Exo_Planet_Data_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://www.kaggle.com/mrisdal/open-exoplanet-catalogue/version/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Nasa continues to report new findings of planets as reported recently in the New York Times:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"700\"\n",
       "            height=\"200\"\n",
       "            src=\"https://www.nytimes.com/2019/01/07/science/exoplanets-tess-nasa.html?action=click&module=Well&pgtype=Homepage&section=Science\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x7fbc867e1eb8>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import IFrame\n",
    "\n",
    "\n",
    "IFrame(src= 'https://www.nytimes.com/2019/01/07/science/exoplanets-tess-nasa.html?action=click&module=Well&pgtype=Homepage&section=Science', width = 700, height = 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "planet = pd.read_csv('exos_new-imp2.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PlanetIdentifier</th>\n",
       "      <th>TypeFlag</th>\n",
       "      <th>PlanetaryMassJpt</th>\n",
       "      <th>RadiusJpt</th>\n",
       "      <th>PeriodDays</th>\n",
       "      <th>SemiMajorAxisAU</th>\n",
       "      <th>Eccentricity</th>\n",
       "      <th>PeriastronDeg</th>\n",
       "      <th>LongitudeDeg</th>\n",
       "      <th>AscendingNodeDeg</th>\n",
       "      <th>...</th>\n",
       "      <th>RightAscension</th>\n",
       "      <th>Declination</th>\n",
       "      <th>DistFromSunParsec</th>\n",
       "      <th>HostStarMassSlrMass</th>\n",
       "      <th>HostStarRadiusSlrRad</th>\n",
       "      <th>HostStarMetallicity</th>\n",
       "      <th>HostStarTempK</th>\n",
       "      <th>HostStarAgeGyr</th>\n",
       "      <th>ListsPlanetIsOn</th>\n",
       "      <th>Probability_of_life</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11 Com b</td>\n",
       "      <td>0</td>\n",
       "      <td>19.400</td>\n",
       "      <td>3.179701</td>\n",
       "      <td>326.03</td>\n",
       "      <td>1.290</td>\n",
       "      <td>0.231</td>\n",
       "      <td>94.800</td>\n",
       "      <td>412.169515</td>\n",
       "      <td>-84.182702</td>\n",
       "      <td>...</td>\n",
       "      <td>12 20 43</td>\n",
       "      <td>+17 47 34</td>\n",
       "      <td>88.900</td>\n",
       "      <td>2.70</td>\n",
       "      <td>19.000</td>\n",
       "      <td>-0.350</td>\n",
       "      <td>4742.0</td>\n",
       "      <td>5.246499</td>\n",
       "      <td>Confirmed planets</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11 UMi b</td>\n",
       "      <td>0</td>\n",
       "      <td>11.200</td>\n",
       "      <td>3.117097</td>\n",
       "      <td>516.22</td>\n",
       "      <td>1.540</td>\n",
       "      <td>0.080</td>\n",
       "      <td>117.630</td>\n",
       "      <td>433.233616</td>\n",
       "      <td>237.056359</td>\n",
       "      <td>...</td>\n",
       "      <td>15 17 05.88899</td>\n",
       "      <td>+71 49 26.0466</td>\n",
       "      <td>122.100</td>\n",
       "      <td>1.80</td>\n",
       "      <td>24.080</td>\n",
       "      <td>0.040</td>\n",
       "      <td>4340.0</td>\n",
       "      <td>1.311730</td>\n",
       "      <td>Confirmed planets</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14 And b</td>\n",
       "      <td>0</td>\n",
       "      <td>4.800</td>\n",
       "      <td>2.063338</td>\n",
       "      <td>185.84</td>\n",
       "      <td>0.830</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>573.470239</td>\n",
       "      <td>-95.384355</td>\n",
       "      <td>...</td>\n",
       "      <td>23 31 17.41346</td>\n",
       "      <td>+39 14 10.3092</td>\n",
       "      <td>79.200</td>\n",
       "      <td>2.20</td>\n",
       "      <td>11.000</td>\n",
       "      <td>-0.240</td>\n",
       "      <td>4813.0</td>\n",
       "      <td>-2.180942</td>\n",
       "      <td>Confirmed planets</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14 Her b</td>\n",
       "      <td>0</td>\n",
       "      <td>4.975</td>\n",
       "      <td>0.538465</td>\n",
       "      <td>1766.00</td>\n",
       "      <td>2.864</td>\n",
       "      <td>0.359</td>\n",
       "      <td>22.230</td>\n",
       "      <td>225.775235</td>\n",
       "      <td>106.852163</td>\n",
       "      <td>...</td>\n",
       "      <td>16 10 23</td>\n",
       "      <td>+43 49 18</td>\n",
       "      <td>18.100</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.708</td>\n",
       "      <td>0.430</td>\n",
       "      <td>5311.0</td>\n",
       "      <td>9.272300</td>\n",
       "      <td>Confirmed planets</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14 Her c</td>\n",
       "      <td>0</td>\n",
       "      <td>7.679</td>\n",
       "      <td>0.333758</td>\n",
       "      <td>9886.00</td>\n",
       "      <td>9.037</td>\n",
       "      <td>0.184</td>\n",
       "      <td>189.076</td>\n",
       "      <td>123.730898</td>\n",
       "      <td>332.066928</td>\n",
       "      <td>...</td>\n",
       "      <td>16 10 23</td>\n",
       "      <td>+43 49 18</td>\n",
       "      <td>18.100</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.708</td>\n",
       "      <td>0.430</td>\n",
       "      <td>5311.0</td>\n",
       "      <td>-0.323214</td>\n",
       "      <td>Controversial</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>16 Cygni B b</td>\n",
       "      <td>2</td>\n",
       "      <td>1.770</td>\n",
       "      <td>0.495553</td>\n",
       "      <td>799.50</td>\n",
       "      <td>1.720</td>\n",
       "      <td>0.689</td>\n",
       "      <td>83.400</td>\n",
       "      <td>138.845836</td>\n",
       "      <td>12.082893</td>\n",
       "      <td>...</td>\n",
       "      <td>19 41 48.95343</td>\n",
       "      <td>+50 31 30.2153</td>\n",
       "      <td>21.146</td>\n",
       "      <td>1.07</td>\n",
       "      <td>1.127</td>\n",
       "      <td>0.052</td>\n",
       "      <td>5750.0</td>\n",
       "      <td>6.800000</td>\n",
       "      <td>Confirmed planets, Planets in binary systems, ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>18 Del b</td>\n",
       "      <td>0</td>\n",
       "      <td>10.300</td>\n",
       "      <td>1.278876</td>\n",
       "      <td>993.30</td>\n",
       "      <td>2.600</td>\n",
       "      <td>0.080</td>\n",
       "      <td>166.100</td>\n",
       "      <td>371.034126</td>\n",
       "      <td>44.065829</td>\n",
       "      <td>...</td>\n",
       "      <td>20 58 25.93397</td>\n",
       "      <td>+10 50 21.4289</td>\n",
       "      <td>75.300</td>\n",
       "      <td>2.30</td>\n",
       "      <td>8.500</td>\n",
       "      <td>-0.052</td>\n",
       "      <td>4979.0</td>\n",
       "      <td>0.730065</td>\n",
       "      <td>Confirmed planets</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  PlanetIdentifier  TypeFlag  PlanetaryMassJpt  RadiusJpt  PeriodDays  \\\n",
       "0         11 Com b         0            19.400   3.179701      326.03   \n",
       "1         11 UMi b         0            11.200   3.117097      516.22   \n",
       "2         14 And b         0             4.800   2.063338      185.84   \n",
       "3         14 Her b         0             4.975   0.538465     1766.00   \n",
       "4         14 Her c         0             7.679   0.333758     9886.00   \n",
       "5     16 Cygni B b         2             1.770   0.495553      799.50   \n",
       "6         18 Del b         0            10.300   1.278876      993.30   \n",
       "\n",
       "   SemiMajorAxisAU  Eccentricity  PeriastronDeg  LongitudeDeg  \\\n",
       "0            1.290         0.231         94.800    412.169515   \n",
       "1            1.540         0.080        117.630    433.233616   \n",
       "2            0.830         0.000          0.000    573.470239   \n",
       "3            2.864         0.359         22.230    225.775235   \n",
       "4            9.037         0.184        189.076    123.730898   \n",
       "5            1.720         0.689         83.400    138.845836   \n",
       "6            2.600         0.080        166.100    371.034126   \n",
       "\n",
       "   AscendingNodeDeg         ...           RightAscension     Declination  \\\n",
       "0        -84.182702         ...                 12 20 43       +17 47 34   \n",
