MySQL NET Connect 6.7.2 in Visual Studio 2012

Connecting to MYSQL with Python 2 in three steps

1 - Setting

You must install a MySQL driver before doing anything. Unlike PHP, Only the SQLite driver is installed by default with Python. The most used package to do so is MySQLdb but it's hard to install it using easy_install. Please note MySQLdb only supports Python 2.

For Windows user, you can get an exe of MySQLdb.

For Linux, this is a casual package (python-mysqldb). (You can use sudo apt-get install python-mysqldb (for debian based distros), yum install MySQL-python (for rpm-based), or dnf install python-mysql (for modern fedora distro) in command line to download.)

For Mac, you can install MySQLdb using Macport.

2 - Usage

After installing, Reboot. This is not mandatory, But it will prevent me from answering 3 or 4 other questions in this post if something goes wrong. So please reboot.

Then it is just like using any other package :

#!/usr/bin/python
import MySQLdb

db = MySQLdb.connect(host="localhost",    # your host, usually localhost
                     user="john",         # your username
                     passwd="megajonhy",  # your password
                     db="jonhydb")        # name of the data base

# you must create a Cursor object. It will let
#  you execute all the queries you need
cur = db.cursor()

# Use all the SQL you like
cur.execute("SELECT * FROM YOUR_TABLE_NAME")

# print all the first cell of all the rows
for row in cur.fetchall():
    print row[0]

db.close()

Of course, there are thousand of possibilities and options; this is a very basic example. You will have to look at the documentation. A good starting point.

3 - More advanced usage

Once you know how it works, You may want to use an ORM to avoid writing SQL manually and manipulate your tables as they were Python objects. The most famous ORM in the Python community is SQLAlchemy.

I strongly advise you to use it: your life is going to be much easier.

I recently discovered another jewel in the Python world: peewee. It's a very lite ORM, really easy and fast to setup then use. It makes my day for small projects or stand alone apps, Where using big tools like SQLAlchemy or Django is overkill :

import peewee
from peewee import *

db = MySQLDatabase('jonhydb', user='john', passwd='megajonhy')

class Book(peewee.Model):
    author = peewee.CharField()
    title = peewee.TextField()

    class Meta:
        database = db

Book.create_table()
book = Book(author="me", title='Peewee is cool')
book.save()
for book in Book.filter(author="me"):
    print book.title

This example works out of the box. Nothing other than having peewee (pip install peewee) is required.

float and double are floating binary point types. In other words, they represent a number like this:

10001.10010110011

The binary number and the location of the binary point are both encoded within the value.

decimal is a floating decimal point type. In other words, they represent a number like this:

12345.65789

Again, the number and the location of the decimal point are both encoded within the value – that's what makes decimal still a floating point type instead of a fixed point type.

The important thing to note is that humans are used to representing non-integers in a decimal form, and expect exact results in decimal representations; not all decimal numbers are exactly representable in binary floating point – 0.1, for example – so if you use a binary floating point value you'll actually get an approximation to 0.1. You'll still get approximations when using a floating decimal point as well – the result of dividing 1 by 3 can't be exactly represented, for example.

As for what to use when:

• For values which are "naturally exact decimals" it's good to use decimal. This is usually suitable for any concepts invented by humans: financial values are the most obvious example, but there are others too. Consider the score given to divers or ice skaters, for example.

• For values which are more artefacts of nature which can't really be measured exactly anyway, float/double are more appropriate. For example, scientific data would usually be represented in this form. Here, the original values won't be "decimally accurate" to start with, so it's not important for the expected results to maintain the "decimal accuracy". Floating binary point types are much faster to work with than decimals.