analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.cluster import KMeans\n",
    "import matplotlib.colors\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', None)  \n",
    "pd.set_option('display.expand_frame_repr', False)\n",
    "pd.set_option('max_colwidth', -1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"oec.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The dataset contains 3584 planets and 25 columns**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</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>InclinationDeg</th>\n",
       "      <th>SurfaceTempK</th>\n",
       "      <th>AgeGyr</th>\n",
       "      <th>DiscoveryMethod</th>\n",
       "      <th>DiscoveryYear</th>\n",
       "      <th>LastUpdated</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HD 143761 b</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0450</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.845800</td>\n",
       "      <td>0.2196</td>\n",
       "      <td>0.037</td>\n",
       "      <td>270.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RV</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>16/07/11</td>\n",
       "      <td>16 01 03</td>\n",
       "      <td>+33 18 13</td>\n",
       "      <td>17.236</td>\n",
       "      <td>0.889</td>\n",
       "      <td>1.362</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>5627.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Confirmed planets</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HD 143761 c</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0790</td>\n",
       "      <td>NaN</td>\n",
       "      <td>102.540000</td>\n",
       "      <td>0.4123</td>\n",
       "      <td>0.050</td>\n",
       "      <td>190.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RV</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>16/07/11</td>\n",
       "      <td>16 01 03</td>\n",
       "      <td>+33 18 13</td>\n",
       "      <td>17.236</td>\n",
       "      <td>0.889</td>\n",
       "      <td>1.362</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>5627.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Confirmed planets</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KOI-1843.03</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0014</td>\n",
       "      <td>0.054</td>\n",
       "      <td>0.176891</td>\n",
       "      <td>0.0048</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>transit</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>13/07/15</td>\n",
       "      <td>19 00 03.14</td>\n",
       "      <td>+40 13 14.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.460</td>\n",
       "      <td>0.450</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3584.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Controversial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>KOI-1843.01</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.114</td>\n",
       "      <td>4.194525</td>\n",
       "      <td>0.0390</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>89.38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>transit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19 00 03.14</td>\n",
       "      <td>+40 13 14.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.460</td>\n",
       "      <td>0.450</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3584.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Controversial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>KOI-1843.02</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.071</td>\n",
       "      <td>6.356006</td>\n",
       "      <td>0.0520</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>88.24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>transit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19 00 03.14</td>\n",
       "      <td>+40 13 14.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.460</td>\n",
       "      <td>0.450</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3584.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Controversial</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  PlanetIdentifier  TypeFlag  PlanetaryMassJpt  RadiusJpt  PeriodDays  SemiMajorAxisAU  Eccentricity  PeriastronDeg  LongitudeDeg  AscendingNodeDeg  InclinationDeg  SurfaceTempK  AgeGyr DiscoveryMethod  DiscoveryYear LastUpdated RightAscension  Declination  DistFromSunParsec  HostStarMassSlrMass  HostStarRadiusSlrRad  HostStarMetallicity  HostStarTempK  HostStarAgeGyr    ListsPlanetIsOn\n",
       "0  HD 143761 b      0         1.0450           NaN         39.845800   0.2196           0.037         270.6         NaN           NaN               NaN             NaN           NaN      RV              2016.0         16/07/11    16 01 03       +33 18 13    17.236             0.889                1.362                -0.31                 5627.0        NaN              Confirmed planets\n",
       "1  HD 143761 c      0         0.0790           NaN         102.540000  0.4123           0.050         190.0         NaN           NaN               NaN             NaN           NaN      RV              2016.0         16/07/11    16 01 03       +33 18 13    17.236             0.889                1.362                -0.31                 5627.0        NaN              Confirmed planets\n",
       "2  KOI-1843.03      0         0.0014            0.054      0.176891    0.0048          NaN           NaN            NaN           NaN                72.00          NaN           NaN      transit         2012.0         13/07/15    19 00 03.14    +40 13 14.7 NaN                 0.460                0.450                 0.00                 3584.0        NaN              Controversial    \n",
       "3  KOI-1843.01      0        NaN                0.114      4.194525    0.0390          NaN           NaN            NaN           NaN                89.38          NaN           NaN      transit        NaN             NaN         19 00 03.14    +40 13 14.7 NaN                 0.460                0.450                 0.00                 3584.0        NaN              Controversial    \n",
       "4  KOI-1843.02      0        NaN                0.071      6.356006    0.0520          NaN           NaN            NaN           NaN                88.24          NaN           NaN      transit        NaN             NaN         19 00 03.14    +40 13 14.7 NaN                 0.460                0.450                 0.00                 3584.0        NaN              Controversial    "
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>InclinationDeg</th>\n",
       "      <th>SurfaceTempK</th>\n",
       "      <th>AgeGyr</th>\n",
       "      <th>DiscoveryYear</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3584.000000</td>\n",
       "      <td>1313.000000</td>\n",
       "      <td>2774.000000</td>\n",
       "      <td>3485.000000</td>\n",
       "      <td>1406.000000</td>\n",
       "      <td>1108.000000</td>\n",
       "      <td>328.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>665.000000</td>\n",
       "      <td>741.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3574.000000</td>\n",
       "      <td>2133.000000</td>\n",
       "      <td>3416.000000</td>\n",
       "      <td>3263.000000</td>\n",
       "      <td>2509.000000</td>\n",
       "      <td>3455.000000</td>\n",
       "      <td>517.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.097656</td>\n",
       "      <td>2.890944</td>\n",
       "      <td>0.371190</td>\n",
       "      <td>537.248317</td>\n",
       "      <td>2.000170</td>\n",
       "      <td>0.166910</td>\n",
       "      <td>150.363823</td>\n",
       "      <td>144.200847</td>\n",
       "      <td>90.624476</td>\n",
       "      <td>82.973840</td>\n",
       "      <td>871.714170</td>\n",
       "      <td>1.502750</td>\n",
       "      <td>2013.300504</td>\n",
       "      <td>554.912346</td>\n",
       "      <td>0.983225</td>\n",
       "      <td>1.495629</td>\n",
       "      <td>0.016228</td>\n",
       "      <td>5505.514501</td>\n",
       "      <td>4.635355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.424554</td>\n",
       "      <td>10.204485</td>\n",
       "      <td>0.416871</td>\n",
       "      <td>7509.660676</td>\n",
       "      <td>19.352699</td>\n",
       "      <td>0.189760</td>\n",
       "      <td>117.859945</td>\n",
       "      <td>127.865952</td>\n",
       "      <td>93.047968</td>\n",
       "      <td>21.402768</td>\n",
       "      <td>699.314463</td>\n",
       "      <td>2.117431</td>\n",
       "      <td>6.159674</td>\n",
       "      <td>847.566803</td>\n",
       "      <td>0.319063</td>\n",
       "      <td>3.071298</td>\n",
       "      <td>0.193495</td>\n",
       "      <td>1204.271777</td>\n",
       "      <td>4.657679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.002300</td>\n",
       "      <td>0.090706</td>\n",
       "      <td>0.004420</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-233.000000</td>\n",
       "      <td>-174.640000</td>\n",
       "      <td>-5.112604</td>\n",
       "      <td>-0.000543</td>\n",
       "      <td>102.200000</td>\n",
       "      <td>0.005500</td>\n",
       "      <td>1781.000000</td>\n",
       "      <td>1.295000</td>\n",
       "      <td>0.012000</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>-2.090000</td>\n",
       "      <td>540.000000</td>\n",
       "      <td>0.002200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.141062</td>\n",
       "      <td>4.757940</td>\n",
       "      <td>0.053000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>66.750000</td>\n",
       "      <td>37.167396</td>\n",
       "      <td>1.509500</td>\n",
       "      <td>85.058000</td>\n",
       "      <td>321.800000</td>\n",
       "      <td>0.754125</td>\n",
       "      <td>2013.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>0.830000</td>\n",
       "      <td>0.810000</td>\n",
       "      <td>-0.050000</td>\n",
       "      <td>5113.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.940000</td>\n",
       "      <td>0.209600</td>\n",
       "      <td>13.071630</td>\n",
       "      <td>0.169500</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>139.700000</td>\n",
       "      <td>162.280000</td>\n",
       "      <td>69.821251</td>\n",
       "      <td>87.800000</td>\n",
       "      <td>719.600000</td>\n",
       "      <td>1.502750</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>333.000000</td>\n",
       "      <td>0.977000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>5634.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>0.321518</td>\n",
       "      <td>49.514000</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>0.247282</td>\n",
       "      <td>243.000000</td>\n",
       "      <td>252.625834</td>\n",
       "      <td>169.175000</td>\n",
       "      <td>89.140000</td>\n",
       "      <td>1310.900000</td>\n",
       "      <td>2.251375</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>773.130000</td>\n",
       "      <td>1.105000</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>5940.000000</td>\n",
       "      <td>6.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>263.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>320000.000000</td>\n",
       "      <td>662.000000</td>\n",
       "      <td>0.956000</td>\n",
       "      <td>791.000000</td>\n",
       "      <td>339.300000</td>\n",
       "      <td>320.800000</td>\n",
       "      <td>305.000000</td>\n",
       "      <td>7112.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>8500.000000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>51.100000</td>\n",
       "      <td>0.560000</td>\n",
       "      <td>29300.000000</td>\n",
       "      <td>80.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          TypeFlag  PlanetaryMassJpt    RadiusJpt     PeriodDays  SemiMajorAxisAU  Eccentricity  PeriastronDeg  LongitudeDeg  AscendingNodeDeg  InclinationDeg  SurfaceTempK    AgeGyr  DiscoveryYear  DistFromSunParsec  HostStarMassSlrMass  HostStarRadiusSlrRad  HostStarMetallicity  HostStarTempK  HostStarAgeGyr\n",
       "count  3584.000000  1313.000000       2774.000000  3485.000000    1406.000000      1108.000000   328.000000     43.000000     46.000000         665.000000      741.000000    2.000000  3574.000000    2133.000000        3416.000000          3263.000000           2509.000000          3455.000000    517.000000    \n",
       "mean   0.097656     2.890944          0.371190     537.248317     2.000170         0.166910      150.363823     144.200847    90.624476         82.973840       871.714170    1.502750  2013.300504    554.912346         0.983225             1.495629              0.016228             5505.514501    4.635355      \n",
       "std    0.424554     10.204485         0.416871     7509.660676    19.352699        0.189760      117.859945     127.865952    93.047968         21.402768       699.314463    2.117431  6.159674       847.566803         0.319063             3.071298              0.193495             1204.271777    4.657679      \n",
       "min    0.000000     0.000008          0.002300     0.090706       0.004420         0.000000     -233.000000    -174.640000   -5.112604         -0.000543        102.200000    0.005500  1781.000000    1.295000           0.012000             0.000014             -2.090000             540.000000     0.002200      \n",
       "25%    0.000000     0.150000          0.141062     4.757940       0.053000         0.020000      66.750000      37.167396     1.509500          85.058000       321.800000    0.754125  2013.000000    60.000000          0.830000             0.810000             -0.050000             5113.000000    2.000000      \n",
       "50%    0.000000     0.940000          0.209600     13.071630      0.169500         0.100000      139.700000     162.280000    69.821251         87.800000       719.600000    1.502750  2014.000000    333.000000         0.977000             1.000000              0.020000             5634.000000    4.000000      \n",
       "75%    0.000000     2.500000          0.321518     49.514000      1.250000         0.247282      243.000000     252.625834    169.175000        89.140000       1310.900000   2.251375  2016.000000    773.130000         1.105000             1.250000              0.100000             5940.000000    6.300000      \n",
       "max    3.000000     263.000000        6.000000     320000.000000  662.000000       0.956000      791.000000     339.300000    320.800000        305.000000      7112.000000   3.000000  2017.000000    8500.000000        4.500000             51.100000             0.560000             29300.000000   80.000000     "
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Let's group the number of discoveries by discovery method and year**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "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>DiscoveryMethod</th>\n",
       "      <th>RV</th>\n",
       "      <th>imaging</th>\n",
       "      <th>microlensing</th>\n",
       "      <th>timing</th>\n",
       "      <th>transit</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DiscoveryYear</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1992.0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994.0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995.0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996.0</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997.0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998.0</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999.0</th>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000.0</th>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001.0</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002.0</th>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003.0</th>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004.0</th>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005.0</th>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006.0</th>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007.0</th>\n",
       "      <td>40</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008.0</th>\n",
       "      <td>35</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009.0</th>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010.0</th>\n",
       "      <td>61</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011.0</th>\n",
       "      <td>97</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012.0</th>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013.0</th>\n",
       "      <td>33</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014.0</th>\n",
       "      <td>57</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015.0</th>\n",
       "      <td>46</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016.0</th>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017.0</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "DiscoveryMethod  RV  imaging  microlensing  timing  transit\n",
       "DiscoveryYear                                              \n",
       "1992.0           1   0        0             3       0      \n",
       "1994.0           0   0        0             1       0      \n",
       "1995.0           1   0        0             0       0      \n",
       "1996.0           6   0        0             0       0      \n",
       "1997.0           1   0        0             0       0      \n",
       "1998.0           5   0        0             0       0      \n",
       "1999.0           10  0        0             0       1      \n",
       "2000.0           20  0        0             0       0      \n",
       "2001.0           13  0        0             0       0      \n",
       "2002.0           29  0        0             0       1      \n",
       "2003.0           25  0        0             0       0      \n",
       "2004.0           20  1        1             0       7      \n",
       "2005.0           26  3        3             0       2      \n",
       "2006.0           22  1        0             0       7      \n",
       "2007.0           40  3        0             1       20     \n",
       "2008.0           35  7        6             1       17     \n",
       "2009.0           67  1        1             2       10     \n",
       "2010.0           61  7        2             1       49     \n",
       "2011.0           97  7        1             6       78     \n",
       "2012.0           40  2        7             4       78     \n",
       "2013.0           33  9        7             1       90     \n",
       "2014.0           57  5        5             2       859    \n",
       "2015.0           46  3        7             3       138    \n",
       "2016.0           27  2        0             0       1347   \n",
       "2017.0           9   1        0             0       6      "
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['DiscoveryMethod', 'DiscoveryYear']).size().unstack().T.fillna(0).astype(int).sort_values(by=['DiscoveryYear'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cuurently Transit method has become the most popular method for finding exoplanets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RV \n",
    "A star with a planet will move in its own small orbit in response to the planet's gravity. This leads to variations in the speed with which the star moves toward or away from Earth, i.e. the variations are in the radial velocity of the star with respect to Earth. The radial velocity can be deduced from the displacement in the parent star's spectral lines due to the Doppler effect.\n",
    "### Direct Imaging\n",
    "(Usually in infrared)\n",
    "### Microlensing\n",
    "Planets found due to gravitational lensing\n",
    "### Timing\n",
    "Similar to transit method checking for time intervals between dips"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking for correlation in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10.5, 0.5)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(10.0, 0)"
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "numericCols=['PlanetaryMassJpt', 'RadiusJpt','PeriodDays', 'SemiMajorAxisAU', 'Eccentricity',\n",
    "                 'SurfaceTempK','HostStarMassSlrMass', 'HostStarRadiusSlrRad',\n",
    "                 'HostStarMetallicity','HostStarTempK','HostStarAgeGyr']\n",
    "fig, ax = plt.subplots(figsize=(10,10))\n",
    "ax=sns.heatmap(df[numericCols].corr(), annot=True, linewidths=.5,square=True)\n",
    "print(ax.get_ylim())\n",
    "ax.set_ylim(10.0, 0) #Due to few cropping issues in seaborn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interesting correlations\n",
    "- Surface Temp and Radius of planet\n",
    "- Host star mass and Radius of planet\n",
    "- Eccentricity and surface temperature\n",
    "\n",
    "**Correlation does not imply causation!**\n",
    "\n",
    "### Obvious correlations\n",
    "- Host star mass and host star temp\n",
    "- Host star mass and host star radius\n",
    "- SurfaceTemp and Host star temp\n",
    "- Period and semi major axis\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Finding the percentage of null values in each column :**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AgeGyr                  99.944196\n",
