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visualizing-real-world-data-project (Daniel Gonzalez) #30

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163 changes: 163 additions & 0 deletions your-code/.ipynb_checkpoints/Projecto Visualizacion-checkpoint.ipynb
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Original file line number Diff line number Diff line change
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{
"cells": [
{
"cell_type": "code",
"execution_count": 29,
"id": "10ca8ff5",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"import matplotlib.pyplot as plt \n",
"import seaborn as sns"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "799cf703",
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv('Accidentes de Tránsito Terrestre en Zonas Urbanas y Suburbanas.csv')"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "271cb4e7",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 301678 entries, 0 to 301677\n",
"Data columns (total 37 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 COBERTURA 301678 non-null object\n",
" 1 ID_ENTIDAD 301678 non-null object\n",
" 2 ID_MUNICIPIO 301678 non-null object\n",
" 3 ANIO 301678 non-null int64 \n",
" 4 MES 301678 non-null int64 \n",
" 5 TIPACCID 301678 non-null object\n",
" 6 AUTOMOVIL 301678 non-null int64 \n",
" 7 CAMPASAJ 301678 non-null int64 \n",
" 8 MICROBUS 301678 non-null int64 \n",
" 9 PASCAMION 301678 non-null int64 \n",
" 10 OMNIBUS 301678 non-null int64 \n",
" 11 TRANVIA 301678 non-null int64 \n",
" 12 CAMIONETA 301678 non-null int64 \n",
" 13 CAMION 301678 non-null int64 \n",
" 14 TRACTOR 301678 non-null int64 \n",
" 15 FERROCARRI 301678 non-null int64 \n",
" 16 MOTOCICLET 301678 non-null int64 \n",
" 17 BICICLETA 301678 non-null int64 \n",
" 18 OTROVEHIC 301678 non-null int64 \n",
" 19 CAUSAACCI 301678 non-null object\n",
" 20 GENERO 301678 non-null object\n",
" 21 ALIENTO 301678 non-null object\n",
" 22 ID_EDAD 301678 non-null int64 \n",
" 23 CONDMUERTO 301678 non-null int64 \n",
" 24 CONDHERIDO 301678 non-null int64 \n",
" 25 PASAMUERTO 301678 non-null int64 \n",
" 26 PASAHERIDO 301678 non-null int64 \n",
