diff --git a/2-Regression/2-Data/notebook.ipynb b/2-Regression/2-Data/notebook.ipynb
index c9b9925b..8dc1ea8a 100644
--- a/2-Regression/2-Data/notebook.ipynb
+++ b/2-Regression/2-Data/notebook.ipynb
@@ -1,5 +1,331 @@
 {
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Build a regression model using Scikit-learn: prepare and visualize data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "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>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>"
+      ],
+      "text/plain": [
+       "   City Name Type       Package      Variety Sub Variety  Grade     Date  \\\n",
+       "0  BALTIMORE  NaN  24 inch bins          NaN         NaN    NaN  4/29/17   \n",
+       "1  BALTIMORE  NaN  24 inch bins          NaN         NaN    NaN   5/6/17   \n",
+       "2  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  9/24/16   \n",
+       "3  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  9/24/16   \n",
+       "4  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  11/5/16   \n",
+       "\n",
+       "   Low Price  High Price  Mostly Low  ...  Unit of Sale Quality Condition  \\\n",
+       "0      270.0       280.0       270.0  ...           NaN     NaN       NaN   \n",
+       "1      270.0       280.0       270.0  ...           NaN     NaN       NaN   \n",
+       "2      160.0       160.0       160.0  ...           NaN     NaN       NaN   \n",
+       "3      160.0       160.0       160.0  ...           NaN     NaN       NaN   \n",
+       "4       90.0       100.0        90.0  ...           NaN     NaN       NaN   \n",
+       "\n",
+       "  Appearance Storage  Crop Repack  Trans Mode  Unnamed: 24  Unnamed: 25  \n",
+       "0        NaN     NaN   NaN      E         NaN          NaN          NaN  \n",
+       "1        NaN     NaN   NaN      E         NaN          NaN          NaN  \n",
+       "2        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "3        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "4        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
+       "\n",
+       "[5 rows x 26 columns]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "pumpkins = pd.read_csv('../data/US-pumpkins.csv')\n",
+    "pumpkins.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Package       0\n",
+       "Low Price     0\n",
+       "High Price    0\n",
+       "Date          0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pumpkins.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pumpkins = pumpkins[pumpkins['Package'].str.contains('bushel', case=True, regex=True)]\n",
+    "columns_to_select = ['Package', 'Low Price', 'High Price', 'Date']\n",
