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+++ b/your-code/main.ipynb
@@ -1,522 +1,2662 @@
-{
- "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": {
+        "id": "mp4RamiSAZnu"
+      },
+      "source": [
+        "# Understanding Descriptive Statistics\n",
+        "\n",
+        "Import the necessary libraries here:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 86,
+      "metadata": {
+        "id": "N3Q9ty_rAZnv"
+      },
+      "outputs": [],
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "GFqFGkOXAZnw"
+      },
+      "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",
+      "source": [
+        "import random\n",
+        "\n",
+        "dice_roll = np.random.randint(1,6,size = 10)\n",
+        "dice_roll_df = pd.DataFrame({\"Rolls\":dice_roll})\n",
+        "display(dice_roll_df)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 363
+        },
+        "id": "K43AMwyBFeB9",
+        "outputId": "eae4390b-24f2-43fc-86ea-6d34a5889deb"
+      },
+      "execution_count": 87,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "   Rolls\n",
+              "0      3\n",
+              "1      3\n",
+              "2      4\n",
+              "3      4\n",
+              "4      2\n",
+              "5      4\n",
+              "6      1\n",
+              "7      2\n",
+              "8      5\n",
+              "9      5"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-598ddc1d-0b4d-4552-91ef-70e60cdafd10\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Rolls</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-598ddc1d-0b4d-4552-91ef-70e60cdafd10')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-598ddc1d-0b4d-4552-91ef-70e60cdafd10 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-598ddc1d-0b4d-4552-91ef-70e60cdafd10');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-070c63a9-6819-4d5e-934e-f0ee20551c7c\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-070c63a9-6819-4d5e-934e-f0ee20551c7c')\"\n",
+              "            title=\"Suggest charts.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-070c63a9-6819-4d5e-934e-f0ee20551c7c button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1xQ-4kRfAZnw"
+      },
+      "source": [
+        "#### 2.- Plot the results sorted by value."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 88,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "w3ui1jB6AZnx",
+        "outputId": "ea76f1e2-3cce-455e-a9f9-7173ec6d6f56"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "1    1\n",
+              "2    2\n",
+              "3    2\n",
+              "4    3\n",
+              "5    2\n",
+              "Name: Rolls, dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 88
+        }
+      ],
+      "source": [
+        "sorted_dice_rolls_df = dice_roll_df['Rolls'].value_counts().sort_index()\n",
+        "sorted_dice_rolls_df"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "plt.plot(dice_roll_df,'b*')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 430
+        },
+        "id": "YHwAbhZBEGr5",
+        "outputId": "a45433fc-bc9b-41e9-f8f4-01d5c23e1901"
+      },
+      "execution_count": 89,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4l29xAMtAZnx"
+      },
+      "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": 90,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 430
+        },
+        "id": "jEsCYzATAZnx",
+        "outputId": "6463faff-e13c-4b98-d853-efb2e11022ad"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "plt.plot(sorted_dice_rolls_df,'b*')\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 91,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 53
+        },
+        "id": "rCAmVllYAZnx",
+        "outputId": "0c31661b-82cb-42e3-ab7c-ad88adb1588a"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "'\\nThe first plot gives the result of each roll. (Roll 1 was 1, Roll 2 was 3, etc...)\\nThe second plot gives the frequency of each number after the 10 rolls. (The dice gave a result of 1 twice, it gave a result of 2 only once, etc... ) \\n'"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            }
+          },
+          "metadata": {},
+          "execution_count": 91
+        }
+      ],
+      "source": [
+        "\"\"\"\n",
+        "The first plot gives the result of each roll. (Roll 1 was 1, Roll 2 was 3, etc...)\n",
+        "The second plot gives the frequency of each number after the 10 rolls. (The dice gave a result of 1 twice, it gave a result of 2 only once, etc... )\n",
+        "\"\"\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "iSTMkIPbAZnx"
+      },
+      "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": 92,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "id": "TZ_NG11MAZnx",
+        "outputId": "230ca515-61cd-4d86-c9cd-773dbffdcd8b"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "3.3"
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "def calculate_mean(df):\n",
+        "  mean = (np.sum(df))/len(df)\n",
+        "  return mean\n",
+        "\n",
+        "result_mean = calculate_mean(dice_roll)\n",
+        "display(result_mean)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6Hpy1FBCAZnx"
+      },
+      "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": 93,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "id": "Rk55Jl1DAZny",
+        "outputId": "8b6d5944-f3f8-49d2-a146-3e78003c06e9"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "{3: 2, 4: 3, 2: 2, 1: 1, 5: 2}"
