Data analysis.ipynb

{
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   "cell_type": "markdown",
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    "<h1>Data Analysis with Python</h1>\n",
    "\n",
    "<h2>Python Packages for Data Science</h2>\n",
    "\n",
    "A Python library is a collection of functions and methods that allow you to perform lots of actions without writing any code. The libraries usually contain built-in modules providing different functionalities which you can use directly. And there are extensive libraries offering a broad range of facilities. We have divided the Python data analysis libraries into three groups.\n",
    "\n",
    "<h3>Scientific Computing Libraries</h3>\n",
    "\n",
    "Examples include:\n",
    "<ol>\n",
    "<li>Pandas - The primary instrument of Pandas is the two-dimensional table consisting of column and row labels, which are called a data frame. It is designed to provide easy indexing functionality.\n",
    "<li>Numpy - uses arrays for its inputs and outputs. It can be extended to objects for matrices and with minor coding changes, developers can perform fast array processing.\n",
    "<li>Scipy - includes functions for some advanced math problems as listed on this slide, as well as data visualization.\n",
    "</ol>\n",
    "<h3>Visualization Libraries</h3>\n",
    "\n",
    "Examples include:\n",
    "<ol>\n",
    "<li>Matplotlib -  It is great for making graphs and plots. The graphs are also highly customizable.\n",
    "<li>Seaborn - It is based on Matplotlib. It's very easy to generate various plots such as heat maps, time series violin plots.\n",
    "</ol>\n",
    "<h3>Algorithmic Libraries</h3>\n",
    "\n",
    "Examples include:\n",
    "<ol>\n",
    "<li>Scikit-learn - contains tools for statistical modelling, including regression, classification, clustering and so on. This library is built on NumPy, SciPy and Matplotib.\n",
    "<li>Statsmodels -  allows users to explore data, estimate statistical models and perform statistical tests.\n",
    "</ol>\n",
    "<h2>Importing Data</h2>\n",
    "\n",
    "The process of loading and reading data into Python from various resources. There are two important aspects to importing the data:\n",
    "<ol>\n",
    "<li>Format - e.g. .csv, .json, .xlsx, .hdf, .nc, .txt …\n",
    "<li>File path\n",
    "</ol>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Importing a CSV into Python</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.data\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the data contains no column headers, the headers option in read_csv has to be disabled."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(url, header=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data is now stored as a dataframe - the primary instrument of pandas. A dataframe is essentially a matrix."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To print the first 5 rows of the dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
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       "      <th>3</th>\n",
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       "      <th>7</th>\n",
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       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>?</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>13495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>?</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>?</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>94.5</td>\n",
       "      <td>...</td>\n",
       "      <td>152</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>9.0</td>\n",
       "      <td>154</td>\n",
       "      <td>5000</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.8</td>\n",
       "      <td>...</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>4wd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.4</td>\n",
       "      <td>...</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115</td>\n",
       "      <td>5500</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   0    1            2    3    4     5            6    7      8     9   ...    \\\n",
       "0   3    ?  alfa-romero  gas  std   two  convertible  rwd  front  88.6  ...     \n",
       "1   3    ?  alfa-romero  gas  std   two  convertible  rwd  front  88.6  ...     \n",
       "2   1    ?  alfa-romero  gas  std   two    hatchback  rwd  front  94.5  ...     \n",
       "3   2  164         audi  gas  std  four        sedan  fwd  front  99.8  ...     \n",
       "4   2  164         audi  gas  std  four        sedan  4wd  front  99.4  ...     \n",
       "\n",
       "    16    17    18    19    20   21    22  23  24     25  \n",
       "0  130  mpfi  3.47  2.68   9.0  111  5000  21  27  13495  \n",
       "1  130  mpfi  3.47  2.68   9.0  111  5000  21  27  16500  \n",
       "2  152  mpfi  2.68  3.47   9.0  154  5000  19  26  16500  \n",
       "3  109  mpfi  3.19  3.40  10.0  102  5500  24  30  13950  \n",
       "4  136  mpfi  3.19  3.40   8.0  115  5500  18  22  17450  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>-1</td>\n",
       "      <td>95</td>\n",
       "      <td>volvo</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>109.1</td>\n",
       "      <td>...</td>\n",
       "      <td>141</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>9.5</td>\n",
       "      <td>114</td>\n",
       "      <td>5400</td>\n",
       "      <td>23</td>\n",
       "      <td>28</td>\n",
       "      <td>16845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>-1</td>\n",
       "      <td>95</td>\n",
       "      <td>volvo</td>\n",
       "      <td>gas</td>\n",
       "      <td>turbo</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>109.1</td>\n",
       "      <td>...</td>\n",
       "      <td>141</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>8.7</td>\n",
       "      <td>160</td>\n",
       "      <td>5300</td>\n",
       "      <td>19</td>\n",
       "      <td>25</td>\n",
       "      <td>19045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>-1</td>\n",
       "      <td>95</td>\n",
       "      <td>volvo</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>109.1</td>\n",
       "      <td>...</td>\n",
       "      <td>173</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.58</td>\n",
       "      <td>2.87</td>\n",
       "      <td>8.8</td>\n",
       "      <td>134</td>\n",
       "      <td>5500</td>\n",
       "      <td>18</td>\n",
       "      <td>23</td>\n",
       "      <td>21485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>-1</td>\n",
       "      <td>95</td>\n",
       "      <td>volvo</td>\n",
       "      <td>diesel</td>\n",
       "      <td>turbo</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>109.1</td>\n",
       "      <td>...</td>\n",
       "      <td>145</td>\n",
       "      <td>idi</td>\n",
       "      <td>3.01</td>\n",
       "      <td>3.40</td>\n",
       "      <td>23.0</td>\n",
       "      <td>106</td>\n",
       "      <td>4800</td>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "      <td>22470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>-1</td>\n",
       "      <td>95</td>\n",
       "      <td>volvo</td>\n",
       "      <td>gas</td>\n",
       "      <td>turbo</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>109.1</td>\n",
       "      <td>...</td>\n",
       "      <td>141</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.78</td>\n",
       "      <td>3.15</td>\n",
       "      <td>9.5</td>\n",
       "      <td>114</td>\n",
       "      <td>5400</td>\n",
       "      <td>19</td>\n",
       "      <td>25</td>\n",
       "      <td>22625</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     0   1      2       3      4     5      6    7      8      9   ...     16  \\\n",
       "200  -1  95  volvo     gas    std  four  sedan  rwd  front  109.1  ...    141   \n",
       "201  -1  95  volvo     gas  turbo  four  sedan  rwd  front  109.1  ...    141   \n",
       "202  -1  95  volvo     gas    std  four  sedan  rwd  front  109.1  ...    173   \n",
       "203  -1  95  volvo  diesel  turbo  four  sedan  rwd  front  109.1  ...    145   \n",
       "204  -1  95  volvo     gas  turbo  four  sedan  rwd  front  109.1  ...    141   \n",
       "\n",
       "       17    18    19    20   21    22  23  24     25  \n",
       "200  mpfi  3.78  3.15   9.5  114  5400  23  28  16845  \n",
       "201  mpfi  3.78  3.15   8.7  160  5300  19  25  19045  \n",
       "202  mpfi  3.58  2.87   8.8  134  5500  18  23  21485  \n",
       "203   idi  3.01  3.40  23.0  106  4800  26  27  22470  \n",
       "204  mpfi  3.78  3.15   9.5  114  5400  19  25  22625  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To assign headers to a dataframe, make a list of strings for headers and use the command df.columns = headers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "headers = [\"symboling\",\"normalized-losses\",\"make\",\"fuel-type\",\"aspiration\", \"num-of-doors\",\"body-style\",\n",
