Cuervo_Mod3Project_Notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Module 3 Final Project\n",
    "\n",
    "* Student name: David Cuervo\n",
    "* Student pace: Self Paced \n",
    "* Scheduled project review date/time: 7/21/2020 ; 3:00PM CST\n",
    "* Instructor name: Jeff Herman\n",
    "* Blog post URL: https://david-cuervo.github.io/northwind_dataset_analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Does discount amount have a statistically significant effect on the quantity of a product in an order? If so, at what level(s) of discount?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Connecting to the database\n",
    "\n",
    "import pandas as pd\n",
    "import sqlite3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "conn = sqlite3.Connection('Northwind_small.sqlite')\n",
    "cur = conn.cursor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>OrderId</th>\n",
       "      <th>ProductId</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>10248/11</td>\n",
       "      <td>10248</td>\n",
       "      <td>11</td>\n",
       "      <td>14.00</td>\n",
       "      <td>12</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>10248/42</td>\n",
       "      <td>10248</td>\n",
       "      <td>42</td>\n",
       "      <td>9.80</td>\n",
       "      <td>10</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>10248/72</td>\n",
       "      <td>10248</td>\n",
       "      <td>72</td>\n",
       "      <td>34.80</td>\n",
       "      <td>5</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>10249/14</td>\n",
       "      <td>10249</td>\n",
       "      <td>14</td>\n",
       "      <td>18.60</td>\n",
       "      <td>9</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>10249/51</td>\n",
       "      <td>10249</td>\n",
       "      <td>51</td>\n",
       "      <td>42.40</td>\n",
       "      <td>40</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2150</td>\n",
       "      <td>11077/64</td>\n",
       "      <td>11077</td>\n",
       "      <td>64</td>\n",
       "      <td>33.25</td>\n",
       "      <td>2</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2151</td>\n",
       "      <td>11077/66</td>\n",
       "      <td>11077</td>\n",
       "      <td>66</td>\n",
       "      <td>17.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2152</td>\n",
       "      <td>11077/73</td>\n",
       "      <td>11077</td>\n",
       "      <td>73</td>\n",
       "      <td>15.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2153</td>\n",
       "      <td>11077/75</td>\n",
       "      <td>11077</td>\n",
       "      <td>75</td>\n",
       "      <td>7.75</td>\n",
       "      <td>4</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2154</td>\n",
       "      <td>11077/77</td>\n",
       "      <td>11077</td>\n",
       "      <td>77</td>\n",
       "      <td>13.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2155 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Id  OrderId  ProductId  UnitPrice  Quantity  Discount\n",
       "0     10248/11    10248         11      14.00        12      0.00\n",
       "1     10248/42    10248         42       9.80        10      0.00\n",
       "2     10248/72    10248         72      34.80         5      0.00\n",
       "3     10249/14    10249         14      18.60         9      0.00\n",
       "4     10249/51    10249         51      42.40        40      0.00\n",
       "...        ...      ...        ...        ...       ...       ...\n",
       "2150  11077/64    11077         64      33.25         2      0.03\n",
       "2151  11077/66    11077         66      17.00         1      0.00\n",
       "2152  11077/73    11077         73      15.00         2      0.01\n",
       "2153  11077/75    11077         75       7.75         4      0.00\n",
       "2154  11077/77    11077         77      13.00         2      0.00\n",
       "\n",
       "[2155 rows x 6 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Source data related to quantity and discount from OrderDetail dataset\n",
    "\n",
    "cur.execute(\"\"\"SELECT *\n",
    "               FROM OrderDetail\n",
    "               ;\n",
    "               \"\"\")\n",
    "df = pd.DataFrame(cur.fetchall())\n",
    "df.columns = [x[0] for x in cur.description]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00    1317\n",
       "0.05     185\n",
       "0.10     173\n",
       "0.20     161\n",
       "0.15     157\n",
       "0.25     154\n",
       "0.03       3\n",
       "0.02       2\n",
       "0.01       1\n",
       "0.04       1\n",
       "0.06       1\n",
       "Name: Discount, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Determine all the discount types\n",
    "\n",
    "df['Discount'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>OrderId</th>\n",
       "      <th>ProductId</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>10248/11</td>\n",
       "      <td>10248</td>\n",
       "      <td>11</td>\n",
       "      <td>14.00</td>\n",
       "      <td>12</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>10248/42</td>\n",
       "      <td>10248</td>\n",
       "      <td>42</td>\n",
       "      <td>9.80</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>10248/72</td>\n",
       "      <td>10248</td>\n",
       "      <td>72</td>\n",
       "      <td>34.80</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>10249/14</td>\n",
       "      <td>10249</td>\n",
       "      <td>14</td>\n",
       "      <td>18.60</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>10249/51</td>\n",
       "      <td>10249</td>\n",
       "      <td>51</td>\n",
       "      <td>42.40</td>\n",
       "      <td>40</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2142</td>\n",
       "      <td>11077/52</td>\n",
       "      <td>11077</td>\n",
       "      <td>52</td>\n",
       "      <td>7.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2143</td>\n",
       "      <td>11077/55</td>\n",
       "      <td>11077</td>\n",
       "      <td>55</td>\n",
       "      <td>24.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2144</td>\n",
       "      <td>11077/66</td>\n",
       "      <td>11077</td>\n",
       "      <td>66</td>\n",
       "      <td>17.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2145</td>\n",
       "      <td>11077/75</td>\n",
       "      <td>11077</td>\n",
       "      <td>75</td>\n",
       "      <td>7.75</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2146</td>\n",
       "      <td>11077/77</td>\n",
       "      <td>11077</td>\n",
       "      <td>77</td>\n",
       "      <td>13.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2147 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Id  OrderId  ProductId  UnitPrice  Quantity  Discount\n",
       "0     10248/11    10248         11      14.00        12       0.0\n",
       "1     10248/42    10248         42       9.80        10       0.0\n",
       "2     10248/72    10248         72      34.80         5       0.0\n",
       "3     10249/14    10249         14      18.60         9       0.0\n",
       "4     10249/51    10249         51      42.40        40       0.0\n",
       "...        ...      ...        ...        ...       ...       ...\n",
       "2142  11077/52    11077         52       7.00         2       0.0\n",
       "2143  11077/55    11077         55      24.00         2       0.0\n",
       "2144  11077/66    11077         66      17.00         1       0.0\n",
       "2145  11077/75    11077         75       7.75         4       0.0\n",
       "2146  11077/77    11077         77      13.00         2       0.0\n",
       "\n",
       "[2147 rows x 6 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Source data that removes outlier discounts\n",
    "\n",
    "cur.execute(\"\"\"SELECT *\n",
    "               FROM OrderDetail\n",
    "               WHERE Discount = 0.00 OR Discount = 0.05 OR Discount = 0.10 OR \n",
    "               Discount = 0.15 OR Discount = 0.20 OR Discount = 0.25\n",
    "               \"\"\")\n",
    "df = pd.DataFrame(cur.fetchall())\n",
    "df.columns = [x[0] for x in cur.description]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00    1317\n",
       "0.05     185\n",
       "0.10     173\n",
       "0.20     161\n",
       "0.15     157\n",
       "0.25     154\n",
       "Name: Discount, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Discount'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20     252\n",
       "30     194\n",
       "10     181\n",
       "15     169\n",
       "40     113\n",
       "12      92\n",
       "6       87\n",
       "25      80\n",
       "50      75\n",
       "35      71\n",
       "5       67\n",
       "24      58\n",
       "60      58\n",
       "4       55\n",
       "21      50\n",
       "8       50\n",
       "2       48\n",
       "18      47\n",
       "3       45\n",
       "14      36\n",
       "16      35\n",
       "9       30\n",
       "28      29\n",
       "70      28\n",
       "7       23\n",
       "36      21\n",
       "42      19\n",
       "80      16\n",
       "1       14\n",
       "45      14\n",
       "100     10\n",
       "55       9\n",
       "120      8\n",
