Assignment pt1_cars.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Second Assignment: Exercise and a Personal Project"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this second and final assignment you have the opportunity to choose a dataset and perform data analysis to answer the type of questions that interest you or will utilise a technique that you want to gain experience in. \n",
    "\n",
    "You may wish to select a dataset that we have already looked at (e.g. Titanic, or IMDB), or select another open-source dataset from Kaggle (or other). Once you've selected a dataset to investigate, proceed to complete the template cells below. In the markdown cells, you should provide a brief introduction to the dataset, and write the questions that you will investigate. Then use the code cells to answer the questions you select.\n",
    "\n",
    "### Logistics\n",
    "\n",
    "**Deadline.** Please see the Assessment deadline in specified in Canvas. Late submissions will not be accepted. This notebook is the Second of two Assignments for this course.\n",
    "\n",
    "**The Assignment Task.** This Second Assignment portfolio will account for the remaining 70 points of the 100 (Assignment 1 was worth 30 marks). This Second Assignment features two parts: \n",
    "* Part 1 is out of 20 marks, based on a defined set of problems. This will extend your application of numpy, pandas, and matplotlib on the iris dataset. \n",
    "* Part 2 is out of 50 marks, and requires you to perform exploratory data analysis on a dataset of your choice. You should start by selecting a dataset and acquiring the data. Describe the context of the dataset with commentary and descriptive statistics. Then you should choose three questions to ask of the dataset and then outline how to answer them. Where appropriate, seek to display the results with an appropriate plot.\n",
    "\n",
    "**Submission.** Please email your solutions and your completed Declaration of Authorship (DoA) form to weeklyclasses@conted.ox.ac.uk "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 1 (Set Exercise) (out of 20 marks)\n",
    "\n",
    "In this first part of Assignment 2, you are asked to continue work on the Iris dataset and further utilise libraries such as NumPy, Pandas and Matplotlib."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set up cells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Installing the python libraries (put this in a separate cell in jupyter notebook)\n",
    "#pip install numpy \n",
    "#pip install pandas \n",
    "#pip install matplotlib \n",
    "## Let's import the libraries we are going to use later on\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "from matplotlib.patches import PathPatch\n",
    "import matplotlib.patches as mpatches \n",
    "import seaborn as sns\n",
    "\n",
    "# Let's set the precision for NumPy and Pandas\n",
    "np.set_printoptions(precision=3)\n",
    "pd.options.display.float_format = '{:,.2f}'.format"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import iris dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Input:\n",
    "# Import iris keeping the text column intact\n",
    "url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data'\n",
    "iris = np.genfromtxt(url, delimiter=',', \n",
    "                     names=['sepal length', 'sepal width', 'petal length', 'petal width', 'species'],\n",
    "                     dtype=[np.float32, np.float32, np.float32, np.float32, '|S15']\n",
    ")\n",
    "iris_df = pd.DataFrame(iris) #I am converting the iris dataset into a pandas dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1A. Produce some descriptive statistics for the iris dataset (5 marks)\n",
    "\n",
    "For example, display the count of records, mean, std, min, max and in addtion to the quartiles for each of the columns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Creating the summary statistic table\n",
    "\n",
    "I have used the `describe()` function to create the summary statistic table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.00</td>\n",
       "      <td>150.00</td>\n",
       "      <td>150.00</td>\n",
       "      <td>150.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.84</td>\n",
       "      <td>3.05</td>\n",
       "      <td>3.76</td>\n",
       "      <td>1.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.83</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.76</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.30</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.10</td>\n",
       "      <td>2.80</td>\n",
       "      <td>1.60</td>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.80</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.35</td>\n",
       "      <td>1.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.40</td>\n",
       "      <td>3.30</td>\n",
       "      <td>5.10</td>\n",
       "      <td>1.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.90</td>\n",
       "      <td>4.40</td>\n",
       "      <td>6.90</td>\n",
       "      <td>2.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sepal_length  sepal_width  petal_length  petal_width\n",
       "count        150.00       150.00        150.00       150.00\n",
       "mean           5.84         3.05          3.76         1.20\n",
       "std            0.83         0.43          1.76         0.76\n",
       "min            4.30         2.00          1.00         0.10\n",
       "25%            5.10         2.80          1.60         0.30\n",
       "50%            5.80         3.00          4.35         1.30\n",
       "75%            6.40         3.30          5.10         1.80\n",
       "max            7.90         4.40          6.90         2.50"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Write your solution here.\n",
    "#print(pd.DataFrame(iris))\n",
    "pd.DataFrame(iris).describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Additional Exploration: Creating a summary statistic table grouped by species\n",
    "\n",
    "I decided to explore further and create a summary statistic table that is grouped by each `species` in the `iris` dataset. I was curious to see how the summary statistic table would look like if I grouped it by each species. I noticed that creating this table helped me answer **question 1.B**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>species</th>\n",
       "      <th>b'Iris-setosa'</th>\n",
       "      <th>b'Iris-versicolor'</th>\n",
       "      <th>b'Iris-virginica'</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">sepal_length</th>\n",
       "      <th>count</th>\n",
       "      <td>50.00</td>\n",
       "      <td>50.00</td>\n",
       "      <td>50.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.01</td>\n",
