{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Ejercicio análisi exploratorio parte 1\n", "\n", "Utilizando los datos sobre automoviles extraídos en un archivo csv via webscraping (están almacenados en un archivo csv en la misma ruta que este notebook), responda a las siguientes preguntas:\n", "\n", "1. [La asimetría de la variable `caballos_potencia` es negativa?](#1)\n", "2. [Entre las variables: `caballos_potencia`, `desplazamiento`, `mpg` y `aceleracion`, ¿cuáles tienen valores atípicos en ambos extremos?](#2)\n", "3. [Entre las variables: `caballos_potencia`, `desplazamiento`, `mpg` y `aceleracion`, ¿cuáles no tienen valores atípicos?](#3)\n", "4. [Entre las variables: `caballos_potencia`, `desplazamiento`, `mpg` y `aceleracion`,, ¿cuáles son las variables con mayor y menor asimetría?](#4)\n", "5. [Entre las variables: `caballos_potencia`, `desplazamiento`, `mpg` y `aceleracion`, muestre los valores atípicos de aquellas variables que los tengan en ambos extremos.](#5)" ] }, { "cell_type": "code", "execution_count": 2, "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": 2, "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", "automoviles = pd.read_csv('./csv/datos_automoviles.csv')\n", "automoviles.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1. La asimetría de la variable `caballos_potencia` es negativa?\n", "\n", "" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Asimetría de caballos_potencia\n" ] }, { "data": { "text/plain": [ "1.034079427703104" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print('Asimetría de caballos_potencia')\n", "automoviles['caballos_potencia'].skew()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2. Entre las variables: `caballos_potencia`, `desplazamiento`, `mpg` y `aceleracion`, ¿cuáles tienen valores atípicos en ambos extremos?\n", "\n", "\n", "\n", "Para saber cúales variables tiene valores atípicos, se puede utilizar gráficos o métido el analítico. En este caso, se utilizarán gráficas de dos tipos:\n", "\n", "- Una será una gráfica de distribución donde se dibujarán líneas verticales a la altura de ambos valores atípicos.\n", "- La otra, un *gráfico de caja* el cual nos señala mediante un círculo si existen o no valores atípicos\n", "\n", "Cabe resaltar que la primera manera no indica de manera precisa la existencia de valores atípicos, pero se lo muestra por propósitos ilustrativos. En cambio, el *gráfico de caja* si indica con exactidud si existen o no valores atípicos." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "def graficar_distribucion_con_valores_atipicos(columna):\n", " q1 = automoviles.describe()[columna]['25%']\n", " q3 = automoviles.describe()[columna]['75%']\n", " iqr = q3 - q1\n", " limite_derecho = q3 + 1.5 * iqr\n", " limite_izquierdo = q1 - 1.5 * iqr\n", "\n", " sns.kdeplot(automoviles[columna], shade=True)\n", " plt.axvline( limite_derecho, color='b')\n", " plt.axvline( limite_izquierdo, color='b')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 