       "65       8\n",
       "49       7\n",
       "48       5\n",
       "32       5\n",
       "90       4\n",
       "44       4\n",
       "77       3\n",
       "110      3\n",
       "56       3\n",
       "13       3\n",
       "27       2\n",
       "39       2\n",
       "33       2\n",
       "130      2\n",
       "11       2\n",
       "84       2\n",
       "22       1\n",
       "66       1\n",
       "52       1\n",
       "54       1\n",
       "63       1\n",
       "91       1\n",
       "Name: Quantity, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking for quantity outliers\n",
    "\n",
    "df['Quantity'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Discount Rates vs Quantity in Order')]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualizing the relationship of discounts and quantities\n",
    "\n",
    "sns.boxplot(x='Discount', y='Quantity', data=df).set(title='Discount Rates vs Quantity in Order')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ho: The difference in means between the quantity of product in an order with no discount and the quantity of product in an order with a 5% discount is zero.\n",
    "\n",
    "### Ha: The difference in means between the quantity of product in an order with no discount and the quantity of product in an order with a 5% discount is not zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating the control and experimental groups\n",
    "\n",
    "no_discount = df[df.Discount == 0.0]\n",
    "\n",
    "five_discount = df[df.Discount == 0.05]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=3.700844260760871, pvalue=0.00027233494432847593)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Conduct two tailed t-test using the scipy function ttest_ind\n",
    "\n",
    "stats.ttest_ind(five_discount['Quantity'], no_discount['Quantity'], equal_var = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Used Cohen' d as a measure of effect size\n",
    "\n",
    "def Cohen_d(group1, group2):\n",
    "\n",
    "    diff = group1.mean() - group2.mean()\n",
    "\n",
    "    n1, n2 = len(group1), len(group2)\n",
    "    var1 = group1.var()\n",
    "    var2 = group2.var()\n",
    "    \n",
    "    pooled_var = (n1 * var1 + n2 * var2) / (n1 + n2)\n",
    "    \n",
    "    d = diff / np.sqrt(pooled_var)\n",
    "    \n",
    "    return d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3468767736284544"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Cohen_d(five_discount['Quantity'], no_discount['Quantity'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ho: The difference in means between the quantity of product in an order with no discount and the quantity of product in an order with a 10% discount is zero.\n",
    "\n",
    "### Ha: The difference in means between the quantity of product in an order with no discount and the quantity of product in an order with a 10% discount is not zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Repeated for the remaining discount amounts \n",
    "ten_discount = df[df.Discount == 0.10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=2.094434281933477, pvalue=0.0374559661063408)"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.ttest_ind(ten_discount['Quantity'], no_discount['Quantity'], equal_var = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.19594203057834086"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Cohen_d(ten_discount['Quantity'], no_discount['Quantity'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ho: The difference in means between the quantity of product in an order with no discount and the quantity of product in an order with a 15% discount is zero.\n",
    "\n",
    "### Ha: The difference in means between the quantity of product in an order with no discount and the quantity of product in an order with a 15% discount is not zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "fifteen_discount = df[df.Discount == 0.15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=3.8356620426605934, pvalue=0.00017220729443627393)"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.ttest_ind(fifteen_discount['Quantity'], no_discount['Quantity'], equal_var = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3724041317472808"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Cohen_d(fifteen_discount['Quantity'], no_discount['Quantity'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ho: The difference in means between the quantity of product in an order with no discount and the quantity of product in an order with a 20% discount is zero.\n",
    "\n",
    "### Ha: The difference in means between the quantity of product in an order with no discount and the quantity of product in an order with a 20% discount is not zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "twenty_discount = df[df.Discount == 0.20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=3.4021239132172787, pvalue=0.0008110033254043408)"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.ttest_ind(twenty_discount['Quantity'], no_discount['Quantity'], equal_var = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3007122542913553"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Cohen_d(twenty_discount['Quantity'], no_discount['Quantity'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ho: The difference in means between the quantity of product in an order with no discount and the quantity of product in an order with a 25% discount is zero.\n",
    "\n",
    "### Ha: The difference in means between the quantity of product in an order with no discount and the quantity of product in an order with a 25% discount is not zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "twentyfive_discount = df[df.Discount == 0.25]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=3.8572676240072523, pvalue=0.00015923050922125637)"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.ttest_ind(twentyfive_discount['Quantity'], no_discount['Quantity'], equal_var = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.36659255652674416"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Cohen_d(twentyfive_discount['Quantity'], no_discount['Quantity'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Based on the results of the t-tests, the means of quantity ordered for every discount amount is significantly different than the mean of quantity ordered with no discount. However, the greatest effect size is seen with the 15% discount. We can conclude that applying a 15% discount to the products is the best discount to increase the quantity of product ordered."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q1. Does the unit price significantly affect the quantity of product ordered?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Product Price')]"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualize the spread of product prices\n",
    "\n",
    "sns.scatterplot(x='ProductId', y='UnitPrice', data=df).set(title='Product Price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Remove outliers by only keeping prices under $50\n",
    "\n",
    "price = df[df.UnitPrice <= 50.00]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "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>Id</th>\n",
       "      <th>OrderId</th>\n",
       "      <th>ProductId</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>10248/11</td>\n",
       "      <td>10248</td>\n",
       "      <td>11</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>10248/42</td>\n",
       "      <td>10248</td>\n",
       "      <td>42</td>\n",
       "      <td>9.8</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>10248/72</td>\n",
       "      <td>10248</td>\n",
       "      <td>72</td>\n",
       "      <td>34.8</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>10249/14</td>\n",
       "      <td>10249</td>\n",
       "      <td>14</td>\n",
       "      <td>18.6</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>10249/51</td>\n",
       "      <td>10249</td>\n",
       "      <td>51</td>\n",
       "      <td>42.4</td>\n",
       "      <td>40</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Id  OrderId  ProductId  UnitPrice  Quantity  Discount\n",
       "0  10248/11    10248         11       14.0        12       0.0\n",
       "1  10248/42    10248         42        9.8        10       0.0\n",
       "2  10248/72    10248         72       34.8         5       0.0\n",
       "3  10249/14    10249         14       18.6         9       0.0\n",
       "4  10249/51    10249         51       42.4        40       0.0"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Quantity vs Price')]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualize the relationship between quantity ordered and product price\n",
    "\n",
    "sns.scatterplot(x='UnitPrice', y='Quantity', data=price).set(title='Quantity vs Price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create two groups of prices to compare means\n",
    "\n",
    "highprice = price[price.UnitPrice >= 25.00]\n",
    "lowprice = price[price.UnitPrice < 25.00]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "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>Id</th>\n",
       "      <th>OrderId</th>\n",
       "      <th>ProductId</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>10248/11</td>\n",
       "      <td>10248</td>\n",
       "      <td>11</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>10248/42</td>\n",
       "      <td>10248</td>\n",
       "      <td>42</td>\n",
       "      <td>9.8</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>10248/72</td>\n",
       "      <td>10248</td>\n",
       "      <td>72</td>\n",
       "      <td>34.8</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>10249/14</td>\n",
       "      <td>10249</td>\n",
       "      <td>14</td>\n",
       "      <td>18.6</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>10249/51</td>\n",
       "      <td>10249</td>\n",
       "      <td>51</td>\n",
       "      <td>42.4</td>\n",
       "      <td>40</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Id  OrderId  ProductId  UnitPrice  Quantity  Discount\n",
       "0  10248/11    10248         11       14.0        12       0.0\n",
       "1  10248/42    10248         42        9.8        10       0.0\n",
       "2  10248/72    10248         72       34.8         5       0.0\n",
       "3  10249/14    10249         14       18.6         9       0.0\n",
       "4  10249/51    10249         51       42.4        40       0.0"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/davidcuervo/opt/anaconda3/envs/learn-env/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "#Create a new price column that is categorical to create a boxplot\n",
    "\n",
    "price['lowhigh'] = ['High' if x >= 25.00 else 'Low' for x in price['UnitPrice']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "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>Id</th>\n",
       "      <th>OrderId</th>\n",
       "      <th>ProductId</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "      <th>lowhigh</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>10248/11</td>\n",
       "      <td>10248</td>\n",
       "      <td>11</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>10248/42</td>\n",
       "      <td>10248</td>\n",
       "      <td>42</td>\n",
       "      <td>9.8</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>10248/72</td>\n",
       "      <td>10248</td>\n",
       "      <td>72</td>\n",
       "      <td>34.8</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>10249/14</td>\n",
       "      <td>10249</td>\n",
       "      <td>14</td>\n",
       "      <td>18.6</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>10249/51</td>\n",
       "      <td>10249</td>\n",
       "      <td>51</td>\n",
       "      <td>42.4</td>\n",
       "      <td>40</td>\n",
       "      <td>0.0</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Id  OrderId  ProductId  UnitPrice  Quantity  Discount lowhigh\n",
       "0  10248/11    10248         11       14.0        12       0.0     Low\n",
       "1  10248/42    10248         42        9.8        10       0.0     Low\n",
       "2  10248/72    10248         72       34.8         5       0.0    High\n",
       "3  10249/14    10249         14       18.6         9       0.0     Low\n",
       "4  10249/51    10249         51       42.4        40       0.0    High"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Prices vs Quantity in Order')]"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/nRNOOCEjR47McccdlyTZYostUldX1/Q1L730UtMlI+ti0aKlaWwsr7dZoajq6ppmYV1dXZO6utcqOBFA+9alS9dmYd2lS1fnTdqVqqrSO27kttkt9ZYuXZrjjz8+p556alNQJ29eFrLxxhtn1qxZSZIJEyZk8ODBbTUWtJoTTjip2XrUqJMrNAlAxzB69CnN1ieffGqFJoF112ZRfcstt+Sll17Kddddl4MPPjgHH3xwrrjizWulLr300lx44YUZOnRoli1blmOPPbatxoJWs/vue6a6+s0fBlVX1+RTn9qjwhMBtG877LBTunTpmuTNXertt9+xwhNBy5XK5fIGcc2Eyz9ojx58cEZ+8pOrMnr010U1QAvMmfNoLrvsovzXf31LVNPurOnyD1ENAAAt0C6uqQYAgA2VqAYAgIJENQAAFCSqAQCgIFENAAAFiWoAAChIVAMAQEGiGgAAChLVAABQkKgGAICCRDUAABQkqgEAoCBRDQAABYlqAAAoSFQDAEBBohoAAAoS1QAAUJCoBgCAgkQ1AAAUJKoBAKAgUQ0AAAWJagAAKEhUAwBAQaIaAAAKEtUAAFCQqAYAgIJENQAAFCSqAQCgIFENrejZZ5/JV796fObNm1vpUQCAViSqoRVdc80Ps3z58vzkJ1dVehQAoBWJamglzz77TJ5/fn6S5Pnn59utBoANmKiGVnLNNT9strZbDQAbLlENrWT1LvU7rQGADYeohlay1VZ917gGADYcohpayahRX222PvHEr1VoEgCgtYlqaCXbbPP+pt3prbbqm6233rbCEwEArUVUQysaNeqr6dy5s11qANjAlcrlcrnSQ6wPixYtTWPjBvGtAADQDlVVldKrV7e3f6yNZwEAgA2OqAYAgIJENQAAFCSqAQCgIFENAAAFiWoAAChIVAMAQEGiGgAAChLVAABQkKgGAICCRDUAABQkqgEAoCBRDQAABYlqAAAoSFQDAEBBohoAAAoS1QAAUJCoBgCAgkQ1AAAUJKoBAKAgUQ0AAAWJagAAKEhUAwBAQaIaAAAKEtUAAFCQqAYAgIJENQAAFCSqAQCgIFENrWjKlN/luOOOytSp91Z6FIAO4cEHZ+S4447KQw89UOlRYJ20alQvXbo0w4YNy3PPPZckmTFjRmpra7Pffvvl8ssvb/q6J554IsOHD8/++++fMWPGpL6+vjXHgjZzww2/SJL88pfXVnYQgA7iZz/7cZLkmmuurvAksG5aLar//Oc/58gjj8wzzzyTJFmxYkW+/e1v5+qrr86dd96Zxx57LFOnTk2SnH766Tn77LNz1113pVwuZ9y4ca01FrSZKVN+l6T8/1dlu9UAa/HggzPS0PDmxlpDQ73dajqUVovqcePG5ZxzzkmfPn2SJI8++mi23XbbbL311qmpqUltbW0mT56c+fPnZ8WKFdlll12SJMOHD8/kyZNbayxoM6t3qVezWw2wZqt3qVezW01HUtNaL3z++ec3Wy9cuDC9e/duWvfp0ycLFix4y/HevXtnwYIF6/x+vXp1e/fDQqsov2Xdu/emFZkEoCNYvUv9z2vnTTqKVovqf9XY2JhSqdS0LpfLKZVK73h8XS1atDSNjf8aMVBJpTQP61Lq6l6r1DAA7V51dU2zsK6urnHepF2pqiq940Zum939Y4sttkhdXV3Tuq6uLn369HnL8ZdeeqnpkhHoyI455kvN1l/84nGVGQSggzjhhJOarUeNOrlCk8C6a7Oo3nnnnfP3v/89c+fOTUNDQ+64444MHjw4ffv2zcYbb5xZs2YlSSZMmJDBgwe31VjQavbee9+8uVudJKUMGbJPJccBaPd2333PVFe/+UP06uqafOpTe1R4Imi5NovqjTfeOGPHjs0pp5ySAw88MB/84AczdOjQJMmll16aCy+8MEOHDs2yZcty7LHHttVY0KpW71bbpQZomdW71Xap6WhK5XJ5g7gQ2TXVAAC0pnZxTTUAAGyoRDUAABQkqgEAoCBRDQAABYlqAAAoSFQDAEBBohoAAAoS1QAAUJCoBgCAgkQ1AAAUJKoBAKAgUQ0AAAWJagAAKEhUAwBAQaIaAAAKEtUAAFCQqAYAgIJENQAAFCSqAQCgIFENAAAFiWoAAChIVAMAQEGiGgAAChLVAABQkKgGAICCRDW0osWLX8nYsedlyZLFlR4FAGhFohpa0aRJt+Uvf3kyEyeOr/QoAEArEtXQShYvfiXTpk1NuVzOtGn32a0GgA2YqIZWMmnSbWlsLCdJGhsb7VYDwAZMVEMrmTlzehoa6pMkDQ31mTlzeoUnAmj/fBaFjkpUQysZMGDgGtcAvJXPotBRiWpoJR/9aL9m6379tq/QJAAdg8+i0JGJamgl119/XbP1L3/58wpNAtAx+CwKHZmohlaybNnra1wD0JzPotCRiWpoJV26dF3jGoDmBgwYmOrqmiRJdXWNz6LQoYhqaCWjR5/SbH3yyadWaBKAjqG29tBUVZWSJFVVVfnc54ZXeCJoOVENrWSHHXZq2p3u0qVrtt9+xwpPBNC+9ejRM4MGDUmpVMqgQYPTvXuPSo8ELSaqoRWNHn1KSqWSXWqAFqqtPTQf+ch2dqnpcErlcrlc6SHWh0WLljZ9YhgAANa3qqpSevXq9vaPtfEsAACwwRHVAABQkKgGAICCRDUAABQkqgEAoCBRDQAABYlqAAAoSFQDAEBBohoAAAoS1QAAUJCoBgCAgkQ1AAAUJKoBAKAgUQ2taPHiVzJ27HlZsmRxpUcB6BCcN+moRDW0okmTbstf/vJkJk4cX+lRADoE5006KlENrWTx4lcybdrUlMvlTJt2n10XgLVw3qQjE9XQSiZNui2NjeUkSWNjo10XgLVw3qQjE9XQSmbOnJ6GhvokSUNDfWbOnF7hiQDaN+dNOjJRDa1kwICBqa6uSZJUV9dkwICBFZ4IoH1z3qQjE9XQSmprD01VVSlJUlVVlc99bniFJwJo35w36chENbSSHj16ZtCgISmVShk0aHC6d+9R6ZEA2jXnTTqymkoPABuy2tpDM3/+c3ZbAFrIeZOOqlQul8uVHmJ9WLRoadMnhgEAYH2rqiqlV69ub/9YG88CAAAbHFENAAAFiWoAAChIVAMAQEGiGgAACqpIVE+YMCEHHXRQDjrooFx00UVJkieeeCLDhw/P/vvvnzFjxqS+vr4So8F6dcklF+a4447K5ZdfVOlRADqEOXMezfHHH53HH3+s0qPAOmnzqF6+fHnOP//8XH/99ZkwYUIefvjhzJgxI6effnrOPvvs3HXXXSmXyxk3blxbjwbr3RNPzE6SzJ795wpPAtAx/OhHV6ZcLufqq6+o9CiwTto8qhsaGtLY2Jjly5envr4+9fX1qampyYoVK7LLLrskSYYPH57Jkye39WiwXl1yyYXN1narAdZszpxHs2zZ60mSZctet1tNh9LmUd2tW7eceuqpOeCAAzJkyJD07ds3nTp1Su/evZu+pnfv3lmwYEFbjwbr1epd6tXsVgOs2Y9+dGWztd1qOpI2/zXl//u//5tbb701v//977PpppvmtNNOy/Tp01MqlZq+plwuN1u3xDv9dhtoT3r33rTSIwC0W6t3qf957bxJR9HmUT1t2rQMGDAgvXr1SvLmpR4///nPU1dX1/Q1L730Uvr06bNOr+vXlNMR1NW9VukRANqtLl26NgvrLl26Om/SrrSrX1Per1+/zJgxI8uWLUu5XM6UKVOy2267ZeONN86sWbOSvHl3kMGDB7f1aLBefexj/Zut+/ffuUKTAHQMo0ef0mx98smnVmgSWHdtvlM9aNCgPP744xk+fHg6deqU/v37Z9SoUdl3331z1llnZenSpdlhhx1y7LHHtvVosF6dfvq3ctxxRzWtv/GNMyo4DUD7t8MOOzXtVnfp0jXbb79jpUeCFiuVy+UN4poJl3/QHl1yyYV54onZ6d9/Z1EN0AJz5jyayy67KP/1X98S1bQ7a7r8Q1QDAEALtKtrqgEAYEMjqqEVPfvsM/nqV4/PvHlzKz0KQIewePErGTv2vCxZsrjSo8A6EdXQiq655odZvnx5fvKTqyo9CkCHMGnSbfnLX57MxInjKz0KrBNRDa3k2WefyfPPz0+SPP/8fLvVAGuxePErmTZtasrlcqZNu89uNR2KqIZWcs01P2y2tlsNsGaTJt3WdNOBxsZGu9V0KKIaWsnqXep3WgPQ3MyZ09PQUJ8kaWioz8yZ0ys8EbScqIZWstVWfde4BqC5AQMGprr6zd9LV11dkwEDBlZ4Img5UQ2tZNSorzZbn3ji1yo0CUDHUFt7aKqqSkmSqqqqfO5zwys8EbScqIZWss0272/and5qq77ZeuttKzwRQPvWo0fPDBo0JKVSKYMGDU737j0qPRK0mKiGVjRq1FfTuXNnu9QALVRbe2g+8pHt7FLT4fg15QAA0AJ+TTkAALQiUQ0AAAWJagAAKEhUAwBAQaIaAAAKEtUAAFCQqAYAgIJENQAAFCSqAQCgIFENAAAFiWoAAChIVAMAQEGiGgAAChLVAABQkKgGAICCRDUAABQkqgEAoKCaSg8AG7JLLrkwTzwxO/3775xvfOOMSo8D0O6NGfPNvPDCc3nf+7bJeeeNrfQ40GIt2qm+/vrrs3Tp0taeBTY4TzwxO0kye/afKzwJQMfwwgvPJUmee+7ZCk8C66ZFUf3kk09m//33z5gxYzJ79uzWngk2CJdccmGz9eWXX1ShSQA6hjFjvtlsffbZZ1ZoElh3Lbr843vf+16WLl2aSZMm5dxzz025XM6RRx6Z2trabLzxxq09I3RIq3epV7NbDbBmq3epV7NbTUfS4g8qduvWLUOHDs2wYcOyePHi/OY3v8nQoUMzZcqU1pwPAADavRbtVM+cOTM33XRTZs6cmf333z8//OEP069fvzz77LM56qijsvfee7f2nAAA0G61aKf63HPPzSc+8Yncc889Oe+889KvX78kyTbbbJPPf/7zrTogdFQf+1j/Zuv+/Xeu0CQAHcOWW76v2fp979umQpPAuiuVy+Xy2r7o17/+dY4++uhmx6655pqMGjWq1QZbV4sWLU1j41q/FWhTxx13VNO/X3vtbyo4CUDH4LxJe1ZVVUqvXt3e9rE1Xv7x29/+NitWrMgvfvGLrFy5sun4qlWrcuONN7arqIb26GMf6990n2oA1m7LLd/XdJ9q6EjWuFN9880355FHHsmUKVOaXTddXV2dwYMHZ7/99muTIVvCTjUAAK1pTTvVLbr845577slnP/vZ9T7Y+iSqAQBoTe/68o+f/vSn+cpXvpKZM2fmgQceeMvjZ5111vqZEAAAOrA1RvWmm26aJOnZs2ebDAMAAB3RGqN6xIgRSZL3vOc9Oeqoo5o9ds0117TeVAAA0IG0+O4fb7zxRtNxd/8AAIB/WGNU19TU5KmnnsqKFSvy1FNPNR2vrq7OmWee2erDAQBAR+DuHwBAu7F48Sv58Y+vzOjRX0/37j0qPQ40867v/rHaLrvskquuuiqLFy9udtzdPwCA9WnSpNvyl788mYkTx2fkyOMqPQ60WFVLvuj000/PnDlz0r179/To0aPpHwCA9WXx4lcybdrUlMvlTJt2X5YsWbz2J0E70aKd6hdffDH/8z//09qzwAbnjjsmZPz4m/L5zx+ZoUNrKz0OQLs2adJtaWxsTJI0NjbYraZDadFO9VZbbZVly5a19iywwRk//qYkybhxv63wJADt38yZ09PQ0JAkaWhoyMyZ0ys8EbRci3aq+/Tpk0MOOSS77bZbNtlkk6bjrqmGd3bHHROarSdPnmS3GmANdtxxpzz88INN6/79d6rgNLBuWhTVffv2Td++fVt7FtigrN6lXm3cuN+KaoA1mDdvbrP1s8/OfYevhPanRVH9ta997S3HXA4CAKxPCxa8uMY1tGctiup77rknP/jBD7Js2bKUy+U0NjZm8eLF+dOf/tTa8wEA/ya22qpvnn9+frM1dBQt+qDixRdfnJNOOilbbrllzjnnnHz605/OiBEjWns26NCGD/9Cs/XnP39khSYB6BhGjfpqs/WJJ771J+XQXrUoqjt37pwDDzwwu+yySzbeeON85zvfyR/+8IdWHg06tmHDDm62djx1evYAABaVSURBVD01wJpts837m3ant9qqb7beetsKTwQt16Ko3njjjbNy5cpss802eeKJJ1JVVZVSqdTas0GHt3q32i41QMsceeTIlEqlHHXUFys9CqyTFl1Tvffee2fUqFG56KKL8oUvfCGzZs1Kz549W3s26PCGDTv4LTvWALyzRx55OEkya9Yfs/32O1Z4Gmi5UrlcLrfkC59//vlstdVWefzxx/PQQw9l2LBh6dWrV2vP12KLFi1NY2OLvhUAoB1avPiVnHHGf2TVqlXp1GmjXHzx99O9e49KjwVNqqpK6dWr29s/1pIXmDNnTl555ZXMmTMn5XI5u+66a1580W1uAID1581fU/7mBlljY2MmThxf4Ymg5Vp0+ccpp5zS9O+rVq3KSy+9lB122CG33HJLqw0GAPx7efPXlNcnSRoa6jNz5vSMHHlchaeClmlRVE+ZMqXZ+sEHH8ykSZNaZSAA4N/TgAEDc999f0hDQ32qq2syYMDASo8ELdaiyz/+1e677545c+as71kAgH9jtbWHpqrqzbuLVVVV5XOfG17hiaDlWrRT/c8BXS6X89hjj2XFihWtNhQA8O+nR4+eGTRoSP7wh3szaNBgH1KkQ2nxNdWr70tdKpXSs2fPnHvuua06GADw76e29tDMn/+cXWo6nLXeUu/111/Pb3/72zzyyCNpbGzMxz/+8Rx99NG55557svnmm2fAgAFtNesauaUeAACtaU231FvjTvXixYvzhS98IR/+8IczcOCbHxZ44IEHcthhh6Vr16751a9+tf6nBQCADmaNUX3llVfmiCOOyAknnNB07Oijj87Xv/711NTUpFu3ty91AAD4d7LGu3/88Y9/zJe//OVmxxYvXpynn3660N0/pkyZkuHDh+eAAw7I9773vSTJjBkzUltbm/322y+XX375u35tAABoa2uM6lKplOrq6mbHunbtmh/84AfZeOON39Ubzps3L+ecc06uvvrqTJw4MY8//nimTp2ab3/727n66qtz55135rHHHsvUqVPf1esDAEBbW+t9qpcuXdps3alTp/Tu3ftdv+Hvfve7HHjggdliiy3SqVOnXH755encuXO23XbbbL311qmpqUltbW0mT578rt8DAADa0hqvqR42bFj++7//OxdddFE22mijJMkbb7yRc845J5/73Ofe1RvOnTs3nTp1ykknnZQXXnghe+21Vz7ykY80C/U+ffpkwYIF7+r1qbzp0+/LtGl+0pAkS5YsThL3Wk0yaNCQDBw4uNJjQLvkvPkPzpvNOXd2HGuM6uOPPz6nnXZa9tlnn+y4445JktmzZ2f33XfP8ccf/67esKGhIQ8//HCuv/76dOnSJaNHj84mm2zSdB/s5M1fMPPP65Z4p9ub0PY226xzOnWqXvsX/ht49dUlSZL3vrdXhSepvM0265zevTet9BjQLjlv/oPzZnPOnR3HGqO6uro6l19+eWbPnp1Zs2YlSUaPHp2ddtrpXb/he9/73gwYMCDvec97kiSf/exnM3ny5GbXbtfV1aVPnz7r9LruU91+9O//qfTv/6lKj9EuXHTRd5Mk//mf367wJO1DXd1rlR4B2iXnzX9w3nwr5872413fp3q1/v37p3///utlmM985jM544wz8uqrr6Zr1665//77M3To0FxzzTWZO3du3ve+9+WOO+7IYYcdtl7eDwAAWluLonp92nnnnXPCCSfkqKOOyqpVqzJw4MAceeSR+eAHP5hTTjklb7zxRoYMGZKhQ4e29WgAAPCutHlUJ8nhhx+eww8/vNmxAQMGZOLEiZUYBwAAClnrLfUAAIA1E9UAAFCQqAYAgIJENQAAFCSqAQCgIFENAAAFiWoAAChIVAMAQEGiGgAAChLVAABQkKgGAICCRDUAABQkqgEAoCBRDQAABYlqAAAoSFQDAEBBohoAAAoS1QAAUJCoBgCAgkQ1AAAUJKoBAKAgUQ0AAAWJagAAKEhUAwBAQaIaAAAKEtUAAFCQqAYAgIJENQAAFCSqAQCgIFENAAAFiWoAAChIVAMAQEGiGgAAChLVAABQkKgGAICCRDUAABQkqgEAoCBRDQAABYlqAAAoSFQDAEBBohoAAAoS1QAAUJCoBgCAgkQ1AAAUJKoBAKAgUQ0AAAWJagAAKEhUAwBAQaIaAAAKEtUAAFCQqAYAgIJENQAAFCSqAQCgIFENAAAFiWoAAChIVAMAQEGiGgAAChLVAABQkKgGAICCRDUAABQkqgEAoCBRDQAABYlqAAAoSFQDAEBBohoAAAoS1QAAUJCoBgCAgkQ1AAAUVLGovuiii3LmmWcmSZ544okMHz48+++/f8aMGZP6+vpKjQUAAOusIlE9c+bM3HbbbU3r008/PWeffXbuuuuulMvljBs3rhJjAQDAu9LmUb148eJcfvnlOemkk5Ik8+fPz4oVK7LLLrskSYYPH57Jkye39VgAAPCu1bT1G5599tn5xje+kRdeeCFJsnDhwvTu3bvp8d69e2fBggXr/Lq9enVbbzPC+tKpU3WSpHfvTSs8CUDH4LxJR9WmUX3zzTdnyy23zIABAzJ+/PgkSWNjY0qlUtPXlMvlZuuWWrRoaRoby+ttVlgfVq1qSJLU1b1W4UkAOgbnTdqzqqrSO27ktmlU33nnnamrq8vBBx+cJUuWZNmyZSmVSqmrq2v6mpdeeil9+vRpy7EAAKCQNo3q6667runfx48fnz/+8Y+58MILM2zYsMyaNSuf/OQnM2HChAwePLgtxwIAgELa/Jrqt3PppZfmrLPOytKlS7PDDjvk2GOPrfRIAADQYhWL6uHDh2f48OFJkn79+uWWW26p1CgAAFCI36gIAAAFiWoAAChIVAMAQEGiGgAAChLVAABQkKgGAICCRDUAABQkqgEAoCBRDQAABYlqAAAoSFQDAEBBohoAAAoS1QAAUJCoBgCAgkQ1AAAUJKoBAKAgUQ0AAAWJagAAKEhUAwBAQaIaAAAKEtUAAFCQqAYAgIJENQAAFCSqAQCgIFENAAAFiWoAAChIVAMAQEGiGgAAChLVAABQkKgGAICCRDUAABRUKpfL5UoPsT4sWrQ0jY2V+1Z+85tfZd68uRV7f9qnZ5998+/ENttsW+FJaE+23nrbHHXUsZUeo+KcN3k7zpu8k/Zw7qyqKqVXr25v+1hNG8+ywZo3b26e/MtfU71Jj0qPQjvS2FCdJPnrvJcqPAntRcOKxZUeod2YN29unvnr/2aLbv5TxD90SWOSZMWLf63wJLQnLy6tr/QIa+VMth5Vb9IjXbbdp9JjAO3Ysrn3VnqEdmWLbjX58k7vqfQYQDt33aMvV3qEtXJNNQAAFCSqAQCgIFENAAAFiWoAAChIVAMAQEGiGgAAChLVAABQkKgGAICCRDUAABQkqgEAoCBRDQAABYlqAAAoSFQDAEBBohoAAAoS1QAAUJCoBgCAgkQ1AAAUJKoBAKAgUQ0AAAWJagAAKEhUAwBAQaIaAAAKEtUAAFCQqAYAgIJENQAAFCSqAQCgIFENAAAFiWoAAChIVAMAQEGiGgAAChLVAABQkKgGAICCRDUAABRUkai+6qqrctBBB+Wggw7KxRdfnCSZMWNGamtrs99+++Xyyy+vxFgAAPCutHlUz5gxI9OmTcttt92W22+/PXPmzMkdd9yRb3/727n66qtz55135rHHHsvUqVPbejQAAHhX2jyqe/funTPPPDMbbbRROnXqlA996EN55plnsu2222brrbdOTU1NamtrM3ny5LYeDQAA3pWatn7Dj3zkI03//swzz+R//ud/cswxx6R3795Nx/v06ZMFCxa09WiFLFmyOA0rFmfZ3HsrPQrQjjWsWJwlS9r81NsuLVmyOK8src91j75c6VGAdu7FpfXpuWRxpcdYo4qd2f/yl7/kxBNPzDe/+c1UV1fnmWeeaXqsXC6nVCqt0+v16tVtPU+4bqqrfeYTaJnq6qr07r1ppceoOOdNYF2093NnRaJ61qxZ+frXv55vf/vbOeigg/LHP/4xdXV1TY/X1dWlT58+6/SaixYtTWNjeX2P2mLdum2W6k1Wpsu2+1RsBqD9Wzb33nTrtlnq6l6r9CgV163bZqnptjBf3uk9lR4FaOeue/TlbNIOzp1VVaV33Mht822CF154IV/96ldz6aWX5qCDDkqS7Lzzzvn73/+euXPnpqGhIXfccUcGDx7c1qMBAMC70uY71T//+c/zxhtvZOzYsU3HRowYkbFjx+aUU07JG2+8kSFDhmTo0KFtPRoAALwrbR7VZ511Vs4666y3fWzixIltPA0AABTnUyIAAFCQqAYAgIJENQAAFCSqAQCgIFENAAAFiWoAAChIVAMAQEGiGgAAChLVAABQkKgGAICCRDUAABQkqgEAoCBRDQAABYlqAAAoSFQDAEBBohoAAAoS1QAAUJCoBgCAgkQ1AAAUJKoBAKAgUQ0AAAWJagAAKEhUAwBAQaIaAAAKEtUAAFCQqAYAgIJENQAAFCSqAQCgIFENAAAFiWoAAChIVAMAQEGiGgAACqqp9AAbkoYVi7Ns7r2VHoN2pLF+RZKkqmaTCk9Ce9GwYnGS91Z6jHbjxaX1ue7Rlys9Bu3I0pWNSZJuG9n34x9eXFqf91d6iLUQ1evJ1ltvW+kRaIeefXZukmSbrUUUq73X+eL/8+fA21n4/8+b793C3w/+4f1p/+eMUrlcLld6iPVh0aKlaWzcIL4VNiAXXfTdJMkZZ/x3hScB6BicN2nPqqpK6dWr29s/1sazAADABkdUAwBAQaIaAAAKEtUAAFCQqAYAgIJENQAAFCSqAQCgIFENAAAFiWoAAChIVAMAQEGiGgAAChLVAABQkKgGAICCRDUAABQkqgEAoCBRDQAABYlqAAAoSFQDAEBBohoAAAoS1QAAUJCoBgCAgkQ1AAAUJKoBAKAgUQ0AAAWJagAAKEhUAwBAQaIaAAAKEtUAAFCQqAYAgIJENQAAFCSqAQCgIFENAAAFlcrlcrnSQ6wPixYtTWPjBvGtdHjTp9+XadOmVnqMduHZZ+cmSbbZZtsKT1J5gwYNycCBgys9BrRLzpv/4LzZnHNn+1JVVUqvXt3e/rE2nmWNJk2alAMPPDD77bdffv3rX1d6HCise/fu6d69e6XHAOgwnDfpqNrNTvWCBQty5JFHZvz48dloo40yYsSIXHbZZfnwhz/coufbqQYAoDV1iJ3qGTNmZI899kiPHj3SpUuX7L///pk8eXKlxwIAgLWqqfQAqy1cuDC9e/duWvfp0yePPvpoi5//Tv/XAAAAra3dRHVjY2NKpVLTulwuN1uvjcs/AABoTR3i8o8tttgidXV1Teu6urr06dOnghMBAEDLtJuo3nPPPTNz5sy8/PLLWb58ee6+++4MHuwWMgAAtH/t5vKPzTffPN/4xjdy7LHHZtWqVTn88MOz0047VXosAABYq3ZzS72iXFMNAEBr6hDXVAMAQEclqgEAoCBRDQAABYlqAAAoSFQDAEBBohoAAAoS1QAAUJCoBgCAgkQ1AAAUJKoBAKAgUQ0AAAWJagAAKEhUAwBAQaIaAAAKqqn0AOtLVVWp0iMAALABW1NvlsrlcrkNZwEAgA2Oyz8AAKAgUQ0AAAWJagAAKEhUAwBAQaIaAAAKEtUAAFCQqAYAgIJENQAAFCSqAQCgIFEN79KDDz6YkSNHVnoMgA7jueeey9577/2W49ttt13uvffeXHHFFWt8/siRI/Pggw+21nhQSE2lBwAA2GeffbLPPvtUegx41+xUw3r24x//OAceeGBqa2szduzYNDQ05KSTTsrUqVOTJJdddllOOOGEJMnChQszbNiwSo4L0C6MHz8+Z555ZpI3fxJYW1ubQw45JN/5znea/VTwlltuyaGHHpp99tknU6ZMqdS48BZ2qmE9mjp1aqZMmZJbb701nTp1yimnnJIbb7wxQ4YMyQMPPJAhQ4bk4YcfzosvvpiGhobcf//9GTJkSKXHBmgzCxcuzMEHH/yOj69atSrf/OY385Of/CT9+vXL9773vWaPb7rpprntttvy+9//PlddddXbXk4ClWCnGtajBx54IAcddFA6d+6cmpqaHHbYYZk5c2b22muvzJw5M0uXLk3y5vWDc+bMyX333ZfPfOYzFZ4aoO306dMnEyZMaPbPP3vqqafSq1ev9OvXL0ly+OGHN3v8s5/9bJLkwx/+cF555ZW2GRpawE41rEeNjY1vOVZfX58tt9wyjY2Nufvuu/OJT3wi733ve/PAAw9kzpw5+fjHP16BSQHap+rq6rc9l/7z40lSKpXaaiRoETvVsB7tscce+b//9/9mxYoVqa+vz6233po99tgjSTJ48OD86Ec/ym677ZY99tgj119/fXbeeeem/0AAkHzwgx/Mq6++mieffDJJMmnSpApPBC1jpxoKePjhh5vtNNfW1mavvfbKYYcdlvr6+gwaNCjHHHNMkmSvvfbKddddl09+8pPp0qVLVq1a5dIPgH+x0UYb5eKLL84ZZ5yRqqqqfOADH8gmm2xS6bFgrUrlcrlc6SEAAJI3L6O79NJL87WvfS1dunTJddddlwULFjTdGQTaKzvVAEC7UVVVlR49euTwww9Pp06d0rdv35x//vmVHgvWyk41AAAU5IOKAABQkKgGAICCRDUAABQkqgE6iAcffDDDhg0r9Brjx4/PiSee+LaPjRkzJjNmzFjj86+88sqcd955hWYA2BC5+wcASeIOCwAF2KkG6GBee+21nHbaaRk2bFhqa2tz8cUXp76+Pueff36+//3vJ0kWLlyY7bbbLg888ECSZMKECfmP//iPJEldXV1GjRqV2traHHLIIXn66aeTJCNHjszkyZOTvLmjPXTo0BxyyCEZO3Zstt9++6b3/9vf/paRI0dm6NChOeaYY7Jw4cK2/PYB2iVRDdDBfO9730uPHj0yadKk3HrrrXnyySdz7bXXZr/99st9992XJLn//vvTu3fvpss5pkyZkv322y9JMm/evIwZMyaTJk3Krrvump///OfNXv+vf/1rLr300vziF7/I7bffnm7duqWhoaHp8Xnz5uWKK67I5MmTs9lmm+Xmm29uo+8coP0S1QAdzH333ZdjjjkmpVIpG220UUaMGJH77rsvn/zkJ7NgwYK89NJLuf/++zN69OhMnz49K1euzEMPPZQhQ4YkSXbaaadsu+22SZKPfexjefnll5u9/rRp0zJw4MBsscUWSZJjjjmm2eMDBw7Me97zniRJv3793vJ8gH9Hohqgg2lsbEypVGq2rq+vT1VVVfbaa69MnTo1jz76aI444ojU1dVl8uTJ+fjHP56uXbsmSWpq/vFxmlKplH/9HWDV1dXNjlVXVzd7fG3PB/h3JKoBOphBgwblhhtuSLlczsqVKzNu3LjsueeeSZL99tsvP/vZz/LRj340G220UfbYY49cdtllTZd+tPT1Z86cmQULFiSJyzsAWkBUA3QwZ511Vl5++eXU1tamtrY2H/jAB3LSSSclSQYMGJCFCxc2RfagQYPy0ksvZe+9927x63/gAx/It771rRx//PEZPnx4nn766XTu3LlVvheADUWp7Od2APyTefPmZcKECTn55JNTVVWVu+++Oz/96U/tWAOsgftUA9DMFltskYULF6a2tjbV1dXZdNNNc8EFF1R6LIB2zU41AAAU5JpqAAAoSFQDAEBBohoAAAoS1QAAUJCoBgCAgkQ1AAAU9P8AVss5B+Of3CUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualize the difference in quantity means between the two price groups\n",
    "\n",
    "sns.boxplot(x='lowhigh', y='Quantity', data=price).set(title='Prices vs Quantity in Order')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ho: The difference in means between the quantity of product with a price below 25 dollars and the quantity of product with a price above 25 dollars is zero.\n",
    "\n",
    "### Ha: The difference in means between the quantity of product with a price below 25 dollars and the quantity of product with a price above 25 dollars is not zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-0.8757605826189973, pvalue=0.3813537001247296)"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.ttest_ind(lowprice['Quantity'], highprice['Quantity'], equal_var = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.04375230097643054"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Cohen_d(lowprice['Quantity'], highprice['Quantity'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Based on the results of the t-test, there is no significant difference between the amount of product ordered in the low and high price groups. We can conclude that the price of the product does not significantly effect the amount of product ordered. More expensive products will mostly likely be sold at the same amount as cheaper products."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q2. Does shipping region have an effect on the quantity of product ordered?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "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>Id</th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>EmployeeId</th>\n",
