blocks|key|94028|text|如果您确定输入中没有逗号，除了分隔类别之外，您可以在,上使用read+the+file+line+by+line和split，然后将结果推送到List|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|94029|也就是说，看起来您正在查看CSV文件，因此您可以考虑使用the+modules|94030|entityMap|0|LINK|mutability|MUTABLE|url|https://stackoverflow.com/questions/3277503/python-read-file-line-by-line-into-array|1|http://www.tutorialspoint.com/python/string_split.htm|2|https://docs.python.org/2/library/csv.html^0|Q|1|1Z|4|U|Q|0|1L|5|1|0|S|B|2|0^^$0|@$1|2|3|4|5|6|7|T|8|@$9|U|A|V|B|C]|$9|W|A|X|B|C]]|D|@$9|Y|A|Z|1|10]|$9|11|A|12|1|13]]|E|$]]|$1|F|3|G|5|6|7|14|8|@]|D|@$9|15|A|16|1|17]]|E|$]]|$1|H|3|-4|5|6|7|18|8|@]|D|@]|E|$]]]|I|$J|$5|K|L|M|E|$N|O]]|P|$5|K|L|M|E|$N|Q]]|R|$5|K|L|M|E|$N|S]]]]

If you are sure there are no commas in your input, other than to separate the category, you can <a href="https://stackoverflow.com/questions/3277503/python-read-file-line-by-line-into-array">read the file line by line</a> and <a href="http://www.tutorialspoint.com/python/string_split.htm" rel="nofollow noreferrer">split</a> on <code>,</code>, then push the result to <code>List</code>

That said, it looks like you are looking at a CSV file, so you might consider using <a href="https://docs.python.org/2/library/csv.html" rel="nofollow noreferrer">the modules</a> for it

blocks|key|94049|text|result+=+[]
for+line+in+text.splitlines():
++++result.append(tuple(line.split(",")))|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript|94050|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>result = []
for line in text.splitlines():
 result.append(tuple(line.split(",")))
</code></pre>

blocks|key|4600181|text|一个简单的循环就足够了：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4600182|lines+=+[]
with+open('test.txt',+'r')+as+f:
++++for+line+in+f.readlines():
++++++++l,name+=+line.strip().split(',')
++++++++lines.append((l,name))

print+lines|code-block|syntax|javascript|4600183|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|$]]

A simple loop would suffice:

<pre><code>lines = []
with open('test.txt', 'r') as f:
 for line in f.readlines():
 l,name = line.strip().split(',')
 lines.append((l,name))

print lines
</code></pre>

blocks|key|4600206|text|稍微扩展一下您的需求，假设您不关心行的顺序，并希望将它们按类别分组，则以下解决方案可能适用于您：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4600207|>>>+fname+=+"lines.txt"
>>>+from+collections+import+defaultdict
>>>+dct+=+defaultdict(list)
>>>+with+open(fname)+as+f:
...+++++for+line+in+f:
...+++++++++text,+cat+=+line.rstrip("\n").split(",",+1)
...+++++++++dct[cat].append(text)
...
>>>+dct
defaultdict(<type+'list'>,+{'+CatA':+['This+is+the+first+line',+'This+is+the+another+line'],+'+CatC':+['This+is+the+third+line'],+'+CatB':+['This+is+the+second+line',+'This+is+the+last+line']})|code-block|syntax|javascript|4600208|这样，您就可以获得字典中key+in+the+category下的所有相关行。|4600209|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|$]]

Extending your requirements a bit and assuming you do not care about the order of lines and want to get them grouped under categories, the following solution may work for you:

<pre><code>&gt;&gt;&gt; fname = "lines.txt"
&gt;&gt;&gt; from collections import defaultdict
&gt;&gt;&gt; dct = defaultdict(list)
&gt;&gt;&gt; with open(fname) as f:
... for line in f:
... text, cat = line.rstrip("\n").split(",", 1)
... dct[cat].append(text)
...
&gt;&gt;&gt; dct
defaultdict(&lt;type 'list'&gt;, {' CatA': ['This is the first line', 'This is the another line'], ' CatC': ['This is the third line'], ' CatB': ['This is the second line', 'This is the last line']})
</code></pre>

