Merge pull request #11 from stevedodson/master

Added DataFrame.__getitem__

eland/__init__.py

@@ -1,4 +1,4 @@
 from .utils import *
 from .frame import *
 from .client import *
-from .mappings import *
+from .mappings import *

eland/client.py

@@ -7,6 +7,8 @@ class Client():
     def __init__(self, es=None):
         if isinstance(es, Elasticsearch):
             self.es = es
+        elif isinstance(es, Client):
+            self.es = es.es
         else:
             self.es = Elasticsearch(es)
             
@@ -21,3 +23,8 @@ class Client():
 
     def field_caps(self, **kwargs):
         return self.es.field_caps(**kwargs)
+
+    def count(self, **kwargs):
+        count_json = self.es.count(**kwargs)
+        return count_json['count']
+

eland/frame.py

@@ -30,6 +30,8 @@ from elasticsearch_dsl import Search
 
 import pandas as pd
 
+from pandas.core.arrays.sparse import BlockIndex
+
 class DataFrame():
     """
     pandas.DataFrame like API that proxies into Elasticsearch index(es).
@@ -42,6 +44,9 @@ class DataFrame():
     index_pattern : str
         An Elasticsearch index pattern. This can contain wildcards (e.g. filebeat-*).
 
+    operations: list of operation
+        A list of Elasticsearch analytics operations e.g. filter, aggregations etc.
+
     See Also
     --------
 
@@ -67,13 +72,26 @@ class DataFrame():
     object is created, the object is not rebuilt and so inconsistencies can occur.
 
     """
-    def __init__(self, client, index_pattern):
+    def __init__(self,
+                 client,
+                 index_pattern,
+                 mappings=None,
+                 operations=None):
         self.client = ed.Client(client)
         self.index_pattern = index_pattern
 
         # Get and persist mappings, this allows us to correctly
         # map returned types from Elasticsearch to pandas datatypes
-        self.mappings = ed.Mappings(self.client, self.index_pattern)
+        if mappings is None:
+            self.mappings = ed.Mappings(self.client, self.index_pattern)
+        else:
+            self.mappings = mappings
+
+        # Initialise a list of 'operations'
+        # these are filters
+        self.operations = []
+        if operations is not None:
+            self.operations.extend(operations)
 
     def _es_results_to_pandas(self, results):
         """
@@ -172,7 +190,7 @@ class DataFrame():
                     is_source_field = False
                     pd_dtype = 'object'
                 else:
-                    is_source_field, pd_dtype = self.mappings.is_source_field(name[:-1])
+                    is_source_field, pd_dtype = self.mappings.source_field_pd_dtype(name[:-1])
 
                 if not is_source_field and type(x) is dict:
                     for a in x:
@@ -180,7 +198,7 @@ class DataFrame():
                 elif not is_source_field and type(x) is list:
                     for a in x:
                         flatten(a, name)
-                else:
+                elif is_source_field == True: # only print source fields from mappings (TODO - not so efficient for large number of fields and filtered mapping)
                     field_name = name[:-1]
 
                     # Coerce types - for now just datetime
@@ -211,6 +229,19 @@ class DataFrame():
         # Create pandas DataFrame
         df = pd.DataFrame(data=rows)
 
+        # _source may not contain all columns in the mapping
+        # therefore, fill in missing columns
+        # (note this returns self.columns NOT IN df.columns)
+        missing_columns = list(set(self.columns) - set(df.columns))
+
+        for missing in missing_columns:
+            is_source_field, pd_dtype = self.mappings.source_field_pd_dtype(missing)
+            df[missing] = None
+            df[missing].astype(pd_dtype)
+
+        # Sort columns in mapping order
+        df = df[self.columns]
+
         return df
 
     def head(self, n=5):
@@ -251,3 +282,89 @@ class DataFrame():
         df = pd.DataFrame(data=results, index=['count', 'mean', 'std', 'min', '25%', '50%', '75%', 'max'])
             
