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@SilvaEmerson
Last active September 25, 2017 11:32
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Data analysis from the data of probably impacts of asteroids with earth, source: NASA(https://www.kaggle.com/nasa/asteroid-impacts/data)
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{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Possible Impacts</th>\n",
" <th>Cumulative Impact Probability</th>\n",
" <th>Asteroid Velocity</th>\n",
" <th>Asteroid Diameter (km)</th>\n",
" <th>Cumulative Palermo Scale</th>\n",
" <th>Period</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>5.200000e-09</td>\n",
" <td>17.77</td>\n",
" <td>0.007</td>\n",
" <td>-8.31</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>23</td>\n",
" <td>7.600000e-05</td>\n",
" <td>8.98</td>\n",
" <td>0.002</td>\n",
" <td>-6.60</td>\n",
" <td>29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>30</td>\n",
" <td>1.600000e-05</td>\n",
" <td>18.33</td>\n",
" <td>0.002</td>\n",
" <td>-6.48</td>\n",
" <td>45</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>24</td>\n",
" <td>2.000000e-07</td>\n",
" <td>4.99</td>\n",
" <td>0.016</td>\n",
" <td>-6.83</td>\n",
" <td>59</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>85</td>\n",
" <td>2.300000e-08</td>\n",
" <td>19.46</td>\n",
" <td>0.497</td>\n",
" <td>-3.85</td>\n",
" <td>79</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Possible Impacts Cumulative Impact Probability Asteroid Velocity \\\n",
"0 1 5.200000e-09 17.77 \n",
"1 23 7.600000e-05 8.98 \n",
"2 30 1.600000e-05 18.33 \n",
"3 24 2.000000e-07 4.99 \n",
"4 85 2.300000e-08 19.46 \n",
"\n",
" Asteroid Diameter (km) Cumulative Palermo Scale Period \n",
"0 0.007 -8.31 0 \n",
"1 0.002 -6.60 29 \n",
"2 0.002 -6.48 45 \n",
"3 0.016 -6.83 59 \n",
"4 0.497 -3.85 79 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_impacts = pd.read_csv('./DataSets/impacts.csv')\n",
"df_impacts.head()\n",
"\n",
"torino = df_impacts['Maximum Torino Scale']\n",
"\n",
"names = df_impacts['Object Name']\n",
"\n",
"torino_scale = df_impacts['Maximum Torino Scale']\n",
"\n",
"df_impacts.drop(['Object Name','Maximum Torino Scale','Asteroid Magnitude'], axis=1, inplace=True)\n",
"\n",
"df_impacts['Period'] = df_impacts['Period End']-df_impacts['Period Start']\n",
"df_impacts.drop(['Period End', 'Period Start','Maximum Palermo Scale'], axis=1, inplace=True)\n",
"\n",
"df_impacts.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Possible Impacts</th>\n",
" <th>Cumulative Impact Probability</th>\n",
" <th>Asteroid Velocity</th>\n",
" <th>Asteroid Diameter (km)</th>\n",
" <th>Cumulative Palermo Scale</th>\n",
" <th>Period</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>683.000000</td>\n",
" <td>6.830000e+02</td>\n",
" <td>683.000000</td>\n",
" <td>683.000000</td>\n",
" <td>683.000000</td>\n",
" <td>683.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>43.348463</td>\n",
" <td>1.539476e-04</td>\n",
" <td>11.462577</td>\n",
" <td>0.049378</td>\n",
" <td>-6.511552</td>\n",
" <td>37.106881</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>113.773280</td>\n",
" <td>2.519607e-03</td>\n",
" <td>6.067772</td>\n",
" <td>0.156403</td>\n",
" <td>1.509189</td>\n",
" <td>32.930861</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.000000</td>\n",
" <td>1.100000e-10</td>\n",
" <td>0.340000</td>\n",
" <td>0.002000</td>\n",
" <td>-10.980000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>2.000000</td>\n",
" <td>1.100000e-07</td>\n",
" <td>7.240000</td>\n",
" <td>0.010000</td>\n",
" <td>-7.490000</td>\n",
" <td>5.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>6.000000</td>\n",
" <td>1.700000e-06</td>\n",
" <td>10.500000</td>\n",
" <td>0.017000</td>\n",
" <td>-6.460000</td>\n",
" <td>31.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>29.000000</td>\n",
" <td>1.550000e-05</td>\n",
" <td>14.810000</td>\n",
" <td>0.033000</td>\n",
" <td>-5.490000</td>\n",
" <td>64.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>1144.000000</td>\n",
" <td>6.500000e-02</td>\n",
" <td>39.470000</td>\n",
" <td>2.579000</td>\n",
" <td>-1.420000</td>\n",
" <td>98.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Possible Impacts Cumulative Impact Probability Asteroid Velocity \\\n",
"count 683.000000 6.830000e+02 683.000000 \n",
"mean 43.348463 1.539476e-04 11.462577 \n",
"std 113.773280 2.519607e-03 6.067772 \n",
"min 1.000000 1.100000e-10 0.340000 \n",
"25% 2.000000 1.100000e-07 7.240000 \n",
"50% 6.000000 1.700000e-06 10.500000 \n",
"75% 29.000000 1.550000e-05 14.810000 \n",
"max 1144.000000 6.500000e-02 39.470000 \n",
"\n",
" Asteroid Diameter (km) Cumulative Palermo Scale Period \n",
"count 683.000000 683.000000 683.000000 \n",
"mean 0.049378 -6.511552 37.106881 \n",
"std 0.156403 1.509189 32.930861 \n",
"min 0.002000 -10.980000 0.000000 \n",
"25% 0.010000 -7.490000 5.000000 \n",
"50% 0.017000 -6.460000 31.000000 \n",
"75% 0.033000 -5.490000 64.000000 \n",
"max 2.579000 -1.420000 98.000000 "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_impacts.describe()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
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9M2ag9xevsvanIDYv2OCxmNzdjnOcK28uvLxcaaFgEX8KFyvEsUPHU+Bo3PwH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\nuRj7fx8LOpMy1I5yAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f62e5971cd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.heatmap(df_impacts.corr(), annot=True)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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1cvHN0uD7o4EgXxi3EAaKwrbpEoPO37RyTA3h4b293PnIgWKT0IpEhPq4Syqb\nJ+MF6bGTOdQ/gucrf/ujZ+fqJQLworZ6Ljt7DVu3dFAfD+aumg8WOMyiVGnOvJk7s2kinIvmRVUt\nBoDCgLbSDmfPV/xZZosWOpMLHcmFGsLmVQ20NsTYf3yYrr40x4eHy5riwhsXVVxHaKmLFmsIhZpB\nS3j9aP8IX/33vaTzUz/7/7162gSoqrDAYRalpb5Ww0I1mybCch87lyOcx/N8Hc0kCjOM+koyjXqH\nM8Xrc9mZvKoxjq/Kyvo4n/lv/4nGZARnXO2gUKM5PJAu6/gznS/KdQRHBNeRMdcnab2akAUOsygt\nh7UaFqLZNBFu37GXiAOJqIuGKaeen+dL9+3hjFMawyAxsxHO6bAfYXxQKPYlpLL0DmXpT+cqmwSP\n0c7koHYQZeWYy7APoSHG//rxb9l/fIihjFc8hgDxiBCLOOR95ZpXb+C3RwaLAeKUpiRvf0Uwo9JN\n9wVjNZoSEY4OZKYtVyziTBoEXBEcZzRIlO4zFyxwmEWpnFz8uWZZXNM3EZZ2OJf2KeQ8n33HhmhM\nRMiVZAdFXeHgiRTp7Ni1twF8VU6kcuNqBGODQuFv/Nrd5YhFHFbWxUY7k+uCAFBJZ3Kpd7zyVG74\n111EIz6+r0EPuUAyFqG1Pj5hgDg+nOGm+3aTjDjhwMEI5XRTOAINcZfTWqdP9a0GCxxm0ZrJWg0z\nPflbFlfQ4by2OUn34AjJaARVRYF0Nk9bQ5x9x4anrC2sDlNO4xGn2EGdynokoy5f/fd9Y/sTwiak