       "      <td>5.94</td>\n",
       "      <td>6.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.35</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.30</td>\n",
       "      <td>4.90</td>\n",
       "      <td>4.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.80</td>\n",
       "      <td>5.60</td>\n",
       "      <td>6.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.00</td>\n",
       "      <td>5.90</td>\n",
       "      <td>6.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.20</td>\n",
       "      <td>6.30</td>\n",
       "      <td>6.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.80</td>\n",
       "      <td>7.00</td>\n",
       "      <td>7.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">sepal_width</th>\n",
       "      <th>count</th>\n",
       "      <td>50.00</td>\n",
       "      <td>50.00</td>\n",
       "      <td>50.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.42</td>\n",
       "      <td>2.77</td>\n",
       "      <td>2.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.30</td>\n",
       "      <td>2.00</td>\n",
       "      <td>2.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.12</td>\n",
       "      <td>2.52</td>\n",
       "      <td>2.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.40</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.68</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.40</td>\n",
       "      <td>3.40</td>\n",
       "      <td>3.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">petal_length</th>\n",
       "      <th>count</th>\n",
       "      <td>50.00</td>\n",
       "      <td>50.00</td>\n",
       "      <td>50.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.46</td>\n",
       "      <td>4.26</td>\n",
       "      <td>5.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.17</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.40</td>\n",
       "      <td>4.00</td>\n",
       "      <td>5.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.50</td>\n",
       "      <td>4.35</td>\n",
       "      <td>5.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.58</td>\n",
       "      <td>4.60</td>\n",
       "      <td>5.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.90</td>\n",
       "      <td>5.10</td>\n",
       "      <td>6.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">petal_width</th>\n",
       "      <th>count</th>\n",
       "      <td>50.00</td>\n",
       "      <td>50.00</td>\n",
       "      <td>50.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.24</td>\n",
       "      <td>1.33</td>\n",
       "      <td>2.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.10</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.20</td>\n",
       "      <td>1.20</td>\n",
       "      <td>1.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.20</td>\n",
       "      <td>1.30</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.30</td>\n",
       "      <td>1.50</td>\n",
       "      <td>2.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.60</td>\n",
       "      <td>1.80</td>\n",
       "      <td>2.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "species             b'Iris-setosa'  b'Iris-versicolor'  b'Iris-virginica'\n",
       "sepal_length count           50.00               50.00              50.00\n",
       "             mean             5.01                5.94               6.59\n",
       "             std              0.35                0.52               0.64\n",
       "             min              4.30                4.90               4.90\n",
       "             25%              4.80                5.60               6.22\n",
       "             50%              5.00                5.90               6.50\n",
       "             75%              5.20                6.30               6.90\n",
       "             max              5.80                7.00               7.90\n",
       "sepal_width  count           50.00               50.00              50.00\n",
       "             mean             3.42                2.77               2.97\n",
       "             std              0.38                0.31               0.32\n",
       "             min              2.30                2.00               2.20\n",
       "             25%              3.12                2.52               2.80\n",
       "             50%              3.40                2.80               3.00\n",
       "             75%              3.68                3.00               3.18\n",
       "             max              4.40                3.40               3.80\n",
       "petal_length count           50.00               50.00              50.00\n",
       "             mean             1.46                4.26               5.55\n",
       "             std              0.17                0.47               0.55\n",
       "             min              1.00                3.00               4.50\n",
       "             25%              1.40                4.00               5.10\n",
       "             50%              1.50                4.35               5.55\n",
       "             75%              1.58                4.60               5.88\n",
       "             max              1.90                5.10               6.90\n",
       "petal_width  count           50.00               50.00              50.00\n",
       "             mean             0.24                1.33               2.03\n",
       "             std              0.11                0.20               0.27\n",
       "             min              0.10                1.00               1.40\n",
       "             25%              0.20                1.20               1.80\n",
       "             50%              0.20                1.30               2.00\n",
       "             75%              0.30                1.50               2.30\n",
       "             max              0.60                1.80               2.50"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# I decided to create a class called quantiles because I was creating functions for the 25th and 75th percentile. I thought that this would be good practice just incase I wanted to add more quntiles in my summary statistic\n",
    "class quantiles: \n",
    "    def quantile_25(self,x): \n",
    "        return x.quantile(0.25) \n",
    "    \n",
    "    def quantile_75(self,x):\n",
    "        return x.quantile(0.75) \n",
    "\n",
    "#I am creating my summary statisitc by using the `groupby()`function\n",
    "#`agg()` is a aggregate function and is used to generate the summary statistics\n",
    "#I use the `transpose()` function to display the summary statistic table vertically so it can be easier to read\n",
