diff --git a/your-code/data/.ipynb_checkpoints/main-checkpoint.ipynb b/your-code/data/.ipynb_checkpoints/main-checkpoint.ipynb
new file mode 100644
index 0000000..c6676e3
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+++ b/your-code/data/.ipynb_checkpoints/main-checkpoint.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Understanding Descriptive Statistics\n",
+    "\n",
+    "Import the necessary libraries here:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd \n",
+    "import numpy as np\n",
+    "from scipy import stats\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from scipy.stats import pearsonr, spearmanr\n",
+    "import random"
+   ]
+  },
+  {
+   "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": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Roll</th>\n",
+       "      <th>Result</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>8</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>9</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>10</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Roll  Result\n",
+       "0     1       3\n",
+       "1     2       1\n",
+       "2     3       6\n",
+       "3     4       1\n",
+       "4     5       5\n",
+       "5     6       2\n",
+       "6     7       2\n",
+       "7     8       4\n",
+       "8     9       3\n",
+       "9    10       2"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def dice_roll():\n",
+    "    new_dict = {}\n",
+    "    index = 0\n",
+    "    my_list = np.arange(1,7)\n",
+    "    for i in range(1,11):\n",
+    "        random_item = random.choice(my_list)\n",
+    "        new_dict[index] = {\"Roll\" : i, \"Result\" : random_item}\n",
+    "        index += 1\n",
+    "    return new_dict\n",
+    "\n",
+    "df = pd.DataFrame.from_dict(dice_roll(), orient=\"index\")\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.- Plot the results sorted by value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 184,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "my_df = df.sort_values(by=\"Result\")\n",
+    "plt.plot(my_df[\"Result\"].values)\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": 187,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sorted_df = my_df.groupby(\"Result\").agg(\"count\").reset_index()\n",
+    "sorted_df\n",
+    "plt.plot(sorted_df[\"Result\"],sorted_df[\"Roll\"])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nIn the plot above you can see on which roll(s) the number was returned. In contrast on the second Plot you can see how often\\nthe number was returned in total, for 10 rolls.\\n'"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "In the plot above you can see on which roll(s) the number was returned. In contrast on the second Plot you can see how often\n",
+    "the number was returned in total, for 10 rolls.\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": 133,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Enter name of Column: Result\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "3.3"
+      ]
+     },
+     "execution_count": 133,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def summary_stats(df):\n",
+    "    x = 0\n",
+    "    for i in df[input(\"Enter name of Column: \")]:\n",
+    "        x += i\n",
+    "    mean = x/len(df)\n",
+    "    return mean\n",
+    "df = pd.DataFrame.from_dict(dice_roll(), orient=\"index\")\n",
+    "summary_stats(df)"
+   ]
+  },
+  {
+   "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": 139,
+   "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>Result</th>\n",
+       "      <th>Roll</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Result  Roll\n",
+       "0       1     3\n",
+       "1       2     1\n",
+       "2       3     1\n",
+       "3       4     1\n",
+       "4       5     3\n",
+       "5       6     1"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.67\n"
+     ]
+    }
+   ],
+   "source": [
+    "sorted_df = df.groupby(\"Result\").agg(\"count\").reset_index()\n",
+    "display(sorted_df)\n",
+    "mean = sum(sorted_df[\"Roll\"]) / len(sorted_df[\"Roll\"])\n",
+    "print(round(mean,2))"
+   ]
+  },
+  {
+   "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": 107,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "def get_median():\n",
+    "    x = df[\"Result\"].reset_index(drop=True)\n",
+    "    n = len(\"Result\")\n",
+    "    if n%2 == 0:\n",
+    "        return (x.iloc[int(n / 2)] + x.iloc[int(n / 2) - 1]) / 2\n",
+    "    else:\n",
+    "        return x.iloc[int((n-1)/2)]\n",
+    "    \n",
+    "median = get_median()\n",
+    "print(median)"
+   ]
+  },
+  {
+   "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": 116,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Q1: 2\n",
+      "Q2: 2.5\n",
+      "Q3: 4\n"
+     ]
+    }
+   ],
+   "source": [
+    "def quartiles():\n",
+    "    x = df[\"Result\"].reset_index(drop=True)\n",
+    "    x = x.sort_values()\n",
+    "    n = len(x)    \n",
+    "\n",
+    "    if n % 4 == 0:\n",
+    "        q1 = (x.iloc[int(n / 4)] + x.iloc[int(n / 4) - 1]) / 2\n",
+    "        q3 = (x.iloc[int(n * 3 / 4)] + x.iloc[int(n * 3 / 4) - 1]) / 2\n",
+    "    else:\n",
+    "        q1 = x.iloc[int(n / 4)]\n",
+    "        q3 = x.iloc[int(n * 3 / 4)]\n",
+    "        \n",
+    "\n",
+    "    if n % 2 == 0:\n",
+    "        q2 = (x.iloc[int(n / 2)] + x.iloc[int(n / 2) - 1]) / 2\n",
+    "    else:\n",
+    "        q2 = x.iloc[int((n - 1) / 2)]\n",
+    "\n",
+    "    return q1, q2, q3\n",
+    "\n",
+    "    \n",
+    "result_q1, result_q2, result_q3 = quartiles()\n",
+    "print(\"Q1:\", result_q1)\n",
+    "print(\"Q2:\", result_q2)\n",
+    "print(\"Q3:\", result_q3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.0\n",
+      "2.5\n",
+      "3.75\n",
+      "2.9\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Result\"].quantile(0.25))\n",
+    "print(df[\"Result\"].quantile(0.5))\n",
+    "print(df[\"Result\"].quantile(0.75))\n",
+    "print(df[\"Result\"].mean())"
+   ]
+  },
+  {
+   "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": 191,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>roll</th>\n",
+       "      <th>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": 191,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_df = pd.read_csv(\"roll_the_dice_hundred.csv\")\n",
+    "new_df.drop(\"Unnamed: 0\", axis=1, inplace=True)\n",
+    "new_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 198,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "new_df = new_df.sort_values(by=\"value\")\n",
+    "x = new_df[\"value\"]\n",
+    "y = new_df[\"roll\"]\n",
+    "plt.scatter(x,y)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nAll vlaues seem to be quite equally distributed throughout the number of rolls. 2 is a small outlier as it doesent really\\noccur after roll number 80. Also 5 has a gap between 45-65.\\n'"
+      ]
+     },
+     "execution_count": 131,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "All values seem to be quite equally distributed throughout the number of rolls. 2 is a small outlier as it doesent really\n",
+    "occur after roll number 80. Also 5 has a gap between 45-65.\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": 134,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Enter name of Column: value\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "3.74"
+      ]
+     },
+     "execution_count": 134,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "summary_stats(new_df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.- Now, calculate the frequency distribution.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "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>value</th>\n",
+       "      <th>num_of_rolls</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>23</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   value  num_of_rolls\n",
+       "0      1            12\n",
+       "1      2            17\n",
+       "2      3            14\n",
+       "3      4            22\n",
+       "4      5            12\n",
+       "5      6            23"
+      ]
+     },
+     "execution_count": 137,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sorted_df = new_df.groupby(\"value\").agg(\"count\").reset_index()\n",
