{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Ejercicio análisi exploratorio parte 2\n", "\n", "Utilizando los mimos datos sobre automoviles, responda a las siguientes preguntas \n", "\n", "1. [¿Existe alguna relacion entre el territorio y los caballos de potencia?](#1)\n", "2. [¿Cuál es la correlación entre los caballos de potencia y las millas por galón?](#2)\n", "3. [¿Si existiese una relación, ambas variables se mueven en la misma dirección?](#3)\n", "4. [Compruebe la dirección de la relación con una visualización.](#4)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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nombrecilindrospesoanioterritorioaceleracionmpgcaballos_potenciadesplazamiento
0Chevrolet Chevelle Malibu835041970USA12.018.0130.0307.0
1Buick Skylark 320836931970USA11.515.0165.0350.0
2Plymouth Satellite834361970USA11.018.0150.0318.0
3Amc Rebel Sst834331970USA12.016.0150.0304.0
4Ford Torino834491970USA10.517.0140.0302.0
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" ], "text/plain": [ " nombre cilindros peso anio territorio aceleracion \\\n", "0 Chevrolet Chevelle Malibu 8 3504 1970 USA 12.0 \n", "1 Buick Skylark 320 8 3693 1970 USA 11.5 \n", "2 Plymouth Satellite 8 3436 1970 USA 11.0 \n", "3 Amc Rebel Sst 8 3433 1970 USA 12.0 \n", "4 Ford Torino 8 3449 1970 USA 10.5 \n", "\n", " mpg caballos_potencia desplazamiento \n", "0 18.0 130.0 307.0 \n", "1 15.0 165.0 350.0 \n", "2 18.0 150.0 318.0 \n", "3 16.0 150.0 304.0 \n", "4 17.0 140.0 302.0 " ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import os\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "autos = pd.read_csv(os.path.join(\"./csv/\", \"datos_automoviles.csv\"))\n", "autos.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. ¿Existe alguna relacion entre el territorio y los caballos de potencia?\n", "\n", "\n", "\n", "`territorio` es una variable categórica, por tanto, no es posible calcular la correlación con `caballos_potencia`. Pero, como `territorio` solo puede tomar 3 valores (Europe, Japan, USA), convertir cada valor en un número y crear una nueva columna que tenga el `territorio` almacenado como un número. Así, sí es posible calcular la correlación." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-0.030029215705601877" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Se crea la nueva columna\n", "def territorio_a_num(territorio):\n", " serie_paises = ['Europe', 'USA', 'Japan']\n", " if territorio == serie_paises[0]:\n", " return 1\n", " elif territorio == serie_paises[1]:\n", " return 2\n", " elif territorio == serie_paises[2]:\n", " return 3\n", " \n", "autos['territorio_num'] = autos['territorio'].map(territorio_a_num)\n", "\n", "# La correlación entre las variables\n", "autos.corr()['territorio_num']['caballos_potencia']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. ¿Cuál es la correlación entre los caballos de potencia y las millas por galón?\n", "\n", "" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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cilindrospesoanioaceleracionmpgcaballos_potenciadesplazamientoterritorio_num
cilindros1.0000000.895220-0.360762-0.522452-0.7753960.8441580.951787-0.027908
peso0.8952201.000000-0.315389-0.430086-0.8317410.8665860.932475-0.094713
anio-0.360762-0.3153891.0000000.3019920.579267-0.424419-0.3817140.143544
aceleracion-0.522452-0.4300860.3019921.0000000.420289-0.697124-0.557984-0.062585
mpg-0.775396-0.8317410.5792670.4202891.000000-0.778427-0.8042030.127164
caballos_potencia0.8441580.866586-0.424419-0.697124-0.7784271.0000000.898326-0.030029
desplazamiento0.9517870.932475-0.381714-0.557984-0.8042030.8983261.000000-0.040180
territorio_num-0.027908-0.0947130.143544-0.0625850.127164-0.030029-0.0401801.000000