       "      <th>OrderDate</th>\n",
       "      <th>RequiredDate</th>\n",
       "      <th>ShippedDate</th>\n",
       "      <th>ShipVia</th>\n",
       "      <th>Freight</th>\n",
       "      <th>ShipName</th>\n",
       "      <th>ShipAddress</th>\n",
       "      <th>ShipCity</th>\n",
       "      <th>ShipRegion</th>\n",
       "      <th>ShipPostalCode</th>\n",
       "      <th>ShipCountry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>10248</td>\n",
       "      <td>VINET</td>\n",
       "      <td>5</td>\n",
       "      <td>2012-07-04</td>\n",
       "      <td>2012-08-01</td>\n",
       "      <td>2012-07-16</td>\n",
       "      <td>3</td>\n",
       "      <td>32.38</td>\n",
       "      <td>Vins et alcools Chevalier</td>\n",
       "      <td>59 rue de l'Abbaye</td>\n",
       "      <td>Reims</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>51100</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>10249</td>\n",
       "      <td>TOMSP</td>\n",
       "      <td>6</td>\n",
       "      <td>2012-07-05</td>\n",
       "      <td>2012-08-16</td>\n",
       "      <td>2012-07-10</td>\n",
       "      <td>1</td>\n",
       "      <td>11.61</td>\n",
       "      <td>Toms Spezialitäten</td>\n",
       "      <td>Luisenstr. 48</td>\n",
       "      <td>Münster</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>44087</td>\n",
       "      <td>Germany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>10250</td>\n",
       "      <td>HANAR</td>\n",
       "      <td>4</td>\n",
       "      <td>2012-07-08</td>\n",
       "      <td>2012-08-05</td>\n",
       "      <td>2012-07-12</td>\n",
       "      <td>2</td>\n",
       "      <td>65.83</td>\n",
       "      <td>Hanari Carnes</td>\n",
       "      <td>Rua do Paço, 67</td>\n",
       "      <td>Rio de Janeiro</td>\n",
       "      <td>South America</td>\n",
       "      <td>05454-876</td>\n",
       "      <td>Brazil</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>10251</td>\n",
       "      <td>VICTE</td>\n",
       "      <td>3</td>\n",
       "      <td>2012-07-08</td>\n",
       "      <td>2012-08-05</td>\n",
       "      <td>2012-07-15</td>\n",
       "      <td>1</td>\n",
       "      <td>41.34</td>\n",
       "      <td>Victuailles en stock</td>\n",
       "      <td>2, rue du Commerce</td>\n",
       "      <td>Lyon</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>69004</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>10252</td>\n",
       "      <td>SUPRD</td>\n",
       "      <td>4</td>\n",
       "      <td>2012-07-09</td>\n",
       "      <td>2012-08-06</td>\n",
       "      <td>2012-07-11</td>\n",
       "      <td>2</td>\n",
       "      <td>51.30</td>\n",
       "      <td>Suprêmes délices</td>\n",
       "      <td>Boulevard Tirou, 255</td>\n",
       "      <td>Charleroi</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>B-6000</td>\n",
       "      <td>Belgium</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id CustomerId  EmployeeId   OrderDate RequiredDate ShippedDate  ShipVia  \\\n",
       "0  10248      VINET           5  2012-07-04   2012-08-01  2012-07-16        3   \n",
       "1  10249      TOMSP           6  2012-07-05   2012-08-16  2012-07-10        1   \n",
       "2  10250      HANAR           4  2012-07-08   2012-08-05  2012-07-12        2   \n",
       "3  10251      VICTE           3  2012-07-08   2012-08-05  2012-07-15        1   \n",
       "4  10252      SUPRD           4  2012-07-09   2012-08-06  2012-07-11        2   \n",
       "\n",
       "   Freight                   ShipName           ShipAddress        ShipCity  \\\n",
       "0    32.38  Vins et alcools Chevalier    59 rue de l'Abbaye           Reims   \n",
       "1    11.61         Toms Spezialitäten         Luisenstr. 48         Münster   \n",
       "2    65.83              Hanari Carnes       Rua do Paço, 67  Rio de Janeiro   \n",
       "3    41.34       Victuailles en stock    2, rue du Commerce            Lyon   \n",
       "4    51.30           Suprêmes délices  Boulevard Tirou, 255       Charleroi   \n",
       "\n",
       "       ShipRegion ShipPostalCode ShipCountry  \n",
       "0  Western Europe          51100      France  \n",
       "1  Western Europe          44087     Germany  \n",
       "2   South America      05454-876      Brazil  \n",
       "3  Western Europe          69004      France  \n",
       "4  Western Europe         B-6000     Belgium  "
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Source data from the Order table\n",
    "\n",
    "cur.execute(\"\"\"SELECT *\n",
    "               FROM 'Order'\n",
    "               ;\n",
    "               \"\"\")\n",
    "order = pd.DataFrame(cur.fetchall())\n",
    "order.columns = [x[0] for x in cur.description]\n",
    "order.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Western Europe     276\n",
       "North America      152\n",
       "South America      145\n",
       "British Isles       75\n",
       "Southern Europe     64\n",
       "Northern Europe     55\n",
       "Central America     28\n",
       "Scandinavia         28\n",
       "Eastern Europe       7\n",
       "Name: ShipRegion, dtype: int64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "order['ShipRegion'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "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>Id</th>\n",
       "      <th>CustomerId</th>\n",
       "      <th>EmployeeId</th>\n",
       "      <th>OrderDate</th>\n",
       "      <th>RequiredDate</th>\n",
       "      <th>ShippedDate</th>\n",
       "      <th>ShipVia</th>\n",
       "      <th>Freight</th>\n",
       "      <th>ShipName</th>\n",
       "      <th>ShipAddress</th>\n",
       "      <th>ShipCity</th>\n",
       "      <th>ShipRegion</th>\n",
       "      <th>ShipPostalCode</th>\n",
       "      <th>ShipCountry</th>\n",
       "      <th>Id</th>\n",
       "      <th>OrderId</th>\n",
       "      <th>ProductId</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>10248</td>\n",
       "      <td>VINET</td>\n",
       "      <td>5</td>\n",
       "      <td>2012-07-04</td>\n",
       "      <td>2012-08-01</td>\n",
       "      <td>2012-07-16</td>\n",
       "      <td>3</td>\n",
       "      <td>32.38</td>\n",
       "      <td>Vins et alcools Chevalier</td>\n",
       "      <td>59 rue de l'Abbaye</td>\n",
       "      <td>Reims</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>51100</td>\n",
       "      <td>France</td>\n",
       "      <td>10248/11</td>\n",
       "      <td>10248</td>\n",
       "      <td>11</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>10248</td>\n",
       "      <td>VINET</td>\n",
       "      <td>5</td>\n",
       "      <td>2012-07-04</td>\n",
       "      <td>2012-08-01</td>\n",
       "      <td>2012-07-16</td>\n",
       "      <td>3</td>\n",
       "      <td>32.38</td>\n",
       "      <td>Vins et alcools Chevalier</td>\n",
       "      <td>59 rue de l'Abbaye</td>\n",
       "      <td>Reims</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>51100</td>\n",
       "      <td>France</td>\n",
       "      <td>10248/42</td>\n",
       "      <td>10248</td>\n",
       "      <td>42</td>\n",
       "      <td>9.8</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>10248</td>\n",
       "      <td>VINET</td>\n",
       "      <td>5</td>\n",
       "      <td>2012-07-04</td>\n",
       "      <td>2012-08-01</td>\n",
       "      <td>2012-07-16</td>\n",
       "      <td>3</td>\n",
       "      <td>32.38</td>\n",
       "      <td>Vins et alcools Chevalier</td>\n",
       "      <td>59 rue de l'Abbaye</td>\n",
       "      <td>Reims</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>51100</td>\n",
       "      <td>France</td>\n",
       "      <td>10248/72</td>\n",
       "      <td>10248</td>\n",
       "      <td>72</td>\n",
       "      <td>34.8</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>10249</td>\n",
       "      <td>TOMSP</td>\n",
       "      <td>6</td>\n",
       "      <td>2012-07-05</td>\n",
       "      <td>2012-08-16</td>\n",
       "      <td>2012-07-10</td>\n",
       "      <td>1</td>\n",
       "      <td>11.61</td>\n",
       "      <td>Toms Spezialitäten</td>\n",
       "      <td>Luisenstr. 48</td>\n",
       "      <td>Münster</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>44087</td>\n",
       "      <td>Germany</td>\n",
       "      <td>10249/14</td>\n",
       "      <td>10249</td>\n",
       "      <td>14</td>\n",
       "      <td>18.6</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>10249</td>\n",
       "      <td>TOMSP</td>\n",
       "      <td>6</td>\n",
       "      <td>2012-07-05</td>\n",
       "      <td>2012-08-16</td>\n",
       "      <td>2012-07-10</td>\n",
       "      <td>1</td>\n",
       "      <td>11.61</td>\n",
