finding_donors_EDA_univariate.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Finding Donors for *CharityML*\n",
    "## EDA: univariate\n",
    "### Kebei Jiang 05/17/2019"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Goal\n",
    "\n",
    "The goal of this notebook is to do initial EDA of the data. More importantly, I am trying to follow this [instruction](https://www.analyticsvidhya.com/blog/2016/01/guide-data-exploration/#one) and later build my own EDA flow/template."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get Started\n",
    "\n",
    "The dataset for this project originates from the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Census+Income). The datset was donated by Ron Kohavi and Barry Becker, after being published in the article [_\"Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid\"_](https://www.aaai.org/Papers/KDD/1996/KDD96-033.pdf). \n",
    "\n",
    "First thing first, I double checked the [origin](https://www.kaggle.com/uciml/adult-census-income) of the original dataset and made following observations.  \n",
    "1. It was extracted from 1994 Census bureau database and was used as an example in aforementioned paper.  \n",
    "2. The _'fnlwgt'_ feature is the number of people in full population the census takers believe that observation represents. It can be ignored for the purpose of this project.  \n",
    "3. The _'census.csv'_ dataset is about the same size as the dataset used in the paper, minus some records with missing or ill-formatted entries."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 0, data head and description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libraries necessary for this project\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from time import time\n",
    "from IPython.display import display # Allows the use of display() for DataFrames\n",
    "\n",
    "# Import libraries for visulization\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set(color_codes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>education_level</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>11th</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          workclass education_level  education-num       marital-status  \\\n",
       "0   39          State-gov       Bachelors           13.0        Never-married   \n",
       "1   50   Self-emp-not-inc       Bachelors           13.0   Married-civ-spouse   \n",
       "2   38            Private         HS-grad            9.0             Divorced   \n",
       "3   53            Private            11th            7.0   Married-civ-spouse   \n",
       "4   28            Private       Bachelors           13.0   Married-civ-spouse   \n",
       "\n",
       "           occupation    relationship    race      sex  capital-gain  \\\n",
       "0        Adm-clerical   Not-in-family   White     Male        2174.0   \n",
       "1     Exec-managerial         Husband   White     Male           0.0   \n",
       "2   Handlers-cleaners   Not-in-family   White     Male           0.0   \n",
       "3   Handlers-cleaners         Husband   Black     Male           0.0   \n",
       "4      Prof-specialty            Wife   Black   Female           0.0   \n",
       "\n",
       "   capital-loss  hours-per-week  native-country income  \n",
       "0           0.0            40.0   United-States  <=50K  \n",
       "1           0.0            13.0   United-States  <=50K  \n",
       "2           0.0            40.0   United-States  <=50K  \n",
       "3           0.0            40.0   United-States  <=50K  \n",
       "4           0.0            40.0            Cuba  <=50K  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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       "    }\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>age</th>\n",
       "      <th>education-num</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>45222.000000</td>\n",
       "      <td>45222.000000</td>\n",
       "      <td>45222.000000</td>\n",
       "      <td>45222.000000</td>\n",
       "      <td>45222.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>38.547941</td>\n",
       "      <td>10.118460</td>\n",
       "      <td>1101.430344</td>\n",
       "      <td>88.595418</td>\n",
       "      <td>40.938017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>13.217870</td>\n",
       "      <td>2.552881</td>\n",
       "      <td>7506.430084</td>\n",
       "      <td>404.956092</td>\n",
       "      <td>12.007508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>37.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>47.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>45.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>99999.000000</td>\n",
       "      <td>4356.000000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                age  education-num  capital-gain  capital-loss  hours-per-week\n",
       "count  45222.000000   45222.000000  45222.000000  45222.000000    45222.000000\n",
       "mean      38.547941      10.118460   1101.430344     88.595418       40.938017\n",
       "std       13.217870       2.552881   7506.430084    404.956092       12.007508\n",
       "min       17.000000       1.000000      0.000000      0.000000        1.000000\n",
       "25%       28.000000       9.000000      0.000000      0.000000       40.000000\n",
       "50%       37.000000      10.000000      0.000000      0.000000       40.000000\n",
       "75%       47.000000      13.000000      0.000000      0.000000       45.000000\n",
       "max       90.000000      16.000000  99999.000000   4356.000000       99.000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Raw data has 45222 rows and 14 columns.\n"
     ]
    }
   ],
   "source": [
    "# Load the Census dataset\n",
    "data = pd.read_csv(\"census.csv\")\n",
    "\n",
    "display(data.head(n=5))\n",
    "display(data.describe())\n",
    "print('Raw data has {} rows and {} columns.'.format(data.shape[0], data.shape[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**observation**: no missing or ill-formatted entries. no cleansing needed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1, Variable Identification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                  int64\n",
       "workclass           object\n",
       "education_level     object\n",
       "education-num      float64\n",
       "marital-status      object\n",
       "occupation          object\n",
