US - SCFP 2019 EDA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "024e7a00",
   "metadata": {},
   "source": [
    "link to Data Description : https://sda.berkeley.edu/sdaweb/docs/scfcomb2019/DOC/hcbkx01.htm#1.HEADING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cda5d919",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ca3359f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def wrangle(filepath):\n",
    "    \n",
    "    df = pd.read_csv(filepath)\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7e22965b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = wrangle(\"SCFP2019.csv\")\n",
    "\n",
    "mask = df[\"TURNFEAR\"] == 1\n",
    "df_fear = df[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "238ec097",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df_fear shape: (4623, 351)\n"
     ]
    },
    {
     "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>1</td>\n",
       "      <td>50</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</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>1</td>\n",
       "      <td>50</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</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>1</td>\n",
       "      <td>50</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</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>1</td>\n",
       "      <td>50</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</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>1</td>\n",
       "      <td>50</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</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  MARRIED  KIDS  ...   \n",
       "5    2  21  3790.476607      1   50      3     8     2        1     3  ...  \\\n",
       "6    2  22  3798.868505      1   50      3     8     2        1     3  ...   \n",
       "7    2  23  3799.468393      1   50      3     8     2        1     3  ...   \n",
       "8    2  24  3788.076005      1   50      3     8     2        1     3  ...   \n",
       "9    2  25  3793.066589      1   50      3     8     2        1     3  ...   \n",
       "\n",
       "   NWCAT  INCCAT  ASSETCAT  NINCCAT  NINC2CAT  NWPCTLECAT  INCPCTLECAT   \n",
       "5      1       2         1        2         1           1            4  \\\n",
       "6      1       2         1        2         1           1            4   \n",
       "7      1       2         1        2         1           1            4   \n",
       "8      1       2         1        2         1           1            4   \n",
       "9      1       2         1        2         1           1            4   \n",
       "\n",
       "   NINCPCTLECAT  INCQRTCAT  NINCQRTCAT  \n",
       "5             4          2           2  \n",
       "6             3          2           2  \n",
       "7             4          2           2  \n",
       "8             4          2           2  \n",
       "9             4          2           2  \n",
       "\n",
       "[5 rows x 351 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"df_fear shape:\", df_fear.shape)\n",
    "df_fear.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35a22d3f",
   "metadata": {},
   "source": [
    "# AGE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b63dea3c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Age groups: [3 5 1 2 4 6]\n"
     ]
    }
   ],
   "source": [
    "df_fear[\"AGECL\"].head()\n",
    "age_groups = df_fear[\"AGECL\"].unique()\n",
    "print(\"Age groups:\", age_groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "259720bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    45-54\n",
       "6    45-54\n",
       "7    45-54\n",
       "8    45-54\n",
       "9    45-54\n",
       "Name: AGECL, dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agecl_dict = {\n",
    "    1: \"Under 35\",\n",
    "    2: \"35-44\",\n",
    "    3: \"45-54\",\n",
    "    4: \"55-64\",\n",
    "    5: \"65-74\",\n",
    "    6: \"75or older\", \n",
    "}\n",
    "age_cl = df_fear[\"AGECL\"].replace(agecl_dict)\n",
    "age_cl.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b599c721",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "age_cl.value_counts().plot(kind = \"bar\",\n",
    "                          xlabel = \"Age Group\",\n",
    "                          ylabel = \"Frequency [count]\",\n",
    "                          title = \"Credit Fearful: Age Groups\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "be35fafa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_fear[\"AGE\"].hist(bins = 10)\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel(\"Frequency [count]\")\n",
    "plt.title(\"Credit Fearful: Age Distribution\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "722ba709",
   "metadata": {},
   "source": [
    "This shows that most of the credit fearful households are between their 30's and 40's"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2932c074",
   "metadata": {},
   "source": [
    "# RACE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "66078695",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "race_dict = {\n",
    "    1: \"White/ Non - Hispanic\",\n",
    "    2: \"Black/ African - American\",\n",
    "    3: \"Hispanic\",\n",
    "    5: \"Other\"\n",
    "}\n",
    "race = df_fear[\"RACE\"].replace(race_dict)\n",
    "\n",
    "race.value_counts(normalize = True).plot(kind = \"barh\")\n",
    "#plt.xlim((0,1))\n",
    "plt.xlabel(\"Frequency [%]\")\n",
    "plt.ylabel(\"Race\")\n",
    "plt.title(\"Credit Fearful: Racial Groups\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f8ec688b",
   "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>RACE</th>\n",
       "      <th>White/ Non - Hispanic</th>\n",
       "      <th>Black/ African - American</th>\n",
       "      <th>Hispanic</th>\n",
       "      <th>Other</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>scf_respondent</th>\n",
       "      <td>0.716739</td>\n",
       "      <td>0.130068</td>\n",
       "      <td>0.096625</td>\n",
       "      <td>0.056569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>credit_fear</th>\n",
       "      <td>0.539477</td>\n",
       "      <td>0.237508</td>\n",
       "      <td>0.162232</td>\n",
       "      <td>0.060783</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "RACE            White/ Non - Hispanic  Black/ African - American  Hispanic   \n",
       "scf_respondent               0.716739                   0.130068  0.096625  \\\n",
       "credit_fear                  0.539477                   0.237508  0.162232   \n",
       "\n",
       "RACE               Other  \n",
       "scf_respondent  0.056569  \n",
       "credit_fear     0.060783  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scf_respondent = df[\"RACE\"].replace(race_dict).value_counts(normalize = True).rename(\"scf_respondent\")\n",
    "credit_fear = df_fear[\"RACE\"].replace(race_dict).value_counts(normalize = True).rename(\"credit_fear\")\n",
    "\n",
    "race_df = pd.DataFrame(data=[scf_respondent, credit_fear])\n",
    "race_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1b1a5d4",
   "metadata": {},
   "source": [
    "This suggests that White/Non-Hispanic people worry more about being denied credit, but thinking critically about what we're seeing, that might be because there are more White/Non-Hispanic in the population of the United States than there are other racial groups, and the sample for this survey was specifically drawn to be representative of the population as a whole."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f6cf744a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "race = df[\"RACE\"].replace(race_dict)\n",
    "\n",
    "race.value_counts(normalize = True).plot(kind = \"barh\")\n",
    "#plt.xlim((0,1))\n",
    "plt.xlabel(\"Frequency [%]\")\n",
    "plt.ylabel(\"Race\")\n",
    "plt.title(\"SCF Respondents: Racial Groups\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef194daa",
   "metadata": {},
   "source": [
    "# INCOME"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "35a0c749",
   "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>TURNFEAR</th>\n",
       "      <th>INCCAT</th>\n",
       "      <th>frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>90-100</td>\n",
       "      <td>0.297296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>60-79.9</td>\n",
       "      <td>0.174841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>40-59.9</td>\n",
       "      <td>0.143146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0-19.9</td>\n",
       "      <td>0.140343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>20-39.9</td>\n",
       "      <td>0.135933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>80-89.9</td>\n",
       "      <td>0.108441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>0-19.9</td>\n",
       "      <td>0.288125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>20-39.9</td>\n",
       "      <td>0.256327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>40-59.9</td>\n",
       "      <td>0.228856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>60-79.9</td>\n",
       "      <td>0.132598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>90-100</td>\n",
