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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Ex2 - Getting and Knowing your Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This time we are going to pull data directly from the internet.\n",
+    "Special thanks to: https://github.com/justmarkham for sharing the dataset and materials.\n",
+    "\n",
+    "### Step 1. Import the necessary libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/justmarkham/DAT8/master/data/chipotle.tsv). "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 3. Assign it to a variable called chipo."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# it's tsv file (tab separated file)\n",
+    "# specify sep = \"\\t\" in argument of read_csv\n",
+    "chipo = pd.read_csv(\"https://raw.githubusercontent.com/justmarkham/DAT8/master/data/chipotle.tsv\", sep=\"\\t\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 4. See the first 10 entries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "scrolled": false
+   },
+   "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>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>"
+      ],
+      "text/plain": [
+       "   order_id  quantity                              item_name  \\\n",
+       "0         1         1           Chips and Fresh Tomato Salsa   \n",
+       "1         1         1                                   Izze   \n",
+       "2         1         1                       Nantucket Nectar   \n",
+       "3         1         1  Chips and Tomatillo-Green Chili Salsa   \n",
+       "4         2         2                           Chicken Bowl   \n",
+       "5         3         1                           Chicken Bowl   \n",
+       "6         3         1                          Side of Chips   \n",
+       "7         4         1                          Steak Burrito   \n",
+       "8         4         1                       Steak Soft Tacos   \n",
+       "9         5         1                          Steak Burrito   \n",
+       "\n",
+       "                                  choice_description item_price  \n",
+       "0                                                NaN     $2.39   \n",
+       "1                                       [Clementine]     $3.39   \n",
+       "2                                            [Apple]     $3.39   \n",
+       "3                                                NaN     $2.39   \n",
+       "4  [Tomatillo-Red Chili Salsa (Hot), [Black Beans...    $16.98   \n",
+       "5  [Fresh Tomato Salsa (Mild), [Rice, Cheese, Sou...    $10.98   \n",
+       "6                                                NaN     $1.69   \n",
+       "7  [Tomatillo Red Chili Salsa, [Fajita Vegetables...    $11.75   \n",
+       "8  [Tomatillo Green Chili Salsa, [Pinto Beans, Ch...     $9.25   \n",
+       "9  [Fresh Tomato Salsa, [Rice, Black Beans, Pinto...     $9.25   "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chipo.head(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 5. What is the number of observations in the dataset?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4622"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution 1\n",
+    "chipo.shape[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4622"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution 2\n",
+    "len(chipo)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 6. What is the number of columns in the dataset?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chipo.shape[1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 7. Print the name of all the columns."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['order_id', 'quantity', 'item_name', 'choice_description',\n",
+       "       'item_price'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chipo.columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 8. How is the dataset indexed?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "RangeIndex(start=0, stop=4622, step=1)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# integer in range(0, 4622)\n",
+    "chipo.index"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 9. Which was the most-ordered item? "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "item_name\n",
+       "Chicken Bowl                             761\n",
+       "Chicken Burrito                          591\n",
+       "Chips and Guacamole                      506\n",
+       "Steak Burrito                            386\n",
+       "Canned Soft Drink                        351\n",
+       "Chips                                    230\n",
+       "Steak Bowl                               221\n",
+       "Bottled Water                            211\n",
+       "Chips and Fresh Tomato Salsa             130\n",
+       "Canned Soda                              126\n",
+       "Chicken Salad Bowl                       123\n",
+       "Chicken Soft Tacos                       120\n",
+       "Side of Chips                            110\n",
+       "Veggie Burrito                            97\n",
+       "Barbacoa Burrito                          91\n",
+       "Veggie Bowl                               87\n",
+       "Carnitas Bowl                             71\n",
+       "Barbacoa Bowl                             66\n",
+       "Carnitas Burrito                          60\n",
+       "Steak Soft Tacos                          56\n",
+       "6 Pack Soft Drink                         55\n",
+       "Chips and Tomatillo Red Chili Salsa       50\n",
+       "Chicken Crispy Tacos                      50\n",
+       "Chips and Tomatillo Green Chili Salsa     45\n",
+       "Carnitas Soft Tacos                       40\n",
+       "Steak Crispy Tacos                        36\n",
+       "Chips and Tomatillo-Green Chili Salsa     33\n",
+       "Steak Salad Bowl                          31\n",
+       "Nantucket Nectar                          29\n",
+       "Chips and Tomatillo-Red Chili Salsa       25\n",
+       "Barbacoa Soft Tacos                       25\n",
+       "Chips and Roasted Chili Corn Salsa        23\n",
+       "Izze                                      20\n",
+       "Veggie Salad Bowl                         18\n",
+       "Chips and Roasted Chili-Corn Salsa        18\n",
+       "Barbacoa Crispy Tacos                     12\n",
+       "Barbacoa Salad Bowl                       10\n",
+       "Chicken Salad                              9\n",
+       "Carnitas Crispy Tacos                      8\n",
+       "Veggie Soft Tacos                          8\n",
+       "Burrito                                    6\n",
+       "Veggie Salad                               6\n",
+       "Carnitas Salad Bowl                        6\n",
+       "Bowl                                       4\n",
+       "Steak Salad                                4\n",
+       "Salad                                      2\n",
+       "Crispy Tacos                               2\n",
+       "Chips and Mild Fresh Tomato Salsa          1\n",
+       "Carnitas Salad                             1\n",
