banditkings

# --- NUMPY ---
import numpy as np

np.random.seed(42)
np.random.binomial(n=1, p=0.5, size=(10,))

# array([0, 1, 1, 1, 0, 0, 0, 1, 1, 1])

# --- SCIPY ---
import numpy as np

banditkings

Quarto with Python, Poetry, and VSCode

If you're having issues with getting pyenv + poetry + quarto + vscode to work together, the solution is to set the QUARTO_PYTHON environment variable to the location of the specific python version you want to use.

For instance, in bash we'd do the following within the project directory:

poetry shell
export QUARTO_PYTHON=$(which python)

banditkings

def clean_annotations(fig):
  """Removes the annoying 'Feature=' label that clutters plotly graphs when you do facet_row or facet_col"""
  fig.for_each_annotation(lambda a: a.update(text=a.text.split("=")[1]))
  return fig

banditkings

Supplemental WSL2 dev setups

Been a few years since I had to make a new dev environment in WSL2, and my previous article is a little outdated from 2021.

Install Script updated

# Update apt-get if this is a fresh Linux distro install
sudo apt update && sudo apt upgrade
# Install Git and zsh

banditkings

import pandas as pd

def create_week_feature(df:pd.DataFrame, date_col='DateTime'):
  """
  Given a dataframe with a column `date_col` of type pd.DateTime
  """
  if type(df[date_col]) != pd.DateTime:
    df[date_col] = pd.to_datetime(df[date_col])
    
  # Use the pd.Series.dt.isocalendar() method to access

banditkings

# Simple example of using graphviz to draw DAGs while working in a jupyter notebook environment
import graphviz as gr

# Simple DAG with a confounder
h = gr.Digraph('Simple DAG with confounder')
h.edge('Treatment', 'Outcome')
h.edge('Confounder', 'Treatment')
h.edge('Confounder', 'Outcome')
display(h)

banditkings

import jax.numpy as jnp
from jax import random

import numpyro
from numpyro.diagnostics import hpdi
import numpyro.distributions as dist
from numpyro.infer import MCMC, NUTS, Predictive

# Simulated Data
# 3000 samples, 3 coefficients

banditkings

# ------- STATSMODELS ---------
# Statsmodels has a few built in datasets as well as a utility for R-Datasets

# Yearly Nile River flows at Ashwan 1871-1970
import statsmodels.api as sm
df = sm.datasets.nile.load().data
df['ds'] = pd.date_range(start='1871', end='1970', freq='AS')

# Mauna Loa Weekly Atmospheric CO2 Data
import statsmodels.api as sm

banditkings

import numpy as np
import plotly.graph_objects as go

# A Random Walk animation in Plotly using a line and a marker for the current position
n = 200

# Random Walk with n points
x = np.arange(n)
i = 0
state = 0 # initial state

banditkings

Project Scaffolding

Let's start a brand new project here with:

• cookiecutter: project templating
• pyenv: managing virtual environments for different python versions
• poetry: dependency management
• pytest: testing
• git: version control