diff --git a/your-code/main.ipynb b/your-code/main.ipynb
index a0a5b66..921edfd 100644
--- a/your-code/main.ipynb
+++ b/your-code/main.ipynb
@@ -1,6 +1,7 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -11,14 +12,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Libraries"
+    "# Libraries\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import random\n",
+    "import matplotlib.pyplot as plt "
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -29,14 +35,87 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "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>Dice Number</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Dice Number\n",
+       "0            2\n",
+       "1            4\n",
+       "2            5\n",
+       "3            6\n",
+       "4            5"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "def rolling_dice():\n",
+    "    dice_numbers = []\n",
+    "    for item in range(5):\n",
+    "        x = random.randint(1,6)\n",
+    "        dice_numbers.append(x)\n",
+    "    return pd.DataFrame(dice_numbers, columns = [\"Dice Number\"])\n",
+    "\n",
+    "dice = rolling_dice()\n",
+    "dice"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -45,14 +124,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1a90681fad0>]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "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",
+    "sorted_dice = pd.DataFrame(sorted(dice[\"Dice Number\"]))\n",
+    "\n",
+    "plt.plot(sorted_dice)"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -61,25 +165,77 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{2: 1, 4: 1, 5: 2, 6: 1}"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "dice_dict = {}\n",
+    "for item in list(dice[\"Dice Number\"]):\n",
+    "    if item not in dice_dict:\n",
+    "        dice_dict[item] = 1\n",
+    "    else:\n",
+    "        dice_dict[item] += 1\n",
+    "\n",
+    "dice_dict"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(dice_dict)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nWith the histogram we can visualize better teh frequency of the results\\n'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "\"\"\"\n",
-    "your comments here\n",
+    "With the histogram we can visualize better teh frequency of the results\n",
     "\"\"\""
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -91,14 +247,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "def average1(x):\n",
+    "    a=0\n",
+    "    for item in x:\n",
+    "        a = a + item\n",
+    "    return a/len(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4.4"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "average1(dice[\"Dice Number\"])"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -107,14 +289,51 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{2: 1, 4: 1, 5: 2, 6: 1}"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "dice_dict = {}\n",
+    "for item in list(dice[\"Dice Number\"]):\n",
+    "    if item not in dice_dict:\n",
+    "        dice_dict[item] = 1\n",
+    "    else:\n",
+    "        dice_dict[item] += 1\n",
+    "\n",
+    "dice_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "sum = 0\n",
+    "\n",
+    "def average(x):\n",
+    "    sum = 0\n",
+    "    counter = 0\n",
+    "    for item,item2 in x.items():\n",
+    "        sum = sum + item * item2\n",
+    "        counter=counter+item2\n",
+    "    return sum/counter\n"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -124,14 +343,44 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "def median(x):\n",
+    "    x = sorted(x)\n",
+    "    if len(x)%2 == 0:\n",
+    "        x1= int((len(x)/2)-1)\n",
+    "        x2 = x1+1\n",
+    "        return (x[x1]+x[x2]/2)/2\n",
+    "    else:\n",
+    "        x1= int((len(x)/2))\n",
+    "        return x[x1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "median(dice[\"Dice Number\"])"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -140,14 +389,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "# I DONT KNOW HOW TO DO IT\n",
+    "        "
    ]
   },
   {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -158,25 +417,79 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "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": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "dice = pd.read_csv(r\"C:\\Users\\Bruno Santos\\Desktop\\Iron Hack - Semanas\\4 Semana\\Labs\\Descriptive-Stats\\data\\roll_the_dice_hundred.csv\")\n",
+    "counted_values= dice['value'].value_counts()\n",
+    "counted_values.sort_index()\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(dice[\"value\"])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nWe dont get any insight from the plot\\n'"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "\"\"\"\n",
-    "your comments here\n",
+    "We dont get any insight from the plot\n",
     "\"\"\""
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -185,14 +498,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.74"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "dice_values = dice[\"value\"]\n",
+    "dice_values_list = list(dice_values)\n",
+    "average1(dice_values_list)"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -201,14 +529,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1: 12, 2: 17, 6: 23, 5: 12, 4: 22, 3: 14}"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "dice_dict = {}\n",
