针对Python 3更新:

import csv

with open('file.csv', newline='') as f:
    reader = csv.reader(f)
    your_list = list(reader)

print(your_list)

输出:

[['This is the first line', 'Line1'], ['This is the second line', 'Line2'], ['This is the third line', 'Line3']]

针对Python 3更新:

import csv

with open('file.csv', newline='') as f:
    reader = csv.reader(f)
    your_list = list(reader)

print(your_list)

输出:

[['This is the first line', 'Line1'], ['This is the second line', 'Line2'], ['This is the third line', 'Line3']]

如果确定输入中没有逗号，而不是分隔类别，则可以逐行读取文件并在，，上进行分割，然后将结果推入List

也就是说，看起来您正在查看一个CSV文件，因此您可以考虑为它使用模块

正如在评论中已经说过的，你可以在python中使用csv库。CSV意味着用逗号分隔的值，这似乎正是您的情况:一个标签和一个用逗号分隔的值。

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。

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。

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)

一个简单的循环就足够了:

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

print lines

Pandas非常擅长处理数据。下面是一个如何使用它的例子:

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()

一个很大的优势是pandas自动处理标题行。

如果你没有听说过Seaborn，我建议你去看看。

请参见:如何使用Python读写CSV文件?

熊猫# 2

import pandas as pd

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

# Convert
dicts = df.to_dict('records')

df的内容为:

     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

词典的内容是

[{'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}]

熊猫# 3

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')]

列表的内容是:

[['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]]