pandas-intro.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6e2f69f5",
   "metadata": {},
   "source": [
    "# Pandas/Jupyter Intro\n",
    "Pandas is a Python library that allows us to manipulate tabular (spreadsheet) data. Seaborn is a visualization library that allows us to easily create charts and graphs. Jupyter is a web-based Python interpreter that allows us to create and run python code. \n",
    "\n",
    "Today, we will focus on the basics of Jupyter and pandas/seaborn to create our bar charts and histograms."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf95120d",
   "metadata": {},
   "source": [
    "First, we will need to import our two libraries. [Pandas](https://pandas.pydata.org/docs/) for data analysis and [Seaborn](https://seaborn.pydata.org/) for visualization. We also us the `as` keyword to create a shortcut for them. Now we can use `pd.` and `sns.` to access the methods in the pandas and seaborn libraries. **Click in the cell below and hit command + enter or run in the menu above to run the code in the cell**. You will have to do this on each cell with code in it to make it run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "powered-tennessee",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "403c0d97",
   "metadata": {},
   "source": [
    "Next, we can read in data from a csv to use later. The `df` variable now holds all the data from the `Dogs.csv` file. The `df` variable has a special type called a dataframe. A dataframe is an object in the Pandas library that allows us to store and manipulate tabular data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9ca9cd22",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>Name</th>\n",
       "      <th>Breed Group</th>\n",
       "      <th>Bred For</th>\n",
       "      <th>Minimum Life Span</th>\n",
       "      <th>Maximum Life Span</th>\n",
       "      <th>Minimum Height</th>\n",
       "      <th>Maximum Height</th>\n",
       "      <th>Minimum Weight</th>\n",
       "      <th>Maximum Weight</th>\n",
       "      <th>Temperament</th>\n",
       "      <th>Image</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Affenpinscher</td>\n",
       "      <td>Toy</td>\n",
       "      <td>Small rodent hunting, lapdog</td>\n",
       "      <td>10.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Stubborn, Curious, Playful, Adventurous, Activ...</td>\n",
       "      <td>https://cdn2.thedogapi.com/images/0LJiOVlxp.jpg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Afghan Hound</td>\n",
       "      <td>Hound</td>\n",
       "      <td>Coursing and hunting</td>\n",
