Read, write and update large scale pandas DataFrame with ElasticSearch.
This package should work on Python3(>=3.4) and ElasticSearch should be version 5.x, 6.x or 7.x.
Installation The package is hosted on PyPi and can be installed with pip:
pip install es_pandas
Supporting of ElasticSearch 5.x will by deprecated in future version.
import time
import pandas as pd
from es_pandas import es_pandas
# Information of es cluseter
es_host = 'localhost:9200'
index = 'demo'
# crete es_pandas instance
ep = es_pandas(es_host)
# Example data frame
df = pd.DataFrame({'Num': [x for x in range(100000)]})
df['Alpha'] = 'Hello'
df['Date'] = pd.datetime.now()
# init template if you want
doc_type = 'demo'
ep.init_es_tmpl(df, doc_type)
# Example of write data to es, use the template you create
ep.to_es(df, index, doc_type=doc_type, thread_count=2, chunk_size=10000)
# set use_index=True if you want to use DataFrame index as records' _id
ep.to_es(df, index, doc_type=doc_type, use_index=True, thread_count=2, chunk_size=10000)
# delete records from es
ep.to_es(df.iloc[5000:], index, doc_type=doc_type, _op_type='delete', thread_count=2, chunk_size=10000)
# Update doc by doc _id
df.iloc[:1000, 1] = 'Bye'
df.iloc[:1000, 2] = pd.datetime.now()
ep.to_es(df.iloc[:1000, 1:], index, doc_type=doc_type, _op_type='update')
# Example of read data from es
df = ep.to_pandas(index)
print(df.head())
# return certain fields in es
heads = ['Num', 'Date']
df = ep.to_pandas(index, heads=heads)
print(df.head())
# set certain columns dtype
dtype = {'Num': 'float', 'Alpha': object}
df = ep.to_pandas(index, dtype=dtype)
print(df.dtypes)
# infer dtype from es template
df = ep.to_pandas(index, infer_dtype=True)
print(df.dtypes)
# use query_sql parameter if you want to do query in sql
# Example of write data to es with pandas.io.json
ep.to_es(df, index, doc_type=doc_type, use_pandas_json=True, thread_count=2, chunk_size=10000)
print('write es doc with pandas.io.json finished')
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