    "         \"drive-wheels\",\"engine-location\",\"wheel-base\", \"length\",\"width\",\"height\",\"curb-weight\",\"engine-type\",\n",
    "         \"num-of-cylinders\", \"engine-size\",\"fuel-system\",\"bore\",\"stroke\",\"compression-ratio\",\"horsepower\",\n",
    "         \"peak-rpm\",\"city-mpg\",\"highway-mpg\",\"price\"]\n",
    "df.columns = headers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>make</th>\n",
       "      <th>fuel-type</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>num-of-doors</th>\n",
       "      <th>body-style</th>\n",
       "      <th>drive-wheels</th>\n",
       "      <th>engine-location</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>...</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>fuel-system</th>\n",
       "      <th>bore</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compression-ratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>?</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>13495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>?</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>?</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>94.5</td>\n",
       "      <td>...</td>\n",
       "      <td>152</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>9.0</td>\n",
       "      <td>154</td>\n",
       "      <td>5000</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.8</td>\n",
       "      <td>...</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>4wd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.4</td>\n",
       "      <td>...</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115</td>\n",
       "      <td>5500</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   symboling normalized-losses         make fuel-type aspiration num-of-doors  \\\n",
       "0          3                 ?  alfa-romero       gas        std          two   \n",
       "1          3                 ?  alfa-romero       gas        std          two   \n",
       "2          1                 ?  alfa-romero       gas        std          two   \n",
       "3          2               164         audi       gas        std         four   \n",
       "4          2               164         audi       gas        std         four   \n",
       "\n",
       "    body-style drive-wheels engine-location  wheel-base  ...    engine-size  \\\n",
       "0  convertible          rwd           front        88.6  ...            130   \n",
       "1  convertible          rwd           front        88.6  ...            130   \n",
       "2    hatchback          rwd           front        94.5  ...            152   \n",
       "3        sedan          fwd           front        99.8  ...            109   \n",
       "4        sedan          4wd           front        99.4  ...            136   \n",
       "\n",
       "   fuel-system  bore  stroke compression-ratio horsepower  peak-rpm city-mpg  \\\n",
       "0         mpfi  3.47    2.68               9.0        111      5000       21   \n",
       "1         mpfi  3.47    2.68               9.0        111      5000       21   \n",
       "2         mpfi  2.68    3.47               9.0        154      5000       19   \n",
       "3         mpfi  3.19    3.40              10.0        102      5500       24   \n",
       "4         mpfi  3.19    3.40               8.0        115      5500       18   \n",
       "\n",
       "  highway-mpg  price  \n",
       "0          27  13495  \n",
       "1          27  16500  \n",
       "2          26  16500  \n",
       "3          30  13950  \n",
       "4          22  17450  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h5>Writing the modified data to a csv file</h5>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = \"C:/Users/91963/Desktop/output.csv\"\n",
    "df.to_csv(path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h5>Reading and writing the data in other formats</h5>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar to csv. For reading use, <b>pd.read_xxx()</b> and for writing, use <b>df.to_xxx()</b> where xxx = csv, json, excel, sql"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Analysing the data</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before starting the analysis, check the data type, data distribution, and potential issues with the data. </br></br>\n",
    "<b>Pandas data types:</b> object (numbers and strings), int64, float64, datetime64 (time data)</br>\n",
    "<b>Corresponding native Python data types:</b> string, int, float, datetime (tobe loaded from the datetime module)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To check the data type of all columns in a dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "symboling              int64\n",
       "normalized-losses     object\n",
       "make                  object\n",
       "fuel-type             object\n",
       "aspiration            object\n",
       "num-of-doors          object\n",
       "body-style            object\n",
       "drive-wheels          object\n",
       "engine-location       object\n",
       "wheel-base           float64\n",
       "length               float64\n",
       "width                float64\n",
       "height               float64\n",
       "curb-weight            int64\n",
       "engine-type           object\n",
       "num-of-cylinders      object\n",
       "engine-size            int64\n",
       "fuel-system           object\n",
       "bore                  object\n",
       "stroke                object\n",
       "compression-ratio    float64\n",
       "horsepower            object\n",
       "peak-rpm              object\n",
       "city-mpg               int64\n",
       "highway-mpg            int64\n",
       "price                 object\n",
       "dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "</br>To check the statistical summary (data distribution) of each column of the dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>symboling</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>curb-weight</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>compression-ratio</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.834146</td>\n",
       "      <td>98.756585</td>\n",
       "      <td>174.049268</td>\n",
       "      <td>65.907805</td>\n",
       "      <td>53.724878</td>\n",
       "      <td>2555.565854</td>\n",
       "      <td>126.907317</td>\n",
       "      <td>10.142537</td>\n",
       "      <td>25.219512</td>\n",
       "      <td>30.751220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.245307</td>\n",
       "      <td>6.021776</td>\n",
       "      <td>12.337289</td>\n",
       "      <td>2.145204</td>\n",
       "      <td>2.443522</td>\n",
       "      <td>520.680204</td>\n",
       "      <td>41.642693</td>\n",
       "      <td>3.972040</td>\n",
       "      <td>6.542142</td>\n",
       "      <td>6.886443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-2.000000</td>\n",
       "      <td>86.600000</td>\n",
       "      <td>141.100000</td>\n",
       "      <td>60.300000</td>\n",
       "      <td>47.800000</td>\n",
       "      <td>1488.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>94.500000</td>\n",
       "      <td>166.300000</td>\n",
       "      <td>64.100000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>2145.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>8.600000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>173.200000</td>\n",
       "      <td>65.500000</td>\n",
       "      <td>54.100000</td>\n",
       "      <td>2414.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>102.400000</td>\n",
       "      <td>183.100000</td>\n",
       "      <td>66.900000</td>\n",
       "      <td>55.500000</td>\n",
       "      <td>2935.000000</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>9.400000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>120.900000</td>\n",
       "      <td>208.100000</td>\n",
       "      <td>72.300000</td>\n",
       "      <td>59.800000</td>\n",
       "      <td>4066.000000</td>\n",
       "      <td>326.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>54.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        symboling  wheel-base      length       width      height  \\\n",
       "count  205.000000  205.000000  205.000000  205.000000  205.000000   \n",
       "mean     0.834146   98.756585  174.049268   65.907805   53.724878   \n",
       "std      1.245307    6.021776   12.337289    2.145204    2.443522   \n",
       "min     -2.000000   86.600000  141.100000   60.300000   47.800000   \n",
       "25%      0.000000   94.500000  166.300000   64.100000   52.000000   \n",
       "50%      1.000000   97.000000  173.200000   65.500000   54.100000   \n",
       "75%      2.000000  102.400000  183.100000   66.900000   55.500000   \n",
       "max      3.000000  120.900000  208.100000   72.300000   59.800000   \n",
       "\n",
       "       curb-weight  engine-size  compression-ratio    city-mpg  highway-mpg  \n",
