Assignment_4_Final(seaborn, sunbrust).ipynb

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    "# Title: Assignment 4\n",
    "### Name: Sharmin Akhter\n",
    "### ID: 201891112\n",
    "### Date 08-03-2023\n",
    "### Master of Data Science"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6bbf2ef7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59a65307",
   "metadata": {},
   "source": [
    "# Question 1\n",
    "\n",
    "Use Seaborn to recreate the plot from Question 3 of Assignment 2. This plot shows **the yearly population change (raw value) per continent over the past 60 years.**\n",
    "\n",
    "The line plot should be made using Seaborn’s lineplot() functionality. The **legend** should be in the **centre of the figure and without a border**. The ordering of continents should be alphabetical, and the colours should match the figure above. Make sure to include the axes labels and plot title. There should **not be any shading around the lines (which Seaborn might add depending upon how you use it).**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "236e53f7",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Continent</th>\n",
       "      <th>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>Population</th>\n",
       "      <th>Yearly%Change</th>\n",
       "      <th>YearlyChange</th>\n",
       "      <th>MedianAge</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1955</td>\n",
       "      <td>8270991</td>\n",
       "      <td>1.30</td>\n",
       "      <td>103775</td>\n",
       "      <td>19.2</td>\n",
       "      <td>587818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1960</td>\n",
       "      <td>8996973</td>\n",
       "      <td>1.70</td>\n",
       "      <td>145196</td>\n",
       "      <td>18.8</td>\n",
       "      <td>755797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1965</td>\n",
       "      <td>9956320</td>\n",
       "      <td>2.05</td>\n",
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       "      <td>18.4</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1970</td>\n",
       "      <td>11173642</td>\n",
       "      <td>2.33</td>\n",
       "      <td>243464</td>\n",
       "      <td>17.9</td>\n",
       "      <td>1295433</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1975</td>\n",
       "      <td>12689160</td>\n",
       "      <td>2.58</td>\n",
       "      <td>303104</td>\n",
       "      <td>17.3</td>\n",
       "      <td>1717422</td>\n",
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       "    <tr>\n",
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       "      <th>4225</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2016</td>\n",
       "      <td>14030331</td>\n",
       "      <td>1.56</td>\n",
       "      <td>215702</td>\n",
       "      <td>18.5</td>\n",
       "      <td>5215894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4226</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2017</td>\n",
       "      <td>14236595</td>\n",
       "      <td>1.47</td>\n",
       "      <td>206264</td>\n",
       "      <td>18.5</td>\n",
       "      <td>5328766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4227</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2018</td>\n",
       "      <td>14438802</td>\n",
       "      <td>1.42</td>\n",
       "      <td>202207</td>\n",
       "      <td>18.5</td>\n",
       "      <td>5447513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4228</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2019</td>\n",
       "      <td>14645468</td>\n",
       "      <td>1.43</td>\n",
       "      <td>206666</td>\n",
       "      <td>18.5</td>\n",
       "      <td>5571525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4229</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2020</td>\n",
       "      <td>14862924</td>\n",
       "      <td>1.48</td>\n",
       "      <td>217456</td>\n",
       "      <td>18.7</td>\n",
       "      <td>5700460</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4230 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Continent      Country  Year  Population  Yearly%Change  YearlyChange  \\\n",
       "0         Asia  Afghanistan  1955     8270991           1.30        103775   \n",
       "1         Asia  Afghanistan  1960     8996973           1.70        145196   \n",
       "2         Asia  Afghanistan  1965     9956320           2.05        191869   \n",
       "3         Asia  Afghanistan  1970    11173642           2.33        243464   \n",
       "4         Asia  Afghanistan  1975    12689160           2.58        303104   \n",
       "...        ...          ...   ...         ...            ...           ...   \n",
       "4225    Africa     Zimbabwe  2016    14030331           1.56        215702   \n",
       "4226    Africa     Zimbabwe  2017    14236595           1.47        206264   \n",
       "4227    Africa     Zimbabwe  2018    14438802           1.42        202207   \n",
       "4228    Africa     Zimbabwe  2019    14645468           1.43        206666   \n",
       "4229    Africa     Zimbabwe  2020    14862924           1.48        217456   \n",
       "\n",
       "      MedianAge  UrbanPopulation  \n",
       "0          19.2           587818  \n",
       "1          18.8           755797  \n",
       "2          18.4           984350  \n",
       "3          17.9          1295433  \n",
       "4          17.3          1717422  \n",
       "...         ...              ...  \n",
       "4225       18.5          5215894  \n",
       "4226       18.5          5328766  \n",
       "4227       18.5          5447513  \n",
       "4228       18.5          5571525  \n",
       "4229       18.7          5700460  \n",
       "\n",
       "[4230 rows x 8 columns]"
      ]
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     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('population.csv', na_filter = False)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42e892d5",
   "metadata": {},
   "source": [
    "# Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9cc7deb9",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1975</td>\n",
       "      <td>12689160</td>\n",
       "      <td>2.58</td>\n",
       "      <td>303104</td>\n",
       "      <td>17.3</td>\n",
       "      <td>1717422</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1980</td>\n",
       "      <td>13356511</td>\n",
       "      <td>1.03</td>\n",
       "      <td>133470</td>\n",
       "      <td>16.9</td>\n",
       "      <td>2119078</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1985</td>\n",
       "      <td>11938208</td>\n",
       "      <td>-2.22</td>\n",
       "      <td>-283661</td>\n",
       "      <td>16.2</td>\n",
       "      <td>2238468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1990</td>\n",
       "      <td>12412308</td>\n",
       "      <td>0.78</td>\n",
       "      <td>94820</td>\n",
       "      <td>15.8</td>\n",
       "      <td>2593947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1995</td>\n",
       "      <td>18110657</td>\n",
       "      <td>7.85</td>\n",
       "      <td>1139670</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3697570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2000</td>\n",
       "      <td>20779953</td>\n",
       "      <td>2.79</td>\n",
       "      <td>533859</td>\n",
       "      <td>15.5</td>\n",
       "      <td>4436282</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "  Continent      Country  Year  Population  Yearly%Change  YearlyChange  \\\n",
       "0      Asia  Afghanistan  1955     8270991           1.30        103775   \n",
       "1      Asia  Afghanistan  1960     8996973           1.70        145196   \n",
       "2      Asia  Afghanistan  1965     9956320           2.05        191869   \n",
       "3      Asia  Afghanistan  1970    11173642           2.33        243464   \n",
       "4      Asia  Afghanistan  1975    12689160           2.58        303104   \n",
       "5      Asia  Afghanistan  1980    13356511           1.03        133470   \n",
       "6      Asia  Afghanistan  1985    11938208          -2.22       -283661   \n",
       "7      Asia  Afghanistan  1990    12412308           0.78         94820   \n",
       "8      Asia  Afghanistan  1995    18110657           7.85       1139670   \n",
       "9      Asia  Afghanistan  2000    20779953           2.79        533859   \n",
       "\n",
       "   MedianAge  UrbanPopulation  \n",
       "0       19.2           587818  \n",
       "1       18.8           755797  \n",
       "2       18.4           984350  \n",
       "3       17.9          1295433  \n",
       "4       17.3          1717422  \n",
       "5       16.9          2119078  \n",
       "6       16.2          2238468  \n",
       "7       15.8          2593947  \n",
       "8       16.0          3697570  \n",
       "9       15.5          4436282  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "62b148c8",
   "metadata": {},
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Continent</th>\n",
       "      <th>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>Population</th>\n",
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       "      <td>17.6</td>\n",
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       "      <td>Africa</td>\n",
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       "      <td>11881477</td>\n",
       "      <td>0.81</td>\n",
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       "      <td>18.2</td>\n",
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       "      <td>Africa</td>\n",
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       "      <td>12076699</td>\n",
       "      <td>0.33</td>\n",
       "      <td>39044</td>\n",
       "      <td>18.3</td>\n",
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       "    <tr>\n",
       "      <th>4223</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2010</td>\n",
       "      <td>12697723</td>\n",
       "      <td>1.01</td>\n",
       "      <td>124205</td>\n",
       "      <td>18.6</td>\n",
       "      <td>4676106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4224</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2015</td>\n",
       "      <td>13814629</td>\n",
       "      <td>1.70</td>\n",
       "      <td>223381</td>\n",
       "      <td>18.4</td>\n",
       "      <td>5109485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4225</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2016</td>\n",
       "      <td>14030331</td>\n",
       "      <td>1.56</td>\n",
       "      <td>215702</td>\n",
       "      <td>18.5</td>\n",
       "      <td>5215894</td>\n",
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       "    <tr>\n",
       "      <th>4226</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2017</td>\n",
       "      <td>14236595</td>\n",
       "      <td>1.47</td>\n",
       "      <td>206264</td>\n",
       "      <td>18.5</td>\n",
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       "      <th>4227</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2018</td>\n",
       "      <td>14438802</td>\n",
       "      <td>1.42</td>\n",
       "      <td>202207</td>\n",
       "      <td>18.5</td>\n",
       "      <td>5447513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4228</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2019</td>\n",
       "      <td>14645468</td>\n",
       "      <td>1.43</td>\n",
       "      <td>206666</td>\n",
       "      <td>18.5</td>\n",
       "      <td>5571525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4229</th>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2020</td>\n",
       "      <td>14862924</td>\n",
       "      <td>1.48</td>\n",
       "      <td>217456</td>\n",
       "      <td>18.7</td>\n",
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      "text/plain": [
       "     Continent   Country  Year  Population  Yearly%Change  YearlyChange  \\\n",
       "4220    Africa  Zimbabwe  1995    11410714           1.81        195659   \n",
       "4221    Africa  Zimbabwe  2000    11881477           0.81         94153   \n",
       "4222    Africa  Zimbabwe  2005    12076699           0.33         39044   \n",
       "4223    Africa  Zimbabwe  2010    12697723           1.01        124205   \n",
       "4224    Africa  Zimbabwe  2015    13814629           1.70        223381   \n",
       "4225    Africa  Zimbabwe  2016    14030331           1.56        215702   \n",
       "4226    Africa  Zimbabwe  2017    14236595           1.47        206264   \n",
       "4227    Africa  Zimbabwe  2018    14438802           1.42        202207   \n",
       "4228    Africa  Zimbabwe  2019    14645468           1.43        206666   \n",
       "4229    Africa  Zimbabwe  2020    14862924           1.48        217456   \n",
       "\n",
       "      MedianAge  UrbanPopulation  \n",
       "4220       17.6          3592151  \n",
       "4221       18.2          4125989  \n",
       "4222       18.3          4413866  \n",
       "4223       18.6          4676106  \n",
       "4224       18.4          5109485  \n",
       "4225       18.5          5215894  \n",
       "4226       18.5          5328766  \n",
       "4227       18.5          5447513  \n",
       "4228       18.5          5571525  \n",
       "4229       18.7          5700460  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "084d5880",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4230 entries, 0 to 4229\n",
      "Data columns (total 8 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   Continent        4230 non-null   object \n",
      " 1   Country          4230 non-null   object \n",
      " 2   Year             4230 non-null   int64  \n",
      " 3   Population       4230 non-null   int64  \n",
      " 4   Yearly%Change    4230 non-null   float64\n",
      " 5   YearlyChange     4230 non-null   int64  \n",
      " 6   MedianAge        4230 non-null   float64\n",
      " 7   UrbanPopulation  4230 non-null   int64  \n",
      "dtypes: float64(2), int64(4), object(2)\n",
      "memory usage: 264.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f73b74ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Continent          0\n",
       "Country            0\n",
       "Year               0\n",
       "Population         0\n",
       "Yearly%Change      0\n",
       "YearlyChange       0\n",
       "MedianAge          0\n",
       "UrbanPopulation    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8fee6146",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Continent', 'Country', 'Year', 'Population', 'Yearly%Change',\n",
       "       'YearlyChange', 'MedianAge', 'UrbanPopulation'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a407bc0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Population</th>\n",
       "      <th>Yearly%Change</th>\n",
       "      <th>YearlyChange</th>\n",
       "      <th>MedianAge</th>\n",
       "      <th>UrbanPopulation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4230.000000</td>\n",
       "      <td>4.230000e+03</td>\n",
       "      <td>4230.000000</td>\n",
       "      <td>4.230000e+03</td>\n",
       "      <td>4230.000000</td>\n",
       "      <td>4.230000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1994.166667</td>\n",
       "      <td>2.423329e+07</td>\n",
       "      <td>1.659778</td>\n",
       "      <td>3.271283e+05</td>\n",
       "      <td>21.789243</td>\n",
       "      <td>1.167059e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>21.723653</td>\n",
       "      <td>1.063217e+08</td>\n",
       "      <td>1.650514</td>\n",
       "      <td>1.329164e+06</td>\n",
       "      <td>11.751355</td>\n",
       "      <td>4.665141e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1955.000000</td>\n",
       "      <td>6.440000e+02</td>\n",
       "      <td>-12.930000</td>\n",
       "      <td>-6.730240e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1975.000000</td>\n",
       "      <td>2.733908e+05</td>\n",
       "      <td>0.610000</td>\n",
       "      <td>1.201750e+03</td>\n",
       "      <td>17.200000</td>\n",
       "      <td>1.148200e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1997.500000</td>\n",
       "      <td>3.603262e+06</td>\n",
       "      <td>1.530000</td>\n",
       "      <td>3.730850e+04</td>\n",
       "      <td>20.700000</td>\n",
       "      <td>1.479994e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2016.000000</td>\n",
       "      <td>1.276953e+07</td>\n",
       "      <td>2.590000</td>\n",
       "      <td>2.038582e+05</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>6.588019e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2020.000000</td>\n",
       "      <td>1.439324e+09</td>\n",
       "      <td>23.300000</td>\n",
       "      <td>2.067648e+07</td>\n",
       "      <td>48.400000</td>\n",
       "      <td>8.750759e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Year    Population  Yearly%Change  YearlyChange    MedianAge  \\\n",
       "count  4230.000000  4.230000e+03    4230.000000  4.230000e+03  4230.000000   \n",
       "mean   1994.166667  2.423329e+07       1.659778  3.271283e+05    21.789243   \n",
       "std      21.723653  1.063217e+08       1.650514  1.329164e+06    11.751355   \n",
       "min    1955.000000  6.440000e+02     -12.930000 -6.730240e+05     0.000000   \n",
       "25%    1975.000000  2.733908e+05       0.610000  1.201750e+03    17.200000   \n",
       "50%    1997.500000  3.603262e+06       1.530000  3.730850e+04    20.700000   \n",
       "75%    2016.000000  1.276953e+07       2.590000  2.038582e+05    30.000000   \n",
       "max    2020.000000  1.439324e+09      23.300000  2.067648e+07    48.400000   \n",
       "\n",
       "       UrbanPopulation  \n",
       "count     4.230000e+03  \n",
       "mean      1.167059e+07  \n",
       "std       4.665141e+07  \n",
       "min       0.000000e+00  \n",
       "25%       1.148200e+05  \n",
       "50%       1.479994e+06  \n",
       "75%       6.588019e+06  \n",
       "max       8.750759e+08  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "02f36dfc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Continent           object\n",
       "Country             object\n",
       "Year                 int64\n",
       "Population           int64\n",
       "Yearly%Change      float64\n",
       "YearlyChange         int64\n",
       "MedianAge          float64\n",
       "UrbanPopulation      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f6b67cf",
   "metadata": {},
   "source": [
    "# Numerical and Categorical Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4a5974cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Continent', 'Country']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_columns = df.select_dtypes(include = 'object').columns.tolist()\n",
    "cat_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2ae67f2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Year',\n",
       " 'Population',\n",
       " 'Yearly%Change',\n",
       " 'YearlyChange',\n",
       " 'MedianAge',\n",
       " 'UrbanPopulation']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_columns = df.select_dtypes(include = ['float64', 'int64']).columns.tolist()\n",
    "num_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e7ba9b8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Continent</th>\n",
       "      <th>Year</th>\n",
       "      <th>YearlyChange</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Africa</td>\n",
       "      <td>1955</td>\n",
       "      <td>4990957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Africa</td>\n",
       "      <td>1960</td>\n",
       "      <td>6122462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Africa</td>\n",
       "      <td>1965</td>\n",
       "      <td>7357208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Africa</td>\n",
       "      <td>1970</td>\n",
       "      <td>8660054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Africa</td>\n",
       "      <td>1975</td>\n",
       "      <td>10245436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>2016</td>\n",
       "      <td>580448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>2017</td>\n",
       "      <td>574701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>2018</td>\n",
       "      <td>568384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>2019</td>\n",
       "      <td>561377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>Oceania</td>\n",
       "      <td>2020</td>\n",
       "      <td>553934</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>108 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Continent  Year  YearlyChange\n",
       "0      Africa  1955       4990957\n",
       "1      Africa  1960       6122462\n",
       "2      Africa  1965       7357208\n",
       "3      Africa  1970       8660054\n",
       "4      Africa  1975      10245436\n",
       "..        ...   ...           ...\n",
       "103   Oceania  2016        580448\n",
       "104   Oceania  2017        574701\n",
       "105   Oceania  2018        568384\n",
       "106   Oceania  2019        561377\n",
       "107   Oceania  2020        553934\n",
       "\n",
