{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SolasAI Disparity Introduction"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quick Start Summary\n",
    "\n",
    "This example serves as an introductory text aiming to teach a user how to measure disparities with the SolasAI library. While the example in this notebook is based on the calculation of a single metric, the Adverse Impact Ratio (AIR), there are numerous other metrics available in SolasAI, as well as a generic metric interface for developing custom disparity metrics. Usage of the other metrics is discussed in other examples.\n",
    "\n",
    "This notebook provides:\n",
    "\n",
    "1. A short background on terms used throughout the SolasAI library.\n",
    "\n",
    "2. An explanation of how to import and call functionality in the SolasAI disparity testing library.\n",
    "\n",
    "3. An example of how to calculate an Adverse Impact Ratio using U.S. Home Mortgage Disclosure Act (HMDA) data.\n",
    "\n",
    "4. An overview of how to make customized or formatted tables and charts using the SolasAI disparity testing library."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Background\n",
    "\n",
    "Before jumping into the code, we provide a short explanation of several terms used in the code."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### \"Protected\" and \"Reference\" Groups\n",
    "\n",
    "In SolasAI's current list of curated metrics, there is an assumption that one will test whether some group(s) achieve outcomes that are _at least as good_ as another group's outcomes. The first group(s), identified in SolasAI as `protected_groups`, typically consists of people who have been or are continuing to experience some form of discrimination or disadvantage.  The outcomes of these groups are then compared to those of the `reference_groups`, which typically have not been subject to the same type of discrimination or disadvantages.\n",
    "\n",
    "Outside SolasAI, there are numerous ways that these classifications are made and described. For example, often the word \"protected\" may be replaced with \"minority\", \"disadvantaged\", \"test\", or some other description, while \"reference\" may be replaced with \"majority\", \"advantaged\", \"control\", or some other description. Our use of the \"protected\" and \"reference\" categorization comes from our experience working in litigation and regulatory compliance in the United States. It is not meant to imply any particular value judgement regarding the classifications used.\n",
    "\n",
    "A third attribute, `group_categories` is also used throughout the SolasAI library to delineate between different protected-reference group combinations. For example, `group_categories` might be race, sex, age, medical diagnoses, etc. By way of example, classifications most commonly used by SolasAI's U.S. customers are:\n",
    "\n",
    "<center/>\n",
    "\n",
    "| `group_categories` | `protected_group` | `reference_group` |\n",
    "|--------------------|-------------------|-------------------|\n",
    "| Race               | Black             | White             |\n",
    "| Race               | Hispanic          | White             |\n",
    "| Race               | Asian             | White             |\n",
    "| Sex / Gender       | Female            | Male              |\n",
    "| Age                | Age >= X          | Age < X           |\n",
    "</center>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "### Outcomes, Labels, and Segments\n",
    "\n",
    "SolasAI can be used to measure disparities created as the result of automated systems, semi-automated systems, or entirely subjective processes.  Regardless of the use case, when the user calculates disparity, they will need to specify the `outcome` attribute.  In some cases, this will be a binary (i.e., \"Yes\" or \"No\") outcome, such as whether a person was offered a job, loan, or sent a marketing offer.  In other cases, it may be a continuous value, such as a model's probability of loan default, the amount of time it takes for a person to be promoted, or an employee's pay rate.  Of the metrics SolasAI provides, some are appropriate for the binary case (e.g., `adverse_impact_ratio`), while others are appropriate for analyses of continuous values (e.g., `standardized_mean_difference`).\n",
    "\n",
    "When measuring disparities that arise from the use of a model, and when the true outcome is known, a user can specify the `label` attribute.  Certain metrics, such as the `residual_standardized_mean_difference`, require the label to be present because the disparity measurement incorporates the label.\n",
    "\n",
    "Some metrics, including the `segmented_adverse_impact_ratio` perform analyses on subsets of the data and then aggregate the results.  SolasAI refers to these subsets using the `segment` attribute.  Examples of segments might be different store locations, job openings, or job types.  Care should be used when deciding whether to incorporate segmentation into an analysis."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Statistical Significance and Practical Significance\n",
    "\n",
    "SolasAI uses thresholds of statistical significance and \"practical\" significance to determine whether any potential disparities found are sufficiently large to warrant further review.  The SolasAI does not provide guidance as to which standards are appropriate.  What constitute appropriate and reasonable standards may be driven by regulatory and legal requirements, business decisions, or other factors.  We suggest consulting with one's compliance department, legal advisors, or consultants such as [BLDS, LLC](https://www.bldsllc.com/) (the consultancy from which SolasAI was founded) for advice.\n",
    "\n",
    "That said, after one decides what thresholds to use, SolasAI allows users to test whether the disparity metrics exceed those thresholds.  In the case of statistical significance, each metric uses a measurement of statistical significance that is appropriate or commonly used for that metric.  As an example, in the case of the AIR, statistical significance is calculated using either a Fisher's Exact test or a Chi-Squared test (depending on the size of the data).  For the SMD, a t-test is used.  In certain cases, the user can specify either the test itself or certain attributes of the test.  In United States legal and regulatory standards, a two-sided p-value less than 5% (or, equivilently, a one-sided p-value less than 2.5%) is generally considered statistically significant.  SolasAI does not provide guidance on whether this standard is reasonable or appropriate for any particular use case.\n",
    "\n",
    "Importantly, for SolasAI to identify that a result is \"practically significant\", it must _both_ be found to be statistically significant and exceed the practical significance thresholds set.  Most metrics in SolasAI provide the option to specify two thresholds.  The first is a `percent_difference_threshold`.  If a user sets a value greater than zero, then the raw difference in outcomes between the protected and reference groups must exceed a particular value before a result is considered practically significant.  For example, if `percent_difference_threshold = 0.02` for the AIR, and we find that Black applicants receive offers 1% of the time, but White applicants receive them 2.5% of the time, then this disparity will not be significant because the difference in outcomes is only 1.5%, which is less thna the 2% threshold.\n",