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" ], "text/plain": [ " cilindros peso anio aceleracion mpg \\\n", "cilindros 1.000000 0.895220 -0.360762 -0.522452 -0.775396 \n", "peso 0.895220 1.000000 -0.315389 -0.430086 -0.831741 \n", "anio -0.360762 -0.315389 1.000000 0.301992 0.579267 \n", "aceleracion -0.522452 -0.430086 0.301992 1.000000 0.420289 \n", "mpg -0.775396 -0.831741 0.579267 0.420289 1.000000 \n", "caballos_potencia 0.844158 0.866586 -0.424419 -0.697124 -0.778427 \n", "desplazamiento 0.951787 0.932475 -0.381714 -0.557984 -0.804203 \n", "territorio_num -0.027908 -0.094713 0.143544 -0.062585 0.127164 \n", "\n", " caballos_potencia desplazamiento territorio_num \n", "cilindros 0.844158 0.951787 -0.027908 \n", "peso 0.866586 0.932475 -0.094713 \n", "anio -0.424419 -0.381714 0.143544 \n", "aceleracion -0.697124 -0.557984 -0.062585 \n", "mpg -0.778427 -0.804203 0.127164 \n", "caballos_potencia 1.000000 0.898326 -0.030029 \n", "desplazamiento 0.898326 1.000000 -0.040180 \n", "territorio_num -0.030029 -0.040180 1.000000 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "autos_corr = autos.corr()\n", "autos_corr" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-0.7784267838977761" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "autos_corr['caballos_potencia']['mpg']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. ¿Si existiese una relación, ambas variables se mueven en la misma dirección?\n", "\n", "\n", "\n", "Como se puede apreciar, la covarianza es negativa, lo cual indica que las variables se mueven en direcciones contrarias." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-233.85792577900716" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "autos.cov()['mpg']['caballos_potencia']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4. Compruebe la dirección de la relación con una visualización\n", "\n", "\n", "\n", "Las gráficas de regresión, se observa que la pendiente de la curva es negativa, lo cual indica que las variables son inversamente proporcionales, lo cual significa que las variables se mueven en direcciones contrarias." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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SyruEEL8CUA7gY99Tb5FSvmFWHInwn9cT74U++HUXX3xxyPym4uJi3ZaM/663/mNKyvk574iIMomZhQ6fApgtpTwuhBgO4GUhxHO+x34gpfytiec2VLzbPhixXUSsKzFwiwoiSmdJ6b4TQpwK4GUA/+m7PRtLUkr1Jn/hutDCtVLifV0qYiWipGL3nQ5TS8KFEDkAXgNQBKBRSvkXIcR/AlghhKgD8EcAP5JSfqrx2oUAFgLA2WefbWaYEekVG+it0hDpdWaWZ6finERkLP/rH+BNMHEeR/exvLw8dHV1xXVcM5malKSUQwBmCCFOB/CkEGIagCUADgMYAWAtgB8CWK7x2rW+x1FWVpbSdK+Uh/tf7JVVGi677DLdbjL/1zkcDlRVVSEnJwejRo2Cx+MxpTtNK1aWhBOlF//rX7wtpUjiTXRmS8ogg5TyKIAdAK6QUnZLr08BbADwuWTEkAitbR8aGxsDVmnIzc3F3r178dxzz6mrLiivKyoqQnV1NRoaGlBfX4/y8nLTlv7hFhVElM7MrL4bD+CklPKoEMIO4DIA9woh8qSU3cLbrpwLYL9ZMRhFq9igq6tLTUgOhwPV1dWor68PaTVVVlZi4sSJKC8vD5kAW1paqtulFm+1H7eoIKJ0Zmb3XR6AZt+4kg3AZinls0KIP/kSlgDwBoD/MDEGw2iVXyvdZFVVVWpCAv6VdKZNmwYhBNxuN+644w40NzfD7Xarz9Eb50m0go6l4kSUrkxLSlLKvQCcGvfPNuucyeQ/f0hvwda9e/filltuQX9/P4qKivCLX/wCBw4cwKeffopNmzZhwoQJmsfWW1ooXMvKHyfPElG64oKscfLvJuvs7MT9998fUlywf/9+tchhwYIFmD9/vtryeeCBBzB8+HDNYydSQcd5SkSUzniVSoDSTXbKKaeELNj63//939iwYQMAaHbv3X777WpXXvDWFAUFBXEvqqrXyuLWFUSUDthSMsC4ceOwfv161NTUYOTIkZg6dSoOHTqE3t5eANDt3uvr69Nt2WzatAnXX399yNJCkXCeEhGlM7aUDFBcXIxVq1ahoaEBJ06cwPz58/HAAw+EtJ782e12TJkyRbdlU1xcHLLaePDW68Eb/wGBW274n0sIEfA8IiIrYlIygP+WFWVlZepme8pmf2PHjsUvf/nLgAT10EMPoaioSLdlc/jwYZSUlKCiogIlJSUhCUlvy3OteUp1dXW46aabuDU6EVket64wmN7ac/fccw+OHj0KIQSklNi8eTOeffZZAIh5rbpI69sp1XcdHR149dVXsWHDBnX8iuvgEVmC6WvfRZLitfFSs/ZdNtLaauLhhx/G0qVL1cSg6O7uxsUXX6y7NYWeSONGSgFGd3c36uvrdZ9HRGQ1WZ+UBgcH4XK54Ha74XA44HQ6MWxY/B+L1ooKNptNLXpQ+FfTTZ8+Hc8995zmJn9aol3fzoh18DjniYiSKWuvLh6PB2+//bbajfbd734X5eXlaGlpweDgYELHVloqynhQYWGh5np0hYWF2Lp1K6ZPn46Kigpcd9112L8/8qpL0a5vl+g6eOHGroiIzJCVY0paZdi1tbVoampCb28vdu7ciVmzZhl2PuWcwS