       "LongitudeDeg            98.800223\n",
       "AscendingNodeDeg        98.716518\n",
       "PeriastronDeg           90.848214\n",
       "HostStarAgeGyr          85.574777\n",
       "InclinationDeg          81.445312\n",
       "SurfaceTempK            79.324777\n",
       "Eccentricity            69.084821\n",
       "PlanetaryMassJpt        63.364955\n",
       "SemiMajorAxisAU         60.770089\n",
       "DistFromSunParsec       40.485491\n",
       "HostStarMetallicity     29.994420\n",
       "RadiusJpt               22.600446\n",
       "HostStarRadiusSlrRad    8.956473 \n",
       "HostStarMassSlrMass     4.687500 \n",
       "dtype: float64"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().mean().sort_values(ascending=False).head(15)*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**We find AgeGyr, LongitudeDeg, AscendingNodeDeg, PeriastronDeg have more than 75% null values and hence won't be of much use**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Let's try finding the distribution of some columns like radius, mass, time period, semi major axis**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/kousik/.local/lib/python3.6/site-packages/numpy/lib/histograms.py:829: RuntimeWarning: invalid value encountered in greater_equal\n",
      "  keep = (tmp_a >= first_edge)\n",
      "/home/kousik/.local/lib/python3.6/site-packages/numpy/lib/histograms.py:830: RuntimeWarning: invalid value encountered in less_equal\n",
      "  keep &= (tmp_a <= last_edge)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 2,figsize=(18,12))\n",
    "axes[0,0].hist(df[\"PlanetaryMassJpt\"],bins=100)\n",
    "axes[0,1].hist(df[\"RadiusJpt\"],bins=100)\n",
    "axes[1,0].hist(df[\"PeriodDays\"],bins=100)\n",
    "axes[1,1].hist(df[\"SemiMajorAxisAU\"],bins=100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**As seen from histograms data is highly skewed**\n",
    "\n",
    "\n",
    "**If we attempt regression on such data it will fail miserably**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Attempting to plot a scatter of Mass vs Radius we find that the skew ruins the plot**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fb2865997b8>"
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df=df.dropna(subset=['PlanetaryMassJpt', 'RadiusJpt'])#Remove null values\n",
    "plt.scatter(df[\"RadiusJpt\"],df[\"PlanetaryMassJpt\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Removing outliers to get a good scatter plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.972030000000331"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"RadiusJpt\"].quantile(0.999)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Removing outliers with values more than the 99.9th percentile we get an approximately polynomial graph between radius and mass as expected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fb286b51748>"
      ]
     },
     "execution_count": 285,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfremoved=df[df[\"RadiusJpt\"]<df[\"RadiusJpt\"].quantile(0.999)]\n",
    "dfremoved=dfremoved[dfremoved[\"PlanetaryMassJpt\"]<dfremoved[\"RadiusJpt\"].quantile(0.999)]\n",
    "plt.scatter(dfremoved[\"RadiusJpt\"],dfremoved[\"PlanetaryMassJpt\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Log transformation\n",
    "#### Instead of trying to fit a polynomial we fill log transform the variables(Take their log values) and fit a straight line through them\n",
    "#### The slope of the line would give the degree of the polynomial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fb288dcba20>"
      ]
     },
     "execution_count": 286,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"PlanetaryMassJptlog\"]=df[\"PlanetaryMassJpt\"].apply(np.log)\n",
    "df[\"RadiusJptlog\"]=df[\"RadiusJpt\"].apply(np.log)\n",
    "\n",
    "plt.scatter(df[\"RadiusJptlog\"],df[\"PlanetaryMassJptlog\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [],
   "source": [
    "linearRegressor = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [],
   "source": [
    "xTrain=df[[\"RadiusJptlog\"]]\n",
    "yTrain=df[\"PlanetaryMassJptlog\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linearRegressor.fit(xTrain,yTrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OwUGkmdV6veD+/sIjjkuZQrsaQ4TXrEl+na6u0JU2XZ78Do1bj8M2upnx48NgNoAf/zgEhQMOqDzLo5WCg0gzS9IuUEvpbxfFlPvmkO311+P39fSMvOX87Ge5wSzrbejrG55F58rXuPSO8Pv6zGdCHJw1qzpZHc20EpxIM8ue8A4at/pZR0f8tBf1kvD+v/xl5q9n333hT38qrcaqHWglOJFWlaRdoF4avG5Ckqk6Uh2mUr+ejTaCF1+EP/9ZgaFUCg4iza5Yu0C9lLqEZ7W9+mpsL63HHw9BYc89R9Ieeyx0TZ0woU75G2UUHEQkmVLaH2ph1arQSytt3MdL097GJhNWstVWI4f99a+h9mnLLRuTzdFCwUGkxmsAjCqpt5hSBrtVUzTOY/XCp9nPr+VNT87n+ZfHATAwEILCHnukHa+/bdkUHKS91XKQWer6o7FwalD7g3d0ctKK79DFaq4nLK7wVU7Fe/tya9tq/bcd7eLm1WilTXMrSdmqMJNnrFJnPW0lAwOlzYFUhe0cPpmR9CF+62vo8Jz5oFJq+bcdJSgwt5K6skp7i+ueaRYagCvR15d/HeTe3lA10+rqVLV0LftzANcOf96Gh7mLXViPtO69+daWnjGjdn/bUUJdWUXi1HKQWa1HN9dKkqqwwcGaB4cH2BbDMwLDU2zOw7w1MzBAmIAvvfooLjBA47vktggFB2lvSSafK1ejRzeXo1g9/YknhqBxzDE1GxC3mEmMYwXb88Bw2p3sgmNszjPJLhKXt2r9bduAgoO0t1oOMqtl4KmVQhP9nXginHNOzYLC66zDO7idjVjM64QeSJdzKI6xC3dXfoNGDiBsQWpzEKmlwcFQsKbXhTdz4VSoDaajo3rzJ6Vx4Hgu4OccN5x2JidzMt+r3k3UzpBXS7Y5mNmBZvawmT1qZl9udH5EytIso5uTKlQVVoPAcDpfoAMfDgwf4zyGsOoGBmjuqrwm1ZTBwcw6gR8B7wW2A442s+0amyuRNlCoKqyjesXFZXwAw/kSpwOwG7exknU5j49Tk2bu117T+IYSNWVwAHYDHnX3x919FXARcGiD8yQy+vX3w3HHjayp0NkZPvf3w7hxFV/+Ct6H4XyIywDoZjkvMJnbeCfr8kbF14+1dKkGwJWoWYPD5sCTaZ+fitKGmdlMM5tnZvMWL15c18yJtKSkXVQvvHCkCmnt2vB5cDC3oboEqW6ph3BFRtpyxjOZJWVftyTVWkGvTTRrcCjK3ee6+3R3nz558uRGZ0ekuSWdSqJQb6Uy1ndexpswPKNb6k+YiWNsy0PlPEllmn2MSRNp1uDwNDA17fOUKE1EypF0LeoqDdxbQyeG08Oy4bRPci6OMZOflnStqlLDdGLNGhzuAN5sZluYWRdwFPD7BudJpHUlLfQL9VZatiz/vizv4m+MZc3w5+24nyGMc2mCtTmbeYxJk2nK4ODua4BPA1cDDwIXu/v9jc2VSAtLOlo7X2+lrq7Q26fImKjPcwaGcyvvGk57gy7uZ4fa9EAq1X77NX9X4ibSlMEBwN2vcvdt3H0rd1e4F6lE0tHa2SPGe3pCUFi6NPbSA/RjOP/D54fTnmNjHKOL1dV8itKkut52dsKsWXDddY3LSwtq2uAgIlVUyjQh6QP3xo+H1fkL+DuYjuHMYGA47S7ejmNszAs1epASpN50pkzJWgFIktD0GSISL890Gs+wac4EeL/mCI7gN/XMWWm6uzWvUh4tOX2GiNRYknEPaW0Sr7MOhmcEhv9iDo41d2AAjXEog4KDSDtKOu5hzhx8XDd9PME4Xh9O3ofrcYw5fKXOGa+AxjiUZEyjMyAiDRA37uG4aGbUqPrl+J+8kwtXLh8+xBhiNWPppAVnONUYh5LozUGkHcV9i167FmbM4If2aczgwr9sNbzrRSYwRGdrBgbQ5HslUnAQaUcx36KvZx/Mh/gPfjic9jDb4BgTeLleuatMb2/outrTk5muyfdKouAg0o6yxj08zhYYzn5cP5z2J96DY2zDI/HXqfE60iXp7oaBgdAN98c/Dt1ws6lhOjEFB5FyJOnp08yicQ+vdmyI4WzF48O7zuRkHOM9XFP4Gt3dsO++Nc5omp6ezLeB9dYLn+PGbVRpnqh2pQZpkVKlevqkGnRTPX2gZfrRDw3B+E/0s3JoJL9HchEXcXSyC6TWebjwwhrlMI9ly0pb6nPatPC3yZcuRenNQaRUSWc4bVIHHRTK9pUrw+dNJ6xgaFpf8sAAoeF67tyK1niIFbfiXKmFetIpQyQvBQeRUhWrrii3yqlaVVUx1zn11FAD88c/jhy6fDk882I3tnBB0Yn1ctRgTWkAPvnJ6hTqpUwZIrncveW3XXfd1UXqprfXPRSlmVtvr/vAgHt3d2Z6d3dIL6Tc8xJc57KuI3KyumhRzLlm+Z+tnltvr/usWeFfs5Hfq1QdMM9jytWGF+zV2BQcpK4KFeSFAkchSc9L3SOu0Ey7znx2yLncLbcUuEZcHhqxdXUpINSBgoNItcUVsHHfvM0KXy/JeUneLsx8MT05lzmPjxW/RtK3hnLeLtZdd+Tn9dZz7+kpfk5PTzX/YpKHgoNIvdTyzSHumM5OdzNfNW2rnF2f4geZ1yl0nyRvDmbu++1X+Jj11hsJnD094S2g3GAkNVUoOKhBWqSayu0hk+S8AlNe7Op30LXo0eGknbkbx/gh/5F5nUKN6fnykM0dHn00d/Rxihn85CeZ60GsWpV5TKpnl7qUNrWmCw5mdoaZPWRm883sMjOb0Og8iSRWbg+ZJOflKUxP4iwM5y52HU5bNXET7u79YP7rxBXIqe6j6XmIkxo70NWVmW4GJ5yQfCBasWAUF4CkPuJeKRq1AQcAY6Kf/xv472LnqFpJ2kJae8EFHJtTA/MCk0aqfgpdY+zYwlU5nZ0jvYUKHTd2bKg2KtSjqFh12cBA/vaHsWPVIF0HtFK1krtf4+5roo+3AlMamR+RptHfz61fvATDOZ6Rkcn/4G04xmSWhISJEwuPlyg2H9LatXDOOWF6ikLHrl4NL71U+FrFqsv6+2HJkjAnUvpb0/nnazxCgzX1MqFm9gfg1+4+kGffTGAmwLRp03ZdmG+YvMgo8fTTYSnkdL/9zM0c9n/vzRylPHZsKGDT6/nNwvfx3t4wbfXSpbXJZNxSnIODoY1h0aJQrTVnjgr+JtF0y4Sa2XVmdl+e7dC0Y2YDa4C8w0Tdfa67T3f36ZMnT65X1qUd1HtSvQL3W7kylO3pgeFrXwMfGOSw3x0bAkNnZ9jR2wsbbJDbAJz6ArhwYe0CA8RPIdLfP9JAvWCBAkOriKtvauQGHA/8HehOcrzaHKRqqjVSOf16hQat5bsf+NDEHt/8Tcszkg84IEEeGz3Cudh4DmkqtNI4B+BA4AFgctJzFBykasodp5BPvkI8VXinAkWe+x3NYEZSF6/7GjoLnjN8zVqPco7GVHhnZ/V+T9IwhYJD0zVIAz8E1geuNbN7zOzcRmdI2kg11wDIN3urp1XxHHNMxpTSZ3EShvMrPjKc9jIb8gbr0snacOyMGfmnoU7lMclYhZ6ekcbfVJVUPtldVbu7wxTdQ0PhX814Oqo1XXBw963dfaq77xxtJzQ6T9IGUvX+qcI7WzkDthIGlGvZH8P5T84aTnuErXGMDXgl8+C4/KXymD5eAnJ7G3V3w9lnj7QBFFofYezYkeCRWr8h1V6gGU9HvaYLDiJ1l1q8J+4bebnfiIsElEfYGsM5gGuH065lfxxjax4rfO3sQt8s5L+vL3yeMycU2O6ZDdYJBtYNW758ZFrutWvD20J647wamke1RF1ZzWwjYA9gM2AlcB+hrqqEZZlqZ/r06T5v3rxGZ0NaVV9ffGDo7S2/62X2inGRl9mACbyckXYWJ3ES/5t5fqoLapze3pDv7OO6usLn1atH0gp1M50xo/B9su+5YEGyY6Xpld2V1cz2MbOrgSuB9wKbAtsBXwHuNbNvmtkG1c6wSF3FVf+YZX4jLrWLa1YVz1o66WRNRmDoZwDHcgNDb2+YiiJuEFqqkE69HaRbtSozMEDhbqaF7pNN6y+3j7iW6uiN4gxgWsy+McAHgMMKXaMem3orSUXievikTxldYRfX/ffP6tTDEz4U1yMovcfPrFm53VO7u5NNbxHX1TSuS216l9u4KbXVG2lUoZW6spazKThIRQrNNzRrVjimyHTZcXMLffWruaesYN3810oPSgMDmd1WU11HU6uk5RkbUdJWLLBVe7yHNKWKgwNwErABYMB5wF3AAUnOrcem4CAVi/umbJZ8cFla4fmb3+Tufuopjx8fkL11deUGrGKrzZW6FXsLKDaAT1peoeCQtEH6H+6+k5m9B/gk8FXgF+6+S5VrucqiBmmpWEdHbt19SqpbaIL5u/6x6YHs/OwfM9JuvRV23z36kLRuP05vb6j3j8trZ+dID6NizAp3ZZVRrxpzK6X+iz6IEBTuT0sTaS7lzI1UqEtngsFli5mE4RmB4cKez+HWwe5HRnkYHKw8OCxcGJ/X3t78g9Pi7qnFdqSQuFeK9A04H7gGeAToJoxgvjPJufXYVK3URsqZq8hspO2g0HULVR2l6vpT946qh95gbM6hnz3wgfz19UnWTS62pZ6lUHtA9u+o2PHStqhCm0MHsAswIfo8EXhbknPrsSk4tIlijaQDA/F1+qm2g0Ly9QxK39IWoBn6xYDvaPdm7J7O7eGHpO0KlbQVlNoeoPYDyaNQcEja5rAHcI+7LzezY6JAcba7N8UiCmpzaBNxg9VSA9XyDDjLOa7QAK7BQTjppMLTWvf0cOIRSzjnnMzkVXQxltX5z0mqoyNZG4DaCqRKqtHmcA6wwsx2Ak4GHgN+XqX8iSRTaFK8fJPcJTk/1T5hFkYKFwgM5/ExbGlmYFiyBLy3r/LAAPGNzNnUViB1kDQ4rIleQQ4FfujuPyK0O0i7qveCOBBfKE6cmGzk7sSJIz8PDsL48Zkzo8YUzrfwbgzn45w3nHbvveHwnh6qM2q4tzdZoa+ZT6VOkgaHV83sFOAY4Eoz6wDG1i5b0tTSJ6pzD//OnFn7ADFnTu400gCvvJJZ8MdZujQEhFRQWL684OGLmIrh7Mktw2mXrz8Dd9hhh7QDK/0m39UVlu9MzZOUbuzYEIE086nUWdLgcCTwBvDv7v4cMIUwtYa0o3xVOHFz95Qj7q2kvx/Wz/PCmppHqNg6BhACQpGgsIJxGE4vI28Ep/JVfMxYDj3nwNwTDjoo/yyp220Xf5PU8T09IcCmqrPcR/b19sL554e6q2aY+bQRb4vSOHEt1a20qbdSncX16KnGEpHFeiQV6k3U0xO2MpfKHAKfzPMZye/jDyPXztfDJ2aZz+F877df4fy2yopqmk5jVKIKXVnfCdwBvAasAtYCLyc5tx6bgkOdVXMpzaTXTto9tMwpJg7n4oyk8bzia7HcZ0rvEtrT497RUfjaqWNLDVblBtqBgczxFNlBrdwurbX8m0vDVCM4zAO2Bu4GOoGPAt9Ncm65G6FXlAOTih2r4FBntfwWWea3/pwCq9A3+rTtDE7OSX6F8fkL6YTXzCnky5kLqdw1q7u6cq+VGp9Ryd+tlm+L0jBVCQ7Rv/PT0u5Ocm45GzAVuBpYqODQpGo1qKoak8qlCqzsb9Fp21UcmJP8GFsULqTLLeRLDSrlBtpC+evtrezbv94cRqVCwSFpg/QKM+sC7jGz083sP6ntEqPfB74IeA3vIZWo1RKRReYwSsR9ZLnMJUui/qbBw2yD4RzEyBxI17MPjrElT+TPT0qpXVZT3U7zrbeclqcMnZ3l90gqlL9FiwqPEykm399F3WpHt7iokb4BvcA4wrTdXwe+B2yd5NxSN8JYirOjnxcQ8+YAzCRUd82bNm1arQKrlKoabxTp16hkKorUN/CBAX9x3KY5u3/IiYXPHzMmM/+lvDmkP3u+30klVTxxv+NavjkUuq+0LJptsR/gOsI61NnbocBtwIZeJDikb6pWahK1aIsop54/bVszbYuc5OPt/NIL+VTBWqxNJH31uGK/k3IK22LXq1Wbg4xKZQcHYH6hrdC55WzAjsALUVBYAKwBFgGbFDpPwaFJ1KpeukDbQaFtL27ISNpqzH20q9IAABUaSURBVBM+RBUavAsFiOwG2mr/Topdr5q9lfSmMOpVEhzuIfRQ+gLw1qh6aXgrdG41Nr05tJhyerQkKYBKfHv4Mt/JSV45dv3Kg0LSN41KfyfV/h2XQ28ZbaFQcCjYqOzuOwNHA+OBXwJzgO2Bp71JZmSVJhI3jURcetJpOJJMqgf8miMwnNM4ZTjtmR9cgvdMYt3VryZ9ivKZ5TbQlvo7Kaba14tT61Hw0vzioka+jTCNxhLgC6WcV+tNbw5NotRvm0mrXIrU89/J23OSb9/kkJH7lvrNv5KxFpX+Tqr9Oy6XxjW0BSppkAY2JwxI+yvwB2AGML7YefXcFByaSCn11EkLoJgg8hwb5SQPnvjX3PuUUyVUzYFr1a67r0dbgMY1tIWygwNwE3AX8GXgzYQV4Ia3QufWc1NwaJBKC6mkBdDAQMb+1+nKOeXzY74ff/8kjdnZ375L7SWVtkrcqKA2h7ZQSXBYADwRbY+nbU8Ajxc6t56bgkMDVKPwKOUaZj4Evg0PZRz+bv4axkIUWiN6YCAU3ukndnSMTNJXqCE8ey3muLed7C6so4F6K416FVUrtcKm4NAA1ap2SFgAfXzbv+TcajVpA+SKBaZqFXRxbw6qi5cWVCg4FFxD2sz63H1Bgf0GbO7uT5XaEF5NWkO6ATo6QrGYrcrrG//kJ3DCCZlpS5nIRF7MPbjYGtHVUGgd61rfu1KDg6G30aJFoXdTamoPaVuVrCF9hpldYmbHmtn2ZraRmU0zs33N7FvALcC2Vc+xNL9qdamMWUDm5ptDnEkPDA88EOLRRHsp/7WqsVxnkXy17BxDjVq9T1pX3CtFagO2I4xvuBF4mDAw7leEJUPXLXZ+PTZVKzVAtdocstoCnhizdU6NzRVXZJ2XZJRwJVVIxZ6tFevi1ftI8kBtDlITlRaSab2IXqM7p9z67ncL3LfQ/EKVBq0kBWm1AkS9Ao3GLUgeFQcH4EN5tv2AjZKcX+tNwaFFga/FfALLMsqrD3Bp8XNLnZm0lG/IxQrSanXzrGd3Ub05SB7VCA5XAsuAS6JtKXAN8AgwI8k1arkpOLSYaHK4D3BpRjk1gWVhaU4o/9rV+IZcrCCtVkFbzwJb4xYkj0LBIemCPWOAbd39MHc/LGqHcGB34EtlN3hI+0g18Jrx3WPuw5Yu4XI+OLz7VcbzIhPpwOMXwkmiGg3lxRqdK1k0J8nx1WxYT8m34FC5iwpJW0gaHKa6+/Npn1+I0pYBq6ufLRlVop4yVyzcAcP5L747vOsJ+nCM8SwfOf7ss8u/VzV6ExUrSKvVU6tek+il1Gr1PhmVkgaHG83sCjM7zsyOA34fpa0HxPQrFAke+ML52IrlHMIVw2k38a84Rh9ZYwZ6eiortKr1DblQQVqt7qyt2i1W2kNcfVP6BhhwGGFt5+8Dh0MYQNcMm9ocmtPSpbnV6ecyM389e6vVgbdabyWRPCh3hHQ6M9sY2I3Q1nC7u79Qi2BVDo2Qbi5r1sDYsZlpn2Auc/lk4RMHBlTVIVJHlYyQTl3gCOB2whvDEcBtZnZ49bIoo8W7350ZGN7KgwxhxQPDrFkKDCJNZEzC42YD70i9LZjZZOA64Le1ypi0li98Ac48MzPtddZhHVYVP3m//eDHP65NxkSkLEkbpDuyqpGWlnBuyczsP8zsITO738xOr9V9pHKDg6HdNz0wPMfGOFY8MJiFN4brrqttJuslbj4mkRaU9M3hT2Z2NWFOJQjLhV5ViwyZ2T7AocBO7v6GmW1Ui/tIZe64A3bbLTPtzk3exy7PlfCfhTtcVZP/jOovNbFdat3l1MR2oOoyaUmlNEgfBuwRffyLu19WkwyZXQzMdffEXyfVIF0/zz4Lm22WmXYRR3Fk7635p7IupspTfDdMK0/lLW2r4gZpAHe/xN0/F201CQyRbYB/MbPbzOwmM3tHvoPMbKaZzTOzeYsXL65hdgTg9ddDOZ4eGE4ZcwaOcSS/Li8wQBjwNRqqY+o52lmkDgpWK5nZq4Suqzm7CPPfbFDOTc3sOmCTPLtmR3maCLwTeAdwsZlt6VmvOO4+F5gL4c2hnHxIce6w9dbw+OMjaXvtBTcu6Cs/IKR0d8NBB42O6php0/L/Pmo12lmkxgq+Obj7+u6+QZ5t/XIDQ3Td/d19hzzb74CnYHhaztuBIWBSufeS8n30o+HLfHpgWLMGbryR6nwjnjs3tDmkAkPKihVhxbJWotHOMsrUrMdRBS4H9gEws22ALmBJQ3PUZn70o1CFdMEFI2kvvhjeIjo7o4RKvxH39oY3g9FSHaOJ7WSUacbg8DNgSzO7D7gIOC67SkkqUKB+//rrQ7n26U+PHP7Q6b/He/uYMDHBcpn59PQU/kZd78nnakkT28loEjevRittmlspoZg5/R/73uU50xz98Y8xx0NYwS216lpqXqCenpwlPzNWZ4ubP0jrDIg0DFomVNw9Z3GZVxifU+6fcUb88UUL8HInkdPkcyINUSg4JB7n0Mw0ziGhjg5wZwhjfV5lBesN7/rwh+Hii/MfH0t9+EVaWlXGOcgoMG0ap/ElOhkaDgwb8TxD0/pyA0N0fEGt1mgsIoklnT5DWtwll8DhCxdkpC2nm+5ug+/MzX/SnDmZYxCytWKjsYgkojeHUe6OO0IPpMOjCdbXX3c1i6e8HbcOuns3Guluma8XU6p7Zr41ndWHX2RU05vDKLVoUWgSSPfQQ/CWt4wF7s7cUWzSuFTwmD07XHjatBAY1FVTZNRSg/Qo8+qr8La3ZbYT//nPsO++BU7SpHEibUkN0m1g7Vo45BDYYIOR8vynPw2djQoGBhg9o5RFpGoUHEaBU06BMWPgiivC589/PgSFj3884QVG0yhlEakKBYcWdsEFobH5tNPC5wMPhNWr4YwzSryQJo0TkSxqkG5BN90Ee+898nnKFLj//lClVJZUw7IanEUkouDQQh55BLbZJjPtiSdCe3LFUr2SRERQtVJLWLYM3vSmzMDw97+HdoWqBAYRkSwKDk1s1Sr4l38JY9BeeimkXXRRCArvfGdj8yYio5uCQxNyh1mzYJ114K9/DWnf+lZIP/LIxuZNRNqD2hyazA9+AJ/5zMjno44Kg5M7FMZFpI4UHJrEVVfB+9438nmHHeC225IttiYiUm1N933UzHY2s1vN7B4zm2dmuzU6T7U0f34Yq5AKDGbwzDNw771VCAwFlgQVESmkGd8cTge+6e5/NLODos97NzZL1ffcc7Dppplp//hHmBepKopNpiciUkDTvTkADqSGc20IPNPAvFTdihWw446ZgeGKK0Jjc9UCA4QBbdnrMKxYEdJFRIpoxuDwWeAMM3sSOBM4Jd9BZjYzqnaat3jx4rpmsBxDQ3D00bDeenDffSHt7LNDUEhva6gaTaYnIhVoSHAws+vM7L4826HALOA/3X0q8J/Aefmu4e5z3X26u0+fPHlyPbNfsm9/Gzo7wxgFgBNOCMEivVdS1WkyPRGpQEPaHNx9/7h9ZvZz4KTo42+A/6tLpmrg4oszxyXsuWdYW6Grqw43z7fEpybTE5GEmrFB+hlgL+BGYF/gkYbmpgy33ZY5gnnCBHj00fyrbdbUuHEjwaGnJ9RjqTFaRBJoxuDwCeBsMxsDvA7MbHB+Elu4MHeuo4cfzp0sr+ayeyoBrFxZ50yISCtrugZpd/+ru+/q7ju5++7ufmej81TMK6/A1KmZgeHGG0Njc9mBoZIxCuqpJCIVarrg0ErWrIH3vhc23BCeeiqknX9+CAp77VXBhVPf/BcuDBdLjVFIGiDUU0lEKqTgUKYvfAHGjoU//Sl8/tKXQjl+/PFVuHil3/zVU0lEKqTgUKLzzgtTXJx5Zvh88MHhDSK1VGdVVPrNX8t+ikiFFBwSuuGGEBQ+/vHwua8vtDX84Q9hDENVVfrNv78f5s6F3t6Q6d7e8Fk9lUQkoWbsrdRUHnoItt02M23hwhrX0FRjjIKW/RSRCujNIcaSJbD++pmB4fbbQ7tCzavu9c1fRBpMbw5Z3ngD9t4bbr11JO03v4HDD69zRvTNX0QaSG8OEXf4xCdg3XVHAsN3vhPS6x4YREQaTG8OwPe/D5/73MjnY46BCy/U0pwi0r7aPjh89KNwwQXh5513hr/9LUxJJCLSztr+u/Fhh8H06WFltrvvVmAQEQEFBw4+GO64AzbeuNE5ERFpHm0fHEREJJeCg4iI5FBwEBGRHAoOIiKSQ8FBRERyNCQ4mNmHzex+Mxsys+lZ+04xs0fN7GEze08j8ici0u4aNQjuPuBDwE/SE81sO+AoYHtgM+A6M9vG3dfWP4siIu2rIW8O7v6guz+cZ9ehwEXu/oa7PwE8CuxW39yJiEiztTlsDjyZ9vmpKC2Hmc00s3lmNm/x4sV1yZyISLuoWbWSmV0HbJJn12x3/12l13f3ucBcgOnTp3ul1xMRkRE1Cw7uvn8Zpz0NTE37PCVKExGROmq2aqXfA0eZ2TpmtgXwZuD2BudJRKTtNKor6wfN7CngXcCVZnY1gLvfD1wMPAD8CfiUeiqJiNRfQ7qyuvtlwGUx++YAc+qbIxERSdds1UoiItIEFBxERCSHgoOIiORQcBARkRwKDiIikkPBQUREcig4iIhIDgUHERHJoeAgIiI5FBxERCSHgoOIiORQcBARkRwKDiIikkPBQUREcrR3cBgchL4+6OgI/w4ONjpHIiJNoSHrOTSFwUGYORNWrAifFy4MnwH6+xuXLxGRJtC+bw6zZ48EhpQVK0K6iEiba9QyoR82s/vNbMjMpqel/5uZ3Wlm90b/7luzTCxaVFq6iEgbadSbw33Ah4Cbs9KXAIe4+47AccAvapaDadNKSxcRaSMNCQ7u/qC7P5wn/W53fyb6eD8wzszWqUkm5syB7u7MtO7ukC4i0uaauc3hMOAud38j304zm2lm88xs3uLFi0u/en8/zJ0Lvb1gFv6dO1eN0SIigLl7bS5sdh2wSZ5ds939d9ExNwKfd/d5WeduD/weOMDdHyt2r+nTp/u8efOKHSYiImnM7E53n55vX826srr7/uWcZ2ZTgMuAY5MEBhERqb6mqlYyswnAlcCX3f2WRudHRKRdNaor6wfN7CngXcCVZnZ1tOvTwNbA18zsnmjbqBF5FBFpZw0ZIe3ulxGqjrLTvw18u/45EhGRdE1VrSQiIs2hZr2V6snMFgMLa3DpSYSBeaOBnqV5jabnGU3PAqPrefI9S6+7T8538KgIDrViZvPiunm1Gj1L8xpNzzOangVG1/OU+iyqVhIRkRwKDiIikkPBobC5jc5AFelZmtdoep7R9Cwwup6npGdRm4OIiOTQm4OIiORQcBARkRwKDgmY2clm5mY2qdF5qYSZfcvM5kfTklxjZps1Ok/lMrMzzOyh6Hkui+blallxqyO2EjM70MweNrNHzezLjc5PJczsZ2b2gpnd1+i8VMrMpprZDWb2QPTf2ElJzlNwKMLMpgIHAKNh/dAz3P1t7r4zcAXwtUZnqALXAju4+9uAfwKnNDg/lYpbHbElmFkn8CPgvcB2wNFmtl1jc1WRC4ADG52JKlkDnOzu2wHvBD6V5G+j4FDc94EvAi3fcu/ur6R9XI8WfiZ3v8bd10QfbwWmNDI/lYpbHbGF7AY86u6Pu/sq4CLg0AbnqWzufjOwrNH5qAZ3f9bd74p+fhV4ENi82HkNmXivVZjZocDT7v4PM2t0dqrCzOYAxwIvA/s0ODvV8jHg143ORJvbHHgy7fNTwO4NyovEMLM+4O3AbcWObfvgUGjFOuC/CFVKLaPYCnzuPhuYbWanEKZI/3pdM1iChKsJzia8Ng/WM2/lSPI8IrViZuOBS4DPZtUi5NX2wSFuxToz2xHYAki9NUwB7jKz3dz9uTpmsSQlrMA3CFxFEweHYs9iZscDBwP7eQsM2Cl3dcQW8TQwNe3zlChNmoCZjSUEhkF3vzTJOW0fHOK4+73A8EJDZrYAmO7uLTtDo5m92d0fiT4eCjzUyPxUwswOJLQF7eXuKxqdH+EO4M1mtgUhKBwFfKSxWRIAC99uzwMedPfvJT1PDdLt5TQzu8/M5hOqyxJ1aWtSPwTWB66Nuuae2+gMVaLA6ogtIeoc8GngakKD58Xufn9jc1U+M/sV8HfgLWb2lJn9e6PzVIE9gBnAvmkrbB5U7CRNnyEiIjn05iAiIjkUHEREJIeCg4iI5FBwEBGRHAoOIiKSQ8FB2o6ZvVbh+b81sy2jnxeY2b3R7LA3mVlvidc63sx+GP18gpkdW0Z+uszsZjPTuCWpGgUHkRKY2fZAp7s/npa8TzQ77I3AV8q9truf6+4/L+O8VcCfgSPLvbdINgUHaVsWnBENDLzXzI6M0jvM7MfRehHXmtlVZnZ4dFo/EDcP0t9Jm+3SzC43szujOfRnpqV/1Mz+aWa3EwYopdK/YWafj36+MbWug5lNikboY2bbm9nt0UCm+Wb25uj0y6O8iVSFXkOlnX0I2BnYCZgE3GFmNxMK7D7CugQbEUb8/iw6Zw/gVzHXO5BQSKd8zN2Xmdm46NqXAF3AN4FdCTPj3gDcXUKeTwDOdvdBM+sCOqP0+4B3lHAdkYIUHKSd7Qn8yt3XAs+b2U2EAnZP4DfuPgQ8Z2Y3pJ2zKbA46zo3mNlE4DXgq2npnzGzD0Y/TwXeTJiV9UZ3XwxgZr8Gtikhz38nzKo7Bbg0NVeWu681s1Vmtn40Z79IRVStJFKalcC6WWn7AL3APYS3Asxsb2B/4F3uvhPh7SD7vELWMPL/5/B57v5L4P1RPq4ys33TzlkHeL2Ee4jEUnCQdvYX4Egz6zSzycC/ArcDtwCHRW0PGwN7p53zILB19oWiiec+CxwbvUVsCLzo7ivM7K2E5RkhLLKyl5n1RNMofzgmbwsIVU8AqfYOol5Sj7v7/xLaPt4WpfcAS9x9dYm/A5G8FByknV0GzAf+AVwPfDFaq+MSwkpmDwADwF2E9gGAK8kMFsPc/VlCe8SngD8BY8zsQeA0wlKmqWO+QageuoUQbPI5E5hlZncT2kNSjgDuM7N7gB2AVO+mfaK8iVSFZmUVycPMxrv7a9E38tuBPdz9uahx+Ybo89rG5nKEmV0KfNnd/9novMjooAZpkfyuMLMJhN5F30qt/ufuK83s64Quq4samcGUqNfS5QoMUk16cxARkRxqcxARkRwKDiIikkPBQUREcig4iIhIDgUHERHJ8f87igMmOoxe4QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(xTrain, yTrain, color = 'red')\n",
    "plt.plot(xTrain, linearRegressor.predict(xTrain), color = 'blue')\n",
    "plt.title('Radius vs Mass of planets')\n",
    "plt.xlabel('log(Radius)')\n",
    "plt.ylabel('log(Mass)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Correlation coefficient**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7419577761281136"
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linearRegressor.score(xTrain,yTrain)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Slope of line**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.1513155])"
      ]
     },
     "execution_count": 292,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linearRegressor.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Intercept**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.3983624209761425"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linearRegressor.intercept_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$log(Mass)=2.2*log(Radius)-0.45$\n",
    "\n",
    "$Predicted : Mass=0.637*Radius^{2.2}$\n",
    "\n",
    "\n",
    "$Actual : Mass=c*Radius^3$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Why is this incorrect?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K-means Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**We will try clustering the planets into two groups based on mass and radius naturallywe expect them to be either rocky or gas giant**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [],
   "source": [
    "km = KMeans(\n",
    "    n_clusters=2, init='random',\n",
    "    n_init=10, max_iter=300, \n",
    "    tol=1e-04, random_state=0\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=np.array(df[[\"RadiusJptlog\",\"PlanetaryMassJptlog\"]])\n",
    "X=X.reshape(-1,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [],
   "source": [
    "km.fit(X)\n",
    "y_km = km.fit_predict(X)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(\n",
    "    X[y_km == 0, 0], X[y_km == 0, 1],\n",
    "    s=50, c='lightgreen',\n",
    "    edgecolor='black',\n",
    "    label='gas giants'\n",
    ")\n",
    "\n",
    "plt.scatter(\n",
    "    X[y_km == 1, 0], X[y_km == 1, 1],\n",
    "    s=50, c='orange',\n",
    "    edgecolor='black',\n",
    "    label='rocky planets'\n",
    ")\n",
    "plt.title('Radius vs Mass of planets')\n",
    "plt.xlabel('log(Radius)')\n",
    "plt.ylabel('log(Mass)')\n",
    "\n",
    "plt.legend(scatterpoints=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TASK: Try checking if 2 is the best value for k (i.e are there only 2 clusters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"cluster\"]=y_km"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"MassRadiusLogRatio\"]=df[\"PlanetaryMassJptlog\"]/df[\"RadiusJptlog\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "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>MassRadiusLogRatio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.351834</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         MassRadiusLogRatio\n",
       "cluster                    \n",
       "0       NaN                \n",
       "1        3.351834          "
      ]
     },
     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"cluster\").agg({\"MassRadiusLogRatio\":\"mean\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"MassRadiusCubeRatio\"]=df[\"PlanetaryMassJpt\"]/df[\"RadiusJpt\"]**3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb28884abe0>"
