diff --git a/your-code/main.ipynb b/your-code/main.ipynb
index a0a5b66..90fcb90 100644
--- a/your-code/main.ipynb
+++ b/your-code/main.ipynb
@@ -11,11 +11,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Libraries"
+    "import numpy as np\n",
+    "import pandas as pd\n"
    ]
   },
   {
@@ -29,11 +30,111 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 60,
    "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>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   value\n",
+       "0      6\n",
+       "1      5\n",
+       "2      4\n",
+       "3      6\n",
+       "4      2\n",
+       "5      2\n",
+       "6      6\n",
+       "7      4\n",
+       "8      1\n",
+       "9      1"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "import random\n",
+    "dice = [1,2,3,4,5,6]\n",
+    "\n",
+    "def dice_stimulation(dice):\n",
+    "    results = []\n",
+    "    for i in range(0,10):\n",
+    "        x = random.choices(dice)\n",
+    "        results.append(int(x[0]))\n",
+    "    dict = {\"value\": results}\n",
+    "    return pd.DataFrame(dict)\n",
+    "\n",
+    "dicing_results = dice_stimulation(dice)\n",
+    "dicing_results"
    ]
   },
   {
@@ -45,11 +146,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 61,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here"
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "dicing_results.plot()"
    ]
   },
   {
@@ -61,21 +185,55 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 63,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([2., 0., 2., 0., 0., 0., 2., 0., 1., 3.]),\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": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here"
+    "plt.hist(dicing_results['value'])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 64,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nthe first one makes more sense in time dependent variavles whilst the second suits better the dice results\\n'"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "\"\"\"\n",
-    "your comments here\n",
+    "the first one makes more sense in time dependent variavles whilst the second suits better the dice results\n",
     "\"\"\""
    ]
   },
@@ -91,11 +249,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 67,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'the mean of the observations is 3.7'"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "\n",
+    "def mean_dicing_results(df):\n",
+    "    x = list(df['value'].values)\n",
+    "    return f'the mean of the observations is {sum(x) / len(x)}'\n",
+    "\n",
+    "mean_dicing_results(dicing_results)"
    ]
   },
   {
@@ -107,30 +281,153 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 66,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'the mean of the frequency distribution is 1.67'"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here"
+    "frequency = list(plt.hist(dicing_results)[0])\n",
+    "\n",
+    "f'the mean of the frequency distribution is {round(sum(frequency)/6, 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."
+    "#### 3.- Define a function to calculate the median. You are not allowed to use any methods or functions that directly calculate the median value. "
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "# your code here"
+    "**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": 136,
+   "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",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>97</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>98</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>100 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    value\n",
+       "0       1\n",
+       "1       2\n",
+       "2       6\n",
+       "3       1\n",
+       "4       6\n",
+       "..    ...\n",
+       "95      4\n",
+       "96      6\n",
+       "97      1\n",
+       "98      3\n",
+       "99      6\n",
+       "\n",
+       "[100 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 136,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -158,11 +455,132 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 102,
    "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",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>95</td>\n",
+       "      <td>95</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>96</td>\n",
+       "      <td>96</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>97</th>\n",
+       "      <td>97</td>\n",
+       "      <td>97</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>98</th>\n",
+       "      <td>98</td>\n",
+       "      <td>98</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>99</td>\n",
+       "      <td>99</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>100 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Unnamed: 0  roll  value\n",
+       "0            0     0      1\n",
+       "1            1     1      2\n",
+       "2            2     2      6\n",
+       "3            3     3      1\n",
+       "4            4     4      6\n",
+       "..         ...   ...    ...\n",
+       "95          95    95      4\n",
+       "96          96    96      6\n",
+       "97          97    97      1\n",
+       "98          98    98      3\n",
+       "99          99    99      6\n",
+       "\n",
+       "[100 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 102,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "roll_hundred = pd.read_csv('roll_the_dice_hundred.csv')\n",
+    "roll_hundred"
    ]
   },
   {
@@ -185,11 +603,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 68,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'the mean of the observations is 3.74'"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "mean_dicing_results(roll_hundred)"
    ]
   },
   {
@@ -201,11 +630,108 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 100,
    "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>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>97</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>98</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>100 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    value\n",
+       "0       1\n",
+       "1       2\n",
+       "2       6\n",
+       "3       1\n",
+       "4       6\n",
+       "..    ...\n",
+       "95      4\n",
+       "96      6\n",
+       "97      1\n",
+       "98      3\n",
+       "99      6\n",
+       "\n",
+       "[100 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 100,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "frequency = pd.DataFrame(roll_hundred['value'])\n",
+    "frequency"
    ]
   },
   {
@@ -217,11 +743,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 101,
    "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": 101,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here"
+    "plt.hist(frequency)"
    ]
   },
   {
@@ -244,11 +793,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 104,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([175.,   0., 167.,   0., 175.,   0., 168.,   0., 149., 166.]),\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": 104,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here"
+    "roll_thousand = pd.read_csv(\"roll_the_dice_thousand.csv\")\n",
+    "\n",
+    "plt.hist(roll_thousand['value'])"
    ]
   },
   {
@@ -274,11 +848,127 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 109,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
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+       "        vertical-align: top;\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>observation</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>39.0</th>\n",
+       "      <td>45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41.0</th>\n",
+       "      <td>36</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30.0</th>\n",
+       "      <td>34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35.0</th>\n",
+       "      <td>33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43.0</th>\n",
