Exploratory Data Analysis for US Survey Data 2019.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "652dd6ba",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis for US Survey Data 2019"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "3236c43b",
   "metadata": {},
   "source": [
    "## Importing Neccesary Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1020e0dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "04162b93",
   "metadata": {},
   "source": [
    "## Reading our Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6843a526",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"C:/Users/Admin 21/Downloads/Customer Segmentation/Data/SCFP2019.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "508549aa",
   "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>YY1</th>\n",
       "      <th>Y1</th>\n",
       "      <th>WGT</th>\n",
       "      <th>HHSEX</th>\n",
       "      <th>AGE</th>\n",
       "      <th>AGECL</th>\n",
       "      <th>EDUC</th>\n",
       "      <th>EDCL</th>\n",
       "      <th>MARRIED</th>\n",
       "      <th>KIDS</th>\n",
       "      <th>...</th>\n",
       "      <th>NWCAT</th>\n",
       "      <th>INCCAT</th>\n",
       "      <th>ASSETCAT</th>\n",
       "      <th>NINCCAT</th>\n",
       "      <th>NINC2CAT</th>\n",
       "      <th>NWPCTLECAT</th>\n",
       "      <th>INCPCTLECAT</th>\n",
       "      <th>NINCPCTLECAT</th>\n",
       "      <th>INCQRTCAT</th>\n",
       "      <th>NINCQRTCAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>6119.779308</td>\n",
       "      <td>2</td>\n",
       "      <td>75</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>4712.374912</td>\n",
       "      <td>2</td>\n",
       "      <td>75</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>5145.224455</td>\n",
       "      <td>2</td>\n",
       "      <td>75</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>5297.663412</td>\n",
       "      <td>2</td>\n",
       "      <td>75</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>4761.812371</td>\n",
       "      <td>2</td>\n",
       "      <td>75</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 351 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   YY1  Y1          WGT  HHSEX  AGE  AGECL  EDUC  EDCL  MARRIED  KIDS  ...  \\\n",
       "0    1  11  6119.779308      2   75      6    12     4        2     0  ...   \n",
       "1    1  12  4712.374912      2   75      6    12     4        2     0  ...   \n",
       "2    1  13  5145.224455      2   75      6    12     4        2     0  ...   \n",
       "3    1  14  5297.663412      2   75      6    12     4        2     0  ...   \n",
       "4    1  15  4761.812371      2   75      6    12     4        2     0  ...   \n",
       "\n",
       "   NWCAT  INCCAT  ASSETCAT  NINCCAT  NINC2CAT  NWPCTLECAT  INCPCTLECAT  \\\n",
       "0      5       3         6        3         2          10            6   \n",
       "1      5       3         6        3         1          10            5   \n",
       "2      5       3         6        3         1          10            5   \n",
       "3      5       2         6        2         1          10            4   \n",
       "4      5       3         6        3         1          10            5   \n",
       "\n",
       "   NINCPCTLECAT  INCQRTCAT  NINCQRTCAT  \n",
       "0             6          3           3  \n",
       "1             5          2           2  \n",
       "2             5          2           2  \n",
       "3             4          2           2  \n",
       "4             5          2           2  \n",
       "\n",
       "[5 rows x 351 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head() "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "fffa63e7",
   "metadata": {},
   "source": [
    "## Gender "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e5620f9f",
   "metadata": {},
   "source": [
    "HHSEX - Gender of the reference person\n",
    "\n",
    "Gender identity includes:\n",
    "\n",
    "\t1\tmale\n",
    "\t2\tfemale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "72b9eb84",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"HHSEX\"] = df[\"HHSEX\"].replace([1,2],[\"male\",\"female\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dfeab8ec",
   "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>YY1</th>\n",
       "      <th>Y1</th>\n",
       "      <th>WGT</th>\n",
       "      <th>HHSEX</th>\n",
       "      <th>AGE</th>\n",
