cs229updated.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9297\n",
      "9297\n",
      "5410\n",
      "3886\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sklearn\n",
    "from sklearn.linear_model import LinearRegression\n",
    "#parse data \n",
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "#label encoding on categorical data \n",
    "\n",
    "df = pd.read_csv('ee6d2f60cdafb550.csv')\n",
    "df = df.dropna()\n",
    "label_encoder = LabelEncoder()\n",
    "integer_encoded = label_encoder.fit_transform(df.FFI49_desc)\n",
    "print(len(integer_encoded))\n",
    "print(len(df.FFI49_desc))\n",
    "df.FFI49_desc = integer_encoded\n",
    "df.divyield_Median = [float(x.strip('%'))/100 for x in df.divyield_Median]\n",
    "\n",
    "print(len(np.where(df.indret_vw > 0)[0]))\n",
    "print(len(np.where(df.indret_vw < 0)[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "output = df.indret_vw\n",
    "outputtwo = df.indret_ew\n",
    "data = df.drop(columns = ['indret_ew','indret_vw','staff_sale_Median'])\n",
    "data = data.iloc[:,3:]\n",
    "\n",
    "x_train, x_test, y_train, y_test = sklearn.model_selection.train_test_split(data, output, test_size=0.20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mse: 1.0116136332759775\n",
      "r2: -0.028452476517936587\n",
      "mse: 1.5505376344086022\n",
      "r2: -0.5990584935897432\n",
      "0.6123655913978494\n",
      "[-1  1]\t[ 275 1585]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import average_precision_score, precision_recall_curve\n",
    "from sklearn.utils.fixes import signature\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score \n",
    "\n",
    "reg = LinearRegression().fit(x_train, y_train)\n",
    "reg.score(x_train, y_train)\n",
    "reg.coef_\n",
    "\n",
    "y_pred = reg.predict(x_test)\n",
    "print('mse: {}'.format(mean_squared_error(y_test, y_pred)))\n",
    "print(\"r2: \" + str(sklearn.metrics.r2_score(y_test,y_pred)))\n",
    "\n",
    "#Binary classification-Logistic Regression\n",
    "transformed_output = [1 if o >= 0 else -1 for o in output]\n",
    "x_train, x_test, y_train, y_test = sklearn.model_selection.train_test_split(data, transformed_output, test_size=0.20)\n",
    "\n",
    "clf = LogisticRegression(random_state=229, solver='liblinear').fit(x_train, y_train)\n",
    "y_pred = clf.predict(x_test)\n",
    "\n",
    "print(\"mse: \" + str(mean_squared_error(y_test, y_pred)))\n",
    "print(\"r2: \" + str(sklearn.metrics.r2_score(y_test,y_pred)))\n",
    "\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "\n",
    "print(accuracy)\n",
    "\n",
    "unique, counts = np.unique(y_pred, return_counts=True)\n",
    "print('{}\\t{}'.format(unique, counts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "70\n",
      "[5.64942249e-03 1.74122500e-02 2.86883937e-06 2.13799981e-03\n",
      " 2.27093860e-05 1.12558040e-04 3.79013755e-07 2.75974588e-10\n",
      " 1.94471490e-02 8.31776138e-03 4.56087094e-02 2.97819002e-02\n",
      " 4.19216164e-02 9.34962829e-03 2.58606264e-02 2.26140756e-03\n",
      " 1.41616714e-02 1.34832595e-03 3.99279253e-03 6.47800694e-03\n",
      " 1.43580810e-03 1.34416543e-09]\n",
      "['divyield_Median' 'evm_Median' 'pcf_Median' 'pe_exi_Median'\n",
      " 'pe_op_basic_Median' 'pe_op_dil_Median' 'ps_Median' 'ptb_Median'\n",
      " 'aftret_eq_Median' 'aftret_equity_Median' 'aftret_invcapx_Median'\n",
      " 'npm_Median' 'pretret_earnat_Median' 'pretret_noa_Median' 'roce_Median'\n",
      " 'roe_Median' 'rect_act_Median' 'fcf_ocf_Median' 'int_debt_Median'\n",
      " 'int_totdebt_Median' 'PEG_1yrforward_Median' 'PEG_ltgforward_Median']\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_selection import f_regression\n",
    "\n",
    "f_score, pvals = f_regression(x_train, y_train)\n",
    "print(len(pvals))\n",
    "\n",
    "# print(f_score)\n",
    "# print(f_score)\n",
    "# print(pvals)\n",
    "print(pvals[pvals < 0.05])\n",
    "print(data.columns.values[pvals < 0.05])\n",
    "\n",
    "ftest_data = data[data.columns.values[(pvals < 0.05)]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mse: 0.004589386037878954\n",
      "r2: 0.04226377749332966\n"
     ]
    }
   ],
   "source": [
    "x_train, x_test, y_train, y_test = sklearn.model_selection.train_test_split(ftest_data, output, test_size=0.20)\n",
    "\n",
    "reg = LinearRegression().fit(x_train, y_train)\n",
    "reg.score(x_train, y_train)\n",
    "reg.coef_\n",
    "\n",
    "y_pred = reg.predict(x_test)\n",
    "print('mse: {}'.format(mean_squared_error(y_test, y_pred)))\n",
    "print(\"r2: \" + str(sklearn.metrics.r2_score(y_test,y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mse: 1.5870967741935484\n",
      "r2: -0.6260821356987507\n",
      "0.603225806451613\n",
      "[-1  1]\t[ 154 1706]\n"
     ]
    }
   ],
   "source": [
    "#Binary classification-Logistic Regression\n",
    "transformed_output = [1 if o >= 0 else -1 for o in output]\n",
    "x_train, x_test, y_train, y_test = sklearn.model_selection.train_test_split(ftest_data, transformed_output, test_size=0.20)\n",
    "\n",
    "clf = LogisticRegression(random_state=229, solver='liblinear').fit(x_train, y_train)\n",
    "y_pred = clf.predict(x_test)\n",
    "\n",
    "print(\"mse: \" + str(mean_squared_error(y_test, y_pred)))\n",
    "print(\"r2: \" + str(sklearn.metrics.r2_score(y_test,y_pred)))\n",
    "\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "\n",
    "print(accuracy)\n",
    "\n",
    "unique, counts = np.unique(y_pred, return_counts=True)\n",
    "print('{}\\t{}'.format(unique, counts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mse: 0.97867564384463\n",
      "r2: -0.0034051906641430207\n",
      "mse: 1.6150537634408602\n",
      "r2: -0.6793704074173812\n",
      "0.5962365591397849\n",
      "[-1  1]\t[  31 1829]\n"
     ]
    }
   ],
   "source": [
    "reverse_ftest_data = data[data.columns.values[~(pvals < 0.05)]]\n",
    "\n",
    "x_train, x_test, y_train, y_test = sklearn.model_selection.train_test_split(reverse_ftest_data, \n",
    "                                                                            transformed_output, test_size=0.20)\n",
    "\n",
    "reg = LinearRegression().fit(x_train, y_train)\n",
    "reg.score(x_train, y_train)\n",
    "reg.coef_\n",
    "\n",
    "y_pred = reg.predict(x_test)\n",
    "print('mse: {}'.format(mean_squared_error(y_test, y_pred)))\n",
    "print(\"r2: \" + str(sklearn.metrics.r2_score(y_test,y_pred)))\n",
    "\n",
    "#Binary classification-Logistic Regression\n",
    "transformed_output = [1 if o >= 0 else -1 for o in output]\n",
    "\n",
    "x_train, x_test, y_train, y_test = sklearn.model_selection.train_test_split(reverse_ftest_data, transformed_output, test_size=0.20)\n",
    "\n",
    "clf = LogisticRegression(random_state=229, solver='liblinear').fit(x_train, y_train)\n",
    "y_pred = clf.predict(x_test)\n",
    "\n",
    "print(\"mse: \" + str(mean_squared_error(y_test, y_pred)))\n",
    "print(\"r2: \" + str(sklearn.metrics.r2_score(y_test,y_pred)))\n",
    "\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "\n",
    "print(accuracy)\n",
    "\n",
    "unique, counts = np.unique(y_pred, return_counts=True)\n",
    "print('{}\\t{}'.format(unique, counts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of increasing: 1690\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of decreasing: 170\n",
      "Average precision-recall score: 0.59\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import average_precision_score, precision_recall_curve\n",
    "from sklearn.utils.fixes import signature\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "print('number of increasing: {}'.format(len(np.where(y_pred > 0)[0])))\n",
    "\n",
    "plt.plot(df.public_date, output)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "print('number of decreasing: {}'.format(len(np.where(y_pred < 0)[0])))\n",
    "      \n",
    "average_precision = average_precision_score(y_test, y_pred)\n",
    "\n",
    "print('Average precision-recall score: {0:0.2f}'.format(\n",
    "      average_precision))\n",
    "precision, recall, _ = precision_recall_curve(y_test, y_pred)\n",
    "step_kwargs = ({'step': 'post'}\n",
    "               if 'step' in signature(plt.fill_between).parameters\n",
    "               else {})\n",
    "plt.step(recall, precision, color='b', alpha=0.1,\n",
    "         where='post')\n",
    "plt.fill_between(recall, precision, alpha=0.1, color='b', **step_kwargs)\n",
    "\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.title('2-class Precision-Recall curve: AP={0:0.2f}'.format(\n",
    "          average_precision))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfsp = pd.read_csv('eafe6a287f1adb2b.csv')\n",
    "outputsp = df.indret_ew\n",
    "outputtwosp = df.indret_vw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>public_date</th>\n",
       "      <th>FFI49_desc</th>\n",
       "      <th>NFIRM</th>\n",
       "      <th>indret_ew</th>\n",
       "      <th>indret_vw</th>\n",
       "      <th>dpr_Median</th>\n",
       "      <th>PEG_trailing_Median</th>\n",
       "      <th>bm_Median</th>\n",
       "      <th>CAPEI_Median</th>\n",
       "      <th>divyield_Median</th>\n",
       "      <th>...</th>\n",
       "      <th>rect_turn_Median</th>\n",
       "      <th>sale_equity_Median</th>\n",
       "      <th>sale_invcap_Median</th>\n",
       "      <th>sale_nwc_Median</th>\n",
       "      <th>accrual_Median</th>\n",
       "      <th>rd_sale_Median</th>\n",
       "      <th>adv_sale_Median</th>\n",
       "      <th>staff_sale_Median</th>\n",
       "      <th>PEG_1yrforward_Median</th>\n",
       "      <th>PEG_ltgforward_Median</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19700131</td>\n",
       "      <td>AERO</td>\n",
       "      <td>8</td>\n",
       "      <td>-0.117325</td>\n",
       "      <td>-0.149955</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.356</td>\n",
       "      <td>0.696</td>\n",
       "      <td>8.490</td>\n",
       "      <td>4.76%</td>\n",
       "      <td>...</td>\n",
       "      <td>8.843</td>\n",
       "      <td>4.416</td>\n",
       "      <td>3.019</td>\n",
       "      <td>7.479</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.388</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>19700131</td>\n",
       "      <td>HSHLD</td>\n",
       "      <td>25</td>\n",
       "      <td>-0.070517</td>\n",
       "      <td>-0.068231</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.131</td>\n",
       "      <td>0.351</td>\n",
       "      <td>18.974</td>\n",
       "      <td>3.20%</td>\n",
