Exploratory Data Analysis Sprint/Exploratory Data Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading all the libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   price  model_year           model  condition  cylinders fuel  odometer  \\\n",
      "0   9400      2011.0          bmw x5       good        6.0  gas  145000.0   \n",
      "1  25500         NaN      ford f-150       good        6.0  gas   88705.0   \n",
      "2   5500      2013.0  hyundai sonata   like new        4.0  gas  110000.0   \n",
      "3   1500      2003.0      ford f-150       fair        8.0  gas       NaN   \n",
      "4  14900      2017.0    chrysler 200  excellent        4.0  gas   80903.0   \n",
      "\n",
      "  transmission    type paint_color  is_4wd date_posted  days_listed  \n",
      "0    automatic     SUV         NaN     1.0  2018-06-23           19  \n",
      "1    automatic  pickup       white     1.0  2018-10-19           50  \n",
      "2    automatic   sedan         red     NaN  2019-02-07           79  \n",
      "3    automatic  pickup         NaN     NaN  2019-03-22            9  \n",
      "4    automatic   sedan       black     NaN  2019-04-02           28  \n"
     ]
    }
   ],
   "source": [
    "# Load the data file into a DataFrame\n",
    "vehicle_price = pd.read_csv(\"/datasets/vehicles_us.csv\")\n",
    "print(vehicle_price.head())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explore initial data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                price    model_year       model  condition     cylinders  \\\n",
      "count    51525.000000  47906.000000       51525      51525  46265.000000   \n",
      "unique            NaN           NaN         100          6           NaN   \n",
      "top               NaN           NaN  ford f-150  excellent           NaN   \n",
      "freq              NaN           NaN        2796      24773           NaN   \n",
      "mean     12132.464920   2009.750470         NaN        NaN      6.125235   \n",
      "std      10040.803015      6.282065         NaN        NaN      1.660360   \n",
      "min          1.000000   1908.000000         NaN        NaN      3.000000   \n",
      "25%       5000.000000   2006.000000         NaN        NaN      4.000000   \n",
      "50%       9000.000000   2011.000000         NaN        NaN      6.000000   \n",
      "75%      16839.000000   2014.000000         NaN        NaN      8.000000   \n",
      "max     375000.000000   2019.000000         NaN        NaN     12.000000   \n",
      "\n",
      "         fuel       odometer transmission   type paint_color   is_4wd  \\\n",
      "count   51525   43633.000000        51525  51525       42258  25572.0   \n",
      "unique      5            NaN            3     13          12      NaN   \n",
      "top       gas            NaN    automatic    SUV       white      NaN   \n",
      "freq    47288            NaN        46902  12405       10029      NaN   \n",
      "mean      NaN  115553.461738          NaN    NaN         NaN      1.0   \n",
      "std       NaN   65094.611341          NaN    NaN         NaN      0.0   \n",
      "min       NaN       0.000000          NaN    NaN         NaN      1.0   \n",
      "25%       NaN   70000.000000          NaN    NaN         NaN      1.0   \n",
      "50%       NaN  113000.000000          NaN    NaN         NaN      1.0   \n",
      "75%       NaN  155000.000000          NaN    NaN         NaN      1.0   \n",
      "max       NaN  990000.000000          NaN    NaN         NaN      1.0   \n",
      "\n",
      "       date_posted  days_listed  \n",
      "count        51525  51525.00000  \n",
      "unique         354          NaN  \n",
      "top     2019-03-17          NaN  \n",
      "freq           186          NaN  \n",
      "mean           NaN     39.55476  \n",
      "std            NaN     28.20427  \n",
      "min            NaN      0.00000  \n",
      "25%            NaN     19.00000  \n",
      "50%            NaN     33.00000  \n",
      "75%            NaN     53.00000  \n",
      "max            NaN    271.00000  \n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 51525 entries, 0 to 51524\n",
      "Data columns (total 13 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   price         51525 non-null  int64  \n",
      " 1   model_year    47906 non-null  float64\n",
      " 2   model         51525 non-null  object \n",
      " 3   condition     51525 non-null  object \n",
      " 4   cylinders     46265 non-null  float64\n",
      " 5   fuel          51525 non-null  object \n",
      " 6   odometer      43633 non-null  float64\n",
      " 7   transmission  51525 non-null  object \n",
      " 8   type          51525 non-null  object \n",
      " 9   paint_color   42258 non-null  object \n",
      " 10  is_4wd        25572 non-null  float64\n",
      " 11  date_posted   51525 non-null  object \n",
      " 12  days_listed   51525 non-null  int64  \n",
      "dtypes: float64(4), int64(2), object(7)\n",
      "memory usage: 5.1+ MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# print the general/summary information about the DataFrame\n",
    "print(vehicle_price.describe(include=\"all\"))\n",
    "print(vehicle_price.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   price  model_year           model  condition  cylinders fuel  odometer  \\\n",
      "0   9400      2011.0          bmw x5       good        6.0  gas  145000.0   \n",
      "1  25500         NaN      ford f-150       good        6.0  gas   88705.0   \n",
      "2   5500      2013.0  hyundai sonata   like new        4.0  gas  110000.0   \n",
      "3   1500      2003.0      ford f-150       fair        8.0  gas       NaN   \n",
      "4  14900      2017.0    chrysler 200  excellent        4.0  gas   80903.0   \n",
      "5  14990      2014.0    chrysler 300  excellent        6.0  gas   57954.0   \n",
      "6  12990      2015.0    toyota camry  excellent        4.0  gas   79212.0   \n",
      "7  15990      2013.0     honda pilot  excellent        6.0  gas  109473.0   \n",
      "8  11500      2012.0     kia sorento  excellent        4.0  gas  104174.0   \n",
      "9   9200      2008.0     honda pilot  excellent        NaN  gas  147191.0   \n",
      "\n",
      "  transmission    type paint_color  is_4wd date_posted  days_listed  \n",
      "0    automatic     SUV         NaN     1.0  2018-06-23           19  \n",
      "1    automatic  pickup       white     1.0  2018-10-19           50  \n",
      "2    automatic   sedan         red     NaN  2019-02-07           79  \n",
      "3    automatic  pickup         NaN     NaN  2019-03-22            9  \n",
      "4    automatic   sedan       black     NaN  2019-04-02           28  \n",
      "5    automatic   sedan       black     1.0  2018-06-20           15  \n",
      "6    automatic   sedan       white     NaN  2018-12-27           73  \n",
      "7    automatic     SUV       black     1.0  2019-01-07           68  \n",
      "8    automatic     SUV         NaN     1.0  2018-07-16           19  \n",
      "9    automatic     SUV        blue     1.0  2019-02-15           17  \n"
     ]
    }
   ],
   "source": [
    "# print a sample of data\n",
    "print(vehicle_price.head(10))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Based on the sample of data and general information, it appears that there are both categorical and numerical data here. Some of these columns seem to vary greatly, as I notice for instance, the standard deviation for \"days_listed\" is very high at 28\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**At first glance, it looks like the column \"date_posted\" has a dtype of \"object\", which should be converted to datetime. \n",
    "**It also seems like the columns \"cylinders\", \"model_year\" and \"is_4wd\" are float64, when I think it might be better to convert them to int64 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Five columns seem to have several missing values:   model_year,  cylinders, odometer, paint_color, is_4wd.\n",
    "**The column with the largest missing values, where nearly 50% of the data is missing. (49.63% of data missing in this column)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusions and further steps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**I will go through each column that contains missing values and look through the values to check if there are any values that I can fill in immediately vs values in columns that I will need to do further analysis on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "price           25572\n",
      "model_year      23764\n",
      "model           25572\n",
      "condition       25572\n",
      "cylinders       22993\n",
      "fuel            25572\n",
      "odometer        21696\n",
      "transmission    25572\n",
      "type            25572\n",
      "paint_color     20942\n",
      "is_4wd          25572\n",
      "date_posted     25572\n",
      "days_listed     25572\n",
      "dtype: int64\n",
      "price           0\n",
      "model_year      0\n",
      "model           0\n",
      "condition       0\n",
      "cylinders       0\n",
      "fuel            0\n",
      "odometer        0\n",
      "transmission    0\n",
      "type            0\n",
      "paint_color     0\n",
      "is_4wd          0\n",
      "date_posted     0\n",
      "days_listed     0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(vehicle_price[vehicle_price[\"is_4wd\"]== 1].count())\n",
    "print(vehicle_price[vehicle_price[\"is_4wd\"]== 0].count())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Here, I have found that all 25572 rows in vehicle[\"is_4wd\"] have  a Boolean value of 1, indicating (True) for 4-wheel drive. As mentioned before, about half of the data is missing and since 100% of the available data in this column indicate \"True\", I think it is reasonable to assume that the other half of the data which is missing should have been recorded as \"0\" (False), but that it is likely that this information got lost in translation. I make this assumption due to the large amount of missing data coupled with the fact that all of the present values in this column are recorded as \"1\" (True)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Treat missing values (if any)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "0      25953\n",
      "1.0    25572\n",
      "Name: is_4wd, dtype: int64\n",
      "\n",
      "\n",
      "3619\n",
      "0.07023774866569626\n",
      "0\n",
      "4897\n",
      "0.10222101615664009\n",
      "12.0        2\n",
      "3.0        33\n",
      "5.0       248\n",
      "10.0      517\n",
      "4.0     12856\n",
      "6.0     14648\n",
      "8.0     14705\n",
      "Name: cylinders, dtype: int64\n",
      "0\n",
      "\n",
      "7716\n",
      "0.17940431072566207\n",
      "0\n",
      "white     8409\n",
      "black     6446\n",
      "silver    5182\n",
      "grey      4222\n",
      "red       3720\n",
      "blue      3716\n",
      "green     1148\n",
      "brown      996\n",
      "custom     974\n",
      "yellow     210\n",
      "orange     186\n",
      "purple      84\n",
      "Name: paint_color, dtype: int64\n",
      "\n",
      "5377\n",
      "0.1523531578499986\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 35293 entries, 2 to 51523\n",
      "Data columns (total 13 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   price         35293 non-null  int64  \n",
      " 1   model_year    35293 non-null  float64\n",
      " 2   model         35293 non-null  object \n",
      " 3   condition     35293 non-null  object \n",
      " 4   cylinders     35293 non-null  float64\n",
      " 5   fuel          35293 non-null  object \n",
      " 6   odometer      35293 non-null  float64\n",
      " 7   transmission  35293 non-null  object \n",
      " 8   type          35293 non-null  object \n",
      " 9   paint_color   35293 non-null  object \n",
      " 10  is_4wd        35293 non-null  object \n",
      " 11  date_posted   35293 non-null  object \n",
      " 12  days_listed   35293 non-null  int64  \n",
      "dtypes: float64(3), int64(2), object(8)\n",
      "memory usage: 3.8+ MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(vehicle_price.duplicated().sum())\n",
    "vehicle_price[\"is_4wd\"] = vehicle_price[\"is_4wd\"].fillna(\"0\")\n",
    "print(vehicle_price[\"is_4wd\"].value_counts())\n",
    "print()\n",
    "print()\n",
    "print(vehicle_price[\"model_year\"].isna().sum())\n",
    "print(vehicle_price[\"model_year\"].isna().sum()/len(vehicle_price))\n",
    "vehicle_price.dropna(subset=[\"model_year\"],inplace=True)\n",
    "print(vehicle_price[\"model_year\"].isna().sum())\n",
    "\n",
    "#print()\n",
    "#print()\n",
    "print(vehicle_price[\"cylinders\"].isna().sum())\n",
    "print(vehicle_price[\"cylinders\"].isna().sum()/len(vehicle_price))\n",
    "#print(vehicle_price.pivot_table(index=[\"cylinders\"], values=[\"model_year\"], aggfunc=\"mean\", margins=True))\n",
    "print(vehicle_price[\"cylinders\"].value_counts().sort_values())\n",
    "count = vehicle_price.loc[vehicle_price[\"cylinders\"] != vehicle_price[\"cylinders\"].isna() & (vehicle_price[\"fuel\"]== \"gas\"),:] \n",
    "count.count()\n",
    "vehicle_price[\"fuel\"].value_counts()\n",
    "vehicle_price.dropna(subset=[\"cylinders\"], inplace=True)\n",
    "print(vehicle_price[\"cylinders\"].isna().sum())\n",
    "print()\n",
    "print(vehicle_price[\"paint_color\"].isna().sum())\n",
