{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "sns.set()\n",
    "# Better aesthetics - https://seaborn.pydata.org/tutorial/aesthetics.html\n",
    "sns.set_style(\"whitegrid\", {'axes.grid': False})\n",
    "sns.set_color_codes('dark')\n",
    "sns.set_context(\"paper\")\n",
    "sns.despine()\n",
    "%run ./utils.ipynb\n",
    "%run ./feature_engineering.ipynb\n",
    "\n",
    "from IPython.core.display import display, HTML\n",
    "from IPython.display import IFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<hr/><h2>Useful tips & hints</h2> \n",
       "<li>See <a href=\"https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Python_Seaborn_Cheat_Sheet.pdf\" target=\"_blank\">Seaborn Cheat Sheet</a>\n",
       "for quick tips on plotting.</li>\n",
       "<li>Try to create functions and methods in way they could be reused for other data sets.</li>\n",
       "<li>Extract common functions and utilites away to separate notebooks. This way it's easier to just lift-and-shift code to FanSifter codebase.</li>\n",
       "<li>The * symbol before the cell means it is currently being computed.</li>\n",
       "<hr/>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(HTML(\"\"\"<hr/><h2>Useful tips & hints</h2> \n",
    "<li>See <a href=\"https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Python_Seaborn_Cheat_Sheet.pdf\" target=\"_blank\">Seaborn Cheat Sheet</a>\n",
    "for quick tips on plotting.</li>\n",
    "<li>Try to create functions and methods in way they could be reused for other data sets.</li>\n",
    "<li>Extract common functions and utilites away to separate notebooks. This way it's easier to just lift-and-shift code to FanSifter codebase.</li>\n",
    "<li>The * symbol before the cell means it is currently being computed.</li>\n",
    "<hr/>\n",
    "\"\"\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "engine = get_rds_engine()\n",
    "schema = 'a441ffb172866cb4928c84a73de403ca15da4a54cc535e704413ab620' # unique customer schema id\n",
    "\n",
    "query = f\"\"\"\n",
    "SELECT *\n",
    "FROM a441ffb172866cb4928c84a73de403ca15da4a54cc535e704413ab620.collection_24_source;\n",
    "\"\"\"\n",
    "\n",
    "query2 = f\"\"\"\n",
    "SELECT *\n",
    "FROM a441ffb172866cb4928c84a73de403ca15da4a54cc535e704413ab620.collection_0_source c0s\n",
    "LEFT JOIN a441ffb172866cb4928c84a73de403ca15da4a54cc535e704413ab620.collection_1_source c1s\n",
    "on c0s.fan_id = c1s.fan_id;\n",
    "\"\"\"\n",
    "\n",
    "df = pd.read_sql(query, engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(500, 16)\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 500 entries, 0 to 499\n",
      "Data columns (total 16 columns):\n",
      "fan_id               500 non-null object\n",
      "userEmail            500 non-null object\n",
      "userState            431 non-null object\n",
      "userFullName         500 non-null object\n",
      "eventPurchaseType    500 non-null object\n",
      "userGender           439 non-null object\n",
      "userDob              440 non-null datetime64[ns]\n",
      "eventName            500 non-null object\n",
      "eventCountry         500 non-null object\n",
      "eventDate            500 non-null object\n",
      "ticket_group_name    500 non-null object\n",
      "admit_name           2 non-null object\n",
      "admit_email          2 non-null object\n",
      "scan_code            500 non-null object\n",
      "ticket_state         500 non-null object\n",
      "newsletter           2 non-null object\n",
      "dtypes: datetime64[ns](1), object(15)\n",
      "memory usage: 62.6+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# Show some stats of our dataset.\n",
    "print(df.shape)\n",
    "print(df.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 500 entries, 0 to 499\n",
      "Data columns (total 16 columns):\n",
      "fan_id               500 non-null object\n",
      "userEmail            500 non-null object\n",
      "userState            500 non-null object\n",
      "userFullName         500 non-null object\n",
      "eventPurchaseType    500 non-null object\n",
      "userGender           500 non-null object\n",
      "userDob              500 non-null object\n",
      "eventName            500 non-null object\n",
      "eventCountry         500 non-null object\n",
      "eventDate            500 non-null object\n",
      "ticket_group_name    500 non-null object\n",
      "admit_name           500 non-null object\n",
      "admit_email          500 non-null object\n",
      "scan_code            500 non-null object\n",
      "ticket_state         500 non-null object\n",
      "newsletter           500 non-null object\n",
      "dtypes: object(16)\n",
      "memory usage: 62.6+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# Clean data (e.g. replace NaN with \"missing\" for better plotting)\n",
    "df = clean_features(df)\n",
    "\n",
    "# Extract date features. This can take a bit of time.\n",
    "date_cols = df.select_dtypes(['datetime64', 'datetime64[ns]']).columns.tolist()\n",
    "df = generate_date_features(df, date_cols)\n",
    "\n",
    "print(df.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>fan_id</th>\n",
       "      <th>userEmail</th>\n",
       "      <th>userState</th>\n",
       "      <th>userFullName</th>\n",
       "      <th>eventPurchaseType</th>\n",
       "      <th>userGender</th>\n",
       "      <th>userDob</th>\n",
       "      <th>eventName</th>\n",
       "      <th>eventCountry</th>\n",
       "      <th>eventDate</th>\n",
       "      <th>ticket_group_name</th>\n",
       "      <th>admit_name</th>\n",
       "      <th>admit_email</th>\n",
       "      <th>scan_code</th>\n",
       "      <th>ticket_state</th>\n",
       "      <th>newsletter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>374</td>\n",
       "      <td>374</td>\n",
       "      <td>12</td>\n",
       "      <td>373</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>500</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>0e63179b896528abfbe72ff354ece9081b0335503647fe...</td>\n",
       "      <td>dankers700@gmail.com</td>\n",
       "      <td>otago</td>\n",
       "      <td>Daniel Keeler</td>\n",
       "      <td>General Admission</td>\n",
       "      <td>female</td>\n",
       "      <td>1998-01-01 00:00:00</td>\n",
       "      <td>Ocean Alley | Dunedin</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2017-08-19 08:00:00+00</td>\n",
       "      <td>Ocean Alley</td>\n",
       "      <td>missing</td>\n",
       "      <td>missing</td>\n",
       "      <td>1565F817E4</td>\n",
       "      <td>redeemed</td>\n",
       "      <td>missing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>352</td>\n",
       "      <td>10</td>\n",
       "      <td>498</td>\n",
       "      <td>259</td>\n",
       "      <td>126</td>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>498</td>\n",
       "      <td>498</td>\n",
       "      <td>1</td>\n",
       "      <td>450</td>\n",
       "      <td>498</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   fan_id  \\\n",
       "count                                                 500   \n",
       "unique                                                374   \n",
       "top     0e63179b896528abfbe72ff354ece9081b0335503647fe...   \n",
       "freq                                                   10   \n",
       "\n",
       "                   userEmail userState   userFullName  eventPurchaseType  \\\n",
       "count                    500       500            500                500   \n",
       "unique                   374        12            373                  2   \n",
       "top     dankers700@gmail.com     otago  Daniel Keeler  General Admission   \n",
       "freq                      10       352             10                498   \n",
       "\n",
       "       userGender              userDob              eventName eventCountry  \\\n",
       "count         500                  500                    500          500   \n",
       "unique          5                   18                      1            1   \n",
       "top        female  1998-01-01 00:00:00  Ocean Alley | Dunedin  New Zealand   \n",
       "freq          259                  126                    500          500   \n",
       "\n",
       "                     eventDate ticket_group_name admit_name admit_email  \\\n",
       "count                      500               500        500         500   \n",
       "unique                       1                 1          3           2   \n",
       "top     2017-08-19 08:00:00+00       Ocean Alley    missing     missing   \n",
       "freq                       500               500        498         498   \n",
       "\n",
       "         scan_code ticket_state newsletter  \n",
       "count          500          500        500  \n",
       "unique         500            2          2  \n",
       "top     1565F817E4     redeemed    missing  \n",
       "freq             1          450        498  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Only describes numeric fields\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>fan_id</th>\n",
       "      <th>userEmail</th>\n",
       "      <th>userState</th>\n",
       "      <th>userFullName</th>\n",
       "      <th>eventPurchaseType</th>\n",
       "      <th>userGender</th>\n",
       "      <th>userDob</th>\n",
       "      <th>eventName</th>\n",
       "      <th>eventCountry</th>\n",
       "      <th>eventDate</th>\n",
       "      <th>ticket_group_name</th>\n",
       "      <th>admit_name</th>\n",
       "      <th>admit_email</th>\n",
       "      <th>scan_code</th>\n",
       "      <th>ticket_state</th>\n",
       "      <th>newsletter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>374</td>\n",
       "      <td>374</td>\n",
       "      <td>12</td>\n",
       "      <td>373</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>500</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>0e63179b896528abfbe72ff354ece9081b0335503647fe...</td>\n",
       "      <td>dankers700@gmail.com</td>\n",
       "      <td>otago</td>\n",
       "      <td>Daniel Keeler</td>\n",
       "      <td>General Admission</td>\n",
       "      <td>female</td>\n",
