rating.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div align=\"center\">\n",
    "    <img src=\"images/logo.png\" width=\"250\" height=\"200\">\n",
    "</div>\n",
    "\n",
    "<div align=\"center\">\n",
    "    To download the previously cleaned dataset click <a href=\"https://drive.google.com/file/d/1mYJws7uPjbPqPszUQHpf4PfrHSEjAVIu/view?usp=sharing\" target=\"__blank\">here</a>.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "XbxxfCPcpgC9"
   },
   "outputs": [],
   "source": [
    "# import packages\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# set max rows displayed in output to 25\n",
    "pd.set_option(\"display.max_rows\", 25)\n",
    "\n",
    "df = pd.read_pickle('clean_complaints.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 686
    },
    "id": "HxgPxD8rsbjV",
    "outputId": "68872cd1-f5cd-487e-df72-653942184a3d"
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date received</th>\n",
       "      <th>Product</th>\n",
       "      <th>Sub-product</th>\n",
       "      <th>Issue</th>\n",
       "      <th>Sub-issue</th>\n",
       "      <th>Consumer complaint narrative</th>\n",
       "      <th>Company public response</th>\n",
       "      <th>Company</th>\n",
       "      <th>State</th>\n",
       "      <th>ZIP code</th>\n",
       "      <th>Consumer consent provided?</th>\n",
       "      <th>Submitted via</th>\n",
       "      <th>Date sent to company</th>\n",
       "      <th>Company response to consumer</th>\n",
       "      <th>Timely response?</th>\n",
       "      <th>Consumer disputed?</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Complaint ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3379500</th>\n",
       "      <td>2019-09-19</td>\n",
       "      <td>Credit reporting, credit repair services, or o...</td>\n",
       "      <td>Credit reporting</td>\n",
       "      <td>Incorrect information on your report</td>\n",
       "      <td>Information belongs to someone else</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>Company has responded to the consumer and the ...</td>\n",
       "      <td>Experian Information Solutions Inc.</td>\n",
       "      <td>PA</td>\n",
       "      <td>15206</td>\n",
       "      <td>Consent not provided</td>\n",
       "      <td>Web</td>\n",
       "      <td>2019-09-20</td>\n",
       "      <td>Closed with non-monetary relief</td>\n",
       "      <td>Yes</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3255455</th>\n",
       "      <td>2019-05-23</td>\n",
       "      <td>Checking or savings account</td>\n",
       "      <td>Checking account</td>\n",
       "      <td>Managing an account</td>\n",
       "      <td>Deposits and withdrawals</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>Company has responded to the consumer and the ...</td>\n",
       "      <td>MIDFIRST BANK</td>\n",
       "      <td>AZ</td>\n",
       "      <td>85254</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>Referral</td>\n",
       "      <td>2019-05-28</td>\n",
       "      <td>Closed with explanation</td>\n",
       "      <td>Yes</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4267123</th>\n",
       "      <td>2021-04-02</td>\n",
       "      <td>Credit reporting, credit repair services, or o...</td>\n",
       "      <td>Credit reporting</td>\n",
       "      <td>Incorrect information on your report</td>\n",
       "      <td>Information belongs to someone else</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>EQUIFAX, INC.</td>\n",
       "      <td>PA</td>\n",
       "      <td>19403</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>Web</td>\n",
       "      <td>2021-04-02</td>\n",
       "      <td>Closed with explanation</td>\n",
       "      <td>Yes</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3446074</th>\n",
       "      <td>2019-11-20</td>\n",
       "      <td>Credit card or prepaid card</td>\n",
       "      <td>General-purpose credit card or charge card</td>\n",
       "      <td>Closing your account</td>\n",
       "      <td>Company closed your account</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>Company has responded to the consumer and the ...</td>\n",
       "      <td>PENTAGON FEDERAL CREDIT UNION</td>\n",
       "      <td>VA</td>\n",
       "      <td>22304</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>Referral</td>\n",
       "      <td>2019-11-21</td>\n",
       "      <td>Closed with explanation</td>\n",
       "      <td>Yes</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4239229</th>\n",
       "      <td>2021-03-23</td>\n",
       "      <td>Money transfer, virtual currency, or money ser...</td>\n",
       "      <td>Foreign currency exchange</td>\n",
       "      <td>Fraud or scam</td>\n",
       "      <td>NaN</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>Square Inc.</td>\n",
       "      <td>CA</td>\n",
       "      <td>91387</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>Web</td>\n",
       "      <td>2021-03-23</td>\n",
       "      <td>Closed with explanation</td>\n",