    "\n",
    "The second practical significance metric relates to the value of the metric itself.  For example, when calling the `adverse_impact_ratio`, one sets the `air_threshold` to a particular value.  In the example below, we use `air_threshold=0.80`.  This means that only results that are statistically significantly different from parity _and_ that have AIR values less than 0.80 will be considered practically significant."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing the Library and Data for Use in the Analysis\n",
    "\n",
    "Below, we import the `pandas` and `plotly` libraries used to prepare and graph the data.  SolasAI relies on the plotly library for graphing.  The final line of code, `pio.renderers.default = 'svg'` is only necessary because this workbook is hosted in GitHub, which cannot render plotly graphics in their native format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pathlib import Path"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Certain notebook environments have limited rendering functionality.\n",
    "Uncomment this cell as a potential workaround if plots are not\n",
    "displaying."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import plotly.io as pio\n",
    "# pio.renderers.default = \"png\""
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's preferable to explicitly and specifically handle warnings. For the\n",
    "purposes of this notebook, we will filter out all warnings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from warnings import simplefilter\n",
    "simplefilter(\"ignore\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SolasAI's disparity library is imported just like any standard Python package.  Here, we import the library itself, which will be accessed as `sd`.  Within sd, we will also access several other types of functions, including:\n",
    "\n",
    "1. The interface functionality, `sd.ui`, which allows users to do things like create nicely formatted tables.\n",
    "2. The SolasAI constants file, `sd.const`, which allows users to customize numerous settings including column names and plot headings.\n",
    "3. Access to a set of utility functions, `sd.util`, which provide additional useful functionality.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import solas_disparity as sd"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Preparation\n",
    "\n",
    "The following code imports a sample of the 2018 Home Mortgage Disclosure Act (HMDA) data.  The HMDA dataset includes information about nearly every home mortgage application in the United States.  This dataset includes information about the mortgage itself, such as the loan term and APR; information about credit characteristics of the borrowers themselves, including the borrowers income and debt-to-income (DTI) ratio; and information about the home being purchased, such as its location and the value of the property.  Importantly, it also includes information about each borrower's race, gender, and ethnicity.  The data we are using is based only for applications where the borrower was approved for the loan."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Low-Priced</th>\n",
       "      <th>Interest Rate</th>\n",
       "      <th>Rate Spread</th>\n",
       "      <th>Loan Amount</th>\n",
       "      <th>Loan-to-Value Ratio</th>\n",
       "      <th>No Intro Rate Period</th>\n",
       "      <th>Intro Rate Period</th>\n",
       "      <th>Property Value</th>\n",
       "      <th>Income</th>\n",
       "      <th>Debt-to-Income Ratio</th>\n",
       "      <th>...</th>\n",
       "      <th>Hawaiian Or Pacific Islander</th>\n",
       "      <th>Hispanic</th>\n",
       "      <th>Non-Hispanic</th>\n",
       "      <th>Male</th>\n",
       "      <th>Female</th>\n",
       "      <th>Age &gt;= 62</th>\n",
       "      <th>Age &lt; 62</th>\n",
       "      <th>Race</th>\n",
       "      <th>Ethnicity</th>\n",
       "      <th>Sex</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13451</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.04875</td>\n",
       "      <td>0.00596</td>\n",
       "      <td>155000.0</td>\n",
       "      <td>0.9700</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>165000.0</td>\n",
       "      <td>35000.0</td>\n",
       "      <td>0.33</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>White</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18248</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.05750</td>\n",
       "      <td>0.01268</td>\n",
       "      <td>305000.0</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>295000.0</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>0.55</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19610</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.05500</td>\n",
       "      <td>0.01214</td>\n",
       "      <td>485000.0</td>\n",
       "      <td>0.9500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>515000.0</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>0.43</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>White</td>\n",
       "      <td>Non-Hispanic</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3339</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.03875</td>\n",
       "      <td>-0.00087</td>\n",
       "      <td>675000.0</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>675000.0</td>\n",
       "      <td>190000.0</td>\n",
       "      <td>0.33</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Black</td>\n",
       "      <td>Non-Hispanic</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19675</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.04375</td>\n",
       "      <td>0.00076</td>\n",
       "      <td>275000.0</td>\n",
       "      <td>0.3507</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>775000.0</td>\n",
       "      <td>209000.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>White</td>\n",
       "      <td>Non-Hispanic</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Low-Priced  Interest Rate  Rate Spread  Loan Amount  \\\n",
       "id                                                           \n",
       "13451         1.0        0.04875      0.00596     155000.0   \n",
       "18248         1.0        0.05750      0.01268     305000.0   \n",
       "19610         1.0        0.05500      0.01214     485000.0   \n",
       "3339          1.0        0.03875     -0.00087     675000.0   \n",
       "19675         1.0        0.04375      0.00076     275000.0   \n",
       "\n",
       "       Loan-to-Value Ratio  No Intro Rate Period  Intro Rate Period  \\\n",
       "id                                                                    \n",
       "13451               0.9700                     1                  0   \n",
       "18248               1.0000                     1                  0   \n",
       "19610               0.9500                     1                  0   \n",
