all_code.ipynb

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    "# Plotting with Python\n",
    "\n",
    "## Introduction\n",
    "\n",
    "Graphs are extremely important for communicating data quickly and effectively. You might have created a graph in MicroSoft Excel before. When you do, you have to click around the software to modify the graph's appearance and what data it uses. This might not take too much time if you're only doing it once but what if you had to make similar graphs another ten times? A hundred times? Or a thousand times? The process would quickly get very boring, take a lot of time, and you'd be more likely to make mistakes!\n",
    "\n",
    "We can use Python to write programs that plot our data. The program acts as instructions to create the graph. It's very customisable and you can use the same code over and over again! Many of the graphs and visualisations you see in magazines, newspapers and social media are created using programming. Data scientists and statisticians create graphs to communicate data to doctors, politicians, CEOs, etc., to influence important decisions. \n",
    "\n",
    "In this activity you are going to create your own graphs using Python. \n",
    "\n",
    "Don't feel nervous if this is your first time using Python and you don't understand all the code (*this is a normal feeling for programmers too*). You won't be asked to write your own from scratch, only to edit what we give you. \n",
    "\n",
    "<br> <br>"
   ]
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    "## Animal ageing\n",
    "\n",
    "The oldest human ever was a French woman named Jeanne Calment. She lived to the age of <b>122 years and 164 days.</b> Whilst humans can be very long-lived, some animals can live even longer. Understanding what makes these animals live for so long could be important for letting us live longer and healthier lifespans. Below are some examples of long-lived animals:\n",
    "\n",
    "<br>\n",
    "<center>\n",
    "\n",
    "<img src=\"images/long_lived_animals.png\" alt=\"Long lived animals\"/>\n",
    "\n",
    "</center>\n",
    "<br>\n",
    "\n",
    "\n",
    "We are going to create plots to show the maximum lifespan of various animals, both long-lived and short-lived. We will customise our plots in various ways.\n",
    "\n",
    "Learning objectives:\n",
    "- To understand which types of animals live the longest\n",
    "- To have an introduction to Python\n",
    "- To use Python to create plots of animal maximum ages\n",
    "\n",
    "\n",
    "<br>\n",
    "\n",
    "<br>\n",
    "\n",
    "### Load packages\n",
    "\n",
    "Python does not know how to process and plot data on its own. Python packages contain additional commands that don’t come installed with Python, and allow us to carry out certain extra tasks. In this activity we're going to need to load two known as `pandas` and `matplotlib`. `Pandas` is a package that lets python read and edit data, much like you'd use Excel to process raw data. `Matplotlib` is the package that then lets us plot the data. We can load them by pasting the following code into our first chunk and pressing the play button:\n"
   ]
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
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   "source": [
    "<br>\n",
    "\n",
    "As part of this, we rename the packages to something shorter. 'Matplotlib.pyplot' is quite a lot of letters to type every time! Each time we used it we'd have to type:\n",
    "\n",
    "`matplotlib.pyplot.plot`\n",
    "\n",
    "<br>\n",
    "\n",
    "Instead, we can rename the package as we load it to something simple and easy to remember. In this case 'plt'. So the above line of code would become:\n",
    "\n",
    "`plt.plot`\n",
    "\n",
    "<br>\n",
    "\n",
    "In summary, this is how we load and rename a package:\n",
    "\n",
    "<br>\n",
    "<center>\n",
    "\n",
    "<img src=\"https://github.com/ejjohnson93/outreach_jupyter/blob/main/images/import2.png?raw=true\" alt=\"Alternative text\" width=\"400\"/>\n",
    "\n",
    "</center>\n",
    "<br><br>"
   ]
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   "source": [
    "### Load the data\n",
    "\n",
    "Next we are going to load our animal ageing data. The data we're going to be using can be found [here](https://raw.githubusercontent.com/CBFLivUni/scholars_event/refs/heads/main/data/animal_ageing_data.csv). We are going to load it using the package mentioned earlier 'pandas'. Remember, we have loaded 'pandas' and renamed it to 'pd'.\n",
    "\n",
    "<br>\n",
    "\n",
    "The code to load the data is below. We have left the space for the URL blank \"____\" for you to fill in. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"https://raw.githubusercontent.com/CBFLivUni/scholars_event/refs/heads/main/data/animal_ageing_data.csv\") # ← Enter the URL of the .csv file here"
   ]
  },
  {
   "attachments": {},
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   "metadata": {},
   "source": [
    "We use the `read_csv` function contained inside the 'pandas' package to load the data at the url. We then store it in a *variable* called 'data' using the equals sign.\n",
    "\n",
    "We can view the data by typing the name of the variable in a code chunk:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Type</th>\n",
       "      <th>Common name</th>\n",
       "      <th>Female maturity (days)</th>\n",
       "      <th>Male maturity (days)</th>\n",
       "      <th>Gestation/Incubation (days)</th>\n",
       "      <th>Litter/Clutch size</th>\n",
       "      <th>Litters/Clutches per year</th>\n",
       "      <th>Birth weight (g)</th>\n",
       "      <th>Adult weight (g)</th>\n",
       "      <th>Maximum longevity (yrs)</th>\n",
       "      <th>Specimen origin</th>\n",
       "      <th>Sample size</th>\n",
       "      <th>Data quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Molluscs</td>\n",
       "      <td>Ocean quahog clam</td>\n",
       "      <td>4562.0</td>\n",
       "      <td>4780.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.260000e+02</td>\n",
       "      <td>507.00</td>\n",
       "      <td>wild</td>\n",
       "      <td>medium</td>\n",
       "      <td>acceptable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Fish</td>\n",
       "      <td>Greenland shark</td>\n",
       "      <td>56940.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.000000e+05</td>\n",
       "      <td>392.00</td>\n",
       "      <td>wild</td>\n",
       "      <td>small</td>\n",
       "      <td>acceptable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mammals</td>\n",
       "      <td>Bowhead whale</td>\n",
       "      <td>8212.0</td>\n",
       "      <td>8212.0</td>\n",
       "      <td>396.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.30</td>\n",
       "      <td>900000.00</td>\n",
       "      <td>1.000000e+08</td>\n",
       "      <td>211.00</td>\n",
       "      <td>wild</td>\n",
       "      <td>medium</td>\n",
       "      <td>acceptable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fish</td>\n",
       "      <td>Rougheye rockfish</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>4.950000e+02</td>\n",
       "      <td>205.00</td>\n",
       "      <td>wild</td>\n",
       "      <td>medium</td>\n",
       "      <td>acceptable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Echinoids</td>\n",
       "      <td>Red sea urchin</td>\n",
       "      <td>550.0</td>\n",
       "      <td>550.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.540000e+02</td>\n",
       "      <td>200.00</td>\n",
       "      <td>wild</td>\n",
       "      <td>medium</td>\n",
       "      <td>acceptable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Reptiles</td>\n",
       "      <td>Galapagos tortoise</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>2.270000e+05</td>\n",
       "      <td>177.00</td>\n",
       "      <td>captivity</td>\n",
       "      <td>medium</td>\n",
       "      <td>acceptable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Fish</td>\n",
       "      <td>Lake sturgeon</td>\n",
       "      <td>9490.0</td>\n",
       "      <td>2920.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>350000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.000000e+04</td>\n",
       "      <td>152.00</td>\n",
       "      <td>wild</td>\n",
       "      <td>medium</td>\n",
       "      <td>acceptable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Mammals</td>\n",
       "      <td>Human</td>\n",
       "      <td>4745.0</td>\n",
       "      <td>5110.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.30</td>\n",
       "      <td>3312.50</td>\n",
       "      <td>6.203500e+04</td>\n",
       "      <td>122.50</td>\n",
       "      <td>captivity</td>\n",
       "      <td>huge</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Mammals</td>\n",
       "      <td>Blue whale</td>\n",
       "      <td>1827.0</td>\n",
       "      <td>1827.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.40</td>\n",
       "      <td>2000000.00</td>\n",
       "      <td>1.360000e+08</td>\n",
       "      <td>110.00</td>\n",
       "      <td>wild</td>\n",
       "      <td>medium</td>\n",
       "      <td>acceptable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Arthropods</td>\n",
       "      <td>Lobster</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>5.000000e+02</td>\n",
       "      <td>100.00</td>\n",
       "      <td>wild</td>\n",
       "      <td>medium</td>\n",
       "      <td>acceptable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Mammals</td>\n",
       "      <td>Killer whale</td>\n",
       "      <td>3780.0</td>\n",
       "      <td>4930.0</td>\n",
       "      <td>435.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.20</td>\n",
       "      <td>180000.00</td>\n",
       "      <td>3.987500e+06</td>\n",
       "      <td>90.00</td>\n",
       "      <td>wild</td>\n",
       "      <td>medium</td>\n",
       "      <td>acceptable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Birds</td>\n",
       "      <td>Pink cockatoo</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>3.900000e+02</td>\n",
       "      <td>83.00</td>\n",
       "      <td>captivity</td>\n",
       "      <td>medium</td>\n",
       "      <td>acceptable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Mammals</td>\n",
       "      <td>Asiatic elephant</td>\n",
       "      <td>3287.0</td>\n",
       "      <td>3287.0</td>\n",
       "      <td>644.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.20</td>\n",
       "      <td>107000.00</td>\n",
       "      <td>3.178000e+06</td>\n",
       "      <td>79.60</td>\n",
       "      <td>captivity</td>\n",
       "      <td>large</td>\n",
       "      <td>acceptable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Birds</td>\n",
       "      <td>Laysan albatross</td>\n",
       "      <td>3141.0</td>\n",
       "      <td>3068.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>167.20</td>\n",
       "      <td>3.150000e+03</td>\n",
       "      <td>70.00</td>\n",
       "      <td>wild</td>\n",
       "      <td>large</td>\n",
       "      <td>acceptable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Reptiles</td>\n",
       "      <td>West African dwarf crocodile</td>\n",
       "      <td>1825.0</td>\n",
       "      <td>1825.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.000000e+04</td>\n",
       "      <td>70.00</td>\n",
       "      <td>captivity</td>\n",
       "      <td>medium</td>\n",
       "      <td>acceptable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Birds</td>\n",
       "      <td>Common raven</td>\n",
       "      <td>1095.0</td>\n",
       "      <td>1095.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>25.00</td>\n",
