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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# update the pandas package - optional\n",
    "# !pip install --upgrade pandas\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-17T05:43:33.890006Z",
     "start_time": "2023-09-17T05:43:33.866292Z"
    }
   },
   "outputs": [],
   "source": [
    "import sqlite3\n",
    "import sys\n",
    "import pandas as pd\n",
    "\n",
    "# Add the hamilton module to your path - optional\n",
    "# project_dir = \"### ADD PATH HERE ###\"\n",
    "# sys.path.append(project_dir)\n",
    "\n",
    "from hamilton import base, driver\n",
    "from hamilton.io.materialization import to"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-17T05:43:33.890336Z",
     "start_time": "2023-09-17T05:43:33.869093Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# We use the autoreload extension that comes with ipython to automatically reload modules when\n",
    "# the code in them changes.\n",
    "\n",
    "# import the jupyter extension\n",
    "%load_ext autoreload\n",
    "# set it to only reload the modules imported\n",
    "%autoreload 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-17T05:43:33.930823Z",
     "start_time": "2023-09-17T05:43:33.875941Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting spend_calculations.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile spend_calculations.py\n",
    "# Define your new Hamilton functions.\n",
    "# The %%writefile magic command creates a new Python module with the functions below.\n",
    "# We will import this later and pass it into our Driver.\n",
    "\n",
    "import pandas as pd\n",
    " \n",
    "# Look at `my_functions` to see how these functions connect.\n",
    "def avg_3wk_spend(spend: pd.Series) -> pd.Series:\n",
    "    \"\"\"Rolling 3 week average spend.\"\"\"\n",
    "    return spend.rolling(3).mean()\n",
    "\n",
    "\n",
    "def spend_per_signup(spend: pd.Series, signups: pd.Series) -> pd.Series:\n",
    "    \"\"\"The cost per signup in relation to spend.\"\"\"\n",
    "    return spend / signups\n",
    "\n",
    "\n",
    "def spend_mean(spend: pd.Series) -> float:\n",
    "    \"\"\"Shows function creating a scalar. In this case it computes the mean of the entire column.\"\"\"\n",
    "    return spend.mean()\n",
    "\n",
    "\n",
    "def spend_zero_mean(spend: pd.Series, spend_mean: float) -> pd.Series:\n",
    "    \"\"\"Shows function that takes a scalar. In this case to zero mean spend.\"\"\"\n",
    "    return spend - spend_mean\n",
    "\n",
    "\n",
    "def spend_std_dev(spend: pd.Series) -> float:\n",
    "    \"\"\"Function that computes the standard deviation of the spend column.\"\"\"\n",
    "    return spend.std()\n",
    "\n",
    "\n",
    "def spend_zero_mean_unit_variance(spend_zero_mean: pd.Series, spend_std_dev: float) -> pd.Series:\n",
    "    \"\"\"Function showing one way to make spend have zero mean and unit variance.\"\"\"\n",
    "    return spend_zero_mean / spend_std_dev"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-17T05:43:33.931096Z",
     "start_time": "2023-09-17T05:43:33.881858Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "initial_columns = {  # load from actuals or wherever -- this is our initial data we use as input.\n",
    "    # Note: these values don't have to be all series, they could be a scalar.\n",
    "    \"signups\": pd.Series([1, 10, 50, 100, 200, 400]),\n",
    "    \"spend\": pd.Series([10, 10, 20, 40, 40, 50]),\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-17T05:43:33.932468Z",
     "start_time": "2023-09-17T05:43:33.887774Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "%aimport spend_calculations\n",
    "\n",
    "df_builder = base.PandasDataFrameResult()\n",
    "dr = driver.Driver({}, spend_calculations)  # can pass in multiple modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-17T05:43:33.932900Z",
     "start_time": "2023-09-17T05:43:33.892681Z"
    }
   },
   "outputs": [],
   "source": [
    "# we need to specify what we want in the final dataframe. These can be string names, or function references.\n",
    "output_columns = [\n",
    "    \"spend\",\n",
