From 1246ac2f05fd321f39a1f652434390f25cf29433 Mon Sep 17 00:00:00 2001
From: pgakafonso <pgak.afonso@gmail.com>
Date: Wed, 3 Nov 2021 19:49:32 +0000
Subject: [PATCH] [DAPT21_Pedro Afonso] add exercises but some are incomplete

---
 your-code/main.ipynb | 956 ++++++++++++++++++++++++++++++++++++++++---
 1 file changed, 897 insertions(+), 59 deletions(-)

diff --git a/your-code/main.ipynb b/your-code/main.ipynb
index a0a5b66..b2bb7f2 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",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from scipy import stats\n",
+    "import scipy.stats as sp\n",
+    "import math"
    ]
   },
   {
@@ -29,11 +36,115 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[5, 2, 5, 6, 5, 4, 3, 2, 3, 5]\n"
+     ]
+    },
+    {
+     "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>0</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>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   0\n",
+       "0  5\n",
+       "1  1\n",
+       "2  1\n",
+       "3  1\n",
+       "4  5\n",
+       "5  3\n",
+       "6  2\n",
+       "7  3\n",
+       "8  5\n",
+       "9  4"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "def rolling_dice(n):\n",
+    "\n",
+    "    dice = [1,2,3,4,5,6]\n",
+    "    roll = np.random.choice(dice,n)\n",
+    "    return (list(roll))\n",
+    "    \n",
+    "print(rolling_dice(10))\n",
+    "rolling_dice_df = pd.DataFrame(rolling_dice(10))\n",
+    "rolling_dice_df.head(10)"
    ]
   },
   {
@@ -45,11 +156,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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DzEVyNTZ3oDjPgsuWFSsdSkJix9Y9mOKxdXEndCIyEtH7RLR9iseIiB4iojYiaiaiS5IbZvyiWwBIHV1/jvcOYzwYgrMsNQn98uXFKMqznJ0gL9RjeDyAVw53Y3ONAyaj9s5Dr77Ijk+tW4RHXz+KPSf6UvY8ibwydwI4PM1jmwFUR25bAfxinnHNWbQ/Wero+jPfoRazMRsN2LTGgZcOeVQ1VkwALx3uxshEEA212im3TPat+pWRsXVNKfv5iiuhE9FCAFsA/HKaQ24E8BiH7QJQSESK9BWVWC0oyrPIGboOtXT5QASsmMNg6Hg11FZgZCKIV450p+w5ROK2N3XAnp+FS6uKlA5lzmzZZtx/Sx2O9w7j/ufdKXmOeM/QHwRwD4DpmikrAZyM+fhU5HPnIaKtRLSbiHZ7vampUxIRqsusktB1qLXbh6rivJQuKFm/tAiltizpdlGRwdEJvNbixZaaChgMcxtoohaXLS/G925ag89vrErJ9581oRNRPYBuZt4z02FTfO6CNhNm3sbM65h5XWlp6vpIXQ4b3B6/dLroTHioRerOzoHwXhxbasrxaosXvlHZQkINXjjowXgwlPZB0KnymQ1LsKgoNyXfO54z9I0AbiCi4wB+D+BqIvrNpGNOAVgU8/FCAIqd4jjtNvjHAugYGFUqBJFkoxNBHO8dTuoeLtNpqKvAeCCEFw95Uv5cYnbbmztQWZiDixcVKh2K6s2a0Jn5H5l5ITNXAbgNwCvM/JlJhz0D4HORbpcNAAaYWbFWgbOdLnJhVDfavUMIhjgtCf2SxYWoLMyRsosKnBkax1utPWioq5jz/NhMMuf+HyK6nYhuj3y4A0A7gDYA/w7gi0mIbc6ibW2yN7p+pLrDJRYRob62HG+29qB/WNYzKOm5g10IhFj1e7eoRUIJnZlfY+b6yP1HmPmRyH1m5juYeTkz1zDz7lQEG6+CXDPs+VlyYVRH3B4fzEZCVXFeWp6vvrYCgRArPvQ30zU2dWBZSR5WV+QrHYomaK9DP04y7EJf3B4flpVY07aHx5rKfFQV58qWugrq9o1iV3sv6mvLpdwSJ90mdJfdhlaPX6bQ6ESLx4fqFHe4xCIiNNRV4C9He+H1KTfFPZPt3N+FEENTe7coTbcJ3emwYSwQwgd9w0qHIuZpaCyAk30jSR1qEY/62gqEGNh5QLYCUEJjUwcuctiSuve93uk2obtkCwDdaO0OT3xJ1aZc03E5bHDardjeJAk93Tr6R7D7xBm5GJog3Sb06PLwVqmja97ZDhcFztTqayvw7vE+dA6kf4J7Jns2skFavYb3blGCbhN6XpYJi4pypHVRB9xdPmSbDSlbXTeT6Bnis7IDY1o1NnegprIAVSXp6WrSC90mdCB8RiedLtrX4vFhRZkVRgX28VhWasWaynw0SkJPmxO9Q2g+NYAGnSz1TyddJ3Sn3YZ27xDGAzL4V8vcHl9aVohOp762Ak0n+/FBr1xgT4fofvRbpNySMF0ndJfDhkCIcaxnSOlQxBwNDE/AMzimSP08aktN+Exx+37pSU+HxqYOfGjJAlQW5igdiuboOqFXl8n0Iq1zd4f/79Ld4RJrUVEuLl5ciEbpdkm5Vo8PR7p8aJDuljnRdUJfVpoHo4EkoWtYtO1UyTN0IDz44nDnINoiLZQiNRqbO0EEXF8jCX0udJ3Qs81GVBXnSi+6hrk9PtiyTCgvyFY0ji215SAKb+UqUoOZsb25AxuWFqMsX9n/b63SdUIHosMuJKFrVUtXeMm/0nt52POzsb6qCI1NHTI4JUUOdQ6i3Tukm0EWStB9QnfabTjRN4zRiaDSoYgEMTPcHl9atsyNR0NdBY56h3BE3vGlxPbmThgNhM1rJKHPVUYkdGZI7VODevzjODM8oWjLYqzNaxwwGkgGX6QAM6OxqQMfXlGCojyL0uFoVjwzRbOJ6F0iaiKig0R03xTHXElEA0S0L3K7NzXhJs4pe7polpJL/qdSbM3C5cuL0dgsZZdk23eyH6fOjMjeLfMUzxn6GICrmbkOwFoAmyJj5iZ7k5nXRm7fSWaQ81FVnAuL0SB1dA2K/hFW0257DXUVONk3guZTA0qHoivbmzthMRpw3WqH0qFoWjwzRZmZo/UKc+SmmdMTk9GA5WVW2dNFg9weH4ryLCixquct+MdXOWA2StklmUIhxrPNnbjCWYqCHLPS4WhaXDV0IjIS0T4A3QBeZOZ3pjjsskhZZicRrZ7m+2wlot1EtNvr9c496gS57Fa0eqSGrjUtHh+cKuhwiVWQa8ZHnaV4dn8nQjI8JSl2nziDrsFR2bslCeJK6MwcZOa1ABYCWE9EayYdshfAkkhZ5mcA/jTN99nGzOuYeV1paenco05Qtd2G0/0j8I1OpO05xfwwM1o9ftXUz2PV11agc2AUez44o3QoutDY1IFsswHXrrQrHYrmJTokuh/AawA2Tfr8YLQsw8w7AJiJqCRJMc5bNCm45SxdMzoGRuEfC6iqfh517So7skwGbJeyy7wFgiHsPNCJay6yIy/LpHQ4mhdPl0spERVG7ucAuBbAkUnHOCjyvpiI1ke+b2/So52jaB+zXBjVDnd0yb9KetBjWbNMuGZlGZ7d34lAUHbynI9d7X3o8Y9LuSVJ4jlDLwfwKhE1A3gP4Rr6diK6nYhujxxzM4ADRNQE4CEAt7GK+roqC3OQazFK66KGRC9iO8vUl9CBcNmlxz+Od471KR2KpjU2dSDPYsSVrjKlQ9GFWd/jMHMzgIun+PwjMfcfBvBwckNLHoOBUG23obVbErpWuLt8cORnoyBXnV0PV7nKkGcxYntzBzauUE11UVPGAyE8d7AL1612INtsVDocXdD9StEoZ5kVLV1SQ9cKd7dP0S1zZ5NjMeLaVXbsPNCFCSm7zMlbbV4MjExIuSWJMiahuxw29PjH0OsfUzoUMYtgKNzh4owM+larhtoK9A9P4