01-Indexing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Structures and Indexing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas provides high-performance containers for tabular data,\n",
    "and methods for operating on that data. We'll get our first look\n",
    "at pandas' data structures. Then we'll focus on indexing, selecting subsets of data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.display.max_rows = 10\n",
    "pd.options.display.float_format = '{:,.2f}'.format\n",
    "plt.rcParams['figure.figsize'] = (16, 12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading Data\n",
    "\n",
    "You can create a `pd.Series` or `pd.DataFrame` directly from lists or dictionaries,\n",
    "but most of the time you'll be reading from an external data source. Pandas can read\n",
    "from many formats, including including\n",
    "\n",
    "- `pd.read_parquet`\n",
    "- `pd.read_csv`\n",
    "- `pd.read_excel`\n",
    "- `pd.read_html`\n",
    "- `pd.read_json`\n",
    "- `pd.read_hdf`\n",
    "- `pd.read_sql`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this section we'll work with some flights data from the US Bureau of Transportation and Statistics.\n",
    "The file includes all the flights from New York airports in January 2014.\n",
    "Each record has information about a single flight including the carrier, tail number, origin, destination, and delays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fl_date</th>\n",
       "      <th>unique_carrier</th>\n",
       "      <th>airline_id</th>\n",
       "      <th>tail_num</th>\n",
       "      <th>fl_num</th>\n",
       "      <th>origin</th>\n",
       "      <th>dest</th>\n",
       "      <th>dep_time</th>\n",
       "      <th>dep_delay</th>\n",
       "      <th>arr_time</th>\n",
       "      <th>arr_delay</th>\n",
       "      <th>cancelled</th>\n",
       "      <th>arr</th>\n",
       "      <th>dep</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>AA</td>\n",
       "      <td>19805</td>\n",
       "      <td>N338AA</td>\n",
       "      <td>1</td>\n",
       "      <td>JFK</td>\n",
       "      <td>LAX</td>\n",
       "      <td>914.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>1,238.00</td>\n",
       "      <td>13.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2014-01-01 12:38:00</td>\n",
       "      <td>2014-01-01 09:14:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>AA</td>\n",
       "      <td>19805</td>\n",
       "      <td>N335AA</td>\n",
       "      <td>3</td>\n",
       "      <td>JFK</td>\n",
       "      <td>LAX</td>\n",
       "      <td>1,157.00</td>\n",
       "      <td>-3.00</td>\n",
       "      <td>1,523.00</td>\n",
       "      <td>13.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2014-01-01 15:23:00</td>\n",
       "      <td>2014-01-01 11:57:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>AA</td>\n",
       "      <td>19805</td>\n",
       "      <td>N327AA</td>\n",
       "      <td>21</td>\n",
       "      <td>JFK</td>\n",
       "      <td>LAX</td>\n",
       "      <td>1,902.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2,224.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2014-01-01 22:24:00</td>\n",
       "      <td>2014-01-01 19:02:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>AA</td>\n",
       "      <td>19805</td>\n",
       "      <td>N3EHAA</td>\n",
       "      <td>29</td>\n",
       "      <td>LGA</td>\n",
       "      <td>PBI</td>\n",
       "      <td>722.00</td>\n",
       "      <td>-8.00</td>\n",
       "      <td>1,014.00</td>\n",
       "      <td>-26.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2014-01-01 10:14:00</td>\n",
       "      <td>2014-01-01 07:22:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>AA</td>\n",
       "      <td>19805</td>\n",
       "      <td>N319AA</td>\n",
       "      <td>117</td>\n",
       "      <td>JFK</td>\n",
       "      <td>LAX</td>\n",
       "      <td>1,347.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1,706.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2014-01-01 17:06:00</td>\n",
       "      <td>2014-01-01 13:47:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20812</th>\n",
       "      <td>2014-01-31</td>\n",
       "      <td>UA</td>\n",
       "      <td>19977</td>\n",
       "      <td>N54711</td>\n",
       "      <td>1253</td>\n",
       "      <td>ROC</td>\n",
       "      <td>ORD</td>\n",
       "      <td>801.00</td>\n",
       "      <td>-4.00</td>\n",
       "      <td>908.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2014-01-31 09:08:00</td>\n",
       "      <td>2014-01-31 08:01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20813</th>\n",
       "      <td>2014-01-31</td>\n",
       "      <td>UA</td>\n",
       "      <td>19977</td>\n",
       "      <td>N77525</td>\n",
       "      <td>1429</td>\n",
       "      <td>LGA</td>\n",
       "      <td>CLE</td>\n",
       "      <td>1,522.00</td>\n",
       "      <td>-10.00</td>\n",
       "      <td>1,649.00</td>\n",
       "      <td>-31.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2014-01-31 16:49:00</td>\n",
       "      <td>2014-01-31 15:22:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20814</th>\n",
       "      <td>2014-01-31</td>\n",
       "      <td>UA</td>\n",
       "      <td>19977</td>\n",
       "      <td>N37293</td>\n",
       "      <td>1456</td>\n",
       "      <td>LGA</td>\n",
       "      <td>IAH</td>\n",
       "      <td>719.00</td>\n",
       "      <td>-6.00</td>\n",
       "      <td>1,006.00</td>\n",
       "      <td>-20.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2014-01-31 10:06:00</td>\n",
       "      <td>2014-01-31 07:19:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20815</th>\n",
       "      <td>2014-01-31</td>\n",
       "      <td>UA</td>\n",
       "      <td>19977</td>\n",
       "      <td>N24729</td>\n",
       "      <td>1457</td>\n",
       "      <td>LGA</td>\n",
       "      <td>IAH</td>\n",
       "      <td>852.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>1,156.00</td>\n",
       "      <td>-6.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2014-01-31 11:56:00</td>\n",
       "      <td>2014-01-31 08:52:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20816</th>\n",
       "      <td>2014-01-31</td>\n",
       "      <td>MQ</td>\n",
       "      <td>20398</td>\n",
       "      <td>N609MQ</td>\n",
       "      <td>3699</td>\n",
       "      <td>BUF</td>\n",
       "      <td>ORD</td>\n",
       "      <td>1,208.00</td>\n",
       "      <td>-12.00</td>\n",
       "      <td>1,251.00</td>\n",
       "      <td>-19.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2014-01-31 12:51:00</td>\n",
       "      <td>2014-01-31 12:08:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20817 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         fl_date unique_carrier  airline_id tail_num  fl_num origin dest  \\\n",
       "0     2014-01-01             AA       19805   N338AA       1    JFK  LAX   \n",
       "1     2014-01-01             AA       19805   N335AA       3    JFK  LAX   \n",
       "2     2014-01-01             AA       19805   N327AA      21    JFK  LAX   \n",
       "3     2014-01-01             AA       19805   N3EHAA      29    LGA  PBI   \n",
       "4     2014-01-01             AA       19805   N319AA     117    JFK  LAX   \n",
       "...          ...            ...         ...      ...     ...    ...  ...   \n",
       "20812 2014-01-31             UA       19977   N54711    1253    ROC  ORD   \n",
       "20813 2014-01-31             UA       19977   N77525    1429    LGA  CLE   \n",
       "20814 2014-01-31             UA       19977   N37293    1456    LGA  IAH   \n",
       "20815 2014-01-31             UA       19977   N24729    1457    LGA  IAH   \n",
       "20816 2014-01-31             MQ       20398   N609MQ    3699    BUF  ORD   \n",
       "\n",
       "       dep_time  dep_delay  arr_time  arr_delay  cancelled  \\\n",
       "0        914.00      14.00  1,238.00      13.00       0.00   \n",
       "1      1,157.00      -3.00  1,523.00      13.00       0.00   \n",
       "2      1,902.00       2.00  2,224.00       9.00       0.00   \n",
       "3        722.00      -8.00  1,014.00     -26.00       0.00   \n",
       "4      1,347.00       2.00  1,706.00       1.00       0.00   \n",
       "...         ...        ...       ...        ...        ...   \n",
       "20812    801.00      -4.00    908.00       4.00       0.00   \n",
       "20813  1,522.00     -10.00  1,649.00     -31.00       0.00   \n",
       "20814    719.00      -6.00  1,006.00     -20.00       0.00   \n",
       "20815    852.00       7.00  1,156.00      -6.00       0.00   \n",
       "20816  1,208.00     -12.00  1,251.00     -19.00       0.00   \n",
       "\n",
       "                      arr                 dep  \n",
       "0     2014-01-01 12:38:00 2014-01-01 09:14:00  \n",
       "1     2014-01-01 15:23:00 2014-01-01 11:57:00  \n",
       "2     2014-01-01 22:24:00 2014-01-01 19:02:00  \n",
       "3     2014-01-01 10:14:00 2014-01-01 07:22:00  \n",
       "4     2014-01-01 17:06:00 2014-01-01 13:47:00  \n",
       "...                   ...                 ...  \n",
       "20812 2014-01-31 09:08:00 2014-01-31 08:01:00  \n",
       "20813 2014-01-31 16:49:00 2014-01-31 15:22:00  \n",
       "20814 2014-01-31 10:06:00 2014-01-31 07:19:00  \n",
       "20815 2014-01-31 11:56:00 2014-01-31 08:52:00  \n",
       "20816 2014-01-31 12:51:00 2014-01-31 12:08:00  \n",
       "\n",
       "[20817 rows x 14 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights = pd.read_parquet(\"data/ny-flights.parq\")\n",
    "flights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`read_parquet` returned a `DataFrame`, which is somewhat similar to a spreadsheet or database table.\n",
    "`pd.DataFrame` is the data container you'll work most with, and consists of a few components:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DataFrame Components\n",
    "\n",
    "\n",
    "![A dataframe is made up of data, row labels, and column labels](figures/dataframe-diagram.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data are in the middle of the table.\n",
    "Each column of the data is a `pd.Series`, kind of like a 1-dimensional version of a DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        14.00\n",
       "1        -3.00\n",
       "2         2.00\n",
       "3        -8.00\n",
       "4         2.00\n",
       "         ...  \n",
       "20812    -4.00\n",
       "20813   -10.00\n",
       "20814    -6.00\n",
       "20815     7.00\n",
       "20816   -12.00\n",
       "Name: dep_delay, Length: 20817, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights['dep_delay']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both `pd.DataFrame`s and `pd.Series` have *row labels*, which can be accessed with the `.index` attribute:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([    0,     1,     2,     3,     4,     5,     6,     7,     8,\n",
       "                9,\n",
       "            ...\n",
       "            20807, 20808, 20809, 20810, 20811, 20812, 20813, 20814, 20815,\n",
       "            20816],\n",
       "           dtype='int64', length=20817)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`flights.index` is a `pd.Index` (there are many specialized index types, like `pd.RangeIndex`, but we'll talk about those later).\n",
    "\n",
    "DataFrames store their column labels in a `.columns` attribute, which is also a `pd.Index`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['fl_date', 'unique_carrier', 'airline_id', 'tail_num', 'fl_num',\n",
       "       'origin', 'dest', 'dep_time', 'dep_delay', 'arr_time', 'arr_delay',\n",
       "       'cancelled', 'arr', 'dep'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The row and column labels help out with indexing and alignemnt, our firs two topics."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Types\n",
    "\n",
    "DataFrames are a **columnar** data structure, which mean they're a container for *heterogenous* data. Each column can have it's own data type:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fl_date           datetime64[ns]\n",
       "unique_carrier            object\n",
       "airline_id                 int64\n",
       "tail_num                  object\n",
       "fl_num                     int64\n",
       "                       ...      \n",
       "arr_time                 float64\n",
       "arr_delay                float64\n",
       "cancelled                float64\n",
       "arr               datetime64[ns]\n",
       "dep               datetime64[ns]\n",
       "Length: 14, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This differs from a NumPy array, which are *homogenous*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\" data-title=\"Data Containers\">\n",
    "  <h1><i class=\"fa fa-tasks\" aria-hidden=\"true\"></i> Exercise: Pandas to NumPy</h1>\n",
    "</div>\n",
    "\n",
    "You can get a NumPy array from a pandas `DataFrame` with the `.values` attribute.\n",
    "What's the data type of that NumPy array?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load solutions/indexing_to_numpy.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preview\n",
    "\n",
    "To get a bit of intuition about the data we're working with, let's to a brief bit of exploratory analysis.\n",
    "We'll see all this in more detail later, but I wanted to demonstrate some of the capabilities of pandas upfront."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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x9We5/5YB8Wv7E+hPmpiOmSey5ESrAfSe4aSlo+6zARE42jOcsnSnSc2/zWRs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\n/wEmZZ1EPRH4ogAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1ca72b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(flights['dep']\n",
    "    .value_counts()           # Series with flights per second\n",
    "    .resample('H').sum()      # Resample to flights per hour\n",
    "    .rolling(8).mean()        # 8 hour rolling mean\n",
    "    .plot (figsize=(12, 6),    # plot\n",
    "          title=\"Number of Flights (8H Rolling Mean)\"))\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And the joint distribution of departure and arrival delays:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GkxnS2fzi8nWPa8ny9UrvpZbKkvbWEdk4hR0RWaGdPmCrTY1Vm9ZaXskpf8xQ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\nSZIS39U4C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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1cdaad30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot('dep_delay', 'arr_delay',\n",
    "              flights.loc[(flights['dep_delay'] > 1) &      # This notebook!\n",
    "                          (flights['dep_delay'] < 500)],\n",
    "              alpha=.25, marker='.', size=8);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll explore some of those methods and visualization techniques later.\n",
    "For now we'll turn to the more fundamental operation of indexing.\n",
    "\n",
    "By indexing, we mean selecting subsets of your data.\n",
    "It's a good starting point, because it comes up in so many other places;\n",
    "It's a terrible starting point because it's somewhat complicated, and somewhat boring."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Goal of Indexing: subset the data\n",
    "\n",
    "There are many ways you might want to specify which subset you want to select:\n",
    "\n",
    "- Like lists, you can index by integer position.\n",
    "- Like dictionaries, you can index by label.\n",
    "- Like NumPy arrays, you can index by boolean masks.\n",
    "- You can index with a scalar, `slice`, or array\n",
    "- Any of these should work on the index (row labels), or columns of a DataFrame, or both\n",
    "- And any of these should work on hierarchical indexes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Basic Rules\n",
    "\n",
    "1. Use `__getitem__` (square brackets) to select columns of a `DataFrame`\n",
    "\n",
    "    ```python\n",
    "    >>> df[['a', 'b', 'c']]\n",
    "    ```\n",
    "\n",
    "2. Use `.loc` for label-based indexing (rows and columns)\n",
    "\n",