+       "Veggie Crispy Tacos                        1\n",
+       "Name: quantity, dtype: int64"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grouped_chipo = chipo.groupby(\"item_name\")[\"quantity\"].sum()\n",
+    "grouped_chipo.sort_values(ascending = False, inplace=True)\n",
+    "grouped_chipo"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Chicken Bowl'"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grouped_chipo.index[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 10. For the most-ordered item, how many items were ordered?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "761"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grouped_chipo[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 11. What was the most ordered item in the choice_description column?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "choice_description\n",
+       "[Diet Coke]                                                                                                                134\n",
+       "[Coke]                                                                                                                     123\n",
+       "[Sprite]                                                                                                                    77\n",
+       "[Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]]                                                      42\n",
+       "[Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]]                                           40\n",
+       "                                                                                                                          ... \n",
+       "[Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Cheese, Guacamole, Lettuce]]                                            1\n",
+       "[Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Cheese, Sour Cream, Guacamole]]                                         1\n",
+       "[Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Guacamole, Lettuce]]                                                    1\n",
+       "[Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Guacamole]]                                                             1\n",
+       "[[Tomatillo-Red Chili Salsa (Hot), Tomatillo-Green Chili Salsa (Medium)], [Rice, Pinto Beans, Fajita Veggies, Lettuce]]      1\n",
+       "Name: choice_description, Length: 1043, dtype: int64"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grouped_by_desc = chipo.groupby(\"choice_description\")[\"choice_description\"].count()\n",
+    "grouped_by_desc.sort_values(ascending=False, inplace=True)\n",
+    "grouped_by_desc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'[Diet Coke]'"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grouped_by_desc.index[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 12. How many items were orderd in total?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4972"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(chipo[\"quantity\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 13. Turn the item price into a float"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Step 13.a. Check the item price type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dtype('O')"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chipo[\"item_price\"].dtype"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Step 13.b. Create a lambda function and change the type of item price"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# note that there were currency symbol\n",
+    "chipo[\"item_price\"] = chipo[\"item_price\"].apply(lambda x: float(x[1:]) )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Step 13.c. Check the item price type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dtype('float64')"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chipo[\"item_price\"].dtype"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 14. How much was the revenue for the period in the dataset?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "39237.02"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chipo[\"total_price\"] = chipo[\"item_price\"] * chipo[\"quantity\"]\n",
+    "chipo[\"total_price\"].sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 15. How many orders were made in the period?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1834"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# note that one order id can have several item\n",
+    "# group by order id\n",
+    "len(chipo.groupby(\"order_id\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 16. What is the average revenue amount per order?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "21.394231188658654"
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Solution 1\n",
+    "group_by_id = chipo.groupby(\"order_id\")[\"total_price\"].sum()\n",
+    "sum(group_by_id) / len(chipo.groupby(\"order_id\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Solution 2\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 17. How many different items are sold?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "50"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chipo[\"item_name\"].nunique()"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/01_Getting_&_Knowing_Your_Data/Occupation/Exercisess_solved.ipynb b/01_Getting_&_Knowing_Your_Data/Occupation/Exercisess_solved.ipynb
new file mode 100644
index 000000000..db8c76192
--- /dev/null
+++ b/01_Getting_&_Knowing_Your_Data/Occupation/Exercisess_solved.ipynb
@@ -0,0 +1,1057 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Ex3 - Getting and Knowing your Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This time we are going to pull data directly from the internet.\n",
+    "Special thanks to: https://github.com/justmarkham for sharing the dataset and materials.\n",
+    "\n",
+    "### Step 1. Import the necessary libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/justmarkham/DAT8/master/data/u.user). "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 3. Assign it to a variable called users and use the 'user_id' as index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "user_id = pd.read_csv(\"https://raw.githubusercontent.com/justmarkham/DAT8/master/data/u.user\", sep=\"|\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 4. See the first 25 entries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "scrolled": false
+   },
+   "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>user_id</th>\n",
+       "      <th>age</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>occupation</th>\n",
+       "      <th>zip_code</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>24</td>\n",
+       "      <td>M</td>\n",
+       "      <td>technician</td>\n",
+       "      <td>85711</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>53</td>\n",
+       "      <td>F</td>\n",
+       "      <td>other</td>\n",
+       "      <td>94043</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>23</td>\n",
+       "      <td>M</td>\n",
+       "      <td>writer</td>\n",