+       "      <td>32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73.0</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>82.0</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>70.0</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>71.0</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>69.0</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>72 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             count\n",
+       "observation       \n",
+       "39.0            45\n",
+       "41.0            36\n",
+       "30.0            34\n",
+       "35.0            33\n",
+       "43.0            32\n",
+       "...            ...\n",
+       "73.0             1\n",
+       "82.0             1\n",
+       "70.0             1\n",
+       "71.0             1\n",
+       "69.0             1\n",
+       "\n",
+       "[72 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 109,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "ages_population = pd.read_csv(\"ages_population.csv\")\n",
+    "\n",
+    "plt.hist(ages_population)\n",
+    "plt.show()\n",
+    "\n",
+    "pd.DataFrame(ages_population[\"observation\"].value_counts())"
    ]
   },
   {
@@ -290,11 +980,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 123,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "36.56\n",
+      "12.81649962597677\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "import statistics as stats\n",
+    "\n",
+    "print(ages_population[\"observation\"].mean())\n",
+    "print(stats.stdev(ages_population[\"observation\"]))\n",
+    "\n"
    ]
   },
   {
@@ -317,11 +1020,146 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 125,
    "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>count</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>observation</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>28.0</th>\n",
+       "      <td>139</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27.0</th>\n",
+       "      <td>125</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26.0</th>\n",
+       "      <td>120</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29.0</th>\n",
+       "      <td>115</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25.0</th>\n",
+       "      <td>98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30.0</th>\n",
+       "      <td>90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24.0</th>\n",
+       "      <td>78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31.0</th>\n",
+       "      <td>61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23.0</th>\n",
+       "      <td>41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22.0</th>\n",
+       "      <td>35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32.0</th>\n",
+       "      <td>31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33.0</th>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21.0</th>\n",
+       "      <td>17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20.0</th>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34.0</th>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19.0</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35.0</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36.0</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             count\n",
+       "observation       \n",
+       "28.0           139\n",
+       "27.0           125\n",
+       "26.0           120\n",
+       "29.0           115\n",
+       "25.0            98\n",
+       "30.0            90\n",
+       "24.0            78\n",
+       "31.0            61\n",
+       "23.0            41\n",
+       "22.0            35\n",
+       "32.0            31\n",
+       "33.0            22\n",
+       "21.0            17\n",
+       "20.0            13\n",
+       "34.0             7\n",
+       "19.0             3\n",
+       "35.0             3\n",
+       "36.0             2"
+      ]
+     },
+     "execution_count": 125,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "ages_pop2 = pd.read_csv(\"ages_population2.csv\")\n",
+    "\n",
+    "pd.DataFrame(ages_pop2[\"observation\"].value_counts())"
    ]
   },
   {
@@ -351,11 +1189,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 128,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "27.155\n",
+      "2.9698139326891835\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "print(ages_pop2[\"observation\"].mean())\n",
+    "print(stats.stdev(ages_pop2[\"observation\"]))"
    ]
   },
   {
@@ -381,11 +1229,146 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 129,
    "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>count</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>observation</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>28.0</th>\n",
+       "      <td>139</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27.0</th>\n",
+       "      <td>125</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26.0</th>\n",
+       "      <td>120</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29.0</th>\n",
+       "      <td>115</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25.0</th>\n",
+       "      <td>98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30.0</th>\n",
+       "      <td>90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24.0</th>\n",
+       "      <td>78</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31.0</th>\n",
+       "      <td>61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23.0</th>\n",
+       "      <td>41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22.0</th>\n",
+       "      <td>35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32.0</th>\n",
+       "      <td>31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33.0</th>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21.0</th>\n",
+       "      <td>17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20.0</th>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34.0</th>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19.0</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35.0</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36.0</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             count\n",
+       "observation       \n",
+       "28.0           139\n",
+       "27.0           125\n",
+       "26.0           120\n",
+       "29.0           115\n",
+       "25.0            98\n",
+       "30.0            90\n",
+       "24.0            78\n",
+       "31.0            61\n",
+       "23.0            41\n",
+       "22.0            35\n",
+       "32.0            31\n",
+       "33.0            22\n",
+       "21.0            17\n",
+       "20.0            13\n",
+       "34.0             7\n",
+       "19.0             3\n",
+       "35.0             3\n",
+       "36.0             2"
+      ]
+     },
+     "execution_count": 129,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "ages_pop3 = pd.read_csv(\"ages_population2.csv\")\n",
+    "\n",
+    "pd.DataFrame(ages_pop3[\"observation\"].value_counts())"
    ]
   },
   {
@@ -397,11 +1380,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 130,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "27.155\n",
+      "2.9698139326891835\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "print(ages_pop3[\"observation\"].mean())\n",
+    "print(stats.stdev(ages_pop3[\"observation\"]))"
    ]
   },
   {
@@ -424,11 +1417,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 133,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "q1 = 25.0\n",
+      "q2 = 27.0\n",
+      "q3 = 29.0\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "import numpy as np\n",
+    "\n",
+    "print(f'q1 = {np.quantile(ages_pop3, 0.25)}')\n",
+    "print(f'q2 = {np.quantile(ages_pop3, 0.50)}')\n",
+    "print(f'q3 = {np.quantile(ages_pop3, 0.75)}')"
    ]
   },
   {
@@ -451,11 +1458,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 135,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "top10% = 31.0\n",
+      "bottom10% = 23.0\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "print(f'top10% = {np.quantile(ages_pop3, 0.90)}')\n",
+    "print(f'bottom10% = {np.quantile(ages_pop3, 0.10)}')"
    ]
   },
   {
@@ -500,9 +1517,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "ironhack-3.7",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "ironhack-3.7"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -514,7 +1531,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,