AllieUbisse

"""
Python script for batch geocoding of addresses using the Google Geocoding API.
This script allows for massive lists of addresses to be geocoded for free by pausing when the 
geocoder hits the free rate limit set by Google (2500 per day).  If you have an API key for paid
geocoding from Google, set it in the API key section.
Addresses for geocoding can be specified in a list of strings "addresses". In this script, addresses
come from a csv file with a column "Address". Adjust the code to your own requirements as needed.
After every 500 successul geocode operations, a temporary file with results is recorded in case of 
script failure / loss of connection later.
Addresses and data are held in memory, so this script may need to be adjusted to process files line

AllieUbisse

import numpy as np

EPSILON = 1e-10


def _error(actual: np.ndarray, predicted: np.ndarray):
    """ Simple error """
    return actual - predicted

AllieUbisse

import boto3

lamba_client = boto3.client('lambda', region_name='REGION_NAME')

lamba_client.add_permission(
    FunctionName='create_lab',
    StatementId='AWSEventsRule',
    Action='lambda:InvokeFunction',
    Principal='events.amazonaws.com',
    SourceArn='arn:aws:events:REGION_NAME:ACCOUNT_NUMBER:rule/*',

AllieUbisse

import numpy as np

EPSILON = 1e-10


def _error(actual: np.ndarray, predicted: np.ndarray):
    """ Simple error """
    return actual - predicted

AllieUbisse

#Import All Functions
from pyspark.sql import SQLContext
from pyspark.sql import functions as F
from pyspark.sql import SparkSession
from pyspark.sql.functions import unix_timestamp, to_date, date_format, month, year, dayofyear, dayofweek, col
from pyspark.sql.types import TimestampType
from pyspark.sql import functions as F
from pyspark.sql import SparkSession
from pyspark.sql.functions import unix_timestamp, to_date, date_format, month, year, dayofyear, dayofweek, col
from pyspark.sql.types import TimestampType

AllieUbisse

Common Alias Functions

These functions are exactly equivalent

		Reference
filter	where	pyspark.sql.DataFrame.filter
drop_duplicates	dropDuplicates	pyspark.sql.DataFrame.drop_duplicates
avg	mean	pyspark.sql.GroupedData.avg

AllieUbisse

AllieUbisse

#!/bin/bash
########################################################################################
#                                   START, STOP or STATUS                              #
#                               ----------------------------                           #
#       This scrip is intended to help you start, stop or get the IP address of        #
#       Current running EC2.                                                           #
#       This will require you to 1st configure your AWC-CLI, namualy to ensure safety  #
#                                                                                      #
#       Please read the code to ensure that It does not cause any security issues      #
#                                                                                      #

AllieUbisse

# A simple cheat sheet of Spark Dataframe syntax
# Current for Spark 1.6.1

# import statements
from pyspark.sql import SQLContext
from pyspark.sql.types import *
from pyspark.sql.functions import *

#creating dataframes
df = sqlContext.createDataFrame([(1, 4), (2, 5), (3, 6)], ["A", "B"]) # from manual data

AllieUbisse

#!/bin/bash


##################################################################################
#     ----------------------------------------------------------------
#     THIS SCRIPT WILL HELP YOUR AUTOMATE THE DOCKER INSTALATION STEPS
#     ----------------------------------------------------------------
# Test was ran on aws ec2 instance. 
#
# AUTHOR: