analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import math\n",
    "import hashlib\n",
    "import re\n",
    "\n",
    "import scipy.stats as stats\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "from patsy import dmatrices\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn import metrics\n",
    "from sklearn.linear_model import Lasso\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Working Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(86140, 113)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load working dataframe\n",
    "df = pd.read_csv('Datasets/working_df.csv')\n",
    "\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(76581, 113)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop passwords leaked 100000 times or more\n",
    "df = df[df['Total'] < 10000]\n",
    "\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    76581.000000\n",
       "mean      2461.055040\n",
       "std       2054.224553\n",
       "min          0.000000\n",
       "25%        938.000000\n",
       "50%       1900.000000\n",
       "75%       3433.000000\n",
       "max       9999.000000\n",
       "Name: Total, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Total'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Length', 'Length_Pp', 'Only_Let', 'Only_Dig', 'Only_Spc',\n",
       "       'Let_And_Dig', 'Let_And_Spc', 'Dig_And_Spc', 'Date', 'Year', 'Rep_2',\n",
       "       'Rep_3', 'Rep_4', 'Rep_5', 'Rep_6', 'Rep_7', 'Rep_8', 'Rep_9', 'Rep_10',\n",
       "       'Asc_Dig_2', 'Asc_Dig_3', 'Asc_Dig_4', 'Asc_Dig_5', 'Asc_Dig_6',\n",
       "       'Asc_Dig_7', 'Asc_Dig_8', 'Asc_Dig_9', 'Asc_Dig_10', 'Dsc_Dig_2',\n",
       "       'Dsc_Dig_3', 'Dsc_Dig_4', 'Dsc_Dig_5', 'Dsc_Dig_6', 'Dsc_Dig_7',\n",
       "       'Dsc_Dig_8', 'Dsc_Dig_9', 'Dsc_Dig_10', 'Asc_Let_2', 'Asc_Let_3',\n",
       "       'Asc_Let_4', 'Asc_Let_5', 'Asc_Let_6', 'Asc_Let_7', 'Asc_Let_8',\n",
       "       'Asc_Let_9', 'Asc_Let_10', 'Dsc_Let_2', 'Dsc_Let_3', 'Dsc_Let_4',\n",
       "       'Dsc_Let_5', 'Dsc_Let_6', 'Dsc_Let_7', 'Dsc_Let_8', 'Dsc_Let_9',\n",
       "       'Dsc_Let_10'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create list of features\n",
    "ignore = ['Password', 'SHA_1', 'Total', 'Length_Sq', 'Length_Cb',\n",
    "          'Only_Let_Div_Len', 'Only_Dig_Div_Len', 'Only_Spc_Div_Len',\n",
    "          'Let_And_Dig_Div_Len', 'Let_And_Spc_Div_Len', 'Dig_And_Spc_Div_Len',\n",
    "          'Date_Div_Len', 'Year_Div_Len', 'Rep_2_Div_Len', 'Rep_3_Div_Len',\n",
    "          'Rep_4_Div_Len', 'Rep_5_Div_Len', 'Rep_6_Div_Len', 'Rep_7_Div_Len',\n",
    "          'Rep_8_Div_Len', 'Rep_9_Div_Len', 'Rep_10_Div_Len', 'Asc_Dig_2_Div_Len',\n",
    "          'Asc_Dig_3_Div_Len', 'Asc_Dig_4_Div_Len', 'Asc_Dig_5_Div_Len', 'Asc_Dig_6_Div_Len',\n",
    "          'Asc_Dig_7_Div_Len', 'Asc_Dig_8_Div_Len', 'Asc_Dig_9_Div_Len', 'Asc_Dig_10_Div_Len',\n",
    "          'Dsc_Dig_2_Div_Len', 'Dsc_Dig_3_Div_Len', 'Dsc_Dig_4_Div_Len', 'Dsc_Dig_5_Div_Len',\n",
    "          'Dsc_Dig_6_Div_Len', 'Dsc_Dig_7_Div_Len', 'Dsc_Dig_8_Div_Len', 'Dsc_Dig_9_Div_Len',\n",
    "          'Dsc_Dig_10_Div_Len', 'Asc_Let_2_Div_Len', 'Asc_Let_3_Div_Len', 'Asc_Let_4_Div_Len',\n",
    "          'Asc_Let_5_Div_Len', 'Asc_Let_6_Div_Len', 'Asc_Let_7_Div_Len', 'Asc_Let_8_Div_Len',\n",
    "          'Asc_Let_9_Div_Len', 'Asc_Let_10_Div_Len', 'Dsc_Let_2_Div_Len', 'Dsc_Let_3_Div_Len',\n",
    "          'Dsc_Let_4_Div_Len', 'Dsc_Let_5_Div_Len', 'Dsc_Let_6_Div_Len', 'Dsc_Let_7_Div_Len',\n",
    "          'Dsc_Let_8_Div_Len', 'Dsc_Let_9_Div_Len', 'Dsc_Let_10_Div_Len']\n",
    "features = df.drop(columns=ignore, axis=1).keys()\n",
    "features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Create list of features\n",
    "# ignore = ['Password', 'SHA_1', 'Total']\n",
    "# features = df.drop(columns=ignore, axis=1).keys()\n",
    "# features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# # Function to create feature pplots\n",
    "# def pplot_features(df, features):\n",
    "#     pp_rows = [features[i:i+4] for i in range(0, len(features), 4)]\n",
    "#     for row in pp_rows:\n",
    "#         pp = sns.pairplot(data=df, y_vars=['Total'], x_vars=row, kind='reg', height=3)\n",
    "#     return None\n",
    "\n",
    "# # Create feature pplots for training df\n",
    "# pplot_features(df, features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/eric/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:2: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  \n",
      "/Users/eric/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:3: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "# Filters for password length and composition\n",
    "let_dig_spc = (df[df['Only_Let']==0][df['Only_Dig']==0][df['Only_Spc']==0][df['Let_And_Dig']==0]\n",
    "               [df['Let_And_Spc']==0][df['Dig_And_Spc']==0])\n",
    "\n",
    "filt_letdig = df['Let_And_Dig'] == 1\n",
    "filt_letspc = df['Let_And_Spc'] == 1\n",
    "filt_6 = df['Length'] == 6\n",
    "filt_7 = df['Length'] == 7\n",
    "filt_8 = df['Length'] == 8\n",
    "filt_9 = df['Length'] == 9\n",
    "filt_10 = df['Length'] == 10\n",
    "filt_11 = df['Length'] == 11\n",
    "filt_12 = df['Length'] == 12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Letter and digit passwords by length\n",
    "# print(df[filt_letdig][filt_8]['Total'].mean(), df[filt_letdig][filt_9]['Total'].mean(),\n",
    "#       df[filt_letdig][filt_10]['Total'].mean(), df[filt_letdig][filt_11]['Total'].mean(),\n",
    "#       df[filt_letdig][filt_12]['Total'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Letter and special character passwords by length\n",
    "# print(df[filt_letspc][filt_8]['Total'].mean(), df[filt_letspc][filt_9]['Total'].mean(),\n",
    "#       df[filt_letspc][filt_10]['Total'].mean(), df[filt_letspc][filt_11]['Total'].mean(),\n",
    "#       df[filt_letspc][filt_12]['Total'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Letter, digit and special character passwords by length\n",
    "# print(let_dig_spc[filt_8]['Total'].mean(), let_dig_spc[filt_9]['Total'].mean(),\n",
    "#       let_dig_spc[filt_10]['Total'].mean(), let_dig_spc[filt_11]['Total'].mean(),\n",
    "#       let_dig_spc[filt_12]['Total'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Filters for repeated characters\n",
    "# filt_r2 = df['Rep_2'] = 1\n",
    "# filt_r3 = df['Rep_3'] = 1\n",
    "# filt_r4 = df['Rep_4'] = 1\n",
    "# filt_r5 = df['Rep_5'] = 1\n",
    "# filt_r6 = df['Rep_6'] = 1\n",
    "# filt_r7 = df['Rep_7'] = 1\n",
    "# filt_r8 = df['Rep_8'] = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test/Train Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fucntion to split data into test and train data\n",
    "def tt_split(df):\n",
    "    ignore = ['Password', 'SHA_1', 'Total']\n",
    "    Y = df['Total']\n",
    "    for col in ignore:\n",
    "        df = df.drop(columns=col, axis=1)\n",
    "    x_train, x_test, y_train, y_test = train_test_split(df, Y, random_state=22,test_size=0.2)\n",
    "    return x_train, x_test, y_train, y_test\n",
    "\n",
    "# Split training data into test and train data\n",
    "x_train, x_test, y_train, y_test = tt_split(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simple Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/eric/opt/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:2495: FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.\n",
      "  return ptp(axis=axis, out=out, **kwargs)\n",
      "/Users/eric/opt/anaconda3/lib/python3.7/site-packages/statsmodels/base/model.py:1294: RuntimeWarning: invalid value encountered in true_divide\n",
      "  return self.params / self.bse\n",
      "/Users/eric/opt/anaconda3/lib/python3.7/site-packages/scipy/stats/_distn_infrastructure.py:903: RuntimeWarning: invalid value encountered in greater\n",
      "  return (a < x) & (x < b)\n",
      "/Users/eric/opt/anaconda3/lib/python3.7/site-packages/scipy/stats/_distn_infrastructure.py:903: RuntimeWarning: invalid value encountered in less\n",
      "  return (a < x) & (x < b)\n",
      "/Users/eric/opt/anaconda3/lib/python3.7/site-packages/scipy/stats/_distn_infrastructure.py:1912: RuntimeWarning: invalid value encountered in less_equal\n",
      "  cond2 = cond0 & (x <= _a)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>Total</td>      <th>  R-squared:         </th>  <td>   0.050</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.048</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>   36.71</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 08 May 2020</td> <th>  Prob (F-statistic):</th>   <td>  0.00</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>12:36:31</td>     <th>  Log-Likelihood:    </th> <td>-5.5277e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td> 61264</td>      <th>  AIC:               </th>  <td>1.106e+06</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td> 61176</td>      <th>  BIC:               </th>  <td>1.107e+06</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    87</td>      <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "           <td></td>              <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>               <td>-6473.0438</td> <td> 3855.196</td> <td>   -1.679</td> <td> 0.093</td> <td> -1.4e+04</td> <td> 1083.150</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Length</th>              <td> 1134.7225</td> <td>  855.707</td> <td>    1.326</td> <td> 0.185</td> <td> -542.466</td> <td> 2811.911</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Length_Sq</th>           <td>  -20.0238</td> <td>   60.304</td> <td>   -0.332</td> <td> 0.740</td> <td> -138.219</td> <td>   98.172</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Length_Cb</th>           <td>   -0.7011</td> <td>    1.546</td> <td>   -0.454</td> <td> 0.650</td> <td>   -3.731</td> <td>    2.328</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Length_Pp</th>           <td>   56.7361</td> <td>   42.785</td> <td>    1.326</td> <td> 0.185</td> <td>  -27.123</td> <td>  140.596</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Only_Let</th>            <td>-3300.8556</td> <td> 1524.141</td> <td>   -2.166</td> <td> 0.030</td> <td>-6288.176</td> <td> -313.535</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Only_Dig</th>            <td>-5925.5984</td> <td> 1529.505</td> <td>   -3.874</td> <td> 0.000</td> <td>-8923.433</td> <td>-2927.764</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Only_Spc</th>            <td> 8710.0959</td> <td> 2.22e+04</td> <td>    0.392</td> <td> 0.695</td> <td>-3.49e+04</td> <td> 5.23e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Let_And_Dig</th>         <td>-3853.7225</td> <td> 1526.656</td> <td>   -2.524</td> <td> 0.012</td> <td>-6845.973</td> <td> -861.472</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Let_And_Spc</th>         <td>-4205.4798</td> <td> 3183.787</td> <td>   -1.321</td> <td> 0.187</td> <td>-1.04e+04</td> <td> 2034.752</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dig_And_Spc</th>         <td>-1164.7212</td> <td> 4711.349</td> <td>   -0.247</td> <td> 0.805</td> <td>-1.04e+04</td> <td> 8069.536</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date</th>                <td>-1.169e-09</td> <td> 8.91e-10</td> <td>   -1.313</td> <td> 0.189</td> <td>-2.91e-09</td> <td> 5.76e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Year</th>                <td> -683.4197</td> <td>  862.044</td> <td>   -0.793</td> <td> 0.428</td> <td>-2373.029</td> <td> 1006.190</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Rep_2</th>               <td>  539.1346</td> <td>  128.382</td> <td>    4.199</td> <td> 0.000</td> <td>  287.506</td> <td>  790.763</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Rep_3</th>               <td>-1351.3438</td> <td>  326.668</td> <td>   -4.137</td> <td> 0.000</td> <td>-1991.615</td> <td> -711.073</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Rep_4</th>               <td> 1682.4835</td> <td>  809.076</td> <td>    2.080</td> <td> 0.038</td> <td>   96.692</td> <td> 3268.275</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Rep_5</th>               <td>-4013.1066</td> <td> 1259.057</td> <td>   -3.187</td> <td> 0.001</td> <td>-6480.862</td> <td>-1545.351</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Rep_6</th>               <td> -1.55e+04</td> <td> 2440.888</td> <td>   -6.351</td> <td> 0.000</td> <td>-2.03e+04</td> <td>-1.07e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Rep_7</th>               <td>-9226.8468</td> <td> 5623.290</td> <td>   -1.641</td> <td> 0.101</td> <td>-2.02e+04</td> <td> 1794.816</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Rep_8</th>               <td>-2.071e+04</td> <td> 1.03e+04</td> <td>   -2.019</td> <td> 0.043</td> <td>-4.08e+04</td> <td> -608.363</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Rep_9</th>               <td>  174.3998</td> <td>  468.320</td> <td>    0.372</td> <td> 0.710</td> <td> -743.509</td> <td> 1092.309</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Rep_10</th>              <td> 2679.0611</td> <td> 3114.904</td> <td>    0.860</td> <td> 0.390</td> <td>-3426.160</td> <td> 8784.282</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Asc_Dig_2</th>           <td> -449.7850</td> <td>  168.283</td> <td>   -2.673</td> <td> 0.008</td> <td> -779.620</td> <td> -119.950</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Asc_Dig_3</th>           <td> 1042.2164</td> <td>  387.435</td> <td>    2.690</td> <td> 0.007</td> <td>  282.843</td> <td> 1801.590</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Asc_Dig_4</th>           <td>  530.8278</td> <td>  982.642</td> <td>    0.540</td> <td> 0.589</td> <td>-1395.154</td> <td> 2456.810</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Asc_Dig_5</th>           <td>-3506.3226</td> <td> 1475.136</td> <td>   -2.377</td> <td> 0.017</td> <td>-6397.594</td> <td> -615.052</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Asc_Dig_6</th>           <td>-1117.4879</td> <td> 2415.345</td> <td>   -0.463</td> <td> 0.644</td> <td>-5851.570</td> <td> 3616.594</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Asc_Dig_7</th>           <td>-3589.5761</td> <td> 3723.051</td> <td>   -0.964</td> <td> 0.335</td> <td>-1.09e+04</td> <td> 3707.613</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Asc_Dig_8</th>           <td> -888.7591</td> <td> 4416.700</td> <td>   -0.201</td> <td> 0.841</td> <td>-9545.504</td> <td> 7767.986</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Asc_Dig_9</th>           <td> 1.119e+04</td> <td> 3908.545</td> <td>    2.863</td> <td> 0.004</td> <td> 3527.695</td> <td> 1.88e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Asc_Dig_10</th>          <td> 1.062e+04</td> <td> 5606.483</td> <td>    1.894</td> <td> 0.058</td> <td> -368.648</td> <td> 2.16e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dsc_Dig_2</th>           <td> 1366.4462</td> <td>  171.636</td> <td>    7.961</td> <td> 0.000</td> <td> 1030.040</td> <td> 1702.852</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dsc_Dig_3</th>           <td> 1250.0683</td> <td>  389.982</td> <td>    3.205</td> <td> 0.001</td> <td>  485.703</td> <td> 2014.434</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dsc_Dig_4</th>           <td>-3949.9430</td> <td> 2314.360</td> <td>   -1.707</td> <td> 0.088</td> <td>-8486.095</td> <td>  586.209</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dsc_Dig_5</th>           <td> 1546.2689</td> <td> 3618.797</td> <td>    0.427</td> <td> 0.669</td> <td>-5546.584</td> <td> 8639.122</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dsc_Dig_6</th>           <td>-5178.2419</td> <td> 4900.211</td> <td>   -1.057</td> <td> 0.291</td> <td>-1.48e+04</td> <td> 4426.186</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dsc_Dig_7</th>           <td> 1.612e+04</td> <td> 6518.279</td> <td>    2.472</td> <td> 0.013</td> <td> 3339.740</td> <td> 2.89e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dsc_Dig_8</th>           <td> -3.24e+04</td> <td> 1.38e+04</td> <td>   -2.356</td> <td> 0.018</td> <td>-5.94e+04</td> <td>-5443.260</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dsc_Dig_9</th>           <td> 2.975e+04</td> <td> 1.34e+04</td> <td>    2.229</td> <td> 0.026</td> <td> 3586.668</td> <td> 5.59e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dsc_Dig_10</th>          <td> 3.768e-10</td> <td> 3.92e-10</td> <td>    0.962</td> <td> 0.336</td> <td>-3.91e-10</td> <td> 1.14e-09</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Asc_Let_2</th>           <td>  246.1342</td> <td>  186.348</td> <td>    1.321</td> <td> 0.187</td> <td> -119.109</td> <td>  611.377</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Asc_Let_3</th>           <td>  648.2944</td> <td>  784.085</td> <td>    0.827</td> <td> 0.408</td> <td> -888.514</td> <td> 2185.102</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Asc_Let_4</th>           <td> 2.339e+04</td> <td> 1.14e+04</td> <td>    2.051</td> <td> 0.040</td> <td> 1037.070</td> <td> 4.57e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Asc_Let_5</th>           <td>-1.066e+04</td> <td> 6123.363</td> <td>   -1.741</td> <td> 0.082</td> <td>-2.27e+04</td> <td> 1341.785</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Asc_Let_6</th>           <td>-5650.8380</td> <td> 7278.164</td> <td>   -0.776</td> <td> 0.438</td> <td>-1.99e+04</td> <td> 8614.383</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Asc_Let_7</th>           <td>-5282.6445</td> <td> 8319.518</td> <td>   -0.635</td> <td> 0.525</td> <td>-2.16e+04</td> <td>  1.1e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Asc_Let_8</th>           <td>-5282.6445</td> <td> 8319.518</td> <td>   -0.635</td> <td> 0.525</td> <td>-2.16e+04</td> <td>  1.1e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Asc_Let_9</th>           <td>-5282.6445</td> <td> 8319.518</td> <td>   -0.635</td> <td> 0.525</td> <td>-2.16e+04</td> <td>  1.1e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Asc_Let_10</th>          <td>-5282.6445</td> <td> 8319.518</td> <td>   -0.635</td> <td> 0.525</td> <td>-2.16e+04</td> <td>  1.1e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dsc_Let_2</th>           <td> -111.3688</td> <td>  192.894</td> <td>   -0.577</td> <td> 0.564</td> <td> -489.441</td> <td>  266.703</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dsc_Let_3</th>           <td>-2992.5642</td> <td>  875.281</td> <td>   -3.419</td> <td> 0.001</td> <td>-4708.118</td> <td>-1277.010</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dsc_Let_4</th>           <td> -278.6627</td> <td>  648.672</td> <td>   -0.430</td> <td> 0.667</td> <td>-1550.062</td> <td>  992.736</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dsc_Let_5</th>           <td> -278.6627</td> <td>  648.672</td> <td>   -0.430</td> <td> 0.667</td> <td>-1550.062</td> <td>  992.736</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dsc_Let_6</th>           <td> -278.6627</td> <td>  648.672</td> <td>   -0.430</td> <td> 0.667</td> <td>-1550.062</td> <td>  992.736</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dsc_Let_7</th>           <td> -603.7018</td> <td> 2774.044</td> <td>   -0.218</td> <td> 0.828</td> <td>-6040.836</td> <td> 4833.433</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dsc_Let_8</th>           <td>  -57.0941</td> <td> 2790.814</td> <td>   -0.020</td> <td> 0.984</td> <td>-5527.097</td> <td> 5412.908</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dsc_Let_9</th>           <td> -248.6472</td> <td> 2801.612</td> <td>   -0.089</td> <td> 0.929</td> <td>-5739.815</td> <td> 5242.521</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dsc_Let_10</th>          <td> 2.334e-10</td> <td> 9.96e-10</td> <td>    0.234</td> <td> 0.815</td> <td>-1.72e-09</td> <td> 2.19e-09</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Only_Let_Div_Len</th>    <td> 3.803e+04</td> <td> 1.19e+04</td> <td>    3.185</td> <td> 0.001</td> <td> 1.46e+04</td> <td> 6.14e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Only_Dig_Div_Len</th>    <td> 5.326e+04</td> <td>  1.2e+04</td> <td>    4.454</td> <td> 0.000</td> <td> 2.98e+04</td> <td> 7.67e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Only_Spc_Div_Len</th>    <td>-4.835e+04</td> <td> 1.66e+05</td> <td>   -0.292</td> <td> 0.770</td> <td>-3.73e+05</td> <td> 2.76e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Let_And_Dig_Div_Len</th> <td> 4.598e+04</td> <td>  1.2e+04</td> <td>    3.846</td> <td> 0.000</td> <td> 2.26e+04</td> <td> 6.94e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Let_And_Spc_Div_Len</th> <td> 4.751e+04</td> <td> 2.25e+04</td> <td>    2.115</td> <td> 0.034</td> <td> 3475.885</td> <td> 9.15e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dig_And_Spc_Div_Len</th> <td> 2.737e+04</td> <td> 3.42e+04</td> <td>    0.800</td> <td> 0.424</td> <td>-3.97e+04</td> <td> 9.44e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date_Div_Len</th>        <td>-2.986e-11</td> <td> 9.69e-11</td> <td>   -0.308</td> <td> 0.758</td> <td> -2.2e-10</td> <td>  1.6e-10</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Year_Div_Len</th>        <td>-4360.1610</td> <td> 6957.545</td> <td>   -0.627</td> <td> 0.531</td> <td> -1.8e+04</td> <td> 9276.646</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Rep_2_Div_Len</th>       <td>-3682.5903</td> <td>  890.683</td> <td>   -4.135</td> <td> 0.000</td> <td>-5428.331</td> <td>-1936.850</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Rep_3_Div_Len</th>       <td> 8275.1373</td> <td> 2398.212</td> <td>    3.451</td> <td> 0.001</td> <td> 3574.635</td> <td>  1.3e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Rep_4_Div_Len</th>       <td>-1.616e+04</td> <td> 5362.411</td> <td>   -3.013</td> <td> 0.003</td> <td>-2.67e+04</td> <td>-5649.197</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Rep_5_Div_Len</th>       <td> 2.436e+04</td> <td> 8365.135</td> <td>    2.912</td> <td> 0.004</td> <td> 7964.895</td> <td> 4.08e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Rep_6_Div_Len</th>       <td> 1.104e+05</td> <td>  1.8e+04</td> <td>    6.138</td> <td> 0.000</td> <td> 7.51e+04</td> <td> 1.46e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Rep_7_Div_Len</th>       <td> 8.726e+04</td> <td> 4.18e+04</td> <td>    2.088</td> <td> 0.037</td> <td> 5338.311</td> <td> 1.69e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Rep_8_Div_Len</th>       <td> 1.986e+05</td> <td> 8.44e+04</td> <td>    2.351</td> <td> 0.019</td> <td> 3.31e+04</td> <td> 3.64e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Rep_9_Div_Len</th>       <td>   19.3778</td> <td>   52.036</td> <td>    0.372</td> <td> 0.710</td> <td>  -82.612</td> <td>  121.368</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Rep_10_Div_Len</th>      <td>-7531.7588</td> <td> 3.35e+04</td> <td>   -0.225</td> <td> 0.822</td> <td>-7.32e+04</td> <td> 5.82e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Asc_Dig_2_Div_Len</th>   <td> 3315.2162</td> <td> 1164.741</td> <td>    2.846</td> <td> 0.004</td> <td> 1032.320</td> <td> 5598.113</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Asc_Dig_3_Div_Len</th>   <td>-1943.9501</td> <td> 2733.232</td> <td>   -0.711</td> <td> 0.477</td> <td>-7301.093</td> <td> 3413.193</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Asc_Dig_4_Div_Len</th>   <td>-1973.6460</td> <td> 7169.243</td> <td>   -0.275</td> <td> 0.783</td> <td> -1.6e+04</td> <td> 1.21e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Asc_Dig_5_Div_Len</th>   <td> 2.756e+04</td> <td> 1.11e+04</td> <td>    2.494</td> <td> 0.013</td> <td> 5898.560</td> <td> 4.92e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Asc_Dig_6_Div_Len</th>   <td> 1.654e+04</td> <td> 1.97e+04</td> <td>    0.839</td> <td> 0.402</td> <td>-2.21e+04</td> <td> 5.52e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Asc_Dig_7_Div_Len</th>   <td>  3.92e+04</td> <td> 3.15e+04</td> <td>    1.246</td> <td> 0.213</td> <td>-2.25e+04</td> <td> 1.01e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Asc_Dig_8_Div_Len</th>   <td> 1.577e+04</td> <td> 4.03e+04</td> <td>    0.391</td> <td> 0.696</td> <td>-6.33e+04</td> <td> 9.48e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Asc_Dig_9_Div_Len</th>   <td>-1.072e+05</td> <td> 4.04e+04</td> <td>   -2.655</td> <td> 0.008</td> <td>-1.86e+05</td> <td>-2.81e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Asc_Dig_10_Div_Len</th>  <td>-8.048e+04</td> <td> 7.14e+04</td> <td>   -1.128</td> <td> 0.259</td> <td> -2.2e+05</td> <td> 5.94e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dsc_Dig_2_Div_Len</th>   <td>-7445.5311</td> <td> 1194.606</td> <td>   -6.233</td> <td> 0.000</td> <td>-9786.962</td> <td>-5104.100</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dsc_Dig_3_Div_Len</th>   <td>-5813.7219</td> <td> 2764.660</td> <td>   -2.103</td> <td> 0.035</td> <td>-1.12e+04</td> <td> -394.980</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dsc_Dig_4_Div_Len</th>   <td> 3.111e+04</td> <td> 1.84e+04</td> <td>    1.692</td> <td> 0.091</td> <td>-4937.688</td> <td> 6.72e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dsc_Dig_5_Div_Len</th>   <td>-4600.4869</td> <td> 2.61e+04</td> <td>   -0.177</td> <td> 0.860</td> <td>-5.57e+04</td> <td> 4.65e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dsc_Dig_6_Div_Len</th>   <td> 3.303e+04</td> <td> 3.55e+04</td> <td>    0.931</td> <td> 0.352</td> <td>-3.65e+04</td> <td> 1.03e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dsc_Dig_7_Div_Len</th>   <td> -1.21e+05</td> <td> 5.18e+04</td> <td>   -2.336</td> <td> 0.020</td> <td>-2.23e+05</td> <td>-1.95e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dsc_Dig_8_Div_Len</th>   <td> 2.718e+05</td> <td> 1.21e+05</td> <td>    2.247</td> <td> 0.025</td> <td> 3.47e+04</td> <td> 5.09e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dsc_Dig_9_Div_Len</th>   <td>-2.667e+05</td> <td> 1.21e+05</td> <td>   -2.196</td> <td> 0.028</td> <td>-5.05e+05</td> <td>-2.87e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dsc_Dig_10_Div_Len</th>  <td>         0</td> <td>        0</td> <td>      nan</td> <td>   nan</td> <td>        0</td> <td>        0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Asc_Let_2_Div_Len</th>   <td>-1782.4925</td> <td> 1319.901</td> <td>   -1.350</td> <td> 0.177</td> <td>-4369.503</td> <td>  804.518</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Asc_Let_3_Div_Len</th>   <td>-5949.7981</td> <td> 5670.207</td> <td>   -1.049</td> <td> 0.294</td> <td>-1.71e+04</td> <td> 5163.823</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Asc_Let_4_Div_Len</th>   <td>-1.639e+05</td> <td> 8.97e+04</td> <td>   -1.827</td> <td> 0.068</td> <td> -3.4e+05</td> <td>  1.2e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Asc_Let_5_Div_Len</th>   <td> 5.701e+04</td> <td>    5e+04</td> <td>    1.141</td> <td> 0.254</td> <td>-4.09e+04</td> <td> 1.55e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Asc_Let_6_Div_Len</th>   <td> 5.772e+04</td> <td> 4.98e+04</td> <td>    1.160</td> <td> 0.246</td> <td>-3.98e+04</td> <td> 1.55e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Asc_Let_7_Div_Len</th>   <td>  5.38e+04</td> <td> 9.43e+04</td> <td>    0.570</td> <td> 0.568</td> <td>-1.31e+05</td> <td> 2.39e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Asc_Let_8_Div_Len</th>   <td>  5.38e+04</td> <td> 9.43e+04</td> <td>    0.570</td> <td> 0.568</td> <td>-1.31e+05</td> <td> 2.39e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Asc_Let_9_Div_Len</th>   <td>  5.38e+04</td> <td> 9.43e+04</td> <td>    0.570</td> <td> 0.568</td> <td>-1.31e+05</td> <td> 2.39e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Asc_Let_10_Div_Len</th>  <td>  5.38e+04</td> <td> 9.43e+04</td> <td>    0.570</td> <td> 0.568</td> <td>-1.31e+05</td> <td> 2.39e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dsc_Let_2_Div_Len</th>   <td>  790.8344</td> <td> 1355.150</td> <td>    0.584</td> <td> 0.560</td> <td>-1865.264</td> <td> 3446.933</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dsc_Let_3_Div_Len</th>   <td> 2.077e+04</td> <td> 6396.135</td> <td>    3.248</td> <td> 0.001</td> <td> 8236.245</td> <td> 3.33e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dsc_Let_4_Div_Len</th>   <td>  -27.5970</td> <td>  157.824</td> <td>   -0.175</td> <td> 0.861</td> <td> -336.932</td> <td>  281.738</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dsc_Let_5_Div_Len</th>   <td>  -27.5970</td> <td>  157.824</td> <td>   -0.175</td> <td> 0.861</td> <td> -336.932</td> <td>  281.738</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dsc_Let_6_Div_Len</th>   <td>  -27.5970</td> <td>  157.824</td> <td>   -0.175</td> <td> 0.861</td> <td> -336.932</td> <td>  281.738</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dsc_Let_7_Div_Len</th>   <td>  -81.7701</td> <td>  422.777</td> <td>   -0.193</td> <td> 0.847</td> <td> -910.414</td> <td>  746.874</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dsc_Let_8_Div_Len</th>   <td>   -3.6833</td> <td>  369.766</td> <td>   -0.010</td> <td> 0.992</td> <td> -728.426</td> <td>  721.059</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dsc_Let_9_Div_Len</th>   <td>  -27.6275</td> <td>  311.290</td> <td>   -0.089</td> <td> 0.929</td> <td> -637.757</td> <td>  582.502</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dsc_Let_10_Div_Len</th>  <td>         0</td> <td>        0</td> <td>      nan</td> <td>   nan</td> <td>        0</td> <td>        0</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>11405.909</td> <th>  Durbin-Watson:     </th> <td>   2.002</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>   <th>  Jarque-Bera (JB):  </th> <td>20321.225</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 1.194</td>   <th>  Prob(JB):          </th> <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td> 4.502</td>   <th>  Cond. No.          </th> <td>1.16e+16</td> \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The smallest eigenvalue is 9.73e-23. This might indicate that there are<br/>strong multicollinearity problems or that the design matrix is singular."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  Total   R-squared:                       0.050\n",
       "Model:                            OLS   Adj. R-squared:                  0.048\n",
       "Method:                 Least Squares   F-statistic:                     36.71\n",
       "Date:                Fri, 08 May 2020   Prob (F-statistic):               0.00\n",
       "Time:                        12:36:31   Log-Likelihood:            -5.5277e+05\n",
       "No. Observations:               61264   AIC:                         1.106e+06\n",
       "Df Residuals:                   61176   BIC:                         1.107e+06\n",
       "Df Model:                          87                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=======================================================================================\n",
       "                          coef    std err          t      P>|t|      [0.025      0.975]\n",
       "---------------------------------------------------------------------------------------\n",
       "const               -6473.0438   3855.196     -1.679      0.093    -1.4e+04    1083.150\n",
       "Length               1134.7225    855.707      1.326      0.185    -542.466    2811.911\n",
       "Length_Sq             -20.0238     60.304     -0.332      0.740    -138.219      98.172\n",
       "Length_Cb              -0.7011      1.546     -0.454      0.650      -3.731       2.328\n",
       "Length_Pp              56.7361     42.785      1.326      0.185     -27.123     140.596\n",
       "Only_Let            -3300.8556   1524.141     -2.166      0.030   -6288.176    -313.535\n",
       "Only_Dig            -5925.5984   1529.505     -3.874      0.000   -8923.433   -2927.764\n",
       "Only_Spc             8710.0959   2.22e+04      0.392      0.695   -3.49e+04    5.23e+04\n",
       "Let_And_Dig         -3853.7225   1526.656     -2.524      0.012   -6845.973    -861.472\n",
       "Let_And_Spc         -4205.4798   3183.787     -1.321      0.187   -1.04e+04    2034.752\n",
       "Dig_And_Spc         -1164.7212   4711.349     -0.247      0.805   -1.04e+04    8069.536\n",
       "Date                -1.169e-09   8.91e-10     -1.313      0.189   -2.91e-09    5.76e-10\n",
       "Year                 -683.4197    862.044     -0.793      0.428   -2373.029    1006.190\n",
       "Rep_2                 539.1346    128.382      4.199      0.000     287.506     790.763\n",
       "Rep_3               -1351.3438    326.668     -4.137      0.000   -1991.615    -711.073\n",
       "Rep_4                1682.4835    809.076      2.080      0.038      96.692    3268.275\n",
       "Rep_5               -4013.1066   1259.057     -3.187      0.001   -6480.862   -1545.351\n",
       "Rep_6                -1.55e+04   2440.888     -6.351      0.000   -2.03e+04   -1.07e+04\n",
       "Rep_7               -9226.8468   5623.290     -1.641      0.101   -2.02e+04    1794.816\n",
       "Rep_8               -2.071e+04   1.03e+04     -2.019      0.043   -4.08e+04    -608.363\n",
       "Rep_9                 174.3998    468.320      0.372      0.710    -743.509    1092.309\n",
       "Rep_10               2679.0611   3114.904      0.860      0.390   -3426.160    8784.282\n",
       "Asc_Dig_2            -449.7850    168.283     -2.673      0.008    -779.620    -119.950\n",
       "Asc_Dig_3            1042.2164    387.435      2.690      0.007     282.843    1801.590\n",
       "Asc_Dig_4             530.8278    982.642      0.540      0.589   -1395.154    2456.810\n",
       "Asc_Dig_5           -3506.3226   1475.136     -2.377      0.017   -6397.594    -615.052\n",
       "Asc_Dig_6           -1117.4879   2415.345     -0.463      0.644   -5851.570    3616.594\n",
       "Asc_Dig_7           -3589.5761   3723.051     -0.964      0.335   -1.09e+04    3707.613\n",
       "Asc_Dig_8            -888.7591   4416.700     -0.201      0.841   -9545.504    7767.986\n",
       "Asc_Dig_9            1.119e+04   3908.545      2.863      0.004    3527.695    1.88e+04\n",
       "Asc_Dig_10           1.062e+04   5606.483      1.894      0.058    -368.648    2.16e+04\n",
       "Dsc_Dig_2            1366.4462    171.636      7.961      0.000    1030.040    1702.852\n",
       "Dsc_Dig_3            1250.0683    389.982      3.205      0.001     485.703    2014.434\n",
       "Dsc_Dig_4           -3949.9430   2314.360     -1.707      0.088   -8486.095     586.209\n",