    "vehicle_price[\"paint_color\"].value_counts()\n",
    "print(vehicle_price[\"paint_color\"].isna().sum()/len(vehicle_price))\n",
    "vehicle_price.dropna(subset=[\"paint_color\"], inplace=True)\n",
    "print(vehicle_price[\"paint_color\"].isna().sum())\n",
    "print(vehicle_price[\"paint_color\"].value_counts())\n",
    "print()\n",
    "print(vehicle_price[\"odometer\"].isna().sum())\n",
    "print(vehicle_price[\"odometer\"].isna().sum()/len(vehicle_price))\n",
    "odometer_mean = vehicle_price[\"odometer\"].mean()\n",
    "vehicle_price[\"odometer\"].fillna(odometer_mean, inplace=True)\n",
    "vehicle_price[\"odometer\"].value_counts()\n",
    "print(vehicle_price.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Note- Data contains no duplicates\n",
    "1. All misssing values in vehicle_price[\"is_4wd\"] were filled in with \"0\" to represent the \"False\" value for reasons above.\n",
    "2. All missing values in vehicle_price[\"model_year\"] were removed from the dataframe, as they only accounted for 7% of the entire data, which is not a significant amount. Missing values may have been due to print\n",
    "3. Note that 4487/4897 of the missing values in vehicle_price[\"cylinders\"] also have a value of \"gas\" in the vehicle[\"fuel\"] column \n",
    "4. The missing data in vehicle_price[\"cylinders\"] makes up about 10% of the entire data. This is also not a signficant amount, and the missing data in this column does not appear to have been influenced by any other column here. Thus, I justify removing the data rather than filling in.\n",
    "5. The missing data in vehicle_price[\"paint_color\"] makes up nearly 18% of the data. It would not make sense to fill these values in as this may risk being wrong about the data, so they will be removed as well.\n",
    "6. The missing data in vehicle_price[\"odometer\"] consists of about 15% of the entire dataframe which is a considerable amount. Since the distribution of values in this column did not seem to be impacted by extreme outliers, I chose to fill in the values with the mean of this column.\n",
    "**It is important to note that after all missing values have been removed, 68% of the original dataset has been retained."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fix data types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 35293 entries, 2 to 51523\n",
      "Data columns (total 13 columns):\n",
      " #   Column        Non-Null Count  Dtype         \n",
      "---  ------        --------------  -----         \n",
      " 0   price         35293 non-null  int64         \n",
      " 1   model_year    35293 non-null  float64       \n",
      " 2   model         35293 non-null  object        \n",
      " 3   condition     35293 non-null  object        \n",
      " 4   cylinders     35293 non-null  float64       \n",
      " 5   fuel          35293 non-null  object        \n",
      " 6   odometer      35293 non-null  float64       \n",
      " 7   transmission  35293 non-null  object        \n",
      " 8   type          35293 non-null  object        \n",
      " 9   paint_color   35293 non-null  object        \n",
      " 10  is_4wd        35293 non-null  object        \n",
      " 11  date_posted   35293 non-null  datetime64[ns]\n",
      " 12  days_listed   35293 non-null  int64         \n",
      "dtypes: datetime64[ns](1), float64(3), int64(2), object(7)\n",
      "memory usage: 3.8+ MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "vehicle_price[\"date_posted\"] = pd.to_datetime(vehicle_price[\"date_posted\"], format=\"%Y-%m-%d\")\n",
    "print(vehicle_price.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Enrich data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2        3\n",
      "4        1\n",
      "5        2\n",
      "6        3\n",
      "7        0\n",
      "        ..\n",
      "51518    4\n",
      "51520    2\n",
      "51521    2\n",
      "51522    3\n",
      "51523    0\n",
      "Name: day_of_week_posted, Length: 35293, dtype: int64\n",
      "2         2\n",
      "4         4\n",
      "5         6\n",
      "6        12\n",
      "7         1\n",
      "         ..\n",
      "51518     8\n",
      "51520    10\n",
      "51521    11\n",
      "51522    11\n",
      "51523     7\n",
      "Name: month_posted, Length: 35293, dtype: int64\n",
      "2        2019\n",
      "4        2019\n",
      "5        2018\n",
      "6        2018\n",
      "7        2019\n",
      "         ... \n",
      "51518    2018\n",
      "51520    2018\n",
      "51521    2018\n",
      "51522    2018\n",
      "51523    2018\n",
      "Name: year_posted, Length: 35293, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Add datetime values for when the ad was placed\n",
    "vehicle_price[\"day_of_week_posted\"] = vehicle_price[\"date_posted\"].dt.dayofweek\n",
    "print(vehicle_price[\"day_of_week_posted\"])\n",
    "vehicle_price['year_posted'] = pd.to_datetime(vehicle_price['date_posted']).dt.year\n",
    "vehicle_price['month_posted'] = pd.to_datetime(vehicle_price['date_posted']).dt.month\n",
    "print(vehicle_price[\"month_posted\"])\n",
    "print(vehicle_price[\"year_posted\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add the vehicle's age when the ad was placed\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "vehicle_price[\"vehicle_age_in_years\"] = vehicle_price[\"year_posted\"] - vehicle_price[\"model_year\"] \n",
    "\n",
    "\n",
    "\n",
    "#vehicle_price.loc[(vehicle_price[\"vehicle_age_in_years\"]== 0) & (vehicle_price[\"odometer\"] > float(\"0\")), \"vehicle_age_in_years\"] = 1\n",
    "\n",
    "vehicle_price[\"age_in_years\"] = np.where((vehicle_price[\"vehicle_age_in_years\"] == 0) & (vehicle_price[\"odometer\"] > float(\"0\")), 1, vehicle_price[\"vehicle_age_in_years\"])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   price  model_year           model  condition  cylinders fuel  odometer  \\\n",
      "2   5500      2013.0  hyundai sonata   like new        4.0  gas  110000.0   \n",
      "4  14900      2017.0    chrysler 200  excellent        4.0  gas   80903.0   \n",
      "5  14990      2014.0    chrysler 300  excellent        6.0  gas   57954.0   \n",
      "6  12990      2015.0    toyota camry  excellent        4.0  gas   79212.0   \n",
      "7  15990      2013.0     honda pilot  excellent        6.0  gas  109473.0   \n",
      "\n",
      "  transmission   type paint_color is_4wd date_posted  days_listed  \\\n",
      "2    automatic  sedan         red      0  2019-02-07           79   \n",
      "4    automatic  sedan       black      0  2019-04-02           28   \n",
      "5    automatic  sedan       black    1.0  2018-06-20           15   \n",
      "6    automatic  sedan       white      0  2018-12-27           73   \n",
      "7    automatic    SUV       black    1.0  2019-01-07           68   \n",
      "\n",
      "   day_of_week_posted  year_posted  month_posted  vehicle_age_in_years  \\\n",
      "2                   3         2019             2                   6.0   \n",
      "4                   1         2019             4                   2.0   \n",
      "5                   2         2018             6                   4.0   \n",
      "6                   3         2018            12                   3.0   \n",
      "7                   0         2019             1                   6.0   \n",
      "\n",
      "   age_in_years   avg_mileage  \n",
      "2           6.0  18333.333333  \n",
      "4           2.0  40451.500000  \n",
      "5           4.0  14488.500000  \n",
      "6           3.0  26404.000000  \n",
      "7           6.0  18245.500000  \n"
     ]
    }
   ],
   "source": [
    "# Add the vehicle's average mileage per year\n",
    "\n",
    "#avg_mileage_calc = vehicle_price.pivot_table(\n",
    "    #index=['vehicle_age_in_years'], values=['odometer'], aggfunc=['mean'])\n",
    "#avg_mileage_calc.columns =[\"mean_mileage\"]\n",
    "#print(avg_mileage_calc)\n",
    "#vehicle_price[\"avg_mileage\"] = avg_mileage_calc[\"mean_mileage\"]\n",
    "vehicle_price.head(20)\n",
    "vehicle_price[\"avg_mileage\"] = vehicle_price[\"odometer\"] / vehicle_price[\"vehicle_age_in_years\"]\n",
    "print(vehicle_price.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2         like new\n",
      "4        excellent\n",
      "5        excellent\n",
      "6        excellent\n",
      "7        excellent\n",
      "           ...    \n",
      "51518    excellent\n",
      "51520     like new\n",
      "51521      salvage\n",
      "51522    excellent\n",
      "51523         good\n",
      "Name: condition, Length: 35293, dtype: object\n",
      "2        4\n",
      "4        3\n",
      "5        3\n",
      "6        3\n",
      "7        3\n",
      "        ..\n",
      "51518    3\n",
      "51520    4\n",
      "51521    0\n",
      "51522    3\n",
      "51523    2\n",
      "Name: new_condition, Length: 35293, dtype: int64\n",
      "excellent    16957\n",
      "good         13808\n",
      "like new      3251\n",
      "fair          1095\n",
      "new            104\n",
      "salvage         78\n",
      "Name: condition, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# It might help to replace the condition values with something that can be manipulated more easily\n",
    "def replace(row):\n",
    "    value= row[\"condition\"]\n",
    "    if value == \"new\": \n",
    "        return 5\n",
    "    elif value == \"like new\":\n",
    "        return 4\n",
    "    elif value == \"excellent\": \n",
    "        return 3\n",
    "    elif value == \"good\": \n",
    "        return 2\n",
    "    elif value == \"fair\":\n",
    "        return 1\n",
    "    elif value == \"salvage\": \n",
    "        return 0\n",
    "vehicle_price[\"new_condition\"] = vehicle_price.apply(replace, axis=1)\n",
    "\n",
    "\n",
    "\n",
    "vehicle_price[[\"condition\", \"new_condition\"]]\n",
    "\n",
    "print(vehicle_price[\"condition\"])\n",
    "print(vehicle_price[\"new_condition\"])\n",
    "print(vehicle_price[\"condition\"].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check clean data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Given the data is ready for the analysis, take just another quick look at it. It is helpful to see what exactly you are going to use for the analysis.]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 35293 entries, 2 to 51523\n",
      "Data columns (total 20 columns):\n",
      " #   Column                Non-Null Count  Dtype         \n",
      "---  ------                --------------  -----         \n",
      " 0   price                 35293 non-null  int64         \n",
      " 1   model_year            35293 non-null  float64       \n",
      " 2   model                 35293 non-null  object        \n",
      " 3   condition             35293 non-null  object        \n",
      " 4   cylinders             35293 non-null  float64       \n",
      " 5   fuel                  35293 non-null  object        \n",
      " 6   odometer              35293 non-null  float64       \n",
      " 7   transmission          35293 non-null  object        \n",
      " 8   type                  35293 non-null  object        \n",
      " 9   paint_color           35293 non-null  object        \n",
      " 10  is_4wd                35293 non-null  object        \n",
      " 11  date_posted           35293 non-null  datetime64[ns]\n",
      " 12  days_listed           35293 non-null  int64         \n",
      " 13  day_of_week_posted    35293 non-null  int64         \n",
      " 14  year_posted           35293 non-null  int64         \n",
      " 15  month_posted          35293 non-null  int64         \n",
      " 16  vehicle_age_in_years  35293 non-null  float64       \n",
      " 17  age_in_years          35293 non-null  float64       \n",
      " 18  avg_mileage           35292 non-null  float64       \n",
      " 19  new_condition         35293 non-null  int64         \n",
      "dtypes: datetime64[ns](1), float64(6), int64(6), object(7)\n",
      "memory usage: 5.7+ MB\n"
     ]
    }
   ],
   "source": [
    "# print the general/summary information about the DataFrame\n",
    "vehicle_price.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    price  model_year                     model  condition  cylinders fuel  \\\n",
      "2    5500      2013.0            hyundai sonata   like new        4.0  gas   \n",
      "4   14900      2017.0              chrysler 200  excellent        4.0  gas   \n",
      "5   14990      2014.0              chrysler 300  excellent        6.0  gas   \n",
      "6   12990      2015.0              toyota camry  excellent        4.0  gas   \n",
      "7   15990      2013.0               honda pilot  excellent        6.0  gas   \n",
      "10  19500      2011.0  chevrolet silverado 1500  excellent        8.0  gas   \n",
      "11   8990      2012.0              honda accord  excellent        4.0  gas   \n",
      "13  16500      2018.0            hyundai sonata  excellent        4.0  gas   \n",
      "14  12990      2009.0                 gmc yukon  excellent        8.0  gas   \n",
      "15  17990      2013.0                  ram 1500  excellent        8.0  gas   \n",
      "\n",
      "         odometer transmission    type paint_color is_4wd date_posted  \\\n",
      "2   110000.000000    automatic   sedan         red      0  2019-02-07   \n",
      "4    80903.000000    automatic   sedan       black      0  2019-04-02   \n",
      "5    57954.000000    automatic   sedan       black    1.0  2018-06-20   \n",