       "      <td>1.041200e+03</td>\n",
       "      <td>69.00</td>\n",
       "      <td>captivity</td>\n",
       "      <td>medium</td>\n",
       "      <td>acceptable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Birds</td>\n",
       "      <td>Eurasian eagle-owl</td>\n",
       "      <td>730.0</td>\n",
       "      <td>730.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.686000e+03</td>\n",
       "      <td>68.00</td>\n",
       "      <td>captivity</td>\n",
       "      <td>large</td>\n",
       "      <td>acceptable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Mammals</td>\n",
       "      <td>Chimpanzee</td>\n",
       "      <td>3376.0</td>\n",
       "      <td>2920.0</td>\n",
       "      <td>229.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.20</td>\n",
       "      <td>1821.00</td>\n",
       "      <td>4.498350e+04</td>\n",
       "      <td>68.00</td>\n",
       "      <td>captivity</td>\n",
       "      <td>large</td>\n",
       "      <td>acceptable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Mammals</td>\n",
       "      <td>Hippopotamus</td>\n",
       "      <td>1279.0</td>\n",
       "      <td>1279.0</td>\n",
       "      <td>234.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.60</td>\n",
       "      <td>40000.00</td>\n",
       "      <td>3.750000e+06</td>\n",
       "      <td>61.20</td>\n",
       "      <td>captivity</td>\n",
       "      <td>large</td>\n",
       "      <td>acceptable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Reptiles</td>\n",
       "      <td>Painted turtle</td>\n",
       "      <td>2750.0</td>\n",
       "      <td>1642.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.718000e+02</td>\n",
       "      <td>61.00</td>\n",
       "      <td>wild</td>\n",
       "      <td>medium</td>\n",
       "      <td>acceptable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Mammals</td>\n",
       "      <td>Gorilla</td>\n",
       "      <td>2829.0</td>\n",
       "      <td>4015.0</td>\n",
       "      <td>256.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2061.40</td>\n",
       "      <td>1.398420e+05</td>\n",
       "      <td>60.10</td>\n",
       "      <td>captivity</td>\n",
       "      <td>large</td>\n",
       "      <td>acceptable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Mammals</td>\n",
       "      <td>Horse</td>\n",
       "      <td>914.0</td>\n",
       "      <td>973.0</td>\n",
       "      <td>337.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>38000.00</td>\n",
       "      <td>3.000000e+05</td>\n",
       "      <td>57.00</td>\n",
       "      <td>captivity</td>\n",
       "      <td>large</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Fish</td>\n",
       "      <td>Great white shark</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3285.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.870000e+06</td>\n",
       "      <td>50.00</td>\n",
       "      <td>wild</td>\n",
       "      <td>medium</td>\n",
       "      <td>acceptable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Birds</td>\n",
       "      <td>Golden eagle</td>\n",
       "      <td>1460.0</td>\n",
       "      <td>1460.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>110.60</td>\n",
       "      <td>4.800000e+03</td>\n",
       "      <td>48.00</td>\n",
       "      <td>captivity</td>\n",
       "      <td>medium</td>\n",
       "      <td>acceptable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Mammals</td>\n",
       "      <td>Polar bear</td>\n",
       "      <td>1734.0</td>\n",
       "      <td>1734.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.40</td>\n",
       "      <td>665.00</td>\n",
       "      <td>4.750000e+05</td>\n",
       "      <td>43.80</td>\n",
       "      <td>captivity</td>\n",
       "      <td>medium</td>\n",
       "      <td>acceptable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Mammals</td>\n",
       "      <td>Indian rhinoceros</td>\n",
       "      <td>1678.0</td>\n",
       "      <td>2557.0</td>\n",
       "      <td>479.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.30</td>\n",
       "      <td>58000.00</td>\n",
       "      <td>1.602330e+06</td>\n",
       "      <td>43.50</td>\n",
       "      <td>captivity</td>\n",
       "      <td>medium</td>\n",
       "      <td>acceptable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Mammals</td>\n",
       "      <td>Naked mole-rat</td>\n",
       "      <td>228.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.50</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.500000e+01</td>\n",
       "      <td>31.00</td>\n",
       "      <td>captivity</td>\n",
       "      <td>large</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Mammals</td>\n",
       "      <td>Domestic dog</td>\n",
       "      <td>510.0</td>\n",
       "      <td>510.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.000000e+04</td>\n",
       "      <td>27.00</td>\n",
       "      <td>captivity</td>\n",
       "      <td>large</td>\n",
       "      <td>acceptable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Mammals</td>\n",
       "      <td>Tiger</td>\n",
       "      <td>1268.0</td>\n",
       "      <td>1415.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1190.00</td>\n",
       "      <td>1.197000e+05</td>\n",
       "      <td>26.30</td>\n",
       "      <td>captivity</td>\n",
       "      <td>large</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Mammals</td>\n",
       "      <td>Snow leopard</td>\n",
       "      <td>730.0</td>\n",
       "      <td>730.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>475.00</td>\n",
       "      <td>5.000000e+04</td>\n",
       "      <td>21.20</td>\n",
       "      <td>captivity</td>\n",
       "      <td>large</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Mammals</td>\n",
       "      <td>Gray wolf</td>\n",
       "      <td>669.0</td>\n",
       "      <td>669.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.80</td>\n",
       "      <td>450.00</td>\n",
       "      <td>2.662500e+04</td>\n",
       "      <td>20.60</td>\n",
       "      <td>captivity</td>\n",
       "      <td>large</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Mammals</td>\n",
       "      <td>European badger, or Old World badger</td>\n",
       "      <td>365.0</td>\n",
       "      <td>365.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>80.00</td>\n",
       "      <td>1.300000e+04</td>\n",
       "      <td>18.60</td>\n",
       "      <td>captivity</td>\n",
       "      <td>medium</td>\n",
       "      <td>acceptable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Mammals</td>\n",
       "      <td>Capybara</td>\n",
       "      <td>456.0</td>\n",
       "      <td>456.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1500.00</td>\n",
       "      <td>5.500000e+04</td>\n",
       "      <td>15.10</td>\n",
       "      <td>captivity</td>\n",
       "      <td>large</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Birds</td>\n",
       "      <td>Powerful owl</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>1.324930e+03</td>\n",
       "      <td>6.10</td>\n",
       "      <td>wild</td>\n",
       "      <td>small</td>\n",
       "      <td>questionable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Mammals</td>\n",
       "      <td>Golden hamster</td>\n",
       "      <td>48.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.45</td>\n",
       "      <td>1.050000e+02</td>\n",
       "      <td>3.90</td>\n",
       "      <td>captivity</td>\n",
       "      <td>medium</td>\n",
       "      <td>acceptable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Mammals</td>\n",
       "      <td>Rat</td>\n",
       "      <td>90.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>9.9</td>\n",
       "      <td>3.70</td>\n",
       "      <td>5.81</td>\n",
       "      <td>3.000000e+02</td>\n",
       "      <td>3.80</td>\n",
       "      <td>captivity</td>\n",
       "      <td>large</td>\n",
       "      <td>acceptable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Fish</td>\n",
       "      <td>Pink salmon</td>\n",
       "      <td>730.0</td>\n",
       "      <td>730.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.740000e+03</td>\n",
       "      <td>3.00</td>\n",
       "      <td>wild</td>\n",
       "      <td>medium</td>\n",
       "      <td>acceptable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Mammals</td>\n",
       "      <td>Star-nosed mole</td>\n",
       "      <td>304.0</td>\n",
       "      <td>304.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.50</td>\n",
       "      <td>5.530000e+01</td>\n",
       "      <td>2.50</td>\n",
       "      <td>captivity</td>\n",
       "      <td>small</td>\n",
       "      <td>questionable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Amphibians</td>\n",
       "      <td>Rainbow frog</td>\n",
       "      <td>365.0</td>\n",
       "      <td>365.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.000000e+00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>wild</td>\n",
       "      <td>small</td>\n",
       "      <td>questionable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Fish</td>\n",
       "      <td>Dwarf seahorse</td>\n",
       "      <td>118.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.000000e+01</td>\n",
       "      <td>1.00</td>\n",
       "      <td>wild</td>\n",
       "      <td>medium</td>\n",
       "      <td>acceptable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Birds</td>\n",
       "      <td>Bassian thrush</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>1.295000e+02</td>\n",
       "      <td>0.60</td>\n",
       "      <td>wild</td>\n",
       "      <td>tiny</td>\n",
       "      <td>acceptable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Arthropods</td>\n",
       "      <td>Fruit fly</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.000000e-03</td>\n",
       "      <td>0.30</td>\n",
       "      <td>captivity</td>\n",
       "      <td>large</td>\n",
       "      <td>acceptable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Arthropods</td>\n",
       "      <td>Bumblebee</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>7.000000e-01</td>\n",
       "      <td>0.27</td>\n",
       "      <td>captivity</td>\n",
       "      <td>small</td>\n",
       "      <td>acceptable</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Type                           Common name  Female maturity (days)  \\\n",
       "0     Molluscs                     Ocean quahog clam                  4562.0   \n",
       "1         Fish                       Greenland shark                 56940.0   \n",
       "2      Mammals                         Bowhead whale                  8212.0   \n",
       "3         Fish                     Rougheye rockfish                     NaN   \n",
       "4    Echinoids                        Red sea urchin                   550.0   \n",
       "5     Reptiles                    Galapagos tortoise                     NaN   \n",
       "6         Fish                         Lake sturgeon                  9490.0   \n",
       "7      Mammals                                 Human                  4745.0   \n",
       "8      Mammals                            Blue whale                  1827.0   \n",
       "9   Arthropods                               Lobster                     NaN   \n",
       "10     Mammals                          Killer whale                  3780.0   \n",
       "11       Birds                         Pink cockatoo                     NaN   \n",
       "12     Mammals                      Asiatic elephant                  3287.0   \n",
       "13       Birds                      Laysan albatross                  3141.0   \n",