       "      <th>AGECL</th>\n",
       "      <th>EDUC</th>\n",
       "      <th>EDCL</th>\n",
       "      <th>MARRIED</th>\n",
       "      <th>KIDS</th>\n",
       "      <th>...</th>\n",
       "      <th>NWCAT</th>\n",
       "      <th>INCCAT</th>\n",
       "      <th>ASSETCAT</th>\n",
       "      <th>NINCCAT</th>\n",
       "      <th>NINC2CAT</th>\n",
       "      <th>NWPCTLECAT</th>\n",
       "      <th>INCPCTLECAT</th>\n",
       "      <th>NINCPCTLECAT</th>\n",
       "      <th>INCQRTCAT</th>\n",
       "      <th>NINCQRTCAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>6119.779308</td>\n",
       "      <td>female</td>\n",
       "      <td>75</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>4712.374912</td>\n",
       "      <td>female</td>\n",
       "      <td>75</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>5145.224455</td>\n",
       "      <td>female</td>\n",
       "      <td>75</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>5297.663412</td>\n",
       "      <td>female</td>\n",
       "      <td>75</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>4761.812371</td>\n",
       "      <td>female</td>\n",
       "      <td>75</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 351 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   YY1  Y1          WGT   HHSEX  AGE  AGECL  EDUC  EDCL  MARRIED  KIDS  ...  \\\n",
       "0    1  11  6119.779308  female   75      6    12     4        2     0  ...   \n",
       "1    1  12  4712.374912  female   75      6    12     4        2     0  ...   \n",
       "2    1  13  5145.224455  female   75      6    12     4        2     0  ...   \n",
       "3    1  14  5297.663412  female   75      6    12     4        2     0  ...   \n",
       "4    1  15  4761.812371  female   75      6    12     4        2     0  ...   \n",
       "\n",
       "   NWCAT  INCCAT  ASSETCAT  NINCCAT  NINC2CAT  NWPCTLECAT  INCPCTLECAT  \\\n",
       "0      5       3         6        3         2          10            6   \n",
       "1      5       3         6        3         1          10            5   \n",
       "2      5       3         6        3         1          10            5   \n",
       "3      5       2         6        2         1          10            4   \n",
       "4      5       3         6        3         1          10            5   \n",
       "\n",
       "   NINCPCTLECAT  INCQRTCAT  NINCQRTCAT  \n",
       "0             6          3           3  \n",
       "1             5          2           2  \n",
       "2             5          2           2  \n",
       "3             4          2           2  \n",
       "4             5          2           2  \n",
       "\n",
       "[5 rows x 351 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "6b9da830",
   "metadata": {},
   "source": [
    "## AGE GROUP"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c2705c41",
   "metadata": {},
   "source": [
    "AGECL - Age group of the reference person\n",
    "\n",
    "Age groups are:\n",
    "\n",
    "```\n",
    "1.  <35      \n",
    "2.  35-44    \n",
    "3.  45-54 \n",
    "4.  55-64\n",
    "5.  65-74 \n",
    "6.  (>=75) \n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "59318281",
   "metadata": {},
   "outputs": [],
   "source": [
    "age_group = {\n",
    "    \n",
    "    1: \"under 35\",\n",
    "    2: \"35-44\",\n",
    "    3: \"45-54\",\n",
    "    4: \"55-64\",\n",
    "    5: \"65-74\",\n",
    "    6: \"75 and Above\"\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c7240f34",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"AGECL\"] = df[\"AGECL\"].replace(age_group)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "447e81c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: title={'center': 'Age Distribution'}, xlabel='Age Group', ylabel='Percentage proportion'>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(df[\"AGECL\"].value_counts(normalize=True)*100).plot(kind=\"bar\",\n",
    "                                                    xlabel=\"Age Group\",\n",
    "                                                    ylabel= \"Percentage proportion\",\n",
    "                                                    title= \"Age Distribution\",\n",
    "                                                    color=\"darkblue\"\n",
    "                                                    )\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "696c0c6b",
   "metadata": {},
   "source": [