\nqTqcJ9OUiNAyLpto7PU4Kxti1Meq1JmswWp8EUeoj0f45FvO5MIXt+OKsO3rO0lEnKDGJkGtK5XN\nc/DECJs7GorlcYRJX7sjsCIZYfOqprkve5kscJhlYzYn/6WUxTVR8Py9M9pHs48mGOFc6HD+r+es\n5ab7dpP3ciSiDiO5oFZxxXnrGEhPUDMYGg0EB0+kOTaUmbCp6BsPvjBlmQudyYmIy8BIjpzn05SI\n8qoXtXLeaS1BzaA+qDXEInO3WkSx/T/sAwiagGT0sqRZ6Pu/OUhLXXRMP1sqm+dbO7t467nrADg8\nEEy5XhqwCt+pdM4rXm9viHN08OTmKid8LwZGPF61aSVf/Nlz3PqrfQxnPepjLu++cCPve92L5+z1\nT8YCh1kwqt0UNJuT/2LP4io0G/18Vzd/98NncV2hPuayp3uQbd/YSX08wmkr64uD1AodtYf6U7Q1\nJLj4jA5LpZGoAAAa10lEQVROaU5wfDjLWWua+PULJ+gOT2yer3z8+0/NqFzxiEN7Y3yCWkFsTK2h\nKRll574+brpvNyuSURJRh77hLD988jC/eK6nWPbVmxJjnr+0w/mUFUnecf6pvHpz22gfwJjAICdt\nr6RG0nUiPel3pPDd7hnMcGwwU1yICYKAsamtnuGsV+yza0hEGMl7ZPM+6Vwwaj3iACLEXIfGRIRv\n7zzA4YFgKpSIEzzPTfftAah68LDAYSpWjRP8fDQFzebkPz6LayCd4+hgMGCrdCbTWphwzIIfTDkx\nfszC9l/sRQSiIvSFtQIlmMTvdz1DfPoHT9NSF6N7MIOvwZKk3YNZnjk8MHUhSkzemRyMTi4GiIbY\nmLmVpnPnIweIOMEEgkOZPCfSwUjpdDZPXzrDl36+h48ntnDRGUGz0L/vPsaX799D1BXa6mMMjuT4\n0s/30N4Yr8pnNVmmX33MLX63VzfF6epLs/94KjzhC0rQvNbRmEBV6U/nWNdSx2Vnr+GBvb08vL+X\nRMShrSFOU/j9VVWePjyAA3gKeQWR4L2/9Vf7LHCYhaVaJ/iZ1AYqDWCzSeG99qJNfPiuxzl4Ik3O\nU3w/mAdo/cpkVfs7CmMWSoPB+DEMhTELfnjS6Q2ziEpHJheakJ45PIA/ZmQy4WNhMJMP3pP+kUnL\nE3MdVq9IcFprHc8eGiDrBRPruY4UR1S3N8S58e3nlP0aJ8oMCqbtGG0Wch2hZyho5nEch4Mn0rji\nIE5Q42lKxEhl83zzoRd4y9lrAPjqf+wnVuhPAOpiTlWbF6da7jXiCKtXJIo1GCF4z3PhfFIN8QhZ\nzyfnKX9z2UuB0Zl2466Q9XwO9QfThzQlo6RzXjDKPHwuCPpVfII5q6rNAoepSLXa+iutDcwkgM02\nhVcAdHQwV5BpI7N6D4Iawdg+hbznjxmzkM374wJAONagNECkZt6ZPOb1BS+RtoZYEAg8nxPhHEgK\nrGqKk/N83vzSU3ju6CCr6uOjU3lLcPLqHhwhEQaTuUwPPXVlPfuPDzGQzjOc9YLmG4KmGzj5+zLf\nzYulmX6FadJb6qIcH86S83yeP54KEq3CpqW8HwRiBI4NZdnU3lD8HgHF/2cdTQkOnRhBUY4NZYi4\nQs7T0Q700rdQmZfR4xY4TEWq9Z+x0trATALYbFJ4t+/YS1MyyuoVSXYdGcB1BPXh2FCGpmR0zHtQ\nqAm90DtMYzyCr8pQ1mPNiiR/dMFpnL9pJbm8T386R89Qht6hidNNC2moM/kF6TpSTC9dWRfD85Xn\njw8zOJIj4/k0xFz6R0ZTWKPhr3vPD6bOSEZd6mIRXugdxnEEFCKu0BCPkM55/POvuzhtZT3PHx9i\ncCRP1vOJR1waExE2tjWwpjlZcZmn86pNK3l4fy+OBCdfDX+xr6wb7Vwu/b7UYpDoRMu99g1nyZUE\ndFWKtzPhrLsZYNeRAXKe8rueYUSgrT5KXSxCYyLKmmboHhhhJO8TdYJgWfiRML72WBedu+SAyVjg\nMBU1+VTrP2OltYGZBrDSFN7C6/7E3U9N+7pLjxdzHXKF//D5oLZw6ESKgZE8G677IQD1MZd4RDh0\nImj2ScaCztwP3/U49fEIw5n8jEYmC8H4gzUrkpzWWnfSuIPWMN20dGTyw/t6+eK9u4lHHVbU1XEi\nlS3WIgBcCVI8NTwT1UUdRIS875PzfBwRVKC9MUHEdWhwhCP9I7ztvHXsfL63eALLZz3SOY8/PP/U\nil9Xqcm+jw/s7aW9IcbgSB7PD4KeI0H/TEMictL3pbR50QubFgvNeZfc+AtEhMFMviqJGAf6UrgC\ne3uGyEzzORfuLf0+qELPUI6+4X4kDBQr6qI0J2MMZ73gR1PUIZ3zi48vpOluWb1izl7HZCxwLHOV\nNvlUa8R2pbWB6QLYdMFwutc9ZtqLsN3+6EAa13GIRZxiG7Mj8NzRweJsqQXDWY/SdXFS2dFBbyfG\nTX9dOs312GyiOK0NwbxF//xYV9he75LJ+2TyHv/f2adw4eb2SZuDCtv+8ntPkYy5xfdqVVOSxkSU\nnsEMdTG3WGMIalFKzh8NHq7jIAKrGhPFjtljQxlSWY8v3benGDQk/EeAHz91ZMads1N9Lgf6UrQ1\nxGlvDDKnBtI5jg1lGMn7dDQmJvy+FJoXfV/xNAiSgrK7ewgv/Py6B0b4k2/00pSIFFfzm20QaYi5\n7OkZxp1ls1FeAU/JeR7DWY/ugQzRiMOqxsSYJqxC39N8zZ5gS8cuc+OXyIQg97yjMcEd2yZecrJw\nUp6vEduTlWGyZTph7BKepct8tjcE02DvPTYMqrQ3JohFHHK+ks7miUVcLtjUetK8RSO5mc+PVDh1\nJGMu2bCGsqG1nre87BS2ntHO7qNDfPvRAxzuH2HNiiRXv/o0Lnpxe7Fj+JqvPsKxoeAzKrRfT/cZ\nlbrwhvtOGjugqhzpT1MXjxJ1g76Mg2HtaG1zULPIecrl567lrscOFt/LY0MZeoaydDTGONw/Os4g\n6ggR18HzfUSE7VedN6PMu6m+j0BF39XS59rbMxQEd4Vc2BxX2qcDQWrwupbkmO/RTLMH3/iFHfz2\nyCAqJzclzVbMDTKx1qxIImHgy3jK+RtWzvr/oojY0rHLUaWZRjNp8pnJiO25Km9pGSaroVxx83+Q\nyuTCX9Ja0hTgc3x47K/9gydlEOX43q8PTnlsEYi7wUlmQ1s9P3u2G4EJB7bFXClOPZHNB80riYgg\nonz/N8EJuXBibq2Pcag/xUfueqIY5ESE57qHiLtCR9No7n8l/UrrW+rYdaSf/nS+eMJ0BCKuQ3uY\nAtrVF3S6ltYsUtk8D+ztHbMcairr0dEYo60hwZH+0cF8OV/J+4U5mpQ/+cbOYtPQscEMH7nrcT57\n+dmzGi/zN5e9tOLmzEJz0XDWKw7gK60cFvqWFcjm/WJf2Q0/2VVsEppJ9mD34EgxWWC2xo8iz/mK\nK8KxoQyb2htwHSn7R8RcscCxhMwk06iaHYizbS4q3e8ffvE7XugNmipeu6WDVc0JuvszvKijnqZk\nhJ7BDJ/6l6c5fsevJ+1Mni7jyAmngDhnfTOtDTFGch6P7OslFnFIxlx8PxjX8Ik3vwRHhK/8+36e\nDcc3TPbUhXTLqBsMJhOEVSsS1MejpLJ5bv3VPurjLseH8ozkgzmOHAeGMtCfHgaCXP+crxw6McKa\n5uB5j/SPoJw8hmSi93x1U4wH9o6+J2HrBw1hmmfOUxriLg3xCMeGMhzqTxNzHdoaYmOCkxL06bTU\nBSf2QnZP6f2lrxtGfx33pXLc8JNdPNF1ojjSOeYKbQ1xFGiMB1OY9AxmODaUGRPACt/HSpszG+MR\ndncPFWd+DTqlTy7v+M8uGXXZ3T3EupbkjLMH07nRWs345y8E7nK7uMZ/b1Uhr4rmfVLZfE0m97Sm\nqiVkps1OH77rcYYy+TEdiCuS0Vm1907XlLR9x14ee6EP3w86X/OqRESIRx0aEzFec3orPYMZ9vYM\nc6AvNas000o0xBxa6uMcG8qQDpunom7QqBH8Wg1qMI4EU2C0NcR55tAAkzVkuU6wIhwEJ4yOxjgd\nTUGzi6ryzOGB4vQVOc9HdfREE5EgT19LawmOFE84pU1Khff1I3c9zuBInrzvE3GCEcbDGY+RvDfh\ne1gfc2lKRuhPhf0cIohQnKjQAaIRh5X1wYC+PT1DZHI+MsVcSuMloy6e7+OFfQ0w9mS6IhFhOJzk\nsKUuQl8qP+Hrq3TNj0f2HyfvB++ZEPxSn4ojcNaaFaSyebr60qxuinNsKEvW84m5DnUxh4ERj/bG\nOPg+vek8mbw/4VQfm//iR+TC/0+lRxXgtNY6jvQHGVKVCn6EOMEa6AIXbGyd06bicpuqLHDMg3Ka\nYybbp5LHPry/l0j408onyP6JOMLASB7HkQm/4Pfv6uZ9dzzGUNYb09F56srkmP+0d/+mi3ueOILn\nK64jXPqy1dz49nMnfc1X/MMDHO5PB2syez7DmTz96VzxGEHq58y/e44EzxFxhHiYCy8IV3Su42v/\nsZ/sDNdScCv4JVgox1QvI+Y6KFp83wRhTXMCVTg6OMJIcTqJ0eVIJ3q6SFguBRJRZ1xn9QjDGY90\n1iPvK64TNEPlPf+kZUzHi46rNUTCX+eF908IahaFcvcNZejPVDYTbSFwTPWZREq+D6U1maZEpKL5\nl0p/sLzQmyJf0lQ5WXNiqdVNcfpSOfLhin6l72Uw7kJYkYzQMxQ0eUbDaUB8hfdffDovW9fM9h17\neWDv8UmP96L2enKe8kLvzFLY466Dj7IiEWHnX71+Rs8xGQscMwgc1Z5KY7Jf3qWDhdoa4sV9xndM\ndvWl6B/JF0eavvvCjbxsXXPxV2bpL5ioAz6j/xnjESGX1+Iv48J/yB89ebiY/ZH1/DFf9EK+vK8T\nt9UWypUKT1jF/ac5WU1HgLPXr+DQiZHifEiFX75+uKAPQCLijJnzp1C7enh/76yC0lwqBAUfigvl\niCp+2DXr+VrWe1V6EiqtuQyO5DjQmzop2DkwaS1osueNOKPrZYtARIImsqgT1AjRmX2ucdfBC0fA\nw+gYjOk4ErzOvlSOxkSEzR2NvGrTSh7Y28uBvhQN4ey2g5l8salr3/FUUIt1HDIl/x/KCRqF/Vrq\nogxn8sU02mg4WE8JvnNZb7QZSsJ+k8Jri0UcWuqi9A1nxzw+WEs8+C50hp3Y7/zaI1OWo/RHSWlf\nTH1sdLzMXPdrLLnAISKXADcBLnCrqn5msn1nEjju39XNn/3TY6RK1gWoi7n8/R+eW9aUF0KQppgN\np6MofVcjAi9ZE+RW//ZwP9kp/kcXTi6l573pftWWo/QX7UTK/Y81V+b6eE44FmEGtf95M/5k7jpy\n0nelEqsa4/SnczNq8phIoXaR95R41EF9yPp+salspuWMuUETX6XFLJQHgqytFXVRugeztDfEiEcc\nXuhNz/l3NhrWrjwN5vcq/S9TKE+hNjRRACw0a7YkoxwrGcnvSHDC/9KVo+eTwpifidTH3GITWVtD\nvDjdSNQRTmlOVtx8V64llVUlIi7wZeD3gS7gERG5R1WfmatjfPiux8cEDYBU1uOdX3tk1ie5vMKT\nB/vL2neic/tc/HieKmjA/AaNahzP17l5n6pp/HlztrWiiabdng1ltFO7OK0KkGfsL95KiQSTDB4b\nylQUPBRwEMQJ+icG0vlwwF+eY3mvKt9ZV4LOfN87OaArgJZM9TEuqCgUZxQYGMnjhH1iEDRZJiqY\n0LEpGaF7MEtjIkJjIkJrPkZfKkddPDLpmJX5tCgCB3A+sEdV9wKIyJ3AZcCcBY5jQ9lJ71vg5yNj\n5kS0pFZa6Pw/PpzFDbPJZtIvFXelmJ2UiLoTNqtNpfCrPhZ2CAf9L/6c1ywLWdOqIM7k/+fzPrQ1\nROkZGrsErRLUNlSDMmfyfrC0qwgRV8bMQ1U44U/VkrChtYErXxE0y3X1pdjY1sBnFtCKk4slcKwF\nDpTc7gJeWaOyGLPoTVR7cESIuEGW0+fedg7bd+ylL9WH60qxL2nXkQE8T6ftP3EE2upjHE/lilOn\nNyairF9ZR/fACKmcX+yjOnwiPem0HJ4qgtDeGKdnMFNsvsl5lS8VO5l4xClmXfnohJ1DTjgq3lNI\nRCOsW+HQm86TCidb7GiMk4y5HDoxQqGnsLBCYltDkEU3fuxNQzzCcDYfZNKFAUcE6mORYt/F++bs\nVc6txRI4piUi24BtAKeeOru5ckx1TdSPYypXmOaj0m7KQlbWsaEMmZyHB8VJDBsTUTa2NRQHeRaS\nO1xHUA0GnqkD65uTxTTp8R33UpLWOpL3x6xs15iI4jpCV1+a08OlUnVFMFivkIpc6BNJRh2yHqys\nj9IQj5DJe8Xmm1R2bpqqCuM7fJREJEhf7kvlkPDZI+GIeIC871Mfdfnlxy4uPn588ktrQ5Te4Ryu\noziOTDgepeDdF27kpvv24DqjtQ9fg+0LXfWnUZwbB4H1JbfXhduKVPUWVe1U1c729vZ5LZypTGFl\ntQpm1K5eWRZAGWaqvSFGRziFynSC7B9Y35KkozFBxBXaGmIQrnp36sokq1ckiEXcMYPJtm7p4PpL\nz6KjMUF/OsfGtnqa66JEXKG1PlxUiNETiRLUNAoD09594UZynpLK5lHV4vZNbfWkc0GtoSkZZX1L\nHfGoQzzicN5pK7n1j1/Bk59+I9uvOo8NrQ30p3NsaG3g/Refzsa2Bhri5Z+6XEdoik/cv9BWH6O1\nIYojQjLqsLGtge1XnccHXrc5mBVYwFeffDhNyfiT+vj3Z0Nr8Phb//gVxfe59HWXvrfve92Lef/F\np5OMuuT9oEby/otPn5elX2drUWRViUgEeA54LUHAeAT4Q1V9eqL9Z5JVNVWGg5k7joymEgPc+LPd\nVe1DGj9OAYIst7UtyeLcTPUxl/6RYNBZxBn95deUiLAiGQ1+mef94qSGWtKa4UowICvvj213L50H\nyXWCKxO1xrjh4LR8OFgMgeZkhLXNdew7NsRwSQpexBkdgFjYJ53zgmy+vD9hRl/hNRUGA3728rOB\n0dHX9WFK61AmX/a8Y6VzlakqvcPBVO2FkeDAmOeaaG4zYNI09XLb8UvX245HHFYmI4jjFF9Tz1Bm\nTJp7aTp7PFxRb3xZJ3v+maznvRDmdKvUUkzHfRPwBYJEj6+o6t9Ntm9nZ6c+/PAjeKqM5Dx6BjN0\nD4xwdCBDz1CG7nDd32NDGY4NjU5mN5NprieTiEg4j36SF44P0Zcuv01WBBwFJuiMdAhOeqlMnr5w\nQJ0jQfpfXTxoDiikBdfHXNasiLO7J1UcFe46oAS/9it5vaWD/j5w52MTDga85Mb72XV0uPiYdSvi\nrG9tmPI/zmT/+UtfR8QRfN/HZ+wgxqn+Y0/2n7Z0e+lJc/z7NtFJYrLHTnVSKH1MQzjWYDjrjbk+\n3Ul2sgGf052QFvqJaz7Kt9Dfg4VmyQWOSqw87SV6zntv5vhwlv50bvoHjCME01y3NcTDvxgdTQk6\nGuOsakqwqinOKSuCaY1Lp/cwxpjFbEmN46jUcDYfTJs9TjziFBe8KQSE9sY4HY0JOprirG5KsGpF\nnFUNCWIV5FwbY8xysiQDR2t9jPdefHoYFOKsbkqyakWc5mSUqOsUsySMMcZUbkkGjjXNST70+jNq\nXQxjjFmS7Ke3McaYiljgMMYYUxELHMYYYypigcMYY0xFLHAYY4ypiAUOY4wxFbHAYYwxpiIWOIwx\nxlTEAocxxpiKLMlJDkWkB3h+Fk/RBhybo+IsJva6lxd73ctLOa/7NFWddkGjJRk4ZktEdpYzQ+RS\nY697ebHXvbzM5eu2pipjjDEVscBhjDGmIhY4JnZLrQtQI/a6lxd73cvLnL1u6+MwxhhTEatxGGOM\nqYgFjhIicomI/FZE9ojIdbUuT7WIyHoR+bmIPCMiT4vI+8PtK0XkpyKyO7xsqXVZq0FEXBH5tYj8\nILy9UUQeCj/3b4lIrNZlnGsi0iwid4nILhF5VkRetRw+bxH5QPgdf0pE7hCRxFL9vEXkKyLSLSJP\nlWyb8DOWwBfD9+AJETm3kmNZ4AiJiAt8GXgjcCZwpYicWdtSVU0e+JCqnglcALwnfK3XAfeq6mbg\n3vD2UvR+4NmS2zcAN6rq6UAf8K6alKq6bgJ+oqpbgLMJXv+S/rxFZC3wPqBTVV8KuMDbWbqf99eA\nS8Ztm+wzfiOwOfzbBtxcyYEscIw6H9ijqntVNQvcCVxW4zJVhaoeVtXHwuuDBCeRtQSv9/Zwt9uB\nt9amhNUjIuuANwO3hrcFuBi4K9xlyb1uEVkBXATcBqCqWVU9wTL4vAmWx06KSASoAw6zRD9vVd0B\n9I7bPNlnfBnwdQ08CDSLyCnlHssCx6i1wIGS213htiVNRDYALwceAlap6uHwriPAqhoVq5q+AHwU\n8MPbrcAJVc2Ht5fi574R6AG+GjbR3Soi9Szxz1tVDwKfA14gCBj9wKMs/c+71GSf8azOdxY4ljER\naQD+GfgfqjpQep8G6XZLKuVORN4CdKvqo7UuyzyLAOcCN6vqy4FhxjVLLdHPu4Xgl/VGYA1Qz8lN\nOcvGXH7GFjhGHQTWl9xeF25bkkQkShA0vqmq3w03Hy1UV8PL7lqVr0peA1wqIvsJmiIvJmj7bw6b\nMmBpfu5dQJeqPhTevosgkCz1z/t1wD5V7VHVHPBdgu/AUv+8S032Gc/qfGeBY9QjwOYw4yJG0Il2\nT43LVBVhu/5twLOq+vmSu+4Brg6vXw3cPd9lqyZV/biqrlPVDQSf732q+g7g58Dl4W5L8XUfAQ6I\nyBnhptcCz7DEP2+CJqoLRKQu/M4XXveS/rzHmewzvgf44zC76gKgv6RJa1o2ALCEiLyJoA3cBb6i\nqn9X4yJVhYhcCPwSeJLRtv6/IOjn+DZwKsHswleo6vjOtiVBRLYCH1bVt4jIJoIayErg18BVqpqp\nZfnmmoicQ5AQEAP2AtcQ/HBc0p+3iHwa+AOCTMJfA+8maMtfcp+3iNwBbCWYBfco8Cng+0zwGYeB\n9P8QNN2lgGtUdWfZx7LAYYwxphLWVGWMMaYiFjiMMcZUxAKHMcaYiljgMMYYUxELHMYYYypigcOY\nGRART0R+E866+h0Rqavw8bdWMommiLxTRP5P5SU1Zu5Z4DBmZtKqek4462oW+NNyHygirqq+W1Wf\nqV7xjKkeCxzGzN4vgdMBROQqEXk4rI1sD6frR0SGROR/i8jjwKtE5H4R6Qzvu1JEngxrLzcUnlRE\nrhGR50TkYYKpMoxZECxwGDML4ZxHbwSeFJGXEIxSfo2qngN4wDvCXeuBh1T1bFX9Vcnj1xCsD3Ex\ncA7wChF5aziv0KcJAsaFBGvEGLMgRKbfxRgzgaSI/Ca8/kuCub+2AecBjwQzOpBkdFI5j2BSyfFe\nAdyvqj0AIvJNgrUzGLf9W8CLq/A6jKmYBQ5jZiYd1iqKwvl/blfVj0+w/4iqevNTNGOqy5qqjJk7\n9wKXi0gHFNd7Pm2axzwM/J6ItIX9IVcCvyCYcPL3RKQ1nAL/bdUsuDGVsBqHMXNEVZ8RkU8A/yYi\nDpAD3kMwK+lkjzksItcRTPUtwA9V9W4AEflr4AHgBPCbyZ7DmPlms+MaY4ypiDVVGWOMqYgFDmOM\nMRWxwGGMMaYiFjiMMcZUxAKHMcaYiljgMMYYUxELHMYYYypigcMYY0xF/h+Z1fWJg8CMNAAAAABJ\nRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f62e3813310>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x = df_impacts['Period']\n",
