Metadata-Version: 2.1
Name: cloudy_warehouses
Version: 0.0.3.6
Summary: DO NOT USE - This is a sample program
Home-page: https://github.com/hashmapinc/sales-and-marketing/pocs/sam-kohlleffel-poc/pandas-cloudy-extension
Author: Hashmap, Inc
Author-email: accelerators@hashmapinc.com
License: UNKNOWN
Description: <!---
        # Modifications © 2020 Hashmap, Inc
        #
        # Licensed under the Apache License, Version 2.0 (the "License");
        # you may not use this file except in compliance with the License.
        # You may obtain a copy of the License at
        #
        #     http://www.apache.org/licenses/LICENSE-2.0
        #
        # Unless required by applicable law or agreed to in writing, software
        # distributed under the License is distributed on an "AS IS" BASIS,
        # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
        # See the License for the specific language governing permissions and
        # limitations under the License.
        -->
        # cloudy_warehouses
        
        Extension to pandas to allow for simplified interactions with cloud-based data-platforms.
        
        ## Snowflake Support
        The cloudy_warehouses pandas extension currently supports: 
        - reading from a Snowflake table
        - writing to an existing Snowflake table from a pandas dataframe
        - creating a new Snowflake table from a pandas dataframe
        - creating a clone of an existing Snowflake table
        - creating an empty clone of an existing Snowflake table (clones columns, not column data)
        - listing Snowflake tables in a database
        ## How To Use cloudy_warehouses
        
        **Installation**
        
        `pip install cloudy_warehouses`
        
        **Configuration** 
        
        Upon installation, create an empty `.py` file. Then, configure the python file in the following way:
        ```python
        import pandas as pd 
        import cloudy_warehouses
        
        ```
        Run this empty file. After you run the file, a configuration file will be created in your HOME directory.
        The path to the configuration file is: `$HOME/.cloudy_warehouses/configuration_profiles.yml`
        
        For Windows user: use $USERPROFILE instead of $HOME variable
        
        The configuration file is a YAML file with the following format
        ```yaml
        profiles:
          snowflake:
            user: <your snowflake username>
            pass: <your snowflake password>
            acct: <your snowflake account>
        ```
        The user, pass, and acct values all need to be filled in with your Snowflake credentials.
        
        
        
        ## API
        The intent has been to keep the API as simple as possible by minimally extending the pandas API and supporting, for the most part, the same functionality.
        
        ### Reading a from a Snowflake table
        ```python
        pandas.read_snowflake(database: str, 
                              schema: str, 
                              table: str, 
                              sf_username: str = None, 
                              sf_password: str = None, 
                              sf_account: str = None
                              )
        ```
        This method reads from a Snowflake table and returns the data in said tables as a pandas DataFrame. This method uses your Snowflake credentials stored in the configuration file.
        However, you can pass optional arguments (sf_username, sf_password, sf_account) to connect to Snowflake as well.
        
        ### Listing all tables in a Snowflake database
        ```python
        pandas.list_snowflake_tables(database: str, 
                                     sf_username: str = None, 
                                     sf_password: str = None, 
                                     sf_account: str = None
                                     )
        ```
        This method returns all of the tables in a Snowflake database as a pandas DataFrame. 
        There is an option to pass arguments (sf_username, sf_password, sf_account) to connect to Snowflake.
        
        ### Writing to an existing Snowflake table
        ```python
        pandas.DataFrame.cloudy_warehouses.write_snowflake(database: str,
                                                           schema: str, 
                                                           table: str, 
                                                           sf_username: str=None, 
                                                           sf_password: str=None, 
                                                           sf_account: str=None
                                                           )
        ```
        This method writes to an existing Snowflake table and informs you on success. Like the read_snowflake method, write_snowflake uses your Snowflake credentials stored in the configuration file.
        However, you can pass optional arguments (sf_username, sf_password, sf_account) to connect to Snowflake as well.
        
