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MANIFEST.in
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include LICENSE.txt
include README.md

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README.md
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_Note, this project is still very much a work in progress and in an alpha state; input and contributions welcome!_
<p align="center">
<a href="https://github.com/elastic/eland">
<img src="./docs/source/logo/eland.png" width="30%" alt="eland" />
<img src="https://raw.githubusercontent.com/elastic/eland/master/docs/source/logo/eland.png" width="30%" alt="Eland" />
</a>
</p>
<table>
<tr>
<td>PyPI</td>
<td>
<a href="https://pypi.org/project/eland/">
<img src="https://img.shields.io/pypi/v/eland.svg" alt="latest release"/>
</a>
</td>
</tr>
<tr>
<td>Conda Forge</td>
<td>
<a href="https://anaconda.org/conda-forge/eland">
<img src="https://img.shields.io/conda/vn/conda-forge/eland" alt="latest release"/>
</a>
</td>
</tr>
<tr>
<td>Package Status</td>
<td>
<a href="https://pypi.org/project/eland/">
<img src="https://img.shields.io/pypi/status/eland.svg" alt="status" />
</a>
</td>
</tr>
<tr>
<td>License</td>
<td>
<a href="https://github.com/elastic/eland/blob/master/LICENSE.txt">
<img src="https://img.shields.io/pypi/l/eland.svg" alt="license" />
</a>
</td>
</tr>
<tr>
<td>Build Status</td>
<td>
<a href="https://clients-ci.elastic.co/job/elastic+eland+master/">
<img src="https://clients-ci.elastic.co/buildStatus/icon?job=elastic%2Beland%2Bmaster" alt="Build Status" />
</a>
</td>
</tr>
</table>
<p align="center">
<a href="https://pypi.org/project/eland"><img src="https://img.shields.io/pypi/v/eland.svg" alt="PyPI Version"></a>
<a href=""><img src="https://img.shields.io/conda/vn/conda-forge/eland" alt="Conda Version"></a>
<a href="https://pepy.tech/project/eland"><img src="https://pepy.tech/badge/eland" alt="Downloads"></a>
<a href="https://pypi.org/project/eland"><img src="https://img.shields.io/pypi/status/eland.svg" alt="Package Status"></a>
<a href="https://clients-ci.elastic.co/job/elastic+eland+master"><img src="https://clients-ci.elastic.co/buildStatus/icon?job=elastic%2Beland%2Bmaster" alt="Build Status"></a>
<a href="https://github.com/elastic/eland/blob/master/LICENSE.txt"><img src="https://img.shields.io/pypi/l/eland.svg" alt="License"></a>
</p>
# What is it?
Eland is a Python Elasticsearch client for exploring and analyzing data
residing in Elasticsearch with a familiar Pandas-compatible API.
Eland is a Python Elasticsearch client for exploring and
analyzing data in Elasticsearch with a familiar Pandas-compatible API.
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:
Eland also provides tools to upload trained machine learning models from your
common libraries like [scikit-learn](https://scikit-learn.org), [XGBoost](https://xgboost.readthedocs.io),
and [LightGBM](https://lightgbm.readthedocs.io) into Elasticsearch.
```
>>> import eland as ed
## Getting Started
>>> # Connect to 'flights' index via localhost Elasticsearch node
>>> df = ed.DataFrame('localhost:9200', 'flights')
>>> df.head()
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]
>>> df.describe()
AvgTicketPrice DistanceKilometers ... FlightTimeMin dayOfWeek
count 13059.000000 13059.000000 ... 13059.000000 13059.000000
mean 628.253689 7092.142457 ... 511.127842 2.835975
std 266.386661 4578.263193 ... 334.741135 1.939365
min 100.020531 0.000000 ... 0.000000 0.000000
25% 410.008918 2470.545974 ... 251.739008 1.000000
50% 640.387285 7612.072403 ... 503.148975 3.000000
75% 842.262193 9735.660463 ... 720.505705 4.239865
max 1199.729004 19881.482422 ... 1902.901978 6.000000
[8 rows x 7 columns]
>>> df[['Carrier', 'AvgTicketPrice', 'Cancelled']]
Carrier AvgTicketPrice Cancelled
0 Kibana Airlines 841.265642 False
1 Logstash Airways 882.982662 False
2 Logstash Airways 190.636904 False
3 Kibana Airlines 181.694216 True
4 Kibana Airlines 730.041778 False
... ... ... ...
13054 Logstash Airways 1080.446279 False
13055 Logstash Airways 646.612941 False
13056 Logstash Airways 997.751876 False
13057 JetBeats 1102.814465 False
13058 JetBeats 858.144337 False
[13059 rows x 3 columns]
>>> df[(df.Carrier=="Kibana Airlines") & (df.AvgTicketPrice > 900.0) & (df.Cancelled == True)].head()
AvgTicketPrice Cancelled ... dayOfWeek timestamp
8 960.869736 True ... 0 2018-01-01 12:09:35
26 975.812632 True ... 0 2018-01-01 15:38:32
311 946.358410 True ... 0 2018-01-01 11:51:12
651 975.383864 True ... 2 2018-01-03 21:13:17
950 907.836523 True ... 2 2018-01-03 05:14:51
[5 rows x 27 columns]
>>> df[['DistanceKilometers', 'AvgTicketPrice']].aggregate(['sum', 'min', 'std'])
DistanceKilometers AvgTicketPrice
sum 9.261629e+07 8.204365e+06
min 0.000000e+00 1.000205e+02
std 4.578263e+03 2.663867e+02
>>> df[['Carrier', 'Origin', 'Dest']].nunique()
Carrier 4
Origin 156
Dest 156
dtype: int64
>>> s = df.AvgTicketPrice * 2 + df.DistanceKilometers - df.FlightDelayMin
>>> s
0 18174.857422
1 10589.365723
2 381.273804
3 739.126221
4 14818.327637
...
13054 10219.474121
13055 8381.823975
13056 12661.157104
13057 20819.488281
13058 18315.431274
Length: 13059, dtype: float64
>>> print(s.es_info())
index_pattern: flights
Index:
index_field: _id
is_source_field: False
Mappings:
capabilities:
es_field_name is_source es_dtype es_date_format pd_dtype is_searchable is_aggregatable is_scripted aggregatable_es_field_name
NaN script_field_None False double None float64 True True True script_field_None
Operations:
tasks: []
size: None
sort_params: None
_source: ['script_field_None']
body: {'script_fields': {'script_field_None': {'script': {'source': "(((doc['AvgTicketPrice'].value * 2) + doc['DistanceKilometers'].value) - doc['FlightDelayMin'].value)"}}}}
post_processing: []
>>> pd_df = ed.eland_to_pandas(df)
>>> pd_df.head()
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]
```
See [docs](https://eland.readthedocs.io/en/latest) and [demo_notebook.ipynb](https://eland.readthedocs.io/en/latest/examples/demo_notebook.html) for more examples.
## Where to get it
Eland can be installed from [PyPI](https://pypi.org/project/eland) via pip:
Eland can be installed from [PyPI](https://pypi.org/project/eland) with Pip:
```bash
$ python -m pip install eland
@ -185,88 +37,129 @@ Eland can also be installed from [Conda Forge](https://anaconda.org/conda-forge/
$ conda install -c conda-forge eland
```
The [source code](https://github.com/elastic/eland) is currently available on GitHub.
### Supported Versions
## Versions and Compatibility
- Supports Python 3.6+ and Pandas 1.0.0+
- Supports Elasticsearch clusters that are 7.x+, recommended 7.6 or later for all features to work.
### Python Version Support
### Connecting to Elasticsearch
Officially Python 3.6 and above.
Eland uses the [Elasticsearch low level client](https://elasticsearch-py.readthedocs.io) to connect to Elasticsearch.
This client supports a range of [connection options and authentication options](https://elasticsearch-py.readthedocs.io/en/master/api.html#elasticsearch).
eland depends on pandas version 1.0.0+.
You can pass either an instance of `elasticsearch.Elasticsearch` to Eland APIs
or a string containing the host to connect to:
### Elasticsearch Versions
```python
import eland as ed
eland is versioned like the Elastic stack (eland 7.5.1 is compatible with Elasticsearch 7.x up to 7.5.1)
# Connecting to an Elasticsearch instance running on 'localhost:9200'
df = ed.DataFrame("localhost:9200", es_index_pattern="flights")
A major version of the client is compatible with the same major version of Elasticsearch.
No compatibility assurances are given between different major versions of the client and Elasticsearch.
Major differences likely exist between major versions of Elasticsearch,
particularly around request and response object formats, but also around API urls and behaviour.
## Connecting to Elasticsearch
eland uses the [Elasticsearch low level client](https://elasticsearch-py.readthedocs.io/) to connect to Elasticsearch.
This client supports a range of [connection options and authentication mechanisms]
(https://elasticsearch-py.readthedocs.io/en/master/api.html#elasticsearch).
### Basic Connection Options
# Connecting to an Elastic Cloud instance
from elasticsearch import Elasticsearch
es = Elasticsearch(
cloud_id="cluster-name:...",
http_auth=("elastic", "<password>")
)
df = ed.DataFrame(es, es_index_pattern="flights")
```
## DataFrames in Eland
`eland.DataFrame` wraps an Elasticsearch index in a Pandas-like API
and defers all processing and filtering of data to Elasticsearch
instead of your local machine. This means you can process large
amounts of data within Elasticsearch from a Jupyter Notebook
without overloading your machine.
➤ [Eland DataFrame API documentation](https://eland.readthedocs.io/en/latest/reference/dataframe.html)
➤ [Advanced examples in a Jupyter Notebook](https://eland.readthedocs.io/en/latest/examples/demo_notebook.html)
```python
>>> import eland as ed
>>> # Connect to flights index via localhost Elasticsearch node