       "relationship        object\n",
       "race                object\n",
       "sex                 object\n",
       "capital-gain       float64\n",
       "capital-loss       float64\n",
       "hours-per-week     float64\n",
       "native-country      object\n",
       "income              object\n",
       "dtype: object"
      ]
     },
     "execution_count": 362,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "age has 74 unique values:\n",
      " [39 50 38 53 28 37 49 52 31 42 30 23 32 34 25 43 40 54 35 59 56 19 20 45\n",
      " 22 48 21 24 57 44 41 29 47 46 36 79 27 18 33 76 55 61 70 64 71 66 51 58\n",
      " 26 17 60 90 75 65 77 62 63 67 74 72 69 68 73 81 78 88 80 84 83 85 82 86\n",
      " 89 87]\n",
      " \n",
      "workclass has 7 unique values:\n",
      " [' State-gov' ' Self-emp-not-inc' ' Private' ' Federal-gov' ' Local-gov'\n",
      " ' Self-emp-inc' ' Without-pay']\n",
      " \n",
      "education_level has 16 unique values:\n",
      " [' Bachelors' ' HS-grad' ' 11th' ' Masters' ' 9th' ' Some-college'\n",
      " ' Assoc-acdm' ' 7th-8th' ' Doctorate' ' Assoc-voc' ' Prof-school'\n",
      " ' 5th-6th' ' 10th' ' Preschool' ' 12th' ' 1st-4th']\n",
      " \n",
      "education-num has 16 unique values:\n",
      " [13.  9.  7. 14.  5. 10. 12.  4. 16. 11. 15.  3.  6.  1.  8.  2.]\n",
      " \n",
      "marital-status has 7 unique values:\n",
      " [' Never-married' ' Married-civ-spouse' ' Divorced'\n",
      " ' Married-spouse-absent' ' Separated' ' Married-AF-spouse' ' Widowed']\n",
      " \n",
      "occupation has 14 unique values:\n",
      " [' Adm-clerical' ' Exec-managerial' ' Handlers-cleaners' ' Prof-specialty'\n",
      " ' Other-service' ' Sales' ' Transport-moving' ' Farming-fishing'\n",
      " ' Machine-op-inspct' ' Tech-support' ' Craft-repair' ' Protective-serv'\n",
      " ' Armed-Forces' ' Priv-house-serv']\n",
      " \n",
      "relationship has 6 unique values:\n",
      " [' Not-in-family' ' Husband' ' Wife' ' Own-child' ' Unmarried'\n",
      " ' Other-relative']\n",
      " \n",
      "race has 5 unique values:\n",
      " [' White' ' Black' ' Asian-Pac-Islander' ' Amer-Indian-Eskimo' ' Other']\n",
      " \n",
      "sex has 2 unique values:\n",
      " [' Male' ' Female']\n",
      " \n",
      "capital-gain has 121 unique values:\n",
      " [ 2174.     0. 14084.  5178.  5013.  2407. 14344. 15024.  7688.  4064.\n",
      "  4386.  7298.  1409.  3674.  1055.  2050.  2176.   594. 20051.  6849.\n",
      "  4101.  8614.  3411.  2597. 25236.  4650.  2463.  3103. 10605.  2964.\n",
      "  3325.  2580.  3471.  4865. 99999.  6514.  1471.  2329.  2105.  2885.\n",
      " 10520.  2202.  2961. 27828.  6767.  2228.  1506. 13550.  2635.  5556.\n",
      "  4787.  3781.  3137.  3818.   914.   401.  2829.  2977.  4934.  2062.\n",
      "  2354.  3464.  5455. 15020.  1424.  3273. 22040.  4416. 10566.  4931.\n",
      "  7430. 34095.  6497.  3908.   114.  2346.  2907.  1151.  9386.  2414.\n",
      "  2290.  3418. 15831. 41310.  4508.  2538.  3456.  1848.  3887.  7896.\n",
      "  5721.  9562.  6418.  1455.  2036.  3942.  1831. 11678.  2936.  2993.\n",
      "  7443.  6360.  4687.  1797.  6723.  2009.  3432.  6097.  1639.  2653.\n",
      " 18481. 25124.  7978.   991.  1173.  2387.  5060.  1086.  1264.  7262.\n",
      "  1731.]\n",
      " \n",
      "capital-loss has 97 unique values:\n",
      " [   0. 2042. 1408. 1902. 1887. 1719. 1762. 1564. 2179. 1816. 1980. 1977.\n",
      " 1876. 1340. 2206. 1741. 1485. 2339. 2415. 1380. 1721. 2051. 2377. 1669.\n",
      " 2352. 1672.  653. 2392. 1504. 1590. 1651. 1628. 1848. 2001. 1740. 2002.\n",
      " 1579. 2258. 1602. 2547. 2205. 1726. 2444. 1138. 2238.  625.  213. 1539.\n",
      "  880. 1668. 1092. 1594. 2231. 1844.  810. 2824. 2559. 2057. 1573. 1974.\n",
      "  974. 2174. 2149. 1735. 1258. 2603. 2282.  323. 2246. 1617. 1648. 1825.\n",
      " 3770. 1755. 3683. 2129. 4356. 2267. 2080. 2457.  419.  155. 3900. 2201.\n",
      " 1944. 2467. 2754. 2472. 3004. 1411. 1429. 3175. 1870. 1911. 1510. 1421.\n",
      " 2163.]\n",
      " \n",
      "hours-per-week has 96 unique values:\n",
      " [40. 13. 16. 45. 50. 80. 30. 35. 60. 20. 52. 44. 15. 25. 43. 38. 55. 48.\n",
      " 58. 32. 70. 22. 56. 41. 28. 36. 24. 46.  2. 42. 12. 65.  1. 34. 75. 98.\n",
      " 33. 54. 10.  6. 64. 19. 18. 72.  8.  9. 47. 37. 21. 26. 14.  5.  7. 99.\n",
      " 53. 39. 62. 59. 57. 78. 90. 66. 11. 49. 84. 17. 68.  3. 27. 85. 31. 51.\n",
      " 77. 63. 23.  4. 87. 88. 73. 89. 97. 94. 29. 96. 67. 82. 86. 91. 81. 76.\n",
      " 92. 61. 74. 95. 79. 69.]\n",
      " \n",
      "native-country has 41 unique values:\n",
      " [' United-States' ' Cuba' ' Jamaica' ' India' ' Mexico' ' Puerto-Rico'\n",
      " ' Honduras' ' England' ' Canada' ' Germany' ' Iran' ' Philippines'\n",
      " ' Poland' ' Columbia' ' Cambodia' ' Thailand' ' Ecuador' ' Laos'\n",
      " ' Taiwan' ' Haiti' ' Portugal' ' Dominican-Republic' ' El-Salvador'\n",
      " ' France' ' Guatemala' ' Italy' ' China' ' South' ' Japan' ' Yugoslavia'\n",
      " ' Peru' ' Outlying-US(Guam-USVI-etc)' ' Scotland' ' Trinadad&Tobago'\n",
      " ' Greece' ' Nicaragua' ' Vietnam' ' Hong' ' Ireland' ' Hungary'\n",
      " ' Holand-Netherlands']\n",
      " \n",
      "income has 2 unique values:\n",
      " ['<=50K' '>50K']\n",
      " \n"
     ]
    }
   ],
   "source": [
    "for col in data.columns.values:\n",
    "    print('{} has {} unique values:\\n {}\\n '.format(col, data[col].nunique(), data[col].unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "ft_num = data.select_dtypes(include=['int64','float64']).columns.values\n",
    "ft_cat = data.select_dtypes(exclude=['int64','float64']).columns.values\n",
    "\n",
    "#print(ft_num)\n",
    "#print(ft_cat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "count     45222\n",
       "unique        2\n",
       "top       <=50K\n",
       "freq      34014\n",
       "Name: income, dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "u, u_count = np.unique(data['income'], return_counts=True)\n",
    "plt.pie(u_count, labels=['<=50K', '>50K'], pctdistance=1.1, labeldistance=1.2)\n",
    "plt.show()\n",
    "data['income'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**observation**:  \n",
    " * skewed target variable, need to pay attetion to the skewness while modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ### 2a, Univariate Analysis: continuous"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numerical features are: ['age' 'education-num' 'capital-gain' 'capital-loss' 'hours-per-week']\n"
