When appending to a table, load if the dataframe contains a subset of…

… the existing schema (googleapis#24)

* Improvements discused in PR conversation

Accidentally left a duplicate test in

Correcting change to schema made by auto-rebase

Fixing missing assertTrue and reversion to not checking subset on append (both from rebase)

Replacing AssertEqual

Shortening line to pass flake

* Making updates per jreback's requested changes

* Fixing trailing whitespace

* Adding detail to changelog

* Use wait_for_job rather than sleep

* Revert "Use wait_for_job rather than sleep"

This reverts commit 8726a01.

* Minor tweaks before merging

* Update the to_gbq doc-string as suggested by @jreback

* Make travis happy

docs/source/changelog.rst

@@ -6,6 +6,8 @@ Changelog
 
 - Resolve issue where the optional ``--noauth_local_webserver`` command line argument would not be propagated during the authentication process. (:issue:`35`)
 - Drop support for Python 3.4 (:issue:`40`)
+- The dataframe passed to ```.to_gbq(...., if_exists='append')``` needs to contain only a subset of the fields in the BigQuery schema. (:issue:`24`)
+
 
 0.1.6 / 2017-05-03
 ------------------

docs/source/writing.rst

@@ -40,7 +40,7 @@ a ``TableCreationError`` if the destination table already exists.
 
    If the ``if_exists`` argument is set to ``'append'``, the destination dataframe will
    be written to the table using the defined table schema and column types. The
-   dataframe must match the destination table in structure and data types.
+   dataframe must contain fields (matching name and type) currently in the destination table.
    If the ``if_exists`` argument is set to ``'replace'``, and the existing table has a
    different schema, a delay of 2 minutes will be forced to ensure that the new schema
    has propagated in the Google environment. See

pandas_gbq/gbq.py

@@ -557,7 +557,25 @@ def load_data(self, dataframe, dataset_id, table_id, chunksize):
 
         self._print("\n")
 
-    def verify_schema(self, dataset_id, table_id, schema):
+    def schema(self, dataset_id, table_id):
+        """Retrieve the schema of the table
+
+        Obtain from BigQuery the field names and field types
+        for the table defined by the parameters
+
+        Parameters
+        ----------
+        dataset_id : str
+            Name of the BigQuery dataset for the table
+        table_id : str
+            Name of the BigQuery table
+
+        Returns
+        -------
+        list of dicts
+            Fields representing the schema
+        """
+
         try:
             from googleapiclient.errors import HttpError
         except:
@@ -573,15 +591,67 @@ def verify_schema(self, dataset_id, table_id, schema):
                               'type': field_remote['type']}
                              for field_remote in remote_schema['fields']]
 
-            fields_remote = set([json.dumps(field_remote)
-                                 for field_remote in remote_fields])
-            fields_local = set(json.dumps(field_local)
-                               for field_local in schema['fields'])
-
-            return fields_remote == fields_local
+            return remote_fields
         except HttpError as ex:
             self.process_http_error(ex)
 
+    def verify_schema(self, dataset_id, table_id, schema):
+        """Indicate whether schemas match exactly
+
+        Compare the BigQuery table identified in the parameters with
+        the schema passed in and indicate whether all fields in the former
+        are present in the latter. Order is not considered.
+
+        Parameters
+        ----------
+        dataset_id :str
+            Name of the BigQuery dataset for the table
+        table_id : str
+            Name of the BigQuery table
+        schema : list(dict)
+            Schema for comparison. Each item should have
+            a 'name' and a 'type'
+
+        Returns
+        -------
+        bool
+            Whether the schemas match
+        """
+
+        fields_remote = sorted(self.schema(dataset_id, table_id),
+                               key=lambda x: x['name'])
+        fields_local = sorted(schema['fields'], key=lambda x: x['name'])
+
+        return fields_remote == fields_local
+
+    def schema_is_subset(self, dataset_id, table_id, schema):
+        """Indicate whether the schema to be uploaded is a subset
+
+        Compare the BigQuery table identified in the parameters with
+        the schema passed in and indicate whether a subset of the fields in
+        the former are present in the latter. Order is not considered.
+
+        Parameters
+        ----------
+        dataset_id : str
+            Name of the BigQuery dataset for the table
+        table_id : str
+            Name of the BigQuery table
+        schema : list(dict)
+            Schema for comparison. Each item should have
+            a 'name' and a 'type'
+
+        Returns
+        -------
+        bool
+            Whether the passed schema is a subset
+        """
+
+        fields_remote = self.schema(dataset_id, table_id)
+        fields_local = schema['fields']
+
+        return all(field in fields_remote for field in fields_local)
+
     def delete_and_recreate_table(self, dataset_id, table_id, table_schema):
         delay = 0
 
