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I've been using cassandra a lot lately. It's very common to denote ID fields in cassanda as a time-based uuid field called timeuuid docs. In python, a timeuuuid is mearly uuid.uuid1 from the built-in uuid package.

Using the cassandra-driver, you might end up with a model like:

import uuid
from cassandra.cqlengine import columns
from cassandra.cqlengine.models import Model

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from fractions import Fraction

def number_str_to_float(amount_str:str) -> (any, bool):
    """
    Take in an amount string to return float (if possible).
    
    Valid string returns:
    Float
    Boolean -> True

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Django Custom Context Processors Example

In cfehome/context_processors.py add:

def my_context(request):
    # caching
    return {
 "my_var": "hello world"

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# Get version at https://knative.dev/docs/install/yaml-install/serving/install-serving-with-yaml/
export KNATIVE_VERSION="v1.10.1" # ensure ISTIO install matches this version too

# Install knative serving
# Ref: https://knative.dev/docs/install/yaml-install/serving/install-serving-with-yaml/#install-the-knative-serving-component
kubectl apply -f https://github.com/knative/serving/releases/download/knative-$KNATIVE_VERSION/serving-crds.yaml
kubectl apply -f https://github.com/knative/serving/releases/download/knative-$KNATIVE_VERSION/serving-core.yaml


# install istio

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Django for Jupyter

It's true packages exist to make it "easy" to use Django inside of a jupyter notebook. I seem to always run into issues successfully running these packages. I've found the below method useful although I cannot recall how I discovered how this works (aka attribution needed).

Requirements

• Virtual Environment (virtualenv, venv, pipenv, etc)
• Django installed & project created (we'll use the project name cfehome)
• Jupyter installed at least in the virtual environment

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/* 
Next.js/Edge function method for hasing passwords 
Using the Web API Crypto feature instead of
Built-in Node.js Crypto
*/

export default async function pbkdf2(password, salt, iterations, keylen) {
    const enc = new TextEncoder();
    const passwordBuffer = enc.encode(password);
    const saltBuffer = enc.encode(salt);

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export default async function isValidURL(url, disallowedDomains) {
    // Construct a regular expression pattern to match disallowed domains
    const disallowedPattern = `^https?:\\/\\/(?:${disallowedDomains.join('|')})\\b`;
    let disallowedRegex = new RegExp(disallowedPattern, 'i');
  
    // Regular expression pattern to match a URL (excluding localhost)
    const urlPattern = /^(https?:\/\/)?((?!localhost)[\w.-]+)\.([a-z]{2,})(:\d{1,5})?(\/.*)?$/i;
    let urlRegex = new RegExp(urlPattern);
  
    // Test the URL against both URL pattern and disallowed domain pattern

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Extract Endpoint from Serverless Framework

The serverless framework lacks native support for outputting the endpoint url for a deployment:

serverless info --stage prod --region us-east-2

Outputs

DOTENV: Loading environment variables from .env, .env.prod:

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Export Pandas Dataframe to a PostgreSQL Database Table

Export your Pandas analysis really easily to a PostgresSQL database table with this tutorial. We used Docker Compose to create the postgres database with docker compose up and the related compose.yaml file.

Step 1 - Install Requirements

Add requirements.txt from below.

python3 -m pip install -r requirements.txt