Question

I have a Pandas dataframe ‘dt = myfunc()’ , and copy the screen output from IDLE as below:

>>> from __future__ import division
>>> dt = __get_stk_data__(['*'], frq='CQQ', from_db=False) # my function
>>> dt = dt[dt['ebt']==0][['tax','ebt']]
>>> type(dt)
<class 'pandas.core.frame.DataFrame'>
>>> dt
                tax ebt
STK_ID RPT_Date        
000719 20100331   0   0
       20100630   0   0
       20100930   0   0
       20110331   0   0
002164 20080331   0   0
300155 20120331   0   0
600094 20090331   0   0
       20090630   0   0
       20090930   0   0
600180 20090331   0   0
600757 20110331   0   0
>>> dt['tax_rate'] = dt.tax/dt.ebt
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "D:\Python\Lib\site-packages\pandas\core\series.py", line 72, in wrapper
    return Series(na_op(self.values, other.values),
  File "D:\Python\Lib\site-packages\pandas\core\series.py", line 53, in na_op
    result = op(x, y)
ZeroDivisionError: float division
>>> 

It costs me a lot time to figure out why Pandas raises the ‘ZeroDivisionError: float division’ , while Pandas works very well for below sample code:

tuples = [('000719','20100331'),('000719','20100930'),('002164','20080331')]
index = MultiIndex.from_tuples(tuples, names=['STK_ID', 'RPT_Date'])
dt =DataFrame({'tax':[0,0,0],'ebt':[0,0,0]},index=index)
dt['tax_rate'] = dt.tax/dt.ebt

>>> dt
                 ebt  tax  tax_rate
STK_ID RPT_Date                    
000719 20100331    0    0       NaN
       20100930    0    0       NaN
002164 20080331    0    0       NaN
>>> 

I expect Pandas offer ‘NaN’ for both cases, why ‘ZeroDivisionError’ happens in first case ? How to fix it ?

---

below codes & screen output is attached to provide further information to debug

def __by_Q__(df):
    ''' this function transforms the input financial report data (which
        is accumulative) to qurterly data
    '''
    df_q1=df[df.index.map(lambda x: x[1].endswith("0331"))]

    print 'before diff:\n'
    print df.dtypes
    df_delta = df.diff()
    print '\nafter diff: \n'
    print df_delta.dtypes


    q1_mask = df_delta.index.map(lambda x: x[1].endswith("0331"));
    df_q234 = df_delta[~q1_mask]

    rst = concat([df_q1,df_q234])

    rst=rst.sort_index()
    return rst

screen output:

before diff:

sales                      float64
discount                    object
net_sales                  float64
cogs                       float64
ebt                        float64
tax                        float64

after diff: 

sales                      object
discount                   object
net_sales                  object
cogs                       object
ebt                        object
tax                        object

Answer 1

@bigbug, how are you getting the data out of the SQLite backend? If you look in pandas.io.sql, the read_frame method has a coerce_float parameter that should convert numerical data to float if possible.

Your second example works because the DataFrame constructor tries to be clever about types. If you set the dtype to object then it fails:

In [16]: dt = DataFrame({'tax':[0,0,0], 'ebt':[0,0,0]},index=index,dtype=object)

In [17]: dt.tax/dt.ebt
---------------------------------------------------------------------------
ZeroDivisionError                         Traceback (most recent call last)

Check your data importing code again and let me know what you find?