Module 6 - Clustering for Diversification analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Module - 6 Clustering for Diversification analysis\n",
    "Clustering is a method of unsupervised learning and is a common technique for statistical data analysis used in many fields.\n",
    "\n",
    "Clustering is a Machine Learning technique that involves the grouping of data points. Given a set of data points, we can use a clustering algorithm to classify each data point into a specific group. In theory, data points that are in the same group should have similar properties and/or features, while data points in different groups should have highly dissimilar properties and/or features.\n",
    "\n",
    "In financial Markets, Cluster analysis is a technique used to group sets of objects that share similar characteristics. It is common in statistics, but investors will use the approach to build a diversified portfolio. Stocks that exhibit high correlations in returns fall into one basket, those slightly less correlated in another, and so on, until each stock is placed into a category."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------#"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Query 6.1 \n",
    "Create a table/dataframe with the closing prices of 30 different stocks, with 10 from each of the caps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pylab import plot,show\n",
    "from numpy import vstack,array\n",
    "from numpy.random import rand\n",
    "import numpy as np\n",
    "from scipy.cluster.vq import kmeans,vq\n",
    "import pandas as pd\n",
    "from math import sqrt\n",
    "from sklearn.cluster import KMeans\n",
    "from matplotlib import pyplot as plt\n",
    "import warnings \n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "large = 'AXISBANK,CIPLA,HDFCBANK,INFRATEL,INFY,ITC,LT,M&M,RELIANCE,TITAN'\n",
    "mid = 'ADANIPOWER,APOLLOTYRE,BERGEPAINT,GMRINFRA,IDBI,IGL,JINDALSTEL,MINDTREE,RELINFRA,TATAPOWER'\n",
    "small = 'BOMDYEING,FORTIS,IDFC,JETAIRWAYS,LALPATHLAB,NCC,PVR,RAYMOND,RCOM,SUZLON'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "large = large.split(',')\n",
    "mid = mid.split(',')\n",
    "small = small.split(',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "for x in large:\n",
    "    df[x] = pd.read_csv(\"Datasets/LARGE_CAP/\"+x+\".csv\", sep=',', usecols=['Close Price'], squeeze=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "for x in mid:\n",
    "    df[x] = pd.read_csv(\"Datasets/MID_CAP/\"+x+\".csv\", sep=',', usecols=['Close Price'], squeeze=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "for x in small:\n",
    "    df[x] = pd.read_csv(\"Datasets/SMALL_CAP/\"+x+\".csv\", sep=',', usecols=['Close Price'], squeeze=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(494,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Date'] = pd.read_csv(\"Datasets/MID_CAP/GMRINFRA.csv\", sep=',', usecols=['Date'], squeeze=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Date'] = df['Date'].astype('datetime64[ns]')\n",
    "df = df.set_index('Date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AXISBANK</th>\n",
       "      <th>CIPLA</th>\n",
       "      <th>HDFCBANK</th>\n",
       "      <th>INFRATEL</th>\n",
       "      <th>INFY</th>\n",
       "      <th>ITC</th>\n",
       "      <th>LT</th>\n",
       "      <th>M&amp;M</th>\n",
       "      <th>RELIANCE</th>\n",
       "      <th>TITAN</th>\n",
       "      <th>...</th>\n",
       "      <th>BOMDYEING</th>\n",
       "      <th>FORTIS</th>\n",
       "      <th>IDFC</th>\n",
       "      <th>JETAIRWAYS</th>\n",
       "      <th>LALPATHLAB</th>\n",
       "      <th>NCC</th>\n",
       "      <th>PVR</th>\n",
       "      <th>RAYMOND</th>\n",
       "      <th>RCOM</th>\n",
       "      <th>SUZLON</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>2019-05-07</th>\n",
       "      <td>747.45</td>\n",
       "      <td>564.50</td>\n",
       "      <td>1975.15</td>\n",
       "      <td>268.00</td>\n",
       "      <td>723.60</td>\n",
       "      <td>304.25</td>\n",
       "      <td>1366.60</td>\n",
       "      <td>645.90</td>\n",
       "      <td>1384.90</td>\n",
       "      <td>1145.05</td>\n",
       "      <td>...</td>\n",
       "      <td>121.45</td>\n",
       "      <td>136.35</td>\n",
       "      <td>37.75</td>\n",
       "      <td>127.30</td>\n",
       "      <td>1024.95</td>\n",
       "      <td>97.55</td>\n",
       "      <td>1802.30</td>\n",
       "      <td>808.95</td>\n",
       "      <td>2.1</td>\n",
       "      <td>6.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-08</th>\n",
       "      <td>741.85</td>\n",
       "      <td>563.35</td>\n",
       "      <td>2004.30</td>\n",
       "      <td>266.70</td>\n",
       "      <td>718.40</td>\n",
       "      <td>307.00</td>\n",
       "      <td>1360.35</td>\n",
       "      <td>645.80</td>\n",
       "      <td>1343.50</td>\n",
       "      <td>1082.85</td>\n",
       "      <td>...</td>\n",
       "      <td>119.35</td>\n",
       "      <td>135.20</td>\n",
       "      <td>37.10</td>\n",
       "      <td>131.35</td>\n",
