diff --git a/your-code/main.ipynb b/your-code/main.ipynb
index a0a5b66..b8adaf0 100644
--- a/your-code/main.ipynb
+++ b/your-code/main.ipynb
@@ -1,6 +1,7 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -11,14 +12,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 101,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Libraries"
+    "# Libraries\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import random\n",
+    "\n",
+    "import matplotlib.pyplot as plt"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -29,14 +37,133 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 102,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "\n",
+    "dice_faces = [1, 2, 3, 4, 5, 6]\n",
+    "\n",
+    "def simulate_dice_rolling(num_rolls):\n",
+    "    rolls = random.choices(dice_faces, k = num_rolls)\n",
+    "    data = {\"Roll\": range(1, num_rolls + 1), \"Result\": rolls}\n",
+    "    df = pd.DataFrame(data)\n",
+    "\n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "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>2</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>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>8</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>9</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>10</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Roll  Result\n",
+       "0     1       2\n",
+       "1     2       1\n",
+       "2     3       3\n",
+       "3     4       5\n",
+       "4     5       1\n",
+       "5     6       5\n",
+       "6     7       6\n",
+       "7     8       5\n",
+       "8     9       2\n",
+       "9    10       4"
+      ]
+     },
+     "execution_count": 103,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dice_rolls_df = simulate_dice_rolling(10)\n",
+    "dice_rolls_df"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -45,14 +172,60 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 104,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   Roll  Result\n",
+      "1     2       1\n",
+      "4     5       1\n",
+      "0     1       2\n",
+      "8     9       2\n",
+      "2     3       3\n",
+      "9    10       4\n",
+      "3     4       5\n",
+      "5     6       5\n",
+      "7     8       5\n",
+      "6     7       6\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "\n",
+    "sorted_df = dice_rolls_df.sort_values(\"Result\")\n",
+    "print(sorted_df)"
    ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.bar(sorted_df[\"Result\"], sorted_df[\"Roll\"])\n",
+    "plt.xlabel(\"Result\")\n",
+    "plt.ylabel(\"Roll\")\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -61,25 +234,65 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 106,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Result\n",
+      "1    2\n",
+      "2    2\n",
+      "3    1\n",
+      "4    1\n",
+      "5    3\n",
+      "6    1\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "\n",
+    "freq_distr = dice_rolls_df[\"Result\"].value_counts().sort_index()\n",
+    "print(freq_distr)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.bar(freq_distr.index, freq_distr.values)\n",
+    "plt.xlabel(\"Result\")\n",
+    "plt.ylabel(\"Frequency\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
    "metadata": {},
    "outputs": [],
    "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
+    "# Both plots are based on the Result. The first one displaying the roll attempt that showed a certain result. The second displaying the frequency of each result being displayed by roll."
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -91,14 +304,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 109,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.4"
+      ]
+     },
+     "execution_count": 109,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "\n",
+    "def calculate_mean(data):\n",
+    "    total_sum = 0\n",
+    "    n = 0\n",
+    "\n",
+    "    for observation in data:\n",
+    "        total_sum += observation\n",
+    "        n += 1\n",
+    "\n",
+    "    mean = total_sum / n\n",
+    "    return mean\n",
+    "\n",
+    "calculate_mean(dice_rolls_df[\"Result\"])"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -107,14 +345,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 110,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.4"
+      ]
+     },
+     "execution_count": 110,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "\n",
+    "def calculate_mean_from_freq_distr(freq_distr):\n",
+    "    total_sum = 0\n",
+    "    n = 0\n",
+    "\n",
+    "    for value, frequency in freq_distr.items():\n",
+    "        total_sum += value * frequency\n",
+    "        n += frequency\n",
+    "\n",
+    "    mean = total_sum / n\n",
+    "    return mean\n",
+    "\n",
+    "calculate_mean_from_freq_distr(freq_distr)"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -124,14 +387,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 111,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.5"
+      ]
+     },
+     "execution_count": 111,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "\n",
+    "def calculate_median(data):\n",
+    "    sorted_data = sorted(data)\n",
+    "\n",
+    "    n = len(sorted_data)\n",
+    "\n",
+    "    if n%2 == 1:\n",
+    "        median = sorted_data[n // 2]\n",
+    "    else:\n",
+    "        pt1 = sorted_data[n // 2 - 1]\n",
+    "        pt2 = sorted_data[n // 2]\n",
+    "        median = (pt1 + pt2) / 2\n",
+    "\n",
+    "    return median\n",
+    "\n",
+    "calculate_median(dice_rolls_df[\"Result\"])"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -140,14 +431,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 112,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2, 5, 3.5)"
+      ]
+     },
+     "execution_count": 112,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "\n",
+    "def calculate_quartiles(data):\n",
+    "    sorted_data = sorted(data)\n",
+    "    n = len(sorted_data)\n",
+    "    median = calculate_median(sorted_data)\n",
+    "\n",
+    "    index_q1 = n // 4\n",
+    "    index_q3 = (3 * n) // 4\n",
+    "\n",
+    "    q1 = sorted_data[index_q1]\n",
+    "    q3 = sorted_data[index_q3]\n",
+    "\n",
+    "\n",
+    "    return q1, q3, median\n",
+    "\n",