    "    ```python\n",
    "    >>> df.loc[row_labels, column_labels]\n",
    "    ```\n",
    "\n",
    "3. Use `.iloc` for position-based indexing (rows and columns)\n",
    "\n",
    "    ```python\n",
    "    >>> df.iloc[row_positions, column_positions]\n",
    "    ```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "The arguments to `.loc` and `.iloc` are `.loc[row_indexer, column_indexer]`. An indexer can be one of\n",
    "\n",
    "- A scalar or array (of labels or integer positions)\n",
    "- A `slice` object (including `:` for everything)\n",
    "- A boolean mask\n",
    "\n",
    "The column indexer is optional.\n",
    "We'll walk through all the combinations below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's get a DataFrame with a labeled index by selecting the\n",
    "first flight for each carrier. We'll talk about `groupby` later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>fl_date</th>\n",
       "      <th>airline_id</th>\n",
       "      <th>tail_num</th>\n",
       "      <th>fl_num</th>\n",
       "      <th>origin</th>\n",
       "      <th>dest</th>\n",
       "      <th>dep_time</th>\n",
       "      <th>dep_delay</th>\n",
       "      <th>arr_time</th>\n",
       "      <th>arr_delay</th>\n",
       "      <th>cancelled</th>\n",
       "      <th>arr</th>\n",
       "      <th>dep</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique_carrier</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AA</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>19805</td>\n",
       "      <td>N338AA</td>\n",
       "      <td>1</td>\n",
       "      <td>JFK</td>\n",
       "      <td>LAX</td>\n",
       "      <td>914.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>1,238.00</td>\n",
       "      <td>13.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2014-01-01 12:38:00</td>\n",
       "      <td>2014-01-01 09:14:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B6</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>20409</td>\n",
       "      <td>N318JB</td>\n",
       "      <td>115</td>\n",
       "      <td>SYR</td>\n",
       "      <td>JFK</td>\n",
       "      <td>720.00</td>\n",
       "      <td>90.00</td>\n",
       "      <td>808.00</td>\n",
       "      <td>55.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2014-01-01 08:08:00</td>\n",
       "      <td>2014-01-01 07:20:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DL</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>19790</td>\n",
       "      <td>N930DL</td>\n",
       "      <td>61</td>\n",
       "      <td>LGA</td>\n",
       "      <td>ATL</td>\n",
       "      <td>1,810.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>2,054.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2014-01-01 20:54:00</td>\n",
       "      <td>2014-01-01 18:10:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EV</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>20366</td>\n",
       "      <td>N14977</td>\n",
       "      <td>3259</td>\n",
       "      <td>HPN</td>\n",
       "      <td>ORD</td>\n",
       "      <td>731.00</td>\n",
       "      <td>-5.00</td>\n",
       "      <td>940.00</td>\n",
       "      <td>31.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2014-01-01 09:40:00</td>\n",
       "      <td>2014-01-01 07:31:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F9</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>20436</td>\n",
       "      <td>N209FR</td>\n",
       "      <td>507</td>\n",
       "      <td>LGA</td>\n",
       "      <td>DEN</td>\n",
       "      <td>1,804.00</td>\n",
       "      <td>35.00</td>\n",
       "      <td>2,047.00</td>\n",
       "      <td>47.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2014-01-01 20:47:00</td>\n",
       "      <td>2014-01-01 18:04:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OO</th>\n",
       "      <td>2014-01-07</td>\n",
       "      <td>20304</td>\n",
       "      <td>N732SK</td>\n",
       "      <td>6258</td>\n",
       "      <td>SYR</td>\n",
       "      <td>ORD</td>\n",
       "      <td>1,721.00</td>\n",
       "      <td>49.00</td>\n",
       "      <td>1,834.00</td>\n",
       "      <td>47.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2014-01-08 18:34:00</td>\n",
       "      <td>2014-01-08 17:21:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UA</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>19977</td>\n",
       "      <td>N413UA</td>\n",
       "      <td>255</td>\n",
       "      <td>LGA</td>\n",
       "      <td>ORD</td>\n",
       "      <td>824.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1,006.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2014-01-01 10:06:00</td>\n",
       "      <td>2014-01-01 08:24:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>US</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>20355</td>\n",
       "      <td>N546UW</td>\n",
       "      <td>629</td>\n",
       "      <td>JFK</td>\n",
       "      <td>PHX</td>\n",
       "      <td>1,624.00</td>\n",
       "      <td>-6.00</td>\n",
       "      <td>1,932.00</td>\n",
       "      <td>-39.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2014-01-01 19:32:00</td>\n",
       "      <td>2014-01-01 16:24:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VX</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>21171</td>\n",
       "      <td>N361VA</td>\n",
       "      <td>11</td>\n",
       "      <td>JFK</td>\n",
       "      <td>SFO</td>\n",
       "      <td>730.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1,056.00</td>\n",
       "      <td>-14.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2014-01-01 10:56:00</td>\n",
       "      <td>2014-01-01 07:30:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WN</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>19393</td>\n",
       "      <td>N625SW</td>\n",
       "      <td>613</td>\n",
       "      <td>ALB</td>\n",
       "      <td>BWI</td>\n",
       "      <td>1,502.00</td>\n",
       "      <td>17.00</td>\n",
       "      <td>1,615.00</td>\n",
       "      <td>15.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2014-01-01 16:15:00</td>\n",
       "      <td>2014-01-01 15:02:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  fl_date  airline_id tail_num  fl_num origin dest  dep_time  \\\n",
       "unique_carrier                                                                 \n",
       "AA             2014-01-01       19805   N338AA       1    JFK  LAX    914.00   \n",
       "B6             2014-01-01       20409   N318JB     115    SYR  JFK    720.00   \n",
       "DL             2014-01-01       19790   N930DL      61    LGA  ATL  1,810.00   \n",
       "EV             2014-01-01       20366   N14977    3259    HPN  ORD    731.00   \n",
       "F9             2014-01-01       20436   N209FR     507    LGA  DEN  1,804.00   \n",
       "...                   ...         ...      ...     ...    ...  ...       ...   \n",
       "OO             2014-01-07       20304   N732SK    6258    SYR  ORD  1,721.00   \n",
       "UA             2014-01-01       19977   N413UA     255    LGA  ORD    824.00   \n",
       "US             2014-01-01       20355   N546UW     629    JFK  PHX  1,624.00   \n",
       "VX             2014-01-01       21171   N361VA      11    JFK  SFO    730.00   \n",
       "WN             2014-01-01       19393   N625SW     613    ALB  BWI  1,502.00   \n",
       "\n",
       "                dep_delay  arr_time  arr_delay  cancelled                 arr  \\\n",
       "unique_carrier                                                                  \n",
       "AA                  14.00  1,238.00      13.00       0.00 2014-01-01 12:38:00   \n",
       "B6                  90.00    808.00      55.00       0.00 2014-01-01 08:08:00   \n",
       "DL                  10.00  2,054.00      10.00       0.00 2014-01-01 20:54:00   \n",
       "EV                  -5.00    940.00      31.00       0.00 2014-01-01 09:40:00   \n",
       "F9                  35.00  2,047.00      47.00       0.00 2014-01-01 20:47:00   \n",
       "...                   ...       ...        ...        ...                 ...   \n",
       "OO                  49.00  1,834.00      47.00       1.00 2014-01-08 18:34:00   \n",
       "UA                   0.00  1,006.00       8.00       0.00 2014-01-01 10:06:00   \n",
       "US                  -6.00  1,932.00     -39.00       0.00 2014-01-01 19:32:00   \n",
       "VX                   0.00  1,056.00     -14.00       0.00 2014-01-01 10:56:00   \n",
       "WN                  17.00  1,615.00      15.00       0.00 2014-01-01 16:15:00   \n",
       "\n",
       "                               dep  \n",
       "unique_carrier                      \n",
       "AA             2014-01-01 09:14:00  \n",
       "B6             2014-01-01 07:20:00  \n",
       "DL             2014-01-01 18:10:00  \n",
       "EV             2014-01-01 07:31:00  \n",
       "F9             2014-01-01 18:04:00  \n",
       "...                            ...  \n",
       "OO             2014-01-08 17:21:00  \n",
       "UA             2014-01-01 08:24:00  \n",
       "US             2014-01-01 16:24:00  \n",
       "VX             2014-01-01 07:30:00  \n",
       "WN             2014-01-01 15:02:00  \n",
       "\n",
       "[13 rows x 13 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first = flights.groupby(\"unique_carrier\").first()\n",
    "first"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Selecting Columns with `__getitem__`\n",
    "\n",
    "Let's select the two delay columns. Since we're *only* filtering the columns (not rows), we can use dictionary-like `[]` to do the slicing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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",
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       "    }\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>dep_delay</th>\n",
       "      <th>arr_delay</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique_carrier</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AA</th>\n",
       "      <td>14.00</td>\n",
       "      <td>13.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B6</th>\n",
       "      <td>90.00</td>\n",
       "      <td>55.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DL</th>\n",
       "      <td>10.00</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EV</th>\n",
       "      <td>-5.00</td>\n",
       "      <td>31.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F9</th>\n",
       "      <td>35.00</td>\n",
       "      <td>47.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OO</th>\n",
       "      <td>49.00</td>\n",
       "      <td>47.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UA</th>\n",
       "      <td>0.00</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>US</th>\n",
       "      <td>-6.00</td>\n",
       "      <td>-39.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VX</th>\n",
       "      <td>0.00</td>\n",
       "      <td>-14.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WN</th>\n",
       "      <td>17.00</td>\n",
       "      <td>15.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                dep_delay  arr_delay\n",
       "unique_carrier                      \n",
       "AA                  14.00      13.00\n",
       "B6                  90.00      55.00\n",
       "DL                  10.00      10.00\n",
       "EV                  -5.00      31.00\n",
       "F9                  35.00      47.00\n",
       "...                   ...        ...\n",
       "OO                  49.00      47.00\n",
       "UA                   0.00       8.00\n",
       "US                  -6.00     -39.00\n",
       "VX                   0.00     -14.00\n",
       "WN                  17.00      15.00\n",
       "\n",
       "[13 rows x 2 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first[['dep_delay', 'arr_delay']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One potential source of confusion: python uses `[]` for two purposes\n",
    "\n",
    "1. building a list\n",
    "2. slicing with `__getitem__`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dep_delay</th>\n",
       "      <th>arr_delay</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique_carrier</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AA</th>\n",
       "      <td>14.00</td>\n",
       "      <td>13.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B6</th>\n",
       "      <td>90.00</td>\n",
       "      <td>55.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DL</th>\n",
       "      <td>10.00</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EV</th>\n",
       "      <td>-5.00</td>\n",
       "      <td>31.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F9</th>\n",
       "      <td>35.00</td>\n",
       "      <td>47.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OO</th>\n",
       "      <td>49.00</td>\n",
       "      <td>47.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UA</th>\n",
       "      <td>0.00</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>US</th>\n",
       "      <td>-6.00</td>\n",
       "      <td>-39.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VX</th>\n",
       "      <td>0.00</td>\n",
       "      <td>-14.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WN</th>\n",
       "      <td>17.00</td>\n",
       "      <td>15.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                dep_delay  arr_delay\n",
       "unique_carrier                      \n",
       "AA                  14.00      13.00\n",
       "B6                  90.00      55.00\n",
       "DL                  10.00      10.00\n",
       "EV                  -5.00      31.00\n",
       "F9                  35.00      47.00\n",
       "...                   ...        ...\n",
       "OO                  49.00      47.00\n",
       "UA                   0.00       8.00\n",
       "US                  -6.00     -39.00\n",
       "VX                   0.00     -14.00\n",
       "WN                  17.00      15.00\n",
       "\n",
       "[13 rows x 2 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. build the list cols\n",
    "cols = ['dep_delay', 'arr_delay']\n",
    "# 2. slice, with cols as the argument to `__getitem__`\n",
    "first[cols]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\" data-title=\"Select Columns by Name\">\n",
    "  <h1><i class=\"fa fa-tasks\" aria-hidden=\"true\"></i> Exercise: Select Columns by Name</h1>\n",
    "</div>\n",
    "<p>Select the two airport-name columns, `'origin'` and `'dest'`, from `first`</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": [
     "solution"
    ]
   },
   "outputs": [],
   "source": [
    "# Your code here...\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "tags": [
     "solution"
    ]
   },
   "outputs": [
    {
     "data": {
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       "    }\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>origin</th>\n",
       "      <th>dest</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique_carrier</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AA</th>\n",
       "      <td>JFK</td>\n",
       "      <td>LAX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B6</th>\n",
       "      <td>SYR</td>\n",
       "      <td>JFK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DL</th>\n",
       "      <td>LGA</td>\n",
       "      <td>ATL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EV</th>\n",
       "      <td>HPN</td>\n",
       "      <td>ORD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F9</th>\n",
       "      <td>LGA</td>\n",
       "      <td>DEN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OO</th>\n",
       "      <td>SYR</td>\n",
       "      <td>ORD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UA</th>\n",
       "      <td>LGA</td>\n",
       "      <td>ORD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>US</th>\n",
       "      <td>JFK</td>\n",
       "      <td>PHX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VX</th>\n",
       "      <td>JFK</td>\n",
       "      <td>SFO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WN</th>\n",
       "      <td>ALB</td>\n",
