mirror of
https://github.com/elastic/eland.git
synced 2025-07-11 00:02:14 +08:00
First prototype code commit
Experimental prototype, for internal development use only!
This commit is contained in:
Stephen Dodson
parent
90649f66f0
commit
f1e27f1dda
19
eland/Client.py
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19
eland/Client.py
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from elasticsearch import Elasticsearch
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# eland client - implement as facade to control access to Elasticsearch methods
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class Client(object):
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def __init__(self, es=None):
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if isinstance(es, Elasticsearch):
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self.es = es
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else:
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self.es = Elasticsearch(es)
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def info(self):
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return self.es.info()
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def indices(self):
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return self.es.indices
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def search(self, **kwargs):
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return self.es.search(**kwargs)
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100
eland/DataFrame.py
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100
eland/DataFrame.py
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import eland
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from elasticsearch import Elasticsearch
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from elasticsearch_dsl import Search
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import pandas as pd
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class DataFrame():
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def __init__(self, client, index_pattern):
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self.client = eland.Client(client)
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self.index_pattern = index_pattern
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self.client.indices().exists(index_pattern)
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@staticmethod
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def _es_results_to_pandas(results):
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# TODO - resolve nested fields
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rows = []
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for hit in results['hits']['hits']:
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row = {}
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for k in hit.keys():
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if k == '_source':
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row.update(hit['_source'])
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rows.append(row)
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return pd.DataFrame(data=rows)
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@staticmethod
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def _flatten_mapping(prefix, properties, result):
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for k, v in properties.items():
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if 'properties' in v:
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if(prefix == ''):
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prefix = k
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else:
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prefix = prefix + '.' + k
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DataFrame._flatten_mapping(prefix, v['properties'], result)
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else:
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if(prefix == ''):
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key = k
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else:
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key = prefix + '.' + k
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type = v['type']
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result.append((key, type))
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@staticmethod
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def _es_mappings_to_pandas(mappings):
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fields = []
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for index in mappings:
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if 'properties' in mappings[index]['mappings']:
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properties = mappings[index]['mappings']['properties']
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DataFrame._flatten_mapping('', properties, fields)
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return pd.DataFrame(data=fields, columns=['field', 'datatype'])
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def head(self, n=5):
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results = self.client.search(index=self.index_pattern, size=n)
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return DataFrame._es_results_to_pandas(results)
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def describe(self):
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# First get all types
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#mapping = self.client.indices().get_mapping(index=self.index_pattern)
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mapping = self.client.indices().get_mapping(index=self.index_pattern)
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fields = DataFrame._es_mappings_to_pandas(mapping)
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# Get numeric types (https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#the-where-method-and-masking)
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# https://www.elastic.co/guide/en/elasticsearch/reference/current/number.html
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# TODO refactor this list out of method
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numeric_fields = fields.query('datatype == ["long", "integer", "short", "byte", "double", "float", "half_float", "scaled_float"]')
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# for each field we copute:
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# count, mean, std, min, 25%, 50%, 75%, max
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search = Search(using=self.client, index=self.index_pattern).extra(size=0)
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for field in numeric_fields.field:
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search.aggs.metric('extended_stats_'+field, 'extended_stats', field=field)
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search.aggs.metric('percentiles_'+field, 'percentiles', field=field)
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response = search.execute()
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results = pd.DataFrame(index=['count', 'mean', 'std', 'min', '25%', '50%', '75%', 'max'])
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for field in numeric_fields.field:
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values = []