       "Dsc_Dig_5            1546.2689   3618.797      0.427      0.669   -5546.584    8639.122\n",
       "Dsc_Dig_6           -5178.2419   4900.211     -1.057      0.291   -1.48e+04    4426.186\n",
       "Dsc_Dig_7            1.612e+04   6518.279      2.472      0.013    3339.740    2.89e+04\n",
       "Dsc_Dig_8            -3.24e+04   1.38e+04     -2.356      0.018   -5.94e+04   -5443.260\n",
       "Dsc_Dig_9            2.975e+04   1.34e+04      2.229      0.026    3586.668    5.59e+04\n",
       "Dsc_Dig_10           3.768e-10   3.92e-10      0.962      0.336   -3.91e-10    1.14e-09\n",
       "Asc_Let_2             246.1342    186.348      1.321      0.187    -119.109     611.377\n",
       "Asc_Let_3             648.2944    784.085      0.827      0.408    -888.514    2185.102\n",
       "Asc_Let_4            2.339e+04   1.14e+04      2.051      0.040    1037.070    4.57e+04\n",
       "Asc_Let_5           -1.066e+04   6123.363     -1.741      0.082   -2.27e+04    1341.785\n",
       "Asc_Let_6           -5650.8380   7278.164     -0.776      0.438   -1.99e+04    8614.383\n",
       "Asc_Let_7           -5282.6445   8319.518     -0.635      0.525   -2.16e+04     1.1e+04\n",
       "Asc_Let_8           -5282.6445   8319.518     -0.635      0.525   -2.16e+04     1.1e+04\n",
       "Asc_Let_9           -5282.6445   8319.518     -0.635      0.525   -2.16e+04     1.1e+04\n",
       "Asc_Let_10          -5282.6445   8319.518     -0.635      0.525   -2.16e+04     1.1e+04\n",
       "Dsc_Let_2            -111.3688    192.894     -0.577      0.564    -489.441     266.703\n",
       "Dsc_Let_3           -2992.5642    875.281     -3.419      0.001   -4708.118   -1277.010\n",
       "Dsc_Let_4            -278.6627    648.672     -0.430      0.667   -1550.062     992.736\n",
       "Dsc_Let_5            -278.6627    648.672     -0.430      0.667   -1550.062     992.736\n",
       "Dsc_Let_6            -278.6627    648.672     -0.430      0.667   -1550.062     992.736\n",
       "Dsc_Let_7            -603.7018   2774.044     -0.218      0.828   -6040.836    4833.433\n",
       "Dsc_Let_8             -57.0941   2790.814     -0.020      0.984   -5527.097    5412.908\n",
       "Dsc_Let_9            -248.6472   2801.612     -0.089      0.929   -5739.815    5242.521\n",
       "Dsc_Let_10           2.334e-10   9.96e-10      0.234      0.815   -1.72e-09    2.19e-09\n",
       "Only_Let_Div_Len     3.803e+04   1.19e+04      3.185      0.001    1.46e+04    6.14e+04\n",
       "Only_Dig_Div_Len     5.326e+04    1.2e+04      4.454      0.000    2.98e+04    7.67e+04\n",
       "Only_Spc_Div_Len    -4.835e+04   1.66e+05     -0.292      0.770   -3.73e+05    2.76e+05\n",
       "Let_And_Dig_Div_Len  4.598e+04    1.2e+04      3.846      0.000    2.26e+04    6.94e+04\n",
       "Let_And_Spc_Div_Len  4.751e+04   2.25e+04      2.115      0.034    3475.885    9.15e+04\n",
       "Dig_And_Spc_Div_Len  2.737e+04   3.42e+04      0.800      0.424   -3.97e+04    9.44e+04\n",
       "Date_Div_Len        -2.986e-11   9.69e-11     -0.308      0.758    -2.2e-10     1.6e-10\n",
       "Year_Div_Len        -4360.1610   6957.545     -0.627      0.531    -1.8e+04    9276.646\n",
       "Rep_2_Div_Len       -3682.5903    890.683     -4.135      0.000   -5428.331   -1936.850\n",
       "Rep_3_Div_Len        8275.1373   2398.212      3.451      0.001    3574.635     1.3e+04\n",
       "Rep_4_Div_Len       -1.616e+04   5362.411     -3.013      0.003   -2.67e+04   -5649.197\n",
       "Rep_5_Div_Len        2.436e+04   8365.135      2.912      0.004    7964.895    4.08e+04\n",
       "Rep_6_Div_Len        1.104e+05    1.8e+04      6.138      0.000    7.51e+04    1.46e+05\n",
       "Rep_7_Div_Len        8.726e+04   4.18e+04      2.088      0.037    5338.311    1.69e+05\n",
       "Rep_8_Div_Len        1.986e+05   8.44e+04      2.351      0.019    3.31e+04    3.64e+05\n",
       "Rep_9_Div_Len          19.3778     52.036      0.372      0.710     -82.612     121.368\n",
       "Rep_10_Div_Len      -7531.7588   3.35e+04     -0.225      0.822   -7.32e+04    5.82e+04\n",
       "Asc_Dig_2_Div_Len    3315.2162   1164.741      2.846      0.004    1032.320    5598.113\n",
       "Asc_Dig_3_Div_Len   -1943.9501   2733.232     -0.711      0.477   -7301.093    3413.193\n",
       "Asc_Dig_4_Div_Len   -1973.6460   7169.243     -0.275      0.783    -1.6e+04    1.21e+04\n",
       "Asc_Dig_5_Div_Len    2.756e+04   1.11e+04      2.494      0.013    5898.560    4.92e+04\n",
       "Asc_Dig_6_Div_Len    1.654e+04   1.97e+04      0.839      0.402   -2.21e+04    5.52e+04\n",
       "Asc_Dig_7_Div_Len     3.92e+04   3.15e+04      1.246      0.213   -2.25e+04    1.01e+05\n",
       "Asc_Dig_8_Div_Len    1.577e+04   4.03e+04      0.391      0.696   -6.33e+04    9.48e+04\n",
       "Asc_Dig_9_Div_Len   -1.072e+05   4.04e+04     -2.655      0.008   -1.86e+05   -2.81e+04\n",
       "Asc_Dig_10_Div_Len  -8.048e+04   7.14e+04     -1.128      0.259    -2.2e+05    5.94e+04\n",
       "Dsc_Dig_2_Div_Len   -7445.5311   1194.606     -6.233      0.000   -9786.962   -5104.100\n",
       "Dsc_Dig_3_Div_Len   -5813.7219   2764.660     -2.103      0.035   -1.12e+04    -394.980\n",
       "Dsc_Dig_4_Div_Len    3.111e+04   1.84e+04      1.692      0.091   -4937.688    6.72e+04\n",
       "Dsc_Dig_5_Div_Len   -4600.4869   2.61e+04     -0.177      0.860   -5.57e+04    4.65e+04\n",
       "Dsc_Dig_6_Div_Len    3.303e+04   3.55e+04      0.931      0.352   -3.65e+04    1.03e+05\n",
       "Dsc_Dig_7_Div_Len    -1.21e+05   5.18e+04     -2.336      0.020   -2.23e+05   -1.95e+04\n",
       "Dsc_Dig_8_Div_Len    2.718e+05   1.21e+05      2.247      0.025    3.47e+04    5.09e+05\n",
       "Dsc_Dig_9_Div_Len   -2.667e+05   1.21e+05     -2.196      0.028   -5.05e+05   -2.87e+04\n",
       "Dsc_Dig_10_Div_Len           0          0        nan        nan           0           0\n",
       "Asc_Let_2_Div_Len   -1782.4925   1319.901     -1.350      0.177   -4369.503     804.518\n",
       "Asc_Let_3_Div_Len   -5949.7981   5670.207     -1.049      0.294   -1.71e+04    5163.823\n",
       "Asc_Let_4_Div_Len   -1.639e+05   8.97e+04     -1.827      0.068    -3.4e+05     1.2e+04\n",
       "Asc_Let_5_Div_Len    5.701e+04      5e+04      1.141      0.254   -4.09e+04    1.55e+05\n",
       "Asc_Let_6_Div_Len    5.772e+04   4.98e+04      1.160      0.246   -3.98e+04    1.55e+05\n",
       "Asc_Let_7_Div_Len     5.38e+04   9.43e+04      0.570      0.568   -1.31e+05    2.39e+05\n",
       "Asc_Let_8_Div_Len     5.38e+04   9.43e+04      0.570      0.568   -1.31e+05    2.39e+05\n",
       "Asc_Let_9_Div_Len     5.38e+04   9.43e+04      0.570      0.568   -1.31e+05    2.39e+05\n",
       "Asc_Let_10_Div_Len    5.38e+04   9.43e+04      0.570      0.568   -1.31e+05    2.39e+05\n",
       "Dsc_Let_2_Div_Len     790.8344   1355.150      0.584      0.560   -1865.264    3446.933\n",
       "Dsc_Let_3_Div_Len    2.077e+04   6396.135      3.248      0.001    8236.245    3.33e+04\n",
       "Dsc_Let_4_Div_Len     -27.5970    157.824     -0.175      0.861    -336.932     281.738\n",
       "Dsc_Let_5_Div_Len     -27.5970    157.824     -0.175      0.861    -336.932     281.738\n",
       "Dsc_Let_6_Div_Len     -27.5970    157.824     -0.175      0.861    -336.932     281.738\n",
       "Dsc_Let_7_Div_Len     -81.7701    422.777     -0.193      0.847    -910.414     746.874\n",
       "Dsc_Let_8_Div_Len      -3.6833    369.766     -0.010      0.992    -728.426     721.059\n",
       "Dsc_Let_9_Div_Len     -27.6275    311.290     -0.089      0.929    -637.757     582.502\n",
       "Dsc_Let_10_Div_Len           0          0        nan        nan           0           0\n",
       "==============================================================================\n",
       "Omnibus:                    11405.909   Durbin-Watson:                   2.002\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            20321.225\n",
       "Skew:                           1.194   Prob(JB):                         0.00\n",
       "Kurtosis:                       4.502   Cond. No.                     1.16e+16\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The smallest eigenvalue is 9.73e-23. This might indicate that there are\n",
       "strong multicollinearity problems or that the design matrix is singular.\n",
       "\"\"\""
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Simple regression model on training data\n",
    "model = sm.OLS(y_train, sm.add_constant(x_train[[key for key in x_train]])).fit()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lasso Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/eric/opt/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/_coordinate_descent.py:476: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Duality gap: 123494855802.03212, tolerance: 25932767.989275213\n",
      "  positive)\n"
     ]
    }
   ],
   "source": [
    "lasso = Lasso(alpha=0.01, normalize=False)\n",
    "lasso.fit(x_train, y_train)\n",
    "y_train_pred = lasso.predict(x_train)\n",
    "y_test_pred = lasso.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Error: 1540.62263939417\n",
      "Testing Error: 1996.6547874002874\n"
     ]
    }
   ],
   "source": [
    "train_rmse = metrics.mean_absolute_error(y_train, y_train_pred)\n",
    "test_rmse = np.sqrt(metrics.mean_squared_error(y_test, y_test_pred))\n",
    "print('Training Error: '+ str(train_rmse) )\n",
    "print('Testing Error: '+ str(test_rmse) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Rep_4_Div_Len     Length_Pp        Rep_6  Dsc_Dig_2_Div_Len    Asc_Let_5  \\\n",
      "0  -10618.611279 -10120.162009 -7885.888014       -7172.809228 -4284.543259   \n",
      "\n",
      "   Dsc_Dig_3_Div_Len  Rep_2_Div_Len    Asc_Dig_5        Rep_5    Dsc_Let_3  \\\n",
      "0       -3664.645416    -3482.53777 -2694.890261 -2315.608978 -2069.610852   \n",
      "\n",
      "   ...        Rep_8   Asc_Dig_10  Asc_Dig_6_Div_Len  Rep_3_Div_Len  \\\n",
      "0  ...  3414.101387  4158.799882        6317.601145    7360.151879   \n",
      "\n",
      "   Let_And_Dig_Div_Len  Rep_5_Div_Len  Dsc_Let_3_Div_Len  Only_Dig_Div_Len  \\\n",
      "0          8400.747836   12985.320131       13910.867601      15710.094079   \n",
      "\n",
      "   Asc_Dig_5_Div_Len  Rep_6_Div_Len  \n",
      "0       21600.776613   53958.866013  \n",
      "\n",
      "[1 rows x 110 columns]\n"
     ]
    }
   ],
   "source": [
    "lasso_coef = pd.DataFrame(data=lasso.coef_).T\n",
    "lasso_coef.columns = x_train.columns\n",
    "lasso_coef = lasso_coef.T.sort_values(by=0).T\n",
    "\n",
    "print(lasso_coef)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Poisson Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create poisson dataframe\n",
    "pois = df.copy(deep=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data set length = 61217\n",
      "Testing data set length = 15364\n"
     ]
    }
   ],
   "source": [
    "# Create poisson variables\n",
    "pois_ds = pois.index.to_series()\n",
    "pois_mask = np.random.rand(len(df)) < 0.8\n",
    "pois_train = pois[pois_mask]\n",
    "pois_test = pois[~pois_mask]\n",
    "print('Training data set length = '+str(len(pois_train)))\n",
    "print('Testing data set length = '+str(len(pois_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create poisson features\n",
    "ignore = ['Password', 'SHA_1', 'Total', 'Length_Sq', 'Length_Cb',\n",
    "          'Only_Let_Div_Len', 'Only_Dig_Div_Len', 'Only_Spc_Div_Len',\n",
