From 3dcde65e39eeeba8f43d3b19db3dbd776022e339 Mon Sep 17 00:00:00 2001
From: Guoxuan Xu <guoxuan.xu8@gmail.com>
Date: Mon, 14 Oct 2024 20:36:54 -0700
Subject: [PATCH] data evaluation notebook

---
 data_eda.ipynb                                | 1034 -----------------
 .../eda_average_ticketPrice.ipynb             |  276 +++++
 .../eval_impact_of_zero ticket price.ipynb    |  129 ++
 3 files changed, 405 insertions(+), 1034 deletions(-)
 delete mode 100644 data_eda.ipynb
 create mode 100644 impact_evaluation/eda_average_ticketPrice.ipynb
 create mode 100644 impact_evaluation/eval_impact_of_zero ticket price.ipynb

diff --git a/data_eda.ipynb b/data_eda.ipynb
deleted file mode 100644
index 3622756..0000000
--- a/data_eda.ipynb
+++ /dev/null
@@ -1,1034 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 279,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import re"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 280,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/opt/anaconda3/envs/tongConsultinInc/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3508: DtypeWarning: Columns (3,13,14) have mixed types.Specify dtype option on import or set low_memory=False.\n",
-      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Load the data\n",
-    "routes = pd.read_csv('data/Flights.csv')\n",
-    "ticket_price = pd.read_csv('data/Tickets.csv')\n",
-    "airportsInfo = pd.read_csv('data/Airport_Codes.csv')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Flights\n",
-    "- FL_DATE: string to stanarded datetime object\n",
-    "- ORIGIN_CITY_NAME: split into city and state\n",
-    "- DEST_CITY_NAME: split into city and state\n",
-    "- AIR_TIME: \n",
-    "    - Two; NAN; negative number; number in str\n",
-    "- DISTANCE: to float"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 281,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "routes['FL_DATE'] = pd.to_datetime(routes['FL_DATE'])\n",
-    "\n",
-    "# \n",
-    "routes['ORIGIN_STATE_NAME'] = routes['ORIGIN_CITY_NAME'].str.split(', ').str[1]\n",
-    "routes['ORIGIN_CITY_NAME'] = routes['ORIGIN_CITY_NAME'].str.split(', ').str[0]\n",
-    "routes['DEST_STATE_NAME'] = routes['DEST_CITY_NAME'].str.split(', ').str[1]\n",
-    "routes['DEST_CITY_NAME'] = routes['DEST_CITY_NAME'].str.split(', ').str[0]\n",
-    "\n",
-    "# air time column adjustments\n",
-    "routes['AIR_TIME'] = routes['AIR_TIME'].apply(lambda x: 2.0 if x == 'Two' else x)\n",
-    "routes['AIR_TIME'] = routes['AIR_TIME'].apply(lambda x: np.nan if x == 'NAN' or x == '$$$' else x)\n",
-    "routes['AIR_TIME'] = routes['AIR_TIME'].apply(lambda x: 121.0 if x == '121.0' else x)\n",
-    "routes['AIR_TIME'] = routes['AIR_TIME'].astype(float)\n",
-    "\n",
-    "# clean and convert distance to float\n",
-    "def distance_to_float(val):\n",
-    "    try:\n",
-    "        float_val = float(val)\n",
-    "        if float_val < 0:\n",
-    "            return -1 * float_val\n",
-    "        return float_val\n",
-    "    except:\n",
-    "        return np.nan\n",
-    "    \n",
-    "routes['DISTANCE'] = routes['DISTANCE'].apply(distance_to_float)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 282,
-   "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>OP_CARRIER</th>\n",
-       "      <th>TAIL_NUM</th>\n",
-       "      <th>OP_CARRIER_FL_NUM</th>\n",
-       "      <th>ORIGIN_AIRPORT_ID</th>\n",
-       "      <th>ORIGIN</th>\n",
-       "      <th>ORIGIN_CITY_NAME</th>\n",
-       "      <th>DEST_AIRPORT_ID</th>\n",
-       "      <th>DESTINATION</th>\n",
-       "      <th>DEST_CITY_NAME</th>\n",
-       "      <th>DEP_DELAY</th>\n",
-       "      <th>ARR_DELAY</th>\n",
-       "      <th>CANCELLED</th>\n",
-       "      <th>AIR_TIME</th>\n",
-       "      <th>DISTANCE</th>\n",
-       "      <th>OCCUPANCY_RATE</th>\n",
-       "      <th>ORIGIN_STATE_NAME</th>\n",
-       "      <th>DEST_STATE_NAME</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2019-03-02</td>\n",
-       "      <td>WN</td>\n",
-       "      <td>N955WN</td>\n",
-       "      <td>4591</td>\n",
-       "      <td>14635</td>\n",
-       "      <td>RSW</td>\n",
-       "      <td>Fort Myers</td>\n",
-       "      <td>11042</td>\n",
-       "      <td>CLE</td>\n",
-       "      <td>Cleveland</td>\n",
-       "      <td>-8.0</td>\n",
-       "      <td>-6.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>143.0</td>\n",
-       "      <td>1025.0</td>\n",
-       "      <td>0.970000</td>\n",
-       "      <td>FL</td>\n",
-       "      <td>OH</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2019-03-02</td>\n",
-       "      <td>WN</td>\n",
-       "      <td>N8686A</td>\n",
-       "      <td>3231</td>\n",
-       "      <td>14635</td>\n",
-       "      <td>RSW</td>\n",
-       "      <td>Fort Myers</td>\n",
-       "      <td>11066</td>\n",
-       "      <td>CMH</td>\n",
-       "      <td>Columbus</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>135.0</td>\n",
-       "      <td>930.0</td>\n",
-       "      <td>0.550000</td>\n",
-       "      <td>FL</td>\n",
-       "      <td>OH</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2019-03-02</td>\n",
-       "      <td>WN</td>\n",
-       "      <td>N201LV</td>\n",
-       "      <td>3383</td>\n",
-       "      <td>14635</td>\n",