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values.append(response.aggregations['extended_stats_'+field]['count'])
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values.append(response.aggregations['extended_stats_'+field]['avg'])
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values.append(response.aggregations['extended_stats_'+field]['std_deviation'])
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values.append(response.aggregations['extended_stats_'+field]['min'])
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values.append(response.aggregations['percentiles_'+field]['values']['25.0'])
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values.append(response.aggregations['percentiles_'+field]['values']['50.0'])
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values.append(response.aggregations['percentiles_'+field]['values']['75.0'])
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values.append(response.aggregations['extended_stats_'+field]['max'])
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# if not None
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if (values.count(None) < len(values)):
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results = results.assign(**{field: values})
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return results
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3
eland/__init__.py
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3
eland/__init__.py
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from .utils import *
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from .DataFrame import *
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from .Client import *
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4
eland/utils.py
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4
eland/utils.py
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import eland
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def from_es(es_params, index_pattern):
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return eland.DataFrame(es_params, index_pattern)
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429
test.ipynb
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429
test.ipynb
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@ -0,0 +1,429 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import eland as ed"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"df = ed.from_es('localhost', 'kibana_sample_data_flights')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
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||||
" }\n",
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||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
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||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
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||||
" text-align: right;\n",
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||||
" }\n",
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||||
"</style>\n",
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||||
"<table border=\"1\" class=\"dataframe\">\n",
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||||
" <thead>\n",
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||||
" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>AvgTicketPrice</th>\n",
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" <th>Cancelled</th>\n",
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" <th>Carrier</th>\n",
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" <th>Dest</th>\n",
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" <th>DestAirportID</th>\n",
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" <th>DestCityName</th>\n",
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" <th>DestCountry</th>\n",
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" <th>DestLocation</th>\n",
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" <th>DestRegion</th>\n",
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" <th>DestWeather</th>\n",
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" <th>...</th>\n",
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" <th>FlightTimeMin</th>\n",
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" <th>Origin</th>\n",
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" <th>OriginAirportID</th>\n",
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" <th>OriginCityName</th>\n",
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" <th>OriginCountry</th>\n",
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" <th>OriginLocation</th>\n",
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" <th>OriginRegion</th>\n",
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" <th>OriginWeather</th>\n",
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" <th>dayOfWeek</th>\n",
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" <th>timestamp</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>841.265642</td>\n",
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" <td>False</td>\n",
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" <td>Kibana Airlines</td>\n",
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" <td>Sydney Kingsford Smith International Airport</td>\n",
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" <td>SYD</td>\n",
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" <td>Sydney</td>\n",
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" <td>AU</td>\n",
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" <td>{'lat': '-33.94609833', 'lon': '151.177002'}</td>\n",
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" <td>SE-BD</td>\n",
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" <td>Rain</td>\n",
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" <td>...</td>\n",
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" <td>1030.770416</td>\n",
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" <td>Frankfurt am Main Airport</td>\n",
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" <td>FRA</td>\n",
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" <td>Frankfurt am Main</td>\n",
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" <td>DE</td>\n",
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" <td>{'lat': '50.033333', 'lon': '8.570556'}</td>\n",
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" <td>DE-HE</td>\n",
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" <td>Sunny</td>\n",
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" <td>0</td>\n",
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" <td>2019-05-27T00:00:00</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>882.982662</td>\n",
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" <td>False</td>\n",
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" <td>Logstash Airways</td>\n",
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" <td>Venice Marco Polo Airport</td>\n",
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" <td>VE05</td>\n",
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" <td>Venice</td>\n",
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" <td>IT</td>\n",
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" <td>{'lat': '45.505299', 'lon': '12.3519'}</td>\n",
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" <td>IT-34</td>\n",
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" <td>Sunny</td>\n",
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" <td>...</td>\n",
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" <td>464.389481</td>\n",
