python pandas滾動功能，在一個分組數據框中有兩個參數

Question

這是對我以前的問題 python pandas rolling function with two arguments的擴展。

如何按組執行相同操作？假設下面的'C'列用於分組。

我很努力：

1. 集團通過列 'C'
2. 在每個組，排序由 'A'
3. Withing每個組，採用滾動操作以兩個參數，像kendalltau ，參數'A'和'B'。

預期的結果將是象下面這樣一個數據幀：

我一直在努力的「傳遞一個索引」的解決方法，如上面的鏈接的說明，但這樣的複雜性情況超出我:-(技能，這是一個玩具例子，而不是遠離什麼，我有工作，所以爲了簡單起見，我用隨機生成的數據。

rand = np.random.RandomState(1) 
dff = pd.DataFrame({'A' : np.arange(20), 
        'B' : rand.randint(100, 120, 20), 
        'C' : rand.randint(0, 2, 20)}) 

def my_tau_indx(indx): 
    x = dff.iloc[indx, 0] 
    y = dff.iloc[indx, 1] 
    tau = sp.stats.mstats.kendalltau(x, y)[0] 
    return tau 

dff['tau'] = dff.sort_values(['C', 'A']).groupby('C').rolling(window = 5).apply(my_tau_indx, args = ([dff.index.values])) 

每修復我使C reates又一個bug ...

上述問題已被Nickil Maveli解決，它可以與numpy 1.11.0，pandas 0.18.1，scipy 0.17.1和conda 4.1.4一起使用。它會產生一些警告，但是會起作用。

---

在我的另一臺機器上使用最新&最大numpy的1.12.0，熊貓0.19.2，SciPy的0.18.1，暢達版本3.10.0和BLAS/LAPACK - 它不工作，我得到下面的回溯。這似乎是版本相關的，因爲我升級第一臺機器，它也停止工作...以科學的名義... ;-)

由於尼克爾建議，這是由於1.11和1.12之間的不兼容。降級numpy幫助。由於我在Windows上安裝了BLAS/LAPACK，因此我從http://www.lfd.uci.edu/~gohlke/pythonlibs/安裝了numpy 1.11.3 + mkl。

Traceback (most recent call last): 

File "<ipython-input-4-bbca2c0e986b>", line 16, in <module> 
t = grp.apply(func) 

File "C:\Apps\Anaconda\v2_1_0_x64\envs\python35\lib\site-packages\pandas\core\groupby.py", line 651, in apply 
return self._python_apply_general(f) 

File "C:\Apps\Anaconda\v2_1_0_x64\envs\python35\lib\site-packages\pandas\core\groupby.py", line 655, in _python_apply_general 
self.axis) 

File "C:\Apps\Anaconda\v2_1_0_x64\envs\python35\lib\site-packages\pandas\core\groupby.py", line 1527, in apply 
res = f(group) 

File "C:\Apps\Anaconda\v2_1_0_x64\envs\python35\lib\site-packages\pandas\core\groupby.py", line 647, in f 
return func(g, *args, **kwargs) 

File "<ipython-input-4-bbca2c0e986b>", line 15, in <lambda> 
func = lambda x: pd.Series(pd.rolling_apply(np.arange(len(x)), 5, my_tau_indx), x.index) 

File "C:\Apps\Anaconda\v2_1_0_x64\envs\python35\lib\site-packages\pandas\stats\moments.py", line 584, in rolling_apply 
kwargs=kwargs) 

File "C:\Apps\Anaconda\v2_1_0_x64\envs\python35\lib\site-packages\pandas\stats\moments.py", line 240, in ensure_compat 
result = getattr(r, name)(*args, **kwds) 

File "C:\Apps\Anaconda\v2_1_0_x64\envs\python35\lib\site-packages\pandas\core\window.py", line 863, in apply 
return super(Rolling, self).apply(func, args=args, kwargs=kwargs) 

File "C:\Apps\Anaconda\v2_1_0_x64\envs\python35\lib\site-packages\pandas\core\window.py", line 621, in apply 
center=False) 

File "C:\Apps\Anaconda\v2_1_0_x64\envs\python35\lib\site-packages\pandas\core\window.py", line 560, in _apply 
result = calc(values) 

File "C:\Apps\Anaconda\v2_1_0_x64\envs\python35\lib\site-packages\pandas\core\window.py", line 555, in calc 
return func(x, window, min_periods=self.min_periods) 

File "C:\Apps\Anaconda\v2_1_0_x64\envs\python35\lib\site-packages\pandas\core\window.py", line 618, in f 
kwargs) 

File "pandas\algos.pyx", line 1831, in pandas.algos.roll_generic (pandas\algos.c:51768) 

File "<ipython-input-4-bbca2c0e986b>", line 8, in my_tau_indx 
x = dff.iloc[indx, 0] 

File "C:\Apps\Anaconda\v2_1_0_x64\envs\python35\lib\site-packages\pandas\core\indexing.py", line 1294, in __getitem__ 
return self._getitem_tuple(key) 

File "C:\Apps\Anaconda\v2_1_0_x64\envs\python35\lib\site-packages\pandas\core\indexing.py", line 1560, in _getitem_tuple 
retval = getattr(retval, self.name)._getitem_axis(key, axis=axis) 

File "C:\Apps\Anaconda\v2_1_0_x64\envs\python35\lib\site-packages\pandas\core\indexing.py", line 1614, in _getitem_axis 
return self._get_loc(key, axis=axis) 

File "C:\Apps\Anaconda\v2_1_0_x64\envs\python35\lib\site-packages\pandas\core\indexing.py", line 96, in _get_loc 
return self.obj._ixs(key, axis=axis) 

File "C:\Apps\Anaconda\v2_1_0_x64\envs\python35\lib\site-packages\pandas\core\frame.py", line 1908, in _ixs 
label = self.index[i] 

File "C:\Apps\Anaconda\v2_1_0_x64\envs\python35\lib\site-packages\pandas\indexes\range.py", line 510, in __getitem__ 
return super_getitem(key) 

File "C:\Apps\Anaconda\v2_1_0_x64\envs\python35\lib\site-packages\pandas\indexes\base.py", line 1275, in __getitem__ 
result = getitem(key) 

IndexError: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices 

最後的檢查：

Answer 1

的一種方式，以實現將通過各組進行迭代，並在每一個這樣的羣體使用pd.rolling_apply。

import scipy.stats as ss 

def my_tau_indx(indx): 
    x = dff.iloc[indx, 0] 
    y = dff.iloc[indx, 1] 
    tau = ss.mstats.kendalltau(x, y)[0] 
    return tau 

grp = dff.sort_values(['A', 'C']).groupby('C', group_keys=False) 
func = lambda x: pd.Series(pd.rolling_apply(np.arange(len(x)), 5, my_tau_indx), x.index) 
t = grp.apply(func) 
dff.reindex(t.index).assign(tau=t) 

---

編輯：

def my_tau_indx(indx): 
    x = dff.ix[indx, 0] 
    y = dff.ix[indx, 1] 
    tau = ss.mstats.kendalltau(x, y)[0] 
    return tau 

grp = dff.sort_values(['A', 'C']).groupby('C', group_keys=False) 
t = grp.rolling(5).apply(my_tau_indx).get('A') 

grp.head(dff.shape[0]).reindex(t.index).assign(tau=t)