K22HqVD0aTtTZ0oyDHjwyvS1/GmdxmT0J3S6aIZH/QNYywQUvUZOgB8xFmC/GyTLDKag9GJIF445MGm1Q5YTBmThlIuY15J6XTRDrUMtZhNlsmIj6924IWDHtnNM0GvtXTDPxaQrXKTLGMSepktCwU5ZknoGhD9P6q2q7vkAoT3dvGPBfC6O30rn/WgsbkTxXkWXLasWOlQdCVjEjoRwWm3SkLXALfHh8VFuci1qH+hyeXLi1GUZ5GySwKGxgJ4+bAHm2scMBkzJgWlRUa9mk67DS1dPtn6VOXckT1ctMBkNGDzGgdePtyN4fGA0uFowstHujE6EUJDbYXSoehORiV0l8OGwdEAPIPS6aJW44EQ2r1DqhlqEY+GugqMTATx8uFupUPRhMamDtjzs3BpVZHSoehORiX0s8MupOyiWsd6hhAIsSqX/E/n0qoilNmypOwSh4GRCbze4sWWmgoYDOrZRVMvMjKht0pCV62zS/41dIZuNBC21JbjNbcXg7Kj54xePOTBeDAki4lSJKMSenhYQpbs6aJirR4fjAbCstI8pUNJSH1tBcYDIbx40KN0KKrW2NSBhQtysHZRodKh6FJGJXQAcDmk00XNWrp8qCrORZZJW3t7XLK4EJWFOdjeLGWX6fQNjePtth7U11aoamiJnmRcQnfabXB7/DJtRqXcHp+m6udRRIT6unK82dqDM0PjSoejSs8d6EIgxFJuSaGMS+guuw0jE0GcOjOidChikpHxIE70DWuqfh6robYCgRDjuYNdSoeiSo1NHVhWkodV5flKh6JbGZfQq+2yBYBatXX7waz+Jf/TWV2Rj6UleVJ2mUL34Ch2HetFfZ2UW1Ip4xJ6dMGKtC6qT/SPrNo35ZoOEaG+thx/OdoLr0/WOsTasb8TzEBDrZRbUimeEXTZRPQuETUR0UEium+KY4iIHiKiNiJqJqJLUhPu/NmyzagszJEzdBVye3ywGA1YUpSrdChz1lBXgRADOw90Kh2Kqmxv7sRFDpsqZ8TqSTxn6GMArmbmOgBrAWwiog2TjtkMoDpy2wrgF8kMMtmcdqu0LqpQi8eH5WVWTe/v4bTb4LLbZJFRjI7+Eew+cQYNdbLUP9Vm/c3hsOgm4ubIbXKLyI0AHoscuwtAIRGp9r2V02FDu3dIBvyqjLvLB5dG9nCZSX1tOd47fgYn+4aVDkUVnon8cauXckvKxXUqRERGItoHoBvhIdHvTDqkEsDJmI9PRT43+ftsJaLdRLTb61Vuu1FnmQ3jwRCO98ovnFoMjk6gY2BUs/XzWH99SSWyTAbc+98HMn4juNP9I/j5K224fHkxlhRra7GYFsWV0Jk5yMxrASwEsJ6I1kw6ZKrL1hf8JDPzNmZex8zrSkuVGzslwy7UpzUySUqrHS6xFi7Ixf/dfBFebfHiD++dnP0LdCoUYtzzZBOCzPjhJ2qVDicjJFSsZOZ+AK8B2DTpoVMAFsV8vBCAaouIK8qsIILU0VXErcE9XGbyt5dV4bJlxfju9kMZW3r5r10n8HZbL761ZRUWF2v3QreWxNPlUkpEhZH7OQCuBXBk0mHPAPhcpNtlA4ABZlbtZf5ssxFVxXlyhq4iLV0+5FqMqCzMUTqUpDAYCP96Sy2ICF99oinjVia3e/34wc7DuNJVik+vXzT7F4ikiOcMvRzAq0TUDOA9hGvo24nodiK6PXLMDgDtANoA/DuAL6Yk2iSS6UXq4vb4UG236WpL1YULcnFvwyq8c6wPv377mNLhpE0gGMLdTzQhy2TEjz5ZKwuJ0mjWGV/M3Azg4ik+/0jMfQZwR3JDSy2n3YaXDndjdCKIbLO2NoLSI7fHh6svKlM6jKS75UML8cLBLvz4+RZ81FmaEX3Yj77Rjvc/6Me/3bYW9vxspcPJKNpt+J0np92GYIjR7h1SOpSM1+sfQ49/XDf181hEhH/5RA3yLEbc9XgTJnTeKnuoYxAPvuTGltpy3CB952mXsQldOl3Uwx3pcNFjQgeAMls2/uWva7D/9AB+/mqb0uGkzFggiLse34fCXAu+d+MaKbUoIGMTelVxHsxGkj1dVCD6R1WL2+bGa3NNOW5aW4GHX2nD/lMDSoeTEg++1IojXT786JM1WJBnUTqcjJSxCd1iMmBZiVXG0alAi8eHghwzymxZSoeSUvfdsAYl1izc9fg+jE4ElQ4nqfac6MOjrx/Fp9YtwtUX2ZUOJ2NlbEIHgGq7Vc7QVSC85N+m+7foBblm/OjmWrR2+/GTF1qUDidphscDuPvxJpQX5OBb9SuVDiejZXRCd9ltONk3gqGxgNKhZCxmjrQsan8Pl3h81FmK//FXi/HLt45hV3uv0uEkxQ92HMGJvmH85NY62LLNSoeT0TI6oUf3DWnt9s9ypEgVz+AYBkcDuq6fT/aN61dicVEuvvpEE/waP5l4s9WL/9p1Al/YuBQblhUrHU7Gy+iEHt03xC1bACimRWdL/uORl2XCT26pw+n+EXz/2UNKhzNnAyMT+NoTzVhRZsXXPu5SOhyBDE/oi4pykW02SOuigqJ/TDMpoQPAuqoibL1iGX737km8eqRb6XDm5L5nDsLrH8MDt9bJ4jyVyOiEbjQQVpTJhVEltXh8KLVloSgD29zu+pgTLrsNX3+qGWeGxpUOJyHPHejE0++fxh1XrUDtwkKlwxERGZ3QgfCZoZyhK6fV4zs75zXTZJmM+MmtdegbGse3//uA0uHEzesbwzf+eABrKvPx5atXKB2OiJHxCd1lt8EzOIb+YW2dIelBKMRwe/wZV26JtaayAHdeU43tzZ2aGFvHzPjGH/fDPxbAT29dC7OGxwXqUcb/bzjPbgEgnS7pdurMCEYmgroYajEff3/lctQtKsS3//sAugdHlQ5nRk/tPY0XD3nwtetcGbHRmNZkfEI/2+kiZZe0O9vhkkEti1MxGQ144NY6jE4E8fWnmlU7tu50/wjue+Yg1lcV4QsfXqp0OGIKGZ/QywuyYcsySUJXQPQ1ry7LzBp6rOWlVnx9k3rH1sWOk7v/ljoYdbRvvZ5kfEInovAWANKLnnZujw+VhTmyujBCzWProuPkvl0v4+TULJ4RdIuI6FUiOkxEB4nozimOuZKIBohoX+R2b2rCTQ2XI9zpota3unrV0pW5HS5TiR1bd7eKxtbFjpO77VIZJ6dm8ZyhBwDczcwrAWwAcAcRrZriuDeZeW3k9p2kRpliTrsNZ4Yn4PWPKR1KxpgIhtDuHcr4+vlkCxfk4p8aVuFdlYytCwRDuOtxGSenFbMmdGbuZOa9kfs+AIcBVKY6sHSKXhhtlU6XtDnRO4TxYCjjO1ymcvOHFuLalXb8+PkWxbd3fvSNduw72Y/v3rRGxslpQEI1dCKqQni+6DtTPHwZETUR0U4iWj3N128lot1EtNvr9SYebYpE26+kjp4+LV36nlI0H0SEH3yiBtYsk6Jj62ScnPbEndCJyArgKQBfYebBSQ/vBbCEmesA/AzAn6b6Hsy8jZnXMfO60tLSOYacfCVWC4ryLNLpkkZujw8GAlZIh8uUSm1Z+P5NaxQbWzd5nJzQhrgSOhGZEU7mv2Xmpyc/zsyDzOyP3N8BwExEJUmNNIWICE4ZdpFWbo8PS4rzZFOnGUTH1v3slTY0n+pP63P/9MXwOLkff7JWxslpSDxdLgTgVwAOM/MD0xzjiBwHIlof+b6a2r3fZbeh1eOXTpc0acngPVwScd8Na1BqzcJdjzelbWzdnhN92PbGUdx26SJcdVFZWp5TJEc8Z+gbAXwWwNUxbYnXE9HtRHR75JibARwgoiYADwG4jTWWGZ0OG/xjAXQMqHvptR6MTgRxvGdILojGoSDXjB/fXIu2bj/ufz71Y+uGxwO46/EmVBTm4Fv1UzWzCTUzzXYAM78FYMZeJWZ+GMDDyQpKCc6YYReVhTkKR6NvR71+hFiW/MfrCmcpPrNhMX719jFcu8qe0slAP9hxBB/0DeN3/3sDrFmzpgehMhm/UjTKWRbpdJE6espF20OlwyV+6RhbFx0n93cyTk6zJKFHFOSa4cjPlnF0adDi8cFsJFQV5ykdimbkWlI7tm5g+Nw4ua/KODnNkoQew+mwyRl6Gri7fFhWYoXFJD9+iYgdW/fKEU9Sv/c/N8o4OT2Q36gYLrsVbd1+BFWyh4ZetXh8Uj+fo3Nj6/YnbWzdcwc68cf3T+NLMk5O8yShx6i22zAWCOEDle10pydDYwGcOjMCl7QszkmWyYgHPlWH/uHkjK2LjpOrqSzAl2ScnOZJQo/hki0AUq61O3xBVKbdzN3qiuSMrYsdJ/fArXUyTk4H5H8wRnXkrFG2AEid6EVn6UGfn9s/uhxrI2PrPHMcWxcdJ3fPx2WcnF5IQo+RazFhcVGuJPQUavH4kG02YFGRDEmYD5PRgJ/MY2zd2XFyS4vwhY0yTk4vJKFP4rTbJKGnkNvjQ3WZTUaYJUF0bN1rLV78PoGxdaEQ42tPNCHEjJ/cUgeD/F/ohiT0SZx2K9q9QxgPKLNlqd6FpxTJ2/tk+dvLqnD58mJ8L4GxdY/95Tj+fLQX36pfJe+UdEYS+iQuhw2BEONYz