+       "      <td>32067</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>24</td>\n",
+       "      <td>M</td>\n",
+       "      <td>technician</td>\n",
+       "      <td>43537</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>33</td>\n",
+       "      <td>F</td>\n",
+       "      <td>other</td>\n",
+       "      <td>15213</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>42</td>\n",
+       "      <td>M</td>\n",
+       "      <td>executive</td>\n",
+       "      <td>98101</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7</td>\n",
+       "      <td>57</td>\n",
+       "      <td>M</td>\n",
+       "      <td>administrator</td>\n",
+       "      <td>91344</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>8</td>\n",
+       "      <td>36</td>\n",
+       "      <td>M</td>\n",
+       "      <td>administrator</td>\n",
+       "      <td>05201</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>9</td>\n",
+       "      <td>29</td>\n",
+       "      <td>M</td>\n",
+       "      <td>student</td>\n",
+       "      <td>01002</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>10</td>\n",
+       "      <td>53</td>\n",
+       "      <td>M</td>\n",
+       "      <td>lawyer</td>\n",
+       "      <td>90703</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>11</td>\n",
+       "      <td>39</td>\n",
+       "      <td>F</td>\n",
+       "      <td>other</td>\n",
+       "      <td>30329</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>12</td>\n",
+       "      <td>28</td>\n",
+       "      <td>F</td>\n",
+       "      <td>other</td>\n",
+       "      <td>06405</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>13</td>\n",
+       "      <td>47</td>\n",
+       "      <td>M</td>\n",
+       "      <td>educator</td>\n",
+       "      <td>29206</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>14</td>\n",
+       "      <td>45</td>\n",
+       "      <td>M</td>\n",
+       "      <td>scientist</td>\n",
+       "      <td>55106</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>15</td>\n",
+       "      <td>49</td>\n",
+       "      <td>F</td>\n",
+       "      <td>educator</td>\n",
+       "      <td>97301</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>16</td>\n",
+       "      <td>21</td>\n",
+       "      <td>M</td>\n",
+       "      <td>entertainment</td>\n",
+       "      <td>10309</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>17</td>\n",
+       "      <td>30</td>\n",
+       "      <td>M</td>\n",
+       "      <td>programmer</td>\n",
+       "      <td>06355</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>18</td>\n",
+       "      <td>35</td>\n",
+       "      <td>F</td>\n",
+       "      <td>other</td>\n",
+       "      <td>37212</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>19</td>\n",
+       "      <td>40</td>\n",
+       "      <td>M</td>\n",
+       "      <td>librarian</td>\n",
+       "      <td>02138</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>20</td>\n",
+       "      <td>42</td>\n",
+       "      <td>F</td>\n",
+       "      <td>homemaker</td>\n",
+       "      <td>95660</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>21</td>\n",
+       "      <td>26</td>\n",
+       "      <td>M</td>\n",
+       "      <td>writer</td>\n",
+       "      <td>30068</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>22</td>\n",
+       "      <td>25</td>\n",
+       "      <td>M</td>\n",
+       "      <td>writer</td>\n",
+       "      <td>40206</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>23</td>\n",
+       "      <td>30</td>\n",
+       "      <td>F</td>\n",
+       "      <td>artist</td>\n",
+       "      <td>48197</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>24</td>\n",
+       "      <td>21</td>\n",
+       "      <td>F</td>\n",
+       "      <td>artist</td>\n",
+       "      <td>94533</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>25</td>\n",
+       "      <td>39</td>\n",
+       "      <td>M</td>\n",
+       "      <td>engineer</td>\n",
+       "      <td>55107</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    user_id  age gender     occupation zip_code\n",
+       "0         1   24      M     technician    85711\n",
+       "1         2   53      F          other    94043\n",
+       "2         3   23      M         writer    32067\n",
+       "3         4   24      M     technician    43537\n",
+       "4         5   33      F          other    15213\n",
+       "5         6   42      M      executive    98101\n",
+       "6         7   57      M  administrator    91344\n",
+       "7         8   36      M  administrator    05201\n",
+       "8         9   29      M        student    01002\n",
+       "9        10   53      M         lawyer    90703\n",
+       "10       11   39      F          other    30329\n",
+       "11       12   28      F          other    06405\n",
+       "12       13   47      M       educator    29206\n",
+       "13       14   45      M      scientist    55106\n",
+       "14       15   49      F       educator    97301\n",
+       "15       16   21      M  entertainment    10309\n",
+       "16       17   30      M     programmer    06355\n",
+       "17       18   35      F          other    37212\n",
+       "18       19   40      M      librarian    02138\n",
+       "19       20   42      F      homemaker    95660\n",
+       "20       21   26      M         writer    30068\n",
+       "21       22   25      M         writer    40206\n",
+       "22       23   30      F         artist    48197\n",
+       "23       24   21      F         artist    94533\n",
+       "24       25   39      M       engineer    55107"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "user_id.head(25)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 5. See the last 10 entries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "scrolled": true
+   },
+   "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>user_id</th>\n",
+       "      <th>age</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>occupation</th>\n",
+       "      <th>zip_code</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>933</th>\n",
+       "      <td>934</td>\n",
+       "      <td>61</td>\n",
+       "      <td>M</td>\n",
+       "      <td>engineer</td>\n",
+       "      <td>22902</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>934</th>\n",
+       "      <td>935</td>\n",
+       "      <td>42</td>\n",
+       "      <td>M</td>\n",
+       "      <td>doctor</td>\n",
+       "      <td>66221</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>935</th>\n",
+       "      <td>936</td>\n",
+       "      <td>24</td>\n",
+       "      <td>M</td>\n",
+       "      <td>other</td>\n",
+       "      <td>32789</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>936</th>\n",
+       "      <td>937</td>\n",
+       "      <td>48</td>\n",
+       "      <td>M</td>\n",
+       "      <td>educator</td>\n",
+       "      <td>98072</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>937</th>\n",
