Jason Shih jasonshih

Archs

	import numpy as np
	import pandas as pd
	import datetime
	import urllib

	from bokeh.plotting import *
	from bokeh.models import HoverTool
	from collections import OrderedDict

	## Read in our data. We've aggregated it by date already, so we don't need to worry about paging

	#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)]

	"""
	required packages:
	numpy
	matplotlib
	basemap: http://matplotlib.org/basemap/users/installing.html
	shapely: https://pypi.python.org/pypi/Shapely
	descartes: https://pypi.python.org/pypi/descartes
	random

	import sys


	def progress(count, total, suffix=''):
	bar_len = 60
	filled_len = int(round(bar_len * count / float(total)))

	percents = round(100.0 * count / float(total), 1)
	bar = '=' * filled_len + '-' * (bar_len - filled_len)

	"""An example of how to perform a multi-threaded unit test of a web service.

	The particulars of this example make use of some conventions used when
	developing a web service when using the Flask microframework, but can be
	modified to fit most needs.
	"""

	import json
	import threading
	import time

	import logging
	from StringIO import StringIO

	from five import grok

	from xxx.objects.interfaces import IXXXResearcher
	from Products.CMFCore.interfaces import ISiteRoot
	from Products.statusmessages.interfaces import IStatusMessage

	from xxx.objects.sync import sync_with_xxx

	"""
	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