pycof 1.1.10

A package for commonly used functions

PYCOF (PYthon COmmon Functions)

1. Installation

You can get pycof from PyPI with:

pip install pycof

The library is supported on Windows, Linux and MacOs.

2. Usage

2.1 Config file for credentials

2.1.1 Save your credentials locally

The function remote_execute_sql will by default look for the credentials located in /etc/config.json. On Windows, save the config file as C:/Windows/config.json.

The file follows the below structure:

{
	"DB_USER": "",
	"DB_PASSWORD": "",
	"DB_HOST": "",
	"DB_PORT": "",
	"DB_DATABASE": "",
	"__COMMENT_1__": "__ IAM specific, if useIAM=True __",
	"CLUSTER_NAME": "",
	"AWS_ACCESS_KEY_ID": "",
	"AWS_SECRET_ACCESS_KEY": "",
	"REGION": ""
}

On Unix based server, run:

sudo nano /etc/config.json

and paste the above json after filling the empty strings.

Reminder: To save the file, with nano press CTRL + O and y then CTRL + X to exit.

On Windows, use the path C:/Windows/config.json.

Pass your credentials in you code

Though it is highly not recommended, you can pass your credentials locally to the remote_execute_sql with the argument credentials. You can then create a dictionnary using the same keys as described in previous section.

2.2 Load pycof

To load pycof in your script, you can use:

# Load pycof
import pycof as pc
# Or, load a specific or all functions from pycof
from pycof import *

To execute an SQL query, follow the below steps:

from pycof import remote_execute_sql

## Set up the SQL query
sql = "SELECT * FROM SCHEMA.TABLE LIMIT 10"

## The function will return a pandas dataframe
remote_execute_sql(sql)

2.3 Available functions

The current version of the library provides:

• verbose_display: extended function for print that can print strings, lists, data frames and uses tqdm is used in for loops.
• remote_execute_sql: aggragated function for SQL queries to SELECT, INSERT or DELETE.
• add_zero: simple function to convert int to str by adding a 0 is less than 10.
• OneHotEncoding: perform One Hot Encoding on a dataframe for the provided column names. Will keep the original categorical variables if drop is set to False.
• create_dataset: function to format a Pandas dataframe for keras format for LSTM.
• group: will convert an int to a str with thousand seperator.
• replace_zero: will transform 0 values to - for display purposes.
• week_sunday: will return week number of last sunday date of a given date.
• display_name: displays the current user name. Will display either first, last or full name.
• write: writes a str to a specific file (usually .txt) in one line of code.
• str2bool: converts string to boolean.
• wmape: computes the Weighted Mean Absolute Percentage Error between two columns.
• mse: computes the Mean Squared Error between two columns. Returns the RMSE (Root MSE) if root is set to True.

3. FAQ

3.1. How to use multiple credentials for remote_execute_sql?

The credentials argument can take the path or json file name into account to load them. You can have multiple credential files such as /etc/config.json, /etc/MyNewHost.json and /home/OtherHost.json. In remote_execute_sql you can play with the arguments.

• To use the /etc/config.json credentials you can use the default arguments by not providing anything.
• To use /etc/MyNewHost.json you can either pass MyNewHost.json or the whole path to use them.
• To use /home/OtherHost.json you need to pass the whole path.

3.2. Can I query a Reshift cluster with IAM user credentials?

The function remote_execute_sql can take into account IAM user's credentials. You need to ensure that your credentials file /etc/config.json includes the IAM access and secret keys with the Redshift cluster information. The only argument to change when calling the function is to set useIAM=True.

The function will then use the AWS access and secret keys to ask AWS to provide the user name and password to connect to the cluster. This is a much safer approach to connect to a Redshift cluster than using direct cluster's credentials.