+    "sorted_df.rename(columns={\"roll\":\"num_of_rolls\"},inplace=True)\n",
+    "sorted_df"
+   ]
+  },
+  {
+   "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": 140,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(new_df['value'], bins='auto', edgecolor='black')  \n",
+    "plt.title('Histogram of Frequency Distribution')\n",
+    "plt.xlabel('Values')\n",
+    "plt.ylabel('Frequency')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "As 3.5 would be the mean if each value had the same Frequency Distribution, but as it can be seen the Freqeuncy for 4 and 6\n",
+    "are the two highest, which is why the mean shift a little bit into their direction (3.7).\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": 199,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dice_df = pd.read_csv(\"roll_the_dice_thousand.csv\")\n",
+    "dice_df.drop(\"Unnamed: 0\", axis=1, inplace=True)\n",
+    "\n",
+    "sorted_dice = dice_df.groupby(\"value\").agg(\"count\").reset_index()\n",
+    "sorted_dice.rename(columns={\"roll\":\"num_of_rolls\"},inplace=True)\n",
+    "x = sorted_dice[\"value\"]\n",
+    "y = sorted_dice[\"num_of_rolls\"]\n",
+    "plt.scatter(x,y)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 200,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nDue to the higher lenght of rolls, the Graphs seems more equally distributed even though the deviation are actually higher.\\n'"
+      ]
+     },
+     "execution_count": 200,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "Due to the higher lenght of rolls, the Graphs seems more equally distributed even though the deviation are actually higher.\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": 201,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "age_pop = pd.read_csv(\"ages_population.csv\")\n",
+    "plt.hist(age_pop['observation'], bins='auto', edgecolor='black')  \n",
+    "plt.title('Histogram of Frequency Distribution')\n",
+    "plt.xlabel('Values')\n",
+    "plt.ylabel('Frequency')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 202,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Guesses:\\nmean: 40\\nstd: 18'"
+      ]
+     },
+     "execution_count": 202,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"Guesses:\n",
+    "mean: 40\n",
+    "std: 18\"\"\""
+   ]
+  },
+  {
+   "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": 203,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean : 36.56\n",
+      "Standard Deviation: 12.816499625976762\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"\"\"Mean : {age_pop[\"observation\"].mean()}\n",
+    "Standard Deviation: {age_pop[\"observation\"].std()}\"\"\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 157,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nBoth a bit smaller than I anticipated\\n'"
+      ]
+     },
+     "execution_count": 157,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "Both a bit smaller than I anticipated\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": 206,
+   "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",
+       "      <th>count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>28.0</td>\n",
+       "      <td>139</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>27.0</td>\n",
+       "      <td>125</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>26.0</td>\n",
+       "      <td>120</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>29.0</td>\n",
+       "      <td>115</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>25.0</td>\n",
+       "      <td>98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>30.0</td>\n",
+       "      <td>90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>24.0</td>\n",
+       "      <td>78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>31.0</td>\n",
+       "      <td>61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>23.0</td>\n",
+       "      <td>41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>22.0</td>\n",
+       "      <td>35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>32.0</td>\n",
+       "      <td>31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>33.0</td>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>21.0</td>\n",
+       "      <td>17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>20.0</td>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>34.0</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>35.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>19.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>36.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    observation  count\n",
+       "0          28.0    139\n",
+       "1          27.0    125\n",
+       "2          26.0    120\n",
+       "3          29.0    115\n",
+       "4          25.0     98\n",
+       "5          30.0     90\n",
+       "6          24.0     78\n",
+       "7          31.0     61\n",
+       "8          23.0     41\n",
+       "9          22.0     35\n",
+       "10         32.0     31\n",
+       "11         33.0     22\n",
+       "12         21.0     17\n",
+       "13         20.0     13\n",
+       "14         34.0      7\n",
+       "15         35.0      3\n",
+       "16         19.0      3\n",
+       "17         36.0      2"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "age_pop2 = pd.read_csv(\"ages_population2.csv\")\n",
+    "freq = age_pop2.value_counts().reset_index()\n",
+    "freq.columns = [\"observation\", \"count\"]\n",
+    "y = freq[\"observation\"]\n",
+    "x = freq[\"count\"]\n",
+    "display(freq)\n",
+    "plt.plot(x,y,c=\"skyblue\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(age_pop2['observation'], bins='auto', edgecolor='black')  \n",
+    "plt.title('Histogram of Frequency Distribution')\n",
+    "plt.xlabel('Values')\n",
+    "plt.ylabel('Frequency')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "####  4.- What do you see? Is there any difference with the frequency distribution in step 1?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "The range is a lot smaller, for example the max is now where the mean in step 1 was. New mean should be at around 27.\n",
+    "General Distribution is again shaped similiar to a hyperbel.\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": 173,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean : 27.155\n",
+      "Standard Deviation: 2.969813932689186\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"\"\"Mean : {age_pop2[\"observation\"].mean()}\n",
+    "Standard Deviation: {age_pop2[\"observation\"].std()}\"\"\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "Both, Mean and Stdev are a lot smaller. This Neighborhood is a lot younger also their are almost no older people living there.\n",
+    "Most People There are between 22 -32 years old.\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": 175,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "age_pop3 = pd.read_csv(\"ages_population3.csv\")\n",
+    "plt.hist(age_pop3['observation'], bins='auto', edgecolor='black')  \n",
+    "plt.title('Histogram of Frequency Distribution')\n",
+    "plt.xlabel('Values')\n",
+    "plt.ylabel('Frequency')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.- Calculate the mean and standard deviation. Compare the results with the plot in step 1. What is happening?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 176,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean : 41.989\n",
+      "Standard Deviation: 16.144705959865934\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"\"\"Mean : {age_pop3[\"observation\"].mean()}\n",
+    "Standard Deviation: {age_pop3[\"observation\"].std()}\"\"\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "The Ages in this neighborhood are a little bit higher than the ones in step 1 on average. Also the Stdev is a bit higher.\n",
+    "However in General it can be said that these two neighborhoods are rather similiar to each other.\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": 178,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "30.0\n",
+      "40.0\n",
+      "53.0\n",
+      "77.0\n",
+      "41.989\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(age_pop3[\"observation\"].quantile(0.25))\n",
