{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-19T18:51:04.827436Z",
     "start_time": "2017-12-19T18:51:02.272120Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "import pandas as pd\n",
    "import mca\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-19T18:51:04.949409Z",
     "start_time": "2017-12-19T18:51:04.836604Z"
    }
   },
   "outputs": [],
   "source": [
    "data = pd.read_table('./cookieclassifier_data_matrix.tsv',\n",
    "                     sep='\\t', header=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-19T18:51:04.961927Z",
     "start_time": "2017-12-19T18:51:04.954714Z"
    }
   },
   "outputs": [],
   "source": [
    "data.columns = ['category'] + data.columns.tolist()[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-19T18:51:04.996635Z",
     "start_time": "2017-12-19T18:51:04.971398Z"
    }
   },
   "outputs": [],
   "source": [
    "data['category'] = data.category.astype('category')\n",
    "data['category'] = data.category.cat.rename_categories([1,2,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-19T18:51:08.252442Z",
     "start_time": "2017-12-19T18:51:06.026019Z"
    }
   },
   "outputs": [],
   "source": [
    "data#\n",
    "X = data.drop('category', axis=1)\n",
    "\n",
    "mca_ben = mca.MCA(X)\n",
    "mca_ind = mca.MCA(X, benzecri=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-19T18:54:15.844622Z",
     "start_time": "2017-12-19T18:54:15.822963Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<mca.MCA at 0x2b6b24bb5390>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mca_ben"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-19T18:58:55.353240Z",
     "start_time": "2017-12-19T18:58:49.607166Z"
    }
   },
   "outputs": [],
   "source": [
    "fs, cos, cont = 'Factor score','Squared cosines', 'Contributions x 1000'\n",
    "table3 = pd.DataFrame(columns=X.index, index=pd.MultiIndex\n",
    "                      .from_product([[fs, cos, cont], range(1, 3)]))\n",
    "\n",
    "table3.loc[fs,    :] = mca_ben.fs_r(N=2).T\n",
    "table3.loc[cos,   :] = mca_ben.cos_r(N=2).T\n",
    "table3.loc[cont,  :] = mca_ben.cont_r(N=2).T * 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-19T18:59:00.476228Z",
     "start_time": "2017-12-19T18:59:00.402096Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>1921</th>\n",
       "      <th>1922</th>\n",
       "      <th>1923</th>\n",
       "      <th>1924</th>\n",
       "      <th>1925</th>\n",
       "      <th>1926</th>\n",
       "      <th>1927</th>\n",
       "      <th>1928</th>\n",
       "      <th>1929</th>\n",
       "      <th>1930</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Factor score</th>\n",
       "      <th>1</th>\n",
       "      <td>-0.421929</td>\n",
       "      <td>-0.296647</td>\n",
       "      <td>-0.449088</td>\n",
       "      <td>-0.412061</td>\n",
       "      <td>-0.479269</td>\n",
       "      <td>-0.350682</td>\n",
       "      <td>-0.504301</td>\n",
       "      <td>-0.498747</td>\n",
       "      <td>-0.514678</td>\n",
       "      <td>-0.533952</td>\n",
       "      <td>...</td>\n",
       "      <td>1.10075</td>\n",
       "      <td>0.777899</td>\n",
       "      <td>1.08087</td>\n",
       "      <td>0.874154</td>\n",
       "      <td>0.989452</td>\n",
       "      <td>0.985762</td>\n",
       "      <td>0.989452</td>\n",
       "      <td>0.985762</td>\n",
       "      <td>0.597031</td>\n",
       "      <td>0.617106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.459544</td>\n",
       "      <td>0.00390408</td>\n",
       "      <td>-0.115709</td>\n",
       "      <td>0.056708</td>\n",
       "      <td>-0.130495</td>\n",
       "      <td>-0.0765635</td>\n",
       "      <td>-0.0590306</td>\n",
       "      <td>-0.123805</td>\n",
       "      <td>-0.423026</td>\n",
       "      <td>0.0243784</td>\n",
       "      <td>...</td>\n",
       "      <td>0.114587</td>\n",
       "      <td>0.102167</td>\n",
       "      <td>0.14361</td>\n",
       "      <td>0.0416651</td>\n",
       "      <td>0.0646777</td>\n",
       "      <td>0.0525748</td>\n",
       "      <td>0.0646777</td>\n",
       "      <td>0.0525748</td>\n",
       "      <td>0.0426535</td>\n",
       "      <td>0.0172379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Squared cosines</th>\n",
       "      <th>1</th>\n",
       "      <td>0.108207</td>\n",
       "      <td>0.145579</td>\n",
       "      <td>0.0966167</td>\n",
       "      <td>0.18672</td>\n",
       "      <td>0.391949</td>\n",
       "      <td>0.120913</td>\n",
       "      <td>0.463594</td>\n",
       "      <td>0.163296</td>\n",
       "      <td>0.194268</td>\n",
       "      <td>0.444923</td>\n",
       "      <td>...</td>\n",
       "      <td>0.139675</td>\n",
       "      <td>0.115483</td>\n",
       "      <td>0.229065</td>\n",
       "      <td>0.58557</td>\n",
       "      <td>0.367409</td>\n",
       "      <td>0.301467</td>\n",
       "      <td>0.367409</td>\n",
       "      <td>0.301467</td>\n",
       "      <td>0.315981</td>\n",
       "      <td>0.534713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.12836</td>\n",
       "      <td>2.52149e-05</td>\n",
       "      <td>0.00641393</td>\n",
       "      <td>0.00353637</td>\n",
       "      <td>0.0290574</td>\n",
       "      <td>0.00576356</td>\n",
       "      <td>0.00635202</td>\n",
       "      <td>0.0100621</td>\n",
       "      <td>0.13124</td>\n",
       "      <td>0.000927455</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00151362</td>\n",
       "      <td>0.00199203</td>\n",
       "      <td>0.00404374</td>\n",
       "      <td>0.00133029</td>\n",
       "      <td>0.00156989</td>\n",
       "      <td>0.000857534</td>\n",
       "      <td>0.00156989</td>\n",
       "      <td>0.000857534</td>\n",
       "      <td>0.00161279</td>\n",
       "      <td>0.000417224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Contributions x 1000</th>\n",
       "      <th>1</th>\n",
       "      <td>0.339312</td>\n",
       "      <td>0.167726</td>\n",
       "      <td>0.349454</td>\n",
       "      <td>0.235364</td>\n",
       "      <td>0.318401</td>\n",
       "      <td>0.191777</td>\n",
       "      <td>0.396597</td>\n",
       "      <td>0.431009</td>\n",
       "      <td>0.504883</td>\n",
       "      <td>0.494005</td>\n",
       "      <td>...</td>\n",
       "      <td>1.67955</td>\n",
       "      <td>0.943661</td>\n",
       "      <td>2.22671</td>\n",
       "      <td>1.98607</td>\n",
       "      <td>2.3749</td>\n",
       "      <td>1.01024</td>\n",
       "      <td>2.3749</td>\n",
       "      <td>1.01024</td>\n",
       "      <td>0.185286</td>\n",
       "      <td>0.329926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.53226</td>\n",
       "      <td>0.00011059</td>\n",
       "      <td>0.0883121</td>\n",
       "      <td>0.0169693</td>\n",
       "      <td>0.0898589</td>\n",
       "      <td>0.0347994</td>\n",
       "      <td>0.0206863</td>\n",
       "      <td>0.101102</td>\n",
       "      <td>1.29841</td>\n",
       "      <td>0.0039201</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0692867</td>\n",
       "      <td>0.0619659</td>\n",
       "      <td>0.149639</td>\n",
       "      <td>0.017176</td>\n",
       "      <td>0.0386298</td>\n",
       "      <td>0.0109394</td>\n",
       "      <td>0.0386298</td>\n",
       "      <td>0.0109394</td>\n",
       "      <td>0.00360012</td>\n",
       "      <td>0.000979994</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6 rows × 1931 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            0            1           2           3     \\\n",
       "Factor score         1 -0.421929    -0.296647   -0.449088   -0.412061   \n",
       "                     2 -0.459544   0.00390408   -0.115709    0.056708   \n",
       "Squared cosines      1  0.108207     0.145579   0.0966167     0.18672   \n",
       "                     2   0.12836  2.52149e-05  0.00641393  0.00353637   \n",
       "Contributions x 1000 1  0.339312     0.167726    0.349454    0.235364   \n",
       "                     2   1.53226   0.00011059   0.0883121   0.0169693   \n",
       "\n",
       "                             4           5           6          7     \\\n",
       "Factor score         1  -0.479269   -0.350682   -0.504301  -0.498747   \n",
       "                     2  -0.130495  -0.0765635  -0.0590306  -0.123805   \n",
       "Squared cosines      1   0.391949    0.120913    0.463594   0.163296   \n",
       "                     2  0.0290574  0.00576356  0.00635202  0.0100621   \n",
       "Contributions x 1000 1   0.318401    0.191777    0.396597   0.431009   \n",
       "                     2  0.0898589   0.0347994   0.0206863   0.101102   \n",
       "\n",
       "                            8            9        ...             1921  \\\n",