       "14    Reptiles          West African dwarf crocodile                  1825.0   \n",
       "15       Birds                          Common raven                  1095.0   \n",
       "16       Birds                    Eurasian eagle-owl                   730.0   \n",
       "17     Mammals                            Chimpanzee                  3376.0   \n",
       "18     Mammals                          Hippopotamus                  1279.0   \n",
       "19    Reptiles                        Painted turtle                  2750.0   \n",
       "20     Mammals                               Gorilla                  2829.0   \n",
       "21     Mammals                                 Horse                   914.0   \n",
       "22        Fish                     Great white shark                     NaN   \n",
       "23       Birds                          Golden eagle                  1460.0   \n",
       "24     Mammals                            Polar bear                  1734.0   \n",
       "25     Mammals                     Indian rhinoceros                  1678.0   \n",
       "26     Mammals                        Naked mole-rat                   228.0   \n",
       "27     Mammals                          Domestic dog                   510.0   \n",
       "28     Mammals                                 Tiger                  1268.0   \n",
       "29     Mammals                          Snow leopard                   730.0   \n",
       "30     Mammals                             Gray wolf                   669.0   \n",
       "31     Mammals  European badger, or Old World badger                   365.0   \n",
       "32     Mammals                              Capybara                   456.0   \n",
       "33       Birds                          Powerful owl                     NaN   \n",
       "34     Mammals                        Golden hamster                    48.0   \n",
       "35     Mammals                                   Rat                    90.0   \n",
       "36        Fish                           Pink salmon                   730.0   \n",
       "37     Mammals                       Star-nosed mole                   304.0   \n",
       "38  Amphibians                          Rainbow frog                   365.0   \n",
       "39        Fish                        Dwarf seahorse                   118.0   \n",
       "40       Birds                        Bassian thrush                     NaN   \n",
       "41  Arthropods                             Fruit fly                     7.0   \n",
       "42  Arthropods                             Bumblebee                     NaN   \n",
       "\n",
       "    Male maturity (days)  Gestation/Incubation (days)  Litter/Clutch size  \\\n",
       "0                 4780.0                          NaN                 NaN   \n",
       "1                    NaN                          NaN                 NaN   \n",
       "2                 8212.0                        396.0                 1.0   \n",
       "3                    NaN                          NaN                 NaN   \n",
       "4                  550.0                          NaN                 NaN   \n",
       "5                    NaN                          NaN                 NaN   \n",
       "6                 2920.0                          6.0            350000.0   \n",
       "7                 5110.0                        280.0                 1.0   \n",
       "8                 1827.0                        350.0                 1.0   \n",
       "9                    NaN                          NaN                 NaN   \n",
       "10                4930.0                        435.0                 1.0   \n",
       "11                   NaN                          NaN                 NaN   \n",
       "12                3287.0                        644.0                 1.0   \n",
       "13                3068.0                         64.0                 1.0   \n",
       "14                1825.0                          NaN                10.0   \n",
       "15                1095.0                         19.0                 5.0   \n",
       "16                 730.0                          NaN                 NaN   \n",
       "17                2920.0                        229.0                 1.0   \n",
       "18                1279.0                        234.0                 1.0   \n",
       "19                1642.0                          NaN                 NaN   \n",
       "20                4015.0                        256.0                 1.0   \n",
       "21                 973.0                        337.0                 1.0   \n",
       "22                3285.0                          NaN                 6.0   \n",
       "23                1460.0                         40.0                 2.0   \n",
       "24                1734.0                         65.0                 2.0   \n",
       "25                2557.0                        479.0                 1.0   \n",
       "26                   NaN                         70.0                 7.0   \n",
       "27                 510.0                         63.0                 6.0   \n",
       "28                1415.0                        105.0                 2.5   \n",
       "29                 730.0                         96.0                 2.0   \n",
       "30                 669.0                         62.0                 6.0   \n",
       "31                 365.0                         49.0                 3.0   \n",
       "32                 456.0                        150.0                 4.8   \n",
       "33                   NaN                          NaN                 NaN   \n",
       "34                  48.0                         16.0                 9.0   \n",
       "35                  70.0                         21.0                 9.9   \n",
       "36                 730.0                          NaN                 NaN   \n",
       "37                 304.0                         40.0                 4.4   \n",
       "38                 365.0                          NaN                 NaN   \n",
       "39                 118.0                          NaN                 NaN   \n",
       "40                   NaN                          NaN                 NaN   \n",
       "41                   7.0                          NaN                 NaN   \n",
       "42                   NaN                          NaN                 NaN   \n",
       "\n",
       "    Litters/Clutches per year  Birth weight (g)  Adult weight (g)  \\\n",
       "0                         NaN               NaN      2.260000e+02   \n",
       "1                         NaN               NaN      4.000000e+05   \n",
       "2                        0.30         900000.00      1.000000e+08   \n",
       "3                         NaN               NaN      4.950000e+02   \n",
       "4                         NaN               NaN      4.540000e+02   \n",
       "5                         NaN               NaN      2.270000e+05   \n",
       "6                         NaN               NaN      7.000000e+04   \n",
       "7                        0.30           3312.50      6.203500e+04   \n",
       "8                        0.40        2000000.00      1.360000e+08   \n",
       "9                         NaN               NaN      5.000000e+02   \n",
       "10                       0.20         180000.00      3.987500e+06   \n",
       "11                        NaN               NaN      3.900000e+02   \n",
       "12                       0.20         107000.00      3.178000e+06   \n",
       "13                       1.00            167.20      3.150000e+03   \n",
       "14                        NaN               NaN      4.000000e+04   \n",
       "15                       1.00             25.00      1.041200e+03   \n",
       "16                        NaN               NaN      2.686000e+03   \n",
       "17                       0.20           1821.00      4.498350e+04   \n",
       "18                       0.60          40000.00      3.750000e+06   \n",
       "19                        NaN               NaN      3.718000e+02   \n",
       "20                       0.30           2061.40      1.398420e+05   \n",
       "21                       1.00          38000.00      3.000000e+05   \n",
       "22                        NaN               NaN      1.870000e+06   \n",
       "23                       1.00            110.60      4.800000e+03   \n",
       "24                       0.40            665.00      4.750000e+05   \n",
       "25                       0.30          58000.00      1.602330e+06   \n",
       "26                       3.50              2.00      3.500000e+01   \n",
       "27                        NaN               NaN      4.000000e+04   \n",
       "28                       0.40           1190.00      1.197000e+05   \n",
       "29                       1.00            475.00      5.000000e+04   \n",
       "30                       0.80            450.00      2.662500e+04   \n",
       "31                       1.00             80.00      1.300000e+04   \n",
       "32                       1.25           1500.00      5.500000e+04   \n",
       "33                        NaN               NaN      1.324930e+03   \n",
       "34                       3.00              2.45      1.050000e+02   \n",
       "35                       3.70              5.81      3.000000e+02   \n",
       "36                        NaN               NaN      3.740000e+03   \n",
       "37                       1.00              1.50      5.530000e+01   \n",
       "38                        NaN               NaN      7.000000e+00   \n",
       "39                        NaN               NaN      5.000000e+01   \n",
       "40                        NaN               NaN      1.295000e+02   \n",
       "41                        NaN               NaN      7.000000e-03   \n",
       "42                        NaN               NaN      7.000000e-01   \n",
       "\n",
       "    Maximum longevity (yrs) Specimen origin Sample size  Data quality  \n",
       "0                    507.00            wild      medium    acceptable  \n",
       "1                    392.00            wild       small    acceptable  \n",
       "2                    211.00            wild      medium    acceptable  \n",
       "3                    205.00            wild      medium    acceptable  \n",
       "4                    200.00            wild      medium    acceptable  \n",
       "5                    177.00       captivity      medium    acceptable  \n",
       "6                    152.00            wild      medium    acceptable  \n",
       "7                    122.50       captivity        huge          high  \n",
       "8                    110.00            wild      medium    acceptable  \n",
       "9                    100.00            wild      medium    acceptable  \n",
       "10                    90.00            wild      medium    acceptable  \n",
       "11                    83.00       captivity      medium    acceptable  \n",
       "12                    79.60       captivity       large    acceptable  \n",
       "13                    70.00            wild       large    acceptable  \n",
       "14                    70.00       captivity      medium    acceptable  \n",
       "15                    69.00       captivity      medium    acceptable  \n",
       "16                    68.00       captivity       large    acceptable  \n",
       "17                    68.00       captivity       large    acceptable  \n",
       "18                    61.20       captivity       large    acceptable  \n",
       "19                    61.00            wild      medium    acceptable  \n",
       "20                    60.10       captivity       large    acceptable  \n",