       "      <td>Yes</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Date received                                            Product  \\\n",
       "Complaint ID                                                                    \n",
       "3379500         2019-09-19  Credit reporting, credit repair services, or o...   \n",
       "3255455         2019-05-23                        Checking or savings account   \n",
       "4267123         2021-04-02  Credit reporting, credit repair services, or o...   \n",
       "3446074         2019-11-20                        Credit card or prepaid card   \n",
       "4239229         2021-03-23  Money transfer, virtual currency, or money ser...   \n",
       "\n",
       "                                             Sub-product  \\\n",
       "Complaint ID                                               \n",
       "3379500                                 Credit reporting   \n",
       "3255455                                 Checking account   \n",
       "4267123                                 Credit reporting   \n",
       "3446074       General-purpose credit card or charge card   \n",
       "4239229                        Foreign currency exchange   \n",
       "\n",
       "                                             Issue  \\\n",
       "Complaint ID                                         \n",
       "3379500       Incorrect information on your report   \n",
       "3255455                        Managing an account   \n",
       "4267123       Incorrect information on your report   \n",
       "3446074                       Closing your account   \n",
       "4239229                              Fraud or scam   \n",
       "\n",
       "                                        Sub-issue  \\\n",
       "Complaint ID                                        \n",
       "3379500       Information belongs to someone else   \n",
       "3255455                  Deposits and withdrawals   \n",
       "4267123       Information belongs to someone else   \n",
       "3446074               Company closed your account   \n",
       "4239229                                       NaN   \n",
       "\n",
       "             Consumer complaint narrative  \\\n",
       "Complaint ID                                \n",
       "3379500                              <NA>   \n",
       "3255455                              <NA>   \n",
       "4267123                              <NA>   \n",
       "3446074                              <NA>   \n",
       "4239229                              <NA>   \n",
       "\n",
       "                                        Company public response  \\\n",
       "Complaint ID                                                      \n",
       "3379500       Company has responded to the consumer and the ...   \n",
       "3255455       Company has responded to the consumer and the ...   \n",
       "4267123                                                    <NA>   \n",
       "3446074       Company has responded to the consumer and the ...   \n",
       "4239229                                                    <NA>   \n",
       "\n",
       "                                          Company State ZIP code  \\\n",
       "Complaint ID                                                       \n",
       "3379500       Experian Information Solutions Inc.    PA    15206   \n",
       "3255455                             MIDFIRST BANK    AZ    85254   \n",
       "4267123                             EQUIFAX, INC.    PA    19403   \n",
       "3446074             PENTAGON FEDERAL CREDIT UNION    VA    22304   \n",
       "4239229                               Square Inc.    CA    91387   \n",
       "\n",
       "             Consumer consent provided? Submitted via Date sent to company  \\\n",
       "Complaint ID                                                                 \n",
       "3379500            Consent not provided           Web           2019-09-20   \n",
       "3255455                            <NA>      Referral           2019-05-28   \n",
       "4267123                            <NA>           Web           2021-04-02   \n",
       "3446074                            <NA>      Referral           2019-11-21   \n",
       "4239229                            <NA>           Web           2021-03-23   \n",
       "\n",
       "                 Company response to consumer Timely response?  \\\n",
       "Complaint ID                                                     \n",
       "3379500       Closed with non-monetary relief              Yes   \n",
       "3255455               Closed with explanation              Yes   \n",
       "4267123               Closed with explanation              Yes   \n",
       "3446074               Closed with explanation              Yes   \n",
       "4239229               Closed with explanation              Yes   \n",
       "\n",
       "             Consumer disputed?  \n",
       "Complaint ID                     \n",
       "3379500                    <NA>  \n",
       "3255455                    <NA>  \n",
       "4267123                    <NA>  \n",
       "3446074                    <NA>  \n",
