{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.var of                                      D.O   pH  CONDUCTIVITY  B.O.D.  NITRATE  \\\n",
       "\"CAUVERY NAPOKULU\"                   6.9  7.5            59     1.0     0.14   \n",
       "\"CAUVERY AT KUSHAL NAGAR \"           7.0  7.8            93     1.0     0.13   \n",
       "\"CAUVERY AT KRS DAM\"                 7.0  8.5           235     1.0     0.31   \n",
       "\"CAUVERY AT KAREKUARA\"               6.9  8.5           243     1.0     0.33   \n",
       "\"CAUVERY AT SRIRANGAPATTANNA.\"       6.1  8.1           280     1.6     0.38   \n",
       "\"CAUVERY AT SATHYAGALAM \"            7.6  8.1           336     1.0     0.46   \n",
       "\"CAUVERY AT BANNUR \"                 7.0  8.1           331     1.3     0.39   \n",
       "\"CAUVERY AT RANGANATHITTU\"           6.7  8.1           277     1.1     0.35   \n",
       "\"CAUVERY AT METTUR\"                  6.5  8.1           417     1.5     0.20   \n",
       "\"CAUVERY AT BHAVANI \"                6.2  8.0           475     1.1     0.25   \n",
       "\"CAUVERY AT PALLIPPALAYAM\"           6.7  7.9           425     1.2     0.20   \n",
       "\"CAUVERY AT ERODE\"                   5.6  7.7           475     1.7     0.25   \n",
       "\"CAUVERY AT VELORE \"                 6.9  7.9           442     1.3     0.17   \n",
       "\"CAUVERY AT KARUR\"                   7.4  8.2           727     1.2     0.22   \n",
       "\"CAUVERY AT MOHANUR \"                6.8  7.8           500     1.3     0.16   \n",
       "\"CAUVERY AT THIRUMUKKUDALI\"          6.5  8.1           635     0.8     0.17   \n",
       "\"CAUVERY AT MUSIRI\"                  8.0  8.1           598     0.9     0.18   \n",
       "\"CAUVERY AT Trichy\"                  7.6  7.8           670     0.9     0.23   \n",
       "\"CAUVERY AT GRAND ANAICUT\"           6.4  8.1           752     1.9     0.20   \n",
       "\"CAUVERY AT THANJAVUR\"               6.5  8.0           657     1.3     0.21   \n",
       "\"CAUVERY AT MAYILADUTHURAI\"          6.8  8.1           576     1.9     0.22   \n",
       "\"CAUVERY AT NAMAKAL\"                 6.4  7.8           431     1.3     0.25   \n",
       "\"CAUVERY AT URRACHIKOTTAI\"           5.8  7.9           290     1.2     0.19   \n",
       "\"CAUVERY AT VAIRAPALAYAM\"            6.9  7.8           399     1.9     0.21   \n",
       "\"CAUVERY AT KUMBAKONAM,THANJAVUR\"    7.0  8.1           513     1.7     0.22   \n",
       "\"CAUVERY AT COLEROON\"                8.1  8.0          1133     0.8     0.20   \n",
       "\"CAUVERY AT PITCHAVARAM\"             7.8  8.1          1073     1.6     0.16   \n",
       "\"SHIMSHA AT YEDIYAR\"                 6.7  7.5           571     2.5     1.38   \n",
       "\"SHIMSHA AT HALAGUR\"                 7.1  8.3           556     1.0     0.71   \n",
       "\"ARKAVATHI AT  KANAKAPURATOWN\"       3.7  7.7          1000     2.1    21.34   \n",
       "...                                  ...  ...           ...     ...      ...   \n",
       "\"ARKAVATHI AT HESARAGHATTA\"          9.9  9.6           545     2.5     5.76   \n",
       "\"LAKSHMANTIRTHA AT HUNSUR TOWN\"      4.5  7.6           502     3.0     0.74   \n",
       "\"LAKSHMANTIRTHA AT KATTEMALAVADI\"    4.3  7.8           557     2.7     0.96   \n",
       "\"KABBANI AT MUTHANKARA\"              6.7  7.0            76     0.7     0.32   \n",
       "\"KABBANI AT SARAGUR \"                6.8  8.1           146     1.0     0.17   \n",
       "\"KABBANI AT CAUSE WAY\"               6.9  8.0           282     1.0     0.35   \n",
       "\"KABBANI AT NANJANGUD\"               7.4  8.0           295     1.0     0.36   \n",
       "\"KABINI AT BATHING GHAT\"             5.8  7.6           277     1.9     0.43   \n",