    "## EDCL"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "cac3ac14",
   "metadata": {},
   "source": [
    "Education category of reference person\n",
    "\n",
    "Text of this Question or Item:\n",
    "```\n",
    "1. no high school diploma/GED\n",
    "2. high school diploma or GED\n",
    "3. some college\n",
    "4. college degree\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23e1f65c",
   "metadata": {},
   "outputs": [],
   "source": [
    "edu_cat = {\n",
    "    \n",
    "    1: \"no high school diploma/GED\",\n",
    "    2: \"high school diploma or GED\",\n",
    "    3: \"some college\",\n",
    "    4: \"college degree\"\n",
    "    \n",
    "    \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "182ee810",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"EDCL\"] = df[\"EDCL\"].replace(edu_cat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5d7363ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "college degree                13270\n",
       "some college                   7032\n",
       "high school diploma or GED     6016\n",
       "no high school diploma/GED     2567\n",
       "Name: EDCL, dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"EDCL\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97c42b52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2.888500e+04\n",
       "mean     1.375703e+07\n",
       "std      7.921992e+07\n",
       "min      0.000000e+00\n",
       "25%      5.020000e+04\n",
       "50%      3.676000e+05\n",
       "75%      1.831400e+06\n",
       "max      1.967199e+09\n",
       "Name: ASSET, dtype: float64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"ASSET\"].describe()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "2e6662f6",
   "metadata": {},
   "source": [
    "## INCCAT"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "8cb7479a",
   "metadata": {},
   "source": [
    "Income percentile groups\n",
    "\n",
    "Text of this Question or Item\n",
    "\n",
    "Income percentile groups:\n",
    "```\n",
    "1 = 0-20 \n",
    "\n",
    "2 = 20-39.9\n",
    "\n",
    "3 = 40-59.9\n",
    "\n",
    "4 = 60-79.9\n",
    "\n",
    "5 = 80-89.9\n",
    "\n",
    "6 = 90-100\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "639fc525",
   "metadata": {},
   "outputs": [],
   "source": [
    "inc_group={\n",
    "    \n",
    "    1: \"0-20\",\n",
    "\n",
    "    2: \"20-39.9\",\n",
    "\n",
    "    3: \"40-59.9\",\n",
    "\n",
    "    4: \"60-79.9\",\n",
    "\n",
    "    5: \"80-89.9\",\n",
    "\n",
    "    6: \"90-100\"\n",
    "\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1083f7fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"INCCAT\"] = df[\"INCCAT\"].replace(inc_group)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9938659d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "90-100     0.257539\n",
       "60-79.9    0.168080\n",
       "0-20       0.163995\n",
       "40-59.9    0.156863\n",
       "20-39.9    0.155202\n",
       "80-89.9    0.098321\n",
       "Name: INCCAT, dtype: float64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"INCCAT\"].value_counts(normalize=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "710539b3",
   "metadata": {},
   "source": [
    "## DEBT"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b5a32955",
   "metadata": {},
   "source": [
    "Total value of debt held by household, 2019 dollars\n",
    "\n",
    "Text of this Question or Item\n",
    "\n",
    "Includes principal `residence debt` (mortgages and HELOCs), `other lines of credit`, `debt for other residential`,`property`, `credit card debt`, `installment loans`,and `other debt`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ce1ba54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2.888500e+04\n",
       "mean     2.986258e+05\n",
       "std      3.532772e+06\n",
       "min      0.000000e+00\n",
       "25%      0.000000e+00\n",
       "50%      2.703000e+04\n",
       "75%      1.900000e+05\n",
       "max      2.640600e+08\n",
       "Name: DEBT, dtype: float64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"DEBT\"].describe()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "3322ba58",
   "metadata": {},
   "source": [
    "## TURNFEAR"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "29b7d87b",
   "metadata": {},
   "source": [
    "Household has been turned down for credit or feared being denied credit in the past 5 years\n",
    "\n",
    "Label\n",
    "\n",