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"MassRadiusCubeRatio\"].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "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>MassRadiusCubeRatio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cluster</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.264171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.575266</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         MassRadiusCubeRatio\n",
       "cluster                     \n",
       "0        2.264171           \n",
       "1        3.575266           "
      ]
     },
     "execution_count": 303,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"cluster\").agg({\"MassRadiusCubeRatio\":\"mean\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot([df[df[\"cluster\"]==0][\"MassRadiusCubeRatio\"],df[df[\"cluster\"]==1][\"MassRadiusCubeRatio\"]])\n",
    "positions = (1, 2)\n",
    "labels = (\"Rocky planets\", \"Gas giants\")\n",
    "plt.ylabel('Density')\n",
    "plt.xticks(positions, labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"oec.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Verifying keplers 3rd law"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Keplers 3rd Law\n",
    "$T^2\\propto a^3$\n",
    "\n",
    "where a is semi major axis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Plotting a scatter plot of period vs semi major axis**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fb28877d5c0>"
      ]
     },
     "execution_count": 306,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df[\"SemiMajorAxisAU\"],df[\"PeriodDays\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.dropna(subset=['SemiMajorAxisAU', 'PeriodDays']) #Removing null values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Doing log transformation and linear regression similar to before"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fb2890668d0>"
      ]
     },
     "execution_count": 308,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"SemiMajorAxisAUlog\"]=df[\"SemiMajorAxisAU\"].apply(np.log)\n",
    "df[\"PeriodDayslog\"]=df[\"PeriodDays\"].apply(np.log)\n",
    "plt.scatter(df[\"SemiMajorAxisAUlog\"],df[\"PeriodDayslog\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [],
   "source": [
    "linearRegressor = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [],
   "source": [
    "xTrain=df[[\"SemiMajorAxisAUlog\"]]\n",
    "yTrain=df[\"PeriodDayslog\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1359,)"
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yTrain.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "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>SemiMajorAxisAUlog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1359.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-1.417422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.744565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-5.421616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-2.951716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-1.857899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.173953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.176150</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       SemiMajorAxisAUlog\n",
       "count  1359.000000       \n",
       "mean  -1.417422          \n",
       "std    1.744565          \n",
       "min   -5.421616          \n",
       "25%   -2.951716          \n",
       "50%   -1.857899          \n",
       "75%    0.173953          \n",
       "max    5.176150          "
      ]
     },
     "execution_count": 312,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xTrain.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1359.000000\n",
       "mean     3.790160   \n",
       "std      2.571380   \n",
       "min     -2.400129   \n",
       "25%      1.487257   \n",
       "50%      3.196671   \n",
       "75%      6.069350   \n",
       "max      12.676076  \n",
       "Name: PeriodDayslog, dtype: float64"
      ]
     },
     "execution_count": 313,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yTrain.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 314,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linearRegressor.fit(xTrain,yTrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(xTrain, yTrain, color = 'blue')\n",
    "plt.plot(xTrain, linearRegressor.predict(xTrain), color = 'red',linewidth=5.0)\n",
    "plt.title('Semi major axis vs time of orbit')\n",
    "plt.xlabel('log(Semimajor axis)')\n",
    "plt.ylabel('log(Time of orbit)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Correlation coefficient**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9884410393078242"
      ]
     },
     "execution_count": 316,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linearRegressor.score(xTrain,yTrain)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Slope**\n",
    "\n",
    "**(Expected 1.5)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.46539408])"
      ]
     },
     "execution_count": 317,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linearRegressor.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.867242385585538"
      ]
     },
     "execution_count": 318,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linearRegressor.intercept_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Again this is an incorrect analysis!\n",
    "## The proportionality constant depends on mass of host star\n",
    "## Hence Keplers law should be verified within a star system\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "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>InclinationDeg</th>\n",
       "      <th>SurfaceTempK</th>\n",
       "      <th>AgeGyr</th>\n",
       "      <th>DiscoveryMethod</th>\n",
       "      <th>DiscoveryYear</th>\n",
       "      <th>LastUpdated</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>SemiMajorAxisAUlog</th>\n",
       "      <th>PeriodDayslog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HD 143761 b</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0450</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.845800</td>\n",
       "      <td>0.2196</td>\n",
       "      <td>0.037</td>\n",
       "      <td>270.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RV</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>16/07/11</td>\n",
       "      <td>16 01 03</td>\n",
       "      <td>+33 18 13</td>\n",
       "      <td>17.236</td>\n",
       "      <td>0.889</td>\n",
       "      <td>1.362</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>5627.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Confirmed planets</td>\n",
       "      <td>-1.515948</td>\n",
       "      <td>3.685017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HD 143761 c</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0790</td>\n",
       "      <td>NaN</td>\n",
       "      <td>102.540000</td>\n",
       "      <td>0.4123</td>\n",
       "      <td>0.050</td>\n",
       "      <td>190.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RV</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>16/07/11</td>\n",
       "      <td>16 01 03</td>\n",
       "      <td>+33 18 13</td>\n",
       "      <td>17.236</td>\n",
       "      <td>0.889</td>\n",
       "      <td>1.362</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>5627.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Confirmed planets</td>\n",
       "      <td>-0.886004</td>\n",
       "      <td>4.630253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KOI-1843.03</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0014</td>\n",
       "      <td>0.054</td>\n",
       "      <td>0.176891</td>\n",
       "      <td>0.0048</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>transit</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>13/07/15</td>\n",
       "      <td>19 00 03.14</td>\n",
       "      <td>+40 13 14.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.460</td>\n",
       "      <td>0.450</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3584.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Controversial</td>\n",
       "      <td>-5.339139</td>\n",
       "      <td>-1.732220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>KOI-1843.01</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.114</td>\n",
       "      <td>4.194525</td>\n",
       "      <td>0.0390</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>89.38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>transit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19 00 03.14</td>\n",
       "      <td>+40 13 14.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.460</td>\n",
       "      <td>0.450</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3584.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Controversial</td>\n",
       "      <td>-3.244194</td>\n",
       "      <td>1.433780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>KOI-1843.02</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.071</td>\n",
       "      <td>6.356006</td>\n",
       "      <td>0.0520</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>88.24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>transit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19 00 03.14</td>\n",
       "      <td>+40 13 14.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.460</td>\n",
       "      <td>0.450</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3584.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Controversial</td>\n",
       "      <td>-2.956512</td>\n",
       "      <td>1.849400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  PlanetIdentifier  TypeFlag  PlanetaryMassJpt  RadiusJpt  PeriodDays  SemiMajorAxisAU  Eccentricity  PeriastronDeg  LongitudeDeg  AscendingNodeDeg  InclinationDeg  SurfaceTempK  AgeGyr DiscoveryMethod  DiscoveryYear LastUpdated RightAscension  Declination  DistFromSunParsec  HostStarMassSlrMass  HostStarRadiusSlrRad  HostStarMetallicity  HostStarTempK  HostStarAgeGyr    ListsPlanetIsOn  SemiMajorAxisAUlog  PeriodDayslog\n",
       "0  HD 143761 b      0         1.0450           NaN         39.845800   0.2196           0.037         270.6         NaN           NaN               NaN             NaN           NaN      RV              2016.0         16/07/11    16 01 03       +33 18 13    17.236             0.889                1.362                -0.31                 5627.0        NaN              Confirmed planets -1.515948            3.685017     \n",
       "1  HD 143761 c      0         0.0790           NaN         102.540000  0.4123           0.050         190.0         NaN           NaN               NaN             NaN           NaN      RV              2016.0         16/07/11    16 01 03       +33 18 13    17.236             0.889                1.362                -0.31                 5627.0        NaN              Confirmed planets -0.886004            4.630253     \n",
       "2  KOI-1843.03      0         0.0014            0.054      0.176891    0.0048          NaN           NaN            NaN           NaN                72.00          NaN           NaN      transit         2012.0         13/07/15    19 00 03.14    +40 13 14.7 NaN                 0.460                0.450                 0.00                 3584.0        NaN              Controversial     -5.339139           -1.732220     \n",
       "3  KOI-1843.01      0        NaN                0.114      4.194525    0.0390          NaN           NaN            NaN           NaN                89.38          NaN           NaN      transit        NaN             NaN         19 00 03.14    +40 13 14.7 NaN                 0.460                0.450                 0.00                 3584.0        NaN              Controversial     -3.244194            1.433780     \n",
       "4  KOI-1843.02      0        NaN                0.071      6.356006    0.0520          NaN           NaN            NaN           NaN                88.24          NaN           NaN      transit        NaN             NaN         19 00 03.14    +40 13 14.7 NaN                 0.460                0.450                 0.00                 3584.0        NaN              Controversial     -2.956512            1.849400     "
      ]
     },
     "execution_count": 319,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making a linear regression function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {},
   "outputs": [],
   "source": [
    "def linreg(x,y):\n",
    "    x=np.array(x).reshape(-1,1)\n",
    "    y=np.array(y)\n",
    "    linearRegressor = LinearRegression()\n",
    "    linearRegressor.fit(x,y)\n",
    "    return len(y),linearRegressor.score(x,y),linearRegressor.coef_[0],linearRegressor.intercept_\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initializing variables for iterating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [],
   "source": [
    "ra=\"\"\n",
    "dec=\"\"\n",
    "x=[]\n",
    "y=[]\n",
    "counts=[]\n",
    "scores=[]\n",
    "slopes=[]\n",
    "intercepts=[]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Planets from the same system occur together\n",
    "**So we'll do linear regression on the planets in the same star system and form a new dataframe with the results**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "outputs": [],
   "source": [
    "for row in df.itertuples(index=True, name='Pandas'):\n",
    "    if(ra==getattr(row, \"RightAscension\") and dec==getattr(row, \"Declination\")):\n",
    "        x.append(getattr(row, \"SemiMajorAxisAUlog\"))\n",
    "        y.append(getattr(row, \"PeriodDayslog\"))\n",
    "    else:\n",
    "        if(len(x)>=2):\n",
    "            count,score,slope,intercept=linreg(x,y)\n",
    "            counts.append(count)\n",
    "            scores.append(score)\n",
    "            slopes.append(slope)\n",
    "            intercepts.append(intercept)\n",
    "\n",
    "        ra=getattr(row, \"RightAscension\")\n",
    "        dec=getattr(row, \"Declination\")\n",
    "        x=[getattr(row, \"SemiMajorAxisAUlog\")]\n",
    "        y=[getattr(row, \"PeriodDayslog\")]\n",
    "        \n",
    "linreg_results=pd.DataFrame({\"Count\":counts,\"Score\":scores,\"Slope\":slopes,\"Intercept\":intercepts})  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**This method is inefficient as it is not vectorised**\n",
    "### TASK : Try vectorising the above code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "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>Count</th>\n",
       "      <th>Score</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Intercept</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.500509</td>\n",
       "      <td>5.959710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>0.999979</td>\n",
       "      <td>1.506220</td>\n",
       "      <td>6.310834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>1.498470</td>\n",
       "      <td>5.872017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.534681</td>\n",
       "      <td>5.834513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.499987</td>\n",
       "      <td>5.880752</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Count     Score     Slope  Intercept\n",
       "0  2      1.000000  1.500509  5.959710 \n",
       "1  3      0.999979  1.506220  6.310834 \n",
       "2  3      0.999999  1.498470  5.872017 \n",
       "3  2      1.000000  1.534681  5.834513 \n",
       "4  2      1.000000  1.499987  5.880752 "
      ]
     },
     "execution_count": 323,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linreg_results.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "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>Count</th>\n",
       "      <th>Score</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Intercept</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>217.00000</td>\n",
       "      <td>217.000000</td>\n",
       "      <td>217.000000</td>\n",
       "      <td>217.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.62212</td>\n",
       "      <td>0.998907</td>\n",
       "      <td>1.518852</td>\n",
       "      <td>6.026499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.11599</td>\n",
       "      <td>0.009595</td>\n",
       "      <td>0.342881</td>\n",
       "      <td>1.134709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.00000</td>\n",
       "      <td>0.888038</td>\n",
       "      <td>0.818987</td>\n",
       "      <td>4.502366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.497343</td>\n",
       "      <td>5.838710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.499910</td>\n",
       "      <td>5.901609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.501965</td>\n",
       "      <td>6.027985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.437564</td>\n",
       "      <td>21.895963</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Count       Score       Slope   Intercept\n",
       "count  217.00000  217.000000  217.000000  217.000000\n",
       "mean   2.62212    0.998907    1.518852    6.026499  \n",
       "std    1.11599    0.009595    0.342881    1.134709  \n",
       "min    2.00000    0.888038    0.818987    4.502366  \n",
       "25%    2.00000    1.000000    1.497343    5.838710  \n",
       "50%    2.00000    1.000000    1.499910    5.901609  \n",
       "75%    3.00000    1.000000    1.501965    6.027985  \n",
       "max    9.00000    1.000000    6.437564    21.895963 "
      ]
     },
     "execution_count": 324,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linreg_results.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The standard deviation of slope is unnaturally high lets make a boxplot to find the problem**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(linreg_results[\"Slope\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**We find one outlier having a very high slope so let's check after removing it**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "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>Count</th>\n",
       "      <th>Score</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Intercept</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>216.000000</td>\n",
       "      <td>216.000000</td>\n",
       "      <td>216.000000</td>\n",
       "      <td>216.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.625000</td>\n",
       "      <td>0.998902</td>\n",
       "      <td>1.496080</td>\n",
       "      <td>5.953029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.117774</td>\n",
       "      <td>0.009617</td>\n",
       "      <td>0.071163</td>\n",
       "      <td>0.341733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.888038</td>\n",
       "      <td>0.818987</td>\n",
       "      <td>4.502366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.497307</td>\n",
       "      <td>5.838097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.499906</td>\n",
       "      <td>5.901068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.501951</td>\n",
       "      <td>6.023135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.892012</td>\n",
       "      <td>8.426045</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Count       Score       Slope   Intercept\n",
       "count  216.000000  216.000000  216.000000  216.000000\n",
       "mean   2.625000    0.998902    1.496080    5.953029  \n",
       "std    1.117774    0.009617    0.071163    0.341733  \n",
       "min    2.000000    0.888038    0.818987    4.502366  \n",
       "25%    2.000000    1.000000    1.497307    5.838097  \n",
       "50%    2.000000    1.000000    1.499906    5.901068  \n",
       "75%    3.000000    1.000000    1.501951    6.023135  \n",
       "max    9.000000    1.000000    1.892012    8.426045  "
      ]
     },
     "execution_count": 326,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linreg_results=linreg_results[linreg_results[\"Slope\"]<6]\n",
    "linreg_results.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The standard deviation of slope is now reasonably good**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(linreg_results[\"Slope\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# HR Diagram"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"hrdiag.png\" style=\"height:700px\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Luminosity\"]=df[\"HostStarRadiusSlrRad\"]**2*df[\"HostStarTempK\"]**4\n",
    "df[\"Luminositylog\"]=np.log(df[\"Luminosity\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting the HR Diagram we find a few outliers ruining the plot so we remove them\n",
    "### The dot size in the scatterplot is mapped to the size of the star"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [],
   "source": [
    "cmap = matplotlib.colors.ListedColormap([\"blue\",\"blue\",\"blue\",\"blue\",\"yellow\",\"darkorange\",\"red\",\"red\",\"red\"][::-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5, 5))\n",
    "plt.scatter(df[\"HostStarTempK\"],df[\"Luminositylog\"],c=df[\"HostStarTempK\"],s=100*df[\"HostStarRadiusSlrRad\"], cmap=\"coolwarm_r\",edgecolor='black', linewidth=0.2)\n",
    "plt.gca().invert_xaxis()\n",
    "plt.title('HR Diagram')\n",
    "plt.xlabel('HostStarTempK')\n",