+    "calculate_quartiles(dice_rolls_df[\"Result\"])"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -158,25 +477,131 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 113,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>roll</th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Unnamed: 0  roll  value\n",
+       "0           0     0      1\n",
+       "1           1     1      2\n",
+       "2           2     2      6\n",
+       "3           3     3      1\n",
+       "4           4     4      6"
+      ]
+     },
+     "execution_count": 113,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "\n",
+    "\n",
+    "data = pd.read_csv('/Users/pedroblanc/Ironhack/Course/WeekFour/Labs/Descriptive-Stats/data/roll_the_dice_hundred.csv')\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sorted_dice = data.sort_values(\"value\")[[\"roll\", \"value\"]]\n",
+    "\n",
+    "plt.hist(data[\"value\"])\n",
+    "\n",
+    "# set labels and title\n",
+    "plt.xlabel(\"Result\")\n",
+    "plt.ylabel(\"Frequency\")\n",
+    "\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 115,
    "metadata": {},
    "outputs": [],
    "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
+    "# It appears that rolling an odd number is less frequent. Possibly because of uneven dice."
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -185,14 +610,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 116,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.74"
+      ]
+     },
+     "execution_count": 116,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "\n",
+    "calculate_mean(data[\"value\"])\n"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -201,14 +640,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 117,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "value\n",
+       "1    12\n",
+       "2    17\n",
+       "3    14\n",
+       "4    22\n",
+       "5    12\n",
+       "6    23\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 117,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "\n",
+    "frequency_distr = data[\"value\"].value_counts().sort_index()\n",
+    "frequency_distr"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -217,25 +678,53 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 138,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "\n",
+    "plt.hist(data[\"value\"], bins=len(frequency_distr))\n",
+    "\n",
+    "plt.xlabel(\"Result\")\n",
+    "plt.ylabel(\"Frequency\")\n",
+    "\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 119,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nyour comments here\\n'"
+      ]
+     },
+     "execution_count": 119,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
+    "# This histogram is very similar to the one produced previously. Not sure what I can take away besides the mean being 3.74."
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -244,25 +733,137 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 144,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>roll</th>\n",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Unnamed: 0  roll  value\n",
+       "0           0     0      5\n",
+       "1           1     1      6\n",
+       "2           2     2      1\n",
+       "3           3     3      6\n",
+       "4           4     4      5"
+      ]
+     },
+     "execution_count": 144,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "\n",
+    "thousand_df = pd.read_csv(\"/Users/pedroblanc/Ironhack/Course/WeekFour/Labs/Descriptive-Stats/data/roll_the_dice_thousand.csv\")\n",
+    "thousand_df.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 145,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
+    "values = sorted(thousand_df[\"value\"])\n",
+    "\n",
+    "plt.hist(values)\n",
+    "\n",
+    "plt.show()"
    ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 121,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nyour comments here\\n'"
+      ]
+     },
+     "execution_count": 121,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# A much higher number of rolls will provide more ambiguous results"
+   ]
+  },
+  {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -274,14 +875,114 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 146,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>observation</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>68.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>12.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>45.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>38.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>49.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   observation\n",
+       "0         68.0\n",
+       "1         12.0\n",
+       "2         45.0\n",
+       "3         38.0\n",
+       "4         49.0"
+      ]
+     },
+     "execution_count": 146,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "\n",
+    "ages = pd.read_csv(\"/Users/pedroblanc/Ironhack/Course/WeekFour/Labs/Descriptive-Stats/data/ages_population.csv\")\n",
+    "ages.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "frequency_distr = ages[\"observation\"].value_counts().sort_index()\n",
+    "\n",
+    "plt.bar(frequency_distr.index, frequency_distr.values)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "# The mean appears to be around 37\n",
+    "# The standard deviation appears to be around 10"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -290,25 +991,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 153,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The mean is:  observation    36.56\n",
+      "dtype: float64\n",
+      "The standard deviation is:  observation    12.8165\n",
+      "dtype: float64\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "\n",
+    "print(\"The mean is: \", ages.mean())\n",
+    "print(\"The standard deviation is: \", ages.std())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 154,
    "metadata": {},
    "outputs": [],
    "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
+    "# The actual values for the mean and standard deviation were close to the ones guessed, with a slight margin of error"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -317,14 +1031,104 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 158,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>observation</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>25.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>31.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>29.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>31.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>29.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   observation\n",
