cloudy-sql 0.0.0.4
A Jupyter magics extension designed for seamless interaction with Snowflake
Requires Python: >=3.7

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# cloudy_sql

## Snowflake Support
The cloudy_sql is an Ipython Magics library and pandas extension that currently supports: 
- a Magics function that allows users to easily execute SQL Queries in Snowflake
- writing to an existing Snowflake table from a pandas dataframe
- creating a new Snowflake table from a pandas dataframe
## How To Use cloudy_sql

**Installation**

`pip install cloudy-sql`

**Configuration** 

Upon installation, open a jupyter notebook and run the following cell.
```python
%load_ext cloudy_sql
```
After you run the cell, a configuration file will be created in your HOME directory.
The path to the configuration file is: `$HOME/.cloudy_sql/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>
    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_sql 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 and IPython Magics APIs.

### 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`.
### IPython Magics
#### %%sql_to_snowflake
IPython Cell Magic to seamlessly connect to Snowflake and run a query in Snowflake and return a pandas DataFrame as the result.
```
%%sql_to_snowflake [<destination_var>] [--username <snowflake_username>]
                   [--password <snowflake_password>] [--account <snowflake_account>]
                   [--role <snowflake_role>] [--warehouse <snowflake_warehouse>]
<SQL query>
```
#### Parameters
    * <destination_var> (Optional [IPython line argument]): 
        Variable to store the query results. If none is given, the magic will return
        the first 10 rows of the pandas DataFrame if applicable.

    * --params <params> (Optional [IPython line argument]):
        Parameters to be used in the SQL Query. Params must be passed in as a 
        dictionary string in the format {"param_name": "param_value"} or reference a 
        dictionary string defined in a previous cell. The use of the parameter in the 
        query should be indicated with {{param_name}}.

    * --username <username> (Optional [IPython line argument]):
        If provided, the called method will connect to Snowflake with this username 
        instead of the default in the configuration file.
    
    * --password <password> (Optional [IPython line argument]):
        If provided, the called method will connect to Snowflake with this password
        instead of the default in the configuration file.

    * --account <account> (Optional [IPython line argument]):
        If provided, the called method will connect to Snowflake with this account 
        instead of the default in the configuration file.

    * --role <role> (Optional [IPython line argument]):
        If provided, the called method will connect to Snowflake with this role 
        instead of the default in the configuration file.

    * --warehouse <warehouse> (Optional [IPython line argument]):
        If provided, the called method will use this warehouse instead of the 
        default in the configuration file.

### write_snowflake
```python
pd.DataFrame.cloudy_sql.write_snowflake(table: str, 
                                        database: str = None, 
                                        schema: str = None, 
                                        overwrite: bool = False, 
                                        username: str = None,
                                        password: str = None, 
                                        account: str = None, 
                                        role: str = None, 
                                        warehouse: str = None
                                       )
```
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. You can configure your `database` and `schema` default values in the configuration file. You can also pass them in directly when calling the method.
The passed in values are used instead of the defaults in the configuration file.

The goal of this method is to be used in tandem with `%%sql_to_snowflake`. You use the magic function to run your SQL query
that returns a pandas DataFrame. Then, you can transform the DataFrame and write the DataFrame to a Snowflake using this method.


## Examples

__Using `%%sql_to_snowflake` magic and `write_snowflake` method__
```
In [1]:  %load_ext cloudy_sql

In [2]:  %%sql_to_snowflake df 
         SELECT * from db.schema.table
         
         Query successfully ran and results were stored to the 'df' destination variable.

In [3]:  df.head()
Out[3]:  
         CUSTOMER_ID	FIRST_NAME	LAST_NAME	FIRST_ORDER_DATE	MOST_RECENT_ORDER_DATE	NUMBER_OF_ORDERS
     0   	   1	   Michael	       P.	      2018-01-01	            2018-02-10	               2
     1   	   2	     Shawn	       M.	      2018-01-11	            2018-01-11	               1
     2   	   3	  Kathleen	       P.	      2018-01-02	            2018-03-11	               3
     3   	   4	     Jimmy	       C.	            None	                  None	               0
     4   	   5	 Katherine	       R.	            None	                  None	               0
     
In [4]: df.cloudy_sql.write_snowflake(table="test_cloudy_sql")

        Successfully wrote to the test_cloudy_sql Snowflake table

In [5]: %%sql_to_snowflake
        drop table if exists db.schema.test_cloudy_sql
        
        Successfully ran SQL Query in Snowflake
        
In [6]: %close_connection
```
The above example runs a SQL query with `%%sql_to_snowflake` and saves the results as a pandas DataFrame by passing
in the destination variable `df`. The example then shows how to easily write that `df` to a Snowflake table `In [4]`. Also, the created table is dropped `In [5]`.
The connection is then closed by calling the `%close_connection` magic `In [6]`.

__Using `%%sql_to_snowflake` magic with the `--params` inline argument__
```
In [1]:  %load_ext cloudy_sql

In [2]:  parameters = {'firstname': 'Michael', 'orders': '2'}
         
In [3]:  %%sql_to_snowflake --params $parameters
         SELECT * from db.schema.table
         WHERE FIRST_NAME = {{firstname}} and NUMBER_OF_ORDERS = {{orders}}
Out[3]:  
         CUSTOMER_ID	FIRST_NAME	LAST_NAME	FIRST_ORDER_DATE	MOST_RECENT_ORDER_DATE	NUMBER_OF_ORDERS
     0   	   1	   Michael	       P.	      2018-01-01	            2018-02-10	               2
     
```
The above example runs a SQL query with passed-in variables. The variables are used directly in the SQL query by placing each one inside `{{  }}`. A dictionary string `parameters` is passed in when the 
magic is called by including a `--params` inline argument and placing a `$` to reference the dictionary string creating in the previous cell. There is no specified `destination variable`. Therefore, the magic prints out the resulting pandas dataframe.

__Using `%%sql_to_snowflake` magic with the `--params` inline argument__
```
In [1]:  %load_ext cloudy_sql
         
In [2]:  %%sql_to_snowflake --params {'firstname': 'Michael', 'orders': '2'}
         SELECT * from db.schema.table
         WHERE FIRST_NAME = {{firstname}} and NUMBER_OF_ORDERS = {{orders}}
Out[2]:  
         CUSTOMER_ID	FIRST_NAME	LAST_NAME	FIRST_ORDER_DATE	MOST_RECENT_ORDER_DATE	NUMBER_OF_ORDERS
     0   	   1	   Michael	       P.	      2018-01-01	            2018-02-10	               2
     
```
This example directly passes in the dictionary string when calling the `--params` inline argument.