Question

我对我处理这个问题的方式有疑问这段代码的作用基本上是：

waps_df2 =  waps_df1-waps_df1.shift(1)
waps_df2 = waps_df2.fillna(0)
waps_x = waps_df2.div(waps_df1.shift(1))
waps_ad = waps_x.add(1)
waps_x3 = waps_ad.shift(+1)

其中 x1 是天 d 的值其中 x2 是天 d + 1 的值 x3 是我根据以前的值计算的值

当我的师出现以下情况时：例如，当2017-09-010和POS_16_20_和2017-09-011这一天发生时（0.5-0）/ 0，它将是无限的。我想在我的部门使用一个条件，如果我划分的值为零，则设置 x3 = x2 因为我不想要无限值

我想用我的最后一个值替换它。

代码：

produktname  POS_00_04  POS_04_08  POS_08_12  POS_12_16  POS_16_20  POS_20_24  
datum_von                                                                      
2017-09-09         0.0        0.0        0.0        0.0       0.00        0.0  
2017-09-10         0.0        0.0        0.0        0.0       0.00        0.0  
2017-09-11         0.0        0.0        0.0        0.0       0.05        0.0  
2017-09-12         0.0        0.0        0.0        0.0       0.06        0.0  
2017-09-13         0.0        0.0        0.0        0.0       0.00        0.0

我的约会对象：

waps_pos = waps_pos.mask((waps_df1!=0), waps_pos.div(waps_df1.shift(1))

waps_x = np.where(waps_df1.shift(1)>0, waps_pos.div(waps_df1.shift(1), waps_df1)

我尝试使用面具

waps_x = np.where(waps_df1.shift(1)>0, waps_pos.div(waps_df1.shift(1), waps_df1)

或

The mandatory tag '%1' is missing or incorrect.

Answer 1

waps_df2 = waps_df1.sub(waps_df1.shift(1)).fillna(0)
print (waps_df2)
            POS_00_04  POS_04_08  POS_08_12  POS_12_16  POS_16_20  POS_20_24
datum_von                                                                   
2017-09-09        0.0        0.0        0.0        0.0       0.00        0.0
2017-09-10        0.0        0.0        0.0        0.0       0.00        0.0
2017-09-11        0.0        0.0        0.0        0.0       0.05        0.0
2017-09-12        0.0        0.0        0.0        0.0       0.01        0.0
2017-09-13        0.0        0.0        0.0        0.0      -0.06        0.0

waps_x = waps_df2.div(waps_df1.shift(1))
print (waps_x)
            POS_00_04  POS_04_08  POS_08_12  POS_12_16  POS_16_20  POS_20_24
datum_von                                                                   
2017-09-09        NaN        NaN        NaN        NaN        NaN        NaN
2017-09-10        NaN        NaN        NaN        NaN        NaN        NaN
2017-09-11        NaN        NaN        NaN        NaN        inf        NaN
2017-09-12        NaN        NaN        NaN        NaN   0.200000        NaN
2017-09-13        NaN        NaN        NaN        NaN  -1.000000        NaN

您可以按numpy.isinf检查inf值，并将waps_df1替换为mask：

print (np.isinf(waps_x))
            POS_00_04  POS_04_08  POS_08_12  POS_12_16  POS_16_20  POS_20_24
datum_von                                                                   
2017-09-09      False      False      False      False      False      False
2017-09-10      False      False      False      False      False      False
2017-09-11      False      False      False      False       True      False
2017-09-12      False      False      False      False      False      False
2017-09-13      False      False      False      False      False      False

waps_x = waps_x.mask(np.isinf(waps_x), waps_df1)
print (waps_x)
            POS_00_04  POS_04_08  POS_08_12  POS_12_16  POS_16_20  POS_20_24
datum_von                                                                   
2017-09-09        NaN        NaN        NaN        NaN        NaN        NaN
2017-09-10        NaN        NaN        NaN        NaN        NaN        NaN
2017-09-11        NaN        NaN        NaN        NaN       0.05        NaN
2017-09-12        NaN        NaN        NaN        NaN       0.20        NaN
2017-09-13        NaN        NaN        NaN        NaN      -1.00        NaN

分裂过程与条件避免无限蟒蛇熊猫

1 个答案: