KULKARNI_MANASI-PS1.ipynb

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    "# Problem Set 1\n",
    "Due April 11th at 5:30pm -- at the start of the class so we can\n",
    "discuss the solutions in class.\n",
    "\n",
    "### Before You Start\n",
    "\n",
    "Make sure to at least take a basic tutorial in the IPython notebook,\n",
    "otherwise you'll be totally lost.  For this problem set, you should\n",
    "download flights.csv.bz2 and weather.csv.bz2 datasets from\n",
    "Canvas (files-data).   Rename the notebook to\n",
    "LASTNAME_FIRSTNAME-PS1.ipynb.\n",
    "\n",
    "When done, please submit\n",
    "1. the notebook itself\n",
    "2. the rendered html or pdf of it.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "## About the Problem Set: \n",
    "\n",
    "This problem set is rather similar to ones used for R, dplyr, and\n",
    "flights data.  Just we use python and pandas.\n",
    "\n",
    "In this problem set you will perform a basic exploratory analysis on\n",
    "an example dataset, bringing to bear all of your new skills in data\n",
    "manipulation and visualization. You will be required to submit well\n",
    "commented python code, documenting all code used in this problem set,\n",
    "along with a write up answering all questions below. Use figures as\n",
    "appropriate to support your answers, and when required by the problem.\n"
   ]
  },
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   "source": [
    "## Preliminaries\n",
    "\n",
    "You should also make sure the following libraries load correctly (click on the box below and hit Ctrl-Enter)"
   ]
  },
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     "output_type": "stream",
     "text": [
      "IPython version:       6.1.0 (need at least 1.0)\n",
      "Numpy version:        1.13.3 (need at least 1.7.1)\n",
      "SciPy version:        0.19.1 (need at least 0.12.0)\n",
      "Pandas version:       0.20.3 (need at least 0.11.0)\n",
      "Mapltolib version:     2.1.0 (need at least 1.2.1)\n",
      "Scikit-Learn version: 0.19.1 (need at least 0.13.1)\n"
     ]
    }
   ],
   "source": [
    "#IPython is what you are using now to run the notebook\n",
    "import IPython\n",
    "print( \"IPython version:      %6.6s (need at least 1.0)\" % IPython.__version__)\n",
    "\n",
    "# Numpy is a library for working with Arrays\n",
    "import numpy as np\n",
    "print( \"Numpy version:        %6.6s (need at least 1.7.1)\" % np.__version__)\n",
    "\n",
    "# SciPy implements many different numerical algorithms\n",
    "import scipy as sp\n",
    "print( \"SciPy version:        %6.6s (need at least 0.12.0)\" % sp.__version__)\n",
    "\n",
    "# Pandas makes working with data tables easier\n",
    "import pandas as pd\n",
    "print( \"Pandas version:       %6.6s (need at least 0.11.0)\" % pd.__version__)\n",
    "\n",
    "# Module for plotting\n",
    "import matplotlib\n",
    "print( \"Mapltolib version:    %6.6s (need at least 1.2.1)\" %\n",
    "       matplotlib.__version__)\n",
    "%matplotlib inline\n",
    "# necessary for in-line graphics\n",
    "\n",
    "# SciKit Learn implements several Machine Learning algorithms\n",
    "import sklearn\n",
    "print( \"Scikit-Learn version: %6.6s (need at least 0.13.1)\" % sklearn.__version__)"
   ]
  },
  {
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   "execution_count": 142,
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   "source": [
    "## Let's read the data and print some summary information\n",
    "## adjust the path for your file system!\n",
    "flights_df= pd.read_csv('/Users/manasi/Desktop/flights.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
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    {
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     "output_type": "stream",
     "text": [
      "(336776, 17)\n",
      "Index(['Unnamed: 0', 'year', 'month', 'day', 'dep_time', 'dep_delay',\n",
      "       'arr_time', 'arr_delay', 'carrier', 'tailnum', 'flight', 'origin',\n",
      "       'dest', 'air_time', 'distance', 'hour', 'minute'],\n",
      "      dtype='object')\n",
      "Unnamed: 0      int64\n",
      "year            int64\n",
      "month           int64\n",
      "day             int64\n",
      "dep_time      float64\n",
      "dep_delay     float64\n",
      "arr_time      float64\n",
      "arr_delay     float64\n",
      "carrier        object\n",
      "tailnum        object\n",
      "flight          int64\n",
      "origin         object\n",
      "dest           object\n",
      "air_time      float64\n",
      "distance        int64\n",
      "hour          float64\n",
      "minute        float64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print (flights_df.shape)\n",
    "print (flights_df.columns)\n",
    "print (flights_df.dtypes)"
   ]
  },
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   "execution_count": 118,
   "metadata": {
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   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr 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>Unnamed: 0</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>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>517.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>830.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>UA</td>\n",
