diff --git a/your-code/main.ipynb b/your-code/main.ipynb
index a0a5b66..7ba9540 100644
--- a/your-code/main.ipynb
+++ b/your-code/main.ipynb
@@ -11,11 +11,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Libraries"
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import random\n",
+    "import seaborn as sns"
    ]
   },
   {
@@ -29,11 +33,106 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "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>Roll</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>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Roll\n",
+       "0     1\n",
+       "1     1\n",
+       "2     5\n",
+       "3     4\n",
+       "4     5\n",
+       "5     5\n",
+       "6     4\n",
+       "7     6\n",
+       "8     1\n",
+       "9     6"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "def dice_roll(num_rolls = 10, num_sides = 6):\n",
+    "    rolls = [random.choice(range(1, num_sides + 1)) for _ in range(num_rolls)]\n",
+    "    df = pd.DataFrame({'Roll': rolls})\n",
+    "    return df\n",
+    "\n",
+    "\n",
+    "dice_df = dice_roll(num_rolls = 10, num_sides = 6)\n",
+    "dice_df"
    ]
   },
   {
@@ -45,11 +144,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here"
+    "sorted_dice = dice_df.sort_values(by = \"Roll\", ascending = False)\n",
+    "\n",
+    "plt.bar(sorted_dice.index, sorted_dice[\"Roll\"])\n",
+    "\n",
+    "plt.xlabel(\"Roll Index\")\n",
+    "plt.ylabel(\"Roll Value\")\n",
+    "\n",
+    "plt.xticks(sorted_dice.index, sorted_dice.index + 1)\n",
+    "\n",
+    "plt.show()"
    ]
   },
   {
@@ -61,11 +180,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 94,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5    3\n",
+      "1    3\n",
+      "6    2\n",
+      "4    2\n",
+      "Name: Roll, dtype: int64\n"
+     ]
+    },
+    {
+     "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"
+    "freq_distribution = sorted_dice[\"Roll\"].value_counts()\n",
+    "print(freq_distribution)\n",
+    "\n",
+    "plt.bar(freq_distribution.index, freq_distribution.values)\n",
+    "plt.xlabel(\"Value\")\n",
+    "plt.ylabel(\"Frequency\")\n",
+    "plt.show()"
    ]
   },
   {
@@ -91,11 +238,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.8"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "def mean_dice(data):\n",
+    "    mean = data.sum()/len(data)\n",
+    "    return mean\n",
+    "\n",
+    "mean_dice(dice_df[\"Roll\"])"
    ]
   },
   {
@@ -107,11 +269,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 45,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Frequency Distribution:\n",
+      "1    3\n",
+      "5    3\n",
+      "4    2\n",
+      "6    2\n",
+      "Name: Roll, dtype: int64\n",
+      "The mean is 3.8\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "freq_distribution = dice_df[\"Roll\"].value_counts()\n",
+    "\n",
+    "sum_values = 0\n",
+    "total_count = 0\n",
+    "\n",
+    "for value, count in freq_distribution.items():\n",
+    "    sum_values += value * count\n",
+    "    total_count += count\n",
+    "\n",
+    "mean_value = sum_values / total_count\n",
+    "\n",
+    "print(\"Frequency Distribution:\")\n",
+    "print(freq_distribution)\n",
+    "print(f\"The mean is {mean_value}\")"
    ]
   },
   {
@@ -124,11 +313,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 43,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The median is 4.5\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "def median_dice(data):\n",
+    "    sorted_data = sorted(data)\n",
+    "    n = len(sorted_data)\n",
+    " \n",
+    "    if n % 2 == 1:\n",
+    "        median_index = n // 2\n",
+    "        median = sorted_data[median_index]\n",
+    "    else:\n",
+    "        upper_median_index = n // 2\n",
+    "        lower_median_index = upper_median_index - 1\n",
+    "        median = (sorted_data[lower_median_index] + sorted_data[upper_median_index]) / 2.0\n",
+    "    \n",
+    "    return median\n",
+    "\n",
+    "\n",
+    "median_dice = median_dice(dice_df[\"Roll\"])\n",
+    "\n",
+    "print(f\"The median is {median_dice}\")"
    ]
   },
   {
@@ -140,11 +354,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 41,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The first quartile is 1\n",
+      "The second quartile is 4.5\n",
+      "The third quartile is 5\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "def quartiles_dice(data):\n",
+    "    sorted_data = sorted(data)\n",
+    "    n = len(sorted_data)\n",
+    "    \n",
+    "    q1 = calculate_median(sorted_data[:n//2])\n",
+    "    q2 = calculate_median(sorted_data)\n",
+    "    q3 = calculate_median(sorted_data[n//2:]) if n % 2 == 0 else calculate_median(sorted_data[n//2 + 1:])\n",
+    "    \n",
+    "    return q1, q2, q3\n",
+    "\n",
+    "q1, q2, q3 = quartiles_dice(dice_df[\"Roll\"])\n",
+    "\n",
+    "print(f\"The first quartile is {q1}\")\n",
+    "print(f\"The second quartile is {q2}\")\n",
+    "print(f\"The third quartile is {q3}\")"
    ]
   },
   {
@@ -158,11 +396,46 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 53,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\carol\\AppData\\Local\\Temp\\ipykernel_15912\\3273671583.py:5: UserWarning: \n",
+      "\n",
+      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
+      "\n",
+      "Please adapt your code to use either `displot` (a figure-level function with\n",
+      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "\n",
+      "For a guide to updating your code to use the new functions, please see\n",
+      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
+      "\n",
+      "  sns.distplot(sorted_values)\n"
+     ]
+    },
+    {
+     "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"
+    "data = pd.read_csv(\"roll_the_dice_hundred.csv\")\n",
+    "\n",
+    "sorted_values = data.sort_values(by = \"value\", ascending = False)\n",
+    "\n",
+    "sns.distplot(sorted_values)\n",