       "\"BHAVANI AT ELACHIVAZHY\"             7.1  7.3           110     0.4     0.31   \n",
       "\"BHAVANI AT PATHIRAKALIAMMAN KOIL\"   8.1  8.1           276     0.8     0.29   \n",
       "\"BHAVANI AT SIRUMUGAI\"               8.0  8.0           193     0.9     0.18   \n",
       "\"BHAVANI AT BHAVANI SAGAR\"           6.7  8.0           255     1.0     0.18   \n",
       "\"BHAVANI AT BHAVANI\"                 5.9  8.0           343     1.3     0.17   \n",
       "\"BHAVANI AT  SATHYAMANGALAM\"         6.7  7.9           237     0.9     0.16   \n",
       "\"AMRAVATI AT MADHUTHUKKULAM\"         7.6  7.9           186     1.0     0.15   \n",
       "\"KALINGARAYAN CANAL , ERODE\"         5.8  8.0           423     1.3     0.26   \n",
       "\"SARABANGA AT SALEM\"                 2.9  7.6          3413    35.0     0.43   \n",
       "\"THIRUMANIMUTHAR AT SALEM\"           2.0  7.5          2306    40.3     0.51   \n",
       "\"TAMBIRAPARANI AT  MADURACOATS\"      7.4  7.6           104     1.2     0.09   \n",
       "\"TAMBIRAPARANI ATCHERANMADEVI\"       7.1  7.7           103     1.2     0.21   \n",
       "\"TAMBIRAPARANI AT TIRUNELVELI,.\"     6.4  7.7           140     2.0     0.16   \n",
       "\"TAMBIRAPARANI AT MURAPPANADU\"       6.6  7.0           342     1.3     0.11   \n",
       "\"TAMBIRAPARANI ATPAPPANKULAM\"        7.2  7.7            87     1.1     0.14   \n",
       "\"TAMBIRAPARANI AT AMBASAMUDAM\"       7.2  7.5            88     1.1     0.12   \n",
       "\"TAMBIRAPARANI AT ARUMUGANERI\"       6.9  7.6           787     3.2     0.14   \n",
       "\"TAMBIRAPARANI AT ERAL\"              6.9  7.2           338     1.3     0.13   \n",
       "\"TAMBIRAPARANI AT KALLIDAI KURICHI\"  7.6  7.6            86     1.7     0.10   \n",
       "\"TAMBIRAPARANI AT SRIVAIKUNTAM\"      6.4  7.0           264     1.3     0.13   \n",
       "\"TAMBIRAPARANI AT VELLAKOIL\"         6.2  7.6           170     1.8     0.23   \n",
       "\"TAMBIRAPARANI AT SIVALAPERI\"        6.3  7.6           200     2.0     1.42   \n",
       "\n",
       "                                     COLIFORM  \n",
       "\"CAUVERY NAPOKULU\"                       1600  \n",
       "\"CAUVERY AT KUSHAL NAGAR \"               1600  \n",
       "\"CAUVERY AT KRS DAM\"                     1600  \n",
       "\"CAUVERY AT KAREKUARA\"                   1600  \n",
       "\"CAUVERY AT SRIRANGAPATTANNA.\"           1600  \n",
       "\"CAUVERY AT SATHYAGALAM \"                1600  \n",
       "\"CAUVERY AT BANNUR \"                     1600  \n",
       "\"CAUVERY AT RANGANATHITTU\"               1600  \n",
       "\"CAUVERY AT METTUR\"                       334  \n",
       "\"CAUVERY AT BHAVANI \"                     324  \n",
       "\"CAUVERY AT PALLIPPALAYAM\"                543  \n",
       "\"CAUVERY AT ERODE\"                        673  \n",
       "\"CAUVERY AT VELORE \"                      347  \n",
       "\"CAUVERY AT KARUR\"                        323  \n",
       "\"CAUVERY AT MOHANUR \"                     368  \n",
       "\"CAUVERY AT THIRUMUKKUDALI\"               325  \n",
       "\"CAUVERY AT MUSIRI\"                       303  \n",
       "\"CAUVERY AT Trichy\"                       305  \n",
       "\"CAUVERY AT GRAND ANAICUT\"                503  \n",
       "\"CAUVERY AT THANJAVUR\"                    317  \n",
       "\"CAUVERY AT MAYILADUTHURAI\"               319  \n",
       "\"CAUVERY AT NAMAKAL\"                      348  \n",
       "\"CAUVERY AT URRACHIKOTTAI\"                288  \n",
       "\"CAUVERY AT VAIRAPALAYAM\"                 352  \n",
       "\"CAUVERY AT KUMBAKONAM,THANJAVUR\"         864  \n",
       "\"CAUVERY AT COLEROON\"                     303  \n",
