{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "85\n",
      "63\n",
      "51\n",
      "26\n",
      "30\n",
      "4\n",
      "7\n",
      "1\n",
      "17\n",
      "81\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([2, 2])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "np.random.randint(0.5, 3.5)\n",
    "p_remove = 0.9\n",
    "np.random.choice([True, False], p=[p_remove, 1-p_remove])\n",
    "rng = np.random.default_rng(0)\n",
    "for _ in range(10):\n",
    "    print(rng.integers(0,100))\n",
    "np.random.choice([1, 2, 3], 2, p=[0.4, 0.4, 0.2])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "share = 0.8\n",
    "list_succ_firms = [1, 1]\n",
    "round(share * len(list_succ_firms)) if round(share * len(list_succ_firms)) > 0 else 1"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
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   "file_extension": ".py",
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