       "      <td>...</td>\n",
       "      <td>7.856</td>\n",
       "      <td>2.443</td>\n",
       "      <td>2.057</td>\n",
       "      <td>3.981</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>19700131</td>\n",
       "      <td>LABEQ</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.083478</td>\n",
       "      <td>-0.083478</td>\n",
       "      <td>0.000</td>\n",
       "      <td>8.505</td>\n",
       "      <td>0.191</td>\n",
       "      <td>44.643</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>5.992</td>\n",
       "      <td>1.585</td>\n",
       "      <td>1.580</td>\n",
       "      <td>2.582</td>\n",
       "      <td>-0.058</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.445</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>19700131</td>\n",
       "      <td>MACH</td>\n",
       "      <td>40</td>\n",
       "      <td>-0.038664</td>\n",
       "      <td>-0.071795</td>\n",
       "      <td>0.048</td>\n",
       "      <td>1.270</td>\n",
       "      <td>0.524</td>\n",
       "      <td>10.474</td>\n",
       "      <td>4.68%</td>\n",
       "      <td>...</td>\n",
       "      <td>5.539</td>\n",
       "      <td>2.145</td>\n",
       "      <td>1.697</td>\n",
       "      <td>3.062</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>19700131</td>\n",
       "      <td>MEALS</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.326923</td>\n",
       "      <td>-0.326923</td>\n",
       "      <td>0.448</td>\n",
       "      <td>0.053</td>\n",
       "      <td>0.266</td>\n",
       "      <td>3.652</td>\n",
       "      <td>3.43%</td>\n",
       "      <td>...</td>\n",
       "      <td>13.913</td>\n",
       "      <td>2.512</td>\n",
       "      <td>1.757</td>\n",
       "      <td>7.331</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>19700131</td>\n",
       "      <td>MEDEQ</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.007937</td>\n",
       "      <td>-0.007937</td>\n",
       "      <td>0.000</td>\n",
       "      <td>7.421</td>\n",
       "      <td>0.423</td>\n",
       "      <td>34.144</td>\n",
       "      <td>1.07%</td>\n",
       "      <td>...</td>\n",
       "      <td>4.370</td>\n",
       "      <td>2.041</td>\n",
       "      <td>1.355</td>\n",
       "      <td>1.788</td>\n",
       "      <td>0.044</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>19700131</td>\n",
       "      <td>MINES</td>\n",
       "      <td>7</td>\n",
       "      <td>-0.046483</td>\n",
       "      <td>-0.064723</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.348</td>\n",
       "      <td>0.463</td>\n",
       "      <td>12.518</td>\n",
       "      <td>5.73%</td>\n",
       "      <td>...</td>\n",
       "      <td>7.728</td>\n",
       "      <td>0.969</td>\n",
       "      <td>0.884</td>\n",
       "      <td>2.951</td>\n",
       "      <td>0.049</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>19700131</td>\n",
       "      <td>OIL</td>\n",
       "      <td>21</td>\n",
       "      <td>-0.122596</td>\n",
       "      <td>-0.128312</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.369</td>\n",
       "      <td>0.534</td>\n",
       "      <td>10.543</td>\n",
       "      <td>5.10%</td>\n",
       "      <td>...</td>\n",
       "      <td>5.704</td>\n",
       "      <td>1.080</td>\n",
       "      <td>0.824</td>\n",
       "      <td>5.185</td>\n",
       "      <td>0.059</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.115</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>19700131</td>\n",
       "      <td>RTAIL</td>\n",
       "      <td>27</td>\n",
       "      <td>-0.056199</td>\n",
       "      <td>-0.059770</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.129</td>\n",
       "      <td>0.673</td>\n",
       "      <td>13.637</td>\n",
       "      <td>3.57%</td>\n",
       "      <td>...</td>\n",
       "      <td>30.907</td>\n",
       "      <td>4.600</td>\n",
       "      <td>3.510</td>\n",
       "      <td>8.853</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>19700131</td>\n",
       "      <td>SHIPS</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.050288</td>\n",
       "      <td>-0.056947</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.190</td>\n",
       "      <td>0.795</td>\n",
       "      <td>9.208</td>\n",
       "      <td>5.18%</td>\n",
       "      <td>...</td>\n",
       "      <td>5.147</td>\n",
       "      <td>3.072</td>\n",
       "      <td>2.410</td>\n",
       "      <td>4.715</td>\n",
       "      <td>-0.019</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>19700131</td>\n",
       "      <td>SMOKE</td>\n",
       "      <td>6</td>\n",
       "      <td>-0.027493</td>\n",
       "      <td>-0.071631</td>\n",
       "      <td>0.009</td>\n",
       "      <td>1.413</td>\n",
       "      <td>0.755</td>\n",
       "      <td>11.354</td>\n",
       "      <td>5.90%</td>\n",
       "      <td>...</td>\n",
       "      <td>9.311</td>\n",
       "      <td>1.858</td>\n",
       "      <td>1.306</td>\n",
       "      <td>2.043</td>\n",
       "      <td>-0.056</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>19700131</td>\n",
       "      <td>SODA</td>\n",
       "      <td>8</td>\n",
       "      <td>-0.011130</td>\n",
       "      <td>-0.026951</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3.000</td>\n",
       "      <td>0.283</td>\n",
       "      <td>15.213</td>\n",
       "      <td>3.98%</td>\n",
       "      <td>...</td>\n",
       "      <td>15.519</td>\n",
       "      <td>2.559</td>\n",
       "      <td>2.541</td>\n",
       "      <td>6.140</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>19700131</td>\n",
       "      <td>STEEL</td>\n",
       "      <td>25</td>\n",
       "      <td>-0.068294</td>\n",
       "      <td>-0.063238</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.331</td>\n",
       "      <td>0.834</td>\n",
       "      <td>9.967</td>\n",
       "      <td>5.45%</td>\n",
       "      <td>...</td>\n",
       "      <td>8.878</td>\n",
       "      <td>1.442</td>\n",
       "      <td>0.993</td>\n",
       "      <td>4.014</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>19700131</td>\n",
       "      <td>TELCM</td>\n",
       "      <td>8</td>\n",
       "      <td>-0.067678</td>\n",
       "      <td>-0.023545</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.785</td>\n",
       "      <td>0.292</td>\n",
       "      <td>14.958</td>\n",
       "      <td>3.14%</td>\n",
       "      <td>...</td>\n",
       "      <td>7.764</td>\n",
       "      <td>1.416</td>\n",
       "      <td>0.901</td>\n",
       "      <td>4.028</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>19700131</td>\n",
       "      <td>TOYS</td>\n",
       "      <td>4</td>\n",
       "      <td>-0.044872</td>\n",
       "      <td>-0.035847</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.900</td>\n",
       "      <td>0.198</td>\n",
       "      <td>17.178</td>\n",
       "      <td>3.57%</td>\n",
       "      <td>...</td>\n",
       "      <td>6.496</td>\n",
       "      <td>3.381</td>\n",
       "      <td>3.155</td>\n",
       "      <td>4.597</td>\n",
       "      <td>0.026</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>19700131</td>\n",
       "      <td>TRANS</td>\n",
       "      <td>14</td>\n",
       "      <td>-0.068535</td>\n",
       "      <td>-0.073527</td>\n",
       "      <td>0.082</td>\n",
       "      <td>0.773</td>\n",
       "      <td>1.301</td>\n",
       "      <td>9.410</td>\n",
       "      <td>5.74%</td>\n",
       "      <td>...</td>\n",
       "      <td>9.358</td>\n",
       "      <td>1.348</td>\n",
       "      <td>0.820</td>\n",
       "      <td>27.673</td>\n",
       "      <td>0.043</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.190</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>19700131</td>\n",
       "      <td>TXTLS</td>\n",
       "      <td>10</td>\n",
       "      <td>-0.053081</td>\n",
       "      <td>-0.051588</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.717</td>\n",
       "      <td>1.147</td>\n",
       "      <td>8.795</td>\n",
       "      <td>5.77%</td>\n",
       "      <td>...</td>\n",
       "      <td>6.231</td>\n",
       "      <td>2.432</td>\n",
       "      <td>1.777</td>\n",
       "      <td>3.482</td>\n",
       "      <td>-0.011</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>19700131</td>\n",
       "      <td>UTIL</td>\n",
       "      <td>47</td>\n",
       "      <td>-0.032949</td>\n",
       "      <td>-0.043141</td>\n",
       "      <td>0.106</td>\n",
       "      <td>1.344</td>\n",
       "      <td>0.622</td>\n",
       "      <td>11.110</td>\n",
       "      <td>6.64%</td>\n",
       "      <td>...</td>\n",
       "      <td>8.225</td>\n",
       "      <td>0.760</td>\n",
       "      <td>0.364</td>\n",
       "      <td>25.478</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.209</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>19700131</td>\n",
       "      <td>HARDW</td>\n",
       "      <td>10</td>\n",
       "      <td>-0.142658</td>\n",
       "      <td>-0.091570</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.275</td>\n",
       "      <td>0.275</td>\n",
       "      <td>31.436</td>\n",
       "      <td>1.32%</td>\n",
       "      <td>...</td>\n",
       "      <td>5.347</td>\n",
       "      <td>2.264</td>\n",
       "      <td>1.573</td>\n",
       "      <td>3.484</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>19700131</td>\n",
       "      <td>GUNS</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.160494</td>\n",
       "      <td>-0.160494</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.395</td>\n",
       "      <td>0.612</td>\n",
       "      <td>9.773</td>\n",
       "      <td>6.47%</td>\n",
       "      <td>...</td>\n",
       "      <td>9.993</td>\n",
       "      <td>1.984</td>\n",
       "      <td>1.247</td>\n",
       "      <td>4.993</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>19700131</td>\n",
       "      <td>PAPER</td>\n",
       "      <td>9</td>\n",
       "      <td>-0.085591</td>\n",
       "      <td>-0.098263</td>\n",
       "      <td>0.004</td>\n",
       "      <td>1.045</td>\n",
       "      <td>0.671</td>\n",
       "      <td>15.005</td>\n",
       "      <td>4.04%</td>\n",
       "      <td>...</td>\n",
       "      <td>9.149</td>\n",
       "      <td>1.581</td>\n",
       "      <td>1.116</td>\n",
       "      <td>5.706</td>\n",
       "      <td>0.038</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.293</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19700131</td>\n",
