From 8189ded29767f0d8463ab3874ef6467343ac8e81 Mon Sep 17 00:00:00 2001
From: Jim Bennett <jim.bennett@microsoft.com>
Date: Tue, 14 Mar 2023 17:27:18 -0700
Subject: [PATCH] Revert "Adding demo for more scripts"

This reverts commit 45b82dfab1b7273e52b3f974b52be1b90bedf086.
---
 2-Regression/3-Linear/notebook.ipynb  | 764 +-------------------------
 5-Clustering/2-K-Means/notebook.ipynb | 379 ++-----------
 2 files changed, 61 insertions(+), 1082 deletions(-)

diff --git a/2-Regression/3-Linear/notebook.ipynb b/2-Regression/3-Linear/notebook.ipynb
index 4da05ffb..b01f1ee8 100644
--- a/2-Regression/3-Linear/notebook.ipynb
+++ b/2-Regression/3-Linear/notebook.ipynb
@@ -16,209 +16,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": null,
    "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": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import pandas as pd\n",
     "import matplotlib.pyplot as plt\n",
@@ -232,121 +32,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": null,
    "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>Month</th>\n",
-       "      <th>DayOfYear</th>\n",
-       "      <th>Variety</th>\n",
-       "      <th>City</th>\n",
-       "      <th>Package</th>\n",
-       "      <th>Low Price</th>\n",
-       "      <th>High Price</th>\n",
-       "      <th>Price</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>70</th>\n",
-       "      <td>9</td>\n",
-       "      <td>267</td>\n",
-       "      <td>PIE TYPE</td>\n",
-       "      <td>BALTIMORE</td>\n",
-       "      <td>1 1/9 bushel cartons</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>13.636364</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>71</th>\n",
-       "      <td>9</td>\n",
-       "      <td>267</td>\n",
-       "      <td>PIE TYPE</td>\n",
-       "      <td>BALTIMORE</td>\n",
-       "      <td>1 1/9 bushel cartons</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>16.363636</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>72</th>\n",
-       "      <td>10</td>\n",
-       "      <td>274</td>\n",
-       "      <td>PIE TYPE</td>\n",
-       "      <td>BALTIMORE</td>\n",
-       "      <td>1 1/9 bushel cartons</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>16.363636</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>73</th>\n",
-       "      <td>10</td>\n",
-       "      <td>274</td>\n",
-       "      <td>PIE TYPE</td>\n",
-       "      <td>BALTIMORE</td>\n",
-       "      <td>1 1/9 bushel cartons</td>\n",
-       "      <td>17.0</td>\n",
-       "      <td>17.0</td>\n",
-       "      <td>15.454545</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>74</th>\n",
-       "      <td>10</td>\n",
-       "      <td>281</td>\n",
-       "      <td>PIE TYPE</td>\n",
-       "      <td>BALTIMORE</td>\n",
-       "      <td>1 1/9 bushel cartons</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>13.636364</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    Month  DayOfYear   Variety       City               Package  Low Price  \\\n",
-       "70      9        267  PIE TYPE  BALTIMORE  1 1/9 bushel cartons       15.0   \n",
-       "71      9        267  PIE TYPE  BALTIMORE  1 1/9 bushel cartons       18.0   \n",
-       "72     10        274  PIE TYPE  BALTIMORE  1 1/9 bushel cartons       18.0   \n",
-       "73     10        274  PIE TYPE  BALTIMORE  1 1/9 bushel cartons       17.0   \n",
-       "74     10        281  PIE TYPE  BALTIMORE  1 1/9 bushel cartons       15.0   \n",
-       "\n",
-       "    High Price      Price  \n",
-       "70        15.0  13.636364  \n",
-       "71        18.0  16.363636  \n",
-       "72        18.0  16.363636  \n",
-       "73        17.0  15.454545  \n",
-       "74        15.0  13.636364  "
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "pumpkins = pumpkins[pumpkins['Package'].str.contains('bushel', case=True, regex=True)]\n",
     "\n",
@@ -383,30 +71,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x14131e0e0>"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "plt.scatter('Month','Price',data=new_pumpkins)"
@@ -414,425 +81,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x1413b82e0>"
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "\n",
-    "plt.scatter('DayOfYear','Price',data=new_pumpkins)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "-0.14878293554077535\n",
-      "-0.16673322492745407\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Print the correlation between month and price\n",
-    "print(new_pumpkins['Month'].corr(new_pumpkins['Price']))\n",