       "      <td>10.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>Aloof, Clownish, Dignified, Independent, Happy</td>\n",
       "      <td>https://cdn2.thedogapi.com/images/tChrH8dDJ.jpg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Airedale Terrier</td>\n",
       "      <td>Terrier</td>\n",
       "      <td>Badger, otter hunting</td>\n",
       "      <td>10.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>Outgoing, Friendly, Alert, Confident, Intellig...</td>\n",
       "      <td>https://cdn2.thedogapi.com/images/PG8UPLSVU.jpg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Akbash Dog</td>\n",
       "      <td>Working</td>\n",
       "      <td>Sheep guarding</td>\n",
       "      <td>10.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>Loyal, Independent, Intelligent, Brave</td>\n",
       "      <td>https://cdn2.thedogapi.com/images/SyfsC19NQ_12...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Akita</td>\n",
       "      <td>Working</td>\n",
       "      <td>Hunting bears</td>\n",
       "      <td>10.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>Docile, Alert, Responsive, Dignified, Composed...</td>\n",
       "      <td>https://cdn2.thedogapi.com/images/36TXlWMDf.jpg</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id              Name Breed Group                      Bred For  \\\n",
       "0   1     Affenpinscher         Toy  Small rodent hunting, lapdog   \n",
       "1   2      Afghan Hound       Hound          Coursing and hunting   \n",
       "2   3  Airedale Terrier     Terrier         Badger, otter hunting   \n",
       "3   4        Akbash Dog     Working                Sheep guarding   \n",
       "4   5             Akita     Working                 Hunting bears   \n",
       "\n",
       "   Minimum Life Span  Maximum Life Span  Minimum Height  Maximum Height  \\\n",
       "0               10.0               12.0             9.0            12.0   \n",
       "1               10.0               13.0            25.0            27.0   \n",
       "2               10.0               13.0            21.0            23.0   \n",
       "3               10.0               12.0            28.0            34.0   \n",
       "4               10.0               14.0            24.0            28.0   \n",
       "\n",
       "   Minimum Weight  Maximum Weight  \\\n",
       "0             6.0            13.0   \n",
       "1            50.0            60.0   \n",
       "2            40.0            65.0   \n",
       "3            90.0           120.0   \n",
       "4            65.0           115.0   \n",