This way you get all relevant lines available in the dictionary under key being the category.

blocks|key|94129|text|Pandas非常擅长处理数据。下面是一个如何使用它的示例：|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|94130|import+pandas+as+pd

#+Read+the+CSV+into+a+pandas+data+frame+(df)
#+++With+a+df+you+can+do+many+things
#+++most+important:+visualize+data+with+Seaborn
df+=+pd.read_csv('filename.csv',+delimiter=',')

#+Or+export+it+in+many+ways,+e.g.+a+list+of+tuples
tuples+=+[tuple(x)+for+x+in+df.values]

#+or+export+it+as+a+list+of+dicts
dicts+=+df.to_dict().values()|code-block|syntax|javascript|94131|一个很大的优点是，pandas可以自动处理标题行。|94132|如果你没有听说过Seaborn，我建议你去看看。|94133|另请参阅：How+do+I+read+and+write+CSV+files+with+Python?+|94134|熊猫#2|94135|import+pandas+as+pd

#+Get+data+-+reading+the+CSV+file
import+mpu.pd
df+=+mpu.pd.example_df()

#+Convert
dicts+=+df.to_dict('records')|94136|df的内容是：|94137|+++++country+++population+population_time++++EUR
0++++Germany+++82521653.0++++++2016-12-01+++True
1+++++France+++66991000.0++++++2017-01-01+++True
2++Indonesia++255461700.0++++++2017-01-01++False
3++++Ireland++++4761865.0+++++++++++++NaT+++True
4++++++Spain+++46549045.0++++++2017-06-01+++True
5++++Vatican++++++++++NaN+++++++++++++NaT+++True|94138|dicts的内容是|94139|[{'country':+'Germany',+'population':+82521653.0,+'population_time':+Timestamp('2016-12-01+00:00:00'),+'EUR':+True},
+{'country':+'France',+'population':+66991000.0,+'population_time':+Timestamp('2017-01-01+00:00:00'),+'EUR':+True},
+{'country':+'Indonesia',+'population':+255461700.0,+'population_time':+Timestamp('2017-01-01+00:00:00'),+'EUR':+False},
+{'country':+'Ireland',+'population':+4761865.0,+'population_time':+NaT,+'EUR':+True},
+{'country':+'Spain',+'population':+46549045.0,+'population_time':+Timestamp('2017-06-01+00:00:00'),+'EUR':+True},
+{'country':+'Vatican',+'population':+nan,+'population_time':+NaT,+'EUR':+True}]|94140|熊猫#3|94141|import+pandas+as+pd

#+Get+data+-+reading+the+CSV+file
import+mpu.pd
df+=+mpu.pd.example_df()