    "plt.ylabel('log(luminosity)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df[df[\"HostStarTempK\"]<25000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(18, 12))\n",
    "points=plt.scatter(df[\"HostStarTempK\"],df[\"Luminositylog\"],c=df[\"HostStarTempK\"],s=100*df[\"HostStarRadiusSlrRad\"], cmap=cmap,edgecolor='black', linewidth=0.5)\n",
    "plt.colorbar(points)\n",
    "plt.gca().invert_xaxis()\n",
    "plt.title('HR Diagram')\n",
    "plt.xlabel('HostStarTempK')\n",
    "plt.ylabel('log(luminosity)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### As you can see the HR diagram provides a clean separation between the main sequence stars and giants"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TASK: Try clustering the HR diagram into the main sequence and giants"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Habitability"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### A planet is habitable if it falls inside the habitable zone of the host star, where liquid water"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$\\large inner radius(AU)=\\sqrt{\\frac{L}{1.1}}$\n",
    "\n",
    "$\\large outer radius(AU)=\\sqrt{\\frac{L}{0.53}}$\n",
    "\n",
    "$\\small L = Luminosity\\:of\\:host\\:star$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**If you want to know where these values came from check out this paper**\n",
    "\n",
    "Kasting, James; Whitmire, Daniel; and Reynolds, Ray (1993). Habitable zones around main sequence stars. Icarus 101: 108-128."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Luminosity'] = df['HostStarRadiusSlrRad']**2  * (df['HostStarTempK']/5777)**4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {},
   "outputs": [],
   "source": [
    "#add habitable zone boundaries\n",
    "df['HabZoneOut'] = np.sqrt(df['Luminosity']/0.53)\n",
    "df['HabZoneIn'] = np.sqrt(df['Luminosity']/1.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    .dataframe thead th {\n",
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       "    }\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>InclinationDeg</th>\n",
       "      <th>SurfaceTempK</th>\n",
       "      <th>AgeGyr</th>\n",
       "      <th>DiscoveryMethod</th>\n",
       "      <th>DiscoveryYear</th>\n",
       "      <th>LastUpdated</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>SemiMajorAxisAUlog</th>\n",
       "      <th>PeriodDayslog</th>\n",
       "      <th>Luminosity</th>\n",
       "      <th>Luminositylog</th>\n",
       "      <th>HabZoneOut</th>\n",
       "      <th>HabZoneIn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>38 Vir b</td>\n",
       "      <td>0</td>\n",
       "      <td>4.510000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>825.900000</td>\n",
       "      <td>1.820000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>17/02/20</td>\n",
       "      <td>12 53 11.16</td>\n",
       "      <td>-03 33 11.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.1800</td>\n",
       "      <td>1.4500</td>\n",
       "      <td>7.000000e-02</td>\n",
       "      <td>6557.0</td>\n",
       "      <td>1.90</td>\n",
       "      <td>Confirmed planets</td>\n",
       "      <td>5.988365e-01</td>\n",
       "      <td>6.716474</td>\n",
       "      <td>3.489372</td>\n",
       "      <td>35.896281</td>\n",
       "      <td>2.565876</td>\n",
       "      <td>1.781055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>HD 183263 b</td>\n",
       "      <td>0</td>\n",
       "      <td>3.635000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>625.100000</td>\n",
       "      <td>1.486000</td>\n",
       "      <td>0.372800</td>\n",
       "      <td>232.900000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RV</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>15/08/15</td>\n",
       "      <td>19 28 24.6</td>\n",
       "      <td>+08 21 28.9</td>\n",
       "      <td>55.1000</td>\n",
       "      <td>1.1210</td>\n",
       "      <td>1.1170</td>\n",
       "      <td>3.020000e-01</td>\n",
       "      <td>5936.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Confirmed planets</td>\n",
       "      <td>3.960879e-01</td>\n",
       "      <td>6.437912</td>\n",
       "      <td>1.390825</td>\n",
       "      <td>34.976456</td>\n",
       "      <td>1.619938</td>\n",
       "      <td>1.124449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>Gliese 3293 b</td>\n",
       "      <td>0</td>\n",
       "      <td>0.076000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.600000</td>\n",
       "      <td>0.143400</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RV</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>14/11/25</td>\n",
       "      <td>04 28 35.72</td>\n",
       "      <td>-25 10 08.9</td>\n",
       "      <td>18.2000</td>\n",
       "      <td>0.4200</td>\n",
       "      <td>0.4040</td>\n",
       "      <td>2.000000e-02</td>\n",
       "      <td>3466.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Confirmed planets</td>\n",
       "      <td>-1.942117e+00</td>\n",
       "      <td>3.421000</td>\n",
       "      <td>0.021148</td>\n",
       "      <td>30.790345</td>\n",
       "      <td>0.199754</td>\n",
       "      <td>0.138656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>Gliese 3293 c</td>\n",
       "      <td>0</td>\n",
       "      <td>0.027000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48.140000</td>\n",
       "      <td>0.193900</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RV</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>14/11/25</td>\n",
       "      <td>04 28 35.72</td>\n",
       "      <td>-25 10 08.9</td>\n",
       "      <td>18.2000</td>\n",
       "      <td>0.4200</td>\n",
       "      <td>0.4040</td>\n",
       "      <td>2.000000e-02</td>\n",
       "      <td>3466.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Controversial</td>\n",
       "      <td>-1.640413e+00</td>\n",
       "      <td>3.874113</td>\n",
       "      <td>0.021148</td>\n",
       "      <td>30.790345</td>\n",
       "      <td>0.199754</td>\n",
       "      <td>0.138656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>HD 165155 b</td>\n",
       "      <td>0</td>\n",
       "      <td>2.890000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>434.500000</td>\n",
       "      <td>1.130000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>211.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RV</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>16/04/02</td>\n",
       "      <td>18 05 57</td>\n",
       "      <td>-29 04 58</td>\n",
       "      <td>64.9800</td>\n",
       "      <td>1.0200</td>\n",
       "      <td>0.9500</td>\n",
       "      <td>9.000000e-02</td>\n",
       "      <td>5426.0</td>\n",
       "      <td>11.00</td>\n",
       "      <td>Confirmed planets</td>\n",
       "      <td>1.222176e-01</td>\n",
       "      <td>6.074196</td>\n",
       "      <td>0.702355</td>\n",
       "      <td>34.293243</td>\n",
       "      <td>1.151173</td>\n",
       "      <td>0.799065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>HD 210277 b</td>\n",
       "      <td>0</td>\n",
       "      <td>1.230000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>442.100000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>0.472000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>240.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RV</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>06/09/06</td>\n",
       "      <td>22 09 29</td>\n",
       "      <td>-07 32 32</td>\n",
       "      <td>21.2900</td>\n",
       "      <td>1.0900</td>\n",
       "      <td>1.1000</td>\n",
       "      <td>1.900000e-01</td>\n",
       "      <td>5532.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Confirmed planets</td>\n",
       "      <td>9.531018e-02</td>\n",
       "      <td>6.091536</td>\n",
       "      <td>1.017430</td>\n",
       "      <td>34.663839</td>\n",
       "      <td>1.385525</td>\n",
       "      <td>0.961736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>HD 125612 b</td>\n",
       "      <td>0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>502.000000</td>\n",
       "      <td>1.370000</td>\n",
       "      <td>0.460000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>224.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RV</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>10/12/06</td>\n",
       "      <td>14 20 54</td>\n",
       "      <td>-17 28 53</td>\n",
       "      <td>52.8200</td>\n",
       "      <td>1.1000</td>\n",
       "      <td>1.0500</td>\n",
       "      <td>2.400000e-01</td>\n",
       "      <td>5897.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Confirmed planets</td>\n",
       "      <td>3.148107e-01</td>\n",
       "      <td>6.218600</td>\n",
       "      <td>1.196999</td>\n",
       "      <td>34.826376</td>\n",
       "      <td>1.502827</td>\n",
       "      <td>1.043159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>HD 4732 c</td>\n",
       "      <td>0</td>\n",
       "      <td>2.370000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2732.000000</td>\n",
       "      <td>4.600000</td>\n",
       "      <td>0.230000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RV</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>12/10/26</td>\n",
       "      <td>00 49 13</td>\n",
       "      <td>-24 08 12</td>\n",
       "      <td>56.5000</td>\n",
       "      <td>1.7400</td>\n",
       "      <td>5.4000</td>\n",
       "      <td>1.000000e-02</td>\n",
       "      <td>4959.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Confirmed planets</td>\n",
       "      <td>1.526056e+00</td>\n",
       "      <td>7.912789</td>\n",
       "      <td>15.832682</td>\n",
       "      <td>37.408635</td>\n",
       "      <td>5.465618</td>\n",
       "      <td>3.793856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>Gliese 876 c</td>\n",
       "      <td>0</td>\n",
       "      <td>0.842900</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.076600</td>\n",
       "      <td>0.135985</td>\n",
       "      <td>0.253900</td>\n",
       "      <td>117.120000</td>\n",
       "      <td>-104.600000</td>\n",
       "      <td>-1.290000</td>\n",
       "      <td>53.060000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RV</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>15/06/08</td>\n",
       "      <td>22 53 13</td>\n",
       "      <td>-14 15 13</td>\n",
       "      <td>4.6900</td>\n",
       "      <td>0.3700</td>\n",
       "      <td>0.3761</td>\n",
       "      <td>1.900000e-01</td>\n",
       "      <td>3129.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Confirmed planets</td>\n",
       "      <td>-1.995211e+00</td>\n",
       "      <td>3.403747</td>\n",
       "      <td>0.012174</td>\n",
       "      <td>30.238075</td>\n",
       "      <td>0.151556</td>\n",
       "      <td>0.105199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>Earth</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003146</td>\n",
       "      <td>0.091130</td>\n",
       "      <td>365.242200</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.016732</td>\n",
       "      <td>102.930059</td>\n",
       "      <td>100.466916</td>\n",
       "      <td>-5.112604</td>\n",
       "      <td>-0.000543</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12/01/01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.000000e-08</td>\n",
       "      <td>5778.0</td>\n",
       "      <td>4.57</td>\n",
       "      <td>Solar System</td>\n",
       "      <td>1.800000e-07</td>\n",
       "      <td>5.900561</td>\n",
       "      <td>1.000693</td>\n",
       "      <td>34.647252</td>\n",
       "      <td>1.374081</td>\n",
       "      <td>0.953793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>Kepler-454 c</td>\n",
       "      <td>0</td>\n",
       "      <td>4.460000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>523.900000</td>\n",
       "      <td>1.286000</td>\n",
       "      <td>0.021400</td>\n",
       "      <td>337.400000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RV</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>16/01/09</td>\n",
       "      <td>19 09 54.841</td>\n",
       "      <td>+38 13 43.95</td>\n",
       "      <td>200.0000</td>\n",
       "      <td>1.0280</td>\n",
       "      <td>1.0660</td>\n",
       "      <td>2.700000e-01</td>\n",
       "      <td>5687.0</td>\n",
       "      <td>5.23</td>\n",
       "      <td>Confirmed planets</td>\n",
       "      <td>2.515366e-01</td>\n",
       "      <td>6.261301</td>\n",
       "      <td>1.067180</td>\n",
       "      <td>34.711579</td>\n",
       "      <td>1.418995</td>\n",
       "      <td>0.984969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>532</th>\n",
       "      <td>HD 216435 b</td>\n",
       "      <td>0</td>\n",
       "      <td>1.260000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1311.000000</td>\n",
       "      <td>2.560000</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>224.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RV</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>09/10/06</td>\n",
       "      <td>22 53 37</td>\n",
       "      <td>-48 35 53</td>\n",
       "      <td>33.3000</td>\n",
       "      <td>1.3000</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>2.440000e-01</td>\n",
       "      <td>5767.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Confirmed planets</td>\n",
       "      <td>9.400073e-01</td>\n",
       "      <td>7.178545</td>\n",
       "      <td>3.972376</td>\n",
       "      <td>36.025924</td>\n",
       "      <td>2.737709</td>\n",
       "      <td>1.900329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>HD 23596 b</td>\n",
       "      <td>0</td>\n",
       "      <td>8.100000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1565.000000</td>\n",
       "      <td>2.880000</td>\n",
       "      <td>0.292000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>220.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RV</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>10/12/30</td>\n",
       "      <td>03 48 00</td>\n",
       "      <td>+40 31 50</td>\n",
       "      <td>52.0000</td>\n",
       "      <td>1.2700</td>\n",
       "      <td>2.0900</td>\n",
       "      <td>3.200000e-01</td>\n",
       "      <td>5888.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Confirmed planets</td>\n",
       "      <td>1.057790e+00</td>\n",
       "      <td>7.355641</td>\n",
       "      <td>4.713617</td>\n",
       "      <td>36.197015</td>\n",
       "      <td>2.982217</td>\n",
       "      <td>2.070050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>839</th>\n",
       "      <td>HD 137388 b</td>\n",
       "      <td>0</td>\n",
       "      <td>0.223000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>0.890000</td>\n",
       "      <td>0.360000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>219.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RV</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>11/08/02</td>\n",
       "      <td>15 35 40</td>\n",
       "      <td>-80 12 17</td>\n",
       "      <td>38.0000</td>\n",
       "      <td>0.8600</td>\n",
       "      <td>0.8200</td>\n",
       "      <td>1.800000e-01</td>\n",
       "      <td>5240.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Confirmed planets</td>\n",
       "      <td>-1.165338e-01</td>\n",
       "      <td>5.799093</td>\n",
       "      <td>0.455138</td>\n",
       "      <td>33.859405</td>\n",
       "      <td>0.926688</td>\n",
       "      <td>0.643243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>873</th>\n",
       "      <td>HD 23079 b</td>\n",
       "      <td>0</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>626.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>223.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RV</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>10/12/30</td>\n",
       "      <td>03 39 43</td>\n",
       "      <td>-52 54 57</td>\n",
       "      <td>34.8000</td>\n",
       "      <td>1.1000</td>\n",
       "      <td>1.1300</td>\n",
       "      <td>-2.400000e-01</td>\n",
       "      <td>5848.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Confirmed planets</td>\n",
       "      <td>4.054651e-01</td>\n",
       "      <td>6.439350</td>\n",
       "      <td>1.340840</td>\n",
       "      <td>34.939855</td>\n",
       "      <td>1.590562</td>\n",
       "      <td>1.104059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>913</th>\n",
       "      <td>WASP-41 c</td>\n",
       "      <td>0</td>\n",
       "      <td>3.180000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>421.000000</td>\n",
       "      <td>1.070000</td>\n",
       "      <td>0.294000</td>\n",
       "      <td>353.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RV</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>15/10/16</td>\n",
       "      <td>12 42 28.497</td>\n",
       "      <td>-30 38 23.55</td>\n",
       "      <td>180.0000</td>\n",
       "      <td>0.9300</td>\n",
       "      <td>0.8700</td>\n",
       "      <td>6.000000e-02</td>\n",
       "      <td>5545.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Confirmed planets</td>\n",
       "      <td>6.765865e-02</td>\n",
       "      <td>6.042633</td>\n",
       "      <td>0.642444</td>\n",
       "      <td>34.204083</td>\n",
       "      <td>1.100981</td>\n",
       "      <td>0.764225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>921</th>\n",
       "      <td>HD 43197 b</td>\n",
       "      <td>0</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>327.800000</td>\n",
       "      <td>0.920000</td>\n",
       "      <td>0.830000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>239.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RV</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>10/09/08</td>\n",
       "      <td>06 13 36</td>\n",
       "      <td>-29 53 50</td>\n",
       "      <td>54.9000</td>\n",
       "      <td>0.9600</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>4.000000e-01</td>\n",
       "      <td>5508.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Confirmed planets</td>\n",
       "      <td>-8.338161e-02</td>\n",
       "      <td>5.792404</td>\n",
       "      <td>0.826354</td>\n",
       "      <td>34.455827</td>\n",
       "      <td>1.248663</td>\n",
       "      <td>0.866736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>960</th>\n",
       "      <td>HD 1605 c</td>\n",
       "      <td>0</td>\n",
       "      <td>3.480000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2111.000000</td>\n",
       "      <td>3.520000</td>\n",
       "      <td>0.098000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RV</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>15/03/26</td>\n",
       "      <td>00 20 31.51872</td>\n",
       "      <td>+30 58 29.2948</td>\n",
       "      <td>84.6000</td>\n",
       "      <td>1.3100</td>\n",
       "      <td>3.8000</td>\n",
       "      <td>2.100000e-01</td>\n",
       "      <td>4757.0</td>\n",
       "      <td>4.59</td>\n",
       "      <td>Confirmed planets</td>\n",
       "      <td>1.258461e+00</td>\n",
       "      <td>7.654917</td>\n",
       "      <td>6.638811</td>\n",
       "      <td>36.539492</td>\n",
       "      <td>3.539217</td>\n",
       "      <td>2.456681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1123</th>\n",
       "      <td>Wolf 1061 c</td>\n",
       "      <td>0</td>\n",
       "      <td>0.013400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.867000</td>\n",
       "      <td>0.084270</td>\n",
       "      <td>0.190000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RV</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>15/12/27</td>\n",
       "      <td>16 30 18.05803</td>\n",
       "      <td>-12 39 45.3232</td>\n",
       "      <td>4.2922</td>\n",
       "      <td>0.2500</td>\n",
       "      <td>0.2600</td>\n",
       "      <td>-5.000000e-02</td>\n",
       "      <td>3345.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Confirmed planets</td>\n",
       "      <td>-2.473729e+00</td>\n",
       "      <td>2.882955</td>\n",
       "      <td>0.007598</td>\n",
       "      <td>29.766741</td>\n",
       "      <td>0.119735</td>\n",
       "      <td>0.083112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1174</th>\n",
       "      <td>KELT-6 c</td>\n",
       "      <td>0</td>\n",
       "      <td>3.710000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1276.000000</td>\n",
       "      <td>2.390000</td>\n",
       "      <td>0.210000</td>\n",
       "      <td>268.700000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RV</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>15/08/27</td>\n",
       "      <td>13 03 55.648</td>\n",
       "      <td>+30 38 24.18</td>\n",
       "      <td>222.0000</td>\n",
       "      <td>1.1260</td>\n",
       "      <td>1.5290</td>\n",
       "      <td>-2.700000e-01</td>\n",
       "      <td>6272.0</td>\n",