"y = df_impacts['Possible Impacts']\n",
"sns.regplot(x,y)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"image/png": 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3e6xiaD8Xg6o2Hqv3ejxWkhrywm1Kx0XSRyR9KdU+qt9sqStv3TWS7onLo3Ive6xKzceZ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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f62e36f34d0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7f62e35dbf50>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7f62e33bac10>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7f62e338b390>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7f62e3552910>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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VHSJyn4jc6DR7DMgRkSrgbuDktM67gBLgXhHZ7DzyQ34WEa6yrp2G9h4uLsl1\nOxQTAj/8zDxSErz8w6+320VdE3V8wTRS1VXAqiH77g3Y7gZuGeZ9/wT80xhjjHrrnfF8S/qxIW9C\nEn973Vx++NJ2fr35MJ87t8DtkIwJWlBJ34zN+qoGZuamMS0zxe1QTJCe3nDotK+rwvSsFP7+pe1c\nPjuf7LTEcYrMmLGxMgxh1jcwyPv7G62XH2M8Itx07jS6+wb5nyt3uB2OMUGzpB9mW2pa6Ogd4OIS\nm6oZa6ZkpHDl3Hxe3nKE32076nY4xgTFkn6YratqQAQunGk9/Vh0+ew8FkzL4O9/vZ3G9ri879BE\nGUv6Yba+qoGF0zLISE1wOxQTBl6P8C+3nMOJ7n7u/Y0N85jIZ0k/jNp7+vnoUIuN58e4OZMn8O1r\nSvnttqM8b3X3TYSzpB9GG/Y30j+olvTjwDcun8WFM3P4h99sZ88xq8RpIpcl/TB6a89xUhO9lBVl\nuR2KCTOvR3hw+SLSkxL4y6c20dHT73ZIxgzLkn6YqCqrdx3nkpJcknxet8Mx4yB/QjL/unwR1Q0d\n/PClbXa3rolIlvTDZE/dCY60dnPV3LirOhHXLpqVy3eumc2vNx/hkbX73Q7HmE+wO3LDZPVuf831\nKy3px527rixhT90J/s/vdjM5I5mli4YuP2GMeyzph8lbu48zf+pEWxoxDnk8wk8+fw6N7T18//kt\n5KYn2cV8EzFseCcMWjp72XSw2YZ24liSz8t/fKmMmbnpfP3JTXx4qNntkIwBLOmHxduV9QyqDe3E\nu4yUBH5x+/nkpCdy639u4K09tsyicZ8l/TBYvfs42WmJnFOQ6XYoxmVTMlJ44RsXMSs/ja89UcGL\nm2rdDsnEOUv6ITYwqLxdWc8Vs/PwemyVLOOvv//snRdywcxsvvf8Fv7Pql309Nsau8YddiE3xD46\n1ExLZ58N7cSRkWrvn/TpeZOZkZPGf6zdz9q9DTy4bBGzbc1kM86spx9ir24/RoJXuGx2bC7wbs6c\nz+vhnz+3gEe/XMbxtm5u+Ld1PPjGXjp77e5dM36CSvoiskRE9ohIlYjcM8zrSSKywnl9g4gUOftz\nROQtEWkXkZ+FNvTIMziovLL1KJfPziMjxapqmuFdM28Sr37nMq45K58H3qjkyn9Zw3MVNQwM2h28\nJvxGHN4RES/wEHAtUAtsFJGVqrozoNkdQLOqlojIMuB+4AtAN/APwNnOI6ZVHGzmWFs3P7h+rtuh\nmAgVOBRDouXFAAAQ4UlEQVR0SUke07NSWbXtKH/zwlb+36t7uGpuPgsKMvizC2a4GKWJZcH09MuB\nKlXdr6q9wLPA0iFtlgJPONsvAFeLiKhqh6quw5/8Y94rW4+QnODhmrMmuR2KiRIzctL4xuWzWF5e\niAisqKjhwTf28vKWIwxaz9+EQTAXcqcBgUXCa4HFp2qjqv0i0grkAA2hCDIaPPneQV788DAl+RP4\nzeYjbodjooiIsGBaBvOnTmTHkTbe3FXHN5/5iB//bjefnj+Zkvz0077/i4sLxylSEwsiYvaOiNwJ\n3AlQWBidP8DVDR109PSzcFqG26GYKOUJSP6ba1p4Y2cdj6+vZu7kCSxdNM2uE5mQCGZ45zAwPeB5\ngbNv2DYi4gMygMZgg1DVR1S1TFXL8vKic9bL1toWEn0e5ky2KXhmbDwinFeYxXevnc2S+ZPZV9/O\ng29Wsulgk5VrNmMWTNLfCJSKSLGIJALLgJVD2qwEbnO2bwZWaxz9dPb2D7LjSBvzpkwkwWuzYE1o\nJHg9XDY7j29dVcrkicm8+OFhfvX+Qbr77MYuc+ZGzFCq2g/cBbwG7AKeU9UdInKfiNzoNHsMyBGR\nKuBu4ONpnSJyAPgJ8BURqRWReSE+B9etq6qnq2+AhQU2tGNCLyc9ia9dOpPPLJjCnroT/PztfTS2\n97gdlolSQY3pq+oqYNWQffcGbHcDt5zivUVjiC8qPPNBDamJ3hEvuBlzpjwiXFySy+SMZJ7ecIiH\n1+xjeXmh/cyZUbOxiDGqaerkzV11lBdl4/PYX6cJr1l56fzVlSVMSPbxxLsH2Hmkze2QTJSxLDVG\nv3r/ICLC4pk5bodi4kR2WiJfv2wWUzOTefqDg7yy1aYIm+BZ0h+Drt4Bnt1Yw6fnT7LpdGZcpSR6\nuf3iYgqzU/nWMx9ZyWYTNEv6Y7Byy2Fau/q47cIit0MxcSgpwctXLirmwlk5fP+FLazcYj1+MzJL\n+mdIVfnFuweZO3kC5cXZbodj4lSiz8OjXz6f84uy+e6Kzby+45jbIZkIZ0n/DG080Myuo23cdlER\nIrZYinFPSqKXx79yPgumZXDX0x+xtrLe7ZBMBLOkfwZUlX9bvZfM1ARuWjTN7XCMIT3JxxNfLWdW\nfjp3PlnB+/uDviHexBlL+mdgTWU97+xt4JtXlZKS6HU7HGMAyEhN4Fd3lFOQlcrtv9jIpoNNbodk\nIpAl/VHqHxjkn3+7i6KcVL5kNc9NhMlJT+Lpry1m0sRkvvL4RrbWtrgdkokwlvRH6dmNNew93s49\n180l0Wd/fSby5E9M5uk/X0xmWgJ/9ugGNh1sdjskE0EiorRytDjR3ccDv6+kvCibT8+f7HY4xgCn\nXph9WVkhj6+vZtkj73Hr4hn86Mb54xyZiUTWVR2FB36/l8aOXv7+hrNsxo6JeFlpidx52Uxy05N4\n8r2DvGzz+A2W9IO2attRHl9fzZcumMHCgky3wzEmKBOSE/jzS2cyPTuVbz7zEf/31d30Dwy6HZZx\nkSX9IFQdP8FfP7+Fcwsz+YcbYq4ytIlxyQlevnpxEcvOn87Da/bxhUfe53BLl9thGZdY0h/Bie4+\n7nxyEymJXn5+66fs4q2JSgleDz/+04U8uGwRu4+2cf2D7/DYump6+m1BlnhjF3JPo7Wrj7/41SYO\nNnby1NcWMzkj2e2QjBmTpYumsbAgkx++tI3/9cpOHl9Xzd3XzuaGc6aQ5Ivte05OdcF7qFhfaN66\nradwsLGD//HwejYeaOL/3byQC6x0sokRxblpPPW1xTx5RzlZaQl87/ktnP9Pb/C3L2xl3d4GW44x\nxllPfxjr9jbwzWc+RIEn71hsCd/EHBHh0tI8Lp6Vy9q99azcfIRXth5hRUUNPo+QNyGJaZkp5E1I\nIis1kay0RDJSEkhN9OIJmLkW673iWBRU0heRJcCDgBd4VFV/POT1JOCXwKeARuALqnrAee0HwB3A\nAPAtVX0tZNGH2LbaVv7l9T28XVnPzLw0Hr/tfIpy09wOy5iw8XiEK+bkc8WcfLr7Bli3t4GPapp5\nfUcdO4+20Xnwj3v9XhEmJPuYmJLAxJQE9tW3MzUzhYIs/2N6dioTk91bW2JgUGnt6qOpo4fG9l6a\nO3tp7uyjpbOP9/Y10N0/SF//IH0DgwwonPz68nmFJJ+X5AQPLV29TJ6YzOSJyUzJTGFaZkpMXcsT\nVT19AxEvUAlcC9QCG4HlqrozoM1fAgtV9Rsisgz4nKp+wVkE/RmgHJgKvAHMVtVT/v5YVlamFRUV\nYzyt4B1r7ebN3XW8vqOOtyvryUxN4C8un8WXLywaVV2dYMcLjYkmXb0DNHf20tTRS1t3Hye6+2nr\n6qO1u4+2rj46egboGjIclJGSQGF2KgVZKUzN9D8mT0wmOy2RnPREMlMTSE30kZLgxev54/tdBgaV\nnv4Bnnr/ED39g/T0D9DTN0h3/wDdfQN09Q3S1ets9w6QlZZIS2dgcu9l8BQpzesRkn0eEnweEr2e\nj39jUZT+AaW7f5CevgH6h3yAR2BKRgozclIpzk37+DEjJ5WCrFSSEyLjWoiIbFLVspHaBdPTLweq\nVHW/88HPAkuBnQFtlgI/crZfAH4m/ruXlgLPqmoPUC0iVc7nvRfsiZyJwUGlb3CQnv5BuvsG6O4d\npKXL/4Pb1NHLgYYOqurbqaxrp+p4OwDTs1P4zjWl3H5Jsas9FWMiSUqil5REf+IezvLy6bR09lHb\n3EVNcyc1TZ3UNHdyqKmLyroTrNlT/4kvhUAJXkGc/vaAKgOnythDJHo9pCR6mdKbTFZqInMmTyAr\nNZGctESy0xLJTk8iO9XZdoam/vvD2qBuqrzp3Kkca+3mWFs3R1q6OdTYwcGmTg40dvLyliO0dff/\nUXv/bwTJTMlIZvLEFHLS/3DcCUk+0pN9pCX5v+QSfR6SfB4SvB58HsHrkXG/0TOYpD8NqAl4Xgss\nPlUbVe0XkVYgx9n//pD3hqUW8ZaaFm7+93fpH1RG+OUFj0Bhdiol+en8j/Omcc1ZkyjNT7e7bI0Z\nJREhK80/5r+gIOMTr6v6h1uOtXXT1N5LQ0cvrZ29dPUN0Nk7QE//H24UE/z3FCT5PGw73EqSz7+d\n5POQnOAlJcFLUoI/2fs8/uGW0VxTCPb/d2qij5l56czMSx/2fJo7+6hu6KCmqZODjf4vuaOtXew+\n5v+S6+wd3YVwj4BHBI8I1y+YzE+XnTuq949WRFzIFZE7gTudp+0isifcx6wG3gYeG/tH5QINY/+Y\nqGPnHV+GPe9bXQgk3Mcf5jPH7d/8QeDB5Wf89qDK/gaT9A8D0wOeFzj7hmtTKyI+IAP/Bd1g3ouq\nPgI8EkzAkUZEKoIZR4s1dt7xJV7PG2Lv3IO5JL0RKBWRYhFJBJYBK4e0WQnc5mzfDKxW/xXilcAy\nEUkSkWKgFPggNKEbY4wZrRF7+s4Y/V3Aa/inbD6uqjtE5D6gQlVX4h8ledK5UNuE/4sBp91z+C/6\n9gN/dbqZO8YYY8JrxCmb5vRE5E5neCqu2HnHl3g9b4i9c7ekb4wxcSR2bjMzxhgzIkv6YyAiS0Rk\nj4hUicg9bscTLiLyuIgcF5H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"text/plain": [