       "      <td>1013.10</td>\n",
       "      <td>96.75</td>\n",
       "      <td>1780.10</td>\n",
       "      <td>807.10</td>\n",
       "      <td>2.2</td>\n",
       "      <td>5.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-09</th>\n",
       "      <td>735.50</td>\n",
       "      <td>557.95</td>\n",
       "      <td>2013.90</td>\n",
       "      <td>267.10</td>\n",
       "      <td>724.55</td>\n",
       "      <td>301.90</td>\n",
       "      <td>1356.45</td>\n",
       "      <td>638.50</td>\n",
       "      <td>1299.45</td>\n",
       "      <td>1086.50</td>\n",
       "      <td>...</td>\n",
       "      <td>117.75</td>\n",
       "      <td>133.60</td>\n",
       "      <td>37.60</td>\n",
       "      <td>147.40</td>\n",
       "      <td>1006.10</td>\n",
       "      <td>93.70</td>\n",
       "      <td>1748.50</td>\n",
       "      <td>805.30</td>\n",
       "      <td>2.3</td>\n",
       "      <td>5.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-10</th>\n",
       "      <td>731.00</td>\n",
       "      <td>558.00</td>\n",
       "      <td>2012.45</td>\n",
       "      <td>262.25</td>\n",
       "      <td>719.35</td>\n",
       "      <td>300.65</td>\n",
       "      <td>1355.50</td>\n",
       "      <td>636.00</td>\n",
       "      <td>1256.45</td>\n",
       "      <td>1088.70</td>\n",
       "      <td>...</td>\n",
       "      <td>117.05</td>\n",
       "      <td>133.00</td>\n",
       "      <td>37.60</td>\n",
       "      <td>151.75</td>\n",
       "      <td>982.90</td>\n",
       "      <td>92.65</td>\n",
       "      <td>1758.45</td>\n",
       "      <td>804.40</td>\n",
       "      <td>2.4</td>\n",
       "      <td>6.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-05-13</th>\n",
       "      <td>735.05</td>\n",
       "      <td>557.75</td>\n",
       "      <td>2018.75</td>\n",
       "      <td>265.45</td>\n",
       "      <td>721.05</td>\n",
       "      <td>299.85</td>\n",
       "      <td>1317.65</td>\n",
       "      <td>621.65</td>\n",
       "      <td>1251.15</td>\n",
       "      <td>1104.45</td>\n",
       "      <td>...</td>\n",
       "      <td>109.85</td>\n",
       "      <td>131.90</td>\n",
       "      <td>38.10</td>\n",
       "      <td>139.10</td>\n",
       "      <td>980.65</td>\n",
       "      <td>93.60</td>\n",
       "      <td>1715.40</td>\n",
       "      <td>779.35</td>\n",
       "      <td>2.3</td>\n",
       "      <td>5.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            AXISBANK   CIPLA  HDFCBANK  INFRATEL    INFY     ITC       LT  \\\n",
       "Date                                                                        \n",
       "2019-05-07    747.45  564.50   1975.15    268.00  723.60  304.25  1366.60   \n",
       "2019-05-08    741.85  563.35   2004.30    266.70  718.40  307.00  1360.35   \n",
       "2019-05-09    735.50  557.95   2013.90    267.10  724.55  301.90  1356.45   \n",
       "2019-05-10    731.00  558.00   2012.45    262.25  719.35  300.65  1355.50   \n",
       "2019-05-13    735.05  557.75   2018.75    265.45  721.05  299.85  1317.65   \n",
       "\n",
       "               M&M  RELIANCE    TITAN  ...  BOMDYEING  FORTIS   IDFC  \\\n",
       "Date                                   ...                             \n",
       "2019-05-07  645.90   1384.90  1145.05  ...     121.45  136.35  37.75   \n",
       "2019-05-08  645.80   1343.50  1082.85  ...     119.35  135.20  37.10   \n",
       "2019-05-09  638.50   1299.45  1086.50  ...     117.75  133.60  37.60   \n",
       "2019-05-10  636.00   1256.45  1088.70  ...     117.05  133.00  37.60   \n",
       "2019-05-13  621.65   1251.15  1104.45  ...     109.85  131.90  38.10   \n",
       "\n",
       "            JETAIRWAYS  LALPATHLAB    NCC      PVR  RAYMOND  RCOM  SUZLON  \n",
       "Date                                                                       \n",
       "2019-05-07      127.30     1024.95  97.55  1802.30   808.95   2.1    6.35  \n",
       "2019-05-08      131.35     1013.10  96.75  1780.10   807.10   2.2    5.95  \n",
       "2019-05-09      147.40     1006.10  93.70  1748.50   805.30   2.3    5.65  \n",
       "2019-05-10      151.75      982.90  92.65  1758.45   804.40   2.4    6.40  \n",
       "2019-05-13      139.10      980.65  93.60  1715.40   779.35   2.3    5.60  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Query 6.2 \n",
    "Calculate average annual percentage return and volatility of all 30 stocks over a theoretical one year period"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "returns = df.pct_change().mean() * 252\n",
    "returns = pd.DataFrame(returns)\n",
    "returns.columns = ['Returns']\n",
    "returns['Volatility'] = df.pct_change().std() * sqrt(252)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Returns</th>\n",
       "      <th>Volatility</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AXISBANK</th>\n",
       "      <td>0.235246</td>\n",
       "      <td>0.277896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CIPLA</th>\n",
       "      <td>0.021976</td>\n",
