataFrame({'r': (x.y + x.z).sum() / x.z.sum(), 's': (x.y + x.z ** 2).sum() / x.z.sum()}))
/usr/local/lib/python3.2/site-packages/pandas-0.8.2.dev-py3.2-linux-x86_64.egg/pandas/core/frame.py in __init__(self, data, index, columns, dtype, copy)
371 mgr = self._init_mgr(data, index, columns, dtype=dtype, copy=copy)
372 elif isinstance(data, dict):
--> 373 mgr = self._init_dict(data, index, columns, dtype=dtype)
374 elif isinstance(data, ma.MaskedArray):
375 mask = ma.getmaskarray(data)
/usr/local/lib/python3.2/site-packages/pandas-0.8.2.dev-py3.2-linux-x86_64.egg/pandas/core/frame.py in _init_dict(self, data, index, columns, dtype)
454 # figure out the index, if necessary
455 if index is None:
--> 456 index = extract_index(data)
457 else:
458 index = _ensure_index(index)
/usr/local/lib/python3.2/site-packages/pandas-0.8.2.dev-py3.2-linux-x86_64.egg/pandas/core/frame.py in extract_index(data)
4719
4720 if not indexes and not raw_lengths:
-> 4721 raise ValueError('If use all scalar values, must pass index')
4722
4723 if have_series or have_dicts:
ValueError: If use all scalar values, must pass index
In [362]: df.groupby('x').apply(lambda x: pandas.DataFrame({'r': (x.y + x.z).sum() / x.z.sum(), 's': (x.y + x.z ** 2).sum() / x.z.sum()}, index=[0]))
Out[362]:
r s
x
a 0 1.316605 -1.672293
b 0 1.608606 -0.972593
有没有什么方法可以使用 apply() 或其他一些函数来获得相同的结果而无需额外的零索引?
Is there any way to use apply() or some other function to get the same results without the extra index of zeros?
推荐答案
您正在为每个组生成聚合 r 和 s 值，因此您应该在此处使用 Series:
You're producing an aggregate r and s value per group, so you should be using Series here:
In [26]: df.groupby('x').apply(lambda x:
Series({'r': (x.y + x.z).sum() / x.z.sum(),
's': (x.y + x.z ** 2).sum() / x.z.sum()}))
Out[26]:
r s
x
a -0.338590 -0.916635
b 66.655533 102.566146
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