+       "      <td>938</td>\n",
+       "      <td>38</td>\n",
+       "      <td>F</td>\n",
+       "      <td>technician</td>\n",
+       "      <td>55038</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>938</th>\n",
+       "      <td>939</td>\n",
+       "      <td>26</td>\n",
+       "      <td>F</td>\n",
+       "      <td>student</td>\n",
+       "      <td>33319</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>939</th>\n",
+       "      <td>940</td>\n",
+       "      <td>32</td>\n",
+       "      <td>M</td>\n",
+       "      <td>administrator</td>\n",
+       "      <td>02215</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>940</th>\n",
+       "      <td>941</td>\n",
+       "      <td>20</td>\n",
+       "      <td>M</td>\n",
+       "      <td>student</td>\n",
+       "      <td>97229</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>941</th>\n",
+       "      <td>942</td>\n",
+       "      <td>48</td>\n",
+       "      <td>F</td>\n",
+       "      <td>librarian</td>\n",
+       "      <td>78209</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>942</th>\n",
+       "      <td>943</td>\n",
+       "      <td>22</td>\n",
+       "      <td>M</td>\n",
+       "      <td>student</td>\n",
+       "      <td>77841</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     user_id  age gender     occupation zip_code\n",
+       "933      934   61      M       engineer    22902\n",
+       "934      935   42      M         doctor    66221\n",
+       "935      936   24      M          other    32789\n",
+       "936      937   48      M       educator    98072\n",
+       "937      938   38      F     technician    55038\n",
+       "938      939   26      F        student    33319\n",
+       "939      940   32      M  administrator    02215\n",
+       "940      941   20      M        student    97229\n",
+       "941      942   48      F      librarian    78209\n",
+       "942      943   22      M        student    77841"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "user_id.tail(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 6. What is the number of observations in the dataset?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "943"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "user_id.shape[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 7. What is the number of columns in the dataset?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "user_id.shape[1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 8. Print the name of all the columns."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['user_id', 'age', 'gender', 'occupation', 'zip_code'], dtype='object')"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "user_id.columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 9. How is the dataset indexed?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "RangeIndex(start=0, stop=943, step=1)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# in range(0, 943)\n",
+    "user_id.index"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 10. What is the data type of each column?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 943 entries, 0 to 942\n",
+      "Data columns (total 5 columns):\n",
+      " #   Column      Non-Null Count  Dtype \n",
+      "---  ------      --------------  ----- \n",
+      " 0   user_id     943 non-null    int64 \n",
+      " 1   age         943 non-null    int64 \n",
+      " 2   gender      943 non-null    object\n",
+      " 3   occupation  943 non-null    object\n",
+      " 4   zip_code    943 non-null    object\n",
+      "dtypes: int64(2), object(3)\n",
+      "memory usage: 37.0+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "user_id.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 11. Print only the occupation column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0         technician\n",
+       "1              other\n",
+       "2             writer\n",
+       "3         technician\n",
+       "4              other\n",
+       "           ...      \n",
+       "938          student\n",
+       "939    administrator\n",
+       "940          student\n",
+       "941        librarian\n",
+       "942          student\n",
+       "Name: occupation, Length: 943, dtype: object"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "user_id[\"occupation\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 12. How many different occupations are in this dataset?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "21"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "user_id[\"occupation\"].nunique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 13. What is the most frequent occupation?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'student'"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m= user_id.groupby(\"occupation\")[\"occupation\"].count()\n",
+    "m.sort_values(ascending=False, inplace=True)\n",
+    "m.index[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 14. Summarize the DataFrame."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "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>user_id</th>\n",
+       "      <th>age</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>943.000000</td>\n",
+       "      <td>943.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>472.000000</td>\n",
+       "      <td>34.051962</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>272.364951</td>\n",
+       "      <td>12.192740</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>7.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>236.500000</td>\n",
+       "      <td>25.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>472.000000</td>\n",
+       "      <td>31.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>707.500000</td>\n",
+       "      <td>43.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>943.000000</td>\n",
+       "      <td>73.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          user_id         age\n",
+       "count  943.000000  943.000000\n",
+       "mean   472.000000   34.051962\n",
+       "std    272.364951   12.192740\n",
+       "min      1.000000    7.000000\n",
+       "25%    236.500000   25.000000\n",
+       "50%    472.000000   31.000000\n",
+       "75%    707.500000   43.000000\n",
+       "max    943.000000   73.000000"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "user_id.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 15. Summarize all the columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "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>gender</th>\n",
+       "      <th>occupation</th>\n",
+       "      <th>zip_code</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>943</td>\n",
+       "      <td>943</td>\n",
+       "      <td>943</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>2</td>\n",
+       "      <td>21</td>\n",
+       "      <td>795</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>M</td>\n",
+       "      <td>student</td>\n",
+       "      <td>55414</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>670</td>\n",
+       "      <td>196</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       gender occupation zip_code\n",
+       "count     943        943      943\n",
+       "unique      2         21      795\n",
+       "top         M    student    55414\n",
+       "freq      670        196        9"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "obj_cols = user_id.select_dtypes(include=\"object\")\n",
+    "obj_cols.describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 16. Summarize only the occupation column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count         943\n",
+       "unique         21\n",
+       "top       student\n",
+       "freq          196\n",
+       "Name: occupation, dtype: object"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "user_id[\"occupation\"].describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 17. What is the mean age of users?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "34.05196182396607"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "user_id[\"age\"].mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 18. What is the age with least occurrence?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "30    39\n",
+       "25    38\n",
+       "22    37\n",
+       "28    36\n",
+       "27    35\n",
+       "      ..\n",
+       "7      1\n",
+       "66     1\n",
+       "11     1\n",
+       "10     1\n",
+       "73     1\n",
+       "Name: age, Length: 61, dtype: int64"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "user_id.age.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7     1\n",
+       "66    1\n",
+       "11    1\n",
+       "10    1\n",
+       "73    1\n",
+       "Name: age, dtype: int64"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "user_id.age.value_counts().tail()"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/01_Getting_&_Knowing_Your_Data/World Food Facts/Exercises_solved.ipynb b/01_Getting_&_Knowing_Your_Data/World Food Facts/Exercises_solved.ipynb
new file mode 100644
index 000000000..0b2e77779
--- /dev/null
+++ b/01_Getting_&_Knowing_Your_Data/World Food Facts/Exercises_solved.ipynb	
@@ -0,0 +1,563 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exercise 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 1. Go to https://www.kaggle.com/openfoodfacts/world-food-facts/data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 2. Download the dataset to your computer and unzip it."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 3. Use the tsv file and assign it to a dataframe called food"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Please provide your Kaggle credentials to download this dataset. Learn more: http://bit.ly/kaggle-creds\n",
+      "Your Kaggle username: \n",
+      "Your Kaggle username: \n",
+      "Your Kaggle username: chewziqing\n",
+      "Your Kaggle Key: ········\n",
+      "Downloading world-food-facts.zip to .\\world-food-facts\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|███████████████████████████████████████████████████████████████████████████████| 109M/109M [01:27<00:00, 1.30MB/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import opendatasets as od\n",
+    "od.download(\"https://www.kaggle.com/openfoodfacts/world-food-facts/data\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\USER\\AppData\\Local\\Temp\\ipykernel_12124\\2931642990.py:5: DtypeWarning: Columns (0,3,5,19,20,24,25,26,27,28,36,37,38,39,48) have mixed types. Specify dtype option on import or set low_memory=False.\n",
+      "  df = pd.read_csv(path, sep=\"\\t\")\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "\n",
+    "path = \"world-food-facts/en.openfoodfacts.org.products.tsv\"\n",
+    "os.listdir(\"./world-food-facts/\")\n",
+    "df = pd.read_csv(path, sep=\"\\t\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 4. See the first 5 entries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "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>code</th>\n",
+       "      <th>url</th>\n",
+       "      <th>creator</th>\n",
+       "      <th>created_t</th>\n",
+       "      <th>created_datetime</th>\n",
+       "      <th>last_modified_t</th>\n",
+       "      <th>last_modified_datetime</th>\n",
+       "      <th>product_name</th>\n",
+       "      <th>generic_name</th>\n",
+       "      <th>quantity</th>\n",
+       "      <th>...</th>\n",
+       "      <th>fruits-vegetables-nuts_100g</th>\n",
+       "      <th>fruits-vegetables-nuts-estimate_100g</th>\n",
+       "      <th>collagen-meat-protein-ratio_100g</th>\n",
+       "      <th>cocoa_100g</th>\n",
+       "      <th>chlorophyl_100g</th>\n",
+       "      <th>carbon-footprint_100g</th>\n",
+       "      <th>nutrition-score-fr_100g</th>\n",
+       "      <th>nutrition-score-uk_100g</th>\n",
+       "      <th>glycemic-index_100g</th>\n",
+       "      <th>water-hardness_100g</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3087</td>\n",
+       "      <td>http://world-en.openfoodfacts.org/product/0000...</td>\n",
+       "      <td>openfoodfacts-contributors</td>\n",
+       "      <td>1474103866</td>\n",
+       "      <td>2016-09-17T09:17:46Z</td>\n",
+       "      <td>1474103893</td>\n",
+       "      <td>2016-09-17T09:18:13Z</td>\n",
+       "      <td>Farine de blé noir</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1kg</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>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4530</td>\n",
+       "      <td>http://world-en.openfoodfacts.org/product/0000...</td>\n",
+       "      <td>usda-ndb-import</td>\n",
+       "      <td>1489069957</td>\n",
+       "      <td>2017-03-09T14:32:37Z</td>\n",
+       "      <td>1489069957</td>\n",
+       "      <td>2017-03-09T14:32:37Z</td>\n",
+       "      <td>Banana Chips Sweetened (Whole)</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</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>14.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4559</td>\n",
+       "      <td>http://world-en.openfoodfacts.org/product/0000...</td>\n",
+       "      <td>usda-ndb-import</td>\n",
+       "      <td>1489069957</td>\n",
+       "      <td>2017-03-09T14:32:37Z</td>\n",
+       "      <td>1489069957</td>\n",
+       "      <td>2017-03-09T14:32:37Z</td>\n",
+       "      <td>Peanuts</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</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>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>16087</td>\n",
+       "      <td>http://world-en.openfoodfacts.org/product/0000...</td>\n",
+       "      <td>usda-ndb-import</td>\n",
+       "      <td>1489055731</td>\n",
+       "      <td>2017-03-09T10:35:31Z</td>\n",
+       "      <td>1489055731</td>\n",
+       "      <td>2017-03-09T10:35:31Z</td>\n",
+       "      <td>Organic Salted Nut Mix</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</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>12.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>16094</td>\n",