       "      <td>N14228</td>\n",
       "      <td>1545</td>\n",
       "      <td>EWR</td>\n",
       "      <td>IAH</td>\n",
       "      <td>227.0</td>\n",
       "      <td>1400</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>533.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>850.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>UA</td>\n",
       "      <td>N24211</td>\n",
       "      <td>1714</td>\n",
       "      <td>LGA</td>\n",
       "      <td>IAH</td>\n",
       "      <td>227.0</td>\n",
       "      <td>1416</td>\n",
       "      <td>5.0</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>542.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>923.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>AA</td>\n",
       "      <td>N619AA</td>\n",
       "      <td>1141</td>\n",
       "      <td>JFK</td>\n",
       "      <td>MIA</td>\n",
       "      <td>160.0</td>\n",
       "      <td>1089</td>\n",
       "      <td>5.0</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>544.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1004.0</td>\n",
       "      <td>-18.0</td>\n",
       "      <td>B6</td>\n",
       "      <td>N804JB</td>\n",
       "      <td>725</td>\n",
       "      <td>JFK</td>\n",
       "      <td>BQN</td>\n",
       "      <td>183.0</td>\n",
       "      <td>1576</td>\n",
       "      <td>5.0</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>554.0</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>812.0</td>\n",
       "      <td>-25.0</td>\n",
       "      <td>DL</td>\n",
       "      <td>N668DN</td>\n",
       "      <td>461</td>\n",
       "      <td>LGA</td>\n",
       "      <td>ATL</td>\n",
       "      <td>116.0</td>\n",
       "      <td>762</td>\n",
       "      <td>5.0</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>554.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>740.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>UA</td>\n",
       "      <td>N39463</td>\n",
       "      <td>1696</td>\n",
       "      <td>EWR</td>\n",
       "      <td>ORD</td>\n",
       "      <td>150.0</td>\n",
       "      <td>719</td>\n",
       "      <td>5.0</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>555.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>913.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>B6</td>\n",
       "      <td>N516JB</td>\n",
       "      <td>507</td>\n",
       "      <td>EWR</td>\n",
       "      <td>FLL</td>\n",
       "      <td>158.0</td>\n",
       "      <td>1065</td>\n",
       "      <td>5.0</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>557.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>709.0</td>\n",
       "      <td>-14.0</td>\n",
       "      <td>EV</td>\n",
       "      <td>N829AS</td>\n",
       "      <td>5708</td>\n",
       "      <td>LGA</td>\n",
       "      <td>IAD</td>\n",
       "      <td>53.0</td>\n",
       "      <td>229</td>\n",
       "      <td>5.0</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>557.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>838.0</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>B6</td>\n",
       "      <td>N593JB</td>\n",
       "      <td>79</td>\n",
       "      <td>JFK</td>\n",
       "      <td>MCO</td>\n",
       "      <td>140.0</td>\n",
       "      <td>944</td>\n",
       "      <td>5.0</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>2013</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>558.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>753.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>AA</td>\n",
       "      <td>N3ALAA</td>\n",
       "      <td>301</td>\n",
       "      <td>LGA</td>\n",
       "      <td>ORD</td>\n",
       "      <td>138.0</td>\n",
       "      <td>733</td>\n",
       "      <td>5.0</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  year  month  day  dep_time  dep_delay  arr_time  arr_delay  \\\n",
       "0           1  2013      1    1     517.0        2.0     830.0       11.0   \n",
       "1           2  2013      1    1     533.0        4.0     850.0       20.0   \n",
       "2           3  2013      1    1     542.0        2.0     923.0       33.0   \n",
       "3           4  2013      1    1     544.0       -1.0    1004.0      -18.0   \n",
       "4           5  2013      1    1     554.0       -6.0     812.0      -25.0   \n",
       "5           6  2013      1    1     554.0       -4.0     740.0       12.0   \n",
       "6           7  2013      1    1     555.0       -5.0     913.0       19.0   \n",
       "7           8  2013      1    1     557.0       -3.0     709.0      -14.0   \n",
       "8           9  2013      1    1     557.0       -3.0     838.0       -8.0   \n",
       "9          10  2013      1    1     558.0       -2.0     753.0        8.0   \n",
       "\n",
       "  carrier tailnum  flight origin dest  air_time  distance  hour  minute  \n",
       "0      UA  N14228    1545    EWR  IAH     227.0      1400   5.0    17.0  \n",
       "1      UA  N24211    1714    LGA  IAH     227.0      1416   5.0    33.0  \n",
       "2      AA  N619AA    1141    JFK  MIA     160.0      1089   5.0    42.0  \n",
       "3      B6  N804JB     725    JFK  BQN     183.0      1576   5.0    44.0  \n",
       "4      DL  N668DN     461    LGA  ATL     116.0       762   5.0    54.0  \n",