2O9vb2uPc+irYFk0hLJ1P2ZmJrjyyIY0o6svJ/plYZdn19vXpf8NiPEYJbTzabLezYUDzHS+R5WhKJzyrY2iNKL1k5pqR3sRVCwG63w+FwmHp+5S93m80Gu92O3NxcdQ29nJwcFBQUmHr+aGXC3kyJriNIRMmVlUlJ72Jrs9nQ1NQEpzNkHdmYhOsu8u86zM3Nxb333ou+vj4sX75c7UosLS1FYWFhyruYtCoJ021vJq5wQZResjIpaV1sm5qaMH36dJSWliZUfae3bNDcuXPR0dGBw4cPB3QTHjt2DCtWrLDkX/KZsDdTJrT2iLJJVialRC62eq0g5f7Dhw9jyZIlqKmpgXcfQ2DJkiUYO3Ys5s6dizvuuCPgAtnf35/QiuBGDOCHO066782UCa09omySlUkJiO9iG64V9NRTT6Gqqgp33303FixYELALbW1tLfbs2aMmH62/3IO/HzVqVFyxxLpFRaZvdZEJrT2irCKltPztggsukFbw9ttvS7vdLgGoN7vdLv/617/KoqIiuXTpUvn8889rPufxxx+XAKTD4ZArV65Un1NUVCTXrl2rfm+32+XKlSvlyy+/HFcsb7/9tiHvKdbjEFFMor7++f/fTOYtLy8vJe8/a1tK8dAbND9y5IjaOho5cqTmc959910AUBdqXbx4MSZOnIhp06bh1VdfxYMPPogxY8bgvffew7p161BZWRlXLLEO4LMQgMj6zJinFElra2vSzwlk6TyleOltC3Haaaep3XWffvqp5nOmT5+u3t/b2wu73Y5f/vKXaG9vx5IlS/Dtb38bt956K6SUuP/++yOOeejFEusAvlHHISIyApNSDPSW7RkYGFBbG83NzSH7KNXW1uLDDz/E4sWLsWzZMtTU1KCpqQlXXnklFi5cGFB5t3z58qjGPBJdQsjo4xARGYHddzHQGzRvb29XixWCu+feffddNDU14bbbbsOpp54aMB9pypQpunspnXvuuXHFEusAPgsBiMhKmJRipFW1F1x23Nvbi+Li4oDtKj755BM0NzerpeJSSnR1dWlW3g0MDMDj8URMDEaVa6d72TcRZY6sTUpGLtJps9kwd+5c7Ny5E263G2eddRaGhoYwcuRILF26FEIIlJaWYuTIkVi5cqX6uqKiIqxdu1btwlO6+hYtWoRnn31WM0loxQ2AC44SUUbIyqRk9Nwcj8ejzlM677zzsHDhQmzcuBF33nknbr/9dvUca9aswapVq3Dw4EHY7XasWrUKEyZMCGg9NTU1we12a1a/6cV9yimn4Prrr8/IeUZElF2ycusKo7dk8D/e448/jgULFqCmpgYNDQ0h59i5cyf6+vrUFs0777yDmTNnor+/Hw6HA1VVVcjJycGcOXNQVlYWkFj04l68eHHADrPpuL0EUZZJ+dYVkZi8tQW3rvBn9JYM/sfr6+tTVxzXOkdfX1/ANhI5OTmoq6tDUVERqqur0dDQgPr6elRUVIRssaAXd/A2DOm2vQQRkSIrk5JRc3M8Hg/a2trQ19eHuro6OBwOjB49OqC8OtI5Ojs70djYiPr6enWuE/CvhVlfeukltLW1wePx6MYd3E3HeUZElK6yMikZMTfHf/O4OXPm4L777sOiRYuwceNGPPDAA9i0aVPIfCWtc+Tl5aG3txf79+/XbAVt375d3ZhOb1v1WbNmcZ4REWWErCx0MGJujtbmccuXL8dzzz0Hh8OB6dOno6enB9u3b8fAwADy8/M1z6EkyH379mmWh0sp1VaTy+XSjBsAXC4Xq++IKO2ZlpSEECMBvAjgFN95fiulvEsIMRnAbwDkAngNwHwp5YBZcehJdG6O3viOlBITJ07E3//+dwwMDEAIgeHDh0NKiY6ODnR2dgYkDiVBTp8+HUVFRfjOd74TUB7e1NSkHvvAgQMAvImspKQkpDz84osvZjIiorRm5hXsUwCzpZTTAcwAcIUQ4kIA9wJokFIWAfgHgG+aGINpgsd3HA4Hamtr8c9//hN/+MMfcOONN+Laa6/FpZdeipdeeglXX301tmzZgvnz56vdcUqBgs1mQ2FhIU4//XQsXrwY69atw+LFi9XycMDbatq9e7f62sHBQbX78JJLLgk5JhFROjItKfmWJz/u+3a47yYBzAbwW9/9zQDmmhWDWTweD6SU+NWvfoW6ujqUlZVh0aJFWL16Nb7yla/guuuuw4IFC+BwONDf34/6+nrMmzcPy5cvV7v8qqqq0N7erh6zvb0d119/Perr63H33XfDbrejt7cXwL/Wz9u4cWNAV15w92HwMZVY29ra0NraqhZMEBFZlaljSkKIHHi76IoANALoAHBUSjnoe4obQIHOaxcCWAgAZ599tplhxkRrAuuWLVvw1a9+NSBB1NfXo6amBitXrgwoEVd2ow3eHsK/O1BZP6+mpgYzZszAG2+8EdBqUtbYi7TlRKZv4EeUqfyvf9nG1KQkpRwCMEMIcTqAJwGEX2U08LVrAawFvJNnTQkwDloFDq+88opmglASkN1ux7Rp01BUVIRp06Zh2bJlyMnJQUHBv/Kx0h3on5gaGhqwc+dO3HzzzSEFEA6HQ7Mwwr8UXCvWqqoqlJaWcmItkYX5X/+EEJa5/iVDUv5cllIeBbADwOcBnC6EUJKhA0BnMmIwilaBg8fj0Zw/JKVUu95qa2vxox/9CI2NjVixYgXuu+8+7NmzR+1O0ytTdzqdMd3vXwpu9CRhIiKzmbbMkBBiPICTUsqjQgg7gD/AW+RwM4AtUsrfCCF+CWCvlLIp3LGMXmYoEVpL/RQVFeGee+7Bt771LbWbbO3ateju7sbRo0exceNGuN1u2O12tUsPCF0OSG+R2FjvDxcrlyAisgQuM6TDzO67PADNvnElG4DNUspnhRAHAPxGCPETAC4Aj5gYg+GCt6lQFladO3cuLrjgAjVB9PT0YP78+QGv9e/SU773HwPSK1OP9f5wsXJiLRFZmWlJSUq5F4BT4/53AXzOrPPqCW5VFBYWoqOjI+YJp+Em3gYnCL3JsP7f5+XlxbUdhcfjQUdHB7q6unD8+HEUFhbinHPOCXgON/AjorQjpbT87YILLpCJGBoakk888YS02+0SgCwqKpIbNmxQv7fb7fKJJ56QQ0NDCZ0n3Dntdrtcu3atLCoqCjjnyZMnQ573zDPPhNznH9/Q0JB85pln5KpVq0x9D0Rkmqivf/BOpUn5LS8vLynvPyu2rggeW1m6dKnmthJGj7UoLaC2tjbs3r0bzz33HL785S9DCAGbzYZrr70Wp556asi4T21tLVavXq0bX1tbGx599NGwz0kHRm60SJRmLD+mFMzgMaaUjClZRnAVmt62Elob6yVC6dI7cOCAut+Rf3J1Op0444wzNKv5wsXX3d0d8TlWxzlURKQlqqQkhKjUuPtjAPuklB8ZG5LxgucAAdrjPWZt96A3p8jhcGDs2LEhj+Xk5ISNLy8vL+JzrI5zqIhIS7R/kn4TwDoA3/Dd/gfADwH8WQgxP9wLrSB4DtCmTZvQ1NSUtO0enE5nyPmamprgdDo15yeVlZWFnYNUXFyMsrIy1NXVRXwPVl1miHOoiEhLVGNKQogXAFRJKT/0fX8mgI0Avg7gRSnlNDODNGKeklHVd/EaHByEy+WC2+2Gw+GA0+nEsGHDNGOLtfqur68PU6ZMCam+s3IXGedQUZbjmJLeA1EmpQNSyql+3wsAb0oppwohXFLKkNJvI1lp8mwqxFsQYOULv5UTJlESMCnpiLbQoVUI8SyAJ3zfX+e7bxSAo4nFRnqtKI/Hg3feeQd79+7Fm2++