       "Factor score         1 -0.514678    -0.533952     ...          1.10075   \n",
       "                     2 -0.423026    0.0243784     ...         0.114587   \n",
       "Squared cosines      1  0.194268     0.444923     ...         0.139675   \n",
       "                     2   0.13124  0.000927455     ...       0.00151362   \n",
       "Contributions x 1000 1  0.504883     0.494005     ...          1.67955   \n",
       "                     2   1.29841    0.0039201     ...        0.0692867   \n",
       "\n",
       "                              1922        1923        1924        1925  \\\n",
       "Factor score         1    0.777899     1.08087    0.874154    0.989452   \n",
       "                     2    0.102167     0.14361   0.0416651   0.0646777   \n",
       "Squared cosines      1    0.115483    0.229065     0.58557    0.367409   \n",
       "                     2  0.00199203  0.00404374  0.00133029  0.00156989   \n",
       "Contributions x 1000 1    0.943661     2.22671     1.98607      2.3749   \n",
       "                     2   0.0619659    0.149639    0.017176   0.0386298   \n",
       "\n",
       "                               1926        1927         1928        1929  \\\n",
       "Factor score         1     0.985762    0.989452     0.985762    0.597031   \n",
       "                     2    0.0525748   0.0646777    0.0525748   0.0426535   \n",
       "Squared cosines      1     0.301467    0.367409     0.301467    0.315981   \n",
       "                     2  0.000857534  0.00156989  0.000857534  0.00161279   \n",
       "Contributions x 1000 1      1.01024      2.3749      1.01024    0.185286   \n",
       "                     2    0.0109394   0.0386298    0.0109394  0.00360012   \n",
       "\n",
       "                               1930  \n",
       "Factor score         1     0.617106  \n",
       "                     2    0.0172379  \n",
       "Squared cosines      1     0.534713  \n",
       "                     2  0.000417224  \n",
       "Contributions x 1000 1     0.329926  \n",
       "                     2  0.000979994  \n",
       "\n",
       "[6 rows x 1931 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-19T19:09:48.329791Z",
     "start_time": "2017-12-19T19:09:47.823608Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEKCAYAAAASByJ7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b6b3b510a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "points = table3.loc[fs].values\n",
    "labels = table3.columns.values\n",
    "colors = ['#66c2a5', '#fc8d62','#8da0cb']\n",
    "\n",
    "plt.figure()\n",
    "plt.margins(0.1)\n",
    "plt.axhline(0, color='gray')\n",
    "plt.axvline(0, color='gray')\n",
    "plt.xlabel('Factor 1')\n",
    "plt.ylabel('Factor 2')\n",
    "plt.scatter(*points, s=20, marker='o', c='r', alpha=.5, linewidths=0)\n",
    "for label, x, y in zip(labels, *points):\n",
    "    if y>3:\n",
    "        plt.annotate(label, xy=(x, y), xytext=(x + .03, y + .03))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-19T19:03:54.882982Z",
     "start_time": "2017-12-19T19:03:54.442110Z"
    }
   },
   "outputs": [],
   "source": [
    "table4 = pd.DataFrame(columns=X.columns, index=pd.MultiIndex\n",
    "                      .from_product([[fs, cos, cont], range(1, 3)]))\n",
    "table4.loc[fs,  :] = mca_ben.fs_c(N=2).T\n",
    "table4.loc[cos, :] = mca_ben.cos_c(N=2).T\n",
    "table4.loc[cont,:] = mca_ben.cont_c(N=2).T * 1000\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-12-19T19:03:59.153915Z",
     "start_time": "2017-12-19T19:03:59.080024Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Italian seasoning</th>\n",
       "      <th>Worcestershire sauce</th>\n",
       "      <th>allspice</th>\n",
       "      <th>almonds</th>\n",
       "      <th>anchovies</th>\n",
       "      <th>anise</th>\n",
       "      <th>apples</th>\n",
       "      <th>apricots</th>\n",
       "      <th>arugula</th>\n",
       "      <th>bacon</th>\n",
       "      <th>...</th>\n",
       "      <th>tortillas</th>\n",
       "      <th>vanilla</th>\n",
       "      <th>vinegar</th>\n",
       "      <th>wafer cookies</th>\n",
       "      <th>walnuts</th>\n",
       "      <th>water</th>\n",
       "      <th>whipping cream</th>\n",
       "      <th>wine</th>\n",
       "      <th>yeast</th>\n",
       "      <th>zucchini</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Factor score</th>\n",