       "3339                1.0000                     1                  0   \n",
       "19675               0.3507                     1                  0   \n",
       "\n",
       "       Property Value    Income  Debt-to-Income Ratio  ...  \\\n",
       "id                                                     ...   \n",
       "13451        165000.0   35000.0                  0.33  ...   \n",
       "18248        295000.0   60000.0                  0.55  ...   \n",
       "19610        515000.0  100000.0                  0.43  ...   \n",
       "3339         675000.0  190000.0                  0.33  ...   \n",
       "19675        775000.0  209000.0                  0.25  ...   \n",
       "\n",
       "       Hawaiian Or Pacific Islander  Hispanic Non-Hispanic Male  Female  \\\n",
       "id                                                                        \n",
       "13451                           0.0       1.0          0.0  1.0     0.0   \n",
       "18248                           NaN       NaN          NaN  NaN     NaN   \n",
       "19610                           0.0       0.0          1.0  NaN     NaN   \n",
       "3339                            0.0       0.0          1.0  NaN     NaN   \n",
       "19675                           0.0       0.0          1.0  NaN     NaN   \n",
       "\n",
       "       Age >= 62  Age < 62     Race     Ethnicity      Sex  \n",
       "id                                                          \n",
       "13451        NaN       NaN    White      Hispanic     Male  \n",
       "18248        NaN       NaN  Unknown       Unknown  Unknown  \n",
       "19610        1.0       0.0    White  Non-Hispanic  Unknown  \n",
       "3339         0.0       1.0    Black  Non-Hispanic  Unknown  \n",
       "19675        0.0       1.0    White  Non-Hispanic  Unknown  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"hmda.csv.gz\", index_col=\"id\")\n",
    "df.sample(random_state=161803, n=5)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We next specify the groups that we will use to test disparities.  In SolasAI, each protected and reference group must be its own variable in the input data.  These variable names are then included in the `protected_group`, `reference_group`, and `group_categories` lists that are used throughout the SolasAI library.\n",
    "\n",
    "For example, if we are going to test for evidence of disparities between Black and White applicants, we must have one variable that identifies whether the person represented by the observation is Black and one variable that identifies whether the person is White.  Importantly, missing values for these characteristics are generally allowed in SolasAI.\n",
    "\n",
    "While more groups are available for analysis in the HMDA data, we limit the analysis in order to make the output more tractable.  The categorization used in this example is as follows:\n",
    "\n",
    "<center/>\n",
    "\n",
    "| `group_categories` | `protected_group` | `reference_group` |\n",
    "|--------------------|-------------------|-------------------|\n",
    "| Race               | Black             | White             |\n",
    "| Race               | Native American   | White             |\n",
    "| Race               | Asian             | White             |\n",
    "| Sex                | Female            | Male              |\n",
    "| Ethnicity          | Hispanic          | Non-Hispanic      |\n",
    "\n",
    "</center>\n",
    "\n",
    "The three lists must all be the same length, with each element of the list corresponding to the same comparison (e.g., the first element of the lists below have `Black`, `White` and `Race`, meaning that Black applicants are being compared to White applicants, which is a comparison by race. The fifth elements of each list are `Female`, `Male`, and `Sex`, which means that women are being compared to men, and the type of comparison is by Sex).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "protected_groups = [\"Black\", \"Asian\", \"Native American\", \"Hispanic\", \"Female\"]\n",
    "reference_groups = [\"White\", \"White\", \"White\", \"Non-Hispanic\", \"Male\"]\n",
    "group_categories = [\"Race\", \"Race\", \"Race\", \"Ethnicity\", \"Sex\"]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While not demonstrated in this notebook, a key benefit to the SolasAI library is its ability to calculate disparities on groups where the characteristics are estimated, rather than known.\n",
    "\n",
    "In this case, each person's probability estimates are put into the fields identifying group membership.  For example, if the estimation procedure finds that a person has a 75% chance of being Black and a 25% chance of being White, then that person's Black and White variables would have values of 0.75 and 0.25, respectively.\n",
    "\n",
    "A common example of this occurs in race and ethnicity estimation, where a person's home address and last name are used to calculate the probability that the person is Black or White (This is known as the Bayesian Improved Surname Geocoding (\"BISG\") method.  See [here](https://github.com/cfpb/proxy-methodology) for more detail)."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculating the Adverse Impact Ratio on Prior Lending Decisions\n",
    "\n",
    "Determining whether there is evidence of discrimination requires, to the extent possible, testing a model or process before it is put into use as well as testing it when it is being used in production (i.e., having an effective monitoring process).  In this example, we focus on how an organization would monitor a process that is already in production.  Here, we use the HMDA data to test whether there is evidence that members of the protected groups were less likely to receive low-priced loans than members of the reference groups.\n",
    "\n",
    "This type of analysis can be performed on subjective decisions, such as loan officer decisions to underwrite a loan, or a manager deciding whom to promote.  It can also be performed on the outcomes of automated decisioning processes, such as the use of a model to screen applicants, give job offers, or some other similar process.  It can also be performed on a model's training or validation datasets prior to the model being used in production.\n",
    "\n",
    "Below, we use the `sd.adverse_impact_ratio` function to calculate the AIR.  More detail about the AIR can be found in the API documentation for <a href=\"../_api/solas_disparity.adverse_impact_ratio.html\">solas_disparity.adverse_impact_ratio</a>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "air = sd.adverse_impact_ratio(\n",
    "    group_data=df,  # dataset containing the protected and reference group information\n",
    "    protected_groups=protected_groups,\n",
    "    reference_groups=reference_groups,\n",
    "    group_categories=group_categories,\n",
    "    outcome=df[\"Low-Priced\"],\n",
    "    sample_weight=None,\n",
    "    air_threshold=0.80,\n",
    "    percent_difference_threshold=0,\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overview of SolasAI Disparity Objects with the AIR as an Example\n",
    "\n",
