Python elasticsearch client to analyse, explore and manipulate data that resides in elasticsearch.
Project description
What is it?
eland is a Elasticsearch client Python package to analyse, explore and manipulate data that resides in Elasticsearch. Where possible the package uses existing Python APIs and data structures to make it easy to switch between numpy, pandas, scikit-learn to their Elasticsearch powered equivalents. In general, the data resides in Elasticsearch and not in memory, which allows eland to access large datasets stored in Elasticsearch.
For example, to explore data in a large Elasticsearch index, simply create an eland DataFrame from an Elasticsearch index pattern, and explore using an API that mirrors a subset of the pandas.DataFrame API:
>>> import eland as ed
>>> df = ed.read_es('http://localhost:9200', 'reviews')
>>> df.head()
reviewerId vendorId rating date
0 0 0 5 2006-04-07 17:08
1 1 1 5 2006-05-04 12:16
2 2 2 4 2006-04-21 12:26
3 3 3 5 2006-04-18 15:48
4 3 4 5 2006-04-18 15:49
>>> df.describe()
reviewerId vendorId rating
count 578805.000000 578805.000000 578805.000000
mean 174124.098437 60.645267 4.679671
std 116951.972209 54.488053 0.800891
min 0.000000 0.000000 0.000000
25% 70043.000000 20.000000 5.000000
50% 161052.000000 44.000000 5.000000
75% 272697.000000 83.000000 5.000000
max 400140.000000 246.000000 5.000000
See docs and demo_notebook.ipynb for more examples.
Where to get it
The source code is currently hosted on GitHub at: https://github.com/elastic/eland
Binary installers for the latest released version are available at the Python package index.
pip install eland
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