Overviewedit
Eland is a Python client and toolkit for DataFrames and machine learning in Elasticsearch. Full documentation is available on Read the Docs. Source code is available on GitHub.
Compatibilityedit
- Supports Python 3.8+ and Pandas 1.5
- Supports Elasticsearch clusters that are 7.11+, recommended 7.14 or later for all features to work. Make sure your Eland major version matches the major version of your Elasticsearch cluster.
The recommended way to set your requirements in your setup.py or
requirements.txt is::
# Elasticsearch 8.x eland>=8,<9
# Elasticsearch 7.x eland>=7,<8
Getting Startededit
Create a DataFrame object connected to an Elasticsearch cluster running on http://localhost:9200:
>>> import eland as ed
>>> df = ed.DataFrame(
... es_client="http://localhost:9200",
... es_index_pattern="flights",
... )
>>> df
AvgTicketPrice Cancelled ... dayOfWeek timestamp
0 841.265642 False ... 0 2018-01-01 00:00:00
1 882.982662 False ... 0 2018-01-01 18:27:00
2 190.636904 False ... 0 2018-01-01 17:11:14
3 181.694216 True ... 0 2018-01-01 10:33:28
4 730.041778 False ... 0 2018-01-01 05:13:00
... ... ... ... ... ...
13054 1080.446279 False ... 6 2018-02-11 20:42:25
13055 646.612941 False ... 6 2018-02-11 01:41:57
13056 997.751876 False ... 6 2018-02-11 04:09:27
13057 1102.814465 False ... 6 2018-02-11 08:28:21
13058 858.144337 False ... 6 2018-02-11 14:54:34
[13059 rows x 27 columns]
Elastic Cloudedit
You can also connect Eland to an Elasticsearch instance in Elastic Cloud:
>>> import eland as ed
>>> from elasticsearch import Elasticsearch
# First instantiate an 'Elasticsearch' instance connected to Elastic Cloud
>>> es = Elasticsearch(cloud_id="...", api_key="...")
# then wrap the client in an Eland DataFrame:
>>> df = ed.DataFrame(es, es_index_pattern="flights")
>>> df.head(5)
AvgTicketPrice Cancelled ... dayOfWeek timestamp
0 841.265642 False ... 0 2018-01-01 00:00:00
1 882.982662 False ... 0 2018-01-01 18:27:00
2 190.636904 False ... 0 2018-01-01 17:11:14
3 181.694216 True ... 0 2018-01-01 10:33:28
4 730.041778 False ... 0 2018-01-01 05:13:00
[5 rows x 27 columns]
Eland can be used for complex queries and aggregations:
>>> df[df.Carrier != "Kibana Airlines"].groupby("Carrier").mean(numeric_only=False)
AvgTicketPrice Cancelled timestamp
Carrier
ES-Air 630.235816 0.129814 2018-01-21 20:45:00.200000000
JetBeats 627.457373 0.134698 2018-01-21 14:43:18.112400635
Logstash Airways 624.581974 0.125188 2018-01-21 16:14:50.711798340