我有一个 Pandas 数据框 'dt = myfunc()' ,并从 IDLE 复制屏幕输出,如下所示:
>>> 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 在下面的示例代码中效果很好:
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 为这两种情况都提供“NaN”,为什么在第一种情况下会发生“ZeroDivisionError”?如何解决?
附上以下代码和屏幕输出以提供更多信息以进行调试
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