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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Understanding Descriptive Statistics\n",
+    "\n",
+    "Import the necessary libraries here:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 481,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Libraries\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import random \n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy import stats"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Challenge 1\n",
+    "#### 1.- Define a function that simulates rolling a dice 10 times. Save the information in a dataframe.\n",
+    "**Hint**: you can use the *choices* function from module *random* to help you with the simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 482,
+   "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>Rolls</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Rolls\n",
+       "0      2\n",
+       "1      2\n",
+       "2      5\n",
+       "3      4\n",
+       "4      3\n",
+       "5      2\n",
+       "6      2\n",
+       "7      4\n",
+       "8      3\n",
+       "9      6"
+      ]
+     },
+     "execution_count": 482,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "\n",
+    "numbers = [1,2,3,4,5,6]\n",
+    "dice_roll = random.choices(numbers, k=10)\n",
+    "dice_roll = pd.DataFrame({\"Rolls\":dice_roll})\n",
+    "dice_roll"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.- Plot the results sorted by value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 483,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "\n",
+    "sorted_dice_roll = dice_roll.sort_values('Rolls')\n",
+    "plt.plot(sorted_dice_roll,'ro')\n",
+    "plt.xlabel(\"Roll number\")\n",
+    "plt.ylabel(\"Dice Value\")\n",
+    "plt.title(\"Sorted Dice Rolls\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.- Calculate the frequency distribution and plot it. What is the relation between this plot and the plot above? Describe it with words."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 484,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "freq_dist_dice_roll = dice_roll['Rolls'].value_counts().sort_index()\n",
+    "plt.bar(freq_dist_dice_roll.index, freq_dist_dice_roll)\n",
+    "\n",
+    "plt.xlabel('Dice Value')\n",
+    "plt.ylabel('Frequency')\n",
+    "plt.title('Dice Rolls Frequency Distribution')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 485,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nyour comments here\\n\\nThe first plot displays the relationship between the roll number and the value.\\nThe second plot displays how frequently each dice value appeared in the rolls, regardless of the order they appeared in.It provides an overview of the distribution of dice values, indicating which values were more or less frequent in the rolls.\\n'"
+      ]
+     },
+     "execution_count": 485,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "your comments here\n",
+    "\n",
+    "The first plot displays the relationship between the roll number and the value.\n",
+    "The second plot displays how frequently each dice value appeared in the rolls, regardless of the order they appeared in.It provides an overview of the distribution of dice values, indicating which values were more or less frequent in the rolls.\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Challenge 2\n",
+    "Now, using the dice results obtained in *challenge 1*, your are going to define some functions that will help you calculate the mean of your data in two different ways, the median and the four quartiles. \n",
+    "\n",
+    "#### 1.- Define a function that computes the mean by summing all the observations and dividing by the total number of observations. You are not allowed to use any methods or functions that directly calculate the mean value. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 486,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.3"
+      ]
+     },
+     "execution_count": 486,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "def mean(col):\n",
+    "    return col.sum()/len(col)\n",
+    "mean_value = mean(dice_roll['Rolls'])\n",
+    "mean_value"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.- First, calculate the frequency distribution. Then, calculate the mean using the values of the frequency distribution you've just computed. You are not allowed to use any methods or functions that directly calculate the mean value. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 487,
+   "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>Rolls</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Rolls\n",
+       "2      4\n",
+       "4      2\n",
+       "3      2\n",
+       "5      1\n",
+       "6      1"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "3.3"
+      ]
+     },
+     "execution_count": 487,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "frequency_distribution = pd.DataFrame(dice_roll['Rolls'].value_counts())\n",
+    "display(frequency_distribution)\n",
+    "\n",
+    "def mean(col):\n",
+    "    return ((col.index)*col).sum()/col.sum()\n",
+    "\n",
+    "mean_value_with_frequency = mean(frequency_distribution['Rolls'])\n",
+    "mean_value_with_frequency "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.- Define a function to calculate the median. You are not allowed to use any methods or functions that directly calculate the median value. \n",
+    "**Hint**: you might need to define two computation cases depending on the number of observations used to calculate the median."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 488,
+   "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>Rolls</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Rolls\n",
+       "0      2\n",
+       "1      2\n",
+       "5      2\n",
+       "6      2\n",
+       "4      3\n",
+       "8      3\n",
+       "3      4\n",
+       "7      4\n",
+       "2      5\n",
+       "9      6"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "3.0"
+      ]
+     },
+     "execution_count": 488,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "def median(col):\n",
+    "    sorted_dice_roll = col.sort_values().reset_index(drop=True)\n",
+    "    n = len(col)\n",
+    "    \n",
+    "    if n % 2 != 0:  \n",
+    "         return sorted_dice_roll[n // 2]\n",
+    "    else:  \n",
+    "        return (sorted_dice_roll[n / 2 - 1] + sorted_dice_roll[n / 2]) / 2\n",
+    "    \n",
+    "display(sorted_dice_roll)    \n",
+    "median_value = median(dice_roll['Rolls'])\n",
+    "median_value\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 4.- Define a function to calculate the four quartiles. You can use the function you defined above to compute the median but you are not allowed to use any methods or functions that directly calculate the quartiles. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 503,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "the first quartile is 2\n",
+      "the second quartile is 3.0\n",
+      "the third quartile is 4\n"
+     ]
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "\n",
+    "def quartile(col):\n",
+    "    sorted_dice_roll = col.sort_values().reset_index(drop=True)\n",
+    "    n=len(col)\n",
+    "    mid = n // 2\n",
+    "\n",
+    "    if n % 2 == 0:  \n",
+    "        lower_half = sorted_dice_roll[:mid]\n",
+    "        upper_half = sorted_dice_roll[mid:]\n",
+    "        Q1 = median(lower_half)\n",
+    "        Q3 = median(upper_half)\n",
+    "    else:  \n",
+    "        lower_half = sorted_dice_roll[:mid]\n",
+    "        upper_half = sorted_dice_roll[mid + 1:]\n",
+    "        Q1 = median(lower_half)\n",
+    "        Q3 = median(upper_half)\n",
+    "\n",
+    "    Q2 = median(col)  \n",
+    "    print(\"the first quartile is\", Q1)\n",
+    "    print(\"the second quartile is\",Q2)\n",
+    "    print(\"the third quartile is\", Q3)\n",
+    "\n",
+    "\n",
+    "quartile_values = quartile(dice_roll['Rolls'])\n",
+    "quartile_values\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Challenge 3\n",
+    "Read the csv `roll_the_dice_hundred.csv` from the `data` folder.\n",
+    "#### 1.- Sort the values and plot them. What do you see?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 490,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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T4zs1lpPzavu2Or39Z47nVD82zIlToqHGQETjdoRacyy/z0Zy28JdY0XnNpig4uqvfnbv3q1p06apWbNmWrJkiYYMGaKRI0fqlVdeKbV9dna2UlNTfY/MzMyI1pufb2dfofTrxPhOjRWuuXCy/3DOa0XqO3Ndp/pxep1w718nxnazxkBE43aEWnMsv89GctvCXWMkjzVXg0pRUZE6dOigJ554Qu3bt9f//u//6u6779a0adNKbT9+/Hh5vV7fIy8vL6L1pqXZ2Vco/ToxvlNjhWsunOw/nPNakfrOXNepfpxeJ9z714mx3awxENG4HaHWHMvvs5HctnDXGNFjrWIf3lRMVlaWueuuu/yWPf/88yY9PT2g9d26RqWsixXP/H6xvDaR+O40EuM7NZYT8xot2xpK34GO51Q/NsyJU6KhxkBE43aEWnMsv89GctvCWWOkr1Fx9ROVLl26aPv27X7LduzYoUaNGrlUUfni46UpU4r/7fH4P3f67ylTpKlTy28zeXL4foseSI1Oje/UWBWd19LYuq3B9l2assZzqp9ARfJYC1U01BiIaNyOUGuO5ffZSG5bOGuM+LFWsUxUMR9//LFJSEgwkyZNMjt37jSvvfaaqV69upk9e3ZA69t0H5XMzHP/Bv7sNm7XaNtYoc5rafcIsX1bA+07lG2L9By5fawHIhpqDEQ0bkeoNcfy+2wkty2cNVZEMOdvjzHGRDAXlfDOO+9o/Pjx2rlzp5o0aaKxY8fq7rvvDmjdgoICpaamyuv1KiUlJcyV+ovlOya6ORZ3puXOtOESDTUGIhq3Ixru3hqqaNg2G+9MG8z52/WgUhFuBhUAABCaYM7f/KeEAADAWgQVAABgLYIKAACwFkEFAABYi6ACAACsRVABAADWIqgAAABrEVQAAIC1CCoAAMBaBBUAAGAtggoAALAWQQUAAFiLoAIAAKxFUAEAANYiqAAAAGsRVAAAgLUIKgAAwFoEFQAAYC2CCgAAsBZBBQAAWIugAgAArEVQAQAA1iKoAAAAaxFUAACAtQgqAADAWgQVAABgLYIKAACwFkEFAABYi6ACAACsRVABAADWIqgAAABrEVQAAIC1CCoAAMBaBBUAAGAtggoAALAWQQUAAFiLoAIAAKxFUAEAANYiqAAAAGsRVAAAgLUIKgAAwFoEFQAAYC2CCgAAsBZBBQAAWIugAgAArEVQAQAA1iKoAAAAaxFUAACAtQgqAADAWgQVAABgLYIKAACwFkEFAABYi6ACAACsRVABAADWIqgAAABrJbhdQGVy6pS0erWUny+lpUlXXCHFx0eub6faoKSz561zZ+nDD6NvHtn/OBe330fcPEYDHZvXkcOMiyZOnGgk+T3q168f8Pper9dIMl6vN4xVOmPuXGMyMoyRfnxkZBQvj0TfTrVBSaXNW3x89M0j+x/n4vb7iJvHaKBj8zoKTDDnb9eDSuvWrU1+fr7vcfjw4YDXj5agMneuMR6P/4ErFS/zeCp2AAfSt1NtUFJZ8xZt88j+x7m4/T7i5jEa6Ni8jgIXzPnbY4wxbn2a88gjj2jBggXKyckJaf2CggKlpqbK6/UqJSXF2eIccuqU1LixtH9/6c97PFJGhrRnT/AfDQbSd8OGxf8+VxtjpAMHnK8xlp1r/s9m6zyG8xhFbAj0vSZc7yNuHqOBjv3FF1LTpryOAhXM+dv1i2l37typ9PR0NWnSRLfeeqt2795dZtsTJ06ooKDA72G71avLP5EZI+XlFbcLR9/79wfWpqw3l4rWGMvONf9ns3Uew3mMIjYE+l4TrvcRN4/RQMd+/nleR+HialC57LLL9Morr2jJkiV68cUXdejQIXXu3FlHjhwptX12drZSU1N9j8zMzAhXHLz8fGfbVXSdioj0eLYLdT5sm8dwHqOIDU7u+3C+14XjGA20z127nO0PP3I1qPTu3Vu/+MUv1LZtW11zzTV69913JUkvv/xyqe3Hjx8vr9fre+Tl5UWy3JCkpTnbrqLrVESkx7NdqPNh2zyG8xhFbHBy34fzvS4cx2igfTZt6mx/+JGr16iUpkePHrrgggs0bdq0c7aNpmtUDhwo/ujvbE58b1te36evUTlXG2OkgwedrzGWnWv+z2brPIbzGEVsCPS9JlzvI24eo4GOffoaFV5HgYmqa1TOdOLECW3btk1pMRQ54+OlKVOK/+3x+D93+u/Jk0M7cAPpe8qUwNpMnRqeGmNZefN/NpvnMZzHKGJDoO814XofcfMYDXTsKlV4HYVNmH+BVK57773XrFixwuzevdusXbvWXHfddSY5Odnk5uYGtH60/DzZmNJ/W5+ZGb57C5zdt1NtUFIg91GJhnlk/+Nc3H4fcfMYDXRsXkeBiZqfJ996661atWqVvvrqK9WtW1c//elP9fvf/14XXnhhQOtHw1c/Z3L7bo1u31EylnFnWlQWbr+PcGfa2BDM+du6a1SCEW1BBQAARPE1KgAAAGciqAAAAGsRVAAAgLUIKgAAwFoEFQAAYC2CCgAAsBZBBQAAWIugAgAArEVQAQAA1iKoAAAAaxFUAACAtQgqAADAWgQVAABgLYIKAACwFkEFAABYi6ACAACsRVABAADWIqgAAABrEVQAAIC1CCoAAMBaBBUAAGAtggoAALAWQQUAAFiLoAIAAKxFUAEAANYiqAAAAGsRVAAAgLUIKgAAwFoEFQAAYC2CCgAAsBZBBQAAWIugAgAArEVQAQAA1iKoAAAAaxFUAACAtQgqAADAWgQVAABgLYIKAACwFkEFAABYi6ACAACsRVABAADWIqgAAABrEVQAAIC1CCoAAMBaBBUAAGAtggoAALAWQQUAAFiLoAIAAKxFUAEAANYiqAAAAGsRVAAAgLUIKgAAwFoEFQAAYC2CCgAAsBZBBQAAWCvB7QJwbqdOSatXS/n5UlqadMUVUnx85PourY0UfE1O9RPObevcWfrww/DUEyucOh7dPq6dEupxHcntL+24DqTGSHJyPiK5/yMp1PfrqN92Y4knnnjCSDKjRo0KeB2v12skGa/XG77CXDZ3rjEZGcZIPz4yMoqXR6Lv0trUrl38CKYmp/oJ97bFx4ennljh1PHo9nHtlFCP60hv/9nHdThfe07VHGo9kdz/kRTqe5qt2x7M+duKoPLxxx+bxo0bm3bt2hFUzjB3rjEej/9BJxUv83gqdvAF0ndZbUp7lFeTU/24sW1O1BMrnDoe3T6unRLqce3G9kfqtedkzaHUE8n9H0kVeU+zddujKqgcO3bMNGvWzCxbtsx069aNoPL/CgtLJuOzD77MzOJ24eg7I6P8NoHWdK6xomHbKlJPrHDqeHT7uHZqP4Z6XJ/r2Avn9tt4rDu5zyK5/yPJifc0G7c9mPN3SBfTfv3115o+fbrGjx+vo0ePSpI2bNigAwcOBN3XsGHD1LdvX11zzTXnbHvixAkVFBT4PWLV6tXS/v1lP2+MlJdX3C4cfe/fX36bQGs611iB9hOocGxbReqJFU4dj24f107tx1CP63Mde+Hc/kBE+lh3cp9Fcv9HkhPvadG67acFfTHtpk2bdM011yg1NVW5ubm6++67VatWLc2fP1979+7VK6+8EnBfb775pjZs2KD169cH1D47O1uPPvposCVHpfx8Z9tVdJ1Q+6/IWLZtW7jnzWZOHY82HNdO7MdIvobCuU4k+nJinEDaRXL/R1I07lenBf2JytixYzVo0CDt3LlTVatW9S3v3bu3Vq1aFXA/eXl5GjVqlGbPnu3XT3nGjx8vr9fre+Tl5QVbftRIS3O2XUXXCbX/ioxl27aFe95s5tTxaMNx7cR+jORrKJzrRKIvJ8YJpF0k938kReN+dVyw3yulpKSYL774whhjTM2aNc2uXbuMMcbk5uaapKSkgPuZP3++kWTi4+N9D0nG4/GY+Ph4UxjAl2mV4RqVsi6Mc+K77PL6Pv2dZzAX5pV3jUpF+3Fz22z8fjfSnDoe3T6unb5GJdjj+lzHXji338Zj3cl9Fsn9H0lOvKfZuO1hvUalatWqpV4bsn37dtWtWzfgfq6++mpt3rxZOTk5vkenTp3Uv39/5eTkKD7qf/hdMfHx0pQpxf/2ePyfO/335Mmh/T4+kL6nTCm7TWnKqqm8sYLpJ1BOb1tF64kVTh2Pbh/XTu3HUI/r8o69cG5/INw41p3cZ5Hc/5FU0fe0aN52n2BT0N13321uuOEG88MPP5iaNWua3bt3m71795r27dsH9Yud0vCrn5JK+118Zmb47rdwdt+B3ifiXDU51U+4t+3s+004VU+scOp4dPu4dkqox3Wktz+Q+6i4eaw7OR+R3P+RFOp7mq3bHsz522OMMcEEm4KCAvXp00dbtmzRsWPHlJ6erkOHDunyyy/XokWLVKNGjZBD01VXXaWLL75YkydPDriW1NRUeb1epaSkhDyu7dy+gyd3pnWmnljBnWnPPZbEnWkrWjN3pi0plu5MG8z5O+igctoHH3ygDRs2qKioSB06dAjo58VOqyxBBQCAWBKRoGIDggoAANEnmPN30PdReeyxx8p9/uGHHw62SwAAgFIFHVTmz5/v9/fJkye1Z88eJSQkqGnTpgQVAADgmKCDysaNG0ssKygo0KBBg3TjjTc6UhQAAIAUwp1pS5OSkqLHHntMDz30kBPdAQAASHIoqEjF/1Gh1+t1qjsAAIDgv/qZOnWq39/GGOXn5+vVV1/Vtdde61hhAAAAQQeVP/3pT35/x8XFqW7duho4cKDGjx/vWGEAAABBB5U9e/aEow4AAIASHLtGBQAAwGkBfaJy0003BdzhvHnzQi4GAADgTAEFldTU1HDXAQAAUEJAQWXmzJnhrgMAAKAErlEBAADWCvpXP5L0j3/8Q3//+9+1b98+/fDDD37PbdiwwZHCAAAAgv5EZerUqfrVr36levXqaePGjbr00ktVu3Zt7d69W7179w5HjQAAoJIKOqg8//zzeuGFF/SXv/xFVapU0bhx47Rs2TKNHDmSW+gDAABHBR1U9u3bp86dO0uSqlWrpmPHjkmSBgwYoDfeeMPZ6gAAQKUWdFBp0KCBjhw5Iklq1KiR1q5dK6n4jrXGGGerAwAAlVrQQeVnP/uZ3n77bUnSXXfdpTFjxqhHjx665ZZbdOONNzpeIAAAqLw8JsCPQRYsWKB+/frJ4/GoqKhICQnFPxj6+9//rjVr1uiCCy7QkCFDVKVKlbAWfKaCggKlpqbK6/UqJSUlYuMCAIDQBXP+DjioJCQkqE6dOho4cKAGDx6sFi1aOFJsRRBUAACIPsGcvwP+6mffvn0aMWKE5s+frwsvvFBdu3bVzJkz9e2331a4YAAAgNIEHFTS09P1wAMPaMeOHfrggw/UtGlTjRw5Umlpafr1r3+tjz76KJx1AgCASiikW+h369ZNL7/8svLz8/Xss89q27Zt6tq1q1q3bu10fQAAoBIL6Rb6p9WsWVPdu3dXbm6uPv/8c+3YscOpugAAAEL7ROW7777Tyy+/rG7duql58+aaM2eOxo4dq9zcXIfLAwAAlVlQn6j861//0owZM/TWW2+psLBQN910k9577z117949XPUBAIBKLOCg0rx5c+3atUvt27fXU089pdtvv12pqanhrA0AAFRyAQeVa6+9VnfddZcuuuiicNYDAADgE3BQmTp1ajjrAAAAKCGki2kBAAAigaACAACsRVABAADWqlBQOX78uFN1AAAAlBB0UCkqKtLvf/97NWzYUDVr1tTu3bslSQ899JBeeuklxwsEAACVV9BB5fHHH9esWbP09NNPq0qVKr7lbdu21fTp0x0tDgAAVG5BB5VXXnlFL7zwgvr376/4+Hjf8nbt2unzzz93tDgAAFC5BR1UDhw4oAsuuKDE8qKiIp08edKRogAAAKQQgkrr1q21evXqEsvfeusttW/f3pGiAAAApCD/U0JJmjhxogYMGKADBw6oqKhI8+bN0/bt2/XKK6/onXfeCUeNAACgkgr6E5V+/fppzpw5WrRokTwejx5++GFt27ZNb7/9tnr06BGOGgEAQCXlMcYYt4sIVUFBgVJTU+X1epWSkuJ2OQAAIADBnL+D/kRl/fr1WrduXYnl69at07///e9guwMAAChT0EFl2LBhysvLK7H8wIEDGjZsmCNFAQAASCEEla1bt6pDhw4llrdv315bt251pCgAAAAphKCSlJSkL7/8ssTy/Px8JSQE/SMiAACAMgUdVHr06KHx48fL6/X6ln399deaMGECv/oBAACOCvojkGeeeUZXXnmlGjVq5LvBW05OjurXr69XX33V8QIBAEDlFXRQadiwoTZt2qTXXntNn376qapVq6Zf/epXuu2225SYmBiOGgEAQCUV0kUlNWrU0G9+8xunawEAAPATUFBZuHChevfurcTERC1cuLDcttdff70jhQEAAAR0Z9q4uDgdOnRI9erVU1xc2dffejwenTp1ytECy8OdaQEAiD7BnL8D+kSlqKio1H8DAACEU9A/TwYAAIiUoC6mLSoq0qxZszRv3jzl5ubK4/GoSZMmuvnmmzVgwAB5PJ5w1QkAACqhgIOKMUbXX3+9Fi1apIsuukht27aVMUbbtm3ToEGDNG/ePC1YsCCowadNm6Zp06YpNzdXktS6dWs9/PDD6t27d1D9ILadOiWtXi3l50tpadIVV0jx8W5X5Qynti2cc+Tm/Lu9750cP5S+AlnH7TmKVmfPW+fO0ocf+s+jFLm5dXs/uj1+uUyAZsyYYZKTk80HH3xQ4rn333/fJCcnm5dffjnQ7owxxixcuNC8++67Zvv27Wb79u1mwoQJJjEx0Xz22WcBre/1eo0k4/V6gxoX0WPuXGMyMoyRfnxkZBQvj3ZObVs458jN+Xd73zs5fih9BbKO23MUrUqbt/h4/79r1y5+RGJu3d6PbowfzPk74KDSo0cPk52dXebzkyZNMj179gy0uzKdd955Zvr06QG1JajEtrlzjfF4/F88UvEyjye634yd2rZwzpGb8+/2vndy/FD6CmQdt+coWpU1b4E8wjG3bu9Ht8YPS1CpX7++2bhxY5nPb9iwwdSvXz/Q7kooLCw0b7zxhqlSpYrZsmVLQOsQVGJXYWHJhH/2iygzs7hdtHFq28I5R27Ov9v73snxQ+krkHUyMmL39RFO55rbQMOKU3MbS8d6sII5fwf8q5+jR4+qfv36ZT5fv359/fe//w36q6fNmzerZs2aSkpK0pAhQzR//nxdeOGFpbY9ceKECgoK/B6ITatXS/v3l/28MVJeXnG7aOPUtoVzjtycf7f3vZPjh9JXIOvs3x+7r49wOtfcBsLJuY2lYz2cAg4qp06dUkJC2dfexsfHq7CwMOgCWrRooZycHK1du1ZDhw7VwIEDtXXr1lLbZmdnKzU11ffIzMwMejxEh/x8Z9vZxKltC+ccuTn/bu97J8cPpS8ntysaXx/hZNvcxtKxHk5B/epn0KBBSkpKKvX5EydOhFRAlSpVdMEFF0iSOnXqpPXr12vKlCn629/+VqLt+PHjNXbsWN/fBQUFhJUYlZbmbDubOLVt4ZwjN+ff7X3v5Pih9OXkdkXj6yOcbJvbWDrWwymgW+hL0q9+9auAOpw5c2aFCrr66quVmZmpWbNmnbMtt9CPXadOSY0bSwcOFH/8eDaPR8rIkPbssegndAFyatvCOUduzr/b+97J8UPpK5B1GjYs/ncsvj7C6VxzGwgn5zaWjvVgBXX+dv4SmcCNHz/erFq1yuzZs8ds2rTJTJgwwcTFxZmlS5cGtD4X08a201ejn31Feiz8qsGpbQvnHLk5/27veyfHD6WvQNZxe46iVVnzFuiFtOH61U8sHOvBCMuvfsJh8ODBplGjRqZKlSqmbt265uqrrw44pBhDUKkMSvt9f2ZmbLwJO7Vt4ZwjN+ff7X3v5Pih9BXIOm7PUbQK9T4q4Zpbt/ejG+MHc/4O+KsfG/HVT+Vg9R0TK4g709o7ttPjc2dau3BnWnfHD+b8TVABAAARFcz5m/89GQAAWIugAgAArEVQAQAA1iKoAAAAaxFUAACAtQgqAADAWgQVAABgLYIKAACwFkEFAABYi6ACAACsRVABAADWIqgAAABrEVQAAIC1CCoAAMBaBBUAAGAtggoAALAWQQUAAFiLoAIAAKxFUAEAANYiqAAAAGsRVAAAgLUIKgAAwFoEFQAAYC2CCgAAsBZBBQAAWIugAgAArEVQAQAA1iKoAAAAaxFUAACAtQgqAADAWgQVAABgLYIKAACwFkEFAABYi6ACAACsRVABAADWIqgAAABrEVQAAIC1CCoAAMBaBBUAAGAtggoAALAWQQUAAFiLoAIAAKxFUAEAANYiqAAAAGsRVAAAgLUIKgAAwFoEFQAAYC2CCgAAsBZBBQAAWIugAgAArEVQAQAA1iKoAAAAaxFUAACAtQgqAADAWgQVAABgrQQ3B8/Ozta8efP0+eefq1q1aurcubOeeuoptWjRws2ySjh1Slq9WsrPl9LSpCuukOLjw9e3FL7xIimc8xZJTm1HrPbjZN+BrOfUayacNUars7etc2fpww/tOkZQCRkX9erVy8ycOdN89tlnJicnx/Tt29dkZWWZb775JqD1vV6vkWS8Xm/Yapw715iMDGOkHx8ZGcXLw9F37drFj3CMF0nhnLdIcmo7YrUfJ/sOZD2nXjPhrDFalbZt8fF2HSOIHcGcv10NKmc7fPiwkWRWrlwZUPtwB5W5c43xePxfTFLxMo+nYi+qsvou7eHEeJEUznmLJKe2I1b7cbLvQNZz6jUTzhqjVaBz6+YxgtgStUFl586dRpLZvHlzQO3DGVQKC0sm/rNfVJmZxe2c7tvp8SIpnPMWSU5tR6z242TfgayXkWFMw4YVf82Es8ZoOK5LE+z7kRvHCGJPMOdvay6mNcZo7Nix6tq1q9q0aVNqmxMnTqigoMDvES6rV0v795f9vDFSXl5xO6f7dnq8SArnvEWSU9sRq/042Xcg6+3fLx04EFw9pY0Xzhqj4bguTbDvR24cI6jcrAkqw4cP16ZNm/TGG2+U2SY7O1upqam+R2ZmZtjqyc93tl1F13Fi3UgI57xFklPbEav9ONl3uI+FM/sPd422H9elCbXmSB4jqNysCCojRozQwoULtXz5cmVkZJTZbvz48fJ6vb5HXl5e2GpKS3O2XUXXcWLdSAjnvEWSU9sRq/042Xe4j4Uz+w93jbYf16UJteZIHiOo5ML/TVTZioqKzLBhw0x6errZsWNH0OtH4hqVsi4wc+J72kAvDIym727DOW+R5NR2xGo/TvYdyHqnr1Gp6GsmnDVGw3FdmmDfj9w4RhB7ouYalWHDhmn27Nl6/fXXlZycrEOHDunQoUP6/vvv3SxLUvHv+adMKf63x+P/3Om/J08O7Xf/5fVdmoqOF0nhnLdIcmo7YrUfJ/sOZL0pU6SpU0tvU5qyxgtnjdFwXJcmmPcjt44RVHIRCE5lklTqY+bMmQGt79Z9VDIzI3sfFafGi6RwzlskObUdsdqPk30Hsp5Tr5lw1hitArmPitvHCGJHMOdvjzHGuBeTKqagoECpqanyer1KSUkJ2zjcmTY0sXLnSdvuBGtbP072zZ1p3cWdaREpwZy/CSoAACCigjl/W/GrHwAAgNIQVAAAgLUIKgAAwFoEFQAAYC2CCgAAsBZBBQAAWIugAgAArEVQAQAA1iKoAAAAaxFUAACAtQgqAADAWgQVAABgLYIKAACwFkEFAABYi6ACAACsRVABAADWIqgAAABrEVQAAIC1CCoAAMBaBBUAAGAtggoAALAWQQUAAFiLoAIAAKxFUAEAANYiqAAAAGsRVAAAgLUIKgAAwFoEFQAAYC2CCgAAsBZBBQAAWIugAgAArEVQAQAA1iKoAAAAaxFUAACAtQgqAADAWgQVAABgLYIKAACwFkEFAABYi6ACAACsRVABAADWIqgAAABrEVQAAIC1CCoAAMBaBBUAAGAtggoAALAWQQUAAFiLoAIAAKxFUAEAANYiqAAAAGsRVAAAgLUIKgAAwFoEFQAAYC2CCgAAsBZBBQAAWIugAgAArJXgdgE2OnVKWr1ays+X0tKkK66Q4uOdXyfSNVZ2zJm7omH+3a7R7fFDEQ01u11jOMePhvNVhRkXrVy50lx33XUmLS3NSDLz588Pan2v12skGa/X61hNc+cak5FhjPTjIyOjeLmT60S6xsqOOXNXNMy/2zW6PX4ooqFmt2sM5/jRcL4qSzDnb1eDyqJFi8wDDzxg5s6da0VQmTvXGI/HfwdKxcs8ntJ3ZCjrRLrGyo45c1c0zL/bNbo9fiiioWa3awzn+NFwvipPMOdvjzHGuPmJzmkej0fz58/XDTfcEPA6BQUFSk1NldfrVUpKSoXGP3VKatxY2r+/rPqkjAxpz54fPyILZZ1I11jZMWfuiob5d7tGt8cPRTTU7HaN4Rw/Gs5X5xLM+TuqLqY9ceKECgoK/B5OWb267B0oFefOvLzidhVZJ9I1VnbMmbuiYf7drtHt8UMRDTW7XWM4x4+G85WToiqoZGdnKzU11ffIzMx0rO/8/ODbhbJORUR6vFjAnLkrGubf7RrdHj8U0VCz2zWGc/xoOF85KaqCyvjx4+X1en2PvLw8x/pOSwu+XSjrVESkx4sFzJm7omH+3a7R7fFDEQ01u11jOMePhvOVk7hG5f+d/v7uwIHij8BK1lf2d37BrBPpGis75sxd0TD/btfo9vihiIaa3a4xnONHw/nqXGL2GpVwio+Xpkwp/rfH4//c6b8nT/bfgaGsE+kaKzvmzF3RMP9u1+j2+KGIhprdrjGc40fD+cpRYf4FUrmOHTtmNm7caDZu3GgkmWeffdZs3LjR7N27N6D1I3UflczM4H+Xfq51Il1jZcecuSsa5t/tGt0ePxTRULPbNYZz/Gg4X5Ulan6evGLFCnXv3r3E8oEDB2rWrFnnXN/Jr37OFA13+ou6OwtagDlzVzTMv9s1uj1+KKKhZrdr5M60JQVz/rbmGpVQhCuoAACA8OEaFQAAEBMIKgAAwFoEFQAAYC2CCgAAsBZBBQAAWIugAgAArEVQAQAA1iKoAAAAaxFUAACAtRLcLqAiTt9Ut6CgwOVKAABAoE6ftwO5OX5UB5Vjx45JkjIzM12uBAAABOvYsWNKTU0tt01U/18/RUVFOnjwoJKTk+U5+/+trqCCggJlZmYqLy+P/0cozJjryGGuI4e5jhzmOnKcmmtjjI4dO6b09HTFxZV/FUpUf6ISFxenjIyMsI6RkpLCgR8hzHXkMNeRw1xHDnMdOU7M9bk+STmNi2kBAIC1CCoAAMBaBJUyJCUlaeLEiUpKSnK7lJjHXEcOcx05zHXkMNeR48ZcR/XFtAAAILbxiQoAALAWQQUAAFiLoAIAAKxFUAEAANYiqJTi+eefV5MmTVS1alV17NhRq1evdrukqJedna1LLrlEycnJqlevnm644QZt377dr40xRo888ojS09NVrVo1XXXVVdqyZYtLFceO7OxseTwejR492reMuXbOgQMHdMcdd6h27dqqXr26Lr74Yn3yySe+55lrZxQWFurBBx9UkyZNVK1aNZ1//vl67LHHVFRU5GvDXIdm1apV6tevn9LT0+XxeLRgwQK/5wOZ1xMnTmjEiBGqU6eOatSooeuvv1779+93pkADP2+++aZJTEw0L774otm6dasZNWqUqVGjhtm7d6/bpUW1Xr16mZkzZ5rPPvvM5OTkmL59+5qsrCzzzTff+No8+eSTJjk52cydO9ds3rzZ3HLLLSYtLc0UFBS4WHl0+/jjj03jxo1Nu3btzKhRo3zLmWtnHD161DRq1MgMGjTIrFu3zuzZs8e899575osvvvC1Ya6d8fjjj5vatWubd955x+zZs8e89dZbpmbNmmby5Mm+Nsx1aBYtWmQeeOABM3fuXCPJzJ8/3+/5QOZ1yJAhpmHDhmbZsmVmw4YNpnv37uaiiy4yhYWFFa6PoHKWSy+91AwZMsRvWcuWLc3999/vUkWx6fDhw0aSWblypTHGmKKiItOgQQPz5JNP+tocP37cpKammr/+9a9ulRnVjh07Zpo1a2aWLVtmunXr5gsqzLVz7rvvPtO1a9cyn2eundO3b18zePBgv2U33XSTueOOO4wxzLVTzg4qgczr119/bRITE82bb77pa3PgwAETFxdnFi9eXOGa+OrnDD/88IM++eQT9ezZ0295z5499eGHH7pUVWzyer2SpFq1akmS9uzZo0OHDvnNfVJSkrp168bch2jYsGHq27evrrnmGr/lzLVzFi5cqE6dOul//ud/VK9ePbVv314vvvii73nm2jldu3bV+++/rx07dkiSPv30U61Zs0Z9+vSRxFyHSyDz+sknn+jkyZN+bdLT09WmTRtH5j6q/1NCp3311Vc6deqU6tev77e8fv36OnTokEtVxR5jjMaOHauuXbuqTZs2kuSb39Lmfu/evRGvMdq9+eab2rBhg9avX1/iOebaObt379a0adM0duxYTZgwQR9//LFGjhyppKQk3Xnnncy1g+677z55vV61bNlS8fHxOnXqlCZNmqTbbrtNEsd1uAQyr4cOHVKVKlV03nnnlWjjxLmToFIKj8fj97cxpsQyhG748OHatGmT1qxZU+I55r7i8vLyNGrUKC1dulRVq1Ytsx1zXXFFRUXq1KmTnnjiCUlS+/bttWXLFk2bNk133nmnrx1zXXFz5szR7Nmz9frrr6t169bKycnR6NGjlZ6eroEDB/raMdfhEcq8OjX3fPVzhjp16ig+Pr5EAjx8+HCJNInQjBgxQgsXLtTy5cuVkZHhW96gQQNJYu4d8Mknn+jw4cPq2LGjEhISlJCQoJUrV2rq1KlKSEjwzSdzXXFpaWm68MIL/Za1atVK+/btk8Rx7aTf/e53uv/++3Xrrbeqbdu2GjBggMaMGaPs7GxJzHW4BDKvDRo00A8//KD//ve/ZbapCILKGapUqaKOHTtq2bJlfsuXLVumzp07u1RVbDDGaPjw4Zo3b54++OADNWnSxO/5Jk2aqEGDBn5z/8MPP2jlypXMfZCuvvpqbd68WTk5Ob5Hp06d1L9/f+Xk5Oj8889nrh3SpUuXEj+z37Fjhxo1aiSJ49pJ3333neLi/E9Z8fHxvp8nM9fhEci8duzYUYmJiX5t8vPz9dlnnzkz9xW+HDfGnP558ksvvWS2bt1qRo8ebWrUqGFyc3PdLi2qDR061KSmppoVK1aY/Px83+O7777ztXnyySdNamqqmTdvntm8ebO57bbb+GmhQ8781Y8xzLVTPv74Y5OQkGAmTZpkdu7caV577TVTvXp1M3v2bF8b5toZAwcONA0bNvT9PHnevHmmTp06Zty4cb42zHVojh07ZjZu3Gg2btxoJJlnn33WbNy40XdbjkDmdciQISYjI8O89957ZsOGDeZnP/sZP08Op+eee840atTIVKlSxXTo0MH3E1qETlKpj5kzZ/raFBUVmYkTJ5oGDRqYpKQkc+WVV5rNmze7V3QMOTuoMNfOefvtt02bNm1MUlKSadmypXnhhRf8nmeunVFQUGBGjRplsrKyTNWqVc35559vHnjgAXPixAlfG+Y6NMuXLy/1/XngwIHGmMDm9fvvvzfDhw83tWrVMtWqVTPXXXed2bdvnyP1eYwxpuKfywAAADiPa1QAAIC1CCoAAMBaBBUAAGAtggoAALAWQQUAAFiLoAIAAKxFUAEAANYiqAAIWW5urjwej3JyciRJK1askMfj0ddff+1qXWXxeDxasGCB22UACAJBBaikBg0aJI/HI4/Ho4SEBGVlZWno0KEl/mMxAHATQQWoxK699lrl5+crNzdX06dP19tvv6177rnH7bKiyg8//OB2CUBMI6gAlVhSUpIaNGigjIwM9ezZU7fccouWLl3qe76oqEiPPfaYMjIylJSUpIsvvliLFy+u0Jgej0fTp0/XjTfeqOrVq6tZs2ZauHCh7/lZs2bpJz/5id86CxYskMfj8f39yCOP6OKLL9aMGTOUlZWlmjVraujQoTp16pSefvppNWjQQPXq1dOkSZNKjJ+fn6/evXurWrVqatKkid566y2/5w8cOKBbbrlF5513nmrXrq2f//znys3N9T0/aNAg3XDDDcrOzlZ6erqaN29eofkAUD6CCgBJ0u7du7V48WIlJib6lk2ZMkXPPPOM/vjHP2rTpk3q1auXrr/+eu3cubNCYz366KP65S9/qU2bNqlPnz7q37+/jh49GlQfu3bt0j//+U8tXrxYb7zxhmbMmKG+fftq//79WrlypZ566ik9+OCDWrt2rd96Dz30kH7xi1/o008/1R133KHbbrtN27ZtkyR999136t69u2rWrKlVq1ZpzZo1qlmzpq699lq/T07ef/99bdu2TcuWLdM777xTobkAcA6O/NeGAKLOwIEDTXx8vKlRo4apWrWq739MffbZZ31t0tPTzaRJk/zWu+SSS8w999xjjDFmz549RpLZuHGjMebH/4X1v//9b5njSjIPPvig7+9vvvnGeDwe889//tMYY8zMmTNNamqq3zrz5883Z75dTZw40VSvXt3vv5nv1auXady4sTl16pRvWYsWLUx2drbf2EOGDPHr+7LLLjNDhw41xhjz0ksvmRYtWpiioiLf8ydOnDDVqlUzS5Ys8c1b/fr1/f7XXgDhk+BiRgLgsu7du2vatGn67rvvNH36dO3YsUMjRoyQJBUUFOjgwYPq0qWL3zpdunTRp59+WqFx27Vr5/t3jRo1lJycrMOHDwfVR+PGjZWcnOz7u379+oqPj1dcXJzfsrP7vfzyy0v8ffpXS5988om++OILv34l6fjx49q1a5fv77Zt26pKlSpB1QsgNAQVoBKrUaOGLrjgAknS1KlT1b17dz366KP6/e9/72tz5rUhkmSMKbEsWGd+vXR6jKKiIklSXFycjDF+z588eTKgPsrrtzynt6eoqEgdO3bUa6+9VqJN3bp1ff+uUaPGOfsE4AyuUQHgM3HiRP3xj3/UwYMHlZKSovT0dK1Zs8avzYcffqhWrVqFrYa6devq2LFj+vbbb33LTn/i4YSzr1lZu3atWrZsKUnq0KGDdu7cqXr16umCCy7we6SmpjpWA4DAEVQA+Fx11VVq3bq1nnjiCUnS7373Oz311FOaM2eOtm/frvvvv185OTkaNWpU2Gq47LLLVL16dU2YMEFffPGFXn/9dc2aNcux/t966y3NmDFDO3bs0MSJE/Xxxx9r+PDhkqT+/furTp06+vnPf67Vq1drz549WrlypUaNGqX9+/c7VgOAwBFUAPgZO3asXnzxReXl5WnkyJG69957de+996pt27ZavHixFi5cqGbNmoVt/Fq1amn27NlatGiR2rZtqzfeeEOPPPKIY/0/+uijevPNN9WuXTu9/PLLeu2113ThhRdKkqpXr65Vq1YpKytLN910k1q1aqXBgwfr+++/V0pKimM1AAicx5z9ZTAAAIAl+EQFAABYi6ACAACsRVABAADWIqgAAABrEVQAAIC1CCoAAMBaBBUAAGAtggoAALAWQQUAAFiLoAIAAKxFUAEAANYiqAAAAGv9H7MYlHQPEQqLAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "roll_the_dice_hundred = pd.read_csv('../data/roll_the_dice_hundred.csv')\n",
+    "roll_the_dice_hundred.head()\n",
+    "sorted_roll_the_dice_hundred = roll_the_dice_hundred.sort_values('value')\n",
+    "plt.plot(sorted_roll_the_dice_hundred[\"roll\"],sorted_roll_the_dice_hundred[\"value\"],'bo')\n",
+    "plt.xlabel(\"Roll number\")\n",
+    "plt.ylabel(\"Dice Value\")\n",
+    "plt.title(\"Sorted Dice Rolls\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 491,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nyour comments here\\nAlthough the dice values may appear random, the frequencies of the values are quite close, indicating that the likelihood of each number appearing when rolling the dice is almost the same\\n'"
+      ]
+     },
+     "execution_count": 491,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "your comments here\n",
+    "Although the dice values may appear random, the frequencies of the values are quite close, indicating that the likelihood of each number appearing when rolling the dice is almost the same\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.- Using the functions you defined in *challenge 2*, calculate the mean value of the hundred dice rolls."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 492,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.74"
+      ]
+     },
+     "execution_count": 492,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "def mean(col):\n",
+    "    return col.sum()/len(col)\n",
+    "mean_value = mean(roll_the_dice_hundred['value'])\n",
+    "mean_value"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.- Now, calculate the frequency distribution.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 493,
+   "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>frequency</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   frequency\n",
+       "6         23\n",
+       "4         22\n",
+       "2         17\n",
+       "3         14\n",
+       "1         12\n",
+       "5         12"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "frequency_distribution = pd.DataFrame(roll_the_dice_hundred['value'].value_counts())\n",
+    "frequency_distribution = frequency_distribution.rename(columns={'value': 'frequency'})\n",
+    "display(frequency_distribution)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 4.- Plot the histogram. What do you see (shape, values...) ? How can you connect the mean value to the histogram? "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 494,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "\n",
+    "plt.hist(roll_the_dice_hundred['value'])\n",
+    "\n",
+    "plt.xlabel('Dice Value')\n",
+    "plt.ylabel('Frequency')\n",
+    "plt.title('Dice Rolls Frequency Distribution')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 495,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nyour comments here\\nIf the numbers appearing when rolling the dice were evenly distributed, we would expect the mean to be 3. However, the calculated mean is 3.75, suggesting that the numbers 4, 5, and 6 appear more frequently than 1, 2, and 3. This is also evident in the plot.\\n'"
+      ]
+     },
+     "execution_count": 495,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "your comments here\n",
+    "If the numbers appearing when rolling the dice were evenly distributed, we would expect the mean to be 3. However, the calculated mean is 3.75, suggesting that the numbers 4, 5, and 6 appear more frequently than 1, 2, and 3. This is also evident in the plot.\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 5.- Read the `roll_the_dice_thousand.csv` from the `data` folder. Plot the frequency distribution as you did before. Has anything changed? Why do you think it changed?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 496,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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7dq2WLVumO+64Q8uWLStx/SFDhsjT01ORkZEKCgpSenq6kpKS5OvrqxYtWkj67fOoJOmtt96Sj4+PPDw8FBYWVugSz/Vyc3NTx44dNXLkSOXn52vy5MnKzMzU+PHjbX369OmjV155RX379tX//d//6eLFi3rjjTeKvKencePG2rhxoz788EMFBQXJx8enyJGxSpUqacqUKerXr5+6deumJ598Urm5uZo6darOnj2rSZMmOXQ8MCkX3hwNOEXBUzoFk7u7u+Hv72+0bdvWmDhxopGRkVFonaKeAjEMw1i4cKHRokULw8PDw6hatarRrFkzuyedDMMwNm3aZHTt2tXw8/MzqlSpYvzpT38yunbtaixfvrzEOgueXpk6dWqRyx9++GGjcuXKxpEjRwzDMIy9e/casbGxhq+vr+Hu7m40adKkUC3X8zTWf//7X+ORRx4xbr31VsPT09Pw9fU17rnnHiMlJaXEeg3j2k8IFZzHn3/+ucjl8+fPN1q2bGl4e3sbnp6exq233moMGDDA2LFjh61Pfn6+kZSUZISEhBju7u7GXXfdZXz44YeFnv4xDMM4dOiQ0alTJ6NatWpG7dq1jeHDhxtr1qwp8umi63mdiqu/qCe/8vLyjNdee80IDw833N3dDV9fX6N169bGhx9+WOi4N27caEgyJk2aVOy5u9rVT755enoadevWNWJjY4358+cbubm5hda5+hwtWLDAiI6ONgICAgx3d3cjODjY6N27t/HNN9/YrTdjxgwjLCzMcHNzs3v/tG3b1mjUqFGR9RX3NNbkyZON8ePHG3Xq1DHc3d2NZs2aGevWrSu0/scff2w0bdrU8PT0NG655RZj1qxZRf4c7t6924iMjDS8vLzsnsi7+mmsAqtWrTJatmxpeHh4GN7e3kb79u2NL7/80q7PjbzOMCeLYVzHLf4AUMZ+/2FyFc2oUaM0Z84cnTx50uEREwDOw2UsAHCSbdu26dChQ5o9e7aefPJJgg5QThB2AMBJCm6K7datmyZMmODqcgD8f1zGAgAApsaj5wAAwNQIOwAAwNQIOwAAwNS4QVm/fZDXjz/+KB8fnxv+qHQAAOAahmEoKytLwcHBdl8IezXCjn77HpXivg8JAACUbydPnizxi5wJO5J8fHwk/XayqlWr5uJqAADA9cjMzFRISIjt93hxCDv637f8VqtWjbADAEAFc61bULhBGQAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmJpLw87mzZsVGxur4OBgWSwWrVq1ym65xWIpcpo6daqtT1RUVKHlffv2LeMjAQAA5ZVLw05OTo6aNGmiWbNmFbk8LS3Nbpo/f74sFot69epl12/IkCF2/ebOnVsW5QMAgArApV8EGhMTo5iYmGKXBwYG2s1/8MEHio6O1i233GLX7uXlVagvAACAVIHu2fnpp5+0Zs0aDR48uNCyRYsWqVatWmrUqJFGjx6trKwsF1QIAADKI5eO7NyIBQsWyMfHR3FxcXbt/fr1U1hYmAIDA7Vv3z6NGTNGe/bsUWpqarHbys3NVW5urm0+MzOz1OoGAACuVWHCzvz589WvXz95eHjYtQ8ZMsT27/DwcDVo0EARERHatWuXmjdvXuS2kpKSNH78+FKtt0C9F9eUyX6c7dikrq4uAeVURXxP834G/tgqxGWsL774QgcPHtTjjz9+zb7NmzdXlSpVdPjw4WL7jBkzRufOnbNNJ0+edGa5AACgHKkQIzvz5s3T3XffrSZNmlyz7/79+3X58mUFBQUV28dqtcpqtTqzRAAAUE65NOxkZ2fryJEjtvmjR49q9+7d8vPzU926dSX9dj/N8uXLNW3atELrf/fdd1q0aJG6dOmiWrVq6cCBAxo1apSaNWumyMjIMjsOAABQfrk07OzYsUPR0dG2+ZEjR0qS4uPjlZKSIklasmSJDMPQI488Umh9d3d3ff7553r99deVnZ2tkJAQde3aVePGjZObm1uZHAMAACjfXBp2oqKiZBhGiX2eeOIJPfHEE0UuCwkJ0aZNm0qjNAAAYBIV4gZlAAAARxF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqVWI78YCgD8avl0ecB5GdgAAgKkxsgPT4C9hAEBRGNkBAACmxsgOAAAVCKPYN46RHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGouDTubN29WbGysgoODZbFYtGrVKrvlCQkJslgsdlOrVq3s+uTm5mr48OGqVauWvL299eCDD+rUqVNleBQAAKA8c2nYycnJUZMmTTRr1qxi+3Tu3FlpaWm26eOPP7Zb/uyzz2rlypVasmSJtmzZouzsbHXr1k15eXmlXT4AAKgAKrty5zExMYqJiSmxj9VqVWBgYJHLzp07p3nz5undd99Vhw4dJEnvvfeeQkJC9Nlnn+mBBx5wes0AAKBiKff37GzcuFH+/v5q2LChhgwZooyMDNuynTt36vLly+rUqZOtLTg4WOHh4frqq6+K3WZubq4yMzPtJgAAYE7lOuzExMRo0aJFWr9+vaZNm6bt27erXbt2ys3NlSSlp6fL3d1dNWrUsFsvICBA6enpxW43KSlJvr6+tikkJKRUjwMAALiOSy9jXUufPn1s/w4PD1dERIRCQ0O1Zs0axcXFFbueYRiyWCzFLh8zZoxGjhxpm8/MzCTwAABgUuU67FwtKChIoaGhOnz4sCQpMDBQly5d0pkzZ+xGdzIyMtSmTZtit2O1WmW1Wku9XgBA+VbvxTWuLgFloFxfxrra6dOndfLkSQUFBUmS7r77blWpUkWpqam2Pmlpadq3b1+JYQcAAPxxuHRkJzs7W0eOHLHNHz16VLt375afn5/8/PyUmJioXr16KSgoSMeOHdPYsWNVq1Yt9ezZU5Lk6+urwYMHa9SoUapZs6b8/Pw0evRoNW7c2PZ0FgAA+GNzadjZsWOHoqOjbfMF99HEx8drzpw52rt3rxYuXKizZ88qKChI0dHRWrp0qXx8fGzrvPbaa6pcubJ69+6tCxcuqH379kpJSZGbm1uZHw8AACh/XBp2oqKiZBhGscvXrVt3zW14eHho5syZmjlzpjNLAwAAJlGh7tkBAAC4UYQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgai4NO5s3b1ZsbKyCg4NlsVi0atUq27LLly/rhRdeUOPGjeXt7a3g4GANGDBAP/74o902oqKiZLFY7Ka+ffuW8ZEAAIDyyqVhJycnR02aNNGsWbMKLTt//rx27dqlv/zlL9q1a5dWrFihQ4cO6cEHHyzUd8iQIUpLS7NNc+fOLYvyAQBABVDZlTuPiYlRTExMkct8fX2Vmppq1zZz5kzdc889OnHihOrWrWtr9/LyUmBgYKnWCgAAKqYKdc/OuXPnZLFYVL16dbv2RYsWqVatWmrUqJFGjx6trKysEreTm5urzMxMuwkAAJiTS0d2bsTFixf14osv6tFHH1W1atVs7f369VNYWJgCAwO1b98+jRkzRnv27Ck0KvR7SUlJGj9+fFmUDQAAXKxChJ3Lly+rb9++ys/P1+zZs+2WDRkyxPbv8PBwNWjQQBEREdq1a5eaN29e5PbGjBmjkSNH2uYzMzMVEhJSOsUDAACXKvdh5/Lly+rdu7eOHj2q9evX243qFKV58+aqUqWKDh8+XGzYsVqtslqtpVEuAAAoZ8p12CkIOocPH9aGDRtUs2bNa66zf/9+Xb58WUFBQWVQIQAAKO9cGnays7N15MgR2/zRo0e1e/du+fn5KTg4WA899JB27dqljz76SHl5eUpPT5ck+fn5yd3dXd99950WLVqkLl26qFatWjpw4IBGjRqlZs2aKTIy0lWHBQAAyhGXhp0dO3YoOjraNl9wH018fLwSExO1evVqSVLTpk3t1tuwYYOioqLk7u6uzz//XK+//rqys7MVEhKirl27aty4cXJzcyuz4wAAAOWXS8NOVFSUDMModnlJyyQpJCREmzZtcnZZAADARCrU5+wAAADcKMIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNYfCztGjR51dBwAAQKlwKOzUr19f0dHReu+993Tx4kVn1wQAAOA0DoWdPXv2qFmzZho1apQCAwP15JNP6j//+Y+zawMAALhpDoWd8PBwTZ8+XT/88IOSk5OVnp6ue++9V40aNdL06dP1888/O7tOAAAAh9zUDcqVK1dWz549tWzZMk2ePFnfffedRo8erTp16mjAgAFKS0tzVp0AAAAOuamws2PHDg0dOlRBQUGaPn26Ro8ere+++07r16/XDz/8oO7duzurTgAAAIdUdmSl6dOnKzk5WQcPHlSXLl20cOFCdenSRZUq/ZadwsLCNHfuXN1+++1OLRYAAOBGORR25syZo0GDBmngwIEKDAwssk/dunU1b968myoOAADgZjkUdg4fPnzNPu7u7oqPj3dk8wAAAE7j0D07ycnJWr58eaH25cuXa8GCBTddFAAAgLM4FHYmTZqkWrVqFWr39/fXxIkTb7ooAAAAZ3Eo7Bw/flxhYWGF2kNDQ3XixImbLgoAAMBZHAo7/v7++uabbwq179mzRzVr1rzu7WzevFmxsbEKDg6WxWLRqlWr7JYbhqHExEQFBwfL09NTUVFR2r9/v12f3NxcDR8+XLVq1ZK3t7cefPBBnTp1ypHDAgAAJuRQ2Onbt6/+/Oc/a8OGDcrLy1NeXp7Wr1+vESNGqG/fvte9nZycHDVp0kSzZs0qcvmUKVM0ffp0zZo1S9u3b1dgYKA6duyorKwsW59nn31WK1eu1JIlS7RlyxZlZ2erW7duysvLc+TQAACAyTj0NNaECRN0/PhxtW/fXpUr/7aJ/Px8DRgw4Ibu2YmJiVFMTEyRywzD0IwZM/TSSy8pLi5OkrRgwQIFBARo8eLFevLJJ3Xu3DnNmzdP7777rjp06CBJeu+99xQSEqLPPvtMDzzwgCOHBwAATMShkR13d3ctXbpU//3vf7Vo0SKtWLFC3333nebPny93d3enFHb06FGlp6erU6dOtjar1aq2bdvqq6++kiTt3LlTly9ftusTHBys8PBwWx8AAPDH5tDIToGGDRuqYcOGzqrFTnp6uiQpICDArj0gIEDHjx+39XF3d1eNGjUK9SlYvyi5ubnKzc21zWdmZjqrbAAAUM44FHby8vKUkpKizz//XBkZGcrPz7dbvn79eqcUJ0kWi8Vu3jCMQm1Xu1afpKQkjR8/3in1AQCA8s2hy1gjRozQiBEjlJeXp/DwcDVp0sRucoaCr6G4eoQmIyPDNtoTGBioS5cu6cyZM8X2KcqYMWN07tw523Ty5Emn1AwAAMofh0Z2lixZomXLlqlLly7OrscmLCxMgYGBSk1NVbNmzSRJly5d0qZNmzR58mRJ0t13360qVaooNTVVvXv3liSlpaVp3759mjJlSrHbtlqtslqtpVY7AAAoPxwKO+7u7qpfv/5N7zw7O1tHjhyxzR89elS7d++Wn5+f6tatq2effVYTJ05UgwYN1KBBA02cOFFeXl569NFHJUm+vr4aPHiwRo0apZo1a8rPz0+jR49W48aNbU9nAQCAPzaHws6oUaP0+uuva9asWde8f6YkO3bsUHR0tG1+5MiRkqT4+HilpKTo+eef14ULFzR06FCdOXNGLVu21KeffiofHx/bOq+99poqV66s3r1768KFC2rfvr1SUlLk5ubmcF0AAMA8HAo7W7Zs0YYNG7R27Vo1atRIVapUsVu+YsWK69pOVFSUDMModrnFYlFiYqISExOL7ePh4aGZM2dq5syZ17VPAADwx+JQ2Klevbp69uzp7FoAAACczqGwk5yc7Ow6AAAASoVDj55L0pUrV/TZZ59p7ty5tu+q+vHHH5Wdne204gAAAG6WQyM7x48fV+fOnXXixAnl5uaqY8eO8vHx0ZQpU3Tx4kW9+eabzq4TAADAIQ5/qGBERITOnDkjT09PW3vPnj31+eefO604AACAm+Xw01hffvlloS/9DA0N1Q8//OCUwgAAAJzBoZGd/Px85eXlFWo/deqU3WfgAAAAuJpDYadjx46aMWOGbd5isSg7O1vjxo0r1a+QAAAAuFEOXcZ67bXXFB0drTvvvFMXL17Uo48+qsOHD6tWrVr65z//6ewaAQAAHOZQ2AkODtbu3bv1z3/+U7t27VJ+fr4GDx6sfv362d2wDAAA4GoOhR1J8vT01KBBgzRo0CBn1gMAAOBUDoWdhQsXlrh8wIABDhUDAADgbA6FnREjRtjNX758WefPn5e7u7u8vLwIOwAAoNxw6GmsM2fO2E3Z2dk6ePCg7r33Xm5QBgAA5YrD3411tQYNGmjSpEmFRn0AAABcyWlhR5Lc3Nz0448/OnOTAAAAN8Whe3ZWr15tN28YhtLS0jRr1ixFRkY6pTAAAABncCjs9OjRw27eYrGodu3aateunaZNm+aMugAAAJzCobCTn5/v7DoAAABKhVPv2QEAAChvHBrZGTly5HX3nT59uiO7AAAAcAqHws7XX3+tXbt26cqVK7rtttskSYcOHZKbm5uaN29u62exWJxTJQAAgIMcCjuxsbHy8fHRggULVKNGDUm/fdDgwIEDdd9992nUqFFOLRIAAMBRDt2zM23aNCUlJdmCjiTVqFFDEyZM4GksAABQrjgUdjIzM/XTTz8Vas/IyFBWVtZNFwUAAOAsDoWdnj17auDAgfrXv/6lU6dO6dSpU/rXv/6lwYMHKy4uztk1AgAAOMyhe3befPNNjR49Wv3799fly5d/21Dlyho8eLCmTp3q1AIBAABuhkNhx8vLS7Nnz9bUqVP13XffyTAM1a9fX97e3s6uDwAA4Kbc1IcKpqWlKS0tTQ0bNpS3t7cMw3BWXQAAAE7hUNg5ffq02rdvr4YNG6pLly5KS0uTJD3++OM8dg4AAMoVh8LOc889pypVqujEiRPy8vKytffp00effPKJ04oDAAC4WQ7ds/Ppp59q3bp1qlOnjl17gwYNdPz4cacUBgAA4AwOjezk5OTYjegU+OWXX2S1Wm+6KAAAAGdxKOzcf//9WrhwoW3eYrEoPz9fU6dOVXR0tNOKAwAAuFkOXcaaOnWqoqKitGPHDl26dEnPP/+89u/fr19//VVffvmls2sEAABwmEMjO3feeae++eYb3XPPPerYsaNycnIUFxenr7/+WrfeequzawQAAHDYDY/sXL58WZ06ddLcuXM1fvz40qgJAADAaW54ZKdKlSrat2+fLBZLadQDAADgVA5dxhowYIDmzZvn7FoAAACczqEblC9duqR33nlHqampioiIKPSdWNOnT3dKcQAAADfrhkZ2vv/+e+Xn52vfvn1q3ry5qlWrpkOHDunrr7+2Tbt373ZqgfXq1ZPFYik0DRs2TJKUkJBQaFmrVq2cWgMAAKi4bmhkp0GDBkpLS9OGDRsk/fb1EG+88YYCAgJKpThJ2r59u/Ly8mzz+/btU8eOHfXwww/b2jp37qzk5GTbvLu7e6nVAwAAKpYbCjtXf6v52rVrlZOT49SCrla7dm27+UmTJunWW29V27ZtbW1Wq1WBgYGlWgcAAKiYHLpBucDV4ae0Xbp0Se+9954GDRpk9zTYxo0b5e/vr4YNG2rIkCHKyMgocTu5ubnKzMy0mwAAgDndUNgpuCfm6raysmrVKp09e1YJCQm2tpiYGC1atEjr16/XtGnTtH37drVr1065ubnFbicpKUm+vr62KSQkpAyqBwAArnDDl7ESEhJsX/Z58eJFPfXUU4WexlqxYoXzKvydefPmKSYmRsHBwba2Pn362P4dHh6uiIgIhYaGas2aNYqLiytyO2PGjNHIkSNt85mZmQQeAABM6obCTnx8vN18//79nVpMSY4fP67PPvvsmkEqKChIoaGhOnz4cLF9rFYr384OAMAfxA2Fnd8/8VTWkpOT5e/vr65du5bY7/Tp0zp58qSCgoLKqDIAAFCe3dQNymUlPz9fycnJio+PV+XK/8tn2dnZGj16tLZu3apjx45p48aNio2NVa1atdSzZ08XVgwAAMoLhz5Buax99tlnOnHihAYNGmTX7ubmpr1792rhwoU6e/asgoKCFB0draVLl8rHx8dF1QIAgPKkQoSdTp06FfmYu6enp9atW+eCigAAQEVRIS5jAQAAOIqwAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATK1ch53ExERZLBa7KTAw0LbcMAwlJiYqODhYnp6eioqK0v79+11YMQAAKG/KddiRpEaNGiktLc027d2717ZsypQpmj59umbNmqXt27crMDBQHTt2VFZWlgsrBgAA5Um5DzuVK1dWYGCgbapdu7ak30Z1ZsyYoZdeeklxcXEKDw/XggULdP78eS1evNjFVQMAgPKi3Iedw4cPKzg4WGFhYerbt6++//57SdLRo0eVnp6uTp062fparVa1bdtWX331VYnbzM3NVWZmpt0EAADMqVyHnZYtW2rhwoVat26d3n77baWnp6tNmzY6ffq00tPTJUkBAQF26wQEBNiWFScpKUm+vr62KSQkpNSOAQAAuFa5DjsxMTHq1auXGjdurA4dOmjNmjWSpAULFtj6WCwWu3UMwyjUdrUxY8bo3LlztunkyZPOLx4AAJQL5TrsXM3b21uNGzfW4cOHbU9lXT2Kk5GRUWi052pWq1XVqlWzmwAAgDlVqLCTm5urb7/9VkFBQQoLC1NgYKBSU1Ntyy9duqRNmzapTZs2LqwSAACUJ5VdXUBJRo8erdjYWNWtW1cZGRmaMGGCMjMzFR8fL4vFomeffVYTJ05UgwYN1KBBA02cOFFeXl569NFHXV06AAAoJ8p12Dl16pQeeeQR/fLLL6pdu7ZatWqlbdu2KTQ0VJL0/PPP68KFCxo6dKjOnDmjli1b6tNPP5WPj4+LKwcAAOVFuQ47S5YsKXG5xWJRYmKiEhMTy6YgAABQ4VSoe3YAAABuFGEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYWrkOO0lJSWrRooV8fHzk7++vHj166ODBg3Z9EhISZLFY7KZWrVq5qGIAAFDelOuws2nTJg0bNkzbtm1Tamqqrly5ok6dOiknJ8euX+fOnZWWlmabPv74YxdVDAAAypvKri6gJJ988ondfHJysvz9/bVz507df//9tnar1arAwMCyLg8AAFQA5Xpk52rnzp2TJPn5+dm1b9y4Uf7+/mrYsKGGDBmijIyMEreTm5urzMxMuwkAAJhThQk7hmFo5MiRuvfeexUeHm5rj4mJ0aJFi7R+/XpNmzZN27dvV7t27ZSbm1vstpKSkuTr62ubQkJCyuIQAACAC5Try1i/98wzz+ibb77Rli1b7Nr79Olj+3d4eLgiIiIUGhqqNWvWKC4urshtjRkzRiNHjrTNZ2ZmEngAADCpChF2hg8frtWrV2vz5s2qU6dOiX2DgoIUGhqqw4cPF9vHarXKarU6u0wAAFAOleuwYxiGhg8frpUrV2rjxo0KCwu75jqnT5/WyZMnFRQUVAYVAgCA8q5c37MzbNgwvffee1q8eLF8fHyUnp6u9PR0XbhwQZKUnZ2t0aNHa+vWrTp27Jg2btyo2NhY1apVSz179nRx9QAAoDwo1yM7c+bMkSRFRUXZtScnJyshIUFubm7au3evFi5cqLNnzyooKEjR0dFaunSpfHx8XFAxAAAob8p12DEMo8Tlnp6eWrduXRlVAwAAKqJyfRkLAADgZhF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqZkm7MyePVthYWHy8PDQ3XffrS+++MLVJQEAgHLAFGFn6dKlevbZZ/XSSy/p66+/1n333aeYmBidOHHC1aUBAAAXM0XYmT59ugYPHqzHH39cd9xxh2bMmKGQkBDNmTPH1aUBAAAXq/Bh59KlS9q5c6c6depk196pUyd99dVXLqoKAACUF5VdXcDN+uWXX5SXl6eAgAC79oCAAKWnpxe5Tm5urnJzc23z586dkyRlZmY6vb783PNO32ZZKI1zUdoq4rnmPJcNznPZ4DyjOKX13ijYrmEYJfar8GGngMVisZs3DKNQW4GkpCSNHz++UHtISEip1FYR+c5wdQV/DJznssF5LhucZxSntN8bWVlZ8vX1LXZ5hQ87tWrVkpubW6FRnIyMjEKjPQXGjBmjkSNH2ubz8/P166+/qmbNmsUGJEdkZmYqJCREJ0+eVLVq1Zy2XRTGuS4bnOeywXkuG5znslGa59kwDGVlZSk4OLjEfhU+7Li7u+vuu+9WamqqevbsaWtPTU1V9+7di1zHarXKarXatVWvXr3UaqxWrRo/SGWEc102OM9lg/NcNjjPZaO0znNJIzoFKnzYkaSRI0fqscceU0REhFq3bq233npLJ06c0FNPPeXq0gAAgIuZIuz06dNHp0+f1l//+lelpaUpPDxcH3/8sUJDQ11dGgAAcDFThB1JGjp0qIYOHerqMuxYrVaNGzeu0CUzOB/numxwnssG57lscJ7LRnk4zxbjWs9rAQAAVGAV/kMFAQAASkLYAQAApkbYAQAApkbYAQAApkbYKSWbN29WbGysgoODZbFYtGrVKleXZDpJSUlq0aKFfHx85O/vrx49eujgwYOuLst05syZo7vuusv2gWCtW7fW2rVrXV2W6SUlJclisejZZ591dSmmk5iYKIvFYjcFBga6uixT+uGHH9S/f3/VrFlTXl5eatq0qXbu3FnmdRB2SklOTo6aNGmiWbNmuboU09q0aZOGDRumbdu2KTU1VVeuXFGnTp2Uk5Pj6tJMpU6dOpo0aZJ27NihHTt2qF27durevbv279/v6tJMa/v27Xrrrbd01113uboU02rUqJHS0tJs0969e11dkumcOXNGkZGRqlKlitauXasDBw5o2rRppfqNBcUxzefslDcxMTGKiYlxdRmm9sknn9jNJycny9/fXzt37tT999/voqrMJzY21m7+1Vdf1Zw5c7Rt2zY1atTIRVWZV3Z2tvr166e3335bEyZMcHU5plW5cmVGc0rZ5MmTFRISouTkZFtbvXr1XFILIzswjXPnzkmS/Pz8XFyJeeXl5WnJkiXKyclR69atXV2OKQ0bNkxdu3ZVhw4dXF2KqR0+fFjBwcEKCwtT37599f3337u6JNNZvXq1IiIi9PDDD8vf31/NmjXT22+/7ZJaCDswBcMwNHLkSN17770KDw93dTmms3fvXlWtWlVWq1VPPfWUVq5cqTvvvNPVZZnOkiVLtGvXLiUlJbm6FFNr2bKlFi5cqHXr1untt99Wenq62rRpo9OnT7u6NFP5/vvvNWfOHDVo0EDr1q3TU089pT//+c9auHBhmdfCZSyYwjPPPKNvvvlGW7ZscXUppnTbbbdp9+7dOnv2rN5//33Fx8dr06ZNBB4nOnnypEaMGKFPP/1UHh4eri7H1H5/i0Hjxo3VunVr3XrrrVqwYIFGjhzpwsrMJT8/XxEREZo4caIkqVmzZtq/f7/mzJmjAQMGlGktjOygwhs+fLhWr16tDRs2qE6dOq4ux5Tc3d1Vv359RUREKCkpSU2aNNHrr7/u6rJMZefOncrIyNDdd9+typUrq3Llytq0aZPeeOMNVa5cWXl5ea4u0bS8vb3VuHFjHT582NWlmEpQUFChP4juuOMOnThxosxrYWQHFZZhGBo+fLhWrlypjRs3KiwszNUl/WEYhqHc3FxXl2Eq7du3L/RE0MCBA3X77bfrhRdekJubm4sqM7/c3Fx9++23uu+++1xdiqlERkYW+jiQQ4cOKTQ0tMxrIeyUkuzsbB05csQ2f/ToUe3evVt+fn6qW7euCyszj2HDhmnx4sX64IMP5OPjo/T0dEmSr6+vPD09XVydeYwdO1YxMTEKCQlRVlaWlixZoo0bNxZ6Gg43x8fHp9D9Zt7e3qpZsyb3oTnZ6NGjFRsbq7p16yojI0MTJkxQZmam4uPjXV2aqTz33HNq06aNJk6cqN69e+s///mP3nrrLb311ltlX4yBUrFhwwZDUqEpPj7e1aWZRlHnV5KRnJzs6tJMZdCgQUZoaKjh7u5u1K5d22jfvr3x6aefurqsP4S2bdsaI0aMcHUZptOnTx8jKCjIqFKlihEcHGzExcUZ+/fvd3VZpvThhx8a4eHhhtVqNW6//XbjrbfeckkdFsMwjLKPWAAAAGWDG5QBAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAuJTFYtGqVatcXUaJEhIS1KNHD1eXAcBBhB0ATpeQkCCLxSKLxaIqVaooICBAHTt21Pz585Wfn2/XNy0tze5bqJ1p+PDhatCgQZHLfvjhB7m5uWnFihWlsm8A5QdhB0Cp6Ny5s9LS0nTs2DGtXbtW0dHRGjFihLp166YrV67Y+gUGBspqtZZKDYMHD9aRI0f0xRdfFFqWkpKimjVrKjY2tlT2DaD8IOwAKBVWq1WBgYH605/+pObNm2vs2LH64IMPtHbtWqWkpNj6XX0Z69SpU+rbt6/8/Pzk7e2tiIgI/fvf/7Yt//DDD3X33XfLw8NDt9xyi8aPH28Xnn6vadOmat68uebPn19oWUpKigYMGKBKlSpp8ODBCgsLk6enp2677Ta9/vrrJR5bvXr1NGPGjEL7SkxMtM2fO3dOTzzxhPz9/VWtWjW1a9dOe/bsKXG7AEoHYQdAmWnXrp2aNGlS7KWj7OxstW3bVj/++KNWr16tPXv26Pnnn7dd+lq3bp369++vP//5zzpw4IDmzp2rlJQUvfrqq8Xuc/DgwVq+fLmys7NtbZs2bdKRI0c0aNAg5efnq06dOlq2bJkOHDigV155RWPHjtWyZcscPk7DMNS1a1elp6fr448/1s6dO9W8eXO1b99ev/76q8PbBeAYwg6AMnX77bfr2LFjRS5bvHixfv75Z61atUr33nuv6tevr969e6t169aSpFdffVUvvvii4uPjdcstt6hjx47629/+prlz5xa7v0cffVR5eXlavny5rW3+/Plq3bq17rzzTlWpUkXjx49XixYtFBYWpn79+ikhIeGmws6GDRu0d+9eLV++XBEREWrQoIH+/ve/q3r16vrXv/7l8HYBOKayqwsA8MdiGIYsFkuRy3bv3q1mzZrJz8+vyOU7d+7U9u3b7UZy8vLydPHiRZ0/f15eXl6F1qlevbri4uI0f/58DRw4UFlZWXr//fftLkO9+eabeuedd3T8+HFduHBBly5dUtOmTR0+xp07dyo7O1s1a9a0a79w4YK+++47h7cLwDGEHQBl6ttvv1VYWFiRyzw9PUtcNz8/X+PHj1dcXFyhZR4eHsWuN3jwYLVv316HDx/Wpk2bJEl9+vSRJC1btkzPPfecpk2bptatW8vHx0dTp061u0/oapUqVZJhGHZtly9ftqszKChIGzduLLRu9erVSzpEAKWAsAOgzKxfv1579+7Vc889V+Tyu+66S++8845+/fXXIkd3mjdvroMHD6p+/fo3tN/o6GjdcsstSklJ0YYNG9S7d2/5+PhIkr744gu1adNGQ4cOtfW/1uhL7dq1lZaWZpvPzMzU0aNH7epMT09X5cqVVa9evRuqFYDzcc8OgFKRm5ur9PR0/fDDD9q1a5cmTpyo7t27q1u3bhowYECR6zzyyCMKDAxUjx499OWXX+r777/X+++/r61bt0qSXnnlFS1cuFCJiYnav3+/vv32Wy1dulQvv/xyibVYLBYNHDhQc+bM0datWzV48GDbsvr162vHjh1at26dDh06pL/85S/avn17idtr166d3n33XX3xxRfat2+f4uPj5ebmZlveoUMHtW7dWj169NC6det07NgxffXVV3r55Ze1Y8eO6z2FAJyEsAOgVHzyyScKCgpSvXr11LlzZ23YsEFvvPGGPvjgA7tg8Hvu7u769NNP5e/vry5duqhx48aaNGmSrf8DDzygjz76SKmpqWrRooVatWql6dOnKzQ09Jr1JCQk6Ny5c7rtttsUGRlpa3/qqacUFxenPn36qGXLljp9+rTdKE9RxowZo/vvv1/dunVTly5d1KNHD91666225RaLRR9//LHuv/9+DRo0SA0bNlTfvn117NgxBQQEXM/pA+BEFuPqC88AAAAmwsgOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwtf8H/RH03tWbS+kAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "\n",
+    "roll_the_dice_thousand = pd.read_csv('../data/roll_the_dice_thousand.csv')\n",
+    "roll_the_dice_thousand.head()\n",
+    "\n",
+    "plt.hist(roll_the_dice_thousand['value'])\n",
+    "\n",
+    "plt.xlabel('Dice Value')\n",
+    "plt.ylabel('Frequency')\n",
+    "plt.title('Dice Rolls Frequency Distribution')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 497,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"\\nyour comments here\\nThe numbers that appear when rolling the dice seem to be more evenly distributed, indicating that as the number of rolls increases, there's a higher possibility of having equal frequencies for the numbers appearing. \\n\""
+      ]
+     },
+     "execution_count": 497,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "your comments here\n",
+    "The numbers that appear when rolling the dice seem to be more evenly distributed, indicating that as the number of rolls increases, there's a higher possibility of having equal frequencies for the numbers appearing. \n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Challenge 4\n",
+    "In the `data` folder of this repository you will find three different files with the prefix `ages_population`. These files contain information about a poll answered by a thousand people regarding their age. Each file corresponds to the poll answers in different neighbourhoods of Barcelona.\n",
+    "\n",
+    "#### 1.- Read the file `ages_population.csv`. Calculate the frequency distribution and plot it as we did during the lesson. Try to guess the range in which the mean and the standard deviation will be by looking at the plot. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 504,
+   "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>observation</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>68.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>12.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>45.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>38.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>49.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   observation\n",
+       "0         68.0\n",
+       "1         12.0\n",
+       "2         45.0\n",
+       "3         38.0\n",
+       "4         49.0"
+      ]
+     },
+     "execution_count": 504,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "ages_population = pd.read_csv('../data/ages_population.csv')\n",
+    "ages_population.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 505,
+   "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>frequency</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>39.0</th>\n",
+       "      <td>45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41.0</th>\n",
+       "      <td>36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30.0</th>\n",
+       "      <td>34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35.0</th>\n",
+       "      <td>33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43.0</th>\n",
+       "      <td>32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73.0</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>82.0</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>70.0</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>71.0</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>69.0</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>72 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      frequency\n",
+       "39.0         45\n",
+       "41.0         36\n",
+       "30.0         34\n",
+       "35.0         33\n",
+       "43.0         32\n",
+       "...         ...\n",
+       "73.0          1\n",
+       "82.0          1\n",
+       "70.0          1\n",
+       "71.0          1\n",
+       "69.0          1\n",
+       "\n",
+       "[72 rows x 1 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "frequency_distribution = pd.DataFrame(ages_population['observation'].value_counts())\n",
+    "frequency_distribution = frequency_distribution.rename(columns={'observation': 'frequency'})\n",
+    "display(frequency_distribution)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 506,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.bar(frequency_distribution.index, frequency_distribution['frequency'])\n",
+    "plt.xlabel('Ages')\n",
+    "plt.ylabel('Frequency')\n",
+    "plt.title('Population Age Frequency Distribution')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 507,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "incomplete input (1764359519.py, line 1)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  Cell \u001b[0;32mIn[507], line 1\u001b[0;36m\u001b[0m\n\u001b[0;31m    ''''\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m incomplete input\n"
+     ]
+    }
+   ],
+   "source": [
+    "''''\n",
+    "We see the symmetrical distribution. Judging from the plot, I believe the mean is approximately 40. However, I don't have an estimate for the standard deviation.\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.- Calculate the exact mean and standard deviation and compare them with your guesses. Do they fall inside the ranges you guessed?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 508,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The mean is : 36.56\n",
+      "The standard deviation is: 12.816499625976762\n"
+     ]
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "print('The mean is :', ages_population['observation'].mean())\n",
+    "print('The standard deviation is:', ages_population['observation'].std())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 509,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nyour comments here\\n\\nThe exact mean value is 36,56 which is  pretty close to my estimate.\\n'"
+      ]
+     },
+     "execution_count": 509,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "your comments here\n",
+    "\n",
+    "The exact mean value is 36,56 which is  pretty close to my estimate.\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.- Now read the file `ages_population2.csv` . Calculate the frequency distribution and plot it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 510,
+   "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>observation</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>25.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>31.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>29.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>31.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>29.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   observation\n",
+       "0         25.0\n",
+       "1         31.0\n",
+       "2         29.0\n",
+       "3         31.0\n",
+       "4         29.0"
+      ]
+     },
+     "execution_count": 510,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "ages_population2 = pd.read_csv('../data/ages_population2.csv')\n",
+    "ages_population2.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 511,
+   "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>frequency</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>28.0</th>\n",
+       "      <td>139</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27.0</th>\n",
+       "      <td>125</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26.0</th>\n",
+       "      <td>120</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29.0</th>\n",
+       "      <td>115</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25.0</th>\n",
+       "      <td>98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30.0</th>\n",
+       "      <td>90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24.0</th>\n",
+       "      <td>78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31.0</th>\n",
+       "      <td>61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23.0</th>\n",
+       "      <td>41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22.0</th>\n",
+       "      <td>35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32.0</th>\n",
+       "      <td>31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33.0</th>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21.0</th>\n",
+       "      <td>17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20.0</th>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34.0</th>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19.0</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35.0</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36.0</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      frequency\n",
+       "28.0        139\n",
+       "27.0        125\n",
+       "26.0        120\n",
+       "29.0        115\n",
+       "25.0         98\n",
+       "30.0         90\n",
+       "24.0         78\n",
+       "31.0         61\n",
+       "23.0         41\n",
+       "22.0         35\n",
+       "32.0         31\n",
+       "33.0         22\n",
+       "21.0         17\n",
+       "20.0         13\n",
+       "34.0          7\n",
+       "19.0          3\n",
+       "35.0          3\n",
+       "36.0          2"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "frequency_distribution2 = pd.DataFrame(ages_population2['observation'].value_counts())\n",
+    "frequency_distribution2 = frequency_distribution2.rename(columns={'observation': 'frequency'})\n",
+    "display(frequency_distribution2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 512,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.bar(frequency_distribution2.index, frequency_distribution2['frequency'])\n",
+    "plt.xlabel('Ages')\n",
+    "plt.ylabel('Frequency')\n",
+    "plt.title('Population Age Frequency Distribution')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "####  4.- What do you see? Is there any difference with the frequency distribution in step 1?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "your comments here\n",
+    "I observe a symmetrical distribution which is same with previous plot. However, the population in this survey was significantly younger, as all values fall between 20 and 35 years old. I estimate the mean to be around 28\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 5.- Calculate the mean and standard deviation. Compare the results with the mean and standard deviation in step 2. What do you think?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 513,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The mean is : 27.155\n",
+      "The standard deviation is: 2.969813932689186\n"
+     ]
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "print('The mean is :', ages_population2['observation'].mean())\n",