       "1        237.056359         ...           15 17 05.88899  +71 49 26.0466   \n",
       "2        -95.384355         ...           23 31 17.41346  +39 14 10.3092   \n",
       "3        106.852163         ...                 16 10 23       +43 49 18   \n",
       "4        332.066928         ...                 16 10 23       +43 49 18   \n",
       "5         12.082893         ...           19 41 48.95343  +50 31 30.2153   \n",
       "6         44.065829         ...           20 58 25.93397  +10 50 21.4289   \n",
       "\n",
       "   DistFromSunParsec HostStarMassSlrMass  HostStarRadiusSlrRad  \\\n",
       "0             88.900                2.70                19.000   \n",
       "1            122.100                1.80                24.080   \n",
       "2             79.200                2.20                11.000   \n",
       "3             18.100                1.00                 0.708   \n",
       "4             18.100                1.00                 0.708   \n",
       "5             21.146                1.07                 1.127   \n",
       "6             75.300                2.30                 8.500   \n",
       "\n",
       "  HostStarMetallicity HostStarTempK HostStarAgeGyr  \\\n",
       "0              -0.350        4742.0       5.246499   \n",
       "1               0.040        4340.0       1.311730   \n",
       "2              -0.240        4813.0      -2.180942   \n",
       "3               0.430        5311.0       9.272300   \n",
       "4               0.430        5311.0      -0.323214   \n",
       "5               0.052        5750.0       6.800000   \n",
       "6              -0.052        4979.0       0.730065   \n",
       "\n",
       "                                     ListsPlanetIsOn  Probability_of_life  \n",
       "0                                  Confirmed planets                    0  \n",
       "1                                  Confirmed planets                    0  \n",
       "2                                  Confirmed planets                    0  \n",
       "3                                  Confirmed planets                    0  \n",
       "4                                      Controversial                    0  \n",
       "5  Confirmed planets, Planets in binary systems, ...                    1  \n",
       "6                                  Confirmed planets                    0  \n",
       "\n",
       "[7 rows x 26 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planet.head(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3584, 26)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planet.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PlanetIdentifier', 'TypeFlag', 'PlanetaryMassJpt', 'RadiusJpt',\n",
       "       'PeriodDays', 'SemiMajorAxisAU', 'Eccentricity', 'PeriastronDeg',\n",
       "       'LongitudeDeg', 'AscendingNodeDeg', 'InclinationDeg', 'SurfaceTempK',\n",
       "       'AgeGyr', 'DiscoveryMethod', 'DiscoveryYear', 'LastUpdated',\n",
       "       'RightAscension', 'Declination', 'DistFromSunParsec',\n",
       "       'HostStarMassSlrMass', 'HostStarRadiusSlrRad', 'HostStarMetallicity',\n",
       "       'HostStarTempK', 'HostStarAgeGyr', 'ListsPlanetIsOn',\n",
       "       'Probability_of_life'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planet.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3584 entries, 0 to 3583\n",
      "Data columns (total 26 columns):\n",
      "PlanetIdentifier        3584 non-null object\n",
      "TypeFlag                3584 non-null int64\n",
      "PlanetaryMassJpt        3584 non-null float64\n",
      "RadiusJpt               3584 non-null float64\n",
      "PeriodDays              3584 non-null float64\n",
      "SemiMajorAxisAU         3584 non-null float64\n",
      "Eccentricity            3584 non-null float64\n",
      "PeriastronDeg           3584 non-null float64\n",
      "LongitudeDeg            3584 non-null float64\n",
      "AscendingNodeDeg        3584 non-null float64\n",
      "InclinationDeg          3584 non-null float64\n",
      "SurfaceTempK            3584 non-null float64\n",
      "AgeGyr                  3584 non-null float64\n",
      "DiscoveryMethod         3521 non-null object\n",
      "DiscoveryYear           3584 non-null float64\n",
      "LastUpdated             3576 non-null object\n",
      "RightAscension          3574 non-null object\n",
      "Declination             3574 non-null object\n",
      "DistFromSunParsec       3584 non-null float64\n",
      "HostStarMassSlrMass     3584 non-null float64\n",
      "HostStarRadiusSlrRad    3584 non-null float64\n",
      "HostStarMetallicity     3584 non-null float64\n",
      "HostStarTempK           3584 non-null float64\n",
      "HostStarAgeGyr          3584 non-null float64\n",
      "ListsPlanetIsOn         3584 non-null object\n",
      "Probability_of_life     3584 non-null int64\n",
      "dtypes: float64(18), int64(2), object(6)\n",
      "memory usage: 728.1+ KB\n"
     ]
    }
   ],
   "source": [
    "planet.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3584"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(planet.PlanetaryMassJpt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "mass = planet['PlanetaryMassJpt'].dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(mass, bins = 100)\n",
    "plt.xlabel('Planetary Mass in relation to Jupyter')\n",
    "plt.ylabel('Counts of Planets')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    3584.000000\n",
       "mean        2.605599\n",
       "std        10.124907\n",
       "min       -31.843139\n",
       "25%        -0.586597\n",
       "50%         1.220000\n",
       "75%         6.500000\n",
       "max       263.000000\n",
       "Name: PlanetaryMassJpt, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planet.PlanetaryMassJpt.describe()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Most of the planets we are seeing are larger than Jupyter and as a result they will\n",
    "not be able to support life.  This is concluded due to the mean value of 2.89.  Large\n",
    "planets will not be able to support life due to increased radiation.  Also, our telescopes\n",
    "may not be sensitive enough to find smaller planets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3584"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(planet.RadiusJpt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "radius = planet['RadiusJpt'].dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3584"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(radius)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(radius, bins = 50)\n",
    "plt.xlabel('Radius of Planet in relation to Jupyter')\n",
    "plt.ylabel('Counts of Planets')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    3584.000000\n",
       "mean        0.443712\n",
       "std         0.608361\n",
       "min        -3.460331\n",
       "25%         0.142265\n",
       "50%         0.228740\n",
       "75%         0.560455\n",
       "max         7.680271\n",
       "Name: RadiusJpt, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "radius.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PlanetIdentifier</th>\n",
       "      <th>TypeFlag</th>\n",
       "      <th>PlanetaryMassJpt</th>\n",
       "      <th>RadiusJpt</th>\n",
       "      <th>PeriodDays</th>\n",
       "      <th>SemiMajorAxisAU</th>\n",
       "      <th>Eccentricity</th>\n",
       "      <th>PeriastronDeg</th>\n",
       "      <th>LongitudeDeg</th>\n",
       "      <th>AscendingNodeDeg</th>\n",
       "      <th>...</th>\n",
       "      <th>RightAscension</th>\n",
       "      <th>Declination</th>\n",
       "      <th>DistFromSunParsec</th>\n",
       "      <th>HostStarMassSlrMass</th>\n",
       "      <th>HostStarRadiusSlrRad</th>\n",
       "      <th>HostStarMetallicity</th>\n",
       "      <th>HostStarTempK</th>\n",
       "      <th>HostStarAgeGyr</th>\n",
       "      <th>ListsPlanetIsOn</th>\n",
       "      <th>Probability_of_life</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>Earth</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003146</td>\n",
       "      <td>0.09113</td>\n",
       "      <td>365.2422</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.016732</td>\n",
       "      <td>102.930059</td>\n",
       "      <td>100.466916</td>\n",
       "      <td>-5.112604</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2254.262308</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000e-08</td>\n",
