simple Django-like ORM for MySQL for use with Pandas

LICENCE

The MIT License (MIT)
Copyright (c) 2015  miraculixx at github.com

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of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
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The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.

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THE SOFTWARE.

miniorm.py

"""
simple Django-like ORM for MySQL for use with Pandas
(c) 2015 miraculixx at github.com

Purpose
-------

Access MySQL tables as if they were Python objects. In principle
works like the Django ORM, however it only supports the very basics:

* filter and exclude by kwargs
* Q objects
* no joins
* no aggregates

And most importantly, directly transform a query to a Pandas dataframe like so:

  sql.objects('auth_user').filter(username='admin').as_dataframe()

Mini Tutorial
-------------

# setup
import pandas as pd
from miniorm import SQL

sql = SQL('mysql://admin:1234@localhost/mydb')

# get all tables in database as a dataframe
tables = sql.tables.as_dataframe()

# get a specific table and filter for certain objects
sql.objects('auth_user').filter(username='admin').as_dataframe()

# get rows in table
rows = sql.objects('auth_user').execute().rows()
=> dict of rows [{ 'col1' : val1 }, ... ]

# or process Resultset row by row, implies execute()
for row in sql.objects('auth_user'):
   print row

# as in Django, you can also create Q objects
q1 = Q(foo='bar', baz=5)
q2 = Q(foz='baz')
rows = sql.objects('auth_user').filter(q1 | q2)


Why?
----

* I am familiar with the Django ORM and how to use it with Pandas.
* I didn't want to learn yet another syntax using either pd.read_sql, MySQLDb or SQLAlchemy.
* It's fun to write your own ORM once in a while 

Licence
-------

MIT license. Detalis see LICENSE file
"""
import MySQLdb as db
import re
import copy

class Q(object):
    def __init__(self, *args, **kwargs):
        self._conditions = kwargs
        self.qlist = [('', self)]
        # should we return ~(conditions)
        self._inv = False
    
    def __repr__(self):
        return 'Q: %s' % self.as_sql()
    
    def negate(self):
        self._inv = True
        return self
    
    def __and__(self, other):
        andq = Q()
        q = copy.deepcopy(self)
        q.qlist.append(('AND', other))
        andq.qlist.append(('', q))
        return q
    
    def __or__(self, other):
        q = copy.deepcopy(self)
        q.qlist.append(('OR', other))
        return q
    
    def __invert__(self):
        """
        return an inverted version of this object
        """
        notq = Q().negate()
        q = copy.deepcopy(self)
        notq.qlist.append(('', q))
        return notq
    
    def as_sql(self):
        r = []
        for op, q in self.qlist:
            if q == self:
                r.append(self.build_conditions())
            else:
                r.append('%s %s' % (op, q.as_sql()))
        sql = (' '.join(r)).strip()
        return ('(%s)' % sql) if sql else ''
            
    def build_conditions(self):
        """
        transform queryset into actual sql
        """
        cond = []
        def add_cond(k, v, pattern):
            cond.append(pattern.format(k=k, v=v))
        for k, v in self._conditions.iteritems():
            if '__' in k:
                k, op = k.split('__')
            else:
                op = 'eq'
            if not str(v).isdigit():
                val = '"%s"' % v
            else:
                val = v
            # standard logical operators
            if op == 'eq':
                add_cond(k, val, '{k}={v}')
            elif op == 'lt':
                add_cond(k, val, '{k}<{v}')
            elif op == 'lte':
                add_cond(k, val, '{k}<={v}')
            elif op == 'gt':
                add_cond(k, val, '{k}>{v}')
            elif op == 'gte':
                add_cond(k, val, '{k}>={v}')
            elif op == 'ne':
                add_cond(k, val, '{k}<>{v}')
            elif op == 'not':
                add_cond(k, val, 'is not {k}')
            elif op in ['in', 'isin']:
                add_cond(k, val.join(','), '{k} in ({v})')
            elif op == 'contains':
                add_cond(k, v, '{k} like "%{v}%"')
        sql = (' AND '.join(cond)).strip()
        return '%s%s' % ('NOT' if self._inv else '', '(%s)' % sql if (sql and len(self.qlist) > 1) else sql)
       
        
class Resultset(list):
    def __init__(self, cursor):
        self._cur = cursor
        
