diff --git a/your-code/main.ipynb b/your-code/main.ipynb
index 5759add..067e206 100644
--- a/your-code/main.ipynb
+++ b/your-code/main.ipynb
@@ -1,522 +1,988 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Understanding Descriptive Statistics\n",
-    "\n",
-    "Import the necessary libraries here:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Libraries"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Challenge 1\n",
-    "#### 1.- Define a function that simulates rolling a dice 10 times. Save the information in a dataframe.\n",
-    "**Hint**: you can use the *choices* function from module *random* to help you with the simulation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 2.- Plot the results sorted by value."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 3.- Calculate the frequency distribution and plot it. What is the relation between this plot and the plot above? Describe it with words."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Challenge 2\n",
-    "Now, using the dice results obtained in *challenge 1*, your are going to define some functions that will help you calculate the mean of your data in two different ways, the median and the four quartiles. \n",
-    "\n",
-    "#### 1.- Define a function that computes the mean by summing all the observations and dividing by the total number of observations. You are not allowed to use any methods or functions that directly calculate the mean value. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 2.- First, calculate the frequency distribution. Then, calculate the mean using the values of the frequency distribution you've just computed. You are not allowed to use any methods or functions that directly calculate the mean value. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 3.- Define a function to calculate the median. You are not allowed to use any methods or functions that directly calculate the median value. \n",
-    "**Hint**: you might need to define two computation cases depending on the number of observations used to calculate the median."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 4.- Define a function to calculate the four quartiles. You can use the function you defined above to compute the median but you are not allowed to use any methods or functions that directly calculate the quartiles. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Challenge 3\n",
-    "Read the csv `roll_the_dice_hundred.csv` from the `data` folder.\n",
-    "#### 1.- Sort the values and plot them. What do you see?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 2.- Using the functions you defined in *challenge 2*, calculate the mean value of the hundred dice rolls."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 3.- Now, calculate the frequency distribution.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 4.- Plot the histogram. What do you see (shape, values...) ? How can you connect the mean value to the histogram? "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 5.- Read the `roll_the_dice_thousand.csv` from the `data` folder. Plot the frequency distribution as you did before. Has anything changed? Why do you think it changed?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Challenge 4\n",
-    "In the `data` folder of this repository you will find three different files with the prefix `ages_population`. These files contain information about a poll answered by a thousand people regarding their age. Each file corresponds to the poll answers in different neighbourhoods of Barcelona.\n",
-    "\n",
-    "#### 1.- Read the file `ages_population.csv`. Calculate the frequency distribution and plot it as we did during the lesson. Try to guess the range in which the mean and the standard deviation will be by looking at the plot. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 2.- Calculate the exact mean and standard deviation and compare them with your guesses. Do they fall inside the ranges you guessed?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 3.- Now read the file `ages_population2.csv` . Calculate the frequency distribution and plot it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "####  4.- What do you see? Is there any difference with the frequency distribution in step 1?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 5.- Calculate the mean and standard deviation. Compare the results with the mean and standard deviation in step 2. What do you think?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Challenge 5\n",
-    "Now is the turn of `ages_population3.csv`.\n",
-    "\n",
-    "#### 1.- Read the file `ages_population3.csv`. Calculate the frequency distribution and plot it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 2.- Calculate the mean and standard deviation. Compare the results with the plot in step 1. What is happening?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 3.- Calculate the four quartiles. Use the results to explain your reasoning for question in step 2. How much of a difference is there between the median and the mean?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 4.- Calculate other percentiles that might be useful to give more arguments to your reasoning."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Bonus challenge\n",
-    "Compare the information about the three neighbourhoods. Prepare a report about the three of them. Remember to find out which are their similarities and their differences backing your arguments in basic statistics."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "ironhack-3.7",
-   "language": "python",
-   "name": "ironhack-3.7"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Understanding Descriptive Statistics\n",
+    "\n",
+    "Import the necessary libraries here:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Libraries\n",
+    "from random import choices\n",
+    "import pandas as pd \n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Challenge 1\n",