       "      <td>FUN</td>\n",
       "      <td>4</td>\n",
       "      <td>-0.080100</td>\n",
       "      <td>-0.078507</td>\n",
       "      <td>0.515</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.448</td>\n",
       "      <td>20.514</td>\n",
       "      <td>2.73%</td>\n",
       "      <td>...</td>\n",
       "      <td>4.995</td>\n",
       "      <td>2.081</td>\n",
       "      <td>1.133</td>\n",
       "      <td>1.544</td>\n",
       "      <td>-0.068</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19700131</td>\n",
       "      <td>GOLD</td>\n",
       "      <td>3</td>\n",
       "      <td>0.048893</td>\n",
       "      <td>0.054816</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.302</td>\n",
       "      <td>27.360</td>\n",
       "      <td>2.19%</td>\n",
       "      <td>...</td>\n",
       "      <td>17.107</td>\n",
       "      <td>0.564</td>\n",
       "      <td>0.564</td>\n",
       "      <td>1.273</td>\n",
       "      <td>-0.004</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19700131</td>\n",
       "      <td>AGRIC</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.006061</td>\n",
       "      <td>-0.006061</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.475</td>\n",
       "      <td>0.780</td>\n",
       "      <td>19.165</td>\n",
       "      <td>2.81%</td>\n",
       "      <td>...</td>\n",
       "      <td>12.686</td>\n",
       "      <td>5.288</td>\n",
       "      <td>3.942</td>\n",
       "      <td>6.452</td>\n",
       "      <td>0.099</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19700131</td>\n",
       "      <td>AUTOS</td>\n",
       "      <td>18</td>\n",
       "      <td>-0.108604</td>\n",
       "      <td>-0.091909</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.806</td>\n",
       "      <td>0.684</td>\n",
       "      <td>10.561</td>\n",
       "      <td>5.40%</td>\n",
       "      <td>...</td>\n",
       "      <td>8.466</td>\n",
       "      <td>2.870</td>\n",
       "      <td>2.233</td>\n",
       "      <td>4.639</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.278</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19700131</td>\n",
       "      <td>BANKS</td>\n",
       "      <td>11</td>\n",
       "      <td>-0.052531</td>\n",
       "      <td>-0.069837</td>\n",
       "      <td>0.204</td>\n",
       "      <td>0.429</td>\n",
       "      <td>0.568</td>\n",
       "      <td>11.940</td>\n",
       "      <td>6.57%</td>\n",
       "      <td>...</td>\n",
       "      <td>0.218</td>\n",
       "      <td>1.065</td>\n",
       "      <td>0.330</td>\n",
       "      <td>0.356</td>\n",
       "      <td>-0.081</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19700131</td>\n",
       "      <td>BEER</td>\n",
       "      <td>5</td>\n",
       "      <td>-0.000440</td>\n",
       "      <td>-0.003983</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3.647</td>\n",
       "      <td>0.630</td>\n",
       "      <td>42.057</td>\n",
       "      <td>1.77%</td>\n",
       "      <td>...</td>\n",
       "      <td>10.156</td>\n",
       "      <td>1.943</td>\n",
       "      <td>1.972</td>\n",
       "      <td>7.738</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>19700131</td>\n",
       "      <td>BLDMT</td>\n",
       "      <td>23</td>\n",
       "      <td>-0.051627</td>\n",
       "      <td>-0.087953</td>\n",
       "      <td>0.015</td>\n",
       "      <td>1.023</td>\n",
       "      <td>0.675</td>\n",
       "      <td>13.450</td>\n",
       "      <td>4.10%</td>\n",
       "      <td>...</td>\n",
       "      <td>7.080</td>\n",
       "      <td>1.601</td>\n",
       "      <td>1.339</td>\n",
       "      <td>3.643</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>19700131</td>\n",
       "      <td>BOXES</td>\n",
       "      <td>11</td>\n",
       "      <td>-0.055760</td>\n",
       "      <td>-0.081525</td>\n",
       "      <td>0.041</td>\n",
       "      <td>1.272</td>\n",
       "      <td>0.500</td>\n",
       "      <td>17.200</td>\n",
       "      <td>2.67%</td>\n",
       "      <td>...</td>\n",
       "      <td>9.077</td>\n",
       "      <td>2.265</td>\n",
       "      <td>1.663</td>\n",
       "      <td>4.838</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.279</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>19700131</td>\n",
       "      <td>BUSSV</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.060979</td>\n",
       "      <td>-0.022816</td>\n",
       "      <td>0.159</td>\n",
       "      <td>1.882</td>\n",
       "      <td>0.547</td>\n",
       "      <td>11.474</td>\n",
       "      <td>4.76%</td>\n",
       "      <td>...</td>\n",
       "      <td>0.131</td>\n",
       "      <td>0.823</td>\n",
       "      <td>0.266</td>\n",
       "      <td>0.300</td>\n",
       "      <td>-0.090</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25182</th>\n",
       "      <td>20170131</td>\n",
       "      <td>TELCM</td>\n",
       "      <td>15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.291</td>\n",
       "      <td>0.835</td>\n",
       "      <td>0.502</td>\n",
       "      <td>22.429</td>\n",
       "      <td>1.78%</td>\n",
       "      <td>...</td>\n",
       "      <td>6.513</td>\n",
       "      <td>1.331</td>\n",
       "      <td>0.642</td>\n",
       "      <td>5.943</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.040</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.478</td>\n",
       "      <td>1.077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25183</th>\n",
       "      <td>20170131</td>\n",
       "      <td>TOYS</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.454</td>\n",
       "      <td>0.634</td>\n",
       "      <td>0.233</td>\n",
       "      <td>26.780</td>\n",
       "      <td>2.47%</td>\n",
       "      <td>...</td>\n",
       "      <td>5.333</td>\n",
       "      <td>2.850</td>\n",
       "      <td>1.539</td>\n",
       "      <td>4.376</td>\n",
       "      <td>0.042</td>\n",
       "      <td>0.053</td>\n",
       "      <td>0.092</td>\n",
       "      <td>0.000</td>\n",
       "      <td>16.290</td>\n",
       "      <td>1.969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25184</th>\n",
       "      <td>20170131</td>\n",
       "      <td>TRANS</td>\n",
       "      <td>15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.294</td>\n",
       "      <td>1.172</td>\n",
       "      <td>0.432</td>\n",
       "      <td>22.045</td>\n",
       "      <td>1.54%</td>\n",
       "      <td>...</td>\n",
       "      <td>11.577</td>\n",
       "      <td>3.411</td>\n",
       "      <td>1.975</td>\n",
       "      <td>22.973</td>\n",
       "      <td>0.054</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.245</td>\n",
       "      <td>2.634</td>\n",
       "      <td>2.189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25166</th>\n",
       "      <td>20170131</td>\n",
       "      <td>INSUR</td>\n",
       "      <td>23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.258</td>\n",
       "      <td>1.522</td>\n",
       "      <td>0.756</td>\n",
       "      <td>19.602</td>\n",
       "      <td>1.89%</td>\n",
       "      <td>...</td>\n",
       "      <td>2.424</td>\n",
       "      <td>1.408</td>\n",
       "      <td>0.934</td>\n",
       "      <td>9.083</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.746</td>\n",
       "      <td>1.736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25176</th>\n",
       "      <td>20170131</td>\n",
       "      <td>RLEST</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.185</td>\n",
       "      <td>1.322</td>\n",
       "      <td>0.297</td>\n",
       "      <td>21.573</td>\n",
       "      <td>2.53%</td>\n",
       "      <td>...</td>\n",
       "      <td>4.821</td>\n",
       "      <td>5.618</td>\n",
       "      <td>1.642</td>\n",
       "      <td>30.581</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.330</td>\n",
       "      <td>1.854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25165</th>\n",
       "      <td>20170131</td>\n",
       "      <td>HSHLD</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.443</td>\n",
       "      <td>3.728</td>\n",
       "      <td>0.212</td>\n",
       "      <td>26.705</td>\n",
       "      <td>2.44%</td>\n",
       "      <td>...</td>\n",
       "      <td>7.705</td>\n",
       "      <td>3.133</td>\n",
       "      <td>1.532</td>\n",
       "      <td>22.271</td>\n",
       "      <td>0.045</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.056</td>\n",
       "      <td>0.000</td>\n",
       "      <td>3.102</td>\n",
       "      <td>3.187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25154</th>\n",
       "      <td>20170131</td>\n",
       "      <td>CLTHS</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.278</td>\n",
       "      <td>0.841</td>\n",
       "      <td>0.210</td>\n",
       "      <td>18.922</td>\n",
       "      <td>1.81%</td>\n",
       "      <td>...</td>\n",
       "      <td>9.741</td>\n",
       "      <td>2.378</td>\n",
       "      <td>1.694</td>\n",
       "      <td>4.177</td>\n",
       "      <td>0.023</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.047</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.408</td>\n",
       "      <td>2.204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25163</th>\n",
       "      <td>20170131</td>\n",
       "      <td>HARDW</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.510</td>\n",
       "      <td>2.271</td>\n",
       "      <td>0.411</td>\n",
       "      <td>19.769</td>\n",
       "      <td>2.95%</td>\n",
       "      <td>...</td>\n",
       "      <td>5.850</td>\n",
       "      <td>1.397</td>\n",
       "      <td>0.955</td>\n",
       "      <td>2.551</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.130</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.686</td>\n",
       "      <td>1.933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25186</th>\n",
       "      <td>20170131</td>\n",
       "      <td>UTIL</td>\n",
       "      <td>33</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.641</td>\n",
       "      <td>2.111</td>\n",
       "      <td>0.798</td>\n",
       "      <td>23.106</td>\n",
       "      <td>3.35%</td>\n",
       "      <td>...</td>\n",
       "      <td>9.204</td>\n",
       "      <td>0.835</td>\n",
       "      <td>0.412</td>\n",
       "      <td>28.583</td>\n",
       "      <td>0.048</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.232</td>\n",