       "21                    57.00       captivity       large          high  \n",
       "22                    50.00            wild      medium    acceptable  \n",
       "23                    48.00       captivity      medium    acceptable  \n",
       "24                    43.80       captivity      medium    acceptable  \n",
       "25                    43.50       captivity      medium    acceptable  \n",
       "26                    31.00       captivity       large          high  \n",
       "27                    27.00       captivity       large    acceptable  \n",
       "28                    26.30       captivity       large          high  \n",
       "29                    21.20       captivity       large          high  \n",
       "30                    20.60       captivity       large          high  \n",
       "31                    18.60       captivity      medium    acceptable  \n",
       "32                    15.10       captivity       large          high  \n",
       "33                     6.10            wild       small  questionable  \n",
       "34                     3.90       captivity      medium    acceptable  \n",
       "35                     3.80       captivity       large    acceptable  \n",
       "36                     3.00            wild      medium    acceptable  \n",
       "37                     2.50       captivity       small  questionable  \n",
       "38                     2.00            wild       small  questionable  \n",
       "39                     1.00            wild      medium    acceptable  \n",
       "40                     0.60            wild        tiny    acceptable  \n",
       "41                     0.30       captivity       large    acceptable  \n",
       "42                     0.27       captivity       small    acceptable  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inspect and format data"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are lots of rows and columns in our data. In the code below we will extract just the oldest animals from the data, and then we will print our their names and lifespans. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The 8 longest-living animals in this dataset are:\n",
      "          Common name  Maximum longevity (yrs)\n",
      "0   Ocean quahog clam                    507.0\n",
      "1     Greenland shark                    392.0\n",
      "2       Bowhead whale                    211.0\n",
      "3   Rougheye rockfish                    205.0\n",
      "4      Red sea urchin                    200.0\n",
      "5  Galapagos tortoise                    177.0\n",
      "6       Lake sturgeon                    152.0\n",
      "7               Human                    122.5\n"
     ]
    }
   ],
   "source": [
    "# Find the top 8 longest living animals\n",
    "oldest_animals = data.sort_values(\"Maximum longevity (yrs)\", ascending=False).head(8)\n",
    "\n",
    "print(\"\\nThe 8 longest-living animals in this dataset are:\")\n",
    "print(oldest_animals[[\"Common name\", \"Maximum longevity (yrs)\"]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this code we take our data and sort it in order of longevity. We tell it NOT to do it in ascending order by saying `ascending=False`. We take the top 8 by including `head(8)`. We then save it in a new variable called 'oldest animals'. \n",
    "\n",
    "After that, we print the two colums we are interested in (\"Common name\" and \"Maximum longevity\" (yrs)\"). Do any of these animals surprise you?\n",
    "\n",
    "> **Challenge 1**: Can you modify the above code so that 'oldest_animals' has the 12 most long-lived animals instead?\n",
    "\n",
    "> **Challenge 2**: Can you make another variable called 'shortest_lived_animals' that has the 12 most short-lived animals?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The 12 longest-living animals in this dataset are:\n",
      "           Common name  Maximum longevity (yrs)\n",
      "0    Ocean quahog clam                    507.0\n",
      "1      Greenland shark                    392.0\n",
      "2        Bowhead whale                    211.0\n",
      "3    Rougheye rockfish                    205.0\n",
      "4       Red sea urchin                    200.0\n",
      "5   Galapagos tortoise                    177.0\n",
      "6        Lake sturgeon                    152.0\n",
      "7                Human                    122.5\n",
      "8           Blue whale                    110.0\n",
      "9              Lobster                    100.0\n",
      "10        Killer whale                     90.0\n",
      "11       Pink cockatoo                     83.0\n"
     ]
    }
   ],
   "source": [
    "# Challenge 1\n",
    "oldest_animals = data.sort_values(\"Maximum longevity (yrs)\", ascending=False).head(12)\n",
    "\n",
    "print(\"\\nThe 12 longest-living animals in this dataset are:\")\n",
    "print(oldest_animals[[\"Common name\", \"Maximum longevity (yrs)\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The 12 shortest-lived animals in this dataset are:\n",
      "                             Common name  Maximum longevity (yrs)\n",
      "42                             Bumblebee                     0.27\n",
      "41                             Fruit fly                     0.30\n",
      "40                        Bassian thrush                     0.60\n",
      "39                        Dwarf seahorse                     1.00\n",
      "38                          Rainbow frog                     2.00\n",
      "37                       Star-nosed mole                     2.50\n",
      "36                           Pink salmon                     3.00\n",
      "35                                   Rat                     3.80\n",
      "34                        Golden hamster                     3.90\n",
      "33                          Powerful owl                     6.10\n",
      "32                              Capybara                    15.10\n",
      "31  European badger, or Old World badger                    18.60\n"
     ]
    }
   ],
   "source": [
    "# Challenge 2 \n",
    "shortest_lived_animals = data.sort_values(\"Maximum longevity (yrs)\", ascending=True).head(12)\n",
    "\n",
    "print(\"\\nThe 12 shortest-lived animals in this dataset are:\")\n",
    "print(shortest_lived_animals[[\"Common name\", \"Maximum longevity (yrs)\"]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the data!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are now going to create a simple horizontal bar plot of the 12 most long-lived animals. In the code below:\n",
    "\n",
    "- The first line sets the size of the figure.\n",
    "- The second line creates a *horiztonal* bar plot (this is a normal bar plot rotated 90 degrees).\n",
    "- The third line tells Python to display the plot. \n",
    "\n",
    "We have left the name of the column containing the lifespan data blank \"______\" can you fill it in below with the correct column name? (Hint: look at what columns we printed above.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.barh(oldest_animals[\"Common name\"], oldest_animals[\"Maximum longevity (yrs)\"]) # ← Which column shows lifespan?\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So far this is quite a simple plot, and it doesn't have any labels or units for the x- and y- axes - this would be considered a poor graph in reality!\n",
    "\n",
    "With matplotlib (our Python plotting package) we can keep adding layers of new information to our plot. We are now going to fill in the x- and y- axis. However, we have left the y-axis blank for you to fill in with a suitable name. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0btw4+fj4aMKECQoJCVH58uVVrVo1q31KlChh+WwymSRdeN5Ykt555x1NnTpVU6ZMUXBwsFxdXRUREWF1+/blY7G3t9eOHTtkb29vte1K/2AwfPhwDRkyxPI9MzOzwHPNAAAAAFAUwvBtVLlyZcXHx1sC8erVq68rENevX1+LFy/WPffcIw8PjxuqycvLS3Xq1NH06dNVokQJ1ahRQ/fcc4+eeuoprVixosAt0leTkJCgzp0769lnn5V0IST/8ssvqlmzZqHt69Wrp7y8PB09elQPPvhgsY7h5OQkJyena6oLAAAAAC7FM8O3mZ+fn+Li4nT06FGFhYVd1/Ou3bt3V5kyZdS5c2dt3LhRBw8eVFxcnF5++WX9/vvv19xfaGioFixYYAm+3t7eCgoK0uLFi685DFerVk2xsbHavHmzkpOT9cILL+ivv/66YvvAwEB1795dPXv21NKlS3Xw4EFt27ZN48eP18qVK695LAAAAABQHIRhG6hYsaLi4uL0zz//XFcgdnFx0Q8//KBKlSrp0UcfVVBQkPr27atz585d10xxixYtlJeXZ/VscGhoaIF1xTFixAjVr19fYWFhCg0NlY+Pj7p06VLkPtHR0erZs6deffVVVa9eXV26dFFiYqIqVap0zWMBAAAAgOIwmc1ms62LAG5EZmamPD095RexRHZOhb+oC8aRPqGDrUsAAACAjVzMBidPnrzqRCEzwwAAAAAAwyEMAwAAAAAMhzAMAAAAADAcwjAAAAAAwHAIwwAAAAAAwyEMAwAAAAAMhzAMAAAAADAcwjAAAAAAwHAcbF0AcLPsiQq76g9rAwAAAIDEzDAAAAAAwIAIwwAAAAAAwyEMAwAAAAAMhzAMAAAAADAcwjAAAAAAwHAIwwAAAAAAw+GnlXDXqD1yjeycXGxdBu5g6RM62LoEAAAA3CbMDAMAAAAADIcwDAAAAAAwHMIwAAAAAMBwCMMAAAAAAMMhDAMAAAAADIcwDAAAAAAwHMIwAAAAAMBwCMMAAAAAAMMhDKOA0NBQRURE2LoMAAAAALhlCMN3qfDwcHXp0sUmxyZMAwAAAPivIwzjPysnJ8fWJQAAAAC4SxGGDSg+Pl6NGzeWk5OTfH19NWzYMJ0/f96qzfnz5zVw4EB5enqqTJkyevPNN2U2my3bZ8yYoWrVqsnZ2VnlypXT448/LunCjHR8fLymTp0qk8kkk8mk9PR0SdKePXvUvn17ubm5qVy5curRo4f++ecfS5+hoaEaOHCgIiIiVKZMGYWFhd36kwEAAADAkAjDBvPHH3/o4YcfVqNGjbRz507NnDlTn3zyicaMGWPVbu7cuXJwcNC2bds0depUTZ48WXPmzJEkbd++XS+//LLefvttpaSkaPXq1WrevLkkaerUqWratKn69eunw4cP6/Dhw/Lz89OJEyfUqlUr1atXT9u3b9fq1av1119/6cknnyxwXEdHRyUkJGjWrFmFjiE7O1uZmZlWCwAAAABcCwdbF4Dba8aMGfLz89P06dNlMplUo0YN/fnnn3r99df11ltvyc7uwr+P+Pn56f3335fJZFL16tW1e/duvf/+++rXr58yMjLk6uqqjh07yt3dXZUrV1a9evUkSZ6ennJ0dJSLi4t8fHwsx50+fbrq1auncePGWdZ9+umn8vPz0y+//KLAwEBJUrVq1TRp0qQixzB+/HhFRUXd7FMDAAAAwECYGTaY5ORkNW3aVCaTybKuWbNmysrK0u+//25Zd99991m1adq0qVJTU5WXl6e2bduqcuXKqlKlinr06KEFCxbozJkzRR53586d+v777+Xm5mZZatSoIUlKS0uztGvQoMFVxzB8+HCdPHnSshw6dKjY4wcAAAAAiZlhXAd3d3f99NNPiouL09q1a/XWW29p1KhRSkxMlJeXV6H7ZGVlqVOnTpo4cWKBbb6+vpbPrq6uVz2+k5OTnJycrrt+AAAAAGBm2GCCgoK0ZcsWq5dhJSQkyN3dXRUrVrSs27p1q9V+P/74o6pVqyZ7e3tJkoODg9q0aaNJkyZp165dSk9P14YNGyRJjo6OysvLs9q/fv362rt3r/z9/RUQEGC1FCcAAwAAAMDNRBi+i508eVJJSUlWy/PPP69Dhw5p0KBB2r9/v77++muNHDlSQ4YMsTwvLEkZGRkaMmSIUlJStHDhQk2bNk2DBw+WJK1YsUIffPCBkpKS9Ntvv2nevHnKz89X9erVJUn+/v7aunWr0tPT9c8//yg/P18DBgzQ8ePH1a1bNyUmJiotLU1r1qxR7969CwRnAAAAALjVuE36LhYXF2d5sdVFffv21apVq/Taa68pJCRE3t7e6tu3r0aMGGHVrmfPnjp79qwaN24se3t7DR48WM8//7wkycvLS0uXLtWoUaN07tw5VatWTQsXLlStWrUkSZGRkerVq5dq1qyps2fP6uDBg/L391dCQoJef/11PfTQQ8rOzlblypXVrl07qxAOAAAAALeDyXzp/bLAHSgzM1Oenp7yi1giOycXW5eDO1j6hA62LgEAAAA34GI2OHnypDw8PIpsy5QcAAAAAMBwCMMAAAAAAMMhDAMAAAAADIcwDAAAAAAwHMIwAAAAAMBwCMMAAAAAAMMhDAMAAAAADMfB1gUAN8ueqLCr/pYYAAAAAEjMDAMAAAAADIgwDAAAAAAwHMIwAAAAAMBwCMMAAAAAAMMhDAMAAAAADIcwDAAAAAAwHMIwAAAAAMBw+J1h3DVqj1wjOycXW5cBg0qf0MHWJQAAAOAaMDMMAAAAADAcwjAAAAAAwHAIwwAAAAAAwyEMAwAAAAAMhzAMAAAAADAcwjAAAAAAwHAIwwAAAAAAwyEMAwAAAAAMhzD8H5Weni6TyaSkpCRbl2IRFxcnk8mkEydO3FA//v7+mjJlyk2pCQAAAACuB2HYBsLDw2UymSxL6dKl1a5dO+3atcvWpQEAAACAIRCGbaRdu3Y6fPiwDh8+rPXr18vBwUEdO3a0dVkAAAAAYAiEYRtxcnKSj4+PfHx8VLduXQ0bNkyHDh3S33//XWj7mJgYeXl5Wa1bvny5TCaT1bqvv/5a9evXl7Ozs6pUqaKoqCidP3++0D737NkjOzs7yzGPHz8uOzs7Pf3005Y2Y8aM0QMPPGC1344dO9SwYUO5uLjo/vvvV0pKimVbWlqaOnfurHLlysnNzU2NGjXSunXrijwXJ06c0HPPPaeyZcvKw8NDrVq10s6dO4vcBwAAAABuBGH4PyArK0ufffaZAgICVLp06evuZ+PGjerZs6cGDx6sffv26aOPPlJMTIzGjh1baPtatWqpdOnSio+Pt+x/6XdJio+PV2hoqNV+b7zxht577z1t375dDg4O6tOnj9VYHn74Ya1fv14///yz2rVrp06dOikjI+OKdT/xxBM6evSovvvuO+3YsUP169dX69atdfz48ULbZ2dnKzMz02oBAAAAgGtBGLaRFStWyM3NTW5ubnJ3d9c333yjxYsXy87u+v8kUVFRGjZsmHr16qUqVaqobdu2Gj16tD766KNC25tMJjVv3lxxcXGSLrwgq3fv3srOztb+/fuVm5urzZs3q0WLFlb7jR07Vi1atFDNmjU1bNgwbd68WefOnZMkhYSE6IUXXlDt2rVVrVo1jR49WlWrVtU333xTaA2bNm3Stm3b9MUXX6hhw4aqVq2a3n33XXl5eenLL78sdJ/x48fL09PTsvj5+V3nGQMAAABgVIRhG2nZsqWSkpKUlJSkbdu2KSwsTO3bt9dvv/123X3u3LlTb7/9tiVku7m5qV+/fjp8+LDOnDlT6D4tWrSwhOH4+Hi1atXKEpATExOVm5urZs2aWe1Tp04dy2dfX19J0tGjRyVdmBmOjIxUUFCQvLy85ObmpuTk5CvODO/cuVNZWVkqXbq0Vd0HDx5UWlpaofsMHz5cJ0+etCyHDh26pvMEAAAAAA62LsCoXF1dFRAQYPk+Z84ceXp6avbs2RozZkyB9nZ2djKbzVbrcnNzrb5nZWUpKipKjz76aIH9nZ2dC60jNDRUERERSk1N1b59+/TAAw9o//79iouL07///mt5NvhSJUqUsHy++Mxyfn6+JCkyMlKxsbF69913FRAQoJIlS+rxxx9XTk5