From 4fcbf7a6edeb52acd79a0c18b5efb9921e2b90a5 Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Mon, 10 May 2021 18:30:44 -0400
Subject: [PATCH] refreshing notebooks

---
 Regression/1-Tools/solution/notebook.ipynb    | 18 +++---
 Regression/2-Data/solution/notebook.ipynb     | 38 ++++++------
 Regression/3-Linear/solution/notebook.ipynb   | 60 +++++++++----------
 Regression/4-Logistic/notebook.ipynb          | 16 +++--
 Regression/4-Logistic/solution/notebook.ipynb | 14 ++---
 5 files changed, 79 insertions(+), 67 deletions(-)

diff --git a/Regression/1-Tools/solution/notebook.ipynb b/Regression/1-Tools/solution/notebook.ipynb
index 76a1634a6..e7d80492a 100644
--- a/Regression/1-Tools/solution/notebook.ipynb
+++ b/Regression/1-Tools/solution/notebook.ipynb
@@ -10,13 +10,17 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.3-final"
+   "version": "3.7.0"
   },
   "orig_nbformat": 2,
   "kernelspec": {
-   "name": "python3",
-   "display_name": "Python 3",
-   "language": "python"
+   "name": "python37364bit8d3b438fb5fc4430a93ac2cb74d693a7",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
   }
  },
  "nbformat": 4,
@@ -121,7 +125,7 @@
      "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "LinearRegression()"
+       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
       ]
      },
      "metadata": {},
@@ -165,8 +169,8 @@
      "output_type": "display_data",
      "data": {
       "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
      },
      "metadata": {
       "needs_background": "light"
diff --git a/Regression/2-Data/solution/notebook.ipynb b/Regression/2-Data/solution/notebook.ipynb
index 4d8a3ea63..44b1e349d 100644
--- a/Regression/2-Data/solution/notebook.ipynb
+++ b/Regression/2-Data/solution/notebook.ipynb
@@ -10,13 +10,17 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.3-final"
+   "version": "3.7.0"
   },
   "orig_nbformat": 2,
   "kernelspec": {
-   "name": "python3",
-   "display_name": "Python 3",
-   "language": "python"
+   "name": "python37364bit8d3b438fb5fc4430a93ac2cb74d693a7",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
   }
  },
  "nbformat": 4,
@@ -31,7 +35,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
@@ -71,7 +75,7 @@
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      },
      "metadata": {},
-     "execution_count": 22
+     "execution_count": 1
     }
    ],
    "source": [
@@ -86,7 +90,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -107,7 +111,7 @@
        "Origin               0\n",
        "Origin District    396\n",
        "Item Size          114\n",
-       "Color              180\n",
+       "Color              145\n",
        "Environment        415\n",
        "Unit of Sale       404\n",
        "Quality            415\n",
@@ -123,7 +127,7 @@
       ]
      },
      "metadata": {},
-     "execution_count": 23
+     "execution_count": 2
     }
    ],
    "source": [
@@ -132,7 +136,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -169,15 +173,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "output_type": "display_data",
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\n"
      },
      "metadata": {
       "needs_background": "light"
@@ -194,7 +198,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -205,14 +209,14 @@
       ]
      },
      "metadata": {},
-     "execution_count": 48
+     "execution_count": 5
     },
     {
      "output_type": "display_data",
      "data": {
       "text/plain": "<Figure size 432x288 with 1 Axes>",
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diff --git a/Regression/3-Linear/solution/notebook.ipynb b/Regression/3-Linear/solution/notebook.ipynb
index 438f3ef2c..19c540069 100644
--- a/Regression/3-Linear/solution/notebook.ipynb
+++ b/Regression/3-Linear/solution/notebook.ipynb
@@ -42,7 +42,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
@@ -75,7 +75,7 @@
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@@ -90,7 +90,7 @@
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+   "execution_count": 2,
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@@ -107,7 +107,7 @@