       "      <th>1</th>\n",
       "      <td>1.31865</td>\n",
       "      <td>1.08229</td>\n",
       "      <td>-0.500185</td>\n",
       "      <td>-0.549462</td>\n",
       "      <td>1.25571</td>\n",
       "      <td>-0.595048</td>\n",
       "      <td>-0.359006</td>\n",
       "      <td>-0.630577</td>\n",
       "      <td>1.11671</td>\n",
       "      <td>1.1155</td>\n",
       "      <td>...</td>\n",
       "      <td>1.63448</td>\n",
       "      <td>-0.582597</td>\n",
       "      <td>0.974167</td>\n",
       "      <td>-0.72474</td>\n",
       "      <td>-0.515376</td>\n",
       "      <td>0.202715</td>\n",
       "      <td>-0.686558</td>\n",
       "      <td>0.71113</td>\n",
       "      <td>0.702307</td>\n",
       "      <td>1.29419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.223061</td>\n",
       "      <td>0.00238838</td>\n",
       "      <td>-0.513981</td>\n",
       "      <td>0.0240526</td>\n",
       "      <td>0.104641</td>\n",
       "      <td>-0.307751</td>\n",
       "      <td>-0.41343</td>\n",
       "      <td>-0.167947</td>\n",
       "      <td>0.147043</td>\n",
       "      <td>0.161577</td>\n",
       "      <td>...</td>\n",
       "      <td>0.396923</td>\n",
       "      <td>0.0052275</td>\n",
       "      <td>0.0157832</td>\n",
       "      <td>1.59897</td>\n",
       "      <td>-0.374316</td>\n",
       "      <td>-0.0623389</td>\n",
       "      <td>0.398752</td>\n",
       "      <td>-0.0460187</td>\n",
       "      <td>0.020879</td>\n",
       "      <td>0.252668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Squared cosines</th>\n",
       "      <th>1</th>\n",
       "      <td>0.176655</td>\n",
       "      <td>0.125145</td>\n",
       "      <td>0.0781151</td>\n",
       "      <td>0.180203</td>\n",
       "      <td>0.053564</td>\n",
       "      <td>0.0177813</td>\n",
       "      <td>0.0359906</td>\n",
       "      <td>0.0361362</td>\n",
       "      <td>0.177083</td>\n",
       "      <td>0.165842</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0540342</td>\n",
       "      <td>0.727684</td>\n",
       "      <td>0.370515</td>\n",
       "      <td>0.00920505</td>\n",
       "      <td>0.0994238</td>\n",
       "      <td>0.104016</td>\n",
       "      <td>0.0137529</td>\n",
       "      <td>0.181779</td>\n",
       "      <td>0.388619</td>\n",
       "      <td>0.114999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.00505495</td>\n",
       "      <td>6.09442e-07</td>\n",
       "      <td>0.0824836</td>\n",
       "      <td>0.000345311</td>\n",
       "      <td>0.00037196</td>\n",
       "      <td>0.0047562</td>\n",
       "      <td>0.04773</td>\n",
       "      <td>0.00256336</td>\n",
       "      <td>0.00307032</td>\n",
       "      <td>0.00347949</td>\n",
       "      <td>...</td>\n",
       "      <td>0.00318654</td>\n",
       "      <td>5.8586e-05</td>\n",
       "      <td>9.72594e-05</td>\n",
       "      <td>0.0448069</td>\n",
       "      <td>0.0524468</td>\n",
       "      <td>0.00983664</td>\n",
       "      <td>0.00463921</td>\n",
       "      <td>0.000761226</td>\n",
       "      <td>0.00034347</td>\n",
       "      <td>0.00438326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Contributions x 1000</th>\n",
       "      <th>1</th>\n",
       "      <td>7.23098</td>\n",
       "      <td>2.43554</td>\n",
       "      <td>2.0808</td>\n",
       "      <td>8.31763</td>\n",
       "      <td>2.18572</td>\n",
       "      <td>0.797581</td>\n",
       "      <td>1.40692</td>\n",
       "      <td>1.79133</td>\n",
       "      <td>6.69836</td>\n",
       "      <td>5.39018</td>\n",
       "      <td>...</td>\n",
       "      <td>2.3145</td>\n",
       "      <td>38.1688</td>\n",
       "      <td>12.6615</td>\n",
       "      <td>1.00112</td>\n",
       "      <td>3.58979</td>\n",
       "      <td>3.0119</td>\n",
       "      <td>0.653389</td>\n",
       "      <td>4.81934</td>\n",
       "      <td>17.0927</td>\n",
       "      <td>4.06306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.787672</td>\n",
       "      <td>4.51518e-05</td>\n",
       "      <td>8.36412</td>\n",
       "      <td>0.0606744</td>\n",
       "      <td>0.0577798</td>\n",
       "      <td>0.812136</td>\n",
       "      <td>7.10282</td>\n",
       "      <td>0.48373</td>\n",
       "      <td>0.442114</td>\n",
       "      <td>0.430509</td>\n",
