将 bar 放在包含 foo 的每一行的末尾

Question

我有一个包含大量行的列表，每行都采用主语-动词-宾语形式，例如：

Jane likes Fred
Chris dislikes Joe
Nate knows Jill

要绘制一个网络图来表达有向颜色编码边中节点之间的不同关系，我需要替换动词用箭头并在每行末尾放置一个颜色代码，因此，有些简化：

Jane -> Fred red;
Chris -> Joe blue;
Nate -> Jill black;

只有少量动词，因此用箭头替换它们只需几个搜索和替换命令即可。然而，在此之前，我需要在每行的末尾放置一个与该行的动词相对应的颜色代码。我想使用 Python 来完成此操作。

这些是我在编程中的初步步骤，因此请明确并包含在文本文件中读取的代码。

感谢您的帮助！

Answer 1

听起来您想要研究 字典 和 字符串格式。一般来说，如果您需要编程帮助，只需将遇到的任何问题分解为非常小的、离散的块，独立搜索这些块，然后您应该能够将其全部表述为更大的答案。 Stack Overflow 是此类搜索的绝佳资源。

另外，如果您对 Python 有任何一般性的好奇，请搜索或浏览官方 Python 文档。如果您发现自己总是不知道从哪里开始，请阅读 Python 教程 或查找书要经过。花一两周的时间来获得对正在做的事情的良好基础知识的投资将在您完成工作时一次又一次地得到回报。

verb_color_map = {
    'likes': 'red',
    'dislikes': 'blue',
    'knows': 'black',
}

with open('infile.txt') as infile: # assuming you've stored your data in 'infile.txt'
    for line in infile:
        # Python uses the name object, so I use object_
        subject, verb, object_ = line.split()
        print "%s -> %s %s;" % (subject, object_, verb_color_map[verb])

Answer 2

足够简单；假设动词列表是固定的并且很小，使用字典和 for 循环很容易做到这一点：

VERBS = {
    "likes": "red"
  , "dislikes": "blue"
  , "knows": "black"
  }

def replace_verb (line):
    for verb, color in VERBS.items():
        if verb in line:
            return "%s %s;" % (
                  line.replace (verb, "->")
                , color
                )
    return line

def main ():
    filename = "my_file.txt"
    with open (filename, "r") as fp:
        for line in fp:
            print replace_verb (line)

# Allow the module to be executed directly on the command line
if __name__ == "__main__":
    main ()

Answer 3

verbs = {"dislikes":"blue", "knows":"black", "likes":"red"}
for s in open("/tmp/infile"):
  s = s.strip()
  for verb in verbs.keys():
    if (s.count(verb) > 0):
      print s.replace(verb,"->")+" "+verbs[verb]+";"
      break

编辑：而是使用“for s in open”

Answer 4

你确定这不是一点家庭作业吗:) 如果是这样，那就坦白吧。无需考虑太多细节，请考虑您要执行的任务：

对于每一行：

1. 读取它，
2. 将其拆分为单词（在空白上 - .split() ）
3. 将中间单词转换为颜色（基于映射 - > cf: python dict()
4. 打印第一个单词、箭头、第三个单词和颜色代码

使用 NetworkX (networkx.lanl.gov/)

'''
plot relationships in a social network
'''

import networkx
## make a fake file 'ex.txt' in this directory
## then write fake relationships to it.
example_relationships = file('ex.txt','w') 
print >> example_relationships, '''\
Jane Doe likes Fred
Chris dislikes Joe
Nate knows Jill \
'''
example_relationships.close()

rel_colors = {
    'likes':  'blue',
    'dislikes' : 'black',
    'knows'   : 'green',
}

def split_on_verb(sentence):
    ''' we know the verb is the only lower cased word

    >>> split_on_verb("Jane Doe likes Fred")
    ('Jane Does','Fred','likes')

    '''
    words = sentence.strip().split()  # take off any outside whitespace, then split
                                       # on whitespace
    if not words:
        return None  # if there aren't any words, just return nothing

    verbs = [x for x in words if x.islower()]
    verb = verbs[0]  # we want the '1st' one (python numbers from 0,1,2...)
    verb_index = words.index(verb) # where is the verb?
    subject = ' '.join(words[:verb_index])
    obj =  ' '.join(words[(verb_index+1):])  # 'object' is already used in python
    return (subject, obj, verb)


def graph_from_relationships(fh,color_dict):
    '''
    fh:  a filehandle, i.e., an opened file, from which we can read lines
        and loop over
    '''
    G = networkx.DiGraph()

    for line in fh:
        if not line.strip():  continue # move on to the next line,
                                         # if our line is empty-ish
        (subj,obj,verb) = split_on_verb(line)
        color = color_dict[verb]
        # cf: python 'string templates', there are other solutions here
        # this is the 
        print "'%s' -> '%s' [color='%s'];" % (subj,obj,color)
        G.add_edge(subj,obj,color)
        # 

    return G

G = graph_from_relationships(file('ex.txt'),rel_colors)
print G.edges()
# from here you can use the various networkx plotting tools on G, as you're inclined.