" 27 PEATMUERTO 301678 non-null int64 \n",
" 28 PEATHERIDO 301678 non-null int64 \n",
" 29 CICLMUERTO 301678 non-null int64 \n",
" 30 CICLHERIDO 301678 non-null int64 \n",
" 31 OTROMUERTO 301678 non-null int64 \n",
" 32 OTROHERIDO 301678 non-null int64 \n",
" 33 NEMUERTO 301678 non-null int64 \n",
" 34 NEHERIDO 301678 non-null int64 \n",
" 35 CLASACC 301678 non-null object\n",
" 36 ESTATUS 301678 non-null object\n",
"dtypes: int64(28), object(9)\n",
"memory usage: 85.2+ MB\n"
]
}
],
"source": [
"df.info()"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "d113895e",
"metadata": {},
"outputs": [
{
"data": {
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O+qRQ8F3nw1vixTQZlt+8nC4HdaFsRNkX79n88maK9ytm6FVDadrexNLrl9JtcjcicyJ0P6o73Q7vxspbV9J1Qle2z9tO6dDSdAcGuLCFnGj3pLqV58qBC3HOP2c6NBpw4SmsbDAzeujwZE5o79/YNPSybZGhl21zJkdfU+Bd+0J5efil8tLYkqKiQY0iKe1cKCJ4S5y/FybqtDZanr8TEWJ1MYwxxOpjeMu9iEfAC6bRYJoMeJzlN7+4maH/k5EOqK4LjXbvnohIF+BKnMB4DLjVGLMxjbXtxRcIrcUZvUulwZLi74eLpMmXinVt9Ho3vlheuuzF8rLGj4qKBtSLjECkU6fpTcyw7JfLaNjYQM/jetL/u3v2X4zWRll12yrq19cTq4sx+NLBdBnXhWhNlNX/t5qm7U30/25/6tfW4yn10GNKRnolvFk1rcpVc6AkHIRHRHoCVwPnAP8EDo13XrNhAxoaafOpGbh2jKzypWJdfaPRvudu39n33O07Adjq8Wx5ubz00xnl5bVVxUV9a0RGIpLUrqZ4hBE3jSBaHWXVHauoW1NHyaDdDc+di3ZSMqQE33U+GjY2EL4lzIhRI/CWefFd7XysaHWUTaFNDLliCGvvXUu0JkrvE3rvsZuTYuvTtWJbEh3TuAU4FfgbcJAxZmdGqmqb6/4DssmrsfGxMZ5VaVl3j1is52k7qiedtsPZ490hsv21stIlz1eUV88vLu61wyOjEGnXDpK33Ev56HJ2Vu3cIzS2vr6VPv4+iAjF/Yop6lNE/fp6yvbfHQgbn9lI32/2JfJOhFJfKd0md2PVbasYFkjbUC3r0rXidBCRM4A5rc1CsEuii7t+gvOX/QZgnYhsj992iEirfVLSTEMjjaZHJw7I1La6GNP1pOqaw+78bNPUt1atGfvuyjWNf/xs07yvVtfM6h6NLiA+JMMuTdubiFY71/fFGmLs/GgnRQP2nGivqFcROz9y/q41RZqoX19PUZ/d76nfUE/jtkbKR5cTa4h98dsfa4yl86O263dWRP5XRD4UkYUiMl9E2j2/j4j0EZE5IjJPRDo88bSInAsM2VdgQIKWhjEm2+Z61dBIo0Vm2HBjiIhkvjdxqTFlx9fUjj++phaABmiYU1qyMFRRvnVOSUnF6q0NB669Z12ZiRkw0G1SN7qO68qWV7YA0PPYnvQ5uQ9r/rGGpTcsBQP9v9ufgi67f8U/e+Iz+n3HOU3b/YjurLx9JZtf3EzfU/qm86MlbGmIyGTgJJxd/3oR6U1yU48eB3xsjJnWwRoBMMY81J737fNAqIgca4x5JX5/mDFmRbPXTjXGPNmZIpPlC4QuBe7M5DbzzZziH33QT7ZNsF1HS03QNLekeMnz5eWb3iwrKdvg9Y5EJBeGSvhq1bSqlp099yAipwLnG2O+2cprE4A/4vTw/hw4zxizvtnr44BncfpmrQUmA5uMMRXx108DTjLGnCciw4F/4Vy2MB242hhTIc4B6t8DJ+J0D/m1MebRtupN1JL4Q7P7T7R47YYEy6ZDTu0f5qI5sQNtH7dqVQEUTKqrH1O5ecvUl1avm7ggvLrLQ+s2LDk7smP2oMbGd8SYz23X2IZl7XjPi8BgEVkiIneKyFQAcY7x3AGcZoyZgDNe72+aL2iMmQ/8AnjUGDPOGFO7j+3cBtxmjJnInt+lU4FxOLMXfhW4RUTa3FVNdPZE2rjf2uNMWGJhm3llenRS15O9b9suIyEPeA6pbxh5SH3DyOudPRQ+LipcNr28fN2rZaXelYUFw2L7+MXPkAYg4ZFlY8zOeIviaJx5hR4VkQDwPjAWeCl+ttpL53bRJwPfjt//N7sbBVOAh40xUeAzEXkNZ1K0Z/daA4lDI5t6uQIsBeqBYgvbzguzYwePMIaoSO5deTu6oXH46IZtw6+KX7W6orBg1YzyslUzy8pkeVHhkCaRwRkuaUXVtKp2HWWNf2FnAbNEpAqYhjOr4YfGmMlJbrf5d7M9F0Mm1QBItHuyv4g8KyLPNbu/63HGpxMIB/1NwOJMbzefVFPapZai9jSps96wxqYhl27bPuWJdRuOmhdePXjG6rXrrtqy9a2x9fWvFzY7PpdGn7bnTSIySkSaTwkyDlgJfAL0iR8oRUQKReRL7VjlZyJyoIh4gFOaPf8OznQkAGc2e342cIaIeEWkD3AM8G5bK09mjNBdTRnT4nGmVeH8o6o0WWyGbpggS0fariPV9muKDrwgsmPgBZEdAHzu9Wx6sazs0xcqyho/KirqXydyQGevWm2hvbvTFcAdItIdZ2L1T4GLjTEN8QOZt4tz0LcA+DPwYYL1BYD/AquBReweJvN/gIdE5CdACIjEn38KZ9dlAc73+1pjTJuzwiUKje7AIGPMXwFE5F2ccTQMdiaABic0VBq9FJ1QMMFjc6aKzOgdjfU5e8fOPmfvcI79RjyebS+XlS6ZUVFWu6C4uE+NyKhkr1pt4b32vMkY8wFwZBuvzcf5y7+v5e/HmWt51+PHgcdbeeta4AhjjBGRM3GOmWCcU6jXxG8JJQqNa9mzGVMEHIbTae0+4D/t2UiKLbKwzbzyQmzi4ACP2C4j47rFYt1P3Vk96dSdzlWrO0V2vFZWumR6RdmOucUlu65aTeb6iWw7ojwB+Ev8FOs24IKOrCRRaBQZY1Y3e/yGMWYzsFlEyjuywRSYb2m7eWOFGTA4amSjV0xar3rKdhXGdPFX10zwV9cAUCdS+2ZpybznK8q3v1dS3G2rxzMKkbbGrt1QNa0qnLFi28EY8zrOadVOSXRx16fGmBFtvLbMGGNlQmZfILQM2N/GtvPF7KIr3xni2XSE7TqyWQM0vFta8snz5WWb3y4t7fK51zMSpzc4wFNV06pOtVpgmiRqacwRkR8YY/7e/EkR+SH7OLqaAbPR0Eirt2Jj64d4XrVdRlYrgqIptXUHTal1uslEITqvpPij58vLNi0rKnzJcnlpkyg0rgKeFpGzgbnx5ybgXCfx7TTWlcgs4DyL23e952OTep6JhkYyvOA9rK5+zGF19dDOg4q5KFGHtY3AkSJyLLDr/HBoV38Ui/Z5Lb/qvHdiY0YaQ4NIUh2nlGM7u//Iuk5ODSzcnC8QWgy4asDWbLOw+MJFXaV2rO06ctBzVEYyPTl6xmRb1/dkvGi7ALdbEBu+2XYNOeoZ2wWkUy6Hhqv/Y7LBi7GJ2scneVFc/ruZy6HxGvCZ7SLc7KXohIz3L3KBN6iMZGs3/ZTI2dAIB/1RWr9UVqXIBnr2azTeNbbryDEZHZjKhpwNjbg2RxdSqRE2/dMz0rA7GTQ0st4bOJ1wVJrMih3SZLuGHPIOlRHXt8xyOjTCQb/BmbhJpcn06KSMztma4zI9KboVOR0acQ/aLsDN5psRI4whK8cNzTLbyZPd5ZwPjXDQPw94y3YdbhXD491MV/cPrtF5/6Yy0trcx66TcFrGHHE7bQxiojrv/djI7Sd43+/Qshc8U8t/lzTRt1xY9CNnAKkzHq/hk8+doTO31Rm6lwjzL6nYa9ltdYaLnq1l0cYYInDvySVMHlzAdS/VMf3TJsb19/LAKU7P9AcXNLCl1nDlEdYuLfmbrQ1nWs63NOKeQA+Ips2M6KQOj51y3rhCZpy75zypj55WxvxLKph/SQXfObCQUw9sfTbGK2fUccKIAj6+vIIFl5