├── README.md ├── df └── Advertising.csv ├── IntroMatplotlib.ipynb └── .ipynb_checkpoints └── IntroMatplotlib-checkpoint.ipynb /README.md: -------------------------------------------------------------------------------- 1 | # tut_matplotlib 2 | tutorial introductorio para usar matplotlib.pyplot 3 | -------------------------------------------------------------------------------- /df/Advertising.csv: -------------------------------------------------------------------------------- 1 | ,TV,radio,newspaper,sales 2 | 1,230.1,37.8,69.2,22.1 3 | 2,44.5,39.3,45.1,10.4 4 | 3,17.2,45.9,69.3,9.3 5 | 4,151.5,41.3,58.5,18.5 6 | 5,180.8,10.8,58.4,12.9 7 | 6,8.7,48.9,75,7.2 8 | 7,57.5,32.8,23.5,11.8 9 | 8,120.2,19.6,11.6,13.2 10 | 9,8.6,2.1,1,4.8 11 | 10,199.8,2.6,21.2,10.6 12 | 11,66.1,5.8,24.2,8.6 13 | 12,214.7,24,4,17.4 14 | 13,23.8,35.1,65.9,9.2 15 | 14,97.5,7.6,7.2,9.7 16 | 15,204.1,32.9,46,19 17 | 16,195.4,47.7,52.9,22.4 18 | 17,67.8,36.6,114,12.5 19 | 18,281.4,39.6,55.8,24.4 20 | 19,69.2,20.5,18.3,11.3 21 | 20,147.3,23.9,19.1,14.6 22 | 21,218.4,27.7,53.4,18 23 | 22,237.4,5.1,23.5,12.5 24 | 23,13.2,15.9,49.6,5.6 25 | 24,228.3,16.9,26.2,15.5 26 | 25,62.3,12.6,18.3,9.7 27 | 26,262.9,3.5,19.5,12 28 | 27,142.9,29.3,12.6,15 29 | 28,240.1,16.7,22.9,15.9 30 | 29,248.8,27.1,22.9,18.9 31 | 30,70.6,16,40.8,10.5 32 | 31,292.9,28.3,43.2,21.4 33 | 32,112.9,17.4,38.6,11.9 34 | 33,97.2,1.5,30,9.6 35 | 34,265.6,20,0.3,17.4 36 | 35,95.7,1.4,7.4,9.5 37 | 36,290.7,4.1,8.5,12.8 38 | 37,266.9,43.8,5,25.4 39 | 38,74.7,49.4,45.7,14.7 40 | 39,43.1,26.7,35.1,10.1 41 | 40,228,37.7,32,21.5 42 | 41,202.5,22.3,31.6,16.6 43 | 42,177,33.4,38.7,17.1 44 | 43,293.6,27.7,1.8,20.7 45 | 44,206.9,8.4,26.4,12.9 46 | 45,25.1,25.7,43.3,8.5 47 | 46,175.1,22.5,31.5,14.9 48 | 47,89.7,9.9,35.7,10.6 49 | 48,239.9,41.5,18.5,23.2 50 | 49,227.2,15.8,49.9,14.8 51 | 50,66.9,11.7,36.8,9.7 52 | 51,199.8,3.1,34.6,11.4 53 | 52,100.4,9.6,3.6,10.7 54 | 53,216.4,41.7,39.6,22.6 55 | 54,182.6,46.2,58.7,21.2 56 | 55,262.7,28.8,15.9,20.2 57 | 56,198.9,49.4,60,23.7 58 | 57,7.3,28.1,41.4,5.5 59 | 58,136.2,19.2,16.6,13.2 60 | 59,210.8,49.6,37.7,23.8 61 | 60,210.7,29.5,9.3,18.4 62 | 61,53.5,2,21.4,8.1 63 | 62,261.3,42.7,54.7,24.2 64 | 63,239.3,15.5,27.3,15.7 65 | 64,102.7,29.6,8.4,14 66 | 65,131.1,42.8,28.9,18 67 | 66,69,9.3,0.9,9.3 68 | 67,31.5,24.6,2.2,9.5 69 | 68,139.3,14.5,10.2,13.4 70 | 69,237.4,27.5,11,18.9 71 | 70,216.8,43.9,27.2,22.3 72 | 71,199.1,30.6,38.7,18.3 73 | 72,109.8,14.3,31.7,12.4 74 | 73,26.8,33,19.3,8.8 75 | 74,129.4,5.7,31.3,11 76 | 75,213.4,24.6,13.1,17 77 | 76,16.9,43.7,89.4,8.7 78 | 77,27.5,1.6,20.7,6.9 79 | 78,120.5,28.5,14.2,14.2 80 | 79,5.4,29.9,9.4,5.3 81 | 80,116,7.7,23.1,11 82 | 81,76.4,26.7,22.3,11.8 83 | 82,239.8,4.1,36.9,12.3 84 | 83,75.3,20.3,32.5,11.3 85 | 84,68.4,44.5,35.6,13.6 86 | 85,213.5,43,33.8,21.7 87 | 86,193.2,18.4,65.7,15.2 88 | 87,76.3,27.5,16,12 89 | 