    "    \"signups\",\n",
    "    \"avg_3wk_spend\",\n",
    "    \"spend_per_signup\",\n",
    "    \"spend_zero_mean_unit_variance\",\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-17T05:43:33.933121Z",
     "start_time": "2023-09-17T05:43:33.896779Z"
    }
   },
   "outputs": [],
   "source": [
    "# set up db connection for sql materializer below\n",
    "conn = sqlite3.connect(\"df.db\")\n",
    "\n",
    "# remove an previous instances of the 'test' table that will be created next\n",
    "conn.cursor().execute(\"DROP TABLE IF EXISTS test;\")\n",
    "conn.commit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-17T05:43:34.014270Z",
     "start_time": "2023-09-17T05:43:33.912979Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "More than one applicable adapter detected for <class 'pandas.core.frame.DataFrame'>. Using the last one registered <class 'hamilton.plugins.pandas_extensions.PandasPickleWriter'>.\n"
     ]
    },
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       "<title>_df_to_excel_build_result_inputs&#45;&gt;df_to_excel_build_result</title>\n",
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       "<title>_df_to_csv_build_result_inputs&#45;&gt;df_to_csv_build_result</title>\n",
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       "<title>_df_to_xml_build_result_inputs&#45;&gt;df_to_xml_build_result</title>\n",
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       "<title>function</title>\n",
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       "<graphviz.graphs.Digraph at 0x15ff50d50>"
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     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "materializers = [\n",
    "    # materialize the dataframe to a pickle file\n",
    "    to.pickle(\n",
    "        dependencies=output_columns,\n",
    "        id=\"df_to_pickle\",\n",
    "        path=\"./df.pkl\",\n",
    "        combine=df_builder,\n",
    "    ),\n",
    "    # materialize the dataframe to a JSON file\n",
    "    to.json(\n",
    "        dependencies=output_columns,\n",
    "        id=\"df_to_json\",\n",
    "        filepath_or_buffer=\"./df.json\",\n",
    "        combine=df_builder,\n",
    "    ),\n",
    "    to.sql(\n",
    "        dependencies=output_columns,\n",
    "        id=\"df_to_sql\",\n",
    "        table_name=\"test\",\n",
    "        db_connection=conn,\n",
    "        combine=df_builder,\n",
    "    ),\n",
    "    # materialize the dataframe to a XML file\n",
    "    to.xml(\n",
    "        dependencies=output_columns,\n",
    "        id=\"df_to_xml\",\n",
    "        path_or_buffer=\"./df.xml\",\n",
    "        combine=df_builder,\n",
    "    ),\n",
    "    to.html(\n",
    "        dependencies=output_columns,\n",
    "        id=\"df_to_html\",\n",
    "        buf=\"./df.html\",\n",
    "        combine=df_builder,\n",
    "    ),\n",
    "    to.stata(\n",
    "        dependencies=output_columns,\n",
    "        id=\"df_to_stata\",\n",
    "        path=\"./df.dta\",\n",
    "        combine=df_builder,\n",
    "    ),\n",
    "    to.feather(\n",
    "        dependencies=output_columns,\n",
    "        id=\"df_to_feather\",\n",
    "        path=\"./df.feather\",\n",
    "        combine=df_builder,\n",
    "    ),\n",
    "    to.parquet(\n",
    "        dependencies=output_columns,\n",
    "        id=\"df_to_parquet\",\n",
    "        path=\"./df.parquet.gzip\",\n",
    "        combine=df_builder,\n",
    "    ),\n",
    "    to.csv(\n",
    "        dependencies=output_columns,\n",
    "        id=\"df_to_csv\",\n",
    "        path=\"./df.csv\",\n",
    "        combine=df_builder,\n",
    "    ),\n",
    "    to.orc(\n",
    "        dependencies=output_columns,\n",
    "        id=\"df_to_orc\",\n",
    "        path=\"./df.orc\",\n",
    "        combine=df_builder,\n",
    "    ),\n",
    "    to.excel(\n",
    "        dependencies=output_columns,\n",
    "        id=\"df_to_excel\",\n",
    "        path=\"./df.xlsx\",\n",
    "        combine=df_builder,\n",
    "    ),\n",
    "]\n",
    "# Visualize what is happening\n",
    "dr.visualize_materialization(\n",
    "    *materializers,\n",
