From c7236335263cb1930309331068662323f68638c5 Mon Sep 17 00:00:00 2001
From: Stephen Dodson <steve.dodson@elastic.co>
Date: Sat, 22 Jun 2019 06:55:30 +0000
Subject: [PATCH] Resolving merge issue

---
 eland/dataframe.py | 100 ---------------------------------------------
 1 file changed, 100 deletions(-)
 delete mode 100644 eland/dataframe.py

diff --git a/eland/dataframe.py b/eland/dataframe.py
deleted file mode 100644
index 1f49216..0000000
--- a/eland/dataframe.py
+++ /dev/null
@@ -1,100 +0,0 @@
-import eland
-
-from elasticsearch import Elasticsearch
-from elasticsearch_dsl import Search
-
-import pandas as pd
-
-class DataFrame():
-    
-    def __init__(self, client, index_pattern):
-        self.client = eland.Client(client)
-        self.index_pattern = index_pattern
-        
-        self.client.indices().exists(index_pattern)
-        
-    @staticmethod
-    def _es_results_to_pandas(results):
-        # TODO - resolve nested fields
-        rows = []
-        for hit in results['hits']['hits']:
-            row = {}
-            for k in hit.keys():
-                if k == '_source':
-                    row.update(hit['_source'])
-            rows.append(row)
-        return pd.DataFrame(data=rows)
-    
-    @staticmethod
-    def _flatten_mapping(prefix, properties, result):
-        for k, v in properties.items():
-            if 'properties' in v:
-                if(prefix == ''):
-                    prefix = k
-                else:
-                    prefix = prefix + '.' + k
-                DataFrame._flatten_mapping(prefix, v['properties'], result)
-            else:
-                if(prefix == ''):
-                    key = k
-                else:
-                    key = prefix + '.' + k
-                type = v['type']
-                result.append((key, type))
-    
-    @staticmethod
-    def _es_mappings_to_pandas(mappings):
-        fields = []
-        for index in mappings:            
-            if 'properties' in mappings[index]['mappings']:
-                properties = mappings[index]['mappings']['properties']
-                
-                DataFrame._flatten_mapping('', properties, fields)
-                
-        return pd.DataFrame(data=fields, columns=['field', 'datatype'])
-        
-    def head(self, n=5):
-        results = self.client.search(index=self.index_pattern, size=n)
-        
-        return DataFrame._es_results_to_pandas(results)
-    
-    def describe(self):
-        # First get all types
-        #mapping = self.client.indices().get_mapping(index=self.index_pattern)
-        mapping = self.client.indices().get_mapping(index=self.index_pattern)
-                
-        fields = DataFrame._es_mappings_to_pandas(mapping)
-        
-        # Get numeric types (https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#the-where-method-and-masking)
-        # https://www.elastic.co/guide/en/elasticsearch/reference/current/number.html
-        # TODO refactor this list out of method
-        numeric_fields = fields.query('datatype == ["long", "integer", "short", "byte", "double", "float", "half_float", "scaled_float"]')
-                
-        # for each field we copute:
-        # count, mean, std, min, 25%, 50%, 75%, max
-        search = Search(using=self.client, index=self.index_pattern).extra(size=0)
-        
-        for field in numeric_fields.field:
-            search.aggs.metric('extended_stats_'+field, 'extended_stats', field=field)
-            search.aggs.metric('percentiles_'+field, 'percentiles', field=field)
-
-        response = search.execute()
-        
-        results = pd.DataFrame(index=['count', 'mean', 'std', 'min', '25%', '50%', '75%', 'max'])
-        
-        for field in numeric_fields.field:
-            values = []
-            values.append(response.aggregations['extended_stats_'+field]['count'])
-            values.append(response.aggregations['extended_stats_'+field]['avg'])
-            values.append(response.aggregations['extended_stats_'+field]['std_deviation'])
-            values.append(response.aggregations['extended_stats_'+field]['min'])
-            values.append(response.aggregations['percentiles_'+field]['values']['25.0'])
-            values.append(response.aggregations['percentiles_'+field]['values']['50.0'])
-            values.append(response.aggregations['percentiles_'+field]['values']['75.0'])
-            values.append(response.aggregations['extended_stats_'+field]['max'])
-            
-            # if not None
-            if (values.count(None) < len(values)):
-                results = results.assign(**{field: values})
-            
-        return results