    "\t0\tno\n",
    "\t1\tyes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2044edd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    24262\n",
       "1     4623\n",
       "Name: TURNFEAR, dtype: int64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"TURNFEAR\"].value_counts()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "616ee730",
   "metadata": {},
   "source": [
    "## EDUC"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "417e896c",
   "metadata": {},
   "source": [
    "Highest completed grade by reference person\n",
    "```\n",
    "    Value\tLabel\n",
    "\n",
    "       -1\tLESS THAN 1ST GRADE \n",
    "        1\t1ST, 2ND, 3RD, OR 4TH GRADE\n",
    "        2\t5TH OR 6TH GRADE\n",
    "        4\t9TH GRADE\n",
    "        5\t10TH GRADE\n",
    "        6\t11TH GRADE\n",
    "        7\t12TH GRADE, NO DIPLOMA\n",
    "        8\tHIGH SCHOOL GRADUATE - HIGH SCHOOL DIPLOMA OR EQUIVALENT\n",
    "        9\tSOME COLLEGE BUT NO DEGREE\n",
    "        10\tASSOCIATE DEGREE IN COLLEGE - OCCUPATION/VOCATION PROGRAM\n",
    "        11\tASSOCIATE DEGREE IN COLLEGE - ACADEMIC PROGRAM\n",
    "        12\tBACHELOR'S DEGREE (FOR EXAMPLE: BA, AB, BS)\n",
    "        13\tMASTER'S DEGREE\n",
    "        14\tDOCTORATE OR PROFESSIONAL SCHOOL DEGREE\n",
    "\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "00747cea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 12    6887\n",
       " 8     6016\n",
       " 9     4131\n",
       " 13    3936\n",
       " 14    2447\n",
       " 10    1523\n",
       " 11    1378\n",
       " 7      676\n",
       " 6      480\n",
       " 4      372\n",
       " 5      332\n",
       " 3      327\n",
       " 2      200\n",
       " 1      135\n",
       "-1       45\n",
       "Name: EDUC, dtype: int64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"EDUC\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74ecfff9",
   "metadata": {},
   "source": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "cc30b0dd",
   "metadata": {},
   "source": [
    "## ASSET\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "881f8093",
   "metadata": {},
   "source": [
    "\n",
    "Total value of assets held by household, 2019 dollars\n",
    "\n",
    "The sum of financial assets and nonfinancial assets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "41074ab6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2.888500e+04\n",
       "mean     1.375703e+07\n",
       "std      7.921992e+07\n",
       "min      0.000000e+00\n",
       "25%      5.020000e+04\n",
       "50%      3.676000e+05\n",
       "75%      1.831400e+06\n",
       "max      1.967199e+09\n",
       "Name: ASSET, dtype: float64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"ASSET\"].describe()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "f4507ad7",
   "metadata": {},
   "source": [
    "## HOUSES\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "1e456461",
   "metadata": {},
   "source": [
    "Total value of primary residence of household, 2019 dollars\n",
    "\n",
    "Value of primary residence. Excludes the part of a farm  or ranch used in a farming or ranching business."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19861049",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2.888500e+04\n",
       "mean     7.245646e+05\n",
       "std      2.618799e+06\n",
       "min      0.000000e+00\n",
       "25%      0.000000e+00\n",
       "50%      1.800000e+05\n",
       "75%      4.930000e+05\n",
       "max      1.064800e+08\n",
       "Name: HOUSES, dtype: float64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"HOUSES\"].describe()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "01b02354",
   "metadata": {},
   "source": [
    "# Comparison between Low Credit Score Households and High Credit Score Households"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "7d2bb716",
   "metadata": {},
   "source": [
    "### Education"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "46d065a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_education = (df[\"EDUC\"]\n",
    ".groupby(df[\"TURNFEAR\"])\n",
    ".value_counts(normalize=True)\n",
    ".rename(\"frequency\")\n",
    ".to_frame()\n",
    ".reset_index()\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "5a473fbf",
   "metadata": {},
   "source": [