"<matplotlib.figure.Figure at 0x7f62e33e2690>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7f62e605add0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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riNg/xfMbJPVK6u3r66t3d8eo3NiiniGXyptB5c3BzKwZ1RXoklrIwvzWiLh9qnUiYmNE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\nSUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f62e37d2e90>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7f62e3957990>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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Hz3c5XY4JsWBCvxioD7jf4J824Tyq6gG6gFz/Y2Uisl9E/iAi18+wXhNCr51u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\nRK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f62e3616150>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7f62e309f0d0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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cc5+4wIA4TwJXnc4H6Qn21TWwurSG6cOy/S7ltE0Y2IdbpxZQdaiRh94rYVe1Ll4TCXXB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\nUSCIiAgA/x87/Z1/gQlVjwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f62e2fec050>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for i in df_impacts.columns:\n",
" sns.boxplot(df_impacts[i])\n",
" plt.show()\n",
" plt.clf()\n",
" sns.distplot(df_impacts[i])\n",
" plt.show()\n",
" plt.clf()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"568\n"
]
},
{
"data": {
"text/plain": [
"Possible Impacts 52.000\n",
"Cumulative Impact Probability 0.065\n",
"Asteroid Velocity 5.100\n",
"Asteroid Diameter (km) 0.007\n",
"Cumulative Palermo Scale -3.200\n",
"Period 20.000\n",
"Name: 568, dtype: float64"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"max_prob = df_impacts['Cumulative Impact Probability'].max()\n",
"\n",
"j = 0\n",
"for i in df_impacts['Cumulative Impact Probability']:\n",
" if i == max_prob:\n",
" print(j)\n",
" j += 1\n",
"\n",
"df_impacts.loc[568]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2011 SR52\n"
]
},
{
"data": {
"text/plain": [
"Possible Impacts 4.000000e+00\n",
"Cumulative Impact Probability 7.600000e-10\n",
"Asteroid Velocity 1.355000e+01\n",
"Asteroid Diameter (km) 2.579000e+00\n",
"Cumulative Palermo Scale -4.350000e+00\n",
"Period 8.100000e+01\n",
"Name: 173, dtype: float64"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#the biggest asteroid\n",
"max_diam = df_impacts['Asteroid Diameter (km)'].max()\n",
"\n",
"j = 0\n",
"for i in df_impacts['Asteroid Diameter (km)']:\n",
" if i == max_diam:\n",
" break\n",
" j += 1\n",
"\n",
"print(names[j])\n",
"df_impacts.loc[j]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2008 EM68\n"
]
},
{
"data": {
"text/plain": [
"Possible Impacts 1144.000000\n",
"Cumulative Impact Probability 0.000013\n",
"Asteroid Velocity 14.540000\n",
"Asteroid Diameter (km) 0.010000\n",
"Cumulative Palermo Scale -5.310000\n",
"Period 98.000000\n",
"Name: 22, dtype: float64"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#the asteroid with higher number of possible impacts\n",
"max_impacts = df_impacts['Possible Impacts'].max()\n",
"\n",
"j = 0\n",
"for i in df_impacts['Possible Impacts']:\n",
" if i == max_impacts:\n",
" break\n",
" j += 1\n",
"\n",
"print(names[j]) \n",
"df_impacts.loc[j]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12+"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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