>>> ed.DataFrame('localhost', 'flights')
>>> # Connect to 'flights' index via localhost Elasticsearch node
>>> df = ed.DataFrame('localhost:9200', 'flights')
>>> # Connect to flights index via localhost Elasticsearch node on port 9200
>>> ed.DataFrame('localhost:9200', 'flights')
# eland.DataFrame instance has the same API as pandas.DataFrame
# except all data is in Elasticsearch. See .info() memory usage.
>>> df.head()
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
>>> # Connect to flights index via localhost Elasticsearch node on port 9200 with <user>:<password> credentials
>>> ed.DataFrame('http://<user>:<password>@localhost:9200', 'flights')
[5 rows x 27 columns]
>>> # Connect to flights index via ssl
>>> es = Elasticsearch(
'https://<user>:<password>@localhost:443',
use_ssl=True,
verify_certs=True,
ca_certs='/path/to/ca.crt'
>>> df.info()
<class 'eland.dataframe.DataFrame'>
Index: 13059 entries, 0 to 13058
Data columns (total 27 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 AvgTicketPrice 13059 non-null float64
1 Cancelled 13059 non-null bool
2 Carrier 13059 non-null object
...
24 OriginWeather 13059 non-null object
25 dayOfWeek 13059 non-null int64
26 timestamp 13059 non-null datetime64[ns]
dtypes: bool(2), datetime64[ns](1), float64(5), int64(2), object(17)
memory usage: 80.0 bytes
# Filtering of rows using comparisons
>>> df[(df.Carrier=="Kibana Airlines") & (df.AvgTicketPrice > 900.0) & (df.Cancelled == True)].head()
AvgTicketPrice Cancelled ... dayOfWeek timestamp
8 960.869736 True ... 0 2018-01-01 12:09:35
26 975.812632 True ... 0 2018-01-01 15:38:32
311 946.358410 True ... 0 2018-01-01 11:51:12
651 975.383864 True ... 2 2018-01-03 21:13:17
950 907.836523 True ... 2 2018-01-03 05:14:51
[5 rows x 27 columns]
# Running aggregations across an index
>>> df[['DistanceKilometers', 'AvgTicketPrice']].aggregate(['sum', 'min', 'std'])
DistanceKilometers AvgTicketPrice
sum 9.261629e+07 8.204365e+06
min 0.000000e+00 1.000205e+02
std 4.578263e+03 2.663867e+02
```
## Machine Learning in Eland
Eland allows transforming trained models from scikit-learn, XGBoost, and LightGBM libraries
to be serialized and used as an inference model in Elasticsearch
➤ [Eland Machine Learning API documentation](https://eland.readthedocs.io/en/latest/reference/ml.html)
➤ [Read more about Machine Learning in Elasticsearch](https://www.elastic.co/guide/en/machine-learning/current/ml-getting-started.html)
```python
>>> from xgboost import XGBClassifier
>>> from eland.ml import ImportedMLModel
# Train and exercise an XGBoost ML model locally
>>> xgb_model = XGBClassifier(booster="gbtree")
>>> xgb_model.fit(training_data[0], training_data[1])
>>> xgb_model.predict(training_data[0])
[0 1 1 0 1 0 0 0 1 0]
# Import the model into Elasticsearch
>>> es_model = ImportedMLModel(
es_client="localhost:9200",
model_id="xgb-classifier",
model=xgb_model,
feature_names=["f0", "f1", "f2", "f3", "f4"],
)
>>> ed.DataFrame(es, 'flights')
>>> # Connect to flights index via ssl using Urllib3HttpConnection options
>>> es = Elasticsearch(
['localhost:443', 'other_host:443'],
use_ssl=True,
verify_certs=True,
ca_certs='/path/to/CA_certs',
client_cert='/path/to/clientcert.pem',
client_key='/path/to/clientkey.pem'
)
>>> ed.DataFrame(es, 'flights')
# Exercise the ML model in Elasticsearch with the training data
>>> es_model.predict(training_data[0])
[0 1 1 0 1 0 0 0 1 0]
```
### Connecting to an Elasticsearch Cloud Cluster
```
>>> import eland as ed
>>> from elasticsearch import Elasticsearch
>>> es = Elasticsearch(cloud_id="<cloud_id>", http_auth=('<user>','<password>'))
>>> es.info()
{'name': 'instance-0000000000', 'cluster_name': 'bf900cfce5684a81bca0be0cce5913bc', 'cluster_uuid': 'xLPvrV3jQNeadA7oM4l1jA', 'version': {'number': '7.4.2', 'build_flavor': 'default', 'build_type': 'tar', 'build_hash': '2f90bbf7b93631e52bafb59b3b049cb44ec25e96', 'build_date': '2019-10-28T20:40:44.881551Z', 'build_snapshot': False, 'lucene_version': '8.2.0', 'minimum_wire_compatibility_version': '6.8.0', 'minimum_index_compatibility_version': '6.0.0-beta1'}, 'tagline': 'You Know, for Search'}
>>> df = ed.read_es(es, 'reviews')
```
## Why eland?
Naming is difficult, but as we had to call it something:
* eland: elastic and data
* eland: 'Elk/Moose' in Dutch (Alces alces)
* [Elandsgracht](https://goo.gl/maps/3hGBMqeGRcsBJfKx8): Amsterdam street near Elastic's Amsterdam office
[Pronunciation](https://commons.wikimedia.org/wiki/File:Nl-eland.ogg): /ˈeːlɑnt/

