diff --git a/your-code/main.ipynb b/your-code/main.ipynb
index 5759add..a2555bb 100644
--- a/your-code/main.ipynb
+++ b/your-code/main.ipynb
@@ -1,522 +1,1359 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Understanding Descriptive Statistics\n",
-    "\n",
-    "Import the necessary libraries here:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Libraries"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Challenge 1\n",
-    "#### 1.- Define a function that simulates rolling a dice 10 times. Save the information in a dataframe.\n",
-    "**Hint**: you can use the *choices* function from module *random* to help you with the simulation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 2.- Plot the results sorted by value."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 3.- Calculate the frequency distribution and plot it. What is the relation between this plot and the plot above? Describe it with words."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Challenge 2\n",
-    "Now, using the dice results obtained in *challenge 1*, your are going to define some functions that will help you calculate the mean of your data in two different ways, the median and the four quartiles. \n",
-    "\n",
-    "#### 1.- Define a function that computes the mean by summing all the observations and dividing by the total number of observations. You are not allowed to use any methods or functions that directly calculate the mean value. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 2.- First, calculate the frequency distribution. Then, calculate the mean using the values of the frequency distribution you've just computed. You are not allowed to use any methods or functions that directly calculate the mean value. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 3.- Define a function to calculate the median. You are not allowed to use any methods or functions that directly calculate the median value. \n",
-    "**Hint**: you might need to define two computation cases depending on the number of observations used to calculate the median."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 4.- Define a function to calculate the four quartiles. You can use the function you defined above to compute the median but you are not allowed to use any methods or functions that directly calculate the quartiles. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Challenge 3\n",
-    "Read the csv `roll_the_dice_hundred.csv` from the `data` folder.\n",
-    "#### 1.- Sort the values and plot them. What do you see?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 2.- Using the functions you defined in *challenge 2*, calculate the mean value of the hundred dice rolls."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 3.- Now, calculate the frequency distribution.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 4.- Plot the histogram. What do you see (shape, values...) ? How can you connect the mean value to the histogram? "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 5.- Read the `roll_the_dice_thousand.csv` from the `data` folder. Plot the frequency distribution as you did before. Has anything changed? Why do you think it changed?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Challenge 4\n",
-    "In the `data` folder of this repository you will find three different files with the prefix `ages_population`. These files contain information about a poll answered by a thousand people regarding their age. Each file corresponds to the poll answers in different neighbourhoods of Barcelona.\n",
-    "\n",
-    "#### 1.- Read the file `ages_population.csv`. Calculate the frequency distribution and plot it as we did during the lesson. Try to guess the range in which the mean and the standard deviation will be by looking at the plot. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 2.- Calculate the exact mean and standard deviation and compare them with your guesses. Do they fall inside the ranges you guessed?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 3.- Now read the file `ages_population2.csv` . Calculate the frequency distribution and plot it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "####  4.- What do you see? Is there any difference with the frequency distribution in step 1?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 5.- Calculate the mean and standard deviation. Compare the results with the mean and standard deviation in step 2. What do you think?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Challenge 5\n",
-    "Now is the turn of `ages_population3.csv`.\n",
-    "\n",
-    "#### 1.- Read the file `ages_population3.csv`. Calculate the frequency distribution and plot it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 2.- Calculate the mean and standard deviation. Compare the results with the plot in step 1. What is happening?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 3.- Calculate the four quartiles. Use the results to explain your reasoning for question in step 2. How much of a difference is there between the median and the mean?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 4.- Calculate other percentiles that might be useful to give more arguments to your reasoning."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Bonus challenge\n",
-    "Compare the information about the three neighbourhoods. Prepare a report about the three of them. Remember to find out which are their similarities and their differences backing your arguments in basic statistics."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "ironhack-3.7",
-   "language": "python",
-   "name": "ironhack-3.7"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Understanding Descriptive Statistics\n",
+    "\n",
+    "Import the necessary libraries here:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Libraries\n",
+    "from scipy import stats\n",
+    "import random\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from matplotlib import pyplot as plt"
+   ]
+  },
+  {
+   "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": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# your code here\n",
+    "\n",
+    "def dice_throw(throws=10, dice_faces = 6):\n",
+    "    dice_list = []\n",
+    "    \n",
+    "    for i in range(throws):\n",
+    "        dice_list.append(random.choice(np.arange(1,(dice_faces +1),1)))\n",
+    "    \n",
+    "    throws_df = pd.DataFrame(dice_list, columns=['Number'])\n",
+    "    return throws_df\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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>Number</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Number\n",
+       "0       1\n",
+       "1       6\n",
+       "2       2\n",
+       "3       4\n",
+       "4       1\n",
+       "5       2\n",
+       "6       6\n",
+       "7       3\n",
+       "8       4\n",
+       "9       4"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dice = dice_throw()\n",
+    "dice"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.- Plot the results sorted by value."