       "      <td>5778.0</td>\n",
       "      <td>4.57</td>\n",
       "      <td>Solar System</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    PlanetIdentifier  TypeFlag  PlanetaryMassJpt  RadiusJpt  PeriodDays  \\\n",
       "104            Earth         0          0.003146    0.09113    365.2422   \n",
       "\n",
       "     SemiMajorAxisAU  Eccentricity  PeriastronDeg  LongitudeDeg  \\\n",
       "104              1.0      0.016732     102.930059    100.466916   \n",
       "\n",
       "     AscendingNodeDeg         ...           RightAscension  Declination  \\\n",
       "104         -5.112604         ...                      NaN          NaN   \n",
       "\n",
       "     DistFromSunParsec HostStarMassSlrMass  HostStarRadiusSlrRad  \\\n",
       "104        2254.262308                 1.0                   1.0   \n",
       "\n",
       "    HostStarMetallicity HostStarTempK HostStarAgeGyr  ListsPlanetIsOn  \\\n",
       "104        1.000000e-08        5778.0           4.57     Solar System   \n",
       "\n",
       "     Probability_of_life  \n",
       "104                    1  \n",
       "\n",
       "[1 rows x 26 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planet[planet.PlanetIdentifier == 'Earth']"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Most of the planets found on average have a much larger radius than Earth (Earth is 0.09113 and the mean is 0.037119).  We are expecting to find life on a planet that is from one half to twice the size of Earth."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      3584.000000\n",
       "mean        566.043417\n",
       "std        7538.172337\n",
       "min      -18307.599127\n",
       "25%           4.639546\n",
       "50%          13.163515\n",
       "75%          52.637830\n",
       "max      320000.000000\n",
       "Name: PeriodDays, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planet.PeriodDays.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3584"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(planet.PeriodDays)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "days = planet.PeriodDays.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3584"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(days)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(days, bins=len(days))\n",
    "plt.xlim(0,1000)\n",
    "plt.xlabel('Period of Days')\n",
    "plt.ylabel('Counts of Planets')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      3584.000000\n",
       "mean        566.043417\n",
       "std        7538.172337\n",
       "min      -18307.599127\n",
       "25%           4.639546\n",
       "50%          13.163515\n",
       "75%          52.637830\n",
       "max      320000.000000\n",
       "Name: PeriodDays, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "days.describe()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "We expect to find life on planets that are located similar to Venus to Mars from their sun.  The sun would also have to have similar proportions to our as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    3584.000000\n",
       "mean      769.007793\n",
       "std       580.083926\n",
       "min     -4720.494459\n",
       "25%       381.537939\n",
       "50%       733.879200\n",
       "75%      1101.434164\n",
       "max      7112.000000\n",
       "Name: SurfaceTempK, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planet.SurfaceTempK.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3584"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(planet.SurfaceTempK)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp = planet.SurfaceTempK.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3584"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEKCAYAAADjDHn2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAGcZJREFUeJzt3XuULWV55/Hvz4OAyl0OhBHIATWoIEFsDOIlirok6AJN1EDU4IieGU2MVwgsNNGV6Bh1DOo46hlFUBkUEaIRlYsK6FKBwx0EBRERZTgHjYAxQZFn/tjVsGm7u6q79605389avXbVu6vqffo93f2ct6r2U6kqJEmazwPGHYAkafKZLCRJrUwWkqRWJgtJUiuThSSplclCktTKZCFJamWykCS1MllIklptNO4Auth2221r1apV4w5DkpaViy666NaqWjmIYy2LZLFq1SrWrl077jAkaVlJ8qNBHcvTUJKkViYLSVIrk4UkqZXJQpLUymQhSWplspAktTJZSJJamSwkSa1MFpKkViaLZWTVUacv6j1JWiqThSSplclCktTKZCFJamWykCS1MllIklqZLCRJrUwWkqRWQ0sWSY5Lsi7JlTPaX5Pke0muSvKuYfUvSRqcYc4sjgcO6G9I8nTgYGDPqtodeM8Q+5ckDcjQkkVVnQf8fEbzq4B3VtWdzTbrhtW/JGlwRn3N4g+ApyQ5P8m5SfYZcf+SpEXYaAz9bQ3sC+wDnJxk16qqmRsmWQ2sBth5551HGqQk6b5GPbO4CTi1ei4A7ga2nW3DqlpTVVNVNbVy5cqRBilJuq9RJ4t/AfYHSPIHwMbArSOOQZK0QEM7DZXkJOBpwLZJbgL+HjgOOK65nfbXwGGznYKSJE2WoSWLqjp0jrdeMqw+JUnD4Se4JUmtTBaSpFYmC0lSK5OFJKmVyUKS1MpkIUlqZbKQJLUyWSzQqqNOH3cIkjRyJgtJUiuThSSplclCktTKZCFJamWykCS1MllIklqZLCRJrUwWkqRWQ0sWSY5Lsq55Kt7M996UpJLM+vxtSdJkGebM4njggJmNSXYCngXcOMS+JUkDNLRkUVXnAT+f5a1/Bo4EfPa2JC0TI71mkeQg4CdVdVmHbVcnWZtk7fr160cQ3WjrPlljStJyMrJkkeTBwDHA33XZvqrWVNVUVU2tXLlyuMFJkuY1ypnFw4FdgMuS3ADsCFyc5PdGGIMkaRE2GlVHVXUFsN30epMwpqrq1lHFIElanGHeOnsS8G1gtyQ3JTl8WH1JkoZraDOLqjq05f1Vw+pbkjRYfoJbktTKZCFJamWykCS1MllIklqZLCRJrUwWkqRWJotlzPpSkkbFZCFJamWykCS1MllIklqZLCRJrUwWkqRWJgtJUiuThSSpVWuySPLCJJs3y29OcmqSvYcfmiRpUnSZWbylqu5I8mTg2cAJwIfadkpyXJJ1Sa7sa3t3kmuSXJ7ktCRbLT50SdKodEkWv21enwN8qKo+D2zcYb/jgQNmtJ0F7FFVewLfB47uGKckaYy6JIufJPkI8CLgS0k26bJfVZ0H/HxG25lVdVez+h1gxwXGK0kagy7J4kXAGcABVfULYBvgiAH0/XLgywM4jiRpyLoki49U1alVdS1AVd0MvHQpnSY5BrgLOHGebVYnWZtk7fr165fSXatJL8jXH9+kxyrp/qlLsti9fyXJCuDxi+0wyWHAc4EXV1XNtV1VramqqaqaWrly5WK7kyQNwJzJIsnRSe4A9kxye5I7mvV1wOcX01mSA4C/BQ6qql8tKmJJ0sjNmSyq6n9U1ebAu6tqi6ravPl6aFW13sWU5CTg28BuSW5Kcjjwv4DNgbOSXJrkw4P6RiRJw7NRh22OSfISYJeq+ockOwE7VNUF8+1UVYfO0vyxxQQpSRqvLtcsPgg8EfiLZv2XTZskaQPRZWbxR1W1d5JLAKrq35J0+VCeJOl+osvM4jfNHVAFkGQlcPdQo5IkTZQuyeL9wGnAdkneDnwTeMdQo5IkTZTW01BVdWKSi4BnAAGeV1VXDz0ySdLE6HLNAuBa4Pbp7ZPsXFU3Di0qSdJEaU0WSV4D/D1wC70KtKF3/WLP4YYmSZoUXa5ZvBbYrap2r6o9q+qxTYnxiTZbDaXF1lVa6H6rjjq98z6D3q7rMawxJWkhuiSLHwO3DTsQSdLk6nLN4nrgnCSnA3dON1bVe4cWlSRponRJFjc2XxvT7Ql5kqT7mS63zr5tFIFIkiZXl7uhVgJH0nuuxabT7VW1/xDjkiRNkC4XuE8ErgF2Ad4G3ABcOMSYJEkTpkuyeGhVfQz4TVWdW1UvB/YdclySpAnSqZBg83pzkuckeRywY9tOSY5Lsi7JlX1t2yQ5K8m1zevWi4xbkjRCXZLFPybZEngj8Cbgo8DrO+x3PHDAjLajgK9W1SOBrzbrkqQJ1+VuqC82i7cBT+964Ko6L8mqGc0HA09rlk8AzqH3TG5J0gSbM1kk+QDNMyxmU1V/s4j+tq+qm5v9b06y3SKOIUkasflOQ60FLprna6iSrE6yNsna9evXD7u7oZuuxTRfTaaZ7y2lftNc+85VM2vQtaKsPSXdv8w5s6iqE5rPWPw+cF1V/WIA/d2SZIdmVrEDsG6e/tcAawCmpqbmnOFIkoZvzplFklcAVwEfAK5JctAA+vsCcFizfBjw+QEcU5I0ZPOdhnodsHtVPRHYDzh6IQdOchLwbWC3JDclORx4J/CsJNcCz2rWJUkTbr67oX5dVesBqur6JJss5MBVdegcbz1jIceRJI3ffMlixyTvn2t9kXdDSZKWofmSxREz1od+B5QkaTLNezfUKAORJE2uLuU+JEkbOJOFJKnVfJ+z+Kfm9YWjC0eSNInmm1kcmOSBLPDzFZKk+5/5ksVXgFuBPZPcnuSO/tcRxbdks9VkaqtbtNi6RqPcbyG1n5bKOk+S5kwWVXVEVW0JnF5VW1TV5v2vI4xRkjRmXZ5ncXCS7YF9mqbzpz/ZLUnaMLTeDdVc4L4AeCHwIuCCJC8YdmCSpMnROrMA3gzsU1XrAJqy5WcDpwwzMEnS5OjyOYsHTCeKxs867idJup/oMrP4SpIzgJOa9T8HvjS8kCRJk6bLBe4jkvwp8GQgwJqqOm3okUmSJkaXmQVVdSpw6pBjkSRNqLFce0jy+iRXJbkyyUlJNh1HHJKkbkaeLJI8DPgbYKqq9gBWAIeMOg5JUncLShZJtk6y5wD63Qh4UJKNgAcDPx3AMSVJQ9LlQ3nnJNkiyTbAZcDHk7x3sR1W1U+A9wA3AjcDt1XVmbP0uzrJ2iRr169f2AfGZ9YyGkRto1VHnX6fOlNz1ZqauTzbe/PFs5Bt5lpvO9ZCjr0Ywxh/SePVZWaxZVXdDvwp8PGqejzwzMV2mGRr4GBgF+C/AA9J8pKZ21XVmqqaqqqplStXLrY7SdIAdEkWGyXZgV6pjy8OoM9nAj+sqvVV9Rt6d1ntN4DjSpKGpEuyeBtwBnBdVV2YZFfg2iX0eSOwb5IHJwnwDODqJRxPkjRkXT5ncXNV3XNRu6quX+I1i/OTnAJcDNwFXAKsWezxJEnD12Vm8YGObZ1V1d9X1aOqao+qemlV3bmU40mShmvOmUWSJ9K7lrAyyRv63tqC3mcjJEkbiPlOQ20MbNZss3lf++2Az7OQpA3InMmiqs4Fzk1yfFX9aIQxSZImTJcL3JskWQOs6t++qvYfVlCSpMnSJVl8Fvgw8FHgt8MNR5I0iboki7uq6kNDj0SSNLG63Dr7r0lenWSHJNtMfw09MknSxOiSLA4DjgC+BVzUfK0dZlDjMFvRvZmFALvsv9D3hm2QfS/0WBYQlO4/ujxWdZdRBCJJmlytySLJX87WXlWfGHw4kqRJ1OUC9z59y5vSK/x3MWCykKQNRJfTUK/pX0+yJfDJoUUkSZo4i3kG96+ARw46EEnS5OpyzeJfgWpWVwCPBk4eZlCSpMnS5ZrFe/qW7wJ+VFU3DSkeSdIEaj0N1RQUvIZe5dmtgV8vtdMkWyU5Jck1Sa5uyqFLkiZUa7JI8iLgAuCF9J7DfX6SpZYofx/wlap6FPCH+FhVSZpoXU5DHQPsU1XrAJKsBM4GTllMh0m2AJ4KvAygqn7NAGYrkqTh6XI31AOmE0XjZx33m8uuwHrg40kuSfLRJA9ZwvEkSUPW5Y/+V5KckeRlSV4GnA58eQl9bgTsDXyoqh4H/Dtw1MyNkqxOsjbJ2vXr13c6cJdaRF1rPw2iJtRCzIxrMccfRiyjstQ+rUMlDVeXC9xHAB8B9qR3fWFNVR25hD5vAm6qqvOb9VPoJY+Z/a6pqqmqmlq5cuUSupMkLdWcySLJI5I8CaCqTq2qN1TV64GfJXn4Yjusqv8H/DjJbk3TM4DvLvZ4kqThm29mcSxwxyztv2reW4rXACcmuRzYC3jHEo8nSRqi+e6GWlVVl89srKq1SVYtpdOquhSYWsoxJEmjM9/MYtN53nvQoAORJE2u+ZLFhUleObMxyeH0npYnSdpAzHca6nXAaUlezL3JYQrYGHj+sAOTJE2OOZNFVd0C7Jfk6cAeTfPpVfW1kUQmSZoYXR5+9HXg6yOIRZI0oZZStkOStIEwWUiSWt0vksUg6iqNw7Dim+24c7UtZuxmq6k1vb7Q72mx/UgarftFspAkDZfJQpLUymQhSWplspAktTJZSJJamSwkSa1MFpKkVmNLFklWJLkkyRfHFYMkqZtxzixeC1w9xv4lSR2NJVkk2RF4DvDRcfQvSVqYcc0sjgWOBO4eU/+SpAUYebJI8lxgXVXN+7S9JKuTrE2ydv369fe