       "      <td>BWI</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               origin dest\n",
       "unique_carrier            \n",
       "AA                JFK  LAX\n",
       "B6                SYR  JFK\n",
       "DL                LGA  ATL\n",
       "EV                HPN  ORD\n",
       "F9                LGA  DEN\n",
       "...               ...  ...\n",
       "OO                SYR  ORD\n",
       "UA                LGA  ORD\n",
       "US                JFK  PHX\n",
       "VX                JFK  SFO\n",
       "WN                ALB  BWI\n",
       "\n",
       "[13 rows x 2 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%load solutions/indexing_00.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Column `.` lookup\n",
    "\n",
    "As a convenience, pandas attaches the column names to your `DataFrame` when they're valid [python identifiers](https://docs.python.org/3/reference/lexical_analysis.html), and don't override one of the ([many](http://pandas.pydata.org/pandas-docs/stable/api.html#dataframe)) methods on `DataFrame`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "N292JB    76\n",
       "N258JB    69\n",
       "N306JB    68\n",
       "N523MQ    59\n",
       "N324JB    58\n",
       "          ..\n",
       "N265WN     1\n",
       "N910DN     1\n",
       "N370AA     1\n",
       "N11194     1\n",
       "N838AS     1\n",
       "Name: tail_num, Length: 2734, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Same as flights['tail_num'].value_counts()\n",
    "flights.tail_num.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is nice when working interactively, especially as tab completion works with `flights.tail_num.<TAB>`, but doesn't with `flights['tail_num'].<TAB>`.\n",
    "Still, since pandas could add methods in the future that clash with your column names, it's recommended to stick with `__getitem__` for production code.\n",
    "This will always work, even when you shadow a DataFrame method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean\n",
       "0     1\n",
       "1     2\n",
       "2     3"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = pd.DataFrame({\"mean\": [1, 2, 3]})\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.mean of    mean\n",
       "0     1\n",
       "1     2\n",
       "2     3>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# returns the method, not the column\n",
    "x.mean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, you can't *assign* with `.`, while you can with `__setitem__` (square brackets on the left-hand side of an `=`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/taugspurger/miniconda3/envs/ph2t2/lib/python3.6/site-packages/ipykernel_launcher.py:1: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>right</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>c</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "   mean right\n",
       "0     1     a\n",
       "1     2     b\n",
       "2     3     c"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.wrong = ['a', 'b', 'c']\n",
    "x['right'] = ['a', 'b', 'c']\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a', 'b', 'c']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.wrong"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`DataFrame`s, like most python objects, allow you to attach arbitrary attributes to any instance.\n",
    "This means `x.wrong = ...` attaches the thing on the right-hand side to the object on the left."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Label-Based Indexing with `.loc`\n",
    "\n",
    "You can slice rows by label (and optionally the columns too) with `.loc`.\n",
    "Let's select the rows for the carriers 'AA', 'DL', 'US', and 'WN'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>fl_date</th>\n",
       "      <th>airline_id</th>\n",
       "      <th>tail_num</th>\n",
       "      <th>fl_num</th>\n",
       "      <th>origin</th>\n",
       "      <th>dest</th>\n",
       "      <th>dep_time</th>\n",
       "      <th>dep_delay</th>\n",
       "      <th>arr_time</th>\n",
       "      <th>arr_delay</th>\n",
       "      <th>cancelled</th>\n",
       "      <th>arr</th>\n",
       "      <th>dep</th>\n",
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       "    <tr>\n",
       "      <th>unique_carrier</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AA</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>19805</td>\n",
       "      <td>N338AA</td>\n",
       "      <td>1</td>\n",
       "      <td>JFK</td>\n",
       "      <td>LAX</td>\n",
       "      <td>914.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>1,238.00</td>\n",
       "      <td>13.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2014-01-01 12:38:00</td>\n",
       "      <td>2014-01-01 09:14:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DL</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>19790</td>\n",
       "      <td>N930DL</td>\n",
       "      <td>61</td>\n",
       "      <td>LGA</td>\n",
       "      <td>ATL</td>\n",
       "      <td>1,810.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>2,054.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2014-01-01 20:54:00</td>\n",
       "      <td>2014-01-01 18:10:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>US</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>20355</td>\n",
       "      <td>N546UW</td>\n",
       "      <td>629</td>\n",
       "      <td>JFK</td>\n",
       "      <td>PHX</td>\n",
       "      <td>1,624.00</td>\n",
       "      <td>-6.00</td>\n",
       "      <td>1,932.00</td>\n",
       "      <td>-39.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2014-01-01 19:32:00</td>\n",
       "      <td>2014-01-01 16:24:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WN</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>19393</td>\n",
       "      <td>N625SW</td>\n",
       "      <td>613</td>\n",
       "      <td>ALB</td>\n",
       "      <td>BWI</td>\n",
       "      <td>1,502.00</td>\n",
       "      <td>17.00</td>\n",
       "      <td>1,615.00</td>\n",
       "      <td>15.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2014-01-01 16:15:00</td>\n",
       "      <td>2014-01-01 15:02:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  fl_date  airline_id tail_num  fl_num origin dest  dep_time  \\\n",
       "unique_carrier                                                                 \n",
       "AA             2014-01-01       19805   N338AA       1    JFK  LAX    914.00   \n",
       "DL             2014-01-01       19790   N930DL      61    LGA  ATL  1,810.00   \n",
       "US             2014-01-01       20355   N546UW     629    JFK  PHX  1,624.00   \n",
       "WN             2014-01-01       19393   N625SW     613    ALB  BWI  1,502.00   \n",
       "\n",
       "                dep_delay  arr_time  arr_delay  cancelled                 arr  \\\n",
       "unique_carrier                                                                  \n",
       "AA                  14.00  1,238.00      13.00       0.00 2014-01-01 12:38:00   \n",
       "DL                  10.00  2,054.00      10.00       0.00 2014-01-01 20:54:00   \n",
       "US                  -6.00  1,932.00     -39.00       0.00 2014-01-01 19:32:00   \n",
       "WN                  17.00  1,615.00      15.00       0.00 2014-01-01 16:15:00   \n",
       "\n",
       "                               dep  \n",
       "unique_carrier                      \n",
       "AA             2014-01-01 09:14:00  \n",
       "DL             2014-01-01 18:10:00  \n",
       "US             2014-01-01 16:24:00  \n",
       "WN             2014-01-01 15:02:00  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "carriers = ['AA', 'DL', 'US', 'WN']\n",
    "# Select those carriers by label\n",
    "first.loc[carriers]  # no column indexer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Indexing both rows and columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "  <thead>\n",
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       "      <th>origin</th>\n",
       "      <th>dest</th>\n",
       "      <th>dep_delay</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique_carrier</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AA</th>\n",
       "      <td>JFK</td>\n",
       "      <td>LAX</td>\n",
       "      <td>14.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DL</th>\n",
       "      <td>LGA</td>\n",
       "      <td>ATL</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>US</th>\n",
       "      <td>JFK</td>\n",
       "      <td>PHX</td>\n",
       "      <td>-6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WN</th>\n",
       "      <td>ALB</td>\n",
       "      <td>BWI</td>\n",
       "      <td>17.00</td>\n",
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      ],
      "text/plain": [
       "               origin dest  dep_delay\n",
       "unique_carrier                       \n",
       "AA                JFK  LAX      14.00\n",
       "DL                LGA  ATL      10.00\n",
       "US                JFK  PHX      -6.00\n",
       "WN                ALB  BWI      17.00"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select just `carriers` and origin, dest, and dep_delay\n",
    "first.loc[carriers, ['origin', 'dest', 'dep_delay']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas will *reduce dimensionality* when possible, so slicing with a scalar on either axis will return a `Series`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "unique_carrier\n",
       "AA    N338AA\n",
       "DL    N930DL\n",
       "US    N546UW\n",
       "WN    N625SW\n",
       "Name: tail_num, dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select just tail_num for those `carriers`\n",
    "first.loc[carriers, 'tail_num']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And scalars on both axes will return a scalar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'N338AA'"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first.loc['AA', 'tail_num']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `slice` objects\n",
    "\n",
    "You can pass a `slice` object (made with a `:`). They make sense when your index is sorted, which ours is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fl_date</th>\n",
       "      <th>airline_id</th>\n",
       "      <th>tail_num</th>\n",
       "      <th>fl_num</th>\n",
       "      <th>origin</th>\n",
       "      <th>dest</th>\n",
       "      <th>dep_time</th>\n",
       "      <th>dep_delay</th>\n",
       "      <th>arr_time</th>\n",
       "      <th>arr_delay</th>\n",
       "      <th>cancelled</th>\n",
       "      <th>arr</th>\n",
       "      <th>dep</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique_carrier</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AA</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>19805</td>\n",
       "      <td>N338AA</td>\n",
       "      <td>1</td>\n",
       "      <td>JFK</td>\n",
       "      <td>LAX</td>\n",
       "      <td>914.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>1,238.00</td>\n",
       "      <td>13.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2014-01-01 12:38:00</td>\n",
       "      <td>2014-01-01 09:14:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B6</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>20409</td>\n",
       "      <td>N318JB</td>\n",
       "      <td>115</td>\n",
       "      <td>SYR</td>\n",
       "      <td>JFK</td>\n",
       "      <td>720.00</td>\n",
       "      <td>90.00</td>\n",
       "      <td>808.00</td>\n",
       "      <td>55.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2014-01-01 08:08:00</td>\n",
       "      <td>2014-01-01 07:20:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DL</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>19790</td>\n",
       "      <td>N930DL</td>\n",
       "      <td>61</td>\n",
       "      <td>LGA</td>\n",
       "      <td>ATL</td>\n",
       "      <td>1,810.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>2,054.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2014-01-01 20:54:00</td>\n",
       "      <td>2014-01-01 18:10:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  fl_date  airline_id tail_num  fl_num origin dest  dep_time  \\\n",
       "unique_carrier                                                                 \n",
       "AA             2014-01-01       19805   N338AA       1    JFK  LAX    914.00   \n",
       "B6             2014-01-01       20409   N318JB     115    SYR  JFK    720.00   \n",
       "DL             2014-01-01       19790   N930DL      61    LGA  ATL  1,810.00   \n",
       "\n",
       "                dep_delay  arr_time  arr_delay  cancelled                 arr  \\\n",
       "unique_carrier                                                                  \n",
       "AA                  14.00  1,238.00      13.00       0.00 2014-01-01 12:38:00   \n",
       "B6                  90.00    808.00      55.00       0.00 2014-01-01 08:08:00   \n",
       "DL                  10.00  2,054.00      10.00       0.00 2014-01-01 20:54:00   \n",
       "\n",
       "                               dep  \n",
       "unique_carrier                      \n",
       "AA             2014-01-01 09:14:00  \n",
       "B6             2014-01-01 07:20:00  \n",
       "DL             2014-01-01 18:10:00  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# slice from AA to DL\n",
    "first.loc['AA':'DL']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the slice is inclusive on *both* sides *when using* `.loc` (`.iloc` follows the usual python semantics of closed on the left, open on the right)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\" data-title=\"Index Rows and Columns\">\n",
    "  <h1><i class=\"fa fa-tasks\" aria-hidden=\"true\"></i> Exercise: Index Rows and Columns</h1>\n",
    "</div>\n",
    "<p>\n",
    "Select the columns `tail_num`, `origin`, and `dest` for the carriers `US`, `VX`, and `WN` from `first`.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "tags": [
     "solution"
    ]
   },
   "outputs": [],
   "source": [
    "# Your code here...\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>tail_num</th>\n",
       "      <th>origin</th>\n",
       "      <th>dest</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique_carrier</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>US</th>\n",
       "      <td>N546UW</td>\n",
       "      <td>JFK</td>\n",
       "      <td>PHX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VX</th>\n",
       "      <td>N361VA</td>\n",
       "      <td>JFK</td>\n",
       "      <td>SFO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WN</th>\n",
       "      <td>N625SW</td>\n",
       "      <td>ALB</td>\n",
       "      <td>BWI</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               tail_num origin dest\n",
       "unique_carrier                     \n",
       "US               N546UW    JFK  PHX\n",
       "VX               N361VA    JFK  SFO\n",
       "WN               N625SW    ALB  BWI"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%load solutions/indexing_rows_columns.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Boolean Indexing\n",
    "\n",
    "Filter using a *1-dimensional* boolean array with the same length.\n",
    "This is esstentially a SQL `WHERE` clause.\n",
    "For example,\n",
    "\n",
    "```sql\n",
    "select * from flights\n",
    "where dep_delay > 10\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>fl_date</th>\n",
       "      <th>unique_carrier</th>\n",
       "      <th>airline_id</th>\n",
       "      <th>tail_num</th>\n",
       "      <th>fl_num</th>\n",
       "      <th>origin</th>\n",
       "      <th>dest</th>\n",
       "      <th>dep_time</th>\n",
       "      <th>dep_delay</th>\n",
       "      <th>arr_time</th>\n",
       "      <th>arr_delay</th>\n",
       "      <th>cancelled</th>\n",
       "      <th>arr</th>\n",
       "      <th>dep</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>AA</td>\n",
       "      <td>19805</td>\n",
       "      <td>N338AA</td>\n",
       "      <td>1</td>\n",
       "      <td>JFK</td>\n",
       "      <td>LAX</td>\n",