       "count   205.000000   205.000000         205.000000  205.000000   205.000000  \n",
       "mean   2555.565854   126.907317          10.142537   25.219512    30.751220  \n",
       "std     520.680204    41.642693           3.972040    6.542142     6.886443  \n",
       "min    1488.000000    61.000000           7.000000   13.000000    16.000000  \n",
       "25%    2145.000000    97.000000           8.600000   19.000000    25.000000  \n",
       "50%    2414.000000   120.000000           9.000000   24.000000    30.000000  \n",
       "75%    2935.000000   141.000000           9.400000   30.000000    34.000000  \n",
       "max    4066.000000   326.000000          23.000000   49.000000    54.000000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " 25, 50, and 75% refer to the quartiles. </br> Note that only the columns with numbers are summarised. To include all columns, even the ones containing objects (ie strings), use:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>make</th>\n",
       "      <th>fuel-type</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>num-of-doors</th>\n",
       "      <th>body-style</th>\n",
       "      <th>drive-wheels</th>\n",
       "      <th>engine-location</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>...</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>fuel-system</th>\n",
       "      <th>bore</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compression-ratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>205.000000</td>\n",
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       "      <td>205</td>\n",
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       "      <td>205</td>\n",
       "      <td>205</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>52</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8</td>\n",
       "      <td>39</td>\n",
       "      <td>37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60</td>\n",
       "      <td>24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>?</td>\n",
       "      <td>toyota</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.62</td>\n",
       "      <td>3.40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68</td>\n",
       "      <td>5500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>41</td>\n",
       "      <td>32</td>\n",
       "      <td>185</td>\n",
       "      <td>168</td>\n",
       "      <td>114</td>\n",
       "      <td>96</td>\n",
       "      <td>120</td>\n",
       "      <td>202</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94</td>\n",
       "      <td>23</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19</td>\n",
       "      <td>37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.834146</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98.756585</td>\n",
       "      <td>...</td>\n",
       "      <td>126.907317</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.142537</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.219512</td>\n",
       "      <td>30.751220</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.245307</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.021776</td>\n",
       "      <td>...</td>\n",
       "      <td>41.642693</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.972040</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.542142</td>\n",
       "      <td>6.886443</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>86.600000</td>\n",
       "      <td>...</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.600000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>102.400000</td>\n",
       "      <td>...</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.400000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>120.900000</td>\n",
       "      <td>...</td>\n",
       "      <td>326.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         symboling normalized-losses    make fuel-type aspiration  \\\n",
       "count   205.000000               205     205       205        205   \n",
       "unique         NaN                52      22         2          2   \n",
       "top            NaN                 ?  toyota       gas        std   \n",
       "freq           NaN                41      32       185        168   \n",
       "mean      0.834146               NaN     NaN       NaN        NaN   \n",
       "std       1.245307               NaN     NaN       NaN        NaN   \n",
       "min      -2.000000               NaN     NaN       NaN        NaN   \n",
       "25%       0.000000               NaN     NaN       NaN        NaN   \n",
       "50%       1.000000               NaN     NaN       NaN        NaN   \n",
       "75%       2.000000               NaN     NaN       NaN        NaN   \n",
       "max       3.000000               NaN     NaN       NaN        NaN   \n",
       "\n",
       "       num-of-doors body-style drive-wheels engine-location  wheel-base  ...   \\\n",
       "count           205        205          205             205  205.000000  ...    \n",
       "unique            3          5            3               2         NaN  ...    \n",
       "top            four      sedan          fwd           front         NaN  ...    \n",
       "freq            114         96          120             202         NaN  ...    \n",
       "mean            NaN        NaN          NaN             NaN   98.756585  ...    \n",
       "std             NaN        NaN          NaN             NaN    6.021776  ...    \n",
       "min             NaN        NaN          NaN             NaN   86.600000  ...    \n",
       "25%             NaN        NaN          NaN             NaN   94.500000  ...    \n",
       "50%             NaN        NaN          NaN             NaN   97.000000  ...    \n",
       "75%             NaN        NaN          NaN             NaN  102.400000  ...    \n",
       "max             NaN        NaN          NaN             NaN  120.900000  ...    \n",
       "\n",
       "        engine-size  fuel-system  bore  stroke compression-ratio horsepower  \\\n",
       "count    205.000000          205   205     205        205.000000        205   \n",
       "unique          NaN            8    39      37               NaN         60   \n",
       "top             NaN         mpfi  3.62    3.40               NaN         68   \n",
       "freq            NaN           94    23      20               NaN         19   \n",
       "mean     126.907317          NaN   NaN     NaN         10.142537        NaN   \n",
       "std       41.642693          NaN   NaN     NaN          3.972040        NaN   \n",
       "min       61.000000          NaN   NaN     NaN          7.000000        NaN   \n",
       "25%       97.000000          NaN   NaN     NaN          8.600000        NaN   \n",
       "50%      120.000000          NaN   NaN     NaN          9.000000        NaN   \n",
       "75%      141.000000          NaN   NaN     NaN          9.400000        NaN   \n",
       "max      326.000000          NaN   NaN     NaN         23.000000        NaN   \n",
       "\n",
       "        peak-rpm    city-mpg highway-mpg price  \n",
       "count        205  205.000000  205.000000   205  \n",
       "unique        24         NaN         NaN   187  \n",
       "top         5500         NaN         NaN     ?  \n",
       "freq          37         NaN         NaN     4  \n",
       "mean         NaN   25.219512   30.751220   NaN  \n",
       "std          NaN    6.542142    6.886443   NaN  \n",
       "min          NaN   13.000000   16.000000   NaN  \n",
       "25%          NaN   19.000000   25.000000   NaN  \n",
       "50%          NaN   24.000000   30.000000   NaN  \n",
       "75%          NaN   30.000000   34.000000   NaN  \n",
       "max          NaN   49.000000   54.000000   NaN  \n",
       "\n",
       "[11 rows x 26 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, for object columns, some other parameters are calculated. </br>\n",
    "<b>unique</b> = no. of unique objects in the column</br>\n",
    "<b>top</b> = most frequently occurring object</br>\n",
    "<b>freq</b> = no. of occurrences of the top object</br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get a concise summary of the DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 205 entries, 0 to 204\n",
      "Data columns (total 26 columns):\n",
      "symboling            205 non-null int64\n",
      "normalized-losses    205 non-null object\n",
      "make                 205 non-null object\n",
      "fuel-type            205 non-null object\n",
      "aspiration           205 non-null object\n",
      "num-of-doors         205 non-null object\n",
      "body-style           205 non-null object\n",
      "drive-wheels         205 non-null object\n",
      "engine-location      205 non-null object\n",
      "wheel-base           205 non-null float64\n",
      "length               205 non-null float64\n",
      "width                205 non-null float64\n",
      "height               205 non-null float64\n",