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@@ -143,24 +143,24 @@
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+   "execution_count": 3,
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\n"
      },
      "metadata": {
@@ -182,7 +182,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -208,7 +208,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -236,7 +236,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -261,7 +261,7 @@
       "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>Package</th>\n      <th>Price</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>70</th>\n      <td>0</td>\n      <td>13.636364</td>\n    </tr>\n    <tr>\n      <th>71</th>\n      <td>0</td>\n      <td>16.363636</td>\n    </tr>\n    <tr>\n      <th>72</th>\n      <td>0</td>\n      <td>16.363636</td>\n    </tr>\n    <tr>\n      <th>73</th>\n      <td>0</td>\n      <td>15.454545</td>\n    </tr>\n    <tr>\n      <th>74</th>\n      <td>0</td>\n      <td>13.636364</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1738</th>\n      <td>2</td>\n      <td>30.000000</td>\n    </tr>\n    <tr>\n      <th>1739</th>\n      <td>2</td>\n      <td>28.750000</td>\n    </tr>\n    <tr>\n      <th>1740</th>\n      <td>2</td>\n      <td>25.750000</td>\n    </tr>\n    <tr>\n      <th>1741</th>\n      <td>2</td>\n      <td>24.000000</td>\n    </tr>\n    <tr>\n      <th>1742</th>\n      <td>2</td>\n      <td>24.000000</td>\n    </tr>\n  </tbody>\n</table>\n<p>415 rows × 2 columns</p>\n</div>"
      },
      "metadata": {},
-     "execution_count": 7
+     "execution_count": 6
     }
    ],
    "source": [
@@ -280,7 +280,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -290,7 +290,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -319,14 +319,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "output_type": "display_data",
      "data": {
       "text/plain": "<Figure size 432x288 with 1 Axes>",
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eHeBqh4/ukZTqugRJ/bM3fUk6ADid8vw+t1K+KgjgXOC7aT7XrBHmX1//7LPV7+fr621/jY2N0dfXt1dbX18fY2NLXkiZSrWJ/z8AP5H0C0n3Sbpf0n3L7LMKuDXZ7m7g5oi4Cfg08AlJD1O+pPPyWoM3q6daJj9L9uSQQw51sre6GB4eplAoMDg4iCQGBwdrGthdypJX9ezZSBqs1B4Rk3WLZAm+qsey8PzzsKBjVZXx8fJShGatbrGrepYc3E1uwroAOA64H7h83hU5Zm1ndBT+8i/T7zczA4vcn2XWdpa7c/dq4PfAHcCfAquBj2UdlFk91bJYCbhOb51ruRr/6mQK5q9RHpB9UwNiMttvtdbrPThrrWD9+vVI2vNYv359XT9/ucT/+9knLvFYq6sl2Z90kpO9tZb169ezefPmvdo2b95c1+S/3JQNJeB3sy+BA4Dp5HlExB/ULZIleHDXKrn7bjj55PT7PfwwHHts/eMxqwct0XOp5mKcBZ+VfnA3IjycZS2lt7c8a2Va7s2bzfGc+tbyPDhrVl+e+NVakgdnrVutW7cuVXstnPitJfzud7Ul+8suc7K3zrJp06Z9kvy6devYtGlT3Y7hUo81zWc+A1/8Yvr9fDOVdbp6JvlKnPitoVyvN2s+J37LnJO9WWtxjd8yUUu9/uSTXa83awQnfquLf/u32pL99u1zif6uuzILz8zmcanHauYSjll7cuK3VJzszdpfZqUeSa+UdKukbZIelPSxpP0Lkh6VtDV5vD2rGKw+fDOVWWfJssY/A/x5RKwGTgEulLQ6ee+SiFibPL6fYQxWg+eeqy3ZX3GFk71ZO8is1BMRO4AdyfPfSnoIOCqr49n+ufhi+NKX0u9XKsEKXyJg1lYaUuOXlANeC9wFvBG4SNIHgQnKfxU81Yg4bG+u15t1p8z7apIOBL4FfDwingXGgWOBtZT/IqjYz5SUlzQhaWLXrl1Zh9k1XK83s0wTv6SVlJN+MSK+DRARj0VEKSJ2A18HKi6lERGFiBiKiKH+/v4sw+x4tST7oSEne7NOleVVPQIuBx6KiC/Pa181b7N3Aw9kFUO3+tGPakv2k5Nzif7uu7OLrxMUi0VyuRwrVqwgl8tRLBabHZJZ1bKs8b8R+ABwv6StSdvngPdLWgsEsB34aIYxdA3X6xunWCySz+eZnp4GYHJyknw+D8Dw8HAzQzOrypJr7rYKr7lbmZN9c+RyOSYnJ/dpHxwcZPv27Y0PyGwRNa25a63Hyb75pqamUrWbtRpfgd3inn22tnr9Ndd4cDYrAwMDqdrNWo0Tfwu68MK5RH/wwdXvVyrNJfoPfCC7+Lrd2NgYfX19e7X19fUxNjbWpIjM0nGpp0W4hNM+ZgdwR0dHmZqaYmBggLGxMQ/sWtvw4G4TOdmbWZYWG9x1qafBaqnXv+UtrtebWf048WfstttqS/ZTU3OJ/rbbsorOzLqRa/wZcAnHzFqZE3+dONmbWbtw4t8PTvZm1o5c40/hmWdqq9dfe60HZ82sdbjHv4yRERgfT7+fV6Yys1blxF+BSzhm1smc+BNO9mbWLTq6GDEyMkJvby+S6O3tZWRkZK/3a6nXn3GG6/Vm1t46NvGPjIwwPj5OqVQCoFQqMT7+cE3J/te/nkv0P/hBRgGbmTVIZqUeSa8ErgGOpLzaViEiLpV0GHA9kKO8Atf7IuKpeh+/UCgkz2rrlrs3b2adKsse/wzw5xGxGjgFuFDSauAzwOaIeDWwOXldd+Wefrrs7RKOmXWDzBJ/ROyIiHuS578FHgKOAt4FXJ1sdjVwVhbH7+npqWo7J3sz6zYNqfFLygGvBe4CjoyIHclbv6FcCqq0T17ShKSJXbt2pT7m7OLXC51++mVO9mbW1TJP/JIOBL4FfDwinp3/XpQXA6iYfiOiEBFDETHU39+f+rgbN25kw4YRenp6AdHT08uGDSP88IcfruGnMDPrHJkmfkkrKSf9YkR8O2l+TNKq5P1VwM6sjr9x40ZmZmaICGZmZti4cWNWhzIzq5tisUgul2PFihXkcjmKxWJdPz+zxC9JwOXAQxHx5Xlv3Qicmzw/F/huVjGYmbWbYrFIPp9ncnKSiGBycpJ8Pl/X5J/Z0ouS/iNwB3A/sDtp/hzlOv83gQFgkvLlnE8u9VmduvSimdlCuVyOycnJfdoHBwfZvn17qs9abOnFzK7jj4g7gcVuk1qX1XHNzNrZ1NRUqvZadOydu2Zm7WhgYCBVey06OvFnPUBiZlZvY2Nj9PX17dXW19fH2NhY3Y7RsYm/EQMkZmb1Njw8TKFQYHBwEEkMDg5SKBQYHh6u2zEyG9ytp1oGd+s5QGJm1o4WG9zt2B5/IwZIzMzaUccm/kYMkJiZtaOOTfyNGCAxM8tC296522yNGCAxM6u3tr5zt558566ZdYtG3LnbsT1+M7N25Dt399OaNWuQtOexZs2aZofU0pZbnN7Msuc7d/fDmjVr2LZt215t27Ztc/JfROXF6ced/M0arBEXpnRsjb88K3Rl7fAzN1pvb++epD9fT08PMzMzTYjIrHsVi0VGR0eZmppiYGCAsbGxmi5MWazG78RvgM+XWSfy4K4tabHF6atdtN7M2ocTvwGLL06/WLuZta8sl168QtJOSQ/Ma/uCpEclbU0eb8/q+JZOeXH6DXt6+D09PWzYsMHrFJt1oCyXXnwz8BxwTUSckLR9AXguIv4mzWe5xm9mll7Da/wRcTuw5Fq6ZmbWeM2o8V8k6b6kFHToYhtJykuakDSxa9euRsZnZtbRGp34x4FjgbXADuBLi20YEYWIGIqIof7+/tQHevnLX56q3cysWzQ08UfEYxFRiojdwNeBk7M61vPPP5+q3WD9+vV7TXGxfv36ZodkZhloaOKXtGrey3cDDyy27f7avXt3qvZut379ejZv3rxX2+bNm538zTpQb1YfLOkfgFOBIyQ9AnweOFXSWiCA7cBHszq+pbMw6S/XbmbtK7PEHxHvr9B8eVbHMzOz6vjOXTOzLuPEbwCsW7cuVbuZtS8nfgNg06ZN+yT5devWsWnTpiZFZGZZyazG32yHH344TzzxRMV2q8xJ3qw7dGyP/9JLL2XlypV7ta1cuZJLL720SRGZmbWGjk38w8PDXHnllQwODiKJwcFBrrzyyppWsTEz6yQduwKXmVm368oVuIrFIrlcjhUrVpDL5SgWi80Oycys6Tp2cLdYLJLP55mengZgcnJyz2pSLveYWTfr2B7/6OjonqQ/a3p6mtHR0SZFZGbWGjo28U9NTaVqNzPrFh2b+AcGBlK1m5l1i45N/GNjYxWv4x8bG2tSRGZmraFjEz/su+D6Uguwm5l1i45N/KOjo7z44ot7tb344ose3DWzrtexid+Du2ZmlWWW+CVdIWmnpAfmtR0m6WZJP0/+PTSr43tw18yssix7/FcBZyxo+wywOSJeDWxOXmdibGyMvr6+vdr6+vo8uGtmXS+zxB8RtwNPLmh+F3B18vxq4Kysjj88PEyhUNhrkrZCoeC7ds2s62U6SZukHHBTRJyQvH46Ig5Jngt4avZ1hX3zQB5gYGDgpMnJycziNDPrRC03SVuUf+Ms+lsnIgoRMRQRQ/39/Q2MzMysszU68T8maRVA8u/OBh/fzKzrNTrx3wicmzw/F/hug49vZtb1sryc8x+AHwP/XtIjks4H/ho4XdLPgfXJazMza6DM5uOPiPcv8ta6rI5pZmbLa4ulFyXtAvbnsp4jgMfrFE49Oa7qtWJM4LjSclzp7G9cgxGxz9UxbZH495ekiUqXNDWb46peK8YEjistx5VOVnF17Fw9ZmZWmRO/mVmX6ZbEX2h2AItwXNVrxZjAcaXluNLJJK6uqPGbmdmcbunxm5lZwonfzKzLdFTil3SGpP8r6WFJ+8z1L+mlkq5P3r8rmT202TF9SNIuSVuTx4ezjik57j4L5Sx4X5L+VxL3fZJe1yJxnSrpmXnn6783IKZXSrpV0jZJD0r6WIVtGn6+qoyrGefrZZL+j6SfJnH9jwrbNOO7WE1czfo+9ki6V9JNFd6r/7mKiI54AD3AL4BXAS8BfgqsXrDNCPB3yfOzgetbIKYPAX/bhPP1ZuB1wAOLvP924AeAgFOAu1okrlMpT/XdyHO1Cnhd8vwg4P9V+O/Y8PNVZVzNOF8CDkyerwTuAk5ZsE1Dv4sp4mrW9/ETwDcq/bfK4lx1Uo//ZODhiPhlRLwIXEd54Zf55i8EcwOwLlkXoJkxNUVUXihnvncB10TZT4BDZmdWbXJcDRcROyLinuT5b4GHgKMWbNbw81VlXA2XnIPnkpcrk8fCq0ga/V2sNq6Gk3Q08A7gskU2qfu56qTEfxTw63mvH2HfL8GebSJiBngGOLzJMQG8JykP3CDplRnGk0a1sTfDG5I/138gaU0jD5z8mf1ayr3F+Zp6vpaIC5pwvpLSxVbKU6/fHBGLnq8GfRerjQsa/338CvApYPci79f9XHVS4m9X/xvIRcQfATcz95vdKruH8vwjJwJfBf6pUQeWdCDwLeDjEfFso467nGXiasr5iohSRKwFjgZOlnRCI467nCriauj3UdKZwM6I2JLlcRbqpMT/KDD/t/PRSVvFbST1AgcDTzQzpoh4IiJeSF5eBpyUYTxpVHM+Gy4inp39cz0ivg+slHRE1seVtJJyci1GxLcrbNKU87VcXM06X/OO/zRwK3DGgrca/V2sKq4mfB/fCLxT0nbKpeDTJP39gm3qfq46KfHfDbxa0jGSXkJ5EOTGBdvMXwjmvcAtkYyYNCumBXXgd1Ku07aCG4EPJlernAI8ExE7mh2UpD+crW9KOpny/8OZJozkeJcDD0XElxfZrOHnq5q4mnS++iXNrq19AHA68LMFmzX6u1hVXI3+PkbEZyPi6IjIUc4Pt0TEOQs2q/u5ymw+/kaLiBlJFwH/Qvlqmisi4kFJ/xOYiIgbKX9JrpX0MOUBxLNbIKb/KumdwEwS04eyjGmWygvlnAo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      },
      "metadata": {
@@ -348,7 +348,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -359,7 +359,7 @@
       ]
      },
      "metadata": {},
-     "execution_count": 12
+     "execution_count": 10
     }
    ],
    "source": [
@@ -368,7 +368,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -393,7 +393,7 @@
       "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>Month</th>\n      <th>Variety</th>\n      <th>City</th>\n      <th>Package</th>\n      <th>Price</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>70</th>\n      <td>1</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n      <td>13.636364</td>\n    </tr>\n    <tr>\n      <th>71</th>\n      <td>1</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n      <td>16.363636</td>\n    </tr>\n    <tr>\n      <th>72</th>\n      <td>2</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n      <td>16.363636</td>\n    </tr>\n    <tr>\n      <th>73</th>\n      <td>2</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n      <td>15.454545</td>\n    </tr>\n    <tr>\n      <th>74</th>\n      <td>2</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n      <td>13.636364</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1738</th>\n      <td>1</td>\n      <td>1</td>\n      <td>9</td>\n      <td>2</td>\n      <td>30.000000</td>\n    </tr>\n    <tr>\n      <th>1739</th>\n      <td>1</td>\n      <td>1</td>\n      <td>9</td>\n      <td>2</td>\n      <td>28.750000</td>\n    </tr>\n    <tr>\n      <th>1740</th>\n      <td>1</td>\n      <td>1</td>\n      <td>9</td>\n      <td>2</td>\n      <td>25.750000</td>\n    </tr>\n    <tr>\n      <th>1741</th>\n      <td>1</td>\n      <td>1</td>\n      <td>9</td>\n      <td>2</td>\n      <td>24.000000</td>\n    </tr>\n    <tr>\n      <th>1742</th>\n      <td>1</td>\n      <td>1</td>\n      <td>9</td>\n      <td>2</td>\n      <td>24.000000</td>\n    </tr>\n  </tbody>\n</table>\n<p>415 rows × 5 columns</p>\n</div>"
      },
      "metadata": {},
-     "execution_count": 13
+     "execution_count": 11
     }
    ],
    "source": [
@@ -405,19 +405,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "output_type": "execute_result",
      "data": {
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x7fef29154a58>"
+       "<pandas.io.formats.style.Styler at 0x7fe5709abfd0>"
       ],
-      "text/html": "<style  type=\"text/css\" >\n#T_20a68c20_aecd_11eb_be4e_f218988e26cdrow0_col0,#T_20a68c20_aecd_11eb_be4e_f218988e26cdrow1_col1,#T_20a68c20_aecd_11eb_be4e_f218988e26cdrow2_col2,#T_20a68c20_aecd_11eb_be4e_f218988e26cdrow3_col3,#T_20a68c20_aecd_11eb_be4e_f218988e26cdrow4_col4{\n            background-color:  #b40426;\n            color:  #f1f1f1;\n        }#T_20a68c20_aecd_11eb_be4e_f218988e26cdrow0_col1{\n            background-color:  #ead4c8;\n            color:  #000000;\n        }#T_20a68c20_aecd_11eb_be4e_f218988e26cdrow0_col2{\n            background-color:  #4961d2;\n            color:  #f1f1f1;\n        }#T_20a68c20_aecd_11eb_be4e_f218988e26cdrow0_col3{\n            background-color:  #9bbcff;\n            color:  #000000;\n        }#T_20a68c20_aecd_11eb_be4e_f218988e26cdrow0_col4{\n            background-color:  #bbd1f8;\n            color:  #000000;\n        }#T_20a68c20_aecd_11eb_be4e_f218988e26cdrow1_col0{\n            background-color:  #9fbfff;\n            color:  #000000;\n        }#T_20a68c20_aecd_11eb_be4e_f218988e26cdrow1_col2,#T_20a68c20_aecd_11eb_be4e_f218988e26cdrow1_col3,#T_20a68c20_aecd_11eb_be4e_f218988e26cdrow1_col4,#T_20a68c20_aecd_11eb_be4e_f218988e26cdrow2_col0,#T_20a68c20_aecd_11eb_be4e_f218988e26cdrow4_col1{\n            background-color:  #3b4cc0;\n            color:  #f1f1f1;\n        }#T_20a68c20_aecd_11eb_be4e_f218988e26cdrow2_col1{\n            background-color:  #a9c6fd;\n            color:  #000000;\n        }#T_20a68c20_aecd_11eb_be4e_f218988e26cdrow2_col3{\n            background-color:  #edd2c3;\n            color:  #000000;\n        }#T_20a68c20_aecd_11eb_be4e_f218988e26cdrow2_col4{\n            background-color:  #f5c0a7;\n            color:  #000000;\n        }#T_20a68c20_aecd_11eb_be4e_f218988e26cdrow3_col0{\n            background-color:  #455cce;\n            color:  #f1f1f1;\n        }#T_20a68c20_aecd_11eb_be4e_f218988e26cdrow3_col1{\n            background-color:  #6485ec;\n            color:  #000000;\n        }#T_20a68c20_aecd_11eb_be4e_f218988e26cdrow3_col2{\n            background-color:  #ccd9ed;\n            color:  #000000;\n        }#T_20a68c20_aecd_11eb_be4e_f218988e26cdrow3_col4{\n            background-color:  #f08a6c;\n            color:  #000000;\n        }#T_20a68c20_aecd_11eb_be4e_f218988e26cdrow4_col0{\n            background-color:  #445acc;\n            color:  #f1f1f1;\n        }#T_20a68c20_aecd_11eb_be4e_f218988e26cdrow4_col2{\n            background-color:  #d2dbe8;\n            color:  #000000;\n        }#T_20a68c20_aecd_11eb_be4e_f218988e26cdrow4_col3{\n            background-color:  #f39778;\n            color:  #000000;\n        }</style><table id=\"T_20a68c20_aecd_11eb_be4e_f218988e26cd\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Month</th>        <th class=\"col_heading level0 col1\" >Variety</th>        <th class=\"col_heading level0 col2\" >City</th>        <th class=\"col_heading level0 col3\" >Package</th>        <th class=\"col_heading level0 col4\" >Price</th>    </tr></thead><tbody>\n                <tr>\n                        <th id=\"T_20a68c20_aecd_11eb_be4e_f218988e26cdlevel0_row0\" class=\"row_heading level0 row0\" >Month</th>\n                        <td id=\"T_20a68c20_aecd_11eb_be4e_f218988e26cdrow0_col0\" class=\"data row0 col0\" >1.000000</td>\n                        <td id=\"T_20a68c20_aecd_11eb_be4e_f218988e26cdrow0_col1\" class=\"data row0 col1\" >0.171330</td>\n                        <td id=\"T_20a68c20_aecd_11eb_be4e_f218988e26cdrow0_col2\" class=\"data row0 col2\" >-0.188728</td>\n                        <td id=\"T_20a68c20_aecd_11eb_be4e_f218988e26cdrow0_col3\" class=\"data row0 col3\" >-0.144847</td>\n                        <td id=\"T_20a68c20_aecd_11eb_be4e_f218988e26cdrow0_col4\" class=\"data row0 col4\" >-0.148783</td>\n            </tr>\n            <tr>\n                        <th id=\"T_20a68c20_aecd_11eb_be4e_f218988e26cdlevel0_row1\" class=\"row_heading level0 row1\" >Variety</th>\n                        <td id=\"T_20a68c20_aecd_11eb_be4e_f218988e26cdrow1_col0\" class=\"data row1 col0\" >0.171330</td>\n                        <td id=\"T_20a68c20_aecd_11eb_be4e_f218988e26cdrow1_col1\" class=\"data row1 col1\" >1.000000</td>\n                        <td id=\"T_20a68c20_aecd_11eb_be4e_f218988e26cdrow1_col2\" class=\"data row1 col2\" >-0.248441</td>\n                        <td id=\"T_20a68c20_aecd_11eb_be4e_f218988e26cdrow1_col3\" class=\"data row1 col3\" >-0.614855</td>\n                        <td id=\"T_20a68c20_aecd_11eb_be4e_f218988e26cdrow1_col4\" class=\"data row1 col4\" >-0.863479</td>\n            </tr>\n            <tr>\n                        <th id=\"T_20a68c20_aecd_11eb_be4e_f218988e26cdlevel0_row2\" class=\"row_heading level0 row2\" >City</th>\n                        <td id=\"T_20a68c20_aecd_11eb_be4e_f218988e26cdrow2_col0\" class=\"data row2 col0\" >-0.188728</td>\n                        <td id=\"T_20a68c20_aecd_11eb_be4e_f218988e26cdrow2_col1\" class=\"data row2 col1\" >-0.248441</td>\n                        <td id=\"T_20a68c20_aecd_11eb_be4e_f218988e26cdrow2_col2\" class=\"data row2 col2\" >1.000000</td>\n                        <td id=\"T_20a68c20_aecd_11eb_be4e_f218988e26cdrow2_col3\" class=\"data row2 col3\" >0.301604</td>\n                        <td id=\"T_20a68c20_aecd_11eb_be4e_f218988e26cdrow2_col4\" class=\"data row2 col4\" >0.323640</td>\n            </tr>\n            <tr>\n                        <th id=\"T_20a68c20_aecd_11eb_be4e_f218988e26cdlevel0_row3\" class=\"row_heading level0 row3\" >Package</th>\n                        <td id=\"T_20a68c20_aecd_11eb_be4e_f218988e26cdrow3_col0\" class=\"data row3 col0\" >-0.144847</td>\n                        <td id=\"T_20a68c20_aecd_11eb_be4e_f218988e26cdrow3_col1\" class=\"data row3 col1\" >-0.614855</td>\n                        <td id=\"T_20a68c20_aecd_11eb_be4e_f218988e26cdrow3_col2\" class=\"data row3 col2\" >0.301604</td>\n                        <td id=\"T_20a68c20_aecd_11eb_be4e_f218988e26cdrow3_col3\" class=\"data row3 col3\" >1.000000</td>\n                        <td id=\"T_20a68c20_aecd_11eb_be4e_f218988e26cdrow3_col4\" class=\"data row3 col4\" >0.606171</td>\n            </tr>\n            <tr>\n                        <th id=\"T_20a68c20_aecd_11eb_be4e_f218988e26cdlevel0_row4\" class=\"row_heading level0 row4\" >Price</th>\n                        <td id=\"T_20a68c20_aecd_11eb_be4e_f218988e26cdrow4_col0\" class=\"data row4 col0\" >-0.148783</td>\n                        <td id=\"T_20a68c20_aecd_11eb_be4e_f218988e26cdrow4_col1\" class=\"data row4 col1\" >-0.863479</td>\n                        <td id=\"T_20a68c20_aecd_11eb_be4e_f218988e26cdrow4_col2\" class=\"data row4 col2\" >0.323640</td>\n                        <td id=\"T_20a68c20_aecd_11eb_be4e_f218988e26cdrow4_col3\" class=\"data row4 col3\" >0.606171</td>\n                        <td id=\"T_20a68c20_aecd_11eb_be4e_f218988e26cdrow4_col4\" class=\"data row4 col4\" >1.000000</td>\n            </tr>\n    </tbody></table>"