       "      <td>914.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>1,238.00</td>\n",
       "      <td>13.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2014-01-01 12:38:00</td>\n",
       "      <td>2014-01-01 09:14:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>AA</td>\n",
       "      <td>19805</td>\n",
       "      <td>N336AA</td>\n",
       "      <td>181</td>\n",
       "      <td>JFK</td>\n",
       "      <td>LAX</td>\n",
       "      <td>1,907.00</td>\n",
       "      <td>142.00</td>\n",
       "      <td>2,223.00</td>\n",
       "      <td>133.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2014-01-01 22:23:00</td>\n",
       "      <td>2014-01-01 19:07:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>AA</td>\n",
       "      <td>19805</td>\n",
       "      <td>N3HPAA</td>\n",
       "      <td>199</td>\n",
       "      <td>JFK</td>\n",
       "      <td>ORD</td>\n",
       "      <td>1,733.00</td>\n",
       "      <td>18.00</td>\n",
       "      <td>2,024.00</td>\n",
       "      <td>69.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2014-01-01 20:24:00</td>\n",
       "      <td>2014-01-01 17:33:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>AA</td>\n",
       "      <td>19805</td>\n",
       "      <td>N3HFAA</td>\n",
       "      <td>211</td>\n",
       "      <td>JFK</td>\n",
       "      <td>IAH</td>\n",
       "      <td>1,640.00</td>\n",
       "      <td>25.00</td>\n",
       "      <td>2,001.00</td>\n",
       "      <td>36.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2014-01-01 20:01:00</td>\n",
       "      <td>2014-01-01 16:40:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>AA</td>\n",
       "      <td>19805</td>\n",
       "      <td>N3KDAA</td>\n",
       "      <td>317</td>\n",
       "      <td>LGA</td>\n",
       "      <td>ORD</td>\n",
       "      <td>1,029.00</td>\n",
       "      <td>44.00</td>\n",
       "      <td>1,212.00</td>\n",
       "      <td>42.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2014-01-01 12:12:00</td>\n",
       "      <td>2014-01-01 10:29:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20779</th>\n",
       "      <td>2014-01-31</td>\n",
       "      <td>UA</td>\n",
       "      <td>19977</td>\n",
       "      <td>N597UA</td>\n",
       "      <td>303</td>\n",
       "      <td>JFK</td>\n",
       "      <td>SFO</td>\n",
       "      <td>624.00</td>\n",
       "      <td>20.00</td>\n",
       "      <td>1,026.00</td>\n",
       "      <td>40.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2014-01-31 10:26:00</td>\n",
       "      <td>2014-01-31 06:24:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20793</th>\n",
       "      <td>2014-01-31</td>\n",
       "      <td>UA</td>\n",
       "      <td>19977</td>\n",
       "      <td>N509UA</td>\n",
       "      <td>509</td>\n",
       "      <td>LGA</td>\n",
       "      <td>DEN</td>\n",
       "      <td>1,740.00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>2,024.00</td>\n",
       "      <td>22.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2014-01-31 20:24:00</td>\n",
       "      <td>2014-01-31 17:40:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20795</th>\n",
       "      <td>2014-01-31</td>\n",
       "      <td>UA</td>\n",
       "      <td>19977</td>\n",
       "      <td>N558UA</td>\n",
       "      <td>518</td>\n",
       "      <td>LGA</td>\n",
       "      <td>DEN</td>\n",
       "      <td>1,518.00</td>\n",
       "      <td>16.00</td>\n",
       "      <td>1,753.00</td>\n",
       "      <td>17.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2014-01-31 17:53:00</td>\n",
       "      <td>2014-01-31 15:18:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20798</th>\n",
       "      <td>2014-01-31</td>\n",
       "      <td>MQ</td>\n",
       "      <td>20398</td>\n",
       "      <td>N544MQ</td>\n",
       "      <td>3199</td>\n",
       "      <td>LGA</td>\n",
       "      <td>CLT</td>\n",
       "      <td>1,513.00</td>\n",
       "      <td>23.00</td>\n",
       "      <td>1,702.00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2014-01-31 17:02:00</td>\n",
       "      <td>2014-01-31 15:13:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20811</th>\n",
       "      <td>2014-01-31</td>\n",
       "      <td>UA</td>\n",
       "      <td>19977</td>\n",
       "      <td>N37255</td>\n",
       "      <td>1206</td>\n",
       "      <td>LGA</td>\n",
       "      <td>DEN</td>\n",
       "      <td>1,225.00</td>\n",
       "      <td>73.00</td>\n",
       "      <td>1,543.00</td>\n",
       "      <td>121.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2014-01-31 15:43:00</td>\n",
       "      <td>2014-01-31 12:25:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6199 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         fl_date unique_carrier  airline_id tail_num  fl_num origin dest  \\\n",
       "0     2014-01-01             AA       19805   N338AA       1    JFK  LAX   \n",
       "11    2014-01-01             AA       19805   N336AA     181    JFK  LAX   \n",
       "13    2014-01-01             AA       19805   N3HPAA     199    JFK  ORD   \n",
       "14    2014-01-01             AA       19805   N3HFAA     211    JFK  IAH   \n",
       "21    2014-01-01             AA       19805   N3KDAA     317    LGA  ORD   \n",
       "...          ...            ...         ...      ...     ...    ...  ...   \n",
       "20779 2014-01-31             UA       19977   N597UA     303    JFK  SFO   \n",
       "20793 2014-01-31             UA       19977   N509UA     509    LGA  DEN   \n",
       "20795 2014-01-31             UA       19977   N558UA     518    LGA  DEN   \n",
       "20798 2014-01-31             MQ       20398   N544MQ    3199    LGA  CLT   \n",
       "20811 2014-01-31             UA       19977   N37255    1206    LGA  DEN   \n",
       "\n",
       "       dep_time  dep_delay  arr_time  arr_delay  cancelled  \\\n",
       "0        914.00      14.00  1,238.00      13.00       0.00   \n",
       "11     1,907.00     142.00  2,223.00     133.00       0.00   \n",
       "13     1,733.00      18.00  2,024.00      69.00       0.00   \n",
       "14     1,640.00      25.00  2,001.00      36.00       0.00   \n",
       "21     1,029.00      44.00  1,212.00      42.00       0.00   \n",
       "...         ...        ...       ...        ...        ...   \n",
       "20779    624.00      20.00  1,026.00      40.00       0.00   \n",
       "20793  1,740.00      12.00  2,024.00      22.00       0.00   \n",
       "20795  1,518.00      16.00  1,753.00      17.00       0.00   \n",
       "20798  1,513.00      23.00  1,702.00      12.00       0.00   \n",
       "20811  1,225.00      73.00  1,543.00     121.00       0.00   \n",
       "\n",
       "                      arr                 dep  \n",
       "0     2014-01-01 12:38:00 2014-01-01 09:14:00  \n",
       "11    2014-01-01 22:23:00 2014-01-01 19:07:00  \n",
       "13    2014-01-01 20:24:00 2014-01-01 17:33:00  \n",
       "14    2014-01-01 20:01:00 2014-01-01 16:40:00  \n",
       "21    2014-01-01 12:12:00 2014-01-01 10:29:00  \n",
       "...                   ...                 ...  \n",
       "20779 2014-01-31 10:26:00 2014-01-31 06:24:00  \n",
       "20793 2014-01-31 20:24:00 2014-01-31 17:40:00  \n",
       "20795 2014-01-31 17:53:00 2014-01-31 15:18:00  \n",
       "20798 2014-01-31 17:02:00 2014-01-31 15:13:00  \n",
       "20811 2014-01-31 15:43:00 2014-01-31 12:25:00  \n",
       "\n",
       "[6199 rows x 14 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights[flights.dep_delay > 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     True\n",
       "1    False\n",
       "2    False\n",
       "3    False\n",
       "4    False\n",
       "Name: dep_delay, dtype: bool"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask = flights.dep_delay > 10\n",
    "mask.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\" data-title=\"Top Airports\">\n",
    "  <h1><i class=\"fa fa-tasks\" aria-hidden=\"true\"></i> Exercise: Top Airports</h1>\n",
    "</div>\n",
    "<p>\n",
    "Select all the flights departing from the 5 busiest airports</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LGA    8530\n",
       "JFK    7878\n",
       "BUF    1348\n",
       "ROC     676\n",
       "HPN     615\n",
       "       ... \n",
       "SYR     554\n",
       "ISP     398\n",
       "ELM     105\n",
       "SWF      62\n",
       "ART      58\n",
       "Name: origin, Length: 11, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the counts for `flights.origin`\n",
    "origin_counts = flights.origin.value_counts()\n",
    "origin_counts "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[`Series.value_counts`](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.value_counts.html) will return a Series where the index is the set of unique values, and the values are the number of occurrances of that value.\n",
    "It's sorted in descending order, so we can get the 5 most common labels with:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['LGA', 'JFK', 'BUF', 'ROC', 'HPN'], dtype='object')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get a boolean mask for whether `counts` is in the top 5.\n",
    "top5 = origin_counts.index...  # how to get the first 5? How would you do it for a list?\n",
    "top5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll test whether any given row from `filghts` is in the top 5 using the `.isin` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        True\n",
       "1        True\n",
       "2        True\n",
       "3        True\n",
       "4        True\n",
       "         ... \n",
       "20812    True\n",
       "20813    True\n",
       "20814    True\n",
       "20815    True\n",
       "20816    True\n",
       "Name: origin, Length: 20817, dtype: bool"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask = flights.origin.isin(top5)\n",
    "mask"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a *boolean mask*, which can be passed into `.loc`.\n",
    "We want to select the rows where `mask` is true (in the top 5) and the columns `['origin', 'dest']`.\n",
    "Do we want to use `.loc` or `.iloc`? Remember, both accept boolean masks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>origin</th>\n",
       "      <th>dest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>JFK</td>\n",
       "      <td>LAX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>JFK</td>\n",
       "      <td>LAX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>JFK</td>\n",
       "      <td>LAX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LGA</td>\n",
       "      <td>PBI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>JFK</td>\n",
       "      <td>LAX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20812</th>\n",
       "      <td>ROC</td>\n",
       "      <td>ORD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20813</th>\n",
       "      <td>LGA</td>\n",
       "      <td>CLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20814</th>\n",
       "      <td>LGA</td>\n",
       "      <td>IAH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20815</th>\n",
       "      <td>LGA</td>\n",
       "      <td>IAH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20816</th>\n",
       "      <td>BUF</td>\n",
       "      <td>ORD</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19047 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      origin dest\n",
       "0        JFK  LAX\n",
       "1        JFK  LAX\n",
       "2        JFK  LAX\n",
       "3        LGA  PBI\n",
       "4        JFK  LAX\n",
       "...      ...  ...\n",
       "20812    ROC  ORD\n",
       "20813    LGA  CLE\n",
       "20814    LGA  IAH\n",
       "20815    LGA  IAH\n",
       "20816    BUF  ORD\n",
       "\n",
       "[19047 rows x 2 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights...   # Do we want flights.loc or flights.loc?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load solutions/indexing_mask.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Boolean indexers are useful because so many operations can produce an array of booleans.\n",
    "\n",
    "- missing data checks (`.isna`, `.notna`)\n",
    "- container checks (`.isin`)\n",
    "- boolean aggregations (`.any`, `.all`)\n",
    "- comparisions (`.gt`, `.lt`, etc.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>fl_date</th>\n",
       "      <th>unique_carrier</th>\n",
       "      <th>airline_id</th>\n",
       "      <th>tail_num</th>\n",
       "      <th>fl_num</th>\n",
       "      <th>origin</th>\n",
       "      <th>dest</th>\n",
       "      <th>dep_time</th>\n",
       "      <th>dep_delay</th>\n",
       "      <th>arr_time</th>\n",
       "      <th>arr_delay</th>\n",
       "      <th>cancelled</th>\n",
       "      <th>arr</th>\n",
       "      <th>dep</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>AA</td>\n",
       "      <td>19805</td>\n",
       "      <td>N3EAAA</td>\n",
       "      <td>359</td>\n",
       "      <td>LGA</td>\n",
       "      <td>ORD</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>AA</td>\n",
       "      <td>19805</td>\n",
       "      <td>N542AA</td>\n",
       "      <td>371</td>\n",
       "      <td>LGA</td>\n",
       "      <td>ORD</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>B6</td>\n",
       "      <td>20409</td>\n",
       "      <td>N913JB</td>\n",
       "      <td>1103</td>\n",
       "      <td>JFK</td>\n",
       "      <td>SJU</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>B6</td>\n",
       "      <td>20409</td>\n",
       "      <td>N193JB</td>\n",
       "      <td>518</td>\n",
       "      <td>JFK</td>\n",
       "      <td>BOS</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>EV</td>\n",
       "      <td>20366</td>\n",
       "      <td>N17159</td>\n",
       "      <td>4130</td>\n",
       "      <td>SYR</td>\n",
       "      <td>CLE</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20645</th>\n",
       "      <td>2014-01-31</td>\n",
       "      <td>DL</td>\n",
       "      <td>19790</td>\n",
       "      <td>N908DA</td>\n",
       "      <td>2129</td>\n",
       "      <td>ROC</td>\n",
       "      <td>ATL</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20653</th>\n",
       "      <td>2014-01-31</td>\n",
       "      <td>DL</td>\n",
       "      <td>19790</td>\n",
       "      <td>N965DL</td>\n",
       "      <td>2165</td>\n",
       "      <td>JFK</td>\n",
       "      <td>TPA</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20702</th>\n",
       "      <td>2014-01-31</td>\n",
       "      <td>MQ</td>\n",
       "      <td>20398</td>\n",
       "      <td>N672MQ</td>\n",
       "      <td>3075</td>\n",
       "      <td>SYR</td>\n",
       "      <td>ORD</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20716</th>\n",
       "      <td>2014-01-31</td>\n",
       "      <td>MQ</td>\n",
       "      <td>20398</td>\n",
       "      <td>N507MQ</td>\n",
       "      <td>3281</td>\n",
       "      <td>LGA</td>\n",
       "      <td>CMH</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20744</th>\n",
       "      <td>2014-01-31</td>\n",
       "      <td>MQ</td>\n",
       "      <td>20398</td>\n",
       "      <td>N502MQ</td>\n",
       "      <td>3531</td>\n",
       "      <td>LGA</td>\n",
       "      <td>RDU</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2355 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         fl_date unique_carrier  airline_id tail_num  fl_num origin dest  \\\n",
       "29    2014-01-01             AA       19805   N3EAAA     359    LGA  ORD   \n",
       "31    2014-01-01             AA       19805   N542AA     371    LGA  ORD   \n",
       "195   2014-01-01             B6       20409   N913JB    1103    JFK  SJU   \n",
       "209   2014-01-01             B6       20409   N193JB     518    JFK  BOS   \n",
       "359   2014-01-01             EV       20366   N17159    4130    SYR  CLE   \n",
       "...          ...            ...         ...      ...     ...    ...  ...   \n",
       "20645 2014-01-31             DL       19790   N908DA    2129    ROC  ATL   \n",
       "20653 2014-01-31             DL       19790   N965DL    2165    JFK  TPA   \n",
