我有一个如下[72行x 25列]的数据框：
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
我只对包含BP100_Fast，100HiBW和100HiBW字符串的行感兴趣。所以我使用了以下命令：
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[36行x 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，如果我更改了最后一个命令：
slew = general.ix[['BP100_Fast', '100LoBW_Fast', '100HiBW_Fast'], :]
我的结果是NAN。 [3行x 25列]
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
有没有办法用df.ix来完成这个？非常感谢你。
答案 0 :(得分：2)
尝试这种方法：
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)
或者你可以改变：
slew = general.ix[['BP100_Fast', '100LoBW_Fast', '100HiBW_Fast'], :]
为：
slew = general.loc[general['CPULabel'].isin(['BP100_Fast','100LoBW_Fast','100HiBW_Fast'])]
答案 1 :(得分：2)
.ix索引器已弃用，支持更严格的.iloc和.loc索引器。 .ix在推断用户想要做的事情上提供了很多魔力。也就是说，.ix可以决定根据索引的数据类型在位置上或通过标签进行索引。多年来，这引起了相当多的用户混淆。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