       "      <td>3.477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25143</th>\n",
       "      <td>20170131</td>\n",
       "      <td>AERO</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.273</td>\n",
       "      <td>1.591</td>\n",
       "      <td>0.241</td>\n",
       "      <td>22.261</td>\n",
       "      <td>1.68%</td>\n",
       "      <td>...</td>\n",
       "      <td>6.365</td>\n",
       "      <td>2.672</td>\n",
       "      <td>1.552</td>\n",
       "      <td>6.191</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.042</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.184</td>\n",
       "      <td>2.741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25144</th>\n",
       "      <td>20170131</td>\n",
       "      <td>AGRIC</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.591</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.100</td>\n",
       "      <td>21.886</td>\n",
       "      <td>1.99%</td>\n",
       "      <td>...</td>\n",
       "      <td>4.556</td>\n",
       "      <td>3.063</td>\n",
       "      <td>1.123</td>\n",
       "      <td>6.193</td>\n",
       "      <td>0.053</td>\n",
       "      <td>0.109</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25145</th>\n",
       "      <td>20170131</td>\n",
       "      <td>AUTOS</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.349</td>\n",
       "      <td>0.458</td>\n",
       "      <td>0.518</td>\n",
       "      <td>12.274</td>\n",
       "      <td>1.84%</td>\n",
       "      <td>...</td>\n",
       "      <td>3.471</td>\n",
       "      <td>3.140</td>\n",
       "      <td>1.390</td>\n",
       "      <td>9.378</td>\n",
       "      <td>0.055</td>\n",
       "      <td>0.031</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.266</td>\n",
       "      <td>-1.358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25146</th>\n",
       "      <td>20170131</td>\n",
       "      <td>BANKS</td>\n",
       "      <td>28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.377</td>\n",
       "      <td>3.041</td>\n",
       "      <td>0.877</td>\n",
       "      <td>16.459</td>\n",
       "      <td>1.83%</td>\n",
       "      <td>...</td>\n",
       "      <td>0.092</td>\n",
       "      <td>0.422</td>\n",
       "      <td>0.242</td>\n",
       "      <td>2.369</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.306</td>\n",
       "      <td>0.840</td>\n",
       "      <td>1.939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25147</th>\n",
       "      <td>20170131</td>\n",
       "      <td>BEER</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.425</td>\n",
       "      <td>7.994</td>\n",
       "      <td>0.194</td>\n",
       "      <td>27.518</td>\n",
       "      <td>1.65%</td>\n",
       "      <td>...</td>\n",
       "      <td>6.947</td>\n",
       "      <td>1.608</td>\n",
       "      <td>0.768</td>\n",
       "      <td>5.668</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.092</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.777</td>\n",
       "      <td>3.260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25148</th>\n",
       "      <td>20170131</td>\n",
       "      <td>BLDMT</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.274</td>\n",
       "      <td>0.586</td>\n",
       "      <td>0.293</td>\n",
       "      <td>42.560</td>\n",
       "      <td>1.56%</td>\n",
       "      <td>...</td>\n",
       "      <td>7.212</td>\n",
       "      <td>1.779</td>\n",
       "      <td>1.288</td>\n",
       "      <td>5.502</td>\n",
       "      <td>0.033</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.745</td>\n",
       "      <td>1.730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25149</th>\n",
       "      <td>20170131</td>\n",
       "      <td>BOOKS</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.111</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.440</td>\n",
       "      <td>-45.240</td>\n",
       "      <td>1.63%</td>\n",
       "      <td>...</td>\n",
       "      <td>6.154</td>\n",
       "      <td>0.707</td>\n",
       "      <td>0.679</td>\n",
       "      <td>6.057</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.073</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25150</th>\n",
       "      <td>20170131</td>\n",
       "      <td>BOXES</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.404</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.658</td>\n",
       "      <td>33.301</td>\n",
       "      <td>1.84%</td>\n",
       "      <td>...</td>\n",
       "      <td>7.294</td>\n",
       "      <td>2.437</td>\n",
       "      <td>0.920</td>\n",
       "      <td>16.036</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-37.821</td>\n",
       "      <td>2.443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25164</th>\n",
       "      <td>20170131</td>\n",
       "      <td>HLTH</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.026</td>\n",
       "      <td>1.256</td>\n",
       "      <td>0.396</td>\n",
       "      <td>21.464</td>\n",
       "      <td>1.16%</td>\n",
       "      <td>...</td>\n",
       "      <td>7.180</td>\n",
       "      <td>1.817</td>\n",
       "      <td>0.932</td>\n",
       "      <td>13.276</td>\n",
       "      <td>0.053</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.228</td>\n",
       "      <td>1.605</td>\n",
       "      <td>1.916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25151</th>\n",
       "      <td>20170131</td>\n",
       "      <td>BUSSV</td>\n",
       "      <td>17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.316</td>\n",
       "      <td>1.834</td>\n",
       "      <td>0.219</td>\n",
       "      <td>33.236</td>\n",
       "      <td>1.50%</td>\n",
       "      <td>...</td>\n",
       "      <td>4.239</td>\n",
       "      <td>2.062</td>\n",
       "      <td>0.837</td>\n",
       "      <td>9.093</td>\n",
       "      <td>0.043</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.163</td>\n",
       "      <td>2.244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25153</th>\n",
       "      <td>20170131</td>\n",
       "      <td>CHIPS</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.417</td>\n",
       "      <td>2.016</td>\n",
       "      <td>0.262</td>\n",
       "      <td>24.266</td>\n",
       "      <td>2.03%</td>\n",
       "      <td>...</td>\n",
       "      <td>7.178</td>\n",
       "      <td>0.982</td>\n",
       "      <td>0.838</td>\n",
       "      <td>2.092</td>\n",
       "      <td>0.052</td>\n",
       "      <td>0.099</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.721</td>\n",
       "      <td>2.151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25155</th>\n",
       "      <td>20170131</td>\n",
       "      <td>CNSTR</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.087</td>\n",
       "      <td>0.548</td>\n",
       "      <td>0.703</td>\n",
       "      <td>15.229</td>\n",
       "      <td>1.43%</td>\n",
       "      <td>...</td>\n",
       "      <td>12.980</td>\n",
       "      <td>2.066</td>\n",
       "      <td>1.686</td>\n",
       "      <td>9.334</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.157</td>\n",
       "      <td>1.781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25156</th>\n",
       "      <td>20170131</td>\n",
       "      <td>DRUGS</td>\n",
       "      <td>17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.320</td>\n",
       "      <td>1.310</td>\n",
       "      <td>0.176</td>\n",
       "      <td>24.904</td>\n",
       "      <td>2.65%</td>\n",
       "      <td>...</td>\n",
       "      <td>5.781</td>\n",
       "      <td>1.169</td>\n",
       "      <td>0.774</td>\n",
       "      <td>2.366</td>\n",
       "      <td>0.040</td>\n",
       "      <td>0.174</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.390</td>\n",
       "      <td>2.815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25157</th>\n",
       "      <td>20170131</td>\n",
       "      <td>ELCEQ</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.519</td>\n",
       "      <td>17.343</td>\n",
       "      <td>0.164</td>\n",
       "      <td>24.542</td>\n",
       "      <td>2.05%</td>\n",
       "      <td>...</td>\n",
       "      <td>5.547</td>\n",
       "      <td>2.105</td>\n",
       "      <td>1.677</td>\n",
       "      <td>5.558</td>\n",
       "      <td>0.032</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25158</th>\n",
       "      <td>20170131</td>\n",
       "      <td>FIN</td>\n",
       "      <td>13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.370</td>\n",
       "      <td>1.497</td>\n",
       "      <td>0.596</td>\n",
       "      <td>23.131</td>\n",
       "      <td>2.01%</td>\n",
       "      <td>...</td>\n",
       "      <td>4.244</td>\n",
       "      <td>0.499</td>\n",
       "      <td>0.336</td>\n",
       "      <td>7.101</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.242</td>\n",
       "      <td>1.047</td>\n",
       "      <td>1.771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25159</th>\n",
       "      <td>20170131</td>\n",
       "      <td>FOOD</td>\n",
       "      <td>14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.616</td>\n",
       "      <td>1.413</td>\n",
       "      <td>0.278</td>\n",
       "      <td>26.146</td>\n",
       "      <td>2.23%</td>\n",
       "      <td>...</td>\n",
       "      <td>11.033</td>\n",
       "      <td>2.712</td>\n",
       "      <td>1.572</td>\n",
       "      <td>9.750</td>\n",
       "      <td>0.041</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.029</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.844</td>\n",
       "      <td>3.933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25160</th>\n",
       "      <td>20170131</td>\n",
       "      <td>FUN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.732</td>\n",
       "      <td>9.119</td>\n",
       "      <td>0.060</td>\n",
       "      <td>224.618</td>\n",
       "      <td>1.97%</td>\n",
       "      <td>...</td>\n",
       "      <td>22.613</td>\n",
       "      <td>3.448</td>\n",
       "      <td>1.079</td>\n",
       "      <td>4.524</td>\n",
       "      <td>-0.027</td>\n",
       "      <td>0.049</td>\n",
       "      <td>0.056</td>\n",
       "      <td>0.000</td>\n",
       "      <td>4.572</td>\n",
       "      <td>2.715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25161</th>\n",
       "      <td>20170131</td>\n",
       "      <td>GOLD</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.269</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.559</td>\n",