       "20702 2014-01-31             MQ       20398   N672MQ    3075    SYR  ORD   \n",
       "20716 2014-01-31             MQ       20398   N507MQ    3281    LGA  CMH   \n",
       "20744 2014-01-31             MQ       20398   N502MQ    3531    LGA  RDU   \n",
       "\n",
       "       dep_time  dep_delay  arr_time  arr_delay  cancelled arr dep  \n",
       "29          nan        nan       nan        nan       1.00 NaT NaT  \n",
       "31          nan        nan       nan        nan       1.00 NaT NaT  \n",
       "195         nan        nan       nan        nan       1.00 NaT NaT  \n",
       "209         nan        nan       nan        nan       1.00 NaT NaT  \n",
       "359         nan        nan       nan        nan       1.00 NaT NaT  \n",
       "...         ...        ...       ...        ...        ...  ..  ..  \n",
       "20645       nan        nan       nan        nan       1.00 NaT NaT  \n",
       "20653       nan        nan       nan        nan       1.00 NaT NaT  \n",
       "20702       nan        nan       nan        nan       1.00 NaT NaT  \n",
       "20716       nan        nan       nan        nan       1.00 NaT NaT  \n",
       "20744       nan        nan       nan        nan       1.00 NaT NaT  \n",
       "\n",
       "[2355 rows x 14 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select rows where `dep` is null\n",
    "\n",
    "flights.loc[flights.dep.isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\" data-title=\"Boolean Indexing\">\n",
    "  <h1><i class=\"fa fa-tasks\" aria-hidden=\"true\"></i> Exercise: Boolean Indexing</h1>\n",
    "</div>\n",
    "\n",
    "<p>Select the rows of `flights` where the flight was cancelled (`cancelled == 1`)</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true,
    "tags": [
     "solution"
    ]
   },
   "outputs": [],
   "source": [
    "# Your code here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "tags": [
     "solution"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>fl_date</th>\n",
       "      <th>unique_carrier</th>\n",
       "      <th>airline_id</th>\n",
       "      <th>tail_num</th>\n",
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       "      <th>arr</th>\n",
       "      <th>dep</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>AA</td>\n",
       "      <td>19805</td>\n",
       "      <td>N3EAAA</td>\n",
       "      <td>359</td>\n",
       "      <td>LGA</td>\n",
       "      <td>ORD</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>AA</td>\n",
       "      <td>19805</td>\n",
       "      <td>N542AA</td>\n",
       "      <td>371</td>\n",
       "      <td>LGA</td>\n",
       "      <td>ORD</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>B6</td>\n",
       "      <td>20409</td>\n",
       "      <td>N913JB</td>\n",
       "      <td>1103</td>\n",
       "      <td>JFK</td>\n",
       "      <td>SJU</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>B6</td>\n",
       "      <td>20409</td>\n",
       "      <td>N193JB</td>\n",
       "      <td>518</td>\n",
       "      <td>JFK</td>\n",
       "      <td>BOS</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>EV</td>\n",
       "      <td>20366</td>\n",
       "      <td>N17159</td>\n",
       "      <td>4130</td>\n",
       "      <td>SYR</td>\n",
       "      <td>CLE</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20645</th>\n",
       "      <td>2014-01-31</td>\n",
       "      <td>DL</td>\n",
       "      <td>19790</td>\n",
       "      <td>N908DA</td>\n",
       "      <td>2129</td>\n",
       "      <td>ROC</td>\n",
       "      <td>ATL</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20653</th>\n",
       "      <td>2014-01-31</td>\n",
       "      <td>DL</td>\n",
       "      <td>19790</td>\n",
       "      <td>N965DL</td>\n",
       "      <td>2165</td>\n",
       "      <td>JFK</td>\n",
       "      <td>TPA</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20702</th>\n",
       "      <td>2014-01-31</td>\n",
       "      <td>MQ</td>\n",
       "      <td>20398</td>\n",
       "      <td>N672MQ</td>\n",
       "      <td>3075</td>\n",
       "      <td>SYR</td>\n",
       "      <td>ORD</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20716</th>\n",
       "      <td>2014-01-31</td>\n",
       "      <td>MQ</td>\n",
       "      <td>20398</td>\n",
       "      <td>N507MQ</td>\n",
       "      <td>3281</td>\n",
       "      <td>LGA</td>\n",
       "      <td>CMH</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20744</th>\n",
       "      <td>2014-01-31</td>\n",
       "      <td>MQ</td>\n",
       "      <td>20398</td>\n",
       "      <td>N502MQ</td>\n",
       "      <td>3531</td>\n",
       "      <td>LGA</td>\n",
       "      <td>RDU</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>1.00</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2395 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         fl_date unique_carrier  airline_id tail_num  fl_num origin dest  \\\n",
       "29    2014-01-01             AA       19805   N3EAAA     359    LGA  ORD   \n",
       "31    2014-01-01             AA       19805   N542AA     371    LGA  ORD   \n",
       "195   2014-01-01             B6       20409   N913JB    1103    JFK  SJU   \n",
       "209   2014-01-01             B6       20409   N193JB     518    JFK  BOS   \n",
       "359   2014-01-01             EV       20366   N17159    4130    SYR  CLE   \n",
       "...          ...            ...         ...      ...     ...    ...  ...   \n",
       "20645 2014-01-31             DL       19790   N908DA    2129    ROC  ATL   \n",
       "20653 2014-01-31             DL       19790   N965DL    2165    JFK  TPA   \n",
       "20702 2014-01-31             MQ       20398   N672MQ    3075    SYR  ORD   \n",
       "20716 2014-01-31             MQ       20398   N507MQ    3281    LGA  CMH   \n",
       "20744 2014-01-31             MQ       20398   N502MQ    3531    LGA  RDU   \n",
       "\n",
       "       dep_time  dep_delay  arr_time  arr_delay  cancelled arr dep  \n",
       "29          nan        nan       nan        nan       1.00 NaT NaT  \n",
       "31          nan        nan       nan        nan       1.00 NaT NaT  \n",
       "195         nan        nan       nan        nan       1.00 NaT NaT  \n",
       "209         nan        nan       nan        nan       1.00 NaT NaT  \n",
       "359         nan        nan       nan        nan       1.00 NaT NaT  \n",
       "...         ...        ...       ...        ...        ...  ..  ..  \n",
       "20645       nan        nan       nan        nan       1.00 NaT NaT  \n",
       "20653       nan        nan       nan        nan       1.00 NaT NaT  \n",
       "20702       nan        nan       nan        nan       1.00 NaT NaT  \n",
       "20716       nan        nan       nan        nan       1.00 NaT NaT  \n",
       "20744       nan        nan       nan        nan       1.00 NaT NaT  \n",
       "\n",
       "[2395 rows x 14 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%load solutions/indexing_cancelled.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\" data-title=\"Boolean Indexing (2)\">\n",
    "  <h1><i class=\"fa fa-tasks\" aria-hidden=\"true\"></i> Exercise: Boolean Indexing (2)</h1>\n",
    "</div>\n",
    "\n",
    "<p>Filter down to rows where the departure **`hour`** is before 6:00 or after 18:00.</p>\n",
    "\n",
    "- Hint: Use the `flights.dep.dt` namespace. See the attributes [here](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DatetimeIndex.html) or use tab-completion on `flights.dep.dt.<tab>` to find an attribute giving the hour component\n",
    "- Hint: NumPy and pandas use `|` for elementwise `or`, and `&` for elementwise `and` between two boolean arrays\n",
    "- Hint: Be careful with [Python's order of operations](https://docs.python.org/3/reference/expressions.html#operator-precedence) between comparison operators (`<`, `>`) and NumPy's logical operators `|` and `&`. If your first attempt raises a `TypeError`, try including some parenthesis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true,
    "tags": [
     "solution"
    ]
   },
   "outputs": [],
   "source": [
    "# Your code here...\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "solution"
    ]
   },
   "outputs": [],
   "source": [
    "%load solutions/indexing_01.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Position-Based Indexing with `.iloc`\n",
    "\n",
    "This takes the same basic forms as `.loc`, except you use integers to designate *positions* instead of labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>airline_id</th>\n",
       "      <th>tail_num</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique_carrier</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AA</th>\n",
       "      <td>19805</td>\n",
       "      <td>N338AA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B6</th>\n",
       "      <td>20409</td>\n",
       "      <td>N318JB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DL</th>\n",
       "      <td>19790</td>\n",
       "      <td>N930DL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                airline_id tail_num\n",
       "unique_carrier                     \n",
       "AA                   19805   N338AA\n",
       "B6                   20409   N318JB\n",
       "DL                   19790   N930DL"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first.iloc[[0, 1, 2], [1, 2]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can use scalars, arrays, boolean masks, or slices.\n",
    "You can also use negative indicies to slice from the end like regular python lists or numpy arrays.\n",
    "\n",
    "The one notable difference compared to label-based indexing is with `slice` objects.\n",
    "Recall that `.loc` included both ends of the slice.\n",
    "`.iloc` uses the usual python behavior of slicing up to, but not including, the end point:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>fl_num</th>\n",
       "      <th>origin</th>\n",
       "      <th>dest</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique_carrier</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AA</th>\n",
       "      <td>1</td>\n",
       "      <td>JFK</td>\n",
       "      <td>LAX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B6</th>\n",
       "      <td>115</td>\n",
       "      <td>SYR</td>\n",
       "      <td>JFK</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                fl_num origin dest\n",
       "unique_carrier                    \n",
       "AA                   1    JFK  LAX\n",
       "B6                 115    SYR  JFK"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select the first two rows, and the 3rd, 4th, and 5th columns\n",
    "first.iloc[:2, 3:6]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dropping rows or columns\n",
    "\n",
    "What if you want all items *except* for some?\n",
    "\n",
    "```\n",
    "DataFrame.drop(labels, axis=0, ...)\n",
    "\n",
    "Parameters\n",
    "----------\n",
    "labels : single label or list-like\n",
    "axis : int or axis name\n",
    "    - 0 / 'index', look in the index.\n",
    "    - 1 / 'columns', look in the columns\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\" data-title=\"Dropping Row Labels\">\n",
    "  <h1><i class=\"fa fa-tasks\" aria-hidden=\"true\"></i> Exercise: Dropping Row Labels</h1>\n",
    "</div>\n",
    "<p>Use `first.drop` to select all the rows *except* `EV` and `F9`.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true,
    "tags": [
     "solution"
    ]
   },
   "outputs": [],
   "source": [
    "# Your code here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "tags": [
     "solution"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fl_date</th>\n",
       "      <th>airline_id</th>\n",
       "      <th>tail_num</th>\n",
       "      <th>fl_num</th>\n",
       "      <th>origin</th>\n",
       "      <th>dest</th>\n",
       "      <th>dep_time</th>\n",
       "      <th>dep_delay</th>\n",
       "      <th>arr_time</th>\n",
       "      <th>arr_delay</th>\n",
       "      <th>cancelled</th>\n",
       "      <th>arr</th>\n",
       "      <th>dep</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique_carrier</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AA</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>19805</td>\n",
       "      <td>N338AA</td>\n",
       "      <td>1</td>\n",
       "      <td>JFK</td>\n",
       "      <td>LAX</td>\n",
       "      <td>914.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>1,238.00</td>\n",
       "      <td>13.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2014-01-01 12:38:00</td>\n",
       "      <td>2014-01-01 09:14:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B6</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>20409</td>\n",
       "      <td>N318JB</td>\n",
       "      <td>115</td>\n",
       "      <td>SYR</td>\n",
       "      <td>JFK</td>\n",
       "      <td>720.00</td>\n",
       "      <td>90.00</td>\n",
       "      <td>808.00</td>\n",
       "      <td>55.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2014-01-01 08:08:00</td>\n",
       "      <td>2014-01-01 07:20:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DL</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>19790</td>\n",
       "      <td>N930DL</td>\n",
       "      <td>61</td>\n",
       "      <td>LGA</td>\n",
       "      <td>ATL</td>\n",
       "      <td>1,810.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>2,054.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2014-01-01 20:54:00</td>\n",
       "      <td>2014-01-01 18:10:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FL</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>20437</td>\n",
       "      <td>N952AT</td>\n",
       "      <td>63</td>\n",
       "      <td>LGA</td>\n",
       "      <td>ATL</td>\n",
       "      <td>1,146.00</td>\n",
       "      <td>-5.00</td>\n",
       "      <td>1,409.00</td>\n",
       "      <td>-10.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2014-01-01 14:09:00</td>\n",
       "      <td>2014-01-01 11:46:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HA</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>19690</td>\n",
       "      <td>N382HA</td>\n",
       "      <td>51</td>\n",
       "      <td>JFK</td>\n",
       "      <td>HNL</td>\n",
       "      <td>906.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>1,503.00</td>\n",
       "      <td>-7.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2014-01-01 15:03:00</td>\n",
       "      <td>2014-01-01 09:06:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OO</th>\n",
       "      <td>2014-01-07</td>\n",
       "      <td>20304</td>\n",
       "      <td>N732SK</td>\n",
       "      <td>6258</td>\n",
       "      <td>SYR</td>\n",
       "      <td>ORD</td>\n",
       "      <td>1,721.00</td>\n",
       "      <td>49.00</td>\n",
       "      <td>1,834.00</td>\n",
       "      <td>47.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2014-01-08 18:34:00</td>\n",
       "      <td>2014-01-08 17:21:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UA</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>19977</td>\n",
       "      <td>N413UA</td>\n",
       "      <td>255</td>\n",
       "      <td>LGA</td>\n",
       "      <td>ORD</td>\n",
       "      <td>824.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1,006.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2014-01-01 10:06:00</td>\n",
       "      <td>2014-01-01 08:24:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>US</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>20355</td>\n",
       "      <td>N546UW</td>\n",
       "      <td>629</td>\n",
       "      <td>JFK</td>\n",
       "      <td>PHX</td>\n",
       "      <td>1,624.00</td>\n",
       "      <td>-6.00</td>\n",
       "      <td>1,932.00</td>\n",
       "      <td>-39.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2014-01-01 19:32:00</td>\n",
       "      <td>2014-01-01 16:24:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VX</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>21171</td>\n",
       "      <td>N361VA</td>\n",
       "      <td>11</td>\n",
       "      <td>JFK</td>\n",