       "\"CAUVERY AT PITCHAVARAM\"                  288  \n",
       "\"SHIMSHA AT YEDIYAR\"                       39  \n",
       "\"SHIMSHA AT HALAGUR\"                     1600  \n",
       "\"ARKAVATHI AT  KANAKAPURATOWN\"             70  \n",
       "...                                       ...  \n",
       "\"ARKAVATHI AT HESARAGHATTA\"                18  \n",
       "\"LAKSHMANTIRTHA AT HUNSUR TOWN\"          1600  \n",
       "\"LAKSHMANTIRTHA AT KATTEMALAVADI\"        1600  \n",
       "\"KABBANI AT MUTHANKARA\"                   435  \n",
       "\"KABBANI AT SARAGUR \"                    1600  \n",
       "\"KABBANI AT CAUSE WAY\"                   1600  \n",
       "\"KABBANI AT NANJANGUD\"                   1600  \n",
       "\"KABINI AT BATHING GHAT\"                 1600  \n",
       "\"BHAVANI AT ELACHIVAZHY\"                  315  \n",
       "\"BHAVANI AT PATHIRAKALIAMMAN KOIL\"        220  \n",
       "\"BHAVANI AT SIRUMUGAI\"                    219  \n",
       "\"BHAVANI AT BHAVANI SAGAR\"                285  \n",
       "\"BHAVANI AT BHAVANI\"                      348  \n",
       "\"BHAVANI AT  SATHYAMANGALAM\"              278  \n",
       "\"AMRAVATI AT MADHUTHUKKULAM\"              211  \n",
       "\"KALINGARAYAN CANAL , ERODE\"              289  \n",
       "\"SARABANGA AT SALEM\"                   306750  \n",
       "\"THIRUMANIMUTHAR AT SALEM\"             351617  \n",
       "\"TAMBIRAPARANI AT  MADURACOATS\"            94  \n",
       "\"TAMBIRAPARANI ATCHERANMADEVI\"            131  \n",
       "\"TAMBIRAPARANI AT TIRUNELVELI,.\"          368  \n",
       "\"TAMBIRAPARANI AT MURAPPANADU\"            103  \n",
       "\"TAMBIRAPARANI ATPAPPANKULAM\"             113  \n",
       "\"TAMBIRAPARANI AT AMBASAMUDAM\"             96  \n",
       "\"TAMBIRAPARANI AT ARUMUGANERI\"            178  \n",
       "\"TAMBIRAPARANI AT ERAL\"                   135  \n",
       "\"TAMBIRAPARANI AT KALLIDAI KURICHI\"       103  \n",
       "\"TAMBIRAPARANI AT SRIVAIKUNTAM\"           160  \n",
       "\"TAMBIRAPARANI AT VELLAKOIL\"              277  \n",
       "\"TAMBIRAPARANI AT SIVALAPERI\"             298  \n",
       "\n",
       "[61 rows x 6 columns]>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "from io import StringIO\n",
    "import requests\n",
    "import json\n",
    "import pandas as pd\n",
    "\n",
    "# @hidden_cell\n",
    "# This function accesses a file in your Object Storage. The definition contains your credentials.\n",
    "# You might want to remove those credentials before you share your notebook.\n",
    "def get_object_storage_file_with_credentials_e0afbdba7e6e4e2896dfe96facb8625f(container, filename):\n",
    "    \"\"\"This functions returns a StringIO object containing\n",
    "    the file content from Bluemix Object Storage.\"\"\"\n",
    "\n",
    "    url1 = ''.join(['https://identity.open.softlayer.com', '/v3/auth/tokens'])\n",
    "    data = {'auth': {'identity': {'methods': ['password'],\n",
    "            'password': {'user': {'name': 'member_1fb655b970e4bae50e20c7d51f190258e4dd6ac3','domain': {'id': '8a56ebd98b3d4dbab5551fbc0ea9acde'},\n",
    "            'password': 'Cka5.cw0ID9}DLqe'}}}}}\n",
    "    headers1 = {'Content-Type': 'application/json'}\n",
    "    resp1 = requests.post(url=url1, data=json.dumps(data), headers=headers1)\n",
    "    resp1_body = resp1.json()\n",
    "    for e1 in resp1_body['token']['catalog']:\n",
    "        if(e1['type']=='object-store'):\n",
    "            for e2 in e1['endpoints']:\n",
    "                        if(e2['interface']=='public'and e2['region']=='dallas'):\n",
    "                            url2 = ''.join([e2['url'],'/', container, '/', filename])\n",
