如何跳过for循环pandas read_csv中的行？
如何跳过for循环pandas read_csv中的行？
提问于 2018-07-25 09:26:41
我有一个由3个表组成的文件。我想定义skiprows参数来将这些表作为一个表读取。该文件如下所示。
0 results
1 bla bla
2 bla bla
3 bla bla
4 bla bla
5 bla bla
6 bla bla
7 Leximin
8 Rule 0 1 2 3
9 Plurality 0 0.222297 0.222297 0.222297
10 Approval q=2 0 0.111312 0.111312 0.111312
11 Inverse Plurality 0 0.111312 0.111312 0.111312
12 Borda 0 0.305553 0.305553 0.305553
13 Black 0 0.05533 0.05533 0.05533
14 Minimal dominant set 0 0.05533 0.05533 0.05533
15 Minimal undominated set 0 0.05533 0.05533 0.05533
16 Minimal weekly stable set 0 0.05533 0.05533 0.05533
17 Fishburn 0 0.05533 0.05533 0.05533
18 Uncovered set I 0 0.05533 0.05533 0.05533
19 Uncovered set II 0 0.05533 0.05533 0.05533
20 Richelson 0 0.05533 0.05533 0.05533
21 Copeland I 0 0.05533 0.05533 0.05533
22 Copeland II 0 0.05533 0.05533 0.05533
23 Copeland III 0 0.05533 0.05533 0.05533
24 Simpson Multy 0 0.05533 0.05533 0.05533
25 MinMax 0 0.05533 0.05533 0.05533
26 Strong q-Paretian simple majority 0 0.222297 0.222297 0.222297
27 Strong q-Paretian plurality 0 0.05533 0.05533 0.05533
28 Strongest q-Paretian simple majority 0 0.05533 0.05533 0.05533
29 Condorset practical 0 0.083825 0.083825 0.083825
30 Threshold 0 0.305553 0.305553 0.305553
31 Nanson Multy 0 0.05533 0.05533 0.05533
32 Nanson alphabet 0 0.111156 0.111156 0.111156
33 Inverse Borda 0 0.05533 0.05533 0.05533
34 Hare 0 0.222297 0.222297 0.222297
35 Simpson Alphabet 0 0.111156 0.111156 0.111156
36 1-stable set 0 0.05533 0.05533 0.05533
37 2-stable set 0 0.05533 0.05533 0.05533
38 3-stable set 0 0.05533 0.05533 0.05533
39 Approval q=3 0 0 0 0
40 Coombs 0 0.222363 0.222363 0.222363
41
42 Leximax
43 Rule 0 1 2 3
44 Plurality 0 0 0 0
45 Approval q=2 0 0.611112 0.611112 0.611112
46 Inverse Plurality 0 0.611112 0.611112 0.611112
47 Borda 0 0.41719 0.41719 0.41719
48 Black 0 0.166967 0.166967 0.166967
49 Minimal dominant set 0 0.166967 0.166967 0.166967
50 Minimal undominated set 0 0.166967 0.166967 0.166967
51 Minimal weekly stable set 0 0.166967 0.166967 0.166967
52 Fishburn 0 0.166967 0.166967 0.166967
53 Uncovered set I 0 0.166967 0.166967 0.166967
54 Uncovered set II 0 0.166967 0.166967 0.166967
55 Richelson 0 0.166967 0.166967 0.166967
56 Copeland I 0 0.166967 0.166967 0.166967
57 Copeland II 0 0.166967 0.166967 0.166967
58 Copeland III 0 0.166967 0.166967 0.166967
59 Simpson Multy 0 0.166967 0.166967 0.166967
60 MinMax 0 0.166967 0.166967 0.166967
61 Strong q-Paretian simple majority 0 0.166967 0.166967 0.166967
62 Strong q-Paretian plurality 0 0.166967 0.166967 0.166967
63 Strongest q-Paretian simple majority 0 0.166967 0.166967 0.166967
64 Condorset practical 0 0.583625 0.583625 0.583625
65 Threshold 0 0.41719 0.41719 0.41719
66 Nanson Multy 0 0.166967 0.166967 0.166967
67 Nanson alphabet 0 0.111156 0.111156 0.111156
68 Inverse Borda 0 0.166967 0.166967 0.166967
69 Hare 0 0 0 0
70 Simpson Alphabet 0 0.111156 0.111156 0.111156
71 1-stable set 0 0.166967 0.166967 0.166967
72 2-stable set 0 0.166967 0.166967 0.166967
73 3-stable set 0 0.166967 0.166967 0.166967
74 Approval q=3 0 0 0 0
75 Coombs 0 0.500435 0.500435 0.500435
76
77 Riskophob
78 Rule 0 1 2 3
79 Plurality 0 0.222297 0.222297 0.222297
80 Approval q=2 0 0.111312 0.111312 0.111312
81 Inverse Plurality 0 0.111312 0.111312 0.111312
82 Borda 0 0.305553 0.305553 0.305553
83 Black 0 0.05533 0.05533 0.05533
84 Minimal dominant set 0 0.05533 0.05533 0.05533
85 Minimal undominated set 0 0.05533 0.05533 0.05533
86 Minimal weekly stable set 0 0.05533 0.05533 0.05533
87 Fishburn 0 0.05533 0.05533 0.05533
88 Uncovered set I 0 0.05533 0.05533 0.05533
89 Uncovered set II 0 0.05533 0.05533 0.05533
90 Richelson 0 0.05533 0.05533 0.05533
91 Copeland I 0 0.05533 0.05533 0.05533
92 Copeland II 0 0.05533 0.05533 0.05533
93 Copeland III 0 0.05533 0.05533 0.05533
94 Simpson Multy 0 0.05533 0.05533 0.05533
95 MinMax 0 0.05533 0.05533 0.05533
96 Strong q-Paretian simple majority 0 0.222297 0.222297 0.222297
97 Strong q-Paretian plurality 0 0.05533 0.05533 0.05533
98 Strongest q-Paretian simple majority 0 0.05533 0.05533 0.05533
99 Condorset practical 0 0.083825 0.083825 0.083825
100 Threshold 0 0.305553 0.305553 0.305553
101 Nanson Multy 0 0.05533 0.05533 0.05533
102 Nanson alphabet 0 0.111156 0.111156 0.111156
103 Inverse Borda 0 0.05533 0.05533 0.05533
104 Hare 0 0.222297 0.222297 0.222297
105 Simpson Alphabet 0 0.111156 0.111156 0.111156
106 1-stable set 0 0.05533 0.05533 0.05533
107 2-stable set 0 0.05533 0.05533 0.05533
108 3-stable set 0 0.05533 0.05533 0.05533
109 Approval q=3 0 0 0 0
110 Coombs 0 0.222363 0.222363 0.222363所以我正在使用pandas并且把我放到了skiprows。但是这个代码pandas不会跳过任何行。能否检查下面的代码并提供建议?
import pandas as pd
file = "C:\\Users\\..."
for i in range (1,3):
df = pd.read_csv(file, header = 8, delimiter='\t', skiprows = lambda x: x in range(9 + i*len(rule_id),12 + i*len(rule_id)), usecols=['Rule', '0', '1', '2', '3'], skip_blank_lines = False)
print (df)回答 1
Stack Overflow用户
发布于 2018-07-25 19:07:02
根据你的评论回答你的具体问题：
import csv
DESIRED_SKIP_ROWS = [41,42,43,76,77,78]
with open(file) as input_file:
reader = csv.reader(input_file)
desired_rows = [row for row_number, row in enumerate(reader)
if row_number not in DESIRED_SKIP_ROWS]如果这和你要找的东西相似，请告诉我。可以使用在问题定义中使用的函数生成DESIRED_SKIP_ROWS列表。
页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/-100005757
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