      "6    79212.000000    automatic   sedan       white      0  2018-12-27   \n",
      "7   109473.000000    automatic     SUV       black    1.0  2019-01-07   \n",
      "10  128413.000000    automatic  pickup       black    1.0  2018-09-17   \n",
      "11  111142.000000    automatic   sedan        grey      0  2019-03-28   \n",
      "13   22104.000000    automatic   sedan      silver      0  2019-01-14   \n",
      "14  132285.000000    automatic     SUV       black    1.0  2019-01-31   \n",
      "15  115458.763003    automatic  pickup         red    1.0  2018-05-15   \n",
      "\n",
      "    days_listed  day_of_week_posted  year_posted  month_posted  \\\n",
      "2            79                   3         2019             2   \n",
      "4            28                   1         2019             4   \n",
      "5            15                   2         2018             6   \n",
      "6            73                   3         2018            12   \n",
      "7            68                   0         2019             1   \n",
      "10           38                   0         2018             9   \n",
      "11           29                   3         2019             3   \n",
      "13           29                   0         2019             1   \n",
      "14           24                   3         2019             1   \n",
      "15          111                   1         2018             5   \n",
      "\n",
      "    vehicle_age_in_years  age_in_years   avg_mileage  new_condition  \n",
      "2                    6.0           6.0  18333.333333              4  \n",
      "4                    2.0           2.0  40451.500000              3  \n",
      "5                    4.0           4.0  14488.500000              3  \n",
      "6                    3.0           3.0  26404.000000              3  \n",
      "7                    6.0           6.0  18245.500000              3  \n",
      "10                   7.0           7.0  18344.714286              3  \n",
      "11                   7.0           7.0  15877.428571              3  \n",
      "13                   1.0           1.0  22104.000000              3  \n",
      "14                  10.0          10.0  13228.500000              3  \n",
      "15                   5.0           5.0  23091.752601              3  \n"
     ]
    }
   ],
   "source": [
    "# print a sample of data\n",
    "print(vehicle_price.head(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Study core parameters\n",
    "\n",
    "The parameters are\n",
    "- Price\n",
    "- The vehicle's age when the ad was placed\n",
    "- Mileage\n",
    "- Number of cylinders\n",
    "- Condition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vehicle_price.boxplot(figsize=(10, 10), color=\"purple\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12170.271895276685\n",
      "9000.0\n",
      "Upper & Lower Bound: [-12850.0, 34750.0]\n"
     ]
    },
    {
     "data": {
      "image/png": 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W4OXSIjIzs4ZT5MjigxXTG4FHyU5FmZlZkyhyzeLkegRiZmaNq8diIelLvawXEfHVEuIxM7MG1NuRxQvdtG1D9k7s7QEXCzOzJtFjsYiIb3dOS9qW7NWnJ5O9b+LbPa1nZmZDT6/XLCSNAT4LnAjMB6ZGxPp6BGZmZo2jt2sW3wKOJnsx0d4RsaFuUZmZDRLN8mxJb0OUfw7YBTgLeELSc+nzvKTn6hOemZk1gt6uWRR514WZmTWBHguCpKWSzpM0XdKIegZlZmaNpbcL3PsD7wamA1+R9DRwHXBtRPyhHsGZWc+qPVe+S3t7SZFYM+jtNNRGoCN9kLQLWeH4mqTdgdsjYnYdYjQzswFWZCDBvSPi/oh4ApgHzJO0GfA3te5U0ijgJ8BeZO/K+BjwEHAJMJFs/KnjImJ9Ghb9POBw4EXgpIi4q9Z9F7Fu+XIWzJlTuP9gvbvBzKyoIhexL5B0h6TZkrYDiIjXI+I/+7Df84BfR8TbgHcAD5INeX5jREwGbmTTEOiHAZPTZxbwwz7s18zMapBbLCLib8keyhsPLJV0saRDat1hKjjvAS5M2381Ip4hG8l2fuo2HzgqTc8ALorMbcAoSTvXun8zM6uesvcZFegoDSP7BX4+8Bwg4MyIuKKqHUr7kD3o9wDZUcVSsqFEVkfEqNRHwPqIGCXpauDciPhtWnYjcFpELOmy3VlkRx60tLTsu3DhwmrCeoNnnnqK19euLdx/TJUXGhvRhg0bGDly5ECHUTdDId91VZ7+HD5hQuk5VxtTtf93nHO+vuQ8bdq0pRHR2t2y3GIh6e1kY0IdASwGLoyIu9IF799FxIRqgpHUCtwGHBQRt0s6j6z4fKazWKR+6yNidNFiUam1tTWWLOlxca4r2tt5+YILCvcfCtcsOjo6aGtrG+gw6mYo5FvL3VBl51z208zOOV9fcpbUY7Eo8vKj75NdjD4zIl7qbIyIJySdVUM8q4BVEXF7mr+c7PrEnyXtHBFr0mmmzj/tV5OdAus0LrWZNbRmGQbCmkORC9xHAAs6C4WkzSRtDRARP692hxHxJPC4pLempoPJTkktAmamtpnAVWl6EfBRZQ4Ano2INdXu18zMalfkyOIG4H1A50CCWwPXAwf2Yb+fAX4haTiwkuw012bApZJOAR4Djkt9ryG7bXYF2a2zfnOfmVmdFSkWIypHnI2IDZ1HFrWKiHuA7s6LHdxN3wCKP/RgZmb9rshpqBckTe2ckbQv8FIv/c3MbIgpcmTxj8Blkp4gu132zcCHygzKzMwaS26xiIg7Jb0N6Lwg/VBE/He5YZmZWSMpcmQB8C6yMZs2B6ZKIiIuKi0qMzNrKEUGEvw5sDtwD/Baag7AxcLMrEkUObJoBfaMouOCmJnZkFPkbqhlZBe1zcysSRU5stgBeEDSHcArnY0RcWRpUZmZWUMpUiy+XHYQZmbW2IrcOnuLpAnA5Ii4IT29Paz80MzMrFHkXrOQ9AmykWF/nJrGAleWGJOZmTWYIhe45wAHkb1zgoh4GNipzKDMzKyxFCkWr0TEq50zkjYne87CzMyaRJFicYukM4GtJL0fuAz4j3LDMjOzRlKkWJwOPAXcD3yS7P0Stbwhz8zMBqkid0O9Dvxr+piZWRMqMjbUH+nmGkVE7FZKRGZm1nCKjg3VaQRwLDCmnHDMzKwR5V6ziIinKz6rI+J7wBHlh2ZmZo2iyGmoqRWzm5EdaRR9D4aZmQ0BRX7pf7tieiPwKHBcKdGYmVlDKnI31LQydixpGLAEWB0RH5A0CVgIbA8sBT4SEa9K2pLsRUv7Ak8DH4qIR8uIyczMulfkNNRne1seEd+pcd+nAg8Cb0rz3wS+GxELJf0IOAX4Yfq6PiL2kHR86vehGvdpZmY1KPJQXivwD2QDCI4FPgVMBbZNn6pJGkd2kfwnaV7Ae8kGLASYDxyVpmekedLyg1N/MzOrE+W9LVXSb4AjIuL5NL8t8KuIeE/NO5UuB75BVmz+CTgJuC0i9kjLxwPXRsRekpYB0yNiVVr2CLB/RPylyzZnAbMAWlpa9l24cGGt4fHMU0/x+tq1hfuPmTKl5n01ig0bNjBy5MiBDqNu6pHvuuXLq+pf7c9RtdsfPmGCcy7BUMp52rRpSyOitbtlRS5wtwCvVsy/mtpqIukDwNqIWCqprdbtdBURc4G5AK2trdHWVvumr2hv5+ULLijcv63Kf8xG1NHRQV++Z4NNPfJdMGdOVf2r/Tmqdvtj2tudcwmaJecixeIi4A5Jv0zzR7HptFAtDgKOlHQ42UN+bwLOA0ZJ2jwiNgLjgNWp/2pgPLAqjXi7HdmFbjMzq5MiD+WdA5wMrE+fkyPi67XuMCLOiIhxETEROB64KSJOBG4GjkndZgJXpelFaZ60/KbIO3dmZmb9qsgFboCtgeci4jyyv/AnlRDLacBnJa0gu332wtR+IbB9av8s2Si4ZmZWR0VunT2b7I6otwI/BbYA/o3sdFKfREQH0JGmVwL7ddPnZbLxqMzMbIAUObL4O+BI4AWAiHiCGm+ZNTOzwalIsXg1XSMIAEnblBuSmZk1miLF4lJJPya7W+kTwA34RUhmZk2l12sW6UnpS4C3Ac+RXbf4UkQsrkNsZmbWIHotFhERkq6JiL0BFwgzsyZV5DTUXZLeVXokZmbWsIo8wb0/8PeSHiW7I0pkBx1vLzMwMzNrHD0WC0m7RsSfgEPrGI+ZmTWg3o4srgSmRsRjkv49Iv53nWIyM7MG01uxqHxnxG5lB2Jm5Vq3fHlVI5h+eAiMpmz9p7diET1MmzWcBVW+I2CX9vaSIrFG4gLZf3orFu+Q9BzZEcZWaRo2XeB+U8+rmplZd6r9w6ZR9FgsImJYPQMxs8ZSj19qjfaLs9HiaSRFhyg3M7Mm5mJhZma5XCzMzCyXi4WZmeVysTAzs1wuFmZmlqvIQIJmVge+bdMamY8szMwsV92LhaTxkm6W9ICk5ZJOTe1jJC2W9HD6Ojq1S9L5klZIuk/S1HrHbGbW7AbiNNRG4HMRcZekbYGlkhYDJwE3RsS5kk4HTgdOAw4DJqfP/sAP01cbRKo9xeIxeswaS92PLCJiTUTclaafBx4ExgIzgPmp23zgqDQ9A7goMrcBoyTtXN+ozcya24Bes5A0EXgncDvQEhFr0qIngZY0PRZ4vGK1VanNzMzqRBEDM/q4pJHALcA5EXGFpGciYlTF8vURMVrS1cC5EfHb1H4jcFpELOmyvVnALICWlpZ9Fy5cWHNszzz1FK+vXVu4/5ghcBfLhg0bGDlyZGnbX1flaaVqv6fVbn/4hAml5gvVx1S2zXbaqaqf66GgGXPuy8/2tGnTlkZEa3fLBqRYSNoCuBq4LiK+k9oeAtoiYk06zdQREW+V9OM0fXHXfj1tv7W1NZYsWdLT4lxXtLfz8gUXFO4/FM6vd3R00NbWVtr2y75mUe32R8yeXdW/MZQfU9lqyXmwa8acd2lvr/n/sqQei0XdL3BLEnAh8GBnoUgWATOBc9PXqyraPy1pIdmF7Wd7KxRmZWm0X/5m9TQQd0MdBHwEuF/SPantTLIicamkU4DHgOPSsmuAw4EVwIvAyXWN1szM6l8s0rUH9bD44G76B1D8vYhmZtbv/AS3mZnlcrEwM7NcLhZmZpbLxcLMzHK5WJiZWS6/z8Jq4mcOzJqLi4UB2dAUC+b4DmUz655PQ5mZWS4fWVhD8mkus8biIwszM8vlYmFmZrl8GqofNNorQ2s5hTNi9uwSIjGzocJHFmZmlstHFgOg0Y5EzMzyuFgMAr4zyMwGmk9DmZlZLhcLMzPL5WJhZma5XCzMzCyXi4WZmeVysTAzs1wuFmZmlmvQFAtJ0yU9JGmFpNMHOh4zs2YyKIqFpGFAO3AYsCdwgqQ9BzYqM7PmMSiKBbAfsCIiVkbEq8BCYMYAx2Rm1jQUEQMdQy5JxwDTI+Ljaf4jwP4R8emKPrOAWWn2rcBDfdjlDsBf+rD+YNRsOTdbvuCcm0Vfcp4QETt2t2DIjA0VEXOBuf2xLUlLIqK1P7Y1WDRbzs2WLzjnZlFWzoPlNNRqYHzF/LjUZmZmdTBYisWdwGRJkyQNB44HFg1wTGZmTWNQnIaKiI2SPg1cBwwD5kVEmS956JfTWYNMs+XcbPmCc24WpeQ8KC5wm5nZwBosp6HMzGwAuViYmVkuF4sKQ2lIEUnzJK2VtKyibYykxZIeTl9Hp3ZJOj/lfZ+kqRXrzEz9H5Y0cyByKUrSeEk3S3pA0nJJp6b2IZu3pBGS7pB0b8r5K6l9kqTbU26XpBtDkLRlml+Rlk+s2NYZqf0hSYcOUEqFSBom6W5JV6f5oZ7vo5Lul3SPpCWprb4/1xHhT3bdZhjwCLAbMBy4F9hzoOPqQz7vAaYCyyra/h9wepo+Hfhmmj4cuBYQcABwe2ofA6xMX0en6dEDnVsvOe8MTE3T2wJ/IBseZsjmnWIfmaa3AG5PuVwKHJ/afwT8Q5qeDfwoTR8PXJKm90w/81sCk9L/hWEDnV8veX8WWABcneaHer6PAjt0aavrz7WPLDYZUkOKRMRvgHVdmmcA89P0fOCoivaLInMbMErSzsChwOKIWBcR64HFwPTSg69RRKyJiLvS9PPAg8BYhnDeKfYNaXaL9AngvcDlqb1rzp3fi8uBgyUptS+MiFci4o/ACrL/Ew1H0jjgCOAnaV4M4Xx7UdefaxeLTcYCj1fMr0ptQ0lLRKxJ008CLWm6p9wH7fcknW54J9lf2kM673RK5h5gLdkvgEeAZyJiY+pSGf9fc0vLnwW2Z3Dl/D3g88DraX57hna+kP0BcL2kpcqGNoI6/1wPiucsrP9FREgakvdNSxoJ/DvwjxHxXPaHZGYo5h0RrwH7SBoF/BJ428BGVB5JHwDWRsRSSW0DHE49vTsiVkvaCVgs6feVC+vxc+0ji02aYUiRP6fDUdLXtam9p9wH3fdE0hZkheIXEXFFah7yeQNExDPAzcDfkJ166PxjsDL+v+aWlm8HPM3gyfkg4EhJj5KdKn4vcB5DN18AImJ1+rqW7A+C/ajzz7WLxSbNMKTIIqDzDoiZwFUV7R9Nd1EcADybDm+vAw6RNDrdaXFIamtI6Vz0hcCDEfGdikVDNm9JO6YjCiRtBbyf7FrNzcAxqVvXnDu/F8cAN0V29XMRcHy6e2gSMBm4oy5JVCEizoiIcRExkez/6E0RcSJDNF8ASdtI2rZzmuzncRn1/rke6Kv8jfQhu4vgD2TnfL8w0PH0MZeLgTXAf5OdmzyF7FztjcDDwA3AmNRXZC+XegS4H2it2M7HyC7+rQBOHui8cnJ+N9m53fuAe9Ln8KGcN/B24O6U8zLgS6l9N7JffiuAy4AtU/uINL8iLd+tYltfSN+Lh4DDBjq3Arm3seluqCGbb8rt3vRZ3vm7qd4/1x7uw8zMcvk0lJmZ5XKxMDOzXC4WZmaWy8XCzMxyuViYmVkuFwsb0iS9lkbqXCbpMklb99Dvv/phX0eqytGKK+Jbrmzk2M9J6vX/paQ2bRpt9SRJP+hL3GZFuFjYUPdSROwTEXsBrwKfqlzY+dRvRBzY1x1FxKKIOLfG+KaQPVB3GHB2X2PpScVTzmZVcbGwZnIrsEf6y/xWSYuABwAkdY7ciqTT0rsD7pV0bmrbXdKv00But0r6H+MvVf6VL+ln6Z0C/yVppaRjuvbvKrKhHGYBn05P346Q9NMUy92SpvW2vqQPKntnw92SbpDUktq/LOnnkv4T+LmkKcregXGPsvcdTC78HbSm5b8yrCmkv6gPA36dmqYCe0U2PHVlv8PIhnjePyJelDQmLZoLfCoiHpa0P3AB2bhEvdmZ7Knyt5ENwXB5790hIlZKGgbsBPx91hR7p+J0vaS39LL6b4EDIiIkfZxsZNbPpWV7kg1G95Kk7wPnRcQv0tA2w/LiMnOxsKFuK2XDd0N2ZHEhcCBwR9dCkbwP+GlEvAgQEeuUjWJ7IHCZNo1gu2WBfV8ZEa8DD3T+lV+ldwPfT3H8XtJjQG/FYhxwSRpUbjhQmd+iiHgpTf8O+IKy90JcEREP1xCbNRkXCxvqXoqIfSob0i/8F6rYxmZk70vYJ69jF69U7rbICpJ2A15j0wii1fg+8J2IWKRs+O4vVyz7a74RsUDS7WQvELpG0icj4qYa9mdNxNcszN5oMXBy511TksZExHPAHyUdm9ok6R39vWNJO5K9EvQHkQ3aditwYlr2FmBXskHverIdm4ac7vH9yqkgrYyI88lGKn1736O3oc7FwqxCRPya7PrCknT66p/SohOBUyR1jvzZX6/c3arz1lmykUOvB76Sll0AbCbpfuAS4KSIeKWH7UB2JHGZpKXAX3rpdxywLOW3F3BR31KwZuBRZ83MLJePLMzMLJeLhZmZ5XKxMDOzXC4WZmaWy8XCzMxyuViYmVkuFwszM8v1/wHuZYrM0cylHAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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DGRya+kRVvZjB5dvPb6ttBD7Upje3edryT9TguNdm4EXtqqZDGBzeunRcdUuSpH6M8oiC+fZyYFOS1wKfB85q42cB706yFbiVdvfgqvpykvOAq4C7gVOq6scLX7YkSVpqpg0ySS6uqmckeUNVvXx3PqSqtgBb2vT17OSqo6r6IfCCad7/OuB1u1ODJEna88y0R2ZNkiczOM9lE5DhhVX1ubFWJkmSNIuZgsyfAK9mcHLtG3dYVgxO3pUkSVo00waZdp+W85O8uqpes4A1SZIkjWSU+8i8JslzgKe1oS1V9eHxliVJkjS7WS+/TvLnDJ6XdFV7vTTJn427MEmSpNmMcvn1s4DDq+onAEnOYXDZ9CvGWZgkSdJsRr0h3qqh6f3GUIckSdKcjbJH5s+Bzyf5JINLsJ8GnDrWqiRJkkYwysm+5ybZAvxyG3p5VX1zrFVJkiSNYKRHFFTVNgbPPJIkSVoyxvbQSEmSpHEzyEiSpG7NGGSS7JXkKwtVjCRJ0lzMGGSq6sfANUkeukD1SJIkjWyUk333B76c5FLgjqnBqnrO2KqSJEkawShB5tVjr0KSJGkXjHIfmU8leRhwWFV9PMkDgL3GX5okSdLMRnlo5G8D5wPvaEMPAT44xpokSZJGMsrl16cATwFuB6iq64CfH2dRkiRJoxglyNxVVT+amkmyAqjxlSRJkjSaUYLMp5K8Arh/kl8F3gf8z/GWJUmSNLtRgsypwLeBLwG/A1wAvGqcRUmSJI1ilKuWfpLkHOASBoeUrqkqDy1JkqRFN2uQSfIs4O3AV4EAhyT5nar6yLiLkyRJmskoN8T7S+DoqtoKkOThwD8ABhlJkrSoRjlH5vtTIaa5Hvj+mOqRJEka2bR7ZJI8r01enuQC4DwG58i8ALhsAWqTJEma0UyHlp49NP0t4N+06W8D9x9bRZIkSSOaNshU1YkLWYgkSdJcjXLV0iHAHwAHD69fVc8ZX1mSJEmzG+WqpQ8CZzG4m+9PxlqNJEnSHIwSZH5YVW8eeyWSJElzNEqQOSPJBPAx4K6pwar63NiqkiRJGsEoQeZxwH8Ens5PDy1Vm5ckSVo0owSZFwCHVtWPxl2MJEnSXIxyZ98rgVVjrkOSJGnORtkjswr4SpLL+NlzZLz8WpIkLapRgszE2KuQJEnaBbMGmar61EIUIkmSNFej3Nn3+wyuUgK4H3Bf4I6q+lfjLEySJGk2o+yReeDUdJIAJwBHjbMoSZKkUYxy1dI9auCDwLHjKUeSJGl0oxxaet7Q7H2AdcAPx1aRJEnSiEa5aunZQ9N3AzcwOLwkSZK0qEY5R+bEhShEkiRprqYNMkn+ZIb3VVW9Zgz1SJIkjWymPTJ37GRsH+Ak4OcAg4wkSVpU0waZqvrLqekkDwReCpwIbAL+crr3SZIkLZQZz5FJcgDwR8CLgXOAI6rqtoUoTJIkaTYznSPzF8DzgDOBx1XV9gWrSpIkaQQz3RDvZcAvAK8CvpHk9vb6fpLbF6Y8SZKk6c10jsyc7vorSZK00KYNK0muSHJGkuOS7L2QRUmSJI1ipr0uRwIfANYDn0pyQZKXJlm7IJVJkiTNYqZDS3cDW9qLJL8AHAe8NsnDgUuq6vcWoEZJkqSdGuWhkY+rqi9V1TeAs4Gzk9wHeNLYq5MkSZrBKCf0vjXJpUl+L8l+AFX1k6r6xzHXJkmSNKNZg0xV/QqDG+IdBFyR5Nwkx4y9MkmSpFmMdIl1VV3H4H4yLweeBpyR5CtJnjfO4iRJkmYya5BJ8otJ3gRcDTwdeHZV/T9t+k1jrk+SJGlas57sC/wV8DfAK6rqzqnBqvpGkleNrTJ1YXJycre3MTExMQ+VSJKWo1GCzLOAO6vqxwDtiqW9q+oHVfXusVYnSZI0g1HOkfk4cP+h+Qe0MUmSpEU1SpDZe/jJ1236AeMrSZIkaTSjBJk7khwxNZPkCcCdM6w/td7e7f4z/5Tky0km2/ghSS5JsjXJe5Pcr42vbPNb2/KDh7Z1Whu/Jsmxc/6WkiRpjzRKkPlD4H1JPp3kM8B7gd8f4X13AU+vqscDhwPHJTkKeAPwpqp6BHAbcFJb/yTgtjb+prYeSR4NvAh4DINHJLw1yV6jfT1JkrQnm/Vk36q6LMmjgEe2oWuq6v+M8L4Cpg5J3be9isFl2/9vGz8HOB14G3BCmwY4H3hLkrTxTVV1F/C1JFuBJwL/e7Ya1AevfJIk7aoM8sYsKyVPBg5mKPhU1btGeN9ewBXAI4C/Bv4C+Gzb60KSg4CPVNVjk1wJHFdVN7VlX2XwBO7T23v+to2f1d5z/g6fdTJwMsDq1aufsGnTplm/167Yvn07++67725tY9u2bfNUzeJYuXIld91112KX8TPWrFmz4J85H78LvbMH9gDsAdiDKePsw9FHH31FVa3bcXyUh0a+G3g48AXgx224gFmDTLtk+/Akq4APAI8aveS5qaozgTMB1q1bV+vXrx/L52zZsoXd3fZ87IFYTGvXruXaa69d7DJ+xoYNGxb8M+fjd6F39sAegD0AezBlMfowyn1k1gGPrlF23Uyjqr6b5JMMnpi9KsmKqrobOBC4ua12M4PnOd2UZAWwH/CdofEpw++RJEnL2Cgn+14J/Ou5bjjJg9ueGJLcH/hVBo85+CTw/LbaRuBDbXpzm6ct/0QLT5uBF7Wrmg4BDgMunWs9kiRpzzPKHpkHAVcluZTBlUgAVNVzZnnfGuCcdp7MfYDzqurDSa4CNiV5LfB54Ky2/lnAu9vJvLcyuFKJqvpykvOAq4C7gVOm7jIsSZKWt1GCzOm7suGq+iLwSzsZv57BVUc7jv8QeME023od8LpdqUOSJO25Rrn8+lNJHgYcVlUfT/IAwPu4SJKkRTfrOTJJfpvBfV3e0YYeAnxwjDVJkiSNZJSTfU8BngLcDlBV1wE/P86iJEmSRjFKkLmrqn40NdMujd7lS7ElSZLmyyhB5lNJXgHcP8mvAu8D/ud4y5IkSZrdKEHmVODbwJeA3wEuAF41zqIkSZJGMcpVSz8B/nt7SZIkLRmjPGvpa+zknJiqOnQsFUmSJI1o1GctTdmbwU3rDhhPOZIkSaOb9RyZqvrO0OvmqvpvwLPGX5okSdLMRjm0dMTQ7H0Y7KEZZU+OJEnSWI0SSP5yaPpu4AbghWOpRpIkaQ5GuWrp6IUoRJIkaa5GObT0RzMtr6o3zl85S9+2bduYnJxc7DIkSRKjX7X0y8DmNv9s4FLgunEVJUmSNIpRgsyBwBFV9X2AJKcD/1BVvz7OwiRJkmYzyiMKVgM/Gpr/URuTJElaVKPskXkXcGmSD7T55wLnjK0iSZKkEY1y1dLrknwE+JU2dGJVfX68ZUmSJM1ulENLAA8Abq+qM4CbkhwyxpokSZJGMmuQSTIBvBw4rQ3dF/jbcRYlSZI0ilH2yPw74DnAHQBV9Q3ggeMsSpIkaRSjBJkfVVUBBZBkn/GWJEmSNJpRgsx5Sd4BrEry28DHgf8+3rIkSZJmN+NVS0kCvBd4FHA78EjgT6rqogWoTZIkaUYzBpmqqiQXVNXjAMOLJElaUkY5tPS5JL889kokSZLmaJQ7+x4J/HqSGxhcuRQGO2t+cZyFSZIkzWbaIJPkoVX1z8CxC1iPJEnSyGbaI/NBBk+9vjHJ31fVv1+gmiRJkkYy0zkyGZo+dNyFSJIkzdVMQaammZYkSVoSZjq09PgktzPYM3P/Ng0/Pdn3X429OkmSpBlMG2Sqaq+FLESSJGmuRrmPjCRJ0pJkkJEkSd0yyEiSpG4ZZCRJUrcMMpIkqVsGGUmS1K1RHhopLXmTk5O7vY2JiYl5qESStJDcIyNJkrplkJEkSd0yyEiSpG4ZZCRJUrcMMpIkqVsGGUmS1C2DjCRJ6pZBRpIkdcsgI0mSumWQkSRJ3TLISJKkbhlkJElStwwykiSpWwYZSZLULYOMJEnqlkFGkiR1yyAjSZK