    "Before jumping into the results of the analysis, below we discuss common elements of the SolasAI disparity objects."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Disparity Object Output\n",
    "\n",
    "In a notebook, a user can display a formatted summary of results by displaying the results object using standard IPython notebook methods such as referencing the object in the last line of a cell or by calling the `display` function. This summary includes three elements:\n",
    "1. The disparity card, which summarizes information about the inputs and results of the test run.\n",
    "2. A summary table, which prints more detailed results (and is discussed below).\n",
    "3. A Plotly graph of the AIR metric.\n",
    "\n",
    "Two examples are shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "## Disparity Calculation: Adverse Impact Ratio"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┌───────────────────────────────────────────┬─────────────────────────────────────────────────────────────────────┐\n",
       "│ Protected Groups                          │ Black, Asian, Native American, Hispanic, Female                     │\n",
       "│ Reference Groups                          │ White, White, White, Non-Hispanic, Male                             │\n",
       "│ Group Categories                          │ Race, Race, Race, Ethnicity, Sex                                    │\n",
       "│ AIR Threshold                             │ 0.8                                                                 │\n",
       "│ Percent Difference Threshold              │ 0.0                                                                 │\n",
       "│ Shortfall Method                          │ to_reference_mean                                                   │\n",
       "│ Affected Groups                           │                                                                     │\n",
       "│ Affected Reference                        │                                                                     │\n",
       "│ Affected Categories                       │                                                                     │\n",
       "└───────────────────────────────────────────┴─────────────────────────────────────────────────────────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┌───────────────────────────────────────────┬─────────────────────────────────────────────────────────────────────┐\n",
       "│ Protected Groups                          │ Black, Asian, Native American, Hispanic, Female                     │\n",
       "│ Reference Groups                          │ White, White, White, Non-Hispanic, Male                             │\n",
       "│ Group Categories                          │ Race, Race, Race, Ethnicity, Sex                                    │\n",
       "│ AIR Threshold                             │ 0.8                                                                 │\n",
       "│ Percent Difference Threshold              │ 0.0                                                                 │\n",
       "│ Shortfall Method                          │ to_reference_mean                                                   │\n",
       "│ Affected Groups                           │                                                                     │\n",
       "│ Affected Reference                        │                                                                     │\n",
       "│ Affected Categories                       │                                                                     │\n",
       "└───────────────────────────────────────────┴─────────────────────────────────────────────────────────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "## Adverse Impact Ratio Summary Table"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "\\* Percent Missing: Ethnicity: 13.97%, Race: 13.88%, Sex: 46.40%"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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 class=\"dataframe disparity-table\" id=\"disp-output\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Group</th>\n",
       "      <th>Reference Group</th>\n",
       "      <th>Group Category</th>\n",
       "      <th>Total</th>\n",
       "      <th>Favorable</th>\n",
       "      <th>Percent Favorable</th>\n",
       "      <th>Percent Difference Favorable</th>\n",
       "      <th>AIR</th>\n",
       "      <th>P-Values</th>\n",
       "      <th>Practically Significant</th>\n",
       "      <th>Shortfall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Black</td>\n",
       "      <td>White</td>\n",
       "      <td>Race</td>\n",
       "      <td>1,337.0</td>\n",
       "      <td>1,065.0</td>\n",
       "      <td>79.66%</td>\n",
       "      <td>11.22%</td>\n",
       "      <td>0.877</td>\n",
       "      <td>0.000</td>\n",
       "      <td>No</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Asian</td>\n",
       "      <td>White</td>\n",
       "      <td>Race</td>\n",
       "      <td>1,286.0</td>\n",
       "      <td>1,224.0</td>\n",
       "      <td>95.18%</td>\n",
       "      <td>-4.30%</td>\n",
       "      <td>1.047</td>\n",
       "      <td>0.000</td>\n",
       "      <td>No</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Native American</td>\n",
       "      <td>White</td>\n",
       "      <td>Race</td>\n",
       "      <td>94.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>86.17%</td>\n",
       "      <td>4.71%</td>\n",
       "      <td>0.948</td>\n",
       "      <td>0.147</td>\n",
       "      <td>No</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>White</td>\n",
       "      <td></td>\n",
       "      <td>Race</td>\n",
       "      <td>14,461.0</td>\n",
       "      <td>13,142.0</td>\n",
       "      <td>90.88%</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Hispanic</td>\n",
       "      <td>Non-Hispanic</td>\n",
       "      <td>Ethnicity</td>\n",
       "      <td>2,032.0</td>\n",
       "      <td>1,593.0</td>\n",
       "      <td>78.40%</td>\n",
       "      <td>13.49%</td>\n",
       "      <td>0.853</td>\n",
       "      <td>0.000</td>\n",
       "      <td>No</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Non-Hispanic</td>\n",
       "      <td></td>\n",
       "      <td>Ethnicity</td>\n",
       "      <td>15,175.0</td>\n",
       "      <td>13,943.0</td>\n",
       "      <td>91.88%</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Female</td>\n",
       "      <td>Male</td>\n",
       "      <td>Sex</td>\n",
       "      <td>4,222.0</td>\n",
       "      <td>3,719.0</td>\n",
       "      <td>88.09%</td>\n",
       "      <td>1.28%</td>\n",
       "      <td>0.986</td>\n",
       "      <td>0.043</td>\n",
       "      <td>No</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Male</td>\n",
       "      <td></td>\n",
       "      <td>Sex</td>\n",
       "      <td>6,497.0</td>\n",
       "      <td>5,806.0</td>\n",
       "      <td>89.36%</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "air"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "## Disparity Calculation: Adverse Impact Ratio"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┌───────────────────────────────────────────┬─────────────────────────────────────────────────────────────────────┐\n",
