eda.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MW-oQIM2TdtR"
   },
   "source": [
    "# Exploratory Data Analysis\n",
    "Demo from lecture 2 of [ORIE 4741](https://people.orie.cornell.edu/mru8/orie4741)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "ViUHHFTETdtV"
   },
   "outputs": [],
   "source": [
    "# math \n",
    "import numpy as np\n",
    "\n",
    "# dataframes\n",
    "import pandas as pd\n",
    "\n",
    "# plotting\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dLTyUpfQTdtX"
   },
   "source": [
    "# Load data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DDGZMQ7xTdtY"
   },
   "source": [
    "The data is available on the course website, and is about 200MB.\n",
    "\n",
    "If you point your browser to the url below, it will be downloaded to your standard download folder. On Mac, you can access it at `/Users/yourusername/Downloads/acs_2013.csv`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "emzOH7t9TdtY"
   },
   "outputs": [],
   "source": [
    "url = \"https://people.orie.cornell.edu/mru8/orie4741/data/acs_2013.csv\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7nzN8MNITdtZ"
   },
   "source": [
    "Or we can read it directly into python:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 225
    },
    "id": "V7chxYZdTdtZ",
    "outputId": "67436aa9-4aeb-4279-ab6b-12f9a81d3d5e"
   },
   "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>ACREHOUS</th>\n",
       "      <th>CLASSWKR</th>\n",
       "      <th>COMMUSE</th>\n",
       "      <th>COSTELEC</th>\n",
       "      <th>COSTGAS</th>\n",
       "      <th>COSTWATR</th>\n",
       "      <th>EDUC</th>\n",
       "      <th>EMPSTAT</th>\n",
       "      <th>FOODSTMP</th>\n",
       "      <th>GRADEATT</th>\n",
       "      <th>...</th>\n",
       "      <th>HHINCOME</th>\n",
       "      <th>HHTYPE</th>\n",
       "      <th>LABFORCE</th>\n",
       "      <th>LOOKING</th>\n",
       "      <th>METRO</th>\n",
       "      <th>OWNERSHP</th>\n",
       "      <th>SCHOOL</th>\n",
       "      <th>STATEICP</th>\n",
       "      <th>UHRSWORK</th>\n",
       "      <th>WKSWORK2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>9999999.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>9993.0</td>\n",
       "      <td>480.0</td>\n",
       "      <td>8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>151000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>41.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>9993.0</td>\n",
       "      <td>480.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>151000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>41.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>9993.0</td>\n",
       "      <td>480.0</td>\n",
       "      <td>7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>151000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>9993.0</td>\n",
       "      <td>480.0</td>\n",
       "      <td>7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>151000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ACREHOUS  CLASSWKR  COMMUSE  COSTELEC  COSTGAS  COSTWATR  EDUC  EMPSTAT  \\\n",
       "0         0         0        0       0.0      0.0       0.0     7      3.0   \n",
       "1         1         2        1    4200.0   9993.0     480.0     8      1.0   \n",
       "2         1         1        1    4200.0   9993.0     480.0     6      1.0   \n",
       "3         1         0        1    4200.0   9993.0     480.0     7      3.0   \n",
       "4         1         0        1    4200.0   9993.0     480.0     7      3.0   \n",
       "\n",
       "   FOODSTMP  GRADEATT  ...   HHINCOME  HHTYPE  LABFORCE  LOOKING  METRO  \\\n",
       "0         1         6  ...  9999999.0     0.0         1        1    4.0   \n",
       "1         1         0  ...   151000.0     1.0         2        3    0.0   \n",
       "2         1         0  ...   151000.0     1.0         2        3    0.0   \n",
       "3         1         6  ...   151000.0     1.0         1        1    0.0   \n",
       "4         1         6  ...   151000.0     1.0         1        1    0.0   \n",
       "\n",
       "   OWNERSHP  SCHOOL  STATEICP  UHRSWORK  WKSWORK2  \n",
       "0         0       2      41.0       0.0         0  \n",
       "1         1       1      41.0      40.0         6  \n",
       "2         1       1      41.0      40.0         6  \n",
       "3         1       2      41.0       0.0         0  \n",
       "4         1       2      41.0       0.0         0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acs = pd.read_csv(url, nrows=10000) # read 10000 rows from the csv\n",
    "acs.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TsHxNTCGTdta"
   },
   "source": [
    "# Summary statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "aZzDVjmqTdtb",
    "outputId": "22a7c6e7-e4e4-4b6b-b70f-d03d75e6113c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 21)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# how big is this table?\n",
    "acs.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "GNKBGLtpTdtb"
   },
   "outputs": [],
   "source": [
    "# select just the income column\n",
    "income = acs[\"HHINCOME\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "AK5EPNhCBQPm"
   },
   "outputs": [],
   "source": [
    "# a synonym\n",
    "income = acs.HHINCOME"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "iUdHCXD3Tdtb",
    "outputId": "41d35a46-93de-4cac-979c-75d2acb13c43"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "568847.3694"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "income.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "G8azkQVLTdtc"
   },
   "source": [
    "**Poll**: Is mean income computed here plausible for US income distribution?\n",
    "* A) No, this value is too high \n",
    "* B) Plausible\n",
    "* C) No, this value is too low"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "i0nmMGwHTdtd",
    "outputId": "10a2268d-68ab-4f62-d92d-9b3866ac5045"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2176047.95112544"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "income.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "MrJvpSCdTdtd",
    "outputId": "8fb11541-14f1-4daf-ee05-3f41e3597cc0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9999999.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "income.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "84WVk6y05eaw"
   },
   "source": [
    "# Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "i6so18yj5qdn",
    "outputId": "0a19e6cf-070f-440c-defb-28e023e8b0eb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1.000000e+04\n",
       "mean     5.688474e+05\n",
       "std      2.176048e+06\n",
       "min      0.000000e+00\n",
       "25%      2.767500e+04\n",
       "50%      5.500000e+04\n",
       "75%      9.500000e+04\n",
       "max      9.999999e+06\n",
       "Name: HHINCOME, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "income.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "b4ao-udo6Ig3",
    "outputId": "a044bd16-1418-4746-dd8b-97f297cdbd45"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       False\n",
       "1       False\n",