0D6o20P2lxtAwv4+2Yy+kHlR/nae2ulJzHa9rP13cX/79pVEZx8ziScBBSW4APg3sn+RTMzeqqjVVNVVVUytXrhx1jJKkPiNPFlV1dFXtWFWrgEOAr1XVS0YdhySpOz9nIUlq1fpY1WGqqnOAc8YZgySpnTMLSVIrk4UkqZXJQpLUymQhSWplspAktTJZSJJamSwkSa2WTbKYq57QOE1CDMO2kO9xtm3b6kAt5rj9daYWeixJi7NskoUkaXxMFpKkViYLSVIrk4UkqZXJQpLUymQhSWplspAktTJZSJJajTxZJNkpydeTXJ3kqiSvHXUMkqSFGceT8u4C3lhVFyfZHLgoyVlV9d0xxCJJ6mDkM4uqurmqLm6W7wCuBh426jgkSd2N9ZpFklXA44DzZ3lvdZK1SdZeeu2NwO/WFbIm0OjMNebD+jfoctz5tpmuHzVb3P1tw/wZaotPWk7GliySbAZ8DnhdVd0+8/2qWlNVU1U1teLBW44+QEnSPcaSLJI8kF6iOLGqTh1HDJKk7sZxN1SAjwFXV9V7R92/JGnhxjGzeBLwUmD/JJc2XweOIQ5JUkcjv3W2qr4JZNT9SpIWz09wS5JamSwkSa1MFpKkViYLSVIrk4UkqZXJQpLUymQhSWq17JOFRQWXr9n+zeb6d1xIYcH5jtGlz9kKVnaJq60wYdv30CX+Lv0sps+F8Hdtw7Tsk4UkafhMFpKkViYLSVIrk4UkqZXJQpLUymQhSWplspAktRrXY1UPSPK9JNclOWocMUiSuhvHY1VXAB8E/gR4DHBokseMOg5JUnfjmFk8Abiuqq6vql8DnwYOHkMckqSOxpEsHgb8uG/9pqZNkjShUlWj7TB5IfDsqnpFs/5S4AlV9ZoZ260GVjerewBXjjTQxdkWuHXcQXRgnIOzHGIE4xy05RLnblW1+SAOtNEgDrJANwE79a3vCPx05kZVtQZYA5BkbVVNjSa8xTPOwVoOcS6HGME4B205xTmoY43jNNSFwCOT7JJkY+AQ4AtjiEOS1NHIZxZVdVeSvwbOAFYAx1XVVaOOQ5LU3ThOQ1FVXwK+tIBd1gwrlgEzzsFaDnEuhxjBOAdtg4tz5Be4JUnLj+U+JEmtJjpZTFpZkCQ3JLkiyaXTdxkk2SbJWUmubV63btqT5P1N7Jcn2XuIcR2XZF2SK/vaFhxXksOa7a9NctiI4nxrkp80Y3ppkgP73ju6ifN7SZ7d1z7Un4skOyX5epKrk1yV5LVN+8SM6TwxTtR4Jtk0yQVJLmvifFvTvkuS85tx+UxzswtJNmnWr2veX9UW/5DjPD7JD/vGc6+mfWy/R00fK5JckuSLzfrwx7OqJvKL3sXvHwC7AhsDlwGPGXNMNwDbzmh7F3BUs3wU8E/N8oHAl4EA+wLnDzGupwJ7A1cuNi5gG+D65nXrZnnrEcT5VuBNs2z7mObffBNgl+ZnYcUofi6AHYC9m+XNge838UzMmM4T40SNZzMmmzXLDwTOb8boZOCQpv3DwKua5VcDH26WDwE+M1/8I4jzeOAFs2w/tt+jpp83AP8X+GKzPvTxnOSZxXIpC3IwcEKzfALwvL72T1TPd4CtkuwwjACq6jzg50uM69nAWVX186r6N+As4IARxDmXg4FPV9WdVfVD4Dp6PxND/7moqpur6uJm+Q7ganpVBiZmTOeJcS5jGc9mTH7ZrD6w+Spgf+CUpn3mWE6P8SnAM5JknviHHedcxvZ7lGRH4DnAR5v1MILxnORkMYllQQo4M8lF6X3CHGD7qroZer/AwHZN+7jjX2hc44z3r5up/HHTp3bmiWekcTbT9sfR+5/mRI7pjBhhwsazOWVyKbCO3h/PHwC/qKq7Zunznnia928DHjqOOKtqejzf3oznPyfZZGacM+IZxb/5scCRwN3N+kMZwXhOcrLILG3jvnXrSVW1N72KuX+V5KnzbDuJ8cPccY0r3g8BDwf2Am4G/mfTPvY4k2wGfA54XVXdPt+mc8Q09FhniXHixrOqfltVe9Gr1vAE4NHz9DkxcSbZAzgaeBSwD71TS387zjiTPBdYV1UX9TfP0+fA4pzkZNGpLMgoVdVPm9d1wGn0fvBvmT691LyuazYfd/wLjWss8VbVLc0v6d3A/+HeqfBY40zyQHp/hE+sqlOb5oka09linNTxbGL7BXAOvXP8WyWZ/pxXf5/3xNO8vyW9U5fjiPOA5nRfVdWdwMcZ/3g+CTgoyQ30ThnuT2+mMfzxHPSFl0F90fvA4PX0Lr5MX3jbfYzxPATYvG/5W/TORb6b+170fFez/BzuewHsgiHHt4r7XjheUFz0/tf0Q3oX5bZulrcZQZw79C2/nt55VIDdue8FuOvpXYwd+s9FMzafAI6d0T4xYzpPjBM1nsBKYKtm+UHAN4DnAp/lvhdkX90s/xX3vSB78nzxjyDOHfrG+1jgnZPwe9T09TTuvcA99PEc+Dcw4ME4kN5dHj8AjhlzLLs2g3sZcNV0PPTO/30VuLZ53abvh+uDTexXAFNDjO0keqccfkPvfwyHLyYu4OX0LnRdB/zXEcX5ySaOy+nVCOv/Y3dME+f3gD8Z1c8F8GR6U/LLgUubrwMnaUzniXGixhPYE7ikiedK4O/6fp8uaMbls8AmTfumzfp1zfu7tsU/5Di/1oznlcCnuPeOqbH9HvX18zTuTRZDH08/wS1JajXJ1ywkSRPCZCFJamWykCS1MllIklqZLCRJrUwWGqkkxzRVPS9vqnj+0SKOcVKz/+uHEN9pTVzXJbmtr9rofoPua1CS7J9k33HHofu3sTwpTxumJE+k90GnvavqziTb0vsgWNf9NwK2Bfarqt8fRoxV9fymr6fRq9763GH0s1BJNqp7a//MtD9wK/CdAR1P+h3OLDRKOwC3Vq90AlV1azUlVNJ7Vsi2zfJUknOa5bcmWZPkTHqfWD4T2K753/5TkrwyyYXNcwg+l+TBzX7bN7OEy5qv/Zr2lzTPLbg0yUeSrOgafJJ9kpzbFJL8cpLtm/ZvJnlvkm8k+W4T/2nNswXe2mzziGZG9cn0nolycpIHdTju25OcR6844MHpPZPgkiRnJtkuycOBVwBHTM+AknwqyfP64v5l8/rMJGcn+TS9D6BNP3thejz+dxL/JmhW/mBolM4Edkry/eYP0x933O/xwMFV9RfAQcAPqmqvqvoGcGpV7VNVf0ivTPfhzT7vB85t2vcGrkryaODP6RWE3Av4LfDiLgE01UbfB/xZVT2e3qd5/6Fvk/+oqqcAHwP+BfjvwGOB1Um2arZ5DPDBqnos8J/Af+tw3C2q6qlVdSxwHrBvVT0OOBV4Y1X9gF6p6nc3Y/Ktlm9lX+DIqnpsUyjv+fRmanvRO9NwSJfx0IbH01Aamar6ZZLHA08Bng58JslRVXV8y65fqKr/mOO9PZL8I7AVsBlwRtO+P/CXTb+/BW5L8lJ6iefCJNCrAbTud444u0fTq6dzdrPvCnolS+6JsXm9Ariiqm6B3oyJXpG2/wR+WL1nH0AvKaymV7BuvuN+um95Z+DkJL9Hr6bP9zvG3u/bVXVjs/xMetVU1/aNx4/n2lEbNpOFRqr5w30OcE6SK4DD6D2N7C7uneluOmO3f5/nkMcDz6uqy5K8jF69nLkEOKGqjl5o3M2+lzezh9nc2bze3bc8vT79ezazts50qej5jtv/vX8QeEdVfSnJM+kVM5zNPWPZnGbr/z3vP16A46rqLXMcR7qHp6E0Mkl2S/LIvqa9gB81yzfQ+18/wJ8t4LCbAzc35br7Tyl9FXhV0++KJFs0bS9Isl3Tvk2SrhfKvws8LMkTmn03TrL7AuIE2CXJPs3yocA3F3jcLYGfpDcNOKyv/Q564zDtBu4dy+fTm63M5mzgRX3Xih6aZOeFfUvaUJgsNEqbASc0F4Ev595nRgO8DXhfkm/Qu5bQ1VvoPSHuLOCavvbXAk9vZi8X0Su7/V3gzfSednh5s0+nR902F+VfALw3yWX0LhAv9Lbfq4BXNn0/BFizwOO+ld5zVM4Fbulr/zy9P/qXNBfyPwI8K8kF9BLynTMP1HxPV9Ab97ObmM4Etl/g96QNhFVnpRFI8gjglOZCsrTsOLOQJLVyZiFJauXMQpLUymQhSWplspAktTJZSJJamSwkSa1MFpKkVv8fg/Do1fInTLcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(temp, bins=len(temp))\n",
    "plt.xlim(0,4000)\n",
    "plt.xlabel('Surface Temperature')\n",
    "plt.ylabel('Counts of Planets')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "transit         2712\n",
       "RV               692\n",
       "imaging           52\n",
       "microlensing      40\n",
       "timing            25\n",
       "Name: DiscoveryMethod, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planet.DiscoveryMethod.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dmethod = planet.DiscoveryMethod.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dmethod.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "planet.DiscoveryMethod.value_counts().plot.bar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = planet[['DiscoveryMethod', 'DiscoveryYear']].dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAELCAYAAADQsFGkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAHUBJREFUeJzt3X2cXVV97/HP1wQQREmAETAJBjUKtqDQeSGKrUAUg3oJrdKCvRIVTatYvMVWot4WqpVCbUWpFm80EXjVihatREUxBXwuSHhKgASJiGR4HBvAB7Qa+7t/rDXmeGbvMzP7zJw5k/V9v177NfusvX57rT1nn/07+/EoIjAzs/I8bro7YGZm08MJwMysUE4AZmaFcgIwMyuUE4CZWaGcAMzMCuUEYGZWKCcAM7NCOQGYmRVq9nR3oJO99947Fi5cON3dMDObUW644YYfRsTAWPX6OgEsXLiQdevWTXc3zMxmFEk/GE89HwIyMyuUE4CZWaHGTACSVkt6SNKtFdP+QlJI2ju/lqQLJG2WtF7SYS11l0m6Mw/LJncxzMxsosazB3ARsKS9UNIC4CXAPS3FxwGL8rAcuDDX3RM4C3gecDhwlqS53XTczMy6M2YCiIivA1srJp0PvB1o/UGBpcAlkVwLzJG0H/BSYG1EbI2Ih4G1VCQVMzPrnUbnACQdD9wbEbe0TZoHbGl5PZTL6sqr5r1c0jpJ64aHh5t0z8zMxmHCCUDSbsC7gL+umlxRFh3KRxdGrIyIwYgYHBgY8zJWMzNrqMkewNOBA4BbJN0NzAdulLQv6Zv9gpa684H7OpSbmdk0mfCNYBGxAXjyyOucBAYj4oeS1gBvkXQp6YTvoxFxv6QrgXNaTvweC7xjom0vXPHFyvK7z335RGdlZla88VwG+kngP4FnSRqSdGqH6lcAdwGbgY8CbwaIiK3Ae4Dr8/DuXGZmZtNkzD2AiDh5jOkLW8YDOK2m3mpg9QT7Z2ZmU8R3ApuZFcoJwMysUE4AZmaFcgIwMyuUE4CZWaGcAMzMCuUEYGZWKCcAM7NCOQGYmRXKCcDMrFBOAGZmhXICMDMrlBOAmVmhnADMzArlBGBmVignADOzQjkBmJkVygnAzKxQTgBmZoVyAjAzK5QTgJlZocZMAJJWS3pI0q0tZe+TtEnSekn/LmlOy7R3SNos6Q5JL20pX5LLNktaMfmLYmZmEzGePYCLgCVtZWuB346IQ4DvAu8AkPRs4CTgt3LMP0uaJWkW8GHgOODZwMm5rpmZTZMxE0BEfB3Y2lb2lYjYll9eC8zP40uBSyPivyPi+8Bm4PA8bI6IuyLiF8Clua6ZmU2TyTgH8HrgS3l8HrClZdpQLqsrH0XScknrJK0bHh6ehO6ZmVmVrhKApHcB24BPjBRVVIsO5aMLI1ZGxGBEDA4MDHTTPTMz62B200BJy4BXAIsjYmRjPgQsaKk2H7gvj9eVm5nZNGi0ByBpCXAmcHxEPNYyaQ1wkqRdJB0ALAK+A1wPLJJ0gKSdSSeK13TXdTMz68aYewCSPgkcBewtaQg4i3TVzy7AWkkA10bEn0bEbZI+DdxOOjR0WkT8Ks/nLcCVwCxgdUTcNgXLY2Zm4zRmAoiIkyuKV3Wo/17gvRXlVwBXTKh3ZmY2ZXwnsJlZoZwAzMwK5QRgZlYoJwAzs0I5AZiZFcoJwMysUE4AZmaFcgIwMyuUE4CZWaGcAMzMCuUEYGZWKCcAM7NCOQGYmRXKCcDMrFBOAGZmhXICMDMrlBOAmVmhnADMzArlBGBmVignADOzQjkBmJkVaswEIGm1pIck3dpStqektZLuzH/n5nJJukDSZknrJR3WErMs179T0rKpWRwzMxuv8ewBXAQsaStbAVwVEYuAq/JrgOOARXlYDlwIKWEAZwHPAw4HzhpJGmZmNj3GTAAR8XVga1vxUuDiPH4xcEJL+SWRXAvMkbQf8FJgbURsjYiHgbWMTipmZtZDTc8B7BMR9wPkv0/O5fOALS31hnJZXfkokpZLWidp3fDwcMPumZnZWCb7JLAqyqJD+ejCiJURMRgRgwMDA5PaOTMz265pAngwH9oh/30olw8BC1rqzQfu61BuZmbTpGkCWAOMXMmzDLi8pfyUfDXQEcCj+RDRlcCxkubmk7/H5jIzM5sms8eqIOmTwFHA3pKGSFfznAt8WtKpwD3Aibn6FcDLgM3AY8DrACJiq6T3ANfneu+OiPYTy2Zm1kNjJoCIOLlm0uKKugGcVjOf1cDqCfXOzMymjO8ENjMrlBOAmVmhnADMzArlBGBmVignADOzQo15FdCMd/YeNeWP9rYfZmZ9xnsAZmaFcgIwMyuUE4CZWaGcAMzMCuUEYGZWKCcAM7NCOQGYmRXKCcDMrFBOAGZmhXICMDMrlBOAmVmhnADMzArlBGBmVignADOzQjkBmJkVqqsEIOnPJd0m6VZJn5T0eEkHSLpO0p2SPiVp51x3l/x6c56+cDIWwMzMmmmcACTNA04HBiPit4FZwEnAecD5EbEIeBg4NYecCjwcEc8Azs/1zMxsmnR7CGg2sKuk2cBuwP3AMcBlefrFwAl5fGl+TZ6+WJK6bN/MzBpqnAAi4l7gH4B7SBv+R4EbgEciYluuNgTMy+PzgC05dluuv1f7fCUtl7RO0rrh4eGm3TMzszF0cwhoLulb/QHAU4AnAMdVVI2RkA7TthdErIyIwYgYHBgYaNo9MzMbQzeHgF4MfD8ihiPil8BngRcAc/IhIYD5wH15fAhYAJCn7wFs7aJ9MzPrQjcJ4B7gCEm75WP5i4HbgWuAV+U6y4DL8/ia/Jo8/eqIGLUHYGZmvdHNOYDrSCdzbwQ25HmtBM4EzpC0mXSMf1UOWQXslcvPAFZ00W8zM+vS7LGr1IuIs4Cz2orvAg6vqPtz4MRu2jMzs8njO4HNzArlBGBmVignADOzQjkBmJkVygnAzKxQTgBmZoVyAjAzK5QTgJlZoZwAzMwK5QRgZlYoJwAzs0I5AZiZFcoJwMysUE4AZmaFcgIwMyuUE4CZWaGcAMzMCuUEYGZWKCcAM7NCOQGYmRXKCcDMrFBdJQBJcyRdJmmTpI2Sni9pT0lrJd2Z/87NdSXpAkmbJa2XdNjkLIKZmTXR7R7AB4EvR8SBwHOAjcAK4KqIWARclV8DHAcsysNy4MIu2zYzsy40TgCSngT8HrAKICJ+ERGPAEuBi3O1i4ET8vhS4JJIrgXmSNqvcc/NzKwr3ewBPA0YBj4u6SZJH5P0BGCfiLgfIP99cq4/D9jSEj+Uy36DpOWS1klaNzw83EX3zMysk24SwGzgMODCiDgU+CnbD/dUUUVZjCqIWBkRgxExODAw0EX3zMysk24SwBAwFBHX5deXkRLCgyOHdvLfh1rqL2iJnw/c10X7ZmbWhcYJICIeALZIelYuWgzcDqwBluWyZcDleXwNcEq+GugI4NGRQ0VmZtZ7s7uM/zPgE5J2Bu4CXkdKKp+WdCpwD3BirnsF8DJgM/BYrmtmZtOkqwQQETcDgxWTFlfUDeC0btozM7PJ4zuBzcwK5QRgZlYoJwAzs0I5AZiZFcoJwMysUE4AZmaFcgIwMyuUE4CZWaGcAMzMCuUEYGZWKCcAM7NCOQGYmRXKCcDMrFBOAGZmhXICMDMrlBOAmVmhnADMzArlBGBmVignADOzQjkBmJkVygnAzKxQXScASbMk3STpC/n1AZKuk3SnpE9J2jmX75Jfb87TF3bbtpmZNTcZewBvBTa2vD4POD8iFgEPA6fm8lOBhyPiGcD5uZ6ZmU2TrhKApPnAy4GP5dcCjgEuy1UuBk7I40vza/L0xbm+mZlNg273AD4AvB34n/x6L+CRiNiWXw8B8/L4PGALQJ7+aK7/GyQtl7RO0rrh4eEuu2dmZnUaJwBJrwAeiogbWosrqsY4pm0viFgZEYMRMTgwMNC0e2ZmNobZXcQeCRwv6WXA44EnkfYI5kianb/lzwfuy/WHgAXAkKTZwB7A1i7aNzOzLjTeA4iId0TE/IhYCJwEXB0RfwxcA7wqV1sGXJ7H1+TX5OlXR8SoPQAzM+uNqbgP4EzgDEmbScf4V+XyVcBeufwMYMUUtG1mZuPUzSGgX4uIrwJfzeN3AYdX1Pk5cOJktGdmZt3zncBmZoVyAjAzK5QTgJlZoZwAzMwK5QRgZlYoJwAzs0I5AZiZFcoJwMysUE4AZmaFcgIwMyuUE4CZWaGcAMzMCuUEYGZWKCcAM7NCOQGYmRXKCcDMrFBOAGZmhXICMDMrlBOAmVmhnADMzArlBGBmVqjGCUDSAknXSNoo6TZJb83le0paK+nO/HduLpekCyRtlrRe0mGTtRBmZjZx3ewBbAPeFhEHAUcAp0l6NrACuCoiFgFX5dcAxwGL8rAcuLCLts3MrEuNE0BE3B8RN+bxHwMbgXnAUuDiXO1i4IQ8vhS4JJJrgTmS9mvcczMz68qknAOQtBA4FLgO2Cci7oeUJIAn52rzgC0tYUO5rH1eyyWtk7RueHh4MrpnZmYVZnc7A0m7A58B/k9E/EhSbdWKshhVELESWAkwODg4avpUO/jig2unbVi2oYc9MTObWl0lAEk7kTb+n4iIz+biByXtFxH350M8D+XyIWBBS/h84L5u2u8XGw88qHbaQZs29rAnZmbj181VQAJWARsj4v0tk9YAy/L4MuDylvJT8tVARwCPjhwqMjOz3utmD+BI4DXABkk357J3AucCn5Z0KnAPcGKedgXwMmAz8Bjwui7aNjOzLjVOABHxTaqP6wMsrqgfwGlN2zMzs8nlO4HNzArlBGBmVignADOzQnV9H4A18+E/vbp22mkfOaaHPTGzUnkPwMysUN4DmEH+8Y9eUVn+tk99occ9MbMdgRPADm5oxTcqy+ef+7s97omZ9RsfAjIzK5QTgJlZoZwAzMwK5QRgZlYoJwAzs0L5KiAb5eyzz55QuZnNTN4DMDMrlPcAbFJcdfXTK8sXH/O9HvfEzMbLCcCmzb7X3FxZ/sDRz+1xT8zK5ENAZmaFcgIwMyuUE4CZWaF8DsBmjIUrvlg77e5zX97DnpjtGJwAbIfWKGmcvUf9DM9+tLL44IsPrg3ZsGxD/fzMplHPE4CkJcAHgVnAxyLi3F73wawfbDzwoMrygzZtrI2p+yU5/4qcNdHTBCBpFvBh4CXAEHC9pDURcXsv+2FWkiY/JOTfkShDr/cADgc2R8RdAJIuBZYCTgBmM1inx4TUTau7eRB8A2GvKCJ615j0KmBJRLwhv34N8LyIeEtLneXA8vzyWcAdNbPbG/jhBLvQrzH92i/H9G+/HNO//eqHmKdGxMCY0RHRswE4kXTcf+T1a4B/ajivdTtKTL/2yzH92y/H9G+/+j2mdej1fQBDwIKW1/OB+3rcBzMzo/c3gl0PLJJ0gKSdgZOANT3ug5mZ0eOTwBGxTdJbgCtJl4GujojbGs5u5Q4U06/9ckz/9ssx/duvfo/5tZ6eBDYzs/7hZwGZmRXKCcDMrFBOAGZmhXICMDMrVFFPA5V0SUSc0gf9OByIiLhe0rOBJcCmiLhikts5kPSojXlAkO65WBMRtU8bk7R77s8CYBtwJ/CViPifDjG/BzwYEXdIeiFwBLAxIiofxSnp+DzPnzdcrgOAQ4HbI2JTh3pPAgYi4ntt5YdExPqK+vsDD0XEzyUJeC1wGOlRJR+NiG3j7N85EfHOMersCxARD0gaAH4XuKPTVXGS9iC9N63v55UR8ch4+tUtSS+JiLW9aGu6tFyefl9E/IekVwMvADYCKyPil9PawUk2I64CkvQ80gblR5J2BVaw/YN5TkSMekavpPb7CwQcDVwNEBHHj9HmC0nPLro1Ir7SoM+vi4iPV5SfBRxHSr5rgecBXwVeTPowv7dmfgeSPvjXRcRPWsqXRMSXK+qfCZwMXEq6AQ/SjXcnAZdGxVNYJf0h8JfALaT/1bdJe4kHA38cEaOeayzpA6T/02zS5b2LgS8BLwJuioi/rIj5GfDTXO+Tebl/VbXcuf7nIuKEPL4U+ADpf/YC4O8i4qKaZfkA8BCwE/DaiLg+T7sxIg6riLkVODwiHpN0HvB04HPAMQAR8fqKmAvai0h3uF+SY06viPkT0jos4DxSorkNOBL4+4hYVRFzCnAW8BXg3lw8n/Rgxb+JiEsqYk4H/j0itrRPa0LSPRGx/2TMq19J+gRpXd4NeATYHfgsab1WRCybxu5Nvm5uI+7VQPpwzM7jK0kf7BeSPhCfrYm5EfgX4CjSxugo4P48/qKK+t9pGX8jcHOe/7eAFQ36fE9N+QbSPRC7AT8CnpTLdwXW18ScTnom0ueAu4GlrctZE/NdYKeK8p2BO2ti1gO75fG9SRtmgEOAb3d4b5SX5+GW+J1IybMq5iZgbv4/XwU8CHyk6n0Zqd8y/m3ggJY+3lITczOwXx4/HNgE/EH7/Npibm8ZvwF4XMvrunaG8np2CrAsD8Mj4x3Wgd2AvYCfAPvm8rnAzTUxdwBzKsrnAt+tiXmUtJfwDeDNpL2hsdbbNTXD54Gfdog7GvgQcDnwGeBc4Bkd6v8+sGceHyAlzA3Ap4D5NTF7An8NvCGvc+8CvgC8D5hbE/NS4FRgYVv56+s+A/nv7LxezsqvRf3nc++21/8buID0TDPVxLwfOHKs92Mc79fVXcV324FeDKRv/yPjN7ZNq/vAPA74c9K37Ofmsrs6tNG6kbl+5MMCPAHYULey1AwbgP8eRzs3jXNZNgC75/GFwDrgrVXzaInZRHogVHv5U0mHGuraGdkr3LWtr3Ub81vz38eTEsCu+fUsWjaobTHt7+G+pCT3n8CWTvVpSdRjLP+Gttf7kTbqp7e331LnSuCYPP6Zkf8faUNdlwCeSPpC8q/AvLHWs4rluaVtWt3yfBfYo6J8D+oT+k35c3AssIqUmL5MSk5PrIl5GHg5+YtSy3AU6TBfVcy5wMdJG77LSBvkN+b2T6yJaU22nyJ9VueT9obW1sRcQdpjupC0B/hPpENn7wYur6h/DvD1/P58D/izunWwdX0mfUmaC/yY7Unq8bRshzq8n/83r0fLgH8Dzq+JGSZ9jn8A/D1waKd1JsdUbmdGXo8VXznPJkG9HvI/8nV5/OPAYB5/JnD9GLHzc/yHqPlWnuvdkt/0vWh7wFKHD+WDwHNJG9XWYSHpGGJVzHVs/5bc+g1zjw4r5e1tr3fPH+T3U580lgCbSYdZVubhy7lsSU3MeXnlfSfpW+M7c/mewG0dYr5BSprvI31TfBfpUMVHamIq/5952lMryn5F2lv6MfALtn9j3rluxSftKTy9reyJpD2OuuS8ALiGtNH4PGljeDVpQ7Z4jPXsd3LsXwB3j1F3HXnvjJZvu6SNTF2iWUbaiF2Y3593kvaavkc6vFUV055odwKOJx12G66J+RJwdM20r9eUb2gZnw18K4/Ppf6Lwx0t4ze0Tatbp2/OfwXcO1YMaQM5cuRgDimBnN9pHSQlortIG+bT8/ry0Tyvs8Zan0lHHp7Q8v+u+/J4U/67CPgr0p70JtJRh2fWxKwh7W0eyPbtzJY8PupzM55hwgHTMZA2jhfllf064Jf5Tfoa8JxxzuPlpPMFddPvzvP8fv47spHZvcMKuQp4Yc20f60p36WmfG/g4JppV5P3YlrKZpN2m3/VYZkeRzoh+0rgVXl81hj/p5eRNmIvaZtPZb/z9OcDR+Txp+f4P6QlwbXVP2qS1os5wPNrpj0HWFRRvhPpfEan+R5EOnn+StI5msrlqIgTcBrwL2PU25/qw3PzgBd3iJtLOofztvw/PomaQx+5fqdEu+tkvAd5Xrew/Zvy/sC1LdPqvjj8P9I3912BfwROyOVHA1+riVmf/wf7kw5vLczle1Gxt0nbN3bSXukq0hfCyn7lek8BntKyjr2KdG6orv4m0kUJv8PoPbq6bceoL3ukQ61/R/rNlLq2fp/0BeX4/Lrj3uZYw4w4CTxC0hOBp5E2fkMR8WDD+eweLSdSx6i7G7BPRHy/SVsTUdcvSfOBbRHxQMW0IyPiW5PRTsv0fWi50mQ8/+dexPRrv/o1RtIzI+K7Y81zEtr5I9JhjDtI307fFBFfzFc3fTAiXl0RsxNpT3HkxPp80oUBnyedc7unIuZk0uEcSOc03pT792zSifCVbfW/ALwvIr7WVv63pL3bSbkMXtI1bUWvjoj7Je1FOo82WBFzU0Qc2rC9JwDvAZ4BHBYR85vMB2bIVUCdTGRj3hIzoasZGrYx5f2a7HYkPZd0WGEPfvNKk0eAN0fEjdMRI+lQ0qGPqvpvioibJthGk5heLf94+jZE2tvo2E4nHb5sdPpf17YjaU/Sl7PNMcHLUvPlrbMj4r/GUXcWabu1TdJs0iHYeyPi/oq6uwJExM8qps2LiHsryg8hHS6dRzocdmZEPJynfSciDp/Acs0i7Tk/VjFtwp/bink8h7QH/JGm89gR7gO4nbRL+BsknVFTX6TDOl230Qf9mux2LgL+JCKua5vXEaRzL8+ZppiPd6h/UYM2msT0avkns2+d1K3Tnf7Xte1ExFZJTwOOlrSNdGK69h6NlvkOku85kTRmTET8StKgpF/fp1K18c91f9bexki/qjb+2T8DZwPXkq42+qak4yPdS7LTeJeF7cs/auOf+/aTDjEdtcV8daz6ncyIBNBwY3YO6aRk1c07o3b9mrTRi371sh3Syavr2gsj4tq82zldMf3ar76OabjeNGnnRaTj+I+QjoN/C5gr6ZfAa6LiPoRexDRpg3S13ch9Nf8g6Qbgy0o/X1t5uKRfl388ZkQCoNnG7EbgcxFxQ/sESW+YpDZ60a9etvMlSV8knVweWZkWkK5xH3WzWQ9j+rVf/R7TZL1p0s4HgGMjYljpLu33R8SRkl5COul67DTFNGlDkvaIfHNpRFwj6ZWky4L3nGHLP6YZcQ5A0rdJ1/BWbcy2RMSCivJnAVsjYrhi2j7tJ7UatjHl/eplO3nacWx/fIRIx5vXRIfHVPQipl/71c8xTdabhu2sj4hD8vgs0qXZh+XXt0XEb01HTMM2Xk26subatvL9gb+KiDfOlOUfj5mSABptzKa6jV70q5ft2I6lh+vnatLhkatIiePeiDhD6Qq6GyPiwOmIadLGjrT84xKTdC1wvw2kqxjOJV2j+1952JjLRt1Sv6P1q0k7LTEb+ymmX/vV7zFdrjcT6dtOpMsyP0S6A3jk8Qm7UnODUi9iGrbR5HPTl8s/rvd7slacqRwavilXAmeSb+jKZfvmslG3mjdsY8r71eN26mJWTGdMv/ZrBsRM5npT286ONDT53MzkYdo7MFVvCjXPu6mbNskbzEnrV4/b6cuYfu3XDIjp1XqzO+mu3ltJd+gOky6jfG2HeU15TMM2dpjlH88wU34QZmFEnBctd8JGxAMRcR711+f/QNLble5oBNJxT6XHJFddLtWkjV70q5ft9GtMv/ar32N6td58gvT4lCXA35CehPka0j0B50xjTJM2dqTlH1vTzNHLgfRgsbeTHskwUrYP6ZvMf9TEzCU9qGwT6aFeW0m7v+eRn1syCW1Meb963E5fxvRrv2ZATK/Wm/bn31yf/z6O9ENH0xLTsI0dZvnHM0w4YDqGJm9KjjuQ9EMru7eVj3oaZq8+lBPtVy/b6eeYfu1XP8f0cP38NvmhiMD/Iv+ORH5dd9hkymOatLEjLf94hgkHTNfQ4E1p8iMqvfhQTrhfPVz+vozp1371e0wP15tDgO+Q7lD9JvlxxqQfejl9umIatrHDLP94hgkHTMfQ8E2Z0I+o9OpDOdF+9bidvozp137NgJierDedBvLvePRbTF39Upb/13FNgno9NFz5J/QjKj38UDb5cZdetdOXMf3arxkQ05P1ptNAhx9hms6YuvqlLP/IMFOeBTQr8tPzIuJuSUcBl0l6KulW9SoPSHpuRNyc434i6RXAatKPnE9GG73oVy/b6deYfu1Xv8f0ZL2RtL5mXiKddJ6WmCZtsAMt/7g0yRq9Hmjwi1ikZ5jvWzNt1I8xN2xjyvvV43b6MqZf+zUDYnq13jT5adQpj2nYxg6z/OMZJhwwHUOTN6UXbfSiX71sx8OONfRw/Wzy06hTHtOkjR1p+cczzIiHwZmZ2eSbKXcCm5nZJHMCMDMrlBOAmVmhnADMzAr1/wEHEfbEB+tc3wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(x.DiscoveryYear.astype(int)).value_counts().plot.bar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "planet.DiscoveryYear.hist(bins =200)\n",
    "plt.xlim(1990,2020)\n",
    "plt.ylabel('Number of Planets Discovered')\n",
    "plt.xlabel('Year')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Most of the planets were dicovered by Kepler Telescope which was launched into space\n",
    "in 2013.  Most of the planets were discovered between 2015 and 2017."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PlanetIdentifier</th>\n",
       "      <th>DiscoveryYear</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3419</th>\n",
       "      <td>Uranus</td>\n",
       "      <td>1781.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PlanetIdentifier  DiscoveryYear\n",
       "3419           Uranus         1781.0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planet[['PlanetIdentifier','DiscoveryYear']][planet.DiscoveryYear == planet.DiscoveryYear.min()]"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "1781 was the first year in which another planet was discovered.  This planet is Uranus."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      3584.000000\n",
       "mean        566.043417\n",
       "std        7538.172337\n",
       "min      -18307.599127\n",
       "25%           4.639546\n",
       "50%          13.163515\n",
       "75%          52.637830\n",
       "max      320000.000000\n",
       "Name: PeriodDays, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planet.PeriodDays.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PlanetIdentifier</th>\n",
       "      <th>PeriodDays</th>\n",
       "      <th>SemiMajorAxisAU</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>Fomalhaut b</td>\n",
       "      <td>320000.0</td>\n",