88,110.7,40.6,63.2,16 90 | 89,88.3,25.5,73.4,12.9 91 | 90,109.8,47.8,51.4,16.7 92 | 91,134.3,4.9,9.3,11.2 93 | 92,28.6,1.5,33,7.3 94 | 93,217.7,33.5,59,19.4 95 | 94,250.9,36.5,72.3,22.2 96 | 95,107.4,14,10.9,11.5 97 | 96,163.3,31.6,52.9,16.9 98 | 97,197.6,3.5,5.9,11.7 99 | 98,184.9,21,22,15.5 100 | 99,289.7,42.3,51.2,25.4 101 | 100,135.2,41.7,45.9,17.2 102 | 101,222.4,4.3,49.8,11.7 103 | 102,296.4,36.3,100.9,23.8 104 | 103,280.2,10.1,21.4,14.8 105 | 104,187.9,17.2,17.9,14.7 106 | 105,238.2,34.3,5.3,20.7 107 | 106,137.9,46.4,59,19.2 108 | 107,25,11,29.7,7.2 109 | 108,90.4,0.3,23.2,8.7 110 | 109,13.1,0.4,25.6,5.3 111 | 110,255.4,26.9,5.5,19.8 112 | 111,225.8,8.2,56.5,13.4 113 | 112,241.7,38,23.2,21.8 114 | 113,175.7,15.4,2.4,14.1 115 | 114,209.6,20.6,10.7,15.9 116 | 115,78.2,46.8,34.5,14.6 117 | 116,75.1,35,52.7,12.6 118 | 117,139.2,14.3,25.6,12.2 119 | 118,76.4,0.8,14.8,9.4 120 | 119,125.7,36.9,79.2,15.9 121 | 120,19.4,16,22.3,6.6 122 | 121,141.3,26.8,46.2,15.5 123 | 122,18.8,21.7,50.4,7 124 | 123,224,2.4,15.6,11.6 125 | 124,123.1,34.6,12.4,15.2 126 | 125,229.5,32.3,74.2,19.7 127 | 126,87.2,11.8,25.9,10.6 128 | 127,7.8,38.9,50.6,6.6 129 | 128,80.2,0,9.2,8.8 130 | 129,220.3,49,3.2,24.7 131 | 130,59.6,12,43.1,9.7 132 | 131,0.7,39.6,8.7,1.6 133 | 132,265.2,2.9,43,12.7 134 | 133,8.4,27.2,2.1,5.7 135 | 134,219.8,33.5,45.1,19.6 136 | 135,36.9,38.6,65.6,10.8 137 | 136,48.3,47,8.5,11.6 138 | 137,25.6,39,9.3,9.5 139 | 138,273.7,28.9,59.7,20.8 140 | 139,43,25.9,20.5,9.6 141 | 140,184.9,43.9,1.7,20.7 142 | 141,73.4,17,12.9,10.9 143 | 142,193.7,35.4,75.6,19.2 144 | 143,220.5,33.2,37.9,20.1 145 | 144,104.6,5.7,34.4,10.4 146 | 145,96.2,14.8,38.9,11.4 147 | 146,140.3,1.9,9,10.3 148 | 147,240.1,7.3,8.7,13.2 149 | 148,243.2,49,44.3,25.4 150 | 149,38,40.3,11.9,10.9 151 | 150,44.7,25.8,20.6,10.1 152 | 151,280.7,13.9,37,16.1 153 | 152,121,8.4,48.7,11.6 154 | 153,197.6,23.3,14.2,16.6 155 | 154,171.3,39.7,37.7,19 156 | 155,187.8,21.1,9.5,15.6 157 | 156,4.1,11.6,5.7,3.2 158 | 157,93.9,43.5,50.5,15.3 159 | 158,149.8,1.3,24.3,10.1 160 | 159,11.7,36.9,45.2,7.3 161 | 160,131.7,18.4,34.6,12.9 162 | 161,172.5,18.1,30.7,14.4 163 | 162,85.7,35.8,49.3,13.3 164 | 163,188.4,18.1,25.6,14.9 165 | 164,163.5,36.8,7.4,18 166 | 165,117.2,14.7,5.4,11.9 167 | 166,234.5,3.4,84.8,11.9 168 | 167,17.9,37.6,21.6,8 169 | 168,206.8,5.2,19.4,12.2 170 | 169,215.4,23.6,57.6,17.1 171 | 170,284.3,10.6,6.4,15 172 | 171,50,11.6,18.4,8.4 173 | 172,164.5,20.9,47.4,14.5 174 | 173,19.6,20.1,17,7.6 175 | 174,168.4,7.1,12.8,11.7 176 | 175,222.4,3.4,13.1,11.5 177 | 176,276.9,48.9,41.8,27 178 | 177,248.4,30.2,20.3,20.2 179 | 178,170.2,7.8,35.2,11.7 180 | 179,276.7,2.3,23.7,11.8 181 | 180,165.6,10,17.6,12.6 182 | 181,156.6,2.6,8.3,10.5 183 | 182,218.5,5.4,27.4,12.2 184 | 183,56.2,5.7,29.7,8.7 185 | 184,287.6,43,71.8,26.2 186 | 185,253.8,21.3,30,17.6 187 | 186,205,45.1,19.6,22.6 188 | 187,139.5,2.1,26.6,10.3 