       "      <td>-246.100</td>\n",
       "      <td>.551%</td>\n",
       "      <td>...</td>\n",
       "      <td>14.636</td>\n",
       "      <td>0.564</td>\n",
       "      <td>0.325</td>\n",
       "      <td>1.656</td>\n",
       "      <td>0.102</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-2.739</td>\n",
       "      <td>-7.860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25162</th>\n",
       "      <td>20170131</td>\n",
       "      <td>GUNS</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.560</td>\n",
       "      <td>2.542</td>\n",
       "      <td>0.033</td>\n",
       "      <td>23.474</td>\n",
       "      <td>2.90%</td>\n",
       "      <td>...</td>\n",
       "      <td>5.158</td>\n",
       "      <td>15.531</td>\n",
       "      <td>2.612</td>\n",
       "      <td>29.236</td>\n",
       "      <td>0.046</td>\n",
       "      <td>0.018</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.718</td>\n",
       "      <td>2.736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25152</th>\n",
       "      <td>20170131</td>\n",
       "      <td>CHEMS</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.484</td>\n",
       "      <td>1.236</td>\n",
       "      <td>0.295</td>\n",
       "      <td>24.752</td>\n",
       "      <td>2.32%</td>\n",
       "      <td>...</td>\n",
       "      <td>5.477</td>\n",
       "      <td>1.787</td>\n",
       "      <td>0.819</td>\n",
       "      <td>5.277</td>\n",
       "      <td>0.045</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.760</td>\n",
       "      <td>2.525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25187</th>\n",
       "      <td>20170131</td>\n",
       "      <td>WHLSL</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.392</td>\n",
       "      <td>3.060</td>\n",
       "      <td>0.244</td>\n",
       "      <td>23.094</td>\n",
       "      <td>2.31%</td>\n",
       "      <td>...</td>\n",
       "      <td>9.550</td>\n",
       "      <td>4.712</td>\n",
       "      <td>4.343</td>\n",
       "      <td>9.503</td>\n",
       "      <td>0.040</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-4.347</td>\n",
       "      <td>2.146</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25188 rows × 76 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       public_date FFI49_desc  NFIRM  indret_ew  indret_vw  dpr_Median  \\\n",
       "0         19700131       AERO      8  -0.117325  -0.149955       0.000   \n",
       "22        19700131      HSHLD     25  -0.070517  -0.068231       0.000   \n",
       "23        19700131      LABEQ      1  -0.083478  -0.083478       0.000   \n",
       "24        19700131       MACH     40  -0.038664  -0.071795       0.048   \n",
       "25        19700131      MEALS      1  -0.326923  -0.326923       0.448   \n",
       "26        19700131      MEDEQ      1  -0.007937  -0.007937       0.000   \n",
       "27        19700131      MINES      7  -0.046483  -0.064723       0.000   \n",
       "28        19700131        OIL     21  -0.122596  -0.128312       0.000   \n",
       "30        19700131      RTAIL     27  -0.056199  -0.059770       0.000   \n",
       "31        19700131      SHIPS      3  -0.050288  -0.056947       0.000   \n",
       "32        19700131      SMOKE      6  -0.027493  -0.071631       0.009   \n",
       "33        19700131       SODA      8  -0.011130  -0.026951       0.000   \n",
       "34        19700131      STEEL     25  -0.068294  -0.063238       0.011   \n",
       "35        19700131      TELCM      8  -0.067678  -0.023545       0.000   \n",
       "36        19700131       TOYS      4  -0.044872  -0.035847       0.000   \n",
       "37        19700131      TRANS     14  -0.068535  -0.073527       0.082   \n",
       "38        19700131      TXTLS     10  -0.053081  -0.051588       0.000   \n",
       "39        19700131       UTIL     47  -0.032949  -0.043141       0.106   \n",
       "21        19700131      HARDW     10  -0.142658  -0.091570       0.000   \n",
       "20        19700131       GUNS      1  -0.160494  -0.160494       0.009   \n",
       "29        19700131      PAPER      9  -0.085591  -0.098263       0.004   \n",
       "18        19700131        FUN      4  -0.080100  -0.078507       0.515   \n",
       "19        19700131       GOLD      3   0.048893   0.054816       0.000   \n",
       "1         19700131      AGRIC      1  -0.006061  -0.006061       0.000   \n",
       "2         19700131      AUTOS     18  -0.108604  -0.091909       0.000   \n",
       "3         19700131      BANKS     11  -0.052531  -0.069837       0.204   \n",
       "4         19700131       BEER      5  -0.000440  -0.003983       0.000   \n",
       "5         19700131      BLDMT     23  -0.051627  -0.087953       0.015   \n",
       "7         19700131      BOXES     11  -0.055760  -0.081525       0.041   \n",
       "8         19700131      BUSSV      2  -0.060979  -0.022816       0.159   \n",
       "...            ...        ...    ...        ...        ...         ...   \n",
       "25182     20170131      TELCM     15        NaN        NaN       0.291   \n",
       "25183     20170131       TOYS      3        NaN        NaN       0.454   \n",
       "25184     20170131      TRANS     15        NaN        NaN       0.294   \n",
       "25166     20170131      INSUR     23        NaN        NaN       0.258   \n",
       "25176     20170131      RLEST      2        NaN        NaN       0.185   \n",
       "25165     20170131      HSHLD     10        NaN        NaN       0.443   \n",
       "25154     20170131      CLTHS      7        NaN        NaN       0.278   \n",
       "25163     20170131      HARDW      6        NaN        NaN       0.510   \n",
       "25186     20170131       UTIL     33        NaN        NaN       0.641   \n",
       "25143     20170131       AERO      6        NaN        NaN       0.273   \n",
       "25144     20170131      AGRIC      1        NaN        NaN       0.591   \n",
       "25145     20170131      AUTOS      4        NaN        NaN       0.349   \n",
       "25146     20170131      BANKS     28        NaN        NaN       0.377   \n",
       "25147     20170131       BEER      4        NaN        NaN       0.425   \n",
       "25148     20170131      BLDMT      5        NaN        NaN       0.274   \n",
       "25149     20170131      BOOKS      1        NaN        NaN       0.111   \n",
       "25150     20170131      BOXES      2        NaN        NaN       1.404   \n",
       "25164     20170131       HLTH      6        NaN        NaN       0.026   \n",
       "25151     20170131      BUSSV     17        NaN        NaN       0.316   \n",
       "25153     20170131      CHIPS     20        NaN        NaN       0.417   \n",
       "25155     20170131      CNSTR      6        NaN        NaN       0.087   \n",
       "25156     20170131      DRUGS     17        NaN        NaN       0.320   \n",
       "25157     20170131      ELCEQ      3        NaN        NaN       0.519   \n",
       "25158     20170131        FIN     13        NaN        NaN       0.370   \n",
       "25159     20170131       FOOD     14        NaN        NaN       0.616   \n",
       "25160     20170131        FUN      2        NaN        NaN       0.732   \n",
       "25161     20170131       GOLD      1        NaN        NaN       0.269   \n",
       "25162     20170131       GUNS      1        NaN        NaN       0.560   \n",
       "25152     20170131      CHEMS     12        NaN        NaN       0.484   \n",
       "25187     20170131      WHLSL      9        NaN        NaN       0.392   \n",
       "\n",
       "       PEG_trailing_Median  bm_Median  CAPEI_Median divyield_Median  \\\n",
       "0                    1.356      0.696         8.490           4.76%   \n",
       "22                   2.131      0.351        18.974           3.20%   \n",
       "23                   8.505      0.191        44.643             NaN   \n",
       "24                   1.270      0.524        10.474           4.68%   \n",
       "25                   0.053      0.266         3.652           3.43%   \n",
       "26                   7.421      0.423        34.144           1.07%   \n",
       "27                   0.348      0.463        12.518           5.73%   \n",
       "28                   1.369      0.534        10.543           5.10%   \n",
       "30                   2.129      0.673        13.637           3.57%   \n",
       "31                   0.190      0.795         9.208           5.18%   \n",
       "32                   1.413      0.755        11.354           5.90%   \n",
       "33                   3.000      0.283        15.213           3.98%   \n",
       "34                   0.331      0.834         9.967           5.45%   \n",
       "35                   1.785      0.292        14.958           3.14%   \n",
       "36                   0.900      0.198        17.178           3.57%   \n",
       "37                   0.773      1.301         9.410           5.74%   \n",
       "38                   0.717      1.147         8.795           5.77%   \n",
       "39                   1.344      0.622        11.110           6.64%   \n",
       "21                   2.275      0.275        31.436           1.32%   \n",
       "20                   0.395      0.612         9.773           6.47%   \n",
       "29                   1.045      0.671        15.005           4.04%   \n",
       "18                     NaN      0.448        20.514           2.73%   \n",
       "19                     NaN      0.302        27.360           2.19%   \n",
       "1                    0.475      0.780        19.165           2.81%   \n",
       "2                    0.806      0.684        10.561           5.40%   \n",
       "3                    0.429      0.568        11.940           6.57%   \n",
       "4                    3.647      0.630        42.057           1.77%   \n",
       "5                    1.023      0.675        13.450           4.10%   \n",
       "7                    1.272      0.500        17.200           2.67%   \n",
       "8                    1.882      0.547        11.474           4.76%   \n",
       "...                    ...        ...           ...             ...   \n",
       "25182                0.835      0.502        22.429           1.78%   \n",
       "25183                0.634      0.233        26.780           2.47%   \n",
       "25184                1.172      0.432        22.045           1.54%   \n",
       "25166                1.522      0.756        19.602           1.89%   \n",
       "25176                1.322      0.297        21.573           2.53%   \n",