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "def calculate_fd(df):\n",
+        "  frequency_distribution = {}\n",
+        "  for value in df:\n",
+        "    if value in frequency_distribution:\n",
+        "        frequency_distribution[value] += 1\n",
+        "    else:\n",
+        "        frequency_distribution[value] = 1\n",
+        "  return frequency_distribution\n",
+        "\n",
+        "result_fd = calculate_fd(dice_roll)\n",
+        "display(result_fd)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def calculate_mean2(freq_dict):\n",
+        "    sum_of_products = sum(k * v for k, v in freq_dict.items())\n",
+        "    mean2 = sum_of_products / sum(freq_dict.values())\n",
+        "    return mean2\n",
+        "\n",
+        "result_mean2 = calculate_mean2(result_fd)\n",
+        "display(result_mean2)\n",
+        "\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "id": "ZLnrCfYDRJ9m",
+        "outputId": "5dbcf803-cda4-4984-f33c-28ff38a68208"
+      },
+      "execution_count": 94,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "3.3"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "UPg9ErK_AZny"
+      },
+      "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": 95,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "id": "Lwrl72eEAZny",
+        "outputId": "1fda3a84-84c2-44f9-bdcd-21c4f77f3cae"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "3.5"
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "def calculate_median(data):\n",
+        "    data.sort()\n",
+        "    num_observations = len(data)\n",
+        "\n",
+        "    if num_observations % 2 == 0:\n",
+        "        mid_idx = num_observations // 2\n",
+        "        median = (data[mid_idx - 1] + data[mid_idx]) / 2\n",
+        "    else:\n",
+        "        mid_idx = num_observations // 2\n",
+        "        median = data[mid_idx]\n",
+        "    return median\n",
+        "\n",
+        "result_median = calculate_median(dice_roll)\n",
+        "display(result_median)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "eufZOnCbAZny"
+      },
+      "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": 96,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "id": "tVZ-VH19AZny",
+        "outputId": "4fc5d8c8-80ff-43d0-8d7e-ad9565ed77eb"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "(2, 3.5, 4.5)"
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "def calculate_quartiles(data):\n",
+        "    data.sort()\n",
+        "\n",
+        "    num_observations = len(data)\n",
+        "    q2 = calculate_median(data)\n",
+        "\n",
+        "    lower_half = data[:num_observations // 2] if num_observations % 2 == 0 else data[:num_observations // 2]\n",
+        "    q1 = calculate_median(lower_half)\n",
+        "\n",
+        "    upper_half = data[num_observations // 2 + 1:] if num_observations % 2 == 0 else data[num_observations // 2 + 1:]\n",
+        "    q3 = calculate_median(upper_half)\n",
+        "\n",
+        "    return q1, q2, q3\n",
+        "\n",
+        "result_quartiles = calculate_quartiles(dice_roll)\n",
+        "display(result_quartiles)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "NeCXgT16AZny"
+      },
+      "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": 97,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 424
+        },
+        "id": "Lw4nsJZ3AZny",
+        "outputId": "7ff74ccf-4597-4c73-9880-43da5258067f"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "    Unnamed: 0  roll  value\n",
+              "0            0     0      1\n",
+              "1            1     1      2\n",
+              "2            2     2      6\n",
+              "3            3     3      1\n",
+              "4            4     4      6\n",
+              "..         ...   ...    ...\n",
+              "95          95    95      4\n",
+              "96          96    96      6\n",
+              "97          97    97      1\n",
+              "98          98    98      3\n",
+              "99          99    99      6\n",
+              "\n",
+              "[100 rows x 3 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-8d13966e-3480-4f7a-9d03-7d917b4efea8\" class=\"colab-df-container\">\n",
+              "    <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>Unnamed: 0</th>\n",
+              "      <th>roll</th>\n",
+              "      <th>value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>3</td>\n",
+              "      <td>3</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>4</td>\n",
+              "      <td>4</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>95</th>\n",
+              "      <td>95</td>\n",
+              "      <td>95</td>\n",
+              "      <td>4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>96</th>\n",
+              "      <td>96</td>\n",
+              "      <td>96</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>97</th>\n",
+              "      <td>97</td>\n",
+              "      <td>97</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>98</th>\n",
+              "      <td>98</td>\n",
+              "      <td>98</td>\n",
+              "      <td>3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>99</th>\n",
+              "      <td>99</td>\n",