        ### Creating a new Snowflake table from a pandas dataframe
        ```python
        pandas.DataFrame.cloudy_warehouses.create_snowflake(database: str,
                                                            schema: str, 
                                                            table: str, 
                                                            sf_username: str=None, 
                                                            sf_password: str=None, 
                                                            sf_account: str=None
                                                            )
        ```
        This method creates a new Snowflake table from a pandas dataframe. It uses the column names of the pandas dataframe as the columns in the 
        new Snowflake table. It then writes the data of the pandas dataframe to the newly created Snowflake table. You can use the configuration file or pass optional arguments (sf_username, sf_password, sf_account) to connect to Snowflake.
        
        ### Creating a clone of an existing Snowflake table
        ```python
        pandas.clone_snowflake(database: str,
                               schema: str,
                               new_table: str,
                               source_table: str,
                               source_schema: str = None,
                               source_database: str = None,
                               sf_username: str = None,
                               sf_password: str = None,
                               sf_account: str = None
                               )           
        ```
        This method creates a clone of an existing Snowflake table. The `new_table` variable is the new table that will
        be created after the method is called. The `source_table` variable is the existing Snowflake table that is being cloned.
        The optional `source_database` and `source_schema` variables are the database and schema in which the `source_table` resides.
        If you plan to clone an existing table from the schema and database that the `new_table` will reside in, you do not need to 
        include `source_database` and `source_schema` variables.
        You can use the configuration file or pass optional arguments (sf_username, sf_password, sf_account) to connect to Snowflake.
        
        ### Creating an empty clone of an existing Snowflake table
        ```python
        pandas.clone_empty_snowflake(database: str,
                                     schema: str,
                                     new_table: str,
                                     source_table: str,
                                     source_schema: str = None,
                                     source_database: str = None,
                                     sf_username: str = None,
                                     sf_password: str = None,
                                     sf_account: str = None
                                     )           
        ```
        This method creates an empty clone of an existing Snowflake table. This means that of the columns from the `source_table`
        are copied into the `new_table`, but the `new_table` does not have any data within its columns. The `new_table` variable is the new table that will
        be created after the method is called. The `source_table` variable is the existing Snowflake table that is being cloned. 
        The optional `source_database` and `source_schema` variables are the database and schema in which the `source_table` resides.
        If you plan to clone an existing table from the schema and database that the `new_table` will reside in, you do not need to 
        include `source_database` and `source_schema` variables.
        You can use the configuration file or pass optional arguments (sf_username, sf_password, sf_account) to connect to Snowflake.
        
        ## Examples
        
        __Reading, Writing, and Listing (using configuration file)__
        ```python
        import pandas as pd 
        import cloudy_warehouses
        
        pd.list_snowflake_tables(database='SNOWFLAKE_DATABASE')
        
        df_to_write = pd.DataFrame.from_dict({'a': [1, 2, 3], 'b': [2, 3, 5]})
        
        df_to_write.cloudy_warehouses.write_snowflake(
            database='SNOWFLAKE_DATABASE', 
            schema='SNOWFLAKE_SCHEMA', 
            table='SNOWFLAKE_TABLE'
            )
        
        df_read_from_snowflake = pd.read_snowflake(
            database='SNOWFLAKE_DATABASE', 
            schema='SNOWFLAKE_SCHEMA', 
            table='SNOWFLAKE_TABLE'
            )
        ```
        
        __Creating, Cloning, and Empty Cloning (using configuration file)__
        ```python
        import pandas as pd 
        import cloudy_warehouses
        
        df = pd.DataFrame.from_dict({'COL_1': ['hello', 'there'], 'COL_2': [10, 20], 'COL_3': [10, 20]})
        
        df.cloudy_warehouses.create_snowflake(
            database='SNOWFLAKE_DATABASE', 
            schema='SNOWFLAKE_SCHEMA', 
            table='SNOWFLAKE_TABLE'
            )
        