       "      <td>177.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    PlanetIdentifier  PeriodDays  SemiMajorAxisAU\n",
       "174      Fomalhaut b    320000.0            177.0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planet[['PlanetIdentifier','PeriodDays','SemiMajorAxisAU']][planet.PeriodDays == planet.PeriodDays.max()]"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "The planet 'Fomalhaut b' is 177 times the axis of Earth and takes 320,000 days to go around its star."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    3584.000000\n",
       "mean        1.061272\n",
       "std        18.331247\n",
       "min       -53.951694\n",
       "25%        -3.825256\n",
       "50%         0.189656\n",
       "75%         5.321737\n",
       "max       662.000000\n",
       "Name: SemiMajorAxisAU, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planet.SemiMajorAxisAU.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PlanetIdentifier</th>\n",
       "      <th>SemiMajorAxisAU</th>\n",
       "      <th>PeriodDays</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>CVSO 30 c</td>\n",
       "      <td>662.0</td>\n",
       "      <td>53538.098155</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    PlanetIdentifier  SemiMajorAxisAU    PeriodDays\n",
       "101        CVSO 30 c            662.0  53538.098155"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planet[['PlanetIdentifier','SemiMajorAxisAU','PeriodDays']][planet.SemiMajorAxisAU == planet.SemiMajorAxisAU.max()]"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "The planet 'CVSO 30 c' is 662 times the axis of Earth and takes 53,538 days to go around its star."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PlanetIdentifier</th>\n",
       "      <th>TypeFlag</th>\n",
       "      <th>PlanetaryMassJpt</th>\n",
       "      <th>RadiusJpt</th>\n",
       "      <th>PeriodDays</th>\n",
       "      <th>SemiMajorAxisAU</th>\n",
       "      <th>Eccentricity</th>\n",
       "      <th>PeriastronDeg</th>\n",
       "      <th>LongitudeDeg</th>\n",
       "      <th>AscendingNodeDeg</th>\n",
       "      <th>...</th>\n",
       "      <th>RightAscension</th>\n",
       "      <th>Declination</th>\n",
       "      <th>DistFromSunParsec</th>\n",
       "      <th>HostStarMassSlrMass</th>\n",
       "      <th>HostStarRadiusSlrRad</th>\n",
       "      <th>HostStarMetallicity</th>\n",
       "      <th>HostStarTempK</th>\n",
       "      <th>HostStarAgeGyr</th>\n",
       "      <th>ListsPlanetIsOn</th>\n",
       "      <th>Probability_of_life</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>Earth</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003146</td>\n",
       "      <td>0.09113</td>\n",
       "      <td>365.2422</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.016732</td>\n",
       "      <td>102.930059</td>\n",
       "      <td>100.466916</td>\n",
       "      <td>-5.112604</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2254.262308</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000e-08</td>\n",
       "      <td>5778.0</td>\n",
       "      <td>4.57</td>\n",
       "      <td>Solar System</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    PlanetIdentifier  TypeFlag  PlanetaryMassJpt  RadiusJpt  PeriodDays  \\\n",
       "104            Earth         0          0.003146    0.09113    365.2422   \n",
       "\n",
       "     SemiMajorAxisAU  Eccentricity  PeriastronDeg  LongitudeDeg  \\\n",
       "104              1.0      0.016732     102.930059    100.466916   \n",
       "\n",
       "     AscendingNodeDeg         ...           RightAscension  Declination  \\\n",
       "104         -5.112604         ...                      NaN          NaN   \n",
       "\n",
       "     DistFromSunParsec HostStarMassSlrMass  HostStarRadiusSlrRad  \\\n",
       "104        2254.262308                 1.0                   1.0   \n",
       "\n",
       "    HostStarMetallicity HostStarTempK HostStarAgeGyr  ListsPlanetIsOn  \\\n",
       "104        1.000000e-08        5778.0           4.57     Solar System   \n",
       "\n",
       "     Probability_of_life  \n",
       "104                    1  \n",
       "\n",
       "[1 rows x 26 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planet[planet.PlanetIdentifier =='Earth']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "SemiAU = (planet.SemiMajorAxisAU.dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "pdays =(planet.PeriodDays.dropna())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "pdaysemi = planet[['PeriodDays','SemiMajorAxisAU']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "pdaysemi.PeriodDays = np.log(pdaysemi.PeriodDays)\n",
    "pdaysemi.SemiMajorAxisAU = np.log(pdaysemi.SemiMajorAxisAU)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "pdaysemi = pdaysemi.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "g = sns.pairplot(pdaysemi, x_vars = ['PeriodDays'], y_vars = ['SemiMajorAxisAU'], kind = 'reg')\n",
    "g.fig.set_size_inches(8, 6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "The linear relationship shown in the graph proves the larger the distance of the planet from their star the longer it will take to rotate around their sun and therefore having a longer year relative to Earth.  The greater the distance from the star the less likelihood of life on that planet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    3584.000000\n",
       "mean        0.983766\n",
       "std         0.316873\n",
       "min         0.012000\n",
       "25%         0.830000\n",
       "50%         0.977000\n",
       "75%         1.110000\n",
       "max         4.500000\n",
       "Name: HostStarMassSlrMass, dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planet.HostStarMassSlrMass.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PlanetIdentifier</th>\n",
       "      <th>HostStarMassSlrMass</th>\n",
       "      <th>HostStarTempK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>443</th>\n",
       "      <td>HD 13189 b</td>\n",
       "      <td>4.5</td>\n",
       "      <td>5150.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    PlanetIdentifier  HostStarMassSlrMass  HostStarTempK\n",
       "443       HD 13189 b                  4.5         5150.0"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planet[['PlanetIdentifier','HostStarMassSlrMass','HostStarTempK']][planet.HostStarMassSlrMass == planet.HostStarMassSlrMass.max()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     3584.000000\n",
       "mean      5520.376527\n",
       "std       1210.211876\n",
       "min        540.000000\n",
       "25%       5106.000000\n",
       "50%       5636.000000\n",
       "75%       5944.250000\n",
       "max      29300.000000\n",
       "Name: HostStarTempK, dtype: float64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planet.HostStarTempK.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PlanetIdentifier</th>\n",
       "      <th>HostStarTempK</th>\n",
       "      <th>HostStarMassSlrMass</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3422</th>\n",
       "      <td>V391 Peg b</td>\n",
       "      <td>29300.0</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PlanetIdentifier  HostStarTempK  HostStarMassSlrMass\n",
       "3422       V391 Peg b        29300.0                  0.5"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planet[['PlanetIdentifier','HostStarTempK','HostStarMassSlrMass']][planet.HostStarTempK == planet.HostStarTempK.max()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "startemp = planet[['HostStarTempK', 'HostStarMassSlrMass']]\n",
    "startemp = startemp.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g1 = sns.pairplot(startemp, x_vars = ['HostStarMassSlrMass'], y_vars = ['HostStarTempK'], kind = 'reg')\n",
    "g1.fig.set_size_inches(8, 6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "The relationship between the star mass and its temperature shows that there is a limit to how hot a star will become based on its size.  These stars appear to be able to support life as they have a similar mass and temperature to our sun."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    3584.000000\n",
       "mean        2.605599\n",
       "std        10.124907\n",
       "min       -31.843139\n",
       "25%        -0.586597\n",
       "50%         1.220000\n",
       "75%         6.500000\n",
       "max       263.000000\n",