+              "      <td>99</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>100 rows × 3 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8d13966e-3480-4f7a-9d03-7d917b4efea8')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-8d13966e-3480-4f7a-9d03-7d917b4efea8 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-8d13966e-3480-4f7a-9d03-7d917b4efea8');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-044cafd7-80da-42c5-bd43-b96178ee9b79\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-044cafd7-80da-42c5-bd43-b96178ee9b79')\"\n",
+              "            title=\"Suggest charts.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-044cafd7-80da-42c5-bd43-b96178ee9b79 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 97
+        }
+      ],
+      "source": [
+        "hundred_rolls = pd.read_csv(\"roll_the_dice_hundred.csv\")\n",
+        "hundred_rolls"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "hd_rolls = hundred_rolls.sort_values(by=['value'])"
+      ],
+      "metadata": {
+        "id": "2HKK8zpsVEka"
+      },
+      "execution_count": 98,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.bar(hd_rolls[\"roll\"],hd_rolls[\"value\"])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 447
+        },
+        "id": "djvsXylDVsME",
+        "outputId": "764ceaff-d21a-49b3-f868-72f1f2709eba"
+      },
+      "execution_count": 99,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<BarContainer object of 100 artists>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 99
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 100,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 36
+        },
+        "id": "sp6vrB1gAZny",
+        "outputId": "5437ceb4-f1e4-4da9-c155-da381ad5ad85"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "\"\\nThe rolls aren't following any logic, there values are very random. There are two many values for the chart to be readable. \\n\""
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            }
+          },
+          "metadata": {},
+          "execution_count": 100
+        }
+      ],
+      "source": [
+        "\"\"\"\n",
+        "The rolls aren't following any logic, there values are very random. There are two many values for the chart to be readable.\n",
+        "\"\"\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "GtaEFdFzAZny"
+      },
+      "source": [
+        "#### 2.- Using the functions you defined in *challenge 2*, calculate the mean value of the hundred dice rolls."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 101,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "id": "rPX1TftXAZny",
+        "outputId": "7dd52ddd-9d23-45ba-f293-c3e60da1bb27"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "3.74"
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "result_mean = calculate_mean(hd_rolls[\"value\"])\n",
+        "display(result_mean)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "uSfMuE9xAZny"
+      },
+      "source": [
+        "#### 3.- Now, calculate the frequency distribution.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 102,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "id": "zR0u7EYMAZny",
+        "outputId": "1505ae64-15ea-43fd-d344-b3d18538b5df"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "{1: 12, 2: 17, 3: 14, 4: 22, 5: 12, 6: 23}"
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "result_fd = calculate_fd(hd_rolls[\"value\"])\n",
+        "display(result_fd)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Q8WvZQezAZnz"
+      },
+      "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": 103,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "id": "JWPvyLspAZnz",
+        "outputId": "130a44e9-5137-4a39-81ec-8054a81e2745"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "plt.hist(hd_rolls[\"value\"])\n",
+        "plt.ylabel(\"Repetition\")\n",
+        "plt.xlabel(\"Dice number\")\n",
+        "plt.title('Frequency of a hundred dice rolls')\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 104,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 36
+        },
+        "id": "zYkzBJS9AZnz",
+        "outputId": "fc3e6925-dbbd-4563-9354-0f9f96b08035"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "'\\nValues are between 0 and 25 more or less. \\nA little tendency where the highest the number the more often it appears but nothing very clear. \\n'"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            }
+          },
+          "metadata": {},
+          "execution_count": 104
+        }
+      ],
+      "source": [
+        "\"\"\"\n",
+        "Values are between 0 and 25 more or less.\n",
+        "A little tendency where the highest the number the more often it appears but nothing very clear.\n",
+        "The standard deviation is quite large.\n",
+        "\"\"\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lDPHYzYlAZnz"
+      },
+      "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": 125,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 424
+        },
+        "id": "Hrgdhoi7AZnz",
+        "outputId": "8f6e0c87-d155-43c2-92cf-1cdd32e99c2f"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "     Unnamed: 0  roll  value\n",
+              "0             0     0      5\n",
+              "1             1     1      6\n",
+              "2             2     2      1\n",
+              "3             3     3      6\n",
+              "4             4     4      5\n",
+              "..          ...   ...    ...\n",
+              "995         995   995      1\n",
+              "996         996   996      4\n",
+              "997         997   997      4\n",
+              "998         998   998      3\n",
+              "999         999   999      6\n",
+              "\n",