       "      <td>0.048886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>80-89.9</td>\n",
       "      <td>0.045209</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    TURNFEAR   INCCAT  frequency\n",
       "0          0   90-100   0.297296\n",
       "1          0  60-79.9   0.174841\n",
       "2          0  40-59.9   0.143146\n",
       "3          0   0-19.9   0.140343\n",
       "4          0  20-39.9   0.135933\n",
       "5          0  80-89.9   0.108441\n",
       "6          1   0-19.9   0.288125\n",
       "7          1  20-39.9   0.256327\n",
       "8          1  40-59.9   0.228856\n",
       "9          1  60-79.9   0.132598\n",
       "10         1   90-100   0.048886\n",
       "11         1  80-89.9   0.045209"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# INCOME\n",
    "\n",
    "inccat_dict = {\n",
    "    1: \"0-19.9\",\n",
    "    2: \"20-39.9\",\n",
    "    3: \"40-59.9\",\n",
    "    4: \"60-79.9\",\n",
    "    5: \"80-89.9\",\n",
    "    6: \"90-100\",\n",
    "}\n",
    "inccat = (df[\"INCCAT\"].replace(inccat_dict).groupby(df[\"TURNFEAR\"]).value_counts(normalize = True)\n",
    "          .rename(\"frequency\").to_frame().reset_index()\n",
    "         )\n",
    "\n",
    "inccat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "66b5ea30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x = \"INCCAT\", y = \"frequency\", \n",
    "           data = inccat, hue = \"TURNFEAR\",\n",
    "           order = inccat_dict.values())\n",
    "plt.xlabel(\"Income Category\")\n",
    "plt.ylabel(\"Frequency [%]\")\n",
    "plt.title(\"Income Distribution: Credit Fearful Vs. Non Fearful\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e222c0a",
   "metadata": {},
   "source": [
    "# ASSET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "21772d51",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SCF Respondent asset house corr 0.5198273544779253\n",
      "Credit Fearful asset house corr 0.583287973597916\n"
     ]
    }
   ],
   "source": [
    "asset_house_corr = df[\"ASSET\"].corr(df[\"HOUSES\"])\n",
    "asset_house_fear_corr = df_fear[\"ASSET\"].corr(df_fear[\"HOUSES\"])\n",
    "print(\"SCF Respondent asset house corr\", asset_house_corr)\n",
    "print(\"Credit Fearful asset house corr\", asset_house_fear_corr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5ebb5201",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_775db_row0_col0, #T_775db_row1_col1, #T_775db_row2_col2, #T_775db_row3_col3, #T_775db_row4_col4 {\n",
       "  background-color: #023858;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_775db_row0_col1, #T_775db_row1_col0 {\n",
       "  background-color: #76aad0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_775db_row0_col2, #T_775db_row2_col0 {\n",
       "  background-color: #4295c3;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_775db_row0_col3, #T_775db_row3_col0 {\n",
       "  background-color: #d7d6e9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_775db_row0_col4, #T_775db_row2_col3, #T_775db_row3_col2, #T_775db_row4_col0 {\n",
       "  background-color: #f5eff6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_775db_row1_col2, #T_775db_row2_col1 {\n",
       "  background-color: #dad9ea;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_775db_row1_col3, #T_775db_row3_col1 {\n",
       "  background-color: #d6d6e9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_775db_row1_col4, #T_775db_row4_col1 {\n",
       "  background-color: #ede7f2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_775db_row2_col4, #T_775db_row4_col2 {\n",
       "  background-color: #fdf5fa;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_775db_row3_col4, #T_775db_row4_col3 {\n",
       "  background-color: #fff7fb;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_775db\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_775db_level0_col0\" class=\"col_heading level0 col0\" >ASSET</th>\n",
       "      <th id=\"T_775db_level0_col1\" class=\"col_heading level0 col1\" >HOUSES</th>\n",
       "      <th id=\"T_775db_level0_col2\" class=\"col_heading level0 col2\" >INCOME</th>\n",
       "      <th id=\"T_775db_level0_col3\" class=\"col_heading level0 col3\" >DEBT</th>\n",
       "      <th id=\"T_775db_level0_col4\" class=\"col_heading level0 col4\" >EDUC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_775db_level0_row0\" class=\"row_heading level0 row0\" >ASSET</th>\n",
       "      <td id=\"T_775db_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "      <td id=\"T_775db_row0_col1\" class=\"data row0 col1\" >0.519827</td>\n",
