diff --git a/your-code/main.ipynb b/your-code/main.ipynb
index a0a5b66..1fea71f 100644
--- a/your-code/main.ipynb
+++ b/your-code/main.ipynb
@@ -11,11 +11,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Libraries"
+    "# Libraries\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import scipy.stats as stats\n",
+    "import random\n",
+    "import pandas as pd"
    ]
   },
   {
@@ -29,11 +36,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "\n",
+    "def dice_roll():\n",
+    "    # define list of possible outcomes\n",
+    "    dice = [1, 2, 3, 4, 5, 6]\n",
+    "    # randomly choose between possible outcomes, do that 10 time (k argument)\n",
+    "    # random.choices returns a list\n",
+    "    r = random.choices(dice, k=10)\n",
+    "    # save it in a dataframe\n",
+    "    r = pd.DataFrame(r)\n",
+    "    return r\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "r = dice_roll()"
    ]
   },
   {
@@ -45,11 +71,478 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>result</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   result\n",
+       "0       5\n",
+       "1       4\n",
+       "2       1\n",
+       "3       2\n",
+       "4       4\n",
+       "5       3\n",
+       "6       3\n",
+       "7       2\n",
+       "8       5\n",
+       "9       6"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# change the name of the column\n",
+    "# I don't want a column name to be the same as an index value\n",
+    "r.rename({0 : \"result\"}, axis=1, inplace=True)\n",
+    "r"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "# sort the values in 'result' column\n",
+    "r.sort_values(by='result', ascending=True, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>result</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   result\n",
+       "2       1\n",
+       "3       2\n",
+       "7       2\n",
+       "5       3\n",
+       "6       3\n",
+       "1       4\n",
+       "4       4\n",
+       "0       5\n",
+       "8       5\n",
+       "9       6"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "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>index</th>\n",
+       "      <th>result</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>8</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>9</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   index  result\n",
+       "0      2       1\n",
+       "1      3       2\n",
+       "2      7       2\n",
+       "3      5       3\n",
+       "4      6       3\n",
+       "5      1       4\n",
+       "6      4       4\n",
+       "7      0       5\n",
+       "8      8       5\n",
+       "9      9       6"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# I want the index to match the sorted values\n",
+    "r.reset_index(inplace=True)\n",
+    "r"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>result</th>\n",
+       "    </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>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   result\n",
+       "0       1\n",
+       "1       2\n",
+       "2       2\n",
+       "3       3\n",
+       "4       3\n",
+       "5       4\n",
+       "6       4\n",
+       "7       5\n",
+       "8       5\n",
+       "9       6"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# I want the old index to be dropped\n",
+    "r.drop('index', axis=1, inplace=True)\n",
+    "r"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot the results\n",
+    "\n",
+    "# xticks are not aligned with bars\n",
+    "\n",
+    "x = r['result']\n",
+    "\n",
+    "sns.distplot(x, kde=False, hist_kws={\"rwidth\":0.75}, bins=6)\n",