       "[108 rows x 3 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df.groupby(['Continent', 'Year'])['YearlyChange'].sum().reset_index()\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f0e21c3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GgwcPKF26NBMnTtR1SLezs9P75n3ixAnOnDnDnDlzMjp0kc3ky5cPgK+++irFASsAvr6+APz222/Url2b2bNn6x2Pjo5O8TxD5u7s27cvEyZMYMGCBTx9+pSEhAT69OmT6vOLFi1KhQoVUjyW9ActIiIi2R/1Gzdu6N6DJEnJ+otu3rypd62kP7ovD+ZITQKzc+dObty4we7du3W1pAAPHjx447mv4+zsTERERLL9N27cAEj2OtOqYcOG/PzzzwQFBaXriOzffvuNIkWKsGrVKr2fnZff49RydHREo9G89vNMki9fPjQaDXv37k3xy1BqvyDdvHkTd3d33fOEhATu3bunl1SZmJjQv39/vv76a6ZOncqsWbOoW7eu7vfsVVxcXACSDdh70Ys/6y+7ceMGJiYmODo6puq1ZLR8+fLh7OzMli1bUjz+Ym24ENlVjqkxfZMPP/yQ/fv3s3LlSk6ePEm7du1o1KgR58+fT7H8vHnz8PHxoUaNGpkcqcjqfH19KV68OCdOnKBChQopPpL+QGg0mmR/oE+ePJlstG9auLm50a5dO2bNmsWcOXNo3ry5XrPd23jvvfcANfF50eHDhwkJCaFu3bp6+6Ojo/nzzz/19i1fvhwTExNq1qwJqKOEQX39L3r5vJQkJV0vv5e//PJLsrKG1F7VrVuXM2fOcOzYMb39SbMb1KlT543XSI3PPvsMW1tb+vXrpzd6O4miKGmaLkqj0WBhYaGXlN68eTPFUfmpYWtrS4UKFVi3bh1xcXG6/Y8ePUo2er9Zs2YoisL169dT/B3w9/dP1T2XLVum93z16tUkJCToKg2SfPzxx1hYWNC5c2dCQ0MZMGDAG69drVo1HBwcmDNnDoqipFjG19cXd3d3li9frlcmJiaGNWvW6EbqGyojaiabNWvGvXv3SExMTPE9f1Oi/qo44e1qe4VITzmmxvR1wsPDWbFiBdeuXdM10X3++eds2bKFhQsXMm7cOL3ysbGxLFu2jC+//NIY4Yps4JdffqFx48Y0bNiQHj164O7uzv379wkJCeHYsWP8/vvvgPqHZMyYMYwaNYpatWoRGhrK999/T5EiRdJljtLBgwdTuXJlABYuXPjW10vi6+vLJ598ws8//4yJiQmNGzfm0qVLjBgxAg8PDz777DO98s7OzvTt25crV67g4+PDpk2bmDt3Ln379tUlywUKFKBevXqMHz8eR0dHPD092bFjh645/nWqVauGo6Mjffr0YdSoUZibm7Ns2TJOnDiRrGxSQjRx4kQaN26MqakpZcqU0eszmeSzzz5jyZIlNG3alO+//x5PT082btzIrFmz6Nu3Lz4+Pml5+5IpUqQIK1eupEOHDpQrV44BAwbo5lQ9c+YMCxYsQFEUWrdubdB1mzVrxtq1a+nXrx9t27bl6tWrjBkzBjc3t1d+6X6T77//nqZNm9KwYUMGDx5MYmIikydPxs7Ojvv37+vKVa9enU8++YQPP/yQI0eOULNmTWxtbYmIiGDfvn34+/vTt2/fN95v7dq1mJmZUb9+fU6fPs2IESMoW7Ys7du31yuXN29eunXrxuzZs/H09EzWHzMldnZ2TJ06lY8//ph69erRq1cvXF1dCQsL48SJE8ycORMTExMmTZpE586dadasGb179yY2NpbJkyfz4MEDJkyYYPibiPpzuHv3bv766y/c3Nywt7dPU+L4oo4dO7Js2TKaNGnC4MGDqVSpEubm5ly7do1du3bRsmVLg3+GklYY+/XXX7G3t8fKyooiRYqk2A1AiExhvHFXGQdQAgMDdc9Xr16tAIqtra3ew8zMTGnfvn2y85cvX66YmZkpERERmRi1yMpq1aqllCpVSm/fiRMnlPbt2yv58+dXzM3NlQIFCijvvfeeMmfOHF2Z2NhY5fPPP1fc3d0VKysrpXz58sq6deuU7t27K56enrpySaO+J0+enOzeKY1af5GXl5fi5+eX6tfyqumiXpaYmKhMnDhR8fHxUczNzZV8+fIpXbp0Ua5evapXLum92b17t1KhQgXF0tJScXNzU77++utksxZEREQobdu2VZycnBQHBwelS5cuypEjR1I1Kv/AgQNK1apVFRsbG8XFxUX5+OOPlWPHjiU7NzY2Vvn4448VFxcXRaPRKIBy8eJFRVGSj8pXFEW5fPmy0qlTJ8XZ2VkxNzdXfH19lcmTJ+uN0H7d5wMoo0aNeu17mSQ8PFzp16+f4u3trVhaWirW1tZKyZIllSFDhuhifPE9fdnLPzeKoigTJkxQvLy8FEtLS8XPz0+ZO3duiu8fL03flSSl9yQwMFDx9/dXLCwslMKFCysTJkxQBg0apDg6OiY7f8GCBUrlypUVW1tbxdraWilWrJjSrVs35ciRI699L5JiPHr0qNK8eXPFzs5Osbe3Vz744APl1q1bKZ6ze/duBVAmTJjw2mu/bNOmTUqtWrUUW1tbxcbGRilZsqQyceJEvTLr1q1TKleurFhZWSm2trZK3bp1lf3796cY84vTpSnK81kyXvwMg4ODlerVqys2NjYKoNSqVUtRlFePyre1tU0Wd0qfY3x8vDJlyhSlbNmyipWVlWJnZ6eUKFFC6d27t3L+/HldOU9PT6Vp06bJrlmrVi1dLEmmT5+uFClSRDE1NX3t/zVCZAaNoryifSMb02g0BAYG6gZyrFq1is6dO3P69Gm9EbWgfqN+ufN93bp1yZMnj6zEIrK8kydPUrZsWf73v//pBl5lttq1a3P37l3+++8/o9xfZLz4+HjKlSuHu7s7f//9t9HiGDp0KLNnz+bq1atSoydEDpUrmvIDAgJITEzk9u3bb+wzevHiRXbt2pWqfm9CGEt4eDiXL1/m66+/xs3NLdm0TkK8jZ49e1K/fn3c3Ny4efMmc+bMISQkhJ9++sko8QQFBXHu3DlmzZpF7969JSkVIgfLMYnpo0ePCAsL0z2/ePEiwcHBODk54ePjQ+fOnenWrRtTp04lICCAu3fvsnPnTvz9/WnSpInuvAULFuDm5vbaEf5CGNuYMWN0S0v+/vvvaRqcIcSrREdH8/nnn3Pnzh3Mzc0pX748mzZtol69ekaJJ2kAUrNmzRg7dqxRYhBCZI4c05S/e/fuFEfQdu/enUWLFhEfH8/YsWNZsmQJ169fx9nZmapVqzJ69GjdYAmtVounpyfdunXjhx9+yOyXIIQQQgiRq+WYxFQIIYQQQmRvuWYeUyGEEEIIkbVJYiqEEEIIIbKEbD34SavVcuPGDezt7Q1aylEIIYQQQmQORVGIjo6mYMGCmJi8vk40WyemN27cwMPDw9hhCCGEEEKIN7h69SqFChV6bZlsnZgmrUd+9epV8uTJY+RohBBCCCHEy6KiovDw8NDlba+TrRPTpOb7PHnySGIqhBBCCJGFpabbpQx+EkIIIYQQWYIkpkIIIYQQIkuQxFQIIYQQQmQJkpgKIYQQQogsQRJTIYQQQgiRJUhiKoQQQgghsgRJTIUQQgghRJYgiakQQgghhMgSJDEVQgghhBBZgiSmQgghhBAiS5DEVAghhBBCZAmSmAohhBBCiCxBElMhhBBCCJElSGIqhBBCCCGyBElMhRBCCCFEliCJqRBCCCGEyBIkMRVCiNzgdgg8iTR2FEII8VqSmAohRE53ZAHMqgL/qwJRN4wdjRBCvJIkpkIIkZOdWAUbhqjbj27Cio4Q99i4MQkhxCtIYiqEEDlVyF+wri+gQJkOYOMMESdgXR/Qao0dnRBCJCOJqRBC5ERhO+CPj0BJhLKdoNUc6PAbmJjDmfWwZ4KxIxRCiGSMnphev36dLl264OzsjI2NDeXKlePo0aPGDksIIbKvywdgZWdIjAO/FtDiZzAxAc9q0Hy6WmbPRDj1h1HDFEKIl5kZ8+aRkZFUr16dOnXqsHnzZvLnz094eDh58+Y1ZlhCCJF9XT8Gy9pDwhPwrg/vzwfTF/6rD+iijtA/OBPW9wfHIlDoHePFK4QQLzBqYjpx4kQ8PDxYuHChbp+Xl9cry8fGxhIbG6t7HhUVlZHhCSFE9nLrDPzWBuKiwfNd6LAUzCySl6v/Pdw9D+e3wspO0GsnOLhnfrxCCPESozbl//nnn1SoUIF27dqRP39+AgICmDt37ivLjx8/HgcHB93Dw8MjE6MVQogs7F44LG2lzlXq/g50Wgnm1imXNTGF9+eBi586Un/lBxAXk6nhCiFESjSKoijGurmVlRUAQ4YMoV27dvz77798+umn/PLLL3Tr1i1Z+ZRqTD08PHj48CF58uTJtLiFECJLeXAVFjaGh1fBtTR0/wtsnN58XuQlmPsePL4HJVtC20VqX1QhhEhHUVFRODg4pCpfM2piamFhQYUKFThw4IBu36BBgzh8+DAHDx584/mGvFAhhMiRHt2GBY3gfjg4e8OHm8Euf+rPv3wQFjcHbTzUGg51vs64WIUQuZIh+ZpRvxq7ublRsmRJvX1+fn5cuXLFSBEJIUQ28vg+LGmlJqUOHtBtvWFJKYBnVRmpL4TIMoyamFavXp3Q0FC9fefOncPT09NIEQkhRDYRGw3L2sLt02DnqialDoXSdq2ALlBtoLq9vj9ckyn7hBDGYdTE9LPPPiMoKIhx48YRFhbG8uXL+fXXX+nfv78xwxJCiKwt7jEs7wjXj4K1I3RdB87F3u6a9UaDTyNIeKoOhnp4PV1CFUIIQxg1Ma1YsSKBgYGsWLGC0qVLM2bMGKZPn07nzp2NGZYQQmRdCXGwuhtc3gcW9tBlLbiWfPN5b2JiCm3mQv6S8OiWjNQXQhiFUQc/vS0Z/CSEyFUSE+CPDyHkTzCzhq5r1dWc0lPkZZhbR0bqCyHSTbYZ/CSEECKVtFr4c4CalJpaQMdl6Z+UAjh6QodlYGIOZ9bD7vHpfw8hhHgFSUyFECKrUxTYPAxOrACNKbRdCN51M+5+nlWh+U/q9j+TZKS+ECLTSGIqhBBZmaLA9u/g8DxAA63ngF+zjL9vQOfnI/XX9ZOR+kKITCGJqRBCZGV7p8D+6ep2sx+hTPvMu3fSSP3EWBmpL4TIFJKYCiFEVhU0B3aOVbcbjIUKH2bu/U1M4f15MlJfCJFpJDEVQois6NhS2DJc3a715fNm9cxmaQ8frASbfBBxAgL7qAOxhBAiA0hiKoQQWc1/a+GvQep21QFQ+0vjxuPoCR1+U0fqh/wpI/WFEBlGElMhhMhKQrfA2l6gaKF8d7UJX6MxdlQyUl8IkSkkMRVCiKziwh51VSdtAvi3Uwc7ZYWkNElAZ6j2rCZXRuoLITKAJKZCCJEVXP0XVnygjoD3bQqtZquDj7Kaet/JSH0hRIaRxFQIIYwt4iT81hbiY6BobWi7AEzNjR1Vyl4eqb+io4zUF0KkG0lMhRDCmO6cg6WtIfYheFSBjsvB3MrYUb3eiyP1b56UkfpCiHQjiakQQhhL5CVY0hIe3wW3stB5NVjYGjuq1Ek2Un+csSMSQuQAkpgKIYQxRN2AxS0g+ga4lIAugWDlYOyoDKM3Un+yjNQXQrw1SUyFECKzxdyFJa3gwWVwLAJd14Gts7GjSptkI/WPGDceIUS2JompEEJkpicP1D6ld0Mhjzt0Ww953Iwd1dup9x34NH42Ur8TPLxm7IiEENmUJKZCCJFZ4mJgeXt1wJCti5qUOnoaO6q3Z2IK78+F/KWejdT/QEbqCyHSRBJTIYTIDPFP1YTt6iG1L2nXQMhX3NhRpR9Le/hgxQsj9XvLSH0hhMEkMRVCiIyWGA+/94CLe8DcFjqvgQL+xo4q/SWN1De1gJC/ZKS+EMJgkpgKIURG0iaqtYfnNoOZFXRaCR4VjR1Vxnl5pP7J340bjxAiW5HEVAghMoqiwIZP4b81YGIG7ZdAkZrGjirjlesE1Qer2+v7y0h9IUSqSWIqhBAZQVFg69dwbAloTNRlPH0aGjuqzFN3lIzUF0IYTBJTIYTICLvGQdAsdbvFTCjV2rjxZLZkI/U7ykh9IcQbSWIqhBDpbf9P8M8kdbvxZHUS+txIb6T+KRmpL4R4I0lMhRAiPR2eD9tGqtt1R0LlT4wbj7E5ekLHZTJSXwiRKpKYCiFEejmxCjYOVbffHQI1hho3nqyicBUZqS+ESBVJTIUQIj2E/AXr+gIKVOqt1paK52SkvhAiFSQxFUKItxW2HX7/EJREKNcZGk0AjcbYUWU9L47UX/GBjNQXQiQjiakQQryNS/thZRfQxkPJVtDiZzCR/1pT9OJI/ZjbMlJfCJGM/O8phBBpdf0oLO8ACU+geANoM1dNvsSrWdqrq1/JSH0hRAokMRVCiLS4cRx+ex/iosGrhrqqk5mFsaPKHvIW1h+pv+sHY0ckhMgiJDEVQghDKAoEzYb5DeBJJLhXUOfqNLc2dmTZS+Eq0HyGur13iozUF0IAkpgKIUTqxdxTB+1s+RIS48C3KXRZozZPC8OV+0BG6gsh9EhiKoQQqXFpH8x5F85tVpugm0xRm6Ot8xo7suyt7ijwbSIj9YUQgCSmQgjxetpE2DUeFjeH6BvgXBw+3gGVesmUUOnBxBTa/Coj9YUQgCSmQgjxag+vqwnpngmgaNU5Sj/ZDW5ljB1ZzpI0Ut/WRR2pv/YTGakvRC4liakQQqQkdDPMqQ6X94OFnToVVKtZYGln7MhypryFocOzkfpnN8CuscaOSAhhBJKYCiHEixJiYfNwtUn5SSS4lYXe/0CZ9saOLOcrXPmFkfpT1Tli718wbkxCiEwliakQQiS5Gwbz6sGhOerzKv2h5zZwLmbcuHKTch9A/e/BxAzObYH/VYGdYyHusbEjE0JkAklMhRACIHgF/FITbp4EG2fotBoajQMzS2NHlvtUHwx9D0LROupo/X8mw/8qwZk/1XlkhRA5liSmQojcLTYa1vaGdX0gPkZdxanPPvBpaOzIcjcXH+gaCO2XgoMHPLwKq7vC0tZw97yxoxNCZBBJTIUQudeNYPilFpxcCRoTqPMtdFsPeQoaOzIB6nRcJVtA/3+h5jB1YNSFXTCrKmwbqX6pEELkKJKYCiFyH92yovXhfjjkcYceG6HWMHVeTZG1WNjAe99CvyDwaQTaeNj/E8ysCKf+kOZ9IXIQSUyFELlLSsuK9tkHntWMHZl4E+di0GkVfLAKHL0gOgLW9IRFzeDWaWNHJ4RIB5KYCiFyj1ctK2rjZOzIhCF8G0G/Q2rXCzNruLwP5tSAzV/CkwfGjk4I8RYkMRVC5HyJCbKsaE5jbqV2vRjwL/i1ACURDs2GmRXg+DJZOUqIbEoSUyFEzvbwOixp8cKyol2g9x5ZVjSnyFsYOiyFLmvVLxwxd2B9P1jQUB3cJoTIViQxFULkXGc3pbCs6P/AwtbYkYn05l0X+h5QJ+c3t4Vr/8KvtWHDZ/D4vrGjE0KkkkZRsu9wxqioKBwcHHj48CF58uQxdjhCZB2KAjeOqyOWz28FuwLqtDt+zXPHVEgJsep0QkkrOLmVg7YLZAWn3CLqBvw9Av77Q31u7QR1R0L5bjLrghBGYEi+JompEDnJvXA49bv6uBeWcplClZ4lqS3A0TNz48sMd8Pgjw/VFZwAqg6AuqPAzMK4cYnMd2kfbBoGt8+oz93KqQPePCoaNSwhchtJTIXITaJvwem1cHI13Dj2fL+ZNfg2hlKt1VVzzqyHq4f0z3Ur9yxJbQn5vDM17AwRvAI2DlVXcLJxhlazZQWn3C4xHg7Pg13jIDZK3RfQBep+B3YuRg1NiNxCElMhcrqnURDyl1ozenGPOqgHQGMKRWtDmfZQoilY2uufFxUBZzeoSerl/c/PA8hfEkq2VGtS8/tlr9HqsdGw8XN1BSdQlxVt82vu6LYgUufRbdj+HQQvU59bOsB730CFnmBqZtTQhMjpJDEVIidKiIXz29Rk9NwWSHj6/FihiuDfTq0dtcufuus9ugOhG+HMn2pyq014fszZW01QS7ZQa1WzcpJ6Ixj++EhdwUljArW/hhpDpC+hSNmVQ7Dp8+ddPfKXgiaTwau6ceMSIgeTxFSInEKrVWs2T61WazmfPnx+LJ8P+LcH//fBqejb3edJJIRuVpPU8J2QGPv8WN7Cz5LUluBeAUyyyGQeiqIObvp7hLpEZZ5C8P488Kxq7MhEVqdNhKOLYMf38PSBus+/vTqiP4+bMSMTIkeSxFSI7ExR4OYpNRk9tUadED6JvRuUfl9tqi9QJmNqMmOj4dxWNREO2w7xj1+4f0F1ZH/JFlC4qvFqJWPuqXNVntuiPi/RDFr8LCs4CcPE3IOd38PRxYCiTilWazhU6Qum5saOTogcQxJTIbKj+xfV6W1O/g53Q5/vt3JQayv924Fn9cxNBuMeq8lpyJ8QugXiop8fs3VR+7GWbKn26cysP+SX9sGaj9V10k0toeEPUPHjrN3dQGRt14+po/evH1Gf5/OFxhOhWB3jxiVEDiGJqRDZxaM7cDpQ7Td67d/n+00t1fXA/dtD8fpgZmm8GJMkxEL4LjVJPbvxeRMogFVeNUn1a6H+Mc+IeBMT4J/J8M8kddCWc3FotxAK+Kf/vUTuo9XCieWwbRQ8vqvuK9kSGvwAeT2MG5sQ2Vy2SUy/++47Ro8erbfP1dWVmzdvpup8SUxFthQbra5IdGq1mugpiep+jQkUqaXWjPo1U2tKs6rEeLi0V23uD9nw/A85gGUedYomvxbgXQ8sbN7+fg+vw9pean9bUJcVbTJJVnAS6e9JJOwaD4fnql+AzG2gxlCoNjBrfEEUIhvKVonpH3/8wfbt23X7TE1NcXFJ3dxykpiKbCMhDsJ3qDWjZzdBwpPnxwqWV/uMlmoN9gWMF2NaaRPhykF14FTIn2oTexJzG7XG16+Fmqy+PH1VapzdpPYnfRKp9gFs9qP6fgmRkW6egk1fwJUD6nOnotBoIvg0MG5cQmRD2SoxXbduHcHBwWk6XxJTkaVptXA1SJ34/sw6NbFK4lRMTa5Kt80ZE9sn0WrVfnpn1qtJ6oMrz4+ZWkKx99SBU76Nwdrx9deKf6ouK/rvL+pzWVZUZDZFUb9M/v0tPLql7vNpDI3Gg1MR48YmRDaSrRLTyZMn4+DggKWlJZUrV2bcuHEULZry1DexsbHExj6fxiYqKgoPDw9JTEXWcvM/9Y/Zf2vUFZeS2LmqI+r920HBgJw/WEdRICL4eU3qi0ukmpip3RZKtlBH1Nvm0z/37vlny4qeUp/LsqLCmJ5GwZ6J6vRk2gT1S9a7n0L1T9Onq4oQOVy2SUw3b97M48eP8fHx4datW4wdO5azZ89y+vRpnJ2dk5VPqU8qIImpML4HV56tUf/H83W5Qe1v6dcC/NtCkZq5d9J3RYHbIWqCema9/nukMVFnGyjZUk1SL+x+aVnROdJ8KrKG22dh8xfqghQADoXV2tMSTXP+F00h3kK2SUxfFhMTQ7Fixfjiiy8YMmRIsuNSYyqylJh7cCZQnd7patDz/aYWULyB2lRfvAGYWxsvxqzqbhiErFdrUyOCUy7jVQPazJUJz0XWoijql6ut30DUNXVfsbrqamOFq2WdBSiEyEKybWIKUL9+fby9vZk9e/Yby0ofU2EU98Jhy1fqYCbdMp4a8HpXTUb9mr+5/6R4LvIShPylJqnX/pVlRUX2EBcDe6fCgZ8hMU7d51AYyrSDMh3Bxce48QmRhWTbxDQ2NpZixYrxySefMHLkyDeWl8RUZDptIvxSC2496/voVlbtM1r6fchT0Lix5QRREepyqI5exo5EiNS5Fw77pqlfrGKjnu93KwdlO6oDHO1SN9OMEDlVtklMP//8c5o3b07hwoW5ffs2Y8eOZc+ePZw6dQpPT883ni+Jqch0wcthXV+wdICPtoBrSWNHJITICuKfQOhmOLlKXS0tqTVFY6rORlG2I/g2kcFSIlcyJF8zy6SYUnTt2jU++OAD7t69i4uLC1WqVCEoKChVSakQmS7+Cewcq27XGCJJqRDiOXNrKN1GfcTchf/WwsmVcP0ohG1THxb26kwUZdqrfailq4oQyWSppnxDSY2pyFR7p8GO0eDgAQOOgLmVsSMSQmR1d8+rcxmfXAUPLj/fb1/weX9U+ZIrcrhs05T/tiQxFZkm5h7MKKf2IWv9K5TtYOyIhBDZiaLAlSC1FvV0IDx9+PyYq7/6f4p/u+y5+psQbyCJqRDpbfNwdXLtAmXgkz0yJYwQIu0SYuHcVrUW9dxW0Mar+zUm6sITZTuqc/pa2hk3TiHSiSSmQqSne+Hwv0rqYIZu66FobWNHJITIKR7fV2tQT66Cq4ee7ze3UaeeK9MeitQGU6MOCRHirUhiKkR6Wt1dXeveuz50+cPY0Qghcqr7F9QFO06uVLeT2Lmqzfxl2qutNrLKlMhmJDEVIr1cPQzz66lNbH32gWspY0ckhMjpFAWuHVET1P/WwpP7z4+5+D3rj9oeHNyNF6MQBpDEVIj0oCiwsDFcOQgBXaDl/4wdkRAit0mIU+dFPbkSQreoC1AAoIEiNaBMB/BrAVbyN1BkXZKYCpEeQjbAqs5gZg2DjsnKTkII43ryAM6sV/ujXt7/fL+ZFZRoqiapxd4DU3OjhShESiQxFeJtJcbDrCpwLwxqfA51Rxg7IiGEeC7yMpz6XU1S7557vt8mH/i3VfujFiwv/VFFliCJqRBv6/A82DhU/U9+0HFpJhNCZE2KAjeOq5P4//cHxNx5fsy5+PP+qI6yoqIwHklMhXgbsdEwI0D9D77JFKjUy9gRCSHEmyXGQ/gutT/q2Y2Q8PT5scLV1CS1ZCuwzmusCEUuJYmpEG9j51j4ZzI4FYP+h6S/lhAi+3kaBSF/qUnqxb3Asz/1ppbgUQny+0E+H3DxBZcSYOsizf4iw0hiKkRaRUWotaUJT6D9UijZwtgRCSHE23l4/Xl/1NtnUi5jlVdNUF18IJ/v8+08hWSlO/HWJDEVIq3WD4DjS8GjCny0RWoQhBA5h6LA7RC1T+rdULhzDu6chchL6GpUX2ZuC/mKP6tZ9X2etDp6yWpUItUMydfkp0qIJLfOQPAydbvBGElKhRA5i0YDriXVx4vin6gzkNwJVUf43zmrJq33wiA+BiKC1ceLTC3A2fuF7gDPklZnbzC3yqxXJHIgSUyFSLJ9FChadbJqj0rGjkYIITKHuTUU8FcfL0qMV2tT75zVT1rvnof4x2q3gJe7BmhM1NrUfL7PE1YXXzWBtbTPrFcksjFpyhcC4MIeWNICTMyg/7/gXMzYEQkhRNak1cLDqy/Urr6QtD59+Orz8ri/0B3ghVpWW+fMi10YhTTlC2EIrRa2PZtAv0JPSUqFEOJ1TEzUeVEdPaF4/ef7FQUe3X5Wq3pOTViTth/dgqjr6iN8p/71bPK91If12cPeTbpU5UKSmArx3x8QcQIs80CtL4wdjRBCZE8aDdi7qo+itfSPPYlU+63eDX2WsD57PLwCj+/C5bv6y6yC+n9yfj8oXAU834XClcHKIfNejzAKacoXuVv8U5hZQW2WqjsSagw1dkRCCJF7xMU8q119KWm9fwGURP2yGhO1H6xn9WePamDjZJy4hUGkKV+I1Pr3VzUptS8IlfsaOxohhMhdLGyhYID6eFFCHNwPhxvBcHkfXD6gJqsRJ9RH0Cy1XP6SaoKalKzau2b6SxDpS2pMRe71+D7MKKd21m85CwI6GzsiIYQQrxJ1Q01QL+9X/71zNnkZZ+/nSapXdXAolPlximRkgn0hUmPrN3BwJriWht7/gImpsSMSQgiRWjF3X0hU98PN/0i2UEDewmr/VM9qaqLqWEQGVBmBJKZCvEnkJZhZERLjoMsa8K5n7IiEEEK8jSeRcOWQ2vR/ab/a5P9yP1X7gs+a/quB17vq/KqSqGY4SUyFeJM/PoL/1kDROtBtnbGjEUIIkd5io+HqIbVW9dJ+uH4UtPH6ZWzyPU9SPatB/lLqdFgiXUliKsTrXD8Kc98DNGoTvlsZY0ckhBAio8U/gWuH1ST18n51O+GpfhkrByj8rNnfsxoUKAumMk78bcmofCFeRVHg75HqdtmOkpQKIURuYW4NRWqqD4CEWLhxHC49G/V/9ZA6GPbcZvUBYGEHHpWfJarVoWB5MLMw3mvIBSQxFbnLua1q/yNTS3jvW2NHI4QQwljMLNXJ+wtXUZ8nJsDNE89qVA/AlQNqohq+Q30AmFlDoQrPm/4LVVQTXpFuJDEVuUdiAmx7Vltapa9MIyKEEOI5UzNwf0d9VB8E2kS4feZ50//lA+oqVZf2qg8AUwu1fFI/1SK1pY/qW5I+piL3OLIQNnwK1k4wOFiWthNCCJF6iqKuSpU0PdWl/fDopn6Zkq2g3SIZ6f8S6WMqxMtiH8Hu8ep2reGSlAohhDCMRgP5S6iPij3VRPX+hedzqZ76A86sU+fHrjbQ2NFmW1LfLHKHgzPh0S11cuUKHxk7GiGEENmdRgPOxaB8V2g9Bxo9q/zYNkpNVkWaSGIqcr7oW7B/hrpdb5SMqBRCCJH+Kn4Mpduqk/r//qH6t0cYTBJTkfPtHg/xMeBeQe3/I4QQQqQ3jQaa/wT5fNW+p2t6qoNuhUEkMRU5251QOLZE3W4wVjqkCyGEyDiWdtBhKZjbqiP3d401dkTZjiSmImfb/p3arFKiGXhWNXY0QgghcjoXX2j5s7q970c4u8m48WQzkpiKnOvSfgjdBBpTqPedsaMRQgiRW5R+Hyr1VrcD+8D9i8aNJxuRxFTkTFot/P1sZad3ekC+4kYNRwghRC7TYKy6MlTsQ1jdDeKfGjuibEESU5EznQmEG8fUdY5rf2nsaIQQQuQ2ZhbqZPs2znDzJGweZuyIsgVJTEXOkxAL20er29UHg11+48YjhBAid3IoBO/PAzTqQNzjy4wdUZYnianIeQ7PhweXwa4AVO1v7GiEEELkZsXegzpfq9sbh8ChXyAx3rgxZWGSmIqc5ckD+GeSul3na7CwNWo4QgghBDU+B59GkPAUNn8B/6sMIX+py5oKPW+VmD59Kh15RRazdyo8iQQXPwjoYuxohBBCCDAxgQ7LoOk0sHWB++GwqgssaATXjhg7uizF4MRUq9UyZswY3N3dsbOz48KFCwCMGDGC+fPnp3uAQqTagytqEwlA/e/BxNS48QghhBBJTM2gYk8YdBxqDgMza7gaBPPqqkuYRl4ydoRZgsGJ6dixY1m0aBGTJk3CwuL5muP+/v7MmzcvXYMTwiA7x0JiLHjVgOL1jR2NEEIIkZylPbz3LQw8CuU6Axo4vRZmVoSt38Dj+8aO0KgMTkyXLFnCr7/+SufOnTE1fV4jVaZMGc6ePZuuwQmRajeC4eQqdbvBGFl6VAghRNbm4A6tZkGfvVC0NiTGwcGZMLMCnFiVa/ufGpyYXr9+HW9v72T7tVot8fEyykwYgaLAthHqtn87KBhg3HiEEEKI1CrgD13XQec16viIx/cg8BP47f1c2bxvcGJaqlQp9u7dm2z/77//TkCAJATCCMK2w8V/wNQC3hth7GiEEEIIw2g0ULyeWnv63ggwtYTwHTCrKhz4GRITjB1hpjEz9IRRo0bRtWtXrl+/jlarZe3atYSGhrJkyRI2bNiQETEK8WraRNg2Ut2u3BscPY0bjxBCCJFWpuZQ83Mo2Qo2fAqX9qrLa5/6A1rMALeyxo4wwxlcY9q8eXNWrVrFpk2b0Gg0jBw5kpCQEP766y/q15cBJyKTBS+H22fAKi/UGGrsaIQQQoi3l88buv8FLX4GKweICIZf68DfIyDusbGjy1AaRcm+vWujoqJwcHDg4cOH5MmTx9jhiMwWFwM/vwPREdDgB6g2wNgRCSGEEOkr+hZsGQ6nA9Xnjl7Q8n/g9a5RwzKEIfmarPwksq+gWWpSmrcwVOpl7GiEEEKI9GfvCu0WwQerII+7OiBqUbNnUyTmvL6nBiemjo6OODk5JXs4Ozvj7u5OrVq1WLhwYUbEKsRzj+7Avp/U7bqjwMzSuPEIIYQQGcm3EfQ/9GxVQwX+mQyLmqqLy+QgBiemI0eOxMTEhKZNmzJ69Gi+++47mjZtiomJCf3798fHx4e+ffsyd+7cjIhXCNWeCRAXrU4NVaqNsaMRQgghMp6lvdqM//58sMyjrhw15104s97YkaUbg0fl79u3j7Fjx9KnTx+9/b/88gt///03a9asoUyZMsyYMYNevaR5VWSAu+fhyLNa+fpj1DWIhRBCiNzCvy24vwNrPobrR2B1N3jnQ2g0HsytjR3dWzH4L/rWrVupV69esv1169Zl69atADRp0oQLFy68fXRCpGT7d6Akgk8jKFLD2NEIIYQQmc+pCHy0Bap/qj4/ulAduX/rjFHDelsGJ6ZOTk789ddfyfb/9ddfODk5ARATE4O9vf3bRyfEy64EwdkNoDGBeqONHY0QQghhPKbmUH80dA0EO1e4EwJz68Dh+dl2SVODm/JHjBhB37592bVrF5UqVUKj0fDvv/+yadMm5syZA8C2bduoVatWugcrcjlFUedwAwjoCvlLGDceIYQQIiso9h702Q/r+qirIW4cAtcOQ7Mfs13TfprmMd2/fz8zZ84kNDQURVEoUaIEAwcOpFq1ahkR4yvJPKa5zOl18Ht3MLeFQcfAvoCxIxJCCCGyDq0WDs583uWtYAB0+A0cChk1LEPytSwzwf748eP5+uuvGTx4MNOnT0/VOZKY5iIJcTCrMty/ALW+hDpfGTsiIYQQImu6sBt+7wFPIsHWBdovBc+qRgvHkHzN4KZ8AK1WS1hYGLdv30ar1eodq1mzpsHXO3z4ML/++itlypRJSzgiNzi6UE1KbfNDtYHGjkYIIYTIuorWhk92w8rOcOs/WNwMGk+Cij2NHdkbGZyYBgUF0alTJy5fvszLla0ajYbExESDrvfo0SM6d+7M3LlzGTt2rKHhiNzg6UPYM1HdrvMVWNoZNx4hhBAiq3P0gp5/w/r+6nKmG4fAzZPQeDKYWRg7ulcyeFR+nz59qFChAv/99x/3798nMjJS97h//77BAfTv35+mTZumOAXVy2JjY4mKitJ7iFxg33R4fA/y+UBAN2NHI4QQQmQPFrbQdqG6QiIaOLpIrT2NvmnsyF7J4BrT8+fP88cff+Dt7f3WN1+5ciXHjh3j8OHDqSo/fvx4Ro+WKYJylYfXIGiWul1vNJimqfeJEEIIkTtpNFBjCLiWVifkv3oIfq0NHZZBoXeMHV0yBteYVq5cmbCwsLe+8dWrVxk8eDC//fYbVlZWqTrnq6++4uHDh7rH1atX3zoOkcXtGgcJT8GzOvg2NnY0QgghRPbk0wB67YR8vhAdAQsbQ8QJY0eVjMHVTwMHDmTo0KHcvHkTf39/zM3N9Y6ndgDT0aNHuX37Nu+88zxbT0xM5J9//mHmzJnExsZiamqqd46lpSWWlpaGhiyyq5unIHi5ul1/jPqtTwghhBBpk88bPt4Ogb0BDbj6GzuiZAyeLsokhXXJNRoNiqIYNPgpOjqay5cv6+378MMPKVGiBMOHD6d06dJvvIZMF5XDLW0N4TuhVBtot9DY0QghhBA5g1artkZa2GTK7TJ0uqiLFy+mObAX2dvbJ0s+bW1tcXZ2TlVSKnK4sB1qUmpiDnVHGjsaIYQQIucwMcm0pNRQBiemnp6eGRGHEM9pE2HbKHW7Ui9wKmLceIQQQgiRKdI8xPnMmTNcuXKFuLg4vf0tWrRIczC7d+9O87kiBzm5Gm6dAksHqDnM2NEIIYQQIpMYnJheuHCB1q1bc+rUKV3fUlD7mQIGT7AvhJ74J7Dz2UILNYaAjZNx4xGvpSgKvXv35o8//iAyMpLjx49Trly5FMtqNBoCAwNp1apVpsYohBAi+zB4uqjBgwdTpEgRbt26hY2NDadPn+aff/6hQoUKUuMp3l7QbIi6Bg4eULmPsaMRzxw4cABTU1MaNWqkt3/Lli0sWrSIDRs2EBER8dr+4RERETRuLFN+CSGEeDWDE9ODBw/y/fff4+LigomJCSYmJrz77ruMHz+eQYMGZUSMIreIuQf7flS33xsB5qmb31ZkvAULFjBw4ED27dvHlStXdPvDw8Nxc3OjWrVqFChQADOz5I0wSd19ChQoINO9CSGEeC2DE9PExETs7NS1yvPly8eNGzcAdVBUaGho+kYncpd/JkFsFBQoA/7tjB1NhlIUhcdxCZn+MHB2OABiYmJYvXo1ffv2pVmzZixatAiAHj16MHDgQK5cuYJGo8HLywuA2rVrM2DAAIYMGUK+fPmoX78+oDblr1u3Tnfda9eu0bFjR5ycnLC1taVChQocOnQIUBPeli1b4urqip2dHRUrVmT79u1v9Z4LIYTI+gzuY1q6dGlOnjxJ0aJFqVy5MpMmTcLCwoJff/2VokWLZkSMIje4Fw6H56nbDcaoU1nkYE/iEyk5cmum3/fM9w2xsTDs137VqlX4+vri6+tLly5dGDhwICNGjOCnn36iWLFi/Prrrxw+fFhvQYzFixfTt29f9u/fn2Iy/OjRI2rVqoW7uzt//vknBQoU4NixY2i1Wt3xJk2aMHbsWKysrFi8eDHNmzcnNDSUwoULv92bIIQQIssyODH99ttviYmJAWDs2LE0a9aMGjVq4OzszKpVq9I9QJELKApsHwXaBPCuB0VrGzsi8YL58+fTpUsXABo1asSjR4/YsWMH9erVw97eHlNTUwoUKKB3jre3N5MmTXrlNZcvX86dO3c4fPgwTk5OunOSlC1blrJly+qejx07lsDAQP78808GDBiQni9PCCFEFmJwYtqwYUPddtGiRTlz5gz379/H0dFRNzJfCIPsngAhf4HGBOp/b+xoMoW1uSlnvm/45oIZcF9DhIaG8u+//7J27VoAzMzM6NChAwsWLKBevXqvPK9ChQqvvW5wcDABAQG6pPRlMTExjB49mg0bNnDjxg0SEhJ48uSJXv9WIYQQOU+a5zF90av+uAjxRkGzYc8EdbvxJHAtZdx4MolGozG4Sd0Y5s+fT0JCAu7u7rp9iqJgbm5OZGTkK8+ztbV97XWtra1fe3zYsGFs3bqVKVOm4O3tjbW1NW3btk02b7IQQoicxeC/jDExMUyYMIEdO3Zw+/ZtXZ+wJBcuXEi34EQOF7wctnypbtf5Vl3lSWQZCQkJLFmyhKlTp9KgQQO9Y++//z7Lli1L87XLlCnDvHnzuH//fopfbPfu3UuPHj1o3bo1oPY5vXTpUprvJ4QQInswODH9+OOP2bNnD127dsXNzU2a70XahGyA9c/6ClYdADU/N248IpkNGzYQGRlJz549cXBw0DvWtm1b5s+fT/fu3dN07Q8++IBx48bRqlUrxo8fj5ubG8ePH6dgwYJUrVoVb29v1q5dS/PmzdFoNIwYMSLZl2AhhBA5j8GJ6ebNm9m4cSPVq1fPiHhEbnBhN/zxISiJENAFGowF+YKT5cyfP5969eolS0pBrTEdN24cXbt2TdO1LSws+Pvvvxk6dChNmjQhISGBkiVL8r///Q+AH3/8kY8++ohq1aqRL18+hg8fTlRU1Fu9HiGEEFmfRjFwYsMiRYqwadMm/Pz8MiqmVIuKisLBwYGHDx+SJ08eY4cjUuPaUVjcHOJjwK85tF0Eplm/r6UQQggh0saQfM3gySLHjBnDyJEjefz4cZoDFLnU7RBY9r6alBatDe/Pl6RUCCGEEDqpygoCAgL0+pKGhYXh6uqKl5cX5ubmemWPHTuWvhGKnCHyEixtDU8ioVBF6LAMzGR5SiGEEEI8l6rEtFWrVhkchsjRom/CkpYQHQH5S0Kn1WBpZ+yohBBCCJHFGNzHNCuRPqbZwOP7sKgp3D4Djl7w0VawL/DG04QQQgiRM2RIH9PIyEh+/vnnFEfGPnz48JXHRC4W+wiWt1eTUrsC0G29JKVCCCGEeKVUJ6YzZ87kn3/+STHTdXBwYO/evfz888/pGpzIxhJiYVUXuHYYrB2h2zq1xlQIIYQQ4hVSnZiuWbOGPn36vPJ47969+eOPP9IlKJHNJSbAmo/hwi4wt4XOf0B+408vJoQQQoisLdWJaXh4OMWLF3/l8eLFixMeHp4uQYlsTFFgw2AI+RNMLeCD5VCogrGjEkIIIUQ2kOrE1NTUlBs3brzy+I0bNzAxMXhaVJGTKAr8/S0c/w00JtB2gTpfqRBCCCFEKqQ6kwwICGDdunWvPB4YGEhAQEB6xCSyq71T4OBMdbvFTHVlJ5FrXbp0CY1GQ3BwsLFDEUIIkU2kOjEdMGAAU6dOZebMmSQmJur2JyYm8vPPP/Pjjz/Sv3//DAlSZAP/zoWdY9XthuMhoLNx4xHp6sCBA5iamtKoUaNUn+Ph4UFERASlS5fOwMiEEELkJAbNY/rNN98wfvx47O3tKVq0KBqNhvDwcB49esSwYcOYMGFCRsaajMxjmkWc/B3W9gIUqDUc6nxt7IhEOvv444+xs7Nj3rx5nDlzhsKFCxs7JCGEENlEhsxjCvDDDz8QFBREjx49KFiwIAUKFODDDz/k4MGDmZ6UiiwidAsE9gYUqNQban9l7IiyB0WBuJjMf6RhPY2YmBhWr15N3759adasGYsWLdIdi4yMpHPnzri4uGBtbU3x4sVZuHAhkLwpPzExkZ49e1KkSBGsra3x9fXlp59+So93UwghRA6RqiVJX1SpUiUqVaqUEbGI7ObSPvi9OyiJUKYjNJoAGo2xo8oe4h/DuIKZf9+vb4CFrUGnrFq1Cl9fX3x9fenSpQsDBw5kxIgRaDQaRowYwZkzZ9i8eTP58uUjLCyMJ0+epHgdrVZLoUKFWL16Nfny5ePAgQN88sknuLm50b59+/R4dUIIIbI5gxNTIQC4cRyWd4SEp+DbBFrOBJmVIUeaP38+Xbp0AaBRo0Y8evSIHTt2UK9ePa5cuUJAQAAVKqhTgnl5eb3yOubm5owePVr3vEiRIhw4cIDVq1dLYiqEEAKQxFSkxZ1z8Nv7EBcNXjWg7UIwNTd2VNmLuY1ae2mM+xogNDSUf//9l7Vr1wJgZmZGhw4dWLBgAfXq1aNv3768//77HDt2jAYNGtCqVSuqVav2yuvNmTOHefPmcfnyZZ48eUJcXBzlypV7m1ckhBAiB5HEVBjmwRVY2goe34OCAfDBCjC3MnZU2Y9GY3CTujHMnz+fhIQE3N3ddfsURcHc3JzIyEgaN27M5cuX2bhxI9u3b6du3br079+fKVOmJLvW6tWr+eyzz5g6dSpVq1bF3t6eyZMnc+jQocx8SUIIIbIwaXsVqffoDixpBVHXIZ8vdF4DlvbGjkpkkISEBJYsWcLUqVMJDg7WPU6cOIGnpyfLli0DwMXFhR49evDbb78xffp0fv311xSvt3fvXqpVq0a/fv0ICAjA29tbVosTQgihR2pMReo8eQC/tYb74eBQGLoGgq2zsaMSGWjDhg1ERkbSs2dPHBwc9I61bduW+fPnc/v2bd555x1KlSpFbGwsGzZswM/PL8XreXt7s2TJErZu3UqRIkVYunQphw8fpkiRIpnxcoQQQmQDBteY3rp1i65du1KwYEHMzMwwNTXVe4gcKO4xrOgIN0+BbX7otg4c3N94msje5s+fT7169ZIlpQDvv/8+wcHBmJmZ8dVXX1GmTBlq1qyJqakpK1euTPF6ffr0oU2bNnTo0IHKlStz7949+vXrl9EvQwghRDZi0AT7AI0bN+bKlSsMGDAANzc3NC9ND9SyZct0DfB1ZIL9TJAQBys7Qdg2sHKAHhuhgL+xoxJCCCFENmFIvmZwU/6+ffvYu3evjKTNDbSJ6uT5YdvAzBo6/S5JqRBCCCEyjMFN+R4eHhhYySqyI0WBjUPh9FowMYeOv0HhysaOSgghhBA5mMGJ6fTp0/nyyy+5dOlSBoQjsowdo+HoQkAD788F73rGjkgIIYQQOZzBTfkdOnTg8ePHFCtWDBsbG8zN9SdWv3//froFJ4xk33TY96O63Xw6lGptzGiEEEIIkUsYnJhOnz49A8IQWcbRRbB9lLpd/3t4p4cxoxFCCCFELmJwYtq9e/eMiENkBf+thb8+Vbff/QyqDzZqOEIIIYTIXdI0wX5iYiLr1q0jJCQEjUZDyZIladGihcxjmp2d3w5rPwEUqPAR1B1l7IiEEEIIkcsYnJiGhYXRpEkTrl+/jq+vL4qicO7cOTw8PNi4cSPFihXLiDhFRroSBKu6gDYeSr8PTaaoa7kLIYQQQmQig0flDxo0iGLFinH16lWOHTvG8ePHuXLlCkWKFGHQoEEZEaPISBEnYVl7SHgC3vWh1RwwkZpvIYQQQmQ+g2tM9+zZQ1BQEE5OTrp9zs7OTJgwgerVq6drcCKD3QuH39pA7EMoXBXaLwEzC2NHJYQQQogMoigKp64/5OLdGFqWy3rLixucmFpaWhIdHZ1s/6NHj7CwkKQm23h4HZa0hJg76mpOH6wECxtjRyWymB49erB48eJk+xs2bMiWLVuMEJEQQghDxSVoOXjhHtvO3GT7mdvcjHqKtbkpDUsVwMo8a7WSGpyYNmvWjE8++YT58+dTqVIlAA4dOkSfPn1o0aJFugcoMkDMPVjaCh5eBWdv6BII1nmNHZXIoho1asTChQv19llaWqbpWoqikJiYiJlZmsZdCiGESKWHT+LZHXqbv8/cYk/oHR7FJuiO2ViYUsvHhYdP4rNcYmpwH9MZM2ZQrFgxqlatipWVFVZWVlSvXh1vb29++umnjIhRpKenUWrz/d1zkMcduq4DOxdjR5XrKIrC4/jHmf5Iy3LClpaWFChQQO/h6OjIpUuX0Gg0BAcH68o+ePAAjUbD7t27Adi9ezcajYatW7dSoUIFLC0t2bt3L7GxsQwaNIj8+fNjZWXFu+++y+HDh3XXSTpv48aNlC1bFisrKypXrsypU6f0Yjtw4AA1a9bE2toaDw8PBg0aRExMTJo+EyGEyO4iY+JYdugynecF8c6YbQxeGczGkxE8ik3Axd6SDyoVZmGPihwbUZ/ZXd7BNY+VsUNOxuBqi7x587J+/XrOnz/P2bNnURSFkiVL4u3tnRHxifQU/wRWfAARwWDjrCaleT2MHVWu9CThCZWXV870+x7qdAgb88zvsvHFF18wZcoUihYtSt68efniiy9Ys2YNixcvxtPTk0mTJtGwYUPCwsL0+q8PGzaMn376iQIFCvD111/TokULzp07h7m5OadOnaJhw4aMGTOG+fPnc+fOHQYMGMCAAQOS1fAKIURO9TgugW1nbvFn8A32nLtDgvZ5BYR3fjsalHSlfklXyhbKi4lJ1p9xJ83tacWLF6d48eLpGYvISInx8PuHcHkfWNhDl7Xg4mPsqEQ2sGHDBuzs7PT2DR8+nK5du6b6Gt9//z3169cHICYmhtmzZ7No0SIaN24MwNy5c9m2bRvz589n2LBhuvNGjRqlO2/x4sUUKlSIwMBA2rdvz+TJk+nUqROffvopoP6fNGPGDGrVqsXs2bOxssp6NQFCCJEe4hO17D1/h/XBN/j79C2exCfqjpV0y0OLcgVpWKoARfLZGjHKtElVYjpkyBDGjBmDra0tQ4YMeW3ZadOmpUtgIh1ptbC+P5zbDGZW0GkVFCxn7KhyNWszaw51OmSU+xqqTp06zJ49W2+fk5MTUVFRqb5GhQoVdNvh4eHEx8frzeJhbm5OpUqVCAkJ0TuvatWqevf09fXVlTl69ChhYWEsW7ZMV0ZRFLRaLRcvXsTPzy/V8QkhRHZw6tpDVh6+wqZTEUQ+jtft93S2oUXZgrQoW5DirvZGjPDtpSoxPX78OPHx8bptkY0oCmwZDidXgYmZOiWUl0zrZWwajcYoTeppYWtrm2JXnUePHgHo9VtN+n8ipWskSSqveWkRB0VRku1LSVIZrVZL7969U5w/uXDhwm+8jhBCZAdP4hL56+QNlgVd5sS1h7r9+ewsaV7WjZbl3ClbyCFV/39mB6lKTHft2pXitsgGdo2Df38FNOrk+T4NjR2RyCFcXNRBcxEREQQEBADoDYR6FW9vbywsLNi3bx+dOnUC1IT2yJEjumb5JEFBQbokMzIyknPnzlGiRAkAypcvz+nTp6V/uxAiRwq7Hc2yQ1dYc/QaUU/VEfXmphoal3ajXYVCVC3qjJmpwWPYszyD+5h+9NFH/PTTT9jb61cVx8TEMHDgQBYsWJBuwYm3dPB/8M8kdbvpFCjTzrjxiGwpNjaWmzdv6u0zMzMjX758VKlShQkTJuDl5cXdu3f59ttv33g9W1tb+vbty7Bhw3BycqJw4cJMmjSJx48f07NnT72y33//Pc7Ozri6uvLNN9+QL18+WrVqBaj9XKtUqUL//v3p1asXtra2hISEsG3bNn7++ed0e/1CCJFZ4hK0/H3mJr8FXSbown3dfg8nazpV8qRdhULks0vbdH3ZhcGJ6eLFi5kwYUKyxPTJkycsWbJEElNje/IATq+F4OVw7dn0O+99CxU/NmpYIvvasmULbm5uevt8fX05e/YsCxYs4KOPPqJChQr4+voyadIkGjRo8MZrTpgwAa1WS9euXYmOjqZChQps3boVR0fHZOUGDx7M+fPnKVu2LH/++aduIY8yZcqwZ88evvnmG2rUqIGiKBQrVowOHTqk34sXQohM8PBJPMsOXWbR/kvcjo4FwEQD75VwpUuVwtQs7pItRtSnB42SyokNo6KiUBQFR0dHzp8/r2vGA0hMTOSvv/7iyy+/5MaNGxkWbEoxOTg48PDhQ/LkyZNp981ytIlwYZeajIZsgET1hxqNKdQYCnW+hhzS90TkDrt376ZOnTpERkaSN29eY4cjhBAZ4saDJyzYd5EV/14hJk4dWZ/f3pKOFT3oWKkwBfMaPmA1KzIkX0t1jWnevHnRaDRoNBp8fJJPM6TRaBg9erTh0Yq0u3MOTiyHEyshOuL5/vwloVxnKNMe7PIbLz4hhBBCJBMSEcXcfy7w54kbunlHfV3t+aRmUZqXLYiFWc7rO5paqU5Md+3ahaIovPfee6xZs0ZvEmwLCws8PT0pWLBghgQpXpBSUz2AtSP4t4dyncCtrNSQCiGEEFmIoigcDL/HnH8u8M+5O7r9VYs680mtotT2cckxI+vfRqqb8pNcvnwZDw8PTEyMn83nmqb81zXVF6+vJqM+jcAsZ3eIFkIIIbKrrwNPsfzQFUDtP9rY343eNYtSplBe4waWCTKkKT+Jp6cnAI8fP+bKlSvExcXpHS9TpkyqrzV79mxmz57NpUuXAChVqhQjR47UrQaT6+ma6ldB9At9d138IKCzWkNq72q8+IQQQgjxRrtCb7P80BVMNNCliicfv1uUws7ZYy7rzGZwYnrnzh0+/PBDNm/enOLxxMTEFPenpFChQkyYMEE3D+HixYtp2bIlx48fp1SpUoaGljO8tqm+3bOm+nLSVC+EEEJkAzGxCXwb+B8AH1UvwrfNSho5oqzN4MT0008/JTIykqCgIOrUqUNgYCC3bt1i7NixTJ061aBrNW/eXO/5Dz/8wOzZswkKCspdiak2ES7sVpPRsxsg4am6X5rqhRBCiGxtyt+hXH/whEKO1gxpkHzwuNBncGK6c+dO1q9fT8WKFTExMcHT05P69euTJ08exo8fT9OmTdMUSGJiIr///jsxMTF662O/KDY2ltjYWN1zQ9bqzpLunleT0RMrpaleCCGEyGGOX4lk0YFLAIxr7Y+NhcFpV65j8DsUExND/vzqFEROTk7cuXMHHx8f/P39OXbsmMEBnDp1iqpVq/L06VPs7OwIDAykZMmUq7nHjx+f/aekevIATgc+a6r/9/l+aaoXQgghcoy4BC1frjmFokCb8u7U9HF580nC8MTU19eX0NBQvLy8KFeuHL/88gteXl7MmTMn2eowqb1ecHAwDx48YM2aNXTv3p09e/akmJx+9dVXDBkyRPc8KioKDw8Pg++Z6V7XVO9dT01GfRtLU70QQgiRQ/yyJ5zQW9E421owoqn0K00tg+d8+vTTT4mIUCdzHzVqFFu2bKFw4cLMmDGDcePGGRyAhYUF3t7eVKhQgfHjx1O2bFl++umnFMtaWlqSJ08evUeWdvc8bB8NP5aG39rAf3+oSalLCag/Boacgc6roVQrSUpFruLl5cX06dONHUa669GjB61atTJ2GGlSu3ZtPv30U4PP02g0rFu3Lt3jyUi7d+9Go9Hw4MEDABYtWvTGFca+++47ypUrl+GxiZwh7PYjft4ZBsDI5iVxtLUwckTZh8E1pp07d9ZtBwQEcOnSJc6ePUvhwoXJly/fWwekKIpeP9Js5+lD+G9t8qZ6q7zPm+oLBkhTvcgWevTowYMHD9KceCxatIhPP/1UlwAkOXz4MLa2tm8fILB8+XK6du1Kr169mDNnTrpcM61++uknDJwaOtv47rvvWLduHcHBwZlyP0VRmDt3LvPnz+f06dOYmZnh7e1Nly5d+OSTT7CxSftUO9WqVSMiIgIHB4d0jFgIlVar8PXaU8Qlaqnj60KLsrL4kCHeuheujY0N5cuXT9O5X3/9NY0bN8bDw4Po6GhWrlzJ7t272bJly9uGlble2VRvAt71paleiJe4uKRfX6sFCxbwxRdfMHv2bKZNm/ZWCUtaJSYmotFoJNFJR127dmXt2rV8++23zJw5ExcXF06cOMH06dPx8vJKc810fHw8FhYWFChQIH0DFuKZFYev8O+l+9hYmDK2tb+s5mSgVDXlDxkyJNUPQ9y6dYuuXbvi6+tL3bp1OXToEFu2bKF+/fppejGZ7o1N9SHSVC9SpCgK2sePM/2R3rV506ZNw9/fH1tbWzw8POjXrx+PHj0C1ObSDz/8kIcPH6LRaNBoNHz33XdA8qZ8jUbDvHnzaN26NTY2NhQvXpw///zzjfe/dOkSBw4c4Msvv6REiRL88ccfeseTmmg3bNiAr68vNjY2tG3blpiYGBYvXoyXlxeOjo4MHDhQbw7muLg4vvjiC9zd3bG1taVy5crs3r07xeuWLFkSS0tLLl++nKwpX6vVMnHiRLy9vbG0tKRw4cL88MMPuuPDhw/Hx8cHGxsbihYtyogRI4iPj3/ta37TOUlNzkuXLsXLywsHBwc6duxIdHS0rkxMTAzdunXDzs4ONze3N071t2jRIkaPHs2JEyd0n+WiRYt0x+/evfvaz+7MmTM0adIEOzs7XF1d6dq1K3fv3n3l/VavXs2yZctYsWIFX3/9NRUrVsTLy4uWLVuyc+dO6tSpA6g17/Xr1ydfvnw4ODhQq1atZINwNRoNc+bMoWXLltja2jJ27NhkTflJ1q1bh4+PD1ZWVtSvX5+rV68mi+2XX37Bw8MDGxsb2rVrl+waCxcuxM/PDysrK0qUKMGsWbP0jqfH5yeyrpsPnzJh01kAvmjoi3teayNHlP2kqsb0+PHjqbqYod8K5s+fb1D5LOG1TfVtoVxnaaoXb6Q8eUJo+Xcy/b6+x46iSccaRRMTE2bMmIGXlxcXL16kX79+fPHFF8yaNYtq1aoxffp0Ro4cSWhoKAB2dnavvNbo0aOZNGkSkydP5ueff6Zz585cvnwZJyenV56zYMECmjZtioODA126dGH+/Pl069ZNr8zjx4+ZMWMGK1euJDo6mjZt2tCmTRvy5s3Lpk2buHDhAu+//z7vvvsuHTp0AODDDz/k0qVLrFy5koIFCxIYGEijRo04deoUxYsX1113/PjxzJs3D2dnZ91sJS/66quvmDt3Lj/++CPvvvsuERERnD17Vnfc3t6eRYsWUbBgQU6dOkWvXr2wt7fniy++eOVrTs054eHhrFu3jg0bNhAZGUn79u2ZMGGCLikeNmwYu3btIjAwkAIFCvD1119z9OjRV/ah7NChA//99x9btmxh+/btAHq1w6/77CIiIqhVqxa9evVi2rRpPHnyhOHDh9O+fXt27tyZ4v2WLVuGr68vLVu2THbsxZrp6OhounfvzowZMwCYOnUqTZo04fz589jb2+vOGTVqFOPHj+fHH3/E1NSUixcvJrvu48eP+eGHH1i8eDEWFhb069ePjh07sn//fl2ZsLAwVq9ezV9//UVUVBQ9e/akf//+LFu2DIC5c+cyatQoZs6cSUBAAMePH6dXr17Y2trSvXv3dPv8RNakKAoj1v9HdGwCAYXz0rWql7FDyp6UbOzhw4cKoDx8+DDjb6bVKsq6fooyJr+ijMqjPr7Lqyi/tVWU/9YqStyTjI9B5BiJMTHKGd8Smf5IjIkxKM7u3bsrLVu2THX51atXK87OzrrnCxcuVBwcHJKV8/T0VH788Ufdc0D59ttvdc8fPXqkaDQaZfPmza+8V2JiouLh4aGsW7dOURRFuXPnjmJubq6cP39e7/6AEhYWptvXu3dvxcbGRomOjtbta9iwodK7d29FURQlLCxM0Wg0yvXr1/XuV7duXeWrr77Su25wcLBemRffr6ioKMXS0lKZO3fuK1/DyyZNmqS88847qS6f0jmjRo1SbGxslKioKN2+YcOGKZUrV1YURVGio6MVCwsLZeXKlbrj9+7dU6ytrZXBgwe/8j6jRo1SypYtm2z/mz67ESNGKA0aNNA75+rVqwqghIaGpngvPz8/pUWLFq9+0a+QkJCg2NvbK3/99ZdefJ9++qleuV27dimAEhkZqSjK888zKChIVyYkJEQBlEOHDimKor5+U1NT5erVq7oymzdvVkxMTJSIiAhFURTFw8NDWb58ud69xowZo1StWvWVMRv6+Ymsa9PJG4rn8A2K99cblbMRUW8+IRcxJF+TmV5TS6OB+CdqU30+X3UC/DIdwF76KQnDaayt8T121Cj3TU+7du1i3LhxnDlzhqioKBISEnj69CkxMTEGD24qU6aMbtvW1hZ7e3tu3779yvJ///03MTExNG7cGIB8+fLRoEEDFixYoDdDiI2NDcWKFdM9d3V1xcvLS6/21tXVVXevY8eOoSgKPj76K7TExsbi7Oyse25hYaEX88tCQkKIjY2lbt26ryzzxx9/MH36dMLCwnj06BEJCQlvnG0kNed4eXnp1Ri6ubnpXl94eDhxcXF6C5k4OTnh6+v72vu+zus+u6NHj7Jr164Ua8vDw8OTvc+g1jylpgXu9u3bjBw5kp07d3Lr1i0SExN5/PgxV65c0StXoUKFN17LzMxMr1yJEiXImzcvISEhVKpUCYDChQtTqFAhXZmqVaui1WoJDQ3F1NSUq1ev0rNnT3r16qUrk5CQoFe7/Lafn8ia7kTHMvLP0wD0rVUM3wL2bzhDvIrBiWmdOnVe+x/Gq5pmcoQan0PV/lCwvDTVi7ei0WjStUndGC5fvkyTJk3o06cPY8aMwcnJiX379tGzZ8839pNMibm5ud5zjUaDVqt9ZfkFCxZw//59vcFOWq2W48ePM2bMGExNTV953dfdS6vVYmpqytGjR3XXSPJicmVtbf3a/wut3/AlICgoiI4dOzJ69GgaNmyIg4MDK1eufG1/z9Se87rXp2TArAFvej+bN2/OxIkTk533qrmvfXx8CAkJeeN9e/TowZ07d5g+fTqenp5YWlpStWpV4uLi9Mql9ktSSp/n6z7jpGMvvt65c+dSuXJlvXJJP0fp8fmJrEVRFNYcu87YjWd48DieYi629H/P29hhZWsGJ6Yv90GKj48nODiY//77T9eHJsdylQlyhUhy5MgREhISmDp1KiYm6jjK1atX65WxsLDQG1SUXu7du8f69etZuXIlpUqV0u3XarXUqFGDzZs306xZszRdOyAggMTERG7fvk2NGjXSHGPx4sWxtrZmx44dfPzxx8mO79+/H09PT7755hvdvsuXL7/2mmk552Xe3t6Ym5sTFBRE4cKFAYiMjOTcuXPUqlXrleel9bMsX748a9aswcvLCzOz1P3J6dSpEx07dmT9+vXJ+pkqikJUVBQODg7s3buXWbNm0aRJEwCuXr362kFVr5OQkMCRI0d0taOhoaE8ePCAEiVK6MpcuXKFGzduULCgOv3PwYMHMTExwcfHB1dXV9zd3blw4YLetIovSo/PT2QdF+/G8E3gKQ6E3wPAzy0PP39QDksz0zecKV7H4MT0xx9/THH/d999pxuNK4TIOR4+fJhs7konJyeKFStGQkICP//8M82bN2f//v3J5hH18vLi0aNH7Nixg7Jly2JjY5Mu0zktXboUZ2dn2rVrp0uKkzRr1oz58+enOTH18fGhc+fOdOvWjalTpxIQEMDdu3fZuXMn/v7+uiToTaysrBg+fDhffPEFFhYWVK9enTt37nD69Gl69uyJt7c3V65cYeXKlVSsWJGNGzcSGBj42mum5ZyX2dnZ0bNnT4YNG4azszOurq588803yd7HlyUNcAsODqZQoULY29tjafnm2Ub69+/P3Llz+eCDDxg2bBj58uUjLCyMlStXMnfu3GS10gDt27cnMDCQDz74gBEjRlC/fn1cXFw4deoUP/74IwMHDqRVq1Z4e3uzdOlSKlSoQFRUFMOGDXtjTfWrmJubM3DgQGbMmIG5uTkDBgygSpUqukQV1M+0e/fuTJkyhaioKAYNGkT79u11U0999913DBo0iDx58tC4cWNiY2M5cuQIkZGRDBkyJF0+P2F8cQla5u69wE87zhOXoMXK3IRP6/nQ890imJsavG6ReEm6vYNdunRhwYIF6XU5IUQWsXv3bgICAvQeI0eOpFy5ckybNo2JEydSunRpli1bxvjx4/XOrVatGn369KFDhw64uLgwadKkdIlpwYIFtG7dOsVk6v3332fDhg3cunUrzddfuHAh3bp1Y+jQofj6+tKiRQsOHTpk8BLII0aMYOjQoYwcORI/Pz86dOig6yvYsmVLPvvsMwYMGEC5cuU4cOAAI0aMeO310nJOSiZPnkzNmjVp0aIF9erV49133+Wdd14/S8T7779Po0aNqFOnDi4uLqxYsSJV9ypYsCD79+8nMTGRhg0bUrp0aQYPHoyDg8Mrk2GNRsPy5cuZNm0agYGB1KpVizJlyvDdd9/RsmVLGjZsCKg/B5GRkQQEBNC1a1cGDRqU4uwIqWFjY8Pw4cPp1KkTVatWxdrampUrV+qV8fb2pk2bNjRp0oQGDRpQunRpvemgPv74Y+bNm8eiRYvw9/enVq1aLFq0iCJFigDp9/kJ4zl6OZLmP+9j8tZQ4hK01Ciej78/rUWfWsUkKU0nGiWdOhwtXbqU4cOHc+PGjfS4XKokNec8fPgw6y9PKoQQQohsKeppPJO3hPLbocsoCjjZWjCyWUlalisoE+ingiH5msFN+W3atNF7rigKERERHDlyRL75CSGEECJH2fLfTUb9+R+3otTl0tu+U4hvmvjhaGth5MhyJoMT05eX3DMxMcHX15fvv/+eBg0apFtgQgghhBDGEvHwCSPXn2bbGbVbkJezDeNa+1PNO5+RI8vZDE5MFy5cmBFxCCGEEEIYXaJW4begy0zeGsqj2ATMTDT0qVWMAe95Y2UuI+4zWpon2D9y5AghISFoNBr8/Pze2HFeCCGEECIrC4mI4qu1pwi++gCA8oXzMr5NGZkwPxMZnJheu3aNDz74gP3795M3b14AHjx4QLVq1VixYoXBo1aFEEIIIYzpaXwiP+04z9x/LpCgVbC3NOOLxiXoXKkwJiYyuCkzGTy3wUcffUR8fDwhISHcv3+f+/fvExISgqIo9OzZMyNiFEIIIYTIEPvO36Xh9H+YvTucBK1Co1IF2DakFl2reEpSagQGTxdlbW3NgQMHCAgI0Nt/7NgxqlevzpMnT9I1wNeR6aKEEEIIkRb3Y+IYu/EMa49dB6BAHiu+b1mKBqUKGDmynCdDp4sqXLhwiutgJyQk4O7ubujlhBBCCCEyjaIorH22vn3k43g0Guhe1YuhDXywtzI3dni5nsGJ6aRJkxg4cCD/+9//eOedd9BoNBw5coTBgwczZcqUjIhRCCGEEOKtXbobwzfrTrE/TF3fvkQBe8a38SegsKORIxNJDG7Kd3R05PHjxyQkJGBmpua1Sdu2trZ6Ze/fv59+kaZAmvKFyJl69OjBgwcPWLdunbFDeSu7d++mTp06REZG6gaLCiEyX3yill//ucCMHeeJTdBiaaaub/9xDVnfPjNkaFP+9OnT0xqXECKb6dGjB4sXL2b8+PF8+eWXuv3r1q2jdevWvO2KxpcuXaJIkSIcP36ccuXKvWW0qidPnlCwoLpM4PXr17G2tk6X66ZFtWrViIiISLYwiRAi8xy7EsnXa09x9mY0AO965+OH1qXxdLZ9w5nCGAxOTLt3754RcQghsigrKysmTpxI7969cXRMv+auuLi4dLvWi9asWUPp0qXVfmRr19K5c+cMuc+bxMfHY2FhQYECMpBCCGOIfhrPlK2hLAl6vr79t039aB3gLuvbZ2Fpqr9OTExkzZo1jB07lh9++IHAwEASExPTOzYhcixFUYiPTcz0R1pqOOvVq0eBAgUYP378a8utWbOGUqVKYWlpiZeXF1OnTtU77uXlxdixY+nRowcODg706tWLIkWKABAQEIBGo6F27dp650yZMgU3NzecnZ3p379/igMvXzZ//ny6dOlCly5dmD9/frLjGo2GX375hWbNmmFjY4Ofnx8HDx4kLCyM2rVrY2trS9WqVQkPD9c776+//uKdd97BysqKokWLMnr0aBISEvSuO2fOHFq2bImtrS1jx45l9+7daDQaHjx4oCu3f/9+atWqhY2NDY6OjjRs2JDIyEgAtmzZwrvvvkvevHlxdnamWbNmyeIQQrzZ1tM3qT/tHxYfVJPS98sXYvuQWrQpX0iS0izO4BrTsLAwmjRpwvXr1/H19UVRFM6dO4eHhwcbN26kWLFiGRGnEDlKQpyWXwfvyfT7fvJTLcwtDVtSz9TUlHHjxtGpUycGDRpEoUKFkpU5evQo7du357vvvqNDhw4cOHCAfv364ezsTI8ePXTlJk+ezIgRI/j2228BGDBgAJUqVWL79u2UKlUKCwsLXdldu3bh5ubGrl27CAsLo0OHDpQrV45evXq9Mtbw8HAOHjzI2rVrURSFTz/9lAsXLlC0aFG9cmPGjGHatGlMmzaN4cOH06lTJ4oWLcpXX31F4cKF+eijjxgwYACbN28GYOvWrXTp0oUZM2ZQo0YNwsPD+eSTTwAYNWqU7rqjRo1i/Pjx/Pjjj5iamnLx4kW9+wYHB1O3bl0++ugjZsyYgZmZGbt27dJ9sY+JiWHIkCH4+/sTExPDyJEjad26NcHBwZiYSD84Id7k5sOnjPrzP7aeVte393y2vn11Wd8+2zB48FOTJk1QFIVly5bh5OQEwL179+jSpQsmJiZs3LgxQwJNiQx+EtlVfGxitkhMXxyEVLVqVUqWLMn8+fOT9THt3Lkzd+7c4e+//9ad+8UXX7Bx40ZOnz4NqDWmAQEBBAYG6sq8qo9pjx492L17N+Hh4ZiaqvG2b98eExMTVq5c+cp4v/nmG86cOaO7R6tWrShdujRjx47VldFoNHz77beMGTMGgKCgIKpWrcr8+fP56KOPAFi5ciUffvihbl7mmjVr0rhxY7766ivddX777Te++OILbty4obvup59+yo8//qgr8/Lgp06dOnHlyhX27duXqvf/zp075M+fn1OnTlG6dOlUnSNEbqTVKiw7dJmJW56vb9+7VlEGvldc1rfPAjJ08NOePXsICgrSJaUAzs7OTJgwgerVqxserRC5kJmFCZ/8VMso902riRMn8t577zF06NBkx0JCQmjZsqXevurVqzN9+nQSExN1yWWFChVSfb9SpUrpzgNwc3Pj1KlTryyfmJjI4sWL+emnn3T7unTpwmeffcbo0aP1rlWmTBndtqurKwD+/v56+54+fUpUVBR58uTh6NGjHD58mB9++EHvfk+fPuXx48fY2Nik6vUFBwfTrl27Vx4PDw9nxIgRBAUFcffuXbRaLQBXrlyRxFSIVwi9Gc1Xa09y7MoDAAIK52V8G39KFJAKq+zI4MTU0tKS6OjoZPsfPXqk1wwnhHg1jUZjcJO6sdWsWZOGDRvy9ddf6zXPg9pn9uV+Wyk1xrw8pdzrmJvrT3St0Wh0iVpKtm7dyvXr1+nQoYPe/sTERP7++28aN26c4rWT4k5pX9L9tFoto0ePpk2bNsnua2Vlpdt+0+t70wwBzZs3x8PDg7lz51KwYEG0Wi2lS5fOsIFiQmRnT+MT+XnneX7Zo65vb2dpxvBGvnSq7ImpLCWabRmcmDZr1oxPPvmE+fPnU6lSJQAOHTpEnz59aNGiRboHKITIOiZMmEC5cuXw8fHR21+yZMlkzdMHDhzAx8dHr6byZUlfZtNj8OT8+fPp2LEj33zzTbKY58+fr5eYGqp8+fKEhobi7e39VjGWKVOGHTt2MHr06GTH7t27R0hICL/88gs1atQASHWTvxC5zYGwu3wdeIpL9x4D0LCUK6NblKaAg9UbzhRZncGJ6YwZM+jevTtVq1bV1TAkJCTQokULvSY0IUTO4+/vT+fOnfn555/19g8dOpSKFSsyZswYOnTowMGDB5k5cyazZs167fXy58+PtbU1W7ZsoVChQlhZWaVpzs87d+7w119/8eeffyZr8u7evTtNmzblzp07uLi4GHxtgJEjR9KsWTM8PDxo164dJiYmnDx5klOnTun1X32Tr776Cn9/f/r160efPn2wsLBg165dtGvXDicnJ5ydnfn1119xc3PjypUrenPHCiHU9e1/2BjCmmPXAHDNY8noFqVpVFqmZcspDO5wljdvXtavX8+5c+f4448/+P333wkNDSUwMFAmkRYiFxgzZkyyZvry5cuzevVqVq5cSenSpRk5ciTff/99sib/l5mZmTFjxgx++eUXChYsmKyfamotWbIEW1tb6tatm+xYnTp1sLe3Z+nSpWm6NkDDhg3ZsGED27Zto2LFilSpUoVp06bh6elp0HV8fHz4+++/OXHiBJUqVaJq1aqsX78eMzMz3cCuo0ePUrp0aT777DMmT56c5piFyEkURSHw+DXqTdvDmmPX0GigW1VPtg+pJUlpDpPqUflarZapU6eybt064uPjqVevHiNHjtTrX5XZZFS+EEIIkbNdvhfDt+v+Y+/5uwD4utoz/n1/ysv69tlGhozKnzhxIt9++y1169bF2tqaadOmcffuXX799de3DlgIIYQQ4kXxiVrm7b3I9O3ndOvbD6pbnE9qFpX17XOwVNeY+vr6MnjwYPr16weoK5S0atWKJ0+eGG0VBakxFUIIIXKePefuMHbDGc7ffgRAdW9nfmjlj1c+Wd8+O8qQGtPLly/TrFkz3fOGDRuiKAo3btzA3d097dEKIYQQQgBht6MZuzGE3aF3AHC0MefbpiVpU17Wt88tUp2YxsXF6c3Bp9FosLCwIDY2NkMCE0IIIUTuEBkTx087zrM06DKJWgVzUw3dq3oxsG5xHKzN33wBkWMYNF3UiBEjdCucgJqs/vDDD3qj8adNm5Z+0QkhhBAix4pL0LI06DIzdpzn4ZN4AOqXdOXrJn4UkWb7XCnViWnNmjUJDQ3V21etWjUuXLigey7V7EIIIYR4E0VR2Hn2Nj9sDOHC3RgAShSwZ2SzklTzzmfk6IQxpTox3b17dwaGIYQQQojc4OzNKMZuCGFfmDr9Uz47Cz5v4Eu7Ch6ylKgwfOUnIYQQQghD3XsUy7Rt51jx7xW0CliYmvDRu0XoX6cY9lbSj1SoJDEVQgghRIaJTUhk0f5LzNwZRnRsAgBN/d34snEJPJxs3nC2yG0kMRVC5AiLFi3i008/5cGDB8YORQiB2o906+lbjNsUwpX7jwEo7Z6Hkc1KUamIk5GjE1mVLJ0ghHitq1ev0rNnTwoWLIiFhQWenp4MHjyYe/fuGTs0PR06dODcuXPGDkMIAfx3/SEdfw2iz29HuXL/MfntLZnSrix/9n9XklLxWlJjKoR4pQsXLlC1alV8fHxYsWIFRYoU4fTp0wwbNozNmzcTFBSEk1PW+CNjbW2tN9eyECLz3Y5+ypStofx+9BqKApZmJvSuWZTetYphaykph3gzg2tMvby8+P7777ly5UpGxCNErqAoCvFPn2b6I5UrEOv0798fCwsL/v77b2rVqkXhwoVp3Lgx27dv5/r163zzzTcAxMbG8sUXX+Dh4YGlpSXFixdn/vz5uuucOXOGJk2aYGdnh6urK127duXu3bu641u2bOHdd98lb968ODs706xZM8LDw3XHL126hEajYe3atdSpUwcbGxvKli3LwYMHdWUWLVpE3rx5dc/Dw8Np2bIlrq6u2NnZUbFiRbZv327oRyWESIWn8Yn8b1cYdSbvZvURNSltWa4gOz+vzZAGvpKUilQz+Cdl6NChLFq0iO+//546derQs2dPWrdujaWlZUbEJ0SOlBAby4zubTP9voMW/4G5lVWqyt6/f5+tW7fyww8/JKuJLFCgAJ07d2bVqlXMmjWLbt26cfDgQWbMmEHZsmW5ePGiLvGMiIigVq1a9OrVi2nTpvHkyROGDx9O+/bt2blzJwAxMTEMGTIEf39/YmJiGDlyJK1btyY4OBgTk+ffn7/55humTJlC8eLF+eabb/jggw8ICwvDzCz5f2WPHj2iSZMmjB07FisrKxYvXkzz5s0JDQ2lcOHCaX0LhRAvUBSFDScjmLD5LNcfPAGgnEdeRjYvSfnCjkaOTmRHBiemAwcOZODAgZw4cYIFCxYwaNAg+vXrR6dOnfjoo48oX758RsQphMhk58+fR1EU/Pz8Ujzu5+dHZGQkhw8fZvXq1Wzbto169eoBULRoUV252bNnU758ecaNG6fbt2DBAjw8PDh37hw+Pj68//77eteeP38++fPn58yZM5QuXVq3//PPP6dp06YAjB49mlKlShEWFkaJEiWSxVe2bFnKli2rez527FgCAwP5888/GTBgQBreESHEi05cfcCYDWc4cjkSgIIOVgxvXIIWZQvKgjsizdJct162bFl++uknpkyZwqxZsxg+fDizZ8+mdOnSDB48mA8//FB+MIV4BTNLSwYt/sMo900vSd0CLl68iKmpKbVq1Uqx3NGjR9m1axd2dnbJjoWHh+Pj40N4eDgjRowgKCiIu3fvotVqAbhy5YpeYlqmTBndtpubGwC3b99OMTGNiYlh9OjRbNiwgRs3bpCQkMCTJ0+kG5IQbyni4RMmbwll7fHrAFibm9K3djF61SiKtYWpkaMT2V2aE9P4+HgCAwNZuHAh27Zto0qVKvTs2ZMbN27wzTffsH37dpYvX56esQqRY2g0mlQ3qRuLt7c3Go2GM2fO0KpVq2THz549i6OjIzY2r5+HUKvV0rx5cyZOnJjsWFJy2bx5czw8PJg7dy4FCxZEq9VSunRp4uLi9Mqbmz+fhDvpi29SEvuyYcOGsXXrVqZMmYK3tzfW1ta0bds22TWFEKnzJC6RX/4JZ86ecJ7Gq79375cvxLCGvhRwyNr/n4nsw+DE9NixYyxcuJAVK1ZgampK165d+fHHH/VqLBo0aEDNmjXTNVAhROZydnamfv36zJo1i88++0yvn+nNmzdZtmwZ3bp1w9/fH61Wy549e3RN+S8qX748a9aswcvLK8W+oPfu3SMkJIRffvmFGjVqALBv3763jn/v3r306NGD1q1bA2qf00uXLr31dYXIbbRahfUnrjNpSygRD58CUNHLkRHNSlKmUF7jBidyHINH5VesWJHz588ze/Zsrl27xpQpU5I1o5UsWZKOHTumW5BCCOOYOXMmsbGxNGzYkH/++YerV6+yZcsW6tevj7u7Oz/88ANeXl50796djz76iHXr1nHx4kV2797N6tWrAXVk//379/nggw/4999/uXDhAn///TcfffQRiYmJODo64uzszK+//kpYWBg7d+5kyJAhbx27t7c3a9euJTg4mBMnTtCpU6dX1q4KIVJ29HIkrWcf4LNVJ4h4+JRCjtbM6lye1b2rSlIqMoTBiemFCxfYsmUL7dq102tWe5GtrS0LFy586+CEEMZVvHhxjhw5QrFixejQoQPFihXjk08+oU6dOhw8eFA3h+ns2bNp27Yt/fr1o0SJEvTq1YuYmBgAChYsyP79+0lMTKRhw4a6fugODg6YmJhgYmLCypUrOXr0KKVLl+azzz5j8uTJbx37jz/+iKOjI9WqVaN58+Y0bNhQBmcKkUrXIh8zcMVx3p99gBNXH2BnacYXjXzZPqQWTfzdZAyJyDAaxdCJDbOQqKgoHBwcePjwIXny5DF2OEIIIUS2FhObwOzd4czde4HYBC0aDXSs6MGQ+r642Mu0kCJtDMnXUtXH1NHRMdXfju7fv5+qckIIIYTIGrRahT+OXWPy1lDuRMcCULWoM98286NUQQcjRydyk1QlptOnT8/gMIQQQghhDIcu3GPMxjP8dz0KAC9nG75u4kf9kq7SZC8yXaoS0+7duwOQkJDAsmXLaNiwIQUKFMjQwIQQQgiRca7ce8z4zSFs/u8mAPZWZgyuW5xuVb2wMDN4CIoQ6cKg6aLMzMzo27cvISEhGRWPEEIIITJQ9NN4Zu4KY+G+S8QlajHRQOfKnnxarzjOdtKPVBiXwfOYVq5cmePHj+Pp6ZkR8QghhBAiAzx8Es+649eZseM892LUhSZqFM/HiGYl8XG1N3J0QqgMTkz79evH0KFDuXbtGu+88w62trZ6x19cMlAIIYQQxhOXoGXPuTsEHr/G9pDbxCWoc/kWc7Hl26Ylqe3rIv1IRZZi8HRRJibJ+51oNBoURUGj0ZCYmJhuwb2JTBclhBBC6FMUheNXHxB47DobTt4g8nG87pivqz2dqxTmg0qFMTeVfqQic6T7dFEvunjxYpoDE0IIIUTGuHwvhnXHb7Au+DoX78bo9ue3t6RluYK0DiiEn5u91JCKLM3gxFT6lgohhBBZw4PHcWw4GUHg8escvRyp229jYUqjUgVoFeBOde98mJpIMiqyB4MT0yRnzpzhypUrxMXF6e1v0aJFqq8xfvx41q5dy9mzZ7G2tqZatWpMnDgRX1/ftIYlhBBC5GixCYnsOnubtceusyv0NvGJao88Ew1U985Hm/LuNChZAFvLNP+JF8JoDP6pvXDhAq1bt+bUqVO6vqWArmnAkD6me/bsoX///lSsWJGEhAS++eYbGjRowJkzZ5INqhJCCCFyK0VROHI5krXHrrPx5A2inibojpV0y0Ob8u60KFuQ/HmsjBilEG/P4MR08ODBFClShO3bt1O0aFH+/fdf7t27x9ChQ5kyZYpB19qyZYve84ULF5I/f36OHj1KzZo1DQ1NCCGEyFEu3HlE4PHrBB6/zrXIJ7r9bg5WtCznTusAd3wLyFRPIucwODE9ePAgO3fuxMXFBRMTE0xMTHj33XcZP348gwYN4vjx42kO5uHDhwA4OTmleDw2NpbY2Fjd86ioqDTfSwghhMiK7j2KZcPJCNYev86Jqw90++0szWhcugCtA9ypXNRZ+o2KHMngxDQxMRE7OzsA8uXLx40bN/D19cXT05PQ0NA0B6IoCkOGDOHdd9+ldOnSKZYZP348o0ePTvM9hBBCiKzoaXwi20NuEXjsOnvO3SFBq3aTMzXRULN4PlqXL0R9P1esLUyNHKkQGcvgxLR06dKcPHmSokWLUrlyZSZNmoSFhQW//vorRYsWTXMgAwYM4OTJk+zbt++VZb766iuGDBmiex4VFYWHh0ea7ymEEEIYi1arcOjifQKPX2PzqZtExz7vN1qmkAOtA9xpVqYgLvayTKjIPQxOTL/99ltiYtT50caOHUuzZs2oUaMGzs7OrFq1Kk1BDBw4kD///JN//vmHQoUKvbKcpaUllpbyCyqEECL7On8rmrXHr7P++HVuPHyq2++e15rWAe60CnDHO7+dESMUwngMXvkpJffv38fR0dHgSXsVRWHgwIEEBgaye/duihcvbtD5svKTEEKI7OB29FP+DFYnv//v+vPxEfZWZjT1d6N1gDsVvZwwkX6jIgfK0JWfkoSFhREeHk7NmjVxcnIiLflt//79Wb58OevXr8fe3p6bN28C4ODggLW1dVpDE0IIIYzucVwCf5++ReDx6+w9f4dn3UYxM9FQ2zc/bcq7816J/FiZS79RIZIYXGN679492rdvz65du9BoNJw/f56iRYvSs2dP8ubNy9SpU1N/81fUsC5cuJAePXq88XypMRVCCJGVJGoVDobfY+3xa2z97yYxcc/n9g4onFfXb9TJ1sKIUQqRuTK0xvSzzz7D3NycK1eu4Ofnp9vfoUMHPvvsM4MS03ToRSCEEEIYXUhEFIHHr7M++Dq3op5Pa1jYyYZWAep8o0XyycIxQryJwYnp33//zdatW5MNUipevDiXL19Ot8CEEEKIrEhRFK7cf0zw1Qccv/KAoAv3OHszWnfcwdqcZmXcaFPenfKFDR9/IURuZnBiGhMTg42NTbL9d+/elRHzQgghcpyHj+MJvvaA4CsPCL4ayYlrD7kfE6dXxsLUhPdK5Kd1eXdq+7pgaSb9RoVIi1QnpteuXaNQoULUqFGDJUuWMGbMGEDtJ6rVapk8eTJ16tTJsECFEEKIjBaXoCUkIorgqw84cfUBwVcfcOFuTLJyFqYmlCyYh3IeeQkonJdaPi7ktZF+o0K8rVQnpqVLl+bnn39m6tSp1KpViyNHjhAXF8cXX3zB6dOnuX//Pvv378/IWIUQQoh0oygKV+8/4fjVSIKfJaGnb0QRl6BNVtbL2YZyHnnVR2FH/NzspVZUiAyQ6sR03Lhx9O/fn/r163P06FHmzZuHqakpMTExtGnThv79++Pm5paRsQohhBBp9vBJvK4WNKlG9N5LTfKg9hF9noTmpVyhvDjKKHohMoVB00VdvHiRnj17cubMGX755RdatmyZkbG9kUwXJYQQIiXxiVrORkQTfDWS40lN8neSN8mbm2oo6ZbneRLq4YiXs40MWBIiHWXYdFFFihRh586dzJw5k7Zt2+Ln54eZmf4ljh07ZnjEQgghRBopisK1yCe6mtDgqw/47/pDYlNoki/sZKNXG1rSLY9McC9EFmLwqPzLly+zZs0anJycaNmyZbLEVAghhMhIUU/jOXn1IcEv9A29+yh5k3weKzPKeuQl4FkSWrZQXpztZPYYIbIyg7LKuXPnMnToUOrVq8d///2Hi4tLRsUlhBBCkJCo5ezNaL3a0PA7j3i5E5qZiQa/pCb5Z4loEWdbWXteiGwm1Ylpo0aN+Pfff5k5cybdunXLyJiEEELkQk/iEgm/84iw2484feMhwVcfcOr6Q57GJ2+SL+RorUtCAwrnpVRBB2mSFyIHSHVimpiYyMmTJ5Ot+CSEEEIYIiY2gfA7jzh36xHnb0cTdusR528/4mrk42Q1oQD2lmqTfFIiWtYjLy720iQvRE6U6sR027ZtGRmHEEKIHCbqaTxhtx89SzyjOX/7EedvPeL6gyevPMfB2hwfVzt8XO0p65GX8oXzUjSfnTTJC5FLyMglIYQQb+XB4zhd0nn+djRhz7ZvRj195Tn57Czwzm9H8fz2FHe1023ns7OQqZqEyMUkMRVCCJEq9x7Fqgno7UeE3YrWbd+Jjn3lOa55LCme315NPF3tdNtOMmG9ECIFkpgKIYTQURSFO9HPEtAXks+w24+4n8IqSUkKOljh7WpP8fx26sPVDu/89jhYm2di9EKI7E4SUyGEyIUUReFm1NNnze+PCLsdrdt++CT+led5OFmrze/5nzW/u9pTzMUWeytJQIUQb08SUyGEyOFiExI5fDGSMxEPX0hEH/EoNiHF8iYadYWk4kk1oM+a4Iu62GJjIX82hBAZR/6HEUKIHOjuo1h2nb3NjpDb7D1/h5i4xGRlTE00eDnbJBuAVNTFVuYEFUIYhSSmQgiRAyiKQuitaHaE3GZ7yC2Crz7QmxPUxd6SSl5OutrP4q52eDnbYmFmYryghRDiJZKYCiFENhWbkEjQhfvsCLnFjpDbyeYHLVUwD3X9XKnnl5/SBR1kLlAhRJYniakQQmQjdx/FsvPsbXaE3GLv+bs8fqGJ3tLMhOre+ajrl5+6JVwp4GBlxEiFEMJwkpgKIUQWpigKZ29Gq7WiZ28na6LPb2+pS0Sre+fD2kL6hgohsi9JTIUQIouJTUjkYPi9ZzWjyZvoS7vnoW4JV+pKE70QIoeRxFQIIbKANzXRv+udj/ekiV4IkcNJYiqEEEbwYhP99pDbnLgmTfRCCCGJqRBCZJKn8YkEXbjHjpDb7Dz76ib6en6ulCqYR5rohRC5jiSmQgiRge5EP5vo/uyrm+jr+rnyXon80kQvhMj1JDEVQoh0pCgKIRHR7DybchO9ax5L3ivhSt0S+aWJXgghXiKJqRBCvKU3NdH7uzvwXon81PNzpbR7HjQaaaIXQoiUSGIqhBBpcD8mju1nbrE95Bb7wqSJXggh0oMkpkIIkUr3Y+LYevomG09GcPDCPRK1z9vok5ro6/nlp1oxaaIXQoi0kMRUCCFe43XJaEm3PDQo9XwUvTTRCyHE25HEVAghXnLvUSxbT99i06nkyWipgnlo4u9GU383vPLZGjFKIYTIeSQxFUII3pyMNi3jRpPSkowKIURGksRUCJFrJSWjG0/dIOjCfb1ktLS7WjMqyagQQmQeSUyFELnKvUexbDl9k02nIl6ZjDb1d8PTWZJRIYTIbJKYCiFyvBeT0YPh93ghF6W0ex6a+hekiX8BSUaFEMLIJDEVQuRIr0tG/d0d1GZ6SUaFECJLkcRUCJFj3H0Uq5vaKehCysloU383CjvbGC9IIYQQrySJqRAiW7v7KJYt/yX1GU2ejCaNppdkVAghsj5JTIUQ2c7rktEyhRx0o+klGRVCiOxFElMhRLaQlIxuPBnBoYuSjAohRE4kiakQIsu6E/1sAJMko0IIkStIYiqEyFLelIw29Xejib8bHk6SjAohRE4jiakQwuhuRz9l63832XTqZrJktGxSzej/27vz4DjOAm/8377mlGZ0jmRZ8hHnsi3fBidsWMKxJoYEQqiFAsqwQLGwP8LCunYrla3UBpY/Astdlc0CWY7a5d1NCCEs+wJmwxtICOscdiTHjoNvx4eO0Tm3Znq6n98f3dPToxlZsixpRtL3UzXV3U8/09PzWJ75ztNPdzOMEhEteQymRFQV0cSEM2b0hXOjEAyjRETLHoMpES2YwfgEfnWkH788OoAXJ4fRrga8c1M79nQzjBIRLVcMpkQ0rwZiE/jV0X788kg/Dr42VhJGt61qwDs3rcBt3e3obGQYJSJa7hhMiWjO9ccy+NWRASeMum1f1YB3bFqBPZtWYGWDv0p7SEREtYjBlIjmRN94Br88YvWMvnR+vGTdjtWNVhjtbkcHwygREU2BwZSIZu3SeAa/OtKPXxzpR48rjEoSsNMJoyvQHvZVbyeJiGjRYDAloityYTSNXx3txy+ODODwhXGnXJKA161uwjs2tWPPphVoCzGMEhHRlWEwJaJpXRhNO4fpD1+MOeWSBLx+TRPeuXkFbtvYjgjDKBERXQUGUyKq6PxIGr+ww+iRS8UwKkvA69c24Z2bVuDt3e2I1DOMEhHR3GAwJSLHayMpJ4wevRR3ymUJuOmaZrxj0wq8fWM7Wuu9VdxLIiJaqhhMiZa5s8Mp5zD9K32lYfTmdcUw2lLHMEpERPOLwZRoGTozlMQvj1gnML3aXwyjiizhDXYY3b2hDc0Mo0REtICqGkyfeeYZfOUrX8GhQ4fQ39+PJ554AnfeeWc1d4loyTo9lMQvX7Yu7fTHgYRTXgij79y0Ars3tqMp6KniXhIR0XJW1WCaSqWwZcsWfPSjH8V73/veau4K0ZKTzRt4+WIMz54cxv6jAzg+WAyjqizhT65twTs2tWP3hnY0MowSEVENqGow3bNnD/bs2VPNXSBaMnJ5E4cvjuO50yN47uwIDr02hgnddNarsoRbrmtxDtM3BBhGiYiotiyqMabZbBbZbNZZjsfjl6lNtLTl8iZevjiO586M4MCZ8iAKAC11Huy6phm3Xt+K3RvaEQ5oVdpbIiKi6S2qYPrAAw/gC1/4QrV3g6gq3EH0uTOjOPjaaFkQbQ56cNM1zbhpXTNuvqYJ61rrIElSlfaYiIjoyiyqYHrvvfdi3759znI8HkdXV1cV94ho/uTyJo5cGsdzZ0bx3JkRHDw3hoxulNRxgug1TbjpmmZcG2EQJSKixWtRBVOv1wuvl5evoaVJN0y8fDFm94hWDqJNQY8TQm9mECUioiVmUQVToqVkchA99NoY0rnyILprbRNuXteMm65pxnUMokREtIRVNZgmk0mcOnXKWT579ix6e3vR1NSEVatWVXHPiOaebpg4cilWHCN6brQsiDYGNPvQfDGIyjKDKBERLQ9VDaYHDx7Em9/8Zme5MH70Ix/5CH74wx9Waa+I5oZumDh6KYYD0wTRXWvtMaLrmnF9pJ5BlIiIlq2qBtNbb70VQohq7gLRnMk7PaKFk5VGkZoURBsCmnVo3j5znkGUiIioiGNMiWYpb5g42hd3xoi+eHbqIFo4NH9DG4MoERHRVBhMiWZoNJXD4YvjOHIxhpfOj+HguTEks/mSOmF/aRC9sZ1BlIiIaKYYTIkqSEzoOHIphpcvxvDyxXG8fDGGi2OZsnohn4pddgi9mUGUiIjoqjCY0rI3oRt4pS/uBNDDF8dxZihVse41LUFs7gxjc2cDdl3ThBvbQ1AYRImIiOYEgyktK7ph4vhAwukJPXwxhhODCRhm+Ul4Kxv82NIVxqaVDdjSGUZ3ZxghH+81v9wJISAyGRixGIx4AhBmYUXp1K5bXCjZSHmhqDA/7bamef7keVmGpGmQVBVQVUiqBklTIamqq9xVpiiVmoCIaN4wmNKSZZgCZ4aSOOw6HH+sP45c3iyr21LnxRa7J3RzZxibOsNoqeNdxpY6M5eDMT5efMRiMMbHYdrTwrIx5pqPxSByuWrv+sKQpGJgtafOQ7MCLNTycmh26HWXe7RiGHbq2vXd5YVQ7PFA8vsh+wOQA37IfushuZYllV9hREsN/1fTkiCEwIXRDA5fHHd6Ql+5FCs7Sx6wxoUWAmhhuiLs4x2VFjGRz8OIx+2AGSsLmkbMVe4OmOn07F9U06DU15f2Khb+htx/S1POFybT1L3a57vnDQNC1yHy+ZIHdB1C18vfoxAQuVzNBnFJ0yAFAk5olf1+SAE7zFZaDviLYddelgOBygHY46n22yNalhhMaVEaiE2UjAk9cimG8XT5F6tfU7BppdUDurkzjC2dDVjdHGAIrVHCNGEmEqUBcrxyqHSHTzORmP2LyjKUcBhKQ0P5tLFCmT2VAkvr70gIYQXXQmDVdcCeFsvyEPlJ5XaZyOeL5Xoh9FaqO6lcd4dkuzynQ+g5mOkMzEzhkYawl2FaRz2ErkPEYjBjsblvEFWdFHhdAbgs4AagtDRDbW2FFolAbW2F2tLCcEs0CwymVPPGXJdpKhyWjyayZfU8ioz1K+rtENqALZ0NuDZSx5OTqkToeslh8vzYWHmPplNWDJmF0DEbcihUGiIrhc2G0mW5rg6SLM/hO1+cJEmyD6nX9teCsHtxzXQaohBa03ZwrbScdgXbkuXSsFt4oNBznM/DTCSu6keP0tgItRBUnWlrSYBVWlshM8ASOWr7E4iWnWQ2jyOuMaEvXxrHhdHyyzTJEnB9W33J4fgb2uvhVXmyxlxzTvYpC5iu3szJZWNjMFOVr2wwE3IgUAyWdpCUywLmpPlQiCfrLAOSJEHyeiF7vUBj45xvX+h6MajOMPwaySSMkRHo0SjyQ0PIDw0Dum79vxgbQ/b48cu+ptLQUCHAuoNsBGqEAZaWBwZTqprEhI5jfXEc7YvjlUv2ZZqGU6h0l9q1rss0bekMY0NHCAEP/3yvVMmh8rEx5EsCpn2ST1nZ2OzHGMoylEIvZmOjK2wWlovBUi2sD4d5CJSqRtI0KJoGJRSa9TaEacKIxZCPRpGPDlnTofKpPjRkBVj7/1r2xInLblcJhy8TYCNOb6zs5YmbtHjxm50WxFgqh6N9MRy9FMfRvhiO9cVxdrhyj9rKBj82rQxjc5c1JrR7ZRhhPy/TBNjBMp2GGY/DSCRhJhMwEgmYiSSMRBymuyyegJG018XsXs2rOFQuadqkUDk5ZBZ7NwshUw6FeJiclh1JlqE2NkJtbARuuGHKekII6yhEdKhicHVPha5b/49jMWRPnrzs68vhMDQ7pBZ6W51pJAKloRFyXdA6ec/HEz+ptjCY0pyLxieKIfRSDK/0xXFpvPxwPGCF0I0dIXSvDKN7ZQibVjagtX5p/toXQkCk0zCSSavXMpGAmUzCiLsDZRJmwg6dCVewdEJnEhW7lK9QyaHyKUNmg6s3c+md7ENUbZIkuQLs9VPWE0LAjMWKQwUuE2RFLgczFkM2FkP25Knpd0JVoQSDkOvrIdfXQ6mrg1xXB7m+DkpdfXG+vh5y0DVfV2fVra+HHAxyGA3NGQZTmjUhBC6NZ3D0Uhyv9MVw9FIMR/viGKpwYhIArGkOYOPKMLo7rBC6sSOMpuDiOWQrDMMKi3avhRFPVO6xTCRKw6f9MJJJwCi/fNWs2Jcqcr48QvXWtL4eSn0d5Lp6KKF6yHV2nbA7eDZwrBrRIiJJkvP/F9dPE2DjceSj0cuGWCMWK/7Izeedz7SrIQcCVkh1B9a6OufzqNBD63wm1dmfU/WFIFzPIQgEgMGUZsg0BV4bTdvhM4ZX7EPylS7RJEvAutY6dK8MO72hGzpCNXPXpOI1L2PW9S3ty80UL0MUK4ZP18OMx+ektxKKUvzgdgdKu6wkUBZ6MewPdKXeqiN5vey9JKISkiRZJwSGw/Bed91l61rDgjIwk8UfzmbS/cM6aR2xSaaKR28qzBfGn5vpNMx0GhgcnP3+a5oTUp2e27o6+8YKPsg+P2S/z7pU11Tzfj9k36R5n489uosIgymVyRsmzgynrBDqGhOazObL6mqKhOvb6p0AurEjjPUr6hfkxCSRy5UHSCdUTr6LjytgJpNX9bpyMAg5HIJSHyr2WBbC4+QeS6fXoFhH8vsZKomoqiRZhlIXhFIXBNrbZ70dM5crBlp7SJKZTDrDkczUTOat8w2E60oGFW73cFUkr7cYWH0+68YLvknzfh8kX+HatZXmfVZIds+7wy/H088JBtNlLpc3cWIwYR+Kt0Loq/1xTOjlJ8h4VRnrV4TQvTJkH44P47q2uqu+RJP7BICSHsypei/n4q49sK95afcuOI+GsHVZonDYPvztnoatSxJptdHzS0RUbbLHA7mpCWhqmvU2hGnCTKUq99wmUxATGZiZCZiZTHF+ImNfusueT2dgThTKC3Uniq+RzcLIZoH5uBmDTfL74bvhBgRetxOBnTvh374dSn39vL3eUiUJMRfHJqsjHo8jHA4jFoshdBWX9lguMjkDrw7E8Yp9eaajfTEcH0hAN8r/BIIeBRs7wtjoCqHrWoNQldn9IhS6Dr2/H7nzF6BfvIDchQvQz19A7uJF6BcuzL4Xs3A5onAYcoMrUE4Km+6LrMuFgMlDO0RES5YwTYhs1gqpGTu4ZiYgMml7flK4nZiwhje4wq0zPzFhX7fWmhdpaxsiW/mcCgCAJMG7/kYEdu5EYMdOBHbugNrcvHANUEOuJK8xmC5RsbSOPw4UrxF6tC+GU9EkzAr/2mG/5vSCWicnhbCmOQj5Cu+YZMTjFYLnBegXLkLv75/2xB+lqal40fSpejDdd+8Jh3nXHiIiqhphmlagzWRgxGLIHH4Z6YMvIn3wIPTXzpfV17q6oLW325fyarGnrVBaXPMNDUtuuBeD6TKiGybODqfwan8cfxxI4PhAAn/sj6MvNlGxfkudx7o0k+vM+M7GmY15FPk89IFBK3iePw/9wkUreNo9n9Pdr1ry+eDp6oTW2QXPqi5onV3QujrhWbUK2sqVPCOTiIiWDH0wisyhg0gfPIj0wUPT3kDBoapQGhugNjZBaWqC2mRNlaZGqE3NUJoaoYTC1nkMoZBzDkMtd9IwmC5BQggMJbP4Y38CfxyI29METkWTyBmVL5i+ssGPDR0hJ4R2rwwjUn/5s7mNZBL6BavHM3fhArLnX7N6Py9ehNHXD+Qv3+tpNoVhrGiBvqIZelsTsu0NmGhrwEQkjImwD7rII2/mkRd56KYO3dCRt8savA3Y0roFm1o2IaAFrqq9iIiIakl+bAzZEyeRH7Yv4TU0BGN42JnPDw3DGB+f3cYlqXiibShkXdUgGIQcDFjTgGs+GLQu7xUMou5Nb4Kkzv/pRgymi9yEbuDkYBKvDsRxrG8crw4O4eTQKMYyKUhyFpKsA3IOkpwDpBy8njwiYRnN9QLhgEDQb8LnMZAXE1b4M3UrDJp55PM6/ONphIYyaBieQMNIFo2jOTSP6Ggey6M+ffk/B10BomFgsFHCYAMQbZAw0GhNo2Eg67n6ww+KpOD6xuuxLbINWyNbsS2yDe3B2Z81SkREtBiYuZx1ZYKREeRHx2CMjSI/MgKjMD9qrbPu7heHkUiUnOR1pW58+fCC3AKawXSB5c08MvlM2SOtp8vL8umSdaPpJIbTScQySSRyVpkuJpzQKclXeEF2IRCcAFriQGtMoG0ciIwLtI0BbeMCkRigTbPJuB9O2BxsKIRQaz4eUqAqHqiy6jw0Wau8LNnzigpN0qat35/sR+9QL/pT/WX71BZoc4Lq1shW3NB4A1SZF5UgIqLlzczlrKsYxOP21Lr5i5lKWY90ujjvXp7IYs1//J8F2UcG03ny5Re+jJeiL5WGTT2DnJmb99eWJQV+1Y+g7EPrhAftCQWRuISWuEDTWB7hsRzqRycQGElDm7j8FeCEIsNsb4HoiAAr2yF1tEPp7IDa2Ql1ZQe0ULgkPGqyBk3WoMgKZGn+x7AMpAbQG+1F71AveqI9OD56HIYoTdN+1Y9NLZucHtXNrZsR8iydXnMiIqKlgsF0ntz9/+7G0xefnnK9LMnwq354ZB9k4YVpeqDnVWSyCiZyCmB6IExPcSo8kOFFpC6EznAYa5oacG24ATcoQCSRgWcoBnlwGNLgMIy+Aej9fcj3Dzh32rgcpakJWkeHdXKR+ySjzi5o7W0LMqZkrqT1NI4OH0VPtAe9Q704HD2MhJ4oqSNBwrqGdU5Q3dq6FV31XUvuzEYiIqLFhsF0nvRGexHPxeFX/fCrfkzkFPSNmnhtWMeZqI4TgxmcHEwhl698MlJH2IeNzR5s9WZxPZLoysXRkBiB0d8Pva8Pel8f8oODgFn5+Q5ZhtrWZgXPyY+VHdBWrIDs989DC9QGU5g4PX4avUO9Vs9qtBfnE+WX5Wj2NVuH/lutw/8bmjfAo/Ae8URERAuJwXSe/PJIP156bQzHBxN4tT+B4WT5hXWDuQxW6zFs9WRwg5RCVy6OltQoAmNDMAf6YYyOTvs6kqZB7VjhCpsrXeFzJbS2CO8+NMlwZhiHo4edw//HRo5BN0uHNHhkDza2bCwJq02+2d+thIiIiKbHYDpPPvTwARx75Rwi6TFEMmNoS49hnUhitW6Fz+D4MOR0atrtyMFgaQ+nq8dT7eiA2tJS09cjWwyyRhbHRo5Zh//tXtWx7FhZvTWhNdjSusU5sWpteO2CjKMlIiJaLhhM58kL734f6o8fmbaeM75z8iF2e14OhTj2cYEJIXA+cb4kqJ6OnS6rF/KESoJqd0s3/OrSHRZBREQ03xhM58mlffsQ3/9rqJFIhUPsy2N851ISy8ZweOgweqPW4f+jw0cxYZReD06VVNzYdKNzmaptkW2IBCJV2mMiIqLFh8F0nhjxOGS/n+M7lyjd1HF89HhJr2o0Ey2r1xHswKbWTWjxtyDkCSHsDZdMQ96QtewJQ1P4t0JERMsbgynRHBBCoD/Vj55oD3qiPTg8dBgnxk7AFNNcNcHFr/rLgqsTYCeF2bAn7ITaek89x7oSEdGSwGBKNE+SuSSODB/B8dHjiOViiGVjiOfiZdNELjH9xi5DgoR6T/2Uobasd9ZV7lN8HMNMREQ1g8GUqMoM00BST04ZXKeaxrPxsnGuV0qTtbIA2+xvRiQQQSQQQVugDa3+VkQCETT6GtkzS0RE8+pK8triuf0P0SKiyArC3jDC3vAVPzdn5K44zBaWDWFAN3UMZ4YxnBme9rVUWUXEH0FroNUJrYUA637wygRERLQQGEyJaoxH8aDF34IWf8sVPU8IgXQ+XRZcx7PjGM4MI5qOIpqOYig9hMH0IEYnRpE38+hL9aEv1XfZbdd76hHxlwdWd5ht8jVBkZWreetERLTMMZgSLRGSJCGoBRHUguhAx7T1dcPqWR1MDzqhNZqJFuftRyafQSKXQCKXqHjt1wJFUtDsb3aCaqu/FW1BVw+sHWzrPHVz+baJ5pVpCmTiOaRiWSTHskiN24+YNU2O5zCRzMEb0BAMexAIexEIexAMeRFssJYL5R6fwvHfRNNgMF1EhBDQJwxMpHRkkjomUjomkvYj5VpOFR+aR4GvToMvqMFXp8Ffp8EbtKZWmQe+oAp/nQfegApJ5ofmcqEpGlbUrcCKuhVT1hFCIKknEU1HMZgexFB6yJkv9L5G01EMTwzDEIYTZi8noAYq9rwWHq3+VjT5muBTfXP9lolK5DJ5JCcFzdRY1iqL5ZAazyIdz0GY05+KkUnoGB9MX7aO6pGLQTVkTYMNxSAbCHsQDHvhDaoMsLRs8eSnKhGmQDaTLwbJpCtsugPmpKlpzOM/lwT4AlpJkPXVafC75p1yO9x6AypkhSfPLHd5M4+RzAiGMkMlodXdGzuUHkJCn/nVCvyqH02+JufR6GuccrnR1wiv4p3Hd0iLiWGYSNvB0urVzCIdyxZD6Li1Ts8aM9qeJAGBkBUiSx5hL+oavPDVa8im80jHrEBbaZqbmNlrAYCsSiVBdaqpv05bMp0JhmHCyJnQcwYM3UQ+ZyKvGxAC8AZU+IIaPH4V8hJ5v8sNz8pfYIZhIpvKl4fJqXo2kzqyaR2zbXlVk0uDYrDCvB0a8zmzdL+Sk/crh4lUHrlMftbvv/Ch4Q6y3jp3r6y7p9YDb1CFwjC7LKX19GWHDUTTUQxnhqGb+hVvO6gFKwdYbyOa/E1o8jahyW8v+5p484NFSAiBbDpfEjiLh9ZzTlkmkQNm+Pnq8SllgbPOFTyDDV4EQtpV/wDXswZSsawVmO1pOl7aM5uKZZFNzfyzWJYl+EOe0iEErqEDhZ7Z2ey/aQrkc4YTEIth0bTK7amhu8rsunm7rlGoN2m9O3gWtjmTXmlI9vdNwPo+8QWt7x5vUIMvoNplml2mOh0tDLTVx2A6Tw7/vwuIno9jIpmfs1Cn+ZRigAuW90pWmmqeuT/BpBCuM8kcsoXg6gqx2aSOzKRwm03P/n17/Kr1oVLncXpf/fVaSU+ENfVAnYf3S7WrMHxgbGIMoxOjzmOq5bGJMeTFlf8t1mv1JUH1cj2yDb4GaDKD7Fww8sWQohfCSs4oK8um9dIAagc4Q5/ZDS5kWbKCmjtoTgqfgbAHHl9tjWgzdBOpeGmALU4L81lkkvqMwzckwF/vcYKqokqTwqIJQ3f9W+jm/B6dm2ZfVU2GqilQPVaYzqbzM+7dnmqbXr8rxAZVeAtHBxloFwSD6Tz5vw8exmtHRyqvlFw9h64xnCU9hxWCpqIu3p5D0zCRTedLe4UnD0uY1FM8kb6CD1ObN6gWg6odVq0vFS/qGq0Q6w95+AGyTAkhEM/FS4LqyMRIyfLoxChGs6MYzYxiPDsOQ1z5l1zIEyoLrg3eBngVLzRFgya7HvayR/ZAUzSoslq2rlL9wqMaVzcQQjg9WXpZUHQHyErrS8NlPmdAr1CWz5kwZ9IzNg1vULXCZrhy4Aw2LK3D3JUYholMXC/tdY1lkYrnrN7YwjCCGY6RvRxFlaF6ZKiaDMWj2MFRhupR7HIFiiZD81x+veqxy1zBc/J6RZUrjq818tb3TeF7JJvSMZGyOomyKR0T6bxdZnec2OvmItAWg6sdaAvzhTAbUJ3v9FCzj8PbKmAwnScnDw4iMTpREjyLh801BqMZME2BXNrqmZ1I5e1eZyvIZuI555Bc4RDdTHtHysaAOV9YnpJlb4AnFSx3pjARz8adoDqWHcNoZrR02RVox7PjV3Qb2rkgSzI8kgde+OETAfjggxd+eOCDR3itB7zQTC804YUmNKimx3oIDYqhQTFVKKYK2VQhGwpkQ4EPfgRQB8VQkdcnh8qFfY+SBCe4aF6lGFg81rzHrzj/b4s9ntahah5FmTnTFJhI6iW9r6YhrBBZCIeXC5GqvKgDvhNoncBqBdpsutBZUiHQpnXoVzAm2E3VZLR01aF1dQiRVfVoXV2Pxvbgss8HDKa0JDjjyWJZpMftsBpzjSkbv/IeAVWTEXD3urpOYHCHWH7xLU9CCJhG4WH17ul6HrGJOMbT4xhLj2M8E0NsIo54JoFkNmmNpcsLmHkTpg4IHTDzgMgDwpCAvP0wJMCQIRkKJEO2w6IK2VSgmCpUU4NiP1RTgyKqd4jZ6SErhBWPAs1TDI1OmTapjldxPc8Ona7A4y6TVYk/EqlmuQNtMbhePtBmEjnkK3SmqB4ZrV1WSG1eWeecyOX1q66psqR7WhlMaVkxTYFMIlcWWJPjWaTtMJscv7KTCrwBtWToQHDSYcNAyANZsb5YJcm6hqgkW1NIgCRLkCUJsMsKdRYzYQqY9kMY9rwhiuWFeXtd6bzpzFd8zuTnGwKGYZYsm4YJw54XheBoCKesECSFU2a6nlt4DbM0eJasE1d9yHO+SIp1prasApIKSKqApAJQBaCYEIoJqCaEbMJUDAjFgKEYMOU8TCUPQ85Dl7Loz/bhfPocJqQM8nIOeUVHXs5BqCbWNa/B+rYbsWXFZmxr24q2YFu13zbRoiJMgfFoGkPnE4i+lkD0tTiGLyRnPJxA9Srw+hR4/Co0rwLNp8LjU6D5FHi8qjX1KdC8qusHo1L6I1IrLiuaDEWVphwesZAYTIkqyOuGdZmYCr2u7uVKv3jnhISKQVayg2yhDFPVkSS7HiZNrXlIEmR3OHbVF8IaEzx1cJxivhDaTHHFY4OXCkkCZEWGrEjFhyw5ZYVxcYVD0YrmGounylAKh0o1+9BpoY77sKnzvMrr5/IwYM7I4djIMRweOozeaC96h3or3r52RXAFtrZuxZbIFmxt3Yrrm67nCWBEV8g0BcYHC2E1jvHBNLJp66TpXCaPbCa/IMNoZFWyPo806/NKseffd+/roGjz31PLYEo0S0II5DJ55zqHhd7W9KQzgzPJHIRp1V+uga1Akq2gJhUC21TziuTULYQ7yT3veo4VAGWrTJGguIJg6UOeYl6CLE+uL1cMloWH4lqW5OLyYh5fNxNCCPSl+qyQGu3F4aHDOD52vGxcrU/xobulG1sjW7GldQu2tG5Bo6+xSntNtHQYhlkMquk8chMG9Al7mjWgTxjIZfPQC+XZ0rHhed190qE1P9PzM/6/h968IJ9xDKZEC0gIASHskGoCprAPCdtlhQArhHWop2y+bH0x8Jp2HcygfuVtWvOSDCuoTQqKlwuOZfOVwqTMcYJLUVpP4+jwUfQOFcNqPBcvq7cmtMYKqXav6rqGdZClpTtOjmixEKY1HMrIW1fbMPJmcVqYNwQ6b1iYH5cMpkRENGdMYeJc/BwORw87YfVM7ExZvXqtHptbN2NLxOpR3dyyGXWeuirsMRHVEgZTIiKaV7FsDC8PvYzeoV4cjh7Gy8MvI5PPlNSRIOG6xuuwtXWrMwSgq76LvexEywyDKRERLai8mcfJsZPWSVV2r+ql5KWyek2+Jmxp3YKtka3Y2roVG5o3wKf6qrDHVOtMYSKTz0CWZKiSCkVWOFRkkWIwJSKiqhvODJcc/n9l5BXopl5SR5VVrG9a74TVLa1b0B5sr9Ie00LRDR3RTBSDqUFE01EMpgeth2t5KDOEvFl6mT8JEhRZgSqpkCXZmVdkBYqkQJVVKJLiLBfm3XUut67wfFmSS7Y11fNL6sgqGrwNaPW3otnfjBZ/C/yqv0otXFsYTImIqObkjBxeHX3VOaGqJ9pT8VJV7cF2bG3diu1t27GjbQeubbiWPWWLSDKXLAmb0XR5AB2dGK32bi6IoBZEi78Fzb5mtAZa0ehtRJ2nDkEtiIAaQEALIKgFEVSDznyjrxFNvqYl9TfPYEpERDWvcKkqd6/qibETMETpBclDnhC2RbZhR9sObG/bjg1NG6ApvKbqQjOFibGJsZKwWRI+7WlKT81oe5qsoS3QhkgggrZgG9oCbWXLDd4GmMKEIQwYpoG8yMMwDWfZENYjb+ZLypxld71Jzy/UMYVZ8vzJdQqvP+Vr2Ms5M4fxiXEMZYYwkhnBhDEx67ZWZdVpj/ZgO9qCbWgPWNMVwRVYE1qDgBaY9fYXGoMpEREtSoVLVfVEe3Bo8BB6h3rLTqryKT5sbt3sBNXNLZsX1Zd0LdINHUOZIUTTUQykBxBNRcsC52B6sOzQ+lTqtXonYEYCESdwtgfbneUGb8OSPRFOCIGUnsJwZth6TAxjJDOCkcwI0vk0UnoKKT2FdD6NtO5a1tMYz45DTHOBbFmSsTq0Gjc23Yj1TetxQ9MNWN+0vmavLcxgSkRES0LezOP46HEcHDyIlwZfwkvRlzCeHS+po0oq1jevt4JqZDu2t21H2Buuzg7XqEw+g3OxczgdO43+ZH/ZYfbRidFpwxBgjfFs9jdPGTYLU/5QmD3d1DGcHsZAegADqQEMpgYxkLanqQH0pfqmHArRFmjD+qb12NC8AdvatmFL65aaGOfKYEpEREuSEAJnYmdwaPAQXoq+hEODhzCQGiird23DtSVBdbmcUJXSUzgzfganY6dxZvwMzsTO4PT4aVxKXpo2eGqyVhYwJx9mbwm0lN2a1jQNGLqOvK7DsB95PefMG7qOfF6HYZdVrJd3l+ecedMw7Ns0lz6sMtm+/bJs37bZtSxLAOybgEByLcuu50/e3uRtTlVPtm8DXXzdwvZlRbEesgzJnpdkGbJcnC/ZH/t20hIk53ULt6V27z8m7ed4dhxn4+dwNnYWZ+JncDZ+Dv2pPggAwu6EFhKgyAqub7wemyNbsKl1Eza1bkJACzrvxx8KL0ivNYMpkYt19yMTpmFYU9O0p0ZxahTKDWtqGBXqucqFgCRZt72UZBmSrLjm5Qrz5eute9srU9RfOoPelzLrrlyFu3mZ9p23TPuuX6b9tycgYP0NOnVN0y4rPN+07+Dl3oZw3b3LrPhasLfhfq3JZe7nFLnu2OVMrC89p8hZb38x2iuKX2ISirOSaxuo8DxX3Umv7a5jTVxfkpXKnFXF5w2lozg2cgyvjr6KV0aOlVymqvAF1xaMYEPzRmxo3oCNzRuxsq7D9f9Mcm23wnsq25+SPXFeqdjuKP4bmqYVFqb4NyyWTVpf6e/Ate1UNoXB9AAGkgMYTA4gmo4imh5EbCIGSdh7Jez9t5f9qh8RXysatDDq5SDq5AACkg9+yQsvNKimDCOfnzpY5nIw8npJuMzrOetvmxalv/q3RxHwBuf9da4kr6nzvjfTeOihh/CVr3wF/f392LhxI775zW/ijW98Y7V3q6Kh8+cwkYgXv0CEAAofOlN8yBS/KCZ/KE3/5QT3cskHVoUPuArrCw8Iex+sHbV+NRdue+kqt+btdShsDyXlxXn7t3fhNYCy8ilfC+79Kv1yd4fHQiAsC4VThcXCuknbKLzOYuMOtVOF19J5ZVJYnmqdBEj2OsnqQUBhnVQsk1zrCz0CTlnJcvlzrOAtl+xP4Ze+fNltW0Gh+G9oFOcNo/jvbrj+BgzjMvXtHxtG8UeHaRjO34z1o6T0x0hx3lVesn3XNgW/kGtJAMDrALwOHVPUOI4+HEcfnljAvZp/CoAVAFZAA9AyTe0UgBRMAHH7MZckSYbi0aCqGhRNg6J5oGgaVM1eVjWoHg8UVbXLPU49VVOhqO7neCArcvF7w/19ivKwXxriS79DL7+u8D0Lq9z5MSnKX3fSd+vk7+KSzxL7s8P5nLE/O9zfneXfp2bZd3B5mSh/vus7151FTGF/bgnT2U7hh4oECWkjgwDmP5heiaoG00cffRSf+9zn8NBDD+FP/uRP8J3vfAd79uzBsWPHsGrVqmruWkW//z8/wNneQ9XeDZpjzuGVwuGWwnxJub0sFUNfIQg7H0au3tjivGF9SExeP4NAI0wThmkCmNnJBrQ4TD7cOPnQoXPozvkRYId2wFU26RCi5KpbOOTnOmToHAKE5Hy5Ae7fbMUfmM4XYKFCoa7rCe4DbcXnuQ4U2z9EnfXOpie/9qTtTX5t92u6G7HCfpQtT9rvvJm3HvYZ1/aLWf8mdq+n+xqVsmS1ubNJZ3vu13Ctk6Xiv6H732Caf5fCv78hTORFHjmhI2fqyJk55Mwc8iJvvYwE12FaASEBmuKBT/XBr/nh16xLD/m1ADRFK/nRWXx967VkRbUDoeoKhnZIVFUoHo8VIN3B0l1uT92Bs7AdWVFAtWsgNYAXB17EocFDGM4Mo9nfXO1dKlPVQ/m7du3C9u3b8S//8i9O2fr163HnnXfigQcemPb5C30o/8mHH8TFY0ddvUZTf8hM/aVj9QiVbENyfaBV+lJyfTlN/pApGUfj2gZcY1KKX0oojlNxjW0pHpIr1C0e1nMfLivMz3h7zriZ8u1P9VrF8Tj2+BxXYHT3ALrH7ZT1LCqTDpsrk547aVvVUDyUVynMVl4uzhtTPscdfCstOz3qrl/+ph2eIYQ9TMEshmlR4TlTlJum6fQ2FIY7wAnt4jL7UFoOwBr6oJSOy7L+XV3zSnGIRMnfg+Iuc4/3cv3wmGLsl1x43Urryl5fKe9BnjRWrdL/T6q+nJHDKyOv4NDgIevM/2gvknqypI5H9qC7pRs72nZgR9sObI1sRVC7up4lU5joT/Xj9PjpsnGgk1+/QIKElXUrsa5hHa5puAbrwuuwrmEd1obXXvX+EC2URTHGNJfLIRAI4LHHHsN73vMep/yzn/0sent78fTTT5c9J5vNIpvNOsvxeBxdXV0cY0pERLNmmAZOjJ1wTqY6NHio7KxnWZJxY9ON2B6xLvq/LbJtyt6mvJnHpeQlK4DaJx+dHj+Nc/FzZZe+KlAkBV31XVYADV+DdQ1WAF0TWsNbttKityjGmA4PD8MwDLS1tZWUt7W1YWCg/AxLAHjggQfwhS98YSF2j4iIlglFVrC+eT3WN6/Hh9Z/CEIIvBZ/rSSoXkpewrGRYzg2cgw/evVHAIC14bXYHtmOjS0bMZwZdnpBz8XOld16tUCTNawJr7HCZ7jYC7o6tJo3DSBCDZz8NPnQVuFs50ruvfde7Nu3z1ku9JgSERHNFUmSsCa8BmvCa3DXdXcBAAZTgyVB9dT4KZyNncXZ2Fk8fvLxsm34FB/Whtc6wbMw7azvhCpX/auXqGZV7X9HS0sLFEUp6x2NRqNlvagFXq8XXq93IXaPiIjI0RZsw561e7Bn7R4AwPjEOHqiPXgp+hKOjx5HJBBxDr9fE74GHXUdS+pe50QLpWrB1OPxYMeOHXjyySdLxpg++eSTePe7312t3SIiIppWg68Bb171Zrx51ZurvStES0pVjyfs27cPe/fuxc6dO3HzzTfju9/9Ls6fP49PfepT1dwtIiIiIqqCqgbT97///RgZGcE//uM/or+/H93d3fjlL3+J1atXV3O3iIiIiKgKeEtSIiIiIpo3V5LXODKbiIiIiGoCgykRERER1QQGUyIiIiKqCQymRERERFQTGEyJiIiIqCYwmBIRERFRTWAwJSIiIqKawGBKRERERDWBwZSIiIiIagKDKRERERHVBAZTIiIiIqoJDKZEREREVBMYTImIiIioJjCYEhEREVFNYDAlIiIiopqgVnsHroYQAgAQj8ervCdEREREVEkhpxVy2+Us6mCaSCQAAF1dXVXeEyIiIiK6nEQigXA4fNk6kphJfK1Rpmmir68P9fX1kCSp2rtTc+LxOLq6unDhwgWEQqFq786SwXadP2zb+cO2nT9s2/nBdp0/C922QggkEgl0dHRAli8/inRR95jKsozOzs5q70bNC4VC/E89D9iu84dtO3/YtvOHbTs/2K7zZyHbdrqe0gKe/ERERERENYHBlIiIiIhqAoPpEub1enH//ffD6/VWe1eWFLbr/GHbzh+27fxh284Ptuv8qeW2XdQnPxERERHR0sEeUyIiIiKqCQymRERERFQTGEyJiIiIqCYwmBIRERFRTWAwrXHPPPMM7rjjDnR0dECSJPzsZz8rWT84OIi/+Iu/QEdHBwKBAG677TacPHmybDsHDhzAW97yFgSDQTQ0NODWW29FJpNx1o+NjWHv3r0Ih8MIh8PYu3cvxsfH5/ndVc/Vtuu5c+cgSVLFx2OPPebUW27tCszN3+zAwAD27t2L9vZ2BINBbN++HT/5yU9K6rBtZ9e2p0+fxnve8x60trYiFArhfe97HwYHB0vqLLe2feCBB/C6170O9fX1iEQiuPPOO3H8+PGSOkIIfP7zn0dHRwf8fj9uvfVWvPLKKyV1stksPvOZz6ClpQXBYBDvete7cPHixZI6bNvZte13v/td3HrrrQiFQpAkqWKbLae2nYt2HR0dxWc+8xnccMMNCAQCWLVqFf76r/8asVisZDsL3a4MpjUulUphy5YtePDBB8vWCSFw55134syZM/iv//ov9PT0YPXq1Xjb296GVCrl1Dtw4ABuu+027N69Gy+88AJefPFF3H333SW3BfvgBz+I3t5e7N+/H/v370dvby/27t27IO+xGq62Xbu6utDf31/y+MIXvoBgMIg9e/Y421pu7QrMzd/s3r17cfz4cfz85z/HkSNHcNddd+H9738/enp6nDps21IzadtUKoXdu3dDkiQ89dRT+MMf/oBcLoc77rgDpmk621pubfv000/j05/+NJ577jk8+eSTyOfz2L17d8nf5D/90z/h61//Oh588EG8+OKLaG9vx5/92Z8hkUg4dT73uc/hiSeewCOPPIJnn30WyWQSt99+OwzDcOqwbWfXtul0Grfddhv+/u//fsrXWk5tOxft2tfXh76+Pnz1q1/FkSNH8MMf/hD79+/Hxz/+8ZLXWvB2FbRoABBPPPGEs3z8+HEBQBw9etQpy+fzoqmpSTz88MNO2a5du8R999035XaPHTsmAIjnnnvOKTtw4IAAIP74xz/O7ZuoQbNt18m2bt0qPvaxjznLy71dhZh92waDQfFv//ZvJdtqamoS//qv/yqEYNsKMbu2/fWvfy1kWRaxWMypMzo6KgCIJ598UgjBthVCiGg0KgCIp59+WgghhGmaor29XXzpS19y6kxMTIhwOCy+/e1vCyGEGB8fF5qmiUceecSpc+nSJSHLsti/f78Qgm0rxOza1u23v/2tACDGxsZKypd7215tuxb8+Mc/Fh6PR+i6LoSoTruyx3QRy2azAACfz+eUKYoCj8eDZ599FgAQjUbx/PPPIxKJ4A1veAPa2trwpje9yVkPWD2q4XAYu3btcspuuukmhMNh/O///u8CvZvaMZN2nezQoUPo7e0t+aXJdi0307a95ZZb8Oijj2J0dBSmaeKRRx5BNpvFrbfeCoBtW8lM2jabzUKSpJKLavt8Psiy7NRh28I5lNnU1AQAOHv2LAYGBrB7926njtfrxZve9CanTQ4dOgRd10vqdHR0oLu726nDtp1d287Ecm/buWrXWCyGUCgEVVUBVKddGUwXsRtvvBGrV6/Gvffei7GxMeRyOXzpS1/CwMAA+vv7AQBnzpwBAHz+85/HJz7xCezfvx/bt2/HW9/6Vmfs2cDAACKRSNn2I5EIBgYGFu4N1YiZtOtk3/ve97B+/Xq84Q1vcMrYruVm2raPPvoo8vk8mpub4fV68clPfhJPPPEE1q1bB4BtW8lM2vamm25CMBjEPffcg3Q6jVQqhb/7u7+DaZpOneXetkII7Nu3D7fccgu6u7sBwHnfbW1tJXXb2tqcdQMDA/B4PGhsbLxsHbbtlbftTCzntp2rdh0ZGcEXv/hFfPKTn3TKqtGuDKaLmKZpePzxx3HixAk0NTUhEAjgd7/7Hfbs2QNFUQDAGTf2yU9+Eh/96Eexbds2fOMb38ANN9yA73//+862JEkq274QomL5UjeTdnXLZDL4j//4j7JxOQDbdbKZtu19992HsbEx/OY3v8HBgwexb98+/Pmf/zmOHDni1GHblppJ27a2tuKxxx7Df//3f6Ourg7hcBixWAzbt28vaf/l3LZ33303Xn75Zfznf/5n2brJ738mbTK5Dtt27tp2um3MdjuLzVy0azwexzvf+U5s2LAB999//2W3cbntzAV1XrZKC2bHjh3o7e1FLBZDLpdDa2srdu3ahZ07dwIAVqxYAQDYsGFDyfPWr1+P8+fPAwDa29vLzsoFgKGhobJfW8vFdO3q9pOf/ATpdBof/vCHS8rZrpVN17anT5/Ggw8+iKNHj2Ljxo0AgC1btuD3v/89/vmf/xnf/va32bZTmMnf7e7du3H69GkMDw9DVVU0NDSgvb0da9euBbC8/24/85nP4Oc//zmeeeYZdHZ2OuXt7e0ArN6jwmcqYA2VKrRJe3s7crkcxsbGSnpNo9GocySFbTu7tp2J5dq2c9GuiUQCt912G+rq6vDEE09A07SS7Sx0u7LHdIkIh8NobW3FyZMncfDgQbz73e8GAKxZswYdHR1ll5E4ceIEVq9eDQC4+eabEYvF8MILLzjrn3/+ecRisZJD08vRVO3q9r3vfQ/vete70NraWlLOdr28qdo2nU4DQMlVIwBrvGThCADb9vJm8nfb0tKChoYGPPXUU4hGo3jXu94FYHm2rRACd999N37605/iqaeeckJ6wdq1a9He3o4nn3zSKcvlcnj66aedNtmxYwc0TSup09/fj6NHjzp12Laza9uZWG5tO1ftGo/HsXv3bng8Hvz85z8vGaMOVKld5+WUKpoziURC9PT0iJ6eHgFAfP3rXxc9PT3itddeE0JYZ9D99re/FadPnxY/+9nPxOrVq8Vdd91Vso1vfOMbIhQKiccee0ycPHlS3HfffcLn84lTp045dW677TaxefNmceDAAXHgwAGxadMmcfvtty/oe11Ic9GuQghx8uRJIUmS+NWvflXxdZZbuwpx9W2by+XEtddeK974xjeK559/Xpw6dUp89atfFZIkiV/84hdOPbbt7P5uv//974sDBw6IU6dOiX//938XTU1NYt++fSV1llvb/tVf/ZUIh8Pid7/7nejv73ce6XTaqfOlL31JhMNh8dOf/lQcOXJEfOADHxArVqwQ8XjcqfOpT31KdHZ2it/85jfipZdeEm95y1vEli1bRD6fd+qwbWfXtv39/aKnp0c8/PDDAoB45plnRE9PjxgZGXHqLKe2nYt2jcfjYteuXWLTpk3i1KlTJdup5t8sg2mNK1waY/LjIx/5iBBCiG9961uis7NTaJomVq1aJe677z6RzWbLtvPAAw+Izs5OEQgExM033yx+//vfl6wfGRkRH/rQh0R9fb2or68XH/rQh8oux7GUzFW73nvvvaKzs1MYhlHxdZZbuwoxN2174sQJcdddd4lIJCICgYDYvHlz2eWj2Laza9t77rlHtLW1CU3TxHXXXSe+9rWvCdM0S+ost7at1KYAxA9+8AOnjmma4v777xft7e3C6/WKP/3TPxVHjhwp2U4mkxF33323aGpqEn6/X9x+++3i/PnzJXXYtrNr2/vvv3/a7Syntp2Ldp3q8wSAOHv2rFNvodtVst8gEREREVFVcYwpEREREdUEBlMiIiIiqgkMpkRERERUExhMiYiIiKgmMJgSERERUU1gMCUiIiKimsBgSkREREQ1gcGUiIiIiGoCgykRERER1QQGUyKieSCEwNve9ja8/e1vL1v30EMPIRwO4/z581XYMyKi2sVgSkQ0DyRJwg9+8AM8//zz+M53vuOUnz17Fvfccw++9a1vYdWqVXP6mrquz+n2iIgWGoMpEdE86erqwre+9S387d/+Lc6ePQshBD7+8Y/jrW99K17/+tfjHe94B+rq6tDW1oa9e/dieHjYee7+/ftxyy23oKGhAc3Nzbj99ttx+vRpZ/25c+cgSRJ+/OMf49Zbb4XP58OPfvSjarxNIqI5IwkhRLV3gohoKbvzzjsxPj6O9773vfjiF7+IF198ETt37sQnPvEJfPjDH0Ymk8E999yDfD6Pp556CgDw+OOPQ5IkbNq0CalUCv/wD/+Ac+fOobe3F7Is49y5c1i7di3WrFmDr33ta9i2bRu8Xi86Ojqq/G6JiGaPwZSIaJ5Fo1F0d3djZGQEP/nJT9DT04Pnn38ev/71r506Fy9eRFdXF44fP47rr7++bBtDQ0OIRCI4cuQIuru7nWD6zW9+E5/97GcX8u0QEc0bHsonIppnkUgEf/mXf4n169fjPe95Dw4dOoTf/va3qKurcx433ngjADiH60+fPo0PfvCDuOaaaxAKhbB27VoAKDthaufOnQv7ZoiI5pFa7R0gIloOVFWFqlofuaZp4o477sCXv/zlsnorVqwAANxxxx3o6urCww8/jI6ODpimie7ubuRyuZL6wWBw/neeiGiBMJgSES2w7du34/HHH8eaNWucsOo2MjKCV199Fd/5znfwxje+EQDw7LPPLvRuEhEtOB7KJyJaYJ/+9KcxOjqKD3zgA3jhhRdw5swZ/M///A8+9rGPwTAMNDY2orm5Gd/97ndx6tQpPPXUU9i3b1+1d5uIaN4xmBIRLbCOjg784Q9/gGEYePvb347u7m589rOfRTgchizLkGUZjzzyCA4dOoTu7m78zd/8Db7yla9Ue7eJiOYdz8onIiIioprAHlMiIiIiqgkMpkRERERUExhMiYiIiKgmMJgSERERUU1gMCUiIiKimsBgSkREREQ1gcGUiIiIiGoCgykRERER1QQGUyIiIiKqCQymRERERFQTGEyJiIiIqCb8/1nqR9PHa0vBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "\n",
    "sns.lineplot(x = 'Year', y = 'YearlyChange', hue = 'Continent', data = df1)\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Yearly Population Change')\n",
    "plt.title('Yearly Population Change by continent')\n",
    "\n",
    "# Set the legend in the center without a border\n",
    "plt.legend(loc = 'center', framealpha = 0);\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15806c6a",
   "metadata": {},
   "source": [
    "# Question 2\n",
    "\n",
    "Use Seaborn to recreate the plot from Question 2 of Assignment 3. This plot shows the average **height of male and female Olympians over the history of the Summer Olympics.** The line plot should be made using Seaborn’s lineplot() functionality. Do not use Matplotlib’s fill_between(). Include the axes labels, plot title, and legend in the lower left corner. The average male heights should be blue and average female heights red. Each **athlete should only be represented once in a given year.** The width of the shading should be equal to 1 standard deviation on either side of the line."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1e87295",
   "metadata": {},
   "source": [
    "# Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "26f0e054",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Team</th>\n",
       "      <th>NOC</th>\n",
       "      <th>Games</th>\n",
       "      <th>Year</th>\n",
       "      <th>Season</th>\n",
       "      <th>City</th>\n",
       "      <th>Sport</th>\n",
       "      <th>Event</th>\n",
       "      <th>Medal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>A Dijiang</td>\n",
       "      <td>M</td>\n",
       "      <td>24.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>China</td>\n",
       "      <td>CHN</td>\n",
       "      <td>1992 Summer</td>\n",
       "      <td>1992</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>Basketball</td>\n",
       "      <td>Basketball Men's Basketball</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>A Lamusi</td>\n",
       "      <td>M</td>\n",
       "      <td>23.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>China</td>\n",
       "      <td>CHN</td>\n",
       "      <td>2012 Summer</td>\n",
       "      <td>2012</td>\n",
       "      <td>Summer</td>\n",
       "      <td>London</td>\n",
       "      <td>Judo</td>\n",
       "      <td>Judo Men's Extra-Lightweight</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Gunnar Nielsen Aaby</td>\n",
       "      <td>M</td>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>DEN</td>\n",
       "      <td>1920 Summer</td>\n",
       "      <td>1920</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Antwerpen</td>\n",
       "      <td>Football</td>\n",
       "      <td>Football Men's Football</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Edgar Lindenau Aabye</td>\n",
       "      <td>M</td>\n",
       "      <td>34.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Denmark/Sweden</td>\n",
       "      <td>DEN</td>\n",
       "      <td>1900 Summer</td>\n",
       "      <td>1900</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Paris</td>\n",
       "      <td>Tug-Of-War</td>\n",
       "      <td>Tug-Of-War Men's Tug-Of-War</td>\n",
       "      <td>Gold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Christine Jacoba Aaftink</td>\n",
       "      <td>F</td>\n",
       "      <td>21.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>NED</td>\n",
       "      <td>1988 Winter</td>\n",
       "      <td>1988</td>\n",
       "      <td>Winter</td>\n",
       "      <td>Calgary</td>\n",
       "      <td>Speed Skating</td>\n",
       "      <td>Speed Skating Women's 500 metres</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271111</th>\n",
       "      <td>135569</td>\n",
       "      <td>Andrzej ya</td>\n",
       "      <td>M</td>\n",
       "      <td>29.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>Poland-1</td>\n",
       "      <td>POL</td>\n",
       "      <td>1976 Winter</td>\n",
       "      <td>1976</td>\n",
       "      <td>Winter</td>\n",
       "      <td>Innsbruck</td>\n",
       "      <td>Luge</td>\n",
       "      <td>Luge Mixed (Men)'s Doubles</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271112</th>\n",
       "      <td>135570</td>\n",
       "      <td>Piotr ya</td>\n",
       "      <td>M</td>\n",
       "      <td>27.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>Poland</td>\n",
       "      <td>POL</td>\n",
       "      <td>2014 Winter</td>\n",
       "      <td>2014</td>\n",
       "      <td>Winter</td>\n",
       "      <td>Sochi</td>\n",
       "      <td>Ski Jumping</td>\n",
       "      <td>Ski Jumping Men's Large Hill, Individual</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271113</th>\n",
       "      <td>135570</td>\n",
       "      <td>Piotr ya</td>\n",
       "      <td>M</td>\n",
       "      <td>27.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>Poland</td>\n",
       "      <td>POL</td>\n",
       "      <td>2014 Winter</td>\n",
       "      <td>2014</td>\n",
       "      <td>Winter</td>\n",
       "      <td>Sochi</td>\n",
       "      <td>Ski Jumping</td>\n",
       "      <td>Ski Jumping Men's Large Hill, Team</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271114</th>\n",
       "      <td>135571</td>\n",
       "      <td>Tomasz Ireneusz ya</td>\n",
       "      <td>M</td>\n",
       "      <td>30.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>Poland</td>\n",
       "      <td>POL</td>\n",
       "      <td>1998 Winter</td>\n",
       "      <td>1998</td>\n",
       "      <td>Winter</td>\n",
       "      <td>Nagano</td>\n",
       "      <td>Bobsleigh</td>\n",
       "      <td>Bobsleigh Men's Four</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271115</th>\n",
       "      <td>135571</td>\n",
       "      <td>Tomasz Ireneusz ya</td>\n",
       "      <td>M</td>\n",
       "      <td>34.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>Poland</td>\n",
       "      <td>POL</td>\n",
       "      <td>2002 Winter</td>\n",
       "      <td>2002</td>\n",
       "      <td>Winter</td>\n",
       "      <td>Salt Lake City</td>\n",
       "      <td>Bobsleigh</td>\n",
       "      <td>Bobsleigh Men's Four</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>271116 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ID                      Name Sex   Age  Height  Weight  \\\n",
       "0            1                 A Dijiang   M  24.0   180.0    80.0   \n",
       "1            2                  A Lamusi   M  23.0   170.0    60.0   \n",
       "2            3       Gunnar Nielsen Aaby   M  24.0     NaN     NaN   \n",
       "3            4      Edgar Lindenau Aabye   M  34.0     NaN     NaN   \n",
       "4            5  Christine Jacoba Aaftink   F  21.0   185.0    82.0   \n",
       "...        ...                       ...  ..   ...     ...     ...   \n",
       "271111  135569                Andrzej ya   M  29.0   179.0    89.0   \n",
       "271112  135570                  Piotr ya   M  27.0   176.0    59.0   \n",
       "271113  135570                  Piotr ya   M  27.0   176.0    59.0   \n",
       "271114  135571        Tomasz Ireneusz ya   M  30.0   185.0    96.0   \n",
       "271115  135571        Tomasz Ireneusz ya   M  34.0   185.0    96.0   \n",
       "\n",
       "                  Team  NOC        Games  Year  Season            City  \\\n",
       "0                China  CHN  1992 Summer  1992  Summer       Barcelona   \n",
       "1                China  CHN  2012 Summer  2012  Summer          London   \n",
       "2              Denmark  DEN  1920 Summer  1920  Summer       Antwerpen   \n",
       "3       Denmark/Sweden  DEN  1900 Summer  1900  Summer           Paris   \n",
       "4          Netherlands  NED  1988 Winter  1988  Winter         Calgary   \n",
       "...                ...  ...          ...   ...     ...             ...   \n",
       "271111        Poland-1  POL  1976 Winter  1976  Winter       Innsbruck   \n",
       "271112          Poland  POL  2014 Winter  2014  Winter           Sochi   \n",
       "271113          Poland  POL  2014 Winter  2014  Winter           Sochi   \n",
       "271114          Poland  POL  1998 Winter  1998  Winter          Nagano   \n",
       "271115          Poland  POL  2002 Winter  2002  Winter  Salt Lake City   \n",
       "\n",
       "                Sport                                     Event Medal  \n",
       "0          Basketball               Basketball Men's Basketball   NaN  \n",
       "1                Judo              Judo Men's Extra-Lightweight   NaN  \n",
       "2            Football                   Football Men's Football   NaN  \n",
       "3          Tug-Of-War               Tug-Of-War Men's Tug-Of-War  Gold  \n",
       "4       Speed Skating          Speed Skating Women's 500 metres   NaN  \n",
       "...               ...                                       ...   ...  \n",
       "271111           Luge                Luge Mixed (Men)'s Doubles   NaN  \n",
       "271112    Ski Jumping  Ski Jumping Men's Large Hill, Individual   NaN  \n",
       "271113    Ski Jumping        Ski Jumping Men's Large Hill, Team   NaN  \n",
       "271114      Bobsleigh                      Bobsleigh Men's Four   NaN  \n",
       "271115      Bobsleigh                      Bobsleigh Men's Four   NaN  \n",
       "\n",
       "[271116 rows x 15 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.read_csv(\"olympic_athletes.csv\")\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d95a7475",
   "metadata": {},
   "source": [
    "# Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "603139ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 271116 entries, 0 to 271115\n",
      "Data columns (total 15 columns):\n",
      " #   Column  Non-Null Count   Dtype  \n",
      "---  ------  --------------   -----  \n",
      " 0   ID      271116 non-null  int64  \n",
      " 1   Name    271116 non-null  object \n",
      " 2   Sex     271116 non-null  object \n",
      " 3   Age     261642 non-null  float64\n",
      " 4   Height  210945 non-null  float64\n",
      " 5   Weight  208241 non-null  float64\n",
      " 6   Team    271116 non-null  object \n",
      " 7   NOC     271116 non-null  object \n",
      " 8   Games   271116 non-null  object \n",
      " 9   Year    271116 non-null  int64  \n",
      " 10  Season  271116 non-null  object \n",
      " 11  City    271116 non-null  object \n",
      " 12  Sport   271116 non-null  object \n",
      " 13  Event   271116 non-null  object \n",
      " 14  Medal   39783 non-null   object \n",
      "dtypes: float64(3), int64(2), object(10)\n",
      "memory usage: 31.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3052af19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID             0\n",
       "Name           0\n",
       "Sex            0\n",
       "Age         9474\n",
       "Height     60171\n",
       "Weight     62875\n",
       "Team           0\n",
       "NOC            0\n",
       "Games          0\n",
       "Year           0\n",
       "Season         0\n",
       "City           0\n",
       "Sport          0\n",
       "Event          0\n",
       "Medal     231333\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa2bf9c2",
   "metadata": {},
   "source": [
    "# Numerical and Categorical Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b1933228",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Name',\n",
       " 'Sex',\n",
       " 'Team',\n",
       " 'NOC',\n",
       " 'Games',\n",
       " 'Season',\n",
       " 'City',\n",
       " 'Sport',\n",
       " 'Event',\n",
       " 'Medal']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_df2 = df2.select_dtypes(include = ['object']).columns.tolist()\n",
    "cat_df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "346c316e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ID', 'Age', 'Height', 'Weight', 'Year']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_df2 = df2.select_dtypes(include = ['float64', 'int64']).columns.tolist()\n",
    "num_df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2694d369",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([180., 170.,  nan, 185., 188., 183., 168., 186., 182., 172., 159.,\n",
       "       171., 184., 175., 189., 176., 163., 187., 169., 174., 190., 194.,\n",
       "       177., 167., 164., 198., 165., 156., 179., 178., 166., 181., 155.,\n",
       "       160., 173., 192., 195., 191., 203., 158., 154., 153., 152., 193.,\n",
       "       162., 197., 157., 207., 147., 150., 202., 161., 210., 205., 200.,\n",
       "       148., 208., 140., 213., 204., 151., 196., 146., 201., 132., 149.,\n",
       "       199., 211., 215., 139., 206., 135., 142., 217., 209., 145., 221.,\n",
       "       144., 214., 143., 218., 127., 223., 216., 141., 212., 131., 136.,\n",
       "       137., 138., 220., 130., 219., 133., 128., 226.])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.Height.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f380920",
   "metadata": {},
   "source": [
    "# Step 1\n",
    "\n",
    "The average height of male and female Olympians over the history of the Summer Olympics."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63dbbcf2",
   "metadata": {},
   "source": [
    "## Filter only Summer Olympics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c0cd89d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Team</th>\n",
       "      <th>NOC</th>\n",
       "      <th>Games</th>\n",
       "      <th>Year</th>\n",
       "      <th>Season</th>\n",
       "      <th>City</th>\n",
       "      <th>Sport</th>\n",
       "      <th>Event</th>\n",
       "      <th>Medal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>A Dijiang</td>\n",
       "      <td>M</td>\n",
       "      <td>24.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>China</td>\n",
       "      <td>CHN</td>\n",
       "      <td>1992 Summer</td>\n",
       "      <td>1992</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>Basketball</td>\n",
       "      <td>Basketball Men's Basketball</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>A Lamusi</td>\n",
       "      <td>M</td>\n",
       "      <td>23.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>China</td>\n",
       "      <td>CHN</td>\n",
       "      <td>2012 Summer</td>\n",
       "      <td>2012</td>\n",
       "      <td>Summer</td>\n",
       "      <td>London</td>\n",
       "      <td>Judo</td>\n",
       "      <td>Judo Men's Extra-Lightweight</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Gunnar Nielsen Aaby</td>\n",
       "      <td>M</td>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>DEN</td>\n",
       "      <td>1920 Summer</td>\n",
       "      <td>1920</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Antwerpen</td>\n",
       "      <td>Football</td>\n",
       "      <td>Football Men's Football</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Edgar Lindenau Aabye</td>\n",
       "      <td>M</td>\n",
       "      <td>34.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Denmark/Sweden</td>\n",
       "      <td>DEN</td>\n",
       "      <td>1900 Summer</td>\n",
       "      <td>1900</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Paris</td>\n",
       "      <td>Tug-Of-War</td>\n",
       "      <td>Tug-Of-War Men's Tug-Of-War</td>\n",
       "      <td>Gold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>8</td>\n",
       "      <td>Cornelia \"Cor\" Aalten (-Strannood)</td>\n",
       "      <td>F</td>\n",
       "      <td>18.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>NED</td>\n",
       "      <td>1932 Summer</td>\n",
       "      <td>1932</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Athletics</td>\n",
       "      <td>Athletics Women's 100 metres</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>8</td>\n",
       "      <td>Cornelia \"Cor\" Aalten (-Strannood)</td>\n",
       "      <td>F</td>\n",
       "      <td>18.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>NED</td>\n",
       "      <td>1932 Summer</td>\n",
       "      <td>1932</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Athletics</td>\n",
       "      <td>Athletics Women's 4 x 100 metres Relay</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>10</td>\n",
       "      <td>Einar Ferdinand \"Einari\" Aalto</td>\n",
       "      <td>M</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Finland</td>\n",
       "      <td>FIN</td>\n",
       "      <td>1952 Summer</td>\n",
       "      <td>1952</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Swimming</td>\n",
       "      <td>Swimming Men's 400 metres Freestyle</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>12</td>\n",
       "      <td>Jyri Tapani Aalto</td>\n",
