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85 lines
3.2 KiB
Plaintext
85 lines
3.2 KiB
Plaintext
[[overview]]
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== Overview
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Eland is a Python client and toolkit for DataFrames and {ml} in {es}.
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Full documentation is available on https://eland.readthedocs.io[Read the Docs].
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Source code is available on https://github.com/elastic/eland[GitHub].
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[discrete]
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=== Compatibility
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- Supports Python 3.7+ and Pandas 1.3
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- Supports {es} clusters that are 7.11+, recommended 7.14 or later for all features to work.
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The recommended way to set your requirements in your `setup.py` or
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`requirements.txt` is::
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# Elasticsearch 7.x
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eland>=7,<8
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[discrete]
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=== Getting Started
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Create a `DataFrame` object connected to an {es} cluster running on `http://localhost:9200`:
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[source,python]
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------------------------------------
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>>> import eland as ed
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>>> df = ed.DataFrame(
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... es_client="http://localhost:9200",
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... es_index_pattern="flights",
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... )
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>>> df
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AvgTicketPrice Cancelled ... dayOfWeek timestamp
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0 841.265642 False ... 0 2018-01-01 00:00:00
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1 882.982662 False ... 0 2018-01-01 18:27:00
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2 190.636904 False ... 0 2018-01-01 17:11:14
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3 181.694216 True ... 0 2018-01-01 10:33:28
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4 730.041778 False ... 0 2018-01-01 05:13:00
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... ... ... ... ... ...
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13054 1080.446279 False ... 6 2018-02-11 20:42:25
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13055 646.612941 False ... 6 2018-02-11 01:41:57
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13056 997.751876 False ... 6 2018-02-11 04:09:27
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13057 1102.814465 False ... 6 2018-02-11 08:28:21
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13058 858.144337 False ... 6 2018-02-11 14:54:34
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[13059 rows x 27 columns]
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------------------------------------
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[discrete]
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==== Elastic Cloud
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You can also connect Eland to an Elasticsearch instance in Elastic Cloud:
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[source,python]
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------------------------------------
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>>> import eland as ed
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>>> from elasticsearch import Elasticsearch
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# First instantiate an 'Elasticsearch' instance connected to Elastic Cloud
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>>> es = Elasticsearch(cloud_id="...", api_key=("...", "..."))
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# then wrap the client in an Eland DataFrame:
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>>> df = ed.DataFrame(es, es_index_pattern="flights")
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>>> df.head(5)
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AvgTicketPrice Cancelled ... dayOfWeek timestamp
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0 841.265642 False ... 0 2018-01-01 00:00:00
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1 882.982662 False ... 0 2018-01-01 18:27:00
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2 190.636904 False ... 0 2018-01-01 17:11:14
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3 181.694216 True ... 0 2018-01-01 10:33:28
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4 730.041778 False ... 0 2018-01-01 05:13:00
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[5 rows x 27 columns]
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------------------------------------
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Eland can be used for complex queries and aggregations:
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[source,python]
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------------------------------------
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>>> df[df.Carrier != "Kibana Airlines"].groupby("Carrier").mean(numeric_only=False)
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AvgTicketPrice Cancelled timestamp
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Carrier
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ES-Air 630.235816 0.129814 2018-01-21 20:45:00.200000000
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JetBeats 627.457373 0.134698 2018-01-21 14:43:18.112400635
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Logstash Airways 624.581974 0.125188 2018-01-21 16:14:50.711798340
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------------------------------------
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