df.ix得到NAN作为集

Question

我有一个像下面[72行×25列]一个数据帧：

 Pin  CPULabel Freq(MHz) DCycle  Skew(1-3)min Skew(1-3)mean 
0 Dif0 BP100_Fast 99.9843 0.492   0    0 
1 Dif0 BP100_Slow 100.011 0.493   0    0 
2 Dif0 100HiBW_Fast 100.006 0.503   0    0 
3 Dif0 100HiBW_Slow 100.007 0.504   0    0 
4 Dif0 100LoBW_Fast 100.005 0.503   0    0 
5 Dif0 100LoBW_Slow 99.9951 0.504   0    0 
8 Dif1 BP100_Fast 99.9928 0.492   7   10 
9 Dif1 BP100_Slow 99.9962 0.492   11   12 
10 Dif1 100HiBW_Fast 100.014 0.502   10   11 
11 Dif1 100HiBW_Slow 100.006 0.503   6   13 
12 Dif1 100LoBW_Fast 99.9965 0.502   5   10 
13 Dif1 100LoBW_Slow 99.9946 0.503   12   14 
16 Dif2 BP100_Fast 99.9929 0.493   2    6 
17 Dif2 BP100_Slow 99.997 0.493   8   13 
18 Dif2 100HiBW_Fast 100.002 0.504   4    9 
19 Dif2 100HiBW_Slow 99.9964 0.504   13   17 
20 Dif2 100LoBW_Fast 100.021 0.504   8    9 

我在其中包含BP100_Fast，100HiBW和100HiBW串行只是有兴趣。所以我用下面的命令：

excel = pd.read_excel('25C_3.3V.xlsx', skiprows=1) 
excel.fillna(value=0, inplace=True) 
general = excel[excel['Pin'] != 'Clkin'] 
general.drop_duplicates(keep=False, inplace=True) 
slew = general[(general['CPULabel']=='BP100_Fast') | (general['CPULabel']=='100LoBW_Fast') | (general['CPULabel']=='100HiBW_Fast')] 

我能够得到我想要的东西[36行×25列]：

 Pin  CPULabel Freq(MHz) DCycle  Skew(1-3)min Skew(1-3)mean 
0 Dif0 BP100_Fast 99.9843 0.492   0    0 
2 Dif0 100HiBW_Fast 100.006 0.503   0    0 
4 Dif0 100LoBW_Fast 100.005 0.503   0    0 
8 Dif1 BP100_Fast 99.9928 0.492   7   10 
10 Dif1 100HiBW_Fast 100.014 0.502   10   11 
12 Dif1 100LoBW_Fast 99.9965 0.502   5   10 
16 Dif2 BP100_Fast 99.9929 0.493   2    6 
18 Dif2 100HiBW_Fast 100.002 0.504   4    9 
20 Dif2 100LoBW_Fast 100.021 0.504   8    9 

但是，如果我改变了最后的命令：

slew = general.ix[['BP100_Fast', '100LoBW_Fast', '100HiBW_Fast'], :] 

我得到了NAN作为我的结果。 [3 rows x 25 columns]

   Pin CPULabel Freq(MHz) DCycle Skew(1-3)min Skew(1-3)mean 
BP100_Fast NaN  NaN  NaN  NaN  NaN   NaN 
100LoBW_Fast NaN  NaN  NaN  NaN  NaN   NaN 
100HiBW_Fast NaN  NaN  NaN  NaN  NaN   NaN 

有什么办法可以用df.ix来完成这个吗？非常感谢你。

Answer 1

Per Docs

.ix索引器已弃用，有利于更严格的.iloc和.loc索引器。 .ix为推断用户想要做什么提供了很多魔力。换句话说，.ix可以根据索引的数据类型决定在位置或通过标签进行索引。多年来这已经导致了用户很多的困惑。完整的索引文档在这里。 （GH14218）

选项1
isin

general[general.CPULabel.isin(['BP100_Fast', '100LoBW_Fast', '100HiBW_Fast'])] 

    Pin  CPULabel Freq(MHz) DCycle Skew(1-3)min Skew(1-3)mean 
0 Dif0 BP100_Fast 99.9843 0.492    0    0 
2 Dif0 100HiBW_Fast 100.0060 0.503    0    0 
4 Dif0 100LoBW_Fast 100.0050 0.503    0    0 
8 Dif1 BP100_Fast 99.9928 0.492    7    10 
10 Dif1 100HiBW_Fast 100.0140 0.502   10    11 
12 Dif1 100LoBW_Fast 99.9965 0.502    5    10 
16 Dif2 BP100_Fast 99.9929 0.493    2    6 
18 Dif2 100HiBW_Fast 100.0020 0.504    4    9 
20 Dif2 100LoBW_Fast 100.0210 0.504    8    9 

---

选项2
query

general.query('CPULabel in ["BP100_Fast", "100LoBW_Fast", "100HiBW_Fast"]') 

    Pin  CPULabel Freq(MHz) DCycle Skew(1-3)min Skew(1-3)mean 
0 Dif0 BP100_Fast 99.9843 0.492    0    0 
2 Dif0 100HiBW_Fast 100.0060 0.503    0    0 
4 Dif0 100LoBW_Fast 100.0050 0.503    0    0 
8 Dif1 BP100_Fast 99.9928 0.492    7    10 
10 Dif1 100HiBW_Fast 100.0140 0.502   10    11 
12 Dif1 100LoBW_Fast 99.9965 0.502    5    10 
16 Dif2 BP100_Fast 99.9929 0.493    2    6 
18 Dif2 100HiBW_Fast 100.0020 0.504    4    9 
20 Dif2 100LoBW_Fast 100.0210 0.504    8    9 

---

选项3
pd.Series.str.endswith

general[general.CPULabel.str.endswith('Fast')] 

    Pin  CPULabel Freq(MHz) DCycle Skew(1-3)min Skew(1-3)mean 
0 Dif0 BP100_Fast 99.9843 0.492    0    0 
2 Dif0 100HiBW_Fast 100.0060 0.503    0    0 
4 Dif0 100LoBW_Fast 100.0050 0.503    0    0 
8 Dif1 BP100_Fast 99.9928 0.492    7    10 
10 Dif1 100HiBW_Fast 100.0140 0.502   10    11 
12 Dif1 100LoBW_Fast 99.9965 0.502    5    10 
16 Dif2 BP100_Fast 99.9929 0.493    2    6 
18 Dif2 100HiBW_Fast 100.0020 0.504    4    9 
20 Dif2 100LoBW_Fast 100.0210 0.504    8    9 

Answer 2

试试这个办法：

labels = ['BP100_Fast', '100HiBW', '100HiBW'] 

slew = \ 
pd.read_excel('25C_3.3V.xlsx', skiprows=1) \ 
    .fillna(value=0) \ 
    .query("Pin != Clkin and CPULabel in @labels") \ 
    .drop_duplicates(keep=False) 

或者您可以更改：

slew = general.ix[['BP100_Fast', '100LoBW_Fast', '100HiBW_Fast'], :] 

到：

slew = general.loc[general['CPULabel'].isin(['BP100_Fast','100LoBW_Fast','100HiBW_Fast'])]