+    "print('The standard deviation is:', ages_population2['observation'].std())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "your comments here\n",
+    "Since the population is younger than in the previous survey, the mean value is smaller, as expected. The standard deviation is also smaller, given the narrower range (indicates less variation) in the population's age.\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Challenge 5\n",
+    "Now is the turn of `ages_population3.csv`.\n",
+    "\n",
+    "#### 1.- Read the file `ages_population3.csv`. Calculate the frequency distribution and plot it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 514,
+   "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>observation</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>21.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>21.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>24.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>31.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>54.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   observation\n",
+       "0         21.0\n",
+       "1         21.0\n",
+       "2         24.0\n",
+       "3         31.0\n",
+       "4         54.0"
+      ]
+     },
+     "execution_count": 514,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "ages_population3 = pd.read_csv('../data/ages_population3.csv')\n",
+    "ages_population3.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 515,
+   "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>frequency</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>32.0</th>\n",
+       "      <td>37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35.0</th>\n",
+       "      <td>31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37.0</th>\n",
+       "      <td>31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39.0</th>\n",
+       "      <td>29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36.0</th>\n",
+       "      <td>26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>76.0</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8.0</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9.0</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1.0</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7.0</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>75 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      frequency\n",
+       "32.0         37\n",
+       "35.0         31\n",
+       "37.0         31\n",
+       "39.0         29\n",
+       "36.0         26\n",
+       "...         ...\n",
+       "76.0          1\n",
+       "8.0           1\n",
+       "9.0           1\n",
+       "1.0           1\n",
+       "7.0           1\n",
+       "\n",
+       "[75 rows x 1 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "frequency_distribution3 = pd.DataFrame(ages_population3['observation'].value_counts())\n",
+    "frequency_distribution3 = frequency_distribution3.rename(columns={'observation': 'frequency'})\n",
+    "display(frequency_distribution3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 516,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.bar(frequency_distribution3.index, frequency_distribution3['frequency'])\n",
+    "plt.xlabel('Ages')\n",
+    "plt.ylabel('Frequency')\n",
+    "plt.title('Population Age Frequency Distribution')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.- Calculate the mean and standard deviation. Compare the results with the plot in step 1. What is happening?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 517,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The mean is : 41.989\n",
+      "The standard deviation is: 16.144705959865934\n"
+     ]
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "print('The mean is :', ages_population3['observation'].mean())\n",
+    "print('The standard deviation is:', ages_population3['observation'].std())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "your comments here\n",
+    "In this survey, we observe a highly diverse population. Estimating the mean value based on the plot is challenging, as we do not observe a symmetric distribution. The standard deviation is also higher, as expected, due to the substantial variation within the data.\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.- Calculate the four quartiles. Use the results to explain your reasoning for question in step 2. How much of a difference is there between the median and the mean?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 520,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "the first quartile is 30.0\n",
+      "the second quartile is 40.0\n",
+      "the third quartile is 53.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "Q1 = np.quantile(ages_population3['observation'], 0.25)\n",
+    "print(\"the first quartile is\", Q1)\n",
+    "Q2 = np.quantile(ages_population3['observation'], 0.50)\n",
+    "print(\"the second quartile is\",Q2)\n",
+    "Q3 = np.quantile(ages_population3['observation'], 0.75)\n",
+    "print(\"the third quartile is\", Q3)\n",
+    "\n",
+    "plt.boxplot(ages_population3['observation'])\n",
+    "plt.title('Box Plot')\n",
+    "plt.ylabel('Values')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "your comments here\n",
+    "The mean value is slightly higher than the median, indicating that there are more individuals with higher ages than younger individuals(slightly positive skewdness). "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 4.- Calculate other percentiles that might be useful to give more arguments to your reasoning."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 522,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "P1: 28.0\n",
+      "P2: 36.0\n",
+      "P3: 45.0\n",
+      "P4: 57.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "P1 = np.percentile(ages_population3['observation'], 20)\n",
+    "P2 = np.percentile(ages_population3['observation'], 40)\n",
+    "P3 = np.percentile(ages_population3['observation'], 60)\n",
+    "P4 = np.percentile(ages_population3['observation'], 80)\n",
+    "\n",
+    "print(\"P1:\", P1)\n",
+    "print(\"P2:\", P2)\n",
+    "print(\"P3:\", P3)\n",
+    "print(\"P4:\", P4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "your comments here\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Bonus challenge\n",
+    "Compare the information about the three neighbourhoods. Prepare a report about the three of them. Remember to find out which are their similarities and their differences backing your arguments in basic statistics."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# your code here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "your comments here\n",
+    "\"\"\""
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/your-code/main.ipynb b/your-code/main.ipynb
index 5759add..c098d54 100644
--- a/your-code/main.ipynb
+++ b/your-code/main.ipynb
@@ -1,522 +1,1902 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Understanding Descriptive Statistics\n",
-    "\n",
-    "Import the necessary libraries here:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Libraries"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Challenge 1\n",
-    "#### 1.- Define a function that simulates rolling a dice 10 times. Save the information in a dataframe.\n",
-    "**Hint**: you can use the *choices* function from module *random* to help you with the simulation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 2.- Plot the results sorted by value."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 3.- Calculate the frequency distribution and plot it. What is the relation between this plot and the plot above? Describe it with words."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Challenge 2\n",
-    "Now, using the dice results obtained in *challenge 1*, your are going to define some functions that will help you calculate the mean of your data in two different ways, the median and the four quartiles. \n",
-    "\n",
-    "#### 1.- Define a function that computes the mean by summing all the observations and dividing by the total number of observations. You are not allowed to use any methods or functions that directly calculate the mean value. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 2.- First, calculate the frequency distribution. Then, calculate the mean using the values of the frequency distribution you've just computed. You are not allowed to use any methods or functions that directly calculate the mean value. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 3.- Define a function to calculate the median. You are not allowed to use any methods or functions that directly calculate the median value. \n",
-    "**Hint**: you might need to define two computation cases depending on the number of observations used to calculate the median."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 4.- Define a function to calculate the four quartiles. You can use the function you defined above to compute the median but you are not allowed to use any methods or functions that directly calculate the quartiles. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Challenge 3\n",
-    "Read the csv `roll_the_dice_hundred.csv` from the `data` folder.\n",
-    "#### 1.- Sort the values and plot them. What do you see?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 2.- Using the functions you defined in *challenge 2*, calculate the mean value of the hundred dice rolls."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 3.- Now, calculate the frequency distribution.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 4.- Plot the histogram. What do you see (shape, values...) ? How can you connect the mean value to the histogram? "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 5.- Read the `roll_the_dice_thousand.csv` from the `data` folder. Plot the frequency distribution as you did before. Has anything changed? Why do you think it changed?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Challenge 4\n",
-    "In the `data` folder of this repository you will find three different files with the prefix `ages_population`. These files contain information about a poll answered by a thousand people regarding their age. Each file corresponds to the poll answers in different neighbourhoods of Barcelona.\n",
-    "\n",
-    "#### 1.- Read the file `ages_population.csv`. Calculate the frequency distribution and plot it as we did during the lesson. Try to guess the range in which the mean and the standard deviation will be by looking at the plot. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 2.- Calculate the exact mean and standard deviation and compare them with your guesses. Do they fall inside the ranges you guessed?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 3.- Now read the file `ages_population2.csv` . Calculate the frequency distribution and plot it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "####  4.- What do you see? Is there any difference with the frequency distribution in step 1?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 5.- Calculate the mean and standard deviation. Compare the results with the mean and standard deviation in step 2. What do you think?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Challenge 5\n",
-    "Now is the turn of `ages_population3.csv`.\n",
-    "\n",
-    "#### 1.- Read the file `ages_population3.csv`. Calculate the frequency distribution and plot it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 2.- Calculate the mean and standard deviation. Compare the results with the plot in step 1. What is happening?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 3.- Calculate the four quartiles. Use the results to explain your reasoning for question in step 2. How much of a difference is there between the median and the mean?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 4.- Calculate other percentiles that might be useful to give more arguments to your reasoning."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Bonus challenge\n",
-    "Compare the information about the three neighbourhoods. Prepare a report about the three of them. Remember to find out which are their similarities and their differences backing your arguments in basic statistics."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "ironhack-3.7",
-   "language": "python",
-   "name": "ironhack-3.7"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Understanding Descriptive Statistics\n",
+    "\n",
+    "Import the necessary libraries here:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Libraries\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import random \n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy import stats"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Challenge 1\n",
+    "#### 1.- Define a function that simulates rolling a dice 10 times. Save the information in a dataframe.\n",
+    "**Hint**: you can use the *choices* function from module *random* to help you with the simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "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>Rolls</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Rolls\n",
+       "0      4\n",
+       "1      5\n",
+       "2      5\n",
+       "3      6\n",
+       "4      4\n",
+       "5      2\n",
+       "6      6\n",
+       "7      4\n",
+       "8      1\n",
+       "9      3"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "\n",
+    "numbers = [1,2,3,4,5,6]\n",
+    "dice_roll = random.choices(numbers, k=10)\n",
+    "dice_roll = pd.DataFrame({\"Rolls\":dice_roll})\n",
+    "dice_roll"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.- Plot the results sorted by value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "\n",
+    "sorted_dice_roll = dice_roll.sort_values('Rolls')\n",
+    "plt.plot(sorted_dice_roll,'ro')\n",
+    "plt.xlabel(\"Roll number\")\n",
+    "plt.ylabel(\"Dice Value\")\n",
+    "plt.title(\"Sorted Dice Rolls\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.- Calculate the frequency distribution and plot it. What is the relation between this plot and the plot above? Describe it with words."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "freq_dist_dice_roll = dice_roll['Rolls'].value_counts().sort_index()\n",
+    "plt.bar(freq_dist_dice_roll.index, freq_dist_dice_roll)\n",
+    "\n",
+    "plt.xlabel('Dice Value')\n",
+    "plt.ylabel('Frequency')\n",
+    "plt.title('Dice Rolls Frequency Distribution')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nyour comments here\\n\\nThe first plot displays the relationship between the roll number and the value.\\nThe second plot displays how frequently each dice value appeared in the rolls, regardless of the order they appeared in.It provides an overview of the distribution of dice values, indicating which values were more or less frequent in the rolls.\\n'"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "your comments here\n",
+    "\n",
+    "The first plot displays the relationship between the roll number and the value.\n",
+    "The second plot displays how frequently each dice value appeared in the rolls, regardless of the order they appeared in.It provides an overview of the distribution of dice values, indicating which values were more or less frequent in the rolls.\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Challenge 2\n",
+    "Now, using the dice results obtained in *challenge 1*, your are going to define some functions that will help you calculate the mean of your data in two different ways, the median and the four quartiles. \n",
+    "\n",
+    "#### 1.- Define a function that computes the mean by summing all the observations and dividing by the total number of observations. You are not allowed to use any methods or functions that directly calculate the mean value. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4.0"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "def mean(col):\n",
+    "    return col.sum()/len(col)\n",
+    "mean_value = mean(dice_roll['Rolls'])\n",
+    "mean_value"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.- First, calculate the frequency distribution. Then, calculate the mean using the values of the frequency distribution you've just computed. You are not allowed to use any methods or functions that directly calculate the mean value. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "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>Rolls</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Rolls\n",
+       "4      3\n",
+       "5      2\n",
+       "6      2\n",
+       "2      1\n",
+       "1      1\n",
+       "3      1"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "4.0"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "frequency_distribution = pd.DataFrame(dice_roll['Rolls'].value_counts())\n",
+    "display(frequency_distribution)\n",
+    "\n",
+    "def mean(col):\n",
+    "    return ((col.index)*col).sum()/col.sum()\n",
+    "\n",
+    "mean_value_with_frequency = mean(frequency_distribution['Rolls'])\n",
+    "mean_value_with_frequency "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.- Define a function to calculate the median. You are not allowed to use any methods or functions that directly calculate the median value. \n",
+    "**Hint**: you might need to define two computation cases depending on the number of observations used to calculate the median."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "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>Rolls</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Rolls\n",