OocfPysqSr6+vJZBf6vJnpC9ycnKSk5NTodsAAAAAoDgIw/8RJpNJdnZ2Onv2bKHby5Ytq1OnTun06dNydXWVpAK/QVy/fn2lpKRYheyrCQ4OVqlSpTRmzBjVrVtXbm5uCg0N1cSJE/Xvv/8WeF74ahISEhQeHq6uXbtKuhB209PTr9i+fv36OnLkiBwcHOTv739NxwIAAACA68Vt0jaSnZ2tI0eO6MiRI0pOTtagQYOUlZWlTp06Fdq+SZMmcnFx0f/+9z+lpaXp888/V0xMjFWbt956S/PmzVNUVJT27t2r5ORkLVq0SCNGjLhiHRefG16wYIEl+NapU0fZ2dlav359geeFr6ZatWpaunSpkpKStHPnTj3zzDOWWePCtGnTRk2bNlWXLl20du1apaena/PmzXrjjTe0ffv2azo2AAAAABQXYdhGVq9eLV9fX/n6+qpJkyZKTEzUF198ccWZWG9vb3322WdatWqVgoODtXDhQo0aNcqqTVhYmFasWKG1a9eqUaNGuu+++/T++++rcuXKRdbSokUL5eXlWY5tZ2en5s2by2QyFXhe+GomT56sUqVK6f7771enTp0UFham+vXrX7G9yWTSqlWr1Lx5c/Xu3VuBgYF6+umn9dtvv6lcuXLXdGwAAAAAKC6T+fIHUYE7TGZm5oW3SkcskZ2Ty9V3AG6B9AkdbF0CAACA4V3MBidPnpSHh0eRbZkZBgAAAAAYDmEYAAAAAGA4hGEAAAAAgOEQhgEAAAAAhkMYBgAAAAAYDmEYAAAAAGA4hGEAAAAAgOE42LoA4GbZExV21d8SAwAAAACJmWEAAAAAgAERhgEAAAAAhkMYBgAAAAAYDmEYAAAAAGA4hGEAAAAAgOEQhgEAAAAAhsNPK+GuUXvkGtk5udi6DOCGpE/oYOsSAAAADIGZYQAAAACA4RCGAQAAAACGQxgGAAAAABgOYRgAAAAAYDiEYQAAAACA4RCGAQAAAACGQxgGAAAAABgOYRgAAAAAYDiEYQAAAACA4RCG73Lh4eHq0qVLgfVxcXEymUw6ceLEba8JAAAAAGyNMAwAAAAAMBzCMDRq1CjVrVvXat2UKVPk7+9v+X5xhnncuHEqV66cvLy89Pbbb+v8+fN67bXX5O3trYoVKyo6Otqqn9dff12BgYFycXFRlSpV9Oabbyo3N7fAsefPny9/f395enrq6aef1qlTp27lkAEAAAAYnIOtC8CdY8OGDapYsaJ++OEHJSQkqG/fvtq8ebOaN2+urVu3avHixXrhhRfUtm1bVaxYUZLk7u6umJgYlS9fXrt371a/fv3k7u6uoUOHWvpNS0vT8uXLtWLFCv3777968sknNWHCBI0dO7bQOrKzs5WdnW35npmZeWsHDgAAAOCuw8ywAaxYsUJubm5WS/v27a+5H29vb33wwQeqXr26+vTpo+rVq+vMmTP63//+p2rVqmn48OFydHTUpk2bLPuMGDFC999/v/z9/dWpUydFRkZqyZIlVv3m5+crJiZGtWvX1oMPPqgePXpo/fr1V6xj/Pjx8vT0tCx+fn7XPBYAAAAAxsbMsAG0bNlSM2fOtFq3detWPfvss9fUT61atWRn93//flKuXDnVrl3b8t3e3l6lS5fW0aNHLesWL16sDz74QGlpacrKytL58+fl4eFh1a+/v7/c3d0t3319fa36uNzw4cM1ZMgQy/fMzEwCMQAAAIBrQhg2AFdXVwUEBFit+/333y2f7ezsZDabrbZf+lzvRSVKlLD6bjKZCl2Xn58vSdqyZYu6d++uqKgohYWFydPTU4sWLdJ777131X4v9lEYJycnOTk5XXE7AAAAAFwNYRgqW7asjhw5IrPZLJPJJElKSkq64X43b96sypUr64033rCs++233264XwAAAAC4UTwzDIWGhurvv//WpEmTlJaWpg8//FDffffdDfdbrVo1ZWRkaNGiRUpLS9MHH3ygZcuW3YSKAQAAAODGEIahoKAgzZgxQx9++KFCQkK0bds2RUZG3nC/jzzyiF555RUNHDhQdevW1ebNm/Xmm2/ehIoBAAAA4MaYzJc/LArcYTIzMy+8VTpiieycXGxdDnBD0id0sHUJAAAAd6yL2eDkyZMFXtx7OWaGAQAAAACGQxgGAAAAABgOYRgAAAAAYDiEYQAAAACA4RCGAQAAAACGQxgGAAAAABgOYRgAAAAAYDiEYQAAAACA4TjYugDgZtkTFXbVH9YGAAAAAImZYQAAAACAARGGAQAAAACGQxgGAAAAABgOYRgAAAAAYDiEYQAAAACA4RCGAQAAAACGw08r4a5Re+Qa2Tm52LoMwDDSJ3SwdQkAAADXjZlhAAAAAIDhEIYBAAAAAIZDGAYAAAAAGA5hGAAAAABgOIRhAAAAAIDhEIYBAAAAAIZDGAYAAAAAGA5hGAAAAABgOIThWygmJkZeXl62LgMAAAAAcBnCcBHCw8PVpUsXW5dRbKGhoYqIiLB1GQAAAADwn3dLwvDvv/+u/Pz8W9E1boOcnBxblwAAAAAAt9QtCcM1a9ZUenr6rej6P2Xy5MkKDg6Wq6ur/Pz81L9/f2VlZV2x/d9//62GDRuqa9euys7OVn5+vsaPH697771XJUuWVEhIiL788ssijzljxgxVq1ZNzs7OKleunB5//HFJF2ax4+PjNXXqVJlMJplMJqWnpxd6q/by5ctlMpks30eNGqW6detqzpw5uvfee+Xs7CxJ2r9/vx544AE5OzurZs2aWrdunUwmk5YvX27Z99ChQ3ryySfl5eUlb29vde7c2epvn5+fr7ffflsVK1aUk5OT6tatq9WrV1u2p6eny2QyaenSpWrZsqVcXFwUEhKiLVu2XO30AwAAAMB1uyVh2Gw234pu/3Ps7Oz0wQcfaO/evZo7d642bNigoUOHFtr20KFDevDBB1W7dm19+eWXcnJy0vjx4zVv3jzNmjVLe/fu1SuvvKJnn31W8fHxhfaxfft2vfzyy3r77beVkpKi1atXq3nz5pKkqVOnqmnTpurXr58OHz6sw4cPy8/Pr9hjOXDggL766istXbpUSUlJysvLU5cuXeTi4qKtW7fq448/1htvvGG1T25ursLCwuTu7q6NGzcqISFBbm5uateunWV2eerUqXrvvff07rvvateuXQoLC9Mjjzyi1NRUq77eeOMNRUZGKikpSYGBgerWrZvOnz9faK3Z2dnKzMy0WgAAAADgWjjYuoA72aXP5/r7+2vMmDF68cUXNWPGDKt2KSkpatu2rbp27aopU6bIZDIpOztb48aN07p169S0aVNJUpUqVbRp0yZ99NFHatGiRYHjZWRkyNXVVR07dpS7u7sqV66sevXqSZI8PT3l6OgoFxcX+fj4XPNYcnJyNG/ePJUtW1aStHr1aqWlpSkuLs7S39ixY9W2bVvLPosXL1Z+fr7mzJljmWmOjo6Wl5eX4uLi9NBDD+ndd9/V66+/rqefflqSNHHiRH3//feaMmWKPvzwQ0tfkZGR6tChgyQpKipKtWrV0oEDB1SjRo0CtY4fP15RUVHXPEYAAAAAuIgXaN2AdevWqXXr1qpQoYLc3d3Vo0cPHTt2TGfOnLG0OXv2rB588EE9+uijlluYpQszsWfOnFHbtm3l5uZmWebNm6e0tLRCj9e2bVtVrlxZVapUUY8ePbRgwQKrY92IypUrW4KwdCHA+/n5WQXrxo0bW+2zc+dOHThwQO7u7pb6vb29de7cOaWlpSkzM1N//vmnmjVrZrVfs2bNlJycbLWuTp06ls++vr6SpKNHjxZa6/Dhw3Xy5EnLcujQoesbNAAAAADDYmb4OqWnp6tjx4566aWXNHbsWHl7e2vTpk3q27evcnJy5OLiIklycnJSmzZttGLFCr322muqUKGCJFmeLV65cqVl3UVOTk6FHtPd3V0//fST4uLitHbtWr311lsaNWqUEhMTr/gTTnZ2dgVuW8/NzS3QztXV9ZrGf3EMDRo00IIFCwpsuzRYF0eJEiUsny/+g8GVXsLm5OR0xXMEAAAAAMXBzPB12rFjh/Lz8/Xee+/pvvvuU2BgoP78888C7ezs7DR//nw1aNBALVu2tLSpWbOmnJyclJGRoYCAAKulqGd9HRwc1KZNG02aNEm7du1Senq6NmzYIElydHRUXl6eVfuyZcvq1KlTOn36tGVdUlLSVcdXvXp1HTp0SH/99ZdlXWJiolWb+vXrKzU1Vffcc0+BMXh6esrDw0Ply5dXQkKC1X4JCQmqWbPmVWsAAAAAgFvllswM36xbd/8LTp48WSA8li5dWgEBAcrNzdW0adPUqVMnJSQkaNasWYX2YW9vrwULFqhbt25q1aqV5TncyMhIvfLKK8rPz9cDDzygkydPKiEhQR4eHurVq1eBflasWKFff/1VzZs3V6lSpbRq1Srl5+erevXqki48t7x161alp6dbbllu0qSJXFxc9L///U8vv/yytm7dqpiYmKuOu23btqpatap69eqlSZMm6dSpUxoxYoSk/5u57d69u9555x117tzZ8sbo3377TUuXLtXQoUNVsWJFvfbaaxo5cqSqVq2qunXrKjo6WklJSYXOJgMAAADA7cLM8FXExcWpXr16VktUVJRCQkI0efJkTZw4UbVr19aCBQs0fvz4K/bj4OCghQsXqlatWmrVqpWOHj2q0aNH680339T48eMVFBSkdu3aaeXKlbr33nsL7cPLy0tLly5Vq1atFBQUpFmzZln6lC68hMre3l41a9ZU2bJllZGRIW9vb3322WdatWqVgoODtXDhQo0aNeqq47a3t9fy5cuVlZWlRo0a6bnnnrO8TfriTy+5uLjohx9+UKVKlfToo48qKChIffv21blz5+Th4SFJevnllzVkyBC9+uqrCg4O1urVq/XNN9+oWrVq1/JnAAAAAICbymS+Bb+D5O7urp07d6pKlSo3u2vYUEJCgh544AEdOHBAVatWtXU5FpmZmfL09JRfxBLZObnYuhzAMNIndLB1CQAAAFYuZoOTJ09aJuiuhBdo4YqWLVsmNzc3VatWTQcOHNDgwYPVrFmz/1QQBgAAAIDrQRjGFZ06dUqvv/66MjIyVKZMGbVp00bvvfeercsCAAAAgBtGGMYV9ezZUz179rR1GQAAAABw0/ECLQAAAACA4dySMHw3/bQSAAAAAODuw8wwAAAAAMBwbkkYdnHh520AAAAAAP9dvEALd409UWFX/S0xAAAAAJC4TRoAAAAAYECEYQAAAACA4RCGAQAAAACGw08rAQAAAAAMh5lhAAAAAIDh8NNKAAAAAADD4aeVcNeoPXKN7Jz4hxjAqNIndLB1CQAA4A7CbdIAAAAAAMMhDAMAAAAADIcwDAAAAAAwHH5aCQAAAABgOMwMAwAAAAAMh59WAgAAAAAYDjPDAAAAAADDIQwDAAAAAAzHEGE4JiZGXl5eti7DUEJDQxUREWHrMgAAAACgUP/pt0kfOXJEgwcPVkBAgJydnVWuXDk1a9ZMM2fO5I3VVzBq1CjVrVv3pvV3vaF26dKlGj169E2rAwAAAABuJodb0enNeIHWr7/+qmbNmsnLy0vjxo1TcHCwnJyctHv3bn388ceqUKGCHnnkkZtQLQqTk5MjR0fH697f29v7JlYDAAAAADfXf3ZmuH///nJwcND27dv15JNPKigoSFWqVFHnzp21cuVKderUydJ28uTJCg4Olqurq/z8/NS/f39lZWVdse+0tDR17txZ5cqVk5ubmxo1aqR169ZZtfH399fo0aPVrVs3ubq6qkKFCvrwww+t2hTnuLNnz5afn59cXFzUtWtXTZ48ucAt2zNnzlTVqlXl6Oio6tWra/78+ZZtZrNZo0aNUqVKleTk5KTy5cvr5ZdfLnRcMTExioqK0s6dO2UymWQymRQTEyNJysjIUOfOneXm5iYPDw89+eST+uuvvyz7XpxRnjNnju699145OzsrPDxc8fHxmjp1qqW/9PR0SVJ8fLwaN24sJycn+fr6atiwYTp//rylv8tnlGfMmKFq1apZZvgff/xxy7b8/HyNHz9e9957r0qWLKmQkBB9+eWXhf/xAAAAAOAm+E/+tNKxY8e0du1aDRgwQK6uroW2MZlMls92dnb64IMPtHfvXs2dO1cbNmzQ0KFDr9h/VlaWHn74Ya1fv14///yz2rVrp06dOikjI8Oq3TvvvKOQkBD9/PPPGjZsmAYPHqzY2NhiHzchIUEvvviiBg8erKSkJLVt21Zjx461OsayZcs0ePBgvfrqq9qzZ49eeOEF9e7dW99//70k6auvvtL777+vjz76SKmpqVq+fLmCg4MLHddTTz2lV199VbVq1dLhw4d1+PBhPfXUU8rPz1fnzp11/PhxxcfHKzY2Vr/++queeuopq/0PHDigr776SkuXLlVSUpKmTp2qpk2bql+/fpb+/Pz89Mcff+jhhx9Wo0aNtHPnTs2cOVOffPKJxowZU2hd27dv18svv6y3335bKSkpWr16tZo3b27ZPn78eM2bN0+zZs3S3r179corr+jZZ59VfHz8Ff+GAAAAAHAjbslt0jfqwIEDMpvNql69utX6MmXK6Ny5c5KkAQMGaOLEiZJkNQPp7++vMWPG6MUXX9SMGTMK7T8kJEQhISGW76NHj9ayZcv0zTffaODAgZb1zZo107BhwyRJgYGBSkhI0Pvvv6+2bdsW67jTpk1T+/btFRkZaelj8+bNWrFihWW/d999V+Hh4erfv78kaciQIfrxxx/17rvvqmXLlsrIyJCPj4/atGmjEiVKqFKlSmrcuHGh4ypZsqTc3Nzk4OAgHx8fy/rY2Fjt3r1bBw8elJ+fnyRp3rx5qlWrlhITE9WoUSNJF26NnjdvnsqWLWvZ19HRUS4uLlb9zZgxQ35+fpo+fbpMJpNq1KihP//8U6+//rreeust2dlZ/xtLRkaGXF1d1bFjR7m7u6ty5cqqV6+eJCk7O1vjxo3TunXr1LRpU0lSlSpVtGnTJn300Udq0aJFgXFmZ2crOzvb8j0zM7PQ8wEAAAAAV3JHvU1627ZtSkpKUq1atazC0Lp169S6dWtVqFBB7u7u6tGjh44dO3bF27WzsrIUGRmpoKAgeXl5yc3NTcnJyQVmhi+Gs0u/JycnF/u4KSkpBYLr5d+Tk5PVrFkzq3XNmjWzHOeJJ57Q2bNnVaVKFfXr10/Lli2zuh25OJKTk+Xn52cJwpJUs2ZNeXl5WY2ncuXKVkG4qP6aNm1qNTvfrFkzZWVl6ffffy/Qvm3btqpcubKqVKmiHj16aMGCBZZzdODAAZ05c0Zt27aVm5ubZZk3b57S0tIKPf748ePl6elpWS4dFwAAAAAUxy0Jw5eGpOsREBAgk8mklJQUq/VVqlRRQECASpYsaVmXnp6ujh07qk6dOvrqq6+0Y8cOy7O9OTk5hfYfGRmpZcuWady4cdq4caOSkpIUHBx8xfaFuZ7jXg8/Pz+lpKRoxowZKlmypPr376/mzZsrNzf3ph3joivdkn6j3N3d9dNPP2nhwoXy9fXVW2+9pZCQEJ04ccLyjPXKlSuVlJRkWfbt23fF54aHDx+ukydPWpZDhw7dkroBAAAA3L1uSRg2m803tH/p0qXVtm1bTZ8+XadPny6y7Y4dO5Sfn6/33ntP9913nwIDA/Xnn38WuU9CQoLCw8PVtWtXBQcHy8fHx/JiqEv9+OOPBb4HBQUV+7jVq1dXYmKi1brLvwcFBSkhIaFAfTVr1rR8L1mypDp16qQPPvhAcXFx2rJli3bv3l3o2BwdHZWXl1fgGIcOHbIKjfv27dOJEyesjnMt/W3ZssXq75yQkCB3d3dVrFix0H4cHBzUpk0bTZo0Sbt27VJ6ero2bNigmjVrysnJSRkZGQoICLBarjTj6+TkJA8PD6sFAAAAAK7FLXlmeN++fSpfvvwN9TFjxgw1a9ZMDRs21KhRo1SnTh3Z2dkpMTFR+/fvV4MGDSRdmEXOzc3VtGnT1KlTJyUkJGjWrFlF9l2tWjUtXbpUnTp1kslk0ptvvqn8/PwC7RISEjRp0iR16dJFsbGx+uKLL7Ry5cpiH3fQoEFq3ry5Jk+erE6dOmnDhg367rvvrGbOX3vtNT355JOqV6+e2rRpo2+//VZLly61vN06JiZGeXl5atKkiVxcXPTZZ5+pZMmSqly5cqFj8/f318GDB5WUlKSKFSvK3d1dbdq0UXBwsLp3764pU6bo/Pnz6t+/v1q0aKGGDRsWea78/f21detWpaeny83NTd7e3urfv7+mTJmiQYMGaeDAgUpJSdHIkSM1ZMiQAs8LS9KKFSv066+/qnnz5ipVqpRWrVql/Px8Va9eXe7u7oqMjNQrr7yi/Px8PfDAAzp58qQSEhLk4eGhXr16FVkfAAAAAFyPWzIz7OfnJ3t7+xvqo2rVqvr555/Vpk0bDR8+XCEhIWrYsKGmTZumyMhIjR49WtKFl2FNnjxZEydOVO3atbVgwQKNHz++yL4nT56sUqVK6f7771enTp0UFham+vXrF2j36quvavv27apXr57GjBmjyZMnKywsrNjHbdasmWbNmqXJkycrJCREq1ev1iuvvCJnZ2dLmy5dumjq1Kl69913VatWLX300UeKjo5WaGioJMnLy0uzZ89Ws2bNVKdOHa1bt07ffvutSpcuXejYHnvsMbVr104tW7ZU2bJltXDhQplMJn399dcqVaqUmjdvrjZt2qhKlSpavHjxVf8OkZGRsre3V82aNVW2bFllZGSoQoUKWrVqlbZt26aQkBC9+OKL6tu3r0aMGFFoH15eXlq6dKlatWqloKAgzZo1SwsXLlStWrUkXXiB2Ztvvqnx48crKChI7dq108qVK3XvvfdetT4AAAAAuB4m843e03yX8vf3V0REhNUbo2+Gfv36af/+/dq4ceNN7dfIMjMzL7xIK2KJ7Jxu7Ge9ANy50id0sHUJAADAxi5mg5MnT171ccr/5E8r3U3effddtW3bVq6urvruu+80d+7cK/7kEwAAAADg9iAM32Lbtm3TpEmTdOrUKVWpUkUffPCBnnvuOVuXBQAAAACGRhi+gsLeLn09lixZclP6AQAAAADcPLfkBVoAAAAAAPyXEYYBAAAAAIZDGAYAAAAAGA5hGAAAAABgOIRhAAAAAIDh8DZp3DX2RIVd9Ye1AQAAAEBiZhgAAAAAYECEYQAAAACA4RCGAQAAAACGQxgGAAAAABgOYRgAAAAAYDiEYQAAAACA4fDTSrhr1B65RnZOLrYuA4DBpU/oYOsSAABAMTAzDAAAAAAwHMIwAAAAAMBwCMMAAAAAAMMhDAMAAAAADIcwDAAAAAAwHMIwAAAAAMBwCMMAAAAAAMMhDAMAAAAADIcwfBvExcXJZDLpxIkTti7llhs1apTq1q1bZJvQ0FBFRETclnoAAAAAoDCE4UuEh4fLZDLJZDKpRIkSuvfeezV06FCdO3fO1qXdVZYuXarRo0fbugwAAAAABuZg6wL+a9q1a6fo6Gjl5uZqx44d6tWrl0wmkyZOnGjr0mwuJydHjo6ON9yPt7f3TagGAAAAAK4fM8OXcXJyko+Pj/z8/NSlSxe1adNGsbGxlu35+fkaP3687r33XpUsWVIhISH68ssvrfpYtWqVAgMDVbJkSbVs2VLp6elFHtNsNmvUqFGqVKmSnJycVL58eb388suW7dnZ2YqMjFSFChXk6uqqJk2aKC4uzrL92LFj6tatmypUqCAXFxcFBwdr4cKFRR6zsNuZp0yZIn9/f8v38PBwdenSRWPHjlX58uVVvXp1SdLvv/+ubt26ydvbW66urmrYsKG2bt1q1df8+fPl7+8vT09PPf300zp16pRl2+W3Sfv7+2vcuHHq06eP3N3dValSJX388cdF1g8AAAAAN4IwXIQ9e/Zo8+bNVrOh48eP17x58zRr1izt3btXr7zyip599lnFx8dLkg4dOqRHH31UnTp1UlJSkp577jkNGzasyON89dVXev/99/XRRx8pNTVVy5cvV3BwsGX7wIEDtWXLFi1atEi7du3SE088oXbt2ik1NVWSdO7cOTVo0EArV67Unj179Pzzz6tHjx7atm3bDZ+D9evXKyUlRbGxsVqxYoWysrLUokUL/fHHH/rmm2+0c+dODR06VPn5+ZZ90tLStHz5cq1YsUIrVqxQfHy8JkyYUORx3nvvPTVs2FA///yz+vfvr5deekkpKSmFts3OzlZmZqbVAgAAAADXgtukL7NixQq5ubnp/Pnzys7Olp2dnaZPny7pQggbN26c1q1bp6ZNm0qSqlSpok2bNumjjz5SixYtNHPmTFWtWlXvvfeeJKl69eravXt3kbdZZ2RkyMfHR23atFGJEiVUqVIlNW7c2LItOjpaGRkZKl++vCQpMjJSq1evVnR0tMaNG6cKFSooMjLS0t+gQYO0Zs0aLVmyxNLP9XJ1ddWcOXMs/yDw8ccf6++//1ZiYqLldueAgACrffLz8xUTEyN3d3dJUo8ePbR+/XqNHTv2isd5+OGH1b9/f0nS66+/rvfff1/ff/+9ZTb6UuPHj1dUVNQNjQsAAACAsRGGL9OyZUvNnDlTp0+f1vvvvy8HBwc99thjkqQDBw7ozJkzatu2rdU+OTk5qlevniQpOTlZTZo0sdp+MThfyRNPPKEpU6aoSpUqateunR5++GF16tRJDg4O2r17t/Ly8hQYGGi1T3Z2tkqXLi1JysvL07hx47RkyRL98ccfysnJUXZ2tlxcXG7oXEhScHCw1cx4UlKS6tWrV+Rzv/7+/pYgLEm+vr46evRokcepU6eO5bPJZJKPj88V9xk+fLiGDBli+Z6ZmSk/P7+rjgUAAAAALiIMX8bV1dUy0/npp58qJCREn3zyifr27ausrCxJ0sqVK1WhQgWr/ZycnK77mH5+fkpJSdG6desUGxur/v3765133lF8fLyysrJkb2+vHTt2yN7e3mo/Nzc3SdI777yjqVOnasqUKQoODparq6siIiKUk5NzxWPa2dnJbDZbrcvNzS3QztXV1ep7yZIlrzqeEiVKWH03mUxWt1Hf6D5OTk43dL4BAAAAgDBcBDs7O/3vf//TkCFD9Mwzz6hmzZpycnJSRkaGWrRoUeg+QUFB+uabb6zW/fjjj1c9VsmSJdWpUyd16tRJAwYMUI0aNbR7927Vq1dPeXl5Onr0qB588MFC901ISFDnzp317LPPSrpwm/Ivv/yimjVrXvF4ZcuW1ZEjR2Q2m2UymSRdmPW9mjp16mjOnDk6fvw4b4UGAAAAcMfiBVpX8cQTT8je3l4ffvih3N3dFRkZqVdeeUVz585VWlqafvrpJ02bNk1z586VJL344otKTU3Va6+9ppSUFH3++eeKiYkp8hgxMTH65JNPtGfPHv3666/67LPPVLJkSVWuXFmBgYHq3r27evbsqaVLl+rgwYPatm2bxo8fr5UrV0qSqlWrptjYWG3evFnJycl64YUX9NdffxV5zNDQUP3999+aNGmS0tLS9OGHH+q777676vno1q2bfHx81KVLFyUkJOjXX3/VV199pS1bthTvhAIAAADAfwBh+CocHBw0cOBATZo0SadPn9bo0aP15ptvavz48QoKClK7du20cuVK3XvvvZKkSpUq6auvvtLy5csVEhKiWbNmady4cUUew8vLS7Nnz1azZs1Up04drVu3Tt9++63lmeDo6Gj17NlTr776qqpXr64uXbooMTFRlSpVkiSNGDFC9evXV1hYmEJDQy1htShBQUGaMWOGPvzwQ4WEhGjbtm1WL+G6EkdHR61du1b33HOPHn74YQUHB2vChAkFbuEGAAAAgP8yk/nyB0eBO0xmZqY8PT3lF7FEdk43/tIwALgR6RM62LoEAAAM62I2OHnypDw8PIpsy8wwAAAAAMBwCMMAAAAAAMMhDAMAAAAADIcwDAAAAAAwHMIwAAAAAMBwCMMAAAAAAMMhDAMAAAAADMfB1gUAN8ueqLCr/pYYAAAAAEjMDAMAAAAADIgwDAAAAAAwHMIwAAAAAMBwCMMAAAAAAMMhDAMAAAAADIcwDAAAAAAwHH5aCXeN2iPXyM7JxdZlAMAVpU/oYOsSAADA/8fMMAAAAADAcAjDAAAAAADDIQwDAAAAAAyHMAwAAAAAMBzCMAAAAADAcAjDAAAAAADDIQwDAAAAAAyHMAwAAAAAMBzC8HUIDw9Xly5dbF3GbVOc8X788cfy8/OTnZ2dpkyZolGjRqlu3brF6v9a2gIAAADAzXDHhOHw8HCZTCaZTCaVKFFC9957r4YOHapz587ZujTDy8zM1MCBA/X666/rjz/+0PPPP6/IyEitX7/e1qUBAAAAQKEcbF3AtWjXrp2io6OVm5urHTt2qFevXjKZTJo4caKtS/tPMpvNysvLk4PDrf0zZ2RkKDc3Vx06dJCvr69lvZub2y09LgAAAABcrztmZliSnJyc5OPjIz8/P3Xp0kVt2rRRbGysZXt2drZefvll3XPPPXJ2dtYDDzygxMREy/aYmBh5eXlZ9bl8+XKZTCardWPGjNE999wjd3d3Pffccxo2bFiht/G+++678vX1VenSpTVgwADl5uZa1RIZGakKFSrI1dVVTZo0UVxcnCTp9OnT8vDw0JdfflmgFldXV506dUqSdOjQIT355JPy8vKSt7e3OnfurPT09Cuen7i4OJlMJn333Xdq0KCBnJyctGnTpqueF0nau3evOnbsKA8PD7m7u+vBBx9UWlpaocdJTExU2bJlNXHiRMXExCg4OFiSVKVKFZlMJqWnpxe49TkuLk6NGzeWq6urvLy81KxZM/32229W/c6fP1/+/v7y9PTU008/bTkPAAAAAHCz3VFh+FJ79uzR5s2b5ejoaFk3dOhQffXVV5o7d65++uknBQQEKCwsTMePHy92vwsWLNDYsWM1ceJE7dixQ5UqVdLMmTMLtPv++++Vlpam77//XnPnzlVMTIxiYmIs2wcOHKgtW7Zo0aJF2rVrl5544gm1a9dOqampcnV11dNPP63o6GirPqOjo/X444/L3d1dubm5CgsLk7u7uzZu3KiEhAS5ubmpXbt2ysnJKXIMw4YN04QJE5ScnKw6depc9bz88ccfat68uZycnLRhwwbt2LFDffr00fnz5wv0vWHDBrVt21Zjx47V66+/rqeeekrr1q2TJG3btk2HDx+Wn5+f1T7nz59Xly5d1KJFC+3atUtbtmzR888/b/WPEGlpaVq+fLlWrFihFStWKD4+XhMmTCj6jwUAAAAA1+mOuk16xYoVcnNz0/nz55WdnS07OztNnz5d0oXZ1pkzZyomJkbt27eXJM2ePVuxsbH65JNP9NprrxXrGNOmTVPfvn3Vu3dvSdJbb72ltWvXKisry6pdqVKlNH36dNnb26tGjRrq0KGD1q9fr379+ikjI0PR0dHKyMhQ+fLlJUmRkZFavXq1oqOjNW7cOD333HO6//77dfjwYfn6+uro0aNatWqVJVguXrxY+fn5mjNnjiU0RkdHy8vLS3FxcXrooYeuOIa3335bbdu2LfZ5+fDDD+Xp6alFixapRIkSkqTAwMAC/S5btkw9e/bUnDlz9NRTT0mSSpYsqdKlS0uSypYtKx8fnwL7ZWZm6uTJk+rYsaOqVq0qSQoKCrJqk5+fr5iYGLm7u0uSevToofXr12vs2LEF+svOzlZ2drZV/wAAAABwLe6omeGWLVsqKSlJW7duVa9evdS7d2899thjki7MLObm5qpZs2aW9iVKlFDjxo2VnJxc7GOkpKSocePGVusu/y5JtWrVkr29veX7xUArSbt371ZeXp4CAwPl5uZmWeLj4y23Hjdu3Fi1atXS3LlzJUmfffaZKleurObNm0uSdu7cqQMHDsjd3d2yv7e3t86dO3fF25cvatiwoeVzcc5LUlKSHnzwQUsQLszWrVv1xBNPaP78+ZYgXFze3t4KDw9XWFiYOnXqpKlTp+rw4cNWbfz9/S1BWLI+n5cbP368PD09LcvlM9EAAAAAcDV3VBh2dXVVQECAQkJC9Omnn2rr1q365JNPir2/nZ2dzGaz1bpLn/O9FpcHR5PJpPz8fElSVlaW7O3ttWPHDiUlJVmW5ORkTZ061bLPc889Z7m1Ojo6Wr1797bMAmdlZalBgwZW+yclJemXX37RM888U2Rtrq6u1zSWkiVLXrVN1apVVaNGDX366afXdc6io6O1ZcsW3X///Vq8eLECAwP1448/WrYXdT4vN3z4cJ08edKyHDp06JrrAQAAAGBsd1QYvpSdnZ3+97//acSIETp79qyqVq0qR0dHJSQkWNrk5uYqMTFRNWvWlHThNt5Tp07p9OnTljZJSUlW/VavXr3Ay6Uu/3419erVU15eno4ePaqAgACr5dLbiJ999ln99ttv+uCDD7Rv3z716tXLsq1+/fpKTU3VPffcU6APT0/PYtdSnPNSp04dbdy4sciQW6ZMGW3YsEEHDhzQk08+eV2BuF69eho+fLg2b96s2rVr6/PPP7/mPqQLL1Lz8PCwWgAAAADgWtyxYViSnnjiCdnb2+vDDz+Uq6urXnrpJb322mtavXq19u3bp379+unMmTPq27evJKlJkyZycXHR//73P6Wlpenzzz+3eumVJA0aNEiffPKJ5s6dq9TUVI0ZM0a7du0q8MbpogQGBqp79+7q2bOnli5dqoMHD2rbtm0aP368Vq5caWlXqlQpPfroo3rttdf00EMPqWLFipZt3bt3V5kyZdS5c2dt3LhRBw8eVFxcnF5++WX9/vvvxa6lOOdl4MCByszM1NNPP63t27crNTVV8+fPV0pKilVf99xzjzZs2KD9+/erW7duhb5gqzAHDx7U8OHDtWXLFv32229au3atUlNTCzw3DAAAAAC3yx0dhh0cHDRw4EBNmjRJp0+f1oQJE/TYY4+pR48eql+/vg4cOKA1a9aoVKlSki48u/rZZ59p1apVCg4O1sKFCzVq1CirPrt3767hw4crMjJS9evX18GDBxUeHi5nZ+drqi06Olo9e/bUq6++qurVq6tLly5KTExUpUqVrNr17dtXOTk56tOnj9V6FxcX/fDDD6pUqZIeffRRBQUFqW/fvjp37tw1z4Re7byULl1aGzZsUFZWllq0aKEGDRpo9uzZhT5D7OPjow0bNmj37t3q3r278vLyrnp8FxcX7d+/X4899pgCAwP1/PPPa8CAAXrhhReuaRwAAAAAcLOYzJc/RIsC2rZtKx8fH82fP/+m9z1//ny98sor+vPPP61+JgrFl5mZeeFFWhFLZOfkYutyAOCK0id0sHUJAADc1S5mg5MnT151EvGO+mml2+HMmTOaNWuWwsLCZG9vr4ULF2rdunWKjY296cc5fPiwJkyYoBdeeIEgDAAAAAC30R19m/StYDKZtGrVKjVv3lwNGjTQt99+q6+++kpt2rS5qceZNGmSatSoIR8fHw0fPvym9g0AAAAAKBq3SeOOx23SAO4U3CYNAMCtdS23STMzDAAAAAAwHMIwAAAAAMBwCMMAAAAAAMMhDAMAAAAADIcwDAAAAAAwHH5nGHeNPVFhV31jHAAAAABIzAwDAAAAAAyIMAwAAAAAMBzCMAAAAADAcAjDAAAAAADDIQwDAAAAAAyHMAwAAAAAMBx+Wgl3jdoj18jOycXWZQDADUmf0MHWJQAAYAjMDAMAAAAADIcwDAAAAAAwHMIwAAAAAMBwCMMAAAAAAMMhDAMAAAAADIcwDAAAAAAwHMIwAAAAAMBwCMMAAAAAAMMhDN8GcXFxMplMOnHixG0/dkxMjLy8vG5af6GhoYqIiLihPm52TQAAAABwrQwXhsPDw2UymSxL6dKl1a5dO+3atcvWpQEAAAAAbhPDhWFJateunQ4fPqzDhw9r/fr1cnBwUMeOHW1dFgAAAADgNjFkGHZycpKPj498fHxUt25dDRs2TIcOHdLff/9tabN79261atVKJUuWVOnSpfX8888rKytLkrRnzx7Z2dlZ2h8/flx2dnZ6+umnLfuPGTNGDzzwgNVxd+zYoYYNG8rFxUX333+/UlJSrLZ//fXXql+/vpydnVWlShVFRUXp/Pnzlu2TJ09WcHCwXF1d5efnp/79+1tquigmJkaVKlWSi4uLunbtqmPHjhV5Lh5//HENHDjQ8j0iIkImk0n79++XJOXk5MjV1VXr1q2ztMnPz9fQoUPl7e0tHx8fjRo1yqrP4tR5uauNHQAAAABuJkOG4UtlZWXps88+U0BAgEqXLi1JOn36tMLCwlSqVCklJibqiy++0Lp16yyhsVatWipdurTi4+MlSRs3brT6Lknx8fEKDQ21OtYbb7yh9957T9u3b5eDg4P69Olj2bZx40b17NlTgwcP1r59+/TRRx8pJiZGY8eOtbSxs7PTBx98oL1792ru3LnasGGDhg4datm+detW9e3bVwMHDlRSUpJatmypMWPGFDn+Fi1aKC4uzqruMmXKWNYlJiYqNzdX999/v6XN3Llz5erqqq1bt2rSpEl6++23FRsbW+w6L1ecsV8qOztbmZmZVgsAAAAAXAuT2Ww227qI2yk8PFyfffaZnJ2dJV0Ivr6+vlqxYoXq168vSZo9e7Zef/11HTp0SK6urpKkVatWqVOnTvrzzz9Vrlw5PfbYY/L19dX06dP1yiuvqESJEpozZ442b96sqlWrysvLS8uXL1fbtm0VFxenli1bat26dWrdurWlvw4dOujs2bNydnZWmzZt1Lp1aw0fPtxS62effaahQ4fqzz//LHQsX375pV588UX9888/kqRnnnlGJ0+e1MqVKy1tnn76aa1evfqKL+/avXu3QkJC9Ndff8nBwUE+Pj568803tWfPHi1atEhjx47VqlWrlJCQIOnCC7Ty8vK0ceNGSx+NGzdWq1atNGHChGLVGRMTo4iICEtN1zr2UaNGKSoqqsB6v4glsnNyKbQGALhTpE/oYOsSAAC4Y2VmZsrT01MnT56Uh4dHkW0NOTPcsmVLJSUlKSkpSdu2bVNYWJjat2+v3377TZKUnJyskJAQSxCWpGbNmik/P99ya/OlM6rx8fFq1aqVmjdvrri4OMtsarNmzayOW6dOHctnX19fSdLRo0clSTt37tTbb78tNzc3y9KvXz8dPnxYZ86ckSRLmK5QoYLc3d3Vo0cPHTt2zLI9OTlZTZo0sTpm06ZNizwXtWvXlre3t+Lj47Vx40bVq1dPHTt2tMxyFzbDfek4Lo7l4jiKU+flijP2Sw0fPlwnT560LIcOHSpyjAAAAABwOQdbF2ALrq6uCggIsHyfM2eOPD09NXv27KveVnzRxZ8YSk1N1b59+/TAAw9o//79iouL07///mt5NvhSJUqUsHw2mUySLjx/K124XTsqKkqPPvpogWM5OzsrPT1dHTt21EsvvaSxY8fK29tbmzZtUt++fZWTk1PgWMVlMpksId7JyUmhoaGqU6eOsrOztWfPHm3evFmRkZFXHMfFPi6O43rqvNrYL+fk5CQnJ6frGi8AAAAASAYNw5czmUyys7PT2bNnJUlBQUGKiYnR6dOnLbPDCQkJsrOzU/Xq1SVJwcHBKlWqlMaMGaO6devKzc1NoaGhmjhxov79998Cs6lXU79+faWkpFiF9Evt2LFD+fn5eu+992Rnd2FCf8mSJVZtgoKCtHXrVqt1P/7441WP3aJFC82ePVtOTk4aO3as7Ozs1Lx5c73zzjvKzs4uMMNdlOLUebmrjR0AAAAAbjZD3iadnZ2tI0eO6MiRI0pOTtagQYOUlZWlTp06SZK6d+8uZ2dn9erVS3v27NH333+vQYMGqUePHipXrpyk/5tRXbBggSX4XpxRXb9+vVq0aHFNNb311luaN2+eoqKitHfvXiUnJ2vRokUaMWKEJCkgIEC5ubmaNm2afv31V82fP1+zZs2y6uPll1/W6tWr9e677yo1NVXTp0/X6tWrr3rs0NBQ7du3T3v37rW8ATs0NFQLFixQw4YNrW4Xv5ri1HmtYwcAAACAm82QYXj16tXy9fWVr6+vmjRpYnlj9MVQ6+LiojVr1uj48eNq1KiRHn/8cbVu3VrTp0+36qdFixbKy8uz7HdxRtVkMl3TbKokhYWFacWKFVq7dq0aNWqk++67T++//74qV64sSQoJCdHkyZM1ceJE1a5dWwsWLND48eOt+rjvvvs0e/ZsTZ06VSEhIVq7dm2xAmVwcLC8vLwsM9zS/70o61pnuItT57WOHQAAAABuNsO9TRp3n4tvjONt0gDuBrxNGgCA68fbpAEAAAAAKAJhGAAAAABgOIRhAAAAAIDhEIYBAAAAAIZDGAYAAAAAGA5hGAAAAABgOIRhAAAAAIDhEIYBAAAAAIbjYOsCgJtlT1TYVX9YGwAAAAAkZoYBAAAAAAZEGAYAAAAAGA5hGAAAAABgOIRhAAAAAIDhEIYBAAAAAIZDGAYAAAAAGA4/rYS7Ru2Ra2Tn5GLrMgAAAG669AkdbF0CcNdhZhgAAAAAYDiEYQAAAACA4RCGAQAAAACGQxgGAAAAABgOYRgAAAAAYDiEYQAAAACA4RCGAQAAAACGQxgGAAAAABgOYfg/IDQ0VBEREbf8OKNGjVLdunWva9/09HSZTCYlJSXd1JouMplMWr58+S3pGwAAAAAud9eH4SNHjmjw4MEKCAiQs7OzypUrp2bNmmnmzJk6c+aMrcsDAAAAANiAg60LuJV+/fVXNWvWTF5eXho3bpyCg4Pl5OSk3bt36+OPP1aFChX0yCOPFLpvbm6uSpQocZsrNp6cnBw5OjraugwAAAAABnNXzwz3799fDg4O2r59u5588kkFBQWpSpUq6ty5s1auXKlOnTpZ2ppMJs2cOVOPPPKIXF1dNXbsWEnS119/rfr168vZ2VlVqlRRVFSUzp8/b9nvxIkTeu6551S2bFl5eHioVatW2rlzp2X7xVuT58+fL39/f3l6eurpp5/WqVOnrlj3/Pnz1bBhQ7m7u8vHx0fPPPOMjh49atkeFxcnk8mk9evXq2HDhnJxcdH999+vlJQUq34mTJigcuXKyd3dXX379tW5c+eKPF///vuvunfvrrJly6pkyZKqVq2aoqOjrdr8+uuvatmypVxcXBQSEqItW7ZYth07dkzdunVThQoV5OLiouDgYC1cuNBq/9DQUA0cOFAREREqU6aMwsLCCq1l5MiR8vX11a5du4qsGQAAAACux10bho8dO6a1a9dqwIABcnV1LbSNyWSy+j5q1Ch17dpVu3fvVp8+fbRx40b17NlTgwcP1r59+/TRRx8pJibGEpQl6YknntDRo0f13XffaceOHapfv75at26t48ePW9qkpaVp+fLlWrFihVasWKH4+HhNmDDhirXn5uZq9OjR2rlzp5YvX6709HSFh4cXaPfGG2/ovffe0/bt2+Xg4KA+ffpYti1ZskSjRo3SuHHjtH37dvn6+mrGjBlFnrM333xT+/bt03fffafk5GTNnDlTZcqUKXDMyMhIJSUlKTAwUN26dbP848C5c+fUoEEDrVy5Unv27NHzzz+vHj16aNu2bVZ9zJ07V46OjkpISNCsWbOstpnNZg0aNEjz5s3Txo0bVadOnQJ1ZmdnKzMz02oBAAAAgGthMpvNZlsXcSts3bpV9913n5YuXaquXbta1pcpU8YyQzpgwABNnDhR0oVgHBERoffff9/Stk2bNmrdurWGDx9uWffZZ59p6NCh+vPPP7Vp0yZ16NBBR48elZOTk6VNQECAhg4dqueff16jRo3SO++8oyNHjsjd3V2SNHToUP3www/68ccfJV2YLa1bt66mTJlS6Fi2b9+uRo0a6dSpU3Jzc1NcXJxatmypdevWqXXr1pKkVatWqUOHDjp79qycnZ11//33q169evrwww8t/dx33306d+7cFV+C9cgjj6hMmTL69NNPC2xLT0/Xvffeqzlz5qhv376SpH379qlWrVpKTk5WjRo1Cu2zY8eOqlGjht59913LWDMzM/XTTz9ZtTOZTPriiy+0bNky/fzzz4qNjVWFChUK7XPUqFGKiooqsN4vYonsnFwK3QcAAOBOlj6hg61LAO4ImZmZ8vT01MmTJ+Xh4VFk27t2ZvhKtm3bpqSkJNWqVUvZ2dlW2xo2bGj1fefOnXr77bfl5uZmWfr166fDhw/rzJkz2rlzp7KyslS6dGmrNgcPHlRaWpqlH39/f0sQliRfX1+r254vt2PHDnXq1EmVKlWSu7u7WrRoIUnKyMiwanfprKmvr68kWfpNTk5WkyZNrNo3bdq0yHPz0ksvadGiRapbt66GDh2qzZs3F2hT1DHz8vI0evRoBQcHy9vbW25ublqzZk2Buhs0aFDo8V955RVt3bpVP/zwwxWDsCQNHz5cJ0+etCyHDh0qclwAAAAAcLm79gVaAQEBMplMBZ6jrVKliiSpZMmSBfa5/HbqrKwsRUVF6dFHHy3Q1tnZWVlZWfL19VVcXFyB7V5eXpbPl7+Iy2QyKT8/v9C6T58+rbCwMIWFhWnBggUqW7asMjIyFBYWppycHKu2l/Z78ZbvK/VbHO3bt9dvv/2mVatWKTY2Vq1bt9aAAQMss7pXO+Y777yjqVOnasqUKQoODparq6siIiIK1H2l29bbtm2rhQsXas2aNerevfsV63RycrKaiQcAAACAa3XXhuHSpUurbdu2mj59ugYNGnTFAFaU+vXrKyUlRQEBAVfcfuTIETk4OMjf3/8GK75g//79OnbsmCZMmCA/Pz9JF26TvlZBQUHaunWrevbsaVl38bbsopQtW1a9evVSr1699OCDD+q1116zCsNFSUhIUOfOnfXss89KuhCSf/nlF9WsWbNY+z/yyCPq1KmTnnnmGdnb2+vpp58u1n4AAAAAcK3u6tukZ8yYofPnz6thw4ZavHixkpOTlZKSos8++0z79++Xvb19kfu/9dZbmjdvnqKiorR3714lJydr0aJFGjFihKQLzxQ3bdpUXbp00dq1a5Wenq7NmzfrjTfeuK4AK0mVKlWSo6Ojpk2bpl9//VXffPONRo8efc39DB48WJ9++qmio6P1yy+/aOTIkdq7d+9Vx/v111/rwIED2rt3r1asWKGgoKBiH7NatWqKjY3V5s2blZycrBdeeEF//fXXNdXdtWtXzZ8/X71799aXX355TfsCAAAAQHHd1WG4atWq+vnnn9WmTRsNHz5cISEhatiwoaZNm6bIyMirhsywsDCtWLFCa9euVaNGjXTffffp/fffV+XKlSVduE141apVat68uXr37q3AwEA9/fTT+u2331SuXLnrqrls2bKKiYnRF198oZo1a2rChAnFnpm91FNPPaU333xTQ4cOVYMGDfTbb7/ppZdeKnIfR0dHDR8+XHXq1FHz5s1lb2+vRYsWFfuYI0aMUP369RUWFqbQ0FD5+PioS5cu11z7448/rrlz56pHjx5aunTpNe8PAAAAAFdz175NGsZx8Y1xvE0aAADcrXibNFA8vE0aAAAAAIAiEIYBAAAAAIZDGAYAAAAAGA5hGAAAAABgOIRhAAAAAIDhEIYBAAAAAIZDGAYAAAAAGI6DrQsAbpY9UWFX/S0xAAAAAJCYGQYAAAAAGBBhGAAAAABgOIRhAAAAAIDhEIYBAAAAAIZDGAYAAAAAGA5hGAAAAABgOPy0Eu4atUeukZ2Ti63LAAAAAAwjfUIHW5dw3ZgZBgAAAAAYDmEYAAAAAGA4hGEAAAAAgOEQhgEAAAAAhkMYBgAAAAAYDmEYAAAAAGA4hGEAAAAAgOEQhgEAAAAAhkMYvkOEhoYqIiLC1mUUEB4eri5duti6DAAAAAC4JjYPw4cOHVKfPn1Uvnx5OTo6qnLlyho8eLCOHTtm69IAAAAAAHcpm4bhX3/9VQ0bNlRqaqoWLlyoAwcOaNasWVq/fr2aNm2q48eP27I8AAAAAMBdyqZheMCAAXJ0dNTatWvVokULVapUSe3bt9e6dev0xx9/6I033rC0zc7O1uuvvy4/Pz85OTkpICBAn3zyiWX7nj171L59e7m5ualcuXLq0aOH/vnnH8v21atX64EHHpCXl5dKly6tjh07Ki0tzbI9PT1dJpNJS5cuVcuWLeXi4qKQkBBt2bKlyDGkpqaqefPmcnZ2Vs2aNRUbGyuTyaTly5dLkuLi4mQymXTixAnLPklJSTKZTEpPT5ckHTt2TN26dVOFChXk4uKi4OBgLVy4sMCx8vPzNXToUHl7e8vHx0ejRo2y2p6RkaHOnTvLzc1NHh4eevLJJ/XXX39ZtRkzZozuueceubu767nnntOwYcNUt27dIse4d+9edezYUR4eHnJ3d9eDDz5ode4uVdzzvGTJEj344IMqWbKkGjVqpF9++UWJiYlq2LCh3Nzc1L59e/39999F1gUAAAAA18tmYfj48eNas2aN+vfvr5IlS1pt8/HxUffu3bV48WKZzWZJUs+ePbVw4UJ98MEHSk5O1kcffSQ3NzdJ0okTJ9SqVSvVq1dP27dv1+rVq/XXX3/pySeftPR5+vRpDRkyRNu3b9f69etlZ2enrl27Kj8/3+rYb7zxhiIjI5WUlKTAwEB169ZN58+fL3QM+fn5evTRR+Xo6KitW7dq1qxZev3116/5XJw7d04NGjTQypUrtWfPHj3//PPq0aOHtm3bZtVu7ty5cnV11datWzVp0iS9/fbbio2NtdTSuXNnHT9+XPHx8YqNjdWvv/6qp556yrL/ggULNHbsWE2cOFE7duxQpUqVNHPmzCJr++OPP9S8eXM5OTlpw4YN2rFjh/r06XPFc1Lc8zxy5EiNGDFCP/30kxwcHPTMM89o6NChmjp1qjZu3KgDBw7orbfeKvQY2dnZyszMtFoAAAAA4Fo42OrAqampMpvNCgoKKnR7UFCQ/v33X/399986ceKElixZotjYWLVp00aSVKVKFUvb6dOnq169eho3bpxl3aeffio/Pz/98ssvCgwM1GOPPWbV/6effqqyZctq3759ql27tmV9ZGSkOnToIEmKiopSrVq1dODAAdWoUaNAjevWrdP+/fu1Zs0alS9fXpI0btw4tW/f/prORYUKFRQZGWn5PmjQIK1Zs0ZLlixR48aNLevr1KmjkSNHSpKqVaum6dOna/369Wrbtq3Wr1+v3bt36+DBg/Lz85MkzZs3T7Vq1VJiYqIaNWqkadOmqW/fvurdu7ck6a233tLatWuVlZV1xdo+/PBDeXp6atGiRSpRooQkKTAw8Irtr+U8h4WFSZIGDx6sbt26af369WrWrJkkqW/fvoqJiSn0GOPHj1dUVNQVawAAAACAq7H5C7QuzvwWJSkpSfb29mrRokWh23fu3Knvv/9ebm5uluVieL14i25qaqq6deumKlWqyMPDQ/7+/pIu3Fp8qTp16lg++/r6SpKOHj1a6HGTk5Pl5+dnCcKS1LRp06uO53J5eXkaPXq0goOD5e3tLTc3N61Zs6bI2i7Wd7G2i7VcDMKSVLNmTXl5eSk5OVmSlJKSYhWuJRX4frmkpCQ9+OCDliB8NddznsuVKydJCg4Otlp3pfM+fPhwnTx50rIcOnSoWLUBAAAAwEU2mxkOCAiQyWRScnKyunbtWmB7cnKySpUqpbJlyxa4jfpyWVlZ6tSpkyZOnFhg28VA26lTJ1WuXFmzZ89W+fLllZ+fr9q1aysnJ8eq/aWhz2QySVKBW3yvhZ3dhX9vuDT05+bmWrV55513NHXqVE2ZMkXBwcFydXVVREREkbVdrO9GaiuOq537y93Ieb583ZXG5uTkJCcnp2uqCwAAAAAuZbOZ4dKlS6tt27aaMWOGzp49a7XtyJEjWrBggZ566imZTCYFBwcrPz9f8fHxhfZVv3597d27V/7+/goICLBaXF1ddezYMaWkpGjEiBFq3bq15RbsGxUUFKRDhw7p8OHDlnU//vijVZuyZctKklWbpKQkqzYJCQnq3Lmznn32WYWEhKhKlSr65ZdfrquWS2dJ9+3bpxMnTqhmzZqSpOrVqysxMdFqv8u/X65OnTrauHFjgQBfmFt1ngEAAADgZrPpbdLTp09Xdna2wsLC9MMPP+jQoUNavXq12rZtqwoVKmjs2LGSJH9/f/Xq1Ut9+vTR8uXLdfDgQcXFxWnJkiWSLryV+vjx4+rWrZsSExOVlpamNWvWqHfv3srLy1OpUqVUunRpffzxxzpw4IA2bNigIUOG3HD9bdq0UWBgoHr16qWdO3dq48aNVm/Ali7MgPv5+WnUqFFKTU3VypUr9d5771m1qVatmmJjY7V582YlJyfrhRdeKPAW6OLUEhwcrO7du+unn37Stm3b1LNnT7Vo0UINGzaUdOFZ5E8++URz585VamqqxowZo127dllmZgszcOBAZWZm6umnn9b27duVmpqq+fPnKyUlpUDbW3WeAQAAAOBms2kYrlatmrZv364qVaroySefVNWqVfX888+rZcuW2rJli7y9vS1tZ86cqccff1z9+/dXjRo11K9fP50+fVqSVL58eSUkJCgvL08PPfSQgoODFRERIS8vL9nZ2cnOzk6LFi3Sjh07VLt2bb3yyit65513brh+Ozs7LVu2TGfPnlXjxo313HPPWQL8RSVKlNDChQu1f/9+1alTRxMnTtSYMWOs2owYMUL169dXWFiYQkND5ePjoy5dulxTLSaTSV9//bVKlSql5s2bq02bNqpSpYoWL15sadO9e3cNHz5ckZGRql+/vg4ePKjw8HA5Oztfsd/SpUtrw4YNysrKUosWLdSgQQPNnj270GeIb9V5BgAAAICbzWQuzhuscE1MJpOWLVt2zYHWFtq2bSsfHx/Nnz/f1qVct8zMTHl6esovYonsnFxsXQ4AAABgGOkTOti6BCsXs8HJkyfl4eFRZFubvUALt9+ZM2c0a9YshYWFyd7eXgsXLtS6dessv1UMAAAAAEZBGDYQk8mkVatWaezYsTp37pyqV6+ur776yvLbzQAAAABgFIThW+C/eud5yZIltW7dOluXAQAAAAA2Z9MXaAEAAAAAYAuEYQAAAACA4RCGAQAAAACGQxgGAAAAABgOYRgAAAAAYDi8TRp3jT1RYVf9YW0AAAAAkJgZBgAAAAAYEGEYAAAAAGA4hGEAAAAAgOEQhgEAAAAAhkMYBgAAAAAYDmEYAAAAAGA4hGEAAAAAgOEQhgEAAAAAhkMYBgAAAAAYDmEYAAAAAGA4hGEAAAAAgOEQhgEAAAAAhkMYBgAAAAAYDmEYAAAAAGA4hGEAAAAAgOEQhgEAAAAAhkMYBgAAAAAYDmEYAAAAAGA4hGEAAAAAgOE42LoA4EaZzWZJUmZmpo0rAQAAAGBLFzPBxYxQFMIw7njHjh2TJPn5+dm4EgAAAAD/BadOnZKnp2eRbQjDuON5e3tLkjIyMq56wQNXkpmZKT8/Px06dEgeHh62Lgd3KK4j3CiuIdwMXEe4UXfyNWQ2m3Xq1CmVL1/+qm0Jw7jj2dldePTd09PzjvuPFf89Hh4eXEe4YVxHuFFcQ7gZuI5wo+7Ua6i4E2S8QAsAAAAAYDiEYQAAAACA4RCGccdzcnLSyJEj5eTkZOtScAfjOsLNwHWEG8U1hJuB6wg3yijXkMlcnHdOAwAAAABwF2FmGAAAAABgOIRhAAAAAIDhEIYBAAAAAIZDGAYAAAAAGA5hGHe8Dz/8UP7+/nJ2dlaTJk20bds2W5eE/4gffvhBnTp1Uvny5WUymbR8+XKr7WazWW+99ZZ8fX1VsmRJtWnTRqmpqVZtjh8/ru7du8vDw0NeXl7q27evsrKybuMoYEvjx49Xo0aN5O7urnvuuUddunRRSkqKVZtz585pwIABKl26tNzc3PTYY4/pr7/+smqTkZGhDh06yMXFRffcc49ee+01nT9//nYOBTY0c+ZM1alTRx4eHvLw8FDTpk313XffWbZzDeFaTZgwQSaTSREREZZ1XEe4mlGjRslkMlktNWrUsGw34jVEGMYdbfHixRoyZIhGjhypn376SSEhIQoLC9PRo0dtXRr+A06fPq2QkBB9+OGHhW6fNGmSPvjgA82aNUtbt26Vq6urwsLCdO7cOUub7t27a+/evYqNjdWKFSv0ww8/6Pnnn79dQ4CNxcfHa8CAAfrxxx8VGxur3NxcPfTQQzp9+rSlzSuvvKJvv/1WX3zxheLj4/Xnn3/q0UcftWzPy8tThw4dlJOTo82bN2vu3LmKiYnRW2+9ZYshwQYqVqyoCRMmaMeOHdq+fbtatWqlzp07a+/evZK4hnBtEhMT9dFHH6lOnTpW67mOUBy1atXS4cOHLcumTZss2wx5DZmBO1jjxo3NAwYMsHzPy8szly9f3jx+/HgbVoX/IknmZcuWWb7n5+ebfXx8zO+8845l3YkTJ8xOTk7mhQsXms1ms3nfvn1mSebExERLm++++85sMpnMf/zxx22rHf8dR48eNUsyx8fHm83mC9dMiRIlzF988YWlTXJyslmSecuWLWaz2WxetWqV2c7OznzkyBFLm5kzZ5o9PDzM2dnZt3cA+M8oVaqUec6cOVxDuCanTp0yV6tWzRwbG2tu0aKFefDgwWazmf8XoXhGjhxpDgkJKXSbUa8hZoZxx8rJydGOHTvUpk0byzo7Ozu1adNGW7ZssWFluBMcPHhQR44csbp+PD091aRJE8v1s2XLFnl5ealhw4aWNm3atJGdnZ22bt1622uG7Z08eVKS5O3tLUnasWOHcnNzra6jGjVqqFKlSlbXUXBwsMqVK2dpExYWpszMTMvMIIwjLy9PixYt0unTp9W0aVOuIVyTAQMGqEOHDlbXi8T/i1B8qampKl++vKpUqaLu3bsrIyNDknGvIQdbFwBcr3/++Ud5eXlW/0FKUrly5bR//34bVYU7xZEjRySp0Ovn4rYjR47onnvusdru4OAgb29vSxsYR35+viIiItSsWTPVrl1b0oVrxNHRUV5eXlZtL7+OCrvOLm6DMezevVtNmzbVuXPn5ObmpmXLlqlmzZpKSkriGkKxLFq0SD/99JMSExMLbOP/RSiOJk2aKCYmRtWrV9fhw4cVFRWlBx98UHv27DHsNUQYBgCgGAYMGKA9e/ZYPV8FFFf16tWVlJSkkydP6ssvv1SvXr0UHx9v67Jwhzh06JAGDx6s2NhYOTs727oc3KHat29v+VynTh01adJElStX1pIlS1SyZEkbVmY73CaNO1aZMmVkb29f4C13f/31l3x8fGxUFe4UF6+Roq4fHx+fAi9jO3/+vI4fP841ZjADBw7UihUr9P3336tixYqW9T4+PsrJydGJEyes2l9+HRV2nV3cBmNwdHRUQECAGjRooPHjxyskJERTp07lGkKx7NixQ0ePHlX9+vXl4OAgBwcHxcfH64MPPpCDg4PKlSvHdYRr5uXlpcDAQB04cMCw/y8iDOOO5ejoqAYNGmj9+vWWdfn5+Vq/fr2aNm1qw8pwJ7j33nvl4+Njdf1kZmZq69atluunadOmOnHihHbs2GFps2HDBuXn56tJkya3vWbcfmazWQMHDtSyZcu0YcMG3XvvvVbbGzRooBIlSlhdRykpKcrIyLC6jnbv3m31DyuxsbHy8PBQzZo1b89A8J+Tn5+v7OxsriEUS+vWrbV7924lJSVZloYNG6p79+6Wz1xHuFZZWVlKS0uTr6+vcf9fZOs3eAE3YtGiRWYnJydzTEyMed++febnn3/e7OXlZfWWOxjXqVOnzD///LP5559/NksyT5482fzzzz+bf/vtN7PZbDZPmDDB7OXlZf7666/Nu3btMnfu3Nl87733ms+ePWvpo127duZ69eqZt27dat60aZO5WrVq5m7dutlqSLjNXnrpJbOnp6c5Li7OfPjwYcty5swZS5sXX3zRXKlSJfOGDRvM27dvNzdt2tTctGlTy/bz58+ba9eubX7ooYfMSUlJ5tWrV5vLli1rHj58uC2GBBsYNmyYOT4+3nzw4EHzrl27zMOGDTObTCbz2rVrzWYz1xCuz6VvkzabuY5wda+++qo5Li7OfPDgQXNCQoK5TZs25jJlypiPHj1qNpuNeQ0RhnHHmzZtmrlSpUpmR0dHc+PGjc0//vijrUvCf8T3339vllRg6dWrl9lsvvDzSm+++aa5XLlyZicnJ3Pr1q3NKSkpVn0cO3bM3K1bN7Obm5vZw8PD3Lt3b/OpU6dsMBrYQmHXjyRzdHS0pc3Zs2fN/fv3N5cqVcrs4uJi7tq1q/nw4cNW/aSnp5vbt29vLlmypLlMmTLmV1991Zybm3ubRwNb6dOnj7ly5cpmR0dHc9myZc2tW7e2BGGzmWsI1+fyMMx1hKt56qmnzL6+vmZHR0dzhQoVzE899ZT5wIEDlu1GvIZMZrPZbJs5aQAAAAAAbINnhgEAAAAAhkMYBgAAAAAYDmEYAAAAAGA4hGEAAAAAgOEQhgEAAAAAhkMYBgAAAAAYDmEYAAAAAGA4hGEAAO4y/v7+mjJliq3LuCnS09NlMpmUlJRkWZeQkKDg4GCVKFFCXbp0sVltN8v69esVFBSkvLw8W5dSpH379qlixYo6ffq0rUsBgJuCMAwAwC0UHh4uk8mkF198scC2AQMGyGQyKTw8/KYeMzExUc8///xN7fNWCg0NVURERKHb/Pz8dPjwYdWuXduybsiQIapbt64OHjyomJiY21PkLTR06FCNGDFC9vb2ti6lSDVr1tR9992nyZMn27oUALgpCMMAANxifn5+WrRokc6ePWtZd+7cOX3++eeqVKnSTT9e2bJl5eLictP7tQV7e3v5+PjIwcHBsi4tLU2tWrVSxYoV5eXlZbviboJNmzYpLS1Njz32mK1LUW5u7lXb9O7dWzNnztT58+dvQ0UAcGsRhgEAuMXq168vPz8/LV261LJu6dKlqlSpkurVq2fVdvXq1XrggQfk5eWl0qVLq2PHjkpLS7Nsnzdvntzc3JSammpZ179/f9WoUUNnzpyRVPA2aZPJpI8++kgdO3aUi4uLgoKCtGXLFh04cEChoaFydXXV/fffb3Wc8PDwArcgR0REKDQ01PI9NDRUgwYNUkREhEqVKqVy5cpp9uzZOn36tHr37i13d3cFBATou+++u+5zd+lt0hc/Hzt2TH369JHJZLLMDO/Zs0ft27eXm5ubypUrpx49euiff/6x9PPll18qODhYJUuWVOnSpdWmTRvL7b4XxxoVFaWyZcvKw8NDL774onJycor9d7lY29KlS9WyZUu5uLgoJCREW7ZsKXJ8ixYtUtu2beXs7Gzpx87OTtu3b7dqN2XKFFWuXFn5+fnFGm9x6128eLFatGghZ2dnLViwQL/99ps6deqkUqVKydXVVbVq1dKqVass+7Vt21bHjx9XfHx8sf5+APBfRhgGAOA26NOnj6Kjoy3fP/30U/Xu3btAu9OnT2vIkCHavn271q9fLzs7O3Xt2tUSgnr27KmHH35Y3bt31/nz57Vy5UrNmTNHCxYsKHI2ePTo0erZs6eSkpJUo0YNPfPMM3rhhRc0fPhwbd++XWazWQMHDrzmcc2dO1dlypTRtm3bNGjQIL300kt64okndP/99+unn37SQw89pB49eliC+o24eMu0h4eHpkyZosOHD+upp57SiRMn1KpVK9WrV0/bt2/X6tWr9ddff+nJJ5+UJB0+fFjdunVTnz59lJycrLi4OD366KMym82WvtevX2/ZtnDhQi1dulRRUVGW7Vf7u1z0xhtvKDIyUklJSQoMDFS3bt2KnEXduHGjGjZsaPnu7++vNm3aWF0rkhQdHa3w8HDZ2dlddbzXUu+wYcM0ePBgJScnKywsTAMGDFB2drZ++OEH7d69WxMnTpSbm5ulvaOjo+rWrauNGzcW508GAP9tZgAAcMv06tXL3LlzZ/PRo0fNTk5O5vT0dHN6errZ2dnZ/Pfff5s7d+5s7tWr1xX3//vvv82SzLt377asO378uLlixYrml156yVyuXDnz2LFjrfapXLmy+f3337d8l2QeMWKE5fuWLVvMksyffPKJZd3ChQvNzs7OBeq+1ODBg80tWrSwfG/RooX5gQcesHw/f/682dXV1dyjRw/LusOHD5slmbds2XLFMbZo0cI8ePDgQrcdPHjQLMn8888/W9Z5enqao6OjLd9Hjx5tfuihh6z2O3TokFmSOSUlxbxjxw6zJHN6enqhx+jVq5fZ29vbfPr0acu6mTNnmt3c3Mx5eXmF7nP53+VinXPmzLG02bt3r1mSOTk5+Ypj9/T0NM+bN89q3eLFi82lSpUynzt3zmw2m807duwwm0wm88GDB4s13mupd8qUKVbtgoODzaNGjbpivWaz2dy1a1dzeHh4kW0A4E7AzDAAALdB2bJl1aFDB8XExCg6OlodOnRQmTJlCrRLTU1Vt27dVKVKFXl4eMjf31+SlJGRYWlTqlQpffLJJ5o5c6aqVq2qYcOGXfX4derUsXwuV66cJCk4ONhq3blz55SZmXlN47q0X3t7e5UuXbpAv5J09OjRa+r3WuzcuVPff/+93NzcLEuNGjUkXXi+OCQkRK1bt1ZwcLCeeOIJzZ49W//++69VHyEhIVYz602bNlVWVpYOHTokqXh/F8n6fPj6+koqeuxnz5613CJ9UZcuXWRvb69ly5ZJkmJiYtSyZUvLMa823mup99JZaUl6+eWXNWbMGDVr1kwjR47Url27CtRcsmTJmzLTDwC2RhgGAOA26dOnj2JiYjR37lz16dOn0DadOnXS8ePHNXv2bG3dulVbt26VJKvnVyXphx9+kL29vQ4fPlysn7opUaKE5bPJZLriuou30drZ2VndRiwV/oKlS/u42E9R/d4KWVlZ6tSpk5KSkqyW1NRUNW/eXPb29oqNjdV3332nmjVratq0aapevboOHjxY7GMU9+9yrWMvU6ZMgWDu6Oionj17Kjo6Wjk5Ofr888+trperjfda6nV1dbX6/txzz+nXX39Vjx49tHv3bjVs2FDTpk2zanP8+HGVLVv2yicLAO4QhGEAAG6Tdu3aKScnR7m5uQoLCyuw/dixY0pJSdGIESPUunVrBQUFFQhKkrR582ZNnDhR3377rdzc3K7rWd+rKVu2rA4fPmy17tLf+v0vqV+/vvbu3St/f38FBARYLRfDnslkUrNmzRQVFaWff/5Zjo6OlplX6cJs66Vv+/7xxx/l5uYmPz+/Yv9drke9evW0b9++Auufe+45rVu3TjNmzND58+f16KOPFnu8N1qvn5+fXnzxRS1dulSvvvqqZs+ebbV9z549BV78BgB3IsIwAAC3ib29vZKTk7Vv375Cf1O2VKlSKl26tD7++GMdOHBAGzZs0JAhQ6zanDp1Sj169NDLL7+s9u3ba8GCBVq8eLG+/PLLm1prq1attH37ds2bN0+pqakaOXKk9uzZc1OPcam///67wEznX3/9Vax9BwwYoOPHj6tbt25KTExUWlqa1qxZo969eysvL09bt27VuHHjtH37dmVkZGjp0qX6+++/FRQUZOkjJydHffv21b59+7Rq1SqNHDlSAwcOlJ2dXbH+LtcrLCxMmzZtKrA+KChI9913n15//XV169ZNJUuWLPZ4b6TeiIgIrVmzRgcPHtRPP/2k77//3uo8paen648//lCbNm1ufPAAYGOEYQAAbiMPDw95eHgUus3Ozk6LFi3Sjh07VLt2bb3yyit65513rNoMHjxYrq6uGjdunKQLz/2OGzdOL7zwgv7444+bVmdYWJjefPNNDR06VI0aNdKpU6fUs2fPm9b/5T7//HPVq1fParl8RvJKypcvr4SEBOXl5emhhx5ScHCwIiIi5OXlJTs7O3l4eOiHH37Qww8/rMDAQI0YMULvvfee2rdvb+mjdevWqlatmpo3b66nnnpKjzzyiEaNGiWpeH+X69W9e3ft3btXKSkpBbb17dtXOTk5BW6pv9p4b6TevLw8DRgwQEFBQWrXrp0CAwM1Y8YMy/aFCxfqoYceUuXKlW9s4ADwH2AyX/5AEAAAgIGEh4frxIkTWr58uU2O/9prrykzM1MfffSR1frRo0friy++KPQlVraQk5OjatWq6fPPP1ezZs1sXQ4A3DBmhgEAAGzojTfeUOXKlS0v2srKytKePXs0ffp0DRo0yMbV/Z+MjAz973//IwgDuGswMwwAAAzN1jPDlwsPD9fChQvVpUsXff7554U+Xw4AuHGEYQAAAACA4XCbNAAAAADAcAjDAAAAAADDIQwDAAAAAAyHMAwAAAAAMBzCMAD8v/brQAAAAABAkL/1IJdFAADsyDAAAAA7MgwAAMCODAMAALAjwwAAAOwEckdrIQgUtrMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.barh(oldest_animals[\"Common name\"], oldest_animals[\"Maximum longevity (yrs)\"]) \n",
    "plt.xlabel(\"Maximum Lifespan (years)\") \n",
    "plt.ylabel(\"______\") # ← Label for y-axis\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have now created a simple plot of animal longevity!\n",
    "\n",
    "<br><br>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make it pretty"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our graph above looks quite good but we might want to customise it more to our liking. We could first change the colours. We do this by passing an additional 'argument' to our command `plt` to let it know the colours we want to use. An example of changing all the colours of our graph is below. (Note: the spelling of 'colour' is American in python).\n",
    "\n",
    "We have also added a title to our graph to explain what we are seeing. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.barh(oldest_animals[\"Common name\"], oldest_animals[\"Maximum longevity (yrs)\"], \n",
    "         color=[\"red\", \"orange\", \"yellow\", \"green\", \"blue\", \"purple\", \"pink\", \"brown\", \"gray\", \"cyan\", \"teal\", \"magenta\"])\n",
    "plt.xlabel(\"Maximum Lifespan (years)\")\n",
    "plt.ylabel(\"Animal\")\n",
    "plt.title(\"Top 12 Longest-Living Animals in the Data\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "You can use any colours you like to customise the graph. You just have to change the name of the colours inside the 'color' argument. The ones below all come included with matplotlib.\n",
    "\n",
    "<br>\n",
    "<center>\n",
    "\n",
    "<img src=\"https://github.com/ejjohnson93/outreach_jupyter/blob/main/images/colours.png?raw=true\" alt=\"Alternative text\" width=\"600\"/>\n",
    "\n",
    "</center>\n",
    "\n",
    "<br>\n",
    "\n",
    "As well as the colours that come included, matplotlib lets you to pick any colour using a **hexcode** (a 6 character number/letter code after a hash/#).\n",
    "\n",
    "We can create hexcodes for colours using some of these links:\n",
    "\n",
    "- <https://htmlcolorcodes.com/color-picker/>\n",
    "- https://color.adobe.com/create/color-wheel\n",
    "- https://www.colorhexa.com/6738c9\n",
    "- <https://imagecolorpicker.com/en>\n",
    "\n",
    "The last link will allow you to create a colour palette from an image and give you the hex codes for the palette.\n",
    "\n",
    "<br>\n",
    "<center>\n",
    "\n",
    "<img src=\"https://github.com/ejjohnson93/outreach_jupyter/blob/main/images/paleete.png?raw=true\" alt=\"Alternative text\" width=\"500\"/>\n",
    "\n",
    "</center>\n",
    "\n",
    "<br>\n",
    "\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Challenge: can you change the colours of the graph to ones of your chosing</b>\n",
    "\n",
    "-   Can you change the colours using their names?\n",
    "\n",
    "-   Can you change the colours using hexcodes instead?\n",
    "\n",
    "<br>\n",
    "\n",
    "> **Challenge 1**: Can you change the colours of the graph using the inbuilt colour names in matplotlib? \n",
    "\n",
    "> **Challenge 2**: Can you change the colours using hexcodes instead? Maybe you could pick colours that remind you of the animals? If you're unsure what they look like then use Google to check. Otherwise, just select colours you like. ☺"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Challenge 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Challenge 2"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another way we can modify our graph is by using a different 'theme'. Themes are different ways of styling the plot, for example background colour, default bar colours, fonts, gridlines, etc. Matplotlib comes with prepared styles that you can use to modify your graph.\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then see a list of which styles we can use using this code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Solarize_Light2',\n",