       "Name: PlanetaryMassJpt, dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planet.PlanetaryMassJpt.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PlanetIdentifier</th>\n",
       "      <th>PlanetaryMassJpt</th>\n",
       "      <th>HostStarMassSlrMass</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>511</th>\n",
       "      <td>HD 156846 b</td>\n",
       "      <td>263.0</td>\n",
       "      <td>1.35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    PlanetIdentifier  PlanetaryMassJpt  HostStarMassSlrMass\n",
       "511      HD 156846 b             263.0                 1.35"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planet[['PlanetIdentifier','PlanetaryMassJpt','HostStarMassSlrMass']][planet.PlanetaryMassJpt == planet.PlanetaryMassJpt.max()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    3584.000000\n",
       "mean        0.983766\n",
       "std         0.316873\n",
       "min         0.012000\n",
       "25%         0.830000\n",
       "50%         0.977000\n",
       "75%         1.110000\n",
       "max         4.500000\n",
       "Name: HostStarMassSlrMass, dtype: float64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planet.HostStarMassSlrMass.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PlanetIdentifier</th>\n",
       "      <th>HostStarMassSlrMass</th>\n",
       "      <th>PlanetaryMassJpt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>443</th>\n",
       "      <td>HD 13189 b</td>\n",
       "      <td>4.5</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    PlanetIdentifier  HostStarMassSlrMass  PlanetaryMassJpt\n",
       "443       HD 13189 b                  4.5              14.0"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planet[['PlanetIdentifier','HostStarMassSlrMass','PlanetaryMassJpt']][planet.HostStarMassSlrMass == planet.HostStarMassSlrMass.max()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "plstmass = planet[['HostStarMassSlrMass', 'PlanetaryMassJpt']]\n",
    "plstmass = plstmass.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g2 = sns.pairplot(plstmass, x_vars = ['HostStarMassSlrMass'], y_vars = ['PlanetaryMassJpt'], kind = 'reg')\n",
    "g2.fig.set_size_inches(8, 6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Based on planetary mass versus the star mass it appears that most of the stars are similar to Earth's sun, but the planets on average are much larger than Earth."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     3584.000000\n",
       "mean       665.757924\n",
       "std        960.075884\n",
       "min      -5123.287717\n",
       "25%         89.950000\n",
       "50%        482.880000\n",
       "75%       1025.720000\n",
       "max      16594.866384\n",
       "Name: DistFromSunParsec, dtype: float64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planet.DistFromSunParsec.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "104    2254.262308\n",
       "Name: DistFromSunParsec, dtype: float64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planet[planet.PlanetIdentifier == 'Earth'].DistFromSunParsec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3584"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parsec = planet.DistFromSunParsec.dropna()\n",
    "len(parsec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "parsec.hist(bins = len(parsec))\n",
    "plt.xlim(-2000,2020)\n",
    "plt.xlabel('Number of Planets')\n",
    "plt.ylabel('Distance from the star')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Planets that we are observing are very close to our star the sun.  Therefore, there are many opportunities to find even more planets that could possibly host life."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PlanetIdentifier</th>\n",
       "      <th>TypeFlag</th>\n",
       "      <th>PlanetaryMassJpt</th>\n",
       "      <th>RadiusJpt</th>\n",
       "      <th>PeriodDays</th>\n",
       "      <th>SemiMajorAxisAU</th>\n",
       "      <th>Eccentricity</th>\n",
       "      <th>PeriastronDeg</th>\n",
       "      <th>LongitudeDeg</th>\n",
       "      <th>AscendingNodeDeg</th>\n",
       "      <th>...</th>\n",
       "      <th>RightAscension</th>\n",
       "      <th>Declination</th>\n",
       "      <th>DistFromSunParsec</th>\n",
       "      <th>HostStarMassSlrMass</th>\n",
       "      <th>HostStarRadiusSlrRad</th>\n",
       "      <th>HostStarMetallicity</th>\n",
       "      <th>HostStarTempK</th>\n",
       "      <th>HostStarAgeGyr</th>\n",
       "      <th>ListsPlanetIsOn</th>\n",
       "      <th>Probability_of_life</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3368</th>\n",
       "      <td>Proxima Centauri b</td>\n",
       "      <td>2</td>\n",
       "      <td>0.004089</td>\n",
       "      <td>-0.322824</td>\n",
       "      <td>11.186</td>\n",
       "      <td>0.0485</td>\n",
       "      <td>0.191716</td>\n",
       "      <td>161.715439</td>\n",
       "      <td>110.0</td>\n",
       "      <td>382.144661</td>\n",
       "      <td>...</td>\n",
       "      <td>14 39 36.204</td>\n",
       "      <td>-60 50 08.23</td>\n",
       "      <td>1.295</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.141</td>\n",
       "      <td>0.090978</td>\n",
       "      <td>3050.0</td>\n",
       "      <td>5.611431</td>\n",
       "      <td>Confirmed planets, Planets in binary systems, ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        PlanetIdentifier  TypeFlag  PlanetaryMassJpt  RadiusJpt  PeriodDays  \\\n",
       "3368  Proxima Centauri b         2          0.004089  -0.322824      11.186   \n",
       "\n",
       "      SemiMajorAxisAU  Eccentricity  PeriastronDeg  LongitudeDeg  \\\n",
       "3368           0.0485      0.191716     161.715439         110.0   \n",
       "\n",
       "      AscendingNodeDeg         ...           RightAscension   Declination  \\\n",
       "3368        382.144661         ...             14 39 36.204  -60 50 08.23   \n",
       "\n",
       "      DistFromSunParsec HostStarMassSlrMass  HostStarRadiusSlrRad  \\\n",
       "3368              1.295                0.12                 0.141   \n",
       "\n",
       "     HostStarMetallicity HostStarTempK HostStarAgeGyr  \\\n",
       "3368            0.090978        3050.0       5.611431   \n",
       "\n",
       "                                        ListsPlanetIsOn  Probability_of_life  \n",
       "3368  Confirmed planets, Planets in binary systems, ...                    0  \n",
       "\n",
       "[1 rows x 26 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planet[planet.PlanetIdentifier.str.contains('Proxima')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PlanetIdentifier</th>\n",
       "      <th>TypeFlag</th>\n",
       "      <th>PlanetaryMassJpt</th>\n",
       "      <th>RadiusJpt</th>\n",
       "      <th>PeriodDays</th>\n",
       "      <th>SemiMajorAxisAU</th>\n",
       "      <th>Eccentricity</th>\n",
       "      <th>PeriastronDeg</th>\n",
       "      <th>LongitudeDeg</th>\n",
       "      <th>AscendingNodeDeg</th>\n",
       "      <th>...</th>\n",
       "      <th>RightAscension</th>\n",
       "      <th>Declination</th>\n",
       "      <th>DistFromSunParsec</th>\n",
       "      <th>HostStarMassSlrMass</th>\n",
       "      <th>HostStarRadiusSlrRad</th>\n",
       "      <th>HostStarMetallicity</th>\n",
       "      <th>HostStarTempK</th>\n",
       "      <th>HostStarAgeGyr</th>\n",
       "      <th>ListsPlanetIsOn</th>\n",
       "      <th>Probability_of_life</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>Earth</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003146</td>\n",
       "      <td>0.09113</td>\n",
       "      <td>365.2422</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.016732</td>\n",
       "      <td>102.930059</td>\n",
       "      <td>100.466916</td>\n",
       "      <td>-5.112604</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2254.262308</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000e-08</td>\n",
       "      <td>5778.0</td>\n",
       "      <td>4.57</td>\n",
       "      <td>Solar System</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    PlanetIdentifier  TypeFlag  PlanetaryMassJpt  RadiusJpt  PeriodDays  \\\n",
       "104            Earth         0          0.003146    0.09113    365.2422   \n",
       "\n",
       "     SemiMajorAxisAU  Eccentricity  PeriastronDeg  LongitudeDeg  \\\n",
       "104              1.0      0.016732     102.930059    100.466916   \n",
       "\n",
