{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "91e2b561",
   "metadata": {
    "id": "91e2b561"
   },
   "source": [
    "# Lecture 13 - Exploratory Data Analysis Activity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e1c48732",
   "metadata": {
    "executionInfo": {
     "elapsed": 342,
     "status": "ok",
     "timestamp": 1675706808059,
     "user": {
      "displayName": "Scott Wehrwein",
      "userId": "11327482518794216604"
     },
     "user_tz": 480
    },
    "id": "e1c48732"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0b44608",
   "metadata": {
    "id": "f0b44608"
   },
   "source": [
    "## The Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "416deba7-8fef-4469-be5d-f2f94cc0ee96",
   "metadata": {},
   "source": [
    "### Option 1: Yellow Cab Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4b537b8b-2925-418d-bf37-ed970912ff1b",
   "metadata": {
    "id": "48761b7e",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "yellow_url = \"/cluster/academic/DATA311/202620/yellow_tripdata_2018-06_small.csv\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d52d455f-b215-4b95-b37e-07e994f37527",
   "metadata": {
    "id": "9455cb31"
   },
   "source": [
    "The data came from here: [http://www.nyc.gov/html/tlc/html/about/trip_record_data.shtml](http://www.nyc.gov/html/tlc/html/about/trip_record_data.shtml).\n",
    "\n",
    "It was preprocessed by a friend using [this notebook](https://fw.cs.wwu.edu/~wehrwes/courses/data311_25f/lectures/L14/taxi_data_cleaning.ipynb). I think he told me he pulled out a subset of columns and subsampled the rows, but I don't know any more than that."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d1575f3-f5cc-42b1-91cf-609b8a7d80eb",
   "metadata": {},
   "source": [
    "### Option 2: Flight Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a84b9f7c-86ae-4506-99ff-b961c6884b76",
   "metadata": {},
   "outputs": [],
   "source": [
    "flights_url = \"/cluster/academic/DATA311/202620/nycflights.csv\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aeeec861-6617-464a-8a0a-70f0c6a44ee7",
   "metadata": {},
   "source": [
    "The data came from <https://github.com/tidyverse/nycflights13/tree/main>.\n",
    "\n",
    "If you want some more data to go with it, you can check out <https://github.com/tidyverse/nycflights13/tree/main/data-raw>, which has tables for weather, planes, airports, and airlines."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c77daf88-8878-4bd7-b943-c54db41c9a7f",
   "metadata": {},
   "source": [
    "### The Plan\n",
    "\n",
    "1. For 20 minutes: explore one of the above two datasets. Find something interesting!\n",
    "\n",
    "2. For 5 minutes: brainstorm answers to the following questions in light of your experience.\n",
    "\n",
    "   - What might you want to find out about a dataset *before* you even look at it?\n",
    "\n",
    "   - What are some useful first steps you can take after loading the data to begin exploring it?\n",
    "\n",
    "3. For 10 minutes: collect ideas as a class, see a couple nifty Seaborn moves"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f4388a8-7bfc-4c4b-8e09-ed34ddf2ec43",
   "metadata": {},
   "source": [
    "### Exploratory Analysis - 20 minutes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b075812b-83e8-4a6c-b8dc-b30185b168d2",
   "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>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>dep_time</th>\n",
       "      <th>dep_delay</th>\n",
       "      <th>arr_time</th>\n",
       "      <th>arr_delay</th>\n",
       "      <th>carrier</th>\n",
       "      <th>tailnum</th>\n",
       "      <th>flight</th>\n",
       "      <th>origin</th>\n",
       "      <th>dest</th>\n",
       "      <th>air_time</th>\n",
       "      <th>distance</th>\n",
       "      <th>hour</th>\n",
       "      <th>minute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013</td>\n",
       "      <td>6</td>\n",
       "      <td>30</td>\n",
       "      <td>940</td>\n",
       "      <td>15</td>\n",
       "      <td>1216</td>\n",
       "      <td>-4</td>\n",
       "      <td>VX</td>\n",
       "      <td>N626VA</td>\n",
       "      <td>407</td>\n",
       "      <td>JFK</td>\n",
       "      <td>LAX</td>\n",
