H-R-Diagram-Pandas-Matplotlib.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# reads in a file separated by multiple spaces.\n",
    "# So, sep='\\s+' is needed. \n",
    "df_hip = pd.read_csv( \\\n",
    "'http://bogotobogo.com/python/images/python_Pandas_NumPy_Matplotlib/HIP_star.dat',\\\n",
    "                     sep='\\s+')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2720, 9)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hip.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "HIP       0\n",
       "Vmag      1\n",
       "RA        1\n",
       "DE        1\n",
       "Plx       1\n",
       "pmRA      1\n",
       "pmDE      1\n",
       "e_Plx     1\n",
       "B-V      42\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check if a column has no data (or NaN)\n",
    "df_hip.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# drop any row if any of the column is missing data\n",
    "df_hip = df_hip.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "HIP      0\n",
       "Vmag     0\n",
       "RA       0\n",
       "DE       0\n",
       "Plx      0\n",
       "pmRA     0\n",
       "pmDE     0\n",
       "e_Plx    0\n",
       "B-V      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check if a column has no data (or NaN) again\n",
    "df_hip.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2678, 9)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hip.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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CNAuHHoCq3g+0pRDtyEklah9ZY1DiJussE9ggk1yREyMn2QCKL0KaCwUHBUchxFkg0t6j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\nhBBCCCkdCg5CCCGElA4FByGEEEJK5/8HtXYJxUsJoM8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f17889b1fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = df_hip.iloc[:,8].values \n",
    "y = df_hip.iloc[:,1].values\n",
    "fig = figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.set_xlabel('B-V')\n",
    "ax.set_ylabel('Vmag')\n",
    "scatter(x,y,s=1) # marker size\n",
    "ylim([y.min(axis=0),y.max(axis=0)]) # x-axis range\n",
    "xlim([x.min(axis=0),2]) # x-axis range\n",
    "gca().invert_yaxis()  # y-axis reversed\n",
    "show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# The lines below are just for exercise \n",
    "# to check data and manipulating numpy arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HIP</th>\n",
       "      <th>Vmag</th>\n",
       "      <th>RA</th>\n",
       "      <th>DE</th>\n",
       "      <th>Plx</th>\n",
       "      <th>pmRA</th>\n",
       "      <th>pmDE</th>\n",
       "      <th>e_Plx</th>\n",
       "      <th>B-V</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>9.27</td>\n",
       "      <td>0.003797</td>\n",
       "      <td>-19.498837</td>\n",
       "      <td>21.90</td>\n",
       "      <td>181.21</td>\n",
       "      <td>-0.93</td>\n",
       "      <td>3.10</td>\n",
       "      <td>0.999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38</td>\n",
       "      <td>8.65</td>\n",
       "      <td>0.111047</td>\n",
       "      <td>-79.061831</td>\n",
       "      <td>23.84</td>\n",
       "      <td>162.30</td>\n",
       "      <td>-62.40</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>47</td>\n",
       "      <td>10.78</td>\n",
       "      <td>0.135192</td>\n",
       "      <td>-56.835248</td>\n",
       "      <td>24.45</td>\n",
       "      <td>-44.21</td>\n",
       "      <td>-145.90</td>\n",
       "      <td>1.97</td>\n",
       "      <td>1.150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>54</td>\n",
       "      <td>10.57</td>\n",
       "      <td>0.151656</td>\n",
       "      <td>17.968956</td>\n",
       "      <td>20.97</td>\n",
       "      <td>367.14</td>\n",
       "      <td>-19.49</td>\n",
       "      <td>1.71</td>\n",
       "      <td>1.030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>74</td>\n",
       "      <td>9.93</td>\n",
       "      <td>0.221873</td>\n",
       "      <td>35.752722</td>\n",
       "      <td>24.22</td>\n",
       "      <td>157.73</td>\n",
       "      <td>-40.31</td>\n",
       "      <td>1.36</td>\n",
       "      <td>1.068</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  HIP   Vmag        RA         DE    Plx    pmRA    pmDE  e_Plx    B-V\n",
       "0   2   9.27  0.003797 -19.498837  21.90  181.21   -0.93   3.10  0.999\n",
       "1  38   8.65  0.111047 -79.061831  23.84  162.30  -62.40   0.78  0.778\n",
       "2  47  10.78  0.135192 -56.835248  24.45  -44.21 -145.90   1.97  1.150\n",
       "3  54  10.57  0.151656  17.968956  20.97  367.14  -19.49   1.71  1.030\n",
       "4  74   9.93  0.221873  35.752722  24.22  157.73  -40.31   1.36  1.068"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hip[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# DBSCAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.cluster import DBSCAN\n",
    "from numpy.random import RandomState\n",
    "import pylab as pl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((2678, 9), array([['2', 9.27, 0.003797, ..., -0.93, 3.1, 0.9990000000000001],\n",
       "        ['38', 8.65, 0.111047, ..., -62.4, 0.78, 0.778],\n",
       "        ['47', 10.78, 0.135192, ..., -145.9, 1.97, 1.15],\n",
       "        ..., \n",
       "        ['118251', 8.16, 359.778318, ..., 3.56, 0.82, 0.652],\n",
       "        ['118254', 7.72, 359.787381, ..., 4.4, 0.8, 0.563],\n",
       "        ['118311', 11.85, 359.954685, ..., -112.81, 2.96, 1.391]], dtype=object))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "XX = df_hip.iloc[:,:].values \n",
    "XX.shape, XX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((2678, 9), array([['2', 9.27, 0.003797, ..., -0.93, 3.1, 0.9990000000000001],\n",
       "        ['38', 8.65, 0.111047, ..., -62.4, 0.78, 0.778],\n",