+    "for item in dice_values_list:\n",
+    "    if item not in dice_dict:\n",
+    "        dice_dict[item] = 1\n",
+    "    else:\n",
+    "        dice_dict[item] += 1\n",
+    "\n",
+    "dice_dict"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -217,18 +565,53 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([12.,  0., 17.,  0., 14.,  0., 22.,  0., 12., 23.]),\n",
+       " array([1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5, 5. , 5.5, 6. ]),\n",
+       " <BarContainer object of 10 artists>)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "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",
+    "plt.hist(dice_values_list)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nyour comments here\\n'"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "\"\"\"\n",
     "your comments here\n",
@@ -236,6 +619,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -244,18 +628,190 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "dice2 = pd.read_csv(r\"C:\\Users\\Bruno Santos\\Desktop\\Iron Hack - Semanas\\4 Semana\\Labs\\Descriptive-Stats\\data\\roll_the_dice_thousand.csv\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "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",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>995</th>\n",
+       "      <td>995</td>\n",
+       "      <td>995</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>996</th>\n",
+       "      <td>996</td>\n",
+       "      <td>996</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>997</th>\n",
+       "      <td>997</td>\n",
+       "      <td>997</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>998</th>\n",
+       "      <td>998</td>\n",
+       "      <td>998</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999</th>\n",
+       "      <td>999</td>\n",
+       "      <td>999</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1000 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Unnamed: 0  roll  value\n",
+       "0             0     0      5\n",
+       "1             1     1      6\n",
+       "2             2     2      1\n",
+       "3             3     3      6\n",
+       "4             4     4      5\n",
+       "..          ...   ...    ...\n",
+       "995         995   995      1\n",
+       "996         996   996      4\n",
+       "997         997   997      4\n",
+       "998         998   998      3\n",
+       "999         999   999      6\n",
+       "\n",
+       "[1000 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dice2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<function matplotlib.pyplot.show(close=None, block=None)>"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(dice2[\"value\"])\n",
+    "plt.show"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nyour comments here\\n'"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "\"\"\"\n",
     "your comments here\n",
@@ -263,6 +819,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -274,14 +831,145 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "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>68.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>12.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>45.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>38.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>49.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>995</th>\n",
+       "      <td>27.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>996</th>\n",
+       "      <td>47.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>997</th>\n",
+       "      <td>53.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>998</th>\n",
+       "      <td>33.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999</th>\n",
+       "      <td>31.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1000 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     observation\n",
+       "0           68.0\n",
+       "1           12.0\n",
+       "2           45.0\n",
+       "3           38.0\n",
+       "4           49.0\n",
+       "..           ...\n",
+       "995         27.0\n",
+       "996         47.0\n",
+       "997         53.0\n",
+       "998         33.0\n",
+       "999         31.0\n",
+       "\n",
+       "[1000 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "population = pd.read_csv(r\"C:\\Users\\Bruno Santos\\Desktop\\Iron Hack - Semanas\\4 Semana\\Labs\\Descriptive-Stats\\data\\ages_population.csv\")\n",
+    "population"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([ 17.,  59., 115., 204., 261., 194.,  99.,  36.,  14.,   1.]),\n",
+       " array([ 1. ,  9.1, 17.2, 25.3, 33.4, 41.5, 49.6, 57.7, 65.8, 73.9, 82. ]),\n",
+       " <BarContainer object of 10 artists>)"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(population)"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -290,25 +978,50 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "observation    36.56\n",
+      "dtype: float64\n",
+      "observation    12.8165\n",
+      "dtype: float64\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "print(population.mean())\n",
+    "print(population.std())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nYes, i guessed the mean to be beetwen 30 and 40 and the mean is 36.56\\n'"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "\"\"\"\n",
-    "your comments here\n",