     ]
    }
   ],
   "source": [
    "print('numerical features are: {}'.format(ft_num))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# displot when the variable is continuous\n",
    "\n",
    "fig = plt.figure(figsize=(12, 4))\n",
    "plt.subplot(121)\n",
    "sns.distplot(data['age'], bins=np.linspace(10, 90, 10), kde=False)\n",
    "plt.subplot(122)\n",
    "sns.violinplot(data['age'], orient='h')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**observation**:  \n",
    " * population concentrates around 30~40 years old and skews towards younger end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### educations, both number and level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# countplot when the variable is categorical or discrete enough\n",
    "\n",
    "fig = plt.figure(figsize=(6, 4))\n",
    "sns.countplot(data['education-num'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1.],\n",
       "       [1., 1.]])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_, ct_num = np.unique(data['education-num'], return_counts=True)\n",
    "_, ct_lvl = np.unique(data['education_level'], return_counts=True)\n",
    "np.corrcoef(sorted(ct_num), sorted(ct_lvl))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**observation**:   \n",
    " *  _education-level_ and _education-num_ carry the same information, can drop either one.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### capital investments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 373,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# can we have both gain and loss non-zero?\n",
    "\n",
    "(data['capital-gain']*data['capital-loss']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create figure\n",
    "fig = plt.figure(figsize = (11,5))\n",
    "\n",
    "# Skewed feature plotting\n",
    "for i, feature in enumerate(['capital-gain','capital-loss']):\n",
    "    ax = fig.add_subplot(1, 2, i+1)\n",
    "    ax.hist(data[feature], bins = 25, color = '#00A0A0')\n",
    "    ax.set_title(\"'%s' Feature Distribution\"%(feature), fontsize = 14)\n",
    "    ax.set_xlabel(\"Value\")\n",
    "    ax.set_ylabel(\"Number of Records\")\n",
    "    ax.set_ylim((0, 2000))\n",
    "    ax.set_yticks([0, 500, 1000, 1500, 2000])\n",
    "    ax.set_yticklabels([0, 500, 1000, 1500, \">2000\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations**  \n",
    " * The distribution looks extremely skewed because of the zero values.   \n",
    " * out liers exist.  \n",
    " * gain and loss are the net income from capital investment. People with zero gain and loss are simply outta market. (May consider combining these two into one, i.e., _capital = capital-gain + (-1)*capital-loss._ )  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### hours-per-week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data['hours-per-week'], bins=np.linspace(0,100, 26), kde=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations**  \n",
    " * Most people working around 40 hours per week"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ### 2b, Univariate Analysis: categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['workclass', 'education_level', 'marital-status', 'occupation',\n",
       "       'relationship', 'race', 'sex', 'native-country', 'income'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ft_cat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Wrok class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data['workclass'])\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations** \n",
    "\n",
    "Apparently, we have a lot of 'private' workers. This give me a better understanding of the classes:  \n",
    " * 'gov' related: federal level has higher income than local and state  \n",
    " * 'private' is privately owned companies, they make up most of the population and has the 2nd lowest >50K proportion.  \n",
    " * the difference between 'self-emp-inc' and 'self-emp-not-inc' are summarized in this [article](https://smallbiztrends.com/2013/11/incorporated-versus-unincorporated-self-employment.html).  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### marital-status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='marital-status', data=data)\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>education_level</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>49</td>\n",
       "      <td>Private</td>\n",
       "      <td>9th</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Married-spouse-absent</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>Jamaica</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>27</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Married-spouse-absent</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>56</td>\n",
       "      <td>Private</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Married-spouse-absent</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>Iran</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>22</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Married-spouse-absent</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>40</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Married-spouse-absent</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>2174.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     age          workclass education_level  education-num  \\\n",
       "6     49            Private             9th            5.0   \n",
       "162   27            Private         HS-grad            9.0   \n",
       "322   56            Private      Assoc-acdm           12.0   \n",
       "337   22            Private         HS-grad            9.0   \n",
       "400   40   Self-emp-not-inc       Bachelors           13.0   \n",
       "\n",
       "             marital-status       occupation    relationship    race      sex  \\\n",
       "6     Married-spouse-absent    Other-service   Not-in-family   Black   Female   \n",
       "162   Married-spouse-absent            Sales       Unmarried   White   Female   \n",
       "322   Married-spouse-absent    Other-service   Not-in-family   White     Male   \n",
       "337   Married-spouse-absent            Sales   Not-in-family   White     Male   \n",
       "400   Married-spouse-absent   Prof-specialty   Not-in-family   White   Female   \n",
       "\n",