    "    s_subject_token = resp1.headers['x-subject-token']\n",
    "    headers2 = {'X-Auth-Token': s_subject_token, 'accept': 'application/json'}\n",
    "    resp2 = requests.get(url=url2, headers=headers2)\n",
    "    return StringIO(resp2.text)\n",
    "\n",
    "df_data_1 = pd.read_csv(get_object_storage_file_with_credentials_e0afbdba7e6e4e2896dfe96facb8625f('DefaultProjectjrkumarinibmcom', 'Environmental_Water_Quality_Ker_TN_Kar_total_new.csv'), index_col=0)\n",
    "#df_data_1.head()\n",
    "#df_data_1.info\n",
    "df_data_1.var\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#it is important to standardize the variables to have mean zero and standard deviation 1 before\n",
    "#performing PCA. We can do this using the scale() function from sklearn:\n",
    "from sklearn.preprocessing import scale\n",
    "X = pd.DataFrame(scale(df_data_1), index=df_data_1.index, columns=df_data_1.columns)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>D.O</th>\n",
       "      <td>-0.438988</td>\n",
       "      <td>0.116016</td>\n",
       "      <td>0.384935</td>\n",
       "      <td>0.786719</td>\n",
       "      <td>0.161108</td>\n",
       "      <td>0.027789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pH</th>\n",
       "      <td>-0.128674</td>\n",
       "      <td>0.762261</td>\n",
       "      <td>0.466528</td>\n",
       "      <td>-0.425141</td>\n",
       "      <td>0.062965</td>\n",
       "      <td>-0.006541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CONDUCTIVITY</th>\n",
       "      <td>0.484688</td>\n",
       "      <td>0.231486</td>\n",
       "      <td>0.150338</td>\n",
       "      <td>0.320418</td>\n",
       "      <td>-0.765165</td>\n",
       "      <td>-0.027285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B.O.D.</th>\n",
       "      <td>0.527618</td>\n",
       "      <td>0.004845</td>\n",
       "      <td>0.185019</td>\n",
       "      <td>0.098204</td>\n",
       "      <td>0.387252</td>\n",
       "      <td>0.726468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NITRATE</th>\n",
       "      <td>0.069878</td>\n",
       "      <td>0.593043</td>\n",
       "      <td>-0.730277</td>\n",
       "      <td>0.269548</td>\n",
       "      <td>0.193363</td>\n",
       "      <td>-0.008229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COLIFORM</th>\n",
       "      <td>0.522059</td>\n",
       "      <td>-0.013758</td>\n",
       "      <td>0.209854</td>\n",
       "      <td>0.123939</td>\n",
       "      <td>0.444126</td>\n",
       "      <td>-0.686015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    V1        V2        V3        V4        V5        V6\n",
       "D.O          -0.438988  0.116016  0.384935  0.786719  0.161108  0.027789\n",
       "pH           -0.128674  0.762261  0.466528 -0.425141  0.062965 -0.006541\n",
       "CONDUCTIVITY  0.484688  0.231486  0.150338  0.320418 -0.765165 -0.027285\n",
       "B.O.D.        0.527618  0.004845  0.185019  0.098204  0.387252  0.726468\n",
       "NITRATE       0.069878  0.593043 -0.730277  0.269548  0.193363 -0.008229\n",
       "COLIFORM      0.522059 -0.013758  0.209854  0.123939  0.444126 -0.686015"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Compute Loading Vectors\n",
    "from sklearn.decomposition import PCA\n",
    "#pca_loadings = pd.DataFrame(PCA().fit(X).components_.T, index=df_data_1.columns, columns=['V1', 'V2', 'V3', 'V4'])\n",
    "pca_loadings = pd.DataFrame(PCA().fit(X).components_.T, index=df_data_1.columns, columns=['V1', 'V2', 'V3', 'V4','V5','V6'])\n",
    "pca_loadings\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PC1</th>\n",
       "      <th>PC2</th>\n",
       "      <th>PC3</th>\n",
       "      <th>PC4</th>\n",
       "      <th>PC5</th>\n",