       "5      UA  N39463    1696    EWR  ORD     150.0       719   5.0    54.0  \n",
       "6      B6  N516JB     507    EWR  FLL     158.0      1065   5.0    55.0  \n",
       "7      EV  N829AS    5708    LGA  IAD      53.0       229   5.0    57.0  \n",
       "8      B6  N593JB      79    JFK  MCO     140.0       944   5.0    57.0  \n",
       "9      AA  N3ALAA     301    LGA  ORD     138.0       733   5.0    58.0  "
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights_df.dest.unique()\n",
    "flights_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "## Some Tips\n",
    "\n",
    "* This assignment involves extensive Data frame splitting and aggregation. You should look into the details of the methods groupby, transform, sum, count, mean etc\n",
    "* Many of the tasks in the assignment can be done either through the Pandas Data Frame or by converting the data frames to Series. Many of the methods in the numpy are applicable to Series only. When stuck, try to explore the type of object (Pandas Data Frame or Numpy Series) you are dealing with."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "## Question 1\n",
    "Let’s explore flights from NYC to Seattle. Use the flights dataset to answer the following questions.\n",
    "\n",
    "(a) How many flights were there from NYC airports to Seattle in 2013?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "autoscroll": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3923"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Selecting destination as SEA\n",
    "flights_df[flights_df['dest'] == 'SEA'].count()['dest']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    " There were 3923 flights from NYC airports to Seattle in 2013."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "(b) How many airlines fly from NYC to Seattle?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "autoscroll": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Selecting destination as SEA and finding Unique characters\n",
    "airlines_df=flights_df[flights_df['dest']=='SEA'].carrier.unique()\n",
    "len(airlines_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "There are 5 unique airlines that fly from NYC to Seattle. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "(c) How many unique air planes fly from NYC to Seattle?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "autoscroll": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "935"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#On observation, I found out that the tailnum had NaN values. \n",
    "#Using nunique directly to drop NA values and count the number of unique flights\n",
    "uniqueflights = flights_df[flights_df['dest'] == 'SEA'].tailnum.nunique()\n",
    "uniqueflights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    " 935 unique air planes fly from NYC to Seattle based on the tail number which is unique for every airplane"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "166"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#If the uniqueness of the flight is based on the flights and not on the tail numbers\n",
    "uniqueflights_1 = flights_df[flights_df['dest'] == 'SEA'].flight.unique()\n",
    "len(uniqueflights_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " 166 unique air planes fly from NYC to Seattle based on the flight."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "(d) What is the average arrival delay for flights from NC to Seattle?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "autoscroll": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.0990990990990992"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Using the mean() function to find the average delay\n",
    "flights_df[flights_df['dest']=='SEA'].arr_delay.mean()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "The average arrival delay for flights from NYC to Seattle is -1.099 minutes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "(e) What proportion of flights to Seattle come from each NYC airport?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "autoscroll": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "origin\n",
       "EWR    46.673464\n",
       "JFK    53.326536\n",
       "dtype: float64"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Using groupby by keeping destination as SEA\n",
    "dest_sea = flights_df[flights_df['dest'] == 'SEA']\n",
    "flightagg = dest_sea.groupby([dest_sea.origin]).size()\n",
    "flightagg/len(dest_sea)*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "Based on the above observation, 46.67 percent of the flights come from EWR and 53.32 percent of the flights come from JFK."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "## Question 2\n",
    "Flights are often delayed. Consider the following questions exploring delay patterns.\n",
    "\n",
    "(a) Which date has the largest average departure delay? Which date has the largest average arrival delay?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "autoscroll": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                dep_delay\n",
      "year day month           \n",
      "2013 8   3      83.536921\n"
     ]
    }
   ],
   "source": [
    "flights_df_q2=pd.DataFrame(flights_df.groupby(['year','day','month'])['dep_delay'].mean())\n",
    "print(flights_df_q2.sort_values(by='dep_delay', ascending=False).head(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                arr_delay\n",