+   ]
+  },
+  {
+   "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>Number</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Number\n",
+       "0       1\n",
+       "4       1\n",
+       "2       2\n",
+       "5       2\n",
+       "7       3\n",
+       "3       4\n",
+       "8       4\n",
+       "9       4\n",
+       "1       6\n",
+       "6       6"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dice = dice.sort_values(by='Number')\n",
+    "dice"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "\n",
+    "plt.plot(dice['Number'],\n",
+    "        dice.index,\n",
+    "        color='purple',\n",
+    "        marker='x')\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": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "dice = dice.sort_index()\n",
+    "dice\n",
+    "\n",
+    "plt.hist(dice, bins=6,\n",
+    "        range=(1,7),\n",
+    "        color= 'purple',\n",
+    "        rwidth = 2)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"\\nyour comments here\\nthe first plot was unorganized and it wasn't clear the meaning of it.\\n\\nThe second plot makes sense because every dice number has a unique bar\\nregarding the quantity of the number of times it showed up.\\n\""
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "your comments here\n",
+    "the first plot was unorganized and it wasn't clear the meaning of it.\n",
+    "\n",
+    "The second plot makes sense because every dice number has a unique bar\n",
+    "regarding the quantity of the number of times it showed up.\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": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "33"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(dice['Number'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.3"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "def mean(data):\n",
+    "    sum_data = sum(data)\n",
+    "    len_data = len(data)\n",
+    "    return sum_data/len_data\n",
+    "mean(dice['Number'])\n"
+   ]
+  },
+  {
+   "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": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1: 2, 6: 2, 2: 2, 4: 3, 3: 1}"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "\n",
+    "def freq_d (values):\n",
+    "    freq_d = {}\n",
+    "    for dice_n in values:\n",
+    "        if dice_n in freq_d:\n",
+    "            freq_d[dice_n] += 1\n",
+    "        else:\n",
+    "            freq_d[dice_n] = 1\n",
+    "            \n",
+    "    return freq_d\n",
+    "\n",
+    "freq_d (dice['Number'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1, 1, 2, 2, 3, 4, 4, 4, 6, 6]"
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dice_sorted = list(dice['Number'].sort_values())\n",
+    "dice_sorted"
+   ]
+  },
+  {
+   "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": 92,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.5"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "\n",
+    "def median(data):\n",
+    "    sorted_data = sorted(data)\n",
+    "    len_data = len(sorted_data)\n",
+    "    midle_index = (len_data) // 2\n",
+    "    if len_data %2 == 0:\n",
+    "        return int(((sorted_data[int(midle_index)]) + (sorted_data[(int(midle_index))-1])))/2\n",
+    "    else:\n",
+    "        return int(sorted_data[int(((len_data-1)/2))])\n",
+    "        \n",
+    "\n",
+    "median(dice_sorted)"
+   ]
+  },
+  {
+   "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": 93,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dice_list = sorted(list(dice['Number']))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2, 3.5, 4)"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def quantiles(value_list):\n",
+    "    value_list = sorted(value_list)\n",
+    "    \n",
+    "    # 2q\n",
+    "    second_q = median(value_list)\n",
+    "\n",
+    "    if len(value_list) % 2 == 0:\n",
