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import collections
import MySQLdb as dbapi

__all__ = ['MySql']


class MySql(object):
    def __init__(self, **kwargs):
        self.connection = dbapi.connect(**kwargs)
        self.host_name = kwargs.get('host')

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names = " ".join(d[0] for d in cursor.description)
cls = collections.namedtuple('Results', names)
rows = map(cls._make, cursor.fetchall())

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The Best of the Best Practices (BOBP) Guide for Python

A "Best of the Best Practices" (BOBP) guide to developing in Python.

In General

Values

• "Build tools for others that you want to be built for you." - Kenneth Reitz
• "Simplicity is alway better than functionality." - Pieter Hintjens

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from pyparsing import *
import unittest


class Node(list):
    def __eq__(self, other):
        return list.__eq__(self, other) and self.__class__ == other.__class__
        
    def __repr__(self):
        return '%s(%s)' % (self.__class__.__name__, list.__repr__(self))

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from pyelasticsearch import ElasticSearch, ElasticHttpNotFoundError
from pyparsing import *
import unittest


ELASTICSEARCH_INDEX = 'myindex'
ELASTICSEARCH_URL = 'http://localhost:9200/'

es = ElasticSearch(ELASTICSEARCH_URL)

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tmux shortcuts & cheatsheet

start new:

tmux

start new with session name:

tmux new -s myname

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# from http://pythondoeswhat.blogspot.com/2011/09/namedtupledict.html

def namedtupledict(*a, **kw):
    namedtuple = collections.namedtuple(*a, **kw)
    def getitem(self, key):
        if type(key) == str:
            return getattr(self, key)
        return tuple.__getitem__(self, key)
    namedtuple.__getitem__ = getitem
    return namedtuple

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#!/usr/bin/env python

from gevent import monkey
monkey.patch_all()  # Patch everything
import gevent
import time


class Hub(object):
    """A simple reactor hub... In async!"""

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import pandas as pd
df = pd.DataFrame([['2012', 'A', 1], ['2012', 'B', 4], ['2011', 'A', 5], ['2011', 'B', 4]],
                 columns=['year', 'manager', 'return_pct'])

df['total_return'] = (df
                      .groupby('manager')['return_pct']
                      .transform(lambda group: (1 + group / 100.).cumprod().iat[-1])) - 1
df['ranking'] = df.total_return.rank(ascending=False, method='dense')

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from MySQLdb.cursors import SSDictCursor

def iterate_query(query, connection, arraysize=1):
    c = connection.cursor(cursorclass=SSDictCursor)
    c.execute(query)
    while True:
        nextrows = c.fetchmany(arraysize)
        if not nextrows:
            break