       "4239229                    <NA>  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 402
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    "id": "mig0hUOkswLk",
    "outputId": "9ea430a6-86b6-4ca1-f8bb-c9b7237d40a8"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company response to consumer</th>\n",
       "      <th>Timely response?</th>\n",
       "      <th>Consumer disputed?</th>\n",
       "      <th>Company</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Complaint ID</th>\n",
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       "      <th>3379500</th>\n",
       "      <td>Closed with non-monetary relief</td>\n",
       "      <td>Yes</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>Experian Information Solutions Inc.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3255455</th>\n",
       "      <td>Closed with explanation</td>\n",
       "      <td>Yes</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>MIDFIRST BANK</td>\n",
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       "      <th>4267123</th>\n",
       "      <td>Closed with explanation</td>\n",
       "      <td>Yes</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>EQUIFAX, INC.</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "                 Company response to consumer Timely response?  \\\n",
       "Complaint ID                                                     \n",
       "3379500       Closed with non-monetary relief              Yes   \n",
       "3255455               Closed with explanation              Yes   \n",
       "4267123               Closed with explanation              Yes   \n",
       "\n",
       "             Consumer disputed?                              Company  \n",
       "Complaint ID                                                          \n",
       "3379500                    <NA>  Experian Information Solutions Inc.  \n",
       "3255455                    <NA>                        MIDFIRST BANK  \n",
       "4267123                    <NA>                        EQUIFAX, INC.  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neccesary_df = df[['Company response to consumer','Timely response?', 'Consumer disputed?', 'Company']]\n",
    "neccesary_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "id": "MANl1OA-9oWB"
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Company Name</th>\n",
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       "      <th>Experian Information Solutions Inc.</th>\n",
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       "      <th>MIDFIRST BANK</th>\n",
       "      <td>0</td>\n",
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       "      <th>EQUIFAX, INC.</th>\n",
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       "      <th>PENTAGON FEDERAL CREDIT UNION</th>\n",
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       "      <th>Square Inc.</th>\n",
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      "text/plain": [
       "                                     Rating\n",
       "Company Name                               \n",
       "Experian Information Solutions Inc.       0\n",
       "MIDFIRST BANK                             0\n",
       "EQUIFAX, INC.                             0\n",
       "PENTAGON FEDERAL CREDIT UNION             0\n",
       "Square Inc.                               0"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "company_ratings = pd.DataFrame(\n",
    "    {'Company Name': neccesary_df['Company'].unique().tolist(),\n",
    "     'Rating': 0})\n",
    "\n",
    "company_ratings.set_index('Company Name', inplace=True)\n",
    "\n",
    "# initial ratings for each unique company\n",
    "company_ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "id": "NdcOUNXRiPO3"
   },
   "outputs": [],
   "source": [
    "def rating(company_name):\n",
    "    company_df = df[df['Company'] == company_name]\n",
    "    complaints = company_df.shape[0] # -0.5\n",
    "    timely_responses = company_df[company_df['Timely response?'] == 'Yes'].shape[0] # 0.5\n",
    "    non_timely_responses = complaints - timely_responses # -1\n",
    "    \n",
    "    responses = company_df['Company response to consumer'].dropna().tolist()\n",
    "    \n",
    "    non_monetary_relief = responses.count('Closed with non-monetary relief') # 1\n",
    "    monetary_relief = responses.count('Closed with monetary relief') # 1\n",
    "    untimely_response = responses.count('Untimely response') # -1\n",
    "    relief = responses.count('Closed with relief') # 0.5\n",
    "    explanation = responses.count('Closed with explanation') # -0.5\n",
    "    in_progress = responses.count('In progress') # no value\n",
    "    closed = responses.count('Closed') # no value\n",
    "    no_relief = responses.count('Closed without relief') # -0.5\n",
    "        \n",
    "    consumer_disputes = company_df[company_df['Consumer disputed?'] == 'Yes'].shape[0] # -1\n",
    "    \n",
    "    rating = - 0.5 * complaints + 0.5 * timely_responses - 1 * non_timely_responses\n",
    "    rating += 1 * non_monetary_relief + 1 * monetary_relief - 1 * untimely_response + 0.5 * relief - 0.5 * explanation - 0.5 * no_relief\n",