RzYB8vkTrDW2uiLLy0gqgxVH0WpbbRcP+CRn400Vo3mQ+ojMyztfFMc0VoxAccvst2HW71SmzcAcZ0bPT5Y4YW0LO09WE3jTE89lEjZ43du8G7vd4we2UTF453AqXIK3QvETwCDVGDMYbaRij0wi1vNfDjSUUUem3MqgE4kxnlDVeERtzdQF3Cd6mkbaeiWz2Fy1O93tdXRelXLhzQa+9hOJdvjdGnTDj/mTrG372Ti56tpbrB0KVY+M6BhYy/u5ph3T10KxbeWxflW6PbNXd0OiwjTw6A7uKa0AgH/Z8D7ZqLUiVviRmU8tntHq5q5KyxrX/Zm2Iwd32MSw8rZN4PKygvFIJv1ANw7VHFzL+kglu/XsLPX63nxi8X84+5DXz3PzX8enZ9qstM5HdURqKZ3qhNrgmNuJtwZrVSKTYzemhK2/5NMcOTHzdxRhuhMairMKircPggZ9fltDEFzN2w57xD89Y739WRvTw8sKCRx04vY9HGKEs3Z+w7vAb4Z6Y2li1cFRrhoH8Vzm6KSrEXYhP3S+X6Zi6PMrq3h0FdW/8V7F/hYXA3D5987gTAyyuaGNN7z/f+/NV6bvxKMY0xiMaPuHgEahpTWek+/YHKSN79kXJVaMT9BsiL8+WZ9IkZMixmZEuyy531RA2T76nmk80xBv1xB/fMdb5jjyzae9dk3Y4Y3/hXzReP7zixhHOerOXgu3Yyf0OMnx29+3Tq0x83MnGgl4FdPHQvESYP8nLQXTsRgUP6Z2RGyfXA3xO+y4VyduSuffEFQjcD19uuw23eKr7ivYGyeaLtOrLE+VRG7rddhA1ubGkA/B7YarsIt3k7NqYm8bvywlzy8FjGLq4MjXDQvw242XYdbvN8dFJ32zVkiauojLivid5OrgyNuNuAj2wX4SZvxsaONIZ87yr/BJWR2baLsMm1oREO+huBH9muw03qKC6tpqS9M6G7UR3O/MZ5zbWhARAO+l8jj/c902GRGebq8S8T+DmVkZRfGZtrXB0acVcDG20X4RYvRg+zdr22Ze+QZ31M2uL60AgH/VuAH9uuwy1ejB3ms12DBXU4p1hjCd+ZB1wfGgDhoP9R4BHbdbjBGtNnQJPxrLddR4ZVUhn52HYR2SIvQiPuhzg9ElUnrTJ9w7ZryKA5wB9sF5FN8iY0wkH/duAMtENbp82OHZy53h12bQHOyLderInkTWgAhIP+D4DrbNeR66ZHJ/W2XUMGGOBcKiMrbReSbfIqNADCQf+fgeds15HL3jejRhqD2y8pv5nKyHTbRWSjvAuNuPOAFbaLyFVRvAXbqHDzCOUvA7+wXUS2ysvQiJ+G/Qbaqa3D5sYO2Ga7hjQJA2fr6dW25WVoAISD/o+BU9ADox0yIzaxLPG7cs4W4EQqI3ox4D7kbWjAF5eZX2C7jlw0M3rocNs1pFgdcLJej5FYXocGQDjo/xe6/5q0rXTt2WAK3HJcKAacQ2XkTduF5IK8Dw2AcNB/E3k6dFtnLDX7uWWCqquojDxpu4hcoaGx2w/Jk1m/U+XV2Dg3DERzA5WR220XkUs0NOLCQb8BLiaP5uTsrOnRSQNs19BJv6Ay8hvbReQaDY1m4sFxCTrFY7t8aHzDjSFiu44OuoHKyE22i8hFGhothIN+Ew76fwT81XYt2U9kI90/tV1FB1ylLYyO09BoQzjovxwI2q4j282JHbjTdg1JaMQZF+PPid4oIkZEHmz2uEBENonIf9ux7FudKzO7aWjsQzjovx5nd0V7ObZhenRSV9s1tNNW4OtJzFVSDYwVkdL44+OBdp0tMsYc2d6ixJFT38OcKtaGcNB/N3ASsN12LdloduzgEcZkfaguByZTGXk1yeWmA/74/bOAh3e9ICKVIvLTZo8XiYgvfn9ns+evEZH3RGShiPwq/pxPRBaLyJ04c6gM7siHskVDox3CQf8M4AggF/ff06qa0i61FGXz4EZvAodTGfmkA8s+ApwpIiXAwTgD8rSbiHwNOACYBIwDJojIMfGXRwEPGGPGG2Nyqvu9hkY7hYP+xcDhwAu2a8k2i83QDbZraMOdwHFURjo0groxZiHgw2llPN+BVXwtfpuH06IYjRMiACuNMe90pC7bNDSSEO8deyJwDc5BNQW8FJ1QYLuGFiLAaVRGLqMyUt/JdT2LM9zfwy