-       "      <td>RSW</td>\n",
-       "      <td>Fort Myers</td>\n",
-       "      <td>11066</td>\n",
-       "      <td>CMH</td>\n",
-       "      <td>Columbus</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>132.0</td>\n",
-       "      <td>930.0</td>\n",
-       "      <td>0.910000</td>\n",
-       "      <td>FL</td>\n",
-       "      <td>OH</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2019-03-02</td>\n",
-       "      <td>WN</td>\n",
-       "      <td>N413WN</td>\n",
-       "      <td>5498</td>\n",
-       "      <td>14635</td>\n",
-       "      <td>RSW</td>\n",
-       "      <td>Fort Myers</td>\n",
-       "      <td>11066</td>\n",
-       "      <td>CMH</td>\n",
-       "      <td>Columbus</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>136.0</td>\n",
-       "      <td>930.0</td>\n",
-       "      <td>0.670000</td>\n",
-       "      <td>FL</td>\n",
-       "      <td>OH</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2019-03-02</td>\n",
-       "      <td>WN</td>\n",
-       "      <td>N7832A</td>\n",
-       "      <td>6933</td>\n",
-       "      <td>14635</td>\n",
-       "      <td>RSW</td>\n",
-       "      <td>Fort Myers</td>\n",
-       "      <td>11259</td>\n",
-       "      <td>DAL</td>\n",
-       "      <td>Dallas</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-17.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>151.0</td>\n",
-       "      <td>1005.0</td>\n",
-       "      <td>0.620000</td>\n",
-       "      <td>FL</td>\n",
-       "      <td>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",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1915881</th>\n",
-       "      <td>2019-03-23</td>\n",
-       "      <td>AA</td>\n",
-       "      <td>N903NN</td>\n",
-       "      <td>1433</td>\n",
-       "      <td>15370</td>\n",
-       "      <td>TUL</td>\n",
-       "      <td>Tulsa</td>\n",
-       "      <td>11057</td>\n",
-       "      <td>CLT</td>\n",
-       "      <td>Charlotte</td>\n",
-       "      <td>-9.0</td>\n",
-       "      <td>-6.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>112.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.794884</td>\n",
-       "      <td>OK</td>\n",
-       "      <td>NC</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1915882</th>\n",
-       "      <td>2019-03-24</td>\n",
-       "      <td>AA</td>\n",
-       "      <td>N965AN</td>\n",
-       "      <td>1433</td>\n",
-       "      <td>15370</td>\n",
-       "      <td>TUL</td>\n",
-       "      <td>Tulsa</td>\n",
-       "      <td>11057</td>\n",
-       "      <td>CLT</td>\n",
-       "      <td>Charlotte</td>\n",
-       "      <td>-2.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>106.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.538399</td>\n",
-       "      <td>OK</td>\n",
-       "      <td>NC</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1915883</th>\n",
-       "      <td>2019-03-25</td>\n",
-       "      <td>AA</td>\n",
-       "      <td>N979NN</td>\n",
-       "      <td>1433</td>\n",
-       "      <td>15370</td>\n",
-       "      <td>TUL</td>\n",
-       "      <td>Tulsa</td>\n",
-       "      <td>11057</td>\n",
-       "      <td>CLT</td>\n",
-       "      <td>Charlotte</td>\n",
-       "      <td>-8.0</td>\n",
-       "      <td>-25.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>106.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.955579</td>\n",
-       "      <td>OK</td>\n",
-       "      <td>NC</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1915884</th>\n",
-       "      <td>2019-03-26</td>\n",
-       "      <td>AA</td>\n",
-       "      <td>N872NN</td>\n",
-       "      <td>1433</td>\n",
-       "      <td>15370</td>\n",
-       "      <td>TUL</td>\n",
-       "      <td>Tulsa</td>\n",
-       "      <td>11057</td>\n",
-       "      <td>CLT</td>\n",
-       "      <td>Charlotte</td>\n",
-       "      <td>-9.0</td>\n",
-       "      <td>-6.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>112.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.595344</td>\n",
-       "      <td>OK</td>\n",
-       "      <td>NC</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1915885</th>\n",
-       "      <td>2019-03-27</td>\n",
-       "      <td>AA</td>\n",
-       "      <td>N945AN</td>\n",
-       "      <td>1433</td>\n",
-       "      <td>15370</td>\n",
-       "      <td>TUL</td>\n",
-       "      <td>Tulsa</td>\n",
-       "      <td>11057</td>\n",
-       "      <td>CLT</td>\n",
-       "      <td>Charlotte</td>\n",
-       "      <td>-8.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>117.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.350192</td>\n",
-       "      <td>OK</td>\n",
-       "      <td>NC</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1915886 rows × 18 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           FL_DATE OP_CARRIER TAIL_NUM OP_CARRIER_FL_NUM  ORIGIN_AIRPORT_ID  \\\n",
-       "0       2019-03-02         WN   N955WN              4591              14635   \n",
-       "1       2019-03-02         WN   N8686A              3231              14635   \n",
-       "2       2019-03-02         WN   N201LV              3383              14635   \n",
-       "3       2019-03-02         WN   N413WN              5498              14635   \n",