      "curb-weight          205 non-null int64\n",
      "engine-type          205 non-null object\n",
      "num-of-cylinders     205 non-null object\n",
      "engine-size          205 non-null int64\n",
      "fuel-system          205 non-null object\n",
      "bore                 205 non-null object\n",
      "stroke               205 non-null object\n",
      "compression-ratio    205 non-null float64\n",
      "horsepower           205 non-null object\n",
      "peak-rpm             205 non-null object\n",
      "city-mpg             205 non-null int64\n",
      "highway-mpg          205 non-null int64\n",
      "price                205 non-null object\n",
      "dtypes: float64(5), int64(5), object(16)\n",
      "memory usage: 41.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Data Wrangling</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Preprocessing the data in Python</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<ul>\n",
    "<li>Identify and handle missing values</li>\n",
    "<li>Data formatting</li>\n",
    "<li>Data normalization</li>\n",
    "<li>Data binning</li>\n",
    "<li>Turning categorical values to numeric variables</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Simple DataFrame operations</h4>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To add 1 to each value in the first column of \"Symboling\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"symboling\"] = df[\"symboling\"]+1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Dealing with missing values</h4>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A missing value would appear as 0, Nan, ?, or a blank cell. Methods to deal with missing values include:\n",
    "<ol>\n",
    "<li>Check with the data collection source</li>\n",
    "<li>Drop the data</li>\n",
    "    <ol>\n",
    "        <li> drop the entire variable</li>\n",
    "        <li> drop just the missing data entry</li>\n",
    "    </ol>\n",
    "<li>Replace the missing data</li>\n",
    "    <ol>\n",
    "        <li> replace with the average of the column - for numerical variables</li>\n",
    "        <li> replace with the most frequent item - for objects</li>\n",
    "        <li> replace it based on other functions</li>\n",
    "    </ol>\n",
    "<li>Leave it as missing</li>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h5>Dropping the missing values</h5>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "axis = 0 drops all rows with missing values, axis = 1 drops all columns with missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.dropna(subset=[\"price\"], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(205, 26)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instead of dropna, <b>replace</b> function can also be used. To replace missing values in the column \"engine-size\" with the mean of the column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "mean = df[\"engine-size\"].mean()\n",
    "df[\"engine-size\"] = df[\"engine-size\"].replace(np.nan, mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"price\"] = df[\"price\"].replace('?',df[\"price\"][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Data Formatting</h4>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bringing data to a common standard of expression for meaningful comparison. For example, units, abbreviations, variable names, variable codes, data shapes etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Formatted data is:\n",
    "<ul>\n",
    "    <li>More clear</li>\n",
    "    <li>Easy to aggregate</li>\n",
    "    <li>Easy to compare</li>\n",
    "<ul/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert \"<b>city-mpg</b>\" in the units of mpg to L/100km:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"city-mpg\"] = 235/df[\"city-mpg\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rename column name:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(columns = {\"city-mpg\":\"city-L/100km\"}, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>inplace</b> dictates changing the original DataFrame. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sometimes, incorrect datatype is assigned to features. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "203    22470\n",
       "204    22625\n",
       "Name: price, dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"price\"].tail(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To identify datatypes of all columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "symboling              int64\n",
       "normalized-losses     object\n",
       "make                  object\n",
       "fuel-type             object\n",
       "aspiration            object\n",
       "num-of-doors          object\n",
       "body-style            object\n",
       "drive-wheels          object\n",
       "engine-location       object\n",
       "wheel-base           float64\n",
       "length               float64\n",
       "width                float64\n",
       "height               float64\n",
       "curb-weight            int64\n",
       "engine-type           object\n",
       "num-of-cylinders      object\n",
       "engine-size            int64\n",
       "fuel-system           object\n",
       "bore                  object\n",
       "stroke                object\n",
       "compression-ratio    float64\n",
       "horsepower            object\n",
       "peak-rpm              object\n",
       "city-L/100km         float64\n",
       "highway-mpg            int64\n",
       "price                 object\n",
       "dtype: object"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To change the datatype:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"price\"] = df[\"price\"].astype(\"int\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "203    22470\n",
       "204    22625\n",
       "Name: price, dtype: int32"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"price\"].tail(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Data Normalization</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For comparison, change the range of the values of features. Methods include:\n",
    "<ol>\n",
    "    <li>Simple feature Scaling</li>\n",
    "    $$x_{new} = \\frac{x_{old}}{x_{max}}$$\n",
    "    <li>Min-Max</li>\n",
    "    $$x_{new}=\\frac{x_{old}-x_{min}}{x_{max}-x_{min}}$$\n",
    "    <li>Z-score or Standard Score</li>\n",
    "    $$x_{new}=\\frac{x_{old}-\\mu}{\\sigma}$$\n",
    "</ol>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To do Simple feature scaling for the column \"<b>height</b>\" using Pandas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.816054\n",
       "1    0.816054\n",
       "2    0.876254\n",
       "3    0.908027\n",
       "4    0.908027\n",
       "Name: height, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"height\"].head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"height\"] = df[\"height\"]/df[\"height\"].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To do min-max for the column \"<b>length</b>\" using Pandas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"length\"] = (df[\"length\"]-df[\"length\"].min())/(df[\"length\"].max()-df[\"length\"].min())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To do Z-score for the column \"<b>width</b>\" using Pandas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"width\"] = (df[\"width\"]-df[\"width\"].mean())/df[\"width\"].std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Binning</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Grouping of values into bins. Binning can convert numeric to categorical variable, group a set of numerical values into a set of 'bins'."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example: To classify cars according to their prices as \"Low\", \"Medium\", and \"High\", we require 3 intervals. ie, 4 equally spaced values in the range of \"Price\". "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "bins = np.linspace(df[\"price\"].min(),df[\"price\"].max(),4)\n",
    "group_names = [\"Low\", \"Medium\", \"High\"]\n",
    "df[\"price-binned\"] = pd.cut(df[\"price\"],bins,labels=group_names,include_lowest=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Low\n",
       "1    Low\n",
       "2    Low\n",
       "Name: price-binned, dtype: category\n",
       "Categories (3, object): [Low < Medium < High]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"price-binned\"].head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Converting categorical variables to quantitative variables</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Statistical models cannot use objects as inputs. Hence, they have to be chanaged to quantitative variables. Example: the column \"<b>fuel-type</b>\" has 2 categories, Diesel and Gas. To convert, two new features by the names Diesel and Gas are created with either 0 or 1 as values.\n",
    "\n",
    "This method is called \"One-hot encoding\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>diesel</th>\n",