2eb2LP709JK8sK8FtjzLj4bYQx5p74azk7SbmGRpLip2T/AEwG3DymRLu9EJuYTfvk7wLjqYw8kaL13QvcaIypavF8GDgUQEQOBYa1suwLwAUiUhF/334i0jdFdVmjodFB8aEDDwXut1yKdSvMgMFRI7a7k8eAW4EpVEZS1pHOGLPGGHNbKy89AfQUkfnApUDzmedMfNkXgX8Db4tIFfA40CVVtdkixrih+4BdvkDoDOAOoI/tWmyZXXTlnCGeTYdb2vyHwEVURqwfIxCRXsBcY8xQ27Wki7Y0UiA+r8oo4P9w/uLlnTdiB9VZ2Gw9zrAG47MkMAYCb+PyKQ+0pZFivkBoEk7flUNt15JJR3sWLnqwKDg2g5t8A/iBDpqTeRoaaeALhLw4M9bfBHSzXE5GFNFY/0nxNEQoTvOmlgE/ozLyWJq3o9qgoZFGvkCoN3AtcBngxjE197Cw+KJFXaUmXa2Nz4FfA3dRGdFxXS3S0MgAXyDUD7geZ6Cf1s7nu8IDhb997Rhv1dQUr7YGuB0IUhnJ1W74rqKhkUG+QGg/4GfARUCR5XJS7hzvzDm/Kbw3VWdQNuGckbqTysjmFK1TpYCGhgW+QGggzjGPi3HRadp+bNk4p+Tyzl68tAzneov7qYzUpqAslWIaGhb5AqESnEmpL8HpEJfzlhZ/b3WhRJO9QjSKc/XkPcDTOlFRdtPQyBK+QOhg4AfA6UA/y+V02ItF17w10rO2veNJfAzcBzxIZWR9GstSKaShkWV8gZAHOBo4DTgVGGi3ouT8rOBfsy8uCB2zj7eswJmA++FsuCBLJU9DI4v5AiEBjsQJkBNxrjrNauNl6ZKnin85stlTMZxxKJ4FnqMy8qGdylSqaGjkEF8g1B84Jn6bCnwJp/t11vAQa1xa/L15XjHvAa8Dr1AZ2WS7LpU6Gho5zBcI9QKOAg4CxsRvo4DSfS2XQjtwhgdYAszHaVG8Hw76c2mwYZUkDQ2XiR8TGYYTICOAAc1uveK3njgXmXlovaVSjzMQ75b4bdf9DewOiaXhoD9bR+xSaaShoXb1lfHEb4SD/s6OdqVcTENDKZUUHU9DKZUUDQ2lVFI0NJRSSdHQUEolRUNDKZUUDQ2lVFI0NJRSSdHQUEolRUNDKZUUDQ2lVFI0NJRSSdHQUEolRUNDKZUUDQ2lVFI0NJRSSdHQUEolRUNDKZUUDQ2lVFI0NJRSSdHQUEolRUNDKZUUDQ2lVFI0NJRSSdHQUEolRUNDKZUUDQ2lVFI0NJRSSfl/VFuuDj2witgAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"serie = df.GENERO.value_counts()\n",
"serie.plot.pie(autopct='%1.1f%%');"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "deb2877b",
"metadata": {
"scrolled": true
},
"outputs": [
{
"ename": "SyntaxError",
"evalue": "invalid syntax (<ipython-input-11-278289d103c1>, line 1)",
"output_type": "error",
"traceback": [
"\u001b[1;36m File \u001b[1;32m\"<ipython-input-11-278289d103c1>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m df.iplot(kind='pie',labels=,values=)\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
]
}
],
"source": [
"df.iplot(kind='pie',labels=,values=)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "80cd74f2",
"metadata": {},
"outputs": [],
"source": []
}
],
"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": "3.8.8"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
Loading