       "2       False\n",
       "3       False\n",
       "4       False\n",
       "        ...  \n",
       "9995    False\n",
       "9996    False\n",
       "9997    False\n",
       "9998    False\n",
       "9999    False\n",
       "Name: HHINCOME, Length: 10000, dtype: bool"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# identify missing values\n",
    "pd.isna(income)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "i7iI-B2y6O18",
    "outputId": "5bed24c5-1164-4c53-ccf9-014f1b6afaaa"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# count missing values\n",
    "sum(pd.isna(income))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "RtcJBmCnTdte",
    "outputId": "2edef689-c9b9-4872-ee2b-4876bd1d3014"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb86fc7ce80>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the income distribution\n",
    "income.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "qod_rQyq6xmj",
    "outputId": "7ca7f729-5645-46c8-a062-b44927e136f2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb86fca08b0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "income.hist(bins=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "19PK5WSgTdte"
   },
   "source": [
    "**Poll**: Is this plausible for US income distribution?\n",
    "* A) No, not plausible\n",
    "* B) Yes, plausible"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gyRLOB_3Tdtf",
    "outputId": "8c4b0a03-48c5-43e9-ee61-c4fc4e6ab6d5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       False\n",
       "1        True\n",
       "2        True\n",
       "3        True\n",
       "4        True\n",
       "        ...  \n",
       "9995     True\n",
       "9996     True\n",
       "9997     True\n",
       "9998    False\n",
       "9999     True\n",
       "Name: HHINCOME, Length: 10000, dtype: bool"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select only rows with income less than 9999999\n",
    "lower_income = (income < 9999999)\n",
    "lower_income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "CVtqUghD8_8x"
   },
   "outputs": [],
   "source": [
    "acs99 = acs.where(lower_income)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([   1,    2,    3,    4,    5,    6,    7,    8,    9,   10,\n",
       "            ...\n",
       "            9989, 9990, 9991, 9992, 9993, 9994, 9995, 9996, 9997, 9999],\n",
       "           dtype='int64', length=9495)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acs[lower_income]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 21)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acs99.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 225
    },
    "id": "HUQbwu98Tdtf",
    "outputId": "df615ec5-bd0b-4ab6-d617-5c61ffc3446e"
   },
   "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>ACREHOUS</th>\n",
       "      <th>CLASSWKR</th>\n",
       "      <th>COMMUSE</th>\n",
       "      <th>COSTELEC</th>\n",
       "      <th>COSTGAS</th>\n",
       "      <th>COSTWATR</th>\n",
       "      <th>EDUC</th>\n",
       "      <th>EMPSTAT</th>\n",
       "      <th>FOODSTMP</th>\n",
       "      <th>GRADEATT</th>\n",
       "      <th>...</th>\n",
       "      <th>HHINCOME</th>\n",
       "      <th>HHTYPE</th>\n",
       "      <th>LABFORCE</th>\n",
       "      <th>LOOKING</th>\n",
       "      <th>METRO</th>\n",
       "      <th>OWNERSHP</th>\n",
       "      <th>SCHOOL</th>\n",
       "      <th>STATEICP</th>\n",
       "      <th>UHRSWORK</th>\n",
       "      <th>WKSWORK2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>9993.0</td>\n",
       "      <td>480.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>151000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>9993.0</td>\n",
       "      <td>480.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>151000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>9993.0</td>\n",
       "      <td>480.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>151000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>9993.0</td>\n",
       "      <td>480.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>151000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ACREHOUS  CLASSWKR  COMMUSE  COSTELEC  COSTGAS  COSTWATR  EDUC  EMPSTAT  \\\n",
       "0       NaN       NaN      NaN       NaN      NaN       NaN   NaN      NaN   \n",
       "1       1.0       2.0      1.0    4200.0   9993.0     480.0   8.0      1.0   \n",
       "2       1.0       1.0      1.0    4200.0   9993.0     480.0   6.0      1.0   \n",
       "3       1.0       0.0      1.0    4200.0   9993.0     480.0   7.0      3.0   \n",
       "4       1.0       0.0      1.0    4200.0   9993.0     480.0   7.0      3.0   \n",
       "\n",
       "   FOODSTMP  GRADEATT  ...  HHINCOME  HHTYPE  LABFORCE  LOOKING  METRO  \\\n",
       "0       NaN       NaN  ...       NaN     NaN       NaN      NaN    NaN   \n",
       "1       1.0       0.0  ...  151000.0     1.0       2.0      3.0    0.0   \n",
       "2       1.0       0.0  ...  151000.0     1.0       2.0      3.0    0.0   \n",
       "3       1.0       6.0  ...  151000.0     1.0       1.0      1.0    0.0   \n",
       "4       1.0       6.0  ...  151000.0     1.0       1.0      1.0    0.0   \n",
       "\n",
       "   OWNERSHP  SCHOOL  STATEICP  UHRSWORK  WKSWORK2  \n",
       "0       NaN     NaN       NaN       NaN       NaN  \n",
       "1       1.0     1.0      41.0      40.0       6.0  \n",
       "2       1.0     1.0      41.0      40.0       6.0  \n",
       "3       1.0     2.0      41.0       0.0       0.0  \n",
       "4       1.0     2.0      41.0       0.0       0.0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acs99.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 319
    },
    "id": "D3dvSuqf9JFu",
    "outputId": "3760b4f2-505b-4c70-a1b4-91c69ef9d495"
   },
   "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>ACREHOUS</th>\n",
       "      <th>CLASSWKR</th>\n",
       "      <th>COMMUSE</th>\n",
       "      <th>COSTELEC</th>\n",
       "      <th>COSTGAS</th>\n",
       "      <th>COSTWATR</th>\n",
       "      <th>EDUC</th>\n",
       "      <th>EMPSTAT</th>\n",
       "      <th>FOODSTMP</th>\n",
       "      <th>GRADEATT</th>\n",
       "      <th>...</th>\n",
       "      <th>HHINCOME</th>\n",
       "      <th>HHTYPE</th>\n",
       "      <th>LABFORCE</th>\n",
       "      <th>LOOKING</th>\n",
       "      <th>METRO</th>\n",
       "      <th>OWNERSHP</th>\n",
       "      <th>SCHOOL</th>\n",
       "      <th>STATEICP</th>\n",
       "      <th>UHRSWORK</th>\n",
       "      <th>WKSWORK2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9495.000000</td>\n",
       "      <td>9495.000000</td>\n",
       "      <td>9495.000000</td>\n",
       "      <td>9495.000000</td>\n",
       "      <td>9495.000000</td>\n",
       "      <td>9495.000000</td>\n",
       "      <td>9495.000000</td>\n",
       "      <td>9495.000000</td>\n",
       "      <td>9495.000000</td>\n",
       "      <td>9495.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>9495.000000</td>\n",
       "      <td>9495.000000</td>\n",
       "      <td>9495.000000</td>\n",
       "      <td>9495.000000</td>\n",