+    "        # 1q\n",
+    "        first_q = median(value_list[:int(len(value_list)/2)])\n",
+    "        # 3q\n",
+    "        third_q = median(value_list[int(len(value_list)/2):])\n",
+    "\n",
+    "    else:\n",
+    "        # 1q\n",
+    "        first_q = median(value_list[:int(len(value_list)/2 -1)])\n",
+    "        # 3q\n",
+    "        third_q = median(value_list[int(len(value_list)/2-1):])\n",
+    "    \n",
+    "    return first_q, second_q, third_q\n",
+    "\n",
+    "quantiles(dice_list)"
+   ]
+  },
+  {
+   "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": 95,
+   "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>times</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>value</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>23</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       times\n",
+       "value       \n",
+       "1         12\n",
+       "2         17\n",
+       "3         14\n",
+       "4         22\n",
+       "5         12\n",
+       "6         23"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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eXh4jR46Mp59++lP/TFNTUzQ2NrZYAIDDV0Hjo6GhISIiqqqqWqyvqqrKb/ukGTNmRGVlZX6pq6sr5EgAQDvTJp92yeVyLR5nWbbfuo9MnTo1du7cmV/q6+vbYiQAoJ1o9T0fn6e6ujoiPrwCUlNTk1+/ffv2/a6GfKS8vDzKy8sLOQYA0I4V9MpH//79o7q6OpYsWZJft3fv3li+fHmMGDGikIcCAEpUq698vPvuu7Fp06b8482bN8eaNWuiZ8+e0adPn5g8eXJMnz49Bg4cGAMHDozp06dHRUVFXHbZZQUdHAAoTa2Oj1WrVsXo0aPzj6dMmRIRERMnToz77rsvbrrpptizZ09ce+21sWPHjjj11FNj8eLF0a1bt8JNDQCUrFbHx6hRoyLLss/cnsvlYtq0aTFt2rRDmQsAOEz5bhcAICnxAQAkJT4AgKTEBwCQlPgAAJISHwBAUuIDAEhKfAAASYkPACAp8QEAJCU+AICkxAcAkJT4AACSEh8AQFLiAwBISnwAAEmJDwAgKfEBACQlPgCApMQHAJCU+AAAkhIfAEBS4gMASEp8AABJiQ8AICnxAQAkJT4AgKTEBwCQlPgAAJISHwBAUuIDAEhKfAAASYkPACAp8QEAJCU+AICkxAcAkJT4AACSEh8AQFLiAwBISnwAAEmJDwAgKfEBACQlPgCApMQHAJCU+AAAkhIfAEBS4gMASEp8AABJiQ8AICnxAQAkJT4AgKTEBwCQlPgAAJISHwBAUuIDAEhKfAAASYkPACAp8QEAJCU+AICkxAcAkJT4AACSEh8AQFLiAwBISnwAAEmJDwAgKfEBACRV8PiYNm1a5HK5Fkt1dXWhDwMAlKiObfGkJ554Yjz++OP5xx06dGiLwwAAJahN4qNjx44HfLWjqakpmpqa8o8bGxvbYiQAoJ1ok3s+Nm7cGLW1tdG/f/+45JJL4tVXX/3MfWfMmBGVlZX5pa6uri1GAgDaiYLHx6mnnho//vGPY9GiRXH33XdHQ0NDjBgxIt5+++1P3X/q1Kmxc+fO/FJfX1/okQCAdqTgb7uMHTs2/99DhgyJ008/Pb785S/HvHnzYsqUKfvtX15eHuXl5YUeAwBop9r8o7ZdunSJIUOGxMaNG9v6UABACWjz+Ghqaor169dHTU1NWx8KACgBBY+PG2+8MZYvXx6bN2+OX/7ylzFhwoRobGyMiRMnFvpQAEAJKvg9H6+99lpceuml8dZbb8UxxxwTp512WqxcuTL69u1b6EMBACWo4PGxYMGCQj8lAHAY8d0uAEBS4gMASEp8AABJiQ8AICnxAQAkJT4AgKTEBwCQlPgAAJISHwBAUuIDAEhKfAAASYkPACAp8QEAJCU+AICkxAcAkJT4AACSEh8AQFLiAwBISnwAAEmJDwAgKfEBACQlPgCApMQHAJCU+AAAkhIfAEBS4gMASEp8AABJiQ8AICnxAQAkJT4AgKTEBwCQlPgAAJISHwBAUuIDAEhKfAAASYkPACAp8QEAJCU+AICkxAcAkJT4AACSEh8AQFLiAwBISnwAAEmJDwAgKfEBACQlPgCApMQHAJCU+AAAkhIfAEBS4gMASEp8AABJiQ8AICnxAQAkJT4AgKTEBwCQlPgAAJISHwBAUuIDAEhKfAAASYkPACAp8QEAJCU+AICkxAcAkJT4AACSEh8AQFJtFh//8A//EP37948jjzwyTj755HjyySfb6lAAQAlpk/j4yU9+EpMnT45bbrklnn/++TjzzDNj7NixsXXr1rY4HABQQtokPmbNmhV//ud/Ht/61rfi+OOPjzvvvDPq6upizpw5bXE4AKCEdCz0E+7duzdWr14dN998c4v1Y8aMiaeffnq//ZuamqKpqSn/eOfOnRER0djYWNC59jW9V9DnawuFfs1txbksjFI4jxHOZaGUwnmMcC4LpRTOY0Rhz+VHz5Vl2RfvnBXYr3/96ywisqeeeqrF+ttvvz0bNGjQfvvfeuutWURYLBaLxWI5DJb6+vovbIWCX/n4SC6Xa/E4y7L91kVETJ06NaZMmZJ/vG/fvvjNb34TvXr1+tT924vGxsaoq6uL+vr66N69e7HHKVnOY+E4l4XjXBaG81g4pXAusyyLXbt2RW1t7RfuW/D4OProo6NDhw7R0NDQYv327dujqqpqv/3Ly8ujvLy8xboePXoUeqw2071793b7P0IpcR4Lx7ksHOeyMJzHwmnv57KysvKA9iv4DaedOnWKk08+OZYsWdJi/ZIlS2LEiBGFPhwAUGLa5G2XKVOmxBVXXBHDhw+P008/Pf75n/85tm7dGldffXVbHA4AKCFtEh8XX3xxvP322/H9738/tm3bFoMHD45HH300+vbt2xaHK4ry8vK49dZb93vLiNZxHgvHuSwc57IwnMfCOdzOZS7LDuQzMQAAheG7XQCApMQHAJCU+AAAkhIfAEBS4gPgE9yHD21LfAB8Qnl5eaxfv77YY8Bhq82+2+V3SX19fdx6661x7733FnuUdm/Pnj2xevXq6NmzZ5xwwgkttv32t7+Nn/70p3HllVcWabrSsn79+li5cmWcfvrpcdxxx8VLL70Ud911VzQ1NcU3vvGNOOuss4o9Yrv38e+V+rjm5ua44447olevXhERMWvWrJRjHTZ27NgR8+bNi40bN0ZNTU1MnDgx6urqij1Wu/f8889Hjx49on///hER8a//+q8xZ86c2Lp1a/Tt2zeuu+66uOSSS4o85aHxez4K4IUXXohhw4ZFc3NzsUdp1zZs2BBjxoyJrVu3Ri6XizPPPDMeeOCBqKmpiYiIN954I2pra53HA/DYY4/F1772tejatWu899578fDDD8eVV14ZQ4cOjSzLYvny5bFo0SIB8gXKyspi6NCh+32f1PLly2P48OHRpUuXyOVysXTp0uIMWGJqa2tj7dq10atXr9i8eXP+KzWGDBkS69evj127dsXKlSvjuOOOK/Kk7duwYcNi5syZMXr06Ljnnnviu9/9bnz729+O448/Pl5++eW455574q677opvfvObxR71oImPA/DII4987vZXX301brjhBj80v8CFF14YH3zwQcydOzfeeeedmDJlSvzP//xPLFu2LPr06SM+WmHEiBFx1llnxW233RYLFiyIa6+9Nq655pq4/fbbIyLilltuiWeffTYWL15c5EnbtxkzZsTdd98d99xzT4tQO+KII+KFF17Y7+ocn6+srCwaGhqid+/ecemll0ZDQ0MsXLgwKioqoqmpKSZMmBBHHnlk/OxnPyv2qO1aly5dYv369dGnT58YNmxYXH311fEXf/EX+e3z58+P22+/PdatW1fEKQ9RxhfK5XJZWVlZlsvlPnMpKysr9pjtXu/evbNf/epXLdZde+21WZ8+fbJXXnkla2hocB4PUPfu3bONGzdmWZZlzc3NWceOHbPVq1fnt69duzarqqoq1ngl5ZlnnskGDRqU3XDDDdnevXuzLMuyjh07ZuvWrSvyZKUnl8tlb7zxRpZlWda/f//sv/7rv1psX7lyZXbssccWY7SS0qtXr2zVqlVZln349+aaNWtabN+0aVPWuXPnYoxWMG44PQA1NTXx4IMPxr59+z51ee6554o9YknYs2dPdOzY8jajv//7v48LLrggRo4cGRs2bCjSZKWtrKwsjjzyyBZvHXTr1i127txZvKFKyCmnnBKrV6+ON998M4YPHx5r166NXC5X7LFK1kfnrqmpKaqqqlpsq6qqijfffLMYY5WUsWPHxpw5cyIiYuTIkfFv//ZvLbb/9Kc/jQEDBhRjtIJxw+kBOPnkk+O5556Lr3/965+6PZfL+WjeATjuuONi1apVcfzxx7dY/3d/93eRZVlccMEFRZqs9PTr1y82bdqU/wvov//7v6NPnz757fX19fl7afhiXbt2jXnz5sWCBQvinHPO8dbfITj77LOjY8eO0djYGBs2bIgTTzwxv23r1q1x9NFHF3G60vCDH/wgvvKVr8TIkSNj+PDhMXPmzFi2bFn+no+VK1fGww8/XOwxD4n4OAB/+Zd/Gbt37/7M7QMGDIgnnngi4USl6cILL4wHHnggrrjiiv22zZ49O/bt2xf/+I//WITJSs8111zT4gfk4MGDW2z/z//8TzebHoRLLrkkzjjjjFi9evVh9S3cqdx6660tHldUVLR4/Itf/CLOPPPMlCOVpNra2nj++efjjjvuiF/84heRZVk888wzUV9fH1/5ylfiqaeeiuHDhxd7zEPihlMAICn3fAAASYkPACAp8QEAJCU+AICkxAeQTL9+/eLOO+8s9hhAkYkPACAp8QEAJCU+gAPyT//0T/GlL30p9u3b12L9BRdcEBMnToxXXnklvva1r0VVVVV07do1TjnllHj88cc/8/m2bNkSuVwu1qxZk1/3zjvvRC6Xi2XLluXXvfjii3H++edH165do6qqKq644op46623Cv3ygITEB3BA/uRP/iTeeuutFr/Nd8eOHbFo0aK4/PLL4913343zzz8/Hn/88Xj++efj3HPPjXHjxsXWrVsP+pjbtm2LkSNHxh/+4R/GqlWr4rHHHos33ngj/vRP/7QQLwkoEr9eHTggPXv2jPPOOy/mz58fZ599dkRE/OxnP4uePXvG2WefHR06dIihQ4fm97/tttvi4YcfjkceeSSuu+66gzrmnDlzYtiwYTF9+vT8unvvvTfq6upiw4YNMWjQoEN7UUBRuPIBHLDLL788HnzwwWhqaoqIiPvvvz8uueSS6NChQ+zevTtuuummOOGEE6JHjx7RtWvXeOmllw7pysfq1avjiSeeiK5du+aX4447LiIiXnnllYK8JiA9Vz6AAzZu3LjYt29fLFy4ME455ZR48sknY9asWRHx4RcwLlq0KP7mb/4mBgwYEJ07d44JEybE3r17P/W5yso+/LfPx79e6v3332+xz759+2LcuHHxgx/8YL8/71t7oXSJD+CAde7cOcaPHx/3339/bNq0KQYNGhQnn3xyREQ8+eSTcdVVV8WFF14YERHvvvtubNmy5TOf65hjjomID+/rOOmkkyIiWtx8GhExbNiwePDBB6Nfv37RsaO/ruBw4W0XoFUuv/zyWLhwYdx7773xjW98I79+wIAB8dBDD8WaNWvihRdeiMsuu2y/T8Z8XOfOneO0006LO+64I1588cVYsWJF/PVf/3WLfSZNmhS/+c1v4tJLL41nnnkmXn311Vi8eHF885vfjObm5jZ7jUDbEh9Aq5x11lnRs2fPePnll+Oyyy7Lr//Rj34URx11VIwYMSLGjRsX5557bgwbNuxzn+vee++N999/P4YPHx7f+9734rbbbmuxvba2Np566qlobm6Oc889NwYPHhzf+973orKyMv+2DVB6ctnH33AFAGhj/ukAACQlPgCApMQHAJCU+AAAkhIfAEBS4gMASEp8AABJiQ8AICnxAQAkJT4AgKTEBwCQ1P8BFF7HQ9QJhbkAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "\n",
+    "roll_dice = pd.read_csv('data/roll_the_dice_hundred.csv')\n",
+    "rolling_sorted = roll_dice.value_counts(['value'])\n",
+    "roll_dice1 = pd.DataFrame(rolling_sorted)\n",
+    "roll_dice1 = roll_dice1.sort_values('value',ascending=True)\n",
+    "roll_dice1 = roll_dice1.rename(columns={0:'times'})\n",
+    "roll_dice1 = roll_dice1.reset_index().set_index('value')\n",
+    "roll_dice1.plot(kind='bar')\n",
+    "plt.show\n",
+    "\n",
+    "roll_dice1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "16.666666666666668"
+      ]
+     },
+     "execution_count": 106,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(roll_dice)/6"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nyour comments here\\n\\nThe values are around the mean of times that we expect. In this case, 16.6\\n'"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "your comments here\n",
+    "\n",
+    "The values are around the mean of times that we expect. In this case, 16.6\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": 112,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.74"
+      ]
