{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4c8c7cb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyspark.sql as ps\n",
    "import pandas as pd\n",
    "from pyspark.sql.functions import col, mean, stddev"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a85bb3cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "spark_session = ps.SparkSession.builder.master(\"local[1]\").getOrCreate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ac02f09c",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd_df = pd.DataFrame(\n",
    "        {\n",
    "            \"spend\": [\n",
    "                10,\n",
    "                10,\n",
    "                20,\n",
    "                40,\n",
    "                40,\n",
    "                50,\n",
    "                60,\n",
    "                70,\n",
    "                90,\n",
    "                100,\n",
    "                70,\n",
    "                80,\n",
    "                90,\n",
    "                100,\n",
    "                110,\n",
    "                120,\n",
    "                130,\n",
    "                140,\n",
    "                150,\n",
    "                160,\n",
    "            ],\n",
    "            \"signups\": [\n",
    "                1,\n",
    "                10,\n",
    "                50,\n",
    "                100,\n",
    "                200,\n",
    "                400,\n",
    "                600,\n",
    "                800,\n",
    "                1000,\n",
    "                1200,\n",
    "                1400,\n",
    "                1600,\n",
    "                1800,\n",
    "                2000,\n",
    "                2200,\n",
    "                2400,\n",
    "                2600,\n",
    "                2800,\n",
    "                3000,\n",
    "                3200,\n",
    "            ],\n",
    "        }\n",
    "    )\n",
    "ps_df = spark_session.createDataFrame(pd_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "71fd52ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DataFrame[foo: double]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ps_df.select(mean(col(\"spend\")).alias(\"foo\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8986d1ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DataFrame[spend: bigint, signups: bigint, foo: bigint]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ps_df.withColumn(\"foo\", ps_df['signups']*ps_df['spend'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7489e4dd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
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   "codemirror_mode": {
    "name": "ipython",
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   "pygments_lexer": "ipython3",
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