6ZZCRJEndMshIkqRuGWQkSVK3DDKSJKlbBhlJktStsQWZJAcl+WSSq5J8OclL2/gBSS5Kcl37uX8bT5I3J9ma5ItJjhja1sa2/nVJNo6rZkmS1Jdx7pG5G3hZVT0aOAo4JcmjgVOBi6vqMODiNg9wPHBYe50MvA0GwQeYAI4EnghMTIUfSZK0vI0tyFTVtqr6XJv+PnA18BDgBOCctto5wHPb9AnAu2rgs8CqJGuAY4GLqurWqroNuAg4blx1S5KkfizIOTJJDgZ+CbgEWF1V29qibwKr2/RDgK8Pve2mNjbduCRJWuZWjPsDkuwL/D3wh1V1e5J7llVVJal5+pyTGRySYvXq1WzZsmU+NnsvK1euZO3atWPZdi/21B7M9Xdm+/btY/s964U9sAdgD8AeTFmMPow1yCS5L4MQ856qen8b/laSNVW1rR06uqWN3wwcNPT2A9vYzcD6Hca37PhZVXUmcCbAunXrav369TuuMi/OPfdcrr322rFsuxdr167dI3uwYcOGOa2/ZcsWxvV71gt7YA/AHoA9mLIYfRjnVUsBzgKurqo3Di3aDExdebQR+NDQ+Eva1UtHAd9rh6AuBI5Jsn87yfeYNiZJkpa5ce6ReQrwH4EvJflCG3sF8HrgvCQnATcCL2zLLgCeCWwFfgCcCFBVtyZ5DXBZW+9Pq+rWMdYtSZI6MbYgU1WfATLN4mfsZP0CTplmW2cDZ89fdZIkaU/gnX0lSVK3DDKSJKlbBhlJktQtg4wkSeqWQUaSJHXLICNJkrplkJEkSd0yyEiSpG4ZZCRJUrcMMpIkqVsGGUmS1C2DjCRJ6pZBRpIkdcsgI0mSumWQkSRJ3TLISJKkbhlkJElStwwykiSpWwYZSZLULYOMJEnqlkFGkiR1yyAjSZK6ZZCRJEndMshIkqRuGWQkSVK3Vix2AdJSMTk5Oaf1165d+zPvmZiYmO+SJEmzcI+MJEnqlkFGkiR1yyAjSZK6ZZCRJEndMshIkqRuGWQkSVK3DDKSJKlbBhlJktQtg4wkSeqWQUaSJHXLICNJkrplkJEkSd0yyEiSpG4ZZCRJUrcMMpIkqVsGGUmS1K0Vi12AtKeYnJzc7W1MTEzMQyWStHy4R0aSJHXLICNJkrplkJEkSd0yyEiSpG4ZZCRJUrcMMpIkqVsGGUmS1C2DjCRJ6pZBRpIkdcsgI0mSuuUjCqQlxMccSNLcuEdGkiR1yyAjSZK6ZZCRJEndMshIkqRuGWQkSVK3DDKSJKlbBhlJktQtg4wkSeqWQUaSJHXLICNJkro1tiCT5OwktyS5cmjsgCQXJbmu/dy/jSfJm5NsTfLFJEcMvWdjW/+6JBvHVa8kSerPOPfI/A/guB3GTgUurqrDgIvbPMDxwGHtdTLwNhgEH2ACOBJ4IjAxFX4kSZLGFmSq6v8Hbt1h+ATgnDZ9DvDcofF31cBngVVJ1gDHAhdV1a1VdRtwEfcOR5IkaZla6HNkVlfVtjb9TWB1m34I8PWh9W5qY9ONS5IksWKxPriqKknN1/aSnMzgsBSrV69my5Yt87Xpn7Fy5UrWrl07lm33wh4MLNU+jOt3f2e2b9++oJ+3FNkDewD2YMpi9GGhg8y3kqypqm3t0NEtbfxm4KCh9Q5sYzcD63cY37KzDVfVmcCZAOvWrav169fvbLXddu6553LttdeOZdu9WLt27bLvASzdPmzYsGHBPmvLli2M62+tF/bAHoA9mLIYfVjoQ0ubgakrjzYCHxoaf0m7euko4HvtENSFwDFJ9m8n+R7TxiRJksa3RybJuQz2pjwoyU0Mrj56PXBekpOAG4EXttUvAJ4JbAV+AJwIUFW3JnkNcFlb70+rascTiCUNmZyc3O1tTExMzEMlkjR+YwsyVTXd/u1n7GTdAk6ZZjtnA2fPY2mSJGkP4Z19JUlStwwykiSpWwYZSZLULYOMJEnqlkFGkiR1yyAjSZK6ZZCRJEndMshIkqRuGWQkSVK3DDKSJKlbBhlJktQtg4wkSeqWQUaSJHVrbE+/ltSvycnJkdZbu3bttOtOTEzMZ0mStFPukZEkSd0yyEiSpG4ZZCRJUrcMMpIkqVsGGUmS1C2DjCRJ6pZBRpIkdcsgI0mSumWQkSRJ3TLISJKkbhlkJElStwwykiSpWwYZSZLULYOMJEnqlkFGkiR1yyAjSZK6ZZCRJEndMshIkqRuGWQkSVK3DDKSJKlbBhlJktQtg4wkSeqWQUaSJHXLICNJkrplkJEkSd0yyEiSpG6tWOwCJO2ZJicnd3sbExMT81CJpD2ZQUbSkmUYkjQbDy1JkqRuGWQkSVK3DDKSJKlbBhlJktQtg4wkSeqWQUaSJHXLICNJkrplkJEkSd0yyEiSpG55Z19JezTvDizt2dwjI0mSumWQkSRJ3TLISJKkbnmOjCTNYqbzbNauXTvSeTieZyONh3tkJElStwwykiSpWx5akqQF4GXg0ni4R0aSJHXLICNJkrrloSVJ6oSHp6R7M8hI0jKyu2HIIKSlxkNLkiSpW93skUlyHHAGsBfwN1X1+kUuSZK0CzxEpvnURZBJshfw18CvAjcBlyXZXFVXLW5lkrS87CyEjHp343HXMVeGoT1DF0EGeCKwtaquB0iyCTgBMMhIkhbNVKDanTBnoNo9vQSZhwBfH5q/CThykWqRJO0BFnov0nSWSh29BqpegsyskpwMnNxmtye5Zkwf9SDgX8a07V7YgwH7YA/AHoA9gD2gB6effvp8bGacfXjYzgZ7CTI3AwcNzR/Yxu5RVWcCZ467kCSXV9W6cX/OUmYPBuyDPQB7APYA7MGUxehDL5dfXwYcluSQJPcDXgRsXuSaJEnSIutij0xV3Z3k94ELGVx+fXZVfXmRy5IkSYusiyADUFUXABcsdh0swOGrDtiDAftgD8AegD0AezBlwfuQqlroz5QkSZoXvZwjI0mSdC8GmTlIclySa5JsTXLqYtezu5KcneSWJFcOjR2Q5KIk17Wf+7fxJHlz++5fTHLE0Hs2tvWvS7JxaPwJSb7U3vPmJFnYbzi7JAcl+WSSq5J8OclL2/iy6UOSvZNcmuSfWg8m2/ghSS5pdb+3nWhPkpVtfmtbfvDQtk5r49ckOXZovIu/nSR7Jfl8kg+3+WXVgyQ3tN/VLyS5vI0tm7+FKUlWJTk/yVeSXJ3kScupD0ke2X4Hpl63J/nDJduDqvI1wovBScZfBQ4F7gf8E/Doxa5rN7/T04AjgCuHxv4LcGqbPhV4Q5t+JvARIMBRwCVt/ADg+vZz/za9f1t2aVs37b3HL/Z33kkP1gBHtOkHAtcCj15OfWh17dum7wtc0uo9D3hRG3878P+16d8D3t6mXwS8t00/uv1drAQOaX8ve/X0twP8EfB3wIfb/LLqAXAD8KAdxpbN38LQdz4H+K02fT9g1XLsQ6t1L+CbDO7hsiR7sOhN6uUFPAm4cGj+NOC0xa5rHr7XwfxskLkGWNOm1wDXtOl3ABt2XA/YALxjaPwdbWwN8JWh8Z9Zb6m+gA8xeKbXsuwD8ADgcwzunP0vwIo2fs/vP4OrB5/Uple09bLj38TUer387TC4P9XFwNOBD7fvtNx6cAP3DjLL6m8B2A/4Gu0c0uXah6H6jgH+cSn3wENLo9vZYxIeski1jNPqqtrWpr8JrG7T033/mcZv2sn4ktUOD/wSgz0Sy6oP7ZDKF4BbgIsY7D34blXd3VYZrvue79qWfw/4Oebem6XmvwH/GfhJm/85ll8PCvhYkisyuFs6LLO/BQZ70r4NvLMdZvybJPuw/Pow5UXAuW16SfbAIKNp1SAqL4vL2pLsC/w98IdVdfvwsuXQh6r6cVUdzmCvxBOBRy1uRQsrya8Bt1TVFYtdyyJ7alUdARwPnJLkacMLl8PfAoM9bEcAb6uqXwLuYHAY5R7LpA+0c8KeA7xvx2VLqQcGmdHN+piEPcS3kqwBaD9vaePTff+Zxg/cyfiSk+S+DELMe6rq/W142fUBoKq+C3ySwaGQVUmm7jU1XPc937Ut3w/4DnPvzVLyFOA5SW4ANjE4vHQGy6sHVNXN7ectwAcYhNrl9rdwE3BTVV3S5s9nEGyWWx9gEGg/V1XfavNLsgcGmdEtl8ckbAamzizfyOCckanxl7Sz048Cvtd2MV4IHJNk/3YG+zEMzgXYBtye5Kh2NvpLhra1ZLTazgKurqo3Di1aNn1I8uAkq9r0/RmcI3Q1g0Dz/Lbajj2Y6s3zgU+0f51tBl6UwRU9hwCHMTihb8n/7VTVaVV1YFUdzKC+T1TVi1lGPUiyT5IHTk0z+B2+kmX0twBQVd8Evp7kkW3oGcBVLLM+NBv46WElWKo9WOwTiXp6MTgz+1oG5w+8crHrmYfvcy6wDfg/DP4VchKD4/wXA9cBHwcOaOsG+Ov23b8ErBvazm8CW9vrxKHxdQz+Q/hV4C3scPLcUngBT2Wwe/SLwBfa65nLqQ/ALwKfbz24EviTNn4og/8Jb2Wwa3llG9+7zW9tyw8d2tYr2/e8hqGrEHr62wHW89OrlpZND9p3/af2+vJUjcvpb2GozsOBy9vfxAcZXHGzrPoA7MNgL+N+Q2NLsgfe2VeSJHXLQ0uSJKlbBhlJktQtg4wkSeqWQUaSJHXLICNJkrplkJE0siQ/zuBpuFcmeV+SB0yz3v+ah896Tub4lOih+r6cwdO8X5Zkxv/OJVmfnz7t+jeSvGV36pa0sAwykubizqo6vKoeC/wI+N3hhVN3wa2qJ+/uB1XV5qp6/S7W9xgGN/Y7HpjY3VqmM3TXX0mLxCAjaVd9GnhE26Px6SSbGdwBlSTbp1ZK8vIkX2p7SF7fxh6e5KMZPJzw00nu9Wyn4b0jSf5Hkjcn+V9Jrk/y/B3X31ENbrN/MvD77Y6jeyd5Z6vl80mOnun9SZ6d5JK27seTrG7jpyd5d5J/BN6d5DFJLm17gr6Y5LCROyhpt/mvCUlz1vZEHA98tA0dATy2qr62w3rHAycAR1bVD5Ic0BadCfxuVV2X5EjgrQyebzSTNQzuxPwoBrdEP3+2Oqvq+iR7AT8P/PpgqB7XgtPHkqyd4e2fAY6qqkryWwyejP2ytuzRDB6weGeSvwLOqKr3tMcP7DVbXZLmj0FG0lzcP8kX2vSnGTyn6snApTuGmObfAu+sqh8AVNWtGTxp/MnA+waPWQFg5Qif/cGq+glw1dTekTl6KvBXrY6vJLkRmCnIHAi8N4OH490PGP5+m6vqzjb9v4FXJjkQeH9VXbcLtUnaRQYZSXNxZ1UdPjzQwsgdc9jGfYDv7ridEdw1/LGjvCHJocCP+elTeufir4A3VtXmJOuB04eW3fN9q+rvklwCPAu4IMnvVNUnduHzJO0Cz5GRNE4XASdOXd2U5ICquh34WpIXtLEkefx8f3CSBwNvB95Sg4fKfRp4cVu2Fngogwc7Tmc/4OY2vXG6lVpYur6q3szgCb6/uPvVSxqVQUbS2FTVRxmcz3J5OyT1x23Ri4GTkkw9afmEefrI+09dfs3g6bwfAybbsrcC90nyJeC9wG9U1V3TbAcGe2Del+QK4F9mWO+FwJXt+z0WeNfufQVJc+HTryVJUrfcIyNJkrplkJEkSd0yyEiSpG4ZZCRJUrcMMpIkqVsGGUmS1C2DjCRJ6pZBRpIkdev/Al8qj0UycuV8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Pr\n",
    "vehicle_price.hist(\"price\", bins=25, range=(0,5000), color=\"brown\")\n",
    "plt.xlabel(\"Price in Dollars\")\n",
    "plt.ylabel(\"Frequency/Number of Ads\")\n",
    "vehicle_price.hist(\"price\", bins=25, range=(0,20000), color=\"brown\")\n",
    "plt.xlabel(\"Price in Dollars\")\n",
    "plt.ylabel(\"Frequency/Number of Ads\")\n",
    "vehicle_price.hist(\"price\", bins=25, range=(0,50000), color=\"brown\")\n",
    "plt.xlabel(\"Price in Dollars\")\n",
    "plt.ylabel(\"Frequency/Number of Ads\")\n",
    "vehicle_price.hist(\"price\", bins=27, range=(0, 70000), figsize=(9, 9), color=\"gray\")\n",
    "plt.xlabel(\"Price in Dollars\")\n",
    "plt.ylabel(\"Frequency/Number of Ads\")\n",
    "print(vehicle_price[\"price\"].mean())\n",
    "print(vehicle_price[\"price\"].median())\n",
    "Q1c = vehicle_price['price'].quantile(0.25)\n",
    "Q3c = vehicle_price['price'].quantile(0.75)\n",
    "iqr4 = Q3c-Q1c\n",
    "print(\"Upper & Lower Bound:\",[Q1c-(1.5 * iqr4), Q3c+(1.5 * iqr4)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above, the data in the vehicle[\"price\"] column appears to be skewed to the left. Most vehicles seem to be priced on the lower end here. Just by a simple google search, cars sell for about $47000. Also, there seems about 2800 cars appear to be listed at a price of $0, which seems strange."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8.564559544385572\n",
      "7.0\n",
      "Upper & Lower Bound: [-8.0, 24.0]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Vehicle Age\n",
    "vehicle_price.hist(\"vehicle_age_in_years\", bins=19, range=(0, 60), figsize=(10, 10), color=\"blue\")\n",
    "plt.title(\"Frequency of Vehicle Age in Years\")\n",
    "plt.xlabel(\"Vehicle Age in Years\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "print(vehicle_price[\"vehicle_age_in_years\"].mean())\n",
    "print(vehicle_price[\"vehicle_age_in_years\"].median())\n",
    "Q1b = vehicle_price['vehicle_age_in_years'].quantile(0.25)\n",
    "Q3b = vehicle_price['vehicle_age_in_years'].quantile(0.75)\n",
    "iqr3 = Q3b-Q1b\n",
    "print(\"Upper & Lower Bound:\",[Q1b-(1.5 * iqr3), Q3b+(1.5 * iqr3)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of the vehicles are between 0-24 years old. Cars above 30 years old are rare."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "58.0       1\n",
      "71.0       1\n",
      "89.0       1\n",
      "59.0       1\n",
      "60.0       1\n",
      "        ... \n",
      "3.0     2318\n",
      "6.0     2521\n",
      "4.0     2542\n",
      "5.0     2559\n",
      "7.0     2595\n",
      "Name: vehicle_age_in_years, Length: 65, dtype: int64\n",
      "inf\n",
      "15417.42857\n",
      "Upper & Lower Bound: [-8353.843193749999, 41959.11007625]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Mileage\n",
    "\n",
    "\n",
    "vehicle_price[\"odometer\"].value_counts()\n",
    "print(vehicle_price[\"vehicle_age_in_years\"].value_counts().sort_values())\n",
    "vehicle_price[\"avg_mileage\"] = vehicle_price[\"avg_mileage\"].round(5)\n",
    "\n",
    "\n",
    "#vehicle_price[(vehicle_price[\"avg_mileage\"] == float(\"0\")) & (vehicle_price[\"odometer\"] > float(\"0\"))]\n",
    "\n",
    "\n",
    "vehicle_price.hist(\"avg_mileage\", bins=25, range=(0, 150000), figsize=(9,9), color=\"black\")\n",
    "plt.xlabel(\"Average Miles Per Year\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "print(vehicle_price[\"avg_mileage\"].mean())\n",
    "print(vehicle_price[\"avg_mileage\"].median())\n",
    "Q1a = vehicle_price['avg_mileage'].quantile(0.25)\n",
    "Q3a = vehicle_price['avg_mileage'].quantile(0.75)\n",
    "iqr2 = Q3a-Q1a\n",
    "print(\"Upper & Lower Bound:\",[Q1a-(1.5 * iqr2), Q3a+(1.5 * iqr2)])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    35293.000000\n",
       "mean         6.126087\n",
       "std          1.658585\n",
       "min          3.000000\n",
       "25%          4.000000\n",
       "50%          6.000000\n",
       "75%          8.000000\n",
       "max         12.000000\n",
       "Name: cylinders, dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Number of cylinders\n",
    "vehicle_price[\"cylinders\"].value_counts().plot(kind=\"bar\", color=\"tan\")\n",
    "plt.xlabel(\"Number of Cylinders\")\n",
    "plt.ylabel(\"Total\")\n",
    "plt.title(\"Total Number of Cylinders\")\n",
    "vehicle_price[\"cylinders\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Upper & Lower Bound Interval: [-2.0, 14.0]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count    35293.000000\n",
       "mean         6.126087\n",
       "std          1.658585\n",
       "min          3.000000\n",
       "25%          4.000000\n",
       "50%          6.000000\n",
       "75%          8.000000\n",
       "max         12.000000\n",
       "Name: cylinders, dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vehicle_price.boxplot(\"cylinders\", color=\"black\")\n",
    "plt.ylabel(\"Number of Cylinders\")\n",
    "plt.title(\"Number of cylinders Distribution\")\n",
    "Q1 = vehicle_price['cylinders'].quantile(0.25)\n",
    "Q3 = vehicle_price['cylinders'].quantile(0.75)\n",
    "iqr = Q3-Q1\n",
    "print(\"Upper & Lower Bound Interval:\",[Q1-(1.5 * iqr), Q3+(1.5 * iqr)])\n",
    "vehicle_price['cylinders'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Most of the data in the [\"cylinders\"] column consists of 4, 6, and 8 cylinders."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Total Number of Ads')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Condition\n",
    "vehicle_price[\"condition\"].value_counts()\n",
    "vehicle_price[\"condition\"].value_counts().plot(kind=\"bar\", color=\"aqua\")\n",
    "plt.title(\"Vehicle Condition Types\")\n",
    "plt.xlabel(\"Condition Type\")\n",
    "plt.ylabel(\"Total Number of Ads\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='condition'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vehicle_price[\"condition\"].value_counts().plot(kind=\"pie\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Upper & Lower Bound: [0.5, 4.5]\n",
      "count    35293.000000\n",
      "mean         2.638087\n",
      "std          0.712859\n",
      "min          0.000000\n",
      "25%          2.000000\n",
      "50%          3.000000\n",
      "75%          3.000000\n",
      "max          5.000000\n",
      "Name: new_condition, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "3.0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vehicle_price.boxplot(\"new_condition\")\n",
    "plt.ylim(-1, 6)\n",
    "Q1d = vehicle_price['new_condition'].quantile(0.25)\n",
    "Q3d = vehicle_price['new_condition'].quantile(0.75)\n",
    "iqr5 = Q3d-Q1d\n",
    "print(\"Upper & Lower Bound:\",[Q1d-(1.5 * iqr5), Q3d+(1.5 * iqr5)])\n",
    "print(vehicle_price[\"new_condition\"].describe())\n",
    "vehicle_price[\"new_condition\"].median()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Data in [\"condition\"] column is skewed to the right. Most cars in this dataset are either excellent, good or like new. Cars with worse conditions are seldom here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Study and treat outliers\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The following columns contain considerable outliers:\n",
    "(1)[\"price\"]\n",
    "(2)[\"avg_mileage\"]\n",
    "(3) [\"vehicle_age_in_years\"]\n",
    "(4) [\"condiiton\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Determine the lower limits for outliers\n",
    "#Condition:\n",
    "price_lower = 0\n",
    "avg_mileage_lower= 0\n",
    "vehicle_age_lower= 0\n",
    "condition_lower= 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Determine the upper limits for outliers\n",
    "price_upper = 55000\n",
    "avg_mileage_upper= 70000\n",
    "vehicle_age_upper = 35\n",