    "    additional_vars=output_columns,\n",
    "    inputs=initial_columns,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-17T05:43:34.026203Z",
     "start_time": "2023-09-17T05:43:34.016610Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "More than one applicable adapter detected for <class 'pandas.core.frame.DataFrame'>. Using the last one registered <class 'hamilton.plugins.pandas_extensions.PandasPickleWriter'>.\n"
     ]
    }
   ],
   "source": [
    "# Materialize a result, i.e. execute the DAG!\n",
    "materialization_results, additional_outputs = dr.materialize(\n",
    "    *materializers,\n",
    "    additional_vars=[\n",
    "        \"df_to_pickle_build_result\",\n",
    "        \"df_to_json_build_result\",\n",
    "        \"df_to_sql_build_result\",\n",
    "        \"df_to_xml_build_result\",\n",
    "        \"df_to_html_build_result\",\n",
    "        \"df_to_stata_build_result\",\n",
    "        \"df_to_feather_build_result\",\n",
    "        \"df_to_parquet_build_result\",\n",
    "        \"df_to_csv_build_result\",\n",
    "        \"df_to_orc_build_result\",\n",
    "        \"df_to_excel_build_result\",\n",
    "    ],  # because combine is used, we can get that result here.\n",
    "    inputs=initial_columns,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-17T05:43:34.041466Z",
     "start_time": "2023-09-17T05:43:34.028346Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'df_to_pickle': {'file_metadata': {'size': 1088,\n",
       "   'path': './df.pkl',\n",
       "   'last_modified': 1707261374.0912328,\n",
       "   'timestamp': 1707257774.091604},\n",
       "  'dataframe_metadata': {'rows': 6,\n",
       "   'columns': 5,\n",
       "   'column_names': ['spend',\n",
       "    'signups',\n",
       "    'avg_3wk_spend',\n",
       "    'spend_per_signup',\n",
       "    'spend_zero_mean_unit_variance'],\n",
       "   'datatypes': ['int64', 'int64', 'float64', 'float64', 'float64']}},\n",
       " 'df_to_json': {'file_metadata': {'size': 428,\n",
       "   'path': './df.json',\n",
       "   'last_modified': 1707261374.0931628,\n",
       "   'timestamp': 1707257774.093234},\n",
       "  'dataframe_metadata': {'rows': 6,\n",
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       "    'avg_3wk_spend',\n",
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       "    'spend_zero_mean_unit_variance'],\n",
       "   'datatypes': ['int64', 'int64', 'float64', 'float64', 'float64']}},\n",
       " 'df_to_sql': {'sql_metadata': {'rows': 6,\n",
       "   'query': None,\n",
       "   'table_name': 'test',\n",
       "   'timestamp': 1707257774.096384},\n",
       "  'dataframe_metadata': {'rows': 6,\n",
       "   'columns': 5,\n",
       "   'column_names': ['spend',\n",
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       "    'spend_zero_mean_unit_variance'],\n",
       "   'datatypes': ['int64', 'int64', 'float64', 'float64', 'float64']}},\n",
       " 'df_to_xml': {'file_metadata': {'size': 1622,\n",
       "   'path': './df.xml',\n",
       "   'last_modified': 1707261374.1253755,\n",
       "   'timestamp': 1707257774.125475},\n",
       "  'dataframe_metadata': {'rows': 6,\n",
       "   'columns': 5,\n",
       "   'column_names': ['spend',\n",
       "    'signups',\n",
       "    'avg_3wk_spend',\n",
       "    'spend_per_signup',\n",
       "    'spend_zero_mean_unit_variance'],\n",
       "   'datatypes': ['int64', 'int64', 'float64', 'float64', 'float64']}},\n",
       " 'df_to_html': {'file_metadata': {'size': 1145,\n",
       "   'path': './df.html',\n",
       "   'last_modified': 1707261374.1274278,\n",
       "   'timestamp': 1707257774.127508},\n",
       "  'dataframe_metadata': {'rows': 6,\n",
       "   'columns': 5,\n",
       "   'column_names': ['spend',\n",
       "    'signups',\n",
       "    'avg_3wk_spend',\n",
       "    'spend_per_signup',\n",
       "    'spend_zero_mean_unit_variance'],\n",
       "   'datatypes': ['int64', 'int64', 'float64', 'float64', 'float64']}},\n",
       " 'df_to_stata': {'file_metadata': {'size': 1526,\n",
       "   'path': './df.dta',\n",
       "   'last_modified': 1707261374.1300728,\n",