       "      <td>9495.000000</td>\n",
       "      <td>9495.000000</td>\n",
       "      <td>9495.000000</td>\n",
       "      <td>9495.0</td>\n",
       "      <td>9495.000000</td>\n",
       "      <td>9495.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.963560</td>\n",
       "      <td>1.041601</td>\n",
       "      <td>0.920274</td>\n",
       "      <td>2489.090363</td>\n",
       "      <td>5901.423697</td>\n",
       "      <td>1663.254344</td>\n",
       "      <td>5.744286</td>\n",
       "      <td>1.559031</td>\n",
       "      <td>1.186519</td>\n",
       "      <td>0.953028</td>\n",
       "      <td>...</td>\n",
       "      <td>67243.201580</td>\n",
       "      <td>2.092048</td>\n",
       "      <td>1.268246</td>\n",
       "      <td>1.724171</td>\n",
       "      <td>2.371669</td>\n",
       "      <td>1.257609</td>\n",
       "      <td>1.192838</td>\n",
       "      <td>41.0</td>\n",
       "      <td>17.808636</td>\n",
       "      <td>2.380937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.378119</td>\n",
       "      <td>0.975828</td>\n",
       "      <td>0.307659</td>\n",
       "      <td>1484.813450</td>\n",
       "      <td>4489.493535</td>\n",
       "      <td>3174.770482</td>\n",
       "      <td>3.107191</td>\n",
       "      <td>1.153514</td>\n",
       "      <td>0.389546</td>\n",
       "      <td>1.907627</td>\n",
       "      <td>...</td>\n",
       "      <td>64959.369545</td>\n",
       "      <td>1.620569</td>\n",
       "      <td>0.758893</td>\n",
       "      <td>1.202772</td>\n",
       "      <td>1.815580</td>\n",
       "      <td>0.437341</td>\n",
       "      <td>0.466026</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.975824</td>\n",
       "      <td>2.789677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1560.000000</td>\n",
       "      <td>840.000000</td>\n",
       "      <td>210.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>26530.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2280.000000</td>\n",
       "      <td>9993.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>51000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3000.000000</td>\n",
       "      <td>9993.000000</td>\n",
       "      <td>840.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>87200.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>41.0</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>9997.000000</td>\n",
       "      <td>9997.000000</td>\n",
       "      <td>9997.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>664040.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>41.0</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          ACREHOUS     CLASSWKR      COMMUSE     COSTELEC      COSTGAS  \\\n",
       "count  9495.000000  9495.000000  9495.000000  9495.000000  9495.000000   \n",
       "mean      0.963560     1.041601     0.920274  2489.090363  5901.423697   \n",
       "std       0.378119     0.975828     0.307659  1484.813450  4489.493535   \n",
       "min       0.000000     0.000000     0.000000    48.000000    48.000000   \n",
       "25%       1.000000     0.000000     1.000000  1560.000000   840.000000   \n",
       "50%       1.000000     1.000000     1.000000  2280.000000  9993.000000   \n",
       "75%       1.000000     2.000000     1.000000  3000.000000  9993.000000   \n",
       "max       2.000000     2.000000     2.000000  9997.000000  9997.000000   \n",
       "\n",
       "          COSTWATR         EDUC      EMPSTAT     FOODSTMP     GRADEATT  ...  \\\n",
       "count  9495.000000  9495.000000  9495.000000  9495.000000  9495.000000  ...   \n",
       "mean   1663.254344     5.744286     1.559031     1.186519     0.953028  ...   \n",
       "std    3174.770482     3.107191     1.153514     0.389546     1.907627  ...   \n",
       "min       4.000000     0.000000     0.000000     1.000000     0.000000  ...   \n",
       "25%     210.000000     4.000000     1.000000     1.000000     0.000000  ...   \n",
       "50%     480.000000     6.000000     1.000000     1.000000     0.000000  ...   \n",
       "75%     840.000000     7.000000     3.000000     1.000000     0.000000  ...   \n",
       "max    9997.000000    11.000000     3.000000     2.000000     7.000000  ...   \n",
       "\n",
       "            HHINCOME       HHTYPE     LABFORCE      LOOKING        METRO  \\\n",
       "count    9495.000000  9495.000000  9495.000000  9495.000000  9495.000000   \n",
       "mean    67243.201580     2.092048     1.268246     1.724171     2.371669   \n",
       "std     64959.369545     1.620569     0.758893     1.202772     1.815580   \n",
       "min         0.000000     1.000000     0.000000     0.000000     0.000000   \n",
       "25%     26530.000000     1.000000     1.000000     1.000000     0.000000   \n",
       "50%     51000.000000     1.000000     1.000000     1.000000     4.000000   \n",
       "75%     87200.000000     3.000000     2.000000     3.000000     4.000000   \n",
       "max    664040.000000     7.000000     2.000000     3.000000     4.000000   \n",
       "\n",
       "          OWNERSHP       SCHOOL  STATEICP     UHRSWORK     WKSWORK2  \n",
       "count  9495.000000  9495.000000    9495.0  9495.000000  9495.000000  \n",
       "mean      1.257609     1.192838      41.0    17.808636     2.380937  \n",
       "std       0.437341     0.466026       0.0    20.975824     2.789677  \n",
       "min       1.000000     0.000000      41.0     0.000000     0.000000  \n",
       "25%       1.000000     1.000000      41.0     0.000000     0.000000  \n",
       "50%       1.000000     1.000000      41.0     0.000000     0.000000  \n",
       "75%       2.000000     1.000000      41.0    40.000000     6.000000  \n",
       "max       2.000000     2.000000      41.0    99.000000     6.000000  \n",
       "\n",
       "[8 rows x 21 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acs99.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      9495.000000\n",
       "mean      67243.201580\n",
       "std       64959.369545\n",
       "min           0.000000\n",
       "25%       26530.000000\n",
       "50%       51000.000000\n",
       "75%       87200.000000\n",
       "max      664040.000000\n",
       "Name: HHINCOME, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acs99.HHINCOME.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "id": "jLU9cUpgTdtf",
    "outputId": "bab01ff4-a6a9-4c9b-9060-1001d5da45e1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb8700808b0>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "acs99.HHINCOME.hist(bins=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 296
    },
    "id": "Xin7UVFABG4X",
    "outputId": "f60c54bf-cd7c-44c5-b0c8-10a17d43f871"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fb870777dc0>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(acs99.HHINCOME, kde=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 360
    },
    "id": "sGTxMp07Tdtf",
    "outputId": "37b168eb-202f-4eaf-a5cb-e20b86f57fd5",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7fb870d83100>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7fb870da8be0>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# not easy to compare \n",