#+Convert
lists+=+[[row[col]+for+col+in+df.columns]+for+row+in+df.to_dict('records')]|94142|lists的内容是：|style|CODE|94143|[['Germany',+82521653.0,+Timestamp('2016-12-01+00:00:00'),+True],
+['France',+66991000.0,+Timestamp('2017-01-01+00:00:00'),+True],
+['Indonesia',+255461700.0,+Timestamp('2017-01-01+00:00:00'),+False],
+['Ireland',+4761865.0,+NaT,+True],
+['Spain',+46549045.0,+Timestamp('2017-06-01+00:00:00'),+True],
+['Vatican',+nan,+NaT,+True]]|94144|entityMap|0|LINK|mutability|MUTABLE|url|http://pandas.pydata.org/|1|http://seaborn.pydata.org/index.html|2|https://stackoverflow.com/a/41585079/562769^0|0|6|0|0|0|0|8|7|1|0|5|1B|2|0|0|0|0|0|0|0|0|0|0|5|0|0^^$0|@$1|2|3|4|5|6|7|1M|8|@]|9|@$A|1N|B|1O|1|1P]]|C|$]]|$1|D|3|E|5|F|7|1Q|8|@]|9|@]|C|$G|H]]|$1|I|3|J|5|6|7|1R|8|@]|9|@]|C|$]]|$1|K|3|L|5|6|7|1S|8|@]|9|@$A|1T|B|1U|1|1V]]|C|$]]|$1|M|3|N|5|6|7|1W|8|@]|9|@$A|1X|B|1Y|1|1Z]]|C|$]]|$1|O|3|P|5|6|7|20|8|@]|9|@]|C|$]]|$1|Q|3|R|5|F|7|21|8|@]|9|@]|C|$G|H]]|$1|S|3|T|5|6|7|22|8|@]|9|@]|C|$]]|$1|U|3|V|5|F|7|23|8|@]|9|@]|C|$G|H]]|$1|W|3|X|5|6|7|24|8|@]|9|@]|C|$]]|$1|Y|3|Z|5|F|7|25|8|@]|9|@]|C|$G|H]]|$1|10|3|11|5|6|7|26|8|@]|9|@]|C|$]]|$1|12|3|13|5|F|7|27|8|@]|9|@]|C|$G|H]]|$1|14|3|15|5|6|7|28|8|@$A|29|B|2A|16|17]]|9|@]|C|$]]|$1|18|3|19|5|F|7|2B|8|@]|9|@]|C|$G|H]]|$1|1A|3|-4|5|6|7|2C|8|@]|9|@]|C|$]]]|1B|$1C|$5|1D|1E|1F|C|$1G|1H]]|1I|$5|1D|1E|1F|C|$1G|1J]]|1K|$5|1D|1E|1F|C|$1G|1L]]]]

<a href="http://pandas.pydata.org/" rel="noreferrer">Pandas</a> is pretty good at dealing with data. Here is one example how to use it:

<pre><code>import pandas as pd

# Read the CSV into a pandas data frame (df)
# With a df you can do many things
# most important: visualize data with Seaborn
df = pd.read_csv('filename.csv', delimiter=',')

# Or export it in many ways, e.g. a list of tuples
tuples = [tuple(x) for x in df.values]

# or export it as a list of dicts
dicts = df.to_dict().values()
</code></pre>

One big advantage is that pandas deals automatically with header rows.

If you haven't heard of <a href="http://seaborn.pydata.org/index.html" rel="noreferrer">Seaborn</a>, I recommend having a look at it.

See also: <a href="https://stackoverflow.com/a/41585079/562769">How do I read and write CSV files with Python?
</a>

<h2>Pandas #2</h2>

<pre><code>import pandas as pd

# Get data - reading the CSV file
import mpu.pd
df = mpu.pd.example_df()

# Convert
dicts = df.to_dict('records')
</code></pre>

The content of df is:

<pre><code> country population population_time EUR
0 Germany 82521653.0 2016-12-01 True
1 France 66991000.0 2017-01-01 True
2 Indonesia 255461700.0 2017-01-01 False
3 Ireland 4761865.0 NaT True
4 Spain 46549045.0 2017-06-01 True
5 Vatican NaN NaT True
</code></pre>

The content of dicts is

<pre><code>[{'country': 'Germany', 'population': 82521653.0, 'population_time': Timestamp('2016-12-01 00:00:00'), 'EUR': True},
 {'country': 'France', 'population': 66991000.0, 'population_time': Timestamp('2017-01-01 00:00:00'), 'EUR': True},
 {'country': 'Indonesia', 'population': 255461700.0, 'population_time': Timestamp('2017-01-01 00:00:00'), 'EUR': False},
 {'country': 'Ireland', 'population': 4761865.0, 'population_time': NaT, 'EUR': True},
 {'country': 'Spain', 'population': 46549045.0, 'population_time': Timestamp('2017-06-01 00:00:00'), 'EUR': True},
 {'country': 'Vatican', 'population': nan, 'population_time': NaT, 'EUR': True}]
</code></pre>