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@ -1,5 +1,5 @@
=====================
Contributing to eland
Contributing to Eland
=====================
Eland is an open source project and we love to receive contributions

1
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@ -58,4 +58,3 @@ the ``pandas.DataFrame`` API. This resolves some of the issues above as:
* Creating a new ``eland.DataFrame`` API gives us full flexibility in terms of implementation. However,
it does create a large amount of work which may duplicate a lot of the ``pandas`` code - for example,
printing objects etc. - this creates maintenance issues etc.

1
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@ -8,3 +8,4 @@ Development
:maxdepth: 2
contributing.rst
implementation.rst

4
docs/source/examples/demo_notebook.ipynb
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@ -32,7 +32,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"## Compare eland DataFrame vs pandas DataFrame"
"## Compare Eland DataFrame vs pandas DataFrame"
]
},
{
@ -52,7 +52,7 @@
},
"outputs": [],
"source": [
"ed_flights = ed.read_es('localhost', 'flights')"
"ed_flights = ed.DataFrame('localhost', 'flights')"
]
},
{

4
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@ -38,7 +38,7 @@
"metadata": {},
"outputs": [],
"source": [
"df = ed.read_csv(\"data/online-retail.csv.gz\",\n",
"df = ed.csv_to_eland(\"data/online-retail.csv.gz\",\n",
" es_client='localhost', \n",
" es_dest_index='online-retail', \n",
" es_if_exists='replace', \n",
@ -390,7 +390,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"### selecting columns\n",
"### Selecting columns\n",
"\n",
"you can also pass a list of columns to select columns from the data frame in a specified order."
]

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.. _implementation:
====================
Implementation Notes
====================
.. toctree::
:maxdepth: 2
details.rst