+       "8      1\n",
+       "5      2\n",
+       "9      3\n",
+       "0      4\n",
+       "4      4\n",
+       "7      4\n",
+       "1      5\n",
+       "2      5\n",
+       "3      6\n",
+       "6      6"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "4.0"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "def median(col):\n",
+    "    sorted_dice_roll = col.sort_values().reset_index(drop=True)\n",
+    "    n = len(col)\n",
+    "    \n",
+    "    if n % 2 != 0:  \n",
+    "         return sorted_dice_roll[n // 2]\n",
+    "    else:  \n",
+    "        return (sorted_dice_roll[n / 2 - 1] + sorted_dice_roll[n / 2]) / 2\n",
+    "    \n",
+    "display(sorted_dice_roll)    \n",
+    "median_value = median(dice_roll['Rolls'])\n",
+    "median_value\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 4.- Define a function to calculate the four quartiles. You can use the function you defined above to compute the median but you are not allowed to use any methods or functions that directly calculate the quartiles. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "the first quartile is 3\n",
+      "the second quartile is 4.0\n",
+      "the third quartile is 5\n"
+     ]
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "\n",
+    "def quartile(col):\n",
+    "    sorted_dice_roll = col.sort_values().reset_index(drop=True)\n",
+    "    n=len(col)\n",
+    "    mid = n // 2\n",
+    "\n",
+    "    if n % 2 == 0:  \n",
+    "        lower_half = sorted_dice_roll[:mid]\n",
+    "        upper_half = sorted_dice_roll[mid:]\n",
+    "        Q1 = median(lower_half)\n",
+    "        Q3 = median(upper_half)\n",
+    "    else:  \n",
+    "        lower_half = sorted_dice_roll[:mid]\n",
+    "        upper_half = sorted_dice_roll[mid + 1:]\n",
+    "        Q1 = median(lower_half)\n",
+    "        Q3 = median(upper_half)\n",
+    "\n",
+    "    Q2 = median(col)  \n",
+    "    print(\"the first quartile is\", Q1)\n",
+    "    print(\"the second quartile is\",Q2)\n",
+    "    print(\"the third quartile is\", Q3)\n",
+    "\n",
+    "\n",
+    "quartile_values = quartile(dice_roll['Rolls'])\n",
+    "quartile_values\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Challenge 3\n",
+    "Read the csv `roll_the_dice_hundred.csv` from the `data` folder.\n",
+    "#### 1.- Sort the values and plot them. What do you see?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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T4zs1lpPzavu2Or39Z47nVD82zIlToqHGQETjdoRacyy/z0Zy28JdY0XnNpig4uqvfnbv3q1p06apWbNmWrJkiYYMGaKRI0fqlVdeKbV9dna2UlNTfY/MzMyI1pufb2dfofTrxPhOjRWuuXCy/3DOa0XqO3Ndp/pxep1w718nxnazxkBE43aEWnMsv89GctvCXWMkjzVXg0pRUZE6dOigJ554Qu3bt9f//u//6u6779a0adNKbT9+/Hh5vV7fIy8vL6L1pqXZ2Vco/ToxvlNjhWsunOw/nPNakfrOXNepfpxeJ9z714mx3awxENG4HaHWHMvvs5HctnDXGNFjrWIf3lRMVlaWueuuu/yWPf/88yY9PT2g9d26RqWsixXP/H6xvDaR+O40EuM7NZYT8xot2xpK34GO51Q/NsyJU6KhxkBE43aEWnMsv89GctvCWWOkr1Fx9ROVLl26aPv27X7LduzYoUaNGrlUUfni46UpU4r/7fH4P3f67ylTpKlTy28zeXL4foseSI1Oje/UWBWd19LYuq3B9l2assZzqp9ARfJYC1U01BiIaNyOUGuO5ffZSG5bOGuM+LFWsUxUMR9//LFJSEgwkyZNMjt37jSvvfaaqV69upk9e3ZA69t0H5XMzHP/Bv7sNm7XaNtYoc5rafcIsX1bA+07lG2L9By5fawHIhpqDEQ0bkeoNcfy+2wkty2cNVZEMOdvjzHGRDAXlfDOO+9o/Pjx2rlzp5o0aaKxY8fq7rvvDmjdgoICpaamyuv1KiUlJcyV+ovlOya6ORZ3puXOtOESDTUGIhq3Ixru3hqqaNg2G+9MG8z52/WgUhFuBhUAABCaYM7f/KeEAADAWgQVAABgLYIKAACwFkEFAABYi6ACAACsRVABAADWIqgAAABrEVQAAIC1CCoAAMBaBBUAAGAtggoAALAWQQUAAFiLoAIAAKxFUAEAANYiqAAAAGsRVAAAgLUIKgAAwFoEFQAAYC2CCgAAsBZBBQAAWIugAgAArEVQAQAA1iKoAAAAaxFUAACAtQgqAADAWgQVAABgLYIKAACwFkEFAABYi6ACAACsRVABAADWIqgAAABrEVQAAIC1CCoAAMBaBBUAAGAtggoAALAWQQUAAFiLoAIAAKxFUAEAANYiqAAAAGsRVAAAgLUIKgAAwFoEFQAAYC2CCgAAsBZBBQAAWIugAgAArEVQAQAA1iKoAAAAaxFUAACAtQgqAADAWgQVAABgLYIKAACwFkEFAABYi6ACAACsRVABAADWIqgAAABrJbhdQGVy6pS0erWUny+lpUlXXCHFx0eub6faoKSz561zZ+nDD6NvHtn/OBe330fcPEYDHZvXkcOMiyZOnGgk+T3q168f8Pper9dIMl6vN4xVOmPuXGMyMoyRfnxkZBQvj0TfTrVBSaXNW3x89M0j+x/n4vb7iJvHaKBj8zoKTDDnb9eDSuvWrU1+fr7vcfjw4YDXj5agMneuMR6P/4ErFS/zeCp2AAfSt1NtUFJZ8xZt88j+x7m4/T7i5jEa6Ni8jgIXzPnbY4wxbn2a88gjj2jBggXKyckJaf2CggKlpqbK6/UqJSXF2eIccuqU1LixtH9/6c97PFJGhrRnT/AfDQbSd8OGxf8+VxtjpAMHnK8xlp1r/s9m6zyG8xhFbAj0vSZc7yNuHqOBjv3FF1LTpryOAhXM+dv1i2l37typ9PR0NWnSRLfeeqt2795dZtsTJ06ooKDA72G71avLP5EZI+XlFbcLR9/79wfWpqw3l4rWGMvONf9ns3Uew3mMIjYE+l4TrvcRN4/RQMd+/nleR+HialC57LLL9Morr2jJkiV68cUXdejQIXXu3FlHjhwptX12drZSU1N9j8zMzAhXHLz8fGfbVXSdioj0eLYLdT5sm8dwHqOIDU7u+3C+14XjGA20z127nO0PP3I1qPTu3Vu/+MUv1LZtW11zzTV69913JUkvv/xyqe3Hjx8vr9fre+Tl5UWy3JCkpTnbrqLrVESkx7NdqPNh2zyG8xhFbHBy34fzvS4cx2igfTZt6mx/+JGr16iUpkePHrrgggs0bdq0c7aNpmtUDhwo/ujvbE58b1te36evUTlXG2OkgwedrzGWnWv+z2brPIbzGEVsCPS9JlzvI24eo4GOffoaFV5HgYmqa1TOdOLECW3btk1pMRQ54+OlKVOK/+3x+D93+u/Jk0M7cAPpe8qUwNpMnRqeGmNZefN/NpvnMZzHKGJDoO814XofcfMYDXTsKlV4HYVNmH+BVK57773XrFixwuzevdusXbvWXHfddSY5Odnk5uYGtH60/DzZmNJ/W5+ZGb57C5zdt1NtUFIg91GJhnlk/+Nc3H4fcfMYDXRsXkeBiZqfJ996661atWqVvvrqK9WtW1c//elP9fvf/14XXnhhQOtHw1c/Z3L7bo1u31EylnFnWlQWbr+PcGfa2BDM+du6a1SCEW1BBQAARPE1KgAAAGciqAAAAGsRVAAAgLUIKgAAwFoEFQAAYC2CCgAAsBZBBQAAWIugAgAArEVQAQAA1iKoAAAAaxFUAACAtQgqAADAWgQVAABgLYIKAACwFkEFAABYi6ACAACsRVABAADWIqgAAABrEVQAAIC1CCoAAMBaBBUAAGAtggoAALAWQQUAAFiLoAIAAKxFUAEAANYiqAAAAGsRVAAAgLUIKgAAwFoEFQAAYC2CCgAAsBZBBQAAWIugAgAArEVQAQAA1iKoAAAAaxFUAACAtQgqAADAWgQVAABgLYIKAACwFkEFAABYi6ACAACsRVABAADWIqgAAABrEVQAAIC1CCoAAMBaBBUAAGAtggoAALAWQQUAAFiLoAIAAKxFUAEAANYiqAAAAGsRVAAAgLUIKgAAwFoEFQAAYC2CCgAAsBZBBQAAWCvB7QJwbqdOSatXS/n5UlqadMUVUnx85PourY0UfE1O9RPObevcWfrww/DUEyucOh7dPq6dEupxHcntL+24DqTGSHJyPiK5/yMp1PfrqN92Y4knnnjCSDKjRo0KeB2v12skGa/XG77CXDZ3rjEZGcZIPz4yMoqXR6Lv0trUrl38CKYmp/oJ97bFx4ennljh1PHo9nHtlFCP60hv/9nHdThfe07VHGo9kdz/kRTqe5qt2x7M+duKoPLxxx+bxo0bm3bt2hFUzjB3rjEej/9BJxUv83gqdvAF0ndZbUp7lFeTU/24sW1O1BMrnDoe3T6unRLqce3G9kfqtedkzaHUE8n9H0kVeU+zddujKqgcO3bMNGvWzCxbtsx069aNoPL/CgtLJuOzD77MzOJ24eg7I6P8NoHWdK6xomHbKlJPrHDqeHT7uHZqP4Z6XJ/r2Avn9tt4rDu5zyK5/yPJifc0G7c9mPN3SBfTfv3115o+fbrGjx+vo0ePSpI2bNigAwcOBN3XsGHD1LdvX11zzTXnbHvixAkVFBT4PWLV6tXS/v1lP2+MlJdX3C4cfe/fX36bQGs611iB9hOocGxbReqJFU4dj24f107tx1CP63Mde+Hc/kBE+lh3cp9Fcv9HkhPvadG67acFfTHtpk2bdM011yg1NVW5ubm6++67VatWLc2fP1979+7VK6+8EnBfb775pjZs2KD169cH1D47O1uPPvposCVHpfx8Z9tVdJ1Q+6/IWLZtW7jnzWZOHY82HNdO7MdIvobCuU4k+nJinEDaRXL/R1I07lenBf2JytixYzVo0CDt3LlTVatW9S3v3bu3Vq1aFXA/eXl5GjVqlGbPnu3XT3nGjx8vr9fre+Tl5QVbftRIS3O2XUXXCbX/ioxl27aFe95s5tTxaMNx7cR+jORrKJzrRKIvJ8YJpF0k938kReN+dVyw3yulpKSYL774whhjTM2aNc2uXbuMMcbk5uaapKSkgPuZP3++kWTi4+N9D0nG4/GY+Ph4UxjAl2mV4RqVsi6Mc+K77PL6Pv2dZzAX5pV3jUpF+3Fz22z8fjfSnDoe3T6unb5GJdjj+lzHXji338Zj3cl9Fsn9H0lOvKfZuO1hvUalatWqpV4bsn37dtWtWzfgfq6++mpt3rxZOTk5vkenTp3Uv39/5eTkKD7qf/hdMfHx0pQpxf/2ePyfO/335Mmh/T4+kL6nTCm7TWnKqqm8sYLpJ1BOb1tF64kVTh2Pbh/XTu3HUI/r8o69cG5/INw41p3cZ5Hc/5FU0fe0aN52n2BT0N13321uuOEG88MPP5iaNWua3bt3m71795r27dsH9Yud0vCrn5JK+118Zmb47rdwdt+B3ifiXDU51U+4t+3s+004VU+scOp4dPu4dkqox3Wktz+Q+6i4eaw7OR+R3P+RFOp7mq3bHsz522OMMcEEm4KCAvXp00dbtmzRsWPHlJ6erkOHDunyyy/XokWLVKNGjZBD01VXXaWLL75YkydPDriW1NRUeb1epaSkhDyu7dy+gyd3pnWmnljBnWnPPZbEnWkrWjN3pi0plu5MG8z5O+igctoHH3ygDRs2qKioSB06dAjo58VOqyxBBQCAWBKRoGIDggoAANEnmPN30PdReeyxx8p9/uGHHw62SwAAgFIFHVTmz5/v9/fJkye1Z88eJSQkqGnTpgQVAADgmKCDysaNG0ssKygo0KBBg3TjjTc6UhQAAIAUwp1pS5OSkqLHHntMDz30kBPdAQAASHIoqEjF/1Gh1+t1qjsAAIDgv/qZOnWq39/GGOXn5+vVV1/Vtdde61hhAAAAQQeVP/3pT35/x8XFqW7duho4cKDGjx/vWGEAAABBB5U9e/aEow4AAIASHLtGBQAAwGkBfaJy0003BdzhvHnzQi4GAADgTAEFldTU1HDXAQAAUEJAQWXmzJnhrgMAAKAErlEBAADWCvpXP5L0j3/8Q3//+9+1b98+/fDDD37PbdiwwZHCAAAAgv5EZerUqfrVr36levXqaePGjbr00ktVu3Zt7d69W7179w5HjQAAoJIKOqg8//zzeuGFF/SXv/xFVapU0bhx47Rs2TKNHDmSW+gDAABHBR1U9u3bp86dO0uSqlWrpmPHjkmSBgwYoDfeeMPZ6gAAQKUWdFBp0KCBjhw5Iklq1KiR1q5dK6n4jrXGGGerAwAAlVrQQeVnP/uZ3n77bUnSXXfdpTFjxqhHjx665ZZbdOONNzpeIAAAqLw8JsCPQRYsWKB+/frJ4/GoqKhICQnFPxj6+9//rjVr1uiCCy7QkCFDVKVKlbAWfKaCggKlpqbK6/UqJSUlYuMCAIDQBXP+DjioJCQkqE6dOho4cKAGDx6sFi1aOFJsRRBUAACIPsGcvwP+6mffvn0aMWKE5s+frwsvvFBdu3bVzJkz9e2331a4YAAAgNIEHFTS09P1wAMPaMeOHfrggw/UtGlTjRw5Umlpafr1r3+tjz76KJx1AgCASiikW+h369ZNL7/8svLz8/Xss89q27Zt6tq1q1q3bu10fQAAoBIL6Rb6p9WsWVPdu3dXbm6uPv/8c+3YscOpugAAAEL7ROW7777Tyy+/rG7duql58+aaM2eOxo4dq9zcXIfLAwAAlVlQn6j861//0owZM/TWW2+psLBQN910k9577z117949XPUBAIBKLOCg0rx5c+3atUvt27fXU089pdtvv12pqanhrA0AAFRyAQeVa6+9VnfddZcuuuiicNYDAADgE3BQmTp1ajjrAAAAKCGki2kBAAAigaACAACsRVABAADWqlBQOX78uFN1AAAAlBB0UCkqKtLvf/97NWzYUDVr1tTu3bslSQ899JBeeuklxwsEAACVV9BB5fHHH9esWbP09NNPq0qVKr7lbdu21fTp0x0tDgAAVG5BB5VXXnlFL7zwgvr376/4+Hjf8nbt2unzzz93tDgAAFC5BR1UDhw4oAsuuKDE8qKiIp08edKRogAAAKQQgkrr1q21evXqEsvfeusttW/f3pGiAAAApCD/U0JJmjhxogYMGKADBw6oqKhI8+bN0/bt2/XKK6/onXfeCUeNAACgkgr6E5V+/fppzpw5WrRokTwejx5++GFt27ZNb7/9tnr06BGOGgEAQCXlMcYYt4sIVUFBgVJTU+X1epWSkuJ2OQAAIADBnL+D/kRl/fr1WrduXYnl69at07///e9guwMAAChT0EFl2LBhysvLK7H8wIEDGjZsmCNFAQAASCEEla1bt6pDhw4llrdv315bt251pCgAAAAphKCSlJSkL7/8ssTy/Px8JSQE/SMiAACAMgUdVHr06KHx48fL6/X6ln399deaMGECv/oBAACOCvojkGeeeUZXXnmlGjVq5LvBW05OjurXr69XX33V8QIBAEDlFXRQadiwoTZt2qTXXntNn376qapVq6Zf/epXuu2225SYmBiOGgEAQCUV0kUlNWrU0G9+8xunawEAAPATUFBZuHChevfurcTERC1cuLDcttdff70jhQEAAAR0Z9q4uDgdOnRI9erVU1xc2dffejwenTp1ytECy8OdaQEAiD7BnL8D+kSlqKio1H8DAACEU9A/TwYAAIiUoC6mLSoq0qxZszRv3jzl5ubK4/GoSZMmuvnmmzVgwAB5PJ5w1QkAACqhgIOKMUbXX3+9Fi1apIsuukht27aVMUbbtm3ToEGDNG/ePC1YsCCowadNm6Zp06YpNzdXktS6dWs9/PDD6t27d1D9ILadOiWtXi3l50tpadIVV0jx8W5X5Qynti2cc+Tm/Lu9750cP5S+AlnH7TmKVmfPW+fO0ocf+s+jFLm5dXs/uj1+uUyAZsyYYZKTk80HH3xQ4rn333/fJCcnm5dffjnQ7owxxixcuNC8++67Zvv27Wb79u1mwoQJJjEx0Xz22WcBre/1eo0k4/V6gxoX0WPuXGMyMoyRfnxkZBQvj3ZObVs458jN+Xd73zs5fih9BbKO23MUrUqbt/h4/79r1y5+RGJu3d6PbowfzPk74KDSo0cPk52dXebzkyZNMj179gy0uzKdd955Zvr06QG1JajEtrlzjfF4/F88UvEyjye634yd2rZwzpGb8+/2vndy/FD6CmQdt+coWpU1b4E8wjG3bu9Ht8YPS1CpX7++2bhxY5nPb9iwwdSvXz/Q7kooLCw0b7zxhqlSpYrZsmVLQOsQVGJXYWHJhH/2iygzs7hdtHFq28I5R27Ov9v73snxQ+krkHUyMmL39RFO55rbQMOKU3MbS8d6sII5fwf8q5+jR4+qfv36ZT5fv359/fe//w36q6fNmzerZs2aSkpK0pAhQzR//nxdeOGFpbY9ceKECgoK/B6ITatXS/v3l/28MVJeXnG7aOPUtoVzjtycf7f3vZPjh9JXIOvs3x+7r49wOtfcBsLJuY2lYz2cAg4qp06dUkJC2dfexsfHq7CwMOgCWrRooZycHK1du1ZDhw7VwIEDtXXr1lLbZmdnKzU11ffIzMwMejxEh/x8Z9vZxKltC+ccuTn/bu97J8cPpS8ntysaXx/hZNvcxtKxHk5B/epn0KBBSkpKKvX5EydOhFRAlSpVdMEFF0iSOnXqpPXr12vKlCn629/+VqLt+PHjNXbsWN/fBQUFhJUYlZbmbDubOLVt4ZwjN+ff7X3v5Pih9OXkdkXj6yOcbJvbWDrWwymgW+hL0q9+9auAOpw5c2aFCrr66quVmZmpWbNmnbMtt9CPXadOSY0bSwcOFH/8eDaPR8rIkPbssegndAFyatvCOUduzr/b+97J8UPpK5B1GjYs/ncsvj7C6VxzGwgn5zaWjvVgBXX+dv4SmcCNHz/erFq1yuzZs8ds2rTJTJgwwcTFxZmlS5cGtD4X08a201ejn31Feiz8qsGpbQvnHLk5/27veyfHD6WvQNZxe46iVVnzFuiFtOH61U8sHOvBCMuvfsJh8ODBplGjRqZKlSqmbt265uqrrw44pBhDUKkMSvt9f2ZmbLwJO7Vt4ZwjN+ff7X3v5Pih9BXIOm7PUbQK9T4q4Zpbt/ejG+MHc/4O+KsfG/HVT+Vg9R0TK4g709o7ttPjc2dau3BnWnfHD+b8TVABAAARFcz5m/89GQAAWIugAgAArEVQAQAA1iKoAAAAaxFUAACAtQgqAADAWgQVAABgLYIKAACwFkEFAABYi6ACAACsRVABAADWIqgAAABrEVQAAIC1CCoAAMBaBBUAAGAtggoAALAWQQUAAFiLoAIAAKxFUAEAANYiqAAAAGsRVAAAgLUIKgAAwFoEFQAAYC2CCgAAsBZBBQAAWIugAgAArEVQAQAA1iKoAAAAaxFUAACAtQgqAADAWgQVAABgLYIKAACwFkEFAABYi6ACAACsRVABAADWIqgAAABrEVQAAIC1CCoAAMBaBBUAAGAtggoAALAWQQUAAFiLoAIAAKxFUAEAANYiqAAAAGsRVAAAgLUIKgAAwFoEFQAAYC2CCgAAsBZBBQAAWIugAgAArEVQAQAA1iKoAAAAaxFUAACAtQgqAADAWgQVAABgrQQ3B8/Ozta8efP0+eefq1q1aurcubOeeuoptWjRws2ySjh1Slq9WsrPl9LSpCuukOLjw9e3FL7xIimc8xZJTm1HrPbjZN+BrOfUayacNUars7etc2fpww/tOkZQCRkX9erVy8ycOdN89tlnJicnx/Tt29dkZWWZb775JqD1vV6vkWS8Xm/Yapw715iMDGOkHx8ZGcXLw9F37drFj3CMF0nhnLdIcmo7YrUfJ/sOZD2nXjPhrDFalbZt8fF2HSOIHcGcv10NKmc7fPiwkWRWrlwZUPtwB5W5c43xePxfTFLxMo+nYi+qsvou7eHEeJEUznmLJKe2I1b7cbLvQNZz6jUTzhqjVaBz6+YxgtgStUFl586dRpLZvHlzQO3DGVQKC0sm/rNfVJmZxe2c7tvp8SIpnPMWSU5tR6z242TfgayXkWFMw4YVf82Es8ZoOK5LE+z7kRvHCGJPMOdvay6mNcZo7Nix6tq1q9q0aVNqmxMnTqigoMDvES6rV0v795f9vDFSXl5xO6f7dnq8SArnvEWSU9sRq/042Xcg6+3fLx04EFw9pY0Xzhqj4bguTbDvR24cI6jcrAkqw4cP16ZNm/TGG2+U2SY7O1upqam+R2ZmZtjqyc93tl1F13Fi3UgI57xFklPbEav9ONl3uI+FM/sPd422H9elCbXmSB4jqNysCCojRozQwoULtXz5cmVkZJTZbvz48fJ6vb5HXl5e2GpKS3O2XUXXcWLdSAjnvEWSU9sRq/042Xe4j4Uz+w93jbYf16UJteZIHiOo5ML/TVTZioqKzLBhw0x6errZsWNH0OtH4hqVsi4wc+J72kAvDIym727DOW+R5NR2xGo/TvYdyHqnr1Gp6GsmnDVGw3FdmmDfj9w4RhB7ouYalWHDhmn27Nl6/fXXlZycrEOHDunQoUP6/vvv3SxLUvHv+adMKf63x+P/3Om/J08O7Xf/5fVdmoqOF0nhnLdIcmo7YrUfJ/sOZL0pU6SpU0tvU5qyxgtnjdFwXJcmmPcjt44RVHIRCE5lklTqY+bMmQGt79Z9VDIzI3sfFafGi6RwzlskObUdsdqPk30Hsp5Tr5lw1hitArmPitvHCGJHMOdvjzHGuBeTKqagoECpqanyer1KSUkJ2zjcmTY0sXLnSdvuBGtbP072zZ1p3cWdaREpwZy/CSoAACCigjl/W/GrHwAAgNIQVAAAgLUIKgAAwFoEFQAAYC2CCgAAsBZBBQAAWIugAgAArEVQAQAA1iKoAAAAaxFUAACAtQgqAADAWgQVAABgLYIKAACwFkEFAABYi6ACAACsRVABAADWIqgAAABrEVQAAIC1CCoAAMBaBBUAAGAtggoAALAWQQUAAFiLoAIAAKxFUAEAANYiqAAAAGsRVAAAgLUIKgAAwFoEFQAAYC2CCgAAsBZBBQAAWIugAgAArEVQAQAA1iKoAAAAaxFUAACAtQgqAADAWgQVAABgLYIKAACwFkEFAABYi6ACAACsRVABAADWIqgAAABrEVQAAIC1CCoAAMBaBBUAAGAtggoAALAWQQUAAFiLoAIAAKxFUAEAANYiqAAAAGsRVAAAgLUIKgAAwFoEFQAAYC2CCgAAsBZBBQAAWIugAgAArJXgdgE2OnVKWr1ays+X0tKkK66Q4uOdXyfSNVZ2zJm7omH+3a7R7fFDEQ01u11jOMePhvNVhRkXrVy50lx33XUmLS3NSDLz588Pan2v12skGa/X61hNc+cak5FhjPTjIyOjeLmT60S6xsqOOXNXNMy/2zW6PX4ooqFmt2sM5/jRcL4qSzDnb1eDyqJFi8wDDzxg5s6da0VQmTvXGI/HfwdKxcs8ntJ3ZCjrRLrGyo45c1c0zL/bNbo9fiiioWa3awzn+NFwvipPMOdvjzHGuPmJzmkej0fz58/XDTfcEPA6BQUFSk1NldfrVUpKSoXGP3VKatxY2r+/rPqkjAxpz54fPyILZZ1I11jZMWfuiob5d7tGt8cPRTTU7HaN4Rw/Gs5X5xLM+TuqLqY9ceKECgoK/B5OWb267B0oFefOvLzidhVZJ9I1VnbMmbuiYf7drtHt8UMRDTW7XWM4x4+G85WToiqoZGdnKzU11ffIzMx0rO/8/ODbhbJORUR6vFjAnLkrGubf7RrdHj8U0VCz2zWGc/xoOF85KaqCyvjx4+X1en2PvLw8x/pOSwu+XSjrVESkx4sFzJm7omH+3a7R7fFDEQ01u11jOMePhvOVk7hG5f+d/v7uwIHij8BK1lf2d37BrBPpGis75sxd0TD/btfo9vihiIaa3a4xnONHw/nqXGL2GpVwio+Xpkwp/rfH4//c6b8nT/bfgaGsE+kaKzvmzF3RMP9u1+j2+KGIhprdrjGc40fD+cpRYf4FUrmOHTtmNm7caDZu3GgkmWeffdZs3LjR7N27N6D1I3UflczM4H+Xfq51Il1jZcecuSsa5t/tGt0ePxTRULPbNYZz/Gg4X5Ulan6evGLFCnXv3r3E8oEDB2rWrFnnXN/Jr37OFA13+ou6OwtagDlzVzTMv9s1uj1+KKKhZrdr5M60JQVz/rbmGpVQhCuoAACA8OEaFQAAEBMIKgAAwFoEFQAAYC2CCgAAsBZBBQAAWIugAgAArEVQAQAA1iKoAAAAaxFUAACAtRLcLqAiTt9Ut6CgwOVKAABAoE6ftwO5OX5UB5Vjx45JkjIzM12uBAAABOvYsWNKTU0tt01U/18/RUVFOnjwoJKTk+U5+/+trqCCggJlZmYqLy+P/0cozJjryGGuI4e5jhzmOnKcmmtjjI4dO6b09HTFxZV/FUpUf6ISFxenjIyMsI6RkpLCgR8hzHXkMNeRw1xHDnMdOU7M9bk+STmNi2kBAIC1CCoAAMBaBJUyJCUlaeLEiUpKSnK7lJjHXEcOcx05zHXkMNeR48ZcR/XFtAAAILbxiQoAALAWQQUAAFiLoAIAAKxFUAEAANYiqJTi+eefV5MmTVS1alV17NhRq1evdrukqJedna1LLrlEycnJqlevnm644QZt377dr40xRo888ojS09NVrVo1XXXVVdqyZYtLFceO7OxseTwejR492reMuXbOgQMHdMcdd6h27dqqXr26Lr74Yn3yySe+55lrZxQWFurBBx9UkyZNVK1aNZ1//vl67LHHVFRU5GvDXIdm1apV6tevn9LT0+XxeLRgwQK/5wOZ1xMnTmjEiBGqU6eOatSooeuvv1779+93pkADP2+++aZJTEw0L774otm6dasZNWqUqVGjhtm7d6/bpUW1Xr16mZkzZ5rPPvvM5OTkmL59+5qsrCzzzTff+No8+eSTJjk52cydO9ds3rzZ3HLLLSYtLc0UFBS4WHl0+/jjj03jxo1Nu3btzKhRo3zLmWtnHD161DRq1MgMGjTIrFu3zuzZs8e899575osvvvC1Ya6d8fjjj5vatWubd955x+zZs8e89dZbpmbNmmby5Mm+Nsx1aBYtWmQeeOABM3fuXCPJzJ8/3+/5QOZ1yJAhpmHDhmbZsmVmw4YNpnv37uaiiy4yhYWFFa6PoHKWSy+91AwZMsRvWcuWLc3999/vUkWx6fDhw0aSWblypTHGmKKiItOgQQPz5JNP+tocP37cpKammr/+9a9ulRnVjh07Zpo1a2aWLVtmunXr5gsqzLVz7rvvPtO1a9cyn2eundO3b18zePBgv2U33XSTueOOO4wxzLVTzg4qgczr119/bRITE82bb77pa3PgwAETFxdnFi9eXOGa+OrnDD/88IM++eQT9ezZ0295z5499eGHH7pUVWzyer2SpFq1akmS9uzZo0OHDvnNfVJSkrp168bch2jYsGHq27evrrnmGr/lzLVzFi5cqE6dOul//ud/VK9ePbVv314vvvii73nm2jldu3bV+++/rx07dkiSPv30U61Zs0Z9+vSRxFyHSyDz+sknn+jkyZN+bdLT09WmTRtH5j6q/1NCp3311Vc6deqU6tev77e8fv36OnTokEtVxR5jjMaOHauuXbuqTZs2kuSb39Lmfu/evRGvMdq9+eab2rBhg9avX1/iOebaObt379a0adM0duxYTZgwQR9//LFGjhyppKQk3Xnnncy1g+677z55vV61bNlS8fHxOnXqlCZNmqTbbrtNEsd1uAQyr4cOHVKVKlV03nnnlWjjxLmToFIKj8fj97cxpsQyhG748OHatGmT1qxZU+I55r7i8vLyNGrUKC1dulRVq1Ytsx1zXXFFRUXq1KmTnnjiCUlS+/bttWXLFk2bNk133nmnrx1zXXFz5szR7Nmz9frrr6t169bKycnR6NGjlZ6eroEDB/raMdfhEcq8OjX3fPVzhjp16ig+Pr5EAjx8+HCJNInQjBgxQgsXLtTy5cuVkZHhW96gQQNJYu4d8Mknn+jw4cPq2LGjEhISlJCQoJUrV2rq1KlKSEjwzSdzXXFpaWm68MIL/Za1atVK+/btk8Rx7aTf/e53uv/++3Xrrbeqbdu2GjBggMaMGaPs7GxJzHW4BDKvDRo00A8//KD//ve/ZbapCILKGapUqaKOHTtq2bJlfsuXLVumzp07u1RVbDDGaPjw4Zo3b54++OADNWnSxO/5Jk2aqEGDBn5z/8MPP2jlypXMfZCuvvpqbd68WTk5Ob5Hp06d1L9/f+Xk5Oj8889nrh3SpUuXEj+z37Fjhxo1aiSJ49pJ3333neLi/E9Z8fHxvp8nM9fhEci8duzYUYmJiX5t8vPz9dlnnzkz9xW+HDfGnP558ksvvWS2bt1qRo8ebWrUqGFyc3PdLi2qDR061KSmppoVK1aY/Px83+O7777ztXnyySdNamqqmTdvntm8ebO57bbb+GmhQ8781Y8xzLVTPv74Y5OQkGAmTZpkdu7caV577TVTvXp1M3v2bF8b5toZAwcONA0bNvT9PHnevHmmTp06Zty4cb42zHVojh07ZjZu3Gg2btxoJJlnn33WbNy40XdbjkDmdciQISYjI8O89957ZsOGDeZnP/sZP08Op+eee840atTIVKlSxXTo0MH3E1qETlKpj5kzZ/raFBUVmYkTJ5oGDRqYpKQkc+WVV5rNmze7V3QMOTuoMNfOefvtt02bNm1MUlKSadmypXnhhRf8nmeunVFQUGBGjRplsrKyTNWqVc35559vHnjgAXPixAlfG+Y6NMuXLy/1/XngwIHGmMDm9fvvvzfDhw83tWrVMtWqVTPXXXed2bdvnyP1eYwxpuKfywAAADiPa1QAAIC1CCoAAMBaBBUAAGAtggoAALAWQQUAAFiLoAIAAKxFUAEAANYiqAAIWW5urjwej3JyciRJK1askMfj0ddff+1qXWXxeDxasGCB22UACAJBBaikBg0aJI/HI4/Ho4SEBGVlZWno0KEl/mMxAHATQQWoxK699lrl5+crNzdX06dP19tvv6177rnH7bKiyg8//OB2CUBMI6gAlVhSUpIaNGigjIwM9ezZU7fccouWLl3qe76oqEiPPfaYMjIylJSUpIsvvliLFy+u0Jgej0fTp0/XjTfeqOrVq6tZs2ZauHCh7/lZs2bpJz/5id86CxYskMfj8f39yCOP6OKLL9aMGTOUlZWlmjVraujQoTp16pSefvppNWjQQPXq1dOkSZNKjJ+fn6/evXurWrVqatKkid566y2/5w8cOKBbbrlF5513nmrXrq2f//znys3N9T0/aNAg3XDDDcrOzlZ6erqaN29eofkAUD6CCgBJ0u7du7V48WIlJib6lk2ZMkXPPPOM/vjHP2rTpk3q1auXrr/+eu3cubNCYz366KP65S9/qU2bNqlPnz7q37+/jh49GlQfu3bt0j//+U8tXrxYb7zxhmbMmKG+fftq//79WrlypZ566ik9+OCDWrt2rd96Dz30kH7xi1/o008/1R133KHbbrtN27ZtkyR999136t69u2rWrKlVq1ZpzZo1qlmzpq699lq/T07ef/99bdu2TcuWLdM777xTobkAcA6O/NeGAKLOwIEDTXx8vKlRo4apWrWq739MffbZZ31t0tPTzaRJk/zWu+SSS8w999xjjDFmz549RpLZuHGjMebH/4X1v//9b5njSjIPPvig7+9vvvnGeDwe889//tMYY8zMmTNNamqq3zrz5883Z75dTZw40VSvXt3vv5nv1auXady4sTl16pRvWYsWLUx2drbf2EOGDPHr+7LLLjNDhw41xhjz0ksvmRYtWpiioiLf8ydOnDDVqlUzS5Ys8c1b/fr1/f7XXgDhk+BiRgLgsu7du2vatGn67rvvNH36dO3YsUMjRoyQJBUUFOjgwYPq0qWL3zpdunTRp59+WqFx27Vr5/t3jRo1lJycrMOHDwfVR+PGjZWcnOz7u379+oqPj1dcXJzfsrP7vfzyy0v8ffpXS5988om++OILv34l6fjx49q1a5fv77Zt26pKlSpB1QsgNAQVoBKrUaOGLrjgAknS1KlT1b17dz366KP6/e9/72tz5rUhkmSMKbEsWGd+vXR6jKKiIklSXFycjDF+z588eTKgPsrrtzynt6eoqEgdO3bUa6+9VqJN3bp1ff+uUaPGOfsE4AyuUQHgM3HiRP3xj3/UwYMHlZKSovT0dK1Zs8avzYcffqhWrVqFrYa6devq2LFj+vbbb33LTn/i4YSzr1lZu3atWrZsKUnq0KGDdu7cqXr16umCCy7we6SmpjpWA4DAEVQA+Fx11VVq3bq1nnjiCUnS7373Oz311FOaM2eOtm/frvvvv185OTkaNWpU2Gq47LLLVL16dU2YMEFffPGFXn/9dc2aNcux/t966y3NmDFDO3bs0MSJE/Xxxx9r+PDhkqT+/furTp06+vnPf67Vq1drz549WrlypUaNGqX9+/c7VgOAwBFUAPgZO3asXnzxReXl5WnkyJG69957de+996pt27ZavHixFi5cqGbNmoVt/Fq1amn27NlatGiR2rZtqzfeeEOPPPKIY/0/+uijevPNN9WuXTu9/PLLeu2113ThhRdKkqpXr65Vq1YpKytLN910k1q1aqXBgwfr+++/V0pKimM1AAicx5z9ZTAAAIAl+EQFAABYi6ACAACsRVABAADWIqgAAABrEVQAAIC1CCoAAMBaBBUAAGAtggoAALAWQQUAAFiLoAIAAKxFUAEAANYiqAAAAGv9H7MYlHQPEQqLAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "roll_the_dice_hundred = pd.read_csv('../data/roll_the_dice_hundred.csv')\n",
