WK1_Python_Basics_Part2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first need to tell Python that we're going to be using Pandas. We do this with the *import* command."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reading data\n",
    "\n",
    "Let's read the dataset within the csv file \"physics_students.csv\" that is located in the \"readonly\" folder.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('./readonly/physics_students.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Getting a sense of the data\n",
    "Get the first five rows of the dataset, i.e. its head."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>studentID</th>\n",
       "      <th>name</th>\n",
       "      <th>age</th>\n",
       "      <th>grade</th>\n",
       "      <th>degree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>s08549383</td>\n",
       "      <td>John</td>\n",
       "      <td>22</td>\n",
       "      <td>55</td>\n",
       "      <td>Biology</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>s06218474</td>\n",
       "      <td>Rhona</td>\n",
       "      <td>20</td>\n",
       "      <td>86</td>\n",
       "      <td>Physics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>s17758784</td>\n",
       "      <td>Clara</td>\n",
       "      <td>36</td>\n",
       "      <td>62</td>\n",
       "      <td>Physics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>s17439450</td>\n",
       "      <td>Dave</td>\n",
       "      <td>22</td>\n",
       "      <td>38</td>\n",
       "      <td>Medicine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>s17089450</td>\n",
       "      <td>George</td>\n",
       "      <td>19</td>\n",
       "      <td>65</td>\n",
       "      <td>Chemistry</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   studentID    name  age  grade     degree\n",
       "0  s08549383    John   22     55    Biology\n",
       "1  s06218474   Rhona   20     86    Physics\n",
       "2  s17758784   Clara   36     62    Physics\n",
       "3  s17439450    Dave   22     38   Medicine\n",
       "4  s17089450  George   19     65  Chemistry"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And now get the last five rows of the data, i.e. its tail."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>studentID</th>\n",
       "      <th>name</th>\n",
       "      <th>age</th>\n",
       "      <th>grade</th>\n",
       "      <th>degree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>s17439104</td>\n",
       "      <td>Olver</td>\n",
       "      <td>42</td>\n",
       "      <td>23</td>\n",
       "      <td>Biology</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>s12865603</td>\n",
       "      <td>Grace</td>\n",
       "      <td>21</td>\n",
       "      <td>49</td>\n",
       "      <td>Biology</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>s16732219</td>\n",
       "      <td>Max</td>\n",
       "      <td>23</td>\n",
       "      <td>69</td>\n",
       "      <td>Chemistry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>s15349206</td>\n",
       "      <td>Jack</td>\n",
       "      <td>22</td>\n",
       "      <td>58</td>\n",
       "      <td>Chemistry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>s16349365</td>\n",
       "      <td>Jessica</td>\n",
       "      <td>22</td>\n",
       "      <td>88</td>\n",
       "      <td>Physics</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    studentID     name  age  grade     degree\n",
       "20  s17439104    Olver   42     23    Biology\n",
       "21  s12865603    Grace   21     49    Biology\n",
       "22  s16732219      Max   23     69  Chemistry\n",
       "23  s15349206     Jack   22     58  Chemistry\n",
       "24  s16349365  Jessica   22     88    Physics"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the names of the columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['studentID', 'name', 'age', 'grade', 'degree'], dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the number of rows and columns in the data frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25, 5)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Working with data\n",
    "Get the 'degree' column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       Biology\n",
       "1       Physics\n",
       "2       Physics\n",
       "3      Medicine\n",
       "4     Chemistry\n",
       "5     Chemistry\n",
       "6      Medicine\n",
       "7       Physics\n",
       "8       Physics\n",
       "9       Physics\n",
       "10     Medicine\n",
       "11    Chemistry\n",
       "12    Chemistry\n",
       "13      Biology\n",
       "14      Physics\n",
       "15    Chemistry\n",
       "16      Physics\n",
       "17      Physics\n",
       "18      Physics\n",
       "19      Physics\n",
       "20      Biology\n",
       "21      Biology\n",
       "22    Chemistry\n",
       "23    Chemistry\n",
       "24      Physics\n",
       "Name: degree, dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['degree']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get a subset of rows, e.g. all rows with index number between 5 and 10."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>studentID</th>\n",