       "   'timestamp': 1707257774.13016},\n",
       "  'dataframe_metadata': {'rows': 6,\n",
       "   'columns': 5,\n",
       "   'column_names': ['spend',\n",
       "    'signups',\n",
       "    'avg_3wk_spend',\n",
       "    'spend_per_signup',\n",
       "    'spend_zero_mean_unit_variance'],\n",
       "   'datatypes': ['int64', 'int64', 'float64', 'float64', 'float64']}},\n",
       " 'df_to_feather': {'file_metadata': {'size': 3514,\n",
       "   'path': './df.feather',\n",
       "   'last_modified': 1707261374.1384852,\n",
       "   'timestamp': 1707257774.138582},\n",
       "  'dataframe_metadata': {'rows': 6,\n",
       "   'columns': 5,\n",
       "   'column_names': ['spend',\n",
       "    'signups',\n",
       "    'avg_3wk_spend',\n",
       "    'spend_per_signup',\n",
       "    'spend_zero_mean_unit_variance'],\n",
       "   'datatypes': ['int64', 'int64', 'float64', 'float64', 'float64']}},\n",
       " 'df_to_parquet': {'file_metadata': {'size': 4705,\n",
       "   'path': './df.parquet.gzip',\n",
       "   'last_modified': 1707261374.14694,\n",
       "   'timestamp': 1707257774.147069},\n",
       "  'dataframe_metadata': {'rows': 6,\n",
       "   'columns': 5,\n",
       "   'column_names': ['spend',\n",
       "    'signups',\n",
       "    'avg_3wk_spend',\n",
       "    'spend_per_signup',\n",
       "    'spend_zero_mean_unit_variance'],\n",
       "   'datatypes': ['int64', 'int64', 'float64', 'float64', 'float64']}},\n",
       " 'df_to_csv': {'file_metadata': {'size': 335,\n",
       "   'path': './df.csv',\n",
       "   'last_modified': 1707261374.1481698,\n",
       "   'timestamp': 1707257774.148234},\n",
       "  'dataframe_metadata': {'rows': 6,\n",
       "   'columns': 5,\n",
       "   'column_names': ['spend',\n",
       "    'signups',\n",
       "    'avg_3wk_spend',\n",
       "    'spend_per_signup',\n",
       "    'spend_zero_mean_unit_variance'],\n",
       "   'datatypes': ['int64', 'int64', 'float64', 'float64', 'float64']}},\n",
       " 'df_to_orc': {'file_metadata': {'size': 954,\n",
       "   'path': './df.orc',\n",
       "   'last_modified': 1707261374.153472,\n",
       "   'timestamp': 1707257774.15356},\n",
       "  'dataframe_metadata': {'rows': 6,\n",
       "   'columns': 5,\n",
       "   'column_names': ['spend',\n",
       "    'signups',\n",
       "    'avg_3wk_spend',\n",
       "    'spend_per_signup',\n",
       "    'spend_zero_mean_unit_variance'],\n",
       "   'datatypes': ['int64', 'int64', 'float64', 'float64', 'float64']}},\n",
       " 'df_to_excel': {'file_metadata': {'size': 5218,\n",
       "   'path': './df.xlsx',\n",
       "   'last_modified': 1707261374.2337506,\n",
       "   'timestamp': 1707257774.233816},\n",
       "  'dataframe_metadata': {'rows': 6,\n",
       "   'columns': 5,\n",
       "   'column_names': ['spend',\n",
       "    'signups',\n",
       "    'avg_3wk_spend',\n",
       "    'spend_per_signup',\n",
       "    'spend_zero_mean_unit_variance'],\n",
       "   'datatypes': ['int64', 'int64', 'float64', 'float64', 'float64']}}}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "materialization_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-17T05:43:34.051151Z",
     "start_time": "2023-09-17T05:43:34.043320Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <td>23.333333</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.677349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40</td>\n",
       "      <td>200</td>\n",
       "      <td>33.333333</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0.677349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>50</td>\n",
       "      <td>400</td>\n",
       "      <td>43.333333</td>\n",
       "      <td>0.125</td>\n",
       "      <td>1.257934</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   spend  signups  avg_3wk_spend  spend_per_signup  \\\n",
       "0     10        1            NaN            10.000   \n",
       "1     10       10            NaN             1.000   \n",
       "2     20       50      13.333333             0.400   \n",
       "3     40      100      23.333333             0.400   \n",
       "4     40      200      33.333333             0.200   \n",
