From 29f4b6ff12232a92d2697330a897b901ec140af3 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E2=80=9Cdanielmdepaoli=E2=80=9D?=
 <“danielmdepaoli@gmail.com”>
Date: Thu, 3 Aug 2023 19:35:07 +0100
Subject: [PATCH 1/2] Lab Done

---
 your-code/.DS_Store                           |  Bin 0 -> 8196 bytes
 .../DescriptiveStatisticsLab-checkpoint.ipynb | 1334 +++++++++++++++++
 your-code/DescriptiveStatisticsLab.ipynb      | 1334 +++++++++++++++++
 your-code/ages_population.csv                 | 1001 +++++++++++++
 your-code/ages_population2.csv                | 1001 +++++++++++++
 your-code/ages_population3.csv                | 1001 +++++++++++++
 your-code/main.ipynb                          |  522 -------
 your-code/roll_the_dice_hundred.csv           |  101 ++
 your-code/roll_the_dice_thousand.csv          | 1001 +++++++++++++
 9 files changed, 6773 insertions(+), 522 deletions(-)
 create mode 100644 your-code/.DS_Store
 create mode 100644 your-code/.ipynb_checkpoints/DescriptiveStatisticsLab-checkpoint.ipynb
 create mode 100644 your-code/DescriptiveStatisticsLab.ipynb
 create mode 100644 your-code/ages_population.csv
 create mode 100644 your-code/ages_population2.csv
 create mode 100644 your-code/ages_population3.csv
 delete mode 100644 your-code/main.ipynb
 create mode 100644 your-code/roll_the_dice_hundred.csv
 create mode 100644 your-code/roll_the_dice_thousand.csv

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diff --git a/your-code/.ipynb_checkpoints/DescriptiveStatisticsLab-checkpoint.ipynb b/your-code/.ipynb_checkpoints/DescriptiveStatisticsLab-checkpoint.ipynb
new file mode 100644
index 0000000..b64a4f0
--- /dev/null
+++ b/your-code/.ipynb_checkpoints/DescriptiveStatisticsLab-checkpoint.ipynb
@@ -0,0 +1,1334 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Understanding Descriptive Statistics\n",
+    "\n",
+    "Import the necessary libraries here:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import random\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy import stats as st"
+   ]
+  },
+  {
+   "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": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2, 3, 5, 1, 5, 3, 2, 5, 2, 0])"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dice = np.random.randint(0, 7, 10)\n",
+    "dice"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.- Plot the results sorted by value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 1, 2, 2, 2, 3, 3, 5, 5, 5])"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.sort(dice)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "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.plot(np.array([0, 1, 2, 2, 2, 3, 3, 5, 5, 5]), \"o\")\n",
+    "plt.xlabel('Roll')\n",
+    "plt.ylabel('Result')\n",
+    "plt.title('Dice Roll Results (Sorted)')\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": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.array([0, 1, 2, 2, 2, 3, 3, 5, 5, 5])\n",
+    "\n",
+    "plt.hist(x, bins=10)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "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": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.5"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean = dice.sum()/len(dice)\n",
+    "\n",
+    "mean"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.- First, calculate the frequency distribution. Then, calculate the mean using the values of the frequency distribution you've just computed. You are not allowed to use any methods or functions that directly calculate the mean value. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 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": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.0"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(np.sort(dice)[5]+np.sort(dice)[4])/2"
+   ]
+  },
+  {
+   "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": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "the first quartile is 2.0\n",
+      "the second quartile is 3.0\n",
+      "the third quartile is 5.5\n",
+      "the fourth quartile is 6\n"
+     ]
+    }
+   ],
+   "source": [
+    "q1 = np.quantile(dice, 0.25)\n",
+    "print(f\"the first quartile is {q1}\")\n",
+    "\n",
+    "q2 = np.quantile(dice, 0.50)\n",
+    "print(f\"the second quartile is {q2}\")\n",
+    "\n",
+    "q3 = np.quantile(dice, 0.75)\n",
+    "print(f\"the third quartile is {q3}\")\n",
+    "\n",
+    "q4 = np.quantile(dice, 1)\n",
+    "print(f\"the fourth quartile is {q4}\")"
+   ]
+  },
+  {
+   "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": 17,
+   "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>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>"
+      ],
+      "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]"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "roll_dice = pd.read_csv(\"roll_the_dice_hundred.csv\")\n",
+    "roll_dice"
+   ]
+  },
+  {
+   "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": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = roll_dice[\"value\"]\n",
+    "\n",
+    "plt.hist(x, bins=100)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.- Now, calculate the frequency distribution.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "object too deep for desired array",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[26], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m frequency \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbincount\u001b[49m\u001b[43m(\u001b[49m\u001b[43mroll_dice\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28mprint\u001b[39m(frequency)\n",
+      "File \u001b[0;32m<__array_function__ internals>:180\u001b[0m, in \u001b[0;36mbincount\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: object too deep for desired array"
+     ]
+    }
+   ],
+   "source": [
+    "frequency = np.bincount(roll_dice)\n",
+    "\n",
+    "print(frequency)"
+   ]
+  },
+  {
+   "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": 19,
+   "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>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>"
+      ],
+      "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]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "roll_dice = pd.read_csv(\"roll_the_dice_thousand.csv\")\n",
+    "roll_dice"
+   ]
+  },
+  {
+   "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": 20,
+   "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",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>995</th>\n",
+       "      <td>27.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>996</th>\n",
+       "      <td>47.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>997</th>\n",
+       "      <td>53.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>998</th>\n",
+       "      <td>33.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999</th>\n",
+       "      <td>31.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1000 rows × 1 columns</p>\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\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]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "age_pop = pd.read_csv(\"ages_population.csv\")\n",
+    "age_pop"
+   ]
+  },
+  {
+   "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": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "observation    36.56\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "age_pop.mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "observation    12.8165\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "age_pop.std()"
+   ]
+  },
+  {
+   "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": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>observation</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>25.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>31.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>29.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>31.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>29.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>995</th>\n",
+       "      <td>26.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>996</th>\n",
+       "      <td>22.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>997</th>\n",
+       "      <td>21.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>998</th>\n",
+       "      <td>19.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999</th>\n",
+       "      <td>28.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1000 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     observation\n",
+       "0           25.0\n",
+       "1           31.0\n",
+       "2           29.0\n",
+       "3           31.0\n",
+       "4           29.0\n",
+       "..           ...\n",
+       "995         26.0\n",
+       "996         22.0\n",
+       "997         21.0\n",
+       "998         19.0\n",
+       "999         28.0\n",
+       "\n",
+       "[1000 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "age_pop2 = pd.read_csv(\"ages_population2.csv\")\n",
+    "age_pop2"
+   ]
+  },
+  {
+   "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": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "observation    2.969814\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "age_pop2.std()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "observation    27.155\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "age_pop2.mean()"
+   ]
+  },
+  {
+   "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": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>observation</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>21.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>21.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>24.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>31.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>54.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>995</th>\n",
+       "      <td>16.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>996</th>\n",
+       "      <td>55.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>997</th>\n",
+       "      <td>30.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>998</th>\n",
+       "      <td>35.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999</th>\n",
+       "      <td>43.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1000 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     observation\n",
+       "0           21.0\n",
+       "1           21.0\n",
+       "2           24.0\n",
+       "3           31.0\n",
+       "4           54.0\n",
+       "..           ...\n",
+       "995         16.0\n",
+       "996         55.0\n",
+       "997         30.0\n",
+       "998         35.0\n",
+       "999         43.0\n",
+       "\n",
+       "[1000 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "age_pop3 = pd.read_csv(\"ages_population3.csv\")\n",
+    "age_pop3"
+   ]
+  },
+  {
+   "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": 44,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "observation    41.989\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "age_pop3.mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "observation    16.144706\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "age_pop3.std()"
+   ]
+  },
+  {
+   "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": 25,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "the first quartile is 30.0\n",
+      "the second quartile is 40.0\n",
+      "the third quartile is 53.0\n",
+      "the fourth quartile is 77.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "q1 = np.quantile(age_pop3, 0.25)\n",
+    "print(f\"the first quartile is {q1}\")\n",
+    "\n",
+    "q2 = np.quantile(age_pop3, 0.50)\n",
+    "print(f\"the second quartile is {q2}\")\n",
+    "\n",
+    "q3 = np.quantile(age_pop3, 0.75)\n",
+    "print(f\"the third quartile is {q3}\")\n",
+    "\n",
+    "q4 = np.quantile(age_pop3, 1)\n",
+    "print(f\"the fourth quartile is {q4}\")"
+   ]
+  },
+  {
+   "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": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "22.0\n",
+      "67.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "ten_percent = np.quantile(age_pop3, 0.10)\n",
+    "print(ten_percent)\n",
+    "\n",
+    "ninety_percent = np.quantile(age_pop3, 0.90)\n",
+    "print(ninety_percent)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Bonus challenge\n",
+    "Compare the information about the three neighbourhoods. Prepare a report about the three of them. Remember to find out which are their similarities and their differences backing your arguments in basic statistics."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# your code here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "your comments here\n",
+    "\"\"\""
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/your-code/DescriptiveStatisticsLab.ipynb b/your-code/DescriptiveStatisticsLab.ipynb
new file mode 100644
index 0000000..b64a4f0
--- /dev/null
+++ b/your-code/DescriptiveStatisticsLab.ipynb
@@ -0,0 +1,1334 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Understanding Descriptive Statistics\n",
+    "\n",
+    "Import the necessary libraries here:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import random\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy import stats as st"
+   ]
+  },
+  {
+   "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": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2, 3, 5, 1, 5, 3, 2, 5, 2, 0])"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dice = np.random.randint(0, 7, 10)\n",
+    "dice"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.- Plot the results sorted by value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 1, 2, 2, 2, 3, 3, 5, 5, 5])"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.sort(dice)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "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.plot(np.array([0, 1, 2, 2, 2, 3, 3, 5, 5, 5]), \"o\")\n",
+    "plt.xlabel('Roll')\n",
+    "plt.ylabel('Result')\n",
+    "plt.title('Dice Roll Results (Sorted)')\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": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.array([0, 1, 2, 2, 2, 3, 3, 5, 5, 5])\n",
+    "\n",
+    "plt.hist(x, bins=10)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "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": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.5"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean = dice.sum()/len(dice)\n",
+    "\n",
+    "mean"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.- First, calculate the frequency distribution. Then, calculate the mean using the values of the frequency distribution you've just computed. You are not allowed to use any methods or functions that directly calculate the mean value. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 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": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.0"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(np.sort(dice)[5]+np.sort(dice)[4])/2"
+   ]
+  },
+  {
+   "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": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "the first quartile is 2.0\n",
+      "the second quartile is 3.0\n",
+      "the third quartile is 5.5\n",
+      "the fourth quartile is 6\n"
+     ]
+    }
+   ],
+   "source": [
+    "q1 = np.quantile(dice, 0.25)\n",
+    "print(f\"the first quartile is {q1}\")\n",
+    "\n",
+    "q2 = np.quantile(dice, 0.50)\n",
+    "print(f\"the second quartile is {q2}\")\n",
+    "\n",
+    "q3 = np.quantile(dice, 0.75)\n",
+    "print(f\"the third quartile is {q3}\")\n",
+    "\n",
+    "q4 = np.quantile(dice, 1)\n",
+    "print(f\"the fourth quartile is {q4}\")"
+   ]
+  },
+  {
+   "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": 17,
+   "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>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>"
+      ],
+      "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]"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "roll_dice = pd.read_csv(\"roll_the_dice_hundred.csv\")\n",
+    "roll_dice"
+   ]
+  },
+  {
+   "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": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = roll_dice[\"value\"]\n",
+    "\n",
+    "plt.hist(x, bins=100)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.- Now, calculate the frequency distribution.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "object too deep for desired array",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[26], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m frequency \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbincount\u001b[49m\u001b[43m(\u001b[49m\u001b[43mroll_dice\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28mprint\u001b[39m(frequency)\n",
+      "File \u001b[0;32m<__array_function__ internals>:180\u001b[0m, in \u001b[0;36mbincount\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: object too deep for desired array"
+     ]
+    }
+   ],
+   "source": [
+    "frequency = np.bincount(roll_dice)\n",
+    "\n",
+    "print(frequency)"
+   ]
+  },
+  {
+   "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": 19,
+   "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>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>"
+      ],
+      "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]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "roll_dice = pd.read_csv(\"roll_the_dice_thousand.csv\")\n",
+    "roll_dice"
+   ]
+  },
+  {
+   "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": 20,
+   "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",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>995</th>\n",
+       "      <td>27.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>996</th>\n",
+       "      <td>47.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>997</th>\n",
+       "      <td>53.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>998</th>\n",
+       "      <td>33.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999</th>\n",
+       "      <td>31.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1000 rows × 1 columns</p>\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\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]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "age_pop = pd.read_csv(\"ages_population.csv\")\n",
+    "age_pop"
+   ]
+  },
+  {
+   "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": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "observation    36.56\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "age_pop.mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "observation    12.8165\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "age_pop.std()"
+   ]
+  },
+  {
+   "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": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>observation</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>25.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>31.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>29.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>31.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>29.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>995</th>\n",
+       "      <td>26.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>996</th>\n",
+       "      <td>22.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>997</th>\n",
+       "      <td>21.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>998</th>\n",
+       "      <td>19.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999</th>\n",
+       "      <td>28.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1000 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     observation\n",
+       "0           25.0\n",
+       "1           31.0\n",
+       "2           29.0\n",
+       "3           31.0\n",
+       "4           29.0\n",
+       "..           ...\n",
+       "995         26.0\n",
+       "996         22.0\n",
+       "997         21.0\n",
+       "998         19.0\n",
+       "999         28.0\n",
+       "\n",
+       "[1000 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "age_pop2 = pd.read_csv(\"ages_population2.csv\")\n",
+    "age_pop2"
+   ]
+  },
+  {
+   "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": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "observation    2.969814\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "age_pop2.std()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "observation    27.155\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "age_pop2.mean()"
+   ]
+  },
+  {
+   "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": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>observation</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>21.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>21.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>24.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>31.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>54.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>995</th>\n",
+       "      <td>16.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>996</th>\n",
+       "      <td>55.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>997</th>\n",
+       "      <td>30.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>998</th>\n",
+       "      <td>35.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999</th>\n",
+       "      <td>43.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1000 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     observation\n",
+       "0           21.0\n",
+       "1           21.0\n",
+       "2           24.0\n",
+       "3           31.0\n",
+       "4           54.0\n",
+       "..           ...\n",
+       "995         16.0\n",
+       "996         55.0\n",
+       "997         30.0\n",
+       "998         35.0\n",
+       "999         43.0\n",
+       "\n",
+       "[1000 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "age_pop3 = pd.read_csv(\"ages_population3.csv\")\n",
+    "age_pop3"
+   ]
+  },
+  {
+   "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": 44,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "observation    41.989\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "age_pop3.mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "observation    16.144706\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "age_pop3.std()"
+   ]
+  },
+  {
+   "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": 25,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "the first quartile is 30.0\n",
+      "the second quartile is 40.0\n",
+      "the third quartile is 53.0\n",
+      "the fourth quartile is 77.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "q1 = np.quantile(age_pop3, 0.25)\n",
+    "print(f\"the first quartile is {q1}\")\n",
+    "\n",
+    "q2 = np.quantile(age_pop3, 0.50)\n",
+    "print(f\"the second quartile is {q2}\")\n",
+    "\n",
+    "q3 = np.quantile(age_pop3, 0.75)\n",
+    "print(f\"the third quartile is {q3}\")\n",
+    "\n",
+    "q4 = np.quantile(age_pop3, 1)\n",
+    "print(f\"the fourth quartile is {q4}\")"
+   ]
+  },
+  {
+   "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": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "22.0\n",
+      "67.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "ten_percent = np.quantile(age_pop3, 0.10)\n",
+    "print(ten_percent)\n",
+    "\n",
+    "ninety_percent = np.quantile(age_pop3, 0.90)\n",
+    "print(ninety_percent)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Bonus challenge\n",
+    "Compare the information about the three neighbourhoods. Prepare a report about the three of them. Remember to find out which are their similarities and their differences backing your arguments in basic statistics."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# your code here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "your comments here\n",
+    "\"\"\""
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/your-code/ages_population.csv b/your-code/ages_population.csv
new file mode 100644
index 0000000..64d8a0a
--- /dev/null
+++ b/your-code/ages_population.csv
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diff --git a/your-code/ages_population2.csv b/your-code/ages_population2.csv
new file mode 100644
index 0000000..00860cb
--- /dev/null
+++ b/your-code/ages_population2.csv
@@ -0,0 +1,1001 @@
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diff --git a/your-code/ages_population3.csv b/your-code/ages_population3.csv
new file mode 100644
index 0000000..6339a1d
--- /dev/null
+++ b/your-code/ages_population3.csv
@@ -0,0 +1,1001 @@
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diff --git a/your-code/main.ipynb b/your-code/main.ipynb
deleted file mode 100644
index a0a5b66..0000000
--- a/your-code/main.ipynb
+++ /dev/null
@@ -1,522 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Understanding Descriptive Statistics\n",
-    "\n",
-    "Import the necessary libraries here:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Libraries"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Challenge 1\n",
-    "#### 1.- Define a function that simulates rolling a dice 10 times. Save the information in a dataframe.\n",
-    "**Hint**: you can use the *choices* function from module *random* to help you with the simulation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 2.- Plot the results sorted by value."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 3.- Calculate the frequency distribution and plot it. What is the relation between this plot and the plot above? Describe it with words."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Challenge 2\n",
-    "Now, using the dice results obtained in *challenge 1*, your are going to define some functions that will help you calculate the mean of your data in two different ways, the median and the four quartiles. \n",
-    "\n",
-    "#### 1.- Define a function that computes the mean by summing all the observations and dividing by the total number of observations. You are not allowed to use any methods or functions that directly calculate the mean value. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 2.- First, calculate the frequency distribution. Then, calculate the mean using the values of the frequency distribution you've just computed. You are not allowed to use any methods or functions that directly calculate the mean value. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 3.- Define a function to calculate the median. You are not allowed to use any methods or functions that directly calculate the median value. \n",
-    "**Hint**: you might need to define two computation cases depending on the number of observations used to calculate the median."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 4.- Define a function to calculate the four quartiles. You can use the function you defined above to compute the median but you are not allowed to use any methods or functions that directly calculate the quartiles. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Challenge 3\n",
-    "Read the csv `roll_the_dice_hundred.csv` from the `data` folder.\n",
-    "#### 1.- Sort the values and plot them. What do you see?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 2.- Using the functions you defined in *challenge 2*, calculate the mean value of the hundred dice rolls."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 3.- Now, calculate the frequency distribution.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 4.- Plot the histogram. What do you see (shape, values...) ? How can you connect the mean value to the histogram? "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 5.- Read the `roll_the_dice_thousand.csv` from the `data` folder. Plot the frequency distribution as you did before. Has anything changed? Why do you think it changed?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Challenge 4\n",
-    "In the `data` folder of this repository you will find three different files with the prefix `ages_population`. These files contain information about a poll answered by a thousand people regarding their age. Each file corresponds to the poll answers in different neighbourhoods of Barcelona.\n",
-    "\n",
-    "#### 1.- Read the file `ages_population.csv`. Calculate the frequency distribution and plot it as we did during the lesson. Try to guess the range in which the mean and the standard deviation will be by looking at the plot. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 2.- Calculate the exact mean and standard deviation and compare them with your guesses. Do they fall inside the ranges you guessed?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 3.- Now read the file `ages_population2.csv` . Calculate the frequency distribution and plot it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "####  4.- What do you see? Is there any difference with the frequency distribution in step 1?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 5.- Calculate the mean and standard deviation. Compare the results with the mean and standard deviation in step 2. What do you think?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Challenge 5\n",
-    "Now is the turn of `ages_population3.csv`.\n",
-    "\n",
-    "#### 1.- Read the file `ages_population3.csv`. Calculate the frequency distribution and plot it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 2.- Calculate the mean and standard deviation. Compare the results with the plot in step 1. What is happening?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 3.- Calculate the four quartiles. Use the results to explain your reasoning for question in step 2. How much of a difference is there between the median and the mean?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 4.- Calculate other percentiles that might be useful to give more arguments to your reasoning."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Bonus challenge\n",
-    "Compare the information about the three neighbourhoods. Prepare a report about the three of them. Remember to find out which are their similarities and their differences backing your arguments in basic statistics."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# your code here"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "ironhack-3.7",
-   "language": "python",
-   "name": "ironhack-3.7"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/your-code/roll_the_dice_hundred.csv b/your-code/roll_the_dice_hundred.csv
new file mode 100644
index 0000000..50975a2
--- /dev/null
+++ b/your-code/roll_the_dice_hundred.csv
@@ -0,0 +1,101 @@
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diff --git a/your-code/roll_the_dice_thousand.csv b/your-code/roll_the_dice_thousand.csv
new file mode 100644
index 0000000..f820dbb
--- /dev/null
+++ b/your-code/roll_the_dice_thousand.csv
@@ -0,0 +1,1001 @@
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From 9046a54bc5a1ec7ff8a013ac908744d8b1ffc6cd Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E2=80=9Cdanielmdepaoli=E2=80=9D?=
 <“danielmdepaoli@gmail.com”>
Date: Sat, 5 Aug 2023 11:02:36 +0100
Subject: [PATCH 2/2] Lab Done

---
 .../DescriptiveStatisticsLab-checkpoint.ipynb | 1221 +++++++++++++----
 your-code/DescriptiveStatisticsLab.ipynb      | 1221 +++++++++++++----
 2 files changed, 1984 insertions(+), 458 deletions(-)

diff --git a/your-code/.ipynb_checkpoints/DescriptiveStatisticsLab-checkpoint.ipynb b/your-code/.ipynb_checkpoints/DescriptiveStatisticsLab-checkpoint.ipynb
index b64a4f0..5b14d08 100644
--- a/your-code/.ipynb_checkpoints/DescriptiveStatisticsLab-checkpoint.ipynb
+++ b/your-code/.ipynb_checkpoints/DescriptiveStatisticsLab-checkpoint.ipynb
@@ -11,7 +11,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -33,22 +33,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([2, 3, 5, 1, 5, 3, 2, 5, 2, 0])"
+       "array([4, 2, 5, 4, 4, 4, 4, 6, 3, 2])"
       ]
      },
-     "execution_count": 66,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "dice = np.random.randint(0, 7, 10)\n",
+    "dice = np.random.randint(1, 7, 10)\n",
     "dice"
    ]
   },
@@ -61,32 +61,53 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2, 2, 3, 4, 4, 4, 4, 4, 5, 6])"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dice = np.sort(dice)\n",
+    "dice"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([0, 1, 2, 2, 2, 3, 3, 5, 5, 5])"
+       "array([2, 2, 3, 4, 4, 4, 4, 4, 5, 6])"
       ]
      },
-     "execution_count": 67,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "np.sort(dice)"
+    "dice"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -96,10 +117,10 @@
     }
    ],
    "source": [
-    "plt.plot(np.array([0, 1, 2, 2, 2, 3, 3, 5, 5, 5]), \"o\")\n",
+    "plt.plot(dice, \"o\")\n",
     "plt.xlabel('Roll')\n",
     "plt.ylabel('Result')\n",
-    "plt.title('Dice Roll Results (Sorted)')\n",
+    "plt.title('Dice Roll Results')\n",
     "plt.show()"
    ]
   },
@@ -112,12 +133,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -127,9 +148,9 @@
     }
    ],
    "source": [
-    "x = np.array([0, 1, 2, 2, 2, 3, 3, 5, 5, 5])\n",
+    "x = dice\n",
     "\n",
-    "plt.hist(x, bins=10)\n",
+    "plt.hist(x, bins=11)\n",
     "plt.show()"
    ]
   },
@@ -163,16 +184,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "3.5"
+       "3.8"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -183,6 +204,32 @@
     "mean"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.8"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def calc_mean(dice):\n",
+    "    dice.sort()\n",
+    "    for item in dice:\n",
+    "        mean = dice.sum()/len(dice)\n",
+    "    return mean\n",
+    "\n",
+    "calc_mean(dice)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -192,11 +239,93 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 50,
    "metadata": {},
-   "outputs": [],
+   "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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: []\n",
+       "Index: [2, 3, 4, 5, 6]"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "dice = pd.DataFrame(dice, columns=['value'])\n",
+    "\n",
+    "dice_data = dice.groupby('value').count()\n",
+    "\n",
+    "dice_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "value    3.8\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dice.mean()"
    ]
   },
   {
@@ -209,16 +338,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "3.0"
+       "4.0"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -227,6 +356,37 @@
     "(np.sort(dice)[5]+np.sort(dice)[4])/2"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4.0"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def calc_median(dice):\n",
+    "    n = len(dice)\n",
+    "    if n % 2 == 0: \n",
+    "        mid_index = n // 2\n",
+    "        median = (dice[mid_index - 1] + dice[mid_index]) / 2\n",
+    "    else: \n",
+    "        mid_index = n // 2\n",
+    "        median = dice[mid_index]\n",
+    "    \n",
+    "    return median\n",
+    "\n",
+    "calc_median(dice)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -236,16 +396,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "the first quartile is 2.0\n",
-      "the second quartile is 3.0\n",
-      "the third quartile is 5.5\n",
+      "the first quartile is 3.25\n",
+      "the second quartile is 4.0\n",
+      "the third quartile is 4.0\n",
       "the fourth quartile is 6\n"
      ]
     }
@@ -264,6 +424,46 @@
     "print(f\"the fourth quartile is {q4}\")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def calc_median(dice):\n",
+    "    n = len(dice)\n",
+    "    if n % 2 == 0: \n",
+    "        mid_index = n // 2\n",
+    "        median = (dice[mid_index - 1] + dice[mid_index]) / 2\n",
+    "    else: \n",
+    "        mid_index = n // 2\n",
+    "        median = dice[mid_index]\n",
+    "\n",
+    "    median\n",
+    "    q1 = median/2\n",
+    "    q2 = median\n",
+    "    q3 = median*(1.5)\n",
+    "    \n",
+    "    return print(f\"The median is {median}, Q1: {q1}, Q2:{q2}, Q3:{q3}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The median is 3.0, Q1: 1.5, Q2:3.0, Q3:4.5\n"
+     ]
+    }
+   ],
+   "source": [
+    "calc_median(dice)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -275,7 +475,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -393,7 +593,7 @@
        "[100 rows x 3 columns]"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -405,90 +605,574 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "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."