       "\n",
       "                                         Temperament  \\\n",
       "0  Stubborn, Curious, Playful, Adventurous, Activ...   \n",
       "1     Aloof, Clownish, Dignified, Independent, Happy   \n",
       "2  Outgoing, Friendly, Alert, Confident, Intellig...   \n",
       "3             Loyal, Independent, Intelligent, Brave   \n",
       "4  Docile, Alert, Responsive, Dignified, Composed...   \n",
       "\n",
       "                                               Image  \n",
       "0    https://cdn2.thedogapi.com/images/0LJiOVlxp.jpg  \n",
       "1    https://cdn2.thedogapi.com/images/tChrH8dDJ.jpg  \n",
       "2    https://cdn2.thedogapi.com/images/PG8UPLSVU.jpg  \n",
       "3  https://cdn2.thedogapi.com/images/SyfsC19NQ_12...  \n",
       "4    https://cdn2.thedogapi.com/images/36TXlWMDf.jpg  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"Dogs.csv\")\n",
    "# print the first 5 rows using the head() function. The tail() function does the same thing but for the end of the \n",
    "# data.\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f10ea4d0",
   "metadata": {},
   "source": [
    "The `describe()` method will calculate the measures of central tendancy and spread for all the numerical values in a dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "bde07795",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "\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>id</th>\n",
       "      <th>Minimum Life Span</th>\n",
       "      <th>Maximum Life Span</th>\n",
       "      <th>Minimum Height</th>\n",
       "      <th>Maximum Height</th>\n",
       "      <th>Minimum Weight</th>\n",
       "      <th>Maximum Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>105.000000</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>105.000000</td>\n",
       "      <td>105.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>53.000000</td>\n",
       "      <td>10.828571</td>\n",
       "      <td>13.495238</td>\n",
       "      <td>18.466667</td>\n",
       "      <td>21.819048</td>\n",
       "      <td>43.561905</td>\n",
       "      <td>66.304762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>30.454885</td>\n",
       "      <td>1.740121</td>\n",
       "      <td>2.005396</td>\n",
       "      <td>5.781380</td>\n",
       "      <td>6.745057</td>\n",
       "      <td>27.672509</td>\n",