+       "      <td>http://world-en.openfoodfacts.org/product/0000...</td>\n",
+       "      <td>usda-ndb-import</td>\n",
+       "      <td>1489055653</td>\n",
+       "      <td>2017-03-09T10:34:13Z</td>\n",
+       "      <td>1489055653</td>\n",
+       "      <td>2017-03-09T10:34:13Z</td>\n",
+       "      <td>Organic Polenta</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</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>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 163 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    code                                                url  \\\n",
+       "0   3087  http://world-en.openfoodfacts.org/product/0000...   \n",
+       "1   4530  http://world-en.openfoodfacts.org/product/0000...   \n",
+       "2   4559  http://world-en.openfoodfacts.org/product/0000...   \n",
+       "3  16087  http://world-en.openfoodfacts.org/product/0000...   \n",
+       "4  16094  http://world-en.openfoodfacts.org/product/0000...   \n",
+       "\n",
+       "                      creator   created_t      created_datetime  \\\n",
+       "0  openfoodfacts-contributors  1474103866  2016-09-17T09:17:46Z   \n",
+       "1             usda-ndb-import  1489069957  2017-03-09T14:32:37Z   \n",
+       "2             usda-ndb-import  1489069957  2017-03-09T14:32:37Z   \n",
+       "3             usda-ndb-import  1489055731  2017-03-09T10:35:31Z   \n",
+       "4             usda-ndb-import  1489055653  2017-03-09T10:34:13Z   \n",
+       "\n",
+       "  last_modified_t last_modified_datetime                    product_name  \\\n",
+       "0      1474103893   2016-09-17T09:18:13Z              Farine de blé noir   \n",
+       "1      1489069957   2017-03-09T14:32:37Z  Banana Chips Sweetened (Whole)   \n",
+       "2      1489069957   2017-03-09T14:32:37Z                         Peanuts   \n",
+       "3      1489055731   2017-03-09T10:35:31Z          Organic Salted Nut Mix   \n",
+       "4      1489055653   2017-03-09T10:34:13Z                 Organic Polenta   \n",
+       "\n",
+       "  generic_name quantity  ... fruits-vegetables-nuts_100g  \\\n",
+       "0          NaN      1kg  ...                         NaN   \n",
+       "1          NaN      NaN  ...                         NaN   \n",
+       "2          NaN      NaN  ...                         NaN   \n",
+       "3          NaN      NaN  ...                         NaN   \n",
+       "4          NaN      NaN  ...                         NaN   \n",
+       "\n",
+       "  fruits-vegetables-nuts-estimate_100g collagen-meat-protein-ratio_100g  \\\n",
+       "0                                  NaN                              NaN   \n",
+       "1                                  NaN                              NaN   \n",
+       "2                                  NaN                              NaN   \n",
+       "3                                  NaN                              NaN   \n",
+       "4                                  NaN                              NaN   \n",
+       "\n",
+       "  cocoa_100g chlorophyl_100g carbon-footprint_100g nutrition-score-fr_100g  \\\n",
+       "0        NaN             NaN                   NaN                     NaN   \n",
+       "1        NaN             NaN                   NaN                    14.0   \n",
+       "2        NaN             NaN                   NaN                     0.0   \n",
+       "3        NaN             NaN                   NaN                    12.0   \n",
+       "4        NaN             NaN                   NaN                     NaN   \n",
+       "\n",
+       "  nutrition-score-uk_100g glycemic-index_100g water-hardness_100g  \n",
+       "0                     NaN                 NaN                 NaN  \n",
+       "1                    14.0                 NaN                 NaN  \n",
+       "2                     0.0                 NaN                 NaN  \n",
+       "3                    12.0                 NaN                 NaN  \n",
+       "4                     NaN                 NaN                 NaN  \n",
+       "\n",
+       "[5 rows x 163 columns]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 5. What is the number of observations in the dataset?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "356027"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 6. What is the number of columns in the dataset?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "163"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape[1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 7. Print the name of all the columns."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['code', 'url', 'creator', 'created_t', 'created_datetime',\n",
+       "       'last_modified_t', 'last_modified_datetime', 'product_name',\n",
+       "       'generic_name', 'quantity',\n",
+       "       ...\n",
+       "       'fruits-vegetables-nuts_100g', 'fruits-vegetables-nuts-estimate_100g',\n",
+       "       'collagen-meat-protein-ratio_100g', 'cocoa_100g', 'chlorophyl_100g',\n",
+       "       'carbon-footprint_100g', 'nutrition-score-fr_100g',\n",
+       "       'nutrition-score-uk_100g', 'glycemic-index_100g',\n",
+       "       'water-hardness_100g'],\n",
+       "      dtype='object', length=163)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 8. What is the name of 105th column?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'-glucose_100g'"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns[104]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 9. What is the type of the observations of the 105th column?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dtype('float64')"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[df.columns[104]].dtype"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 10. How is the dataset indexed?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "RangeIndex(start=0, stop=356027, step=1)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.index"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 11. What is the product name of the 19th observation?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Lotus Organic Brown Jasmine Rice'"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.loc[18, \"product_name\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/02_Filtering_&_Sorting/Chipotle/Exercises_solved.ipynb b/02_Filtering_&_Sorting/Chipotle/Exercises_solved.ipynb
new file mode 100644
index 000000000..05646c6ed
--- /dev/null
+++ b/02_Filtering_&_Sorting/Chipotle/Exercises_solved.ipynb
@@ -0,0 +1,756 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Ex1 - Filtering and Sorting Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This time we are going to pull data directly from the internet.\n",
+    "Special thanks to: https://github.com/justmarkham for sharing the dataset and materials.\n",
+    "\n",