    "    rating -= consumer_disputes\n",
    "    \n",
    "    return rating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "id": "My-Nfm0-jeCG"
   },
   "outputs": [],
   "source": [
    "for company, row in company_ratings.iterrows():\n",
    "    company_ratings.at[company, 'Rating'] = rating(company)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
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     "base_uri": "https://localhost:8080/",
     "height": 35
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    "id": "fB1z62UJlCkh",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "Rating    float64\n",
       "dtype: object"
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     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "company_ratings.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "id": "fiYQ7mEMWQos"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                      Rating\n",
      "Company Name                                \n",
      "Experian Information Solutions Inc. -12375.5\n",
      "MIDFIRST BANK                          -75.0\n",
      "EQUIFAX, INC.                       -48771.0\n",
      "PENTAGON FEDERAL CREDIT UNION         -196.0\n",
      "Square Inc.                           -344.0\n",
      "...                                      ...\n",
      "Mathew Aaron Holdings Inc.               1.0\n",
      "Pando Mortgage, LLC                     -0.5\n",
      "Square One Financial, LLC               -0.5\n",
      "Cash Security, Inc.                     -0.5\n",
      "Rating                                   0.0\n",
      "\n",
      "[4854 rows x 1 columns]\n"
     ]
    },
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0\n",
       "0  0.746550\n",
       "1  0.985160\n",
       "2  0.040533\n",
       "3  0.982813\n",
       "4  0.979942"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "\n",
    "x = company_ratings\n",
    "\n",
    "print(x)\n",
    "\n",
    "min_max_scaler = preprocessing.MinMaxScaler()\n",
    "x_scaled = min_max_scaler.fit_transform(x)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rating</th>\n",
       "      <th>normalized</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Company Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Experian Information Solutions Inc.</th>\n",
       "      <td>-12375.5</td>\n",
       "      <td>74.654950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MIDFIRST BANK</th>\n",
       "      <td>-75.0</td>\n",
       "      <td>98.516018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EQUIFAX, INC.</th>\n",
       "      <td>-48771.0</td>\n",
       "      <td>4.053307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PENTAGON FEDERAL CREDIT UNION</th>\n",
       "      <td>-196.0</td>\n",
       "      <td>98.281297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Square Inc.</th>\n",
       "      <td>-344.0</td>\n",
       "      <td>97.994200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      Rating  normalized\n",
       "Company Name                                            \n",
       "Experian Information Solutions Inc. -12375.5   74.654950\n",
       "MIDFIRST BANK                          -75.0   98.516018\n",
       "EQUIFAX, INC.                       -48771.0    4.053307\n",
       "PENTAGON FEDERAL CREDIT UNION         -196.0   98.281297\n",
       "Square Inc.                           -344.0   97.994200"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "company_ratings['normalized'] = x_scaled * 100\n",
    "\n",
    "company_ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rating</th>\n",
       "      <th>normalized</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Company Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>IQOR US INC</th>\n",
       "      <td>690.0</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Capio Partners, LLC</th>\n",
       "      <td>420.0</td>\n",
       "      <td>99.476242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The CMI Group, Inc.</th>\n",
       "      <td>377.0</td>\n",
       "      <td>99.392828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CL Holdings LLC</th>\n",
       "      <td>313.0</td>\n",
       "      <td>99.268678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Empowerment Ventures, LLC</th>\n",
       "      <td>250.0</td>\n",
       "      <td>99.146468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dynamic Recovery Solutions, LLC</th>\n",
       "      <td>212.5</td>\n",
       "      <td>99.073724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The Law Offices of Mitchell D. Bluhm &amp; Associates</th>\n",
       "      <td>158.5</td>\n",
       "      <td>98.968972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paypal Holdings, Inc</th>\n",
       "      <td>122.5</td>\n",
       "      <td>98.899138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Windham Professionals, Inc.</th>\n",
       "      <td>120.5</td>\n",
       "      <td>98.895258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Credit Bureau Collection Services, Inc.</th>\n",