-    "\n",
-    "# Print the correlation between Day of the year and price\n",
-    "print(new_pumpkins['DayOfYear'].corr(new_pumpkins['Price']))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Define the colors to use to plot the pumpkins\n",
-    "colors = ['red','blue','green','yellow']\n",
-    "\n",
-    "# Plot the price vs day of year for the pumpkins, using a different\n",
-    "# color for each variety \n",
-    "ax=None\n",
-    "for i,var in enumerate(new_pumpkins['Variety'].unique()):\n",
-    "    df = new_pumpkins[new_pumpkins['Variety']==var]\n",
-    "    ax = df.plot.scatter('DayOfYear','Price', ax=ax, c=colors[i], label=var)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot: xlabel='Variety'>"
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "new_pumpkins.groupby('Variety')['Price'].mean().plot(kind='bar')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot: xlabel='DayOfYear', ylabel='Price'>"
-      ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pie_pumpkins = new_pumpkins[new_pumpkins['Variety']=='PIE TYPE']\n",
-    "pie_pumpkins.plot.scatter('DayOfYear','Price') "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "-0.23841413206125714\n",
-      "-0.2669192282197318\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Print the correlation between month and price\n",
-    "print(pie_pumpkins['Month'].corr(pie_pumpkins['Price']))\n",
-    "\n",
-    "# Print the correlation between Day of the year and price\n",
-    "print(pie_pumpkins['DayOfYear'].corr(pie_pumpkins['Price']))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from sklearn.linear_model import LinearRegression\n",
-    "from sklearn.metrics import mean_squared_error\n",
-    "from sklearn.model_selection import train_test_split"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(144, 1)"
-      ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Get the day of year and price in separate arrays\n",
-    "X = pie_pumpkins['DayOfYear'].to_numpy().reshape(-1,1)\n",
-    "y = pie_pumpkins['Price']\n",
     "\n",
-    "X.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
-      ],
-      "text/plain": [
-       "LinearRegression()"
-      ]
-     },
-     "execution_count": 56,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Create a linear regression object\n",
-    "lin_reg = LinearRegression()\n",
-    "\n",
-    "# Train the model using our training data\n",
-    "lin_reg.fit(X_train,y_train)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Mean error: 2.77 (17.2%)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Test the model using our test data\n",
-    "pred = lin_reg.predict(X_test)\n",
-    "\n",
-    "# Calculate the mean squared error\n",
-    "mse = np.sqrt(mean_squared_error(y_test,pred))\n",
-    "\n",
-    "# Print the mean squared error in an easy to read format\n",
-    "print(f'Mean error: {mse:3.3} ({mse/np.mean(pred)*100:3.3}%)')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Model determination:  0.04460606335028361\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Calculate the coefficient of determination\n",
-    "score = lin_reg.score(X_train,y_train)\n",
-    "print('Model determination: ', score)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x17f976e30>]"
-      ]
-     },
-     "execution_count": 60,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Create a scatter plot using our test data\n",
-    "plt.scatter(X_test,y_test)\n",
-    "\n",
-    "# Draw the plot using our predictions\n",
-    "plt.plot(X_test,pred)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "x = -0.017518760953105y + 21.133734359909326\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"x = {lin_reg.coef_[0]}y + {lin_reg.intercept_}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([16.64893156])"
-      ]
-     },
-     "execution_count": 66,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "lin_reg.predict([[256]])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Mean error: 2.73 (17.0%)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x17fa83c70>]"
-      ]
-     },
-     "execution_count": 67,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from sklearn.preprocessing import PolynomialFeatures\n",
-    "from sklearn.pipeline import make_pipeline\n",
-    "\n",
-    "# Build a polynomial regression pipeline\n",
-    "pipeline = make_pipeline(PolynomialFeatures(2), LinearRegression())\n",
-    "\n",
-    "# Use the pipeline to build the model\n",
-    "pipeline.fit(X_train,y_train)\n",
-    "\n",
-    "# Test the model with our test data\n",