2.1 Valores atípicos `caballos_potencia`" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "graficar_distribucion_con_valores_atipicos('caballos_potencia')" ] }, { "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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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "automoviles['caballos_potencia'].plot.box()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 2.2 Valores atípicos `desplazamiento`" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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4kQXAEeASVe1yx4+8JiL/p6pvTuZNm8hU397n++C/YQmBWGakJbCvsZuFBWl+h2NMSI3XOJ7i/kwF0kZ5jGUZUKmq+1S1H3gMWD6izHLgYXf7SeBScfpaLgcec3ttVQGVwDJ1DM/1EOc+rDP9NDA0pDR29oXNHQc41VU2gtxMR2PecajqT9yfJ1MlVAzUBD2vBc47URlVHRCRdiDH3f/miHOL4e07mQ3APOCHqrpmtBcXkZuBmwFmzpx5EuGbcNLUdYTk+ADxgfCZLq0kK4kdBzv44FnFfodiTEhNdJLDb4tIuojEicgLItIoIh/1OrjRqOqgqi7FabBfJiLvOEG5+1W1XFXL8/LyQhqjmXr17X3MSAufuw1wZsndXt/udxjGhNxEv75drqodwNU4c1XNA/5xnHPqgNKg5yXuvlHLiEgAyACaJ3KuqrYBLwFXTvA9mAgWDpMbjlSWk8LOgzaWw0w/E00cw1VaHwB+raoT+Zq1DpgvIrNFJB6nsXvliDIrgRvd7euAF9WZAGglsMLtdTUbmA+sFZE8EckEEJEknIb3XRN8DyaC1beFz+C/YTkp8QwMDtHQ0ed3KMaE1ESnHHlWRHYBvcAtIpIHjPm/xW2zuA1YDcQCP1XV7SLyTWC9qq4EHgQeEZFKoAUnueCWewLYAQwAt6rqoIgUAg+77RwxwBOq+uxk37SJPLWtPWQnh1dVlYgwOzeF7fUdzEgPj/ElxoTChBKHqt4hIt8G2t0/4N0c30NqtPNWAatG7LsraLsPuP4E594N3D1i3xbgrInEbKJLbWsvpxVl+B3GcWZmJ7Otvp2LF83wOxRjQmaidxwAi3DGcwSf8/MpjseYUdW39fHeBeHXyWFWTgpba62B3EwvE51W/RFgLrAJGHR3K5Y4TIgcbO/1fQGn0ZTlpPD0WyP7fBgT3SZ6x1EOLFFbucb4oO/oIF1HBshICr/FJgszEmnt6aetp5/MMJmA0RivTbRX1TagwMtAjDmRg+195KYmEOPzAk6jiYkR5uSmsLXOqqvM9DHRO45cYIeIrMWZLwoAVb3Wk6iMCRJOCziNpiw3hc01bVw4P/zaYIzxwkQTx9e9DMKYsdS1hWf7xrA5uSm8VdPmdxjGhMyEqqpU9U84I8bj3O11wEYP4zLmbXWtPWE3+C/YnLxU61llppWJzlX19ziz1/7E3VUM/NajmIw5Rk1r+KzDMZoZaQn0HR3ksI0gN9PERBvHbwXeDXQAqOoewEY8mZCobQ3vNg4RYWFBGhv3t/odijEhMdHEccRdUwN4e0JC65prQuJgWy+5YdzGAU511XpLHGaamGji+JOI/DOQJCLvA34NPONdWMY4hoaUwx1Hwm5m3JHmz0hlfXWL32EYExITTRx3AI3AVuBTOPNPfdWroIwZ1tR9hOT4WBICsX6HMqa5ealUHO7kyMDg+IWNiXATneRwSER+C/xWVRu9DcmYv6hv6yMvzBZwGk1iXCylWclsqW3n3LJsv8MxxlNj3nGI4+si0gRUABXu6n93jXWeMVOlrjX8FnA6kQX5aby5r9nvMIzx3HhVVZ/H6U11rqpmq2o2zrrh7xaRz3senZn26tp6wrorbrBFhWn8ea8lDhP9xkscHwNuUNWq4R2qug/4KPB3XgZmDMCB5p6IueNYVJDO5po2jg4O+R2KMZ4aL3HEqWrTyJ1uO0f4TVVqok5Nay95Yd4Vd1hqQoCC9ES21Lb5HYoxnhovcfSf5DFjpkRday+5EdA4PmxJUTqv7j7uu5YxUWW8xHGmiHSM8ugETg9FgGb6UlXq2yPnjgPg9OIMXqpo8DsMYzw1ZuJQ1VhVTR/lkaaqVlVlPNXeexQRSEmYzArH/lpUkM7uw1209xz1OxRjPDPRAYDGhFxtay8z0hL9DmNS4gMxLC5M47VKq64y0csShwlbta29ETH4b6QzSzP5w/aDfodhjGcscZiwVdvaQ04Yr8NxIuWzsnm5opH+AeuWa6KTJQ4TtmpaImfwX7DslHiKMpJsFLmJWpY4TNja39LDjAisqgIoL8ti5eZ6v8MwxhOWOEzYqm3pJS89MhPHu+bmsnr7IfqO2my5JvpY4jBhSVWpa4usMRzBslPimZeXyurth/wOxZgpZ4nDhKXWnqPExkhEjeEY6YL5ufzizf1+h2HMlLPEYcJSTUsP+RFaTTVsWVk2VU3dbK9vn5LrDQwOcaC5h4pDnbT12Iw/xj+efp0TkSuB+4BY4AFV/Y8RxxOAnwPnAM3Ah1W12j12J3ATMAh8VlVXi0ipWz4fZ83z+1X1Pi/fg/FHTWtPxA3+GykQG8Nli/O5/5V93LfirJO+zht7m3n4z9W8sqeRtMQA8YEYmrv6yU9P5ENnF/Oxd5aRkWwTOZjQ8SxxiEgs8EPgfUAtsE5EVqrqjqBiNwGtqjpPRFYA9wAfFpElwArgNKAIeF5EFgADwBdVdaOIpAEbROS5Edc0UaCmJXIWcBrLZYvz+dKvN7PrUAeLCtInde6+xi7+5bfbqGru5srTCvjeh5eSlugkiKEhZW9jFy9VNPDAq1Xcftl8PnZ+GbEx4sXbeFv/wBD7mrpo7uonMS6GObmpZEXgWBtzary841gGVLrrdyAijwHLgeA/8suBr7vbTwI/EBFx9z+mqkeAKhGpBJap6hvAQQBV7RSRnUDxiGuaKLC/uZvcCG0YD5aSEODapUX827M7eeSmZTi/3mMbGlJ+9noV33+xkmvPLOLTF80lEHNsrXJMjDA/P435+WnUtfby0z9X8cyWg9y3YiklWclT+h5UlVf2NPHom/t5rbKJ7JR4spLj6R8Yora1hzl5qXzigjKWn1lMjMeJy4QHLxNHMVAT9LwWZ/XAUcuo6oCItAM57v43R5xbHHyiiJQBZwFrRntxEbkZuBlg5syZJ/sejE/2N/fwngW5focxJd63OJ+vV27nl2sP8JHzZo1ZtqGjj88/vonm7n6+ce1p5KePX11XnJXEV65azO+31nPNf7/Gf/3NmVyyKH9KYn+9som7f7+Tnv4BLlucf8xdD8DA0BBbatu5/0/7+N9Xqvj+DUuZNyNtSl7bhK+I7LIiIqnAb4DbVbVjtDKqej9wP0B5ebmGMDwzBQ609JAf4W0cwwKxMdxy0Tz+9dkdzM5N4V1zj0+IqsqzWw7ytZXbuWThDG65aN6kqp1iYoRrzixmQX46X35yC9eXl/LFyxeedNVVfVsvX1u5na217Xz43FKWzc4mZpS7pUBMDGfPzOKs0kxerGjguv95g29fdwaXn1ZwUq9rIoOXiaMOKA16XuLuG61MrYgEgAycRvITnisicThJ41FVfcqb0I2fBgaHaOjsi6gFnMZTnJnEZy+Zxy2/2MgX37eAFctmEh+IQVVZU9XCd5/bzaH2Pj5/2fxT+sa+sCCNf/3g6fzo5UrWVbdw34qzKMpMmvD5RweHeOj1Kn7w0l7et3gG93zoDOID43e+FBEuXZRPWU4Kdz61lc6+AT50TslJvw8T3rxMHOuA+SIyG+eP/grgb0eUWQncCLwBXAe8qKoqIiuBX4rIvTiN4/OBtW77x4PATlW918PYjY/q2/rISo4nLja6eosvKcrgXz6whJ+/Wc13/lhBcWYSh9r7SE+K4/Il+XzmkvlT0ridkRTHl69YxDNb6vnA91/lS1csZMW5M8e8tqryp92N/OuzO0hNCPC1q5dQOImEM2xuXip3XrWYf1+1k+T4WK46vfBU3ooJU54lDrfN4jZgNU533J+q6nYR+SawXlVX4iSBR9zG7xac5IJb7gmcRu8B4FZVHRSRC4CPAVtFZJP7Uv+sqqu8eh8m9A609FCQER3VVCMVZyVx51WLaevpp6W7n8zkeLI96JUUEyMsX1rM0tJMHn5jPz97vZpPvWcOV76j4Jg2io6+o7yw8zA/e72a1u5+ri8vpXxW1oQa8U+kOCuJL16+kDue2kpBRiJnzcyairdkwoioRn/1f3l5ua5fv37S5110kfPz5ZenNBwzjkfX7OfFnQ188sI5focSFVSVzbVtvLirgW11HeSnJ5CRFEdb71EaOo5wWlE6F87Po3xW1pT2ilpf3cIv3tzPs5+9MCLXVYl0p/r3S0Q2qGr5aMcisnHcRLf9TT1R1b7hNxFhaWkWS0uz6B8Y4nBHH939A6TEByjISPSsSrC8LJt9Td187rG3+MVN51lX3SgSXZXIJipUN3dHTY+qcBMfiKE0O5lFBemUZid73o70obNLaOnu54HX9nn6Oia0LHGYsFPV1B3x81QZR2yMcMt75/LDl/ZS2dDpdzhmiljiMGFFValt7Y3axvHpaEZ6Ih86p5gvPLGZwaHob1OdDixxmLDS0HmExLgYkuOt+S2aXLoon4Eh5ZE3qv0OxUwBSxwmrFQ1dVOYMfnxAya8xYjwiXfP5nvP7+FwR5/f4ZhTZInDhJVqa9+IWsWZSVy8aAbffGa736GYU2SJw4SVfU3d5FmPqqi1fGkR6/e38sbeZr9DMafAEocJK/sauyiYwIywJjIlBGK54dyZ/MvvtjEwOOR3OOYkWeIwYaW6KXqnGzGOZbOzSYqL5VdrD/gdijlJljhM2BgcUg609lBoiSOqiQgfOW8m9z63m/aeo36HY06CJQ4TNupae8lIiiMxLtbvUIzHZuWkcM6sLL7/4h6/QzEnwRKHCRt7m7ooPompvE1k+tDZJTy5oYbqpm6/QzGTZInDhI29DV3WvjGNZCbHc9U7Cvn3VTv9DsVMkiUOEzb2HO6i0HpUTStXvaOQTTVtrKtu8TsUMwmWOEzY2NvYdVKrzpnIFR+I4fryUr6xcjtDNo9VxLDEYcLGvqZuiqyqatp519wcjgwM8cyWer9DMRNkicOEhbaefvqODnqyjKoJbzEi3LBsJt9atYu+o4N+h2MmwBKHCQu7D3dRmp18Smtdm8i1uDCdstxk/vdVW/ApEljiMGFh9+FO64o7zX24fCYPvFpFQ6fNnhvuLHGYsFBxqJMim059WivISOQ9C3L59h8q/A7FjMMShwkLuw51UJJliWO6++DSYl7YeZhtde1+h2LGYInDhIXKhi5LHIbk+ADXnVPCXb/bhqp1zw1XljiM71q6++kfGLIeVQaAixbMoL33KCs3W/fccGWJw/hu18EOynJTrEeVASAmRvi788u4+/c76Toy4Hc4ZhSWOIzvdhy09g1zrAX5aSwpSud7z+32OxQzCkscxnfb69spzU72OwwTZlacO5MnN9RScajT71DMCJY4jO921HcyKzvF7zBMmMlIiuND55Tw5d9ssXmswoyniUNErhSRChGpFJE7RjmeICKPu8fXiEhZ0LE73f0VInJF0P6fikiDiGzzMnYTGkcHh6hu7qY026qqzPEuWTSD3v5Bfrl2v9+hmCCeJQ4RiQV+CFwFLAFuEJElI4rdBLSq6jzgu8A97rlLgBXAacCVwI/c6wE85O4zUWBvYxd5aQkkBGzVP3O8GBFuumA2/7l6N4fabUR5uPDyjmMZUKmq+1S1H3gMWD6izHLgYXf7SeBScbrWLAceU9UjqloFVLrXQ1VfAWzy/iixtbad2blWTWVOrDQ7mcsW53PnU1tsbEeY8DJxFAM1Qc9r3X2jllHVAaAdyJnguWMSkZtFZL2IrG9sbJxk6CZUNte0WfuGGdfypUXsa+rmt5vq/A7FEMWN46p6v6qWq2p5Xl6e3+GYE9hc286cPEscZmyB2Bg+9Z65fPOZHVZlFQa8TBx1QGnQ8xJ336hlRCQAZADNEzzXRLijg0PsaeikLMcShxnf7NwULluczxee2GS9rHzmZeJYB8wXkdkiEo/T2L1yRJmVwI3u9nXAi+pUYq4EVri9rmYD84G1HsZqfLD7cCcz0hJJireGcTMxy5cW09zVz0N/rvI7lGnNs8ThtlncBqwGdgJPqOp2EfmmiFzrFnsQyBGRSuALwB3uuduBJ4AdwB+AW1V1EEBEfgW8ASwUkVoRucmr92C8tbmmnTnWMG4mITZGuOWiudz3QiXb620GXb8EvLy4qq4CVo3Yd1fQdh9w/QnOvRu4e5T9N0xxmMYn66qbmTsj1e8wTITJT0/kI+fN5NZHN/L7z15ISoKnf8bMKKK2cdyEvw3725hvicOchAvn5zEnL4Uv/8a66PrBEofxRUt3P81dRyjNsjmqzMn5u/PL2FbXziNv2KjyULPEYXyxcX8r8/PTiImxqdTNyUkIxPK5Sxdw73O72bDfxgSHkiUO44s1VS3Ms2oqc4oKMhL5+/fM4dOPbLTxHSFkicP44vW9TSwpTPc7DBMFzp6ZxaWLZ/CJh9bR2z/odzjTgiUOE3LtvUepauy2Ow4zZa49s4jctHhuf/wtGxwYApY4TMitrWphYUEacbH262emhojwyQvmUNPSy7f+b5ff4UQ9+59rQu71ykYWFqT5HYaJMnGxMdx+2XxWbT3IQ6/byHIvWeIwIfdSRSNnlmT6HYaJQmmJcfzTFQv57xcreXZLvd/hRC1LHCak9jd309F7lFk5Nn7DeGNGeiL/eMVCvvr0Nl7ZbUsqeMEShwmplysaWVqaSYzY+A3jnVk5Kdx+2QI+86u3WFtlYzymmiUOE1J/3HGI04sz/Q7DTAMLC9K49eJ53Pzz9WzY3+p3OFHFEocJmbaeft460MbS0ky/QzHTxOnFGXzqvXO46eF1bDxgyWOqWOIwIfPcjsOcXpxh62+YkFpamsXNF87hEw+tY321VVtNBUscJmSe2VzPuWXZfodhpqGzZmbx6ffM5ZM/X8+fK5v8DifiWeIwIdHQ2cfGA22cPTPL71DMNHVmaSafuWQ+//DLjTy347Df4UQ0SxwmJJ7eWMe5ZVlWTWV8taQwnS9dvpAv/2YLj6874Hc4EcsSh/GcqvL4uhounJ/ndyjGMDcvla+8fzH3Preb+57fbQtBnQRLHMZzr1c2M6jKIptmxISJoswkvnbNaazcXM8/PrmF/oEhv0OKKJY4jOfuf2Ufly8pQGzQnwkjWcnxfPUDS6hu7uZjD66htbvf75AihiUO46kd9R1srWvjgnm5fodizHES42L5/KULyE9P5NofvMauQx1+hxQRLHEYT/3nHyu45swi4gP2q2bCU0yMcMOymSxfWsyKn7zJys02OeJ4An4HYKLX2qoWtta1c+P5ZX6HYsy43j0vl5KsJL61aidv7m3mrmuWkBhnvQBHY18DjSeODg7xz09v5W+XzbS7DRMxZuWk8G8ffAfVzd1c89+vUXGo0++QwpL9jzaeuO/5PaQnxnHebBspbiJLcnyA2y6ex6WLZ/A3P3mDH71UycCg9boKZonDTLnXK5v41doD/P2Fs60nlYlIIsJ7F8zgX5efxurth7j6v19jU02b32GFDUscZkrtOtTBbb/cyD9cNJfM5Hi/wzHmlOSlJfLlKxdxyaIZfOKhdXzxiU0cau/zOyzfWeIwU2ZrbTsf+d81fOyds1hSlOF3OMZMCRHhwvl5fOe6MxgcUi7/7p/4xjPbOdwxfROIJQ5zylSVX609wEcfXMON7yrj/Lk2ZsNEn+T4AB8+dybf+uszONzRx2X3/onbH3uLjQdap920JZ4mDhG5UkQqRKRSRO4Y5XiCiDzuHl8jImVBx+5091eIyBUTvaYJHVVlXXUL1//4DR54dR9fef9imzbdRL3slHg+9s4y7r1+KSkJAT7zy7e4+D9f5r9WV7C1tp2hoehPIp6N4xCRWOCHwPuAWmCdiKxU1R1BxW4CWlV1noisAO4BPiwiS4AVwGlAEfC8iCxwzxnvmsZDA4ND7DrUySu7G/ntpjo6+wa4+owi3rsgj9gYawg300dqYoCrzyjiA6cXsrexizVVLdzy6Aa6jwxQXpbNuWVZnFaUwfz8VPJSE6Kqo4iXAwCXAZWqug9ARB4DlgPBf+SXA193t58EfiDOv+5y4DFVPQJUiUilez0mcE0zRZ5YV8NLFQ00d/VzsL2Xmtbet48tyE+lfFY2S4rSEWB/c7d/gRrjsxgRzp+Tw/lzcmju6mfXoQ5+s6GO//rjbo4ETaCYlRxHbmoC2SnxZCbHkZIQICU+QGJcDAmBWOJiYwjEClefUcisnBQf39HYvEwcxUBN0PNa4LwTlVHVARFpB3Lc/W+OOLfY3R7vmgCIyM3Aze7TLhGpmGDcucAxS4SF0ReF42LzUlzOzEUSF3/Mb68ODhxBh4bqgZfdfUO9XYGYpNSBUMU1GRbb5IVrXBC+sY0bl8TESkwg/uAE/5Z8rru1frCz+eBUxCZy0n83Zp3oQNROOaKq9wP3T/Y8EVmvquUehHTKwjU2EVk/0NUcdnGBxXYywjUuCN/YwjUu8ObvhpeN43VAadDzEnffqGVEJABkAM1jnDuRaxpjjPGQl4ljHTBfRGaLSDxOY/fKEWVWAje629cBL6rTr20lsMLtdTUbmA+sneA1jTHGeMizqiq3zeI2YDUQC/