       "      <td>42.557003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>35.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>53.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>60.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>79.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>90.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>105.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>200.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               id  Minimum Life Span  Maximum Life Span  Minimum Height  \\\n",
       "count  105.000000         105.000000         105.000000      105.000000   \n",
       "mean    53.000000          10.828571          13.495238       18.466667   \n",
       "std     30.454885           1.740121           2.005396        5.781380   \n",
       "min      1.000000           6.000000           8.000000        8.000000   \n",
       "25%     27.000000          10.000000          12.000000       14.000000   \n",
       "50%     53.000000          10.000000          14.000000       19.000000   \n",
       "75%     79.000000          12.000000          15.000000       23.000000   \n",
       "max    105.000000          15.000000          20.000000       30.000000   \n",
       "\n",
       "       Maximum Height  Minimum Weight  Maximum Weight  \n",
       "count      105.000000      105.000000      105.000000  \n",
       "mean        21.819048       43.561905       66.304762  \n",
       "std          6.745057       27.672509       42.557003  \n",
       "min          9.000000        3.000000        7.000000  \n",
       "25%         16.000000       24.000000       35.000000  \n",
       "50%         24.000000       40.000000       60.000000  \n",
       "75%         27.000000       65.000000       90.000000  \n",
       "max         35.000000      120.000000      200.000000  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a030ac29",
   "metadata": {},
   "source": [
    "# Seaborn bar plots and histograms\n",
    "\n",
    "Seaborn (barplots)[https://seaborn.pydata.org/generated/seaborn.barplot.html] are best for comparing two columns using bars to summarize. We are learning about single column plots today but I wanted you to be aware the Seaborn barplots are not the same as what we are calling bar charts. Instead, we will use the [seaborn.countplot()](https://seaborn.pydata.org/generated/seaborn.countplot.html) to make one column bar charts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a943da99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='id', ylabel='Maximum Life