sankroh

#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)]

sankroh

NAME	EXPLANATION	EXAMPLES
Common Name	The fully qualified domain name (FQDN) of your server. This must match exactly what you type in your web browser or you will receive a name mismatch error.
*.google.com

sankroh

#!/bin/sh
# WARNING: REQUIRES /bin/sh
#
# Install Puppet with shell... how hard can it be?
#
# 0.0.1a - Here Be Dragons
#

# Set up colours
if tty -s;then

sankroh

[MASTER]
profile=no
persistent=yes
ignore=migrations
cache-size=500

[BASIC]
# Regular expression which should only match correct module names
module-rgx=([a-z][a-z0-9_]*)$

sankroh

import pprint
import requests

def get_blackhawks_schedule():
    url = "http://blackhawks.nhl.com/schedule/full.csv"
    response = requests.get(url)
    if response.status_code == 200:
        data = filter(None, response.text.split('\r\n'))
        headers = data[0].split(',')
        data = [dict((headers[i], d) for i, d in enumerate(dt.split(','))) for dt in data]

sankroh

from dateutil import rrule, relativedelta
from django.utils.timezone import now, get_default_timezone
import pytz

def main(datetime, weekdays):
    tz_day = datetime.weekday()
    print "TZ Day:", tz_day
    utc_day = datetime.astimezone(pytz.utc).weekday()
    print "UTC Day:", utc_day
    weekdays = [eval('rrule.%s.weekday' % day) for day in weekdays]

sankroh

Here are the areas I've been researching, some things I've read and some open source packages...

Nearly all text processing starts by transforming text into vectors: http://en.wikipedia.org/wiki/Vector_space_model

Often it uses transforms such as TFIDF to normalise the data and control for outliers (words that are too frequent or too rare confuse the algorithms): http://en.wikipedia.org/wiki/Tf%E2%80%93idf

Collocations is a technique to detect when two or more words occur more commonly together than separately (e.g. "wishy-washy" in English) - I use this to group words into n-gram tokens because many NLP techniques consider each word as if it's independent of all the others in a document, ignoring order: http://matpalm.com/blog/2011/10/22/collocations_1/

sankroh

import requests
import time

def run():
    output = []
    for x in range(100):
        resp = requests.get("http://perf.herokuapp.com")
        output.append(resp.text)

    print output

sankroh

// grab your file object from a file input
$('#fileInput').change(function () {
  sendFile(this.files[0]);
});

// can also be from a drag-from-desktop drop
$('dropZone')[0].ondrop = function (e) {
  e.preventDefault();
  sendFile(e.dataTransfer.files[0]);
};

sankroh

### BEGIN INIT INFO
# Provides:          nginx
# Required-Start:    $all
# Required-Stop:     $all
# Default-Start:     2 3 4 5
# Default-Stop:      0 1 6
# Short-Description: starts the nginx web server
# Description:       starts nginx using start-stop-daemon
### END INIT INFO