xUVbeLyDeB9aq6EngQeMRt/G7BSQS45Z7AafQeAG5V1UGA0a45xaFPunorhMI1tnCNCyy2kxGucUH4xhaucYEHscl0G7hijDHm1NjIcWOMMZNiicMYY8ykTOvEISLfEZFdIrJFRJ4WkcygY75NeSIi14vIdhEZEpHyEcfCaioWv6eAEZGfikiDiGwL2pctIs+JyB73Z5a7X0Tk+26sW0TkbA/jKhWRl0Rkh/tZfi6MYksUkbUistmN7Rvu/tnu1D+V7lRA8e7+E04N5FF8sSLylog8G2ZxVYvIVhHZJCLr3X3h8HlmisiT7t+ynSJyvudxqeq0fQCXAwF3+x7gHnd7CbAZSABmA3txGuNj3e05QLxbZokHcS0GFuKMsSsP2u9rXKPE6cvrjojhPcDZwLagfd8G7nC37wj6XN8P/B8gwDuBNR7GVQic7W6nAbvdzy8cYhMg1d2OA9a4r/kEsMLd/2PgFnf7H4Afu9srgMc9/ky/APwSeNZ9Hi5xVQO5I/aFw+f5MPBJdzseyPQ6Ls/+kSPtAfwV8Ki7fSdwZ9Cx1cD57mN10P5jynkQ08scmzjCIq6g1/HldUeJo4xjE0cFUOhuFwIV7vZPgBtGKxeCGH+HM8daWMUGJAMbcWZgaOIvX6Te/myHf8/c7YBbTjyKpwR4AbgEeNb9A+d7XO5rVHN84vD188QZ+1Y18n17Hde0rqoa4RM4mRhGny6leIz9oRJucfn973Ei+ao6PF3DISDf3fYlXrcK5Sycb/ZhEZtbHbQJaACew7lzbFPV4Wkzgl//mKmBgOGpgbzwPeCfgOEJnnLCJC4ABf4oIhvEmdII/P88ZwONwM/c6r0HRCTF67iidsqRYSLyPFAwyqGvqOrv3DJfwRkv8mg4xWVOnaqqiPjW51xEUoHfALeraocETXzmZ2zqjItaKk673tPAIj/iCCYiVwMNqrpBRC7yOZzRXKCqdSIyA3hORHYFH/Tp8wzgVNV+RlXXiMh9OFVTnsYV9YlDVS8b67iIfBy4GrhU3Xs3xp7aZEqmPBkvrhPwPK4pjMdPh0WkUFUPikghzrdqCHG8IhKHkzQeVdWnwim2YaraJiIv4VQBZYpIwP32Hvz6w7HVyrFTA021dwPXisj7gUQgHbgvDOICQFXr3J8NIvI0zozdfn+etUCtqq5xnz+Jkzg8jWtaV1WJyJU4t8XXqmpP0KFwnfIk3OLy+9/jRIKnsrkRp31heP/fuT1L3gm0B93OTylxbi0eBHaq6r1hFluee6eBiCThtL3sBF7CmfpntNhGmxpoSqnqnapaoqplOL9LL6rqR/yOC0BEUkQkbXgbp2PNNnz+PFX1EFAjIgvdXZfizLjhbVxeNSRFwgOoxKnv2+Q+fhx07Cs49b4VwFVB+9+P00NmL061khdx/RXON4kjwGGObYD2La4TxOrL6wa9/q+Ag8BR99/sJpx67heAPcDzQLZbVnAWAtsLbCWo44EHcV2AUye+Jej36/1hEtsZwFtubNuAu9z9c3C+iFQCvwYS3P2J7vNK9/icEHyuF/GXXlW+x+XGsNl9bB/+XQ+Tz3MpsN79PH8LZHkdl005YowxZlKmdVWVMcaYybPEYYwxZlIscRhjjJkUSxzGGGMmxRKHMcaYSbHEYaYdEfm6iHxpCq/XNVXXmsRrFonIk6dw/u0ikjyVMZnpwxKHMRFIVetV9brxS57Q7TgTHBozaZY4zLQgIl8Rkd0i8hrOlPWIyFwR+YM7ad2rIrLI3X+9iGwTZ72KV9x9HxeR34nIy+4aB18b5TVSReQFEdkozroNy939nxZnDYdNIlLlTvGBiPyPiKyXoDUx3P3VIvItt/x6ETlbRFaLyF4R+bRbpkzcNUjEmbDwOyKyTpw1Fj7l7r/IjXd4rYZH3RHDnwWKgJeCYrnBjXmbiNzj2QdhooPXI0DtYQ+/H8A5OKNkk3HmP6oEvoQzsna+W+Y8nCkrcMsWu9uZ7s+P44xQzwGScEZcl7vHutyfASDd3c51X0eC4ogDXgWucZ8Pj+aNxZlC/wz3eTV/WXPiuzgjgtOAPOCwu78Mdyp54Gbgq+52As4o4tk4o6/bceYjigHewJmob/g1ct3tIuCAe/0A8CLwQb8/N3uE7yPqJzk0BrgQeFrd+chEZCXOdBXvAn4tf5mxNsH9+TrwkIg8ATwVdJ3nVLXZvcZTONOKrA86LsC/i8h7cKYFL8aZzvqQe/w+nOT0jPv8b8SZnjuAs2bCEpwkAX+Z82srzqJLnUCniByRoJUqXZcDZ4jIcNVVBs48Zv3AWlWtdWPehJNwXhtx/rnAy6ra6JZ7FGeBrN9izCgscZjpKgZnnYelIw+o6qdF5DzgA8AGETln+NDIoiOefwTnW/s5qnpURKpxEtTwLMyzgNvc57Nx7nrOVdVWEXlouKzriPtzKGh7+PnI/7eCM6326mN2OlOTB587OMq5xkyatXGY6eAV4IMikuTOcHoN0ANUicj18PZazGe623NVdY2q3oWzSM7wNNTvE2ct5yTggzh3JsEycNaTOCoiF+MkCtzE8yXgo6o6vEBROtANtItIPnDVKby/1cAt4kzjjogscGdwHUsnTvUXOBMEvldEckUkFrgB+NMpxGOinH37MFFPVTeKyOM4M5s24EwHD84dwv+IyFdx2h8ec8t8R0Tm43yTf8HdtxTnD+xvcNoMfqGqwdVU4CwE9oyIbMWpwhpe6Oc2IBunMRpgvap+UkTecsvUcHwSmowHcKqgNorzAo04iW0s9wN/EJF6Vb1YRO7Amb5cgN+rLSZmxmCz4xozAW5VU7mq3uZ3LMb4zaqqjDHGTIrdcRhjjJkUu+MwxhgzKZY4jDHGTIolDmOMMZNiicMYY8ykWOIwxhgzKf8fsurY4mJvrWkAAAAASUVORK5CYII=", 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "automoviles['desplazamiento'].plot.box()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 