    "          'Let_And_Dig_Div_Len', 'Let_And_Spc_Div_Len', 'Dig_And_Spc_Div_Len',\n",
    "          'Date_Div_Len', 'Year_Div_Len', 'Rep_2_Div_Len', 'Rep_3_Div_Len',\n",
    "          'Rep_4_Div_Len', 'Rep_5_Div_Len', 'Rep_6_Div_Len', 'Rep_7_Div_Len',\n",
    "          'Rep_8_Div_Len', 'Rep_9_Div_Len', 'Rep_10_Div_Len', 'Asc_Dig_2_Div_Len',\n",
    "          'Asc_Dig_3_Div_Len', 'Asc_Dig_4_Div_Len', 'Asc_Dig_5_Div_Len', 'Asc_Dig_6_Div_Len',\n",
    "          'Asc_Dig_7_Div_Len', 'Asc_Dig_8_Div_Len', 'Asc_Dig_9_Div_Len', 'Asc_Dig_10_Div_Len',\n",
    "          'Dsc_Dig_2_Div_Len', 'Dsc_Dig_3_Div_Len', 'Dsc_Dig_4_Div_Len', 'Dsc_Dig_5_Div_Len',\n",
    "          'Dsc_Dig_6_Div_Len', 'Dsc_Dig_7_Div_Len', 'Dsc_Dig_8_Div_Len', 'Dsc_Dig_9_Div_Len',\n",
    "          'Dsc_Dig_10_Div_Len', 'Asc_Let_2_Div_Len', 'Asc_Let_3_Div_Len', 'Asc_Let_4_Div_Len',\n",
    "          'Asc_Let_5_Div_Len', 'Asc_Let_6_Div_Len', 'Asc_Let_7_Div_Len', 'Asc_Let_8_Div_Len',\n",
    "          'Asc_Let_9_Div_Len', 'Asc_Let_10_Div_Len', 'Dsc_Let_2_Div_Len', 'Dsc_Let_3_Div_Len',\n",
    "          'Dsc_Let_4_Div_Len', 'Dsc_Let_5_Div_Len', 'Dsc_Let_6_Div_Len', 'Dsc_Let_7_Div_Len',\n",
    "          'Dsc_Let_8_Div_Len', 'Dsc_Let_9_Div_Len', 'Dsc_Let_10_Div_Len']\n",
    "pois_features = pois.drop(columns=ignore, axis=1).keys()\n",
    "\n",
    "# Create poisson expression\n",
    "pois_expr = 'Total~' + '+'.join([f'{f}' for f in pois_features])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Create poisson features\n",
    "# ignore = ['Password', 'SHA_1', 'Total']\n",
    "# pois_features = pois.drop(columns=ignore, axis=1).keys()\n",
    "\n",
    "# # Create poisson expression\n",
    "# pois_expr = 'Total~' + '+'.join([f'{f}' for f in pois_features])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create poisson test/train split\n",
    "pois_y_train, pois_X_train = dmatrices(pois_expr, pois_train, return_type='dataframe')\n",
    "pois_y_test, pois_X_test = dmatrices(pois_expr, pois_test, return_type='dataframe')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/eric/opt/anaconda3/lib/python3.7/site-packages/statsmodels/base/model.py:1294: RuntimeWarning: invalid value encountered in true_divide\n",
      "  return self.params / self.bse\n",
      "/Users/eric/opt/anaconda3/lib/python3.7/site-packages/scipy/stats/_distn_infrastructure.py:903: RuntimeWarning: invalid value encountered in greater\n",
      "  return (a < x) & (x < b)\n",
      "/Users/eric/opt/anaconda3/lib/python3.7/site-packages/scipy/stats/_distn_infrastructure.py:903: RuntimeWarning: invalid value encountered in less\n",
      "  return (a < x) & (x < b)\n",
      "/Users/eric/opt/anaconda3/lib/python3.7/site-packages/scipy/stats/_distn_infrastructure.py:1912: RuntimeWarning: invalid value encountered in less_equal\n",
      "  cond2 = cond0 & (x <= _a)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Generalized Linear Model Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>Total</td>      <th>  No. Observations:  </th>   <td> 61217</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                  <td>GLM</td>       <th>  Df Residuals:      </th>   <td> 61170</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model Family:</th>         <td>Poisson</td>     <th>  Df Model:          </th>   <td>    46</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Link Function:</th>          <td>log</td>       <th>  Scale:             </th>  <td>  1.0000</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>                <td>IRLS</td>       <th>  Log-Likelihood:    </th> <td>-4.7107e+07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Fri, 08 May 2020</td> <th>  Deviance:          </th> <td>9.3651e+07</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>12:36:51</td>     <th>  Pearson chi2:      </th>  <td>9.78e+07</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Iterations:</th>         <td>10</td>        <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>     <td>nonrobust</td>    <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>          <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>   <td>    7.1023</td> <td>    0.007</td> <td>  997.067</td> <td> 0.000</td> <td>    7.088</td> <td>    7.116</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Length</th>      <td>   -0.0420</td> <td> 7.63e-05</td> <td> -550.688</td> <td> 0.000</td> <td>   -0.042</td> <td>   -0.042</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Length_Pp</th>   <td>   -0.0021</td> <td> 3.81e-06</td> <td> -550.688</td> <td> 0.000</td> <td>   -0.002</td> <td>   -0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Only_Let</th>    <td>    1.0017</td> <td>    0.007</td> <td>  141.185</td> <td> 0.000</td> <td>    0.988</td> <td>    1.016</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Only_Dig</th>    <td>    0.8411</td> <td>    0.007</td> <td>  118.519</td> <td> 0.000</td> <td>    0.827</td> <td>    0.855</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Only_Spc</th>    <td>    1.3390</td> <td>    0.011</td> <td>  116.807</td> <td> 0.000</td> <td>    1.317</td> <td>    1.362</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Let_And_Dig</th> <td>    1.2140</td> <td>    0.007</td> <td>  171.078</td> <td> 0.000</td> <td>    1.200</td> <td>    1.228</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Let_And_Spc</th> <td>    1.2357</td> <td>    0.008</td> <td>  157.120</td> <td> 0.000</td> <td>    1.220</td> <td>    1.251</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dig_And_Spc</th> <td>    1.2090</td> <td>    0.009</td> <td>  136.261</td> <td> 0.000</td> <td>    1.192</td> <td>    1.226</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date</th>        <td>-6.165e-15</td> <td> 7.98e-17</td> <td>  -77.221</td> <td> 0.000</td> <td>-6.32e-15</td> <td>-6.01e-15</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Year</th>        <td>   -0.5182</td> <td>    0.001</td> <td> -408.228</td> <td> 0.000</td> <td>   -0.521</td> <td>   -0.516</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Rep_2</th>       <td>   -0.0015</td> <td>    0.000</td> <td>   -8.370</td> <td> 0.000</td> <td>   -0.002</td> <td>   -0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Rep_3</th>       <td>   -0.1775</td> <td>    0.001</td> <td> -350.257</td> <td> 0.000</td> <td>   -0.179</td> <td>   -0.177</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Rep_4</th>       <td>   -0.2580</td> <td>    0.001</td> <td> -190.627</td> <td> 0.000</td> <td>   -0.261</td> <td>   -0.255</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Rep_5</th>       <td>   -0.1260</td> <td>    0.002</td> <td>  -74.694</td> <td> 0.000</td> <td>   -0.129</td> <td>   -0.123</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Rep_6</th>       <td>   -0.2236</td> <td>    0.002</td> <td>  -90.133</td> <td> 0.000</td> <td>   -0.228</td> <td>   -0.219</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Rep_7</th>       <td>    0.6134</td> <td>    0.003</td> <td>  231.638</td> <td> 0.000</td> <td>    0.608</td> <td>    0.619</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Rep_8</th>       <td>    0.7055</td> <td>    0.003</td> <td>  215.674</td> <td> 0.000</td> <td>    0.699</td> <td>    0.712</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Rep_9</th>       <td>    0.2678</td> <td>    0.004</td> <td>   64.348</td> <td> 0.000</td> <td>    0.260</td> <td>    0.276</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Rep_10</th>      <td>    0.6824</td> <td>    0.003</td> <td>  242.223</td> <td> 0.000</td> <td>    0.677</td> <td>    0.688</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Asc_Dig_2</th>   <td>    0.0338</td> <td>    0.000</td> <td>  133.659</td> <td> 0.000</td> <td>    0.033</td> <td>    0.034</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Asc_Dig_3</th>   <td>    0.2552</td> <td>    0.001</td> <td>  476.653</td> <td> 0.000</td> <td>    0.254</td> <td>    0.256</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Asc_Dig_4</th>   <td>    0.0015</td> <td>    0.001</td> <td>    1.124</td> <td> 0.261</td> <td>   -0.001</td> <td>    0.004</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Asc_Dig_5</th>   <td>   -0.0240</td> <td>    0.002</td> <td>  -11.628</td> <td> 0.000</td> <td>   -0.028</td> <td>   -0.020</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Asc_Dig_6</th>   <td>    0.2404</td> <td>    0.003</td> <td>   86.835</td> <td> 0.000</td> <td>    0.235</td> <td>    0.246</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Asc_Dig_7</th>   <td>    0.1698</td> <td>    0.003</td> <td>   48.581</td> <td> 0.000</td> <td>    0.163</td> <td>    0.177</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Asc_Dig_8</th>   <td>   -0.0945</td> <td>    0.005</td> <td>  -20.984</td> <td> 0.000</td> <td>   -0.103</td> <td>   -0.086</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Asc_Dig_9</th>   <td>    0.2329</td> <td>    0.004</td> <td>   57.337</td> <td> 0.000</td> <td>    0.225</td> <td>    0.241</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Asc_Dig_10</th>  <td>    0.8739</td> <td>    0.010</td> <td>   84.203</td> <td> 0.000</td> <td>    0.854</td> <td>    0.894</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dsc_Dig_2</th>   <td>    0.1021</td> <td>    0.000</td> <td>  390.651</td> <td> 0.000</td> <td>    0.102</td> <td>    0.103</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dsc_Dig_3</th>   <td>    0.1695</td> <td>    0.001</td> <td>  312.408</td> <td> 0.000</td> <td>    0.168</td> <td>    0.171</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dsc_Dig_4</th>   <td>    0.0303</td> <td>    0.003</td> <td>    9.183</td> <td> 0.000</td> <td>    0.024</td> <td>    0.037</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dsc_Dig_5</th>   <td>    0.2782</td> <td>    0.005</td> <td>   54.994</td> <td> 0.000</td> <td>    0.268</td> <td>    0.288</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dsc_Dig_6</th>   <td>   -0.3233</td> <td>    0.008</td> <td>  -40.350</td> <td> 0.000</td> <td>   -0.339</td> <td>   -0.308</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dsc_Dig_7</th>   <td>    0.2599</td> <td>    0.009</td> <td>   28.337</td> <td> 0.000</td> <td>    0.242</td> <td>    0.278</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dsc_Dig_8</th>   <td>   -0.2711</td> <td>    0.011</td> <td>  -24.613</td> <td> 0.000</td> <td>   -0.293</td> <td>   -0.249</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dsc_Dig_9</th>   <td>    0.2544</td> <td>    0.011</td> <td>   22.712</td> <td> 0.000</td> <td>    0.232</td> <td>    0.276</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dsc_Dig_10</th>  <td> 1.014e-16</td> <td> 1.99e-17</td> <td>    5.084</td> <td> 0.000</td> <td> 6.23e-17</td> <td>  1.4e-16</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Asc_Let_2</th>   <td>    0.0201</td> <td>    0.000</td> <td>   74.549</td> <td> 0.000</td> <td>    0.020</td> <td>    0.021</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Asc_Let_3</th>   <td>   -0.0351</td> <td>    0.001</td> <td>  -25.875</td> <td> 0.000</td> <td>   -0.038</td> <td>   -0.032</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Asc_Let_4</th>   <td>    0.6595</td> <td>    0.008</td> <td>   82.302</td> <td> 0.000</td> <td>    0.644</td> <td>    0.675</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Asc_Let_5</th>   <td>   -3.2064</td> <td>    0.047</td> <td>  -68.514</td> <td> 0.000</td> <td>   -3.298</td> <td>   -3.115</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Asc_Let_6</th>   <td>    2.7956</td> <td>    0.047</td> <td>   59.374</td> <td> 0.000</td> <td>    2.703</td> <td>    2.888</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Asc_Let_7</th>   <td>    0.1095</td> <td>    0.005</td> <td>   23.928</td> <td> 0.000</td> <td>    0.101</td> <td>    0.118</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Asc_Let_8</th>   <td>    0.1095</td> <td>    0.005</td> <td>   23.928</td> <td> 0.000</td> <td>    0.101</td> <td>    0.118</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Asc_Let_9</th>   <td>    0.1095</td> <td>    0.005</td> <td>   23.928</td> <td> 0.000</td> <td>    0.101</td> <td>    0.118</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Asc_Let_10</th>  <td>    0.1095</td> <td>    0.005</td> <td>   23.928</td> <td> 0.000</td> <td>    0.101</td> <td>    0.118</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dsc_Let_2</th>   <td>    0.0135</td> <td>    0.000</td> <td>   48.692</td> <td> 0.000</td> <td>    0.013</td> <td>    0.014</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dsc_Let_3</th>   <td>   -0.0146</td> <td>    0.002</td> <td>   -9.170</td> <td> 0.000</td> <td>   -0.018</td> <td>   -0.012</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dsc_Let_4</th>   <td>   -0.0579</td> <td>    0.007</td> <td>   -8.048</td> <td> 0.000</td> <td>   -0.072</td> <td>   -0.044</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dsc_Let_5</th>   <td>   -0.0579</td> <td>    0.007</td> <td>   -8.048</td> <td> 0.000</td> <td>   -0.072</td> <td>   -0.044</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dsc_Let_6</th>   <td>   -0.0579</td> <td>    0.007</td> <td>   -8.048</td> <td> 0.000</td> <td>   -0.072</td> <td>   -0.044</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dsc_Let_7</th>   <td>   -0.8176</td> <td>    0.039</td> <td>  -20.708</td> <td> 0.000</td> <td>   -0.895</td> <td>   -0.740</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dsc_Let_8</th>   <td>   -0.5343</td> <td>    0.055</td> <td>   -9.684</td> <td> 0.000</td> <td>   -0.642</td> <td>   -0.426</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dsc_Let_9</th>   <td>   -1.8037</td> <td>    0.120</td> <td>  -15.086</td> <td> 0.000</td> <td>   -2.038</td> <td>   -1.569</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Dsc_Let_10</th>  <td>         0</td> <td>        0</td> <td>      nan</td> <td>   nan</td> <td>        0</td> <td>        0</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                 Generalized Linear Model Regression Results                  \n",
       "==============================================================================\n",
       "Dep. Variable:                  Total   No. Observations:                61217\n",
       "Model:                            GLM   Df Residuals:                    61170\n",
       "Model Family:                 Poisson   Df Model:                           46\n",
       "Link Function:                    log   Scale:                          1.0000\n",
       "Method:                          IRLS   Log-Likelihood:            -4.7107e+07\n",
       "Date:                Fri, 08 May 2020   Deviance:                   9.3651e+07\n",
       "Time:                        12:36:51   Pearson chi2:                 9.78e+07\n",
       "No. Iterations:                    10                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "===============================================================================\n",
       "                  coef    std err          z      P>|z|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------\n",
       "Intercept       7.1023      0.007    997.067      0.000       7.088       7.116\n",
       "Length         -0.0420   7.63e-05   -550.688      0.000      -0.042      -0.042\n",
       "Length_Pp      -0.0021   3.81e-06   -550.688      0.000      -0.002      -0.002\n",
       "Only_Let        1.0017      0.007    141.185      0.000       0.988       1.016\n",
       "Only_Dig        0.8411      0.007    118.519      0.000       0.827       0.855\n",
       "Only_Spc        1.3390      0.011    116.807      0.000       1.317       1.362\n",
       "Let_And_Dig     1.2140      0.007    171.078      0.000       1.200       1.228\n",
       "Let_And_Spc     1.2357      0.008    157.120      0.000       1.220       1.251\n",
       "Dig_And_Spc     1.2090      0.009    136.261      0.000       1.192       1.226\n",
       "Date        -6.165e-15   7.98e-17    -77.221      0.000   -6.32e-15   -6.01e-15\n",
       "Year           -0.5182      0.001   -408.228      0.000      -0.521      -0.516\n",
       "Rep_2          -0.0015      0.000     -8.370      0.000      -0.002      -0.001\n",
       "Rep_3          -0.1775      0.001   -350.257      0.000      -0.179      -0.177\n",
       "Rep_4          -0.2580      0.001   -190.627      0.000      -0.261      -0.255\n",
       "Rep_5          -0.1260      0.002    -74.694      0.000      -0.129      -0.123\n",
       "Rep_6          -0.2236      0.002    -90.133      0.000      -0.228      -0.219\n",
       "Rep_7           0.6134      0.003    231.638      0.000       0.608       0.619\n",
       "Rep_8           0.7055      0.003    215.674      0.000       0.699       0.712\n",
       "Rep_9           0.2678      0.004     64.348      0.000       0.260       0.276\n",
       "Rep_10          0.6824      0.003    242.223      0.000       0.677       0.688\n",
       "Asc_Dig_2       0.0338      0.000    133.659      0.000       0.033       0.034\n",
       "Asc_Dig_3       0.2552      0.001    476.653      0.000       0.254       0.256\n",
       "Asc_Dig_4       0.0015      0.001      1.124      0.261      -0.001       0.004\n",
       "Asc_Dig_5      -0.0240      0.002    -11.628      0.000      -0.028      -0.020\n",
       "Asc_Dig_6       0.2404      0.003     86.835      0.000       0.235       0.246\n",
       "Asc_Dig_7       0.1698      0.003     48.581      0.000       0.163       0.177\n",
       "Asc_Dig_8      -0.0945      0.005    -20.984      0.000      -0.103      -0.086\n",
       "Asc_Dig_9       0.2329      0.004     57.337      0.000       0.225       0.241\n",
       "Asc_Dig_10      0.8739      0.010     84.203      0.000       0.854       0.894\n",
       "Dsc_Dig_2       0.1021      0.000    390.651      0.000       0.102       0.103\n",
       "Dsc_Dig_3       0.1695      0.001    312.408      0.000       0.168       0.171\n",
       "Dsc_Dig_4       0.0303      0.003      9.183      0.000       0.024       0.037\n",
       "Dsc_Dig_5       0.2782      0.005     54.994      0.000       0.268       0.288\n",
       "Dsc_Dig_6      -0.3233      0.008    -40.350      0.000      -0.339      -0.308\n",
       "Dsc_Dig_7       0.2599      0.009     28.337      0.000       0.242       0.278\n",
       "Dsc_Dig_8      -0.2711      0.011    -24.613      0.000      -0.293      -0.249\n",
       "Dsc_Dig_9       0.2544      0.011     22.712      0.000       0.232       0.276\n",
       "Dsc_Dig_10   1.014e-16   1.99e-17      5.084      0.000    6.23e-17     1.4e-16\n",
       "Asc_Let_2       0.0201      0.000     74.549      0.000       0.020       0.021\n",
       "Asc_Let_3      -0.0351      0.001    -25.875      0.000      -0.038      -0.032\n",
       "Asc_Let_4       0.6595      0.008     82.302      0.000       0.644       0.675\n",
       "Asc_Let_5      -3.2064      0.047    -68.514      0.000      -3.298      -3.115\n",
       "Asc_Let_6       2.7956      0.047     59.374      0.000       2.703       2.888\n",
       "Asc_Let_7       0.1095      0.005     23.928      0.000       0.101       0.118\n",
       "Asc_Let_8       0.1095      0.005     23.928      0.000       0.101       0.118\n",
       "Asc_Let_9       0.1095      0.005     23.928      0.000       0.101       0.118\n",
       "Asc_Let_10      0.1095      0.005     23.928      0.000       0.101       0.118\n",
       "Dsc_Let_2       0.0135      0.000     48.692      0.000       0.013       0.014\n",
       "Dsc_Let_3      -0.0146      0.002     -9.170      0.000      -0.018      -0.012\n",
       "Dsc_Let_4      -0.0579      0.007     -8.048      0.000      -0.072      -0.044\n",
       "Dsc_Let_5      -0.0579      0.007     -8.048      0.000      -0.072      -0.044\n",
       "Dsc_Let_6      -0.0579      0.007     -8.048      0.000      -0.072      -0.044\n",
       "Dsc_Let_7      -0.8176      0.039    -20.708      0.000      -0.895      -0.740\n",
       "Dsc_Let_8      -0.5343      0.055     -9.684      0.000      -0.642      -0.426\n",
       "Dsc_Let_9      -1.8037      0.120    -15.086      0.000      -2.038      -1.569\n",
       "Dsc_Let_10           0          0        nan        nan           0           0\n",
       "===============================================================================\n",
       "\"\"\""
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print poisson train results\n",
    "pois_trn_res = sm.GLM(pois_y_train, pois_X_train, family=sm.families.Poisson()).fit()\n",
    "\n",
    "pois_trn_res.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "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>mean</th>\n",
       "      <th>mean_se</th>\n",
       "      <th>mean_ci_lower</th>\n",
       "      <th>mean_ci_upper</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9559</th>\n",
       "      <td>2187.982338</td>\n",
       "      <td>0.494292</td>\n",
       "      <td>2187.013758</td>\n",
       "      <td>2188.951348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9561</th>\n",
       "      <td>2569.180842</td>\n",
       "      <td>0.434545</td>\n",
       "      <td>2568.329291</td>\n",
       "      <td>2570.032676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9562</th>\n",
       "      <td>1832.086759</td>\n",
       "      <td>0.940910</td>\n",
       "      <td>1830.243537</td>\n",
       "      <td>1833.931837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9564</th>\n",
       "      <td>2569.180842</td>\n",
       "      <td>0.434545</td>\n",
       "      <td>2568.329291</td>\n",
       "      <td>2570.032676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9566</th>\n",
       "      <td>2463.230262</td>\n",
       "      <td>0.381121</td>\n",
       "      <td>2462.483392</td>\n",
       "      <td>2463.977359</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             mean   mean_se  mean_ci_lower  mean_ci_upper\n",
       "9559  2187.982338  0.494292    2187.013758    2188.951348\n",
       "9561  2569.180842  0.434545    2568.329291    2570.032676\n",
       "9562  1832.086759  0.940910    1830.243537    1833.931837\n",
       "9564  2569.180842  0.434545    2568.329291    2570.032676\n",
       "9566  2463.230262  0.381121    2462.483392    2463.977359"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create poisson predictions and predictions dataframe\n",
    "pois_pred = pois_trn_res.get_prediction(pois_X_test)\n",
    "pois_pred_df = pois_pred.summary_frame()\n",
    "\n",
    "pois_pred_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create poisson predicted and actual pwned totals to be plotted\n",
    "pois_pred_cnt = pois_pred_df['mean']\n",
    "pois_act_cnt = pois_y_test['Total']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012.525003981384"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate root mean squared error\n",
    "rms = math.sqrt(mean_squared_error(pois_act_cnt, pois_pred_cnt))\n",