       "      <td>4.90</td>\n",
       "      <td>Confirmed planets</td>\n",
       "      <td>8.712934e-01</td>\n",
       "      <td>7.151485</td>\n",
       "      <td>3.248102</td>\n",
       "      <td>35.824630</td>\n",
       "      <td>2.475580</td>\n",
       "      <td>1.718377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1222</th>\n",
       "      <td>HD 218566 b</td>\n",
       "      <td>0</td>\n",
       "      <td>0.210000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>225.700000</td>\n",
       "      <td>0.687300</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RV</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>15/11/03</td>\n",
       "      <td>23 09 10.72771</td>\n",
       "      <td>-02 15 38.6775</td>\n",
       "      <td>28.5500</td>\n",
       "      <td>0.8500</td>\n",
       "      <td>0.8600</td>\n",
       "      <td>3.800000e-01</td>\n",
       "      <td>4820.0</td>\n",
       "      <td>8.50</td>\n",
       "      <td>Confirmed planets</td>\n",
       "      <td>-3.749844e-01</td>\n",
       "      <td>5.419207</td>\n",
       "      <td>0.358406</td>\n",
       "      <td>33.620471</td>\n",
       "      <td>0.822337</td>\n",
       "      <td>0.570810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1240</th>\n",
       "      <td>HD 13908 c</td>\n",
       "      <td>0</td>\n",
       "      <td>5.130000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>931.000000</td>\n",
       "      <td>2.030000</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>185.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RV</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>13/11/22</td>\n",
       "      <td>02 18 14.56056</td>\n",
       "      <td>+65 35 39.6988</td>\n",
       "      <td>71.3000</td>\n",
       "      <td>1.2900</td>\n",
       "      <td>1.6700</td>\n",
       "      <td>1.000000e-02</td>\n",
       "      <td>6255.0</td>\n",
       "      <td>2.90</td>\n",
       "      <td>Confirmed planets</td>\n",
       "      <td>7.080358e-01</td>\n",
       "      <td>6.836259</td>\n",
       "      <td>3.832946</td>\n",
       "      <td>35.990193</td>\n",
       "      <td>2.689233</td>\n",
       "      <td>1.866681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1263</th>\n",
       "      <td>Kepler-446 c</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.101155</td>\n",
       "      <td>3.036179</td>\n",
       "      <td>0.104000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>88.970000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>transit</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>15/01/06</td>\n",
       "      <td>18 49 00.051</td>\n",
       "      <td>+44 55 15.99</td>\n",
       "      <td>120.0000</td>\n",
       "      <td>0.2200</td>\n",
       "      <td>0.2400</td>\n",
       "      <td>-3.000000e-01</td>\n",
       "      <td>3359.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Confirmed planets</td>\n",
       "      <td>-2.263364e+00</td>\n",
       "      <td>1.110600</td>\n",
       "      <td>0.006583</td>\n",
       "      <td>29.623362</td>\n",
       "      <td>0.111452</td>\n",
       "      <td>0.077362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1382</th>\n",
       "      <td>HD 92788 b</td>\n",
       "      <td>0</td>\n",
       "      <td>3.860000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>325.810000</td>\n",
       "      <td>0.970000</td>\n",
       "      <td>0.334000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>245.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RV</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>10/10/19</td>\n",
       "      <td>10 42 48</td>\n",
       "      <td>-02 11 01</td>\n",
       "      <td>32.8200</td>\n",
       "      <td>1.1300</td>\n",
       "      <td>0.9900</td>\n",
       "      <td>3.200000e-01</td>\n",
       "      <td>5559.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Confirmed planets</td>\n",
       "      <td>-3.045921e-02</td>\n",
       "      <td>5.786314</td>\n",
       "      <td>0.840326</td>\n",
       "      <td>34.472593</td>\n",
       "      <td>1.259174</td>\n",
       "      <td>0.874032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1474</th>\n",
       "      <td>HD 16175 b</td>\n",
       "      <td>0</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>990.000000</td>\n",
       "      <td>2.100000</td>\n",
       "      <td>0.590000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>243.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RV</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>09/04/21</td>\n",
       "      <td>02 37 02</td>\n",
       "      <td>+42 03 45</td>\n",
       "      <td>59.8000</td>\n",
       "      <td>1.3500</td>\n",
       "      <td>1.8700</td>\n",
       "      <td>2.300000e-01</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Confirmed planets</td>\n",
       "      <td>7.419373e-01</td>\n",
       "      <td>6.897705</td>\n",
       "      <td>4.068916</td>\n",
       "      <td>36.049936</td>\n",
       "      <td>2.770776</td>\n",
       "      <td>1.923282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1509</th>\n",
       "      <td>HD 28185 b</td>\n",
       "      <td>0</td>\n",
       "      <td>5.700000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>383.000000</td>\n",
       "      <td>1.030000</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>241.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RV</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>10/12/20</td>\n",
       "      <td>04 26 26</td>\n",
       "      <td>-10 33 02</td>\n",
       "      <td>39.4000</td>\n",
       "      <td>1.2400</td>\n",
       "      <td>1.0300</td>\n",
       "      <td>2.400000e-01</td>\n",
       "      <td>5482.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Confirmed planets</td>\n",
       "      <td>2.955880e-02</td>\n",
       "      <td>5.948035</td>\n",
       "      <td>0.860243</td>\n",
       "      <td>34.496019</td>\n",
       "      <td>1.274009</td>\n",
       "      <td>0.884330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1801</th>\n",
       "      <td>7 CMa b</td>\n",
       "      <td>0</td>\n",
       "      <td>2.460000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>796.000000</td>\n",
       "      <td>1.930000</td>\n",
       "      <td>0.220000</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RV</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>15/10/25</td>\n",
       "      <td>06 36 41.038</td>\n",
       "      <td>-19 15 21.17</td>\n",
       "      <td>19.7500</td>\n",
       "      <td>1.5200</td>\n",
       "      <td>2.3000</td>\n",
       "      <td>2.100000e-01</td>\n",
       "      <td>4792.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Confirmed planets</td>\n",
       "      <td>6.575200e-01</td>\n",
       "      <td>6.679599</td>\n",
       "      <td>2.504456</td>\n",
       "      <td>35.564631</td>\n",
       "      <td>2.173796</td>\n",
       "      <td>1.508900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1919</th>\n",
       "      <td>Gliese 667 C f</td>\n",
       "      <td>2</td>\n",
       "      <td>0.008493</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.026000</td>\n",
       "      <td>0.156000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>103.132403</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RV</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>13/06/25</td>\n",
       "      <td>17 18 57</td>\n",
       "      <td>-34 59 23</td>\n",
       "      <td>6.9700</td>\n",
       "      <td>0.3300</td>\n",
       "      <td>0.4100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Controversial, Planets in binary systems, S-type</td>\n",
       "      <td>-1.857899e+00</td>\n",
       "      <td>3.664228</td>\n",
       "      <td>0.019008</td>\n",
       "      <td>30.683666</td>\n",
       "      <td>0.189379</td>\n",
       "      <td>0.131454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1952</th>\n",
       "      <td>HD 20782 b</td>\n",
       "      <td>2</td>\n",
       "      <td>1.430000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>597.065000</td>\n",
       "      <td>1.397000</td>\n",
       "      <td>0.956000</td>\n",
       "      <td>142.100000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RV</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>16/01/10</td>\n",
       "      <td>03 20 03.57791</td>\n",
       "      <td>-28 51 14.6651</td>\n",
       "      <td>35.4900</td>\n",
       "      <td>1.0200</td>\n",
       "      <td>1.0900</td>\n",
       "      <td>1.000000e-02</td>\n",
       "      <td>5798.0</td>\n",
       "      <td>5.53</td>\n",
       "      <td>Confirmed planets, Planets in binary systems, S-type</td>\n",
       "      <td>3.343271e-01</td>\n",
       "      <td>6.392026</td>\n",
       "      <td>1.205470</td>\n",
       "      <td>34.833429</td>\n",
       "      <td>1.508135</td>\n",
       "      <td>1.046844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971</th>\n",
       "      <td>Proxima Centauri b</td>\n",
       "      <td>2</td>\n",
       "      <td>0.004089</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.186000</td>\n",
       "      <td>0.048500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>234.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RV</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>16/08/22</td>\n",
       "      <td>14 39 36.204</td>\n",
       "      <td>-60 50 08.23</td>\n",
       "      <td>1.2950</td>\n",
       "      <td>0.1200</td>\n",
       "      <td>0.1410</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3050.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Confirmed planets, Planets in binary systems, S-type</td>\n",
       "      <td>-3.026191e+00</td>\n",
       "      <td>2.414663</td>\n",
       "      <td>0.001545</td>\n",
       "      <td>28.173597</td>\n",
       "      <td>0.053985</td>\n",
       "      <td>0.037473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>HD 141937 b</td>\n",
       "      <td>0</td>\n",
       "      <td>9.700000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>653.220000</td>\n",
       "      <td>1.520000</td>\n",
       "      <td>0.410000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>215.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RV</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>10/12/29</td>\n",
       "      <td>15 52 17</td>\n",
       "      <td>-18 26 09</td>\n",
       "      <td>33.4600</td>\n",
       "      <td>1.1000</td>\n",
       "      <td>1.0600</td>\n",
       "      <td>1.100000e-01</td>\n",
       "      <td>5925.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Confirmed planets</td>\n",
       "      <td>4.187103e-01</td>\n",
       "      <td>6.481914</td>\n",
       "      <td>1.243242</td>\n",
       "      <td>34.864282</td>\n",
       "      <td>1.531581</td>\n",
       "      <td>1.063118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2025</th>\n",
       "      <td>Kepler-442 b</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>112.305300</td>\n",
       "      <td>0.409000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>89.940000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>transit</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>15/01/28</td>\n",
       "      <td>19 01 27.98</td>\n",
       "      <td>+39 16 48.3</td>\n",
       "      <td>342.0000</td>\n",
       "      <td>0.6090</td>\n",
       "      <td>0.5980</td>\n",
       "      <td>-3.700000e-01</td>\n",
       "      <td>4402.0</td>\n",
       "      <td>2.90</td>\n",
       "      <td>Confirmed planets</td>\n",
       "      <td>-8.940401e-01</td>\n",
       "      <td>4.721221</td>\n",
       "      <td>0.120557</td>\n",
       "      <td>32.530928</td>\n",
       "      <td>0.476934</td>\n",
       "      <td>0.331055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2115</th>\n",
       "      <td>Kepler-443 b</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.208000</td>\n",
       "      <td>177.669300</td>\n",
       "      <td>0.495000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>89.940000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>transit</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>15/01/28</td>\n",
       "      <td>19 14 26.53</td>\n",
       "      <td>+49 58 07.1</td>\n",
       "      <td>779.0000</td>\n",
       "      <td>0.7380</td>\n",
       "      <td>0.7060</td>\n",
       "      <td>-1.000000e-02</td>\n",
       "      <td>4723.0</td>\n",
       "      <td>3.20</td>\n",
       "      <td>Confirmed planets</td>\n",
       "      <td>-7.031975e-01</td>\n",
       "      <td>5.179924</td>\n",
       "      <td>0.222675</td>\n",
       "      <td>33.144518</td>\n",
       "      <td>0.648183</td>\n",
       "      <td>0.449924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2236</th>\n",
       "      <td>HD 86264 b</td>\n",
       "      <td>0</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1475.000000</td>\n",
       "      <td>2.860000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>214.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RV</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>09/08/13</td>\n",
       "      <td>09 56 58</td>\n",
       "      <td>-15 53 42</td>\n",
       "      <td>72.6000</td>\n",
       "      <td>1.4200</td>\n",
       "      <td>1.8800</td>\n",
       "      <td>2.020000e-01</td>\n",
       "      <td>6210.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Confirmed planets</td>\n",
       "      <td>1.050822e+00</td>\n",
       "      <td>7.296413</td>\n",
       "      <td>4.719246</td>\n",
       "      <td>36.198208</td>\n",
       "      <td>2.983997</td>\n",
       "      <td>2.071286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2279</th>\n",
       "      <td>55 Cnc f</td>\n",
       "      <td>2</td>\n",
       "      <td>0.141000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>262.000000</td>\n",
       "      <td>0.788000</td>\n",
       "      <td>0.305000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>185.600000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RV</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>11/06/07</td>\n",
       "      <td>08 52 35.81093</td>\n",
       "      <td>+28 19 50.9511</td>\n",
       "      <td>12.3400</td>\n",
       "      <td>0.9600</td>\n",
       "      <td>0.9600</td>\n",
       "      <td>3.000000e-01</td>\n",
       "      <td>5219.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Confirmed planets, Planets in binary systems, S-type</td>\n",
       "      <td>-2.382572e-01</td>\n",
       "      <td>5.568345</td>\n",
       "      <td>0.613878</td>\n",
       "      <td>34.158600</td>\n",
       "      <td>1.076225</td>\n",
       "      <td>0.747042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2295</th>\n",
       "      <td>Kepler-296 f</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.161000</td>\n",
       "      <td>63.336270</td>\n",
       "      <td>0.255000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>89.700000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>transit</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>15/05/11</td>\n",
       "      <td>19 06 09.602</td>\n",
       "      <td>+49 26 14.37</td>\n",
       "      <td>558.5000</td>\n",
       "      <td>0.4980</td>\n",
       "      <td>0.4800</td>\n",
       "      <td>-8.000000e-02</td>\n",
       "      <td>3740.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Confirmed planets, Planets in binary systems, S-type</td>\n",
       "      <td>-1.366492e+00</td>\n",
       "      <td>4.148458</td>\n",
       "      <td>0.040472</td>\n",
       "      <td>31.439425</td>\n",
       "      <td>0.276339</td>\n",
       "      <td>0.191815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2447</th>\n",
       "      <td>HD 82943 b</td>\n",
       "      <td>0</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>442.400000</td>\n",
       "      <td>1.186600</td>\n",
       "      <td>0.203000</td>\n",
       "      <td>107.000000</td>\n",
       "      <td>333.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.410000</td>\n",
       "      <td>250.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RV</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>13/06/05</td>\n",
       "      <td>09 34 50</td>\n",
       "      <td>-12 07 46</td>\n",
       "      <td>27.4600</td>\n",
       "      <td>1.1300</td>\n",
       "      <td>1.1200</td>\n",
       "      <td>3.200000e-01</td>\n",
       "      <td>5874.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Confirmed planets</td>\n",
       "      <td>1.710921e-01</td>\n",
       "      <td>6.092214</td>\n",
       "      <td>1.340795</td>\n",
       "      <td>34.939822</td>\n",
       "      <td>1.590535</td>\n",
       "      <td>1.104040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2554</th>\n",
       "      <td>Upsilon Andromedae A d</td>\n",
       "      <td>2</td>\n",
       "      <td>8.860000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1282.570000</td>\n",
       "      <td>2.533539</td>\n",
       "      <td>0.249090</td>\n",
       "      <td>253.270000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.970000</td>\n",
       "      <td>28.300000</td>\n",
       "      <td>218.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RV</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>15/03/16</td>\n",
       "      <td>01 36 48</td>\n",
       "      <td>+41 24 38</td>\n",
       "      <td>13.4700</td>\n",
       "      <td>1.2700</td>\n",
       "      <td>1.6310</td>\n",
       "      <td>9.000000e-02</td>\n",
       "      <td>6212.0</td>\n",
       "      <td>3.10</td>\n",
       "      <td>Confirmed planets, Planets in binary systems, S-type</td>\n",
       "      <td>9.296171e-01</td>\n",
       "      <td>7.156621</td>\n",
       "      <td>3.556512</td>\n",
       "      <td>35.915339</td>\n",
       "      <td>2.590444</td>\n",
       "      <td>1.798108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2569</th>\n",
       "      <td>TRAPPIST-1 e</td>\n",
       "      <td>0</td>\n",
       "      <td>0.001950</td>\n",
       "      <td>0.083658</td>\n",
       "      <td>6.099615</td>\n",
       "      <td>0.028170</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>89.860000</td>\n",
       "      <td>251.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>transit</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>17/02/22</td>\n",
       "      <td>23 06 30.34</td>\n",
       "      <td>-05 02 36.44</td>\n",
       "      <td>12.1000</td>\n",
       "      <td>0.0802</td>\n",
       "      <td>0.1170</td>\n",
       "      <td>4.000000e-02</td>\n",
       "      <td>2559.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Confirmed planets</td>\n",
       "      <td>-3.569498e+00</td>\n",
       "      <td>1.808226</td>\n",
       "      <td>0.000527</td>\n",
       "      <td>27.098325</td>\n",
       "      <td>0.031534</td>\n",
       "      <td>0.021889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2656</th>\n",
       "      <td>Gliese 687 b</td>\n",
       "      <td>0</td>\n",
       "      <td>0.058000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.140000</td>\n",
       "      <td>0.163530</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RV</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>14/05/12</td>\n",
       "      <td>17 36 25.89973</td>\n",
       "      <td>+68 20 20.9108</td>\n",
       "      <td>4.5000</td>\n",
       "      <td>0.4130</td>\n",
       "      <td>0.4183</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3413.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Confirmed planets</td>\n",
       "      <td>-1.810759e+00</td>\n",
       "      <td>3.641264</td>\n",
       "      <td>0.021316</td>\n",
       "      <td>30.798275</td>\n",
       "      <td>0.200548</td>\n",
       "      <td>0.139206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2759</th>\n",
       "      <td>Gliese 581 g</td>\n",
       "      <td>0</td>\n",
       "      <td>0.009752</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.500000</td>\n",
       "      <td>0.146000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>230.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RV</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>14/07/04</td>\n",
       "      <td>15 19 26</td>\n",
       "      <td>-07 43 20</td>\n",
       "      <td>6.2100</td>\n",
       "      <td>0.3100</td>\n",
       "      <td>0.3000</td>\n",
       "      <td>-1.350000e-01</td>\n",
       "      <td>3498.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Retracted planet candidate</td>\n",
       "      <td>-1.924149e+00</td>\n",
       "      <td>3.597312</td>\n",
       "      <td>0.012098</td>\n",
       "      <td>30.231841</td>\n",
       "      <td>0.151084</td>\n",
       "      <td>0.104872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2862</th>\n",
       "      <td>mu Arae b</td>\n",
       "      <td>0</td>\n",