+     },
+     "execution_count": 112,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "mean(roll_dice['value'])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.- Now, calculate the frequency distribution.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1: 12, 2: 17, 6: 23, 5: 12, 4: 22, 3: 14}"
+      ]
+     },
+     "execution_count": 113,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "freq_d (roll_dice['value'])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 4.- Plot the histogram. What do you see (shape, values...) ? How can you connect the mean value to the histogram? "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAh8AAAGdCAYAAACyzRGfAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAWfUlEQVR4nO3df2xV9f348ded1Su60gWV/ggVGofTCTIjBsEf4C+yhpA5nNO5HzjnohGdjBgn+ofNopSZaNxC1gSyMMjG8I9NdEEFjArbDBuwEQ0zDiPOTm3ImFLgY0qU8/1jsd91MGdr+7q97PFI3onn3NOelydEnp572lsqiqIIAIAkn6j0AADA/xbxAQCkEh8AQCrxAQCkEh8AQCrxAQCkEh8AQCrxAQCkqqn0AP/u0KFD8eabb0ZtbW2USqVKjwMAfARFUcS+ffuiqakpPvGJD7+3Mezi480334zm5uZKjwEADEBnZ2eMGTPmQ48ZdvFRW1sbEf8cfuTIkRWeBgD4KLq7u6O5ubn37/EPM+zi44O3WkaOHCk+AKDKfJRHJjxwCgCkEh8AQCrxAQCkEh8AQCrxAQCkEh8AQCrxAQCkEh8AQCrxAQCkEh8AQCrxAQCkEh8AQCrxAQCkEh8AQKqaSg8AAJUy7q61lR6hIl5bPKui53fnAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIVVPpAQD+1bi71lZ6hIp4bfGsSo8Aadz5AABSiQ8AIJX4AABSiQ8AIJX4AABSiQ8AIJX4AABSiQ8AIJX4AABSiQ8AIJX4AABSiQ8AIJX4AABSiQ8AIJX4AABSiQ8AIJX4AABSiQ8AIJX4AABSiQ8AIJX4AABSiQ8AIJX4AABSiQ8AIJX4AABS9Ss+2tvb47zzzova2toYPXp0XHnllfHyyy/3OaYoimhra4umpqYYMWJEzJgxI3bs2DGoQwMA1atf8bFx48aYN29ebN68OTZs2BDvvfdezJw5Mw4cONB7zAMPPBAPPfRQLFmyJLZs2RINDQ1xxRVXxL59+wZ9eACg+tT05+Cnnnqqz/by5ctj9OjRsW3btrj44oujKIp4+OGH45577ok5c+ZERMSKFSuivr4+Vq1aFTfddNPgTQ4AVKWP9czH3r17IyJi1KhRERGxa9eu6OrqipkzZ/YeUy6XY/r06fH8888f8Xv09PREd3d3nwUAHL0GHB9FUcSCBQviwgsvjAkTJkRERFdXV0RE1NfX9zm2vr6+97V/197eHnV1db2rubl5oCMBAFVgwPFx6623xgsvvBC/+MUvDnutVCr12S6K4rB9H1i4cGHs3bu3d3V2dg50JACgCvTrmY8P3HbbbfH444/Hpk2bYsyYMb37GxoaIuKfd0AaGxt79+/evfuwuyEfKJfLUS6XBzIGAFCF+nXnoyiKuPXWW+NXv/pVPPPMM9HS0tLn9ZaWlmhoaIgNGzb07jt48GBs3Lgxpk2bNjgTAwBVrV93PubNmxerVq2Kxx57LGpra3uf46irq4sRI0ZEqVSK+fPnx6JFi2L8+PExfvz4WLRoUZxwwglx3XXXDcm/AABQXfoVHx0dHRERMWPGjD77ly9fHtdff31ERNx5553x7rvvxi233BJvv/12TJkyJdavXx+1tbWDMjAAUN36FR9FUfzXY0qlUrS1tUVbW9tAZwIAjmI+2wUASCU+AIBU4gMASCU+AIBU4gMASCU+AIBU4gMASCU+AIBU4gMASCU+AIBU4gMASCU+AIBU4gMASCU+AIBU4gMASCU+AIBU4gMASCU+AIBU4gMASCU+AIBU4gMASCU+AIBU4gMASCU+AIBU4gMASCU+AIBU4gMASCU+AIBU4gMASCU+AIBU4gMASCU+AIBU4gMASCU+AIBU4gMASCU+AIBU4gMASCU+AIBU4gMASFVT6QGoDuPuWlvpESritcWzKj0CwFHHnQ8AIJX4AABSiQ8AIJX4AABSiQ8AIJX4AABSiQ8AIJX4AABSiQ8AIJX4AABSiQ8AIJX4AABSiQ8AIJX4AABSiQ8AIJX4AABSiQ8AIJX4AABSiQ8AIJX4AABSiQ8AIJX4AABSiQ8AIJX4AABSiQ8AIJX4AABS9Ts+Nm3aFLNnz46mpqYolUqxZs2aPq9ff/31USqV+qzzzz9/sOYFAKpcv+PjwIEDMWnSpFiyZMl/PObzn/98vPXWW73riSee+FhDAgBHj5r+fkFra2u0trZ+6DHlcjkaGhoGPBQAcPQakmc+nnvuuRg9enScfvrp8e1vfzt27979H4/t6emJ7u7uPgsAOHoNeny0trbGz3/+83jmmWfiwQcfjC1btsSll14aPT09Rzy+vb096urqeldzc/NgjwQADCP9ftvlv7nmmmt6/3nChAkxefLkGDt2bKxduzbmzJlz2PELFy6MBQsW9G53d3cLEAA4ig16fPy7xsbGGDt2bOzcufOIr5fL5SiXy0M9BgAwTAz57/nYs2dPdHZ2RmNj41CfCgCoAv2+87F///545ZVXerd37doV27dvj1GjRsWoUaOira0trrrqqmhsbIzXXnst7r777jj55JPji1/84qAODgBUp37Hx9atW+OSSy7p3f7geY25c+dGR0dHvPjii7Fy5cp45513orGxMS655JJ45JFHora2dvCmBgCqVr/jY8aMGVEUxX98fd26dR9rIADg6OazXQCAVOIDAEglPgCAVOIDAEglPgCAVOIDAEglPgCAVOIDAEg15B8sB//Lxt21ttIjVMRri2dVegRgGHPnAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFTiAwBIJT4AgFQ1lR4g27i71lZ6hIp4bfGsSo8ADCH/baOauPMBAKQSHwBAKvEBAKQSHwBAKvEBAKQSHwBAKvEBAKQSHwBAKvEBAKQSHwBAKvEBAKQSHwBAKvEBAKQSHwBAKvEBAKQSHwBAKvEBAKQSHwBAKvEBAKQSHwBAKvEBAKQSHwBAKvEBAKQSHwBAKvEBAKTqd3xs2rQpZs+eHU1NTVEqlWLNmjV9Xi+KItra2qKpqSlGjBgRM2bMiB07dgzWvABAlet3fBw4cCAmTZoUS5YsOeLrDzzwQDz00EOxZMmS2LJlSzQ0NMQVV1wR+/bt+9jDAgDVr6a/X9Da2hqtra1HfK0oinj44YfjnnvuiTlz5kRExIoVK6K+vj5WrVoVN91008ebFgCoeoP6zMeuXbuiq6srZs6c2buvXC7H9OnT4/nnnz/i1/T09ER3d3efBQAcvQY1Prq6uiIior6+vs/++vr63tf+XXt7e9TV1fWu5ubmwRwJABhmhuSnXUqlUp/toigO2/eBhQsXxt69e3tXZ2fnUIwEAAwT/X7m48M0NDRExD/vgDQ2Nvbu371792F3Qz5QLpejXC4P5hgAwDA2qHc+WlpaoqGhITZs2NC77+DBg7Fx48aYNm3aYJ4KAKhS/b7zsX///njllVd6t3ft2hXbt2+PUaNGxamnnhrz58+PRYsWxfjx42P8+PGxaNGiOOGEE+K6664b1MEBgOrU7/jYunVrXHLJJb3bCxYsiIiIuXPnxk9/+tO488474913341bbrkl3n777ZgyZUqsX78+amtrB29qAKBq9Ts+ZsyYEUVR/MfXS6VStLW1RVtb28eZCwA4SvlsFwAglfgAAFKJDwAglfgAAFKJDwAglfgAAFKJDwAglfgAAFKJDwAglfgAAFKJDwAglfgAAFKJDwAglfgAAFKJDwAglfgAAFKJDwAglfgAAFKJDwAglfgAAFKJDwAglfgAAFKJDwAglfgAAFKJDwAglfgAAFKJDwAglfgAAFKJDwAglfgAAFKJDwAglfgAAFKJDwAglfgAAFKJDwAglfgAAFKJDwAglfgAAFKJDwAglfgAAFKJDwAglfgAAFKJDwAglfgAAFKJDwAglfgAAFKJDwAglfgAAFKJDwAglfgAAFKJDwAglfgAAFKJDwAglfgAAFKJDwAglfgAAFKJDwAglfgAAFKJDwAglfgAAFKJDwAglfgAAFKJDwAglfgAAFKJDwAglfgAAFINeny0tbVFqVTqsxoaGgb7NABAlaoZim961llnxdNPP927fcwxxwzFaQCAKjQk8VFTU+NuBwBwREPyzMfOnTujqakpWlpa4tprr41XX331Px7b09MT3d3dfRYAcPQa9PiYMmVKrFy5MtatWxfLli2Lrq6umDZtWuzZs+eIx7e3t0ddXV3vam5uHuyRAIBhZNDjo7W1Na666qqYOHFiXH755bF27dqIiFixYsURj1+4cGHs3bu3d3V2dg72SADAMDIkz3z8qxNPPDEmTpwYO3fuPOLr5XI5yuXyUI8BAAwTQ/57Pnp6euKll16KxsbGoT4VAFAFBj0+7rjjjti4cWPs2rUrfv/738eXvvSl6O7ujrlz5w72qQCAKjTob7v87W9/i6985Svx97//PU455ZQ4//zzY/PmzTF27NjBPhUAUIUGPT5Wr1492N8SADiK+GwXACCV+AAAUokPACCV+AAAUokPACCV+AAAUokPACCV+AAAUokPACCV+AAAUokPACCV+AAAUokPACCV+AAAUokPACCV+AAAUokPACCV+AAAUokPACCV+AAAUokPACCV+AAAUokPACCV+AAAUokPACCV+AAAUokPACCV+AAAUokPACCV+AAAUokPACCV+AAAUokPACCV+AAAUokPACCV+AAAUokPACCV+AAAUokPACCV+AAAUokPACCV+AAAUokPACCV+AAAUokPACCV+AAAUokPACCV+AAAUokPACCV+AAAUokPACCV+AAAUokPACCV+AAAUokPACCV+AAAUokPACCV+AAAUokPACCV+AAAUokPACCV+AAAUokPACCV+AAAUokPACDVkMXHj3/842hpaYnjjz8+zj333PjNb34zVKcCAKrIkMTHI488EvPnz4977rkn/vSnP8VFF10Ura2t8frrrw/F6QCAKjIk8fHQQw/Ft771rbjxxhvjzDPPjIcffjiam5ujo6NjKE4HAFSRmsH+hgcPHoxt27bFXXfd1Wf/zJkz4/nnnz/s+J6enujp6end3rt3b0REdHd3D/ZoERFxqOf/huT7Dncf93q6bgPjuvWfazYwrtvAuG6D/z2LovjvBxeD7I033igiovjd737XZ//9999fnH766Ycdf++99xYRYVmWZVnWUbA6Ozv/aysM+p2PD5RKpT7bRVEcti8iYuHChbFgwYLe7UOHDsU//vGPOOmkk454/Ifp7u6O5ubm6OzsjJEjRw5s8P9BrtvAuG7955oNjOs2MK7bwAz0uhVFEfv27Yumpqb/euygx8fJJ58cxxxzTHR1dfXZv3v37qivrz/s+HK5HOVyuc++T33qUx9rhpEjR/qDNgCu28C4bv3nmg2M6zYwrtvADOS61dXVfaTjBv2B0+OOOy7OPffc2LBhQ5/9GzZsiGnTpg326QCAKjMkb7ssWLAgvv71r8fkyZNj6tSpsXTp0nj99dfj5ptvHorTAQBVZEji45prrok9e/bE97///XjrrbdiwoQJ8cQTT8TYsWOH4nS9yuVy3HvvvYe9jcOHc90GxnXrP9dsYFy3gXHdBibjupWK4qP8TAwAwODw2S4AQCrxAQCkEh8AQCrxAQCkOiriY9OmTTF79uxoamqKUqkUa9asqfRIVaG9vT3OO++8qK2tjdGjR8eVV14ZL7/8cqXHGtY6Ojri7LPP7v3lO1OnTo0nn3yy0mNVnfb29iiVSjF//vxKjzKstbW1RalU6rMaGhoqPVZVeOONN+JrX/tanHTSSXHCCSfE5z73udi2bVulxxq2xo0bd9iftVKpFPPmzRuS8x0V8XHgwIGYNGlSLFmypNKjVJWNGzfGvHnzYvPmzbFhw4Z47733YubMmXHgwIFKjzZsjRkzJhYvXhxbt26NrVu3xqWXXhpf+MIXYseOHZUerWps2bIlli5dGmeffXalR6kKZ511Vrz11lu968UXX6z0SMPe22+/HRdccEEce+yx8eSTT8af//znePDBBz/2b88+mm3ZsqXPn7MPflHo1VdfPSTnG7LPdsnU2toara2tlR6j6jz11FN9tpcvXx6jR4+Obdu2xcUXX1yhqYa32bNn99m+//77o6OjIzZv3hxnnXVWhaaqHvv374+vfvWrsWzZsrjvvvsqPU5VqKmpcbejn37wgx9Ec3NzLF++vHffuHHjKjdQFTjllFP6bC9evDhOO+20mD59+pCc76i488Hg2Lt3b0REjBo1qsKTVIf3338/Vq9eHQcOHIipU6dWepyqMG/evJg1a1ZcfvnllR6lauzcuTOampqipaUlrr322nj11VcrPdKw9/jjj8fkyZPj6quvjtGjR8c555wTy5Ytq/RYVePgwYPxs5/9LG644YZ+f8DrRyU+iIh/fhrhggUL4sILL4wJEyZUepxh7cUXX4xPfvKTUS6X4+abb45HH300PvvZz1Z6rGFv9erV8cc//jHa29srPUrVmDJlSqxcuTLWrVsXy5Yti66urpg2bVrs2bOn0qMNa6+++mp0dHTE+PHjY926dXHzzTfHd77znVi5cmWlR6sKa9asiXfeeSeuv/76ITvHUfG2Cx/frbfeGi+88EL89re/rfQow95nPvOZ2L59e7zzzjvxy1/+MubOnRsbN24UIB+is7Mzbr/99li/fn0cf/zxlR6navzr28kTJ06MqVOnxmmnnRYrVqyIBQsWVHCy4e3QoUMxefLkWLRoUUREnHPOObFjx47o6OiIb3zjGxWebvj7yU9+Eq2trdHU1DRk53Dng7jtttvi8ccfj2effTbGjBlT6XGGveOOOy4+/elPx+TJk6O9vT0mTZoUP/zhDys91rC2bdu22L17d5x77rlRU1MTNTU1sXHjxvjRj34UNTU18f7771d6xKpw4oknxsSJE2Pnzp2VHmVYa2xsPOx/Bs4888x4/fXXKzRR9fjrX/8aTz/9dNx4441Deh53Pv6HFUURt912Wzz66KPx3HPPRUtLS6VHqkpFUURPT0+lxxjWLrvsssN+SuOb3/xmnHHGGfG9730vjjnmmApNVl16enripZdeiosuuqjSowxrF1xwwWG/NuAvf/nLkH+46dHggx88mDVr1pCe56iIj/3798crr7zSu71r167Yvn17jBo1Kk499dQKTja8zZs3L1atWhWPPfZY1NbWRldXV0RE1NXVxYgRIyo83fB09913R2trazQ3N8e+ffti9erV8dxzzx32k0P0VVtbe9izRCeeeGKcdNJJnjH6EHfccUfMnj07Tj311Ni9e3fcd9990d3dHXPnzq30aMPad7/73Zg2bVosWrQovvzlL8cf/vCHWLp0aSxdurTSow1rhw4diuXLl8fcuXOjpmaI86A4Cjz77LNFRBy25s6dW+nRhrUjXbOIKJYvX17p0YatG264oRg7dmxx3HHHFaecckpx2WWXFevXr6/0WFVp+vTpxe23317pMYa1a665pmhsbCyOPfbYoqmpqZgzZ06xY8eOSo9VFX79618XEyZMKMrlcnHGGWcUS5curfRIw966deuKiChefvnlIT9XqSiKYmjzBgDg//PAKQCQSnwAAKnEBwCQSnwAAKnEBwCQSnwAAKnEBwCQSnwAAKnEBwCQSnwAAKnEBwCQSnwAAKn+H81hZtP1eHkDAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "\n",