       "      <td>M</td>\n",
       "      <td>31.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>Finland</td>\n",
       "      <td>FIN</td>\n",
       "      <td>2000 Summer</td>\n",
       "      <td>2000</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Sydney</td>\n",
       "      <td>Badminton</td>\n",
       "      <td>Badminton Men's Singles</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>13</td>\n",
       "      <td>Minna Maarit Aalto</td>\n",
       "      <td>F</td>\n",
       "      <td>30.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>55.5</td>\n",
       "      <td>Finland</td>\n",
       "      <td>FIN</td>\n",
       "      <td>1996 Summer</td>\n",
       "      <td>1996</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>Sailing</td>\n",
       "      <td>Sailing Women's Windsurfer</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>13</td>\n",
       "      <td>Minna Maarit Aalto</td>\n",
       "      <td>F</td>\n",
       "      <td>34.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>55.5</td>\n",
       "      <td>Finland</td>\n",
       "      <td>FIN</td>\n",
       "      <td>2000 Summer</td>\n",
       "      <td>2000</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Sydney</td>\n",
       "      <td>Sailing</td>\n",
       "      <td>Sailing Women's Windsurfer</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ID                                Name Sex   Age  Height  Weight  \\\n",
       "0    1                           A Dijiang   M  24.0   180.0    80.0   \n",
       "1    2                            A Lamusi   M  23.0   170.0    60.0   \n",
       "2    3                 Gunnar Nielsen Aaby   M  24.0     NaN     NaN   \n",
       "3    4                Edgar Lindenau Aabye   M  34.0     NaN     NaN   \n",
       "26   8  Cornelia \"Cor\" Aalten (-Strannood)   F  18.0   168.0     NaN   \n",
       "27   8  Cornelia \"Cor\" Aalten (-Strannood)   F  18.0   168.0     NaN   \n",
       "29  10      Einar Ferdinand \"Einari\" Aalto   M  26.0     NaN     NaN   \n",
       "31  12                   Jyri Tapani Aalto   M  31.0   172.0    70.0   \n",
       "32  13                  Minna Maarit Aalto   F  30.0   159.0    55.5   \n",
       "33  13                  Minna Maarit Aalto   F  34.0   159.0    55.5   \n",
       "\n",
       "              Team  NOC        Games  Year  Season         City       Sport  \\\n",
       "0            China  CHN  1992 Summer  1992  Summer    Barcelona  Basketball   \n",
       "1            China  CHN  2012 Summer  2012  Summer       London        Judo   \n",
       "2          Denmark  DEN  1920 Summer  1920  Summer    Antwerpen    Football   \n",
       "3   Denmark/Sweden  DEN  1900 Summer  1900  Summer        Paris  Tug-Of-War   \n",
       "26     Netherlands  NED  1932 Summer  1932  Summer  Los Angeles   Athletics   \n",
       "27     Netherlands  NED  1932 Summer  1932  Summer  Los Angeles   Athletics   \n",
       "29         Finland  FIN  1952 Summer  1952  Summer     Helsinki    Swimming   \n",
       "31         Finland  FIN  2000 Summer  2000  Summer       Sydney   Badminton   \n",
       "32         Finland  FIN  1996 Summer  1996  Summer      Atlanta     Sailing   \n",
       "33         Finland  FIN  2000 Summer  2000  Summer       Sydney     Sailing   \n",
       "\n",
       "                                     Event Medal  \n",
       "0              Basketball Men's Basketball   NaN  \n",
       "1             Judo Men's Extra-Lightweight   NaN  \n",
       "2                  Football Men's Football   NaN  \n",
       "3              Tug-Of-War Men's Tug-Of-War  Gold  \n",
       "26            Athletics Women's 100 metres   NaN  \n",
       "27  Athletics Women's 4 x 100 metres Relay   NaN  \n",
       "29     Swimming Men's 400 metres Freestyle   NaN  \n",
       "31                 Badminton Men's Singles   NaN  \n",
       "32              Sailing Women's Windsurfer   NaN  \n",
       "33              Sailing Women's Windsurfer   NaN  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = df2[df2.Season == 'Summer']\n",
    "df3.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11ddc939",
   "metadata": {},
   "source": [
    "## Drop duplicates\n",
    "\n",
    "Athlete should only be represented once in a given year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "122ba56b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Team</th>\n",
       "      <th>NOC</th>\n",
       "      <th>Games</th>\n",
       "      <th>Year</th>\n",
       "      <th>Season</th>\n",
       "      <th>City</th>\n",
       "      <th>Sport</th>\n",
       "      <th>Event</th>\n",
       "      <th>Medal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>A Dijiang</td>\n",
       "      <td>M</td>\n",
       "      <td>24.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>China</td>\n",
       "      <td>CHN</td>\n",
       "      <td>1992 Summer</td>\n",
       "      <td>1992</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>Basketball</td>\n",
       "      <td>Basketball Men's Basketball</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>A Lamusi</td>\n",
       "      <td>M</td>\n",
       "      <td>23.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>China</td>\n",
       "      <td>CHN</td>\n",
       "      <td>2012 Summer</td>\n",
       "      <td>2012</td>\n",
       "      <td>Summer</td>\n",
       "      <td>London</td>\n",
       "      <td>Judo</td>\n",
       "      <td>Judo Men's Extra-Lightweight</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Gunnar Nielsen Aaby</td>\n",
       "      <td>M</td>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>DEN</td>\n",
       "      <td>1920 Summer</td>\n",
       "      <td>1920</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Antwerpen</td>\n",
       "      <td>Football</td>\n",
       "      <td>Football Men's Football</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Edgar Lindenau Aabye</td>\n",
       "      <td>M</td>\n",
       "      <td>34.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Denmark/Sweden</td>\n",
       "      <td>DEN</td>\n",
       "      <td>1900 Summer</td>\n",
       "      <td>1900</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Paris</td>\n",
       "      <td>Tug-Of-War</td>\n",
       "      <td>Tug-Of-War Men's Tug-Of-War</td>\n",
       "      <td>Gold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>8</td>\n",
       "      <td>Cornelia \"Cor\" Aalten (-Strannood)</td>\n",
       "      <td>F</td>\n",
       "      <td>18.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>NED</td>\n",
       "      <td>1932 Summer</td>\n",
       "      <td>1932</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Athletics</td>\n",
       "      <td>Athletics Women's 100 metres</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271106</th>\n",
       "      <td>135565</td>\n",
       "      <td>Fernando scar Zylberberg</td>\n",
       "      <td>M</td>\n",
       "      <td>27.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>ARG</td>\n",
       "      <td>2004 Summer</td>\n",
       "      <td>2004</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Athina</td>\n",
       "      <td>Hockey</td>\n",
       "      <td>Hockey Men's Hockey</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271107</th>\n",
       "      <td>135566</td>\n",
       "      <td>James Francis \"Jim\" Zylker</td>\n",
       "      <td>M</td>\n",
       "      <td>21.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>USA</td>\n",
       "      <td>1972 Summer</td>\n",
       "      <td>1972</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Munich</td>\n",
       "      <td>Football</td>\n",
       "      <td>Football Men's Football</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271108</th>\n",
       "      <td>135567</td>\n",
       "      <td>Aleksandr Viktorovich Zyuzin</td>\n",
       "      <td>M</td>\n",
       "      <td>24.0</td>\n",
       "      <td>183.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>Russia</td>\n",
       "      <td>RUS</td>\n",
       "      <td>2000 Summer</td>\n",
       "      <td>2000</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Sydney</td>\n",
       "      <td>Rowing</td>\n",
       "      <td>Rowing Men's Lightweight Coxless Fours</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271109</th>\n",
       "      <td>135567</td>\n",
       "      <td>Aleksandr Viktorovich Zyuzin</td>\n",
       "      <td>M</td>\n",
       "      <td>28.0</td>\n",
       "      <td>183.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>Russia</td>\n",
       "      <td>RUS</td>\n",
       "      <td>2004 Summer</td>\n",
       "      <td>2004</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Athina</td>\n",
       "      <td>Rowing</td>\n",
       "      <td>Rowing Men's Lightweight Coxless Fours</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271110</th>\n",
       "      <td>135568</td>\n",
       "      <td>Olga Igorevna Zyuzkova</td>\n",
       "      <td>F</td>\n",
       "      <td>33.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>Belarus</td>\n",
       "      <td>BLR</td>\n",
       "      <td>2016 Summer</td>\n",
       "      <td>2016</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Rio de Janeiro</td>\n",
       "      <td>Basketball</td>\n",
       "      <td>Basketball Women's Basketball</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>158859 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ID                                Name Sex   Age  Height  Weight  \\\n",
       "0            1                           A Dijiang   M  24.0   180.0    80.0   \n",
       "1            2                            A Lamusi   M  23.0   170.0    60.0   \n",
       "2            3                 Gunnar Nielsen Aaby   M  24.0     NaN     NaN   \n",
       "3            4                Edgar Lindenau Aabye   M  34.0     NaN     NaN   \n",
       "26           8  Cornelia \"Cor\" Aalten (-Strannood)   F  18.0   168.0     NaN   \n",
       "...        ...                                 ...  ..   ...     ...     ...   \n",
       "271106  135565            Fernando scar Zylberberg   M  27.0   168.0    76.0   \n",
       "271107  135566          James Francis \"Jim\" Zylker   M  21.0   175.0    75.0   \n",
       "271108  135567        Aleksandr Viktorovich Zyuzin   M  24.0   183.0    72.0   \n",
       "271109  135567        Aleksandr Viktorovich Zyuzin   M  28.0   183.0    72.0   \n",
       "271110  135568              Olga Igorevna Zyuzkova   F  33.0   171.0    69.0   \n",
       "\n",
       "                  Team  NOC        Games  Year  Season            City  \\\n",
       "0                China  CHN  1992 Summer  1992  Summer       Barcelona   \n",
       "1                China  CHN  2012 Summer  2012  Summer          London   \n",
       "2              Denmark  DEN  1920 Summer  1920  Summer       Antwerpen   \n",
       "3       Denmark/Sweden  DEN  1900 Summer  1900  Summer           Paris   \n",
       "26         Netherlands  NED  1932 Summer  1932  Summer     Los Angeles   \n",
       "...                ...  ...          ...   ...     ...             ...   \n",
       "271106       Argentina  ARG  2004 Summer  2004  Summer          Athina   \n",
       "271107   United States  USA  1972 Summer  1972  Summer          Munich   \n",
       "271108          Russia  RUS  2000 Summer  2000  Summer          Sydney   \n",
       "271109          Russia  RUS  2004 Summer  2004  Summer          Athina   \n",
       "271110         Belarus  BLR  2016 Summer  2016  Summer  Rio de Janeiro   \n",
       "\n",
       "             Sport                                   Event Medal  \n",
       "0       Basketball             Basketball Men's Basketball   NaN  \n",
       "1             Judo            Judo Men's Extra-Lightweight   NaN  \n",
       "2         Football                 Football Men's Football   NaN  \n",
       "3       Tug-Of-War             Tug-Of-War Men's Tug-Of-War  Gold  \n",
       "26       Athletics            Athletics Women's 100 metres   NaN  \n",
       "...            ...                                     ...   ...  \n",
       "271106      Hockey                     Hockey Men's Hockey   NaN  \n",
       "271107    Football                 Football Men's Football   NaN  \n",
       "271108      Rowing  Rowing Men's Lightweight Coxless Fours   NaN  \n",
       "271109      Rowing  Rowing Men's Lightweight Coxless Fours   NaN  \n",
       "271110  Basketball           Basketball Women's Basketball   NaN  \n",
       "\n",
       "[158859 rows x 15 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = df3.drop_duplicates(subset = ['ID', 'Year', 'Height'])\n",
    "df3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "daed0cbf",
   "metadata": {},
   "source": [
    "### Height has NA values. Now remove the rows where height is missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5b160d57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([180., 170.,  nan, 168., 172., 159., 175., 189., 163., 187., 174.,\n",
       "       194., 182., 185., 177., 167., 176., 164., 198., 165., 156., 179.,\n",
       "       178., 166., 186., 190., 183., 181., 188., 155., 169., 160., 173.,\n",
       "       171., 192., 195., 184., 191., 203., 158., 154., 153., 152., 193.,\n",
       "       162., 197., 157., 207., 147., 150., 202., 161., 210., 205., 200.,\n",
       "       148., 208., 140., 213., 204., 151., 196., 146., 201., 132., 149.,\n",
       "       199., 211., 215., 139., 206., 135., 142., 217., 209., 145., 221.,\n",
       "       144., 214., 143., 218., 127., 223., 216., 141., 212., 131., 136.,\n",
       "       137., 138., 220., 130., 219., 133., 128., 226.])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3['Height'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "09bc307f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Team</th>\n",
       "      <th>NOC</th>\n",
       "      <th>Games</th>\n",
       "      <th>Year</th>\n",
       "      <th>Season</th>\n",
       "      <th>City</th>\n",
       "      <th>Sport</th>\n",
       "      <th>Event</th>\n",
       "      <th>Medal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>A Dijiang</td>\n",
       "      <td>M</td>\n",
       "      <td>24.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>China</td>\n",
       "      <td>CHN</td>\n",
       "      <td>1992 Summer</td>\n",
       "      <td>1992</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>Basketball</td>\n",
       "      <td>Basketball Men's Basketball</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>A Lamusi</td>\n",
       "      <td>M</td>\n",
       "      <td>23.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>China</td>\n",
       "      <td>CHN</td>\n",
       "      <td>2012 Summer</td>\n",
       "      <td>2012</td>\n",
       "      <td>Summer</td>\n",
       "      <td>London</td>\n",
       "      <td>Judo</td>\n",
       "      <td>Judo Men's Extra-Lightweight</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>8</td>\n",
       "      <td>Cornelia \"Cor\" Aalten (-Strannood)</td>\n",
       "      <td>F</td>\n",
       "      <td>18.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>NED</td>\n",
       "      <td>1932 Summer</td>\n",
       "      <td>1932</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Athletics</td>\n",
       "      <td>Athletics Women's 100 metres</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>12</td>\n",
       "      <td>Jyri Tapani Aalto</td>\n",
       "      <td>M</td>\n",
       "      <td>31.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>Finland</td>\n",
       "      <td>FIN</td>\n",
       "      <td>2000 Summer</td>\n",
       "      <td>2000</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Sydney</td>\n",
       "      <td>Badminton</td>\n",
       "      <td>Badminton Men's Singles</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>13</td>\n",
       "      <td>Minna Maarit Aalto</td>\n",
       "      <td>F</td>\n",
       "      <td>30.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>55.5</td>\n",
       "      <td>Finland</td>\n",
       "      <td>FIN</td>\n",
       "      <td>1996 Summer</td>\n",
       "      <td>1996</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>Sailing</td>\n",
       "      <td>Sailing Women's Windsurfer</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271106</th>\n",
       "      <td>135565</td>\n",
       "      <td>Fernando scar Zylberberg</td>\n",
       "      <td>M</td>\n",
       "      <td>27.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>ARG</td>\n",
       "      <td>2004 Summer</td>\n",
       "      <td>2004</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Athina</td>\n",
       "      <td>Hockey</td>\n",
       "      <td>Hockey Men's Hockey</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271107</th>\n",
       "      <td>135566</td>\n",
       "      <td>James Francis \"Jim\" Zylker</td>\n",
       "      <td>M</td>\n",
       "      <td>21.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>USA</td>\n",
       "      <td>1972 Summer</td>\n",
       "      <td>1972</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Munich</td>\n",
       "      <td>Football</td>\n",
       "      <td>Football Men's Football</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271108</th>\n",
       "      <td>135567</td>\n",
       "      <td>Aleksandr Viktorovich Zyuzin</td>\n",
       "      <td>M</td>\n",
       "      <td>24.0</td>\n",
       "      <td>183.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>Russia</td>\n",
       "      <td>RUS</td>\n",
       "      <td>2000 Summer</td>\n",
       "      <td>2000</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Sydney</td>\n",
       "      <td>Rowing</td>\n",
       "      <td>Rowing Men's Lightweight Coxless Fours</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271109</th>\n",
       "      <td>135567</td>\n",
       "      <td>Aleksandr Viktorovich Zyuzin</td>\n",
       "      <td>M</td>\n",
       "      <td>28.0</td>\n",
       "      <td>183.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>Russia</td>\n",
       "      <td>RUS</td>\n",
       "      <td>2004 Summer</td>\n",
       "      <td>2004</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Athina</td>\n",
       "      <td>Rowing</td>\n",
       "      <td>Rowing Men's Lightweight Coxless Fours</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271110</th>\n",
       "      <td>135568</td>\n",
       "      <td>Olga Igorevna Zyuzkova</td>\n",
       "      <td>F</td>\n",
       "      <td>33.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>Belarus</td>\n",
       "      <td>BLR</td>\n",
       "      <td>2016 Summer</td>\n",
       "      <td>2016</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Rio de Janeiro</td>\n",
       "      <td>Basketball</td>\n",
       "      <td>Basketball Women's Basketball</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>125190 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ID                                Name Sex   Age  Height  Weight  \\\n",
       "0            1                           A Dijiang   M  24.0   180.0    80.0   \n",
       "1            2                            A Lamusi   M  23.0   170.0    60.0   \n",
       "26           8  Cornelia \"Cor\" Aalten (-Strannood)   F  18.0   168.0     NaN   \n",
       "31          12                   Jyri Tapani Aalto   M  31.0   172.0    70.0   \n",
       "32          13                  Minna Maarit Aalto   F  30.0   159.0    55.5   \n",
       "...        ...                                 ...  ..   ...     ...     ...   \n",
       "271106  135565            Fernando scar Zylberberg   M  27.0   168.0    76.0   \n",
       "271107  135566          James Francis \"Jim\" Zylker   M  21.0   175.0    75.0   \n",
       "271108  135567        Aleksandr Viktorovich Zyuzin   M  24.0   183.0    72.0   \n",
       "271109  135567        Aleksandr Viktorovich Zyuzin   M  28.0   183.0    72.0   \n",
       "271110  135568              Olga Igorevna Zyuzkova   F  33.0   171.0    69.0   \n",
       "\n",
       "                 Team  NOC        Games  Year  Season            City  \\\n",
       "0               China  CHN  1992 Summer  1992  Summer       Barcelona   \n",
       "1               China  CHN  2012 Summer  2012  Summer          London   \n",
       "26        Netherlands  NED  1932 Summer  1932  Summer     Los Angeles   \n",
       "31            Finland  FIN  2000 Summer  2000  Summer          Sydney   \n",
       "32            Finland  FIN  1996 Summer  1996  Summer         Atlanta   \n",
       "...               ...  ...          ...   ...     ...             ...   \n",
       "271106      Argentina  ARG  2004 Summer  2004  Summer          Athina   \n",
       "271107  United States  USA  1972 Summer  1972  Summer          Munich   \n",
       "271108         Russia  RUS  2000 Summer  2000  Summer          Sydney   \n",
       "271109         Russia  RUS  2004 Summer  2004  Summer          Athina   \n",
       "271110        Belarus  BLR  2016 Summer  2016  Summer  Rio de Janeiro   \n",
       "\n",
       "             Sport                                   Event Medal  \n",
       "0       Basketball             Basketball Men's Basketball   NaN  \n",
       "1             Judo            Judo Men's Extra-Lightweight   NaN  \n",
       "26       Athletics            Athletics Women's 100 metres   NaN  \n",
       "31       Badminton                 Badminton Men's Singles   NaN  \n",
       "32         Sailing              Sailing Women's Windsurfer   NaN  \n",
       "...            ...                                     ...   ...  \n",
       "271106      Hockey                     Hockey Men's Hockey   NaN  \n",
       "271107    Football                 Football Men's Football   NaN  \n",
       "271108      Rowing  Rowing Men's Lightweight Coxless Fours   NaN  \n",
       "271109      Rowing  Rowing Men's Lightweight Coxless Fours   NaN  \n",
       "271110  Basketball           Basketball Women's Basketball   NaN  \n",
       "\n",
       "[125190 rows x 15 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4 = df3[~df3['Height'].isnull()]\n",
    "df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "67278c86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([180., 170., 168., 172., 159., 175., 189., 163., 187., 174., 194.,\n",
       "       182., 185., 177., 167., 176., 164., 198., 165., 156., 179., 178.,\n",
       "       166., 186., 190., 183., 181., 188., 155., 169., 160., 173., 171.,\n",
       "       192., 195., 184., 191., 203., 158., 154., 153., 152., 193., 162.,\n",
       "       197., 157., 207., 147., 150., 202., 161., 210., 205., 200., 148.,\n",
       "       208., 140., 213., 204., 151., 196., 146., 201., 132., 149., 199.,\n",
       "       211., 215., 139., 206., 135., 142., 217., 209., 145., 221., 144.,\n",
       "       214., 143., 218., 127., 223., 216., 141., 212., 131., 136., 137.,\n",
       "       138., 220., 130., 219., 133., 128., 226.])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4['Height'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8424bc3c",
   "metadata": {},
   "source": [
    "# Extract male and female data separately"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "00eb3ba5",
   "metadata": {},
   "outputs": [],
   "source": [
    "male_data = df4[df4['Sex'] == 'M']\n",
    "female_data = df4[df4['Sex'] == 'F']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7c1ae125",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
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       "      <th>NOC</th>\n",
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       "      <th>City</th>\n",
       "      <th>Sport</th>\n",
       "      <th>Event</th>\n",
       "      <th>Medal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>A Dijiang</td>\n",
       "      <td>M</td>\n",
       "      <td>24.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>China</td>\n",
       "      <td>CHN</td>\n",
       "      <td>1992 Summer</td>\n",
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       "      <td>Barcelona</td>\n",
       "      <td>Basketball</td>\n",
       "      <td>Basketball Men's Basketball</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>A Lamusi</td>\n",
       "      <td>M</td>\n",
       "      <td>23.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>China</td>\n",
       "      <td>CHN</td>\n",
       "      <td>2012 Summer</td>\n",
       "      <td>2012</td>\n",
       "      <td>Summer</td>\n",