       "      <th>gas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     diesel  gas\n",
       "200       0    1\n",
       "201       0    1\n",
       "202       0    1\n",
       "203       1    0\n",
       "204       0    1"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(df[\"fuel-type\"]).tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"diesel\"] = pd.get_dummies(df[\"fuel-type\"])[\"diesel\"]\n",
    "df[\"gas\"] = pd.get_dummies(df[\"fuel-type\"])[\"gas\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(205, 29)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Exploratory Data Analysis</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "EDA is an approach to analyze data in order to: \n",
    "<ul>\n",
    "<li>summarize main characteristics of the data</li>\n",
    "<li>gain better understanding of the data set</li>\n",
    "<li>uncover relationships between different variables</li>\n",
    "<li>extract important variables for the problem we're trying to solve.</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Question: \"<b>what are the characteristics that had the most impact on the car price?</b>\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Descriptive Statistics</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Methods iclude:\n",
    "\n",
    "1. Use the <i>describe()</i> function in Pandas\n",
    "2. use <i>value_counts()</i> for categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "wheel_counts = df[\"drive-wheels\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fwd    120\n",
       "rwd     76\n",
       "4wd      9\n",
       "Name: drive-wheels, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wheel_counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Use box-plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29aae41a908>"
      ]
     },
     "execution_count": 50,
     "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": [
    "import seaborn as sns\n",
    "sns.boxplot(data=df, x=\"drive-wheels\", y=\"price\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. Use Scatter-plots\n",
    "\n",
    "    i. Predictor/independent variable on X-axis. eg, engine-size\n",
    "    \n",
    "    ii. Target/dependent variable on Y-axis. eg, price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29aae4a4b00>"
      ]
     },
     "execution_count": 51,
     "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": [
    "sns.scatterplot(x=\"engine-size\", y=\"price\", data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>GroupBy</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Question: \"<b>How does the average price vary according to drive-wheels and body-style?</b>\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test = df[[\"drive-wheels\", \"body-style\", \"price\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_grp = df_test.groupby([\"drive-wheels\", \"body-style\"], as_index=False).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>drive-wheels</th>\n",
       "      <th>body-style</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4wd</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>10549.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4wd</td>\n",
       "      <td>sedan</td>\n",
       "      <td>12647.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4wd</td>\n",
       "      <td>wagon</td>\n",
       "      <td>9095.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>fwd</td>\n",
       "      <td>convertible</td>\n",
       "      <td>11595.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>fwd</td>\n",
       "      <td>hardtop</td>\n",
       "      <td>8249.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>fwd</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>8396.387755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>fwd</td>\n",
       "      <td>sedan</td>\n",
       "      <td>9941.035088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>fwd</td>\n",
       "      <td>wagon</td>\n",
       "      <td>9997.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>rwd</td>\n",
       "      <td>convertible</td>\n",
       "      <td>23949.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>rwd</td>\n",
       "      <td>hardtop</td>\n",
       "      <td>24202.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>rwd</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>14293.421053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>rwd</td>\n",
       "      <td>sedan</td>\n",
       "      <td>21711.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>rwd</td>\n",
       "      <td>wagon</td>\n",
       "      <td>16994.222222</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   drive-wheels   body-style         price\n",
       "0           4wd    hatchback  10549.000000\n",
       "1           4wd        sedan  12647.333333\n",
       "2           4wd        wagon   9095.750000\n",
       "3           fwd  convertible  11595.000000\n",
       "4           fwd      hardtop   8249.000000\n",
       "5           fwd    hatchback   8396.387755\n",
       "6           fwd        sedan   9941.035088\n",
       "7           fwd        wagon   9997.333333\n",
       "8           rwd  convertible  23949.600000\n",
       "9           rwd      hardtop  24202.714286\n",
       "10          rwd    hatchback  14293.421053\n",
       "11          rwd        sedan  21711.833333\n",
       "12          rwd        wagon  16994.222222"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_grp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another way to visualize is to use <i>pivot()</i> method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pivot = df_grp.pivot(index='drive-wheels', columns='body-style')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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 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 colspan=\"5\" halign=\"left\">price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>body-style</th>\n",
       "      <th>convertible</th>\n",
       "      <th>hardtop</th>\n",
       "      <th>hatchback</th>\n",
       "      <th>sedan</th>\n",
       "      <th>wagon</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>drive-wheels</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4wd</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10549.000000</td>\n",
       "      <td>12647.333333</td>\n",
       "      <td>9095.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fwd</th>\n",
       "      <td>11595.0</td>\n",
       "      <td>8249.000000</td>\n",
       "      <td>8396.387755</td>\n",
       "      <td>9941.035088</td>\n",
       "      <td>9997.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rwd</th>\n",
       "      <td>23949.6</td>\n",
       "      <td>24202.714286</td>\n",
       "      <td>14293.421053</td>\n",
       "      <td>21711.833333</td>\n",
       "      <td>16994.222222</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   price                                            \\\n",
       "body-style   convertible       hardtop     hatchback         sedan   \n",
       "drive-wheels                                                         \n",
       "4wd                  NaN           NaN  10549.000000  12647.333333   \n",
       "fwd              11595.0   8249.000000   8396.387755   9941.035088   \n",
       "rwd              23949.6  24202.714286  14293.421053  21711.833333   \n",
       "\n",
       "                            \n",
       "body-style           wagon  \n",
       "drive-wheels                \n",
       "4wd            9095.750000  \n",
       "fwd            9997.333333  \n",
       "rwd           16994.222222  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pivot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Correlation</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Question: \"<b>How does the price vary according to engine-size?</b>\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91963\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29aae51a5c0>"
      ]
     },
     "execution_count": 57,
     "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": [
    "sns.regplot(x=\"engine-size\",y=\"price\",data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Question: \"<b>How does the price vary according to engine-size?</b>\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29aae5fd9e8>"
      ]
     },
     "execution_count": 58,
     "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": [