       "      <th>name</th>\n",
       "      <th>age</th>\n",
       "      <th>grade</th>\n",
       "      <th>degree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>s16018474</td>\n",
       "      <td>Lucy</td>\n",
       "      <td>24</td>\n",
       "      <td>42</td>\n",
       "      <td>Chemistry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>s06898474</td>\n",
       "      <td>Hannah</td>\n",
       "      <td>22</td>\n",
       "      <td>31</td>\n",
       "      <td>Medicine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>s17768784</td>\n",
       "      <td>Teresa</td>\n",
       "      <td>22</td>\n",
       "      <td>78</td>\n",
       "      <td>Physics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>s09341718</td>\n",
       "      <td>Robert</td>\n",
       "      <td>21</td>\n",
       "      <td>76</td>\n",
       "      <td>Physics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>s16483954</td>\n",
       "      <td>Hugh</td>\n",
       "      <td>33</td>\n",
       "      <td>79</td>\n",
       "      <td>Physics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>s12038588</td>\n",
       "      <td>Elaine</td>\n",
       "      <td>28</td>\n",
       "      <td>93</td>\n",
       "      <td>Medicine</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "    studentID    name  age  grade     degree\n",
       "5   s16018474    Lucy   24     42  Chemistry\n",
       "6   s06898474  Hannah   22     31   Medicine\n",
       "7   s17768784  Teresa   22     78    Physics\n",
       "8   s09341718  Robert   21     76    Physics\n",
       "9   s16483954    Hugh   33     79    Physics\n",
       "10  s12038588  Elaine   28     93   Medicine"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[5:11]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the degree information of all rows with index number between 5 and 10."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5     Chemistry\n",
       "6      Medicine\n",
       "7       Physics\n",
       "8       Physics\n",
       "9       Physics\n",
       "10     Medicine\n",
       "Name: degree, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[5:11]['degree']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filter the data to get all records of students older than 30."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>studentID</th>\n",
       "      <th>name</th>\n",
       "      <th>age</th>\n",
       "      <th>grade</th>\n",
       "      <th>degree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>s17758784</td>\n",
       "      <td>Clara</td>\n",
       "      <td>36</td>\n",
       "      <td>62</td>\n",
       "      <td>Physics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>s16483954</td>\n",
       "      <td>Hugh</td>\n",
       "      <td>33</td>\n",
       "      <td>79</td>\n",
       "      <td>Physics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>s17791784</td>\n",
       "      <td>Christine</td>\n",
       "      <td>52</td>\n",
       "      <td>56</td>\n",
       "      <td>Chemistry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>s17029450</td>\n",
       "      <td>Alison</td>\n",
       "      <td>31</td>\n",
       "      <td>54</td>\n",
       "      <td>Biology</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>s17433250</td>\n",
       "      <td>Sophie</td>\n",
       "      <td>31</td>\n",
       "      <td>63</td>\n",
       "      <td>Physics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>s17439104</td>\n",
       "      <td>Olver</td>\n",
       "      <td>42</td>\n",
       "      <td>23</td>\n",
       "      <td>Biology</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    studentID       name  age  grade     degree\n",
       "2   s17758784      Clara   36     62    Physics\n",
       "9   s16483954       Hugh   33     79    Physics\n",
       "11  s17791784  Christine   52     56  Chemistry\n",
       "13  s17029450     Alison   31     54    Biology\n",
       "16  s17433250     Sophie   31     63    Physics\n",
       "20  s17439104      Olver   42     23    Biology"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.age>30]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Summarising and Visualising Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summarising data\n",
    "Get quick statistical information about all the numeric columns with the use of of the *describe* function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>age</th>\n",
       "      <th>grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>26.080000</td>\n",
       "      <td>61.480000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.740155</td>\n",
       "      <td>17.706214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>23.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>22.000000</td>\n",
       "      <td>53.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>23.000000</td>\n",
       "      <td>61.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>76.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>52.000000</td>\n",