    "acs99[\"HHINCOME\"].hist(by=acs99[\"FOODSTMP\"], bins=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "acs99[\"INCOME+1\"] = acs99.HHINCOME + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 296
    },
    "id": "YnhVq1JNAlc8",
    "outputId": "42cc681a-b113-4849-b694-090edc11d12c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb871bd9130>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# better \n",
    "sns.histplot(\n",
    "    acs99,\n",
    "    x=\"INCOME+1\", hue=\"FOODSTMP\",\n",
    "    # multiple=\"stack\",\n",
    "    # palette=\"light:m_r\",\n",
    "    # edgecolor=\".3\",\n",
    "    # linewidth=.5,\n",
    "    log_scale=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 386
    },
    "id": "ahqAbAp4Dk27",
    "outputId": "a60917b4-23a4-4970-9423-90e455977b3c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fb8710b3f70>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 423.875x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# better \n",
    "sns.displot(\n",
    "    acs99,\n",
    "    x=\"HHINCOME\", hue=\"FOODSTMP\",\n",
    "    kind='kde'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mFlj9vsWEDWP"
   },
   "source": [
    "Tips for making good plots:\n",
    "* label your axes\n",
    "* label each curve or each color\n",
    "* consider semilogy or semilogx\n",
    "* are tick marks useful?\n",
    "* make every pixel informative \n",
    "* choose plot style to allow easy comparisons between interesting quantities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tIVAt-v6EX8p"
   },
   "source": [
    "Quiz: would a semilogx axis help us visualize the income distribution?\n",
    "1. A) yes\n",
    "2. B) no"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ClN8QfTaTdtg"
   },
   "source": [
    "# Now with recoded data\n",
    "The cleaned data set is in the demos repo as `acs_2013_cleaned.csv`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 225
    },
    "id": "AkavEuloTdtg",
    "outputId": "0313f922-6ecf-4e78-f7bd-d45c571d083d"
   },
   "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>ACREHOUS</th>\n",
       "      <th>CLASSWKR</th>\n",
       "      <th>COMMUSE</th>\n",
       "      <th>COSTELEC</th>\n",
       "      <th>COSTGAS</th>\n",
       "      <th>COSTWATR</th>\n",
       "      <th>EDUC</th>\n",
       "      <th>EMPSTAT</th>\n",
       "      <th>FOODSTMP</th>\n",
       "      <th>GRADEATT</th>\n",
       "      <th>...</th>\n",
       "      <th>HHINCOME</th>\n",
       "      <th>HHTYPE</th>\n",
       "      <th>LABFORCE</th>\n",
       "      <th>LOOKING</th>\n",
       "      <th>METRO</th>\n",
       "      <th>OWNERSHP</th>\n",
       "      <th>SCHOOL</th>\n",
       "      <th>STATEICP</th>\n",
       "      <th>UHRSWORK</th>\n",
       "      <th>WKSWORK2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>480.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>151000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>480.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>151000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>480.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>151000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>480.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>151000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ACREHOUS  CLASSWKR  COMMUSE  COSTELEC  COSTGAS  COSTWATR  EDUC  EMPSTAT  \\\n",
       "0       NaN       NaN      NaN       NaN      NaN       NaN   7.0      3.0   \n",
       "1       1.0       2.0      1.0    4200.0      NaN     480.0   8.0      1.0   \n",
       "2       1.0       1.0      1.0    4200.0      NaN     480.0   6.0      1.0   \n",
       "3       1.0       NaN      1.0    4200.0      NaN     480.0   7.0      3.0   \n",
       "4       1.0       NaN      1.0    4200.0      NaN     480.0   7.0      3.0   \n",
       "\n",
       "   FOODSTMP  GRADEATT  ...  HHINCOME  HHTYPE  LABFORCE  LOOKING  METRO  \\\n",
       "0         1       6.0  ...       NaN     NaN       1.0      1.0    4.0   \n",
       "1         1       NaN  ...  151000.0     1.0       2.0      NaN    NaN   \n",
       "2         1       NaN  ...  151000.0     1.0       2.0      NaN    NaN   \n",
       "3         1       6.0  ...  151000.0     1.0       1.0      1.0    NaN   \n",
       "4         1       6.0  ...  151000.0     1.0       1.0      1.0    NaN   \n",
       "\n",
       "   OWNERSHP  SCHOOL  STATEICP  UHRSWORK  WKSWORK2  \n",
       "0       NaN     2.0      41.0       NaN       NaN  \n",
       "1       1.0     1.0      41.0      40.0       6.0  \n",
       "2       1.0     1.0      41.0      40.0       6.0  \n",
       "3       1.0     2.0      41.0       NaN       NaN  \n",
       "4       1.0     2.0      41.0       NaN       NaN  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url_clean = \"https://people.orie.cornell.edu/mru8/orie4741/data/acs_2013_cleaned.csv\"\n",
    "acs = pd.read_csv(url_clean, nrows=10000, na_values='NA') # read 10000 rows from the csv\n",
    "acs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "IzlNWw9-Tdtg",
    "outputId": "09023f12-40fa-4419-9389-865f4fe61cbf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      9394.000000\n",
       "mean      67966.169789\n",
       "std       64930.350456\n",
       "min           1.000000\n",
       "25%       27200.000000\n",
       "50%       52000.000000\n",
       "75%       87960.000000\n",
       "max      664040.000000\n",
       "Name: HHINCOME, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acs.HHINCOME.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Xx9z_p7jTdtg"
   },
   "source": [
    "**Poll**: Is mean income computed here plausible for US income distribution?\n",
    "* A) No, this value is too high \n",
    "* B) Plausible\n",
    "* C) No, this value is too low"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "id": "b6G34EocTdth",
    "outputId": "db715590-0a84-4059-864a-749949f07672"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa2b53e1610>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# histogram automatically drops missing values\n",
    "acs.HHINCOME.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "id": "vBHFIbuqTdth",
    "outputId": "51995169-bd0f-4f18-852f-25b59831535c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa2a3c33550>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "acs.COSTGAS.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "id": "0crmewUTTdth",
    "outputId": "f9393b5a-2da5-440f-a2bc-27eb413117d5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa2a3d64910>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "acs.EDUC.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OXBPnymjTdti"
   },
   "source": [
    "Look at income distribution by :FOODSTMP, :LABFORCE, :OWNERSHP, ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 386
    },
    "id": "KynGskroTdti",
    "outputId": "45ca2246-58fa-4157-c380-826afb683d93"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fa2a3daf9d0>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 420.5x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(\n",
    "    acs,\n",