       "      <td>313</td>\n",
       "      <td>2475</td>\n",
       "      <td>9</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>1657</td>\n",
       "      <td>-3</td>\n",
       "      <td>2104</td>\n",
       "      <td>10</td>\n",
       "      <td>DL</td>\n",
       "      <td>N3760C</td>\n",
       "      <td>329</td>\n",
       "      <td>JFK</td>\n",
       "      <td>SJU</td>\n",
       "      <td>216</td>\n",
       "      <td>1598</td>\n",
       "      <td>16</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>859</td>\n",
       "      <td>-1</td>\n",
       "      <td>1238</td>\n",
       "      <td>11</td>\n",
       "      <td>DL</td>\n",
       "      <td>N712TW</td>\n",
       "      <td>422</td>\n",
       "      <td>JFK</td>\n",
       "      <td>LAX</td>\n",
       "      <td>376</td>\n",
       "      <td>2475</td>\n",
       "      <td>8</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>1841</td>\n",
       "      <td>-4</td>\n",
       "      <td>2122</td>\n",
       "      <td>-34</td>\n",
       "      <td>DL</td>\n",
       "      <td>N914DL</td>\n",
       "      <td>2391</td>\n",
       "      <td>JFK</td>\n",
       "      <td>TPA</td>\n",
       "      <td>135</td>\n",
       "      <td>1005</td>\n",
       "      <td>18</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013</td>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>1102</td>\n",
       "      <td>-3</td>\n",
       "      <td>1230</td>\n",
       "      <td>-8</td>\n",
       "      <td>9E</td>\n",
       "      <td>N823AY</td>\n",
       "      <td>3652</td>\n",
       "      <td>LGA</td>\n",
       "      <td>ORF</td>\n",
       "      <td>50</td>\n",
       "      <td>296</td>\n",
       "      <td>11</td>\n",
       "      <td>2</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32730</th>\n",
       "      <td>2013</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>752</td>\n",
       "      <td>-8</td>\n",
       "      <td>921</td>\n",
       "      <td>-28</td>\n",
       "      <td>9E</td>\n",
       "      <td>N8505Q</td>\n",
       "      <td>3611</td>\n",
       "      <td>JFK</td>\n",
       "      <td>PIT</td>\n",
       "      <td>63</td>\n",
       "      <td>340</td>\n",
       "      <td>7</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32731</th>\n",
       "      <td>2013</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>812</td>\n",
       "      <td>-3</td>\n",
       "      <td>1043</td>\n",
       "      <td>8</td>\n",
       "      <td>DL</td>\n",
       "      <td>N6713Y</td>\n",
       "      <td>1429</td>\n",
       "      <td>JFK</td>\n",
       "      <td>LAS</td>\n",
       "      <td>286</td>\n",
       "      <td>2248</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32732</th>\n",
       "      <td>2013</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>1057</td>\n",
       "      <td>-1</td>\n",
       "      <td>1319</td>\n",
       "      <td>-19</td>\n",
       "      <td>UA</td>\n",
       "      <td>N77871</td>\n",
       "      <td>1545</td>\n",
       "      <td>EWR</td>\n",
       "      <td>IAH</td>\n",
       "      <td>180</td>\n",
       "      <td>1400</td>\n",
       "      <td>10</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32733</th>\n",
       "      <td>2013</td>\n",
       "      <td>10</td>\n",
       "      <td>15</td>\n",
       "      <td>844</td>\n",
       "      <td>56</td>\n",
       "      <td>1045</td>\n",
       "      <td>60</td>\n",
       "      <td>B6</td>\n",
       "      <td>N258JB</td>\n",
       "      <td>1273</td>\n",
       "      <td>JFK</td>\n",
       "      <td>CHS</td>\n",
       "      <td>93</td>\n",
       "      <td>636</td>\n",
       "      <td>8</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32734</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>28</td>\n",
       "      <td>1813</td>\n",
       "      <td>-3</td>\n",
       "      <td>1942</td>\n",
       "      <td>-23</td>\n",
       "      <td>UA</td>\n",
       "      <td>N36272</td>\n",
       "      <td>1053</td>\n",
       "      <td>EWR</td>\n",
       "      <td>CLE</td>\n",
       "      <td>59</td>\n",
       "      <td>404</td>\n",