    "rms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create poisson predicted and actual pwned totals to be plotted\n",
    "pois_pred_cnt = pois_pred_df['mean']\n",
    "pois_act_cnt = pois_y_test['Total']\n",
    "\n",
    "# Plot poisson predicted vs actual pwned totals\n",
    "pois_fig = plt.figure(figsize=(10,6))\n",
    "pois_fig.suptitle('Poisson Predicted vs Actual\\nTotal Times Password Pwned')\n",
    "pois_pred_plt, = plt.plot(pois_X_test.index, pois_pred_cnt, 'go-', label='Predicted')\n",
    "pois_act_plt, = plt.plot(pois_X_test.index, pois_act_cnt, 'ro-', label='Actual')\n",
    "plt.xlabel('Index')\n",
    "plt.ylabel('Times Pwned')\n",
    "plt.legend(handles=[pois_pred_plt, pois_act_plt])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Save poisson figure\n",
    "# pois_fig.savefig('Visualizations/poisson.png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0.5, 'Frequency'),\n",
       " Text(0.5, 0, 'Times Pwned'),\n",
       " Text(0.5, 1.0, 'Distribution: Number of Times\\nPasswords Pwned')]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot distribution of num of times passwrods pwned\n",
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "ax = sns.distplot(df['Total'])\n",
    "ax.set(title='Distribution: Number of Times\\nPasswords Pwned',\n",
    "       xlabel='Times Pwned', ylabel='Frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Save figure\n",
    "# fig.savefig('Visualizations/pwned_dist.png', dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Distribution is skewed right."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Times Pwned (Mean)')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0V/11N/AnYBHwb1VZ/A1Ylzw+65CqLK6vyuJy4AvkpALynpyjwC1VWdxclcUXyZvR30ZOJucBf6jLTgN2Aq6q4xwiJ0OQk6wfUCdw9Tkursriz8CBwDlVWXykKovfVmVxMrk17j1tz2M5cGRVFonc+rU/8OGqLM6uyuJK4LXAyma398FCTOsA76Degqwqi6ursngbedur17RVjVVZfLHezuu99XPfLsT0cPLv6R1VWZxflcUl5MRyCKBOru8G7h3TnX1mVRafq8/3YXLL6PZTeQ6Sps4ETtJ0+ceeoPUWSTfzQEJ1ErBH3SL3EnLS8sMJzrWEnBRsT245+qeqLBZUZfHj+vx/Iu9b+pYQ03+HmH4CfI0H3tPOBn4JXBhiSiGmo4Fbq7K4g9xq923gtBDTTSGmz5PH3P2p3iM0Ai8MMW1M3ij9GHKr3BA5gftBfY0F5K7idj8FHhdiWr/++aZWtzF5X9jHAJe23affk8f1TcUTgbWAc9uS3bvq49u01bu27XrL6m/n1XXm07Zhe1UWVwN3dLnu9W31R4A7yd23kmaQY+AkTZexLUnDwH3196cDnyevcL8ncEpVFvdPcK57q7K4rlNhiOlx5M3TryF3e55CTpBOBqjK4u4Q02Jyt+2u5A3Z/zPE9PKqLM4Fdg8xbQe8rP7aP8S0b72Ux1nAvwK/JSeBPyEnKNsDLySPuwO4Z5zQWgnknPrfu9vKWonc2IkB9zE1rffvFwN/HlO2rO378c4/xANjCif7H/nxWgyd7CDNMBM4SdNlO+AC+EeCtTm5JY2qLJaFmL5H7jp9KfAvq3mtV5MH3b+obWLEwfW/Q+QxZy+pyuIDwM+Bw0JMPwN2CzHdQu52PJA88eGIENPJwP8HnEhO4D5FnvhwflUW94aYLiSPlbsPuKSOIQHPHhPXs8nJ1F/Hifkv5AkTO9QxEWLakDzJYiquq+/BRlVZtM43TE5ivwF8r8Hj7yGPWVxaP35LYP22Om7VI81SJnCSpssHQkw3AzcBnwYuaXV51k4iz+hc2popuhqWkrsjdw0xXUWeMfneumwt8sK/7wkx/QUI5IkHC4CvkGdO7htiWk7uht2Y3FJ3EkBVFjeEmG4C9iInm5AnInwA+Gpbl+gngItDTO8lD/Z/Onn821H1LNQHBVwf+zQ5mbyBnEB9nO7vw48JMb1kzLG/VmVxUYjpC8BnQkz3krtKDyK3KB7W5ZxUZfH3ENNxwCdDTHeQu0I/Vxe3nuNdwLYhpn+uyuKmbueUNHMcAydpuhxBTmp+Se463H1M+dnkQf0nT8O1vkVOvk4ErgTeBOxHTjyeXk8Q+HfgjcDVwH8Dn6zK4oR6/FwFvIg8ceEMcpJ3RNv5z6r/bY1xa01uaI1/o544sRu5u/XX5GVJPkheJqSTo8lj6r5EboW7jLYxah0sruNp/2pttH4wcBpwQn0fnkpuebyhyzlbDiG3mn6fPCbxdPI9bHW7nkhOcJfUYwIlzRJuZi9pRoSYHkXuQtx2ovFtmjkhplcB57UmN9RbZ/2ZvGbf7/oanKQJmcBJ6qkQ07rkrsi9gEdWZfGiPoekWojpUvJYvg+SZ6Z+ENikKotnTfhASX1nF6qkXltJ7u7cFjigz7HowfYCHg1cDPwMGAFe1deIJDViC5wkSdKAsQVOkiRpwDxklhE56rhzhoDH8+AFLiVJkmarRwC/P3i/XVbpLn3IJHDk5M1ZVZIkaZBsBtwy9uBDKYFbBvCDb5/A/SumunPNgw0ND7PJFgVLb0yMjoxMyzl7xVh7w1h7w1h7w1h7w1h7Y1Bi7VWcc+fN51922wc69Bw+lBI4AO5fcd+0JnArV67k/hX3zeoXFxhrrxhrbxhrbxhrbxhrbwxKrP2Kc0YTuBDTnsCHgMeSV0d/e1UWPwsxHURevbw9s9qqKos/hJh2AI4lb4VzObB3VRbX1ufrWCZJkrSmmrFZqCGmrYEvA3tWZbEeOfE6rS7eHjiwKouHt339IcS0Nnmbm0+RN1g+h7yXIhOVSZIkrclmLIGryuIa4HFVWVwaYpoPPAr4S128PXDFOA8rgWVVWZxUlcUK8l6Fm4eYntqlTJIkaY01o12oVVncFWJaSF71+36gqlvStgYODTHtSN4r8T1VWZwJbEPuam09fmWI6fr6+CYTlF3ZKYah4WGGhqcnb22dZ7rO10vG2hvG2hvG2hvG2hvG2huDEmuv4ux2vn5MYrgKWBt4DbkLdVvyFi6fA/YAdgZOCTEtAh4GLB/z+OXAul3KOtpki4KVK1eu5lMYe84F03q+XjLW3jDW3jDW3jDW3jDW3hiUWKc7zjlz5kxYPuMJXN3dCXBCiOmdwI5VWbygrcr3Q0yRvPn1cmCdMadYF7irS1lHS29M07yMyAKW3rhkVs+QAWPtFWPtDWPtDWPtDWPtjUGJtVdxzp03n6ctWty5fNqu1EWIaVfgzVVZvKzt8Hzgn0NMB1dlcVTb8bWAe4GbgH3bzjEH2BK4hpzAdSrraHRkZNpfCL04Z68Ya28Ya28Ya28Ya28Ya28MSqzTHWe3c81kC9wlwOIQ06uA7wH7A/OAs4ALQ0wJOBPYHdgReB1wB7BBiGkf4GTgUOBmcjfsdROUSZIkrbFmchbqH4FXAYcDt9ffv7QqiyuBPYEjgTuBw4CqKotbq7K4G9gVeAt5xurOwB5VWYxOVDZTz0mSJKkfZnoWagS2G+d4AEKHx1wCPGOyZZIkSWuq2T03V5IkSaswgZMkSRowD7nN7KUmRhYd1qje0OgKWHY2IwsPYnRoXtf6wxcfsbqhSZJkC5wkSdKgMYGTJEkaMCZwkiRJA8YETpIkacCYwEmSJA0YEzhJkqQBYwInSZI0YEzgJEmSBowJnCRJ0oAxgZMkSRowJnCSJEkDxgROkiRpwJjASZIkDRgTOEmSpAFjAidJkjRg5vY7AEmrZ2TRYY3qDY2ugGVnM7LwIEaH5nWtP3zxEasbmiSpR2yBkyRJGjAmcJIkSQPGBE6SJGnAmMBJkiQNGBM4SZKkAWMCJ0mSNGBM4CRJkgaMCZwkSdKAMYGTJEkaMCZwkiRJA8YETpIkacCYwEmSJA0YEzhJkqQBYwInSZI0YEzgJEmSBowJnCRJ0oCZO5MXCzHtCXwIeCxwNfD2qix+FmLaATgW2Aq4HNi7Kotr68dMqUySJGlNNWMtcCGmrYEvA3tWZbEeOfE6LcS0NnAG8ClgfeAc4NT6MVMqkyRJWpPNWAJXlcU1wOOqsrg0xDQfeBTwF6AEllVlcVJVFiuAI4DNQ0xPXY0ySZKkNdaMdqFWZXFXiGkhcDFwP1ABC8jdqa06K0NM1wPbAJtMsezKTjEMDQ8zNDw9eWvrPNN1vl4y1knGMLpiUvUa1+/BcxqkWJuaDa+Bpoy1N4y1N4x1+vUqzm7nm9EErnYVsDbwGuA04Ghg+Zg6y4F1gYdNsayjTbYoWLly5ZQC73zOBdN6vl4y1oaWnT2p6pveeV6zik/cdgrBdDFIsU6Sr9feMNbeMNbeGJRYpzvOOXPmTFg+4wlc3d0JcEKI6Z3APcA6Y6qtC9xFTsimUtbR0hsT96+4bwqRr2poeJhNtljA0huXMDoyMi3n7BVjnZyRhQc1qjc0uoJN7zyPW9bbidGheV3rD1929OqGtopBirWp2fAaaMpYe8NYe8NYp1+v4pw7bz5PW7S4c/m0XamLENOuwJursnhZ2+H55G7Q17bVmwNsCVxDTtL2nUJZR6MjI9P+QujFOXvFWBteu0GCM7Z+k8f04vkMUqyT5eu1N4y1N4y1NwYl1umOs9u5ZrIF7hJgcYjpVcD3gP2BecD/Al8KMe0DnAwcCtxM7mq9DthgCmWSJElrrJmchfpH4FXA4cDt9fcvrcri78CuwFvIs1J3BvaoymK0Kou7p1I2U89JkiSpH2Z6FmoEthvn+CXAMzo8ZkplkiRJa6rZPTdXkiRJqzCBkyRJGjAmcJIkSQPGBE6SJGnAmMBJkiQNGBM4SZKkAWMCJ0mSNGBM4CRJkgaMCZwkSdKAmdGdGCRpUIwsOqxRvaHRFbDsbEYWHsTo0Lyu9YcvPmJ1Q5NW0YvXq6/V2c0ETtKMMSnSIDEp0mxmF6okSdKAMYGTJEkaMCZwkiRJA8YETpIkacCYwEmSJA0YEzhJkqQBYwInSZI0YEzgJEmSBowJnCRJ0oAxgZMkSRowJnCSJEkDxgROkiRpwJjASZIkDRgTOEmSpAFjAidJkjRgTOAkSZIGjAmcJEnSgDGBkyRJGjBz+x2AVt/IosO61hkaXQHLzmZk4UGMDs3rWn/44iOmIzRJktQDtsBJkiQNGFvgOrBVS5IkzVa2wEmSJA0YEzhJkqQBYwInSZI0YEzgJEmSBsyMTmIIMe0EfALYErgFeE9VFt8NMR0EfBS4r636VlVZ/CHEtANwLLAVcDmwd1UW19bn61gmSZK0ppqxBC7EtBFwGvBa4ExgJ+DbIabtge2BA6uy+OKYx6wNnAEcAnwTeDdwKrD9RGUz8oQkSZL6ZFIJXIhpXWAjYCXwx6osVkzi4ZsDp1Rl8b3653NDTL8FFpGTri+M85gSWFaVxUn19Y8A3hFieiqwSaeyqiyunMzzkiRJGiRdE7gQ0z8DBwD/AmwzpuzXwPeB46qyuHmi81Rl8SvgV22PfQKwALgK2Bo4NMS0I3AruWv1zPp6V7edY2WI6fr6+CYTlHVM4IaGhxka7j70b2i0e27aqtOkbuvavTBIsU7m2n2Noel9mgX31ViNtanZ8LfV1GyItRevgX5+DrTX62esTc2G10ATvYqz2/mGRkdHxy0IMT0KOBrYHTgXOBtYAvwFmANsCGwHPA94CXA6cFBVFn/pFlSIaWMgAj8CPg58DTgCOB/YmdwlugjYA1hQlcVebY+9ADgeeHynsqosThx7zaOOO+eRwB2XXvxTVq5c2S1ESZKkvpkzZw5PW7QYYP2D99vlb2PLJ2qBOx/4InBAVRb3dKjzE+C/QkyPAF5fP2bbiQIKMT2ZPAbuHOCtVVmMAC9oq/L9EFMEXgosB9YZc4p1gbu6lHW09MbE/Svum6gKACMLD+paZ2h0BZveeR63rLdTs50YLju6a52pGKRYmxgaHmaTLRaw9MYljI6M9CWGJvcUZsd9NVZjbWo2/G01NRti7cVroJ+fAzA7Ym1qNrwGmuhVnHPnzW8lcOOXT/DYHauyWN7kIlVZLAOOCTF9eaJ6IabFQAA+XpXFx+tj2wM7V2VxVFvVtYB7gZuAfdseP4c8g/UacgLXqayj0ZGRRje4yRtxe90m9Xv1AhykWCej6e+qJ9eexD1t1e/XfTVWY52sfv5tTdaa9j4wGz4HWvX7FetkDcrrdbrj7Haujglc0+St6WNCTI8nJ28HVWVxfFvRXcDhIaZEbpnbHdgReB1wB7BBiGkf4GTgUOBm8ri56yYokyRJWmM1moUaYnoScBTwDGAeMNReXpXFRg1O8wbgUcBnQ0yfbTt+ALAncCR57Nu1QFWVxa31tXclr/X2WeAKYI+qLEaBuycokyRJWmM1XUbkePKkhY8Dy6ZyoaosDgcOn6BK6PC4S8iJ46TKJEmS1lRNE7hFwKKqLH7Ty2AkSZLUXdNFS24CHtnDOCRJktRQ0xa49wJfCDF9hDx54EHrcFRlsWS6A5MkSdL4miZwp9b/fqvt2Ch5MsMoeWFfSZIkzYCmCdwWPY1CkiRJjTVK4Cba5zTE9LjpC0eSJEndNF0HbkvgE+RtslrdpUPkHRM2anoeSZIkrb6mideXgH8i7436UeB95G7VNwD79SY0SdKaZmTRYY3qDY2ugGVnM7LwoGZ7zF58xOqGJg2UpsuI7Ai8qSqLTwGXAxdWZfEW4BBgr14FJ0mSpFU1TeCGgD/W318DbF9//x3gadMdlCRJkjprmsBdAexWf38V8Pz6+00Ysy+qJEmSeqvpGLjDge+GmFYAXwfeG2L6MbAN8IPehCZJkqTxNGqBq8riHGBr4JyqLG4FFgNXAp/GSQySJEkzqvHyH1VZ3BxieniIaSGwBDisKos7exeaJEmSxtN0Hbj5wDHAG+tDWwFHh5jmAq+pymJZj+KTJEnSGE1b4D4MPBt4HvDD+thRwPHAJ7EbVZL6xrXVpIdKe5MrAAAgAElEQVSeprNQ/w34z6osfk7evJ6qLC4kL+Rb9Sg2SZIkjaNpArcRD6wD1+5vwMOmLxxJkiR10zSBuwD4z7afR+txce8DfjrtUUmSJKmjpmPg3gacE2LaBVgbOBF4ErAS2Lk3oUmSJGk8TdeBuxYoyBMXjgEuJS/uu3VVFqln0UmSJGkVk1kH7l7ghB7GIkmSpAY6JnAhpouanqQqi2dOTziSJEnqZqIWuEXkJUN+DkTyeDdptTRZr8q1qiRJmthECdxTgd2B3YA3AwE4HTi3KosVMxCbJEmSxtExgavK4irgKuCDIaYtyYnc+4GTQ0xnkpO5s6qyuGdGIpUkSRLQfBbqdVVZHFWVxY7AU4ALgbcCfwwxfauXAUqSJOnBmi7k2+5u4O/AMmAeeaycJEmSZkijZURCTJsDr6q/ngNcDZwBfKgqi0t7F54kSZLGmmgZkacCryQnbU8BLiEnbW+syuKamQlPkiRJY03UAnc5cB/wY+BY4Jb6+BNDTE9sr1iVxQ96Ep0kSZJW0a0LdT55r9OJ9jsdBeZMW0SSJEma0ETLiExlgoMkSZJ6rGOSVq/9NikhpietXjiSJEnqZqIu1JNCTJcDn6vKYslEJwkxLQQOIE922GEa45MkSdIYEyVwzwEOBGKI6f+As4HfALeTW+42BLYDng9sAHwCeFNPo5UkSQOtyZ7YMLl9sR+Ke2JPNAZuBPh0iOnzwJ7ArkAFPAYYAf5IXlrkSOC0qizu7naxENNO5ERvS/Ks1vdUZfHdENMO5JmuW5Fnv+5dlcW19WOmVCZJkrSm6rqQb1UW9wFfq7+mLMS0EXAa8FrgTGAn4Nshph3J68sdAnwTeDdwKrB9iGntqZStTpySJEmz3UzONN0cOKUqi+9VZTFSlcW5wG/JW3Etq8ripKosVgBHAJvXCwmXUyyTJElaYzXaSms6VGXxK+BXrZ9DTE8AFgCPJ2/N1aq3MsR0PbANsMkUy67sFMfQ8DBDw93z1qHRFY3rNKnbunYvGOv0x9r42sY6KcZqrA/1WPv53tpez1inT+va0x1Dt/MNjY6OTusFmwgxbQxE4EfArcCCqiz2aiu/ADienNxNuqwqixPHXvOo4855JHDHpRf/lJUrV/bkeUmSJE2HOXPm8LRFiwHWP3i/Xf42tnzGWuBaQkxPJo+BOwd4K/B2YJ0x1dYF7gKWT7Gso6U3Ju5fcV/XOEcWHtS1ztDoCja98zxuWW+nrjNkAIYvO7prnakw1umPtUmcYKyTZazG+lCPtZ/vrWCsvTA0PMwmWyxg6Y1LGB0Zmbbzzp03v5XAjV/eqSDEtFnTi1Rl8bsm9UJMi4EAfLwqi4/Xx64G9m2rM4c8S/UacpI2lbKORkdGGt3gJm8Y7XWb1J/OX+zY60+mrrE2u/Zk6xtrs2tPtr6xNrv2ZOsba7NrT7Z+t8fMhvfWVn1jnV5N84vJnG8iE7XA3UTe57SJrnuhhpgeT07eDqrK4vi2oghsEGLaBzgZOBS4GbgKuG6KZZIkSWusiRK4p7R9/3zgYPKSHRcB9wFPBz4OfKbhtd4APAr4bIjps23HDyCvMXcs8FngCmCPqixGgbtDTFMpkyRJWmNNtJDvb1rfh5i+A7y+KosftVVZGmL6K7n169huF6rK4nDg8AmqPKPD4y6ZSpkkSdKaqumc142B28Y5fg+w/vSFI0mSpG6aJnDnAl8MMW3TOhBi2o7c8vbdXgQmSZKk8TVdRuSN5G2wloSY7gGGgPnAWcBbehSbJEmSxtEogavK4nbgBXUL3Lbk2am/rsrit70MTpIkSatqvO9DiGkY2ALYFDgPWD/EtF6vApMkSdL4GiVwIabHApcDpwCfAP4JOAxI7ePiJEmS1HtNW+A+AywBHg3cXR/bC7gYOKYHcUmSJKmDpgncC4EPVWVxb+tAVRZ/B94NPKsXgUmSJGl8TRO4IWCtcY5vQN6VQZIkSTOkaQJ3OnB0iGlj8gzU0RDTU4HP4zpwkiRJM6ppAvdO4M/AH4CHk8fDXQbcWJdJkiRphjRdB+4u4NUhpvcAC+rHJdeBkyRJmnlNd2IgxDQXmAPcQB4TNzfEtACgKoslvQlPkiRJYzVK4EJMLwP+G9hwTNEQeUzcnGmOS5IkSR00bYH7FHAB8CFgWe/CkSRJUjdNE7hNgV2qsrixl8FIkiSpu6azUC8Ent7LQCRJktRM0xa4M4AvhZheAFzHmMV7q7L4wjTHJUmSpA6aJnDvII9923WcslHABE6SJGmGNF0HboteByJJkqRmmi4jch1wZv3146os3P9UkiSpT5p2oR4G7ExeC+5RIaYfAT8AzqzK4pZeBSdJkqRVNZqFWpXFKVVZ7FuVxabADkAEdgNuCDFd2csAJUmS9GCNt9ICCDFtDzwPeA7wVGAF8JcexCVJkqQOmo6B+z45aRsGLiLvyvBZ4ELHw0mSJM2spgv5/jOwHnApOXk7H5M3SZKkvmg6Bm5b4LHk9d4eC3wJuCPE9OMQ0+G9C0+SJEljNW2BoyqL26qyOBU4ENgf+BqwI3mGqiRJkmZI0zFwzwReWH89G7gTOAfYG/hhr4KTJEnSqprOQv058EvgLODdVVlc0ruQJEmSNJGuCVyI6ZHAZlVZ/GEG4pEkSVIXHRO4ENNGwNeBnYDRENMPgf2qslg6U8FJkiRpVRNNYvgI8Hjg34E9gccAx89EUJIkSepsoi7UCqiqsrgIIMT0G+DKENPaVVncMyPRSZIkaRUTtcBtCPyu9UNVFgkYATbqdVCSJEnqbKIWuGFywtbuvi6PaSTEtDvw9qosnlv/vAfwP8C9bdVeXJXFL0JMTyJ33S4ErgfeWJXFL+vHdSyTJElaU3VLxkan82IhpiFgX+C/gIvbirYHPlWVxSHjPOwU4HTyGnSvAb4TYnpCVRZ3dymTJElaI3VL4D4QYlre9vN84NAQ0x3tlaqyOLjh9Y4kLwT8SeD5bce3B74xtnKIqQCeBBxVlcUK4IQQ0zuBF4eYru1UBoSG8UiSJA2ciRK4C4Anjzn2c3LS1G4yrXTHVGVxSIhpb1ZN4OaEmD4JLAOOrMriBGAb4Po6QWu5pj4+Z4Kyjgnc0PAwQ8PddxAbGl3RuE6Tuq1r94KxTn+sja9trJNirMb6UI+1n++t7fWMdfq0rj3dMXQ739Do6LT2kjZSJ3BvqMpicYhpbeBs4FjgDODpwPeBfwUeC7ylKotntz32a8ANwHWdyqqyOHzsNY867pxHAndcevFPWblyZa+emiRJ0mqbM2cOT1u0GGD9g/fb5W9jy1d7QsLqqpckeUHboZ+HmE4mL2NyPrDOmIesC9wFLJ+grKOlNybuX3Ff17hGFh7Utc7Q6Ao2vfM8bllvJ0aH5nWtP3zZ0V3rTIWxTn+sTeIEY50sYzXWh3qs/XxvBWPthaHhYTbZYgFLb1zC6MjYuZ9TN3fe/FYCN375tF1pikJMmwNvqsriPW2H1wLuAK4GnhBimlOVRavZbGvgq+RZp53KOhodGWl0g5u8YbTXbVJ/On+xY68/mbrG2uzak61vrM2uPdn6xtrs2pOtb6zNrj3Z+t0eMxveW1v1jXV6Nc0vJnO+ifQ9gSMnavuHmG4BvgQ8l7zzw3OrslgSYroJeG+I6WPAXsCjgfOqsljeqawPz0GSJGnG9G/UX60qi78BLwf2IU9gOA7YtyqLK+oqu5EnPNwOvB14RVUWyxuUSZIkrZEat8CFmJ4