       "      <td>London</td>\n",
       "      <td>Judo</td>\n",
       "      <td>Judo Men's Extra-Lightweight</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>12</td>\n",
       "      <td>Jyri Tapani Aalto</td>\n",
       "      <td>M</td>\n",
       "      <td>31.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>Finland</td>\n",
       "      <td>FIN</td>\n",
       "      <td>2000 Summer</td>\n",
       "      <td>2000</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Sydney</td>\n",
       "      <td>Badminton</td>\n",
       "      <td>Badminton Men's Singles</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>17</td>\n",
       "      <td>Paavo Johannes Aaltonen</td>\n",
       "      <td>M</td>\n",
       "      <td>28.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Finland</td>\n",
       "      <td>FIN</td>\n",
       "      <td>1948 Summer</td>\n",
       "      <td>1948</td>\n",
       "      <td>Summer</td>\n",
       "      <td>London</td>\n",
       "      <td>Gymnastics</td>\n",
       "      <td>Gymnastics Men's Individual All-Around</td>\n",
       "      <td>Bronze</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>17</td>\n",
       "      <td>Paavo Johannes Aaltonen</td>\n",
       "      <td>M</td>\n",
       "      <td>32.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Finland</td>\n",
       "      <td>FIN</td>\n",
       "      <td>1952 Summer</td>\n",
       "      <td>1952</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Helsinki</td>\n",
       "      <td>Gymnastics</td>\n",
       "      <td>Gymnastics Men's Individual All-Around</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271105</th>\n",
       "      <td>135565</td>\n",
       "      <td>Fernando scar Zylberberg</td>\n",
       "      <td>M</td>\n",
       "      <td>23.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>ARG</td>\n",
       "      <td>2000 Summer</td>\n",
       "      <td>2000</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Sydney</td>\n",
       "      <td>Hockey</td>\n",
       "      <td>Hockey Men's Hockey</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271106</th>\n",
       "      <td>135565</td>\n",
       "      <td>Fernando scar Zylberberg</td>\n",
       "      <td>M</td>\n",
       "      <td>27.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>ARG</td>\n",
       "      <td>2004 Summer</td>\n",
       "      <td>2004</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Athina</td>\n",
       "      <td>Hockey</td>\n",
       "      <td>Hockey Men's Hockey</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271107</th>\n",
       "      <td>135566</td>\n",
       "      <td>James Francis \"Jim\" Zylker</td>\n",
       "      <td>M</td>\n",
       "      <td>21.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>USA</td>\n",
       "      <td>1972 Summer</td>\n",
       "      <td>1972</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Munich</td>\n",
       "      <td>Football</td>\n",
       "      <td>Football Men's Football</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271108</th>\n",
       "      <td>135567</td>\n",
       "      <td>Aleksandr Viktorovich Zyuzin</td>\n",
       "      <td>M</td>\n",
       "      <td>24.0</td>\n",
       "      <td>183.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>Russia</td>\n",
       "      <td>RUS</td>\n",
       "      <td>2000 Summer</td>\n",
       "      <td>2000</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Sydney</td>\n",
       "      <td>Rowing</td>\n",
       "      <td>Rowing Men's Lightweight Coxless Fours</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271109</th>\n",
       "      <td>135567</td>\n",
       "      <td>Aleksandr Viktorovich Zyuzin</td>\n",
       "      <td>M</td>\n",
       "      <td>28.0</td>\n",
       "      <td>183.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>Russia</td>\n",
       "      <td>RUS</td>\n",
       "      <td>2004 Summer</td>\n",
       "      <td>2004</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Athina</td>\n",
       "      <td>Rowing</td>\n",
       "      <td>Rowing Men's Lightweight Coxless Fours</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>87712 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ID                          Name Sex   Age  Height  Weight  \\\n",
       "0            1                     A Dijiang   M  24.0   180.0    80.0   \n",
       "1            2                      A Lamusi   M  23.0   170.0    60.0   \n",
       "31          12             Jyri Tapani Aalto   M  31.0   172.0    70.0   \n",
       "41          17       Paavo Johannes Aaltonen   M  28.0   175.0    64.0   \n",
       "49          17       Paavo Johannes Aaltonen   M  32.0   175.0    64.0   \n",
       "...        ...                           ...  ..   ...     ...     ...   \n",
       "271105  135565      Fernando scar Zylberberg   M  23.0   168.0    76.0   \n",
       "271106  135565      Fernando scar Zylberberg   M  27.0   168.0    76.0   \n",
       "271107  135566    James Francis \"Jim\" Zylker   M  21.0   175.0    75.0   \n",
       "271108  135567  Aleksandr Viktorovich Zyuzin   M  24.0   183.0    72.0   \n",
       "271109  135567  Aleksandr Viktorovich Zyuzin   M  28.0   183.0    72.0   \n",
       "\n",
       "                 Team  NOC        Games  Year  Season       City       Sport  \\\n",
       "0               China  CHN  1992 Summer  1992  Summer  Barcelona  Basketball   \n",
       "1               China  CHN  2012 Summer  2012  Summer     London        Judo   \n",
       "31            Finland  FIN  2000 Summer  2000  Summer     Sydney   Badminton   \n",
       "41            Finland  FIN  1948 Summer  1948  Summer     London  Gymnastics   \n",
       "49            Finland  FIN  1952 Summer  1952  Summer   Helsinki  Gymnastics   \n",
       "...               ...  ...          ...   ...     ...        ...         ...   \n",
       "271105      Argentina  ARG  2000 Summer  2000  Summer     Sydney      Hockey   \n",
       "271106      Argentina  ARG  2004 Summer  2004  Summer     Athina      Hockey   \n",
       "271107  United States  USA  1972 Summer  1972  Summer     Munich    Football   \n",
       "271108         Russia  RUS  2000 Summer  2000  Summer     Sydney      Rowing   \n",
       "271109         Russia  RUS  2004 Summer  2004  Summer     Athina      Rowing   \n",
       "\n",
       "                                         Event   Medal  \n",
       "0                  Basketball Men's Basketball     NaN  \n",
       "1                 Judo Men's Extra-Lightweight     NaN  \n",
       "31                     Badminton Men's Singles     NaN  \n",
       "41      Gymnastics Men's Individual All-Around  Bronze  \n",
       "49      Gymnastics Men's Individual All-Around     NaN  \n",
       "...                                        ...     ...  \n",
       "271105                     Hockey Men's Hockey     NaN  \n",
       "271106                     Hockey Men's Hockey     NaN  \n",
       "271107                 Football Men's Football     NaN  \n",
       "271108  Rowing Men's Lightweight Coxless Fours     NaN  \n",
       "271109  Rowing Men's Lightweight Coxless Fours     NaN  \n",
       "\n",
       "[87712 rows x 15 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "male_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "fb12b0ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Team</th>\n",
       "      <th>NOC</th>\n",
       "      <th>Games</th>\n",
       "      <th>Year</th>\n",
       "      <th>Season</th>\n",
       "      <th>City</th>\n",
       "      <th>Sport</th>\n",
       "      <th>Event</th>\n",
       "      <th>Medal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>8</td>\n",
       "      <td>Cornelia \"Cor\" Aalten (-Strannood)</td>\n",
       "      <td>F</td>\n",
       "      <td>18.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>NED</td>\n",
       "      <td>1932 Summer</td>\n",
       "      <td>1932</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Athletics</td>\n",
       "      <td>Athletics Women's 100 metres</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>13</td>\n",
       "      <td>Minna Maarit Aalto</td>\n",
       "      <td>F</td>\n",
       "      <td>30.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>55.5</td>\n",
       "      <td>Finland</td>\n",
       "      <td>FIN</td>\n",
       "      <td>1996 Summer</td>\n",
       "      <td>1996</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>Sailing</td>\n",
       "      <td>Sailing Women's Windsurfer</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>13</td>\n",
       "      <td>Minna Maarit Aalto</td>\n",
       "      <td>F</td>\n",
       "      <td>34.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>55.5</td>\n",
       "      <td>Finland</td>\n",
       "      <td>FIN</td>\n",
       "      <td>2000 Summer</td>\n",
       "      <td>2000</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Sydney</td>\n",
       "      <td>Sailing</td>\n",
       "      <td>Sailing Women's Windsurfer</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>21</td>\n",
       "      <td>Ragnhild Margrethe Aamodt</td>\n",
       "      <td>F</td>\n",
       "      <td>27.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Norway</td>\n",
       "      <td>NOR</td>\n",
       "      <td>2008 Summer</td>\n",
       "      <td>2008</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>Handball</td>\n",
       "      <td>Handball Women's Handball</td>\n",
       "      <td>Gold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>22</td>\n",
       "      <td>Andreea Aanei</td>\n",
       "      <td>F</td>\n",
       "      <td>22.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>Romania</td>\n",
       "      <td>ROU</td>\n",
       "      <td>2016 Summer</td>\n",
       "      <td>2016</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Rio de Janeiro</td>\n",
       "      <td>Weightlifting</td>\n",
       "      <td>Weightlifting Women's Super-Heavyweight</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271080</th>\n",
       "      <td>135553</td>\n",
       "      <td>Galina Ivanovna Zybina (-Fyodorova)</td>\n",
       "      <td>F</td>\n",
       "      <td>33.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>Soviet Union</td>\n",
       "      <td>URS</td>\n",
       "      <td>1964 Summer</td>\n",
       "      <td>1964</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>Athletics</td>\n",
       "      <td>Athletics Women's Shot Put</td>\n",
       "      <td>Bronze</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271099</th>\n",
       "      <td>135560</td>\n",
       "      <td>Stavroula Zygouri</td>\n",
       "      <td>F</td>\n",
       "      <td>36.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>Greece</td>\n",
       "      <td>GRE</td>\n",
       "      <td>2004 Summer</td>\n",
       "      <td>2004</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Athina</td>\n",
       "      <td>Wrestling</td>\n",
       "      <td>Wrestling Women's Middleweight, Freestyle</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271102</th>\n",
       "      <td>135563</td>\n",
       "      <td>Olesya Nikolayevna Zykina</td>\n",
       "      <td>F</td>\n",
       "      <td>19.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Russia</td>\n",
       "      <td>RUS</td>\n",
       "      <td>2000 Summer</td>\n",
       "      <td>2000</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Sydney</td>\n",
       "      <td>Athletics</td>\n",
       "      <td>Athletics Women's 4 x 400 metres Relay</td>\n",
       "      <td>Bronze</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271103</th>\n",
       "      <td>135563</td>\n",
       "      <td>Olesya Nikolayevna Zykina</td>\n",
       "      <td>F</td>\n",
       "      <td>23.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>Russia</td>\n",
       "      <td>RUS</td>\n",
       "      <td>2004 Summer</td>\n",
       "      <td>2004</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Athina</td>\n",
       "      <td>Athletics</td>\n",
       "      <td>Athletics Women's 4 x 400 metres Relay</td>\n",
       "      <td>Silver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271110</th>\n",
       "      <td>135568</td>\n",
       "      <td>Olga Igorevna Zyuzkova</td>\n",
       "      <td>F</td>\n",
       "      <td>33.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>Belarus</td>\n",
       "      <td>BLR</td>\n",
       "      <td>2016 Summer</td>\n",
       "      <td>2016</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Rio de Janeiro</td>\n",
       "      <td>Basketball</td>\n",
       "      <td>Basketball Women's Basketball</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>37478 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ID                                 Name Sex   Age  Height  Weight  \\\n",
       "26           8   Cornelia \"Cor\" Aalten (-Strannood)   F  18.0   168.0     NaN   \n",
       "32          13                   Minna Maarit Aalto   F  30.0   159.0    55.5   \n",
       "33          13                   Minna Maarit Aalto   F  34.0   159.0    55.5   \n",
       "79          21            Ragnhild Margrethe Aamodt   F  27.0   163.0     NaN   \n",
       "80          22                        Andreea Aanei   F  22.0   170.0   125.0   \n",
       "...        ...                                  ...  ..   ...     ...     ...   \n",
       "271080  135553  Galina Ivanovna Zybina (-Fyodorova)   F  33.0   168.0    80.0   \n",
       "271099  135560                    Stavroula Zygouri   F  36.0   171.0    63.0   \n",
       "271102  135563            Olesya Nikolayevna Zykina   F  19.0   171.0    64.0   \n",
       "271103  135563            Olesya Nikolayevna Zykina   F  23.0   171.0    64.0   \n",
       "271110  135568               Olga Igorevna Zyuzkova   F  33.0   171.0    69.0   \n",
       "\n",
       "                Team  NOC        Games  Year  Season            City  \\\n",
       "26       Netherlands  NED  1932 Summer  1932  Summer     Los Angeles   \n",
       "32           Finland  FIN  1996 Summer  1996  Summer         Atlanta   \n",
       "33           Finland  FIN  2000 Summer  2000  Summer          Sydney   \n",
       "79            Norway  NOR  2008 Summer  2008  Summer         Beijing   \n",
       "80           Romania  ROU  2016 Summer  2016  Summer  Rio de Janeiro   \n",
       "...              ...  ...          ...   ...     ...             ...   \n",
       "271080  Soviet Union  URS  1964 Summer  1964  Summer           Tokyo   \n",
       "271099        Greece  GRE  2004 Summer  2004  Summer          Athina   \n",
       "271102        Russia  RUS  2000 Summer  2000  Summer          Sydney   \n",
       "271103        Russia  RUS  2004 Summer  2004  Summer          Athina   \n",
       "271110       Belarus  BLR  2016 Summer  2016  Summer  Rio de Janeiro   \n",
       "\n",
       "                Sport                                      Event   Medal  \n",
       "26          Athletics               Athletics Women's 100 metres     NaN  \n",
       "32            Sailing                 Sailing Women's Windsurfer     NaN  \n",
       "33            Sailing                 Sailing Women's Windsurfer     NaN  \n",
       "79           Handball                  Handball Women's Handball    Gold  \n",
       "80      Weightlifting    Weightlifting Women's Super-Heavyweight     NaN  \n",
       "...               ...                                        ...     ...  \n",
       "271080      Athletics                 Athletics Women's Shot Put  Bronze  \n",
       "271099      Wrestling  Wrestling Women's Middleweight, Freestyle     NaN  \n",
       "271102      Athletics     Athletics Women's 4 x 400 metres Relay  Bronze  \n",
       "271103      Athletics     Athletics Women's 4 x 400 metres Relay  Silver  \n",
       "271110     Basketball              Basketball Women's Basketball     NaN  \n",
       "\n",
       "[37478 rows x 15 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "female_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d8d36f5",
   "metadata": {},
   "source": [
    "# Recreate the plot using seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e6427c83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a figure and axis\n",
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "\n",
    "\n",
    "sns.lineplot(x = 'Year', y = 'Height', \n",
    "             color = 'blue', label = \"Male\", \n",
    "             errorbar = \"sd\",\n",
    "             data = male_data, ax = ax)\n",
    "\n",
    "sns.lineplot(x = 'Year', y = 'Height', \n",
    "             color = 'red', label = \"Female\", \n",
    "             errorbar = \"sd\",\n",
    "             data = female_data, ax = ax)\n",
    "\n",
    "# set the axis labels, limits, and title\n",
    "ax.set_xlabel('Year')\n",
    "ax.set_ylabel('Height (cm)')\n",
    "ax.set_ylim(140, 200)\n",
    "ax.set_yticks([140, 150, 160, 170, 180, 190, 200])\n",
    "yticklabels = ['140', '150', '160', '170', '180', '190', '200']\n",
    "ax.set_yticklabels(yticklabels)\n",
    "ax.set_title('Average Olympian Height (+/- std dev) in the Summer Olympics')\n",
    "\n",
    "# add the legend\n",
    "ax.legend(loc = 'lower left')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "157dd9db",
   "metadata": {},
   "source": [
    "# Question 3\n",
    "\n",
    "The figure below is a sunburst plot showing the **total number of video game consoles sold by Nintendo, Microsoft and Sony**. The **inner ring** shows the total number of **consoles** sold per company, and the **outer ring** refines each company into their individual consoles.\n",
    "\n",
    "Recreate the above sunburst plot using Matplotlib (hint: use nested pie charts). The arc length of each wedge should be proportional to the number of units sold. Importantly, the outer ring should be aligned with the inner ring. For example, all of the Nintendo consoles on the outer ring should be within the arc spanned by Nintendo.\n",
    "The consoles on the outer ring should be ordered from largest number of units sold to smallest number of units sold in a clockwise fashion. The three consoles with the smallest number of units sold for Nintendo are not displayed (since that would create visual clutter).\n",
    "\n",
    "The colours of the inner ring are [#156EAF, #DB2018, #56B45B] for Microsoft, Nintendo and Sony, and [#5599CC, #EA6727, #83C143] for the outer ring. The edge colours of the wedges should be white. The labels of the inner ring should be white in colour and on the interior, and the labels of the outer ring on the exterior."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6d64923",
   "metadata": {},
   "source": [
    "# Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "cde14885",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Console</th>\n",
       "      <th>Type</th>\n",
       "      <th>Company</th>\n",
       "      <th>Release</th>\n",
       "      <th>Units_sold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PlayStation 2</td>\n",
       "      <td>Home</td>\n",
       "      <td>Sony</td>\n",
       "      <td>2000</td>\n",
       "      <td>155000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Nintendo DS</td>\n",
       "      <td>Handheld</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>2004</td>\n",
       "      <td>154002000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Nintendo Switch</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>2017</td>\n",
       "      <td>122550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Game Boy</td>\n",
       "      <td>Handheld</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>1989</td>\n",
       "      <td>118690000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PlayStation 4</td>\n",
       "      <td>Home</td>\n",
       "      <td>Sony</td>\n",
       "      <td>2013</td>\n",
       "      <td>117200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>PlayStation</td>\n",
       "      <td>Home</td>\n",
       "      <td>Sony</td>\n",
       "      <td>1994</td>\n",
       "      <td>102490000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Wii</td>\n",
       "      <td>Home</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>2006</td>\n",
       "      <td>101630000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>PlayStation 3</td>\n",
       "      <td>Home</td>\n",
       "      <td>Sony</td>\n",
       "      <td>2006</td>\n",
       "      <td>87400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Xbox 360</td>\n",
       "      <td>Home</td>\n",
       "      <td>Microsoft</td>\n",
       "      <td>2005</td>\n",
       "      <td>84000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>PlayStation Portable</td>\n",
       "      <td>Handheld</td>\n",
       "      <td>Sony</td>\n",
       "      <td>2004</td>\n",
       "      <td>82000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Game Boy Advance</td>\n",
       "      <td>Handheld</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>2001</td>\n",
       "      <td>81510000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Nintendo 3DS</td>\n",
       "      <td>Handheld</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>2011</td>\n",
       "      <td>75940000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>NES</td>\n",
       "      <td>Home</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>1983</td>\n",
       "      <td>61910000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Xbox One</td>\n",
       "      <td>Home</td>\n",
       "      <td>Microsoft</td>\n",
       "      <td>2013</td>\n",
       "      <td>58500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>SNES</td>\n",
       "      <td>Home</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>1990</td>\n",
       "      <td>49100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Nintendo 64</td>\n",
       "      <td>Home</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>1996</td>\n",
       "      <td>32930000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>PlayStation 5</td>\n",
       "      <td>Home</td>\n",
       "      <td>Sony</td>\n",
       "      <td>2020</td>\n",
       "      <td>32100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Xbox</td>\n",
       "      <td>Home</td>\n",
       "      <td>Microsoft</td>\n",
       "      <td>2001</td>\n",
       "      <td>24000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>GameCube</td>\n",
       "      <td>Home</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>2001</td>\n",
       "      <td>21740000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Xbox Series X/S</td>\n",
       "      <td>Home</td>\n",
       "      <td>Microsoft</td>\n",
       "      <td>2020</td>\n",
       "      <td>18500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>PlayStation Vita</td>\n",
       "      <td>Handheld</td>\n",
       "      <td>Sony</td>\n",
       "      <td>2011</td>\n",
       "      <td>15000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Wii U</td>\n",
       "      <td>Home</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>2012</td>\n",
       "      <td>13560000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>SNES Classic</td>\n",
       "      <td>Dedicated</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>2017</td>\n",
       "      <td>5280000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>NES Classic</td>\n",
       "      <td>Dedicated</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>2016</td>\n",
       "      <td>3560000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Color TV-Game</td>\n",
       "      <td>Dedicated</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>1977</td>\n",
       "      <td>3000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Console       Type    Company  Release  Units_sold\n",
       "0          PlayStation 2       Home       Sony     2000   155000000\n",
       "1            Nintendo DS   Handheld   Nintendo     2004   154002000\n",
       "2        Nintendo Switch     Hybrid   Nintendo     2017   122550000\n",
       "3               Game Boy   Handheld   Nintendo     1989   118690000\n",
       "4          PlayStation 4       Home       Sony     2013   117200000\n",
       "5            PlayStation       Home       Sony     1994   102490000\n",
       "6                    Wii       Home   Nintendo     2006   101630000\n",
       "7          PlayStation 3       Home       Sony     2006    87400000\n",
       "8               Xbox 360       Home  Microsoft     2005    84000000\n",