+    "### Step 1. Import the necessary libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 2. Import the dataset from this [address](https://raw.githubusercontent.com/justmarkham/DAT8/master/data/chipotle.tsv). "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 3. Assign it to a variable called chipo."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "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>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>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4617</th>\n",
+       "      <td>1833</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Steak Burrito</td>\n",
+       "      <td>[Fresh Tomato Salsa, [Rice, Black Beans, Sour ...</td>\n",
+       "      <td>$11.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4618</th>\n",
+       "      <td>1833</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Steak Burrito</td>\n",
+       "      <td>[Fresh Tomato Salsa, [Rice, Sour Cream, Cheese...</td>\n",
+       "      <td>$11.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4619</th>\n",
+       "      <td>1834</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Chicken Salad Bowl</td>\n",
+       "      <td>[Fresh Tomato Salsa, [Fajita Vegetables, Pinto...</td>\n",
+       "      <td>$11.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4620</th>\n",
+       "      <td>1834</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Chicken Salad Bowl</td>\n",
+       "      <td>[Fresh Tomato Salsa, [Fajita Vegetables, Lettu...</td>\n",
+       "      <td>$8.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4621</th>\n",
+       "      <td>1834</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Chicken Salad Bowl</td>\n",
+       "      <td>[Fresh Tomato Salsa, [Fajita Vegetables, Pinto...</td>\n",
+       "      <td>$8.75</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>4622 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      order_id  quantity                              item_name  \\\n",
+       "0            1         1           Chips and Fresh Tomato Salsa   \n",
+       "1            1         1                                   Izze   \n",
+       "2            1         1                       Nantucket Nectar   \n",
+       "3            1         1  Chips and Tomatillo-Green Chili Salsa   \n",
+       "4            2         2                           Chicken Bowl   \n",
+       "...        ...       ...                                    ...   \n",
+       "4617      1833         1                          Steak Burrito   \n",
+       "4618      1833         1                          Steak Burrito   \n",
+       "4619      1834         1                     Chicken Salad Bowl   \n",
+       "4620      1834         1                     Chicken Salad Bowl   \n",
+       "4621      1834         1                     Chicken Salad Bowl   \n",
+       "\n",
+       "                                     choice_description item_price  \n",
+       "0                                                   NaN     $2.39   \n",
+       "1                                          [Clementine]     $3.39   \n",
+       "2                                               [Apple]     $3.39   \n",
+       "3                                                   NaN     $2.39   \n",
+       "4     [Tomatillo-Red Chili Salsa (Hot), [Black Beans...    $16.98   \n",
+       "...                                                 ...        ...  \n",
+       "4617  [Fresh Tomato Salsa, [Rice, Black Beans, Sour ...    $11.75   \n",
+       "4618  [Fresh Tomato Salsa, [Rice, Sour Cream, Cheese...    $11.75   \n",
+       "4619  [Fresh Tomato Salsa, [Fajita Vegetables, Pinto...    $11.25   \n",
+       "4620  [Fresh Tomato Salsa, [Fajita Vegetables, Lettu...     $8.75   \n",
+       "4621  [Fresh Tomato Salsa, [Fajita Vegetables, Pinto...     $8.75   \n",
+       "\n",
+       "[4622 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "path = \"https://raw.githubusercontent.com/justmarkham/DAT8/master/data/chipotle.tsv\"\n",
+    "chipo = pd.read_csv(path, sep=\"\\t\")\n",
+    "chipo"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 4. How many products cost more than $10.00?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dtype('O')"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chipo.item_price.dtype"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# item_price != unit_price\n",
+    "def clean_price_col(chipo, col):\n",
+    "    # remove $ symbol and convert to float\n",
+    "    chipo[col] = chipo[col].apply(lambda x: x[1:].rstrip())\n",
+    "    chipo[col] = chipo[col].astype(\"float\") \n",
+    "    return chipo\n",
+    "\n",
+    "def remove_duplicate(chipo, cols):\n",
+    "    return chipo.drop_duplicates(subset=cols)\n",
+    "\n",
+    "chipo = clean_price_col(chipo, \"item_price\")\n",
+    "new_chipo = remove_duplicate(chipo, [\"item_name\", \"quantity\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "12"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len( new_chipo[ (new_chipo.item_price > 10) & (new_chipo.quantity==1)] )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 5. What is the price of each item? \n",
+    "###### print a data frame with only two columns item_name and item_price"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "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>item_name</th>\n",
+       "      <th>item_price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>606</th>\n",
+       "      <td>Steak Salad Bowl</td>\n",
+       "      <td>11.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1229</th>\n",
+       "      <td>Barbacoa Salad Bowl</td>\n",
+       "      <td>11.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1132</th>\n",
+       "      <td>Carnitas Salad Bowl</td>\n",
+       "      <td>11.89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>93</th>\n",
+       "      <td>Carnitas Burrito</td>\n",
+       "      <td>11.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1008</th>\n",
+       "      <td>Carnitas Crispy Tacos</td>\n",
+       "      <td>11.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Side of Chips</td>\n",
+       "      <td>1.69</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>329</th>\n",
+       "      <td>Bottled Water</td>\n",
+       "      <td>1.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263</th>\n",
+       "      <td>Canned Soft Drink</td>\n",
+       "      <td>1.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>Canned Soda</td>\n",
+       "      <td>1.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>Bottled Water</td>\n",
+       "      <td>1.09</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>135 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  item_name  item_price\n",
+       "606        Steak Salad Bowl       11.89\n",
+       "1229    Barbacoa Salad Bowl       11.89\n",
+       "1132    Carnitas Salad Bowl       11.89\n",
+       "93         Carnitas Burrito       11.75\n",
+       "1008  Carnitas Crispy Tacos       11.75\n",
+       "...                     ...         ...\n",
+       "6             Side of Chips        1.69\n",
+       "329           Bottled Water        1.50\n",
+       "263       Canned Soft Drink        1.25\n",