+              "[1000 rows x 3 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-3622346b-0702-472b-821d-7f0904800a8e\" class=\"colab-df-container\">\n",
+              "    <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>Unnamed: 0</th>\n",
+              "      <th>roll</th>\n",
+              "      <th>value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>3</td>\n",
+              "      <td>3</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>4</td>\n",
+              "      <td>4</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>995</th>\n",
+              "      <td>995</td>\n",
+              "      <td>995</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>996</th>\n",
+              "      <td>996</td>\n",
+              "      <td>996</td>\n",
+              "      <td>4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>997</th>\n",
+              "      <td>997</td>\n",
+              "      <td>997</td>\n",
+              "      <td>4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>998</th>\n",
+              "      <td>998</td>\n",
+              "      <td>998</td>\n",
+              "      <td>3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>999</th>\n",
+              "      <td>999</td>\n",
+              "      <td>999</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>1000 rows × 3 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3622346b-0702-472b-821d-7f0904800a8e')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-3622346b-0702-472b-821d-7f0904800a8e button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-3622346b-0702-472b-821d-7f0904800a8e');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-4ece1803-2251-4e6a-86e6-c50b33e9f0c8\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4ece1803-2251-4e6a-86e6-c50b33e9f0c8')\"\n",
+              "            title=\"Suggest charts.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-4ece1803-2251-4e6a-86e6-c50b33e9f0c8 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 125
+        }
+      ],
+      "source": [
+        "thsd_rolls = pd.read_csv(\"roll_the_dice_thousand.csv\")\n",
+        "thsd_rolls"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "result_fd = calculate_fd(thsd_rolls[\"value\"])\n",
+        "display(result_fd)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "id": "cXnipAP5affs",
+        "outputId": "12973558-967c-48f3-db84-bd83287c087e"
+      },
+      "execution_count": 126,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "{5: 149, 6: 166, 1: 175, 2: 167, 4: 168, 3: 175}"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.hist(thsd_rolls[\"value\"])\n",
+        "plt.ylabel(\"Repetition\")\n",
+        "plt.xlabel(\"Dice number\")\n",
+        "plt.title('Frequency of a thousand dice rolls')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "id": "PgTOIrBfaqro",
+        "outputId": "0bfbbe7d-d2fb-4afe-cf66-f5cba5766196"
+      },
+      "execution_count": 130,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 106,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 36
+        },
+        "id": "nyOUcUVkAZnz",
+        "outputId": "f55234f4-c9e5-480d-b7bc-57e5000e05fb"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "'\\nyour comments here\\n'"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            }
+          },
+          "metadata": {},
+          "execution_count": 106
+        }
+      ],
+      "source": [
+        "\"\"\"\n",
+        "The frequency are a lot more similar than in the previous example.\n",
+        "The amount of repetition kind of equalize it.\n",
+        "\"\"\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lUowSTu9AZnz"
+      },
+      "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": 133,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 424
+        },
+        "id": "2TWHihlkAZnz",
+        "outputId": "5de35d08-9ba5-4954-889c-9ccf376c65d1"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "     observation\n",
+              "0           68.0\n",
+              "1           12.0\n",
+              "2           45.0\n",
+              "3           38.0\n",
+              "4           49.0\n",
+              "..           ...\n",
+              "995         27.0\n",
+              "996         47.0\n",
+              "997         53.0\n",
+              "998         33.0\n",
+              "999         31.0\n",
+              "\n",
+              "[1000 rows x 1 columns]"
+            ],
+            "text/html": [
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+              "    <tr>\n",
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+              "      <td>68.0</td>\n",
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+              "      <td>49.0</td>\n",
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+              "      <td>...</td>\n",
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+              "      <th>996</th>\n",
+              "      <td>47.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>997</th>\n",
+              "      <td>53.0</td>\n",
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+              "      <td>33.0</td>\n",
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+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-731db5a8-0a13-4d71-bd6b-49feb352fde1');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
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+              "    </g>\n",
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+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 133