    "condition_upper = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       price  model_year           model  condition  cylinders fuel  odometer  \\\n",
      "2       5500      2013.0  hyundai sonata   like new        4.0  gas  110000.0   \n",
      "4      14900      2017.0    chrysler 200  excellent        4.0  gas   80903.0   \n",
      "5      14990      2014.0    chrysler 300  excellent        6.0  gas   57954.0   \n",
      "6      12990      2015.0    toyota camry  excellent        4.0  gas   79212.0   \n",
      "7      15990      2013.0     honda pilot  excellent        6.0  gas  109473.0   \n",
      "...      ...         ...             ...        ...        ...  ...       ...   \n",
      "51517  11000      2012.0   dodge charger  excellent        8.0  gas   81000.0   \n",
      "51518   3750      2005.0     ford taurus  excellent        6.0  gas  110200.0   \n",
      "51520   9249      2013.0   nissan maxima   like new        6.0  gas   88136.0   \n",
      "51522   3950      2009.0  hyundai sonata  excellent        4.0  gas  128000.0   \n",
      "51523   7455      2013.0  toyota corolla       good        4.0  gas  139573.0   \n",
      "\n",
      "      transmission   type paint_color is_4wd date_posted  days_listed  \\\n",
      "2        automatic  sedan         red      0  2019-02-07           79   \n",
      "4        automatic  sedan       black      0  2019-04-02           28   \n",
      "5        automatic  sedan       black    1.0  2018-06-20           15   \n",
      "6        automatic  sedan       white      0  2018-12-27           73   \n",
      "7        automatic    SUV       black    1.0  2019-01-07           68   \n",
      "...            ...    ...         ...    ...         ...          ...   \n",
      "51517    automatic  sedan       black      0  2019-04-19           44   \n",
      "51518    automatic  sedan      silver      0  2018-08-10           63   \n",
      "51520    automatic  sedan       black      0  2018-10-03           37   \n",
      "51522    automatic  sedan        blue      0  2018-11-15           32   \n",
      "51523    automatic  sedan       black      0  2018-07-02           71   \n",
      "\n",
      "       day_of_week_posted  year_posted  month_posted  vehicle_age_in_years  \\\n",
      "2                       3         2019             2                   6.0   \n",
      "4                       1         2019             4                   2.0   \n",
      "5                       2         2018             6                   4.0   \n",
      "6                       3         2018            12                   3.0   \n",
      "7                       0         2019             1                   6.0   \n",
      "...                   ...          ...           ...                   ...   \n",
      "51517                   4         2019             4                   7.0   \n",
      "51518                   4         2018             8                  13.0   \n",
      "51520                   2         2018            10                   5.0   \n",
      "51522                   3         2018            11                   9.0   \n",
      "51523                   0         2018             7                   5.0   \n",
      "\n",
      "       age_in_years  avg_mileage  new_condition  \n",
      "2               6.0  18333.33333              4  \n",
      "4               2.0  40451.50000              3  \n",
      "5               4.0  14488.50000              3  \n",
      "6               3.0  26404.00000              3  \n",
      "7               6.0  18245.50000              3  \n",
      "...             ...          ...            ...  \n",
      "51517           7.0  11571.42857              3  \n",
      "51518          13.0   8476.92308              3  \n",
      "51520           5.0  17627.20000              4  \n",
      "51522           9.0  14222.22222              3  \n",
      "51523           5.0  27914.60000              2  \n",
      "\n",
      "[31627 rows x 20 columns]\n"
     ]
    }
   ],
   "source": [
    "# Store data without outliers in a separate DataFrame\n",
    "data_filtered = vehicle_price.query('price < 40000 & avg_mileage < 45000 & vehicle_age_in_years < 35 & new_condition <= 4 & new_condition > 0')\n",
    "print(data_filtered)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Study core parameters without outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    31627.000000\n",
       "mean     10862.926961\n",
       "std       7876.460224\n",
       "min          1.000000\n",
       "25%       4995.000000\n",
       "50%       8500.000000\n",
       "75%      14995.000000\n",
       "max      39999.000000\n",
       "Name: price, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_filtered[\"price\"].plot(kind=\"hist\", bins=20, range=(0,50000), color=\"salmon\")\n",
    "plt.title(\"Frequency of Vehicle Price\")\n",
    "plt.xlabel(\"Vehicle Price in Dollars\")\n",
    "data_filtered[\"price\"].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After removing the outliers, the data is not as skewed to the left as it was before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Vehicle Age in Years')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_filtered[\"vehicle_age_in_years\"].plot(kind=\"hist\", bins=20, range=(0,40), color=\"green\")\n",
    "plt.title(\"Frequency of Vehicle Age\")\n",
    "plt.xlabel(\"Vehicle Age in Years\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distribution of vehicle age in the filtered dataset looks less skewed than the unfiltered dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    31627.000000\n",
       "mean     16094.754053\n",
       "std       8496.787695\n",
       "min          0.000000\n",
       "25%      10272.727270\n",
       "50%      14471.600000\n",
       "75%      20203.666670\n",
       "max      44999.000000\n",
       "Name: avg_mileage, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GNgHur9R3qs5jZmYDoKEji4h4BfB2ypf2Akk/lvS6mnmOiYgtImISpYP6txHxduBS4M052UzgwixflMPk+N9GRGT9jDxbamtgMnBVoxtoZmZ91+iRBRGxUNJngHbgJGDH7ID+dET8dDXW+SlgjqTjgWuB07L+NOBMSYuA5ZQEQ0TcLGkucAvwBHBkRPxnNdZnTac+zBv9FoWZNY/Kj/eaiaSXAodTzmyaD5wWEddI2hy4PCK2am6Yq6etrS3a29sHO4w10Jcv3aHKycKsVUhaEBFt3Y1r9MjiW5TTXz8dEY91VkbE3Xm0YWZmw1ijyWJ/4LHO5h9JawHrRcSjEXFm06IzM7OW0Oh1Fr8B1q8Mb5B1ZmY2AjSaLNaLiIc7B7K8QXNCMjOzVtNosnhE0tTOAUk7AY/1Mr2ZmQ0jjfZZHAWcI+luyik7zwXe2qygzMystTSULCLiakkvBLbLqtsi4t/NC8vMzFpJwxflAS8HJuU8UyUREWc0JSozM2spDSULSWcCzweuAzqvng7AycLMbARo9MiiDZgSjVzubWZmw06jZ0PdROnUNjOzEajRI4tNgVskXQX8q7MyIg5sSlRmZtZSGk0Wn29mEGZm1toaPXX295K2AiZHxG8kbQCMam5oZmbWKhp9rOp7gHOBU7JqInBBk2IyM7MW02gH95GUx6Q+COVBSMBzmhWUmZm1lkaTxb8i4vHOgXxGtk+jNTMbIRpNFr+X9Glg/Xz29jnAz5oXlpmZtZJGk8XRQAdwI/Be4GLAT8gzMxshGj0b6knge/kyM7MRptF7Q91BN30UEbFNv0dkZmYtZ3XuDdVpPeAtwLj+D8fMzFpRQ30WEXF/5bU0Ir4J7N/c0MzMrFU02gw1tTK4FuVIY3WehWFmZkNYo1/4X6uUnwDuBA7ubQZJ6wF/ANbN9ZwbEcdK2hqYAzwbWAC8MyIel7Qu5fkYOwH3A2+NiDtzWccAR1CepfHhiJjXYNxmZtYPGj0b6tVrsOx/AXtFxMOS1gYuk/RL4KPANyJijqTvUpLAyfm+IiK2lTQDOAF4q6QpwAxge2Bz4DeSXhAR/+lupWZm1v8abYb6aG/jI+Lr3dQF8HAOrp2vAPYC3pb1syl3tD0ZmM7Td7c9F/i2JGX9nIj4F3CHpEXAzsDljcRuZmZ91+hFeW3A+yk3EJwIvA+YCmycr25JGiXpOmAZMB/4G7AyIp7ISZbk8sj3xQA5/gFKU9VT9d3MU13XLEntkto7Ojoa3CwzM2tEo30WWwBTI+IhAEmfB34REe/obaZsKtpB0hjgfOCFax5q7yLiVOBUgLa2Nt+3ysysHzV6ZDEBeLwy/HjWNSQiVgKXArsBY/JGhFCS0NIsLwW2hKduVLgJpaP7qfpu5jEzswHQaLI4A7hK0ufzqOJKSn9DjySNzyMKJK0PvA64lZI03pyTzQQuzPJFOUyO/232e1wEzJC0bp5JNRm4qsG4zcysHzR6NtQX80ymV2TV4RFxbc1smwGzJY2iJKW5EfFzSbcAcyQdD1wLnJbTnwacmR3YyylnQBERN0uaC9xCOW33SJ8JZWY2sFR+vDcwobQH5bGqP5A0HtgoIu5oanRrqK2tLdrb2wc7jDWgwQ5gELh7yaxVSFoQEW3djWv0sarHAp8CjsmqtYEf9U94ZmbW6hrts3gDcCDwCEBE3E0vp8yamdnw0miyeDw7mwNA0obNC8nMzFpNo8lirqRTKKe9vgf4DX4QkpnZiFF7NlTecuNsygV1DwLbAZ+LiPlNjs3MzFpEbbKIiJB0cUS8hHLLDjMzG2EabYa6RtLLmxqJmZm1rEbvDbUL8A5Jd1LOiBLloOOlzQrMzMxaR6/JQtLzIuLvwN4DFI+NOH25ENEX9JkNlLojiwsod5u9S9J5EfGmAYjJzMxaTF2fRfVn3zbNDMTMzFpXXbKIHspmZjaC1DVDvUzSg5QjjPWzDE93cD+rqdGZmVlL6DVZRMSogQrEzMxaV6PXWZiZ2QjmZGFmZrWcLMzMrJaThZmZ1XKyMDOzWk4WZmZWy8nCzMxqOVmYmVmtRm9Rbg3ry11Uzcxak48szMysVtOShaQtJV0q6RZJN0v6SNaPkzRf0sJ8H5v1knSSpEWSbpA0tbKsmTn9QkkzmxWzmZl1r5lHFk8AH4uIKcCuwJGSpgBHA5dExGTgkhwG2BeYnK9ZwMlQkgtwLOVpfTsDx3YmGDMzGxhNSxYRcU9EXJPlh4BbgYnAdGB2TjYbOCjL04EzorgCGCNpM8pT+uZHxPKIWAHMB/ZpVtxmZvZMA9JnIWkSsCNwJTAhIu7JUfcCE7I8EVhcmW1J1vVU33UdsyS1S2rv6Ojo3w0wMxvhmp4sJG0EnAccFREPVsdFRNBPD1WKiFMjoi0i2saPH98fizQzs9TUZCFpbUqiOCsifprV92XzEvm+LOuXAltWZt8i63qqNzOzAdLMs6EEnAbcGhFfr4y6COg8o2kmcGGl/tA8K2pX4IFsrpoHTJM0Nju2p2WdmZkNkGZelLc78E7gRknXZd2ngS8DcyUdAdwFHJzjLgb2AxYBjwKHA0TEcklfAK7O6Y6LiOVNjNvMzLpQ6TYYXtra2qK9vX2Q1u4ruAfO8Pvsmg0mSQsioq27cb6C28zMajlZmJlZLScLMzOr5WRhZma1nCzMzKyWk4WZmdVysjAzs1pOFmZmVsvJwszMajlZmJlZLScLMzOr5WRhZma1nCzMzKyWk4WZmdVysjAzs1rNfPiRWZP15dkhfhaG2epwsrARqq8PqXKysZHFzVBmZlbLycLMzGo5WZiZWS0nCzMzq+VkYWZmtZwszMyslpOFmZnValqykHS6pGWSbqrUjZM0X9LCfB+b9ZJ0kqRFkm6QNLUyz8ycfqGkmc2K18zMetbMI4sfAvt0qTsauCQiJgOX5DDAvsDkfM0CToaSXIBjgV2AnYFjOxOMmZkNnKYli4j4A7C8S/V0YHaWZwMHVerPiOIKYIykzYC9gfkRsTwiVgDzeWYCMjOzJhvoPosJEXFPlu8FJmR5IrC4Mt2SrOup3szMBtCgdXBHRNCPN9iRNEtSu6T2jo6Ovi6tDy8zs+FnoJPFfdm8RL4vy/qlwJaV6bbIup7qnyEiTo2ItohoGz9+fL8HbmY2kg10srgI6DyjaSZwYaX+0DwralfggWyumgdMkzQ2O7anZZ2ZmQ2gpt2iXNJPgD2BTSUtoZzV9GVgrqQjgLuAg3Pyi4H9gEXAo8DhABGxXNIXgKtzuuMiomunuZmZNZlK18Hw0tbWFu3t7X1YgvserM7w+78xk7QgItq6G+cruM3MrJaThZmZ1fJjVc0GnJ8dbkOPjyzMzKyWk4WZmdVysjAzs1ruszBbIz692kYWH1mYmVktJwszM6vlZGFmZrWcLMzMrJaThZmZ1XKyMDOzWk4WZmZWy8nCzMxqOVmYmVktJwszM6vl232YDSm+vbkNDh9ZmJlZLScLMzOr5WYosxHDTVi25nxkYWZmtZwszMyslpOFmZnVcrIwM7NaQyZZSNpH0m2SFkk6erDjMTMbSYZEspA0CvhfYF9gCnCIpCmDG5WZ2cgxJJIFsDOwKCJuj4jHgTnA9EGOycxsxBgq11lMBBZXhpcAu1QnkDQLmJWDD0u6rQ/r2xT4Rx/mH0hDKVYYWvE61qf05RqNbnnfNk9f4t2qpxFDJVnUiohTgVP7Y1mS2iOirT+W1WxDKVYYWvE61uYZSvEOpVihefEOlWaopcCWleEtss7MzAbAUEkWVwOTJW0taR1gBnDRIMdkZjZiDIlmqIh4QtIHgXnAKOD0iLi5iavsl+asATKUYoWhFa9jbZ6hFO9QihWaFK8ifIMwMzPr3VBphjIzs0HkZGFmZrWcLCoG65Yikk6XtEzSTZW6cZLmS1qY72OzXpJOyhhvkDS1Ms/MnH6hpJmV+p0k3ZjznCSpTyfNS9pS0qWSbpF0s6SPtGrMktaTdJWk6zPW/8n6rSVdmcs/O0+cQNK6Obwox0+qLOuYrL9N0t6V+n793EgaJelaST8fArHemX+n6yS1Z13LfQ5yWWMknSvpL5JulbRbC8e6Xe7TzteDko4a1Hgjwq/SbzMK+BuwDbAOcD0wZYDW/UpgKnBTpe4rwNFZPho4Icv7Ab+kXCW1K3Bl1o8Dbs/3sVkem+OuymmV8+7bx3g3A6ZmeWPgr5TbsLRczDn/RlleG7gylzsXmJH13wXen+UPAN/N8gzg7CxPyc/EusDW+VkZ1YzPDfBR4MfAz3O4lWO9E9i0S13LfQ5yWbOBd2d5HWBMq8baJe5RwL2UC+YGLd6mfxEOlRewGzCvMnwMcMwArn8SqyaL24DNsrwZcFuWTwEO6TodcAhwSqX+lKzbDPhLpX6V6fop9guB17V6zMAGwDWUq///AYzu+rennHG3W5ZH53Tq+nnonK6/PzeUa4guAfYCfp7rbslYcxl38sxk0XKfA2AT4A7ypJ5WjrWb2KcBfxrseN0M9bTubikycZBiAZgQEfdk+V5gQpZ7irO3+iXd1PeLbPrYkfKLvSVjzmad64BlwHzKr+uVEfFEN8t/KqYc/wDw7DXYhjX1TeCTwJM5/OwWjhXK81Z/LWmByi13oDU/B1sDHcAPsonv+5I2bNFYu5oB/CTLgxavk8UQECX1t9w5zpI2As4DjoqIB6vjWinmiPhPROxA+dW+M/DCwY2oe5IOAJZFxILBjmU17BERUyl3hD5S0iurI1voczCa0tR7ckTsCDxCacZ5SgvF+pTsnzoQOKfruIGO18niaa12S5H7JG0GkO/Lsr6nOHur36Kb+j6RtDYlUZwVET8dCjFHxErgUkpzzBhJnRelVpf/VEw5fhPg/jXYhjWxO3CgpDspd1beCzixRWMFICKW5vsy4HxKMm7Fz8ESYElEXJnD51KSRyvGWrUvcE1E3JfDgxdvf7SpDYcX5ZfH7ZTD1c7Ov+0HcP2TWLXP4v+zakfWV7K8P6t2ZF2V9eMobbJj83UHMC7Hde3I2q+PsQo4A/hml/qWixkYD4zJ8vrAH4EDKL/Uqp3GH8jykazaaTw3y9uzaqfx7ZSOx6Z8boA9ebqDuyVjBTYENq6U/wzs04qfg1zWH4Htsvz5jLMlY63EPAc4vBX+xwbki3CovChnFPyV0qb93wO43p8A9wD/pvwCOoLS9nwJsBD4TeUPLMqDoP4G3Ai0VZbzLmBRvqofsDbgppzn23Tp5FuDePegHP7eAFyXr/1aMWbgpcC1GetNwOeyfpv8Z1lE+TJeN+vXy+FFOX6byrL+O+O5jcqZI8343LBqsmjJWDOu6/N1c+fyWvFzkMvaAWjPz8IFlC/Plow1l7ch5Uhxk0rdoMXr232YmVkt91mYmVktJwszM6vlZGFmZrWcLMzMrJaThZmZ1XKysGFH0kGSQlJLXqldlXdt/WOXuuuUdyCW1CbppCwfJunbgxGnmZOFDUeHAJfle59JGtUfy+nFxpI6r8R+UXVERLRHxIebvH6zWk4WNqzk/ar2oFzYOCPr9pF0TmWaPfX0syKmSbpc0jWSzsn5O3/xnyDpGuAtkt4j6WqV52KcJ2mDnO75kq7I5wIcL+nhyno+kfPcoHyORg/mAm/N8iE8fdO4VWLtsp3jM46r87V71u+c23OtpD9L2i7rN5A0V+UZJOerPP+irbd9YFblZGHDzXTgVxHxV+B+STtRrnTdJe8yCuWLeY6kTYHPAK+NcjO8dsqzJDrdHxFTI2IO8NOIeHlEvAy4lZKMoNy76cSIeAmVu3hKmgZMptwraQdgp6432as4D3hjll8P/KyB7TwR+EZEvBx4E/D9rP8L8IooN8v7HPD/sv4DwIqImAJ8Ftgp46zbB2ZAuVeM2XByCOWLFMp9dQ6JiAWSfgW8XtK5lPvofBJ4FeVBQX/Kh4StA1xeWdbZlfKLJR1PeWDORpRnREC5KeFBWf4x8NUsT8vXtTm8ESV5/KGbmO8HVkiaQUlEjzawna8FplQebvasPCLYBJgtaTLllixr5/g9yP0SETdJuiHrd6X3fWAGOFnYMCJpHOVOrS+RFJSb54WkT1ASxweB5UB7RDyUj5GcHxE99W08Uin/EDgoIq6XdBjl3k29hgN8KSJOaTD8syn39jmswenXAnaNiH+ustLSAX5pRLwhnzXyuwbi7G0fmAFuhrLh5c3AmRGxVURMiogtKXfZfAXwe8otqd9DSRwAVwC7S9oWQNKGkl7Qw7I3Bu7JW7O/vVJ/BaUZCLKPJM0D3lXpA5ko6Tm9xH4+5ZGZ83qZpurXwIc6ByTtkMVNePpW04dVpv8TcHBOOwV4SSX+RveBjWBOFjacHEL50q06j9IU9R/KY0r3zXciooPyhfqTbJa5nJ4fjPRZytMA/0TpF+h0FPDRnH9bytPqiIhfU5qlLpd0I+X5CRv3FHhEPBQRJ0TE4w1u64eBtuw8vwV4X9Z/BfiSpGtZteXgO8D4nPZ4yl1iH1jNfWAjmO86a9YHeVbUYxER2edwSERMH+y4usrTf9eOiH9Kej6l03+71UhONsK5z8Ksb3YCvp39Hyspzw5oRRsAl2YzmigPUHKisIb5yMLMzGq5z8LMzGo5WZiZWS0nCzMzq+VkYWZmtZwszMys1v8BUIkjJuLmbtYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_filtered[\"avg_mileage\"].plot(kind=\"hist\", bins=20, range=(0,70000), color=\"yellow\")\n",
    "plt.title(\"Frequency of Average Mileage\")\n",
    "plt.xlabel(\"Average Mileage\")\n",
    "data_filtered[\"avg_mileage\"].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The average mileage data here looks more normally distributed as compared to the unfiltered dataset. The extented tail on the right is much shorter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of Vehicle Ads')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_filtered[\"condition\"].value_counts().plot(kind=\"bar\", color=\"orange\")\n",
    "plt.title(\"Condition Types vs. Number of Vehicle Ads\")\n",
    "plt.xlabel(\"Condition Type\")\n",