Span'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6P0fVMO5rgcNX6XYB8JC79wMeyt+rltcFuBw4AtgdOMnMdq+hPBERaUe1PEi4n7uvcQPu7o+ZWd9VOh8LHJzp64BHgPNXybMv8Fr+NzpmdmsO939rWgcREamfWgLIU2a2u7u3RQPey92nAbj7NDPr2Uye7YG3qn5PBj7e0gjNbDAwGGCH92/TBlVcv9107Wdb7Df0+viP9NNPafn/0Wv1o9uinO9+/oG1Hpe0neNHPAHAyIGf7OCawG9vbwDgjBOaawbKe+zGyr9PtNerNqb/bBIAvf9z55bzXPJSU7r3eR+ue53aQy3z9zoiiEwHlhBPoru771mnOlkz3d7zOvmmHu5XAVcB7LXzri3mExGRtlVLABkGnAy8BKxYy/JmmFmfPProAzQ0k2cysGPV7x2AqWtZroiItLFa7sJ6093vcvc33H1S8SlZ3l3AqZk+FbizmTzPAv3MbBcz6w6cmMOJiEgnUssRyMtmdjNwN3EKCwB3v721gczsFuKC+bZmNhn4HnAx8HszOx14E/hc5t0OuNrdB7h7o5mdDTwAdAGGufvYNZ4yERGpq1oCyKZE4PhMVTcHWg0g7n5SC70ObSbvVGBA1e9RwKga6iYiIh1ktQHE3U9rj4qIiMi6ZbUBxMyuoZm7oNz9y3WpkYiIrBNqOYV1T1V6E+B4dFeUiMgGr5ZTWCOqf+fF8T/VrUYiIrJOKPM23n7ATm1dERERWbfUcg1kPnENxPJ7Ou99f5WIiGxgajmFtWV7VERERNYtNb1rzMz2BPpW51/dg4QiIrJ+q+UU1jBgT2AslXdhrfZBQhERWb/V7f9ARERk/VbLXVhP6R8BRURkVZ3x/0BERGQd0N7/ByIiIuuJWgLIm+6u/+MQEZGV1O3/QEREZP1Wt/8DEZH12xdvjz8mvemEnetWxu3DZzWlTxi0bd3KkXL0fyAiIlJKiwHEzL7l7j81s8to/v9AzilToJntBtxW1WlX4CJ3/1VVnoOJ/0t/Izvd7u4/LFOeiIjUR2tHIOPye3RbFujurwB7AZhZF2AKMLKZrI+7+1FtWbaIiLSdFgOIu9+dydvcfXF1PzNrq5ORhwIT3H1SG41PRETaSS1Poj9jZvsVP8xsIPBkG5V/InBLC/32N7MXzew+M/tQSyMws8FmNtrMRs9eMK+NqiUiIqtTy11YXwSGmdkjwHbANsCn17ZgM+sOHANc2Ezv54Gd3X2BmQ0A7iD+yOo93P0q4CqAvXbe9T3XakREpD5WewTi7i8B/wOcCRwCnO3uk9ug7COA5919RjNlznP3BZkeBXRrw9NmIiLSBmp5nftQ4APEK93/CbjbzIa4++VrWfZJtHD6ysx6AzPc3c1sXyLQzV7L8kREpA3VcgprDPAVd3fgjbwecsnaFGpmmwGHAWdUdTsTwN2vBAYBZ5lZI7AIODHLFxGRTqKWBwl/ucrvd4DT16ZQd3+XuJZS3e3KqvQQYMjalCEiIvVVyymsfsCPgd2BTYru7r5rHeslIiKdXC238V4DXAE0EhfRrwduqGelRESk86slgGzq7g8B5u6T3P37tMFtvCIism6r5SL6YjPbCBhvZmcTrx7pWd9qiYhIZ1fLEci5wGbAOcBHiX8nPLWOdRIRkXVALXdhPZvJBYBe7S4iIkDrr3Nv9W9s3f2Ytq+OiIisK1o7AtkfeIt4WvyvgLVLjUREZJ3QWgDpTTwtfhLwBeBe4BZ3H9seFRMRkc6txYvo7r7c3e9391OB/YDXgEfM7OvtVjsREem0Wr2IbmYbA0cSRyF9gUuB2+tfLRER6exau4h+HbAHcB/wA3cf0261EhGRTq+1I5CTgYXEK9zPMWu6hm6Au/tWda6biIh0Yq39J3otDxmKiMgGSkFCRERKUQAREZFSFEBERKSUDgkgZjbRzF4ysxfMbHQz/c3MLjWz18zs72a2T0fUU0REWlbL69zr5RB3n9VCvyOAfvn5OPGHVh9vr4qJiMjqddZTWMcC13t4GtjazPp0dKVERKSiowKIA380s+fMbHAz/bcnXuRYmJzd3sPMBpvZaDMbPXvBvDpUVUREmtNRp7AOcPepZtYTeNDMXnb3x6r6N/fmX29uRO5+FXAVwF4779psHhERaXsdcgTi7lPzuwEYCey7SpbJwI5Vv3cAprZP7UREpBbtHkDMbHMz27JIA58BVn3P1l3AKXk31n7AO+4+rZ2rKiIireiIU1i9gJH5bq2uwM3ufr+ZnQng7lcCo4ABxCvk30V/pSsi0um0ewBx99eBjzTT/cqqtANfa896iYjImumst/GKiEgnpwAiIiKlKICIiEgpCiAiIlKKAoiIiJSiACIiIqUogIiISCkKICIiUooCiIiIlKIAIiIipSiAiIhIKQogIiJSigKIiIiUogAiIiKlKICIiEgpCiAiIlKKAoiIiJTSEf+JvqOZPWxm48xsrJl9o5k8B5vZO2b2Qn4uau96iohI6zriP9EbgW+6+/NmtiXwnJk96O7/t0q+x939qA6on4iI1KDdj0DcfZq7P5/p+cA4YPv2roeIiKydDr0GYmZ9gb2BvzbTe38ze9HM7jOzD7UyjsFmNtrMRs9eMK9eVRURkVV0WAAxsy2AEcC57r5qy/88sLO7fwS4DLijpfG4+1Xu3t/d+2+zxVZ1q6+IiKysQwKImXUjgsdN7n77qv3dfZ67L8j0KKCbmW3bztUUEZFWdMRdWAYMBca5+yUt5Omd+TCzfYl6zm6/WoqIyOp0xF1YBwAnAy+Z2QvZ7dvATgDufiUwCDjLzBqBRcCJ7u4dUFcREWlBuwcQd38CsNXkGQIMaZ8aiYhIGXoSXURESlEAERGRUhRARESkFAUQEREpRQFERERKUQAREZFSFEBERKQUBRARESlFAUREREpRABERkVIUQEREpBQFEBERKUUBRERESlEAERGRUhRARESkFAUQEREpRQFERERK6ZAAYmaHm9krZvaamV3QTH8zs0uz/9/NbJ+OqKeIiLSs3QOImXUBLgeOAHYHTjKz3VfJdgTQLz+DgSvatZIiIrJaHXEEsi/wmru/7u5LgVuBY1fJcyxwvYenga3NrE97V1RERFpm7t6+BZoNAg5396/k75OBj7v72VV57gEudvcn8vdDwPnuPrqZ8Q0mjlIAdgNmA7Py97ZV6VV/r016Qx++M9VF09I569LRw3emunT2adnc3XtQhru36wf4HHB11e+TgctWyXMv8Mmq3w8BH61x/KObS7fWb03TG/rwnakumpbOWZeOHr4z1WVdmZYyn444hTUZ2LHq9w7A1BJ5RESkA3VEAHkW6Gdmu5hZd+BE4K5V8twFnJJ3Y+0HvOPu09q7oiIi0rKu7V2guzea2dnAA0AXYJi7jzWzM7P/lcAoYADwGvAucNoaFHFVC+nW+q1pekMfvjPVZW2H70x1WdvhO1NdOnr4zlSXtR2+veqyxtr9IrqIiKwf9CS6iIiUogAiIiKltPs1kHoxs2HAUUAD8ST79UBvwIHNgbnE9A4HfgiMBqYAewDzgeWZdwKwD7Ad8CZxDeafgbeJZ0x6A1sDjcALwK7ZfSugF7AMWAL8Q5ZpwBY5/vFAn/z9BjAP+BhxT/ZmQPcc70TiKXwDllJZTstyPN2zrkX/acTOwHb5bZlnYtZjC2BFdieHXQ7MyHnTM383Zr+Xcr5snL8bc7zF+c5iXF2yX2OWV4x7PnF/+ds5/o1yPi7L+jRmefMzf58c59Icx1jgQ1XTuaJq3DNzfu2W45sE7JzDvk3csddIZZl3B14H/inzvE6sI32y/h/M31sBr2S+BmIZ/mOOa1zWx7IuK7IuGwMLgLdyPMuy38aZ921g0yynepiFWe9+wBxinQJYnN/FetC1ajjL+bgk8y0m1sNi3lhVnmL5zwM2yXp1y/FMBrbMeb8V0CPrsFWOs1vV9yY5zKJMd8l5WNRn48w7OedVsQ5tnHV6h5WXd7f8XgxMz2GM2H62qZp/3TPfRvlZlvm6Vo1rcdZri1X6zc1xbZLpTavmT7HeLiO2jZ75WZzz4P3Eero5FSuyDl3ye2mOY0lO5zJi+3tf1mWjnM7Xc/l0z49lOcW0PQbsTSz7ebk8tslp6lb1KbbZYn54VTfL+s6jsg13yc8SKuvB8hxv96xvMT1LgbOyLuOy/9gc99PufiarsT4dgVwLHJ7pRuCb7v5B4OPEjDoJ2CvzXELMsMIh7r4X8Hfgfnffldig9gWOJlbOg4g7xjYBLiYu8HcFvko0lndn/oXA48BzxEI7PrvPJ55vOR74NZVGY3pV/T+evxcCB2QdP5/dnbi54CCi8ZmQeS8mVv73Ec/UXA7cQays3wLOI1bwfYCbsvs+wC+IlW48lRV4H2AgETxOc/cuwCeIQHMT0VDMAj5KBNelwCHAX3K8BxGvnXHgReC7RAPyAnFn3U2ZPgT4DrGBvEk0NNOz+9FEw3pslr9Tjms40VAvAB4lNgQHrsluZNkTiYb79ZxHj+a8nQg05HJeSGzsRZCbTQT064EniQbkauC2HP56YoOcTtxOPhl4Oev+naz/O8D2wG9y/E/m9G+Zw1wDXJrD/C/wBBGoxuWwc4lb1wfkfN0lp3/7HH4acDPwB+CGrP/jwIX5PQ84n1j+bxLr57cz/4VZr8uAZ4A/A08R28kcYCix7n+b2PlaBHwnyx+Z3acAtxDr7VtZ7xnARURQXwB8IMtZBDyd82YGESiuzO79svszuQyn5LyZn/2OzO67Ew3azOw+nVjeH8rpg9j+niTWqUbixpzHMt+rwM+IxvPrwHVEo/l14KfETuHMnPeTsqzjgP+rGmZfIoB8PZfVcuAbWZctiJ2/vxA7jm8TF6TvyfL/Kz8bZb7fE8FsX2AIcBixjr6Ty+5dom14lWj4Pwb8P2K7mpDzaS6xHRU7ukOIbWga0U50JQLA0Tk9k4h1DWCMu29M7CR1yfSlwG+BXwGPALPcfa/8rDZ4wHoUQNz9MWJjwN2nufvzmZ5PrBTbEzN7M6JRvLp6eDPbCvgUsTHh7kvdfS7wSWLFmUWsvHOIhQBwJ7AfEWx+kP1mEXuxjcC77v5wdl8I7JD1dGJl+mqOG6IBK/aCLiA2PNz9ruzeFXg8p2sHolF9IYfbm7g9eh7wcyJQLgR6uPu1mW97IsjNy/QiYkW9IeszPbt/Cfhb1bx8Joc/kmjIpma+HYhG1XN6i6OVy3J+LAf+SmVv5yViQ+uS+bYiGtfLikWQ3c8iNtZ3svvcHObQnCebEsuvO7HRHZn5Ns30d4k9uW2IZblxdr8Z2MTMdgA+nN0n5HB/z/l7ZH6/RexoXJ31P5JYd+ZmWdtR2QF5hAioRRD7ObGTAfBXd2/M9FM5zyDWQSeW9Q9Y2Vk57UsB3L0hu/fIefgponHelJjvQ4ijsW0zfSVxRDYFGEPsiAwhGtadgP1zeg+gcrTYHeib+b6e5f