ifXr16O3t1fz4u2fuAoKCjA0NIS33noLP/7xj5GTk4NPPvkEANDc3BywL1MiCSCR6jnOoSIiLdFeARYB2ADvvkgz4N1yYpGUsk9KeYk5oWWHwcFBtLS0oLy8HJWVlSgvL0dLSwsGBgawdetWzJw5E9dffz3uu+8+VFdXIzc3F1VVVXjnnXfUY/hvzT5//nxs2bIFM2fORGVlJX7yk59ASonm5mbcf//9WLRoEd55552E91/yP2e8+zkp1YkVFRUoKSlhQiKi6JKS9LbZ/gygFcCfALws02GCUxpwuVyorq4OqEKrrq7Grl27QqrT6uvr1fveffdd9Rj+lWxVVVVYvnx5yDbtyuPLly/H4OCg7v5L0dKrnov3eEREQJRJSQjxLQB/BXAtvF13rwohFpgZWLbQ2xeps7NTdzsMu92OUaNGqff7V7LpzcHy38fJP5H09PTEVZ3H6jkiMkO0/SU/AOCUUt4ipbwZwAXwloRTgpQ5TP7890sKvt9ms2H58uXIz89X7w/eml1vG43gr4uKivD+++/H1QUXfE7l2OkyT4qIrCnapNQL4BO/7z/x3UcJ0pvDpDVXqaGhAWPGjMFZZ52FyZMnq8fwn+vU3NwcMn+prq5Ofdz/68bGRnW7DSC2Lji9/Z+4AjkRJSLakvCNAEoBPA3v4nzXANjru0FKeb+JMWZMSbhetVq46rvXXnsNv/vd7zA0NBSwL5PL5UJxcbF6vAkTJiAnJwednZ1q9d3hw4dx5pln4tixY/jb3/4Gh8OB3NxcdHV1IS8vD11dXZg9e7Yan8PhQFVVFcrKyjB16tSI1XBcu44obiwJ1xFtSXiH76ZEpCSnMYnFlT4SvQCHm5czbNgwzJo1C7NmzQp4jc1mQ19fn7punkIZC3rttdfUlk5RUREaGxsxfPhwnDx5EseOHcOHH36IMWPGQAiBYcOGYdiwYeokW4WyVJHD4UB1dTXq6+ujnjcUaT8nIqJYRXtV/T28peDXAvgavMUO10kp75FS3mNSbJZhRPlzvNVqemM3AwMDakJyOBxYsGAB5s6di9mzZ6OsrAytra3YuHEjXn/9dVRUVASUmw8ODgII7IKrqqpSE1Is8RERGSnapNQCYD2ASgBzfLerzQrKaowof463Wk1r7GbdunV45ZVX1ONpJZTly5fjv/7rv3D77beHlJu7XC4A/5rA6nK5UFZWlvRqOquuy0dEqRNt990RKeXvTI3EwsIllGi7rvRWKo9Uraa18kFPTw8OHDiAoqIizJs3D1OmTNGMr7e3V/N+t9utdhUqXXBKPLHEl0iXJpcZIiIt0f7vv0sIsU4I8XUhRKVyMzUyCzGi/FmrxfPwww/DZrNFbCEEr3wwbtw47Ny5E3feeScaGhpw6NAhzfjGjRunW24eTXzhqukS7dLk5Fsi0hJtUroV3jGlK+Dttrsa3i68rGBE+bPS4nn99dexadMmLF68GEuXLsX06dNjHp8qLi5GbW2t2jXX3NyM2trakDLwBx98EA888IDmlhl68blcLuzYsQMulytsqyXRpMLJt0SkJdruu1lSyqwtsTJq8VCbzQYhBG655ZaAC3Ksm9vZbDYMHz5cPYbb7UZTUxNqampQVlaGMWPGwOVywel04uTJk/j973+Pf/zjHyFbZmgdN9pqukS7NOPtziSizBZtUvpfIcRUKeUBU6OxMKPKn40YnwKA/Pz8gIu62+1GQ0MDdu7cifLyctO3q0g0qSitz+AxJU6+Jcpu0SalCwG8IYQ4BOBTeCc+SSnl+aZFloBwG+olg38BgDKp1e12Y/To0Rg2bFjIxbyoqAjDhw/Hk08+qcZrs9nUTfyOHz+OM888E59++inGjRuH4uJi3Yv6wMCAZtI7cOAAbDYbhoaG0N3djdGjR2NgYEA9XqytvniSSnBhxNy5c+FyuTj5liiC1tbWVIcAAOoamnqUSfkJnSPKFR0mat0vpXw/obNHKZYVHZStIJSVt5VxlJtuuikpiUmrqqyurg6NjY3o7e3FT3/6UwwMDKCurk6d9LpkyRLcdtttAfFOnDgRf/nLX9QVv+12O+655x6sXbsWq1atQmWlt84kuPqtvb1dc2O/u+66Czk5Oep5tY4Xa0KIpfqO1XZEAdJuRYdoxLDqQ2I7z6ZaLElp165dmt1XO3fuDFkxwQx6u73W1NRg5cqVaoLo6+vDhRdeiNNOOw2XXXZZyPOffPJJXHvttZrHaWho0O2O07r419bW4pRTTsGPf/zjkOMtW7YMK1asMH03WivvgkuUAkxKOjLuT1S9rSDcbndSzq83ZuS/dcQnn3yC+vp6nHrqqfjoo480n9/T06N7nHBVav5VdFu3bkVNTQ2ampowduxYzeNNmDAhKVVvrLYjomhkXFIKtxVEMujNaQreOkIpCgiO1+FwoLa2FiNHjkRdXV1A3MGv1aMUZUybNg05OTm4+eabdeMaNWoUioqKMGrUKFNXVtA7f0FBAVd1ICJVxiUlva0gtObmmEFrTpP/dhG1tbXYvHmzWhTgH6/D4cCiRYuwevVqXHfddbjvvvuwaNEiNXHdc889Aa8Nx+PxYM+ePVi9ejVWrFiBt956S3NLiyNHjmDZsmUoLy+Pe12/eD+XTZs2Yc+ePQmtKUhEmSXjxpQAa1bfdXZ2YtSoUZoVb0q8R48exTXXXBMy7vK73/0OY8eOjalaLngMx+Fw4M4778SRI0fg8Xhgs9lQWlqKoqIiXHTRRUkZ6wkujJBSYubMmRxnomzEMSUdpl2phRBnAdgI4Ex4t7lYK6V8QAhxN4BvAzjie+pSKeXvjTy33lYQyaI1p6m4uDjgoqzcZ7PZYLPZMHbsWN3xsJycHIwdOxY9PT04duwYdu7cifz8fM3k5PF40NHRgb/97W+44447AADNzc1wu9342c9+hubmZnz88cdqsn7ppZcMmTelRas6z/9z2bFjh2nnJqL0ZGb33SCA70spp8I7z2mREGKq77EGKeUM383QhGRVemvFDQ4Oqve/9tpruttUzJkzBy+++CLKy8sxe/Zsza4uj8eDbdu2YcuWLbjmmmuwYsUK3H///aiurobD4UBvby9aW1vVbSyeeuopnDx50pRtzSOtjefxeDA4OMgt1YkogGlJSUrZLaV83ff1JwDeAlBg1vmsTm+tOJfLpd6vtYbdunXrsGjRIsybNy/ifkft7e3YtWuXOrdJeV59fT1uvfVW1NXVYcOGDQGv37NnT8g5165dm/DKCpHWxmtvb0d1dbXm++WqDkTZKykDLUKISQCcAP4C4CIAtwkhqgDshrc19Q+N1ywEsBAAzj777GSEaSq9kmj/LrvgNeymTp2Knp4eHDx4UC0FD369f1dXd3c3PB6P5vOcTie++93vBpTG9/f34+OPP0ZzczNqamoghICUEp/5zGcSntAaaTml7u5uHDx4UH2/yrknTpzIybSU9fyvf9nG9KQkhBgNYAuA70kpjwkhHgJQD+84Uz2AnwNYEPw6KeVaAGsBb6GD2XFGkuh26HprxSmVdcFr2PkP9vu3JMKtNZeXl4ecnBzd8/T29gbEZLfbYbPZ4Ha7sXLlSvW+uXPnRv2+Yn2/SrzK48HnrqqqSvjcROnO//onhEj59S+ZTP2TVAgxHN6E9KiUcisASCk/lFIOSSk9AP4HwOfMjMEIRmyHrrf9hdPpDLsthvK6TZs2hXR1BZeGFxcX4wtf+ILmdhXTp0/XPM95551nSvl8pO0+jNgOhIgyj2kl4cK7hEEzgL9LKb/nd3+elLLb93UNgP8rpbwh3LFiLQk3mlFL5Oi1tiK1wpTHe3p6MGLECPT19em21t5++21cffXVmDdvHoQQGDNmDKSUKC0tRWFhoVqerrze4/GYVj4f7fvSejzRlimRxbEkXO8BE5PSvwF4CcA+AEqTYimAr8O7YaAE8B6A7yhJSk+qk1JraysuueSSkPt37NgBq/2y+MfqcDhQXV2tFkikyyKoXLyVsgCTkg7TxpSklC/rnDjtSsDN3pAu2sm+0bQe/GOtqqoKqNjLzc3F3r17YbfbUVRUpLaU9u3bh56eHng8HowePRpHjhxBQUFBwq2meFs7epV7sWyESETpiX92RsHM8Q9lq43y8nJ1/lBLSwsGBwcDnhduXMvj8ajrx0kpsWnTJtjt9oCKPaXVtHr1asyZM0d9/bZt2/DCCy+guroabW1teOGFF7B79248++yz2LJlS0gc0UpkHI6LtxJlr4xcZsgMZo1xRLvVht641p49e7Bnz56Qrq7p06ejs7MTV111Ffr7+7F06VI0NDSEvH7Dhg249dZbcdddd0FKGbB/U11dHa666iqccsopMb/vRMbhuM0FZQF23+lgSylKytJBFRUVKCkpMWxsI9qtNvRaD11dXZpdXR6PByNGjFAXYdWb53T8+HH09/dj4sSJIZNuH3nkEbzxxhtJb+2wMo8oeyVvlVLSFDxPCdDeakNvXEtJKv6Ui39fXx8aGxtRU1OD0tJSzdePHj0adrsdfX19IceZN28e/uM//iOusZ28vDwUFRWpVYAAsGnTpqjG4ZQ9oUpLS1l9R5RlmJRSTNm6Inj79uC5QkrrIbibrrCwULcIQ0qJ3t5erFy5Ut2nKbgSb/jw4airq0NPT0/IcXJycuJeMLWwsBDLli0LeV+FhYVRfS5ai9oSUebjmJIFJFJ9B0C3fDr4saKiIjQ2NmLEiBEB85T27duHY8eO4f3331dbRna7HU8//bTmVhrK2E64cTaOCxGFxTElvQeYlKw5UTOWiacFBQUYGhrCu+++i1GjRsHhcODkyZM4fPiw+tjhw4c1j+OfECdOnAiPx4MPPvgADocD06dPxzPPPBNVwgueS2T23C4r/syIYhBTUjIzECPl5eWhq6srmqcmf55SurDiRM1IMWk93tTUhBUrVuDEiRNYtGhRQBWd3vtRytH9u9geeOABrF27Fm+++SY2btyIuXPnwuVyhVz829raws4lMnNulxV/ZkRmSnVLKYYWUMKyvqVkxW6mSDHpPV5TUwMAmqXfWu9Hrxx9/fr1+PrXvx72c4jUEjIzcVjxZ0YUo7TqvjMhKbEkXI8VJ2pGiknvcSFE2C0ugumVo/f19YV9HfCvakB//i0hpYLO5XJhx44dcLlchrVkrPgzIyJjZH1SinRxTYVIMek9rvwl4/+YUnXX19eHtra2gDlGSjl68HFGjRoVcs5g0cwlMmtulxV/ZkRkjKxPSlacqBnPtg9NTU3YvHkzmpub1QmzDocDixYtCllaSElMSjm6/3EeeOAB/PznP4/4OUTTEvJf/ig4IZr5+RBR+sr6MSXAmpVcsW77UFhYiI6ODnR3d+Oss85Cb28vOjs78cYbb2D9+vXqChHBYy/+1XcFBQXIzc3FBx98kPDnYHYxghV/ZkQx4JiS3gNMSplFKxnU1taiqalJTUzJ2HKDxQhEYTEp6cj6kvBUiHezv2iOefjw4ZBS7fr6etTU1GDlypXq2Ivy/K6uLowePRoDAwP47Gc/i6GhIXR3d4fcpzXPKVy8wcUIDocDVVVVOHDgAACwZUNEmnhVSDK9LR0GBwfj3urB/5jbt2/XrczzX5pIef7s2bNRXl6O1157DU8//TRmzpyp3ve///u/eP755zFz5kzN7TLCxetfjKBsm9HQ0IDKysq4tpMnouzApJRkehvYuVwuzfvb29tjPqZWZdpll12mFiN0dHSEnKunpwd1dXUB99111104cuSIZkx670OJ178YIXizwVjeGxFlFyalJNObY6M3ZyiauTf+x2xubkZtbW1IZdoXv/hFtSxbKwaPx6N5/uDWjBJTpLlC/tV5ZWVlus81ozqPiNIXx5SSTG/5Hb0tLKKZe+N/TLfbjaamJixevBgXXnghCgsLQ8ZvtGLIycnRPH/wuI9/TJHi9Xg8OHbsGMaMGaP53KGhITz66KPweDzIyclBWVkZvvKVr3CsiSiL8X9/kunNsXE6nXHPvQk+Zm9vL84//3xcccUVmpNWtWLIzc3F8uXLA+675557MH78eM2YIs0V8t/m/dZbb1XnTinPfeSRR3DgwAGsXr0aK1aswH333Yc333wTHR0dCX2+RJTeWBKeAmZW30X7Wv/njxo1KqTSTuu+WKrvgtfVczgcuPXWW1FWVoaSkhL09vbi0ksvDWk9PffccygvL4/3oyVKFywJ13uASSm7mTUJ9cknn1S3uPC3detWXHvttdi2bRvmzJkT8vi2bdtw1VVXJXx+IotjUtLBMaUUMDoReDwedHR0oKurCwMDAzjttNPQ19eH/Pz8gJUetFo6Rq26EPyeCgoKwm7zrrdj7pQpU+L+HIgo/XFMKckize+J53jbt2/H3r178ec//xnHjh3DN77xDcyePRtOpxMtLS2YM2eO5rkilXUn8p76+vqwZs2agHGkNWvWYPr06QCAc845R3NM6pxzzonrcyCizMCWUpLpJQJlc7xYdXR04L333kNNTY3mskLV1dXqag7B5wpX1h1LLFrvaceOHdixYwfWr1+Pvr4+jBo1Co2NjbjooovU4ovKykqUlpZy/ToiUvEKkGRG7wXU1dWlJiTlWPX19aiqqlK/F+Jf3bf+54pmC4hwK30PDg5i165d2LNnDzZs2ICysrKA17333nvYt28fDh06hP379+O9994LeJ9mbW1BROmLLaUkM3qb8OPHj+suK6Qc23+A0v9chYWFaGpqCtgOvampCYWFhQDCjzl5PB7NrdQBYPfu3Tj99NNDtmWvq6tDQUFBXO+TiLID/zRNMqP3AlIKBvwpich/nyWtc3V0dGDFihWoqanBsmXLUFNTgxUrVqhzhbS65ZYsWYLXXnsN27Ztw6FDh5Cbm6s+dvvtt+P73/8+7HY7nE6nmpCUx5cvX46hoaG43icRZQe2lJLM6LEUpWDAvzXz61//Gp/97GfxhS98AVOmTMHmzZvx3nvvweFwwOl0BqzkffDgQaxcuTLgmMqYktZK3wsWLFDnHwWPX/X39yMnJwculwtdXV2aLbjDhw/j3HPPjeu9mo17NJFVtba2pjqEgGGAeOXl5aGrqyvsc0xLSkKIswBsBHAmAAlgrZTyASHEZwBsAjAJwHsA5kkp/2FWHFakjKUYta/QKaecgsWLF8Pj8eC0007D8ePHMX/+/IBus8bGRvT29uLhhx9Wlx+K1JUY/LjWwqrB22JMmjRJfV9GdVMmI1mYvSkhUSJSPU/JKNEkVzP/tw0C+L6UciqACwEsEkJMBfAjAH+UUhYD+KPve4pTe3s7rr/+etTX12PFihU4evQo/vM//zOk20y52H7nO9/Br3/9a2zduhWTJ08O2Q7df0xp8uTJAWXdOTk5YbfFaGpqgtPpBODtVnzooYcCjv3QQw+px46W0SX0eowqjyeixJjWUpJSdgPo9n39iRDiLQAFAK4BUOF7WjOAVgA/NCuOTNfd3Y3c3FxUVVVBCIHJkyeHLXxQVv6uqqrCzp071TElIQSklFixYgU+//nPo6SkBHv27MGqVatQU1ODKVOm6C6sWl5ejmuuuQZOpxPDhnl/pQ4dOoRjx46pLTibzYZjx47h0KFDMY2fGV1Cr8eo8ngiSkxSxpSEEJMAOAH8BcCZvoQFAIfh7d7Tes1CAAsB4Oyzz05ClOmpoKAgoMpNWfg0OHEoFXjK18qK4uHGlPwfX7ZsmbothtKFZ7fb8fDDD+NLX/pSSBdXV1cXfvjDH4bEcf7558eUlJKVLIyuiiRKhP/1L9uY3lkuhBgNYAuA70kpj/k/Jr1XSs0FlaSUa6WUZVLKsvHjx5sdZtoaGhoKqHJbv359yIrcdXV1asVfbW2t+rWyXYY//wtx8OO9vb1oampSq/WU7TG0xlz0StX7+vpien/RzKUygtFVkUSJ8L/+pTqWZDO1pSSEGA5vQnpUSrnVd/eHQog8KWW3ECIPwEdmxpDpDh8+HHDxd7vdaGxsxIYNG7Bv3z6MHz8eM2bMwNlnn42cnBysXr0avb29AdtlBA/uKxdip9OpzmNqbm5GXV0dli9frhY1KFurazFqbTslWejFaJTgqkhllfT29nZW4RElkZnVdwLAIwDeklLe7/fQMwBuBvBT379PmxVDNtDqdurt7cWUKVPgcDjw/vvv48orrwzoblOq7yKVpw8bNgw33XQTzjvvPLjdbkyaNAlz585Vt7EoLCzUrYrTKlWPZ207vRgBoK2tzdCKPJvNhuLiYuzbtw9XXXWVZhUey8aJzGXa1hVCiH8D8BKAfQCUUqml8I4rbQZwNoD34S0J/3u4Y3HrCn3hSpnb29sxZ84czJs3Ty102LRpE5599tmEx2OiKaE26wJuZvl2W1sbnE5nSAvP5XKhuLiYZeNklLTausIofltgcD+lTDY4OAiXywW32w2Hw4HS0lLs27cP3d3dePPNNwMKE2pra3HxxRfjoosuAhCYOCZMmICcnBx0dnaqSWRgYAC7du1CV1cXCgoKUFZWhpEjR+pevF9//XUIIQzdwDBYuMSRaLJtbW3FJZdcEnL/jh07kJeXZ9p5KeswKengn3dpzuPx4KmnnkJ5eTkqKytx44034rHHHkN5eTn++c9/ak52HT58uPpa/zlAM2fOxJYtWzB//nw4nU5s374djz32GC6//HLccMMN+PKXv4zHHnsMJ06c0K2K27t3b8icosHBQUPnGhm9qK2/cIUVZp6XiLyYlNJc8DyeefPm4bbbbkN/fz/a29s1L6I9PT2arw2eaHv06FH1WMrjt912G3bt2qV78d6/f3/InCKXy2XoxFQzK/LCVeElqxKQKJsxKaW54L/ehRDq959++qnmRXTUqFGarwUCJ9rqlXV3d3drXrwffvhhbNiwQX2uw+FATU0NDh06hDvuuEPdddb/OPEws3xbKaxwuVzYsWMHXC6XOmbEsnEi83FB1jSnN+mzv79fc7JrXV0d8vPzw752zJgxWLp0KUaPHo26ujqsX78ebrdbfTwvL0+zKs5ms6G3txeANyFVV1cHnPuee+7Bgw8+CLfbnVALw+wNAvXWJozmvKzOI0oMCx3SXHAlWlFREZYsWaJ2uxUVFeGnP/0pDhw4gJMnT2LWrFn4yle+ohYfBFeT3XvvvThx4gTuuusuzQVd16xZgxtvvBEjR44MG0tNTQ0aGhpCEt6yZcuwYsWKjKxa46KuFAMWOuhgUsoAwX+dT548GXv27FGr8U477TR0dXVF/Mt+woQJ+OSTT9StKRR2ux1PPvkkTj31VMyaNUszIQUfb9++ffja174W8vjWrVsxderUlLYgzGrNmFkVSBmHSUkHu+8sKpYLZ3B30+DgoPrYsGHDoq5ys9lsuvsgHT16FGPGjAmIQSkX7+zshMPhQFlZGUaMGIGSkhJ8+OGHml2Do0ePNvz9x/LacK0ZAAklKy7qSpQ49ilYUCLbNQwODqKlpQXl5eX47ne/ixdeeAEzZ87UPI7/eebPn4+tW7fi9ddf1yyO2Lt3Ly699FK0tLRgYGAAAwMDaGlpwWWXXYbrr78+4DEAmDBhguYafK+//nrE95PI+4/0Wr1Vx995552Ey9YnTJig+dlNmDAh6mMQZTt231lQIt1Au3btUrvfli5dqjmuoxzH/zzKc3Nzc0MKFPx3l7Xb7di+fTsA4LLLLgs59vbt23HRRRehvb0dzz//PI4cOaJuXTFu3Djcd9996nH03k8i7z/Sa/Umx27btg3XXXddQl1v7e3t2LJli7pArpKIv/rVr7JCj4Kx+04Hu+8sKNZuIP/uqsHBQeTm5sLtdgeUh2sdx/88ynPdbre6ErgQAtOmTcMPfvADtfquv78fXV1d6vYXwcdWtjru7OzEz372M1RVVWHKlCl499131YQU6f0k0g3W09Ojxg4Azc3NcLvd6mv1Kg5HjRqVcNdbZ2cnGhsbA/anamxsxIUXXmhIUmJlH2UDJiULimVvH60xEqVaTnlduO3Oi4qKMG/ePEyePDmg/FtZCXzx4sVqIlFen5+fr+42G3xs/3Jz/4IIZfO/SO8n1vcf/Fm8//77autQaeWtX79efa3equP5+fnqOR0OB6qqqpCTk4MRI0ZgcHBQjT9cYsjLy0Nvb2/A/lRGTa5lZR9lC3bfWVAsFyC97qrFixdjw4YNARsABh9HGX+qrq7WLP9++OGHMWzYMHzzm99UH1+zZg1uuukmAEBLS4taeu7/mHIhD3fscBfUeC/Aep/FU089hUsvvTTsQrGAtzJwyZIlWLBgQUD3ZVNTE2666SbYbLawcaVqoVgWUaQldt/pYFKyqGi7avTGSJ5++mkMDQ1h0qRJsNvt6nYT/sfRu9A9/fTTOP300+F0OuHxeNQFWfPz8zFr1iyMGDECAAIWaw1+TO/Yzz33HCZMmBCx6ymerqpwi6lG85/a4/HglVde0Rwr27lzJ8aOHRsxMZjVxZboeyPLiSkpmRlIMuXl5Sld/BxTSjd6qwoE0+vqCn7tueeeG/JavbGb4cOHY9asWep9yoriwUaMGKH7mN6xpZRR/WUf7fv3l+iW5jabDR999JFm3G63G2eccUbEcad44o4Gt2vPbun0h4dfaygu7IxOc4msxxbNAqMejwdtbW1obW1FW1tb1CXSZi9eqhWX1mexbt063d1xtehtEe9wOEz9vCLhunuULZiU0ly4BUQjiXShS2S+0OTJk7FmzZqAY69ZswaTJ09O4N0ibFwAMHfuXDz11FOora1FTU0N7rrrLjz11FNRJ4fp06drxj19+nRDPq94k1YiP2eidMIxpSwXbgwk0flSN954o7rrrZQSmzdvxmOPPRbQNRiPcHEBSKggoK2tDd/61rewaNEi9PX1YdSoUWhsbMS6detQUlKS0OfFCjryk7GFDlF233FMibSFGwNJZL6Q2+3GwYMHA8qjlfsTTUqRNttLZL5Rd3c3Xn75Zbz88ssh95eUlCT0eemtJlFaWsoKOiIf/nlGuhIZFwo3NhONcN1c4eKKN2blfH19fairqwuIM9r3HOncVt651qyxMKJYMSmRrkQG151OJ5qamgJe29TUBKfTGfG1kcZmwsUVT8z+55szZw7uu+8+LFq0SE2s0b7nSOe26s61iYwdEhmNY0oUViLzbgYHB+FyudQtNJxOZ8jKDlqiGcuKtBK41uTYWMeCop1TFe3nZdUxJU7MTQmOKengmBKFlci8m2HDhmHWrFkxjyFFM5YVLq7gxyIlg0TnVIU7d/BjZu6YGy9uuUFWwqREYYVr7URqCcXbyjJ6omikAgO98wkh0NbWFnXcyrhMW1sbTj31VJx++umYMWOGusoFYN7kWuX88X7eyhqIykK2mzZtSnm3opVxcVzz8FMkXf57M1VWVqK8vBwtLS0YHBwM+xiQ2DiF0RNFIxUYaJ2vrq4ON910U9RxezwebNmyBRdccAGuvfZazJ07F3/605+wZcsWdY8pMyXyeRcWFmLZsmVoaGjAihUrcP/992PZsmUxTTrOJhyDMxfHlEiX/95MCmUdOAC6j82aNSvhcQoj/xKNZYyqo6MDr776KjZs2KCujh5N3OEWxr388st1l2Myipl7UFEggz4vjinpYEuJdLndbt114MI9Blir/DmalpfSrXbqqaeivr5efR8OhwM1NTU4cOAA2traMDg4qFk6rfd+PR6PuseUmbq7u5Gbm4ulS5di2bJlWLZsGXJzc6P6vBP9WWVbObmVfrczEceUSJdSEh38F6EyhyfcY4mMCxldpRZLgYF/3A6HI2QX3qamJqxYsQIHDx4MiEvv/dpsNnWPKTMVFBSEbFNSV1eHgoKCiK+10s8qHXBxXHNl5m8NGSLcXKNI85ASGRfSK0xob2+P+70oLaGKigp1ZQYt/nFXVVWpCUmJo7q6GvPmzQuJq7i4GM3NzSHjUv/n//yfhFewiMbQ0JCakJTYli9fjqGhoYivtdrPyuq4OK652FIiXcOGDcNNN92E8847T7PCLtxjiZQ/p7JE2T/uAwcOaMahVKgFx/XVr34V06ZNQ1tbG+x2u7onlX/1nVkOHz6sGevhw4c1ty3xl64/q1Sxaml/pjAtKQkh1gOYA+AjKeU03313A/g2gCO+py2VUv7erBisKt5Jpalgs9kwduxYnHHGGRg7dmzAf7x45yFFkmj3SKJFEkqrSjlvcBz+g7j+cdlsNkyaNAlHjhxBd3c3Tj311KhaKtGKtBV7ontJxVOqHs95M6Gc2szS/mxn5m/CrwBcoXF/g5Ryhu+WlQkpXCm1lSRS+pqqknAjy3W14lizZg02b94c8L2yHUd/fz8ef/xxXHHFFbjhhhtw+eWX4/HHHw9pScQj0vsqLCzU7E41u6w71vOynJoiMbUkXAgxCcCzQS2l41LK1bEcJ5NKwsOVWSdj7CEWqSwzjvevaaPLm/3jGD58OH70ox/h4osv1tyO48UXX8QVV1wRcu7nn38eF198ccznjuV9tbW1Yc6cOSFbhTz77LOm/jUf63lZfq5iSbiOVPQZ3SaEqAKwG8D3pZT/0HqSEGIhgIUAcPbZZycxPHOFK6W2WlJKZLwg0bGGeLtHjB7j8I/jySef1NzWQvnZmVkqHOl9dXd3a24VYvbYTqznzcYxqHj4X/+yTbI7ch8CUAhgBoBuAD/Xe6KUcq2UskxKWTZ+/PgkhWe+RLd0SKZEVrVO1YrYZp430s8uPz8/4HGHw4Ha2lqMGDEi4fk7kd5XunzeVl0p3Wr8r3+pjiXppJSm3QBMArA/1seCbxdccIHMFCdPnpQbNmyQdrtdApB2u11u2LBBnjx5MtWhhRgaGpJPPPFEQKxPPPGEHBoaMvW1qYo5kkg/u3/+85/ykUcekXa7XTocDrlq1SrD4oj0vtLl805VnBYUy3VUptMtLy8vofef7DGlPCllt+/rGgD/V0p5Q6TjZNKYEpBe1XeJVEqlqsrKzPNG+tn19/dj165d6O/vx7XXXmvo2Emk95Uun3cmVN8ZIO3HlKIcO9Kj+/5NS0pCiMcBVAAYB+BDAHf5vp8Bb0Z9D8B3lCQVTqYlJcp8ra2tuOSSS0Lu37FjB6x4gaGkY1LSYdqf51LKr2vc/YhZ5yOKR6wbAkY6xvjx43Hs2DEMDAxozt8ZPnw4BgcHNVvGZraE0ql1bpREP89s/MysIOvazEQKrTkz27Zti2keTfAxZs2ahZ07d2LZsmWoq6sLmL9TW1uLW265RXNeWqT5O4nM70mnuXFGSfTzzMbPzCq4dQVlLa05M7W1tVi9enXUY0F6825qamqwceNG3HrrrXA6ndi9ezc2btwIt9utOS8tmnlI8c7vSae5cUZJ9PNMwmfG7jsdbClR1tKaM+PxeGKaa6Q370YIAbfbjfr6egwMDGDlypXqdhj+W3xEOo5y3kTmQEXaZiQTJfp5ZuNnZhVMSpS1tObM5OTkGDLvRvkL0m63Y8KECSGPB89LM3MeUjrNjTNKop9nNn5mVsGkRFlLa227srKymNbd09tKXblvzZo1AYkueIuPcMfxP28i6wFG2mYkEyX6eWbjZ2YVHFOirGZW9d3f/vY35OXloaysDCNGjIiqiovVd8ayePUdx5R0ZPZvJWUdj8eDgwcP4oMPPsCxY8dw1llnYcaMGboXE6019sJVtOldqJRjnDhxArt27VKPbbPZArb4CL4QFhYWoqOjI+DCGO/k2nAXUY/Hg4GBAZw8eRInT540dFXudJ0MG2l9RbO2ZqHwrP+bQxQlj8ejlnRfffXVqKysxMUXX4xHH3006lLecKXCkcqET5w4gcceewyXX345brjhBnz5y1/GY489hhMnTmgee86cOWhpaYmqzDuREuaBgQG0tLTgsssuw/XXX49LL70ULS0tGBgYMOQzt+JWFFaNiyJj9x1ljLa2Njz66KOaJd3RlvKGKxU+duxY2DLhl156CZdffnnI4y+88AK++MUvhhx76dKlaGhoiKrMO5ES5oGBAVx22WUhj23fvh0XXXRRxM8k3s8rlat+WzUuP+y+08GWEmWM7u5u3ZLuaEt5w5UKRyoT7urqiqkMWQgRdZl3IiXMnZ2dmo91dXVpfwgxMHO7jkRYNS6KjEmJMkZeXp5uSXe0pbzhSoUjlQkXFBTEVYas9/xo4wLClzDrPZafnx/6AcTIqltRWDUuioxJiTJGcXExysrKQpb3eeihh6Iu5Q1XKhypTLisrAxr1qwJ2T5d6TYMPvamTZtCjqdX5p1ICXOkuBKRSKm6mawaF0XGMSXKKEr1ndvtxscffxxXKW+4arJIZcJK9Z3y2lmzZmHkyJG6x9aqvtOrXEukhHlgYAC7du1CV1cX8vPzMWvWLIwYMSKmzzaezyuVrBqXD8eU9B5gUiIiSjomJR2cp0RkEbH8ZW90KyCZk2st3oJRpUucmYafMJEFxDKvxug5OMncpiFd5g+lS5yZiN13RBYQy7wao+fgJHNrizSYPwQgKXGy+04HW0pEFhDLvBqj5+Akc5uGdJk/lC5xZiKOKRFZgDKvJvgv83BzlqJ5bjSUeUzBxzNjmwajYzeL1eJsbW1NyXnDMeuzYEuJyAJimVdj9BycZG7TkC7zh6wWZ0VFRcgNAKSUKbsZsSKIFo4pEVkEq++sxeQ4Ex5TSnBMJ9U4T4koGdLlghssneL2j3XChAnIyclBZ2en5eMOwqSkIy1+ekTpIF3LiNMp7uBYZ86ciS1btmD+/PmWjpuix5YSkUHSpdw5WDrFrRdrTU0NVq5cadm4NbClpIMtJSKDpGsZcTrFrRerEEL92opxU/SYlIgMkq7bJaRT3HqxKi0Gq8ZN0WNSIjKI1cqIo5VOcWvFWldXp95n1bgpehxTIjJQOlWx+UunuFl955WpY0pMSkREycekpMO0PymEEOuFEB8JIfb73fcZIcR2IUS7798zzDo/EaUHj8eDtrY2tLa2oq2tzbCSbrOOS+Yys537KwBXBN33IwB/lFIWA/ij73siylJmzZFKp7lXFMi0pCSlfBHA34PuvgZAs+/rZgBzzTo/EVlfe3s7qqqq1DLv/v5+VFVVob293ZLHJfMle0TwTCmlMongMIAz9Z4ohFgohNgthNh95MiR5ERHREll1hypdJp7pcX/+pfqWJItZWUq0jtCpztKJ6VcK6Usk1KWjR8/PomREVGymDVHKp3mXmnxv/6lOpZkS3ZS+lAIkQcAvn8/SvL5ichCzJojlU5zryhQsjf5ewbAzQB+6vv36SSfn4