From 2535abdfe243529ef945ee20b3acd03cab411d09 Mon Sep 17 00:00:00 2001 From: jgoncsilva Date: Sat, 1 Aug 2020 18:42:44 +0100 Subject: [PATCH] =?UTF-8?q?[lab-understanding-descriptive-stats]Jhonatas?= =?UTF-8?q?=20Gon=C3=A7alo?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- your-code/main.ipynb | 712 ++++++++++++++++++++++++++++++++++++++----- 1 file changed, 628 insertions(+), 84 deletions(-) diff --git a/your-code/main.ipynb b/your-code/main.ipynb index a0a5b66..658e6fa 100644 --- a/your-code/main.ipynb +++ b/your-code/main.ipynb @@ -11,11 +11,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ - "# Libraries" + "# Libraries\n", + "import pandas as pd \n", + "import random \n", + "import matplotlib.pyplot as plt \n", + "import seaborn as sns\n", + "import numpy as np\n", + "import math" ] }, { @@ -29,11 +35,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ - "# your code here" + "# your code here\n", + "def dice():\n", + " return np.random.choice(range(1, 7), 10)\n", + "\n", + "df = pd.DataFrame(dice())" ] }, { @@ -45,11 +55,46 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 4, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": "0\n2 1\n5 1\n7 1\n9 2\n0 3\n6 4\n8 4\n4 5\n1 6\n3 6\n" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": "" + }, + "metadata": {}, + "execution_count": 4 + }, + { + "output_type": "display_data", + "data": { + "text/plain": "
", + "image/svg+xml": "\r\n\r\n\r\n\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n\r\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAD4CAYAAADmWv3KAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAANe0lEQVR4nO3df6zd9V3H8eeLFsLPBTOuC6Ncu8WJQwyj3tTNJp3CnLBNFg2akYDZsuX6BxDQxclcosxkiUYlM7osNsDE8CusQDKJMlAGZIvjR/khLWVuYwwqYCFz8sNEfr3943wZt6W0rO3new4fno/kpufce3reb25un/32e36QqkKS1J99pr2AJKkNAy9JnTLwktQpAy9JnTLwktSp5dNeYKnDDjusVq5cOe01JOl1Y8OGDU9U1dyOvjZTgV+5ciV33HHHtNeQpNeNJN9/ta95ikaSOmXgJalTBl6SOmXgJalTBl6SOmXgJalTTQOf5NAk65Pcn2Rzkve0nCdJelnr58H/NXBdVZ2SZD/gwMbzJEmDZoFP8iZgLfBRgKp6Fni21TxJ0rZaHsG/HXgc+FKSY4ENwNlV9czSGyVZBBYB5ufnG67z43voT39+tFnzf3zvaLN2x81r3zvarPfecvNos6SetTwHvxxYBXyxqo4DngHO3f5GVbWuqhaqamFubodvpyBJ2g0tA78F2FJVtw7X1zMJviRpBM0CX1WPAQ8nOWr41AnAfa3mSZK21fpZNGcBlw7PoHkA+FjjeZKkQdPAV9XdwELLGZKkHfOVrJLUKQMvSZ0y8JLUKQMvSZ0y8JLUKQMvSZ0y8JLUKQMvSZ0y8JLUKQMvSZ0y8JLUKQMvSZ0y8JLUKQMvSZ0y8JLUKQMvSZ0y8JLUKQMvSZ0y8JLUKQMvSZ0y8JLUKQMvSZ0y8JLUKQMvSZ0y8JLUqeUt7zzJg8BTwAvA81W10HKeJOllTQM/+JWqemKEOZKkJTxFI0mdan0EX8D1SQr4u6pat/0NkiwCiwDz8/ON19Hr3d9+8h9Hm3XmX/36aLOkFlofwa+pqlXAScAZSdZuf4OqWldVC1W1MDc313gdSXrjaBr4qnpk+HUrcA2wuuU8SdLLmgU+yUFJDnnpMvB+YGOreZKkbbU8B/8W4JokL825rKquazhPkrREs8BX1QPAsa3uX5K0cz5NUpI6ZeAlqVMGXpI6ZeAlqVMGXpI6ZeAlqVMGXpI6ZeAlqVMGXpI6ZeAlqVMGXpI6ZeAlqVMGXpI6ZeAlqVMGXpI6ZeAlqVMGXpI6ZeAlqVMGXpI6ZeAlqVMGXpI6ZeAlqVMGXpI6ZeAlqVMGXpI6ZeAlqVPNA59kWZK7klzbepYk6WVjHMGfDWweYY4kaYmmgU+yAvggcEHLOZKkV1re+P4/D3wKOOTVbpBkEVgEmJ+fb7yO1I/Nn7txtFnv/Mzxo83aHVd+efVos377t24bbdaeanYEn+RDwNaq2rCz21XVuqpaqKqFubm5VutI0htOy1M0a4CTkzwIXAEcn+SShvMkSUs0C3xVfbqqVlTVSuAjwI1VdVqreZKkbfk8eEnqVOsHWQGoqpuAm8aYJUma8Ahekjpl4CWpUwZekjpl4CWpUwZekjpl4CWpUwZekjpl4CWpUwZekjpl4CWpUwZekjpl4CWpUwZekjpl4CWpUwZekjpl4CWpUwZekjpl4CWpUwZekjpl4CWpUwZekjpl4CWpU8t3dYMkPwt8GDgCKOAR4CtVtbnxbpKkPbDTI/gkfwhcAQS4Dbh9uHx5knPbrydJ2l27OoL/OPBzVfXc0k8mOR/YBPxZq8UkSXtmV+fgXwTeuoPPHz587VUl2T/JbUnuSbIpyWd3d0lJ0o9vV0fw5wD/muTbwMPD5+aBnwbO3MXv/T/g+Kp6Osm+wNeT/HNVfXOPNpYkvSY7DXxVXZfkZ4DVTB5kDbAFuL2qXtjF7y3g6eHqvsNH7fHGkqTXZJfPoqmqF4HdOupOsgzYwOSI/wtVdesObrMILALMz8//6PO/8Af/sDsjd8uGv/id0WZJ0liaPg++ql6oqncBK4DVSY7ZwW3WVdVCVS3Mzc21XEeS3lBGeaFTVf0QuAk4cYx5kqSGgU8yl+TQ4fIBwPuA+1vNkyRta5fn4PfA4cDFw3n4fYArq+rahvMkSUs0C3xV/TtwXKv7lyTtnG82JkmdMvCS1CkDL0mdMvCS1CkDL0mdMvCS1CkDL0mdMvCS1CkDL0mdMvCS1CkDL0mdMvCS1CkDL0mdMvCS1CkDL0mdMvCS1CkDL0mdMvCS1CkDL0mdMvCS1CkDL0mdMvCS1CkDL0mdMvCS1CkDL0mdMvCS1KlmgU9yZJKvJdmcZFOSs1vNkiS90vKG9/088MmqujPJIcCGJDdU1X0NZ0qSBs2O4Kvq0aq6c7j8FLAZOKLVPEnStloewf9IkpXAccCtO/jaIrAIMD8/P8Y6krTXHbv+q6PNuueUX3tNt2v+IGuSg4GrgHOq6sntv15V66pqoaoW5ubmWq8jSW8YTQOfZF8mcb+0qq5uOUuStK2Wz6IJcCGwuarObzVHkrRjLY/g1wCnA8cnuXv4+EDDeZKkJZo9yFpVXwfS6v4lSTvnK1klqVMGXpI6ZeAlqVMGXpI6ZeAlqVMGXpI6ZeAlqVMGXpI6ZeAlqVMGXpI6ZeAlqVMGXpI6ZeAlqVMGXpI6ZeAlqVMGXpI6ZeAlqVMGXpI6ZeAlqVMGXpI6ZeAlqVMGXpI6ZeAlqVMGXpI6ZeAlqVPNAp/koiRbk2xsNUOS9OpaHsH/PXBiw/uXJO1Es8BX1S3AD1rdvyRp55ZPe4Eki8AiwPz8/JS3mU1r/mbNaLO+cdY3Rpv1evW5004ZbdZnLlk/2qzdcd5553U5qxdTf5C1qtZV1UJVLczNzU17HUnqxtQDL0lqw8BLUqdaPk3ycuDfgKOSbEny8VazJEmv1OxB1qo6tdV9S5J2zVM0ktQpAy9JnTLwktQpAy9JnTLwktQpAy9JnTLwktQpAy9JnTLwktQpAy9JnTLwktQpAy9JnTLwktQpAy9JnTLwktQpAy9JnTLwktQpAy9JnTLwktQpAy9JnTLwktQpAy9JnTLwktQpAy9JnTLwktQpAy9JnWoa+CQnJvlWku8kObflLEnStpoFPsky4AvAScDRwKlJjm41T5K0rZZH8KuB71TVA1X1LHAF8OGG8yRJS6Sq2txxcgpwYlV9Yrh+OvCLVXXmdrdbBBaHq0cB39qDsYcBT+zB799bZmGPWdgBZmOPWdgBZmOPWdgBZmOPWdgB9nyPn6qquR19Yfke3OmuZAefe8XfJlW1Dli3VwYmd1TVwt64r9f7HrOww6zsMQs7zMoes7DDrOwxCzu03qPlKZotwJFLrq8AHmk4T5K0RMvA3w68I8nbkuwHfAT4SsN5kqQlmp2iqarnk5wJfBVYBlxUVZtazRvslVM9e8Es7DELO8Bs7DELO8Bs7DELO8Bs7DELO0DDPZo9yCpJmi5fySpJnTLwktSpbgI/C2+LkOSiJFuTbJzG/GGHI5N8LcnmJJuSnD2FHfZPcluSe4YdPjv2DtvtsyzJXUmundL8B5Pcm+TuJHdMY4dhj0OTrE9y//Dz8Z6R5x81fA9e+ngyyTlj7rBkl98bfjY3Jrk8yf5T2OHsYf6mZt+HqnrdfzB5EPe7wNuB/YB7gKOnsMdaYBWwcYrfi8OBVcPlQ4D/GPt7weQ1EAcPl/cFbgXePcXvye8DlwHXTmn+g8Bh0/rvX7LHxcAnhsv7AYdOcZdlwGNMXqQz9uwjgO8BBwzXrwQ+OvIOxwAbgQOZPNnlX4B37O05vRzBz8TbIlTVLcAPxp673Q6PVtWdw+WngM1MfqDH3KGq6unh6r7Dx1QezU+yAvggcME05s+KJG9icgByIUBVPVtVP5ziSicA362q709p/nLggCTLmUR27NfovBP4ZlX9b1U9D9wM/MbeHtJL4I8AHl5yfQsjR20WJVkJHMfkCHrs2cuS3A1sBW6oqtF3GHwe+BTw4pTmw+Qvt+uTbBjemmMa3g48DnxpOF11QZKDprQLTF4Xc/k0BlfVfwJ/CTwEPAr8T1VdP/IaG4G1Sd6c5EDgA2z7wtC9opfAv6a3RXgjSXIwcBVwTlU9Ofb8qnqhqt7F5BXMq5McM/YOST4EbK2qDWPP3s6aqlrF5J1Vz0iydgo7LGdy+vCLVXUc8Awwrceq9gNOBr48pfk/weRf+G8D3goclOS0MXeoqs3AnwM3ANcxOa38/N6e00vgfVuEJZLsyyTul1bV1dPcZTgNcBNw4hTGrwFOTvIgk9N2xye5ZOwlquqR4detwDVMTimObQuwZcm/pNYzCf40nATcWVX/NaX57wO+V1WPV9VzwNXAL429RFVdWFWrqmotk1O7397bM3oJvG+LMEgSJudZN1fV+VPaYS7JocPlA5j8gbp/7D2q6tNVtaKqVjL5mbixqkY9UktyUJJDXroMvJ/JP89HVVWPAQ8nOWr41AnAfWPvMTiVKZ2eGTwEvDvJgcOflxOYPFY1qiQ/Ofw6D/wmDb4nLd9NcjQ1nbdFeIUklwO/DByWZAvwJ1V14chrrAFOB+4dzoED/FFV/dOIOxwOXDz8T1/2Aa6sqqk8RXEGvAW4ZtIRlgOXVdV1U9rlLODS4SDoAeBjYy8wnG/+VeB3x579kqq6Ncl64E4mp0XuYjpvW3BVkjcDzwFnVNV/7+0BvlWBJHWql1M0kqTtGHhJ6pSBl6ROGXhJ6pSBl6ROGXhJ6pSBl6RO/T+yJAx/gBlszAAAAABJRU5ErkJggg==\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], "source": [ - "# your code here" + "# your code here\n", + "df.sort_values(by=0, ascending=True,inplace=True)\n", + "print(df)\n", + "sns.barplot(df.index,df[0])\n", + "#plt.hist(df[0])\n", + "\n", + "#Tempo - Line Graph\n", + "#Frequencia de uma coluna - Histogram ou Bar\n", + "#Mais de uma coluna - Scatterplot" ] }, { @@ -61,21 +106,56 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 5, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": "1 3\n6 2\n4 2\n5 1\n3 1\n2 1\nName: 0, dtype: int64\n" + }, + { + "output_type": "display_data", + "data": { + "text/plain": "
", + "image/svg+xml": "\r\n\r\n\r\n\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n\r\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD4CAYAAAD8Zh1EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAOCUlEQVR4nO3dbYild3nH8e/P7PpQnwLdoS774FgSBJVq0iEqAQlqy2qCKTSFCI0PKAti2kiFEn0R0Vf6RotGDFuTmtjUBxKVra61FhX1hdHZ7SYxWYWtpGRISjZGE1Otsvbqi7ktw+TMnDM7Z3IyV74fGPacc//nnOtmyTf33nOfM6kqJEnb31NmPYAkaToMuiQ1YdAlqQmDLklNGHRJamLHrF54165dNT8/P6uXl6Rt6ejRow9W1dyobTML+vz8PIuLi7N6eUnalpL851rbPOUiSU0YdElqwqBLUhMGXZKaMOiS1IRBl6QmxgY9ydOTfD/J7UnuSvL+EWueluRzSU4muS3J/FYMK0la2yRH6L8GXl1VLwVeBhxI8opVa94G/KyqzgE+AnxoumNKksYZG/Ra9uhwd+fwtfpD1C8Fbhxu3wK8JkmmNqUkaayJ3ima5CzgKHAO8PGqum3Vkj3AvQBVdTrJw8DvAw+uep6DwEGA/fv3n/HQ81d/5Yy/9/F0zwcvnvUIkp5EJvqhaFX9tqpeBuwFLkjyklVLRh2NP+ZXIVXVoapaqKqFubmRH0UgSTpDG7rKpap+DnwLOLBq0xKwDyDJDuC5wENTmE+SNKFJrnKZS3L2cPsZwGuBH61adhh483D7MuAb5S8rlaTH1STn0HcDNw7n0Z8CfL6qvpzkA8BiVR0Grgc+neQky0fml2/ZxJKkkcYGvaruAM4b8fg1K27/D/AX0x1NkrQRvlNUkpow6JLUhEGXpCYMuiQ1YdAlqQmDLklNGHRJasKgS1ITBl2SmjDoktSEQZekJgy6JDVh0CWpCYMuSU0YdElqwqBLUhMGXZKaMOiS1IRBl6QmDLokNWHQJakJgy5JTRh0SWrCoEtSEwZdkpoYG/Qk+5J8M8mJJHcluWrEmouSPJzk+PB1zdaMK0lay44J1pwG3l1Vx5I8Gzia5OtVdfeqdd+pqkumP6IkaRJjj9Cr6v6qOjbc/gVwAtiz1YNJkjZmQ+fQk8wD5wG3jdj8yiS3J/lqkhev8f0HkywmWTx16tSGh5UkrW3ioCd5FnAr8K6qemTV5mPA86vqpcDHgC+Neo6qOlRVC1W1MDc3d6YzS5JGmCjoSXayHPObq+oLq7dX1SNV9ehw+wiwM8muqU4qSVrXJFe5BLgeOFFVH15jzfOGdSS5YHjen05zUEnS+ia5yuVC4ArgziTHh8feC+wHqKrrgMuAdyQ5DfwKuLyqagvmlSStYWzQq+q7QMasuRa4dlpDSZI2zneKSlITBl2SmjDoktSEQZekJgy6JDVh0CWpCYMuSU0YdElqwqBLUhMGXZKaMOiS1IRBl6QmDLokNWHQJakJgy5JTRh0SWrCoEtSEwZdkpow6JLUhEGXpCYMuiQ1YdAlqQmDLklNGHRJasKgS1ITBl2Smhgb9CT7knwzyYkkdyW5asSaJPlokpNJ7khy/taMK0lay44J1pwG3l1Vx5I8Gzia5OtVdfeKNa8Dzh2+Xg58YvhTkvQ4GXuEXlX3V9Wx4fYvgBPAnlXLLgVuqmXfA85Osnvq00qS1jTJEfr/SzIPnAfctmrTHuDeFfeXhsfuX/X9B4GDAPv379/YpNKMzV/9lVmPMJF7PnjxrEeYmSf739HEPxRN8izgVuBdVfXI6s0jvqUe80DVoapaqKqFubm5jU0qSVrXREFPspPlmN9cVV8YsWQJ2Lfi/l7gvs2PJ0ma1CRXuQS4HjhRVR9eY9lh4E3D1S6vAB6uqvvXWCtJ2gKTnEO/ELgCuDPJ8eGx9wL7AarqOuAI8HrgJPBL4K3TH1WStJ6xQa+q7zL6HPnKNQW8c1pDSZI2zneKSlITBl2SmjDoktSEQZekJgy6JDVh0CWpCYMuSU0YdElqwqBLUhMGXZKaMOiS1IRBl6QmDLokNWHQJakJgy5JTRh0SWrCoEtSEwZdkpow6JLUhEGXpCYMuiQ1YdAlqQmDLklNGHRJasKgS1ITY4Oe5IYkDyT54RrbL0rycJLjw9c10x9TkjTOjgnWfAq4FrhpnTXfqapLpjKRJOmMjD1Cr6pvAw89DrNIkjZhWufQX5nk9iRfTfLitRYlOZhkMcniqVOnpvTSkiSYTtCPAc+vqpcCHwO+tNbCqjpUVQtVtTA3NzeFl5Yk/c6mg15Vj1TVo8PtI8DOJLs2PZkkaUM2HfQkz0uS4fYFw3P+dLPPK0namLFXuST5DHARsCvJEvA+YCdAVV0HXAa8I8lp4FfA5VVVWzaxJGmksUGvqjeO2X4ty5c1SpJmyHeKSlITBl2SmjDoktSEQZekJgy6JDVh0CWpCYMuSU0YdElqwqBLUhMGXZKaMOiS1IRBl6QmDLokNWHQJakJgy5JTRh0SWrCoEtSEwZdkpow6JLUhEGXpCYMuiQ1YdAlqQmDLklNGHRJasKgS1ITBl2Smhgb9CQ3JHkgyQ/X2J4kH01yMskdSc6f/piSpHEmOUL/FHBgne2vA84dvg4Cn9j8WJKkjRob9Kr6NvDQOksuBW6qZd8Dzk6ye1oDSpIms2MKz7EHuHfF/aXhsftXL0xykOWjePbv3z+Fl+5j/uqvzHqEidzzwYsnWtdtfzry76ifafxQNCMeq1ELq+pQVS1U1cLc3NwUXlqS9DvTCPoSsG/F/b3AfVN4XknSBkwj6IeBNw1Xu7wCeLiqHnO6RZK0tcaeQ0/yGeAiYFeSJeB9wE6AqroOOAK8HjgJ/BJ461YNK0la29igV9Ubx2wv4J1Tm0iSdEZ8p6gkNWHQJakJgy5JTRh0SWrCoEtSEwZdkpow6JLUhEGXpCYMuiQ1YdAlqQmDLklNGHRJasKgS1ITBl2SmjDoktSEQZekJgy6JDVh0CWpCYMuSU0YdElqwqBLUhMGXZKaMOiS1IRBl6QmDLokNTFR0JMcSPLjJCeTXD1i+1uSnEpyfPh6+/RHlSStZ8e4BUnOAj4O/AmwBPwgyeGqunvV0s9V1ZVbMKMkaQKTHKFfAJysqp9U1W+AzwKXbu1YkqSNmiToe4B7V9xfGh5b7c+T3JHkliT7Rj1RkoNJFpMsnjp16gzGlSStZZKgZ8Rjter+PwPzVfVHwL8BN456oqo6VFULVbUwNze3sUklSeuaJOhLwMoj7r3AfSsXVNVPq+rXw92/B/54OuNJkiY1SdB/AJyb5AVJngpcDhxeuSDJ7hV33wCcmN6IkqRJjL3KpapOJ7kS+BpwFnBDVd2V5APAYlUdBv46yRuA08BDwFu2cGZJ0ghjgw5QVUeAI6seu2bF7fcA75nuaJKkjfCdopLUhEGXpCYMuiQ1YdAlqQmDLklNGHRJasKgS1ITBl2SmjDoktSEQZekJgy6JDVh0CWpCYMuSU0YdElqwqBLUhMGXZKaMOiS1IRBl6QmDLokNWHQJakJgy5JTRh0SWrCoEtSEwZdkpow6JLUhEGXpCYmCnqSA0l+nORkkqtHbH9aks8N229LMj/tQSVJ6xsb9CRnAR8HXge8CHhjkhetWvY24GdVdQ7wEeBD0x5UkrS+SY7QLwBOVtVPquo3wGeBS1etuRS4cbh9C/CaJJnemJKkcVJV6y9ILgMOVNXbh/tXAC+vqitXrPnhsGZpuP8fw5oHVz3XQeDgcPeFwI+ntSNTsAt4cOyq7aXbPnXbH+i3T932B554+/T8qpobtWHHBN886kh79f8FJllDVR0CDk3wmo+7JItVtTDrOaap2z512x/ot0/d9ge21z5NcsplCdi34v5e4L611iTZATwXeGgaA0qSJjNJ0H8AnJvkBUmeClwOHF615jDw5uH2ZcA3aty5HEnSVI095VJVp5NcCXwNOAu4oaruSvIBYLGqDgPXA59OcpLlI/PLt3LoLfKEPBW0Sd32qdv+QL996rY/sI32aewPRSVJ24PvFJWkJgy6JDXxpA96khuSPDBcS7/tJdmX5JtJTiS5K8lVs55ps5I8Pcn3k9w+7NP7Zz3TNCQ5K8m/J/nyrGeZhiT3JLkzyfEki7OeZ7OSnJ3kliQ/Gv57euWsZxrnSX8OPcmrgEeBm6rqJbOeZ7OS7AZ2V9WxJM8GjgJ/VlV3z3i0Mza86/iZVfVokp3Ad4Grqup7Mx5tU5L8DbAAPKeqLpn1PJuV5B5gYfUbCrerJDcC36mqTw5X+P1eVf181nOt50l/hF5V36bRNfNVdX9VHRtu/wI4AeyZ7VSbU8seHe7uHL629ZFIkr3AxcAnZz2LHivJc4BXsXwFH1X1myd6zMGgtzZ86uV5wG2znWTzhtMTx4EHgK9X1Xbfp78D/hb431kPMkUF/GuSo8PHfGxnfwicAv5hOC32ySTPnPVQ4xj0ppI8C7gVeFdVPTLreTarqn5bVS9j+Z3KFyTZtqfHklwCPFBVR2c9y5RdWFXns/zJrO8cTmduVzuA84FPVNV5wH8Dj/no8Ccag97QcJ75VuDmqvrCrOeZpuGfvd8CDsx4lM24EHjDcM75s8Crk/zjbEfavKq6b/jzAeCLLH9S63a1BCyt+JfgLSwH/gnNoDcz/ADxeuBEVX141vNMQ5K5JGcPt58BvBb40WynOnNV9Z6q2ltV8yy/q/obVfWXMx5rU5I8c/ghPMOpiT8Ftu2VY1X1X8C9SV44PPQa4Al/YcEkn7bYWpLPABcBu5IsAe+rqutnO9WmXAhcAdw5nHMGeG9VHZnhTJu1G7hx+GUrTwE+X1UtLvVr5A+ALw6/BmEH8E9V9S+zHWnT/gq4ebjC5SfAW2c8z1hP+ssWJakLT7lIUhMGXZKaMOiS1IRBl6QmDLokNWHQJakJgy5JTfwfaQSQKi1B+UIAAAAASUVORK5CYII=\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], "source": [ - "# your code here" + "# your code here\n", + "freq = df[0].value_counts()\n", + "print(freq)\n", + "\n", + "plt.bar(freq.index, freq)\n", + "plt.show()\n", + "\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "\"\\nyour comments here\\n# They have the same values but the 2nd plot organizes the data by bin and count the frequency that the number appears. So it's like when we use barplot we have the times for X axis. \\n\"" + }, + "metadata": {}, + "execution_count": 6 + } + ], "source": [ "\"\"\"\n", "your comments here\n", + "# They have the same values but the 2nd plot organizes the data by bin and count the frequency that the number appears. So it's like when we use barplot we have the times for X axis. \n", "\"\"\"" ] }, @@ -91,11 +171,24 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 7, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": "Dice results mean: 3.3\n" + } + ], "source": [ - "# your code here" + "# your code here\n", + "#Mean - 2 Ways - Median / Quantiles (0.25,0.50,0.75,1.0) \n", + "def mean_func (n):\n", + " return n.sum()/len(n)\n", + "\n", + "print('Dice results mean:', mean_func(df[0]))\n" ] }, { @@ -107,11 +200,21 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 8, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": "Dice results mean: 1.6666666666666667\n" + } + ], "source": [ - "# your code here" + "# your code here\n", + "freq_distr = pd.DataFrame(freq)\n", + "print('Dice results mean:', mean_func(freq_distr[0]))" ] }, { @@ -124,11 +227,31 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 9, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": "Median for the results of the dice(df): 3.5\nMedian for frequency Distribution: 1.5\n" + } + ], "source": [ - "# your code here" + "# your code here\n", + "import math\n", + "def median_value(n):\n", + " sorted_n = n.sort_values()\n", + " if (len(n)%2 == 0):\n", + " first = sorted_n.values[int(len(sorted_n)*.5)]\n", + " second = sorted_n.values[int(len(sorted_n)*.5-1)]\n", + " return (first+second)/2\n", + " else:\n", + " return sorted_n.values[math.floor(len(sorted_n)*.5)]\n", + "\n", + "print('Median for the results of the dice(df):', median_value(df[0]))\n", + "print('Median for frequency Distribution:', median_value(freq_distr[0]))" ] }, { @@ -140,13 +263,100 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": "First Quartile: 1.0\nSecond Quartile: 3.5\nThird Quartile : 4.5\nFouth Quartile: 6\n" + } + ], + "source": [ + "# your code here\n", + "#(0.25,0.50,0.75, 10)\n", + "def quartiles_pri(n):\n", + " sorted_n = n.sort_values()\n", + " if (len(n)%2 == 0):\n", + " first = sorted_n.values[int(len(sorted_n)*.25)]\n", + " second = sorted_n.values[int(len(sorted_n)*.25 - 1)]\n", + " print('First Quartile:',(first+second)/2)\n", + " else:\n", + " print(' 1º Quartile:', sorted_n.values[math.floor(len(sorted_n)*.25)])\n", + "\n", + " if (len(n)%2 == 0):\n", + " first = sorted_n.values[int(len(sorted_n)*.5)]\n", + " second = sorted_n.values[int(len(sorted_n)*.5-1)]\n", + " print('Second Quartile:',(first+second)/2)\n", + " else:\n", + " print('2º Quartile:', sorted_n.values[math.floor(len(sorted_n)*.5)])\n", + "\n", + " if (len(n)%2 == 0):\n", + " first = sorted_n.values[int(len(sorted_n)*.75)]\n", + " second = sorted_n.values[int(len(sorted_n)*.75 - 1)]\n", + " print('Third Quartile :',(first+second)/2)\n", + " else:\n", + " print('3 º Quartile:', sorted_n.values[math.floor(len(sorted_n)*.75)])\n", + " \n", + " print('Fouth Quartile:', sorted_n.values[int(len(sorted_n))-1])\n", + "\n", + "\n", + "'''def quartiles_func (x):\n", + " fifty = len(x)//2\n", + " split_x = x[:fifty], x[fifty:]\n", + " print('1º Quartile:', [median_value(i) for i in split_x][0][0])\n", + " print('2º Quartile:', [median_value(i) for i in split_x][1])\n", + "\n", + "quartiles_func(df[0])'''\n", + "quartiles_pri(df[0])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "'def quartiles(x):\\n import math\\n df=pd.Series(x)\\n sort=df.sort_values().reset_index()[0]\\n