Python将csv导入lis

Question

我有一个CSV文件，大约有2000条记录。

每条记录都有一个字符串和一个类别。

This is the first line, Line1
This is the second line, Line2
This is the third line, Line3

我需要把这个文件读成这样的列表

List = [('This is the first line', 'Line1'),
        ('This is the second line', 'Line2'),
        ('This is the third line', 'Line3')]

如何使用Python将这个csv导入到我需要的列表中？

Answer 1

为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']]

Answer 2

Pandas非常擅长处理数据。下面是一个如何使用它的示例：

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。

如果你没听说过Seaborn，我建议你看看。

另请参见：How do I read and write CSV files with Python?

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的内容是：

     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

口述的内容是

[{'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')]

lists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]]

Answer 3

使用^{}模块（Python 2.x）：

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

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

如果需要元组：

import csv
with open('test.csv', 'rb') as f:
    reader = csv.reader(f)
    your_list = map(tuple, reader)

print your_list
# [('This is the first line', ' Line1'),
#  ('This is the second line', ' Line2'),
#  ('This is the third line', ' Line3')]

Python 3.x版本（作者@seokhoonlee）

import csv

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

print(your_list)
# [['This is the first line', 'Line1'],
#  ['This is the second line', 'Line2'],
#  ['This is the third line', 'Line3']]