       "      <td>Toms Spezialitäten</td>\n",
       "      <td>Luisenstr. 48</td>\n",
       "      <td>Münster</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>44087</td>\n",
       "      <td>Germany</td>\n",
       "      <td>10249/51</td>\n",
       "      <td>10249</td>\n",
       "      <td>51</td>\n",
       "      <td>42.4</td>\n",
       "      <td>40</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id CustomerId  EmployeeId   OrderDate RequiredDate ShippedDate  ShipVia  \\\n",
       "0  10248      VINET           5  2012-07-04   2012-08-01  2012-07-16        3   \n",
       "1  10248      VINET           5  2012-07-04   2012-08-01  2012-07-16        3   \n",
       "2  10248      VINET           5  2012-07-04   2012-08-01  2012-07-16        3   \n",
       "3  10249      TOMSP           6  2012-07-05   2012-08-16  2012-07-10        1   \n",
       "4  10249      TOMSP           6  2012-07-05   2012-08-16  2012-07-10        1   \n",
       "\n",
       "   Freight                   ShipName         ShipAddress ShipCity  \\\n",
       "0    32.38  Vins et alcools Chevalier  59 rue de l'Abbaye    Reims   \n",
       "1    32.38  Vins et alcools Chevalier  59 rue de l'Abbaye    Reims   \n",
       "2    32.38  Vins et alcools Chevalier  59 rue de l'Abbaye    Reims   \n",
       "3    11.61         Toms Spezialitäten       Luisenstr. 48  Münster   \n",
       "4    11.61         Toms Spezialitäten       Luisenstr. 48  Münster   \n",
       "\n",
       "       ShipRegion ShipPostalCode ShipCountry        Id  OrderId  ProductId  \\\n",
       "0  Western Europe          51100      France  10248/11    10248         11   \n",
       "1  Western Europe          51100      France  10248/42    10248         42   \n",
       "2  Western Europe          51100      France  10248/72    10248         72   \n",
       "3  Western Europe          44087     Germany  10249/14    10249         14   \n",
       "4  Western Europe          44087     Germany  10249/51    10249         51   \n",
       "\n",
       "   UnitPrice  Quantity  Discount  \n",
       "0       14.0        12       0.0  \n",
       "1        9.8        10       0.0  \n",
       "2       34.8         5       0.0  \n",
       "3       18.6         9       0.0  \n",
       "4       42.4        40       0.0  "
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Join the Order and OrderDetail tables to obtain quantity and shipregion data\n",
    "\n",
    "cur.execute(\"\"\"SELECT *\n",
    "               FROM 'Order'\n",
    "               JOIN OrderDetail\n",
    "               ON 'Order'.Id = OrderDetail.OrderID\n",
    "               ;\n",
    "               \"\"\")\n",
    "region = pd.DataFrame(cur.fetchall())\n",
    "region.columns = [i[0] for i in cur.description]\n",
    "region.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Shipping Region vs Quantity in Order')]"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualize the differences in quantity ordered and shipping region\n",
    "\n",
    "sns.boxplot(x='ShipRegion', y='Quantity', data=region).set(title='Shipping Region vs Quantity in Order')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ho: The means of quantity of product ordered from any two regions are equal. \n",
    "\n",
    "### Ha: The means of quantity of product ordered from any two regions are not equal. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Multiple Comparison of Means - Tukey HSD, FWER=0.05</caption>\n",
       "<tr>\n",
       "      <th>group1</th>          <th>group2</th>      <th>meandiff</th>  <th>p-adj</th>   <th>lower</th>   <th>upper</th>  <th>reject</th>\n",
       "</tr>\n",
       "<tr>\n",
       "   <td>British Isles</td>  <td>Central America</td>  <td>-9.0586</td> <td>0.0129</td> <td>-17.0414</td> <td>-1.0759</td>  <td>True</td> \n",
       "</tr>\n",
       "<tr>\n",
       "   <td>British Isles</td>  <td>Eastern Europe</td>  <td>-10.4822</td> <td>0.4305</td> <td>-25.4978</td> <td>4.5333</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "   <td>British Isles</td>   <td>North America</td>   <td>3.2018</td>  <td>0.5516</td>  <td>-1.8286</td> <td>8.2321</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "   <td>British Isles</td>  <td>Northern Europe</td>  <td>0.5165</td>    <td>0.9</td>   <td>-5.8694</td> <td>6.9023</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "   <td>British Isles</td>    <td>Scandinavia</td>    <td>-8.3519</td> <td>0.0358</td> <td>-16.4169</td> <td>-0.2869</td>  <td>True</td> \n",
       "</tr>\n",
       "<tr>\n",
       "   <td>British Isles</td>   <td>South America</td>   <td>-2.106</td>    <td>0.9</td>   <td>-7.291</td>   <td>3.079</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "   <td>British Isles</td>  <td>Southern Europe</td>  <td>-8.1633</td> <td>0.0029</td> <td>-14.6285</td> <td>-1.6982</td>  <td>True</td> \n",
       "</tr>\n",
       "<tr>\n",
       "   <td>British Isles</td>  <td>Western Europe</td>   <td>3.9549</td>  <td>0.1787</td>  <td>-0.7332</td>  <td>8.643</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Central America</td> <td>Eastern Europe</td>   <td>-1.4236</td>   <td>0.9</td>  <td>-17.3662</td> <td>14.519</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Central America</td>  <td>North America</td>   <td>12.2604</td>  <td>0.001</td>  <td>4.9116</td>  <td>19.6091</td>  <td>True</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Central America</td> <td>Northern Europe</td>  <td>9.5751</td>  <td>0.0111</td>  <td>1.2396</td>  <td>17.9105</td>  <td>True</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Central America</td>   <td>Scandinavia</td>    <td>0.7067</td>    <td>0.9</td>   <td>-8.9754</td> <td>10.3889</td>  <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Central America</td>  <td>South America</td>   <td>6.9526</td>   <td>0.09</td>   <td>-0.5029</td> <td>14.4081</td>  <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Central America</td> <td>Southern Europe</td>  <td>0.8953</td>    <td>0.9</td>   <td>-7.5011</td> <td>9.2916</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Central America</td> <td>Western Europe</td>   <td>13.0136</td>  <td>0.001</td>  <td>5.8947</td>  <td>20.1324</td>  <td>True</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Eastern Europe</td>   <td>North America</td>   <td>13.684</td>  <td>0.0907</td>  <td>-1.0043</td> <td>28.3723</td>  <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Eastern Europe</td>  <td>Northern Europe</td>  <td>10.9987</td>  <td>0.378</td>  <td>-4.2073</td> <td>26.2047</td>  <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Eastern Europe</td>    <td>Scandinavia</td>    <td>2.1304</td>    <td>0.9</td>  <td>-13.8536</td> <td>18.1143</td>  <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Eastern Europe</td>   <td>South America</td>   <td>8.3762</td>  <td>0.6801</td>  <td>-6.3658</td> <td>23.1182</td>  <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Eastern Europe</td>  <td>Southern Europe</td>  <td>2.3189</td>    <td>0.9</td>  <td>-12.9206</td> <td>17.5583</td>  <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Eastern Europe</td>  <td>Western Europe</td>   <td>14.4372</td> <td>0.0546</td>  <td>-0.1375</td> <td>29.0118</td>  <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "   <td>North America</td>  <td>Northern Europe</td>  <td>-2.6853</td> <td>0.8426</td>  <td>-8.2584</td> <td>2.8878</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "   <td>North America</td>    <td>Scandinavia</td>   <td>-11.5536</td>  <td>0.001</td> <td>-18.9917</td> <td>-4.1156</td>  <td>True</td> \n",
       "</tr>\n",
       "<tr>\n",
       "   <td>North America</td>   <td>South America</td>   <td>-5.3078</td> <td>0.0023</td>  <td>-9.4508</td> <td>-1.1647</td>  <td>True</td> \n",
       "</tr>\n",
       "<tr>\n",
       "   <td>North America</td>  <td>Southern Europe</td> <td>-11.3651</td>  <td>0.001</td> <td>-17.0289</td> <td>-5.7013</td>  <td>True</td> \n",
       "</tr>\n",
       "<tr>\n",
       "   <td>North America</td>  <td>Western Europe</td>   <td>0.7532</td>    <td>0.9</td>   <td>-2.748</td>  <td>4.2544</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Northern Europe</td>   <td>Scandinavia</td>    <td>-8.8683</td> <td>0.0299</td> <td>-17.2826</td> <td>-0.4541</td>  <td>True</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Northern Europe</td>  <td>South America</td>   <td>-2.6225</td> <td>0.8856</td>  <td>-8.3356</td> <td>3.0907</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Northern Europe</td> <td>Southern Europe</td>  <td>-8.6798</td> <td>0.0031</td> <td>-15.5758</td> <td>-1.7838</td>  <td>True</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Northern Europe</td> <td>Western Europe</td>   <td>3.4385</td>  <td>0.5207</td>  <td>-1.8278</td> <td>8.7048</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>Scandinavia</td>    <td>South America</td>   <td>6.2459</td>  <td>0.1995</td>  <td>-1.2977</td> <td>13.7894</td>  <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>Scandinavia</td>   <td>Southern Europe</td>  <td>0.1885</td>    <td>0.9</td>   <td>-8.2861</td> <td>8.6631</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>Scandinavia</td>   <td>Western Europe</td>   <td>12.3068</td>  <td>0.001</td>  <td>5.0958</td>  <td>19.5178</td>  <td>True</td> \n",
       "</tr>\n",
       "<tr>\n",
       "   <td>South America</td>  <td>Southern Europe</td>  <td>-6.0573</td>  <td>0.033</td>  <td>-11.859</td> <td>-0.2557</td>  <td>True</td> \n",
       "</tr>\n",
       "<tr>\n",
       "   <td>South America</td>  <td>Western Europe</td>   <td>6.0609</td>   <td>0.001</td>  <td>2.3409</td>   <td>9.781</td>   <td>True</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Southern Europe</td> <td>Western Europe</td>   <td>12.1183</td>  <td>0.001</td>  <td>6.7561</td>  <td>17.4804</td>  <td>True</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.table.SimpleTable'>"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Using a tukey test, each region can be tested for their differences in quantity \n",
    "\n",
    "from statsmodels.stats.multicomp import pairwise_tukeyhsd\n",
    "from statsmodels.formula.api import ols\n",
    "from statsmodels.base.model import Model\n",
    "\n",
    "tukey_test = pairwise_tukeyhsd(region['Quantity'], region['ShipRegion'] )\n",
    "tukey_test._results_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## From the results of the tukey test, there are many significant differences in quantity ordered by region. However, the largest effect size is seen between Central America and Western Europe. The company can think about what strategies from Western Europe they apply to Central America to increase sales. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q3. Does the office where an order was processed have an effect on the quantity of product ordered?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "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>Id</th>\n",
       "      <th>LastName</th>\n",
       "      <th>FirstName</th>\n",
       "      <th>Title</th>\n",
       "      <th>TitleOfCourtesy</th>\n",
       "      <th>BirthDate</th>\n",
       "      <th>HireDate</th>\n",
       "      <th>Address</th>\n",
       "      <th>City</th>\n",
       "      <th>Region</th>\n",
       "      <th>PostalCode</th>\n",
       "      <th>Country</th>\n",
       "      <th>HomePhone</th>\n",
       "      <th>Extension</th>\n",
       "      <th>Photo</th>\n",
       "      <th>Notes</th>\n",
       "      <th>ReportsTo</th>\n",
       "      <th>PhotoPath</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Davolio</td>\n",
       "      <td>Nancy</td>\n",
       "      <td>Sales Representative</td>\n",
       "      <td>Ms.</td>\n",
       "      <td>1980-12-08</td>\n",
       "      <td>2024-05-01</td>\n",
       "      <td>507 - 20th Ave. E. Apt. 2A</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>North America</td>\n",
       "      <td>98122</td>\n",
       "      <td>USA</td>\n",
       "      <td>(206) 555-9857</td>\n",
       "      <td>5467</td>\n",
       "      <td>None</td>\n",
       "      <td>Education includes a BA in psychology from Col...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>http://accweb/emmployees/davolio.bmp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Fuller</td>\n",
       "      <td>Andrew</td>\n",
       "      <td>Vice President, Sales</td>\n",
       "      <td>Dr.</td>\n",
       "      <td>1984-02-19</td>\n",
       "      <td>2024-08-14</td>\n",
       "      <td>908 W. Capital Way</td>\n",
       "      <td>Tacoma</td>\n",
       "      <td>North America</td>\n",
       "      <td>98401</td>\n",
       "      <td>USA</td>\n",
       "      <td>(206) 555-9482</td>\n",
       "      <td>3457</td>\n",
       "      <td>None</td>\n",
       "      <td>Andrew received his BTS commercial in 1974 and...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://accweb/emmployees/fuller.bmp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Leverling</td>\n",
       "      <td>Janet</td>\n",
       "      <td>Sales Representative</td>\n",
       "      <td>Ms.</td>\n",
       "      <td>1995-08-30</td>\n",
       "      <td>2024-04-01</td>\n",
       "      <td>722 Moss Bay Blvd.</td>\n",
       "      <td>Kirkland</td>\n",
       "      <td>North America</td>\n",
       "      <td>98033</td>\n",
       "      <td>USA</td>\n",
       "      <td>(206) 555-3412</td>\n",
       "      <td>3355</td>\n",
       "      <td>None</td>\n",
       "      <td>Janet has a BS degree in chemistry from Boston...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>http://accweb/emmployees/leverling.bmp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Peacock</td>\n",
       "      <td>Margaret</td>\n",
       "      <td>Sales Representative</td>\n",
       "      <td>Mrs.</td>\n",
       "      <td>1969-09-19</td>\n",
       "      <td>2025-05-03</td>\n",
       "      <td>4110 Old Redmond Rd.</td>\n",
       "      <td>Redmond</td>\n",
       "      <td>North America</td>\n",
       "      <td>98052</td>\n",
       "      <td>USA</td>\n",
       "      <td>(206) 555-8122</td>\n",
       "      <td>5176</td>\n",
       "      <td>None</td>\n",
       "      <td>Margaret holds a BA in English literature from...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>http://accweb/emmployees/peacock.bmp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>Buchanan</td>\n",
       "      <td>Steven</td>\n",
       "      <td>Sales Manager</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>1987-03-04</td>\n",
       "      <td>2025-10-17</td>\n",
       "      <td>14 Garrett Hill</td>\n",
       "      <td>London</td>\n",
       "      <td>British Isles</td>\n",
       "      <td>SW1 8JR</td>\n",
       "      <td>UK</td>\n",
       "      <td>(71) 555-4848</td>\n",
       "      <td>3453</td>\n",
       "      <td>None</td>\n",
       "      <td>Steven Buchanan graduated from St. Andrews Uni...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>http://accweb/emmployees/buchanan.bmp</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id   LastName FirstName                  Title TitleOfCourtesy   BirthDate  \\\n",
       "0   1    Davolio     Nancy   Sales Representative             Ms.  1980-12-08   \n",
       "1   2     Fuller    Andrew  Vice President, Sales             Dr.  1984-02-19   \n",
       "2   3  Leverling     Janet   Sales Representative             Ms.  1995-08-30   \n",
       "3   4    Peacock  Margaret   Sales Representative            Mrs.  1969-09-19   \n",
       "4   5   Buchanan    Steven          Sales Manager             Mr.  1987-03-04   \n",
       "\n",
       "     HireDate                     Address      City         Region PostalCode  \\\n",
       "0  2024-05-01  507 - 20th Ave. E. Apt. 2A   Seattle  North America      98122   \n",
       "1  2024-08-14          908 W. Capital Way    Tacoma  North America      98401   \n",
       "2  2024-04-01          722 Moss Bay Blvd.  Kirkland  North America      98033   \n",
       "3  2025-05-03        4110 Old Redmond Rd.   Redmond  North America      98052   \n",
       "4  2025-10-17             14 Garrett Hill    London  British Isles    SW1 8JR   \n",
       "\n",
       "  Country       HomePhone Extension Photo  \\\n",
       "0     USA  (206) 555-9857      5467  None   \n",
       "1     USA  (206) 555-9482      3457  None   \n",
       "2     USA  (206) 555-3412      3355  None   \n",
       "3     USA  (206) 555-8122      5176  None   \n",
       "4      UK   (71) 555-4848      3453  None   \n",
       "\n",
       "                                               Notes  ReportsTo  \\\n",
       "0  Education includes a BA in psychology from Col...        2.0   \n",
       "1  Andrew received his BTS commercial in 1974 and...        NaN   \n",
       "2  Janet has a BS degree in chemistry from Boston...        2.0   \n",
       "3  Margaret holds a BA in English literature from...        2.0   \n",
       "4  Steven Buchanan graduated from St. Andrews Uni...        2.0   \n",
       "\n",
       "                                PhotoPath  \n",
       "0    http://accweb/emmployees/davolio.bmp  \n",
       "1     http://accweb/emmployees/fuller.bmp  \n",
       "2  http://accweb/emmployees/leverling.bmp  \n",
       "3    http://accweb/emmployees/peacock.bmp  \n",
       "4   http://accweb/emmployees/buchanan.bmp  "
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Source data from Employee table\n",
    "\n",
    "cur.execute(\"\"\"SELECT *\n",
    "               FROM Employee\n",
    "               ;\n",
    "               \"\"\")\n",
    "employee = pd.DataFrame(cur.fetchall())\n",
    "employee.columns = [i[0] for i in cur.description]\n",
    "employee.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>LastName</th>\n",
       "      <th>FirstName</th>\n",
       "      <th>Title</th>\n",
       "      <th>TitleOfCourtesy</th>\n",
       "      <th>BirthDate</th>\n",
       "      <th>HireDate</th>\n",
       "      <th>Address</th>\n",
       "      <th>City</th>\n",
       "      <th>Region</th>\n",
       "      <th>...</th>\n",
       "      <th>ShipCity</th>\n",
       "      <th>ShipRegion</th>\n",
       "      <th>ShipPostalCode</th>\n",
       "      <th>ShipCountry</th>\n",
       "      <th>Id</th>\n",
       "      <th>OrderId</th>\n",
       "      <th>ProductId</th>\n",