       "      <td>SFO</td>\n",
       "      <td>730.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1,056.00</td>\n",
       "      <td>-14.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2014-01-01 10:56:00</td>\n",
       "      <td>2014-01-01 07:30:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WN</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>19393</td>\n",
       "      <td>N625SW</td>\n",
       "      <td>613</td>\n",
       "      <td>ALB</td>\n",
       "      <td>BWI</td>\n",
       "      <td>1,502.00</td>\n",
       "      <td>17.00</td>\n",
       "      <td>1,615.00</td>\n",
       "      <td>15.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2014-01-01 16:15:00</td>\n",
       "      <td>2014-01-01 15:02:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  fl_date  airline_id tail_num  fl_num origin dest  dep_time  \\\n",
       "unique_carrier                                                                 \n",
       "AA             2014-01-01       19805   N338AA       1    JFK  LAX    914.00   \n",
       "B6             2014-01-01       20409   N318JB     115    SYR  JFK    720.00   \n",
       "DL             2014-01-01       19790   N930DL      61    LGA  ATL  1,810.00   \n",
       "FL             2014-01-01       20437   N952AT      63    LGA  ATL  1,146.00   \n",
       "HA             2014-01-01       19690   N382HA      51    JFK  HNL    906.00   \n",
       "...                   ...         ...      ...     ...    ...  ...       ...   \n",
       "OO             2014-01-07       20304   N732SK    6258    SYR  ORD  1,721.00   \n",
       "UA             2014-01-01       19977   N413UA     255    LGA  ORD    824.00   \n",
       "US             2014-01-01       20355   N546UW     629    JFK  PHX  1,624.00   \n",
       "VX             2014-01-01       21171   N361VA      11    JFK  SFO    730.00   \n",
       "WN             2014-01-01       19393   N625SW     613    ALB  BWI  1,502.00   \n",
       "\n",
       "                dep_delay  arr_time  arr_delay  cancelled                 arr  \\\n",
       "unique_carrier                                                                  \n",
       "AA                  14.00  1,238.00      13.00       0.00 2014-01-01 12:38:00   \n",
       "B6                  90.00    808.00      55.00       0.00 2014-01-01 08:08:00   \n",
       "DL                  10.00  2,054.00      10.00       0.00 2014-01-01 20:54:00   \n",
       "FL                  -5.00  1,409.00     -10.00       0.00 2014-01-01 14:09:00   \n",
       "HA                   6.00  1,503.00      -7.00       0.00 2014-01-01 15:03:00   \n",
       "...                   ...       ...        ...        ...                 ...   \n",
       "OO                  49.00  1,834.00      47.00       1.00 2014-01-08 18:34:00   \n",
       "UA                   0.00  1,006.00       8.00       0.00 2014-01-01 10:06:00   \n",
       "US                  -6.00  1,932.00     -39.00       0.00 2014-01-01 19:32:00   \n",
       "VX                   0.00  1,056.00     -14.00       0.00 2014-01-01 10:56:00   \n",
       "WN                  17.00  1,615.00      15.00       0.00 2014-01-01 16:15:00   \n",
       "\n",
       "                               dep  \n",
       "unique_carrier                      \n",
       "AA             2014-01-01 09:14:00  \n",
       "B6             2014-01-01 07:20:00  \n",
       "DL             2014-01-01 18:10:00  \n",
       "FL             2014-01-01 11:46:00  \n",
       "HA             2014-01-01 09:06:00  \n",
       "...                            ...  \n",
       "OO             2014-01-08 17:21:00  \n",
       "UA             2014-01-01 08:24:00  \n",
       "US             2014-01-01 16:24:00  \n",
       "VX             2014-01-01 07:30:00  \n",
       "WN             2014-01-01 15:02:00  \n",
       "\n",
       "[11 rows x 13 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%load solutions/indexing_drop_index.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\" data-title=\"Drop a column\">\n",
    "  <h1><i class=\"fa fa-tasks\" aria-hidden=\"true\"></i> Exercise: Drop a column</h1>\n",
    "</div>\n",
    "<p>`flights.airline_id` is redundent with `unique_carrier`. Drop `airline_id`.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "tags": [
     "solution"
    ]
   },
   "outputs": [],
   "source": [
    "# your code here\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "tags": [
     "solution"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fl_date</th>\n",
       "      <th>unique_carrier</th>\n",
       "      <th>tail_num</th>\n",
       "      <th>fl_num</th>\n",
       "      <th>origin</th>\n",
       "      <th>dest</th>\n",
       "      <th>dep_time</th>\n",
       "      <th>dep_delay</th>\n",
       "      <th>arr_time</th>\n",
       "      <th>arr_delay</th>\n",
       "      <th>cancelled</th>\n",
       "      <th>arr</th>\n",
       "      <th>dep</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>AA</td>\n",
       "      <td>N338AA</td>\n",
       "      <td>1</td>\n",
       "      <td>JFK</td>\n",
       "      <td>LAX</td>\n",
       "      <td>914.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>1,238.00</td>\n",
       "      <td>13.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2014-01-01 12:38:00</td>\n",
       "      <td>2014-01-01 09:14:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>AA</td>\n",
       "      <td>N335AA</td>\n",
       "      <td>3</td>\n",
       "      <td>JFK</td>\n",
       "      <td>LAX</td>\n",
       "      <td>1,157.00</td>\n",
       "      <td>-3.00</td>\n",
       "      <td>1,523.00</td>\n",
       "      <td>13.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2014-01-01 15:23:00</td>\n",
       "      <td>2014-01-01 11:57:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>AA</td>\n",
       "      <td>N327AA</td>\n",
       "      <td>21</td>\n",
       "      <td>JFK</td>\n",
       "      <td>LAX</td>\n",
       "      <td>1,902.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2,224.00</td>\n",
       "      <td>9.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2014-01-01 22:24:00</td>\n",
       "      <td>2014-01-01 19:02:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>AA</td>\n",
       "      <td>N3EHAA</td>\n",
       "      <td>29</td>\n",
       "      <td>LGA</td>\n",
       "      <td>PBI</td>\n",
       "      <td>722.00</td>\n",
       "      <td>-8.00</td>\n",
       "      <td>1,014.00</td>\n",
       "      <td>-26.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2014-01-01 10:14:00</td>\n",
       "      <td>2014-01-01 07:22:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>AA</td>\n",
       "      <td>N319AA</td>\n",
       "      <td>117</td>\n",
       "      <td>JFK</td>\n",
       "      <td>LAX</td>\n",
       "      <td>1,347.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1,706.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2014-01-01 17:06:00</td>\n",
       "      <td>2014-01-01 13:47:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20812</th>\n",
       "      <td>2014-01-31</td>\n",
       "      <td>UA</td>\n",
       "      <td>N54711</td>\n",
       "      <td>1253</td>\n",
       "      <td>ROC</td>\n",
       "      <td>ORD</td>\n",
       "      <td>801.00</td>\n",
       "      <td>-4.00</td>\n",
       "      <td>908.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2014-01-31 09:08:00</td>\n",
       "      <td>2014-01-31 08:01:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20813</th>\n",
       "      <td>2014-01-31</td>\n",
       "      <td>UA</td>\n",
       "      <td>N77525</td>\n",
       "      <td>1429</td>\n",
       "      <td>LGA</td>\n",
       "      <td>CLE</td>\n",
       "      <td>1,522.00</td>\n",
       "      <td>-10.00</td>\n",
       "      <td>1,649.00</td>\n",
       "      <td>-31.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2014-01-31 16:49:00</td>\n",
       "      <td>2014-01-31 15:22:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20814</th>\n",
       "      <td>2014-01-31</td>\n",
       "      <td>UA</td>\n",
       "      <td>N37293</td>\n",
       "      <td>1456</td>\n",
       "      <td>LGA</td>\n",
       "      <td>IAH</td>\n",
       "      <td>719.00</td>\n",
       "      <td>-6.00</td>\n",
       "      <td>1,006.00</td>\n",
       "      <td>-20.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2014-01-31 10:06:00</td>\n",
       "      <td>2014-01-31 07:19:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20815</th>\n",
       "      <td>2014-01-31</td>\n",
       "      <td>UA</td>\n",
       "      <td>N24729</td>\n",
       "      <td>1457</td>\n",
       "      <td>LGA</td>\n",
       "      <td>IAH</td>\n",
       "      <td>852.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>1,156.00</td>\n",
       "      <td>-6.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2014-01-31 11:56:00</td>\n",
       "      <td>2014-01-31 08:52:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20816</th>\n",
       "      <td>2014-01-31</td>\n",
       "      <td>MQ</td>\n",
       "      <td>N609MQ</td>\n",
       "      <td>3699</td>\n",
       "      <td>BUF</td>\n",
       "      <td>ORD</td>\n",
       "      <td>1,208.00</td>\n",
       "      <td>-12.00</td>\n",
       "      <td>1,251.00</td>\n",
       "      <td>-19.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2014-01-31 12:51:00</td>\n",
       "      <td>2014-01-31 12:08:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20817 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         fl_date unique_carrier tail_num  fl_num origin dest  dep_time  \\\n",
       "0     2014-01-01             AA   N338AA       1    JFK  LAX    914.00   \n",
       "1     2014-01-01             AA   N335AA       3    JFK  LAX  1,157.00   \n",
       "2     2014-01-01             AA   N327AA      21    JFK  LAX  1,902.00   \n",
       "3     2014-01-01             AA   N3EHAA      29    LGA  PBI    722.00   \n",
       "4     2014-01-01             AA   N319AA     117    JFK  LAX  1,347.00   \n",
       "...          ...            ...      ...     ...    ...  ...       ...   \n",
       "20812 2014-01-31             UA   N54711    1253    ROC  ORD    801.00   \n",
       "20813 2014-01-31             UA   N77525    1429    LGA  CLE  1,522.00   \n",
       "20814 2014-01-31             UA   N37293    1456    LGA  IAH    719.00   \n",
       "20815 2014-01-31             UA   N24729    1457    LGA  IAH    852.00   \n",
       "20816 2014-01-31             MQ   N609MQ    3699    BUF  ORD  1,208.00   \n",
       "\n",
       "       dep_delay  arr_time  arr_delay  cancelled                 arr  \\\n",
       "0          14.00  1,238.00      13.00       0.00 2014-01-01 12:38:00   \n",
       "1          -3.00  1,523.00      13.00       0.00 2014-01-01 15:23:00   \n",
       "2           2.00  2,224.00       9.00       0.00 2014-01-01 22:24:00   \n",
       "3          -8.00  1,014.00     -26.00       0.00 2014-01-01 10:14:00   \n",
       "4           2.00  1,706.00       1.00       0.00 2014-01-01 17:06:00   \n",
       "...          ...       ...        ...        ...                 ...   \n",
       "20812      -4.00    908.00       4.00       0.00 2014-01-31 09:08:00   \n",
       "20813     -10.00  1,649.00     -31.00       0.00 2014-01-31 16:49:00   \n",
       "20814      -6.00  1,006.00     -20.00       0.00 2014-01-31 10:06:00   \n",
       "20815       7.00  1,156.00      -6.00       0.00 2014-01-31 11:56:00   \n",
       "20816     -12.00  1,251.00     -19.00       0.00 2014-01-31 12:51:00   \n",
       "\n",
       "                      dep  \n",
       "0     2014-01-01 09:14:00  \n",
       "1     2014-01-01 11:57:00  \n",
       "2     2014-01-01 19:02:00  \n",
       "3     2014-01-01 07:22:00  \n",
       "4     2014-01-01 13:47:00  \n",
       "...                   ...  \n",
       "20812 2014-01-31 08:01:00  \n",
       "20813 2014-01-31 15:22:00  \n",
       "20814 2014-01-31 07:19:00  \n",
       "20815 2014-01-31 08:52:00  \n",
       "20816 2014-01-31 12:08:00  \n",
       "\n",
       "[20817 rows x 13 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%load solutions/indexing_drop_columns.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is our first time seeing the `axis` keyword argument.\n",
    "It comes up a lot in pandas and NumPy. `axis='index'` (or 0) means\n",
    "operate on the index (e.g. drop labels from the index).\n",
    "`axis='columns'` (or 1) means operate on the columns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Special Case: `DateTimeIndex`\n",
    "\n",
    "> Easier slicing with strings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas has really good support for time series data, including a few conveniences to make indexing easier.\n",
    "First let's get a DataFrame with a `DatetimeIndex`, another specialied index type like we saw with `RangeIndex`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dep_delay</th>\n",
       "      <th>arr_delay</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arr</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-01 00:00:00</th>\n",
       "      <td>32.69</td>\n",
       "      <td>41.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 01:00:00</th>\n",
       "      <td>96.75</td>\n",
       "      <td>99.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 02:00:00</th>\n",
       "      <td>103.75</td>\n",
       "      <td>112.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 03:00:00</th>\n",
       "      <td>110.00</td>\n",
       "      <td>116.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01 04:00:00</th>\n",
       "      <td>13.50</td>\n",
       "      <td>10.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     dep_delay  arr_delay\n",
       "arr                                      \n",
       "2014-01-01 00:00:00      32.69      41.56\n",
       "2014-01-01 01:00:00      96.75      99.00\n",
       "2014-01-01 02:00:00     103.75     112.75\n",
       "2014-01-01 03:00:00     110.00     116.00\n",
       "2014-01-01 04:00:00      13.50      10.25"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We'll talk about resample later\n",
    "# This gets the average delays per hour\n",
    "delays = flights.resample(\"H\", on=\"arr\")[['dep_delay', 'arr_delay']].mean()\n",
    "delays.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`delays` has a `DatetimeIndex`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2014-01-01 00:00:00', '2014-01-01 01:00:00',\n",
       "               '2014-01-01 02:00:00', '2014-01-01 03:00:00',\n",
       "               '2014-01-01 04:00:00', '2014-01-01 05:00:00',\n",
       "               '2014-01-01 06:00:00', '2014-01-01 07:00:00',\n",
       "               '2014-01-01 08:00:00', '2014-01-01 09:00:00',\n",
       "               ...\n",
       "               '2014-01-31 15:00:00', '2014-01-31 16:00:00',\n",
       "               '2014-01-31 17:00:00', '2014-01-31 18:00:00',\n",
       "               '2014-01-31 19:00:00', '2014-01-31 20:00:00',\n",
       "               '2014-01-31 21:00:00', '2014-01-31 22:00:00',\n",
       "               '2014-01-31 23:00:00', '2014-02-01 00:00:00'],\n",
       "              dtype='datetime64[ns]', name='arr', length=745, freq=None)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delays.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VhiQBAAAAmAgCau46avEtGJIEAAAAYMIIqLmzroYkDVt8GZIEAAAAYBIIqLnzHW8zoxWG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\nvgAweJT4ArFCiS8ADFVNQQa1enJh5wEAQ5rzF2RQgVj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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1fb64d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "delays.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since `delays.index` is a `DatetimeIndex`, we can use [partial string indexing](http://pandas.pydata.org/pandas-docs/stable/timeseries.html#datetimeindex-partial-string-indexing) to easily select subsets of the data.\n",
    "The basic idea is to specify the datetime up to whatever resolution you care about.\n",
    "For example, to select all the flights on the 12th (a daily resolution):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>dep_delay</th>\n",