-       "4       2019-03-02         WN   N7832A              6933              14635   \n",
-       "...            ...        ...      ...               ...                ...   \n",
-       "1915881 2019-03-23         AA   N903NN              1433              15370   \n",
-       "1915882 2019-03-24         AA   N965AN              1433              15370   \n",
-       "1915883 2019-03-25         AA   N979NN              1433              15370   \n",
-       "1915884 2019-03-26         AA   N872NN              1433              15370   \n",
-       "1915885 2019-03-27         AA   N945AN              1433              15370   \n",
-       "\n",
-       "        ORIGIN ORIGIN_CITY_NAME  DEST_AIRPORT_ID DESTINATION DEST_CITY_NAME  \\\n",
-       "0          RSW       Fort Myers            11042         CLE      Cleveland   \n",
-       "1          RSW       Fort Myers            11066         CMH       Columbus   \n",
-       "2          RSW       Fort Myers            11066         CMH       Columbus   \n",
-       "3          RSW       Fort Myers            11066         CMH       Columbus   \n",
-       "4          RSW       Fort Myers            11259         DAL         Dallas   \n",
-       "...        ...              ...              ...         ...            ...   \n",
-       "1915881    TUL            Tulsa            11057         CLT      Charlotte   \n",
-       "1915882    TUL            Tulsa            11057         CLT      Charlotte   \n",
-       "1915883    TUL            Tulsa            11057         CLT      Charlotte   \n",
-       "1915884    TUL            Tulsa            11057         CLT      Charlotte   \n",
-       "1915885    TUL            Tulsa            11057         CLT      Charlotte   \n",
-       "\n",
-       "         DEP_DELAY  ARR_DELAY  CANCELLED  AIR_TIME  DISTANCE  OCCUPANCY_RATE  \\\n",
-       "0             -8.0       -6.0        0.0     143.0    1025.0        0.970000   \n",
-       "1              1.0        5.0        0.0     135.0     930.0        0.550000   \n",
-       "2              0.0        4.0        0.0     132.0     930.0        0.910000   \n",
-       "3             11.0       14.0        0.0     136.0     930.0        0.670000   \n",
-       "4              0.0      -17.0        0.0     151.0    1005.0        0.620000   \n",
-       "...            ...        ...        ...       ...       ...             ...   \n",
-       "1915881       -9.0       -6.0        0.0     112.0       NaN        0.794884   \n",
-       "1915882       -2.0       -1.0        0.0     106.0       NaN        0.538399   \n",
-       "1915883       -8.0      -25.0        0.0     106.0       NaN        0.955579   \n",
-       "1915884       -9.0       -6.0        0.0     112.0       NaN        0.595344   \n",
-       "1915885       -8.0        5.0        0.0     117.0       NaN        0.350192   \n",
-       "\n",
-       "        ORIGIN_STATE_NAME DEST_STATE_NAME  \n",
-       "0                      FL              OH  \n",
-       "1                      FL              OH  \n",
-       "2                      FL              OH  \n",
-       "3                      FL              OH  \n",
-       "4                      FL              TX  \n",
-       "...                   ...             ...  \n",
-       "1915881                OK              NC  \n",
-       "1915882                OK              NC  \n",
-       "1915883                OK              NC  \n",
-       "1915884                OK              NC  \n",
-       "1915885                OK              NC  \n",
-       "\n",
-       "[1915886 rows x 18 columns]"
-      ]
-     },
-     "execution_count": 282,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "routes"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Tickets\n",
-    "- YEAR to int\n",
-    "- clean itin_fare"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 283,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# year column to int year\n",
-    "ticket_price['YEAR'] = ticket_price['YEAR'].astype(int)\n",
-    "\n",
-    "# clean and convert price to float\n",
-    "def find_number(text):\n",
-    "    if type(text) != str:\n",
-    "        return np.nan\n",
-    "    re_result = re.search(r'[\\d\\.]+', text)\n",
-    "    if re_result is not None:\n",
-    "        return float(re_result.group(0))\n",
-    "    return np.nan\n",
-    "\n",
-    "ticket_price['ITIN_FARE'] = ticket_price['ITIN_FARE'].apply(find_number)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 284,
-   "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>ITIN_ID</th>\n",
-       "      <th>YEAR</th>\n",
-       "      <th>QUARTER</th>\n",
-       "      <th>ORIGIN</th>\n",
-       "      <th>ORIGIN_COUNTRY</th>\n",
-       "      <th>ORIGIN_STATE_ABR</th>\n",
-       "      <th>ORIGIN_STATE_NM</th>\n",
-       "      <th>ROUNDTRIP</th>\n",
-       "      <th>REPORTING_CARRIER</th>\n",
-       "      <th>PASSENGERS</th>\n",
-       "      <th>ITIN_FARE</th>\n",
-       "      <th>DESTINATION</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>201912723049</td>\n",
-       "      <td>2019</td>\n",
-       "      <td>1</td>\n",
-       "      <td>ABI</td>\n",
-       "      <td>US</td>\n",
-       "      <td>TX</td>\n",