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#000000;\n        }#T_374540ec_b1df_11eb_9c12_acde48001122row3_col2{\n            background-color:  #ccd9ed;\n            color:  #000000;\n        }#T_374540ec_b1df_11eb_9c12_acde48001122row3_col4{\n            background-color:  #f08a6c;\n            color:  #000000;\n        }#T_374540ec_b1df_11eb_9c12_acde48001122row4_col0{\n            background-color:  #445acc;\n            color:  #f1f1f1;\n        }#T_374540ec_b1df_11eb_9c12_acde48001122row4_col2{\n            background-color:  #d2dbe8;\n            color:  #000000;\n        }#T_374540ec_b1df_11eb_9c12_acde48001122row4_col3{\n            background-color:  #f39778;\n            color:  #000000;\n        }</style><table id=\"T_374540ec_b1df_11eb_9c12_acde48001122\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Month</th>        <th class=\"col_heading level0 col1\" >Variety</th>        <th class=\"col_heading level0 col2\" >City</th>        <th class=\"col_heading level0 col3\" >Package</th>        <th class=\"col_heading level0 col4\" >Price</th>    </tr></thead><tbody>\n                <tr>\n                        <th id=\"T_374540ec_b1df_11eb_9c12_acde48001122level0_row0\" class=\"row_heading level0 row0\" >Month</th>\n                        <td id=\"T_374540ec_b1df_11eb_9c12_acde48001122row0_col0\" class=\"data row0 col0\" >1.000000</td>\n                        <td id=\"T_374540ec_b1df_11eb_9c12_acde48001122row0_col1\" class=\"data row0 col1\" >0.171330</td>\n                        <td id=\"T_374540ec_b1df_11eb_9c12_acde48001122row0_col2\" class=\"data row0 col2\" >-0.188728</td>\n                        <td id=\"T_374540ec_b1df_11eb_9c12_acde48001122row0_col3\" class=\"data row0 col3\" >-0.144847</td>\n                        <td id=\"T_374540ec_b1df_11eb_9c12_acde48001122row0_col4\" class=\"data row0 col4\" >-0.148783</td>\n            </tr>\n            <tr>\n                        <th id=\"T_374540ec_b1df_11eb_9c12_acde48001122level0_row1\" class=\"row_heading level0 row1\" >Variety</th>\n                        <td id=\"T_374540ec_b1df_11eb_9c12_acde48001122row1_col0\" class=\"data row1 col0\" >0.171330</td>\n                        <td id=\"T_374540ec_b1df_11eb_9c12_acde48001122row1_col1\" class=\"data row1 col1\" >1.000000</td>\n                        <td id=\"T_374540ec_b1df_11eb_9c12_acde48001122row1_col2\" class=\"data row1 col2\" >-0.248441</td>\n                        <td id=\"T_374540ec_b1df_11eb_9c12_acde48001122row1_col3\" class=\"data row1 col3\" >-0.614855</td>\n                        <td id=\"T_374540ec_b1df_11eb_9c12_acde48001122row1_col4\" class=\"data row1 col4\" >-0.863479</td>\n            </tr>\n            <tr>\n                        <th id=\"T_374540ec_b1df_11eb_9c12_acde48001122level0_row2\" class=\"row_heading level0 row2\" >City</th>\n                        <td id=\"T_374540ec_b1df_11eb_9c12_acde48001122row2_col0\" class=\"data row2 col0\" >-0.188728</td>\n                        <td id=\"T_374540ec_b1df_11eb_9c12_acde48001122row2_col1\" class=\"data row2 col1\" >-0.248441</td>\n                        <td id=\"T_374540ec_b1df_11eb_9c12_acde48001122row2_col2\" class=\"data row2 col2\" >1.000000</td>\n                        <td id=\"T_374540ec_b1df_11eb_9c12_acde48001122row2_col3\" class=\"data row2 col3\" >0.301604</td>\n                        <td id=\"T_374540ec_b1df_11eb_9c12_acde48001122row2_col4\" class=\"data row2 col4\" >0.323640</td>\n            </tr>\n            <tr>\n                        <th id=\"T_374540ec_b1df_11eb_9c12_acde48001122level0_row3\" class=\"row_heading level0 row3\" >Package</th>\n                        <td id=\"T_374540ec_b1df_11eb_9c12_acde48001122row3_col0\" class=\"data row3 col0\" >-0.144847</td>\n                        <td id=\"T_374540ec_b1df_11eb_9c12_acde48001122row3_col1\" class=\"data row3 col1\" >-0.614855</td>\n                        <td id=\"T_374540ec_b1df_11eb_9c12_acde48001122row3_col2\" class=\"data row3 col2\" >0.301604</td>\n                        <td id=\"T_374540ec_b1df_11eb_9c12_acde48001122row3_col3\" class=\"data row3 col3\" >1.000000</td>\n                        <td id=\"T_374540ec_b1df_11eb_9c12_acde48001122row3_col4\" class=\"data row3 