189 | 188,191.1,28.7,18.2,17.3 190 | 189,286,13.9,3.7,15.9 191 | 190,18.7,12.1,23.4,6.7 192 | 191,39.5,41.1,5.8,10.8 193 | 192,75.5,10.8,6,9.9 194 | 193,17.2,4.1,31.6,5.9 195 | 194,166.8,42,3.6,19.6 196 | 195,149.7,35.6,6,17.3 197 | 196,38.2,3.7,13.8,7.6 198 | 197,94.2,4.9,8.1,9.7 199 | 198,177,9.3,6.4,12.8 200 | 199,283.6,42,66.2,25.5 201 | 200,232.1,8.6,8.7,13.4 202 | -------------------------------------------------------------------------------- /IntroMatplotlib.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import pandas as pd\n", 10 | "import numpy as np\n", 11 | "import matplotlib.pyplot as plt\n" 12 | ] 13 | }, 14 | { 15 | "cell_type": "markdown", 16 | "metadata": {}, 17 | "source": [ 18 | "# Figure and Axes" 19 | ] 20 | }, 21 | { 22 | "cell_type": "markdown", 23 | "metadata": {}, 24 | "source": [ 25 | "* Fig = liezo o marco en el que se va a generar el eje o ejes (puede ser mas de uno)\n", 26 | "* Axes= Ejes que delimitan las dimensiones del gráfico" 27 | ] 28 | }, 29 | { 30 | "cell_type": "code", 31 | "execution_count": 6, 32 | "metadata": {}, 33 | "outputs": [ 34 | { 35 | "data": { 36 | "text/plain": [ 37 | "(
,\n", 38 | " )" 39 | ] 40 | }, 41 | "execution_count": 6, 42 | "metadata": {}, 43 | "output_type": "execute_result" 44 | }, 45 | { 46 | "data": { 47 | "image/png": "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\n", 48 | "text/plain": [ 49 | "
" 50 | ] 51 | }, 52 | "metadata": { 53 | "needs_background": "light" 54 | }, 55 | "output_type": "display_data" 56 | } 57 | ], 58 | "source": [ 59 | "#ejemplo de un figure y un axe\n", 60 | "plt.subplots()\n", 61 | "\n" 62 | ] 63 | }, 64 | { 65 | "cell_type": "code", 66 | "execution_count": 7, 67 | "metadata": {}, 68 | "outputs": [], 69 | "source": [ 70 | "df=pd.read_csv('df/Advertising.csv')" 71 | ] 72 | }, 73 | { 74 | "cell_type": "code", 75 | "execution_count": 8, 76 | "metadata": {}, 77 | "outputs": [ 78 | { 79 | "data": { 80 | "text/html": [ 81 | "
\n", 82 | "\n", 95 | "\n", 96 | " \n", 97 | " \n", 98 | " \n", 99 | " \n", 100 | " \n", 101 | " \n", 102 | " \n", 103 | " \n", 104 | " \n", 105 | " \n", 106 | " \n", 107 | " \n", 108 | " \n", 109 | " \n", 110 | " \n", 111 | " \n", 112 | " \n", 113 | " \n", 114 | " \n", 115 | " \n", 116 | " \n", 117 | " \n", 118 | " \n", 119 | " \n", 120 | " \n", 121 | " \n", 122 | " \n", 123 | " \n", 124 | " \n", 125 | " \n", 126 | " \n", 127 | " \n", 128 | " \n", 129 | " \n", 130 | " \n", 131 | " \n", 132 | " \n", 133 | " \n", 134 | " \n", 135 | " \n", 136 | " \n", 137 | " \n", 138 | " \n", 139 | " \n", 140 | " \n", 141 | " \n", 142 | " \n", 143 | " \n", 144 | " \n", 145 | " \n", 146 | " \n", 147 | " \n", 148 | "
Unnamed: 0TVradionewspapersales
01230.137.869.222.1
1244.539.345.110.4
2317.245.969.39.3
34151.541.358.518.5
45180.810.858.412.9
\n", 149 | "
" 150 | ], 151 | "text/plain": [ 152 | " Unnamed: 0 TV radio newspaper sales\n", 153 | "0 1 230.1 37.8 69.2 22.1\n", 154 | "1 2 44.5 39.3 45.1 10.4\n", 155 | "2 3 17.2 45.9 69.3 9.3\n", 156 | "3 4 151.5 41.3 58.5 18.5\n", 157 | "4 5 180.8 10.8 58.4 12.9" 158 | ] 159 | }, 160 | "execution_count": 8, 161 | "metadata": {}, 162 | "output_type": "execute_result" 163 | } 164 | ], 165 | "source": [ 166 | "df.head()" 167 | ] 168 | }, 169 | { 170 | "cell_type": "markdown", 171 | "metadata": {}, 172 | "source": [ 173 | "# Generar gráficos simples" 174 | ] 175 | }, 176 | { 177 | "cell_type": "markdown", 178 | "metadata": {}, 179 | "source": [ 180 | "* Gráficos sencillos de linea, barras" 181 | ] 182 | }, 183 | { 184 | "cell_type": "code", 185 | "execution_count": 12, 186 | "metadata": {}, 187 | "outputs": [], 188 | "source": [ 189 | "#definir variables de gráficos\n", 190 | "x=[1,2,3,4,5,6,7]\n", 191 | "y=[i*2 for i in x] " 192 | ] 193 | }, 194 | { 195 | "cell_type": "code", 196 | "execution_count": 15, 197 | "metadata": {}, 198 | "outputs": [ 199 | { 200 | "data": { 201 | "text/plain": [ 202 | "[]" 203 | ] 204 | }, 205 | "execution_count": 15, 206 | "metadata": {}, 207 | "output_type": "execute_result" 208 | }, 209 | { 210 | "data": { 211 | "image/png": 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1/OWzbJ7/fBttIsJ47pphXBjfSUfdEnB8KnBjzN3ArYAF0oGbrLUV/ggm4oS1OwuZlpzGtoJSLh/WlQcuHkhbDZ+SAOV1gRtjugK/AwZaa8uNMe8CVwGv+imbSIMprazhqY8389o3O+jSOpxXbxrB2P4aPiWBzddTKE2AcGNMNRAB7PE9kkjD+mJrATNS08k7VM71Z/Rg2oQ4WjTT2UUJfF5/l1prdxtjngZygXJgmbV22bHPM8ZMBaYCxMTEeLs5Eb87UlbNIx9mkrw2j15Rkbx7+xmM7NnO6Vgip8zrdb/GmLbApUBPoAsQaYy59tjnWWvnW2sTrbWJ0dHR3icV8aOlGXsZP/dzFvywmzvH9mbJXb9QeYvr+PJz4nhgu7W2AMAYkwqMBv7hj2Ai9SG/uIKH3t/IRxn7GNi5Fa/cOIL4rq2djiXiFV8KPBc43RgTwdFTKOOANX5JJeJn1lpS1u3mkcWZlFfXcu8F/Zl6Vi8NnxJX8+Uc+LfGmGRgHVAD/ADM91cwEX/JO1TGfQsyWLWlgOE92vLElAT6dGjhdCwRn/n0Vru19iHgIT9lEfErj8fyxuqdPLE0C4CHJw3iutN70EjDpyRI6FopCUrZ+SUkpaSxZuchftE3iscma/iUBB8VuASV6loP81flMG/5VsKbNubpK4YwZVhXLYOXoKQCl6CRsfsI05LTyNxbxEWDOzFr0iA6tNTwKQleKnBxvYrqWuZ9upX5q3JoG9GU568dxoT4zk7HEql3KnBxte93FDI9OY2cA6VcMbwbMy8eSOuIMKdjiTQIFbi4UkllDU8uzeL1b3bStU04r988krP6aaWvhBYVuLjOys353L8ggz1HyrlxdCz3XtCfSA2fkhCk73pxjUOlVTzyYSap63bTOzqS5DvOYHgPzS+R0KUCl4BnreWjjH08+H4Gh8uq+c05ffjNuX1oHtbY6WgijlKBS0DLL6rggfcz+HjjfuK7tuK1m0cyqIuGT4mAClwClLWW99bmMXtxJhU1HqZPiOO2X/SkiYZPifybClwCzq7CMmakpvNl9gFGxrZjzpTB9IrW8CmRY6nAJWDUeiyvfb2Dpz7eTCMDj1w6iGtGafiUyImowCUgbN1fzPSUNNblHmZs/2genTyYrm3CnY4lEtBU4OKo6loPz6/cxrOfZRPRrDFzrxzCZUM1fErkVKjAxTHpeUe4N3kDWfuKuTihMw9PGkRUi2ZOxxJxDRW4NLiK6lrmLt/CC6tyiGrRjL9fN5wLBnVyOpaI66jApUGtzjnIjNR0th8o5crE7tx38QBah2v4lIg3VODSIIorqpnzURZvfptL93bhvHnrKMb0iXI6loirqcCl3q3Iyue+BensK6rgljN78ofz+xHRVN96Ir7S/yKpN4WlVfzxg40sXL+Hvh1akPLr0QyLaet0LJGgoQIXv7PWsjhtL7MWbeRIeTW/G9eX/z6nN82aaPiUiD+pwMWv9hdVcP+CDJZv2k9Ct9b849ZRDOjcyulYIkFJBS5+Ya3lne938eiSTVTVeLjvojhuHqPhUyL1yacCN8a0AV4E4gEL3Gyt/cYfwcQ9dh4sJSklnW9yDjKqZzuemJJAbFSk07FEgp6vR+DzgKXW2l8aY5oCEX7IJC5R67G