       "      <td>0.254183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HDFCBANK</th>\n",
       "      <td>0.166872</td>\n",
       "      <td>0.256821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INFRATEL</th>\n",
       "      <td>-0.124187</td>\n",
       "      <td>0.306023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INFY</th>\n",
       "      <td>-0.022672</td>\n",
       "      <td>0.418963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ITC</th>\n",
       "      <td>0.064014</td>\n",
       "      <td>0.223863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LT</th>\n",
       "      <td>-0.085904</td>\n",
       "      <td>0.314323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M&amp;M</th>\n",
       "      <td>-0.269158</td>\n",
       "      <td>0.440357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RELIANCE</th>\n",
       "      <td>0.095121</td>\n",
       "      <td>0.439091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TITAN</th>\n",
       "      <td>0.473109</td>\n",
       "      <td>0.324361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADANIPOWER</th>\n",
       "      <td>0.305273</td>\n",
       "      <td>0.610532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APOLLOTYRE</th>\n",
       "      <td>-0.064303</td>\n",
       "      <td>0.310445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BERGEPAINT</th>\n",
       "      <td>0.108935</td>\n",
       "      <td>0.267668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GMRINFRA</th>\n",
       "      <td>0.043816</td>\n",
       "      <td>0.479169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IDBI</th>\n",
       "      <td>-0.296008</td>\n",
       "      <td>0.453787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IGL</th>\n",
       "      <td>-0.131762</td>\n",
       "      <td>0.651128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JINDALSTEL</th>\n",
       "      <td>0.275371</td>\n",
       "      <td>0.492980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MINDTREE</th>\n",
       "      <td>0.392570</td>\n",
       "      <td>0.341485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RELINFRA</th>\n",
       "      <td>-0.750074</td>\n",
       "      <td>0.547576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TATAPOWER</th>\n",
       "      <td>-0.090950</td>\n",
       "      <td>0.308297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BOMDYEING</th>\n",
       "      <td>0.327621</td>\n",
       "      <td>0.602869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FORTIS</th>\n",
       "      <td>-0.137297</td>\n",
       "      <td>0.421483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IDFC</th>\n",
       "      <td>-0.200489</td>\n",
       "      <td>0.353990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JETAIRWAYS</th>\n",
       "      <td>-0.481019</td>\n",
       "      <td>0.624854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LALPATHLAB</th>\n",
       "      <td>0.078122</td>\n",
       "      <td>0.264419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NCC</th>\n",
       "      <td>0.077142</td>\n",
       "      <td>0.441393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PVR</th>\n",
       "      <td>0.108530</td>\n",
       "      <td>0.310191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RAYMOND</th>\n",
       "      <td>0.070021</td>\n",
       "      <td>0.363834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RCOM</th>\n",
       "      <td>-0.881729</td>\n",
       "      <td>0.985366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SUZLON</th>\n",
       "      <td>-0.468152</td>\n",
       "      <td>0.593556</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Returns  Volatility\n",
       "AXISBANK    0.235246    0.277896\n",
       "CIPLA       0.021976    0.254183\n",
       "HDFCBANK    0.166872    0.256821\n",
       "INFRATEL   -0.124187    0.306023\n",
       "INFY       -0.022672    0.418963\n",
       "ITC         0.064014    0.223863\n",
       "LT         -0.085904    0.314323\n",
       "M&M        -0.269158    0.440357\n",
       "RELIANCE    0.095121    0.439091\n",
       "TITAN       0.473109    0.324361\n",
       "ADANIPOWER  0.305273    0.610532\n",
       "APOLLOTYRE -0.064303    0.310445\n",
       "BERGEPAINT  0.108935    0.267668\n",
       "GMRINFRA    0.043816    0.479169\n",
       "IDBI       -0.296008    0.453787\n",
       "IGL        -0.131762    0.651128\n",
       "JINDALSTEL  0.275371    0.492980\n",
       "MINDTREE    0.392570    0.341485\n",
       "RELINFRA   -0.750074    0.547576\n",
       "TATAPOWER  -0.090950    0.308297\n",
       "BOMDYEING   0.327621    0.602869\n",
       "FORTIS     -0.137297    0.421483\n",
       "IDFC       -0.200489    0.353990\n",
       "JETAIRWAYS -0.481019    0.624854\n",
       "LALPATHLAB  0.078122    0.264419\n",
       "NCC         0.077142    0.441393\n",
       "PVR         0.108530    0.310191\n",
       "RAYMOND     0.070021    0.363834\n",
       "RCOM       -0.881729    0.985366\n",
       "SUZLON     -0.468152    0.593556"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "returns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Query 6.3 \n",