+    "roll_dice.drop('Unnamed: 0', axis= 1, inplace=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "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": [
-    "x = roll_dice[\"value\"]\n",
-    "\n",
-    "plt.hist(x, bins=100)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 3.- Now, calculate the frequency distribution.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "object too deep for desired array",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[26], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m frequency \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbincount\u001b[49m\u001b[43m(\u001b[49m\u001b[43mroll_dice\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28mprint\u001b[39m(frequency)\n",
-      "File \u001b[0;32m<__array_function__ internals>:180\u001b[0m, in \u001b[0;36mbincount\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
-      "\u001b[0;31mValueError\u001b[0m: object too deep for desired array"
-     ]
-    }
-   ],
-   "source": [
-    "frequency = np.bincount(roll_dice)\n",
-    "\n",
-    "print(frequency)"
-   ]
-  },
-  {
-   "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": [],
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>roll</th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>95</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>96</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>97</th>\n",
+       "      <td>97</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>98</th>\n",
+       "      <td>98</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>99</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>100 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    roll  value\n",
+       "0      0      1\n",
+       "1      1      2\n",
+       "2      2      6\n",
+       "3      3      1\n",
+       "4      4      6\n",
+       "..   ...    ...\n",
+       "95    95      4\n",
+       "96    96      6\n",
+       "97    97      1\n",
+       "98    98      3\n",
+       "99    99      6\n",
+       "\n",
+       "[100 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "roll_dice"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "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.plot(roll_dice[\"roll\"], roll_dice[\"value\"])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "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": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "roll     49.50\n",
+       "value     3.74\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def calc_mean(dice):\n",
+    "    for item in dice:\n",
+    "        mean = dice.sum()/len(dice)\n",
+    "    return mean\n",
+    "\n",
+    "calc_mean(roll_dice)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.- Now, calculate the frequency distribution.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>roll</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>value</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>23</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       roll\n",
+       "value      \n",
+       "1        12\n",
+       "2        17\n",
+       "3        14\n",
+       "4        22\n",
+       "5        12\n",
+       "6        23"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "roll_dice.groupby(\"value\").agg(\"count\")"
+   ]
+  },
+  {
+   "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": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAh8AAAGdCAYAAACyzRGfAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy88F64QAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAVwklEQVR4nO3dfWxV9f3A8c+d1Su60gWVPoSKzYbTCTIjBsEnfCJrDJnDbTr3gHuKRnSyxjjRP2wWpc5E4xayJvAHg2QM/9h8WFCBRS3bDBuwEQ0zDiPGTm3ImFLgZ2qU8/tjsb9fLT5Ubz+XXl6v5Jt4zj3t+Xhi4O25p72loiiKAABI8qlqDwAAHF7EBwCQSnwAAKnEBwCQSnwAAKnEBwCQSnwAAKnEBwCQqq7aA7zXgQMH4tVXX436+voolUrVHgcA+AiKooi9e/dGS0tLfOpTH3xv45CLj1dffTVaW1urPQYA8DH09vbGpEmTPvCYQy4+6uvrI+K/w48fP77K0wAAH0V/f3+0trYO/j3+QQ65+Hj3rZbx48eLDwAYYz7KIxMeOAUAUokPACCV+AAAUokPACCV+AAAUokPACCV+AAAUokPACCV+AAAUokPACCV+AAAUokPACCV+AAAUokPACBVXbUHAABGz0m3rh2276W7L6vCJP/HnQ8AIJX4AABSiQ8AIJX4AABSiQ8AIJX4AABSiQ8AIJX4AABSiQ8AIJX4AABSiQ8AIJX4AABSiQ8AIJX4AABSiQ8AIFVdtQcAGAtOunXtkO2X7r6sSpPA2OfOBwCQSnwAAKnEBwCQSnwAAKnEBwCQSnwAAKnEBwCQSnwAAKnEBwCQSnwAAKnEBwCQSnwAAKnEBwCQSnwAAKnEBwCQSnwAAKnEBwCQSnwAAKnEBwCQSnwAAKnEBwCQSnwAAKnEBwCQSnwAAKnEBwCQakTx0dXVFWeddVbU19fHxIkT4/LLL4/nn39+yDFFUURnZ2e0tLTEuHHjYs6cObF9+/aKDg0AjF0jio+enp5YuHBhbNq0KTZs2BBvv/12zJ07N/bv3z94zD333BP33XdfLF26NDZv3hxNTU1x6aWXxt69eys+PAAw9tSN5ODHH398yPaKFSti4sSJsXXr1jj//POjKIq4//774/bbb4/58+dHRMTKlSujsbExVq9eHddee23lJgcAxqRP9MzHnj17IiJiwoQJERGxc+fO6Ovri7lz5w4eUy6X44ILLoinn376oN9jYGAg+vv7hywAoHZ97PgoiiI6Ojri3HPPjalTp0ZERF9fX0RENDY2Djm2sbFx8LX36urqioaGhsHV2tr6cUcCAMaAjx0fN9xwQzzzzDPxm9/8ZthrpVJpyHZRFMP2vWvx4sWxZ8+ewdXb2/txRwIAxoARPfPxrhtvvDEeeeSR2LhxY0yaNGlwf1NTU0T89w5Ic3Pz4P5du3YNuxvyrnK5HOVy+eOMAQCMQSO681EURdxwww3xu9/9Lp544oloa2sb8npbW1s0NTXFhg0bBve99dZb0dPTE7Nnz67MxADAmDaiOx8LFy6M1atXx8MPPxz19fWDz3E0NDTEuHHjolQqxaJFi2LJkiUxZcqUmDJlSixZsiSOOeaYuPrqq0flXwAAGFtGFB/d3d0RETFnzpwh+1esWBHXXHNNRETccsst8eabb8b1118fr7/+esycOTPWr18f9fX1FRkYABjbRhQfRVF86DGlUik6Ozujs7Pz484EANQwn+0CAKQSHwBAKvEBAKQSHwBAKvEBAKQSHwBAKvEBAKQSHwBAKvEBAKQSHwBAKvEBAKQSHwBAKvEBAKQSHwBAKvEBAKQSHwBAKvEBAKQSHwBAKvEBAKQSHwBAKvEBAKQSHwBAKvEBAKQSHwBAKvEBAKQSHwBAKvEBAKQSHwBAKvEBAKQSHwBAKvEBAKQSHwBAKvEBAKQSHwBAKvEBAKQSHwBAKvEBAKQSHwBAKvEBAKSqq/YA1KaTbl07bN9Ld19WhUkAONS48wEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAECqEcfHxo0bY968edHS0hKlUikeeuihIa9fc801USqVhqyzzz67UvMCAGPciONj//79MX369Fi6dOn7HvOlL30pXnvttcH16KOPfqIhAYDaUTfSL2hvb4/29vYPPKZcLkdTU9PHHgoAqF2j8szHU089FRMnToyTTz45fvjDH8auXbve99iBgYHo7+8fsgCA2lXx+Ghvb49f//rX8cQTT8S9994bmzdvjosuuigGBgYOenxXV1c0NDQMrtbW1kqPBAAcQkb8tsuHufLKKwf/eerUqTFjxoyYPHlyrF27NubPnz/s+MWLF0dHR8fgdn9/vwABgBpW8fh4r+bm5pg8eXLs2LHjoK+Xy+Uol8ujPQYAcIgY9d/zsXv37ujt7Y3m5ubRPhUAMAaM+M7Hvn374oUXXhjc3rlzZ2zbti0mTJgQEyZMiM7Ozrjiiiuiubk5Xnrppbjtttvi+OOPj6985SsVHRwAGJtGHB9btmyJCy+8cHD73ec1FixYEN3d3fHss8/GqlWr4o033ojm5ua48MIL44EHHoj6+vrKTQ0AjFkjjo85c+ZEURTv+/q6des+0UAAQG3z2S4AQCrxAQCkEh8AQCrxAQCkEh8AQCrxAQCkEh8AQCrxAQCkGvUPlgNGz0m3rh2276W7L6vCJAAfnTsfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApBIfAEAq8QEApKqr9gDZTrp17bB9L919WRUmAeC93vtntD+fa5M7HwBAKvEBAKQSHwBAKvEBAKQSHwBAKvEBAKQSHwBAKvEBAKQSHwBAKvEBAKQSHwBAKvEBAKQSHwBAKvEBAKQSHwBAKvEBAKQSHwBAKvEBAKQSHwBAKvEBAKQSHwBAKvEBAKQSHwBAKvEBAKQSHwBAqhHHx8aNG2PevHnR0tISpVIpHnrooSGvF0URnZ2d0dLSEuPGjYs5c+bE9u3bKzUvADDGjTg+9u/fH9OnT4+lS5ce9PV77rkn7rvvvli6dGls3rw5mpqa4tJLL429e/d+4mEBgLGvbqRf0N7eHu3t7Qd9rSiKuP/+++P222+P+fPnR0TEypUro7GxMVavXh3XXnvtJ5sWABjzKvrMx86dO6Ovry/mzp07uK9cLscFF1wQTz/99EG/ZmBgIPr7+4csAKB2VTQ++vr6IiKisbFxyP7GxsbB196rq6srGhoaBldra2slRwIADjGj8tMupVJpyHZRFMP2vWvx4sWxZ8+ewdXb2zsaIwEAh4gRP/PxQZqamiLiv3dAmpubB/fv2rVr2N2Qd5XL5SiXy5UcAwA4hFX0zkdbW1s0NTXFhg0bBve99dZb0dPTE7Nnz67kqQCAMWrEdz727dsXL7zwwuD2zp07Y9u2bTFhwoQ48cQTY9GiRbFkyZKYMmVKTJkyJZYsWRLHHHNMXH311RUdHAAYm0YcH1u2bIkLL7xwcLujoyMiIhYsWBC/+tWv4pZbbok333wzrr/++nj99ddj5syZsX79+qivr6/c1ADAmDXi+JgzZ04URfG+r5dKpejs7IzOzs5PMhcAUKN8tgsAkEp8AACpxAcAkEp8AACpxAcAkEp8AACpxAcAkEp8AACpxAcAkEp8AACpxAcAkEp8AACpxAcAkEp8AACpxAcAkEp8AACpxAcAkEp8AACpxAcAkEp8AACpxAcAkEp8AACpxAcAkEp8AACpxAcAkEp8AACpxAcAkEp8AACpxAcAkEp8AACpxAcAkEp8AACpxAcAkEp8AACpxAcAkEp8AACpxAcAkEp8AACpxAcAkEp8AACpxAcAkEp8AACpxAcAkEp8AACpxAcAkEp8AACpxAcAkEp8AACpxAcAkEp8AACpxAcAkEp8AACpxAcAkEp8AACpxAcAkEp8AACpxAcAkEp8AACpxAcAkEp8AACpxAcAkEp8AACpxAcAkEp8AACpxAcAkEp8AACpKh4fnZ2dUSqVhqympqZKnwYAGKPqRuObnnbaafGHP/xhcPuII44YjdMAAGPQqMRHXV2dux0AwEGNyjMfO3bsiJaWlmhra4urrroqXnzxxfc9dmBgIPr7+4csAKB2VTw+Zs6cGatWrYp169bF8uXLo6+vL2bPnh27d+8+6PFdXV3R0NAwuFpbWys9EgBwCKl4fLS3t8cVV1wR06ZNi0suuSTWrl0bERErV6486PGLFy+OPXv2DK7e3t5KjwQAHEJG5ZmP/+/YY4+NadOmxY4dOw76erlcjnK5PNpjAACHiFH/PR8DAwPx3HPPRXNz82ifCgAYAyoeHzfffHP09PTEzp074y9/+Ut89atfjf7+/liwYEGlTwUAjEEVf9vlX//6V3zjG9+If//733HCCSfE2WefHZs2bYrJkydX+lQAwBhU8fhYs2ZNpb8lAFBDfLYLAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBKfAAAqcQHAJBq1OLjl7/8ZbS1tcXRRx8dZ555Zvzxj38crVMBAGPIqMTHAw88EIsWLYrbb789/v73v8d5550X7e3t8fLLL4/G6QCAMWRU4uO+++6L73//+/GDH/wgTj311Lj//vujtbU1uru7R+N0AMAYUlfpb/jWW2/F1q1b49Zbbx2yf+7cufH0008PO35gYCAGBgYGt/fs2RMREf39/ZUeLSIiDgz8z7B9o3Wuw5nrnMN1zvPea+06jw7XufKy/px493sWRfHhBxcV9sorrxQRUfz5z38esv+uu+4qTj755GHH33HHHUVEWJZlWZZVA6u3t/dDW6Hidz7eVSqVhmwXRTFsX0TE4sWLo6OjY3D7wIED8Z///CeOO+64gx7/SfT390dra2v09vbG+PHjK/q9+T+ucw7XOY9rncN1zjFa17koiti7d2+0tLR86LEVj4/jjz8+jjjiiOjr6xuyf9euXdHY2Djs+HK5HOVyeci+z3zmM5Uea4jx48f7DzuB65zDdc7jWudwnXOMxnVuaGj4SMdV/IHTo446Ks4888zYsGHDkP0bNmyI2bNnV/p0AMAYMypvu3R0dMS3v/3tmDFjRsyaNSuWLVsWL7/8clx33XWjcToAYAwZlfi48sorY/fu3fHTn/40XnvttZg6dWo8+uijMXny5NE43UdWLpfjjjvuGPY2D5XlOudwnfO41jlc5xyHwnUuFcVH+ZkYAIDK8NkuAEAq8QEApBIfAEAq8QEApDos4mPjxo0xb968aGlpiVKpFA899FC1R6pJXV1dcdZZZ0V9fX1MnDgxLr/88nj++eerPVbN6e7ujtNPP33wFwTNmjUrHnvssWqPVfO6urqiVCrFokWLqj1KTens7IxSqTRkNTU1VXusmvTKK6/Et771rTjuuOPimGOOiS9+8YuxdevWqsxyWMTH/v37Y/r06bF06dJqj1LTenp6YuHChbFp06bYsGFDvP322zF37tzYv39/tUerKZMmTYq77747tmzZElu2bImLLroovvzlL8f27durPVrN2rx5cyxbtixOP/30ao9Sk0477bR47bXXBtezzz5b7ZFqzuuvvx7nnHNOHHnkkfHYY4/FP/7xj7j33ntH/TeKv59R+2yXQ0l7e3u0t7dXe4ya9/jjjw/ZXrFiRUycODG2bt0a559/fpWmqj3z5s0bsn3XXXdFd3d3bNq0KU477bQqTVW79u3bF9/85jdj+fLlceedd1Z7nJpUV1fnbsco+9nPfhatra2xYsWKwX0nnXRS1eY5LO58UB179uyJiIgJEyZUeZLa9c4778SaNWti//79MWvWrGqPU5MWLlwYl112WVxyySXVHqVm7dixI1paWqKtrS2uuuqqePHFF6s9Us155JFHYsaMGfG1r30tJk6cGGeccUYsX768avOID0ZFURTR0dER5557bkydOrXa49ScZ599Nj796U9HuVyO6667Lh588MH4whe+UO2xas6aNWvib3/7W3R1dVV7lJo1c+bMWLVqVaxbty6WL18efX19MXv27Ni9e3e1R6spL774YnR3d8eUKVNi3bp1cd1118WPfvSjWLVqVVXmOSzediHfDTfcEM8880z86U9/qvYoNenzn/98bNu2Ld5444347W9/GwsWLIienh4BUkG9vb1x0003xfr16+Poo4+u9jg16/+/JT5t2rSYNWtWfPazn42VK1dGR0dHFSerLQcOHIgZM2bEkiVLIiLijDPOiO3bt0d3d3d85zvfSZ/HnQ8q7sYbb4xHHnkknnzyyZg0aVK1x6lJRx11VHzuc5+LGTNmRFdXV0yfPj1+/vOfV3usmrJ169bYtWtXnHnmmVFXVxd1dXXR09MTv/jFL6Kuri7eeeedao9Yk4499tiYNm1a7Nixo9qj1JTm5uZh/3Ny6qmnxssvv1yVedz5oGKKoogbb7wxHnzwwXjqqaeira2t2iMdNoqiiIGBgWqPUVMuvvjiYT918d3vfjdOOeWU+MlPfhJHHHFElSarbQMDA/Hcc8/FeeedV+1Raso555wz7Fcf/POf/6zaB74eFvGxb9++eOGFFwa3d+7cGdu2bYsJEybEiSeeWMXJasvChQtj9erV8fDDD0d9fX309fVFRERDQ0OMGzeuytPVjttuuy3a29ujtbU19u7dG2vWrImnnnpq2E8b8cnU19cPe17p2GOPjeOOO85zTBV08803x7x58+LEE0+MXbt2xZ133hn9/f2xYMGCao9WU3784x/H7NmzY8mSJfH1r389/vrXv8ayZcti2bJl1RmoOAw8+eSTRUQMWwsWLKj2aDXlYNc4IooVK1ZUe7Sa8r3vfa+YPHlycdRRRxUnnHBCcfHFFxfr16+v9liHhQsuuKC46aabqj1GTbnyyiuL5ubm4sgjjyxaWlqK+fPnF9u3b6/2WDXp97//fTF16tSiXC4Xp5xySrFs2bKqzVIqiqKoTvYAAIcjD5wCAKnEBwCQSnwAAKnEBwCQSnwAAKnEBwCQSnwAAKnEBwCQSnwAAKnEBwCQSnwAAKnEBwCQ6n8BTzsythAcSMIAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = roll_dice[\"value\"]\n",
+    "\n",
+    "plt.hist(x, bins=100)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "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": 29,
+   "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>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>"
+      ],
+      "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]"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "roll_dice1 = pd.read_csv(\"roll_the_dice_thousand.csv\")\n",
+    "roll_dice1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "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>Unnamed: 0</th>\n",
+       "      <th>roll</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>value</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>175</td>\n",
+       "      <td>175</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>167</td>\n",
+       "      <td>167</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>175</td>\n",
+       "      <td>175</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>168</td>\n",
+       "      <td>168</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>149</td>\n",
+       "      <td>149</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>166</td>\n",
+       "      <td>166</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Unnamed: 0  roll\n",
+       "value                  \n",
+       "1             175   175\n",
+       "2             167   167\n",
+       "3             175   175\n",
+       "4             168   168\n",
+       "5             149   149\n",
+       "6             166   166"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "roll_dice1.groupby(\"value\").agg(\"count\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "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"
+    "x = roll_dice1[\"value\"]\n",
+    "\n",
+    "plt.hist(x, bins=1000)\n",
+    "plt.show()"
    ]
   },
   {
@@ -506,12 +1190,15 @@
    "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?"
+    "## 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": 19,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [
     {
@@ -535,134 +1222,89 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Unnamed: 0</th>\n",
-       "      <th>roll</th>\n",
-       "      <th>value</th>\n",
+       "      <th>observation</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",
+       "      <td>68.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>6</td>\n",
+       "      <td>12.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
+       "      <td>45.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>6</td>\n",
+       "      <td>38.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>4</td>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
+       "      <td>49.0</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",
+       "      <td>27.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>996</th>\n",
-       "      <td>996</td>\n",
-       "      <td>996</td>\n",
-       "      <td>4</td>\n",
+       "      <td>47.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>997</th>\n",
-       "      <td>997</td>\n",
-       "      <td>997</td>\n",
-       "      <td>4</td>\n",
+       "      <td>53.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>998</th>\n",
-       "      <td>998</td>\n",
-       "      <td>998</td>\n",
-       "      <td>3</td>\n",
+       "      <td>33.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>999</th>\n",
-       "      <td>999</td>\n",
-       "      <td>999</td>\n",
-       "      <td>6</td>\n",
+       "      <td>31.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>1000 rows × 3 columns</p>\n",
+       "<p>1000 rows × 1 columns</p>\n",
        "</div>"
       ],
       "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",
+       "     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 3 columns]"
+       "[1000 rows x 1 columns]"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "roll_dice = pd.read_csv(\"roll_the_dice_thousand.csv\")\n",
-    "roll_dice"
-   ]
-  },
-  {
-   "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. "
+    "age_pop = pd.read_csv(\"ages_population.csv\")\n",
+    "age_pop"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [
     {
@@ -686,84 +1328,95 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
        "      <th>observation</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>68.0</td>\n",
+       "      <th>1.0</th>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>12.0</td>\n",
+       "      <th>2.0</th>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>45.0</td>\n",
+       "      <th>4.0</th>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>38.0</td>\n",
+       "      <th>5.0</th>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>49.0</td>\n",
+       "      <th>6.0</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
-       "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>995</th>\n",
-       "      <td>27.0</td>\n",
+       "      <th>69.0</th>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>996</th>\n",
-       "      <td>47.0</td>\n",
+       "      <th>70.0</th>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>997</th>\n",
-       "      <td>53.0</td>\n",
+       "      <th>71.0</th>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>998</th>\n",
-       "      <td>33.0</td>\n",
+       "      <th>73.0</th>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>999</th>\n",
-       "      <td>31.0</td>\n",
+       "      <th>82.0</th>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>1000 rows × 1 columns</p>\n",
+       "<p>72 rows × 0 columns</p>\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\n",
-       "..           ...\n",
-       "995         27.0\n",
-       "996         47.0\n",
-       "997         53.0\n",
-       "998         33.0\n",
-       "999         31.0\n",
+       "Empty DataFrame\n",
+       "Columns: []\n",