       "     capital-gain  capital-loss  hours-per-week  native-country income  \n",
       "6             0.0           0.0            16.0         Jamaica  <=50K  \n",
       "162           0.0           0.0            25.0   United-States  <=50K  \n",
       "322           0.0           0.0            25.0            Iran  <=50K  \n",
       "337           0.0           0.0            55.0   United-States  <=50K  \n",
       "400        2174.0           0.0            40.0   United-States  <=50K  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# understand ' Married-spouse-absent'\n",
    "data[data['marital-status']==' Married-spouse-absent'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**observations**  \n",
    " * Married-AF-spouse = Armed Forces, Married-civ-spouse = Civilian   \n",
    " * see how 'Married-civ-spouse' earns way more than any other category? We are dealing with **househould** income!  \n",
    " * So the person listed is the bread winner of the household? Do we have people from the same family?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  relationship"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='relationship', data=data)\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "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>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>education_level</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>23</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>25</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Farming-fishing</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>19</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>20</td>\n",
       "      <td>Private</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>45</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1408.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age          workclass education_level  education-num  marital-status  \\\n",
       "12   23            Private       Bachelors           13.0   Never-married   \n",
       "15   25   Self-emp-not-inc         HS-grad            9.0   Never-married   \n",
       "25   19            Private         HS-grad            9.0   Never-married   \n",
       "29   20            Private    Some-college           10.0   Never-married   \n",
       "30   45            Private       Bachelors           13.0        Divorced   \n",
       "\n",
       "          occupation relationship    race      sex  capital-gain  \\\n",
       "12      Adm-clerical    Own-child   White   Female           0.0   \n",
       "15   Farming-fishing    Own-child   White     Male           0.0   \n",
       "25      Craft-repair    Own-child   White     Male           0.0   \n",
       "29             Sales    Own-child   Black     Male           0.0   \n",
       "30   Exec-managerial    Own-child   White     Male           0.0   \n",
       "\n",
       "    capital-loss  hours-per-week  native-country income  \n",
       "12           0.0            30.0   United-States  <=50K  \n",
       "15           0.0            35.0   United-States  <=50K  \n",
       "25           0.0            40.0   United-States  <=50K  \n",
       "29           0.0            44.0   United-States  <=50K  \n",
       "30        1408.0            40.0   United-States  <=50K  "
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# understand 'own-child'\n",
    "data[(data['relationship']==' Own-child')].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations**  \n",
    " * People who are listed as 'Own-child' are single. \n",
    " * 'Other-relative' might be grandparents.  \n",
    " * We can actually divide this feature into 'complete' and 'incomplete' where the former overlaps with 'Married'. Therefore we can **combine 'relationship' and 'Martial-Status'**  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  occupation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='occupation', data=data)\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**observation**  \n",
    " * will it help to cap them with bigger groups?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  race"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hw4YBMG3aNEJCQli8eDFubm7Mnz9fra4IIYS4SxpFUW6dmKgDbjcnM3ji11aoSF1r5g2RORkhxF2rlnMyQggh6jYJGSGEEKqRkBFCCKEaCRkhhBCqkZARQgihGgkZIYQQqqlUyPzZumC//vrrPS9GCCFE7XLbkLl69SpXr15l+PDhXLt2zXI7KyuLsWPHVlWNQgghaqjbnvH/zjvv8MMPPwDwr3/96/8epNValtkXQggh/sptQ+aLL74AblzNcs6cOVVSkBBCiNqjUmuXzZkzh9TUVK5du0bFVWg8PT1VK0wIIUTNV6mQWbhwIV988QVNmza1tGk0Gvbs2aNaYUIIIWq+SoVMdHQ0O3fuxMXFRe16hBBC1CKVOoTZzc1NAkYIIcQdq9SeTLdu3Zg3bx49e/akXr16lnaZkxFCCHE7lQqZTZs2AWA0Gi1tMicjhBDi71QqZL777ju16xBCCFELVSpkIiMj/7T91Vdfve3jPvvsM7Zv3w6Aj48PEydOJD4+njlz5lBcXEzv3r2ZMGECAMnJyUyePJnCwkK8vLyYMWMGWq2WtLQ0goODyc7OplWrVoSFhdGwYUPy8vJ49913uXjxIs7OzixYsAC9Xn8nfRdCCKGySk38nzp1yvKVlJREZGQkKSkpt31MfHw833//PZs3byY6OpoTJ06wbds2QkNDiYiIIDY2lqSkJOLi4gAIDg5m6tSp7NixA0VRiIqKAmDGjBkMHjwYo9FI+/btiYiIAGDBggV4eXmxfft2BgwYwKxZs/6b50EIIYQKKhUyc+bMsXx9/PHHfPPNN2RnZ9/2MXq9npCQEOzs7NDpdLRu3Zrz58/TsmVLPDw80Gq1BAYGYjQaSU1NpaioiM6dOwMQFBSE0WiktLSUhIQEyxI25e0Ae/fuJTAwEIC+ffuyb98+SktL7/qJEEIIce/d1VL/Li4upKam3vY+bdq0sYTG+fPn2b59OxqN5qYhLYPBQHp6OhkZGTe16/V60tPTyc3NxcHBAa1We1M7cNNjtFotDg4O5OTk3E13hBBCqOSO52QURSEpKemms/9v5/Tp04wcOZKJEydia2vL+fPnb/pdGo0Gs9mMRqO5pb38e0V/vF3xMTY2lc/Mpk0dKn3f2kKvb2TtEoQQdUylQubUqVM33XZzc2PixIl/+7jExETGjx9PaGgoAQEBHDp0iMzMTMvPMzMzMRgMuLq63tSelZWFwWDA2dmZ/Px8TCYTtra2lvvDjb2grKwsXF1dKSsro7CwECcnp0p1GiA7uwCzWbmlvTZviDMz861dghCihrKx0dzVh/NKL5AJkJqaSllZGS1btvzbx1y+fJkxY8YQHh5Ot27dAOjUqRPnzp3jwoULuLu7s23bNp5//nmaN2+Ovb09iYmJdOnShZiYGLy9vdHpdHh5eREbG0tgYCDR0dF4e3sDN45Wi46O5o033iA2NhYvLy90Ot0dPwFCCCHUo1EqLqv8Fy5cuMDo0aPJyMjAbDbTpEkTlixZQuvWrf/yMR988AEbN26kRYsWlraBAwdy//33Ww5h9vHxYdKkSWg0GlJSUpgyZQoFBQV4enoyZ84c7OzsSE1NJSQkhOzsbNzc3Jg/fz6Ojo5cvXqVkJAQLl68SKNGjQgLC8Pd3b3SHb/dnszgiV9X+vfUFGvmDZE9GSHEXbvbPZlKhczrr79O3759ee655wDYuHEjMTExrFy58s4rrSYkZIQQovLuNmQqNVOenZ1tCRiA559/ntzc3Dv+Y0IIIeqWSoWMyWTi6tWrlttyqLAQQojKqNTE/7///W9eeuklevfujUajITY2lpdfflnt2oQQQtRwldqT8fHxAaC0tJQzZ86Qnp5Or169VC1MCCFEzVepPZmQkBCGDBnCsGHDKC4uZu3atYSGhrJs2TK16xNCCFGDVWpPJjc3l2HDhgFgb2/PK6+8ctPJk0IIIcSfqfTEf/maYXDjjPxKHPkshBCijqvUcNkrr7xC//79efLJJ9FoNMTHx1dqWRkhhBB1W6VC5oUXXqB9+/YcPHgQW1tbXn/9dR566CG1axNCCFHDVSpkANq2bUvbtm3VrEUIIUQtc1fXkxFCCCEqQ0JGCCGEaiRkhBBCqEZCRgghhGokZIQQQqhGQkYIIYRqJGSEEEKoRkJGCCGEalQPmYKCAvr27culS5cAmDRpEr6+vvTr149+/fqxa9cuAOLj4wkMDMTX15fw8HDL45OTkwkKCsLPz4/JkydTVlYGQFpaGkOGDMHf359Ro0ZRWFiodleEEELcIVVD5tixYwwaNIjz589