       "      <td>18</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32735 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       year  month  day  dep_time  dep_delay  arr_time  arr_delay carrier  \\\n",
       "0      2013      6   30       940         15      1216         -4      VX   \n",
       "1      2013      5    7      1657         -3      2104         10      DL   \n",
       "2      2013     12    8       859         -1      1238         11      DL   \n",
       "3      2013      5   14      1841         -4      2122        -34      DL   \n",
       "4      2013      7   21      1102         -3      1230         -8      9E   \n",
       "...     ...    ...  ...       ...        ...       ...        ...     ...   \n",
       "32730  2013     10    8       752         -8       921        -28      9E   \n",
       "32731  2013      7    7       812         -3      1043          8      DL   \n",
       "32732  2013      9    3      1057         -1      1319        -19      UA   \n",
       "32733  2013     10   15       844         56      1045         60      B6   \n",
       "32734  2013      3   28      1813         -3      1942        -23      UA   \n",
       "\n",
       "      tailnum  flight origin dest  air_time  distance  hour  minute  \n",
       "0      N626VA     407    JFK  LAX       313      2475     9      40  \n",
       "1      N3760C     329    JFK  SJU       216      1598    16      57  \n",
       "2      N712TW     422    JFK  LAX       376      2475     8      59  \n",
       "3      N914DL    2391    JFK  TPA       135      1005    18      41  \n",
       "4      N823AY    3652    LGA  ORF        50       296    11       2  \n",
       "...       ...     ...    ...  ...       ...       ...   ...     ...  \n",
       "32730  N8505Q    3611    JFK  PIT        63       340     7      52  \n",
       "32731  N6713Y    1429    JFK  LAS       286      2248     8      12  \n",
       "32732  N77871    1545    EWR  IAH       180      1400    10      57  \n",
       "32733  N258JB    1273    JFK  CHS        93       636     8      44  \n",
       "32734  N36272    1053    EWR  CLE        59       404    18      13  \n",
       "\n",
       "[32735 rows x 16 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights = pd.read_csv(flights_url)\n",
    "flights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0fe72bde-9756-4c2f-bbfc-658c4558ebec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.DataFrame'>\n",
      "RangeIndex: 32735 entries, 0 to 32734\n",
      "Data columns (total 16 columns):\n",
      " #   Column     Non-Null Count  Dtype\n",
      "---  ------     --------------  -----\n",
      " 0   year       32735 non-null  int64\n",
      " 1   month      32735 non-null  int64\n",
      " 2   day        32735 non-null  int64\n",
      " 3   dep_time   32735 non-null  int64\n",
      " 4   dep_delay  32735 non-null  int64\n",
      " 5   arr_time   32735 non-null  int64\n",
      " 6   arr_delay  32735 non-null  int64\n",
      " 7   carrier    32735 non-null  str  \n",
      " 8   tailnum    32735 non-null  str  \n",
      " 9   flight     32735 non-null  int64\n",
      " 10  origin     32735 non-null  str  \n",
      " 11  dest       32735 non-null  str  \n",
      " 12  air_time   32735 non-null  int64\n",
      " 13  distance   32735 non-null  int64\n",
      " 14  hour       32735 non-null  int64\n",
      " 15  minute     32735 non-null  int64\n",
      "dtypes: int64(12), str(4)\n",
      "memory usage: 4.4 MB\n"
     ]
    }
   ],
   "source": [
    "flights.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4f7a9aec-4825-40ce-9e83-027499b5bd18",
   "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>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>dep_time</th>\n",
       "      <th>dep_delay</th>\n",
       "      <th>arr_time</th>\n",
       "      <th>arr_delay</th>\n",
       "      <th>carrier</th>\n",
       "      <th>tailnum</th>\n",
       "      <th>flight</th>\n",
       "      <th>origin</th>\n",
       "      <th>dest</th>\n",
       "      <th>air_time</th>\n",
       "      <th>distance</th>\n",
       "      <th>hour</th>\n",
       "      <th>minute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7898</th>\n",
       "      <td>2013</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>1930</td>\n",