    "    x=\"HHINCOME\", hue=\"LABFORCE\",\n",
    "    kind='kde', # also try hist\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5P03PaDjd9EW"
   },
   "source": [
    "Which makes a nicer plot here?\n",
    "1. A) kde\n",
    "2. B) hist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "PynC_nxZeGR7",
    "outputId": "7ef63365-f701-4e2f-e43b-961f4199daaf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5395604395604395"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# what fraction of people are working? \n",
    "sum(acs.LABFORCE == 2) / sum(~pd.isna(acs.LABFORCE))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 315
    },
    "id": "2IMmw-A-fMeZ",
    "outputId": "72d4e53f-08ec-4a95-f10f-c535b7707019"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fa2a3e67ac0>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3600x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(\n",
    "    acs,\n",
    "    x=\"HHINCOME\", row=\"LABFORCE\", col=\"EDUC\",\n",
    "    kind='hist', # also try kde\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 296
    },
    "id": "tM0poqSkf32W",
    "outputId": "532d9c62-9708-4c9d-9597-bd7b57447bce"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa2a6bd3370>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PmFjk2JXAF0TkL4FcoFhEfgc0iki1Zb1UA6et4+uBeTH55wInrfS5CdJj89SLiA+YAbRa6deMyPPWBMo+YcxIfkMiTMNjNOYdyQ7sOgsgGAw6IjDJ+mBmAf8DuAD4BXA90Kyqb6vq2+NlVNUfqOpcVV1ItPP+DVX9D8B6wI7qWgWss7bXAyutyLBFRDvzN1vutC4RudzqX/naiDz2uW6zrqHAq8ANIlJqde7fYKU5hqlIRmPuhQl4MCQmG56LYDA4tG3POJFukvXBhIFXgFeskfxfAd4SkR+q6v2TvOaPgbUicjtwDPiyda3dIrIW2AOEgDut6wN8G1gN5AEvWx+Ah4HHRKSOqOWy0jpXq4j8CPjQOu6Hqto6yfKmhLFgRmPuhWl4GBKTDc9FrAXjisDA0BQxNxMVl4XAfcAzE7mIqr6F5aJS1Rbg2jGOuxe4N0H6FqJW1Mj0fiyBSrDvEeCRiZRzKmTDA2MwfBww70h24LoFIyJriFbsLwP/pKq7HCnFNMDuMDOt9mFMRWJIhHlHhnHzHYkVlVixSSfJLJivAj3A2cA/iEhs9JaqarEjpfoYYl6a0Zh7Eu+GMBhG4uY7EiswbvXBmGHIBsMUcKpl+HHGWLbDuHkvYp9NtywYAETks0SncFFgt9WnYkiAabWb/qhYjMCMxrwjw5zRFoyIzCHaod9PdIoYAf4PEckDvqSqJxwp1ccYU6kaYjECYxiPbOmDccqVm8yC+TfgQVVdHZsoIl8juh7MrY6UyjBlwuEwXq83+YEOkA0x/tnC4OAgYBoeYCzbWLJhaqlMWDDJ+ljOGykuAKr6W+Dc0YefudgvTba8PE49MKmQbcLi5mqjtgWTbffETbLlHXET22LIlnEwTlkwyQQmYRNYRDxj7TtTsR+UbFk62bhmhnHzXrgp9NmKEdvh58LNe5GJgZbJBOZ5Efm1iBTYCdb2Q8BLjpToY0q2WTDZUKlmy72w3VRuYMKUhzGu02Gy4bmIbQw79T9JJjDfBzqAoyKyVUS2AEeATuC/OVKijynZ9vK4Walmm/Xk5r3IlufBkF3YjTA3LdyRsyk7QbJxMEHgv4nI/w0sIRpFVqeqvY6UZhqQLa12Nyt5u0LPlso1G9xU2XIvsoFseUfcxK7Q3bRkMvFMJgtT/g+AqOpjQE1M+t8BPar6uMPlM0wSNytVNy2GRLgpttnmOs0GjNgOWw/Z4CpzkmQusv8KPJcg/Slrn8Ei2yoSN4MNsi00182X2K1Q8WzEhCkPkw1hypkgaRSZqnaNTFTVTsDvTJE+nmRbH0y2rDORDbgptn5/9DUxlaohlmyoJ+zVVsG55zOZwPhjI8hiClMEBBwp0ceUbLNg3CTbxn64+T+xBSZbwtfdJNsaYW6SDfVF7LVjxSadJDvrw8DTIrIwplALgSetfQaLbFt73U3XTDYMIovFzXIEAqYdZhhNNghMbB3hVH2RLIrsn0WkG3hbRAqt5G7gx6r6oCMl+phiC0y2VKpuCky2rY3jxFrjqWIExpAI+/108z3NhMAktYtU9SFVXQAsABaq6oJUxEVEckVks4jsEJHdIvJPVnqZiGwQkQPW39KYPD8QkToRqRWRG2PSLxWRGmvffWLV4iKSIyJPWekfjLC0VlnXOCAiqyZyUyZDtlkwblaq2UY2CIxTLoiPI9nSCHMTu0J387mIFRWnypEsTPlrCdKGtq05ycZiAPicqnaLiB94V0ReBv498Lqq/lhE7gLuAv67iJwHrCS6LMBs4E8icraqhoEHgTuATURnELiJ6CqbtwNtqrpERFYCPwH+WkTKgHuA5USXGNgqIutVtS35LZkcxoIZJtvuhd0P4ua1TR/MMNnSCHMTY8FE+VSCzwrgRyRZ716jdFtf/dZHic7AvMZKXwN80dq+FXhSVQdU9TBQB6wQkWqgWFXf1+iT+dsReexzPQ1ca1k3NwIbVLXVEpUNREXJMbLNgnHzwbUthmy5F24KjMGQiGwIX4+1WlyxYFT1u/a2VXH/LfDfiVoS9yY7uYh4ia4jswT4pap+ICJVqnrKOv8pEZlpHT7HOq9NvZUWtLZHptt5jlvnColIB1Aem54gjyPYD0y2tNrdNL1tgcmWe2H6QbKLbHku3CQbrPzYOsKVTn4AEfEBXyc6sPID4DZVrU3l5JZ76xIRKQGeFZELxrtUolOMkz7ZPMMXFLmDqOuN+fPnj1O01MmWVruJnBrGzT6YbIgWyjay5R1xk2zweGSiD2bcs4rIncAe4FLgJlX9eqriEouqtgNvEXVTNVpuL6y/p63D6oF5MdnmAiet9LkJ0uPyWEI4A2gd51wjy/UrVV2uqssrKysn+rPiMBXJMLbAZEtF4qY7IlvugSG7yLb6wq2BlvcDxcBVRKfu32l9akRk53gZRaTSslyQ6BLL1wH7gPWAHdW1Clhnba8HVlqRYYuApcBmy53WJSKXW266r43IY5/rNuANq5/mVeAGESm1otRusNIcJ1sqFDfLYfo8DIbxyQZhycRI/mS+g0VTOHc1sMbqh/EAa1X1BRF5H1grIrcDx4AvA6jqbhFZS9RiCgF3Wi42gG8Dq4E8otFjL1vpDwOPiUgdUctlpXWuVhH5EfChddwPVbV1Cr8lKdk2z5KbAmNbMNlyL9zE3IPRmHuSHcT+H1wRGFU9OtkTq+pO4BMJ0luAa8fIcy8JggdUdQswqv9GVfuxBCrBvkdIEunmBNliwbiJbcGYe3HmTGo4Ecy9yI7nIvbaTpUj2TiYLhJ0jhPtRFdVLXakVIYpky1TUJzpmNa6IRH2uCg3nw/XBUZVixy56jQkG1oksWRD+KOpXLPPdWrIDrJhHZhMLJmczIIpG2+/0/0aHyeyrSLJBisiW8TWkF1kyzviJtkwX5/rSyYTHSRpjyupJhrqaz8dCpzlSKk+hmRbZWrmvsoOzBT1ozH3YrhCd3MKoVhRcaocyVxkQ1FkIvKRqo7qtDdkJ9kw2aVpqRoMibGXNHdTYGKv7VQ5JtLMNc2OcTAustGYlmr2PRfZgLkXw9aDLTRuEHttp8ph/ChpIts6+bNhPRhTkRgSkS3viJvYFbqbnf2ZEJhknfz/JebrzBHfUdV/daRUH0OyraXqZjmybUVLNzF9MIZEZIMFEytubnXyx4Yp/3rEd0MM2WbBuEkwGATMGiixGLE1xLJjxw4geywY+51NN8k6+f/JkatOQ4xbaBinHtaPM6bhMYx5R6CjowNw14IZHBwc2nZFYETkvvH2q+r30lucjy/ZENeeLdgPrqlIhjH3YhjzjgzfAzctmFiBid1OJ6mMg7H5J6LLEBsSYCyYYezWkKlIhjH3Ivv6Kd3EvhfZYsEMDAw4co1kLjJ7OWJE5D/FfjfEk219MJFIxLXBlk61hj7OmEo1+wiHw65FW2aDwPT39w9tOyUwZhxMmsg2C8bNB9f0wQyTLQ0Ow2icqlRTwa4vBgb6kxzpHLEC09fX58g1zDiYNGH7UrOlQnGzkjd9MKMx9yI7QrZjr50NAhNbyWeanp6eoe3e3l5HrjGR6frzRaTT3oWZrj+ObLNgskFgDMNkS8PjTCf2vXCzcrfri74+Zyr2VOjp6QEREIkTm3RiputPE9kWReZm6ywbJvIzZB/Z0AiLfS+y4R1xqmJPha6uLvB4QaxtB3DMRSYi80TkTRHZKyK7ReQfrPQyEdkgIgesv6UxeX4gInUiUisiN8akXyoiNda++8R6QkUkR0SestI/EJGFMXlWWdc4ICKrnPqdNm72eSTCzZdnKIrMdNtlnchmw/TwbpYhewQmWl90d/e4dj86OjpQjwcVD+3t7Y5cw8k+mBDwX1V1GXA5cKeInAfcBbyuqkuB163vWPtWAucDNwEPiIgd4vEgcAew1PrcZKXfDrSp6hLg58BPrHOVEQ2pvgxYAdwTK2SO/FhLYLLFgnGq0y4VsmGepWwh2wIesmXkuFtkIjQ3GaFQiHA4gogSCoXp7u52pRytbW1RgfF6af24CYyqnlLVbdZ2F7AXmAPcCtjhzmuAL1rbtwJPquqAqh4G6oAVIlINFKvq+xqtvX87Io99rqeBay3r5kZgg6q2qmobsIFhUXIEuyLJlj6YbBCYbKhQwF0rItsExs3yZMO9yMTo9WS0tbUB4PdHv7e0tLhSjuaWVvB6weNxrAwZiSKzXFefAD4AqlT1FERFCJhpHTYHOB6Trd5Km2Ntj0yPy6OqIaADKB/nXI7hprmdCLdaRTDcSs4WC8YEPAzjpuhnwzuSDQLT1NQEQCAQ/X769OmMlyEUCtHeZgmM10dne7sjz6rjAiMihcAfgf+kqp3jHZogTcdJn2ye2LLdISJbRGSL/U+fLHZEipsOstgX2KlOu1TIhpliY3EzWigbKtVYsmFgn5sWZSYmeExGQ0MDAIFAtLZobGzMeBmam5vRSAT1+lBrsKkTQueowIiIn6i4/F5Vn7GSGy23F9Zf+1fVA/Niss8lukRzvbU9Mj0uj4j4gBlA6zjnikNVf6Wqy1V1eWVl5WR/JjDsklIXX57Yjjp7Mj03GHKRhY3ADDU8sqRvzk1rzraqnRpzkQqZmH8rGadOnQIgkAMez/D3THLypFUd+nzRD86Uw8koMgEeBvaOWDdmPWBHda0C1sWkr7QiwxYR7czfbLnRukTkcuucXxuRxz7XbcAbVj/Nq8ANIlJqde7fYKU5hh1uGIm4V5HEiopTUSGpMNTJH8oOF5mb/VHZZsG46bKzBaar0z3rOhtcZCdOnMDjiYpLcbFw4sQJV8oARC0YX7QzqL6+frwsk8LJhduvBL4K1IjIdivtfwA/BtaKyO3AMeDLAKq6W0TWAnuIRqDdqap2DfVtYDWQB7xsfSAqYI+JSB1Ry2Wlda5WEfkR8KF13A9VtdWpHwrDAtPX715llnUWTJa4yNxsMdsC46blENsX5qbYdnZGPeRuPpvZEKZ8/PhxfL5oQ3TGjBDHjx/NeBmOHj2K+PxD1osEAhw7dizt13FMYFT1XRL3hQBcO0aee4F7E6RvAS5IkN6PJVAJ9j0CPJJqeaeK/dL09fa6NomeLTABjw5FqrhBNvTBxPr53RQY20XmpiUT+/vdvBft1jPppsDECqxb9+Lo0cN2vU5JqbJ3z/GMT057+MgRwmXlQ9/DpRUcPnIk7dcxc5GlCfulUdWhllqmsQVmTkGY9nb3BMZ2Q7jpIsvERH6pYAtLf597/UCxv9/Ne9HeFn0+u7q7XGt8xAa/uBFp2d7eTnt755AFU1amDA4Ghzr+M8WBujpC5TOHvofKKzlw4EDa+wqNwKSJvfv2DW23tjrqjRuTzs5ORGBmfoQuF1uJtjsoFAq51rmdLZWqLTBuWjCxYutmwMPp5iYEQVVdi3K0G4Iej3Oj18fj0KFDwPAYmPLyqKV98ODBjJWhra2N9tZWwjECE66ooquzk+bm5rReywhMmuju6katiQfS/U9Klc7OTgr9QqE/QodLVhTE9ze41fcQW6G72bGdDQKTDaPX+/v7CYfDBLzRmtWtRlhLSwsigoi4MsCxrq4OiBUYRSSzArN//34AwjNnDaXZ2wcOHEjrtYzApIHBwUHC4RDqi46ccmPgFERN/ny/UuBTunt6XbMesqFyz5ZW+5DAuChysSLvlmvKFpQclwWmsbERj8eDiGTcLQXRCrywUPBYXbR+P5SUDFf6maC2thaAcGXVUFq4ogpEhvalCyMwaWBokKbXDyKuDJyCqMD0BZV9bT4ikYhrFWtsJJ1bZcgGkYsth5sWTKyouCUwtrWQ48sB3LPyT548icfjwePxcOrUqYw3wmpr91JeEf8/qKwMs2/fnoyVYd++fVBaDjm5w4mBAFpWEd2XRozApAG7JaQeH5JT6MrAKYiGSociSuegZ+i7G8RG57jV/5ENbiEYFthgcNC1EezZMHrdtupzvVEr3w33VCgU4tSpU3g8HrxeL319fRmNaOvt7eXo0ePMnBkvapUzIzQ3t2bsnuzZt4/Bylmj0oOVs9izb19aRdcITBoYEhTxEvIXuCYwvT1deAQ8VnC4W2GYzU3NqFddLUM29AMB9PW576rLBmvOFpiA109hTr4rVn5jYyPhcHjIggEcGfsxFnV1dagqM2fGNzTs75lwk7W2ttLa3LLTVpoAACAASURBVEx4ZvWofeGZ1XS0taXVujQCkwaGBMXjJZJTxImTo2alyQi9vX14cFdgVDVaiVllcCtaKBsG1EF2jEGJva5bVm1DQwMeEbzioSx3hiuNsCPWOA+v1zs0Tu3o0cwNcrQFZKTAVFZq3P5MlCG2g98mXBUVnXT2wxiBSQOnTp0CK4JMc4poa211pVLr6+21LBj3rIchl5jVienWoLpYa8FNgenp6SHHlwe4N8N17LgstwT/1KlT+D3RDv6K3FJOnXRXYGw32REHBheOxf79+ykoEAoK4tMDASgtTX8H+1hlgGgHf+47r+FtasTb1EjBHx/Dv28XiKQ1kswITBo4efIU6hkWGHBnhtTevv44F5kb/R9DMwhYI5Xdihayf7sX9yyHUChEb28PYr1mblXutm/f7+C6H8moP3Ycvyf6UFTml3Lq1KmMBxwcOXKE3NzcoTWbCgoKhsalZIK6uv2Ulyf+zeXlIerqnLdgDhw4ADNKIScXb1MjMjiADA7gO3EMb2sTlJYbgck2Tpw8OSQwkdyowGQ6BDIUCjEYDOIRHRIYN9whQxWYD/C4Fy1kV+alIq5ZDkMD+az/h1tiG3VPRd2XbrimgsEgJxtODY2BqSqoIBQOZbwshw4dIj8/f+h7fn5+xgQmFApx5MgxyisSd6BXVERoaDjt+LO6/8ABghUzx9w/WF5JrRGY7KG/v5+O9rY4FxmQ8RlS7QfTK9FPbFomGQrZ9oAn38NU19mZLB0dHXhFmKHq2szS9m/3eaKRU26J7fHjx/EgeEWor6/PeGjusWPHiEQiQwIzuyC6NEYm3VPRCv4IBTH+qcLCQtra2jLixj127BihUIiKiggb3/HR3CQ0NwnPPBNg4zu+IeE5fPiwY2Xo6+uj4dQpwuMITKSiiqbGxrQ1To3ATJEhIbHMf/XnI15fxgXG9rN7Y/pg3HDJDFluXgjnhV0bE9Ta2opXlQ6gxSWRs++F3xvA5/W7Yj1EIhEOHzqEVwSvR+jv78/8vFdWizjHClGeXTgTSbOvPxn19fUEg0EKCwuH0myxsUfXO4l9jYoKpanZw+CgMDgonDzhoanZQ0VFtOPfyXty+PBhVHVcgQmXR8U/XZadEZgpcvx4dGVm20WGCJpbPJSeKey+D69HEaDAL664ZE6ePBl1CQlogVJ/Iv1rTKRCY0MDAgSBlrY2VwYY2s+A1+OjMKck488ERDvX+/r78XqiFgw4W4klYs+ePeT6coYsmBxfgNmFlezZnbnBhfZvLioqGkqztzMVveXzCaWlia3HwkLIyxNHy2KLRqR8HIGxxMcITJYwZNJ6hlc+COWWUHcwc52HMOx+8VnusZKciCsumfr6+qEIMgqhrbXNlU7248eO4wP8RFvxboXFesSLiIeinDIOH3LO/TEW9shsn2XBiAPTgSRjx/YdLJoxJ27tjsUl89m1qyZjwr937168Xm9cH0wgECAvLy/to9cTsWfPbioqIow1I78IVFaG2LNnt2NlOHToEOL3E5lROuYxWjQDCeQYgckW6urqkLwZ0SfEIpJXRtPpxoz2gdhLoPo90RZSZW6Qky5YDwcPHRwaZKnF0b+ZHMwGUddgc2sLfqICA5n199vsr92PzwrNnZE/k4bGhoz3i9XW1kbHn3gEAYoCPmozUKHaNDc3c/jIYc4tOysu/dyyRfT29bFnT2asmJ07d1JUVDRqzZWioiJqamoc7ZcaGBhg3759VM8ef/mK2bMjHDly1LE+oYOHDhEuq4yrq0YhQqi8koNGYNxHVanZtYtgfkVcergw6sfM1MsD0Uq8LE+GIshm5Ueor6+PW83QaVpaWujs6Bxexm5G9E8mfNyx2DPT5gABoh67TLuFenp6OF5/HJ/V71BWEJ1YMNPWw759+ygKDFvXRT4ve/fuzVhH//vvvw/ABZVL4tKXlZ+FVzz8+c9/drwMXV1dHDhwgJKSklH7SktLaW5udmS5YJudO3cSCoWZM2f8qYLs/R999FHay6Cq1NXVEbL6WMYjVF45NOvAVDECMwVOnjxJe1sb4aKquPRIYbSVsGPHjoyVZX/tPuYXDA8oXFAYZmAwmFG//969e6MbttlQABKQ4fQMsWvXLmBYXKrEw24rLVPs2bMHVcU/JDDVgAyVLROEQiH27dtHsX9YYGbk+Onq7na0Qo3l7bffprKgjDmF8e9Ivj+Ps8sW8vZbbzsudps3b0ZVKSsrG7WvtDTqLtq0aZNj13///ffx+SSpwFTNUnJzZUiU00lLSwtdnZ3RWZOTEKmYSW9PT1pmhXdMYETkERE5LSK7YtLKRGSDiByw/pbG7PuBiNSJSK2I3BiTfqmI1Fj77hNrlJSI5IjIU1b6ByKyMCbPKusaB0RklVO/0X4owzPmxO/wBogUzuT99517aGPp7u7m6NFjLCoatlbOmhH1be/e7ZxPdyQ7d+5EPDJswQhEyiJ8tD39LbLx2L59OzM9nqGuoAUaoaYmc/5+uwwiHvze6OzBAV8upQWVfLQtc/di79699Pf3U5brH0ory4lub9u2zfHrd3R0sHXrVj45c9nQ4MZYLq06nxMnTzjeyf7222+Tk5PDjBkzRu3Lz8+nsLCQt956y5FrqyobN77DnLmhoTVgxsLjgfnzQ7z33rtp9zzYfSrjRZDZ2CKUjjVqnLRgVgM3jUi7C3hdVZcCr1vfEZHzgJXA+VaeB0TErh8eBO4Allof+5y3A22qugT4OfAT61xlwD3AZcAK4J5YIUsn77yzEfJK0NzRD26oZD51dQcyEqa7c+dOIqosKx2uQGfnRyjOccbcHovNH25Gy5XY3lydqdQfr89YuHJfXx/bP/qIs2JmLl4M9A8MZNSi/PDDDykvmIXI8CtWWTiPml01GZv08t1330VEKM8NDKXl+7zk+31s3LjR8eu/9dZbhMNhPjXrgoT7P1G1DK94eP311x0rQ2dnJ++99x4VFRUJRQ5g5syZ1NTUODK0YP/+/TQ2nmbx4tRm0j5rcZjOzi527tyZ1nLYbuqUBMaKMstqgVHVd4CRcbK3Amus7TXAF2PSn1TVAVU9DNQBK0SkGihW1fc1akf/dkQe+1xPA9da1s2NwAZVbVXVNmADo4Vuypw+fZrt2z9isGxRwv0hK33Dhg3pvvQoNm3aRI5PWFoyLDAicEHpAJs3vZ+RaeIbGxs5dPAQkVnx19LqqPvDCbM/EVu2bGEwGOTcmLTFgF+Ed999NyNlaGtro7a2lqri+Gdj1oxFBIPBjIh+OBzmTxs2UJ7rxx/TsS0iVOUF2LJly/C0Pg7x2muvUV1Yydyi0RMrAhQG8jmvYgl/2vAnx/oKX3rpJYLBILNnzx7zmOrqakSE5557Lu3Xj4o8LFqU2u9bsCCCzye88847aS3HwYMHkeIZkJuX/OCcHJhRmpa+00z3wVSp6ikA668tp3OA2M6CeittjrU9Mj0uj6qGgA6gfJxzjUJE7hCRLSKyZaIjzl955RVUlVDFkoT7NbeYSPEsXnjxRUcr+Egkwrsb3+HC0gH8I/6bn6gI0t7ZlRG/v+1i0Dkj/OlFIMXCG2++4XgZIFqpFXg8LIxJCyAsVeX1P/0pI26yP//5z6gqs0sXx6VXFs3D78vJiNBt3bqVpuZmZufnjto3uyCXSCTCK6+84tj1T5w4QU1NDSuqLxzTcgC4fPZFNLc0O+KyGxwcZO3atZSWlsaNeenq6qKrq4tt27axf/9+cnJymDlzJuvXr0/74OR3391IdbWSl0K9DtEVLufMjbrJ0tk3VXfwIIOlFckPtAiWVVCXhkiybOnkT/QE6jjpk80Tn6j6K1VdrqrLKyuTR1fYhEIhnn3uOcIz5iR0j9kMVp7LyRMn+PDDD1M+90TZvXs3zS2trKgavc7HJRVB/B548803Hbu+zSuvvoKUCRSN2CEQnhtmx/YdjrvJOjo6eO+997goEsE74jG4BGjv6HC0M9fmjTfeoDC3hJK8eHeE1+NjVvEi3n77bceFbv369eT4vFTl54zaV+j3UZobYP26dY41fl566SVEhCtmXwzAU3tf5nhXA8e7GviXzY/y1N6XAbi48hwKAnm8+OKLaS/DunXraG5uZsGCBUNp3d3dhMNhwuEw7e3tQ2HjCxYsoK+vj8cffzxt129ra+PgwUMsWDix//XChREaGhrT5rILhULR6XpGuscGBsjLy+O2224jLy8PYmYdD5dXcsKa/WAqZFpgGi23F9ZfO0yhHpgXc9xc4KSVPjdBelweEfERDYptHedcaePtt9+mpbmZYNV5AASOvo+ntwVPbwu5e14gcDTqDgqXLUIC+Tz11Np0Xj6O1157jYBX+ETF6AchzweXVAzyxp82OFqh7d+/n4N1BwkvSOwG0IWKqvLyyy87VgaIVqqhUIhLE+w7Gyj2ePjjH//oaBmam5vZunUrc0vPSdhyn1+2jM7OTkcbHY2Njbz77rvMzs/BM4b1MLcglxMnT7Jly5a0X39gYIDn16/nwoqllFoNsPquBvpCA/SFBtjfdpT6LnsaHT+XV1/M22+/ndaBwV1dXaxZs4bS0tKhSLHxKCwspKqqirVr16ZtKp2amhogOr5lItjH2/mnyqlTpwiHQoRLy+PSZbCfm2++me9973vcfPPNyOBw32CkrIJIODxlkcu0wKwH7KiuVcC6mPSVVmTYIqKd+ZstN1qXiFxu9a98bUQe+1y3AW9Y/TSvAjeISKnVuX+DlZYWVJXHfvd7yC8hXDIfAE9PCxIOIuEg3q4GPD3WjMIeLwNV57Nly4eORMoMDAzw+p82sLyynzxf4mOuqh6kraPT0Zb7s88+i/gEnT+GSV8AVMFz655zTOgGBwf549NPsxihKoER60W4LBJh69atjo7Lee2114hEIiwsPz/h/lkzFpHrz+eFF15wrAx/+MMfUFXmFY7tl5mVn0Ouz5fWFrvNa6+9RntHB59bcHlKx392/goi4Qh/+MMf0laGRx55hK6uLpYsWTKuiy6Ws846i0gkwgMPPJCWMhw4cAARRi2RPDhAnOUwOGK5orIyxe9P37Qxdkh6ZITAaCCXF198kfvuu48XX3wRDQy7UyMlZXF5J4uTYcpPAO8D54hIvYjcDvwYuF5EDgDXW99R1d3AWmAP8Apwp6razeFvA78h2vF/ELCbwQ8D5SJSB/wXrIg0VW0FfgR8aH1+aKWlhc2bN3PoYB0DVReOPyLWIjhzGeILOPIiv/nmm3T39HLN7Kh77LHaPI52eTna5eV/bSnksdo8Li4PUpILz69fn/brQ3RK+tc2vEZ4XhgCINsF2oF28LzliX4HwkvCtLa0pr3z0ub555+nta2NqxN7QwH4FJArwqOPPupIGcLhMOueW0dl0VyK88oTHuP1eFlQfj7vvfeeIzNNNzU18dxzz1Gdl0OezzvmcR4R5hfmsm3bNrZv35626weDQX732GMsLJnDuWMEwIykMr+M5bPO57lnn0vLzNeHDx/m2Wefpbq6Om7usWTk5eUxf/583nrrrbQEYpw8eZLiYsE74t8wMEic5TAwwrstAjNmpG9pBXs8S6SoOH5HTg59fX08/fTT0fWTcobdqZHi6KDUqbq1nYwi+4qqVquqX1XnqurDqtqiqteq6lLrb2vM8feq6mJVPUdVX45J36KqF1j7vmNZKahqv6p+WVWXqOoKVT0Uk+cRK32Jqqa1NnnssceQnMIxO/dH4QswUHkub775ZloHt6kqT/9hLbMLh8OTj3Z56Qt76At72Nfu52iXF58HrqnuY9MHmxwZdLlu3TqCg0F0abRil3ZBgtanSZB2S4SrQYqEJ554Iu0D6/r6+nhszRoWIpw1znF5CFeosnHjRkfmn3rvvfc41XCKJTM/Oe5xi2degqo64q779a9/TSgYZHHJ8LT0e9u66BoM0TUYYnNjG3vboh3Z8wvzyPP7+OUvf5m2vpgXXniBUw0N/LuzrknZcgC4efFf0D/Qz+9+97spl+H+++/H6/WyePHi5AePYP78+eTl5fGLX/xiyvekubmZ/PzRbuOcAHGWQ05gdN6CgjDNzelpgDQ3N0cn4c0vTH6wheblg8c75UZQtnTyfyzYt28fO3fupL/qfPCM3TocSWjWBSCetLoAtm3bxv4Dddw4tzepIXXd3AG8Ak8++WTarg/Riv2ptU9FQ5HHjnWIIhBeGqa2tjbtEUNPPPEEre3tXIciCWM8hvk0UODx8G/3359WoVNVHnvsMQpzS5hTuhSAj469QXvvadp7T/Pmvif56Fg0kq4wp4S5JWfz3LPPpTVqqaamhldeeYX5hXnkx1gvXYMhQqqEVGkbCNI1GG2QeD3CkuJ8amtref7556d8/d7eXlY/upqlpQs4P9UGmEV1YSWXV1/Ms88+O6VW87Zt29iyZQsLFizAn2xkYwK8Xi+LFi3i0KFDvPHG1CIf+/p68ftHP2OBHOIsh8DoOAz8/mj+dNDV1YXk5DLmTJuJEEFyc6c8d54RmAnwxz/+EfH5CVWeM6F8GshnsOwsXnr55bRMdqiqPPrII5TmwtXVo6PHRlKSo1wzu5+XX3oprWuBPPfcc3R3dRM5N7WWni5UJE94dHX6jMrGxkae+P3vuQBYECMuL6GcAk4BD6O8ZLnOchE+F4mws6aGt99+O23l2LRpE7W1tZxTtQKPNbiyvfc0wfAAwfAATV3Hae8dnnpj2ezL6e3rTVujIxgM8tOf/pQ8v4/FMwqSZ7Cozs+hLDfAQw89OOXllJ955hna2tv44tJrJ2S92Py7Jdeg4QirV6+edBmeeOIJcnJymDMn4ciElKiqqqKwsJDHH398So2QcDiMTLKG9XhIW39lX18fSacRSITfP+VBwUZgUqS3t5c33niTwbLF4Etg0yYhVLWMgf7+tExJsWnTJnbW1PCFBb0EUjSk/t3CfkTDPPzww1O+PkTvx+8f/z1UAamG13shfE6YnTt2snXr1rSU45e//CWRUIgbRqSfAgaszxHru82lwCwR/u2++9Myqj4SifCrX/2awtwSFlYkHrU+kpL8mcwtPZsnn3wqLev2PP744xw9epRzZxTg86