Exploration/recommended_exploration.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Exploratory Data Analysis**\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "plt.style.use('dark_background')\n",
    "\n",
    "import scipy.stats as st\n",
    "from sklearn import preprocessing as pre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "RANDOM_STATE = 42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['fl_date', 'mkt_unique_carrier', 'branded_code_share', 'mkt_carrier',\n",
       "       'mkt_carrier_fl_num', 'op_unique_carrier', 'tail_num',\n",
       "       'op_carrier_fl_num', 'origin_airport_id', 'origin', 'origin_city_name',\n",
       "       'dest_airport_id', 'dest', 'dest_city_name', 'crs_dep_time', 'dep_time',\n",
       "       'dep_delay', 'taxi_out', 'wheels_off', 'wheels_on', 'taxi_in',\n",
       "       'crs_arr_time', 'arr_time', 'arr_delay', 'cancelled',\n",
       "       'cancellation_code', 'diverted', 'dup', 'crs_elapsed_time',\n",
       "       'actual_elapsed_time', 'air_time', 'flights', 'distance',\n",
       "       'carrier_delay', 'weather_delay', 'nas_delay', 'security_delay',\n",
       "       'late_aircraft_delay'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights = pd.read_csv('../Data/files/flights_no_missing.csv') # run data_overview.ipynb to obtain this file.\n",
    "flights.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **2) Recommended exploration tasks**\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Motivation:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our primary objective with this exploratory analysis is to identify the features in our data that best represent our understanding of the [root causes of flight delays](../Research/flight_delay_reasons.md) and in so doing, provide the basis for the **accurate** and **reliable** prediction of commercial flight delays. We aim to develop a sense for how well our feature space captures common flight delay reasons in order to determine which features to select, which additional data to gather, and which features should be re-engineered or excluded."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### [Suggested considerations](https://github.com/lighthouse-labs/mid-term-project-I/blob/master/exploratory_analysis.ipynb) synopsis:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. [Test the hypothesis that the arrival delay is from Normal distribution and that mean of the delay is 0. Be careful about the outliers.](#task1)\n",
    "2. [Is average/median monthly delay different during the year? If so, which months have the biggest delays and what could be the reason?](#task2)\n",
    "3. [Does the weather affect the delay?](#task3)\n",
    "4. [How are taxi times changing during the day? Does higher traffic lead to longer taxi times?](#task4)\n",
    "5. [What is the average percentage of delays that exist prior to departure (*i.e.* are arrival delays caused by departure delays)? Are airlines able to lower the delay during the flights?](#task5)\n",
    "6. [How many states cover 50% of US air traffic?](#task6)\n",
    "7. Test the hypothesis that planes fly faster when there is a departure delay.\n",
    "8. When (which hour) do most 'LONG', 'SHORT', 'MEDIUM' haul flights take off?\n",
    "9. Find the top 10 the bussiest airports. Does the greatest number of flights mean that the majority of passengers went through a given airport? How much traffic do these 10 airports cover?\n",
    "10. Do bigger delays lead to bigger fuel consumption per passenger?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1) Is the arrival delay normally distributed with a mean of 0?<a id='task1'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `arr_delay` distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2.339962e+06\n",
       "mean     5.466329e+00\n",
       "std      4.992268e+01\n",
       "min     -1.290000e+03\n",
       "25%     -1.500000e+01\n",
       "50%     -6.000000e+00\n",
       "75%      8.000000e+00\n",
       "max      2.692000e+03\n",
       "Name: arr_delay, dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights['arr_delay'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='arr_delay'>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=flights['arr_delay'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NormaltestResult(statistic=3414284.7469730703, pvalue=0.0)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st.normaltest(flights['arr_delay'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2339962"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights['arr_delay'].shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have strong evidence to reject the null hypothesis that the data is sampled from a normal distribution. Let us attempt a robust [quantile transformation](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.QuantileTransformer.html) to see if we can better normalize the data. [Google](https://developers.google.com/machine-learning/clustering/prepare-data) suggests using ~10x fewer quantiles than the number of observations, so we will attempt that. (Note that this is not a requirement. [Jason Brownlee](https://machinelearningmastery.com/quantile-transforms-for-machine-learning/) found reasonable success using ~2x fewer quantiles than observations, but that was with a much smaller dataset and the number of rows we have here is so large that we should avoid too high of a subsample to prevent the operation from becoming prohibitively computationally expensive for the limited time we have to complete this project.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "quantile_transformer = pre.QuantileTransformer(n_quantiles=250000,\n",
    "                                               subsample=250000,\n",
    "                                               output_distribution='normal',\n",
    "                                               random_state=RANDOM_STATE,\n",
    "                                               )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "quantile_delay = quantile_transformer.fit_transform(flights['arr_delay'].to_numpy().reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1c9e0cb59a0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 402.375x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(quantile_delay, kde=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NormaltestResult(statistic=array([16.40402169]), pvalue=array([0.0002741]))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st.normaltest(quantile_delay)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Outlier inspection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A quantile transformation on its own is insufficient to justify the assumption of normality using a reasonable subsample size. We should first deal with outliers. To do so we will compare them on the basis of z score to filter out values with exceedingly anomolous deviations from the mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Return only rows with flights where the z score of the arrival delay is within 3 standard deviations of the mean.\n",
    "flights_no_arrival_outliers = flights[(np.abs(st.zscore(flights['arr_delay'])) < 3)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's check the lost data to ensure that we only eliminated outliers and not actual components of a trend in a wide distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "37906 \"outliers\" were eliminated, retaining 98.38005916335393 of the original DataFrame.\n"
     ]
    }
   ],
   "source": [
    "percent_retained = flights_no_arrival_outliers.shape[0] / flights.shape[0] * 100\n",
    "observations_lost = flights.shape[0] - flights_no_arrival_outliers.shape[0]\n",
    "\n",
    "print(f'{observations_lost} \"outliers\" were eliminated, retaining {percent_retained} of the original DataFrame.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Less than 2% of the data were removed by the above operation. Let us test the assumption of normality again now. We should try to include as much data as possible while supporting the null hypothesis that out data was drawn from a normal distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NormaltestResult(statistic=1108764.9326740047, pvalue=0.0)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st.normaltest(flights_no_arrival_outliers.arr_delay)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It appears that the assumption of normality remains invalid. Let us check the distribution of the pruned DataFrame to get a better intuition for why this may be."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1c9e0cb5940>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(flights_no_arrival_outliers.arr_delay, kde=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='arr_delay'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=flights_no_arrival_outliers.arr_delay)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The assumption of normality is most likely violated on the bases that:\n",
    "1) the right and left tails are asymmetric because more flights are delayed than are early.\n",
    "2) there are appreciable gaps in the sampled data.  \n",
    "\n",
    "Let us try applying a quantile transformation on the distribution with outliers removed and see if that helps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "quantile_transformed_arrival_delay_no_outliers = quantile_transformer.fit_transform(\n",
    "    flights_no_arrival_outliers['arr_delay'].to_numpy().reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1c9e6e62dc0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sbN1///3yer1qb2/X448/rubmZpWXl0uSysvLderUKUlSc3OzvF6vDMNQbm6uPB6Pzp07p1AopJGRERUVFUmSysrK7hgzua2dO3eqvb3drukAAKaYV4B85StfmVfffOzbt08PP/ywLl26pIcfflj79u2TJHV1dampqUldXV06ffq09uzZEz53Ul1drUOHDqm3t1e//vWv1dLSIkk6fPiwMjIyFAgE9L3vfS98RRcAwH7zOoT10ksv6aGHHpqzbyY///nP9fOf/1ySNDQ0pC1btkz7upqaGtXU1ET0nz9/XuvXr4/ov3Hjhnbv3j2vGgAAi2vWANm4caO+8pWv6POf/7yeffbZcP+qVavkcDhsLw4AEL9mDRDDMJSamqqUlBTdd9994f7//d//1c6dO20vDgAQv2YNkLfffltvv/22jhw5oqtXr0arJgBAApjXOZB77rlH//AP/6Dc3FylpHw6hJXfiCZuEwLEl3kFyCuvvKKXX35Zhw4d0vj4uN01AREmn4XeUnc41qUA+K15BcjY2Jhefvllu2sBZpUIz0Kf3EsaHR2NcSWA/ea1DuS1115TdXW1nE6n0tPTww0AsHzNaw9kcrX3c889F+4zTVNf+tKX7KkKABD35hUgX/ziF+2uAwCQYOYVII8//vi0/ceOHVvUYgAAiWNeAVJQUBD+87333qvNmzfrwoULBAgALGPzCpCnnnrqjq8nb80OAFi+LN3O/Te/+Y08Hs9i1wIASCDz2gNpbm4OP+nP4XAoLy9PTU1NthYGAIhv8wqQv//7vw//eWxsTO+//74++OAD24oCAETX1q1bVVtbK4fDoUOHDmn//v1zjpnXIay3335bPT09uu+++5Sens4qWwCw0ftXr8o0zUVr789xM9zk5GT96Ec/0rZt2/TAAw/oscceU15e3px1zmsPZNeuXfq7v/s7vfXWW0pKStJLL72k5557Tq+++ur83g0AwLx9ITtbW//PXy7a9t44eGjW7xcWFqq3t1eXL1+WJDU2Nqq0tFTd3d2zjptXgPzgBz9QQUGBPvroI0lSZmam3nzzTQIEmAb3w0Kicblc6uvrC38dDAZVVFQ057h5HcJKTk4Oh4ck/c///I+Sky1dwAUAiDNJSUkRfZMXTs1mXnsgp0+f1unTp/XTn/5UkvTtb39br7/++gJLBADEo2AwqOzs7PDXbrdb/f39c46bNUC+9KUvKSsrS88//7weffRR/cEf/IGSkpL0i1/8QsePH7/7qgEAMef3++XxeJSbm6sPPvhAXq9X3/nOd+YcN2uAvPjii9q7d68k6eTJkzp58qQk6aGHHtKLL76oRx55ZBFKB2bHkwgBe42Pj+vJJ5/UG2+8IYfDoR//+Mfq6uqac9ysAZKbm6t33303ov/8+fPKzc21XCwwXzyJEMvR1b6+Oa+cWuj25tLS0qKWlpYFbXfWALn33ntn/N7KlSsX9IMAqxLhSYTAYsr5whdiXcK8zHopld/v11/+ZeS1yE888YTOnz9vW1EAgPg36x7IM888o5MnT+pP//RPw4Hx+7//+zIMQ48++mhUCgQAxKdZA2RwcFCbNm3S1772NX35y1+WJP3sZz/Tv/zLv0SlOABA/JrXOpC33npLb731ls2lAAASCcvJAQCWECCADQzDYP0KljwCBABgCQECALCEAAEAWEKAAAAsIUAAAJYQIAAAS2wLkHvuuUdnz57VO++8o/fee09/8zd/I0lKT09Xa2urLl26pNbWVq1evTo8xufzKRAIqKenR8XFxeH+/Px8dXZ2KhAIqLa2NtxvGIYaGxsVCATU0dGhnJwcu6YDAJjCtgC5ceOGvv71r+vBBx/Ugw8+qJKSEhUVFcnn86mtrU1r165VW1ubfD6fJCkvL09er1fr1q1TSUmJDh48GH5sbl1dnaqqquTxeOTxeFRSUiJJqqio0PXr1+XxeHTgwAHt37/frukgBlhHAcQ3Ww9hffLJJ5KkFStWaMWKFTJNU6WlpWpoaJAkNTQ0aMeOHZKk0tJSNTY2anR0VFeuXFFvb68KCwvldDq1atUqdXR0SJKOHj16x5jJbZ04cUKbN2+2czqIosnngCRyiLCYEEudrQGSnJysixcvanBwUGfOnNG5c+eUlZWlUCgkSQqFQlqzZo0kyeVyqe+2h54Eg0G5XC65XC4Fg8GI/qljxsfHNTw8rIyMjIg6Kisr5ff75ff7lZmZadt8sbh4DggQ32wNkImJCW3YsEFut1uFhYVat27djK9NSkqK6DNNc8b+2cZMVV9fr4KCAhUUFOjatWsLmQIAYAZRuQpreHhYb731lkpKSjQwMCCn0ylJcjqdGhwclHRrzyI7Ozs8xu12q7+/X8FgUG63O6J/6hiHw6G0tDQNDQ1FY0oAsOzZFiCZmZlKS0uTdOvRuFu2bFFPT4+am5tVXl4uSSovL9epU6ckSc3NzfJ6vTIMQ7m5ufJ4PDp37pxCoZBGRkZUVFQkSSorK7tjzOS2du7cqfb2drumAwCYYl7PA7Hid37nd9TQ0CCHw6Hk5GQ1NTXpZz/7mX7xi1+oqalJFRUVunr1qnbt2iVJ6urqUlNTk7q6ujQ2NqY9e/ZoYmJCklRdXa0jR45o5cqVdzz4/fDhwzp27JgCgYCGhobk9Xrtmg4AYArbAuTdd99Vfn5+RP/Q0JC2bNky7ZiamhrV1NRE9J8/f17r16+P6L9x44Z2795998UCABaMlegAAEsIEMBGrAXBUkaAAAAsIUAAAJYQIAAASwgQAIAlBAgAwBICBABgCQGCuMNlr0BiIEAQV5bCc0CA5YIAQdzhOSBAYiBAAJuxGh1LFQECALCEAAEAWEKAAAAsIUAAAJYQIAAASwgQIAq4CgtLEQEC2MwwDG1/sooQwZJDgABRwOJILEUECADAEgIEAGAJAQIAsIQAQdzgJDOQWAgQxAVu4w4kHgIEcWOpX6lEOGKpIUCAKEhxOFgLgiWHAAGiZKnvYWH5IUAAAJYQIAAASwgQAIAlBAgAwBICBIgirsLCUkKAAFHCpbxYaggQIIq4lBdLiW0B4na71d7erq6uLr333nt66qmnJEnp6elqbW3VpUuX1NraqtWrV4fH+Hw+BQIB9fT0qLi4ONyfn5+vzs5OBQIB1dbWhvsNw1BjY6MCgYA6OjqUk5Nj13QAAFPYFiBjY2P6/ve/rwceeEAbN27Unj17lJeXJ5/Pp7a2Nq1du1ZtbW3y+XySpLy8PHm9Xq1bt04lJSU6ePCgkpNvlVdXV6eqqip5PB55PB6VlJRIkioqKnT9+nV5PB4dOHBA+/fvt2s6sBGHdIDEZFuAhEIhXbx4UZL08ccfq7u7Wy6XS6WlpWpoaJAkNTQ0aMeOHZKk0tJSNTY2anR0VFeuXFFvb68KCwvldDq1atUqdXR0SJKOHj16x5jJbZ04cUKbN2+2azqwCTdRBBJXVM6B5OTkaMOGDTp79qyysrIUCoUk3QqZNWvWSJJcLpf6+vrCY4LBoFwul1wul4LBYET/1DHj4+MaHh5WRkZGxM+vrKyU3++X3+9XZmambfOENcvpvECKw0FYYsmwPUA++9nP6tVXX9UzzzyjkZGRGV+XlJQU0Wea5oz9s42Zqr6+XgUFBSooKNC1a9cWUj4AYAa2BkhKSopeffVVHT9+XCdPnpQkDQwMyOl0SpKcTqcGBwcl3dqzyM7ODo91u93q7+9XMBiU2+2O6J86xuFwKC0tTUNDQ3ZOCQDwW7YGyOHDh9Xd3a0DBw6E+5qbm1VeXi5JKi8v16lTp8L9Xq9XhmEoNzdXHo9H586dUygU0sjIiIqKiiRJZWVld4yZ3NbOnTvV3t5u53QAALdJsWvDmzZtUllZmTo7O8Mn0/fu3at9+/apqalJFRUVunr1qnbt2iVJ6urqUlNTk7q6ujQ2NqY9e/ZoYmJCklRdXa0jR45o5cqVamlpUUtLi6RbAXXs2DEFAgENDQ3J6/XaNR0AwBS2Bci///u/T3uOQpK2bNkybX9NTY1qamoi+s+fP6/169dH9N+4cUO7d+++u0IBAJawEh0AYAkBAkSZYRhcyoslgQABAFhCgAAALCFAAACWECCImeV8LmC5zhtLCwECRJlhGDxYCksCAQLEwHK6gSSWLgIEAGAJAQIAsIQAAWKA54JgKSBAAACWECAAAEsIEACAJQQIAMASAgRRx8njW2Zbic97hERAgCCqDMNQ6dPV/IKcBe8REgUBgqhjFfanZgoJ3iMkAgIEiBHuiYVER4AAMcSeBhIZAQIAsIQAAQBYQoAAACwhQIAY4qaKSGQECKKGX5TA0kKAICpYHDcz3hMkKgIEUcMlq5FSHA6V/FUFtzRBQiJAgBibKVjZa0O8I0CAOMZeG+IZAQLEWIrDodTU1FiXASwYAQLE2FznQYB4RYAAcYBDVUhEBAgAwBICBABgCQEC28326FbcMtstTXj/EK9sC5DDhw9rYGBA7777brgvPT1dra2tunTpklpbW7V69erw93w+nwKBgHp6elRcXBzuz8/PV2dnpwKBgGpra8P9hmGosbFRgUBAHR0dysnJsWsqAIBp2BYgR44cUUlJyR19Pp9PbW1tWrt2rdra2uTz+SRJeXl58nq9WrdunUpKSnTw4EElJ98qra6uTlVVVfJ4PPJ4POFtVlRU6Pr16/J4PDpw4ID2799v11SAqGAvA4nGtgD513/9Vw0NDd3RV1paqoaGBklSQ0ODduzYEe5vbGzU6Oiorly5ot7eXhUWFsrpdGrVqlXq6OiQJB09evSOMZPbOnHihDZv3mzXVADbcSkvElFUz4FkZWUpFApJkkKhkNasWSNJcrlc6uvrC78uGAzK5XLJ5XIpGAxG9E8dMz4+ruHhYWVkZEz7cysrK+X3++X3+5WZmWnL3IC7xaW8SDRxcRI9KSkpos80zRn7Zxsznfr6ehUUFKigoEDXrl27y2oBAFKUA2RgYEBOp1OS5HQ6NTg4KOnWnkV2dnb4dW63W/39/QoGg3K73RH9U8c4HA6lpaVFHDJDbHH10MLMdksT3kvEo6gGSHNzs8rLyyVJ5eXlOnXqVLjf6/XKMAzl5ubK4/Ho3LlzCoVCGhkZUVFRkSSprKzsjjGT29q5c6fa29ujORVg0XEeBIkmxa4N/+QnP9HXvvY1ZWZmqq+vT3/913+tffv2qampSRUVFbp69ap27dolSerq6lJTU5O6uro0NjamPXv2aGJiQpJUXV2tI0eOaOXKlWppaVFLS4ukW5cJHzt2TIFAQENDQ/J6vXZNBYga0zRnDRDDMDQ6OhrFioCZ2RYg3/nOd6bt37Jly7T9NTU1qqmpieg/f/681q9fH9F/48YN7d69++6KBOLQbAsKt1VX6FRtHSGCuBAXJ9EB3JLicKi48s+V7HBM+32u1EI8IUBgC47jWzdXSPDeIl4QIFh0PIr17qQ4HHIkT78HkuJI0fYnq3hvERcIENiCQy324b1FvCBAAACWECBAAuIQFuIBAQLEodmeD5LicHAeBHGBAMGi4pfa4pntvZxrwSEQDQQIFg1XXy2eudaDAPGAAMGi4gqhxWOa5oyX80rcYBGxR4AACYwAQSwRIECCMgyDk+mIKQIEi4LDKfaY7RkhEifTEVsECBDHJk+mz3WLdyAWCBAgzs12YcLkQ6hm20sB7EKAAAlgrjUhnAtBLBAguGv84rLXfA5j3Rwb4+8BUUeA4K6weDA65gqIew1DjzzF3wOiiwDBXWPxoP0+XZk+8/+y7IUg2ggQWMYvq+i6OTY268OmJvdCOKGOaCFAYAmHruITJ9QRTQQILOPQVXwyTXPavRBCBYuNAAESzGyHsSa//8d7qvS5z30u3De5x8jhLSwmAgQLxm1L4sNca0OmLjA0TXF4C4uKAAESUIrDoS1PPD7rnogjOTnipDqHHbGYCBAsCJ9e48d8wsCRnKw/efbJOw5nAYuFAMG8ceVVYjJNc841JIAV/IvCvEye9+AQSPyZ66S6dOfeCh8AsFgIEMyJXzhLR4rDwdVYWDQECGbFYavEMt+9EcMwlJqaqtTUVK6qg2UpsS4A8ckwDI2Ojkriyp2lyjAMbauukCSNjY/r5P/9oUZHR+/4uwdmwx4IIty+6IxPpkvbzbGxcJvcK2GPE/PFHgimxV7H0pLiuHVYa3xiXNLs57WmO+HOHgmmQ4DgjkMWHA9fulIcjnCQfOOvbh26+n83bswrVIDpECDLXGpqqrZVV6il7jCfMpeRyb2MyVC5Z4W046lqSdLIb34TDpPJDxczhcvtHzyW4r+fpTqvxZLwAbJ161bV1tbK4XDo0KFD2r9/f6xLilu3H46Y3NP45pNVusHhKujOUPnW956UI9mhsfFxtdYf0daqP5dpSklJt+6pNbnXMvnBY/JDyMcff3zHNqceAkukX8iT5wJP1dYlTM3RltAn0ZOTk/WjH/1I27Zt0wMPPKDHHntMeXl5sS4r6qb7dDj1UJRhGPrW957Uo9978o6T4zfHxqNWJxLH7SfXp3499XvSpzdqvP2y4Kn/LhPxknDOBc4uofdACgsL1dvbq8uXL0uSGhsbVVpaqu7u7kX9OfH8D94wDP3JM3vUeqjhjk9Jf/x/KjUhU68fPDTtmEkrUhyamNJnGIZWpKSE++b681x9U7d5+/em67/b/97ubsbO/V58+t7NNP72bcz0Xsy1bmNSUlKSkpI079dbdfvPSUpKCtd5+x5I8kSSJE37Xszk9r/zRDD17w2RkiSZsS7Cqm9961sqKSlRZWWlJOnP/uzPVFRUpO9+97t3vK6yslJVVVWSpN/93d/Vr371q6jXulCZmZm6du1arMu4a0thHswhPiTSHK5du6Zt27bFugzbJXS8JiUlRfSZZmQe1tfXq76+PholLRq/36+CgoJYl3HXlsI8mEN8WApzWGoS+hxIMBhUdnZ2+Gu3263+/v4YVgQAy0dCB4jf75fH41Fubq5WrFghr9er5ubmWJcFAMtCQh/CGh8f15NPPqk33nhDDodDP/7xj9XV1RXrshbFP/7jP8a6hEWxFObBHOLDUpjDUpPQJ9EBALGT0IewAACxQ4AAACwhQBLA97//fZmmqYyMjFiXsmB/+7d/q+7ubv3yl7/UP//zPystLS3WJc3b1q1b1dPTo0AgoBdeeCHW5VjidrvV3t6urq4uvffee3rqqadiXZJlycnJunDhgl577bVYl4LbmLT4bW632zx9+rR55coVMyMjI+b1LLQ9/PDDpsPhMCWZ+/btM/ft2xfzmubTkpOTzd7eXvP+++83V6xYYb7zzjtmXl5ezOtaaHM6neaGDRtMSWZqaqr5q1/9KiHnIcl89tlnzePHj5uvvfZazGuh3WrsgcS5AwcO6Pnnn592gWQiOHPmjMbHb91vq6OjQ263O8YVzc/tt8m5efNm+DY5iSYUCunixYuSpI8//ljd3d1yuVwxrmrhXC6Xtm/frkOHIm/Ng9ghQOLYN7/5TX3wwQfq7OyMdSmL4oknnlBLS0usy5gXl8ulvr6+8NfBYDAhf/HeLicnRxs2bNDZs2djXcqCvfjii3r++ec1MTER61Jwm4ReB7IUnDlzRk6nM6L/Bz/4gfbu3avi4uIYVLUws81hcmHn3r17NTY2puPHj0e7PEvme5ucRPHZz35Wr776qp555hmNjIzEupwF2b59uwYHB3XhwgV99atfjXU5mCLmx9Foke3LX/6yOTAwYF6+fNm8fPmyefPmTfP99983s7KyYl7bQltZWZn5H//xH+bKlStjXst828aNG83Tp0+Hv/b5fKbP54t5XVZaSkqKefr0afPZZ5+NeS1WWk1NjdnX12devnzZ/PDDD81PPvnEPHbsWMzroslUHBRAm0e7fPlyQp5E37p1q/lf//VfZmZmZsxrWUhzOBzmr3/9azM3Nzd8Ev2BBx6IeV1WWkNDg3ngwIGY17EY7atf/Son0eOocQ4EtvrhD3+o++67T2fOnNHFixdVV1cX65Lm5fbb5HR3d6upqSkhb5OzadMmlZWV6etf/7ouXryoixcvLovbjCM6uJUJAMAS9kAAAJYQIAAASwgQAIAlBAgAwBICBABgCQECALCEAAEAWPL/AaRoBtPJ47AdAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 402.375x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(quantile_transformed_arrival_delay_no_outliers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAD4CAYAAADSIzzWAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAAsTAAALEwEAmpwYAAAK+ElEQVR4nO3dX2iX9R7A8c/cPJyRudPxhwnbOZrnqugigyJYYEQ1QsL7BJGgQIhIAhO7D+nGLgIhuyhiEUFBeeFiIQTaHwabRbkicw4X1RjHMzV2OGs950J+Ozp/2/L0PPu4/V4vePD5/fvu8+iz9x5/m9gSEUUAsORWZQ8A0KwEGCCJAAMkEWCAJAIMkKTtep48Pj4eo6OjVc0CsCJt3Lgx1q9ff8391xXg0dHRuOeee0obCqAZDAwMNLzfWxAASQQYIIkAAyQRYIAkAgyQRIABkggwQBIBBkgiwABJBBggiQADJBFggCQCDJBEgAGSCDBAEgEGSCLAAEkEGCCJAAMkua7/Ew7K0N3dHbVardQ1Ozo6IiJicnKylPUmJibixIkTpawF8xFgllytVou/bdoUf/7rX0pb89///FdERKz/x22lrQVVE2BS/Pmvf4nNjz5Y2npnjh6LiChlzTNHj0Wc/cPLwKK8BwyQRIABkggwQBIBBkgiwABJBBggiQADJBFggCQCDJBEgAGSCDBAEgEGSCLAAEkEGCCJAAMkEWCAJAIMkESAAZIIMEASAQZIIsAASQQYIIkAAyQRYIAkAgyQRIABkggwQBIBBkgiwABJBBggiQADJBFggCQCDJBEgAGSCDBAEgEGSCLAAEkEGCCJAAMkEWCAJAIMkESAbxDd3d3R3d2dPQZNyLmXpy17AC6r1WrZI9CknHt5XAEDJBFggCQCDJBEgAGSCDBAEgEGSCLAAEkEGCCJAAMkEWCAJAIMkESAAZIIMEASAQZIIsAASQQYIIkAAyQRYIAkAgyQRIABkggwQBIBBkgiwABJBBggiQADJBFggCQCDJBEgAGSCDBAEgEGSCLAAEkEGCCJAAMkEWCAJAIMkESAAZIIMEASAQZI0rYUH2THjh2xdu3amJycjLfeeqvUtTdv3hw9PT0xPT0dbW1t0dLSEhERx48fjzVr1sRdd90Vg4ODceHChXjggQdibGwsurq6Zl8/NTUV7e3t16xbv3+xxxut0ei1i+1PTU3F+fPnS/pdgetzyy23xO7duxs+Nt/5Wv81Imb3X3/99di1a9dVt5e7Ko9nSQK8du3aiIjo6Ogofe2HHnooIiJWr1591f3333//7P7dd98dRVFERFwV34hoGNcr71/s8UbPafTaxfbb29sFmDTznedzH5t7bv/ex5azKo+n8rcgduzYERExe2X6+OOPl7b25s2bo7W1NVpaWq7Z6ur7V94/93nzvf73PL7QmvM9p9F+xOWrEFhq9fOu0Xm+0Pna6PNs165dDW8vV1UfT+VXwPWr37oyr4LrV78rRXt7e2zfvj17jMrVarX4z8VL2WPM6z8XL0WtVmuKP4uIcq/s5vvb4HJV9fEsGuAnn3wynnrqqYi4/Inz/7jyK2b9rYAy1K9+F/qYC+03uj33/utdf77XLrZf9u8NXI/5zvO5jy12FVwUxYo7p6s8nkUDfPjw4Th8+HBERAwMDPxfH6T+h1L28DMzM/NGuP6xrvy4c0+QK2dbaOaFHm+0RqPXLrZfn/H999+//t+IZWb79u1xy9+7Fn9ikj/dvCZGvzvdFH8WERG7d++e9zyPmP98bfS5Nfc5K0GVx1P5e8AXLly46vbk5GRpa3/00UelrXUjqH9HGZZSmefd3LWW+zld9fFUHuDe3t6I+N8VaZk/hnbmzJmYmZmJoiiu2equ/Ko8d7/R7bn3L/b4QmvO95xG+xHhpyBIUT/vGp3nC52vjT7P6j+mNff2clX18SzJP8SoXwWXefVbV78Knp6evuqEOH78eJw8eTIiIgYHB+Pjjz+OiIixsbGrXj/fV7T6/Ys93ug5jV672P5yv1JgeVvo/FvofJ2ammp432JrLidVHs+S/Bxw/Sq4CmfOnIlDhw7N+/inn346uz88PFzZHH9Us3zHnRvT+fPnS7u6W+5XvXNVeTz+KTJAEgEGSCLAAEkEGCCJAAMkEWCAJAIMkESAAZIIMEASAQZIIsAASQQYIIkAAyQRYIAkAgyQRIABkggwQBIBBkgiwABJBBggiQADJBFggCQCDJBEgAGSCDBAEgEGSCLAAEkEGCCJAAMkEWCAJAIMkESAAZIIMEASAQZIIsAASQQYIIkAAyQRYIAkbdkDcNnExET2CDQp514eAb5BnDhxInsEmpRzL4+3IACSCDBAEgEGSCLAAEkEGCCJAAMkEWCAJAIMkESAAZIIMEASAQZIIsAASQQYIIkAAyQRYIAkAgyQRIABkggwQBIBBkgiwABJBBggiQADJBFggCQCDJBEgAGSCDBAEgEGSCLAAEkEGCCJAAMkEWCAJAIMkESAAZIIMEASAQZIIsAASQQYIIkAAyQRYIAkAgyQpC17AJrTv//5rzhz9Fip60VEKWvW14KqCTBLbmJi4vLO2fLW7OjoiIiIc2fLWXR2RqiQALPkTpw4kT0C3BC8BwyQRIABkggwQBIBBkgiwABJBBggiQADJBFggCQCDJBEgAGSCDBAEgEGSCLAAEkEGCCJAAMkEWCAJAIMkESAAZIIMEASAQZI0hIRxe998vj4eIyOjlY4ztKo1WpN87/eNsuxNstxRjTPsa6k49y4cWOsX7++4WNFs20DAwPpMzhWx+lYHae3IACSCDBAkqYM8Kuvvpo9wpJplmNtluOMaJ5jbYbjvK5vwgFQnqa8Aga4EQgwQJKmD/Bzzz0XRVHEunXrskepxEsvvRTDw8PxxRdfxHvvvRcdHR3ZI5Wup6cnvvnmm/juu+/i+eefzx6nEl1dXXHs2LE4depUfPXVV/HMM89kj1SpVatWxeDgYBw5ciR7lMql/yxc1tbV1VX09fUVZ8+eLdatW5c+TxXbww8/XLS2thYRURw4cKA4cOBA+kxlbqtWrSpOnz5d3HbbbcXq1auLkydPFrfffnv6XGVvGzZsKLZs2VJERLFmzZri22+/XZHHWd/27NlT9Pb2FkeOHEmfpcqtqa+ADx48GHv37o2iKLJHqUx/f3/MzMxERMRnn30WXV1dyROV6957743Tp0/HyMhITE9Px9tvvx3bt2/PHqt0P/30UwwNDUVExKVLl2J4eDg6OzuTp6pGZ2dnbNu2LV577bXsUSrXtAF+7LHH4ocffogvv/wye5Ql88QTT8TRo0ezxyhVZ2dnnDt3bvb22NjYig1T3caNG2PLli3x+eefZ49SiZdffjn27t0bv/32W/YolWvLHqBK/f39sWHDhmvuf+GFF2L//v3xyCOPJExVvoWO84MPPoiIiP3798evv/4avb29Sz1epVpaWq65byX/jeamm26Kd999N5599tm4ePFi9jil27ZtW4yPj8fg4GBs3bo1e5wlkf4+yFJvd955Z/Hzzz8XIyMjxcjISDE9PV2Mjo4Wt956a/psVWw7d+4sPvnkk6K9vT19lrK3++67r+jr65u9vW/fvmLfvn3pc1WxtbW1FX19fcWePXvSZ6lqe/HFF4tz584VIyMjxY8//lj88ssvxZtvvpk+V4Vb+gDp28jIyIr9JlxPT0/x9ddfF7VaLX2WKrbW1tbi+++/LzZt2jT7Tbg77rgjfa4qtjfeeKM4ePBg+hxLtW3dutU34VjeXnnllbj55pujv78/hoaG4tChQ9kjlWpmZiaefvrp+PDDD2N4eDjeeeedOHXqVPZYpevu7o6dO3fGgw8+GENDQzE0NBSPPvpo9lj8Qf4pMkASV8AASQQYIIkAAyQRYIAkAgyQRIABkggwQJL/AjN5AjYBGHuKAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=quantile_transformed_arrival_delay_no_outliers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NormaltestResult(statistic=array([1251.82619391]), pvalue=array([1.47705219e-272]))"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st.normaltest(quantile_transformed_arrival_delay_no_outliers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is clear that there is sufficent statistical evidence to reject the null hypothesis that the underlying distribution of the sample from the flights table is normally distributed when outliers are considered to be values 3 or more standard deviations from the mean; however, statistical tests that are robust with respect to the assumption of normality (such as the Student's t-test) may still be applicable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_1sampResult(statistic=56.64040377294951, pvalue=0.0)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st.ttest_1samp(flights_no_arrival_outliers['arr_delay'], 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_1sampResult(statistic=array([3.07636692]), pvalue=array([0.00209542]))"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st.ttest_1samp(quantile_transformed_arrival_delay_no_outliers, 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see above, even using our quantile transformed data with outliers removed provides inadequate support for the null hypothesis that our data is drawn from a normal distribution centered around a mean of 0."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### monthly comparison"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we need to establish a column for the month of the year, then we can compare mean and median arrival delays between months. I predict that winter months will have longer delays on average than warmer months because factors like de-icing planes, clearing runways, and visibility issues are likely to be prevalent during these months."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2) How do arrival delays differ throughout the year?<a id='task2'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### monthly comparison"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we need to establish a column for the month of the year, then we can compare mean and median arrival delays between months. I predict that winter months will have longer delays on average than warmer months because factors like de-icing planes, clearing runways, and visibility issues are likely to be prevalent during these months."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "flights['flight_month'] = pd.DatetimeIndex(flights['fl_date']).month # save the month to its own column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">arr_delay</th>\n",
       "      <th colspan=\"2\" halign=\"left\">dep_delay</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flight_month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.923047</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>9.769483</td>\n",