       "      <td>1998-01-01 00:00:00</td>\n",
       "      <td>Ocean Alley | Dunedin</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>2017-08-19 08:00:00+00</td>\n",
       "      <td>Ocean Alley</td>\n",
       "      <td>missing</td>\n",
       "      <td>missing</td>\n",
       "      <td>1565F817E4</td>\n",
       "      <td>redeemed</td>\n",
       "      <td>missing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>352</td>\n",
       "      <td>10</td>\n",
       "      <td>498</td>\n",
       "      <td>259</td>\n",
       "      <td>126</td>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>500</td>\n",
       "      <td>498</td>\n",
       "      <td>498</td>\n",
       "      <td>1</td>\n",
       "      <td>450</td>\n",
       "      <td>498</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   fan_id  \\\n",
       "count                                                 500   \n",
       "unique                                                374   \n",
       "top     0e63179b896528abfbe72ff354ece9081b0335503647fe...   \n",
       "freq                                                   10   \n",
       "\n",
       "                   userEmail userState   userFullName  eventPurchaseType  \\\n",
       "count                    500       500            500                500   \n",
       "unique                   374        12            373                  2   \n",
       "top     dankers700@gmail.com     otago  Daniel Keeler  General Admission   \n",
       "freq                      10       352             10                498   \n",
       "\n",
       "       userGender              userDob              eventName eventCountry  \\\n",
       "count         500                  500                    500          500   \n",
       "unique          5                   18                      1            1   \n",
       "top        female  1998-01-01 00:00:00  Ocean Alley | Dunedin  New Zealand   \n",
       "freq          259                  126                    500          500   \n",
       "\n",
       "                     eventDate ticket_group_name admit_name admit_email  \\\n",
       "count                      500               500        500         500   \n",
       "unique                       1                 1          3           2   \n",
       "top     2017-08-19 08:00:00+00       Ocean Alley    missing     missing   \n",
       "freq                       500               500        498         498   \n",
       "\n",
       "         scan_code ticket_state newsletter  \n",
       "count          500          500        500  \n",
       "unique         500            2          2  \n",
       "top     1565F817E4     redeemed    missing  \n",
       "freq             1          450        498  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Describes other types as well\n",
    "df.describe(include=['object', 'bool'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'CONFIRM_TIME'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-32-61cb2bb6124a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Sort by time and plot timeseries\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mby\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'CONFIRM_TIME'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mascending\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlineplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"CONFIRM_DATA\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"fan_id\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/python3/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36msort_values\u001b[0;34m(self, by, axis, ascending, inplace, kind, na_position)\u001b[0m\n\u001b[1;32m   4717\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4718\u001b[0m             \u001b[0mby\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mby\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4719\u001b[0;31m             \u001b[0mk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_label_or_level_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mby\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4720\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4721\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mascending\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtuple\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/python3/lib/python3.6/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_get_label_or_level_values\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1704\u001b[0m             \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_level_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1705\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1706\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1707\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1708\u001b[0m         \u001b[0;31m# Check for duplicates\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'CONFIRM_TIME'"
     ]
    }
   ],
   "source": [
    "# Sort by time and plot timeseries\n",
    "df.sort_values(by='CONFIRM_TIME', ascending=False).head()\n",
    "sns.lineplot(data = df[[\"CONFIRM_DATA\", \"fan_id\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "g = sns.relplot(x=\"time\", y=\"value\", kind=\"line\", data=df)\n",
    "g.fig.autofmt_xdate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.catplot(x=\"userState\", kind=\"count\", data=df)\n",
    "# We rotate the X axis labels for better readability. This is also why we define \"g\" (graph) variable here.\n",
    "g = [plt.setp(ax.get_xticklabels(), rotation=45, horizontalalignment='right') for ax in g.axes.flat]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.catplot(x=\"CLEAN_CAMPAIGN_TITLE\", kind=\"count\", data=df)\n",
    "# We rotate the X axis labels for better readability. This is also why we define \"g\" (graph) variable here.\n",
    "g = [plt.setp(ax.get_xticklabels(), rotation=45, horizontalalignment='right') for ax in g.axes.flat]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 425.43x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Using the \"hue\" argument, we can add an additional variable to the graph\n",
    "g = sns.catplot(x=\"userState\", kind=\"count\", hue=\"userGender\", data=df)\n",
    "# We rotate the X axis labels for better readability. This is also why we define \"g\" (graph) variable here.\n",
    "g = [plt.setp(ax.get_xticklabels(), rotation=45, horizontalalignment='right') for ax in g.axes.flat]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 425.43x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Using the \"hue\" argument, we can add an additional variable to the graph\n",
    "g = sns.catplot(x=\"CLEAN_CAMPAIGN_TITLE\", kind=\"count\", hue=\"gender\", data=df)\n",
    "# We rotate the X axis labels for better readability. This is also why we define \"g\" (graph) variable here.\n",
    "g = [plt.setp(ax.get_xticklabels(), rotation=45, horizontalalignment='right') for ax in g.axes.flat]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fb6d0197cc0>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vr9crv98vSdq0aZNSUlL061//WmVlZZKksrIy9ezZM5QjAYCVQnraZPfu3SosLFR8fLyio6P10EMPKTk5WQUFBcrJyVHnzp119dVXh3IkALBSSON98cUXa+nSpScsLy4uDuUYAGA9PlANABYi3gBgIeINABYi3gBgIeINABYi3gBgIeINABYi3gBgIeINABYi3gBgIeINABYi3gBgIeINABYi3gBgIeINABYi3gBgIeINABYi3gBgIeINABYi3gBgIeINABYi3gBgIeINABYi3gBgIeINABYi3gBgIeINABYi3gBgIeINABYi3gBgoahwDwDg9NUfOaKGY8fCPcYZi2jRQtHx8eEew0rEG7BQw7FjemPchHCPccZ+V/y3cI9gLU6bAICFiDcAWIh4A4CFiDcAWIh4A4CFiDcAWIh4A4CFiDcAWIh4A4CFiDcAWIh4A4CFiDcAWIh4A4CFiDcAWIh4A4CFiDcAWKjJxPuVV15Rdna2cnNztW/fvnCPAwBNWpP4TTrV1dVavHixSkpK9NFHH6moqEjFxcXhHgtAE3PUWyefryHcY5yRqKgIxbWMOfPtnIVZzlh5ebkuv/xyRUVFKT09XXv27An3SACaIJ+vQY/NXBnuMc5I/j39z8p2HMYYc1a2dAaWL1+uL7/8Urfccosk6cYbb9Ty5ctP+ZytW7eGYjQACKmMjIxGrdckjrydTqc++eSTwOOIiJ8/Fd/YHQSA5qhJ/MCye/fu2rx5s/x+vz788EOlpKSEeyQAaNKaxJF3UlKSsrKylJOTo6ioKE2bNi3cIwFAk9YkznkDAE5PkzhtAgA4PcQbACxEvAHAQud0vO+//36NGDFCmzZtOmvbHDBgwFnbVqhNmzZNHo+n0etPnDgxiNM0TaWlpXrmmWfCPQbOgoULFwa+fuyxx7Rq1aowTnP6zul4b9u2TQsWLNDll18e7lGahPvuu08JCQmNXn/69OlBnAYIrpKSkjPeRkND+C7VbxIfFQyHadOmad++fcrNzdWVV16pt956S36/X7feeqv69Omj3NxcpaWl6cMPP1Tnzp11/vnnq6ysTB07dtT06dO1c+dOPfjgg2poaJDT6VRxcbGio6MD23e73Zo0aZKqq6sVExOjmTNnqnXr1mHcY2njxo2aO3eu4uPjtWvXLuXn52vZsmWqrKzUzJkzNWPGDBUXF2vfvn2aOnWq4uLilJKSooceekizZ8/Whg0bFBsbq5ycHF133XUaMGCAVqxYoYKCAkVHR2v//v2qq6vTvHnzFBMTo2nTpmn79u3q2rWrNm7c2OSObH7u9Vi5cqW2b9+u2tpajR07VpmZmcc9f+XKlVq4cKEaGho0ePBg/fGPfwzTnpyejRs36qmnnlKrVq20e/duTZ48WZGRkZo1a5YcDod69+6t22+/XaWlpVq/fr38fr8qKyv1t7/9Tb/85S/1zjvv6PHHH5cxRr1791ZeXl64d+lnNTQ0qLCwUJ9//rkiIiJ01VVXqbKyUrm5uerbt68kae3atVq6dKkOHz6suXPnqk2bNj/5HhcUFCg2NlZffPGFJkyYoG7duoVnp8w5rH///uazzz4z+fn5xhhjvv32WzNw4EBjjDEjRowwb7/9tjHGmKysLLNmzRpjjDHDhw83Bw8eNEePHjV+v98YY0xRUZFZuXJlYJvGGDNr1iyzdu1aY4wx69atM0VFRaHbsZN49913TXZ2tmloaDCvv/66ycrKMn6/3/znP/8xkyZNMiNGjDBff/21+fvf/25Wr15tjDGBfbzppptMXV3dccu+39d77rnHvPTSS8YYY6ZOnWrWrVtntm/fbv76178aY4zZu3evycjICOm+NsbPvR61tbXGGGOqq6sDfy+WLFli5s2bZ6qqqkxOTo7x+XzG7/ebYcOGGY/HE87dabQf7vcHH3xg8vLyzODBg82BAweMMcaMGTPGfPTRR2bJkiVmwoQJxhhjVq9ebaZPn24aGhrMoEGDAq9Nfn6+2bVrV9j2pbHWrFljpkyZYowx5r///a+57bbbAn9/jTFm9uzZ5pFHHjHGGPPss8+aF1988aTv8T333GOee+65cOzGcc7ZI+/vffbZZ/roo4+Um5srSfJ6vTpy5Igk6aKLLpIktW3bVmlpaYGv3W63jhw5ohkzZsjr9erw4cNKTk4+Ybtbt27V888/L5/Ppy5duoRwr04uLS1NDodDLpdLF154oSIiItS2bVtVV1cH1snJydHcuXO1evVqXXHFFRo0aJDuuusuTZo0SQ6HQ2PGjFHnzp2P2+73Rx/nn3++3G63jh49qksuuUSS1L59+xNen6biVK/H/Pnz9eabbyoqKkr79+8/7nl79+7V559/rj/96U+SpJqaGh08eFDx8fFh2IvT9/1+n3/++aqurlZdXZ1cLpckqUePHoGbw/34fa2qqlJlZaX+8pe/SPpuv7/88kt16tQpPDvSSHv27FH37t0lSenp6dq7d+8J6/xwX3fu3HnS91iSLr300tAMfgrnfLw7deqk7t2765FHHpEk1dfXH3f643sOhyPwtTFGixYt0pAhQ9S3b18VFRXJ/Ohap86dO6t379666qqrAtttCn64Hz/8+odatWqlwsJCGWPUr18/DRgwQJdddpmuuOIKbdmyRXPmzAm8Xj+1LWOMOnTooH//+9+SpMrKSlVVVQVhb87cyV6P6upqrVq1SkuWLFFNTY1uuOGG457Xvn17de7cWc8++6wiIiJ07NgxtWjRImRzn6kfv/cxMTE6ePCgXC6X3n//ffXp00cff/zxCe9rcnKyUlNT9fTTTys2NlYNDQ0n/N1vilJTU7Vx40b94Q9/UHl5udq3b3/C7w348b6e6j1uzP2Xgu2cj3fXrl2Vnp6unJwcRUREqF27dpo5c+bPPq9Pnz6aNm2alixZooSEhBOOLG+99VZNnjxZ8+bNU0NDg4YMGaIbb7wxWLtxVr388stas2aNjDG66qqrFB0drdGjR6u+vl719fXKz8//2W1ccsklcrlcys7O1oUXXqhf/OIXIZj87ElMTFRCQoJycnJ00UUXnfCD3OTkZA0ZMkS5ubmKiIhQTEyMnnrqqSbxP/X/oqCgQPn5+XI4HOrVq5e6deumjz/++IT1HA6Hxo8frzFjxkiSoqKiVFRUpPPOOy/UI5+Wvn376o033tDw4cPlcDg0ZcoULV26VLfccot+//vf/+RzTvYeNxVcHo+g+f5