diff --git a/your-code/.ipynb_checkpoints/LAB_main_Matheus_Freire-checkpoint.ipynb b/your-code/.ipynb_checkpoints/LAB_main_Matheus_Freire-checkpoint.ipynb
new file mode 100644
index 0000000..eae5c8e
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+++ b/your-code/.ipynb_checkpoints/LAB_main_Matheus_Freire-checkpoint.ipynb
@@ -0,0 +1,902 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Understanding Descriptive Statistics\n",
+    "\n",
+    "Import the necessary libraries here:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import random\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "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": 4,
+   "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>Roll</th>\n",
+       "      <th>Result</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>8</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>9</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>10</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Roll  Result\n",
+       "0     1       4\n",
+       "1     2       6\n",
+       "2     3       1\n",
+       "3     4       4\n",
+       "4     5       6\n",
+       "5     6       1\n",
+       "6     7       1\n",
+       "7     8       2\n",
+       "8     9       3\n",
+       "9    10       2"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dice = [1, 2, 3, 4, 5, 6]\n",
+    "\n",
+    "def rolling_dice(n_rolls):\n",
+    "    rolls = random.choices(dice, k = n_rolls)\n",
+    "    data = {\"Roll\": range(1, n_rolls + 1), \"Result\": rolls}\n",
+    "    df = pd.DataFrame(data)\n",
+    "    return df\n",
+    "\n",
+    "dice_df = rolling_dice(10)\n",
+    "dice_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.- Plot the results sorted by value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dice_df = dice_df.sort_values(\"Result\")\n",
+    "y = dice_df[[\"Roll\"]]\n",
+    "x = dice_df[\"Result\"]\n",
+    "\n",
+    "plt.plot(x,y)\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": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "freq_table = dice_df.groupby(\"Result\").size().reset_index(name=\"Frequency\")\n",
+    "\n",
+    "plt.bar(freq_table[\"Result\"], freq_table[\"Frequency\"])\n",
+    "plt.xlabel(\"Result\")\n",
+    "plt.ylabel(\"Frequency\")\n",
+    "plt.title(\"Frequency Distribution of Rolling a 10-Sided Dice\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "Both provide information about the distribution of the result values when rolling a 10-sided die\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": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sum_obs = 0\n",
+    "num_obs = len(dice_df)\n",
+    "\n",
+    "for result in dice_df[\"Result\"]:\n",
+    "    sum_obs += result\n",
+    "\n",
+    "mean = sum_obs / num_obs\n",
+    "\n",
+    "print(\"Mean:\", mean)"
+   ]
+  },
+  {
+   "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": 20,
+   "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": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "unsupported operand type(s) for /: 'str' and 'int'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_396\\1801346641.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      9\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mx1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 11\u001b[1;33m \u001b[0mmedian\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdice_df\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_396\\1801346641.py\u001b[0m in \u001b[0;36mmedian\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m      4\u001b[0m         \u001b[0mx1\u001b[0m\u001b[1;33m=\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m         \u001b[0mx2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mx1\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mx1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mx2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m         \u001b[0mx1\u001b[0m\u001b[1;33m=\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mTypeError\u001b[0m: unsupported operand type(s) for /: 'str' and 'int'"
+     ]
+    }
+   ],
+   "source": [
+    "def median(x):\n",
+    "    x = sorted(x)\n",
+    "    if len(x)%2 == 0:\n",
+    "        x1= int((len(x)/2)-1)\n",
+    "        x2 = x1+1\n",
+    "        return (x[x1]+x[x2]/2)/2\n",
+    "    else:\n",
+    "        x1= int((len(x)/2))\n",
+    "        return x[x1]\n",
+    "    \n",
+    "median(dice_df)"
+   ]
+  },
+  {
+   "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": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dice = pd.read_csv(\"roll_the_dice_hundred.csv\")\n",
+    "sorted_values = dice['value'].value_counts()\n",
+    "sorted_values.sort_index()\n",
+    "\n",
+    "plt.plot(dice[\"value\"])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "The plot does not give any insights\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": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'average1' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_396\\3746702648.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mdice_values\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdice\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"value\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mdice_values_list\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdice_values\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0maverage1\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdice_values_list\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m: name 'average1' is not defined"
+     ]
+    }
+   ],
+   "source": []
+  },
+  {
+   "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": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<function matplotlib.pyplot.show(close=None, block=None)>"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAigAAAGdCAYAAAA44ojeAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAfG0lEQVR4nO3df2xV9f3H8dddSy8/VqqltPfecKmdq9PRWoE6fqm0gOgV6xQnoGyWyeqMyGSlUasxlsVR54LiIDZCsIBASpYJsqFCUVokSAZlKKDRonXU2btGBr1tZZdazvePzfv12oKr3tvz6eX5SE7C+XHPfd8TEp+ce6/XYVmWJQAAAIN8x+4BAAAAvopAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGCceLsH+CbOnDmjTz75RImJiXI4HHaPAwAA/geWZam1tVUej0ff+c6575H0yUD55JNP5PV67R4DAAB8A42NjRo2bNg5j+mTgZKYmCjpPy9w8ODBNk8DAAD+F4FAQF6vN/Tf8XPpk4Hyxds6gwcPJlAAAOhj/pePZ/AhWQAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABinx4Gya9cuFRQUyOPxyOFwaPPmzWH7HQ5Ht8vvf//70DF5eXld9s+aNetbvxgAABAbehwo7e3tysnJ0fLly7vd39TUFLY8//zzcjgcuvXWW8OOKyoqCjvuueee+2avAAAAxJwe/1igz+eTz+c7636XyxW2/tJLLyk/P1/f+973wrYPHDiwy7EAAABSlD+D8s9//lNbt27V3Llzu+xbv369UlJSNGLECJWUlKi1tfWs5wkGgwoEAmELAACIXT2+g9ITa9asUWJioqZPnx62ffbs2crIyJDL5dLhw4dVWlqqt956S9XV1d2ep7y8XIsWLYrmqGEuemhrrz1XpHz0xDS7R4Ch+PsMoC+KaqA8//zzmj17tvr37x+2vaioKPTnrKwsZWZmKjc3VwcOHNCoUaO6nKe0tFTFxcWh9UAgIK/XG73BAQCAraIWKG+88Ybee+89bdy48WuPHTVqlPr166f6+vpuA8XpdMrpdEZjTAAAYKCofQZl1apVGj16tHJycr722CNHjqijo0Nutzta4wAAgD6kx3dQ2tradPTo0dB6Q0ODDh48qOTkZA0fPlzSf96C+eMf/6glS5Z0efwHH3yg9evX64YbblBKSoreeecdLVy4UCNHjtSECRO+xUsBAACxoseBsn//fuXn54fWv/hsSGFhoVavXi1JqqqqkmVZuv3227s8PiEhQa+99pqeeeYZtbW1yev1atq0aXrssccUFxf3DV8GAACIJT0OlLy8PFmWdc5j7r77bt19993d7vN6vaqtre3p0wIAgPMIv8UDAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDhR/bFAADhf9MVfjZb45WiYizsoAADAONxBgW364r84+dcmAPQO7qAAAADjcAcFAIAo445xz3EHBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcXocKLt27VJBQYE8Ho8cDoc2b94ctn/OnDlyOBxhy9ixY8OOCQaDmj9/vlJSUjRo0CDddNNN+vjjj7/VCwEAALGjx4HS3t6unJwcLV++/KzHXH/99WpqagotL7/8ctj+BQsWaNOmTaqqqtLu3bvV1tamG2+8UZ2dnT1/BQAAIObE9/QBPp9PPp/vnMc4nU65XK5u97W0tGjVqlV64YUXNGXKFEnSunXr5PV6tWPHDl133XU9HQkAAMSYqHwGpaamRqmpqbrkkktUVFSk5ubm0L66ujp1dHRo6tSpoW0ej0dZWVnas2dPNMYBAAB9TI/voHwdn8+n2267Tenp6WpoaNCjjz6qSZMmqa6uTk6nU36/XwkJCbrwwgvDHpeWlia/39/tOYPBoILBYGg9EAhEemwAAGCQiAfKzJkzQ3/OyspSbm6u0tPTtXXrVk2fPv2sj7MsSw6Ho9t95eXlWrRoUaRHBQAAhor614zdbrfS09NVX18vSXK5XDp9+rROnDgRdlxzc7PS0tK6PUdpaalaWlpCS2NjY7THBgAANor4HZSvOn78uBobG+V2uyVJo0ePVr9+/VRdXa0ZM2ZIkpqamnT48GE9+eST3Z7D6XTK6XRGe1QAQB9w0UNb7R4BvaDHgdLW1qajR4+G1hsaGnTw4EElJycrOTlZZWVluvXWW+V2u/XRRx/p4YcfVkpKim655RZJUlJSkubOnauFCxdqyJAhSk5OVklJibKzs0Pf6gEAAOe3HgfK/v37lZ+fH1ovLi6WJBUWFqqiokKHDh3S2rVrdfLkSbndbuXn52vjxo1KTEwMPebpp59WfHy8ZsyYoVOnTmny5MlavXq14uLiIvCSAABAX9fjQMnLy5NlWWfdv23btq89R//+/bVs2TItW7asp08PAADOA/wWDwAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOD0OlF27dqmgoEAej0cOh0ObN28O7evo6NCDDz6o7OxsDRo0SB6PR3feeac++eSTsHPk5eXJ4XCELbNmzfrWLwYAAMSGHgdKe3u7cnJytHz58i77PvvsMx04cECPPvqoDhw4oBdffFHvv/++brrppi7HFhUVqampKbQ899xz3+wVAACAmBPf0wf4fD75fL5u9yUlJam6ujps27Jly/SjH/1Ix44d0/Dhw0PbBw4cKJfL1dOnBwAA54GofwalpaVFDodDF1xwQdj29evXKyUlRSNGjFBJSYlaW1vPeo5gMKhAIBC2AACA2NXjOyg98e9//1sPPfSQ7rjjDg0ePDi0ffbs2crIyJDL5dLhw4dVWlqqt956q8vdly+Ul5dr0aJF0RwVAAAYJGqB0tHRoVmzZunMmTN69tlnw/YVFRWF/pyVlaXMzEzl5ubqwIEDGjVqVJdzlZaWqri4OLQeCATk9XqjNToAALBZVAKlo6NDM2bMUENDg15//fWwuyfdGTVqlPr166f6+vpuA8XpdMrpdEZjVAAAYKCIB8oXcVJfX6+dO3dqyJAhX/uYI0eOqKOjQ263O9LjAACAPqjHgdLW1qajR4+G1hsaGnTw4EElJyfL4/HoJz/5iQ4cOKC//OUv6uzslN/vlyQlJycrISFBH3zwgdavX68bbrhBKSkpeuedd7Rw4UKNHDlSEyZMiNwrAwAAfVaPA2X//v3Kz88PrX/x2ZDCwkKVlZVpy5YtkqQrrrgi7HE7d+5UXl6eEhIS9Nprr+mZZ55RW1ubvF6vpk2bpscee0xxcXHf4qUAAIBY0eNAycvLk2VZZ91/rn2S5PV6VVtb29OnBQAA5xF+iwcAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHF6HCi7du1SQUGBPB6PHA6HNm/eHLbfsiyVlZXJ4/FowIABysvL05EjR8KOCQaDmj9/vlJSUjRo0CDddNNN+vjjj7/VCwEAALGjx4HS3t6unJwcLV++vNv9Tz75pJ566iktX75c+/btk8vl0rXXXqvW1tbQMQsWLNCmTZtUVVWl3bt3q62tTTfeeKM6Ozu/+SsBAAAxI76nD/D5fPL5fN3usyxLS5cu1SOPPKLp06dLktasWaO0tDRt2LBBv/zlL9XS0qJVq1bphRde0JQpUyRJ69atk9fr1Y4dO3Tdddd9i5cDAABiQUQ/g9LQ0CC/36+pU6eGtjmdTk2cOFF79uyRJNXV1amjoyPsGI/Ho6ysrNAxXxUMBhUIBMIWAAAQuyIaKH6/X5KUlpYWtj0tLS20z+/3KyEhQRdeeOFZj/mq8vJyJSUlhRav1xvJsQEAgGGi8i0eh8MRtm5ZVpdtX3WuY0pLS9XS0hJaGhsbIzYrAAAwT0QDxeVySVKXOyHNzc2huyoul0unT5/WiRMnznrMVzmdTg0ePDhsAQAAsSuigZKRkSGXy6Xq6urQttOnT6u2tlbjx4+XJI0ePVr9+vULO6apqUmHDx8OHQMAAM5vPf4WT1tbm44ePRpab2ho0MGDB5WcnKzhw4drwYIFWrx4sTIzM5WZmanFixdr4MCBuuOOOyRJSUlJmjt3rhYuXKghQ4YoOTlZJSUlys7ODn2rBwAAnN96HCj79+9Xfn5+aL24uFiSVFhYqNWrV+uBBx7QqVOndO+99+rEiRMaM2aMtm/frsTExNBjnn76acXHx2vGjBk6deqUJk+erNWrVysuLi4CLwkAAPR1PQ6UvLw8WZZ11v0Oh0NlZWUqKys76zH9+/fXsmXLtGzZsp4+PQAAOA/wWzwAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIwT8UC56KKL5HA4uizz5s2TJM2ZM6fLvrFjx0Z6DAAA0IfFR/qE+/btU2dnZ2j98OHDuvbaa3XbbbeFtl1//fWqrKwMrSckJER6DAAA0IdFPFCGDh0atv7EE0/o4osv1sSJE0PbnE6nXC5XpJ8aAADEiKh+BuX06dNat26d7rrrLjkcjtD2mpoapaam6pJLLlFRUZGam5vPeZ5gMKhAIBC2AACA2BXVQNm8ebNOnjypOXPmhLb5fD6tX79er7/+upYsWaJ9+/Zp0qRJCgaDZz1PeXm5kpKSQovX643m2AAAwGYRf4vny1atWiWfzyePxxPaNnPmzNCfs7KylJubq/T0dG3dulXTp0/v9jylpaUqLi4OrQcCASIFAIAYFrVA+fvf/64dO3boxRdfPOdxbrdb6enpqq+vP+sxTqdTTqcz0iMCAABDRe0tnsrKSqWmpmratGnnPO748eNqbGyU2+2O1igAAKCPiUqgnDlzRpWVlSosLFR8/P/fpGlra1NJSYnefPNNffTRR6qpqVFBQYFSUlJ0yy23RGMUAADQB0XlLZ4dO3bo2LFjuuuuu8K2x8XF6dChQ1q7dq1Onjwpt9ut/Px8bdy4UYmJidEYBQAA9EFRCZSpU6fKsqwu2wcMGKBt27ZF4ykBAEAM4bd4AACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGiXiglJWVyeFwhC0ulyu037IslZWVyePxaMCAAcrLy9ORI0ciPQYAAOjDonIHZcSIEWpqagothw4dCu178skn9dRTT2n58uXat2+fXC6Xrr32WrW2tkZjFAAA0AdFJVDi4+PlcrlCy9ChQyX95+7J0qVL9cgjj2j69OnKysrSmjVr9Nlnn2nDhg3RGAUAAPRBUQmU+vp6eTweZWRkaNasWfrwww8lSQ0NDfL7/Zo6dWroWKfTqYkTJ2rPnj1nPV8wGFQgEAhbAABA7Ip4oIwZM0Zr167Vtm3btHLlSvn9fo0fP17Hjx+X3++XJKWlpYU9Ji0tLbSvO+Xl5UpKSgotXq830mMDAACDRDxQfD6fbr31VmVnZ2vKlCnaunWrJGnNmjWhYxwOR9hjLMvqsu3LSktL1dLSEloaGxsjPTYAADBI1L9mPGjQIGVnZ6u+vj70bZ6v3i1pbm7uclfly5xOpwYPHhy2AACA2BX1QAkGg3r33XfldruVkZEhl8ul6urq0P7Tp0+rtrZW48ePj/YoAACgj4iP9AlLSkpUUFCg4cOHq7m5WY8//rgCgYAKCwvlcDi0YMECLV68WJmZmcrMzNTixYs1cOBA3XHHHZEeBQAA9FERD5SPP/5Yt99+uz799FMNHTpUY8eO1d69e5Weni5JeuCBB3Tq1Cnde++9OnHihMaMGaPt27crMTEx0qMAAIA+KuKBUlVVdc79DodDZWVlKisri/RTAwCAGMFv8QAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAME7EA6W8vFxXXnmlEhMTlZqaqptvvlnvvfde2DFz5syRw+EIW8aOHRvpUQAAQB8V8UCpra3VvHnztHfvXlVXV+vzzz/X1KlT1d7eHnbc9ddfr6amptDy8ssvR3oUAADQR8VH+oSvvvpq2HplZaVSU1NVV1ena665JrTd6XTK5XJF+ukBAEAMiPpnUFpaWiRJycnJYdtramqUmpqqSy65REVFRWpubj7rOYLBoAKBQNgCAABiV1QDxbIsFRcX66qrrlJWVlZou8/n0/r16/X6669ryZIl2rdvnyZNmqRgMNjtecrLy5WUlBRavF5vNMcGAAA2i/hbPF9233336e2339bu3bvDts+cOTP056ysLOXm5io9PV1bt27V9OnTu5yntLRUxcXFofVAIECkAAAQw6IWKPPnz9eWLVu0a9cuDRs27JzHut1upaenq76+vtv9TqdTTqczGmMCAAADRTxQLMvS/PnztWnTJtXU1CgjI+NrH3P8+HE1NjbK7XZHehwAANAHRfwzKPPmzdO6deu0YcMGJSYmyu/3y+/369SpU5KktrY2lZSU6M0339RHH32kmpoaFRQUKCUlRbfcckukxwEAAH1QxO+gVFRUSJLy8vLCtldWVmrOnDmKi4vToUOHtHbtWp08eVJut1v5+fnauHGjEhMTIz0OAADog6LyFs+5DBgwQNu2bYv00wIAgBjCb/EAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADCOrYHy7LPPKiMjQ/3799fo0aP1xhtv2DkOAAAwhG2BsnHjRi1YsECPPPKI/va3v+nqq6+Wz+fTsWPH7BoJAAAYwrZAeeqppzR37lz94he/0GWXXaalS5fK6/WqoqLCrpEAAIAh4u140tOnT6uurk4PPfRQ2PapU6dqz549XY4PBoMKBoOh9ZaWFklSIBCIynxngp9F5bzRFK1rEU1c597Bde4dffE6S1xrnF00/m58cU7Lsr72WFsC5dNPP1VnZ6fS0tLCtqelpcnv93c5vry8XIsWLeqy3ev1Rm3GviZpqd0TnB+4zr2D69x7uNY4m2j+3WhtbVVSUtI5j7ElUL7gcDjC1i3L6rJNkkpLS1VcXBxaP3PmjP71r39pyJAh3R7/bQQCAXm9XjU2Nmrw4MERPTf+H9e5d3CdewfXufdwrXtHtK6zZVlqbW2Vx+P52mNtCZSUlBTFxcV1uVvS3Nzc5a6KJDmdTjmdzrBtF1xwQTRH1ODBg/nL3wu4zr2D69w7uM69h2vdO6Jxnb/uzskXbPmQbEJCgkaPHq3q6uqw7dXV1Ro/frwdIwEAAIPY9hZPcXGxfvaznyk3N1fjxo3TihUrdOzYMd1zzz12jQQAAAxhW6DMnDlTx48f129+8xs1NTUpKytLL7/8stLT0+0aSdJ/3k567LHHurylhMjiOvcOrnPv4Dr3Hq517zDhOjus/+W7PgAAAL2I3+IBAADGIVAAAIBxCBQAAGAcAgUAABiHQPmvXbt2qaCgQB6PRw6HQ5s3b7Z7pJhUXl6uK6+8UomJiUpNTdXNN9+s9957z+6xYk5FRYUuv/zy0P9kady4cXrllVfsHivmlZeXy+FwaMGCBXaPElPKysrkcDjCFpfLZfdYMekf//iHfvrTn2rIkCEaOHCgrrjiCtXV1dkyC4HyX+3t7crJydHy5cvtHiWm1dbWat68edq7d6+qq6v1+eefa+rUqWpvb7d7tJgybNgwPfHEE9q/f7/279+vSZMm6cc//rGOHDli92gxa9++fVqxYoUuv/xyu0eJSSNGjFBTU1NoOXTokN0jxZwTJ05owoQJ6tevn1555RW98847WrJkSdT/z+1nY+tv8ZjE5/PJ5/PZPUbMe/XVV8PWKysrlZqaqrq6Ol1zzTU2TRV7CgoKwtZ/+9vfqqKiQnv37tWIESNsmip2tbW1afbs2Vq5cqUef/xxu8eJSfHx8dw1ibLf/e538nq9qqysDG276KKLbJuHOyiwVUtLiyQpOTnZ5kliV2dnp6qqqtTe3q5x48bZPU5MmjdvnqZNm6YpU6bYPUrMqq+vl8fjUUZGhmbNmqUPP/zQ7pFizpYtW5Sbm6vbbrtNqampGjlypFauXGnbPAQKbGNZloqLi3XVVVcpKyvL7nFizqFDh/Td735XTqdT99xzjzZt2qQf/vCHdo8Vc6qqqlRXV6fy8nK7R4lZY8aM0dq1a7Vt2zatXLlSfr9f48eP1/Hjx+0eLaZ8+OGHqqioUGZmprZt26Z77rlHv/rVr7R27Vpb5uEtHtjmvvvu09tvv63du3fbPUpM+sEPfqCDBw/q5MmT+tOf/qTCwkLV1tYSKRHU2Nio+++/X9u3b1f//v3tHidmffnt9+zsbI0bN04XX3yx1qxZo+LiYhsniy1nzpxRbm6uFi9eLEkaOXKkjhw5ooqKCt155529Pg93UGCL+fPna8uWLdq5c6eGDRtm9zgxKSEhQd///veVm5ur8vJy5eTk6JlnnrF7rJhSV1en5uZmjR49WvHx8YqPj1dtba3+8Ic/KD4+Xp2dnXaPGJMGDRqk7Oxs1dfX2z1KTHG73V3+AXPZZZfp2LFjtszDHRT0KsuyNH/+fG3atEk1NTXKyMiwe6TzhmVZCgaDdo8RUyZPntzl2yQ///nPdemll+rBBx9UXFycTZPFtmAwqHfffVdXX3213aPElAkTJnT53z68//77tv2IL4HyX21tbTp69GhovaGhQQcPHlRycrKGDx9u42SxZd68edqwYYNeeuklJSYmyu/3S5KSkpI0YMAAm6eLHQ8//LB8Pp+8Xq9aW1tVVVWlmpqaLt+iwreTmJjY5fNTgwYN0pAhQ/hcVQSVlJSooKBAw4cPV3Nzsx5//HEFAgEVFhbaPVpM+fWvf63x48dr8eLFmjFjhv76179qxYoVWrFihT0DWbAsy7J27txpSeqyFBYW2j1aTOnuGkuyKisr7R4tptx1111Wenq6lZCQYA0dOtSaPHmytX37drvHOi9MnDjRuv/+++0eI6bMnDnTcrvdVr9+/SyPx2NNnz7dOnLkiN1jxaQ///nPVlZWluV0Oq1LL73UWrFihW2zOCzLsuxJIwAAgO7xIVkAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBx/g+81u+XV22VPgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dice_thousand = pd.read_csv(\"roll_the_dice_thousand.csv\")\n",