       "      <td>-3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.693371</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>11.603780</td>\n",
       "      <td>-2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.902349</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>8.409256</td>\n",
       "      <td>-2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.071800</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>9.264988</td>\n",
       "      <td>-3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6.448396</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>11.205167</td>\n",
       "      <td>-2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10.562826</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>14.698757</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8.871938</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>13.661959</td>\n",
       "      <td>-2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8.883695</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>13.198901</td>\n",
       "      <td>-2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.694430</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>6.941369</td>\n",
       "      <td>-3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2.810083</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>7.441231</td>\n",
       "      <td>-3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3.071748</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>8.201969</td>\n",
       "      <td>-3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>5.087656</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>10.785105</td>\n",
       "      <td>-2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              arr_delay         dep_delay       \n",
       "                   mean median       mean median\n",
       "flight_month                                    \n",
       "1              3.923047   -7.0   9.769483   -3.0\n",
       "2              6.693371   -6.0  11.603780   -2.0\n",
       "3              2.902349   -7.0   8.409256   -2.0\n",
       "4              4.071800   -7.0   9.264988   -3.0\n",
       "5              6.448396   -6.0  11.205167   -2.0\n",
       "6             10.562826   -4.0  14.698757   -1.0\n",
       "7              8.871938   -5.0  13.661959   -2.0\n",
       "8              8.883695   -5.0  13.198901   -2.0\n",
       "9              1.694430   -8.0   6.941369   -3.0\n",
       "10             2.810083   -6.0   7.441231   -3.0\n",
       "11             3.071748   -7.0   8.201969   -3.0\n",
       "12             5.087656   -6.0  10.785105   -2.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_average_delays = flights[['flight_month', 'arr_delay', 'dep_delay']].groupby('flight_month').agg(['mean', 'median'])\n",
    "monthly_average_delays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
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       "        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>level_0</th>\n",
       "      <th>level_1</th>\n",
       "      <th>flight_month</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>arr_delay</td>\n",
       "      <td>mean</td>\n",
       "      <td>1</td>\n",
       "      <td>3.923047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>arr_delay</td>\n",
       "      <td>mean</td>\n",
       "      <td>2</td>\n",
       "      <td>6.693371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>arr_delay</td>\n",
       "      <td>mean</td>\n",
       "      <td>3</td>\n",
       "      <td>2.902349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>arr_delay</td>\n",
       "      <td>mean</td>\n",
       "      <td>4</td>\n",
       "      <td>4.071800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>arr_delay</td>\n",
       "      <td>mean</td>\n",
       "      <td>5</td>\n",
       "      <td>6.448396</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     level_0 level_1  flight_month         0\n",
       "0  arr_delay    mean             1  3.923047\n",
       "1  arr_delay    mean             2  6.693371\n",
       "2  arr_delay    mean             3  2.902349\n",
       "3  arr_delay    mean             4  4.071800\n",
       "4  arr_delay    mean             5  6.448396"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove multilevel column and row indices.\n",
    "monthly_average_delays = monthly_average_delays.unstack().reset_index()\n",
    "monthly_average_delays.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>delay_type</th>\n",
       "      <th>metric</th>\n",
       "      <th>flight_month</th>\n",
       "      <th>delay (mins)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>arr_delay</td>\n",
       "      <td>mean</td>\n",
       "      <td>1</td>\n",
       "      <td>3.923047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>arr_delay</td>\n",
       "      <td>mean</td>\n",
       "      <td>2</td>\n",
       "      <td>6.693371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>arr_delay</td>\n",
       "      <td>mean</td>\n",
       "      <td>3</td>\n",
       "      <td>2.902349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>arr_delay</td>\n",
       "      <td>mean</td>\n",
       "      <td>4</td>\n",
       "      <td>4.071800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>arr_delay</td>\n",
       "      <td>mean</td>\n",
       "      <td>5</td>\n",
       "      <td>6.448396</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  delay_type metric  flight_month  delay (mins)\n",
       "0  arr_delay   mean             1      3.923047\n",
       "1  arr_delay   mean             2      6.693371\n",
       "2  arr_delay   mean             3      2.902349\n",
       "3  arr_delay   mean             4      4.071800\n",
       "4  arr_delay   mean             5      6.448396"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Assign more meaningful column names.\n",
    "monthly_average_delays = monthly_average_delays.rename(columns={\n",
    "    'level_0': 'delay_type',\n",
    "    'level_1': 'metric',\n",
    "    0: 'delay (mins)',\n",
    "    })\n",
    "\n",
    "monthly_average_delays.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=monthly_average_delays[monthly_average_delays.metric == 'mean'],\n",
    "            x='flight_month',\n",
    "            y='delay (mins)',\n",
    "            hue='delay_type',\n",
    "            palette='colorblind',\n",
    "            )\n",
    "plt.title('Mean flight delay')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VWKnuJNGyZUs4OjpCofj/1YOCgvTWFBERUYkBtWnTJjg7OyMhIQG5ubkAACEEA4qIiPSqxIByc3PDO++8Y4heiIiIdEr8Dkqr1cLW1tYQvRAREemUuAdlY2ODc+fO4dSpU3j69KluPk+UICIifSoxoPgYDCIikkKJARUdHW2IPoiIiPIp8juoo0ePAgAePHiAzMxM3fTyNRERkT4VuQfl4eEBALCysjJYM0RERC+V6kJda2trNGzYMN+Fumq1Wm9NERERlRhQ8+fPx6hRo3D58mXk5eUBeHGhbvfu3fXeHBERVV4lBtTQoUPh7OyM58+fl3vxKVOmYPny5bCxscFff/1V7uMTEVHFVaoLda2trcu9sL29Pby8vHD16tVyH5uIiCq+EvegFi1aBLVaDa1WW64X6q5YsQLTpk3Dnj17yjQOERG9nUoMqI0bN2LJkiXQaDS676DKqn///vjzzz9x5syZEtf19fWFn58fgBd3tSCSwq3Vn0ndAlGlU2JAZWRkYPXq1a89sEqlKvQefv7+/pg1axZ69uxZqnGUSiWUSiUAIDY29rX7ICKiiqnEgDp9+jQWLlyIsLCwfIf4SjrN3MvLq9D5rVu3RuPGjZGYmAjgxXdR8fHx6NixI27duvU6vRMR0VusxIBydXUFAHTq1Ek3ryynmWu1WtSrV0/3OjU1FW5ubjyLj4iI8ikxoLp162aIPoiIiPIp8jTzESNGwMjIqMgNnZyc0KVLlzI30LhxY+49ERFRAUXuQdWuXRtqtRqnT5/G6dOncefOHZiZmaFJkybw9PRERkYGZsyYYcheiYioEikyoFatWoUff/wR3bp1Q5cuXdC2bVtkZ2cjKSkJn376Ka5fv27IPomIqJIp9juovLw8HDhwAAcOHDBUP0RERABKcasjIiIiKTCgiIhIlkoMKGNjZhgRERleiemTkpKCpUuXomXLloboh4iICEApAqpt27a4cOEC1q9fj5MnT8LX1xeWlpaG6I2IiCqxEgPq4cOHWL9+Pbp06YJp06Zh7ty5SE9Px4YNG+Ds7GyIHomIqBIq1XdQ/fv3R0hICH744Qd8//33cHJyQnh4OH7//XdD9EhERJVQiffiu3jxIqKiorBs2TKcPHlSN3/37t3o2rWrXpsjIqLKq8SAatu2LR49elTosi+//LLcGyIiIgJKEVA5OTkYP348WrVqBTMzM938sWPH6rUxIiKq3Er8DiooKAi2trbo1asXjhw5Ant7e2RlZRmiNyIiqsRKDKgmTZpgzpw5ePToETZt2oS+ffuiTZs2huiNiIgqsRID6vnz5wCA+/fvo1WrVqhRowYcHR313RcREVVyJX4HtW7dOlhbW+Obb75BWFgYLCwsMGfOHEP0RkRElViJARUQEAAAiI6OLrcLc+fOnQtfX1/cuXMHADBr1ixERESUy9hERPR2KDKg/v3vfxe74YoVK8pUeMWKFfj+++/LNAYREb29igwo3m+PiIikVGRAzZ8/X6+FJ0yYgJEjRyIuLg5TpkzB/fv3C13P19cXfn5+AAAbGxu99kQkR7dWf1ap61PlVeJZfE2bNsWBAweg0WgAAG3atIG/v3+JA6tUKmg0mgKTt7c31qxZA2dnZ7Rr1w7p6enFHupTKpVwd3eHu7s7MjIyXuOtERFRRVbiSRJKpRJff/01fv75ZwCARqPB1q1b8d133xW7nZeXV6kaUCqV+O2330q1LhERVR4l7kFVq1YNsbGx+ebl5OSUqaitra3u5w8//BBarbZM4xER0dunxD2ojIwMODk5QQgBABg8eDDS09PLVHTp0qVo164dhBC4cuUKPv/88zKNR0REb58SA+pf//oX1q1bhxYtWuDGjRtITU3FJ598UqaiI0eOLNP2RET09isxoFJTU+Hl5YVq1arB2NgYDx8+NERfRERUyUl2oS4REVFxSrxQt3nz5nB3d0dYWBgAoH///oiOjjZMd0REVGmVeKFuZGQk2rdvrzu0N2/ePAQHBxumOyIiqrRKPM28UaNGePbsme71s2fP+LgNIiLSuxJPkggKCsKpU6cQGhoKIQQ+/PBDbNy40RC9ERFRJVZiQC1cuBARERHw8PAAAIwePRoJCQn67ouIiCq5EgMKANRqNdRqtb57ISIi0inxOygiIiIpMKCIiEiWGFBERCRLDCgiIpIlBhQREckSA4qIiGSJAUVERLLEgCIiIlliQBERkSxJFlATJkxAcnIytFotlixZIlUbREQkU6W61VF5+8c//oEBAwagbdu2ePbsGerUqSNFG0REJGOS7EGNGzcOixcv1j3G486dO1K0QUREMibJHlSzZs3g4eGB7777Dk+ePMHUqVMRFxdX6Lq+vr7w8/MDANjY2BiyzSLdWv2Z1C1USvzcyZAq8983ubx3vQWUSqWCra1tgfn+/v5QKBSoWbMmOnXqBHd3d+zcuRNOTk6FjqNUKqFUKgEAsbGx+mqXiIhkRm8B5eXlVeSycePGISQkBMCL0MnLy4ONjQ0yMjL01Q4REVUwknwH9euvv6Jbt24AgKZNm6JKlSoMJyIiykeS76B++eUX/PLLL9BoNHj27Bl8fHykaIOIiGRMkoB6/vw5Pv30UylKExFRBcE7SRARkSxJsgdFJIWaNWti8uTJcHR0hJGRkdTtvBWEELhy5QpWrlyJe/fuSd0OvWUYUFRpTJ48GXFxcZg/fz5yc3OlbuetYGJigr59+2Ly5MmYO3eu1O3QW4aH+KjScHR0xO+//85wKke5ubnYu3cvHB0dpW6F3kIMKKo0jIyMGE56kJuby0OmpBcMKCIikiUGFBERyRIDiqgM5s6diylTprzx8tfl4+OD+vXrl9t4RHLGgCKqQEaNGgU7Ozup2yAyCAYU0WuaNWsWkpOToVKp0Lx5cwCAk5MTIiIiEBcXh+joaN38V3322Wc4deoUEhISsGvXLpibm8PCwgKXL1+GQvHiig9LS0ukpqbqXr9q8ODBcHNzw5YtW6BWq9GnTx/dTZcBoEePHti9ezcAICsrC8uXL8fp06dx4MAB3aNqStMnkVwwoIheQ/v27TFs2DC4urpi0KBBcHd3BwCsW7cOEydOhJubG6ZOnYqffvqpwLYhISHo2LEj2rVrh6SkJIwdOxYPHz7E4cOH0bdvXwDAsGHDsHv3buTk5BTYfvfu3YiLi8OIESPg6uqK33//HS1bttSFz+jRoxEYGAgAsLCwQHx8PDp06IAjR47orlEqTZ9EcsELdYleg4eHB0JDQ5GdnQ0ACAsLg5mZGd577z0EBwfr1qtatWqBbVu3bo3//Oc/sLa2hoWFBSIjIwEA69evx7Rp07Bnzx6MHj0avr6+pe4nKCgIn3zyCQIDA9G5c2eMHDkSwItTv3fs2AEA2Lx5M0JCQlC9evVS9UkkFwwootckhMj32tjYGPfv34erq2ux223YsAEDBw7EmTNn4OPjg3/84x8AgBMnTsDR0RFdu3aFiYkJzp49W+peAgMDER4ejidPniA4OLjI67yEEKXuk0gueIiP6DVER0fjww8/hJmZGSwsLNC/f388fvwYqampGDJkiG69tm3bFtjW0tIS6enpUCgUGDFiRL5lmzZtwrZt23SH6IqSlZUFS0tL3ev09HSkpaVh9uzZ2LBhg26+iYmJrp+PP/4Yx44dQ1ZWVqn6JJILBhTRa1Cr1dixYwcSEhKwe/duHD16FAAwYsQIjB07FgkJCTh79iwGDBhQYNtvvvkGf/zxB1QqFZKTk/Mt27JlC2rWrIlt27YVW3/Dhg1Yu3Yt1Go1zMzMdNtev34dSUlJuvUePnyIVq1aIS4uDt26dcP8+fNL3SeRXPAQH9FrWrhwIRYuXFhgfu/evQvM+/bbb3U/r127FmvXri10zPfffx+7du1CZmZmsbVDQkLynbn3clulUllg3Tlz5mDOnDn55l25cqXQPonkiAFFJLFVq1ahd+/e6NOnz2tvGxcXh0ePHpXrxcBEciFJQG3fvl13/YW1tTW/uKVKbdKkSQXm/fjjj+jSpUu+eT/88EO+75kAwM3NrdAxX/2eiqiikiSghg0bpvt5+fLlJR7WIKpsJkyYIHULRJKT/BDf0KFD0a1bN6nbICIimZE0oDw8PHDr1i2kpKQUuY6vry/8/PwAQHfFPADcWv2Z3vuTKynfe2X+3IkMhf/OXtBbQKlUKtja2haY7+/vj7CwMADA8OHDSzytVqlU6s5Qio2NLf9GiYhIlvQWUF5eXsUuNzExwaBBg9ChQwd9tUBUrHoT15frePxfL1H5kuxC3R49eiA5ORl//vmnVC0QvRU8PT0RHh5e5nWI5EaygBo2bFiJh/eIKhtjY+NiXxNVJpL97R89ejR+/vlnqcoTSSI0NBRxcXHQarW6u5ZnZWXh22+/RUxMDDp37lzgdWF69eqFpKQkHD16FIMGDdLNr1atGgICAnDq1CnEx8fD29u7wLbu7u44fvw44uPjcfz4cTRr1gzAi/sMuri46NY7duwY2rRpU55vn+i18L9nRAY0ZswYuLm5wc3NDZMmTUKtWrVgYWEBrVaLTp064fjx4wVe/13VqlWhVCrRv39/eHh45DsZyd/fH4cOHULHjh3xwQcfYNmyZahWrVq+7ZOTk9G1a1e0b98ec+bM0d22af369Rg1ahQAoGnTpqhatSo0Go3+PgyiEjCgiAxo0qRJSEhIQExMDBo2bIimTZsiJydH9yRcAAVe/12LFi2Qmpqquzxj8+bNumU9e/bEjBkzoFarcfjwYZiZmaFRo0b5tq9RowaCg4Oh0WiwYsUKtGrVCgAQHByMfv36QaFQYMyYMQXuWkFkaJJfqEtUWXh6eqJHjx7o3LkzsrOzERUVBTMzMzx58gR5eXm69f7+ujB/fybVS0ZGRhg8eDAuXLiQb369evV0Py9YsABRUVEYNGgQHBwccPjwYQBAdnY2VCoVBgwYgKFDhxZ5GyUiQ2FAUaVl6NPCa9SogXv37iE7OxvNmzdHp06d3mic5ORkNG7cGE5OTrh8+TKGDx+uWxYZGYmJEydi4sSJAIB27dohISGhQB8vz559eUjvpfXr1yM8PBxHjx7FvXv33qg/ovLCQ3xEBrJv3z4oFAokJiZiwYIFiImJeaNxnj59Cj8/P+zduxdHjx7F1atXdcsWLFgAU1NTnDlzBhqNBgsWLCiw/dKlS7Fo0SIcO3YMJiYm+ZbFx8fjwYMHJT44kcgQuAdFZCDPnj0r9JEaf7/zeGnuRB4ZGYmWLVsWmP/kyRN88cUXBeYfOXIER44cAQDExMToniYAIN8zo+rXrw9jY2Ps37+/xB6I9I17UEQEAPj000/xxx9/wN/fv8jvuIgMiXtQRDIWEhKCxo0b55s3ffp0vezhBAUFISgoqNzHJXpTDCgiGXv1IlyiyoaH+IiISJYYUEREJEsMKCIikiV+B0WV1tHFjuU6nseMK+U6HlFlxz0oIonMnTsXU6ZM0cvYUVFRJT4MtDTrEEmJAUVERLLEgCIyoFmzZiE5ORkqlUp3NwcnJydEREQgLi4O0dHRuvmBgYFYs2YNoqOjcf78efTt27fIcc3MzLBt2zYkJiZi+/btMDc31y3z8vLCiRMncPr0aezcuRPVq1cvsP1PP/2E2NhYaLVazJs3DwDQrVs3hISE6Nbp0aNHsXdZJypv/A6KyEDat2+PYcOGwdXVFQqFAvHx8Th9+jTWrVuHL774AikpKejYsSN++ukndO/eHQDg6OgIT09PODs7IyoqCk2aNMHTp08LjD1u3Dg8fvwYLi4uaNOmDeLj4wEAtWvXxuzZs9GjRw88fvwY06ZNw1dffVXgHn3+/v64d+8ejI2NcfDgQbRp0waHDh3Cf//7X9jY2CAjIwOjR