         return df
+
+    @property
+    def shape(self):
+        """
+        Return a tuple representing the dimensionality of the DataFrame.
+
+        Returns
+        -------
+        shape: tuple
+            0 - number of rows
+            1 - number of columns
+        """
+        num_rows = len(self)
+        num_columns = len(self.columns)
+
+        return num_rows, num_columns
+
+    @property
+    def columns(self):
+        return self.mappings.source_fields()
+
+    def __getitem__(self, item):
+        # df['a'] -> item == str
+        # df['a', 'b'] -> item == (str, str) tuple
+        columns = []
+        if isinstance(item, str):
+            if not self.mappings.is_source_field(item):
+                raise TypeError('Column does not exist: [{0}]'.format(item))
+            columns.append(item)
+        elif isinstance(item, tuple):
+            columns.extend(list(item))
+
+        if len(columns) > 0:
+            # Return new eland.DataFrame with modified mappings
+            mappings = ed.Mappings(mappings=self.mappings, columns=columns)
+
+            return DataFrame(self.client, self.index_pattern, mappings=mappings)
+        """
+        elif isinstance(item, BooleanFilter):
+            self._filter = item.build()
+            return self
+        else:
+            raise TypeError('Unsupported expr: [{0}]'.format(item))
+        """
+
+    def __len__(self):
+        """
+        Returns length of info axis, but here we use the index.
+        """
+        return self.client.count(index=self.index_pattern)
+
+    # ----------------------------------------------------------------------
+    # Rendering Methods
+
+    def __repr__(self):
+        return self.to_string()
+
+
+    def to_string(self):
+        # The return for this is display.options.max_rows
+        max_rows = 60
+        head_rows = max_rows / 2
+        tail_rows = max_rows - head_rows
+
+        head = self.head(max_rows)
+
+        num_rows = len(self)
+
+        if (num_rows > max_rows):
+            # If we have a lot of rows, create a SparseDataFrame and use
+            # pandas to_string logic
+            # NOTE: this sparse DataFrame can't be used as the middle
+            # section is all NaNs. However, it gives us potentially a nice way
+            # to use the pandas IO methods.
+            # TODO - if data is indexed by time series, return top/bottom of
+            #   time series, rather than first max_rows items
+            sdf = pd.DataFrame({item: pd.SparseArray(data=head[item],
+                                                     sparse_index=
+                                                     BlockIndex(
+                                                         num_rows, [0, num_rows-tail_rows], [head_rows, tail_rows]))
+                                for item in self.columns})
+
+            # TODO - don't hard code max_rows - use pandas default/ES default
+            return sdf.to_string(max_rows=max_rows)
+
+        return head.to_string(max_rows=max_rows)

eland/mappings.py

@@ -26,7 +26,11 @@ class Mappings():
         origin_location.lat             True    text        object      True        False
 
     """
-    def __init__(self, client, index_pattern):
+    def __init__(self,
+                 client=None,
+                 index_pattern=None,
+                 mappings=None,
+                 columns=None):
         """
         Parameters
         ----------
@@ -35,29 +39,38 @@ class Mappings():
 
         index_pattern: str
             Elasticsearch index pattern
+
+        Copy constructor arguments
+
+        mappings: Mappings
+            Object to copy
+
+        columns: list of str
+            Columns to copy
         """
-        # persist index_pattern for debugging
-        self.index_pattern = index_pattern
+        if (client is not None) and (index_pattern is not None):
+            get_mapping = client.indices().get_mapping(index=index_pattern)
 
-        mappings = client.indices().get_mapping(index=index_pattern)
+            # Get all fields (including all nested) and then field_caps
+            # for these names (fields=* doesn't appear to work effectively...)
+            all_fields = Mappings._extract_fields_from_mapping(get_mapping)
+            all_fields_caps = client.field_caps(index=index_pattern, fields=list(all_fields.keys()))
 
-        # Get all fields (including all nested) and then field_caps
-        # for these names (fields=* doesn't appear to work effectively...)
-        all_fields = Mappings._extract_fields_from_mapping(mappings)
-        all_fields_caps = client.field_caps(index=index_pattern, fields=list(all_fields.keys()))
+            # Get top level (not sub-field multifield) mappings
+            source_fields = Mappings._extract_fields_from_mapping(get_mapping, source_only=True)
 