5DSoehO//A4un1jsilXEoXH1tXBEOfYunavHz987giuknFyuiQJfZLo2aMsMEo+d3TJv/SgJ1VlYQ7ujWNsXXScXLZZxsnplST0SZaV5sFoIMVHf+lR9I+kdLgkXzxj686Ok7txDcpknJwuSUKfJMtkxNKSPOlFTwF3lw+2LBPKCySZJFvs2Lp/eHzfBWPrDnYM4MGX3KivLUeDjJPTLUnoU3DardLpkgLRJf/yVj81omPrDpwexMOvnBtbNxYI4q4/NKEw14Lvyjg5XYtnYtEiInqViA4T0UEiunOKY4iIHiKiNiJqJqJLUhNuejjtNpzoG8bIeHomxGQCZkarTClKuc015fjriyvx8Kvnxtb99MVWtHhknFwmiOcMPQDgbmZeCWADgDuIaPIok80AqiO3rQB+kdQo08xlt4EZaIssUxfz5/WP4czwhLQspsE/37D67Ni6P7f1YNsbR/Hp9TJOLhPMmtCZuZOZ90bu+wAcBlA56bAbATzGYbsAFBJRedKjTZNoF4aUXZLH3RX+4ygXRFOvIOfc2LrP/fpdVC7IwTe3yDi5TJBQDZ2IqgBcDOCdSQ9VAohdqnYKFyZ9ENFWItpNRLu9Xm+CoabPkqJcWEwGSehJFO1wkZbF9LjCWYrPbliCEDPuv7lOxslliLj/l4nICuApAF9h5sHJD0/xJRescGDmbQC2AcC6detUu+m4yWjA8lKr9KInkbvLh6I8C0qsWUqHkjHuu2E1/v7K5aiQGbkZI64zdCIyI5zMf8vMT09xyCkAscvOFgKY+76eKuCyW2UcXRK1yAXRtDMYSJJ5homny4UA/ArAYWZ+YJrDngHwuUi3ywYAA8zcmcQ4087psKFjYBSDoxNKh6J50Q4XqZ8LkVrxlFw2AvgsgP1EtC/yuW8AWAwAzPwIgB0ArgfQBmAYwOeTHmmaRZNPq8ePDy1ZoHA02na6fwRD40GpnwuRYrMmdGZ+C1PXyGOPYQB3JCsoNYi217k9Pkno8+SWJf9CpIWsFJ1GZWEOci1G2QIgCVq6ZOycEOkgCX0aBgOhWoZdJIXb44MjPxsFOWalQxFC1yShz8Ale7okhTuyh4sQIrUkoc/Aabehxz+OXv+Y0qFoVjDEaO32wyUti0KknCT0GbjObgEge7rM1Yne8Dg/2cNFiNSThD6D2E4XMTfR104SuhCpJwl9BmW2LBTkmGULgHk41+EiJRchUk0S+gyICC67TbYAmAd3tw+Li3KRa5HNoYRINUnos3A6wp0u4bVTIlHuLp+UW4RIE0nos3DZbRgcDcAzKJ0uiRoLBHGsZwguh5RbhEgHSeiziK5ulDp64o71DCEQYjlDFyJNJKHP4myni9TRExbdNkESuhDpIQl9FkV5FpTasuQMfQ5aPX4YDYRlpXlKhyJERpCEHgeX3YZWSegJa/H4sLQkD1kmo9KhCJERJKHHodpuhdvjRygknS6JcMtQCyHSShJ6HFx2G0Ymgjh1ZkTpUDRjeDyAD/qGZUGREGkUzwi6XxNRNxEdmObxK4logIj2RW73Jj9MZUV3CpQ6evzauv1glqEWQqRTPGfo/wlg0yzHvMnMayO378w/LHWpLgufZcqeLvGLbmgm2+YKkT6zJnRmfgNAXxpiUS1bthmVhTmS0BPg9vhgMRmwpChX6VCEyBjJqqFfRkRNRLSTiFZPdxARbSWi3US02+v1Jump08Npt8o4ugS0dPmwotQKk1Eu0wiRLsn4bdsLYAkz1wH4GYA/TXcgM29j5nXMvK60tDQJT50+TocN7d4hTARDSoeiCW6PD065ICpEWs07oTPzIDP7I/d3ADATUcm8I1MZl92G8WAIJ3qHlA5F9QZGJtA5MCr1cyHSbN4JnYgcRESR++sj37N3vt9Xbc4Nu5DpRbNp6w6XpqTDRYj0mnWTaiL6HYArAZQQ0SkA/wTADADM/AiAmwH8PREFAIwAuI11uNfsijIrDBSuDV9fU650OKoWHWohe7gIkV6zJnRm/vQsjz8M4OGkRaRS2WYjlhTnSadLHNweH/IsRlQW5igdihAZRVoQEuC0W2VxURxaunxYYbfBYCClQxEio0hCT4DLbsPxniGMTgSVDkXVwnu4SIeLEOkmCT0BTocNIQbavdLpMp0e/xh6h8alfi6EAiShJ8B1ttNFyi7Tib42LmlZFCLtJKEnoKokD2YjSR19Bm6ZUiSEYiShJ8BsNGBZiVXG0c2gxeNHQY4ZZbYspUMRIuNIQk+Q02GTM/QZRIdaRNaaCSHSSBJ6glx2K06dGcHQWEDpUFSHmcN7uDikw0UIJUhCT1C0NtzaLVsATNY1OArfaECW/AuhEEnoCTq7p4vU0S8Q3V64WhK6EIqQhJ6gRUW5yDYbpI4+hWjLonS4CKEMSegJMhoI1WU26UWfQkuXH6W2LBTlWZQORYiMJAl9Dpx2SehTae32Sf1cCAVJQp8Dl8MKz+AY+ofHlQ5FNUKhSIeLJHQhFCMJfQ6qZdjFBU6eGcboREjGzgmhIEnocxAtK8iF0XOiHS4ydk4I5cya0Ino10TUTUQHpnmciOghImojomYiuiT5YapLeUE2bFkmaV2MEb2mUF0mZ+hCKCWeM/T/BLBphsc3A6iO3LYC+MX8w1I3IoLTIRdGY7k9flQW5sCWbVY6FCEyVjwj6N4goqoZDrkRwGOROaK7iKiQiMqZuTNZQaqR027Dk3tO4mMPvK50KKpw8swwLl9eonQYQmS0WRN6HCoBnIz5+FTkcxckdCLaivBZPBYvXpyEp1bOp9cvwuDoBHQ4D3tOqu1W/M36JUqHIURGS0ZCn2pbvSmzHDNvA7ANANatW6fpTFi7sBA//xvdXy4QQmhIMrpcTgFYFPPxQgAdSfi+QgghEpCMhP4MgM9Ful02ABjQe/1cCCHUaNaSCxH9DsCVAEqI6BSAfwJgBgBmfgTADgDXA2gDMAzg86kKVgghxPTi6XL59CyPM4A7khaREEKIOZGVokIIoROS0IUQQickoQshhE5IQhdCCJ0gpVY6EpEXwIk5fnkJgJ4khqN18nqcT16Pc+S1OJ8eXo8lzFw61QOKJfT5IKLdzLxO6TjUQl6P88nrcY68FufT++shJRchhNAJSehCCKETWk3o25QOQGXk9TifvB7nyGtxPl2/HpqsoQshhLiQVs/QhRBCTCIJXQghdEJzCZ2INhFRS2Qo9f9VOh4lEdEiInqViA4T0UEiulPpmJRGREYiep+Itisdi9Ii4yCfJKIjkZ+Ry5SOSSlE9A+R35EDRPQ7IspWOqZU0FRCJyIjgJ8jPJh6FYBPE9EqZaNSVADA3cy8EsAGAHdk+OsBAHcCOKx0ECrxbwCeY+aLANQhQ18XIqoE8H8ArGPmNQCMAG5TNqrU0FRCB7AeQBsztzPzOIDfIzykOiMxcycz743c9yH8C1upbFTKIaKFALYA+KXSsSiNiPIBXAHgVwDAzOPM3K9oUMoyAcghIhOAXOh0qprWEvp0A6kzHhFVAbgYwDsKh6KkBwHcAyCkcBxqsAyAF8B/REpQvySiPKWDUgIznwZwP4APEB5eP8DMLygbVWpoLaHHPZA6kxCRFcBTAL7CzINKx6MEIqoH0M3Me5SORSVMAC4B8AtmvhjAEICMvOZERAsQfie/FEAFgDwi+oyyUaWG1hK6DKSehIjMCCfz3zLz00rHo6CNAG4gouMIl+KuJqLfKBuSok4BOMXM0XdsTyKc4DPRtQCOMbOXmScAPA3gcoVjSgmtJfT3AFQT0VIisiB8YeMZhWNSDBERwjXSw8z8gNLxKImZ/5GZFzJzFcI/F68wsy7PwuLBzF0AThKRK/KpawAcUjAkJX0AYAMR5UZ+Z66BTi8QzzpTVE2YOUBEXwLwPMJXqn/NzAcVDktJGwF8FsB+ItoX+dw3mHmHciEJFfkygN9GTn7akaED3Jn5HSJ6EsBehDvD3odOtwCQpf9CCKETWiu5CCGEmIYkdCGE0AlJ6EIIoROS0IUQQickoQshhE5IQhdCCJ2QhC6EEDrx/wHWZr937ic6DwAAAABJRU5ErkJggg==\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",
+    "plt.plot(rolling_dice_df).sort()"
    ]
   },
   {
@@ -61,11 +186,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[<AxesSubplot:title={'center':'0'}>]], dtype=object)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "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",
+    "rolling_dice_df.hist()"
    ]
   },
   {
@@ -75,7 +224,8 @@
    "outputs": [],
    "source": [
     "\"\"\"\n",
-    "your comments here\n",