    "sns.regplot(x=\"highway-mpg\",y=\"price\",data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Question: \"<b>How does the price vary according to peak-rpm?</b>\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"peak-rpm\"] = df[\"peak-rpm\"].replace('?',df[\"peak-rpm\"][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"peak-rpm\"]=df[\"peak-rpm\"].astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29aae6657f0>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GYRSEub+qgKMnIgQEAgF/S44IqKccPWE5nwzDqC3MUjEMwzCKhimVKmDt0lY8BU8VRfFU8dSXG4Zh1BKmVKqAWzatp7MljADxhIcAnS1hbtm0vtJdMwzDKAhTKlXAxvXL+PK1F/CusztZfkYz7zq7ky9fe4GFFBuGUXPkvVAvIucA61T1SZd2JaSqw6Xr2sLCEgkahlEP5GWpiMhv4tc42eZEK4H/U6pOGYZhGLVJvu6vm4DLgdMAqnoYsGm1YRiGkUG+SmXCVVcEUqnpF1bSMMMwDGNW8lUq/yIifwA0i8gvAf8I/N/SdcswDMOoRfJVKrcCA8BLwBbgUeAPS9UpwzAMozbJN/qrGb9eyVfBrz3vZJZHpEhYlmLDMOqBfC2V7+MrkSTNwJPF787CZNehfj6/8wD9w+N0NIfpHx7n8zsPWJZiwzBqjnyVSpOqjiS/uM8tM50gIqtE5AciclBEDojIZ5z8iyLymoi84F4fSjvnNhE5IiI/EZEPpsk3OdkREbk1Tb5GRJ4RkcMi8i1XVrjm2La7h3BQaGkIIeK/h4PCtt09le6aYRhGQeSrVEZF5KLkFxG5GBib5Zw48Huqei5wGXCTiJznjt2lqhe616PumufhlxA+H9gE/I2IBJ2r7a+BK4HzgOvTrnOnu9Y6YAj4ZJ7jqSp6hyI0h4MZsuZwkL4h8y4ahlFb5Lum8jvAP4rI6+77cuCXZzpBVY8Bx9znYRE5CKyY4ZSrge2qOgEcdbXqL3HHjqhqD4CIbAeudte7AvgV1+ZB4IvAPXmOqaQUskayqrOF/uFxWhomf46xWIKVnTMag4ZhGFVHXpaKqu4F1gOfAn4bOFdV9+d7ExFZDbwLeMaJbhaRH4nI/SLS6WQrgN600/qcLJd8CXBSVeNT5Nnuv1lE9onIvoGBgXy7PWcKXSPZsmEtsYQSicZR9d9jCWXLhrUl76thGEYxmVGpiMgV7v2/AP8ZeDuwDvjPTjYrItIG/BPwO6p6Gt+SeCtwIb4l8+fJpllO1znIpwtV71XVblXt7urqyqfb86LQNZKN65ex9arzWdbexKmxGMvam9h61fkW/WUYRs0xm/vrPwFP4SuUqSjw7ZlOFpEwvkL5B1X9NoCqvpl2/KvAI+5rH7Aq7fSVQNLdlk1+HOgQkZCzVtLbV5TeoQgdzeEM2WxrJJZQ0jCMemBGpaKqXxCRAPCYqj5cyIVFRICvAQdV9S/S5MvdegvAR4Efu887gW+IyF8Ab8G3iJ7Ft0jWicga4DX8xfxfUVUVkR8A1wLbgRuB7xbSx1JhayRGObE9TkY1Meuaiqp6wM1zuPblwMeBK6aED/+piLwkIj8CfhH4H+4+B4CHgZeB7wE3qWrCWSE3A48DB4GHXVuAW4DfdYv6S/CVWMWxNRKjXNgeJ6PaENXZ80KKyB/hhxB/CxhNylV1sHRdKw3d3d26b9++kt8nOXvsG4qw0maPRom4/t4906ziSDTOsvYmvrn5sgr2zKg3RGS/qnbP1i7fkOJfx19D+e0pcpt658DWSIxyMJf1O8MoJflufjwPfwPii8ALwF/hb1I0DKOCrOpsYSyWyJDZ+p1RSfJVKg8C5wJ34yuUc53MMIwKYut3RrWRr/vrHap6Qdr3H4jIi6XokGEsZAqN5Nq4fhlbwdbvjKohX6XyvIhcpqp7AETkUuDfStcto96wsNfZSUZyhYOSEcm1FWZVLPb/0qgW8nV/XQr8u4i8IiKvAE8D/yktNNgwcmJhr/lh2aqNeiBfS2VTSXth1DXpD0uAloYQkWicbbt7bIadxlwjucwKNKqJvJSKqv6s1B0x6hcLe82PuWRimKvLzDBKRb7uL8OYMxb2mh9zieQyl5lRbZhSMUqOhb3mx1yyVfcORYgnPHoGRjj0xml6BkaIJzyzAo2Kke+ailEg5ueexMJe86fQSK62hiBHBkYJihAUIZ5QXjs5ztu6WkvYS8PIjSmVErDrUD+f2/Eiw+Nx4p7H8eEJPrfjRb587QUL9kFqYa+lwU8Gjp/LO1lhSNPkhlFmzP1VAu783iGGIjEUCAUDKDAUiXHn9w5VumtGnTE8EWdFRxOhgJDwlFBAWNHRxMhEfPaTDaMEmKVSAnqOjxIQCLjZogioKD3HR2c50zAKIxkxtrarLSVLZik2jEpgloph1DAWBGFUGyVTKiKySkR+ICIHReSAiHzGyReLyBMicti9dzq5iMjdInJERH4kIhelXetG1/6wiNyYJr/Y7eo/4s6tCkfymiUteAqep6gqnqd46ssNo5jMJWLMMEpJKd1fceD3VPU5EWkH9ovIE8CvAd9X1TtE5FbgVvwKjlfilxBeh58W5h7gUhFZDHwB6Mav6bJfRHaq6pBrsxnYAzyKv/P/sRKOKS9uvfJcPrvjRUYm4iQ8JRgQOhrD3HrluZXumlGHWBCEUU2UTKm4OvTH3OdhETkIrACuBja6Zg8Cu/CVytXAQ+qXotwjIh0isty1fSJZZdIppk0isgtYpKpPO/lDwDVUgVLZuH4Zf3btBRZCW2dYmLhhzE5ZFupFZDXwLuAZ4EyncFDVYyKS/KtcAfSmndbnZDPJ+7LIqwKbPdYXlg7FMPKj5Av1ItIG/BPwO6p6eqamWWQ6B3m2PmwWkX0ism9gYGC2LhvGNCwdimHkR0mVioiE8RXKP6jqt534TefWwr0n85/3AavSTl8JvD6LfGUW+TRU9V5V7VbV7q6urvkNyliQ9A5FaA4HM2SWFNMwplMy95eLxPoacFBV/yLt0E7gRuAO9/7dNPnNIrIdf6H+lHOPPQ78STJKDPgAcJuqDorIsIhchu9WuwG/1LFh5EUhayRzySBc6D3KMQ7DKDWltFQuBz4OXCEiL7jXh/CVyS+JyGHgl9x38KO3eoAjwFeB3wZwC/S3A3vda2ty0R74FHCfO+c/qIJFeqM2KLRw2Fz2g5SjOJkVQDOqDfGDrRYO3d3dum/fvkp3w6gw19+7Z5rlkdyJ/s3Nl2U9J2kR5BvRN5d7zGUcR4+PMDweJ5rwaAgGaG8KsWZpW9HuYRgAIrJfVbtna2dpWuaJuR6qh0J+i96hCEGBnoGR1MN4aVtDXmsk+U7DylGc7Kdvnub0eJwAk1mKT4xGiSdmiokxjNJhaVrmgbkeqodCf4v2xhCvnRwn7janxj0/ZXxbY/Z51lx+63IUJ4sl/IwNMc9jIu4R8zw8T4kmcqu+XYf6uf7ePbz3zqe4/t499u/VKCqmVPIk2x+ihZlWD4X+Fim3r6a90uXzvD6ULy9XQiE1HPW/58ImQkapMfdXHuTa+DY6EWP5Gc0ZbS3MNDuldhMW6moaiSZY0dHE8ZFoyv11Vlsjo9FE1vZzcWWVqzhZQMDTzO+5SFeOAC0NISLRONt295jb1igKC06pqPqz0UJyT+b6Q4wllLFYouAw04VGOXajFxryW2jK+LmGFJc6s4KqZigU8BVMLourHOs8xsJmwbm/ogmPo8dH6R2McOzUGMdHJjgViTkl4WU9J9fGt4ZQwNKO50E53ISFuppK3b5c5HJ15ZKXY53HWNgsOEslSSzhEUvAGJl/YCJCKCA0hAKEAkI4FOAtZzTz6uAIw+OJlKtkUXOIdcva2bJhrSWOnIVyzI4LdTXNpf21fSe574dHGY0maG0I8hvvXVPx3zoaz+6uyyXfsmEtn995gEg0TnM4yFgsURXK0agfFqxSyYWqEktohtVy/vJ29r4ymAoljSUSRKIJrr1oJZevW1rxB0u1M1fXUaEU6moqpP2uQ/3seO41utobOds9jHc89xo/v7KjqL9/oWtPwUAAVc+PNVC/yqg4eTbKtc5jofYLF1MqefAvh49nlf+/l97goxetJCC+ZdMQChAOBAgFhVBQaAgGClq7qVfKNTu++8mfTrMkPv3+txfl2uVY4J7L2tOaJS0cGRglJOKXrVZIqM5YEK7U6zyW0XlhY0olD3qHIoQCEEib/XmeR69z33iqjMcSjMemuxzCQadsUu8LT9mUY3Z895M/5a4nD6esydPjce568jBAURTLfDZL5su23T3EEglOjMQz3KwzKa5qLAg3l3EY9YMplRLjr91kBgCkr9uEg4GUVRMOBgjOFA9aw5R6dvxXPzgybae7OnkxlEpbQ5AjA6MEZXLn+msnx3lbV+u8r53kcP8wpyIxAgFJbcg8PhwllhjOeU41FoSbyziM+sGUSh6s6mjmZ4MR8DTlYvAUzulsnv3kLGRbt0kSED84IBwQQkFn2YQCC866KZRYjnCnXPJCERE8T0mITq5dKEX9TaJxD8T/N+DfEzxRXz4D1VYQbq7jMOoDUyp5sHnDW7nz8UOMRuN4nhIICIsawmze8Nai38tTZSKWYCLLsamutFDAt3hCwQUXGV52+ofHkQBo8rmoIAEYGB4v2j3CQWE06rtSk1XoAgFoCNbWZCIcFMZi4KVNwqD2xmHMDVMqeXDJ2sXc8sH1bN/byxunxzhrUTPXvXsVl6xdXNZ+ZHOlwaQ7LRQUQoHJUOhQQOrapVZOYgkl/X+9AgmPGXNsFcqy9iaGRmO+NnFaRT3oyrEhs1p5+5mLsmRODrNmadvsJ+eJRZdVL6ZU8uSStYvLrkTyZdKdBjA9WCAYmHShhZPvdaZsPnrhcr7zwrGs8mIw236QYjzkVH0rODglkqvWylMko/3OOiNUkmg/iy6rbkypLAASnjIWTUzb6Bl0C6nhYNKVFiAYlJq0cK6+cCWPv9xPJC13V0tDkKsvXDnDWfkTy7EcEPOK95ArNB9ZkmqbtZc62s/yl1U3pSwnfD/wEaBfVd/pZF8EfhMYcM3+QFUfdcduAz6JP9X+tKo+7uSbgK8AQeA+Vb3DydcA24HFwHPAx1U1Wqrx1CMJT0l4uRdQg+nBAs66Cbvv1RY0sG13D8vPaJpWEKtYD5qktSDJ/6hLbqxatBDaQvORQfXO2ksZPGD5y6qbUq7wPgBsyiK/S1UvdK+kQjkPuA44353zNyISFJEg8NfAlcB5wPWuLcCd7lrrgCF8hWQUkYTnBw2MjMcZHI3y5ulx+oYiqdxpb5wa58TIBKfG/Nxp0bhXMVdN71CEeMKjZ2CEQ2+cpmdghHjCK9qDpq0xRDDgRzLhor+CAV9+uH+Y48PRjNosx4ejHO4vLIR2LvnFFmL5BctfVt2UzFJR1d0isjrP5lcD21V1AjgqIkeAS9yxI6raAyAi24GrReQgcAXwK67Ng8AXgXtmu9GrgxFu+/ZLtDeFaGsM0dYUor0xRFtTmLbGUIa8rTFEa0Ow6mbl1UCuoAHADxYISsqqCQVLH6nW3hjicP9IyqWXLLq1bllxFod/471r+MpTRwgGJlPNe+rL7/vh0aKE0M7FbbQQZ+2Wv6y6qcSays0icgOwD/g9VR0CVgB70tr0ORlA7xT5pcAS4KSqxrO0n4aIbAY2AzSc9TaeOTqYd2cD4s9GW7MonPbU53DqWHtTZtvwAgz3jXsecY+sGQaSkWrBQNq7W7+Zj+JRt6AdjetkOK7kTgFfKJ9+/9s5enyEnT96g1jCt0iu+vmz+PT7386DT79StBDaQt1G5cqrVk2UK3+ZMTfKrVTuAW7Hd0ffDvw58Os4V/UUlOzuueQzI5s8K6p6L3AvwFvWna8fu3glIxNxhsfjjEzEGRmPMzwRY2QizuhE5oPQUz/lx+nxOMdO5THCKTSFArQ6a2hS4YRTSin9WIbCavIjZ+rNSsqMVMtOeoh0MCCT+dQCARdYMH1NZ2BkIuuO+uMj2Xb8FM6uQ/3sf/UUq5e0pGbH+189xa5D/WUJoc3FQp21V9uGT2OSsioVVX0z+VlEvgo84r72AavSmq4EXnefs8mPAx0iEnLWSnr7GVnS2sinNubetJjwlNEJp2xSCif9PcbIRILh8ViqzfD45PHElIpJ43GP8ZEoJ0YKjyFIWkm+iy6coXDaGkNZ3XXpx2p1U2Q+iieYsnZ8hTMR9wgIGWOOu7rtxWCmiKNSh9DOhM3ajbngeYqnmspurWhGSerk92QwSkMo/2dJWZWKiCxX1eRmgo8CP3afdwLfEJG/AN4CrAOexbdI1rlIr9fwF/N/RVVVRH4AXIsfAXYj8N1i9DEYEBY1h1k0xU/9bM8g3z/Yz7HTYyyGdoTyAAAgAElEQVTPsflRVRmPe4xMs4ASjIzHJi2jKcoqKYtEc1tJUPjObQFCQWFJayNd7Y20N01aS1MVUFvKYvKtqKZwdaeFSUWu4SuNcDDAeDRBQtNcUOormZ+dGCUgk5ZPQPxXUAQJ4L777yJMHk8LqZ5p7aJaHuy1tZuldklW29S0h7KnmvEwznhg6+QDOr1d8gfT1HUzH+6p+xXQr/RzpykL5u4O7mxpyLttKUOKvwlsBJaKSB/wBWCjiFyIP75XgC0AqnpARB4GXgbiwE2qmnDXuRl4HD+k+H5VPeBucQuwXUS+BDwPfK1UY3m2Z5CvPHWYUEBY1BTixOgEX3nqMJ9hXYZiERGaw0Gaw0G62hsLvk/C05SSGZ7wldBomjWU/D7sFFL6sZEsVpLi7wR/4/Q4b5wuTCkFA5K5RpSuiLJYRVOtqXLvcTlncSuvnRxlZCJBLOERDgZoaw6xoqPVV0DMbPnkImkRLWtv5MTIBM0NoZTvdSyW4MxFTZyKxLhodSf3rr44QxGPTsRTSiwUmFzE99+L8/+nWkOK50ryoaea/rDVaQ/W3T8Z4Kv/Ork3Z/P/t5YN7+jyH/bkfrhq2vXUm64EPNcoXQGkv89UqtnwkYX2P+jnLrxIv/vE7oLO+d1vvciJ0YmMksJjsQRLWhv5i1++oNhdnBOqynjM43P/+CNORCYIBwJ46s/mJ+IejaEAl79taeZaUpo1NZ5rd98caWkI5nTPtTeGM4IZpgY3NIYKt5Ke7Rn087OlpYBvbQxxywfXFyUTwrM9g9z+yAEiMS+1qNcSDvBHHzl/XtfPsI4Cwu07f8w/H+zHU9/9uen8Zdx+zc+nHqopqyogCPDrD+zlZydGGUlbz2lrCnHO4lbuu/HdgP/glLRlyPQHbBJJHXPvU1wj6Y2TH//9yHG+vudnHDs1zvIzmvjVS8/hPW9b4s6fbJuuKEi/JtOVRz6kT/KawgHGYx5xT/nMFeuqNutFrdPZ0sDitsb9qto9W1vbUZ8Hx06PERToHZpIzYI7W8K8cXqs0l1LISI0NwQ5EZlgUVNo2kNkeDzOzVe8Lef58YSXuUaUw0Xnrx+5daWJWOrYFCOJSNSvjtk/XPhCeTgoWdaMwtMsp3SFdWI06mabvvsrayjHPPjJG6dTCgX8B2Ek5vGTN07P60GWnBknUP5k58s8eWgg7Rg8+uN+ovEX+YMPn5f1/MNvDnNqLJ7qV9zzGItFicU9TowWJ0ghG+kP9rbGIAPDE3z5n3/CZ+Klf7Bv39tLKCCpSV5yHWv73l5TKlWAKZU8aG0I+X75gD+bjHvKm6cnOGdJ8WppFIvli5qnWVXjMY+zFs2cpj8UDNDR0kBHAb7TJKrKWCyRqYxSbrw4o0nlNBH3AxymHBufspgeSyhDkRhDkVjBfUkinnLai/GlRw9y4aqOGQMaJi2mcM4FyYf39xEMZJbpTXgeD+/v4+O/sHrO/UznqZ8M5JT/wYeznzPmMhqn4yu8Ofj6CmC2B/uzPYNs39s74xrkXDl2eoxFTZmPrqZwoKomeQsZUyr5kG7LZ7xXn+vwunev4itPHWYslshwDVz37lWznzxHkju5WxpCnDmH86Nxj9HoZBTdpLUUm241TWS2GZ2IT3uowmQW4ZGJOD88kr0cdDbCQUkFK6QrnNFowt/34mmGqygSTfDGqXHaGkO0NAZTGyDnwlRrbzY5MG0dbTZ5sZjpwZ7vGuRcmevEySgPplTyYDSW4MxFjQxFYmnur4aSzwbnwiVrF/MZ1lU8TX8hNIQCNIQaCoowSeKpEplIcO3f/hvZ8i4GBT5ywVumKaJhZzVNLeIVSyiDo1EGR6eHgHvJRYYp/Mp9zwC+x601yxpRezYLKXVsMrhhLogIQXELyExu+ix15N5MD/ZSu6cqMXEy8seUSh4k/4BWpe1SHoslWNZaeIRXOajmNP3FJiBCW1OIHJnpUYXPvG9dzvOjcY/h8Vj2taM0V97Lx07x6uDM7hWF1PnFJCDwd/92NLW2lFRS7Y0hli9qom9ozE8fExA/SsnTjH+rpWCmB/td3/9pSd1TtThxWkiYUskDmxlVP7li12aLaWsIBVjS1siSttknCF//91d4eH8fY7EEzeEg1160gv9y8UqGx+M80zPIoy8d4/joBIuawvzcijPobG2Ysmk2M/Iu31LHnsLX97w6cxuPlJ8suefmf/3fl6dZR+lWU/qxQjfKzvRgX7639O6phTRxqjVMqeSBzYwMgI//wuqsi/IHXx9mx3N9hNx+lvGYx/O9J2cMcfXzlHnODee75Pa/MsT/efH1VBnhaNwj4cGapa2Eg5KypkYn4jPWWPEUjgyMcGRgJO+xNYUDGa64GbM1OOW0emkLf/zRd07bKGuTsIWNKZUsJPcOJMNyReDydUu5fN1S0l3VIjItBj/5Obk3YHKzW+aehGRdjvR7OBHijovzjafv9k6/Zi5yxQ+kbyFLDzlOv17GOKYsgWcemyTV17S+JResM3YQp90362Y0twt+cjfy9L0S2Ta3ifhrJ9km/uUoi759by/xRIKTkbSNl43BGdcQRITGcJDGcJClzkp654ozOHf5otTk5ezO1pyTl2Q6odu+/RKDo1FCQcHz/EqRE/EEjaEgl65dnCUcPEc6oZjHeGyCgTnkSktulE1XOF1tjbx2cozB0SiLmsJctnYxkVicva8MpvYqJdvPZaNsKaPLjPmx4JRKQzDAqsUtqYc3TG5pSD7EjeJz95M/5b4fHmU0mqC1IchvvHcNn37/24t2/bd0NNM7NN1nv/yMJlYvaU0pqXSlNNtOa0/9h3f6rmvPrVmkl/r92eAow2MxJC3kfGg0RtwbLXgc+bp1kumEBiNRggEYHI1m7KHyFP5Hjv+/6emEhrO45dKj8DKCG3JslE14yqmxGKfGsoeAj0YTfOeF1/nOC9nT8zWHg3m56JKyV45