【问题标题】:Printing a properly formatted SQLite table in Python在 Python 中打印格式正确的 SQLite 表
【发布时间】:2016-05-05 22:48:32
【问题描述】:

我编写了一个 Python 脚本来向我的表中添加行。我决定如果我也可以使用相同的脚本查看我的表,而不必退出脚本并运行 sqlite3 或切换到另一个 shell 并运行 sqlite3,那就太好了。所以我写了我期望的东西,它会给我我想要的东西,它确实......这是有问题的脚本的一部分:

import sqlite3

conn = sqlite3.connect('stu.db')
c = conn.cursor()

var = 1
while var == 1:

    enquiry = raw_input("What would you like to do?> ")

    enquiry == 'stu db' or enquiry == 'sd':
    c.execute("SELECT * FROM stu")
    conn.commit

在 sqlite3 中,当您运行 SELECT * FROM stu 时,您会得到一个格式精美的表格,其中包含统一的行和列。当我在这里运行它时,我会得到一长串括号中的信息。它看起来有点像这样(我没有打印实际结果,因为这会违反某些联邦法律):

[(1234567890, u'John Doe', 3852, u'DEGR-AA', 4, None), (1234567890, u'John Doe', 3852, u'DEGR-AA', 4, None),(1234567890, u'John Doe', 3852, u'DEGR-AA', 4, None),(1234567890, u'John Doe', 3852, u'DEGR-AA', 4, None),(1234567890, u'John Doe', 3852, u'DEGR-AA', 4, None),(1234567890, u'John Doe', 3852, u'DEGR-AA', 4, None),(1234567890, u'John Doe', 3852, u'DEGR-AA', 4, None),(1234567890, u'John Doe', 3852, u'DEGR-AA', 4, None),(1234567890, u'John Doe', 3852, u'DEGR-AA', 4, None),(1234567890, u'John Doe', 3852, u'DEGR-AA', 4, None)]

我想我知道发生了什么。 Python 只是吐出对 sqlite 的查询返回的内容,但是有没有办法对这些信息进行格式化以便于阅读?

【问题讨论】:

标签: python sqlite


【解决方案1】:

您可以为此使用pandas

print pd.read_sql_query("SELECT * FROM stu", conn)

示例程序(python 2.7.6、pandas 0.18.0):

import sqlite3
import pandas as pd

conn = sqlite3.connect(':memory:')
c = conn.cursor()

c.execute('create table stu ( ID, Name, ShoeSize, Course, IQ, Partner )')
conn.commit()
c.executemany('insert into stu VALUES (?, ?, ?, ?, ?, ?)',
    [(1234567890, u'John Doe', 3852, u'DEGR-AA', 4, None),
     (1234567890, u'John Doe', 3852, u'DEGR-AA', 4, None),
     (1234567890, u'John Doe', 3852, u'DEGR-AA', 4, None),
     (1234567890, u'John Doe', 3852, u'DEGR-AA', 4, None),
     (1234567890, u'John Doe', 3852, u'DEGR-AA', 4, None),
     (1234567890, u'John Doe', 3852, u'DEGR-AA', 4, None),
     (1234567890, u'John Doe', 3852, u'DEGR-AA', 4, None),
     (1234567890, u'John Doe', 3852, u'DEGR-AA', 4, None),
     (1234567890, u'John Doe', 3852, u'DEGR-AA', 4, None),
     (1234567890, u'John Doe', 3852, u'DEGR-AA', 4, None)])
conn.commit()


# Ugly way
print list(c.execute("SELECT * FROM stu"))

# Pretty way
print pd.read_sql_query("SELECT * FROM stu", conn)

结果,包括丑陋和漂亮的输出:

[(1234567890, u'John Doe', 3852, u'DEGR-AA', 4, None), (1234567890, u'John Doe', 3852, u'DEGR-AA', 4, None), (1234567890, u'John Doe', 3852, u'DEGR-AA', 4, None), (1234567890, u'John Doe', 3852, u'DEGR-AA', 4, None), (1234567890, u'John Doe', 3852, u'DEGR-AA', 4, None), (1234567890, u'John Doe', 3852, u'DEGR-AA', 4, None), (1234567890, u'John Doe', 3852, u'DEGR-AA', 4, None), (1234567890, u'John Doe', 3852, u'DEGR-AA', 4, None), (1234567890, u'John Doe', 3852, u'DEGR-AA', 4, None), (1234567890, u'John Doe', 3852, u'DEGR-AA', 4, None)]
           ID      Name  ShoeSize   Course  IQ Partner
0  1234567890  John Doe      3852  DEGR-AA   4    None
1  1234567890  John Doe      3852  DEGR-AA   4    None
2  1234567890  John Doe      3852  DEGR-AA   4    None
3  1234567890  John Doe      3852  DEGR-AA   4    None
4  1234567890  John Doe      3852  DEGR-AA   4    None
5  1234567890  John Doe      3852  DEGR-AA   4    None
6  1234567890  John Doe      3852  DEGR-AA   4    None
7  1234567890  John Doe      3852  DEGR-AA   4    None
8  1234567890  John Doe      3852  DEGR-AA   4    None
9  1234567890  John Doe      3852  DEGR-AA   4    None

【讨论】:

  • 太棒了!这实际上减少了行数!谢谢!
【解决方案2】:

我过去的做法是简单地使用 pandas 数据框。

import pandas as pd

data = [(1234567890, u'John Doe', 3852, u'DEGR-AA', 4, None), (1234567890, u'John Doe', 3852, u'DEGR-AA', 4, None),(1234567890, u'John Doe', 3852, u'DEGR-AA', 4, None),(1234567890, u'John Doe', 3852, u'DEGR-AA', 4, None),(1234567890, u'John Doe', 3852, u'DEGR-AA', 4, None),(1234567890, u'John Doe', 3852, u'DEGR-AA', 4, None),(1234567890, u'John Doe', 3852, u'DEGR-AA', 4, None),(1234567890, u'John Doe', 3852, u'DEGR-AA', 4, None),(1234567890, u'John Doe', 3852, u'DEGR-AA', 4, None),(1234567890, u'John Doe', 3852, u'DEGR-AA', 4, None)]

pd.DataFrame(data)

【讨论】:

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