       "5     50      400      43.333333             0.125   \n",
       "\n",
       "   spend_zero_mean_unit_variance  \n",
       "0                      -1.064405  \n",
       "1                      -1.064405  \n",
       "2                      -0.483821  \n",
       "3                       0.677349  \n",
       "4                       0.677349  \n",
       "5                       1.257934  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "additional_outputs[\"df_to_pickle_build_result\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-17T05:43:34.058608Z",
     "start_time": "2023-09-17T05:43:34.048662Z"
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>spend</th>\n",
       "      <th>signups</th>\n",
       "      <th>avg_3wk_spend</th>\n",
       "      <th>spend_per_signup</th>\n",
       "      <th>spend_zero_mean_unit_variance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.000</td>\n",
       "      <td>-1.064405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000</td>\n",
       "      <td>-1.064405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>50</td>\n",
       "      <td>13.333333</td>\n",
       "      <td>0.400</td>\n",
       "      <td>-0.483821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>100</td>\n",
       "      <td>23.333333</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.677349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40</td>\n",
       "      <td>200</td>\n",
       "      <td>33.333333</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0.677349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>50</td>\n",
       "      <td>400</td>\n",
       "      <td>43.333333</td>\n",
       "      <td>0.125</td>\n",
       "      <td>1.257934</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   spend  signups  avg_3wk_spend  spend_per_signup  \\\n",
       "0     10        1            NaN            10.000   \n",
       "1     10       10            NaN             1.000   \n",
       "2     20       50      13.333333             0.400   \n",
       "3     40      100      23.333333             0.400   \n",
       "4     40      200      33.333333             0.200   \n",
       "5     50      400      43.333333             0.125   \n",
       "\n",
       "   spend_zero_mean_unit_variance  \n",
       "0                      -1.064405  \n",
       "1                      -1.064405  \n",
       "2                      -0.483821  \n",
       "3                       0.677349  \n",
       "4                       0.677349  \n",
       "5                       1.257934  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "additional_outputs[\"df_to_json_build_result\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-17T05:43:34.089706Z",
     "start_time": "2023-09-17T05:43:34.060251Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   spend  signups  avg_3wk_spend  spend_per_signup  \\\n",
      "0     10        1            NaN            10.000   \n",
      "1     10       10            NaN             1.000   \n",
      "2     20       50      13.333333             0.400   \n",
      "3     40      100      23.333333             0.400   \n",
      "4     40      200      33.333333             0.200   \n",
      "5     50      400      43.333333             0.125   \n",
      "\n",
      "   spend_zero_mean_unit_variance  \n",
      "0                      -1.064405  \n",
      "1                      -1.064405  \n",
      "2                      -0.483821  \n",
      "3                       0.677349  \n",
      "4                       0.677349  \n",
      "5                       1.257934  \n"
     ]
    }
   ],
   "source": [
    "print(additional_outputs[\"df_to_sql_build_result\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   spend  signups  avg_3wk_spend  spend_per_signup  \\\n",
      "0     10        1            NaN            10.000   \n",
      "1     10       10            NaN             1.000   \n",
      "2     20       50      13.333333             0.400   \n",
      "3     40      100      23.333333             0.400   \n",
      "4     40      200      33.333333             0.200   \n",
