{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/konstantin/.miniconda3/envs/hamilton-vaex/lib/python3.9/site-packages/dask/dataframe/_pyarrow_compat.py:17: FutureWarning: Minimal version of pyarrow will soon be increased to 14.0.1. You are using 11.0.0. Please consider upgrading.\n",
      "  warnings.warn(\n",
      "/Users/konstantin/.miniconda3/envs/hamilton-vaex/lib/python3.9/site-packages/scipy/__init__.py:155: UserWarning: A NumPy version >=1.18.5 and <1.25.0 is required for this version of SciPy (detected version 1.26.3\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
     ]
    }
   ],
   "source": [
    "from hamilton import base, driver\n",
    "from hamilton.plugins import vaex_extensions, h_vaex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import my_functions\n",
    "\n",
    "config = {\n",
    "    \"base_df_location\": \"dummy_value\",\n",
    "}\n",
    "adapter = base.SimplePythonGraphAdapter(result_builder=h_vaex.VaexDataFrameResult())\n",
    "dr = driver.Driver(config, my_functions, adapter=adapter)\n",
    "output_columns = [\n",
    "    \"spend\",\n",
    "    \"signups\",\n",
    "    \"spend_per_signup\",\n",
    "    \"spend_std_dev\",\n",
    "    \"spend_mean\",\n",
    "    \"spend_zero_mean_unit_variance\",\n",
    "]\n",
    "df = dr.execute(output_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  #    spend    signups    spend_per_signup    spend_zero_mean_unit_variance    spend_std_dev    spend_mean\n",
      "  0       10          1              10                            -1.166             15.7233       28.3333\n",
      "  1       10         10               1                            -1.166             15.7233       28.3333\n",
      "  2       20         50               0.4                          -0.529999          15.7233       28.3333\n",
      "  3       40        100               0.4                           0.741999          15.7233       28.3333\n",
      "  4       40        200               0.2                           0.741999          15.7233       28.3333\n",
      "  5       50        400               0.125                         1.378             15.7233       28.3333\n"
     ]
    }
   ],
   "source": [
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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     "execution_count": 6,
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