-       "      <td>Texas</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>MQ</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>736.0</td>\n",
-       "      <td>DAB</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>201912723085</td>\n",
-       "      <td>2019</td>\n",
-       "      <td>1</td>\n",
-       "      <td>ABI</td>\n",
-       "      <td>US</td>\n",
-       "      <td>TX</td>\n",
-       "      <td>Texas</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>MQ</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>570.0</td>\n",
-       "      <td>COS</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>201912723491</td>\n",
-       "      <td>2019</td>\n",
-       "      <td>1</td>\n",
-       "      <td>ABI</td>\n",
-       "      <td>US</td>\n",
-       "      <td>TX</td>\n",
-       "      <td>Texas</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>MQ</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>564.0</td>\n",
-       "      <td>MCO</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>201912723428</td>\n",
-       "      <td>2019</td>\n",
-       "      <td>1</td>\n",
-       "      <td>ABI</td>\n",
-       "      <td>US</td>\n",
-       "      <td>TX</td>\n",
-       "      <td>Texas</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>MQ</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>345.0</td>\n",
-       "      <td>LGA</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>201912723509</td>\n",
-       "      <td>2019</td>\n",
-       "      <td>1</td>\n",
-       "      <td>ABI</td>\n",
-       "      <td>US</td>\n",
-       "      <td>TX</td>\n",
-       "      <td>Texas</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>MQ</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>309.0</td>\n",
-       "      <td>MGM</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1167280</th>\n",
-       "      <td>201911284909</td>\n",
-       "      <td>2019</td>\n",
-       "      <td>1</td>\n",
-       "      <td>YAK</td>\n",
-       "      <td>US</td>\n",
-       "      <td>AK</td>\n",
-       "      <td>Alaska</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>AS</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>244.0</td>\n",
-       "      <td>ANC</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1167281</th>\n",
-       "      <td>201911284959</td>\n",
-       "      <td>2019</td>\n",
-       "      <td>1</td>\n",
-       "      <td>YAK</td>\n",
-       "      <td>US</td>\n",
-       "      <td>AK</td>\n",
-       "      <td>Alaska</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>AS</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>371.0</td>\n",
-       "      <td>JNU</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1167282</th>\n",
-       "      <td>201911284940</td>\n",
-       "      <td>2019</td>\n",
-       "      <td>1</td>\n",
-       "      <td>YAK</td>\n",
-       "      <td>US</td>\n",
-       "      <td>AK</td>\n",
-       "      <td>Alaska</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>AS</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>271.0</td>\n",
-       "      <td>JNU</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1167283</th>\n",
-       "      <td>201911284914</td>\n",
-       "      <td>2019</td>\n",
-       "      <td>1</td>\n",
-       "      <td>YAK</td>\n",
-       "      <td>US</td>\n",
-       "      <td>AK</td>\n",
-       "      <td>Alaska</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>AS</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>603.0</td>\n",
-       "      <td>ANC</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1167284</th>\n",
-       "      <td>201911284952</td>\n",
-       "      <td>2019</td>\n",
-       "      <td>1</td>\n",
-       "      <td>YAK</td>\n",
-       "      <td>US</td>\n",
-       "      <td>AK</td>\n",
-       "      <td>Alaska</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>AS</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>299.0</td>\n",
-       "      <td>JNU</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1167285 rows × 12 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              ITIN_ID  YEAR  QUARTER ORIGIN ORIGIN_COUNTRY ORIGIN_STATE_ABR  \\\n",
-       "0        201912723049  2019        1    ABI             US               TX   \n",
-       "1        201912723085  2019        1    ABI             US               TX   \n",
-       "2        201912723491  2019        1    ABI             US               TX   \n",
-       "3        201912723428  2019        1    ABI             US               TX   \n",
-       "4        201912723509  2019        1    ABI             US               TX   \n",
-       "...               ...   ...      ...    ...            ...              ...   \n",
-       "1167280  201911284909  2019        1    YAK             US               AK   \n",
-       "1167281  201911284959  2019        1    YAK             US               AK   \n",
-       "1167282  201911284940  2019        1    YAK             US               AK   \n",
-       "1167283  201911284914  2019        1    YAK             US               AK   \n",
-       "1167284  201911284952  2019        1    YAK             US               AK   \n",
-       "\n",
-       "        ORIGIN_STATE_NM  ROUNDTRIP REPORTING_CARRIER  PASSENGERS  ITIN_FARE  \\\n",