       " '_classic_test_patch',\n",
       " '_mpl-gallery',\n",
       " '_mpl-gallery-nogrid',\n",
       " 'bmh',\n",
       " 'classic',\n",
       " 'dark_background',\n",
       " 'fast',\n",
       " 'fivethirtyeight',\n",
       " 'ggplot',\n",
       " 'grayscale',\n",
       " 'seaborn',\n",
       " 'seaborn-bright',\n",
       " 'seaborn-colorblind',\n",
       " 'seaborn-dark',\n",
       " 'seaborn-dark-palette',\n",
       " 'seaborn-darkgrid',\n",
       " 'seaborn-deep',\n",
       " 'seaborn-muted',\n",
       " 'seaborn-notebook',\n",
       " 'seaborn-paper',\n",
       " 'seaborn-pastel',\n",
       " 'seaborn-poster',\n",
       " 'seaborn-talk',\n",
       " 'seaborn-ticks',\n",
       " 'seaborn-white',\n",
       " 'seaborn-whitegrid',\n",
       " 'tableau-colorblind10']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.style.available"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An example of one of the styles applied to our graph is below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('ggplot')\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.barh(oldest_animals[\"Common name\"], oldest_animals[\"Maximum longevity (yrs)\"])\n",
    "plt.xlabel(\"Maximum Lifespan (years)\")\n",
    "plt.ylabel(\"Animal\")\n",
    "plt.title(\"Top 12 Longest-Living Animals in the Data\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "> <b>Challenge 1:</b> pick a style! Apply it to your graph instead of the one used in the example above. Try a few, which do you like best? \n",
    "\n",
    "> <b>Challenge 2:</b> Can you include a theme AND your custom colours?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Challenge 1 \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Challenge 2\n",
    "\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.barh(oldest_animals[\"Common name\"], oldest_animals[\"Maximum longevity (yrs)\"], \n",
    "         color=[\"red\", \"orange\", \"yellow\", \"green\", \"blue\", \"purple\", \"pink\", \"brown\", \"gray\", \"cyan\", \"teal\", \"magenta\"])\n",
    "plt.xlabel(\"Maximum Lifespan (years)\")\n",
    "plt.ylabel(\"Animal\")\n",
    "plt.title(\"Top 12 Longest-Living Animals in the Data\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you don't want to use a theme and prefer the default appearance, you can change it back at any time by running this block of code. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('default')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### To finish\n",
    "\n",
    "Well done! You have now learned how to create a plot of using Python. \n",
    "\n",
    "Now, using everything we've learned, can you modify the code to create a plot of the *shortest-lived* animals in the dataset - remember that earlier in the activity we created a variable storing the data called `shortest_lived_animals`. \n",
    "\n",
    "Don't worry if this part proves tricky and ask the teachers for help at any point if you get stuck! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Advanced/additional modification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This section is entirely optional and you should only attempt it if you have completed all the challenges above!\n",
    "\n",
    "When we first looked at our dataset at the start it had columns containing extra information. One column of interest is \"type\", which lets us know what type of animal they are (mammal, reptile, etc). Let's manually inspect what type of animal the longest-lived animals are..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          Type         Common name  Maximum longevity (yrs)\n",
      "0     Molluscs   Ocean quahog clam                    507.0\n",
      "1         Fish     Greenland shark                    392.0\n",
      "2      Mammals       Bowhead whale                    211.0\n",
      "3         Fish   Rougheye rockfish                    205.0\n",
      "4    Echinoids      Red sea urchin                    200.0\n",
      "5     Reptiles  Galapagos tortoise                    177.0\n",
      "6         Fish       Lake sturgeon                    152.0\n",
      "7      Mammals               Human                    122.5\n",
      "8      Mammals          Blue whale                    110.0\n",
      "9   Arthropods             Lobster                    100.0\n",
      "10     Mammals        Killer whale                     90.0\n",
      "11       Birds       Pink cockatoo                     83.0\n"
     ]
    }
   ],
   "source": [
    "print(oldest_animals[[\"Type\", \"Common name\", \"Maximum longevity (yrs)\"]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Are there any of these you haven't heard of before?\n",
    "\n",
    " For example, echinoids are animals that have a spikey hard shell. Echinoids evolved about 450 million years ago, which is about 220 million years before first dinosaurs appeared! Today we would commonly think of them as 'sea urchins' but there are plenty of fossils of ancient echinoids. Below is an artist's rendition of one vs some red and black sea urchins. \n",
    "\n",
    "<br>\n",
    "<center>\n",
    "\n",
    "<img src=\"images/urchins.png\" alt=\"Echinoids\"/>\n",
    "\n",
    "</center>\n",
    "<br>\n",
    "\n",
    "It would be useful to colour our graph by the different animal types. This would give our colours more meaning and also allow us to see if there are any patterns in the data. First, we will store all the unique types of animals in a new variable called 'types'. We can print 'types' to see what this includes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       Molluscs\n",
      "1           Fish\n",
      "2        Mammals\n",
      "3           Fish\n",
      "4      Echinoids\n",
      "5       Reptiles\n",
      "6           Fish\n",
      "7        Mammals\n",
      "8        Mammals\n",
      "9     Arthropods\n",
      "10       Mammals\n",
      "11         Birds\n",
      "Name: Type, dtype: object\n"
     ]
    }
   ],
   "source": [
    "types = oldest_animals['Type']\n",
    "\n",
    "print(types)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are going to create a colour map for the different types of animals. \n",
    "\n",
    "This code is a little more complex so don't try to understand all of it. To summarise, we first manually create a colour map for our different types of animals. Then we create a 'list', which is a type of information Python can work with to set the colours. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pick a color for each type of animal\n",
    "color_map = {\n",
    "    'Molluscs': 'purple',\n",
    "    'Fish': 'blue',\n",
    "    'Mammals': 'orange',\n",
    "    'Echinoids': 'teal',\n",
    "    'Reptiles': 'green',\n",
    "    'Arthropods': 'pink',\n",
    "    'Birds': 'red'\n",
    "}\n",
    "\n",
    "# Create a list of colors based on each animal's type\n",
    "bar_colors = [color_map.get(t, 'gray') for t in types]\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then use our colour map in our plotting code. This includes an aditional step where we manually add a legend too so we can see which colour corresponds to which type of animal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.barh(oldest_animals[\"Common name\"], oldest_animals[\"Maximum longevity (yrs)\"], color=bar_colors)\n",
    "plt.xlabel(\"Animal\")\n",
    "plt.ylabel(\"Maximum Lifespan (years)\")\n",
    "plt.title(\"How Long Do These Animals Live?\")\n",
    "\n",
    "# Add a legend manually\n",
    "legend_labels = {v: k for k, v in color_map.items()}\n",
    "for color in legend_labels:\n",
    "    plt.bar(0, 0, color=color, label=legend_labels[color])  # invisible bars for legend\n",
    "plt.legend()\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this, we can see that many of the most long-lived animals in our dataset are mammals, in particular whales and humans! The longest-lived animal is a mollusc, but there is only one of them.\n",
    "\n",
    "The full code to generate the graph is below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "types = oldest_animals['Type']\n",
    "\n",
    "# Pick a color for each type of animal\n",
    "color_map = {\n",
    "    'Molluscs': 'purple',\n",
    "    'Fish': 'blue',\n",
    "    'Mammals': 'orange',\n",
    "    'Echinoids': 'teal',\n",
    "    'Reptiles': 'green',\n",
    "    'Arthropods': 'pink',\n",
    "    'Birds': 'red'\n",
    "}\n",
    "\n",
    "# Create a list of colors based on each animal's type\n",
    "bar_colors = [color_map.get(t, 'gray') for t in types]\n",
    "\n",
    "# Plot\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.barh(oldest_animals[\"Common name\"], oldest_animals[\"Maximum longevity (yrs)\"], color=bar_colors)\n",
    "plt.xlabel(\"Animal\")\n",
    "plt.ylabel(\"Maximum Lifespan (years)\")\n",
    "plt.title(\"How Long Do These Animals Live?\")\n",
    "\n",
    "# Add a legend manually\n",
    "legend_labels = {v: k for k, v in color_map.items()}\n",
    "for color in legend_labels:\n",
    "    plt.bar(0, 0, color=color, label=legend_labels[color])  # invisible bars for legend\n",
    "plt.legend()\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> <b>Challenge 1:</b> Using the above code, can you modify the colour map and assign your own colours to the types of animals?\n",
    "\n",
    "> <b>Challenge 2:</b> Can you repeat the same steps as above to colour code the data for the shortest-lived animals?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Challenge 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "42    Arthropods\n",
      "41    Arthropods\n",
      "40         Birds\n",
      "39          Fish\n",
      "38    Amphibians\n",
      "37       Mammals\n",
      "36          Fish\n",
      "35       Mammals\n",
      "34       Mammals\n",
      "33         Birds\n",
      "32       Mammals\n",
      "31       Mammals\n",
      "Name: Type, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Challenge 2 - check what types of animals there are\n",
    "types = shortest_lived_animals['Type']\n",
    "\n",
    "print(types)\n",
    "# 5 unique types of animal: Arthropods, Birds, Fish, Amphibians, Mammals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Challenge 2 - create the colour map and graph\n",
    "\n",
    "color_map = {\n",
    "    'Arthropods': 'pink',\n",
    "    'Birds': 'red',\n",
    "    'Fish': 'blue',\n",
    "    'Amphibians': 'brown',\n",
    "    'Mammals': 'orange'\n",
    "}\n",
    "\n",
    "# Create a list of colors based on each animal's type\n",
    "bar_colors = [color_map.get(t, 'gray') for t in types]\n",
    "\n",
    "# Plot\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.barh(shortest_lived_animals[\"Common name\"], shortest_lived_animals[\"Maximum longevity (yrs)\"], color=bar_colors)\n",
    "plt.xlabel(\"Animal\")\n",
    "plt.ylabel(\"Maximum Lifespan (years)\")\n",
    "plt.title(\"How Long Do These Animals Live?\")\n",
    "\n",
    "# Add a legend manually\n",
    "legend_labels = {v: k for k, v in color_map.items()}\n",
    "for color in legend_labels:\n",
    "    plt.bar(0, 0, color=color, label=legend_labels[color])  # invisible bars for legend\n",
    "plt.legend()\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you got this far then well done, that concludes all of our activities! "
   ]
  }
 ],
 "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": "3.10.5"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}