    "plt.ylabel(\"Number of Vehicle Ads\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The bar chart displaying the conditions in this filtered dataset is more useful than the chart in the unfiltered dataset because it focuses on the conditions that are most prevelant in the dataset. The conditions \"new\" and \"salvage\" have been removed, as they appeared to be outliers. In this dataset, most vehicles fell into the above four categories."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ads lifetime\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "39.695039048913905\n",
      "count    31627.000000\n",
      "mean        39.695039\n",
      "std         28.319722\n",
      "min          0.000000\n",
      "25%         19.000000\n",
      "50%         33.000000\n",
      "75%         53.000000\n",
      "max        267.000000\n",
      "Name: days_listed, dtype: float64\n",
      "Upper & Lower Bound: [-32.0, 104.0]\n"
     ]
    },
    {
     "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>days_listed</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>51</td>\n",
       "      <td>9424.392157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>42</td>\n",
       "      <td>11300.904762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>51</td>\n",
       "      <td>11335.156863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>58</td>\n",
       "      <td>10773.758621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>49</td>\n",
       "      <td>11096.102041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>1</td>\n",
       "      <td>5995.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>1</td>\n",
       "      <td>11500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>1</td>\n",
       "      <td>3800.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>1</td>\n",
       "      <td>5995.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>1</td>\n",
       "      <td>5500.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>114 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            count          mean\n",
       "            price         price\n",
       "days_listed                    \n",
       "101            51   9424.392157\n",
       "102            42  11300.904762\n",
       "103            51  11335.156863\n",
       "104            58  10773.758621\n",
       "105            49  11096.102041\n",
       "...           ...           ...\n",
       "244             1   5995.000000\n",
       "252             1  11500.000000\n",
       "261             1   3800.000000\n",
       "263             1   5995.000000\n",
       "267             1   5500.000000\n",
       "\n",
       "[114 rows x 2 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_filtered[\"days_listed\"].hist(bins=20, color=\"red\")\n",
    "plt.xlabel(\"Number of days listed\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.title(\"Frequency of Number of Days Listed\")\n",
    "print(data_filtered[\"days_listed\"].mean())\n",
    "data_filtered[\"days_listed\"].median()\n",
    "print(data_filtered[\"days_listed\"].describe())\n",
    "short_listings = data_filtered.query('days_listed == 0')\n",
    "long_listings = data_filtered.query('days_listed > 100')\n",
    "Q1e = data_filtered['days_listed'].quantile(0.25)\n",
    "Q3e = data_filtered['days_listed'].quantile(0.75)\n",
    "iqr6 = Q3e-Q1e\n",
    "print(\"Upper & Lower Bound:\",[Q1e-(1.5 * iqr6), Q3e+(1.5 * iqr6)])\n",
    "short_listings.pivot_table(index=\"days_listed\", values=\"price\", aggfunc=\"mean\")\n",
    "long_listings.pivot_table(index=\"days_listed\", values=\"price\", aggfunc=[\"count\",\"mean\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Average price per each type of vehicle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Average Price per Type of Vehicle')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_filtered.pivot_table(index=\"type\", values=\"price\", aggfunc=\"mean\").sort_values(by=\"price\", ascending=False).plot(kind=\"bar\", color=\"pink\")\n",
    "plt.ylabel(\"Average Price in Dollars\")\n",
    "plt.title(\"Average Price per Type of Vehicle\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Number of Ads per Type of Vehicle')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_filtered[\"type\"].value_counts().sort_values(ascending=False).plot(kind=\"bar\", color=\"purple\")\n",
    "plt.xlabel(\"Type of Vehicle\")\n",
    "plt.ylabel(\"Number of Ads\")\n",
    "plt.title(\"Number of Ads per Type of Vehicle\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* SUV and Sedan type vehicles have the greatest number of ads. However, buses and trucks and pickups are the top three highest priced vehicles. While buses are the highest priced, there are not a lot of ads for this type of vehicle. I will select trucks and pickups to analyze, as they have sufficient number of ads and are on the higher end in terms of price."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Price factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": false
   },
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th colspan=\"4\" halign=\"left\">price</th>\n",
       "      <th colspan=\"4\" halign=\"left\">model_year</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"2\" halign=\"left\">age_in_years</th>\n",
       "      <th colspan=\"4\" halign=\"left\">avg_mileage</th>\n",
       "      <th colspan=\"4\" halign=\"left\">new_condition</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>std</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>std</th>\n",
       "      <th>count</th>\n",
       "      <th>...</th>\n",
       "      <th>median</th>\n",
       "      <th>std</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>std</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>wagon</td>\n",
       "      <td>1019</td>\n",
       "      <td>8659.842983</td>\n",
       "      <td>6995.0</td>\n",
       "      <td>5330.111923</td>\n",
       "      <td>1019</td>\n",
       "      <td>2009.422964</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>4.389519</td>\n",
       "      <td>1019</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.397868</td>\n",
       "      <td>1019</td>\n",
       "      <td>16202.452937</td>\n",
       "      <td>14735.600000</td>\n",
       "      <td>7622.252900</td>\n",
       "      <td>1019</td>\n",
       "      <td>2.548577</td>\n",
       "      <td>3</td>\n",
       "      <td>0.612850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>van</td>\n",
       "      <td>390</td>\n",
       "      <td>9930.794872</td>\n",
       "      <td>7572.5</td>\n",
       "      <td>8774.917486</td>\n",
       "      <td>390</td>\n",
       "      <td>2009.341026</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>4.821131</td>\n",
       "      <td>390</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.840816</td>\n",
       "      <td>390</td>\n",
       "      <td>16728.613128</td>\n",
       "      <td>15203.669935</td>\n",
       "      <td>8005.605827</td>\n",
       "      <td>390</td>\n",
       "      <td>2.489744</td>\n",
       "      <td>2</td>\n",
       "      <td>0.656114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>truck</td>\n",
       "      <td>7519</td>\n",
       "      <td>14722.203219</td>\n",
       "      <td>13950.0</td>\n",
       "      <td>8668.775892</td>\n",
       "      <td>7519</td>\n",
       "      <td>2008.758213</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>5.521542</td>\n",
       "      <td>7519</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.511377</td>\n",
       "      <td>7519</td>\n",
       "      <td>16601.602535</td>\n",
       "      <td>14800.000000</td>\n",
       "      <td>9054.196075</td>\n",
       "      <td>7519</td>\n",
       "      <td>2.536109</td>\n",
       "      <td>3</td>\n",
       "      <td>0.678984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>sedan</td>\n",
       "      <td>7575</td>\n",
       "      <td>6578.542310</td>\n",
       "      <td>5900.0</td>\n",
       "      <td>3826.599237</td>\n",
       "      <td>7575</td>\n",
       "      <td>2010.231023</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>4.467553</td>\n",
       "      <td>7575</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.463797</td>\n",
       "      <td>7575</td>\n",
       "      <td>16444.246721</td>\n",
       "      <td>14943.666670</td>\n",
       "      <td>8263.188057</td>\n",
       "      <td>7575</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>3</td>\n",
       "      <td>0.684944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>pickup</td>\n",
       "      <td>4091</td>\n",
       "      <td>14249.503300</td>\n",
       "      <td>12850.0</td>\n",
       "      <td>9108.885508</td>\n",
       "      <td>4091</td>\n",
       "      <td>2008.373747</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>6.063711</td>\n",
       "      <td>4091</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.054599</td>\n",
       "      <td>4091</td>\n",
       "      <td>15912.505420</td>\n",
       "      <td>14432.345380</td>\n",
       "      <td>8601.489061</td>\n",
       "      <td>4091</td>\n",
       "      <td>2.549254</td>\n",
       "      <td>3</td>\n",
       "      <td>0.703958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>other</td>\n",
       "      <td>153</td>\n",
       "      <td>10004.320261</td>\n",
       "      <td>8795.0</td>\n",
       "      <td>7374.213496</td>\n",
       "      <td>153</td>\n",
       "      <td>2007.496732</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>6.969977</td>\n",
       "      <td>153</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.980001</td>\n",
       "      <td>153</td>\n",
       "      <td>14872.818544</td>\n",
       "      <td>13050.750000</td>\n",
       "      <td>9233.901489</td>\n",
       "      <td>153</td>\n",
       "      <td>2.431373</td>\n",
       "      <td>2</td>\n",
       "      <td>0.636238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>offroad</td>\n",
       "      <td>143</td>\n",
       "      <td>13334.944056</td>\n",
       "      <td>11450.0</td>\n",
       "      <td>8501.345973</td>\n",
       "      <td>143</td>\n",
       "      <td>2005.174825</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>7.087297</td>\n",
       "      <td>143</td>\n",
       "      <td>...</td>\n",
       "      <td>13.0</td>\n",
       "      <td>7.118670</td>\n",
       "      <td>143</td>\n",
       "      <td>11874.282325</td>\n",
       "      <td>9296.296300</td>\n",
       "      <td>7721.526795</td>\n",
       "      <td>143</td>\n",
       "      <td>2.741259</td>\n",
       "      <td>3</td>\n",
       "      <td>0.784653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>mini-van</td>\n",
       "      <td>724</td>\n",
       "      <td>7265.324586</td>\n",
       "      <td>5995.0</td>\n",
       "      <td>4933.486514</td>\n",
       "      <td>724</td>\n",
       "      <td>2009.211326</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>4.271988</td>\n",
       "      <td>724</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.274022</td>\n",
       "      <td>724</td>\n",
       "      <td>16763.546499</td>\n",
       "      <td>15104.166665</td>\n",
       "      <td>7561.772182</td>\n",
       "      <td>724</td>\n",
       "      <td>2.618785</td>\n",
       "      <td>3</td>\n",
       "      <td>0.694560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>hatchback</td>\n",
       "      <td>621</td>\n",
       "      <td>6407.442834</td>\n",
       "      <td>5795.0</td>\n",
       "      <td>3605.310667</td>\n",
       "      <td>621</td>\n",
       "      <td>2009.597424</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>5.254699</td>\n",
       "      <td>621</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.235818</td>\n",
       "      <td>621</td>\n",
       "      <td>15918.389499</td>\n",
       "      <td>14908.800000</td>\n",
       "      <td>8063.298141</td>\n",
       "      <td>621</td>\n",
       "      <td>2.692432</td>\n",
       "      <td>3</td>\n",
       "      <td>0.688915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>coupe</td>\n",
       "      <td>1369</td>\n",
       "      <td>12423.834186</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>8930.944445</td>\n",
       "      <td>1369</td>\n",
       "      <td>2008.978817</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>7.208587</td>\n",
       "      <td>1369</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.191242</td>\n",
       "      <td>1369</td>\n",
       "      <td>12976.143219</td>\n",
       "      <td>11000.000000</td>\n",
       "      <td>8967.533158</td>\n",
       "      <td>1369</td>\n",
       "      <td>2.541271</td>\n",
       "      <td>2</td>\n",
       "      <td>0.712215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>convertible</td>\n",
       "      <td>277</td>\n",
       "      <td>11954.194946</td>\n",
       "      <td>10995.0</td>\n",
       "      <td>7297.232456</td>\n",
       "      <td>277</td>\n",
       "      <td>2004.036101</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>8.373015</td>\n",
       "      <td>277</td>\n",
       "      <td>...</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.361715</td>\n",
       "      <td>277</td>\n",
       "      <td>9810.510391</td>\n",
       "      <td>7413.388890</td>\n",
       "      <td>8276.921516</td>\n",
       "      <td>277</td>\n",
       "      <td>2.703971</td>\n",
       "      <td>3</td>\n",
       "      <td>0.701256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bus</td>\n",