+    "plt.hist(roll_dice['value'],bins = 6, range=(1,7), rwidth = 0.8)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nyour comments here\\n\\nWe are expecting a mean of 3.5\\nAnd our mean is 3.74, meaning that we have more values on the right side of the graph.\\n'"
+      ]
+     },
+     "execution_count": 141,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "your comments here\n",
+    "\n",
+    "We are expecting a mean of 3.5\n",
+    "And our mean is 3.74, meaning that we have more values on the right side of the graph.\n",
+    "And indeed, we can see in the histogram that we have many values on the 4 and the 6, which skew the data towards the right.\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": 145,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "roll_dice_thousand = pd.read_csv('data/roll_the_dice_thousand.csv')\n",
+    "roll_dice_thousand\n",
+    "\n",
+    "\n",
+    "plt.hist(roll_dice_thousand['value'],bins = 6, range=(1,7), rwidth = 0.8)\n",
+    "\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nyour comments here\\n'"
+      ]
+     },
+     "execution_count": 142,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "your comments here\n",
+    "Now we have more data and the values are more close to each other, so there is less deviation, and it is less skewed.\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": 154,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "age = pd.read_csv('data/ages_population.csv')\n",
+    "age.hist()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nThe mean would be around 38, and the standard deviation to be 15\\n'"
+      ]
+     },
+     "execution_count": 155,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "The mean could be around 38, and the standard deviation could be around 15\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "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": 156,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "36.56\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "12.816499625976762"
+      ]
+     },
+     "execution_count": 156,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "\n",
+    "print(mean(age['observation']))\n",
+    "age['observation'].std()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 157,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nyour comments here\\n'"
+      ]
+     },
+     "execution_count": 157,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "your comments here\n",
+    "\n",
+    "They were approximate, actually the standard deviation was a little bit below what we expected.So most data is compiled in a way smaller range than what we thought<.\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": 159,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[<AxesSubplot:title={'center':'observation'}>]], dtype=object)"
+      ]
+     },
+     "execution_count": 159,
+     "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": [
+    "# your code here\n",
+    "age2 = pd.read_csv('data/ages_population2.csv')\n",
+    "age2.hist()"
+   ]
+  },
+  {
+   "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": 160,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nyour comments here\\nThe mean could be around 27\\nThe data is less disperse, so the standard deviation probably has a fewer value.\\nAnd the range is shorted\\n'"