+    "roll_the_dice_hundred.head()\n",
+    "sorted_roll_the_dice_hundred = roll_the_dice_hundred.sort_values('value')\n",
+    "plt.plot(sorted_roll_the_dice_hundred[\"roll\"],sorted_roll_the_dice_hundred[\"value\"],'bo')\n",
+    "plt.xlabel(\"Roll number\")\n",
+    "plt.ylabel(\"Dice Value\")\n",
+    "plt.title(\"Sorted Dice Rolls\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nyour comments here\\nAlthough the dice values may appear random, the frequencies of the values are close, indicating that the likelihood of each number appearing when rolling the dice is almost the same\\n'"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "your comments here\n",
+    "Although the dice values may appear random, the frequencies of the values are close, indicating that the likelihood of each number appearing when rolling the dice is almost the same\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.- Using the functions you defined in *challenge 2*, calculate the mean value of the hundred dice rolls."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.74"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "def mean(col):\n",
+    "    return col.sum()/len(col)\n",
+    "mean_value = mean(roll_the_dice_hundred['value'])\n",
+    "mean_value"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.- Now, calculate the frequency distribution.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "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>frequency</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   frequency\n",
+       "6         23\n",
+       "4         22\n",
+       "2         17\n",
+       "3         14\n",
+       "1         12\n",
+       "5         12"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "frequency_distribution = pd.DataFrame(roll_the_dice_hundred['value'].value_counts())\n",
+    "frequency_distribution = frequency_distribution.rename(columns={'value': 'frequency'})\n",
+    "display(frequency_distribution)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 4.- Plot the histogram. What do you see (shape, values...) ? How can you connect the mean value to the histogram? "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "\n",
+    "plt.hist(roll_the_dice_hundred['value'])\n",
+    "\n",
+    "plt.xlabel('Dice Value')\n",
+    "plt.ylabel('Frequency')\n",
+    "plt.title('Dice Rolls Frequency Distribution')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nyour comments here\\nIf the numbers appearing when rolling the dice were evenly distributed, we would expect the mean to be 3. However, the calculated mean is 3.75, suggesting that the numbers 4, 5, and 6 appear more frequently than 1, 2, and 3. This is also evident in the plot.\\n'"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "your comments here\n",
+    "If the numbers appearing when rolling the dice were evenly distributed, we would expect the mean to be 3. However, the calculated mean is 3.75, suggesting that the numbers 4, 5, and 6 appear more frequently than 1, 2, and 3. This is also evident in the plot.\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 5.- Read the `roll_the_dice_thousand.csv` from the `data` folder. Plot the frequency distribution as you did before. Has anything changed? Why do you think it changed?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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7dq2WLVumO+64Q8uWLStx/SFDhsjT01ORkZEKCgpSenq6kpKS5OvrqxYtWkj67fOoJOmtt96Sj4+PPDw8FBYWVugSz/Vyc3NTx44dNXLkSOXn52vy5MnKzMzU+PHjbX369OmjV155RX379tX//d//6eLFi3rjjTeKvKencePG2rhxoz788EMFBQXJx8enyJGxSpUqacqUKerXr5+6deumJ598Urm5uZo6darOnj2rSZMmOXQ8MCkX3hwNOEXBUzoFk7u7u+Hv72+0bdvWmDhxopGRkVFonaKeAjEMw1i4cKHRokULw8PDw6hatarRrFkzuyedDMMwNm3aZHTt2tXw8/MzqlSpYvzpT38yunbtaixfvrzEOgueXpk6dWqRyx9++GGjcuXKxpEjRwzDMIy9e/casbGxhq+vr+Hu7m40adKkUC3X8zTWf//7X+ORRx4xbr31VsPT09Pw9fU17rnnHiMlJaXEeg3j2k8IFZzHn3/+ucjl8+fPN1q2bGl4e3sbnp6exq233moMGDDA2LFjh61Pfn6+kZSUZISEhBju7u7GXXfdZXz44YeFnv4xDMM4dOiQ0alTJ6NatWpG7dq1jeHDhxtr1qwp8umi63mdiqu/qCe/8vLyjNdee80IDw833N3dDV9fX6N169bGhx9+WOi4N27caEgyJk2aVOy5u9rVT755enoadevWNWJjY4358+cbubm5hda5+hwtWLDAiI6ONgICAgx3d3cjODjY6N27t/HNN9/YrTdjxgwjLCzMcHNzs3v/tG3b1mjUqFGR9RX3NNbkyZON8ePHG3Xq1DHc3d2NZs2aGevWrSu0/scff2w0bdrU8PT0NG655RZj1qxZRf4c7t6924iMjDS8vLzsnsi7+mmsAqtWrTJatmxpeHh4GN7e3kb79u2NL7/80q7PjbzOMCeLYVzHLf4AUMZ+/2FyFc2oUaM0Z84cnTx50uEREwDOw2UsAHCSbdu26dChQ5o9e7aefPJJgg5QThB2AMBJCm6K7datmyZMmODqcgD8f1zGAgAApsaj5wAAwNQIOwAAwNQIOwAAwNS4QVm/fZDXjz/+KB8fnxv+qHQAAOAahmEoKytLwcHBdl8IezXCjn77HpXivg8JAACUbydPnizxi5wJO5J8fHwk/XayqlWr5uJqAADA9cjMzFRISIjt93hxCDv637f8VqtWjbADAEAFc61bULhBGQAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmJpLw87mzZsVGxur4OBgWSwWrVq1ym65xWIpcpo6daqtT1RUVKHlffv2LeMjAQAA5ZVLw05OTo6aNGmiWbNmFbk8LS3Nbpo/f74sFot69epl12/IkCF2/ebOnVsW5QMAgArApV8EGhMTo5iYmGKXBwYG2s1/8MEHio6O1i233GLX7uXlVagvAACAVIHu2fnpp5+0Zs0aDR48uNCyRYsWqVatWmrUqJFGjx6trKwsF1QIAADKI5eO7NyIBQsWyMfHR3FxcXbt/fr1U1hYmAIDA7Vv3z6NGTNGe/bsUWpqarHbys3NVW5urm0+MzOz1OoGAACuVWHCzvz589WvXz95eHjYtQ8ZMsT27/DwcDVo0EARERHatWuXmjdvXuS2kpKSNH78+FKtt0C9F9eUyX6c7dikrq4uAeVURXxP834G/tgqxGWsL774QgcPHtTjjz9+zb7NmzdXlSpVdPjw4WL7jBkzRufOnbNNJ0+edGa5AACgHKkQIzvz5s3T3XffrSZNmlyz7/79+3X58mUFBQUV28dqtcpqtTqzRAAAUE65NOxkZ2fryJEjtvmjR49q9+7d8vPzU926dSX9dj/N8uXLNW3atELrf/fdd1q0aJG6dOmiWrVq6cCBAxo1apSaNWumyMjIMjsOAABQfrk07OzYsUPR0dG2+ZEjR0qS4uPjlZKSIklasmSJDMPQI488Umh9d3d3ff7553r99deVnZ2tkJAQde3aVePGjZObm1uZHAMAACjfXBp2oqKiZBhGiX2eeOIJPfHEE0UuCwkJ0aZNm0qjNAAAYBIV4gZlAAAARxF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqVWI78YCgD8avl0ecB5GdgAAgKkxsgPT4C9hAEBRGNkBAACmxsgOAAAVCKPYN46RHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGqEHQAAYGouDTubN29WbGysgoODZbFYtGrVKrvlCQkJslgsdlOrVq3s+uTm5mr48OGqVauWvL299eCDD+rUqVNleBQAAKA8c2nYycnJUZMmTTRr1qxi+3Tu3FlpaWm26eOPP7Zb/uyzz2rlypVasmSJtmzZouzsbHXr1k15eXmlXT4AAKgAKrty5zExMYqJiSmxj9VqVWBgYJHLzp07p3nz5undd99Vhw4dJEnvvfeeQkJC9Nlnn+mBBx5wes0AAKBiKff37GzcuFH+/v5q2LChhgwZooyMDNuynTt36vLly+rUqZOtLTg4WOHh4frqq6+K3WZubq4yMzPtJgAAYE7lOuzExMRo0aJFWr9+vaZNm6bt27erXbt2ys3NlSSlp6fL3d1dNWrUsFsvICBA6enpxW43KSlJvr6+tikkJKRUjwMAALiOSy9jXUufPn1s/w4PD1dERIRCQ0O1Zs0axcXFFbueYRiyWCzFLh8zZoxGjhxpm8/MzCTwAABgUuU67FwtKChIoaGhOnz4sCQpMDBQly5d0pkzZ+xGdzIyMtSmTZtit2O1WmW1Wku9XgBA+VbvxTWuLgFloFxfxrra6dOndfLkSQUFBUmS7r77blWpUkWpqam2Pmlpadq3b1+JYQcAAPxxuHRkJzs7W0eOHLHNHz16VLt375afn5/8/PyUmJioXr16KSgoSMeOHdPYsWNVq1Yt9ezZU5Lk6+urwYMHa9SoUapZs6b8/Pw0evRoNW7c2PZ0FgAA+GNzadjZsWOHoqOjbfMF99HEx8drzpw52rt3rxYuXKizZ88qKChI0dHRWrp0qXx8fGzrvPbaa6pcubJ69+6tCxcuqH379kpJSZGbm1uZHw8AACh/XBp2oqKiZBhGscvXrVt3zW14eHho5syZmjlzpjNLAwAAJlGh7tkBAAC4UYQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgaoQdAABgai4NO5s3b1ZsbKyCg4NlsVi0atUq27LLly/rhRdeUOPGjeXt7a3g4GANGDBAP/74o902oqKiZLFY7Ka+ffuW8ZEAAIDyyqVhJycnR02aNNGsWbMKLTt//rx27dqlv/zlL9q1a5dWrFihQ4cO6cEHHyzUd8iQIUpLS7NNc+fOLYvyAQBABVDZlTuPiYlRTExMkct8fX2Vmppq1zZz5kzdc889OnHihOrWrWtr9/LyUmBgYKnWCgAAKqYKdc/OuXPnZLFYVL16dbv2RYsWqVatWmrUqJFGjx6trKysEreTm5urzMxMuwkAAJiTS0d2bsTFixf14osv6tFHH1W1atVs7f369VNYWJgCAwO1b98+jRkzRnv27Ck0KvR7SUlJGj9+fFmUDQAAXKxChJ3Lly+rb9++ys/P1+zZs+2WDRkyxPbv8PBwNWjQQBEREdq1a5eaN29e5PbGjBmjkSNH2uYzMzMVEhJSOsUDAACXKvdh5/Lly+rdu7eOHj2q9evX243qFKV58+aqUqWKDh8+XGzYsVqtslqtpVEuAAAoZ8p12CkIOocPH9aGDRtUs2bNa66zf/9+Xb58WUFBQWVQIQAAKO9cGnays7N15MgR2/zRo0e1e/du+fn5KTg4WA899JB27dqljz76SHl5eUpPT5ck+fn5yd3dXd99950WLVqkLl26qFatWjpw4IBGjRqlZs2aKTIy0lWHBQAAyhGXhp0dO3YoOjraNl9wH018fLwSExO1evVqSVLTpk3t1tuwYYOioqLk7u6uzz//XK+//rqys7MVEhKirl27aty4cXJzcyuz4wAAAOWXS8NOVFSUDMModnlJyyQpJCREmzZtcnZZAADARCrU5+wAAADcKMIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNYfCztGjR51dBwAAQKlwKOzUr19f0dHReu+993Tx4kVn1wQAAOA0DoWdPXv2qFmzZho1apQCAwP15JNP6j//+Y+zawMAALhpDoWd8PBwTZ8+XT/88IOSk5OVnp6ue++9V40aNdL06dP1888/O7tOAAAAh9zUDcqVK1dWz549tWzZMk2ePFnfffedRo8erTp16mjAgAFKS0tzVp0AAAAOuamws2PHDg0dOlRBQUGaPn26Ro8ere+++07r16/XDz/8oO7duzurTgAAAIdUdmSl6dOnKzk5WQcPHlSXLl20cOFCdenSRZUq/ZadwsLCNHfuXN1+++1OLRYAAOBGORR25syZo0GDBmngwIEKDAwssk/dunU1b968myoOAADgZjkUdg4fPnzNPu7u7oqPj3dk8wAAAE7j0D07ycnJWr58eaH25cuXa8GCBTddFAAAgLM4FHYmTZqkWrVqFWr39/fXxIkTb7ooAAAAZ3Eo7Bw/flxhYWGF2kNDQ3XixImbLgoAAMBZHAo7/v7++uabbwq179mzRzVr1rzu7WzevFmxsbEKDg6WxWLRqlWr7JYbhqHExEQFBwfL09NTUVFR2r9/v12f3NxcDR8+XLVq1ZK3t7cefPBBnTp1ypHDAgAAJuRQ2Onbt6/+/Oc/a8OGDcrLy1NeXp7Wr1+vESNGqG/fvte9nZycHDVp0kSzZs0qcvmUKVM0ffp0zZo1S9u3b1dgYKA6duyorKwsW59nn31WK1eu1JIlS7RlyxZlZ2erW7duysvLc+TQAACAyTj0NNaECRN0/PhxtW/fXpUr/7aJ/Px8DRgw4Ibu2YmJiVFMTEyRywzD0IwZM/TSSy8pLi5OkrRgwQIFBARo8eLFevLJJ3Xu3DnNmzdP7777rjp06CBJeu+99xQSEqLPPvtMDzzwgCOHBwAATMShkR13d3ctXbpU//3vf7Vo0SKtWLFC3333nebPny93d3enFHb06FGlp6erU6dOtjar1aq2bdvqq6++kiTt3LlTly9ftusTHBys8PBwWx8AAPDH5tDIToGGDRuqYcOGzqrFTnp6uiQpICDArj0gIEDHjx+39XF3d1eNGjUK9SlYvyi5ubnKzc21zWdmZjqrbAAAUM44FHby8vKUkpKizz//XBkZGcrPz7dbvn79eqcUJ0kWi8Vu3jCMQm1Xu1afpKQkjR8/3in1AQCA8s2hy1gjRozQiBEjlJeXp/DwcDVp0sRucoaCr6G4eoQmIyPDNtoTGBioS5cu6cyZM8X2KcqYMWN07tw523Ty5Emn1AwAAMofh0Z2lixZomXLlqlLly7OrscmLCxMgYGBSk1NVbNmzSRJly5d0qZNmzR58mRJ0t13360qVaooNTVVvXv3liSlpaVp3759mjJlSrHbtlqtslqtpVY7AAAoPxwKO+7u7qpfv/5N7zw7O1tHjhyxzR89elS7d++Wn5+f6tatq2effVYTJ05UgwYN1KBBA02cOFFeXl569NFHJUm+vr4aPHiwRo0apZo1a8rPz0+jR49W48aNbU9nAQCAPzaHws6oUaP0+uuva9asWde8f6YkO3bsUHR0tG1+5MiRkqT4+HilpKTo+eef14ULFzR06FCdOXNGLVu21KeffiofHx/bOq+99poqV66s3r1768KFC2rfvr1SUlLk5ubmcF0AAMA8HAo7W7Zs0YYNG7R27Vo1atRIVapUsVu+YsWK69pOVFSUDMModrnFYlFiYqISExOL7ePh4aGZM2dq5syZ17VPAADwx+JQ2Klevbp69uzp7FoAAACczqGwk5yc7Ow6AAAASoVDj55L0pUrV/TZZ59p7ty5tu+q+vHHH5Wdne204gAAAG6WQyM7x48fV+fOnXXixAnl5uaqY8eO8vHx0ZQpU3Tx4kW9+eabzq4TAADAIQ5/qGBERITOnDkjT09PW3vPnj31+eefO604AACAm+Xw01hffvlloS/9DA0N1Q8//OCUwgAAAJzBoZGd/Px85eXlFWo/deqU3WfgAAAAuJpDYadjx46aMWOGbd5isSg7O1vjxo0r1a+QAAAAuFEOXcZ67bXXFB0drTvvvFMXL17Uo48+qsOHD6tWrVr65z//6ewaAQAAHOZQ2AkODtbu3bv1z3/+U7t27VJ+fr4GDx6sfv362d2wDAAA4GoOhR1J8vT01KBBgzRo0CBn1gMAAOBUDoWdhQsXlrh8wIABDhUDAADgbA6FnREjRtjNX758WefPn5e7u7u8vLwIOwAAoNxw6GmsM2fO2E3Z2dk6ePCg7r33Xm5QBgAA5YrD3411tQYNGmjSpEmFRn0AAABcyWlhR5Lc3Nz0448/OnOTAAAAN8Whe3ZWr15tN28YhtLS0jRr1ixFRkY6pTAAAABncCjs9OjRw27eYrGodu3aateunaZNm+aMugAAAJzCobCTn5/v7DoAAABKhVPv2QEAAChvHBrZGTly5HX3nT59uiO7AAAAcAqHws7XX3+tXbt26cqVK7rtttskSYcOHZKbm5uaN29u62exWJxTJQAAgIMcCjuxsbHy8fHRggULVKNGDUm/fdDgwIEDdd9992nUqFFOLRIAAMBRDt2zM23aNCUlJdmCjiTVqFFDEyZM4GksAABQrjgUdjIzM/XTTz8Vas/IyFBWVtZNFwUAAOAsDoWdnj17auDAgfrXv/6lU6dO6dSpU/rXv/6lwYMHKy4uztk1AgAAOMyhe3befPNNjR49Wv3799fly5d/21Dlyho8eLCmTp3q1AIBAABuhkNhx8vLS7Nnz9bUqVP13XffyTAM1a9fX97e3s6uDwAA4Kbc1IcKpqWlKS0tTQ0bNpS3t7cMw3BWXQAAAE7hUNg5ffq02rdvr4YNG6pLly5KS0uTJD3++OM8dg4AAMoVh8LOc889pypVqujEiRPy8vKytffp00effPKJ04oDAAC4WQ7ds/Ppp59q3bp1qlOnjl17gwYNdPz4cacUBgAA4AwOjezk5OTYjegU+OWXX2S1Wm+6KAAAAGdxKOzcf//9WrhwoW3eYrEoPz9fU6dOVXR0tNOKAwAAuFkOXcaaOnWqoqKitGPHDl26dEnPP/+89u/fr19//VVffvmls2sEAABwmEMjO3feeae++eYb3XPPPerYsaNycnIUFxenr7/+WrfeequzawQAAHDYDY/sXL58WZ06ddLcuXM1fvz40qgJAADAaW54ZKdKlSrat2+fLBZLadQDAADgVA5dxhowYIDmzZvn7FoAAACczqEblC9duqR33nlHqampioiIKPSdWNOnT3dKcQAAADfrhkZ2vv/+e+Xn52vfvn1q3ry5qlWrpkOHDunrr7+2Tbt373ZqgfXq1ZPFYik0DRs2TJKUkJBQaFmrVq2cWgMAAKi4bmhkp0GDBkpLS9OGDRsk/fb1EG+88YYCAgJKpThJ2r59u/Ly8mzz+/btU8eOHfXwww/b2jp37qzk5GTbvLu7e6nVAwAAKpYbCjtXf6v52rVrlZOT49SCrla7dm27+UmTJunWW29V27ZtbW1Wq1WBgYGlWgcAAKiYHLpBucDV4ae0Xbp0Se+9954GDRpk9zTYxo0b5e/vr4YNG2rIkCHKyMgocTu5ubnKzMy0mwAAgDndUNgpuCfm6raysmrVKp09e1YJCQm2tpiYGC1atEjr16/XtGnTtH37drVr1065ubnFbicpKUm+vr62KSQkpAyqBwAArnDDl7ESEhJsX/Z58eJFPfXUU4WexlqxYoXzKvydefPmKSYmRsHBwba2Pn362P4dHh6uiIgIhYaGas2aNYqLiytyO2PGjNHIkSNt85mZmQQeAABM6obCTnx8vN18//79nVpMSY4fP67PPvvsmkEqKChIoaGhOnz4cLF9rFYr384OAMAfxA2Fnd8/8VTWkpOT5e/vr65du5bY7/Tp0zp58qSCgoLKqDIAAFCe3dQNymUlPz9fycnJio+PV+XK/8tn2dnZGj16tLZu3apjx45p48aNio2NVa1atdSzZ08XVgwAAMoLhz5Buax99tlnOnHihAYNGmTX7ubmpr1792rhwoU6e/asgoKCFB0draVLl8rHx8dF1QIAgPKkQoSdTp06FfmYu6enp9atW+eCigAAQEVRIS5jAQAAOIqwAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATK1ch53ExERZLBa7KTAw0LbcMAwlJiYqODhYnp6eioqK0v79+11YMQAAKG/KddiRpEaNGiktLc027d2717ZsypQpmj59umbNmqXt27crMDBQHTt2VFZWlgsrBgAA5Um5DzuVK1dWYGCgbapdu7ak30Z1ZsyYoZdeeklxcXEKDw/XggULdP78eS1evNjFVQMAgPKi3Iedw4cPKzg4WGFhYerbt6++//57SdLRo0eVnp6uTp062fparVa1bdtWX331VYnbzM3NVWZmpt0EAADMqVyHnZYtW2rhwoVat26d3n77baWnp6tNmzY6ffq00tPTJUkBAQF26wQEBNiWFScpKUm+vr62KSQkpNSOAQAAuFa5DjsxMTHq1auXGjdurA4dOmjNmjWSpAULFtj6WCwWu3UMwyjUdrUxY8bo3LlztunkyZPOLx4AAJQL5TrsXM3b21uNGzfW4cOHbU9lXT2Kk5GRUWi052pWq1XVqlWzmwAAgDlVqLCTm5urb7/9VkFBQQoLC1NgYKBSU1Ntyy9duqRNmzapTZs2LqwSAACUJ5VdXUBJRo8erdjYWNWtW1cZGRmaMGGCMjMzFR8fL4vFomeffVYTJ05UgwYN1KBBA02cOFFeXl569NFHXV06AAAoJ8p12Dl16pQeeeQR/fLLL6pdu7ZatWqlbdu2KTQ0VJL0/PPP68KFCxo6dKjOnDmjli1b6tNPP5WPj4+LKwcAAOVFuQ47S5YsKXG5xWJRYmKiEhMTy6YgAABQ4VSoe3YAAABuFGEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYWrkOO0lJSWrRooV8fHzk7++vHj166ODBg3Z9EhISZLFY7KZWrVq5qGIAAFDelOuws2nTJg0bNkzbtm1Tamqqrly5ok6dOiknJ8euX+fOnZWWlmabPv74YxdVDAAAypvKri6gJJ988ondfHJysvz9/bVz507df//9tnar1arAwMCyLg8AAFQA5Xpk52rnzp2TJPn5+dm1b9y4Uf7+/mrYsKGGDBmijIyMEreTm5urzMxMuwkAAJhThQk7hmFo5MiRuvfeexUeHm5rj4mJ0aJFi7R+/XpNmzZN27dvV7t27ZSbm1vstpKSkuTr62ubQkJCyuIQAACAC5Try1i/98wzz+ibb77Rli1b7Nr79Olj+3d4eLgiIiIUGhqqNWvWKC4urshtjRkzRiNHjrTNZ2ZmEngAADCpChF2hg8frtWrV2vz5s2qU6dOiX2DgoIUGhqqw4cPF9vHarXKarU6u0wAAFAOleuwYxiGhg8frpUrV2rjxo0KCwu75jqnT5/WyZMnFRQUVAYVAgCA8q5c37MzbNgwvffee1q8eLF8fHyUnp6u9PR0XbhwQZKUnZ2t0aNHa+vWrTp27Jg2btyo2NhY1apVSz179nRx9QAAoDwo1yM7c+bMkSRFRUXZtScnJyshIUFubm7au3evFi5cqLNnzyooKEjR0dFaunSpfHx8XFAxAAAob8p12DEMo8Tlnp6eWrduXRlVAwAAKqJyfRkLAADgZhF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqRF2AACAqZkm7MyePVthYWHy8PDQ3XffrS+++MLVJQEAgHLAFGFn6dKlevbZZ/XSSy/p66+/1n333aeYmBidOHHC1aUBAAAXM0XYmT59ugYPHqzHH39cd9xxh2bMmKGQkBDNmTPH1aUBAAAXq/Bh59KlS9q5c6c6depk196pUyd99dVXLqoKAACUF5VdXcDN+uWXX5SXl6eAgAC79oCAAKWnpxe5Tm5urnJzc23z586dkyRlZmY6vb783PNO32ZZKI1zUdoq4rnmPJcNznPZ4DyjOKX13ijYrmEYJfar8GGngMVisZs3DKNQW4GkpCSNHz++UHtISEip1FYR+c5wdQV/DJznssF5LhucZxSntN8bWVlZ8vX1LXZ5hQ87tWrVkpubW6FRnIyMjEKjPQXGjBmjkSNH2ubz8/P166+/qmbNmsUGJEdkZmYqJCREJ0+eVLVq1Zy2XRTGuS4bnOeywXkuG5znslGa59kwDGVlZSk4OLjEfhU+7Li7u+vuu+9WamqqevbsaWtPTU1V9+7di1zHarXKarXatVWvXr3UaqxWrRo/SGWEc102OM9lg/NcNjjPZaO0znNJIzoFKnzYkaSRI0fqscceU0REhFq3bq233npLJ06c0FNPPeXq0gAAgIuZIuz06dNHp0+f1l//+lelpaUpPDxcH3/8sUJDQ11dGgAAcDFThB1JGjp0qIYOHerqMuxYrVaNGzeu0CUzOB/numxwnssG57lscJ7LRnk4zxbjWs9rAQAAVGAV/kMFAQAASkLYAQAApkbYAQAApkbYAQAApkbYKSWbN29WbGysgoODZbFYtGrVKleXZDpJSUlq0aKFfHx85O/vrx49eujgwYOuLst05syZo7vuusv2gWCtW7fW2rVrXV2W6SUlJclisejZZ591dSmmk5iYKIvFYjcFBga6uixT+uGHH9S/f3/VrFlTXl5eatq0qXbu3FnmdRB2SklOTo6aNGmiWbNmuboU09q0aZOGDRumbdu2KTU1VVeuXFGnTp2Uk5Pj6tJMpU6dOpo0aZJ27NihHTt2qF27durevbv279/v6tJMa/v27Xrrrbd01113uboU02rUqJHS0tJs0969e11dkumcOXNGkZGRqlKlitauXasDBw5o2rRppfqNBcUxzefslDcxMTGKiYlxdRmm9sknn9jNJycny9/fXzt37tT999/voqrMJzY21m7+1Vdf1Zw5c7Rt2zY1atTIRVWZV3Z2tvr166e3335bEyZMcHU5plW5cmVGc0rZ5MmTFRISouTkZFtbvXr1XFILIzswjXPnzkmS/Pz8XFyJeeXl5WnJkiXKyclR69atXV2OKQ0bNkxdu3ZVhw4dXF2KqR0+fFjBwcEKCwtT37599f3337u6JNNZvXq1IiIi9PDDD8vf31/NmjXT22+/7ZJaCDswBcMwNHLkSN17770KDw93dTmms3fvXlWtWlVWq1VPPfWUVq5cqTvvvNPVZZnOkiVLtGvXLiUlJbm6FFNr2bKlFi5cqHXr1untt99Wenq62rRpo9OnT7u6NFP5/vvvNWfOHDVo0EDr1q3TU089pT//+c9auHBhmdfCZSyYwjPPPKNvvvlGW7ZscXUppnTbbbdp9+7dOnv2rN5//33Fx8dr06ZNBB4nOnnypEaMGKFPP/1UHh4eri7H1H5/i0Hjxo3VunVr3XrrrVqwYIFGjhzpwsrMJT8/XxEREZo4caIkqVmzZtq/f7/mzJmjAQMGlGktjOygwhs+fLhWr16tDRs2qE6dOq4ux5Tc3d1Vv359RUREKCkpSU2aNNHrr7/u6rJMZefOncrIyNDdd9+typUrq3Llytq0aZPeeOMNVa5cWXl5ea4u0bS8vb3VuHFjHT582NWlmEpQUFChP4juuOMOnThxosxrYWQHFZZhGBo+fLhWrlypjRs3KiwszNUl/WEYhqHc3FxXl2Eq7du3L/RE0MCBA3X77bfrhRdekJubm4sqM7/c3Fx9++23uu+++1xdiqlERkYW+jiQQ4cOKTQ0tMxrIeyUkuzsbB05csQ2f/ToUe3evVt+fn6qW7euCyszj2HDhmnx4sX64IMP5OPjo/T0dEmSr6+vPD09XVydeYwdO1YxMTEKCQlRVlaWlixZoo0bNxZ6Gg43x8fHp9D9Zt7e3qpZsyb3oTnZ6NGjFRsbq7p16yojI0MTJkxQZmam4uPjXV2aqTz33HNq06aNJk6cqN69e+s///mP3nrrLb311ltlX4yBUrFhwwZDUqEpPj7e1aWZRlHnV5KRnJzs6tJMZdCgQUZoaKjh7u5u1K5d22jfvr3x6aefurqsP4S2bdsaI0aMcHUZptOnTx8jKCjIqFKlihEcHGzExcUZ+/fvd3VZpvThhx8a4eHhhtVqNW6//XbjrbfeckkdFsMwjLKPWAAAAGWDG5QBAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAAICpEXYAuJTFYtGqVatcXUaJEhIS1KNHD1eXAcBBhB0ATpeQkCCLxSKLxaIqVaooICBAHTt21Pz585Wfn2/XNy0tze5bqJ1p+PDhatCgQZHLfvjhB7m5uWnFihWlsm8A5QdhB0Cp6Ny5s9LS0nTs2DGtXbtW0dHRGjFihLp166YrV67Y+gUGBspqtZZKDYMHD9aRI0f0xRdfFFqWkpKimjVrKjY2tlT2DaD8IOwAKBVWq1WBgYH605/+pObNm2vs2LH64IMPtHbtWqWkpNj6XX0Z69SpU+rbt6/8/Pzk7e2tiIgI/fvf/7Yt//DDD3X33XfLw8NDt9xyi8aPH28Xnn6vadOmat68uebPn19oWUpKigYMGKBKlSpp8ODBCgsLk6enp2677Ta9/vrrJR5bvXr1NGPGjEL7SkxMtM2fO3dOTzzxhPz9/VWtWjW1a9dOe/bsKXG7AEoHYQdAmWnXrp2aNGlS7KWj7OxstW3bVj/++KNWr16tPXv26Pnnn7dd+lq3bp369++vP//5zzpw4IDmzp2rlJQUvfrqq8Xuc/DgwVq+fLmys7NtbZs2bdKRI0c0aNAg5efnq06dOlq2bJkOHDigV155RWPHjtWyZcscPk7DMNS1a1elp6fr448/1s6dO9W8eXO1b99ev/76q8PbBeAYwg6AMnX77bfr2LFjRS5bvHixfv75Z61atUr33nuv6tevr969e6t169aSpFdffVUvvvii4uPjdcstt6hjx47629/+prlz5xa7v0cffVR5eXlavny5rW3+/Plq3bq17rzzTlWpUkXjx49XixYtFBYWpn79+ikhIeGmws6GDRu0d+9eLV++XBEREWrQoIH+/ve/q3r16vrXv/7l8HYBOKayqwsA8MdiGIYsFkuRy3bv3q1mzZrJz8+vyOU7d+7U9u3b7UZy8vLydPHiRZ0/f15eXl6F1qlevbri4uI0f/58DRw4UFlZWXr//fftLkO9+eabeuedd3T8+HFduHBBly5dUtOmTR0+xp07dyo7O1s1a9a0a79w4YK+++47h7cLwDGEHQBl6ttvv1VYWFiRyzw9PUtcNz8/X+PHj1dcXFyhZR4eHsWuN3jwYLVv316HDx/Wpk2bJEl9+vSRJC1btkzPPfecpk2bptatW8vHx0dTp061u0/oapUqVZJhGHZtly9ftqszKChIGzduLLRu9erVSzpEAKWAsAOgzKxfv1579+7Vc889V+Tyu+66S++8845+/fXXIkd3mjdvroMHD6p+/fo3tN/o6GjdcsstSklJ0YYNG9S7d2/5+PhIkr744gu1adNGQ4cOtfW/1uhL7dq1lZaWZpvPzMzU0aNH7epMT09X5cqVVa9evRuqFYDzcc8OgFKRm5ur9PR0/fDDD9q1a5cmTpyo7t27q1u3bhowYECR6zzyyCMKDAxUjx499OWXX+r777/X+++/r61bt0qSXnnlFS1cuFCJiYnav3+/vv32Wy1dulQvv/xyibVYLBYNHDhQc+bM0datWzV48GDbsvr162vHjh1at26dDh06pL/85S/avn17idtr166d3n33XX3xxRfat2+f4uPj5ebmZlveoUMHtW7dWj169NC6det07NgxffXVV3r55Ze1Y8eO6z2FAJyEsAOgVHzyyScKCgpSvXr11LlzZ23YsEFvvPGGPvjgA7tg8Hvu7u769NNP5e/vry5duqhx48aaNGmSrf8DDzygjz76SKmpqWrRooVatWql6dOnKzQ09Jr1JCQk6Ny5c7rtttsUGRlpa3/qqacUFxenPn36qGXLljp9+rTdKE9RxowZo/vvv1/dunVTly5d1KNHD91666225RaLRR9//LHuv/9+DRo0SA0bNlTfvn117NgxBQQEXM/pA+BEFuPqC88AAAAmwsgOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwtf8H/RH03tWbS+kAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "\n",