-       "0                 Texas        1.0                MQ         1.0      736.0   \n",
-       "1                 Texas        1.0                MQ         1.0      570.0   \n",
-       "2                 Texas        1.0                MQ         1.0      564.0   \n",
-       "3                 Texas        1.0                MQ         1.0      345.0   \n",
-       "4                 Texas        0.0                MQ         1.0      309.0   \n",
-       "...                 ...        ...               ...         ...        ...   \n",
-       "1167280          Alaska        0.0                AS         1.0      244.0   \n",
-       "1167281          Alaska        1.0                AS         1.0      371.0   \n",
-       "1167282          Alaska        0.0                AS         1.0      271.0   \n",
-       "1167283          Alaska        0.0                AS         1.0      603.0   \n",
-       "1167284          Alaska        1.0                AS         1.0      299.0   \n",
-       "\n",
-       "        DESTINATION  \n",
-       "0               DAB  \n",
-       "1               COS  \n",
-       "2               MCO  \n",
-       "3               LGA  \n",
-       "4               MGM  \n",
-       "...             ...  \n",
-       "1167280         ANC  \n",
-       "1167281         JNU  \n",
-       "1167282         JNU  \n",
-       "1167283         ANC  \n",
-       "1167284         JNU  \n",
-       "\n",
-       "[1167285 rows x 12 columns]"
-      ]
-     },
-     "execution_count": 284,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ticket_price"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## AirportsInfo\n",
-    "- COORDINATES into atomic data set\n",
-    "    - first one is longitude\n",
-    "    - second one is latitude"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 285,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# clean coordinates\n",
-    "\n",
-    "airportsInfo['COORDINATES_LONGITUDE'] = airportsInfo['COORDINATES'].apply(lambda x: x.split(', ')[0]).astype(float)\n",
-    "airportsInfo['COORDINATES_LATITUDE'] = airportsInfo['COORDINATES'].apply(lambda x: x.split(', ')[1]).astype(float)\n",
-    "airportsInfo.drop(columns=['COORDINATES'], inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 286,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 55369 entries, 0 to 55368\n",
-      "Data columns (total 9 columns):\n",
-      " #   Column                 Non-Null Count  Dtype  \n",
-      "---  ------                 --------------  -----  \n",
-      " 0   TYPE                   55369 non-null  object \n",
-      " 1   NAME                   55369 non-null  object \n",
-      " 2   ELEVATION_FT           48354 non-null  float64\n",
-      " 3   CONTINENT              27526 non-null  object \n",
-      " 4   ISO_COUNTRY            55122 non-null  object \n",
-      " 5   MUNICIPALITY           49663 non-null  object \n",
-      " 6   IATA_CODE              9182 non-null   object \n",
-      " 7   COORDINATES_LONGITUDE  55369 non-null  float64\n",
-      " 8   COORDINATES_LATITUDE   55369 non-null  float64\n",
-      "dtypes: float64(3), object(6)\n",
-      "memory usage: 3.8+ MB\n"
-     ]
-    }
-   ],
-   "source": [
-    "airportsInfo.info()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 287,
-   "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>TYPE</th>\n",
-       "      <th>NAME</th>\n",
-       "      <th>ELEVATION_FT</th>\n",
-       "      <th>CONTINENT</th>\n",
-       "      <th>ISO_COUNTRY</th>\n",
-       "      <th>MUNICIPALITY</th>\n",
-       "      <th>IATA_CODE</th>\n",
-       "      <th>COORDINATES_LONGITUDE</th>\n",
-       "      <th>COORDINATES_LATITUDE</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>heliport</td>\n",
-       "      <td>Total Rf Heliport</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>US</td>\n",
-       "      <td>Bensalem</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-74.933601</td>\n",
-       "      <td>40.070801</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>small_airport</td>\n",
-       "      <td>Aero B Ranch Airport</td>\n",
-       "      <td>3435.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>US</td>\n",
-       "      <td>Leoti</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-101.473911</td>\n",
-       "      <td>38.704022</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>small_airport</td>\n",
-       "      <td>Lowell Field</td>\n",
-       "      <td>450.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>US</td>\n",
-       "      <td>Anchor Point</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-151.695999</td>\n",
-       "      <td>59.949200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>small_airport</td>\n",
-       "      <td>Epps Airpark</td>\n",
-       "      <td>820.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>US</td>\n",
-       "      <td>Harvest</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-86.770302</td>\n",
-       "      <td>34.864799</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>closed</td>\n",
-       "      <td>Newport Hospital &amp; Clinic Heliport</td>\n",
-       "      <td>237.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>US</td>\n",