+       "28              Canned Soda        1.09\n",
+       "34            Bottled Water        1.09\n",
+       "\n",
+       "[135 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_chipo_2 = chipo.drop_duplicates(subset=[\"item_name\", \"item_price\"])\n",
+    "new_chipo_2 = new_chipo_2[new_chipo_2.quantity==1]\n",
+    "\n",
+    "new_chipo_2.loc[:, [\"item_name\", \"item_price\"]].sort_values(by=\"item_price\", ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 6. Sort by the name of the item"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "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>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>3389</th>\n",
+       "      <td>1360</td>\n",
+       "      <td>2</td>\n",
+       "      <td>6 Pack Soft Drink</td>\n",
+       "      <td>[Diet Coke]</td>\n",
+       "      <td>12.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>341</th>\n",
+       "      <td>148</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6 Pack Soft Drink</td>\n",
+       "      <td>[Diet Coke]</td>\n",
+       "      <td>6.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1849</th>\n",
+       "      <td>749</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6 Pack Soft Drink</td>\n",
+       "      <td>[Coke]</td>\n",
+       "      <td>6.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1860</th>\n",
+       "      <td>754</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6 Pack Soft Drink</td>\n",
+       "      <td>[Diet Coke]</td>\n",
+       "      <td>6.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2713</th>\n",
+       "      <td>1076</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6 Pack Soft Drink</td>\n",
+       "      <td>[Coke]</td>\n",
+       "      <td>6.49</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>2384</th>\n",
+       "      <td>948</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Veggie Soft Tacos</td>\n",
+       "      <td>[Roasted Chili Corn Salsa, [Fajita Vegetables,...</td>\n",
+       "      <td>8.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>781</th>\n",
+       "      <td>322</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Veggie Soft Tacos</td>\n",
+       "      <td>[Fresh Tomato Salsa, [Black Beans, Cheese, Sou...</td>\n",
+       "      <td>8.75</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2851</th>\n",
+       "      <td>1132</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Veggie Soft Tacos</td>\n",
+       "      <td>[Roasted Chili Corn Salsa (Medium), [Black Bea...</td>\n",
+       "      <td>8.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1699</th>\n",
+       "      <td>688</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Veggie Soft Tacos</td>\n",
+       "      <td>[Fresh Tomato Salsa, [Fajita Vegetables, Rice,...</td>\n",
+       "      <td>11.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1395</th>\n",
+       "      <td>567</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Veggie Soft Tacos</td>\n",
+       "      <td>[Fresh Tomato Salsa (Mild), [Pinto Beans, Rice...</td>\n",
+       "      <td>8.49</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>4622 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      order_id  quantity          item_name  \\\n",
+       "3389      1360         2  6 Pack Soft Drink   \n",
+       "341        148         1  6 Pack Soft Drink   \n",
+       "1849       749         1  6 Pack Soft Drink   \n",
+       "1860       754         1  6 Pack Soft Drink   \n",
+       "2713      1076         1  6 Pack Soft Drink   \n",
+       "...        ...       ...                ...   \n",
+       "2384       948         1  Veggie Soft Tacos   \n",
+       "781        322         1  Veggie Soft Tacos   \n",
+       "2851      1132         1  Veggie Soft Tacos   \n",
+       "1699       688         1  Veggie Soft Tacos   \n",
+       "1395       567         1  Veggie Soft Tacos   \n",
+       "\n",
+       "                                     choice_description  item_price  \n",
+       "3389                                        [Diet Coke]       12.98  \n",
+       "341                                         [Diet Coke]        6.49  \n",
+       "1849                                             [Coke]        6.49  \n",
+       "1860                                        [Diet Coke]        6.49  \n",
+       "2713                                             [Coke]        6.49  \n",
+       "...                                                 ...         ...  \n",
+       "2384  [Roasted Chili Corn Salsa, [Fajita Vegetables,...        8.75  \n",
+       "781   [Fresh Tomato Salsa, [Black Beans, Cheese, Sou...        8.75  \n",
+       "2851  [Roasted Chili Corn Salsa (Medium), [Black Bea...        8.49  \n",
+       "1699  [Fresh Tomato Salsa, [Fajita Vegetables, Rice,...       11.25  \n",
+       "1395  [Fresh Tomato Salsa (Mild), [Pinto Beans, Rice...        8.49  \n",
+       "\n",
+       "[4622 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chipo.sort_values(\"item_name\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 7. What was the quantity of the most expensive item ordered?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    4622.000000\n",
+       "mean        7.464336\n",
+       "std         4.245557\n",
+       "min         1.090000\n",
+       "25%         3.390000\n",
+       "50%         8.750000\n",
+       "75%         9.250000\n",
+       "max        44.250000\n",
+       "Name: item_price, dtype: float64"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chipo[\"item_price\"].describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "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>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>3598</th>\n",
+       "      <td>1443</td>\n",
+       "      <td>15</td>\n",
+       "      <td>Chips and Fresh Tomato Salsa</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>44.25</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      order_id  quantity                     item_name choice_description  \\\n",
+       "3598      1443        15  Chips and Fresh Tomato Salsa                NaN   \n",
+       "\n",
+       "      item_price  \n",
+       "3598       44.25  "
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "max_price = chipo.item_price.max()\n",
+    "\n",
+    "max_price_item = chipo[chipo.item_price == max_price]\n",
+    "max_price_item"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 8. How many times was a Veggie Salad Bowl ordered?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "18"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(chipo[chipo.item_name == \"Veggie Salad Bowl\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 9. How many times did someone order more than one Canned Soda?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "20"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(  chipo[(chipo.item_name == \"Canned Soda\") & (chipo.quantity>1)] )"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}