IZd++fbrrrrv00ksvhXskoNk454+8ETxTp07V7t275fV6z8nPhAPBxJE3AFjIzhN0AHCOI94AYCHiDQAWIt7A/7N///7AZfCDBg3S6NGjtWPHjjPebllZmQoKCs7ChMD/x6dNcM7z+/1yOBwaO3asRo4cqSeeeEKSVF5err179waurg3lPJGRkSH9M2Ef4g0rVVZWasKECXrllVckfXeRyXXXXacFCxboq6++kiQVFhbqN7/5jd58803NmTNHdXV16t69ux544IHAUXZqaqp27NihyZMnKyEhQVlZWYE/Iz09Xenp6ZKkiooKPfjgg3K73UpOTtbMmTPVpk0b/e53v1NWVpbWrl2rhIQE/eMf/1B8fLzee+89FRYWKjo6Wj169Ahs8/Dhw5o0aZIOHjyo2NhYTZs2TR07dlRubq66deumLVu2aPTo0erfv38IX03YiNMmaDY8Ho/atGmj5cuXa9myZfrVr36lb775Ri+88ILmz5+vZcuWKTIyUmvWrJEk7dy5U3l5eVq9erV27dp1yrvDTZkyRQ899JBKS0t18803a86cOYHvdejQQf/617+Ulpam1157TZI0adIkFRUVqbS0VIcPHw6sO336dOXl5am0tFQFBQWaMWNG4HsxMTEqLS0l3GgUjrzRbFRXV2vTpk16+OGHdd111yk9PV1vvPGGPvnkEw0ZMkSS9O2336pdu3a6+OKL1alTp5PeMGzs2LHas2ePevfurfHjx2vbtm267bbbJH13e9H27dsH1v3+VrHdunXTvn37VFNTo4aGhsA/Btdff73KysokSe+++6527twZeO4PT4/069fvLL4aaO6IN6wUGRl53I3w6+vrlZqaqiVLlujNN9/UlClTNGzYMCUlJSkzM1NTp0497vmVlZWKi4sLPO7UqZPWrVsXePzEE0+orKxMr732mowxcrlcWrZs2U/O8v2NzCIiIuT3+yWd/IZXDodDr7766k/eAyU2NvZ0XgKc4zhtAiudd955OnDggLxer2pqarRlyxa53W61atVKgwYNUk5Ojnbs2KEePXronXfe0YEDByRJVVVVga9/qHfv3qqurj4u0HV1dZKkhIQEOZ1ObdiwQdJ392zZtWvXSWdzOp2KiIjQjh07ZIwJ3F1RkjIyMrR48WJJ3x3Bf/LJJ2f+YuCcxJE3rBQdHa2RI0fqpptuUocOHZSWlqa4uDjdfPPNioiIUFxcnGbOnKnzzjtPkydP1m233Ra4UdbUqVOVmJh43PYiIiI0Z86cwG8NatOmjZKSkjR27FhJUlFRkR544AHNmjVLfr9fo0ePPuGe5j80ZcoU3XnnnYqOjtall14qr9cr6btz4ffff78WLlwon8+nrKwsde3aNXgvFJot7m0CABbitAkAWIh4A4CFiDcAWIh4A4CFiDcAWIh4A4CFiDcAWIh4A4CF/i9Us3FfEqWTWwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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GAoAWoUnPeVdWViokJESS5Ha75fF45PF45Ha7nWXcbrfKy8ubciwAsE6TxrtNmzbOUbXX61V4eLjCw8Pl9XqdZbxeryIiIppyLACwTpPGOy4uThs3bpQk5efnq2fPnoqJiVFJSYkqKyt16NAhBQYGKjg4uCnHAgDrBPlz5Q8++KB27dql0NBQFRYWavTo0XrkkUe0dOlS9erVS7GxsZKkjIwMjRw5Ui6XS1OnTvXnSADQIriMMaa5hzgfBQUFiouLO+/nH/FUKmPuK404ES5kz05PVmR4aHOPATQaLtIBAAsRbwCwEPEGAAsRbwCwEPEGAAsRbwCwEPEGAAsRbwCwEPEGAAsRbwCwEPEGAAsRbwCwEPEGAAsRbwCwEPEGAAsRbwCwEPEGAAsRbwCwEPEGAAsRbwCwEPEGAAsRbwCwEPEGAAsRbwCwEPEGAAsRbwCwEPEGAAsRbwCwEPEGAAsRbwCwEPEGAAsRbwCwEPEGAAsRbwCwEPEGAAsRbwCwEPEGAAsRbwCwEPEGAAsRbwCwEPEGAAsRbwCwEPEGAAsRbwCwEPEGAAsRbwCwEPEGAAsRbwCwEPEGAAsRbwCw0AUT75deekmpqalKT0/Xvn37mnscALigBTX3AJJUXl6uvLw85ebm6uOPP1Z2drZycnKaeyzge6s5fky1NTXNPQaaSEBQkILahDXJti6IeBcWFuqGG25QUFCQfv7zn+vf//53c48ENIramhrt/PNvm3sMNJHu47KbbFsXRLw9Ho/Cw8Od940xDXpeQUHB99ruw8O6fq/nwx7/Li5Ssx0S3DquubaMJrZzz+ffex1xcXENWu6CiLfb7dann37qvB8Q8N2n4hv6BwSAluiC+AfLa665Rlu3bpXP59Pu3bsVExPT3CMBwAXtgjjyjoiI0KBBgzR8+HAFBQVp7ty5zT0SAFzQXKahJ5gBABeMC+K0CQDg3BBvALAQ8QYACxHvRrZr1y7de++9Sk9PV1pamp5++ukm2/aXX36pMWPG1PmxFStWqHfv3qqtra3z4+np6Tpy5EiDtjN16tQGz3To0CE9+eSTDV4e0s6dOzVy5EiNGDFCaWlpeueddxp9G6c+30VFRVq+fHmjr3/dunU6ePBgo6zr4MGDSk9PV3p6uq699lrn7eLi4rPui5mZmdq5c+d3rv/LL7/Uxo0bG2XWpnRB/LRJS3H06FFNnTpVixYtUrt27SRJH3zwQaNvx+fzKTAw8Jyes27dOiUkJGjLli3q1avX99p+VlZWg5e99NJL9fDDD3+v7f2QHD16VNOmTdPixYsVHR2t6upq7dixw2/b69atm7p169bo6123bp2ioqIUFRX1nct+1/4cFRWlFStWSJL69+/vvC2d275Yn6+++kobN25UQkLC915XUyLejei9997Trbfe6oRbkm688UZJJ68iffTRR1VeXq7g4GD94Q9/UGVlpR566CFdeeWV2rNnj8aOHaukpCR99dVX+v3vf6+qqipFRERo/vz52rFjh5YsWaLg4GBdddVVateunV566SUdP35cvXv31oMPPljvXF9++aUiIyM1YsQIrVy50on38uXL9dprr+nKK6/U0aNHJUlr1qzRu+++q9raWu3fv19jxozR6tWrdfjwYf3pT39SdHS0+vfvrzfeeEPr16/XwoULFRoaqri4OE2cOFHTp0/X3r17FRQUpIkTJyoqKkqPPfaYFi9erIKCAj3xxBNyuVy66aabNGHCBK1Zs0YbNmyQz+fTl19+qaeeekqdO3f242fpwnZqH4qOjpYktW7dWjfccIMk6emnn9aHH34oSZoyZYp69Oih9PR0de3aVbt371anTp10+eWXKz8/Xx06dFBWVpbWrFmjd955Rz6fT4cPH1ZWVpZ++tOfOtv78MMPtXbtWs2cOVNJSUm66aabtH37diUmJqq8vFwfffSREhISNGHChAbvw926ddOmTZu0Z88edenSRZMnT9akSZNUW1srl8ulP/7xj4qMjFRSUpLi4+NVVFSkbt266cYbb1R8fLy+/vprzZgxQ0uWLPnO1+vUvlheXq5p06bJ6/UqICBAzz//vLPMvn37NHPmTM2aNUuBgYFnfG298MILKiws1GeffaZZs2ZpyZIlp+3DF+wFgQaNZvHixSY3N9cYY0xJSYkZMWKEue2220x5ebl54oknzLp164wxxrz77rsmOzvb7Nu3zyQmJpqqqipz+PBhk5ycbIwx5sEHHzRFRUXGGGNeeOEFk5ubazZv3mySk5NNTU2NMcaYiooKY4wxtbW1ZtiwYaa0tNTs27fP3H///XXOdWrb6enpprq62nzzzTcmJSXFnDhxwng8HtOzZ09z+PBhs3r1avPQQw8ZY4xZtmyZGTt2rDHGmJdeesksWrTIGGNMUlKSMcaYyZMnm127dhljjPH5fKa6utoMGjTI+Hw+57Fvz5SSkmIOHDhgjDFm9OjR5uOPPz5te2vXrjVZWVmN8Jmw17f3oW/bvXu3GTNmjDHGmP3795shQ4YYY4wZMWKE2bRpkzHGmEGDBpm3337bGGPMr371K3Pw4EGzevVqM27cuDPWMWLECHP48GGzefNmM2vWLGOMMYmJiWbv3r3mxIkTplevXmbXrl2mtrbW3Hbbbaa2tvac9uEpU6aYwsJCY4wx1dXVprq62hhjTG5urlm8eLGzvY8//tgYY0xxcbGZNGmSMcaYP/3pT+b111+v8/U5te/97/uPP/64eeWVV4wxxtn/pkyZYvLy8szIkSPNwYMHjTH1f22deg3q2ocvVBx5N6If//jH+vzzk/c2iImJ0YoVK5Seni6fz6fPPvtMBQUFWrZsmWpqahQbGytJ6ty5s1q3bq3IyEhVV1dLkj7//HPnQqWqqiolJCSoQ4cO6t69u/Pt5ebNm7V06VIZY7Rv3z4dPHhQERERdc719ttvq23btlq1apVKS0v1/vvvKzIyUl27dlVQUJDcbrc6dOjgLH/q2+ioqKjT3t67d+9p6x0/fryWLFmiiooK9e/fX4mJiRo9erSmTJmikJAQZWRknLZ8VVWV8230tdde69yA7NQ2Lr/8cnk8nvN56VuMb+9D31ZSUqJrrrlG0snX6b///a/zsZ/97GeSpMsuu0xdu3Z13j71Wnbv3t1Z7quvvqp3223atHH2gx/96Ef62c9+JpfLJbfbrePHj5/TPvxtHo9Hs2bN0pEjR3Ts2DH17NlTkhQcHOx87jt16qTDhw/L4/Fo/fr1evHFF8/hVZOKi4t19913Szr99hrPPPOMZs+ercsuu0xS/V9bp7Rq1eqMfbghp36aA/FuRH369NFzzz2noUOH6oorrpAk1fz/24F26tRJN910k3r37i1Jqq6uVmlpqVwu1xnr6dChgyZOnKiOHTs6y3700UennRfMycnR8uXLFR4errS0tHpv5lVcXKwOHTpo/vz5kqQvvvhCzzzzjKZNm6ZPP/1UPp9PlZWVKikpcZ7z7Znqmu+Uyy67TLNnz1Z1dbX69++v+Ph49e3bV/369dOrr76qlStX6q677nKWDw4O1sGDBxUVFaUdO3YoMTFRRUVFp22jvj/HD8WpfWjYsGGKjo7WiRMntGPHDsXExOi1116TJH399dcKCQmp8/l1vZa7d++WJH3yySenndI723PrWte57MOtWrWSz+eTJL3++uuKi4vTr3/9a+Xm5uqzzz6TpDPOcw8YMEBz5szRVVddpeDg4HrnrEvnzp21ZcsWDRw4ULW1tU7AH3/8cS1evFgRERG69tpr6/za2rVrl/N16vP5ztiHH3rooXOapakQ70bkdruVlZWlRx99VDU1NWrVqpWuu+46tW3bVmPHjtXMmTP1/PPPq7a2VnfddZd69OhR53qmTJmixx57TJWVlZKkcePGnXGzrqSkJN19993q3LlzvV/IkvTGG2/oF7/4hfN+TEyM9uzZo9DQUPXr109Dhw5Vhw4dzvpFXZ+FCxfqo48+Uk1NjYYMGaKKigpn1hMnTmjmzJmnLZ+ZmakHHnhALpdLvXr1Urdu3VRUVHTO223J3G635s2bp2nTpqmmpkY+n0/33nuvrr/+esXGxio1NVXGGGVmZjZ4nQEBARo9erQOHz6sefPmnfds57IPJyQkKDs7W1dddZWSk5M1efJkffDBB4qKilJQUN3ZueOOOzR37ly98MIL5zzbmDFjNHXqVOXl5SkwMNA55922bVstWLBAEyZM0KRJk+r82urevbuKi4s1YcIEjRkzRvPmzat3H76QcHk80IKtWbNGZWVluu+++5p7lO9UWVmp0aNHa+XKlc09ihX4OW8Aza6wsFD33nuv7r333uYexRoceQOAhTjyBgALEW8AsBDxBgAL8aOCsN6HH36o0NBQ52KUzMxMbdu2TaGhoQoJCVFWVpY6der0vbaxYMECXXLJJUpLS2uMkR0bN27UU089JenkBSQdO3aUy+VSSkqKc9EJUBfiDett2bJFl1xyiRNvSZo5c6bi4+OVl5en+fPna9GiRQ1a1/nc9Ov7SEhIcG6IdMsttygvL++cL1DBDxOnTdBkXn75ZaWkpOjOO+/Us88+q4yMDG3bts35+NChQ7V//35VVFRo8uTJSklJUUpKinNXvczMTM2ZM0dDhgzRHXfcoaKiIn399df629/+pkWLFmngwIFnXMJ/3XXX6T//+Y+kk3GsqqqSdPLnn7OzsyWdvD3qvHnzNHjwYL311lv6/PPPNWLECN15550aOnSoc8n37t27lZaWpr59++r999+XJP3nP/9RWlqakpOTNWzYMOfS9s2bN2vAgAEaOHCgUlNTJZ282nbOnDlKSUnRwIEDtX79+npfq/LycvXr1895f9euXbr//vslSfHx8crOzlb//v11zz33OLfy3bt3r379619r8ODBGjVqVINv8Qs7ceSNJlFcXKxNmzbppZdeksvl0m9+8xt1795da9euVc+ePZ17bkRHRys7O1u33Xab+vbtqwMHDug3v/mN1qxZI+nkhRwvv/yy1q5dq+eff15PPvmkUlNT6z2lkZ+f36C7FAYHBzvbSElJ0eTJk9WrVy95vV7nisD9+/frxRdfVFFRkebMmaObb75Zl156qZYvX67WrVursLBQTz31lBYuXKhly5Zp+vTpzjokKS8vT1dccYVmzJihY8eOadiwYerdu7datWp1xjwRERHq3LmzCgoKFBcXp3/84x8aNGiQ8/HLL79cb7zxhv7617/qz3/+s6ZPn67Zs2crKyvL+dipx9EyEW80iQ8++EA7duzQ4MGDJZ2M8O23367XXntN06ZN09q1a/XLX/5SkvSvf/1L+fn5WrBggaSTR6Gn7j1xyy23SDp5k6WlS5fWu73Zs2crNDRU0dHRDbrE+fbbb5ckHTt2TBUVFc5tc9u2bessk5CQoMDAwNNu8FRdXa3Zs2fr008/VUBAgHNk36NHD82fP1/Jycm64447nD9XcXGxXnnlFUlSRUWFSktL6701weDBg/Xqq6/qmmuuUX5+viZPnux8LCkpyfn/uHHjdPToUe3YsUNjx46VJNXW1p52wyW0PMQbTcIYo7S0NCcup7z99tsqLCzU2rVrndMYxhg999xzdd7NrXXr1pJO3jSpvt8KJP3fOe9vCwgIcJ7zv3e/O9v9Yf53299ez/Lly9WhQwdlZ2errKxMQ4YMkXTyXhvx8fFav369hg4dqtWrV8sYo7lz5+q66677zm1JUu/evTV//ny9++67uv766087F37qZlAul8t5LaKjo/Xqq682aN2wH+e80SR69eqlf/7zn84vfThw4IDKysp0++23a9myZTpx4oSuvPJKSSd/gcWqVauc537yySdnXfdFF12kioqK75whOjpaRUVFqq2t1XvvvVfnMmFhYWrbtq02b94sSfJ6vWf9S+LYsWO65JJL5HK5nCNq6eQvAOjWrZvGjx+vdu3a6cCBA7rxxhuVm5vrrO+7bsoVGBio+Ph4PfbYY6edMpGkN9980/l/XFycIiIiFBIS4vzmpurq6jpvLYuWg3ijSfz0pz/VfffdpxEjRmjAgAGaOHGijh8/rltuuUXr1q1zTplIJ+8TXlpaqgEDBqhfv37Ky8s767oTExP16quvatCgQWf8g+W3jRs3TlOmTFFr2UzKAAAAz0lEQVRqaqpzf+e6/OEPf9CCBQt05513atSoUc4pm7qkpqZq5cqVGjhw4Gl/gSxdulRJSUkaMGCAOnbsqK5duzrn5gcOHKikpCQtXLjwrH8u6eRpkdatWzv3wD5l//796t+/vzZs2OB8N/Pkk09q0aJFuvPOOzV48OAG/f5G2It7mwAXsBdffFFlZWV64IEHnMdOnY6p79aq+GHgsw9coKZOnaqdO3ee1/2t0fJx5A0AFuKcNwBYiHgDgIWINwBYiHgDgIWINwBYiHgDgIX+H+mghH9j9lSvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sns.catplot(x=\"gender\", kind=\"count\", data=df)\n",
    "sns.catplot(x=\"userGender\", kind=\"count\", data=df)\n",
    "sns.catplot(x=\"eventPurchaseType\", kind=\"count\", data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ffa903bff98>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JiWlsn9gcNCfK8LrIynDT0NafkvM9si9KSRYsKDx5lsoFRTEMYF9Xaj59CDFdJRPwW4CLlVJOpdQ5wIFh+1YCc5RSjwLvBG5TSi1OQznFGI13HdbTMQyDiqIsjrROPOBN0+KpQ1EurTU41T1YLgesKIuyrVXWoxFiIkZ9B2mtjyqlfgk8DUSA9yilbgTqtNYPAA8AKKVuB17WWu9OX3FFMmKmRXNHagMeoKoki/rWvgmfZ3ebyWAYVlYadHaf+piVZVGea/LROmgwY8JXFGJ6SqqKpLW+G7h72KaDpzjm9hSVSUxQR7efcNRMaRs8wIzSHDbtbJ7weZ4+FOPcmU58I/Sj5nktZuWZ7O10sbpmwpcUYlqSz8A21Ng+QHaGm7xsT0rPO6M0m8b2fiJRE7drfDckBUIxXjgS42MXexjqznFEAriDR3FG/BixEKbLi+nKZGl+KZvacrCs1I3cEWI6kYC3oaE7WCc6ydiJqkqziZkWR9r6qa3KG9c5nt/fTYYbFpdCX/0r5B/Zh8ffjun0EnNnYbq8OPwhnJFBLouFmO0upfvwHPIWLcZwSqerEGMhAW9DqR4iOcTncVFelEVdc++4A36LPsrl5V30b95CzN9HNGcWAyXLiPryjz/QsnCFemk/1MrM5u10d+4ic+45eGcoDEOmMxAiGfJOsaFUzkFzotrKPOqax9fRGgyFqWzcwIV9D+PKK8W1+i0MlC4/OdwBDIOoL5/O/CX8IHINGbOXMqifp/e5PxPtPzrB70KI6UEC3oZSPURyuJrKXOqae8f8upi/j8P3/DcrXYfIWfOvZC+5CMM9+kIk8wtjHO51MVCyhIL1b8WZlUfPpj/iP7Rd2uaFGIU00dhMvz9Mz0CIGWWpb6IBqKnM489PHcKyrKTb+CM97bT85nb6g262VLyTfytKvl5R4LMozYKdLSaXzfORs/w1eMprGdi1kWh3G9nLLhnndyKGi/n7MUOpm2vI4c3CmZme/4MieRLwNtPYNoDLaVBWmJmW88+uzKXfH6arN0hxfsaox4e7mmn57RfxlNXwnV1LuPX8aqBxTNdcVGawsznGZfPi/129ZbNx5byZvpf+Qc+zD+AuqsRdUD6eb0ckmKFBAnWpuyE9o2aZBPxZQJpobOZwSy8zSnNwOdPzqy3JzyAn080rjT2jHhvuaqLl/30e38wFdK64CX/YYuWcsXfOLi41eLnVJBp7tUnGmZlL/tqrcBdW0v7Hr9P/8lNjPq8QdicBbzN1zX3UVOam7fyGYaBmFbL38MgdndHeDlp+ewcZNcsofdNHeG5POytVKT7P2Ic6ziuGmAW64/jZxwyni5ylF5O/7no6//oDOv/+M6xYdMznF8KuJOBtpq65l5rK8Q1hTNaimkL21J0+4GODvbT89g68ZbMpeeMHwXCw+eUW1i4ZXzOKx2mwsNTBzpZTTy+ZvegCKv/tywzqLbT89g5ig2PvBBbCjiTgbcQ0LQ63pLcGD7CopoiDjT1EoifPBGmGg7T+/is4swsoveY/MJwuXmnqpasnwLmLxt9OvrzSyY7mk683xFs5l6qb/xcsk8aff5pQyyvjvlbM30+kuzVlXzF/ambgFGKspJPVRtqO+gmGY8yuSG8Nft7MfCwLDh7pZWFN4bHtlhmj/U/fxowEqXz7F3C4vQBs3tXC4tpi8rK9RE4zudhoVlQ6+H/bLLr8FkWZpx6948rOp+Idt9O14R6af/VfFF/5PnKWXjLma0mHo7ALqcHbSF1zLwU5XvJzvGm9jsftZO6MPPbUdR23vWvDPYSaD1D+tttw+uJLBVqWxbO7msfdPDOkLMegNNtg1wi1eIi3yxe//haK33ArnX+7m46//RAzHJzQtYWYqiTgbSTewZre2vuQRTVFx3W09j7/V/q3P0759Z/FnVd6XJka2wdYt6JqQtczDIPllQ62jxLwQ3KWv4bKG79C8Mhemn7+KUKthyZ0/TMlFjN5blcLz+xoZk9dF8GQdBqL8ZOAt5F4B2t629+HLEx0tJqmxeC+LXQ9/itKr/443sq5xx335IuNLJtbTGHu6HetjmZ5pTM+XDLJdWG95bVU3fy/+KoX03TPZ+nZ8hCWdfauA3ikrZ8P/O8/uW/DfvoHw7ywt53fP76fzp7AZBdNTFES8DZS19LH7DNUg186p5hAKMq+F16g/c/fpujym8iaf+5xx5imxVMvNXLxytQs2bGwzEEkBgc6kg9ph8dHyZX/TtnVH6fnmT/S+rsvE+lpT0l5UqmrN8AXfvwc86sL+OoHLuSKC2bzjjcoqsty+OMTB1O2Fq6YXpLqZFVK3QrcBISBm7XWhxLb84E/Ah7AAD6itX4xTWUVI+gdCNF+1M+ccc7yOFZZGW4umePE8c+7yD33DeStvuKkY3bXddEzEOb8ZZUpuabPZbAgMVxyYdnYxtNnLViLt3IeHX/7IY0//hgF699K3po3YjgmfwriSNTkiz/dTHV5Dh9920o6uuM1dqfDwSXnxP84PralgeteOw+PKz3lDYai9A6ERz3O63Hg88jYjKli1Bq8UqoQuAVYD3wKuHPY7hDwb1rrixLHfC0dhRSj29/QTXaGO6ULbY8k2tfFFQMPsDdSRd4l7zzlMY9trmfN4jKyM0ZYummMVowyXHIkrtwiyt92GyVv/CC9mx+i8ccfZ3D/1kmftOyBjQfoHwzzn+9afdIdyIZhsG55FYYDntk+8dW0TiccNWls7x/1KxQ+e5u4xMmSaaJZA2zUWke11lsBNbRDax3QWjclnoYB6RGaJPvqu5k/qwCHI7WLfJxKLDBAy71fJrO8mt8MXMDeupPHPrZ2DfLU9iauuXjuKc4wfssrHdR3W3QHxhfKhmGQvehCZr7/u2QtWEv7g9+k5ddfINh80iqUZ0RL5yD3/WM/771mKZmnWcPQ7XLwujWz2Fd/lJbO1E0IJuwvmYAvBIa/g096jVLKAL4J/G+KyiXGSNcfZUF1QdqvY0ZCtP3hThwuD5XXfYpzFlbw5EsnTx72wMaDLKopZMHswlOcZfwqcg2Ks+KTj02Ew5tJ4SVvZ+b7v4e7oJzmez5L24PfJNxxJEUlTc7PHnqZFfNLWbukYsTjivMzWFRTxDM7mif9E4eYOpIJ+G5g+IoMp3pn3UW8lv9ESkolxiRmWuxv6EGlOExPNHQjU2ywh/K3fg6HJ4Mrzp/N41sbONzy6iIgnT0BNjzfwHWvmZ/yMgwNl9zRnJqmAlduESVv/CAzbvk/rEiIxh9/nM5HfkSkuy0l5x/JwSM9bN3Tyo1vXJTU1MtrFpXR3R/kwJHRJ3oTApIL+C3AxUopp1LqHODA8J1Kqc8BUa31t9NRQDG6I239BEJR5qezBm/GiPzzR4SaD1J+w+dxZsU7c1eqUi5aUcVdv3+JWMxkMBDhjp9tZkltEStVSVqKsrzSya7WGLEkh0smw1M6i/LrP8uMW7+B4fLSu+XP9G75C+GOhrTVmO/9h2bd8ipmJjl3f6bPzaoFpWzZ3Uo0Jm3hYnSjBrzW+ijwS+Bp4FvAZ5VSNyqlLlZKzQS+BKxSSm1USv0mvcUVp6LrjzKzLDulnZnHMWOU7/ktsVZN5bu+iDu/7Ljdt1y1lM7uAB/55kY+9d2ncDoMPvPuc1O+6PeQxWUOwlE40Jn6kPOUzqLo8hspWP82nNkF9L34GN1P/Z5A3U7MSChl1znYGK+9v/XysX3KWTa3mEjUZNPO9HW4CvtIaryT1vpu4O5hm4b3SE3+OLNpbt/hblR1eppnjFiEit2/xtt/BO81/4W78OQhj7lZHr724XVs399BT3+IKy+oOW2HYSr43AZLyh1sbYixoDQ9//2cmblkL15H5rzVBJs0gYbdDO7firdyLhnVi3DmFif9B8yKRYh0tx637Xd/28/5Cwup8PqJnDDG3RMMU+U7/bj3S5aV8LdNdbz5krl43OP7/s1wgFDzQcLt9YTbGwi0H6GoqxUjFiL+XVmYDg+my0fMnUXEl0/UV4hlZY3remJyyIDWKc6yLLYf6OBdVyxM+bkdET9VO3+GMzzAkXM+yOK8stMeW1mcTWXxmRmiCbCm2sn9O6O8Y1V6OxwdHh+ZNcvJmL2MSOcRAvW76Xn2QZzZ+Xgr5+OtnIszY+Tv2wwFCbftPfb88FGTrQdC3Hml95STmvkHwgy0n34GyrVzl/LcvqM88txhrlo/J6nvwwz5CR7ZS6BhD8H63YRaXsFwufGUVOMpqcZZPpdBKxfTOXRLCzjMCI5oAGd4gIzeBlztO4k2e+gvnYm3Yi7u4pkYDrlX8mwmAT/FHWnrp7MnkPL2bre/g8qdv8B0eWlY9WFMz5kL72SsnuHkZ1sivNJpsqw2/dczDONYGJrBQYItBwk17ce/fwvuoiq8FXNwl1Qfm2RtJA/uirBmppMZ+eMLR7fTwb9cWMP9jx/gdefNIsN78tvYikUINu0nULeTQN0uQs0HMDw+MmYuJGvBWope9x68FbXHbvTqamwg6N808s/AjFDh6oO+Jvq3P47h8uCdqcioXoLDO/ryjeLMk4Cf4l7UHdRW5VGQM/G5XoZkde6hfM9v8BcqWhe+FcuZ3tkpxyPLa7C43MGWBpNla87stR2+LDJrlpNZs5xoXxeh5v34X9mO+fJTuHKLcZfOwlNSjSuvGMM4PsQPHzXZ1mTy1Ssn9jO9cFklG55v4K/PHOK6y+ZjmTHCHUcIHN5JoG4nwYY9YJr4qheSNf9cil93M57ymgnduWs53DiKZ5Mzez7mogsJtxwiUL+LwKGd+GYqMmpXJvUHTpw5EvBT3Iv72li1oHT0A5NgmFGKDj1KwZEn6ay9ku7qSyBNHaWpcN4sJw/sjPKeSRwX7sotwpV7PplqLbHBHsLtDYQ76gm88iI4nLjySvBWzMPh9uDKK+FPu9ysmelk5jhr7wBEQzh6m7l1aT/7n/k1RxqiRDsOY0XCeMpryahZSt55/4pvxoJjc/KnmsPlwTdzAd4ZikjnEfyvvET3k78jY/ZSMmpXpOWaYuwk4KewYDjKy4e6uP61r47EiPn7MUNju9vREwwzI3wI7477MaIhgmtuJLOohkwCRJ0+2gbPznbWc2c6uWdrhJ11fZxbOPKNQulmGAau7AJc2QVk1i7HjISI9nYQ7W0n1tdJoP0wZsjPDZaB4cuiZ3MOzoxsHL4sDIcLnE4MhwvD6cQMxcjoHcBhRjHMKIYZi7eFR/04I4M49P2EgIr8Mrrc2Rw05rL2+hvwlNee8Rr0UNOVu3gmkY4GBvXzBI/sJTbYQ8FF16ftD4xIjgT8FPbyK124nI7j7hYd62pEZiRM/97nyWjaQzC3mv6qtVg9fdCzA4DsOcuBzFQXPSWyPAbnVTvZ8FI7565So7/gDHK4vXiKZ8S/ymoIt9Xxw43d5DLItSpILDCAGRggNtiLZUYhFsMyo1ixGDHTxBdzYDlciS8nMXcW4cwSYu4svLUrKZy3jMqKIuq3N/Hd+7ZzzrXzyfBNXpgahoGndBbukpmEmg8ysOtJBl5+msKL30b20ovPikndpiMJ+Cns6e1NrF5YdtIEVcmwzBjBRk3g4DZMh5vumRcRyUxNU8+ZdOlcJ199vJvegRB52WdvbfFgp8mmZh93XpmHd5Tmmd6BMG0jjKLx5JRhuON9Lhcuq+QPj+/nwY0HufGNiydcTsuC1kGDfV0u2vwOwjHI81rMzDFZXDz6VFOG4cBXNZ/8tVfhP/QSXRt+Se/zf6XwNe8ic87KCZdPjM3Z+dlbjMofjLBpZzOXnTtzTK+zYlGCDXvofur3+A+8QEbtClznXDUlwx1AlTgoK/Dy+NYzO4fMWFiWxb0vRVhfM/6RM6fjcBi864qFPPT0IRpH+KOQjPZBg3v3erlnl4+WQQdV2TEWFMbwOi02N7v4/osZPLo/uQVXDJeb/PPexMwPfJ+M2uW0/uFOWn57B6HWugmVUYyN1OCnqGd3NpPlc7NifnLBHPP3EWzcR7BhL4bDgW/2UjKqF2O43ISTmAf8bGUYBm9YVcqfnznEv15Ui9t19tVZXqob4GCXyfsvSM8njHMXlbNmUTnf+8MOvvL+C8c8o6hpWjy8tY37d/lYUBjj/SuD5PuOD3HTirCn08mTdV52t4f4wIXOVrK6AAAQQUlEQVQeynNG/1k7M7Ipuuzd5K66gu4nf0fTzz9N9pKLKLz4Blx56ZnKQrzq7Hs3iKRs2HqE16yeiXOEN3MsOEigYQ+9W/5C95O/I9LZRNbC8ym45O1k1q7AcKXvbtMz6bUrS4jGTDZsbZjsopwkHLO4Z2Mbb1AuirLS93Z73zVLqW/p46+bxrb2bE9/iC/+dDN/e6GNt6gQV88PnxTuAA4DlpTEuO1SB4WZBp9/NMTu1uRn9HTnl1J61UepuulOon1dHPnhh+l89CdEetI/qdt0JgE/BTW09rH7UBevWX1884xlmYQ7j+A/uI2eZx+g+4lfE6jbiSuvhPx115N/wTX4qubbrsPL63bylkvncd+G/USiZ9ckXH9+OUrMsrh6aXo/LBfk+vjQdSv4xV92s2N/R1Kv2XGgg4984wlCkRhfeucC5hWM/rPL9hh89CIPVyxw8bUnwjxxcGxLQHgr5lDxjtspu/4zhDsbOfKDD9H+p28Tajs8pvOI5EgTzRT060f3sXZJOVUFbgKHdxFs1AQb9xFq2o8Z9OPKL8VTXkv2sktxZad/jvizwRUXzOaBJw7w8LN1Sd++n24N3SZ/2RPlM1dX43MlF7oTceHyShrbFV/95fPc8b4LTju7aCRq8vsNmvsfP8C1l83jhssVPS2NdCZ5HcMwePNSNxU5Bj96LkJzn8UNK1xJNw0ZhkFm7Qoya1cQbNpPz3N/oumnnySjdjm5Ky8nc94qDKc9Pl1ONgn4SZTsmHUzHCDa10W0u422+sMsqjvA0sIAh7/eheFy4Smdjae8liy1FndhJZGukxfgsDuv28nNb1rC9/6wnXMXlZ3ReXFOxR+2uOvpMOtqnKyYnU24Lf0BD3D9a+fT74/wn997hve8aTGvXzv7WL9ELGbywt42fvHXPQRCUb743vNZPm/87eDnz3ZRnO3gm0+GaO03+eAFHnzusbX/+6rmU37tpwl3NtK39WE6/vYDMBxkL15HzrJL8ZTXpm1W0ulAAn4SDY1Zt2IRYv4BzEAfsUA/ZqCfmL//2GMrMU2tw5dNUygfT04BObVzcWUX4swpeLXJxTJt064+HhevrOLZnc3cde9LfOUD60bsn0gn07S4+7kwPjfcuPrM/j4Mw+CWq5agqgv4/h938JtH97G4tgjTstjf0E0gFONNF9Vy3WXzUjLj57xiB3e83sv/bQxzxz9CfOISL0WZY/+5e4pnUHzFeym8/Eb8+7fSv/MJmn7xGdyFFWTOW0Xm3FX4ZizAcEpkjYX8tM4QMzhIpLs1/nW0hUh3G5GOeiJHWzBD8alhDbcPZ2YOjowcnJm5uIuqEs9zcWZk86c9Fo/qKF+50odvHG8iuzMMg/e/ZRkf/cZGfnD/Dj503fIzXvuLmRY/ei7CwU6T21/vxeOanN/TRSurOG9JOS/sbePAkR6cToNLzpnBuYvKTzk52USUZDv479d7+d4zYb7waJCPrfeytPrkKZKT5a2Yg7diDrHBHvyHthOs303f1kfA5SazZhkZNcvxzVC4i2fYrj8p1STgU8SyLMxA/7AAbyU6FOjdrZj+PjAc8cmoCstxFZTjm70MT8UcnJm5ODJycLg8pz3/i40xHtwV4dOXesZVQ5ouCnJ83PG+C/jcD57B5TS49eql47oRbDwGQxY/3hzmlS6T/7rcS2n25I5h8LidXLCskguWnTyHf6plug0+cbGH370U5UuPhfjXo/VcVTs44T9wruxCshdfRNHrbyXW14H/4Iv0vvAwnY/8GMPjw1c1H2/VfLwVc/CUzcKVWyJNOsNIwI+BZVnEBnqIdLfEwzsR5ENhbob84HDizi/FVVCOu6CC7MXrcOeX4yosx51XelwTSqS7NalpBTbsj/KrbRFuOMfNkgqpsYxmdkUuX3zv+Xz551s42NjDR966klnluWm7nmlZbDti8qttEXJ98PnLvZQlMUbcbpwOg3eucrOyysFPtvbx1O4o1yx1sW62c8JB73B78M6NN9VAfAhwqGk/wUZNqGk/fS/+HdPfh+HNxJuYzdNdVIm7oOKU773pIqmAV0rdCtwEhIGbtdaHhu07l/ii2wbwP1rrv6ajoGeCFY0Q8/cR7esg2tNBpLfj2IRR0cRjKxrGcLpxFZThLqjAXVhOxqzFuAoqcBeU4corSdnHxle6TO7fEWFfu8kHL/RwXrWEe7LmzSzgu598DXc/sJMP/98T8bVjl1exuLaI0sLMCbfPR2IWjT0WO1pibKqLcdRvccVCF1cvduFyTu8a5OJyJ9++cRaPPPMKf9gR4d6XIqyd5WR5pRNV4iDbO/Gfj9OXReaclcemPxiqfIXbD8dXqepoYGDPphM+PRfhLijHVVARnwU0pxBndgHOxCRxjsyck6Z3nupGDXilVCFwC3AhsBK4E7h+2CHfAq4DeoGnlVKPaK2TvwNijMxICCscxDJjYMawLDP+r3nCv7EoZiSIFQ7F/42EXn0eDsRHsPh7ifn74l+BfqxEWzgOV/w/QH4prtwSfDMUrsXrcOWV4C6oiHdspvk/Qsy0+ObGEEsrnHz1X7xJ3TUojpeb5eHT71rNWy+fz8Ob6rhvw35augZxOQ3ysr143U48bidul+O4IX5GLEIsGMBhGDhO+Lgfiln0B6HTb4EFc4sdrK91culcV0qCyy48LgdXLHRx+Xwn2xpNNjdEufu5MP4w5GdAQYZBlscgw23hMuJz4Fic7l8L91aN09uAx+XE43bgdTvxeuK/v/iXI/H7LMabVYYn//zEcwduM4Qn0Ikx0Emkp5VQbxtW204sfw+WvwcjHB/JZuLAmZ2POzsfhzcThy8LhzcLpy8ThzcLhy8Tw5OBw+3BcHowXO7EV+Kx0w0ORzwbDGPY4/jzoceOjOwz1oyUTA1+DbBRax0Ftiqljk3bp5TyAS6tdVPi+X5gHrAvHYUFaPzJfxBNpvPG4cLh8WG4vTg8Xgy3D4c78a/XhzMjF1d5Dc7MvHgbeGbOscfOrLxJ77xxOgy+/xZZJScVZpXn8v63LAeguy/Iy4e62L6/g2jMJBozT7o5Ks/lJNx1lLwcLxmexB/WxPvR7TDI9cU7FqtyjTEPC5xuXE6D82Y5OW+WE9O0aBuIf/LpDVoMhi26BmIcHQhjEL9b1iCejQavPgYozPWQkZNFJGoSisQYDEbo7g8RisQIR2LH/o0/No89DkdiHD91TiZQk/iKn78wy0F1rkltAbxuaR7ZzjBmcBAzNIgZ9BMd7MHsasIM+TFDAYhFMCNhrFgEKxrGikbATL5OW/yG95K76vWp+PGOKpmALwS6hz13nLCvZ9jznsS2UW3bti2Zw0523s3je91IgkAwCke7gK7Un39ERak9XWvf2M+ZBZ6aU8+nHjYDlHgCADTXd9FcP8HyAcfKN8J1h2sH2kea3+pQE9CU9NUzgQuO3QtlcPK68U5g5HUATeAIxKuZoxnr72SUn0vYDNBcvy9Fv4tXeWrmj3rMqL+LIaf6ng0gB/JzIH8c5Yu/UYdzJL5S17beAvHfqbcIvEAaum66Acabf2OUTMB3A8uGPY+dsG/47yoPODraCVetWiXVHiGESLNkAn4L8AWllBNYDhwY2qG1DiilokqpCqCPePPMwbSUVAghxJgYVhLrWSql/h34NyACvAdYB9RprZ9USp1HvKPVAL6qtX4ojeUVQgiRpKQCXgghxNQjY++EEMKmJOCFEMKmJOCFEMKmJOCFEMKmZLKxSTLS/D7TjVLqfOCbxH8WA8A7tNY9I7/K3pRS64CngRKtdbKLLdmSUmoN8GXidzQ9rLX++iQXacqQgJ8ESczvM93UA5dprf2JIbkfBP5nkss02T4OvDDZhZhsSikv8N/A1Vpr/2SXZ6qRgJ8cp53fZzrSWjcPexoGxraSs80opd4IPANcNdllOQusBQLA/UopF/BJrfXoc2wLQAJ+sow0v8+0pZQqAj4AvGGyyzJZlFIO4j+DNyMBD1AJLALOBaqBnxC/0VIkQYJlcpw4h0/apleeKpRSmcAfgI9M8zbntwMPaa1PnFlruuoGNmmtB7XWe4nPdyWSJDX4yXHa+X2mo8RH73uB72qtn53s8kyypcAqpdTVxCf5+y3wuskt0qTaAnwu8cmmlJOnlBQjkICfBFrro0qpXxIfJTE0v890dgOwHshVSn0U+Nt0HSmhtf7PocdKqY3Ea/TTlta6Wyl1D/Ak8VE0n5jcEk0tMheNEELYlLTBCyGETUnACyGETUnACyGETUnACyGETUnACyGETUnACyGETck4eJFySqmZwHeJ37TTA+wDPgScA/wfkEH8hpVPaK0fT7xmI2BprS9NPL8RWKC1/oxS6nbi9wp0JS5xt9b6bqVUq9a6XCk1G9hN/IYxH/Co1vpjw87zC2CV1vrFxLavAp8BMk68Y1QpVQo8lnhaTfxOyn5gJ/Bz4N+11m9L5rxKqUiiXEO+rLW+P4mf372J73HjaMcKMRIJeJFSSikDeBD4jtb66sS21xGfT+QXwBu01nuUUouAR5VS52utmxIvn6eUWqO1fv4Up/4frfXdI1x6l9Z6rVLKB2xXSq3TWj+T2PcycC3wYuL5ZUDzqU6itW4HViTKfQ9wr9b60cTzS044fLTzdmmtV4xQZiHSSppoRKpdBvRrrX81tEFr/RhwJfAzrfWexLY9xGvEHxj22m8B/8kEJGrk24GqYZv/AbwWQCm1kngwRyZynVSfVyn1JaWUVko9BhQN236TUurlxNd7E9tuU0rdlHj8Z6XUXYnHX1RKTes7X8XxJOBFqi0iHrCn2v7SCdteAhYOe/4wMFcpNf8Ur79NKbU98XXa6ZWVUvmAIj4NxJAwsFcptRy4Dhi1mSRJo523aFiZtyulVp+mzGuAy4ElxBeBWZvYPgO4jfjsiecD/5FojtoEXJB4eTHxOWsgvr7AphR9b8IGpIlGpMN457+wiLfRf4qTg2q0JpqlSqntwFzgJyfMMQ/xmSqvJV7jvn2c5TuVkc6bbBPNBcADWusI0KSUGvrjtBp4bGh1K6XUw8B5wF+AuxJ/6PYAM5RSucAMrXX9RL8hYR9Sgxeptpf4KlXJbF+R2D7c74CLgYoxXndXIkwXAG9LNJkM9xjwDmCv1jo8xnOPJFXntU7z+CSJlY2CwJuILwyyBXgv8Y5gIY6RgBeptgHIU0q9c2iDUuq1wKPAe5RSCxPbFhAfGfPD4S9OrHL1fY5vm0+a1roR+ArwyRO2h4mPcPnGeM47wvVScd5ngWuUUm6lVCVwUWL7VuAypVSuUiobuIJ4mAM8B3yU+CedTcDHkOYZcQJpohEppbW2EnOZf08p9UXiNc0XgQ8TD/TfJUa6BIFbEoF8op8Cn59AMX4KfFopNbyjFa31fRM452mNcN6iRLPRkO9prX96itc/r5R6nHgn7WFgc2J7k1Lqa8T/AAB8S2t9OPF4E3CD1vqgUqoNKB92nBCATBcshBC2JU00QghhU9JEI6atxCLfj5+wuVNr/do0XnML4D1h85oUd/wKAUgTjRBC2JY00QghhE1JwAshhE1JwAshhE1JwAshhE39f5EHurSxVypIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot clean_time & confirm_time distribution together (TODO! fix the axis labelling)\n",
    "sns.distplot(df['CLEAN_TIME_dow'])\n",