       "      <td id=\"T_775db_row0_col2\" class=\"data row0 col2\" >0.622429</td>\n",
       "      <td id=\"T_775db_row0_col3\" class=\"data row0 col3\" >0.261250</td>\n",
       "      <td id=\"T_775db_row0_col4\" class=\"data row0 col4\" >0.116673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_775db_level0_row1\" class=\"row_heading level0 row1\" >HOUSES</th>\n",
       "      <td id=\"T_775db_row1_col0\" class=\"data row1 col0\" >0.519827</td>\n",
       "      <td id=\"T_775db_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "      <td id=\"T_775db_row1_col2\" class=\"data row1 col2\" >0.247852</td>\n",
       "      <td id=\"T_775db_row1_col3\" class=\"data row1 col3\" >0.266661</td>\n",
       "      <td id=\"T_775db_row1_col4\" class=\"data row1 col4\" >0.169300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_775db_level0_row2\" class=\"row_heading level0 row2\" >INCOME</th>\n",
       "      <td id=\"T_775db_row2_col0\" class=\"data row2 col0\" >0.622429</td>\n",
       "      <td id=\"T_775db_row2_col1\" class=\"data row2 col1\" >0.247852</td>\n",
       "      <td id=\"T_775db_row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
       "      <td id=\"T_775db_row2_col3\" class=\"data row2 col3\" >0.114646</td>\n",
       "      <td id=\"T_775db_row2_col4\" class=\"data row2 col4\" >0.069400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_775db_level0_row3\" class=\"row_heading level0 row3\" >DEBT</th>\n",
       "      <td id=\"T_775db_row3_col0\" class=\"data row3 col0\" >0.261250</td>\n",
       "      <td id=\"T_775db_row3_col1\" class=\"data row3 col1\" >0.266661</td>\n",
       "      <td id=\"T_775db_row3_col2\" class=\"data row3 col2\" >0.114646</td>\n",
       "      <td id=\"T_775db_row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
       "      <td id=\"T_775db_row3_col4\" class=\"data row3 col4\" >0.054179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_775db_level0_row4\" class=\"row_heading level0 row4\" >EDUC</th>\n",
       "      <td id=\"T_775db_row4_col0\" class=\"data row4 col0\" >0.116673</td>\n",
       "      <td id=\"T_775db_row4_col1\" class=\"data row4 col1\" >0.169300</td>\n",
       "      <td id=\"T_775db_row4_col2\" class=\"data row4 col2\" >0.069400</td>\n",
       "      <td id=\"T_775db_row4_col3\" class=\"data row4 col3\" >0.054179</td>\n",
       "      <td id=\"T_775db_row4_col4\" class=\"data row4 col4\" >1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2154394f910>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = [\"ASSET\", \"HOUSES\", \"INCOME\", \"DEBT\", \"EDUC\"]\n",
    "corr = df[cols].corr()\n",
    "corr.style.background_gradient(axis = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1d1d6071",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_6157f_row0_col0, #T_6157f_row1_col1, #T_6157f_row2_col2, #T_6157f_row3_col3, #T_6157f_row4_col4 {\n",
       "  background-color: #023858;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6157f_row0_col1, #T_6157f_row1_col0 {\n",
       "  background-color: #65a3cb;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6157f_row0_col2, #T_6157f_row2_col0 {\n",
       "  background-color: #1e80b8;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6157f_row0_col3, #T_6157f_row3_col0 {\n",
       "  background-color: #99b8d8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_6157f_row0_col4, #T_6157f_row4_col0 {\n",
       "  background-color: #fff7fb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_6157f_row1_col2, #T_6157f_row2_col1 {\n",
       "  background-color: #e2dfee;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_6157f_row1_col3, #T_6157f_row3_col1 {\n",
       "  background-color: #034369;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_6157f_row1_col4, #T_6157f_row4_col1 {\n",
       "  background-color: #f7f0f7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_6157f_row2_col3, #T_6157f_row3_col2 {\n",
       "  background-color: #f5eff6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_6157f_row2_col4, #T_6157f_row4_col2 {\n",
       "  background-color: #fcf4fa;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_6157f_row3_col4, #T_6157f_row4_col3 {\n",
       "  background-color: #f4eef6;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_6157f\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_6157f_level0_col0\" class=\"col_heading level0 col0\" >ASSET</th>\n",
       "      <th id=\"T_6157f_level0_col1\" class=\"col_heading level0 col1\" >HOUSES</th>\n",
       "      <th id=\"T_6157f_level0_col2\" class=\"col_heading level0 col2\" >INCOME</th>\n",
       "      <th id=\"T_6157f_level0_col3\" class=\"col_heading level0 col3\" >DEBT</th>\n",
