alpoza

listFilter = []
jarfile = ""
fileList = []

cli = new CliBuilder(usage:"jinspect [-hHvxXoawmISMWjJrgGcCpPlLZZZ] jarfile [filename ...]")
cli.with {
  h(longOpt:'help', 'show help')
  v(longOpt:'verbose', 'be more verbose')
  X(longOpt:'save', 'save specified files (including path) to the current directory instead of printing them')
  l(longOpt:'war', 'list war contents; specify again to include more information')

alpoza

import org.codehaus.groovy.scriptom.*
import org.codehaus.groovy.scriptom.tlb.office.*
import org.codehaus.groovy.scriptom.tlb.office.excel.*
import org.codehaus.groovy.scriptom.tlb.office.word.WdSaveOptions

def waitTime = 10000
Scriptom.inApartment {
  def dir = new File("c:/temp")
  def xlApp = new ActiveXObject('Excel.Application')
  def wdApp = new ActiveXObject('Word.Application')

alpoza

import com.jcraft.jsch.JSch
import com.jcraft.jsch.Session
import com.jcraft.jsch.UserInfo
import com.jcraft.jsch.Channel
import com.jcraft.jsch.ChannelExec

def sshHost = '10.1.2.132'
def sshUser = 'root'
def sshPass = '******'
def sshPort = 22

alpoza

The pyspark documentation doesn't include an example for the aggregateByKey RDD method. I didn't find any nice examples online, so I wrote my own.

Here's what the documetation does say:

aggregateByKey(self, zeroValue, seqFunc, combFunc, numPartitions=None)

Aggregate the values of each key, using given combine functions and a neutral "zero value". This function can return a different result type, U, than the type of the values in this RDD, V. Thus, we need one operation for merging a V into a U and one operation for merging two U's, The former operation is used for merging values within a partition, and the latter is used for merging values between partitions. To avoid memory allocation, both of these functions are allowed to modify and return their first argument instead of creating a new U.

reduceByKey and aggregateByKey are much more efficient than groupByKey and should be used for aggregations as much as possible.

alpoza

import pandas as pd
import numpy as np
from sklearn.feature_extraction import DictVectorizer
 
def encode_onehot(df, cols):
    """
    One-hot encoding is applied to columns specified in a pandas DataFrame.
    
    Modified from: https://gist.github.com/kljensen/5452382
    

alpoza

import groovy.json.JsonOutput

/**
 * A simple CSV file to Json converter
 *
 * The CSV file is expected to have a header row to identify the columns. These
 * columns will be used to generate the corresponding Json field.
 *
 * @author Marco Pas
 */

alpoza

package groovy.csv

/**
 * CSV slurper which parses text or reader content into a data strucuture of lists and maps.
 * <p>
 * Example usage:
 * <code><pre>
 * def slurper = new CsvSlurper()
 * def result = slurper.parseText('''
 *     name,   age

alpoza

#!/usr/bin/env python
import sys
import pandas as pd
import pymongo
import json



def import_content(filepath):
    mng_client = pymongo.MongoClient('localhost', 27017)

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ORIGINAL_JENKINS_SERVER=
ORIGINAL_SERVER_USER=

NEW_JENKINS_SERVER=
NEW_SERVER_USER=

# ON THE ORIGINAL JENKINS SERVER
ssh $ORIGINAL_SERVER_USER@$ORIGINAL_JENKINS_SERVER
cd /var/lib/jenkins/
for i in `ls jobs`; do echo "jobs/$i/config.xml";done > config.totar

alpoza

library(RCurl)
val <- getURL('ldap://ldap.domain.net/DC=domain,DC=net?sAMAccountName?sub?(employeeID=0123456)', 
 .opts=list(userpwd = "DOMAIN\\domainid:password"))