tpjfern03

"""
Patched version to support PostgreSQL 
(original version: https://github.com/pydata/pandas/blob/v0.13.1/pandas/io/sql.py)

Adapted functions are:
- added _write_postgresql
- updated table_exist
- updated get_sqltype
- updated get_schema

tpjfern03

#A SQLAlchemy Cheat Sheet
###Introduction

##Basic Models
One model is used to describe one database table. For example:

from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import scoped_session,sessionmaker
from zope.sqlalchemy import ZopeTransactionExtension
from sqlalchemy import (

tpjfern03

import cx_Oracle
import pandas

connection = cx_Oracle.connect('username/pwd@host:port/dbname')

def read_query(connection, query):
    cursor = connection.cursor()
    try:
        cursor.execute( query )
        names = [ x[0] for x in cursor.description]

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Introduction to cx_Oracle

cx_Oracle is a third-party Python library that facilitates Oracle-to-Python database communication. A cursor is a control structure that enables traversal over the records in a database. This is Python's primary means of accessing database table data. The setup is:

• Create a connection object
• Define a cursor and call the cursor() method on the connection object
• Construct a query string for the data of interest
• Pass the query string to the cursor's execute method

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# Note: it is a copy of great answer by "mgoldwasser" from Stackoverflow
# Check the original answer here: http://stackoverflow.com/a/26018934/1032439

# Imagine that post1, post5, and post1000 are posts objects with ids 1, 5 and 1000 respectively
# The goal is to "upsert" these posts.
# we initialize a dict which maps id to the post object

my_new_posts = {1: post1, 5: post5, 1000: post1000} 

for each in posts.query.filter(posts.id.in_(my_new_posts.keys())).all():

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import csv
import sqlalchemy as sa
import os
import pytz
from datetime import datetime
import itertools

tz = pytz.timezone('America/Chicago')

# DB_CONN = os.environ['DATABASE_URL']

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"""
tiny script to convert a pandas data frame into a JSON object
"""

import ujson as json
import pandas
import numpy as np

df = pandas.DataFrame({
    "time" : [1,2,3,4,5],

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#List unique values in a DataFrame column
pd.unique(df.column_name.ravel())

#Convert Series datatype to numeric, getting rid of any non-numeric values
df['col'] = df['col'].astype(str).convert_objects(convert_numeric=True)

#Grab DataFrame rows where column has certain values
valuelist = ['value1', 'value2', 'value3']
df = df[df.column.isin(value_list)]

tpjfern03

# -*- coding: utf-8 -*-
"""
LICENSE: BSD (same as pandas)
example use of pandas with oracle mysql postgresql sqlite
    - updated 9/18/2012 with better column name handling; couple of bug fixes.
    - used ~20 times for various ETL jobs.  Mostly MySQL, but some Oracle. 

    to do:  
            save/restore index (how to check table existence? just do select count(*)?), 
            finish odbc,