+    "#### 1.- Define a function that simulates rolling a dice 10 times. Save the information in a dataframe.\n",
+    "**Hint**: you can use the *choices* function from module *random* to help you with the simulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    3\n",
+       "1    5\n",
+       "2    4\n",
+       "3    1\n",
+       "4    3\n",
+       "5    3\n",
+       "6    2\n",
+       "7    4\n",
+       "8    6\n",
+       "9    1\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "def roll_dice(n):\n",
+    "    return choices(range(1,7), k=n)\n",
+    "\n",
+    "rolls = pd.Series(roll_dice(10))\n",
+    "rolls"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.- Plot the results sorted by value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "rolls = rolls.sort_values()\n",
+    "plt.plot(rolls)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.- Calculate the frequency distribution and plot it. What is the relation between this plot and the plot above? Describe it with words."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "plt.hist(rolls, bins=6)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nThe both show the numbers that occured more often.\\n'"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "The both show the numbers that occured more often.\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Challenge 2\n",
+    "Now, using the dice results obtained in *challenge 1*, your are going to define some functions that will help you calculate the mean of your data in two different ways, the median and the four quartiles. \n",
+    "\n",
+    "#### 1.- Define a function that computes the mean by summing all the observations and dividing by the total number of observations. You are not allowed to use any methods or functions that directly calculate the mean value. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.2"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "def list_avg(lst):\n",
+    "    lst_sum = 0\n",
+    "    for i in lst:\n",
+    "        lst_sum += i\n",
+    "    return lst_sum / len(lst)\n",
+    "\n",
+    "list_avg(rolls.tolist())"
+   ]
+  },
+  {
+   "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": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[2, 1, 3, 2, 1, 1]\n",
+      "Mean of frequency dist. is 1.6666666666666667\n"
+     ]
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "freq_dist = [0,0,0,0,0,0]\n",
+    "for i in rolls:\n",
+    "    freq_dist[i-1] += 1\n",
+    "print(freq_dist)\n",
+    "print('Mean of frequency dist. is', list_avg(freq_dist))"
+   ]
+  },
+  {
+   "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": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4.0 [1, 5, 0, 3, 6, 6]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "def list_median(lst):\n",
+    "    sorted_list = list(lst)\n",
+    "    sorted_list.sort()\n",
+    "    list_len = len(sorted_list)\n",
+    "    mid_point = list_len // 2\n",
+    "    if list_len % 2 == 0:\n",
+    "        return (sorted_list[mid_point] + sorted_list[mid_point - 1]) / 2\n",
+    "    return sorted_list[mid_point]\n",
+    "\n",
+    "a = [1,5,0,3,6,6]\n",
+    "print(list_median(a), a)"
+   ]
+  },
+  {
+   "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": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[4, 5, 9.0, 12]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "def list_quantiles(lst):\n",
+    "    result = [0,0,0,0]\n",
+    "    sorted_list = list(lst)\n",
+    "    sorted_list.sort()\n",
+    "    list_len = len(sorted_list)\n",
+    "    mid_point = list_len // 2\n",
+    "    #first quantile is the median of first half of the list\n",
+    "    result[0] = list_median(sorted_list[0:mid_point])\n",
+    "    # second quantile is the median of the full list\n",
+    "    result[1] = list_median(sorted_list)\n",
+    "    # third quantile is the median of the second half of the list\n",
+    "    result[2] = list_median(sorted_list[mid_point:])\n",
+    "    # last quantile is the last value of the list\n",
+    "    result[3] = sorted_list[-1]\n",
+    "    \n",
+    "    return result\n",
+    "\n",
+    "a = [2,2,4,5,5,5,8,9,9,9,12]\n",
+    "print(list_quantiles(a))"
+   ]
+  },
+  {
+   "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": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "data = pd.read_csv('../data/roll_the_dice_hundred.csv')\n",
+    "data = data['value'].tolist()\n",
+    "\n",
+    "data.sort()\n",
+    "\n",
+    "plt.plot(data)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nThere are different frequency for each of the numbers, like 4 and 6 has around 30% of the value each, while others\\nseem to be smaller\\n'"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "There are different frequency for each of the numbers, like 4 and 6 has showed up more than others\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": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.74"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "list_avg(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.- Now, calculate the frequency distribution.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[12, 17, 14, 22, 12, 23]\n",
+      "Average frequency:  16.666666666666668\n"
+     ]
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "def get_freq_dist(d):\n",
+    "    freq_dist = [0,0,0,0,0,0]\n",
+    "    for i in d:\n",
+    "        freq_dist[i-1] += 1\n",
+    "    return freq_dist\n",
+    "\n",
+    "print (get_freq_dist(data))\n",
+    "print ('Average frequency: ', list_avg(get_freq_dist(data)))"
+   ]
+  },
+  {
+   "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": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "plt.hist(data, bins=6)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nBecause we have more frequency on 4 and 6, the mean value for the whole list is more biased towards those numbers.\\n'"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "Because we have more frequency on 4 and 6, the mean value for the whole list is more biased towards those numbers.\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": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# your code here\n",
