blocks|key|769469|text|这会将密钥保留为字符串：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|769470|with+open('infile.txt')+as+f:
++d+=+dict(x.rstrip().split(None,+1)+for+x+in+f)|code-block|syntax|javascript|769471|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

This will leave the key as a string:

<pre><code>with open('infile.txt') as f:
 d = dict(x.rstrip().split(None, 1) for x in f)
</code></pre>

blocks|key|2837164|text|import+re

my_file+=+open('file.txt','r')
d+=+{}
for+i+in+my_file:
++g+=+re.search(r'(\d%2B)\s%2B(.*)',+i)+#+glob+line+containing+an+int+and+a+string
++d[int(g.group(1))]+=+g.group(2)|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript|2837165|unstyled|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|G|8|@]|9|@]|A|$B|C]]|$1|D|3|-4|5|E|7|H|8|@]|9|@]|A|$]]]|F|$]]

<pre><code>import re

my_file = open('file.txt','r')
d = {}
for i in my_file:
 g = re.search(r'(\d+)\s+(.*)', i) # glob line containing an int and a string
 d[int(g.group(1))] = g.group(2)
</code></pre>

blocks|key|769491|text|d+=+{}
with+open("file.txt")+as+f:
++++for+line+in+f:
+++++++(key,+val)+=+line.split()
+++++++d[int(key)]+=+val|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript|769492|unstyled|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|G|8|@]|9|@]|A|$B|C]]|$1|D|3|-4|5|E|7|H|8|@]|9|@]|A|$]]]|F|$]]

<pre><code>d = {}
with open("file.txt") as f:
 for line in f:
 (key, val) = line.split()
 d[int(key)] = val
</code></pre>

blocks|key|4437895|text|def+get_pair(line):
++++key,+sep,+value+=+line.strip().partition("+")
++++return+int(key),+value

with+open("file.txt")+as+fd:++++
++++d+=+dict(get_pair(line)+for+line+in+fd)|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript|4437896|unstyled|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|G|8|@]|9|@]|A|$B|C]]|$1|D|3|-4|5|E|7|H|8|@]|9|@]|A|$]]]|F|$]]

<pre><code>def get_pair(line):
 key, sep, value = line.strip().partition(" ")
 return int(key), value

with open("file.txt") as fd: 
 d = dict(get_pair(line) for line in fd)
</code></pre>

blocks|key|2837202|text|您还可以使用如下所示的dict+comprehension：|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|2837203|with+open("infile.txt")+as+f:
++++d+=+{int(k):+v+for+line+in+f+for+(k,+v)+in+[line.strip().split(None,+1)]}|code-block|syntax|javascript|2837204|entityMap|0|LINK|mutability|MUTABLE|url|http://www.python.org/dev/peps/pep-0274/^0|B|I|0|0|0^^$0|@$1|2|3|4|5|6|7|Q|8|@]|9|@$A|R|B|S|1|T]]|C|$]]|$1|D|3|E|5|F|7|U|8|@]|9|@]|C|$G|H]]|$1|I|3|-4|5|6|7|V|8|@]|9|@]|C|$]]]|J|$K|$5|L|M|N|C|$O|P]]]]

You can also use a <a href="http://www.python.org/dev/peps/pep-0274/" rel="nofollow noreferrer">dict comprehension</a> like:
<pre><code>with open(&quot;infile.txt&quot;) as f:
 d = {int(k): v for line in f for (k, v) in [line.strip().split(None, 1)]}
</code></pre>

blocks|key|4437968|text|我更倾向于使用生成器(你可能需要用到2.7%2B+)：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4437969|with+open('infile.txt')+as+fd:
++++pairs+=+(line.split(None)+for+line+in+fd)
++++res+++=+{int(pair[0]):pair[1]+for+pair+in+pairs+if+len(pair)+==+2+and+pair[0].isdigit()}|code-block|syntax|javascript|4437970|这也会过滤掉不是以整数开头的行，或者不包含两个项目的行|4437971|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|K|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|L|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|M|8|@]|9|@]|A|$]]|$1|I|3|-4|5|6|7|N|8|@]|9|@]|A|$]]]|J|$]]

IMHO a bit more pythonic to use generators (probably you need 2.7+ for this):

<pre><code>with open('infile.txt') as fd:
 pairs = (line.split(None) for line in fd)
 res = {int(pair[0]):pair[1] for pair in pairs if len(pair) == 2 and pair[0].isdigit()}
</code></pre>

This will also filter out lines not starting with an integer or not containing exactly two items

blocks|key|2837252|text|如果你喜欢one+liners，试试：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2837253|d=eval('{'%2Bre.sub('\'[\s]*?\'','\':\'',re.sub(r'([%5E'%2Binput('SEP:+')%2B',]%2B)','\''%2Br'\1'%2B'\'',open(input('FILE:+')).read().rstrip('\n').replace('\n',',')))%2B'}')|code-block|syntax|javascript|2837254|输入文件=文件路径，SEP+=键值分隔符|2837255|这不是最优雅或最有效的方式，但仍然很有趣:)|2837256|entityMap^0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|N|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|O|8|@]|9|@]|A|$]]|$1|I|3|J|5|6|7|P|8|@]|9|@]|A|$]]|$1|K|3|-4|5|6|7|Q|8|@]|9|@]|A|$]]]|L|$]]

If you love one liners, try:

<pre><code>d=eval('{'+re.sub('\'[\s]*?\'','\':\'',re.sub(r'([^'+input('SEP: ')+',]+)','\''+r'\1'+'\'',open(input('FILE: ')).read().rstrip('\n').replace('\n',',')))+'}')
</code></pre>

Input FILE = Path to file, SEP = Key-Value separator character

Not the most elegant or efficient way of doing it, but quite interesting nonetheless :)

blocks|key|4438019|text|这是另一个选择。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4438020|events+=+{}
for+line+in+csv.reader(open(os.path.join(path,+'events.txt'),+"rb")):
++++if+line[0][0]+==+"#":
++++++++continue
++++events[line[0]]+=+line[1]+if+len(line)+==+2+else+line[1:]|code-block|syntax|javascript|4438021|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

Here's another option...