+        }
+      ],
+      "source": [
+        "age_pop = pd.read_csv(\"ages_population.csv\")\n",
+        "age_pop"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "result_fd = calculate_fd(age_pop[\"observation\"])\n",
+        "\n",
+        "plt.hist(age_pop[\"observation\"])\n",
+        "plt.ylabel(\"age\")\n",
+        "plt.xlabel(\"population\")\n",
+        "plt.title('Frequency of each age in the population')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "id": "Kuonl9Libzlg",
+        "outputId": "393fd5ef-78b6-47e5-c6f8-1cad55ccba6f"
+      },
+      "execution_count": 137,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\"\"\"\n",
+        "By looking at the plot, I think that the mean will be between 30 and 40 years old.\n",
+        "I'm also guessing that the standard deviation will be around 10 years.\n",
+        "\"\"\""
+      ],
+      "metadata": {
+        "id": "MMb_Rw1CcoOA"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "le9MEHg-AZnz"
+      },
+      "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": 139,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "usmggQS0AZnz",
+        "outputId": "5c106f06-ac7a-43ef-d523-35597246c0b2"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "observation    36.56\n",
+            "dtype: float64\n",
+            "observation    12.81009\n",
+            "dtype: float64\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(age_pop.mean())\n",
+        "print(np.std(age_pop))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 109,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 36
+        },
+        "id": "6Oe16RVVAZnz",
+        "outputId": "811f2877-2e22-4dbd-c070-d9d9d209eec1"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "'\\nyour comments here\\n'"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            }
+          },
+          "metadata": {},
+          "execution_count": 109
+        }
+      ],
+      "source": [
+        "\"\"\"\n",
+        "Indeed, the mean is 36.56 and the standard deviation is 12.81.\n",
+        "\"\"\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zns1SZ6TAZnz"
+      },
+      "source": [
+        "#### 3.- Now read the file `ages_population2.csv` . Calculate the frequency distribution and plot it."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 140,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "id": "mWimSwx-AZnz",
+        "outputId": "f3505110-60f4-4384-a61b-2f9f9cda35e9"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "age_pop2 = pd.read_csv(\"ages_population2.csv\")\n",
+        "result_fd = calculate_fd(age_pop2[\"observation\"])\n",
+        "\n",
+        "plt.hist(age_pop2[\"observation\"])\n",
+        "plt.ylabel(\"age\")\n",
+        "plt.xlabel(\"population\")\n",
+        "plt.title('Frequency of each age in the population')\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xfa9popdAZnz"
+      },
+      "source": [
+        "####  4.- What do you see? Is there any difference with the frequency distribution in step 1?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 141,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 53
+        },
+        "id": "vAZzBwE2AZn0",
+        "outputId": "212106ee-6c9e-4c1a-ddb5-dd4115e7b2fd"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "'\\nThe population in this survey were a lot younger as all values are between 20 and 35 years old. \\nThe mean seems to be around 27.5 years old. The standard deviation will also be a lower than in the previous example as values are closer to each other. \\n'"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            }
+          },
+          "metadata": {},
+          "execution_count": 141
+        }
+      ],
+      "source": [
+        "\"\"\"\n",
+        "The population in this survey were a lot younger as all values are between 20 and 35 years old.\n",
+        "The mean seems to be around 27.5 years old. The standard deviation will also be a lower than in the previous example as values are closer to each other.\n",
+        "\"\"\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tYE7gaP4AZn5"
+      },
+      "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": 142,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "nFGqxrtrAZn5",
+        "outputId": "680b52c9-aaa0-4f82-b0b9-fe08017a1097"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "observation    27.155\n",
+            "dtype: float64\n",
+            "observation    2.968329\n",
+            "dtype: float64\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(age_pop2.mean())\n",
+        "print(np.std(age_pop2))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 113,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 36
+        },
+        "id": "cJ0yjyTdAZn6",
+        "outputId": "60d96e5b-cb5a-4242-ffe7-a83a916f2877"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "'\\nyour comments here\\n'"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            }
+          },
+          "metadata": {},
+          "execution_count": 113
+        }
+      ],
+      "source": [
+        "\"\"\"\n",
+        "As expected, the mean is a bit more than 27 years old.\n",
+        "The standard deviation is now 2.97.\n",
+        "\"\"\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Mg0fxBIxAZn6"
+      },
+      "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": 143,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "id": "MZdmS0R8AZn6",