+    "plt.show()"
    ]
   },
   {
@@ -185,11 +458,110 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 56,
    "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": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.74"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def mean_dice(data):\n",
+    "    mean = data.sum()/len(data)\n",
+    "    return mean\n",
+    "\n",
+    "mean_dice(data[\"value\"])"
    ]
   },
   {
@@ -201,11 +573,41 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 77,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "6    23\n",
+      "4    22\n",
+      "2    17\n",
+      "3    14\n",
+      "1    12\n",
+      "5    12\n",
+      "Name: value, dtype: int64\n"
+     ]
+    },
+    {
+     "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"
+    "freq_distribution = data[\"value\"].value_counts()\n",
+    "print(freq_distribution)\n",
+    "\n",
+    "plt.bar(freq_distribution.index, freq_distribution.values)\n",
+    "plt.xlabel(\"Value\")\n",
+    "plt.ylabel(\"Frequency\")\n",
+    "plt.show()"
    ]
   },
   {
@@ -217,11 +619,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 62,
    "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": 62,
+     "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(data[\"value\"])"
    ]
   },
   {
@@ -244,11 +669,43 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 79,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1    175\n",
+      "3    175\n",
+      "4    168\n",
+      "2    167\n",
+      "6    166\n",
+      "5    149\n",
+      "Name: value, dtype: int64\n"
+     ]
+    },
+    {
+     "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"
+    "another_data = pd.read_csv(\"roll_the_dice_thousand.csv\")\n",
+    "\n",
+    "freq_distribution = another_data[\"value\"].value_counts()\n",
+    "print(freq_distribution)\n",
+    "\n",
+    "plt.bar(freq_distribution.index, freq_distribution.values)\n",
+    "plt.xlabel(\"Value\")\n",
+    "plt.ylabel(\"Frequency\")\n",
+    "plt.show()"
    ]
   },
   {
@@ -258,7 +715,7 @@
    "outputs": [],
    "source": [
     "\"\"\"\n",
-    "your comments here\n",
+    "It changed because there are more values\n",
     "\"\"\""
    ]
   },
@@ -274,11 +731,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 81,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here"
+    "age = pd.read_csv(\"ages_population.csv\")\n",
+    "\n",
+    "freq_distribution = age[\"observation\"].value_counts()\n",
+    "\n",
+    "plt.bar(freq_distribution.index, freq_distribution.values)\n",
+    "plt.xlabel(\"Age\")\n",
+    "plt.ylabel(\"Frequency\")\n",
+    "plt.show()"
    ]
   },
   {
@@ -290,11 +765,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 83,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The mean is 36.56\n",
+      "The standard deviation is 12.816499625976762\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "mean = age[\"observation\"].mean()\n",
+    "print(f\"The mean is {mean}\")\n",
+    "\n",
+    "std_dev = age[\"observation\"].std()\n",
+    "print(f\"The standard deviation is {std_dev}\")"
    ]
   },
   {
@@ -317,11 +805,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 84,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here"
+    "age_pop = pd.read_csv(\"ages_population2.csv\")\n",
+    "\n",
+    "freq_distribution = age_pop[\"observation\"].value_counts()\n",
+    "\n",
+    "plt.bar(freq_distribution.index, freq_distribution.values)\n",
+    "plt.xlabel(\"Age\")\n",
+    "plt.ylabel(\"Frequency\")\n",
+    "plt.show()"
    ]
   },
   {
@@ -351,11 +857,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 86,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The mean is 27.155\n",
+      "The standard deviation is 2.969813932689186\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "mean = age_pop[\"observation\"].mean()\n",
+    "print(f\"The mean is {mean}\")\n",
+    "\n",
+    "std_dev = age_pop[\"observation\"].std()\n",
+    "print(f\"The standard deviation is {std_dev}\")"
    ]
   },
   {
@@ -381,11 +900,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 87,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here"
+    "age_pop_3 = pd.read_csv(\"ages_population3.csv\")\n",
+    "\n",
+    "freq_distribution = age_pop_3[\"observation\"].value_counts()\n",
+    "\n",
+    "plt.bar(freq_distribution.index, freq_distribution.values)\n",
+    "plt.xlabel(\"Age\")\n",
+    "plt.ylabel(\"Frequency\")\n",
+    "plt.show()"
    ]
   },
   {
@@ -397,11 +934,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 89,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The mean is 41.989\n",
+      "The standard deviation is 16.144705959865934\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "mean = age_pop_3[\"observation\"].mean()\n",
+    "print(f\"The mean is {mean}\")\n",
+    "\n",
+    "std_dev = age_pop_3[\"observation\"].std()\n",
+    "print(f\"The standard deviation is {std_dev}\")"
    ]
   },
   {
@@ -424,11 +974,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 90,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "the first quartile is 30.0\n",
+      "the second quartile is 40.0\n",
+      "the third quartile is 53.0\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "q1 = np.quantile(age_pop_3, 0.25)\n",
+    "print(f\"the first quartile is {q1}\")\n",
+    "\n",
+    "q2 = np.quantile(age_pop_3, 0.50)\n",
+    "print(f\"the second quartile is {q2}\")\n",
+    "\n",
+    "q3 = np.quantile(age_pop_3, 0.75)\n",
+    "print(f\"the third quartile is {q3}\")"
    ]
   },
   {
@@ -451,11 +1018,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 91,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "23.0"
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "#Interquartile Range\n",
+    "iqr = q3 - q1\n",
+    "iqr"
    ]
   },
   {
@@ -500,9 +1080,9 @@
  ],
  "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 +1094,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,