      "5     50      400      43.333333             0.125   \n",
      "\n",
      "   spend_zero_mean_unit_variance  \n",
      "0                      -1.064405  \n",
      "1                      -1.064405  \n",
      "2                      -0.483821  \n",
      "3                       0.677349  \n",
      "4                       0.677349  \n",
      "5                       1.257934  \n"
     ]
    }
   ],
   "source": [
    "print(additional_outputs[\"df_to_xml_build_result\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   spend  signups  avg_3wk_spend  spend_per_signup  \\\n",
      "0     10        1            NaN            10.000   \n",
      "1     10       10            NaN             1.000   \n",
      "2     20       50      13.333333             0.400   \n",
      "3     40      100      23.333333             0.400   \n",
      "4     40      200      33.333333             0.200   \n",
      "5     50      400      43.333333             0.125   \n",
      "\n",
      "   spend_zero_mean_unit_variance  \n",
      "0                      -1.064405  \n",
      "1                      -1.064405  \n",
      "2                      -0.483821  \n",
      "3                       0.677349  \n",
      "4                       0.677349  \n",
      "5                       1.257934  \n"
     ]
    }
   ],
   "source": [
    "print(additional_outputs[\"df_to_html_build_result\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   spend  signups  avg_3wk_spend  spend_per_signup  \\\n",
      "0     10        1            NaN            10.000   \n",
      "1     10       10            NaN             1.000   \n",
      "2     20       50      13.333333             0.400   \n",
      "3     40      100      23.333333             0.400   \n",
      "4     40      200      33.333333             0.200   \n",
      "5     50      400      43.333333             0.125   \n",
      "\n",
      "   spend_zero_mean_unit_variance  \n",
      "0                      -1.064405  \n",
      "1                      -1.064405  \n",
      "2                      -0.483821  \n",
      "3                       0.677349  \n",
      "4                       0.677349  \n",
      "5                       1.257934  \n"
     ]
    }
   ],
   "source": [
    "print(additional_outputs[\"df_to_stata_build_result\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   spend  signups  avg_3wk_spend  spend_per_signup  \\\n",
      "0     10        1            NaN            10.000   \n",
      "1     10       10            NaN             1.000   \n",
      "2     20       50      13.333333             0.400   \n",
      "3     40      100      23.333333             0.400   \n",
      "4     40      200      33.333333             0.200   \n",
      "5     50      400      43.333333             0.125   \n",
      "\n",
      "   spend_zero_mean_unit_variance  \n",
      "0                      -1.064405  \n",
      "1                      -1.064405  \n",
      "2                      -0.483821  \n",
      "3                       0.677349  \n",
      "4                       0.677349  \n",
      "5                       1.257934  \n"
     ]
    }
   ],
   "source": [
    "print(additional_outputs[\"df_to_feather_build_result\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   spend  signups  avg_3wk_spend  spend_per_signup  \\\n",
      "0     10        1            NaN            10.000   \n",
      "1     10       10            NaN             1.000   \n",
      "2     20       50      13.333333             0.400   \n",
      "3     40      100      23.333333             0.400   \n",
      "4     40      200      33.333333             0.200   \n",
      "5     50      400      43.333333             0.125   \n",
      "\n",
      "   spend_zero_mean_unit_variance  \n",
      "0                      -1.064405  \n",
      "1                      -1.064405  \n",
      "2                      -0.483821  \n",
      "3                       0.677349  \n",
      "4                       0.677349  \n",
      "5                       1.257934  \n"
     ]
    }
   ],
   "source": [
    "print(additional_outputs[\"df_to_parquet_build_result\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   spend  signups  avg_3wk_spend  spend_per_signup  \\\n",