       "│ Protected Groups                          │ Black, Asian, Native American, Hispanic, Female                     │\n",
       "│ Reference Groups                          │ White, White, White, Non-Hispanic, Male                             │\n",
       "│ Group Categories                          │ Race, Race, Race, Ethnicity, Sex                                    │\n",
       "│ AIR Threshold                             │ 0.8                                                                 │\n",
       "│ Percent Difference Threshold              │ 0.0                                                                 │\n",
       "│ Shortfall Method                          │ to_reference_mean                                                   │\n",
       "│ Affected Groups                           │                                                                     │\n",
       "│ Affected Reference                        │                                                                     │\n",
       "│ Affected Categories                       │                                                                     │\n",
       "└───────────────────────────────────────────┴─────────────────────────────────────────────────────────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┌───────────────────────────────────────────┬─────────────────────────────────────────────────────────────────────┐\n",
       "│ Protected Groups                          │ Black, Asian, Native American, Hispanic, Female                     │\n",
       "│ Reference Groups                          │ White, White, White, Non-Hispanic, Male                             │\n",
       "│ Group Categories                          │ Race, Race, Race, Ethnicity, Sex                                    │\n",
       "│ AIR Threshold                             │ 0.8                                                                 │\n",
       "│ Percent Difference Threshold              │ 0.0                                                                 │\n",
       "│ Shortfall Method                          │ to_reference_mean                                                   │\n",
       "│ Affected Groups                           │                                                                     │\n",
       "│ Affected Reference                        │                                                                     │\n",
       "│ Affected Categories                       │                                                                     │\n",
       "└───────────────────────────────────────────┴─────────────────────────────────────────────────────────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "## Adverse Impact Ratio Summary Table"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "\\* Percent Missing: Ethnicity: 13.97%, Race: 13.88%, Sex: 46.40%"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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 class=\"dataframe disparity-table\" id=\"disp-output\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Group</th>\n",
       "      <th>Reference Group</th>\n",
       "      <th>Group Category</th>\n",
       "      <th>Total</th>\n",
       "      <th>Favorable</th>\n",
       "      <th>Percent Favorable</th>\n",
       "      <th>Percent Difference Favorable</th>\n",
       "      <th>AIR</th>\n",
       "      <th>P-Values</th>\n",
       "      <th>Practically Significant</th>\n",
       "      <th>Shortfall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Black</td>\n",
       "      <td>White</td>\n",
       "      <td>Race</td>\n",
       "      <td>1,337.0</td>\n",
       "      <td>1,065.0</td>\n",
       "      <td>79.66%</td>\n",
       "      <td>11.22%</td>\n",
       "      <td>0.877</td>\n",
       "      <td>0.000</td>\n",
       "      <td>No</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Asian</td>\n",
       "      <td>White</td>\n",
       "      <td>Race</td>\n",
       "      <td>1,286.0</td>\n",
       "      <td>1,224.0</td>\n",
       "      <td>95.18%</td>\n",
       "      <td>-4.30%</td>\n",
       "      <td>1.047</td>\n",
       "      <td>0.000</td>\n",
       "      <td>No</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Native American</td>\n",
       "      <td>White</td>\n",
       "      <td>Race</td>\n",
       "      <td>94.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>86.17%</td>\n",
       "      <td>4.71%</td>\n",
       "      <td>0.948</td>\n",
       "      <td>0.147</td>\n",
       "      <td>No</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>White</td>\n",
       "      <td></td>\n",
       "      <td>Race</td>\n",
       "      <td>14,461.0</td>\n",
       "      <td>13,142.0</td>\n",
       "      <td>90.88%</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Hispanic</td>\n",
       "      <td>Non-Hispanic</td>\n",
       "      <td>Ethnicity</td>\n",
       "      <td>2,032.0</td>\n",
       "      <td>1,593.0</td>\n",
       "      <td>78.40%</td>\n",
       "      <td>13.49%</td>\n",
       "      <td>0.853</td>\n",
       "      <td>0.000</td>\n",
       "      <td>No</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Non-Hispanic</td>\n",
       "      <td></td>\n",
       "      <td>Ethnicity</td>\n",
       "      <td>15,175.0</td>\n",
       "      <td>13,943.0</td>\n",
       "      <td>91.88%</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Female</td>\n",
       "      <td>Male</td>\n",
       "      <td>Sex</td>\n",
       "      <td>4,222.0</td>\n",
       "      <td>3,719.0</td>\n",
       "      <td>88.09%</td>\n",
       "      <td>1.28%</td>\n",
       "      <td>0.986</td>\n",
       "      <td>0.043</td>\n",
       "      <td>No</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Male</td>\n",
       "      <td></td>\n",
       "      <td>Sex</td>\n",
       "      <td>6,497.0</td>\n",
       "      <td>5,806.0</td>\n",
       "      <td>89.36%</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import display\n",
    "\n",
    "display(air)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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 class=\"dataframe disparity-table\" id=\"disp-output\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Group</th>\n",
       "      <th>Reference Group</th>\n",
       "      <th>Group Category</th>\n",
       "      <th>Observations</th>\n",
       "      <th>Percent Missing</th>\n",
       "      <th>Total</th>\n",
       "      <th>Favorable</th>\n",
       "      <th>Percent Favorable</th>\n",
       "      <th>Percent Difference Favorable</th>\n",
       "      <th>AIR</th>\n",
       "      <th>P-Values</th>\n",
       "      <th>Practically Significant</th>\n",
       "      <th>Shortfall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Black</td>\n",
       "      <td>White</td>\n",
       "      <td>Race</td>\n",
       "      <td>17,224</td>\n",
       "      <td>13.88%</td>\n",
       "      <td>1,337.0</td>\n",
       "      <td>1,065.0</td>\n",
       "      <td>79.66%</td>\n",
       "      <td>11.22%</td>\n",
       "      <td>0.877</td>\n",
       "      <td>0.000</td>\n",