<h2>Pandas #3</h2>

<pre><code>import pandas as pd

# Get data - reading the CSV file
import mpu.pd
df = mpu.pd.example_df()

# Convert
lists = [[row[col] for col in df.columns] for row in df.to_dict('records')]
</code></pre>

The content of <code>lists</code> is:

<pre><code>[['Germany', 82521653.0, Timestamp('2016-12-01 00:00:00'), True],
 ['France', 66991000.0, Timestamp('2017-01-01 00:00:00'), True],
 ['Indonesia', 255461700.0, Timestamp('2017-01-01 00:00:00'), False],
 ['Ireland', 4761865.0, NaT, True],
 ['Spain', 46549045.0, Timestamp('2017-06-01 00:00:00'), True],
 ['Vatican', nan, NaT, True]]
</code></pre>

blocks|key|94161|text|接下来是一段代码，它使用csv模块，但使用第一行将file.csv内容提取到字典列表中，这是csv表的标题|type|unstyled|depth|inlineStyleRanges|entityRanges|data|94162|import+csv
def+csv2dicts(filename):
++with+open(filename,+'rb')+as+f:
++++reader+=+csv.reader(f)
++++lines+=+list(reader)
++++if+len(lines)+<+2:+return+None
++++names+=+lines[0]
++++if+len(names)+<+1:+return+None
++++dicts+=+[]
++++for+values+in+lines[1:]:
++++++if+len(values)+!=+len(names):+return+None
++++++d+=+{}
++++++for+i,_+in+enumerate(names):
++++++++d[names[i]]+=+values[i]
++++++dicts.append(d)
++++return+dicts
++return+None

if+__name__+==+'__main__':
++your_list+=+csv2dicts('file.csv')
++print+your_list|code-block|syntax|javascript|94163|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|$]]

Next is a piece of code which uses csv module but extracts file.csv contents to a list of dicts using the first line which is a header of csv table

<pre><code>import csv
def csv2dicts(filename):
 with open(filename, 'rb') as f:
 reader = csv.reader(f)
 lines = list(reader)
 if len(lines) &lt; 2: return None
 names = lines[0]
 if len(names) &lt; 1: return None
 dicts = []
 for values in lines[1:]:
 if len(values) != len(names): return None
 d = {}
 for i,_ in enumerate(names):
 d[names[i]] = values[i]
 dicts.append(d)
 return dicts
 return None

if __name__ == '__main__':
 your_list = csv2dicts('file.csv')
 print your_list
</code></pre>

blocks|key|94189|text|Python3更新：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|94190|import+csv
from+pprint+import+pprint

with+open('text.csv',+newline='')+as+file:
++++reader+=+csv.reader(file)
++++res+=+list(map(tuple,+reader))

pprint(res)|code-block|syntax|javascript|94191|输出：|94192|[('This+is+the+first+line',+'+Line1'),
+('This+is+the+second+line',+'+Line2'),
+('This+is+the+third+line',+'+Line3')]|94193|如果csvfile是文件对象，则应使用newline=''打开它。|offset|length|style|CODE|94194|csv+module|94195|entityMap|0|LINK|mutability|MUTABLE|url|https://docs.python.org/3.6/library/csv.html#id3^0|0|0|0|0|J|A|0|0|A|0|0^^$0|@$1|2|3|4|5|6|7|10|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|11|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|12|8|@]|9|@]|A|$]]|$1|I|3|J|5|D|7|13|8|@]|9|@]|A|$E|F]]|$1|K|3|L|5|6|7|14|8|@$M|15|N|16|O|P]]|9|@]|A|$]]|$1|Q|3|R|5|6|7|17|8|@]|9|@$M|18|N|19|1|1A]]|A|$]]|$1|S|3|-4|5|6|7|1B|8|@]|9|@]|A|$]]]|T|$U|$5|V|W|X|A|$Y|Z]]]]