+    "print(age_pop3[\"observation\"].quantile(0.5))\n",
+    "print(age_pop3[\"observation\"].quantile(0.75))\n",
+    "print(age_pop3[\"observation\"].quantile(1))\n",
+    "print(age_pop3[\"observation\"].mean())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "The difference between mean and medain is around 1.9 years. 50 % of the people in this neighborhood are between 30 and 53 \n",
+    "years old.\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": 182,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.0\n",
+      "25.0\n",
+      "64.0\n",
+      "70.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(age_pop3[\"observation\"].quantile(0))\n",
+    "print(age_pop3[\"observation\"].quantile(0.15))\n",
+    "print(age_pop3[\"observation\"].quantile(0.85))\n",
+    "print(age_pop3[\"observation\"].quantile(0.95))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "only 5% under 25, means not a lot children\n",
+    "15% over 64% so more Senior than children\n",
+    "\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Bonus challenge\n",
+    "Compare the information about the three neighbourhoods. Prepare a report about the three of them. Remember to find out which are their similarities and their differences backing your arguments in basic statistics."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# your code here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "your comments here\n",
+    "\"\"\""
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/data/ages_population.csv b/your-code/data/ages_population.csv
similarity index 100%
rename from data/ages_population.csv
rename to your-code/data/ages_population.csv
diff --git a/data/ages_population2.csv b/your-code/data/ages_population2.csv
similarity index 100%
rename from data/ages_population2.csv
rename to your-code/data/ages_population2.csv
diff --git a/data/ages_population3.csv b/your-code/data/ages_population3.csv
similarity index 100%
rename from data/ages_population3.csv
rename to your-code/data/ages_population3.csv
diff --git a/your-code/data/main.ipynb b/your-code/data/main.ipynb
new file mode 100644
index 0000000..c6676e3
--- /dev/null
+++ b/your-code/data/main.ipynb
@@ -0,0 +1,1467 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Understanding Descriptive Statistics\n",
+    "\n",
+    "Import the necessary libraries here:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd \n",
+    "import numpy as np\n",
+    "from scipy import stats\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from scipy.stats import pearsonr, spearmanr\n",
+    "import random"
+   ]
+  },
+  {
+   "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": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Roll</th>\n",
+       "      <th>Result</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>8</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>9</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>10</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Roll  Result\n",
+       "0     1       3\n",
+       "1     2       1\n",
+       "2     3       6\n",
+       "3     4       1\n",
+       "4     5       5\n",
+       "5     6       2\n",
+       "6     7       2\n",
+       "7     8       4\n",
+       "8     9       3\n",
+       "9    10       2"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def dice_roll():\n",
+    "    new_dict = {}\n",
+    "    index = 0\n",
+    "    my_list = np.arange(1,7)\n",
+    "    for i in range(1,11):\n",
+    "        random_item = random.choice(my_list)\n",
+    "        new_dict[index] = {\"Roll\" : i, \"Result\" : random_item}\n",
+    "        index += 1\n",
+    "    return new_dict\n",
+    "\n",
+    "df = pd.DataFrame.from_dict(dice_roll(), orient=\"index\")\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.- Plot the results sorted by value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 184,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "my_df = df.sort_values(by=\"Result\")\n",
+    "plt.plot(my_df[\"Result\"].values)\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": 187,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sorted_df = my_df.groupby(\"Result\").agg(\"count\").reset_index()\n",
+    "sorted_df\n",
+    "plt.plot(sorted_df[\"Result\"],sorted_df[\"Roll\"])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nIn the plot above you can see on which roll(s) the number was returned. In contrast on the second Plot you can see how often\\nthe number was returned in total, for 10 rolls.\\n'"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "In the plot above you can see on which roll(s) the number was returned. In contrast on the second Plot you can see how often\n",
+    "the number was returned in total, for 10 rolls.\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": 133,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Enter name of Column: Result\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "3.3"
+      ]
+     },
+     "execution_count": 133,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def summary_stats(df):\n",
+    "    x = 0\n",
+    "    for i in df[input(\"Enter name of Column: \")]:\n",
+    "        x += i\n",
+    "    mean = x/len(df)\n",
+    "    return mean\n",
+    "df = pd.DataFrame.from_dict(dice_roll(), orient=\"index\")\n",
+    "summary_stats(df)"
+   ]
+  },
+  {
+   "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": 139,
+   "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>Result</th>\n",
+       "      <th>Roll</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Result  Roll\n",
+       "0       1     3\n",
+       "1       2     1\n",
+       "2       3     1\n",
+       "3       4     1\n",
+       "4       5     3\n",
+       "5       6     1"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.67\n"
+     ]
+    }
+   ],
+   "source": [
+    "sorted_df = df.groupby(\"Result\").agg(\"count\").reset_index()\n",
+    "display(sorted_df)\n",
+    "mean = sum(sorted_df[\"Roll\"]) / len(sorted_df[\"Roll\"])\n",
+    "print(round(mean,2))"
+   ]
+  },
+  {
+   "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": 107,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "def get_median():\n",
+    "    x = df[\"Result\"].reset_index(drop=True)\n",
+    "    n = len(\"Result\")\n",
+    "    if n%2 == 0:\n",
+    "        return (x.iloc[int(n / 2)] + x.iloc[int(n / 2) - 1]) / 2\n",
+    "    else:\n",
+    "        return x.iloc[int((n-1)/2)]\n",
+    "    \n",
+    "median = get_median()\n",
+    "print(median)"
+   ]
+  },
+  {
+   "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": 116,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Q1: 2\n",
+      "Q2: 2.5\n",
+      "Q3: 4\n"
+     ]
+    }
+   ],
+   "source": [
+    "def quartiles():\n",
+    "    x = df[\"Result\"].reset_index(drop=True)\n",
+    "    x = x.sort_values()\n",
+    "    n = len(x)    \n",
+    "\n",
+    "    if n % 4 == 0:\n",
+    "        q1 = (x.iloc[int(n / 4)] + x.iloc[int(n / 4) - 1]) / 2\n",
+    "        q3 = (x.iloc[int(n * 3 / 4)] + x.iloc[int(n * 3 / 4) - 1]) / 2\n",
+    "    else:\n",
+    "        q1 = x.iloc[int(n / 4)]\n",
+    "        q3 = x.iloc[int(n * 3 / 4)]\n",
+    "        \n",
+    "\n",
+    "    if n % 2 == 0:\n",
+    "        q2 = (x.iloc[int(n / 2)] + x.iloc[int(n / 2) - 1]) / 2\n",
+    "    else:\n",
+    "        q2 = x.iloc[int((n - 1) / 2)]\n",
+    "\n",
+    "    return q1, q2, q3\n",
+    "\n",
+    "    \n",
+    "result_q1, result_q2, result_q3 = quartiles()\n",
+    "print(\"Q1:\", result_q1)\n",
+    "print(\"Q2:\", result_q2)\n",
+    "print(\"Q3:\", result_q3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.0\n",
+      "2.5\n",
+      "3.75\n",
+      "2.9\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Result\"].quantile(0.25))\n",
+    "print(df[\"Result\"].quantile(0.5))\n",
+    "print(df[\"Result\"].quantile(0.75))\n",
+    "print(df[\"Result\"].mean())"
+   ]
+  },
+  {
+   "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": 191,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>roll</th>\n",
+       "      <th>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": 191,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_df = pd.read_csv(\"roll_the_dice_hundred.csv\")\n",