4/mPfrIoCQJKBcXF6xduxZmZmbIycnB+PHjERsbK0UrRAbj4eGB0NBQZGdnAwDCwsJgZmaG9957D8HBwbr1qlatqvt5586dEEIgJSUFly9fRosWLZCYmFhg7K5du2LVqlUAAI1GgzNnzgAAOnXqhHfeeUf34MMqVarg5MmTBbYfOnQo/Pz8oFAoUL9+fbzzzjvQaDQICgrCJ598gsDAQHTu3BkjR44svw+EqASSBNTSpUvx7bffYt++fejduzeWLl2KDz74QIpWiAzq7/e4MzY2xv379+Hq6lqq9Yu7R15hy4yMjKBSqfDxxx8XuZ2joyOmTp0Kd3d33L9/H4GBgTAzMwPw4jBjeHg4njx5guDgYOTm5hY5DlF5kySghBCwsrIC8OLZNGlpaVK0QZWcoU8Lj46OxoYNG7B48WIoFAr0798fP//8M1JTUzFkyBDs2rULANC2bVvdHtBHH32EjRs36p7/dP78+SLHHjFiBA4fPoxWrVqhbdu2AF7cufy///0vnJ2dcenSJZibm8Pe3h4XL17UbWtlZYVHjx4hMzMTdevWRe/evXUPMUxPT0daWhpmz54NLy8vPX46RAVJElCTJ09GZGQkli9fDmNjY7z33ntFruvr6ws/Pz8AgI2NjaFaJCp3arUaO3bsQEJCAq5evYqjR48CAEaMGIE1a9Zg9uzZMDU1xfbt23UBdf78eRw5cgT16tXDF198Uej3TwCwZs0aBAYGIjExEQkJCTh16hQAICMjA6NGjcK2bdt0hw5nz56dL6DOnDkDtVqNs2fP4vLly7rDgS9t2bIFderUQVJSUrl/JiUx9EMlSX6EPiaVSiU0Gk2BydvbW/zwww9i0KBBAoD46KOPhEqlKtWYsbGxeumVU+WYNm3aJHkPrzMFBgaKwYMHS97H6tWrxZgxY96qz5aTvKaifrfrbQ+quMMBmzZtwpdffgkACA4Oxvr16/XVBhGVQVxcHB49eqS3C4qJiiPJIb60tDR4enriyJEj6NatW77DDUT0wujRowvM69mzJ5YsWZJvXmpqqt4ey+Hm5qaXcYlKQ5KA8vX1xQ8//ACFQoEnT57ovmMi0ichBExMTCr0mWj79++X3ePYTUxM+ARe0gtJAur48eP8nxkZ3JUrV9C3b1/s3bu3QoeUnJiYmKBv3764cuWK1K3QW4h3kqBKY+XKlZg8eTIGDx4MIyMjqdt5KwghcOXKFaxcuVLqVugtxICiSuPevXuYO3eu1G0QUSnxZrFERCRLDCgiIpIlBhQREcmSEV5csVsh3L59G1evXn3j7V8+NkAKrF25aktdn7WlUVnfe1lrOzg4oG7duoUuk/w2F4aapLxVEmtXrtpS12ftylf/bazNQ3xERCRLDCgiIpKlShVQ69atY23WrhT1Wbvy1X8ba1eokySIiKjyqFR7UEREVHEwoIiISJbe+oAKCAjArVu3oNFoDF7b3t4ehw4dwrlz56DVajFp0iSD1q9atSr++OMPJCQkQKvVYt68eQatb2xsjPj4eISHhxu0LvDiGUkvH2UeGxtr0No1atRAcHAwkpKScO7cOXTq1MlgtZs1awa1Wq2bMjMzdQ8HNYTJkydDq9VCo9Fg69atusfMG8KkSZOg0Wig1Wr1/p4L+71Ss2ZN7N+/HxcuXMD+/fthbW1t0PpDhgyBVqtFbm4uOnToYNDaS5cuRVJSEhITExESEoIaNWqUWz1JrxvQ9+Th4SFcXV2FRqMxeG1bW1vh6uoqAAgLCwtx/vx50bJlS4P2UL16dQFAKBQKERMTI959912D1f73v/8ttmzZIsLDww3+2aemporatWsbvC4AsWHDBjF27FgBQJiamooaNWpI0oexsbFIT08XjRo1Mkg9Ozs7cfnyZWFmZiYAiB07dggfHx+D1G7VqpXQaDTC3NxcmJiYCJVKJZo0aaK3eoX9XlmyZImYPn26ACCmT58uFi9ebND6LVq0EM2aNRNRUVGiQ4cOBq3t5eUlTExMBACxePHicnvvb/0e1NGjR3H37l1Jat+8eRNqtRoA8PDhQyQlJaFBgwYG7eHRo0cAAFNTU5iamhrswXINGjRA3759sX79eoPUkwtLS0t07doVAQEBAIDnz58jMzNTkl66d++OS5cu4dq1awarqVAoYG5uDhMTE1SrVg1paWkGqduyZUvExMQgOzsbubm5OHLkCD788EO91Svs98qAAQOwceNGAMDGjRsxcOBAg9ZPTk7GhQsX9FazuNoqlUr3jLWYmBjY29uXS623PqDkwsHBAa6urvjjjz8MWtfY2BhqtRq3b9+GSqXCqVOnDFJ35cqVmDZtGvLy8gxS7++EENi/fz/i4uLg6+trsLpOTk64c+cOAgMDER8fD6VSiWrVqhms/quGDRuGbdu2GaxeWloali9fjmvXriE9PR2ZmZlQqVQGqa3VatG1a1fUqlUL5ubm6NOnDxo2bGiQ2i/Vq1cPN2/eBPDiP6dF3brnbTdmzBhERESUy1gMKAOoXr06du/ejcmTJyMrK8ugtfPy8uDq6gp7e3t07NgRrVq10nvNvn374vbt24iPj9d7raJ06dIFHTp0QO/evfGvf/0LHh4eBqmrUCjQvn17rFmzBu3bt8ejR48wY8YMg9R+lampKby9vREcHGywmtbW1hgwYAAaN24MOzs7VK9eHSNGjDBI7eTkZCxZsgQqlQr79u1DYmIicnJyDFKb/t+sWbOQk5ODLVu2lMt4DCg9UygU2L17N7Zs2YLQ0FDJ+sjMzMThw4fxz3/+U++1unTpAm9vb6SmpmL79u3o1q0bgoKC9F73Venp6QCAO3fuIDQ0FB07djRI3Rs3buDGjRu6PdVdu3ahffv2Bqn9qt69eyM+Ph63b982WM0ePXogNTUVGRkZyMnJQUhICN577z2D1f/ll1/QoUMHeHp64u7du7h48aLBagPArVu3YGtrCwCwtbU16GcvByNHjkS/fv3K9T8lDCg9CwgIQFJSElasWGHw2jY2NrqzaczMzNCjRw8kJyfrve6sWbPQsGFDNG7cGMOGDcOhQ4fw6aef6r3uS9WqVYOFhYXu5549e0Kr1Rqk9q1bt3D9+nU0a9YMwIvvgc6dO2eQ2q8aPny4QQ/vAcC1a9fQqVMnmJubA3jx3pOSkgxWv06dOgCAhg0bYtCgQQZ//2FhYfDx8QEA+Pj4YM+ePQatL6VevXph+vTp8Pb2RnZ2drmObZCzbKSatm7dKtLS0sSzZ8/E9evXxZgxYwxWu0uXLkIIIRITE4VarRZqtVr07t3bYPXbtGkj4uPjRWJiotBoNOKbb74x+Ofv6elp8LP4GjduLBISEkRCQoLQarVi1qxZBq3v4uIiYmNjRWJioggNDRXW1tYGrW9ubi4yMjKElZWVwf+8582bJ5KSkoRGoxGbNm0SVapUMVjt6OhocfbsWZGQkCC6deum11qF/V6pVauWOHDggLhw4YI4cOCAqFmzpkHrDxw4UFy/fl08efJE3Lx5U+zbt89gtS9evCiuXbum+z23Zs2acqnFWx0REZEs8RAfERHJEgOKiIhkiQFFRESyxIAiIiJZYkAREZEsMaCIiEiWGFBExZg4cSLOnTuHGzduYPXq1QCAzz//vMQLj318fHTr/93MmTPLvc/SqFGjBsaNG6d77enpKcmjUIhKiwFFVIzx48ejT58+8Pf31837+eefy3TrplmzZpVHa6/N2toa48ePl6Q20ZtgQBEVYc2aNXByckJYWBhq1qypmz937lxMmTIFAODm5obExEScOHECS5cuzfcQNzs7O0RERODChQtYsmQJAGDRokUwNzeHWq3G5s2bC63r4OCApKQkKJVKaDQabN68Gd27d8exY8dw4cIFuLu7A3jxgLzQ0FAkJibi5MmTaNOmja6/gIAAREVF4dKlS5g4cSIAYPHixXB2doZarcbSpUsBABYWFrqHKxbVD5GUDHYrEk6cKtr08sGHPj4+YvXq1QKAmDt3rpgyZYoAIDQajejcubMAIBYtWqR7iJuPj4+4dOmSsLKyElWrVhVXrlwR9vb2AoDIysoqtqaDg4N4/vy5aN26tTAyMhJxcXEiICBAABDe3t4iNDRUABCrVq0Sc+bMEQDEBx98INRqta6/48ePiypVqojatWuLjIwMoVAohIODQ76HzHl6eor79++LBg0aCCMjI3HixAnRpUsXyT9zTpxeTtyDInpDNWrUgKWlJU6ePAkA2Lp1a77lBw8exIMHD/D06VOcO3cODg4OpR47NTUVWq0WQgicPXsWBw8eBABoNBo4OjoCAN5//33docaoqCjUrl0bVlZWAIC9e/fi2bNn+Ouvv3D79m3Uq1ev0DqnTp3Cn3/+CSEEEhISdGMTyQEDiugNGRkZFbv86dOnup9zc3OhUChKPfar2+bl5ele5+Xl6cYprP7LJyaXtnZZeiTSNwYU0Ru6f/8+srKy8O677wJ48QTb0nj+/Hm5BEF0dLTu2Tuenp7IyMgo9oGYWVlZsLS0LHNdIkNhQBGVwdixY7Fu3TqcOHECRkZGyMzMLHGbdevW4cyZM2U+KWHevHm6kzQWL16sexZRUe7evYvjx49Do9HoTpIgkjM+boOoDKpXr45Hjx4BAKZPn4769etj8uTJ0jZF9JbgAWeiMujbty9mzpwJhUKBq1evYtSoUVK3RPTW4B4UkURq1aqlOzvvVd27d8fdu3cl6IhIXhhQREQkSzxJgoiIZIkBRUREssSAIiIiWWJAERGRLP0fLaIHe5thYRcAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(data=monthly_average_delays[monthly_average_delays.metric == 'median'], \n",
    "            x='flight_month',\n",
    "            y='delay (mins)',\n",
    "            hue='delay_type',\n",
    "            palette='colorblind',\n",
    "            )\n",
    "plt.title('Median flight delay')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Taken together, these figures reinforce the idea that many flights are not delayed (suggested by the median [i.e the midpoint of the flight delay observations accounting for 50% of the data]) and even arrive early. This observation is consistent with the statistics reported by the US Department of Transportation. Conversely, the higher mean delay values suggest that the low proportion of flights that are delayed substantially impact the average delay times, thus the median is a better measure of the central tendency of the distribution because it is not skewed by large outlying values, but the mean better captures the effect that long delays are having on flights monthly. Regardless of which metric is used, the departure delay is always greater than the arrival delay. This makes sense because most of the uncertainty regarding delays is encountered prior to departure.\n",
    "\n",
    "Interestingly, it would appear that my prediction was unsupported by the data, and it is the summer months that experience the highest periods of delay on average. There are two hypotheses that immediately come to mind to explain this divergence from my initial prediction. 1) Winter schedules are typically quite well managed by airlines insofar as they more rigorously adjust their schedules to account for weather related delays. 2) Commercial flights are more frequent in the summer months than in others, and schedulling more flights over a shorter time period creates more opportunity for unexpected circumstances to cause delays.\n",
    "\n",
    "The first hypothesis can be addressed by comparing the scheduled times of flights between the same origins and destinations in the winter vs. the summer. For hypothesis 1 to gain support, would predict longer scheduled flights than in summer months, and fewer flights scheduled during periods where inclement weather is forcasted. Hypothesis 2 can be addressed by inpecting the overall number of flights in each month and to also consider data regarding demand for flights (number of passengers, cargo, *etc*.).\n",
    "\n",
    "There are likely many more hypotheses one could test for here as well, but in the interest of time and scope, we will focus on the weather for the time being as we were recommended to explore that factor; however, it would be interesting to return to some of these other analyses if time permits."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3) How does the weather affect the delay?<a id='task3'></a>\n",
    "**Note**: While it is suggested to examine this closely by finding additional data from a weather API for a sample of the flights, in the interest of saving time early on we will only consider the relative delay of weather overall using the data we already have for our first pass and we will return to address this point more rigorously in the future if time permits."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to appreciate the impact of weather on flight delays, we should consider it in the context of all features that affect flight delays. Unfortunately, the descriptions of the data we have do not clearly state whether the durations listed on the factor-specific columns contribute to the departure or arrival delay. Hence, we should begin by establishing this.  \n",
    "\n",
    "Given that our goal is to predict arrival delays, it is sensible to begin by tesing the hypothesis that the specific delay columns should be interpreted in the context of an arrival delay; thus, in observations where they are known to contribute to the delay (i.e. non-zero), they should always sum to a number that is $\\leq$ the arrival delay ($<$ to account for missing values). Conversely, there should be no such restriction for the departure delay."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Arrival delay hypothesis test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Colums where the contribution to the delay is specifically known.\n",
    "specific_delay_columns = ['carrier_delay', 'weather_delay', 'nas_delay', 'security_delay', 'late_aircraft_delay']\n",
    "# All delay features.\n",
    "all_delay_columns = ['dep_delay', 'arr_delay'] + specific_delay_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_specific_delay = flights[specific_delay_columns].sum(axis=1) # Calculate the sum across columns for each observation\n",
    "positive_total_specific_delay = total_specific_delay[total_specific_delay > 0] # Keep observations where we have delay information.\n",
    "known_specific_delay_indices = positive_total_specific_delay.index # Store the indices where we know what factors contributed to the delay."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1921207    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test our hypothesis that the specific delay information columns are addressing arrival delay. We expact all true if so.\n",
    "arr_test_results = np.less_equal(positive_total_specific_delay, flights.loc[known_specific_delay_indices]['arr_delay'])\n",
    "\n",
    "arr_true = arr_test_results.sum()\n",
    "arr_false = arr_test_results.count() - arr_test_results.sum()\n",
    "\n",
    "arr_test_results[arr_test_results == False]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Only 1 index failed the positive control test for our hypothesis. Let us check to see if there is a likely data entry error that may explain this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dep_delay              96\n",
       "arr_delay              67\n",
       "carrier_delay          67\n",
       "weather_delay           0\n",
       "nas_delay              67\n",
       "security_delay          0\n",
       "late_aircraft_delay     0\n",
       "Name: 1921207, dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights.iloc[1921207][all_delay_columns]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here both carrier and nas delays are listed as 67. This may simply be a data entry error such that the value was mistakenly recorded twice. Let us move on with our next test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total True: 226840\n",
      "Total False: 223662\n"
     ]
    }
   ],
   "source": [
    "# Negative control for our hypothesis test. This should give a mix of true and false values.\n",
    "dep_test_results = np.less_equal(positive_total_specific_delay, flights.loc[known_specific_delay_indices]['dep_delay'])\n",
    "\n",
    "dep_true = dep_test_results.sum()\n",
    "dep_false = dep_test_results.count() - dep_test_results.sum()\n",
    "\n",
    "print(f'Total True: {dep_true}\\nTotal False: {dep_false}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>Test</th>\n",
       "      <th>Condition</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Positive Control</td>\n",
       "      <td>True</td>\n",
       "      <td>450501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Positive Control</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Negative Control</td>\n",
       "      <td>True</td>\n",
       "      <td>226840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Negative Control</td>\n",
       "      <td>False</td>\n",
       "      <td>223662</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Test  Condition   Count\n",