<h3>Update for Python3:</h3>

<pre class="lang-py prettyprint-override"><code>import csv
from pprint import pprint

with open('text.csv', newline='') as file:
 reader = csv.reader(file)
 res = list(map(tuple, reader))

pprint(res)
</code></pre>

Output:

<pre class="lang-py prettyprint-override"><code>[('This is the first line', ' Line1'),
 ('This is the second line', ' Line2'),
 ('This is the third line', ' Line3')]
</code></pre>

If csvfile is a file object, it should be opened with <code>newline=''</code>. 
<a href="https://docs.python.org/3.6/library/csv.html#id3" rel="nofollow noreferrer">csv module</a>

blocks|key|4600318|text|正如在注释中所说的，您可以在python中使用csv库。csv的意思是逗号分隔的值，这看起来就是您的情况:一个标签和一个用逗号分隔的值。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|4600319|作为一个类别和值类型，我宁愿使用字典类型，而不是元组列表。|4600320|无论如何，在下面的代码中，我展示了两种方式：d是字典，l是元组列表。|4600321|import+csv

file_name+=+"test.txt"
try:
++++csvfile+=+open(file_name,+'rt')
except:
++++print("File+not+found")
csvReader+=+csv.reader(csvfile,+delimiter=",")
d+=+dict()
l+=++list()
for+row+in+csvReader:
++++d[row[1]]+=+row[0]
++++l.append((row[0],+row[1]))
print(d)
print(l)|code-block|syntax|javascript|4600322|entityMap^0|N|3|0|0|M|1|R|1|0|0^^$0|@$1|2|3|4|5|6|7|Q|8|@$9|R|A|S|B|C]]|D|@]|E|$]]|$1|F|3|G|5|6|7|T|8|@]|D|@]|E|$]]|$1|H|3|I|5|6|7|U|8|@$9|V|A|W|B|C]|$9|X|A|Y|B|C]]|D|@]|E|$]]|$1|J|3|K|5|L|7|Z|8|@]|D|@]|E|$M|N]]|$1|O|3|-4|5|6|7|10|8|@]|D|@]|E|$]]]|P|$]]

As said already in the comments you can use the <code>csv</code> library in python. csv means comma separated values which seems exactly your case: a label and a value separated by a comma.

Being a category and value type I would rather use a dictionary type instead of a list of tuples.

Anyway in the code below I show both ways: <code>d</code> is the dictionary and <code>l</code> is the list of tuples.

<pre><code>import csv

file_name = "test.txt"
try:
 csvfile = open(file_name, 'rt')
except:
 print("File not found")
csvReader = csv.reader(csvfile, delimiter=",")
d = dict()
l = list()
for row in csvReader:
 d[row[1]] = row[0]
 l.append((row[0], row[1]))
print(d)
print(l)
</code></pre>

blocks|key|4600370|text|这是在Python3.x中导入CSV到多维数组的最简单的方法，它只有4行代码，而不需要导入任何东西！|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4600371|#pull+a+CSV+into+a+multidimensional+array+in+4+lines!

L=[]++++++++++++++++++++++++++++#Create+an+empty+list+for+the+main+array
for+line+in+open('log.txt'):++++#Open+the+file+and+read+all+the+lines
++++x=line.rstrip()+++++++++++++#Strip+the+\n+from+each+line
++++L.append(x.split(','))++++++#Split+each+line+into+a+list+and+add+it+to+the
++++++++++++++++++++++++++++++++#Multidimensional+array
print(L)|code-block|syntax|javascript|4600372|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 is the easiest way in Python 3.x to import a CSV to a multidimensional array, and its only 4 lines of code without importing anything!

<pre><code>#pull a CSV into a multidimensional array in 4 lines!

