Solution to Exercise 1 - Pandas.ipynb

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   "source": [
    "# Solution to Exercise: Grabbing/Understanding Data with __`pandas`__\n",
    "* We're going to grab some data from the Internet and do a bit of analysis of it\n",
    "* Start by importing the necessary modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## In addition to reading local files, __`read_csv`__ can import files from a URL, such as this [one](https://raw.githubusercontent.com/justmarkham/DAT8/master/data/chipotle.tsv)\n",
    "* This is data from a Chipotle restaurant, representing sales for a small period\n",
    "* (Note: this is a tab-separated file as opposed to comma-separated)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://raw.githubusercontent.com/justmarkham/DAT8/master/data/chipotle.tsv'\n",
    "chip = pd.read_csv(url, sep='\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Examine the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chip.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How many observations are in the dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4622"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chip.shape[0] # or chip.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How many columns?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chip.shape[1] # or len(chip.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What are the names of the columns?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['order_id', 'quantity', 'item_name', 'choice_description',\n",
       "       'item_price'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chip.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How many Items were ordered in total?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4972"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chip.quantity.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Slightly More Advanced Exercises\n",
    "* these will likely require reading the documentation if this is your first exposure to __`pandas`__\n",
    "* there are some hints..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What's the total revenue for the period in the dataset?\n",
    "* Hints\n",
    "  * __`item_price`__ is not a float\n",
    "  * check out the __`apply()`__ method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Revenue was: $39,237.02\n"
     ]
    }
   ],
   "source": [
    "chip['item_price'] = chip['item_price'].apply(lambda x: float(x[1:]))\n",
    "revenue = (chip['quantity'] * chip['item_price']).sum()\n",
    "print('Revenue was: ${:,.2f}'.format(revenue))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How many orders are in the Dataset?\n",
    "* Hint: check out __`value_counts()`__ method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1834"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chip.order_id.value_counts().count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How many different items were sold?\n",
    "* Hint: similar to above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chip.item_name.value_counts().count()"
   ]
  }
 ],
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