       "      <th>PC6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>\"CAUVERY NAPOKULU\"</th>\n",
       "      <td>-0.612562</td>\n",
       "      <td>-1.005936</td>\n",
       "      <td>-0.377983</td>\n",
       "      <td>0.207874</td>\n",
       "      <td>0.388113</td>\n",
       "      <td>-0.029555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"CAUVERY AT KUSHAL NAGAR \"</th>\n",
       "      <td>-0.715999</td>\n",
       "      <td>-0.399931</td>\n",
       "      <td>0.023699</td>\n",
       "      <td>-0.031312</td>\n",
       "      <td>0.398551</td>\n",
       "      <td>-0.034005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"CAUVERY AT KRS DAM\"</th>\n",
       "      <td>-0.807499</td>\n",
       "      <td>1.062962</td>\n",
       "      <td>0.850648</td>\n",
       "      <td>-0.684390</td>\n",
       "      <td>0.312451</td>\n",
       "      <td>-0.053738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"CAUVERY AT KAREKUARA\"</th>\n",
       "      <td>-0.762713</td>\n",
       "      <td>1.061112</td>\n",
       "      <td>0.815504</td>\n",
       "      <td>-0.743338</td>\n",
       "      <td>0.288465</td>\n",
       "      <td>-0.056547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"CAUVERY AT SRIRANGAPATTANNA.\"</th>\n",
       "      <td>-0.252636</td>\n",
       "      <td>0.233632</td>\n",
       "      <td>0.096573</td>\n",
       "      <td>-0.799667</td>\n",
       "      <td>0.100589</td>\n",
       "      <td>-0.003513</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     PC1       PC2       PC3       PC4  \\\n",
       "\"CAUVERY NAPOKULU\"             -0.612562 -1.005936 -0.377983  0.207874   \n",
       "\"CAUVERY AT KUSHAL NAGAR \"     -0.715999 -0.399931  0.023699 -0.031312   \n",
       "\"CAUVERY AT KRS DAM\"           -0.807499  1.062962  0.850648 -0.684390   \n",
       "\"CAUVERY AT KAREKUARA\"         -0.762713  1.061112  0.815504 -0.743338   \n",
       "\"CAUVERY AT SRIRANGAPATTANNA.\" -0.252636  0.233632  0.096573 -0.799667   \n",
       "\n",
       "                                     PC5       PC6  \n",
       "\"CAUVERY NAPOKULU\"              0.388113 -0.029555  \n",
       "\"CAUVERY AT KUSHAL NAGAR \"      0.398551 -0.034005  \n",
       "\"CAUVERY AT KRS DAM\"            0.312451 -0.053738  \n",
       "\"CAUVERY AT KAREKUARA\"          0.288465 -0.056547  \n",
       "\"CAUVERY AT SRIRANGAPATTANNA.\"  0.100589 -0.003513  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the PCA model and transform X to get the principal components\n",
    "pca = PCA()\n",
    "df_plot = pd.DataFrame(pca.fit_transform(X), columns=['PC1', 'PC2', 'PC3', 'PC4','PC5','PC6'], index=X.index)\n",
    "df_plot.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.patches.FancyArrow at 0x7f963a270410>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4HhlpANFAIw0AAABEgIw0PI2MNAAA3kJGGgAAAHA5GmkAgOuRkQYQDTTSAAAAQATISMPT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0J7zpWVlR3+G+NwfivckeZwOJw/CVq9cnl5Obp37w4vLy8dnayenp7Oi4HHjx+HVCrFlClTcP78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w88QJUqVcjKynKdEx4efsrmIA899BAvvfRSiXWMHTuWuLg44uLiqFGjBm3btiU+Pp7HHnuM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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9638361a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# construct a biplot of the first two principal components using our loading vectors:\n",
    "#fig = plt.figure(figsize=(9,9))\n",
    "fig , ax1 = plt.subplots(figsize=(9,20))\n",
    "ax1.set_xlim(-3.5,3.5)\n",
    "ax1.set_ylim(-3.5,3.5)\n",
    "#print(df_plot.index)\n",
    "# Plot Principal Components 1 and 2\n",
    "for i in df_plot.index:\n",
    "    ax1.annotate(i, (-df_plot.PC1.loc[i], -df_plot.PC2.loc[i]), ha='center')\n",