    "#I use the `rename()` function to rename some of the default index names that were set during the `agg()` function\n",
    "pd.DataFrame(iris).groupby('species').agg(['count','mean','std',\n",
    "                                           'min',quantiles().quantile_25,'quantile',quantiles().quantile_75,'max']\n",
    "                                         ).transpose().rename(index={'quantile_25': '25%','quantile': '50%','quantile_75': '75%'})\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1B. Calculate the maximum petal width for each species of flower (5 marks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Approach one: using a summary statistic table\n",
    "Using the code from the previous problem I decided to make my summary statistic table,which was grouped by each species, shorter. I did this by only specifying the `petal_width` column and then only calculating the maximum `petal_width` for each species through the `agg()` function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>species</th>\n",
       "      <th>b'Iris-setosa'</th>\n",
       "      <th>b'Iris-versicolor'</th>\n",
       "      <th>b'Iris-virginica'</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>petal_width</th>\n",
       "      <th>max</th>\n",
       "      <td>0.60</td>\n",
       "      <td>1.80</td>\n",
       "      <td>2.50</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "species          b'Iris-setosa'  b'Iris-versicolor'  b'Iris-virginica'\n",
       "petal_width max            0.60                1.80               2.50"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Write your solution here\n",
    "pd.DataFrame(iris)[['petal_width','species']].groupby('species').agg(['max']).transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Approach two (straightforward version): Generating the max `petal_width` through the `groupby()` function\n",
    "\n",
    "An alternative way to list out the maximum `petal_width` if to use the `groupby()` and `max()` functions. This method is similar to my first approach although this second approach is more straight forward."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "species\n",
       "b'Iris-setosa'       0.60\n",
       "b'Iris-versicolor'   1.80\n",
       "b'Iris-virginica'    2.50\n",
       "Name: petal_width, dtype: float32"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df.groupby('species').max()['petal_width']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1C. Data Visualisation (5 marks)\n",
    "\n",
    "Produce a boxplot for each of the four columns of the iris dataset (sepal_length, sepal_width, petal_length and petal_width) in addition to the volume column (if you completed Assignment 1)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Creating the volume column, changing the flower species names, and re-naming some columns\n",
    "\n",
    "I am creating the volume column and will be replacing the old/default species name with new names that will make the graph legends look neat. I am also re-naming the `sepal_length, sepal_width, petal_length, petal_width` columns because I want to remove the underscore in the names (this step is not necessary but I want to make this change so the labels in the graph look nice)\n",
    "\n",
    "* **Note**: While I am making some big changes to the iris dataset I would like to point out that the dataframe that is being edited is `iris_df` which is an alternative dataframe that I made to make these additional edits and to not mess up the original dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sepal Length</th>\n",
       "      <th>Sepal Width</th>\n",
       "      <th>Petal Length</th>\n",
       "      <th>Petal Width</th>\n",
       "      <th>species</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.10</td>\n",
       "      <td>3.50</td>\n",
       "      <td>1.40</td>\n",
       "      <td>0.20</td>\n",
       "      <td>Setosa</td>\n",
       "      <td>38.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.90</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1.40</td>\n",
       "      <td>0.20</td>\n",
       "      <td>Setosa</td>\n",
       "      <td>35.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.70</td>\n",
       "      <td>3.20</td>\n",
       "      <td>1.30</td>\n",
       "      <td>0.20</td>\n",
       "      <td>Setosa</td>\n",
       "      <td>30.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.60</td>\n",
       "      <td>3.10</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0.20</td>\n",
       "      <td>Setosa</td>\n",
       "      <td>33.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.00</td>\n",
       "      <td>3.60</td>\n",
       "      <td>1.40</td>\n",
       "      <td>0.20</td>\n",
       "      <td>Setosa</td>\n",
       "      <td>36.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.70</td>\n",
       "      <td>3.00</td>\n",
       "      <td>5.20</td>\n",
       "      <td>2.30</td>\n",
       "      <td>Virginica</td>\n",
       "      <td>244.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.30</td>\n",
       "      <td>2.50</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1.90</td>\n",
       "      <td>Virginica</td>\n",
       "      <td>207.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.50</td>\n",
       "      <td>3.00</td>\n",
       "      <td>5.20</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Virginica</td>\n",
       "      <td>230.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.20</td>\n",
       "      <td>3.40</td>\n",
       "      <td>5.40</td>\n",
       "      <td>2.30</td>\n",
       "      <td>Virginica</td>\n",
       "      <td>217.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.90</td>\n",
       "      <td>3.00</td>\n",
       "      <td>5.10</td>\n",
       "      <td>1.80</td>\n",
       "      <td>Virginica</td>\n",
       "      <td>185.91</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Sepal Length  Sepal Width  Petal Length  Petal Width    species  Volume\n",
       "0            5.10         3.50          1.40         0.20     Setosa   38.13\n",
       "1            4.90         3.00          1.40         0.20     Setosa   35.20\n",
       "2            4.70         3.20          1.30         0.20     Setosa   30.07\n",
       "3            4.60         3.10          1.50         0.20     Setosa   33.24\n",
       "4            5.00         3.60          1.40         0.20     Setosa   36.65\n",
       "..            ...          ...           ...          ...        ...     ...\n",
       "145          6.70         3.00          5.20         2.30  Virginica  244.45\n",