@@ -810,7 +880,9 @@ def to_gbq(dataframe, destination_table, project_id, chunksize=10000,
     if_exists : {'fail', 'replace', 'append'}, default 'fail'
         'fail': If table exists, do nothing.
         'replace': If table exists, drop it, recreate it, and insert data.
-        'append': If table exists, insert data. Create if does not exist.
+        'append': If table exists and the dataframe schema is a subset of
+        the destination table schema, insert data. Create destination table
+        if does not exist.
     private_key : str (optional)
         Service account private key in JSON format. Can be file path
         or string contents. This is useful for remote server
@@ -844,7 +916,9 @@ def to_gbq(dataframe, destination_table, project_id, chunksize=10000,
             connector.delete_and_recreate_table(
                 dataset_id, table_id, table_schema)
         elif if_exists == 'append':
-            if not connector.verify_schema(dataset_id, table_id, table_schema):
+            if not connector.schema_is_subset(dataset_id,
+                                              table_id,
+                                              table_schema):
                 raise InvalidSchema("Please verify that the structure and "
                                     "data types in the DataFrame match the "
                                     "schema of the destination table.")

pandas_gbq/tests/test_gbq.py

@@ -1071,6 +1071,31 @@ def test_upload_data_if_table_exists_append(self):
                        _get_project_id(), if_exists='append',
                        private_key=_get_private_key_path())
 
+    def test_upload_subset_columns_if_table_exists_append(self):
+        # Issue 24: Upload is succesful if dataframe has columns
+        # which are a subset of the current schema
+        test_id = "16"
+        test_size = 10
+        df = make_mixed_dataframe_v2(test_size)
+        df_subset_cols = df.iloc[:, :2]
+
+        # Initialize table with sample data
+        gbq.to_gbq(df, self.destination_table + test_id, _get_project_id(),
+                   chunksize=10000, private_key=_get_private_key_path())
+
+        # Test the if_exists parameter with value 'append'
+        gbq.to_gbq(df_subset_cols,
+                   self.destination_table + test_id, _get_project_id(),
+                   if_exists='append', private_key=_get_private_key_path())
+
+        sleep(30)  # <- Curses Google!!!
+
+        result = gbq.read_gbq("SELECT COUNT(*) AS num_rows FROM {0}"
+                              .format(self.destination_table + test_id),
+                              project_id=_get_project_id(),
+                              private_key=_get_private_key_path())
+        assert result['num_rows'][0] == test_size * 2
+
     def test_upload_data_if_table_exists_replace(self):
         test_id = "4"
         test_size = 10
@@ -1258,6 +1283,66 @@ def test_verify_schema_ignores_field_mode(self):
         assert self.sut.verify_schema(
             self.dataset_prefix + "1", TABLE_ID + test_id, test_schema_2)
 
+    def test_retrieve_schema(self):
+        # Issue #24 schema function returns the schema in biquery
+        test_id = "15"
+        test_schema = {'fields': [{'name': 'A', 'type': 'FLOAT'},
+                                  {'name': 'B', 'type': 'FLOAT'},
+                                  {'name': 'C', 'type': 'STRING'},
+                                  {'name': 'D', 'type': 'TIMESTAMP'}]}
+
+        self.table.create(TABLE_ID + test_id, test_schema)
+        actual = self.sut.schema(self.dataset_prefix + "1", TABLE_ID + test_id)
+        expected = test_schema['fields']
+        assert expected == actual, 'Expected schema used to create table'
+
+    def test_schema_is_subset_passes_if_subset(self):
+        # Issue #24 schema_is_subset indicates whether the schema of the
+        # dataframe is a subset of the schema of the bigquery table
+        test_id = '16'
+
+        table_name = TABLE_ID + test_id
+        dataset = self.dataset_prefix + '1'
+
+        table_schema = {'fields': [{'name': 'A',
+                                    'type': 'FLOAT'},
+                                   {'name': 'B',
+                                    'type': 'FLOAT'},
+                                   {'name': 'C',
+                                    'type': 'STRING'}]}
+        tested_schema = {'fields': [{'name': 'A',
+                                     'type': 'FLOAT'},
+                                    {'name': 'B',
+                                     'type': 'FLOAT'}]}
+
+        self.table.create(table_name, table_schema)
+
+        assert self.sut.schema_is_subset(
+            dataset, table_name, tested_schema) is True
+
+    def test_schema_is_subset_fails_if_not_subset(self):
+        # For pull request #24
+        test_id = '17'
+
+        table_name = TABLE_ID + test_id
+        dataset = self.dataset_prefix + '1'
+
+        table_schema = {'fields': [{'name': 'A',
+                                    'type': 'FLOAT'},
+                                   {'name': 'B',
+                                    'type': 'FLOAT'},
+                                   {'name': 'C',
+                                    'type': 'STRING'}]}
+        tested_schema = {'fields': [{'name': 'A',
+                                     'type': 'FLOAT'},
+                                    {'name': 'C',
+                                     'type': 'FLOAT'}]}
+
+        self.table.create(table_name, table_schema)
+
+        assert self.sut.schema_is_subset(
+            dataset, table_name, tested_schema) is False
+
     def test_list_dataset(self):
         dataset_id = self.dataset_prefix + "1"
         assert dataset_id in self.dataset.datasets()