+    "dice_thousand.head()\n",
+    "\n",
+    "plt.hist(dice_thousand[\"value\"])\n",
+    "plt.show"
+   ]
+  },
+  {
+   "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": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([ 17.,  59., 115., 204., 261., 194.,  99.,  36.,  14.,   1.]),\n",
+       " array([ 1. ,  9.1, 17.2, 25.3, 33.4, 41.5, 49.6, 57.7, 65.8, 73.9, 82. ]),\n",
+       " <BarContainer object of 10 artists>)"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "age_pop = pd.read_csv(\"ages_population.csv\")\n",
+    "age_pop.head()\n",
+    "\n",
+    "plt.hist(age_pop)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Mean is between 30 and 40"
+   ]
+  },
+  {
+   "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": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "observation    36.56\n",
+      "dtype: float64\n",
+      "observation    12.8165\n",
+      "dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(age_pop.mean())\n",
+    "print(age_pop.std())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "The mean is very close to my guess\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": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([ 16.,  52., 119.,  98., 245., 254.,  90.,  92.,  29.,   5.]),\n",
+       " array([19. , 20.7, 22.4, 24.1, 25.8, 27.5, 29.2, 30.9, 32.6, 34.3, 36. ]),\n",
+       " <BarContainer object of 10 artists>)"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "age_pop_2 = pd.read_csv(\"ages_population2.csv\")\n",
+    "age_pop_2.head()\n",
+    "\n",
+    "plt.hist(age_pop_2)"
+   ]
+  },
+  {
+   "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": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "observation    27.155\n",
+      "dtype: float64\n",
+      "observation    2.969814\n",
+      "dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(age_pop_2.mean())\n",
+    "print(age_pop_2.std())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nThe age_pop_2 data set is smaller than the first one\\n'"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "The age_pop_2 data set is smaller than the first one\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": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([  8.,  33.,  78., 158., 187., 174., 133.,  57., 117.,  55.]),\n",
+       " array([ 1. ,  8.6, 16.2, 23.8, 31.4, 39. , 46.6, 54.2, 61.8, 69.4, 77. ]),\n",
+       " <BarContainer object of 10 artists>)"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "age_pop_3 = pd.read_csv(\"ages_population3.csv\")\n",
+    "age_pop_3.head()\n",
+    "\n",
+    "plt.hist(age_pop_3)"
+   ]
+  },
+  {
+   "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": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "observation    41.989\n",
+      "dtype: float64\n",
+      "observation    16.144706\n",
+      "dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(age_pop_3.mean())\n",
+    "print(age_pop_3.std())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "There are more 60 and 70 year olds in this data set, which makesthe standard deviation and mean be higher on this one\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": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[30.]\n",
+      "[40.]\n",
+      "[53.]\n",
+      "[77.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "quartile_1 = np.quantile(age_pop_3, [0.25])\n",
+    "quartile_2 = np.quantile(age_pop_3, [0.5])\n",
+    "quartile_3 = np.quantile(age_pop_3, [0.75])\n",
+    "quartile_4 = np.quantile(age_pop_3, [1])\n",
+    "\n",
+    "print(quartile_1)\n",
+    "print(quartile_2)\n",
+    "print(quartile_3)\n",
+    "print(quartile_4)"
+   ]
+  },
+  {
+   "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": "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": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/your-code/LAB_main_Matheus_Freire.ipynb b/your-code/LAB_main_Matheus_Freire.ipynb
new file mode 100644
index 0000000..eae5c8e
--- /dev/null
+++ b/your-code/LAB_main_Matheus_Freire.ipynb
@@ -0,0 +1,902 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Understanding Descriptive Statistics\n",
+    "\n",
+    "Import the necessary libraries here:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import random\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "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": 4,
+   "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>Roll</th>\n",
+       "      <th>Result</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>8</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>9</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>10</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Roll  Result\n",
+       "0     1       4\n",
+       "1     2       6\n",
+       "2     3       1\n",
+       "3     4       4\n",
+       "4     5       6\n",
+       "5     6       1\n",
+       "6     7       1\n",
+       "7     8       2\n",
+       "8     9       3\n",
+       "9    10       2"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dice = [1, 2, 3, 4, 5, 6]\n",
+    "\n",
+    "def rolling_dice(n_rolls):\n",
+    "    rolls = random.choices(dice, k = n_rolls)\n",
+    "    data = {\"Roll\": range(1, n_rolls + 1), \"Result\": rolls}\n",
+    "    df = pd.DataFrame(data)\n",
+    "    return df\n",
+    "\n",
+    "dice_df = rolling_dice(10)\n",
+    "dice_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.- Plot the results sorted by value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dice_df = dice_df.sort_values(\"Result\")\n",
+    "y = dice_df[[\"Roll\"]]\n",
+    "x = dice_df[\"Result\"]\n",
+    "\n",
+    "plt.plot(x,y)\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": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "freq_table = dice_df.groupby(\"Result\").size().reset_index(name=\"Frequency\")\n",
+    "\n",
+    "plt.bar(freq_table[\"Result\"], freq_table[\"Frequency\"])\n",
+    "plt.xlabel(\"Result\")\n",
+    "plt.ylabel(\"Frequency\")\n",
+    "plt.title(\"Frequency Distribution of Rolling a 10-Sided Dice\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "Both provide information about the distribution of the result values when rolling a 10-sided die\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": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sum_obs = 0\n",
+    "num_obs = len(dice_df)\n",
+    "\n",
+    "for result in dice_df[\"Result\"]:\n",
+    "    sum_obs += result\n",
+    "\n",
+    "mean = sum_obs / num_obs\n",
+    "\n",
+    "print(\"Mean:\", mean)"
+   ]
+  },
+  {
+   "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": 20,