    "####  Visualization Education comparison between Low Credit Score Households and High Credit Score Households"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5889326c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Education Level: Low Credit Score Households VS  High Credit Score Households')"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"EDUC\",\n",
    "            y=\"frequency\",\n",
    "            hue=\"TURNFEAR\",\n",
    "            data = df_education\n",
    "            )\n",
    "plt.xlabel(\"Education Level\")\n",
    "plt.ylabel(\"Frequency [%]\")\n",
    "plt.title(\"Education Level: Low Credit Score Households VS  High Credit Score Households\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "15791241",
   "metadata": {},
   "source": [
    "From the visualization we can see the following levels probability of being turned down for credit is very low compared to the other levels.\n",
    "\n",
    "```\n",
    " \t12\tBACHELOR'S DEGREE (FOR EXAMPLE: BA, AB, BS)\n",
    "        13\tMASTER'S DEGREE\n",
    "        14\tDOCTORATE OR PROFESSIONAL SCHOOL DEGREE\n",
    "```\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e45f9778",
   "metadata": {},
   "source": [
    "### ASSET VS DEBT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c6e8c671",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'ASSET VS DEBT')"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter(x=\"DEBT\", y= \"ASSET\",c=\"darkblue\")\n",
    "plt.title(\"ASSET VS DEBT\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "99694065",
   "metadata": {},
   "source": [
    "We can note that people with assets tends to have smaller debts."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "d73d6a5c",
   "metadata": {},
   "source": [
    "A deeper look in households with low credit score asset vs debt comparison below shows most have debt equal to their assets or lesser."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df34f8c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Low Credit Score: ASSET VS DEBT')"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df[\"TURNFEAR\"]==1].plot.scatter(x=\"DEBT\", y= \"ASSET\",c=\"darkblue\")\n",
    "plt.title(\"Low Credit Score: ASSET VS DEBT\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "2a6720e9",
   "metadata": {},
   "source": [
    "The Asset vs Debt doesnot have any clustering. Next we'll look House vs Debt."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "bfa1be72",
   "metadata": {},
   "source": [
    "### HOUSE VS DEBT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "57c4fb98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'HOUSE VS DEBT')"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter(x=\"DEBT\", y=\"HOUSES\")\n",
    "plt.title(\"HOUSE VS DEBT\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "ea553957",
   "metadata": {},
   "source": [
    "The graph doesn't show any clustering and most of the households with less debt their house values are high."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce1602f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Low Credit Score: HOUSE VS DEBT')"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df[\"TURNFEAR\"]==1].plot.scatter(x=\"DEBT\", y= \"HOUSES\",c=\"darkblue\")\n",
    "plt.title(\"Low Credit Score: HOUSE VS DEBT\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "48b81f4a",
   "metadata": {},
   "source": [
    "The graph exhibits several clusters in those households with low credit score."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "ea59a8d8",
   "metadata": {},
   "source": [
    "# Low Credit Score Households\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "5e30ccd5",
   "metadata": {},
   "source": [
    "Subset of our dataset with households that have been turned down for credit or feared being denied credit in the past 5 years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "88da1e42",
   "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>YY1</th>\n",
       "      <th>Y1</th>\n",
       "      <th>WGT</th>\n",
       "      <th>HHSEX</th>\n",
       "      <th>AGE</th>\n",
       "      <th>AGECL</th>\n",
       "      <th>EDUC</th>\n",
       "      <th>EDCL</th>\n",
       "      <th>MARRIED</th>\n",
       "      <th>KIDS</th>\n",
       "      <th>...</th>\n",
       "      <th>NWCAT</th>\n",
       "      <th>INCCAT</th>\n",
       "      <th>ASSETCAT</th>\n",
       "      <th>NINCCAT</th>\n",
       "      <th>NINC2CAT</th>\n",
       "      <th>NWPCTLECAT</th>\n",
       "      <th>INCPCTLECAT</th>\n",
       "      <th>NINCPCTLECAT</th>\n",
       "      <th>INCQRTCAT</th>\n",