-        # Get top level (not sub-field multifield) mappings
-        source_fields = Mappings._extract_fields_from_mapping(mappings, source_only=True)
-
-        # Populate capability matrix of fields
-        # field_name, es_dtype, pd_dtype, is_searchable, is_aggregtable, is_source
-        self.mappings_capabilities = Mappings._create_capability_matrix(all_fields, source_fields, all_fields_caps)
+            # Populate capability matrix of fields
+            # field_name, es_dtype, pd_dtype, is_searchable, is_aggregtable, is_source
+            self.mappings_capabilities = Mappings._create_capability_matrix(all_fields, source_fields, all_fields_caps)
+        else:
+            # Copy object and restrict mapping columns
+            self.mappings_capabilities = mappings.mappings_capabilities.loc[columns]
 
         # Cache source field types for efficient lookup
         # (this massively improves performance of DataFrame.flatten)
         self.source_field_pd_dtypes = {}
 
-        for field_name in source_fields:
+        for field_name in self.source_fields():
             pd_dtype = self.mappings_capabilities.loc[field_name]['pd_dtype']
             self.source_field_pd_dtypes[field_name] = pd_dtype
 
@@ -265,7 +278,7 @@ class Mappings():
     def pd_dtype
     """
 
-    def is_source_field(self, field_name):
+    def source_field_pd_dtype(self, field_name):
         """
         Parameters
         ----------
@@ -287,6 +300,24 @@ class Mappings():
 
         return is_source_field, pd_dtype
 
+    def is_source_field(self, field_name):
+        """
+        Parameters
+        ----------
+        field_name: str
+
+        Returns
+        -------
+        is_source_field: bool
+            Is this field name a top-level source field?
+        """
+        is_source_field = False
+
+        if field_name in self.source_field_pd_dtypes:
+            is_source_field = True
+
+        return is_source_field
+
     def numeric_source_fields(self):
         """
         Returns
@@ -298,3 +329,21 @@ class Mappings():
                                           ((self.mappings_capabilities.pd_dtype == 'int64') |
                                            (self.mappings_capabilities.pd_dtype == 'float64'))].index.tolist()
 
+    def source_fields(self):
+        """
+        Returns
+        -------
+        source_fields: list of str
+            List of source fields
+        """
+        return self.mappings_capabilities[self.mappings_capabilities._source == True].index.tolist()
+
+    def count_source_fields(self):
+        """
+        Returns
+        -------
+        count_source_fields: int
+            Number of source fields in mapping
+        """
+        return len(self.mappings_capabilities[self.mappings_capabilities._source == True].index)
+

eland/tests/__init__.py

@@ -438,6 +438,7 @@ TEST_MAPPING1_EXPECTED = {
 }
 
 TEST_MAPPING1_EXPECTED_DF = pd.DataFrame.from_dict(data=TEST_MAPPING1_EXPECTED, orient='index', columns=['es_dtype'])
+TEST_MAPPING1_EXPECTED_SOURCE_FIELD_COUNT = len(TEST_MAPPING1_EXPECTED_DF.index) - 4
 
 TEST_NESTED_USER_GROUP_INDEX_NAME = 'nested_user_group'
 TEST_NESTED_USER_GROUP_MAPPING = {

eland/tests/client/test_mappings_pytest.py

@@ -12,11 +12,36 @@ class TestMapping():
 
     # Requires 'setup_tests.py' to be run prior to this
     def test_mapping(self):
-        mapping = ed.Mappings(ed.Client(ELASTICSEARCH_HOST), TEST_MAPPING1_INDEX_NAME)
+        mappings = ed.Mappings(ed.Client(ELASTICSEARCH_HOST), TEST_MAPPING1_INDEX_NAME)
 
-        assert mapping.all_fields() == TEST_MAPPING1_EXPECTED_DF.index.tolist()
+        assert mappings.all_fields() == TEST_MAPPING1_EXPECTED_DF.index.tolist()
 
-        assert_frame_equal(TEST_MAPPING1_EXPECTED_DF, pd.DataFrame(mapping.mappings_capabilities['es_dtype']))
+        assert_frame_equal(TEST_MAPPING1_EXPECTED_DF, pd.DataFrame(mappings.mappings_capabilities['es_dtype']))
 
+        assert mappings.count_source_fields() == TEST_MAPPING1_EXPECTED_SOURCE_FIELD_COUNT
 
+    def test_copy(self):
+        mappings = ed.Mappings(ed.Client(ELASTICSEARCH_HOST), TEST_MAPPING1_INDEX_NAME)
 