0qt4n+2b0X8ExuEzsQDeYE4lVE/YFx7j6JCAzFHroDE7L7XGBRpjcHPk1sl41Z39nZ7yJgvLuPJ5b5eCJg9CQa81lZ/iIq6952xDoyitjZKo4y/k4svznE9lGkt8j8w3Jai6Ozs4gAWOSbm/nm5PxdQOyI9Ml+xTv7LH9fTLQH7+b8nUPsaBxBbF/zM70jsX08k+XPzc+XgT9WDTON2BHYMuf/08Ty34g4g3JD1n1LIjAVZ1vG5Hg3y+VkxPoDsUPX1cw2JtqvTYhANp4y1uYpxM72ITaCMc10e5NYmRbkwvkocDCxt/AG8DyxMr9B7K3+jWg8NidWsuE57ByiAd6LiPRPZf7l1eXnQnuaeOdX0X1eLrC3iD3YOdlvMrEnNJFolBqBn2QdFhJ7Iv9K7Im8mcMvIfY23yT2Lj3TW+U436FyRFBM/1bAn4iVe3J+FmT3eVluUfd5xAr6KHAM0XA/T2zQjVROY9xHBBfPOr2Q41xMNKyv5O+fVtXpyRx+ReYvhp9HpVF4mgg+C7K+N+f3QqJhmJ7jmEssyyU5vmK5rsj6n0FshB/NcqfneKYRwXQaERBvyekan/VcQDTWL+Sy+QqxbizKfCtyec3K+eiZfp448lxBNBbjiNfsvEGlkShOJxTje57KaZli/MW0zgf+J+u4KKe9MfstyrreSOW02o3E+r0ip/UNIhjfmHmn5nxbQCznhkzPzvLfrip/VpbzNtGoLcq8k3N+PwGcTRzhOnB2rnvDcvhHiPW96F5sg28Re8Yv5/ATs/vs/F4MPEgs/2nEuvOprMdCKqf0niaOBopTnKOzfkW6mJ6i+5L8vSL7zazKv5AIuJ51X7DK8O9SWe5Tch4uBX6X86XIu03O5+XEkdve2f93xLZUnLadmuP7HbFOLM1+c4ijsRU57V/M+TGPaG+KoLyCWCf7UtkJuC67v53dV+R0/EfRPZfDwix7bNV0bkEchSwh2p1HgQNranM7utGvZwDJGfMccEL+/jyV6x4HEwFku+x3WC6MM/L3r4mNdzZxiqAHEe2fJzaChcTe3lBWH0B+liuBEXsFU4CZVQFkPLGns0vVwu1L7OG9QTQAC4GBOcy3Mt+ErKNXTeMWVILBgcX0E3u6b2e6R660t2f+pcSRx5ZEI/Vm1vWgXKnuJwJVA/DTLOfCzDuWSuO2B3Hedxmxx9c/f79OBK+tgYeBPak0JHtk+gniyGBmTusexF7VpJxfPyP2MGdnv3uIlf+5LG95Vb2WEw3YEKKB+q9chs8QpxnezP4T83NJfm9EbFTFee7Lc9xXEI3baGJv91xiY19ENJgrcj58ithTbiQ2xMOII8WbiAalZ+b7LdGIjMthJhPB9qCc/sXZ/bNV8//xXIae0/AilcC1ONNDc557jv9kKg3qSCJQFsHlWiqBblCmh+Z0FeM6NdPjsvx7gB8B38z8LxLXE53YQ+6e9ZlLNEJzs/tFOR29iPW/uLbYi8opxz7EUeEKIrh0z+En5fxalvXoQQStpTn/F2WdZxJHEHMz/SrwL5m+kzh9PBP4GrG+vZv53yQC5NjsPy6/L86yZ+a0PEGsx28Q290yKtckF+c4ns9uc7NOjxHr2YKcL48QO2OLc9xF+tocfhmx7j+Swy8m2pEVRGB6IOfjNGI7eCnn/WziNGurAYQ4g7KceDAbYt1dThyF/Ai4KLt/NOfRVhtsACEa+weA86r6/zgXxBwqe6M3Zr/eueD/I38fSGx4LwJDq8ZxCnHoOIY4v/idXKh9svyXicbgaeLw89Rcscbm8B/O8ou92GKPvncOvzBX0r5ZxoRc+ZYTjVoxXcWFzsdzxeuTvx8mGvpHiSOk84hD47nEUUs34rxt0VAWe1Bv5rBF0CrKmZXjeyTzFs8OFRcF/4PYsKZluk8OM4FozE7NlfHCHO57xGmmWVTOwRcXP3+Q9X67ajlMyLr+KMsrlt8CYuMp9iKL9EIqNwm8W5WenvPpXSo3TBSfYg9+QVW/BVVlFHneJYK5ETsE3ycamkXEqavmpn8ksZw3y+n5fuYtprmY/ndamP45RCM/j6p1NLtNIxrGSTlt9xLBuZFoVD6b+d8mGu5js66ziGVf7FVPJhqze4kjtcZM9870kiz/wOx+LNCY9Tsr51mf7P4Isf6/RGwDpxLbxJ+q1v95VNb/4sirdw4/h1j/jiUa1QlUjtyGVq17S4jGvmjUi2tdUzI9hVjfXyaW/Rez+8NV5RdH3vOJgPNozodHiVNZ04ltqLhQfUp2L8r/9yx/eqb75LRPIl4GC3Gk+3rO//8ljpqK7Xk2lWV/QZb7Rub795z2opGfl91n5jQVN+CsIHZWfpLjeo1KAJlMrCvLc16/mt2KHdsiOE2lsi4WR4yPAP1X1+auN9dAqpmZEXtQ44AbzGzr7PVDYk/yFOKC+KPEBgCxEm1E5Tz3ocQprLuA/cxssxzvkcRC6kbszd9FLMxTc7itiT0eiJXmfOJwsxuAu79EHN3Md/e+xMo3yd2Li+nFKQqIvbCtsrxlxB7qUCp7/kOJw+Y/ZflDs5xrifPibxCn5i4hLgjfnnmeJfbUp1HZG36KCJbPZ/lDs3/ReL5ABILimsNXsx6vULnL52XgdCp3Cu1P7