+    "plt.title(\"Histogram of simulated dice rolls\")\n",
+    "plt.xlim([0,7])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# xticks are not aligned with bars\n",
+    "\n",
+    "x = r['result']\n",
+    "\n",
+    "plt.hist(x, bins=6, width=0.7)\n",
+    "plt.title(\"Histogram of simulated dice rolls\")\n",
+    "plt.xlim([0,7])\n",
+    "plt.show()"
    ]
   },
   {
@@ -61,21 +554,50 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "\n",
+    "freq = r['result'].value_counts()\n",
+    "\n",
+    "plt.bar(freq.index, freq.values)\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nThe two plots show the same information: number of occurrences of each side of the dice. \\n'"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "\"\"\"\n",
-    "your comments here\n",
+    "The two plots show the same information: number of occurrences of each side of the dice. \n",
     "\"\"\""
    ]
   },
@@ -91,11 +613,56 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "\n",
+    "def mean_func(x):\n",
+    "    x = sum(x)/len(x)\n",
+    "    return x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.5"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mean_func(r['result'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.5"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# check the mean is correct\n",
+    "np.mean(r['result'])"
    ]
   },
   {
@@ -107,11 +674,66 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "# value_counts()\n",
+    "\n",
+    "def freq_func(x):\n",
+    "    result_dict = {}\n",
+    "    for result in x:\n",
+    "        if result not in result_dict.keys():\n",
+    "            # add result of dice roll as key and assign value 1\n",
+    "            result_dict[result] = 1\n",
+    "        else:\n",
+    "            # if a result of dice roll occurs once more, increment value in dict by 1\n",
+    "            result_dict[result] += 1            \n",
+    "    return result_dict\n",
+    "\n",
+    "result_dict = freq_func(r['result'])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1: 1, 2: 2, 3: 2, 4: 2, 5: 2, 6: 1}"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.6666666666666667"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "mean_func(result_dict.values())\n"
    ]
   },
   {
@@ -124,11 +746,63 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "\n",
+    "def median_func(lst):\n",
+    "    sortedLst = sorted(list(lst))\n",
+    "    lstLen = len(lst)\n",
+    "\n",
+    "    if (lstLen % 2) == 0:\n",
+    "        index1 = int(lstLen / 2)\n",
+    "        index2 = int((lstLen / 2) - 1)\n",
+    "        return (sortedLst[index1] + sortedLst[index2]) / 2, [index1, index2]\n",
+    "    else:\n",
+    "        index1 = int(lstLen / 2)\n",
+    "        return sortedLst[index1], [index1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3.5, [5, 4])"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "median_func(r['result'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.5"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.median(r['result'])"
    ]
   },
   {
@@ -140,11 +814,67 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(Q1, median, Q3): [13.0, 14, 21.5]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "\n",
+    "num_list = [28, 12, 8, 27, 16, 31, 14, 13, 19, 1, 1, 22, 13]\n",
+    "\n",
+    "med, median_indices = median_func(num_list)\n",
+    "\n",
+    "Q2, Q1_indices = median_func(num_list[:median_indices[0]])\n",
+    "Q1, Q2_indices = median_func(num_list[median_indices[-1] + 1:])\n",
+    "\n",
+    "quartiles = [Q1, med, Q2]\n",
+    "\n",
