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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)]

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# to execute this gist, run the line bellow in terminal
\curl -L https://raw.github.com/gist/3875946/install_source_code_pro.sh | sh

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// flatMap
// This code is equivalent to:
// option.flatMap(foo(_))
option match {
  case None => None
  case Some(x) => foo(x)
}

// flatten
// This code is equivalent to:

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set daemon 30 with start delay 60
set logfile syslog facility log_daemon
set httpd port 2812
    allow localhost

set mailserver localhost
set alert alert@example.com but not on { instance }

mail-format {
    From: noreply@example.com

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Installing Kerberos on Redhat 7

This installation is going to require 2 servers one acts as kerberos KDC server and the other machine is going to be client. Lets assume the FQDN's are (here cw.com is the domain name, make a note of the domain name here):

• Kerberos KDC Server: kdc.cw.com
• Kerberos Client: kclient.cw.com

Important: Make sure that both systems have their hostnames properly set and both systems have the hostnames and IP addresses of both systems in

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command-shift-P > Package > Package Generator: Generate Syntax Theme > mypackage
cd ~/.atom/packages/mypackage
apm login
apm develop mypackage
cd ~/github/mypackage
sudo chown -R username:wheel .

git commit -a -m 'checking everything in'
apm publish --tag v2.5.0 minor

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<VirtualHost *>
    ServerName example.com

    WSGIDaemonProcess www user=max group=max threads=5
    WSGIScriptAlias / /home/max/Projekte/flask-upload/flask-upload.wsgi

    <Directory /home/max/Projekte/flask-upload>
        WSGIProcessGroup www
        WSGIApplicationGroup %{GLOBAL}
        Order deny,allow

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#!/usr/bin/env python
# -*- coding: utf-8 -*-

# Example of `builder' design pattern
# Copyright (C) 2011 Radek Pazdera

# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.

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• With Docker 1.8.0 shipped new log-driver for GELF via UDP, this means that the logs from Docker Container(s) can be shipped directly to the ELK stack for further analysis.
• This tutorial will illustrate how to use the GELF log-driver with Docker engine.
• Step 1: Setup ELK Stack:
  • docker run -d --name es elasticsearch
  • docker run -d --name logstash --link es:elasticsearch logstash -v /tmp/logstash.conf:/config-dir/logstash.conf logstash logstash -f /config-dir/logstash.conf
  • Note the config for Logstash can be found at this link
  • docker run --link es:elasticsearch -d kibana
• Once the ELK stack is up now let's fire up our nginx container which ships its logs to ELK stack.

• LOGSTASH_ADDRESS=$(docker inspect --format '{{ .NetworkSettings.IPAddress }}' logstash)
• `docker run -d --net=host --log-driver=gelf --log-opt gelf-address=u

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input {
  gelf {}
}
output {
  elasticsearch {
        host => "elasticsearch"
        protocol => "http"
        workers=> 10
        }
  stdout {