P1PB14yswOJRu1vOQ/m5rJoIFbuZ4mNqzvwlpn9E3HOtrieMpHYOE+hclpsq+w23d23IPb2JxCB9XdEEOhN7DU/4O6bEUcQj1FpfL8MjMrhH89lswVwJtHQPpbT/hKxvpyQ8/ZwIkg8SZxmHJP9l+Y8PCin93vA9mbWI4fZjDiaehn4AtFojc9lMJfYEx9jZh8hjgqLo7C9iHW0N9EoFXfgLahKD8iylxDXBzbL6dyeOKUymziiuivHuTDruDiHn0LlFMpWRGM5J7ufTqxP/wlMNrONiKPhscT6dxKVPf5exHp7PrEdPghN6/944Llc/xcD/53r/0m57BZm+s9Zh6VZh0+b2WY5/4u70BbnsvgIcZ3iz5leRByt9CMa/SXZ/W7iCK6RCObTcr5vmfNmQeabm9P+YWL7mEIcpRenJZ24/tOY8/jBnAd/znIfyDeNfyY//YkXyV5PXLx+l7i5oCE/I/J7aeZ7hrjONSCncQbwb8RRYO+ch8WR4wSiDbCsa3GtbUJ+r8h5c2FOx5Ts/kjWY8+cJz929yFmtmvOt9dZjfXmSXQzu4U4LVXcWVNcdOxOXDycQayYv3f3H5rZwcSh9TY5iq7Ewt+fyh0MBxCnlc4jTn81Egu9S+YvGsriVlV47612ULkV0IkVu7jVbhmVC65UDbeCyi2ZK7Kc4pbWrqx8W+M7ROP2AWKFW5zdu1K59c+o3A5c3Ba5NOs9MedP0X1RprfIca0gNpCJ2X1HKrcCGpU9/c2qpqHYqIoL48up3JYKlXP+C3O5fCjLacxxNxANHlXdJ2WdNiEaqV5E4zKZ2Ck4mFjuG1G5nbko7yViuXUnGu1JxDnjLxGnw94gDtsnEg3ILkRj0p1opC4iAuT/5HeXHO9GVBqdYqMly7X8NFK5zbk4mnk3h9s0p3WPqulcXjV+iODbjVgW22dZm+Y4tsg6zM9hiovzRuXW0WL92TjH/Raxzk/Nef8JKutksW4tJhpUspx3cxzbEo3PP+f3kpzuDxLn7j9FNLDvJ/bGl1PZuSiOFKcQOzd7Eke984lg1i/H+2CWfzQRkBuyjgOzjPdTuZ22+lbVblSOaHtVLQPPvMWF8uKIf3JO8z9mHZbk/FtB5TbkacQOzNKcB8XF72Jbd2Ln8a2cB0Zl2y62+eIor7r8JTn/PefFW0R7VVyf2aQq7+ZUtrNlVLbtwoqq9OoOCIpxdK+qx7+7+41mdhuxwzMj833P3e9ezfjWnwAiIiLta708hSUiIvWnACIiIqUogIiISCkKICIiUooCiIiIlKIAIlIHZvZkC92vNbNB7V0fkXpQABGpA3f/REfXQaTeuq4+i4isKTNb4O5b5NsLLiPePvsGKz9gKrJO0xGISH0dTzxt/GHi9S86MpH1hgKISH19CrjF3Ze7+1TidTki6wUFEJH60/uCZL2kACJSX48BJ5pZFzPrQ/xtr8h6QRfRReprJHEB/SUq/zkhsl7Q23hFRKQUncISEZFSFEBERKQUBRARESlFAUREREpRABERkVIUQEREpBQFEBERKeX/A19Bdyuw0dJYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='id', y = 'Maximum Life Span', data = df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "be2b3a01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Max Life Span Counts')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p1 = sns.countplot(x='Maximum Life Span', data=df, color='lightblue')\n",
    "p1.set_ylabel('Amount')\n",
    "p1.set_title('Max Life Span Counts')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f724d001",
   "metadata": {},
   "source": [
    "## Histograms\n",
    "A histogram is a popular chart to check the distibution for one column of data. The [seaborn.histplot()](https://seaborn.pydata.org/generated/seaborn.histplot.html) function will take care of all we need."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c3db9759",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Maximum Life Span', ylabel='Count'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data=df, x='Maximum Life Span', binwidth = 2)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}