+    "data = pd.read_csv('../data/roll_the_dice_thousand.csv')\n",
+    "data = data['value'].tolist()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[175, 167, 175, 168, 149, 166]\n",
+      "Average frequency:  166.66666666666666\n"
+     ]
+    }
+   ],
+   "source": [
+    "print (get_freq_dist(data))\n",
+    "print ('Average frequency: ', list_avg(get_freq_dist(data)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(data, bins=6)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nThe more we throw the dice, the closer frequency distribution will get\\n'"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "The more we throw the dice, the closer frequency distribution will get\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": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>observation</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>68.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>12.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>45.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>38.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>49.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   observation\n",
+       "0         68.0\n",
+       "1         12.0\n",
+       "2         45.0\n",
+       "3         38.0\n",
+       "4         49.0"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "ages = pd.read_csv('../data/ages_population.csv')\n",
+    "ages.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(ages, bins=10)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "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": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean is: observation    36.56\n",
+      "dtype: float64 \n",
+      "STD is: observation    12.8165\n",
+      "dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "print('Mean is:', ages.mean() , '\\nSTD is:', ages.std())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "Looking at the histogram, the mean seems to be around 40s, which is close to the value calculated 36.56\n",
+    "And also the STD from the graph looks like within the 15 which is close to 12.8 as we calculated\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": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "ages2 = pd.read_csv('../data/ages_population2.csv')\n",
+    "plt.hist(ages2, bins=10)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "####  4.- What do you see? Is there any difference with the frequency distribution in step 1?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "The population here seems to be young people in the age of 20 to 35, with majority at their 25 to 30s. \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": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean is: observation    27.155\n",
+      "dtype: float64 \n",
+      "STD is: observation    2.969814\n",
+      "dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "print('Mean is:', ages2.mean() , '\\nSTD is:', ages2.std())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "The majority of the population are on similar age range, close to the mean value\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": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "ages3 = pd.read_csv('../data/ages_population3.csv')\n",
+    "plt.hist(ages3, bins=10)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.- Calculate the mean and standard deviation. Compare the results with the plot in step 1. What is happening?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean is: observation    41.989\n",
+      "dtype: float64 \n",
+      "STD is: observation    16.144706\n",
+      "dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "print('Mean is:', ages3.mean() , '\\nSTD is:', ages3.std())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "The distribution of the data is more biased towards the right side of the range, but majority of the population\n",
+    "are below 50\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": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[30.0, 40.0, 53.0, 77.0]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "print(list_quantiles(ages3['observation'].tolist()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "The first 25% are a wide age range group, hence 30 is the first quantile, where as the median is around 40, that \n",
+    "is less than the mean value of 41.9. That is because the population distribution is more biased towards the \n",
+    "end of the range, which makes the average value to increase and become more than median value\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": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "57.0"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "np.quantile(ages3, 0.80)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "If we use 80% of the data, we can remove the abnormal number of people between age of 60 to 70, which caused the \n",
+    "mean value get biased\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",
+    "Based on the age distributions in the area, we can make following claims:\n",
+    "\n",
+    "The frist group, is more of a family based area, hence the distribution of the age range is more uniformed across\n",
+    "all age groups.\n",
+    "\n",
+    "In the second group, it seems to have more university students, hence the data is more biased towards young group\n",
+    "of people.\n",
+    "\n",
+    "In the last group, based on the frequency distribution and mean value, it is more likely an area with elderly\n",
+    "homes, hence the increase of population at late 60s\n",
+    "\"\"\""
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}