-       "      <td>Newport</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-91.254898</td>\n",
-       "      <td>35.608700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>55364</th>\n",
-       "      <td>medium_airport</td>\n",
-       "      <td>Yingkou Lanqi Airport</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>AS</td>\n",
-       "      <td>CN</td>\n",
-       "      <td>Yingkou</td>\n",
-       "      <td>YKH</td>\n",
-       "      <td>122.358600</td>\n",
-       "      <td>40.542524</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>55365</th>\n",
-       "      <td>medium_airport</td>\n",
-       "      <td>Shenyang Dongta Airport</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>AS</td>\n",
-       "      <td>CN</td>\n",
-       "      <td>Shenyang</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>123.496002</td>\n",
-       "      <td>41.784401</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>55366</th>\n",
-       "      <td>heliport</td>\n",
-       "      <td>Sealand Helipad</td>\n",
-       "      <td>40.0</td>\n",
-       "      <td>EU</td>\n",
-       "      <td>GB</td>\n",
-       "      <td>Sealand</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.482500</td>\n",
-       "      <td>51.894444</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>55367</th>\n",
-       "      <td>small_airport</td>\n",
-       "      <td>Glorioso Islands Airstrip</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>AF</td>\n",
-       "      <td>TF</td>\n",
-       "      <td>Grande Glorieuse</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>47.296389</td>\n",
-       "      <td>-11.584278</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>55368</th>\n",
-       "      <td>small_airport</td>\n",
-       "      <td>Satsuma Iōjima Airport</td>\n",
-       "      <td>338.0</td>\n",
-       "      <td>AS</td>\n",
-       "      <td>JP</td>\n",
-       "      <td>Mishima-Mura</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>130.270556</td>\n",
-       "      <td>30.784722</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>55369 rows × 9 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 TYPE                                NAME  ELEVATION_FT  \\\n",
-       "0            heliport                   Total Rf Heliport          11.0   \n",
-       "1       small_airport                Aero B Ranch Airport        3435.0   \n",
-       "2       small_airport                        Lowell Field         450.0   \n",
-       "3       small_airport                        Epps Airpark         820.0   \n",
-       "4              closed  Newport Hospital & Clinic Heliport         237.0   \n",
-       "...               ...                                 ...           ...   \n",
-       "55364  medium_airport               Yingkou Lanqi Airport           0.0   \n",
-       "55365  medium_airport             Shenyang Dongta Airport           NaN   \n",
-       "55366        heliport                     Sealand Helipad          40.0   \n",
-       "55367   small_airport           Glorioso Islands Airstrip          11.0   \n",
-       "55368   small_airport             Satsuma Iōjima Airport         338.0   \n",
-       "\n",
-       "      CONTINENT ISO_COUNTRY      MUNICIPALITY IATA_CODE  \\\n",
-       "0           NaN          US          Bensalem       NaN   \n",
-       "1           NaN          US             Leoti       NaN   \n",
-       "2           NaN          US      Anchor Point       NaN   \n",
-       "3           NaN          US           Harvest       NaN   \n",
-       "4           NaN          US           Newport       NaN   \n",
-       "...         ...         ...               ...       ...   \n",
-       "55364        AS          CN           Yingkou       YKH   \n",
-       "55365        AS          CN          Shenyang       NaN   \n",
-       "55366        EU          GB           Sealand       NaN   \n",
-       "55367        AF          TF  Grande Glorieuse       NaN   \n",
-       "55368        AS          JP      Mishima-Mura       NaN   \n",
-       "\n",
-       "       COORDINATES_LONGITUDE  COORDINATES_LATITUDE  \n",
-       "0                 -74.933601             40.070801  \n",
-       "1                -101.473911             38.704022  \n",
-       "2                -151.695999             59.949200  \n",
-       "3                 -86.770302             34.864799  \n",
-       "4                 -91.254898             35.608700  \n",
-       "...                      ...                   ...  \n",
-       "55364             122.358600             40.542524  \n",
-       "55365             123.496002             41.784401  \n",
-       "55366               1.482500             51.894444  \n",
-       "55367              47.296389            -11.584278  \n",
-       "55368             130.270556             30.784722  \n",
-       "\n",
-       "[55369 rows x 9 columns]"
-      ]
-     },
-     "execution_count": 287,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "airportsInfo"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "tongConsultinInc",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.19"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/impact_evaluation/eda_average_ticketPrice.ipynb b/impact_evaluation/eda_average_ticketPrice.ipynb