col4\" >0.606171</td>\n            </tr>\n            <tr>\n                        <th id=\"T_374540ec_b1df_11eb_9c12_acde48001122level0_row4\" class=\"row_heading level0 row4\" >Price</th>\n                        <td id=\"T_374540ec_b1df_11eb_9c12_acde48001122row4_col0\" class=\"data row4 col0\" >-0.148783</td>\n                        <td id=\"T_374540ec_b1df_11eb_9c12_acde48001122row4_col1\" class=\"data row4 col1\" >-0.863479</td>\n                        <td id=\"T_374540ec_b1df_11eb_9c12_acde48001122row4_col2\" class=\"data row4 col2\" >0.323640</td>\n                        <td id=\"T_374540ec_b1df_11eb_9c12_acde48001122row4_col3\" class=\"data row4 col3\" >0.606171</td>\n                        <td id=\"T_374540ec_b1df_11eb_9c12_acde48001122row4_col4\" class=\"data row4 col4\" >1.000000</td>\n            </tr>\n    </tbody></table>"
      },
      "metadata": {},
-     "execution_count": 14
+     "execution_count": 12
     }
    ],
    "source": [
@@ -434,7 +434,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -451,14 +451,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "output_type": "display_data",
      "data": {
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\n"
      },
      "metadata": {
@@ -494,7 +494,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -513,7 +513,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -524,7 +524,7 @@
       ]
      },
      "metadata": {},
-     "execution_count": 18
+     "execution_count": 16
     }
    ],
    "source": [
diff --git a/Regression/4-Logistic/notebook.ipynb b/Regression/4-Logistic/notebook.ipynb
index a08ea65fd..c151ea782 100644
--- a/Regression/4-Logistic/notebook.ipynb
+++ b/Regression/4-Logistic/notebook.ipynb
@@ -10,13 +10,17 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.3-final"
+   "version": "3.7.0"
   },
   "orig_nbformat": 2,
   "kernelspec": {
-   "name": "python3",
-   "display_name": "Python 3",
-   "language": "python"
+   "name": "python37364bit8d3b438fb5fc4430a93ac2cb74d693a7",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
   }
  },
  "nbformat": 4,
@@ -35,7 +39,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
@@ -68,7 +72,7 @@
       "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>City Name</th>\n      <th>Type</th>\n      <th>Package</th>\n      <th>Variety</th>\n      <th>Sub Variety</th>\n      <th>Grade</th>\n      <th>Date</th>\n      <th>Low Price</th>\n      <th>High Price</th>\n      <th>Mostly Low</th>\n      <th>...</th>\n      <th>Unit of Sale</th>\n      <th>Quality</th>\n      <th>Condition</th>\n      <th>Appearance</th>\n      <th>Storage</th>\n      <th>Crop</th>\n      <th>Repack</th>\n      <th>Trans Mode</th>\n      <th>Unnamed: 24</th>\n      <th>Unnamed: 25</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>BALTIMORE</td>\n      <td>NaN</td>\n      <td>24 inch bins</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>4/29/17</td>\n      <td>270.0</td>\n      <td>280.0</td>\n      <td>270.0</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>E</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>BALTIMORE</td>\n      <td>NaN</td>\n      <td>24 inch bins</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>5/6/17</td>\n      <td>270.0</td>\n      <td>280.0</td>\n      <td>270.0</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>E</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>BALTIMORE</td>\n      <td>NaN</td>\n      <td>24 inch bins</td>\n      <td>HOWDEN TYPE</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>9/24/16</td>\n      <td>160.0</td>\n      <td>160.0</td>\n      <td>160.0</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>N</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>BALTIMORE</td>\n      <td>NaN</td>\n      <td>24 inch bins</td>\n      <td>HOWDEN TYPE</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>9/24/16</td>\n      <td>160.0</td>\n      <td>160.0</td>\n      <td>160.0</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>N</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>BALTIMORE</td>\n      <td>NaN</td>\n      <td>24 inch bins</td>\n      <td>HOWDEN TYPE</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>11/5/16</td>\n      <td>90.0</td>\n      <td>100.0</td>\n      <td>90.0</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>N</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 26 columns</p>\n</div>"