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@ -1,5 +1,3 @@
.. eland documentation master file, created by
.. module:: eland
**************************************************************
@ -11,10 +9,10 @@ Eland: DataFrames and Machine Learning backed by Elasticsearch
**Useful links**:
`Source Repository <https://github.com/elastic/eland>`__ |
`Issues & Ideas <https://github.com/elastic/eland/issues>`__ |
`Q&A Support <https://discuss.elastic.co>`__ |
`Q&A Support <https://discuss.elastic.co>`__
Eland is a Python Elasticsearch client for exploring and analyzing data
residing in Elasticsearch with a familiar Pandas-compatible API.
in Elasticsearch with a familiar Pandas-compatible API.
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
@ -48,30 +46,27 @@ If you're new to Elasticsearch we recommend `reading the documentation <https://
:hidden:
reference/index
implementation/index
development/index
reference/ml
examples/index
development/index
* :doc:`reference/index`
* :doc:`reference/supported_apis`
* :doc:`reference/io`
* :doc:`reference/general_utility_functions`
* :doc:`reference/dataframe`
* :doc:`reference/series`
* :doc:`reference/indexing`
* :doc:`reference/ml`
* :doc:`implementation/index`
* :doc:`implementation/details`
* :doc:`reference/indexing`
* :doc:`reference/general_utility_functions`
* :doc:`reference/io`
* :doc:`development/index`
* :doc:`development/contributing`
* :doc:`development/implementation`
* :doc:`examples/index`
* :doc:`examples/demo_notebook`
* :doc:`examples/introduction_to_eland_webinar`
* :doc:`examples/online_retail_analysis`

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eland.DataFrame.ndim
====================
.. currentmodule:: eland
.. autoattribute:: DataFrame.ndim

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@ -0,0 +1,6 @@
eland.DataFrame.size
====================
.. currentmodule:: eland
.. autoattribute:: DataFrame.size

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@ -0,0 +1,6 @@
eland.Series.dtype
==================
.. currentmodule:: eland
.. autoattribute:: Series.dtype

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@ -0,0 +1,6 @@
eland.Series.dtypes
===================
.. currentmodule:: eland
.. autoattribute:: Series.dtypes

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@ -0,0 +1,6 @@
eland.Series.ndim
=================
.. currentmodule:: eland
.. autoattribute:: Series.ndim

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@ -0,0 +1,6 @@
eland.Series.size
=================
.. currentmodule:: eland
.. autoattribute:: Series.size

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@ -0,0 +1,6 @@
eland.ml.MLModel.delete_model
=============================
.. currentmodule:: eland.ml
.. automethod:: MLModel.delete_model

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@ -0,0 +1,6 @@
eland.ml.MLModel.exists_model
=============================
.. currentmodule:: eland.ml
.. automethod:: MLModel.exists_model

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@ -14,8 +14,6 @@ Constructor
Attributes and underlying data
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
**Axes**
.. autosummary::
:toctree: api/
@ -26,6 +24,8 @@ Attributes and underlying data
DataFrame.values
DataFrame.empty
DataFrame.shape
DataFrame.ndim
DataFrame.size
Indexing, iteration
~~~~~~~~~~~~~~~~~~~

8
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@ -1,7 +1,7 @@
.. _api:
=============
API reference
API Reference
=============
This page gives an overview of all public eland objects, functions and
@ -11,9 +11,9 @@ methods. All classes and functions exposed in ``eland.*`` namespace are public.
:maxdepth: 2
supported_apis
io
general_utility_functions
dataframe
series
indexing
ml
indexing
general_utility_functions
io

16
docs/source/reference/ml.rst
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@ -13,9 +13,10 @@ To use the Elastic Stack machine learning features, you must have the appropriat
learning node in your Elasticsearch cluster. If Elastic Stack security features are enabled, you must also ensure
your users have the necessary privileges.
The fastest way to get started with machine learning features is to start a free 14-day trial of Elasticsearch Service in the cloud.
The fastest way to get started with machine learning features is to
`start a free 14-day trial of Elastic Cloud <https://www.elastic.co/cloud/elasticsearch-service/signup>`_.
See https://www.elastic.co/guide/en/machine-learning/current/setup.html and other documentation for more detail.
See `Elasticsearch Machine Learning documentation <https://www.elastic.co/guide/en/machine-learning/current/setup.html>`_ more details.
ImportedMLModel
~~~~~~~~~~~~~~~
@ -28,10 +29,17 @@ Constructor
ImportedMLModel
Learning API
^^^^^^^^^^^^
Predictions
^^^^^^^^^^^
.. autosummary::
:toctree: api/
ImportedMLModel.predict
Manage Models
^^^^^^^^^^^^^
.. autosummary::
:toctree: api/
MLModel.exists_model
MLModel.delete_model