88tV2nl62mSaNGvHo5HiuHhGj4VMiDcTrAjfGtALOAm4EsNZWAVX+iSWBbvO+o8On1u86zLlxHXh0cjydW2v4lEhD8uUIvBdQALxijBkCrAXustaW/vhJxpipwFSAmJgYHzYngaCqxsNzK7P564psWjRrwryrhjJpSBcNnxJxgC/vMDUBhgF/s9aeBpQCScc+yVo731qbaK1NjI6O9mFz4rQNuw5zybNf8uflW7kwvjPL7zmbSzU5UMQxvhyB5wF51tpv6z5P5jgFLu5XXlXLnz7ZzEtfbqdDy+a8eH0i4wd2dDqWSMjzusCttfuMMbuMMf2ttZuBcUCm/6JJIPh62wFmpKaz82AZvxoVQ9KFcbRqruFTIoHA16tQfgu8WXcFSg5wk++RJBAUVVTz+JIs3voulx7tI/jnbaMY3VvDp0QCiU8Fbq1dDyT6KYsEiOWZ+7l/YToFxZVMPasXd4/vR3hTLYMXCTRaiSn/drCkkoc/yGTRhj3079iSv1+XyNDubZyOJSInoAIXrLUs2rCHWYs2UlJZw93j+/Hrsb1p2kTL4EUCmQo8xO09Us7MBRl8mpXPkO5teHJKAv07tXQ6loicAhV4iPJ4LG99n8vjS7Ko8XiYefEAbhrTk8ZaBi/iGirwELTjQClJqWmszilkdO/2zLk8gZj2GmMj4jYq8BBSU+vh5a+288yyLTRt3Ig5lw/myhHdtZJSxKVU4CFi094ipqekkZZ3hPEDOjL7sng6tW7udCwR8YEKPMhV1tTy1xXbeG5FNq3Dw3j26tOYmNBZR90iQUAFHsTW5R5ienIaW/NLmHxaVx6YOJB2kU2djiUifqICD0JlVTU8s2wLL3+1nU6tmvPKjSM4J66D07FExM9U4EHmq+wDJKWmsauwnGtPj2H6hDhaaviUSFBSgQeJI+XVPL5kE29/v4ueUZG8M/V0RvVq73QsEalHKvAgsGzjPmYuzOBASSW3n310+FTzMA2fEgl2KnAXO1BSyaxFG1mctpe4Ti158YZEErpp+JRIqFCBu5C1loXrd/PwB5mUVdbyh/P6ccfY3oQ11vApkVCiAneZ3YfLuX9BOis3FzAspg1PTEmgb0cNnxIJRSpwl/B4LG9+l8ucJZvwWHjokoFcf0ashk+JhDAVuAvkFJSQlJLOdzsKObNPFI9fPpju7TR8SiTUqcADWE2thxe/3M7cT7bQrEkjnvxlAlcM76Zl8CICqMADVuaeIqalbCBjdxEXDOrII5fG06GVhk+JyP9RgQeYyppa/vJZNn9buY02EWE8d80wLozvpKNuEfl/VOABZO3OQqanpJOdX8KUYd2YefEA2mr4lIicgAo8AJRW1vDUx5t57ZsddGkdzms3j+TsftFOxxKRAKcCd9gXWwuYkZpO3qFybjijB/dOiKNFM/2ziMjJ+dwUxpjGwBpgt7V2ou+RQsORsmpmf5jJe2vz6BUdyXt3nMGI2HZOxxIRF/HHod5dwCaglR9eKyQszdjHA+9nUFhaxZ1je/O7cX01fEpEfjafCtwY0w24GHgUuMcviYJYQXElDy3KYEn6PgZ2bsUrN44gvmtrp2OJiEv5egT+Z2AacMJhHMaYqcBUgJiYGB83507WWlLX7eaPizMpr67l3gv6M/WsXho+JSI+8brAjTETgXxr7VpjzNgTPc9aOx+YD5CYmGi93Z5b5R0q474FGazaUkBij7bMmZJAnw4tnI4lIkHAlyPwMcAkY8xFQHOglTHmH9baa/0Tzd08Hssbq3fyxNIsAB6eNIjrTu9BIw2fEhE/8brArbUzgBkAdUfg/6PyPmpbQQnTk9NYs/MQZ/WL5rHJ8XRrq+FTIuJfuuDYj6prPcxflcO8T7cSHtaYp68YwpRhXbUMXkTqhV8K3Fq7Eljpj9dyq4zdR5iWnEbm3iIuGtyJWZMG0aGlhk+JSP3REbiPKqprmffpVuavyqFdZFOev3YYE+I7Ox1LREKACtwH3+8oZHpyGjkHSrlieDdmXjyQ1hFhTscSkRChAvdCSWUNTy7N4vVvdtKtbThv3DKSX/TV8CkRaVgq8J/p8y0F3Jeazp4j5dw4OpZ7L+hPpIZPiYgD1Dyn6FBpFY98mEnqut30jo4k+Y4zGN5Dw6dExDkq8JOw1vJRxj4efD+Dw2XV/PbcPvzm3D40a6LhUyLiLBX4T8gvquCB9zP4eON+Bndtzes3j2JgFw1dFJHAoAI/Dmst763NY/biTCprPCRdGMetZ/akiYZPiUgAUYEfY1dhGTNS0/ky+wAjY9sxZ8pgekVr+JSIBB4VeJ1aj+W1r3fw1MebaWTgkcviuWZkjIZPiUjAUoEDW/cXMz0ljXW5hxnbP5pHJw+ma5twp2OJiPykkC7w6loPz6/cxrOfZRPZrDF/vnIolw7touFTIuIKIVvg6XlHuDd5A1n7ipmY0JlZkwYR1aKZ07FERE5ZyBV4RXUtc5dv4YVVOUS1aMb864Zz/qBOTscSEfnZQqrAV+ccJCkljR0Hy7hqRHdmXDSA1uEaPiUi7hQSBV5cUc2cj7J489tcYtpF8OatoxjTJ8rpWCIiPgn6Al+Rlc99C9LZX1TBrWf25J7z+xHRNOh3W0RCQNA2WWFpFX/8YCML1++hb4cWPPfr0ZwW09bpWCIifhN0BW6t5YO0vcxatJGi8mruGteXO8/preFTIhJ0gqrA9x2pYObCDJZv2k9Ct9Y8edso4jpp+JSIBKegKHBrLW9/v4vHPtxEtcfD/RcN4KYxsRo+JSJBzfUFvvNgKUkp6XyTc5DTe7VjzuUJxEZFOh1LRKTeubbAaz2WV77aztPLNhPWqBGPTR7MVSO6a/iUiIQMVxb45n3FTEtJY8Ouw4yL68DsyfF0bq3hUyISWrwucGNMd+B1oBPgAeZba+f5K9jxVNV4eG5lNn9dkU3L5mHMu2ook4Zo+JSIhCZfjsBrgD9Ya9cZY1oCa40xn1hrM/2U7T+s33WY6clpbN5fzKVDu/DgxIG01/ApEQlhXhe4tXYvsLfu42JjzCagK+D3An/2063MXb6FDi2b89INiYwb0NHfmxARcR2/nAM3xsQCpwHfHudrU4GpADExMV69fkz7CK4aGUPShXG0aq7hUyIiAMZa69sLGNMC+Bx41Fqb+lPPTUxMtGvWrPFpeyIiocYYs9Zam3js4z6tdDHGhAEpwJsnK28REfEvrwvcHL304yVgk7X2T/6LJCIip8KXI/AxwHXAucaY9XW/LvJTLhEROQlfrkL5EtAF2CIiDtG0JxERl1KBi4i4lApcRMSlVOAiIi7l80Ken7UxYwqAnV7+8SjggB/jOEn7EniCZT9A+xKofNmXHtba6GMfbNAC94UxZs3xViK5kfYl8ATLfoD2JVDVx77oFIqIiEupwEVEXMpNBT7f6QB+pH0JPMGyH6B9CVR+3xfXnAMXEZH/5KYjcBER+REVuIiISwV8gRtjXjbG5BtjMpzO4gtjTHdjzApjzCZjzEZjzF1OZ/KWMaa5MeY7Y8yGun152OlMvjLGNDbG/GCMWex0Fl8YY3YYY9LrpoO69u4pxpg2xphkY0xW3f+ZM5zO5A1jTP8fTWtdb4wpMsb83m+vH+jnwI0xZwElwOvW2nin83jLGNMZ6Pzjm0ADl9XXTaDrU90s+EhrbUndTT2+BO6y1q52OJrXjDH3AIlAK2vtRKfzeMsYswNItNa6evGLMeY14Atr7YvGmKZAhLX2sNO5fGGMaQzsBkZZa71d0PgfAv4I3Fq7Cih0OoevrLV7rbXr6j4uBv51E2jXsUeV1H0aVvcrsI8EfoIxphtwMfCi01kEjDGtgLM4esMYrLVVbi/vOuOAbf4qb3BBgQejn7oJtFvUnXJYD+QDn1hrXbsvwJ+BaYDH6SB+YIFlxpi1dTcUd6NeQAHwSt1prReNMZFOh/KDq4C3/PmCKvAGVncT6BTg99baIqfzeMtaW2utHQp0A0YaY1x5essYMxHIt9audTqLn4yx1g4DLgT+u+4UpNs0AYYBf7PWngaUAknORvJN3WmgScB7/nxdFXgDCsabQNf9aLsSmOBwFG+NASbVnTt+m6O3CPyHs5G8Z63dU/d7PrAAGOlsIq/kAXk/+qkumaOF7mYXAuustfv9+aIq8AYSTDeBNsZEG2Pa1H0cDowHspxN5R1r7QxrbTdrbSxHf8T9zFp7rcOxvGKMiax7g5y6Uw7nA667estauw/YZYzpX/fQOMB1b/Yf42r8fPoEfLgnZkMxxrwFjAWijDF5wEPW2pecTeWVf90EOr3u3DHAfdbaJQ5m8lZn4LW6d9UbAe9aa119+V2Q6AgsOHqsQBPgn9bapc5G8tpvgTfrTj3kADc5nMdrxpgI4Dzgdr+/dqBfRigiIsenUygiIi6lAhcRcSkVuIiIS6nARURcSgUuIuJSKnAREZdSgYuIuNT/Ak52KTJ7veuaAAAAAElFTkSuQmCC\n", 212 | "text/plain": [ 213 | "
" 214 | ] 215 | }, 216 | "metadata": { 217 | "needs_background": "light" 218 | }, 219 | "output_type": "display_data" 220 | } 221 | ], 222 | "source": [ 223 | "plt.plot(x,y)" 224 | ] 225 | }, 226 | { 227 | "cell_type": "markdown", 228 | "metadata": {}, 229 | "source": [ 230 | "# Manejo de variables para gráficos" 231 | ] 232 | }, 233 | { 234 | "cell_type": "code", 235 | "execution_count": 18, 236 | "metadata": {}, 237 | "outputs": [ 238 | { 239 | "data": { 240 | "text/plain": [ 241 | "" 242 | ] 243 | }, 244 | "execution_count": 18, 245 | "metadata": {}, 246 | "output_type": "execute_result" 247 | }, 248 | { 249 | "data": { 250 | "image/png": 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251 | "text/plain": [ 252 | "
" 253 | ] 254 | }, 255 | "metadata": { 256 | "needs_background": "light" 257 | }, 258 | "output_type": "display_data" 259 | } 260 | ], 261 | "source": [ 262 | "plt.bar(x,y)" 263 | ] 264 | }, 265 | { 266 | "cell_type": "code", 267 | "execution_count": null, 268 | "metadata": {}, 269 | "outputs": [], 270 | "source": [] 271 | } 272 | ], 273 | "metadata": { 274 | "kernelspec": { 275 | "display_name": "Python 3", 276 | "language": "python", 277 | "name": "python3" 278 | }, 279 | "language_info": { 280 | "codemirror_mode": { 281 | "name": "ipython", 