2.3 Valores atípicos `mpg`" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "automoviles['mpg'].plot.box()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 2.4 Valores atípicos `aceleracion`" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "graficar_distribucion_con_valores_atipicos('aceleracion')" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "automoviles['aceleracion'].plot.box()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Respuesta pregunta 2: la `aceleración` es la única variable que tiene valores atípicos en ambos extremos" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3. Entre las variables: `caballos_potencia`, `desplazamiento`, `mpg` y `aceleracion`, ¿cuáles no tienen valores atípicos?\n", "\n", "\n", "\n", "Sobre la base de los gráficos hechos en el anterior inciso, `desplazamiento` es la variable que no tiene valores atípicos" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4. Entre las variables: `caballos_potencia`, `desplazamiento`, `mpg` y `aceleracion`, ¿cuáles son las variables con mayor y menor asimetría?\n", "\n", "" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "caballos_potencia: 1.034079427703104\n", "desplazamiento: 0.6941299865999901\n", "mpg: 0.45706634399491936\n", "aceleracion: 0.23022375946556034\n" ] } ], "source": [ "print('caballos_potencia: ', automoviles['caballos_potencia'].skew())\n", "print('desplazamiento: ', automoviles['desplazamiento'].skew())\n", "print('mpg: ', automoviles['mpg'].skew())\n", "print('aceleracion:', automoviles['aceleracion'].skew())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 5. Entre las variables: `caballos_potencia`, `desplazamiento`, `mpg` y `aceleracion`, muestre los valores atípicos de aquellas variables que los tengan en ambos extremos\n", "\n", "\n", "\n", "Como se pudo observar en el __[inciso 2](#2)__ la variable `aceleracion` es la única que tiene valores atípicos en ambos extremos." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "q1 = automoviles.describe()['aceleracion']['25%']\n", "q3 = automoviles.describe()['aceleracion']['75%']\n", "iqr = q3 - q1\n", "limite_derecho = q3 + 1.5 * iqr\n", "limite_izquierdo = q1 - 1.5 * iqr" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Valores atípicos menores o iguales al límite inferior:" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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nombrecilindrospesoanioterritorioaceleracionmpgcaballos_potenciadesplazamiento
16Plymouth 'Cuda 340836091970USA8.014.0160.0340.0
17Ford Mustang Boss 302833531970USA8.0NaN140.0302.0