       "      <td>-5</td>\n",
       "      <td>2102</td>\n",
       "      <td>-30</td>\n",
       "      <td>EV</td>\n",
       "      <td>N708EV</td>\n",
       "      <td>5287</td>\n",
       "      <td>LGA</td>\n",
       "      <td>MSN</td>\n",
       "      <td>118</td>\n",
       "      <td>812</td>\n",
       "      <td>19</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12189</th>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>1011</td>\n",
       "      <td>71</td>\n",
       "      <td>1312</td>\n",
       "      <td>52</td>\n",
       "      <td>AA</td>\n",
       "      <td>N5BSAA</td>\n",
       "      <td>647</td>\n",
       "      <td>JFK</td>\n",
       "      <td>MIA</td>\n",
       "      <td>150</td>\n",
       "      <td>1089</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4401</th>\n",
       "      <td>2013</td>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "      <td>1443</td>\n",
       "      <td>-3</td>\n",
       "      <td>1708</td>\n",
       "      <td>-6</td>\n",
       "      <td>UA</td>\n",
       "      <td>N14228</td>\n",
       "      <td>1222</td>\n",
       "      <td>EWR</td>\n",
       "      <td>LAS</td>\n",
       "      <td>303</td>\n",
       "      <td>2227</td>\n",
       "      <td>14</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>830</th>\n",
       "      <td>2013</td>\n",
       "      <td>7</td>\n",
       "      <td>25</td>\n",
       "      <td>658</td>\n",
       "      <td>-2</td>\n",
       "      <td>1012</td>\n",
       "      <td>3</td>\n",
       "      <td>DL</td>\n",
       "      <td>N905DE</td>\n",
       "      <td>1879</td>\n",
       "      <td>LGA</td>\n",
       "      <td>FLL</td>\n",
       "      <td>154</td>\n",
       "      <td>1076</td>\n",
       "      <td>6</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2215</th>\n",
       "      <td>2013</td>\n",
       "      <td>12</td>\n",
       "      <td>29</td>\n",
       "      <td>905</td>\n",
       "      <td>-3</td>\n",
       "      <td>1211</td>\n",
       "      <td>7</td>\n",
       "      <td>B6</td>\n",
       "      <td>N828JB</td>\n",
       "      <td>653</td>\n",
       "      <td>JFK</td>\n",
       "      <td>PBI</td>\n",
       "      <td>154</td>\n",
       "      <td>1028</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7033</th>\n",
       "      <td>2013</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>1341</td>\n",
       "      <td>26</td>\n",
       "      <td>1457</td>\n",
       "      <td>24</td>\n",
       "      <td>EV</td>\n",
       "      <td>N12540</td>\n",
       "      <td>5793</td>\n",
       "      <td>EWR</td>\n",
       "      <td>RIC</td>\n",
       "      <td>58</td>\n",
       "      <td>277</td>\n",
       "      <td>13</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18186</th>\n",
       "      <td>2013</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>2043</td>\n",
       "      <td>-2</td>\n",
       "      <td>2335</td>\n",
       "      <td>-9</td>\n",
       "      <td>UA</td>\n",
       "      <td>N38268</td>\n",
       "      <td>1115</td>\n",
       "      <td>EWR</td>\n",
       "      <td>TPA</td>\n",
       "      <td>147</td>\n",
       "      <td>997</td>\n",
       "      <td>20</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25194</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>746</td>\n",
       "      <td>1</td>\n",
       "      <td>1032</td>\n",
       "      <td>22</td>\n",
       "      <td>DL</td>\n",
       "      <td>N784NC</td>\n",
       "      <td>807</td>\n",
       "      <td>EWR</td>\n",
       "      <td>ATL</td>\n",
       "      <td>138</td>\n",
       "      <td>746</td>\n",
       "      <td>7</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23982</th>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>1715</td>\n",
       "      <td>75</td>\n",
       "      <td>2024</td>\n",
       "      <td>69</td>\n",
       "      <td>AA</td>\n",
       "      <td>N3FAAA</td>\n",
       "      <td>565</td>\n",
       "      <td>JFK</td>\n",
       "      <td>DFW</td>\n",
       "      <td>226</td>\n",
       "      <td>1391</td>\n",
       "      <td>17</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20614</th>\n",