H+Mazr/qReo3BokeXGfNjwSkVERZkOvpKcveTP+UrTx3xEz4G/CCHrzx1BKBoiqW1IUhQmBYF1dYYIpCaCRd/wqCqxBMeCATTzM24KLGER2tjKJWnLJnhoJjMZQ/VfNMJ5doom1U5pefAy7FRdiyWYCw2t42y4P9/F4Gtj7zM+SsWZSqnHLWS2l05i1oqmV0rLDilUmyqrT54NXLfD4/ieUr6KoA4ebGUykg0wcrO5oz67kvbGmat7z5fRISGUIDxuIcqqWSWIkJTKMCZizJLASetpbjnkfCUuKd47j35PZHQlBU0KxXYQ1WMjbIZ2b+zJVqdslE2eWzqRlnwLUYU4l6Cva8MFdSf1obgtFpJ7cn6SDlz3Ll0QmmBCMYkplTmwa5D/Xxux4sMj8eJex7Hhyf43I4X+fK1F5hiSWN4fPoGRXXyYrGqs4VXTmQuTEcTHquXlL6mSSH1VPx9JRAMzP5A8pWMl1orSSqblEJKKJEa2kMFmRtl5/IXEkt4/O63XuT1UxEiEwliLs9bY0hob2xgw9uX5twkm81KGo0mGJ1nOiF/s2xw0iKaGtwwZb9Se2N43htlqxlTKvPgzu8dYigSS1UvVIWhSIw7v3eookqlWNZTsa6Ta85czLn0e9Yu5tlXBl1Qg69Q+oejXP/u0vvYS1VPxc+QPLPyWbO0jTdPj/HWrkb//6dCJBZnaWsjZzSHiXt+lFncy9PyqXLCwQCXrO7kwT2nCQgEA771E4kqH7vozBlT5niqjEUTGamDJl10OdaW0qynqeWh55NOaKaNssnvM22ULaS+SbkxpTIPeo6PpiKzwLk+ROk5XvgCbbEoVir0Wkup/nTPIF1tDVOshRBP9wzy6RLfu5L1VJIKLbl3ZiyeIOHBTb/4tml7b+IJzy9+5nnE4u5zovYUzvO9p1jcEmY0Ohlt19oQ5PneU3x8hvMC4meubm0MwaLC7xuNe9MqxaZbQ5nZGtKOTcSmVZSd70bZxlAgZy67qetJU5VTS0NprSRTKnXGTBUKC3nIFes64FsO2dani7lG2jsUYWlbI13tk2sYqkrfUKR4N5mBSpW33bh+Gdf2nZwWWZetL6FggFAQmplu/cSdcokmPOIJTX1PrvMUqnRKnVCys7WBxa2ZmbhLnVCyIRRgcaiBxa2FryUlPCUSnR5dl1EbKSO/XWaF2akbZSfiHhPxKCeypBOajYCQKtSXy0XX1pgZ3LCiI/+Nq6Us0nU/8BGgX1Xf6WSLgW8Bq/GLdP1XVR0SP473K8CHgAjwa6r6nDvnRuAP3WW/pKoPOvnFwANAM/Ao8Bkt83RrzZIWjgyMIt5ktUFP4W1LS5siYyZmqlBYiesAdDaHOBGZPiPrbC7eP79VnS30D4+nlCD4UUUri5iupBqDMnYd6mfHc6/R1d7I2c71tuO51/j5lR0F9S2pcJpyLD6nWzmzKR1LKDmdYMBPVNreFGb5GYWdm75RdmoW8MzouoSzijJdd1ODVTwlZVmVglJaKg8A/xt4KE12K/B9Vb1DRG51328BrsQvIbwOuBS4B7jUKaEvAN34FuN+EdmpqkOuzWZgD75S2QQ8VsLxTOPWK8/l0998jpFoIlVUqa0hyK1XnlvObmRQrIdrUR/SOUztYu4J2rJhLZ/d8SKvnRxLFelqawzxRznqkBRKtQZlbNvdw/HhcSJpe0dawoE5WZQzMZOVA5NBBQlP+afn+mgMBWgKB1GF5gZhLGoJJedKto2yhZDcKJu7NlI8wyqaujcpXmAYfMmUiqruFpHVU8RX45cYBngQ2IWvVK4GHnKWxh4R6RCR5a7tE6o6CCAiTwCbRGQXsEhVn3byh4BrKLNSAWgIB2lwM7ZgQGiocJhh0sceicbntWhcrOuAnx4+G8UO9xXwS8GqgkpRd6VUa1DGc6+eYKpbPhLzeP7VE2XtR3pQwbHT43Q0h1OTBlUl1CQMjIyzuLUhtZYTS3hz2rdjaZMKI7lRdtEUz0M+JDfKBkU49878zin3msqZqnoMQFWPiUjyr3EF0JvWrs/JZpL3ZZGXlW27ezijOczyMybN7rmuOxSLQnzss12nWIvPuWY6hc6AZmLb7h4WNYc5q0S/hR98ocQSmtqPEhAqGpQBTFMoSUrk2ciLqVauiDARS3D24tZpe1uSbrVo3COa8F+xuOdXxJwBSyiZP/NZ30pulC2kLEW1LNRnm1TqHOTZLy6yGd9Vxtlnnz2X/mWlmOsOxaJYPnYo4uJzGWKKe4ciBAV6BkYyNj8W67fwVIl7pHK2qUJcQaR2oqbKRSFWbsqt1pBp4cdTCkaZSPjn56NsjEye7RnkzscPMeryvQ2NRrnz8VFu+eD6kinlcgc7v+ncWrj3fifvA9IdoiuB12eRr8wiz4qq3quq3ara3dXVNe9BJFnV2cLYlE1mxV4cLpT0qK3kRrNwUNi2u6difQrniKnPJZ8L7Y0hXjs5Tty5IeOe8trJ8TkXv5pKKJBMhJnMGZYprxS57l7JXm1cv4xrL1rBwPAEB98YZmB4gmsvWlFw4EBLQ4gzWsIsa29iRUczq5e2cvbiFs46o4klrY20N4VpCgct1coM3PuvPZwei6FAMCgocHosxr3/WrrnQbmVyk7gRvf5RuC7afIbxOcy4JRzkz0OfEBEOkWkE/gA8Lg7Niwil7nIsRvSrlU2tmxYy+mxGIffHObgsVMcfnOY02OxeW96mw+9Q5GMqBiovPWUKyivmMF6fvoTZSLuMR7zmHCz2mLdo6UhOO1BLfhpPipJrudpJZ+z6dbyuWe109XeyI7nXmPXof7ZT56FdGXT1d7IWzqaOWdJK+csaeUtHc2cuchXOIuaw25CFVjQSWJ7hyKTWc4RAi7reW8JnwelDCn+Jv5C+1IR6cOP4roDeFhEPgm8CnzMNX8UP5z4CH5I8ScAVHVQRG4H9rp2W5OL9sCnmAwpfowKLNKDm7EmsxtLcXeJz4VyhNYWSjRHMapc8rnw+qnxaXthPPXlxWBZexNDo7GU60sERMnYF1MRkrHs2eQVYtvuHqLxBCdGMjeilnKtcbbsA8kQ6GRutZiLVIu5NZ1a2vxZ7ZQy+uv6HIfel6WtAjfluM79wP1Z5PuAd86nj/OlGhfqixm1VSxyRfgUM2NvrkiyYkWYqSqBgKQy4qanvofK7WFpbQgyGo2nUvn75Rsqa0H99M3TnB6PE8D//xVPKCdGo8QTpyvWp+TaTTZUNSMiLRk0UA/KZlVHMz8bjMCUvXTndJZuT0/1JpCpAarR1bRx/TK2XnU+y9qbODUWY1l7E1uvOr/im/RKTakV10g0QWdLiJjnMR73iHkenS0hRqOJVEqb/uHxjJQ2xXD3zMb71neR8CZT/vs1YHx5pUju/g4ExC8y53xxxbRMi0ky03RrY4iOlgaWLWpiZWcLq5e0sLKzhTMXNbG4tSGVc6uW3GmbN7yVRc1hJOAHm0gAFjWH2bzhrSW7Z7VEf9Uk1ehqgsqlDKkkwYBkVSDFWsRtbwzxxqlx56PH7VOJs25ZE9t29xBLZLp7FjWX1t2T5I3TUTqaQ5wej6c24C5qCvHG6cLTdxSLhlCAsWjCf4glvXNKVSdBzIavbCRrv9Otmvnuuykll6xdzC0fXF/WPT2mVOZBNbqaoDrTiZSacMCfoWeTFwN1rq5oXDOKgKkqh/uHGRqNpqyFeCLBuPu3UGp6hyKEp9RMDgelotbyumXt/OSN05wci6UUXUdzmHXL2ivWp2ITDgYIBwNM3b7heS5hpwuBTn6OV1DhlHtPjymVeVCO7LSFKohdh/r57I4XGXFx6cdHJvjsjhf5szqv8dIQCjIRz6zbIk5eDI6nKQ2YdDUdH40SiSZIKK5EtX8wobkzCRQVVQZGJlOvewoDIzFWdlRuTSVZhiAYEMIumeip8TjvWQCbFQMBoTEQpDEETMmoMhkYMKlokgk862n/jSmVeVJKV9Nc8k3d8dhBTkZiBMVfJFUPTkZi3PHYwTpXKgFCrqRuakHS06K5XCLRRNZCY5Fogmg8kfqe3igpLyUDOYpL5ZKXg0qWIahmkhFq2ZJ2pmeJTpYl8OvgZDG/qxxTKlXMXPJNHT3h4tIDaTVePOXoicq5Q8pBqV0u4zmsjvFognAogKqX2hjpossJBkq/hjCRw8WWS14OylGG4O4nfzotFVGxSlNXglxZoj2naCbiyfUb/72aLRtTKlVMteabqkZK7XLJNV/0mCyBEJoSbrxmSWUDNipFqQNY7n7yp/zl9w+n9iWdHo/zl98/DFDTiiUbgYDQlMW6mVpwrZoqfNZWOMYCI5lvSt3KsCrEPWacpaxd2oqnfhvF99V66svrmad7BmlvDJLwlIm4nzW6vTHI0z2Ds588T2698lw6WvywzYQL2+xoCVe0BEIl2bJhLbGEX5TKL/UbL2oAy9/u7sm60fVvK5iKqNyEggGaG4IsagqzpK2RMxc1sWpxC2uWtrJqcQvLz2hmabtfUrq1sbyZBcxSqWJCAfGtFMjw1c+Ub+qWTesn12ESHqFAgM6WMLdsWl/y/laS5Ia75MPGU38Ge/jN0m+427h+GX927QUVKSfcEg4SiU13zbVUsARDqQNYcgVAlCUwogbwI9NyV/gstYVjSqWKaWkIMhH3UgkMk9FFM+2W3rh+GV+u0AOukoxMJLLOXocnyvOgKVbARqHRfmcvbuLQm9PdoWcvrmz6mIW4V6oWmK2k9FSFk4xUK+gexeqsUXzefuYijh4fmRZFs2Zp24znLcQ/6Gg8+6pHLnmhBMi+rlJM//Fcov0OD2Rf/M4lN4xczKRwCrFibE2litmyYS0NoSBnndHEO85s56wzmmgIBSu+ubIaKXXJlnAokDVLcTHT9yej/RT/D1yZjPbLRTnyqlUbzTl2tOaSG/OnkPUY+xWqmIWax2suNASz/6PPJS+UNUtaCAaExmCAplCAxmCAYECKGuHVc3w0a5pyi/bLpLUxRIDJRMwi/oOstUi1c4z5Yb9ClVMPriy3yTyrvFgsa2ug79T0DX/L2vIvgzoTt155bkamgmBA6GhcuBFelWTdsnZeCY5weiwz19rqJTO7hY3yYJaKUXJWdGRfNM4lnxOBAF1t4VRxqoBAV1sYKdIGxI3rl3HDZefQEAzgKTQEA9xw2TlFVfhrlrT44eAuEsfz/HDwhbrfJRdbNqwlHMx0C4eD5hauFkypGCXnS9f8HC1T/N0t4QBfuubninaPVZ0tfux+OEg4KDSHg4SCgaJtuCtlNcMkc9nvkisOsLL1KEuLuYWrm4q4v0TkFWAYSABxVe0WkcXAt4DVwCvAf1XVIVcu+Cv4lSEjwK+p6nPuOjcCf+gu+yVVfbCc4zDyp6UxhMek66hlFv93oaG1yR31AZd1IJrw6B+Ocv27c++oL+Qe23b3EA5Kapd4S0Oo6AXZ5rLfJVfAdL3v2KgHt3C9Usk1lV9U1eNp328Fvq+qd4jIre77LcCVwDr3uhS4B7jUKaEvAN34Lvv9IrJTVYfKOQhjdgqtkJksehUOSkbRq62Q80FSaBLDQu/ROxShozmcIStFQTZ7WBq1TjW5v64GkpbGg8A1afKH1GcP0CEiy4EPAk+o6qBTJE8Am8rdaWN2Cq2QmW4ViPjv4aCwbYY0HMkkhmu72lh/1iLWdrWxtK2xaPdY1dnC2JSd69VQkM0wqo1KKRUF/llE9ovIZic7U1WPAbj35HRtBdCbdm6fk+WST0NENovIPhHZNzAwUMRhGPlQ6AN5LmWaS32PUuezmivvWdNZkNwwSk2llMrlqnoRvmvrJhHZMEPbbJGnOoN8ulD1XlXtVtXurq7K1e5eqBT6QJ6LVVDqe1Tr4vA3t/zCNAXynjWdfHPLL1SoR8ZCpyJrKqr6unvvF5HvAJcAb4rIclU95txbybCaPmBV2ukrgdedfOMU+a4Sd92YA4UmGJxLmeZy3aPSSiQbpkCMakLKnXtfRFqBgKoOu89PAFuB9wEn0hbqF6vq74vIh4Gb8aO/LgXuVtVL3EL9fuAid+nngItVdcZc593d3bpv377SDM4oGsnIrFImxSzHPQyjXhCR/araPVu7SlgqZwLfcblkQsA3VPV7IrIXeFhEPgm8CnzMtX8UX6EcwQ8p/gSAqg6KyO3AXtdu62wKxagdymEVVKvlYRi1TNktlUpjlophGEbh5GupVFNIsWEYhlHjmFIxDMMwioYpFcMwDKNomFIxDMMwisaCW6gXkQHgZ5XuR5lYChyftVX9YeNeOCzEMUNlxn2Oqs66e3zBKZWFhIjsyydao96wcS8cFuKYobrHbe4vwzAMo2iYUjEMwzCKhimV+ubeSnegQti4Fw4LccxQxeO2NRXDMAyjaJilYhiGYRQNUyo1iIgEReR5EXnEfX9ARI6KyAvudaGTi4jcLSJHRORHInJR2jVuFJHD7nVjpcaSLyLyioi85Ma3z8kWi8gTbgxPiEink9f7uL8oIq+l/d4fSmt/mxv3T0Tkg2nyTU52xGUBr2pEpENEdojIIRE5KCLvqfffO8eYa++3VlV71dgL+F3gG8Aj7vsDwLVZ2n0IeAy/oNllwDNOvhjoce+d7nNnpcc1y5hfAZZOkf0pcKv7fCtw5wIZ9xeBz2Zpex7wItAIrAH+Awi6138Aa4EG1+a8So9tlnE/CPyG+9wAdNT7751jzDX3W5ulUmOIyErgw8B9eTS/GnhIffYAHa4A2geBJ1R1UFWH8GvabCpZp0vH1fh/iLj3a9Lk9TzuXFwNbFfVCVU9il8u4hL3OqKqPaoaBba7tlWJiCwCNgBfA1DVqKqepI5/7xnGnIuq/a1NqdQefwn8PuBNkf+xM/3vEpFGJ1sB9Ka16XOyXPJqRoF/FpH9IrLZyc5U1WMA7j1ZHKXexw1ws/u970+6gaifca8FBoC/c27e+8Qv6FfPv3euMUON/damVGoIEfkI0K+q+6ccug1YD7wb39S/JXlKlsvoDPJq5nJVvQi4ErhJRDbM0Lbex30P8FbgQuAY8Oeubb2MO4Rf0fUeVX0XMIrv7spFPYw715hr7rc2pVJbXA5cJSKv4Ju1V4jI36vqMWf6TwB/h28Cgz9LWZV2/krg9RnkVYuqvu7e+4Hv4I/xTefmwL33u+Z1PW5VfVNVE6rqAV+l/n7vPqBPVZ9x33fgP3Dr+ffOOuZa/K1NqdQQqnqbqq5U1dXAdcBTqvqraX9ogu9n/rE7ZSdwg4uOuQw45dwGjwMfEJFOZ05/wMmqEhFpFZH25Gf8/v4Yf3zJiJ4bge+6z3U97uTv7fgomb/3dSLSKCJrgHXAs/glt9eJyBoRacD/t7OzXOMoFFV9A+gVkXc40fuAl6nj3zvXmGvxt65EjXqj+PyDiHThm74vAL/l5I/iR8YcASLAJwBUdVBEbsf/BwiwVVUHy9vlgjgT+I6vMwkB31DV74nIXuBhEfkk8CrwMde+3sf9dfHDxhU/OmwLgKoeEJGH8R/AceAmVU0AiMjN+A/UIHC/qh4o92AK5L/j/7tuwI/a+gT+JLief+9sY7671n5r21FvGIZhFA1zfxmGYRhFw5SKYRiGUTRMqRiGYRhFw5SKYRiGUTRMqRiGYRhFw5SKYVQA8TNLX1vpfhhGsTGlYhg1gIgEK90Hw8gHUyqGUQAistrVu3jQJfnbISItInKxiPyLS/z4eFqWg98Ukb0i8qKI/JOItGS55u3OcglMkW8UkR+IyDeAl3Ld27V9RUT+RESeFpF9InKR68d/iMhvTb2nYZQKUyqGUTjvAO5V1Z8HTgM3AX+FX9PmYuB+4I9d22+r6rtV9QLgIPDJ9AuJyJ/iZ9v9hMvvNJVLgP+pqufluPdvp7XtVdX3AP+Kq7GDX19k6zzHaxh5Y0rFMAqnV1X/zX3+e/y6He8EnhCRF4A/xE/kB/BOEflXEXkJ+G/A+WnX+SOgQ1W3aO7UFs+6ehm57v3etGPJHE8v4ReqGlbVAWBcRDrmME7DKBjL/WUYhTNVAQwDB5yVMJUHgGtU9SNUubMAAAD5SURBVEUR+TVgY9qxvcDFIrLY5am6FNjmjn0e3xIZneXe6d8n3LuX9jn53f7WjbJglophFM7ZIpJUINcDe4CupExEwiKStEjagWMiEsa3VNL5HnAH8P9EpF1Vn1HVC90rV2bZqff+YbEGZRjFwJSKYRTOQeBGEfkRflG0v8Jfv7hTRF7EzxT9C67tHwHP4JeyPTT1Qqr6j/h1MnaKSPMc7n3PPMdiGEXFshQbRgGIyGrgEVV950K6t2Hki1kqhmEYRtEwS8UwDMMoGmapGIZhGEXDlIphGIZRNEypGIZhGEXDlIphGIZRNEypGIZhGEXDlIphGIZRNP5/dr1C0fG7QW8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x=\"peak-rpm\",y=\"price\",data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Pearson Correlation</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.8617685921390482, 9.562625728254391e-62)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "\n",
    "stats.pearsonr(df[\"engine-size\"], df[\"price\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To look at correlations among all features, a correlation heatmap can be created."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Analysis of Variance (ANOVA)</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Statistical comparison of groups. Eg. Average price of different vehicle makes.\n",
    "\n",
