尝试将日志方法应用于Python中的 pandas 数据框列时出错 [英] Error when trying to apply log method to pandas data frame column in Python
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问题描述
spydata = pd.DataFrame（row，columns = ['date'，'ticker'，'close'，'iv1m'，'iv3m']）
tickerlist = unique（spydata [spydata ['date'] =='2013-05-31']）
之后，我有写了一个函数，使用已经存在的数据在数据框中创建一些新列：
def demean（arr）：
arr ['retlog'] = log（arr ['close'] / arr ['close']。shift（1））
arr ['10dvol'] = sqrt（252） * sqrt（pd.rolling_std（arr ['ret']，10））
arr ['60dvol'] = sqrt（252）* sqrt（pd.rolling_std（arr ['ret']，10））
arr ['90dvol'] = sqrt（252）* sqrt（pd.rolling_std（arr ['ret']，10））
arr ['1060rat'] = arr ['10dvol'] / arr ['60dvol']
arr ['1090rat'] = arr ['10dvol'] / arr ['90dvol' ]
arr ['60dis'] =（arr ['1060rat'] - arr ['1060rat']。mean（））/ arr ['1060rat']。std（）
arr ['90dis '] =（arr ['1090rat'] - arr ['1090rat']。mean（））/ arr ['1090rat']。std（）
return arr
我遇到问题的唯一部分是功能的第一行：
arr ['retlog'] = log（arr ['close'] / arr ['close']。shift（1））
当我运行这个命令时，我收到一个错误：
result = spydata.groupby（['ticker']）。apply（demean）
错误：
--------------------- -------------------------------------------------- ----
AttributeError追溯（最近的最后一次调用）
< ipython-input-196-4a66225e12ea>在< module>（）
----> 1 result = spydata.groupby（['ticker']）。apply（demean）
2 results2 = result [result.date == result.date.max（）]
3
C：\Python27\lib\site-packages\pandas-0.11.0-py2.7-win32.egg\pandas\core\groupby.pyc in apply（self，func，* args，** kwargs）
323 func = _intercept_function（func）
324 f = lambda g：func（g，* args，** kwargs）
- > 325 return self._python_apply_general（f）
326
327 def _python_apply_general（self，f）：
C：\Python27\lib\site-packages\pandas -0.11.0-py2.7-win32.egg\pandas\core\groupby.pyc in _python_apply_general（self，f）
326
327 def _python_apply_general（self，f）：
- > 328键，值，mutated = self.grouper.apply（f，self.obj，self.axis）
329
330 return self._wrap_applied_output（keys，values，
C：\Python27\lib\site-packages\pandas-0.11.0-py2.7-win32.egg\pandas\core\groupby.pyc in apply（self，f，data，axis， keep_internal）
632＃group可能被修改
633 group_axes = _get_axes（group）
- > 634 res = f（group）
635 if not _is_indexed_like（res，group_axes ）：
636 mutated = True
C：\Python27\lib\site-packages\pandas-0.11.0-py2.7-win32.egg\pandas\\ < lambda>（g）中的\\core\groupby.pyc
322
323 func = _intercept_function（func）
- > 324 f = lambda g：func g，* args，** kwargs）
325 return self._python_apply_general（f）
326
< ipython-input-195-47b6fa a3f43c> in demean（arr）
1 def demean（arr）：
----> 2 arr ['retlog'] = log（arr ['close'] / arr ['close']。shift（1））
3 arr ['10dvol'] = sqrt（252）* sqrt（pd。 rolling_std（arr ['ret']，10））
4 arr ['60dvol'] = sqrt（252）* sqrt（pd.rolling_std（arr ['ret']，10））
5 arr ['90dvol'] = sqrt（252）* sqrt（pd.rolling_std（arr ['ret']，10））
AttributeError：log
我已经尝试将功能更改为np.log以及math.log，在这种情况下，我收到错误
TypeError：只有length-1数组可以转换为Python标量
我已经尝试了这个，但没有找到任何直接适用的。任何线索？
解决方案
当列的数据类型不是数字时，会发生这种情况。尝试
arr ['retlog'] = log（arr ['close']。astype（'float64'）/ arr [ 'close']。astype（'float64'）。shift（1））
我怀疑数字存储为通用的对象类型，我知道导致日志抛出该错误。这是一个简单的例子：
在[15]中：np.log（系列（[1,2,3 ，4]，dtype ='object'））
----------------------------------- ----------------------------------------
AttributeError Traceback（最近的调用最后）
< ipython-input-15-25deca6462b7>在< module>（）
----> 1 np.log（系列（[1,2,3,4]，dtype ='object'））
AttributeError：log
在[16]中：np。 log（系列（[1,2,3,4]，dtype ='float64'））
输出[16]：
0 0.000000
1 0.693147
2 1.098612
3 1.386294
dtype：float64
您尝试使用数学.log 不起作用，因为该函数仅用于单个数字（标量），而不是列表或数组。
我认为这是一个令人困惑的错误信息;无论如何，它曾经让我忍受了一段时间。我想知道是否可以改善。
So, I am very new to Python and Pandas (and programming in general), but am having trouble with a seemingly simple function. So I created the following dataframe using data pulled with a SQL query (if you need to see the SQL query, let me know and I'll paste it)
spydata = pd.DataFrame(row,columns=['date','ticker','close', 'iv1m', 'iv3m'])