+   "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": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "unsupported operand type(s) for /: 'str' and 'int'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_396\\1801346641.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      9\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mx1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 11\u001b[1;33m \u001b[0mmedian\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdice_df\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_396\\1801346641.py\u001b[0m in \u001b[0;36mmedian\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m      4\u001b[0m         \u001b[0mx1\u001b[0m\u001b[1;33m=\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m         \u001b[0mx2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mx1\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mx1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mx2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m         \u001b[0mx1\u001b[0m\u001b[1;33m=\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mTypeError\u001b[0m: unsupported operand type(s) for /: 'str' and 'int'"
+     ]
+    }
+   ],
+   "source": [
+    "def median(x):\n",
+    "    x = sorted(x)\n",
+    "    if len(x)%2 == 0:\n",
+    "        x1= int((len(x)/2)-1)\n",
+    "        x2 = x1+1\n",
+    "        return (x[x1]+x[x2]/2)/2\n",
+    "    else:\n",
+    "        x1= int((len(x)/2))\n",
+    "        return x[x1]\n",
+    "    \n",
+    "median(dice_df)"
+   ]
+  },
+  {
+   "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": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dice = pd.read_csv(\"roll_the_dice_hundred.csv\")\n",
+    "sorted_values = dice['value'].value_counts()\n",
+    "sorted_values.sort_index()\n",
+    "\n",
+    "plt.plot(dice[\"value\"])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "The plot does not give any insights\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": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'average1' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_396\\3746702648.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mdice_values\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdice\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"value\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mdice_values_list\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdice_values\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0maverage1\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdice_values_list\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m: name 'average1' is not defined"
+     ]
+    }
+   ],
+   "source": []
+  },
+  {
+   "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": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<function matplotlib.pyplot.show(close=None, block=None)>"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAigAAAGdCAYAAAA44ojeAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAfG0lEQVR4nO3df2xV9f3H8dddSy8/VqqltPfecKmdq9PRWoE6fqm0gOgV6xQnoGyWyeqMyGSlUasxlsVR54LiIDZCsIBASpYJsqFCUVokSAZlKKDRonXU2btGBr1tZZdazvePzfv12oKr3tvz6eX5SE7C+XHPfd8TEp+ce6/XYVmWJQAAAIN8x+4BAAAAvopAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGCceLsH+CbOnDmjTz75RImJiXI4HHaPAwAA/geWZam1tVUej0ff+c6575H0yUD55JNP5PV67R4DAAB8A42NjRo2bNg5j+mTgZKYmCjpPy9w8ODBNk8DAAD+F4FAQF6vN/Tf8XPpk4Hyxds6gwcPJlAAAOhj/pePZ/AhWQAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABinx4Gya9cuFRQUyOPxyOFwaPPmzWH7HQ5Ht8vvf//70DF5eXld9s+aNetbvxgAABAbehwo7e3tysnJ0fLly7vd39TUFLY8//zzcjgcuvXWW8OOKyoqCjvuueee+2avAAAAxJwe/1igz+eTz+c7636XyxW2/tJLLyk/P1/f+973wrYPHDiwy7EAAABSlD+D8s9//lNbt27V3Llzu+xbv369UlJSNGLECJWUlKi1tfWs5wkGgwoEAmELAACIXT2+g9ITa9asUWJioqZPnx62ffbs2crIyJDL5dLhw4dVWlqqt956S9XV1d2ep7y8XIsWLYrmqGEuemhrrz1XpHz0xDS7R4Ch+PsMoC+KaqA8//zzmj17tvr37x+2vaioKPTnrKwsZWZmKjc3VwcOHNCoUaO6nKe0tFTFxcWh9UAgIK/XG73BAQCAraIWKG+88Ybee+89bdy48WuPHTVqlPr166f6+vpuA8XpdMrpdEZjTAAAYKCofQZl1apVGj16tHJycr722CNHjqijo0Nutzta4wAAgD6kx3dQ2tradPTo0dB6Q0ODDh48qOTkZA0fPlzSf96C+eMf/6glS5Z0efwHH3yg9evX64YbblBKSoreeecdLVy4UCNHjtSECRO+xUsBAACxoseBsn//fuXn54fWv/hsSGFhoVavXi1JqqqqkmVZuv3227s8PiEhQa+99pqeeeYZtbW1yev1atq0aXrssccUFxf3DV8GAACIJT0OlLy8PFmWdc5j7r77bt19993d7vN6vaqtre3p0wIAgPMIv8UDAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDhR/bFAADhf9MVfjZb45WiYizsoAADAONxBgW364r84+dcmAPQO7qAAAADjcAcFAIAo445xz3EHBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcXocKLt27VJBQYE8Ho8cDoc2b94ctn/OnDlyOBxhy9ixY8OOCQaDmj9/vlJSUjRo0CDddNNN+vjjj7/VCwEAALGjx4HS3t6unJwcLV++/KzHXH/99WpqagotL7/8ctj+BQsWaNOmTaqqqtLu3bvV1tamG2+8UZ2dnT1/BQAAIObE9/QBPp9PPp/vnMc4nU65XK5u97W0tGjVqlV64YUXNGXKFEnSunXr5PV6tWPHDl133XU9HQkAAMSYqHwGpaamRqmpqbrkkktUVFSk5ubm0L66ujp1dHRo6tSpoW0ej0dZWVnas2dPNMYBAAB9TI/voHwdn8+n2267Tenp6WpoaNCjjz6qSZMmqa6uTk6nU36/XwkJCbrwwgvDHpeWlia/39/tOYPBoILBYGg9EAhEemwAAGCQiAfKzJkzQ3/OyspSbm6u0tPTtXXrVk2fPv2sj7MsSw6Ho9t95eXlWrRoUaRHBQAAhor614zdbrfS09NVX18vSXK5XDp9+rROnDgRdlxzc7PS0tK6PUdpaalaWlpCS2NjY7THBgAANor4HZSvOn78uBobG+V2uyVJo0ePVr9+/VRdXa0ZM2ZIkpqamnT48GE9+eST3Z7D6XTK6XRGe1QAQB9w0UNb7R4BvaDHgdLW1qajR4+G1hsaGnTw4EElJycrOTlZZWVluvXWW+V2u/XRRx/p4YcfVkpKim655RZJUlJSkubOnauFCxdqyJAhSk5OVklJibKzs0Pf6gEAAOe3HgfK/v37lZ+fH1ovLi6WJBUWFqqiokKHDh3S2rVrdfLkSbndbuXn52vjxo1KTEwMPebpp59WfHy8ZsyYoVOnTmny5MlavXq14uLiIvCSAABAX9fjQMnLy5NlWWfdv23btq89R//+/bVs2TItW7asp08PAADOA/wWDwAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOD0OlF27dqmgoEAej0cOh0ObN28O7evo6NCDDz6o7OxsDRo0SB6PR3feeac++eSTsHPk5eXJ4XCELbNmzfrWLwYAAMSGHgdKe3u7cnJytHz58i77PvvsMx04cECPPvqoDhw4oBdffFHvv/++brrppi7HFhUVqampKbQ899xz3+wVAACAmBPf0wf4fD75fL5u9yUlJam6ujps27Jly/SjH/1Ix44d0/Dhw0PbBw4cKJfL1dOnBwAA54GofwalpaVFDodDF1xwQdj29evXKyUlRSNGjFBJSYlaW1vPeo5gMKhAIBC2AACA2NXjOyg98e9//1sPPfSQ7rjjDg0ePDi0ffbs2crIyJDL5dLhw4dVWlqqt956q8vdly+Ul5dr0aJF0RwVAAAYJGqB0tHRoVmzZunMmTN69tlnw/YVFRWF/pyVlaXMzEzl5ubqwIEDGjVqVJdzlZaWqri4OLQeCATk9XqjNToAALBZVAKlo6NDM2bMUENDg15//fWwuyfdGTVqlPr166f6+vpuA8XpdMrpdEZjVAAAYKCIB8oXcVJfX6+dO3dqyJAhX/uYI0eOqKOjQ263O9LjAACAPqjHgdLW1qajR4+G1hsaGnTw4EElJyfL4/HoJz/5iQ4cOKC//OUv6uzslN/vlyQlJycrISFBH3zwgdavX68bbrhBKSkpeuedd7Rw4UKNHDlSEyZMiNwrAwAAfVaPA2X//v3Kz88PrX/x2ZDCwkKVlZVpy5YtkqQrrrgi7HE7d+5UXl6eEhIS9Nprr+mZZ55RW1ubvF6vpk2bpscee0xxcXHf4qUAAIBY0eNAycvLk2VZZ91/rn2S5PV6VVtb29OnBQAA5xF+iwcAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHF6HCi7du1SQUGBPB6PHA6HNm/eHLbfsiyVlZXJ4/FowIABysvL05EjR8KOCQaDmj9/vlJSUjRo0CDddNNN+vjjj7/VCwEAALGjx4HS3t6unJwcLV++vNv9Tz75pJ566iktX75c+/btk8vl0rXXXqvW1tbQMQsWLNCmTZtUVVWl3bt3q62tTTfeeKM6Ozu/+SsBAAAxI76nD/D5fPL5fN3usyxLS5cu1SOPPKLp06dLktasWaO0tDRt2LBBv/zlL9XS0qJVq1bphRde0JQpUyRJ69atk9fr1Y4dO3Tdddd9i5cDAABiQUQ/g9LQ0CC/36+pU6eGtjmdTk2cOFF79uyRJNXV1amjoyPsGI/Ho6ysrNAxXxUMBhUIBMIWAAAQuyIaKH6/X5KUlpYWtj0tLS20z+/3KyEhQRdeeOFZj/mq8vJyJSUlhRav1xvJsQEAgGGi8i0eh8MRtm5ZVpdtX3WuY0pLS9XS0hJaGhsbIzYrAAAwT0QDxeVySVKXOyHNzc2huyoul0unT5/WiRMnznrMVzmdTg0ePDhsAQAAsSuigZKRkSGXy6Xq6urQttOnT6u2tlbjx4+XJI0ePVr9+vULO6apqUmHDx8OHQMAAM5vPf4WT1tbm44ePRpab2ho0MGDB5WcnKzhw4drwYIFWrx4sTIzM5WZmanFixdr4MCBuuOOOyRJSUlJmjt3rhYuXKghQ4YoOTlZJSUlys7ODn2rBwAAnN96HCj79+9Xfn5+aL24uFiSVFhYqNWrV+uBBx7QqVOndO+99+rEiRMaM2aMtm/frsTExNBjnn76acXHx2vGjBk6deqUJk+erNWrVysuLi4CLwkAAPR1PQ6UvLw8WZZ11v0Oh0NlZWUqKys76zH9+/fXsmXLtGzZsp4+PQAAOA/wWzwAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIwT8UC56KKL5HA4uizz5s2TJM2ZM6fLvrFjx0Z6DAAA0IfFR/qE+/btU2dnZ2j98OHDuvbaa3XbbbeFtl1//fWqrKwMrSckJER6DAAA0IdFPFCGDh0atv7EE0/o4osv1sSJE0PbnE6nXC5XpJ8aAADEiKh+BuX06dNat26d7rrrLjkcjtD2mpoapaam6pJLLlFRUZGam5vPeZ5gMKhAIBC2AACA2BXVQNm8ebNOnjypOXPmhLb5fD6tX79er7/+upYsWaJ9+/Zp0qRJCgaDZz1PeXm5kpKSQovX643m2AAAwGYRf4vny1atWiWfzyePxxPaNnPmzNCfs7KylJubq/T0dG3dulXTp0/v9jylpaUqLi4OrQcCASIFAIAYFrVA+fvf/64dO3boxRdfPOdxbrdb6enpqq+vP+sxTqdTTqcz0iMCAABDRe0tnsrKSqWmpmratGnnPO748eNqbGyU2+2O1igAAKCPiUqgnDlzRpWVlSosLFR8/P/fpGlra1NJSYnefPNNffTRR6qpqVFBQYFSUlJ0yy23RGMUAADQB0XlLZ4dO3bo2LFjuuuuu8K2x8XF6dChQ1q7dq1Onjwpt9ut/Px8bdy4UYmJidEYBQAA9EFRCZSpU6fKsqwu2wcMGKBt27ZF4ykBAEAM4bd4AACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBxCBQAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGiXiglJWVyeFwhC0ulyu037IslZWVyePxaMCAAcrLy9ORI0ciPQYAAOjDonIHZcSIEWpqagothw4dCu178skn9dRTT2n58uXat2+fXC6Xrr32WrW2tkZjFAAA0AdFJVDi4+PlcrlCy9ChQyX95+7J0qVL9cgjj2j69OnKysrSmjVr9Nlnn2nDhg3RGAUAAPRBUQmU+vp6eTweZWRkaNasWfrwww8lSQ0NDfL7/Zo6dWroWKfTqYkTJ2rPnj1nPV8wGFQgEAhbAABA7Ip4oIwZM0Zr167Vtm3btHLlSvn9fo0fP17Hjx+X3++XJKWlpYU9Ji0tLbSvO+Xl5UpKSgotXq830mMDAACDRDxQfD6fbr31VmVnZ2vKlCnaunWrJGnNmjWhYxwOR9hjLMvqsu3LSktL1dLSEloaGxsjPTYAADBI1L9mPGjQIGVnZ6u+vj70bZ6v3i1pbm7uclfly5xOpwYPHhy2AACA2BX1QAkGg3r33XfldruVkZEhl8ul6urq0P7Tp0+rtrZW48ePj/YoAACgj4iP9AlLSkpUUFCg4cOHq7m5WY8//rgCgYAKCwvlcDi0YMECLV68WJmZmcrMzNTixYs1cOBA3XHHHZEeBQAA9FERD5SPP/5Yt99+uz799FMNHTpUY8eO1d69e5Weni5JeuCBB3Tq1Cnde++9OnHihMaMGaPt27crMTEx0qMAAIA+KuKBUlVVdc79DodDZWVlKisri/RTAwCAGMFv8QAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAME7EA6W8vFxXXnmlEhMTlZqaqptvvlnvvfde2DFz5syRw+EIW8aOHRvpUQAAQB8V8UCpra3VvHnztHfvXlVXV+vzzz/X1KlT1d7eHnbc9ddfr6amptDy8ssvR3oUAADQR8VH+oSvvvpq2HplZaVSU1NVV1ena665JrTd6XTK5XJF+ukBAEAMiPpnUFpaWiRJycnJYdtramqUmpqqSy65REVFRWpubj7rOYLBoAKBQNgCAABiV1QDxbIsFRcX66qrrlJWVlZou8/n0/r16/X6669ryZIl2rdvnyZNmqRgMNjtecrLy5WUlBRavF5vNMcGAAA2i/hbPF9233336e2339bu3bvDts+cOTP056ysLOXm5io9PV1bt27V9OnTu5yntLRUxcXFofVAIECkAAAQw6IWKPPnz9eWLVu0a9cuDRs27JzHut1upaenq76+vtv9TqdTTqczGmMCAAADRTxQLMvS/PnztWnTJtXU1CgjI+NrH3P8+HE1NjbK7XZHehwAANAHRfwzKPPmzdO6deu0YcMGJSYmyu/3y+/369SpU5KktrY2lZSU6M0339RHH32kmpoaFRQUKCUlRbfcckukxwEAAH1QxO+gVFRUSJLy8vLCtldWVmrOnDmKi4vToUOHtHbtWp08eVJut1v5+fnauHGjEhMTIz0OAADog6LyFs+5DBgwQNu2bYv00wIAgBjCb/EAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADAOgQIAAIxDoAAAAOMQKAAAwDgECgAAMA6BAgAAjEOgAAAA4xAoAADAOAQKAAAwDoECAACMQ6AAAADjECgAAMA4BAoAADCOrYHy7LPPKiMjQ/3799fo0aP1xhtv2DkOAAAwhG2BsnHjRi1YsECPPPKI/va3v+nqq6+Wz+fTsWPH7BoJAAAYwrZAeeqppzR37lz94he/0GWXXaalS5fK6/WqoqLCrpEAAIAh4u140tOnT6uurk4PPfRQ2PapU6dqz549XY4PBoMKBoOh9ZaWFklSIBCIynxngp9F5bzRFK1rEU1c597Bde4dffE6S1xrnF00/m58cU7Lsr72WFsC5dNPP1VnZ6fS0tLCtqelpcnv93c5vry8XIsWLeqy3ev1Rm3GviZpqd0TnB+4zr2D69x7uNY4m2j+3WhtbVVSUtI5j7ElUL7gcDjC1i3L6rJNkkpLS1VcXBxaP3PmjP71r39pyJAh3R7/bQQCAXm9XjU2Nmrw4MERPTf+H9e5d3CdewfXufdwrXtHtK6zZVlqbW2Vx+P52mNtCZSUlBTFxcV1uVvS3Nzc5a6KJDmdTjmdzrBtF1xwQTRH1ODBg/nL3wu4zr2D69w7uM69h2vdO6Jxnb/uzskXbPmQbEJCgkaPHq3q6uqw7dXV1Ro/frwdIwEAAIPY9hZPcXGxfvaznyk3N1fjxo3TihUrdOzYMd1zzz12jQQAAAxhW6DMnDlTx48f129+8xs1NTUpKytLL7/8stLT0+0aSdJ/3k567LHHurylhMjiOvcOrnPv4Dr3Hq517zDhOjus/+W7PgAAAL2I3+IBAADGIVAAAIBxCBQAAGAcAgUAABiHQPmvXbt2qaCgQB6PRw6HQ5s3b7Z7pJhUXl6uK6+8UomJiUpNTdXNN9+s9957z+6xYk5FRYUuv/zy0P9kady4cXrllVfsHivmlZeXy+FwaMGCBXaPElPKysrkcDjCFpfLZfdYMekf//iHfvrTn2rIkCEaOHCgrrjiCtXV1dkyC4HyX+3t7crJydHy5cvtHiWm1dbWat68edq7d6+qq6v1+eefa+rUqWpvb7d7tJgybNgwPfHEE9q/f7/279+vSZMm6cc//rGOHDli92gxa9++fVqxYoUuv/xyu0eJSSNGjFBTU1NoOXTokN0jxZwTJ05owoQJ6tevn1555RW98847WrJkSdT/z+1nY+tv8ZjE5/PJ5/PZPUbMe/XVV8PWKysrlZqaqrq6Ol1zzTU2TRV7CgoKwtZ/+9vfqqKiQnv37tWIESNsmip2tbW1afbs2Vq5cqUef/xxu8eJSfHx8dw1ibLf/e538nq9qqysDG276KKLbJuHOyiwVUtLiyQpOTnZ5kliV2dnp6qqqtTe3q5x48bZPU5MmjdvnqZNm6YpU6bYPUrMqq+vl8fjUUZGhmbNmqUPP/zQ7pFizpYtW5Sbm6vbbrtNqampGjlypFauXGnbPAQKbGNZloqLi3XVVVcpKyvL7nFizqFDh/Td735XTqdT99xzjzZt2qQf/vCHdo8Vc6qqqlRXV6fy8nK7R4lZY8aM0dq1a7Vt2zatXLlSfr9f48eP1/Hjx+0eLaZ8+OGHqqioUGZmprZt26Z77rlHv/rVr7R27Vpb5uEtHtjmvvvu09tvv63du3fbPUpM+sEPfqCDBw/q5MmT+tOf/qTCwkLV1tYSKRHU2Nio+++/X9u3b1f//v3tHidmffnt9+zsbI0bN04XX3yx1qxZo+LiYhsniy1nzpxRbm6uFi9eLEkaOXKkjhw5ooqKCt155529Pg93UGCL+fPna8uWLdq5c6eGDRtm9zgxKSEhQd///veVm5ur8vJy5eTk6JlnnrF7rJhSV1en5uZmjR49WvHx8YqPj1dtba3+8Ic/KD4+Xp2dnXaPGJMGDRqk7Oxs1dfX2z1KTHG73V3+AXPZZZfp2LFjtszDHRT0KsuyNH/+fG3atEk1NTXKyMiwe6TzhmVZCgaDdo8RUyZPntzl2yQ///nPdemll+rBBx9UXFycTZPFtmAwqHfffVdXX3213aPElAkTJnT53z68//77tv2IL4HyX21tbTp69GhovaGhQQcPHlRycrKGDx9u42SxZd68edqwYYNeeuklJSYmyu/3S5KSkpI0YMAAm6eLHQ8//LB8Pp+8Xq9aW1tVVVWlmpqaLt+iwreTmJjY5fNTgwYN0pAhQ/hcVQSVlJSooKBAw4cPV3Nzsx5//HEFAgEVFhbaPVpM+fWvf63x48dr8eLFmjFjhv76179qxYoVWrFihT0DWbAsy7J27txpSeqyFBYW2j1aTOnuGkuyKisr7R4tptx1111Wenq6lZCQYA0dOtSaPHmytX37drvHOi9MnDjRuv/+++0eI6bMnDnTcrvdVr9+/SyPx2NNnz7dOnLkiN1jxaQ///nPVlZWluV0Oq1LL73UWrFihW2zOCzLsuxJIwAAgO7xIVkAAGAcAgUAABiHQAEAAMYhUAAAgHEIFAAAYBwCBQAAGIdAAQAAxiFQAACAcQgUAABgHAIFAAAYh0ABAADGIVAAAIBx/g+81u+XV22VPgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dice_thousand = pd.read_csv(\"roll_the_dice_thousand.csv\")\n",
+    "dice_thousand.head()\n",
+    "\n",
+    "plt.hist(dice_thousand[\"value\"])\n",
+    "plt.show"
+   ]
+  },
+  {
+   "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": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([ 17.,  59., 115., 204., 261., 194.,  99.,  36.,  14.,   1.]),\n",
+       " array([ 1. ,  9.1, 17.2, 25.3, 33.4, 41.5, 49.6, 57.7, 65.8, 73.9, 82. ]),\n",
+       " <BarContainer object of 10 artists>)"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAigAAAGdCAYAAAA44ojeAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAgq0lEQVR4nO3dfWyV9f3/8dexpQeo7RmltKdnlFIdzptW5ooDOr4CgsWOmyhmoE6FiEYnMLrCuF1CZ5QyE4EtTBYd4VYGWQRkg03KgCIhTGxgFDSIoWCZ7c5EOKdFPMXy+f3hjyseijeF1vO5Ds9HciWe6/r08HkHtc9cPafHY4wxAgAAsMh1sd4AAADApQgUAABgHQIFAABYh0ABAADWIVAAAIB1CBQAAGAdAgUAAFiHQAEAANZJjPUGrsSFCxf04YcfKiUlRR6PJ9bbAQAA34AxRg0NDQoEArruuq++R+LKQPnwww+VnZ0d620AAIArUFtbq+7du3/lGlcGSkpKiqTPB0xNTY3xbgAAwDcRDoeVnZ3tfB//Kq4MlIs/1klNTSVQAABwmW/y8gxeJAsAAKxDoAAAAOsQKAAAwDoECgAAsA6BAgAArEOgAAAA6xAoAADAOgQKAACwDoECAACsQ6AAAADrECgAAMA6BAoAALAOgQIAAKxDoAAAAOskxnoDANpXz5mbY72FVjs+f3istwAgxriDAgAArEOgAAAA6xAoAADAOgQKAACwDoECAACsQ6AAAADrECgAAMA6BAoAALAOgQIAAKxDoAAAAOu0KlDKy8t15513KiUlRRkZGbrvvvt05MiRqDXjx4+Xx+OJOvr16xe1JhKJaPLkyUpPT1dycrJGjRqlkydPXv00AAAgLrQqUCorKzVx4kTt3btXFRUV+uyzz1RUVKSzZ89Grbv33ntVV1fnHFu2bIm6XlJSog0bNmjt2rXavXu3GhsbNWLECDU3N1/9RAAAwPVa9WGB//jHP6IeL1u2TBkZGaqqqtJdd93lnPd6vfL7/Zd9jlAopKVLl2rVqlUaOnSoJGn16tXKzs7Wtm3bNGzYsNbOAAAA4sxVvQYlFApJktLS0qLO79y5UxkZGbrpppv05JNPKhgMOteqqqp0/vx5FRUVOecCgYDy8vK0Z8+ey/45kUhE4XA46gAAAPHrigPFGKPS0lINGDBAeXl5zvni4mK9+uqr2r59u1588UXt27dPd999tyKRiCSpvr5eSUlJ6tKlS9TzZWZmqr6+/rJ/Vnl5uXw+n3NkZ2df6bYBAIALtOpHPF80adIkHTx4ULt37446P3bsWOef8/Ly1KdPH+Xk5Gjz5s0aPXr0lz6fMUYej+ey12bNmqXS0lLncTgcJlIAAIhjV3QHZfLkydq0aZN27Nih7t27f+XarKws5eTk6OjRo5Ikv9+vpqYmnT59OmpdMBhUZmbmZZ/D6/UqNTU16gAAAPGrVYFijNGkSZO0fv16bd++Xbm5uV/7NadOnVJtba2ysrIkSQUFBerQoYMqKiqcNXV1dTp06JAKCwtbuX0AABCPWvUjnokTJ2rNmjV6/fXXlZKS4rxmxOfzqVOnTmpsbFRZWZkeeOABZWVl6fjx45o9e7bS09N1//33O2snTJigqVOnqmvXrkpLS9O0adOUn5/vvKsHAABc21oVKEuWLJEkDRo0KOr8smXLNH78eCUkJKi6ulorV67UmTNnlJWVpcGDB2vdunVKSUlx1i9cuFCJiYkaM2aMzp07pyFDhmj58uVKSEi4+okAAIDreYwxJtabaK1wOCyfz6dQKMTrUYCv0XPm5lhvodWOzx8e6y0AaAet+f7NZ/EAAADrECgAAMA6BAoAALAOgQIAAKxDoAAAAOsQKAAAwDoECgAAsA6BAgAArEOgAAAA6xAoAADAOgQKAACwDoECAACsQ6AAAADrECgAAMA6BAoAALAOgQIAAKxDoAAAAOsQKAAAwDoECgAAsA6BAgAArEOgAAAA6xAoAADAOgQKAACwDoECAACsQ6AAAADrECgAAMA6BAoAALAOgQIAAKxDoAAAAOsQKAAAwDoECgAAsA6BAgAArEOgAAAA6xAoAADAOgQKAACwDoECAACsQ6AAAADrECgAAMA6BAoAALAOgQIAAKxDoAAAAOsQKAAAwDoECgAAsA6BAgAArEOgAAAA6xAoAADAOgQKAACwDoECAACsQ6AAAADrECgAAMA6BAoAALAOgQIAAKxDoAAAAOsQKAAAwDoECgAAsE5irDcAuEnPmZtjvQUAuCa06g5KeXm57rzzTqWkpCgjI0P33Xefjhw5ErXGGKOysjIFAgF16tRJgwYN0uHDh6PWRCIRTZ48Wenp6UpOTtaoUaN08uTJq58GAADEhVYFSmVlpSZOnKi9e/eqoqJCn332mYqKinT27FlnzQsvvKAFCxZo8eLF2rdvn/x+v+655x41NDQ4a0pKSrRhwwatXbtWu3fvVmNjo0aMGKHm5ua2mwwAALiWxxhjrvSL//e//ykjI0OVlZW66667ZIxRIBBQSUmJZsyYIenzuyWZmZn67W9/q6eeekqhUEjdunXTqlWrNHbsWEnShx9+qOzsbG3ZskXDhg372j83HA7L5/MpFAopNTX1SrcPtBo/4vl2HJ8/PNZbANAOWvP9+6peJBsKhSRJaWlpkqSamhrV19erqKjIWeP1ejVw4EDt2bNHklRVVaXz589HrQkEAsrLy3PWAACAa9sVv0jWGKPS0lINGDBAeXl5kqT6+npJUmZmZtTazMxMnThxwlmTlJSkLl26tFhz8esvFYlEFIlEnMfhcPhKtw0AAFzgiu+gTJo0SQcPHtSf//znFtc8Hk/UY2NMi3OX+qo15eXl8vl8zpGdnX2l2wYAAC5wRYEyefJkbdq0STt27FD37t2d836/X5Ja3AkJBoPOXRW/36+mpiadPn36S9dcatasWQqFQs5RW1t7JdsGAAAu0apAMcZo0qRJWr9+vbZv367c3Nyo67m5ufL7/aqoqHDONTU1qbKyUoWFhZKkgoICdejQIWpNXV2dDh065Ky5lNfrVWpqatQBAADiV6tegzJx4kStWbNGr7/+ulJSUpw7JT6fT506dZLH41FJSYnmzZunXr16qVevXpo3b546d+6shx9+2Fk7YcIETZ06VV27dlVaWpqmTZum/Px8DR06tO0nBAAArtOqQFmyZIkkadCgQVHnly1bpvHjx0uSpk+frnPnzumZZ57R6dOn1bdvX23dulUpKSnO+oULFyoxMVFjxozRuXPnNGTIEC1fvlwJCQlXNw0AAIgLV/V7UGKF34OCWOH3oHw7+D0oQHz61n4PCgAAQHsgUAAAgHX4NGMA1nHjj9L4sRTQtriDAgAArEOgAAAA6xAoAADAOgQKAACwDoECAACsQ6AAAADrECgAAMA6BAoAALAOgQIAAKxDoAAAAOsQKAAAwDoECgAAsA6BAgAArEOgAAAA6xAoAADAOgQKAACwDoECAACsQ6AAAADrECgAAMA6BAoAALAOgQIAAKxDoAAAAOsQKAAAwDoECgAAsA6BAgAArEOgAAAA6xAoAADAOgQKAACwDoECAACsQ6AAAADrECgAAMA6BAoAALAOgQIAAKxDoAAAAOsQKAAAwDoECgAAsA6BAgAArEOgAAAA6xAoAADAOgQKAACwDoECAACsQ6AAAADrECgAAMA6BAoAALAOgQIAAKxDoAAAAOsQKAAAwDoECgAAsA6BAgAArEOgAAAA6xAoAADAOgQKAACwDoECAACsQ6AAAADrtDpQdu3apZEjRyoQCMjj8Wjjxo1R18ePHy+PxxN19OvXL2pNJBLR5MmTlZ6eruTkZI0aNUonT568qkEAAED8aHWgnD17Vr1799bixYu/dM29996ruro659iyZUvU9ZKSEm3YsEFr167V7t271djYqBEjRqi5ubn1EwAAgLiT2NovKC4uVnFx8Veu8Xq98vv9l70WCoW0dOlSrVq1SkOHDpUkrV69WtnZ2dq2bZuGDRvW2i0BAIA40y6vQdm5c6cyMjJ000036cknn1QwGHSuVVVV6fz58yoqKnLOBQIB5eXlac+ePZd9vkgkonA4HHUAAID41eaBUlxcrFdffVXbt2/Xiy++qH379unuu+9WJBKRJNXX1yspKUldunSJ+rrMzEzV19df9jnLy8vl8/mcIzs7u623DQAALNLqH/F8nbFjxzr/nJeXpz59+ignJ0ebN2/W6NGjv/TrjDHyeDyXvTZr1iyVlpY6j8PhMJECAEAca/e3GWdlZSknJ0dHjx6VJPn9fjU1Nen06dNR64LBoDIzMy/7HF6vV6mpqVEHAACIX+0eKKdOnVJtba2ysrIkSQUFBerQoYMqKiqcNXV1dTp06JAKCwvbezsAAMAFWv0jnsbGRr3//vvO45qaGh04cEBpaWlKS0tTWVmZHnjgAWVlZen48eOaPXu20tPTdf/990uSfD6fJkyYoKlTp6pr165KS0vTtGnTlJ+f77yrBwAAXNtaHShvv/22Bg8e7Dy++NqQcePGacmSJaqurtbKlSt15swZZWVlafDgwVq3bp1SUlKcr1m4cKESExM1ZswYnTt3TkOGDNHy5cuVkJDQBiMBAAC38xhjTKw30VrhcFg+n0+hUIjXo+Bb1XPm5lhvAZY6Pn94rLcAWK8137/5LB4AAGAdAgUAAFiHQAEAANYhUAAAgHUIFAAAYB0CBQAAWIdAAQAA1iFQAACAdQgUAABgHQIFAABYh0ABAADWIVAAAIB1CBQAAGAdAgUAAFiHQAEAANYhUAAAgHUIFAAAYB0CBQAAWIdAAQAA1iFQAACAdQgUAABgHQIFAABYh0ABAADWIVAAAIB1CBQAAGAdAgUAAFiHQAEAANYhUAAAgHUIFAAAYB0CBQAAWIdAAQAA1iFQAACAdQgUAABgHQIFAABYh0ABAADWIVAAAIB1CBQAAGAdAgUAAFiHQAEAANYhUAAAgHUSY70BXLt6ztwc6y0AACzFHRQAAGAdAgUAAFiHQAEAANYhUAAAgHUIFAAAYB0CBQAAWIdAAQAA1iFQAACAdQgUAABgHQIFAABYh0ABAADWIVAAAIB1CBQAAGAdAgUAAFiHQAEAANZpdaDs2rVLI0eOVCAQkMfj0caNG6OuG2NUVlamQCCgTp06adCgQTp8+HDUmkgkosmTJys9PV3JyckaNWqUTp48eVWDAACA+NHqQDl79qx69+6txYsXX/b6Cy+8oAULFmjx4sXat2+f/H6/7rnnHjU0NDhrSkpKtGHDBq1du1a7d+9WY2OjRowYoebm5iufBAAAxI3E1n5BcXGxiouLL3vNGKNFixZpzpw5Gj16tCRpxYoVyszM1Jo1a/TUU08pFApp6dKlWrVqlYYOHSpJWr16tbKzs7Vt2zYNGzbsKsYBAADxoE1fg1JTU6P6+noVFRU557xerwYOHKg9e/ZIkqqqqnT+/PmoNYFAQHl5ec6aS0UiEYXD4agDAADErzYNlPr6eklSZmZm1PnMzEznWn19vZKSktSlS5cvXXOp8vJy+Xw+58jOzm7LbQMAAMu0y7t4PB5P1GNjTItzl/qqNbNmzVIoFHKO2traNtsrAACwT6tfg/JV/H6/pM/vkmRlZTnng8Ggc1fF7/erqalJp0+fjrqLEgwGVVhYeNnn9Xq98nq9bblVAGhTPWdujvUWrsjx+cNjvQXgstr0Dkpubq78fr8qKiqcc01NTaqsrHTio6CgQB06dIhaU1dXp0OHDn1poAAAgGtLq++gNDY26v3333ce19TU6MCBA0pLS1OPHj1UUlKiefPmqVevXurVq5fmzZunzp076+GHH5Yk+Xw+TZgwQVOnTlXXrl2VlpamadOmKT8/33lXDwAAuLa1OlDefvttDR482HlcWloqSRo3bpyWL1+u6dOn69y5c3rmmWd0+vRp9e3bV1u3blVKSorzNQsXLlRiYqLGjBmjc+fOaciQIVq+fLkSEhLaYCQAAOB2HmOMifUmWiscDsvn8ykUCik1NTXW28EVcuvP7IF4wmtQ8G1qzfdvPosHAABYh0ABAADWIVAAAIB1CBQAAGAdAgUAAFiHQAEAANYhUAAAgHUIFAAAYB0CBQAAWIdAAQAA1iFQAACAdQgUAABgHQIFAABYh0ABAADWIVAAAIB1CBQAAGAdAgUAAFiHQAEAANYhUAAAgHUIFAAAYB0CBQAAWIdAAQAA1iFQAACAdQgUAABgHQIFAABYh0ABAADWIVAAAIB1CBQAAGAdAgUAAFiHQAEAANYhUAAAgHUIFAAAYB0CBQAAWIdAAQAA1iFQAACAdQgUAABgHQIFAABYh0ABAADWIVAAAIB1CBQAAGAdAgUAAFiHQAEAANYhUAAAgHUIFAAAYB0CBQAAWIdAAQAA1iFQAACAdQgUAABgHQIFAABYJzHWG0Db6Dlzc6y3AABAm+EOCgAAsA6BAgAArEOgAAAA6xAoAADAOgQKAACwDoECAACsQ6AAAADrtHmglJWVyePxRB1+v9+5boxRWVmZAoGAOnXqpEGDBunw4cNtvQ0AAOBi7XIH5bbbblNdXZ1zVFdXO9deeOEFLViwQIsXL9a+ffvk9/t1zz33qKGhoT22AgAAXKhdAiUxMVF+v985unXrJunzuyeLFi3SnDlzNHr0aOXl5WnFihX65JNPtGbNmvbYCgAAcKF2CZSjR48qEAgoNzdXDz74oI4dOyZJqqmpUX19vYqKipy1Xq9XAwcO1J49e770+SKRiMLhcNQBAADiV5sHSt++fbVy5Uq98cYbeuWVV1RfX6/CwkKdOnVK9fX1kqTMzMyor8nMzHSuXU55ebl8Pp9zZGdnt/W2AQCARdo8UIqLi/XAAw8oPz9fQ4cO1ebNn3+I3YoVK5w1Ho8n6muMMS3OfdGsWbMUCoWco7a2tq23DQAALNLubzNOTk5Wfn6+jh496ryb59K7JcFgsMVdlS/yer1KTU2NOgAAQPxq90CJRCJ69913lZWVpdzcXPn9flVUVDjXm5qaVFlZqcLCwvbeCgAAcInEtn7CadOmaeTIkerRo4eCwaCee+45hcNhjRs3Th6PRyUlJZo3b5569eqlXr16ad68eercubMefvjhtt4KAABwqTYPlJMnT+qhhx7SRx99pG7duqlfv37au3evcnJyJEnTp0/XuXPn9Mwzz+j06dPq27evtm7dqpSUlLbeCgAAcCmPMcbEehOtFQ6H5fP5FAqFeD3K/9dz5uZYbwGACx2fPzzWW8A1pDXfv/ksHgAAYB0CBQAAWIdAAQAA1iFQAACAdQgUAABgHQIFAABYh0ABAADWIVAAAIB1CBQAAGAdAgUAAFiHQAEAANYhUAAAgHXa/NOMAQDu4cYPGuUDDq8N3EEBAADWIVAAAIB1CBQAAGAdAgUAAFiHQAEAANYhUAAAgHUIFAAAYB0CBQAAWIdAAQAA1iFQAACAdQgUAABgHQIFAABYh0ABAADWIVAAAIB1CBQAAGAdAgUAAFiHQAEAANYhUAAAgHUIFAAAYB0CBQAAWIdAAQAA1iFQAACAdQgUAABgHQIFAABYJzHWG7BRz5mbY70FAACuadxBAQAA1iFQAACAdQgUAABgHV6DAgBwFTe+TvD4/OGx3oLrcAcFAABYh0ABAADWIVAAAIB1CBQAAGAdAgUAAFiHQAEAANYhUAAAgHUIFAAAYB0CBQAAWIdAAQAA1iFQAACAdQgUAABgHQIFAABYh0ABAADWiWmgvPTSS8rNzVXHjh1VUFCgN998M5bbAQAAlohZoKxbt04lJSWaM2eO9u/fr//7v/9TcXGxPvjgg1htCQAAWCJmgbJgwQJNmDBBTzzxhG655RYtWrRI2dnZWrJkSay2BAAALJEYiz+0qalJVVVVmjlzZtT5oqIi7dmzp8X6SCSiSCTiPA6FQpKkcDjcLvu7EPmkXZ4XAHBt6vHLv8R6C6126DfD2vw5L37fNsZ87dqYBMpHH32k5uZmZWZmRp3PzMxUfX19i/Xl5eX6zW9+0+J8dnZ2u+0RAIBrmW9R+z13Q0ODfD7fV66JSaBc5PF4oh4bY1qck6RZs2aptLTUeXzhwgV9/PHH6tq162XXfxPhcFjZ2dmqra1VamrqFT2HzZjPveJ5Non53CyeZ5OY79tgjFFDQ4MCgcDXro1JoKSnpyshIaHF3ZJgMNjirookeb1eeb3eqHPf+c532mQvqampcfkv4kXM517xPJvEfG4Wz7NJzNfevu7OyUUxeZFsUlKSCgoKVFFREXW+oqJChYWFsdgSAACwSMx+xFNaWqpHH31Uffr0Uf/+/fXyyy/rgw8+0NNPPx2rLQEAAEvELFDGjh2rU6dO6dlnn1VdXZ3y8vK0ZcsW5eTkfCt/vtfr1dy5c1v86CheMJ97xfNsEvO5WTzPJjGfbTzmm7zXBwAA4FvEZ/EAAADrECgAAMA6BAoAALAOgQIAAKxzzQbKSy+9pNzcXHXs2FEFBQV68803Y72lK7Jr1y6NHDlSgUBAHo9HGzdujLpujFFZWZkCgYA6deqkQYMG6fDhw7HZbCuVl5frzjvvVEpKijIyMnTffffpyJEjUWvcPN+SJUt0++23O780qX///vr73//uXHfzbJcqLy+Xx+NRSUmJc87N85WVlcnj8UQdfr/fue7m2STpP//5jx555BF17dpVnTt31g9+8ANVVVU51908X8+ePVv83Xk8Hk2cOFGSu2eTpM8++0y//vWvlZubq06dOumGG27Qs88+qwsXLjhrXDOjuQatXbvWdOjQwbzyyivmnXfeMVOmTDHJycnmxIkTsd5aq23ZssXMmTPHvPbaa0aS2bBhQ9T1+fPnm5SUFPPaa6+Z6upqM3bsWJOVlWXC4XBsNtwKw4YNM8uWLTOHDh0yBw4cMMOHDzc9evQwjY2Nzho3z7dp0yazefNmc+TIEXPkyBEze/Zs06FDB3Po0CFjjLtn+6K33nrL9OzZ09x+++1mypQpznk3zzd37lxz2223mbq6OucIBoPOdTfP9vHHH5ucnBwzfvx4869//cvU1NSYbdu2mffff99