espandas

Reading and writing pandas DataFrames in ElasticSearch

Requirements.

This package should work on both python 2(>=2.7) and 3(>=3.4) but has primarily been tested on python 2.7. ElasticSearch is of course required and should be version 2.x.

Installation

The package is hosted on PyPi and can be installed with pip:

pip install espandas

Alternatively, the development version from Github can be installed:

pip install git+git://github.com/dashaub/espandas.git

Tests

Unit tests can be run with pytest or nosetests. Code coverage can be established with pytest-cov from PyPi:

py.test --cov=espandas

Usage

This example assumes ElasticSearch is running on localhost on the standard port. If different connection infromation needs to be specified, it can be passed to the Espandas() constructor as keyward arguments. The DataFrame to insert must have a column that will be used for the unique identifier id in ElasticSearch: the default value is uidname = 'indexId'.

import pandas as pd
import numpy as np
from espandas import Espandas
Example data frame
df = (100 * pd.DataFrame(np.round(np.random.rand(100, 5), 2))).astype(int)
df.columns = ['A', 'B', 'C', 'D', 'E']
df['indexId'] = (df.index + 100).astype(str)
Create a client and write the DataFrame. If necessary, connection
information to the ES cluster can be passed in the espandas constructor
as keyword arguments.
INDEX = 'foo_index'
TYPE = 'bar_type'
esp = Espandas()
esp.es_write(df, INDEX, TYPE)
Query for the first ten rows and see that they match the original
k = df.indexId[0:10]
res = esp.es_read(k, INDEX, TYPE)
res == df.iloc[0:10].astype('str')

License

(c) 2017 David Shaub

This package is free software released under the GPL-3 license.