       "      <th>UnitPrice</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Buchanan</td>\n",
       "      <td>Steven</td>\n",
       "      <td>Sales Manager</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>1987-03-04</td>\n",
       "      <td>2025-10-17</td>\n",
       "      <td>14 Garrett Hill</td>\n",
       "      <td>London</td>\n",
       "      <td>British Isles</td>\n",
       "      <td>...</td>\n",
       "      <td>Reims</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>51100</td>\n",
       "      <td>France</td>\n",
       "      <td>10248/11</td>\n",
       "      <td>10248</td>\n",
       "      <td>11</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Buchanan</td>\n",
       "      <td>Steven</td>\n",
       "      <td>Sales Manager</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>1987-03-04</td>\n",
       "      <td>2025-10-17</td>\n",
       "      <td>14 Garrett Hill</td>\n",
       "      <td>London</td>\n",
       "      <td>British Isles</td>\n",
       "      <td>...</td>\n",
       "      <td>Reims</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>51100</td>\n",
       "      <td>France</td>\n",
       "      <td>10248/42</td>\n",
       "      <td>10248</td>\n",
       "      <td>42</td>\n",
       "      <td>9.8</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>Buchanan</td>\n",
       "      <td>Steven</td>\n",
       "      <td>Sales Manager</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>1987-03-04</td>\n",
       "      <td>2025-10-17</td>\n",
       "      <td>14 Garrett Hill</td>\n",
       "      <td>London</td>\n",
       "      <td>British Isles</td>\n",
       "      <td>...</td>\n",
       "      <td>Reims</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>51100</td>\n",
       "      <td>France</td>\n",
       "      <td>10248/72</td>\n",
       "      <td>10248</td>\n",
       "      <td>72</td>\n",
       "      <td>34.8</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>Suyama</td>\n",
       "      <td>Michael</td>\n",
       "      <td>Sales Representative</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>1995-07-02</td>\n",
       "      <td>2025-10-17</td>\n",
       "      <td>Coventry House Miner Rd.</td>\n",
       "      <td>London</td>\n",
       "      <td>British Isles</td>\n",
       "      <td>...</td>\n",
       "      <td>Münster</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>44087</td>\n",
       "      <td>Germany</td>\n",
       "      <td>10249/14</td>\n",
       "      <td>10249</td>\n",
       "      <td>14</td>\n",
       "      <td>18.6</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>Suyama</td>\n",
       "      <td>Michael</td>\n",
       "      <td>Sales Representative</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>1995-07-02</td>\n",
       "      <td>2025-10-17</td>\n",
       "      <td>Coventry House Miner Rd.</td>\n",
       "      <td>London</td>\n",
       "      <td>British Isles</td>\n",
       "      <td>...</td>\n",
       "      <td>Münster</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>44087</td>\n",
       "      <td>Germany</td>\n",
       "      <td>10249/51</td>\n",
       "      <td>10249</td>\n",
       "      <td>51</td>\n",
       "      <td>42.4</td>\n",
       "      <td>40</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Id  LastName FirstName                 Title TitleOfCourtesy   BirthDate  \\\n",
       "0  5  Buchanan    Steven         Sales Manager             Mr.  1987-03-04   \n",
       "1  5  Buchanan    Steven         Sales Manager             Mr.  1987-03-04   \n",
       "2  5  Buchanan    Steven         Sales Manager             Mr.  1987-03-04   \n",
       "3  6    Suyama   Michael  Sales Representative             Mr.  1995-07-02   \n",
       "4  6    Suyama   Michael  Sales Representative             Mr.  1995-07-02   \n",
       "\n",
       "     HireDate                   Address    City         Region  ... ShipCity  \\\n",
       "0  2025-10-17           14 Garrett Hill  London  British Isles  ...    Reims   \n",
       "1  2025-10-17           14 Garrett Hill  London  British Isles  ...    Reims   \n",
       "2  2025-10-17           14 Garrett Hill  London  British Isles  ...    Reims   \n",
       "3  2025-10-17  Coventry House Miner Rd.  London  British Isles  ...  Münster   \n",
       "4  2025-10-17  Coventry House Miner Rd.  London  British Isles  ...  Münster   \n",
       "\n",
       "       ShipRegion ShipPostalCode ShipCountry        Id OrderId  ProductId  \\\n",
       "0  Western Europe          51100      France  10248/11   10248         11   \n",
       "1  Western Europe          51100      France  10248/42   10248         42   \n",
       "2  Western Europe          51100      France  10248/72   10248         72   \n",
       "3  Western Europe          44087     Germany  10249/14   10249         14   \n",
       "4  Western Europe          44087     Germany  10249/51   10249         51   \n",
       "\n",
       "  UnitPrice  Quantity Discount  \n",
       "0      14.0        12      0.0  \n",
       "1       9.8        10      0.0  \n",
       "2      34.8         5      0.0  \n",
       "3      18.6         9      0.0  \n",
       "4      42.4        40      0.0  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Join employee, order, and orderdetail tables to obtain the necessary data\n",
    "\n",
    "cur.execute(\"\"\"SELECT *\n",
    "               FROM Employee\n",
    "               JOIN 'Order'\n",
    "               ON Employee.Id = 'Order'.EmployeeId\n",
    "               JOIN OrderDetail\n",
    "               ON 'Order'.Id = OrderDetail.OrderID\n",
    "               ;\n",
    "               \"\"\")\n",
    "city = pd.DataFrame(cur.fetchall())\n",
    "city.columns = [i[0] for i in cur.description]\n",
    "city.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Seattle     605\n",
       "London      568\n",
       "Redmond     420\n",
       "Kirkland    321\n",
       "Tacoma      241\n",
       "Name: City, dtype: int64"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Determine the number of cities \n",
    "\n",
    "city['City'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Office Location vs Quantity in Order')]"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualize the differences in quantity and cities\n",
    "\n",
    "sns.boxplot(x='City', y='Quantity', data=city).set(title='Office Location vs Quantity in Order')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ho: The means of quantity of product ordered through any two offices are equal. \n",
    "\n",
    "### Ha: The means of quantity of product ordered through any two offices are not equal. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Multiple Comparison of Means - Tukey HSD, FWER=0.05</caption>\n",
       "<tr>\n",
       "   <th>group1</th>  <th>group2</th>  <th>meandiff</th>  <th>p-adj</th>  <th>lower</th>   <th>upper</th> <th>reject</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Kirkland</td> <td>London</td>   <td>-0.0121</td>   <td>0.9</td>  <td>-3.6379</td> <td>3.6137</td>  <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Kirkland</td> <td>Redmond</td>  <td>-1.1325</td>   <td>0.9</td>  <td>-4.982</td>  <td>2.7171</td>  <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Kirkland</td> <td>Seattle</td>  <td>-1.7751</td> <td>0.6383</td> <td>-5.3606</td> <td>1.8104</td>  <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <td>Kirkland</td> <td>Tacoma</td>   <td>0.6634</td>    <td>0.9</td>  <td>-3.7623</td> <td>5.0891</td>  <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "   <td>London</td>  <td>Redmond</td>  <td>-1.1204</td> <td>0.8858</td> <td>-4.462</td>  <td>2.2212</td>  <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "   <td>London</td>  <td>Seattle</td>  <td>-1.763</td>  <td>0.5051</td> <td>-4.7967</td> <td>1.2707</td>  <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "   <td>London</td>  <td>Tacoma</td>   <td>0.6755</td>    <td>0.9</td>  <td>-3.3163</td> <td>4.6673</td>  <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "   <td>Redmond</td> <td>Seattle</td>  <td>-0.6426</td>   <td>0.9</td>  <td>-3.9405</td> <td>2.6553</td>  <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "   <td>Redmond</td> <td>Tacoma</td>   <td>1.7959</td>  <td>0.7422</td> <td>-2.4002</td>  <td>5.992</td>  <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "   <td>Seattle</td> <td>Tacoma</td>   <td>2.4385</td>  <td>0.4462</td> <td>-1.5167</td> <td>6.3938</td>  <td>False</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.table.SimpleTable'>"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tukey_test_city = pairwise_tukeyhsd(city['Quantity'], city['City'] )\n",
    "tukey_test_city._results_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## From the results of the tukey test, no city's quantity of orders is significantly different from any other city. Every office is perfoming equally. Best practices can not be taken from any one office. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "learn-env",
   "language": "python",
   "name": "learn-env"
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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