    "sns.distplot(df['CONFIRM_TIME_dow'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "float() argument must be a string or a number, not 'Timestamp'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-37-13765123bf2f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m# g = (g.map(sns.distplot, \"CLEAN_TIME_dow\", hist=False, rug=True))\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFacetGrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"userGender\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdistplot\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"userDob\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhist\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrug\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/envs/python3/lib/python3.6/site-packages/seaborn/axisgrid.py\u001b[0m in \u001b[0;36mmap\u001b[0;34m(self, func, *args, **kwargs)\u001b[0m\n\u001b[1;32m    760\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    761\u001b[0m             \u001b[0;31m# Draw the plot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 762\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_facet_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mplot_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    763\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    764\u001b[0m         \u001b[0;31m# Finalize the annotations and layout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/python3/lib/python3.6/site-packages/seaborn/axisgrid.py\u001b[0m in \u001b[0;36m_facet_plot\u001b[0;34m(self, func, ax, plot_args, plot_kwargs)\u001b[0m\n\u001b[1;32m    844\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    845\u001b[0m         \u001b[0;31m# Draw the plot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 846\u001b[0;31m         \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mplot_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mplot_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    847\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    848\u001b[0m         \u001b[0;31m# Sort out the supporting information\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/python3/lib/python3.6/site-packages/seaborn/distributions.py\u001b[0m in \u001b[0;36mdistplot\u001b[0;34m(a, bins, hist, kde, rug, fit, hist_kws, kde_kws, rug_kws, fit_kws, color, vertical, norm_hist, axlabel, label, ax)\u001b[0m\n\u001b[1;32m    175\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    176\u001b[0m     \u001b[0;31m# Make a a 1-d float array\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 177\u001b[0;31m     \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    178\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    179\u001b[0m         \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/python3/lib/python3.6/site-packages/numpy/core/numeric.py\u001b[0m in \u001b[0;36masarray\u001b[0;34m(a, dtype, order)\u001b[0m\n\u001b[1;32m    490\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    491\u001b[0m     \"\"\"\n\u001b[0;32m--> 492\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    493\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    494\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/python3/lib/python3.6/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m__array__\u001b[0;34m(self, dtype)\u001b[0m\n\u001b[1;32m    726\u001b[0m             \u001b[0mwarnings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mFutureWarning\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    727\u001b[0m             \u001b[0mdtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'M8[ns]'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 728\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    729\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    730\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__array_wrap__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/python3/lib/python3.6/site-packages/numpy/core/numeric.py\u001b[0m in \u001b[0;36masarray\u001b[0;34m(a, dtype, order)\u001b[0m\n\u001b[1;32m    490\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    491\u001b[0m     \"\"\"\n\u001b[0;32m--> 492\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    493\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    494\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/python3/lib/python3.6/site-packages/pandas/core/arrays/numpy_.py\u001b[0m in \u001b[0;36m__array__\u001b[0;34m(self, dtype)\u001b[0m\n\u001b[1;32m    169\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    170\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__array__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 171\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ndarray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    172\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    173\u001b[0m     \u001b[0m_HANDLED_TYPES\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNumber\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/python3/lib/python3.6/site-packages/numpy/core/numeric.py\u001b[0m in \u001b[0;36masarray\u001b[0;34m(a, dtype, order)\u001b[0m\n\u001b[1;32m    490\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    491\u001b[0m     \"\"\"\n\u001b[0;32m--> 492\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    493\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    494\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: float() argument must be a string or a number, not 'Timestamp'"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show distribution of \"CLEAN_TIME_dow\" over gender\n",
    "# g = sns.FacetGrid(df, hue=\"gender\")\n",
    "# g = (g.map(sns.distplot, \"CLEAN_TIME_dow\", hist=False, rug=True))\n",
    "g = sns.FacetGrid(df, hue=\"userGender\")\n",
    "g = (g.map(sns.distplot, \"userDob\", hist=False, rug=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\" Generate plots for all columns that have \"_dow\" in their name \"\"\"\n",
    "\n",
    "# Extract columns which have \"_dow\" in their name\n",
    "columns = df.filter(regex=(\"_dow\")).columns\n",
    "for col in columns:\n",
    "    g = sns.FacetGrid(df, hue=\"gender\")\n",
    "    g = (g.map(sns.distplot, col, hist=False, rug=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\" Generate plots for all columns that have \"_hour\" in their name \"\"\"\n",
    "\n",
    "# Extract columns which have \"_dow\" in their name\n",
    "columns = df.filter(regex=(\"_hour\")).columns\n",
    "for col in columns:\n",
    "    g = sns.FacetGrid(df, hue=\"gender\")\n",
    "    g = (g.map(sns.distplot, col, hist=False, rug=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\" Generate plots for all columns that have \"_dow\" in their name, \n",
    "but in a single chart, side by side. For some reason, we also get one empty\n",
    "graph, I couldn't figure out how to lose this, if we still want to use the \"hue\" and\n",
    "plot some cateogrical variable in addition, e.g. \"gender\"\n",
    "\"\"\"\n",
    "\n",
    "# Extract columns which have \"_dow\" in their name\n",
    "columns = df.filter(regex=(\"_dow\")).columns\n",
    "\n",
    "# Create as many subplots as there are columns, in a signle row\n",
    "f, axes = plt.subplots(nrows=1, ncols=len(columns), figsize=(12,5))\n",
    "g = sns.FacetGrid(df, hue=\"gender\")\n",
    "for i, col in enumerate(columns):\n",
    "    g = (g.map(sns.distplot, col, hist=False, rug=True, ax=axes[i]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XlFLPKaW+QXVPwE/3ZPEWE01kOXZ4tOn3TYz4pPxpAFg2ME6lC1cm3G3F55VSCmF6J4CP1Ibs3MY1GSjAobUucH0G6mNU+5JLBsoA0UR1A91Ik4FxfXz0cizDgWkJjAfBTjMBgAe2ec9HgI90cFkDJ7qebfpCFqT8aVBYNjBOZwv4timjgGrGWEophJlJBqo/RNez+DxDeN3N/Tr1uIYI+6u9jA9MBzu0OiHEtaqlFM11kYHqBryXzq51YEWin1g3MM4V8e1wohr2OklLVwphcpKB6r1W6hXrJiJSsyhEN+ULJZLpQtN7AqCaMX70qUsdWJXoJ5bdfJfJFXfM4FQ330kphRCiPWuJNgJjqVkUoqui67Wpdy0cs9XjNUO5XDF6WaKPWDcwzhbxenbIGHucZHJFSvIXXAjRhngyx0hAAmMhzCC2nmPIYSOwzVTcnUxGfBRL5SsjpYU1WTYwTu+WMfZWn8tInbEQog3xZI5woPHRsptVp99JYCxEt8RTWUJ+d0vT60aCHoYcNplYaXGWDYx3K6Wod6yQcgohRDviqdYD4+oudznJCtEt7VzIVnsZ+1iKbhi8KtFPrBsYZ3evMYZq9wohhGhVLJkj7G8xYzziJZnOy+8hIbok3sbxCjA5Ii3brM66gfEuGWPnkB2X03FldLQQQrSirVKKER8AKzHJGgvRDbFU63sCoNp/fFmOV0uzdGC8U7s2qPUylsBYCNGiYqlMMp1vOTD2uIcIDrtkA54QXdLOhSzU9gXI8Wpplg6Md+pKAdWTUjZ/3RRdIYRoSCKVA2CknROtZKCE6Jq2A+MRLysSGFuatQPjXTLGXvcQmZxsvhNCtCaWrAbGoTZqFsdHfHKiFaJL2q0xnhjxsRLLUKlIq1erGvjAOJuXwFgI0Zp4MkfA52TI0fqv0vERr9QYC9El8VS2zYyxj3yxTLx2t0hYjyUD40qlQraBwNjjcpDJSSmFEKI17d6WBRnyIUS3ZPNFMrlSW8fsaMiD3W6Ti1kLs2RgnMuXKFeQUgohREfFUznC/tZ3uIPUGAvRLfFa6VM7pRQOh53RkEcuZi3MkoFxPdj1unce+eh1D5GVwFgI0SIjMsbjIz5iySyFYtmgVQkhthJP5bDbbQR8rrY+Z2JEJlZambUD4wa6UmSkxlgI0SKjSikqFViNS9ZYiE6KrecI+13Y7c2Pg95MehlbmyUD43Q9MHY5dnydx+WQjLEQomXxVLat27IAAZ8Tt8vBSlwyUEJ0UnV8e3ulTyD7Aqxu55RqjVLqA8B7gDzwXq312U3PTQC/D4wCi1rrd3dioc3I5Iq4nA4cu+wUlxpjIUQ74slcWz2MAWw2W7XOOCoZKCE6yYg7PFDdF/CtlxYNWJHoR7sGxkqpCPB+4B7gDuDjwI9seslvA/9Za32mIytsQSNT76BeYyxdKYQQrYmncoQMONFKL2MhOi+ebP8OD0jG2OoaKaW4GziutS5qrU8Cqv6EUsoB3Aj8mlLqMaXUj3ZonU3JZHdv1QbgcUmNsRCiNaVyheRG3sATrWSMheikhFHHa8RHOlsklSkYsCrRbxoJjCNAbJv3TAC3Af8NeBfwn2sZ5p5qZLgHVDfnSSmFEKIVqXSecqW9qXd11ZZtkoESopMSqRwhf3sdKQDGw14AuctjUY0ExjEgvOnn0jXPXdBVG8DTwA0Grq8lmVxx144UAF6XtGsTQrQmUZt8FRo24EQ74mNFulII0VHVwLj9C1mX00E44JaWbRbVyOa7E8BHamUTtwGn6k9orbNKqTml1CSwCtwCXOjISpvQaMbY45bJd0KI1iRSedwuB54GftfsZjxcHQtdLlfabiUlhNhaIpU3JDCG6l2eJckYW9KuGWOtdRT4HPA48CngQ0qpB5VS99de8svAF4BvAn+mtV7q1GIb1XAphXuIYqksjfWFEE1LbBiTfYJqjXGxVCZey0ILIYxVKpVJpvOGlFJAfcOs3OWxooZSHVrrh4CHNj10etNzTwH3GbyutmRyRXyNlFLUgudsvohzyJiDRQgxGBLJnCFlFACRkAeH3cZyLE0k2H6fVSHE1dbTeSoG7QmA6sXsUnTDkM8S/cWSAz6a6UoByAY8IUTTEhvG3ZZ12G2Mhr2sSC9jITpiPZUHjAyMZfqdVVkyME43UWMMyAY8IUTT4gbtcK+bGPHK9DshOiSeyuF1O3A7d56I26gJ6T1uWZYMjDO54pVs8E7cTgd2G2TzsgFPCNGc9VSe0LAx2SeQXsZCdNK6gRvvoNrLOJHKk5VZCJZjycA4m2+sXZvNZsPjHiKTlb/YQojmxA1q/VQ3HpZexkJ0SjyVM/hCtt7LWC5mrab9PkN9KJsr4nU1drtEpt8JIVqxvmFsKcX4iI8TLy0a9nmi/yilPgC8B8gD79Van609HgYeBlyADfhFrfW3lVIPAH/G621Sf0xrLX9JWmBUD+M6n8fJsNfJSizD3smAYZ8res+agXG+1HBvUa/bITXGQoimxZMG35qV6XeWVpsK+37gHuAO4OPAj9SezgE/rbWeU0rdCPwe8Pbac1/UWv9Ct9drNdXNssZ2n5qUzhSWZM3AOFfE02DG2OuWsdDCvCQD1RulUplUxtgT7WTERzpbJJXO4/dJ+0gLuhs4rrUuAieVUqr+hNY6A8zVfswDm09K36eUuh14DPhVrXWlWwu2kkQqx+y439DPnBrzsbAmF7NWY7ka40qlQqaJjLHHPSTT74QpbcpA3Qd8kGoGqq6egbq39ppPbHrui1rrB2r/SFDcAqN7okK1lMJug0U50VpVBIht+vm6869SygZ8EvjN2kNPAYrqMT4D/GiH12hZ8aSxpRQAU5FhFtckY2w1lssYF4plyuUK3ga6UkC1xlh2lQqTkgxUjxjdExXAOWRnLOxlYW2DG/aGDftc0TdiwBs2/bxVRuZ3qB7TjwJorVP1J5RSXwDuB/6ik4u0qvWNHGGDSymmxoZ55rVlQz9T9J7lMsb11mtuKaUQ1icZqB4xuidq3dSoZKAs7ARwv1LKoZS6k9fLmQBQSv0KUNRaf3rTY6FNL7n/2veIxsVTeYIGZ4ynR30srqWpVCS3YCWWyxjXN9I1MuCj/joJjIVJSQaqR9ZTeYIGtn6qqwbGUkphRVrrqFLqc8DjQAF4n1LqQeAccBb4DeAbSqnjwJzW+ieAH6vtI8gAZ4Bf7cXaza5QLLORKRA2upRidJhMrsi6gVMwRe9ZLjCut15rvMbYQSKZ7+SShOiUE8BHlFIO4DYazEBprRO1HyUD1aJ4Kmf4SRZgatTHs6+tGP65oj9orR8CHtr00OlNf77u9oPW+rPAZzu9Lqtb38gBGN6VYjzsxW63sbC2IYGxhVguMM7lS9hs4BpqrErE6xpiKS8ZGmE+koHqncRGjqDBJ1moZYyjFwz/XCEGWaK2J8DouzwOh52JES+La2lu3B8x9LNF71guMK6Pg7bZbA29XkophJlJBqo3Eql8xzLGq7E0hWIZZ4MX90KInSVSOYa9zo4cU7IvwHos95s3myvidTe+IcYjgbEQokmJVI7gcGcyxuUKrMTlLpYQRkmkcoQ6cLwCTEtgbDnWC4zzJdwNtmqD2uQ7adcmhGhCIpUjHDA+YxzwuRj2OllclcBYCKPEU53bHDdV60whrMOCgXGx4R7GUO1jLAM+hBDNSHSoKwXUTrQyZlYIw6xvdOZCFqp3eRZW5Xi1EssFxplcCU8TpRRSYyyEaFaiQ10pQE60QhgtnuxM6RPA9Ngw0fUsuYIk2KzCcoFxNl9suFUbVAPjbK4oDbqFEA0plsqkMoWOdKUAmIr4WIrKrVkhjLK+0ZnNsgCTER8AS1JnbBnWC4xzRTwNTr2D6ua7UrlCsVTu4KqEEFaxvlFt/dSpE+30mGSMhTBSPJXrWI2xz+Mk5HexKBezlmG9wDhfwtNUjXE1iJY6YyFEIxKpzgwLqJuKDLMU3ZC7WEIYJJHKdex4heoxuygXs5ZhucA4kys2PA4aXh8dLXXGQohGrKfy+DxDOIcavzPVjKmxYTK50pWhBEKI9iQ62JUCavsCpJTCMiwXGOfypaZKKeqBcVYCYyFEA+KpHKEOdaQAGAt5cNht0plCCAPkCiUyuWJnA+MxadlmJZYLjDNNbr5zDtmx221kpJexEKIBiY3O3patjpn1ya1ZIQzQ6dInqJVSSMbYMiwXGGdrI6EbZbPZqi3bshIYCyF21+nbslDvZSwZKCHatZ7KY7NB0Ne5wHh6bJilaJpyWfYFWIH1AuMmSykAvC6ZfieEaEyigzvc66SXsRDGiKdy+L0uHI7OhTtToz4KxTJriWzHvkN0T0OpVaXUB4D3AHngvVrrs5ueOw44gQLwZa31xzuwzoZlc82VUkC1ZZt0pRBCNCKRyrF3MtDR75gaHeZbLy929DuEGATVqXedyxYDRIIe3C4H86spxke8Hf0u0Xm7RpBKqQjwfuAe4A7g48CPXPOy79darxq/vOZVR0I3lzH2uIckYyyEaEgilefYoc5mjKfHfMyvpDr6HUIMgurUu84erzabjdlxP3MrKW47Mt7R7xKd18i9hbuB41rrotb6JKCueb4CfFEp9RWl1O2Gr7BJ1ZHQzWWMfVJjLIRoUDyVIxzo7Il2z0SAWDJHOlvo6PcIYXXxVL7jxyvAnnE/l5flYtYKGgmMI0Bsh/f8sNb6XuA/An9i1MJaUalUyOWbm3wH4HENSVcKIURDEqkcI12oMbbbbXKiFaJN8WS248crwOyEnzk5Xi2hkcA4BoQ3/XxVMW69hEJr/RJQUEr1rMAmXyxTrtBCjbGDrNQYCyF2kS+USGeLhDqcgXIO2ZmM+JiTcgoh2pJI5Tt+vALsmfBzWY5XS2gkMD4B3K+Uciil7gRObX5SKRWs/e8UMKy1zhi/zMbUh3R4m2jXBtUhHzL5Tgixm3gXeqLWzcqtWSHaFk/mCHcjYzzuZyWWJleQJJvZ7RpBaq2jSqnPAY9T7TzxPqXUg8C52mOPKqXStc/6dx1c666y+epfSHez7drcQ0SlzYoQYhfxZK7aE7XDm3mgmoGSW7NCtCfehfaKUA2MKxWYX0lxcCbU8e8TndNQalVr/RDw0KaHTm/6812GrqgN9Yxx06UUUmMshGhAIpUjOOzCYbd1/Lv2TPh59rWVjn+PEFZVLleqewK6UErhcQ8xFvZyeVkCY7Oz1ICPTL6I3W7DNdTcv5ZXaoyFEA1IpLpzWxaqGaj5lRQlmaYlREs2sgVK5UpXMsZQ7Uwh+wLMz1KBcTZX7WFsszWXzanWGEtbJCHEzmLJ7tyWheou93yxzEpMRkML0Yp4sronoBvt2qB6l+fSYrIr3yU6x1KBcSs9jEEm3wkhGpNI5buWMQ773Qx7nbIBT4gWxVM5XE5H0y1cW7VvOsjFJQmMzc5SgXG1h3ELgbFLJt8JIXaX6MJwjzqbzca+yQAXJQMlREvqx2uzd5FbtW8ywOXlFKVSuSvfJzrDUoFxJl/C627+ytDrdki7NiHEruJdLKUA2DcV4OLSete+TwgrqbZq63xrxbr9UwGKpTLzqxtd+05hPEsFxtlcEXcLGWOve4hsrkilIptchBDb61brp7p9U5IxFqJV8VSOsN/Tte/z+1xEgm4ppzA5ywXG3hZrjMuV6uQ8IYTYTrdaP9Xtn6zWLJalM4UQTave4elexhhg31RQLmZNzlKBcSZfaqnIvj4pLyvlFEKIbZTLFRIb+a6eaPdNBcjlSyxLZwohmtbNPQF11bs8Uv5kZs2nV/tYqxljr6f6nkyu2NXbpEII80im85S72BMVqm2mAj4nF5eSTI0Od+17RWcopT4AvAfIA+/VWp+tPR4GHgZcgA34Ra31t5VSduD3gVuAeeBBrXWmJ4s3oXgyx62Hx7r6nfsmgzKYx+QsljEuttauzfV6YCyEEFuJp2o9UbsYGNtsNrk1axFKqQjwfuA+4IPAxzc9nQN+Wmt9b+01n6g9/k6gXHv8KapBtWhQIpXverJr/3SAueUUhaK0gDUry2WMx8Pept/nHLIz5LDJ9DthOpKB6p5EKofH5Wjp4rsd+6YCXJBbs1ZwN3Bca10ETiqlVP2J2jE4V/sxD9SzNPcCj9T+/AjwX4A/6M5yzS+eynbv8q7yAAAgAElEQVS9lOLAVJBypcLFxSSH94S7+t3CGJbKGGdzpZZKKaA+/U4yxsI8JAPVXYlkvusnWYCDMyHOzSW6/r3CcBEgtunn686/Sikb8EngN7d4T7z2s2hANl8kkyt19Q4PVDfzz4z5OSvHrGlZKjDOtDjgA2rT72TIhzCXKxkorfVJ4KoMlNa6kQzUfd1arNnFUtme7EE4PBvi0nKKXEHuaJlcDNicQtzq/9DfoXpMP7rFe0JAtHPLs5ZEKg90bxz0ZodnQxIYm5ilAuNciwM+oFpnnMlKYCxMRTJQXdTNcdCb7Z8OAnBhQcopTO4EcL9SyqGUuhM4tflJpdSvAEWt9ac3Pfx14F21P78LeLwrK7WARCqH3VbtLdxth2ZDnJHA2LQsFRhncq1tvgPwuWUstDAdyUB1US9aPwG4nQ72TMitWbPTWkeBz1ENbj8FfEgp9aBS6n6l1F7gN4C7lFLHlVJ/VnvblwC3Uupx4C3An/Ri7WYUT+YI+t047N0ZB73ZodkQ5xcS0n/cpKy1+a6tUgoZCy1M5wTwEaWUA7iN5jJQX0YyUE2JJ3NXsrfddkhuzVqC1voh4KFND53e9OfrbndqrcvAz3Z6XVZUnXrXm/arh2ZDZHIlFtc2mBn392QNonWWCowzudYGfIBsvhPmo7WOKqXqGagC8D6l1IPAOeAs1QzUN5RSx4E5rfVPUM1A/etaBmoBeLAHSzeleCrHG7o8Ravu8GyIbzw735PvFsKM4sneBcYhv5uxkIczcwkJjE3IMoFxqVwhXyi1XErhcQ+RzcvmFmEukoHqnngyx4jf05PvPjQb4n986VVK5UpPbg0LYTaJVK6nA7sOzYY5O5fg3ttne7YG0RrL1BjnavXBLbdrcw3JSGghxLYSqRyhQG8yxodmQuQLJeaWZdCHEI2IJ3uzJ6Du4GxQyp9MyjKBcb0Mop1SirRBgXG5XGEtITMThLCKbK5INl/qWQbK73MxEfHJiVaIBsVTOUI9Kn2C11u2VSqyAc9sLBMY18sgWs0Ye9zGZYy//M/nef9H/5HHn5nb/cVCiL7Xi3HQ1zosLaCEaFg8lWOkhxnjQ7Nh4qkc0fVsz9YgWmOZwLieMXa32JXCa2BXiq8/M8cNe0L89uef5rWLsd3fIIToa4lUDrvdRqAHPVHrpDOFEI3rdY3xxIgXv9cpx6wJWSYwzuVLuJyOljemeN1DZHPtb76Lrmd5+dwa/9sP3YbaP8Lzp1fb/kwhRG8lUnlCwy7sPdz4dkhuzQrRkFK5wvpGvqeBsc1mk4tZk7JMYJzJFVueege1yXcGDPh48oUFpkaHOTAd5Oi+EckYC2EBsWRvs09QLaVIZQqsxGX/ghA7Wd/IUan0Zhz0ZodmQ5ydl8DYbCwTGGfzxZbLKAC8HmP6GJ94cYF73jCDzWbj6N4RTklgLITpJXo4LKAuEvQQ8rskAyXELhKpPNDbPQFQvZg9fSne0zWI5jUUGCulPqCUeqI2qvLQFs+HlFKrSqkfMn6Jjcnminhb7EgBxrVru7C4zpG91Ym7R/aFWU1kpUOFECYX73G9IlRvzR6eDXP6spxohdhJbD2LzzOEy9l6TGAEtT/CcixDTDbgmcqugbFSKgK8H7gP+CDw8S1e9stUx9P2TCbX+nAPqI6EzuZLbc02T2cLRNdzzE5UJ91MRnwEh12ckitGIUwtup5lJNjbwBiolmddkLtQQuwklswyEujNMJ7NpkarMYCWO8em0kjG+G7guNa6qLU+CajNTyqlJoFDwMkOrK9h2XwRbzulFLWgOttGnfHcSgq7DWbGhoFqhkfqjIUwv9h6lkiw9ydatb/6+6SdC3ghrC66nuuL47UeA2i5mDWVRgLjCLD5/9Vr3/Nh4BOGrahFmVwRTxub714PjFvvTHF5OcVkZBjn0OvrODwb4tz8esufKYTovdh6jpE+ONEe3TfCRrbI3Eqq10sRom/F+uQOD8CN+yUwNptGAuMYEN7085XIUSl1EAhrrZ83emHNyubbLKWoZZvb2YA3t5y6UkZRNzXqYymabvkzhRC9ValUiCazRPrgRBscdjEzNoy+EO31UoToW9E+ucMD1bs8py7FKJXKvV6KaFAjgfEJ4H6llEMpdSdwatNzdwCHlVJfBn4S+LBS6lgH1rmr6ua7dmqM2w+ML6+k2HNNYDwR8bESS0vvUSFMKpMrksuX+qJmEaon2lclAyXEtmLJXN8cr0f2jpArlLi4lOz1UkSDdo0ktdZRpdTngMeBAvA+pdSDwDmt9d8AfwOglPo14EWt9UudW+72Mrki4yO+lt/vsNtwOR1tdaaYW05x+5Hxqx6bGPGRzZd63mxcCNGaWLI6Drp/MlARvvzk+V4vQ4i+Vc0Y98f5dtjrZO9kAH0hxsGZUK+XIxrQUIpVa/0Q8NCmh05v8ZpfM2hNLcnmS221awPwuVvvZVwuV5hfub6UYizsxW6D5VhaAmMhTCi6nsU1ZMfnaf2OlJHU/hH+ry8+TzpbwOdx9no5QvSdao1xf1zIAqjaBrzveeuBXi9FNMBSAz7aqTGGWsu2FsdCr8Qz5Itl9oxfHRgPOeyMhr0sR6WXsRBmVD/J2my9Gwe92YHpIENDDulnLMQW0tkC2Xypb+7wQPUuj74o+wLMwjqBca6Ip82McTtjoZdjaVxD9i1HUE6MyAY8IcyqX1o/1Q057BzZG5ad7kJsoV761E8Z4xv3j3BpKUUqU+j1UkQDLBMYtzvgA6ot21otpViLZxgNe7fMKk2MeFmOSWAshBn1U+unOiW9UYXYUr30abhPSp8A9kwG8LodMtPAJCwTGGfzxSst11rldbc+Fno1kWUs5N3yuYmIZIyFMKtoMkukT3a416lab1TpdiPE1fqt9Amqm/uP7O2Pi9nVeIZvvbwoQfoO+ueSqk3ZXBFvGwM+oBoYp7PtZIy3PnlOjvj45xcW2lmaEKJHYutZ9k4Eer2Mq6j9I8RTOZaiaaZGh3u9HCH6Rr+VPtXddCDCK+fWerqGc/MJPvQH38Rht7GRKfCLP3oH3/nGvT1dUz+yRMa4UqmQaXPAB4DP03opxWoiw3h4+4zxsvQyFsKUqifa/iqlGA15GR/x8sp52dAjxGb9WPoEcOzQKK+cj1Ls0aCP6HqW//OzT3Dv7bP86X/9Hv79j93B7/7lM7x0trfBej+yRGBcKJYplyttDfgA8HpazxivJrKMbldKMeIjkyuRTEvhvRBm02+tn+qOHRrlxTO9P6nNr6R47rUVEqlcr5ciRF+WPkE1Y1woljnTo24yf/4PmunRYX7uB9+AzWbjbXft5bvu3sdfflX3ZD39zBKBcT3L23bG2O0knWsteF2LZxgLbX0wRmqPx9azLa9NCNF9uUKJVKbQl7dmbzk0xotnVnu6hi8/eZ7/9RNf49f/+AT/8dOPsbi20dP1CLEWzzK6zd3bXvK4h7hhT7gnF7OXl5P8w4kLPPh9x7DbX6+9/oEHbuC5UyvS+vEalgiMs/lq7+F227X5WswYF4pl4qnctgej2+lg2DNEVAJjIUwlmqges2N9eKK99fAo86sbPfu98s3n5/nsF5/n//ipN/EX/+1d3LA3zIf/8JvkCq31ghfCCGuJ7ZNUvXbL4VFe7EHpwhe+doq7bpzg2KHRqx6fHffz1ltn+LvHznR9Tf3MIoFxNZj1GlFj3EJgHFvPUqmwbVcKgHDAc6W/ohDCHFYTGVxDdvze/pswNz02TCTo7knWOJsv8kd/+wI/8T03cc9tMziH7PzyT9xFqVzhH09c6Pp6hIDqfqOdyhp77ZbDY7x8bo1SuXv7jdY38jz+7Bzff9/hLZ//l2/ay4mXFikU5YK2zhqBca6I3W7DOdTev06rpRSriQxDDjvBYde2r4kEPVJKIYTJ7NSfvNdsNlutnKL7Gai/+/oZHA4733/foSuPOYcc/OADN/Dw8dMUir3ZYCQG20amQL5Q2rZDVK/dfDBCNlfk3Hyia9/5tZMXmRjx8oYbxrZ8/rYj41QqFV443fv9Cv3CIoFxCY/L0fbJq9XNd2vxLKMhz1W1O9caCbglYyyEyawlqsd2v6remu1uxjiTK/I3j57mp995E86hq8vXvvst+8kXSnzz+fmurkkIqG6CB/o2Y+zzODk0G+raxWylUuHLT57ne956cNv4yOV0cOeNE/zzi9JSts4SgXHGgOEe0HqN8Wois2sN4ohkjIUwndVEZscSqV675fAYl5ZSxLt40X386Uv4PE6+47aZ657zuIb4jttm+eZzc11bjxB1q/EMAZ8Tt7O9/UaddKyLm2ZfuxhjMZrmbXft2fF1b7llmhMvLVDuYolHP7NEYGzEcA+oXs0VS+Wma21WExlGd9m1LhljIcyn3zPGeyb8hP3urvUirVQqPPLNc7zzrQdwOLY+fbz11mm+/epyy1NErUwp9QGl1BNKqeNKqUPXPPcZpdSCUuozmx57QCk1V3v9caXUVPdXbR5rfVxfXHfL4VFePrfWlSD068/OcfvRcUL+nfs6v/GmSWLJHBcW1zu+JjOwxOQ7I4Z7APhqn5HOFgn5Gw+04+u5Ky3ZtjMS9EhXCmE4pdQHgPcAeeC9Wuuzm577DPC/AA9rrX+h9tgDwJ8Bp2ov+zGt9WJXF20ia4kMtx7eujavH9hsNo4dHuXFM6vcs0UG12gvnV1jfmWD737z/m1fc8uhUdwuB0/rZe55Q+fXZBZKqQjwfuAe4A7g48CPbHrJx4CHqR6zm32xfvyKna0lMn19IQvV/uOpTIELi+scnAl17HtK5QrfeHaOn/nem3d9bcDnYv9UkJfOrnV0TWZhmYyxIaUUtZ3nG9nmNuDFkjlGAjtfkY0E3MSTEhgL42w60d4HfJDqiXazjwHv3uKtX9RaP1D7R4LiHazGs4z16UaeulsPda8F1D89dYk3H5sivMPvO4fDzpuPTfOE1Blf627guNa6qLU+CajNT2qt54Gt0ojfp5T6hlLqo0qp/tsF2kfWEtm+bK24WT0IfaHD5RQvn1sjmS7w5mPTDb3+2KFRmYJXY5nAuN1WbfB6u7dm64xjySzhXSbtRIIeNrJF6fEpjCQn2g4qlcrEk2a4NTvG+YV11jfyHf2efKHEE8/P88Au9YoAb7x5kudOrVCpSM3iJhEgtunnRs6/T1E9ru8DZoAf7cC6LGM1ken74xXg9qPjPPvaSke/4/Fn5njjTZMMN9hq8tjBaomHHLMWCYwz+VLbwz0AHHYbHpej6V7GjWSM6xkW2YAnDCQn2g6KJXOUK/T9rdm9kwHCATfPnersifbkK0vY7TbuunFy19fecmiURCrPpaVkR9dkMjEgvOnnXbMkWuuU1jqntS4DX6BagiG2sRbv/1IKgDuOTvDC6dWOtTUslsp88/l57r19tuH33HwoQnQ9x+JauiNrMhNLBMbZvDEZY6h3pmi8lCJfKLHRwMjYgM/FkMNGbF024AnDyIm2g9YSGex22653g3rNbrdxx9FxntHLHf2e409f4jtum22oX3zI72bvZKAnU7762AngfqWUQyl1J6/X+W9LKbW54PP+Rt4zyNYS2b7uIlN37PAo5XKFVy9EO/L5z59aJV8o8aabd7+IrRsNeZka9Uk5BVYJjHNFQzbfAXjdTtJN7Kaud5rYqeYOqievsN9NTOqMhXHkRNtBq4kskYAbxw79yfvFHWqCZ/Ryx26DJtN5nnplqaEyirpbD4/2ZPhIv9JaR4HPAY8DnwI+pJR6UCl1P4BS6sPAbwPfr5T6Su1tP6aUekop9TgwWXu/2EI2VySVKZgiY+x2Ojh2aLRjF7OPPXOZNx+bbnrv1U0HIh0L1s3EEl0psvkSY2Fj+hY228s4lszisNsI+LafelcnvYyFkbTWUaVU/URbAN6nlHoQOKe1fqx2ov1BYEIpdURr/Q6qJ9oPABngDPCrPVp+31uJ7d6fvF/ccXSCT37+21xaSrJvKmj453/zuXkiIS83HYg0/J5bDo/xR3/7ApVKpS8nB/aC1voh4KFND53e9NxHgY9e8/rPAp/tzurMbSWeAWB8xCTHrJrgsWcu89Pv2r1rRDNyhRJPvrDAL737zqbfe8PeMI8+dcnQ9ZiRJQLjjEFdKaD5UorYeo5wwL3j1Lu6kYBHehkLQ8mJtnNWYmkmRny9XkZDwgE3h2ZDfFuvdCQwPv7tyzxw556mAtxbDo0SS+aYX91gdtxv+JqE2GwpmsbvdeLzNLbZrNfeeNMkf/w/X6q1mDMumH/qlSWGHDbubGAvwLWO7BnhT/7nSxSKpeumWg4SC5VSGJUxdpJpopQinszuuvGuLuR3kejwznEhhDGWY2nTZJ+geqI9+bLx3feWomleOrvGA3c2XkYB1TtkExEfr12M7f5iIdq0EkszETHHhSxUh/PMjA3zrZeXDP3cx759mXsa3AtwrYOzQcrlChcWBnvTrDUC43wJr0EZY6+72VKKXMObc0J+N4mUZIyFMIPlWMZUJ9o3H5vixbNrpNLGXnw/+vQljuwNs3cy0PR7j+wNc+pS3ND1CLGV5ViGCRNdyNpsNu4+NsW3XjLuYjaVKXDy5SXuv6PxbhSbeVxD7J0McPryYB+zlgiMMwZuvmu6lKKBVm11Ib+buJRSCGEKZiqlALhhT5iw38VTrxq3oadSqfBPT13iO9+4t6X3H90b5pRkjEUXLJvseIXqxexzp1aauku9kyefnyfsd3HzwdGWP+PwnrAExo28aJf57g8rpR5TSp1USv1wZ5a5M2PbtTmbyxivZxnZpVVbXdjvkoyxECaQyRVJpgumykDZ7TbedPMUJ15cMOwzXzkfZSWW5r47miujqDuyd4SzcwmKpc70axWibjmaZtxkgfFNByJ4XEM8/aox5RSPPXOZ++7Y09Cep+3IXZ4GAuMGxs7+uNb6fuA7gV83fIUNyOSMGfAB4GuylCLeRMY46HdLjbEQJrAcqza5N1sG6i23TPP0q8uGTdj8p6cu8aabpwgO7951ZyuH94QolMpcXBzsmkXReWYrpYDq+PR7bpvh68/Mtf1Z0fUsz59e5f4m9wJc69BsiIuL6wN9MdtIxni3sbP1SM8PvGTw+nZVKlfIF0rGdaXwOknnmimlyDLSYI1x2O9mI1Po2LQbIYQxlqNpAj6XYSVa3XLbkXEcdhtPvdJ+BipXKPH4s3Mtl1FA9Q7cngk/py5JOYXonEKxTCyZNdWegLr77pjlqVeW2Mg0Hnds5evPzLFnIsDBmfa60uyfClIsVZhbSbX1OWbWSGC869hZpdRXgeeALxu0robVa3N8XoMC4yYyxpVKpbb5rvGuFADrG1JOIUQ/W45lmIyYK/sE4ByqZ6Aut/1Z33pxEYfd3tAI6J0c2Tsy8LdmRWetxjNUKua7wwNw7OAowWEXT77QeglUpVLhH05c4G13NddScSvDXifjI14uLKy39Tlm1khgvOvYWa3124GjwAevmazVcfWNcj63Mb0LfZ4hMg0GxhvZIoVimZFgg6UUw9XXJVJSTiFEP1uJma9ese6+O2Y5+fJSU5uIt/K1py5y/52ttX3a7OBMkPMDfJIVnbccTeNxOQj4zNHDeDO73ca9t8/y6NOtD9Z46ewaC