+        assert mappings.all_fields() == TEST_MAPPING1_EXPECTED_DF.index.tolist()
+        assert_frame_equal(TEST_MAPPING1_EXPECTED_DF, pd.DataFrame(mappings.mappings_capabilities['es_dtype']))
+        assert mappings.count_source_fields() == TEST_MAPPING1_EXPECTED_SOURCE_FIELD_COUNT
+
+        # Pick 1 source field
+        columns = ['dest_location']
+        mappings_copy1 = ed.Mappings(mappings=mappings, columns=columns)
+
+        assert mappings_copy1.all_fields() == columns
+        assert mappings_copy1.count_source_fields() == len(columns)
+
+        # Pick 3 source fields (out of order)
+        columns = ['dest_location', 'city', 'user_name']
+        mappings_copy2 = ed.Mappings(mappings=mappings, columns=columns)
+
+        assert mappings_copy2.all_fields() == columns
+        assert mappings_copy2.count_source_fields() == len(columns)
+
+        # Check original is still ok
+        assert mappings.all_fields() == TEST_MAPPING1_EXPECTED_DF.index.tolist()
+        assert_frame_equal(TEST_MAPPING1_EXPECTED_DF, pd.DataFrame(mappings.mappings_capabilities['es_dtype']))
+        assert mappings.count_source_fields() == TEST_MAPPING1_EXPECTED_SOURCE_FIELD_COUNT

eland/tests/frame/common.py

@@ -23,6 +23,8 @@ _pd_ecommerce['order_date'] = \
     pd.to_datetime(_pd_ecommerce['order_date'])
 _pd_ecommerce['products.created_on'] = \
     _pd_ecommerce['products.created_on'].apply(lambda x: pd.to_datetime(x))
+_pd_ecommerce.insert(2, 'customer_birth_date', None)
+_pd_ecommerce['customer_birth_date'].astype('datetime64')
 _ed_ecommerce = ed.read_es(ELASTICSEARCH_HOST, ECOMMERCE_INDEX_NAME)
 
 class TestData:

eland/tests/frame/test_indexing_pytest.py

@@ -52,3 +52,47 @@ class TestDataFrameIndexing(TestData):
         # don't match the mapping types. This is mainly because the products field is
         # nested and so can be treated as a multi-field in ES, but not in pandas
 
+    def test_size(self):
+        assert self.pd_flights().shape == self.ed_flights().shape
+        assert len(self.pd_flights()) == len(self.ed_flights())
+
+    def test_to_string(self):
+        print(self.ed_flights())
+
+    def test_get_item(self):
+        # Test 1 attribute
+        ed_carrier = self.ed_flights()['Carrier']
+
+        carrier_head = ed_carrier.head(5)
+
+        carrier_head_expected = pd.DataFrame(
+            {'Carrier':[
+                'Kibana Airlines',
+                'Logstash Airways',
+                'Logstash Airways',
+                'Kibana Airlines',
+                'Kibana Airlines'
+            ]})
+
+        assert_frame_equal(carrier_head_expected, carrier_head)
+
+        #carrier_to_string = ed_carrier.to_string()
+        #print(carrier_to_string)
+
+        # Test multiple attributes (out of order)
+        ed_3_items = self.ed_flights()['Dest','Carrier','FlightDelay']
+
+        ed_3_items_head = ed_3_items.head(5)
+
+        ed_3_items_expected = pd.DataFrame(dict(
+            Dest={0: 'Sydney Kingsford Smith International Airport', 1: 'Venice Marco Polo Airport',
+                  2: 'Venice Marco Polo Airport', 3: "Treviso-Sant'Angelo Airport",
+                  4: "Xi'an Xianyang International Airport"},
+            Carrier={0: 'Kibana Airlines', 1: 'Logstash Airways', 2: 'Logstash Airways', 3: 'Kibana Airlines',
+                     4: 'Kibana Airlines'},
+            FlightDelay={0: False, 1: False, 2: False, 3: True, 4: False}))
+
+        assert_frame_equal(ed_3_items_expected, ed_3_items_head)
+
+        #ed_3_items_to_string = ed_3_items.to_string()
+        #print(ed_3_items_to_string)

eland/utils.py

@@ -1,4 +1,4 @@
-import eland
+import eland as ed
 
 def read_es(es_params, index_pattern):
-    return eland.DataFrame(es_params, index_pattern)
+    return ed.DataFrame(client=es_params, index_pattern=index_pattern)