+    "print(\"(Q1, median, Q3): {}\".format(quartiles))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "num_list = [28, 12, 8, 27, 16, 31, 14, 13, 19, 1, 1, 22, 13]\n",
+    "\n",
+    "def quantiles_func(x):\n",
+    "    med, median_indices = median_func(x)\n",
+    "    Q2, Q1_indices = median_func(x[:median_indices[0]])\n",
+    "    Q1, Q2_indices = median_func(x[median_indices[-1] + 1:])\n",
+    "    quartiles = [Q1, med, Q2]\n",
+    "    return \"(Q1, median, Q3): {}\".format(quartiles)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'(Q1, median, Q3): [13.0, 14, 21.5]'"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "quartiles = quantiles_func(num_list)\n",
+    "quartiles"
    ]
   },
   {
@@ -158,11 +888,182 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "\n",
+    "roll_hundred = pd.read_csv('/Users/celinaagostinho/Desktop/lab-understanding-descriptive-stats/data/roll_the_dice_hundred.csv')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "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>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": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "roll_hundred"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "roll_hundred.drop('Unnamed: 0', axis=1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "roll_hundred = roll_hundred.sort_values(by='value')\n",
+    "roll_hundred_hist = roll_hundred.groupby('value')['value'].count()\n",
+    "\n",
+    "x = roll_hundred_hist.index\n",
+    "y = roll_hundred_hist.values\n",
+    "\n",
+    "plt.bar(x,y)\n",
+    "\n",
+    "plt.show()\n"
    ]
   },
   {
@@ -172,7 +1073,7 @@
    "outputs": [],
    "source": [
     "\"\"\"\n",
-    "your comments here\n",
+    "the most frequent values are 4 and 6\n",
     "\"\"\""
    ]
   },
@@ -185,11 +1086,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 57,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.74"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "\n",
+    "mean_func(roll_hundred['value'])"
    ]
   },
   {
@@ -201,11 +1115,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 76,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1: 12, 2: 17, 6: 23, 5: 12, 4: 22, 3: 14}"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "frequencies = freq_func(roll_hundred['value'])\n",
+    "frequencies"
    ]
   },
   {
@@ -217,11 +1143,62 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 86,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "# histogram of what?\n",
+    "\n",
+    "x = list(frequencies.keys())\n",
+    "y = list(frequencies.values())\n",
+    "\n",
+    "sns.barplot(x, y)\n",
+    "plt.title(\"Bar plot of dice rolls\")\n",
+    "#plt.xlim([0,7])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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z0LVMVZK7ktyU5MbJnj63rkiyWZKLktza/R/Yb9A1TVaSXbv3fPXPiiQnTbjNXI+hd1ME/A9wML1THX8IHF1VP5nTjmdRkgOAVcCXqmqPQdczFUm2AraqquuTbAJcBxwxLO9/kgAbVdWqJBsCVwHvqqprBlzalCR5D7AQeF5VHTboeqYiyV3AwqoaugtzkpwDfL+qPt+djffcqnpk0HVNVZej99G7ePPu8dqtjT30oZ8ioKquBB4edB3TUVVLq+r67v5K4BZgm8FWNXnVs6pb3LD7Gaoj+Um2Bf4Q+Pyga1mfJHkecABwFkBVPTqMYd45CLhjojCHtRPo2wD3jlpewhAFSkuSLAD2Aq4dbCVT0w1X3AgsAy6vqqGqH/gk8D7gyUEXMk0FXJbkum4qj2GxI7Ac+EI33PX5JBsNuqhpOgo4v1+jtRHok5oiQHMrycbAxcBJVbVi0PVMRVU9UVV70rsaeZ8kQzPsleQwYFlVXTfoWmZg/6p6Kb2ZVd/eDUEOg3nAS4HPVtVewP8Cw3gM75nAa4B/7dd2bQS6UwQMWDf2fDFwblV9ddD1TFf3dfl7wCEDLmUq9gde041DXwD8QZIvD7akqamq+7vbZcAl9IZRh8ESYMmob3QX0Qv4YXMocH1VPdiv4doIdKcIGKDuoOJZwC1Vdfqg65mqJCNJNuvuPwd4BXDrYKuavKp6f1VtW1UL6P3b/8+qesOAy5q0JBt1B9PphiteyZDMmFpVDwD3Jtm1W3UQMBQnA4xxNJMYboGZzbY4KdOcImCdkuR84EBgfpIlwKlVddZgq5q0/YE3Ajd149AAp1TVtwZY01RsBZzTHeV/BnBhVQ3dqX9DbEvgkt5+AfOA86rq24MtaUreAZzb7UzeCbxpwPVMSZLn0jtD8K2Tau+l/5LUBq8UlaRGGOiS1AgDXZIaYaBLUiMMdElqhIGu9V6SVf1bSes+A12SGmGgqzlJPprkbaOWT0tyapIrklzfze39tBk/kxw4er7yJGckOb67v3eS/+omqPpONy2xtE4x0NWiC4A/HrX8euALwGu7SaZeDny8mxahr24unH8EjqyqvYGzgQ/PbsnSzM35pf/S2lZVNyTZIsnWwAjwC2Ap8IlupsAn6U3hvCXwwCSecldgD+Dy7jNgg+75pHWKga5WXQQcCbyQ3h77sfTCfe+qeqyb/fDZY7Z5nKd+a139eIDFVTU0f75M6yeHXNSqC+jNbngkvXDflN685I8leTmwwxq2uRvYLcmzkmxKb3Y+gJ8CI6v/HmWSDZPsPuevQJoi99DVpKpa3E37el9VLU1yLvCN7o8c38gapuCtqnuTXAj8GLgNuKFb/2iSI4F/6IJ+Hr2/QjRUs4aqfc62KEmNcMhFkhphoEtSIwx0SWqEgS5JjTDQJakRBrokNcJAl6RG/B/G2P2CY7d76gAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# xticks are not aligned with bars\n",
+    "\n",
+    "x = roll_hundred['value']\n",
+    "\n",
+    "sns.distplot(x, kde=False, hist_kws={\"rwidth\":0.75}, bins=6)\n",
+    "plt.title(\"Histogram of dice rolls\")\n",
+    "plt.xlim([0,7])\n",
+    "plt.show()"
    ]
   },
   {
@@ -231,7 +1208,7 @@
    "outputs": [],
    "source": [
     "\"\"\"\n",
-    "your comments here\n",
+    "Mean is close to 4, most frequent values are 4 and 6\n",
     "\"\"\""
    ]
   },
@@ -244,11 +1221,116 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 98,
    "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",
+       "      <th>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   roll  value\n",
+       "0     0      5\n",
+       "1     1      6\n",
+       "2     2      1\n",
+       "3     3      6\n",
+       "4     4      5"
+      ]
+     },
+     "execution_count": 98,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "\n",
+    "roll_thousand = pd.read_csv('/Users/celinaagostinho/Desktop/lab-understanding-descriptive-stats/data/roll_the_dice_thousand.csv')\n",
+    "\n",
+    "roll_thousand.drop('Unnamed: 0', axis=1, inplace=True)\n",
+    "\n",
+    "roll_thousand.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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7Gl1Vva2qzqiqWZa+puNfqurXp9xWL0lO6j68pxu+eDFwwt6BVlX/BXw1yTld6SJgzd+QMKlvhTwhJdkFXAhsSLIAXFlV10y3q97OBy4FvtCNUwO8vapunWJPfWwErut+EOZJwE1V1cTtgw05Dbh56bqC9cANVfWx6bbU2+8A13d3yjwEvHbK/SzLWyElqUEOy0hSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1KD/A0Lg07au0yK+AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "roll_thousand = roll_thousand.sort_values(by='value')\n",
+    "roll_thousand_hist = roll_thousand.groupby('value')['value'].count()\n",
+    "\n",
+    "x = roll_thousand_hist.index\n",
+    "y = roll_thousand_hist.values\n",
+    "\n",
+    "plt.bar(x,y)\n",
+    "\n",
+    "plt.show()"
    ]
   },
   {
@@ -258,7 +1340,11 @@
    "outputs": [],
    "source": [
     "\"\"\"\n",
-    "your comments here\n",
+    "\n",
+    "frequencies are balanced between the different possible values\n",
+    "\n",
+    "Roughly same frequency for each value with increase in dice rolls (larger sample of dice rolls)\n",
+    "\n",
     "\"\"\""
    ]
   },
@@ -274,11 +1360,232 @@
   },
   {
    "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>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": 102,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "\n",
+    "ages = pd.read_csv('/Users/celinaagostinho/Desktop/lab-understanding-descriptive-stats/data/ages_population.csv')\n",
+    "ages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{68.0: 3,\n",
+       " 12.0: 11,\n",
+       " 45.0: 29,\n",
+       " 38.0: 30,\n",
+       " 49.0: 19,\n",
+       " 27.0: 25,\n",
+       " 39.0: 45,\n",
+       " 42.0: 32,\n",
+       " 33.0: 22,\n",
+       " 30.0: 34,\n",
+       " 25.0: 19,\n",
+       " 44.0: 23,\n",
+       " 53.0: 12,\n",
+       " 46.0: 23,\n",
+       " 50.0: 16,\n",
+       " 22.0: 16,\n",
+       " 6.0: 2,\n",
+       " 29.0: 26,\n",