    "Cluster the 30 stocks according to their mean annual Volatilities and Returns using K-means clustering. Identify the optimum number of clusters using the Elbow curve method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#format the data as a numpy array to feed into the K-Means algorithm\n",
    "data = np.asarray([np.asarray(returns['Returns']),np.asarray(returns['Volatility'])]).T\n",
    "X = data\n",
    "distorsions = []\n",
    "for k in range(2, 20):\n",
    "    k_means = KMeans(n_clusters=k)\n",
    "    k_means.fit(X)\n",
    "    distorsions.append(k_means.inertia_)\n",
    "fig = plt.figure(figsize=(15, 5))\n",
    "plt.plot(range(2, 20), distorsions)\n",
    "plt.grid(True)\n",
    "plt.title('Elbow curve')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 7 Clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Query 6.4\n",
    " Prepare a separate Data frame to show which stocks belong to the same cluster "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": [
    "#computing K-Means with K = 7 (7 clusters)\n",
    "centroids,_ = kmeans(data,7)\n",
    "# assign each sample to a cluster\n",
    "idx,_ = vq(data,centroids)\n",
    "# some plotting using numpy's logical indexing\n",
    "plot(data[idx==0,0],data[idx==0,1],'ob',\n",
    "     data[idx==1,0],data[idx==1,1],'oy',\n",
    "     data[idx==2,0],data[idx==2,1],'or',\n",
    "     data[idx==3,0],data[idx==3,1],'og',\n",
    "     data[idx==4,0],data[idx==4,1],'om',\n",
    "     data[idx==5,0],data[idx==5,1],'*',\n",
    "     data[idx==6,0],data[idx==6,1],'^')\n",
    "#plot(centroids[:,0],centroids[:,1],'sg',markersize=8)\n",
    "show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "details = [(name,cluster) for name, cluster in zip(returns.index,idx)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2['Name'] = returns.index\n",
    "df2['Cluster'] = idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Name</th>\n",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AXISBANK</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CIPLA</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HDFCBANK</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>INFRATEL</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>INFY</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ITC</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>LT</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>M&amp;M</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>RELIANCE</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>TITAN</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>ADANIPOWER</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>APOLLOTYRE</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>BERGEPAINT</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>GMRINFRA</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>IDBI</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>IGL</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>JINDALSTEL</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>MINDTREE</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>RELINFRA</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>TATAPOWER</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>BOMDYEING</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>FORTIS</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>IDFC</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>JETAIRWAYS</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>LALPATHLAB</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>NCC</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>PVR</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>RAYMOND</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>RCOM</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>SUZLON</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Name  Cluster\n",
       "0     AXISBANK        4\n",
       "1        CIPLA        6\n",
       "2     HDFCBANK        6\n",
       "3     INFRATEL        3\n",
       "4         INFY        2\n",
       "5          ITC        6\n",
       "6           LT        3\n",
       "7          M&M        3\n",
       "8     RELIANCE        2\n",
       "9        TITAN        4\n",
       "10  ADANIPOWER        1\n",
       "11  APOLLOTYRE        3\n",
       "12  BERGEPAINT        6\n",
       "13    GMRINFRA        2\n",
       "14        IDBI        3\n",
       "15         IGL        3\n",
       "16  JINDALSTEL        1\n",
       "17    MINDTREE        4\n",
       "18    RELINFRA        5\n",
       "19   TATAPOWER        3\n",
       "20   BOMDYEING        1\n",
       "21      FORTIS        3\n",
       "22        IDFC        3\n",
       "23  JETAIRWAYS        5\n",
       "24  LALPATHLAB        6\n",
       "25         NCC        2\n",
       "26         PVR        6\n",
       "27     RAYMOND        2\n",
       "28        RCOM        0\n",
       "29      SUZLON        5"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  }
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