       "0  Positive Control       True  450501\n",
       "1  Positive Control      False       1\n",
       "2  Negative Control       True  226840\n",
       "3  Negative Control      False  223662"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delay_results = pd.DataFrame({\n",
    "    'Test': ['Positive Control', 'Positive Control', 'Negative Control', 'Negative Control'],\n",
    "    'Condition': [True, False, True, False],\n",
    "    'Count': [arr_true, arr_false, dep_true, dep_false],\n",
    "})\n",
    "delay_results.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Test summary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='Test',\n",
    "            y='Count',\n",
    "            data=delay_results,\n",
    "            hue='Condition',\n",
    "            palette='colorblind',\n",
    "            )\n",
    "plt.title('Arrival Delay hypothesis test')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see from the above results that our hypothesis that the specific delay columns sum to the arrival delay holds (data entry errors and missing data notwithstanding)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Impact of weather on flight delays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "34.05% of flights were late departing.\n",
      "35.07% of flights were late reaching their destination.\n",
      "25.00% of flights were late departing and reaching their destination.\n"
     ]
    }
   ],
   "source": [
    "flight_departure_delayed = flights[flights['dep_delay'] > 0][all_delay_columns] # Observations where flight departure is delayed.\n",
    "flight_arrival_delayed = flights[flights['arr_delay'] > 0][all_delay_columns] # Observations where flight arrival is delayed.\n",
    "# Observations where flights were delayed departing and arriving. \n",
    "flight_delayed = flights[(flights['arr_delay'] > 0) & (flights['dep_delay'] > 0)][all_delay_columns]\n",
    "\n",
    "flight_departure_delayed_percentage = flight_departure_delayed.shape[0] / flights.shape[0] * 100\n",
    "flight_arrival_delayed_percentage = flight_arrival_delayed.shape[0] / flights.shape[0] * 100\n",
    "flight_both_delayed_percentage = flight_delayed.shape[0] / flights.shape[0] * 100\n",
    "\n",
    "print(f'{flight_departure_delayed_percentage:.2f}% of flights were late departing.')\n",
    "print(f'{flight_arrival_delayed_percentage:.2f}% of flights were late reaching their destination.')\n",
    "print(f'{flight_both_delayed_percentage:.2f}% of flights were late departing and reaching their destination.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have some information about the flights experiencing some form of delay, in order to appreciate the impact of the weather on delay, we should find:  \n",
    "1. In what proportion of these delayed flights do we know (from our sample) that weather was a factor in the delay?\n",
    "2. How much of a factor was weather in each of these cases (e.g. on average, relative to other factors, *etc*.)?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4) How taxi times changing during the day? Does higher traffic lead to bigger taxi times?<a id='task4'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A simple way to define **taxi time** is the amount of time an aircraft spends in movement on the surface of an airport.\n",
    "Source: [Predicting Aircraft Taxi Time](https://towardsdatascience.com/predicting-aircraft-taxi-time-6c4eaa402d77)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Departure taxi-time (or taxi-out time) is the period of time between an aircraft leaving a terminal gate and actually taking off from an airport.\n",
    "\n",
    "Vice versa, arrival taxi-time (or taxi-in time) is the period of time between an aircraft touch down and getting to the gate."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We preview the departure and arrival times (scheduled and actual) along with their taxi times."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>taxi_out</th>\n",
       "      <th>crs_dep_time</th>\n",
       "      <th>dep_time</th>\n",
       "      <th>taxi_in</th>\n",
       "      <th>crs_arr_time</th>\n",
       "      <th>arr_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>346168</th>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2002</td>\n",
       "      <td>2153.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2200</td>\n",
       "      <td>2344.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345836</th>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1555</td>\n",
       "      <td>1550.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1722</td>\n",
       "      <td>1708.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345837</th>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1738</td>\n",
       "      <td>1735.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1913</td>\n",
       "      <td>1913.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345838</th>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1800</td>\n",
       "      <td>1754.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1930</td>\n",
       "      <td>1931.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345839</th>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1955</td>\n",
       "      <td>2113.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2210</td>\n",
       "      <td>2329.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           fl_date  taxi_out  crs_dep_time  dep_time  taxi_in  crs_arr_time  \\\n",
       "346168  2018-01-01      20.0          2002    2153.0      4.0          2200   \n",
       "345836  2018-01-01      18.0          1555    1550.0      4.0          1722   \n",
       "345837  2018-01-01      17.0          1738    1735.0     15.0          1913   \n",
       "345838  2018-01-01      32.0          1800    1754.0      4.0          1930   \n",
       "345839  2018-01-01      42.0          1955    2113.0      3.0          2210   \n",
       "\n",
       "        arr_time  \n",
       "346168    2344.0  \n",
       "345836    1708.0  \n",
       "345837    1913.0  \n",
       "345838    1931.0  \n",
       "345839    2329.0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights[['fl_date', 'taxi_out', 'crs_dep_time', 'dep_time', 'taxi_in', 'crs_arr_time', 'arr_time']].sort_values(by='fl_date').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are going to plot **taxi out times** vs **departure times**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Taxi-Out** vs **CRS Dep Time**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='crs_dep_time', ylabel='taxi_out'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=flights, x=\"crs_dep_time\", y=\"taxi_out\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Taxi-Out** vs **Actual Dep Time**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='dep_time', ylabel='taxi_out'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=flights, x=\"dep_time\", y=\"taxi_out\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are also check **taxi in times** vs **arrival times**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Taxi-In** vs **CRS Arr Time**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='crs_arr_time', ylabel='taxi_in'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=flights, x=\"crs_arr_time\", y=\"taxi_in\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Taxi-In** vs **Actual Arr Time**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='arr_time', ylabel='taxi_in'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=flights, x=\"arr_time\", y=\"taxi_in\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we could see that taxi times tend to be low during red eye flights flights (flights that leave in the evening or at night, and arrives at the end destination during the day (normally in the morning)). We could notice a drop in values for taxi times which happens around midnight (0000) to 5AM (0500)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5) What is the average percentage of delays that is already created before departure? (aka are arrival delays caused by departure delays?) Are airlines able to lower the delay during the flights?<a id='task5'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We preview the data for relevant columns to tackle this task below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>crs_dep_time</th>\n",
       "      <th>dep_time</th>\n",
       "      <th>wheels_off</th>\n",
       "      <th>taxi_out</th>\n",
       "      <th>dep_delay</th>\n",
       "      <th>carrier_delay</th>\n",
       "      <th>weather_delay</th>\n",
       "      <th>nas_delay</th>\n",
       "      <th>security_delay</th>\n",
       "      <th>late_aircraft_delay</th>\n",
       "      <th>crs_arr_time</th>\n",
       "      <th>wheels_on</th>\n",
       "      <th>arr_time</th>\n",
       "      <th>taxi_in</th>\n",
       "      <th>arr_delay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1155</td>\n",
       "      <td>1151.0</td>\n",
       "      <td>1224.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1551</td>\n",
       "      <td>1547.0</td>\n",
       "      <td>1555.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1730</td>\n",
       "      <td>1729.0</td>\n",
       "      <td>1830.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1917</td>\n",
       "      <td>1945.0</td>\n",
       "      <td>1955.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1830</td>\n",
       "      <td>1835.0</td>\n",
       "      <td>1854.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2050</td>\n",
       "      <td>2058.0</td>\n",
       "      <td>2106.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>740</td>\n",
       "      <td>736.0</td>\n",
       "      <td>749.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>923</td>\n",
       "      <td>930.0</td>\n",
       "      <td>935.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1400</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>1436.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1724</td>\n",
       "      <td>1731.0</td>\n",
       "      <td>1735.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>610</td>\n",
       "      <td>849.0</td>\n",
       "      <td>902.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>704</td>\n",
       "      <td>914.0</td>\n",
       "      <td>924.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>140.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>715</td>\n",
       "      <td>733.0</td>\n",
       "      <td>748.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>855</td>\n",
       "      <td>855.0</td>\n",
       "      <td>901.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2130</td>\n",
       "      <td>2120.0</td>\n",
       "      <td>2134.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2236</td>\n",
       "      <td>2241.0</td>\n",
       "      <td>2248.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1940</td>\n",
       "      <td>2104.0</td>\n",
       "      <td>2127.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>2037</td>\n",
       "      <td>2154.0</td>\n",
       "      <td>2159.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1833</td>\n",
       "      <td>1934.0</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2151</td>\n",
       "      <td>2306.0</td>\n",
       "      <td>2311.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>1528</td>\n",
       "      <td>1550.0</td>\n",
       "      <td>1601.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1727</td>\n",
       "      <td>1731.0</td>\n",
       "      <td>1737.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1120</td>\n",
       "      <td>1121.0</td>\n",
       "      <td>1136.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1406</td>\n",
       "      <td>1347.0</td>\n",
       "      <td>1410.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1820</td>\n",
       "      <td>1910.0</td>\n",
       "      <td>1953.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1956</td>\n",
       "      <td>2112.0</td>\n",
       "      <td>2118.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2205</td>\n",
       "      <td>2225.0</td>\n",
       "      <td>2243.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2328</td>\n",
       "      <td>2341.0</td>\n",