Z4+b5gsFg1N9bRUWFkWR27NhhjHH3bMYY89xzz5muXbuav/3tb6ampsb85S9/Mddff71ZtGiRs8YtM16TgfKjH/3IPP3001Hnbr75ZjNz5swY7ahtXBooFy5cMH6/38yfP9859+mnnxqfz2f++Mc/xmCHVycYDBpJprKy0hgTf/MZY0yXLl3Mn/70p7iZraGhwfTq1ctUVFSYgQMHOoHi9vnmzp1revfufdlrbp9txowZZsCAAV963e3zXWrKlCnmxhtvNBcuXIiL2YYPH24ef/zxqHOjR482jzzyiDHGXX9/19yPeJqamlRVVaWioqKo80VFRdqzZ0+MdtU+ampqVF9fHzWr1+vVwIEDXTlrKBSSJKWlpUmKr/mam5u1du1anT17Vv3794+b2SZOnKjhw4dr6NChUefjYb6jR48qEAgoNzdXDz74oI4dOybJ/bNt2rRJffr00U9/+lNlZGTojjvu0CuvvOJcd/t8X9TU1KTVq1fr8ccfl8fjiYvZBgwYoH/+85967733JEn//ve/tXv3bv3kJz+R5K6/v5h+mnEsfPTRR2pubm7xoYSZmZktPrzQ7S7Oc7lZT5w4EYstXTFjjEpLSzVgwADl5eVJio/5qqur1b9/f3366ae6/vrrtWHDBt16663O/yjcPNvatWtVVVWlt99+u8U1t//d9e3bVytXrtRNN92k//73v3ruuedUWFiow4cPu362Y8eOacmSJSotLdXs2bP11ltv6Re/+IW8Xq8ee+wx18/3RRs3btSZM2c0fvx4Se7/91KSZsyYoVAopJtvvlkJCQlqbm7W888/r4ceekiSu2a85gLlIo/HE/XYGNPiXLyIh1knTZqkgwcPavfu3S2uuXm+73//+zpw4IDOnDmj1157TePGjVNlZaVz3a2z1dbWasqUKdq6das6duz4pevcOl9xcbHzz/n5+erfv79uvPFGrVixQv369ZPk3tkuXLigPn36aN68eZKkO+64Q4cPH9aSJUv02GOPOevcOt8XLV26VMXFxQoEAlHn3TzbunXrtHr1aq1Zs0a33XabDhw4oJKSEgUCAY0bN85Z54YZr7kf8aSnpyshIaHF3ZJgMNiiKN3u4rsK3D7r5MmTtWnTJu3YsUPdu3d3zsfDfElJSfre976nPn36qLy8XL1799bvfvc7189WVVWlYDCogoICJSYmKjExUZWVlfr973+vxMREZwa3znep5ORk5efn6+jRo67/u8vKytKtt94ade6WW27RBx98ICk+/ruTpBMnTmjbtm164oknnHPxMNuvfvUrzZw5Uw8++KDy8/P16KOP6pe//KXKy8sluWvGay5QkpKSVFBQoIqKiqjzFRUVKiwsjNGu2kdubq78fn/UrE1NTaqsrHTFrMYYTZo0SevXr9f27duVm5sbdd3t812OMUaRSMT1sw0ZMkTV1dU6cOCAc/Tp00c/+9nPdODAAd1www2unu9SkUhE7777rrKyslz/d/fjH/+4xdv533vvPeeDXN0+30XLli1TRkaGhg8f7pyLh9k++eQTXXdd9Lf2hIQE523GrpoxNq/Nja2LbzNeunSpeeedd0xJSYlJTk42x48fj/XWWq2hocHs37/f7N+/30gyCxYsMPv373feMj1//nzj8/nM+vXrTXV1tXnooYesfDvZ5fz85z83Pp/P7Ny5M+ptgZ988omzxs3zzZo1y+zatcvU1NSYgwcPmtmzZ5vrrrvObN261Rjj7tku54vv4jHG3fNNnTrV7Ny50xw7dszs3bvXjBgxwqSkpDj/D3HzbG+99ZZJTEw0zz//vDl69Kh59dVXTefOnc3q1audNW6ezxhjmpubTY8ePcyMGTNaXHP7bOPGjTPf/e53nbcZr1+/3qSnp5vp06c7a9wy4zUZKMYY84c//MHk5OSYpKQk88Mf/tB566rb7Nixw0hqcYwbN84Y8/lbyubOnWv8fr/xer3mrrvuMtXV1bHd9Dd0ubkkmWXLljlr3Dzf448/7vw72K1bNzNkyBAnToxx92yXc2mguHm+i783okOHDiYQCJjRo0ebw4cPO9fdPJsxxvz1r381eXl5xuv1mptvvtm8/PLLUdfdPt8bb7xhJJkjR460uOb22cLhsJkyZYrp0aOH6dixo7nhhhvMnDlzTCQScda4ZUaPMcbE5NYNAADAl7jmXoMCAADsR6AAAADrECgAAMA6BAoAALAOgQIAAKxDoAAAAOsQKAAAwDoECgAAsA6BAgAArEOgAAAA6xAoAADAOgQKAACwzv8Drudy/CJ04+AAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "age_pop = pd.read_csv(\"ages_population.csv\")\n",
+    "age_pop.head()\n",
+    "\n",
+    "plt.hist(age_pop)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Mean is between 30 and 40"
+   ]
+  },
+  {
+   "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": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "observation    36.56\n",
+      "dtype: float64\n",
+      "observation    12.8165\n",
+      "dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(age_pop.mean())\n",
+    "print(age_pop.std())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "The mean is very close to my guess\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": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([ 16.,  52., 119.,  98., 245., 254.,  90.,  92.,  29.,   5.]),\n",
+       " array([19. , 20.7, 22.4, 24.1, 25.8, 27.5, 29.2, 30.9, 32.6, 34.3, 36. ]),\n",
+       " <BarContainer object of 10 artists>)"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "age_pop_2 = pd.read_csv(\"ages_population2.csv\")\n",
+    "age_pop_2.head()\n",
+    "\n",
+    "plt.hist(age_pop_2)"
+   ]
+  },
+  {
+   "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": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "observation    27.155\n",
+      "dtype: float64\n",
+      "observation    2.969814\n",
+      "dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(age_pop_2.mean())\n",
+    "print(age_pop_2.std())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nThe age_pop_2 data set is smaller than the first one\\n'"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "The age_pop_2 data set is smaller than the first one\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": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([  8.,  33.,  78., 158., 187., 174., 133.,  57., 117.,  55.]),\n",
+       " array([ 1. ,  8.6, 16.2, 23.8, 31.4, 39. , 46.6, 54.2, 61.8, 69.4, 77. ]),\n",
+       " <BarContainer object of 10 artists>)"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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6Oe8aAgAA+L4WB8u2bds0dOhQ53FBQYEkKT8/Xy+++KIefvhhHTt2TA8++KAOHTqk/v3766233lJycrLznKeeekqJiYm6/fbbdezYMV1zzTV68cUXlZCQEIWRAACA23iMMSbei2ipUCgkn8+nmpoa7mfBT6rbtOJ4LwGIqr2z8+K9hBZrjf8dtsZ/zrFwJj+/+SwhAABgPYIFAABYj2ABAADWI1gAAID1CBYAAGA9ggUAAFiPYAEAANYjWAAAgPUIFgAAYD2CBQAAWI9gAQAA1iNYAACA9QgWAABgPYIFAABYj2ABAADWI1gAAID1CBYAAGA9ggUAAFiPYAEAANYjWAAAgPUIFgAAYD2CBQAAWI9gAQAA1iNYAACA9QgWAABgPYIFAABYj2ABAADWI1gAAID1CBYAAGA9ggUAAFiPYAEAANYjWAAAgPUIFgAAYD2CBQAAWC8x3gtA29VtWnG8lwAAaCW4wgIAAKwX9WDp1q2bPB5Pk238+PGSpDFjxjQ5NmDAgGgvAwAAuEjUfyW0detWNTQ0OI937typ6667Trfddpuz7/rrr9eiRYucx0lJSdFeBgAAcJGoB0uXLl0iHs+ePVvnn3++Bg8e7Ozzer3y+/3R/tIAAMClYnoPS319vZYuXaqxY8fK4/E4+zds2KD09HRdcMEFGjdunKqrq3/wdcLhsEKhUMQGAADajpgGy+rVq3X48GGNGTPG2Zebm6tly5Zp3bp1mjNnjrZu3aqrr75a4XD4lK9TVFQkn8/nbMFgMJbLBgAAlvEYY0ysXnz48OFKSkrSa6+9dspzKisrlZWVpeXLl2vkyJHNnhMOhyOCJhQKKRgMqqamRikpKVFfN34avK0ZiL+9s/PivYQWa43/72iN/5xjIRQKyefzndbP75j9PSz79u3T2rVrtXLlyh88LzMzU1lZWSovLz/lOV6vV16vN9pLBAAArUTMfiW0aNEipaenKy/vh6vy4MGDOnDggDIzM2O1FAAA0MrFJFgaGxu1aNEi5efnKzHx/1/Eqaur05QpU/Tuu+9q79692rBhg0aMGKG0tDTdcsstsVgKAABwgZj8Smjt2rXav3+/xo4dG7E/ISFBZWVlWrJkiQ4fPqzMzEwNHTpUK1asUHJyciyWAgAAXCAmwTJs2DA1dy9vhw4d9Oabb8biSwIAABfjs4QAAID1CBYAAGA9ggUAAFiPYAEAANYjWAAAgPUIFgAAYD2CBQAAWI9gAQAA1iNYAACA9QgWAABgPYIFAABYj2ABAADWI1gAAID1CBYAAGA9ggUAAFiPYAEAANYjWAAAgPUIFgAAYD2CBQAAWI9gAQAA1iNYAACA9QgWAABgPYIFAABYj2ABAADWI1gAAID1CBYAAGA9ggUAAFgvMd4LAADET7dpxfFeAvA/4QoLAACwHsECAACsR7AAAADrcQ8LAAAx1hrvFdo7Oy/eS4jAFRYAAGA9ggUAAFiPYAEAANYjWAAAgPWiHiwzZ86Ux+OJ2Px+v3PcGKOZM2cqEAioQ4cOGjJkiHbt2hXtZQAAABeJyRWWnj17qrKy0tnKysqcY08++aTmzp2r+fPna+vWrfL7/bruuutUW1sbi6UAAAAXiEmwJCYmyu/3O1uXLl0k/ffqyrx58zR9+nSNHDlSOTk5Wrx4sY4ePaqXX345FksBAAAuEJNgKS8vVyAQUHZ2tkaNGqXPP/9cklRRUaGqqioNGzbMOdfr9Wrw4MHasmXLKV8vHA4rFApFbAAAoO2IerD0799fS5Ys0Ztvvqnnn39eVVVVGjRokA4ePKiqqipJUkZGRsRzMjIynGPNKSoqks/nc7ZgMBjtZQMAAItFPVhyc3N16623qlevXrr22mtVXPzfv91v8eLFzjkejyfiOcaYJvu+r7CwUDU1Nc524MCBaC8bAABYLOZva+7UqZN69eql8vJy591CJ19Nqa6ubnLV5fu8Xq9SUlIiNgAA0HbEPFjC4bA++eQTZWZmKjs7W36/XyUlJc7x+vp6bdy4UYMGDYr1UgAAQCsV9Q8/nDJlikaMGKGuXbuqurpajz/+uEKhkPLz8+XxeDRp0iTNmjVLPXr0UI8ePTRr1ix17NhRd955Z7SXAgAAXCLqwfLFF19o9OjR+vrrr9WlSxcNGDBA7733nrKysiRJDz/8sI4dO6YHH3xQhw4dUv/+/fXWW28pOTk52ksBAAAu4THGmHgvoqVCoZB8Pp9qamq4n6UVa40ftw4AbcXe2XlRf80z+fnNZwkBAADrESwAAMB6BAsAALAewQIAAKxHsAAAAOsRLAAAwHoECwAAsB7BAgAArEewAAAA6xEsAADAegQLAACwHsECAACsR7AAAADrESwAAMB6BAsAALAewQIAAKxHsAAAAOsRLAAAwHoECwAAsB7BAgAArEewAAAA6xEsAADAegQLAACwHsECAACsR7AAAADrJcZ7AYiObtOK470EAABihissAADAegQLAACwHsECAACsR7AAAADrESwAAMB6BAsAALAewQIAAKxHsAAAAOsRLAAAwHoECwAAsF7Ug6WoqEiXX365kpOTlZ6erptvvlm7d++OOGfMmDHyeDwR24ABA6K9FAAA4BJRD5aNGzdq/Pjxeu+991RSUqLvvvtOw4YN05EjRyLOu/7661VZWelsb7zxRrSXAgAAXCLqH374r3/9K+LxokWLlJ6eru3bt+uXv/yls9/r9crv90f7ywMAABeK+T0sNTU1kqTU1NSI/Rs2bFB6erouuOACjRs3TtXV1ad8jXA4rFAoFLEBAIC2I6bBYoxRQUGBrrrqKuXk5Dj7c3NztWzZMq1bt05z5szR1q1bdfXVVyscDjf7OkVFRfL5fM4WDAZjuWwAAGAZjzHGxOrFx48fr+LiYm3evFnnnXfeKc+rrKxUVlaWli9frpEjRzY5Hg6HI2ImFAopGAyqpqZGKSkpMVl7a9NtWnG8lwAAcJG9s/Oi/pqhUEg+n++0fn5H/R6WEyZMmKA1a9Zo06ZNPxgrkpSZmamsrCyVl5c3e9zr9crr9cZimQAAoBWIerAYYzRhwgStWrVKGzZsUHZ29o8+5+DBgzpw4IAyMzOjvRwAAOACUb+HZfz48Vq6dKlefvllJScnq6qqSlVVVTp27Jgkqa6uTlOmTNG7776rvXv3asOGDRoxYoTS0tJ0yy23RHs5AADABaJ+hWXBggWSpCFDhkTsX7RokcaMGaOEhASVlZVpyZIlOnz4sDIzMzV06FCtWLFCycnJ0V4OAABwgZj8SuiHdOjQQW+++Wa0vywAAHAxPksIAABYj2ABAADWI1gAAID1CBYAAGA9ggUAAFiPYAEAANYjWAAAgPUIFgAAYD2CBQAAWI9gAQAA1iNYAACA9QgWAABgPYIFAABYj2ABAADWI1gAAID1CBYAAGA9ggUAAFiPYAEAANYjWAAAgPUIFgAAYD2CBQAAWI9gAQAA1iNYAACA9QgWAABgPYIFAABYLzHeC7BRt2nF8V4CAAD4Hq6wAAAA6xEsAADAegQLAACwHsECAACsR7AAAADrESwAAMB6BAsAALAewQIAAKxHsAAAAOsRLAAAwHpxDZZnnnlG2dnZat++vfr27at33nknnssBAACWiluwrFixQpMmTdL06dP1wQcf6P/+7/+Um5ur/fv3x2tJAADAUnELlrlz5+q+++7T/fffr4suukjz5s1TMBjUggUL4rUkAABgqbh8WnN9fb22b9+uadOmRewfNmyYtmzZ0uT8cDiscDjsPK6pqZEkhUKhmKyvMXw0Jq8LAEBrEYufsSde0xjT4ufGJVi+/vprNTQ0KCMjI2J/RkaGqqqqmpxfVFSkRx99tMn+YDAYszUCANCW+ebF7rVra2vl8/la9Jy4BMsJHo8n4rExpsk+SSosLFRBQYHzuLGxUd988406d+7c7Pn/i1AopGAwqAMHDiglJeW0XqO1aEuzSm1rXmZ1r7Y0L7O618nzGmNUW1urQCDQ4teKS7CkpaUpISGhydWU6urqJlddJMnr9crr9UbsO+ecc6KylpSUlDbxL43UtmaV2ta8zOpebWleZnWv78/b0isrJ8TlptukpCT17dtXJSUlEftLSko0aNCgeCwJAABYLG6/EiooKNA999yjfv36aeDAgXruuee0f/9+PfDAA/FaEgAAsFTcguWOO+7QwYMH9dhjj6myslI5OTl64403lJWV9ZN8fa/XqxkzZjT5VZMbtaVZpbY1L7O6V1ual1ndK5rzeszpvLcIAADgJ8RnCQEAAOsRLAAAwHoECwAAsB7BAgAArNcmg+WZZ55Rdna22rdvr759++qdd96J95KiYtOmTRoxYoQCgYA8Ho9Wr14dcdwYo5kzZyoQCKhDhw4aMmSIdu3aFZ/FnqGioiJdfvnlSk5OVnp6um6++Wbt3r074hy3zLtgwQJdcsklzl+8NHDgQP3zn/90jrtlzuYUFRXJ4/Fo0qRJzj43zTtz5kx5PJ6Ize/3O8fdNKsk/ec//9Hdd9+tzp07q2PHjrr00ku1fft257ib5u3WrVuT763H49H48eMluWvW7777Tn/4wx+UnZ2tDh06qHv37nrsscfU2NjonBOVeU0bs3z5ctOuXTvz/PPPm48//thMnDjRdOrUyezbty/eSztjb7zxhpk+fbp59dVXjSSzatWqiOOzZ882ycnJ5tVXXzVlZWXmjjvuMJmZmSYUCsVnwWdg+PDhZtGiRWbnzp2mtLTU5OXlma5du5q6ujrnHLfMu2bNGlNcXGx2795tdu/ebR555BHTrl07s3PnTmOMe+Y82fvvv2+6detmLrnkEjNx4kRnv5vmnTFjhunZs6eprKx0turqaue4m2b95ptvTFZWlhkzZoz597//bSoqKszatWvNnj17nHPcNG91dXXE97WkpMRIMuvXrzfGuGvWxx9/3HTu3Nm8/vrrpqKiwrzyyivm7LPPNvPmzXPOica8bS5YrrjiCvPAAw9E7LvwwgvNtGnT4rSi2Dg5WBobG43f7zezZ8929n377bfG5/OZv/3tb3FYYXRVV1cbSWbjxo3GGPfPe+6555q///3vrp2ztrbW9OjRw5SUlJjBgwc7weK2eWfMmGF69+7d7DG3zTp16lRz1VVXnfK42+Y92cSJE835559vGhsbXTdrXl6eGTt2bMS+kSNHmrvvvtsYE73vbZv6lVB9fb22b9+uYcOGRewfNmyYtmzZEqdV/TQqKipUVVUVMbvX69XgwYNdMXtNTY0kKTU1VZJ7521oaNDy5ct15MgRDRw40LVzjh8/Xnl5ebr22msj9rtx3vLycgUCAWVnZ2vUqFH6/PPPJblv1jVr1qhfv3667bbblJ6erssuu0zPP/+8c9xt835ffX29li5dqrFjx8rj8bhu1quuukpvv/22Pv30U0nShx9+qM2bN+uGG26QFL3vbVw/rfmn9vXXX6uhoaHJByxmZGQ0+SBGtzkxX3Oz79u3Lx5LihpjjAoKCnTVVVcpJydHkvvmLSsr08CBA/Xtt9/q7LPP1qpVq3TxxRc7/7G7ZU5JWr58ubZv365t27Y1Oea272v//v21ZMkSXXDBBfrqq6/0+OOPa9CgQdq1a5frZv3888+1YMECFRQU6JFHHtH777+v3/72t/J6vbr33ntdN+/3rV69WocPH9aYMWMkue/f46lTp6qmpkYXXnihEhIS1NDQoCeeeEKjR4+WFL1521SwnODxeCIeG2Oa7HMrN87+0EMP6aOPPtLmzZubHHPLvL/4xS9UWlqqw4cP69VXX1V+fr42btzoHHfLnAcOHNDEiRP11ltvqX379qc8zy3z5ubmOn/u1auXBg4cqPPPP1+LFy/WgAEDJLln1sbGRvXr10+zZs2SJF122WXatWuXFixYoHvvvdc5zy3zft/ChQuVm5urQCAQsd8ts65YsUJLly7Vyy+/rJ49e6q0tFSTJk1SIBBQfn6+c96ZztumfiWUlpamhISEJldTqqurm5Sf25x454HbZp8wYYLWrFmj9evX67zzznP2u23epKQk/fznP1e/fv1UVFSk3r176+mnn3bdnNu3b1d1dbX69u2rxMREJSYmauPGjfrzn/+sxMREZya3zHuyTp06qVevXiovL3fd9zYzM1MXX3xxxL6LLrpI+/fvl+S+/2ZP2Ldvn9auXav777/f2ee2WX//+99r2rRpGjVqlHr16qV77rlHv/vd71RUVCQpevO2qWBJSkpS3759VVJSErG/pKREgwYNitOqfhrZ2dny+/0Rs9fX12vjxo2tcnZjjB566CGtXLlS69atU3Z2dsRxt817MmOMwuGw6+a85pprVFZWptLSUmfr16+f7rrrLpWWlqp79+6umvdk4XBYn3zyiTIzM133vb3yyiub/NUDn376qfOBt26b94RFixYpPT1deXl5zj63zXr06FGddVZkTiQkJDhva47avKd/X3DrdOJtzQsXLjQff/yxmTRpkunUqZPZu3dvvJd2xmpra80HH3xgPvjgAyPJzJ0713zwwQfOW7Znz55tfD6fWblypSkrKzOjR49utW+j+81vfmN8Pp/ZsGFDxFsHjx496pzjlnkLCwvNpk2bTEVFhfnoo4/MI488Ys466yzz1ltvGWPcM+epfP9dQsa4a97JkyebDRs2mM8//9y899575sYbbzTJycnO/4/cNOv7779vEhMTzRNPPGHKy8vNsmXLTMeOHc3SpUudc9w0rzHGNDQ0mK5du5qpU6c2OeamWfPz883PfvYz523NK1euNGlpaebhhx92zonGvG0uWIwx5q9//avJysoySUlJpk+fPs5bYVu79evXG0lNtvz8fGPMf99aNmPGDOP3+43X6zW//OUvTVlZWXwXfZqam1OSWbRokXOOW+YdO3as8+9rly5dzDXXXOPEijHumfNUTg4WN8174u+iaNeunQkEAmbkyJFm165dznE3zWqMMa+99prJyckxXq/XXHjhhea5556LOO62ed98800jyezevbvJMTfNGgqFzMSJE03Xrl1N+/btTffu3c306dNNOBx2zonGvB5jjDndy0AAAAA/hTZ1DwsAAGidCBYAAGA9ggUAAFiPYAEAANYjWAAAgPUIFgAAYD2CBQAAWI9gAQAA1iNYAACA9QgWAABgPYIFAABYj2ABAADW+3+Qqn8LvoaQ1wAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "age_pop_3 = pd.read_csv(\"ages_population3.csv\")\n",
+    "age_pop_3.head()\n",
+    "\n",
+    "plt.hist(age_pop_3)"
+   ]
+  },
+  {
+   "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": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "observation    41.989\n",
+      "dtype: float64\n",
+      "observation    16.144706\n",
+      "dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(age_pop_3.mean())\n",
+    "print(age_pop_3.std())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "There are more 60 and 70 year olds in this data set, which makesthe standard deviation and mean be higher on this one\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": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[30.]\n",
+      "[40.]\n",
+      "[53.]\n",
+      "[77.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "quartile_1 = np.quantile(age_pop_3, [0.25])\n",
+    "quartile_2 = np.quantile(age_pop_3, [0.5])\n",
+    "quartile_3 = np.quantile(age_pop_3, [0.75])\n",
+    "quartile_4 = np.quantile(age_pop_3, [1])\n",
+    "\n",
+    "print(quartile_1)\n",
+    "print(quartile_2)\n",
+    "print(quartile_3)\n",
+    "print(quartile_4)"
+   ]
+  },
+  {
+   "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": "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": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/your-code/ages_population.csv b/your-code/ages_population.csv
new file mode 100644
index 0000000..64d8a0a
--- /dev/null
+++ b/your-code/ages_population.csv
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diff --git a/your-code/ages_population2.csv b/your-code/ages_population2.csv
new file mode 100644
index 0000000..00860cb
--- /dev/null
+++ b/your-code/ages_population2.csv
@@ -0,0 +1,1001 @@
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diff --git a/your-code/ages_population3.csv b/your-code/ages_population3.csv
new file mode 100644
index 0000000..6339a1d
--- /dev/null
+++ b/your-code/ages_population3.csv
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diff --git a/your-code/main.ipynb b/your-code/main.ipynb
deleted file mode 100644
index a0a5b66..0000000
--- a/your-code/main.ipynb
+++ /dev/null
@@ -1,522 +0,0 @@
-{
- "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
-}
diff --git a/your-code/roll_the_dice_hundred.csv b/your-code/roll_the_dice_hundred.csv
new file mode 100644
index 0000000..50975a2
--- /dev/null
+++ b/your-code/roll_the_dice_hundred.csv
@@ -0,0 +1,101 @@
+,roll,value
+0,0,1
+1,1,2
+2,2,6
+3,3,1
+4,4,6
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diff --git a/your-code/roll_the_dice_thousand.csv b/your-code/roll_the_dice_thousand.csv
new file mode 100644
index 0000000..f820dbb
--- /dev/null
+++ b/your-code/roll_the_dice_thousand.csv
@@ -0,0 +1,1001 @@
+,roll,value
+0,0,5
+1,1,6
+2,2,1
+3,3,6
+4,4,5
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