tickerlist = unique(spydata[spydata['date'] == '2013-05-31'])
After that, I have written a function to create some new columns in the dataframe using the data already held in it:
def demean(arr):
arr['retlog'] = log(arr['close']/arr['close'].shift(1))
arr['10dvol'] = sqrt(252)*sqrt(pd.rolling_std(arr['ret'] , 10 ))
arr['60dvol'] = sqrt(252)*sqrt(pd.rolling_std(arr['ret'] , 10 ))
arr['90dvol'] = sqrt(252)*sqrt(pd.rolling_std(arr['ret'] , 10 ))
arr['1060rat'] = arr['10dvol']/arr['60dvol']
arr['1090rat'] = arr['10dvol']/arr['90dvol']
arr['60dis'] = (arr['1060rat'] - arr['1060rat'].mean())/arr['1060rat'].std()
arr['90dis'] = (arr['1090rat'] - arr['1090rat'].mean())/arr['1090rat'].std()
return arr
The only part that I'm having a problem with is the first line of the function:
arr['retlog'] = log(arr['close']/arr['close'].shift(1))
Which, when I run, with this command, I get an error:
result = spydata.groupby(['ticker']).apply(demean)
Error:
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-196-4a66225e12ea> in <module>()
----> 1 result = spydata.groupby(['ticker']).apply(demean)
2 results2 = result[result.date == result.date.max()]
3
C:\Python27\lib\site-packages\pandas-0.11.0-py2.7-win32.egg\pandas\core\groupby.pyc in apply(self, func, *args, **kwargs)
323 func = _intercept_function(func)
324 f = lambda g: func(g, *args, **kwargs)
--> 325 return self._python_apply_general(f)
326
327 def _python_apply_general(self, f):
C:\Python27\lib\site-packages\pandas-0.11.0-py2.7-win32.egg\pandas\core\groupby.pyc in _python_apply_general(self, f)
326
327 def _python_apply_general(self, f):
--> 328 keys, values, mutated = self.grouper.apply(f, self.obj, self.axis)
329
330 return self._wrap_applied_output(keys, values,
C:\Python27\lib\site-packages\pandas-0.11.0-py2.7-win32.egg\pandas\core\groupby.pyc in apply(self, f, data, axis, keep_internal)
632 # group might be modified
633 group_axes = _get_axes(group)
--> 634 res = f(group)
635 if not _is_indexed_like(res, group_axes):
636 mutated = True
C:\Python27\lib\site-packages\pandas-0.11.0-py2.7-win32.egg\pandas\core\groupby.pyc in <lambda>(g)
322 """
323 func = _intercept_function(func)
--> 324 f = lambda g: func(g, *args, **kwargs)
325 return self._python_apply_general(f)
326
<ipython-input-195-47b6faa3f43c> in demean(arr)
1 def demean(arr):
----> 2 arr['retlog'] = log(arr['close']/arr['close'].shift(1))
3 arr['10dvol'] = sqrt(252)*sqrt(pd.rolling_std(arr['ret'] , 10 ))
4 arr['60dvol'] = sqrt(252)*sqrt(pd.rolling_std(arr['ret'] , 10 ))
5 arr['90dvol'] = sqrt(252)*sqrt(pd.rolling_std(arr['ret'] , 10 ))
AttributeError: log
I have tried changing the function to np.log as well as math.log, in which case I get the error
TypeError: only length-1 arrays can be converted to Python scalars
I've tried looking this up, but haven't found anything directly applicable. Any clues?
解决方案
This happens when the datatype of the column is not numeric. Try
arr['retlog'] = log(arr['close'].astype('float64')/arr['close'].astype('float64').shift(1))
I suspect that the numbers are stored as generic 'object' types, which I know causes log to throw that error. Here is a simple illustration of the problem:
In [15]: np.log(Series([1,2,3,4], dtype='object'))
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-15-25deca6462b7> in <module>()
----> 1 np.log(Series([1,2,3,4], dtype='object'))
AttributeError: log
In [16]: np.log(Series([1,2,3,4], dtype='float64'))
Out[16]:
0 0.000000
1 0.693147
2 1.098612
3 1.386294
dtype: float64
Your attempt with math.log did not work because that function is designed for single numbers (scalars) only, not lists or arrays.
For what it's worth, I think this is a confusing error message; it once stumped me for awhile, anyway. I wonder if it can be improved.
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