282 | "version": 3 283 | }, 284 | "file_extension": ".py", 285 | "mimetype": "text/x-python", 286 | "name": "python", 287 | "nbconvert_exporter": "python", 288 | "pygments_lexer": "ipython3", 289 | "version": "3.8.3" 290 | } 291 | }, 292 | "nbformat": 4, 293 | "nbformat_minor": 4 294 | } 295 | -------------------------------------------------------------------------------- /.ipynb_checkpoints/IntroMatplotlib-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import pandas as pd\n", 10 | "import numpy as np\n", 11 | "import matplotlib.pyplot as plt\n" 12 | ] 13 | }, 14 | { 15 | "cell_type": "markdown", 16 | "metadata": {}, 17 | "source": [ 18 | "# Figure and Axes" 19 | ] 20 | }, 21 | { 22 | "cell_type": "markdown", 23 | "metadata": {}, 24 | "source": [ 25 | "* Fig = liezo o marco en el que se va a generar el eje o ejes (puede ser mas de uno)\n", 26 | "* Axes= Ejes que delimitan las dimensiones del gráfico" 27 | ] 28 | }, 29 | { 30 | "cell_type": "code", 31 | "execution_count": 6, 32 | "metadata": {}, 33 | "outputs": [ 34 | { 35 | "data": { 36 | "text/plain": [ 37 | "(
,\n", 38 | " )" 39 | ] 40 | }, 41 | "execution_count": 6, 42 | "metadata": {}, 43 | "output_type": "execute_result" 44 | }, 45 | { 46 | "data": { 47 | "image/png": "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\n", 48 | "text/plain": [ 49 | "
" 50 | ] 51 | }, 52 | "metadata": { 53 | "needs_background": "light" 54 | }, 55 | "output_type": "display_data" 56 | } 57 | ], 58 | "source": [ 59 | "#ejemplo de un figure y un axe\n", 60 | "plt.subplots()\n", 61 | "\n" 62 | ] 63 | }, 64 | { 65 | "cell_type": "code", 66 | "execution_count": 7, 67 | "metadata": {}, 68 | "outputs": [], 69 | "source": [ 70 | "df=pd.read_csv('df/Advertising.csv')" 71 | ] 72 | }, 73 | { 74 | "cell_type": "code", 75 | "execution_count": 8, 76 | "metadata": {}, 77 | "outputs": [ 78 | { 79 | "data": { 80 | "text/html": [ 81 | "
\n", 82 | "\n", 95 | "\n", 96 | " \n", 97 | " \n", 98 | " \n", 99 | " \n", 100 | " \n", 101 | " \n", 102 | " \n", 103 | " \n", 104 | " \n", 105 | " \n", 106 | " \n", 107 | " \n", 108 | " \n", 109 | " \n", 110 | " \n", 111 | " \n", 112 | " \n", 113 | " \n", 114 | " \n", 115 | " \n", 116 | " \n", 117 | " \n", 118 | " \n", 119 | " \n", 120 | " \n", 121 | " \n", 122 | " \n", 123 | " \n", 124 | " \n", 125 | " \n", 126 | " \n", 127 | " \n", 128 | " \n", 129 | " \n", 130 | " \n", 131 | " \n", 132 | " \n", 133 | " \n", 134 | " \n", 135 | " \n", 136 | " \n", 137 | " \n", 138 | " \n", 139 | " \n", 140 | " \n", 141 | " \n", 142 | " \n", 143 | " \n", 144 | " \n", 145 | " \n", 146 | " \n", 147 | " \n", 148 | "
Unnamed: 0TVradionewspapersales
01230.137.869.222.1
1244.539.345.110.4
2317.245.969.39.3
34151.541.358.518.5
45180.810.858.412.9
\n", 149 | "