       "      <td>2348.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>1230</td>\n",
       "      <td>1313.0</td>\n",
       "      <td>1328.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1354</td>\n",
       "      <td>1425.0</td>\n",
       "      <td>1428.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>1940</td>\n",
       "      <td>1930.0</td>\n",
       "      <td>1949.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2107</td>\n",
       "      <td>2100.0</td>\n",
       "      <td>2113.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>521</td>\n",
       "      <td>516.0</td>\n",
       "      <td>532.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>800</td>\n",
       "      <td>732.0</td>\n",
       "      <td>816.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>1200</td>\n",
       "      <td>1514.0</td>\n",
       "      <td>1551.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1345</td>\n",
       "      <td>1718.0</td>\n",
       "      <td>1724.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>219.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>609</td>\n",
       "      <td>639.0</td>\n",
       "      <td>650.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>905</td>\n",
       "      <td>912.0</td>\n",
       "      <td>922.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>500</td>\n",
       "      <td>508.0</td>\n",
       "      <td>519.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>647</td>\n",
       "      <td>648.0</td>\n",
       "      <td>652.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    crs_dep_time  dep_time  wheels_off  taxi_out  dep_delay  carrier_delay  \\\n",
       "2           1155    1151.0      1224.0      33.0       -4.0            0.0   \n",
       "7           1730    1729.0      1830.0      61.0       -1.0            0.0   \n",
       "8           1830    1835.0      1854.0      19.0        5.0            5.0   \n",
       "10           740     736.0       749.0      13.0       -4.0            0.0   \n",
       "14          1400    1400.0      1436.0      36.0        0.0            0.0   \n",
       "15           610     849.0       902.0      13.0      159.0          140.0   \n",
       "17           715     733.0       748.0      15.0       18.0            0.0   \n",
       "19          2130    2120.0      2134.0      14.0      -10.0            0.0   \n",
       "23          1940    2104.0      2127.0      23.0       84.0            0.0   \n",
       "26          1833    1934.0      2011.0      37.0       61.0            0.0   \n",
       "33          1528    1550.0      1601.0      11.0       22.0            0.0   \n",
       "34          1120    1121.0      1136.0      15.0        1.0            0.0   \n",
       "35          1820    1910.0      1953.0      43.0       50.0            0.0   \n",
       "37          2205    2225.0      2243.0      18.0       20.0            5.0   \n",
       "38          1230    1313.0      1328.0      15.0       43.0            0.0   \n",
       "39          1940    1930.0      1949.0      19.0      -10.0            0.0   \n",
       "41           521     516.0       532.0      16.0       -5.0            0.0   \n",
       "44          1200    1514.0      1551.0      37.0      194.0          142.0   \n",
       "46           609     639.0       650.0      11.0       30.0            0.0   \n",
       "53           500     508.0       519.0      11.0        8.0            0.0   \n",
       "\n",
       "    weather_delay  nas_delay  security_delay  late_aircraft_delay  \\\n",
       "2             0.0        0.0             0.0                  0.0   \n",
       "7             0.0       38.0             0.0                  0.0   \n",
       "8             0.0       11.0             0.0                  0.0   \n",
       "10            0.0        0.0             0.0                  0.0   \n",
       "14            0.0        0.0             0.0                  0.0   \n",
       "15            0.0        0.0             0.0                  0.0   \n",
       "17            0.0        0.0             0.0                  0.0   \n",
       "19            0.0        0.0             0.0                  0.0   \n",
       "23            0.0        0.0             0.0                 82.0   \n",
       "26            0.0       61.0             0.0                 19.0   \n",
       "33            0.0        0.0             0.0                  0.0   \n",
       "34            0.0        0.0             0.0                  0.0   \n",
       "35            0.0       32.0             0.0                 50.0   \n",
       "37            0.0        0.0             0.0                 15.0   \n",
       "38           34.0        0.0             0.0                  0.0   \n",
       "39            0.0        0.0             0.0                  0.0   \n",
       "41            0.0       16.0             0.0                  0.0   \n",
       "44           52.0       25.0             0.0                  0.0   \n",
       "46           17.0        0.0             0.0                  0.0   \n",
       "53            0.0        0.0             0.0                  0.0   \n",
       "\n",
       "    crs_arr_time  wheels_on  arr_time  taxi_in  arr_delay  \n",
       "2           1551     1547.0    1555.0      8.0        4.0  \n",
       "7           1917     1945.0    1955.0     10.0       38.0  \n",
       "8           2050     2058.0    2106.0      8.0       16.0  \n",
       "10           923      930.0     935.0      5.0       12.0  \n",
       "14          1724     1731.0    1735.0      4.0       11.0  \n",
       "15           704      914.0     924.0     10.0      140.0  \n",
       "17           855      855.0     901.0      6.0        6.0  \n",
       "19          2236     2241.0    2248.0      7.0       12.0  \n",
       "23          2037     2154.0    2159.0      5.0       82.0  \n",
       "26          2151     2306.0    2311.0      5.0       80.0  \n",
       "33          1727     1731.0    1737.0      6.0       10.0  \n",
       "34          1406     1347.0    1410.0     23.0        4.0  \n",
       "35          1956     2112.0    2118.0      6.0       82.0  \n",
       "37          2328     2341.0    2348.0      7.0       20.0  \n",
       "38          1354     1425.0    1428.0      3.0       34.0  \n",
       "39          2107     2100.0    2113.0     13.0        6.0  \n",
       "41           800      732.0     816.0     44.0       16.0  \n",
       "44          1345     1718.0    1724.0      6.0      219.0  \n",
       "46           905      912.0     922.0     10.0       17.0  \n",
       "53           647      648.0     652.0      4.0        5.0  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights[['crs_dep_time', 'dep_time', 'wheels_off', 'taxi_out', 'dep_delay', 'carrier_delay', 'weather_delay', 'nas_delay', 'security_delay', 'late_aircraft_delay', 'crs_arr_time', 'wheels_on','arr_time', 'taxi_in', 'arr_delay']].query('arr_delay > 0').head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As per our analysis in [task 3](#task3), specific delay columns (`carrier_delay`, `weather_delay`, `nas_delay`, `security_delay`, `late_aircraft_delay`) most likely add up into arrival delay data. Looking at the preview above, most of the specific delay columns that are non-zero add up into arrival delays (`arr_delay` feature).\n",
    "\n",
    "To answer the question \"are arrival delays caused by departure delays?\", we first examine how much of the data has been affected by the specific delays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19.252534870224387"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((flights[['carrier_delay', 'weather_delay', 'nas_delay', 'security_delay', 'late_aircraft_delay']].sum(axis=1) > 0).sum() / flights.shape[0]) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80.7474651297756"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((flights[['carrier_delay', 'weather_delay', 'nas_delay', 'security_delay', 'late_aircraft_delay']].sum(axis=1) <= 0).sum() / flights.shape[0]) * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are around 19% of observations that were affected by the specific delays and 81% that were not affected by these.\n",
    "\n",
    "It is, however, important to recall that we have imputed a chunk (81%) of missing values for these specific delays with 0."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have decided to continue this study at a later time but we've provided some numbers below:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Percentage of flights that had departure delays = 34%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34.05243333011391"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(flights['dep_delay'] > 0).sum() / flights.shape[0] * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Percentage of flights that had arrival delays = 35%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35.07206527285486"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(flights['arr_delay'] > 0).sum() / flights.shape[0] * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Percentage of flights that had arrival delays and had non-zero specific delays = 55%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "54.894214869990854"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((flights[['carrier_delay', 'weather_delay', 'nas_delay', 'security_delay', 'late_aircraft_delay']].sum(axis=1) > 0).sum() / (flights['arr_delay'] > 0).sum()) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "56.53791223547779"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((flights[['carrier_delay', 'weather_delay', 'nas_delay', 'security_delay', 'late_aircraft_delay']].sum(axis=1) > 0).sum() / (flights['dep_delay'] > 0).sum()) * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ratio of flight affected by departure delays vs Ratio of flight affected by arrival delays = 97%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "97.09275192433527"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(flights['dep_delay'] > 0).sum() / (flights['arr_delay'] > 0).sum() * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6) How many states cover 50% of US air traffic?<a id='task6'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        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>origin</th>\n",
       "      <th>origin_airport_id</th>\n",
       "      <th>origin_city_name</th>\n",
       "      <th>dest</th>\n",
       "      <th>dest_airport_id</th>\n",
       "      <th>dest_city_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IAH</td>\n",
       "      <td>12266</td>\n",
       "      <td>Houston, TX</td>\n",
       "      <td>LCH</td>\n",
       "      <td>12915</td>\n",
       "      <td>Lake Charles, LA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MEM</td>\n",
       "      <td>13244</td>\n",
       "      <td>Memphis, TN</td>\n",
       "      <td>IAH</td>\n",
       "      <td>12266</td>\n",
       "      <td>Houston, TX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>IAH</td>\n",
       "      <td>12266</td>\n",
       "      <td>Houston, TX</td>\n",
       "      <td>CLE</td>\n",
       "      <td>11042</td>\n",
       "      <td>Cleveland, OH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CLE</td>\n",
       "      <td>11042</td>\n",
       "      <td>Cleveland, OH</td>\n",
       "      <td>DCA</td>\n",
       "      <td>11278</td>\n",
       "      <td>Washington, DC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LRD</td>\n",
       "      <td>13061</td>\n",
       "      <td>Laredo, TX</td>\n",
       "      <td>IAH</td>\n",
       "      <td>12266</td>\n",
       "      <td>Houston, TX</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2339957</th>\n",
       "      <td>DFW</td>\n",
       "      <td>11298</td>\n",
       "      <td>Dallas/Fort Worth, TX</td>\n",
       "      <td>MIA</td>\n",
       "      <td>13303</td>\n",
       "      <td>Miami, FL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2339958</th>\n",
       "      <td>MYR</td>\n",
       "      <td>13577</td>\n",
       "      <td>Myrtle Beach, SC</td>\n",
       "      <td>PHL</td>\n",
       "      <td>14100</td>\n",
       "      <td>Philadelphia, PA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2339959</th>\n",
       "      <td>BUF</td>\n",
       "      <td>10792</td>\n",