       "     AscendingNodeDeg         ...           RightAscension  Declination  \\\n",
       "104         -5.112604         ...                      NaN          NaN   \n",
       "\n",
       "     DistFromSunParsec HostStarMassSlrMass  HostStarRadiusSlrRad  \\\n",
       "104        2254.262308                 1.0                   1.0   \n",
       "\n",
       "    HostStarMetallicity HostStarTempK HostStarAgeGyr  ListsPlanetIsOn  \\\n",
       "104        1.000000e-08        5778.0           4.57     Solar System   \n",
       "\n",
       "     Probability_of_life  \n",
       "104                    1  \n",
       "\n",
       "[1 rows x 26 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planet[planet.PlanetIdentifier.str.contains('Earth')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3584 entries, 0 to 3583\n",
      "Data columns (total 26 columns):\n",
      "PlanetIdentifier        3584 non-null object\n",
      "TypeFlag                3584 non-null int64\n",
      "PlanetaryMassJpt        3584 non-null float64\n",
      "RadiusJpt               3584 non-null float64\n",
      "PeriodDays              3584 non-null float64\n",
      "SemiMajorAxisAU         3584 non-null float64\n",
      "Eccentricity            3584 non-null float64\n",
      "PeriastronDeg           3584 non-null float64\n",
      "LongitudeDeg            3584 non-null float64\n",
      "AscendingNodeDeg        3584 non-null float64\n",
      "InclinationDeg          3584 non-null float64\n",
      "SurfaceTempK            3584 non-null float64\n",
      "AgeGyr                  3584 non-null float64\n",
      "DiscoveryMethod         3521 non-null object\n",
      "DiscoveryYear           3584 non-null float64\n",
      "LastUpdated             3576 non-null object\n",
      "RightAscension          3574 non-null object\n",
      "Declination             3574 non-null object\n",
      "DistFromSunParsec       3584 non-null float64\n",
      "HostStarMassSlrMass     3584 non-null float64\n",
      "HostStarRadiusSlrRad    3584 non-null float64\n",
      "HostStarMetallicity     3584 non-null float64\n",
      "HostStarTempK           3584 non-null float64\n",
      "HostStarAgeGyr          3584 non-null float64\n",
      "ListsPlanetIsOn         3584 non-null object\n",
      "Probability_of_life     3584 non-null int64\n",
      "dtypes: float64(18), int64(2), object(6)\n",
      "memory usage: 728.1+ KB\n"
     ]
    }
   ],
   "source": [
    "planet.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "dum = pd.get_dummies(planet.DiscoveryMethod)\n",
    "planet = pd.concat([planet, dum], axis = 1)\n",
    "planet = planet.drop('DiscoveryMethod', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "dum = pd.get_dummies(planet.ListsPlanetIsOn)\n",
    "planet = pd.concat([planet, dum], axis = 1)\n",
    "planet = planet.drop('ListsPlanetIsOn', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "planet = planet.drop(['LastUpdated', 'RightAscension', 'Declination'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = planet.drop(['Probability_of_life','PlanetIdentifier'], axis = 1).values\n",
    "y = planet.Probability_of_life.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.2, random_state = 1)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Start of KNN model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier #importing KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9274755927475593\n",
      "0.9274755927475593\n",
      "0.9246861924686193\n",
      "0.9260808926080892\n",
      "0.9288702928870293\n",
      "0.9288702928870293\n",
      "0.9330543933054394\n",
      "0.9302649930264993\n",
      "0.9344490934449093\n"
     ]
    }
   ],
   "source": [
    "for i in range(1,10): \n",
    "    knn = KNeighborsClassifier(n_neighbors = i)\n",
    "    knn.fit(X_train, y_train)\n",
    "    y_pred = knn.predict(X_test)\n",
    "    \n",
    "    print(accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "We were able to see that 9 was a better value giving us the highest score after using a loop using a range 1 to 10."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors = 9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=None, n_neighbors=9, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = knn.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9344490934449093\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.98      0.96       642\n",
      "           1       0.79      0.51      0.62        75\n",
      "\n",
      "   micro avg       0.93      0.93      0.93       717\n",
      "   macro avg       0.87      0.75      0.79       717\n",
      "weighted avg       0.93      0.93      0.93       717\n",
      "\n",
      "[[632  10]\n",
      " [ 37  38]]\n"
     ]
    }
   ],
   "source": [
    "print(accuracy_score(y_test, y_pred))\n",
    "print(classification_report(y_test, y_pred))\n",
    "print(confusion_matrix(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "We are now going to use the Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "logreg = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
       "          tol=0.0001, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = logreg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9665271966527197\n",
      "[[636   6]\n",
      " [ 18  57]]\n"
     ]
    }
   ],
   "source": [
    "print(accuracy_score(y_test, y_pred))\n",
    "print(confusion_matrix(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "The accuracy score tells us whether the planet has a chance of life with 94.97% accuracy."
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "We will now be using the naive bayes model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "gnb = GaussianNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB(priors=None, var_smoothing=1e-09)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gnb.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = gnb.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9497907949790795\n",
      "[[639   3]\n",
      " [ 33  42]]\n"
     ]
    }
   ],
   "source": [
    "print(accuracy_score(y_test, y_pred))\n",
    "print(confusion_matrix(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "We will now make a model decsion tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = tree.DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9497907949790795\n",
      "[[639   3]\n",
      " [ 33  42]]\n"
     ]
    }
   ],
   "source": [
    "print(accuracy_score(y_test, y_pred))\n",
    "print(confusion_matrix(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "We will now make a new model called random forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "list=[]\n",
    "ival = range(1, 100)\n",
    "jval = range(1,100)\n",
    "for i,j in zip(ival, jval): \n",
    "    clfr = RandomForestClassifier(n_estimators = i, max_depth = j, random_state = 1)\n",
    "    clfr.fit(X_train, y_train)\n",
    "    y_pred = clfr.predict(X_test)\n",
    "    \n",
    "    list.append((accuracy_score(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "list = pd.DataFrame(list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>53</th>\n",
       "      <td>0.95537</td>\n",
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       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>0.95537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>0.95537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>0.95537</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0\n",
       "53  0.95537\n",
       "55  0.95537\n",
       "57  0.95537\n",
       "58  0.95537"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list[list == list.max()].dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "clfr = RandomForestClassifier(n_estimators = 57, max_depth =57, random_state = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=57, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=57, n_jobs=None,\n",
       "            oob_score=False, random_state=1, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clfr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = clfr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9539748953974896"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "The accuracy score that produced the best result is Random Forest Classifier with an accuracy score of 95.4%.\n",
    "This model will tell us iof a new planet can host life with a 95.4% accuracy."
   ]
  }
 ],
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