       "146          6.30         2.50          5.00         1.90  Virginica  207.82\n",
       "147          6.50         3.00          5.20         2.00  Virginica  230.07\n",
       "148          6.20         3.40          5.40         2.30  Virginica  217.37\n",
       "149          5.90         3.00          5.10         1.80  Virginica  185.91\n",
       "\n",
       "[150 rows x 6 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#making the volume column\n",
    "iris_df['Volume'] = (math.pi * iris_df['sepal_length']**2 * iris_df['petal_length'])/3\n",
    "\n",
    "#I am also replacing the values under the `species` column in the iris_df dataframe just to display the species name neatly\n",
    "iris_df = iris_df.replace({b'Iris-setosa': 'Setosa',b'Iris-versicolor': 'Versicolor',b'Iris-virginica': 'Virginica'})\n",
    "\n",
    "#re-naming the `sepal_length, sepal_width, petal_length, petal_width` columns\n",
    "iris_df.rename(columns= {'sepal_length' : 'Sepal Length',\n",
    "                         'sepal_width' : 'Sepal Width',\n",
    "                         'petal_length' : 'Petal Length',\n",
    "                        'petal_width' : 'Petal Width'},  inplace=True)\n",
    "iris_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Creating the boxplot using `seaborn`\n",
    "\n",
    "`seaborn` is a python visualisation library based on `matplotlib`. I am using it to create the boxplots for the `iris_df` dataframe. I am using `seaborn` since it is a popular python library used for data visualisation and to create graphs. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "my_pal = {'Setosa': \"#5975a4\", 'Versicolor': \"#cc8963\" , 'Virginica' :\"#5f9e6e\"} #This is the color palette that I am defining to color in the boxes in the boxplot\n",
    "\n",
    "#I am using this for loop to go through the list each column in the `iris_df` dataframe\n",
    "for x in list(iris_df.columns):\n",
    "    y_axis_val = iris_df[x] #I am creating the `y_axis_val` variable to store the y values of the chart\n",
    "    \n",
    "    ###this if statement goes through all the numeric columns and leaves out the `species` column since the graph is got going to display the flower species as a numeric value\n",
    "    if x != 'species':\n",
    "        plt.figure(figsize=(8,8))#defining the figure size of the graph\n",
    "        sns.set_style(\"whitegrid\")#defining the background of the graph to be single-lined and horizontal\n",
    "\n",
    "        if x == 'Sepal Length' or x == 'Sepal Width' or x == 'Petal Length' or x == 'Petal Width':\n",
    "            sns.boxplot(x=iris_df[\"species\"], #the x-axis will only include the species\n",
    "                        y=y_axis_val, \n",
    "                        hue=iris_df[\"species\"], #`hue` is used to visualise the data of different categories into one graph\n",
    "                        palette=my_pal, # I am defining the colors of each boxplot\n",
    "                       flierprops={\"marker\": \"D\",'markersize': 5, 'markerfacecolor': '#4c4c4c'} #I am defining the markers color and size\n",
    "                       ).set(title= (x +' by Species'), ylabel = (x + ' (cm.)')) #I am setting the title for each graph and the y-axis label\n",
    "        \n",
    "        elif x == 'Volume': #I am creating a separate boxplot for the volume column so I can edit the y-axis unit label\n",
    "                    sns.boxplot(x=iris_df[\"species\"], #the x-axis will only include the species\n",
    "                        y=y_axis_val, \n",
    "                        hue=iris_df[\"species\"], #`hue` is used to visualise the data of different categories into one graph\n",
    "                        palette=my_pal, # I am defining the colors of each boxplot\n",
    "                       flierprops={\"marker\": \"D\",'markersize': 5, 'markerfacecolor': '#4c4c4c'} #I am defining the markers color and size\n",
    "                       ).set(title= (x +' by Species'), ylabel = (x + ' (cubic centimeters)')) #I am setting the title for each graph and the y-axis label\n",
    "\n",
    "        ### manually creating the color legend labels and colors\n",
    "        setosa_color_legend = mpatches.Patch(color='#5975a4', label='Setosa')\n",
    "        versicolor_color_legend = mpatches.Patch(color='#cc8963', label='Versicolor')\n",
    "        virginica_color_legend = mpatches.Patch(color='#5f9e6e', label='Virginica')\n",
    "\n",
    "        ### creating the color legend and defining its position\n",
    "        plt.legend(handles = [setosa_color_legend, versicolor_color_legend, virginica_color_legend], # the handles create the legend \n",
    "                   bbox_to_anchor=(1, 1), loc='upper left') #I am defining the legend position\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1D. Provide a short intrepretation of the data visualised by your 1C solution. (5 marks)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on my observations and analysis of the boxplots, I believe that the **Iris-Virginica** flower species is a large flower since it has the largest sepal length, petal length, petal width, and volume as compared to the other flower species. \n",
    "\n",
    "**Iris-Setosa** is the smallest flower species and **Iris-Versicolor** is the flower species that falls in the middle when compared with the other two species. I reached my conclusion based on my observations of each graph which I have desribed below:\n",
    "\n",
    "#### Sepal Length Observations\n",
    "\n",
    "*   **Iris-Setosa** has the **smallest median sepal length of 5 cm.**. There is one outlier which is close to 6 cm. but that is not representative of the whole sample.\n",
    "*   **Iris-Versicolor** has a **sepal length median that is below 6 cm.**. Although the median sepal length of Iris-Versicolor is not too big, I did notice that there was a **right-skew** in the boxplot which might suggest that most of the sepal length values of Iris-Versicolor are small but there were some large values that skewed the distribution\n",
    "*    **Iris-Virginica** has a **sepal length median of 6.5 cm.** which is the largest medain as compared to the other flower species. This boxplot also has a **right-skew** distribution which might imply that most of the sepal length values were small, but there were some large values in the distribution.\n",
    "\n",
    "#### Sepal Width Observations\n",
    "\n",
    "*   **Iris-Setosa** has the **highest median sepal width that is close to 3.5 cm.**. There appears to be a equal/normal distribution which means that the values close to the mean occur frequently as opposed to values that are farther from the mean.\n",