       "      <td>17</td>\n",
       "      <td>18575.823529</td>\n",
       "      <td>28990.0</td>\n",
       "      <td>11454.846836</td>\n",
       "      <td>17</td>\n",
       "      <td>2008.823529</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>4.260799</td>\n",
       "      <td>17</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.182421</td>\n",
       "      <td>17</td>\n",
       "      <td>11186.488069</td>\n",
       "      <td>5939.125000</td>\n",
       "      <td>10886.835549</td>\n",
       "      <td>17</td>\n",
       "      <td>2.176471</td>\n",
       "      <td>2</td>\n",
       "      <td>0.392953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SUV</td>\n",
       "      <td>7729</td>\n",
       "      <td>10186.178548</td>\n",
       "      <td>8450.0</td>\n",
       "      <td>7046.364292</td>\n",
       "      <td>7729</td>\n",
       "      <td>2009.143097</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>5.076023</td>\n",
       "      <td>7729</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.080597</td>\n",
       "      <td>7729</td>\n",
       "      <td>16151.628519</td>\n",
       "      <td>14500.000000</td>\n",
       "      <td>8029.490274</td>\n",
       "      <td>7729</td>\n",
       "      <td>2.663734</td>\n",
       "      <td>3</td>\n",
       "      <td>0.671538</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13 rows × 49 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           type price                                      model_year  \\\n",
       "                count          mean   median           std      count   \n",
       "12        wagon  1019   8659.842983   6995.0   5330.111923       1019   \n",
       "11          van   390   9930.794872   7572.5   8774.917486        390   \n",
       "10        truck  7519  14722.203219  13950.0   8668.775892       7519   \n",
       "9         sedan  7575   6578.542310   5900.0   3826.599237       7575   \n",
       "8        pickup  4091  14249.503300  12850.0   9108.885508       4091   \n",
       "7         other   153  10004.320261   8795.0   7374.213496        153   \n",
       "6       offroad   143  13334.944056  11450.0   8501.345973        143   \n",
       "5      mini-van   724   7265.324586   5995.0   4933.486514        724   \n",
       "4     hatchback   621   6407.442834   5795.0   3605.310667        621   \n",
       "3         coupe  1369  12423.834186  10000.0   8930.944445       1369   \n",
       "2   convertible   277  11954.194946  10995.0   7297.232456        277   \n",
       "1           bus    17  18575.823529  28990.0  11454.846836         17   \n",
       "0           SUV  7729  10186.178548   8450.0   7046.364292       7729   \n",
       "\n",
       "                                  cylinders  ... age_in_years            \\\n",
       "           mean  median       std     count  ...       median       std   \n",
       "12  2009.422964  2010.0  4.389519      1019  ...          8.0  4.397868   \n",
       "11  2009.341026  2010.0  4.821131       390  ...          8.0  4.840816   \n",
       "10  2008.758213  2010.0  5.521542      7519  ...          9.0  5.511377   \n",
       "9   2010.231023  2011.0  4.467553      7575  ...          7.0  4.463797   \n",
       "8   2008.373747  2009.0  6.063711      4091  ...          9.0  6.054599   \n",
       "7   2007.496732  2008.0  6.969977       153  ...         10.0  6.980001   \n",
       "6   2005.174825  2006.0  7.087297       143  ...         13.0  7.118670   \n",
       "5   2009.211326  2009.0  4.271988       724  ...          9.0  4.274022   \n",
       "4   2009.597424  2010.0  5.254699       621  ...          8.0  5.235818   \n",
       "3   2008.978817  2011.0  7.208587      1369  ...          7.0  7.191242   \n",
       "2   2004.036101  2004.0  8.373015       277  ...         14.0  8.361715   \n",
       "1   2008.823529  2011.0  4.260799        17  ...          8.0  4.182421   \n",
       "0   2009.143097  2010.0  5.076023      7729  ...          8.0  5.080597   \n",
       "\n",
       "   avg_mileage                                           new_condition  \\\n",
       "         count          mean        median           std         count   \n",
       "12        1019  16202.452937  14735.600000   7622.252900          1019   \n",
       "11         390  16728.613128  15203.669935   8005.605827           390   \n",
       "10        7519  16601.602535  14800.000000   9054.196075          7519   \n",
       "9         7575  16444.246721  14943.666670   8263.188057          7575   \n",
       "8         4091  15912.505420  14432.345380   8601.489061          4091   \n",
       "7          153  14872.818544  13050.750000   9233.901489           153   \n",
       "6          143  11874.282325   9296.296300   7721.526795           143   \n",
       "5          724  16763.546499  15104.166665   7561.772182           724   \n",
       "4          621  15918.389499  14908.800000   8063.298141           621   \n",
       "3         1369  12976.143219  11000.000000   8967.533158          1369   \n",
       "2          277   9810.510391   7413.388890   8276.921516           277   \n",
       "1           17  11186.488069   5939.125000  10886.835549            17   \n",
       "0         7729  16151.628519  14500.000000   8029.490274          7729   \n",
       "\n",
       "                               \n",
       "        mean median       std  \n",
       "12  2.548577      3  0.612850  \n",
       "11  2.489744      2  0.656114  \n",
       "10  2.536109      3  0.678984  \n",
       "9   2.666667      3  0.684944  \n",
       "8   2.549254      3  0.703958  \n",
       "7   2.431373      2  0.636238  \n",
       "6   2.741259      3  0.784653  \n",
       "5   2.618785      3  0.694560  \n",
       "4   2.692432      3  0.688915  \n",
       "3   2.541271      2  0.712215  \n",
       "2   2.703971      3  0.701256  \n",
       "1   2.176471      2  0.392953  \n",
       "0   2.663734      3  0.671538  \n",
       "\n",
       "[13 rows x 49 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_filtered.groupby(['type']).agg(['count', 'mean', 'median', 'std']).reset_index().sort_values(by='type', ascending=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                         price  model_year  cylinders  odometer  days_listed  \\\n",
      "price                 1.000000    0.639875   0.015171 -0.443910     0.014390   \n",
      "model_year            0.639875    1.000000  -0.021671 -0.464116    -0.003253   \n",
      "cylinders             0.015171   -0.021671   1.000000  0.002141     0.005700   \n",
      "odometer             -0.443910   -0.464116   0.002141  1.000000     0.008235   \n",
      "days_listed           0.014390   -0.003253   0.005700  0.008235     1.000000   \n",
      "day_of_week_posted   -0.000943   -0.005185  -0.011223 -0.000218     0.003992   \n",
      "year_posted           0.034799    0.064045   0.009234 -0.017824    -0.000065   \n",
      "month_posted         -0.030026   -0.051387  -0.000195  0.016815    -0.006220   \n",
      "vehicle_age_in_years -0.638105   -0.996415   0.022493  0.463461     0.003253   \n",
      "age_in_years         -0.638105   -0.996415   0.022493  0.463461     0.003253   \n",
      "avg_mileage           0.332731    0.605505  -0.033999  0.198562     0.007826   \n",
      "new_condition         0.309494    0.321893  -0.004325 -0.274407     0.013834   \n",
      "\n",
      "                      day_of_week_posted  year_posted  month_posted  \\\n",
      "price                          -0.000943     0.034799     -0.030026   \n",
      "model_year                     -0.005185     0.064045     -0.051387   \n",
      "cylinders                      -0.011223     0.009234     -0.000195   \n",
      "odometer                       -0.000218    -0.017824      0.016815   \n",
      "days_listed                     0.003992    -0.000065     -0.006220   \n",
      "day_of_week_posted              1.000000    -0.015525      0.016832   \n",
      "year_posted                    -0.015525     1.000000     -0.821202   \n",
      "month_posted                    0.016832    -0.821202      1.000000   \n",
      "vehicle_age_in_years            0.003878     0.020610     -0.018134   \n",
      "age_in_years                    0.003878     0.020610     -0.018134   \n",
      "avg_mileage                    -0.012816    -0.068961      0.063739   \n",
      "new_condition                   0.003596     0.030448     -0.027129   \n",
      "\n",
      "                      vehicle_age_in_years  age_in_years  avg_mileage  \\\n",
      "price                            -0.638105     -0.638105     0.332731   \n",
      "model_year                       -0.996415     -0.996415     0.605505   \n",
      "cylinders                         0.022493      0.022493    -0.033999   \n",
      "odometer                          0.463461      0.463461     0.198562   \n",
      "days_listed                       0.003253      0.003253     0.007826   \n",
      "day_of_week_posted                0.003878      0.003878    -0.012816   \n",
      "year_posted                       0.020610      0.020610    -0.068961   \n",
      "month_posted                     -0.018134     -0.018134     0.063739   \n",
      "vehicle_age_in_years              1.000000      1.000000    -0.612468   \n",
      "age_in_years                      1.000000      1.000000    -0.612468   \n",
      "avg_mileage                      -0.612468     -0.612468     1.000000   \n",
      "new_condition                    -0.319905     -0.319905     0.095910   \n",
      "\n",
      "                      new_condition  \n",
      "price                      0.309494  \n",
      "model_year                 0.321893  \n",
      "cylinders                 -0.004325  \n",
      "odometer                  -0.274407  \n",
      "days_listed                0.013834  \n",
      "day_of_week_posted         0.003596  \n",
      "year_posted                0.030448  \n",
      "month_posted              -0.027129  \n",
      "vehicle_age_in_years      -0.319905  \n",
      "age_in_years              -0.319905  \n",
      "avg_mileage                0.095910  \n",
      "new_condition              1.000000  \n",
      "\n",
      "                         price  model_year  cylinders  odometer  days_listed  \\\n",
      "price                 1.000000    0.742953  -0.026353 -0.530908     0.031064   \n",
      "model_year            0.742953    1.000000  -0.095892 -0.496267     0.032174   \n",
      "cylinders            -0.026353   -0.095892   1.000000  0.103354     0.005253   \n",
      "odometer             -0.530908   -0.496267   0.103354  1.000000    -0.015673   \n",
      "days_listed           0.031064    0.032174   0.005253 -0.015673     1.000000   \n",
      "day_of_week_posted   -0.032606   -0.024939  -0.020576  0.033743    -0.006348   \n",
      "year_posted           0.051610    0.057908   0.003302 -0.032765     0.063815   \n",
      "month_posted         -0.053363   -0.060924   0.010214  0.049729    -0.043340   \n",
      "vehicle_age_in_years -0.740111   -0.997061   0.096290  0.494500    -0.027325   \n",
      "age_in_years         -0.740111   -0.997061   0.096290  0.494500    -0.027325   \n",
      "avg_mileage           0.410150    0.598081  -0.040171  0.155113     0.009661   \n",
      "new_condition         0.316975    0.287549   0.046141 -0.212092     0.006063   \n",
      "\n",
      "                      day_of_week_posted  year_posted  month_posted  \\\n",
      "price                          -0.032606     0.051610     -0.053363   \n",
      "model_year                     -0.024939     0.057908     -0.060924   \n",
      "cylinders                      -0.020576     0.003302      0.010214   \n",
      "odometer                        0.033743    -0.032765      0.049729   \n",
      "days_listed                    -0.006348     0.063815     -0.043340   \n",
      "day_of_week_posted              1.000000    -0.001743     -0.006461   \n",
      "year_posted                    -0.001743     1.000000     -0.823552   \n",
      "month_posted                   -0.006461    -0.823552      1.000000   \n",
      "vehicle_age_in_years            0.024843     0.018741     -0.002180   \n",
      "age_in_years                    0.024843     0.018741     -0.002180   \n",
      "avg_mileage                    -0.010130    -0.066881      0.046427   \n",
      "new_condition                  -0.030903     0.013464     -0.016095   \n",
      "\n",
      "                      vehicle_age_in_years  age_in_years  avg_mileage  \\\n",
      "price                            -0.740111     -0.740111     0.410150   \n",
      "model_year                       -0.997061     -0.997061     0.598081   \n",
      "cylinders                         0.096290      0.096290    -0.040171   \n",
      "odometer                          0.494500      0.494500     0.155113   \n",
      "days_listed                      -0.027325     -0.027325     0.009661   \n",
      "day_of_week_posted                0.024843      0.024843    -0.010130   \n",
      "year_posted                       0.018741      0.018741    -0.066881   \n",
      "month_posted                     -0.002180     -0.002180     0.046427   \n",
      "vehicle_age_in_years              1.000000      1.000000    -0.604113   \n",
      "age_in_years                      1.000000      1.000000    -0.604113   \n",
      "avg_mileage                      -0.604113     -0.604113     1.000000   \n",