       "9   PlayStation Portable   Handheld       Sony     2004    82000000\n",
       "10      Game Boy Advance   Handheld   Nintendo     2001    81510000\n",
       "11          Nintendo 3DS   Handheld   Nintendo     2011    75940000\n",
       "12                   NES       Home   Nintendo     1983    61910000\n",
       "13              Xbox One       Home  Microsoft     2013    58500000\n",
       "14                  SNES       Home   Nintendo     1990    49100000\n",
       "15           Nintendo 64       Home   Nintendo     1996    32930000\n",
       "16         PlayStation 5       Home       Sony     2020    32100000\n",
       "17                  Xbox       Home  Microsoft     2001    24000000\n",
       "18              GameCube       Home   Nintendo     2001    21740000\n",
       "19       Xbox Series X/S       Home  Microsoft     2020    18500000\n",
       "20      PlayStation Vita   Handheld       Sony     2011    15000000\n",
       "21                 Wii U       Home   Nintendo     2012    13560000\n",
       "22          SNES Classic  Dedicated   Nintendo     2017     5280000\n",
       "23           NES Classic  Dedicated   Nintendo     2016     3560000\n",
       "24         Color TV-Game  Dedicated   Nintendo     1977     3000000"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5 = pd.read_csv(\"game_consoles.csv\", na_filter = False)\n",
    "df5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "badf09e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25, 5)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9208492e",
   "metadata": {},
   "source": [
    "# Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8588e13f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 25 entries, 0 to 24\n",
      "Data columns (total 5 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   Console     25 non-null     object\n",
      " 1   Type        25 non-null     object\n",
      " 2   Company     25 non-null     object\n",
      " 3   Release     25 non-null     int64 \n",
      " 4   Units_sold  25 non-null     int64 \n",
      "dtypes: int64(2), object(3)\n",
      "memory usage: 1.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df5.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "30425689",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Console</th>\n",
       "      <th>Type</th>\n",
       "      <th>Company</th>\n",
       "      <th>Release</th>\n",
       "      <th>Units_sold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PlayStation 2</td>\n",
       "      <td>Home</td>\n",
       "      <td>Sony</td>\n",
       "      <td>2000</td>\n",
       "      <td>155000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Nintendo DS</td>\n",
       "      <td>Handheld</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>2004</td>\n",
       "      <td>154002000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Nintendo Switch</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>2017</td>\n",
       "      <td>122550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Game Boy</td>\n",
       "      <td>Handheld</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>1989</td>\n",
       "      <td>118690000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PlayStation 4</td>\n",
       "      <td>Home</td>\n",
       "      <td>Sony</td>\n",
       "      <td>2013</td>\n",
       "      <td>117200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>PlayStation</td>\n",
       "      <td>Home</td>\n",
       "      <td>Sony</td>\n",
       "      <td>1994</td>\n",
       "      <td>102490000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Wii</td>\n",
       "      <td>Home</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>2006</td>\n",
       "      <td>101630000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>PlayStation 3</td>\n",
       "      <td>Home</td>\n",
       "      <td>Sony</td>\n",
       "      <td>2006</td>\n",
       "      <td>87400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Xbox 360</td>\n",
       "      <td>Home</td>\n",
       "      <td>Microsoft</td>\n",
       "      <td>2005</td>\n",
       "      <td>84000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>PlayStation Portable</td>\n",
       "      <td>Handheld</td>\n",
       "      <td>Sony</td>\n",
       "      <td>2004</td>\n",
       "      <td>82000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Console      Type    Company  Release  Units_sold\n",
       "0         PlayStation 2      Home       Sony     2000   155000000\n",
       "1           Nintendo DS  Handheld   Nintendo     2004   154002000\n",
       "2       Nintendo Switch    Hybrid   Nintendo     2017   122550000\n",
       "3              Game Boy  Handheld   Nintendo     1989   118690000\n",
       "4         PlayStation 4      Home       Sony     2013   117200000\n",
       "5           PlayStation      Home       Sony     1994   102490000\n",
       "6                   Wii      Home   Nintendo     2006   101630000\n",
       "7         PlayStation 3      Home       Sony     2006    87400000\n",
       "8              Xbox 360      Home  Microsoft     2005    84000000\n",
       "9  PlayStation Portable  Handheld       Sony     2004    82000000"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "784adc4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Console</th>\n",
       "      <th>Type</th>\n",
       "      <th>Company</th>\n",
       "      <th>Release</th>\n",
       "      <th>Units_sold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>PlayStation Vita</td>\n",
       "      <td>Handheld</td>\n",
       "      <td>Sony</td>\n",
       "      <td>2011</td>\n",
       "      <td>15000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Wii U</td>\n",
       "      <td>Home</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>2012</td>\n",
       "      <td>13560000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>SNES Classic</td>\n",
       "      <td>Dedicated</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>2017</td>\n",
       "      <td>5280000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>NES Classic</td>\n",
       "      <td>Dedicated</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>2016</td>\n",
       "      <td>3560000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Color TV-Game</td>\n",
       "      <td>Dedicated</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>1977</td>\n",
       "      <td>3000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Console       Type   Company  Release  Units_sold\n",
       "20  PlayStation Vita   Handheld      Sony     2011    15000000\n",
       "21             Wii U       Home  Nintendo     2012    13560000\n",
       "22      SNES Classic  Dedicated  Nintendo     2017     5280000\n",
       "23       NES Classic  Dedicated  Nintendo     2016     3560000\n",
       "24     Color TV-Game  Dedicated  Nintendo     1977     3000000"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "12a69054",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Release</th>\n",
       "      <td>25.0</td>\n",
       "      <td>2004.28</td>\n",
       "      <td>1.140731e+01</td>\n",
       "      <td>1977.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>2020.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Units_sold</th>\n",
       "      <td>25.0</td>\n",
       "      <td>64863680.00</td>\n",
       "      <td>4.729924e+07</td>\n",
       "      <td>3000000.0</td>\n",
       "      <td>21740000.0</td>\n",
       "      <td>61910000.0</td>\n",
       "      <td>101630000.0</td>\n",
       "      <td>155000000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            count         mean           std        min         25%  \\\n",
       "Release      25.0      2004.28  1.140731e+01     1977.0      2000.0   \n",
       "Units_sold   25.0  64863680.00  4.729924e+07  3000000.0  21740000.0   \n",
       "\n",
       "                   50%          75%          max  \n",
       "Release         2005.0       2013.0       2020.0  \n",
       "Units_sold  61910000.0  101630000.0  155000000.0  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "6733d960",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Console       object\n",
       "Type          object\n",
       "Company       object\n",
       "Release        int64\n",
       "Units_sold     int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d25d4bf0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Console       0\n",
       "Type          0\n",
       "Company       0\n",
       "Release       0\n",
       "Units_sold    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "aa98b69e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Sony', 'Nintendo', 'Microsoft'], dtype=object)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5['Company'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f14c8c7c",
   "metadata": {},
   "source": [
    "# Categorical and numerical columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "024af4a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Console', 'Type', 'Company']"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_df5 = df5.select_dtypes(include = ['object']).columns.tolist()\n",
    "cat_df5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "055217a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Release', 'Units_sold']"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_df5 = df5.select_dtypes(include = ['float64', 'int64']).columns.tolist()\n",
    "num_df5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87ca8235",
   "metadata": {},
   "source": [
    "# total number of video game consoles sold by Nintendo, Microsoft and Sony."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "7d0c121d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Company\n",
       "Microsoft    185000000\n",
       "Nintendo     845402000\n",
       "Sony         591190000\n",
       "Name: Units_sold, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculate the total number of units sold for each company\n",
    "company_units_sold = df5.groupby(\"Company\")[\"Units_sold\"].sum()\n",
    "company_units_sold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ee9dd203",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Console</th>\n",
       "      <th>Type</th>\n",
       "      <th>Company</th>\n",
       "      <th>Release</th>\n",
       "      <th>Units_sold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>PlayStation Vita</td>\n",
       "      <td>Handheld</td>\n",
       "      <td>Sony</td>\n",
       "      <td>2011</td>\n",
       "      <td>15000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>PlayStation 5</td>\n",
       "      <td>Home</td>\n",
       "      <td>Sony</td>\n",
       "      <td>2020</td>\n",
       "      <td>32100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>PlayStation Portable</td>\n",
       "      <td>Handheld</td>\n",
       "      <td>Sony</td>\n",
       "      <td>2004</td>\n",
       "      <td>82000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>PlayStation 3</td>\n",
       "      <td>Home</td>\n",
       "      <td>Sony</td>\n",
       "      <td>2006</td>\n",
       "      <td>87400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>PlayStation</td>\n",
       "      <td>Home</td>\n",
       "      <td>Sony</td>\n",
       "      <td>1994</td>\n",
       "      <td>102490000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PlayStation 4</td>\n",
       "      <td>Home</td>\n",
       "      <td>Sony</td>\n",
       "      <td>2013</td>\n",
       "      <td>117200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PlayStation 2</td>\n",
       "      <td>Home</td>\n",
       "      <td>Sony</td>\n",
       "      <td>2000</td>\n",
       "      <td>155000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Color TV-Game</td>\n",
       "      <td>Dedicated</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>1977</td>\n",
       "      <td>3000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>NES Classic</td>\n",
       "      <td>Dedicated</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>2016</td>\n",
       "      <td>3560000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>SNES Classic</td>\n",
       "      <td>Dedicated</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>2017</td>\n",
       "      <td>5280000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Wii U</td>\n",
       "      <td>Home</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>2012</td>\n",
       "      <td>13560000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>GameCube</td>\n",
       "      <td>Home</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>2001</td>\n",
       "      <td>21740000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Nintendo 64</td>\n",
       "      <td>Home</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>1996</td>\n",
       "      <td>32930000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>SNES</td>\n",
       "      <td>Home</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>1990</td>\n",
       "      <td>49100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>NES</td>\n",
       "      <td>Home</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>1983</td>\n",
       "      <td>61910000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Nintendo 3DS</td>\n",
       "      <td>Handheld</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>2011</td>\n",
       "      <td>75940000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Game Boy Advance</td>\n",
       "      <td>Handheld</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>2001</td>\n",
       "      <td>81510000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Wii</td>\n",
       "      <td>Home</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>2006</td>\n",
       "      <td>101630000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Game Boy</td>\n",
       "      <td>Handheld</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>1989</td>\n",
       "      <td>118690000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Nintendo Switch</td>\n",
       "      <td>Hybrid</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>2017</td>\n",
       "      <td>122550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Nintendo DS</td>\n",
       "      <td>Handheld</td>\n",
       "      <td>Nintendo</td>\n",
       "      <td>2004</td>\n",
       "      <td>154002000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Xbox Series X/S</td>\n",
       "      <td>Home</td>\n",
       "      <td>Microsoft</td>\n",
       "      <td>2020</td>\n",
       "      <td>18500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Xbox</td>\n",
       "      <td>Home</td>\n",
       "      <td>Microsoft</td>\n",
       "      <td>2001</td>\n",
       "      <td>24000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Xbox One</td>\n",
       "      <td>Home</td>\n",
       "      <td>Microsoft</td>\n",
       "      <td>2013</td>\n",
       "      <td>58500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Xbox 360</td>\n",
       "      <td>Home</td>\n",
       "      <td>Microsoft</td>\n",
       "      <td>2005</td>\n",
       "      <td>84000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Console       Type    Company  Release  Units_sold\n",
       "20      PlayStation Vita   Handheld       Sony     2011    15000000\n",
       "16         PlayStation 5       Home       Sony     2020    32100000\n",
       "9   PlayStation Portable   Handheld       Sony     2004    82000000\n",
       "7          PlayStation 3       Home       Sony     2006    87400000\n",
       "5            PlayStation       Home       Sony     1994   102490000\n",
       "4          PlayStation 4       Home       Sony     2013   117200000\n",
       "0          PlayStation 2       Home       Sony     2000   155000000\n",
       "24         Color TV-Game  Dedicated   Nintendo     1977     3000000\n",
       "23           NES Classic  Dedicated   Nintendo     2016     3560000\n",
       "22          SNES Classic  Dedicated   Nintendo     2017     5280000\n",
       "21                 Wii U       Home   Nintendo     2012    13560000\n",
       "18              GameCube       Home   Nintendo     2001    21740000\n",
       "15           Nintendo 64       Home   Nintendo     1996    32930000\n",
       "14                  SNES       Home   Nintendo     1990    49100000\n",
       "12                   NES       Home   Nintendo     1983    61910000\n",
       "11          Nintendo 3DS   Handheld   Nintendo     2011    75940000\n",
       "10      Game Boy Advance   Handheld   Nintendo     2001    81510000\n",
       "6                    Wii       Home   Nintendo     2006   101630000\n",
       "3               Game Boy   Handheld   Nintendo     1989   118690000\n",
       "2        Nintendo Switch     Hybrid   Nintendo     2017   122550000\n",
       "1            Nintendo DS   Handheld   Nintendo     2004   154002000\n",
       "19       Xbox Series X/S       Home  Microsoft     2020    18500000\n",
       "17                  Xbox       Home  Microsoft     2001    24000000\n",
       "13              Xbox One       Home  Microsoft     2013    58500000\n",
       "8               Xbox 360       Home  Microsoft     2005    84000000"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort the DataFrame by total units sold for each console for each company\n",
    "sorted_df5 = df5.sort_values(by=[\"Company\", \"Units_sold\", \"Console\"], ascending=[False, True, False])\n",
    "sorted_df5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "f9701dea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Company    Console             \n",
       "Microsoft  Xbox                     24000000\n",
       "           Xbox 360                 84000000\n",
       "           Xbox One                 58500000\n",
       "           Xbox Series X/S          18500000\n",
       "Nintendo   Color TV-Game             3000000\n",
       "           Game Boy                118690000\n",
       "           Game Boy Advance         81510000\n",
       "           GameCube                 21740000\n",
       "           NES                      61910000\n",
       "           NES Classic               3560000\n",
       "           Nintendo 3DS             75940000\n",
       "           Nintendo 64              32930000\n",
       "           Nintendo DS             154002000\n",
       "           Nintendo Switch         122550000\n",
       "           SNES                     49100000\n",
       "           SNES Classic              5280000\n",
       "           Wii                     101630000\n",
       "           Wii U                    13560000\n",
       "Sony       PlayStation             102490000\n",
       "           PlayStation 2           155000000\n",
       "           PlayStation 3            87400000\n",
       "           PlayStation 4           117200000\n",
       "           PlayStation 5            32100000\n",
       "           PlayStation Portable     82000000\n",
       "           PlayStation Vita         15000000\n",
       "Name: Units_sold, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the total number of units sold by the three companies\n",
    "console_units_sold = sorted_df5.groupby([\"Company\", \"Console\"])[\"Units_sold\"].sum()\n",
    "console_units_sold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "0b96bbfc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#define the colors for the inner ring and outer ring\n",
    "outer_colors = {\"Microsoft\": \"#5599CC\", \"Nintendo\": \"#EA6727\", \"Sony\": \"#83C143\"}\n",
    "inner_colors = [\"#156EAF\", \"#DB2018\", \"#56B45B\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b0bc0a26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define the figure and subplot\n",
    "fig, ax = plt.subplots(figsize = (8,8))\n",
    "\n",
    "outer_consoles = []\n",
    "outer_values = []\n",
    "outer_colors_list = []\n",
    "\n",
    "for company in sorted_df5[\"Company\"].unique():\n",
    "    company_sales = console_units_sold.loc[company].sort_values(ascending = False)\n",
    "    outer_consoles += list(company_sales.index)\n",
    "    outer_values += list(company_sales.values)\n",
    "    outer_colors_list += [outer_colors[company] for _ in range(len(company_sales))]\n",
    "\n",
    "outer = ax.pie(\n",
    "    outer_values,\n",
    "    radius = 1,\n",
    "    colors = outer_colors_list,\n",
    "    labels = outer_consoles,\n",
    "    counterclock = False,\n",
    "    wedgeprops = dict(edgecolor = \"w\", linewidth = 1.5)\n",
    ")\n",
    "\n",
    "# Create the inner ring for all companies\n",
    "inner = ax.pie(\n",
    "    company_units_sold,\n",
    "    radius = 0.6,\n",
    "    colors = inner_colors,\n",
    "    labels = company_units_sold.index,\n",
    "    labeldistance = 0.5,\n",
    "    wedgeprops = dict(edgecolor = \"w\", linewidth = 1)\n",
    ")\n",
    "ax.set_title(\"Best Selling Video Game Consoles\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ec6b7ec5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define the figure and subplot\n",
    "fig, ax = plt.subplots(figsize = (8,8))\n",
    "\n",
    "\n",
    "# Create the outer ring for all consoles\n",
    "outer_consoles = []\n",
    "outer_values = []\n",
    "outer_colors_list = []\n",
    "\n",
    "for company in sorted_df5[\"Company\"].unique():\n",
    "    company_sales = console_units_sold.loc[company].sort_values(ascending=False)\n",
    "    outer_consoles += list(company_sales.index)\n",
    "    outer_values += list(company_sales.values)\n",
    "    outer_colors_list += [outer_colors[company] for _ in range(len(company_sales))]\n",
    "\n",
    "# Replace label with empty string for the three consoles\n",
    "outer_consoles[outer_consoles.index(\"Color TV-Game\")] = \"\"\n",
    "outer_consoles[outer_consoles.index(\"NES Classic\")] = \"\"\n",
    "outer_consoles[outer_consoles.index(\"SNES Classic\")] = \"\"\n",
    "\n",
    "outer = ax.pie(\n",
    "    outer_values,\n",
    "    radius = 1,\n",
    "    colors = outer_colors_list,\n",
    "    labels = outer_consoles,\n",
    "    counterclock = False,\n",
    "    wedgeprops = dict(edgecolor = \"w\", linewidth = 1.5)\n",
    ")\n",
    "\n",
    "# Create the inner ring for all companies\n",
    "inner = ax.pie(\n",
    "    company_units_sold,\n",
    "    radius = 0.6,\n",
    "    colors = inner_colors,\n",
    "    labels = company_units_sold.index,\n",
    "    labeldistance = 0.5,\n",
    "    wedgeprops = dict(edgecolor = \"w\", linewidth = 1)\n",
    ")\n",
    "ax.set_title(\"Best Selling Video Game Consoles\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eff4bda7",
   "metadata": {},
   "source": [
    "# Question 4\n",
    "\n",
    "The bar plot below shows the number of athletes that competed in the 2012 (dark blue) and 2016 (light blue) Olympics from a set of different countries. The Venn diagram acts as the legend – it informs the meaning of each colour. Additionally, it shows the overlap on the number of athletes (from these countries) that competed in both Olympics, and how many competed in just one of these Olympics.\n",
    "Recreate the above bar plot using Seaborn, Matplotlib and the Matplotlib-venn libraries. The colours are given by the ‘tab20’ colour palette. Make sure to include the axes labels, plot title, legend annotations, and that the countries are listed in descending order based on number of athletes in the 2012 Olympics."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d5fcb02",
   "metadata": {},
   "source": [
    "# Import venn diagram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "3c69af87",
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib_venn import venn2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "7ce34f29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Team</th>\n",
       "      <th>NOC</th>\n",
       "      <th>Games</th>\n",
       "      <th>Year</th>\n",
       "      <th>Season</th>\n",
       "      <th>City</th>\n",
       "      <th>Sport</th>\n",
       "      <th>Event</th>\n",
       "      <th>Medal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>A Dijiang</td>\n",
       "      <td>M</td>\n",
       "      <td>24</td>\n",
       "      <td>180</td>\n",
       "      <td>80</td>\n",
       "      <td>China</td>\n",
       "      <td>CHN</td>\n",
       "      <td>1992 Summer</td>\n",
       "      <td>1992</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>Basketball</td>\n",