    "*   **Iris-Versicolor** has the **lowest median sepal width that is just below 3 cm.**. There appears to be a **left-skew** which mean the median is greater than the mean and implies that most of the sepal width values are big with some small values skewing the distribution. \n",
    "*    **Iris-Virginica** has a **sepal width median of 3 cm.** which falls in the middle of Iris-Setosa and Iris-Versicolor. The distribution of the boxplot appears to be normal with two outliers (one large and one small)\n",
    "\n",
    "#### Petal Length Observations\n",
    "\n",
    "*   **Iris-Setosa** has the **smallest median petal length which is around 1.5 cm.**. There are 3 outliers in this boxplot which shows that there are not many Iris-Setosa flowers that have large petal lengths.\n",
    "*   **Iris-Versicolor** has the **a petal length that is above 4 cm.**. This length falls in the middle when compared to the two species but it is still significantly bigger than the petal length of Iris-Setosa.\n",
    "*   **Iris-Virginica** has the **largest petal length that is around 5.5 cm.**. \n",
    "\n",
    "#### Petal Width Observations\n",
    "\n",
    "*   **Iris-Setosa** has the **smallest median petal width which is around 0.25 cm.**. The median line is not visible in the boxplot which indicates skewness in the data\n",
    "*   **Iris-Versicolor** has a **median petal width that is around 1.25 cm.**. The boxplot appears to have a right skew\n",
    "*   **Iris-Virginica** has the **largest median petal width of 2 cm.**\n",
    "\n",
    "#### Volume Observations\n",
    "*   **Iris-Setosa** has the **smallest median volume which is below 50 cubic centimeters**. The median volume is extremly low as compared to Iris-Versicolor and Iris-Virginica which indicates that the Iris-Setosa is really light in weight\n",
    "*   **Iris-Versicolor** has a **median volume that is slightly above 150 cubic centimeters**. The boxplot indicated a right/positive skew in the data\n",
    "*   **Iris-Virginica** has the **largest median volume that is slightly below 250 cubic centimeters**. This means that the flower is the heaviest out of all the species"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 2 - Personal Project (out of 50 marks)\n",
    "In this second part of Assignment 2 you have the opportunity to perform some data analysis on a dataset of your choice. Choose a dataset or a scenario that interests you and provide an outline of the scenario, clean the dataset (if needed), perform some statistic analysis, visualise this data analysis and comment on what you see.   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [Provide a brief introduction to your dataset here] (5 marks)\n",
    "\n",
    "For this assignment, I will be analysing the mpg dataset which contains information about 38 new car models which have been released every year from 1999 and 2008.\n",
    "\n",
    "I have definded the dataset columns below:\n",
    "\n",
    "*   **mpg**: mpg means miles per gallon and it is the distance of each car measured in miles. It is referred to the *fuel economy* of a car since it represents the amount travelled by the car and the amount of fuel consumed. A *higher* mpg is better since it shows that your car can consume less fuel as you drive.\n",
    "*   **cylinders**: A car cylinder is a important part of the engine. This is where fuel is consumed and the car's energy is generated. If a car has *more* cylinders that means the cars power can be generated *faster*. However, there is a tradeoff to this since you need more fuel to generate more power. So if your care has more cylinders then you will end up paying for more fuel.\n",
    "*    **displacement**: A cars displacement refers to the amount of air that can be pushed into the cylinders. The higher the engine displacement the more power the engine can generate.\n",
    "*    **horsepower**: The cars horsepower is a measurment of how fast a cars engine can work. The more horsepower a car has the faster it can go\n",
    "*    **weight**: Car weight. The heavier a car, the more fuel it requires\n",
    "*    **acceleration**: Acceleration describes the rate at which the car can increase its speed. Having a high car acceleration will use more fuel\n",
    "*    **model_year**: Gives the year the car was made/manufactured\n",
    "*    **origin**: Where the car was manufactured\n",
    "*    **name**: The car model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#installing the mpg python data package which contains information about fuel economy from 1999 and 2008 for 38 popular models of cars\n",
    "#pip install mpg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>model_year</th>\n",
       "      <th>origin</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.00</td>\n",
       "      <td>8</td>\n",
       "      <td>307.00</td>\n",
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       "      <td>3504</td>\n",
       "      <td>12.00</td>\n",
       "      <td>70</td>\n",
       "      <td>usa</td>\n",
       "      <td>chevrolet chevelle malibu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.00</td>\n",
       "      <td>8</td>\n",
       "      <td>350.00</td>\n",
       "      <td>165.00</td>\n",
       "      <td>3693</td>\n",
       "      <td>11.50</td>\n",
       "      <td>70</td>\n",
       "      <td>usa</td>\n",
       "      <td>buick skylark 320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.00</td>\n",
       "      <td>8</td>\n",
       "      <td>318.00</td>\n",
       "      <td>150.00</td>\n",
       "      <td>3436</td>\n",
       "      <td>11.00</td>\n",
       "      <td>70</td>\n",
       "      <td>usa</td>\n",
       "      <td>plymouth satellite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.00</td>\n",
       "      <td>8</td>\n",
       "      <td>304.00</td>\n",
       "      <td>150.00</td>\n",
       "      <td>3433</td>\n",
       "      <td>12.00</td>\n",
       "      <td>70</td>\n",
       "      <td>usa</td>\n",
       "      <td>amc rebel sst</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.00</td>\n",
       "      <td>8</td>\n",
       "      <td>302.00</td>\n",
       "      <td>140.00</td>\n",
       "      <td>3449</td>\n",
       "      <td>10.50</td>\n",
       "      <td>70</td>\n",
       "      <td>usa</td>\n",
       "      <td>ford torino</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mpg  cylinders  displacement  horsepower  weight  acceleration  \\\n",
       "0 18.00          8        307.00      130.00    3504         12.00   \n",
       "1 15.00          8        350.00      165.00    3693         11.50   \n",
       "2 18.00          8        318.00      150.00    3436         11.00   \n",
       "3 16.00          8        304.00      150.00    3433         12.00   \n",