       "25165                3.728      0.212        26.705           2.44%   \n",
       "25154                0.841      0.210        18.922           1.81%   \n",
       "25163                2.271      0.411        19.769           2.95%   \n",
       "25186                2.111      0.798        23.106           3.35%   \n",
       "25143                1.591      0.241        22.261           1.68%   \n",
       "25144                  NaN      0.100        21.886           1.99%   \n",
       "25145                0.458      0.518        12.274           1.84%   \n",
       "25146                3.041      0.877        16.459           1.83%   \n",
       "25147                7.994      0.194        27.518           1.65%   \n",
       "25148                0.586      0.293        42.560           1.56%   \n",
       "25149                  NaN      1.440       -45.240           1.63%   \n",
       "25150                  NaN      0.658        33.301           1.84%   \n",
       "25164                1.256      0.396        21.464           1.16%   \n",
       "25151                1.834      0.219        33.236           1.50%   \n",
       "25153                2.016      0.262        24.266           2.03%   \n",
       "25155                0.548      0.703        15.229           1.43%   \n",
       "25156                1.310      0.176        24.904           2.65%   \n",
       "25157               17.343      0.164        24.542           2.05%   \n",
       "25158                1.497      0.596        23.131           2.01%   \n",
       "25159                1.413      0.278        26.146           2.23%   \n",
       "25160                9.119      0.060       224.618           1.97%   \n",
       "25161                  NaN      0.559      -246.100           .551%   \n",
       "25162                2.542      0.033        23.474           2.90%   \n",
       "25152                1.236      0.295        24.752           2.32%   \n",
       "25187                3.060      0.244        23.094           2.31%   \n",
       "\n",
       "               ...            rect_turn_Median  sale_equity_Median  \\\n",
       "0              ...                       8.843               4.416   \n",
       "22             ...                       7.856               2.443   \n",
       "23             ...                       5.992               1.585   \n",
       "24             ...                       5.539               2.145   \n",
       "25             ...                      13.913               2.512   \n",
       "26             ...                       4.370               2.041   \n",
       "27             ...                       7.728               0.969   \n",
       "28             ...                       5.704               1.080   \n",
       "30             ...                      30.907               4.600   \n",
       "31             ...                       5.147               3.072   \n",
       "32             ...                       9.311               1.858   \n",
       "33             ...                      15.519               2.559   \n",
       "34             ...                       8.878               1.442   \n",
       "35             ...                       7.764               1.416   \n",
       "36             ...                       6.496               3.381   \n",
       "37             ...                       9.358               1.348   \n",
       "38             ...                       6.231               2.432   \n",
       "39             ...                       8.225               0.760   \n",
       "21             ...                       5.347               2.264   \n",
       "20             ...                       9.993               1.984   \n",
       "29             ...                       9.149               1.581   \n",
       "18             ...                       4.995               2.081   \n",
       "19             ...                      17.107               0.564   \n",
       "1              ...                      12.686               5.288   \n",
       "2              ...                       8.466               2.870   \n",
       "3              ...                       0.218               1.065   \n",
       "4              ...                      10.156               1.943   \n",
       "5              ...                       7.080               1.601   \n",
       "7              ...                       9.077               2.265   \n",
       "8              ...                       0.131               0.823   \n",
       "...            ...                         ...                 ...   \n",
       "25182          ...                       6.513               1.331   \n",
       "25183          ...                       5.333               2.850   \n",
       "25184          ...                      11.577               3.411   \n",
       "25166          ...                       2.424               1.408   \n",
       "25176          ...                       4.821               5.618   \n",
       "25165          ...                       7.705               3.133   \n",
       "25154          ...                       9.741               2.378   \n",
       "25163          ...                       5.850               1.397   \n",
       "25186          ...                       9.204               0.835   \n",
       "25143          ...                       6.365               2.672   \n",
       "25144          ...                       4.556               3.063   \n",
       "25145          ...                       3.471               3.140   \n",
       "25146          ...                       0.092               0.422   \n",
       "25147          ...                       6.947               1.608   \n",
       "25148          ...                       7.212               1.779   \n",
       "25149          ...                       6.154               0.707   \n",
       "25150          ...                       7.294               2.437   \n",
       "25164          ...                       7.180               1.817   \n",
       "25151          ...                       4.239               2.062   \n",
       "25153          ...                       7.178               0.982   \n",
       "25155          ...                      12.980               2.066   \n",
       "25156          ...                       5.781               1.169   \n",
       "25157          ...                       5.547               2.105   \n",
       "25158          ...                       4.244               0.499   \n",
       "25159          ...                      11.033               2.712   \n",
       "25160          ...                      22.613               3.448   \n",
       "25161          ...                      14.636               0.564   \n",
       "25162          ...                       5.158              15.531   \n",
       "25152          ...                       5.477               1.787   \n",
       "25187          ...                       9.550               4.712   \n",
       "\n",
       "       sale_invcap_Median  sale_nwc_Median  accrual_Median  rd_sale_Median  \\\n",
       "0                   3.019            7.479           0.021           0.000   \n",
       "22                  2.057            3.981           0.002           0.000   \n",
       "23                  1.580            2.582          -0.058           0.000   \n",
       "24                  1.697            3.062           0.003           0.000   \n",
       "25                  1.757            7.331           0.028           0.000   \n",
       "26                  1.355            1.788           0.044           0.000   \n",
       "27                  0.884            2.951           0.049           0.000   \n",
       "28                  0.824            5.185           0.059           0.000   \n",
       "30                  3.510            8.853           0.008           0.000   \n",
       "31                  2.410            4.715          -0.019           0.000   \n",
       "32                  1.306            2.043          -0.056           0.000   \n",
       "33                  2.541            6.140           0.029           0.000   \n",
       "34                  0.993            4.014           0.014           0.000   \n",
       "35                  0.901            4.028           0.026           0.000   \n",
       "36                  3.155            4.597           0.026           0.000   \n",
       "37                  0.820           27.673           0.043           0.000   \n",
       "38                  1.777            3.482          -0.011           0.000   \n",
       "39                  0.364           25.478           0.033           0.000   \n",
       "21                  1.573            3.484           0.032           0.000   \n",
       "20                  1.247            4.993           0.020           0.000   \n",
       "29                  1.116            5.706           0.038           0.000   \n",
       "18                  1.133            1.544          -0.068           0.000   \n",
       "19                  0.564            1.273          -0.004           0.000   \n",
       "1                   3.942            6.452           0.099           0.000   \n",
       "2                   2.233            4.639           0.005           0.000   \n",
       "3                   0.330            0.356          -0.081           0.000   \n",
       "4                   1.972            7.738           0.034           0.000   \n",
       "5                   1.339            3.643           0.028           0.000   \n",
       "7                   1.663            4.838           0.020           0.000   \n",
       "8                   0.266            0.300          -0.090           0.000   \n",
       "...                   ...              ...             ...             ...   \n",
       "25182               0.642            5.943           0.029           0.000   \n",
       "25183               1.539            4.376           0.042           0.053   \n",
       "25184               1.975           22.973           0.054           0.000   \n",
       "25166               0.934            9.083           0.022           0.000   \n",
       "25176               1.642           30.581           0.017           0.000   \n",
       "25165               1.532           22.271           0.045           0.018   \n",
       "25154               1.694            4.177           0.023           0.000   \n",