+    "new_df.drop(\"Unnamed: 0\", axis=1, inplace=True)\n",
+    "new_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 198,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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mY/TgMpti+NB3UNLJNwEAZiWjh/5GYD2xA9BvyRh+6cxrVTZ1fNbe29AXKOSbAADYJ/QFCvkmANgrGcP3JxnDs83YPaEvUJL5Zn8NrIi6d/OQb/qns8tT/YkWPXv0tOpPtPAPC4B+EcOHV+iHZG3cWuUytnMDSAcxfHiFvoMi2be1ylVs5waQLmL48Ap9ByXJpq1VLmI7N4ChYJtxeNFBuYQtW6tcRI4MYCi4aDS8KFBgBDkygKEihg8nIh4YMeHqqK/PAbbgklEziOHDhwIFZgx2JzE7jpFD2JVmFheNhgsRD4z44FyHr88B2cauNCBYFCgwgq2CcMlAu9IkTjcFMkWBAiM4sRcuYVcaEDwKFBjBVkG4hF1pQPAoUGAMWwXhCiJLIHjs4oFRbBWEC2ZPvkbDItKVRkyGRbqfAzA0FCgwjq2CyHUNJ1uvWJxI3cVLw8lWvtaBISLiAYA0MYMCBI8OCuAoTjgNzoQxgzwZeZDPAeiNAgVwECecBmywdR71IDBkRDyAYzjhNHgfnB3kyciDfA5AbxQogEM44dQMthkDwaNAARzCCadmcDIyXNbZ5an+RIuePXpa9SdasvYDDTMogEPYXWJG8mTkh3cdUUQ9L+HmZGTkMpvm1+igAA4hejCHk5HhGtvm1+igAA5JRg9NbR/3OYcSUfc3UKIHf3AyMlwx0PxaRN3za4tmxI19fdNBARySjB76S4w9ET34LXky8tKZ16ps6njeW+QkG+fXKFAAAAg5G+fXKFAAhyTbtP1JtmnZZgzgUjbOr1GgXMKWrVXAUNnYpgVgPxu3zjMke5FNW6uAobKxTQvAfjZunaeDIvu2VgFDZWObFkBusG3rfOg7KDZurQKGim3G5nFrNFxi09b50Bco6WT2ZVPHm1sYMAQ2tmldRjQMFyW3zmdb6CMeMnu4xrY2rauIhoFghb6DQmYPF91ZWqjbpsf0dP27OvnhR5o8Lk8Plk3RqBGh/5nEF0TDQPBCX6CQ2cNFfUUPTx5oJHrwCdEwELzQ/ziVzOwl9dr/TWaPXET0EDyiYSB4oS9QJDJ7uGOg6EHiJFk/EA0DwQt9xJNk09YqYKiIHswgGgaCF0gH5fTp03rggQc0fvx45eXlaebMmWpoaEh93vM8bdq0SUVFRRo9erQWLlyo48ePB7GUtHArKXId0YMZ3BoNBM/3AqW1tVW33nqrRo4cqeeee06vv/66fvzjH+szn/lM6pnNmzdry5Ytqq6u1uHDhxWPx7Vo0SK1t7f7vRwgVIgeALjC94inqqpKxcXF2rFjR+q1KVOmpP6353naunWrHnvsMd17772SpJ07dyoWi2n37t1auXKl30sCQoPowYzB3hrNNmNg6HzvoOzbt09z5szRl7/8ZU2cOFGzZs1STU1N6vONjY1qamrS4sWLU69Fo1EtWLBABw8e7PPP7OjoUCKR6PEBoDd2pZnBrdFA8HwvUN555x1t27ZN06ZN0wsvvKBVq1bpO9/5jp566ilJUlNTkyQpFov1+H2xWCz1uctVVlaqoKAg9VFcXOz3sgFnsCsteMz6AMHzPeLp6urSnDlzVFFRIUmaNWuWjh8/rm3btulrX/ta6rlIpOdPcJ7n9XotaePGjVq3bl3q14lEgiIFuAJ2pQVrwpior88B6M33AqWwsFAzZszo8dr111+vX/ziF5KkeDwuqbuTUlj46U9yzc3NvboqSdFoVNEo/6G7gttfzbDlwi8nDfbLlS9rYMh8L1BuvfVWvfnmmz1ee+uttzR58mRJUklJieLxuGprazVr1ixJ0oULF1RXV6eqqiq/lwPLcPsrXPDB2Q5fnwPQm+8zKI8++qgOHTqkiooKvf3229q9e7e2b9+uNWvWSOqOdsrLy1VRUaG9e/fq2LFjeuihh5SXl6fly5f7vRxYhCPY4Qq2cwPB872Dcsstt2jv3r3auHGjHn/8cZWUlGjr1q26//77U8+sX79e58+f1+rVq9Xa2qq5c+dq//79ys/P93s5sAS3v5pHlBYctnMDwYt4npdzl3IkEgkVFBSora1NY8eOzfZyMAj1J1q0rObQgM89863PMzfhA6K04CU7gpJ6FCnJEpAdU0Bv6Xz/5rJAGMG2THOI0sxgOzcQLC4LvAQt8eCQ2ZtBlGYW27mB4FCgXERLPFhk9mZwm7F5bOcGgkHEI1riJnAEuxlEaQBcEfoCZaCWuNTdEu/syrlZYuuQ2QePKA2AK0If8dASN+vO0kLdNj2mp+vf1ckPP9LkcXl6sGyKRo0Ifa3sC6I0AK4IfYFCS9ysvmZ9njzQyKyPT5JR2sO7jiiivre/EqUByAWh/7GVlrg5zPqYQZQGwAWh76DQEjeD7a9msf0VwFDZcuRG6AsUWuJmMOtjHttfAaTLpiM3Qh/xSLTETWDWBwDsZlsMH/oOShIt8WAx6wMA9rIxhqdAuQQt8eAw6wNX2ZLXA5mwMYanQIERzPrARTbl9UAmbIzhmUGBMcz6wCW25fVAJmyM4emgwChOkoULbMzrgUzYGMNToMAoTpKFC2zM64FM2BjD82MrjKElDlfYmNcDmbIthqeDAiNoicMlNub1gB9sOnKDAgVG0BKHS2zM6wG/2HLkBhEPjKAlDpck83rp03w+iW3zgD8oUGDEhDFRX58Dss22vB5wDREPzBjsD5L8wOkbTjgNnk15PeAaChQY8cHZDl+fw5Vxwqk5tuT1gGuIeGAEux7MYTs3ABdQoMCI5K6H/hrfEXX/hM+uh8wMtJ1b6t7O3dnV1xMAYA8KFBiR3PXQ37dFT+x68EM627kBwGYUKIBD2M4NwBUUKDAiGT30J3mSLNFDZpj1AeAKChQYQfRgBrM+5nV2eao/0aJnj55W/YkWimzAJ2wzhhFED2bYeCOpy9jODQSHDgqMIHowhxNOzWA7NxAsOiiX4OTN4HC5mlmccBosbucGgkeBchGt2mARPZjHCafB4XZuIHhEPKJVawrRA1zBTBUQvNB3UGjVmnVnaaFumx7T0/Xv6uSHH2nyuDw9WDZFo0ZQKyN3MFMFBC/0BQqtWrP6itKePNBIlIacwkwVELzQ/9hKq9YcojS4IjlTJanXmTPMVAH+CH2BQqvWDC6xg2uYqQKCFfqIh1atGURpcBHbueEiW47cCH2BwvZXM4jS4Cq2c8MlNh25EfqIR6JVawJRGgDYzbY5wdB3UJJo1QaLKA2usqUdDmTCxiM3KFAuQas2OERpcJFN7XAgEzbOCRLxwBiiNLjEtnY4kAkb5wTpoMAoojS4wMZ2OJAJG+cEKVBgHFGaGcxGBMfGdjiQCRvnBClQAAcxGxEsG9vhQCZsnBNkBgVwDLMRwbOxHQ5kyrY5QToogEOYjTDDxnZ4GBBbBs+mOUEKFMAhzEaYYWM73HXElubYMidIxAM4hNkIc2xrh7uM2DKc6KDAONq0wWE2wiyb2uGuIrYMLwoUGEWbNljMRphnSzvcVcSW4UXEA2No0wYvORshfToLkcRsBHIRsWV4UaDAiIHatFJ3m7azq68nkA5mI+ASYsvwIuKBEbRpzWI2Aq4gtjTPljnBwDsolZWVikQiKi8vT73meZ42bdqkoqIijR49WgsXLtTx48eDXgqyiDatecnZiKUzr1XZ1PEUJ8hJxJZmPX/sjOZXvahlNYf0yJ6jWlZzSPOrXsxKBB9ogXL48GFt375dN910U4/XN2/erC1btqi6ulqHDx9WPB7XokWL1N7eHuRykEW0aQEMFbGlGbbNCQYW8Zw9e1b333+/ampq9MQTT6Re9zxPW7du1WOPPaZ7771XkrRz507FYjHt3r1bK1euDGpJyCLatAAyQWwZLBu3cwfWQVmzZo3uvvtu3XHHHT1eb2xsVFNTkxYvXpx6LRqNasGCBTp48GCff1ZHR4cSiUSPD+QW2rQAMkVsGZx05gRNCaRA2bNnj44cOaLKyspen2tqapIkxWKxHq/HYrHU5y5XWVmpgoKC1EdxcbH/i0bgaNMCgJ1snBP0PeI5deqUHnnkEe3fv19XXdX/PEEk0rPy9Tyv12tJGzdu1Lp161K/TiQSFCk5ijYtANhnwtVRX5/zg+8FSkNDg5qbmzV79uzUa52dnXrllVdUXV2tN998U1J3J6Ww8NOfmJubm3t1VZKi0aiiUXNvCoLFyZtm2LJVEEAOGOwRVAaPqvK9QLn99tv12muv9Xjt61//uqZPn64NGzboc5/7nOLxuGprazVr1ixJ0oULF1RXV6eqqiq/lwOEElcKAEjHB+c6fH3OD74XKPn5+SotLe3x2pgxYzR+/PjU6+Xl5aqoqNC0adM0bdo0VVRUKC8vT8uXL/d7OUDoJLcKXv6DTnKrIPM+AC5n41EQWTlJdv369Tp//rxWr16t1tZWzZ07V/v371d+fn42lgPDiB6CY+NWQQD2s/EoiIjneTl3+UkikVBBQYHa2to0duzYbC8HaSB6CFb9iRYtqzk04HPPfOvzzAEB6CHZfZV6jpokf5Txo/uazvdvLguEMbadUugiG7cKAsgNth0FwWWBMILowQwbc2QAuePO0kLdNj2mp+vf1ckPP9LkcXl6sGyKRo0w38+gQIER3GZsho05MoDc0VcM/+SBxqzE8EQ8MILowQyuFAAwVLbF8BQoMILowRzbcmQA9hsohpe6Y/jOLnP7aoh4YATRg1lcKQAgHTbG8BQoMCIZPTy864gi6nsLG9GDv7hSAMBg2RjDE/HAGKIHALCTjTE8HRQYRfRgDif2Ahis2ZOv0bCIdKURk2GR7udMoUCBcUQPwePEXgDpaDjZesXiROouXhpOthr795uIB3CMbVsFAdiPGRRA3dFD/YkWPXv0tOpPtBjdtuY6G7cKArAfMygIPaKHYNm4VRCA/Ww8CoIOCowhegiejW1aAPaz8RRqChQYQfRgho1tWgC5wbajIIh4YATRgxk2tmkBv7B1Png2HQVBgQIjiB7M4MReuIr5NXNsOQqCiAdGED2YY1ubFsgU82vhRAcFRhA9mGVTmxbIxEDzaxF1z68tmhHn69sxdFBghI0T4q5LtmmXzrxWZVPH894iJ6Uzvwa3UKDAGKIHAOlifi28iHhgFNEDgHRMuDrq63PIHRQoMM6WCXEAOWCwRyNxhJJvbNnOTYECALDWB+c6fH0OV2bTdm5mUAAA1uKIAnNs285NgQLjuM0YwGAljyjoL2CIqPsnfI4oyIyN15EQ8cAom9qHAOzH6chm2HgdCR0UGGNb+xBAbuCIguDZuJ2bDgqM4DRIuMqWHQ+u44iCYNk460OBAiNsbB8CmSKyNIsjCoJj43UkRDwwwsb2IZAJIku4xMbrSChQYISN7UNgqGzc8QBkyrZZHyIeGGFj+xAYKiJLuMqmWR8KFBjBVkG4hMgSLrNl1oeIB8bY1j4EhorIEggeHZRLsF0weDa1D4Ghmj35Gg2LSFcaMRkW6X4OwNBQoFzEdkFzbGkfAkPVcLL1isWJ1F28NJxs5WsdGCIiHrFdEEB6mEEBghf6AoXtggDSxQwKELzQFyjpbBcEAIkbdgETQl+g0KoFkK7ktvn++qqe2DYPZCr0BQqtWgAA7BP6AoVWLYB0JWfX+pO8nZvZNeSizi5P9Sda9OzR06o/0ZK1r+PQbzPmhFMA6eKoe7jKpiM3Qt9BkTjhFEB6mF2Di2w7ciP0HZQkTjgFMFjMrsE1Ax25kYwtF82IG/u+SIFyCU44BTAY3M4N19gYWxLxAECakrNrknoN2DO7hlxkY2xJgQIAQ8DsGlwy4eqor8/5gYgHcBS3cweP2TU4Y7A7iQ3uOKZAARxk01ZB1zG7Bhd8cK7D1+f8QMQDOMa2rYIA7GfjzjQKFMAh3M4NYChsPFWdAgVwCLdzAxgKGy/ApEABHGLjVkEAGAoKFMAhNubIAOxn4wWYvhcolZWVuuWWW5Sfn6+JEyfqnnvu0ZtvvtnjGc/ztGnTJhUVFWn06NFauHChjh8/7vdSgNCxMUd2nS03vwKZsDEe9r1Aqaur05o1a3To0CHV1tbqk08+0eLFi3Xu3LnUM5s3b9aWLVtUXV2tw4cPKx6Pa9GiRWpvb/d7OUCocMKpWc8fO6P5VS9qWc0hPbLnqJbVHNL8qhfZKYWcY2M8HPE8L9By/3/+5380ceJE1dXV6Ytf/KI8z1NRUZHKy8u1YcMGSVJHR4disZiqqqq0cuXKAf/MRCKhgoICtbW1aezYsUEuH8hJnIMSvOR27sv/AU2Wfpwmi1xSf6JFy2oODfjcM9/6fEbn/qTz/Tvwg9ra2tokSePGdbeUGxsb1dTUpMWLF6eeiUajWrBggQ4ePDioAgXAlXHCabBsvPkVyISNF2AGOiTreZ7WrVun+fPnq7S0VJLU1NQkSYrFYj2ejcViqc9drqOjQ4lEoscHgCtLnnC6dOa1Kps6nm+UPrIxrwcyYWM8HGiB8u1vf1u///3v9cwzz/T6XCTS8y/peV6v15IqKytVUFCQ+iguLg5kvQAwGDbm9UCmbLsAM7CIZ+3atdq3b59eeeUVTZo0KfV6PB6X1N1JKSz89C/b3Nzcq6uStHHjRq1bty7160QiQZGSw7jEDrnOxptfAT/YFA/7XqB4nqe1a9dq7969evnll1VSUtLj8yUlJYrH46qtrdWsWbMkSRcuXFBdXZ2qqqr6/DOj0aiiUf5DdwHDm3CChTe/An6x5QJM3yOeNWvWaNeuXdq9e7fy8/PV1NSkpqYmnT9/XlJ3tFNeXq6Kigrt3btXx44d00MPPaS8vDwtX77c7+XAIlxiB1fYePMr4BrfOyjbtm2TJC1cuLDH6zt27NBDDz0kSVq/fr3Onz+v1atXq7W1VXPnztX+/fuVn5/v93JgCXY9wCWc2AsEL5CIZyCRSESbNm3Spk2b/P6/h6XS2fVgQ2sRuBIbt2QCfrFlTjDwc1AAiV0PcEtyS+bDu44oop6jJpzYi1xm05wglwXCCFricI1tWzKBTNk2J0gHBUbQEoeL7iwt1G3TY3q6/l2d/PAjTR6XpwfLpmjUCH72Q26xcU6QAgVG0BKHi/pqhz95oJFt88g5Ns4JUubDGFricIlt7XAgEzbOCdJBgVE2nVIIDJWN7XAgEzbOCVKgwDhbTikEhsrGdjiQCRvnBIl4ACBNNrbDgUyE7jZjAHCRje1wIFO2zQkS8QBAmmZPvkbDIlLXFQ7OHhbpfg7+seWEU5fZNCdIgQIAaWo42XrF4kTqLl4aTrYyg+ITm044dZ0tc4JEPACQJmZQzGJLdzhRoABAmphBMWegLd1S95buzoFaWsg5FCgAkKbklswrKeTqBl+ks6UbbqFAAYA0DR8W0ZduvvLcw5duLmSA0wfEaeFFgQIAaers8rTvd1eee9j3uzPEDj4gTgsvChQASNNAsYNE7OCXZJzWXy8qIuI0V1GgAECaiB3MSZ5w2l8vyhM3obuKAgUA0kTsAASPAgUA0kTsYE5ym3F/kjdHM+/jn84uT/UnWvTs0dOqP9GStfeWk2QBIE3J2OHhXUcUkXrED9m6WM1V3Bxtlk0n9tJBAYAhsO1iNVcx72OObSf20kEBgCGy6WI1VzHvY8ZAJ/Ymo7RFM+LGvr4pUAAgA7ZcrOaq5LxPU9vHfX7zjKi7a8W8T2ZsjNKIeAAA1mKbsRk2RmkUKAAAhJyNURoFyiVs2VoFAOjGNmMzbNw6zwzKRTZtrQIAdLNxNsJFNm6dp4Mi+7ZWAQC62Tgb4Srbts6HvoNi49YqALmjs8tjm3GAbJyNcJlNW+dDX6DQPgQwVETDwWObsXm2bJ0PfcRD+xDAUBANm5GcjZDUa4CTawXcFvoChfYhgHQNFA1L7Czxk22zETAj9BEP7UPzyOyR64iGzbNpNgJmhL5AsXFrlcvI7OECouHssGU2AmaEPuKRaB+aQmYPVxANA8ELfQclifZhsNjODZcQDQPBo4NyiWT7cOnMa1U2dTzfKH2UTmYP2I6dJUDwKFBgBJk9XEM0DASLiAdGTLg66utzgA2IhoHgUKDAjMEeB8GxEcgx7CwxhyMKwoUCBUZ8cK7D1+cAhAtHFIQPMygwgm2ZAIaKIwrCiQIFRiS3ZfbXjI2o+6chtmUCuBTXCpjX2eWp/kSLnj16WvUnWrL23hLxwAhO7AUwFFwrYJZNURodFBjDtkwA6eKIAnNsi9LooFyCCfHgsS0TQDqYXzPDxtO+KVAusqmt5Tq2ZQIYLK4VMMPGKI2IR/a1tQAA3bhWwAwbo7TQFyhMiAOA3ZhfC56NUVroIx4b21oAgJ6YXwuWjVFa6AsUG9taAIDemF8LTjJKW7XrSJ+f92Q+Sgt9xGNjWwsAgLALfYHCCafm2XJKIQCgW3Iesz/JbcYm/70OfcTDCadmsZ0bAOxj4zxm6DsoEhPiprCdGwDsZOM8Zug7KEl3lhbqtukxPV3/rk5++JEmj8vTg2VTNGoENZwfbDylEADQzcZ5zKx+9/33f/93lZSU6KqrrtLs2bP1X//1X1lby/PHzmjBj17SP/3nG3qq/qT+6T/f0IIfvcRP9T5Jp30IADDLxnnMrBUoP//5z1VeXq7HHntMv/3tb/WFL3xBd911l9577z3jayF6CJ6N7UMAQDcbT+zNWoGyZcsWffOb39Rf/dVf6frrr9fWrVtVXFysbdu2GV0HJ8maYWP7EADwKdvmMbMyg3LhwgU1NDToe9/7Xo/XFy9erIMHD/Z6vqOjQx0dHalfJxIJ39Zi4+Syi2w8pRAA0JNNJ/ZmpYPywQcfqLOzU7FYrMfrsVhMTU1NvZ6vrKxUQUFB6qO4uNi3tRA9mGFj+xAA0FvyxN6lM69V2dTxWft3OatDspFIz7+053m9XpOkjRs3qq2tLfVx6tQp39ZA9GCObe1DAIC9shLxTJgwQcOHD+/VLWlubu7VVZGkaDSqaDQayFqIHsyyqX0IALBXVjooo0aN0uzZs1VbW9vj9draWs2bN8/oWogezLOlfQgAsFfWIp5169bpySef1H/8x3/ojTfe0KOPPqr33ntPq1atMr4WogcAAOyStZNk77vvPrW0tOjxxx/XmTNnVFpaql//+teaPHlyVtZD9AAAgD0inufl3AEfiURCBQUFamtr09ixY7O9HAAAMAjpfP/mohkAAGAdChQAAGAdChQAAGAdChQAAGAdChQAAGAdChQAAGAdChQAAGAdChQAAGAdChQAAGCdrB11n4nk4beJRCLLKwEAAIOV/L49mEPsc7JAaW9vlyQVFxdneSUAACBd7e3tKigouOIzOXkXT1dXl95//33l5+crEvH3Mr9EIqHi4mKdOnWKe34CxPtsBu+zGbzP5vBemxHU++x5ntrb21VUVKRhw648ZZKTHZRhw4Zp0qRJgf5/jB07li9+A3ifzeB9NoP32RzeazOCeJ8H6pwkMSQLAACsQ4ECAACsQ4FymWg0qu9///uKRqPZXorTeJ/N4H02g/fZHN5rM2x4n3NySBYAALiNDgoAALAOBQoAALAOBQoAALAOBQoAALAOBcpFr7zyipYsWaKioiJFIhH98pe/zPaSnFRZWalbbrlF+fn5mjhxou655x69+eab2V6Wc7Zt26abbropdchSWVmZnnvuuWwvy3mVlZWKRCIqLy/P9lKcsmnTJkUikR4f8Xg828ty0unTp/XAAw9o/PjxysvL08yZM9XQ0JCVtVCgXHTu3DndfPPNqq6uzvZSnFZXV6c1a9bo0KFDqq2t1SeffKLFixfr3Llz2V6aUyZNmqQf/vCHevXVV/Xqq6/qtttu09KlS3X8+PFsL81Zhw8f1vbt23XTTTdleylOuuGGG3TmzJnUx2uvvZbtJTmntbVVt956q0aOHKnnnntOr7/+un784x/rM5/5TFbWk5NH3Qfhrrvu0l133ZXtZTjv+eef7/HrHTt2aOLEiWpoaNAXv/jFLK3KPUuWLOnx6x/84Afatm2bDh06pBtuuCFLq3LX2bNndf/996umpkZPPPFEtpfjpBEjRtA1CVhVVZWKi4u1Y8eO1GtTpkzJ2nrooCCr2traJEnjxo3L8krc1dnZqT179ujcuXMqKyvL9nKctGbNGt1999264447sr0UZ/3hD39QUVGRSkpK9NWvflXvvPNOtpfknH379mnOnDn68pe/rIkTJ2rWrFmqqanJ2nooUJA1nudp3bp1mj9/vkpLS7O9HOe89tpruvrqqxWNRrVq1Srt3btXM2bMyPaynLNnzx4dOXJElZWV2V6Ks+bOnaunnnpKL7zwgmpqatTU1KR58+appaUl20tzyjvvvKNt27Zp2rRpeuGFF7Rq1Sp95zvf0VNPPZWV9RDxIGu+/e1v6/e//70OHDiQ7aU46U/+5E909OhR/e///q9+8YtfaMWKFaqrq6NI8dGpU6f0yCOPaP/+/brqqquyvRxnXRq/33jjjSorK9PUqVO1c+dOrVu3Losrc0tXV5fmzJmjiooKSdKsWbN0/Phxbdu2TV/72teMr4cOCrJi7dq12rdvn1566SVNmjQp28tx0qhRo/RHf/RHmjNnjiorK3XzzTfrX//1X7O9LKc0NDSoublZs2fP1ogRIzRixAjV1dXp3/7t3zRixAh1dnZme4lOGjNmjG688Ub94Q9/yPZSnFJYWNjrB5jrr79e7733XlbWQwcFRnmep7Vr12rv3r16+eWXVVJSku0lhYbneero6Mj2Mpxy++2399pN8vWvf13Tp0/Xhg0bNHz48CytzG0dHR1644039IUvfCHbS3HKrbfe2uvYh7feekuTJ0/OynooUC46e/as3n777dSvGxsbdfToUY0bN07XXXddFlfmljVr1mj37t169tlnlZ+fr6amJklSQUGBRo8eneXVueNv//Zvddddd6m4uFjt7e3as2ePXn755V67qJCZ/Pz8XvNTY8aM0fjx45mr8tF3v/tdLVmyRNddd52am5v1xBNPKJFIaMWKFdlemlMeffRRzZs3TxUVFfrKV76i3/zmN9q+fbu2b9+enQV58DzP81566SVPUq+PFStWZHtpTunrPZbk7dixI9tLc8o3vvENb/Lkyd6oUaO8z372s97tt9/u7d+/P9vLCoUFCxZ4jzzySLaX4ZT77rvPKyws9EaOHOkVFRV59957r3f8+PFsL8tJv/rVr7zS0lIvGo1606dP97Zv3561tUQ8z/OyUxoBAAD0jSFZAABgHQoUAABgHQoUAABgHQoUAABgHQoUAABgHQoUAABgHQoUAABgHQoUAABgHQoUAABgHQoUAABgHQoUAABgHQoUAABgnf8HGimKhzrBaKcAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "new_df = new_df.sort_values(by=\"value\")\n",
+    "x = new_df[\"value\"]\n",
+    "y = new_df[\"roll\"]\n",
+    "plt.scatter(x,y)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nAll vlaues seem to be quite equally distributed throughout the number of rolls. 2 is a small outlier as it doesent really\\noccur after roll number 80. Also 5 has a gap between 45-65.\\n'"
+      ]
+     },
+     "execution_count": 131,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "All values seem to be quite equally distributed throughout the number of rolls. 2 is a small outlier as it doesent really\n",
+    "occur after roll number 80. Also 5 has a gap between 45-65.\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": 134,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Enter name of Column: value\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "3.74"
+      ]
+     },
+     "execution_count": 134,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "summary_stats(new_df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 3.- Now, calculate the frequency distribution.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "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>value</th>\n",
+       "      <th>num_of_rolls</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>23</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   value  num_of_rolls\n",
+       "0      1            12\n",
+       "1      2            17\n",
+       "2      3            14\n",
+       "3      4            22\n",
+       "4      5            12\n",
+       "5      6            23"
+      ]
+     },
+     "execution_count": 137,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sorted_df = new_df.groupby(\"value\").agg(\"count\").reset_index()\n",
+    "sorted_df.rename(columns={\"roll\":\"num_of_rolls\"},inplace=True)\n",
+    "sorted_df"
+   ]
+  },
+  {
+   "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": 140,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(new_df['value'], bins='auto', edgecolor='black')  \n",
+    "plt.title('Histogram of Frequency Distribution')\n",
+    "plt.xlabel('Values')\n",
+    "plt.ylabel('Frequency')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "As 3.5 would be the mean if each value had the same Frequency Distribution, but as it can be seen the Freqeuncy for 4 and 6\n",
+    "are the two highest, which is why the mean shift a little bit into their direction (3.7).\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": 199,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dice_df = pd.read_csv(\"roll_the_dice_thousand.csv\")\n",
+    "dice_df.drop(\"Unnamed: 0\", axis=1, inplace=True)\n",
+    "\n",
+    "sorted_dice = dice_df.groupby(\"value\").agg(\"count\").reset_index()\n",
+    "sorted_dice.rename(columns={\"roll\":\"num_of_rolls\"},inplace=True)\n",
+    "x = sorted_dice[\"value\"]\n",
+    "y = sorted_dice[\"num_of_rolls\"]\n",
+    "plt.scatter(x,y)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 200,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nDue to the higher lenght of rolls, the Graphs seems more equally distributed even though the deviation are actually higher.\\n'"
+      ]
+     },
+     "execution_count": 200,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "Due to the higher lenght of rolls, the Graphs seems more equally distributed even though the deviation are actually higher.\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": 201,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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88uXLValSJd1xxx2S5PiD4eL3rqST7q9mb0v37t317bffOs1N+t/Vbxd/NxSsgT07sJw+ffqoefPmateunWrXrq2UlBTNnj1bdevWVVRUlCQ5vnPkjTfe0KBBg+Tl5aXGjRurcePGevLJJzVnzhxVqlRJvXr1clyNFR4e7vilUatWLY0ZM0ZxcXGqWbOmHnzwQf3000968cUXVadOHadzYC4lJiZGf/rTnzR16lR17dpVSUlJmjZtmiIjI0u8Gs2VKlWqpJkzZ+qxxx5TTEyMhg8frtzcXM2aNUsnT57Uyy+/fE23X5I333xTvXr10t13363Bgwfrpptu0vHjx5WYmKjdu3frgw8+kCT94Q9/0Jo1a3TnnXfqj3/8o/z8/DRv3jydOXOm2JiPP/64pkyZoj/+8Y/q2rWrvv32W82dO7fYIdFp06YpISFBnTp10nPPPafGjRsrJydHhw8f1scff6yFCxde9pDUxebNm6c+ffro9ttv1/PPP6+IiAilpqbq008/1V//+lenvqNGjVKHDh0kqdiVX5dz6NAhbd++XYWFhY4vFVy0aJFOnTqld99913EoriRr167V/Pnz9cADD6h+/foyxmjVqlU6efKkevTo4ejXokULbdy4Uf/85z9Vp04d+fv7O/a0Xa3AwEA9/fTTSk1NVaNGjfTxxx/r7bff1tNPP+0IeaGhobrrrrscP2N169bV559/rlWrVhUbr+jn+ZVXXlGvXr1UuXJltWzZ0nE490LPP/+83n33XfXu3VvTpk1T3bp19a9//Uvz58/X008/fdkvy4SHqtDTo4GLFF29sWPHjhLX9+7d+7JXY7322mumU6dOJigoyHh7e5uIiAgzdOhQc/jwYafnTZw40YSFhZlKlSo5XfVRUFBgXnnlFdOoUSPj5eVlgoKCzIABA0xaWprT8wsLC8306dPNzTffbLy9vU3Lli3N2rVrTatWrZyupLrUFTO5ublm3Lhx5qabbjI+Pj6mTZs2ZvXq1WbQoEFO8yy6MmTWrFlOzy9t7Mu9jhdavXq16dChg/Hx8TFVq1Y13bt3N//5z3+uaDsluVzf0uZSZO/evebhhx82wcHBxsvLy4SGhpo777zTLFy40Knff/7zH3P77bcbu91uQkNDze9//3vz1ltvFbv6Jzc314wfP96Eh4cbX19f07VrV7Nnz55inxtjjPn111/Nc889ZyIjI42Xl5epVauWadu2rZk8ebLJysq6bP0q4cqvbdu2mV69epmAgABjt9tNgwYNzPPPP1/i3OvVq2eaNm1a4rqSXHzlW5UqVUxgYKDp2LGjmTRpUrHPvDHFr5D67rvvzKOPPmoaNGhgfH19TUBAgGnfvr1ZsmSJ0/P27NljOnfubPz8/JyuOLzUZ620q7GaNWtmNm7caNq1a2fsdrupU6eOmTRpUrGr8I4cOWIeeughU6tWLRMQEGAGDBjguHrswquxcnNzzbBhw0zt2