       "4 17.00          8        302.00      140.00    3449         10.50   \n",
       "\n",
       "   model_year origin                       name  \n",
       "0          70    usa  chevrolet chevelle malibu  \n",
       "1          70    usa          buick skylark 320  \n",
       "2          70    usa         plymouth satellite  \n",
       "3          70    usa              amc rebel sst  \n",
       "4          70    usa                ford torino  "
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add setup code here - set the path to the chosen dataset, and import the relevant Python libraries.\n",
    "mpg = sns.load_dataset(\"mpg\") #loading the dataset \n",
    "mpg[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>model_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>398.00</td>\n",
       "      <td>398.00</td>\n",
       "      <td>398.00</td>\n",
       "      <td>392.00</td>\n",
       "      <td>398.00</td>\n",
       "      <td>398.00</td>\n",
       "      <td>398.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>23.51</td>\n",
       "      <td>5.45</td>\n",
       "      <td>193.43</td>\n",
       "      <td>104.47</td>\n",
       "      <td>2,970.42</td>\n",
       "      <td>15.57</td>\n",
       "      <td>76.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.82</td>\n",
       "      <td>1.70</td>\n",
       "      <td>104.27</td>\n",
       "      <td>38.49</td>\n",
       "      <td>846.84</td>\n",
       "      <td>2.76</td>\n",
       "      <td>3.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>9.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>68.00</td>\n",
       "      <td>46.00</td>\n",
       "      <td>1,613.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>70.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>17.50</td>\n",
       "      <td>4.00</td>\n",
       "      <td>104.25</td>\n",
       "      <td>75.00</td>\n",
       "      <td>2,223.75</td>\n",
       "      <td>13.83</td>\n",
       "      <td>73.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>23.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>148.50</td>\n",
       "      <td>93.50</td>\n",
       "      <td>2,803.50</td>\n",
       "      <td>15.50</td>\n",
       "      <td>76.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>29.00</td>\n",
       "      <td>8.00</td>\n",
       "      <td>262.00</td>\n",
       "      <td>126.00</td>\n",
       "      <td>3,608.00</td>\n",
       "      <td>17.18</td>\n",
       "      <td>79.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>46.60</td>\n",
       "      <td>8.00</td>\n",
       "      <td>455.00</td>\n",
       "      <td>230.00</td>\n",
       "      <td>5,140.00</td>\n",
       "      <td>24.80</td>\n",
       "      <td>82.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         mpg  cylinders  displacement  horsepower   weight  acceleration  \\\n",
       "count 398.00     398.00        398.00      392.00   398.00        398.00   \n",
       "mean   23.51       5.45        193.43      104.47 2,970.42         15.57   \n",
       "std     7.82       1.70        104.27       38.49   846.84          2.76   \n",
       "min     9.00       3.00         68.00       46.00 1,613.00          8.00   \n",
       "25%    17.50       4.00        104.25       75.00 2,223.75         13.83   \n",
       "50%    23.00       4.00        148.50       93.50 2,803.50         15.50   \n",
       "75%    29.00       8.00        262.00      126.00 3,608.00         17.18   \n",
       "max    46.60       8.00        455.00      230.00 5,140.00         24.80   \n",
       "\n",
       "       model_year  \n",
       "count      398.00  \n",
       "mean        76.01  \n",
       "std          3.70  \n",
       "min         70.00  \n",
       "25%         73.00  \n",
       "50%         76.00  \n",
       "75%         79.00  \n",
       "max         82.00  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load in your data and display some descriptive statistics and commentary to provide a summary of the dataset.\n",
    "mpg.describe() #creating the summary statistic for the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "## 1. [Choose and write a question here] (15 marks)\n",
    "### (e.g. What percentage of male and female passengers perished?)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Question 1: Is there a realtionship between car origins and the mpg of each car?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I am curious to see if there is a relationship between the origin of where a car is manufactured and how many miles per gallon (mpg) can it ride. Do some countries produce less powerful cars as compared the overall average? To answer this question I am going to create a box plot which displays the median mpgs of cars produced by each country in the mpg dataset.\n",
    "\n",
    "#### Answer\n",
    "There does seem to be some relationship between the origin of a car and the mpg (miles per gallon) that it can run. In the boxplot that I produced below I noticed the the USA has the lowest median of car mpg's which is below 20 mpg (this number is below the average car mpg which is around 25). Japan and Europe manufacture and release cars with mpg's higher than the average (Japan has a median car mpg of around 32 while Europe has a median car mpg of around 27).\n",
    "\n",
    "While my dataset only contained the data of 3 countries it was interesting to see that some contries produce cars that are a bit inefficient and are not in line with the main manufacturing/production market. I am now curious to find out if there are specific cities in which these cars are being produced. Does the specific manufacturing location of cars correlate with a low (or high) mpg rate or overall car build?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2cae4e71610>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Add code here to answer this question\n",
    "\n",
    "\n",
    "sns.boxplot(x=mpg[\"origin\"], #the x-axis will only include the species\n",
    "            y=mpg['mpg'], \n",
    "            hue=mpg[\"origin\"], #`hue` is used to visualise the data of different categories into one graph\n",
    "            palette={'usa': \"brown\", 'japan': \"red\" , 'europe' :\"blue\"}, # I am defining the colors of each boxplot\n",
    "           flierprops={\"marker\": \"o\",'markersize': 5} #I am defining the markers color and size\n",
    "           ).set(title= ('mpg (miles per gallon) of each car compared to the average car mpg'), ylabel = ('mpg (miles per gallon')\n",
    "                ) \n",
    "\n",
    "plt.axhline(mpg.mpg.mean(),color='orange',linestyle='dashed',linewidth=2)\n",
    "plt.legend(handles = [mpatches.Patch(color='orange', label='Average MPG')], # the handles create the legend \n",