       "      <td>No</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Asian</td>\n",
       "      <td>White</td>\n",
       "      <td>Race</td>\n",
       "      <td>17,224</td>\n",
       "      <td>13.88%</td>\n",
       "      <td>1,286.0</td>\n",
       "      <td>1,224.0</td>\n",
       "      <td>95.18%</td>\n",
       "      <td>-4.30%</td>\n",
       "      <td>1.047</td>\n",
       "      <td>0.000</td>\n",
       "      <td>No</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Native American</td>\n",
       "      <td>White</td>\n",
       "      <td>Race</td>\n",
       "      <td>17,224</td>\n",
       "      <td>13.88%</td>\n",
       "      <td>94.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>86.17%</td>\n",
       "      <td>4.71%</td>\n",
       "      <td>0.948</td>\n",
       "      <td>0.147</td>\n",
       "      <td>No</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>White</td>\n",
       "      <td></td>\n",
       "      <td>Race</td>\n",
       "      <td>17,224</td>\n",
       "      <td>13.88%</td>\n",
       "      <td>14,461.0</td>\n",
       "      <td>13,142.0</td>\n",
       "      <td>90.88%</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Hispanic</td>\n",
       "      <td>Non-Hispanic</td>\n",
       "      <td>Ethnicity</td>\n",
       "      <td>17,207</td>\n",
       "      <td>13.97%</td>\n",
       "      <td>2,032.0</td>\n",
       "      <td>1,593.0</td>\n",
       "      <td>78.40%</td>\n",
       "      <td>13.49%</td>\n",
       "      <td>0.853</td>\n",
       "      <td>0.000</td>\n",
       "      <td>No</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Non-Hispanic</td>\n",
       "      <td></td>\n",
       "      <td>Ethnicity</td>\n",
       "      <td>17,207</td>\n",
       "      <td>13.97%</td>\n",
       "      <td>15,175.0</td>\n",
       "      <td>13,943.0</td>\n",
       "      <td>91.88%</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Female</td>\n",
       "      <td>Male</td>\n",
       "      <td>Sex</td>\n",
       "      <td>10,719</td>\n",
       "      <td>46.40%</td>\n",
       "      <td>4,222.0</td>\n",
       "      <td>3,719.0</td>\n",
       "      <td>88.09%</td>\n",
       "      <td>1.28%</td>\n",
       "      <td>0.986</td>\n",
       "      <td>0.043</td>\n",
       "      <td>No</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Male</td>\n",
       "      <td></td>\n",
       "      <td>Sex</td>\n",
       "      <td>10,719</td>\n",
       "      <td>46.40%</td>\n",
       "      <td>6,497.0</td>\n",
       "      <td>5,806.0</td>\n",
       "      <td>89.36%</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from solas_disparity import ui\n",
    "ui.show(air.summary_table)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Disparity Summary Table Output\n",
    "\n",
    "Nearly all the important information about the results of the analysis is contained in the `summary_table`.  The discussion below describes how to access, format, and graph the information in the `summary_table`."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The table can be accessed as a pandas DataFrame by referencing the `summary_table` attribute of the results object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Reference Group</th>\n",
       "      <th>Group Category</th>\n",
       "      <th>Observations</th>\n",
       "      <th>Percent Missing</th>\n",
       "      <th>Total</th>\n",
       "      <th>Favorable</th>\n",
       "      <th>Percent Favorable</th>\n",
       "      <th>Percent Difference Favorable</th>\n",
       "      <th>AIR</th>\n",
       "      <th>P-Values</th>\n",
       "      <th>Practically Significant</th>\n",
       "      <th>Shortfall</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Group</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>Black</th>\n",
       "      <td>White</td>\n",
       "      <td>Race</td>\n",
       "      <td>17224</td>\n",
       "      <td>0.13880</td>\n",
       "      <td>1337.0</td>\n",
       "      <td>1065.0</td>\n",
       "      <td>0.796559</td>\n",
       "      <td>0.112230</td>\n",
       "      <td>0.876506</td>\n",
       "      <td>1.242621e-38</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asian</th>\n",
       "      <td>White</td>\n",
       "      <td>Race</td>\n",
       "      <td>17224</td>\n",
       "      <td>0.13880</td>\n",
       "      <td>1286.0</td>\n",
       "      <td>1224.0</td>\n",
       "      <td>0.951788</td>\n",
       "      <td>-0.042999</td>\n",
       "      <td>1.047315</td>\n",
       "      <td>2.389532e-08</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Native American</th>\n",
       "      <td>White</td>\n",
       "      <td>Race</td>\n",
       "      <td>17224</td>\n",
       "      <td>0.13880</td>\n",
       "      <td>94.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0.861702</td>\n",
       "      <td>0.047087</td>\n",
       "      <td>0.948187</td>\n",
       "      <td>1.467790e-01</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td></td>\n",
       "      <td>Race</td>\n",
       "      <td>17224</td>\n",
       "      <td>0.13880</td>\n",
       "      <td>14461.0</td>\n",
       "      <td>13142.0</td>\n",
       "      <td>0.908789</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hispanic</th>\n",
       "      <td>Non-Hispanic</td>\n",
       "      <td>Ethnicity</td>\n",
       "      <td>17207</td>\n",
       "      <td>0.13965</td>\n",
       "      <td>2032.0</td>\n",
       "      <td>1593.0</td>\n",
       "      <td>0.783957</td>\n",
       "      <td>0.134857</td>\n",
       "      <td>0.853227</td>\n",
       "      <td>1.726213e-82</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Non-Hispanic</th>\n",
       "      <td></td>\n",
       "      <td>Ethnicity</td>\n",
       "      <td>17207</td>\n",
       "      <td>0.13965</td>\n",
       "      <td>15175.0</td>\n",
       "      <td>13943.0</td>\n",
       "      <td>0.918814</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>Male</td>\n",
       "      <td>Sex</td>\n",
       "      <td>10719</td>\n",
       "      <td>0.46405</td>\n",
       "      <td>4222.0</td>\n",
       "      <td>3719.0</td>\n",
       "      <td>0.880862</td>\n",
       "      <td>0.012781</td>\n",
       "      <td>0.985698</td>\n",
       "      <td>4.300645e-02</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td></td>\n",
       "      <td>Sex</td>\n",
       "      <td>10719</td>\n",
       "      <td>0.46405</td>\n",
       "      <td>6497.0</td>\n",
       "      <td>5806.0</td>\n",
       "      <td>0.893643</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Reference Group Group Category  Observations  Percent Missing  \\\n",
       "Group                                                                           \n",
       "Black                     White           Race         17224          0.13880   \n",
       "Asian                     White           Race         17224          0.13880   \n",
       "Native American           White           Race         17224          0.13880   \n",
       "White                                     Race         17224          0.13880   \n",
       "Hispanic           Non-Hispanic      Ethnicity         17207          0.13965   \n",
