Python 导入 CSV 到列表

Question

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对应的类别：

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入一个看起来像这样的列表：

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

我该如何用Python把这个CSV文件导入到我需要的列表中呢？

Answer 1

如果你确定输入中除了用来分隔类别的逗号之外没有其他逗号，你可以逐行读取文件，然后用逗号来分割内容，最后把结果放到一个List里。

不过，看起来你是在处理一个CSV文件，所以你可以考虑使用专门处理CSV的模块。

Answer 2

Python3的更新：

import csv
from pprint import pprint

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

pprint(res)

输出结果：

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

如果csvfile是一个文件对象，那么在打开它的时候应该加上 newline='' 这个参数。
csv模块

Answer 3

Pandas 是一个处理数据非常不错的工具。这里有一个使用它的例子：

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()

它的一个大优点是，pandas 会自动处理表头行。

如果你还没听说过 Seaborn，我建议你去看看。

另外，你也可以参考一下这个链接：如何用 Python 读取和写入 CSV 文件？

Pandas #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

dicts 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}]

Pandas #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 4

更新为Python 3：

import csv

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

print(your_list)

输出结果：

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

Answer 5

使用 csv模块：

import csv

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

print(data)

输出结果：

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

---

如果你需要元组（tuples）：

import csv

with open('file.csv', newline='') as f:
    reader = csv.reader(f)
    data = [tuple(row) for row in reader]

print(data)

输出结果：

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

---

这是旧版Python 2的回答，同样使用 csv 模块：

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']]