ReuYsI55UW0tfbx3333Tfp6/f19fHkE09yfsUSlpTOn9Q5yvNKuGrupbzyyiuTagg1NjayefNmqqur8Y7s+JgAIsLs2bOpq6ub0uSkfr+fyCTHj4bD0eUE0oGIwGSEUnVSDYVYjMCkyHvvvUcwOJh638sIIgWVkFfMn6Y4LUYoFOKBX/4bswqUz84ZSJ7BojxXuXFeP6+99mpaZvR96qmn6OzoJHz+xN4gPUuRAuHBhx6csotq69atvPXWW1ytSmkS11gsXoSbVTnd3MRjjz02pTIAvPrqqxw8WMcFs6/COwHX6YVzr2ZwYGDKon/w4EFWr17NrPwcZiYY95KMAr+Ps4rzefPNNyc9ZmrDhg10dnXy+bOunlR+mxsXfhqNKM8+++yE827ZsgVVZebMsadDCYVCcRFcY1kJVVXR+bg2b9484XLY5OXlEwxOroIOBiE3lVH3KZCbmwuTsYZCIXJyJv48xWIEJkU2bdqEBPKJFCafjTQhIgyWLGD79u1TajWvW7eOo8eOs3JxD74J/ve+sKiPogDcd98vplS5t7S08PgTj0dH7ScOlhobL4SXhdlfu39KA0CDwSA//5d/oczj4apJ5F+IcDHwxONPTCn4obe3l4ceeojywmrmlZ2bPEMMRbllLK68hBdeeGHSodN9fX3cc889+EVYVpp6xNRIFhXnMyPHz09/+pNJRS+9+OKLzC2uYklJYuulLxQ/qK8vlLhxVJZXwkWVZ/PySy9PePqYvXv3EggEhpZCTkQoFIqL4BpLYPx+P4WFhVOaCby4uJiBwclZUgMDHmbMGL28wGQoLS1F+/smJjLhMNrXm3AG6olgBCZFPtq+g2BhVeLQ5PBg/IjYcOJ+kUjxbCLh8KTXienq6uKRh3/D+WUhLq2c+AjbfB/ctqiHmppdU+p/ePTRRxkYHCBy4eSibHShIiVRK2ZwMHkfUiKeeeYZjtXX85eRCP4JWC+x3Ah4I2Hun4JraM2aNbS1tXHJvM9Nyp1w/pwrCfhy+cUvJi76qso///M/c+zYMS4oKyTgnfzr7BHhorIiBvv7+Z//839O6P/S1NTE3r17+VTVBWPeg75g/KC+vuDYjawV1RfS3tE+4SmOWlpayMnJGff/4PP54iK4fL4xBpARdVFNZfBnSUkJvT2Ty9vX50lpgGgq2NaYpyv1hcykuxOSWIOpYAQmBdrb22luOj20kNhIJDQYPyI2lPjltPNPdgDVmjVr6O7u4W+XJo8cG4tr5gwyryjCgw/8clLTQBw5coTnn3+eyFmR0dPCpIpA6KIQjQ2Nk3KFdHR0sObRR1kKnDNJcQEoQrhGlU0ffDCpEe319fWsXfsHFpafT3nh2JMpjkfAl8v5s69ix44dE+6fW7duHRs2bGDxjIK4GZMnS77fx/mlRdTW1k6oP8ae+XdZ+dhhwXn++EF9ef7Rc6TZ2ONnJjr7dSgUSiryPp8vLoJrPIERkSl1tFdVVTEwoEy0DRWJQFf31Ct3m0WLovfT25R6dJ597FlnjRf4nxwjMClw9OhRADQvcYtCfYH4EbFjBQH4cpFA/qSW7z19+jTPPvsMV1UPML9o8jPPegRWLu7hVEPjpEaT//o3vwY/6HlTDPGtAmbBmt+uobd3YuGYv/vd7+jp7eXG5Icm5TKg1OPhwQcemLAF8dBDDyF4uHDeZ6ZUhrMqL6Ikv5IHH0jdojt69Cj3338/FbkBFhfnj3tsKBKJ73cYZ3xHVX4OC4vyWb9+fcqrTR45cgQRYU7R2BVini9+UF+eb2zffkEgn4r80qH3LlUKCgrSOlNEKBSisDD1sSMjsWdwbmubWCOos1PQCOPOAD0RFi9ejM/vx9sQP+1LuLIKDeSggRxCc+YTrhx2//saTuD1+ViyZHJ9zjZGYFLAXu8+kluc+ABvIH5ErHfs1mQ4p2hS8/s8/vjjRMIh/uqssV0LfSEZ4edO/GBfVB7i7JIwj/12zYSsmIMHD7LxnY2El4Sj6xFPkfD5Ybq7uidkxXR1dbHuuee4EBJOCTNR/Ah/EYlwoK5uQpFttbW1vPPOO5xTtZw8f+JKKBiO73cIhhP3O3jEw0Vz/4KGxgZeeumlpNdWVX72s58hkQgXlBclbbUHIxpnYQcj4wvp0pICigJ+fvqTn6TUX9ja2kpRTgE+z9jWwESZESicsHtq7ty59PX1pW3QaH9/P3Pnzk1+4BjYAz1bWiZWzTY3S1z+qeLz+bjwwgsJHD8cl97/mRsIV1YRrqyi56++Sv9nhmMxA8ePcP755085ks0ITArYo1k1kPr4grGIBApoaJzYUqSdnZ289OILfLpqgIq8sV+e3pDEVSS9YwiMCNy6sJfmltYJDSZbt24d4pWhUfsJCcbPs8R4+lUGzIRnnn0m5Q7dl19+mf6BgaQd+/3El2O8avJioMjj4Q9rU5+U9IknniDgy2Vp1fIxjwmGBuIr9jE6tgGqihdSXjibx3//eNIKctu2bezcuZPFxXnkpBCO6/dInIXtTxLG7BFhWUkBrW1tKQ3AVE0+wHWiTOZ8S5cuRVXp7Oyc8vV7e3sZGBiYUgt+9uzZFBTk03Aq/rdUVkQIBJRAQJk9J0JlRfz/+9QpD4GAf8i1lQ6uuPxypKUJ6Uw+FY90dyJNDVx+2WVTvq4RmBRob29HfAFIQwtN/Xl0dKTe2QbROcf6Bwa5Yd74Ycn5Po2rSPJ9YwvBReUhZhUoL6fQYoZoJfLGm28Qnh2dc2xMgvHzLI0rMEBkYYSm000ph07/+b33mCVCdZIKqJ/4cownMD6EC6yJMAcGkod+d3d3s3HjRuaXnUdgHFeP35cTX7GPc6yIsGTmJ2hobEg6i+4LL7xAjs/LnHEms4zF5/HE9zukMKK7NDdASY6f9SnMYVdaWkrXQA/hyQ76SEBHsHvCEUyf+MQnEJEpDxgFhsYmfepTn5r0ObxeL5deupzj9f64YShXfyZERaVSUan8+38/yNWfiXfr1R/3ceGFF005RDiWq6+Oho/7DySPivMfiE6j9JnPTM31C0ZgUqKnp2dSgysTod4AA/0TM+M3vvMO1QXKwiR9L3k+HeHnHltgRODymf18tH17Si2+EydO0NnRObxE3Fj44+dZIskAYq2KljGViKFgMMjOmhoWp9BXkkt8OcbuUo6yBBgMBtm9e3fSc+/Zs4dgMMic0vFbt35vfL+D3zt+hTG7ZAkg4060qKp8sGkT5Tl+vFMcBJeMqrwcjh49OjRZ4lgsWrSIiEY41pmeyRm7Bnto7mmbcAu+uLiYSy+9lNOnT095jFVDQwOLFi1i3rx5yQ8eh09/+tN0dSqNjan9r1pahJYWuOKKK6Z03ZHMmTOHs885h5z9ySNYc/bv5qwlS5g/f3IDZmMxApMCXq93ciNhE6ERRCTlkFZVpbZ2H2fPGJx05NhYnF0SQlVTWnvbnjNLc5PcB3/8PEvJBMbuy0llCp2BgQFCoVBKwWu5xJcjmcDY50ylHPakpWPNmDxZ/N4ABbnF406KGgqF6O7piZtrzCnsayRbWnn58uV4xMO2xsmPGYnlo8a9KMqKFSsmnPeWW26hr69vSpM0dnR00NnZyS233DLlkex/8Rd/QSDgZ+/e1P5ftfu8eDwerrvuuildNxGfv+kmPE0NeJrHjibztDThaTzJ529MRwiNEZiUKCoqQoP9aREZCfZTUFA4IYHp6uqmOJD+lQdnBKK/JxWXXZ615qsMpFnlrKCp3NxkEjCME6u3T+Sc9jQk6sBqkKqRpNOc+P1+BsLOLcNtY18jmaumpKSEqz9zNRtPbKU32Dela4YjYV4/tomlS5ZwzjkTm/MPom6dOXPmcOTIkYRegsLCQrxeL16vl5KSklFRYqrKoUOHKC4u5pZbbpn077ApKCjg2muvLo6NFgAAGqpJREFUY3+tn74kt2ZwEPbs8XPllVdOeYBjIq699lq8Ph+BPTvHPCawdycer5cbbhg5+dLkMAKTAkuXLoVIGE/3xDrnE+HvaeTsc85O+XiPx0NJSTEt/en/V9nnTGVA1/z58ykqLoL0zZUJgFgdoBdddFHSYwsKCpgzezaH0tyhDNGFhoCUKjV7bEBLT3pcQja9A530DnSNGz3k9/u5+uqraewbpDfkhNRGCUUiHOvpZ8GCBXFLDo/FV7/6VQZCgzy7/08J988tmkWeL4c8Xw5nly5gbtGshMf96egmGrqbWfX1r0/KevD5fNxxxx10d3cnjNY8++yzKSoqoqioiE9+8pOcfXb8u9jU1ERbWxtf//rXhxpVU2XlypUEg8qumvEbDnv3eOnvV77yla+k5bojKSkp4aorryS3dld0srORhMPk7Kvh01dckbZBnkZgUuCyyy7D4/Xia0nsSooUlKNeP+r1Ey6aRaQgsevE09MCve1cdeWVE7r+5VdcyZamXLoG01uxvnkihxnFRZx7bvIpTjweDzf/5c146j0wsRiFsYmAt9bL/AXzWbZsWdLDRYTPfu5zHEZpHWdhsYkSQtkuwrnnnDM06nk8li1bRnlZOXWnt6V1yv8Dp7chInz6058e97ivf/3rBHJz2dbUQV8KIlMU8OETwSdCaY6fosD4wSrBSISPmjvpDYW58847U6rozz77bG778m28U7+VbQ2j/fx/vezzzCuaxbyiWfzXFd/gr5d9ftQxh9vrWV/3BldeeeVQp/RkuOaaa/jUpz7F4cOHJzTGanBwkAMHDrBkyRK++MUvTvr6I1m0aBGf/vQV7NgRYKwYklAItm0LcNFFF3LBBalNmDoZbrnlFrSvF9/hA6P2+Y7UQW9PWiw3GyMwKVBUVMSNN9yA//Q+pG+0P3pwwRVE8suJ5JfTf94tDC5I0EGnSs6xTRQWFk3Yv/rXf/3XhCLwm735JBnCkDLvnQrwUbOfv7rtyylHq/zN3/wNBYUF+D70jelT0hJF/danUtGScQINdgnaqfz9f/z7lGe//dKXvkRuXh7PiRBJk8i8AzSrsurrX0/peJ/Px9dWfY2mrnqOtiQPCkiF9t7T1J3+iOuvvz7p2IuFCxfyz//yL0S8Pv7c2Maxrt5xhW5ZaRFFAR9FAR8rqkrHnbPsdN8A7ze20zYQ5O677+byy1Nb9hjgjjvuYNmyZaze/RxHOiY21qu5r42HdqylorKSu+66a0p9HyLC97//fQKBAHv37k0poEZV2bdvH+FwmLvvvnvcEf6T4Rvf+Cb9/cqOHYmf8127vPT0KN/61t+l9bojWb58OaXl5QT2jnaTBfbVMKO0dFJ9X2MxrQVGRG4SkVoRqRORu6Zyrr//+78nPz+P3MMbITLx0cK+xj14Ok/x7W//PTNmJFudK55Fixbxf955J1ubAvx+f96YIrOgKEyeN0KeN8K5JUEWjBF1tqvVx2/2FnDxRRfxt3/7tymXo6SkhLv/x91omyJbhUT1u16iUAKUQOSaSPR7AuS44Kn18IUvfIGrrkp9usrKykq+893vcliV8RZfriYaP5ADLLS+J+IwyjsiXHfddVw5AcvyC1/4AhdffDHbjr0et85LLCX5M/F7c/B7c6gsmkdJfuIQvIFQH5sOPc+MGcV85zvfSen65513HqvXrOHiSy5hb1s3m0+309o/uXndALoHQ3zU1MFHTR1UzZnLQw89xPXXXz+hcwQCAe69915Ky8u4f9vvOZmiS7ljoItfbP0dIW+E//3j/z3h9yMRVVVVfP/736ejo4NDhw4lPf748eM0Nzfz7W9/O20DHGM555xzuPKqK9mxfbQVEwrBR9sCXHLJxVxyySVpv3YsXq+Xm264Af/Rg0jMRGnS34f/SB03Xn99WsV12gqMiHiBXwKfB84DviIi5032fKWlpfz3738fT9dpcg5tnFCHv7f9ODnHNnH5FVdEx4ZMgi9/+cv81V/9Fa8ez+X/251PKEGj7Kvn9LGgKMyCojD/1/JuvnrO6F7FPzf4+dn2IubMm8+P/tf/mvCqf1dddRXf/OY38Rz1IDsTi0xSGsC72cuFF17Id7/73Qlnv/nmm7n+uut4Hdg2RgH+EqGaqLDcjvCXCfptGlAeF2HuvHn85//8nydUBq/Xyz333ENJaTEbD/yR7v7RA9g+Mf9zlOTPpCR/Jp89dyWfmP+5UceEwoO8V/csfcEu/umH/0RJSeoz6FZXV/Ov//pzfvCDH+AtLObD0+1sOd1O52DqszP0hcLUNHfyXkMrXXj41re+xcMPP5ySyzIRFRUV/OvP/5VAQS4/3/JbGrrHj+bqHOjm51t+S2eoh5/89Cdprdw/97nPceutt3Ls2LFxZwXo6Ojg4MGDXHXVVdx2221pu/5IvvmNbzIwMNqK2W1ZL9/85u2OXTuW6667DiIRfIeGx575DtZCOJz26LVpKzDACqBOVQ+p6iDwJHDrVE742c9+lr/7u7/D13IQ//EPUxIZT/dp8g6+weLFi/l/7rkHz0SWLY1BRPje977Ht771Ld5ryOF/f1RMxwT6ZCIKTx/M5YFdhVxwwYX88oEHJ1SZxbJq1Sq+9KUv4dnvQXZNUGQawfdnH4sWLeLHP/7xpAaTiQj/f3v3Hh1lfedx/P2dScCEiQkkMUC4JpBYNItcRCzKYtFAEOutiHGzkVOU2oMe6h4PEK0I2j1UIBJota7W7bZewW7vp9Vqt9bVrZeggIjhLreEJEAgBELI5bd/PM/AECYhl+d5Zorf1zk5mczt+TDDPN/5/Z7f8/stLCriyrFj+S2wqQtVrhrDL3w+Ar17s6K4mISEzs/cmZKSQnFxMf5Y+NvWNRxv6NzBqabmRt7b/isO1ZXz6KJHGTlyZKcziAh5eXm89tprzJ07l1OxPfn7gRo2Hmz/+ExjSwtbaup4r+Iw1Y3N5Ofns3btWgoLC7u01HCoAQMGULKqBH9cLCvXvUj1ifALqh0/dYKSdS9yuLGWZcuXkZOT063thnP//feTkZFBWVlZ2JNom5qa2Lx5M6mpqRQVFXV7WHJ7hg8fztVXX82mz3qc3nW0tMCGDdaxF7dbL0HDhg2jf3o6PewTKgFit5eR1rdvl0butedCLjDpQOhCH/vs604TkTkiUioipR0dN19QUMDNN99Mj4qNxJafme21pVfyOQf35cRh4re+ySUpKSxftoz4+PYnJTwfEaGwsJBFixaxq+4iFn2cxO5jZ38bCrZgQp1sglUbe/GbXXFMnTqVFcVPdWmHGppj3rx5TJ8+HV+ZD9l89ofSJLVx7KXaKi6DBg6iZGVJtzLExsbyxA9+wIjLLuN1wheZYAvm3BiGn/l8+BMSeKqkpEMH9tsydOhQVpashJhm3glTZIItmNaCxaX62D6+//3vM2nSpC5nAGso8cyZM1mzZg0FBQUcbGzh/QOH2W0fnwkegwGorm/gvQM17K6rJ3fKFF599VW++93vOtI1FTRkyBBWlqykOcZQsu5Fjp06zoCEvqdHjzU2N/LjT1+hqv4QS5cudW3n2rNnTx5//HEAysrKMMYQCAROD0/etm0bJ0+eZPHixd36/9hRM2fO5MQJg99vSE1pYedOH7W1hpkz73R920EiwrXXXENM+R6a+6TS3CeVHvt3c+011zheYC/kAhPulTprL2SMec4YM9YYMzY1NfxU/Oc8qQgPPvggubm59NhXSkyV1cw8Nfjqsw7uS0Mdvba+Se+LA6wqWUlKSkfXFT6/66+/nqefeQZfr2SeWJfI+oNn+kz/Nbv+rK6xmgbhB58k8unBnjzwwAMUFRU5MgWFz+fjoYceIi8vD99mH7LlzMttrjDnHns5BDHvxzCg/wBWr1rd5dZTqPj4eFYUF58uMmWtisy0MF1jhzH8l11cVv/oRwwZMqTbObKzsykpKQF/E3/buoYTp46dvm3UoG+c0zXW3NLE+9t/TfWxvTzyyCOdPtbRnkAgwJw5c3jllVcYd9V4ymrq2HiolqykAJcmBdh2pI5Pqo8ycPAQXnjhBR5++GHHpoVvLSMjg+UrlnO0sY7nNrzOt7Jzmfm1PIwxvLT5D+w8so9HFy1i7Ni253NzwqBBg7jvvvs4dOgQlZWVZGVlkZWVRU1NDRUVFdx1112ujtwKNWrUKAYOTMe0+Lh2YhNlZX6Sk3ufd+Sg08aPHw/NzTQNyaRp6HBMU1OnBnR01IVcYPYBofM8DADKnXhin8/HwoULufLKK+n55fv4jrZ62uZTxG17i4t8hqeKix2bdjtUdnY2//Hc8wwcksFTGxL4qPLcbo3DJ4XH1yVxoCGOpT/8ITNmzHD0G4rP52P+/Plc943r8G30IXvaeO46q+WSlpLGqpJVjo2xhzNFJisrizUi7Gqnu6wWw899Pkx8PCWrVjlSXIKCRaZFGnl36+s0NIYfHttiWvj7jt9TWbuboqIix05oay0tLY2lS5cyZ84cDpxoYNuROvYfP8nO2hPk5eXx7LPPdnsq9o4YMWIE8+fPZ+vhL/nL7g8AWF9VxgflG5g1a1a3W24dddtttzFs2DB27dpFS0vL6RksUlNTmdXB0YNOEBFyc6eyf79w+LCwd4+f66/P7fAoSqdcfvnl+GNi8JfvxV+xF5/P50oX5YVcYD4GhovIUBHpAdwJnH/mvg6KiYlhyZIlDB40iPgdf4WQFfp67P4Af30NTzzxeLcX7GlPSkoKP/rx04wYMYKnPw/w2aEzLZm6RuGH6xOpa+lJyapVjs9tFOT3+3nk4UfIycnB/7EfWh/rbraKS3xMPCuWr3C0JRcUHx/P8uJi0gcO5GURDoYpMo0YXhKhPjaW5StWuPK+ZGdn8+SyJ6lvrOXvO39HS5jJHzfu/RvlR7Yzb948pk6d6niGUD6fj4KCAm677TZ2H6vn88PHuOKKK1iwYIGjEymeT25uLhMmTOD3O9/haMMx1m55k8yMTAoLCz3L4PP5+M53vkN9fT2VlZXU1NRQW1vL7NmzPX0tgNOtlY8+jKG5mU6NXnRKz549ycrKIrZiHzEV+8gcPtyxE0tDXbAFxhjTBNwPvAl8Aaw1xjhz0oItEAiwePFjSMspeuz9CABfbQWx1VvJz893dDx5W+Lj43ly2XIGDRrMs5sTTp+M+YstcVTW+3ly2XJGjOjy4LkOCQ5PTUpKIubDs8+RkY2COWpYsniJI5PntSUxMZEVxcX06NWLNSI0tioyfwIqjOHRxx5z9fUYOXIkCxYuoKp2L5v2v3/WbftrtrG1spRbb72V22+/3bUMrc2efWZ00pw5c7o80KSrRIR7772XU02NPP3JqxyuP8Lse2Y7fq7J+YwbN4709HQqKiooLy8nISGByZMne5oBrHVeAoFebN/uJzY2pssj9rorOyuLmENVxB6s4tKsjs8u0hkXbIEBMMb80RiTZYzJNMb8uxvbyMzMZOYddxBbvRWpP8JF+0pJvSSNu+++243NhZWQkMCjix6jrsnH2h1xbDoUw/8d6Elh4d2ejUxJSkqiaGERptYg2+2usqPg2+Hjlltu8aTYpqWlseixxzhgDKGr3GzD8DGQn5/fqXNuuio3N5cbb7yRLZUfUXPcmljwVFMDn+x5i8zMYV0amt0dCQkJjB0zhuTk5A7N2uCGjIwMvnbppeyuLScpMdG1FnV7RIQbbriBI0eOUFVVxaRJkzxvvYDVmho+fDhgDYaIRAaw3hPT0IA5We/o2jOhLugC45U77rgDn89Hjz0fIscqmfGt2zs1eaMThg0bxo03Tue9Axfx611x9E68mIKCAk8zjB8/nnHjxuHf4odmkM3WCpuh36DddtVVVzF16lQ+EOEohhYMfxahX9++3HPPPZ7lmDt3LoFAAp/t/18AtlaWUn/qOAsWzPf8mzvA8hUrWLt2bUS2HTRq9GgALs/J8fyYQ9BoOwPAmDFjIpIBYPDgIWf9joTQY8Pp6ent3LPrtMA4IDk5mXHjriLmiDUq2q0Dt+czffp0GpsNW47EMCVvWrfPZ+iK/Px8TINBdgn+cj83Tb/J0eGvHTF79mzw+XgP2AIcMIZ758zx9PUIBALk59/JgaO7OHKimp3V65kw4ZqItSD8fn9E/j+ECnaRdnTEphtCT+T0YpBDW4LHIr3+bITq169f2MtO0gLjkJwca5hjYlKSK1Ntd0Sw2Q3WcMhIGDVqFL379Mb3qQ/TYlxZ1+J80tLS+PqECXzu87EBSExI8Gy0Uqi8vDxEhNIv3+Bk4wluusm5SQT/EU2cOJFvf/vb3HXXXRHLEHqui1s71Y4IrnXv5omd5xO6n3Jrn6UFxiHBYx2dnSnZSX6/nwceeIApU6ZErMD4fD7GjLa6HuLi484qel667rrrONbSwufAxEmTItI1lJycTHZ2NoePHyA2NjaiXTLRIBAIMGvWrG6d2OqEYKsh0i06iGyBCT3xu/W6OE6JXIfsBSYnJ4c33njDlaF+nTFjxoyIbh+sboi3336bfn37RayvPfTEOa9Oogvnsssuo6ysjIyMzIgdzFVne/7552ls7Ph8bW7o29ea0aAja+24JbS4uTWyUFswDoqPj4/oN5JoMWbMGJJTkpni0LKrXXHJJZcQa7danJ5fqTOCQ6JHjIjMUFR1rr59+zJw4MDz39FF1157LatXr2batGkRzeE2cXLBpH9kY8eONaWlpZGOoRxUXl5ObW1txA6sBx08eJDevXtHrDWnVFsmTpwIwLvvtrf4RftEZJ0xJux8P9pFpi5Y/fv3d2Wans5yY/YCpZyQmZlJU1Pn17fqKC0wSin1FfXMM884uux3a1pglFLqK8rtQUl6kF8ppZQrtMAopZRyhRYYpZRSrtACo5RSyhVaYJRSSrlCC4xSSilXaIFRSinlCp0qxiYi1cDubj5NCnDQgTjdFQ05oiEDREeOaMgA0ZEjGjJAdOSIhgzQ/RyDjTFhF/nRAuMgESlta06er1qOaMgQLTmiIUO05IiGDNGSIxoyuJ1Du8iUUkq5QguMUkopV2iBcdZzkQ5gi4Yc0ZABoiNHNGSA6MgRDRkgOnJEQwZwMYceg1FKKeUKbcEopZRyhRYYpZRSrtAC00ki8p8iUiUim9q4XURktYhsF5GNIjLahQwDReSvIvKFiHwuIvMilOMiEflIRDbYOZZEIoe9Hb+IfCoif4hghi9F5DMRWS8i56y/7dF7kiQivxSRMvv/x9URyJBtvwbBn1oR+V4Ecjxo/7/cJCKvishFXmQIt48QkT4i8paIbLN/927jsVNFZIudaaHDGWbYr0eLiLQ5LNmpDAAYY/SnEz/ARGA0sKmN26cBfwIEGA986EKGfsBo+3ICsBUYEYEcAgTsy7HAh8B4r3PY2/k34BXgD5F4T+ztfAmktHO7F+/Jz4F77Ms9gKRIvBYh2/MDB7BOxvMsB5AO7ALi7L/XArO8yBBuHwEsAxbalxcCT7bxWu0AMuz3bkPrz3U3M3wNyAbeAca28345ksEYoy2YzjLGvAscbucuNwO/MJYPgCQR6edwhgpjzCf25WPAF1gfKK9zGGNMnf1nrP3TetSI6zlEZABwI/DTNu7ieoYOcjWHiFyMtWN5AcAYc8oYc8TLDGFMBnYYY1rPkuFFjhggTkRigHig3IsMbewjbsYq/ti/bwnz0HHAdmPMTmPMKeA1+3GOZDDGfGGM2XKehzqWAbSLzA3pwN6Qv/dx7s7fMSIyBBiF1XrwPIfdNbUeqALeMsZEIkcJMB9oaeN2r94TA/xZRNaJyJwI5MgAqoGf2d2FPxWRXh5naO1O4NUw17uawxizH1gB7AEqgKPGmD97maGVNGNMhZ2tArgkzH28fm/CcTSDFhjnSZjrXBkLLiIB4L+B7xljaiORwxjTbIy5AhgAjBORy73MISLTgSpjzLr27uZmhhATjDGjgTxgrohM9DhHDFa3yE+MMaOA41jdMV5mOLMhkR7AN4HXw93sZg77GMfNwFCgP9BLRAq8zNAF0ZDH0QxaYJy3DxgY8vcAzm2ad5uIxGIVl5eNMb+KVI4guyvmHWCqxzkmAN8UkS+xmvPfEJGXPM4AgDGm3P5dBfwaq7vByxz7gH0hrchfYhUcLzOEygM+McZUhrnN7RzXA7uMMdXGmEbgV8DXPc4QqjLY/Wb/rgpzH08/s21wNIMWGOf9Dii0R6iMx2qaVzi5ARERrH72L4wxT0UwR6qIJNmX47A+1GVe5jDGFBljBhhjhmB1x/yPMab1N1UvXoteIpIQvAzkAq1HGrr9WhwA9opItn3VZGCzlxlaySd895gXOfYA40Uk3v68TMY6Vullhtbbutu+fDfw2zD3+RgYLiJD7dbfnfbjvORshq6ODviq/mB9YCqARqxqPxu4D7jPvl2Ap7FGYnxGG6M1upnhGqxm60Zgvf0zLQI5/gn41M6xCVhkX+9pjpA8k7BHkUXgtcjAGnGzAfgceCRCOa4ASu335DdA70i8H1gH1Q8BiSHXef1aLMH6wrMJeBHo6UWGNvYRycBfgG327z72ffsDfwx57DSsUaE7gv+HHMxwq325AagE3nQzgzFGp4pRSinlDu0iU0op5QotMEoppVyhBUYppZQrtMAopZRyhRYYpZRSrtACo5THRKRZzp5teKF9/Tv2LLYbxZoN+cch5xkNkVYzeIvIYhF5KOTvh+zHbRJrhutCb/9lSp0tJtIBlPoKqjfW9Drh/IsxptQ+yW0p1gl5/3y+JxSR+4AbgHHGmFoRSST8hIpKeUYLjFJRyBhzSkTmA9tFZCRw9DwPeRi4zthz0hljjnJm9l6lIkK7yJTyXlyrLrKZ4e5kjGnGmhng0vaezJ6iJsEYs8OFrEp1mbZglPJee11krQVnt21ryg1j30en5FBRR1swSkUpEfEDOViTNB7CmlcsVB/goN0tdlxEMjyOqFS7tMAoFYXs5RiWAnuNMRuNtXJohYhMtm/vg7U0wnv2Q5YCT9urWiIiF7ex6JlSntEuMqW8F2evAhr0hjEmuDDYyyLSgDXz79ucvVxtIVYRKbb/XhJy3OUnQAD4WEQasWbRLUapCNLZlJVSSrlCu8iUUkq5QguMUkopV2iBUUop5QotMEoppVyhBUYppZQrtMAopZRyhRYYpZRSrvh/FnfHIxHpiSAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(\n",
    "    data=acs,\n",
    "    y=\"HHINCOME\", x=\"EDUC\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 296
    },
    "id": "9kBBT2tigEFR",
    "outputId": "ec882b80-f6be-4491-e405-d3a9e596198c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa2a71413a0>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(\n",
    "    data=acs,\n",
    "    y=\"HHINCOME\", x=\"EDUC\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 296
    },
    "id": "D8zQD-6FTdti",
    "outputId": "7d1bcc4f-27a0-47ee-b973-8d65e11146e0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa2a7c6b370>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# why not a scatterplot?\n",
    "acs.head(500).plot.scatter(x=\"EDUC\", y=\"HHINCOME\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MyI2gV9FTdti"
   },
   "source": [
    "# More great plotting ideas: \n",
    "\n",
    "* [MatPlotLib](http://matplotlib.org/) in Python\n",
    "* [Seaborn](https://seaborn.pydata.org/examples/index.html) in Python\n",
    "* [ggplot2](http://ggplot2.org/) in R\n",
    "* [D3](https://d3js.org/) in JavaScript\n",
    "* Tableau"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "l1sG9goDTdtj"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "@webio": {
   "lastCommId": null,
   "lastKernelId": null
  },
  "colab": {
   "collapsed_sections": [],
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