       "Non-Hispanic                         Ethnicity         17207          0.13965   \n",
       "Female                     Male            Sex         10719          0.46405   \n",
       "Male                                       Sex         10719          0.46405   \n",
       "\n",
       "                   Total  Favorable  Percent Favorable  \\\n",
       "Group                                                    \n",
       "Black             1337.0     1065.0           0.796559   \n",
       "Asian             1286.0     1224.0           0.951788   \n",
       "Native American     94.0       81.0           0.861702   \n",
       "White            14461.0    13142.0           0.908789   \n",
       "Hispanic          2032.0     1593.0           0.783957   \n",
       "Non-Hispanic     15175.0    13943.0           0.918814   \n",
       "Female            4222.0     3719.0           0.880862   \n",
       "Male              6497.0     5806.0           0.893643   \n",
       "\n",
       "                 Percent Difference Favorable       AIR      P-Values  \\\n",
       "Group                                                                   \n",
       "Black                                0.112230  0.876506  1.242621e-38   \n",
       "Asian                               -0.042999  1.047315  2.389532e-08   \n",
       "Native American                      0.047087  0.948187  1.467790e-01   \n",
       "White                                     NaN       NaN           NaN   \n",
       "Hispanic                             0.134857  0.853227  1.726213e-82   \n",
       "Non-Hispanic                              NaN       NaN           NaN   \n",
       "Female                               0.012781  0.985698  4.300645e-02   \n",
       "Male                                      NaN       NaN           NaN   \n",
       "\n",
       "                Practically Significant  Shortfall  \n",
       "Group                                               \n",
       "Black                                No        NaN  \n",
       "Asian                                No        NaN  \n",
       "Native American                      No        NaN  \n",
       "White                                          NaN  \n",
       "Hispanic                             No        NaN  \n",
       "Non-Hispanic                                   NaN  \n",
       "Female                               No        NaN  \n",
       "Male                                           NaN  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air.summary_table"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can also be viewed as a styled Pandas dataframe by using the SolasAI `sd.ui.show` command."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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 class=\"dataframe disparity-table\" id=\"disp-output\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Group</th>\n",
       "      <th>Reference Group</th>\n",
       "      <th>Group Category</th>\n",
       "      <th>Observations</th>\n",
       "      <th>Percent Missing</th>\n",
       "      <th>Total</th>\n",
       "      <th>Favorable</th>\n",
       "      <th>Percent Favorable</th>\n",
       "      <th>Percent Difference Favorable</th>\n",
       "      <th>AIR</th>\n",
       "      <th>P-Values</th>\n",
       "      <th>Practically Significant</th>\n",
       "      <th>Shortfall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Black</td>\n",
       "      <td>White</td>\n",
       "      <td>Race</td>\n",
       "      <td>17,224</td>\n",
       "      <td>13.88%</td>\n",
       "      <td>1,337.0</td>\n",
       "      <td>1,065.0</td>\n",
       "      <td>79.66%</td>\n",
       "      <td>11.22%</td>\n",
       "      <td>0.877</td>\n",
       "      <td>0.000</td>\n",
       "      <td>No</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Asian</td>\n",
       "      <td>White</td>\n",
       "      <td>Race</td>\n",
       "      <td>17,224</td>\n",
       "      <td>13.88%</td>\n",
       "      <td>1,286.0</td>\n",
       "      <td>1,224.0</td>\n",
       "      <td>95.18%</td>\n",
       "      <td>-4.30%</td>\n",
       "      <td>1.047</td>\n",
       "      <td>0.000</td>\n",
       "      <td>No</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Native American</td>\n",
       "      <td>White</td>\n",
       "      <td>Race</td>\n",
       "      <td>17,224</td>\n",
       "      <td>13.88%</td>\n",
       "      <td>94.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>86.17%</td>\n",
       "      <td>4.71%</td>\n",
       "      <td>0.948</td>\n",
       "      <td>0.147</td>\n",
       "      <td>No</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>White</td>\n",
       "      <td></td>\n",
       "      <td>Race</td>\n",
       "      <td>17,224</td>\n",
       "      <td>13.88%</td>\n",
       "      <td>14,461.0</td>\n",
       "      <td>13,142.0</td>\n",
       "      <td>90.88%</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Hispanic</td>\n",
       "      <td>Non-Hispanic</td>\n",
       "      <td>Ethnicity</td>\n",
       "      <td>17,207</td>\n",
       "      <td>13.97%</td>\n",
       "      <td>2,032.0</td>\n",
       "      <td>1,593.0</td>\n",
       "      <td>78.40%</td>\n",
       "      <td>13.49%</td>\n",
       "      <td>0.853</td>\n",
       "      <td>0.000</td>\n",
       "      <td>No</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Non-Hispanic</td>\n",
       "      <td></td>\n",
       "      <td>Ethnicity</td>\n",
       "      <td>17,207</td>\n",
       "      <td>13.97%</td>\n",
       "      <td>15,175.0</td>\n",
       "      <td>13,943.0</td>\n",
       "      <td>91.88%</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Female</td>\n",
       "      <td>Male</td>\n",
       "      <td>Sex</td>\n",
       "      <td>10,719</td>\n",
       "      <td>46.40%</td>\n",
       "      <td>4,222.0</td>\n",
       "      <td>3,719.0</td>\n",
       "      <td>88.09%</td>\n",
       "      <td>1.28%</td>\n",
       "      <td>0.986</td>\n",
       "      <td>0.043</td>\n",
       "      <td>No</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Male</td>\n",
       "      <td></td>\n",
       "      <td>Sex</td>\n",
       "      <td>10,719</td>\n",
       "      <td>46.40%</td>\n",
       "      <td>6,497.0</td>\n",
       "      <td>5,806.0</td>\n",
       "      <td>89.36%</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sd.ui.show(air.summary_table)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The user can generate plots for specific columns of the summary table by using the `plot` method of the results object.  Below, we show examples of plotting the `percent_favorable` and `air` values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "air.plot(column=\"Percent Favorable\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "air.plot(column=\"AIR\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Results can also be exported. Examples of these commands are shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "output = Path(\".output\")\n",