      "year day month           \n",
      "2013 8   3      85.862155\n"
     ]
    }
   ],
   "source": [
    "flights_arr_q2=pd.DataFrame(flights_df.groupby(['year','day','month'])['arr_delay'].mean())\n",
    "print(flights_arr_q2.sort_values(by='arr_delay', ascending=False).head(1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "The largest average departure delay was on 8th March 2013. The largest average arrival delay was on 8th March 2013."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "(b) What was the worst day to fly out of NYC in 2013 if you dislike delayed flights?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "autoscroll": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                dep_delay\n",
      "year day month           \n",
      "2013 8   3      83.536921\n"
     ]
    }
   ],
   "source": [
    "print(flights_df_q2.sort_values(by='dep_delay', ascending=False).head(1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    " The worst day to fly out of NYC in 2013 was 8th March 2013 as the average departure delay was 83.53 minutes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "(c) Are there any seasonal patterns in departure delays for flights from NYC?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "autoscroll": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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8pfQ07V19pMVHcdsludy0MJvFU1Owqx6HtGJmGuFhwraj9ZrcVcDQ5K5CkjGGfVWtvLCn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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1510899f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "flights_df.groupby(['month'])['dep_delay'].mean().plot(title = 'Seasonal Pattern Analysis of Departure Delays')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "The worst months to leave New York City are June and July. There could be a couple of reasons for that: \n",
    "This could be because it’s summer and everyone is going out for a holiday. Also, there is a probability that New York is facing summer thunderstorms. One more cause could be long TSA checking queues.\n",
    "\n",
    "The best month to leave New York City is October/November as the departure delay is the minimum in this month. The possible reason could be because the weather disturbances are minimum."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "(d) On average, how do departure delays vary over the course of a day?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "autoscroll": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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h+5OXGiu9RzpzbpjD9iPbGfTmIGbnzOa+gffRvk17j+3zgcEPsLdoL++vf99j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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1510cd4320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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RNjjo/MPdz7CzrZv/+sszhyY8HK+iIuP/vOs0zIxP/c8zhz0FuoI+w5Lh2bGq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\nOAP4I/p9yXnQ2wjrNN4zcJ6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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1510d04748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "weather_df = pd.read_csv('/Users/manasi/Desktop/weather.csv')\n",
    "weather_df_wflight = pd.merge(flights_df, weather_df, how='inner', on=['origin','year','month','day','hour'])\n",
    "weather_df_wflight.groupby('hour')['visib'].mean().plot(title = 'Average Hourly Visibility', color='green')\n",
    "plt.show()\n",
    "\n",
    "flights_df.groupby('hour')['dep_delay'].mean().plot(title = 'Avg Hourly Departure Delay')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "Departure delays are maximum during 12 am to 3 am when the visibility is very low. When the visibility is high, the delays are very less and the flights are on time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "## Question 3\n",
    "   Which flight departing NYC in 2013 flew the fastest?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "autoscroll": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Unnamed: 0  year  month  day  dep_time  dep_delay  arr_time  \\\n",
      "216447      216448  2013      5   25    1709.0        9.0    1923.0   \n",
      "\n",
      "        arr_delay carrier tailnum  flight origin dest  air_time  distance  \\\n",
      "216447      -14.0      DL  N666DN    1499    LGA  ATL      65.0       762   \n",
      "\n",
      "        hour  minute      speed  \n",
      "216447  17.0     9.0  11.723077  \n"
     ]
    }
   ],
   "source": [
    "#Calculating speed based on distance travelled and air_time\n",
    "flights_df['speed'] = flights_df['distance']/flights_df['air_time']\n",
    "\n",
    "#Filtering based on max overall speed of plane in travelling to its destination\n",
    "print(flights_df[(flights_df.speed == max(flights_df.speed))])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "Flight 1499 departing NYC flew the fastest. It's speed is 11.72 (unit not known)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "## Question 4\n",
    "Which flights (i.e. carrier + flight + dest) happen every day? Where do they fly to?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "autoscroll": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "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>carrier</th>\n",