       "      <th>arr_delay</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arr</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-12 00:00:00</th>\n",
       "      <td>58.75</td>\n",
       "      <td>46.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-12 01:00:00</th>\n",
       "      <td>85.00</td>\n",
       "      <td>71.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-12 02:00:00</th>\n",
       "      <td>33.00</td>\n",
       "      <td>17.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-12 03:00:00</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-12 04:00:00</th>\n",
       "      <td>6.50</td>\n",
       "      <td>-1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-12 19:00:00</th>\n",
       "      <td>10.06</td>\n",
       "      <td>-6.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-12 20:00:00</th>\n",
       "      <td>16.23</td>\n",
       "      <td>-1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-12 21:00:00</th>\n",
       "      <td>8.95</td>\n",
       "      <td>-8.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-12 22:00:00</th>\n",
       "      <td>16.79</td>\n",
       "      <td>-2.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-12 23:00:00</th>\n",
       "      <td>16.15</td>\n",
       "      <td>-0.54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     dep_delay  arr_delay\n",
       "arr                                      \n",
       "2014-01-12 00:00:00      58.75      46.58\n",
       "2014-01-12 01:00:00      85.00      71.00\n",
       "2014-01-12 02:00:00      33.00      17.00\n",
       "2014-01-12 03:00:00        nan        nan\n",
       "2014-01-12 04:00:00       6.50      -1.00\n",
       "...                        ...        ...\n",
       "2014-01-12 19:00:00      10.06      -6.85\n",
       "2014-01-12 20:00:00      16.23      -1.09\n",
       "2014-01-12 21:00:00       8.95      -8.65\n",
       "2014-01-12 22:00:00      16.79      -2.45\n",
       "2014-01-12 23:00:00      16.15      -0.54\n",
       "\n",
       "[24 rows x 2 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delays.loc['2014-01-12']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Without parital string indexing, you'd need to do something like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>dep_delay</th>\n",
       "      <th>arr_delay</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arr</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-12 00:00:00</th>\n",
       "      <td>58.75</td>\n",
       "      <td>46.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-12 01:00:00</th>\n",
       "      <td>85.00</td>\n",
       "      <td>71.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-12 02:00:00</th>\n",
       "      <td>33.00</td>\n",
       "      <td>17.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-12 03:00:00</th>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-12 04:00:00</th>\n",
       "      <td>6.50</td>\n",
       "      <td>-1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-12 19:00:00</th>\n",
       "      <td>10.06</td>\n",
       "      <td>-6.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-12 20:00:00</th>\n",
       "      <td>16.23</td>\n",
       "      <td>-1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-12 21:00:00</th>\n",
       "      <td>8.95</td>\n",
       "      <td>-8.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-12 22:00:00</th>\n",
       "      <td>16.79</td>\n",
       "      <td>-2.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-12 23:00:00</th>\n",
       "      <td>16.15</td>\n",
       "      <td>-0.54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     dep_delay  arr_delay\n",
       "arr                                      \n",
       "2014-01-12 00:00:00      58.75      46.58\n",
       "2014-01-12 01:00:00      85.00      71.00\n",
       "2014-01-12 02:00:00      33.00      17.00\n",
       "2014-01-12 03:00:00        nan        nan\n",
       "2014-01-12 04:00:00       6.50      -1.00\n",
       "...                        ...        ...\n",
       "2014-01-12 19:00:00      10.06      -6.85\n",
       "2014-01-12 20:00:00      16.23      -1.09\n",
       "2014-01-12 21:00:00       8.95      -8.65\n",
       "2014-01-12 22:00:00      16.79      -2.45\n",
       "2014-01-12 23:00:00      16.15      -0.54\n",
       "\n",
       "[24 rows x 2 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delays[(delays.index.year == 2014) & (delays.index.month == 1) & (delays.index.day == 12)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which isn't very fun."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\" data-title=\"Datetime Indexing\">\n",
    "  <h1><i class=\"fa fa-tasks\" aria-hidden=\"true\"></i> Exercise: Datetime Indexing</h1>\n",
    "</div>\n",
    "<p>\n",
    "Slice `delays` to select all rows from 12:00 on January 3rd, to 12:00 on the 10th.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true,
    "tags": [
     "solution"
    ]
   },
   "outputs": [],
   "source": [
    "# Your code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "tags": [
     "solution"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dep_delay</th>\n",
       "      <th>arr_delay</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arr</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-03 12:00:00</th>\n",
       "      <td>131.00</td>\n",
       "      <td>138.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03 13:00:00</th>\n",
       "      <td>87.77</td>\n",
       "      <td>96.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03 14:00:00</th>\n",
       "      <td>67.12</td>\n",
       "      <td>68.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03 15:00:00</th>\n",
       "      <td>119.12</td>\n",
       "      <td>120.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03 16:00:00</th>\n",
       "      <td>93.74</td>\n",
       "      <td>93.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-10 08:00:00</th>\n",
       "      <td>0.07</td>\n",
       "      <td>6.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-10 09:00:00</th>\n",
       "      <td>3.00</td>\n",
       "      <td>7.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-10 10:00:00</th>\n",
       "      <td>8.87</td>\n",
       "      <td>10.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-10 11:00:00</th>\n",
       "      <td>10.76</td>\n",
       "      <td>23.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-10 12:00:00</th>\n",
       "      <td>11.12</td>\n",
       "      <td>23.43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>169 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     dep_delay  arr_delay\n",
       "arr                                      \n",
       "2014-01-03 12:00:00     131.00     138.17\n",
       "2014-01-03 13:00:00      87.77      96.15\n",
       "2014-01-03 14:00:00      67.12      68.19\n",
       "2014-01-03 15:00:00     119.12     120.92\n",
       "2014-01-03 16:00:00      93.74      93.11\n",
       "...                        ...        ...\n",
       "2014-01-10 08:00:00       0.07       6.22\n",
       "2014-01-10 09:00:00       3.00       7.74\n",
       "2014-01-10 10:00:00       8.87      10.53\n",
       "2014-01-10 11:00:00      10.76      23.57\n",
       "2014-01-10 12:00:00      11.12      23.43\n",
       "\n",
       "[169 rows x 2 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%load solutions/indexing_datetime.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\" data-title=\"Thought Exercise\">\n",
    "  <h1><i class=\"fa fa-lightbulb-o\" aria-hidden=\"true\"></i> Thought Exercise</h1>\n",
    "</div>\n",
    "<p>\n",
    "Why does pandas use a property like `.loc[..., ...]`, rather than a method like `.loc(..., ...)`?</p>\n",
    "\n",
    "- Hint: Where in the language can you use `foo.loc[...]`, but not `foo.loc(...)`?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "tags": [
     "solution"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fl_date</th>\n",
       "      <th>airline_id</th>\n",
       "      <th>tail_num</th>\n",
       "      <th>fl_num</th>\n",
       "      <th>origin</th>\n",
       "      <th>dest</th>\n",
       "      <th>dep_time</th>\n",
       "      <th>dep_delay</th>\n",
       "      <th>arr_time</th>\n",
       "      <th>arr_delay</th>\n",
       "      <th>cancelled</th>\n",
       "      <th>arr</th>\n",
       "      <th>dep</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique_carrier</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AA</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>19805</td>\n",
       "      <td>N338AA</td>\n",
       "      <td>-1</td>\n",
       "      <td>JFK</td>\n",
       "      <td>LAX</td>\n",
       "      <td>914.00</td>\n",
       "      <td>14.00</td>\n",
       "      <td>1,238.00</td>\n",
       "      <td>13.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2014-01-01 12:38:00</td>\n",
       "      <td>2014-01-01 09:14:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B6</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>20409</td>\n",
       "      <td>N318JB</td>\n",
       "      <td>115</td>\n",
       "      <td>SYR</td>\n",
       "      <td>JFK</td>\n",
       "      <td>720.00</td>\n",
       "      <td>90.00</td>\n",
       "      <td>808.00</td>\n",
       "      <td>55.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2014-01-01 08:08:00</td>\n",
       "      <td>2014-01-01 07:20:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DL</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>19790</td>\n",
       "      <td>N930DL</td>\n",
       "      <td>61</td>\n",
       "      <td>LGA</td>\n",
       "      <td>ATL</td>\n",
       "      <td>1,810.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>2,054.00</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2014-01-01 20:54:00</td>\n",
       "      <td>2014-01-01 18:10:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EV</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>20366</td>\n",
       "      <td>N14977</td>\n",
       "      <td>3259</td>\n",
       "      <td>HPN</td>\n",
       "      <td>ORD</td>\n",
       "      <td>731.00</td>\n",
       "      <td>-5.00</td>\n",
       "      <td>940.00</td>\n",
       "      <td>31.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2014-01-01 09:40:00</td>\n",
       "      <td>2014-01-01 07:31:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F9</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>20436</td>\n",
       "      <td>N209FR</td>\n",
       "      <td>507</td>\n",
       "      <td>LGA</td>\n",
       "      <td>DEN</td>\n",
       "      <td>1,804.00</td>\n",
       "      <td>35.00</td>\n",
       "      <td>2,047.00</td>\n",
       "      <td>47.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2014-01-01 20:47:00</td>\n",
       "      <td>2014-01-01 18:04:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  fl_date  airline_id tail_num  fl_num origin dest  dep_time  \\\n",
       "unique_carrier                                                                 \n",
       "AA             2014-01-01       19805   N338AA      -1    JFK  LAX    914.00   \n",
       "B6             2014-01-01       20409   N318JB     115    SYR  JFK    720.00   \n",
       "DL             2014-01-01       19790   N930DL      61    LGA  ATL  1,810.00   \n",
       "EV             2014-01-01       20366   N14977    3259    HPN  ORD    731.00   \n",
       "F9             2014-01-01       20436   N209FR     507    LGA  DEN  1,804.00   \n",
       "\n",
       "                dep_delay  arr_time  arr_delay  cancelled                 arr  \\\n",
       "unique_carrier                                                                  \n",
       "AA                  14.00  1,238.00      13.00       0.00 2014-01-01 12:38:00   \n",
       "B6                  90.00    808.00      55.00       0.00 2014-01-01 08:08:00   \n",
       "DL                  10.00  2,054.00      10.00       0.00 2014-01-01 20:54:00   \n",
       "EV                  -5.00    940.00      31.00       0.00 2014-01-01 09:40:00   \n",
       "F9                  35.00  2,047.00      47.00       0.00 2014-01-01 20:47:00   \n",
       "\n",
       "                               dep  \n",
       "unique_carrier                      \n",
       "AA             2014-01-01 09:14:00  \n",
       "B6             2014-01-01 07:20:00  \n",
       "DL             2014-01-01 18:10:00  \n",
       "EV             2014-01-01 07:31:00  \n",
       "F9             2014-01-01 18:04:00  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%load solutions/indexing_thoughts.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "- Introducted to `DataFrame` (2-D tabel) and `Series` (1-D array)\n",
    "- Both have *row labels*, `DataFrame` also has `column labels`\n",
    "- Saw `.loc` for labeled indexing and `.iloc` for positional indexing\n",
    "- `.loc`, `.iloc`, and `__getitem__` all accept boolean masks too"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Additional Exercises\n",
    "\n",
    "Some additional exercises focused on indexing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"750\"\n",
       "            height=\"900\"\n",
       "            src=\"http://vincentarelbundock.github.io/Rdatasets/doc/ISLR/Auto.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x11dd60fd0>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import IFrame\n",
    "\n",
    "IFrame(\"http://vincentarelbundock.github.io/Rdatasets/doc/ISLR/Auto.html\", 750, 900)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "url = \"http://vincentarelbundock.github.io/Rdatasets/csv/ISLR/Auto.csv\"\n",
    "cars = pd.read_csv(url, index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>year</th>\n",
       "      <th>origin</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18.00</td>\n",
       "      <td>8</td>\n",
       "      <td>307.00</td>\n",
       "      <td>130</td>\n",
       "      <td>3504</td>\n",
       "      <td>12.00</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>chevrolet chevelle malibu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15.00</td>\n",
       "      <td>8</td>\n",
       "      <td>350.00</td>\n",
       "      <td>165</td>\n",
       "      <td>3693</td>\n",
       "      <td>11.50</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>buick skylark 320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18.00</td>\n",
       "      <td>8</td>\n",
       "      <td>318.00</td>\n",
       "      <td>150</td>\n",
       "      <td>3436</td>\n",
       "      <td>11.00</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>plymouth satellite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16.00</td>\n",
       "      <td>8</td>\n",
       "      <td>304.00</td>\n",
       "      <td>150</td>\n",
       "      <td>3433</td>\n",
       "      <td>12.00</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>amc rebel sst</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>17.00</td>\n",
       "      <td>8</td>\n",
       "      <td>302.00</td>\n",
       "      <td>140</td>\n",
       "      <td>3449</td>\n",
       "      <td>10.50</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>ford torino</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mpg  cylinders  displacement  horsepower  weight  acceleration  year  \\\n",
       "1 18.00          8        307.00         130    3504         12.00    70   \n",
       "2 15.00          8        350.00         165    3693         11.50    70   \n",
       "3 18.00          8        318.00         150    3436         11.00    70   \n",
       "4 16.00          8        304.00         150    3433         12.00    70   \n",
       "5 17.00          8        302.00         140    3449         10.50    70   \n",
       "\n",
       "   origin                       name  \n",
       "1       1  chevrolet chevelle malibu  \n",
       "2       1          buick skylark 320  \n",
       "3       1         plymouth satellite  \n",
       "4       1              amc rebel sst  \n",
       "5       1                ford torino  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