      },
      "metadata": {},
-     "execution_count": 44
+     "execution_count": 1
     }
    ],
    "source": [
diff --git a/Regression/4-Logistic/solution/notebook.ipynb b/Regression/4-Logistic/solution/notebook.ipynb
index 967dc6e9a..e20d01961 100644
--- a/Regression/4-Logistic/solution/notebook.ipynb
+++ b/Regression/4-Logistic/solution/notebook.ipynb
@@ -37,7 +37,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
@@ -70,7 +70,7 @@
       "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>City Name</th>\n      <th>Type</th>\n      <th>Package</th>\n      <th>Variety</th>\n      <th>Sub Variety</th>\n      <th>Grade</th>\n      <th>Date</th>\n      <th>Low Price</th>\n      <th>High Price</th>\n      <th>Mostly Low</th>\n      <th>...</th>\n      <th>Unit of Sale</th>\n      <th>Quality</th>\n      <th>Condition</th>\n      <th>Appearance</th>\n      <th>Storage</th>\n      <th>Crop</th>\n      <th>Repack</th>\n      <th>Trans Mode</th>\n      <th>Unnamed: 24</th>\n      <th>Unnamed: 25</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>BALTIMORE</td>\n      <td>NaN</td>\n      <td>24 inch bins</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>4/29/17</td>\n      <td>270.0</td>\n      <td>280.0</td>\n      <td>270.0</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>E</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>BALTIMORE</td>\n      <td>NaN</td>\n      <td>24 inch bins</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>5/6/17</td>\n      <td>270.0</td>\n      <td>280.0</td>\n      <td>270.0</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>E</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>BALTIMORE</td>\n      <td>NaN</td>\n      <td>24 inch bins</td>\n      <td>HOWDEN TYPE</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>9/24/16</td>\n      <td>160.0</td>\n      <td>160.0</td>\n      <td>160.0</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>N</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>BALTIMORE</td>\n      <td>NaN</td>\n      <td>24 inch bins</td>\n      <td>HOWDEN TYPE</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>9/24/16</td>\n      <td>160.0</td>\n      <td>160.0</td>\n      <td>160.0</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>N</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>BALTIMORE</td>\n      <td>NaN</td>\n      <td>24 inch bins</td>\n      <td>HOWDEN TYPE</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>11/5/16</td>\n      <td>90.0</td>\n      <td>100.0</td>\n      <td>90.0</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>N</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 26 columns</p>\n</div>"
      },
      "metadata": {},
-     "execution_count": 8
+     "execution_count": 1
     }
    ],
    "source": [
@@ -85,7 +85,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -109,7 +109,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -133,7 +133,7 @@
       ]
      },
      "metadata": {},
-     "execution_count": 10
+     "execution_count": 3
     }
    ],
    "source": [
@@ -149,7 +149,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -159,7 +159,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-11-9d9b5803ae31>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mseaborn\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPairGrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_pumpkins\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatterplot\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-4-9d9b5803ae31>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mseaborn\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPairGrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_pumpkins\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatterplot\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'seaborn'"
      ]
     }