gsxGazobKyEqWlpYbOkTLruGQ+05KSEOJxABUAxgkh3ADugjcZbRZCfBPA+wDmmXV+IkoPNpsNJSUlhi+iatZxyVymJSUp5dd1HvqSWeckImtIpxUiyFr4W0JEhuIcIUoEkxIRGYpzhCgRyS50IKIMF26OEMd3Yjd8+HC0traG3J8u5e2xYkuJiAyV7nOErOb888+HlDLk1tXVlerQTMGkRESG4hwhSgS774jIUJwjRIlgUiIiw3GOEMWLf7oQEZFlMCkREZFlMCkREZFlMCkREZFlMCkREZFlMCkREZFlMCkREZFlMCkREZFlMCkREZFlCCllqmOISAhxBN6dao0yDkCPgcczilXjAqwbG+OKjVXjAqwbmxlx9Ugpr4jmiUKI56N9biZIi6RkNCHEbillWarjCGbVuADrxsa4YmPVuADrxmbVuDIVu++IiMgymJSIiMgysjUprU11ADqsGhdg3dgYV2ysGhdg3disGldGysoxJSIisqZsbSkREZEFMSkREZFlZEVSEkK8J4TYJ4R4Qwix23ffZ4QQ24UQ7b5/z0hyTCW+eJTbMSHE94QQdwshOv3uvyoJsawXQnwkhNjvd5/m5yO8HhRCHBRC7BVCzExyXPcJId72nftJIcTpvvsnCSH6/T63X5oVV5jYdH92Qoglvs+sTQhxeZLj2uQX03tCiDd89yftMxNCnCWE2CGEOCCEeFMIcbvv/pT+noWJyxK/Z1lJSpnxNwDvARgXdN/PAPzI9/WPANybwvhyABwGMBHA3QAWJ/n8FwOYCWB/pM8HwFUAngMgAFwI4C9JjuvLAIb5vr7XL65J/s9L0Wem+bMDMBXAHgCnAJgMoANATrLiCnr85wDqkv2ZAcgDMNP39RgA7/g+l5T+noWJyxK/Z9l4y4qWko5rADT7vm4GMDd1oeBLADqklEauWhE1KeWLAP4edLfe53MNgI3S61UApwsh8pIVl5TyD1LKQd+3rwJwmHHuSHQ+Mz3XAPiNlPJTKeUhAAcBfC7ZcQkhBIB5AB4349zhSCm7pZSv+77+BMBbAAqQ4t8zvbis8nuWjbIlKUkAfxBCvCaEWOi770wpZbfv68MAzkxNaACAGxB4objN122wPtndin70Pp8CAB/4Pc/tuy8VFsD717RishDCJYTYKYT4Yopi0vrZWeUz+yKAD6WU7X73Jf0zE0JMAuAE8BdY6PcsKC5/Vvw9y1jZkpT+TUo5E8CVABYJIS72f1B62+UpqY0XQowA8O8AnvDd9RCAQgAzAHTD292SUqn8fPQIIZYBGATwqO+ubgBnSymdAO4A8JgQYmySw7Lczy7I1xH4x0/SPzMhxGgAWwB8T0p5zP+xFP8/1IzLor9nGS0rkpKUstP370cAnoS36+RDpTvA9+9HKQrvSgCvSyk/9MX4oZRySErpAfA/MKmbJwp6n08ngLP8nufw3Zc0QohbAMwB8A3fhQy+rrFe39evwTtuc04y4wrzs7PCZzYMQCWATcp9yf7MhBDD4b3wPyql3Oq7O+W/ZzpxWfb3LNNlfFISQowSQoxRvoZ3AHM/gGcA3Ox72s0Ank5NhIF/vQb1m18Lb6ypoPf5PAOgylcddSGAj/26X0wnhLgCwJ0A/l1K+U+/+8cLIXJ8X08BUAzg3WTF5Tuv3s/uGQA3CCFOEUJM9sX212TGBuBSAG9LKd3KHcn8zHzjWY8AeEtKeb/fQyn9PdOLy8q/Zxkv1ZUWZt8ATIG38mkPgDcBLPPdnwvgjwDaAfw/AJ9JQWyjAPQCOM3vvl8D2AdgL7z/MfOSEMfj8HZLnIS37/6bep8PvNVQjfD+hbgPQFmS4zoI71jDG77bL33P/arv5/sGgNcBXJ2Cz0z3Zwdgme8zawNwZTLj8t3/KwD/EfTcpH1mAP4N3q65vX4/u6tS/XsWJi5L/J5l443LDBERkWVkfPcdERGlDyYlIiKyDCYlIiKyDCYlIiKyDCYlIiKyDCYlIiKyDCYlsgzh3fphcYyvOe77d5Lw264h2YR325FTTTju75VtE4iyAZMSkTG+B8DwpCSlvEpKedTo4xJZFZMSmU4IUeVbOXuPEOLXQoirhRB/8a20/P+EEP4rtE8XQrzi2/Tt277XjxZC/FEI8brwbtZ4TYTzjRRCbPA91yWEuMR3/3lCiL/6NmfbK4Qo1nn9JN8Gb48KId4SQvxWaQUJIb7kO+Y+30rgpwghvgsgH8AOIcQO3/O+7HsfrwshnvAt+KlsOHmP33s51+89KjHvFUJ81e/543xfPyW8K92/Kf612j1RZkn1khK8ZfYNwHnwbpw2zvf9ZwCcAairiXwLwM99X98N73JQdgDj4F3mJR/AMABjfc8ZB+8SMMrrj/v+nQTf5msAvg9gve/rcwH8DcBIAP8N7+KaADACgF0n5knwLj1zke/79QAW+47xAYBzfPdvhHdVacBvI0lfjC8CGOX7/of418Z67wH4L9/X1QDW+b6+F8Av/GI4Q+O4yhI8dnjX1ctN9c+XN96MvrGlRGabDeAJKWUPAEgp/w7vis8vCCH2AfgBvIlL8bSUst/3/B3wrrQtAKwUQuyFd320AoTf/+rfALT4zvc2gPfhXcn5FQBLhRA/BDBRStkf5hgfSCn/7Pu6xXfMEgCHpJTv+O5vhnen12AXwrt76Z+Fd+vxm+HdVVihrET9GrwJEPAumNqoPEFK+Q+N435XCLEH3k3nzoJ3MVCijMKkRKnw3wDWSClLAXwH3haIIngxRgngGwDGA7hASjkDwIdBr4mKlPIxePeu6gfweyHE7HBPj/B9OALAdinlDN9tqpTym36Pf+r7dwjeVmDkAwpRAW/i+ryUcjoAF+L4DIisjkmJzPYnAF8TQuQCgBDiMwBOw7/2xrk56PnX+MaEcgFUANjle/5HUsqTvvGhiQjvJXgTGYQQ5wA4G0Cbb6uBd6WUD8K7RcL5YY5xthDi876vbwTwMrwrfE8SQhT57p8PYKfv608AjPF9/SqAi5Tn+bZPibTnznYAi5RvROiOw6cB+IeU8p++cagLIxyPKC0xKZGppJRvAlgBYKev6+l+eMeOnhBCvAagJ+gle+HttnsVQL2UsgveXT/LfN19VQDejnDaJgA23/M3AbhFSvkpgHkA9vu61KbBOyakpw3eXYrfgncM7CEp5QkAt/pi3wfAA+CXvuevBfC8EGKHlPIIgFsAPO7rcnwF3rGtcH4C4AwhxH7f53RJ0OPPAxjmi+en8H4+RBmHW1cQBRFCTALwrJRyWqpjIco2bCkREZFlsKVEWcs3bvVHjYe+JKXsTXY8RMSkREREFsLuOyIisgwmJSIisgwmJSIisgwmJSIisoz/D5261YpvMjiuAAAAAElFTkSuQmCC", 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Graficar utilizando seaborn\n", "sns.jointplot(x='caballos_potencia', y='mpg', data=autos, color='#000000')\n", "\n", "# Graficar utilizando pandas\n", "autos.plot.scatter(x='caballos_potencia', y='mpg', color='#000000', title='scatter plot : mpg por caballos_potencia')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.7" } }, "nbformat": 4, "nbformat_minor": 2 }