       "25163               0.955            2.551           0.032           0.130   \n",
       "25186               0.412           28.583           0.048           0.000   \n",
       "25143               1.552            6.191           0.004           0.042   \n",
       "25144               1.123            6.193           0.053           0.109   \n",
       "25145               1.390            9.378           0.055           0.031   \n",
       "25146               0.242            2.369           0.005           0.000   \n",
       "25147               0.768            5.668           0.021           0.000   \n",
       "25148               1.288            5.502           0.033           0.010   \n",
       "25149               0.679            6.057           0.032           0.000   \n",
       "25150               0.920           16.036           0.021           0.002   \n",
       "25164               0.932           13.276           0.053           0.000   \n",
       "25151               0.837            9.093           0.043           0.000   \n",
       "25153               0.838            2.092           0.052           0.099   \n",
       "25155               1.686            9.334           0.018           0.000   \n",
       "25156               0.774            2.366           0.040           0.174   \n",
       "25157               1.677            5.558           0.032           0.022   \n",
       "25158               0.336            7.101           0.007           0.000   \n",
       "25159               1.572            9.750           0.041           0.008   \n",
       "25160               1.079            4.524          -0.027           0.049   \n",
       "25161               0.325            1.656           0.102           0.000   \n",
       "25162               2.612           29.236           0.046           0.018   \n",
       "25152               0.819            5.277           0.045           0.028   \n",
       "25187               4.343            9.503           0.040           0.000   \n",
       "\n",
       "       adv_sale_Median  staff_sale_Median  PEG_1yrforward_Median  \\\n",
       "0                0.000              0.388                    NaN   \n",
       "22               0.000              0.000                    NaN   \n",
       "23               0.000              0.445                    NaN   \n",
       "24               0.000              0.000                    NaN   \n",
       "25               0.000              0.000                    NaN   \n",
       "26               0.000              0.000                    NaN   \n",
       "27               0.000              0.000                    NaN   \n",
       "28               0.000              0.115                    NaN   \n",
       "30               0.000              0.000                    NaN   \n",
       "31               0.000              0.000                    NaN   \n",
       "32               0.000              0.000                    NaN   \n",
       "33               0.000              0.000                    NaN   \n",
       "34               0.000              0.000                    NaN   \n",
       "35               0.000              0.000                    NaN   \n",
       "36               0.000              0.000                    NaN   \n",
       "37               0.000              0.190                    NaN   \n",
       "38               0.000              0.000                    NaN   \n",
       "39               0.000              0.209                    NaN   \n",
       "21               0.000              0.000                    NaN   \n",
       "20               0.000              0.000                    NaN   \n",
       "29               0.000              0.293                    NaN   \n",
       "18               0.000              0.000                    NaN   \n",
       "19               0.000              0.000                    NaN   \n",
       "1                0.000              0.000                    NaN   \n",
       "2                0.000              0.278                    NaN   \n",
       "3                0.000              0.000                    NaN   \n",
       "4                0.000              0.000                    NaN   \n",
       "5                0.000              0.000                    NaN   \n",
       "7                0.000              0.279                    NaN   \n",
       "8                0.000              0.000                    NaN   \n",
       "...                ...                ...                    ...   \n",
       "25182            0.040              0.000                  0.478   \n",
       "25183            0.092              0.000                 16.290   \n",
       "25184            0.000              0.245                  2.634   \n",
       "25166            0.000              0.000                  0.746   \n",
       "25176            0.018              0.000                  2.330   \n",
       "25165            0.056              0.000                  3.102   \n",
       "25154            0.047              0.000                  2.408   \n",
       "25163            0.004              0.000                  1.686   \n",
       "25186            0.000              0.000                  2.232   \n",
       "25143            0.000              0.000                  2.184   \n",
       "25144            0.005              0.000                    NaN   \n",
       "25145            0.012              0.000                  0.266   \n",
       "25146            0.015              0.306                  0.840   \n",
       "25147            0.092              0.000                  2.777   \n",
       "25148            0.000              0.000                  0.745   \n",
       "25149            0.073              0.000                    NaN   \n",
       "25150            0.000              0.000                -37.821   \n",
       "25164            0.000              0.228                  1.605   \n",
       "25151            0.000              0.000                  2.163   \n",
       "25153            0.000              0.000                  1.721   \n",
       "25155            0.002              0.000                  1.157   \n",
       "25156            0.010              0.000                  1.390   \n",
       "25157            0.000              0.000                    NaN   \n",
       "25158            0.000              0.242                  1.047   \n",
       "25159            0.029              0.000                  1.844   \n",
       "25160            0.056              0.000                  4.572   \n",
       "25161            0.000              0.000                 -2.739   \n",
       "25162            0.000              0.000                  2.718   \n",
       "25152            0.000              0.000                  0.760   \n",
       "25187            0.000              0.000                 -4.347   \n",
       "\n",
       "       PEG_ltgforward_Median  \n",
       "0                        NaN  \n",
       "22                       NaN  \n",
       "23                       NaN  \n",
       "24                       NaN  \n",
       "25                       NaN  \n",
       "26                       NaN  \n",
       "27                       NaN  \n",
       "28                       NaN  \n",
       "30                       NaN  \n",
       "31                       NaN  \n",
       "32                       NaN  \n",
       "33                       NaN  \n",
       "34                       NaN  \n",
       "35                       NaN  \n",
       "36                       NaN  \n",
       "37                       NaN  \n",
       "38                       NaN  \n",
       "39                       NaN  \n",
       "21                       NaN  \n",
       "20                       NaN  \n",
       "29                       NaN  \n",
       "18                       NaN  \n",
       "19                       NaN  \n",
       "1                        NaN  \n",
       "2                        NaN  \n",
       "3                        NaN  \n",
       "4                        NaN  \n",
       "5                        NaN  \n",
       "7                        NaN  \n",
       "8                        NaN  \n",
       "...                      ...  \n",
       "25182                  1.077  \n",
       "25183                  1.969  \n",
       "25184                  2.189  \n",
       "25166                  1.736  \n",
       "25176                  1.854  \n",
       "25165                  3.187  \n",
       "25154                  2.204  \n",
       "25163                  1.933  \n",
       "25186                  3.477  \n",
       "25143                  2.741  \n",
       "25144                    NaN  \n",
       "25145                 -1.358  \n",
       "25146                  1.939  \n",
       "25147                  3.260  \n",
       "25148                  1.730  \n",
       "25149                    NaN  \n",
       "25150                  2.443  \n",
       "25164                  1.916  \n",
       "25151                  2.244  \n",
       "25153                  2.151  \n",
       "25155                  1.781  \n",
       "25156                  2.815  \n",
       "25157                    NaN  \n",
       "25158                  1.771  \n",
       "25159                  3.933  \n",
       "25160                  2.715  \n",
       "25161                 -7.860  \n",
       "25162                  2.736  \n",
       "25152                  2.525  \n",
       "25187                  2.146  \n",
       "\n",
       "[25188 rows x 76 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('ee6d2f60cdafb550.csv')\n",
    "df.sort_values(by='public_date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>public_date</th>\n",
       "      <th>NFIRM</th>\n",
       "      <th>indret_ew</th>\n",
       "      <th>indret_vw</th>\n",
       "      <th>dpr_Median</th>\n",
       "      <th>PEG_trailing_Median</th>\n",
       "      <th>bm_Median</th>\n",
       "      <th>CAPEI_Median</th>\n",
       "      <th>evm_Median</th>\n",
       "      <th>pcf_Median</th>\n",
       "      <th>...</th>\n",
       "      <th>rect_turn_Median</th>\n",
       "      <th>sale_equity_Median</th>\n",
       "      <th>sale_invcap_Median</th>\n",
       "      <th>sale_nwc_Median</th>\n",
       "      <th>accrual_Median</th>\n",
       "      <th>rd_sale_Median</th>\n",
       "      <th>adv_sale_Median</th>\n",
       "      <th>staff_sale_Median</th>\n",
       "      <th>PEG_1yrforward_Median</th>\n",