       "      <td>1.676000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>643.250000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.128000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>228.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RV</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>09/04/02</td>\n",
       "      <td>17 44 08</td>\n",
       "      <td>-51 50 02</td>\n",
       "      <td>15.3000</td>\n",
       "      <td>1.0800</td>\n",
       "      <td>1.2450</td>\n",
       "      <td>2.800000e-01</td>\n",
       "      <td>5700.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Confirmed planets</td>\n",
       "      <td>4.054651e-01</td>\n",
       "      <td>6.466533</td>\n",
       "      <td>1.469023</td>\n",
       "      <td>35.031157</td>\n",
       "      <td>1.664855</td>\n",
       "      <td>1.155628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2881</th>\n",
       "      <td>WASP-47 c</td>\n",
       "      <td>0</td>\n",
       "      <td>1.240000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>572.000000</td>\n",
       "      <td>1.360000</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RV</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>15/10/17</td>\n",
       "      <td>22 04 48.731</td>\n",
       "      <td>-12 01 07.99</td>\n",
       "      <td>200.0000</td>\n",
       "      <td>1.0260</td>\n",
       "      <td>1.1500</td>\n",
       "      <td>3.600000e-01</td>\n",
       "      <td>5576.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Confirmed planets</td>\n",
       "      <td>3.074847e-01</td>\n",
       "      <td>6.349139</td>\n",
       "      <td>1.147829</td>\n",
       "      <td>34.784432</td>\n",
       "      <td>1.471637</td>\n",
       "      <td>1.021509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2948</th>\n",
       "      <td>EPIC 201912552 b</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.206000</td>\n",
       "      <td>32.944880</td>\n",
       "      <td>0.149100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>272.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>transit</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>15/07/07</td>\n",
       "      <td>11 30 14.511</td>\n",
       "      <td>+07 35 18.21</td>\n",
       "      <td>34.0000</td>\n",
       "      <td>0.4130</td>\n",
       "      <td>0.3940</td>\n",
       "      <td>9.000000e-02</td>\n",
       "      <td>3503.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Confirmed planets</td>\n",
       "      <td>-1.903138e+00</td>\n",
       "      <td>3.494836</td>\n",
       "      <td>0.020987</td>\n",
       "      <td>30.782691</td>\n",
       "      <td>0.198991</td>\n",
       "      <td>0.138126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3004</th>\n",
       "      <td>HIP 57050 b</td>\n",
       "      <td>0</td>\n",
       "      <td>0.298000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.397000</td>\n",
       "      <td>0.163506</td>\n",
       "      <td>0.314000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>218.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RV</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>10/04/27</td>\n",
       "      <td>11 41 45</td>\n",
       "      <td>+42 45 07</td>\n",
       "      <td>11.0000</td>\n",
       "      <td>0.3400</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>3.200000e-01</td>\n",
       "      <td>3190.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Confirmed planets</td>\n",
       "      <td>-1.810906e+00</td>\n",
       "      <td>3.723208</td>\n",
       "      <td>0.014876</td>\n",
       "      <td>30.438523</td>\n",
       "      <td>0.167532</td>\n",
       "      <td>0.116289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3033</th>\n",
       "      <td>HD 142415 b</td>\n",
       "      <td>0</td>\n",
       "      <td>1.620000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>386.300000</td>\n",
       "      <td>1.050000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>250.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RV</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>10/12/29</td>\n",
       "      <td>15 57 40</td>\n",
       "      <td>-60 12 00</td>\n",
       "      <td>34.2000</td>\n",
       "      <td>1.0900</td>\n",
       "      <td>1.0300</td>\n",
       "      <td>2.100000e-01</td>\n",
       "      <td>5834.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Confirmed planets</td>\n",
       "      <td>4.879016e-02</td>\n",
       "      <td>5.956614</td>\n",
       "      <td>1.103394</td>\n",
       "      <td>34.744950</td>\n",
       "      <td>1.442871</td>\n",
       "      <td>1.001542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3062</th>\n",
       "      <td>HD 564 b</td>\n",
       "      <td>0</td>\n",
       "      <td>0.330000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>492.300000</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>0.096000</td>\n",
       "      <td>314.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RV</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>15/11/17</td>\n",
       "      <td>00 09 52.82123</td>\n",
       "      <td>-50 16 04.1606</td>\n",
       "      <td>53.6000</td>\n",
       "      <td>0.9610</td>\n",
       "      <td>1.0100</td>\n",
       "      <td>-2.000000e-01</td>\n",
       "      <td>5902.0</td>\n",
       "      <td>5.50</td>\n",
       "      <td>Confirmed planets</td>\n",
       "      <td>1.823216e-01</td>\n",
       "      <td>6.199088</td>\n",
       "      <td>1.111297</td>\n",
       "      <td>34.752087</td>\n",
       "      <td>1.448029</td>\n",
       "      <td>1.005122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3126</th>\n",
       "      <td>HD 196885 A b</td>\n",
       "      <td>2</td>\n",
       "      <td>2.980000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1326.000000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>228.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RV</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>12/12/07</td>\n",
       "      <td>20 39 51</td>\n",
       "      <td>+11 14 58</td>\n",
       "      <td>33.0000</td>\n",
       "      <td>1.3300</td>\n",
       "      <td>1.7900</td>\n",
       "      <td>2.900000e-01</td>\n",
       "      <td>6340.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>Confirmed planets, Planets in binary systems, S-type</td>\n",
       "      <td>9.555114e-01</td>\n",
       "      <td>7.189922</td>\n",
       "      <td>4.647866</td>\n",
       "      <td>36.182967</td>\n",
       "      <td>2.961344</td>\n",
       "      <td>2.055561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3219</th>\n",
       "      <td>HD 10697 b</td>\n",
       "      <td>0</td>\n",
       "      <td>6.380000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1076.400000</td>\n",
       "      <td>2.160000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>221.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RV</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>10/10/19</td>\n",
       "      <td>01 44 55</td>\n",
       "      <td>+20 04 59</td>\n",
       "      <td>32.5600</td>\n",
       "      <td>1.1500</td>\n",
       "      <td>1.7200</td>\n",
       "      <td>1.000000e-01</td>\n",
       "      <td>5641.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Confirmed planets</td>\n",
       "      <td>7.701082e-01</td>\n",
       "      <td>6.981377</td>\n",
       "      <td>2.689502</td>\n",
       "      <td>35.635915</td>\n",
       "      <td>2.252672</td>\n",
       "      <td>1.563650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3333</th>\n",
       "      <td>Kepler-68 d</td>\n",
       "      <td>2</td>\n",
       "      <td>0.947000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>580.000000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>0.180000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RV</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>15/06/09</td>\n",
       "      <td>19 24 07.7644</td>\n",
       "      <td>+49 02 24.957</td>\n",
       "      <td>135.0000</td>\n",
       "      <td>1.0790</td>\n",
       "      <td>1.2430</td>\n",
       "      <td>1.200000e-01</td>\n",
       "      <td>5793.0</td>\n",
       "      <td>6.30</td>\n",
       "      <td>Confirmed planets, Planets in binary systems, S-type</td>\n",
       "      <td>3.364722e-01</td>\n",
       "      <td>6.363028</td>\n",
       "      <td>1.562237</td>\n",
       "      <td>35.092678</td>\n",
       "      <td>1.716863</td>\n",
       "      <td>1.191728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3581</th>\n",
       "      <td>HD 108874 b</td>\n",
       "      <td>0</td>\n",
       "      <td>1.360000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>395.400000</td>\n",
       "      <td>1.051000</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>256.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RV</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>10/12/29</td>\n",
       "      <td>12 30 26</td>\n",
       "      <td>+22 52 47</td>\n",
       "      <td>68.5000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.2200</td>\n",
       "      <td>1.400000e-01</td>\n",
       "      <td>5407.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Confirmed planets</td>\n",
       "      <td>4.974209e-02</td>\n",
       "      <td>5.979898</td>\n",
       "      <td>1.142183</td>\n",
       "      <td>34.779500</td>\n",
       "      <td>1.468013</td>\n",
       "      <td>1.018994</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            PlanetIdentifier  TypeFlag  PlanetaryMassJpt  RadiusJpt   PeriodDays  SemiMajorAxisAU  Eccentricity  PeriastronDeg  LongitudeDeg  AscendingNodeDeg  InclinationDeg  SurfaceTempK  AgeGyr DiscoveryMethod  DiscoveryYear LastUpdated  RightAscension     Declination  DistFromSunParsec  HostStarMassSlrMass  HostStarRadiusSlrRad  HostStarMetallicity  HostStarTempK  HostStarAgeGyr                                       ListsPlanetIsOn  SemiMajorAxisAUlog  PeriodDayslog  Luminosity  Luminositylog  HabZoneOut  HabZoneIn\n",
       "55    38 Vir b                0         4.510000         NaN         825.900000   1.820000         0.030000     NaN            NaN           NaN               NaN             NaN           NaN      NaN             2016.0         17/02/20    12 53 11.16     -03 33 11.2    NaN                 1.1800               1.4500                7.000000e-02         6557.0         1.90            Confirmed planets                                     5.988365e-01        6.716474       3.489372    35.896281      2.565876    1.781055 \n",
       "91    HD 183263 b             0         3.635000         NaN         625.100000   1.486000         0.372800      232.900000    NaN           NaN               NaN             NaN           NaN      RV              2004.0         15/08/15    19 28 24.6      +08 21 28.9     55.1000            1.1210               1.1170                3.020000e-01         5936.0        NaN              Confirmed planets                                     3.960879e-01        6.437912       1.390825    34.976456      1.619938    1.124449 \n",
       "230   Gliese 3293 b           0         0.076000         NaN         30.600000    0.143400         0.090000     NaN            NaN           NaN               NaN             NaN           NaN      RV              2014.0         14/11/25    04 28 35.72     -25 10 08.9     18.2000            0.4200               0.4040                2.000000e-02         3466.0        NaN              Confirmed planets                                    -1.942117e+00        3.421000       0.021148    30.790345      0.199754    0.138656 \n",
       "232   Gliese 3293 c           0         0.027000         NaN         48.140000    0.193900         0.130000     NaN            NaN           NaN               NaN             NaN           NaN      RV              2014.0         14/11/25    04 28 35.72     -25 10 08.9     18.2000            0.4200               0.4040                2.000000e-02         3466.0        NaN              Controversial                                        -1.640413e+00        3.874113       0.021148    30.790345      0.199754    0.138656 \n",
       "270   HD 165155 b             0         2.890000         NaN         434.500000   1.130000         0.200000      211.000000    NaN           NaN               NaN             NaN           NaN      RV              2016.0         16/04/02    18 05 57        -29 04 58       64.9800            1.0200               0.9500                9.000000e-02         5426.0         11.00           Confirmed planets                                     1.222176e-01        6.074196       0.702355    34.293243      1.151173    0.799065 \n",
       "271   HD 210277 b             0         1.230000         NaN         442.100000   1.100000         0.472000     NaN            NaN           NaN               NaN              240.3        NaN      RV              1998.0         06/09/06    22 09 29        -07 32 32       21.2900            1.0900               1.1000                1.900000e-01         5532.0        NaN              Confirmed planets                                     9.531018e-02        6.091536       1.017430    34.663839      1.385525    0.961736 \n",
       "314   HD 125612 b             0         3.000000         NaN         502.000000   1.370000         0.460000     NaN            NaN           NaN               NaN              224.5        NaN      RV              2007.0         10/12/06    14 20 54        -17 28 53       52.8200            1.1000               1.0500                2.400000e-01         5897.0        NaN              Confirmed planets                                     3.148107e-01        6.218600       1.196999    34.826376      1.502827    1.043159 \n",
       "334   HD 4732 c               0         2.370000         NaN         2732.000000  4.600000         0.230000     NaN            NaN           NaN               NaN             NaN           NaN      RV              2012.0         12/10/26    00 49 13        -24 08 12       56.5000            1.7400               5.4000                1.000000e-02         4959.0        NaN              Confirmed planets                                     1.526056e+00        7.912789       15.832682   37.408635      5.465618    3.793856 \n",
       "381   Gliese 876 c            0         0.842900         NaN         30.076600    0.135985         0.253900      117.120000    -104.600000   -1.290000          53.060000      NaN           NaN      RV              2000.0         15/06/08    22 53 13        -14 15 13       4.6900             0.3700               0.3761                1.900000e-01         3129.0        NaN              Confirmed planets                                    -1.995211e+00        3.403747       0.012174    30.238075      0.151556    0.105199 \n",
       "410   Earth                   0         0.003146          0.091130   365.242200   1.000000         0.016732      102.930059     100.466916   -5.112604         -0.000543       NaN           NaN      NaN            NaN             12/01/01    NaN             NaN            NaN                 1.0000               1.0000                1.000000e-08         5778.0         4.57            Solar System                                          1.800000e-07        5.900561       1.000693    34.647252      1.374081    0.953793 \n",
       "488   Kepler-454 c            0         4.460000         NaN         523.900000   1.286000         0.021400      337.400000    NaN           NaN               NaN             NaN           NaN      RV              2015.0         16/01/09    19 09 54.841    +38 13 43.95    200.0000           1.0280               1.0660                2.700000e-01         5687.0         5.23            Confirmed planets                                     2.515366e-01        6.261301       1.067180    34.711579      1.418995    0.984969 \n",
       "532   HD 216435 b             0         1.260000         NaN         1311.000000  2.560000         0.070000     NaN            NaN           NaN               NaN              224.7        NaN      RV              2002.0         09/10/06    22 53 37        -48 35 53       33.3000            1.3000               2.0000                2.440000e-01         5767.0        NaN              Confirmed planets                                     9.400073e-01        7.178545       3.972376    36.025924      2.737709    1.900329 \n",
       "612   HD 23596 b              0         8.100000         NaN         1565.000000  2.880000         0.292000     NaN            NaN           NaN               NaN              220.0        NaN      RV              2002.0         10/12/30    03 48 00        +40 31 50       52.0000            1.2700               2.0900                3.200000e-01         5888.0        NaN              Confirmed planets                                     1.057790e+00        7.355641       4.713617    36.197015      2.982217    2.070050 \n",
       "839   HD 137388 b             0         0.223000         NaN         330.000000   0.890000         0.360000     NaN            NaN           NaN               NaN              219.9        NaN      RV              2011.0         11/08/02    15 35 40        -80 12 17       38.0000            0.8600               0.8200                1.800000e-01         5240.0        NaN              Confirmed planets                                    -1.165338e-01        5.799093       0.455138    33.859405      0.926688    0.643243 \n",
       "873   HD 23079 b              0         2.500000         NaN         626.000000   1.500000         0.020000     NaN            NaN           NaN               NaN              223.8        NaN      RV              2001.0         10/12/30    03 39 43        -52 54 57       34.8000            1.1000               1.1300               -2.400000e-01         5848.0        NaN              Confirmed planets                                     4.054651e-01        6.439350       1.340840    34.939855      1.590562    1.104059 \n",
       "913   WASP-41 c               0         3.180000         NaN         421.000000   1.070000         0.294000      353.000000    NaN           NaN               NaN             NaN           NaN      RV              2015.0         15/10/16    12 42 28.497    -30 38 23.55    180.0000           0.9300               0.8700                6.000000e-02         5545.0        NaN              Confirmed planets                                     6.765865e-02        6.042633       0.642444    34.204083      1.100981    0.764225 \n",
       "921   HD 43197 b              0         0.600000         NaN         327.800000   0.920000         0.830000     NaN            NaN           NaN               NaN              239.6        NaN      RV              2009.0         10/09/08    06 13 36        -29 53 50       54.9000            0.9600               1.0000                4.000000e-01         5508.0        NaN              Confirmed planets                                    -8.338161e-02        5.792404       0.826354    34.455827      1.248663    0.866736 \n",
       "960   HD 1605 c               0         3.480000         NaN         2111.000000  3.520000         0.098000     NaN            NaN           NaN               NaN             NaN           NaN      RV              2015.0         15/03/26    00 20 31.51872  +30 58 29.2948  84.6000            1.3100               3.8000                2.100000e-01         4757.0         4.59            Confirmed planets                                     1.258461e+00        7.654917       6.638811    36.539492      3.539217    2.456681 \n",
       "1123  Wolf 1061 c             0         0.013400         NaN         17.867000    0.084270         0.190000      37.000000     NaN           NaN               NaN             NaN           NaN      RV              2015.0         15/12/27    16 30 18.05803  -12 39 45.3232  4.2922             0.2500               0.2600               -5.000000e-02         3345.0        NaN              Confirmed planets                                    -2.473729e+00        2.882955       0.007598    29.766741      0.119735    0.083112 \n",
       "1174  KELT-6 c                0         3.710000         NaN         1276.000000  2.390000         0.210000      268.700000    NaN           NaN               NaN             NaN           NaN      RV              2015.0         15/08/27    13 03 55.648    +30 38 24.18    222.0000           1.1260               1.5290               -2.700000e-01         6272.0         4.90            Confirmed planets                                     8.712934e-01        7.151485       3.248102    35.824630      2.475580    1.718377 \n",
       "1222  HD 218566 b             0         0.210000         NaN         225.700000   0.687300         0.300000      36.000000     NaN           NaN               NaN             NaN           NaN      RV              2010.0         15/11/03    23 09 10.72771  -02 15 38.6775  28.5500            0.8500               0.8600                3.800000e-01         4820.0         8.50            Confirmed planets                                    -3.749844e-01        5.419207       0.358406    33.620471      0.822337    0.570810 \n",
