我有一个.txt文件，如下所示：
08/19/93 UW ARCHIVE 100.0 1962 W IEEE 14 Bus Test Case
BUS DATA FOLLOWS 14 ITEMS
1 Bus 1 HV 1 1 3 1.060 0.0 0.0 0.0 232.4 -16.9 0.0 1.060 0.0 0.0 0.0 0.0 0
2 Bus 2 HV 1 1 2 1.045 -4.98 21.7 12.7 40.0 42.4 0.0 1.045 50.0 -40.0 0.0 0.0 0
3 Bus 3 HV 1 1 2 1.010 -12.72 94.2 19.0 0.0 23.4 0.0 1.010 40.0 0.0 0.0 0.0 0
4 Bus 4 HV 1 1 0 1.019 -10.33 47.8 -3.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0
5 Bus 5 HV 1 1 0 1.020 -8.78 7.6 1.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0
6 Bus 6 LV 1 1 2 1.070 -14.22 11.2 7.5 0.0 12.2 0.0 1.070 24.0 -6.0 0.0 0.0 0
7 Bus 7 ZV 1 1 0 1.062 -13.37 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0
8 Bus 8 TV 1 1 2 1.090 -13.36 0.0 0.0 0.0 17.4 0.0 1.090 24.0 -6.0 0.0 0.0 0
9 Bus 9 LV 1 1 0 1.056 -14.94 29.5 16.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.19 0
10 Bus 10 LV 1 1 0 1.051 -15.10 9.0 5.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0
11 Bus 11 LV 1 1 0 1.057 -14.79 3.5 1.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0
12 Bus 12 LV 1 1 0 1.055 -15.07 6.1 1.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0
13 Bus 13 LV 1 1 0 1.050 -15.16 13.5 5.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0
14 Bus 14 LV 1 1 0 1.036 -16.04 14.9 5.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
我需要从此文件中删除字符，并且只需要矩阵形式的数字数据。我是python的新手，所以非常感谢您提供任何帮助。谢谢。
答案 0 :(得分：0)
我建议读取Pandas数据框中的数据，而不要删除带有文本的列，或者创建第二个没有文本列的Frame。
尝试：
data = pd.read_csv('output_list.txt', sep=" ", header=None)
data.columns = ["a", "b", "c", "etc."]
答案 1 :(得分：0)
因为如果数据正确，在熊猫中执行此操作很简单，这是我的看法：
import pandas as pd
data = '''\
08/19/93 UW ARCHIVE 100.0 1962 W IEEE 14 Bus Test Case
BUS DATA FOLLOWS 14 ITEMS
1 Bus 1 HV 1 1 3 1.060 0.0 0.0 0.0 232.4 -16.9 0.0 1.060 0.0 0.0 0.0 0.0 0
2 Bus 2 HV 1 1 2 1.045 -4.98 21.7 12.7 40.0 42.4 0.0 1.045 50.0 -40.0 0.0 0.0 0
3 Bus 3 HV 1 1 2 1.010 -12.72 94.2 19.0 0.0 23.4 0.0 1.010 40.0 0.0 0.0 0.0 0
4 Bus 4 HV 1 1 0 1.019 -10.33 47.8 -3.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0
5 Bus 5 HV 1 1 0 1.020 -8.78 7.6 1.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0
6 Bus 6 LV 1 1 2 1.070 -14.22 11.2 7.5 0.0 12.2 0.0 1.070 24.0 -6.0 0.0 0.0 0
7 Bus 7 ZV 1 1 0 1.062 -13.37 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0
8 Bus 8 TV 1 1 2 1.090 -13.36 0.0 0.0 0.0 17.4 0.0 1.090 24.0 -6.0 0.0 0.0 0
9 Bus 9 LV 1 1 0 1.056 -14.94 29.5 16.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.19 0
10 Bus 10 LV 1 1 0 1.051 -15.10 9.0 5.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0
11 Bus 11 LV 1 1 0 1.057 -14.79 3.5 1.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0
12 Bus 12 LV 1 1 0 1.055 -15.07 6.1 1.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0
13 Bus 13 LV 1 1 0 1.050 -15.16 13.5 5.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0
'''
fileobj = pd.compat.StringIO(data)
# change fileobj to filepath and sep to `\t`
df = pd.read_csv(fileobj, sep='\s+', header=None, skiprows=2)
df = df.loc[:,df.dtypes != 'object']
print(df)
返回：
0 2 4 5 6 7 8 9 10 11 12 13 14 \
0 1 1 1 1 3 1.060 0.00 0.0 0.0 232.4 -16.9 0.0 1.060
1 2 2 1 1 2 1.045 -4.98 21.7 12.7 40.0 42.4 0.0 1.045
2 3 3 1 1 2 1.010 -12.72 94.2 19.0 0.0 23.4 0.0 1.010
3 4 4 1 1 0 1.019 -10.33 47.8 -3.9 0.0 0.0 0.0 0.000
4 5 5 1 1 0 1.020 -8.78 7.6 1.6 0.0 0.0 0.0 0.000
5 6 6 1 1 2 1.070 -14.22 11.2 7.5 0.0 12.2 0.0 1.070
6 7 7 1 1 0 1.062 -13.37 0.0 0.0 0.0 0.0 0.0 0.000
7 8 8 1 1 2 1.090 -13.36 0.0 0.0 0.0 17.4 0.0 1.090
8 9 9 1 1 0 1.056 -14.94 29.5 16.6 0.0 0.0 0.0 0.000
9 10 10 1 1 0 1.051 -15.10 9.0 5.8 0.0 0.0 0.0 0.000
10 11 11 1 1 0 1.057 -14.79 3.5 1.8 0.0 0.0 0.0 0.000
11 12 12 1 1 0 1.055 -15.07 6.1 1.6 0.0 0.0 0.0 0.000
12 13 13 1 1 0 1.050 -15.16 13.5 5.8 0.0 0.0 0.0 0.000
15 16 17 18 19
0 0.0 0.0 0.0 0.00 0
1 50.0 -40.0 0.0 0.00 0
2 40.0 0.0 0.0 0.00 0
3 0.0 0.0 0.0 0.00 0
4 0.0 0.0 0.0 0.00 0
5 24.0 -6.0 0.0 0.00 0
6 0.0 0.0 0.0 0.00 0
7 24.0 -6.0 0.0 0.00 0
8 0.0 0.0 0.0 0.19 0
9 0.0 0.0 0.0 0.00 0
10 0.0 0.0 0.0 0.00 0
11 0.0 0.0 0.0 0.00 0
12 0.0 0.0 0.0 0.00 0