       "      <th>PEG_ltgforward_Median</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.518800e+04</td>\n",
       "      <td>25188.000000</td>\n",
       "      <td>25143.000000</td>\n",
       "      <td>25143.000000</td>\n",
       "      <td>25034.000000</td>\n",
       "      <td>23865.000000</td>\n",
       "      <td>25186.000000</td>\n",
       "      <td>25187.000000</td>\n",
       "      <td>25186.000000</td>\n",
       "      <td>25186.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>25138.000000</td>\n",
       "      <td>25188.000000</td>\n",
       "      <td>25188.000000</td>\n",
       "      <td>24577.000000</td>\n",
       "      <td>25186.000000</td>\n",
       "      <td>25188.000000</td>\n",
       "      <td>25186.000000</td>\n",
       "      <td>25186.000000</td>\n",
       "      <td>21555.000000</td>\n",
       "      <td>18984.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.993518e+07</td>\n",
       "      <td>10.681753</td>\n",
       "      <td>0.011830</td>\n",
       "      <td>0.010508</td>\n",
       "      <td>0.292932</td>\n",
       "      <td>1.409593</td>\n",
       "      <td>0.635862</td>\n",
       "      <td>21.112411</td>\n",
       "      <td>8.885819</td>\n",
       "      <td>10.261947</td>\n",
       "      <td>...</td>\n",
       "      <td>9.498688</td>\n",
       "      <td>2.798205</td>\n",
       "      <td>1.629473</td>\n",
       "      <td>7.949518</td>\n",
       "      <td>0.035598</td>\n",
       "      <td>0.013711</td>\n",
       "      <td>0.013081</td>\n",
       "      <td>0.039355</td>\n",
       "      <td>0.666897</td>\n",
       "      <td>1.408018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.346762e+05</td>\n",
       "      <td>9.359648</td>\n",
       "      <td>0.075221</td>\n",
       "      <td>0.073355</td>\n",
       "      <td>1.845015</td>\n",
       "      <td>2.816861</td>\n",
       "      <td>0.421867</td>\n",
       "      <td>103.482835</td>\n",
       "      <td>8.215927</td>\n",
       "      <td>11.249100</td>\n",
       "      <td>...</td>\n",
       "      <td>11.207948</td>\n",
       "      <td>13.426756</td>\n",
       "      <td>0.792297</td>\n",
       "      <td>458.055652</td>\n",
       "      <td>0.041333</td>\n",
       "      <td>0.030763</td>\n",
       "      <td>0.024436</td>\n",
       "      <td>0.094464</td>\n",
       "      <td>2.070773</td>\n",
       "      <td>1.854341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.970013e+07</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.477184</td>\n",
       "      <td>-0.477206</td>\n",
       "      <td>-4.003000</td>\n",
       "      <td>-15.636000</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>-1031.000000</td>\n",
       "      <td>-634.290000</td>\n",
       "      <td>-329.770000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.082000</td>\n",
       "      <td>0.417000</td>\n",
       "      <td>0.232000</td>\n",
       "      <td>-40463.000000</td>\n",
       "      <td>-0.389000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-50.389000</td>\n",
       "      <td>-30.091000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.982053e+07</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>-0.028681</td>\n",
       "      <td>-0.028955</td>\n",
       "      <td>0.033000</td>\n",
       "      <td>0.470000</td>\n",
       "      <td>0.335000</td>\n",
       "      <td>12.354500</td>\n",
       "      <td>6.507000</td>\n",
       "      <td>6.056000</td>\n",
       "      <td>...</td>\n",
       "      <td>5.681000</td>\n",
       "      <td>1.783750</td>\n",
       "      <td>1.124000</td>\n",
       "      <td>4.228000</td>\n",
       "      <td>0.014000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.194000</td>\n",
       "      <td>1.016000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.994023e+07</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.011136</td>\n",
       "      <td>0.010303</td>\n",
       "      <td>0.248000</td>\n",
       "      <td>0.867000</td>\n",
       "      <td>0.516000</td>\n",
       "      <td>18.219000</td>\n",
       "      <td>8.433000</td>\n",
       "      <td>9.228500</td>\n",
       "      <td>...</td>\n",
       "      <td>7.010000</td>\n",
       "      <td>2.358000</td>\n",
       "      <td>1.546000</td>\n",
       "      <td>6.693000</td>\n",
       "      <td>0.037000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.713000</td>\n",
       "      <td>1.365000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.005093e+07</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.051200</td>\n",
       "      <td>0.049365</td>\n",
       "      <td>0.390000</td>\n",
       "      <td>1.479000</td>\n",
       "      <td>0.828000</td>\n",
       "      <td>25.026000</td>\n",
       "      <td>10.588750</td>\n",
       "      <td>13.142250</td>\n",
       "      <td>...</td>\n",
       "      <td>9.101000</td>\n",
       "      <td>3.071000</td>\n",
       "      <td>1.989000</td>\n",
       "      <td>11.896000</td>\n",
       "      <td>0.057000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.016000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.257000</td>\n",
       "      <td>1.755000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.017013e+07</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>1.320997</td>\n",
       "      <td>1.239549</td>\n",
       "      <td>163.957000</td>\n",
       "      <td>74.358000</td>\n",
       "      <td>3.768000</td>\n",
       "      <td>7522.850000</td>\n",
       "      <td>216.961000</td>\n",
       "      <td>349.224000</td>\n",
       "      <td>...</td>\n",
       "      <td>300.799000</td>\n",
       "      <td>1209.790000</td>\n",
       "      <td>7.614000</td>\n",
       "      <td>8210.620000</td>\n",
       "      <td>0.655000</td>\n",
       "      <td>0.202000</td>\n",
       "      <td>0.152000</td>\n",
       "      <td>0.477000</td>\n",
       "      <td>32.240000</td>\n",
       "      <td>42.843000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 74 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        public_date         NFIRM     indret_ew     indret_vw    dpr_Median  \\\n",
       "count  2.518800e+04  25188.000000  25143.000000  25143.000000  25034.000000   \n",
       "mean   1.993518e+07     10.681753      0.011830      0.010508      0.292932   \n",
       "std    1.346762e+05      9.359648      0.075221      0.073355      1.845015   \n",
       "min    1.970013e+07      1.000000     -0.477184     -0.477206     -4.003000   \n",
       "25%    1.982053e+07      3.000000     -0.028681     -0.028955      0.033000   \n",
       "50%    1.994023e+07      8.000000      0.011136      0.010303      0.248000   \n",
       "75%    2.005093e+07     15.000000      0.051200      0.049365      0.390000   \n",
       "max    2.017013e+07     51.000000      1.320997      1.239549    163.957000   \n",
       "\n",
       "       PEG_trailing_Median     bm_Median  CAPEI_Median    evm_Median  \\\n",
       "count         23865.000000  25186.000000  25187.000000  25186.000000   \n",
       "mean              1.409593      0.635862     21.112411      8.885819   \n",
       "std               2.816861      0.421867    103.482835      8.215927   \n",
       "min             -15.636000      0.001000  -1031.000000   -634.290000   \n",
       "25%               0.470000      0.335000     12.354500      6.507000   \n",
       "50%               0.867000      0.516000     18.219000      8.433000   \n",
       "75%               1.479000      0.828000     25.026000     10.588750   \n",
       "max              74.358000      3.768000   7522.850000    216.961000   \n",
       "\n",
       "         pcf_Median          ...            rect_turn_Median  \\\n",
       "count  25186.000000          ...                25138.000000   \n",
       "mean      10.261947          ...                    9.498688   \n",
       "std       11.249100          ...                   11.207948   \n",
       "min     -329.770000          ...                    0.082000   \n",
       "25%        6.056000          ...                    5.681000   \n",
       "50%        9.228500          ...                    7.010000   \n",
       "75%       13.142250          ...                    9.101000   \n",
       "max      349.224000          ...                  300.799000   \n",
       "\n",
       "       sale_equity_Median  sale_invcap_Median  sale_nwc_Median  \\\n",
       "count        25188.000000        25188.000000     24577.000000   \n",
       "mean             2.798205            1.629473         7.949518   \n",
       "std             13.426756            0.792297       458.055652   \n",
       "min              0.417000            0.232000    -40463.000000   \n",
       "25%              1.783750            1.124000         4.228000   \n",
       "50%              2.358000            1.546000         6.693000   \n",
       "75%              3.071000            1.989000        11.896000   \n",
       "max           1209.790000            7.614000      8210.620000   \n",
       "\n",
       "       accrual_Median  rd_sale_Median  adv_sale_Median  staff_sale_Median  \\\n",
       "count    25186.000000    25188.000000     25186.000000       25186.000000   \n",
       "mean         0.035598        0.013711         0.013081           0.039355   \n",
       "std          0.041333        0.030763         0.024436           0.094464   \n",
       "min         -0.389000        0.000000         0.000000           0.000000   \n",
       "25%          0.014000        0.000000         0.000000           0.000000   \n",
       "50%          0.037000        0.000000         0.000000           0.000000   \n",
       "75%          0.057000        0.010000         0.016000           0.000000   \n",
       "max          0.655000        0.202000         0.152000           0.477000   \n",
       "\n",
       "       PEG_1yrforward_Median  PEG_ltgforward_Median  \n",
       "count           21555.000000           18984.000000  \n",
       "mean                0.666897               1.408018  \n",
       "std                 2.070773               1.854341  \n",
       "min               -50.389000             -30.091000  \n",
       "25%                 0.194000               1.016000  \n",
       "50%                 0.713000               1.365000  \n",
       "75%                 1.257000               1.755000  \n",
       "max                32.240000              42.843000  \n",
       "\n",
       "[8 rows x 74 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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