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" <td>Cape Town International Airport</td>\n",
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" <td>CPT</td>\n",
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" <td>Cape Town</td>\n",
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" <td>ZA</td>\n",
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" <td>{'lat': '-33.96480179', 'lon': '18.60169983'}</td>\n",
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" <td>SE-BD</td>\n",
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" <td>Clear</td>\n",
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" <td>0</td>\n",
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" <td>2019-05-27T18:27:00</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>190.636904</td>\n",
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" <td>False</td>\n",
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" <td>Logstash Airways</td>\n",
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" <td>Venice Marco Polo Airport</td>\n",
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" <td>VE05</td>\n",
|
||||
" <td>Venice</td>\n",
|
||||
" <td>IT</td>\n",
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" <td>{'lat': '45.505299', 'lon': '12.3519'}</td>\n",
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" <td>IT-34</td>\n",
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" <td>Cloudy</td>\n",
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" <td>...</td>\n",
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" <td>0.000000</td>\n",
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" <td>Venice Marco Polo Airport</td>\n",
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" <td>VE05</td>\n",
|
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" <td>Venice</td>\n",
|
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" <td>IT</td>\n",
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" <td>{'lat': '45.505299', 'lon': '12.3519'}</td>\n",
|
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" <td>IT-34</td>\n",
|
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" <td>Rain</td>\n",
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" <td>0</td>\n",
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" <td>2019-05-27T17:11:14</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>181.694216</td>\n",
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" <td>True</td>\n",
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" <td>Kibana Airlines</td>\n",
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" <td>Treviso-Sant'Angelo Airport</td>\n",
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" <td>TV01</td>\n",
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" <td>Treviso</td>\n",
|
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" <td>IT</td>\n",
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" <td>{'lat': '45.648399', 'lon': '12.1944'}</td>\n",
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" <td>IT-34</td>\n",
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" <td>Clear</td>\n",
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" <td>...</td>\n",
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" <td>222.749059</td>\n",
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" <td>Naples International Airport</td>\n",
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" <td>NA01</td>\n",
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" <td>Naples</td>\n",
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" <td>IT</td>\n",
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" <td>{'lat': '40.886002', 'lon': '14.2908'}</td>\n",
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" <td>IT-72</td>\n",
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" <td>Thunder & Lightning</td>\n",
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" <td>0</td>\n",
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" <td>2019-05-27T10:33:28</td>\n",
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||||
" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>730.041778</td>\n",
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" <td>False</td>\n",
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" <td>Kibana Airlines</td>\n",
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" <td>Xi'an Xianyang International Airport</td>\n",
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" <td>XIY</td>\n",
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" <td>Xi'an</td>\n",
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" <td>CN</td>\n",
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" <td>{'lat': '34.447102', 'lon': '108.751999'}</td>\n",
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" <td>SE-BD</td>\n",
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" <td>Clear</td>\n",
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" <td>...</td>\n",
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" <td>785.779071</td>\n",
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" <td>Licenciado Benito Juarez International Airport</td>\n",
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" <td>AICM</td>\n",
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" <td>Mexico City</td>\n",
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" <td>MX</td>\n",
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" <td>{'lat': '19.4363', 'lon': '-99.072098'}</td>\n",
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" <td>MX-DIF</td>\n",
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" <td>Damaging Wind</td>\n",
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" <td>0</td>\n",
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" <td>2019-05-27T05:13:00</td>\n",
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" </tr>\n",
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||||
" </tbody>\n",
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"</table>\n",
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"<p>5 rows × 27 columns</p>\n",
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"</div>"
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],
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"text/plain": [
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" AvgTicketPrice Cancelled Carrier \\\n",
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"0 841.265642 False Kibana Airlines \n",
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"1 882.982662 False Logstash Airways \n",
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"2 190.636904 False Logstash Airways \n",
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"3 181.694216 True Kibana Airlines \n",
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"4 730.041778 False Kibana Airlines \n",
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"\n",
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" Dest DestAirportID DestCityName \\\n",
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"0 Sydney Kingsford Smith International Airport SYD Sydney \n",
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"1 Venice Marco Polo Airport VE05 Venice \n",
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"2 Venice Marco Polo Airport VE05 Venice \n",
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"3 Treviso-Sant'Angelo Airport TV01 Treviso \n",
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"4 Xi'an Xianyang International Airport XIY Xi'an \n",
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"\n",
