diff --git a/01_Getting_&_Knowing_Your_Data/Chipotle/Exercises.ipynb b/01_Getting_&_Knowing_Your_Data/Chipotle/Exercises.ipynb
index 03e1fc603..d9e2280ed 100644
--- a/01_Getting_&_Knowing_Your_Data/Chipotle/Exercises.ipynb
+++ b/01_Getting_&_Knowing_Your_Data/Chipotle/Exercises.ipynb
@@ -19,12 +19,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {
     "collapsed": false
    },
    "outputs": [],
-   "source": []
+   "source": [
+    "import pandas as pd"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -34,10 +36,22 @@
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 5,
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv('https://raw.githubusercontent.com/justmarkham/DAT8/master/data/chipotle.tsv',delimiter='\\t')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "### Step 3. Assign it to a variable called chipo."
+    "### Step 3. Assign it to a variable called chipo.\n",
+    "chipo = df"
    ]
   },
   {
@@ -58,13 +72,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {
     "collapsed": false,
     "scrolled": false
    },
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "   order_id  quantity                              item_name  \\\n0         1         1           Chips and Fresh Tomato Salsa   \n1         1         1                                   Izze   \n2         1         1                       Nantucket Nectar   \n3         1         1  Chips and Tomatillo-Green Chili Salsa   \n4         2         2                           Chicken Bowl   \n5         3         1                           Chicken Bowl   \n6         3         1                          Side of Chips   \n7         4         1                          Steak Burrito   \n8         4         1                       Steak Soft Tacos   \n9         5         1                          Steak Burrito   \n\n                                  choice_description item_price  \n0                                                NaN     $2.39   \n1                                       [Clementine]     $3.39   \n2                                            [Apple]     $3.39   \n3                                                NaN     $2.39   \n4  [Tomatillo-Red Chili Salsa (Hot), [Black Beans...    $16.98   \n5  [Fresh Tomato Salsa (Mild), [Rice, Cheese, Sou...    $10.98   \n6                                                NaN     $1.69   \n7  [Tomatillo Red Chili Salsa, [Fajita Vegetables...    $11.75   \n8  [Tomatillo Green Chili Salsa, [Pinto Beans, Ch...     $9.25   \n9  [Fresh Tomato Salsa, [Rice, Black Beans, Pinto...     $9.25   ",
+      "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>order_id</th>\n      <th>quantity</th>\n      <th>item_name</th>\n      <th>choice_description</th>\n      <th>item_price</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>1</td>\n      <td>Chips and Fresh Tomato Salsa</td>\n      <td>NaN</td>\n      <td>$2.39</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>1</td>\n      <td>Izze</td>\n      <td>[Clementine]</td>\n      <td>$3.39</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>1</td>\n      <td>Nantucket Nectar</td>\n      <td>[Apple]</td>\n      <td>$3.39</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>1</td>\n      <td>Chips and Tomatillo-Green Chili Salsa</td>\n      <td>NaN</td>\n      <td>$2.39</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2</td>\n      <td>2</td>\n      <td>Chicken Bowl</td>\n      <td>[Tomatillo-Red Chili Salsa (Hot), [Black Beans...</td>\n      <td>$16.98</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>3</td>\n      <td>1</td>\n      <td>Chicken Bowl</td>\n      <td>[Fresh Tomato Salsa (Mild), [Rice, Cheese, Sou...</td>\n      <td>$10.98</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>3</td>\n      <td>1</td>\n      <td>Side of Chips</td>\n      <td>NaN</td>\n      <td>$1.69</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>4</td>\n      <td>1</td>\n      <td>Steak Burrito</td>\n      <td>[Tomatillo Red Chili Salsa, [Fajita Vegetables...</td>\n      <td>$11.75</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>4</td>\n      <td>1</td>\n      <td>Steak Soft Tacos</td>\n      <td>[Tomatillo Green Chili Salsa, [Pinto Beans, Ch...</td>\n      <td>$9.25</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>5</td>\n      <td>1</td>\n      <td>Steak Burrito</td>\n      <td>[Fresh Tomato Salsa, [Rice, Black Beans, Pinto...</td>\n      <td>$9.25</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 7
+    }
+   ],
+   "source": [
+    "chipo.head(10)"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -75,26 +101,41 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 8,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": "4622\n"
+    }
+   ],
    "source": [
     "# Solution 1\n",
-    "\n"
+    "print(len(chipo))\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 12,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": "4622\n"
+    }
+   ],
    "source": [
     "# Solution 2\n",
-    "\n"
+    "\n",
+    "print(chipo.shape[0])"
    ]
   },
   {
@@ -106,12 +147,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "tags": []
    },
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": "5\n"
+    }
+   ],
+   "source": [
+    "print(chipo.shape[1])"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -122,12 +172,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "tags": []
    },
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": "['order_id' 'quantity' 'item_name' 'choice_description' 'item_price']\n"
+    }
+   ],
+   "source": [
+    "print(chipo.columns.values)"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -138,12 +197,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "tags": []
    },
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": "RangeIndex(start=0, stop=4622, step=1)\n"
+    }
+   ],
+   "source": [
+    "print(chipo.index)"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -154,12 +222,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "'Chicken Bowl'"
+     },
+     "metadata": {},
+     "execution_count": 27
+    }
+   ],
+   "source": [
