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.

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

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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.

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
```