       "      <th id=\"T_6157f_level0_col4\" class=\"col_heading level0 col4\" >EDUC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_6157f_level0_row0\" class=\"row_heading level0 row0\" >ASSET</th>\n",
       "      <td id=\"T_6157f_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "      <td id=\"T_6157f_row0_col1\" class=\"data row0 col1\" >0.583288</td>\n",
       "      <td id=\"T_6157f_row0_col2\" class=\"data row0 col2\" >0.722074</td>\n",
       "      <td id=\"T_6157f_row0_col3\" class=\"data row0 col3\" >0.474658</td>\n",
       "      <td id=\"T_6157f_row0_col4\" class=\"data row0 col4\" >0.113536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6157f_level0_row1\" class=\"row_heading level0 row1\" >HOUSES</th>\n",
       "      <td id=\"T_6157f_row1_col0\" class=\"data row1 col0\" >0.583288</td>\n",
       "      <td id=\"T_6157f_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "      <td id=\"T_6157f_row1_col2\" class=\"data row1 col2\" >0.264099</td>\n",
       "      <td id=\"T_6157f_row1_col3\" class=\"data row1 col3\" >0.962629</td>\n",
       "      <td id=\"T_6157f_row1_col4\" class=\"data row1 col4\" >0.160348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6157f_level0_row2\" class=\"row_heading level0 row2\" >INCOME</th>\n",
       "      <td id=\"T_6157f_row2_col0\" class=\"data row2 col0\" >0.722074</td>\n",
       "      <td id=\"T_6157f_row2_col1\" class=\"data row2 col1\" >0.264099</td>\n",
       "      <td id=\"T_6157f_row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
       "      <td id=\"T_6157f_row2_col3\" class=\"data row2 col3\" >0.172393</td>\n",
       "      <td id=\"T_6157f_row2_col4\" class=\"data row2 col4\" >0.133170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6157f_level0_row3\" class=\"row_heading level0 row3\" >DEBT</th>\n",
       "      <td id=\"T_6157f_row3_col0\" class=\"data row3 col0\" >0.474658</td>\n",
       "      <td id=\"T_6157f_row3_col1\" class=\"data row3 col1\" >0.962629</td>\n",
       "      <td id=\"T_6157f_row3_col2\" class=\"data row3 col2\" >0.172393</td>\n",
       "      <td id=\"T_6157f_row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
       "      <td id=\"T_6157f_row3_col4\" class=\"data row3 col4\" >0.177386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6157f_level0_row4\" class=\"row_heading level0 row4\" >EDUC</th>\n",
       "      <td id=\"T_6157f_row4_col0\" class=\"data row4 col0\" >0.113536</td>\n",
       "      <td id=\"T_6157f_row4_col1\" class=\"data row4 col1\" >0.160348</td>\n",
       "      <td id=\"T_6157f_row4_col2\" class=\"data row4 col2\" >0.133170</td>\n",
       "      <td id=\"T_6157f_row4_col3\" class=\"data row4 col3\" >0.177386</td>\n",
       "      <td id=\"T_6157f_row4_col4\" class=\"data row4 col4\" >1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x21543af69b0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr = df_fear[cols].corr()\n",
    "corr.style.background_gradient(axis = None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e22fc8fa",
   "metadata": {},
   "source": [
    "Whoa! There are some pretty important differences here! The relationship between \"DEBT\" and \"HOUSES\" is positive for both datasets, but while the coefficient for df is fairly weak at 0.26, the same number for df_fear is 0.96.\n",
    "\n",
    "Remember, the closer a correlation coefficient is to 1.0, the more exactly they correspond. In this case, that means the value of the primary residence and the total debt held by the household is getting pretty close to being the same. This suggests that the main source of debt being carried by our \"TURNFEAR\" folks is their primary residence, which, again, is an intuitive finding.\n",
    "\n",
    "\"DEBT\" and \"ASSET\" share a similarly striking difference, as do \"EDUC\" and \"DEBT\" which, while not as extreme a contrast as the other, is still big enough to catch the interest of our hypothetical banker.\n",
    "\n",
    "Let's make some visualizations to show these relationships graphically."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d69f523f",
   "metadata": {},
   "outputs": [
    {