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" DestCountry DestLocation DestRegion \\\n",
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||||
"0 AU {'lat': '-33.94609833', 'lon': '151.177002'} SE-BD \n",
|
||||
"1 IT {'lat': '45.505299', 'lon': '12.3519'} IT-34 \n",
|
||||
"2 IT {'lat': '45.505299', 'lon': '12.3519'} IT-34 \n",
|
||||
"3 IT {'lat': '45.648399', 'lon': '12.1944'} IT-34 \n",
|
||||
"4 CN {'lat': '34.447102', 'lon': '108.751999'} SE-BD \n",
|
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"\n",
|
||||
" DestWeather ... FlightTimeMin \\\n",
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||||
"0 Rain ... 1030.770416 \n",
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||||
"1 Sunny ... 464.389481 \n",
|
||||
"2 Cloudy ... 0.000000 \n",
|
||||
"3 Clear ... 222.749059 \n",
|
||||
"4 Clear ... 785.779071 \n",
|
||||
"\n",
|
||||
" Origin OriginAirportID \\\n",
|
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"0 Frankfurt am Main Airport FRA \n",
|
||||
"1 Cape Town International Airport CPT \n",
|
||||
"2 Venice Marco Polo Airport VE05 \n",
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||||
"3 Naples International Airport NA01 \n",
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||||
"4 Licenciado Benito Juarez International Airport AICM \n",
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||||
"\n",
|
||||
" OriginCityName OriginCountry \\\n",
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||||
"0 Frankfurt am Main DE \n",
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||||
"1 Cape Town ZA \n",
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||||
"2 Venice IT \n",
|
||||
"3 Naples IT \n",
|
||||
"4 Mexico City MX \n",
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||||
"\n",
|
||||
" OriginLocation OriginRegion \\\n",
|
||||
"0 {'lat': '50.033333', 'lon': '8.570556'} DE-HE \n",
|
||||
"1 {'lat': '-33.96480179', 'lon': '18.60169983'} SE-BD \n",
|
||||
"2 {'lat': '45.505299', 'lon': '12.3519'} IT-34 \n",
|
||||
"3 {'lat': '40.886002', 'lon': '14.2908'} IT-72 \n",
|
||||
"4 {'lat': '19.4363', 'lon': '-99.072098'} MX-DIF \n",
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||||
"\n",
|
||||
" OriginWeather dayOfWeek timestamp \n",
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||||
"0 Sunny 0 2019-05-27T00:00:00 \n",
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||||
"1 Clear 0 2019-05-27T18:27:00 \n",
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||||
"2 Rain 0 2019-05-27T17:11:14 \n",
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||||
"3 Thunder & Lightning 0 2019-05-27T10:33:28 \n",
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"4 Damaging Wind 0 2019-05-27T05:13:00 \n",
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"\n",
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"[5 rows x 27 columns]"
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]
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},
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"execution_count": 3,
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||||
"metadata": {},
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||||
"output_type": "execute_result"
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||||
}
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||||
],
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"source": [
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"df.head()"
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]
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},
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||||
{
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||||
"cell_type": "code",
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||||
"execution_count": 4,
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||||
"metadata": {},
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||||
"outputs": [
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||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>AvgTicketPrice</th>\n",
|
||||
" <th>DistanceKilometers</th>\n",
|
||||
" <th>DistanceMiles</th>\n",
|
||||
" <th>FlightDelayMin</th>\n",
|
||||
" <th>FlightTimeMin</th>\n",
|
||||
" <th>dayOfWeek</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>count</th>\n",
|
||||
" <td>13059.000000</td>\n",
|
||||
" <td>13059.000000</td>\n",
|
||||
" <td>13059.000000</td>\n",
|
||||
" <td>13059.000000</td>\n",
|
||||
" <td>13059.000000</td>\n",
|
||||
" <td>13059.000000</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>mean</th>\n",
|
||||
" <td>628.253689</td>\n",
|
||||
" <td>7092.142457</td>\n",
|
||||
" <td>4406.853010</td>\n",
|
||||
" <td>47.335171</td>\n",
|
||||
" <td>511.127842</td>\n",
|
||||
" <td>2.835975</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>std</th>\n",
|
||||
" <td>266.386661</td>\n",
|
||||
" <td>4578.263193</td>\n",
|
||||
" <td>2844.800855</td>\n",
|
||||
" <td>96.743006</td>\n",
|
||||
" <td>334.741135</td>\n",
|
||||
" <td>1.939365</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>min</th>\n",
|
||||
" <td>100.020531</td>\n",
|
||||
" <td>0.000000</td>\n",
|
||||
" <td>0.000000</td>\n",
|
||||
" <td>0.000000</td>\n",
|
||||
" <td>0.000000</td>\n",
|
||||
" <td>0.000000</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>25%</th>\n",
|
||||
" <td>410.008918</td>\n",
|
||||
" <td>2470.545974</td>\n",
|
||||
" <td>1535.126118</td>\n",
|
||||
" <td>0.000000</td>\n",
|
||||
" <td>251.773003</td>\n",
|
||||
" <td>1.000000</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>50%</th>\n",
|
||||
" <td>640.362667</td>\n",
|
||||
" <td>7612.072403</td>\n",
|
||||
" <td>4729.922470</td>\n",
|
||||
" <td>0.000000</td>\n",
|
||||
" <td>503.148975</td>\n",
|
||||
" <td>3.000000</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>75%</th>\n",
|
||||
" <td>842.233478</td>\n",
|
||||
" <td>9735.887390</td>\n",
|
||||
" <td>6049.459005</td>\n",
|
||||
" <td>15.000000</td>\n",
|
||||
" <td>720.534532</td>\n",
|
||||
" <td>4.095833</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>max</th>\n",
|
||||
" <td>1199.729004</td>\n",
|
||||
" <td>19881.482422</td>\n",
|
||||
" <td>12353.780273</td>\n",
|
||||
" <td>360.000000</td>\n",
|
||||
" <td>1902.901978</td>\n",
|
||||
" <td>6.000000</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" AvgTicketPrice DistanceKilometers DistanceMiles FlightDelayMin \\\n",
|
||||
"count 13059.000000 13059.000000 13059.000000 13059.000000 \n",
|
||||
"mean 628.253689 7092.142457 4406.853010 47.335171 \n",
|
||||
"std 266.386661 4578.263193 2844.800855 96.743006 \n",
|
||||
"min 100.020531 0.000000 0.000000 0.000000 \n",
|
||||
"25% 410.008918 2470.545974 1535.126118 0.000000 \n",
|
||||
"50% 640.362667 7612.072403 4729.922470 0.000000 \n",
|
||||
"75% 842.233478 9735.887390 6049.459005 15.000000 \n",
|
||||
"max 1199.729004 19881.482422 12353.780273 360.000000 \n",
|
||||
"\n",
|
||||
" FlightTimeMin dayOfWeek \n",
|
||||
"count 13059.000000 13059.000000 \n",
|
||||
"mean 511.127842 2.835975 \n",
|
||||
"std 334.741135 1.939365 \n",
|
||||
"min 0.000000 0.000000 \n",
|
||||
"25% 251.773003 1.000000 \n",
|
||||
"50% 503.148975 3.000000 \n",
|
||||
"75% 720.534532 4.095833 \n",
|
||||
"max 1902.901978 6.000000 "
|
||||
]
|
||||
},
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df.describe()"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.8"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
Loading…
x
Reference in New Issue
Block a user