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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# \"Causality modelling in Python for data scientists\"\n",
    "> \"Data science is increasingly commonplace in industry and the enterprise. Industrial data scientists have a vast toolbox for descriptive and predictive analyses at their disposal. However, data science tools for decision-making in industry and the enterprise are less well established. Here we survey Python packages that can aid industrial data scientists facilitate intelligent decision-making through causality modelling.\"\n",
    "- hidden: true\n",
    "- toc: true\n",
    "- branch: master\n",
    "- badges: true\n",
    "- comments: true\n",
    "- categories: [causal inference, causal discovery, causality modelling, python]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(123)\n",
    "random.seed(123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "no_samples = 10000\n",
    "\n",
    "seasons = np.random.choice(['winter', 'spring', 'summer', 'fall'], size=(no_samples,))\n",
    "color = np.array(\n",
    "    [\n",
    "        random.choice(['yellow', 'pink'])\n",
    "        if season in ['spring', 'summer']\n",
    "        else random.choice(['navy', 'grey'])\n",
    "        for season in seasons\n",
    "    ]\n",
    ")\n",
    "price = np.random.lognormal(size=(no_samples,))\n",
    "rank = np.array(\n",
    "    [\n",
    "        \n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['fall', 'spring', 'spring', ..., 'spring', 'spring', 'spring'],\n",
       "      dtype='<U6')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seasons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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