new file mode 100644
index 0000000..409150b
--- /dev/null
+++ b/impact_evaluation/eda_average_ticketPrice.ipynb
@@ -0,0 +1,276 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## eval_impact_of_tickets_associations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 200,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 201,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load data\n",
+    "\n",
+    "tickets = pd.read_csv('../data/cleaned_data/Tickets.csv').dropna()\n",
+    "ignore_column_tickets = ['ITIN_ID', 'YEAR', 'QUARTER', 'ORIGIN_COUNTRY', 'ORIGIN_STATE_ABR', 'ORIGIN_STATE_NM', 'ROUNDTRIP']\n",
+    "# tickets = tickets.drop(columns=ignore_column_tickets)\n",
+    "# # remove tickets that visit airports outside of the US\n",
+    "# tickets = tickets[(tickets['DEST_AIRPORT_IATA'].isin(airports['IATA_CODE'])) & (tickets['ORIGIN_AIRPORT_IATA'].isin(airports['IATA_CODE']))]\n",
+    "# tickets = tickets.assign(sorted_route = tickets.apply(lambda x : tuple(sorted([x['ORIGIN_AIRPORT_IATA'], x['DEST_AIRPORT_IATA']])), axis=1))\n",
+    "# tickets = tickets.drop(['ORIGIN_AIRPORT_IATA', 'DEST_AIRPORT_IATA'], axis=1)\n",
+    "\n",
+    "tickets = tickets.assign(\n",
+    "        sorted_route=tickets.apply(\n",
+    "            lambda x: tuple(\n",
+    "                sorted([x[\"ORIGIN_AIRPORT_IATA_CODE\"], x[\"DEST_AIRPORT_IATA_CODE\"]])\n",
+    "            ),\n",
+    "            axis=1,\n",
+    "        )\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Checking how many tickets associated with each round-trip routes\n",
+    "\n",
+    "It is critical to check the number of tickets associated with the round trip routes because we calculate average ticket price based on the ticket associated with each round trip route. Specially for round-trip routes associated with only a single ticket, the analysis would be highly biased if we only rely on a single ticket for that round-trip route"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 202,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "roundtrip_route_distribution = tickets.groupby('sorted_route')['ITIN_ID'].count().value_counts(normalize=True)\n",
+    "roundtrip_route_distributions_more_than_10 = roundtrip_route_distribution[roundtrip_route_distribution.index > 9].sum()\n",
+    "roundtrip_route_distribution = roundtrip_route_distribution.reset_index().loc[:8]\n",
+    "roundtrip_route_distribution.loc[9] = {'index' : '10 or more', 'ITIN_ID' : roundtrip_route_distributions_more_than_10}\n",
+    "roundtrip_route_distribution.columns = ['# of tickets on roundtrip route', 'Percetange of roundtrip routes']\n",
+    "\n",
+    "roundtrip_route_distribution['# of tickets on roundtrip route'] = pd.Series(['1 ticket', '2 tickets', '3 tickets', '4 tickets', '5 tickets', '6 tickets', '7 tickets', '8 tickets', '9 tickets', '10 or more'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 240,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.pie(roundtrip_route_distribution['Percetange of roundtrip routes'], labels=roundtrip_route_distribution['# of tickets on roundtrip route'], autopct='%1.1f%%', startangle=140)\n",
+    "\n",
+    "plt.title('Distribution of roundtrip routes categorized by the number of assoicated tickets');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Investiage the round trip route only associated with less than 3 tickets\n",
+    "- we hypothesis that unexpected distribution of single ticket association might be due to certain factors\n",
+    "- potential candidates\n",
+    "    - OP_CARRIER\n",
+    "    - ORIGIN_AIRPORT_IATA_CODE\n",
+    "    - ORIGIN_STATE_ABR\n",
+    "    - DEST_AIRPORT_IATA_CODE\n",
+    "    - ONE_PASSENGERS_FARE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 252,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = tickets.groupby('sorted_route').filter(lambda x: len(x) > 3)['OP_CARRIER'].value_counts(True)\n",
+    "y = tickets.groupby('sorted_route').filter(lambda x: len(x) <= 3)['OP_CARRIER'].value_counts(True)\n",
+    "df = pd.DataFrame({'x': x, 'y': y}).sort_values('y', ascending=False)\n",
+    "ax = df.plot(kind='bar', figsize=(10, 6))\n",
+    "plt.title('Distribution of Operation carrier for roundtrip tickets')\n",
+    "plt.xlabel('Operation Carrier')\n",
+    "plt.ylabel('Percentage of roundtrip tickets')\n",
+    "plt.legend(['Tickets with 3 or more tickets on a single round-trip route', 'Tickets with 3 or less tickets on a single round-trip route'])\n",
+    "plt.grid(axis='y')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Conducting Hypothesis Testing "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We hypothesis that the less ticket association(roundtrips associated with less than 3 tickets) happen due to the Operation Carrier. And we will conduct hypothesis testing on this problem.\n",
+    "\n",
+    "Null Hypothesis: There is no direct correlation between the Operation carrier and less ticket assoication. Thus, any distributions is due to random chance\n",
+    "Alternative Hypothesis: There is some corrleations happening between the Operation carrier and ticket assoications\n",
+    "\n",
