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Code Samples from my blog posts

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This is a repository of code samples from my blogposts. I'm writing on Medium at the moment. You can find me here.

Articles

Link to the article on Medium Sample Code Publish Date Topics
Pandas: apply, map or transform? Code January, 2023 jupyter-notebook, pandas, python
Improving the performance of NumPy code Code October, 2022 jupyter-notebook, numpy, pandas, matplotlib, numba
Dissecting the Birthday Paradox Code April, 2022 jupyter-notebook, statistics, pandas, matplotlib
How do Chatbots Understand? Code February, 2022 rasa, python, chatbot, nlu
Handling Chatbot Failure Gracefully Code December, 2021 rasa, python, chatbot, nlu
Evaluating Multi-label Classifiers Code November, 2021 classification, sklearn, ml, metrics
Rasa Chatbot v2 (not a post) Code October, 2021 rasa, python, chatbot, nlu
Building a Chatbot with Rasa Code September, 2021 rasa, python, chatbot, nlu
How Imports Work in Python Code June, 2021 python, imports
Python: Decorators in OOP Code January, 2021 python, oop, decorators
How Neural Networks Solve the XOR Problem Code November, 2020 python, jupyter-notebook, matplotlib
Understanding Dynamic Programming Code October, 2020 python, algorithms, dynamic programming
Understanding Maximum Likelihood Estimation TBA August, 2020 statistics
Visualizing the Defective Chessboard Problem Code Jan, 2020 algorithms

Star History Chart

Star History Chart

Checkout star-history.com to get a star plot like the one above.

Also, if you found this repository useful, please do leave a star!

Usage

  • Fork this repo
  • Clone it
https://github.com/Polaris000/BlogCode.git
  • Create an environment with the required packages installed. (More info below)
  • Navigate to a project
  • Check the README inside each project for information specific to it.

Managing environments and dependencies

  • Creating an environment is straightforward. Though there are a few ways to do it, conda is a reliable way to go about it. Install conda from here.

  • To create an environment run:

    $ conda create --name <env_name> python=3.8.10

  • After the setup is complete, activate the env.

    $ conda activate <env_name>

  • The packages required to run these code samples are mainly of two kinds:

    • Rasa dependencies
    • Python data visualization and machine learning libraries

  • If you want to install both, use requirements/requirements.txt in your env

    (env)$ pip install -r requirements/requirements.txt

  • If you want to install rasa dependencies, use requirements/rasa_requirements.txt in your env

    (env)$ pip install -r requirements/rasa_requirements.txt

  • If you want to install python machine learning dependencies only, use requirements/non_rasa_requirements.txt in your env

    (env)$ pip install -r requirements/non_rasa_requirements.txt

Notes

  • If you're interested in using Rasa X for a more visual experience while improving and conversing with your bot, you'll require these additional steps:
    • Downgrade pip to fix a circular dependency issue 
      $ pip install pip==20.2
    • Install rasa x 
      $ pip install install rasa-x==0.38.1 --extra-index-url https://pypi.rasa.com/simple