       "      <td>Buffalo, NY</td>\n",
       "      <td>PHL</td>\n",
       "      <td>14100</td>\n",
       "      <td>Philadelphia, PA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2339960</th>\n",
       "      <td>IAH</td>\n",
       "      <td>12266</td>\n",
       "      <td>Houston, TX</td>\n",
       "      <td>JAN</td>\n",
       "      <td>12448</td>\n",
       "      <td>Jackson/Vicksburg, MS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2339961</th>\n",
       "      <td>CAE</td>\n",
       "      <td>10868</td>\n",
       "      <td>Columbia, SC</td>\n",
       "      <td>IAH</td>\n",
       "      <td>12266</td>\n",
       "      <td>Houston, TX</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2339962 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        origin  origin_airport_id       origin_city_name dest  \\\n",
       "0          IAH              12266            Houston, TX  LCH   \n",
       "1          MEM              13244            Memphis, TN  IAH   \n",
       "2          IAH              12266            Houston, TX  CLE   \n",
       "3          CLE              11042          Cleveland, OH  DCA   \n",
       "4          LRD              13061             Laredo, TX  IAH   \n",
       "...        ...                ...                    ...  ...   \n",
       "2339957    DFW              11298  Dallas/Fort Worth, TX  MIA   \n",
       "2339958    MYR              13577       Myrtle Beach, SC  PHL   \n",
       "2339959    BUF              10792            Buffalo, NY  PHL   \n",
       "2339960    IAH              12266            Houston, TX  JAN   \n",
       "2339961    CAE              10868           Columbia, SC  IAH   \n",
       "\n",
       "         dest_airport_id         dest_city_name  \n",
       "0                  12915       Lake Charles, LA  \n",
       "1                  12266            Houston, TX  \n",
       "2                  11042          Cleveland, OH  \n",
       "3                  11278         Washington, DC  \n",
       "4                  12266            Houston, TX  \n",
       "...                  ...                    ...  \n",
       "2339957            13303              Miami, FL  \n",
       "2339958            14100       Philadelphia, PA  \n",
       "2339959            14100       Philadelphia, PA  \n",
       "2339960            12448  Jackson/Vicksburg, MS  \n",
       "2339961            12266            Houston, TX  \n",
       "\n",
       "[2339962 rows x 6 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights[['origin', 'origin_airport_id', 'origin_city_name', 'dest', 'dest_airport_id', 'dest_city_name']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this task, we are particularly interested in US states and we extract that information from the `origin_city_name` and/or the `dest_city_name` features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We extract the set of unique state codes from both columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# origin/departure states\n",
    "city_state = flights[\"origin_city_name\"].apply(lambda x: x.split(\", \"))\n",
    "state = city_state.apply(lambda x: x[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "arrival_city_state = flights[\"dest_city_name\"].apply(lambda x: x.split(\", \"))\n",
    "# destination/arrival states\n",
    "arrival_state = arrival_city_state.apply(lambda x: x[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To check, we compare if both sets is a subset of each other."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(state.unique()).issubset(set(arrival_state.unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(arrival_state.unique()).issubset(set(state.unique()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We preview some columns that we need that are relevant to the origin/departure and destination/arrival airports."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>origin_city_name</th>\n",
       "      <th>origin</th>\n",
       "      <th>origin_airport_id</th>\n",
       "      <th>dest_city_name</th>\n",
       "      <th>dest</th>\n",
       "      <th>dest_airport_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Houston, TX</td>\n",
       "      <td>IAH</td>\n",
       "      <td>12266</td>\n",
       "      <td>Lake Charles, LA</td>\n",
       "      <td>LCH</td>\n",
       "      <td>12915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Memphis, TN</td>\n",
       "      <td>MEM</td>\n",
       "      <td>13244</td>\n",
       "      <td>Houston, TX</td>\n",
       "      <td>IAH</td>\n",
       "      <td>12266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Houston, TX</td>\n",
       "      <td>IAH</td>\n",
       "      <td>12266</td>\n",
       "      <td>Cleveland, OH</td>\n",
       "      <td>CLE</td>\n",
       "      <td>11042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cleveland, OH</td>\n",
       "      <td>CLE</td>\n",
       "      <td>11042</td>\n",
       "      <td>Washington, DC</td>\n",
       "      <td>DCA</td>\n",
       "      <td>11278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Laredo, TX</td>\n",
       "      <td>LRD</td>\n",
       "      <td>13061</td>\n",
       "      <td>Houston, TX</td>\n",
       "      <td>IAH</td>\n",
       "      <td>12266</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2339957</th>\n",
       "      <td>Dallas/Fort Worth, TX</td>\n",
       "      <td>DFW</td>\n",
       "      <td>11298</td>\n",
       "      <td>Miami, FL</td>\n",
       "      <td>MIA</td>\n",
       "      <td>13303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2339958</th>\n",
       "      <td>Myrtle Beach, SC</td>\n",
       "      <td>MYR</td>\n",
       "      <td>13577</td>\n",
       "      <td>Philadelphia, PA</td>\n",
       "      <td>PHL</td>\n",
       "      <td>14100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2339959</th>\n",
       "      <td>Buffalo, NY</td>\n",
       "      <td>BUF</td>\n",
       "      <td>10792</td>\n",
       "      <td>Philadelphia, PA</td>\n",
       "      <td>PHL</td>\n",
       "      <td>14100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2339960</th>\n",
       "      <td>Houston, TX</td>\n",
       "      <td>IAH</td>\n",
       "      <td>12266</td>\n",
       "      <td>Jackson/Vicksburg, MS</td>\n",
       "      <td>JAN</td>\n",
       "      <td>12448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2339961</th>\n",
       "      <td>Columbia, SC</td>\n",
       "      <td>CAE</td>\n",
       "      <td>10868</td>\n",
       "      <td>Houston, TX</td>\n",
       "      <td>IAH</td>\n",
       "      <td>12266</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2339962 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              origin_city_name origin  origin_airport_id  \\\n",
       "0                  Houston, TX    IAH              12266   \n",
       "1                  Memphis, TN    MEM              13244   \n",
       "2                  Houston, TX    IAH              12266   \n",
       "3                Cleveland, OH    CLE              11042   \n",
       "4                   Laredo, TX    LRD              13061   \n",
       "...                        ...    ...                ...   \n",
       "2339957  Dallas/Fort Worth, TX    DFW              11298   \n",
       "2339958       Myrtle Beach, SC    MYR              13577   \n",
       "2339959            Buffalo, NY    BUF              10792   \n",
       "2339960            Houston, TX    IAH              12266   \n",
       "2339961           Columbia, SC    CAE              10868   \n",
       "\n",
       "                dest_city_name dest  dest_airport_id  \n",
       "0             Lake Charles, LA  LCH            12915  \n",
       "1                  Houston, TX  IAH            12266  \n",
       "2                Cleveland, OH  CLE            11042  \n",
       "3               Washington, DC  DCA            11278  \n",
       "4                  Houston, TX  IAH            12266  \n",
       "...                        ...  ...              ...  \n",
       "2339957              Miami, FL  MIA            13303  \n",
       "2339958       Philadelphia, PA  PHL            14100  \n",
       "2339959       Philadelphia, PA  PHL            14100  \n",
       "2339960  Jackson/Vicksburg, MS  JAN            12448  \n",
       "2339961            Houston, TX  IAH            12266  \n",
       "\n",
       "[2339962 rows x 6 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights2[['origin_city_name', 'origin', 'origin_airport_id', 'dest_city_name', 'dest', 'dest_airport_id']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "flights2['dep_state'] = flights[\"origin_city_name\"].apply(lambda x: x.split(\", \")[1])\n",
    "flights2['arr_state'] = flights[\"dest_city_name\"].apply(lambda x: x.split(\", \")[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "dep_states_traffic = flights2['dep_state'].value_counts()\n",
    "arr_states_traffic = flights2['arr_state'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We count the traffic for each state and list them in order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CA    257018\n",
       "TX    230132\n",
       "FL    170341\n",
       "IL    146331\n",
       "GA    125186\n",
       "NY    119871\n",
       "NC    107958\n",
       "CO     91658\n",
       "PA     69668\n",
       "DC     69091\n",
       "WA     67883\n",
       "MI     66087\n",
       "AZ     61273\n",
       "NV     56277\n",
       "OH     53043\n",
       "MN     50891\n",
       "NJ     48041\n",
       "MA     44812\n",
       "MO     42998\n",
       "TN     41334\n",
       "UT     37673\n",
       "HI     35201\n",
       "OR     33702\n",
       "MD     31648\n",
       "LA     25101\n",
       "VA     22621\n",
       "IN     21266\n",
       "WI     21130\n",
       "SC     20401\n",
       "OK     13345\n",
       "AL     12889\n",
       "KY     12195\n",
       "AK     12167\n",
       "ID      9909\n",
       "NE      9849\n",
       "AR      9457\n",
       "NM      9435\n",
       "IA      9140\n",
       "CT      9090\n",
       "MT      8946\n",
       "PR      8579\n",
       "ND      6279\n",
       "ME      6032\n",
       "RI      5840\n",
       "KS      4860\n",
       "SD      4581\n",
       "MS      4567\n",
       "NH      3533\n",
       "VT      3336\n",
       "WY      3033\n",
       "WV      2456\n",
       "VI      1234\n",
       "TT       574\n",
       "Name: dep_state, dtype: int64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dep_states_traffic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We calculate for the cumsum of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CA     257018\n",
       "TX     487150\n",
       "FL     657491\n",
       "IL     803822\n",
       "GA     929008\n",
       "NY    1048879\n",
       "NC    1156837\n",
       "CO    1248495\n",
       "PA    1318163\n",
       "DC    1387254\n",
       "WA    1455137\n",
       "MI    1521224\n",
       "AZ    1582497\n",
       "NV    1638774\n",
       "OH    1691817\n",
       "MN    1742708\n",
       "NJ    1790749\n",
       "MA    1835561\n",
       "MO    1878559\n",
       "TN    1919893\n",
       "UT    1957566\n",
       "HI    1992767\n",
       "OR    2026469\n",
       "MD    2058117\n",
       "LA    2083218\n",
       "VA    2105839\n",
       "IN    2127105\n",
       "WI    2148235\n",
       "SC    2168636\n",
       "OK    2181981\n",
       "AL    2194870\n",
       "KY    2207065\n",
       "AK    2219232\n",
       "ID    2229141\n",
       "NE    2238990\n",
       "AR    2248447\n",
       "NM    2257882\n",
       "IA    2267022\n",
       "CT    2276112\n",
       "MT    2285058\n",
       "PR    2293637\n",
       "ND    2299916\n",
       "ME    2305948\n",
       "RI    2311788\n",
       "KS    2316648\n",
       "SD    2321229\n",
       "MS    2325796\n",
       "NH    2329329\n",
       "VT    2332665\n",
       "WY    2335698\n",
       "WV    2338154\n",
       "VI    2339388\n",
       "TT    2339962\n",
       "Name: dep_state, dtype: int64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dep_states_traffic.cumsum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We filter out the states that make up 50% of US air traffic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CA     10.983854\n",
