CODEWITHSUNDEEP
pythonnumpypandasdataframe
user2460677
user2460677

Reputation: 93

Error when trying to apply log method to pandas data frame column in Python

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?

Upvotes: 9

Views: 20501

Answers (1)

Dan Allan
Dan Allan

Reputation: 35245

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.

Upvotes: 13

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