+    "chipo.item_name.value_counts().index[0]"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -170,12 +249,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "726"
+     },
+     "metadata": {},
+     "execution_count": 28
+    }
+   ],
+   "source": [
+    "chipo.item_name.value_counts()[0]"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -186,12 +276,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "'[Diet Coke]'"
+     },
+     "metadata": {},
+     "execution_count": 29
+    }
+   ],
+   "source": [
+    "chipo.choice_description.value_counts().index[0]"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -202,12 +303,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "4972"
+     },
+     "metadata": {},
+     "execution_count": 30
+    }
+   ],
+   "source": [
+    "chipo.quantity.sum()"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -225,12 +337,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "dtype('O')"
+     },
+     "metadata": {},
+     "execution_count": 32
+    }
+   ],
+   "source": [
+    "chipo.item_price.dtype"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -241,12 +364,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "metadata": {
     "collapsed": true
    },
    "outputs": [],
-   "source": []
+   "source": [
+    "chipo.item_price = chipo.item_price.map(lambda x: float(x.replace('$','')))"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -257,12 +382,41 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 34,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "dtype('float64')"
+     },
+     "metadata": {},
+     "execution_count": 34
+    }
+   ],
+   "source": [
+    "chipo.item_price.dtype"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "0        2.39\n1        3.39\n2        3.39\n3        2.39\n4       16.98\n        ...  \n4617    11.75\n4618    11.75\n4619    11.25\n4620     8.75\n4621     8.75\nName: item_price, Length: 4622, dtype: float64"
+     },
+     "metadata": {},
+     "execution_count": 35
+    }
+   ],
+   "source": [
+    "chipo.item_price"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -273,12 +427,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 36,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "39237.02"
+     },
+     "metadata": {},
+     "execution_count": 36
+    }
+   ],
+   "source": [
+    "revenue = chipo.item_price * chipo.quantity\n",
+    "revenue.sum()"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -289,12 +455,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 37,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "1834"
+     },
+     "metadata": {},
+     "execution_count": 37
+    }
+   ],
+   "source": [
+    "chipo.order_id.nunique()"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -305,14 +482,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 40,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "21.39423118865867"
+     },
+     "metadata": {},
+     "execution_count": 40
+    }
+   ],
    "source": [
     "# Solution 1\n",
-    "\n"
+    "chipo['revenue'] = chipo.item_price * chipo.quantity\n",
+    "chipo.groupby('order_id')['revenue'].sum().mean()\n"
    ]
   },
   {
@@ -336,10 +523,47 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 41,
    "metadata": {
     "collapsed": false
    },
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "   order_id  quantity                              item_name  \\\n0         1         1           Chips and Fresh Tomato Salsa   \n1         1         1                                   Izze   \n2         1         1                       Nantucket Nectar   \n3         1         1  Chips and Tomatillo-Green Chili Salsa   \n4         2         2                           Chicken Bowl   \n\n                                  choice_description  item_price  revenue  \n0                                                NaN        2.39     2.39  \n1                                       [Clementine]        3.39     3.39  \n2                                            [Apple]        3.39     3.39  \n3                                                NaN        2.39     2.39  \n4  [Tomatillo-Red Chili Salsa (Hot), [Black Beans...       16.98    33.96  ",
+      "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>order_id</th>\n      <th>quantity</th>\n      <th>item_name</th>\n      <th>choice_description</th>\n      <th>item_price</th>\n      <th>revenue</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>1</td>\n      <td>Chips and Fresh Tomato Salsa</td>\n      <td>NaN</td>\n      <td>2.39</td>\n      <td>2.39</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>1</td>\n      <td>Izze</td>\n      <td>[Clementine]</td>\n      <td>3.39</td>\n      <td>3.39</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>1</td>\n      <td>Nantucket Nectar</td>\n      <td>[Apple]</td>\n      <td>3.39</td>\n      <td>3.39</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>1</td>\n      <td>Chips and Tomatillo-Green Chili Salsa</td>\n      <td>NaN</td>\n      <td>2.39</td>\n      <td>2.39</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2</td>\n      <td>2</td>\n      <td>Chicken Bowl</td>\n      <td>[Tomatillo-Red Chili Salsa (Hot), [Black Beans...</td>\n      <td>16.98</td>\n      <td>33.96</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "metadata": {},
+     "execution_count": 41
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": "50"
+     },
+     "metadata": {},
+     "execution_count": 42
+    }
+   ],
+   "source": [
+    "chipo.item_name.nunique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": []
   }
@@ -347,9 +571,9 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python [default]",
+   "display_name": "Python 3.7.7 64-bit",
    "language": "python",
-   "name": "python2"
+   "name": "python_defaultSpec_1599526098309"
   },
   "language_info": {
    "codemirror_mode": {
@@ -361,9 +585,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython2",
-   "version": "2.7.12"
+   "version": "3.7.7-final"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 0
-}
+}
\ No newline at end of file