+       "Index: [1.0, 2.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0, 36.0, 37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0, 48.0, 49.0, 50.0, 51.0, 52.0, 53.0, 54.0, 55.0, 56.0, 57.0, 58.0, 59.0, 60.0, 61.0, 62.0, 63.0, 64.0, 65.0, 66.0, 67.0, 68.0, 69.0, 70.0, 71.0, 73.0, 82.0]\n",
        "\n",
-       "[1000 rows x 1 columns]"
+       "[72 rows x 0 columns]"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "age_pop = pd.read_csv(\"ages_population.csv\")\n",
-    "age_pop"
+    "age_pop.groupby(\"observation\").agg(\"count\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x161c913c0>]"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(age_pop, \"o\")"
    ]
   },
   {
@@ -835,7 +1488,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [
     {
@@ -929,7 +1582,7 @@
        "[1000 rows x 1 columns]"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -939,6 +1592,68 @@
     "age_pop2"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "observation\n",
+       "28.0           139\n",
+       "27.0           125\n",
+       "26.0           120\n",
+       "29.0           115\n",
+       "25.0            98\n",
+       "30.0            90\n",
+       "24.0            78\n",
+       "31.0            61\n",
+       "23.0            41\n",
+       "22.0            35\n",
+       "32.0            31\n",
+       "33.0            22\n",
+       "21.0            17\n",
+       "20.0            13\n",
+       "34.0             7\n",
+       "35.0             3\n",
+       "19.0             3\n",
+       "36.0             2\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "age_pop2_freq = age_pop2.value_counts()\n",
+    "age_pop2_freq"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "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(age_pop2)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1029,7 +1744,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [
     {
@@ -1123,7 +1838,7 @@
        "[1000 rows x 1 columns]"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 61,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1133,6 +1848,61 @@
     "age_pop3"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "observation\n",
+       "32.0           37\n",
+       "37.0           31\n",
+       "35.0           31\n",
+       "39.0           29\n",
+       "36.0           26\n",
+       "               ..\n",
+       "8.0             1\n",
+       "7.0             1\n",
+       "5.0             1\n",
+       "76.0            1\n",
+       "1.0             1\n",
+       "Length: 75, dtype: int64"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "age_pop3_freq = age_pop3.value_counts()\n",
+    "age_pop3_freq"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "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(age_pop3)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1274,13 +2044,6 @@
     "print(ninety_percent)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
diff --git a/your-code/DescriptiveStatisticsLab.ipynb b/your-code/DescriptiveStatisticsLab.ipynb
index b64a4f0..5b14d08 100644
--- a/your-code/DescriptiveStatisticsLab.ipynb
+++ b/your-code/DescriptiveStatisticsLab.ipynb
@@ -11,7 +11,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -33,22 +33,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([2, 3, 5, 1, 5, 3, 2, 5, 2, 0])"
+       "array([4, 2, 5, 4, 4, 4, 4, 6, 3, 2])"
       ]
      },
-     "execution_count": 66,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "dice = np.random.randint(0, 7, 10)\n",
+    "dice = np.random.randint(1, 7, 10)\n",
     "dice"
    ]
   },
@@ -61,32 +61,53 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2, 2, 3, 4, 4, 4, 4, 4, 5, 6])"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dice = np.sort(dice)\n",
+    "dice"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([0, 1, 2, 2, 2, 3, 3, 5, 5, 5])"
+       "array([2, 2, 3, 4, 4, 4, 4, 4, 5, 6])"
       ]
      },
-     "execution_count": 67,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "np.sort(dice)"
+    "dice"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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JS0vThAkTPPvJyck6cuSIUlJSlJeXp1dffVVLly5VamqqHVMAAKDJGxkboYzxfRTu8r70FO4KVMb4PhoZG9Gg9dh6K7gkvffee0pLS9PBgwcVHR2tlJQUPfzww57XJ02apMOHD2vDhg2ettzcXE2dOlVffPGFIiMjNX36dCUnJ1fr/bgVHACA+lGfTyiuyee37eGmoRFuAABofBrNc24AAADqGuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAotoab5557Tg6Hw2sLDw+vtP+GDRvK9Xc4HNq/f38DVg0AAHyZv90FxMTE6KOPPvLs+/n5XXXMgQMHFBwc7Nlv3759vdQGAAAaH9vDjb+/f5VnayoSGhqqNm3a1E9BAACgUbN9zc3BgwcVGRmp6OhoPfDAAzp06NBVx8TFxSkiIkKJiYlav359lX1LSkrkdru9NgAAYC5bw80tt9yi5cuX68MPP9SSJUtUWFioAQMG6OTJkxX2j4iI0OLFi5WZmanVq1ere/fuSkxM1MaNGyt9j/T0dLlcLs8WFRVVX9MBAAA+wGFZlmV3EWXOnj2r66+/XtOmTVNKSkq1xiQlJcnhcCgrK6vC10tKSlRSUuLZd7vdioqKUnFxsde6HQAA4LvcbrdcLle1Pr9tvyz1Y61atdKNN96ogwcPVntM//79q+zvdDoVHBzstQEAAHP5VLgpKSlRXl6eIiIiqj1m586dNeoPAADMZuvdUqmpqUpKSlLHjh1VVFSkP/zhD3K73Zo4caIkKS0tTcePH9fy5cslSfPmzVPnzp0VExOjixcvasWKFcrMzFRmZqad0wAAAD7E1nDz1VdfaezYsTpx4oTat2+v/v37a/PmzerUqZMkqaCgQEePHvX0v3jxolJTU3X8+HG1aNFCMTExWrNmjUaPHm3XFAAAgI/xqQXFDaEmC5IAAIBvaLQLigEAAK4V4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFH87S4AAICmrvSKpS35p1R05oJCgwLVLzpEfs0cdpfVaNl65ua5556Tw+Hw2sLDw6sck5ubq759+yowMFBdunTRwoULG6haAADqXvbeAg2as05jl2zWlFW7NHbJZg2as07ZewvsLq3Rsv2yVExMjAoKCjzbnj17Ku2bn5+v0aNH69Zbb9XOnTs1Y8YMPfnkk8rMzGzAigEAqBvZewv06IodKii+4NVeWHxBj67YQcCpJdsvS/n7+1/1bE2ZhQsXqmPHjpo3b54kqUePHtq2bZvmzp2re+65px6rBACgbpVesTTr3X2yKnjNkuSQNOvdfbq9ZziXqGrI9jM3Bw8eVGRkpKKjo/XAAw/o0KFDlfbdtGmThg8f7tU2YsQIbdu2TZcuXapwTElJidxut9cGAIDdtuSfKnfG5scsSQXFF7Ql/1TDFWUIW8PNLbfcouXLl+vDDz/UkiVLVFhYqAEDBujkyZMV9i8sLFRYWJhXW1hYmC5fvqwTJ05UOCY9PV0ul8uzRUVF1fk8AACoqaIzlQeb2vTD/7E13IwaNUr33HOPbrzxRt12221as2aNJOn111+vdIzD4X1qzrKsCtvLpKWlqbi42LMdO3asjqoHAKD2QoMC67Qf/o/ta25+rFWrVrrxxht18ODBCl8PDw9XYWGhV1tRUZH8/f3Vrl27Csc4nU45nc46rxUAgGvRLzpEEa5AFRZfqHDdjUNSuOuH28JRM7avufmxkpIS5eXlKSIiosLXExISlJOT49W2du1axcfHKyAgoCFKBACgTvg1c2hmUk9JPwSZHyvbn5nUk8XEtWBruElNTVVubq7y8/P1v//7v7r33nvldrs1ceJEST9cUpowYYKnf3Jyso4cOaKUlBTl5eXp1Vdf1dKlS5WammrXFAAAqLWRsRHKGN9H4S7vS0/hrkBljO+jkbEV/2MfVbP1stRXX32lsWPH6sSJE2rfvr369++vzZs3q1OnTpKkgoICHT161NM/Ojpa77//vqZOnaoFCxYoMjJS8+fP5zZwAECjNTI2Qrf3DOcJxXXIYZWtyG0i3G63XC6XiouLFRwcbHc5AACgGmry+e1Ta24AAACuFeEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAoPhNu0tPT5XA49NRTT1XaZ8OGDXI4HOW2/fv3N1yhAADAp/nbXYAkbd26VYsXL1avXr2q1f/AgQMKDg727Ldv376+SgMAAI2M7Wduvv/+e40bN05LlixR27ZtqzUmNDRU4eHhns3Pz6+eqwQAAI2F7eFm8uTJGjNmjG677bZqj4mLi1NERIQSExO1fv36KvuWlJTI7XZ7bQAAwFy2XpZatWqVduzYoa1bt1arf0REhBYvXqy+ffuqpKREb7zxhhITE7VhwwYNHjy4wjHp6emaNWtWXZYNAAB8mMOyLMuONz527Jji4+O1du1a3XTTTZKkoUOHqnfv3po3b161j5OUlCSHw6GsrKwKXy8pKVFJSYln3+12KyoqSsXFxV7rdgAAgO9yu91yuVzV+vy27bLU9u3bVVRUpL59+8rf31/+/v7Kzc3V/Pnz5e/vr9LS0modp3///jp48GClrzudTgUHB3ttAADAXLZdlkpMTNSePXu82h566CHdcMMNmj59erUXCe/cuVMRERH1USIAAGiEbAs3QUFBio2N9Wpr1aqV2rVr52lPS0vT8ePHtXz5cknSvHnz1LlzZ8XExOjixYtasWKFMjMzlZmZ2eD1AwAA3+QTz7mpTEFBgY4ePerZv3jxolJTU3X8+HG1aNFCMTExWrNmjUaPHm1jlQAAwJfYtqDYLjVZkAQAAHxDo1hQDAAAUB8INwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRahVunn/+eZ07d65c+/nz5/X8889fc1EAAAC15bAsy6rpID8/PxUUFCg0NNSr/eTJkwoNDVVpaWmdFVjX3G63XC6XiouLFRwcbHc5AACgGmry+V2rMzeWZcnhcJRr3717t0JCQmpzSAAAgDrhX5PObdu2lcPhkMPh0M9//nOvgFNaWqrvv/9eycnJdV4kAABAddUo3MybN0+WZek3v/mNZs2aJZfL5XmtefPm6ty5sxISEuq8SAAAgOqqUbiZOHGiJCk6OloDBgxQQEBAvRQFAABQW9UON2632/PfcXFxOn/+vM6fP19hXxbqAgAAu1Q73LRp06bCRcQ/VrbQ2JfvlgIAAGardrhZv359fdYBAABQJ6odboYMGVKfdQAAANSJGi0oLrNx48YqXx88eHCtigEAALhWtQo3Q4cOLdf202feAAAA2KFWTyj+7rvvvLaioiJlZ2fr5ptv1tq1a+u6RgAAgGqr1ZmbHz+8r8ztt98up9OpqVOnavv27ddcGACp9IqlLfmnVHTmgkKDAtUvOkR+zaq+a9EXMQ/fY8pcTJkH6latwk1l2rdvrwMHDtRqbHp6umbMmKEpU6Zo3rx5lfbLzc1VSkqKvvjiC0VGRmratGl85QOMlL23QLPe3aeC4guetghXoGYm9dTI2AgbK6sZ5uF7TJmLKfNA3avVt4J//vnnXvuWZamgoEB/+tOfdOnSJX366ac1Ot7WrVt1//33Kzg4WMOGDas03OTn5ys2NlYPP/ywHnnkEX366ad67LHH9Oabb+qee+6p1nvxreBoDLL3FujRFTv00z+cZf8ezRjfp1H85c08fI8pczFlHqi+ev9W8N69eysuLk69e/f2/Pfo0aN18eJFLV26tEbH+v777zVu3DgtWbJEbdu2rbLvwoUL1bFjR82bN089evTQb3/7W/3mN7/R3LlzazMNwCeVXrE069195f7SluRpm/XuPpVeqfG/SxoU8/A9pszFlHmg/tQq3OTn5+vQoUPKz89Xfn6+jhw5onPnzumzzz7TDTfcUKNjTZ48WWPGjNFtt9121b6bNm3S8OHDvdpGjBihbdu26dKlSxWOKSkpkdvt9toAX7Yl/5TXafafsiQVFF/QlvxTDVdULTAP32PKXEyZB+pPrdbcdOrUqVzb6dOnFRgYWKPjrFq1Sjt27NDWrVur1b+wsFBhYWFebWFhYbp8+bJOnDihiIjypyDT09M1a9asGtUF2KnoTOV/ademn12Yh+8xZS6mzAP1p1ZnbubMmaO33nrLs3///fcrJCRE1113nXbv3l2tYxw7dkxTpkzRihUrahSKfvr9VmVLhir73qu0tDQVFxd7tmPHjlX7vQA7hAZV789DdfvZhXn4HlPmYso8UH9qFW4WLVqkqKgoSVJOTo5ycnKUnZ2tUaNG6emnn67WMbZv366ioiL17dtX/v7+8vf3V25urubPny9/f/8KHwQYHh6uwsJCr7aioiL5+/urXbt2Fb6P0+lUcHCw1wb4sn7RIYpwBaqym1kd+uGOkH7RIQ1ZVo0xD99jylxMmQfqT63CTUFBgSfcvPfee7r//vs1fPhwTZs2rdqXmBITE7Vnzx7t2rXLs8XHx2vcuHHatWuX/Pz8yo1JSEhQTk6OV9vatWsVHx+vgICA2kwF8Dl+zRyamdRTksr95V22PzOpp88/y4N5+B5T5mLKPFB/ahVu2rZt67m8k52d7VkMbFlWtb96ISgoSLGxsV5bq1at1K5dO8XGxkr64ZLShAkTPGOSk5N15MgRpaSkKC8vT6+++qqWLl2q1NTU2kwD8FkjYyOUMb6Pwl3ep9XDXYGN6hZX5uF7TJmLKfNA/ajVguK7775bv/71r9WtWzedPHlSo0aNkiTt2rVLXbt2rbPiCgoKdPToUc9+dHS03n//fU2dOlULFixQZGSk5s+fX+1n3ACNycjYCN3eM7zRP32VefgeU+ZiyjxQ92r1EL9Lly7ppZde0rFjxzRp0iTFxcVJkubNm6fWrVvrt7/9bZ0XWld4iB8AAI1PTT6/axVuGjPCDQAAjU+9P6FYkt544w0NGjRIkZGROnLkiKQfzty88847tT0kAADANatVuMnIyFBKSopGjRql06dPexYRt2nTpsovvQQAAKhvtQo3L7/8spYsWaJnn33W65bt+Ph47dmzp86KAwAAqKlaf7dU2SLiH3M6nTp79uw1FwUAAFBbtQo30dHR2rVrV7n2Dz74QD169LjWmgAAAGqtVs+5efrppzV58mRduHBBlmVpy5YtevPNNzV79mwtXbq0rmsEAACotlqFm4ceekiXL1/WtGnTdO7cOf3617/Wddddp5dfflm33nprXdcIAABQbbW+Ffzhhx/WkSNHVFRUpMLCQm3ZskU7d+6s0ycUAwAA1FSNws3p06c1btw4tW/f3vPVByEhIVqwYIG6du2qzZs369VXX62vWgEAAK6qRpelZsyYoY0bN2rixInKzs7W1KlTlZ2drQsXLuj999/XkCFD6qtOAACAaqlRuFmzZo2WLVum2267TY899pi6du2qn//85zy4DwAA+IwaXZb6+uuv1bNnT0lSly5dFBgY6NNfkgkAAJqeGoWbK1euKCAgwLPv5+enVq1a1XlRAAAAtVWjy1KWZWnSpElyOp2SpAsXLig5OblcwFm9enXdVQgAAFADNQo3EydO9NofP358nRYDAABwrWoUbpYtW1ZfdQAAANSJWj/EDwAAwBcRbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYxdZwk5GRoV69eik4OFjBwcFKSEjQBx98UGn/DRs2yOFwlNv279/fgFUDAABf5m/nm3fo0EF/+tOf1LVrV0nS66+/rjvvvFM7d+5UTExMpeMOHDig4OBgz3779u3rvVYAANA42BpukpKSvPb/+Mc/KiMjQ5s3b64y3ISGhqpNmzb1XB0AAGiMfGbNTWlpqVatWqWzZ88qISGhyr5xcXGKiIhQYmKi1q9fX2XfkpISud1urw0AAJjL9nCzZ88etW7dWk6nU8nJyXr77bfVs2fPCvtGRERo8eLFyszM1OrVq9W9e3clJiZq48aNlR4/PT1dLpfLs0VFRdXXVAAAgA9wWJZl2VnAxYsXdfToUZ0+fVqZmZl65ZVXlJubW2nA+amkpCQ5HA5lZWVV+HpJSYlKSko8+263W1FRUSouLvZatwMAAHyX2+2Wy+Wq1ue3rWtuJKl58+aeBcXx8fHaunWrXnrpJS1atKha4/v3768VK1ZU+rrT6ZTT6ayTWgEAgO+z/bLUT1mW5XWm5Wp27typiIiIeqwIAAA0JraeuZkxY4ZGjRqlqKgonTlzRqtWrdKGDRuUnZ0tSUpLS9Px48e1fPlySdK8efPUuXNnxcTE6OLFi1qxYoUyMzOVmZlp5zQAAIAPsTXcfPPNN3rwwQdVUFAgl8ulXr16KTs7W7fffrskqaCgQEePHvX0v3jxolJTU3X8+HG1aNFCMTExWrNmjUaPHm3XFAAAgI+xfUFxQ6vJgiQAAOAbavL57XNrbgAAAK4F4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFH87S4AqA+lVyxtyT+lojMXFBoUqH7RIfJr5rC7LABAA7D1zE1GRoZ69eql4OBgBQcHKyEhQR988EGVY3Jzc9W3b18FBgaqS5cuWrhwYQNVi8Yie2+BBs1Zp7FLNmvKql0au2SzBs1Zp+y9BXaXBgBoALaGmw4dOuhPf/qTtm3bpm3btukXv/iF7rzzTn3xxRcV9s/Pz9fo0aN16623aufOnZoxY4aefPJJZWZmNnDl8FXZewv06IodKii+4NVeWHxBj67YQcABgCbAYVmWZXcRPxYSEqI///nP+rd/+7dyr02fPl1ZWVnKy8vztCUnJ2v37t3atGlTtY7vdrvlcrlUXFys4ODgOqsb9iu9YmnQnHXlgk0Zh6RwV6A+mf4LLlEBQCNTk89vn1lQXFpaqlWrVuns2bNKSEiosM+mTZs0fPhwr7YRI0Zo27ZtunTpUoVjSkpK5Ha7vTaYaUv+qUqDjSRZkgqKL2hL/qmGKwoA0OBsDzd79uxR69at5XQ6lZycrLfffls9e/assG9hYaHCwsK82sLCwnT58mWdOHGiwjHp6elyuVyeLSoqqs7nAN9QdKbyYFObfgCAxsn2cNO9e3ft2rVLmzdv1qOPPqqJEydq3759lfZ3OLwvJ5RdVftpe5m0tDQVFxd7tmPHjtVd8fApoUGBddoPANA42X4rePPmzdW1a1dJUnx8vLZu3aqXXnpJixYtKtc3PDxchYWFXm1FRUXy9/dXu3btKjy+0+mU0+ms+8Lhc/pFhyjCFajC4guqaCFZ2ZqbftEhDV0aAKAB2X7m5qcsy1JJSUmFryUkJCgnJ8erbe3atYqPj1dAQEBDlAcf5tfMoZlJP1zS/Ol5vLL9mUk9WUwMAIazNdzMmDFDH3/8sQ4fPqw9e/bo2Wef1YYNGzRu3DhJP1xSmjBhgqd/cnKyjhw5opSUFOXl5enVV1/V0qVLlZqaatcU4GNGxkYoY3wfhbu8Lz2FuwKVMb6PRsZG2FQZAKCh2HpZ6ptvvtGDDz6ogoICuVwu9erVS9nZ2br99tslSQUFBTp69Kinf3R0tN5//31NnTpVCxYsUGRkpObPn6977rnHrinAB42MjdDtPcN5QjEANFE+95yb+sZzbgAAaHwa5XNuAAAA6gLhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKLaGm/T0dN18880KCgpSaGio7rrrLh04cKDKMRs2bJDD4Si37d+/v4GqBgAAvszWcJObm6vJkydr8+bNysnJ0eXLlzV8+HCdPXv2qmMPHDiggoICz9atW7cGqBgAAPg6fzvfPDs722t/2bJlCg0N1fbt2zV48OAqx4aGhqpNmzb1WB0AAGiMfGrNTXFxsSQpJCTkqn3j4uIUERGhxMRErV+/vtJ+JSUlcrvdXhsAADCXz4Qby7KUkpKiQYMGKTY2ttJ+ERERWrx4sTIzM7V69Wp1795diYmJ2rhxY4X909PT5XK5PFtUVFR9TQEAAPgAh2VZlt1FSNLkyZO1Zs0affLJJ+rQoUONxiYlJcnhcCgrK6vcayUlJSopKfHsu91uRUVFqbi4WMHBwddcNwAAqH9ut1sul6tan98+cebmiSeeUFZWltavX1/jYCNJ/fv318GDByt8zel0Kjg42GsDAADmsnVBsWVZeuKJJ/T2229rw4YNio6OrtVxdu7cqYiIiDquDgAANEa2hpvJkydr5cqVeueddxQUFKTCwkJJksvlUosWLSRJaWlpOn78uJYvXy5Jmjdvnjp37qyYmBhdvHhRK1asUGZmpjIzM22bBwAA8B22hpuMjAxJ0tChQ73aly1bpkmTJkmSCgoKdPToUc9rFy9eVGpqqo4fP64WLVooJiZGa9as0ejRoxuqbAAA4MN8ZkFxQ6nJgiQAAOAbGt2CYgAAgLpCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACMQrgBAABGIdwAAACjEG4AAIBRCDcAAMAohBsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwAAwCiEGwAAYBR/uwswRekVS1vyT6nozAWFBgWqX3SI/Jo57C6rxkyZBwCg6bI13KSnp2v16tXav3+/WrRooQEDBmjOnDnq3r17leNyc3OVkpKiL774QpGRkZo2bZqSk5MbqOrysvcWaNa7+1RQfMHTFuEK1MyknhoZG2FbXTVlyjwAAE2brZelcnNzNXnyZG3evFk5OTm6fPmyhg8frrNnz1Y6Jj8/X6NHj9att96qnTt3asaMGXryySeVmZnZgJX/n+y9BXp0xQ6vQCBJhcUX9OiKHcreW2BLXTVlyjwAAHBYlmXZXUSZb7/9VqGhocrNzdXgwYMr7DN9+nRlZWUpLy/P05acnKzdu3dr06ZNV30Pt9stl8ul4uJiBQcHX1O9pVcsDZqzrlwgKOOQFO4K1CfTf+HTl3ZMmQcAwFw1+fz2qQXFxcXFkqSQkJBK+2zatEnDhw/3ahsxYoS2bdumS5culetfUlIit9vttdWVLfmnKg0EkmRJKii+oC35p+rsPeuDKfMAAEDyoXBjWZZSUlI0aNAgxcbGVtqvsLBQYWFhXm1hYWG6fPmyTpw4Ua5/enq6XC6XZ4uKiqqzmovOVB4IatPPLqbMAwAAyYfCzeOPP67PP/9cb7755lX7Ohzel0bKrqz9tF2S0tLSVFxc7NmOHTtWNwVLCg0KrNN+djFlHgAASD5yK/gTTzyhrKwsbdy4UR06dKiyb3h4uAoLC73aioqK5O/vr3bt2pXr73Q65XQ667TeMv2iQxThClRh8QVVtHCpbK1Kv+jKL7P5AlPmAQCAZPOZG8uy9Pjjj2v16tVat26doqOjrzomISFBOTk5Xm1r165VfHy8AgIC6qvUCvk1c2hmUk9JPwSAHyvbn5nU0+cX4ZoyDwAAJJvDzeTJk7VixQqtXLlSQUFBKiwsVGFhoc6fP+/pk5aWpgkTJnj2k5OTdeTIEaWkpCgvL0+vvvqqli5dqtTUVDumoJGxEcoY30fhLu9LNuGuQGWM79Nong9jyjwAALD1VvCK1shI0rJlyzRp0iRJ0qRJk3T48GFt2LDB83pubq6mTp3qeYjf9OnTq/0Qv7q8FfzHTHmyrynzAACYpSaf3z71nJuGUF/hBgAA1J9G+5wbAACAa0W4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABgFMINAAAwCuEGAAAYhXADAACM4hPfCt6Qyh7I7Ha7ba4EAABUV9nndnW+WKHJhZszZ85IkqKiomyuBAAA1NSZM2fkcrmq7NPkvlvqypUr+vrrrxUUFFTpF3fWltvtVlRUlI4dO8b3VvkAfh++hd+H7+F34lv4fVTNsiydOXNGkZGRatas6lU1Te7MTbNmzdShQ4d6fY/g4GD+x/Qh/D58C78P38PvxLfw+6jc1c7YlGFBMQAAMArhBgAAGIVwU4ecTqdmzpwpp9NpdykQvw9fw+/D9/A78S38PupOk1tQDAAAzMaZGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4qSN//etfFR0drcDAQPXt21cff/yx3SU1Wenp6br55psVFBSk0NBQ3XXXXTpw4IDdZeH/S09Pl8Ph0FNPPWV3KU3W8ePHNX78eLVr104tW7ZU7969tX37drvLapIuX76s//iP/1B0dLRatGihLl266Pnnn9eVK1fsLq1RI9zUgbfeektPPfWUnn32We3cuVO33nqrRo0apaNHj9pdWpOUm5uryZMna/PmzcrJydHly5c1fPhwnT171u7SmrytW7dq8eLF6tWrl92lNFnfffedBg4cqICAAH3wwQfat2+fXnzxRbVp08bu0pqkOXPmaOHChfrLX/6ivLw8vfDCC/rzn/+sl19+2e7SGjVuBa8Dt9xyi/r06aOMjAxPW48ePXTXXXcpPT3dxsogSd9++61CQ0OVm5urwYMH211Ok/X999+rT58++utf/6o//OEP6t27t+bNm2d3WU3OM888o08//ZSzyz7ijjvuUFhYmJYuXeppu+eee9SyZUu98cYbNlbWuHHm5hpdvHhR27dv1/Dhw73ahw8frs8++8ymqvBjxcXFkqSQkBCbK2naJk+erDFjxui2226zu5QmLSsrS/Hx8brvvvsUGhqquLg4LVmyxO6ymqxBgwbpn//8p7788ktJ0u7du/XJJ59o9OjRNlfWuDW5L86saydOnFBpaanCwsK82sPCwlRYWGhTVShjWZZSUlI0aNAgxcbG2l1Ok7Vq1Srt2LFDW7dutbuUJu/QoUPKyMhQSkqKZsyYoS1btujJJ5+U0+nUhAkT7C6vyZk+fbqKi4t1ww03yM/PT6WlpfrjH/+osWPH2l1ao0a4qSMOh8Nr37Kscm1oeI8//rg+//xzffLJJ3aX0mQdO3ZMU6ZM0dq1axUYGGh3OU3elStXFB8fr9mzZ0uS4uLi9MUXXygjI4NwY4O33npLK1as0MqVKxUTE6Ndu3bpqaeeUmRkpCZOnGh3eY0W4eYa/exnP5Ofn1+5szRFRUXlzuagYT3xxBPKysrSxo0b1aFDB7vLabK2b9+uoqIi9e3b19NWWlqqjRs36i9/+YtKSkrk5+dnY4VNS0REhHr27OnV1qNHD2VmZtpUUdP29NNP65lnntEDDzwgSbrxxht15MgRpaenE26uAWturlHz5s3Vt29f5eTkeLXn5ORowIABNlXVtFmWpccff1yrV6/WunXrFB0dbXdJTVpiYqL27NmjXbt2ebb4+HiNGzdOu3btItg0sIEDB5Z7NMKXX36pTp062VRR03bu3Dk1a+b9Uezn58et4NeIMzd1ICUlRQ8++KDi4+OVkJCgxYsX6+jRo0pOTra7tCZp8uTJWrlypd555x0FBQV5zqq5XC61aNHC5uqanqCgoHLrnVq1aqV27dqxDsoGU6dO1YABAzR79mzdf//92rJlixYvXqzFixfbXVqTlJSUpD/+8Y/q2LGjYmJitHPnTv3nf/6nfvOb39hdWuNmoU4sWLDA6tSpk9W8eXOrT58+Vm5urt0lNVmSKtyWLVtmd2n4/4YMGWJNmTLF7jKarHfffdeKjY21nE6ndcMNN1iLFy+2u6Qmy+12W1OmTLE6duxoBQYGWl26dLGeffZZq6SkxO7SGjWecwMAAIzCmhsAAGAUwg0AADAK4QYAABiFcAMAAIxCuAEAAEYh3AAAAKMQbgAAgFEINwCMd/jwYTkcDu3atUuStGHDBjkcDp0+fdrWugDUD8INAJ83adIkORwOORwO+fv7q2PHjnr00Uf13Xff2V0aAB9EuAHQKIwcOVIFBQU6fPiwXnnlFb377rt67LHH7C4LgA8i3ABoFJxOp8LDw9WhQwcNHz5cv/rVr7R27VpJ0pUrV/T888+rQ4cOcjqd6t27t7Kzs22uGIBdCDcAGp1Dhw4pOztbAQEBkqSXXnpJL774oubOnavPP/9cI0aM0C9/+UsdPHjQ5koB2IFwA6BReO+999S6dWu1aNFC119/vfbt26fp06dLkubOnavp06frgQceUPfu3TVnzhz17t1b8+bNs7doALbwt7sAAKiOYcOGKSMjQ+fOndMrr7yiL7/8Uk888YTcbre+/vprDRw40Kv/wIEDtXv3bpuqBWAnztwAaBRatWqlrl27qlevXpo/f75KSko0a9Ysz+sOh8Orv2VZ5doANA2EGwCN0syZMzV37lx9//33ioyM1CeffOL1+meffaYePXrYVB0AO3FZCkCjNHToUMXExGj27Nl6+umnNXPmTF1//fXq3bu3li1bpl27dulvf/ub3WUCsAHhBkCjlZKSooceekhffvml3G63/v3f/11FRUXq2bOnsrKy1K1bN7tLBGADh2VZlt1FAAAA1BXW3AAAAKMQbgAAgFEINwAAwCiEGwAAYBTCDQAAMArhBgAAGIVwAwAAjEK4AQAARiHcAAAAoxBuAACAUQg3AADAKIQbAABglP8H7B40lhLNW98AAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -96,10 +117,10 @@