       "      <th>NINCQRTCAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>3790.476607</td>\n",
       "      <td>male</td>\n",
       "      <td>50</td>\n",
       "      <td>45-54</td>\n",
       "      <td>8</td>\n",
       "      <td>high school diploma or GED</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>20-39.9</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>3798.868505</td>\n",
       "      <td>male</td>\n",
       "      <td>50</td>\n",
       "      <td>45-54</td>\n",
       "      <td>8</td>\n",
       "      <td>high school diploma or GED</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>20-39.9</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>23</td>\n",
       "      <td>3799.468393</td>\n",
       "      <td>male</td>\n",
       "      <td>50</td>\n",
       "      <td>45-54</td>\n",
       "      <td>8</td>\n",
       "      <td>high school diploma or GED</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>20-39.9</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>24</td>\n",
       "      <td>3788.076005</td>\n",
       "      <td>male</td>\n",
       "      <td>50</td>\n",
       "      <td>45-54</td>\n",
       "      <td>8</td>\n",
       "      <td>high school diploma or GED</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>20-39.9</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>3793.066589</td>\n",
       "      <td>male</td>\n",
       "      <td>50</td>\n",
       "      <td>45-54</td>\n",
       "      <td>8</td>\n",
       "      <td>high school diploma or GED</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>20-39.9</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 351 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   YY1  Y1          WGT HHSEX  AGE  AGECL  EDUC                        EDCL  \\\n",
       "5    2  21  3790.476607  male   50  45-54     8  high school diploma or GED   \n",
       "6    2  22  3798.868505  male   50  45-54     8  high school diploma or GED   \n",
       "7    2  23  3799.468393  male   50  45-54     8  high school diploma or GED   \n",
       "8    2  24  3788.076005  male   50  45-54     8  high school diploma or GED   \n",
       "9    2  25  3793.066589  male   50  45-54     8  high school diploma or GED   \n",
       "\n",
       "   MARRIED  KIDS  ...  NWCAT   INCCAT  ASSETCAT  NINCCAT  NINC2CAT  \\\n",
       "5        1     3  ...      1  20-39.9         1        2         1   \n",
       "6        1     3  ...      1  20-39.9         1        2         1   \n",
       "7        1     3  ...      1  20-39.9         1        2         1   \n",
       "8        1     3  ...      1  20-39.9         1        2         1   \n",
       "9        1     3  ...      1  20-39.9         1        2         1   \n",
       "\n",
       "   NWPCTLECAT  INCPCTLECAT  NINCPCTLECAT  INCQRTCAT  NINCQRTCAT  \n",
       "5           1            4             4          2           2  \n",
       "6           1            4             3          2           2  \n",
       "7           1            4             4          2           2  \n",
       "8           1            4             4          2           2  \n",
       "9           1            4             4          2           2  \n",
       "\n",
       "[5 rows x 351 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_credit=df[df[\"TURNFEAR\"]==1]\n",
    "df_credit.head(5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a7ca13c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 4623 entries, 5 to 28869\n",
      "Columns: 351 entries, YY1 to NINCQRTCAT\n",
      "dtypes: float64(74), int64(273), object(4)\n",
      "memory usage: 12.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df_credit.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e24b61e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4623, 351)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_credit.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "51f3a592",
   "metadata": {},
   "source": [
    "## Saving our subset data set to ``csv`` file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dccb4978",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_credit.to_csv('C:/Users/Admin 21/Downloads/Customer Segmentation/Data/df_credit.csv')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  },
  "vscode": {
   "interpreter": {
    "hash": "0e2b774fbaab44ff543f02a32ce55460e0d4e68f77640d9e006920ee1173c352"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}