qx=sort[:math.ceil(len(x)*0.5)-1]\\n #print(qx)\\n asn=median_dumb(qx)\\n print(\"Q1 :\", asn)\\n qx=sort\\n asn=median_dumb(qx)\\n print(\"Q2 :\", asn)\\n qx=sort[math.ceil(len(x)*0.5)+1:]\\n #print(qx)\\n asn=median_dumb(qx)\\n print(\"Q3 :\",asn)\\n qx=sort.values[-1]\\n asn=qx\\n print(\"Q4 :\", asn)\\nquartiles(x)'" + }, + "metadata": {}, + "execution_count": 11 + } + ], "source": [ - "# your code here" + "\"JOAO INVERNO SOLUTION \"\n", + "'''def quartiles(x):\n", + " import math\n", + " df=pd.Series(x)\n", + " sort=df.sort_values().reset_index()[0]\n", + " qx=sort[:math.ceil(len(x)*0.5)-1]\n", + " #print(qx)\n", + " asn=median_dumb(qx)\n", + " print(\"Q1 :\", asn)\n", + " qx=sort\n", + " asn=median_dumb(qx)\n", + " print(\"Q2 :\", asn)\n", + " qx=sort[math.ceil(len(x)*0.5)+1:]\n", + " #print(qx)\n", + " asn=median_dumb(qx)\n", + " print(\"Q3 :\",asn)\n", + " qx=sort.values[-1]\n", + " asn=qx\n", + " print(\"Q4 :\", asn)\n", + "quartiles(x)'''" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "metadata": {}, @@ -158,22 +368,96 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": " Unnamed: 0 roll value\n0 0 0 1\n1 1 1 2\n2 2 2 6\n3 3 3 1\n4 4 4 6", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Unnamed: 0rollvalue
0001
1112
2226
3331
4446
\n
" + }, + "metadata": {}, + "execution_count": 12 + } + ], "source": [ - "# your code here" + "# your code here\n", + "data = pd.read_csv('../data/roll_the_dice_hundred.csv')\n", + "data.head()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "
", + "image/svg+xml": "\r\n\r\n\r\n\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n\r\n", + "image/png": "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\n" + }, + "metadata": { + "needs_background": "light" + } + }, + { + "output_type": "display_data", + "data": { + "text/plain": "
", + "image/svg+xml": "\r\n\r\n\r\n\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n\r\n", + "image/png": "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\n" + }, + "metadata": { + "needs_background": "light" + } + }, + { + "output_type": "display_data", + "data": { + "text/plain": "
", + "image/svg+xml": "\r\n\r\n\r\n\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n\r\n", + "image/png": "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\n" + }, + "metadata": { + "needs_background": "light" + } + }, + { + "output_type": "display_data", + "data": { + "text/plain": "
", + "image/svg+xml": "\r\n\r\n\r\n\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n\r\n", + "image/png": "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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], "source": [ "\"\"\"\n", "your comments here\n", - "\"\"\"" + "6 and 4 have more frequency \n", + "\n", + "\"\"\"\n", + "sorted_data = data['value'].sort_values() #Order\n", + "freq_data = sorted_data.value_counts(ascending=True) #Aggregate and COunt \n", + "\n", + "#Plot Aggregata \n", + "freq_data.plot.bar()\n", + "plt.show()\n", + "\n", + "plt.bar(freq_data.index,freq_data) #Got the values of freq, buscar o index(bins), y = Freq calculada \n", + "plt.show()\n", + "#Sorted values without agregation\n", + "plt.hist(sorted_data)\n", + "plt.show()\n", + "\n", + "sorted_data.plot(kind = 'bar')\n", + "plt.show()" ] }, { @@ -185,11 +469,21 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 16, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": "The mean of this values : 3.74\n" + } + ], "source": [ - "# your code here" + "# your code here\n", + "data_mean = mean_func(data['value'])\n", + "print('The mean of this values :',data_mean)\n" ] }, { @@ -201,11 +495,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "1 12\n5 12\n3 14\n2 17\n4 22\n6 23\nName: value, dtype: int64" + }, + "metadata": {}, + "execution_count": 17 + } + ], "source": [ - "# your code here" + "freq_data" ] }, { @@ -217,11 +520,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "
", + "image/svg+xml": "\r\n\r\n\r\n\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n\r\n", + "image/png": "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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], "source": [ - "# your code here" + "# your code here\n", + "#plt.hist(freq_data, median_value(data))\n", + "plt.bar(freq_data.index,freq_data)\n", + "plt.axvline(data_mean, color='red', linestyle='dashed', linewidth=1)\n", + "plt.show()" ] }, { @@ -244,22 +563,46 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "
", + "image/svg+xml": "\r\n\r\n\r\n\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n\r\n", + "image/png": "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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], "source": [ - "# your code here" + "# your code here\n", + "data_thousand = pd.read_csv('../data/roll_the_dice_thousand.csv')\n", + "\n", + "#q1, q3 = np.percentile(data_thousand, 25), np.percentile(data_thousand, 75)\n", + "\n", + "data_thousand['value'].plot(x='value', y='roll', kind='hist')\n", + "plt.axvline(data_mean, color='red', linestyle='dashed', linewidth=1)\n", + "\n", + "\n", + "plt.show()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "\"\"\"\n", "your comments here\n", - "\"\"\"" + "\"\"\"\n", + "def frequency (n):\n", + " return n.value_counts()\n", + "\n" ] }, { @@ -274,11 +617,47 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 24, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": "The frequency is : {73.0: 1, 4.0: 1, 82.0: 1, 69.0: 1, 71.0: 1, 7.0: 1, 70.0: 1, 64.0: 2, 1.0: 2, 2.0: 2, 65.0: 2, 6.0: 2, 5.0: 2, 9.0: 2, 61.0: 2, 66.0: 3, 68.0: 3, 10.0: 3, 11.0: 3, 67.0: 4, 62.0: 4, 60.0: 4, 8.0: 5, 13.0: 6, 58.0: 7, 18.0: 7, 57.0: 7, 63.0: 7, 59.0: 8, 16.0: 8, 15.0: 8, 51.0: 9, 17.0: 10, 14.0: 10, 12.0: 11, 19.0: 11, 53.0: 12, 54.0: 13, 20.0: 13, 55.0: 13, 52.0: 14, 21.0: 14, 56.0: 15, 50.0: 16, 22.0: 16, 23.0: 17, 47.0: 17, 24.0: 18, 48.0: 19, 49.0: 19, 25.0: 19, 28.0: 20, 33.0: 22, 46.0: 23, 26.0: 23, 44.0: 23, 31.0: 24, 27.0: 25, 29.0: 26, 40.0: 27, 45.0: 29, 34.0: 29, 38.0: 30, 32.0: 30, 37.0: 30, 36.0: 31, 42.0: 32, 43.0: 32, 35.0: 33, 30.0: 34, 41.0: 36, 39.0: 45}\nValue of Mean: 36.56\nValue of std 12.816499625976762\n" + }, + { + "output_type": "display_data", + "data": { + "text/plain": "
", + "image/svg+xml": "\r\n\r\n\r\n\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n\r\n", + "image/png": "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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], "source": [ - "# your code here" + "# your code here\n", + "ages_pop = pd.read_csv('../data/ages_population.csv')\n", + "ages_pop.head()\n", + "\n", + "frequencia = frequency(ages_pop['observation']).sort_values()\n", + "#Calculate\n", + "ages_std = ages_pop['observation'].std()\n", + "ages_mean = ages_pop['observation'].mean()\n", + "plt.axvline(ages_std, color='red', linestyle='dashed', linewidth=1)\n", + "plt.axvline(ages_mean, color='yellow', linestyle='dashed', linewidth=1)\n", + "\n", + "plt.hist(ages_pop['observation'])\n", + "print('The frequency is :', dict(frequencia))\n", + "print('Value of Mean:', ages_mean)\n", + "print('Value of std', ages_std )\n", + "#plt.axvline(, color='red', linestyle='dashed', linewidth=1)\n", + "\n", + "\n" ] }, { @@ -294,7 +673,8 @@ "metadata": {}, "outputs": [], "source": [ - "# your code here" + "# your code here\n", + "#I already did" ] }, { @@ -305,6 +685,7 @@ "source": [ "\"\"\"\n", "your comments here\n", + "# Yes they fall but i prefer to plot. \n", "\"\"\"" ] }, @@ -317,11 +698,43 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 25, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": "Resume of population 2 :\n index observation\n0 0 25.0\n1 1 31.0\n2 2 29.0\n3 3 31.0\n4 4 29.0\n.. ... ...\n995 995 26.0\n996 996 22.0\n997 997 21.0\n998 998 19.0\n999 999 28.0\n\n[1000 rows x 2 columns]\n\n\nFrequency of Ages:\n index observation\n0 28.0 139\n1 27.0 125\n2 26.0 120\n3 29.0 115\n4 25.0 98\n5 30.0 90\n6 24.0 78\n7 31.0 61\n8 23.0 41\n9 22.0 35\n10 32.0 31\n11 33.0 22\n12 21.0 17\n13 20.0 13\n14 34.0 7\n15 19.0 3\n16 35.0 3\n17 36.0 2\n\n\n" + }, + { + "output_type": "display_data", + "data": { + "text/plain": "