+    "pumpkins = pumpkins.loc[:, columns_to_select]\n",
+    "price = (pumpkins['Low Price'] + pumpkins['High Price']) / 2\n",
+    "month = pd.DatetimeIndex(pumpkins['Date']).month\n",
+    "new_pumkinns = pd.DataFrame({\n",
+    "    'Month': month, \n",
+    "    'Package': pumpkins['Package'], \n",
+    "    'Low Price': pumpkins['Low Price'],\n",
+    "    'High Price': pumpkins['High Price'],\n",
+    "    'Price': price\n",
+    "    })\n",
+    "new_pumkinns.loc[new_pumkinns['Package'].str.contains('1 1/9'), 'Price'] = price/(1 + 1/9)\n",
+    "new_pumkinns.loc[new_pumkinns['Package'].str.contains('1/2'), 'Price'] = price/(1/2)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "price = new_pumkinns.Price\n",
+    "month = new_pumkinns.Month\n",
+    "plt.scatter (month, price, alpha=0.5)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Pumpkin Price')"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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iU6dO+b5Vq1bFMcccE5MnT44ePXrUR50AALs/Id55550XGzduzFtjsgUisy17vmXLlvwYAEBhW2SmT58ezz//fHz605+u2Zc9/9GPfpRPlAcAUNgWmYqKirxF5sM2b94c5eXldVUXAEDdB5nbb789Lr744ryzb7Xs+T/8wz/Ev/zLv+zsxwEANNytpREjRsR7772Xz+ibra+UyRaLzJ6PHDky36pl/WcAAAoTZMaPH18/lQAA1HeQOeecc3b2LQAAxZoQ7+233863bNj1hxeUBAAoZJCZM2dO3iqTzR1TVVVV61i2sGQ2egkAoJBBJuvMe9BBB8U999wTXbt23e6q2AAAhQwyr7/+ejz44IPRp0+f+qkIAKC+5pE54YQT4qWXXtrZtwEAlL5F5u677877yLz88svRt2/faNWqVa3jp556al3WBwBQd0Fm1qxZ+crXjz/++DbHdPYFAAp9aylbnuCss86KZcuW5UOvt96EGACg0EFmxYoVcemll+YjlgAAkgoyp59+ejzzzDP1Uw0AQH32kcnmkBk7dmzMnDkzDjvssG06+15yySU7+5EAAA03aqldu3Yxffr0fPtwZ19BBgAobJBZtGhR/VQCAFDffWQAAJJea+mj3HvvvbtTDwBA/QWZd999t9brjRs35rP8rlq1KgYPHryzHwcA0HBB5qGHHtpmXzYZ3oUXXhi9e/fe9UoAAErRR6Z58+YxZsyYuPPOO+vi4wAAGraz78KFC2PTpk119XEAAHV/aylredlaVVVVvu7SY489lq+KDQBQ2CDzpz/9aZvbSvvuu2/ccccdHzuiCQCgpEHGOksAQHJ9ZLKRSbfeemsMGDAgjjrqqLjqqqvi/fffr9/qAADqIsjcfPPN8b3vfS9fZ2m//faLH/zgBzFq1KhP+nYAgNIFmfvuuy9+8pOfxBNPPBEPP/xwPPLII/Hzn/88b6kBACh0kHnjjTdi6NChNa9PPPHEfLXrpUuX1ldtAAB1E2SyOWJat25da1+rVq3yJQoAAAo9aimbL2bEiBFRVlZWs++DDz6ICy64INq2bVuzb8qUKXVfJQDA7gSZ7U12d9ZZZ33StwMAlC7ITJw4se6/OgBAEdZaAgBoaIIMAJAsQQYASJYgAwAkS5ABAJIlyAAAyRJkAIBklTTIzJgxI0455ZQoLy/P123KFqP88GzC1157bXTv3j3atGmTr++0YMGCktULABRLSYPMunXr4vDDD4+77rpru8dvu+22+OEPfxg//elP4w9/+EO+FMKQIUPypREAAD7xzL714eSTT8637claY8aPHx9XX311nHbaafm+++67L7p27Zq33Hzzm99s4GoBgKIpbB+ZRYsWxfLly/PbSdU6duwY/fv3j1mzZu3wfevXr4/KyspaGwDQOBU2yGQhJpO1wGwte119bHvGjRuXB57qraKiot5rBQBKo7BBZleNHTs2Vq9eXbMtWbKk1CUBAE0tyHTr1i1/fOutt2rtz15XH9uesrKy6NChQ60NAGicChtkDjjggDywTJs2rWZf1t8lG7109NFHl7Q2AKAYSjpqae3atfHaa6/V6uA7b9686Ny5c+y///4xevTouOmmm+LAAw/Mg80111yTzzkzbNiwUpYNABRESYPM7NmzY9CgQTWvx4wZkz+ec845MWnSpLjiiivyuWa+853vxKpVq+LYY4+NqVOnRuvWrUtYNQBQFCUNMscff3w+X8yOZLP93nDDDfkGAJBMHxkAgI8jyAAAyRJkAIBkCTIAQLIEGQAgWYIMAJAsQQYASJYgAwAkS5ABAJIlyAAAyRJkAIBkCTIAQLIEGQAgWYIMAJAsQQYASJYgAwAkS5ABAJIlyAAAyRJkAIBkCTIAQLIEGQAgWYIMAJAsQQYASJYgAwAkS5ABAJIlyAAAyRJkAIBkCTIAQLIEGQAgWYIMAJAsQQYASJYgAwAkS5ABAJIlyAAAyRJkAIBkCTIAQLIEGQAgWYIMAJAsQQYASJYgAwAkS5ABAJIlyAAAyRJkAIBkCTIAQLIEGQAgWYIMAJAsQQYASJYgAwAkS5ABAJIlyAAAySp0kPn+978fzZo1q7UdfPDBpS4LACiIllFwhx56aDz99NM1r1u2LHzJAEADKXwqyIJLt27dSl0GAFBAhb61lFmwYEGUl5dHr1694swzz4w33njjI89fv359VFZW1toAgMap0EGmf//+MWnSpJg6dWpMmDAhFi1aFF/60pdizZo1O3zPuHHjomPHjjVbRUVFg9YMADScQgeZk08+Ob72ta9Fv379YsiQIfG73/0uVq1aFb/85S93+J6xY8fG6tWra7YlS5Y0aM0AQMMpfB+ZrXXq1CkOOuigeO2113Z4TllZWb4BAI1foVtkPmzt2rWxcOHC6N69e6lLAQAKoNBB5rLLLovp06fH4sWL4/nnn4+vfvWr0aJFizjjjDNKXRoAUACFvrX05ptv5qFlxYoVse+++8axxx4bL7zwQv4cAKDQQWby5MmlLgEAKLBC31oCAPgoggwAkCxBBgBIliADACRLkAEAkiXIAADJEmQAgGQJMgBAsgQZACBZggwAkCxBBgBIliADACRLkAEAkiXIAADJEmQAgGQJMgBAsgQZACBZggwAkCxBBgBIliADACRLkAEAkiXIAADJEmQAgGQJMgBAsgQZACBZggwAkCxBBgBIliADACRLkAEAkiXIAADJEmQAgGQJMgBAsgQZACBZggwAkCxBBgBIliADACRLkAEAkiXIAADJEmQAgGQJMgBAsgQZACBZggwAkCxBBgBIliADACRLkAEAkiXIAADJEmQAgGQJMgBAsgQZACBZggwAkCxBBgBIVhJB5q677oqePXtG69ato3///vHiiy+WuiQAoAAKH2R+8YtfxJgxY+K6666LuXPnxuGHHx5DhgyJt99+u9SlAQAlVvgg86//+q9x/vnnx7e//e045JBD4qc//Wnsueeece+995a6NACgxFpGgW3YsCHmzJkTY8eOrdnXvHnzOPHEE2PWrFnbfc/69evzrdrq1avzx8rKynqtdcv69yJ19f09aiiN4Vo0luvhWhSHa1EsjeF6VNbztaj+/KqqqnSDzDvvvBObN2+Orl271tqfvf6f//mf7b5n3Lhxcf3112+zv6Kiot7qbCw6ji91BWzN9SgO16I4XIumdy3WrFkTHTt2TDPI7Iqs9SbrU1Nty5YtsXLlyth7772jWbNmkaosmWZhbMmSJdGhQ4dSl9OkuRbF4VoUh2tRHJWN5FpkLTFZiCkvL//I8wodZPbZZ59o0aJFvPXWW7X2Z6+7deu23feUlZXl29Y6deoUjUX2jzLlf5iNiWtRHK5FcbgWxdGhEVyLj2qJSaKz7x577BFHHnlkTJs2rVYLS/b66KOPLmltAEDpFbpFJpPdJjrnnHPi85//fHzhC1+I8ePHx7p16/JRTABA01b4IPONb3wj/vrXv8a1114by5cvj89+9rMxderUbToAN3bZ7bJsLp0P3zaj4bkWxeFaFIdrURxlTexaNKv6uHFNAAAFVeg+MgAAH0WQAQCSJcgAAMkSZACAZAkyAECyCj/8Gkpt2bJlMWHChJg5c2b+PFu4tFevXjFs2LAYMWJEPvs0AKWhRaagfvzjH8e3vvWtmDx5cv763//93+OQQw6Jgw8+OL73ve/Fpk2bSl1ikzB79uz4zGc+E7/73e9i48aNsWDBgny26bZt28Zll10Wxx13XL4WCAClIcgU0E033ZSHlffeey8uvfTSuPXWW/PHM888M5/l+O67744bb7yx1GU2CaNHj86/91mg+f3vfx+TJk2KV199NQ+Yr7/+en6Nrr766lKX2WRs2LAhfvnLX+bX5Iwzzsi37PmvfvWr/BjFka2Jd8MNN5S6jCblzTffjLVr126zP/sjbMaMGdFoZRPiUSy9e/euevDBB/Pn8+bNq2rRokXV/fffX3N8ypQpVX369ClhhU1HmzZtqhYuXFjzevPmzVWtWrWqWr58ef76ySefrCovLy9hhU3HggULqnr16lXVunXrqoEDB1Z9/etfz7fsebYv+38iO4diyH52NW/evNRlNAlLly6tOuqoo/Lvd/b74uyzz65as2ZNzfHs51Vjvhb6yBTQ0qVL87WlMocffnjeJyNbmqHa5z73ufwc6l+XLl3yfjFZn5jqvzKz23rVK8oeeOCBsXLlyhJX2TRceOGFcdhhh8Wf/vSnbVb0rayszG/Fjho1Kp544omS1diUzJ8//yOPv/LKKw1WS1N31VVX5b8n/vCHP8SqVavy14MGDYonn3wy9tprr/ycxjyJvyBTQN26dYv//u//jv333z/vk7F58+b89aGHHpof/6//+q/8Fyz1L+vQe8EFF8Ttt9+er1uS3dIbOHBgtGnTpuaH9X777VfqMpuE