I3FaVxbIQ007k5OmXVVl8bbIXrcriRODEtp9/BjyzQ93ryeu8TapMkiRpTdWoBS7E9B/kJTqeEWJ6MvA9YDvgmBDTu3sYnyRJksZo2oX6buCAqizOA/YGfluVxWLyWLX9exSbJEmSxtE0gXsC8IP6+13JLXCQW+UePd1BSZIkqbOmY+BuARaFmDYm73RwZn28Ii/nIUmSpBnSNIH7BHAqMAL8rCqLC0NMhwPvIS/fIUmSpBnSqAu1KosvA88A/o28WTzk7a+eXpXFaT2KTZIkSeNovA5cVRaXkce8vTTE9DDgT8BVvQpMkiRJ42u6jMgjQkxnAr8hd6U+BvgMcFmI6XE9jE+SJEljNG2B+ySwNvB44O762FvJG8cf04O4JEmS1EHTBG5X4F1VWfyhdaAqi5uB/wRe1IvAJEmSNL6mCdzDeaDlbezj+76fqiRJ0kNJ0+TrbOD9IabWsiOjIaZHA0cD5/YkMkmSJI2raQL3VmAz4K/AusD/Ar8DHgkc2JvQJEmSNJ5GC/lWZfEn4DkhphJYUD8uAedWZTHaw/gkSZI0xmTHry0h74N6BnA1sGmIabNpj0qSJEkdNWqBCzFVwFeADcYUDQGjwJxpjkuSJEkdNN0L9RjyRIbPMv5sVEmSJM2Qpgnco4GPVGXx214GI0mSpO6ajoE7Fah6GYgkSZKaadoC937gihDTXsANwEh7YVUW/zbdgUmSJGl8TRO444GVwG9xDJwkSVJfNU3gFgOLq7K4tJfBSJIkqbumY+B+C6zTy0AkSZLUTNMWuI8AXw0xfQ64Ebi/vbAqix9Md2CSJEkaX9ME7lv1v58ep8yFfCVJkmZQ071QJ7vlliRJknqkYwIXYlq3Kovlre8nOkmrniRJknpvopa1O0NMG9Xf3wXcOc5X67gkSZJmyERdqC8E/lp/X85ALJIkSWpgogTu+cCvgPursjh/huKRJElSFxN1oX4AePhMBSJJkqRmJkrghmYsCkmSJDXWbRmRx4eY1u52kqosfjdN8UiSJKmLbgncr7qUD+FCvpIkSTOqWwJXAn+ZiUAkSZLUzEQJ3ChwdVUWf56pYCRJktTdRAlczyYxhJh2B95elcVz65+fBBwPLASuB95YlcUvV6dMkiRpTTXRLNSvAndP58VCTEMhpjcAJ/PgBPEU4GzgUcAxwHdCTOusZpkkSdIaqWMCV5XFPlVZTPc2WUcCrwM+2ToQYiqAJwFHVWWxoiqLE4DbgRdPtWyaY5YkSZpVuk1imG7HVGVxSIhpb/JODwDbANdXZbGird419fE5UywLnQIYGh5maHiihse63uiKxnWa1G1duxeMdfpjbXxtY50UYzXWh3qs/Xxvba9nrNOnde3pjqHb+WY0gavK4tZxDj8MWD7m2HJg3dUo62iTLQpWrlzZPdhlZ3evU9v0zvOaVXzito3POSnGOsVgJjCJOMFYGzNWY32oxzoL3lvBWHthky0WTOv55syZeIW2mW6BG89yYOy4tXWBu1ajrKOlNybuX3Ff16BGFh7Utc7Q6Ao2vfM8bllvJ0aH5nWtP3zZ0V3rTIWxTn+sTeIEY50sYzXWh3qs/XxvBWPthaHhYTbZYgFLb1zC6MjItJ137rz5PG3R4s7l03alqbsaeEKIaU5VFq2msa3Jkyiun2JZR6MjI41ucJM3jPa6TepP5y927PUnU9dYm117svWNtdm1J1vfWJtde7L1jbXZtSdbv9tjZsN7a6u+sU6vpvnFZM43kf51GteqslgC3AS8N8Q0vx4f92jgvKmWzfRzkCRJmkl9T+Bqu5EnNdwOvB14RVUWy1ezTJIkaY3Uly7UqixOBE5s+/l64IUd6k6pTJIkaU01W1rgJEmS1JAJnCRJ0oAxgZMkSRowJnCSJEkDxgROkiRpwJjASZIkDRgTOEmSpAFjAidJkjRgTOAkSZIGjAmcJEnSgDGBkyRJGjB92QtVkiRpthtZdFjXOkOjK2DZ2YwsPIjRoXld6w9ffMR0hGYLnCRJ0qAxgZMkSRowJnCSJEkDxgROkiRpwJjASZIkDRgTOEmSpAFjAidJkjRgTOAkSZIGjAmcJEnSgDGBkyRJGjAmcJIkSQPGBE6SJGnAmMBJkiQNGBM4SZKkAWMCJ0mSNGBM4CRJkgaMCZwkSdKAMYGTJEkaMCZwkiRJA8YETpIkacCYwEmSJA0YEzhJkqQBYwInSZI0YOb2O4CWENNBwEeB+9oObwVsChxbf385sHdVFtfWj9mhU5kkSdKaaja1wG0PHFiVxcNbX8BfgTOATwHrA+cApwKEmNbuVCZJkrQmm20J3BVjjpXAsqosTqrKYgVwBLB5iOmpXcokSZLWWLOiC7VuTdsaODTEtCNwK/Aectfo1a16VVmsDDFdD2wDbDJB2ZWdrjU0PMzQcPe8dWh0ReM6Teq2rt0Lxjr9sTa+trFOirEa60M91n6+t7bXM9Zm+vmZ1a3e0OjoaKMT9VKIaTPga+RWtPOBnYFvAkcCC6qy2Kut7gXA8cDjO5VVZXHi2Gscddw5jwTuuPTin7Jy5coePhtJkqTVM2fOHJ62aDHA+gfvt8vfxpbPiha4qix+B7yg7dD3Q0wRuBtYZ0z1dYG7gOUTlHW09MbE/Svum6gKACMLD+paZ2h0BZveeR63rLcTo0PzutYfvuzornWmwlinP9YmcYKxTpaxGutDPdZ+vreCsU5WPz+z5s6b30rgxi9vdJYeCzFtD+xclcVRbYfXIidwW7XVmwNsCVxDTuD27VDW0ejICKMjI11javJLaK/bpH6T606FsU5/rJOJs1XfWJtde7L1jbXZtSdb31ibXXuy9bs9Zja8t7bqG2uza0+m7nS+VrvVmxUJHLnV7PAQUwLOBHYHdgReB7w/xLQPcDJwKHAzcBVwHbBBhzJJkqQ11qyYhVqVxXXAnuQxb3cChwFVVRa3ArsCbwH+Qh4bt0dVFqNVWdzdqawPT0GSJGnGzJYWOKqyCEAY5/glwDM6PKZjmSRJ0ppqVrTASZIkqTkTOEmSpAFjAidJkjRgTOAkSZIGjAmcJEnSgDGBkyRJGjAmcJIkSQPGBE6SJGnAmMBJkiQNGBM4SZKkAWMCJ0mSNGBM4CRJkgaMCZwkSdKAMYGTJEkaMCZwkiRJA8YETpIkacCYwEmSJA0YEzhJkqQBYwInSZI0YEzgJEmSBowJnCRJ0oAxgZMkSRowJnCSJEkDxgROkiRpwJjASZIkDRgTOEmSpAFjAidJkjRgTOAkSZIGjAmcJEnSgDGBkyRJGjAmcJIkSQPGBE6SJGnAmMBJkiQNGBM4SZKkAWMCJ0mSNGBM4CRJkgbM3H4HsLpCTDsAxwJbAZcDe1dlcW1/o5IkSeqdgW6BCzGtDZwBfApYHzgHOLWvQUmSJPXYQCdwQAksq8ripKosVgBHAJuHmJ7a57gkSZJ6ZtC7ULcBrm79UJXFyhDT9fXxK8d7wLz5azE03D1vHRm6r2udIVYwZ84c5g3dx+jQaNf6w2ut3bXOVBjr9MfaJE4w1skyVmN9qMfaz/dWMNbJ6udn1ty58ya+7uho94vNViGm9wILqrLYq+3YBcDxVVmc2F73qOPO2RT43cxGKEmStFo2O3i/XW4Ze3DQW+CWA+uMObYucNc4dX8PbAYs63VQkiRJ0+AR5PxlFYOewF0N7Nv6IcQ0B9gSuGZsxYP322UUWCWDlSRJmqX+1qlg0BO4CGwQYtoHOBk4FLgZuKqvUUmSJPXQQM9CrcribmBX4C3AX4CdgT2qshjcgX2SJEldDPQkBkmSpIeigW6BkyRJeiga9DFwMyrEtDlwe1UWfw8xPRHYBRgCQlUWs2aCRIjpUODzVVnc2e9YmggxrQVQlcW9IabHArsDK4Azq7IYd/ZNv9Sxvgh4ErA2cAdwWVUWv+xrYJKkhxS7UBsKMf078EVgB+CJwP8APwHmAM8CXl2VxZn9i/ABIaYR8gzd/6jK4pJ+xzORENPOwLeBe4DXAV8DLgPuJ9/rqiqLn/QvwgeEmLYmb9d2FzACtH7eHriNHOvS/kUoSXqosAWuuQ8DO1VlkUJMpwC7V2XxQ4AQ00vJ+7HOigQO+DvwUeDcENO3gCNmUwvhGEcBrwFGgdOB11dl8Q2AEFNFvq/P6F94D/IF4PDWItEhpv8AFgKvBN4PHAu8vG/RjVG3Fr6YvDNJa33Ea4DzqrK4p5+xDbIQ02Lyf9p+XZXF2WPKDqvK4oj+RLaqENN6wIqqLO4JMT0FeAm51fi7VVn8ub/RdRdiOrgqi6P6HUe7ENMO7S3uIaYSqIB7gZOrsvh134IbR4jpOcCSqiz+L8T0XPJ71BBwalUWF/U3ugeEmI4FPlSVxR/6HUsTdY/cyqosfh9i2p78ObYCOL0qi4tnIgbHwDW3YVUWv6q/fwzwo7ayc4CNZz6kjkarsvg6sID8O14SYgohpteEmJ4YYlq3z/G126Iqi+9UZfFdcmvmt1oFVVkE8rp+s8XTyS2ELf8D/HtVFiPAkcDivkQ1jhDTM4EbyfsDt1qNnwV8DLghxLSoj+ENrBDTG4DvkO/pV0NM3wkxzW+rckh/IltViOnlwJ+Bm0JMrwMuBJ4L7AH8Zja9BkJMLxzvC3hviKmsv58tzm19E2LaGziN/N71KOAnIabd+hTXKkJM/4/8H+N/CjG9nvzafTh5cdiz6yW4Zov9gItCTK/qdyDd1D1yV5P/jt5IXtJsffJr4Icz9RqwBa65n4WYPlCVxQfJScabgM/XZf8PmJGMezKqsvgj8MYQ0/uAVwP7kFuRHkZ+w5kNrg8xvQhYixzTzsAPAEJM/8rsWnz5ZuBfgVPqn3chf0ACbMHs2uXjS8B7xm4pB//40Pky8LQZjmlcIaYb6PJeVJXFZjMUTjeHAC+qyuLyENP6QAC+FWJ6Vb180VB/w3uQjwEvAzYkr5O5a1UW5wDUH5L/BezYv/Ae5JvkD79bePA9fBhwPLmF/gl9iGs87fEdArysKotfAISYvkH+2/t2PwIbxzuB51RlcX2I6QfAzq1hNSGm48jJ5wn9DLDNcuDfgRNDTPuTezt+0eeYOnk/8Pz6+18Au1Rl8b8AIaavAl9hBl4DJnDN7Q98t/6f7BJg/xDTu4D1gLuBsp/BjfGgD5GqLP4EHAMcE2IaAjboS1TjO5D8Qv8n8hv1TiGmtwHzyB8ur+5jbGO9C/h2/T+uu8l/wHuFmBYAPwfe3s/gxtgSOKlD2cnAZ2Ywlm5eTU7aP8os/I/QGI8GrgCoyuKOENOuwAXAp5ldv3+AzaqyOK/eoebrwHltZd8l/73NFk8hJz4rgDdXZXE7QIjpr1VZbNHXyFbVPnD8kTz4NXsBsMnMhjOhh/HAHuAPA9q7d68kt8bNFqNVWZwfYirIiecZIaY/kxtMfgHcWpXFkr5G+IDHVGVxUf15OgL8uK3sQmboNWAC11BVFjcD24eYnkUe9/QL8piH3wLn1osKzxZv6lRQtxLcPoOxTKgqi5+EmDYmd1H/KcQ0TJ6F+njg7bNpPElVFj8MMW0LvJTcNf2OqiyuDTE9HFhYlcWN/Y3wQS4D3hVi+kRVFitbB+s3nEOZRYlSVRa/rLtyPlyVxSf7HU8XvwbeABwHUJXFnSGmlwEXhpju4MEf7v12Q4jpxVVZnBti2ghY2VZ2APk/orNC/Z/MV4aYXkPu7XhvVRan9juuDtYOMX2MnMhfRh4D24r1jcyi+0oe3vP5ENOB5Fb3D4WYDiMnbh8Czu9ncOOpx+d+NMR0NPAv5DF7ewKbkxtMZoNf18Mp5pEbTPbigeE1B5K7V3vOWajSGijEtCW5q3dL4Hpy98Q65G6om4DdZlnCSd1tcnpVFrf1O5ZOQkwLyd2mv6zKYo+241sDZwGbV2UxK4Yn1EMTTgceW5XF8rbjl5Bb4V8+m/6D1BJi2oScbNxN7q5ev88hPUiIaXfyzPPW17VVWbwwxHQEuafmH12q/RZiehQ5sXgBcC25pXOEPFzlCuBf6uS570JMd1ZlMVsStAnVvS7fALYij3++n7yKwlxyb9KuVVn8vNdxmMBJa7C6O2JrcvfJcuDqqixSf6MabCGmucDjqrL43ZjjDyNv5ffV/kS2qhDTI6qyWDbm2Hbk18G9fQqrkbqFY4+qLF7S71gmEmJaq17DcjPgjrH3ezb4/9u79xg5qzKO49+CF/jDIhFjCgEXSYFTWlaKlUCk5BQVleRoSOoFUqkiYJEWCyLUhGqpxMRSpVwElSpootYQoqe1gFqPLZcWXUFc4LBt6cXSCEVMq1vEQrv+8ZzpvjSseykzs7P+PslkZ2fed95n5o/Nb5/nfeeUUNyOnWf4H2Bt8O4vza3q1WLK7wvePdDsOoaqXDh2BPBQo0KxApyIiIhIi9E5cCIjUEx5VX/bBO8mN6KW/qjW+lCt9aFa60O1Dp4CnMjItAi78vB6YH2Ta+mPaq0P1VofqrU+VOsgaYQqMkKVc4imBe/O6HfjJlOt9aFa60O11odqHRytxCAyQgXvbgdWx5Tf0exa+qNa60O11odqrQ/VOjjqwImIiIi0GHXgRERERFqMApyIiIhIi9FVqCLSEDHlTdhyODW7gWexFSO+GrzrbkZd9VCW11oavHvNxe1jyj3YSgjLGlvZ3uOfAewI3v05ptwGbAQmBO8eb0Y9IjJ46sCJSCN9BRhTbu8EpgPnATc0sab/R7/H1hsWkRalDpyINNK/gnfPVn7fGlNeBFyFLRIvIiIDoAAnIs32MrY+IwAx5dnADKAN6AaWATOCdztjym8BbgM+BBwE3A/MDN6tK2uUfhuYChwC/Am4HOgAtgGXBu9+Vo6xDHhX8G5c+f0U4HfYIu97gDlYd3BM2f+K4N3DZdtNwM+BT5aS28t+twGnYV/s+ZP9+UBiyscCNwKTgeexMfM1Zc3NNmzkORX4evmcOoALgnddZf924BbgZKALW9B8VvCurdQPsDSmfCfwtfL7WTHlJcAx2ELnnw3ePbE/70NE6kcjVBFpipjyATHlScBM4BflsXOxQHE5MBYLUR8DLiq7zQccMAU4CTuP7gfluUuBj5btTwDWAncBPcCvyz7ElA8ETgeOiykfVvY9C0jBu5eAm7Bu4BfKMZ4AfhNTHlMp/3PAOeXWDSwvP99T6v/yfnwuBwH3AevK8adhgXXRPpvOBS4EJgGHAd8s+x9S3u86YCKwELi2st+k8nMacFnl8Yux4Hwy9pl9b6jvQUTqT1eB1bUAAAQpSURBVAFORBppYUy5O6bcjXXdHgT+iI1QAf4GTA/eLQvebQ7eRWAlML48fzSwE9gUvFuLBZirKs+9BGwO3m3AQuD52N+5eygBDgsoLwBPYR0usAD3q5jyW4ELgNnBu+XBu4yFmi1YQKxZErzrCN51AO8HjsI6Vk8G7+6mhKkh+hSwK3g3M3jXFbxbBXweuDCmPLqy3XXBu/uDd53Ad+gNZp/AAtjFwbscvPsxcHNtp+Dd8+Xu9uDdjsrrXR28W1W6bjdj4VFEhimNUEWkkb6BrSEIsAt4Lni3d3wavEsx5Ykx5fnA8Vgn7XhsBAjWTYrAtrKgdATuKM/dio0Vt8SU1wBLgR8G73bHlO8D7owpHwV4LBTuASbHlFcA7wXOBY4FDgRWV2raE1N+qNRSs6FyfzwWGv9ReewPg/5kep0AHFNCbs0oLIiOxcInWIet5p/AG8v9E4HHgne7Ks+vpnfk25enK/e3AwcPsm4RaSB14ESkkf4evFtfbn+thjeAmPJ0rCv3NuBeLFTF2vOlG3Uk8GlgKzAPWBNTPjh49xTWhTsH6MTGg4/ElA8P3m3DzombQm+AW4mNUs8EuoJ3m7EO3ms5AAt2Nf+u3O/BAlbVLobuDVjgenfl1o6Ftyf/xzFqNbzM0P627x7CPiLSJApwIjKcXAEsCN5dErxbjJ1MP5YSTmLKXwROD94tCd5NB04BxgEnxpSnAR8P3sXg3Qysm/Z2LKSBjVE/iF1oUAtw7djIcXnZZj0WgE6rFRRTHgWcCuQ+au4E2vZZE3HikD8BO85Y4Jla2MXOcVsAvGkA+z8OTIgpV7ed1NfGItKaNEIVkeFkK+BjyuOw0DYbC2id5fkjgJllvLgF+Aw2PuzCLiC4Nqb8AtapOhv7J/XRsu892PefPRe82wgQU96CjV09QPDuxZjyTcC3yjE2ArOwKz2/30fNK7DQ9aOY8pewDuGcAbzXiTHlV/Z5rBMbMc/FRr7zgdHAYmBD8G5HTPnQfl73p8B1wK0x5QXYSHUWvaNXsAsuxpfRsIi0IHXgRGQ4uQwbSXYAvwXejJ03V+toXYONVpdgoelM4Ozg3XbsRP5bsK/z6MJO/J9aLnYAOy+tG+u81azEAuCDlcfmlNe/A3gEOyfNB++q55ztFbx7BfgI1rlbg32VycIBvNd5WKis3j4QvNuJdQoPLTX/EhupnjeA1yR49yIWXidgHcyrgdt59cj1eiwkLh7Ia4rI8DOqp6en2TWIiMjrJKZ8NHBkOV+w9tiVwIeDd1P63lNEWolGqCIiI8toYEW5IOQB4DhsFD2vmUWJyOtLI1QRkREkePcY9sXHc7FR8nexsa6+mFdkBNEIVURERKTFqAMnIiIi0mIU4ERERERajAKciIiISItRgBMRERFpMQpwIiIiIi1GAU5ERESkxfwXwjmmi6nDXjkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot mean total times pwned by password length\n",
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "ax = df.groupby('Length')['Total'].mean().iloc[:-2].plot(\n",
    "    kind='bar', title='Total Times Password Pwned (Mean)\\nby Password Length')\n",
    "ax.set_xlabel('Password Length')\n",
    "ax.set_ylabel('Times Pwned (Mean)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Save figure\n",
    "# fig.savefig('Visualizations/mean_pwned_by_length', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0.5, 'Frequency'),\n",
       " Text(0.5, 0, 'Password Length'),\n",
       " Text(0.5, 1.0, 'Distribution of Pwned Password Lengths')]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the distribution of pwned password lengths\n",
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "ax = sns.countplot(x='Length', data=df)\n",
    "ax.set(title='Distribution of Pwned Password Lengths', xlabel='Password Length',\n",
    "       ylabel='Frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Save figure\n",
    "# fig.savefig('Visualizations/pwd_length_dist.png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "rep_y = [df[df['Rep_2']==1]['Total'].mean(), df[df['Rep_3']==1]['Total'].mean(),\n",
    "         df[df['Rep_4']==1]['Total'].mean(), df[df['Rep_5']==1]['Total'].mean(),\n",
    "         df[df['Rep_6']==1]['Total'].mean(), df[df['Rep_7']==1]['Total'].mean(),\n",
    "         df[df['Rep_8']==1]['Total'].mean(), df[df['Rep_9']==1]['Total'].mean(),\n",
    "         df[df['Rep_10']==1]['Total'].mean()]\n",
    "rep_x = ['One', 'Two', 'Three', 'Four', 'Five', 'Six', 'Seven', 'Eight', 'Nine', 'Ten']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'list' object has no attribute 'set_xlabel'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m----------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-43-7f1c06aa71c0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'bar'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtitle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Total Times Password Pwned (Mean)\\nby Password Length'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_xlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Password Length'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_ylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Times Pwned (Mean)'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'list' object has no attribute 'set_xlabel'"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot mean total times pwned by num of repeated characters\n",
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "ax = plt.plot(x=[1,2,3], y=[2,4,6], kind='bar', title='Total Times Password Pwned (Mean)\\nby Password Length')\n",
    "ax.set_xlabel('Password Length')\n",
    "ax.set_ylabel('Times Pwned (Mean)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}