       "      <th>flight</th>\n",
       "      <th>dest</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>AA</td>\n",
       "      <td>59</td>\n",
       "      <td>SFO</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>775</th>\n",
       "      <td>AA</td>\n",
       "      <td>119</td>\n",
       "      <td>LAX</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>783</th>\n",
       "      <td>AA</td>\n",
       "      <td>181</td>\n",
       "      <td>LAX</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>904</th>\n",
       "      <td>AA</td>\n",
       "      <td>1357</td>\n",
       "      <td>SJU</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>914</th>\n",
       "      <td>AA</td>\n",
       "      <td>1611</td>\n",
       "      <td>MIA</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1118</th>\n",
       "      <td>B6</td>\n",
       "      <td>219</td>\n",
       "      <td>CLT</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1147</th>\n",
       "      <td>B6</td>\n",
       "      <td>359</td>\n",
       "      <td>BUR</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1150</th>\n",
       "      <td>B6</td>\n",
       "      <td>371</td>\n",
       "      <td>FLL</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1169</th>\n",
       "      <td>B6</td>\n",
       "      <td>431</td>\n",
       "      <td>SRQ</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1243</th>\n",
       "      <td>B6</td>\n",
       "      <td>703</td>\n",
       "      <td>SJU</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1379</th>\n",
       "      <td>B6</td>\n",
       "      <td>1783</td>\n",
       "      <td>MCO</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>DL</td>\n",
       "      <td>2159</td>\n",
       "      <td>MCO</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2081</th>\n",
       "      <td>DL</td>\n",
       "      <td>2391</td>\n",
       "      <td>TPA</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4631</th>\n",
       "      <td>EV</td>\n",
       "      <td>5712</td>\n",
       "      <td>IAD</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5116</th>\n",
       "      <td>UA</td>\n",
       "      <td>15</td>\n",
       "      <td>HNL</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10607</th>\n",
       "      <td>VX</td>\n",
       "      <td>251</td>\n",
       "      <td>LAS</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10609</th>\n",
       "      <td>VX</td>\n",
       "      <td>407</td>\n",
       "      <td>LAX</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10613</th>\n",
       "      <td>VX</td>\n",
       "      <td>413</td>\n",
       "      <td>LAX</td>\n",
       "      <td>365</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      carrier  flight dest  Count\n",
       "767        AA      59  SFO    365\n",
       "775        AA     119  LAX    365\n",
       "783        AA     181  LAX    365\n",
       "904        AA    1357  SJU    365\n",
       "914        AA    1611  MIA    365\n",
       "1118       B6     219  CLT    365\n",
       "1147       B6     359  BUR    365\n",
       "1150       B6     371  FLL    365\n",
       "1169       B6     431  SRQ    365\n",
       "1243       B6     703  SJU    365\n",
       "1379       B6    1783  MCO    365\n",
       "2012       DL    2159  MCO    365\n",
       "2081       DL    2391  TPA    365\n",
       "4631       EV    5712  IAD    365\n",
       "5116       UA      15  HNL    365\n",
       "10607      VX     251  LAS    365\n",
       "10609      VX     407  LAX    365\n",
       "10613      VX     413  LAX    365"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eday = pd.DataFrame(flights_df.groupby([flights_df.carrier,flights_df.flight,flights_df.dest]).size().reset_index(name='Count'))\n",
    "\n",
    "eday[eday.Count == max(eday.Count)]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "18 flights happen everyday. They fly to LAX, SJU, MIA, SFO, MCO, CLT, BUR, FLL, SRQ, TPA, IAD, HNL and LAS."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "## Question 5\n",
    "Develop one research question you can address using the nycflights2013 dataset. Provide two visualizations to support your exploration of this question. Discuss what you find.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Did certain carriers have better sense of time while arriving and departing from NYC airports as compared to others?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "autoscroll": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/manasi/anaconda3/lib/python3.6/site-packages/numpy/core/fromnumeric.py:57: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  return getattr(obj, method)(*args, **kwds)\n"
     ]
    },
    {
     "data": {
      "image/png": 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LNadSieLiYjo7OykvL2fGjBmUl5fT2dlJcXFxolUzDCMBmLEIQ2DNqZqaGjZu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\nA0S8byDS0tJIT0+nqqqK7u5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      "text/plain": [
       "<matplotlib.figure.Figure at 0x15137947b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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IvCciWzxht4vIyyLyZxF5TERGe659T0ReE5FXRORcT/gX3bDXROT6+L+KYRiG4RfRtCwe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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1512327e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "flights_df.boxplot(column='dep_delay', by='carrier')\n",