       "1240  HD 13908 c              0         5.130000         NaN         931.000000   2.030000         0.120000      185.000000    NaN           NaN               NaN             NaN           NaN      RV              2013.0         13/11/22    02 18 14.56056  +65 35 39.6988  71.3000            1.2900               1.6700                1.000000e-02         6255.0         2.90            Confirmed planets                                     7.080358e-01        6.836259       3.832946    35.990193      2.689233    1.866681 \n",
       "1263  Kepler-446 c            0        NaN                0.101155   3.036179     0.104000        NaN           NaN            NaN           NaN                88.970000      NaN           NaN      transit         2015.0         15/01/06    18 49 00.051    +44 55 15.99    120.0000           0.2200               0.2400               -3.000000e-01         3359.0        NaN              Confirmed planets                                    -2.263364e+00        1.110600       0.006583    29.623362      0.111452    0.077362 \n",
       "1382  HD 92788 b              0         3.860000         NaN         325.810000   0.970000         0.334000     NaN            NaN           NaN                8.000000        245.9        NaN      RV              2000.0         10/10/19    10 42 48        -02 11 01       32.8200            1.1300               0.9900                3.200000e-01         5559.0        NaN              Confirmed planets                                    -3.045921e-02        5.786314       0.840326    34.472593      1.259174    0.874032 \n",
       "1474  HD 16175 b              0         4.400000         NaN         990.000000   2.100000         0.590000     NaN            NaN           NaN               NaN              243.7        NaN      RV              2007.0         09/04/21    02 37 02        +42 03 45       59.8000            1.3500               1.8700                2.300000e-01         6000.0        NaN              Confirmed planets                                     7.419373e-01        6.897705       4.068916    36.049936      2.770776    1.923282 \n",
       "1509  HD 28185 b              0         5.700000         NaN         383.000000   1.030000         0.070000     NaN            NaN           NaN               NaN              241.6        NaN      RV              2001.0         10/12/20    04 26 26        -10 33 02       39.4000            1.2400               1.0300                2.400000e-01         5482.0        NaN              Confirmed planets                                     2.955880e-02        5.948035       0.860243    34.496019      1.274009    0.884330 \n",
       "1801  7 CMa b                 0         2.460000         NaN         796.000000   1.930000         0.220000      77.000000     NaN           NaN               NaN             NaN           NaN      RV              2011.0         15/10/25    06 36 41.038    -19 15 21.17    19.7500            1.5200               2.3000                2.100000e-01         4792.0        NaN              Confirmed planets                                     6.575200e-01        6.679599       2.504456    35.564631      2.173796    1.508900 \n",
       "1919  Gliese 667 C f          2         0.008493         NaN         39.026000    0.156000         0.030000      103.132403     34.000000    NaN               NaN             NaN           NaN      RV              2013.0         13/06/25    17 18 57        -34 59 23       6.9700             0.3300               0.4100               NaN                   3350.0        NaN              Controversial, Planets in binary systems, S-type     -1.857899e+00        3.664228       0.019008    30.683666      0.189379    0.131454 \n",
       "1952  HD 20782 b              2         1.430000         NaN         597.065000   1.397000         0.956000      142.100000    NaN           NaN               NaN             NaN           NaN      RV              2006.0         16/01/10    03 20 03.57791  -28 51 14.6651  35.4900            1.0200               1.0900                1.000000e-02         5798.0         5.53            Confirmed planets, Planets in binary systems, S-type  3.343271e-01        6.392026       1.205470    34.833429      1.508135    1.046844 \n",
       "1971  Proxima Centauri b      2         0.004089         NaN         11.186000    0.048500        NaN           NaN             110.000000   NaN               NaN              234.0        NaN      RV              2016.0         16/08/22    14 39 36.204    -60 50 08.23    1.2950             0.1200               0.1410               NaN                   3050.0        NaN              Confirmed planets, Planets in binary systems, S-type -3.026191e+00        2.414663       0.001545    28.173597      0.053985    0.037473 \n",
       "2012  HD 141937 b             0         9.700000         NaN         653.220000   1.520000         0.410000     NaN            NaN           NaN               NaN              215.8        NaN      RV              2002.0         10/12/29    15 52 17        -18 26 09       33.4600            1.1000               1.0600                1.100000e-01         5925.0        NaN              Confirmed planets                                     4.187103e-01        6.481914       1.243242    34.864282      1.531581    1.063118 \n",
       "2025  Kepler-442 b            0        NaN                0.120000   112.305300   0.409000        NaN           NaN            NaN           NaN                89.940000      NaN           NaN      transit         2015.0         15/01/28    19 01 27.98     +39 16 48.3     342.0000           0.6090               0.5980               -3.700000e-01         4402.0         2.90            Confirmed planets                                    -8.940401e-01        4.721221       0.120557    32.530928      0.476934    0.331055 \n",
       "2115  Kepler-443 b            0        NaN                0.208000   177.669300   0.495000        NaN           NaN            NaN           NaN                89.940000      NaN           NaN      transit         2015.0         15/01/28    19 14 26.53     +49 58 07.1     779.0000           0.7380               0.7060               -1.000000e-02         4723.0         3.20            Confirmed planets                                    -7.031975e-01        5.179924       0.222675    33.144518      0.648183    0.449924 \n",
       "2236  HD 86264 b              0         7.000000         NaN         1475.000000  2.860000         0.700000     NaN            NaN           NaN               NaN              214.2        NaN      RV              2009.0         09/08/13    09 56 58        -15 53 42       72.6000            1.4200               1.8800                2.020000e-01         6210.0        NaN              Confirmed planets                                     1.050822e+00        7.296413       4.719246    36.198208      2.983997    2.071286 \n",
       "2279  55 Cnc f                2         0.141000         NaN         262.000000   0.788000         0.305000      166.000000     185.600000   NaN               NaN             NaN           NaN      RV              2007.0         11/06/07    08 52 35.81093  +28 19 50.9511  12.3400            0.9600               0.9600                3.000000e-01         5219.0        NaN              Confirmed planets, Planets in binary systems, S-type -2.382572e-01        5.568345       0.613878    34.158600      1.076225    0.747042 \n",
       "2295  Kepler-296 f            2        NaN                0.161000   63.336270    0.255000        NaN           NaN            NaN           NaN                89.700000      NaN           NaN      transit         2014.0         15/05/11    19 06 09.602    +49 26 14.37    558.5000           0.4980               0.4800               -8.000000e-02         3740.0        NaN              Confirmed planets, Planets in binary systems, S-type -1.366492e+00        4.148458       0.040472    31.439425      0.276339    0.191815 \n",
       "2447  HD 82943 b              0         1.750000         NaN         442.400000   1.186600         0.203000      107.000000     333.000000   NaN                19.410000       250.5        NaN      RV              2003.0         13/06/05    09 34 50        -12 07 46       27.4600            1.1300               1.1200                3.200000e-01         5874.0         3.00            Confirmed planets                                     1.710921e-01        6.092214       1.340795    34.939822      1.590535    1.104040 \n",
       "2554  Upsilon Andromedae A d  2         8.860000         NaN         1282.570000  2.533539         0.249090      253.270000    NaN            9.970000          28.300000       218.0        NaN      RV              1999.0         15/03/16    01 36 48        +41 24 38       13.4700            1.2700               1.6310                9.000000e-02         6212.0         3.10            Confirmed planets, Planets in binary systems, S-type  9.296171e-01        7.156621       3.556512    35.915339      2.590444    1.798108 \n",
       "2569  TRAPPIST-1 e            0         0.001950          0.083658   6.099615     0.028170        NaN           NaN            NaN           NaN                89.860000       251.3        NaN      transit         2017.0         17/02/22    23 06 30.34     -05 02 36.44    12.1000            0.0802               0.1170                4.000000e-02         2559.0        NaN              Confirmed planets                                    -3.569498e+00        1.808226       0.000527    27.098325      0.031534    0.021889 \n",
       "2656  Gliese 687 b            0         0.058000         NaN         38.140000    0.163530         0.040000     NaN            NaN           NaN               NaN             NaN           NaN      RV              2014.0         14/05/12    17 36 25.89973  +68 20 20.9108  4.5000             0.4130               0.4183               NaN                   3413.0        NaN              Confirmed planets                                    -1.810759e+00        3.641264       0.021316    30.798275      0.200548    0.139206 \n",
       "2759  Gliese 581 g            0         0.009752         NaN         36.500000    0.146000         0.000000     NaN            NaN           NaN               NaN              230.9        NaN      RV              2010.0         14/07/04    15 19 26        -07 43 20       6.2100             0.3100               0.3000               -1.350000e-01         3498.0        NaN              Retracted planet candidate                           -1.924149e+00        3.597312       0.012098    30.231841      0.151084    0.104872 \n",
       "2862  mu Arae b               0         1.676000         NaN         643.250000   1.500000         0.128000     NaN            NaN           NaN               NaN              228.7        NaN      RV              2000.0         09/04/02    17 44 08        -51 50 02       15.3000            1.0800               1.2450                2.800000e-01         5700.0        NaN              Confirmed planets                                     4.054651e-01        6.466533       1.469023    35.031157      1.664855    1.155628 \n",
       "2881  WASP-47 c               0         1.240000         NaN         572.000000   1.360000         0.130000      144.000000    NaN           NaN               NaN             NaN           NaN      RV              2015.0         15/10/17    22 04 48.731    -12 01 07.99    200.0000           1.0260               1.1500                3.600000e-01         5576.0        NaN              Confirmed planets                                     3.074847e-01        6.349139       1.147829    34.784432      1.471637    1.021509 \n",
       "2948  EPIC 201912552 b        0        NaN                0.206000   32.944880    0.149100        NaN           NaN            NaN           NaN               NaN              272.0        NaN      transit         2015.0         15/07/07    11 30 14.511    +07 35 18.21    34.0000            0.4130               0.3940                9.000000e-02         3503.0        NaN              Confirmed planets                                    -1.903138e+00        3.494836       0.020987    30.782691      0.198991    0.138126 \n",
       "3004  HIP 57050 b             0         0.298000         NaN         41.397000    0.163506         0.314000     NaN            NaN           NaN               NaN              218.6        NaN      RV              2010.0         10/04/27    11 41 45        +42 45 07       11.0000            0.3400               0.4000                3.200000e-01         3190.0        NaN              Confirmed planets                                    -1.810906e+00        3.723208       0.014876    30.438523      0.167532    0.116289 \n",
       "3033  HD 142415 b             0         1.620000         NaN         386.300000   1.050000         0.500000     NaN            NaN           NaN               NaN              250.5        NaN      RV              2003.0         10/12/29    15 57 40        -60 12 00       34.2000            1.0900               1.0300                2.100000e-01         5834.0        NaN              Confirmed planets                                     4.879016e-02        5.956614       1.103394    34.744950      1.442871    1.001542 \n",
       "3062  HD 564 b                0         0.330000         NaN         492.300000   1.200000         0.096000      314.000000    NaN           NaN               NaN             NaN           NaN      RV              2014.0         15/11/17    00 09 52.82123  -50 16 04.1606  53.6000            0.9610               1.0100               -2.000000e-01         5902.0         5.50            Confirmed planets                                     1.823216e-01        6.199088       1.111297    34.752087      1.448029    1.005122 \n",
       "3126  HD 196885 A b           2         2.980000         NaN         1326.000000  2.600000         0.480000     NaN            NaN           NaN               NaN              228.4        NaN      RV              2007.0         12/12/07    20 39 51        +11 14 58       33.0000            1.3300               1.7900                2.900000e-01         6340.0         0.50            Confirmed planets, Planets in binary systems, S-type  9.555114e-01        7.189922       4.647866    36.182967      2.961344    2.055561 \n",
       "3219  HD 10697 b              0         6.380000         NaN         1076.400000  2.160000         0.100000     NaN            NaN           NaN                69.000000       221.8        NaN      RV              2000.0         10/10/19    01 44 55        +20 04 59       32.5600            1.1500               1.7200                1.000000e-01         5641.0        NaN              Confirmed planets                                     7.701082e-01        6.981377       2.689502    35.635915      2.252672    1.563650 \n",
       "3333  Kepler-68 d             2         0.947000         NaN         580.000000   1.400000         0.180000     NaN            NaN           NaN               NaN             NaN           NaN      RV              2013.0         15/06/09    19 24 07.7644   +49 02 24.957   135.0000           1.0790               1.2430                1.200000e-01         5793.0         6.30            Confirmed planets, Planets in binary systems, S-type  3.364722e-01        6.363028       1.562237    35.092678      1.716863    1.191728 \n",
       "3581  HD 108874 b             0         1.360000         NaN         395.400000   1.051000         0.070000     NaN            NaN           NaN               NaN              256.8        NaN      RV              2003.0         10/12/29    12 30 26        +22 52 47       68.5000            1.0000               1.2200                1.400000e-01         5407.0        NaN              Confirmed planets                                     4.974209e-02        5.979898       1.142183    34.779500      1.468013    1.018994 "
      ]
     },
     "execution_count": 335,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "habitable_zone=df[(df[\"SemiMajorAxisAU\"]>df[\"HabZoneIn\"]) & (df[\"SemiMajorAxisAU\"]<df[\"HabZoneOut\"])]\n",
    "habitable_zone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        vertical-align: top;\n",
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       "\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>InclinationDeg</th>\n",
       "      <th>SurfaceTempK</th>\n",
       "      <th>AgeGyr</th>\n",
       "      <th>DiscoveryMethod</th>\n",
       "      <th>DiscoveryYear</th>\n",
       "      <th>LastUpdated</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>SemiMajorAxisAUlog</th>\n",
       "      <th>PeriodDayslog</th>\n",
       "      <th>Luminosity</th>\n",
       "      <th>Luminositylog</th>\n",
       "      <th>HabZoneOut</th>\n",
       "      <th>HabZoneIn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>Gliese 3293 c</td>\n",
       "      <td>0</td>\n",
       "      <td>0.027000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48.140000</td>\n",
       "      <td>0.19390</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RV</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>14/11/25</td>\n",
       "      <td>04 28 35.72</td>\n",
       "      <td>-25 10 08.9</td>\n",
       "      <td>18.2000</td>\n",
       "      <td>0.4200</td>\n",
       "      <td>0.404</td>\n",
       "      <td>2.000000e-02</td>\n",
       "      <td>3466.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Controversial</td>\n",
       "      <td>-1.640413e+00</td>\n",
       "      <td>3.874113</td>\n",
       "      <td>0.021148</td>\n",
       "      <td>30.790345</td>\n",
       "      <td>0.199754</td>\n",
       "      <td>0.138656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>Earth</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003146</td>\n",
       "      <td>0.091130</td>\n",
       "      <td>365.242200</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.016732</td>\n",
       "      <td>102.930059</td>\n",
       "      <td>100.466916</td>\n",
       "      <td>-5.112604</td>\n",
       "      <td>-0.000543</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12/01/01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000000e-08</td>\n",
       "      <td>5778.0</td>\n",
       "      <td>4.57</td>\n",
       "      <td>Solar System</td>\n",
       "      <td>1.800000e-07</td>\n",
       "      <td>5.900561</td>\n",
       "      <td>1.000693</td>\n",
       "      <td>34.647252</td>\n",
       "      <td>1.374081</td>\n",
       "      <td>0.953793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1123</th>\n",
       "      <td>Wolf 1061 c</td>\n",
       "      <td>0</td>\n",
       "      <td>0.013400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.867000</td>\n",
       "      <td>0.08427</td>\n",
       "      <td>0.190000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RV</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>15/12/27</td>\n",
       "      <td>16 30 18.05803</td>\n",
       "      <td>-12 39 45.3232</td>\n",
       "      <td>4.2922</td>\n",
       "      <td>0.2500</td>\n",
       "      <td>0.260</td>\n",
       "      <td>-5.000000e-02</td>\n",
       "      <td>3345.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Confirmed planets</td>\n",
       "      <td>-2.473729e+00</td>\n",
       "      <td>2.882955</td>\n",
       "      <td>0.007598</td>\n",
       "      <td>29.766741</td>\n",
       "      <td>0.119735</td>\n",
       "      <td>0.083112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1919</th>\n",
       "      <td>Gliese 667 C f</td>\n",
       "      <td>2</td>\n",
       "      <td>0.008493</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.026000</td>\n",
       "      <td>0.15600</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>103.132403</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RV</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>13/06/25</td>\n",
       "      <td>17 18 57</td>\n",
       "      <td>-34 59 23</td>\n",
       "      <td>6.9700</td>\n",
       "      <td>0.3300</td>\n",
       "      <td>0.410</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3350.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Controversial, Planets in binary systems, S-type</td>\n",
       "      <td>-1.857899e+00</td>\n",
       "      <td>3.664228</td>\n",
       "      <td>0.019008</td>\n",
       "      <td>30.683666</td>\n",
       "      <td>0.189379</td>\n",
       "      <td>0.131454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971</th>\n",
       "      <td>Proxima Centauri b</td>\n",
       "      <td>2</td>\n",
       "      <td>0.004089</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.186000</td>\n",
       "      <td>0.04850</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>234.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RV</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>16/08/22</td>\n",