       "      <td>2013</td>\n",
       "      <td>11</td>\n",
       "      <td>18</td>\n",
       "      <td>2115</td>\n",
       "      <td>-10</td>\n",
       "      <td>2306</td>\n",
       "      <td>-22</td>\n",
       "      <td>EV</td>\n",
       "      <td>N13903</td>\n",
       "      <td>3845</td>\n",
       "      <td>EWR</td>\n",
       "      <td>GSP</td>\n",
       "      <td>95</td>\n",
       "      <td>594</td>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       year  month  day  dep_time  dep_delay  arr_time  arr_delay carrier  \\\n",
       "7898   2013     11    3      1930         -5      2102        -30      EV   \n",
       "12189  2013      1   14      1011         71      1312         52      AA   \n",
       "4401   2013      7   21      1443         -3      1708         -6      UA   \n",
       "830    2013      7   25       658         -2      1012          3      DL   \n",
       "2215   2013     12   29       905         -3      1211          7      B6   \n",
       "7033   2013      4   25      1341         26      1457         24      EV   \n",
       "18186  2013     11   11      2043         -2      2335         -9      UA   \n",
       "25194  2013      3   23       746          1      1032         22      DL   \n",
       "23982  2013      1   16      1715         75      2024         69      AA   \n",
       "20614  2013     11   18      2115        -10      2306        -22      EV   \n",
       "\n",
       "      tailnum  flight origin dest  air_time  distance  hour  minute  \n",
       "7898   N708EV    5287    LGA  MSN       118       812    19      30  \n",
       "12189  N5BSAA     647    JFK  MIA       150      1089    10      11  \n",
       "4401   N14228    1222    EWR  LAS       303      2227    14      43  \n",
       "830    N905DE    1879    LGA  FLL       154      1076     6      58  \n",
       "2215   N828JB     653    JFK  PBI       154      1028     9       5  \n",
       "7033   N12540    5793    EWR  RIC        58       277    13      41  \n",
       "18186  N38268    1115    EWR  TPA       147       997    20      43  \n",
       "25194  N784NC     807    EWR  ATL       138       746     7      46  \n",
       "23982  N3FAAA     565    JFK  DFW       226      1391    17      15  \n",
       "20614  N13903    3845    EWR  GSP        95       594    21      15  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights.sample(n=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "016aa0ff-3c7c-489d-b501-69a219dabe75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x14b8dfa12900>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data=flights, x=\"dep_time\", col=\"origin\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f5bbe430-2f56-4126-92b2-239f6ce16d8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "carrier\n",
       "HA    28.088235\n",
       "FL    19.553746\n",
       "OO    16.666667\n",
       "EV    16.210424\n",
       "YV    15.094340\n",
       "F9    12.478261\n",
       "MQ    10.092142\n",
       "B6     9.725632\n",
       "WN     8.883426\n",
       "9E     8.038325\n",
       "UA     4.358232\n",
       "VX     2.414487\n",
       "US     1.803970\n",
       "AA     1.398683\n",
       "DL     0.906756\n",
       "AS   -11.333333\n",
       "Name: arr_delay, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights.groupby(\"carrier\")[\"arr_delay\"].mean().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "58c77c07-54f0-4913-9fd2-8ccac1f7e89c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x14b8deeb6cf0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(data=flights, x=\"distance\", y=\"arr_delay\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "979da560-0e6c-4d64-8b05-3e05cef84e61",
   "metadata": {},
   "source": [
    "### Generalization of Questions/Strategies - 5 minutes\n",
    "\n",
    "What might you want to find out about a dataset *before* you even look at it?\n",
    "* *list your responses here*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40eaf319-0a5b-4de8-9430-c98e9f3c77a2",
   "metadata": {},
   "source": [
    "What are some useful first steps you can take after loading the data to begin exploring it?\n",
    "* *list your responses here*"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}