        pd.clone_snowflake(
            database='SNOWFLAKE_DATABASE', 
            schema='SNOWFLAKE_SCHEMA',
            new_table='SNOWFLAKE_TABLE',
            source_table='SNOWFLAKE_TABLE_TO_BE_CLONED',
            source_schema='SNOWFLAKE_SCHEMA_THAT_HOLDS_THE_SOURCE_TABLE'
            )
        
        pd.clone_empty_snowflake(
            database='SNOWFLAKE_DATABASE', 
            schema='SNOWFLAKE_SCHEMA',
            new_table='SNOWFLAKE_TABLE',
            source_table='SNOWFLAKE_TABLE_TO_BE_CLONED',
            source_schema='SNOWFLAKE_SCHEMA_THAT_HOLDS_THE_SOURCE_TABLE',
            source_database='SNOWFLAKE_DATABASE_THAT_HOLDS_THE_SOURCE_SCHEMA'
            )
        ```
        
        __Reading, Writing, and Listing (using optional Snowflake credentials arguments)__
        ```python
        import pandas as pd
        import cloudy_warehouses
        
        pd.list_snowflake_tables(
            database='SNOWFLAKE_DATABASE', 
            sf_username='my_snowflake_username', 
            sf_password='my_snowflake_password', 
            sf_account='my_snowflake_account'
            )
        
        df_to_write = pd.DataFrame.from_dict({'a': [1, 2, 3], 'b': [2, 3, 5]})
        
        df_to_write.cloudy_warehouses.write_snowflake(
            database='SNOWFLAKE_DATABASE', 
            schema='SNOWFLAKE_SCHEMA', 
            table='SNOWFLAKE_TABLE', 
            sf_username='my_snowflake_username', 
            sf_password='my_snowflake_password', 
            sf_account='my_snowflake_account'
            )
        
        df_read_from_snowflake = pd.read_snowflake(
            database='SNOWFLAKE_DATABASE', 
            schema='SNOWFLAKE_SCHEMA', 
            table='SNOWFLAKE_TABLE', 
            sf_username='my_snowflake_username', 
            sf_password='my_snowflake_password', 
            sf_account='my_snowflake_account'
            )
        ```
        
        __Creating, Cloning, and Empty Cloning (using optional Snowflake credentials arguments)__
        ```python
        import pandas as pd 
        import cloudy_warehouses
        
        df = pd.DataFrame.from_dict({'COL_1': ['hello', 'there'], 'COL_2': [10, 20], 'COL_3': [10, 20]})
        
        df.cloudy_warehouses.create_snowflake(
            database='SNOWFLAKE_DATABASE', 
            schema='SNOWFLAKE_SCHEMA', 
            table='SNOWFLAKE_TABLE',
            sf_username='my_snowflake_username', 
            sf_password='my_snowflake_password', 
            sf_account='my_snowflake_account'
            )
        
        pd.clone_snowflake(
            database='SNOWFLAKE_DATABASE', 
            schema='SNOWFLAKE_SCHEMA',
            new_table='SNOWFLAKE_TABLE',
            source_table='SNOWFLAKE_TABLE_TO_BE_CLONED',
            source_schema='SNOWFLAKE_SCHEMA_THAT_HOLDS_THE_SOURCE_TABLE',
            source_database='SNOWFLAKE_DATABASE_THAT_HOLDS_THE_SOURCE_SCHEMA',
            sf_username='my_snowflake_username', 
            sf_password='my_snowflake_password', 
            sf_account='my_snowflake_account'
            )
        
        pd.clone_empty_snowflake(
            database='SNOWFLAKE_DATABASE', 
            schema='SNOWFLAKE_SCHEMA',
            new_table='SNOWFLAKE_TABLE',
            source_table='SNOWFLAKE_TABLE_TO_BE_CLONED',
            sf_username='my_snowflake_username', 
            sf_password='my_snowflake_password', 
            sf_account='my_snowflake_account'
            )
        ```
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Development Status :: 3 - Alpha
Requires-Python: >=3.7
Description-Content-Type: text/markdown