-    "pred = pipeline.predict(X_test)\n",
-    "\n",
-    "# Calculate and print the mean squared error\n",
-    "mse = np.sqrt(mean_squared_error(y_test,pred))\n",
-    "print(f'Mean error: {mse:3.3} ({mse/np.mean(pred)*100:3.3}%)')\n",
-    "\n",
-    "# Plot the results\n",
-    "plt.scatter(X_test,y_test)\n",
-    "plt.plot(sorted(X_test),pipeline.predict(sorted(X_test)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Model determination:  0.07639977655280161\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Score the model\n",
-    "score = pipeline.score(X_train,y_train)\n",
-    "print('Model determination: ', score)"
+    "plt.scatter('DayOfYear','Price',data=new_pumpkins)"
    ]
   }
  ],
@@ -852,7 +106,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.10"
+   "version": "3.8.3-final"
   },
   "orig_nbformat": 2
  },
diff --git a/5-Clustering/2-K-Means/notebook.ipynb b/5-Clustering/2-K-Means/notebook.ipynb
index 8dce4e42..b25d4f18 100644
--- a/5-Clustering/2-K-Means/notebook.ipynb
+++ b/5-Clustering/2-K-Means/notebook.ipynb
@@ -1,11 +1,37 @@
 {
+ "metadata": {
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.0"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python37364bit8d3b438fb5fc4430a93ac2cb74d693a7",
+   "display_name": "Python 3.7.0 64-bit ('3.7')"
+  },
+  "metadata": {
+   "interpreter": {
+    "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
  "cells": [
   {
-   "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "# Nigerian Music scraped from Spotify - an analysis"
-   ]
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
   },
   {
    "cell_type": "code",
@@ -13,8 +39,8 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
      "output_type": "stream",
+     "name": "stdout",
      "text": [
       "Requirement already satisfied: seaborn in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (0.11.1)\n",
       "Requirement already satisfied: numpy>=1.15 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from seaborn) (1.19.2)\n",
@@ -39,11 +65,11 @@
    ]
   },
   {
-   "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "Start where we finished in the last lesson, with data imported and filtered."
-   ]
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
   },
   {
    "cell_type": "code",
@@ -51,144 +77,8 @@
    "metadata": {},
    "outputs": [
     {
+     "output_type": "execute_result",
      "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>name</th>\n",
-       "      <th>album</th>\n",
-       "      <th>artist</th>\n",
-       "      <th>artist_top_genre</th>\n",
-       "      <th>release_date</th>\n",
-       "      <th>length</th>\n",
-       "      <th>popularity</th>\n",
-       "      <th>danceability</th>\n",
-       "      <th>acousticness</th>\n",
-       "      <th>energy</th>\n",
-       "      <th>instrumentalness</th>\n",
-       "      <th>liveness</th>\n",
-       "      <th>loudness</th>\n",
-       "      <th>speechiness</th>\n",
-       "      <th>tempo</th>\n",
-       "      <th>time_signature</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Sparky</td>\n",
-       "      <td>Mandy &amp; The Jungle</td>\n",
-       "      <td>Cruel Santino</td>\n",
-       "      <td>alternative r&amp;b</td>\n",
-       "      <td>2019</td>\n",
-       "      <td>144000</td>\n",
-       "      <td>48</td>\n",
-       "      <td>0.666</td>\n",
-       "      <td>0.8510</td>\n",
-       "      <td>0.420</td>\n",
-       "      <td>0.534000</td>\n",
-       "      <td>0.1100</td>\n",
-       "      <td>-6.699</td>\n",
-       "      <td>0.0829</td>\n",
-       "      <td>133.015</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>shuga rush</td>\n",
-       "      <td>EVERYTHING YOU HEARD IS TRUE</td>\n",
-       "      <td>Odunsi (The Engine)</td>\n",
-       "      <td>afropop</td>\n",
-       "      <td>2020</td>\n",
-       "      <td>89488</td>\n",
-       "      <td>30</td>\n",
-       "      <td>0.710</td>\n",
-       "      <td>0.0822</td>\n",
-       "      <td>0.683</td>\n",
-       "      <td>0.000169</td>\n",
-       "      <td>0.1010</td>\n",
-       "      <td>-5.640</td>\n",
-       "      <td>0.3600</td>\n",
-       "      <td>129.993</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>LITT!</td>\n",
-       "      <td>LITT!</td>\n",
-       "      <td>AYLØ</td>\n",
-       "      <td>indie r&amp;b</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>207758</td>\n",
-       "      <td>40</td>\n",
-       "      <td>0.836</td>\n",
-       "      <td>0.2720</td>\n",
