This interactive Neo4j graph tutorial covers a common credit card fraud detection scenario.
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#credit to mholtzscher: https://gist.github.com/mholtzscher/9648cfd27769d1df6a6ed855fdd7bd7a | |
from airflow.models import DAG | |
from airflow.operators.email_operator import EmailOperator | |
from airflow.operators.python_operator import PythonOperator | |
from datetime import datetime | |
from tempfile import NamedTemporaryFile | |
dag = DAG( | |
"email_example", |
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""" | |
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 |