Answer 5

Python 2.5：

import sys
from collections import defaultdict

codes = defaultdict(lambda: ("---", "Missing action!"))
codes["likes"] =    ("-->", "red")
codes["dislikes"] = ("-/>", "green")
codes["loves"] =    ("==>", "blue")

for line in sys.stdin:
    subject, verb, object_ = line.strip().split(" ")
    arrow, color = codes[verb]
    print subject, arrow, object_, color, ";"

Answer 6

除了这个问题之外，卡尔土还说道（在对一个答案的评论中）：“在实际输入中，主语和宾语在一到两个单词之间发生不可预测的变化。”

好的，这就是我解决这个问题的方法。

color_map = \
{
    "likes" : "red",
    "dislikes" : "blue",
    "knows" : "black",
}

def is_verb(word):
    return word in color_map

def make_noun(lst):
    if not lst:
        return "--NONE--"
    elif len(lst) == 1:
        return lst[0]
    else:
        return "_".join(lst)


for line in open("filename").readlines():
    words = line.split()
    # subject could be one or two words
    if is_verb(words[1]):
        # subject was one word
        s = words[0]
        v = words[1]
        o = make_noun(words[2:])
    else:
        # subject was two words
        assert is_verb(words[2])
        s = make_noun(words[0:2])
        v = words[2]
        o = make_noun(words[3:])
    color = color_map[v]
    print "%s -> %s %s;" % (s, o, color)

一些注意事项：

0）我们实际上并不需要“with”来解决这个问题，并且以这种方式编写它可以使程序更容易移植到旧版本的Python。我认为这应该适用于 Python 2.2 及更高版本（我只在 Python 2.6 上测试过）。

1）您可以更改 make_noun() 以采用您认为对处理多个单词有用的任何策略。我展示的只是用下划线将它们链接在一起，但是你可以有一本包含形容词的字典，然后把它们扔掉，有一本名​​词字典，然后选择它们，或者其他什么。

2）您还可以使用正则表达式进行模糊匹配。您可以拥有一个元组列表，其中包含与替换颜色配对的正则表达式，然后当正则表达式匹配时，替换颜色，而不是简单地使用 color_map 字典。

Answer 7

这是我之前答案的改进版本。这个使用正则表达式匹配来对动词进行模糊匹配。这些都有效：

Steve loves Denise
Bears love honey
Maria interested Anders
Maria interests Anders

正则表达式模式“loves?”匹配“love”加上可选的“s”。模式“interest.*”匹配“interest”加上任何内容。如果任一替代项匹配，则具有由竖线分隔的多个替代项的模式匹配。

import re

re_map = \
[
    ("likes?|loves?|interest.*", "red"),
    ("dislikes?|hates?", "blue"),
    ("knows?|tolerates?|ignores?", "black"),
]

# compile the regular expressions one time, then use many times
pat_map = [(re.compile(s), color) for s, color in re_map]

# We dont use is_verb() in this version, but here it is.
# A word is a verb if any of the patterns match.
def is_verb(word):
    return any(pat.match(word) for pat, color in pat_map)

# Return color from matched verb, or None if no match.
# This detects whether a word is a verb, and looks up the color, at the same time.
def color_from_verb(word):
    for pat, color in pat_map:
        if pat.match(word):
            return color
    return None

def make_noun(lst):
    if not lst:
        return "--NONE--"
    elif len(lst) == 1:
        return lst[0]
    else:
        return "_".join(lst)


for line in open("filename"):
    words = line.split()
    # subject could be one or two words
    color = color_from_verb(words[1])
    if color:
        # subject was one word
        s = words[0]
        o = make_noun(words[2:])
    else:
        # subject was two words
        color = color_from_verb(words[1])
        assert color
        s = make_noun(words[0:2])
        o = make_noun(words[3:])
    print "%s -> %s %s;" % (s, o, color)

我希望清楚如何接受这个答案并扩展它。您可以轻松添加更多模式来匹配更多动词。您可以添加逻辑来检测“是”和“在”并丢弃它们，以便“安德斯对玛丽亚感兴趣”会匹配。等等。

如果您有任何疑问，我很乐意进一步解释。祝你好运。