" 150 | ], 151 | "text/plain": [ 152 | " Unnamed: 0 TV radio newspaper sales\n", 153 | "0 1 230.1 37.8 69.2 22.1\n", 154 | "1 2 44.5 39.3 45.1 10.4\n", 155 | "2 3 17.2 45.9 69.3 9.3\n", 156 | "3 4 151.5 41.3 58.5 18.5\n", 157 | "4 5 180.8 10.8 58.4 12.9" 158 | ] 159 | }, 160 | "execution_count": 8, 161 | "metadata": {}, 162 | "output_type": "execute_result" 163 | } 164 | ], 165 | "source": [ 166 | "df.head()" 167 | ] 168 | }, 169 | { 170 | "cell_type": "markdown", 171 | "metadata": {}, 172 | "source": [ 173 | "# Generar gráficos simples" 174 | ] 175 | }, 176 | { 177 | "cell_type": "markdown", 178 | "metadata": {}, 179 | "source": [ 180 | "* Gráficos sencillos de linea, barras" 181 | ] 182 | }, 183 | { 184 | "cell_type": "code", 185 | "execution_count": 12, 186 | "metadata": {}, 187 | "outputs": [], 188 | "source": [ 189 | "#definir variables de gráficos\n", 190 | "x=[1,2,3,4,5,6,7]\n", 191 | "y=[i*2 for i in x] " 192 | ] 193 | }, 194 | { 195 | "cell_type": "code", 196 | "execution_count": 15, 197 | "metadata": {}, 198 | "outputs": [ 199 | { 200 | "data": { 201 | "text/plain": [ 202 | "[]" 203 | ] 204 | }, 205 | "execution_count": 15, 206 | "metadata": {}, 207 | "output_type": "execute_result" 208 | }, 209 | { 210 | "data": { 211 | "image/png": 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\n", 212 | "text/plain": [ 213 | "
" 214 | ] 215 | }, 216 | "metadata": { 217 | "needs_background": "light" 218 | }, 219 | "output_type": "display_data" 220 | } 221 | ], 222 | "source": [ 223 | "plt.plot(x,y)" 224 | ] 225 | }, 226 | { 227 | "cell_type": "markdown", 228 | "metadata": {}, 229 | "source": [ 230 | "# Manejo de variables para gráficos" 231 | ] 232 | }, 233 | { 234 | "cell_type": "code", 235 | "execution_count": 18, 236 | "metadata": {}, 237 | "outputs": [ 238 | { 239 | "data": { 240 | "text/plain": [ 241 | "" 242 | ] 243 | }, 244 | "execution_count": 18, 245 | "metadata": {}, 246 | "output_type": "execute_result" 247 | }, 248 | { 249 | "data": { 250 | "image/png": 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251 | "text/plain": [ 252 | "
" 253 | ] 254 | }, 255 | "metadata": { 256 | "needs_background": "light" 257 | }, 258 | "output_type": "display_data" 259 | } 260 | ], 261 | "source": [ 262 | "plt.bar(x,y)" 263 | ] 264 | }, 265 | { 266 | "cell_type": "code", 267 | "execution_count": null, 268 | "metadata": {}, 269 | "outputs": [], 270 | "source": [] 271 | } 272 | ], 273 | "metadata": { 274 | "kernelspec": { 275 | "display_name": "Python 3", 276 | "language": "python", 277 | "name": "python3" 278 | }, 279 | "language_info": { 280 | "codemirror_mode": { 281 | "name": "ipython", 282 | "version": 3 283 | }, 284 | "file_extension": ".py", 285 | "mimetype": "text/x-python", 286 | "name": "python", 287 | "nbconvert_exporter": "python", 288 | "pygments_lexer": "ipython3", 289 | "version": "3.8.3" 290 | } 291 | }, 292 | "nbformat": 4, 293 | "nbformat_minor": 4 294 | } 295 | --------------------------------------------------------------------------------