+       "0         25.0\n",
+       "1         31.0\n",
+       "2         29.0\n",
+       "3         31.0\n",
+       "4         29.0"
+      ]
+     },
+     "execution_count": 158,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "\n",
+    "ages_p2 = pd.read_csv(\"/Users/pedroblanc/Ironhack/Course/WeekFour/Labs/Descriptive-Stats/data/ages_population2.csv\")\n",
+    "ages_p2.head()"
    ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 159,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "frequency_distr = ages_p2[\"observation\"].value_counts().sort_index()\n",
+    "\n",
+    "plt.bar(frequency_distr.index, frequency_distr.values)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -333,16 +1137,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 162,
    "metadata": {},
    "outputs": [],
    "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
+    "# The observation sample appears to be narrower in this population than the one on step 1"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -351,25 +1154,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 163,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The mean is:  observation    27.155\n",
+      "dtype: float64\n",
+      "The standard deviation is:  observation    2.969814\n",
+      "dtype: float64\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "\n",
+    "print(\"The mean is: \", ages_p2.mean())\n",
+    "print(\"The standard deviation is: \",ages_p2.std())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 164,
    "metadata": {},
    "outputs": [],
    "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
+    "# This smaller observation sample means a decrease in standard deviation "
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -381,14 +1197,104 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 165,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>observation</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>21.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>21.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>24.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>31.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>54.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   observation\n",
+       "0         21.0\n",
+       "1         21.0\n",
+       "2         24.0\n",
+       "3         31.0\n",
+       "4         54.0"
+      ]
+     },
+     "execution_count": 165,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "\n",
+    "ages_p3 = pd.read_csv(\"/Users/pedroblanc/Ironhack/Course/WeekFour/Labs/Descriptive-Stats/data/ages_population3.csv\")\n",
+    "ages_p3.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 166,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "frequency_distr = ages_p3[\"observation\"].value_counts().sort_index()\n",
+    "\n",
+    "plt.bar(frequency_distr.index, frequency_distr.values)\n",
+    "plt.show()"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -397,25 +1303,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 168,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The mean is:  observation    41.989\n",
+      "dtype: float64\n",
+      "The standard deviation is:  observation    16.144706\n",
+      "dtype: float64\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "\n",
+    "print(\"The mean is: \", ages_p3.mean())\n",
+    "print(\"The standard deviation is: \",ages_p3.std())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 169,
    "metadata": {},
    "outputs": [],
    "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
+    "# The graph proves the existence of a bimodal distribution due to the both peaks present. The first, and wider one, around yound adults and the second, smaller one, around the high 60's"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -424,18 +1343,52 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 173,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{30.0}\n",
+      "{40.0}\n",
+      "{53.0}\n",
+      "{77.0}\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "\n",
+    "q1 = np.quantile(ages_p3, 0.25)\n",
+    "print({q1})\n",
+    "\n",
+    "q2 = np.quantile(ages_p3, 0.50)\n",
+    "print({q2})\n",
+    "\n",
+    "q3 = np.quantile(ages_p3, 0.75)\n",
+    "print({q3})\n",
+    "\n",
+    "q4 = np.quantile(ages_p3, 1)\n",
+    "print({q4})"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 133,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nyour comments here\\n'"
+      ]
+     },
+     "execution_count": 133,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "\"\"\"\n",
     "your comments here\n",
@@ -443,6 +1396,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -451,25 +1405,46 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 174,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{67.0}\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "\n",
+    "q90 = np.percentile(ages_p3, 90)\n",
+    "print({q90})\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 135,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nyour comments here\\n'"
+      ]
+     },
+     "execution_count": 135,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "\"\"\"\n",
-    "your comments here\n",
-    "\"\"\""
+    "# 90% of the population falls bellow 67 years"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -479,7 +1454,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 136,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -488,9 +1463,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 137,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nyour comments here\\n'"
+      ]
+     },
+     "execution_count": 137,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "\"\"\"\n",
     "your comments here\n",
@@ -500,9 +1486,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "ironhack-3.7",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "ironhack-3.7"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -514,7 +1500,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.11.3"
   }
  },
  "nbformat": 4,