rWNzWZz2mZJ73NKSorp37+/CQwMNF5eXqZx48Zm1qxZTlclXu37DPdmM+YKvrgEwBVJTk5WkyZNNHXqVE2aNKmiy7nhLFmyRE888YSSk5NLvSrOXe3bt0+tWrXSvHnzHCdkA3ANDmMBZbR3716tWLFCnTp1UvXq1ZWUlKSZM2eqevXqlz0hFCjyww8/KCUlRZMmTVKdOnW4aSVwDRB2gDKqWrWqdu7cqUWLFunkyZMKCAhQdHS0XnrppVIvPwcu9qc//UnvvfeemjZtqg8++OC6nhQO3Cg4jAUAACyNS88BAIClEXYAAIClEXYAAIClcYKypMLCQv3yyy/y9/e/qq88BwAAFccYo9OnTyssLOySX+ZK2NH5e6JceDdmAADgOdLS0i75DeeEHclxv520tDRVr169gqsBAABX4tSpUwoPD3f8Hi8NYUf/u1tv9erVCTsAAHiYy52CwgnKAADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0qpUdAEArCM1NVUZGRkuGSsoKEgREREuGQvAjY2wA8AlUlNT1bhJU+Vkn3XJeD6+fkr6LpHAA6DcCDsAXCIjI0M52WcVGDNWXoHh5Ror/1iajq19TRkZGYQdAOVG2AHgUl6B4bKHNqzoMgDAgROUAQCApRF2AACApRF2AACApRF2AACApVVo2Nm8ebP69OmjsLAw2Ww2rV692rEuPz9fL7zwglq0aKGqVasqLCxMAwcO1C+//OI0Rm5urp599lkFBQWpatWquu+++/TTTz9d55kAAAB3VaFh58yZM2rVqpXmzp1bbN3Zs2e1e/duTZkyRbt379aqVat08OBB3XfffU79Ro8erY8++kgrV67UF198oaysLMXExKigoOB6TQMAALixCr30vFevXurVq1eJ6wICApSQkODUNmfOHLVv316pqamKiIhQZmamFi1apPfee0933XWXJGnZsmUKDw/XZ599prvvvvuazwEAALg3jzpnJzMzUzabTTVq1JAk7dq1S/n5+erZs6ejT1hYmJo3b66tW7eWOk5ubq5OnTrltAAAAGvymLCTk5OjCRMmqH///qpevbokKT09Xd7e3qpZs6ZT35CQEKWnp5c6VlxcnAICAhxLeHj5vu0VAAC4L48IO/n5+XrkkUdUWFio+fPnX7a/MUY2m63U9RMnTlRmZqZjSUtLc2W5AADAjbh92MnPz9fDDz+s5ORkJSQkOPbqSFJoaKjy8vJ04sQJp+ccPXpUISEhpY5pt9tVvXp1pwUAAFiTW4edoqBz6NAhffbZZwoMDHRa37ZtW3l5eTmdyHzkyBHt379fnTp1ut7lAgAAN1ShV2NlZWXp+++/dzxOTk7Wnj17VKtWLYWFhemhhx7S7t27tXbtWhUUFDjOw6lVq5a8vb0VEBCgoUOHauzYsQoMDFStWrU0btw4tWjRwnF1FgAAuLFVaNjZuXOnunXr5ng8ZswYSdKgQYMUGxurNWvWSJJuvfVWp+dt2LBB0dHRkqTXX39dVapU0cMPP6zs7Gx1795dS5YsUeXKla/LHAAAgHur0LATHR0tY0yp6y+1roiPj4/mzJmjOXPmuLI0AABgEW59zg4AAEB5EXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClVanoAgCgNImJiS4ZJygoSBERES4ZC4DnIewAcDsFWSckm00DBgxwyXg+vn5K+i6RwAPcoAg7ANxOYW6WZIwCY8bKKzC8XGPlH0vTsbWvKSMjg7AD3KAIOwDclldguOyhDSu6DAAejhOUAQCApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApfE9O4CHSk1NVUZGhkvG4nYKAKyMsAN4oNTUVDVu0lQ52WddMh63UwBgZYQdwANlZGQoJ/sst1MAgCtA2AE8GLdTAIDL4wRlAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaRUadjZv3qw+ffooLCxMNptNq1evdlpvjFFsbKzCwsLk6+ur6OhoHThwwKlPbm6unn32WQUFBalq1aq677779NNPP13HWQAAAHdWoWHnzJkzatWqlebOnVvi+pkzZyo+Pl5z587Vjh07FBoaqh49euj06dOOPqNHj9ZHH32klStX6osvvlBWVpZiYmJUUFBwvaYBAADcWIV+z06vXr3Uq1evEtcZYzR79mxNnjxZffv2lSQtXbpUISEhWr58uYYPH67MzEwtWrRI7733nu666y5J0rJlyxQeHq7PPvtMd99993WbCwAAcE9ue85OcnKy0tPT1bNnT0eb3W5X165dtXXrVknSrl27lJ+f79QnLCxMzZs3d/QpSW5urk6dOuW0AAAAa3LbsJOeni5JCgkJcWoPCQlxrEtPT5e3t7dq1qxZap+SxMXFKSAgwLGEh5fv6/YBAID7ctuwU8Rmszk9NsYUa7vY5fpMnDhRmZmZjiUtLc0ltQIAAPfjtmEnNDRUkortoTl69Khjb09oaKjy8vJ04sSJUvuUxG63q3r16k4LAACwJrcNO5GRkQoNDVVCQoKjLS8vT5s2bVKnTp0kSW3btpWXl5dTnyNHjmj//v2OPgAA4MZWoVdjZWVl6fvvv3c8Tk5O1p49e1SrVi1FRERo9OjRmjFjhqKiohQVFaUZM2bIz89P/fv3lyQFBARo6NChGjt2rAIDA1WrVi2NGzdOLVq0cFydBQAAbmwVGnZ27typbt26OR6PGTNGkjRo0CAtWbJE48ePV3Z2tkaMGKETJ06oQ4cOWrdunfz9/R3Pef3111WlShU9/PDDys7OVvfu3bVkyRJVrlz5us8HAAC4nwoNO9HR0TLGlLreZrMpNjZWsbGxpfbx8fHRnDlzNGfOnGtQIQAA8HRue84OAACAKxB2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApVWp6AIAwJOkpqYqIyOj3OMEBQUpIiLCBRUBuBzCDgBcodTUVDVu0lQ52WfLPZaPr5+Svksk8ADXAWEHAK5QRkaGcrLPKjBmrLwCw8s8Tv6xNB1b+5oyMjIIO8B1QNgBcENITEx02RhegeGyhzYs93gArg/CDgBLK8g6IdlsGjBgQEWXAqCCEHYAWFphbpZkTLkPPUlS9o87lbllmYsqA3C9EHYA3BBccegp/1iai6oBcD3xPTsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDS3DrsnDt3Tn/4wx8UGRkpX19f1a9fX9OmTVNhYaGjjzFGsbGxCgsLk6+vr6Kjo3XgwIEKrBoAALgTt77r+SuvvKKFCxdq6dKlatasmXbu3KknnnhCAQEBGjVqlCRp5syZio+P15IlS9SoUSNNnz5dPXr0UFJSkvz9/St4BoDnSExMrNDnA8C14tZhZ9u2bbr//vvVu3dvSVK9evW0YsUK7dy5U9L5vTqzZ8/W5MmT1bdvX0nS0qVLFRISouXLl2v48OEVVjvgKQqyTkg2mwYMGFDRpQDANeHWYadLly5auHChDh48qEaNGmnv3r364osvNHv2bElScnKy0tPT1bNnT8dz7Ha7unbtqq1btxJ2gCtQmJslGaPAmLHyCgwv8zjZP+5U5pZlLqwMAFzDrcPOCy+8oMzMTDVp0kSVK1dWQUGBXnrpJT366KOSpPT0dElSSEiI0/NCQkKUkpJS6ri5ubnKzc11PD516tQ1qB7wLF6B4bKHNizz8/OPpbmwGgBwHbc+Qfn999/XsmXLtHz5cu3evVtLly7Vq6++qqVLlzr1s9lsTo+NMcXaLhQXF6eAgADHEh5e9r9mAQCAe3PrsPP73/9eEyZM0COPPKIWLVro8ccf1/PPP6+4uDhJUmhoqKT/7eEpcvTo0WJ7ey40ceJEZWZmOpa0NP4iBQDAqtw67Jw9e1aVKjmXWLlyZcel55GRkQoNDVVCQoJjfV5enjZt2qROnTqVOq7dblf16tWdFgAAYE1ufc5Onz599NJLLykiIkLNmjXT119/rfj4eA0ZMkTS+cNXo0eP1owZMxQVFaWoqCjNmDFDfn5+6t+/fwVXDwAA3IFbh505c+ZoypQpGjFihI4ePaqwsDANHz5cf/zjHx19xo8fr+zsbI0YMUInTpxQhw4dtG7dOr5jBwAASHLzsOPv76/Zs2c7LjUvic1mU2xsrGJjY69bXQAAwHO49Tk7AAAA5UXYAQAAlkbYAQAAllamsJOcnOzqOgAAAK6JMoWdhg0bqlu3blq2bJlycnJcXRMAAIDLlCns7N27V61bt9bYsWMVGhqq4cOH66uvvnJ1bQAAAOVWprDTvHlzxcfH6+eff9bixYuVnp6uLl26qFmzZoqPj9evv/7q6joBAADKpFwnKFepUkUPPvig/va3v+mVV17RDz/8oHHjxunmm2/WwIEDdeTIEVfVCQAAUCblCjs7d+7UiBEjVKdOHcXHx2vcuHH64YcftH79ev3888+6//77XVUnAABAmZTpG5Tj4+O1ePFiJSUl6d5779W7776re++913HTzsjISL355ptq0qSJS4sFAAC4WmUKOwsWLNCQIUP0xBNPKDQ0tMQ+ERERWrRoUbmKAwAAKK8yhZ1Dhw5dto+3t7cGDRpUluEBAABcpkzn7CxevFgffPBBsfYPPvhAS5cuLXdRAAAArlKmsPPyyy8rKCioWHtwcLBmzJhR7qIAAABcpUxhJyUlRZGRkcXa69atq9TU1HIXBQAA4CplCjvBwcHat29fsfa9e/cqMDCw3EUBAAC4SpnCziOPPKLnnntOGzZsUEFBgQoKCrR+/XqNGjVKjzzyiKtrBAAAKLMyXY01ffp0paSkqHv37qpS5fwQhYWFGjhwIOfsAAAAt1KmsOPt7a33339ff/rTn7R37175+vqqRYsWqlu3rqvrAwAAKJcyhZ0ijRo1UqNGjVxVCwAAgMuVKewUFBRoyZIl+vzzz3X06FEVFhY6rV+/fr1LigMAACivMoWdUaNGacmSJerdu7eaN28um83m6roAAABcokxhZ+XKlfrb3/6me++919X1AAAAuFSZLj339vZWw4YNXV0LAACAy5Up7IwdO1ZvvPGGjDGurgcAAMClynQY64svvtCGDRv0ySefqFmzZvLy8nJav2rVKpcUBwAAUF5lCjs1atTQgw8+6OpaAAAAXK5MYWfx4sWurgMAAOCaKNM5O5J07tw5ffbZZ3rzzTd1+vRpSdIvv/yirKwslxUHAABQXmXas5OSkqJ77rlHqampys3NVY8ePeTv76+ZM2cqJydHCxcudHWdAAAAZVLmLxVs166d9u7dq8DAQEf7gw8+qGHDhrmsOMBqUlNTlZGRUe5xEhMTXVANANwYynw11n/+8x95e3s7tdetW1c///yzSwoDrCY1NVWNmzRVTvbZii4FAG4oZQo7hYWFKigoKNb+008/yd/fv9xFAVaUkZGhnOyzCowZK6/A8HKNlf3jTmVuWeaiygDA2soUdnr06KHZs2frrbfekiTZbDZlZWVp6tSp3EICuAyvwHDZQ8v3DeT5x9JcVA0AWF+Zws7rr7+ubt266ZZbblFOTo769++vQ4cOKSgoSCtWrHB1jQAAAGVWprATFhamPXv2aMWKFdq9e7cKCws1dOhQPfbYY/L19XV1jQAAAGVWprAjSb6+vhoyZIiGDBniynoAAABcqkxh5913373k+oEDB5apGAAAAFcr8/fsXCg/P19nz56Vt7e3/Pz8CDsAAMBtlOl2ESdOnHBasrKylJSUpC5dunCCMgAAcCtlvjfWxaKiovTyyy8X2+sDAABQkVwWdiSpcuXK+uWXX1w5JAAAQLmU6ZydNWvWOD02xujIkSOaO3euOnfu7JLCAAAAXKFMYeeBBx5wemyz2VS7dm3deeedeu2111xRFwAAgEuU+d5YAAAAnsCl5+wAAAC4mzLt2RkzZswV942Pjy/LJhx+/vlnvfDCC/rkk0+UnZ2tRo0aadGiRWrbtq2k8+cLvfjii3rrrbd04sQJdejQQfPmzVOzZs3KtV0AAGANZQo7X3/9tXbv3q1z586pcePGkqSDBw+qcuXKatOmjaOfzWYrV3EnTpxQ586d1a1bN33yyScKDg7WDz/8oBo1ajj6zJw5U/Hx8VqyZIkaNWqk6dOnq0ePHkpKSpK/v3+5tg8AADxfmcJOnz595O/vr6VLl6pmzZqSzgeTJ554Qv/3f/+nsWPHuqS4V155ReHh4Vq8eLGjrV69eo7/N8Zo9uzZmjx5svr27StJWrp0qUJCQrR8+XINHz7cJXUAAADPVaZzdl577TXFxcU5go4k1axZU9OnT3fp1Vhr1qxRu3bt9Nvf/lbBwcFq3bq13n77bcf65ORkpaenq2fPno42u92url27auvWraWOm5ubq1OnTjktAADAmsoUdk6dOqX//ve/xdqPHj2q06dPl7uoIj/++KMWLFigqKgoffrpp3rqqaf03HPPOW5Emp6eLkkKCQlxel5ISIhjXUni4uIUEBDgWMLDw11WMwAAcC9lCjsPPvignnjiCf3973/XTz/9pJ9++kl///vfNXToUMfhJFcoLCxUmzZtNGPGDLVu3VrDhw/X7373Oy1YsMCp38XnBhljLnm+0MSJE5WZmelY0tLSXFYzAABwL2U6Z2fhwoUaN26cBgwYoPz8/PMDVamioUOHatasWS4rrk6dOrrllluc2po2baoPP/xQkhQaGirp/B6eOnXqOPocPXq02N6eC9ntdtntdpfVCQAA3FeZ9uz4+flp/vz5OnbsmOPKrOPHj2v+/PmqWrWqy4rr3LmzkpKSnNoOHjyounXrSpIiIyMVGhqqhIQEx/q8vDxt2rRJnTp1clkdAADAc5XrSwWPHDmiI0eOqFGjRqpataqMMa6qS5L0/PPPa/v27ZoxY4a+//57LV++XG+99ZZGjhwp6fzhq9GjR2vGjBn66KOPtH//fg0ePFh+fn7q37+/S2sBAACeqUyHsY4dO6aHH35YGzZskM1m06FDh1S/fn0NGzZMNWrUcNkVWbfddps++ugjTZw4UdOmTVNkZKRmz56txx57zNFn/Pjxys7O1ogRIxxfKrhu3Tq+YwcAAEgq456d559/Xl5eXkpNTZWfn5+jvV+/fvr3v//tsuIkKSYmRt98841ycnKUmJio3/3ud07rbTabYmNjdeTIEeXk5GjTpk1q3ry5S2sAAACeq0x7dtatW6dPP/1UN998s1N7VFSUUlJSXFIYAACAK5Rpz86ZM2ec9ugUycjI4ConAADgVsoUdu644w7HF/tJ5w8lFRYWatasWerWrZvLigMAACivMh3GmjVrlqKjo7Vz507l5eVp/PjxOnDggI4fP67//Oc/rq4RAACgzMq0Z+eWW27Rvn371L59e/Xo0UNnzpxR37599fXXX6tBgwaurhEAAKDMrnrPTn5+vnr27Kk333xTL7744rWoCQAAwGWues+Ol5eX9u/ff8l7TwEAALiLMh3GGjhwoBYtWuTqWgAAAFyuTCco5+Xl6S9/+YsSEhLUrl27YvfDio+Pd0lxAAAA5XVVYefHH39UvXr1tH//frVp00bS+RtzXojDWwAAwJ1cVdiJiorSkSNHtGHDBknnbw/x5z//WSEhIdekOAAAgPK6qnN2Lr6r+SeffKIzZ864tCAAAABXKtMJykUuDj8AAADu5qrCjs1mK3ZODufoAAAAd3ZV5+wYYzR48GDHzT5zcnL01FNPFbsaa9WqVa6rEAAAoByuKuwMGjTI6fGAAQNcWgwAAICrXVXYWbx48bWqAwAA4Joo1wnKAAAA7q5M36AM3EhSU1OVkZFR7nESExNdUA0A4GoRdoBLSE1NVeMmTZWTfbaiSwEAlBFhB7iEjIwM5WSfVWDMWHkFhpdrrOwfdypzyzIXVQYAuFKEHeAKeAWGyx7asFxj5B9Lc1E1AICrwQnKAADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0rjrOSwpNTVVGRkZ5R4nMTHRBdUAACoSYQeWk5qaqsZNmion+2xFlwIAcAOEHVhORkaGcrLPKjBmrLwCw8s1VvaPO5W5ZZmLKgMAVATCDizLKzBc9tCG5Roj/1iai6oBinPVYdKgoCBFRES4ZCzAigg7AHCdFWSdkGw2DRgwwCXj+fj6Kem7RAIPUArCDgBcZ4W5WZIxLjnUmn8sTcfWvqaMjAzCDlAKwg4AVBBXHGoFcHl8zw4AALA09uwAABxc9R1VEidOw30QdgAAklz/HVWcOA13QdgBAEhy7XdUceI03IlHnbMTFxcnm82m0aNHO9qMMYqNjVVYWJh8fX0VHR2tAwcOVFyRAODhik6cLs9S3rAEuJLHhJ0dO3borbfeUsuWLZ3aZ86cqfj4eM2dO1c7duxQaGioevToodOnT1dQpQAAwJ14RNjJysrSY489prfffls1a9Z0tBtjNHv2bE2ePFl9+/ZV8+bNtXTpUp09e1bLly+vwIoBAIC78IiwM3LkSPXu3Vt33XWXU3tycrLS09PVs2dPR5vdblfXrl21devWUsfLzc3VqVOnnBYAAGBNbn+C8sqVK7V7927t2LGj2Lr09HRJUkhIiFN7SEiIUlJSSh0zLi5OL774omsLBQAAbsmt9+ykpaVp1KhRWrZsmXx8fErtZ7PZnB4bY4q1XWjixInKzMx0LGlp3OwRAACrcus9O7t27dLRo0fVtm1bR1tBQYE2b96suXPnKikpSdL5PTx16tRx9Dl69GixvT0Xstvtstvt165wALjOXHEHdVfdhR1wN24ddrp3765vvvnGqe2JJ55QkyZN9MILL6h+/foKDQ1VQkKCWrduLUnKy8vTpk2b9Morr1REyQBwXbn6DuqAFbl12PH391fz5s2d2qpWrarAwEBH++jRozVjxgxFRUUpKipKM2bMkJ+fn/r3718RJQPAdeXKO6hn/7hTmVuWuagywH24ddi5EuPHj1d2drZGjBihEydOqEOHDlq3bp38/f0rujQAuG5ccQf1/GOcvwhr8riws3HjRqfHNptNsbGxio2NrZB6AACAe3Prq7EAAADKi7ADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsjbADAAAsrUpFFwDPl5qaqoyMDJeMFRQUpIiICJeMBQCARNhBOaWmpqpxk6bKyT7rkvF8fP2U9F0igQcA4DKEHZRLRkaGcrLPKjBmrLwCw8s1Vv6xNB1b+5oyMjIIOwAAlyHswCW8AsNlD23okrESExMr9PkAAGsh7MBtFGSdkGw2DRgwoKJLAQBYCGEHbqMwN0syptyHxLJ/3KnMLctcWBkAwJMRduB2yntILP9YmgurAQB4Or5nBwAAWBphBwAAWBphBwAAWBphBwAAWBphBwAAWBphBwAAWBphBwAAWBphBwAAWBphBwAAWBphBwAAWJpbh524uDjddttt8vf3V3BwsB544AElJSU59THGKDY2VmFhYfL19VV0dLQOHDhQQRUDAAB349ZhZ9OmTRo5cqS2b9+uhIQEnTt3Tj179tSZM2ccfWbOnKn4+HjNnTtXO3bsUGhoqHr06KHTp09XYOUAAMBduPWNQP/97387PV68eLGCg4O1a9cu3XHHHTLGaPbs2Zo8ebL69u0rSVq6dKlCQkK0fPlyDR8+vCLKBgAAbsSt9+xcLDMzU5JUq1YtSVJycrLS09PVs2dPRx+73a6uXbtq69atpY6Tm5urU6dOOS0AAMCaPCbsGGM0ZswYdenSRc2bN5ckpaenS5JCQkKc+oaEhDjWlSQuLk4BAQGOJTw8/NoVDgAAKpTHhJ1nnnlG+/bt04oVK4qts9lsTo+NMcXaLjRx4kRlZmY6lrS0NJfXCwAA3INbn7NT5Nlnn9WaNWu0efNm3XzzzY720NBQSef38NSpU8fRfvTo0WJ7ey5kt9tlt9uvXcEAAMBtuPWeHWOMnnnmGa1atUrr169XZGSk0/rIyEiFhoYqISHB0ZaXl6dNmzapU6dO17tcAADghtx6z87IkSO1fPly/eMf/5C/v7/jPJyAgAD5+vrKZrNp9OjRmjFjhqKiohQVFaUZM2bIz89P/fv3r+DqAQCAO3DrsLNgwQJJUnR0tFP74sWLNXjwYEnS+PHjlZ2drREjRujEiRPq0KGD1q1bJ39//+tcLQAAcEduHXaMMZftY7PZFBsbq9jY2GtfEAAA8Dhufc4OAABAeRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApbn1pecAAEhSamqqMjIyyj1OUFCQIiIiXFARPAlhBwDg1lJTU9W4SVPlZJ8t91g+vn5K+i6RwHODIewAANxaRkaGcrLPKjBmrLwCw8s8Tv6xNB1b+5oyMjIIOzcYwg4A4JpJTEx02RhegeGyhzYs93i48RB2AAAuV5B1QrLZNGDAgIouBSDsAABcrzA3SzKm3IeeJCn7x53K3LLMRZXhRkTYuUG56soGV+yiBmBdrjj0lH8szUXV4EZF2LkBufLKBgAA3B1h5wbkqisbJHYvAwDcH2HnBsbuZQDAjYDbRQAAAEsj7AAAAEsj7AAAAEsj7AAAAEvjBGUAwA3FVd8Pxh3UPQdhBwBwQ3D1LSy4g7rnIOwAAG4IrryFBXdQ9yyEHQDADYW7p994OEEZAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGvfGusZSU1OVkZHhkrFyc3Nlt9vLPU5iYqILqgEAwDMQdq6h1NRUNW7SVDnZZ10zoK2SZApdMxYAADcIws41lJGRoZzsswqMGSuvwPByjZX9405lblnm0rEAALgREHauA6/AcNlDG5ZrjPxjaS4fCwCAGwEnKAMAAEtjzw4AAGXkqgs+goKCFBER4ZKxUBxhBwCAq1SQdUKy2TRgwACXjOfj66ek7xIJPNcIYQcAgKtUmJslGeOSi0byj6Xp2NrXlJGRQdi5Rgg7AACUkSsuGsG1xwnKAADA0iwTdubPn6/IyEj5+Piobdu22rJlS0WXBAAA3IAlDmO9//77Gj16tObPn6/OnTvrzTffVK9evfTtt99y/BMA4BHc8couV93yqKKvNrNE2ImPj9fQoUM1bNgwSdLs2bP16aefasGCBYqLi6vg6gAAKJ27XtnlylseVfTVZh4fdvLy8rRr1y5NmDDBqb1nz57aunVrBVUFAMCVcdcru1x1yyN3uNrM48NORkaGCgoKFBIS4tQeEhKi9PT0Ep+Tm5ur3Nxcx+PMzExJ0qlTp1xaW1ZW1vntpX+vwrycco1VdIsHxvLMmm6EsdyxJncdyx1ruhHGcseaLhyrMD+33GMV5p//3bZr1y7H76CySkpKckldRTVlZWW5/Pds0XjGmEt3NB7u559/NpLM1q1bndqnT59uGjduXOJzpk6daiSxsLCwsLCwWGBJS0u7ZFbw+D07QUFBqly5crG9OEePHi22t6fIxIkTNWbMGMfjwsJCHT9+XIGBgbLZbGWq49SpUwoPD1daWpqqV69epjHcmZXnZ+W5SczPk1l5bhLz82TuMjdjjE6fPq2wsLBL9vP4sOPt7a22bdsqISFBDz74oKM9ISFB999/f4nPsdvtstvtTm01atRwST3Vq1e33If6Qlaen5XnJjE/T2bluUnMz5O5w9wCAgIu28fjw44kjRkzRo8//rjatWunjh076q233lJqaqqeeuqpii4NAABUMEuEnX79+unYsWOaNm2ajhw5oubNm+vjjz9W3bp1K7o0AABQwSwRdiRpxIgRGjFiRIVt3263a+rUqcUOj1mFledn5blJzM+TWXluEvPzZJ42N5sxl7teCwAAwHNZ5t5YAAAAJSHsAAAASyPsAAAASyPsAAAASyPsuMD8+fMVGRkpHx8ftW3bVlu2bKnokspk8+bN6tOnj8LCwmSz2bR69Wqn9cYYxcbGKiwsTL6+voqOjtaBAwcqptirFBcXp9tuu03+/v4KDg7WAw884LjvSxFPnt+CBQvUsmVLxxd8dezYUZ988oljvSfP7WJxcXGy2WwaPXq0o82T5xcbGyubzea0hIaGOtZ78tyK/PzzzxowYIACAwPl5+enW2+9Vbt27XKs9+Q51qtXr9j7Z7PZNHLkSEmePbdz587pD3/4gyIjI+Xr66v69etr2rRpKiwsdPTxmPmV785UWLlypfHy8jJvv/22+fbbb82oUaNM1apVTUpKSkWXdtU+/vhjM3nyZPPhhx8aSeajjz5yWv/yyy8bf39/8+GHH5pvvvnG9OvXz9SpU8ecOnWqYgq+CnfffbdZvHix2b9/v9mzZ4/p3bu3iYiIMFlZWY4+njy/NWvWmH/9618mKSnJJCUlmUmTJhkvLy+zf/9+Y4xnz+1CX331lalXr55p2bKlGTVqlKPdk+c3depU06xZM3PkyBHHcvToUcd6T56bMcYcP37c1K1b1wwePNh8+eWXJjk52Xz22Wfm+++/d/Tx5DkePXrU6b1LSEgwksyGDRuMMZ49t+nTp5vAwECzdu1ak5ycbD744ANTrVo1M3v2bEcfT5kfYaec2rdvb5566imntiZNmpgJEyZUUEWucXHYKSwsNKGhoebll192tOXk5JiAgACzcOHCCqiwfI4ePWokmU2bNhljrDc/Y4ypWbOm+ctf/mKZuZ0+fdpERUWZhIQE07VrV0fY8fT5TZ061bRq1arEdZ4+N2OMeeGFF0yXLl1KXW+FOV5o1KhRpkGDBqawsNDj59a7d28zZMgQp7a+ffuaAQMGGGM8673jMFY55OXladeuXerZs6dTe8+ePbV169YKquraSE5OVnp6utNc7Xa7unbt6pFzzczMlCTVqlVLkrXmV1BQoJUrV+rMmTPq2LGjZeY2cuRI9e7dW3fddZdTuxXmd+jQIYWFhSkyMlKPPPKIfvzxR0nWmNuaNWvUrl07/fa3v1VwcLBat26tt99+27HeCnMskpeXp2XLlmnIkCGy2WweP7cuXbro888/18GDByVJe/fu1RdffKF7771Xkme9d5b5BuWKkJGRoYKCgmJ3Vw8JCSl2F3ZPVzSfkuaakpJSESWVmTFGY8aMUZcuXdS8eXNJ1pjfN998o44dOyonJ0fVqlXTRx99pFtuucXxj44nz23lypXavXu3duzYUWydp793HTp00LvvvqtGjRrpv//9r6ZPn65OnTrpwIEDHj83Sfrxxx+1YMECjRkzRpMmTdJXX32l5557Tna7XQMHDrTEHIusXr1aJ0+e1ODBgyV5/mfzhRdeUGZmppo0aaLKlSuroKBAL730kh599FFJnjU/wo4L2Gw2p8fGmGJtVmGFuT7zzDPat2+fvvjii2LrPHl+jRs31p49e3Ty5El9+OGHGjRokDZt2uRY76lzS0tL06hRo7Ru3Tr5+PiU2s9T59erVy/H/7do0UIdO3ZUgwYNtHTpUt1+++2SPHduklRYWKh27dppxowZkqTWrVvrwIEDWrBggQYOHOjo58lzLLJo0SL16tVLYWFhTu2eOrf3339fy5Yt0/Lly9WsWTPt2bNHo0ePVlhYmAYNGuTo5wnz4zBWOQQFBaly5crF9uIcPXq0WNL1dEVXh3j6XJ999lmtWbNGGzZs0M033+xot8L8vL291bBhQ7Vr105xcXFq1aqV3njjDY+f265du3T06FG1bdtWVapUUZUqVbRp0yb9+c9/VpUqVRxz8NT5Xaxq1apq0aKFDh065PHvnSTVqVNHt9xyi1Nb06ZNlZqaKskaP3uSlJKSos8++0zDhg1ztHn63H7/+99rwoQJeuSRR9SiRQs9/vjjev755xUXFyfJs+ZH2CkHb29vtW3bVgkJCU7tCQkJ6tSpUwVVdW1ERkYqNDTUaa55eXnatGmTR8zVGKNnnnlGq1at0vr16xUZGem03tPnVxJjjHJzcz1+bt27d9c333yjPXv2OJZ27drpscce0549e1S/fn2Pnt/FcnNzlZiYqDp16nj8eydJnTt3LvY1DwcPHlTdunUlWednb/HixQoODlbv3r0dbZ4+t7Nnz6pSJeeYULlyZcel5x41v4o5L9o6ii49X7Rokfn222/N6NGjTdWqVc3hw4crurSrdvr0afP111+br7/+2kgy8fHx5uuvv3ZcRv/yyy+bgIAAs2rVKvPNN9+YRx991C0vMSzJ008/bQICAszGjRudLhM9e/aso48nz2/ixIlm8+bNJjk52ezbt89MmjTJVKpUyaxbt84Y49lzK8mFV2MZ49nzGzt2rNm4caP58ccfzfbt201MTIzx9/d3/BviyXMz5vzXBVSpUsW89NJL5tChQ+avf/2r8fPzM8uWLXP08fQ5FhQUmIiICPPCCy8UW+fJcxs0aJC56aabHJeer1q1ygQFBZnx48c7+njK/Ag7LjBv3jxTt25d4+3tbdq0aeO4nNnTbNiwwUgqtgwaNMgYc/4yw6lTp5rQ0FBjt9vNHXfcYb755puKLfoKlTQvSWbx4sWOPp48vyFDhjg+g7Vr1zbdu3d3BB1jPHtuJbk47Hjy/Iq+l8TLy8uEhYWZvn37mgMHDjjWe/Lcivzzn/80zZs3N3a73TRp0sS89dZbTus9fY6ffvqpkWSSkpKKrfPkuZ06dcqMGjXKREREGB8fH1O/fn0zefJkk5ub6+jjKfOzGWNMhexSAgAAuA44ZwcAAFgaYQcAAFgaYQcAAFgaYQcAAFgaYQcAAFgaYQcAAFgaYQcAAFgaYQeAZURHR2v06NEVXQYAN0PYAeAW+vTpo7vuuqvEddu2bZPNZtPu3buvc1UArICwA8AtDB06VOvXr1dKSkqxde+8845uvfVWtWnTpgIqA+DpCDsA3EJMTIyCg4O1ZMkSp/azZ8/q/fff1wMPPKBHH31UN998s/z8/NSiRQutWLHikmPabDatXr3aqa1GjRpO2/j555/Vr18/1axZU4GBgbr//vt1+PBhx/qNGzeqffv2qlq1qmrUqKHOnTuXGMgAuC/CDgC3UKVKFQ0cOFBLlizRhbfs++CDD5SXl6dhw4apbdu2Wrt2rfbv368nn3xSjz/+uL788ssyb/Ps2bPq1q2bqlWrps2bN+uLL75QtWrVdM899ygvL0/nzp3TAw88oK5du2rfvn3atm2bnnzySdlsNldMGcB1UqWiCwCAIkOGDNGsWbO0ceNGdevWTdL5Q1h9+/bVTTfdpHHjxjn6Pvvss/r3v/+tDz74QB06dCjT9lauXKlKlSrpL3/5iyPALF68WDVq1NDGjRvVrl07ZWZmKiYmRg0aNJAkNW3atJyzBHC9sWcHgNto0qSJOnXqpHfeeUeS9MMPP2jLli0aMmSICgoK9NJLL6lly5YKDAxUtWrVtG7dOqWmppZ5e7t27dL3338vf39/VatWTdWqVVOtWrWUk5OjH374QbVq1dLgwYN19913q0+fPnrjjTd05MgRV00XwHVC2AHgVoYOHaoPP/xQp06d0uLFi1W3bl11795dr732ml5//XWNHz9e69ev1549e3T33XcrLy+v1LFsNpvTITFJys/Pd/x/YWGh2rZtqz179jgtBw8eVP/+/SWd39Ozbds2derUSe+//74aNWqk7du3X5vJA7gmCDsA3MrDDz+sypUra/ny5Vq6dKmeeOIJ2Ww2bdmyRffff78GDBigVq1aqX79+jp06NAlx6pdu7bTnphDhw7p7Nmzjsdt2rTRoUOHFBwcrIYNGzotAQEBjn6tW7fWxIkTtXXrVjVv3lzLly93/cQBXDOEHQBupVq1aurXr58mTZqkX375RYMHD5YkNWzYUAkJCdq6dasSExM1fPhwpaenX3KsO++8U3PnztXu3bu1c+dOPfXUU/Ly8nKsf+yxxxQUFKT7779fW7ZsUXJysjZt2qRRo0bpp59+UnJysiZOnKht27YpJSVF69at08GDBzlvB/AwhB0Abmfo0KE6ceKE7rrrLkVEREiSpkyZojZt2ujuu+9WdHS0QkND9cADD1xynNdee03h4eG644471L9/f40bN05+fn6O9X5+ftq8ebMiIiLUt29fNW3aVEOGDFF2draqV68uPz8/fffdd/rNb36jRo0a6cknn9Qzzzyj4cOHX8vpA3Axm7n4gDYAAICFsGcHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABY2v8Dp2kagJbqbU4AAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "age_pop = pd.read_csv(\"ages_population.csv\")\n",
+    "plt.hist(age_pop['observation'], bins='auto', edgecolor='black')  \n",
+    "plt.title('Histogram of Frequency Distribution')\n",
+    "plt.xlabel('Values')\n",