    "           bbox_to_anchor=(1, 1), loc='upper left') #I am defining the legend position"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Has the distribution of car mpgs increased or decreased throughout the years?  \n",
    "\n",
    "Following the last question, I am curious to see if the car mpgs can increased as they were distributed througout the years. I assume that the answer will be yes but I am curious to see if this growth was fast or slow. I am going to use a line chart for this visualisation. \n",
    "\n",
    "#### Answer and observation\n",
    "Based on the line graph that I have made my initial hypothesis was correct. The distribution of car mps's has increased through the years. I noticed that in the years 1979 and 1980 the care mpg distribution increased drastically but then fell during the following year. One question that I would like to investigate further is why did the car mpg rate drop during that year. In fact, what led to the mpg rate drop for the other years (ex. 1971-1973, 1974-1975)\n",
    "\n",
    "#### Line graph by each country\n",
    "I also decided to go one step further with my line graph and see how the care distribution mpg rates compare for each country. The second line graph perfectly explained the observations that I made during the first question where I analysed the boxplot. The USA has always had the lowest car mpg distribution rates and they were only able to reach the global average in 1979. So it took the USA **9 years** to catch up with Japan and Europe. Even then, Japan and Europe were ahead of the USA by 10 mpg units.\n",
    "\n",
    "Europe and Japan have has similar mpg distribution rates throughout the years. This means that both countries are leaders in the car manufacturing industry (although this might be big thing to say without taking other countries into account. I noticed that both Japan and Europe has a big mpg crash between 1971 and 1973. I wonder what happended during that time where both countries could not release a car model that had a higher mpg that the previous models. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2cae4f04f50>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Add code here to answer this question\n",
    "sns.lineplot(x=mpg[\"model_year\"], #the x-axis will only include the species\n",
    "            y=mpg['mpg'], \n",
    "            #hue=mpg[\"origin\"], #`hue` is used to visualise the data of different categories into one graph\n",
    "            #palette={'usa': \"brown\", 'japan': \"red\" , 'europe' :\"blue\"}, # I am defining the colors of each boxplot\n",
    "           #flierprops={\"marker\": \"o\",'markersize': 5} #I am defining the markers color and size\n",
    "           ).set(title= ('mpg (miles per gallon) throughout the years'), ylabel = ('mpg (miles per gallon')\n",
    "                ) \n",
    "\n",
    "plt.axhline(mpg.mpg.mean(),color='orange',linestyle='dashed',linewidth=2)\n",
    "plt.legend(handles = [mpatches.Patch(color='orange', label='Average MPG')], # the handles create the legend \n",
    "           bbox_to_anchor=(1, 1), loc='upper left') #I am defining the legend position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2cae72a1550>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x=mpg[\"model_year\"], #the x-axis will only include the species\n",
    "            y=mpg['mpg'], \n",
    "            hue=mpg[\"origin\"], #`hue` is used to visualise the data of different categories into one graph\n",
    "              errorbar=None, #removing the confidence interval shadow that is surrounded by each line\n",
    "            palette={'usa': \"brown\", 'japan': \"red\" , 'europe' :\"blue\"}, # I am defining the colors of each line\n",
    "           ).set(title= ('mpg (miles per gallon) throughout the years'), ylabel = ('mpg (miles per gallon'), xlabel = ('Car Model Year')\n",
    "               ) \n",
    "\n",
    "plt.axhline(mpg.mpg.mean(),color='orange',linestyle='dashed',linewidth=2)\n",
    "plt.legend(handles = [mpatches.Patch(color='orange', label='Average MPG'),\n",
    "                     mpatches.Patch(color='brown', label='USA'),\n",
    "                     mpatches.Patch(color='red', label='Japan'),\n",
    "                     mpatches.Patch(color='blue', label='Europe')], # the handles create the legend \n",
    "           bbox_to_anchor=(1, 1), loc='upper left') #I am defining the legend position"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Is there a relationship between the car mpg distribution and car companies?  (15 marks)\n",
    "\n",
    "I want to see if there is a trend with car companies and the mpg distributions that they release. Are a small number of car companies producing cars that have a mpg above the average or are most car companies doing this?\n",
    "\n",
    "To start, I am first going to create a new column called `company_name`. I am going to create this column by spliting the first names of the car models into the  `company_name` column (I am splitting the first name because most cars always start their car model name with the company name). After looking at the different car companies I realised that there are some companies that are worded differently or have abbreviations. My next goal is to make the naming of all companies consistent and to remove any repetitons through the `replace()` function.\n",
    "\n",
    "#### My Answer and Observations\n",
    "After plotting the box plots I realised that out of the 27 companies that I displayed, only **10 of the companies** were able to manufacture care that were above the average mpg rate. I was suprised to see this since I expected at least half of the car companies to be above the average threshold. I am curious to see how much impact these 10 companies played in bringing up the average car mpg rate (the highs in the line graph). But I believe that would require some advanced statisitcs so I will leave that question for another day. \n",
    "\n",
    "I have also included three additional boxplots where I compare the individual car companies from each country to the mpg (miles per gallon) level and the all the USA car companies were below the average mpg level meaning that U.S companies are behind in creating fuel effeicient cars. If you would want to buy a efficient car then it would be best to buy a car from a Japenese or European car company. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is the aproximate number of car companies in this dataset: 37\n",
      "------------------------------------\n",
      "['chevrolet' 'buick' 'plymouth' 'amc' 'ford' 'pontiac' 'dodge' 'toyota'\n",
      " 'datsun' 'volkswagen' 'peugeot' 'audi' 'saab' 'bmw' 'chevy' 'hi'\n",