5557Ll588cVtQkwm25ddm/79+5ektqYo++OqWbNm2/0FWb0/e6T+Pf300/HQQw/V/AGc/b/yta99LQYPHhzTpk3L9zXmayHIFFDWFyb76/K0007L/xFeccUVecfSFStW5P8Yb7755vi7v/u7UpfZZPorZS0yp5xySh4ojz766Lj//vtrjmfXY9y4cSWtsano1KlTLF68OPr27bvd49mx7BwaRufOneO2226LE044YbvHsz+4sv9vqH+rV6+uaXnJZH90TZkyJQ8zWcvM1j+zGiNBpoCuv/76/C/+WbNmxfnnn583E2a3mLJAk3UuzX446OzbMNq1axe/+MUv4oMPPshvKWWvt3bSSSeVrLam5rzzzssD/jXXXJP/8uzatWvN7b4s8Geh8+KLLy51mU1GNnovu8X9qU99arvHs1scjfl2RpH06tUrv9WX3equ1rJly7wTfBZm/vZv/zYaM6tfA8nIRvD94Ac/iOXLl9c0lWc/wrLbsdkIsyzs0zCyWxnr1q2Ls846a7vH33333fjtb3+bj7Skfl155ZUxb9687fYPy/4AGz58eDzyyCOxZcuWaIwEGSA5ixYtysNMJgsxBxxwQKlLgpLZtGlT3lq/vf5j1cf/8pe/7LD1LHXmkQGSkwWXrL9StlWHmCVLlsTIkSNLXRr/z/VoOC1bttxhiMlk/fyyLguNlRYZoFF46aWX8qkJsk7ZlJ7rURwvNfJrobMvkISsv8VHyWZapuG4HsXx2yZ+LbTIAEnIJvza0bwl1bLjjfWvzqJxPYqjeRO/FvrIAEno3r17PjdGNvJie9vcuXNLXWKT4noUR/cmfi0EGSCZeUvmzJmzw+Mf9xcpdcv1KI4jm/i10EcGSMLll1+ez1uyI3369IlnnnmmQWtqylyP4mjq10IfGQAgWW4tAQDJEmQAgGQJMgBAsgQZACBZggzQJGVDUh9++OFSlwHsJkEGaFAjRozIQ8QFF1ywzbFRo0blx7Jz6sr3v//9+OxnP1tnnwcUiyADNLiKioqYPHlyvP/++zX7Pvjgg3jggQdi//33L2ltQFoEGaDBZSvxZmEmm1a9WvY8CzFHHHFEzb7169fHJZdcEl26dInWrVvHscceG3/84x9rjj/77LN5C860adPi85//fOy5555xzDHHxCuvvJIfnzRpUlx//fX56r/ZedmW7av2zjvvxFe/+tX8fQceeODHLr4HFI8gA5TEyJEjY+LEiTWv77333vj2t79d65wrrrgiHnzwwfi3f/u3fL2YbIbSIUOGxMqVK2ud90//9E9xxx13xOzZs6Nly5b5Z2e+8Y1vxD/+4z/GoYceGsuWLcu3bF+1LOR8/etfj/nz58fQoUPjzDPP3OazgWITZICSOOuss2LmzJnxv//7v/n23HPP5fuqZVOuT5gwIW6//fY4+eST45BDDomf/exn0aZNm7jnnntqfdbNN98cAwcOzM+56qqr4vnnn89vVWXntmvXLg833bp1y7dsX7WsL84ZZ5yRB6R//ud/jrVr18aLL77YoN8HYPdYawkoiX333Te+8pWv5Ld6spVSsuf77LNPzfGFCxfGxo0bY8CAATX7WrVqFV/4whfiz3/+c63P6tevX62VgDNvv/32x/a32fp9bdu2jQ4dOuTvA9IhyAAlk90Cuuiii/Lnd9111y5/ThZwqmX9YDJbtmzZqfdVv/eTvA8oDreWgJL58pe/HBs2bMhbXrK+L1vr3bt37LHHHvktp2rZeVln3+wW0ieVfcbmzZvrtG6gOLTIACXTokWLmttE2fOtZbd6Lrzwwrj88sujc+fO+W2i2267Ld57770499xzP/HX6NmzZyxatCjmzZsXPXr0iPbt20dZWVmd/7cApSHIACWV9UvZkVtuuSW/1XP22WfHmjVr8iHWTzzxROy1116f+POHDx+eD+0eNGhQrFq1Kh8pVZcT7gGl1awq62UHAJAgfWQAgGQJMgBAsgQZACBZggwAkCxBBgBIliADACRLkAEAkiXIAADJEmQAgGQJMgBAsgQZACBS9X/BuMI4CdcILgAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "new_pumkinns.groupby(['Month'])['Price'].mean().plot(kind='bar')\n",
+    "plt.ylabel('Pumpkin Price')"
+   ]
+  }
+ ],
  "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
@@ -10,24 +336,10 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.3-final"
+   "version": "3.13.3"
   },
-  "orig_nbformat": 2,
-  "kernelspec": {
-   "name": "python3",
-   "display_name": "Python 3",
-   "language": "python"
-  }
+  "orig_nbformat": 2
  },
  "nbformat": 4,
- "nbformat_minor": 2,
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ]
-}
\ No newline at end of file
+ "nbformat_minor": 2
+}