Skip to main content

A package for commonly used functions

Project description

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.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pycof-1.1.10.tar.gz (9.3 kB view hashes)

Uploaded Source

Built Distribution

pycof-1.1.10-py3-none-any.whl (10.6 kB view hashes)

Uploaded Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page