    "    \n",
    "    # Plot reference lines\n",
    "ax1.hlines(0,-3.5,3.5, linestyles='dotted', colors='grey')\n",
    "ax1.vlines(0,-3.5,3.5, linestyles='dotted', colors='grey')\n",
    "ax1.set_xlabel('First Principal Component')\n",
    "ax1.set_ylabel('Second Principal Component')\n",
    "\n",
    "# Plot Principal Component loading vectors, using a second y-axis.\n",
    "ax2 = ax1.twinx().twiny()\n",
    "ax2.set_ylim(-1,1)\n",
    "ax2.set_xlim(-1,1)\n",
    "ax2.set_xlabel('Principal Component loading vectors', color='red')\n",
    "\n",
    "# Plot labels for vectors. Variable ’a’ is a small offset parameter to separate arrow tip and text.\n",
    "a = 1.07\n",
    "for i in pca_loadings[['V1', 'V2']].index:\n",
    "    ax2.annotate(i, (-pca_loadings.V1.loc[i]*a, -pca_loadings.V2.loc[i]*a), color='red')\n",
    "\n",
    "# Plot vectors\n",
    "ax2.arrow(0,0,-pca_loadings.V1[0], -pca_loadings.V2[0])\n",
    "ax2.arrow(0,0,-pca_loadings.V1[1], -pca_loadings.V2[1])\n",
    "ax2.arrow(0,0,-pca_loadings.V1[2], -pca_loadings.V2[2])\n",
    "ax2.arrow(0,0,-pca_loadings.V1[3], -pca_loadings.V2[3])\n",
    "ax2.arrow(0,0,-pca_loadings.V1[3], -pca_loadings.V2[4])\n",
    "ax2.arrow(0,0,-pca_loadings.V1[3], -pca_loadings.V2[5])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3.35851866,  1.14527897,  1.03806197,  0.28964479,  0.16471375,\n",
       "        0.00378186])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.55975311,  0.19087983,  0.17301033,  0.04827413,  0.02745229,\n",
       "        0.00063031])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " pca.explained_variance_ratio_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x7f963845b410>,\n",
       "  <matplotlib.axis.XTick at 0x7f96384a8490>,\n",
       "  <matplotlib.axis.XTick at 0x7f963a3155d0>,\n",
       "  <matplotlib.axis.XTick at 0x7f963a315d50>,\n",
       "  <matplotlib.axis.XTick at 0x7f96385904d0>,\n",
       "  <matplotlib.axis.XTick at 0x7f9638590c10>],\n",
       " <a list of 6 Text xticklabel objects>)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7kTJVVQVHHAF1dXDffTBnDgwcCOPGheInIutTsRMpYTU1YSmyF14IS5ENGwbH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\nRESk7P0/bth6UHU6X8UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f963a311f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,5))\n",
    "plt.plot([1,2,3,4,5,6], pca.explained_variance_ratio_, '-o')\n",
    "plt.ylabel('Proportion of Variance Explained')\n",
    "plt.xlabel('Principal Component')\n",
    "plt.xlim(0.75,4.25)\n",
    "plt.ylim(0,1.05)\n",
    "plt.xticks([1,2,3,4,5,6])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x7f9653c852d0>,\n",
       "  <matplotlib.axis.XTick at 0x7f9653c7d450>,\n",
       "  <matplotlib.axis.XTick at 0x7f9653d24990>,\n",
       "  <matplotlib.axis.XTick at 0x7f967c0cb150>,\n",
       "  <matplotlib.axis.XTick at 0x7f967c0cb890>,\n",
       "  <matplotlib.axis.XTick at 0x7f967c0cbfd0>],\n",
       " <a list of 6 Text xticklabel objects>)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ws3zzTW/FY2bWV1lqdmdExL9FxOqIWBoRY0mSn+UgAn75Sxg1Kumfe/hhJzozs77qbouf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\n8QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9653d58b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,5))\n",
    "plt.plot([1,2,3,4,5,6], np.cumsum(pca.explained_variance_ratio_), '-s')\n",
    "plt.ylabel('Proportion of Variance Explained')\n",
    "plt.xlabel('Principal Component')\n",
    "plt.xlim(0.75,4.25)\n",
    "plt.ylim(0,1.05)\n",
    "plt.xticks([1,2,3,4,5,6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2 with Spark 2.0",
   "language": "python",
   "name": "python2-spark20"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}