+    "plt.ylabel('Frequency')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 202,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Guesses:\\nmean: 40\\nstd: 18'"
+      ]
+     },
+     "execution_count": 202,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"Guesses:\n",
+    "mean: 40\n",
+    "std: 18\"\"\""
+   ]
+  },
+  {
+   "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": 203,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean : 36.56\n",
+      "Standard Deviation: 12.816499625976762\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"\"\"Mean : {age_pop[\"observation\"].mean()}\n",
+    "Standard Deviation: {age_pop[\"observation\"].std()}\"\"\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 157,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nBoth a bit smaller than I anticipated\\n'"
+      ]
+     },
+     "execution_count": 157,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\"\"\n",
+    "Both a bit smaller than I anticipated\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": 206,
+   "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",
+       "      <th>count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>28.0</td>\n",
+       "      <td>139</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>27.0</td>\n",
+       "      <td>125</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>26.0</td>\n",
+       "      <td>120</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>29.0</td>\n",
+       "      <td>115</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>25.0</td>\n",
+       "      <td>98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>30.0</td>\n",
+       "      <td>90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>24.0</td>\n",
+       "      <td>78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>31.0</td>\n",
+       "      <td>61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>23.0</td>\n",
+       "      <td>41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>22.0</td>\n",
+       "      <td>35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>32.0</td>\n",
+       "      <td>31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>33.0</td>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>21.0</td>\n",
+       "      <td>17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>20.0</td>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>34.0</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>35.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>19.0</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>36.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    observation  count\n",
+       "0          28.0    139\n",
+       "1          27.0    125\n",
+       "2          26.0    120\n",
+       "3          29.0    115\n",
+       "4          25.0     98\n",
+       "5          30.0     90\n",
+       "6          24.0     78\n",
+       "7          31.0     61\n",
+       "8          23.0     41\n",
+       "9          22.0     35\n",
+       "10         32.0     31\n",
+       "11         33.0     22\n",
+       "12         21.0     17\n",
+       "13         20.0     13\n",
+       "14         34.0      7\n",
+       "15         35.0      3\n",
+       "16         19.0      3\n",
+       "17         36.0      2"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "age_pop2 = pd.read_csv(\"ages_population2.csv\")\n",
+    "freq = age_pop2.value_counts().reset_index()\n",
+    "freq.columns = [\"observation\", \"count\"]\n",
+    "y = freq[\"observation\"]\n",
+    "x = freq[\"count\"]\n",
+    "display(freq)\n",
+    "plt.plot(x,y,c=\"skyblue\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(age_pop2['observation'], bins='auto', edgecolor='black')  \n",
+    "plt.title('Histogram of Frequency Distribution')\n",
+    "plt.xlabel('Values')\n",
+    "plt.ylabel('Frequency')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "####  4.- What do you see? Is there any difference with the frequency distribution in step 1?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "The range is a lot smaller, for example the max is now where the mean in step 1 was. New mean should be at around 27.\n",
+    "General Distribution is again shaped similiar to a hyperbel.\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": 173,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean : 27.155\n",
+      "Standard Deviation: 2.969813932689186\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"\"\"Mean : {age_pop2[\"observation\"].mean()}\n",
+    "Standard Deviation: {age_pop2[\"observation\"].std()}\"\"\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "Both, Mean and Stdev are a lot smaller. This Neighborhood is a lot younger also their are almost no older people living there.\n",
+    "Most People There are between 22 -32 years old.\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": 175,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "age_pop3 = pd.read_csv(\"ages_population3.csv\")\n",
+    "plt.hist(age_pop3['observation'], bins='auto', edgecolor='black')  \n",
+    "plt.title('Histogram of Frequency Distribution')\n",
+    "plt.xlabel('Values')\n",
+    "plt.ylabel('Frequency')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.- Calculate the mean and standard deviation. Compare the results with the plot in step 1. What is happening?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 176,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean : 41.989\n",
+      "Standard Deviation: 16.144705959865934\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"\"\"Mean : {age_pop3[\"observation\"].mean()}\n",
+    "Standard Deviation: {age_pop3[\"observation\"].std()}\"\"\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "The Ages in this neighborhood are a little bit higher than the ones in step 1 on average. Also the Stdev is a bit higher.\n",
+    "However in General it can be said that these two neighborhoods are rather similiar to each other.\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": 178,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "30.0\n",
+      "40.0\n",
+      "53.0\n",
+      "77.0\n",
+      "41.989\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(age_pop3[\"observation\"].quantile(0.25))\n",
+    "print(age_pop3[\"observation\"].quantile(0.5))\n",
+    "print(age_pop3[\"observation\"].quantile(0.75))\n",
+    "print(age_pop3[\"observation\"].quantile(1))\n",
+    "print(age_pop3[\"observation\"].mean())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "The difference between mean and medain is around 1.9 years. 50 % of the people in this neighborhood are between 30 and 53 \n",
+    "years old.\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": 182,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.0\n",
+      "25.0\n",
+      "64.0\n",
+      "70.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(age_pop3[\"observation\"].quantile(0))\n",
+    "print(age_pop3[\"observation\"].quantile(0.15))\n",
+    "print(age_pop3[\"observation\"].quantile(0.85))\n",
+    "print(age_pop3[\"observation\"].quantile(0.95))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "only 5% under 25, means not a lot children\n",
+    "15% over 64% so more Senior than children\n",
+    "\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Bonus challenge\n",
+    "Compare the information about the three neighbourhoods. Prepare a report about the three of them. Remember to find out which are their similarities and their differences backing your arguments in basic statistics."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# your code here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\"\"\"\n",
+    "your comments here\n",
+    "\"\"\""
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/data/roll_the_dice_hundred.csv b/your-code/data/roll_the_dice_hundred.csv
similarity index 100%
rename from data/roll_the_dice_hundred.csv
rename to your-code/data/roll_the_dice_hundred.csv
diff --git a/data/roll_the_dice_thousand.csv b/your-code/data/roll_the_dice_thousand.csv
similarity index 100%
rename from data/roll_the_dice_thousand.csv
rename to your-code/data/roll_the_dice_thousand.csv
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",
-    "\"\"\""
-   ]
-  }
- ],
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