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       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>origin</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>17.69</td>\n",
       "      <td>6.76</td>\n",
       "      <td>281.41</td>\n",
       "      <td>147.83</td>\n",
       "      <td>3,372.79</td>\n",
       "      <td>12.95</td>\n",
       "      <td>1.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>21.11</td>\n",
       "      <td>5.63</td>\n",
       "      <td>213.89</td>\n",
       "      <td>107.04</td>\n",
       "      <td>3,030.59</td>\n",
       "      <td>15.00</td>\n",
       "      <td>1.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>18.71</td>\n",
       "      <td>5.82</td>\n",
       "      <td>218.38</td>\n",
       "      <td>120.18</td>\n",
       "      <td>3,237.71</td>\n",
       "      <td>15.12</td>\n",
       "      <td>1.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>17.10</td>\n",
       "      <td>6.38</td>\n",
       "      <td>256.88</td>\n",
       "      <td>130.47</td>\n",
       "      <td>3,419.03</td>\n",
       "      <td>14.31</td>\n",
       "      <td>1.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>22.77</td>\n",
       "      <td>5.23</td>\n",
       "      <td>170.65</td>\n",
       "      <td>94.23</td>\n",
       "      <td>2,878.04</td>\n",
       "      <td>16.17</td>\n",
       "      <td>1.69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       mpg  cylinders  displacement  horsepower   weight  acceleration  origin\n",
       "year                                                                          \n",
       "70   17.69       6.76        281.41      147.83 3,372.79         12.95    1.31\n",
       "71   21.11       5.63        213.89      107.04 3,030.59         15.00    1.44\n",
       "72   18.71       5.82        218.38      120.18 3,237.71         15.12    1.54\n",
       "73   17.10       6.38        256.88      130.47 3,419.03         14.31    1.38\n",
       "74   22.77       5.23        170.65       94.23 2,878.04         16.17    1.69"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yearly = cars.groupby(\"year\").mean()\n",
    "yearly.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " <div class=\"alert alert-success\" data-title=\"\">\n",
    "  <h1><i class=\"fa fa-tasks\" aria-hidden=\"true\"></i> Exercise</h1>\n",
    "</div>\n",
    "<p>\n",
    "Select the engine-related columns (cylinders, displacement, horsepower) from `cars`.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>307.00</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>350.00</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>318.00</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>304.00</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8</td>\n",
       "      <td>302.00</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>4</td>\n",
       "      <td>140.00</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>4</td>\n",
       "      <td>97.00</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>4</td>\n",
       "      <td>135.00</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>4</td>\n",
       "      <td>120.00</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>4</td>\n",
       "      <td>119.00</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>392 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     cylinders  displacement  horsepower\n",
       "1            8        307.00         130\n",
       "2            8        350.00         165\n",
       "3            8        318.00         150\n",
       "4            8        304.00         150\n",
       "5            8        302.00         140\n",
       "..         ...           ...         ...\n",
       "393          4        140.00          86\n",
       "394          4         97.00          52\n",
       "395          4        135.00          84\n",
       "396          4        120.00          79\n",
       "397          4        119.00          82\n",
       "\n",
       "[392 rows x 3 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%load solutions/indexing_ex1_engine_columns.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " <div class=\"alert alert-success\" data-title=\"\">\n",
    "  <h1><i class=\"fa fa-tasks\" aria-hidden=\"true\"></i> Exercise</h1>\n",
    "</div>\n",
    "<p>\n",
    "Select every 5th row of `cars`.\n",
    "</p>\n",
    "\n",
    "Hint: See [here](http://stackoverflow.com/a/509295) for all the options on slicing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>year</th>\n",
       "      <th>origin</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18.00</td>\n",
       "      <td>8</td>\n",
       "      <td>307.00</td>\n",
       "      <td>130</td>\n",
       "      <td>3504</td>\n",
       "      <td>12.00</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>chevrolet chevelle malibu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15.00</td>\n",
       "      <td>8</td>\n",
       "      <td>429.00</td>\n",
       "      <td>198</td>\n",
       "      <td>4341</td>\n",
       "      <td>10.00</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>ford galaxie 500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>15.00</td>\n",
       "      <td>8</td>\n",
       "      <td>383.00</td>\n",
       "      <td>170</td>\n",
       "      <td>3563</td>\n",
       "      <td>10.00</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>dodge challenger se</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>22.00</td>\n",
       "      <td>6</td>\n",
       "      <td>198.00</td>\n",
       "      <td>95</td>\n",
       "      <td>2833</td>\n",
       "      <td>15.50</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>plymouth duster</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>25.00</td>\n",
       "      <td>4</td>\n",
       "      <td>110.00</td>\n",
       "      <td>87</td>\n",
       "      <td>2672</td>\n",
       "      <td>17.50</td>\n",
       "      <td>70</td>\n",
       "      <td>2</td>\n",
       "      <td>peugeot 504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376</th>\n",
       "      <td>37.00</td>\n",
       "      <td>4</td>\n",
       "      <td>91.00</td>\n",
       "      <td>68</td>\n",
       "      <td>2025</td>\n",
       "      <td>18.20</td>\n",
       "      <td>82</td>\n",
       "      <td>3</td>\n",
       "      <td>mazda glc custom l</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>36.00</td>\n",
       "      <td>4</td>\n",
       "      <td>107.00</td>\n",
       "      <td>75</td>\n",
       "      <td>2205</td>\n",
       "      <td>14.50</td>\n",
       "      <td>82</td>\n",
       "      <td>3</td>\n",
       "      <td>honda accord</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>25.00</td>\n",
       "      <td>6</td>\n",
       "      <td>181.00</td>\n",
       "      <td>110</td>\n",
       "      <td>2945</td>\n",
       "      <td>16.40</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>buick century limited</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>36.00</td>\n",
       "      <td>4</td>\n",
       "      <td>135.00</td>\n",
       "      <td>84</td>\n",
       "      <td>2370</td>\n",
       "      <td>13.00</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>dodge charger 2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>28.00</td>\n",
       "      <td>4</td>\n",
       "      <td>120.00</td>\n",
       "      <td>79</td>\n",
       "      <td>2625</td>\n",
       "      <td>18.60</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>ford ranger</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>79 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      mpg  cylinders  displacement  horsepower  weight  acceleration  year  \\\n",
       "1   18.00          8        307.00         130    3504         12.00    70   \n",
       "6   15.00          8        429.00         198    4341         10.00    70   \n",
       "11  15.00          8        383.00         170    3563         10.00    70   \n",
       "16  22.00          6        198.00          95    2833         15.50    70   \n",
       "21  25.00          4        110.00          87    2672         17.50    70   \n",
       "..    ...        ...           ...         ...     ...           ...   ...   \n",
       "376 37.00          4         91.00          68    2025         18.20    82   \n",
       "381 36.00          4        107.00          75    2205         14.50    82   \n",
       "386 25.00          6        181.00         110    2945         16.40    82   \n",
       "391 36.00          4        135.00          84    2370         13.00    82   \n",
       "396 28.00          4        120.00          79    2625         18.60    82   \n",
       "\n",
       "     origin                       name  \n",
       "1         1  chevrolet chevelle malibu  \n",
       "6         1           ford galaxie 500  \n",
       "11        1        dodge challenger se  \n",
       "16        1            plymouth duster  \n",
       "21        2                peugeot 504  \n",
       "..      ...                        ...  \n",
       "376       3         mazda glc custom l  \n",
       "381       3               honda accord  \n",
       "386       1      buick century limited  \n",
       "391       1          dodge charger 2.2  \n",
       "396       1                ford ranger  \n",
       "\n",
       "[79 rows x 9 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%load solutions/indexing_ex2_5th.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " <div class=\"alert alert-success\" data-title=\"\">\n",
    "  <h1><i class=\"fa fa-tasks\" aria-hidden=\"true\"></i> Exercise</h1>\n",
    "</div>\n",
    "\n",
    "<p>Select years `70`, `75`, `80`, and `82` and columns `horsepower` and `weight` from `yearly`.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>147.83</td>\n",
       "      <td>3,372.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>101.07</td>\n",
       "      <td>3,176.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>77.48</td>\n",
       "      <td>2,441.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>81.47</td>\n",
       "      <td>2,434.17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      horsepower   weight\n",
       "year                     \n",
       "70        147.83 3,372.79\n",
       "75        101.07 3,176.80\n",
       "80         77.48 2,441.59\n",
       "82         81.47 2,434.17"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%load solutions/indexing_ex3_years.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " <div class=\"alert alert-success\" data-title=\"\">\n",
    "  <h1><i class=\"fa fa-tasks\" aria-hidden=\"true\"></i> Exercise</h1>\n",
    "</div>\n",
    "\n",
    "<p>Select rows in `cars` with at least 30 MPG.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>year</th>\n",
       "      <th>origin</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>31.00</td>\n",
       "      <td>4</td>\n",
       "      <td>71.00</td>\n",
       "      <td>65</td>\n",
       "      <td>1773</td>\n",
       "      <td>19.00</td>\n",
       "      <td>71</td>\n",
       "      <td>3</td>\n",
       "      <td>toyota corolla 1200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>35.00</td>\n",
       "      <td>4</td>\n",
       "      <td>72.00</td>\n",
       "      <td>69</td>\n",
       "      <td>1613</td>\n",
       "      <td>18.00</td>\n",
       "      <td>71</td>\n",
       "      <td>3</td>\n",
       "      <td>datsun 1200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>31.00</td>\n",
       "      <td>4</td>\n",
       "      <td>79.00</td>\n",
       "      <td>67</td>\n",
       "      <td>1950</td>\n",
       "      <td>19.00</td>\n",
       "      <td>74</td>\n",
       "      <td>3</td>\n",
       "      <td>datsun b210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>32.00</td>\n",
       "      <td>4</td>\n",
       "      <td>71.00</td>\n",
       "      <td>65</td>\n",
       "      <td>1836</td>\n",
       "      <td>21.00</td>\n",
       "      <td>74</td>\n",
       "      <td>3</td>\n",
       "      <td>toyota corolla 1200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>31.00</td>\n",
       "      <td>4</td>\n",
       "      <td>76.00</td>\n",
       "      <td>52</td>\n",
       "      <td>1649</td>\n",
       "      <td>16.50</td>\n",
       "      <td>74</td>\n",
       "      <td>3</td>\n",
       "      <td>toyota corona</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>32.00</td>\n",
       "      <td>4</td>\n",
       "      <td>144.00</td>\n",
       "      <td>96</td>\n",
       "      <td>2665</td>\n",
       "      <td>13.90</td>\n",
       "      <td>82</td>\n",
       "      <td>3</td>\n",
       "      <td>toyota celica gt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>36.00</td>\n",
       "      <td>4</td>\n",
       "      <td>135.00</td>\n",
       "      <td>84</td>\n",
       "      <td>2370</td>\n",
       "      <td>13.00</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>dodge charger 2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>44.00</td>\n",
       "      <td>4</td>\n",
       "      <td>97.00</td>\n",
       "      <td>52</td>\n",
       "      <td>2130</td>\n",
       "      <td>24.60</td>\n",
       "      <td>82</td>\n",
       "      <td>2</td>\n",
       "      <td>vw pickup</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>32.00</td>\n",
       "      <td>4</td>\n",
       "      <td>135.00</td>\n",
       "      <td>84</td>\n",
       "      <td>2295</td>\n",
       "      <td>11.60</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>dodge rampage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>31.00</td>\n",
       "      <td>4</td>\n",
       "      <td>119.00</td>\n",
       "      <td>82</td>\n",
       "      <td>2720</td>\n",
       "      <td>19.40</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>chevy s-10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>83 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      mpg  cylinders  displacement  horsepower  weight  acceleration  year  \\\n",
       "54  31.00          4         71.00          65    1773         19.00    71   \n",
       "55  35.00          4         72.00          69    1613         18.00    71   \n",
       "130 31.00          4         79.00          67    1950         19.00    74   \n",
       "132 32.00          4         71.00          65    1836         21.00    74   \n",
       "145 31.00          4         76.00          52    1649         16.50    74   \n",
       "..    ...        ...           ...         ...     ...           ...   ...   \n",
       "390 32.00          4        144.00          96    2665         13.90    82   \n",
       "391 36.00          4        135.00          84    2370         13.00    82   \n",
       "394 44.00          4         97.00          52    2130         24.60    82   \n",
       "395 32.00          4        135.00          84    2295         11.60    82   \n",
       "397 31.00          4        119.00          82    2720         19.40    82   \n",
       "\n",
       "     origin                 name  \n",
       "54        3  toyota corolla 1200  \n",
       "55        3          datsun 1200  \n",
       "130       3          datsun b210  \n",
       "132       3  toyota corolla 1200  \n",
       "145       3        toyota corona  \n",
       "..      ...                  ...  \n",
       "390       3     toyota celica gt  \n",
       "391       1    dodge charger 2.2  \n",
       "394       2            vw pickup  \n",
       "395       1        dodge rampage  \n",
       "397       1           chevy s-10  \n",
       "\n",
       "[83 rows x 9 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%load solutions/indexing_ex4_mpg.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " <div class=\"alert alert-success\" data-title=\"\">\n",
    "  <h1><i class=\"fa fa-tasks\" aria-hidden=\"true\"></i> Exercise</h1>\n",
    "</div>\n",
    "\n",
    "<p>How many cars have at least 30 MPG and at least 5 cylinders?</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# %load solutions/indexing_ex5_mpg_and_cylinders.py\n",
    "len(cars[(cars.mpg >= 30) & (cars.cylinders >= 5)])"
   ]
  }
 ],
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