       "      <td>114.0</td>\n",
       "      <td>98.882649</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   Rating  normalized\n",
       "Company Name                                                         \n",
       "IQOR US INC                                         690.0  100.000000\n",
       "Capio Partners, LLC                                 420.0   99.476242\n",
       "The CMI Group, Inc.                                 377.0   99.392828\n",
       "CL Holdings LLC                                     313.0   99.268678\n",
       "Empowerment Ventures, LLC                           250.0   99.146468\n",
       "Dynamic Recovery Solutions, LLC                     212.5   99.073724\n",
       "The Law Offices of Mitchell D. Bluhm & Associates   158.5   98.968972\n",
       "Paypal Holdings, Inc                                122.5   98.899138\n",
       "Windham Professionals, Inc.                         120.5   98.895258\n",
       "Credit Bureau Collection Services, Inc.             114.0   98.882649"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "company_ratings.sort_values(by=['normalized'], ascending=False, inplace=True)\n",
    "company_ratings.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rating</th>\n",
       "      <th>normalized</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Company Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Navient Solutions, LLC.</th>\n",
       "      <td>-6803.0</td>\n",
       "      <td>85.464738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CITIBANK, N.A.</th>\n",
       "      <td>-7820.5</td>\n",
       "      <td>83.490946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NATIONSTAR MORTGAGE</th>\n",
       "      <td>-8740.5</td>\n",
       "      <td>81.706288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Experian Information Solutions Inc.</th>\n",
       "      <td>-12375.5</td>\n",
       "      <td>74.654950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ocwen Financial Corporation</th>\n",
       "      <td>-12427.5</td>\n",
       "      <td>74.554078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JPMORGAN CHASE &amp; CO.</th>\n",
       "      <td>-16222.5</td>\n",
       "      <td>67.192365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BANK OF AMERICA, NATIONAL ASSOCIATION</th>\n",
       "      <td>-19060.5</td>\n",
       "      <td>61.687084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WELLS FARGO &amp; COMPANY</th>\n",
       "      <td>-23906.5</td>\n",
       "      <td>52.286593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EQUIFAX, INC.</th>\n",
       "      <td>-48771.0</td>\n",
       "      <td>4.053307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRANSUNION INTERMEDIATE HOLDINGS, INC.</th>\n",
       "      <td>-50860.5</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         Rating  normalized\n",
       "Company Name                                               \n",
       "Navient Solutions, LLC.                 -6803.0   85.464738\n",
       "CITIBANK, N.A.                          -7820.5   83.490946\n",
       "NATIONSTAR MORTGAGE                     -8740.5   81.706288\n",
       "Experian Information Solutions Inc.    -12375.5   74.654950\n",
       "Ocwen Financial Corporation            -12427.5   74.554078\n",
       "JPMORGAN CHASE & CO.                   -16222.5   67.192365\n",
       "BANK OF AMERICA, NATIONAL ASSOCIATION  -19060.5   61.687084\n",
       "WELLS FARGO & COMPANY                  -23906.5   52.286593\n",
       "EQUIFAX, INC.                          -48771.0    4.053307\n",
       "TRANSUNION INTERMEDIATE HOLDINGS, INC. -50860.5    0.000000"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "company_ratings.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# build a horizontal bar chart for top companies with most complaints\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import StrMethodFormatter\n",
    "\n",
    "ax = company_ratings['normalized'].tail(10).plot(kind='barh', figsize=(8, 10), color='#d42c20', zorder=2, width=0.85)\n",
    "\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "\n",
    "# Switch off ticks\n",
    "ax.tick_params(axis=\"both\", which=\"both\", bottom=\"off\", top=\"off\", labelbottom=\"on\", left=\"off\", right=\"off\", labelleft=\"on\")\n",
    "\n",
    "# Draw vertical axis lines\n",
    "vals = ax.get_xticks()\n",
    "for tick in vals:\n",
    "  ax.axvline(x=tick, linestyle='dashed', alpha=0.4, color='#eeeeee', zorder=1)\n",
    "\n",
    "# Set x-axis label\n",
    "ax.set_xlabel(\"Rating\", labelpad=20, weight='bold', size=12)\n",
    "\n",
    "# Set y-axis label\n",
    "ax.set_ylabel(\"Companies\", labelpad=20, weight='bold', size=12)\n",
    "\n",
    "# Format y-axis label\n",
    "ax.xaxis.set_major_formatter(StrMethodFormatter('{x:,g}'))\n",
    "\n",
    "ax.figure.savefig('worst_companies.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "name": "Bank.ipynb",
   "provenance": [],
   "toc_visible": true
  },
  "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": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}