-       "      <td>0.564</td>\n",
-       "      <td>0.000537</td>\n",
-       "      <td>0.1100</td>\n",
-       "      <td>-7.127</td>\n",
-       "      <td>0.0424</td>\n",
-       "      <td>130.005</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Confident / Feeling Cool</td>\n",
-       "      <td>Enjoy Your Life</td>\n",
-       "      <td>Lady Donli</td>\n",
-       "      <td>nigerian pop</td>\n",
-       "      <td>2019</td>\n",
-       "      <td>175135</td>\n",
-       "      <td>14</td>\n",
-       "      <td>0.894</td>\n",
-       "      <td>0.7980</td>\n",
-       "      <td>0.611</td>\n",
-       "      <td>0.000187</td>\n",
-       "      <td>0.0964</td>\n",
-       "      <td>-4.961</td>\n",
-       "      <td>0.1130</td>\n",
-       "      <td>111.087</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>wanted you</td>\n",
-       "      <td>rare.</td>\n",
-       "      <td>Odunsi (The Engine)</td>\n",
-       "      <td>afropop</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>152049</td>\n",
-       "      <td>25</td>\n",
-       "      <td>0.702</td>\n",
-       "      <td>0.1160</td>\n",
-       "      <td>0.833</td>\n",
-       "      <td>0.910000</td>\n",
-       "      <td>0.3480</td>\n",
-       "      <td>-6.044</td>\n",
-       "      <td>0.0447</td>\n",
-       "      <td>105.115</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
       "text/plain": [
        "                       name                         album  \\\n",
        "0                    Sparky            Mandy & The Jungle   \n",
@@ -217,11 +107,11 @@
        "2       0.0424  130.005               4  \n",
        "3       0.1130  111.087               4  \n",
        "4       0.0447  105.115               4  "
-      ]
+      ],
+      "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>name</th>\n      <th>album</th>\n      <th>artist</th>\n      <th>artist_top_genre</th>\n      <th>release_date</th>\n      <th>length</th>\n      <th>popularity</th>\n      <th>danceability</th>\n      <th>acousticness</th>\n      <th>energy</th>\n      <th>instrumentalness</th>\n      <th>liveness</th>\n      <th>loudness</th>\n      <th>speechiness</th>\n      <th>tempo</th>\n      <th>time_signature</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Sparky</td>\n      <td>Mandy &amp; The Jungle</td>\n      <td>Cruel Santino</td>\n      <td>alternative r&amp;b</td>\n      <td>2019</td>\n      <td>144000</td>\n      <td>48</td>\n      <td>0.666</td>\n      <td>0.8510</td>\n      <td>0.420</td>\n      <td>0.534000</td>\n      <td>0.1100</td>\n      <td>-6.699</td>\n      <td>0.0829</td>\n      <td>133.015</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>shuga rush</td>\n      <td>EVERYTHING YOU HEARD IS TRUE</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2020</td>\n      <td>89488</td>\n      <td>30</td>\n      <td>0.710</td>\n      <td>0.0822</td>\n      <td>0.683</td>\n      <td>0.000169</td>\n      <td>0.1010</td>\n      <td>-5.640</td>\n      <td>0.3600</td>\n      <td>129.993</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>LITT!</td>\n      <td>LITT!</td>\n      <td>AYLØ</td>\n      <td>indie r&amp;b</td>\n      <td>2018</td>\n      <td>207758</td>\n      <td>40</td>\n      <td>0.836</td>\n      <td>0.2720</td>\n      <td>0.564</td>\n      <td>0.000537</td>\n      <td>0.1100</td>\n      <td>-7.127</td>\n      <td>0.0424</td>\n      <td>130.005</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Confident / Feeling Cool</td>\n      <td>Enjoy Your Life</td>\n      <td>Lady Donli</td>\n      <td>nigerian pop</td>\n      <td>2019</td>\n      <td>175135</td>\n      <td>14</td>\n      <td>0.894</td>\n      <td>0.7980</td>\n      <td>0.611</td>\n      <td>0.000187</td>\n      <td>0.0964</td>\n      <td>-4.961</td>\n      <td>0.1130</td>\n      <td>111.087</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>wanted you</td>\n      <td>rare.</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2018</td>\n      <td>152049</td>\n      <td>25</td>\n      <td>0.702</td>\n      <td>0.1160</td>\n      <td>0.833</td>\n      <td>0.910000</td>\n      <td>0.3480</td>\n      <td>-6.044</td>\n      <td>0.0447</td>\n      <td>105.115</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
      },
-     "execution_count": 6,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 6
     }
    ],
    "source": [
@@ -236,11 +126,11 @@
    ]
   },
   {
-   "cell_type": "markdown",
-   "metadata": {},
    "source": [
     "We will focus only on 3 genres. Maybe we can get 3 clusters built!"