+    "test stiatistic: The Total variation distance between the distribution of Operation carriers of tickets with less than 3 associations and the distribution of Operation carriers of tickets with more than 3 association"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 255,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# finding sample statistic\n",
+    "def tvd(series1, series2):\n",
+    "    ser_diff = series1.combine(series2, lambda x, y: y  if pd.isnull(x) else x if pd.isnull(y) else x - y)\n",
+    "    return 0.5 * np.sum(np.abs(ser_diff))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 288,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "observed_tvd = tvd(tickets.groupby('sorted_route').filter(lambda x: len(x) > 3)['OP_CARRIER'].value_counts(True), tickets.groupby('sorted_route').filter(lambda x: len(x) <= 3)['OP_CARRIER'].value_counts(True))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 311,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sim_times = 1000\n",
+    "\n",
+    "sim_tvd = []\n",
+    "for i in range(sim_times):\n",
+    "    perm_tickets = tickets.assign(OP_CARRIER = np.random.permutation(tickets['OP_CARRIER']))\n",
+    "    route_less_association = perm_tickets.groupby('sorted_route')['ITIN_ID'].count()\n",
+    "    route_less_association = pd.Series(route_less_association[route_less_association <= 3].index)\n",
+    "\n",
+    "    dis1 = perm_tickets[perm_tickets['sorted_route'].isin(route_less_association)]['OP_CARRIER'].value_counts(True)\n",
+    "    dis2 = perm_tickets[~perm_tickets['sorted_route'].isin(route_less_association)]['OP_CARRIER'].value_counts(True)\n",
+    "\n",
+    "\n",
+    "    sim_tvd.append(tvd(dis1, dis2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 328,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Distribution of simulated TVD')"
+      ]
+     },
+     "execution_count": 328,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(sim_tvd, density=True, bins=20)\n",
+    "plt.axvline(observed_tvd, color='r')\n",
+    "\n",
+    "plt.legend(['Observed statistic', 'Simulated statistic'])\n",
+    "plt.title('Distribution of simulated TVD')\n",
+    "\n",
+    "# plt.grid(axis='both')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "p-value of 0 which reject the null hypothesis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# result"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There might be an association between the operation carrier and associated ticket"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "tongConsultinInc",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.19"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/impact_evaluation/eval_impact_of_zero ticket price.ipynb b/impact_evaluation/eval_impact_of_zero ticket price.ipynb
new file mode 100644
index 0000000..bec50ce
--- /dev/null
+++ b/impact_evaluation/eval_impact_of_zero ticket price.ipynb	
@@ -0,0 +1,129 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# evaluating the degree of impact on removing ticket with 0 price"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "ori_tickets = pd.read_csv('../data/cleaned_data/Tickets.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "free_tickets_proportion = round(ori_tickets['ITIN_FARE'].isnull().sum() / len(ori_tickets) * 100, 2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Compare the impact before and after removing the ticket of 0 price\n",
+    "- will evaluate the distribution of ORIGIN, DEST, ORIGIN_STATE, OPERATION carrier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def tvd(series1, series2):\n",
+    "    ser_diff = series1.combine(series2, lambda x, y: y  if pd.isnull(x) else x if pd.isnull(y) else x - y)\n",
+    "    return 0.5 * np.sum(np.abs(ser_diff))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ori_tickets_no_free = ori_tickets[ori_tickets['ITIN_FARE'].notnull()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tvd_across_columns = {}\n",
+    "for col in ['ORIGIN_AIRPORT_IATA_CODE', 'ORIGIN_STATE_ABR', 'OP_CARRIER', 'DEST_AIRPORT_IATA_CODE']:\n",
+    "    tvd_across_columns[col] = tvd(ori_tickets[col].value_counts(normalize=True), ori_tickets_no_free[col].value_counts(normalize=True))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Conclusion"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.07% of the ticket has price of 0 assoicated with it which is really low\n",
+      "TVD of orginal and modified ticket data on ORIGIN_AIRPORT_IATA_CODE is 0.00018956667384382902\n",
+      "TVD of orginal and modified ticket data on ORIGIN_STATE_ABR is 8.62530261284656e-05\n",
+      "TVD of orginal and modified ticket data on OP_CARRIER is 6.253813875461848e-05\n",
+      "TVD of orginal and modified ticket data on DEST_AIRPORT_IATA_CODE is 0.00016569140934213444\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"{free_tickets_proportion}% of the ticket has price of 0 assoicated with it which is really low\")\n",
+    "for k, v in tvd_across_columns.items():\n",
+    "    print(f\"TVD of orginal and modified ticket data on {k} is {v}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Each of the above values is less than 0.01, so we can conclude that the impact of removing ticket with 0 price has extremely low impact on the data set"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "tongConsultinInc",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.19"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}