    "plt.title('Departure Delay of flights')\n",
    "plt.show()\n",
    "flights_df.boxplot(column='arr_delay', by='carrier')\n",
    "plt.title('Arrival Delay of flights')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "From the boxplots above, we see that most of the airlines seem to have median arrival delay of around 0 minutes, but some carriers such as AA, AS and HA seem to have a smaller interquartile range compared to the others. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'Avg Departure Delay')"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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14Z/HxONaIoc7FHXSpEm89tprNG/ePBHhxSw9PZ3ly5fz3nvvMXHiRMaPHx/Zd+WVVzJ2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\nG6Hk4BUWFjJgwID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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1510fc5be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Help and code from Stackoverflow\n",
    "m_dep_del = flights_df.groupby('carrier').dep_delay.mean()\n",
    "m_arr_del = flights_df.groupby('carrier').arr_delay.mean()\n",
    "carrierlabels = flights_df.carrier.unique()\n",
    "\n",
    "carrierlabels.sort()\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.scatter(m_dep_del, m_arr_del)\n",
    "\n",
    "for i, txt in enumerate(carrierlabels):\n",
    "    ax.annotate(txt, (m_dep_del[i],m_arr_del[i]))\n",
    "plt.ylabel('Avg Arrival Delay')\n",
    "plt.xlabel('Avg Departure Delay')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The graph above proves our understanding that HA, AA and AS has lesser departure delays and arrival delays and therefore have better sense of time than the other carriers. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "## Question 6\n",
    "What weather conditions are associated with flight delays leaving NYC? Use graphics to explore."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "autoscroll": false,
    "ein.tags": "worksheet-0",
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x15137a0898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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p7G9sc1WDxdHZMhA5LfetB5po6+yJuuQOcM6EPH53+1k0d3STlhDHXQvHh/R6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\n7sYYE4UsuRtjTBT6/0sIXt+jdJIvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15126eff28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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3MfdVnCWGW04fzdq9tXy662CfyqhUtOt0GDaV28M+QtRNA3oAeJMYrW9p56F3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\nITaU1XLjKaN8XgYxnDSgq6C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      "text/plain": [
       "<matplotlib.figure.Figure at 0x15137a0ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x15108cef60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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P7DzEgZ7hBetSmUhBn0KxaIXO6EVkWps2N7OquowrT1s288oppqBPIY1iKSLT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\niOQ5Bb2ISJ77//D9fc+dmwpkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1511885a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Considering delayed flights with dep delay greater than 0\n",
    "flights_df_fd = flights_df[(flights_df.dep_delay>0)] \n",
    "\n",
    "#Grouping by origin, hour, day and month\n",
    "groupby_output = flights_df_fd.groupby([flights_df_fd.origin, flights_df_fd.month, flights_df_fd.day, flights_df_fd.hour])\n",
    "\n",
    "#Merging the above output with the weather dataset. This is an inner join and the hour-day-month for which \n",
    "#data is not present in weather dataset are omitted as per the condition of Inner Join\n",
    "df_weather_flights_merged = pd.merge(grouped_origin_time_hour, weather_df, how='inner', on=['origin','hour','day','month'])\n",
    "\n",
    "\n",
    "weather_df_wflight.groupby('hour')['wind_speed'].mean().plot(title = 'Average Hourly Wind Speed')\n",
    "plt.show()\n",
    "weather_df_wflight.groupby('hour')['visib'].mean().plot(title = 'Average Hourly Visibility')\n",
    "plt.show()\n",
    "weather_df_wflight.groupby('hour')['dewp'].mean().plot(title = 'Average Hourly Dew Point')\n",
    "plt.show()\n",
    "weather_df_wflight.groupby('hour')['humid'].mean().plot(title = 'Average Hourly Humidity')\n",
    "plt.show()\n",
    "weather_df_wflight.groupby('hour')['dep_delay'].mean().plot(title = 'Avg Hourly Departure Delay')\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
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   "source": [
    "My idea was to plot the graphs based on hourly data and various factors in the entire day that contribute to the change in the data. Looking at the wind graph, we can see that the with the increase in wind speed, the departure delay also increases. Also, roughly trying to map all the graphs to a common point, we see that the departure delays increases with the increase in the humidity and decreases with the decrease in humidity. Visibility also has a relation with the departure delays. There are more delays when the visibility is low and the dew point is higher. "
   ]
  }
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