elasticsearch client from jupyter

es_client.py

## Import all the needed libraries
from elasticsearch import helpers
from elasticsearch import Elasticsearch
import os
import sys
import json
import logging.config
from elasticsearch import RequestsHttpConnection

## Create connection using proxy
class MyConnection(RequestsHttpConnection):
    def __init__(self, *args, **kwargs):
        proxies = kwargs.pop('proxies', {})
        super(MyConnection, self).__init__(*args, **kwargs)
        self.session.proxies = proxies
        
## Create Elasticsearch client
es = Elasticsearch('es_host:es_port', 
                   connection_class=MyConnection, 
                   proxies = {'http': 'proxy_host:proxy_port'})

## If you want to print all the indexes in Elasticsearch
for index in es.indices.get('*'):
    print(index)
    
## Change index and doc_type depending on the kind of search you want to do
index = 'your_index'
doc_type = 'your_doc_type'
size = 1000 # More info: http://elasticsearch-py.readthedocs.io/en/master/helpers.html#scan

docs = helpers.scan(
            es, 
            query={'query': {'match_all': {}}}, 
                    index=index,
                    doc_type=doc_type,
                    size=size)

# Now that you have the data, you can create a cool DataFrame with pandas
df = pd.DataFrame([{
    'field_1': doc['parent_field']['field_1'],
    'field_2': child_field['field_2'],
    'field_3': child_field['field_3']}
    for doc in docs for child_field in doc['parent_field']['child_field']])

df