b2pKSkli9ejUxMTHExMTQq1cvioqKCA0NJSIigtjYWJKSkoiLiwMgODiYqVOnsmPHDhRFISoqCoAZM2YwePBgjEYj7du3JyIiQs2uCCGEuAuqhkxUVBTTpk3DYDAAcP36ddLS0ggNDSUwMJCFCxdiNps5fvw4LVu2xMPDA61WS2BgIEajkdTUVIqKiujcuTMAQUFBGI1GSktLSUhIwM/P76Z2IYQQ1Uul1y67G7NmzbrpdlZWFl27dmXatGk0atSIkSNHsmHDBho0aIBer7fcz2AwkJ6eTkZGxk3ter2e9PR0cnNzcXBwQKvV3tQuhBCielE1ZP7Iw8ODRYsWWW4PHTqU6Oho/Pz80Gg0lnZFUdBoNJjN5j9tL/9e0R9v/52mTR3ushc1l17fyNolCCHqmCoNmZMnT3L+/HnLMJeiKGi1WlxdXW+60mZmZiYGg+GW9qysLAwGA87OzuTn52MymbC1tbXc/05kZxdgNt964bXavCHOzMy3dglCiBrKxkZzVx/Oq/QQZkVRmD17NteuXaO0tJT169fTq1cvOnXqxLlz57hw4QImk4lt27bh7e1N8+bNsbe3JzExEYCYmBi8vb3R6XR4eXkRGxsLQHR0NN7e3lXZFSGEEJVQpXsybdu2ZcSIEQwaNIiysjJ8fX3p27cvAHPnzmXcuHEUFxfj4+ODv78/AGFhYUyZMoWCggI8PT0ZNmwYANOmTSMkJITFixfj5ubG/Pnzq7IrQgghKkGjKMqtY0Z1wO2GywZP/NoKFalrzbwhMlwmhLhrNWK4TAghRN0iISOEEEI1EjJCCCFUIyEjhBBCNRIyQgghVCMhI4QQQjUSMkIIIVQjISOEEEI1EjJCCCFUIyEjhBBCNRIyQgghVCMhI4QQQjUSMkIIIVQjISOEEEI1EjJCCCFUIyEjhBBCNRIyQgghVKN6yBQUFNC3b18uXboEQHx8PIGBgfj6+hIeHm65X3JyMkFBQfj5+TF58mTKysoASEtLY8iQIfj7+zNq1CgKCwsByMvLY8SIEfTu3ZshQ4aQmZmpdleEEELcIVVD5tixYwwaNIjz588DUFRURGhoKBEREcTGxpKUlERcXBwAwcHBTJ06lR07dqAoClFRUQDMmDGDwYMHYzQaad++PREREQAsWLAALy8vtm/fzoABA5g1a5aaXRFCCHEXVA2ZqKgopk2bhsFgAOD48eO0bNkSDw8PtFotgYGBGI1GUlNTKSoqonPnzgAEBQVhNBopLS0lISEBPz+/m9oB9u7dS2BgIAB9+/Zl3759lJaWqtkdIYQQd0ir5i//495FRkYGer3ecttgMJCenn5Lu16vJz09ndzcXBwcHNBqtTe1//F3abVaHBwcyMnJwcXFRc0uCSGEuAOqhswfmc1mNBqN5baiKGg0mr9sL/9e0R9vV3yMjU3ld8yaNnW4w+prPr2+kbVLEELUMVUaMq6urjdN0GdmZmIwGG5pz8rKwmAw4OzsTH5+PiaTCVtbW8v94cZeUFZWFq6urpSVlVFYWIiTk1Ola8nOLsBsVm5pr80b4szMfGuXIISooWxsNHf14bxKD2Hu1KkT586d48KFC5hMJrZt24a3tzfNmzfH3t6exMREAGJiYvD29kan0+Hl5UVsbCwA0dHReHt7A+Dj40N0dDQAsbGxeHl5odPpqrI7Qggh/kaV7snY29szd+5cxo0bR3FxMT4+Pvj7+wMQFhbGlClTKCgowNPTk2HDhgEwbdo0QkJCWLx4MW5ubsyfPx+AN998k5CQEAICAmjUqBFhYWFV2RUhhBCVoFEU5dYxozrgdsNlgyd+bYWK1LVm3hAZLhNC3LUaMVwmhBCibpGQEUIIoRoJGSGEEKqRkBFCCKEaCRkhhBCqkZARQgihGgkZIYQQqpGQEUIIoRoJGSGEEKqRkBFCCKEaCRkhhBCqkZARQgihGgkZIYQQqpGQEUIIoRoJGSGEEKqRkBFCCKEaCRkhhBCqkZARQgihGq01/ujQoUPJyclBq73x599//31+++03Fi9eTFlZGS+//DJDhgwBID4+njlz5lBcXEzv3r2ZMGECAMnJyUyePJnCwkK8vLyYMWOG5fcJIYSoHqp8T0ZRFM6fP09MTIzly9XVlfDwcNasWUN0dDTr16/n119/paioiNDQUCIiIoiNjSUpKYm4uDgAgoODmTp1Kjt27EBRFKKioqq6K0IIIf5GlYfM2bNnAXjttdd49tlnWb16NfHx8XTt2hUnJycaNGiAn58fRqOR48eP07JlSzw8PNBqtQQGBmI0GklNTaWoqIjOnTsDEBQUhNForOquCCGE+BtVHjJ5eXl069aNRYsWsWLFCtatW0daWhp6vd5yH4PBQHp6OhkZGZVq1+v1pKenV2k/hBBC/L0qn8R45JFHeOSRRyy3X3jhBebMmcOoUaMsbYqioNFoMJvNaDSaSrffiaZNHf6LXtRMen0ja5cghKhjqjxkDh8+TGlpKd26dQNuBETz5s3JzMy03CczMxODwYCrq2ul2rOysjAYDHdUR3Z2AWazckt7bd4QZ2bmW7sEIUQNZWOjuasP51U+XJafn8+8efMoLi6moKCAzZs389FHH3HgwAFycnK4fv06O3fuxNvbm06dOnHu3DkuXLiAyWRi27ZteHt707x5c+zt7UlMTAQgJiYGb2/vqu6KEEKIv1HlezJPP/00x44do3///pjNZgYPHkyXLl2YMGECw4YNo7S0lBdeeIGOHTsCMHfuXMaNG0dxcTE+Pj74+/sDEBYWxpQpUygoKMDT05Nhw4ZVdVeEEEL8DY2iKLeOGdUBtxsuGzzxaytUpK4184bc8XBZE0c7tHb2KlVkPWUlxeReK7F2GULUKHc7XCZnL4q/pLWzJ3He/1i7jHuuy8TlgISMEFVBlpURQgihGgkZIYQQqpGQEUIIoRoJGSGEEKqRkBFCCKEaCRkhhBCqkZARQgihGgkZIYQQqpGQEUIIoRoJGSGEEKqRkBFCCKEaCRkhhBCqkZARQgihGlmFWYhKaOxoj72dnbXLuOeKS0rIu1Zs7TJELSYhI0Ql2NvZ8Urkm9Yu455b8eongISMUI8MlwkhhFCNhIwQQgjV1OiQ2bp1K3369MHX15evv659l0wWQoiarsbOyaSnpxMeHs6mTZuws7Nj4MCB/Otf/+LBBx+0dmlC1GpOjezQ1bO3dhn3XGlRMVfz5bLc91qNDZn4+Hi6du2Kk5MTAH5+fhiNRsaOHVupx9vYaP7yZ82aNLwnNVY3t+vzX7Fr3FSFSqzvbp6LZg7OKlRifXf6XOjq2fOft99VqRrreXp+GDaFpXf0mEYO9bCzr7Gb0b9UUlxGfkHRTW13854B0CiKotyLoqrakiVL+P3335kwYQIA33zzDcePH2fmzJlWrkwIIUS5GjsnYzab0Wj+L1kVRbnpthBCCOursSHj6upKZmam5XZmZiYGg8GKFQkhhPijGhsyjz/+OAcOHCAnJ4fr16+zc+dOvL29rV2WEEKICmrsjJWLiwsTJkxg2LBhlJaW8sILL9CxY0drlyWEEKKCGjvxL4QQovqrscNlQgghqj8JGSGEEKqRkBFCCKEaCRkhhBCqkZARQogaqiYctyUhU43VhBeQWupy36uaPNc1U8VVTuLi4jh79qyVK/pzEjLVyB/f7HV1mZzS0lJL37dv305KSoqVK6q9Km6oVq5cya5du6xcUfVQE4K3/P9t06ZNLF++nKZNq+dithIy1Uj5i2bz5s18/PHHrFixgsTERCtXVbV+/vlnli9fTnp6OgCrVq2ipESWX1dL+WsuNjaW+Ph42rZta+WKrK9i8B44cIAtW7ZuTwrhAAAgAElEQVRw7NgxcnJyrFzZrU6fPs2mTZv4xz/+gaOjI1D9AlJCppoof2GsXLmSDRs20KVLFzZv3sz+/fspLb2z5cdrMq1Wy969e9m4cSO5ubnUr1//T+9X3d5INU3582cymbh+/ToRERFcu3YNR0dHzGazlauzrvKAWbFiBR999BHx8fF88cUXREREcOnSJavW9sfXvV6v55lnniEpKYlvv/0WuFF/dXp/2E6fPn26tYuoyzIyMmjYsCEajYaSkhJiYmIICwsjMTGRrKwsQkJC2LJlCwaDgQYNGtTaIbTyDZter+eRRx5h7dq1aDQajhw5wo8//khqairHjh2jpKSEZs2aodPprFxxzVXxk3pBQQEODg707duX2NhYzp07h7e3d619nVXWmTNnWL58OZGRkfTp04fmzZtz5swZ8vPzad++vVVWfa/4N7du3crBgwe5du0aHTt2pHHjxvzwww9oNBpat25drf7/ZE/GioqKipgzZw5RUVGcOHECW1tbTCYTL774Irt37+bzzz+nsLCQrVu34ujoWK1eOPeSoijY2Nig0WhISUmhXr16TJo0iV27dnH16lUeffRRPDw8OHjwIOvXr+fq1avWLrlGK38drVmzhilTpjB9+nS2b99OREQECQkJzJs3r1p9Eq4Kf+xvkyZNaNCgARcvXgSgY8eONG3alCNHjgDWmS8t/5tr167l66+/pkWLFkyYMIHTp0/z9NNP89hjj7Fx40b27NlT5bXdjoSMlVy8eBGdTsf48eNZsGABY8aMwdbWFl9fXxo3bkzv3r0BSExMRFEUrl+/buWK1VP+5lm6dCnvvfceo0aNQqvVEhoaioeHB05OTgQEBLB8+XLmzp2Lm5ublSuumSoOu27cuJFt27bx7rvvcuXKFQ4ePIijoyNfffUVu3bt4pNPPrFipVWr4h5CUlISKSkpODs7o9frSU5OtswP6vV6bGxsrDZHqCgKWVlZHDhwgIULF5Kbm8vjjz9O165dSUlJoVevXjzzzDO0b9/eKvX9FVkgs4opikJ+fj4fffQR77zzDnl5eUydOpW0tDSGDx/OgAEDWLNmDVu2bEGn03H16lU++uijWj8he+LECaZNm0ZUVBQ2Nv/32efo0aN88MEH+Pv78/rrr9/0M1F5v/76K3v27CEoKAi9Xs+KFSvo2rUrP//8Mzt37mThwoUsXbqUgIAAGjVqRElJCR4eHtYuW3UVA2bFihVs2LDBMkxtMBhYtWoVDRo0oGHDhiQkJLBgwQIefvhhq9RX7sMPPyQtLY3CwkKWLVtGWloar732Gt9++y02NjbV7j1SY5f6r6k0Gg2NGzfmvffe4/jx45w8eZKwsDCuXbvGmDFjKCkpYciQIfTp04eCggLq1atHs2bNrF32PffHN4+trS1arZasrCwMBgNlZWVMmjSJJ554gvDwcLRabbV789QkZ86c4cKFC8TExPDSSy9Rv359Bg8eTKdOnYiMjARufIrv0aMHDz74oJWrrRoVX4NXr17l7NmzbNmyhaSkJIKDgxk/fjzBwcFcvHiRy5cvM3z48CoN3sLCQho2bAjAwYMHKSwspGfPnjRr1gyj0cjKlSvRaDT8/PPPuLu7YzKZ0Gqr3ya9+lVUi5nNZsuG0s7ODrgxgVdUVMRLL73Ehx9+SHBwMHFxcbRu3Zr//d//tWa5qqn45k5JScFgMNC0aVPatGlDfHw8TzzxBM2aNePhhx8mJyeHFi1aWLnimqv8Nefn58fp06dJSEjAxsaGJ598kqCgIAoLCykoKCAuLo7s7Oxqe67FvVbxvbhixQqOHz9OSkoKV65coWPHjsycOZP33nuPQYMG8corr1R5fSdPnmTXrl0MGDCA7777juXLl+Pi4sKSJUuIiori4sWLzJw5E4CcnBw++OAD7O3tq7zOypDhsipSUlJiCZbY2FjMZjPt27fHxsaGyZMn89RTTzF48GDS0tJYsWIFw4YNo02bNlauWl3Lli2zDAuOGzeOtLQ0Tp8+TW5uLg888ABbt25lyZIltG7d2tql1nhfffUVu3fvxmAwUFhYSKdOnWjXrh0//PADP//8MzqdjilTplTpUFB1cODAARYvXszw4cPZunUrhYWFTJ48mfvuu48DBw7w4YcfsmLFiio98MZkMvHrr7+yaNEi3N3dOXPmDPPmzcPR0ZHXXnuNoqIi1qxZw8WLF7l06RL3339/tZ6nlJCpAidPnmT27NksWbKEnTt3Eh4ejo+PDydPnuSdd95Br9czffp0unTpwqhRoyxHWtVmW7duZfPmzXz55ZeMGzeO1NRU3nrrLRwcHLh8+TKpqan4+vpy//33W7vUGi89PZ3g4GAiIiJwcHAgLi6OH3/8Eb1ez8CBA9HpdJSWlv7lOUm1yeHDh0lOTmbo0KEcPnyYpUuX0q5dOyZMmEB6ejqRkZH89ttvhIaG4u7uTlFREfXq1auy+soPwujfvz8//vgjGzZs4OzZs0yZMoVHHnkEgP/5n//h7NmzfPfdd1VW139DzpNR2U8//YSjoyO//PILX375JYWFhcyZM4d+/fqh1Wr54osv6NSpE35+fmzevJknn3yyVr7Zjx49yrJly+jevTs2NjYYjUbat2+Pg4MD6enpPPjggyxbtozmzZvTq1cvHn/8cZycnKxddo30Z5PFmzZtokmTJrRp04b777+fEydOEB0dDUDnzp0te9m1nclk4sEHHyQ7O5u2bdty4sQJzp49i4eHB61bt+ahhx4iKSmJvXv38swzz6DVaqv0A9/Zs2f5z3/+Q3R0NA0bNqRv376cOXOG4uJiHBwcaNasGc8++yyJiYl4enpazvKvziRkVLZ8+XJWrVrF0qVLOXXqFPv27ePpp5/GxcUFd3d36tevT3h4ON27d+fll1+2TPTVNnZ2dnzyySf89ttveHt7U1BQwP33309iYiIeHh4MGTKEb7/9lqKiIrp3714rg7YqVAyY+Ph40tPTady4MXZ2dpw5cwYADw8Prly5Ql5eHq+99hoODg7WLLlKlD8vjo6O2Nra8tJLL1FYWMiECRM4fvw4ycnJODk50bp1azp06ICPjw8ODg5VPqLg4uLC6dOnWbVqFZ06dcLf35+WLVuye/ducnNzadiwIXq9nt69e9eIgAEJGdV5e3tz5MgRcnNzGTVqFCdOnGDv3r08+eSTODo64u7uTrNmzWjdunWt/OSelZVFXl4eBoOBPn36sGTJEi5cuMCLL76Iq6srn3/+ueU4/6SkJEJDQ3FxcbF22TVW+UZx3bp1fPLJJ5azwps1a0ZJSQmrV6/mwIEDbNu2jRkzZuDu7m7litVXXFxsOepq9+7dAPTt25clS5aQn5/PuHHjOHToEAkJCbi4uNCqVSsaNGhQZfUdOnSIPXv2EBUVZTnQ5dlnnyUhIYH09HSefPJJHn74YWJiYgD4xz/+ga2tbZXV99+SkFHB/v37OXPmDK6uruh0OsvyKD4+Pvj6+rJnzx5iY2MtQ0Jt2rShcePG1i77nvvuu+/44IMP2Lp1K9nZ2Xh7e9OzZ08+//xzTp8+zaOPPkp8fDxnzpxh7dq1hIWF0apVK2uXXePt3buX7du3s3btWrp168b333+PVqvFy8uLoKAgWrRowSuvvELLli2tXarqTp48SUxMDE5OTjg7OxMaGsrjjz+Op6cnXbp04dNPP+X69euMGTOGEydO8Pjjj1fpaMLevXuZN28eXl5eZGRkcOXKFVatWsXYsWNxdXXFaDRiNpuxtbXFzc0NHx+fGrMHY6GIeyo/P18ZNGiQ0qNHDyUyMlLZunWroiiK8tZbbynh4eGW+73++uvKmDFjFJPJZK1SVbV//37lueeeU44cOaIkJycrAQEBypkzZxRFufEcvfTSS8qnn36qXLx4UUlJSVHS0tKsXHHNZTabLf8uKChQZs+erXTt2lXJyMhQFEVRkpOTlcmTJysffPCBcu7cOStVaR0HDhxQxowZoyxZskS5cuWKMmHCBOXXX3+1/PzUqVNKjx49lC+++KLKa/vll18Uf39/5dixY5a2oqIi5f3331d69eqlXLt2TTEajUpwcLASEBCgpKenV3mN94LsydxjdnZ2FBQUcOLECZ566il27drFkSNHGD58OKtXr0av19OyZUueffZZunTpQqNGjaxd8j13+PBh3njjDRYuXEinTp0wmUysXLmSX375hejoaOzt7ZkwYQLTp0+npKSEwMDAWvk8VAWlwhzMpUuXLOcbZWRksH37drp27UqLFi1wcXHhxIkTtfbAkj8ym81oNBrc3d0xGAzs3buXsrIyYmJiSElJITk5mStXrqDT6fj3v//Ngw8+WOWjCadOneL69esMHDjQMqSn1Wp55JFHSE5Opl69evj5+dGpUydeeOGFGntStpxCfY+kpqaSmpoKwMsvv8yTTz7J1atXCQsLIysri82bN+Po6MjBgwcta0jV1rmHoqIiWrduzaVLl8jLy+Ptt99m2LBhTJ8+nR49ehATE4NGoyEqKooBAwZYu9warTxgli9fzrhx4xg5ciQZGRmMGDGCli1b8v7775ORkYGnpychISE4OztbuWL1Kf9/wVW4sUZghw4dGD58OPHx8djZ2dG8eXPatGljOQ/Lzs7OKnNT58+f5/z58wDY29tbFuls3LgxZWVlHDt2DACDwUCTJk2qvL57RfZk/kuKonDt2jWCg4M5d+4cmZmZeHp6Uq9ePU6ePMkTTzyBv78/ZrOZrKwstm3bxqBBg2r1IaOurq60atWKr776irCwMIYPH87QoUNxdHSkVatWbN26lc6dO+Ph4VHzxperoejoaHbu3Mny5ctZu3YtP/30Ex07dqRr164kJyfzn//8xyqH41pLeR8jIyNZsWIF+/bt49///jfu7u6kpqbSqlUr+vfvz4ABA+jTp4/VXoMGg4FvvvkGrVaLp6cnGo2G0tJSbG1tuXDhAi1btqwVaxZKyPyXNBoN9erVo127dtjb27Np0ybLUNnGjRvJy8ujY8eOtGjRAm9vb55//vlaOclfka2tLS4uLri6unL69GnatGlDhw4dgBsTnT/++CPPP/98nTh0Vg0JCQns3buXjh07oigKa9aswd/fHwcHB3JycrC3t2fLli20bNmS7t2707t3b8s1i2qzixcvotFosLe357vvvmPz5s2sWLGC5s2bk5GRQVlZGZ6ensTGxpKXl0f79u2t+mFPp9NRUlLCjz/+yNWrV+nQoQO2trbExsaydu1aXn311VpxxKmEzD1SvtaWr68vGzZssFxlcNu2bXTo0MEyNGZvb1/r3+xwI2hcXV1xd3dn48aNlJSUcPnyZb744gtmzZpVJ45sUktaWhozZsygQYMGdOzYkdzcXDw8PDhw4ABeXl4MGjSI5cuXo9Fo8PX1rRUbqr8TFxfHwoUL6d69O40aNeLUqVOkp6dz5coVduzYwe7du1m+fDkBAQF4e3tbLvRlTVqtFoPBQElJCWvXrmXr1q0cOHCAPXv2MG/evNqzUKlVDzuoZcqPFCstLVW2bdumLFu2THn44YeVJUuW1NqjyP5OcXGxEh8fr/Tt21fp2rWrcvr0aWuXVGOZzWbLkWQJCQlKz549laioKKW0tFTJz89XXn75ZSU5OVnZvXu3MmzYMOXSpUtWrrhq7Nu3T+nfv7/yww8/KIpy4/138eJFZfbs2cqIESOU/fv3K4qiKMuXL1ciIyOtWOmfKysrU3JycpRdu3YpJ06cUK5cuWLtku4pWbvsHqu4uivcOCnuX//6V50+/6OkpISjR4/i5uYmKyrfJaXCUWRXrlzB1dWVEydO8Oabb/Lqq6/i5+fHpEmTuO+++9i/fz9Lly6tPZ+EbyMuLo7Fixczfvx4Hn/8cS5dusTy5ct58803LZPl27ZtIy8vj1WrVrF48eJqtx6eYoVLOVclCRkhqrHyt2f5Rmj16tXs2rWLRx55hLfeeovDhw8TEhLC22+/zQMPPEBOTg7u7u51IswvXbrEM888w/Tp0xk4cCAXL14kODiYnj17Mnz4cODGEVybN2/mt99+Y/To0bV+ZfPqSEJGiGqstLQUnU4H3Fi5et26dcydO9cywa8oCg4ODgQFBTFp0iSCgoKsXHHV+vTTT9m7dy8TJkxg2bJl9OzZk2HDhll+fvjwYdq2bUv9+vVr1FIstYmEjBDVkKIonD17lsGDB/Ptt9/SrFkzoqOjycrKwt7enpSUFC5dukRiYiKrVq3C1tYWR0fHOnlAxeeff86CBQt4/fXXCQ4OtgxZR0dHs379esLDw3F1dbV2mXWWHF0mRDWk0Whwdnbm/PnzfPjhh/Tv35/69euzb98+EhMTefHFFy3X3wHo3r17nTiK7M94eXmh0+nYuXMnXl5elkBevXq1HMlYDciejBDVkMlksgzvvP/+++zatYtt27bh6OhIWVkZKSkpHDt2jLVr1/LZZ59Vu8nsqqAoiuXs/vLDgDdt2kSvXr344YcfmDlzZp04+KG601q7ACHErWxtbS3DPlOnTkVRFPz8/Ni6dStXr15l/fr1pKamsmDBgjoRMH92BJZGo0Gj0XD8+HH27NnD6NGjycnJITIykrVr10rAVBOyJyNENaUoCmVlZZaJ//nz57NlyxY2b95MkyZNKC4uxt7e3spVqq9iwHz33XfY2dnh4uJCmzZtOHHiBGPHjmXmzJk88cQTAFy9erXODh1WRxIyQlQDtztX4qeffuLIkSO89tprTJw4kaNHj2I0GtFoNDedk1Xbbd68mc8//xx3d3eaN29Ojx49+P3332nSpAndunWr9eeb1FQSMkJYWcWN45YtW8jJyaF+/fq89NJLHD16lIkTJzJ16lSefPJJ4MbVRmvqsu93a/v27ezatYs5c+aQl5fH+vXrKSgooGvXrjz11FNA7T+psaaqOx+DhKimKl4yeenSpTg5OVGvXj0AfvnlF0vAlJWVAdC0aVOr1VpV/vjZ9+eff2b37t1kZWWh1+vp06cPDg4O7Nixg4MHDwJIwFRTMvEvhJUpikJhYSG7du1i+vTpeHl5AZCenk5JSYllD6b8OvW1fWNacY/kzJkzuLu7M378eH7//XdGjx5NZGQkDzzwAL1792bPnj0ywV/NyZ6MEFZgNptvuu3g4ECzZs24evWqpc1kMrF7925KSkqqujyrKg+YL7/8kvHjx/P222+zb98+3n33XR5//HFGjhxJVlYWrVu35tVXX61zQ4c1jYSMEFVMqXDlxj179rBhwwby8vLo0qULX375Jb/++itwY4iofNisLii/YizcWNQyLi6OdevWUVRUxNq1a9m/fz9jxozB09OTCRMmYDabLXt3ovqS/yEhqlj5J/U1a9bw9ddf06FDBz777DN27NhBYWEhs2fPpnHjxly+fJkZM2bU6quolvvpp584d+4czz77LLa2thw7doy+ffuSn5/Pgw8+iL29PV999RXZ2dmMHj0arVZbp46sq8nk6DIhrODHH39k/vz5LF26lJ9//pmpU6diMpksV228du0aTk5OdWLNrby8PEpKSjCZTBQUFODi4kJiYiINGjQgJSWF+++/nyeffJLnnnuOjh078uabb+Ls7GztskUlyUcBIarA4cOHiY6OJjo6GrgxB9O9e3eysrJISEggJiaGBx98EF9fXzIzM2nbtm2dCJj09HRGjhzJoUOHcHFx4d1332XdunW0a9eOjh07sn37dmxtbYmLi8POzo433nhDAqaGkeEyIVS2f/9+PvjgA3r27Mm6des4evQokydPRlEULl68iNlsplGjRnh5eVFaWlqnNqIODg4MHjyYtWvX4uHhwccff8zMmTNRFAVfX18eeughYmJiOHLkCIsWLcLNzc3aJYs7JMNlQqho//79zJ8/n2nTptG5c2fOnTvH4MGDWbFiBQ8//DChoaE4OTnxz3/+k08++YRFixZx3333WbvsKrV161a2bt1KZmYm7733Hnq9nsmTJxMYGEiLFi1wcHCgcePGeHh4WLtUcRdkuEwIlRw/fpy3336b0NBQOnfuDECLFi145JFHLEdFDRw4kISEBFatWsWsWbPqXMB8/fXXREZG8txzz+Hj40N4eDiXL19m5syZrFmzhlOnTuHp6SkBU4PJcJkQKnFxcaFJkyYcOXKExx57DIDw8HDKysosKyd37NiRFStWYGNjQ/369a1YrXVcunSJSZMm8dhjj+Hj40NcXBzz589n4sSJLFy4UI4gqwVkuEwIFZRfDyY1NZU33ngDPz8/rl+/zpkzZ/j000/R6XQ3XTOmLviztcVmzpzJb7/9xrJlywC4cuUKwcHB6HQ6Fi9eXCdWma7tJGSEUEnFoBk/fjw5OTns3LmzzgfMN998Q2pqKg899BCPPvooCxYswMbGhlmzZrF9+3b27NnD//7v/2IwGKxctbgXJGSEUFFJSQl2dnZkZWUxYsQIevbsyciRI+vsmep79+5l4cKFdO/encuXL+Pm5kbv3r2JiIjg+vXrZGdn8+GHH/Lwww9bu1Rxj0jICHEPlF/FsqLS0lJ0Oh2HDx+msLCQtm3bMmDAAF588UXGjh1rpUqrVlZWFg0aNKBBgwZER0ezceNGZs6cyf33309cXBzfffcd9913HyNHjsRkMnH9+nUcHBysXba4h2RWTYh7oDxgjh8/zpkzZygqKkKn03HgwAFmzZqFyWTCxcWF9evX069fPytXqz5FUcjIyOCtt95i9+7dALi7u5OQkMDOnTsB6NatGz169CA5OZnFixdja2srAVML1c19diHukYpzDWvWrGHJkiV06tQJGxsbZsyYwdGjRxkxYgQ9evSgtLS0zpxMqNFoMBgM9O/fn82bN6PRaAgMDOTLL7/k9ddfx83NjcDAQLp164ZWq6VNmzbWLlmoREJGiP9CecDEx8eTmprKmjVrKCsrIzIykvfff5/33nsPJycnysrK0Ol0Vq626pSH7wsvvIBWqyUqKgqNRkPfvn1ZunQpo0ePpri4mBdeeIHu3btbu1yhItvp06dPt3YRQtQ05RtRs9lMdnY2r7zyimWj6uTkxAMPPEBKSgobN26kZ8+edWrJfsDy3Gg0Gtq2bYtGo+Gbb75Bq9Xi6+tL27Ztef/99xk0aBA6na7WX4itLpOQEeIOVRwiu3btGs7Ozjz99NOsX78ejUbDo48+ipOTEx4eHqSlpeHp6Vkn5hoqPi/l18wpKyvDxsaGdu3a0bRpU9asWYPZbCYgIIAhQ4bQsGFDCZhaTo4uE+IOVNyQrly5EqPRSJs2bRg6dCiKojB69GiGDh3KsGHDgP87wqy2q/i8fPvttzg4OPDEE09ga2vL4cOHiYyMZNGiRaxbt47//Oc/fPzxx3UieIUcXSZEpVXckK5bt44dO3YwduxYLl26xMcff4zJZGLx4sUsWrSINWvWANSJgIH/m5vas2cPn3/+Od26dbNcfCw0NJTnn38euLFWmwRM3SIhI0QlnDp1CqPRiKIoFBUV8dNPPxEWFkbz5s0xGAx06NCBTz/9lOvXr7Nhw4Y6OZl98eJFYmJicHJysrSVlZXx3nvv0aNHD0wmEwANGza0VonCCmRORohKOHr0KI899hg5OTk0atSI5ORk7Ozs+PHHH/Hx8cHDw4MtW7aQmJjIiy++iF6vt3bJqvvjWmSOjo44OTlx4cIFLl26RKtWrWjdujUtW7a0zNEAMgdTx8icjBC3UfFM/suXL/Phhx/yr3/9i+eee46ysjJeeuklYmJi+OGHH9i4cSPTpk2jadOmVq66aq1cuZKLFy+SnZ3N6NGjOXnyJIcOHaJFixb079+/zj0f4mYyXCbEX6j46TsnJwdXV1eeeuopUlJS2LRpE8XFxbRu3Zpx48YRFhbG6NGj69wGNSoqit27dzN+/HgSEhKIjY0lICCAZ555hl9++YVvv/0Ws9ls7TKFFcnJmEL8hfJhndWrV/P999/zj3/8g7Fjx2IymTh27Bgmk4lXXnmFCxcu0LlzZ1q1amXliqveuXPnmDJlCps3b6Zdu3YMHTqU999/n3feeYfS0lI6dOgg14Sp4yRkhLiN2NhYYmNj+eyzz/jll1/IzMyka9eulJSUcPz4cRo1asRzzz1n7TKrRMWhw/JLFZSWljJ9+nTc3Nz47LPPsLOzIykpid9//50ePXpYuWJRHUjICPEnyjeip0+fxtvbm19++YWdO3cydepUWrZsyTvvvIOjoyNdunSxdqlVpjxgNm7cSHZ2Nh07duTll1+mX79+vPjii9ja2rJ161YKCgrq1LVyxO3JfqwQ/19ycjJnzpwBsGwke/XqRXR0NMuXL6dbt27s2rULNzc3srOz6dOnDy4uLtYsuUpUPDYoLi6OiIgIFEVh0qRJnD17llWrVvHVV18xbtw41qxZw4IFC3B2drZixaI6kaPLhODG8jA+Pj60atUKvV7P22+/TZMmTXBxcaG4uBh7e3sOHTpESUkJ8+fPZ+HChbi7u1u77Cr1008/ceHCBTw8PHj00UeJi4tjxowZTJ06laeeeopr165hMpkkYMRN5DwZIYB69epx/fp1Hvt/7d1PSJRbGMfxr75jphlliZFD/8Zdo+UfCgYNiaJFEGoQxCyEIjAkAnGhlYMKzspRwkWgLtokFUJhqLQwaqMxDrjoH0juEjSpJHQWM/qOd5FZeedS9+I7o3d+n+WsngMDP857nvOcY8dITk6mv7+foaEhkpKS2LlzJ3Nzc/h8Pl6/fo3H48HhcMS7ZMutfTLZ6/UyMTHBzMwMR44cwel0cujQIerq6sjKyqKwsJC0tLQ4Vy0bjXYyIiv6+/u5c+cOfX19ZGRkUF9fz+DgICdOnGDbtm3U19eTkZGRcDfWnz17xvj4OFVVVUxOTvL8+XOys7M5f/48WVlZvHz5kpycHA4cOBDvUmUD0pmMyIry8nIKCwsZHBzE7/fz6tUrWlpacLvdzM7OsrS0lFABs7S0RDgc5vbt21Y1pMcAAAOhSURBVIyMjGCz2SgpKeH48ePMzMzQ29vL58+fcblcChj5R9rJiPDj01BfXx/379/n69ev3Lp1K+HacL+Py8nNzV397cuXL9TU1OB0OvF4PAAMDAzw5s0bqquryczMjFe5sgkoZER+sri4iNvtZv/+/bS3twO/3g/5P4vW/LB9+3bsdjufPn3i6tWr5Ofn09TUBMDCwoKmKctv6eBfZIVpmthsNnbt2sXbt29xuVykpaUlzEDHaM0PT58+BcDpdHLhwgXa29uZmpqitLSULVu2xLli2Qx0GVNkxfe7MXv27OHjx48JsXtZy+Fw/K35wePxMDAwQG5uLp2dnaSmpsa7TNlE9LlMJIpgMJhQh/w/a2ho4OjRoxw8eJDm5mauXLlCdnY2XV1d+Hw+cnJy4l2ibCIKGZEo1r6VkgjU/CBWSLzvASJ/INECBn6suaKiAsMwKCgoWA0YjeuX/0ohIyKrTNMkJSWF6upqQqEQc3NzAAl5PiXrQ/8cEVml5gdZbzqTEZGoErn5QdaPQkZEokrE5gdZf9oLi0hUChhZDwoZERGxjEJGREQso5ARERHLKGRERMQyChkREbGMQkZERCyjUf8iMeD3+/F6vaSnpxMMBikqKuLdu3cEg0GWl5dpbW2luLiYYDBIa2sr4+PjGIbB6dOnqa2tZXFxEZ/PRyAQwDRNDh8+TGNjox4Nkw1PISMSI+/fv2d4eJjZ2Vnu3r3Lw4cPSU5Opru7m56eHoqLi+ns7CQUCjE0NIRpmly+fJmxsTECgQCGYfDo0SOSkpLo6OjA5/OhNwdlo1PIiMTI3r17sdvt2O12duzYwYMHD/jw4QN+v391fMvo6Cg3btzAMAwMw+DevXsAtLW1MT8/z+joKPDtmejdu3fHbS0if0ohIxIj6enpALx48QKv18ulS5c4deoUDoeDJ0+eAGCz2X65aT89Pc3WrVuJRCLcvHmTsrIy4NtcsVAoFPtFiPxLOvgXibGRkRFOnjyJ2+0mLy+P4eFhTNMEwOVy8fjxYyKRCOFwmOvXrxMIBCgtLaW3t5dwOEwkEsHj8dDR0RHnlYj8nkJGJMYuXrzI2NgY586do7Kykn379jE1NUUkEuHatWukpKRQXl5ORUUFZWVlnDlzhpqaGux2O5WVlZw9e5bl5WUaGhrivRSR39IUZhERsYx2MiIiYhmFjIiIWEYhIyIillHIiIiIZRQyIiJiGYWMiIhYRiEjIiKWUciIiIhl/gL5VRPjFYBY5wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='race', data=data)\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Where are all the **hispanics**? Let's take a look at the races of people from a latino american country."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "48        White\n",
       "657       White\n",
       "6413      White\n",
       "6667      Black\n",
       "11487     White\n",
       "15704     White\n",
       "16272     White\n",
       "23858     White\n",
       "25127     White\n",
       "25274     White\n",
       "25731     White\n",
       "30069     White\n",
       "31203     White\n",
       "31861     White\n",
       "35405     White\n",
       "38155     White\n",
       "39046     Other\n",
       "39362     Black\n",
       "44209     Black\n",
       "Name: race, dtype: object"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['native-country']==' Honduras']['race']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations**  \n",
    " * 'Hispanic' is NOT considered as a race (https://en.wikipedia.org/wiki/Hispanic)\n",
    " * Current racial distribution in the US from national [demographics](https://statisticalatlas.com/United-States/Race-and-Ethnicity).   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='sex', data=data)\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations**  \n",
    " * the ratio between 'Male' and 'Female' population is about 2:1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Native Country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='native-country', data=data)\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations**  \n",
    " * Over 90% of the samples are US citizens  \n",
    " * The ratio of income classes are similar between people of US origin and people from all origins. \n",
    " * The correlation between country and income is very small. It should be ok to drop this feature (and it will introduce a lot of one-hot-encode features.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Univariate Analysis Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Step               \t| Observation                                                                                                                                                                                                                       \t| Idea                                                                                                                           \t|\n",
    "|--------------------\t|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\t|--------------------------------------------------------------------------------------------------------------------------------\t|\n",
    "| **data overview**  \t| 45222x14, 5 numerical, 9 categorical<br>no missing or ill-formated<br>target variable is skewed (~75% '<50K')                                                                                                                     \t| use part of the data for balanced training?                                                                                    \t|\n",
    "| **ages**           \t| most population falls between 30~60                                                                                                                                                  \t|                                                                                                                                \t|\n",