<pre><code>events = {}
for line in csv.reader(open(os.path.join(path, 'events.txt'), "rb")):
 if line[0][0] == "#":
 continue
 events[line[0]] = line[1] if len(line) == 2 else line[1:]
</code></pre>

blocks|key|4438039|text|字典理解的|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4438040|d+=+{+line.split()[0]+:+line.split()[1]+for+line+in+open("file.txt")+}|code-block|syntax|javascript|4438041|或pandas|offset|length|style|BOLD|4438042|import+pandas+as+pd+
d+=+pd.read_csv("file.txt",+delimiter="+",+header+=+None).to_dict()[0]|4438043|entityMap^0|0|0|0|7|0|0^^$0|@$1|2|3|4|5|6|7|Q|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|R|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|S|8|@$I|T|J|U|K|L]]|9|@]|A|$]]|$1|M|3|N|5|D|7|V|8|@]|9|@]|A|$E|F]]|$1|O|3|-4|5|6|7|W|8|@]|9|@]|A|$]]]|P|$]]

By dictionary comprehension 

<pre><code>d = { line.split()[0] : line.split()[1] for line in open("file.txt") }
</code></pre>

Or By pandas

<pre><code>import pandas as pd 
d = pd.read_csv("file.txt", delimiter=" ", header = None).to_dict()[0]
</code></pre>

blocks|key|2837307|text|简单选项|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2837308|大多数存储字典的方法都使用JSON、Pickle或行读取。如果您不是在Python之外编辑字典，那么这个简单的方法即使对于复杂的字典也应该足够了。虽然Pickle对于更大的字典来说会更好。|2837309|x+=+{1:'a',+2:'b',+3:'c'}
f+=+'file.txt'
print(x,+file=open(f,'w'))++++#+file.txt+>>>+{1:'a',+2:'b',+3:'c'}
y+=+eval(open(f,'r').read())
print(x==y)+++++++++++++++++++#+>>>+True|code-block|syntax|javascript|2837310|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|K|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|L|8|@]|9|@]|A|$]]|$1|D|3|E|5|F|7|M|8|@]|9|@]|A|$G|H]]|$1|I|3|-4|5|6|7|N|8|@]|9|@]|A|$]]]|J|$]]

<h1>Simple Option</h1>

Most methods for storing a dictionary use JSON, Pickle, or line reading. Providing you're not editing the dictionary outside of Python, this simple method should suffice for even complex dictionaries. Although Pickle will be better for larger dictionaries.

<pre><code>x = {1:'a', 2:'b', 3:'c'}
f = 'file.txt'
print(x, file=open(f,'w')) # file.txt &gt;&gt;&gt; {1:'a', 2:'b', 3:'c'}
y = eval(open(f,'r').read())
print(x==y) # &gt;&gt;&gt; True
</code></pre>

blocks|key|2837330|text|我需要从文本文件中获取值，并将其用作键值对。我在文本文件中的内容是key+=+value，所以我使用了带有分隔符的split方法作为"=“，并编写了下面的代码|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2837331|d+=+{}
file+=+open("filename.txt")
for+x+in+file:
++++f+=+x.split("=")
++++d.update({f[0].strip():+f[1].strip()})|code-block|syntax|javascript|2837332|通过使用条带法，"=“分隔符之前或之后的任何空格都会被删除，您将拥有字典格式的预期数据|2837333|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|K|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|L|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|M|8|@]|9|@]|A|$]]|$1|I|3|-4|5|6|7|N|8|@]|9|@]|A|$]]]|J|$]]

I had a requirement to take values from text file and use as key value pair. i have content in text file as key = value, so i have used split method with separator as &quot;=&quot; and
wrote below code
<pre><code>d = {}
file = open(&quot;filename.txt&quot;)
for x in file:
 f = x.split(&quot;=&quot;)
 d.update({f[0].strip(): f[1].strip()})
</code></pre>
By using strip method any spaces before or after the &quot;=&quot; separator are removed and you will have the expected data in dictionary format

I have a file comprising two columns, i.e.,

<pre><code>1 a 
2 b 
3 c
</code></pre>

I wish to read this file to a dictionary such that column 1 is the key and column 2 is the value, i.e., 

<pre><code>d = {1:'a', 2:'b', 3:'c'}
</code></pre>

The file is small, so efficiency is not an issue.

How to convert a file into a dictionary?

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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我有一个包含两列的文件，即，1 a 2 b 3 c我希望将这个文件读到字典中，使得列1是键，列2是值，即，d = {1:'a', 2:'b', 3:'c'}文件很小，所以效率不是问题。

问如何将文件转换为字典？
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问如何将文件转换为字典？EN