       "      <td>14 39 36.204</td>\n",
       "      <td>-60 50 08.23</td>\n",
       "      <td>1.2950</td>\n",
       "      <td>0.1200</td>\n",
       "      <td>0.141</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3050.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Confirmed planets, Planets in binary systems, S-type</td>\n",
       "      <td>-3.026191e+00</td>\n",
       "      <td>2.414663</td>\n",
       "      <td>0.001545</td>\n",
       "      <td>28.173597</td>\n",
       "      <td>0.053985</td>\n",
       "      <td>0.037473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2569</th>\n",
       "      <td>TRAPPIST-1 e</td>\n",
       "      <td>0</td>\n",
       "      <td>0.001950</td>\n",
       "      <td>0.083658</td>\n",
       "      <td>6.099615</td>\n",
       "      <td>0.02817</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>89.860000</td>\n",
       "      <td>251.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>transit</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>17/02/22</td>\n",
       "      <td>23 06 30.34</td>\n",
       "      <td>-05 02 36.44</td>\n",
       "      <td>12.1000</td>\n",
       "      <td>0.0802</td>\n",
       "      <td>0.117</td>\n",
       "      <td>4.000000e-02</td>\n",
       "      <td>2559.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Confirmed planets</td>\n",
       "      <td>-3.569498e+00</td>\n",
       "      <td>1.808226</td>\n",
       "      <td>0.000527</td>\n",
       "      <td>27.098325</td>\n",
       "      <td>0.031534</td>\n",
       "      <td>0.021889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2759</th>\n",
       "      <td>Gliese 581 g</td>\n",
       "      <td>0</td>\n",
       "      <td>0.009752</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.500000</td>\n",
       "      <td>0.14600</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>230.9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RV</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>14/07/04</td>\n",
       "      <td>15 19 26</td>\n",
       "      <td>-07 43 20</td>\n",
       "      <td>6.2100</td>\n",
       "      <td>0.3100</td>\n",
       "      <td>0.300</td>\n",
       "      <td>-1.350000e-01</td>\n",
       "      <td>3498.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Retracted planet candidate</td>\n",
       "      <td>-1.924149e+00</td>\n",
       "      <td>3.597312</td>\n",
       "      <td>0.012098</td>\n",
       "      <td>30.231841</td>\n",
       "      <td>0.151084</td>\n",
       "      <td>0.104872</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        PlanetIdentifier  TypeFlag  PlanetaryMassJpt  RadiusJpt  PeriodDays  SemiMajorAxisAU  Eccentricity  PeriastronDeg  LongitudeDeg  AscendingNodeDeg  InclinationDeg  SurfaceTempK  AgeGyr DiscoveryMethod  DiscoveryYear LastUpdated  RightAscension     Declination  DistFromSunParsec  HostStarMassSlrMass  HostStarRadiusSlrRad  HostStarMetallicity  HostStarTempK  HostStarAgeGyr                                       ListsPlanetIsOn  SemiMajorAxisAUlog  PeriodDayslog  Luminosity  Luminositylog  HabZoneOut  HabZoneIn\n",
       "232   Gliese 3293 c       0         0.027000         NaN         48.140000   0.19390          0.130000     NaN            NaN           NaN               NaN             NaN           NaN      RV              2014.0         14/11/25    04 28 35.72     -25 10 08.9     18.2000            0.4200               0.404                 2.000000e-02         3466.0        NaN              Controversial                                        -1.640413e+00        3.874113       0.021148    30.790345      0.199754    0.138656 \n",
       "410   Earth               0         0.003146          0.091130   365.242200  1.00000          0.016732      102.930059     100.466916   -5.112604         -0.000543       NaN           NaN      NaN            NaN             12/01/01    NaN             NaN            NaN                 1.0000               1.000                 1.000000e-08         5778.0         4.57            Solar System                                          1.800000e-07        5.900561       1.000693    34.647252      1.374081    0.953793 \n",
       "1123  Wolf 1061 c         0         0.013400         NaN         17.867000   0.08427          0.190000      37.000000     NaN           NaN               NaN             NaN           NaN      RV              2015.0         15/12/27    16 30 18.05803  -12 39 45.3232  4.2922             0.2500               0.260                -5.000000e-02         3345.0        NaN              Confirmed planets                                    -2.473729e+00        2.882955       0.007598    29.766741      0.119735    0.083112 \n",
       "1919  Gliese 667 C f      2         0.008493         NaN         39.026000   0.15600          0.030000      103.132403     34.000000    NaN               NaN             NaN           NaN      RV              2013.0         13/06/25    17 18 57        -34 59 23       6.9700             0.3300               0.410                NaN                   3350.0        NaN              Controversial, Planets in binary systems, S-type     -1.857899e+00        3.664228       0.019008    30.683666      0.189379    0.131454 \n",
       "1971  Proxima Centauri b  2         0.004089         NaN         11.186000   0.04850         NaN           NaN             110.000000   NaN               NaN              234.0        NaN      RV              2016.0         16/08/22    14 39 36.204    -60 50 08.23    1.2950             0.1200               0.141                NaN                   3050.0        NaN              Confirmed planets, Planets in binary systems, S-type -3.026191e+00        2.414663       0.001545    28.173597      0.053985    0.037473 \n",
       "2569  TRAPPIST-1 e        0         0.001950          0.083658   6.099615    0.02817         NaN           NaN            NaN           NaN                89.860000       251.3        NaN      transit         2017.0         17/02/22    23 06 30.34     -05 02 36.44    12.1000            0.0802               0.117                 4.000000e-02         2559.0        NaN              Confirmed planets                                    -3.569498e+00        1.808226       0.000527    27.098325      0.031534    0.021889 \n",
       "2759  Gliese 581 g        0         0.009752         NaN         36.500000   0.14600          0.000000     NaN            NaN           NaN               NaN              230.9        NaN      RV              2010.0         14/07/04    15 19 26        -07 43 20       6.2100             0.3100               0.300                -1.350000e-01         3498.0        NaN              Retracted planet candidate                           -1.924149e+00        3.597312       0.012098    30.231841      0.151084    0.104872 "
      ]
     },
     "execution_count": 336,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "habitable_zone=habitable_zone[(habitable_zone[\"PlanetaryMassJpt\"]>0.0015) & (habitable_zone[\"PlanetaryMassJpt\"]<0.03)]\n",
    "habitable_zone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "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>DistFromSunParsec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1971</th>\n",
       "      <td>Proxima Centauri b</td>\n",
       "      <td>1.2950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1123</th>\n",
       "      <td>Wolf 1061 c</td>\n",
       "      <td>4.2922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2759</th>\n",
       "      <td>Gliese 581 g</td>\n",
       "      <td>6.2100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1919</th>\n",
       "      <td>Gliese 667 C f</td>\n",
       "      <td>6.9700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2569</th>\n",
       "      <td>TRAPPIST-1 e</td>\n",
       "      <td>12.1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>Gliese 3293 c</td>\n",
       "      <td>18.2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>Earth</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        PlanetIdentifier  DistFromSunParsec\n",
       "1971  Proxima Centauri b  1.2950           \n",
       "1123  Wolf 1061 c         4.2922           \n",
       "2759  Gliese 581 g        6.2100           \n",
       "1919  Gliese 667 C f      6.9700           \n",
       "2569  TRAPPIST-1 e        12.1000          \n",
       "232   Gliese 3293 c       18.2000          \n",
       "410   Earth              NaN               "
      ]
     },
     "execution_count": 337,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "habitable_zone[[\"PlanetIdentifier\",\"DistFromSunParsec\"]].sort_values(by=[\"DistFromSunParsec\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TASK: Instead of hardcoding the parameters for habitability try finding the similarity of each exoplanet with earth and hence the most earth like exoplanets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>InclinationDeg</th>\n",
       "      <th>SurfaceTempK</th>\n",
       "      <th>AgeGyr</th>\n",
       "      <th>DiscoveryMethod</th>\n",
       "      <th>DiscoveryYear</th>\n",
       "      <th>LastUpdated</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>SemiMajorAxisAUlog</th>\n",
       "      <th>PeriodDayslog</th>\n",
       "      <th>Luminosity</th>\n",
       "      <th>Luminositylog</th>\n",
       "      <th>HabZoneOut</th>\n",
       "      <th>HabZoneIn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>Mercury</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000174</td>\n",
       "      <td>0.034902</td>\n",
       "      <td>87.9700</td>\n",
       "      <td>0.387098</td>\n",
       "      <td>0.205637</td>\n",
       "      <td>77.457719</td>\n",
       "      <td>252.251667</td>\n",
       "      <td>48.339618</td>\n",
       "      <td>7.005594</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12/01/01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>-9.490763e-01</td>\n",
       "      <td>4.476996</td>\n",
       "      <td>1.000693</td>\n",
       "      <td>34.647252</td>\n",
       "      <td>1.374081</td>\n",
       "      <td>0.953793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>Venus</td>\n",
       "      <td>0</td>\n",
       "      <td>0.002564</td>\n",
       "      <td>0.086565</td>\n",
       "      <td>224.7000</td>\n",
       "      <td>0.723321</td>\n",
       "      <td>0.006764</td>\n",
       "      <td>131.767557</td>\n",
       "      <td>181.979708</td>\n",
       "      <td>76.672615</td>\n",
       "      <td>3.397775</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12/01/01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>-3.239021e-01</td>\n",
       "      <td>5.414766</td>\n",
       "      <td>1.000693</td>\n",
       "      <td>34.647252</td>\n",
       "      <td>1.374081</td>\n",
       "      <td>0.953793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>Earth</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003146</td>\n",
       "      <td>0.091130</td>\n",
       "      <td>365.2422</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.016732</td>\n",
       "      <td>102.930059</td>\n",
       "      <td>100.466916</td>\n",
       "      <td>-5.112604</td>\n",
       "      <td>-0.000543</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12/01/01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>1.800000e-07</td>\n",
       "      <td>5.900561</td>\n",
       "      <td>1.000693</td>\n",
       "      <td>34.647252</td>\n",
       "      <td>1.374081</td>\n",
       "      <td>0.953793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>Mars</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000338</td>\n",
       "      <td>0.048489</td>\n",
       "      <td>686.9800</td>\n",
       "      <td>1.523712</td>\n",
       "      <td>0.093365</td>\n",
       "      <td>-23.917448</td>\n",
       "      <td>-4.568132</td>\n",
       "      <td>49.713210</td>\n",
       "      <td>1.851819</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12/01/01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>4.211497e-01</td>\n",
       "      <td>6.532305</td>\n",
       "      <td>1.000693</td>\n",
       "      <td>34.647252</td>\n",
       "      <td>1.374081</td>\n",
       "      <td>0.953793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>Jupiter</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4332.8200</td>\n",
       "      <td>5.202480</td>\n",
       "      <td>0.048536</td>\n",
       "      <td>14.274952</td>\n",
       "      <td>34.334792</td>\n",
       "      <td>100.292827</td>\n",
       "      <td>1.298614</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12/01/01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>1.649135e+00</td>\n",
       "      <td>8.373974</td>\n",
       "      <td>1.000693</td>\n",
       "      <td>34.647252</td>\n",
       "      <td>1.374081</td>\n",
       "      <td>0.953793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>Saturn</td>\n",
       "      <td>0</td>\n",
       "      <td>0.299000</td>\n",
       "      <td>0.832944</td>\n",
       "      <td>10755.6700</td>\n",
       "      <td>9.541499</td>\n",
       "      <td>0.055508</td>\n",
       "      <td>92.861361</td>\n",
       "      <td>50.075713</td>\n",
       "      <td>113.639987</td>\n",
       "      <td>2.494241</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12/01/01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>2.255651e+00</td>\n",
       "      <td>9.283188</td>\n",
       "      <td>1.000693</td>\n",
       "      <td>34.647252</td>\n",
       "      <td>1.374081</td>\n",
       "      <td>0.953793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>Uranus</td>\n",
       "      <td>0</td>\n",
       "      <td>0.045700</td>\n",
       "      <td>0.362775</td>\n",
       "      <td>30687.1530</td>\n",
       "      <td>19.187979</td>\n",
       "      <td>0.046857</td>\n",
       "      <td>172.434044</td>\n",
       "      <td>314.202766</td>\n",
       "      <td>73.962502</td>\n",
       "      <td>0.772981</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1781.0</td>\n",
       "      <td>12/01/01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>2.954284e+00</td>\n",
       "      <td>10.331599</td>\n",
       "      <td>1.000693</td>\n",
       "      <td>34.647252</td>\n",
       "      <td>1.374081</td>\n",
       "      <td>0.953793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>Neptune</td>\n",
       "      <td>0</td>\n",
       "      <td>0.053953</td>\n",
       "      <td>0.352219</td>\n",
       "      <td>60190.0300</td>\n",
       "      <td>30.069528</td>\n",
       "      <td>0.008954</td>\n",
       "      <td>46.681587</td>\n",
       "      <td>304.222893</td>\n",
       "      <td>131.786359</td>\n",
       "      <td>1.770055</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1846.0</td>\n",
       "      <td>12/01/01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>3.403512e+00</td>\n",
       "      <td>11.005262</td>\n",
       "      <td>1.000693</td>\n",
       "      <td>34.647252</td>\n",
       "      <td>1.374081</td>\n",
       "      <td>0.953793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>Pluto</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.016438</td>\n",
       "      <td>90553.0200</td>\n",
       "      <td>39.486860</td>\n",
       "      <td>0.248852</td>\n",
       "      <td>224.097026</td>\n",
       "      <td>238.965350</td>\n",
       "      <td>110.301680</td>\n",
       "      <td>17.141043</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1930.0</td>\n",
       "      <td>12/01/01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>3.675968e+00</td>\n",
       "      <td>11.413691</td>\n",
       "      <td>1.000693</td>\n",
       "      <td>34.647252</td>\n",
       "      <td>1.374081</td>\n",
       "      <td>0.953793</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    PlanetIdentifier  TypeFlag  PlanetaryMassJpt  RadiusJpt  PeriodDays  SemiMajorAxisAU  Eccentricity  PeriastronDeg  LongitudeDeg  AscendingNodeDeg  InclinationDeg  SurfaceTempK  AgeGyr DiscoveryMethod  DiscoveryYear LastUpdated RightAscension Declination  DistFromSunParsec  HostStarMassSlrMass  HostStarRadiusSlrRad  HostStarMetallicity  HostStarTempK  HostStarAgeGyr ListsPlanetIsOn  SemiMajorAxisAUlog  PeriodDayslog  Luminosity  Luminositylog  HabZoneOut  HabZoneIn\n",
       "408  Mercury          0         0.000174          0.034902   87.9700     0.387098         0.205637      77.457719      252.251667    48.339618         7.005594       NaN           NaN      NaN            NaN             12/01/01    NaN            NaN        NaN                 1.0                  1.0                   1.000000e-08         5778.0         4.57            Solar System   -9.490763e-01        4.476996       1.000693    34.647252      1.374081    0.953793 \n",
       "409  Venus            0         0.002564          0.086565   224.7000    0.723321         0.006764      131.767557     181.979708    76.672615         3.397775       NaN           NaN      NaN            NaN             12/01/01    NaN            NaN        NaN                 1.0                  1.0                   1.000000e-08         5778.0         4.57            Solar System   -3.239021e-01        5.414766       1.000693    34.647252      1.374081    0.953793 \n",
       "410  Earth            0         0.003146          0.091130   365.2422    1.000000         0.016732      102.930059     100.466916   -5.112604         -0.000543       NaN           NaN      NaN            NaN             12/01/01    NaN            NaN        NaN                 1.0                  1.0                   1.000000e-08         5778.0         4.57            Solar System    1.800000e-07        5.900561       1.000693    34.647252      1.374081    0.953793 \n",
       "411  Mars             0         0.000338          0.048489   686.9800    1.523712         0.093365     -23.917448     -4.568132      49.713210         1.851819       NaN           NaN      NaN            NaN             12/01/01    NaN            NaN        NaN                 1.0                  1.0                   1.000000e-08         5778.0         4.57            Solar System    4.211497e-01        6.532305       1.000693    34.647252      1.374081    0.953793 \n",
       "412  Jupiter          0         1.000000          1.000000   4332.8200   5.202480         0.048536      14.274952      34.334792     100.292827        1.298614       NaN           NaN      NaN            NaN             12/01/01    NaN            NaN        NaN                 1.0                  1.0                   1.000000e-08         5778.0         4.57            Solar System    1.649135e+00        8.373974       1.000693    34.647252      1.374081    0.953793 \n",
       "413  Saturn           0         0.299000          0.832944   10755.6700  9.541499         0.055508      92.861361      50.075713     113.639987        2.494241       NaN           NaN      NaN            NaN             12/01/01    NaN            NaN        NaN                 1.0                  1.0                   1.000000e-08         5778.0         4.57            Solar System    2.255651e+00        9.283188       1.000693    34.647252      1.374081    0.953793 \n",
       "414  Uranus           0         0.045700          0.362775   30687.1530  19.187979        0.046857      172.434044     314.202766    73.962502         0.772981       NaN           NaN      NaN             1781.0         12/01/01    NaN            NaN        NaN                 1.0                  1.0                   1.000000e-08         5778.0         4.57            Solar System    2.954284e+00        10.331599      1.000693    34.647252      1.374081    0.953793 \n",
       "415  Neptune          0         0.053953          0.352219   60190.0300  30.069528        0.008954      46.681587      304.222893    131.786359        1.770055       NaN           NaN      NaN             1846.0         12/01/01    NaN            NaN        NaN                 1.0                  1.0                   1.000000e-08         5778.0         4.57            Solar System    3.403512e+00        11.005262      1.000693    34.647252      1.374081    0.953793 \n",
       "416  Pluto            0         0.000008          0.016438   90553.0200  39.486860        0.248852      224.097026     238.965350    110.301680        17.141043      NaN           NaN      NaN             1930.0         12/01/01    NaN            NaN        NaN                 1.0                  1.0                   1.000000e-08         5778.0         4.57            Solar System    3.675968e+00        11.413691      1.000693    34.647252      1.374081    0.953793 "
      ]
     },
     "execution_count": 338,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"ListsPlanetIsOn\"]==\"Solar System\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task: Try clustering the planets in our solar system based on density ans verify if they form two clusters for rocky planets and gas giants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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