       "      <td>...</td>\n",
       "      <td>0.519598</td>\n",
       "      <td>0.0116982</td>\n",
       "      <td>0.0126523</td>\n",
       "      <td>18.5507</td>\n",
       "      <td>7.20869</td>\n",
       "      <td>1.08429</td>\n",
       "      <td>0.839036</td>\n",
       "      <td>0.0768275</td>\n",
       "      <td>0.0575089</td>\n",
       "      <td>0.58954</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6 rows × 133 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Italian seasoning Worcestershire sauce   allspice  \\\n",
       "Factor score         1           1.31865              1.08229  -0.500185   \n",
       "                     2          0.223061           0.00238838  -0.513981   \n",
       "Squared cosines      1          0.176655             0.125145  0.0781151   \n",
       "                     2        0.00505495          6.09442e-07  0.0824836   \n",
       "Contributions x 1000 1           7.23098              2.43554     2.0808   \n",
       "                     2          0.787672          4.51518e-05    8.36412   \n",
       "\n",
       "                            almonds   anchovies      anise     apples  \\\n",
       "Factor score         1    -0.549462     1.25571  -0.595048  -0.359006   \n",
       "                     2    0.0240526    0.104641  -0.307751   -0.41343   \n",
       "Squared cosines      1     0.180203    0.053564  0.0177813  0.0359906   \n",
       "                     2  0.000345311  0.00037196  0.0047562    0.04773   \n",
       "Contributions x 1000 1      8.31763     2.18572   0.797581    1.40692   \n",
       "                     2    0.0606744   0.0577798   0.812136    7.10282   \n",
       "\n",
       "                          apricots     arugula       bacon     ...      \\\n",
       "Factor score         1   -0.630577     1.11671      1.1155     ...       \n",
       "                     2   -0.167947    0.147043    0.161577     ...       \n",
       "Squared cosines      1   0.0361362    0.177083    0.165842     ...       \n",
       "                     2  0.00256336  0.00307032  0.00347949     ...       \n",
       "Contributions x 1000 1     1.79133     6.69836     5.39018     ...       \n",
       "                     2     0.48373    0.442114    0.430509     ...       \n",
       "\n",
       "                         tortillas     vanilla      vinegar wafer cookies  \\\n",
       "Factor score         1     1.63448   -0.582597     0.974167      -0.72474   \n",
       "                     2    0.396923   0.0052275    0.0157832       1.59897   \n",
       "Squared cosines      1   0.0540342    0.727684     0.370515    0.00920505   \n",
       "                     2  0.00318654  5.8586e-05  9.72594e-05     0.0448069   \n",
       "Contributions x 1000 1      2.3145     38.1688      12.6615       1.00112   \n",
       "                     2    0.519598   0.0116982    0.0126523       18.5507   \n",
       "\n",
       "                          walnuts       water whipping cream         wine  \\\n",
       "Factor score         1  -0.515376    0.202715      -0.686558      0.71113   \n",
       "                     2  -0.374316  -0.0623389       0.398752   -0.0460187   \n",
       "Squared cosines      1  0.0994238    0.104016      0.0137529     0.181779   \n",
       "                     2  0.0524468  0.00983664     0.00463921  0.000761226   \n",
       "Contributions x 1000 1    3.58979      3.0119       0.653389      4.81934   \n",
       "                     2    7.20869     1.08429       0.839036    0.0768275   \n",
       "\n",
       "                             yeast    zucchini  \n",
       "Factor score         1    0.702307     1.29419  \n",
       "                     2    0.020879    0.252668  \n",
       "Squared cosines      1    0.388619    0.114999  \n",
       "                     2  0.00034347  0.00438326  \n",
       "Contributions x 1000 1     17.0927     4.06306  \n",
       "                     2   0.0575089     0.58954  \n",
       "\n",
       "[6 rows x 133 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [default]",
   "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": "2.7.11"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autocomplete": true,
   "bibliofile": "Zotero.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  },
  "toc": {
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": "block",
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}