+    "The difference is that in this plot we can see the number of times a given side of the dice is revealed. For example, the side with 1 rolled 5 (five) times. \n",
+    "In the above plot, we could only see the numbers that occur one by one.\n",
     "\"\"\""
    ]
   },
@@ -91,11 +241,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.0"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "\n",
+    "#change name of column\n",
+    "rollDice = rolling_dice_df.rename(columns={0:'results'})\n",
+    "\n",
+    "\n",
+    "# dataframe to list\n",
+    "rollDiceList = rollDice['results'].tolist()\n",
+    "\n",
+    "\n",
+    "def mean(x):\n",
+    "    mean = sum(x) / len(x)\n",
+    "    return(mean)\n",
+    "\n",
+    "\n",
+    "mean(rollDiceList)\n",
+    "    "
    ]
   },
   {
@@ -107,11 +284,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.6666666666666667"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "\n",
+    "def frequencyDistribution(x):\n",
+    "    \n",
+    "    dice = [1,2,3,4,5,6]\n",
+    "    frequencyDistributionList = [x.count(i) for i in dice]\n",
+    "    \n",
+    "    return frequencyDistributionList\n",
+    "\n",
+    "# calculating the mean\n",
+    "freq = frequencyDistribution(rollDiceList)\n",
+    "\n",
+    "mean(freq)"
    ]
   },
   {
@@ -128,7 +328,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "# your code here\n"
    ]
   },
   {
@@ -158,11 +358,172 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "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": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "\n",
+    "#read csv\n",
+    "rollDice100 = pd.read_csv(\"path tho csv\")\n",
+    "\n",
+    "#create dataframe\n",
+    "pd.DataFrame(rollDice100)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "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": [
+    "rollDice100sort = rollDice100.sort_values(by='value')\n",
+    "#dice_sorted.head()\n",
+    "\n",
+    "rollDice100sort['value'].plot()"
    ]
   },
   {
@@ -185,11 +546,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.74"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "\n",
+    "rollDice100List = rollDice100sort['value'].tolist()\n",
+    "mean(rollDice100List)"
    ]
   },
   {
@@ -201,11 +576,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[12, 17, 14, 22, 12, 23]"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "\n",
+    "frequencyDistribution(rollDice100List)"
    ]
   },
   {
@@ -217,11 +605,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "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",
+    "plt.hist(rollDice100List,6)\n",
+    "plt.show()"
    ]
   },
   {
@@ -272,13 +676,129 @@
     "#### 1.- Read the file `ages_population.csv`. Calculate the frequency distribution and plot it as we did during the lesson. Try to guess the range in which the mean and the standard deviation will be by looking at the plot. "
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>observation</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>68.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>12.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>45.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>38.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>49.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   observation\n",
+       "0         68.0\n",
+       "1         12.0\n",
+       "2         45.0\n",
+       "3         38.0\n",
+       "4         49.0"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# your code here\n",
+    "\n",
+    "agesPopulationMain = pd.read_csv(\"path tho csv\")\n",
+    "agesPopulationMain.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/pedroafflalo/opt/anaconda3/envs/Ironhack/lib/python3.8/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:ylabel='Density'>"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "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": [
+    "sns.distplot(agesPopulationMain)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "#Mean = 35\n",
+    "#STD = 25"
    ]
   },
   {
@@ -290,11 +810,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 34,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "observation    36.56\n",
+       "dtype: float64"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "observation    12.8165\n",
+       "dtype: float64"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "\n",
+    "agesPopulationMean = np.mean(agesPopulationMain)\n",
+    "agesPopulationSTD = agesPopulationMain.std()\n",
+    "\n",
+    "display(agesPopulationMean)\n",