       "        ['47', 10.78, 0.135192, ..., -145.9, 1.97, 1.15],\n",
       "        ..., \n",
       "        ['118251', 8.16, 359.778318, ..., 3.56, 0.82, 0.652],\n",
       "        ['118254', 7.72, 359.787381, ..., 4.4, 0.8, 0.563],\n",
       "        ['118311', 11.85, 359.954685, ..., -112.81, 2.96, 1.391]], dtype=object), array([[0.9990000000000001, 9.27],\n",
       "        [0.778, 8.65],\n",
       "        [1.15, 10.78],\n",
       "        ..., \n",
       "        [0.652, 8.16],\n",
       "        [0.563, 7.72],\n",
       "        [1.391, 11.85]], dtype=object))"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xnew = XX[:,[8,1]]\n",
    "XX.shape, XX,Xnew"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/k/.local/lib/python2.7/site-packages/sklearn/utils/validation.py:429: DataConversionWarning: Data with input dtype object was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, _DataConversionWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "((2678, 2), array([[ 0.74646047,  0.56791503],\n",
       "        [ 0.05177198,  0.2342337 ],\n",
       "        [ 1.22111189,  1.38059052],\n",
       "        ..., \n",
       "        [-0.34429476, -0.02948219],\n",
       "        [-0.62405619, -0.26628829],\n",
       "        [ 1.97866812,  1.95645991]]))"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = StandardScaler().fit_transform(Xnew)\n",
    "X.shape, X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimated number of clusters: 3\n"
     ]
    }
   ],
   "source": [
    "# db = DBSCAN(eps=0.2, min_samples=8).fit(X)\n",
    "db = DBSCAN(eps=0.20, min_samples=7).fit(X)\n",
    "core_samples_mask = np.zeros_like(db.labels_, dtype=bool)\n",
    "core_samples_mask[db.core_sample_indices_] = True\n",
    "labels = db.labels_\n",
    "\n",
    "# Number of clusters in labels, ignoring noise if present.\n",
    "n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)\n",
    "\n",
    "print('Estimated number of clusters: %d' % n_clusters_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ True,  True,  True, ...,  True,  True,  True], dtype=bool),\n",
       " array([   0,    1,    2, ..., 2675, 2676, 2677]))"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "core_samples_mask, db.core_sample_indices_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({-1, 0, 1, 2}, array([[ 0.61960787,  0.00392157,  0.25882354,  1.        ],\n",
       "        [ 0.99346405,  0.74771243,  0.43529413,  1.        ],\n",
       "        [ 0.74771243,  0.89803922,  0.627451  ,  1.        ],\n",
       "        [ 0.36862746,  0.30980393,  0.63529414,  1.        ]]))"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Black removed and is used for noise instead.\n",
    "unique_labels = set(labels)\n",
    "colors = plt.cm.Spectral(np.linspace(0, 1, len(unique_labels)))\n",
    "unique_labels, colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/matplotlib/lines.py:1145: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.\n",
      "  if self._markerfacecolor != fc:\n"
     ]
    },
    {
     "data": {
      "image/png": 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jIogIjhOl2ItKqgzEnE5njcFC71+05U+j+jF29GU15g5orodSKlAaDATRo5Mn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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1778e7b890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for k, col in zip(unique_labels, colors):\n",
    "    if k == -1:\n",
    "        # Black used for noise.\n",
    "        col = 'k'\n",
    "\n",
    "    class_member_mask = (labels == k)\n",
    "\n",
    "    xy = X[class_member_mask & core_samples_mask]\n",
    "    plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=col,\n",
    "             markeredgecolor='k', markersize=6)\n",
    "\n",
    "    xy = X[class_member_mask & ~core_samples_mask]\n",
    "    plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=col,\n",
    "             markeredgecolor='k', markersize=2)\n",
    "\n",
    "plt.gca().invert_yaxis()  \n",
    "plt.title('Estimated number of clusters: %d' % n_clusters_)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# BreastCancer-Wisconsin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/\\\n",
    "breast-cancer-wisconsin/wdbc.data', header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((569, 30),\n",
       " (569,),\n",
       " array([[  1.79900000e+01,   1.03800000e+01,   1.22800000e+02,\n",
       "           1.00100000e+03,   1.18400000e-01,   2.77600000e-01,\n",
       "           3.00100000e-01,   1.47100000e-01,   2.41900000e-01,\n",
       "           7.87100000e-02,   1.09500000e+00,   9.05300000e-01,\n",
       "           8.58900000e+00,   1.53400000e+02,   6.39900000e-03,\n",
       "           4.90400000e-02,   5.37300000e-02,   1.58700000e-02,\n",
       "           3.00300000e-02,   6.19300000e-03,   2.53800000e+01,\n",
       "           1.73300000e+01,   1.84600000e+02,   2.01900000e+03,\n",
       "           1.62200000e-01,   6.65600000e-01,   7.11900000e-01,\n",
       "           2.65400000e-01,   4.60100000e-01,   1.18900000e-01]]),\n",
       " array(['M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M',\n",
       "        'M', 'M', 'M', 'M', 'M', 'M', 'B', 'B', 'B', 'M', 'M', 'M', 'M',\n",
       "        'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'M', 'B', 'M',\n",
       "        'M', 'M', 'M', 'M', 'M', 'M', 'M', 'B', 'M', 'B', 'B'], dtype=object))"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.loc[:, 2:].values\n",
    "y = df.loc[:, 1].values\n",
    "X.shape, y.shape, X[:1], y[:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       0, 1, 0, 0])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "le = LabelEncoder()\n",
    "y = le.fit_transform(y)\n",
    "y[0:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "le.transform(['B','M'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}