如何使用Python读取和写入CSV文件？ [英] How do I read and write CSV files with Python?

问题描述

我有一个文件 example.csv 和内容

1,"A towel,",1.0
42," it says, ",2.0
1337,is about the most ,-1
0,massively useful thing ,123
-2,an interstellar hitchhiker can have.,3

如何读取 example.csv with Python？

How do I read this example.csv with Python?

同样，如果我有

data = [(1, "A towel,", 1.0),
        (42, " it says, ", 2.0),
        (1337, "is about the most ", -1),
        (0, "massively useful thing ", 123),
        (-2, "an interstellar hitchhiker can have.", 3)]

如何使用Python将 data 写入CSV文件？

How do I write data to a CSV file with Python?

推荐答案

这里有一些最简单的例子，说明如何读取CSV文件以及如何使用Python编写CSV文件。

Here are some minimal complete examples how to read CSV files and how to write CSV files with Python.

纯Python

# -*- coding: utf-8 -*-
import csv

# Define data
data = [(1, "A towel,", 1.0),
        (42, " it says, ", 2.0),
        (1337, "is about the most ", -1),
        (0, "massively useful thing ", 123),
        (-2, "an interstellar hitchhiker can have.", 3)]

# Write CSV file
with open('test.csv', 'w') as fp:
    writer = csv.writer(fp, delimiter=',')
    writer.writerows(data)

# Read CSV file
with open('test.csv', 'r') as fp:
    reader = csv.reader(fp, delimiter=',', quotechar='"')
    # next(reader, None)  # skip the headers
    data_read = [row for row in reader]

print(data_read)

之后， data_read 的内容为

[['1', 'A towel,', '1.0'],
 ['42', ' it says, ', '2.0'],
 ['1337', 'is about the most ', '-1'],
 ['0', 'massively useful thing ', '123'],
 ['-2', 'an interstellar hitchhiker can have.', '3']]

Pandas

Pandas

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('myfile.csv', sep=',')
print(df)

# 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()

查看 read_csv docs 了解更多信息。请注意，大熊猫会自动推断是否有标题行，但您也可以手动设置。

See read_csv docs for more information. Please note that pandas automatically infers if there is a header line, but you can set it manually, too.

如果您没有听说过 Seaborn ，我建议你看看它。

If you haven't heard of Seaborn, I recommend having a look at it.

1,"A towel,",1.0
42," it says, ",2.0
1337,is about the most ,-1
0,massively useful thing ,123
-2,an interstellar hitchhiker can have.,3

公共文件结尾

.csv

将CSV文件读取到元组/它只是工作与这种数据。

After reading the CSV file to a list of tuples / dicts or a Pandas dataframe, it is simply working with this kind of data. Nothing CSV specific.

• JSON：数据;非常常用（阅读和写入）


• CSV：超级简单格式阅读&写）


• YAML：很高兴阅读，类似于JSON 阅读和写入）


• pickle：Python序列化格式（阅读和写入）


• MessagePack （ Python软件包）：更紧凑的表示（阅读）


• HDF5 （ Python包）：适合矩阵（阅读&写）


• XML：存在太*叹息*（阅读） & 写）

• JSON: Nice for writing human-readable data; VERY commonly used (read & write)
• CSV: Super simple format (read & write)
• YAML: Nice to read, similar to JSON (read & write)
• pickle: A Python serialization format (read & write)
• MessagePack (Python package): More compact representation (read)
• HDF5 (Python package): Nice for matrices (read & write)
• XML: exists too *sigh* (read & write)

对于您的应用，以下内容可能很重要：

For your application, the following might be important:

• 支持其他编程语言


• /写入效果


• 压缩（文件大小）

另请参阅：数据序列化格式的比较

如果您是寻找制作配置文件的方法，您可能需要阅读我的简短文章配置文件在Python

In case you are rather looking for a way to make configuration files, you might want to read my short article Configuration files in Python

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