    "| **education**      \t| _education-level_ and _education-num_ carry the same information                                                                                              \t|                                                                                                                                \t|\n",
    "| **capitals**       \t| **net** income from capital investment<br>outliers with high gain \t| feature engineering to combine gain and loss so that we can extract more info?<br>ignore zero values or create a new category? \t|\n",
    "| **hours-per-week** \t| most people work around 40 hours a week                                                                                                                                                                                                \t| correlated with occupation?                                                                                                    \t|\n",
    "| **workclass**      \t|                                                                                                                                                                                    \t| 'without-pay' and no 'capital-gain' but '>50K', outlier?                                                                       \t|\n",
    "| **marital-status** \t| We are dealing with **househould** income                                                                                                                                                                                         \t| Do we have people from the same family? It actually does NOT matter                                                                          \t|\n",
    "| **relationship**   \t| People who are listed as 'Own-child' are single<br>'Other-relative' might be grandparents                                                                                                                                         \t| may combine 'relationship' and 'Martial-Status'                                                                                \t|\n",
    "| **occupation**     \t|                                                                                                                                                                                                                   \t| combine into fewer groups?                                                                                                     \t|\n",
    "| **race**           \t| Hispanic is not a race                                                                                                                               \t|                                                                                                                                \t|\n",
    "| **sex**            \t| ratio between male and female is 2:1                                                                                                                                                                 \t| combine with martial status?                                                                                                                               \t|\n",
    "| **native-country** \t| over 90% are from USA                                                                                                          \t| can be ignored?                                                                                                                \t|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Knowledge and Tricks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " To quickly visualize distribution or frequency of some variable, `seaborn.countplot()` and `seaborn.distplot()`, former for more discrete case and latter for more continuous, work better than `plt.hist()` for several reasons:  \n",
    "  * do NOT require explicity binning  \n",
    "  * come with options like KDE and RUG  \n",
    "  * supports HUE by class very naturally"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### groupby"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " * [unstack()](#another_cell)\n",
    " * https://stackoverflow.com/questions/23377108/pandas-percentage-of-total-with-groupby  \n",
    " * https://stackoverflow.com/questions/19384532/get-statistics-for-each-group-such-as-count-mean-etc-using-pandas-groupby  \n",
    " * https://stackoverflow.com/questions/33217702/groupby-in-pandas-with-different-functions-for-different-columns  \n",
    " * https://stackoverflow.com/questions/22407798/how-to-reset-a-dataframes-indexes-for-all-groups-in-one-step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "relationship     income\n",
       " Husband         <=50K     10159\n",
       "                 >50K       8507\n",
       " Not-in-family   <=50K     10474\n",
       "                 >50K       1228\n",
       " Other-relative  <=50K      1299\n",
       "                 >50K         50\n",
       " Own-child       <=50K      6521\n",
       "                 >50K        105\n",
       " Unmarried       <=50K      4486\n",
       "                 >50K        302\n",
       " Wife            <=50K      1075\n",
       "                 >50K       1016\n",
       "Name: income, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# groupby target variables and get the counts for each label\n",
    "grouped = data.groupby(['relationship', 'income'])['income'].agg('count')\n",
    "print(type(grouped))\n",
    "display(grouped)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "income  relationship   \n",
       "<=50K    Husband           10159\n",
       "         Not-in-family     10474\n",
       "         Other-relative     1299\n",
       "         Own-child          6521\n",
       "         Unmarried          4486\n",
       "         Wife               1075\n",
       ">50K     Husband            8507\n",
       "         Not-in-family      1228\n",
       "         Other-relative       50\n",
       "         Own-child           105\n",
       "         Unmarried           302\n",
       "         Wife               1016\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# unstack to moves the labels to columns\n",
    "# so that we can count with ease\n",
    "grouped = grouped.unstack()   \n",
    "print(type(grouped))\n",
    "display(grouped)     # print won't work here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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