+       " 35.0: 33,\n",
+       " 28.0: 20,\n",
+       " 26.0: 23,\n",
+       " 60.0: 4,\n",
+       " 41.0: 36,\n",
+       " 52.0: 14,\n",
+       " 32.0: 30,\n",
+       " 23.0: 17,\n",
+       " 15.0: 8,\n",
+       " 40.0: 27,\n",
+       " 63.0: 7,\n",
+       " 31.0: 24,\n",
+       " 34.0: 29,\n",
+       " 61.0: 2,\n",
+       " 64.0: 2,\n",
+       " 37.0: 30,\n",
+       " 56.0: 15,\n",
+       " 14.0: 10,\n",
+       " 13.0: 6,\n",
+       " 51.0: 9,\n",
+       " 36.0: 31,\n",
+       " 18.0: 7,\n",
+       " 48.0: 19,\n",
+       " 58.0: 7,\n",
+       " 20.0: 13,\n",
+       " 54.0: 13,\n",
+       " 19.0: 11,\n",
+       " 62.0: 4,\n",
+       " 55.0: 13,\n",
+       " 21.0: 14,\n",
+       " 43.0: 32,\n",
+       " 17.0: 10,\n",
+       " 7.0: 1,\n",
+       " 47.0: 17,\n",
+       " 1.0: 2,\n",
+       " 16.0: 8,\n",
+       " 24.0: 18,\n",
+       " 59.0: 8,\n",
+       " 57.0: 7,\n",
+       " 8.0: 5,\n",
+       " 67.0: 4,\n",
+       " 2.0: 2,\n",
+       " 66.0: 3,\n",
+       " 4.0: 1,\n",
+       " 73.0: 1,\n",
+       " 82.0: 1,\n",
+       " 70.0: 1,\n",
+       " 5.0: 2,\n",
+       " 71.0: 1,\n",
+       " 9.0: 2,\n",
+       " 69.0: 1,\n",
+       " 11.0: 3,\n",
+       " 10.0: 3,\n",
+       " 65.0: 2}"
+      ]
+     },
+     "execution_count": 103,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "frequencies = freq_func(ages['observation'])\n",
+    "frequencies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ages_plot = ages.sort_values(by='observation')\n",
+    "ages_plot = ages_plot.groupby('observation')['observation'].count()\n",
+    "\n",
+    "x = ages_plot.index\n",
+    "y = ages_plot.values\n",
+    "\n",
+    "plt.bar(x,y)\n",
+    "\n",
+    "plt.show()"
    ]
   },
   {
@@ -290,11 +1597,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 105,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The mean is 36.56\n",
+      "The standard deviation is 12.81008977329979\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "\n",
+    "print('The mean is', np.mean(ages['observation']))\n",
+    "print('The standard deviation is', np.std(ages['observation']))"
    ]
   },
   {
@@ -304,7 +1623,7 @@
    "outputs": [],
    "source": [
     "\"\"\"\n",
-    "your comments here\n",
+    "They are consistent with the ranges shown in the plot\n",
     "\"\"\""
    ]
   },
@@ -317,11 +1636,142 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 106,
    "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",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>995</th>\n",
+       "      <td>26.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>996</th>\n",
+       "      <td>22.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>997</th>\n",
+       "      <td>21.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>998</th>\n",
+       "      <td>19.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999</th>\n",
+       "      <td>28.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1000 rows × 1 columns</p>\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\n",
+       "..           ...\n",
+       "995         26.0\n",
+       "996         22.0\n",
+       "997         21.0\n",
+       "998         19.0\n",
+       "999         28.0\n",
+       "\n",
+       "[1000 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 106,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "\n",
+    "ages2 = pd.read_csv('/Users/celinaagostinho/Desktop/lab-understanding-descriptive-stats/data/ages_population2.csv')\n",
+    "ages2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "frequencies = freq_func(ages2['observation'])\n",
+    "\n",