    @property
    def cursor(self):
        return self._cur
    
    @property
    def columns(self):
        return zip(*self._cur.description)[0]
    
    @property
    def rows(self):
        return list(self)
    
    @property
    def count(self):
        return len(list(self))
        
    def __iter__(self):
        self.extend(self._cur.fetchall())
        self._n = 0
        return self
    
    def _row_to_dict(self, row):
        return { k : v for k,v in zip(self.columns, row) }
    
    def next(self):
        if self._n < len(self):
            row = self[self._n]
            self._n += 1
            return self._row_to_dict(row)
        raise StopIteration
        
            
class Queryset(object):
    def __init__(self, sql=None, table=None, columns=None):
        self._q = None
        self._sql = sql
        self._table = table or '<missing table>'
        self._columns = columns or '*'
        
    def __repr__(self):
        return 'QS: %s' % self.as_sql()
    
    def __iter__(self):
        return self.execute().__iter__()
    
    def all(self):
        return self
    
    def filter(self, *args, **kwargs):
        if args and args[0]:
            q = args[0]
        else:
            q = Q(**kwargs)
        if self._q:
            self._q &= q
        else:
            self._q = q
        return self
    
    def exclude(self, *args, **kwargs):
        if args and args[0]:
            q = args[0]
        else:
            q = Q(**kwargs)
        if self._q:
            self._q &= ~q
        else:
            self._q = ~q
        return self
        
    def as_sql(self, table=None, columns=None):
        table = table or self._table
        columns = columns or self._columns
        where = self._q.as_sql() if self._q else '1'
        return sql._prepare_sql(table, columns, where)
    
    def execute(self, sql=None, table=None, columns=None):
        sql = sql or self._sql
        table = table or self._table
        columns = self._columns or columns
        assert sql, "need an SQL connection to perform this query"
        return Resultset(sql._execute_sql(self.as_sql()))
    
    def as_dataframe(self):
        import pandas as pd
        results = self.execute()
        data = list(results) if results.count else None
        return pd.DataFrame(data, columns=list(results.columns))

    
class SQL(object):
    def __init__(self, connstr):
        self.connstr = connstr
        self.conn = None
        
    def connect(self):
        if not self.conn:
            self.conn = db.connect(**self.dbparams)
        return self.conn
    
    @property
    def dbparams(self):
        match = re.match(r'^mysql://(?P<user>.*):(?P<passwd>.*)@(?P<host>.*)/(?P<db>.*)', self.connstr) 
        return match.groupdict()
    
    def objects(self, table):
        return Queryset(sql=self, table=table)
       
    @property
    def tables(self):
        db = self.dbparams['db']
        return Queryset(sql=self, table='information_schema.tables').filter(table_schema=db)
                  
    def _prepare_sql(self, table, columns, where):
        opts=dict(table=table, columns=columns, where=where)
        sql = """
        select {columns}
        from {table}
        where {where}
        """.format(**opts)
        return sql
                  
    def _execute_sql(self, sql):
        #print "execute sql %s" % sql
        conn = self.connect()
        cur = conn.cursor()
        cur.execute(sql)
        return cur
        

sample.py

q1 = Q(foo='bar', baz=5)
q2 = Q(foz='baz')
q3 = Q(baz='foo')
nq1 = ~q1
print "q1", q1
print "q1 & q2", q1 & q2
print "q1 | q2", q1 | q2
print "(q1 | q2) & q3", (q1 | q2) & q3
print "nq1", nq1
print "nq1 & q1", nq1 & q1
print "((q1 | q2) & q3)", ((q1 | q2) & q3)
print "(~q1 | q2)", (~q1 | q2)
print "(~(q1 | q2))", (~(q1 | q2)) 
print "(~(q1 | q2)) | q3", (~(q1 | q2)) | q3
qs = Queryset()
qs.filter(q1)
print qs
qs.filter(q2)
print qs
qs.exclude(q2 | q3)
print qs
import pandas as pd
sql = SQL('mysql://admin:1234@localhost/shrebo')
print sql.tables.filter(table_name__contains='auth_group')
pd.DataFrame(list(sql.tables.filter(table_name='auth_group')))
sql.objects('auth_user').filter(username='admin').as_dataframe()
sql.objects('auth_user').execute().rows
for row in sql.objects('auth_user'):
    print row