+      ]
+     },
+     "execution_count": 160,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "your comments here\n",
+    "\n",
+    "The data is less disperse, so the standard deviation probably has a fewer value.\n",
+    "The mean could be around 27\n",
+    "And the range is shorter\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": 163,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "27.155\n",
+      "2.969813932689186\n"
+     ]
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "\n",
+    "print(age2['observation'].mean())\n",
+    "print(age2['observation'].std())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "your comments here\n",
+    "Indeed, the mean was around 27 and the standard deviation is way less than the first csv.\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": 165,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[<AxesSubplot:title={'center':'observation'}>]], dtype=object)"
+      ]
+     },
+     "execution_count": 165,
+     "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": [
+    "# your code here\n",
+    "age3 = pd.read_csv('data/ages_population3.csv')\n",
+    "age3.hist()"
+   ]
+  },
+  {
+   "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": 166,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "41.989\n",
+      "16.144705959865934\n"
+     ]
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "print(age3['observation'].mean())\n",
+    "print(age3['observation'].std())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nyour comments here\\nThe mean is a little bit higher than 40, because we have a skewed graph.\\n'"
+      ]
+     },
+     "execution_count": 172,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "your comments here\n",
+    "The mean is a little bit higher than 40, because we have a skewed graph.\n",
+    "And the data is a little more disperse, so the standard deviation is a little bit higher.\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": 175,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "observation    1.0\n",
+      "Name: 0.0, dtype: float64\n",
+      "observation    30.0\n",
+      "Name: 0.25, dtype: float64\n",
+      "observation    40.0\n",
+      "Name: 0.5, dtype: float64\n",
+      "observation    53.0\n",
+      "Name: 0.75, dtype: float64\n",
+      "observation    77.0\n",
+      "Name: 1.0, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "\n",
+    "print(age3.quantile(0))\n",
+    "print(age3.quantile(0.25))\n",
+    "print(age3.quantile(0.5))\n",
+    "print(age3.quantile(0.75))\n",
+    "print(age3.quantile(1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 176,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nyour comments here\\n\\nSo our range is between 1 and 77.\\nThe mean was 41 and the median is 40, so the data is very balanced.\\n'"
+      ]
+     },
+     "execution_count": 176,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "your comments here\n",
+    "\n",
+    "So our range is between 1 and 77.\n",
+    "The mean was 41 and the median is 40, so the data is very balanced.\n",
+    "The the percentil 75 is a little bit more distant from the median than the 25 percentil to the median.\n",
+    "It means that we have more disperse data on the right side of the graph.\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": 171,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "observation    36.0\n",
+      "Name: 0.4, dtype: float64\n",
+      "observation    45.0\n",
+      "Name: 0.6, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "\n",
+    "print(age3.quantile(0.4))\n",
+    "print(age3.quantile(0.6))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "your comments here\n",
+    "As we saw earlier, on the right side we have a more disperse data\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
+}