L=[] #Create an empty list for the main array
for line in open('log.txt'): #Open the file and read all the lines
 x=line.rstrip() #Strip the \n from each line
 L.append(x.split(',')) #Split each line into a list and add it to the
 #Multidimensional array
print(L)
</code></pre>

blocks|key|4600419|text|不幸的是，我发现现有的答案都不是特别令人满意。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4600420|下面是一个使用csv模块的简单而完整的Python3解决方案。|offset|length|4600421|import+csv

with+open('../resources/temp_in.csv',+newline='')+as+f:
++++reader+=+csv.reader(f,+skipinitialspace=True)
++++rows+=+list(reader)

print(rows)|code-block|syntax|javascript|4600422|请注意skipinitialspace=True参数。这是必要的，因为不幸的是，OP的CSV在每个逗号后都包含空格。|style|CODE|4600423|输出：|4600424|[['This+is+the+first+line',+'Line1'],+['This+is+the+second+line',+'Line2'],+['This+is+the+third+line',+'Line3']]|4600425|entityMap|0|LINK|mutability|MUTABLE|url|https://docs.python.org/3/library/csv.html^0|0|7|3|0|0|0|3|L|0|0|0^^$0|@$1|2|3|4|5|6|7|10|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|11|8|@]|9|@$D|12|E|13|1|14]]|A|$]]|$1|F|3|G|5|H|7|15|8|@]|9|@]|A|$I|J]]|$1|K|3|L|5|6|7|16|8|@$D|17|E|18|M|N]]|9|@]|A|$]]|$1|O|3|P|5|6|7|19|8|@]|9|@]|A|$]]|$1|Q|3|R|5|H|7|1A|8|@]|9|@]|A|$I|J]]|$1|S|3|-4|5|6|7|1B|8|@]|9|@]|A|$]]]|T|$U|$5|V|W|X|A|$Y|Z]]]]

Unfortunately I find none of the existing answers particularly satisfying. 

Here is a straightforward and complete Python 3 solution, using the <a href="https://docs.python.org/3/library/csv.html" rel="nofollow noreferrer">csv</a> module.

<pre class="lang-py prettyprint-override"><code>import csv

with open('../resources/temp_in.csv', newline='') as f:
 reader = csv.reader(f, skipinitialspace=True)
 rows = list(reader)

print(rows)
</code></pre>

Notice the <code>skipinitialspace=True</code> argument. This is necessary since, unfortunately, OP's CSV contains whitespace after each comma.

Output:

<pre class="lang-py prettyprint-override"><code>[['This is the first line', 'Line1'], ['This is the second line', 'Line2'], ['This is the third line', 'Line3']]
</code></pre>

blocks|key|94308|text|您可以使用list()函数将csv阅读器对象转换为列表|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|94309|import+csv

with+open('input.csv')+as+csv_file:
++++reader+=+csv.reader(csv_file,+delimiter=',')
++++rows+=+list(reader)
++++print(rows)|code-block|syntax|javascript|94310|entityMap^0|5|6|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@$9|N|A|O|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|P|8|@]|D|@]|E|$I|J]]|$1|K|3|-4|5|6|7|Q|8|@]|D|@]|E|$]]]|L|$]]

You can use the <code>list()</code> function to convert csv reader object to list
<pre><code>import csv

with open('input.csv') as csv_file:
 reader = csv.reader(csv_file, delimiter=',')
 rows = list(reader)
 print(rows)
</code></pre>

I have a CSV file with about 2000 records. 

Each record has a string, and a category to it:

<pre class="lang-none prettyprint-override"><code>This is the first line,Line1
This is the second line,Line2
This is the third line,Line3
</code></pre>

I need to read this file into a list that looks like this:

<pre class="lang-py prettyprint-override"><code>data = [('This is the first line', 'Line1'),
 ('This is the second line', 'Line2'),
 ('This is the third line', 'Line3')]
</code></pre>

How can import this CSV to the list I need using Python?

Python import csv to list

Python

我有一个包含大约2000条记录的CSV文件。每条记录都有一个字符串和一个类别：This is the first line,Line1This is the second line,Line2This is the third line,Line3我需要将这个文件读入如下所示的列表中：data = [('This is...

问Python将csv导入列表
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