+    "roll_the_dice_thousand = pd.read_csv('../data/roll_the_dice_thousand.csv')\n",
+    "roll_the_dice_thousand.head()\n",
+    "\n",
+    "plt.hist(roll_the_dice_thousand['value'])\n",
+    "\n",
+    "plt.xlabel('Dice Value')\n",
+    "plt.ylabel('Frequency')\n",
+    "plt.title('Dice Rolls Frequency Distribution')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"\\nyour comments here\\nThe numbers that appear when rolling the dice seem to be more evenly distributed, indicating that as the number of rolls increases, there's a higher possibility of having equal frequencies for the numbers appearing. \\n\""
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "your comments here\n",
+    "The numbers that appear when rolling the dice seem to be more evenly distributed, indicating that as the number of rolls increases, there's a higher possibility of having equal frequencies for the numbers appearing. \n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Challenge 4\n",
+    "In the `data` folder of this repository you will find three different files with the prefix `ages_population`. These files contain information about a poll answered by a thousand people regarding their age. Each file corresponds to the poll answers in different neighbourhoods of Barcelona.\n",
+    "\n",
+    "#### 1.- Read the file `ages_population.csv`. Calculate the frequency distribution and plot it as we did during the lesson. Try to guess the range in which the mean and the standard deviation will be by looking at the plot. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "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>observation</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>68.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>12.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>45.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>38.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>49.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   observation\n",
+       "0         68.0\n",
+       "1         12.0\n",
+       "2         45.0\n",
+       "3         38.0\n",
+       "4         49.0"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "ages_population = pd.read_csv('../data/ages_population.csv')\n",
+    "ages_population.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "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>frequency</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>39.0</th>\n",
+       "      <td>45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41.0</th>\n",
+       "      <td>36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30.0</th>\n",
+       "      <td>34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35.0</th>\n",
+       "      <td>33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43.0</th>\n",
+       "      <td>32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73.0</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>82.0</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>70.0</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>71.0</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>69.0</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>72 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      frequency\n",
+       "39.0         45\n",
+       "41.0         36\n",
+       "30.0         34\n",
+       "35.0         33\n",
+       "43.0         32\n",
+       "...         ...\n",
+       "73.0          1\n",
+       "82.0          1\n",
+       "70.0          1\n",
+       "71.0          1\n",
+       "69.0          1\n",
+       "\n",
+       "[72 rows x 1 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "frequency_distribution = pd.DataFrame(ages_population['observation'].value_counts())\n",
+    "frequency_distribution = frequency_distribution.rename(columns={'observation': 'frequency'})\n",
+    "display(frequency_distribution)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.bar(frequency_distribution.index, frequency_distribution['frequency'])\n",
+    "plt.xlabel('Ages')\n",
+    "plt.ylabel('Frequency')\n",
+    "plt.title('Population Age Frequency Distribution')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\"\"\\nWe see the symmetrical distribution. Judging from the plot, I believe the mean is approximately 40. However, I don\\'t have an estimate for the standard deviation.'"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"\"\"\n",
+    "We see the symmetrical distribution. Judging from the plot, I believe the mean is approximately 40. However, I don't have an estimate for the standard deviation.\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.- Calculate the exact mean and standard deviation and compare them with your guesses. Do they fall inside the ranges you guessed?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The mean is : 36.56\n",
+      "The standard deviation is: 12.816499625976762\n"
+     ]
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "print('The mean is :', ages_population['observation'].mean())\n",
+    "print('The standard deviation is:', ages_population['observation'].std())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nyour comments here\\n\\nThe exact mean value is 36,56 which is  pretty close to my estimate.\\n'"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "your comments here\n",
+    "\n",
+    "The exact mean value is 36,56 which is  pretty close to my estimate.\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.- Now read the file `ages_population2.csv` . Calculate the frequency distribution and plot it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "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>observation</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>25.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>31.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>29.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>31.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>29.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   observation\n",
+       "0         25.0\n",
+       "1         31.0\n",
+       "2         29.0\n",
+       "3         31.0\n",
+       "4         29.0"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "ages_population2 = pd.read_csv('../data/ages_population2.csv')\n",
+    "ages_population2.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "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>frequency</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>28.0</th>\n",
+       "      <td>139</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27.0</th>\n",
+       "      <td>125</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26.0</th>\n",
+       "      <td>120</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29.0</th>\n",
+       "      <td>115</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25.0</th>\n",
+       "      <td>98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30.0</th>\n",
+       "      <td>90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24.0</th>\n",
+       "      <td>78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31.0</th>\n",
+       "      <td>61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23.0</th>\n",
+       "      <td>41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22.0</th>\n",
+       "      <td>35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32.0</th>\n",
+       "      <td>31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33.0</th>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21.0</th>\n",
+       "      <td>17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20.0</th>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34.0</th>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19.0</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35.0</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36.0</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      frequency\n",
+       "28.0        139\n",
+       "27.0        125\n",
+       "26.0        120\n",
+       "29.0        115\n",
+       "25.0         98\n",
+       "30.0         90\n",
+       "24.0         78\n",
+       "31.0         61\n",
+       "23.0         41\n",
+       "22.0         35\n",
+       "32.0         31\n",
+       "33.0         22\n",
+       "21.0         17\n",
+       "20.0         13\n",
+       "34.0          7\n",
+       "19.0          3\n",
+       "35.0          3\n",
+       "36.0          2"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "frequency_distribution2 = pd.DataFrame(ages_population2['observation'].value_counts())\n",
+    "frequency_distribution2 = frequency_distribution2.rename(columns={'observation': 'frequency'})\n",
+    "display(frequency_distribution2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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7d87+OsOiRYscXleoUEFhYWHatWtXppmZjCU3//DfjJYtW2rv3r168MEHszxuRthp2LChzp07l+kcL1iwIFOfZcuW1YEDBxz+0Txz5ow2bdqU6dhnzpxRWlpalsfOTcBr3ry51q1bd1OX/fr06aPly5dr6NChCgwMtD9hlFtpaWnq3bu3zpw5o8GDB9/0fmXKlFHv3r3VtGlTh0vFzp7NmD9/vsPrL7/8UleuXHF4cq5s2bKZ3tdr167V+fPnHdbd7EyodOt/g5A3MLMDt9K0aVM1a9ZMgwcPVlJSkurWrWt/EqJ69erq1KmTQ/vg4GC1bt1akZGRKlmypObNm6fo6GiNHTtW+fPnlyTVqlVLFSpU0MCBA3XlyhUVKVJES5cu1caNG2+qHm9vb7300ksaNGiQLl++rGnTpikhISFT2ypVqmjJkiWaNm2aatSoofvuuy/bGazhw4fb7z95//33VbRoUc2fP1/Lly/P9NRNbq1cuVInT57U2LFjs3y0OmOmYNasWWrZsqW6dOmiiRMnqmPHjhoxYoTKlSunlStX6vvvv5ck3Xff//7faMaMGWrevLmaNWumrl276v7779dff/2l33//Xdu3b9dXX3112/Vn5cMPP1R0dLTq1KmjPn36qEKFCrp8+bIOHz6sFStWaPr06SpVqpQ6d+6siRMnqnPnzho5cqTCwsK0YsUK+1iu1alTJ82YMUMdO3bUq6++qjNnzmjcuHEqVKiQQ7v27dtr/vz5euaZZ9S3b1899thj8vLy0vHjx7Vu3Tq1adNGzz333C2PZ+XKlapXr57eeecdValSRWfPntWqVavUv39/VaxY0d62Y8eOGjp0qDZs2KB3331X3t7eN32c06dPa8uWLTLG6Ny5c/YPFdy1a5fefvttvfrqq9num5iYqIYNG6pDhw6qWLGi/Pz8FBMTo1WrVjnM7N3K7//NWLJkiTw9PdW0aVP701jVqlVTRESEvU2nTp303nvv6f3331f9+vX122+/acqUKZnePxmfGj5z5kz5+fkpX758Cg0NzfIS1K3+DUIe4bp7o3GvyXgqKiYmJsd2ly5dMoMHDzYhISHGy8vLlCxZ0rzxxhsmISHBoV1ISIhp0aKF+frrr02lSpWMt7e3KVu2rJkwYUKmPg8cOGDCw8NNoUKFTPHixc1bb71lli9fflNPY/373/821apVM/ny5TP333+/+dvf/mZWrlyZad+//vrLtGvXzhQuXNjYbDZz7dtLWTw1smfPHtOqVSvj7+9vvL29TbVq1RyeSjHmf09jffXVVw7rs3qK5XrPPvus8fb2NvHx8dm2ad++vfH09DRxcXHGGGOOHj1q2rZtawoWLGj8/PzM888/b1asWJHlk027du0yERERpkSJEsbLy8sEBQWZRo0a2Z/6yolu8IRQxrnNyp9//mn69OljQkNDjZeXlylatKipUaOGGTZsmDl//ry93fHjx83zzz/vMJZNmzZl+XObM2eOeeihh0y+fPnMww8/bBYvXpzl70Jqaqr5xz/+Yf99KFiwoKlYsaLp2bOnOXjw4A3rz+rJr2PHjpnu3buboKAg4+XlZYKDg01ERIQ5ffp0pv27du1qPD09zfHjx7P92V1P1zztdt9995lChQqZKlWqmNdeey3Lp/mu/926fPmyef31103VqlVNoUKFjK+vr6lQoYIZPny4/YlHY7L//c/ob/z48Tc8ljH/expr27ZtplWrVvbz99JLL2X6mSQnJ5tBgwaZ0qVLG19fX1O/fn2zc+fOTE9jGWPMpEmTTGhoqPHw8HA4Zlbn+Vb/Bl0vuyf84Do2Y27iVnzADZUtW1aVK1fWd9995+pSLG3UqFF69913dfToUZd/5cXtOnz4sEJDQzV79uyb+iwgd5KSkqKyZcvqySefdNqHNwL3Ci5jAbCbMmWKJKlixYpKTU3V2rVr9cknn6hjx455PujkVX/++af279+v2bNn6/Tp0xoyZIirSwLyHMIOALv8+fNr4sSJOnz4sJKTk1WmTBkNHjxY7777rqtLu2ctX75c3bp1U8mSJTV16lSnPG4O3Gu4jAUAACyNR88BAIClEXYAAIClEXYAAIClcYOypPT0dJ08eVJ+fn63/JHmAADANcz//6DM4OBghw8+vR5hR1e/j+j6b58GAAB5w7Fjx3L8eAzCjv73BX7Hjh3L9BHxAADAPSUlJal06dI3/D4+wo7+92WPhQoVIuwAAJDH3OgWFG5QBgAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlubSsLNhwwa1atVKwcHBstls+vbbb7Nt27NnT9lsNk2aNMlhfXJyst566y0VK1ZMBQoUUOvWrXX8+PE7WzgAAMgzXBp2Lly4oGrVqmnKlCk5tvv222/1yy+/KDg4ONO2fv36aenSpVq0aJE2btyo8+fPq2XLlkpLS7tTZQMAgDzEpV8X0bx5czVv3jzHNidOnFDv3r31/fffq0WLFg7bEhMTNWvWLP3zn/9UkyZNJEnz5s1T6dKltWbNGjVr1uyO1Q4AAPIGt75nJz09XZ06ddLf/vY3VapUKdP2bdu2KTU1VeHh4fZ1wcHBqly5sjZt2pRtv8nJyUpKSnJYAACANbl12Bk7dqw8PT3Vp0+fLLfHxcXJ29tbRYoUcVgfGBiouLi4bPsdPXq0/P397Uvp0qWdWjcAAHAfbht2tm3bpo8//lhRUVE3/DbT6xljctxn6NChSkxMtC/Hjh273XIBAICbctuw89NPPyk+Pl5lypSRp6enPD09deTIEQ0YMEBly5aVJAUFBSklJUUJCQkO+8bHxyswMDDbvn18fFSoUCGHBQAAWJNLb1DOSadOnew3HWdo1qyZOnXqpG7dukmSatSoIS8vL0VHRysiIkKSdOrUKe3du1fjxo276zUDcG9lhyx3ep+Hx7S4cSMALuXSsHP+/Hn98ccf9texsbHauXOnihYtqjJlyiggIMChvZeXl4KCglShQgVJkr+/v3r06KEBAwYoICBARYsW1cCBA1WlSpVMQQkAANybXBp2tm7dqoYNG9pf9+/fX5LUpUsXRUVF3VQfEydOlKenpyIiInTp0iU1btxYUVFR8vDwuBMlAwCAPMZmjDGuLsLVkpKS5O/vr8TERO7fASyMy1iAtdzsv99ue4MyAACAMxB2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApXm6ugAAkKSyQ5Y7tb/DY1o4tT8AeRczOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNJcGnY2bNigVq1aKTg4WDabTd9++619W2pqqgYPHqwqVaqoQIECCg4OVufOnXXy5EmHPpKTk/XWW2+pWLFiKlCggFq3bq3jx4/f5ZEAAAB35dKwc+HCBVWrVk1TpkzJtO3ixYvavn273nvvPW3fvl1LlizRgQMH1Lp1a4d2/fr109KlS7Vo0SJt3LhR58+fV8uWLZWWlna3hgEAANyYS78uonnz5mrevHmW2/z9/RUdHe2wbvLkyXrsscd09OhRlSlTRomJiZo1a5b++c9/qkmTJpKkefPmqXTp0lqzZo2aNWt2x8cAAADcW566ZycxMVE2m02FCxeWJG3btk2pqakKDw+3twkODlblypW1adOmbPtJTk5WUlKSwwIAAKwpz3wR6OXLlzVkyBB16NBBhQoVkiTFxcXJ29tbRYoUcWgbGBiouLi4bPsaPXq0PvjggztaL2AlfEkngLwsT8zspKamqn379kpPT9fUqVNv2N4YI5vNlu32oUOHKjEx0b4cO3bMmeUCAAA34vZhJzU1VREREYqNjVV0dLR9VkeSgoKClJKSooSEBId94uPjFRgYmG2fPj4+KlSokMMCAACsya3DTkbQOXjwoNasWaOAgACH7TVq1JCXl5fDjcynTp3S3r17VadOnbtdLgAAcEMuvWfn/Pnz+uOPP+yvY2NjtXPnThUtWlTBwcFq166dtm/fru+++05paWn2+3CKFi0qb29v+fv7q0ePHhowYIACAgJUtGhRDRw4UFWqVLE/nQUAAO5tLg07W7duVcOGDe2v+/fvL0nq0qWLIiMjtWzZMknSI4884rDfunXr1KBBA0nSxIkT5enpqYiICF26dEmNGzdWVFSUPDw87soYAACAe3Np2GnQoIGMMdluz2lbhnz58mny5MmaPHmyM0sDAAAW4db37AAAANwuwg4AALA0wg4AALA0wg4AALA0wg4AALC0PPPdWACQV/BdYoB7YWYHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYmkvDzoYNG9SqVSsFBwfLZrPp22+/ddhujFFkZKSCg4Pl6+urBg0aaN++fQ5tkpOT9dZbb6lYsWIqUKCAWrdurePHj9/FUQAAAHfm0rBz4cIFVatWTVOmTMly+7hx4zRhwgRNmTJFMTExCgoKUtOmTXXu3Dl7m379+mnp0qVatGiRNm7cqPPnz6tly5ZKS0u7W8MAAABuzNOVB2/evLmaN2+e5TZjjCZNmqRhw4apbdu2kqQ5c+YoMDBQCxYsUM+ePZWYmKhZs2bpn//8p5o0aSJJmjdvnkqXLq01a9aoWbNmd20sAADAPbntPTuxsbGKi4tTeHi4fZ2Pj4/q16+vTZs2SZK2bdum1NRUhzbBwcGqXLmyvU1WkpOTlZSU5LAAAABrctuwExcXJ0kKDAx0WB8YGGjfFhcXJ29vbxUpUiTbNlkZPXq0/P397Uvp0qWdXD0AAHAXbht2MthsNofXxphM6653ozZDhw5VYmKifTl27JhTagUAAO7HbcNOUFCQJGWaoYmPj7fP9gQFBSklJUUJCQnZtsmKj4+PChUq5LAAAABrctuwExoaqqCgIEVHR9vXpaSkaP369apTp44kqUaNGvLy8nJoc+rUKe3du9feBgAA3Ntc+jTW+fPn9ccff9hfx8bGaufOnSpatKjKlCmjfv36adSoUQoLC1NYWJhGjRql/Pnzq0OHDpIkf39/9ejRQwMGDFBAQICKFi2qgQMHqkqVKvanswAAwL3NpWFn69atatiwof11//79JUldunRRVFSUBg0apEuXLqlXr15KSEhQ7dq1tXr1avn5+dn3mThxojw9PRUREaFLly6pcePGioqKkoeHx10fDwAAcD8uDTsNGjSQMSbb7TabTZGRkYqMjMy2Tb58+TR58mRNnjz5DlQIuLeyQ5Y7vc/DY1o4vU8AcCW3vWcHAADAGQg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0jxdXQAA4NaVHbLcqf0dHtPCqf0B7oSZHQAAYGmEHQAAYGmEHQAAYGmEHQAAYGmEHQAAYGmEHQAAYGmEHQAAYGmEHQAAYGmEHQAAYGm5CjuxsbHOrgMAAOCOyFXYKVeunBo2bKh58+bp8uXLzq4JAADAaXIVdnbt2qXq1atrwIABCgoKUs+ePfXrr786uzYAAIDblquwU7lyZU2YMEEnTpzQ7NmzFRcXpyeffFKVKlXShAkT9Oeffzq7TgAAgFy5rRuUPT099dxzz+nLL7/U2LFjdejQIQ0cOFClSpVS586dderUKWfVCQAAkCu3FXa2bt2qXr16qWTJkpowYYIGDhyoQ4cOae3atTpx4oTatGnjrDoBAAByxTM3O02YMEGzZ8/W/v379cwzz2ju3Ll65plndN99V7NTaGioZsyYoYoVKzq1WAAAgFuVq7Azbdo0de/eXd26dVNQUFCWbcqUKaNZs2bdVnEAAAC3K1dh5+DBgzds4+3trS5duuSmewAAAKfJ1T07s2fP1ldffZVp/VdffaU5c+bcdlEAAADOkquwM2bMGBUrVizT+hIlSmjUqFG3XRQAAICz5CrsHDlyRKGhoZnWh4SE6OjRo7ddVIYrV67o3XffVWhoqHx9ffXAAw/oww8/VHp6ur2NMUaRkZEKDg6Wr6+vGjRooH379jmtBgAAkLflKuyUKFFCu3fvzrR+165dCggIuO2iMowdO1bTp0/XlClT9Pvvv2vcuHEaP368Jk+ebG8zbtw4TZgwQVOmTFFMTIyCgoLUtGlTnTt3zml1AACAvCtXYad9+/bq06eP1q1bp7S0NKWlpWnt2rXq27ev2rdv77TiNm/erDZt2qhFixYqW7as2rVrp/DwcG3dulXS1VmdSZMmadiwYWrbtq0qV66sOXPm6OLFi1qwYIHT6gAAAHlXrsLOiBEjVLt2bTVu3Fi+vr7y9fVVeHi4GjVq5NR7dp588kn98MMPOnDggKSrM0cbN27UM888I+nqt6/HxcUpPDzcvo+Pj4/q16+vTZs2ZdtvcnKykpKSHBYAAGBNuXr03NvbW4sXL9bf//537dq1S76+vqpSpYpCQkKcWtzgwYOVmJioihUrysPDQ2lpaRo5cqReeuklSVJcXJwkKTAw0GG/wMBAHTlyJNt+R48erQ8++MCptQIAAPeUq7CToXz58ipfvryzaslk8eLFmjdvnhYsWKBKlSpp586d6tevn4KDgx0+w8dmsznsZ4zJtO5aQ4cOVf/+/e2vk5KSVLp0aecPAAAAuFyuwk5aWpqioqL0ww8/KD4+3uHpKElau3atU4r729/+piFDhtjvA6pSpYqOHDmi0aNHq0uXLvZPb46Li1PJkiXt+8XHx2ea7bmWj4+PfHx8nFIjAABwb7kKO3379lVUVJRatGihypUr5ziLcjsuXrxo/76tDB4eHvZwFRoaqqCgIEVHR6t69eqSpJSUFK1fv15jx469IzUBAIC8JVdhZ9GiRfryyy/tNwrfKa1atdLIkSNVpkwZVapUSTt27NCECRPUvXt3SVcvX/Xr10+jRo1SWFiYwsLCNGrUKOXPn18dOnS4o7UBAIC8Idc3KJcrV87ZtWQyefJkvffee+rVq5fi4+MVHBysnj176v3337e3GTRokC5duqRevXopISFBtWvX1urVq+Xn53fH6wNyUnbIcqf2d3hMC6f2BwD3ilw9ej5gwAB9/PHHMsY4ux4Hfn5+mjRpko4cOaJLly7p0KFDGjFihLy9ve1tbDabIiMjderUKV2+fFnr169X5cqV72hdAAAg78jVzM7GjRu1bt06rVy5UpUqVZKXl5fD9iVLljilOAAAgNuVq7BTuHBhPffcc86uBQAAwOlyFXZmz57t7DoAAADuiFzdsyNd/UbyNWvWaMaMGfYv3Tx58qTOnz/vtOIAAABuV65mdo4cOaKnn35aR48eVXJyspo2bSo/Pz+NGzdOly9f1vTp051dJwAAQK7kamanb9++qlmzphISEuTr62tf/9xzz+mHH35wWnEAAAC3K9dPY/38888Oj4BLUkhIiE6cOOGUwgAAAJwhVzM76enpSktLy7T++PHjfJgfAABwK7kKO02bNtWkSZPsr202m86fP6/hw4ff8a+QAAAAuBW5uow1ceJENWzYUA8//LAuX76sDh066ODBgypWrJgWLlzo7BoBAAByLVdhJzg4WDt37tTChQu1fft2paenq0ePHnr55ZcdblgGAABwtVyFHUny9fVV9+7d7d9ADgAA4I5yFXbmzp2b4/bOnTvnqhgAAABny1XY6du3r8Pr1NRUXbx4Ud7e3sqfPz9hBwAAuI1cPY2VkJDgsJw/f1779+/Xk08+yQ3KAADAreT6u7GuFxYWpjFjxmSa9QEAAHAlp4UdSfLw8NDJkyed2SUAAMBtydU9O8uWLXN4bYzRqVOnNGXKFNWtW9cphQEAADhDrsLOs88+6/DaZrOpePHiatSokT766CNn1AUAAOAUuQo76enpzq4DAADgjnDqPTsAAADuJlczO/3797/pthMmTMjNIQAAAJwiV2Fnx44d2r59u65cuaIKFSpIkg4cOCAPDw89+uij9nY2m805VQIAAORSrsJOq1at5Ofnpzlz5qhIkSKSrn7QYLdu3fTUU09pwIABTi0SAAAgt3J1z85HH32k0aNH24OOJBUpUkQjRozgaSwAAOBWchV2kpKSdPr06Uzr4+Pjde7cudsuCgAAwFlyFXaee+45devWTV9//bWOHz+u48eP6+uvv1aPHj3Utm1bZ9cIAACQa7m6Z2f69OkaOHCgOnbsqNTU1KsdeXqqR48eGj9+vFMLBAAAuB25Cjv58+fX1KlTNX78eB06dEjGGJUrV04FChRwdn0AAAC35bY+VPDUqVM6deqUypcvrwIFCsgY46y6AAAAnCJXYefMmTNq3Lixypcvr2eeeUanTp2SJL3yyis8dg4AANxKrsLO22+/LS8vLx09elT58+e3r3/xxRe1atUqpxUHAABwu3J1z87q1av1/fffq1SpUg7rw8LCdOTIEacUBgAA4Ay5mtm5cOGCw4xOhv/+97/y8fG57aIAAACcJVdhp169epo7d679tc1mU3p6usaPH6+GDRs6rTgAAIDblavLWOPHj1eDBg20detWpaSkaNCgQdq3b5/++usv/fzzz86uEQAAINdyNbPz8MMPa/fu3XrsscfUtGlTXbhwQW3bttWOHTv04IMPOrtGAACAXLvlmZ3U1FSFh4drxowZ+uCDD+5ETQAAAE5zyzM7Xl5e2rt3r2w2252oBwAAwKlydRmrc+fOmjVrlrNrydKJEyfUsWNHBQQEKH/+/HrkkUe0bds2+3ZjjCIjIxUcHCxfX181aNBA+/btuyu1AQAA95erG5RTUlL0+eefKzo6WjVr1sz0nVgTJkxwSnEJCQmqW7euGjZsqJUrV6pEiRI6dOiQChcubG8zbtw4TZgwQVFRUSpfvrxGjBihpk2bav/+/fLz83NKHQAAIO+6pbDzn//8R2XLltXevXv16KOPSpIOHDjg0MaZl7fGjh2r0qVLa/bs2fZ1ZcuWtf+3MUaTJk3SsGHD1LZtW0nSnDlzFBgYqAULFqhnz55OqwUA7jVlhyx3ep+Hx7Rwep/AjdzSZaywsDD997//1bp167Ru3TqVKFFCixYtsr9et26d1q5d67Tili1bppo1a+qFF15QiRIlVL16dX322Wf27bGxsYqLi1N4eLh9nY+Pj+rXr69NmzY5rQ4AAJB33VLYuf5bzVeuXKkLFy44taBr/ec//9G0adMUFham77//Xq+//rr69Olj/0DDuLg4SVJgYKDDfoGBgfZtWUlOTlZSUpLDAgAArClX9+xkuD78OFt6erpq1qypUaNGSZKqV6+uffv2adq0aercubO93fWXzowxOV5OGz16NI/NAwBwj7ilmR2bzZYpRNzJR9BLliyphx9+2GHdQw89pKNHj0qSgoKCJCnTLE58fHym2Z5rDR06VImJifbl2LFjTq4cAAC4i1ua2THGqGvXrvYv+7x8+bJef/31TE9jLVmyxCnF1a1bV/v373dYd+DAAYWEhEiSQkNDFRQUpOjoaFWvXl3S1SfF1q9fr7Fjx2bbr4+PD19YCgDAPeKWwk6XLl0cXnfs2NGpxVzv7bffVp06dTRq1ChFRETo119/1cyZMzVz5kxJV2eV+vXrp1GjRiksLExhYWEaNWqU8ufPrw4dOtzR2gAAQN5wS2Hn2kfA74ZatWpp6dKlGjp0qD788EOFhoZq0qRJevnll+1tBg0apEuXLqlXr15KSEhQ7dq1tXr1aj5jBwAASLrNG5TvhpYtW6ply5bZbrfZbIqMjFRkZOTdKwoAAOQZufq6CAAAgLyCsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACwtT4Wd0aNHy2azqV+/fvZ1xhhFRkYqODhYvr6+atCggfbt2+e6IgEAgFvJM2EnJiZGM2fOVNWqVR3Wjxs3ThMmTNCUKVMUExOjoKAgNW3aVOfOnXNRpQAAwJ14urqAm3H+/Hm9/PLL+uyzzzRixAj7emOMJk2apGHDhqlt27aSpDlz5igwMFALFixQz549XVUy3FzZIcud2t/hMS2c2h8AwHnyxMzOm2++qRYtWqhJkyYO62NjYxUXF6fw8HD7Oh8fH9WvX1+bNm3Ktr/k5GQlJSU5LAAAwJrcfmZn0aJF2r59u2JiYjJti4uLkyQFBgY6rA8MDNSRI0ey7XP06NH64IMPnFsoAABwS249s3Ps2DH17dtX8+bNU758+bJtZ7PZHF4bYzKtu9bQoUOVmJhoX44dO+a0mgEAgHtx65mdbdu2KT4+XjVq1LCvS0tL04YNGzRlyhTt379f0tUZnpIlS9rbxMfHZ5rtuZaPj498fHzuXOEAAMBtuPXMTuPGjbVnzx7t3LnTvtSsWVMvv/yydu7cqQceeEBBQUGKjo6275OSkqL169erTp06LqwcAAC4C7ee2fHz81PlypUd1hUoUEABAQH29f369dOoUaMUFhamsLAwjRo1Svnz51eHDh1cUTIAAHAzbh12bsagQYN06dIl9erVSwkJCapdu7ZWr14tPz8/V5cGAADcQJ4LOz/++KPDa5vNpsjISEVGRrqkHgAA4N7c+p4dAACA20XYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlpbnHj2HtZUdstzpfR4e08LpfQIA8g5mdgAAgKURdgAAgKURdgAAgKURdgAAgKURdgAAgKURdgAAgKURdgAAgKURdgAAgKURdgAAgKXxCcoAAJdy9ien86npuB4zOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNI8XV0AAAB3Wtkhy53a3+ExLZzaH+4st57ZGT16tGrVqiU/Pz+VKFFCzz77rPbv3+/QxhijyMhIBQcHy9fXVw0aNNC+fftcVDEAAHA3bh121q9frzfffFNbtmxRdHS0rly5ovDwcF24cMHeZty4cZowYYKmTJmimJgYBQUFqWnTpjp37pwLKwcAAO7CrS9jrVq1yuH17NmzVaJECW3btk316tWTMUaTJk3SsGHD1LZtW0nSnDlzFBgYqAULFqhnz56uKBsAALgRt57ZuV5iYqIkqWjRopKk2NhYxcXFKTw83N7Gx8dH9evX16ZNm7LtJzk5WUlJSQ4LAACwpjwTdowx6t+/v5588klVrlxZkhQXFydJCgwMdGgbGBho35aV0aNHy9/f376ULl36zhUOAABcKs+End69e2v37t1auHBhpm02m83htTEm07prDR06VImJifbl2LFjTq8XAAC4B7e+ZyfDW2+9pWXLlmnDhg0qVaqUfX1QUJCkqzM8JUuWtK+Pj4/PNNtzLR8fH/n4+Ny5ggEAgNtw65kdY4x69+6tJUuWaO3atQoNDXXYHhoaqqCgIEVHR9vXpaSkaP369apTp87dLhcAALght57ZefPNN7VgwQL961//kp+fn/0+HH9/f/n6+spms6lfv34aNWqUwsLCFBYWplGjRil//vzq0KGDi6sHAADuwK3DzrRp0yRJDRo0cFg/e/Zsde3aVZI0aNAgXbp0Sb169VJCQoJq166t1atXy8/P7y5XCwAA3JFbhx1jzA3b2Gw2RUZGKjIy8s4XBAAA8hy3vmcHAADgdrn1zA7cC1+kBwDIi5jZAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlsYnKFsEn24MAEDWmNkBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACW5unqAgAAsIKyQ5Y7vc/DY1o4vc97ETM7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0vgE5TuMT9QEADiTs/9duRf+TWFmBwAAWBphBwAAWJplLmNNnTpV48eP16lTp1SpUiVNmjRJTz31lKvLAgAgz7HapTJLzOwsXrxY/fr107Bhw7Rjxw499dRTat68uY4ePerq0gAAgItZIuxMmDBBPXr00CuvvKKHHnpIkyZNUunSpTVt2jRXlwYAAFwsz4edlJQUbdu2TeHh4Q7rw8PDtWnTJhdVBQAA3EWev2fnv//9r9LS0hQYGOiwPjAwUHFxcVnuk5ycrOTkZPvrxMRESVJSUpLT60tPvuj0PrOq09nHscox7tZxrHKMu3Ucqxzjbh3HKse4W8exyjHu1nFcdQxn9muMybmhyeNOnDhhJJlNmzY5rB8xYoSpUKFClvsMHz7cSGJhYWFhYWGxwHLs2LEcs0Ken9kpVqyYPDw8Ms3ixMfHZ5rtyTB06FD179/f/jo9PV1//fWXAgICZLPZ7mi9rpSUlKTSpUvr2LFjKlSokKvLuavu5bFL9/b4Gfu9OXbp3h7/vTJ2Y4zOnTun4ODgHNvl+bDj7e2tGjVqKDo6Ws8995x9fXR0tNq0aZPlPj4+PvLx8XFYV7hw4TtZplspVKiQpX/5c3Ivj126t8fP2O/NsUv39vjvhbH7+/vfsE2eDzuS1L9/f3Xq1Ek1a9bUE088oZkzZ+ro0aN6/fXXXV0aAABwMUuEnRdffFFnzpzRhx9+qFOnTqly5cpasWKFQkJCXF0aAABwMUuEHUnq1auXevXq5eoy3JqPj4+GDx+e6RLeveBeHrt0b4+fsd+bY5fu7fHfy2PPis2YGz2vBQAAkHfl+Q8VBAAAyAlhBwAAWBphBwAAWBphBwAAWBphJw8aPXq0atWqJT8/P5UoUULPPvus9u/f79DGGKPIyEgFBwfL19dXDRo00L59+27Y9zfffKOHH35YPj4+evjhh7V06dI7NYxcu9H4U1NTNXjwYFWpUkUFChRQcHCwOnfurJMnT+bYb1RUlGw2W6bl8uXLd3pIN+1mzn3Xrl0zjeHxxx+/Yd/ufu5vZuxZnT+bzabx48dn229eOO+SNG3aNFWtWtX+IXFPPPGEVq5cad9u5fd8TmO38vtduvF5t+r73dkIO3nQ+vXr9eabb2rLli2Kjo7WlStXFB4ergsXLtjbjBs3ThMmTNCUKVMUExOjoKAgNW3aVOfOncu2382bN+vFF19Up06dtGvXLnXq1EkRERH65Zdf7sawbtqNxn/x4kVt375d7733nrZv364lS5bowIEDat269Q37LlSokE6dOuWw5MuX704P6abdzLmXpKefftphDCtWrMix37xw7m9m7Nefuy+++EI2m03PP/98jn27+3mXpFKlSmnMmDHaunWrtm7dqkaNGqlNmzb2QGPl93xOY7fy+1268XmXrPl+dzpnfBknXCs+Pt5IMuvXrzfGGJOenm6CgoLMmDFj7G0uX75s/P39zfTp07PtJyIiwjz99NMO65o1a2bat29/Zwp3kuvHn5Vff/3VSDJHjhzJts3s2bONv7//Hajwzslq7F26dDFt2rS5pX7y4rm/mfPepk0b06hRoxz7yYvnPUORIkXM559/fs+9543539izYtX3e4Zrx36vvN9vFzM7FpCYmChJKlq0qCQpNjZWcXFxCg8Pt7fx8fFR/fr1tWnTpmz72bx5s8M+ktSsWbMc93EH148/uzY2m+2G34F2/vx5hYSEqFSpUmrZsqV27NjhzFKdLrux//jjjypRooTKly+vV199VfHx8Tn2kxfP/Y3O++nTp7V8+XL16NHjhn3ltfOelpamRYsW6cKFC3riiSfuqff89WPPilXf79mN/V54v98uwk4eZ4xR//799eSTT6py5cqSZP8G+Ou/9T0wMDDTt8NfKy4u7pb3cbWsxn+9y5cva8iQIerQoUOOX4hXsWJFRUVFadmyZVq4cKHy5cununXr6uDBg3eq/NuS3dibN2+u+fPna+3atfroo48UExOjRo0aKTk5Odu+8tq5v5nzPmfOHPn5+alt27Y59pWXzvuePXtUsGBB+fj46PXXX9fSpUv18MMP3xPv+ezGfj0rvt9zGvu98H53CtdOLOF29erVy4SEhJhjx47Z1/38889Gkjl58qRD21deecU0a9Ys2768vLzMggULHNbNmzfP+Pj4OLdoJ8pq/NdKSUkxbdq0MdWrVzeJiYm31HdaWpqpVq2aeeutt5xRqtPdaOwZTp48aby8vMw333yTbZu8du5vZuwVKlQwvXv3vuW+3fm8Jycnm4MHD5qYmBgzZMgQU6xYMbNv37574j2f3divZdX3+82MPYMV3+/OYJnvxroXvfXWW1q2bJk2bNigUqVK2dcHBQVJupreS5YsaV8fHx+fKc1fKygoKFOyv9E+rpTd+DOkpqYqIiJCsbGxWrt2bY7/l5eV++67T7Vq1XLL/9O70divVbJkSYWEhOQ4jrx07m9m7D/99JP279+vxYsX33L/7nzevb29Va5cOUlSzZo1FRMTo48//liDBw+WZO33fHZjnzFjhiRrv99vNPZrWe397ixcxsqDjDHq3bu3lixZorVr1yo0NNRhe2hoqIKCghQdHW1fl5KSovXr16tOnTrZ9vvEE0847CNJq1evznEfV7jR+KX//eE7ePCg1qxZo4CAgFwdZ+fOnQ7/eLjazYz9emfOnNGxY8dyHEdeOPe3MvZZs2apRo0aqlatWq6O427nPTvGGCUnJ1v+PZ+VjLFL1n2/Z+fasV/PKu93p3PVlBJy74033jD+/v7mxx9/NKdOnbIvFy9etLcZM2aM8ff3N0uWLDF79uwxL730kilZsqRJSkqyt+nUqZMZMmSI/fXPP/9sPDw8zJgxY8zvv/9uxowZYzw9Pc2WLVvu6vhu5EbjT01NNa1btzalSpUyO3fudGiTnJxs7+f68UdGRppVq1aZQ4cOmR07dphu3boZT09P88svv9z1MWbnRmM/d+6cGTBggNm0aZOJjY0169atM0888YS5//778/y5v5nfe2OMSUxMNPnz5zfTpk3Lsp+8eN6NMWbo0KFmw4YNJjY21uzevdu888475r777jOrV682xlj7PZ/T2K38fjcm57Fb+f3ubISdPEhSlsvs2bPtbdLT083w4cNNUFCQ8fHxMfXq1TN79uxx6Kd+/fqmS5cuDuu++uorU6FCBePl5WUqVqyY43VfV7nR+GNjY7Nts27dOns/14+/X79+pkyZMsbb29sUL17chIeHm02bNt3dwd3AjcZ+8eJFEx4ebooXL268vLxMmTJlTJcuXczRo0cd+smL5/5mfu+NMWbGjBnG19fXnD17Nst+8uJ5N8aY7t27m5CQEHudjRs3tgcdY6z9ns9p7FZ+vxuT89it/H53NpsxxtzZuSMAAADX4Z4dAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAHnGpk2b5OHhoaefftrVpQDIQ/gEZQB5xiuvvKKCBQvq888/12+//aYyZcq4uiQAeQAzOwDyhAsXLujLL7/UG2+8oZYtWyoqKsph+7JlyxQWFiZfX181bNhQc+bMkc1m09mzZ+1tNm3apHr16snX11elS5dWnz59dOHCBfv2qVOnKiwsTPny5VNgYKDatWt3l0YH4E4i7ADIExYvXqwKFSqoQoUK6tixo2bPnq2MienDhw+rXbt2evbZZ7Vz50717NlTw4YNc9h/z549atasmdq2bavdu3dr8eLF2rhxo3r37i1J2rp1q/r06aMPP/xQ+/fv16pVq1SvXr27Pk4AzsdlLAB5Qt26dRUREaG+ffvqypUrKlmypBYuXKgmTZpoyJAhWr58ufbs2WNv/+6772rkyJFKSEhQ4cKF1blzZ/n6+mrGjBn2Nhs3blT9+vV14cIFrVixQt26ddPx48fl5+fniiECuEOY2QHg9vbv369ff/1V7du3lyR5enrqxRdf1BdffGHfXqtWLYd9HnvsMYfX27ZtU1RUlAoWLGhfmjVrpvT0dMXGxqpp06YKCQnRAw88oE6dOmn+/Pm6ePHi3RkggDvK09UFAMCNzJo1S1euXNH9999vX2eMkZeXlxISEmSMkc1mc9jn+knr9PR09ezZU3369MnUf5kyZeTt7a3t27frxx9/1OrVq/X+++8rMjJSMTExKly48B0ZF4C7g7ADwK1duXJFc+fO1UcffaTw8HCHbc8//7zmz5+vihUrasWKFQ7btm7d6vD60Ucf1b59+1SuXLlsj+Xp6akmTZqoSZMmGj58uAoXLqy1a9eqbdu2zhsQgLuOsAPArX333XdKSEhQjx495O/v77CtXbt2mjVrlpYsWaIJEyZo8ODB6tGjh3bu3Gl/Witjxmfw4MF6/PHH9eabb+rVV19VgQIF9Pvvvys6OlqTJ0/Wd999p//85z+qV6+eihQpohUrVig9PV0VKlS420MG4GTcswPArc2aNUtNmjTJFHSkqzM7O3fuVEJCgr7++mstWbJEVatW1bRp0+xPY/n4+EiSqlatqvXr1+vgwYN66qmnVL16db333nsqWbKkJKlw4cJasmSJGjVqpIceekjTp0/XwoULValSpbs3WAB3BE9jAbCkkSNHavr06Tp27JirSwHgYlzGAmAJU6dOVa1atRQQEKCff/5Z48ePt3+GDoB7G2EHgCUcPHhQI0aM0F9//aUyZcpowIABGjp0qKvLAuAGuIwFAAAsjRuUAQCApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApf0/ySw8roGf3IUAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.bar(frequency_distribution2.index, frequency_distribution2['frequency'])\n",
+    "plt.xlabel('Ages')\n",
+    "plt.ylabel('Frequency')\n",
+    "plt.title('Population Age Frequency Distribution')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "####  4.- What do you see? Is there any difference with the frequency distribution in step 1?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nyour comments here\\nI observe a symmetrical distribution which is same with previous plot. However, the population in this survey was significantly younger, as all values fall between 20 and 35 years old. I estimate the mean to be around 28\\n'"
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "your comments here\n",
+    "I observe a symmetrical distribution which is same with previous plot. However, the population in this survey was significantly younger, as all values fall between 20 and 35 years old. I estimate the mean to be around 28\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 5.- Calculate the mean and standard deviation. Compare the results with the mean and standard deviation in step 2. What do you think?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The mean is : 27.155\n",
+      "The standard deviation is: 2.969813932689186\n"
+     ]
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "print('The mean is :', ages_population2['observation'].mean())\n",
+    "print('The standard deviation is:', ages_population2['observation'].std())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"\\nyour comments here\\nSince the population is younger than in the previous survey, the mean value is smaller, as expected. The standard deviation is also smaller, given the narrower range (indicates less variation) in the population's age.\\n\""
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "your comments here\n",
+    "Since the population is younger than in the previous survey, the mean value is smaller, as expected. The standard deviation is also smaller, given the narrower range (indicates less variation) in the population's age.\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Challenge 5\n",
+    "Now is the turn of `ages_population3.csv`.\n",
+    "\n",
+    "#### 1.- Read the file `ages_population3.csv`. Calculate the frequency distribution and plot it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "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>observation</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>21.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>21.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>24.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>31.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>54.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   observation\n",
+       "0         21.0\n",
+       "1         21.0\n",
+       "2         24.0\n",
+       "3         31.0\n",
+       "4         54.0"
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "ages_population3 = pd.read_csv('../data/ages_population3.csv')\n",
+    "ages_population3.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "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>frequency</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>32.0</th>\n",
+       "      <td>37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35.0</th>\n",
+       "      <td>31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37.0</th>\n",
+       "      <td>31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39.0</th>\n",
+       "      <td>29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36.0</th>\n",
+       "      <td>26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>76.0</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8.0</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9.0</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1.0</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7.0</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>75 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      frequency\n",
+       "32.0         37\n",
+       "35.0         31\n",
+       "37.0         31\n",
+       "39.0         29\n",
+       "36.0         26\n",
+       "...         ...\n",
+       "76.0          1\n",
+       "8.0           1\n",
+       "9.0           1\n",
+       "1.0           1\n",
+       "7.0           1\n",
+       "\n",
+       "[75 rows x 1 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "frequency_distribution3 = pd.DataFrame(ages_population3['observation'].value_counts())\n",
+    "frequency_distribution3 = frequency_distribution3.rename(columns={'observation': 'frequency'})\n",
+    "display(frequency_distribution3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.bar(frequency_distribution3.index, frequency_distribution3['frequency'])\n",
+    "plt.xlabel('Ages')\n",
+    "plt.ylabel('Frequency')\n",
+    "plt.title('Population Age Frequency Distribution')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.- Calculate the mean and standard deviation. Compare the results with the plot in step 1. What is happening?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The mean is : 41.989\n",
+      "The standard deviation is: 16.144705959865934\n"
+     ]
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "print('The mean is :', ages_population3['observation'].mean())\n",
+    "print('The standard deviation is:', ages_population3['observation'].std())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nyour comments here\\nIn this survey, we observe a highly diverse population. Estimating the mean value based on the plot is challenging, as we do not observe a symmetric distribution. The standard deviation is also higher, as expected, due to the substantial variation within the data.\\n'"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "your comments here\n",
+    "In this survey, we observe a highly diverse population. Estimating the mean value based on the plot is challenging, as we do not observe a symmetric distribution. The standard deviation is also higher, as expected, due to the substantial variation within the data.\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.- Calculate the four quartiles. Use the results to explain your reasoning for question in step 2. How much of a difference is there between the median and the mean?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "the first quartile is 30.0\n",
+      "the second quartile is 40.0\n",
+      "the third quartile is 53.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "Q1 = np.quantile(ages_population3['observation'], 0.25)\n",
+    "print(\"the first quartile is\", Q1)\n",
+    "Q2 = np.quantile(ages_population3['observation'], 0.50)\n",
+    "print(\"the second quartile is\",Q2)\n",
+    "Q3 = np.quantile(ages_population3['observation'], 0.75)\n",
+    "print(\"the third quartile is\", Q3)\n",
+    "\n",
+    "plt.boxplot(ages_population3['observation'])\n",
+    "plt.title('Box Plot')\n",
+    "plt.ylabel('Values')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nyour comments here\\nThe mean value is slightly higher than the median, indicating that there are more individuals with higher ages than younger individuals(slightly positive skewdness). '"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "your comments here\n",
+    "The mean value is slightly higher than the median, indicating that there are more individuals with higher ages than younger individuals(slightly positive skewdness). \"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 4.- Calculate other percentiles that might be useful to give more arguments to your reasoning."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "P1: 28.0\n",
+      "P2: 36.0\n",
+      "P3: 45.0\n",
+      "P4: 57.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "P1 = np.percentile(ages_population3['observation'], 20)\n",
+    "P2 = np.percentile(ages_population3['observation'], 40)\n",
+    "P3 = np.percentile(ages_population3['observation'], 60)\n",
+    "P4 = np.percentile(ages_population3['observation'], 80)\n",
+    "\n",
+    "print(\"P1:\", P1)\n",
+    "print(\"P2:\", P2)\n",
+    "print(\"P3:\", P3)\n",
+    "print(\"P4:\", P4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nyour comments here\\n'"
+      ]
+     },
+     "execution_count": 99,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "your comments here\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Bonus challenge\n",
+    "Compare the information about the three neighbourhoods. Prepare a report about the three of them. Remember to find out which are their similarities and their differences backing your arguments in basic statistics."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# your code here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nyour comments here\\n'"
+      ]
+     },
+     "execution_count": 101,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "your comments here\n",
+    "\"\"\""
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}