     "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\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c77f0ec8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OKi+//LKeeeYZp2133XWXevToocsvv1wDBgzQsWPHPF4gAAAIXi4Hlb/97W+KiYlxPF+/fr3S0tK0cuVK7dixQ+3bt9eCBQu8UiQAAAhOLo9Ryc3N1VVXXeV4/u6772ratGmaMWOGpJ9Wqr3jjjs8XyEAAAhaLp9R+f777xUdHe14vnXrVo0ZM8bxvEePHiooKPBsdQAAIKi5HFQSExO1c+dOSVJRUZH27dunkSNHOvYXFBTIbrd7vkIAABC03FpHZe7cudq3b58+/fRT9e3bV0OGDHHs37p1qwYMGOCVIgEAQHByOag8/PDDqqio0OrVq9WlSxe9/fbbTvu3bNmilJQUjxcIAACCl83w49sgl5SUyG63q7i42Gn8DAAAsC53vr9dPqPSkB9++EFvvvmmysvLde2116pnz54X8nYAAABOXA4qDz74oM6fP6+lS5dKks6dO6fk5GTt27dPkZGRevjhh/Xxxx8rOTnZa8UCAIDg4vKsn48++kgTJ050PH/99dd15MgR5ebm6syZM7rpppu0cOFCrxQJAACCk8tB5ejRo+rXr5/j+UcffaRf/OIXSkxMlM1m0/3336/du3d7pUgAABCcXA4qISEhqj3udvv27Ro+fLjjefv27XXmzBnPVgcAAIKay0Hlsssu0z/+8Q9J0r59+3T06FGNGzfOsf/IkSPq3Lmz5ysEAABBy611VG699Va9//772rdvn66//np1797dsf+DDz7QsGHDvFIkAAAITi6fUbnxxhv1wQcfaODAgfrVr36lN99802l/ZGSkRo8e7fECAQBA8LrgBd9KS0uVkZGh5cuXa+fOnaqqqvJUbc1iwTcAAPyPO9/fLp9RqSs7O1upqanq2rWrnn76aV1zzTXavn17S98OAACgHrdWpi0oKFB6erpWrFihkpIS3XzzzaqsrNTatWudpi4DAAB4gstnVKZOnao+ffroyy+/1J///GcdP37csUotAACAN7h8RuXDDz/Ufffdp3vuuUe9evXyZk0AAACS3DijsnnzZpWWlmrIkCFKSkrSX//6VxUVFXmzNgAAEORcDirDhw/XSy+9pBMnTujuu+/WqlWrFB8fr+rqan388ccqLS31Zp0AACAIXdD05AMHDmjFihV69dVXdfbsWU2cOFHr1q3zZH1NYnoyAAD+xyfTkyWpT58+euqpp/TNN98oIyPjQt4KAACgngte8M1MnFEBAMD/+OyMCgAAgDcRVAAAgGURVAAAgGURVAAAgGURVAAAgGURVAAAgGURVAAAgGURVAAAgGURVAAAgGURVAAAgGWZGlQWLVqkoUOHKioqSp06ddL06dN14MABM0sCAAAWYmpQycrK0ty5c7V9+3Z9/PHHOn/+vK699lqVl5ebWRYAALAIS92UsLCwUJ06dVJWVpbGjBnTbHtuSggAgP9x5/s7zEc1uaS4uFiS1KFDhwb3V1ZWqrKy0vG8pKTEJ3UBAABzWGYwbXV1tR544AGNHDlSAwYMaLDNokWLZLfbHY+EhAQfVwkAAHzJMpd+7rnnHn344YfavHmzLr744gbbNHRGJSEhgUs/AAD4Eb+79HPvvffqvffeU3Z2dqMhRZIiIiIUERHhw8oAAICZTA0qhmFo3rx5WrNmjTIzM9W9e3czywEAABZjalCZO3eu3njjDb377ruKiopSQUGBJMlut6tNmzZmlgYAACzA1DEqNputwe1paWmaNWtWs69nejIAAP7Hb8aoWGQcLwAAsCjLTE8GAACoi6ACAAAsi6ACAAAsi6ACAAAsi6ACAAAsi6ACAAAsi6ACAAAsi6ACAAAsi6ACAAAsi6ACAAAsi6ACAAAsi6ACAAAsi6ACAAAsi6ACAAAsK8zsAgAAgOccLizTke8q1K1jW3WPbWt2OReMoAIAQAA4W3FO92XkKDu30LFtTK84LU0ZLHtkKxMruzBc+gEAIADcl5GjLQeLnLZtOVikeRm7TarIMwgqAAD4ucOFZcrOLVSVYThtrzIMZecWKq+o3KTKLhxBBQAAP3fku4om9+efJqgAAACTJHaIbHJ/t47+O6iWoAIAgJ/rEddOY3rFKdRmc9oearNpTK84v579Q1ABACAALE0ZrJE9Y522jewZq6Upg02qyDOYngwAQACwR7bSytnDlFdUrvzT5ayjEugCbcEcAEBw6B4bWN9bBJU6AnXBHABA4AvEX7IJKnU0tWDOytnDTKoKAIDGBfIv2QymrSWQF8wBAASuQF2VViKoOAnkBXMAAIEp0H/JJqjUEsgL5gAAAlOg/5JNUKklkBfMAQAEpkD/JZugUkegLpgDAAhMgf5Lts0w6lzU8iMlJSWy2+0qLi5WdHS0R9870BbMAQAEruKK85qXsdtp1s/QbjFaPnOoJWf9uPP9zRmVRnSPbatxfToRUgAAlmePbKXnUq7Q5Rf960t/R/4ZzcvYreKK8yZWduEIKgAA+LmzFec07ulM7fm2xGn7loOFFzRF+XBhmTYdOGXqzCEWfAMAwM/NeeULnWngzEmVIccUZXeuEFhpATnOqAAA4McOF5bpiyNnmmzj7hRlKy0gR1ABAMCPNbeOiuTeFGWrLSBHUAEAwI81t47K0G4xbl32sdoCcgSVRlhhABEAAM2pWUeloS/0mMhWWj5zqFvvZ7UF5BhMW4eVBhABAOCKpSmD66+jkhij5anur6NSE3y2HCxyuvwTarNpZM9Yny/bwYJvdcxc8XmjB2fl7GEe+QwAALzBU4uVNrSAnCd/aXfn+5szKrXUDCCqq/YAIhaAAwBYVfdYz6ymbo9spZWzh1lilXaCSi2uDCAiqAAAgoWngs+FYDBtLaeKf2hyf1FppY8qAQAAEkHFSUFp00HlePH3PqoEAABIBBUnV1zcvsn9V14S45tCAACAJIKKk6v7dJK9TcPDduxtwjS6V5yPKwIAILgRVOro07nhaVKNbQcAAN5DUKnlcGGZPs//rsF9n+d/xyq1AAD4GEGlFqvd3wAAgGBHUKnFavc3AAAg2BFUaqm5v0Gozea0PdRm05hecaYvegMAQLAhqNSxNGWwRvaMddo2smeslqYMNqkiAACCl6lBJTs7W1OnTlV8fLxsNpvWrl1rZjmS/nV/g00PjdWDE3vrjhHddOeY7tw5GQAAE5h6r5/y8nINGjRI//Ef/6Gf/exnZpbi5Mjpcv3sv7foTMV5SVLa1nzFRLbSurmjlNCx6XEsAADAc0wNKpMnT9bkyZPNLKFBNzy3WaWVPzptO1NxXtc/90/tWXCdSVUBABB8GKNSR9aBU/VCSo3Syh/1z9xCH1cEAEDw8qugUllZqZKSEqeHp722/UiT+1duy/f4ZwIAgIb5VVBZtGiR7Ha745GQkODxzyhr5GxKjfJm9gMAAM/xq6Dy2GOPqbi42PE4duyYxz9j+hUXNbn/xsEXe/wzAQBAw/wqqERERCg6Otrp4Wm3DLtEobaG94XapJuu8vxZHAAA0DBTg0pZWZlycnKUk5MjScrLy1NOTo6OHj1qZln6x72jFBbinFbCQmz6x72jTKoIAIDgZDMMwzDrwzMzMzVu3Lh621NTU5Went7s60tKSmS321VcXOyVsytvf3FMWw4VaeSlsZxJAQDAQ9z5/jY1qFwobwcVAADgee58f/vVGBUAABBcCCoAAMCyTF1CPxA8uW6fth4u0qiecfp/N/QzuxwAAAIKQaWFPtp7Qne9tsvx/OuCMi3fnKcVM6/S+H6dTawMAIDAwaWfFqodUmqbvfILH1cCAEDgIqi0wJPr9jW5f+F7+31UCQAAgY2g0gJbDxc1uX/zQe6wDACAJxBUWmBEj9gm94/qGeejSgAACGwElRZ4/N/6N7mf2T8AAHgGQaWFVsy8yq3tAADAfSyhf4EWvrdfmw8Wso4KAAAucuf7m3VULhDhBAAA7+HSDwAAsCyCCgAAsCyCCgAAsCyCCgAAsCyCCgAAsCyCCgAAsCyCCgAAsCyCCgAAsCyCCgAAsCyCCgAAsCyCCgAAsCyCCgAAsCyCCgAAsCzunuwjT67bp62HizSqZxx3XAYAwEUEFS/7aO8J3fXaLsfzrwvKtHxznlbMvErj+3U2sTIAAKyPSz9eVjuk1DZ75Rc+rgQAAP9DUPGiJ9fta3L/wvf2+6gSAAD8E0HFi7YeLmpy/+aDhT6qBAAA/0RQ8aIRPWKb3D+qZ5yPKgEAwD8RVLzo8X/r3+R+Zv8AANA0gooXPbluny5q37rBfStmXuXjagAA8D9MT/aCulOSa8RHR+j6gfF+fSaF9WAAAL5EUPGCxqYkHy+p9Nsvd9aDAQCYgUs/HhaoU5JZDwYAYAaCiocF4pTkQA1frjhcWKZNB04pr6jc7FIAIChx6cfDRvSI1dcFZY3u98cpyYEYvppztuKc7svIUXbuv/o2pleclqYM1unySh35rkLdOraVYRiO/+8e21bST+Gm7jYAQMsQVBrR0kGjj/9bf728Nb/R/f44RiUQw1dz7svI0ZaDzgFtc26hxj69SWcqzjf4mhGXdpRhSNsOn3ZsG9MrTr++tre+qzhHcAGAFrAZhmGYXURLlZSUyG63q7i4WNHR0R55z8Zm7LgzaHTj/pMNjt3w54Gn3R59v9F9+Yun+LAS7ztcWKZr/pTllfeuOStjj2zllfcHAH/gzvc3Y1Tq8MSg0fH9Oit/8RTNGdVdfbu005xR3ZW/eIrfhhSp8XVfAnE9mCPfVXjtvbccLNK8jN1ee38ACDRc+qnFlUGj7ly68cfLPI2pCV8L39uvzQcLA3odlcQOkV577yrDUHZuofKKyrkMBAAuIKjUEoyDRt0VqOGkth5x7TSmV5y2HCxSlZeujOafJqgAgCu49FMLNxFEjaUpgzWyp/Pfh5jIVgq1eeb9u3UkpACAKzijUosrM3Y8uYR81oFTyvnmrK68JEajexGCrMQe2UorZw9TXlG58k