+    "Yes, i guessed the mean to be beetwen 30 and 40 and the mean is 36.56\n",
     "\"\"\""
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -317,14 +1030,48 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "population2 = pd.read_csv(r\"C:\\Users\\Bruno Santos\\Desktop\\Iron Hack - Semanas\\4 Semana\\Labs\\Descriptive-Stats\\data\\ages_population2.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([ 16.,  52., 119.,  98., 245., 254.,  90.,  92.,  29.,   5.]),\n",
+       " array([19. , 20.7, 22.4, 24.1, 25.8, 27.5, 29.2, 30.9, 32.6, 34.3, 36. ]),\n",
+       " <BarContainer object of 10 artists>)"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(population2)"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -333,16 +1080,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 34,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\n\\n'"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "\"\"\"\n",
-    "your comments here\n",
+    "\n",
     "\"\"\""
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -351,25 +1110,51 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "observation    27.155\n",
+      "dtype: float64\n",
+      "observation    2.969814\n",
+      "dtype: float64\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "\n",
+    "print(population2.mean())\n",
+    "print(population2.std())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 36,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nwe can figure that the range of the data is way smaller in the second dataset.\\n'"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "\"\"\"\n",
-    "your comments here\n",
+    "we can figure that the range of the data is way smaller in the second dataset.\n",
     "\"\"\""
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -381,14 +1166,41 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 37,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([  8.,  33.,  78., 158., 187., 174., 133.,  57., 117.,  55.]),\n",
+       " array([ 1. ,  8.6, 16.2, 23.8, 31.4, 39. , 46.6, 54.2, 61.8, 69.4, 77. ]),\n",
+       " <BarContainer object of 10 artists>)"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "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",
+    "population3 = pd.read_csv(r\"C:\\Users\\Bruno Santos\\Desktop\\Iron Hack - Semanas\\4 Semana\\Labs\\Descriptive-Stats\\data\\ages_population3.csv\")\n",
+    "\n",
+    "plt.hist(population3)"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -397,25 +1209,51 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 38,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "observation    41.989\n",
+      "dtype: float64\n",
+      "observation    16.144706\n",
+      "dtype: float64\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "\n",
+    "print(population3.mean())\n",
+    "print(population3.std())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 39,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nBoth the average age and the standard deviation are higher than in step 1, we can guess that thisd dataset has an higher frequency in the higher ages, around the 60 to 70 years, increasing both the average and the standard deviation\\n'"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "\"\"\"\n",
-    "your comments here\n",
+    "Both the average age and the standard deviation are higher than in step 1, we can guess that thisd dataset has an higher frequency in the higher ages, around the 60 to 70 years, increasing both the average and the standard deviation\n",
     "\"\"\""
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -424,25 +1262,51 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 40,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[30.] [40.] [53.] [77.]\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "q1=np.quantile(population3, [0.25])\n",
+    "q2=np.quantile(population3, [0.5])\n",
+    "q3=np.quantile(population3, [0.75])\n",
+    "q4=np.quantile(population3, [1])\n",
+    "\n",
+    "print(q1, q2, q3, q4)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 41,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nyour comments here\\n'"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "\"\"\"\n",
-    "your comments here\n",
+    "Most of the ages are beetwen 40 and 53 \n",
     "\"\"\""
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -451,25 +1315,51 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 46,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "76.0"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "\n",
+    "range=np.max(population3)-np.min(population3)\n",
+    "range "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 47,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\n\\n'"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "\"\"\"\n",
-    "your comments here\n",
+    "\n",
     "\"\"\""
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -479,7 +1369,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -488,9 +1378,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 45,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nyour comments here\\n'"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "\"\"\"\n",
     "your comments here\n",
@@ -500,9 +1401,9 @@
  ],
  "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 +1415,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.11.2"
   }
  },
  "nbformat": 4,