    "1. Why ANOVA?\n",
    "    \n",
    "    Finding correlation between different groups of a categorical variable.\n",
    "2. What do we obtain from ANOVA?\n",
    "    \n",
    "    F-test score: variation between sample group means divided by the variation within sample group.\n",
    "    \n",
    "    P-value: confidence degree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "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"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.bar(df[\"make\"], df[\"price\"])\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>ANOVA between Honda and Dodge</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=0.19744030127462606, pvalue=0.6609478240622193)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_anova = df[[\"make\", \"price\"]]\n",
    "grouped_anova = df_anova.groupby([\"make\"])\n",
    "\n",
    "stats.f_oneway(grouped_anova.get_group(\"honda\")[\"price\"],grouped_anova.get_group(\"subaru\")[\"price\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>ANOVA between Honda and BMW</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=46.43581434840086, pvalue=1.6638930893773901e-06)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_anova = df[[\"make\", \"price\"]]\n",
    "grouped_anova = df_anova.groupby([\"make\"])\n",
    "\n",
    "stats.f_oneway(grouped_anova.get_group(\"honda\")[\"price\"],grouped_anova.get_group(\"bmw\")[\"price\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Model Development</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A model or estimator can be thought of as a mathematical equation used to predict the value given one or more other values. Relating one or more independent variables or features to dependent variables. For example, you input a car models highway miles per gallon as the independent variable or feature, the output of the model or dependent variable is the price."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Simple Linear Regression</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\\hat{y} = b_0 + b_1x$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "x = predictor/independent variable, y = target/dependent variable, b0 = intercept, and b1 = slope"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Fitting a simple linear model estimator</h4>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import linear_model from scikit-mlearn:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "create a linear regression object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "lm = LinearRegression()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the predictor and target variable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[[\"highway-mpg\"]]\n",
    "Y = df[\"price\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find the LinearModel parameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.fit(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept is 37464.386085555205 and the slope is [-788.63993457]\n"
     ]
    }
   ],
   "source": [
    "yhat = lm.predict(X)\n",
    "b0 = lm.intercept_\n",
    "b1 = lm.coef_\n",
    "\n",
    "print(\"Intercept is \"+str(b0)+\" and the slope is \"+str(b1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Multiple Linear Regression</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To explain the relationship between one Y variable and two or more X variables. $$\\hat{y}=b_0+b_1x_1+b_2x_2+b_3x_3+b_4x_4$$ where $b_0$ is the intercept, and $b_1$ is the coefficient or parameter of $x_1$ and so on."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Fitting a multiple linear model estimator</h4>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract three predictor variables and store them in Z:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "Z = df[[\"curb-weight\", \"engine-size\", \"highway-mpg\"]]\n",
    "\n",
    "lm = LinearRegression()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train the model as before:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.fit(Z, df[\"price\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find the coefficients and intercept:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-6695.29569673281"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   3.59909002,  111.86331866, -113.35950977])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model can be summarized as:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Price = -6695.3 + 3.6*Curb Weight  + 111.86*Engine Size  + -113.36*Highway-mpg\n"
     ]
    }
   ],
   "source": [
    "print(\"Price = \"+str(np.round(lm.intercept_,2))+\" + \"+str(np.round(lm.coef_[0],2))+\"*Curb Weight \"+\" + \"+str(np.round(lm.coef_[1],2))+\"*Engine Size \"+\" + \"+str(np.round(lm.coef_[2],2))+\"*Highway-mpg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Model Evaluation</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\91963\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29aaff4c198>"
      ]
     },
     "execution_count": 76,
     "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": [
    "sns.regplot(df[\"highway-mpg\"], df[\"price\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look at the residuals:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x29aaffbc4a8>"
      ]
     },
     "execution_count": 77,
     "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": [
    "sns.residplot(df[\"highway-mpg\"],df[\"price\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The residuals have a curvature. It implies that the linear assumption is incorrect. Typically, the residual plot should be random with zero mean and no curvature."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Distribution Plots</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x29ab001e898>"
      ]
     },
     "execution_count": 78,
     "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": [
    "ax1 = sns.distplot(df[\"price\"], hist=False, label=\"Actual Value\", color='r')\n",
    "sns.distplot(yhat, color='b', hist=False, label=\"Fitted Values\", ax=ax1)\n",
    "plt.ylabel(\"Proportion of Cars\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Model evaluation using in-sample numerical methods</h4>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Two important methods:\n",
    "1. Mean Squared Error (MSE)\n",
    "2. R-squared ($R^2$)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For MSE:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32266003.534882523"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_squared_error(df[\"price\"], yhat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For $R^2$:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$R^2 = 1 - \\frac{MSE_{regression line}}{MSE_{mean}}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "where $MSE_{regressionline}$ is the MSE of the data with respect to the regression line and $MSE_{mean}$ is the MSE of the data with respect to the mean line."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To find out the $R^2$ value of a linear model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.47634628273144575"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm = LinearRegression()\n",
    "X = df[[\"highway-mpg\"]]\n",
    "Y = df[\"price\"]\n",
    "lm.fit(X,Y)\n",
    "lm.score(X,Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ie, approximately 47.63% of the variation of the price is explained by the linear model. If the $R^2$ value is negative, it would be due to overfitting."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Data Visualization in Python</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. For exploratory data analysis\n",
    "2. Communicate data clearly\n",
    "3. Share unbiased representation of data\n",
    "4. Use them to support proposals/policies/stakeholders"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remember:\n",
    "<ul>\n",
    "    <li>Less is more Effective</li>\n",
    "<li>Less is more Attractive</li>\n",
    "<li>Less is more Impactive</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Matplotlib</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "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"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x = np.random.randn(1000)\n",
    "plt.hist(x, 100)\n",
    "plt.title('Normal distribution')\n",
    "#plt.savefig('plot1.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Line plots</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x29ab0212fd0>"
      ]
     },
     "execution_count": 83,
     "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": [
    "x = np.arange(1901,2001)\n",
    "y = np.random.randn(100)\n",
    "y1 = np.cos(y)\n",
    "y2 = np.sin(y)+1.0\n",
    "\n",
    "plt.plot(x,y1, color='r', label=\"Data1\")\n",
    "plt.plot(x,y2, color='b', label=\"Data2\")\n",
    "\n",
    "plt.title('Time-series')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Anomaly')\n",
    "plt.legend(loc=\"upper right\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}