      " 'mercury' 'opel' 'fiat' 'oldsmobile' 'chrysler' 'mazda' 'volvo' 'renault'\n",
      " 'toyouta' 'maxda' 'honda' 'subaru' 'chevroelt' 'capri' 'vw'\n",
      " 'mercedes-benz' 'cadillac' 'mercedes' 'vokswagen' 'triumph' 'nissan']\n"
     ]
    }
   ],
   "source": [
    "# Add code here to answer this question\n",
    "mpg['name_copy'] = mpg['name'].copy() #I am creating a copy of the 'name' column\n",
    "mpg['company_name'] = mpg['name_copy'].str.split(' ', expand=True)[0] #I am spliting the car model names and am only keepin the column that contain the car company names\n",
    "print('This is the aproximate number of car companies in this dataset:',len(mpg['company_name'].unique())) #output: 37 -there are roughly 37 different car companies represented in this dataset\n",
    "print('------------------------------------')\n",
    "print(mpg['company_name'].unique()) #I am printing out all the car companies names to get a feel of the data. I need to make the naming consistent for some companies\n",
    "\n",
    "mpg['company_name'] = mpg['company_name'].replace(['volkswagen','vokswagen','vw'],'VW') #making the volkswagen naming consistent\n",
    "mpg['company_name'] = mpg['company_name'].replace('maxda','mazda') #making the mazda naming consistent\n",
    "mpg['company_name'] = mpg['company_name'].replace('toyouta','toyota')\n",
    "mpg['company_name'] = mpg['company_name'].replace('mercedes','mercedes-benz')\n",
    "mpg['company_name'] = mpg['company_name'].replace('nissan','datsun')\n",
    "mpg['company_name'] = mpg['company_name'].replace('capri','ford')\n",
    "mpg['company_name'] = mpg['company_name'].replace(['chevroelt','chevy'],'chevrolet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x2caed983150>"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=mpg[\"company_name\"], #the x-axis will only include the species\n",
    "            y=mpg['mpg'], \n",
    "            hue=mpg[\"company_name\"], #`hue` is used to visualise the data of different categories into one graph\n",
    "            #palette={'usa': \"brown\", 'japan': \"red\" , 'europe' :\"blue\"}, # I am defining the colors of each boxplot\n",
    "           flierprops={\"marker\": \"o\",'markersize': 5} #I am defining the markers color and size\n",
    "           ).set(title= ('mpg (miles per gallon) of each car company compared to the average car mpg'), ylabel = ('mpg (miles per gallon)')\n",
    "                ) \n",
    "#plt.set_xticklabels(ax.get_xticklabels(),rotation=30)\n",
    "plt.xticks(rotation=45,size = 7)\n",
    "#plt.axis(ymin=0, ymax=1)\n",
    "plt.axhline(mpg.mpg.mean(),color='orange',linestyle='dashed',linewidth=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], Name: company_name, dtype: object)"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#mpg['origin' == 'japan']#[\"company_name\"]\n",
    "\n",
    "#mpg[mpg[\"origin\"].str.contains(\"japan\")]['company_name']\n",
    "mpg[mpg[\"origin\"].str.contains(\"USA\")]['company_name']\n",
    "#mpg[mpg[\"origin\"].str.contains(\"europe\")]['company_name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x2caeea98a90>"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=mpg[mpg[\"origin\"].str.contains(\"japan\")]['company_name'], #the x-axis will only include the species\n",
    "            y=mpg['mpg'], \n",
    "            hue=mpg[\"company_name\"], #`hue` is used to visualise the data of different categories into one graph\n",
    "            #palette={'usa': \"brown\", 'japan': \"red\" , 'europe' :\"blue\"}, # I am defining the colors of each boxplot\n",
    "           flierprops={\"marker\": \"o\",'markersize': 5} #I am defining the markers color and size\n",
    "           ).set(title= ('mpg (miles per gallon) of each Japanese company compared to the average car mpg'), \n",
    "                 ylabel = ('mpg (miles per gallon)'),\n",
    "                 xlabel = ('Japanese Car Companies')\n",
    "                ) \n",
    "#plt.set_xticklabels(ax.get_xticklabels(),rotation=30)\n",
    "plt.legend([],[], frameon=False)\n",
    "plt.xticks(rotation=45,size = 7)\n",
    "#plt.axis(ymin=0, ymax=1)\n",
    "plt.axhline(mpg.mpg.mean(),color='orange',linestyle='dashed',linewidth=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x2caf05bcdd0>"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=mpg[mpg[\"origin\"].str.contains(\"europe\")]['company_name'], #the x-axis will only include the species\n",
    "            y=mpg['mpg'], \n",
    "            hue=mpg[\"company_name\"], #`hue` is used to visualise the data of different categories into one graph\n",
    "            #palette={'usa': \"brown\", 'japan': \"red\" , 'europe' :\"blue\"}, # I am defining the colors of each boxplot\n",
    "           flierprops={\"marker\": \"o\",'markersize': 5} #I am defining the markers color and size\n",
    "           ).set(title= ('mpg (miles per gallon) of each European company compared to the average car mpg'), \n",
    "                 ylabel = ('mpg (miles per gallon)'),\n",
    "                 xlabel = ('Europeoan Car Companies')\n",
    "                ) \n",
    "#plt.set_xticklabels(ax.get_xticklabels(),rotation=30)\n",
    "plt.legend([],[], frameon=False)\n",
    "plt.xticks(rotation=45,size = 7)\n",
    "#plt.axis(ymin=0, ymax=1)\n",
    "plt.axhline(mpg.mpg.mean(),color='orange',linestyle='dashed',linewidth=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x2caf05fe5d0>"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=mpg[mpg[\"origin\"].str.contains(\"usa\")]['company_name'], #the x-axis will only include the species\n",
    "            y=mpg['mpg'], \n",
    "            hue=mpg[\"company_name\"], #`hue` is used to visualise the data of different categories into one graph\n",
    "            #palette={'usa': \"brown\", 'japan': \"red\" , 'europe' :\"blue\"}, # I am defining the colors of each boxplot\n",
    "           flierprops={\"marker\": \"o\",'markersize': 5} #I am defining the markers color and size\n",
    "           ).set(title= ('mpg (miles per gallon) of each U.S company compared to the average car mpg'), \n",
    "                 ylabel = ('mpg (miles per gallon)'),\n",
    "                 xlabel = ('U.S Car Companies')\n",
    "                ) \n",
    "#plt.set_xticklabels(ax.get_xticklabels(),rotation=30)\n",
    "plt.legend([],[], frameon=False)\n",
    "plt.xticks(rotation=45,size = 7)\n",
    "#plt.axis(ymin=0, ymax=1)\n",
    "plt.axhline(mpg.mpg.mean(),color='orange',linestyle='dashed',linewidth=2)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  },
  "vscode": {
   "interpreter": {
    "hash": "c24ecc2928a1df641fb89905a028e959d05eac4f79c1af53f0bb766b01409011"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}