       "TX     20.818714\n",
       "FL     28.098362\n",
       "IL     34.351925\n",
       "GA     39.701841\n",
       "NY     44.824617\n",
       "NC     49.438281\n",
       "CO     53.355354\n",
       "PA     56.332667\n",
       "DC     59.285322\n",
       "WA     62.186352\n",
       "MI     65.010628\n",
       "AZ     67.629175\n",
       "NV     70.034214\n",
       "OH     72.301046\n",
       "MN     74.475910\n",
       "NJ     76.528978\n",
       "MA     78.444052\n",
       "MO     80.281603\n",
       "TN     82.048042\n",
       "UT     83.658025\n",
       "HI     85.162366\n",
       "OR     86.602646\n",
       "MD     87.955146\n",
       "LA     89.027856\n",
       "VA     89.994581\n",
       "IN     90.903399\n",
       "WI     91.806405\n",
       "SC     92.678257\n",
       "OK     93.248566\n",
       "AL     93.799386\n",
       "KY     94.320549\n",
       "AK     94.840515\n",
       "ID     95.263983\n",
       "NE     95.684887\n",
       "AR     96.089039\n",
       "NM     96.492251\n",
       "IA     96.882855\n",
       "CT     97.271323\n",
       "MT     97.653637\n",
       "PR     98.020267\n",
       "ND     98.288605\n",
       "ME     98.546387\n",
       "RI     98.795963\n",
       "KS     99.003659\n",
       "SD     99.199431\n",
       "MS     99.394606\n",
       "NH     99.545591\n",
       "VT     99.688157\n",
       "WY     99.817775\n",
       "WV     99.922734\n",
       "VI     99.975470\n",
       "TT    100.000000\n",
       "Name: dep_state, dtype: float64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dep_states_traffic_percent = ((dep_states_traffic.cumsum() / dep_states_traffic.sum()) * 100)\n",
    "dep_states_traffic_percent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are the states which cover 50% of US air traffic for departures:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['CA', 'TX', 'FL', 'IL', 'GA', 'NY', 'NC'], dtype='object')"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dep_states_50 = dep_states_traffic_percent[dep_states_traffic_percent <= 50].index\n",
    "dep_states_50"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We do the same for the arr_states data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CA    257081\n",
       "TX    230677\n",
       "FL    170216\n",
       "IL    146086\n",
       "GA    124632\n",
       "NY    119829\n",
       "NC    107780\n",
       "CO     91444\n",
       "DC     69250\n",
       "PA     69115\n",
       "WA     68448\n",
       "MI     65786\n",
       "AZ     61358\n",
       "NV     56133\n",
       "OH     53524\n",
       "MN     51173\n",
       "NJ     48129\n",
       "MA     45461\n",
       "MO     43290\n",
       "TN     40919\n",
       "UT     37416\n",
       "HI     35099\n",
       "OR     33739\n",
       "MD     31641\n",
       "LA     25112\n",
       "VA     22463\n",
       "IN     21496\n",
       "WI     20600\n",
       "SC     20393\n",
       "OK     13447\n",
       "AL     12838\n",
       "AK     12087\n",
       "KY     11983\n",
       "NE      9871\n",
       "ID      9810\n",
       "AR      9769\n",
       "NM      9402\n",
       "CT      9074\n",
       "IA      9017\n",
       "MT      8997\n",
       "PR      8606\n",
       "ND      6318\n",
       "ME      6100\n",
       "RI      5936\n",
       "KS      4971\n",
       "MS      4621\n",
       "SD      4601\n",
       "VT      3479\n",
       "NH      3387\n",
       "WY      3035\n",
       "WV      2500\n",
       "VI      1235\n",
       "TT       588\n",
       "Name: arr_state, dtype: int64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_states_traffic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CA     257081\n",
       "TX     487758\n",
       "FL     657974\n",
       "IL     804060\n",
       "GA     928692\n",
       "NY    1048521\n",
       "NC    1156301\n",
       "CO    1247745\n",
       "DC    1316995\n",
       "PA    1386110\n",
       "WA    1454558\n",
       "MI    1520344\n",
       "AZ    1581702\n",
       "NV    1637835\n",
       "OH    1691359\n",
       "MN    1742532\n",
       "NJ    1790661\n",
       "MA    1836122\n",
       "MO    1879412\n",
       "TN    1920331\n",
       "UT    1957747\n",
       "HI    1992846\n",
       "OR    2026585\n",
       "MD    2058226\n",
       "LA    2083338\n",
       "VA    2105801\n",
       "IN    2127297\n",
       "WI    2147897\n",
       "SC    2168290\n",
       "OK    2181737\n",
       "AL    2194575\n",
       "AK    2206662\n",
       "KY    2218645\n",
       "NE    2228516\n",
       "ID    2238326\n",
       "AR    2248095\n",
       "NM    2257497\n",
       "CT    2266571\n",
       "IA    2275588\n",
       "MT    2284585\n",
       "PR    2293191\n",
       "ND    2299509\n",
       "ME    2305609\n",
       "RI    2311545\n",
       "KS    2316516\n",
       "MS    2321137\n",
       "SD    2325738\n",
       "VT    2329217\n",
       "NH    2332604\n",
       "WY    2335639\n",
       "WV    2338139\n",
       "VI    2339374\n",
       "TT    2339962\n",
       "Name: arr_state, dtype: int64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_states_traffic.cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CA     10.986546\n",
       "TX     20.844697\n",
       "FL     28.119004\n",
       "IL     34.362096\n",
       "GA     39.688337\n",
       "NY     44.809317\n",
       "NC     49.415375\n",
       "CO     53.323302\n",
       "DC     56.282752\n",
       "PA     59.236432\n",
       "WA     62.161608\n",
       "MI     64.973021\n",
       "AZ     67.595200\n",
       "NV     69.994085\n",
       "OH     72.281473\n",
       "MN     74.468389\n",
       "NJ     76.525217\n",
       "MA     78.468026\n",
       "MO     80.318056\n",
       "TN     82.066760\n",
       "UT     83.665760\n",
       "HI     85.165742\n",
       "OR     86.607603\n",
       "MD     87.959804\n",
       "LA     89.032984\n",
       "VA     89.992957\n",
       "IN     90.911605\n",
       "WI     91.791961\n",
       "SC     92.663471\n",
       "OK     93.238138\n",
       "AL     93.786779\n",
       "AK     94.303326\n",
       "KY     94.815429\n",
       "NE     95.237273\n",
       "ID     95.656511\n",
       "AR     96.073996\n",
       "NM     96.475797\n",
       "CT     96.863582\n",
       "IA     97.248930\n",
       "MT     97.633423\n",
       "PR     98.001207\n",
       "ND     98.271211\n",
       "ME     98.531899\n",
       "RI     98.785579\n",
       "KS     98.998018\n",
       "MS     99.195500\n",
       "SD     99.392127\n",
       "VT     99.540805\n",
       "NH     99.685550\n",
       "WY     99.815253\n",
       "WV     99.922093\n",
       "VI     99.974871\n",
       "TT    100.000000\n",
       "Name: arr_state, dtype: float64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_states_traffic_percent = (arr_states_traffic.cumsum() / arr_states_traffic.sum()) * 100\n",
    "arr_states_traffic_percent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are the states which cover 50% of US air traffic for arrivals:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['CA', 'TX', 'FL', 'IL', 'GA', 'NY', 'NC'], dtype='object')"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_states_50 = arr_states_traffic_percent[arr_states_traffic_percent <= 50].index\n",
    "arr_states_50"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could also confirm both have the same set of states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(dep_states_50).issubset(set(arr_states_50))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(arr_states_50).issubset(set(dep_states_50))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We plot them nicely together onto a bar chart along with a line plot for the cumulative sum of flights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:label='7ae01ab8-4572-4863-82ae-ae7f83334d1e'>"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data1 = dep_states_traffic.index\n",
    "data2 = arr_states_traffic.index\n",
    "\n",
    "width =0.3\n",
    "\n",
    "fig = plt.figure(figsize=(15,8))\n",
    "\n",
    "plt.bar(data1, width=width, height=dep_states_traffic)\n",
    "plt.bar(np.arange(len(data2)) + width, width=width, height=arr_states_traffic)\n",
    "plt.xticks(rotation=45)\n",
    "plt.ylabel('Flight Count')\n",
    "plt.xlabel('States')\n",
    "\n",
    "dep_states_traffic.cumsum().plot(secondary_y=True)\n",
    "arr_states_traffic.cumsum().plot(secondary_y=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7) Test the hypothesis whether planes fly faster when there is the departure delay?<a id='task7'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Flights that were not delayed**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's filter out the data for flights that didn't get delayed. These are those whose `dep_delay` values are less than or equal to 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "flights_not_delayed = flights[['dep_delay', 'crs_elapsed_time', 'actual_elapsed_time', 'air_time']].query('dep_delay <= 0')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Flights that were delayed**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To do the same for the flights that didn't get delayed, we filter out the rows with `dep_delay` values that are greater than 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "flights_delayed = flights[['dep_delay', 'crs_elapsed_time', 'actual_elapsed_time', 'air_time']].query('dep_delay > 0')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To test the hypothesis that planes fly faster when there is a departure delay, we perform a t-test on the means of the Actual Elapsed Time of Flight (`actual_elapsed_time`) and Flight Time (`air_time`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dep_delay               -5.013855\n",
       "crs_elapsed_time       135.523854\n",
       "actual_elapsed_time    130.307149\n",
       "air_time               105.335696\n",
       "dtype: float64"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights_not_delayed.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dep_delay               40.448515\n",
       "crs_elapsed_time       143.308628\n",
       "actual_elapsed_time    138.713736\n",
       "air_time               113.070094\n",
       "dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "flights_delayed.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Air Time**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "105.33569625207693"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights_not_delayed.air_time.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "113.07009414995218"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "flights_delayed.air_time.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Actual Elapsed Time**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "130.30714876343683"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights_not_delayed.actual_elapsed_time.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "138.71373620443416"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "flights_delayed.actual_elapsed_time.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We run a t-test on the two means to test our hypothesis that these means are different."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-85.24375804818777, pvalue=0.0)"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st.ttest_ind(flights_on_time.actual_elapsed_time, flights_delayed.actual_elapsed_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-80.5826736445401, pvalue=0.0)"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st.ttest_ind(flights_on_time.air_time, flights_delayed.air_time)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we returned p-values of zero, there was not enough evidence from the data to reject our null hypothesis and conclude that they both have different means.\n",
    "\n",
    "However, statistically, we cannot conclude yet if planes indeed fly faster if there are departure delays.\n",
    "\n",
    "The test needs to be re-done on a recent version of Scipy and adjust the parameters to define the alternative hypothesis with a one-sided configuration (less or greater setting)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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