       "      <td>93.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             age      grade\n",
       "count  25.000000  25.000000\n",
       "mean   26.080000  61.480000\n",
       "std     7.740155  17.706214\n",
       "min    19.000000  23.000000\n",
       "25%    22.000000  53.000000\n",
       "50%    23.000000  61.000000\n",
       "75%    28.000000  76.000000\n",
       "max    52.000000  93.000000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the mean of the 'age' column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26.08"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['age'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the mean of all numeric columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_117688/3698961737.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  df.mean()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "age      26.08\n",
       "grade    61.48\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the standard deviation of the 'age' column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.740155037206942"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['age'].std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the maximum value of 'age'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "52"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['age'].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the minimum value of 'age'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['age'].min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualising data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to visualise our data, we'll be using the matplotlib library. Let's import it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get a histogram for 'age'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 18,
     "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": [
    "student_ages = df['age']\n",
    "student_ages.hist(bins=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get a box plot for 'age'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kRhlwSWqUAZekRhlwSWrU/wNzI6w827KabgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "student_ages.plot.box()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "**And now give yourself a _high five_! You deserve it!** <br> <br> <br> <br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Keep practising"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quiz 1 - Question 7\n",
    "\n",
    "The following code specifies a DataFrame that captures information about patients. Complete the code so as to get the shape of patients."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5 entries, 0 to 4\n",
      "Data columns (total 4 columns):\n",
      " #   Column     Non-Null Count  Dtype \n",
      "---  ------     --------------  ----- \n",
      " 0   patientID  5 non-null      object\n",
      " 1   name       5 non-null      object\n",
      " 2   age        5 non-null      int64 \n",
      " 3   city       5 non-null      object\n",
      "dtypes: int64(1), object(3)\n",
      "memory usage: 288.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = {'patientID': ['pt8794', 'pt1297', 'pt3350', 'pt5986', 'pt8853'],\n",
    "        'name': ['Carlos', 'Amy', 'Ewen', 'Lydia', 'Enrico'],\n",
    "        'city': ['Edinburgh', 'Edinburgh', 'Aberdeen', 'Glasgow', 'Inverness'],\n",
    "        'age': [32, 50, 36, 42, 83]}\n",
    "patients = pd.DataFrame(data, columns=['patientID', 'name', 'age', 'city'])\n",
    "# add your code here\n",
    "patients.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quiz 1 - Question 8\n",
    "\n",
    "The following code specifies a DataFrame that captures information about patients. Complete the code so as to get the mean of the age column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "48.6"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = {'patientID': ['pt8794', 'pt1297', 'pt3350', 'pt5986', 'pt8853'],\n",
    "        'name': ['Carlos', 'Amy', 'Ewen', 'Lydia', 'Enrico'],\n",
    "        'city': ['Edinburgh', 'Edinburgh', 'Aberdeen', 'Glasgow', 'Inverness'],\n",
    "        'age': [32, 50, 36, 42, 83]}\n",
    "patients = pd.DataFrame(data, columns=['patientID', 'name', 'age', 'city'])\n",
    "# add your code here\n",
    "patients['age'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make up your own questions\n",
    "\n",
    "Keep practising with the DataFrames specified above or create your own DataFrame and analyse it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5 entries, 0 to 4\n",
      "Data columns (total 4 columns):\n",
      " #   Column     Non-Null Count  Dtype \n",
      "---  ------     --------------  ----- \n",
      " 0   patientID  5 non-null      object\n",
      " 1   name       5 non-null      object\n",
      " 2   age        5 non-null      int64 \n",
      " 3   city       5 non-null      object\n",
      "dtypes: int64(1), object(3)\n",
      "memory usage: 288.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "#describe the data above\n",
    "patients.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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