python - 为什么 Pandas 在一种情况下会导致 'ZeroDivisionError' 而在另一种情况下不会？

Question

我有一个 Pandas 数据框 'dt = myfunc()' ，并从 IDLE 复制屏幕输出如下:

>>> 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
>>> 

我花了很多时间来弄清楚为什么 Pandas 会引发 'ZeroDivisionError: float division' ，而 Pandas 对于以下示例代码工作得很好:

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
>>> 

我希望 Pandas 在这两种情况下都提供“NaN”，为什么在第一种情况下会出现“ZeroDivisionError”？如何解决？

---

附上以下代码和屏幕输出以提供进一步的调试信息

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

屏幕输出:

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，你如何从 SQLite 后端获取数据？如果您查看 pandas.io.sql，read_frame 方法有一个 coerce_float 参数，如果可能，该参数应将数值数据转换为 float 。

您的第二个示例之所以有效，是因为 DataFrame 构造函数试图巧妙地处理类型。如果将 dtype 设置为 object 则它会失败:

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)

再次检查您的数据导入代码，让我知道您发现了什么？