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import pandas as pd |
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from . import BasicNeeds as ClassBN |
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from . import TypeDetermination as ClassTD |
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from tableauhyperapi import HyperProcess, Telemetry, \ |
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Connection, CreateMode, \ |
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NOT_NULLABLE, NULLABLE, SqlType, TableDefinition, \ |
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Inserter, \ |
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TableName, \ |
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HyperException |
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View Code Duplication |
class TableauHyperApiExtraLogic: |
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def fn_build_hyper_columns_for_csv(self, detected_csv_structure, verbose): |
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list_hyper_table_columns_to_return = [] |
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for current_field_structure in detected_csv_structure: |
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list_hyper_table_columns_to_return.append(current_field_structure['order']) |
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current_column_type = self.fn_convert_to_hyper_types(current_field_structure['type']) |
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ClassBN.fn_optional_print(ClassBN, verbose, 'Column ' |
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+ str(current_field_structure['order']) + ' having name "' |
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+ current_field_structure['name'] + '" and type "' |
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+ current_field_structure['type'] + '" will become "' |
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+ str(current_column_type) + '"') |
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if current_field_structure['nulls'] == 0: |
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list_hyper_table_columns_to_return[current_field_structure['order']] = TableDefinition.Column( |
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name = current_field_structure['name'], |
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type = current_column_type, |
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nullability = NOT_NULLABLE |
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) |
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else: |
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list_hyper_table_columns_to_return[current_field_structure['order']] = TableDefinition.Column( |
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name = current_field_structure['name'], |
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type = current_column_type, |
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nullability = NULLABLE |
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) |
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return list_hyper_table_columns_to_return |
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@staticmethod |
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def fn_convert_to_hyper_types(given_type): |
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switcher = { |
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'empty': SqlType.text(), |
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'int': SqlType.big_int(), |
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'float-USA': SqlType.double(), |
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'date-iso8601': SqlType.date(), |
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'date-USA': SqlType.date(), |
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'time-24': SqlType.time(), |
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'time-24-micro-sec': SqlType.time(), |
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'time-USA': SqlType.time(), |
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'time-USA-micro-sec': SqlType.time(), |
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'datetime-iso8601': SqlType.timestamp(), |
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'datetime-iso8601-micro-sec': SqlType.timestamp(), |
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'str': SqlType.text() |
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} |
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identified_type = switcher.get(given_type) |
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if identified_type is None: |
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identified_type = SqlType.text() |
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return identified_type |
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def fn_create_hyper_file_from_csv(self, input_csv_data_frame, formats_to_evaluate, output_hyper_file, verbose): |
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detected_csv_structure = ClassTD.fn_detect_csv_structure(ClassTD, input_csv_data_frame, |
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formats_to_evaluate, verbose) |
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hyper_table_columns = self.fn_build_hyper_columns_for_csv(self, detected_csv_structure, verbose) |
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# Starts the Hyper Process with telemetry enabled/disabled to send data to Tableau or not |
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# To opt in, simply set telemetry=Telemetry.SEND_USAGE_DATA_TO_TABLEAU. |
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# To opt out, simply set telemetry=Telemetry.DO_NOT_SEND_USAGE_DATA_TO_TABLEAU. |
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with HyperProcess(telemetry = Telemetry.DO_NOT_SEND_USAGE_DATA_TO_TABLEAU) as hyper: |
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# Creates new Hyper file <output_hyper_file> |
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# Replaces file with CreateMode.CREATE_AND_REPLACE if it already exists. |
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with Connection(endpoint = hyper.endpoint, |
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database = output_hyper_file, |
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create_mode = CreateMode.CREATE_AND_REPLACE) as hyper_connection: |
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print(f'Connection to the Hyper engine file "{output_hyper_file}" has been created.') |
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hyper_connection.catalog.create_schema("Extract") |
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print('Hyper schema "Extract" has been created.') |
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hyper_table = TableDefinition( |
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TableName("Extract", "Extract"), |
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columns = hyper_table_columns |
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) |
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hyper_connection.catalog.create_table(table_definition = hyper_table) |
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print('Hyper table "Extract" has been created.') |
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# The rows to insert into the <hyper_table> table. |
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data_to_insert = self.fn_rebuild_csv_content_for_hyper(self, input_csv_data_frame, |
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detected_csv_structure, verbose) |
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# Execute the actual insert |
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with Inserter(hyper_connection, hyper_table) as hyper_inserter: |
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hyper_inserter.add_rows(rows = data_to_insert) |
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hyper_inserter.execute() |
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# Number of rows in the <hyper_table> table. |
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# `execute_scalar_query` is for executing a query that returns exactly one row with one column. |
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row_count = hyper_connection.\ |
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execute_scalar_query(query = f'SELECT COUNT(*) FROM {hyper_table.table_name}') |
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print(f'Number of rows in table {hyper_table.table_name} is {row_count}.') |
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print('Connection to the Hyper engine file has been closed.') |
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print('Hyper engine process has been shut down.') |
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def fn_rebuild_csv_content_for_hyper(self, input_csv_data_frame, detected_fields_type, verbose): |
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input_csv_data_frame.replace(to_replace = [pd.np.nan], value = [None], inplace = True) |
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# Cycle through all found columns |
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for current_field in detected_fields_type: |
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fld_nm = current_field['name'] |
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ClassBN.fn_optional_print(ClassBN, verbose, 'Column ' + fld_nm + ' ' |
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+ 'has panda_type = ' + str(current_field['panda_type']) + ' ' |
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+ 'and ' + str(current_field['type'])) |
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if current_field['panda_type'] == 'float64' and current_field['type'] == 'int': |
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input_csv_data_frame[fld_nm] = input_csv_data_frame[fld_nm].replace(to_replace = [pd.np.nan, '.0'], |
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value = [None, ''], |
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inplace = True) |
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elif current_field['type'] in ('datetime-iso8601', 'datetime-iso8601-micro-sec'): |
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input_csv_data_frame[fld_nm] = pd.to_datetime(input_csv_data_frame[fld_nm]) |
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return input_csv_data_frame.values |
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def fn_run_hyper_creation(self, input_csv_data_frame, formats_to_evaluate, output_hyper_file, verbose): |
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try: |
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self.fn_create_hyper_file_from_csv(self, input_csv_data_frame, formats_to_evaluate, output_hyper_file, |
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verbose) |
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except HyperException as ex: |
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print(ex) |
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exit(1) |
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danielgp /
tableau-hyper-management
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The coding style of this project requires that you add a docstring to this code element. Below, you find an example for methods:
If you would like to know more about docstrings, we recommend to read PEP-257: Docstring Conventions.