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@ -14,15 +14,17 @@ Constructor
Attributes and underlying data
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
**Axes**
.. autosummary::
:toctree: api/
Series.index
Series.dtype
Series.dtypes
Series.shape
Series.name
Series.empty
Series.ndim
Series.size
Indexing, iteration
~~~~~~~~~~~~~~~~~~~

144
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@ -32,152 +32,34 @@ CLASSIFIERS = [
"License :: OSI Approved :: Apache Software License",
"Environment :: Console",
"Operating System :: OS Independent",
"Intended Audience :: Developers",
"Intended Audience :: Science/Research",
"Operating System :: OS Independent",
"Programming Language :: Python",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3 :: Only",
"Programming Language :: Python :: 3.6",
"Programming Language :: Python :: 3.7",
"Programming Language :: Python :: 3.8",
"Programming Language :: Python :: 3.9",
"Topic :: Scientific/Engineering",
]
LONG_DESCRIPTION = """
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.
# Remove all raw HTML from README for long description
with open(path.join(here, "README.md"), "r", "utf-8") as f:
lines = f.read().split("\n")
last_html_index = 0
for i, line in enumerate(lines):
if line == "</p>":
last_html_index = i + 1
long_description = "\n".join(lines[last_html_index:])
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
>>> # Connect to 'flights' index via localhost Elasticsearch node
>>> df = ed.DataFrame('localhost:9200', 'flights')
>>> df.head()
AvgTicketPrice Cancelled Carrier ... OriginWeather dayOfWeek timestamp
0 841.265642 False Kibana Airlines ... Sunny 0 2018-01-01 00:00:00
1 882.982662 False Logstash Airways ... Clear 0 2018-01-01 18:27:00
2 190.636904 False Logstash Airways ... Rain 0 2018-01-01 17:11:14
3 181.694216 True Kibana Airlines ... Thunder & Lightning 0 2018-01-01 10:33:28
4 730.041778 False Kibana Airlines ... Damaging Wind 0 2018-01-01 05:13:00
[5 rows x 27 columns]
>>> df.describe()
AvgTicketPrice DistanceKilometers DistanceMiles FlightDelayMin FlightTimeHour FlightTimeMin dayOfWeek
count 13059.000000 13059.000000 13059.000000 13059.000000 13059.000000 13059.000000 13059.000000
mean 628.253689 7092.142457 4406.853010 47.335171 8.518797 511.127842 2.835975
std 266.386661 4578.263193 2844.800855 96.743006 5.579019 334.741135 1.939365
min 100.020531 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
25% 410.008918 2470.545974 1535.126118 0.000000 4.194976 251.738513 1.000000
50% 640.362667 7612.072403 4729.922470 0.000000 8.385816 503.148975 3.000000
75% 842.254990 9735.082407 6049.459005 15.000000 12.009396 720.534532 4.141221
max 1199.729004 19881.482422 12353.780273 360.000000 31.715034 1902.901978 6.000000
>>> df[['Carrier', 'AvgTicketPrice', 'Cancelled']]
Carrier AvgTicketPrice Cancelled
0 Kibana Airlines 841.265642 False
1 Logstash Airways 882.982662 False
2 Logstash Airways 190.636904 False
3 Kibana Airlines 181.694216 True
4 Kibana Airlines 730.041778 False
... ... ... ...
13054 Logstash Airways 1080.446279 False
13055 Logstash Airways 646.612941 False
13056 Logstash Airways 997.751876 False
13057 JetBeats 1102.814465 False
13058 JetBeats 858.144337 False
[13059 rows x 3 columns]
>>> df[(df.Carrier=="Kibana Airlines") & (df.AvgTicketPrice > 900.0) & (df.Cancelled == True)].head()
AvgTicketPrice Cancelled Carrier ... OriginWeather dayOfWeek timestamp