    "output.mkdir(exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "air.to_excel(file_path=output / \"air_summary_table.xlsx\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Utility Functions"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`utils.pgrg_ordered` returns a unique and ordered list of protected AND reference groups.  This can be helpful when working with group data or with SolasAI results outside of SolasAI."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Black',\n",
       " 'Asian',\n",
       " 'Native American',\n",
       " 'White',\n",
       " 'Hispanic',\n",
       " 'Non-Hispanic',\n",
       " 'Female',\n",
       " 'Male']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "protected_and_reference_groups = sd.utils.pgrg_ordered(\n",
    "    protected_groups=protected_groups,\n",
    "    reference_groups=reference_groups,\n",
    ")\n",
    "protected_and_reference_groups"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Customizing Output\n",
    "\n",
    "A user can change column names used in all downstream results using the `solas_disparity.const` interface.  This can be very helpful when customizing output for a particular use case.  As an example, a lender might want to make the \"Percent Favorable\" column display as \"Loans Underwritten\" or \"Accepted Applications\", while an employer might want to make the \"Percent Favorable\" column be \"Job Offers\" or \"Promotions.\"\n",
    "\n",
    "Below is an example of how column names can be customized for the HMDA data use case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "sd.const.FAVORABLE = \"Total Loan Offers\"\n",
    "sd.const.PERCENT_FAVORABLE = \"Loan Offer Percent\"\n",
    "sd.const.OBSERVATIONS = \"Obs. with Data\"\n",
    "sd.const.PERCENT_MISSING = \"Pct Obs. Missing Data\"\n",
    "sd.const.PERCENT_DIFFERENCE_FAVORABLE = \"Offer Percent Difference\"\n",
    "sd.const.AIR_VALUES = \"Adverse Impact Ratio\""
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Formatting in the table can also be changed, as occurs in the code below.  Here, `sd.ui.AUTO_FORMATTERS` is a dictionary that contains the formats for each of the attributes in the summary table.  When changing the formats, one specifies the key of the dictionary as the attribute from the const file (e.g., \"FAVORABLE\" when changing `sd.const.FAVORABLE`), and the value as desired python formatter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "sd.ui.AUTO_FORMATTERS[\"TOTAL\"] = \"0,.0f\"\n",
    "sd.ui.AUTO_FORMATTERS[\"FAVORABLE\"] = \"0,.0f\"\n",
    "sd.ui.AUTO_FORMATTERS[\"P_VALUES\"] = \"0.1%\"\n",
    "sd.ui.AUTO_FORMATTERS[\"PERCENT_MISSING\"] = \"0.1%\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "const_modified_air = sd.adverse_impact_ratio(\n",
    "    group_data=df,\n",
    "    protected_groups=protected_groups,\n",
    "    reference_groups=reference_groups,\n",
    "    group_categories=group_categories,\n",
    "    outcome=df[\"Low-Priced\"],\n",
    "    sample_weight=None,\n",
    "    air_threshold=0.80,\n",
    "    percent_difference_threshold=0.0,\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The summary table with new variable names and formatted values is printed below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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 class=\"dataframe disparity-table\" id=\"disp-output\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Group</th>\n",
       "      <th>Reference Group</th>\n",
       "      <th>Group Category</th>\n",
       "      <th>Obs. with Data</th>\n",
       "      <th>Pct Obs. Missing Data</th>\n",
       "      <th>Total</th>\n",
       "      <th>Total Loan Offers</th>\n",
       "      <th>Loan Offer Percent</th>\n",
       "      <th>Offer Percent Difference</th>\n",
       "      <th>Adverse Impact Ratio</th>\n",
       "      <th>P-Values</th>\n",
       "      <th>Practically Significant</th>\n",
       "      <th>Shortfall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Black</td>\n",
       "      <td>White</td>\n",
       "      <td>Race</td>\n",
       "      <td>17,224</td>\n",
       "      <td>13.9%</td>\n",
       "      <td>1,337</td>\n",
       "      <td>1,065</td>\n",
       "      <td>79.66%</td>\n",
       "      <td>11.22%</td>\n",
       "      <td>0.877</td>\n",
       "      <td>0.0%</td>\n",
       "      <td>No</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Asian</td>\n",
       "      <td>White</td>\n",
       "      <td>Race</td>\n",
       "      <td>17,224</td>\n",
       "      <td>13.9%</td>\n",
       "      <td>1,286</td>\n",
       "      <td>1,224</td>\n",
       "      <td>95.18%</td>\n",
       "      <td>-4.30%</td>\n",
       "      <td>1.047</td>\n",
       "      <td>0.0%</td>\n",
       "      <td>No</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Native American</td>\n",
       "      <td>White</td>\n",
       "      <td>Race</td>\n",
       "      <td>17,224</td>\n",
       "      <td>13.9%</td>\n",
       "      <td>94</td>\n",
       "      <td>81</td>\n",
       "      <td>86.17%</td>\n",
       "      <td>4.71%</td>\n",
       "      <td>0.948</td>\n",
       "      <td>14.7%</td>\n",
       "      <td>No</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>White</td>\n",
       "      <td></td>\n",
       "      <td>Race</td>\n",
       "      <td>17,224</td>\n",
       "      <td>13.9%</td>\n",
       "      <td>14,461</td>\n",
       "      <td>13,142</td>\n",
       "      <td>90.88%</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Hispanic</td>\n",
       "      <td>Non-Hispanic</td>\n",
       "      <td>Ethnicity</td>\n",
       "      <td>17,207</td>\n",
       "      <td>14.0%</td>\n",
       "      <td>2,032</td>\n",
       "      <td>1,593</td>\n",
       "      <td>78.40%</td>\n",
       "      <td>13.49%</td>\n",
       "      <td>0.853</td>\n",
       "      <td>0.0%</td>\n",
       "      <td>No</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Non-Hispanic</td>\n",
       "      <td></td>\n",
       "      <td>Ethnicity</td>\n",
       "      <td>17,207</td>\n",
       "      <td>14.0%</td>\n",
       "      <td>15,175</td>\n",
       "      <td>13,943</td>\n",
       "      <td>91.88%</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Female</td>\n",
       "      <td>Male</td>\n",
       "      <td>Sex</td>\n",
       "      <td>10,719</td>\n",
       "      <td>46.4%</td>\n",
       "      <td>4,222</td>\n",
       "      <td>3,719</td>\n",
       "      <td>88.09%</td>\n",
       "      <td>1.28%</td>\n",
       "      <td>0.986</td>\n",
       "      <td>4.3%</td>\n",
       "      <td>No</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Male</td>\n",
       "      <td></td>\n",
       "      <td>Sex</td>\n",
       "      <td>10,719</td>\n",
       "      <td>46.4%</td>\n",
       "      <td>6,497</td>\n",
       "      <td>5,806</td>\n",
       "      <td>89.36%</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sd.ui.show(const_modified_air.summary_table)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "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": "3.10.6"
  },
  "vscode": {
   "interpreter": {
    "hash": "ce19b2e0a61d581377ddd9e3832a10d6b5902034b65fa21b321d9eb6a81e8d5e"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}