+    "display(agesPopulationSTD)"
    ]
   },
   {
@@ -304,7 +851,7 @@
    "outputs": [],
    "source": [
     "\"\"\"\n",
-    "your comments here\n",
+    "While the mean is near my guess, i cannot say the same about standard deviation\n",
     "\"\"\""
    ]
   },
@@ -317,11 +864,116 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>observation</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>25.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>31.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>29.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>31.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>29.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   observation\n",
+       "0         25.0\n",
+       "1         31.0\n",
+       "2         29.0\n",
+       "3         31.0\n",
+       "4         29.0"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "agesPopulation2 = pd.read_csv('path tho csv')\n",
+    "agesPopulation2.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/pedroafflalo/opt/anaconda3/envs/Ironhack/lib/python3.8/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:ylabel='Density'>"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "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": [
+    "sns.distplot(agesPopulation2)"
    ]
   },
   {
@@ -338,7 +990,7 @@
    "outputs": [],
    "source": [
     "\"\"\"\n",
-    "your comments here\n",
+    "The population is younger comparing with the first dataset. And there are some gaps in the x axis values.\n",
     "\"\"\""
    ]
   },
@@ -351,11 +1003,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 37,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "observation    27.155\n",
+       "dtype: float64"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "observation    2.969814\n",
+       "dtype: float64"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "\n",
+    "agesPopulationMean2 = np.mean(agesPopulation2)\n",
+    "agesPopulationSTD2 = agesPopulation2.std()\n",
+    "\n",
+    "display(agesPopulationMean2)\n",
+    "display(agesPopulationSTD2)"
    ]
   },
   {
@@ -365,7 +1044,7 @@
    "outputs": [],
    "source": [
     "\"\"\"\n",
-    "your comments here\n",
+    "The average age is 27, below the average age in the first dataset. The standard deviation value is 2.96 wich is much lower than the one in the first dataset\n",
     "\"\"\""
    ]
   },
@@ -381,11 +1060,117 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 38,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>observation</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>21.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>21.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>24.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>31.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>54.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   observation\n",
+       "0         21.0\n",
+       "1         21.0\n",
+       "2         24.0\n",
+       "3         31.0\n",
+       "4         54.0"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "\n",
+    "agesPopulation3 = pd.read_csv('path tho csv')\n",
+    "agesPopulation3.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/pedroafflalo/opt/anaconda3/envs/Ironhack/lib/python3.8/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+      "  warnings.warn(msg, FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:ylabel='Density'>"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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pmStmZtHd6+bVfcecDsWMcJY4TEirae7g9YO1fHZeDuEhPndjMOkJUZw/MZWiI41UnTjpdDhmBBvdnyQT8tZsr6TXrVxbOHovU/V16dRMYiJd/GV3tW03a86aJQ4TslSV1UXlFOaNCYl9N4ZCTKSLS6dmUFrXRkldm9PhmBHKEocJWduOnqCktm3UzBT31Xn5KSRGh7N+/3HrdZiz4lPiEJEXRORKEbFEY0aM54rKiY10ccWsLKdDCSoRrjAuOiedI/Xt1uswZ8XXRPAgsBI4JCI/EZGpAYzJmI+trbOHl3ZWccXMLOKjbGWd/go/6HXUWK/D+M2nxKGq/1DVG4B5wBHgVRF5V0Ru8264ZExQWbe7mrauXr54nl2mGsg/ex12r8P4z+evYiKSCtyIZ5+M7cBTwBI8myldHIjgjDlbq4vKmZgWR+EI3rDpbBf181Vhfgpvvl/L+v01/L9XTQ/oe5nQ4us9jjXA20AscLWqflpV/6iqXwFsuIoJKodrW9lypJFrC8ePqgUN/RXhCuMT3l7HtqONTodjRhBf73E8oqrTVfXHqloNICJRAKpaGLDojDkLq4vKcYUJn5s/+hY09Nf8vDFER4Tx6NulTodiRhBfE8d/DnDsvaEMxJih0N3r5oWtlVwyJYOMhGinwwl6UeEuFuSn8Nc91ZQ3tDsdjhkhzpg4RGSsiMwHYkRkrojM8z4uxnPZypig8sbBWupaO+2muB/On5RGmAi/f/eI06GYEWKwm+OfAm4FcoBf9jneAnwnQDEZc9b+uKWctPgoLp4yuhc09EdSTARXzcrij1vKufeyAhKjbaCkObMz9jhU9XFVvQS4VVUv6fP4tKquGaYYjfGJZ0HDGj43P5uIUb6gob9uXzKR1s4e/ri53OlQzAhwxh6HiNyoqk8C+SLy9f7nVfWXA1QzxhEvbPMsaGhLjPhvZk4SCyek8Lt3Srltcf6oX0nYnNlgfx1x3p/xQMIAD2OCgqryXFE55+XbgoZn61+WTKCqqYP1B2qcDsUEuTP2OFT1t96fPxiecIw5O0VljZTUtfHliyc5HcqItXRqBllJ0Ty5sYxPnTvW6XBMEPN1AuBPRSRRRCJEZL2I1InIjYEOzhhfPbP5KHGRLq6YaQsanq1wVxjXL8jl7UN1lNoyJOYMfF1y5JOqep+IXANUANcCrwNPBiwyY3z0yNslrN1Rxfy8Mby4o8rpcEa0684bz2/WH+LpTWX825W2DIkZmK93wE6Nz7sCeEZVGwIUjzF+21bWSI9bWTAhxelQRryMxGg+de5YVhdV0NHd63Q4Jkj5mjheEpEDQCGwXkTSgY7BKonIMhE5KCLFInL/AOdFRH7jPb9LROb1OfeYiNSIyJ5+db4vIpUissP7uMLHNpgQpKpsKm0gNyWWrKQYp8MJCTcsyqXpZDcv76p2OhQTpHxdVv1+4HygUFW7gTZgxZnqiIgLeABYDkwHrheR/n3f5UCB93EHnn0/Tvk9sOw0L/8rVZ3jfazzpQ0mNL17uJ76ti4WWm9jyJw/MZVJ6XH8YWOZ06GYIOXPYO1pwBdF5Gbg88AnBym/AChW1RJV7QKe5aPJZgXwhHpsBJJFJAtAVd8C7JKYOaOnNpURE+FiRnaS06GEDBHhxkV57Cw/wZ7KJqfDMUHI11FVfwB+jmf/jfO8j8FWxc0G+k5DrfAe87fMQO7xXtp6TEQG3HBBRO4QkSIRKaqtrfXhJc1IU9Pcwd/3Hmd+3hibKT7EPjs3h6jwMJ7eHNg9QczI5OunrRBYrKp3qepXvI+vDlJnoI0Q+u9R6UuZ/h4EJgFzgGrgFwMVUtVVqlqoqoXp6bZuUSj645ZyuykeIEmxEVw1axwvbq+krbPH6XBMkPE1cewB/J0RVAH0XfshB+g/VtKXMh+iqsdVtVdV3cDDeC6JmVGm1608s/koSyankRYf5XQ4IWnlwlzaunpZu9OGOJsP8zVxpAH7ROQVEVl76jFInS1AgYhMEJFI4Dqgf521wM3e0VWLgKZTG0Wdzql7IF7X4ElqZpR5/UANVU0d3Lgo1+lQQta83GSmZCbwjF2uMv34OgHw+/6+sKr2iMg9wCuAC3hMVfeKyJ3e8w8B6/DMDSkG2oHbTtUXkWfw7GWeJiIVwPdU9VHgpyIyB88lrSPA/+1vbGbke2pTGRkJUSydlslzRRVOhxOSRISVC3P53tq97KlssgEI5gM+JQ5VfVNE8oACVf2HiMTiSQaD1VuHJzn0PfZQn98VuPs0da8/zfGbfInZhK6j9e288X4tX7lkst0UD7DPzM3mx3/dz9Obj/Kja2Y6HY4JEr6Oqvq/gOeB33oPZQN/DlBMxpzRY++UEh4m3LAoz+lQQp5nkyfPTfJWu0luvHz9unY3sBhoBlDVQ0BGoIIy5nSaTnazuqicq2ePIzPR9hQfDqdukr9kN8mNl6+Jo9M7iQ8AEQln8GGzxgy5Zzcfpb2rly8tmeh0KKPG3PHJTB2bwNOb7Ca58fD15vibIvIdIEZELgfuAl4KXFjGfFR3r5vfv3uExZNTmT4u0elwRo1TN8m/++Jedlc0MTMnOG+SH65tZWf5CWpaOkmIDmdmdhIz7YZ+QPja47gfqAV24xnFtA7490AFZcxA1u2uprqpw3obDlgxJ5voiOCcSd7Z08vzWyt4dEMpuyubCBOhovEkz24pZ9XbJTS0dQ3+IsYvvo6qcovIn4E/q6qt32GGnary8NslTEqP46JzbCWA4ZYUE8HVs8axdkcl/3blNOKjfL1YEVhdPW5+984RyhvauWRKBhdPSSfCFYZblW1ljazdWcUNj2zi6S8tZExcpNPhhowz9ji8E/O+LyJ1wAHgoIjUish3hyc8Yzw2lzawp7KZ25dMJCxsoJVqTKB9MJM8SDbL6nUrT24qo7yhnesX5HL59MwPhmeHiVCYn8JNi/I4XNvKV57Zjtttt2WHymBfG76GZzTVeapaCiAiE4EHReRfVfVXAY7PjCJnuvn6h/eOEBvporvXbTdpHTLn1E3yzWWsXOj8jP31+49TXNPKZ+dmn3ZyYkFmAt+7ejr/9qc9PLmpjJvPzx/eIEPUYPc4bgauP5U0AFS1BLjRe86YgKtr6eTAsRYWTki1CX8OEhFuWJjLnspmdlc4u9x6cU0rb75fS2HeGArzz7zI5coFuVw8JZ0frdtP5YmTwxRhaBvsUxihqnX9D3rvc0QMUN6YIffO4TrCwoRFE20VXKetmJtNTISLpzc7t8lTV4+bNdsrSI2P4qpZ4wYtLyL88JqZuBV+9er7wxBh6BsscZxpOIINVTAB197Vw7ajjcwZn0xCtH1XcVpidARXz87ixR1Vjs0kX3/gOCfau7lmbjaR4b71QLOTY7h5UR5rtlXw/vGWAEcY+gb7rz5bRJoHeLQAtnCNCbjNpQ109yqLJ6c5HYrxWrkwj/auXl7cUTns7328uYN3iusozBvDhLQ4v+refclk4iLD+fU/rNfxcZ0xcaiqS1UTB3gkqKp9/TMB1eN2815JPQUZ8Yy15UWCxuycJKZlJfKH98rwrFM6fF7Ze4zI8DCWnevv9kAwJi6SGxbl8bc9xyhvaA9AdKNHcAzGNmYAuyqaaOno4XPzrLcRTESE2xbnc9/zu3j3cP2w9QZLals5cKyFZeeOJdaPeSR9R+ElxXi+797/wi6uPMP9kWAYNRbMLHGYoKSqvFNcR0ZCFAUZ8U6HE/L8HeLc0+smPiqch98uGZbE4XYrf91zjOSYCM6flHrWr5MUE8HM7CSKyhpZOi2T6IhBd4cwA7CxjSYoldS1Ud3UwZLJaYjYhL9gE+4KY9HEVN44WMuhYbjZ/NKuKipPnPzQJL+zdcGkNDp73OysODE0wY1CljhMUNpwqI64qHBmj092OhRzGgsnpBAVHsajG0oHL/wxdPb08rNXDjIuKXpI/h5yxsSQmRjF1rLGjx/cKGWJwwSdmuYODh5vYdHEFJvwF8TiosL53Pwc1myvpK61M2Dv88S7ZVQ0nmTZjCzChqD3KSIU5qVQ0XiSY00dQxDh6GOfShN03jlcT3iYsHDC2V/LNsPj9iUT6Opx84f3AjMh8ER7F//92iEuOiedyUN4r2vO+GRcImwtaxiy1xxNLHGYoNLa2cP2o43MzU0OmhVYzelNSo/nsmkZPP7ekYBMCHzg9WJaO3v49hVTh/R146LCmZqVwM6KJnpt8UO/WeIwQWVzaT09bmXxJBuCO1Lcc2kBJ9q7efzdI0P6uqV1bfz+3SN8fn4OU8cO/cZds3KSae3sobSubchfO9RZ4jBBo7vXzXslDUzJTCDDJvyNGHPGJ3Pp1AwefruElo7uIXvdH/5lH1HhLr75qSlD9pp9TR2bQFR4mI2uOguWOEzQ2Fl+grbOHlteZAT62mWeXsfDb5UMyeu99X4t/9hfw1cunUxGQmC+RES4wpielcjeqiZ6et0BeY9QZYnDBAVVZUNxHWMTo5mU7t8aRMZ5s3KSuWpWFqveLqG66eMtXd7d6+Y/Xt5HXmosty7OH5oAT2NWTjId3W4O1bQG9H1CjSUOExTePlRHTUunTfgbwb61bCpuN/zslYMf63We2ljGoZpW/v3K6USFB3Zm96SMOKLCw9hX3RzQ9wk1ljhMUHhkQykJ0eHMGj/wTm4m+I1PieX2CyewZlsl7x7+yDY+Pqk8cZKf//19LixI47JpGUMc4UeFh4UxZWwC+6ubcQ/zgo0jWUATh4gsE5GDIlIsIvcPcF5E5Dfe87tEZF6fc4+JSI2I7OlXJ0VEXhWRQ96fYwLZBhN4B4+18Nb7tZw/MZXwMPsuM5J99dIC8lNj+faa3Zzs6vWrrqryred34VblR9fMHLae5/SsRNq7eimrtxVzfRWwT6mIuIAHgOXAdOB6EZner9hyoMD7uAN4sM+53wPLBnjp+4H1qloArPc+NyPYoxtKiI4IY8EgW4Ca4BcT6eLHn51FWX07P3hpr191H3m7lA3FdXznimmMT4kNUIQfNSUzgfAwYV+Vs9vhjiSB/Hq3AChW1RJV7QKeBVb0K7MCeEI9NgLJIpIFoKpvAQNN61wBPO79/XHgM4EI3gyP2pZO/ry9is/Pz/FrqWwTvM6flMrdl0zi2S3l/HGLb6vubjhUx4//up/lM8ZywzAvaR4V4WJSejz7qpuHfX+RkSqQn9RsoLzP8wpgoQ9lsoHqM7xupqpWA6hqtYgMeCFURO7A04shN9fW1g9Wf9hYRlevm39ZPIGNJbb8w0hzuuXYs5JimJwez7fX7GZXRRPnjvvnvav+e11sLWvgy09upSAjgZ9fO9uRwRHTxyVycHsLx5o7yEqKGfb3H2kC2eMY6F+/fzr3pcxZUdVVqlqoqoXp6elD8ZJmiHV09/LkxjIum5bBxHTbcyOUhImwcmEu2ckxPLP5KO+V1A9483nd7mpuenQzqfGR/O6284hzqNc5LSsRAfZW2egqXwTyX6kCGN/neQ5QdRZl+jsuIlne3kYWUPOxIzWO+NP2ShrauvjShROdDsUEQHSEi9sWT+CZzUd5aWcVuypOcF5eCudkxlPd1MGabRW8frCWmdlJPHpLoaOrBcRHhZObGsv+6mYum5bpWBwjRSATxxagQEQmAJXAdcDKfmXWAveIyLN4LmM1nboMdQZrgVuAn3h/vjikUZth4XYrj7xdwozsRBZOsJvioSo6wsWtF+Sz5Ugjb75fw/PbKnh+WwUAafFR3LdsCndcOJHwIFg+/9ysRNbtOUZDW5fToQS9gCUOVe0RkXuAVwAX8Jiq7hWRO73nHwLWAVcAxUA7cNup+iLyDHAxkCYiFcD3VPVRPAljtYjcDhwFrg1UG0zgvPl+LYdr2/j1F+fYhL8QJyIsmJDCefljqGrqYNHEFM8mXTnJuMKC599+mjdx7LfJgIMK6AVFVV2HJzn0PfZQn98VuPs0da8/zfF6YOkQhmkc8MiGEsYmRnPFzCynQzHDRETITo7hwoLgvOeYGh9FekIUB48Ffivckc75/qEZdfZVNfNOcT23XJBPZLj9CZrgMW1sAqV1bUO6ym8osk+tGXaPbiglNtLFygU2TNoElyljE+lV5a33z27JlNHCEocZVsebO1i7s5IvFI4nKTbC6XCM+ZDclFhiIlysP3Dc6VCCmiUOM6yeeO8IPW7ltgAvl23M2XCFCVPGJvDGwVrbUvYMLHGYYdPe1cNTm47yyemZ5KXanhsmOE0dm0BDWxfbjzY6HUrQssRhhs0L2yo50d5tE/5MUDvHu+jh+gM2t/h0LHGYYeF2K49tKGV2ThKFebYSvgle0REuFkxIYf1+u89xOpY4zLBYf6CG0ro2vnThRJvwZ4LepVMzeP94K+UNtkfHQGwdaxMQ/VdNffjtEpJjIjjR3n3aFVWNCRaXTcvkP/+yn/X7j3Pr4glOhxN0rMdhAq6y8SSldW2cPyk1qJaYMOZ08tPimJQeZ/c5TsMShwm4dw7XERkexnm2w58ZQZZOy2RjSb3NIh+AJQ4TUE0nu73LaY8hOsLldDjG+Gzp1Ay6e5UNh2wWeX+WOExAvXe4DlW4YFKa06EY45f5eWNIiongH/vtclV/ljhMwHT29LL5SAPnZicxJi7S6XCM8Uu4K4yLp6Tz+sEam0XejyUOEzBbyxrp6HazZLL1NszItHRaJg1tXewoP+F0KEHFEocJCLcq7x6uJzclltyUWKfDMeasXFSQjitMbDJgP5Y4TEDsrWqmoa2LxdbbMCNYUmwE5+WP4TUblvshljjMkFNV3j5US2pcJOeOS3Q6HGM+lsumZXLgWAsVjTaL/BRLHGbIbSxpoKLxJEsK0giz5UXMCHfp1AwA1tvoqg9Y4jBDbtVbh4mLdDEv1xYzNCPfxPR4JqbZLPK+LHGYIXXgWDOvH6zl/ElpRLjsz8uEhqXTMth4uJ7Wzh6nQwkK9sk2Q2rVWyXERLhYNNGWFzGh49KpmXT1utlwqNbpUIKCrY5rhkzViZOs3VHFTefnERtpf1pmYCNxdeTC/DEkRoezfn8Ny2ZkOR2O46zHYYbMYxtKUeD2JbYMtQktEa4wLp6SwWsHaujpdTsdjuMscZgh0XSym2c2H+WqWVnkjLEJfyb0LJ8xlvq2LjaXNjgdiuMscZgh8eTGMtq6ernjE7afuAlNF0/JICbCxcu7q50OxXEBTRwiskxEDopIsYjcP8B5EZHfeM/vEpF5g9UVke+LSKWI7PA+rghkG8zg2jp7eHRDKZ84J51zxyU5HY4xARET6WLptAz+tufYqL9cFbDEISIu4AFgOTAduF5Epvcrthwo8D7uAB70se6vVHWO97EuUG0wvnlyYxkNbV3cu7TA6VCMCairZmXR0NbFxpLRfbkqkD2OBUCxqpaoahfwLLCiX5kVwBPqsRFIFpEsH+uaINDe1cOqt0q4sCCN+Xk24c+EtounZBAb6eIvu6ucDsVRgUwc2UB5n+cV3mO+lBms7j3eS1uPiciA/7cSkTtEpEhEimprbex1oDy18Sj11tswo0R0hIvLpmXytz3H6B7Fl6sCmTgGWqSo/24opytzproPApOAOUA18IuB3lxVV6lqoaoWpqen+xSw8c/Jrl5++9ZhFk9OpdD2EzejxJWzsmhs7+a9w/VOh+KYQCaOCmB8n+c5QP/+3enKnLauqh5X1V5VdQMP47msZRzw1KYy6lq7uHfpOU6HYsywueicdOIiXfxl1+gdXRXIxLEFKBCRCSISCVwHrO1XZi1ws3d01SKgSVWrz1TXew/klGuAPQFsgzmNlo5u/veNw1wwKZUFE6y3YUaP6AgXl0/P5G97j9HZ0+t0OI4IWOJQ1R7gHuAVYD+wWlX3isidInKnt9g6oAQoxtN7uOtMdb11fioiu0VkF3AJ8K+BaoM5vd++WUJDWxf3L5/qdCjGDLsVc7JpOtnN66N0xdyALijkHSq7rt+xh/r8rsDdvtb1Hr9piMM0fjrW1MEjG0r49OxxzMpJdjocY4bdhQVppCdE8fzWylG5dpXNHDd++9Wr79PrVv6fT01xOhRjHBHuCuOaudm8cbCG+tZOp8MZdpY4jF8OHmvhua3l3Hx+PuNTbE0qM3p9bl4OPW7lzztG35wOSxzGZ6rKD9ftJz4qnK9cOtnpcIxx1JSxCcwen8yzm4/iueo+etimCcZnL++q5q33a/nuVdNJjo10OhxjAsbXPUMmpcWxZnslP1p3gAlpcaxcmBvgyIKD9TiMT5pOdvODl/YxMzuJWy7IdzocY4LCrJxkoiPC2HJkdK1dZYnD+OSnfztAQ1snP/7sTFxhA03sN2b0iQwPY874MeyubBpV+5HbpSpzRk9vOkpZfRtPbTrKkslp7KpoYldFk9NhGRM0zp+YysaSejaV1o+a/Wisx2HOqLvXzZrtlSTHRLB0WobT4RgTdNITopiSmcDGkgY6ukfHTHJLHOaM/rrnGLUtnVwzL5uocJfT4RgTlBZPTqOts4cXd1Q6HcqwsMRhTuvVfcfZWFLP4kmpFGQkOB2OMUFrUnocWUnRPPRmCb3u0B+aa4nDDKi0ro2v/3EH45Kj+eS5Y50Ox5igJiJcMiWD0ro2XtoZ+hMCLXGYj2ju6ObOP2zF5RJuWJBHhMv+TIwZzPRxiUzJTOC/XzsU8r0O+z+C+ZDuXjd3P7WNw7Wt/M/18xgTZxP9jPFFmAj3XlbA4do2XthW4XQ4AWWJw3yg1618Y/VO3j5Ux4+umcmSgjSnQzJmRFk+Yyxzxifzi78fpL0rdOd1WOIwAPT0urnv+V2s3VnFt5ZN5QvnjR+8kjHmQ0SEf79yGsebO1n1VonT4QSMJQ5DR3cvdz21jRe2VfD1y8/hyxdPcjokY0aswvwUrpyVxf++cZjSujanwwkISxyjXHXTSb64aiN/33ec7189na8uLXA6JGNGvO9eNZ0oVxj/9qfdIblyriWOUey1A8e5+r83UHy8hd/eNJ9bF09wOiRjQkJmYjT3LZ/Ku4fredLHlXZHElurahRqaOvix+v289zWCqaOTeC/r59LQaZN8DNmKN2wIJd/7DvOf768j4UTUjgnhD5j1uMYRVo6uvmf1w5xyc/f4E/bK7nzokm8eM9iSxrGBEBYmPDza2eTEB3OnU9upelkt9MhDRnrcYxAvm4yc0pLRzdbjjSw5UgjTSe7uWxaBvctmxpS34CMCUbpCVH8z8p53PjIJu55ehu/u/U8wkNgQq0ljhDV0+vmUE0rRWWNHDzWjFvhsmkZ3Lv0HGbmJDkdnjGjxqKJqfzwmhl864XdfH31Tn71xTkjfk8bSxwh5GRXL4dqWthX3czBYy109riJjwpnyeQ05uelcO9lNmLKGCd88bxcGtq6+T9/O4AI/Ozzs4kMH7k9D0scI1hPr5uyhnYO17RSXNtKZeNJFIiLdDEzO4lzxyUyOSNhxH+7MSYUfPniSSjKT/92kJrmTh64YR4pI3RJH0scI4jbrRw41sLbh2oprmnlSH0b3b1KmMD4MbFcOjWDyRnxjE+JJUwsWRgTbO66eDJZSdF86/ndLP+vt/jVF+ZwweSRt7SPJY4g1utWDh5rYXNpPVuONLKptJ661i7Ac9OtMD+FyenxTEiLIzpi8E2W/L2pbowZetfMzaEgI4GvPrOdlY9s4pq52XzzU1PITo5xOjSfWeIIIi0d3eytamZH+Qk2lzaw5UgDLR2ehdKyk2O4sCCdxZPTqG3pJCkmwuFojTFna0Z2En/56oX87xvF/PbNEl7eVcU1c7O5cVEeM7OTkCC/YhDQxCEiy4D/AlzAI6r6k37nxXv+CqAduFVVt52proikAH8E8oEjwBdUtTGQ7RhqHd29lDe0U1rXRkldG3urmtlT2fShdW0mpcdx1awsFkxI4bz8FHLGxH5wznoOxox8MZEuvvHJKVy/IJcH3zjM81srWF1UwfiUGC6ZksElUzKYm5tMcmzw3QeRQK2jIiIu4H3gcqAC2AJcr6r7+pS5AvgKnsSxEPgvVV14proi8lOgQVV/IiL3A2NU9VtniqWwsFCLioo+VntUFbdCj9uN2w29qvT2Kr2q9PS6ae/qpa2rx/Oz0/OztaOH2tZOals6qW3tpK6lk4rGk1Q1naTvf/ZxSdHMyE5iZnYSM3KSmJWdRGp81GljscRhTHBauTD3rOs2d3Tz8s5qXjtQwzvFdZzs7gUgKymaKWMTyE2JJTMxmqykaJJjI4iPiiAuykVCVASxUS4iwsJwuYTwMM/DFSYfu+ciIltVtbD/8UD2OBYAxapa4g3gWWAFsK9PmRXAE+rJXhtFJFlEsvD0Jk5XdwVwsbf+48AbwBkTx9n6wUt7eWrjUU+y+Bj5NSEqnLSEKNLjo5ifN4bPp+UwIS2O/LQ4JqTGkRRrl52MGe0SoyNYuTCXlQtz6ejuZWtZI3sqmzhwrIX91c1sK2ukucO/PT7Cw4RHbz2Pi85JH9JYA5k4soHyPs8r8PQqBiuTPUjdTFWtBlDVahHJGOjNReQO4A7v01YROXg2jQgiaUCd00EMs9HW5tHWXgixNt/gW7FhbfPFP/5Y1fMGOhjIxDFQH6n/9/bTlfGl7hmp6ipglT91gpmIFA3UZQxlo63No629YG0eqQI5dbEC6LuNXA5Q5WOZM9U97r2chfdnzRDGbIwxZhCBTBxbgAIRmSAikcB1wNp+ZdYCN4vHIqDJexnqTHXXArd4f78FeDGAbTDGGNNPwC5VqWqPiNwDvIJnSO1jqrpXRO70nn8IWIdnRFUxnuG4t52prvelfwKsFpHbgaPAtYFqQ5AJmctufhhtbR5t7QVr84gUsOG4xhhjQtPIXZ7RGGOMIyxxGGOM8YsljiAnIstE5KCIFHtnyoccERkvIq+LyH4R2Ssi93qPp4jIqyJyyPtzjNOxDiURcYnIdhF52fs81NubLCLPi8gB77/1+aOgzf/q/ZveIyLPiEh0KLTZEkcQ8y698gCwHJgOXC8i052NKiB6gG+o6jRgEXC3t533A+tVtQBY730eSu4F9vd5Hurt/S/gb6o6FZiNp+0h22YRyQa+ChSq6gw8A32uIwTabIkjuH2wbIuqdgGnll4JKapafWpxS1VtwfM/lGw8bX3cW+xx4DOOBBgAIpIDXAk80udwKLc3EfgE8CiAqnap6glCuM1e4UCMiIQDsXjmo434NlviCG6nW5IlZIlIPjAX2ES/5WWAAZeXGaF+DdwHuPscC+X2TgRqgd95L889IiJxhHCbVbUS+DmeaQPVeOap/Z0QaLMljuD2sZdeGUlEJB54AfiaqjY7HU+giMhVQI2qbnU6lmEUDswDHlTVuUAbI/ASjT+89y5WABOAcUCciNzobFRDwxJHcPNl2ZaQICIReJLGU6q6xns4VJeXWQx8WkSO4Ln8eKmIPEnothc8f8sVqrrJ+/x5PIkklNt8GVCqqrWq2g2sAS4gBNpsiSO4+bJsy4jn3dDrUWC/qv6yz6mQXF5GVb+tqjmqmo/n3/Q1Vb2REG0vgKoeA8pFZIr30FI82ySEbJvxXKJaJCKx3r/xpXju3434NtvM8SDn3ezq1/xz6ZUfOhvR0BORJcDbwG7+ec3/O3juc6wGcvEuL6OqDY4EGSAicjHwTVW9SkRSCeH2isgcPIMBIoESPEsMhRHabf4B8EU8Iwe3A18C4hnhbbbEYYwxxi92qcoYY4xfLHEYY4zxiyUOY4wxfrHEYYwxxi+WOIwxxvjFEocxxhi/WOIwxhjjl/8f2gqtixvBGQ0AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(agesPopulation3)"
    ]
   },
   {
@@ -397,11 +1182,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 40,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "observation    41.989\n",
+       "dtype: float64"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "observation    16.144706\n",
+       "dtype: float64"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "\n",
+    "agesPopulationMean3 = np.mean(agesPopulation3)\n",
+    "agesPopulationSTD3 = agesPopulation3.std()\n",
+    "\n",
+    "display(agesPopulationMean3)\n",
+    "display(agesPopulationSTD3)"
    ]
   },
   {
@@ -411,7 +1223,8 @@
    "outputs": [],
    "source": [
     "\"\"\"\n",
-    "your comments here\n",
+    "The average age is 41.9 ~ 42 years, wich is higher than the average ages in the previous datasets.\n",
+    "Standard Deviation is 16.144\n",
     "\"\"\""
    ]
   },
@@ -424,11 +1237,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 44,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1st quartile = 30.0\n",
+      "2nd quartile = 40.0\n",
+      "3rd quartile = 53.0\n"
+     ]
+    },
+    {
+     "ename": "TypeError",
+     "evalue": "unsupported operand type(s) for -: 'float' and 'function'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m/var/folders/mx/c92fsbbj77jdnshv3s_113900000gn/T/ipykernel_8298/3468935160.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"2nd quartile = \"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpercentile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0magesPopulation3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m50\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"3rd quartile = \"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpercentile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0magesPopulation3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m75\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"the difference between median and mean is \"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpercentile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0magesPopulation3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m50\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mmean\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for -: 'float' and 'function'"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "print(\"1st quartile = \" + str(np.percentile(agesPopulation3, 25)))\n",
+    "print(\"2nd quartile = \" + str(np.percentile(agesPopulation3, 50)))\n",
+    "print(\"3rd quartile = \" + str(np.percentile(agesPopulation3, 75)))\n",
+    "print(\"the difference between median and mean is \" + str(np.percentile(agesPopulation3, 50) - mean))"
    ]
   },
   {
@@ -451,11 +1289,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "# your code here\n"
    ]
   },
   {
@@ -500,9 +1338,9 @@
  ],
  "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 +1352,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.8.11"
   }
  },
  "nbformat": 4,