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" ], "text/plain": [ " nombre cilindros peso anio territorio aceleracion \\\n", "16 Plymouth 'Cuda 340 8 3609 1970 USA 8.0 \n", "17 Ford Mustang Boss 302 8 3353 1970 USA 8.0 \n", "\n", " mpg caballos_potencia desplazamiento \n", "16 14.0 160.0 340.0 \n", "17 NaN 140.0 302.0 " ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "automoviles.loc[automoviles['aceleracion'] <= limite_izquierdo]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Valores atípicos mayores o iguales al límite superior:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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nombrecilindrospesoanioterritorioaceleracionmpgcaballos_potenciadesplazamiento
66Volkswagen Type 3422541972Europe23.523.054.097.0
306Peugeot 504431901979Europe24.827.271.0141.0
333Vw Dasher (Diesel)423351980Europe23.743.448.090.0
402Vw Pickup421301982Europe24.644.052.097.0
\n", "
" ], "text/plain": [ " nombre cilindros peso anio territorio aceleracion mpg \\\n", "66 Volkswagen Type 3 4 2254 1972 Europe 23.5 23.0 \n", "306 Peugeot 504 4 3190 1979 Europe 24.8 27.2 \n", "333 Vw Dasher (Diesel) 4 2335 1980 Europe 23.7 43.4 \n", "402 Vw Pickup 4 2130 1982 Europe 24.6 44.0 \n", "\n", " caballos_potencia desplazamiento \n", "66 54.0 97.0 \n", "306 71.0 141.0 \n", "333 48.0 90.0 \n", "402 52.0 97.0 " ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "automoviles.loc[automoviles['aceleracion'] >= limite_derecho]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Hay alguna relacion entre el territorio y los caballos de potencia?\n", "- Hay correlación entre los caballos de potencia y las millas por galón? \n", "- Si existe es un relación, ambas variables se mueven en la misma dirección?\n", "- Compruebe la dirección de la relación con una visualización." ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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nombrecilindrospesoanioterritorioaceleracionmpgcaballos_potenciadesplazamientoterritorio_num
0Chevrolet Chevelle Malibu835041970USA12.018.0130.0307.02
1Buick Skylark 320836931970USA11.515.0165.0350.02
2Plymouth Satellite834361970USA11.018.0150.0318.02
3Amc Rebel Sst834331970USA12.016.0150.0304.02
4Ford Torino834491970USA10.517.0140.0302.02
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401Ford Mustang Gl427901982USA15.627.086.0140.02
402Vw Pickup421301982Europe24.644.052.097.01
403Dodge Rampage422951982USA11.632.084.0135.02
404Ford Ranger426251982USA18.628.079.0120.02
405Chevy S-10427201982USA19.431.082.0119.02
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406 rows × 10 columns

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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", "401 Ford Mustang Gl 4 2790 1982 USA 15.6 \n", "402 Vw Pickup 4 2130 1982 Europe 24.6 \n", "403 Dodge Rampage 4 2295 1982 USA 11.6 \n", "404 Ford Ranger 4 2625 1982 USA 18.6 \n", "405 Chevy S-10 4 2720 1982 USA 19.4 \n", "\n", " mpg caballos_potencia desplazamiento territorio_num \n", "0 18.0 130.0 307.0 2 \n", "1 15.0 165.0 350.0 2 \n", "2 18.0 150.0 318.0 2 \n", "3 16.0 150.0 304.0 2 \n", "4 17.0 140.0 302.0 2 \n", ".. ... ... ... ... \n", "401 27.0 86.0 140.0 2 \n", "402 44.0 52.0 97.0 1 \n", "403 32.0 84.0 135.0 2 \n", "404 28.0 79.0 120.0 2 \n", "405 31.0 82.0 119.0 2 \n", "\n", "[406 rows x 10 