8 960.869736 True Kibana Airlines ... Heavy Fog 0 2018-01-01 12:09:35
26 975.812632 True Kibana Airlines ... Rain 0 2018-01-01 15:38:32
311 946.358410 True Kibana Airlines ... Heavy Fog 0 2018-01-01 11:51:12
651 975.383864 True Kibana Airlines ... Rain 2 2018-01-03 21:13:17
950 907.836523 True Kibana Airlines ... Thunder & Lightning 2 2018-01-03 05:14:51
[5 rows x 27 columns]
>>> df[['DistanceKilometers', 'AvgTicketPrice']].aggregate(['sum', 'min', 'std'])
DistanceKilometers AvgTicketPrice
sum 9.261629e+07 8.204365e+06
min 0.000000e+00 1.000205e+02
std 4.578263e+03 2.663867e+02
>>> df[['Carrier', 'Origin', 'Dest']].nunique()
Carrier 4
Origin 156
Dest 156
dtype: int64
>>> s = df.AvgTicketPrice * 2 + df.DistanceKilometers - df.FlightDelayMin
>>> s
0 18174.857422
1 10589.365723
2 381.273804
3 739.126221
4 14818.327637
...
13054 10219.474121
13055 8381.823975
13056 12661.157104
13057 20819.488281
13058 18315.431274
Length: 13059, dtype: float64
>>> print(s.info_es())
index_pattern: flights
Index:
index_field: _id
is_source_field: False
Mappings:
capabilities:
es_field_name is_source es_dtype es_date_format pd_dtype is_searchable is_aggregatable is_scripted aggregatable_es_field_name
NaN script_field_None False double None float64 True True True script_field_None
Operations:
tasks: []
size: None
sort_params: None
_source: ['script_field_None']
body: {'script_fields': {'script_field_None': {'script': {'source': "(((doc['AvgTicketPrice'].value * 2) + doc['DistanceKilometers'].value) - doc['FlightDelayMin'].value)"}}}}
post_processing: []
>>> pd_df = ed.eland_to_pandas(df)
>>> pd_df.head()
AvgTicketPrice Cancelled Carrier ... OriginWeather dayOfWeek timestamp
0 841.265642 False Kibana Airlines ... Sunny 0 2018-01-01 00:00:00
1 882.982662 False Logstash Airways ... Clear 0 2018-01-01 18:27:00
2 190.636904 False Logstash Airways ... Rain 0 2018-01-01 17:11:14
3 181.694216 True Kibana Airlines ... Thunder & Lightning 0 2018-01-01 10:33:28
4 730.041778 False Kibana Airlines ... Damaging Wind 0 2018-01-01 05:13:00
[5 rows x 27 columns]
```
See [docs](https://eland.readthedocs.io/en/latest) and [demo_notebook.ipynb](https://eland.readthedocs.io/en/latest/examples/demo_notebook.html) 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](https://pypi.org/project/eland).
```sh
pip install eland
```
"""
setup(
name=about["__title__"],
version=about["__version__"],
description=about["__description__"],
long_description=LONG_DESCRIPTION,
long_description=long_description,
long_description_content_type="text/markdown",
url=about["__url__"],
author=about["__author__"],

19
utils/generate-supported-apis.py
View File

@ -21,8 +21,10 @@ import re
import eland
import pandas
import inspect
from pathlib import Path
api_docs_dir = Path(__file__).absolute().parent.parent / "docs/source/reference/api"
is_supported = []
supported_attr = re.compile(
r"(?:[a-zA-Z0-9][a-zA-Z0-9_]*|__[a-zA-Z0-9][a-zA-Z0-9_]*__)"
@ -68,6 +70,23 @@ def main():
)
print(row_delimiter)
for attr, supported in is_supported:
if supported and "__" not in attr:
attr = attr.replace("ed.", "eland.").rstrip("()")
attr_doc_path = api_docs_dir / f"{attr}.rst"
if not attr_doc_path.exists():
with attr_doc_path.open(mode="w") as f:
f.truncate()
f.write(
f"""{attr}
{'=' * len(attr)}
.. currentmodule:: eland
.. automethod:: { attr.replace('eland.', '') }
"""
)
if __name__ == "__main__":
main()