Cloudy Warehouses allows for stream-lined interaction between pandas and cloud data platform providers.

# 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:

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

profiles:
snowflake:
acct: <your snowflake account>
role: <your snowflake role>
warehouse: <your snowflake warehouse>
database: <your snowflake database>
schema: <your snowflake schema>


The user, pass, acct, database, schema, values all should be filled in with your desired Snowflake credentials and connection details. The variables in this file serve as default arguments when calling a cloudy_warehouses method. Role and warehouse can be filled in as well, but they are optional arguments when connecting to Snowflake.

## 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.

### Optional Arguments

There are two methods for passing optional arguments into a method.

1. The configuration file
2. Directly pass in the arguments when calling the method

The variables saved in the configuration file serve as default arguments for the methods to use. However, you tell the method to use different credentials by passing in arguments directly. The method will use the passed in arguments instead of the default arguments saved in configuration_profiles.yml.

For example, if I had the database variable saved in the configuration_profiles.yml as database_1, but passed in database = database_2 directly into the method, the method would use database_2 instead of database_1.

However, if I choose to not directly pass a database argument in, the method will use the database_1 because it is the default. The passed in arguments take priority over the default variables saved in configuration_profiles.yml.

### Reading a from a Snowflake table

pandas.read_snowflake(table: str,
username: str = None,
password: str = None,
account: str = None,
database: str = None,
schema: str = None,
role: str = None,
warehouse: str = None
)


This method reads from a Snowflake table and returns the data in said tables as a pandas DataFrame.

### Listing all tables in a Snowflake database

pandas.list_snowflake_tables(database: str = None,
username: str = None,
password: str = None,
account: str = None,
role: str = None,
warehouse: str = None
)


This method returns all of the tables in a Snowflake database as a pandas DataFrame.

### Writing to a Snowflake table

pandas.DataFrame.cloudy_warehouses.write_snowflake(table: str,
database: str = None,
schema: str = None,
username: str = None,
password: str = None,
account: str = None,
role: str = None,
warehouse: str = None,
overwrite: bool = False
)


This method writes to a Snowflake table and informs you on success. This method works when writing to either an existing Snowflake table or a previously non-existing Snowflake table. If the table that you provide does not exist, this method creates a new Snowflake table and writes to it. If the table already exists, the DataFrame data is appended to the existing table by default. If you would like to replace the table with the pandas DataFrame set overwrite = True when calling the method.

### Creating a clone of an existing Snowflake table

pandas.clone_snowflake(new_table: str,
source_table: str,
source_schema: str = None,
source_database: str = None,
database: str = None,
schema: str = None,
username: str = None,
password: str = None,
account: str = None,
role: str = None,
warehouse: 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.

### Creating an empty clone of an existing Snowflake table

pandas.clone_empty_snowflake(new_table: str,
source_table: str,
source_database: str = None,
source_schema: str = None,
database: str = None,
schema: str = None,
username: str = None,
password: str = None,
account: str = None,
role: str = None,
warehouse: 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.

## Examples

Reading, Writing, and Listing (using configuration file)

import pandas as pd
import cloudy_warehouses

pd.list_snowflake_tables(role='SNOWFLAKE_ROLE')

df_to_write = pd.DataFrame.from_dict({'a': [1, 2, 3], 'b': [2, 3, 5]})

df_to_write.cloudy_warehouses.write_snowflake(table='SNOWFLAKE_TABLE', role='SNOWFLAKE_ROLE')



The methods called in this example use the database, schema, warehouse, and account credentials stored in configuration_profiles.yml. However, the role is directly passed in and therefore overwrites the default role saved in the configuraiton file.

Cloning and Empty Cloning (using configuration file)

import pandas as pd
import cloudy_warehouses

df = pd.DataFrame.from_dict({'COL_1': ['hello', 'there'], 'COL_2': [10, 20], 'COL_3': [10, 20]})

pd.clone_snowflake(
new_table='SNOWFLAKE_TABLE',
source_table='SNOWFLAKE_TABLE_TO_BE_CLONED',
source_schema='SNOWFLAKE_SCHEMA_THAT_HOLDS_THE_SOURCE_TABLE'
)

pd.clone_empty_snowflake(
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'
)


The methods called in this example use the database, schema, warehouse, role and account credentials stored in configuration_profiles.yml. The newly cloned table will be saved in the default database and schema in the configuration file.

Reading, Writing, and Listing (using optional Snowflake credentials arguments)

import pandas as pd
import cloudy_warehouses

pd.list_snowflake_tables(
database='SNOWFLAKE_DATABASE',
account='my_snowflake_account',
warehouse='my_snowflake_warehouse',
role='my_snowflake_role'
)

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',
account='my_snowflake_account',
overwrite=True
)

schema='SNOWFLAKE_SCHEMA',
table='SNOWFLAKE_TABLE',
account='my_snowflake_account',
role='my_snowflake_role'
)


The arguments passed in here will be used instead of the default variables saved in the configuration file. For example, in last method called, the schema, username, password, account, and role are all passed in. However, this method will use the default database variable because there is not one directly passed in.

When the write_snowflake method is called in the above example, overwrite=True. This means that the Snowflake table being uploaded to will replace any existing data with the pandas DataFrame data.

Cloning and Empty Cloning (using optional Snowflake credentials arguments)

import pandas as pd
import cloudy_warehouses

df = pd.DataFrame.from_dict({'COL_1': ['hello', 'there'], 'COL_2': [10, 20], 'COL_3': [10, 20]})

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',
account='my_snowflake_account',
warehouse='my_snowflake_warehouse',
role='my_snowflake_role'
)

pd.clone_empty_snowflake(
database='SNOWFLAKE_DATABASE',
schema='SNOWFLAKE_SCHEMA',
new_table='SNOWFLAKE_TABLE',
source_table='SNOWFLAKE_TABLE_TO_BE_CLONED',
account='my_snowflake_account'
)


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