columns]" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def territorio_escrito_a_num(territorio):\n", " return territorio.map({ 'Europe': 1, 'USA': 2, 'Japan': 3 })\n", "\n", "automoviles['territorio_num'] = territorio_escrito_a_num(automoviles['territorio'])\n", "automoviles" ] }, { "cell_type": "code", "execution_count": 33, "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": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "automoviles.corr()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Los caballos de potención tienen una relación con las millas por galón, debido el valor de correlación es 0.77 y es próximo a -1. Lo cual indica una relación inversa." ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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cilindrospesoanioaceleracionmpgcaballos_potenciadesplazamientoterritorio_num
cilindros2.9314911298.254662-2.315526-2.507662-10.30891156.184831170.982829-0.029265
peso1298.254662717416.332056-1001.421626-1021.220272-5505.21174528538.20640482868.813665-49.132056
anio-2.315526-1001.42162614.0530323.17365616.741163-61.462080-150.1384050.329563
aceleracion-2.507662-1021.2202723.1736567.8588219.058930-75.801907-164.122683-0.107452
mpg-10.308911-5505.21174516.7411639.05893061.089611-233.857926-655.4023180.608486
caballos_potencia56.18483128538.206404-61.462080-75.801907-233.8579261503.0182393666.724846-0.713434
desplazamiento170.98282982868.813665-150.138405-164.122683-655.4023183666.72484611008.722272-2.581919
territorio_num-0.029265-49.1320560.329563-0.1074520.608486-0.713434-2.5819190.375090
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" ], "text/plain": [ " cilindros peso anio aceleracion \\\n", "cilindros 2.931491 1298.254662 -2.315526 -2.507662 \n", "peso 1298.254662 717416.332056 -1001.421626 -1021.220272 \n", "anio -2.315526 -1001.421626 14.053032 3.173656 \n", "aceleracion -2.507662 -1021.220272 3.173656 7.858821 \n", "mpg -10.308911 -5505.211745 16.741163 9.058930 \n", "caballos_potencia 56.184831 28538.206404 -61.462080 -75.801907 \n", "desplazamiento 170.982829 82868.813665 -150.138405 -164.122683 \n", "territorio_num -0.029265 -49.132056 0.329563 -0.107452 \n", "\n", " mpg caballos_potencia desplazamiento \\\n", "cilindros -10.308911 56.184831 170.982829 \n", "peso -5505.211745 28538.206404 82868.813665 \n", "anio 16.741163 -61.462080 -150.138405 \n", "aceleracion 9.058930 -75.801907 -164.122683 \n", "mpg 61.089611 -233.857926 -655.402318 \n", "caballos_potencia -233.857926 1503.018239 3666.724846 \n", "desplazamiento -655.402318 3666.724846 11008.722272 \n", "territorio_num 0.608486 -0.713434 -2.581919 \n", "\n", " territorio_num \n", "cilindros -0.029265 \n", "peso -49.132056 \n", "anio 0.329563 \n", "aceleracion -0.107452 \n", "mpg 0.608486 \n", "caballos_potencia -0.713434 \n", "desplazamiento -2.581919 \n", "territorio_num 0.375090 " ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "automoviles.cov()" ] } ], "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 }