-   ]
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
   },
   {
    "cell_type": "code",
@@ -248,27 +138,25 @@
    "metadata": {},
    "outputs": [
     {
+     "output_type": "execute_result",
      "data": {
       "text/plain": [
        "Text(0.5, 1.0, 'Top genres')"
       ]
      },
-     "execution_count": 7,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 7
     },
     {
+     "output_type": "display_data",
      "data": {
-      "image/png": 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\n"
      },
      "metadata": {
       "needs_background": "light"
-     },
-     "output_type": "display_data"
+     }
     }
    ],
    "source": [
@@ -287,144 +175,8 @@
    "metadata": {},
    "outputs": [
     {
+     "output_type": "execute_result",
      "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>name</th>\n",
-       "      <th>album</th>\n",
-       "      <th>artist</th>\n",
-       "      <th>artist_top_genre</th>\n",
-       "      <th>release_date</th>\n",
-       "      <th>length</th>\n",
-       "      <th>popularity</th>\n",
-       "      <th>danceability</th>\n",
-       "      <th>acousticness</th>\n",
-       "      <th>energy</th>\n",
-       "      <th>instrumentalness</th>\n",
-       "      <th>liveness</th>\n",
-       "      <th>loudness</th>\n",
-       "      <th>speechiness</th>\n",
-       "      <th>tempo</th>\n",
-       "      <th>time_signature</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>shuga rush</td>\n",
-       "      <td>EVERYTHING YOU HEARD IS TRUE</td>\n",
-       "      <td>Odunsi (The Engine)</td>\n",
-       "      <td>afropop</td>\n",
-       "      <td>2020</td>\n",
-       "      <td>89488</td>\n",
-       "      <td>30</td>\n",
-       "      <td>0.710</td>\n",
-       "      <td>0.0822</td>\n",
-       "      <td>0.683</td>\n",
-       "      <td>0.000169</td>\n",
-       "      <td>0.1010</td>\n",
-       "      <td>-5.640</td>\n",
-       "      <td>0.3600</td>\n",
-       "      <td>129.993</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Confident / Feeling Cool</td>\n",
-       "      <td>Enjoy Your Life</td>\n",
-       "      <td>Lady Donli</td>\n",
-       "      <td>nigerian pop</td>\n",
-       "      <td>2019</td>\n",
-       "      <td>175135</td>\n",
-       "      <td>14</td>\n",
-       "      <td>0.894</td>\n",
-       "      <td>0.7980</td>\n",
-       "      <td>0.611</td>\n",
-       "      <td>0.000187</td>\n",
-       "      <td>0.0964</td>\n",
-       "      <td>-4.961</td>\n",
-       "      <td>0.1130</td>\n",
-       "      <td>111.087</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>wanted you</td>\n",
-       "      <td>rare.</td>\n",
-       "      <td>Odunsi (The Engine)</td>\n",
-       "      <td>afropop</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>152049</td>\n",
-       "      <td>25</td>\n",
-       "      <td>0.702</td>\n",
-       "      <td>0.1160</td>\n",
-       "      <td>0.833</td>\n",
-       "      <td>0.910000</td>\n",
-       "      <td>0.3480</td>\n",
-       "      <td>-6.044</td>\n",
-       "      <td>0.0447</td>\n",
-       "      <td>105.115</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>Kasala</td>\n",
-       "      <td>Pioneers</td>\n",
-       "      <td>DRB Lasgidi</td>\n",
-       "      <td>nigerian pop</td>\n",
-       "      <td>2020</td>\n",
-       "      <td>184800</td>\n",
-       "      <td>26</td>\n",
-       "      <td>0.803</td>\n",
-       "      <td>0.1270</td>\n",
-       "      <td>0.525</td>\n",
-       "      <td>0.000007</td>\n",
-       "      <td>0.1290</td>\n",
-       "      <td>-10.034</td>\n",
-       "      <td>0.1970</td>\n",
-       "      <td>100.103</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>Pull Up</td>\n",
-       "      <td>Everything Pretty</td>\n",
-       "      <td>prettyboydo</td>\n",
-       "      <td>nigerian pop</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>202648</td>\n",
-       "      <td>29</td>\n",
-       "      <td>0.818</td>\n",
-       "      <td>0.4520</td>\n",
-       "      <td>0.587</td>\n",
-       "      <td>0.004490</td>\n",
-       "      <td>0.5900</td>\n",
-       "      <td>-9.840</td>\n",
-       "      <td>0.1990</td>\n",
-       "      <td>95.842</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
       "text/plain": [
        "                       name                         album  \\\n",
        "1                shuga rush  EVERYTHING YOU HEARD IS TRUE   \n",
@@ -453,43 +205,16 @@
        "4       0.0447  105.115               4  \n",
        "5       0.1970  100.103               4  \n",
        "6       0.1990   95.842               4  "
-      ]
+      ],
+      "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>name</th>\n      <th>album</th>\n      <th>artist</th>\n      <th>artist_top_genre</th>\n      <th>release_date</th>\n      <th>length</th>\n      <th>popularity</th>\n      <th>danceability</th>\n      <th>acousticness</th>\n      <th>energy</th>\n      <th>instrumentalness</th>\n      <th>liveness</th>\n      <th>loudness</th>\n      <th>speechiness</th>\n      <th>tempo</th>\n      <th>time_signature</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>shuga rush</td>\n      <td>EVERYTHING YOU HEARD IS TRUE</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2020</td>\n      <td>89488</td>\n      <td>30</td>\n      <td>0.710</td>\n      <td>0.0822</td>\n      <td>0.683</td>\n      <td>0.000169</td>\n      <td>0.1010</td>\n      <td>-5.640</td>\n      <td>0.3600</td>\n      <td>129.993</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Confident / Feeling Cool</td>\n      <td>Enjoy Your Life</td>\n      <td>Lady Donli</td>\n      <td>nigerian pop</td>\n      <td>2019</td>\n      <td>175135</td>\n      <td>14</td>\n      <td>0.894</td>\n      <td>0.7980</td>\n      <td>0.611</td>\n      <td>0.000187</td>\n      <td>0.0964</td>\n      <td>-4.961</td>\n      <td>0.1130</td>\n      <td>111.087</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>wanted you</td>\n      <td>rare.</td>\n      <td>Odunsi (The Engine)</td>\n      <td>afropop</td>\n      <td>2018</td>\n      <td>152049</td>\n      <td>25</td>\n      <td>0.702</td>\n      <td>0.1160</td>\n      <td>0.833</td>\n      <td>0.910000</td>\n      <td>0.3480</td>\n      <td>-6.044</td>\n      <td>0.0447</td>\n      <td>105.115</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Kasala</td>\n      <td>Pioneers</td>\n      <td>DRB Lasgidi</td>\n      <td>nigerian pop</td>\n      <td>2020</td>\n      <td>184800</td>\n      <td>26</td>\n      <td>0.803</td>\n      <td>0.1270</td>\n      <td>0.525</td>\n      <td>0.000007</td>\n      <td>0.1290</td>\n      <td>-10.034</td>\n      <td>0.1970</td>\n      <td>100.103</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Pull Up</td>\n      <td>Everything Pretty</td>\n      <td>prettyboydo</td>\n      <td>nigerian pop</td>\n      <td>2018</td>\n      <td>202648</td>\n      <td>29</td>\n      <td>0.818</td>\n      <td>0.4520</td>\n      <td>0.587</td>\n      <td>0.004490</td>\n      <td>0.5900</td>\n      <td>-9.840</td>\n      <td>0.1990</td>\n      <td>95.842</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
      },
-     "execution_count": 8,
      "metadata": {},
-     "output_type": "execute_result"
+     "execution_count": 8
     }
    ],
    "source": [
     "df.head()"
    ]
   }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": ".venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.10"
-  },
-  "metadata": {
-   "interpreter": {
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-   }
-  },
-  "orig_nbformat": 2
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
+ ]
+}
\ No newline at end of file