+    "ages2_plot = ages2.sort_values(by='observation')\n",
+    "ages2_plot = ages2_plot.groupby('observation')['observation'].count()\n",
+    "\n",
+    "x = ages2_plot.index\n",
+    "y = ages2_plot.values\n",
+    "\n",
+    "plt.bar(x,y)\n",
+    "\n",
+    "plt.show()"
    ]
   },
   {
@@ -338,7 +1788,10 @@
    "outputs": [],
    "source": [
     "\"\"\"\n",
-    "your comments here\n",
+    "The ages are in intervals\n",
+    "The mean is around 28, rather than 37.\n",
+    "The standard deviation is not wide.\n",
+    "\n",
     "\"\"\""
    ]
   },
@@ -351,11 +1804,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 109,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The mean is 27.155\n",
+      "The standard deviation is 2.9683286543103704\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "\n",
+    "print('The mean is', np.mean(ages2['observation']))\n",
+    "print('The standard deviation is', np.std(ages2['observation']))"
    ]
   },
   {
@@ -365,7 +1830,7 @@
    "outputs": [],
    "source": [
     "\"\"\"\n",
-    "your comments here\n",
+    "The mean and standard deviation are consistent with what was observed before.\n",
     "\"\"\""
    ]
   },
@@ -381,11 +1846,142 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 110,
    "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",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>995</th>\n",
+       "      <td>16.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>996</th>\n",
+       "      <td>55.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>997</th>\n",
+       "      <td>30.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>998</th>\n",
+       "      <td>35.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999</th>\n",
+       "      <td>43.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1000 rows × 1 columns</p>\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\n",
+       "..           ...\n",
+       "995         16.0\n",
+       "996         55.0\n",
+       "997         30.0\n",
+       "998         35.0\n",
+       "999         43.0\n",
+       "\n",
+       "[1000 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 110,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "\n",
+    "ages3 = pd.read_csv('/Users/celinaagostinho/Desktop/lab-understanding-descriptive-stats/data/ages_population3.csv')\n",
+    "ages3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXIAAAD4CAYAAADxeG0DAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAQFklEQVR4nO3df6xkdX3G8fdTxF9oC5Qr2QK3i4YgxpTF3q5YGqP4oys2golNJI3uHzRrE0ihIW3BJq0kbYKJSvtHY7oWhDQWtYiFoFUJYoxNA11wwV1Xij+2imzZtULRNqGCn/4x5+rt9d6duXNn7pzv7vuVTGbOmTN3njtz5tmz53zPnVQVkqR2/dysA0iS1scil6TGWeSS1DiLXJIaZ5FLUuOetZFPdtJJJ9XmzZs38iklqXn33Xff96pqbrX7N7TIN2/ezK5duzbyKSWpeUn+/XD3u2tFkhpnkUtS4yxySWqcRS5JjbPIJalxFrkkNc4il6TGWeSS1DiLXJIat6Fndkqj2HzVp35ye/+1b55hEqkNbpFLUuMscklqnEUuSY2zyCWpcRa5JDXOIpekxlnkktS4oUWe5LlJ7k3yQJK9Sa7p5t+Y5FtJdneXLdOPK0labpQTgp4Czq+qHyY5FvhSkn/q7vvDqrplevEkScMMLfKqKuCH3eSx3aWmGUqSNLqR9pEnOSbJbuAgcGdV3dPd9RdJHkxyXZLnrPLYHUl2Jdl16NChCcWWJC0aqcir6pmq2gKcCmxN8nLgauClwK8BJwJ/vMpjd1bVQlUtzM3NTSi2JGnRmkatVNUTwBeAbVV1oAaeAj4MbJ1CPknSEKOMWplLcnx3+3nA64GvJdnUzQtwEbBnmkElSSsbZdTKJuCmJMcwKP6PV9UdST6fZA4IsBv4vSnmlCStYpRRKw8C56ww//ypJJIkrYlndkpS4yxySWqcRS5JjbPIJalxFrkkNc4il6TGWeSS1DiLXJIaZ5FLUuMscklqnEUuSY2zyCWpcRa5JDXOIpekxlnkktQ4i1ySGjfKNwRJvbb5qk/95Pb+a9+85vul1rlFLkmNs8glqXFDizzJc5Pcm+SBJHuTXNPNPz3JPUkeTvKxJM+eflxJ0nKjbJE/BZxfVWcDW4BtSc4F3gtcV1VnAI8Dl0wvpiRpNUOLvAZ+2E0e210KOB+4pZt/E3DRVBJKkg5rpH3kSY5Jshs4CNwJfAN4oqqe7hZ5BDhllcfuSLIrya5Dhw5NIrMkaYmRiryqnqmqLcCpwFbgrJUWW+WxO6tqoaoW5ubmxk8qSVrRmkatVNUTwBeAc4HjkyyOQz8VeHSy0SRJoxhl1MpckuO7288DXg/sA+4G3tYtth24bVohJUmrG+XMzk3ATUmOYVD8H6+qO5J8Ffhokj8HvgxcP8WckqRVDC3yqnoQOGeF+d9ksL9capqn8Kt1ntkpSY2zyCWpcRa5JDXOIpekxlnkktQ4i1ySGmeRS1LjLHJJapxFLkmNs8glqXEWuSQ1ziKXpMZZ5JLUOItckhpnkUtS4yxySWrcKN8QJDXFL4rQ0cYtcklqnEUuSY0bWuRJTktyd5J9SfYmubyb/54k302yu7tcMP24kqTlRtlH/jRwZVXdn+SFwH1J7uzuu66q3je9eJKkYYYWeVUdAA50t3+QZB9wyrSDSZJGs6ZRK0k2A+cA9wDnAZcleSewi8FW++MrPGYHsANgfn5+nXHVor6NIllrno3Mv/S5NuL5dGQY+WBnkhcAnwCuqKongQ8CLwG2MNhif/9Kj6uqnVW1UFULc3NzE4gsSVpqpCJPciyDEv9IVd0KUFWPVdUzVfVj4EPA1unFlCStZpRRKwGuB/ZV1QeWzN+0ZLG3AnsmH0+SNMwo+8jPA94BfCXJ7m7eu4GLk2wBCtgPvGsqCSVJhzXKqJUvAVnhrk9PPo6OBn07+DltR9vvq43nmZ2S1DiLXJIaZ5FLUuMscklqnEUuSY2zyCWpcRa5JDXOIpekxlnkktQ4i1ySGmeRS1Lj1vTFEpJ+Vp+/qOJI52s54Ba5JDXOIpekxlnkktQ4i1ySGufBTs1c3w5Y9S3PUkuzQf/yaTbcIpekxlnkktS4oUWe5LQkdyfZl2Rvksu7+ScmuTPJw931CdOPK0labpQt8qeBK6vqLOBc4NIkLwOuAu6qqjOAu7ppSdIGG1rkVXWgqu7vbv8A2AecAlwI3NQtdhNw0bRCSpJWt6ZRK0k2A+cA9wAnV9UBGJR9khet8pgdwA6A+fn59WSVgH6PKpFmYeSDnUleAHwCuKKqnhz1cVW1s6oWqmphbm5unIySpMMYqciTHMugxD9SVbd2sx9Lsqm7fxNwcDoRJUmHM8qolQDXA/uq6gNL7rod2N7d3g7cNvl4kqRhRtlHfh7wDuArSXZ3894NXAt8PMklwLeB355OREnS4Qwt8qr6EpBV7n7dZOOoD/z72uqr9a5rR+q66pmdktQ4i1ySGmeRS1LjLHJJapxFLkmN84slNHFH6siAWVj+RRLSStwil6TGWeSS1DiLXJIaZ5FLUuMscklqnKNWNNSsR6HM+vnXapZ5W3utJm3573+0vB5ukUtS4yxySWqcRS5JjbPIJalxFrkkNc4il6TGWeSS1LihRZ7khiQHk+xZMu89Sb6bZHd3uWC6MSVJqxlli/xGYNsK86+rqi3d5dOTjSVJGtXQIq+qLwLf34AskqQxrOcU/cuSvBPYBVxZVY+vtFCSHcAOgPn5+XU8nfriaDnteSP4xRGH57o2mnEPdn4QeAmwBTgAvH+1BatqZ1UtVNXC3NzcmE8nSVrNWEVeVY9V1TNV9WPgQ8DWycaSJI1qrCJPsmnJ5FuBPastK0marqH7yJPcDLwGOCnJI8CfAa9JsgUoYD/wrilmlCQdxtAir6qLV5h9/RSyaEqWH1DzoNH69OkA3LAsfcqq6fHMTklqnEUuSY2zyCWpcRa5JDXOIpekxq3nFH1JjXNUy5HBLXJJapxFLkmNs8glqXEWuSQ1ziKXpMY5auUI4egDtWj5eut6PB63yCWpcRa5JDXOIpekxlnkktQ4D3YegYZ9M7vf3C4NHCkHV90il6TGWeSS1LihRZ7khiQHk+xZMu/EJHcmebi7PmG6MSVJqxlli/xGYNuyeVcBd1XVGcBd3bQkaQaGFnlVfRH4/rLZFwI3dbdvAi6acC5J0ojGHbVyclUdAKiqA0letNqCSXYAOwDm5+fHfDodKUfX1W+uZ22a+sHOqtpZVQtVtTA3Nzftp5Oko864Rf5Ykk0A3fXByUWSJK3FuEV+O7C9u70duG0ycSRJazXK8MObgX8BzkzySJJLgGuBNyR5GHhDNy1JmoGhBzur6uJV7nrdhLNoiWEHnTwopXG43hyZPLNTkhpnkUtS4yxySWqcRS5JjbPIJalxfrFETziaQH10tI2eavX3cYtckhpnkUtS4yxySWqcRS5JjbPIJalxFrkkNc4il6TGWeSS1DiLXJIaZ5FLUuM8RV/SyNZ6yn6rp7wvWpp/uT79Pm6RS1LjLHJJaty6dq0k2Q/8AHgGeLqqFiYRSpI0uknsI39tVX1vAj9HkjQGd61IUuPWu0VewOeSFPA3VbVz+QJJdgA7AObn59f5dEeO1o/mS+qP9W6Rn1dVrwDeBFya5NXLF6iqnVW1UFULc3Nz63w6SdJy6yryqnq0uz4IfBLYOolQkqTRjV3kSY5L8sLF28AbgT2TCiZJGs169pGfDHwyyeLP+fuq+sxEUkmSRjZ2kVfVN4GzJ5jliOLBTEkbxeGHktQ4i1ySGmeRS1LjLHJJapxFLkmN84slJG0YR3NNh1vkktQ4i1ySGmeRS1LjLHJJapxFLkmNa3bUyrCj39M+Or785886j6SNtfQzDT/7uR52/yS5RS5JjbPIJalxFrkkNc4il6TGNXOwc70HC9d6MHKtBzMlHd2WH9zcSG6RS1LjLHJJaty6ijzJtiQPJfl6kqsmFUqSNLqxizzJMcBfA28CXgZcnORlkwomSRrNerbItwJfr6pvVtX/Ah8FLpxMLEnSqFJV4z0weRuwrap+t5t+B/DKqrps2XI7gB3d5JnAQ2t8qpOA740Vcvr6nA3Mtx59zgbmW48+Z4OV8/1yVc2t9oD1DD/MCvN+5l+FqtoJ7Bz7SZJdVbUw7uOnqc/ZwHzr0edsYL716HM2GC/fenatPAKctmT6VODRdfw8SdIY1lPk/wqckeT0JM8G3g7cPplYkqRRjb1rpaqeTnIZ8FngGOCGqto7sWQ/NfZumQ3Q52xgvvXoczYw33r0ORuMkW/sg52SpH7wzE5JapxFLkmN622R9+30/yQ3JDmYZM+SeScmuTPJw931CTPKdlqSu5PsS7I3yeU9y/fcJPcmeaDLd003//Qk93T5PtYdNJ+ZJMck+XKSO/qUL8n+JF9JsjvJrm5eL97bLsvxSW5J8rVuHXxVX/IlObN73RYvTya5okf5/qD7TOxJcnP3WVnzetfLIu/p6f83AtuWzbsKuKuqzgDu6qZn4Wngyqo6CzgXuLR7vfqS7yng/Ko6G9gCbEtyLvBe4Lou3+PAJTPKt+hyYN+S6T7le21VbVkyvrgv7y3AXwGfqaqXAmczeA17ka+qHupety3ArwL/A3yyD/mSnAL8PrBQVS9nMGjk7Yyz3lVV7y7Aq4DPLpm+Gri6B7k2A3uWTD8EbOpubwIemnXGLsttwBv6mA94PnA/8EoGZ689a6X3fAa5TmXwgT4fuIPBCW+9yAfsB05aNq8X7y3w88C36AZO9C3fskxvBP65L/mAU4DvACcyGEF4B/Cb46x3vdwi56e/4KJHunl9c3JVHQDorl804zwk2QycA9xDj/J1uy12AweBO4FvAE9U1dPdIrN+j/8S+CPgx930L9KffAV8Lsl93Z+8gP68ty8GDgEf7nZL/W2S43qUb6m3Azd3t2eer6q+C7wP+DZwAPgv4D7GWO/6WuQjnf6v/y/JC4BPAFdU1ZOzzrNUVT1Tg//ensrgD66dtdJiG5tqIMlvAQer6r6ls1dYdFbr4HlV9QoGuxovTfLqGeVYybOAVwAfrKpzgP9mtrt5VtTtZ34L8A+zzrKo2y9/IXA68EvAcQze4+WGrnd9LfJWTv9/LMkmgO764KyCJDmWQYl/pKpu7Vu+RVX1BPAFBvvyj0+yeFLaLN/j84C3JNnP4K94ns9gC70X+arq0e76IIP9u1vpz3v7CPBIVd3TTd/CoNj7km/Rm4D7q+qxbroP+V4PfKuqDlXVj4BbgV9njPWur0Xeyun/twPbu9vbGeyb3nBJAlwP7KuqDyy5qy/55pIc391+HoMVeB9wN/C2Weerqqur6tSq2sxgXft8Vf1OH/IlOS7JCxdvM9jPu4eevLdV9R/Ad5Kc2c16HfBVepJviYv56W4V6Ee+bwPnJnl+9xlefO3Wvt7N+gDEYQ4EXAD8G4N9qX/Sgzw3M9iP9SMGWyGXMNiPehfwcHd94oyy/QaD/349COzuLhf0KN+vAF/u8u0B/rSb/2LgXuDrDP7L+5wevM+vAe7oS74uwwPdZe/iZ6Ev722XZQuwq3t//xE4oWf5ng/8J/ALS+b1Ih9wDfC17nPxd8BzxlnvPEVfkhrX110rkqQRWeSS1DiLXJIaZ5FLUuMscklqnEUuSY2zyCWpcf8HNnP04K/5faEAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "frequencies = freq_func(ages3['observation'])\n",
+    "\n",
+    "ages3_plot = ages3.sort_values(by='observation')\n",
+    "ages3_plot = ages3_plot.groupby('observation')['observation'].count()\n",
+    "\n",
+    "x = ages3_plot.index\n",
+    "y = ages3_plot.values\n",
+    "\n",
+    "plt.bar(x,y)\n",
+    "\n",
+    "plt.show()"
    ]
   },
   {
@@ -397,11 +1993,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 113,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The mean is 41.989\n",
+      "The standard deviation is 16.136631587788084\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "\n",
+    "print('The mean is', np.mean(ages3['observation']))\n",
+    "print('The standard deviation is', np.std(ages3['observation']))"
    ]
   },
   {
@@ -411,7 +2019,7 @@
    "outputs": [],
    "source": [
     "\"\"\"\n",
-    "your comments here\n",
+    "There's a wider range of values, and a bimodal distribution, which inflates the mean.\n",
     "\"\"\""
    ]
   },
@@ -424,11 +2032,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 120,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The lower quartile boundary is 30.0\n",
+      "The median is 40.0\n",
+      "The upper quartile boundary is 53.0\n",
+      "The mean is 41.989\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "\n",
+    "#ages3.quantile()\n",
+    "\n",
+    "q1 = ages3['observation'].quantile(q=0.25)\n",
+    "median = ages3['observation'].quantile(q=0.5)\n",
+    "q3 = ages3['observation'].quantile(q=0.75)\n",
+    "mean = ages3['observation'].mean()\n",
+    "\n",
+    "print('The lower quartile boundary is', q1)\n",
+    "print('The median is', median)\n",
+    "print('The upper quartile boundary is', q3)\n",
+    "print('The mean is', mean)"
    ]
   },
   {
@@ -438,7 +2069,7 @@
    "outputs": [],
    "source": [
     "\"\"\"\n",
-    "your comments here\n",
+    "There's a difference of less than one years between the mean and the median.\n",
     "\"\"\""
    ]
   },
@@ -451,11 +2082,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 123,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Percentile 70 is 50.0\n",
+      "Percentile 80 is 57.0\n",
+      "Percentile 90 is 67.0\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "\n",
+    "q7 = ages3['observation'].quantile(q=0.7)\n",
+    "q8 = ages3['observation'].quantile(q=0.8)\n",
+    "q9 = ages3['observation'].quantile(q=0.9)\n",
+    "\n",
+    "print('Percentile 70 is', q7)\n",
+    "print('Percentile 80 is', q8)\n",
+    "print('Percentile 90 is', q9)"
    ]
   },
   {
@@ -465,7 +2114,7 @@
    "outputs": [],
    "source": [
     "\"\"\"\n",
-    "your comments here\n",
+    "10% of the sample is between 57 and 67 years old.\n",
     "\"\"\""
    ]
   },
@@ -500,9 +2149,9 @@
  ],
  "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 +2163,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.6"
   }
  },
  "nbformat": 4,