     }
    ],
    "source": [
-    "plt.plot(np.array([0, 1, 2, 2, 2, 3, 3, 5, 5, 5]), \"o\")\n",
+    "plt.plot(dice, \"o\")\n",
     "plt.xlabel('Roll')\n",
     "plt.ylabel('Result')\n",
-    "plt.title('Dice Roll Results (Sorted)')\n",
+    "plt.title('Dice Roll Results')\n",
     "plt.show()"
    ]
   },
@@ -112,12 +133,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -127,9 +148,9 @@
     }
    ],
    "source": [
-    "x = np.array([0, 1, 2, 2, 2, 3, 3, 5, 5, 5])\n",
+    "x = dice\n",
     "\n",
-    "plt.hist(x, bins=10)\n",
+    "plt.hist(x, bins=11)\n",
     "plt.show()"
    ]
   },
@@ -163,16 +184,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "3.5"
+       "3.8"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -183,6 +204,32 @@
     "mean"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.8"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def calc_mean(dice):\n",
+    "    dice.sort()\n",
+    "    for item in dice:\n",
+    "        mean = dice.sum()/len(dice)\n",
+    "    return mean\n",
+    "\n",
+    "calc_mean(dice)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -192,11 +239,93 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 50,
    "metadata": {},
-   "outputs": [],
+   "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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: []\n",
+       "Index: [2, 3, 4, 5, 6]"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "dice = pd.DataFrame(dice, columns=['value'])\n",
+    "\n",
+    "dice_data = dice.groupby('value').count()\n",
+    "\n",
+    "dice_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "value    3.8\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dice.mean()"
    ]
   },
   {
@@ -209,16 +338,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "3.0"
+       "4.0"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -227,6 +356,37 @@
     "(np.sort(dice)[5]+np.sort(dice)[4])/2"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4.0"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def calc_median(dice):\n",
+    "    n = len(dice)\n",
+    "    if n % 2 == 0: \n",
+    "        mid_index = n // 2\n",
+    "        median = (dice[mid_index - 1] + dice[mid_index]) / 2\n",
+    "    else: \n",
+    "        mid_index = n // 2\n",
+    "        median = dice[mid_index]\n",
+    "    \n",
+    "    return median\n",
+    "\n",
+    "calc_median(dice)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -236,16 +396,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "the first quartile is 2.0\n",
-      "the second quartile is 3.0\n",
-      "the third quartile is 5.5\n",
+      "the first quartile is 3.25\n",
+      "the second quartile is 4.0\n",
+      "the third quartile is 4.0\n",
       "the fourth quartile is 6\n"
      ]
     }
@@ -264,6 +424,46 @@
     "print(f\"the fourth quartile is {q4}\")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def calc_median(dice):\n",
+    "    n = len(dice)\n",
+    "    if n % 2 == 0: \n",
+    "        mid_index = n // 2\n",
+    "        median = (dice[mid_index - 1] + dice[mid_index]) / 2\n",
+    "    else: \n",
+    "        mid_index = n // 2\n",
+    "        median = dice[mid_index]\n",
+    "\n",
+    "    median\n",
+    "    q1 = median/2\n",
+    "    q2 = median\n",
+    "    q3 = median*(1.5)\n",
+    "    \n",
+    "    return print(f\"The median is {median}, Q1: {q1}, Q2:{q2}, Q3:{q3}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The median is 3.0, Q1: 1.5, Q2:3.0, Q3:4.5\n"
+     ]
+    }
+   ],
+   "source": [
+    "calc_median(dice)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -275,7 +475,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -393,7 +593,7 @@
        "[100 rows x 3 columns]"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -405,90 +605,574 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "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."
+    "roll_dice.drop('Unnamed: 0', axis= 1, inplace=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "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": [
-    "x = roll_dice[\"value\"]\n",
-    "\n",
-    "plt.hist(x, bins=100)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 3.- Now, calculate the frequency distribution.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "object too deep for desired array",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[26], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m frequency \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbincount\u001b[49m\u001b[43m(\u001b[49m\u001b[43mroll_dice\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28mprint\u001b[39m(frequency)\n",
-      "File \u001b[0;32m<__array_function__ internals>:180\u001b[0m, in \u001b[0;36mbincount\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
-      "\u001b[0;31mValueError\u001b[0m: object too deep for desired array"
-     ]
-    }
-   ],
-   "source": [
-    "frequency = np.bincount(roll_dice)\n",
-    "\n",
-    "print(frequency)"
-   ]
-  },
-  {
-   "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": [],
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>roll</th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>95</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>96</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>97</th>\n",
+       "      <td>97</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>98</th>\n",
+       "      <td>98</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>99</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>100 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    roll  value\n",
+       "0      0      1\n",
+       "1      1      2\n",
+       "2      2      6\n",
+       "3      3      1\n",
+       "4      4      6\n",
+       "..   ...    ...\n",
+       "95    95      4\n",
+       "96    96      6\n",
+       "97    97      1\n",
+       "98    98      3\n",
+       "99    99      6\n",
+       "\n",
+       "[100 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "roll_dice"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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QRtV1Fj8muvuBf+6NsXBIFYngJ2osWgKmpIMtN12935GzNgGgWcYEU5Z1F7siY2GO5AsaC7QRN62zwN+/iykOB1NCiZ1XhTiUm1K9QlaBhe8NaR3SDOuQruFgHVMh6ubPTemplRBUEK+uq21XhpTW2SrlpjrGAv28TsBUtRyc0ml1gdMmsMABA1e82WaH08l8AadWE/GSs5cTvknx6MSjeK/o4mhOhaillGLjbJOxmC0yOMk0xeGAstGtYulNd0ldMR++y03XoA/HfmEsZuuYCqnMWCipEEtVCED7lSFiTGKdZTMWDPHm1FcqBAVbLmnimAppGfjBMC3Siwb9DkwQkztJAC4680DhZ03AZ3dLTrmpzsNelJw2zViom6fPe0t2Ja3lvNl8E7J12LT3jcaiYOkd5r1WoT4fbI1FBfFm24yFWNcvOcvtAIdZM52DKv5+66VCqj2fsQlZy8CTgstYtJkKEZP70MYQDh9Y5v58iwwxfG4s+Fo6502qJBOgvUZkqkDV53dLNSEbVKjmMAcozWksQm2MNVmDdA0Hs8KGFOa9VqFu/lymRaX/beWmAO02IsMN0PKUM7MqxLXctEbAhO+JG2MRUyGtAi/Mpui7aKTUXmAh3uvw5hC2N4cA0Gxg47MPQ5H90IiaNJF07mXRcCpEGZfP73ZKPMxVRJdkYNFvxtJ7HapC9ioq40NDQd+1LoFFRcZCPMcHR8vn2pQKEa9p09YbN0C7+Cy3VEihspDlY+FHvFm331AXOG0Ci4LGwlRu2pF4U7zX9oEhbB9oPrDwtWhnWcYqp7OlQppWhreRCqlfbkoZbTXvYxHqpr1fmpDth1QI9/6LgPWxhzYAoJxmwFoy8Zo2fGwExOc6YzyAs89wqwrBzwknFVLPeRMxFi6W3rJXSAwsWgH+wrmMReeBRYOMieq8WLVKQs2p6gOL5fXVdr5jmQppdnFRqyp86WcWaSZdXQeUsVUFGhN7fQwbSoXghWsWqH6hoIxfk5M+hXVMhajCdW5gKwLWcw+NAWDZ3VStyAJYasnOOWP5mjZTIXidPbRaZ3f36PbuKvC+wXPeXFReV6tWRMnAohdTIa2Aw1iok6uNRmACgjLcPjCEwy2nQgCqnwjV+6O39F45wvXUVEhb4s3iuHxV/OCFhLTidlgUZHfTXj3mg4NC2/RAT9HrwKpwUCg3XZPAQj3UcANb8Ryfu7Uhf4bTD1hLdmDUThoUQ5Tw4wNclgEc27N7FrkGFllWPZAsHPwcgpNo6d0yOBoLavFui7XQpUKa8tFXS7yqLtwqY6HbAK2pkKbLTRXGxNf3Wgws6jUPk0ZbLWgs1qFt+jroQDiYr1lgsUgzKXAUz4trVcjB8UBqKPCzhg9Q+bPfDWMxHvThwLA8Rh2KGgv6fqgHq6rrWtVycCkkj6mQdlAoN9Vs1jiX5XsDsoEKLOZpBic8+i1gqKeEqgueyjTou5vSqZC2GpGJ7/+cldueL/EmprmLgcWq3XnNVIi8jneNBd60w9zs1qFyhQOcj18Ho69jezMQS6F4XrjzT8yrjWEPDm+KZy1nB8U6d3hz2JpwG2MXrbNiHAA8BhPvGzomQg2Aq65rVX2GYrlpy8APNMeO9WzPG5AN+IE7MOxLIWBTgY064aueiNVFwZYKKVWFDEQjsnYCi7NXeV3fjEW/l8i0BQBiGlzyo5SJWAtt00NNM6yD1wYHONheB42FeDY2R33YWLEOrlUhG8O+1DDgZw0foNpKg2Lg6jsxDnWMOrBSISpjUTFoqtp9OBeSR41FK8Bfjk6og19z5mbz/Tow8AOXJEn+UDYU2HBTGPbrqIyFORUyUERF42E7qRCxMZ/luQ9Lrosofq5KbdM9tV/noLBpB3qKXofgh4N1c97E6QpXHxWxHowHPdg+MAAAOhVyaJWKWP5N+1UhIqAQ6yznAOmqsQConuapOven8+VrB5GxaAczhsYC/9y1FKku1AnvQtFVgXpKqLq5cFMqujIowVg0fWoRAY/v71Vs+GrTnyrGVuK1BT+MNtqmB7ppr0M/Ew6KvULC/xx4LXItd8aMhTD6oxiLw4ixaLMJmXh/EVAcdmAsCm0BNN+jWoZaNc1Tl7HoOhUy6PTdW8SCUW6Kf07lB5tErlYerf5/VQrVWCpEZSz8aCy0xjFzegPO6dCmGYuVxqKhVIgaMFWy9DamQk4/g6xC5UqgOhAOsM4mVD0LBg4shOcEOxWymlcbgx5ZNl8MWpZzu9VUyKnqqRDMdOtKtNWDVdXPVr+7aUyFtAKOeFM8PINeImm8tvqFiBbtYqJvO1B0VdBUVYjWOCalI2nhY9G0Mlx8/02lQtRqlyrNw2iDLP8+FlmWBd82PU2zgh4h1OCHA5wK0WmQQsJRYvNnG2St5tV42CcdhItVGe2kQTFUZthJY8Gw9FYPVtWrQqoxFiIV0jVjcRoFFoxUyGri9HpJK/06BLIsy9XKm+4UXRWUUxh+fCxcnTc3Bu0wFuKBPxsFFi4e/DroqEdXY6ssy0iB67CBVEjZ1Cy8TbukAQpwjFwUfSzC/xy76FQvmTdur5B5XhUiD0dUKqRQFdJiKuQknXLmaNkWlVIhHhiLCqmQqLFoCS4GWUvGonmTKoFTs4XcVA4roqLGAgtPjAU7sNBE0uOWFhc1FZJlIGv160Dnze960sOvI422PJ7Y1U07RGFkKcUW4Bi5wJvNOog38alelDu7tk3fGPTJNbTAWHRQbpprPIopZ29VIaVUSMUUc0XGIjYhaxku5ab9JGmlEZiAeI9BL4HNVXkXFe37RNl5s+oDwDv96k72rRlkrb7bA6O+1HX4SHPNNQ+yq7HVXOuH4V9joYrlQty0y8FP+BuyDjMlpdOU6Z0vYHdK13ksvrcxYiyKVSHLax8KwCBLjAOA6WNRQWNRuSqkJmOhatnaxukTWKCJYCs37feTxjUOGLi8K0mWD3LTtt4qY1GValY3KedUSFsGWajcVaa5PHy3On+O3NiKtyFiPQHlvOnzpFsS7gZ4it5PjIV6f0NPh8jNd3MkGTPu/MOMBZVmEFqywwdGrRtk4QZoeSpEpEYdLb01qRB1HfXCWDg1IYsai1aBFybdIrXAjEXDVRkYO4q+AqD5qhBfp1bXJmTatuktMRaDPn2Sqgrd53JlGnQ9R0SA4pWxUIPKADdtX4FvCFADidDTIVQqxNUgazwsp0JULZnUV7XEWOAGaFsbS3G+yzo7Z6RCVAO06s6b9RiLaOndEgoGWZaqkH4vaaURmIBKz+H/bq4qxE+eXTw4K6KFwVjQ5aZttU33rZ/RMTGu2gh8HcFaqdfxRaGrp6gQNRZqqi7EMXKhbjahBxa0QRY3FYINsorPGdaSYY1FW23TcQO03upz5eusPRWCmW6dg6r4bsUj3HZVSOxu2jLwqUF3+sGBBdY4NJ0TVZXKAC2kQpj+EzaIB+eM8fIEMCUowjTNUIMttQlZS23TF+i79Wg+pq0KcazmEK9Tu7/i++Vrcy2zAeFtdCVR8D7RWACEb+stq0IODKHfd6sKoQyyTs0WMJkvClqyg6N+64zFUWqdXf33ienCGvBxGAvxc7Eetl8VslpnI2PRDrD4S8tYZOXAYtFgIzAB7EYn0HRVStkgqx5jcWhDNE4rP3B4Uyg5b7bGWOSMQBupEFfGYmphPlyuZUNZuBseG6AGmuvMWKjB5dpoLA4MUdm0Y1XIsAdbGwN5ct85NSu1LdhombHApa4Ch9Caa0uH4H1Dd2AQ2guxHlYNmtbdefO0CSx4Govll9LvJa00AhOgUiFiwu/u+fFbUOHLIEs8ACJCpyJ5/BCWnTfb6RcgK356vjUWOvGmW/OwvLMpLQJdXstXKsTPd98kShqgwDdjE1SGIkSGSGC2SOVB6vBmbunNmSNZluVVIYM+9HoJbK3WhZ2TsxJjkPcJaldjgdfZfi+RegtbBZ66h1DrsjhEifWwStA0X6RFQ8dYbhou8MKkrwpZ/n+/lxQagTVt6y0oeUpjkWUAx/bq+y2o8NU2XaZCVg8npZYuChPpctPmq0JyjYU0H/OgX6FanQO49/jQ+XwM0ALha3NVT1Eh5vxVxmIdrLB1UO9viPdbAAfbWxtuGgt8OBBM5GHUzFEVqed9gtq5H7gBGgb3oKHuG1R6TqZCVuthlaCppH+rUBUSy01bAoexEItXPxHCnnJ3viYg7bxXDyHAMuI/sDrNN9GITGzkBx3bIuuuI6J+Kn88RYKmfq+bclMp3uz79SjJu5v6SoUo10lwKsTPAixOUXW/+yahzs8Q0zVclDQWATciE5vv1sYA+r1EBracoBafzsVzjTdtlTFo23mTSjkXxmg5aKhzkEppiQOCWA+rBE0Uo8jV+Qn7+Oi82RIWDEvvFDEWAHm03XTJKUXRLd+/OZ2FiIoPrii76hqLIvVHaiwMtdUbLbVOnqE0l89USC5KrVduKn02FAqz10tAxGK+xZt1v/smoc7PEIMfLlQWbx0YC7H2uKT0xOm8l+R/h6vbVJG6YCvnadZKeqjuOqvOQWrMqnjTB2MBAMCd/jqtVts4bQILHHHrFqk52nwAmi/5FNg5WU6F4H83EVio2oiqp2Hx4GytxEpkKmSud4MbD/NUSFPVN2magbj0AGksfHyv8rOVUiFuxkImClOcPmbeNBZKMBigfmGizs8Ax8iFSpmHnNbZVTZfobHgzD1cESJKpjE7qDIGgrEAaKcRmS6w4K6z6r5hYmfFeliHsUBkJXvOiENCTIW0BE530xRVhQA0X5khQKmVAZDdrOfABousDtZcuMWDI6g/UryZ6qNowVikWXNqefzdD7xXhejEm27GQiY1tzw1ero/kzVgLGQqpGbgGwLWKhWi6L2GDpbesmU6ChhMqZAxEiq3kQ7RrbPcg4a6b1DrlVhH81SI++cSa+omuo+c6b9IM/k9xaqQllAwyNIxFotuA4sSRdfQ++PTwcGxH42F9LGgonhDK1/BWCzH1czigjclbJDlI8U11fhP+Co3LV7Lz+YqFq78uw9v084D33B1IFysVSrkZLFJl5x7DhoLHDDgNUy2Y1+lmHu9RJ6su2UsRoXf66AG9jNizOVUiPvnEuvg5uoaALxnH88rNaXaNk6bwEJtBERBMhaJkgppMLBI06w2RecKLLKSZaIVF24hXjQxFqbTePHU0szigr/vpavqciE5NpnXzu3mFrrFB3noaCxkukfSbMubj0X4aYZyGXN4Y+RCfLdCjB10YLESkgu2dOAQ1LoyFgDFVGjTwA3QMNipkBJjYUqF1GcszkCBBSf2N1XftY3TJrBgMRZp+4zF8elcCnPaCizEAtDvJdJWd1FxscurQlYGWZRS2nAaT5Kk8ZJTfNIY9npwaCN/YHdrlvKKwEHNaTq3TTcIXF1OjRz4Eu42iZxVWX/xpthsROfikAMLNRUycAhqxfOLDwu2wKLNRmS4ARpGPkZz9V2p3NSw1uUaiwqBxWp9PoACNB5jgda5GFi0A6cmZLIqpPlGZIJ6HA96hUgfv78PvwUMwVhsDHrOvQBU7KmW3iRjYc77NW2SJcRzSbKkXwf9njTuqetRMtV8NleNhSkV4rt1uqpfCHHTLutAwt2MbRAB4QEZWIR3vwVUHULfQd8zQQ3IBMR1jp6cSpE61ji01TpdbYCGwa0KKZebEnqy1X2qlQpZ3ccDo74UcHK8LMQhp99LSmX9beP0CSzQJNB9SWpg0UZViC4Ngn/WFGMxHvblBli5u6m09K6WCgFo3iQLNyATOOTp3mrFm/08YONUuxjFm6LCxJvGQkkzBLhpr0PlChfiuz040j8joUCtChHPDGeOyFQIYiwOBcJYqA3QMHylQnBPpEM1fCwmMqXUk2l5ziMaSqkpwOkUWDAsUnWMRZOpEJ1SGQBVhXg2yJJ+/oixqCzeVJ03HVMhAJixaEi8SQgsfQVtus+GgxjOvRVCMKp5kO/W6TIVstroskyfHuwKuUFWnq5puhlgE8CbzeZ4DVIhUrxZTIWwqkJQualAwceCDCzaaUSmNkDDYFeFKGtbqWst2v2l82YljYVYn/tOwm3JDPe639a7H0FLWCiBBbVICfGmau7SRmBBMRa5Ha5fS2+xsWwM+6h7ofuiPVuk8r6egWj1ci7SzFjkjciaFW/izd5X0KjTRrg2D5NGWwSF6VtjkadCcA43rE1brQoB4JsEhQS82YggaRow+6KuR26W3mWNhVjDHj4xJbVkortx043I1AZoGFUZC1NzuXpVISKl1JPPvot4s+vOpgCnUWChnqSp50Q8PL3VxGu6ERgAbuU7Kv3OZ1kkhthYRoMe6l5Y/QEAyMVKAGXaVKdDEMhbpzeVClmOB5dg+QoadVbc+N+cRVl3HQA3ZT4HlOo8NA2Dmq4BCPukrwPefKTGooXSyqoQz4OsCnGobjIxFgKqlmyjpUZkVMt0AaG5mMxTYzpWZW1KPWCI9XCKDl9cVGcsYiqkdai1+tSXLYIH8TA13QgMgKex8N0EDS8A/RoaC/wQFjcANbI3R9KSDm2YsehTqZCa+hndw4wZC47wTTYho1Ihff6pkQNVGOnz2r6wp1SuAIQpMrUBbz6bsu9JuIHFUSU165IqxS3TBQ6O+oVnQV3n8kZk7aRCVOEmAMDWeCDHaDpoWAMLJBLHTJtr0LSHRLCSsWDptMIwxwI4jQILdeGkHhSVscCNwJpKhxhTIaufnZguvJ7WsDho4OCspwIzH7jdt3oikx1AO9dYoAVOqNU9aSzK7c7dmofl94gqN13lub2lQsqMha9r+4LqtQEQXvDDAW7AJzbRUKtC9mYL6UujWnq7dDcVDCTAspwcN/1StWR56/SGUyEGxiJJEim2dAks1JQW3tjxPXBN8+CUkksqypZybhPdj6AlqPkwqjKEqh7YbkhAKSBqp03izeXr/AU2MiIe9HPVd4XARWo1BstcoLhtJQtjjTulQF4V0rDGooFUiGAa1M+WJImTl4WJxqyTrqIg6+SRiC20yhDVawOAbzYWEvBmI4LPaaCpEJEu6PcSGdANnbqblhkLgOJm3jljQQQW+OcmAWeJsVAPUKgnUr+XyHvnmuLFjLI45DoJwGMqpD2oXwx1QhOv6TUg8tPBNOH7vUQ6uPkNLBBjUaMqJL/OcnEQkbLq4mmqeADI696bKzddaSyIgLF2YGHog+IWWIjgx2CQ5c15M1+4XHpBtAk8R/s15mjXkHMftSAPVStCCRxdcvzqeiBwyBBYjFsqN9W1TJfj2rTbepe6myr3RD0c5EGT22eruj6bdFpto/sRtAR1EpCMRVZmLHz5HejAjaR9vn+hKqSWxiJXLwPkNL4uFaItN634AHIhNm2c6xXue940FmSZKN9ciONj4a/ctLxph+YTgTcp34FVm5AN+AY9+XyE+jmotchNYyGY0OIcxmysKlJv2sNGQHUUVcFZZ8X+IJYRNRWibuxV7coxo9xzuP8m99620f0IWoL6MFMR+ILYgJo2yZJqZSIVgt/fp/smtt51UX2rmCD1MoC+VbitlW/TynCq45/vVAjZ7tzBXMikQxFpFl+5eUm1DuobpDWFPM2Wp+tCC344wA34xPwLNxVS7qXhMvcmRK8QAEsqpGHXXQG1B4oKjlBePCOy54syZnVjl6W0jp+toIFzCCxMOq220f0IWoK6KFFr/SKjTrbdMhZNpGIm6DRYx9IbMx8A+QOlGseIhVTXcW/cMGOh9oAB8G+QhVkuARdzIU65qa8upFIcVhDvhrPZZVmWB7/Dnvdy2zaBF3uZKgw8FYLTBX0HtmyPsPQGsAUW7TAWuRFhuawfIP/MptJ+UTUoPl+pKkRNhdRkLDaGboyFWEO67mwKcBoFFiWNhUG82U/8b0A6dJ8Kqa+xEHRmvnBWM8hqanGZE5u/7GFQU5Q7Q1S3Cpc0A6sJmQdWYZFm8vspsAEBMRazRSZ9ZjYGfacALTTgzUZ0wA09sKBSIawmZIRBFgCYq0IaPlQIiD4lddbZEmNREqkrqZCKwlSqKoTHWMRUSOtQ6WiueLOJVAR+P+GPoRUVNZCKwQGBbMldgWZW2yQL1buaVrFRdE3ToXlVSLmHwd4srZWCMaVCXKo5jFUhNb4jFXiRK+gXAkoz4O8Duw+GWqZpAhblDj2ntHyDai8gU2WMYGhCGGQBmMWbbRlkcZlhU/l5zljoDlDFjX2jYinthKoKcWhCFgOLFuHCWDRh/UwB02763J9dreyKokFWDedNeR3BWCyvVfLQtzpvCkvvZjUWOBWyNR7IzoF17q2Jjek7GFsZr+ORVcCBxfJEFJ7GAp9ex8gddr0Zi558PtaKsXBJhRBNyNTr6TQWrVWFaLRsHJG+WCN1jIVqBLhRsdptj/AZcllDRoM1S4X80i/9EiRJUvjfeeed19TYvKLkY0GJNwnGoqlGYAD5JD446ms33WZSIdWoNhU587ESb2pOZPZUSLOW3pQOotdLvLBR5nbnLhoLffDlU2MxQbX2PVQCGZLGAjNqSZKgAC2cMXKBRbliw1mnwMIlqNVpLLCuQRWpt9E2PU0zLylnmZ6zaSx6wmCxWg8kvK7m3U3rrSFtY2B/SRFPfepT4eMf/7j8d7/fN7w6HJTKTYnvmm5W1UwjMABsn0sLigDwhPcX2OAFYKDRRbCug5rlAOSRuq7cVCcqaroJ2YJIhQAs7y3uulgFxjJRB/MxY3fTvr9UABZFFscYDhtQEgUHyKpwUWQswk6FUP00hi6pkBqMRZNNyI5P52QDNIzDjEOGWEc2NKkQdWOv6iicz/+ePOS6MBY6I8I24RxYDAaDtWEpMGzmJgC5Hzsl3vTdCAyg3PCHAjcV8+V7d2D7wBCOnLVpfV9MWdZhLNSc6khzshQPoE5j0XQTMipgBMi/2/9x+wNw1yMn5c+ffelZcNGZ9vsIwBNdsmrQ0+Jpp3gdM/NxarqAm7/5KDznsrOspxVcIw/gvyW7D6ii4BB1IFzgzabLVMhdD5+E45M5POWCQ9rX1GUsdBoLVlVIg4yFCBbUBmgY24x1VgYWAw1joRwOqqZ5MGMh1iwOYyGdPwNIhTgHFnfccQdccMEFMB6P4TnPeQ68+93vhssuu0z7+slkApPJRP57d3e32khrQn0wKFbV2KyqycBiQ/81cN7/kRNT+Ge/9Tdw/vYB+PR/eLH1fakFoArNrKrA83JTnXGMmbForNyU8CcBADjr4JIpet9f31n4+WWPOQif+D9fZL1ummZ5u3MqFeKQH+WkQnTX+Y1P3gG/+cl/hPf86NPgXzz7YuP74Bp5AP+unj6g+iGss/PmHLF1XfpYvPp9n4VvH5/ATb/wEji0QR9kdomDjpPGQmHDBMRz1kuobqfNW3rb0iD4d6zAYmROhYgDVlXzL9LSm9OEjPDr6QpOgcVznvMc+P3f/3144hOfCN/61rfgne98Jzzvec+D22+/Hc4++2zyb6677jp4xzve4WWwdaCedkjGQgYW+c+aVC3P5nSEj8GpCrnv6CmYLTK465GTsEiz0gaqAgcEgqWpVBWiWPjKtErJOEZP8wPkOdnmDLLowObfveg7oJ8k8oHcmy7g8994BB7Y2WNdF1ca0c6b/GoOTipER0ffvxrvfYxxq62thzUM0ppCWRQsrOLDGSMXlI9FF0Hc/TunIM2Wp3ddYCEOAHg9cknnYVMzjMdsjeHa738ibG0MSpte1coJF4h1anOkX2dFgDNPM0jTrKCzE5iXGAtFSyaZUTUVUs0gCxsYuhhkrV1g8dKXvlT+99Oe9jR47nOfC49//OPh937v9+Daa68l/+Ztb3tb4Xe7u7tw5MiRisOtjjnqFzFPM7INLcVY4LxolmXSQ9/3mHTIm6DpAwscdOyemsGZB/WaDYDi5iImfS2DrEExFVKO5HlVIW22TQdYpjyefelZ8t/3HT0Fz3vPJ9hBFl5YhkRe00V0aSo3taWrxHg5VTUqyxQiY5GPUWEs1jwVIi3vWw7iFmnuC2J6b6oLsEuqTNcrBADgzd/3BPJv2mAs1GCaAj6MzdMMRsSaLPYMEQyVqt+Uw0EVS++lORzBWDikQkIwyHJOhWAcPHgQnva0p8Edd9yhfc14PIbxeFznbbxAPDTjQQ/m0wW5eVCMBd4w5mnmtXMc5QipQuT+pvMU9mYL8uHA9N0OI7DAVLOYsLWqQtSTpcWRTkXVsiwuqAWTAnYO5QSR+JRftwmZyfbc1s9F3F/O/VPTYGFqLIqMxVo7b6LNpqtUCH4eTd8ztR5xS6bni1S+RjXIMqGNclNVs0MBrw36AL747Nh6IlXRjmF2Y2NYrW362lt6TyYT+Id/+Ac4//zzfY2nEWRZngsXtLupCVmBsUBCGN8nDaqHhYozRgPZ9EaX/1MDCxsmUsDXq9VxUW5SFudNsZBqy00reupzoRNvqsDBAU8XsRxvktDBoYuxVW57ThhtWdIV4rvj3D9VYxFix01VY+GiVQkNeLPp6l7j9zPdQyplOGRubMUNkV8p2Ea56URWr+nHNSg8+/T3I26B8LFQ74m6sVepdsP3cTzIfYbWrdzUaQT//t//e7jxxhvhzjvvhM997nPwYz/2Y7C7uwvXXHNNU+PzAvydiIlMRaUyYkd7BP6ShMuiL1BdN1UU/BY0QQP22DClTASozpGVqkKUDcDOWOg0FtVETlzItukWtqnwXXNKRNGDTLEbbm3T9ayO7TpiQeHcP9V7JERhpNolM0RWhQsp7u11p7HAgb4pyM1FzvlzgOdHZhAQqsZrXGDGwnT9OjClaARwiabuHomAIy83La4RpXJTyVg4BBarsfaS5VrgxHquq8binnvugVe/+tXw0EMPwWMe8xj47u/+bvjsZz8Ll1xySVPj8wI8AUyBhUyFoC8Gn3LVnFpdLBgaC4ClzuLRkzOtgNOVscgftF6tRVttm64rp7N3N222lp0TwAGogYVDeZfmc1XRWJi6pNooWg6djGvk8bVDYgPyFFsx+AnV/8EEydYNklxj0XIqZF5gLAwaC4LZwxvuIs20wfmeLHXskcJHHTZQBclknjqxHVzoephg4CHrngVx68QY1ZQWrgACqHZgwvqKJMkDC0oTqMKWcm4TToHFDTfc0NQ4GgVekEcsxiL/YpJkuSBMF6n3HG/ew8ISWGyOAB4+qQ0asMfGjqHtr4CkBgf9nJ6tUm6qtE3XlptaREUilTJdpFpFdh3kVu3mSB4/kDzGwvy5XIytTAJXm4lZngrhMxZ5q3t+uqYtqKLgYZ8foIWGgkHWKq2qPh9NAx+ITAeIBbEeDZT04ECz708YOgYKY3TBpgILnb8GRpIkMOwnMFtk+gA+LaZU9OWmRcbCJcU7KYmrHSrL1jUVsq7AEaiYyCbGQmUQ5AbhORVCUY8UrKmQk3zGAoussDioiuJe1zZd14RMnwopLi6+wdVYiMUFwD0VQsGFDZL3iCxbNW+seSrEPmaVZfLdkt0HVFFwiJUrXODNJre8b1tjkZH/XXrdag4UxJtKtYQOnMoLCsN+ItmCpnoF6XqYqMiZsfL3k2UZQ2OhpEIqiNLV+yjiOg5joXZX7RLdj6AFzJmpEDFR1BMzrhbwOi7mhpd7WdBshEsqZE8RWdVZtEtt0zVtoW0bMH7gm9BZiO+/z6AI5eLPCCJtKuxKGgvSedN8HUGtc9TnE00pZ0ibtlxcFXfQkFgVLvLupkln5aacqpA0zUDsXZjZK1RLGO7/niIK5iJJksYrQ9RgWgfTQQD/TJabKocgdWPPxZsOgUXJHI6vy5kHlAo5LQIL2VwMqfepqhARFaobvU6UWH9cPFHhYYutOGYsbO3V8SSva9ojc5fS0pum7G0b8KCfMydNMhaU14QKXYdWCracps3YSgD7DJhSIbqNVaZCHBiLMssUzqatq1wJKfjhgkqFtH2v8QaoS+fie4vXI8xYmNKleaWZeyqj6cqQvIeJeWymeYb3C7uld7Hc1CkVogiXxXLAYz1XWjbHdFQT6H4ELSBnBsxOZuKBVxkL2QPD84LA9VewpUKqMBZCZJX3YahRbippdY1xjGAsDB72Ve1vOaDapusgHkqOnsZGPXKFkXiBMqVCdNcRP+cwFto+HAFt2iU6OMB0DRdUEzLhk9IW8Hdrq3gAKK5HSZKw+glVZSyWf9MsY8HRWACY/VKKjAXtvKkeYDYqiTdV4TI/nRpTIS1jjuhIk5PZgmhCtvy7ZlMhXI2FrpR01yWwmBXzjT7apsvTr0iF6LqbGj5nk63TOQ6nApKdYqRC8nmlS4XwFoVCYGEoN9VqLOZ8xkLVxYSosVCFgCF2YOUCN+DDC36bgRzHxwL/XA3AOcHnZMZjBSg0+ewD8AyyAMzN7qhUSLnctMhgVgmYVD2IG2PBX+eaxukRWCBRkmkj1Z1sm+pKyM2JmTrvzRcpHJvkLd1tgYWk2tT+HpVSIUXabqRJq3Ac4ZosObUFABguehq16VD5Wjw2yGYNPrSkAipVhZQMssLZtEvBT399fSzwZuNadeQLONDX3cOFYQ7qRNkYuQmV+5YiUyFNMRaMnkwALhoLTa8Q1dK7QoqntD57EoC3je5H0ALycsMETIZA+sCiGY0Fx9IbwJwK2d2bF/5tT4UoG0tFxiJNM3lSVvP15VRIMfdIoUmTLPz92+DijmirduGmGcR1+r2ELLW1lZzllt4OVSFr0DY9ZK8NLnJvgyJj4bvCzIRpgbGg54jQTyRJORXMmcdqGbMLxhWqJ1ygzicdco0FNxVirn7zwVi4dDedI3asa3Q/ghYwQydWk3hTtwGJvHtTlt5sjQUhzFQDCX4qpOy86JL3xYuVar2MT0hZlrHqq8cVXOq4mDEDOADEunBsuK3lprygLbc814hAmU3IWL1C1qBtuq4JWUgdWLnAqRD8nLfZqRXPZd28Nq1FLI1FxXJTgHwTbT4VYh6bMRWS5QUAuuoeqbFQDbLmC/baWtYXrd4/aizCgytjoSs39U0Xz5gU/WFDKkSUoJ4xXnqdnZwujE2OVMpSbbLGBWXhSzE7+JqmqowqpVlc6NqmU3Bhp2zdBG3GVvI6TBGobjMSC8o8zaybb6kJ2Rq1TQ+JVeECs3XCbA//vM0xANiDU0oHxWHxVGMnF4wbTIMC8MtNh4bSTsxm68rq1Z5IIpDJMv7eoTPIctJYxHLTdoA1FqYvShe15+KxhspNHVIhauQrgo2LzjwAQnNqYi1UkRX2dnBZuMXD2u8l8kGiNBbFigf956ziq89Fbuntt9xUzKu6lt42y3NTJRNA8R7bStt0qbAgGQulKiSkMXKhbjZSr9VRKkQXnJo8dTjpsj1FG+CCphkLtaeRDqZ5Vggs0IEBr8flVEj+PHM/Wx3GQsypmAppCTlF1ZNOZqaqkJ5SFdJUKsRVYzFPMzgxLU5QEUScuTmCrRVrsXNKb+utTly8kLgs3BPCzY5qC40XUGMqJBCNhQtjYUuFcDdEm+W5SWOBPTAA7PdP9RtwORG1hXITsvBYFS7UzaapCjPzGPLvViveFIevCk3wAPg6BgqtGWRZ2JQ8gNdrLPpJArrqnpmicRj1e/Kwx13XJjM1VVlBvBkDi3aQW2czGQvl4XIpQawyLlsq5MCwLyerTlOxfWBorB4RUKm2QmDhsNhRJxSqegYvoKaNvcnW6dyeLADVUiE6FTbXfMq2IOStq8tjUsdpY3xUxsJ07a5Qapvu4D4YGtS8e25I100qRO9jYU+FmOYx7j/kiqYNslQ3Sx1MZc2YscCMAL636nOcJIlzxYt6H/sO4s2QmpCdFoFFUWOx/JmJ7lIZCxd63AUuPSwOaQSc4t+HN4dw+MBo+TNDYKEzH8Lj4UD1wwCgtSg4XUC1FhdoUmMhLb1ZjAW//NL2IPOrQswUpuk6amBhu3+ltukNmb/VQcnS25IKChlqKmTUQSqE093UZNZnMo4SCJmx4Btk2Q+d/V5SOKDg75HSOMgyenYqhC4H5/Ryik3IWsYMWWcLxiI1MRYa8aZvKnZBNP3RYfvAMs1xVElzFBgL2VPEFFgUGzzh1rwuCzdVG06d9sWDZ2MLZFVIE4GF4TSmwoWxsFmFszUWNvGmYWNVAyDb4qxrmx7Spq1rQhaS1wYX6ilWsFttpkKmhUDfzFhQaxGHjq9VFdLgoQKALyw1aiyEeWKvWN1T0K8QG7vUjjGDJtV00KXcNKZCWsYCifc4jIXex8JzKkShSU04vLlkI9R+IcKNc3uTmwopU5ZVxKniARgVGAt9KsQ22eXi0kAqRJfioiAWftU9lII8jWpEqXk1h0VjYfH5MDXhKqdC3BgL2XQtoMBiHdxBuVA3m6aE4OYxoKoQbbmpnn0bslIhNapCGkyDAvCDHlOFVJ5Oh0J1D2ZxKCPAsVzXmBoLxXSQG/hnWea0nzSN0yKwyE+WiVxIqTa0eVTaUirEoVpBZ5JFMRbmVEiZsqxyalUjawC0KRfaNHMDiwYZCwer26HD6dhqkCXKJK0aCzOj0jdQ0WppsSmXm2VZWbxrEKx1gYLxmireDCj44UJ1123KbM8EHCTrAsjZwsRYcMSb9RmLpgyyVO8WHUzzLG9Qqa/uoYwAqzIWQrvWY67NBffe6LzZDnC5qaCWqjEWvlMh/GoFXZrDPRVSXgCqlPOplDoAbRxjs70WyJuQNSfe5GksxAmeId60dW11dN6sUm6qXtvEWODAWE0zhKKxwKdWWW4aYAdWLtTKIdnkrsXPUvSxoOf1wpAu5DBG3M2bQpOHikWaG/TZDbL0KR/xnInbQ6W0VD0NgHvQpKvac+k3FMtNWwKmwsUibdJY6AIL34vBjOljAWBgLIR488DI2l4dgKYsq3w+ys2OMtLJO5uap9rYUeTkgkVaXNxNyFMhfLGU1iCLWXFhszyX6QpGKsSUp8ZBmzhJmUyBugD+/gVjMVxjxqKkseii3BTdN5vGgprLpvkn4Kdtuv97gjd0W9Bj6u2TKoFXfk+wMLasuXJN86jrM5+x4FXftYXTIrDArnLG7qaoVhmjqSZkLrl/f6kQE2PhoLEgGAvSx4KZhmhSGY57cdjg4oxodcxkVjPY1NymU0spFWJyXV0tsr0kn9OhmU+J73/QS2QZ9n5omz6SgUUHGgs0J7TlpobnlDOP67RNb7JXSIEB41p6mxiL1e0ZkYeo8gHB1Z9Hx1hYvXBW750kvHWuaZwegQViBuqIN0PQWKit08lUiCmwMBhbOVWFEBoLk/OmjS3IUyFdG2TxF357EzIeE8RvZlYeU0m8abh/WLgrSn9D01hQLa7z5lBhBD8uUDebTjQWHEtvY1UIR2PhoW16A4cKMa5hP7FuuKYAPlXuj6on0/VEcv1spbbpq+eU0gRi4Pc2lfW3hdMksMgnRd8k3mxZYyE2iiFjwxP9QnCaYzJfwKnVg8OuCiGMraqU86nqZQBNuak4sVlSIXm9d3MaC07bdBdnRJv/BNd8yuaHYUpVuZSb0sLdZiqeqoIyMzJVxYQMvNnkoj9+qs0XpoSYWsXC8Izk9980t8S6UqUqxK1ywgUuAY+pt0++hyjf4+qeFMSTuCrE8bOp6zOXUZwrzFjXCGMUDQPn2Psm8aamKmTkkHd3gYuokEpziP9OEoCt8YAl3qREVr6qQnKNRe6hzzVtaVIZbjL/UeGiN5laAgJXS2+ONbjaK0Zd7E0LmCkNFoqPBWVmFKLXBgf4ey+lQlrtbmpnLEwpSw4dn2sDwmIs8qaLjMDCkHLL94bia8V9w4cHvB64fjZd92FKE4gRkusmwGkSWOBSKp14M8syA2PRzGJgOiWoEIwFDhqEcPPQxhB6vUQGFrtEszIBKoKv0uHSZJAFkN9zrsaiybbpLgEclTvVXlc67dXTWOBeNuR10LjVS6nMimkBo4S7oZVyUqkQyagFkq7hgmrAlzMW3aRCdN+zaS3qM9aHUNumU/NJB1PKbaGkrdX+UbqeSJWrQgYqY2GeL9M5fy9pA2GMomHgLqK6clP8T1W82RRd7HKSNjEWIugQJlrTRardYNS26QBIC1CBscAPLKbhxIPAT4WsKMNA2qb7SIVw583MwlgULISVcZVTIW6MRWht0/eIE+a6tk2nNpumzPaM48DOm5rv2dRegFOV47KBq2iybXo+5xmBhanZn2AsVrdH/R51PZHyA5ObpbdaDm6LqW0l620jjFE0DFxKNdBQSzgiVDv8ubgxuo2LX60geoXs7s3k2LFwEwDg4Kgvr6VafwuQjIUvgyzCQ1/8P9cgqxGNhVPbdP7Cby8T5QkjbTQm9hZQv6NSuanh/qnmO/jaoWzaandHgPC8NrjADIuYC02Z7ZkwZTAWuFGjCpuld5ZlJIPJxYajO6ULuC3TAcypS7XcVBV5440diyfzAxOPARXv7cpYzB0OT23g9Ags0MbS00we/L2pjIULPe4CF38FETxkGcCxvTkA5GkR8bskSawlp1RXUsp/wgZKvNnvJbJNsFjMbDoEgSarQrjN3gDMtewq2NoIy4bIbb8OUJ631apCiE07kMBijxgj1w8kNOCAUWw2TXnicMYBYG+bTj2ntnQZDmYrGWQ5ulO6gNsyHcBcIVUyyCqJN+n7l3dttq9rxfuodjc1/+2UeYBrC2GMomHgjUWeIlURHGYsdFUhnhdfk42uivGgDwdWk00EDSpjAQDSJEvtgirgW7yJA5QkSaQ5jBQ1BWDp7dQrxKuPBS/FZL0ODiysqRBOVUiZZQomFUKKgt1TdSGAYuu6KDedF6pCbOWmRFWIpVcIPo1XMshqULjtwliY2qaXLb2Lgv7cLK94//LPZv++8ecXgRDXwyWkBmQAp0lgITUW/SR3MlsYGAtdYOGZpnfxVwAo6yyowOKQxctij3DIq3JqzUVGxSlUpgiZqZAGGxHNHFJOucbCfi9sTX+4AVteKkZfp9dLpDGPPRVi0FgQHiahMRaSUifmZyjpGi6oBnyq6K/NcQDoNyhO23Td34o5h43XXCC+63maeQ9wuS3TASyW3gvBWNApLd3G7tI2Xcz9UT9n1vO5b/7bWBXSAfBDw2IsWnLenKOAhwNZGbLST1CBhT0VUmYsJD3rQDXrTgKqcQw3FYLV07qKlipI0wzE5Vht06t0N7WkMGzzxpYKAUDCMmsqxFAVQqXBAtVYjAlGbe00FsRmIz5Lq5beHI2F4ZAjNlwdY4tFwVXMmfAa4vtg4eIIahKpLrLi/clTWqrGgk6FuDAWY0JfxC83DWNLD2MUDQPTfH2NpbeYOEmSR6UCjRlkWbpaqjikYSxEwIH/m+oXohNZVRHH6XKX+tyj+TMKBiXN/Crm8SLBCeBc9DS2z8atZpAluabAQkNHly293RgL7D0SAozl0GumsRDfFd5sunbetFp6G9qm6+bxHlHG7AL8d77TIVRPIx1MZbXis4uqwtz6v1hWr/ZEcknzmNlkWyokaixaxwJR1jpa1ZSWcKHHuciyzMlfAaDc4dTEWFAmWUXffD8aC5WxUHttsAMLFKX7VIfPCWW+CQPL6QzD9jBz0wwcGlO3wIhrHxzZNSpkuWlgfTiorrmhsSpcTImAsYvupvi9all6WzQWVSpCAJYHObFu+GYsJk7lpvp1UN0f1LJ0nXjSpQkZpX+TjIVluugCm64QxigaBm5CZQssegSVp9JePoDfnpsXO6wwFkdPLlMi2wdG2tdgYJEVZZnsIk7VlZepFSby1Gbrblo4tfi7z3hTZ2ksHFIhc0tAkFP4llIxxj3SLXpinFsby+/d2ISMOFmGprGgAtYqlvMhgAoYuy83pd+X0zZd97eUDbsrmhJw7jk4gurSjQDldg+ldU7TqdrFIKtOg0g51wJoQAZwmgQWONrUBRbie6MZC/8aC3wtV8Zit6J4U2ws/V5SiKwFBbhwKTfVGOLojGNsbEGSJI2UnGKR7pDlY8H/rm3aCG7zLErkV74W7a8hxnnGxgAALOWmxMLVRfmjCVSKzUbFhwpaY0F/j22MA0A/F2eajRHAXpVTxxxLYNxQyalLDxNTyk0NLNTnRmcEmH8uRmBBBf6iCZnltnB0Wm0ijFE0DNyEShdYiMmk6isAmnHLw+/P1ViUUyHzws/xf1OMhU4XUcXWeU/DWFTVWOBr+aRDxYJJaWcoeG2bzqTwOfdIx1iIBeWMsQgs9OOmhLuhVVxIVqXCqS00UJtNU2Z7xnEgB1BdACktqwn2Tc4RSyqE049Dh6ZMsmR6gaOxMKR81MBCre7RpULyz8VIhdRhLISQPKZC2sMcnZrlQ6JUHqSK6hejibbprqJCgFyYubPqBbKzqg4pijdH8jUqdJSliQLUgdqkAMriR7Go2VIh+FpeGQvHkl5Th0MVNhtdzFiYKl04GgvdaUr87daKsTAtzLhturxuYJs2rQMJi1XhIu9smn+vTZntmceRv5dNY0GxetLfwZYKqbGpNdWIzKWHydBwEFAbVOrLTdVUCP9zmTQWtukSnTc7QKFtuqYqxCReGg38LwY4766Wt+qAq0JOzRZy4SIZi5NlS++JxnuiiniT2qQAUNtxaRzDn/BjB5c6Llwrb1xSIVxLbwDzveXYnuuCPzGPDq00Fs5t01fvmWb2krY2QNHq3AAtNFBMVFNme5xxLN+Xnh+m9gJijmirQjwwFjIN6pmxcEnT5M3uDIzFaq0eKEaAOtZRtk1naSzKehB+WwBRgRTGlh7GKBrGgpEK4Yk3/adCekyKHgClQk7NJCMx6CWwOeqXXmNiLNQFIC+z4n2+2SKV41cZi5JBlkEUpsLFV58LFztvAH4qJMsydCI1V4XgcVCYpfSiRF2rVG5aSoWYqkL0aQbbGNsCJQo2dXcNGWQqpJPupvaqEBOzZ7f09sdY+G5E5tLDxGTpLddrNRWyOhTMNRoHl/QuVRHVYx76RNk5l/1uGqdFYIEpySrlpoMGUyEubW5FmmP31EzqLA5vDgumNDhdop5AddH70JEOx5uXTmMhu5s65P4kbejx1CIWCSp3TIGrp8GLrO6UgBcZY2DBSYXoqkIU8eZknmpP9bZNOwSdhUkHAtBuCqEuaEvvblMheh8L/XqUB7WaVEjNctPl3zajsdClbCmYUm7qAUV+j6v1RWcEKO7JFB3GrGOt0CAyGmR1gAWi+ayMhaUqxBcV69IyXQCzEYKROITSIPg1aQZwfDov/E5Xb+7aNh1H3+qmKk/8SipEZ1eNkVeFNMFYMFMhA14QiRdrXSoEb4g64RsAMxWiLGTqOITGAkB/OjKlGahrd4E9IsWG70sIwQ8XMyLv3bZBVppmhedad3gwtk23GWR5rQrxLN4kTKd04HQ3VatCSgZZmlQIgD1ooipYerIJGe+gE1MhLQL3dNCJN02MhfiysszfwubSMl1ABA3HJ3N4+Pi08DOBjWFf0nRqIzKdQ97AsZxPPPyjQU/rUlrubupSFdKExoLJWDC9J7DSntU8zLBpc04bso+BEqDMlVQIgJ5ONgkjqWt3AVPbdIAw0jVczCQ9TaRCWrrXarBYzSDLfPCo0zJdwKV6wgWUIFIHU8pHvT9qSksn5C7YlVsOTBPCc4O7NtvaC7SNMEbRMHDb9GqMBY/SdoFLy3SBQ+hUevejJwEgN8TC0Jlk6SjLvKsfNxVCi0AB9AZZnM/ZRC27q7upGGdqCSIxo6ELWpIkMZ6C1GuZUiG6PgbibzdHA9moTBeYUblwPPQQNm1buiaULqwcUIK6thkLldbXfcestunaclP+5q3DRkOMhUuaxqSxSDVVIWqzRVXjsPQMWv7MluIlDbI0xQYqYiqkA3AMsjiW3gD+dBYuLdMFBv0ebK1Opt98eBlYqIwF/pkaWOiid9eqEMprQEAsoqpxDKsqpJFyU7cyLKwFMS3+uLzL1HiJE1hwgi9dPTu+v7bSNkq9nyT5whdCySlFq+MAbZ1SIVTA2ESFmQnq++iCgxk6fKmw9Wpx6cehw7ipclOHPiYmjYXOIEuI000bO/fAZNIXpRmwStajeLNFYLtVWxMyuioE5aE9UXWu/goCQlNx1yMnAMAtsKCa3AC4ayzyyLo8fdRUiJNBVgOt010DOPxdm4JIji4CAAVtJo2FxrWvcB0NJYr/1laypwssqzShawo6Wj0063EOQnDeVOew3dJbz1jogjqqmsEVsizTt0GWU9t0/RxTy011qRBqPeAKU6kS/oJOyygAjxqL1pFXYOhPPvg1KpIkQekCvxoL1whTVH3c9ciKsdgcaV+jNiLTKaTzzpm8DX1CqJcFhsqJzMVqtlmDLKZ4E73OFERytSM2cyF8LVOQqTtN4QDHVrKX+5jQBmkhsAGUMh5gPVunU0xUE2Z7JqjrlU1jQa1HfUswpGtI6ILGDLJc2qYb9AxaxkJJhVCHgzqMBU7N160saxOnR2BBaCzULylVIlIVvnOjrtUKAoKNuO/oXuHfGGp7dQFt4zDH06CMrA2MRRVL7yY1FtwArtfj6SK4n8tmLsS9ls4hE3tg2Mp1cx8TTWDZcSokyzLktUGn67oeowuoxV6kQtrSiqjvM1vQJmPidbRBlq0qRJy06/hY+D9U4Ou5VIVQa7yq1VJTWrl4snz/uJ/N1H0YINd5UIjdTTuAFEr2cuZB/ZJsIj/f9efzChoLgJyNEJ/JSWOhKQtzzV/rTpUAZdW7rQMoRhO17HMGG6BC2vUaGIv8NGq+LifNwOluqgt2cG7V1MRtkeaGXlo2oGPGYp5m0gCrNEZGgBYaKFar9aoQYr2ibqHpoMM1yKrnvOk/DZplmfEQpMLE3KnlpuK1gpHN0+16xsL22ajuwzg1z0mFcBottoEwRtEwZqi0s9ejTwzqxFHhe0GoqrFQAwm6KkT0CynaeusoS1dnUVObZJ1a2qXctMuqEAAeO8VNhdjMx7DPgOlaQ83GKnux9HtyYadSIThY0+oXOk4z4IBI3QjWsXU6qbFoORUirPUPoO/c1L2TToWYWRafBlk+GYvpIgVxfnSpCnEpNxX3xNQTifvZKD0Iuy2Apb1A2zgtAgv80OSMRfE1dsbCbypkVlFjUTLE2qQYi2XlCLfc1LV75MRAfar3ycXHwsVXnwts587FiBFEcnOafcNiBVDUXpiupdtY8ea1YRBv4mCt3N3WTbzbFExjHDqyaiGA2myaMNszjkGWI6PAgnKWNDB7uqBWwKXyQocmDhWm+URhYAig1CaVairEtB7kn82SCjE0IQOwlL9HH4v2gRtRCWpJ3UTVOmUVIhL0ddJYIN2HCwQbkf+bYCw0HU4pqg3AvdzUJNbK1dLFMqwRI5JupG16hVSI6sVhuq5dvGlelHGgwGubrtNYJMaSPWlq1i+bmpnq99sEnp9qCW8/EB2IC2Zpee41YbZnHMOifJjgnMgxbJojl8oLHZqoChHXShJetYTpc4qf9dSqkNXzazpA5Z/NkgohqvaSJJFeM6b5wmE920QYo2gY2OVSLKLq+jS3bPS+G5HlbYrrpUJMGotyVYjIN9L5ay7NvGcUbyqRvEMk3WRViO9UCKdEFMBuPoYrTziW3iWNBa4KMSzOplxzOKkQ/QYVCqviAioN2ITZHmcM+BRsqnqg5qDpJA/gt226zyZkuArK5DUjYGJmUiVVlGssiowFxYzmn82dsQBAGjiGeDOWm7YInArpa7zXxb91bDS36yUXVSy9AcqBhJoawT8rpUI0C4CzQZZhAyj5WDikImRViE/xZoVInpcK4WlkbMJYcartJea50NeVm+JUCIOxoNTxw0A2bVPPiXUsN6Ua8DVhtmccgwyA+0athHgdWRVis/T2qbHw+OzrKox0MAXYc/mc0qkQKcA2pkJ45aZlnyH73I/dTTsAVjzjNrQ4x5mLN+lbIr4wb6kQxzJIgcNIU7Ex7JEPM+5wijHRMBauGgtTThUzO8vW4hWqQnyKNytU37gwFvZyU4vGgilu1SnzsbujqSrE1OUxFPMpU8+JdXTepBrwNWG2xx0DxwCK7u5svvcTTRmzC2TlhE/GQswnpiOoqaRZLAV5d9NioM9LhfAMskqMRUJXMmJES+8OgBtRFeuC0WtkYEFfQ3Va8zcmt68AMxZUGgT//NjevLAY6BgLaenMTYUYNBaY2VkGb8WfmzC2+DBUwYLIc9vA0dPwUyHmFJqYT7b7o9NBUIwFRbmaNu1gNBaG4EfX3TVkzAi2rAmzPeMY0PwwMZNzA7No8ncAwL2DwmQsuI6g4rOnWX7QFBDPRk8JLDgOw94Yi6ixCAtzotwU/xyAId70XG46r7DhAbgFFgAAu4i10FeFuFHhpoUEb8pcYaLARgMGWXlzIP5UtwUDABUsvTUbItfjX5cKwB4Ypg6RnDRD16WcOROm11iE0IGVC6q7KUC7jcimcv4nRr2PqQvw0CJA9uG82UTbdF0LAx1MXXTFtCtZeq+qe0zMI6cH0tIczly1pwY7GNwDSlsIYxQNAwuTCowFer5s4k3ZXMvTiamKvwJAsbxUrRARGPZ7cHBVXnYUBRa+qkJM1GfeCyFVSik5jIV/ZXgVvxCOnoZS/FOwnTa4qRAq+MMeGINeYlycOcLIrtMMea7eoLFY81QIgH+zPRPmmLEwiBONVSEmf4dFKn/uw3nTZ0WYS8t0ALNnxEKxBxDfoajuMRkBcgyyMDta9nCxH/xwABkC9n1ggaPJfi8pOJmRjIXme+G4MbqgStt0AIAzUHtsSrgpQLlv6kRWnPJKjFxtbdZYFCseGBqLANqmA5T7nVCghHnktSzVRNzcKNXHoBC4IcaCun+mRTYUjYXJeM1VBxQCdN9tm+6buFrArLHQb4ymoA5vlsEyFsxx4U1ZnWfiuVPLTZevzYw9kTjVbviZVZlgccm6bQHaRK1RXHfddZAkCbz1rW/1NBz/wN9FSWOBGQureNPvYlClbTrAMscnAgpdKgSArgzRUZaVLb0pjQXalHHlBKfcq4lyUxdLcQGZPzUEkdxugnbGgqfVoPLceC6OsMaCKjc10MKuTeiagom6tpk0hQjdZtNFKmTY7+UusKRBln79w4ymaupVcEv1ZJDlyzjMlP6jgPVu6j0Se4Uq3gQQaV+DxkJWu+m/b6Ev6iXltYrDKO6bctMvfOEL8N73vheuuOIKn+PxDhx54u6m6u9yvwP6Or7LTauICgWEKdZhwnVTvkZ2OM1tvfO2vGoqxG3R5jQhmy4y5yh63IBBVh1LbzP1yAtYbBoLTmfT4nUQY6F4YORVIYTGwsBYhJJmMJUHhuK14YK5jrHwbLZnAmbWTCZjOKWmAm+46hqxh3L7qvGaC/B37uu+mATLFPDw1WdhXhJvFqt7TEaAMsVrODDhlunqIUzcfpOPhexdFIil96DKHx0/fhxe85rXwPve9z545zvf6XtMXoEXokGvJ53M0qz4RS0sjEWVvOjOyRlpub28TvWc2DaDsRC/+8cHj8Md3zoGAHqmwaa4f+j4BB49kQcoR1c9SCjxJtZYcDdfAZFamc5TSNNMu1DNFil846ETymfowePO3iw9lK5t0/F4fZab6pgudrkpEeyoHhimqhpdy3SAfM5zA8udUzPYGg9qbSQUTOWBTXQ33d2bwbd29go/29oYwnnbG8a/O3pyKt1tTdCVWuvSY1mWwZ0PnbB+D4cODOGxh8xjFBD3a9mAUf89G3uFFFIEGeCvZ+LoFaED/s73ZilbcGmCq6g0SRIY9hOYLbKyxmL1TzEPRXXPPF2m2qVQl1hnOIyFqYLFdvDLssypdUIbqBRYvOENb4CXvexl8JKXvMQaWEwmE5hMJvLfu7u7Vd6yMvBCLE49/V4CqTJ5bIyFa170E//zW/BTv3sT/MLLngz/+/dcVvq9LZAxYXu1qJkCCyHs/PVPfB1+/RNfL/xOnbzyNEx8ti/dcxRe+Zt/Q3ZEtKVC5sxNk7reZJ7CgRG9ILz2//ocfO7OR0o//zcvuAze/kNPLvyMK7LE4KRCuJ/NtijIUy3TwXNOpEJE0GFyLzSxAUOHVMhdD5+El/zKjfDyK86HX/nxZ1hf7wLTGG1+IK7YOTmDq//TJ+DYZF763f/1uqvgJU95LPl3H/n7++DNf/RFePc/exr8b8+52PgeuqBRx36+40+/Ar/7t99gjf/9P/ksePGTzrW+Do/BVP2DGzWqGPSKgQWGa+WFDsN+fuCbzBYAhrWNiyqOoP3eMrBQvxu13HQ55h7M08VKqG7SWNj1I6b7aLP0xt9JKN1NnQOLG264AW655Rb4whe+wHr9ddddB+94xzucB+YL+MsYoMBCjUptJ9uBYyrky/cuA6iv3EcHUibq0YYffeaF8O1jE3jhEx+jfc3Ln34+fObrD8EpZTI/97KzSwGJSQfwD/fvQpotH/ytjfzvLji8Ad918Zml1xfEm45RNNYZTBcpHAB6sfrK/ct7un1gCP1eApPZAk5MF/Dle3dKr5U9WSpoLLw0IeNqLCpcRy0xk6kQgrEQG+gZ4/Ij7yLe/Pq3j8F0kcKX7yvf67owlTH7rlz55iMn4NhkDkkCcOYqUD8xmcNknsKX7jmqDSxuvesoACyfCxt0p0id2d4X715ee2s80AaaxydzmM5T+Mr9u6zAQjanGiRaLVWK/GZMbdMByocPU4rNBUmSwGjQg71Z6i8VIsWb/LEtP39KVIWI3+PAIoFTM6En06dCxLrG6ZZsSgPq+w1hEfcapkLuvvtueMtb3gJ/+Zd/CRsbPCrubW97G1x77bXy37u7u3DkyBG3UdaAOIX1kjzalLbeOLDIiqpfFSPXyonVA6d7SGQ3wQqpkFc+80J45TMvNL7me57wGPibn/te1vVMmgJBT3//Ux4Lv/WaK9nXmuJUCHOy44eWk4b48zdfDReduQkf/fL98Po/uIXUZlQJ4Hy2TbcJI01q8uJ1yhurGtyYjHh2Vn1jKJbLpdxYtOH2qYMR4FSu+BJPi/FfctYmfOpnXgwAAP+///FV+I1Pfr3kWIshfsdZB+aazUZntic8Z37nJ58Fz770LPKa/9//9mX4/b/7JrsDcMEgS6OxwM+9qW06QDld6sPOW2DYXwYWvr7jnLHgj03HjAnGoo/2hzxgyIw9keqmVm2pSle/oDbgFFjcfPPN8OCDD8KVV+YbzGKxgE9/+tPwG7/xGzCZTKDfL36J4/EYxuOxn9FWwJxgIqgI0GaxrTqt2SAWd91kquKv0BRMPQRk7o/5cOJN2aUBGUAxx2l+CIsVGWMD1ZgbkfEfuBEjNcDvbmphLDQmSrrr4EVEpdpNTcjEhrhNaANcKp7E5/ZZuSNg9Nrw7A5K5d+l4NkYWCz1RZx1IK+K4pWbCqG1SZQtg0dmYFd03qS1HQWBO7EeYT1BWbzph7EQY8RjrosqQY9Oy0M1MyysdYYDgtrxmYKpC7MsN9WIN/H9CmE/AXAMLL7v+74PbrvttsLP/uW//JfwpCc9CX72Z3+2FFSEAOkohwIGU2ChYywENelqe61brG3lrW3C5KznWgsuo/N5WgoAeH/fg9lioX0IU7S4SW2BoQa+SgA3kEEkQ4VtTWFYNBapWyoEb6xqcDM2aCyE4NbMWHA2SxFYNMBYtOgOSs1rXfM+jJyxYLA7Gv0MZbaXZRns7i3TVSbt1IahHwyFGZqnuiCX0qGp6K8CC/Vv8+/MB2OxShF5YsN0hoAm6KqPqMACp7SmBga6brdkuYZYvHBG/R6rrL8NOAUWW1tbcPnllxd+dvDgQTj77LNLPw8FVBdR+UURVSG6Dcg1mhZUq+71VfwVmoLQH8zIVIjbwynvU5rlwklnfcNCeyIsunkurzs2uPbNa2ks6qdCbMJIbipkSFC05VSIftPZOaXftFw0FuJ++nRHFTA3IfOrsaDmNWUqp0L8ziZ0NTXgGxAb6PFJ3tfHFFiYgkcKOPjUmYzhDUsn/hv0EphA+XPrGmdVgW/GwsSA6aDT8iyIlg9ivPh5ow5RQ42mBmNqaBEgpo+WsZhXrzBsCt0flxsGFTCI7w5HpZKx0AUWlkY8KnLGQhNYVLT0bgKmHLvrw4kXh6ljKgS/VldWSOUTTY6dsjlPhVQIJx1jt+LmpULYlt7o86vBzYbBOljk7w8Tm5bNHRRjihgLX0ZGAqaSOyqwqvde5Xkt7o3Qo1A4epKnsTA14KNSIeK64wHdsVjAJNClgAMLncmYCNaTRL/+UeXOAO6pUhNGmveoCtcmZACg1aFIETi6P2K8Jyf5d2FKhZieL1kWTDqfmoPq0EpNASqWm2J86lOf8jCM5kA1oRJfFG5DaxP5DaXHAjcVIhgL+vUhaSxUZz1Mp7k+nOJhy7L8b10mvNzUNfdZNYXCY6MEbXUMsoxVIUxLb5sw0lWrURRvFoMbU7Mjkb+vy1jgDXUyT72I9gRMTchMOqBK7yU3RMRYbPIZC1OaDEApAWSUm0oNjKXMklO6iIHTkTaa37QW6ebxXoXKCx18d5DWdQs1QXuPDIzFiem89DMMTlWIaR2QBlmWdGpIgUU4I2kI1ENDfVGpZQNyT4WYGYsqXTebQsHKVs2hIkc4DnAFyMnp8h64aCxyfYPuvuVVPuK7Mjl2qs2DXMbAyonanDctQQq3bFVqDNBJSvXAEKfGeZopfhcpnFh9F740FgD+K0M4Tcj8pULKGgucCqHYmL3ZIk9xWj47nr/q3KOqBHaZgcVYCnTdUiGDgsZCqQrRiEwxdK3TJxUqL3TQleFWhckhWAed3owWby7/W6xz+GcY4r6bPpepW7KNsRB/a1uL2kT3u1rDIDUWhnJTXWAx0lBkOohFUidEqmPp7RtYf1A+kbh2CMxfJx44N42FOQ1B0X7SsXNRrj/XKfPrjAEg16PYrmtvm85LqeRVEYTGolcsNwUobjy76ARONa6zuYNS4wUwWxRXgWQsyKqQhmhyxFgIU7l5mslADAMzGbYDRoFZY1SFiEoUU0UIQBXGAlWFaNJJnJJsXRrFVdxtgu/mbFXaucsAShNY4Hsk5qRc5zQ9kdw0W+W/71mC6lw4Gs52Hs5IGoLMsaObbqoK6WtUtfIkzU2FrBZJ3UIYosYCQO+sx9dY5Nc6taIIK2ksdNU0RKVJcUMtLriV2qZzqEtmKoRrkFVNY6GkQtBY8MYjNq2tjQHdFttBGInvie/KkNx9UM9Y+EqFTIhNZ2PYk/OKSocUAgvLvcIN+FTdAsWIuadCuIxF/rxohYni8GU4AOjmsWujLxOkxsKzeNNlbLqyZvG5cdWgGK9tnRsxKgp1fWUAXNKp3e8lAvs/sCBEN8ZyU1+pEIuPxTyoVAgKLDRUJ/fhFF4UACBPfS6pENumPluUN3Q8NlUtX0djYQoiuakQdtt0i4nYkKCx1eCm10vkvcY+BzuW07BLE7JiKsQvYzFhtU33nApBcydJ8s7BlICzEFhYUhGmgJESB4trU4wSRtVUCHbeVBkByutHRR7Y0alSLwZZnpuzmeaTDrrPSaXK1XVOt7FjA8JUGxyUD0wCIpix+VhEjUWLoKtCVpG7U7mpY1WI0FhoFoA6lt6+0TcwFlVMZsQEPzkRkbyDvsGSj6Qow0E/74OgquWrOJzaKlMAXJgGG2PBq1qhrkN5YFCdFG2nYZe26XhTaoqxoPL1vtum62jy3CRrWvqboycdUiGGUyRltieuLdIxOuT9YHhBHa7M0p7GF/a1SFctUaXyQvsesoFhc5U/3DHo0kWUeFOsc5QHBUBx7dE1ejT5YNgCf8niemCNfCGckTQEyktBlps6MBYjy8lThXjgdOrxBaH96ArCWQ+AaotcoWSrVxQ1VUmFWEWvykaso4g5p7HyGPyVm9o1FsyqEEJjMSXuxZgovTXZeRfHaJ/bWDPELXnkgtM23f+mU3wvcY92bakQa2Chnx+U2R4/FaKv/KGA57/NIMu0FunSZb6akAE04LxZxyBL+ZypoSok11hoUiFoDthF3Pq5r2M7Qiw3DWckDSGvP8Yai+V/p4XTn42xMFcrqBAUoe7Um2s/ug8sAAyq7woLh4icT86EeNN/KkSNznOKmNZYVCo35Vjwsqs56qVCKDtmSuchN555mbHQnYb7mlMaBTyfuSZNXJhodZfKFd570aWIJpOsYmBh01joF3tqA82rQswOAGODV4lpHKNBQup0APJ7alqL9KkQf4wF7ozsA5UsvTXsnbEqRJTVa55hPAd0DDYnsLDptEJgvwX2fWBBBQzi+cFflIxIteJNx1TIzJIKIQKeLuGTsShRhB47i+ooZi1jUUHYxAkisY2uCX1B4Ws/jz63WrgOpbEgPhtVNSBodl3+fqihucnxomDLZ7+QNM0kG0K1uXbRgXCgYyyESdZRSmNxMk+P2NYBUwM+ymxPpF4OE71cMFwZC5wK0ZmMzQgdmgpdRUmVdIMO3qtCarCt+HNmWQbin3h/KKd86ffp9xLZ+lyXCjGlM2yMhckOvCuEM5KGMCfKOgcGxkJfbsqn6bIsWyuDLAC7s54LY5Ebx1RJhZgDOB3FrDOHqibetG+0rqkQ3Ybo2iWVKjfFQQkl7rPR7DpTIAqFqhCPqRA8Xqp0UQRo3FSkDbrSVlO/EJdUyJyRCsHiYOeqkApNyKh+M8t/88Wb6t9WSTfo4DMVskgz+Yy6GWSVUz74v/E9Et8jR6RuC5pM5aZsnVZMhbQHqmupmBsFxsJmkDXgR9P4pDtd0NbHs4B8LAA4qm/34OCkKMNyKvcSaQhNYKGxwNZRxJxFU8WIkQphBwQaUyKBvLspLxVi6m4KQIv7uFUhLI0FrgrxmArBKaw2GIuJphTRdyqE7h1R3kBtrJKAGO9CMUHTjyOfI7pnfE6skSp0Ghe/bdNX7+HBeK0wnyqsXfje4jmHlxFp6c0oq7e5iubpDIKxWLEkqbUqJIy9BOA0CCyolANt6W0WU7pE0zoBIYatTXvb0DUoqsNYVBFvjiyRvT4VQjMWXC0EBsd5k5tioZiGwnUInxXyOqSld/mz5fcBMxZ6O+/ltVef19XHwiNjIcY76CWkJse3xmJPU4poap2Of2bTWplU/pTZni34E8Dj5bAWeJ7qWElZOVXFICvQtul4/ley9CbS5ADFjV8VqZvWGCsTK9wzqVRInw4I878Vcy2c7TyckTQE6aNesPQuf1HiGdcHFnyNhVoKRk2I0DQW1MKBUzpVyk1PicCiQhrCVpalbsS61ulVUk6cboT8JmRmCp+t1TBoLApmYZK5cS835Wza+HP4LDe1GS3pcvzV34+e19yqEBtbYGoup5rtLdIMjjFapgPoTdB0mBKMhRoc8KpCdBoLn23Thbap/nec9ylKnNKg1D3SMRaCiT3FOEBV1Y4BIKdoLWPB02m1iXBG0hCoh0ZOnkqMhX3Sq3Q8tUFRAU+XoKhOPG4nv31hHDNxT4XYKjJ0uWtd6/Sc5q0wBo4Fr+WzUcZWheswu5tSRltUcDMmGQt9Z1MAN41FIRXSgMZCF8DaAjT396MDGclYkOLN/GdpZk4dGctNlfmFgxhbYJEkiTzVcipDKI2FOq8XjGckr0pSNRbuqVId8jnuIxUihMBuAQ/V2wdr8QoaC6ElkyJ1TmChYSwMzKVOGyNg0md0hX0fWCyIL6xH9gpZ/r+uKsSl856OjscIydIbgI7U8ebk8oDWSYXYfSzMjIWOLapi6W3axLh5Tb6lN/86QrND901xrwrRmQKZxrt8D/+MhS6wGGpO21Wh0wZwNRYAllSZNC+jAoviBi+ue3DUZz0rQoPCYSwwq6Vtm85Iheg0Lj4ZC8qRtCpyTxTHwILYxAuMBbpFI6klMztvAjDK6A0HjDywoK9tEgp3hXBG0hCoUipqE7X55XPocQF1waUmU2gaC8pZT2zSvcQtGpYbcyoougqpEGtgodNY0IxFFTpU910v28sv/9tGP/rWWACALH2jXEWpTq/WVIiTpXf+Gp9NyGypkLw5lCeNhcYxUhdYZFlW+plpLZgaRLmq2R63IkTApRFZ3jog0Qa5HK+XvmYe68p2qyDvbOwvFeJarUJqLND9SYhyU84zbFtTjKkQUW4aLb3DAdVFlO4VsvqdhrEY9fmnOpUipmh9TqviNkFRzbhlOtW1Twf15FMlDaG39KYfYlkNUTLIcqcJuawJgP2z2WyKuamQgi3w6v2p3OpYOc3iVt86YWDfQWNRZCz8p0J0J0xbgFb1/VQmbntlIra7NytQ4Cemi7L/g4G95KRCxBwXotBti4eFgC7tpyLLsgKrpWvkxtkYKe+N5Rj8ayx8MBZVUzRUylEeThLzulZHY2GqMrOlKmNVSAegSqnowMJMBw4QjWhb3EqMBbFg2zQdbYNOhVRTfKsPh9/upvQDmG+oyqJZQSQ7spQW46DHWhXCtvTmlZsur6WmQnBVSNEoTJyG+70EzhjTro5DB/3CjAg8fcA21yjnUR/vp2qHBGuQZSAFlQD5fRwNetb0FoBZlKua7e0wXTcFdEJlFXh8o77B0pthkEX5O1QVd+tAlXpWRZWW6QA0Y6FjdFQm1vQM5zbu9GczpTNs5abR0rsDUBsLRS3Zu5uWT4w6cDQW4aZCyhoL14dTFTRWSaPYLb3VVAi92FZp9oZz0ZTbHT6pVmkeVriWWFCY7dfxtUyW3uIkKbtmbgy0rJNL59CmGIs9yY7pAguzCNYF80UqP6vKWIwGPTiwmks49YH7rci0qJGx0AeMqtle1VSIzUcEf1fDQZK7wJZSIdU0FkVTs/pbiS2gd0GVlukA9EFAF1i4HKBs+hGepbf733aFcEbSEKiNRYg3qajU1isEwB5YqCc5KhWia6bVFSiadK+iq556SnMpg7J3N9VVhVgMsiroPABotmmGBKG6QFRgYGFg5KJgmQdUa3uKwlabkOX+CHqa3SXN0JR4c2I5Yfpsm47nCPV+VIfTo8gLhEPZc5qQidcIq3BbZ1MBXV+c0hjQuoNTIdq26SaNAOGlMKko7ta+R8+cBnVBlZbpAJhNwsJ+ZmBhWCfzz+aezuCKN6Old4uQ0Thus02lQjIbY5HfKhsdq57kqAclNEtvauGu6qqnfqZqVSHmB1ANyFRtgXx9hZRTMYikgkK9ME+FzdUyZyzM1+qhfgPiWnSvkNV9WC2sHEfHfMNhaCzQpuyz3HTPUh440Jy2K70XmiNU0EwJOHdRya7NxG35OzFH9BqLEmNhMccS0PXFKY0BBcWDXqJNy7HaphN/K75/V3G3Di5eQTZUXbtMlt7qGqI+/ybrABlMalgu3YEJv28sNw0I1EPTIxZ7aZBloIt1deAqVEdCKq8WmsaCKkWrWrKlRu5ObIElF5nT/5pUCHpwU1S94dY2HQUWxELgQj36pDHVslBK+KnS5ByafUAspjrMiFSZD0w0mgcBnR11FYg5Mur3yIME1S8E30ceYyHeo3x9NZBzT4XQQbRuDMP+sppBZzLm0jadSpW6irt1sKVBXVCVbaVSPjJNrnxGlYnlpEKqrAM2xiI09hvgdAgs5EOTf1TaXc2+0dtoegE190mdbEKrPaaCpi5SIbZcZF7CqlaFrOhhtNjixcEluCl0IyTG4eJ0ZxOjcp03Acp0NLUYqTS5zRwLoIbGogmDLA1jYQvQnN7LEsRQHU6PIo0Fp9OxSVCnagnwtTngtk4XqRAxhjpt06nOqD7tvPE4fWgsKrOthIBUxy67pEJsrqLi/VTtGH5frQCc0Fp1jXBG0hBygyxbVUjxdxQ4FChAecFVF6Asy9bCIKvqw6kuUFWcN3UPoG7BltoCtNjOFSrYBXKRo8SblRgLW27Vnf2gAhxVxCry96ZNy0WNX0iFNGKQRd8HnblTtffKT9oUqFQITldw1gF5cCDmvmq258pY6Dr5qlCfFZ3JGKdtOlXy6LMBGR6nT4Ms16CH9jjSaCxKInV7YKFvQqY/aEpDR810y83YwthLAE6DwEJMUjwpZGBRqAqxK6OHTKpOpYhVhgM/16HkxSj3RXkicYyE65SbcrubqgwERQ/jz+IawI0M45DBgEUXAWDWWGRZ3tq5il7D1IRs4rBpuTEW5fnhA7byQF13zUrvZTlpU/1CqqZCaB+LIvNZ3SDLvA7JtgGSsaBZn6pt03WN3KqCwwRxsVfRX4NaB/XiTTU1YmC7LZ9NNq0jvgNbLx+TPqMrhDOShkDRWDRjYRZvAvDFRSWDLOX1+N+hMBZ94tQqT3bOdKIaWPA/I7csq5QKIejhBdqEbFUXKkwLgTxdMK5pug5evFgaC+WkTKdCiozFUVkVUl9jgQ2XAHy3TRcsgllj4YOxsJ20qX4hR3FgMbCnRPNqn/LcV832uJ1NBVyrQsTzZ9NYGMWbBINX1d1ShzyY9/cdu5bBUg7E3HJTU1WNWmKsQldGD0C3oCj8bexu2j6oUipTYMFpHWw7NakLbjmniXL/gQhu6Dr1aoxF2TimQlWIrYqiVG5a1lgIVXySmANG0ziozaOK4JJaFPAiw9JYlBiL8r1Qrc2lj4VJvGlpyyygfgavVSEWoTC14Fd+L4t2yFgVspkzFqb7JTqXUqkQ1WyvKcZCTYXoTMbEYULXzgAArZlEKsT14KGD11SIXLsqGmQtyntDHR8La7WbqRuutbIspkJah+yngBkLsgkZrfzF4E58W7lpVVFhk6A2wMoGWR6cN/W5SPoBpAyy6pT0mhaCvLOp/bpU8zCBos+Ay7WExqLM3qiMBa8qhLdpq/fCbxMy81zz6bxp88ywVoX07OsAJxUCAHByOpdNrNiBhdQT8atCAPQblPRDYaRCfKRKdZCMsI/gseraZSo3TdTAgp8KqdPdlKpiLPxtNMhqH5RI0shYMJTRtg6nJYMsNbDAqRAPZVo+QDIWFanOcmDB/4y2XKS2CRmRCqnTj2VkKHs1nS5U4HGqC4MIUJKElxJTmx5RHhgljcVJflVImgHpNKqOV8BvrxBeEzIqQHN/L7HpaKpCVmZiR0/RVSHifvO6m5a/VzxvHj6+FNcmCcDWhpt4k+u8qWos1I2b04RMGr1hjUVTjIWPVIgltaZDfo/sjIVLuakpjb7UWtHaMQB6bcYwCT+7QjgjaQh5wFBOhZB+8BzGwtorRG1CRj/IvQoUfVPoE3R4vgDX87FwKze15SLNqRBKvFmNsdDn0bkdSQFoK+783yIP32P5AKg0LWUWpmUsTBoLbP5mmNulADnNvPR1ALCfMHGAVldmYWv1bRZvjvI0meGAIVIhJoMsAICHjk8AAGBrPGDrrWTbdAtjoZa060zG5gzhus9UqQ7NVIVUKzf1beltYkBt3ZJjd9MAge2XBeQXtZowWZbJxcr0cNtoegFdvwr13yGJbfJSNHwiqdiErORQVyEV4lhuujEo553FZzHljnUwdSV16SZINQ+T11HEdfZr0VUhVLnpZJ5CivL3Jrto/GyYxJFUGsJXIzLbXMPPZd2Nx9bqe1v6WCzZhDTNYHePqgqxB2E6wyPxcb59bBlYcF03l+MWJmjcctNiKqSssbCvR5TGwmcDMjxOH5be1ctNyyk3rvOmme3WB014HpnKTWN304CwIIyvVMYCL6bmwIJbFVJcuEoaC4aFbtsQ5jm06ruuxoL/OW2nlrkuFYKabwmqvI4jncmCl9vqHKC42KgLQ67V4I1voOSgqbJXrIJ/9ORUznFOuSmAWWch3g8v1r7SITl1bdZYANSvDJlYhH0ibXRiuoDZIoVje3N5otxGlt6ce6Wb+2LuCMaC2ycEgN82XZcK0Zeb2te+OXHw8FUVwhHFcmGbTzpQbLbOc8jFCNCUPit2S9aLN/WMRUyFtI6cts4nhfpFcf0OTJUCGOKBEzlTNWcYmp03AN2IypaL1sHFOEbFyJK/1nUDFQtImuWvqSPeNFnwujzIOLWmXsuVwpR9DBZZwQOjUBWCFtIHV6fhUb9n/A65fXCmiCERi2hbjEUhQKsZWOxZShFxBc3uqZlkfTZHfRgNenIsplSIbAyl+W7Fz7993G5gpoLbNj0vYVy+l85kbKbZODEoS++qqVIdfFp6T2oaZOHPmXKbkLHKTctzd14ILMrfQRRvBgiqbbpaF4wjQVYqxNqEbPlFb40Hq9fTJ4SQJgJFk1btFVKn3NSUglj+vEz/AxQ3CZF7ruNuanIAzYV59s9FNQ+T13GwBgcoLnoFDww0t4f9/P0e2N0DgCXNbtJw4NvD0ViMBj22+yMXtk2qEKDVToWYmbh+L4GtjeWze/TUrNDZFIC3DpgsvQHywFgwFlVSIdYmZCVLb1oEyLH0pp15/Vp64/ltEhFzUDVNQ2ks5DqiVoWoByhjd1N9mgen66nnlF1uyqhSawvh7GwNgTq1qqdzV8bCtrAJqvWMDTqw4Fjotg2q5DC3Pq6WpwQoNm/jQDyc+rbptNAMj1Go5XVpEw5MDqBTaUjD1EZoRL/yszhqLOZpWphTOBWSJIncMB8UgYXlNJwkiXXxAihuVGp79rqwVSCZAjTn92KctLGXhVqy6+K8qftuxZx8SGgsHBgLtkGWIsq0aSz6pnJTIg1cNVWqA96Y65ac2qqMdKA0FiLIKWkqVAaD49pcoamhZCxiKiQcUIrnEmOBAwvDyc5G0wtIxkIGFuoJIVyNBan6rlEV4t6jQyx+lnJTZcFYbqjFU3QdxsLkAOr6IMtNW5kHrhQmDoiLHhiKkHV1gnxwl79pceyUZ2k+Xqxp8YE9xmaflzzWPc3aNx1TYGFzh8W/07FRYgOTjIVLYMFmLIrPis5kjOW8STEWFVOlOuB7Vde6vSpjYdJYqB5HVZqQUXPX1vbc1tk3P6CEs52HM5KGMCeiTXXycBkLsRjoGmQJiMV2a7zSWJTK9NxOqm1AbloeVN/4AXEpNcWvTzP6ZGpKH+QVEcv7z+mBoINZxe2qjaAX9Koai/miaK2tbgjiPohUiMnDIr8GnX8vjBcxNVw6ngupsTCcfn21TufMa2GvvXNyVuo+qlqrU9BpgQSEnuDbUrzpkgrhpaHUJnW675haI1XQbdP99gopan1qprsqdl4dElqzVBN4qWwsT2NR/lxyTdN5uCQ28WasCmkdFM2n1gWnMiIFYy6a77y5/L0uFTKvseE1BfHQYArSR9t011a+w8KpxW1TV+2sbXQ0ZxymclOuha7utOJ8HRSgYJ2HOmfF9/WtCoyFWWORb1SSHfLAWGRZhvQ89jx1XVvvCWPT8ZUK0WosZCrEXbypepXooJ6EcSO3jGjAaDbIojRY1VKlOuAy3Lolp9JuvGJVCP5uJWNB3B+8mZvbzq8OpRVSIaYmgTYPjK4QzkgaghRKEpbeKmNho8xlKsTqvLnSWKzEm7py06A0FoRivHrb9KKY0G0c+eupxcWkbxgPdIxFncCifiqE6j+Ar8OlMPECM5vrT5ni+3rw2JKxMPUJEeBs2ngB5HopcDBPcx8ZI2OhqWpwha1tOkAxsNhVmoS5pEJs5aanZm523gBld1UdVIMsnckYpyzbZJDly3kTgMcG2ZBlWX4oquhjQbV7oNLkeA0wbeymVKNNa6X6LmEUtFYxsGgPMyIaF1+g+KI4lrYAaLMxLGyLNC8DPLRGGgujs17FDoHL6zoyFuj1tNDJlAopMhb1qkJMGgu3nKZOGOlKYWKNhanqQCz03xKpEEbFgS740Y2Xu7lxgE/epo3AV+t0HmOxsvUmUiE8xsIcfKo/r2SQNU+N9uY6HwuAYgDJaWeQt0Eoi7t9pUIAcIfT6vNqukjlKb5qVUjBldnQpA1/j7zupkRwYPHFMTEWhZRoTIW0B+qhkU5mq4ckNUSkGJwFBS+SIhWi5gtnAWos8o2FWDgc6US86evyhjr0egkZ5AiYUyFFxoLjKKiDqdzUNYWhGlup1+GLQPPFyfS3gpr+tkPFAUtjgTZLLh3PAdZpmGj1oSZAcwWHiTOmQgb6TULAtlmowaQbY5GP2xTYlZuQYQ1DWZzI6excSIVUTJWawDUhNAHfE/eqkPI6KD4yzVi4pUJMDKiO8TAyFuizmprItY1wRtIQqEZU0iBr9Z1wT7ZygzA8zHhSn6ERby4I3UfXoERLk4p0Yp1UCP57NR+ZZbl/A3Vd1dab0wPBNgayCVnFgEBn6e3qY7FA5aZUcCM2HvF2bhoLbiqkyA7VAS4NNGmc+owxcsAR9pGBxao5mdQjGTY/m9K/xFhUKDcFMDciK7VN15iMybbpRoMsgtFsgLHgegWZIILdJHHXHVCfUyfeBOCnQowMaGpeT0yMBfbACKXvFMDpEFgQqZBer7hApczAwtYgCyCf1Fjgpp568+ZT4UwEVfWNUzqujAUWbFbJ++nykQVPfeIkopo2cVNcFIwWvMLPgXka0qUZbCZK2uug74Yag9oUylcqBG+WG4qepQ64lPqAqEyo9n52/wVZFXJqKrucynJThkOkGKNus1G/c9FRlYNhvye/L5N4tpQK0ZiMcQz7qHSe77bpeAy1GAvEtHKa+1Hvjz+nSbxZEKobNRb2VIhVY0GkvUJ03QQ4DQILKhUiH5LV95QzFubbwdFYYHW7zhSlTu6/KailfJjirlNuWmXC6/KRBaES8V2NldbpqnjNxxgA3JkQnTBSLO6uRltzlAqhxqCK6Tin4SErFZIvYlwvBQ64PSf8l5vyGItdTVXIVNPee5Fm1s0a/7zfS+DgyO0Z21A8WyioZYw6kzHOepSn4fLvW1ZeeGUsfKRCqjGtAHSALTZ0G2NhOmiYDqU2PQ4vNRzOXgJwGgQWVGln7r2erv5fPFjma3G6m+LmN7p+ExyxVNtQqfCCoM61ZFSxmHaF7tRSVEBTKYDiYlsngMs9S/SpEK5+RFfKacutlseEGQuD1kQZFyewkMJIQ2AxRyyJakZWB9yeE33i1Fzt/eyMBZUKOcwUb+Kf25w3xXu5nqzHw2IQTYFyqaVMxuaGIDX/O4Kx8GzpDYC1TdUDi6raMAD6ECDmPcVYUAdWCjkDatJs0fdRNXQs/m31w1OTGHQ9gKZBdRLNaT1Y/b+93AqAF03jh00nAKzTdbMpqFSnWLBG/Z5z7q5uKkSXhhCLTZLQwYJqM72oo7EwlBZP524PM24eVrhOxVRIQWNBpUJKjIWdZqd6JKiQwUwvKVQm1AW354ROBOsKF8bikRNT+bzmjIVZ68EpAcQ/dzHHEuAwFtSmM+glMAWNeNMwD0mNxbz6Bq6Djw6ndQIecQ/SbJki7/USWW5qZSyMGgt9MGp33tSziTEV0hEojUU/KS6iYuLY9nmOsAjni3X9JupseE2h3yt+No5hkQ51UyG6+4xTG9QJT7WZdvWJwBgRJzsB14d5SJyCCtdhNg/K7c4zY3CjMky8qhAXjUWvxA7VwR6DQViOkQ7QnN+PobEQ5Z94Dh5SGQtNKgTfQ05gwfEZUcFxPlUtvQFoF1hW23Tp+oqrxqqvEdr38dDhtGrLdAC1JFfozZbXUy29AfJ1wtYTieO8qe8VAqtxGNaigBqQAZwGgQWV61QpVT5jYafpsMJdx3AEqbFQqE6OiZAOxVRIhcCCyOfif+tEr+pi68Mgi/qupfiWmebRqbpdNSBYYGvSeWDG4sCwz0rZcISRMyRa9dmEbMJgEJZj1OeauciyjJV6OWM0KHR93doYyO/Rtg6IeWrabHAnSpeKkPzvi0E0hdydtRzo402K0xSxr6Tz5otU/rdXxoJRcWNDLcYC3YN8fyj/TkBs6La1wGSQxXXeXKdUSFij8Ywsy8iHRhdY2PYfW4MsgCJjoYtS64gKm4K6aFc1xwJY5iLFPa7Sylc8rOrCrWtAJuCzCZmptFhtR829lt4gy01jsUAaCzoVgmh2pvGSqaRNANuI+2xCtsdsdqfrueKCgseBYW73eklhw8f/bUuJ6jrwYhRSIQ7mWAIcxmJKpFwpk7EqbdPxfWyi3NTWj8mEvRqi0mJJbjGlSq0j4t7aPCQwC6uamtkEmHK/MlWFBJRWB9jngQVeIwdUYJG5MRZ5mZl+0uMNWUvpB8hYqAZZdV31KFMe/t/SeVabtkHN+9dpm87LiXIDAnoe2HKrpeugDc0U3GCWiXsa5mgsCuWmXqtCBDvGW5zriDex74PtpK0LLGxl5xxRLn4uqjAWnFQUlQqhykY5VXH44IF7uwB4NsgytBfnomrLdADaREx8zdR6Le3SmXMXoBy829YTkbrPsrJJlvzbmAppD/hkQ3U3Fb+WjIXNIMtQKSCAm9/o6K8QNRbqol3XVS/vT1BHY6GmkMzqaXmK9tI2naOxYAYEhB1y8TrujIUpKMGMBTd/PyBOsiry/iR5VYgPxmIic/VMxqLGaVaMt5fYvz+84WNWYaAJfAVszNryd/VSIWpptWkcOBVCGaFRAncVeMNdpJlsc19F3G2CrpLOBXUORfijqBoLah0ZMVMhxZbwGgZbM18K915lOywOr10hrNF4Bn7wKTpwrpSb2jZ6VlUIYix0J5tcVBhOYJGXGy7HWtdVT3z2KqkQ/X0zb+gy7++xbbqpGyG3TFSXZnBNqRQ0FoxeIQD8ioO+QXkux1sQb4omZB4YC2Z1gY/uplg7ZCvx3EamVVQqxKaxMG02eO40xlgQc4TS0rB6hSiunZMGhJt4rPVSIdXbuSdJIr83mSrP9AcU7gEKzwVV9GstN0U/1rW8j4FFi8APD62xgNX/8xgLEVGaTkxyUg/62lTIgkE9tg15Gl7U11gA1GMsxAKnbupTdGKmkDMWy7+b1Sk3NQSRthNG+Vr0vJk5ikCxxsck2sIsE3fTykso7YHFCDUh89E2nVtdwGntbn0vh3mt11jwUiGc8kP12lzkqSi3clOK9aEq51TgZ2ieZrXE3SboKulckFeFVFu71Nbpcr02dDe1HTLwvVUDUpsmR2WLMFwPOW0hrNF4Bv4SChoLpdyU04QHgGfpnbvR9bSlU9z3axPqSYZTkme8ng+NhaPYUS62grEQwt1aGovyJsYR52FoGYuq7detqRDEWLiKNw1zu6kmZFw/BE6jNOt7OTBx2wcG6L9z9sKmteKkuDA7UC0VIlJR9lQI6Tq8uodpmres57RNB1g+Vy4Bmgt8OG/WNe5S55np4Cm1ZJY1JkkSlF5VmFhLi4ACY6GkQqYWO/CusK8DC7FI9pLipFCrQrjdTcXDZdJY4Jp8bbmpo5VzG1BPg3lJXrUpIh6iKqIiWypE11U0rwpxCxgpmLsRVtVGqAtK/aoQU5dXAAfxJsPVEreLb6Jtum2u+Wib7qIdOnxAlwqxMBZzfdAngE+YLn1CBPJUlD0Vgt9LNRnDwS6nbbr42yYakOGxemGlah6KZGktowkZ5xmW+4EmFaK39EaMhcZsMaZCWgRl5w1QDizyJjPm63HMW7BwSJQApVlxwa7jr9AUylUh9R5OLkVI/y2dCrE9RLnNsRBvCmah+hiMbY4dLb11vU/Ylt6IReG0TQdwqQqxL+g4sGuibbrVIItRuWJ/L37+XZcKsXU35VQNeUuFGBkLKhWyOo0viqdxAPN6lCRJIfhsomU6HiulbeICs8ZVoGp5TE0qnQIL2ZRSJ0qn7z9+25J4M6ZC2odOlKSWm6aaAESFPElrHPcAFIMs9NDhRYjb9KxNqE2o9mo+nLlxTBWNBU01W1MhJUvvGoyFge52ZSx0wkjRl4PL6mBL79wLxZwK2WaehgesVAgWbxbZoTrg6nl8GGS5NM7a3qSrQmypEI5PTV0fC5kKMQR2VIAzVO4htke3VU/hVNykhkDSBJORFBeTmoyFqkMxVZe5HKCoRm4Adu1YkiSlw7DAvmhCdv3118MVV1wBhw4dgkOHDsFzn/tc+Iu/+IumxlYb2AEPQ2uQZa0KoXNkGAXGAn3ZOEqt46/QFPqSIi2KN6tqLJooN6Vyxhhq2/R5DY2FuRuhm8ZCt2nLdsnMABNvrKZTMd6guadhjkFWYxoLJq0uq2JaSoXYxJu4iykG5xSpNiFzBcdHRMw3XJlVqojD9uOWeYjncd1UqQ4cHZsNdT14VI1FaqgKESwD53Aw0gRNnINKX9OIbF+kQi666CJ4z3veAzfddBPcdNNN8L3f+73wIz/yI3D77bc3Nb5a0J1Y9ZbetsCCpugxCk3ICLMVgDANstQce22DLOFIV2lT12zElgV7Q6ntl6VYFZghswVvtVSIqxi1fJ18YzWnQtw1FhzzKezPgM3IVCdBV3DbpnMqV+zv5SLeNDtvApgN1EyaBXnSRffSBVJPZKjKoZodqi6w4l4mCcPHB6XL6oq7deD0Y7Khbg8T1etjYVivxT3hHA50LKhNO4bfW8dYVOmH1CScupu+4hWvKPz7Xe96F1x//fXw2c9+Fp761Kd6HZgP6Lr2iegvzZa237IJmUW8yZn0uAFOr7fMS+I21wBhaixwYIGd9SobZNVIhehq2aeW6Nxn23RTqTDVf8YEnTByRpwoudcR6TirpbcjY2E0yEK9WvCiPZmntShx97bpPmhyhnhz08xYAOgM1PipkCqdTQGQnsho6Z0HggIDhfUx9ZxRUdBYMPu7uMImjOWgbtdVNRXCKTfliTfNTKyRsbCtIQGx3wCOgQXGYrGAD3zgA3DixAl47nOfq33dZDKByWQi/727u1v1LZ2hc5RT64K51QOcSa8q3Af9ZWCBWY46XTebQsHKNuU1ajKhVipEV6Zr6xWiLLbS4dRjKqTYEpurjaCDFFugVL5OfpIyeWBsVLH0ZmzaeLz4PSazYmCxN1vAj//238EVFx2GX37l5db35laFcDqwAgA8fHwC//y3/w5+9Lsugje8+DuU93LQWGDGYpMOLGbzFGBc/DuTeVl+jaT0Hi7YYDEW5TmiChPnBKuhA/Z3qKtj0EEcSHRr7Hv+4n/C7/7tnWAiyURAVVkfpik3pTUWy59xDgf6wMK+DvgqWW8LzqO57bbb4IwzzoDxeAyvf/3r4cMf/jA85SlP0b7+uuuug+3tbfm/I0eO1BqwC3TGL/gZmqeZUfWLgalYHf2r5oupyRSipTfWISwQ1Vn1RPLMI2fCsJ/AUy845Py3ug6H1u6mSHWdphlJBbPHoFkETk3zhZwbdOVOfuq15gCw7EDqcp255bMdOjCExz/mIHznY7ece4WYu5vmgd2wn0i1umrrfce3jsPf37MDH7rlHtZ7S92D5T5wKlcAAG656yj847dPwJ988d7ye834jMVjzhjDpecchCedtwVb4/wM1u/ln53aAEVQbtpsnnTeITgw7MOzLj3LOg4KNoOsJfO4/G+cOlQ3KBf2FKfL6jT6Mr6HJggX+Mit98LebKnx0P0vy5ZMXpW1BwC7ENvFm0+7cBsGvQSeceSw9bq6SjObdgy/d6rsO+LZ45r1tQVnxuI7v/M74dZbb4WjR4/CBz/4Qbjmmmvgxhtv1AYXb3vb2+Daa6+V/97d3W0tuNBR1ngxTrOMTZkXqzwycuFQG+BQddkhaywA/DjrveUlT4B/84LL4MCoQtt1bVWILRWCTtHztGbb9Dw1kGWZtH/eOTUDAICDo34FpqH4ecS1+AJLpLEgGkzh9/uLt7yg5N/CvbYOuF18kixLTk/NFiUB4dFTUwAAODFdwGyRWu9T3l+Hx1jYDLLEMyjub/F3fCZu0O/B/3jr8j6q9t/Dfg8m85T0tOGc5i8+exNu+f98f+XA3WaQVWTWeqX/zjUWq8MXg33D87huqlQHm126+Lz/z796Nlx6zkHtdbYPDGFroxobpJY1m8Sbz7nsbLjtl36Qtc7p1zW72FfXJ6du2qcpOAcWo9EIvuM7lvTiVVddBV/4whfg137t1+C3f/u3ydePx2MYj8fk75oG1TIdoDpjoTaSofLbqjiMqsueB5kKQYEFpjpr5FCrBBUA+lTIVG6mmqoQ9H3szRY126bTQeRRx2AAgKbw0zRzDizItumaDYErLC2NkVEVIp6DjWFvGVgojAXe0HdPzeDsM8zPv2vbdFv+XTyDZGDhyMTp7uNoFVhQJ2vuab7q8wFgrwrBGzPVgHG2KJ7GOawensd1U6U62LqbioDm4rM24aIzN72+t4D6vM41+4gA93vUMRaclGiuCyzOt7rMclOoPZosywoaipCgO7EWGIuUz1jg6+gWN9XqlqouCNHSu68wFk0563Fg626qdajr9+Q9nczTWg6nOIjE5cUyGHBwS6Qo/OPTubRSdu5AmqbOrdvtY2RoLKR9sAgsaAEh3tCpzV2FDMYtpy5u23Sx2E7maSlV4KuaQaaOiHWgjcXe5ryJN2ZcFaVqaTidTeXfomqJpj6jzs5fYK+hgAZDbW8gNvO667V2XbNoxwD0rGeX67QJTozF29/+dnjpS18KR44cgWPHjsENN9wAn/rUp+CjH/1oU+OrhZlGvFdwMksztqV3v5dAkgBkmYGqU1III4L+MrXh7QrCWW++qnpoylmPA30usnhiprAx7MPxybzAWNRx3gRYGaKt4oicZeA/OpQwcufkbDVefrkhLhX0zXpxhJGqGFBX8nj0ZB5MHGUEFtxOmVxLb5we2Dk1K6XIAOpviHnlkl5j4bsUE8OeCsk3Q5wO09lVc9YibPTWVBMyc/O/PL3Z5LqkbuLSmdmyP9ggAwudpbfhO9BVhaip91DgFFh861vfgp/4iZ+A+++/H7a3t+GKK66Aj370o/D93//9TY2vFmQTKmVjEU5monSQa6S0bKnbg+k81S7AKmNBRal5q9twAguA5eQVpbEhMBZT5QHkNNzZGPbg+GT5PZjU3Dbogsidk0v9gEsqRBWDAbjrK5bXQRoLz2Vm/Z75pAhQzgXrBIS7rowF8xTKqVxRx7NzagaPPbRR+l3deW0qPW+TsdCJN3UljKqWZmGoLlJRTIU0y1hQXkHYvrxRxkLVWGgcnF2h049I7RiDsVADi7p+Q03BKbD4nd/5nabG0QhMKYd+ksAClh4WCyZjAbBcVKfzVJ8KUahWqnxqrgl4usagl8AEir0Ausjd6RxOOfXe4r5PZinUcThNkgSGvR5MF8XvWmyUuEGVDYJVwC6Hla6Dy009p0KGfXrhwlCtqnVeCqrGwoQUlWLbKjW4lt4TJbDA2GMKRW0YaXRA+D2aXOxVzxYVukoD9R7a9AOFv0VsR1Of0VTSjz9rkyf0gcKM+WYs1PSZ1I5V8LHYtxqLkGEyf8EqW5fUhMmRMctyUdO4xFjgVEh4GguAdpz1OLCVZRkDC7Tg1q2+ocYhaP5th/4O1IZYhbHAqRBXDwwbOMJI1VGSlQo5aQ4s8OnNJnbs94unbR3wyVZ9/wmztNUGE2XfBj09VlxmVejShuppvIp4c5GmjX1GU3dTsSaNBr1SlY5PqL19Fg73yAQqLb78t/0ApBNvtpF2q4J9HVjompABFOuCxdrA2YB0ND3AcpEU37usCiEaz8wC9LEAKFKdTTnrcaBr9jbnaCxED4tCuWm1z0BZ8FZLYeRMg4DY8LjCTYBigMIxYXKBS9v0ciqkungTn0LbZCzqnrQHBr+FNhkLPBcwdEF4qeLBgebHGhdfKSUVplTIhMls1R9DcZ7lvaTqXpfW5Yj3YZWbahiLqmZgTSGs0XiGKeUg9vSlWJG/0Zua5OAFdsOQCjEFPF0Cb4B1m5DVwUDzAHJ6MIgHbDJbsIxnTKBo2SqBBZVmkKkQJ+ajrLHwpdNRlfAqliJnWL3nKrCQAkJ9uamNsRDPzKCXWIWofI1F+fuSv/N00jaVRTalP8AoOJ0SY8jtvIvzQ9XSuKx9uCqnqSZkJka4qWBGRc5mL8fgkio3QffZZkq1lWlMqRpYMEu128b+DiyEMIl4aMSXmKY5Y8ExE8qjWf2CkiT560w+FqFpLIaIaq7bNr3eOKqnQmjGotqCQAWRVXwsKPMpYSJVlfnwbeWrNl5SUTBcGhQ1FnUYCxdKneMOClBMzQixrXw/T2yCrlMlgL+SVvP75/eLKjnVtW5Xg1yd1w8F2iCrvSZkbQkVVfbO10FQpx+ZclIhWsbCj2bIN8IajWeYcuxCiIMZC05EakqFTFA9vsgBUnXZpoCnS4j7dLKCbbVPjDS5dF65aa6xcFk0KVAnjN1KTEN5096trbHwmwrROfsJUD1SZL+KmZ6xsKdC+JsFt236hMFY1KXTOeWmTTIWvV4iBaQUY5GXMKpVIcU5nW+afI3FfJE2lio1McJtlVYOlOBGpkJqMhbi+yqva8UUIwVtuWlLLI4r9nVgYYo0cVS6MFi2lv6OoVrGDxv1oIRo6Q2Q35MTk7n8WRdWsTrnzZmG3sXAojbXLqSlcRCnp0qiS4N4s0oqZIbLTZmdUbnX1mks8D0QmxVunZ6/LoXjaP7snCoyBipc6O2hZnEtXRMxFqqPxoTpimkdi0FImvcjafbZ0QV2APpUiHoadzHrw+myplKleerYcHBrjbFQgq+aDDNVbkqlGCloq0JaMAyrgn0dWJgaNeEvysXvwESBUgpdEdTgVEioGgsxnhOr5lg4pdMmdKdBVioEaSzqV4UQqZCT9ZiGWtdBn2MitQl+xZszbWCRV06JlKE0aUIbm1peak+F8Knc3A/EprHQizd96R9MVSFtpRFNrdNnmhJG1QV2jr5XG/poHnfRNr2t0kpdozZf4s2CkJ9IMZJjWrElC1QVgku1YyqkRZiESTKwyNwCC1djHOr1Lq2K24SgmsWJE6d02sSgRy/anG6l2DjIVG7MgRpETucpnFp9xy7+E1Tb9ErVJSjIE+Pw1dUw3zToTTuvtc/HQJk0qRs5tyqEwyBQARoF1Xmz+H55urIOTBtgW8JnmfYjWqfPNWwdTmeYXkcBP5eNaSyIKjqBtkorVTYqt/T2kz7D1W74c5rWKcpyHx+8ImPRIkylVEXGovgzE8wRdZmWokxRdO3cu4aaCunKdMWaizSmQkSlQioNqaraXqt6GrFJJQnA1gbfW27YKy8KlapLiIXNF6M0tOgXqA1IskPYN2L1ucTctleF8E+hA6bGolAVclINLPyU5+WsWnciQylUJlIhury97jTOSgMXUiHNfEaqik6gK8YiZz7rXVeyXClmLFCK0fAGQt+Bb4tLqXbbCGs0nmGqvigGFg4lVwbHPUpcRKVOQk+FHJ90V2oKoE+FcBzqioxFvaoQVbwp9AJb4wG7HTkArejeqZAKoRZ/k+DLBTrVuQCVhhoTG5sImC468wAA0I3AMFxSIZQIlrymhkGZL1L5+ZpkLJpqKa5iTAR2Ajr7e+kCq6RCeBqLsrjbd7oHM7yZppNnXX2MDTqNRd0qPoq9xilGU3BHlVqLALbPKNVuG2GNxjNslt4AQry5/BlH9TvU0PQAdLvkAXGy4VD6XSAUxkKXv+bQtrgEsi4zpFqL54JLfhoEAJVJIpX5sdU9drkWNY/9dzelAws6FSLEg+Vqlwu2D0ivGJOtt4t40xb8CKipELFB4Z/X7xVSTCkIYPfdthgLqtxUV46ss6vmbEwiXdakuBuPt9TJsyU9QX6gUFgdb03IUDqDeK4o9GRgkf9s4qnCqQmENyKPkEyENRWif50KMwVaXiSpk02olt79UmDRLWOht741BBbItMnFVZCC6gBaRXAJUK64wBvtIYeUSq+XgDplfLFeNjaAZCxEEIerMFb36MyDQ3mfTB1OpaqdsUGpp23tNdFGO08zOLE6YfvsNaFbB4rBS7PLq875FMCeCilXPPAPVWJ96DUg7i50FVaCtrZSTDpLb1uTShuoA5M8LFkOmSbGIjR9BcA+DyxykaQtsODXKQ9l/t8kLjKnQkLVWIjFUog3u1IaY4c/7DQ3Y0T34iE7OV1Ie/XKlt5KSqZKiShA2dJbbLRnjAfOFKb6WWwLkut1bRqLUSEVIqpCaO2ICCxMAk5uy/TlGIvMjw46Xw3xfI76PadUFgVdKgTfi6ZTiTiIVqFzZlXtqt0MstT1wb+4GweuqqV/7kHSTiqkpEPxxFjgFG9eQm+e/z20Xwm0lXKrgvBG5BFzQ26MCix4trYccRHFWJSrQnzR2L6gMhZN5zJ1KJxaCipo+30Tp0RM11ZOhSh6GrFBufT3ACinQqoINwXwZxmg0s+6sGosCH3LBsFY4HskAwuDgHPiwFhQPVdM1xT7gHh/n30VpMhQ0TeITb6J07wKM2NBpzhqtU1XUiFNMDJ4DVY1VpOWyngHSprLxefIBMr7hJsKobxw2kq5VUFYO5tn5AZJvHJTlqW3qfkQkfNSN6csywI2yBKbcrduboVTCyF04rRNF59h+fqKgYVyQq6bChHf+9GT7nbe8lros/gMTG1t06k+LdTGJu7R4QMj2F7pR4ypEAel/5CRCsECzbMPivefFsbpg0nQdeHE9HTTpdq4k68KWxMy1SCLVxUiAovm1ockSdC9VVIhLTMWgjEVzKevwILysbA9y1R307bErFWwrwMLrLhVgRkLl+oBcVIhu+8REbXqyYDXodA0FmI8oaRCAIonwrkmb4whNqjjPhgLXSrEObAoLuZ1GAs8Z3xWFdnaplNiQIqKd06FiPShg3iTCuoFsL31uVsbAJBrWnw2BxPBonqq9tXkjANT6/RcY6FUhSisj4unTlvrg2SF1VRIa+WmedCIDanqBhYjopSW09kUID/0zmMqpHuYUhy4KiR1oLrUSgEMKqIeKYxFwRAlsHJTqfqedive7CORYjG6t4sxxb0XnwGghsZCWQiq9PcAKG/aVfqN5NfC+h1/j6/d0rsc1ElLb6IqZBlYLIWppsAit7/mb2wmxgJXSDz20Ljw/j7FbtpUSIuCug0jY6GpClE0Fi6HKjH3ml4fdN2N26oKwY0m8VyrG1jkwWhZN2bbC8T3k5KpkPC28fBG5BGmUipcXjd3EDCNCM2EALWoqBoLPFFDKzdVVd9dljHJ+7a6X1nGa7wl7r34DEniz9K7SmdTfJ2FTIVUZyyGDaVCbJ1DqT4t1MYm0g6HN4fSnVTtMIpRxXnTpLHYQwLNM0UqRmgsPDIWumZZbbax5lSFqKJAncaCU/GgaiyaouD1wti226ZnynrtS2OBdWO8VAhVbtpWG/kqCGtn8wyT+UuPYiycupvyjHHUh2TuMQL2jZKld4cTNi/1LN8300l9rKRC6iwGalBYvypkafrjS7xpasbmCtXqWQXlvUJR8a6pEJe2zxznTSzQPKS8/8SjxkJXEt0mPS2dN01VIcr8HyqpkJmhck5FW6kQXT+mLtqm43WnfnfT8ufSMUv6MaFy00D7hADs98DCIEyiNBYc8SbVSluAUumqVSR44Q5VY+Gy2DcFVfRabNZjT4VgV7rKY1C+OyG6dK4KQWNYpCiwqJAKwdfyyViI+5RmRbpVgBKZUYyFu8aimkGW6soor4eCBxEA7jSgsbCVm7YhqJPOmybGgmnpzWFP+8r60NQGnz/7qkdIS23ThUBfKXf3J94sC9KtGguiCVmoLdMBAPjuPGuIhSEVgqtCUoc8o2v3PbXxTO6ZwQtk2oSa5+tywqothrme+uqmUcfnQf2ud04tT2rOVSHovs7TrHJKZXktNLc8ptLwdRdZBj0ozgWqT4sI4uZpJqsxxKazvTmUgZO5KoS/SeE0UJoBUOw9TneoBl0+25nrDhhUZVhTkG3TCcZC9LdRUyGqw6qTcF157pr6jLo1tjXGAjX8KjIW9a5r9rGoUW7aUesFE/Y1Y2Gi+UTaY+7IWMhSKIPz5pjQWEyVVEho+gqA8n3qUhQ0UMp6uV0AVZq7jlseDgqzLEOiS0dLb/RdFxiLmlUhTaRCAOi5TVG2eIGfzFN5f3oJwBmjATMVwj+F4hOjrnoF553F++8qBlk+fBB0WivK1r8p5OJZqruprty0uH7JtumM50Q9sTf1GXXdjX36kJiANRZY2F+3fJjyQOK2d5CMIlUVEsWb7WJhcLgUD1Ja0SBLVSwD0KrloSI4mzMqG7qCaiTWVRMyANzhtJgKGfbND7gaDNVJN2GB3qnZQn7nVatCAJbfvwxQHFqvU9dqIhUCQIsj6SZk+X/vzRYFc6xeL3EzyOKIN5UAzXS98aBXSoX4ZCx0jfLa7N9gakKmKzdVTcak149DualAU59RraQTaKttOvad8ek5RBlksX0sokFWOMibVpkZCxfLVlN3U0qlq/abCNXOG6B8n7pkLEqpkHn5xExBPUXVYYYk3Y1Yhn4vgYMjtwd5oGzadapCCqkQrwZZ5k2bslPv9XIzo715KlMOwucDb+w6TYRT23QlpURhQjAWsirEI5ugc+Bts3+DqW26SIWoaWBdKoRlkKWmQhquCpl25GOB0w6pw95ggykVMrKwjybGIjYhaxmmtum4LtjFstXovEksKgMlX5i7gYZ369uiOjlQhU7csiyVUq8n3swrU7A5lislipuHeU2FeGS98G2i5rbuZJULCBelVvDi/+dpVmi1jeFyCsWLu656JX8Ge6VUjM+KDer0id+jjaCcVW6qSYXk3U3LAaMOKmPRtEFWyXmzJbEi1lj4bBiJtSMi0M4tvd0Zi+i82RFMkwI7mS0conbVNAmDUi3rRIghMhZtLRwcDNCmDqDPGasYD3qA9/06KSecCqnDMgDkC/rJ6QJOrRaEKlUhTaVCkiQxGlDp+rSMURWO2kvlwLAv579OwDlx2IjVAI28nnwG+7C9SjXt7s0gTf22M9dWhbRE1y/fQ6RC+E3ISpbehsOXCnXNapqx0N/bdqpC5gs3/Z0N4rvIMko8awksCEvvtu5HFYQ3Io8QmxEVWEjGInMMLIxVISaDrCJjEVqpKUB7VCcHI+XUkqdCzPctSZLCg1bLx2KQB4V1SkQB8gDn4ROT1TgBtsbuRVlNGWQBmA2o5poTsCw5nS/yVMhK3JokidzcdTqLPcfNXsxRXSqEYiyyDODY3twrm6DTWrkESnUxNjAWImDQtU1XNRYu+jKBpj6jWkkHsDTIa0tTgNkBsZH7ZCzEtQHyg5M1FaI0MgSIBlmdIXfeNDAWC5RHcwosCOdNok4+FyGuJpIIdoIUb4ZUbirykW6pEIDiabGOxgIHhXXSFwD5vX34+MoLY2NY6RTUlKU3gNmASncCxrbe+T3KAyabrbdresLWOh0LNEeDHhxYjW/n1MxrEzKd1qrNxd5k6a17XtQUzsywRqpoS9xNBW1YoNr0vcUN+cR98sNY5PcvZ7CZqRDCx6JNPY8r9ndgwdBYLBy7jZraplPOfqpgx8WQpm2ElAoZKqkQ3cZGAQd2dVJOeKNV9QPu1xKMRW55Xec6AH41FgDm1un6VEjOWAjrblztItiLnVO0rbfrKdTWOl1tanZYemlMvRpk6crO26SnOU3I1IBBNRkzVc6paKscnbK+xiZgTd9b3NvHL2OByqVX35nuudKNCacA2zIMq4LwRuQReSkVwViInFXFJmSkpTexcKk12S59SdpGSKmQkhU68wEEoJ1PqwBb8FbtbCog7u3Dx5epkLoBCr6mL5hap+tOVthLgWJ1TF4Ws0UujuOWgNpap6sMCH5/nyc8rUFWIIyFztERz580c+xuqjxLTYkGKY8Qsbb2e0njwnfckE9qLDxUhSRJgg6mRR+RKoFFZCw6wswQjeOSoiruc+qpbpFmcrLQ3U2LKuwgNRYBGWSpzBCXMgQoRvA+qkKmi0w21/LFWFS+ToMaC1Pr9Lk2FSIEhHS6yBRY4A2Ra/Jja52uLra4X4jPE57ugNGJQdY8LZXzykBc47wJsPyeTeliFW2lSgdEKqTN0krckG/hcH9Y1+4VD0xcJpYSVrdZgeSK8EbkEaZJkfdGyCcPJyrVK5bpRRKfsFIcxKyBxqJLgyx9uSknFdKExmJp5+3aJ0RA1VhU12pgjYXfOWRqnS6+h5GysI+Rl8JRQuCqeklgYArfVWNhqwoRi+1h9P4+W5rrnDfbpKfxe6jpEJ3GQjUZczlUqc9Sc+Wm5TVWTXE1CdyQz8XjiAP1wMRNhfSIeT9tsQLJFfs6sDDRfFRrXM4mpHbdFMDK7I0BTcXP0hQWDuVdbSMsgyxdZM9IhWDxZi1L73yB2alo551fa5UKOVEvFYLTes1VhVAaC8G0aapCZtUZi2WJMO97srVOVwWahVRIC03IWjXIwpbqSmWI1FgoAUPRYdVRY9FSLyHq3rbJWGAdikvFIAcqg+0s3oyMRfcwuVySjAXjbuQ0HW2MM+r3CgpiPGFmiyz3YwgwFaIGO102t1FbDOcOdYxUiCfxJmZNhDDRV1VI3esA+NdY9NFJTYW2KgQxFrvOgYX7JoztlimowQPuF5IHMv6cN9VOq202IRv2e3nHUcXLQscwFXvCpE7apbYtvYulle0FbJhl9h1Y6A9MlnJTVGwg4Fqq3Sb2dWDBSYXMC+5q9tvhat5SCCzmqVMFStsoVYUE0IRsKiN7/gJYLDf1obGoX27qrSoEzWX/qRC7eLOUCkGMhUh34M+m9uvAqJI2wEwjBTXdIatCTs5QBUr9eT1QDgzq+7flhihNslTGQuPoqJqMuaxHbTchK5abrg5ubTIWCzdXZg5U/Qg3sCOrQjw6yfpGeCPyCJOrWU4t5VEgh7HIy8xoNbj6sPXRg4xV8CFqLEpUZxAai2JkzwkUClU5NU71lI9F3YDgkbriTTRJvadCDGyALrATQdyjJ6fy75plLHgaC7oqxB9jMSoEFoiyb5GxAEC23ipjYbDqxt+zm0EWnQbzDToV0t7pfFA4dPJTRRyoZfRcfx6yKiQyFt0g11gQ5aaI9hXsEq8JGa1KN+W75GRClSOh+1ioKZ22Ie6z7G4qTmCcVIgnxkJsHlNNxYML+oowcrtCZ9PldVCazfPmZdIv6AI7sah9a3dvOaZ+Ik2pAMyBRRWXyrwxnE5jUSz3PESWm/rQWBSrKwR8CkQ5EAGMWnIqUyHEhiW/50WG/C7s96RUFdLQwUPqEOY4FdKenkDcizTL76OPclMAdDAVzpuuqRD0d66l2m0ivN3NI0waC4puc0mFTBfFEi+TMQ4WfC4CLjfFGouu6TVVdW9aKFV4M8haPewnpwsQBwUf/hO+rtNUuanRebNUFbL8twgstg+MCkJMnIpQUaWvhgzQdKkQJd0hxLZHC+Wm9Rfifi+RPWkoyr6tNOIYlZwKYG0AFTDQaWBOVUg7qVLKhLDNHix4zRCVF77Wa9UwkasdU5m6QkVVFG+2C1MnUTF5cB06h0QYohdRneaokwo20wlaY4Gi5q475qkPYNVy0zqbL+UyWfUk6i2wKLRN9zuHhqZy0zkd2In78eAxUe1S7H8iGAPRCAyjyil0KDdFm0GWUhVycuqVTk+SRN4vUmTY0ilyTDAWeEOmUyH5JlWrbXpjlt45wyvQKmOB7oXYwH2xtzJoEo7Cc/0ehdFTLL0LHjBRY9EesixPO5iqQnBgwWIsULMYrmoZb5IuKuy2gR+orkuYBsoDKNNaLRpkqZto1WAAoKxf8WPp3QxjMTOIN3V59gd36TLaQiOwybzwu7yCw4Wx4JWbqj4WDx3PLcV9nfCok3XbjaGo1uk46KLmyACxgTrjMwq6NJhvqJ2NAdq9r/hZFQyUL8ZioDKxTAY7b+VeDCxcSrXbRHi7myfgtZH60vpEKoTFWBCNZADMCnfc+CdkxgKPqesoWJeL5KVCPFWFDPwEAwDlUl4f5aZN+VgsKI2FRgwomAHxLKg+H+NBP28EpqRD8t467hoLTtt0gPw+42fV10lbbUQ2R4xkW88P1Todb8hkYEEyFu4ai6Y+48iYCmmDsUBr/NyveDNf15RyU8vn6ik+FiG3TAfYx4EFPtGYyk0njowF3qi4oq0RWoBC1ljgRahrpbEuF+nahKyeQZY/xgL7lgx6CWyOqt3fYtv0ZspNSY2FhrJVmS3qHukEnLpKKvMYy+mH4jWLjIXqlNpL/N233Na7nPfukrEQz0q/lxjZWleNBU4DNynuVp99ACz0bf6+4o818RxYyM6tSirEdmBSXXFDbpkOsJ8DC7TwmMpNCxoLxtwpNpIhyszIqpCcHZFVIQGWm/YLqZAwAgvXsiwAf5bevnQRAMV7e3hzWJm+bLJtet9YbqpLhRTniVNgIYSWDgzCwJoKKTIW/V4CWxu57mNj2PdGHaunzy7y3lQjstwllf6cuN/R3GE96hc0WM19PrW1O0C7pZV4jRcHRn+W3qoonbeuicc+DyzCLTUF2M+BBVocORqLpcqbN3nyTY8wxiEWybzxDHZyC+/WDwJKhajBm4s2xZfGAi8wANVLRAGK467ab2R5HcR8ePex0AsjdeJZdZ6QgQVqXY6Rm0nxP4eptXuWZWQbdjwmn/Na7XAq3rvNUm2qdbqtggpX/5gq51QU14fmNjSyV0jLZlA5o70o/Lsu1PQZV5SuMhYht0wH2MeBBc7BcjQWLhFprlqmxEUEYyHrslE3wQBTIfh033UkrI/s3apC6jJDOCDwxVj4uo73VIjQL5DdTXWpkDqMhTCT4s81U9t0XQkeHpPPea2mQvLUTnvLKsVYzC15e7H2zBap1KK5poGbFHfToth2T+jifnhPhfSqHZjEr0VVSNt+Ka7Yt4GFeLh6CV0qpLaIdpk4pDOcoUZeiJHmaSrHFWIqBI+p66oQ9R67pEJ8GWSp71erKgSnQjxdx3cqxMRY2KpCBCiBK+4wilGlhNDUNn2iaQSIx9REYCHuTRf0tGydTqRCdIGneM4L+jLHtulNfsZcY1HuwdLWCX2gaCH89wpxbEKmaItCbkAGsJ8DC0v/DyoVwoVULVOpEIPz5nThZqHbNvotUZ0cqKcWp14hBYOselMcv1+dqpBBIaVS5zr5eHw7b+o0FimqHtBVhQiYGItdhbGoYnqUVzSUWRVBD6sCzaZSIWr1Qhf0dF4VUk6F6J4V8T1jloOzHiVJLgZt8jOaupu25a8zCCwVIhj1VDAWAbdMB9jHgYWtJ8egRmAxkIECId4kvmgsRHQp72obw0IqpGPGQm0v7GDpXWhbX5ux8JXC8J9S8R2cis+qphlwyk+9/z6qQqpYelOsCm6ZjvVShcCiEcaiO0FdXhVSNsjSBRb5ppl/r9z1T/xtk59xpNj5AyBH1dY1Fs2Um5a7m9oYi+K8j4xFR7ClOHpJ9cDCNQeIX+9iSNM2CqrvrhmLXvEBFCIzTkfPgqW3T41FHcYCayw2q4tAWzHIUjQWOO2gpl/UeUKmQjS23lXyxColjKGrzMKiW58bkyreNFWGNQWy3HRuXmPEHMLByJB50MkDi+Y+Ixa7C7RdXik1Fp6rQtRWEq7dTdNU1fNExqJV2FIOMs9YQ2OBFzeTYQm2qA3ZIKug+u6asVAWbZFv5YjMxgXGom4qxFcKw38qxHcLaV3nUHxyLNmcK/OEqng5pGEsqqQOTDoQXaDSPGOxSoV47J7KBWmQZWhlAJDPRbE5JRodGv23vdX7tqGx6FC8KXUoi8K/60IwfvNFZkwxqhBf5TxVUyFhbuFhjsoDpMZC83CVGAuHiHSk5MkAzBE1ToWErLEoqL67ZizqpEI8NSEDaEa86es6/p03aY2FWOCThOhwWacqpErbdIM7qM4ivFAV4lVjoQQWSgO0NjA2Mha6VEhRl+GyFrXBWIwGxUMFQPsbqZoK8dXdFAejphRjeTzL3+eMRawK6QSmlunLnxc/upPGgrADZ7VNLxhkhXfrQyo31eUieamQZspN61RzYI1Fves0WG4q/Q3oVAi1UeEA9MCwT55khc23F42FwR10T+N10HRVyFRV6rcYlMu26XNKY8FLhbiYyIn51+RnVM3xANp13gTImU7f3U2x4NeUYlTRV5uQVSjVbhPh7W6eYDN+UZ+luuWmJpUupvVDtvQuaiy6ToVUEzkBFMdex3lz+X75PfFlbFVHq1G09G5GY6EyFjNk/ESNRxzmdEyMjrGo0zbdKN40pUIa0FiIQGyvguFXXci26YixsJVmq6dxl7VIXLMN582uupsCEIyF53LT6Tyz9nQpjEcRLXfhmeKCMEflAaaW6QDl1IdTuSnKkwlMGIzFNPAmZEUDnG4jYbWO3IXp8dWEDCD/7g6O+rU2cn8GWfkYmkqFlDQWmgZkAMsSxA2l4ZcK8fPjk7mmhJD/OXSVKwB5PlxNdzRlkFVOhYTBWNgEgeLn4v67CJzzctPmPiMWxWYdlVeqGgtv4k0iFUKlGFXIclNFYxEZi5ZhapkOUKbIqzAWXHFRLvZMndp/t42Q2qbrupty6H9flt4AeRCpdu10RRMGWU2lQlTzKeEsqZuzYq7omJhDqFcH9rKo0zZdrVwB4DEWPue1rty0TaW+qQmZjbGokgpppdx0Ne4so5putbMuqSW5davLBLBZoos3Tyw3DQQ2kaQqxnGz9KbERYa26UiMFLKld1gGWTqNhX3KDvo9eX99aSzqpEHEmATqXKuNclNVGGm792qLchWDfg+2xsvgAqdDKrVN11SuAGCNg8JYbOJUiEeNxaDIquk0Hk2CrAoR39fArLGoJN7sC8ai+VQIAAraWmxCBtBcuSmVCuGsaXhtTtOs0rPTJpxGdd1118GznvUs2NragnPPPRde+cpXwle/+tWmxlYLgr7VG2TVEG8S4iITYzFCJ5uFQ9OftpEkSSuqbw7y4C2DLMuconuA/Huoq7EQ92P7wMDySt51xoNercVx0KDGYqjkcQVsbJFkLAwBE1VyWsWbQFe5AoCrMorXO2M0kJ2Lm2AsxFqje/8msUFqLMyl2Wq5qctaJFJxbVh6AyzN2eaLvJquqyZkvg6CRSE/n4XF39E8zfK0236oCrnxxhvhDW94A3z2s5+Fj33sYzCfz+EHfuAH4MSJE02NrzLmMhVCf8Q64s2RQoECmM1xxAM+RYxFiAZZAEj13XUTskHx1OLSKwQg/x5ql5uKVEiNzqZ4HHVswZfXWY6nx8jLVr22WnFhu/dirphSPNIkq5AKqVBuqqlcAdAzBr1eIoOeZnqFdEdPV3HezC293c362jh44PHM5qmcJ8v3bVdjMfUt3kRWBS5rWoGxyLLgy02djmEf/ehHC/9+//vfD+eeey7cfPPN8IIXvMDrwOrClnKow1jI0zSijE1t02UqZJ5aA56uMez3YDJPO6fYsLEVbt7GXQTF91A3gBNBZB3BJUB+0q59ndU89c1W4GuXDbLMbJGYK6bPpvYLybJMLtouc83UNt1Ukrh9YAiPnpx5nddDZfPZM6wBTYHqFTJnp0LcGYs8FdLcZxTM6TxdMpUZ5EFTa03IaqSLTBjKwDhju26q7z9Ps07Sbi6oxe/u7OwAAMBZZ52lfc1kMoHJZCL/vbu7W+ct2bBpLNTvsop48w/+7pvwiX94EAAAThkU7liIGLJBFkBAjEXh1OKeChn7YixW46hTIgqAUyp1A5TldXx3NsXXnms0FrogTYgVTfdIfO5f+djX4P/5u2/KZkoA1VIhprbp1GlavH+jzpsdWnofn8zhx67/WwAAuO/oqcL4VOSpELFpVhFvNvsZh/0ezNPFskQ/W835Qa/QA6ZJqGXN3stNHVMhWBO4SLNO0m4uqDw7siyDa6+9Fq6++mq4/PLLta+77rrrYHt7W/7vyJEjVd/SCSKi09keq4yBywZ04ZkHAADgvp09uOmbj8JN33wUAAC2NgbkxlGYTAH7WAAAXHD4AAx6CZy3vdHpOPq93B9himlDZoR+8VmbALD8PHVw4eHldR7/mIO1riPG8fjHnFHrOo85YwyDXiLnoE+IYAVXGADYqXVxr02fTfzumw+fhJu++SjcctdRAAA4++AIDjgsjmOlVBJjz2CpLd7/yJmb7PeyIYS26Yc3h1IYK9ai+3b2AADgQs3cLxlkObB6Yh4fOcvffaQg2aBFqhXltvH+At4Yi4qpEPz+C8xYBFoVUpmxeOMb3whf+tKX4DOf+YzxdW9729vg2muvlf/e3d1tJbgQIjHdCbHkY+EQCf/r77kMnnrBNpyazgs/f/L5hzQGWbnY09Z1tWv8/k89G46enMI5Z4w7HUeSJDDs92A6T52FTgAAv/bjz4R7jp6EJz52q9Y4/u2LHg/f88Rz4OkXHa51nRc84Rz4kzc8H5742HqBxdlnjOGjb/2eQmMtX9AZWU0tbNE7fvip8BPffQlccdG29tpv+r7vgCsfd6ZMVwhcfuG2U1Cv6zsCgDf28jh/+ZWXwzXPe5xxjK6QTORCeAu0T09vDPvwZ2++Gv7h/iITfGA0gOc9/mzyb/o1XCXf9c+eBv/q6kvhaRf6u48UsFeQYNDaDNjUOenP0juvEHRhYXtqYDHXB9EhoFJg8aY3vQk+8pGPwKc//Wm46KKLjK8dj8cwHre/SVkDCzUiddjoh/0evPCJj2G/Hhu+hK6xeMzWGB6z1W1QITDsJTCF5clKMOfcpmLbm0PY3qy/+I0GPfiui8+sfZ0kSeAZRw7Xvg4AwHecWy9Y0kHVQQjMLWzRwfEAnm75bONBH178ned6GyMVWJiU8pwxumKATtUA1bq1+sAlZx+ES87mM2rDGlUhZ4wHcEXNIJuDvMNpfrJv83SuBlv+q0IyZ92Y0J0sULlp19V7OjiNKssyeOMb3wgf+tCH4BOf+ARceumlTY2rNlwZC18RKQWssZAuhoGmQkKC2MhOTheln0X4xzZRuQHg1qelaZgCi72Wa/tLqZAONBZVIA2yBGMRoFmf9AgppELaC9jUe+KrAosuN+Xdf8FaLLKsUql2m3BiLN7whjfAH/7hH8J/+2//Dba2tuCBBx4AAIDt7W04cMB/zrcOxMKjK+9TJ0qTmofcFCUN2tI7NIj7VggsAtjc9itESe3OqRlkWSaFci4t65uGeJ7VMQLkG3tbzpdq2XkXG2AVyIoH6bwZ3jOF08ddMEHqPfHFMONUiC3FSI1pCgCLRda6YZgrnO7W9ddfDzs7O/CiF70Izj//fPm/P/7jP25qfJVhZSzUHFqjgUU+mULXWIQEsXCfQFoWbiokwh3iWVmkGZxAwZxLy/qmIcY4W2SyEkug7Q1oqLT3lv0sAmcsxGlcbE4hHnJw0NaFdkW9J75IHfy5XJ8rwbLP07RSqXabcGIssqxc4hUqjp6cAgA/sGiDsZih2uUQTn+hQwRkp6b5yarJAPB0x8awB6N+D6aLFI6enMIZq2oDV+Fsk9gc9WWu+ejJGWyO8iVsr+UNCOsAAMxVKSFB9StpwhOlLvIOp2kn1TbqPfHHWORMjGuKUax9mMHdF4zFOiFnLGj1fLuMRb4A2dq5R+QYKKmQyPI0iyRJpM4CaxhE+q4J7wxXJElSSIdgtL0B6ZqQhbrYC5RP4+E9V1LwPk87cTRtirGQnytNZVt47iFTBIQnJjmD22YJrgvCHJUH7Jxa3nxtY6RWGYuYCqmCXGMxL/w7ojlQ4khZlhjInNWVnOrapjeFUSkVErYbooAqTAxaY7HIWm+ZDtCcxqJOKkRlLPq9JEjhLcA+DSyyLIOdU6tUiEa8Wae7qStyU5TcQTKmQuwQFOGJycrsLNCHaD9B9PvYOZlv2q7q9aYhxnj0pBJYdMRYTOcpZGvQv0GgdKgK5HvFGCGWtwszKDWI9t3dFHtRuJSbAuSBRahsBcA+DSxOzRZyA9c1RmozFYIfktAtvUNCZCzaB8VYuLSsbwM6v422N6C8u2lW6NWxLuWmAiGuRZjl7SJgKzsz+7kuZieEdoz7XInDsEiFhBzAhv0EVIRYFAe9BDZH9M1Xn6UmH66CQVbUWLAh7puI0IeapkoR/kAHFmGJ/HReFrJ/QkuUOd78cGARunhTPSGHuBYNCqmQ9lNMTaVC8HVdtWPidaJKLuSUW7gjqwFBkW4fGGqb1iRJUnig2hFvunW0O92h+ljEUtPmQZlkBZcK2VwKso+u0p0CbZsGFb0Wlu/dS8KonjFB3SRDHG8xFdKBj0Up+PJzXfwMyXWNeXGRjpGpkMhYtAtZEWLpSIkDiyYZixHKxUaDLD5GMRXSOkypkJDFm/NF/my17bw5XRT1FW114KyK8mk8vPEWUiHzDpw3G2Is+r38QCvWNW4qpK9UhbRlBFcF+3KltpljCWBBTpOW3mIBEpQeQJh5zdAg7psQb8ZUSPPYJsWb4ZSbAuDgJy+7K2oc2i43TdemIgSAqogLb8y4I7QQ5bYp3ixpLDzuD0NFlM5mLBTxZshzLdyR1cDOSV5gMWiJsRATKUX+YqGc/kJG3iskMhZtgfKImAZkkAWAq0LyVAhuo97Wgpt3N+2Grq8Kde0J8ZAzRN1N91ouIwZoVodSVZSuMhYhi4TDHVkNyD4hlsAC6yqapAOpcq4QTwmhYahE6FFj0TyoVIitu2nboKpChD31qN9rzZ01F2WjzW8NAovSaTyQgBFDPPuzRa5f6bJtejOBhZt4UwYW01gV0gmEqMuFsWgysKAo5BDzmqFBPICnYlVIaxDPDBZGyqqQQAI7SmA66cDrANP1uZ13GPfIBLWzcpCMBU6FdNCDpUkditqqwF1jEf5cC3dkNcDVWLTFWFAUcogPc2gQgcSJmAppDcICnzTICiSwO0ywKl2kIvCGsA6COoGyj0V4z5U0FZxn3bRNL/lY+Gcs8nWNyVjIqpDIWHQCaee9SfcJEWiLsej3EsDan17SbHnrfkGp3DQGFo1DBOPHJnNp5iYsvUO5/zgVkq7G2HYDMoBioHVsb7XYB3yKFFgLjUVg5aY+79FIXde43U0VxqLNQMsV4T8FFWDrbCqAK0GatPROkqRAI4d4QggRarlpKFUJ+xnimckygGN7S0ZgHlgXTFFummbLAAigfTtvgOJzfHyNGIvSaTxAjYWw85+n3VTclJyZvVaF1BNvir9rMzXkinBHVgO7TPEmjkqbfrgw3RUrQngQ90mcWOJ9ax6jQU+61YpUQ2iW3hvDvlTEi2c9F092UzmwToyFummGop3BEIL36Tz3CGkzaFODL59rj7qusVMhUrwZvlA4vBnlAWyDrJYYC4Ai3RWFmzyokXwoJ+b9jm2lyVdo3U0BytUrUrzZIj2cJIncFAS7E/JiL1B2lQznexUopkLaDxrV1EcTjIXu3zqUyk0DDmLDHVkNHOUaZLWksQAoTp4Qc5ohIgYW3UDdtEOz9AYAOLwSmYrgR/YJaZkeFvdEpkICXuwFmjyN+8IIOW920TZdZbCb0FgIcJ8rtbtpyGm38J8CR6Rpxk6FtBlY4MkUYpviEKE+gKMAF8D9iHJgEZbGAqA8xi4qBwDyeyJTIQEv9gLr5Lw5W2SdMBZqeshrVYhSXeXa3VQg5CA23JFVxPHpXDpcHnIILJpmEfCpIDIWPJSU2QFtbPsZuZfFSrwZmMYCoNwvZK8D22cAQKmQ8N0QBdahbbrUWCDGYr8YZFVljNQxhBzEhv8UOELU348HPeuNb6u7KUDxtBdiTjNExFRINxC23oL5m64Yi5Ao88ObRSOvSQeNqgCoVEi4i72A+hyFuB7l/TTyfjCttk1vwdJb928d1DFExqJFSDtvi3AToL1yU4Di5IkbJA8xFdIN1kFjUU6FtF85AIBTIUK8Gc490qHEWAT4XI2U+wrQdilxc+LNkZIKqRpYRMaiRXBdNwHaM8gCKG6KIZ4QQoSaiwxpY9vP2FaafIVWbgpQ7hfSlaW2OFkfjxoLr5BM0Oq+9ntJq89/KV3RIGPhauktEPJcC29G1cRRZmdTgPYsvQFiVUgVlCjDgKm//QThWFtiLAKx9AZAqZBSVUhHjMU6VYWsQ7npoNv7WjLIajIVwnyuVFY95LkW7sgqImcszHbeAO0yFoNokOWMJk8NEXrgNEOWZetVFdJ1uWnAp0gB9bniGjS1CdEo7fikGyZIvSd+GYtqjJG6b0TGokVwO5sCdOdjobYtjqCh5iJHAUfo+wnYIEvYeQOE5dB4SDHx6qKfBEC+SWSr2xTyYi/QZMWDLwjGQt7XjhmLJsWbVctNQ9bzhDuyinDRWLRZboonj9q2OIJGrArpBoeRfkGkQQACS4Wozpsd9JMAKM/JkOlpAXWtC/G5Kt3XjlJcAo1WhTCfK/V7C7kCKbwZVRO7DlUhOGflU/VLIZabuiMGFt0A+1jM5oixCOj+l8Wb3TAWKou2DoxFr5cAXoJCXI/UdEHXGgufVYNV1zVV5xEZixbhIt4sMBYN5xmjxsId5Qcw3rc2IILyk9MFnFppFwDC0rgcXglMj03mMF/kHTDbXmzVexJy/waMQaHbcjjfq4CaHmg7YMP3JEn8ijfVsnlXS2+BkIPY9XgKHFA1FdI0Y1Gw9A4oVx0y1EAipBPzfsbWRv7sPHR8AgDL7yJp+BlxwaGNgfzv3b25bJveNj3cNWVfFcWDTnjPlTqm1gNGzDB7nvfqZ+MemNTgJuS0W7gjqwhuZ1MAVWPR7K2I5abuiKmQbtDvJbC12ri/fUwEFmHd+0G/B2eMl2PcOTXrpG06QLkEOmR6GqNNfVkVlFMh7QZsTQr7q65rkbHoEFVTIU2TCFigE2JOM0TEVEh3EOmQbx8PM7AAKBp5ddWErETZByyow2iz1L4KyqmQ7lJc/gOLaqWs0ceiQ+xWrgppj7EIcZEOEWVL73jf2oJ4fh5ag8Bi59Qsb63dURMygbbfvyoGhfUovMBCnW+taywadErGgt9Rv8dOMWKbgtGA/3ddYD2eAibmi1Q6tdlapgMUI8Cm181YFeKO2N20O8jA4tjSFybEPi04sMgtvdvegPYDYxHec6U++22fzvFB0/d6XRDOOjxXeKqFLhIOe3SO2N3LO+HZWqYDqFFp04xF2DnNEBFTId3h8Mq5VjAWIQZ1Il2zDCw6Kjft+GRdFeFrLLq9r/iW+L4/eB1zYQLxHhX6PAtvtagBIdw8OOqzvrCuupvGclMe1EU7RDp+v0IE5rl4M7w5KxmLk7POm5AJhJz3xgh9Peo6sEiSRH63visGcSrELbDI/zsGFi0ib5lu7xMC0K6AKVp6u0N1pIuW3u1BbNoPnwhfY3EUaSy6akIGsAyEffodNIk22xlUQb+XFMbVRcAm3t8/Y4HnDP/aOMAJPYANe3SOEG2eOWkQgHa7mxZ9LMJ7kENELDftDiLN8NDxlcYiwIVMlJQLVgWguyZkAOsj3AQorkGh+upgNqiLE7q4L76DxYKQ3+G5wt9ZZCxaRG6ONbC8cok2xZvRedMdagAWA7L2INiAR04sA4sQ770Y4wO7e/Jn7Rtkrc9ij7EO6xFuetfFCV3cF+/izYp6u34hsAh76w57dI6QqRBGy3QAgH6r4s3IWLgC5zkBwjw171eo5dohskVijA+uAote0r4WpMBYrNH87Adu6Q1QPM134Wgq7ov3ctOK1gP9QqAVdhC7Pk8CAzsO5lgACmPRoqV31FjwEf0/uoFarh1iUCcOEN/aXaZCNob91mv78ZxcK8YicI0FgMIGdaix8L03FDQWDp+rKN4M73nECHt0jnCx8wZQHq6GTzpYiBiiwj5UFAOLeN/agqpTCjGoEweIUx1VhAAUT9WhL/YYeO0bBnrQ6TpoExqLJp03KzMWgQexYc6oijjq4LoJoIg3Gz7pNGm4sp9R9SGMqIfDm2pgEd6cVcfYxeYzLFQuhL3YY4g8v+/OnT7RdZqpKY1F1bQ4Xv5CT7uFPTpHuHQ2Beiu3DTUnGaIiKmQbqA+QyEaZKmsSieBRX89GQtx+g15LepaGNtKualTKmR90m7r8yQw4BpYtFpuilIhIS7SoUI8hL0kMj1t4ozxoHC/Q+zTsjUeACYaO0+FrBFjMZSbZnjfq0DXqZBhY+WmFVMhaLKHPtfCnVUVIMSbKkWqQ2Qswod4CCNb0S6SJCkE6CGmQnq94hi7yDtjg6N18rFo6jTuE12nQtpgLFyeq4JhWOBzLezROcKZsUi6CSziyZsPcd9CPDHvdxQDizDvPx5jF5UDhVN14KdIDKkfCDBgFBh1Ld4MztI7MhadwNXHoug+13RgEVMhVSAevFBNfPYzDq1bYNHJ5rM+Sn2MgdRYhPm9AhSf+S70K2JP8L32FCpyHJ6rovNmuN8bwD4KLCbzhSw7Y/tYoC/Kd1SqIqZCqiGmQrrD4cBTIQDFZ70TjQVOhQSu1McYrF0qpENLb98+FoNqqRCs9Qh9roU9OgcItiJJALY2mJbeLbrPxVRINYj7FgOL9rF2qZBONBbro9THkOZPAa9FXVfcNKWxqOq8GXuFdIDdVWBxaGPIVvHi77TpWu5o9FQNIh8ZovPjfsf6BRYdaywCp6cxBjJgD3ctwpV0XWosmvSxcHmuMHMSA4uWcNTRzhug3VruaOldDcM1WAD3K3B1VaiBHR5jF3R513R9VTTVB8Mn1Jb0baOpe9TvJSAu6fJcDdYo7Rb26BzgWhECkDMWbTjPVe1od7ojzwXvm6m6NsDPUqhztnvGYn0EdRj9NXiuxNhGg14n7qD9hiy9AXLGyOW5ioxFBzjq6GEBkH9RbSyaUbxZDULoNAw8Qt+PiFUhdnRt4lQVIiAKudpKpEK6KCMGyO9REwzzqIJ2LPpYdADBWKg2vybIJjMtdETEVF7ID3NoEPdtFO9Z6yhUhQQa2G2j0vKunTdDp6cx1sPSeznGrsp48+6m/q8tghanVMga9aVZnyfBgtBTIbi7adRY8BHLTbsDfpZCDey6ZyzWh57GWCeNRVcpJnGPmtgfqmjHiqmQsNfDsEfngNwcK0zxJs5lDgN+mEODzEXGwKJ1HN7M2YBQc/EF8WaH/SQAwl/sMQYyFRLumMUYu3KZrKKD4EIa/zk8V4M1CmKdZ9WnP/1peMUrXgEXXHABJEkCf/Inf9LAsNwRPGPRx4xFDCy4iKmQ7rC9FqmQjg2yCqmQsBd7jHUwyBrJVEi3jEUT67VkYh3mLGYsQk+7OY/uxIkT8PSnPx1+4zd+o4nxVEa1wKI9xiJJkrUQTIWGmArpDjEVYsf6VoU0V/HgCzIV0lHA1qSJ2LDCgWmdDLJ4FpUIL33pS+GlL31pE2OphaMnpwDgVhUiRJtN23kLDPs9mC0WwdLKISI6b3aHjWEPRoMeTOdpsPd/c9SHYT+B2SLrpglZbz0Zi3UI2HONRTf3Vbx/E+L+Kutafz8HFq6YTCYwmUzkv3d3dxt5nypVIbKWu6XT2HISLYI+JYSGGFh0B9E6/dvHJsHefzHGh45PO9FY9HoJDHoJzNNszRiLdRBvLsfWFe2f3yP/7y/L6KuWm+63VIgrrrvuOtje3pb/O3LkSCPvs3NqDgBuqRDx2kMb/L+pgzNXbIrLGE93iHvmwkRF+MP52xsAEPb9P281xrMP8roa+8bhzREkSVHsGjoOr8FadObqfp7V1fcq9ocD/s/fVda1g+MB9Fa9sEIN9AWSLMuyyn+cJPDhD38YXvnKV2pfQzEWR44cgZ2dHTh06FDVty7ht2/8R3j4xBRe/8LHsydilmXwX2++B55ywSF46gXb3saiwxfvehTueuQk/MgzLmz8vfYLju3N4MNfvBf+l8vPg3O3NroezmmHr9y3C1++dwdeddVFkLSUMnTF7fftwO337cKrruxmjJ+/8xF46PgEfuhp57f+3lWxN1vAB26+B773SefChYcPdD0cEqemC/ivN98N3/vkx3YyxkdPTOFPv3QfvOKKC+BMz8HNP377OHzunx6Bf37VRU6VOf/j9gdga2MAz3v8OV7Hw8Xu7i5sb29b9+/GA4uqA4uIiIiIiIgIB9z9O2w+JSIiIiIiImKt4Jw8On78OHz961+X/77zzjvh1ltvhbPOOgsuvvhir4OLiIiIiIiIWC84BxY33XQTvPjFL5b/vvbaawEA4JprroHf/d3f9TawiIiIiIiIiPWDc2Dxohe9CGrIMiIiIiIiIiL2MaLGIiIiIiIiIsIbYmARERERERER4Q0xsIiIiIiIiIjwhhhYRERERERERHhDDCwiIiIiIiIivCEGFhERERERERHeEAOLiIiIiIiICG+IgUVERERERESEN8TAIiIiIiIiIsIb/Deat0C4du7u7rb91hEREREREREVIfZtm/t264HFsWPHAADgyJEjbb91RERERERERE0cO3YMtre3tb9PspYbf6RpCvfddx9sbW1BkiTerru7uwtHjhyBu+++29gnPqI+4r1uD/Fet4d4r9tFvN/twde9zrIMjh07BhdccAH0enolReuMRa/Xg4suuqix6x86dChO0pYQ73V7iPe6PcR73S7i/W4PPu61iakQiOLNiIiIiIiICG+IgUVERERERESEN+ybwGI8HsMv/uIvwng87noo+x7xXreHeK/bQ7zX7SLe7/bQ9r1uXbwZERERERERsX+xbxiLiIiIiIiIiO4RA4uIiIiIiIgIb4iBRURERERERIQ3xMAiIiIiIiIiwhv2TWDxW7/1W3DppZfCxsYGXHnllfDXf/3XXQ9prXHdddfBs571LNja2oJzzz0XXvnKV8JXv/rVwmuyLINf+qVfggsuuAAOHDgAL3rRi+D222/vaMT7B9dddx0kSQJvfetb5c/ivfaLe++9F1772tfC2WefDZubm/CMZzwDbr75Zvn7eL/9YD6fwy/8wi/ApZdeCgcOHIDLLrsM/uN//I+Qpql8TbzX1fDpT38aXvGKV8AFF1wASZLAn/zJnxR+z7mvk8kE3vSmN8E555wDBw8ehB/+4R+Ge+65p/7gsn2AG264IRsOh9n73ve+7Ctf+Ur2lre8JTt48GD2zW9+s+uhrS1+8Ad/MHv/+9+fffnLX85uvfXW7GUve1l28cUXZ8ePH5evec973pNtbW1lH/zgB7Pbbrst+/Ef//Hs/PPPz3Z3dzsc+Xrj85//fPa4xz0uu+KKK7K3vOUt8ufxXvvDI488kl1yySXZT/7kT2af+9znsjvvvDP7+Mc/nn3961+Xr4n32w/e+c53ZmeffXb2Z3/2Z9mdd96ZfeADH8jOOOOM7Fd/9Vfla+K9rob//t//e/bzP//z2Qc/+MEMALIPf/jDhd9z7uvrX//67MILL8w+9rGPZbfcckv24he/OHv605+ezefzWmPbF4HFs5/97Oz1r3994WdPetKTsp/7uZ/raET7Dw8++GAGANmNN96YZVmWpWmanXfeedl73vMe+Zq9vb1se3s7+8//+T93Ncy1xrFjx7InPOEJ2cc+9rHshS98oQws4r32i5/92Z/Nrr76au3v4/32h5e97GXZT/3UTxV+9qM/+qPZa1/72izL4r32BTWw4NzXo0ePZsPhMLvhhhvka+69996s1+tlH/3oR2uNZ+1TIdPpFG6++Wb4gR/4gcLPf+AHfgD+9m//tqNR7T/s7OwAAMBZZ50FAAB33nknPPDAA4X7Ph6P4YUvfGG87xXxhje8AV72spfBS17yksLP4732i4985CNw1VVXwate9So499xz4ZnPfCa8733vk7+P99sfrr76avirv/or+NrXvgYAAH//938Pn/nMZ+CHfuiHACDe66bAua8333wzzGazwmsuuOACuPzyy2vf+9abkPnGQw89BIvFAh772McWfv7Yxz4WHnjggY5Gtb+QZRlce+21cPXVV8Pll18OACDvLXXfv/nNb7Y+xnXHDTfcALfccgt84QtfKP0u3mu/+Kd/+ie4/vrr4dprr4W3v/3t8PnPfx7e/OY3w3g8hte97nXxfnvEz/7sz8LOzg486UlPgn6/D4vFAt71rnfBq1/9agCIc7spcO7rAw88AKPRCM4888zSa+runWsfWAioLdizLPPalv10xhvf+Eb40pe+BJ/5zGdKv4v3vT7uvvtueMtb3gJ/+Zd/CRsbG9rXxXvtB2mawlVXXQXvfve7AQDgmc98Jtx+++1w/fXXw+te9zr5uni/6+OP//iP4Q/+4A/gD//wD+GpT30q3HrrrfDWt74VLrjgArjmmmvk6+K9bgZV7quPe7/2qZBzzjkH+v1+KcJ68MEHS9FahDve9KY3wUc+8hH45Cc/WWh3f9555wEAxPvuATfffDM8+OCDcOWVV8JgMIDBYAA33ngj/Pqv/zoMBgN5P+O99oPzzz8fnvKUpxR+9uQnPxnuuusuAIhz2yd+5md+Bn7u534O/sW/+BfwtKc9DX7iJ34Cfvqnfxquu+46AIj3uilw7ut5550H0+kUHn30Ue1rqmLtA4vRaARXXnklfOxjHyv8/GMf+xg873nP62hU648sy+CNb3wjfOhDH4JPfOITcOmllxZ+f+mll8J5551XuO/T6RRuvPHGeN8d8X3f931w2223wa233ir/d9VVV8FrXvMauPXWW+Gyyy6L99ojnv/855dKp7/2ta/BJZdcAgBxbvvEyZMnodcrbjP9fl+Wm8Z73Qw49/XKK6+E4XBYeM39998PX/7yl+vf+1rSz0Agyk1/53d+J/vKV76SvfWtb80OHjyYfeMb3+h6aGuLf/tv/222vb2dfepTn8ruv/9++b+TJ0/K17znPe/Jtre3sw996EPZbbfdlr361a+OZWKegKtCsizea5/4/Oc/nw0Gg+xd73pXdscdd2T/5b/8l2xzczP7gz/4A/maeL/94JprrskuvPBCWW76oQ99KDvnnHOy//Af/oN8TbzX1XDs2LHsi1/8YvbFL34xA4DsV37lV7IvfvGL0maBc19f//rXZxdddFH28Y9/PLvllluy7/3e743lphi/+Zu/mV1yySXZaDTKvuu7vkuWRUZUAwCQ/3v/+98vX5OmafaLv/iL2XnnnZeNx+PsBS94QXbbbbd1N+h9BDWwiPfaL/70T/80u/zyy7PxeJw96UlPyt773vcWfh/vtx/s7u5mb3nLW7KLL74429jYyC677LLs53/+57PJZCJfE+91NXzyk58k1+hrrrkmyzLefT116lT2xje+MTvrrLOyAwcOZC9/+cuzu+66q/bYYtv0iIiIiIiICG9Ye41FRERERERERDiIgUVERERERESEN8TAIiIiIiIiIsIbYmARERERERER4Q0xsIiIiIiIiIjwhhhYRERERERERHhDDCwiIiIiIiIivCEGFhERERERERHeEAOLiIiIiIiICG+IgUVERERERESEN8TAIiIiIiIiIsIbYmARERERERER4Q3/L7/29lTq6484AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(roll_dice[\"roll\"], roll_dice[\"value\"])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "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": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "roll     49.50\n",
+       "value     3.74\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def calc_mean(dice):\n",
+    "    for item in dice:\n",
+    "        mean = dice.sum()/len(dice)\n",
+    "    return mean\n",
+    "\n",
+    "calc_mean(roll_dice)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.- Now, calculate the frequency distribution.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>roll</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>value</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>23</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       roll\n",
+       "value      \n",
+       "1        12\n",
+       "2        17\n",
+       "3        14\n",
+       "4        22\n",
+       "5        12\n",
+       "6        23"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "roll_dice.groupby(\"value\").agg(\"count\")"
+   ]
+  },
+  {
+   "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": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = roll_dice[\"value\"]\n",
+    "\n",
+    "plt.hist(x, bins=100)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "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": 29,
+   "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>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>"
+      ],
+      "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]"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "roll_dice1 = pd.read_csv(\"roll_the_dice_thousand.csv\")\n",
+    "roll_dice1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "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>Unnamed: 0</th>\n",
+       "      <th>roll</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>value</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>175</td>\n",
+       "      <td>175</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>167</td>\n",
+       "      <td>167</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>175</td>\n",
+       "      <td>175</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>168</td>\n",
+       "      <td>168</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>149</td>\n",
+       "      <td>149</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>166</td>\n",
+       "      <td>166</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Unnamed: 0  roll\n",
+       "value                  \n",
+       "1             175   175\n",
+       "2             167   167\n",
+       "3             175   175\n",
+       "4             168   168\n",
+       "5             149   149\n",
+       "6             166   166"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "roll_dice1.groupby(\"value\").agg(\"count\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "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"
+    "x = roll_dice1[\"value\"]\n",
+    "\n",
+    "plt.hist(x, bins=1000)\n",
+    "plt.show()"
    ]
   },
   {
@@ -506,12 +1190,15 @@
    "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?"
+    "## 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": 19,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [
     {
@@ -535,134 +1222,89 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Unnamed: 0</th>\n",
-       "      <th>roll</th>\n",
-       "      <th>value</th>\n",
+       "      <th>observation</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",
+       "      <td>68.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>6</td>\n",
+       "      <td>12.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
+       "      <td>45.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>6</td>\n",
+       "      <td>38.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>4</td>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
+       "      <td>49.0</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",
+       "      <td>27.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>996</th>\n",
-       "      <td>996</td>\n",
-       "      <td>996</td>\n",
-       "      <td>4</td>\n",
+       "      <td>47.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>997</th>\n",
-       "      <td>997</td>\n",
-       "      <td>997</td>\n",
-       "      <td>4</td>\n",
+       "      <td>53.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>998</th>\n",
-       "      <td>998</td>\n",
-       "      <td>998</td>\n",
-       "      <td>3</td>\n",
+       "      <td>33.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>999</th>\n",
-       "      <td>999</td>\n",
-       "      <td>999</td>\n",
-       "      <td>6</td>\n",
+       "      <td>31.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>1000 rows × 3 columns</p>\n",
+       "<p>1000 rows × 1 columns</p>\n",
        "</div>"
       ],
       "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",
+       "     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 3 columns]"
+       "[1000 rows x 1 columns]"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "roll_dice = pd.read_csv(\"roll_the_dice_thousand.csv\")\n",
-    "roll_dice"
-   ]
-  },
-  {
-   "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. "
+    "age_pop = pd.read_csv(\"ages_population.csv\")\n",
+    "age_pop"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [
     {
@@ -686,84 +1328,95 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
        "      <th>observation</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>68.0</td>\n",
+       "      <th>1.0</th>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>12.0</td>\n",
+       "      <th>2.0</th>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>45.0</td>\n",
+       "      <th>4.0</th>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>38.0</td>\n",
+       "      <th>5.0</th>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>49.0</td>\n",
+       "      <th>6.0</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
-       "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>995</th>\n",
-       "      <td>27.0</td>\n",
+       "      <th>69.0</th>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>996</th>\n",
-       "      <td>47.0</td>\n",
+       "      <th>70.0</th>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>997</th>\n",
-       "      <td>53.0</td>\n",
+       "      <th>71.0</th>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>998</th>\n",
-       "      <td>33.0</td>\n",
+       "      <th>73.0</th>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>999</th>\n",
-       "      <td>31.0</td>\n",
+       "      <th>82.0</th>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>1000 rows × 1 columns</p>\n",
+       "<p>72 rows × 0 columns</p>\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\n",
-       "..           ...\n",
-       "995         27.0\n",
-       "996         47.0\n",
-       "997         53.0\n",
-       "998         33.0\n",
-       "999         31.0\n",
+       "Empty DataFrame\n",
+       "Columns: []\n",
+       "Index: [1.0, 2.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0, 36.0, 37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0, 48.0, 49.0, 50.0, 51.0, 52.0, 53.0, 54.0, 55.0, 56.0, 57.0, 58.0, 59.0, 60.0, 61.0, 62.0, 63.0, 64.0, 65.0, 66.0, 67.0, 68.0, 69.0, 70.0, 71.0, 73.0, 82.0]\n",
        "\n",
-       "[1000 rows x 1 columns]"
+       "[72 rows x 0 columns]"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "age_pop = pd.read_csv(\"ages_population.csv\")\n",
-    "age_pop"
+    "age_pop.groupby(\"observation\").agg(\"count\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x161c913c0>]"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(age_pop, \"o\")"
    ]
   },
   {
@@ -835,7 +1488,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [
     {
@@ -929,7 +1582,7 @@
        "[1000 rows x 1 columns]"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -939,6 +1592,68 @@
     "age_pop2"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "observation\n",
+       "28.0           139\n",
+       "27.0           125\n",
+       "26.0           120\n",
+       "29.0           115\n",
+       "25.0            98\n",
+       "30.0            90\n",
+       "24.0            78\n",
+       "31.0            61\n",
+       "23.0            41\n",
+       "22.0            35\n",
+       "32.0            31\n",
+       "33.0            22\n",
+       "21.0            17\n",
+       "20.0            13\n",
+       "34.0             7\n",
+       "35.0             3\n",
+       "19.0             3\n",
+       "36.0             2\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "age_pop2_freq = age_pop2.value_counts()\n",
+    "age_pop2_freq"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "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(age_pop2)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1029,7 +1744,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [
     {
@@ -1123,7 +1838,7 @@
        "[1000 rows x 1 columns]"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 61,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1133,6 +1848,61 @@
     "age_pop3"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "observation\n",
+       "32.0           37\n",
+       "37.0           31\n",
+       "35.0           31\n",
+       "39.0           29\n",
+       "36.0           26\n",
+       "               ..\n",
+       "8.0             1\n",
+       "7.0             1\n",
+       "5.0             1\n",
+       "76.0            1\n",
+       "1.0             1\n",
+       "Length: 75, dtype: int64"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "age_pop3_freq = age_pop3.value_counts()\n",
+    "age_pop3_freq"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "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(age_pop3)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1274,13 +2044,6 @@
     "print(ninety_percent)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "metadata": {},