+        "outputId": "88374ee1-662c-4105-c8cc-0e8c0503d1ca"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "age_pop3 = pd.read_csv(\"ages_population3.csv\")\n",
+        "result_fd = calculate_fd(age_pop3[\"observation\"])\n",
+        "\n",
+        "plt.hist(age_pop3[\"observation\"])\n",
+        "plt.ylabel(\"age\")\n",
+        "plt.xlabel(\"population\")\n",
+        "plt.title('Frequency of each age in the population')\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "T-jve2nVAZn6"
+      },
+      "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": 144,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "b0YXcryFAZn6",
+        "outputId": "e986dc48-62c3-435d-8fe0-9e7f343d4e9a"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "observation    41.989\n",
+            "dtype: float64\n",
+            "observation    16.136632\n",
+            "dtype: float64\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(age_pop3.mean())\n",
+        "print(np.std(age_pop3))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 145,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 71
+        },
+        "id": "EMYzD26UAZn6",
+        "outputId": "b68fbc4f-08e9-4f1c-db29-74005497331c"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "\"\\nAs in the step1, we have a very diverse population with people between 0 and 80 years old. \\nWe can see that the mean is a bit higher than in the step 1 as more people are aged between 40 and 55 years old. \\n\\nThe standard deviations is also higher because the grap isn't as symetric and values are higher on the right side of the mean. There is also a pic between 65 and 70 years old increasing the gap. \\n\\n\""
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            }
+          },
+          "metadata": {},
+          "execution_count": 145
+        }
+      ],
+      "source": [
+        "\"\"\"\n",
+        "As in the step1, we have a very diverse population with people between 0 and 80 years old.\n",
+        "We can see that the mean is a bit higher than in the step 1 as more people are aged between 40 and 55 years old.\n",
+        "\n",
+        "The standard deviations is also higher because the grap isn't as symetric and values are higher on the right side of the mean. There is also a pic between 65 and 70 years old increasing the gap.\n",
+        "\n",
+        "\"\"\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "96bMUz8FAZn6"
+      },
+      "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": 150,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ESN7EjieAZn6",
+        "outputId": "c7be786d-a6d3-4d5f-db9a-6759d353d193"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Q1: 30.0\n",
+            "Q2 (Median): 40.0\n",
+            "Q3: 53.0\n"
+          ]
+        }
+      ],
+      "source": [
+        "q1 = np.percentile(age_pop3, 25)\n",
+        "q2 = np.percentile(age_pop3, 50)\n",
+        "q3 = np.percentile(age_pop3, 75)\n",
+        "\n",
+        "print(\"Q1:\", q1)\n",
+        "print(\"Q2 (Median):\", q2)\n",
+        "print(\"Q3:\", q3)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 118,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 36
+        },
+        "id": "HBD-IxJqAZn6",
+        "outputId": "3649d5be-42cf-40bb-cd8c-ebc244e28781"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "'\\nyour comments here\\n'"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            }
+          },
+          "metadata": {},
+          "execution_count": 118
+        }
+      ],
+      "source": [
+        "\"\"\"\n",
+        "The median and the mean are very similar.\n",
+        "q1 is also closer to the median than q3 because more people are older than the mean.\n",
+        "\"\"\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-wOwSiJsAZn6"
+      },
+      "source": [
+        "#### 4.- Calculate other percentiles that might be useful to give more arguments to your reasoning."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 152,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "mDQBo1AsAZn6",
+        "outputId": "9b21c535-3b3c-4692-bb08-0ab2fb46962e"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "P1: 28.0\n",
+            "P2: 36.0\n",
+            "P3: 45.0\n",
+            "P4: 57.0\n"
+          ]
+        }
+      ],
+      "source": [
+        "p1 = np.percentile(age_pop3, 20)\n",
+        "p2 = np.percentile(age_pop3, 40)\n",
+        "p3 = np.percentile(age_pop3, 60)\n",
+        "p4 = np.percentile(age_pop3, 80)\n",
+        "\n",
+        "print(\"P1:\", p1)\n",
+        "print(\"P2:\", p2)\n",
+        "print(\"P3:\", p3)\n",
+        "print(\"P4:\", p4)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "R2S3IPAVAZn6"
+      },
+      "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": 121,
+      "metadata": {
+        "id": "BpdyWNxbAZn6"
+      },
+      "outputs": [],
+      "source": [
+        "# your code here"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 122,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 36
+        },
+        "id": "xe66yq3sAZn7",
+        "outputId": "0ae412fc-41e9-40cb-f2df-c84aed778a7b"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "'\\nyour comments here\\n'"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            }
+          },
+          "metadata": {},
+          "execution_count": 122
+        }
+      ],
+      "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"
+    },
+    "colab": {
+      "provenance": []
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file