       "      <td>Basketball Men's Basketball</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>A Lamusi</td>\n",
       "      <td>M</td>\n",
       "      <td>23</td>\n",
       "      <td>170</td>\n",
       "      <td>60</td>\n",
       "      <td>China</td>\n",
       "      <td>CHN</td>\n",
       "      <td>2012 Summer</td>\n",
       "      <td>2012</td>\n",
       "      <td>Summer</td>\n",
       "      <td>London</td>\n",
       "      <td>Judo</td>\n",
       "      <td>Judo Men's Extra-Lightweight</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Gunnar Nielsen Aaby</td>\n",
       "      <td>M</td>\n",
       "      <td>24</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>DEN</td>\n",
       "      <td>1920 Summer</td>\n",
       "      <td>1920</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Antwerpen</td>\n",
       "      <td>Football</td>\n",
       "      <td>Football Men's Football</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Edgar Lindenau Aabye</td>\n",
       "      <td>M</td>\n",
       "      <td>34</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>Denmark/Sweden</td>\n",
       "      <td>DEN</td>\n",
       "      <td>1900 Summer</td>\n",
       "      <td>1900</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Paris</td>\n",
       "      <td>Tug-Of-War</td>\n",
       "      <td>Tug-Of-War Men's Tug-Of-War</td>\n",
       "      <td>Gold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Christine Jacoba Aaftink</td>\n",
       "      <td>F</td>\n",
       "      <td>21</td>\n",
       "      <td>185</td>\n",
       "      <td>82</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>NED</td>\n",
       "      <td>1988 Winter</td>\n",
       "      <td>1988</td>\n",
       "      <td>Winter</td>\n",
       "      <td>Calgary</td>\n",
       "      <td>Speed Skating</td>\n",
       "      <td>Speed Skating Women's 500 metres</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>Christine Jacoba Aaftink</td>\n",
       "      <td>F</td>\n",
       "      <td>21</td>\n",
       "      <td>185</td>\n",
       "      <td>82</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>NED</td>\n",
       "      <td>1988 Winter</td>\n",
       "      <td>1988</td>\n",
       "      <td>Winter</td>\n",
       "      <td>Calgary</td>\n",
       "      <td>Speed Skating</td>\n",
       "      <td>Speed Skating Women's 1,000 metres</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5</td>\n",
       "      <td>Christine Jacoba Aaftink</td>\n",
       "      <td>F</td>\n",
       "      <td>25</td>\n",
       "      <td>185</td>\n",
       "      <td>82</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>NED</td>\n",
       "      <td>1992 Winter</td>\n",
       "      <td>1992</td>\n",
       "      <td>Winter</td>\n",
       "      <td>Albertville</td>\n",
       "      <td>Speed Skating</td>\n",
       "      <td>Speed Skating Women's 500 metres</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5</td>\n",
       "      <td>Christine Jacoba Aaftink</td>\n",
       "      <td>F</td>\n",
       "      <td>25</td>\n",
       "      <td>185</td>\n",
       "      <td>82</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>NED</td>\n",
       "      <td>1992 Winter</td>\n",
       "      <td>1992</td>\n",
       "      <td>Winter</td>\n",
       "      <td>Albertville</td>\n",
       "      <td>Speed Skating</td>\n",
       "      <td>Speed Skating Women's 1,000 metres</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5</td>\n",
       "      <td>Christine Jacoba Aaftink</td>\n",
       "      <td>F</td>\n",
       "      <td>27</td>\n",
       "      <td>185</td>\n",
       "      <td>82</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>NED</td>\n",
       "      <td>1994 Winter</td>\n",
       "      <td>1994</td>\n",
       "      <td>Winter</td>\n",
       "      <td>Lillehammer</td>\n",
       "      <td>Speed Skating</td>\n",
       "      <td>Speed Skating Women's 500 metres</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5</td>\n",
       "      <td>Christine Jacoba Aaftink</td>\n",
       "      <td>F</td>\n",
       "      <td>27</td>\n",
       "      <td>185</td>\n",
       "      <td>82</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>NED</td>\n",
       "      <td>1994 Winter</td>\n",
       "      <td>1994</td>\n",
       "      <td>Winter</td>\n",
       "      <td>Lillehammer</td>\n",
       "      <td>Speed Skating</td>\n",
       "      <td>Speed Skating Women's 1,000 metres</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID                      Name Sex Age Height Weight            Team  NOC  \\\n",
       "0   1                 A Dijiang   M  24    180     80           China  CHN   \n",
       "1   2                  A Lamusi   M  23    170     60           China  CHN   \n",
       "2   3       Gunnar Nielsen Aaby   M  24     NA     NA         Denmark  DEN   \n",
       "3   4      Edgar Lindenau Aabye   M  34     NA     NA  Denmark/Sweden  DEN   \n",
       "4   5  Christine Jacoba Aaftink   F  21    185     82     Netherlands  NED   \n",
       "5   5  Christine Jacoba Aaftink   F  21    185     82     Netherlands  NED   \n",
       "6   5  Christine Jacoba Aaftink   F  25    185     82     Netherlands  NED   \n",
       "7   5  Christine Jacoba Aaftink   F  25    185     82     Netherlands  NED   \n",
       "8   5  Christine Jacoba Aaftink   F  27    185     82     Netherlands  NED   \n",
       "9   5  Christine Jacoba Aaftink   F  27    185     82     Netherlands  NED   \n",
       "\n",
       "         Games  Year  Season         City          Sport  \\\n",
       "0  1992 Summer  1992  Summer    Barcelona     Basketball   \n",
       "1  2012 Summer  2012  Summer       London           Judo   \n",
       "2  1920 Summer  1920  Summer    Antwerpen       Football   \n",
       "3  1900 Summer  1900  Summer        Paris     Tug-Of-War   \n",
       "4  1988 Winter  1988  Winter      Calgary  Speed Skating   \n",
       "5  1988 Winter  1988  Winter      Calgary  Speed Skating   \n",
       "6  1992 Winter  1992  Winter  Albertville  Speed Skating   \n",
       "7  1992 Winter  1992  Winter  Albertville  Speed Skating   \n",
       "8  1994 Winter  1994  Winter  Lillehammer  Speed Skating   \n",
       "9  1994 Winter  1994  Winter  Lillehammer  Speed Skating   \n",
       "\n",
       "                                Event Medal  \n",
       "0         Basketball Men's Basketball    NA  \n",
       "1        Judo Men's Extra-Lightweight    NA  \n",
       "2             Football Men's Football    NA  \n",
       "3         Tug-Of-War Men's Tug-Of-War  Gold  \n",
       "4    Speed Skating Women's 500 metres    NA  \n",
       "5  Speed Skating Women's 1,000 metres    NA  \n",
       "6    Speed Skating Women's 500 metres    NA  \n",
       "7  Speed Skating Women's 1,000 metres    NA  \n",
       "8    Speed Skating Women's 500 metres    NA  \n",
       "9  Speed Skating Women's 1,000 metres    NA  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6 = pd.read_csv(\"olympic_athletes.csv\", na_filter = False)\n",
    "df6.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "29544fbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID        0\n",
       "Name      0\n",
       "Sex       0\n",
       "Age       0\n",
       "Height    0\n",
       "Weight    0\n",
       "Team      0\n",
       "NOC       0\n",
       "Games     0\n",
       "Year      0\n",
       "Season    0\n",
       "City      0\n",
       "Sport     0\n",
       "Event     0\n",
       "Medal     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "2b71dbad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Team</th>\n",
       "      <th>NOC</th>\n",
       "      <th>Games</th>\n",
       "      <th>Year</th>\n",
       "      <th>Season</th>\n",
       "      <th>City</th>\n",
       "      <th>Sport</th>\n",
       "      <th>Event</th>\n",
       "      <th>Medal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>A Lamusi</td>\n",
       "      <td>M</td>\n",
       "      <td>23</td>\n",
       "      <td>170</td>\n",
       "      <td>60</td>\n",
       "      <td>China</td>\n",
       "      <td>CHN</td>\n",
       "      <td>2012 Summer</td>\n",
       "      <td>2012</td>\n",
       "      <td>Summer</td>\n",
       "      <td>London</td>\n",
       "      <td>Judo</td>\n",
       "      <td>Judo Men's Extra-Lightweight</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>22</td>\n",
       "      <td>Andreea Aanei</td>\n",
       "      <td>F</td>\n",
       "      <td>22</td>\n",
       "      <td>170</td>\n",
       "      <td>125</td>\n",
       "      <td>Romania</td>\n",
       "      <td>ROU</td>\n",
       "      <td>2016 Summer</td>\n",
       "      <td>2016</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Rio de Janeiro</td>\n",
       "      <td>Weightlifting</td>\n",
       "      <td>Weightlifting Women's Super-Heavyweight</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>34</td>\n",
       "      <td>Jamale (Djamel-) Aarrass (Ahrass-)</td>\n",
       "      <td>M</td>\n",
       "      <td>30</td>\n",
       "      <td>187</td>\n",
       "      <td>76</td>\n",
       "      <td>France</td>\n",
       "      <td>FRA</td>\n",
       "      <td>2012 Summer</td>\n",
       "      <td>2012</td>\n",
       "      <td>Summer</td>\n",
       "      <td>London</td>\n",
       "      <td>Athletics</td>\n",
       "      <td>Athletics Men's 1,500 metres</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>48</td>\n",
       "      <td>Abdelhak Aatakni</td>\n",
       "      <td>M</td>\n",
       "      <td>24</td>\n",
       "      <td>NA</td>\n",
       "      <td>64</td>\n",
       "      <td>Morocco</td>\n",
       "      <td>MAR</td>\n",
       "      <td>2012 Summer</td>\n",
       "      <td>2012</td>\n",
       "      <td>Summer</td>\n",
       "      <td>London</td>\n",
       "      <td>Boxing</td>\n",
       "      <td>Boxing Men's Light-Welterweight</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>51</td>\n",
       "      <td>Nstor Abad Sanjun</td>\n",
       "      <td>M</td>\n",
       "      <td>23</td>\n",
       "      <td>167</td>\n",
       "      <td>64</td>\n",
       "      <td>Spain</td>\n",
       "      <td>ESP</td>\n",
       "      <td>2016 Summer</td>\n",
       "      <td>2016</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Rio de Janeiro</td>\n",
       "      <td>Gymnastics</td>\n",
       "      <td>Gymnastics Men's Individual All-Around</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271054</th>\n",
       "      <td>135547</td>\n",
       "      <td>Viktoriya Viktorovna Zyabkina</td>\n",
       "      <td>F</td>\n",
       "      <td>23</td>\n",
       "      <td>174</td>\n",
       "      <td>62</td>\n",
       "      <td>Kazakhstan</td>\n",
       "      <td>KAZ</td>\n",
       "      <td>2016 Summer</td>\n",
       "      <td>2016</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Rio de Janeiro</td>\n",
       "      <td>Athletics</td>\n",
       "      <td>Athletics Women's 200 metres</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271055</th>\n",
       "      <td>135547</td>\n",
       "      <td>Viktoriya Viktorovna Zyabkina</td>\n",
       "      <td>F</td>\n",
       "      <td>23</td>\n",
       "      <td>174</td>\n",
       "      <td>62</td>\n",
       "      <td>Kazakhstan</td>\n",
       "      <td>KAZ</td>\n",
       "      <td>2016 Summer</td>\n",
       "      <td>2016</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Rio de Janeiro</td>\n",
       "      <td>Athletics</td>\n",
       "      <td>Athletics Women's 4 x 100 metres Relay</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271090</th>\n",
       "      <td>135557</td>\n",
       "      <td>Dominik ycki</td>\n",
       "      <td>M</td>\n",
       "      <td>38</td>\n",
       "      <td>192</td>\n",
       "      <td>95</td>\n",
       "      <td>Poland</td>\n",
       "      <td>POL</td>\n",
       "      <td>2012 Summer</td>\n",
       "      <td>2012</td>\n",
       "      <td>Summer</td>\n",
       "      <td>London</td>\n",
       "      <td>Sailing</td>\n",
       "      <td>Sailing Men's Two Person Keelboat</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271091</th>\n",
       "      <td>135558</td>\n",
       "      <td>ukasz Tomasz ygado</td>\n",
       "      <td>M</td>\n",
       "      <td>32</td>\n",
       "      <td>200</td>\n",
       "      <td>89</td>\n",
       "      <td>Poland</td>\n",
       "      <td>POL</td>\n",
       "      <td>2012 Summer</td>\n",
       "      <td>2012</td>\n",
       "      <td>Summer</td>\n",
       "      <td>London</td>\n",
       "      <td>Volleyball</td>\n",
       "      <td>Volleyball Men's Volleyball</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271110</th>\n",
       "      <td>135568</td>\n",
       "      <td>Olga Igorevna Zyuzkova</td>\n",
       "      <td>F</td>\n",
       "      <td>33</td>\n",
       "      <td>171</td>\n",
       "      <td>69</td>\n",
       "      <td>Belarus</td>\n",
       "      <td>BLR</td>\n",
       "      <td>2016 Summer</td>\n",
       "      <td>2016</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Rio de Janeiro</td>\n",
       "      <td>Basketball</td>\n",
       "      <td>Basketball Women's Basketball</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26608 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ID                                Name Sex Age Height Weight  \\\n",
       "1            2                            A Lamusi   M  23    170     60   \n",
       "80          22                       Andreea Aanei   F  22    170    125   \n",
       "98          34  Jamale (Djamel-) Aarrass (Ahrass-)   M  30    187     76   \n",
       "134         48                    Abdelhak Aatakni   M  24     NA     64   \n",
       "139         51                   Nstor Abad Sanjun   M  23    167     64   \n",
       "...        ...                                 ...  ..  ..    ...    ...   \n",
       "271054  135547       Viktoriya Viktorovna Zyabkina   F  23    174     62   \n",
       "271055  135547       Viktoriya Viktorovna Zyabkina   F  23    174     62   \n",
       "271090  135557                        Dominik ycki   M  38    192     95   \n",
       "271091  135558                  ukasz Tomasz ygado   M  32    200     89   \n",
       "271110  135568              Olga Igorevna Zyuzkova   F  33    171     69   \n",
       "\n",
       "              Team  NOC        Games  Year  Season            City  \\\n",
       "1            China  CHN  2012 Summer  2012  Summer          London   \n",
       "80         Romania  ROU  2016 Summer  2016  Summer  Rio de Janeiro   \n",
       "98          France  FRA  2012 Summer  2012  Summer          London   \n",
       "134        Morocco  MAR  2012 Summer  2012  Summer          London   \n",
       "139          Spain  ESP  2016 Summer  2016  Summer  Rio de Janeiro   \n",
       "...            ...  ...          ...   ...     ...             ...   \n",
       "271054  Kazakhstan  KAZ  2016 Summer  2016  Summer  Rio de Janeiro   \n",
       "271055  Kazakhstan  KAZ  2016 Summer  2016  Summer  Rio de Janeiro   \n",
       "271090      Poland  POL  2012 Summer  2012  Summer          London   \n",
       "271091      Poland  POL  2012 Summer  2012  Summer          London   \n",
       "271110     Belarus  BLR  2016 Summer  2016  Summer  Rio de Janeiro   \n",
       "\n",
       "                Sport                                    Event Medal  \n",
       "1                Judo             Judo Men's Extra-Lightweight    NA  \n",
       "80      Weightlifting  Weightlifting Women's Super-Heavyweight    NA  \n",
       "98          Athletics             Athletics Men's 1,500 metres    NA  \n",
       "134            Boxing          Boxing Men's Light-Welterweight    NA  \n",
       "139        Gymnastics   Gymnastics Men's Individual All-Around    NA  \n",
       "...               ...                                      ...   ...  \n",
       "271054      Athletics             Athletics Women's 200 metres    NA  \n",
       "271055      Athletics   Athletics Women's 4 x 100 metres Relay    NA  \n",
       "271090        Sailing        Sailing Men's Two Person Keelboat    NA  \n",
       "271091     Volleyball              Volleyball Men's Volleyball    NA  \n",
       "271110     Basketball            Basketball Women's Basketball    NA  \n",
       "\n",
       "[26608 rows x 15 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df7 = df6[df6.Year.isin([2012, 2016])]\n",
    "df7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "68434bb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2012, 2016])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df7.Year.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "f7d1c4f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df8 = df7.groupby(['Team', 'Year']).size().unstack()\n",
    "df9 = df8.sort_values(by = 2012, ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b19c5c4",
   "metadata": {},
   "source": [
    "# Selected column for y axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "5139d22a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Year</th>\n",
       "      <th>2012</th>\n",
       "      <th>2016</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Team</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Great Britain</th>\n",
       "      <td>672.0</td>\n",
       "      <td>470.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>669.0</td>\n",
       "      <td>699.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Russia</th>\n",
       "      <td>541.0</td>\n",
       "      <td>398.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>506.0</td>\n",
       "      <td>510.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>494.0</td>\n",
       "      <td>528.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>463.0</td>\n",
       "      <td>483.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>422.0</td>\n",
       "      <td>504.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>395.0</td>\n",
       "      <td>436.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>374.0</td>\n",
       "      <td>395.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>331.0</td>\n",
       "      <td>353.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>294.0</td>\n",
       "      <td>571.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Year            2012   2016\n",
       "Team                       \n",
       "Great Britain  672.0  470.0\n",
       "United States  669.0  699.0\n",
       "Russia         541.0  398.0\n",
       "Australia      506.0  510.0\n",
       "Germany        494.0  528.0\n",
       "China          463.0  483.0\n",
       "France         422.0  504.0\n",
       "Japan          395.0  436.0\n",
       "Italy          374.0  395.0\n",
       "Spain          331.0  353.0\n",
       "Brazil         294.0  571.0"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df10 = df9.loc[[\"Great Britain\", \"United States\", \"Russia\", \"Australia\", \n",
    "               \"Germany\", \"China\", \"France\", \"Japan\", \"Italy\", \"Spain\", \"Brazil\"]]\n",
    "df10"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "143ac19b",
   "metadata": {},
   "source": [
    "## Convert the data to long format(melt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "694e90e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Team</th>\n",
       "      <th>Year</th>\n",
       "      <th>Number of Athletes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Great Britain</td>\n",
       "      <td>2012</td>\n",
       "      <td>672.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>United States</td>\n",
       "      <td>2012</td>\n",
       "      <td>669.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Russia</td>\n",
       "      <td>2012</td>\n",
       "      <td>541.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Australia</td>\n",
       "      <td>2012</td>\n",
       "      <td>506.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Germany</td>\n",
       "      <td>2012</td>\n",
       "      <td>494.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>China</td>\n",
       "      <td>2012</td>\n",
       "      <td>463.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>France</td>\n",
       "      <td>2012</td>\n",
       "      <td>422.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Japan</td>\n",
       "      <td>2012</td>\n",
       "      <td>395.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Italy</td>\n",
       "      <td>2012</td>\n",
       "      <td>374.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Spain</td>\n",
       "      <td>2012</td>\n",
       "      <td>331.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>2012</td>\n",
       "      <td>294.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Great Britain</td>\n",
       "      <td>2016</td>\n",
       "      <td>470.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>United States</td>\n",
       "      <td>2016</td>\n",
       "      <td>699.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Russia</td>\n",
       "      <td>2016</td>\n",
       "      <td>398.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Australia</td>\n",
       "      <td>2016</td>\n",
       "      <td>510.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Team  Year  Number of Athletes\n",
       "0   Great Britain  2012               672.0\n",
       "1   United States  2012               669.0\n",
       "2          Russia  2012               541.0\n",
       "3       Australia  2012               506.0\n",
       "4         Germany  2012               494.0\n",
       "5           China  2012               463.0\n",
       "6          France  2012               422.0\n",
       "7           Japan  2012               395.0\n",
       "8           Italy  2012               374.0\n",
       "9           Spain  2012               331.0\n",
       "10         Brazil  2012               294.0\n",
       "11  Great Britain  2016               470.0\n",
       "12  United States  2016               699.0\n",
       "13         Russia  2016               398.0\n",
       "14      Australia  2016               510.0"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df10_long = pd.melt(df10.reset_index(), id_vars=['Team'], var_name='Year', value_name='Number of Athletes')\n",
    "df10_long.head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a276cfd3",
   "metadata": {},
   "source": [
    "### The bar plot below shows the number of athletes that competed in the 2012 (dark blue) and 2016 (light blue) Olympics from a set of different countries. The Venn diagram acts as the legend – it informs the meaning of each colour."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "813759de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1400x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define a color palette\n",
    "colors = plt.cm.tab20(range(2))\n",
    "\n",
    "# Create the figure with two subplots\n",
    "fig, (ax1, ax2) = plt.subplots(ncols = 2, figsize = (14, 6), \n",
    "                               gridspec_kw = {'width_ratios': [4, 1], 'height_ratios': [1.3]})\n",
    "\n",
    "# Add the bar plot to the first subplot\n",
    "sns.barplot(x = 'Number of Athletes', y = 'Team', data = df10_long, hue='Year', palette = colors, ax = ax1)\n",
    "\n",
    "ax1.set(xlabel = 'Number of Athletes', xticks = [0, 100, 200, 300, 400, 500], ylabel = '', \n",
    "        title='Change in Number of Athletes per Country from 2012 to 2016')\n",
    "\n",
    "ax1.legend_.remove()\n",
    "\n",
    "# Add the venn diagram to the second subplot\n",
    "venn = venn2(subsets = (2464, 2815, 1358), set_labels = ('2012', '2016'), set_colors = colors, alpha=0.8, ax = ax2)\n",
    "venn.get_label_by_id('10')\n",
    "venn.get_label_by_id('01')\n",
    "venn.get_label_by_id('11')\n",
    "\n",
    "# Adjust the margins to shift the venn diagram to the left\n",
    "fig.subplots_adjust(left = 0.1, right = 0.9, bottom = 0.1, top = 0.9)\n",
    "\n",
    "# Move the second subplot inside the first subplot\n",
    "pos2 = ax2.get_position() # get the position of the second subplot\n",
    "\n",
    "ax2.set_position([ax1.get_position().x0 + ax1.get_position().width - pos2.width, \n",
    "                  ax1.get_position().y0 + 0.08, pos2.width, pos2.height])\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7786ec76",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea724ab6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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