      "0     10        1            NaN            10.000   \n",
      "1     10       10            NaN             1.000   \n",
      "2     20       50      13.333333             0.400   \n",
      "3     40      100      23.333333             0.400   \n",
      "4     40      200      33.333333             0.200   \n",
      "5     50      400      43.333333             0.125   \n",
      "\n",
      "   spend_zero_mean_unit_variance  \n",
      "0                      -1.064405  \n",
      "1                      -1.064405  \n",
      "2                      -0.483821  \n",
      "3                       0.677349  \n",
      "4                       0.677349  \n",
      "5                       1.257934  \n"
     ]
    }
   ],
   "source": [
    "print(additional_outputs[\"df_to_csv_build_result\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   spend  signups  avg_3wk_spend  spend_per_signup  \\\n",
      "0     10        1            NaN            10.000   \n",
      "1     10       10            NaN             1.000   \n",
      "2     20       50      13.333333             0.400   \n",
      "3     40      100      23.333333             0.400   \n",
      "4     40      200      33.333333             0.200   \n",
      "5     50      400      43.333333             0.125   \n",
      "\n",
      "   spend_zero_mean_unit_variance  \n",
      "0                      -1.064405  \n",
      "1                      -1.064405  \n",
      "2                      -0.483821  \n",
      "3                       0.677349  \n",
      "4                       0.677349  \n",
      "5                       1.257934  \n"
     ]
    }
   ],
   "source": [
    "print(additional_outputs[\"df_to_orc_build_result\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>spend</th>\n",
       "      <th>signups</th>\n",
       "      <th>avg_3wk_spend</th>\n",
       "      <th>spend_per_signup</th>\n",
       "      <th>spend_zero_mean_unit_variance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.000</td>\n",
       "      <td>-1.064405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000</td>\n",
       "      <td>-1.064405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>50</td>\n",
       "      <td>13.333333</td>\n",
       "      <td>0.400</td>\n",
       "      <td>-0.483821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>100</td>\n",
       "      <td>23.333333</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.677349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40</td>\n",
       "      <td>200</td>\n",
       "      <td>33.333333</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0.677349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>50</td>\n",
       "      <td>400</td>\n",
       "      <td>43.333333</td>\n",
       "      <td>0.125</td>\n",
       "      <td>1.257934</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   spend  signups  avg_3wk_spend  spend_per_signup  \\\n",
       "0     10        1            NaN            10.000   \n",
       "1     10       10            NaN             1.000   \n",
       "2     20       50      13.333333             0.400   \n",
       "3     40      100      23.333333             0.400   \n",
       "4     40      200      33.333333             0.200   \n",
       "5     50      400      43.333333             0.125   \n",
       "\n",
       "   spend_zero_mean_unit_variance  \n",
       "0                      -1.064405  \n",
       "1                      -1.064405  \n",
       "2                      -0.483821  \n",
       "3                       0.677349  \n",
       "4                       0.677349  \n",
       "5                       1.257934  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "additional_outputs[\"df_to_excel_build_result\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-09-17T05:43:34.090208Z",
     "start_time": "2023-09-17T05:43:34.066483Z"
    }
   },
   "outputs": [],
   "source": [
    "# closing out db connection\n",
    "conn.close()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 },
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