", + "image/svg+xml": "\r\n\r\n\r\n\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n\r\n", + "image/png": "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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], "source": [ - "# your code here" + "# your code here\n", + "ages_pop_2 = pd.read_csv('../data/ages_population2.csv')\n", + "print('Resume of population 2 :\\n ', ages_pop_2.reset_index())\n", + "# Sorting values and creating one index by the number \n", + "#ages_pop_2.sort_values(normalize=False, sort=True, ascending=True).reset_index()\n", + "print('\\n')\n", + "frequencia2 = frequency(ages_pop_2['observation']).sort_values(ascending=False)\n", + "print('Frequency of Ages:\\n',frequencia2.reset_index())\n", + "\n", + "print('\\n')\n", + "\n", + "plt.hist(ages_pop_2['observation'])\n", + "\n", + "plt.show()\n" ] }, { @@ -339,6 +752,7 @@ "source": [ "\"\"\"\n", "your comments here\n", + "Yes any people with more then 36~37 Years, with the frequency indicates a lot of people between 26~29 where the most part of the cases ocurred.\n", "\"\"\"" ] }, @@ -351,11 +765,41 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 38, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": "The frequency is : {28.0: 139, 27.0: 125, 26.0: 120, 29.0: 115, 25.0: 98, 30.0: 90, 24.0: 78, 31.0: 61, 23.0: 41, 22.0: 35, 32.0: 31, 33.0: 22, 21.0: 17, 20.0: 13, 34.0: 7, 19.0: 3, 35.0: 3, 36.0: 2}\nValue of Mean: 28\nValue of std 2.969813932689186\n" + }, + { + "output_type": "display_data", + "data": { + "text/plain": "
", + "image/svg+xml": "\r\n\r\n\r\n\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n\r\n", + "image/png": "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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], "source": [ - "# your code here" + "# your code here\n", + "ages_pop_2 = pd.read_csv('../data/ages_population2.csv')\n", + "ages_std2 = ages_pop_2['observation'].std()\n", + "ages_mean2 = ages_pop_2['observation'].mean()\n", + "plt.axvline(ages_mean2, color='yellow', linestyle='dashed', linewidth=1)\n", + "plt.axvspan( ages_std2, color='red', linestyle='dashed', linewidth=1, xmax=.5)\n", + "\n", + "\n", + "plt.hist(ages_pop_2['observation'])\n", + "print('The frequency is :', dict(frequencia2))\n", + "print('Value of Mean:', math.ceil(ages_mean2))\n", + "print('Value of std', ages_std2 )" ] }, { @@ -366,6 +810,8 @@ "source": [ "\"\"\"\n", "your comments here\n", + "The standard deviation is less spreadout \n", + "\n", "\"\"\"" ] }, @@ -381,11 +827,36 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 6, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": "Sample 3 : index observation\n0 0 21.0\n1 1 21.0\n2 2 24.0\n3 3 31.0\n4 4 54.0\n.. ... ...\n995 995 16.0\n996 996 55.0\n997 997 30.0\n998 998 35.0\n999 999 43.0\n\n[1000 rows x 2 columns]\nFrequency of Count Values index observation\n0 32.0 37\n1 37.0 31\n2 35.0 31\n3 39.0 29\n4 36.0 26\n.. ... ...\n70 8.0 1\n71 7.0 1\n72 5.0 1\n73 76.0 1\n74 1.0 1\n\n[75 rows x 2 columns]\n" + }, + { + "output_type": "display_data", + "data": { + "text/plain": "
", + "image/svg+xml": "\r\n\r\n\r\n\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n\r\n", + "image/png": "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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], "source": [ - "# your code here" + "# your code here\n", + "ages_pop_3 = pd.read_csv('../data/ages_population3.csv')\n", + "print('Sample 3 :', ages_pop_3.reset_index())\n", + "frequencia3 = frequency(ages_pop_3['observation'].sort_values(ascending=False))\n", + "print('Frequency of Count Values', frequencia3.reset_index())\n", + "plt.hist(ages_pop_3['observation'])\n", + "plt.show()" ] }, { @@ -397,11 +868,40 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 9, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": "Frequency (Counts for each age) : {32.0: 37, 37.0: 31, 35.0: 31, 39.0: 29, 36.0: 26, 48.0: 25, 41.0: 25, 29.0: 25, 38.0: 25, 30.0: 24, 45.0: 24, 67.0: 22, 43.0: 22, 46.0: 22, 50.0: 21, 31.0: 21, 40.0: 21, 34.0: 20, 70.0: 19, 24.0: 19, 27.0: 19, 66.0: 19, 28.0: 18, 49.0: 18, 68.0: 17, 33.0: 17, 25.0: 17, 44.0: 17, 52.0: 17, 69.0: 17, 65.0: 15, 47.0: 15, 26.0: 15, 42.0: 14, 53.0: 14, 51.0: 14, 21.0: 14, 55.0: 13, 19.0: 12, 64.0: 12, 63.0: 12, 23.0: 11, 20.0: 11, 71.0: 11, 22.0: 11, 17.0: 10, 56.0: 10, 54.0: 9, 16.0: 9, 18.0: 9, 72.0: 8, 57.0: 8, 59.0: 8, 15.0: 8, 73.0: 6, 74.0: 6, 58.0: 6, 60.0: 6, 61.0: 6, 14.0: 5, 12.0: 4, 62.0: 3, 75.0: 2, 77.0: 2, 2.0: 2, 13.0: 2, 11.0: 2, 10.0: 2, 4.0: 2, 9.0: 1, 8.0: 1, 7.0: 1, 5.0: 1, 76.0: 1, 1.0: 1}\nValue of Mean: 42\nValue of std 16.144705959865934\n" + }, + { + "output_type": "display_data", + "data": { + "text/plain": "
", + "image/svg+xml": "\r\n\r\n\r\n\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n\r\n", + "image/png": "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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], "source": [ - "# your code here" + "# your code here\n", + "ages_std3 = ages_pop_3['observation'].std()\n", + "ages_mean3 = ages_pop_3['observation'].mean()\n", + "plt.axvline(ages_mean3, color='yellow', linestyle='dashed', linewidth=1)\n", + "plt.axvline( ages_std3, color='red', linestyle='dashed', linewidth=1)\n", + "\n", + "\n", + "plt.hist(ages_pop_3['observation'])\n", + "print('Frequency (Counts for each age) :', dict(frequencia3))\n", + "print('Value of Mean:', math.ceil(ages_mean3))\n", + "print('Value of std', ages_std3 )" ] }, { @@ -412,6 +912,7 @@ "source": [ "\"\"\"\n", "your comments here\n", + "WE have more old people between 60~70, but the mean it's around 42. I can see a lot of old people maybe this country is Portugal :D, but this values have influence in the postion value of the mean .\n", "\"\"\"" ] }, @@ -424,11 +925,21 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 10, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": "count 1000.000000\nmean 41.989000\nstd 16.144706\nmin 1.000000\n25% 30.000000\n50% 40.000000\n75% 53.000000\nmax 77.000000\nName: observation, dtype: float64\nDiff between Mean and Median -1.9889999999999972\n" + } + ], "source": [ - "# your code here" + "# your code here\n", + "print(ages_pop_3['observation'].describe())\n", + "print('Diff between Mean and Median', ages_pop_3['observation'].median()-ages_pop_3['observation'].mean())" ] }, { @@ -451,22 +962,55 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, - "outputs": [], + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "count 1000.000000\nmean 41.989000\nstd 16.144706\nmin 1.000000\n25% 30.000000\n50% 40.000000\n75% 53.000000\nmax 77.000000\nName: observation, dtype: float64" + }, + "metadata": {}, + "execution_count": 24 + } + ], "source": [ - "# your code here" + "# your code here\n", + "#They are already calculated by PANDAS :D\n", + "ages_pop_3['observation'].describe()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": "" + }, + "metadata": {}, + "execution_count": 22 + }, + { + "output_type": "display_data", + "data": { + "text/plain": "
", + "image/svg+xml": "\r\n\r\n\r\n\r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n \r\n\r\n", + "image/png": "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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], "source": [ "\"\"\"\n", "your comments here\n", - "\"\"\"" + "\"\"\"\n", + "sns.swarmplot(x=ages_pop_3['observation'])\n", + "sns.boxplot(x=ages_pop_3['observation'], color='yellow', fliersize=1)" ] }, { @@ -500,9 +1044,9 @@ ], "metadata": { "kernelspec": { - "display_name": "ironhack-3.7", + "display_name": "Python 3.7.6 64-bit ('base': conda)", "language": "python", - "name": "ironhack-3.7" + "name": "python_defaultSpec_1596302900397" }, "language_info": { "codemirror_mode": { @@ -519,4 +1063,4 @@ }, "nbformat": 4, "nbformat_minor": 2 -} +} \ No newline at end of file