【pandas】【可视化】一 SQL 数据可视化简单实例 - 使用 pandas.io.sql, MySQL, matplotlib
Overview
- 1 depends
- 2 Prepare
- 2.1 SQL
- 2.2 matplotlib
- 3 pandas 实现简单可视化
- Reference
1 depends
$ pip install numpy, pandas
$
$ sudo apt install python3.6-tk # maybe need
$ pip install matplotlib # x
$ pip uninstall matplotlib
$ sudo apt install python3-matplotlib
$
$ sudo apt install -y python3-dev libpython3.<?>-minimal # maybe need
$ sudo apt install -y libmysqlclient-dev # maybe need
$ pip install mysqlclient
$
注意上面 matplotlib 使用 APT 安装1
2 Prepare
2.1 SQL
$ mysql -u <the username> -p
: <enter the password>
mysql> USE <the database>;
mysql> SELECT i.yuanchuan, i.zhuanzai, i.visitor, i.rank, i.date, u.username
FROM userinfors AS i
INNER JOIN users AS u
ON i.user_id = u.id WHERE u.id = 1;
+-----------+----------+---------+-------+------------+----------+
| yuanchuan | zhuanzai | visitor | rank | date | username |
+-----------+----------+---------+-------+------------+----------+
| 103 | 19 | 36176 | 41440 | 2019-05-07 | RDpWTeHM |
| 103 | 20 | 36402 | 41138 | 2019-05-08 | RDpWTeHM |
| 103 | 20 | 36607 | 40748 | 2019-05-09 | RDpWTeHM |
| 103 | 20 | 37205 | 40478 | 2019-05-13 | RDpWTeHM |
| 103 | 20 | 37205 | 40423 | 2019-05-14 | RDpWTeHM |
| 103 | 20 | 36776 | 40423 | 2019-05-15 | RDpWTeHM |
| 104 | 20 | 36325 | 40090 | 2019-05-16 | RDpWTeHM |
| 104 | 20 | 36325 | 40062 | 2019-05-17 | RDpWTeHM |
| 104 | 20 | 38371 | 40015 | 2019-05-18 | RDpWTeHM |
| 105 | 20 | 38652 | 39437 | 2019-05-20 | RDpWTeHM |
| 105 | 20 | 38921 | 39403 | 2019-05-21 | RDpWTeHM |
| 105 | 20 | 39068 | 39420 | 2019-05-22 | RDpWTeHM |
+-----------+----------+---------+-------+------------+----------+
12 rows in set (0.00 sec)
mysql>
mysql> quit;
$
2.2 matplotlib
运行下面脚本测试 plt.show() 能否工作2
#!/usr/bin/env python3
"""test_plt_show.py
Environment:
- Ubuntu 18.04 + Gnome(Mac Themes)
- sudo apt install python3-matplotlib
- $ export DISPLAY=:0
Usage:
$ ipython
>>> from test_plt_show import *
>>> plt.show()
"""
import matplotlib.pyplot as plt
import numpy as np
t = np.arange(0.0, 2.0, 0.01)
s = 1 + np.sin(2*np.pi*t)
plt.plot(t, s)
plt.title('About as simple as it gets, folks')
if __name__ == '__main__':
plt.show()
如果不能工作则考虑使用 plt.save(" 代替。
如果出现如下错误
Unable to init server: Could not connect: Connection refused
Unable to init server: 无法连接： Connection refused
(ipython:31480): Gdk-CRITICAL **: 16:39:40.005: gdk_cursor_new_for_display: assertion 'GDK_IS_DISPLAY (display)' failed
则需要注意上面代码中给出的方案3：
$ export DISPLAY=:0
3 pandas 实现简单可视化
$ export THE_USERNAME=<the username>
$ export THE_PASSWORD=<the password>
$ export THE_DB = <the database name>
$ ipython
>>> import pandas as pd
>>> import pandas.io.sql as sql
>>> import matplotlib.pyplot as plt
>>> import os, sys
>>> USERNAME = os.environ.get("THE_USERNAME")
>>> PASSWORD = os.environ.get("THE_PASSWORD")
>>> DB_NAME = os.environ.get("THE_DB")
>>> import MySQLdb
>>> conn = MySQLdb.connect(
...: 'localhost',
...: USERNAME, PASSWORD, DB_NAME,
...: charset='utf8')
>>>
>>> dataframe = sql.read_sql_query(
...: "SELECT i.visitor, i.rank, i.date "
...: "FROM userinfors AS i "
...: "INNER JOIN users AS u "
...: "ON i.user_id = u.id WHERE u.id = 1;", conn)
>>> # 注意上面每行 "..." 后面没有逗号(,)，引号（"")里面末尾有空格( )
>>> dataframe
visitor rank date
0 36176 41440 2019-05-07
1 36402 41138 2019-05-08
2 36607 40748 2019-05-09
3 37205 40478 2019-05-13
4 37205 40423 2019-05-14
5 36776 40423 2019-05-15
6 36325 40090 2019-05-16
7 36325 40062 2019-05-17
8 38371 40015 2019-05-18
9 38652 39437 2019-05-20
10 38921 39403 2019-05-21
11 39068 39420 2019-05-22
>>> df = dataframe.set_index('date')
>>> df.plot()
<matplotlib.axes._subplots.AxesSubplot at 0x7f02eb914c88>
>>> plt.show()
>>> # if .show() not work(open Figure window)
>>> plt.savefig('/home//.png' )
>>> exit
$
注意 .set_index 和 .plot 的使用4.
最后使用 matplotlib 绘出的图形：
Reference
https://github.com/NVIDIA/DIGITS/issues/187 ↩︎
https://stackoverflow.com/questions/43397162/show-matplotlib-plots-in-ubuntu-windows-subsystem-for-linux ↩︎
https://blog.csdn.net/qq_29757283/article/details/89463085 > SSH 远程启动 xterm ↩︎
https://stackoverflow.com/questions/38274187/plotting-dataframe-column-datetime ↩︎