+Xq1vHtuoQGa55GbudpizX1tCsn7pCbTaN7BnL2RQAcBGzfupobMbOf17dQ/+ddbje9pbM5DlyulzTl21xmuYaE9lK6+aOUkLHxsdHsD6HNdQOL5Ic/19zTGr2d2wbrqc3/G+Da7Ew6wdAMHPn+5ug0oi6g0Y9OT138JMfNbgWR0xkK+1+/Np625tafIwvPOurHWwImADg3vc3l34aUfuyjidnA2UdONXogmFnKs7rn7mF9S4DNbT4WM0015Wzh7n0uTBP91gCCgC0FEHFBS2ZDdTYWJacb842+V67jp5xCiqHC8saHBNRe5prQ8u4B5LDhWV678vj+q78nMZf1rnen08g9x0Agh1BpRFPrtun9/ceV7vwVopv31pfq/El5Id26+D4/7or235dUKblm/McY1lC1fS0kX8eKNT943s7nje3+NhdK79Q7ql/1RYol4QOF5Zp34kS/S3rkPZ8W+LYnr71iOxtwvTGnCQtWc/4DwAIdIxRqaOxJfSbMqZXnOMSTHNjWZ5Yt0/pTcwsqlETbFqynPvF7dto86PXuPUaq2hoPE5DwkJsMgw5rXNSM6OGy2EAYG0soX8B3A0pkhyXYFwZy9KhrWu/7dfMPKpZfMwd35z9Xt0efV8b959063VW0NB4nIb8WG3UW4yt9uUwAEBgIKjU0lzQaMq8N3bpw70nmmyz+WChbhgY7/J7LnxvvyTp19f2bqZlw9y5P5EV1IzHudDVYPNPE1QAIFAQVGp5dXt+i1+793iJTpRUNtkmRDb95+s71SU6wqX3XL45Txv3n9R3FedaXFdN2PEHnroZIKu+AkDgsERQWbZsmbp166bWrVsrKSlJn3/+uSl1nK/27vvvLyjV1wVlKmgm0NQ2e+UXF3STPH+6P5E7/QwLsSnU5jwwOdRm05heccz+AYAAYnpQefPNN/Xggw9q/vz52rVrlwYNGqTrrrtOp06d8nktYR66j4unvfHZUQ3tFtOi1/rT/YlqxuPUDSB12duEad3ckfXuxTOyZ6yWpgz2ZokAAB8zfdZPUlKShg4dqr/+9a+SpOrqaiUkJGjevHl69NFHm3ytp2f99Hj0fXn5pEqLxLQJU+ZvrtHYpzc1ulhcY9xdNddsxRXn691PZ2hijJK6d1DZuR/rraPCqq8A4H/8ZmXac+fOaefOnXrssccc20JCQjRhwgRt27bN5/VYMaTUsEe2UuZD4zTnlR3aceSMY/vQxBgNSYzRC9kN34fI3zR0M8CmAgirvgJAYDM1qBQVFamqqkqdOzvf1K9z5876+uuv67WvrKxUZeW/xneUlJTUaxOIfj4kQdJPX+Jv3zOiwS/xR6+/rN79ifwZAQQAIPnZyrSLFi3SggULzC7D5+qGjsa+xP09nAAAUJepg2ljY2MVGhqqkyedFyY7efKkunTpUq/9Y489puLiYsfj2LFjvirVNP54+QYAAE8xNaiEh4dryJAh2rhxo2NbdXW1Nm7cqOTk5HrtIyIiFB0d7fTwJCsMPI0IC1HfLu00Z1R35S+eovH9Ojf/IgAAApTpl34efPBBpaam6qqrrtKwYcP05z//WeXl5brjjjvMLs0jQiQZ//doTkxkK62bO0oJHVu+bgoAAIHE9KByyy23qLCwUI8//rgKCgp0xRVXaP369fUG2PpKzVmVpm4u2JQu0eGamdxdl8VHOw12zSsq17JPc3WwsEy9OkWpXUSYOrQLV5fo1jpe/L2uvCTGadotAACwwDoqF8Ibd08GAADexd2TAQBAQCCoAAAAyyKoAAAAyyKoAAAAyyKoAAAAyyKoAAAAyyKoAAAAyyKoAAAAyyKoAAAAyyKoAAAAyzL9Xj8Xomb1/5KSEpMrAQAArqr53nblLj5+HVRKS0slSQkJCSZXAgAA3FVaWiq73d5kG7++KWF1dbWOHz+uqKgo2Ww2j753SUmJEhISdOzYsYC/4SF9DUzB1FcpuPpLXwNTMPXVMAyVlpYqPj5eISFNj0Lx6zMqISEhuvjii736GdHR0QH/F6YGfQ1MwdRXKbj6S18DU7D0tbkzKTUYTAsAACyLoAIAACyLoNKIiIgIzZ8/XxEREWaX4nX0NTAFU1+l4OovfQ1MwdRXd/j1YFoAABDYOKMCAAAsi6ACAAAsi6ACAAAsK2iCyrJly9StWze1bt1aSUlJ+vzzz5ts//bbb6tv375q3bq1Lr/8cn3wwQdO+w3D0OOPP66uXbuqTZs2mjBhgnJzc73ZBZe509eXXnpJo0ePVkxMjGJiYjRhwoR67WfNmiWbzeb0mDRpkre74TJ3+puenl6vL61bt3ZqEyjHduzYsfX6arPZNGXKFEcbqx7b7OxsTZ06VfHx8bLZbFq7dm2zr8nMzNSVV16piIgI9ezZU+np6fXauPvvgC+429fVq1dr4sSJiouLU3R0tJKTk7VhwwanNk888US949q3b18v9sI17vY1MzOzwb/DBQUFTu0C4bg29LNos9nUv39/RxurHldvC4qg8uabb+rBBx/U/PnztWvXLg0aNEjXXXedTp061WD7rVu3KiUlRbNnz9bu3bs1ffp0TZ8+XXv37nW0eeqpp/Tcc8/phRde0Geffaa2bdvquuuu0w8//OCrbjXI3b5mZmYqJSVFmzZt0rZt25SQkKBrr71W3377rVO7SZMm6cSJE45HRkaGL7rTLHf7K/20mFLtvhw5csRpf6Ac29WrVzv1c+/evQoNDdVNN93k1M6Kx7a8vFyDBg3SsmXLXGqfl5enKVOmaNy4ccrJydEDDzygOXPmOH2Bt+Tvii+429fs7GxNnDhRH3zwgXbu3Klx48Zp6tSp2r17t1O7/v37Ox3XzZs3e6N8t7jb1xoHDhxw6kunTp0c+wLluP7lL39x6uOxY8fUoUOHej+vVjyuXmcEgWHDhhlz5851PK+qqjLi4+ONRYsWNdj+5ptvNqZMmeK0LSkpybj77rsNwzCM6upqo0uXLsYf//hHx/6zZ88aERERRkZGhhd64Dp3+1rXjz/+aERFRRmvvPKKY1tqaqoxbdo0T5fqEe72Ny0tzbDb7Y2+XyAf22effdaIiooyysrKHNusfGxrSDLWrFnTZJuHH37Y6N+/v9O2W265xbjuuusczy/0z88XXOlrQ/r162csWLDA8Xz+/PnGoEGDPFeYF7jS102bNhmSjDNnzjTaJlCP65o1awybzWbk5+c7tvnDcfWGgD+jcu7cOe3cuVMTJkxwbAsJCdGECRO0bdu2Bl+zbds2p/aSdN111zna5+XlqaCgwKmN3W5XUlJSo+/pCy3pa10VFRU6f/68OnTo4LQ9MzNTnTp1Up8+fXTPPffo9OnTHq29JVra37KyMiUmJiohIUHTpk3Tvn37HPsC+diuWLFCt956q9q2beu03YrH1l3N/cx64s/Pqqqrq1VaWlrvZzY3N1fx8fHq0aOHZsyYoaNHj5pU4YW74oor1LVrV02cOFFbtmxxbA/k47pixQpNmDBBiYmJTtsD6bi6KuCDSlFRkaqqqtS5c2en7Z07d653nbNGQUFBk+1r/uvOe/pCS/pa1yOPPKL4+HinH/xJkyZp5cqV2rhxo5YsWaKsrCxNnjxZVVVVHq3fXS3pb58+ffTyyy/r3Xff1Wuvvabq6mqNGDFC33zzjaTAPbaff/659u7dqzlz5jhtt+qxdVdjP7MlJSX6/vvvPfKzYVVPP/20ysrKdPPNNzu2JSUlKT09XevXr9fzzz+vvLw8jR492nHHeX/RtWtXvfDCC/r73/+uv//970pISNDYsWO1a9cuSZ75N8+Kjh8/rg8//LDez2ugHFd3+fVNCeFZixcv1qpVq5SZmek0wPTWW291/P/ll1+ugQMH6tJLL1VmZqbGjx9vRqktlpycrOTkZMfzESNG6LLLLtOLL76o3//+9yZW5l0rVqzQ5ZdfrmHDhjltD6RjG4zeeOMNLViwQO+++67TuI3Jkyc7/n/gwIFKSkpSYmKi3nrrLc2ePduMUlukT58+6tOnj+P5iBEjdOjQIT377LN69dVXTazMu1555RW1b99e06dPd9oeKMfVXQF/RiU2NlahoaE6efKk0/aTJ0+qS5cuDb6mS5cuTbav+a877+kLLelrjaefflqLFy/WRx99pIEDBzbZtkePHoqNjdXBgwcvuOYLcSH9rdGqVSsNHjzY0ZdAPLbl5eVatWqVS/+QWeXYuquxn9no6Gi1adPGI39XrGbVqlWaM2eO3nrrrXqXvepq3769evfu7XfHtSHDhg1z9CMQj6thGHr55Zd1++23Kzw8vMm2gXRcmxLwQSU8PFxDhgzRxo0bHduqq6u1ceNGp9+sa0tOTnZqL0kff/yxo3337t3VpUsXpzYlJSX67LPPGn1PX2hJX6WfZrn8/ve/1/r163XVVVc1+znffPONTp8+ra5du3qk7pZqaX9rq6qq0p49exx9CbRjK/001b6yslK33XZbs59jlWPrruZ+Zj3xd8VKMjIydMcddygjI8NpunljysrKdOjQIb87rg3Jyclx9CPQjqskZWVl6eDBgy79YhFIx7VJZo/m9YVVq1YZERERRnp6urF//37jrrvuMtq3b28UFBQYhmEYt99+u/Hoo4862m/ZssUICwsznn76aeOrr74y5s+fb7Rq1crYs2ePo83ixYuN9u3bG++++67x5ZdfGtOmTTO6d+9ufP/99z7vX23u9nXx4sVGeHi48c477xgnTpxwPEpLSw3DMIzS0lLjoYceMrZt22bk5eUZn3zyiXHllVcavXr1Mn744QdT+libu/1dsGCBsWHDBuPQoUPGzp07jVtvvdVo3bq1sW/fPkebQDm2NUaNGmXccsst9bZb+diWlpYau3fvNnbv3m1IMp555hlj9+7dxpEjRwzDMIxHH33UuP322x3tDx8+bERGRhq/+c1vjK+++spYtmyZERoaaqxfv97Rprk/P7O429fXX3/dCAsLM5YtW+b0M3v27FlHm1//+tdGZmamkZeXZ2zZssWYMGGCERsba5w6dcrn/avN3b4+++yzxtq1a43c3Fxjz549xv3332+EhIQYn3zyiaNNoBzXGrfddpuRlJTU4Hta9bh6W1AEFcMwjKVLlxqXXHKJER4ebgwbNszYvn27Y9/VV19tpKamOrV/6623jN69exvh4eFG//79jffff99pf3V1tfG73/3O6Ny5sxEREWGMHz/eOHDggC+60ix3+pqYmGhIqveYP3++YRiGUVFRYVx77bVGXFyc0apVKyMxMdG48847Tf9HoDZ3+vvAAw842nbu3Nm4/vrrjV27djm9X6AcW8MwjK+//tqQZHz00Uf13svKx7ZmWmrdR03/UlNTjauvvrrea6644gojPDzc6NGjh5GWllbvfZv68zOLu329+uqrm2xvGD9Nze7atasRHh5uXHTRRcYtt9xiHDx40Lcda4C7fV2yZIlx6aWXGq1btzY6dOhgjB071vj000/rvW8gHFfD+GkphDZt2hh/+9vfGnxPqx5Xb+PuyQAAwLICfowKAADwXwQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABgWQQVAABQT3Z2tqZOnar4+HjZbDatXbvWrdc/8cQTstls9R5t27Z1630IKgC8YtasWY5/mFq1aqXOnTtr4sSJevnll1VdXe1o161btwb/MVu8eLEkKT8/32l7eHi4evbsqYULF6pmYe3G3qPmMWvWLDP+CAC/Vl5erkGDBmnZsmUtev1DDz2kEydOOD369eunm266ya33CWvRpwOACyZNmqS0tDRVVVXp5MmTWr9+ve6//3698847WrduncLCfvon6Mknn9Sdd97p9NqoqCin55988on69++vyspKbd68WXPmzFHXrl01e/Zs7dixQ1VVVZKkrVu36uc//7kOHDig6OhoSVKbNm180FsgsEyePFmTJ09udH9lZaV++9vfKiMjQ2fPntWAAQO0ZMkSjR07VpLUrl07tWvXztH+f/7nf7R//3698MILbtVBUAHgNREREerSpYsk6aKLLtKVV16p4cOHa/z48UpPT9ecOXMk/RRKato1pmPHjo42iYmJSktL065duzR79mzFxcU52nXo0EGS1KlTJ7Vv394LvQIgSffee6/279+vVatWKT4+XmvWrNGkSZO0Z88e9erVq1775cuXq3fv3ho9erRbn8OlHwA+dc0112jQoEFavXp1i9/jiy++0M6dO5WUlOTBygC46ujRo0pLS9Pbb7+t0aNH69JLL9VDDz2kUaNGKS0trV77H374Qa+//rpmz57t9mcRVAD4XN++fZWfn+94/sgjjzhOE9c8/vnPfzq9ZsSIEWrXrp3Cw8M1dOhQ3XzzzZo5c6aPKwcgSXv27FFVVZV69+7t9HOblZWlQ4cO1Wu/Zs0alZaWKjU11e3P4tIPAJ8zDEM2m83x/De/+U29Aa8XXXSR0/M333xTl112mc6fP6+9e/dq3rx5iomJcQy6BeA7ZWVlCg0N1c6dOxUaGuq0r/a4lBrLly/XDTfcoM6dO7v9WQQVAD731VdfqXv37o7nsbGx6tmzZ5OvSUhIcLS57LLLdOjQIf3ud7/TE088odatW3u1XgDOBg8erKqqKp06darZMSd5eXnatGmT1q1b16LPIqgA8KlPP/1Ue/bs0a9+9asLep/Q0FD9+OOPOnfuHEEF8IKysjIdPHjQ8TwvL085OTnq0KGDevfurRkzZmjmzJn605/+pMGDB6uwsFAbN27UwIEDNWXKFMfrXn75ZXXt2rXJGURNIagA8JrKykoVFBQ4TU9etGiRbrjhBqfxJaWlpSooKHB6bWRkpGN6sSSdPn1aBQUF+vHHH7Vnzx795S9/0bhx45zaAPCcL774QuPGjXM8f/DBByVJqampSk9PV1pamhYuXKhf//rX+vbbbxUbG6vhw4frhhtucLymurpa6enpmjVrVr1LRK6yGTUrJgGAB82aNUuvvPKKJCksLEwxMTEaNGiQ/v3f/12pqakKCflpLH+3bt105MiReq+/++679cILLyg/P9/pMlFoaKjjt7P/+q//cpqaLEmZmZkaN26czpw5w/RkIAAQVAAAgGUxPRkAAFgWQQUAAFgWQQUAAFgWQQUAAFgWQQUAAFgWQQUAAFgWQQUAAFgWQQUAAFgWQQUAAFgWQQUAAFgWQQUAAFgWQQUAAFjW/wf1gp4+4dU70AAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_fear.plot.scatter(x = \"DEBT\", y = \"ASSET\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3f3db4c6",
   "metadata": {},
   "outputs": [
    {
     "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\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f606716c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_fear.plot.scatter(x = \"DEBT\", y = \"HOUSES\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83dc9a60",
   "metadata": {},
   "source": [
    "# EDUCATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2beac500",
   "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>TURNFEAR</th>\n",
       "      <th>EDUC</th>\n",
       "      <th>frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0.257481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0.192029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0.149823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0.129833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0.096117</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   TURNFEAR  EDUC  frequency\n",
       "0         0    12   0.257481\n",
       "1         0     8   0.192029\n",
       "2         0    13   0.149823\n",
       "3         0     9   0.129833\n",
       "4         0    14   0.096117"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_educ = df[\"EDUC\"].groupby(df[\"TURNFEAR\"]).value_counts(normalize = True).rename(\"frequency\").to_frame().reset_index()\n",
    "df_educ.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "70a624f8",
   "metadata": {},
   "outputs": [
    {
     "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\", y = \"frequency\", \n",
    "           data = df_educ, hue = \"TURNFEAR\",\n",
    "           )\n",
    "plt.xlabel(\"Education Level\")\n",
    "plt.ylabel(\"Frequency [%]\")\n",
    "plt.title(\"Educational Attainment: Credit Fearful Vs. Non Fearful\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a349fcc5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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