      "new_condition                    -0.286949     -0.286949     0.159323   \n",
      "\n",
      "                      new_condition  \n",
      "price                      0.316975  \n",
      "model_year                 0.287549  \n",
      "cylinders                  0.046141  \n",
      "odometer                  -0.212092  \n",
      "days_listed                0.006063  \n",
      "day_of_week_posted        -0.030903  \n",
      "year_posted                0.013464  \n",
      "month_posted              -0.016095  \n",
      "vehicle_age_in_years      -0.286949  \n",
      "age_in_years              -0.286949  \n",
      "avg_mileage                0.159323  \n",
      "new_condition              1.000000  \n",
      "                         price  model_year  cylinders  odometer  days_listed  \\\n",
      "price                 1.000000    0.514414   0.321627 -0.403588     0.001509   \n",
      "model_year            0.514414    1.000000  -0.167555 -0.486711    -0.000751   \n",
      "cylinders             0.321627   -0.167555   1.000000  0.111705     0.000463   \n",
      "odometer             -0.403588   -0.486711   0.111705  1.000000     0.006697   \n",
      "days_listed           0.001509   -0.000751   0.000463  0.006697     1.000000   \n",
      "day_of_week_posted   -0.009906   -0.006613  -0.005994  0.011371     0.001292   \n",
      "year_posted           0.032692    0.051454   0.001114 -0.022972     0.011714   \n",
      "month_posted         -0.028714   -0.044792   0.002260  0.024840    -0.012339   \n",
      "vehicle_age_in_years -0.511944   -0.996247   0.167770  0.485064     0.001766   \n",
      "age_in_years         -0.511944   -0.996247   0.167770  0.485064     0.001766   \n",
      "avg_mileage           0.279631    0.591503  -0.058741  0.169604     0.003365   \n",
      "new_condition         0.218649    0.284731  -0.069466 -0.270775    -0.005070   \n",
      "\n",
      "                      day_of_week_posted  year_posted  month_posted  \\\n",
      "price                          -0.009906     0.032692     -0.028714   \n",
      "model_year                     -0.006613     0.051454     -0.044792   \n",
      "cylinders                      -0.005994     0.001114      0.002260   \n",
      "odometer                        0.011371    -0.022972      0.024840   \n",
      "days_listed                     0.001292     0.011714     -0.012339   \n",
      "day_of_week_posted              1.000000    -0.008179      0.007016   \n",
      "year_posted                    -0.008179     1.000000     -0.822731   \n",
      "month_posted                    0.007016    -0.822731      1.000000   \n",
      "vehicle_age_in_years            0.005909     0.035183     -0.026485   \n",
      "age_in_years                    0.005909     0.035183     -0.026485   \n",
      "avg_mileage                    -0.001490    -0.092282      0.078772   \n",
      "new_condition                   0.003150     0.014475     -0.009933   \n",
      "\n",
      "                      vehicle_age_in_years  age_in_years  avg_mileage  \\\n",
      "price                            -0.511944     -0.511944     0.279631   \n",
      "model_year                       -0.996247     -0.996247     0.591503   \n",
      "cylinders                         0.167770      0.167770    -0.058741   \n",
      "odometer                          0.485064      0.485064     0.169604   \n",
      "days_listed                       0.001766      0.001766     0.003365   \n",
      "day_of_week_posted                0.005909      0.005909    -0.001490   \n",
      "year_posted                       0.035183      0.035183    -0.092282   \n",
      "month_posted                     -0.026485     -0.026485     0.078772   \n",
      "vehicle_age_in_years              1.000000      1.000000    -0.599919   \n",
      "age_in_years                      1.000000      1.000000    -0.599919   \n",
      "avg_mileage                      -0.599919     -0.599919     1.000000   \n",
      "new_condition                    -0.283678     -0.283678     0.086337   \n",
      "\n",
      "                      new_condition  \n",
      "price                      0.218649  \n",
      "model_year                 0.284731  \n",
      "cylinders                 -0.069466  \n",
      "odometer                  -0.270775  \n",
      "days_listed               -0.005070  \n",
      "day_of_week_posted         0.003150  \n",
      "year_posted                0.014475  \n",
      "month_posted              -0.009933  \n",
      "vehicle_age_in_years      -0.283678  \n",
      "age_in_years              -0.283678  \n",
      "avg_mileage                0.086337  \n",
      "new_condition              1.000000  \n",
      "Truck Price & Vehicle Age Correlation:\n",
      "                         price  vehicle_age_in_years\n",
      "price                 1.000000             -0.638105\n",
      "vehicle_age_in_years -0.638105              1.000000\n",
      "Truck Price & Vehicle Mileage Correlation:\n",
      "                price  avg_mileage\n",
      "price        1.000000     0.332731\n",
      "avg_mileage  0.332731     1.000000\n",
      "\n",
      "Pickup Price & Vehicle Mileage Correlation:\n",
      "               price  avg_mileage\n",
      "price        1.00000      0.41015\n",
      "avg_mileage  0.41015      1.00000\n",
      "Pickup Price & Vehicle Mileage Correlation:\n",
      "                         price  vehicle_age_in_years\n",
      "price                 1.000000             -0.740111\n",
      "vehicle_age_in_years -0.740111              1.000000\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "truck_data = data_filtered.query('type==\"truck\"')\n",
    "pickup_data = data_filtered.query('type==\"pickup\"')\n",
    "print(truck_data.corr())\n",
    "print()\n",
    "print(pickup_data.corr())\n",
    "print(data_filtered.corr())\n",
    "print(\"Truck Price & Vehicle Age Correlation:\")\n",
    "print(truck_data[[\"price\", \"vehicle_age_in_years\"]].corr())\n",
    "print(\"Truck Price & Vehicle Mileage Correlation:\")\n",
    "print(truck_data[[\"price\", \"avg_mileage\"]].corr())\n",
    "print()\n",
    "print(\"Pickup Price & Vehicle Mileage Correlation:\")\n",
    "print(pickup_data[[\"price\", \"avg_mileage\"]].corr())\n",
    "print(\"Pickup Price & Vehicle Mileage Correlation:\")\n",
    "print(pickup_data[[\"price\", \"vehicle_age_in_years\"]].corr())\n",
    "\n",
    "truck_data.corr().plot(kind=\"scatter\", x=\"avg_mileage\", y=\"price\")\n",
    "plt.title(\"Effect of Mileage on Price for Trucks\")\n",
    "plt.show()\n",
    "print()\n",
    "pickup_data.corr().plot(kind=\"scatter\", x=\"avg_mileage\", y=\"price\")\n",
    "plt.title(\"Effect of Mileage on Price for Pickups\")\n",
    "plt.show()\n",
    "\n",
    "print()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Type of transmission vs. Price')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "truck_data.corr().plot(kind=\"scatter\", x=\"price\", y=\"new_condition\")\n",
    "sb.boxplot( x = 'transmission', y = 'price', data = truck_data )\n",
    "plt.title(\"Type of transmission vs. Price\")\n",
    "plt.show()\n",
    "sb.boxplot( x = 'transmission', y = 'price', data = pickup_data )\n",
    "plt.title(\"Type of transmission vs. Price\")\n",
    "plt.show()\n",
    "sb.boxplot(  x = 'transmission', y = 'price', data = data_filtered)\n",
    "plt.title(\"Type of transmission vs. Price\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Transmission does not seem to have a definite effect on price, as it also appears that the mediating factor between the two variables here might depend on the type of vehicle. As shown above, trucks tend to be higher priced if they are automatic while pickups appear to be more expensive if they fall in the \"other\" transmission category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Pickup Condition vs. Price')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(sb.boxplot( x= 'condition', y=\"price\", data= truck_data))\n",
    "plt.title(\"Truck Condition vs. Price\")\n",
    "plt.show()\n",
    "print(sb.boxplot( x= 'condition', y=\"price\", data= pickup_data))\n",
    "plt.title(\"Pickup Condition vs. Price\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Higher priced vehicles tend to also have a be in better condition, which makes sense. Trucks and pickups in the \"like new\" category of condition tend to be higher priced."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(sb.boxplot( x= 'paint_color', y=\"price\", data= truck_data))\n",
    "plt.title(\"Color of Truck vs. Price\")\n",
    "plt.show()\n",
    "print(sb.boxplot( x= 'paint_color', y=\"price\", data= pickup_data))\n",
    "plt.title(\"Color of Pickup vs. Price\")\n",
    "plt.show()\n",
    "sb.boxplot( x= 'paint_color', y=\"price\", data= data_filtered)\n",
    "plt.title(\"Color of Vehicles vs. Price\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Based on the filtered dataset, it appears that the colors do vary in price, but the type of vehicle also factors in here. For instance, we find that trucks tend to be higher priced in the color black, while pickups tend to be higher priced in the color brown."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## General conclusion\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* This dataset contained a considerable amount of missing values, which were distributed accross 5 different columns. Missing values found in the [\"is_4wd\"] column and [\"odometer\"] column were filled in while all other missing values were removed from the dataset. I justified filling in the missing values in the above named columns as the [\"is_4wd\"] column contained only one type of boolean value \"False\", so it is reasonable to assume that the missing values were likely due to not entering the \"True\" boolean value. \n",
    "* Furthermore, the missing values found in the [\"odometer\"] column were replaced with the median, as that seemed to be the best fit value in this circumstance. Missing values in the [\"paint_color\"], [\"model_year\"] and [\"cylinder\"] columns were removed, as the likely possibility for incorrectly filling in those values might have had an affect on the final analysis of finding out which factors contribute most to price. There were no duplicates in this dataset. It is important to note that when the additional column \"avg_mileage\" was created as result of dividing [\"odometer\"] and [\"model_year\"], it created problematic infinite values, as there some vehicles who were 0 years old but also had a non-zero value for their respective [\"odometer\"] reading. These values were problematic because they did not allow for further analysis/plotting of the data. Thus, I went back and replaced all values in the [\"vehicle_age_in_years\"] column that were equal to \"0\" with \"1\" under the condition that those values also had an [\"odometer\"] values greater than 0.\n",
    "* I chose to replace these values with 1, as an ['odometer'] reading that is greater than 0 indicates usage, which indicates an age that should be greater than 0 as well.\n",
    "\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "* Upon analyzing the different factors that impact vehicle price, I concluded that the two factors that appear to have the most direct impact on price are the vehicles age in years along with mileage. I selected the top two most popular and highest-priced vehicles which were trucks and pickups and analyzed data based on these two types of vehicles. The most significant finding was that a medium-strength negative correlation exists (-.638, -.740) between vehicle age and price for trucks and pickups respectively. This suggests that the younger the car, the higher the price, which is not surprising. \n",
    "* A correlational analysis of price and mileage resulted in a weak positive correlation (.332, .410) for trucks and pickups respectively. This indicates that mileage did not necessarily impact the price of vehicles in this dataset. As stated earlier, it is noteable that the sample of vehicles in this dataset was overall skewed to the left in terms of price and it is possible that a larger and more representative sample of vehicles might provide more insight into the true relationship between mileage and price.\n",
    "* Furthermore, paint color and transmission did not seem to have a direct impact on price either, as both appeared to be mediated by some other variable, such as type of vehicle. Further investigation should be conducted in order to determine if there are other confounding variables that mediate the relationship between these factors and price. However, vehicle condition did appear to impact price, as vehicles with the \"like new\" condition tended to be higher priced as well. This makes sense, as one would expect vehicles in better condition to be priced higher. Another point to make is that in my analysis I compared pickups and trucks in seperate dataframes to compare how these higher-priced vehicles are impacted by other factors.While these findings give insight to some of the factors that might impact vehicle price, further analysis should be conducted in order to confirm these findings and what they suggest. Although the results with in this analysis, a deeper look into the relationship between mileage and price should be taken, as this analysis can serve as a preliminary step to further investigation.\n"
   ]
  }
 ],
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