6sp3n73fkPWtH8qyIUBLn9q5DfetmNnlVJ2pVT9b2IayNb+6Zp6EOvzGLf5LpUpNDRatT7FrtFSCueQnWGvk7hswBOir1XrFc0ZGB87NEZw2MU3nptv+TMW1zZ4Ri/zXW/a1/Z6DkwHubCwTrncmXHVQizXLmTNOmHxHW/ZzwtnVrm01Fow+vdPnOeeN8w2fPd6N/VjdlDtGhjvMt/dA3xVKXUcOA78pta6q1FfOltkyGFrO6tRF/A5KZbKDW1eiSdzeFyOpjpiSGcKIfpftfWT+UopABx2G2+/ez9//8S5hi7wt/LIN85x08FRDu8J7/7iXRyYDpHNl1iKptv+LCG2shRNM2nC+uK6PRMBbrthnC89eb7p967EMjz5wjzvuueAYevZPz3Yd3kaiuh2GjsLPGDkgpqVyRXxup2GXSkGfNU64FS6sOuGvmY23tVVh3xIKYUQ/WxxbYOpseFeL6Nl3/PW/fzV117j1KU4R/eNNPXedLbAP5y4wH/4sTsMWUs44CYccHN+IcG0if+biv61sLrBjMn/br3rngN8+i+e4Se/58amxlo//Ogp1P4INx2IGLaW/VMBlqJp0tmCaUZsG8n0Az7SuYJhZRQA/lqNUrKB6VHNbLyrk+l3QvS3jUyBRCrP9Kh5T7SjIS9vuWWKR75xtun3fumJ8wSHXbz5lmnD1nNgOsj5+cHNQInOWljbMP1F1903TxH2u/n/Hm/8mF1LZPjKP1/gR7/rqKFlJHsmAjjsNi62WNphduYPjLNFQwNj55ADt8tBKr37xpXqcA8JjIWwkoW1DWw2mBo1761ZgH9z3w089swc8020XdrIFHj40VP8+HcrHG0MCbjWgekg5wb41qzorIXVDaZMfCEL1Z7G737HjXzx+OmGEnMAf/qlVzmyN8ztR8cNXYtzyM7shJ8LCxIYm1I1MDY21R/wOkllGssYR5oupXBJKYUQfWxxbYOxsBfnkDFDg3rlpoMR7jg6zp//g274PV88fppwwM0Dd7Xeu3grB2dCA12zKDonmc6TyhRMX0oBcO/ts0yM+Pj8V17d9bUvnlnln56+xM/+wK0d2XR4YCrIhcXBPGZNHxhnsgXDxkHX+X0ukg1mjMPNZoyHJWMsRD9bWN0wdRnFZj/5zpv4+jOXGxqwcXFxnYcfPc2D33vM0GwxVFu2La5tXOk7L4RRFlY3sNttpm2vuJndbuPnf+g2vvTEeZ47tbLt61KZAp/5wrN83z0HDdkgu5X9A9yZwvSBcTpXxGd4YOwk1WCNcbOb78J+t7RrE6KPLayav16x7oY9Yd7xlgN86s+/vWOnnVKpzKf/4hnuecMMdx+bMnwdeyb82G02Lg5oBkp0zsLqBhMjXsM6U/XajQci/ODbbuCTn396yzKoYqnMJ/7fk3g9Tn7qXTd1bB0Hap0pWu1sY2am/5uUzhbxGlhjDNXOFKkGxjPGkzlGmt18F3DJ5Dsh+ti8hTLGAO/5V8coFMs89PDzW/YSLpcr/O5fPUtsPcu//YFbO7IG55CDPRN+KacQhltYs9bxCvDud9zIsUNjfOgPvsHzp1/PHC9F0/zKH3yT+ZUUv/qeu3ftnNWO/dNB1jfyxJODF6907r9ql6SzBcNvofi9zl1LKUqlMomN5jPGoWE3mVyJbL7Y0b/UQojWLFpgh/tmXvcQH37Pm/nwH36Tz3zhWf7tv7kVT+0uWzpb4LNffIGTLy/y8Z//DoLDro6t48B0SDpTCMMtrJq7teJWhhx2fundd/Inj7zMRz77JDPjw7hdQ5y5HOcONcEn33M/Ib8xwzy2Mx724nU7uLC4zkiwuTjH7EwfmaVzxnalgGrGeCm2czP6xEaeSoWWulIArKfyeCKm/88vhKVk80XWEllLBcZQvS360Z+7h4/9ybf42Y9/jTfdPEmlAidfXiQ47OKjP3cP+6aCnV3DTJCnXlnq6HeIwbOwusG/eINxrQX7hcNh5/3ffwvf9x0Hee7UKplckV/8kds5MB3syoQ/u93Gvqkg5xeS3H50ouPf109MH5llsp2pMT4zt3ONcX0cdLNXbYFhFzYbJDZyTJh4Uo8QVrS0Vr0gNnvrp60cmA7y+//pO/nHb13g1KU4xVKZn/nem7nvjtmudOA4MB3kr//pFJVKxbSje0X/mV9NMTPm7/UyOmZqdLhnv48GdTS06QPj6mQW47tS7FZjHEvmCA67GHI0V6btsNsI+KRlmxD96NJyktGQx/BON/3COWTnnf/iIO/swXcfnAmykSmwGs8ybtJx26K/rG/kSaTy7Jm0bmDcS/ungjz69KVeL6PrzL/5LlfEa3QfY9/uNcbxZLbpqXd1Ib97IAvaheh3l5ZS7J0M9HoZlhQJegj4nJxfSPR6KcIiLi0lcQ7ZmYxY7w5PP9g3FeDSUnLLTbtWZvrAuBOlFAGva9d2bWuJLKMtFqSH/W7pTCFEH7q8lJTAuENsNhv7ay2ghDDCpaUks+N+w/tui6p9UwGy+RLLu+y5shpTB8aVSqUjm++GfU7S2SKlUnnb16wlsoyGWrsdGPS7iEsphRB959Jykr0Tclu2U/ZPBQd2zKww3qXlJHvkeO2YsN9NwOfi4tJgHbOmDoxzhRLlcsX4kdC+asuineqMq4Fx6xljmX4nRH8plStcXk6xRzLGHbN/enDHzArjXV5KsU+O146x2WzsmwpwcVECY9PIZKvjRY1v11YNtHcMjNczLQfGIRMHxi+dXeO//+nWE3mEMLPlaJpCsSwn2g7aPxXg8nKS4g5344Ro1MWlpFzIdlg1MB6si1lTB8bpXDUwNnoHudc9hN1uI7lDnfFaIkukxRrjkN9lysC4UCzze3/1LPpilJ//rUc5czne6yUJYZhLy0kCPlfHG+cPsn1TQYqlilxYi7alswVW4xnZE9Bh+ycDUkphJulsAZsNwyfI2Ww2/F4nqW06UxRLZRKpXMs1xiG/25Q1xn/39TNk80V+95fexh1qnK9+62KvlySEYaob76ResZP8XidjYS8XBuzWrDDe5eUUdhvMjktHik7aNx3k0lJqoDpTmDwwLl7J7hot4HOxvrF18Bpdz1Kp0FaN8XoqR6Vinr9olUqFv3/iHD/+3Qqve4i33bmXx5+dk1uiwjIuLEpHim7YPxUYyKEBAEqpDyilnlBKHVdKHbrmuc8opRaUUp/Z9JhdKfWHSqnHlVJ/qZSSBtA1FxbWmR7zd2U4zSDbNxkgXyixFB2czhSmD4yNbtVWFwl6rky3u1Y0kcVht7V8yzU47CJfLJOplYKYwcLqBiuxDG+8aRKANx2bpFAs84xe7vHKBpucaI1zfn6dgzOhXi/D8vZPDeYGPKVUBHg/cHCl1EMAACAASURBVB/wQeDj17zkY8C7r3nsnUBZa30v8BTwnk6v0yzOLaxzeFaO104L+d2E/e6BOmZNHRhncgXDh3vURYIeotsExmuJLCNBT8uZ6vpgEDNNv3vmtRX2TQWulI94XEO89dZpHn92rscrG1xyojVOoVjm4tI6hyQw7rhqZ4qBLKW4GziutS5qrU8CavOTWut54NrbiPcCj9T+/AjVY10AZ+cSHJTAuCsGrTOFqQPjjYzxPYzrIiEPa9sGxq13pAAY9jhx2G0kTDTk47lTK9x+ZPyqx247MsYr56M9WpFATrSGubycpFSusH9aSik6bf9UgMW1DbImumNmkAgQ2/RzI+ffze+J134eeJVKhXPzCQ7OBHu9lIGwb1ICY9NIZQr4vR3MGCe2zxi3Exjb7TaCwy4SJhkLXSqVef7UCrcdvTowPrpvhMW1tCk7bFiEnGgNcnYuwfTosOE90cX19kwGsFHtAjJgYkB408+lJt8TAiQTASxF06SzRbnD0yX7pgJcXJJSClNIZfL4va6OfPZo0EMsuVNg3F5pppk6U5xbWCeTK3LLodGrHp8Z8zPsdfLaxdg27xQdJidag5ydl9uy3eJ2Opge8w/iBLwTwP1KKYdS6k7gVAPv+Trwrtqf3wU83qnFmcm5+QThgJuRFlumiubsmwpyeTlFaUA6U5g6MN7IFBj2dqaUYiToJprIbtk5IrqeZbTNAzLsd7NuklKKs3MJZif812XT7HYbR/eG0RIY94qcaA1ybk7qi7tp/3RgoDbzAGito8DnqB5znwI+pJR6UCl1P4BS6sPAbwPfr5T6Su1tXwLcSqnHgbcAf9L9lfefs3K8dtW+qQCFYpnFtY1eL6UrOhNVdkkqXWB0ujOb6iMhD/limY1MAb/v6qz0UizN+Eh73xv0u4ibpATh3HyCg9Nb/xI6un+E1y5IYNwLWuuoUqp+oi0A71NKPQic01o/VjvR/iAwoZQ6orV+B9UT7b+unWgXgAd7s/r+UalUODuf4AffdkOvlzIw9k8FeXUA9ydorR8CHtr00OlNz30U+Og1ry8DP9ud1ZnH2TmpL+6mgM9FJOjm4uI6s+PW7/Vu7sC4kzXGgWpGeG09e1VgXCiWWI2lmR5rr6l42O8mkTRJKcX8+pU2bddS+0Z45BvnKJcrHeknLXYmJ9r2La6l2cgUpPVTF+2fCvKVf77Q62UIE6pUKrx2McZ33b2310sZKPsmq91k3nprr1fSeQ0FxkqpD1Bt65QH3qu1Plt7PAw8DLgAG/CLWutvd2it19noYGDscQ8x7Bkimsiyf+r1K9OlaJpyBaZH2wuMQ363KcYsVioVzs8n+OF/eWTL52/YG2YjU2A5lmaqzf8mQvSCvhBlIuKTesUu2j8dILqeJZnOE/B1Zp+IsKalaJp4KsfRfSO9XspA2T8d5PyADObZtcZ4l16pOeCnaz1R3w98ohOL3E4qncfv69wu8kjo+l7GC6sbBHyu68ormhXyu0zRzWE5lmEjW9x28EHY7ybgcw1UKxdhLfpCjBvlJNtV06PDOIfsA3OiFcbRF2KMhb1tb4AXzTk4E+TcXKLXy+iKRjbfbdsrVWud0VrXJzzkga42pkxlCgx3KGMMMBLYOjCeabOMAqoZYzMExmfnEoT9bkYCW0/5s9ls7JsavI00wjpevRjj6H4JjLvJ4bCzf3pwTrTCOPpiDCXHa9cdnAmxMCD9x5vte7rle5RSNuCTwG8atK5dFUtlsvlSx9q1wfYZ43bri6FWY5zKb9n1op+cn09wYCaIzbZ9/fC+qQCXTFAWIsS1coUS5+YScqLtgcOzIc5IYCya9NqFGDfK8dp1eycDOOw2zg9AEqyRwLiRXqm/QzWr/Kghq2rARqYA0LEaY6j2Mr42MJ5fMyYwDvpdlMqVK/8e/eriUvKqGuut7J8MmKJeWohrnbkcx2azyca7Hjg4E+LcvATGonGFYokzcwmpL+4B55CdPROBgbjL00hgvGOvVKXUrwBFrfWnO7HA7aTqgXEHa4zHwl6WoumrHjMyYwz0fcu2+ZUNZid2bs+ybyrIpaUU5QFp/i2s49XzUQ7PhnAOOXq9lIFzeDbExcUkhWIjc2mEgNcuxrHZ4IY94d1fLAx3cCbIuXnJGO/YlFwptRf4DeAupdRxpdSfdXa5r0ul89ht4HV3ruPcwZkQFxaSFEtloFq+sRxtv1UbVNftHLKT6OPpd5VKhbnVFLPjO//77psKkC+UrruIEKLfPX96lVsOj+7+QmG4A9NBypWKbNwVDXv+9Co37o/gcsqFbC8cmh2MuzwNRZU79UoFevI3tL7xbqfa13YdnAlSKpe5tJTk4EyIlViGUrnSdqs2qG5aCw33d2eK6HqWXL60a0PvkN9NyO/i4uK6IRcNQnRDsVTm5XNr/Kt7D/V6KQPJ4x5iZszP2bkEhyUDKBrwwulVbpUL2Z45OB3iz778quXnFph2JHQqXejoxjsAn8fJzNgwZy7HAXjtYoxI0E1w2JjvDQX6uzPF3EoKt8tBpIH+rnulzliYzJnLcfKFMjcflBNtrxyeDXF2AGoWRfvyhRKvXohy6w1jvV7KwDowEySbL1l+NLRpA+ONbIHhDtYX1x2eDXPmcvUX97OvrXD70QnDstQhv5vERv+WUsytbDD7/7d35/FxluXCx3+zJJkkk2SyNmvTLM3TPWlDW7pSVlkKiFgVUAQVjyAeFVCPx8971PeIh4P64oYvRz16QMCCKIsIlK0b0IWkbVra5mnSptn3fZlMMsv7RxLfCKXZZvIsub6fD59Pk0meuVrmfuaa+77u605yTurvm5USQ11L3yxEJURwHK1sY2GWK6TlWOL8cjPiODMHlmbFzKnVnVhAOshoKM4ZQWKcw/R1xoZNjEdmjGchMc4caSkUCAQ4cqqFlQXJQbt2XHQ43b06njFu6SN9gvriMRkpTupbJTEWxnGssk1mnzSWM1qzKBt3xUTKKltZnJMgG2U1lpNu/g+zxk2MQ3gc9Hh5GS6qGrqpae6lrXuQwmAmxs4IXXelqG/tm7C+eExGspO6lj7d92UWAmBwyMvxM+0UBXE8i6nLy4jD7fHR1GHupVkxcyUnm1mlzNM6jDlvpDOFJMa6NHIcdGhrjAFyM+Pw+vz8+PFSFqTFEh8zcb3tZLl0nhg3tPaRPsnEODPFSb97WNddNoQYc7SyDbvdKvXFGhtbmpU6Y3E+HT2DnK7rZvUSSYy1lpMWJ6UUetXnHibaEfrawJiocO6/cwOJcQ4uXzM/qNeOj3XQ+b4DRPTC6/PT1DEwYau2McnxUYTZrdS1yAY8oX8lJ5pZqaRgtxn2FmgaOemyAU+cX+nJZuYlRJE5QU99EXo5GbG0dbnpHTDvJJhh3xX63cOzMmMMsCQnke/dsY7rNucF9bojJ+vpc8a4uWMAvz8w6Rljm9VCRrLUGQv9CwQCvHuymdWLZfZJD6QzhZjIuyebuWDxvJC2ZxWTk5bkJDzMZupyCsMmxrNVYxxK8bERuD1e3B6v1qF8QH1rH7HR4cRM4cPHWJ2xEHp2trGH9m43xYskMdaDXEmMxXl4hn0cOdUiZRQ6YbNayEmP/Xu3LjMybGLc0+cJWj9hrYz1B9ZjOUXDFDbejclMkcRY6N/bZQ0syUnEFROhdSiCkcS4s9dDe7db61CEDpWcbMZmtbIiXzbK6kXB/Hgqaru0DiNkDJkYBwIBuvuHiHMa+40tOjKMMLuVDh0mxvWt/ZNu1TZGWrYJvQsEAuw9Us+mwnStQxGj5iVEEecM51RNp9ahCB1660g965anEWY3ZLpiSgVZLlQTj1dDvtLcHi/DXj9xTmPPGFssFhJiHXTqsM54ujPGze39DHt9IYpKiJk5U99NU3s/6yUx1g2LxYIyPwG12rxvtGJ6Bj1e3j3ZzKaiDK1DEeMUzI+npWOALh2fwzAThkyMx1qCuQw+Ywwj5RQdvXqcMZ56YpyR7MQfgMY26Ukq9GnvkXqW5SUFte2imLmCbHPPQInp2X+8CUe4jRVyEI+upCVFEx0ZRkWtOcesQRNjDzarhWiDb76DkQ14eqsxdnu8tHcPTjkxjnKEkRAbIXXGQpd8Pj87S+vYsipT61DE+yyan0BFbRc+n1/rUISOvHagmouLs7BJW0VdsVgsFGS5OFVjzjpjQ77aukc33pmhdUtCjIN2nSXGjW39WCyQmjS1GmOAzJQYqTMWulRyshm3xyvLsjq0cL6LoWEfNc3SB12MaGzr52hlG1eszdY6FHEOBfPjTbsvwJiJsQk23o1JiNPfIR/1rX0kuyKJCJv6mfTSsk3o1Y4D1Vy0KhNHROgPBhJTE+UIIzMlhvKzHVqHInTitYPVLF6QQNa8GK1DEeewaEECanUHPn9A61CCzpiJcZ/H8BvvxsTH6O+Qj6kcBf1+mSlO6iUxFjrT0jFA6clmPiKzT7q1JCeBE1WSGAsYHPKyY381V66T8apXixYkMODxUtNkvuOhDZoYDxEXbZIZYx0eCz2djXdjMlKc1LX0EgiY71OkMK7n95xmcU4i+VkurUMRH2JZbiLvnW6Te4dgZ0ktdpuVTUWyH0CvnJFhZKfGcuJMu9ahBJ1BE2MPcSZpzh8fG0Gfe5ihYf20OGuYRg/jMZkpMfQPeunq09csuJi7+gaGePVANR/bkq91KOI8luYm0dY9SEunHPQxl/n8AZ7bfZrrNuVK72KdM+sqjyFfdd19HuIMfurdmLHT7/RyyEcgEKBuBjPGya5Iwu1WqTMWuvHi21UkxkVywWI5UlbPkuMjSUmI4viZNq1DERp660g9XX0ePrJugdahiAkszU3keFW76VZ5DJoYm2fzXWx0OOF2K21d+pgl6ekfot89PO3E2Gq1kJ4sdcZCH3r6h3h2VyU3XaFgtRq/i43ZjZRTmG9pVkyO1+fniR3l3LAlH6cJ2rGa3ZKcRNq7B2nuGNA6lKAyZmLcb57NdxaLheT4SFp1khg3tPZjt1lIjo+a9jXkaGihF3/ZWUFKfJS0aDMISYznttcP1tDvHua6TblahyImIckVSVpiNEcrzbXKY7i+RYFAgO6+IWJNsvkOINkVRatO6urqW/tIS4rGNoPZtcxkJ6fru4MYlfgwiqLcAdwODAGfU1X1zLjHVgM/AyzA/aqqvqgoygKgFDg2+mNfU1X1yOxGPTsa2/p5Ye8Zvv3Z1TJbbBCFC5P5+dNHaGrvJzVxevsc9EzG64frGxjiDy+f5KYrFKIcMltsFEUFyRw51WqqftOGS4wHBr14fX5cJtl8B+hrxritj/Sk6ZVRjMlMcbLncH2QIhIfRlGUBOALwAZgJfAA8IlxP/IQsA3oBvYqivLy6Pf3qaq6dTZjnW2BQIBfP3eMwoXJUltsICkJUWQkOzmstnDV+hytwwkqGa/n9/gr5STGObhKaosNpaggmYefKcPvD5hmAsJwpRTd/SPdDsyy+Q4gOT6Klk591OjMpFXbmIwUJ80d/Qx79dNpw6TWALtUVfWqqvouoIw9oCiKA7CrqlqvqmofcApYOPrwakVR9iqK8itFUSJnP+zQe+dYI2UVrfzTDctNcULmXLJqUQqHT7VqHUYoyHj9ECerOnh531m+9LEVcvyzwazIT6JvYIiqBvOsEhvuFdjV68FmtRBtosL8ZFekfkopWqZ/uMeYjGQn/gA0tPUHKSrxIRKA8WdyWt/32PiD7LtGv9cI5KuqugloA74a6iBnW2fvIL96poxPX7nYlMvxZrdKSaGsohWfz691KMEm4/UcBoe8/HT7IbZuyGFJTqLW4YgpckaFk5/l4oiJPsxOKjFWFOUORVHeURRll6Ioue977JeKojQqivLL0IT4j9q7B0mMc5hqFig5PpK2rgHNW574fH7qW/vJmjezxDjKEUZCrENatoVeJzD+xArfeR6LAzpUVfWoqto7+r3tjCzpmobfH+AXTx8hM8XJ9RflaR2OmIZluYkMDfspr+6c+IeNRcbrOfz2+fewWOAzVy/WOhQxTSuVFErLW7QOI2gmTIzH1UVtBr7BSF3UeD8Ebg5+aOfW1uUmyWWu1aTk+EjcHh/97mFN42jqGMDr8wflbPpgHA29+1Ad3/rlXu568A3KKszzaTSIDgAXKYpiUxRlFVAx9oCqqm7AqyhKmqIo0Ywsy1YqihI77vcvGv87ZvDnnRWUn+3gnpuLZ7SBVGjHEWFnRX4SB483aR1KsMl4fZ83S2rYWVLLt25djSPccFuexKi1S1M5XtVO78CQ1qEExWRmjD+0LgpAVdUGYNamOs2YGCfFjfx9tN6AV9vci8sZQUzUzOu3x46Gnq59xxp56I+HWJ6XxJolqXz31/t4u6xhxnGZiaqqHcCjwF5GNu58W1GU2xRFuWj0R+4F/gy8DnxfVVUvsFlRlFJFUXYDHwF+rEHoIVF2qpUnXinn3luKmZcw/XaDQnsXLk9j33uNmq+iBZOM1390trGHh585yp03riAnPU7rcMQM5GW4cDkjKD3ZrHUoQTGZj2jnq4uadW3dblITzFU3GB5mwxUTQWunW9MbRG1zb1BmiwGy58Xw6oGaaf1uW5ebHz9RyuevW8a1o/0sXTERPPLsUYoKkk1VXz5Tqqo+Ajwy7luV4x47AKx/38+/CLw4O9HNnrYuNw8+XsInLyugeJF0oTC6tUtT+dUzZdQ29zI/NXbiXzAIGa8jBgaHeeDRg2wuyuCyNeZp8zVXWa0W1ixN5cDxJrYUZ2kdzoxNJsk9X13UrDPjjDFASnwkrRp3pqht7iVzhvXFY/IyXdQ090yrM8X211QWZrnYuvH/t2u6dmMuLmcET+4oD0p8wjyGhn38x6MHWZjl4pOXKxP/gtC9hFgHSnY8+98zXTnFnBcIBPjZU4cJs9v4p48t1zocESRrl6ZSWt5iim5Uk0mMP7QuSgtmTYyTXVE0a9yZoralj6yU4MwYL0iLxecPUN00tXKK+tY+XjtYw61XL/6HDZY2m5XPX7eUl945S1evJygxCuMLBAI88pej9PQPcd8txabpoylg3bI03iqTfuhm85edlZRVtPGvt62RumITKVyYhNVqoeSk8TfhTZgYT1QXpSjKd4CfANcrirIjlMF6fX46ez0kuRyhfBpNpCdH06DhMcp+f4C65l7mB6mUwhFhJzPFyZkpnoD3/O7TFOYnnbNtT+HCZNKTo3n1QHVQYhTG98q+s+w5Us93bl+LMwi18UI/Nq/M5GxjD9WNPVqHIoKk7FQrf3j5JPfdUkxakrlKIue6MLuN9cvT2HO4TutQZmxSH9cmqIu6H7g/yHGdU0f3IIEAppwxTk9ysv+9Rs2ev63bzeCQL2ilFAC56S5O13XBJI+KdHu87DpUx703rzrn4xaLha0bcnj69VPceHG+NIKf405UtfPr545xz83FLEgzTx2qGJEcH8nS3ER2Harjs9cs0TocMUMtnQM8+HgJn7isQE6jNKnNKzP4998dxO3xEhlh3NUAQ2UWrV1u7DYrcdHmOQ56TEayk8a2fs2a2tc09RLtsJMQG7zZ+LzMuCnNGO85XEeUw37em+aW4iwGPF5KTLL7VUxPe7ebBx59l+s357GpKEPrcESIbFmVxe7Ddfj95ulOMRcNe3088Oi7FMyP51OyD8C0lucnE+WwazrJFwyGSozbu90kxjlMWUeYkeLE6wvQolGd8Zn6bnIy4oJ6cEpuRhxVjT34Jvmm9vK+s1yxNvu8M8GREXbWLU9jzxGpPZyrfD4///lYCdlpsXzmaplJNLMNhel093o4Wil9zI3s9y+eoKd/iHtvXmXK928xwma1sGVVJq9NsyOVXhgqMTbrxjuA2OhwYqLCqNeozvhMQze5GcFtFZeX6WJo2EdN08Q1ghW1nVTVd3PFJMouNhdlcuB4E4MebzDCFAbz9BsVNLb3c98tcoiH2Tkjw9hYlMHL+85qHYqYpkNqCy+9XcV9ny6WfQBzwBVrszl2uk2zXCYYDJUYt3a5/34YhhmlJzs124BXVd9NbpB7KDsjw8hJj+NYZduEP/vKvmpWL0md1AefFQuTiAiz8e4JKaeYa8qrO3jqNZWvfWolcU7zlVSJD7pq/QL2v9dEe7e2XXvE1PX0D/Gz7Yf4xGUFLMpO0DocMQuy5sWwNDeRV/cbd5O8oRLj5o4BUhLMmxhnJDup0yAxHhgcprG9P+gzxgAr8pM4OkFi3O8eZvfhOq5ct2BS17TbrGwoTGe3CXa/iskbGBzmJ0+Ucs2GHDnEYw5R5seTnRpj6DfauSgQCPDwM0dIdkXxicsKtA5HzKIrL8zmtYM1eIaN2dPYUIlxXXNf0E5m06MMjWaMzzb2YLNayQxSD+Pxlucn8d7ptvPWGe86VEdcdDgrlZRJX3dzUQal5S30uYeDEaYwgP9+4TgRYTbpUDDHWCwWrtuUx9/eqTLsG+1ctPtwPaXlLdxz8yrs0kFoTtlQmEGY3cLOklqtQ5kWw7xah4Z9NHf0k5kSvHZiepOR7KS+ZfYT46r6buanxhBmD/7LYWlOIm6Pl6oP6U4RCAR4Zd9Zrrgwe0r1oktyEolzhrP/WEOQIhV6VlbRyhvv1vD1m1YRHmbTOhwxyy5alYHNauFNg77RzjXdfR5+89wxbr1qMenJ5n3PFucWZreydWMuz+0+bciOMoZJjBvb+vEHCMmspl7MT42hrXuQ3oGhWX3e0yGoLx4THRlGfpaLsopz7ypXqzupbe7l8jWT63U8xmq1sKkogz2HpTuF2Q0Oefnln47w0YvyyMt0TfwLwnTC7Dau3ZTHs7sqNWtpKSbvv194j9TEKK7ZmKt1KEIjV65bQFu3m4MnjHesu2ES49qWXpJckYZuGj2R9GQnkRG2kUMxZlF5dSdKdnzIrr9ueTo7S2sJBD74yfGvb51h7bLUafVP3lSUQVllmxwRbXJP7lCxWizc9JFFWociNHT1+gX09g/J3gKdO6S2sOdwPXdvK5KuMXNYTFQ416zP4Y+vqud879czwyTGdS19pi6jgJEegLkZLirrpnaM8kz09A9R29zL4pzQ7Ri+9IIs6lr6UGs6/+H7NU09vHWknm2XTG9jxsIsF/Pio3hHyilMq6K2k+f3nObuTxQRISUUc1qUI4yPXZzPH19V8cqssS4Nerw8/EwZN16ykJwQrUIK47hhSz71rX0cPG6sWWPDJMa1zb2m3ng3Jj/TRWXt7M0Yl5/twBkZRlYIS1TiYx2sWZr6gV3lT+5QWbM0lfys6S2PWywWNq2Ucgqz8vr8/PypI1yxNpvleUlahyN0YOvGXNweL68dkA4VevTEjnLCbBY+KV0oBOCKiWDrhhwef6V80gd96YFhEuO5MGMMkJ8ZR+UsllKcqGpncU5CyE8jumrdAnYdqqO8ugOAnaW1HDjeyC1XLp7RdTcXZXCiqp22LulxajZ/3llBT/8Qt0kXCjEqMsLOTVcs4okd5fRLRxpdOVXTyQt7z3D3tiLZICv+7uOXLKS9282b7xrnNDxDJMZ+f4D61r6QzmrqRV6mi+aOAXr6Z2cD3omqDpbkJIb8eVYqKWzdmMv9vzvIr/5cxs+fOsLd24pYkBY7o+tmp8WSNS+GvXJEtKnUNPWw/dVT3HXjCqIjw7QOR+jIlRdmExsdwdOvn9I6FDFq2OvnF0+PrO4sk9UdMY4zKpxPXa7w+CsnGRg0xodZQyTG9a19eIZ8ZM8wiTKCjNENeLMxa+wZ9lFR28XiBbNzItFnr1nC5Wvn4xnycfe2Qi5dPT8o192yKpM3S869uU8Yj88f4OdPHWHDinTWLkvTOhyhMzablTuuX8YLe09TPYnj5kXoPfPGKfoGhrh9q6zuiA+6an0O0ZFh/PFVVetQJsUQifGJqnay5jmJjTb/OetWq4VF2Qm8d3riY5RnqqyilfAwKwXzQ9eRYjyb1cKtVy/h6zetClpSDHDJBVnUNPVwehY3LYrQeX73aZo7BvjiDcu1DkXo1EolhQ0rMnj4T2WG7JNqJlUN3Tz9xim+vK2IKIes7ogPCrNbufPGQv669wxVDfp/nzZIYhyc5f5r730+CNGE/vlWLZpH6cmWKf3OkzvKp/w8B483ccGiedM62GM2/y0neq7EuEhWKim8HoQapun8O4rgqW/t44lXTvKlj62Y9AfhYL4Wg/26luuF7nqfv34pdS29vPjWmaA+p5g8n8/Pz586zOaVmVyweHLHtBv5Naf19fQc20TXW56XxMXFWfx0+2GGvfruKmOQxLh91pb79aB4UQpnGrrp6Bmc9O9MdYnC7w9w8HgTa5amTjU8Xbp8TTa7DtXh9nhndB2jLPWY0bDXz0NPHmL1klQ2FKZrHY7QufgYB3feWMijfztBbXOv1uHMSdtfO0V79yBfuH6Z1qEIA/j89cvo6fPw1Ov6fp/VfWLc0TNIU/vArGwQ04vMFCcpCVEcKm8O2XNU1HbS0z9E8SQ/5evdhctSiXLYDbXzVfyjx146QUfvIF/eVqh1KMIgNhVlsH5FOg/+oYTBoZl9KBZTU3aqlT+9cYp7by4mJsr8ZY5i5pyRYXz1Uyt55o0KjlWGvlx0unSfGJ+s6iA+JoLUxCitQ5k1FouFYiWFkimWU0zFnsP1rMhPwmmSHf82m5VrN+by/N4zhuqXKEbsf6+RF986wzc/c4G8yYopufPGFQx7/fzfPx+VDbizpLNnkB8/Wcq2SwsoLEjWOhxhIEUFKXz8koU8+HjJlFbFZ5PuE+P9xxspXJiMxTK3jpbcWJTOwRNNdPcF/7jjgcFhXn+3hqvWLwj6tbV0xdpsuno9vHNUTsIzkuaOAX66/TCfvWYJi7LnTsmUCI4oRxjfvm01+4418Oyu01qHY3o+f4CfPFlKVkoMn7pC0TocYUA3fWQRC1Jj+cHvDuhypUfXibHb42XfsUa2FGdqHcqsW56XRFpSNDv2B/+Ep50ltTgjw1izxBz1xWOiI8O4YUs+j7109mck5QAACspJREFUgmGvT+twxCT0DQzx/d/uZ3leItdvztM6HGFQ2amxfPMzq3nspRPsPlSndTimFQgE+PWzR6lp6uXeW1ZhC/HBUMKcbFYL37r1AtweLz9+vFR3R7zrOjHe/14jkRF2ihbOvaUai8XC1o25vPROVVBfNMNeH8/vPcPV63Ow2XT9v39abrgoj6FhPy++VaV1KGICgx4vP/j9QRzhNu69uXjOrQqJ4Lpg8Tzu3lbIT7cf4u0yWTUKhadfP8XO0lq++4ULSYyL1DocYWDOqHC+d8c6Ttd18dCTh/DpKDnWdWb0Zkktm1dmmDKBm4yLV2Xi8wV4bnfwlgf/9EYFXp+fqzfkBO2aeuKIsPO5a5fy+CvlVDdK83+9Ghgc5nu/3U/vwBD/9vkLcUTYtQ5JmMBla7L54g0r+NHjJbyy76zW4ZhGIBDgj6+qPP1GBd+5bS15mS6tQxImMC8hivvv3MDxqnZ+8PuDDM6wq1Sw6DbjPFrZyrHKNq68cIHWoWjGEWHny9sKeeKV8qA0xa6o7eRPb5zirhsLiTRxInLRqkw2FaXzwGPv0jcwO0dri8lr7hjgm7/Yi3vQyw/v3IArJkLrkISJXLVuAfd9uphfP3eM//rLUSmrmqGx456f3VXJd7+wVjbbiaBKT3byo69sHnlf+OVeGlr7tA5Jn4mxz+fn188eY+vGXLLmxWgdjqYuXJbGpauz+Lf/2kf52Y5pX6f8bAf/65F3uHpDzqQbsRvZl25YQbQjjG//6m3d7nydawKBALtKa/nqT3aSmhjNA3dvJM4pSbEIvo2FGfzn3Rs5eLKZe366B7V6+vfOuexsYw/3/WwPZZVt/Ogrm1iRL0mxCL7k+Eh+/M+bSE928rWHdvFXjbtLTWraUFGUO4DbgSHgc6qqnhn32GrgZ4AFuF9V1RdnGlRzxwD+QICbZMcrAHfeWIgzMoxvPfwW65ansW5ZGpkpTqIcYTgibESE2QAYGvZhtVqwWixYLNDTP8SZ+m72HK7nzdJaPro5j9vmyFn2jgg7//6l9fzwfw5y14Nv8qnLC7i4OMt0idh0xqaiKN8DLgN6gc+oqhrShpKBQIATVR384eWTVNZ1cfs1S7h6Q47UFIuQWpgVz8/v2cJjL53gm7/Yy/oV6Wy7tIDcjDjNYjLCeAVo6RjgmZ0V7NhfzZZVmXzxo8uJNklrT6FPUY4wvvWZC9hZWsdvn3+Pl/ed5abLFdatSMM+y+W0EybGiqIkAF8ANgArgQeAT4z7kYeAbUA3sFdRlJdVVZ3R2lV6spOHv3GJvHGOslkt3LZ1KetXpPPiW2d47KUTtHS6P/BzN/7LBz+TRITbKMxP5sG7N6LMsVZYkRF2vn/HOt4sqWH7a6f4/V+Pk5YUTXysg0Bg5PS/YZ8fz5AXt8eHZ7RtzGe//wqOcDtxzpH+2WmJ0aQmRZOWGE1aUjSx0eGTem36/AFeP1jD6bou7vp48A+tmM7YBBYBq1VV3agoyjbgm6P/BdWw18/Zxm6OVrSx53A9Nc09XFycxTc+XSybdsSsiY4M484bC7ly3QKe3FHO1x7aRV6mizWL57FqUQr5WfGz1llBz+M1EAjQ1D7AybPtvF3WSEl5M8r8eH545waW5s6dw7WEtiwWC5dckMWapak8t6uSh/9cxm+eP8aapakUL5pH4cIkohyh/4A2mRnjNcAuVVW9wLuKovx9GldRFAdgV1W1fvTrU8BCoHymgUlS/EEF8+O55+ZiYCTxGEvohrw+vvTAG/zyvovxBwL4/AECgQAxUeEkxkUSZtdlxcyssFotXLYmm0tXz+dMfTfVTT109XqwWi1YLBbsNiuRETYc4XYc4Xa++5t93HVjIW6Pl85eD03t/ZRXd7LrUB3NHQP4/AGiHXbSkp2kJ0bjio0gLjoCZ9TIYA0ERjaW1bf2cVhtZdjr53PXhmyWfjpjcxMw9gnqReCeYATyZkkN5dWddPV6aO92U9XQg8/nJzcjjo1F6VxywVpJiIVmctLj+M7ta2lq72fvkXpKy1vY/vopbFYL6UnRpCc7ccVEEBVhxxkVztXrF4TiDVg347WyroudpbV09Xro6vVQ1dBD78AQKfGRrF6Syo++somC+fHBeCohpswZGcanr1rMxy9dyL5jjZScaOYXTx+md2CY5PhIMpJGTgeOjLDjiLCxYUU6OenBWwmaTGKcAHSO+9r6vse6xn3dNfo9EWJhdith9nCc4w4EzE6L1S4gnbNYLORluia1m3rtsrRzft/n89Pa5aahtZ+Gtj6a2gfo6vVQ09hL3+DwyPMwMlM9LyGK269dyurF80K5BDmdsZkAnAFQVdWtKIozGIHUtfThGfKRnhTNstxEbt8aR36mS7pNCF1JTYxm26UFbLu0gH73MFUN3dS39tPQ2kdP/xBN/QP4Wvu4uDgzFImxbsZrZ88gXb0eXDERLEiL5bpNuRTMjyc+1hGMywsRFI5wOxcXZ3FxcRY+f4Caph7qWvpoaO2jtctNV68Hd5uXvAxXUBNjy0RHaCqKchVwkaqq/zL69RFVVYtG/xwJ7FRV9cLRr7cD31NV9bwzxqWlpXJupxDvU1xcPKVlkumMTeBiIKCq6iOjs1Q7VVVdN9FzyZgV4oOmMmZlvAqhrcmO18lM5xwA/k1RFBtQCFSMPTD6CdarKEoa0MPI0k9lsIITQpzXdMamDfgh8AhwNfDWZJ5IxqwQMybjVQgDmHDGGEBRlC8BtwLDwOeBjUCVqqq7FUVZy8imAQvwH6qqvhDCeIUQ40xnbCqK8r+BSxjZ5X6rqqqtmgQvxBwj41UI/ZtUYiyEEEIIIYTZzd12BUIIIYQQQowjibEQQgghhBBIYiyEEEIIIQQgibEQQgghhBDA5Nq1hcz5zo03MkVR1gH/h5G/Vx9wi6qqXef/LeNQFGUjsBdIVlW1Tet4gkVRlDXAD4Aw4CVVVX+kcUjiPPR8/zDKPUDPY1nGo/nImJ0ZGa+zQ7PEeBLnxhtZNXCpqqoDo+15vgzcr3FMwfR1oETrIIJJUZQI4LvAR1VVHdA6HnF+Brh/GOUeoMuxLOPRfGTMBoWM11mg5Yzxh54bb3SqqjaM+3II8GoVS7ApirKVkSbz12sdS5BdCLiBZxRFsQP3qap6VOOYxIfT9f3DCPcAnY9lGY/mI2N2BmS8zh4tE+PznRtvCoqiJAJ3AVdqHUswKIpiZeTv8zH0OThnIh1YAqwG5gO/YaT5vtAnQ9w/9HoPMMBYlvFoPjJmp0nG6+zS8oXZCbjGfe3TKpBQUBQlCvgT8M96qwWagZuBF1RVHdQ6kBDoBN5WVbVfVdWTQJzWAYnz0v39Q+f3AL2PZRmP5iNjdvpkvM4iLWeMP/TceKMbXUrYDvxCVdV3tI4niJYDxYqifBRYATwJXKFtSEFzAPjX0U/mKYBeb0BihK7vHwa4B+h9LMt4NB8Zs9Mn43UWaZYYq6raoSjKo4zssBw7N94sbgI2A7GKonwV+JuRd2iOUVX1W2N/VhRlFyOfYk1BVdVORVH+B9jNyK7ae7WNSJyPAe4fur4H6H0sy3g0Hxmz0yfjdXZZAoGA1jEIIYQQQgihOV0WvwshhBBCCDHbJDEWQgghhBACSYyFEEIIIYQAJDEWQgghhBACkMRYCCGEEEIIQBJjIYQQQgghAEmMhRBCCCGEACQxFkIIIYQQAoD/B9vH5vIoW8ckAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create as many subplots as there are columns, in a signle row\n",
    "columns = df.filter(regex=(\"_dow\")).columns\n",
    "f, axes = plt.subplots(nrows=1, ncols=len(columns), figsize=(12,5))\n",
    "for i, col in enumerate(columns):\n",
    "    sns.distplot(df[[col]], hist=False, rug=True, ax=axes[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "index -1 is out of bounds for axis 0 with size 0",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-39-dd99d8376642>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m sns.pairplot(df[columns],\n\u001b[1;32m      4\u001b[0m              \u001b[0mplot_kws\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'alpha'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;36m0.6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m's'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;36m80\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'edgecolor'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'k'\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m              \u001b[0mdiag_kind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'kde'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m              )\n",
      "\u001b[0;32m~/anaconda3/envs/python3/lib/python3.6/site-packages/seaborn/axisgrid.py\u001b[0m in \u001b[0;36mpairplot\u001b[0;34m(data, hue, hue_order, palette, vars, x_vars, y_vars, kind, diag_kind, markers, height, aspect, corner, dropna, plot_kws, diag_kws, grid_kws, size)\u001b[0m\n\u001b[1;32m   2084\u001b[0m     grid = PairGrid(data, vars=vars, x_vars=x_vars, y_vars=y_vars, hue=hue,\n\u001b[1;32m   2085\u001b[0m                     \u001b[0mhue_order\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhue_order\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpalette\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpalette\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcorner\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcorner\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2086\u001b[0;31m                     height=height, aspect=aspect, dropna=dropna, **grid_kws)\n\u001b[0m\u001b[1;32m   2087\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2088\u001b[0m     \u001b[0;31m# Add the markers here as PairGrid has figured out how many levels of the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/python3/lib/python3.6/site-packages/seaborn/axisgrid.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, hue, hue_order, palette, hue_kws, vars, x_vars, y_vars, corner, diag_sharey, height, aspect, layout_pad, despine, dropna, size)\u001b[0m\n\u001b[1;32m   1314\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1315\u001b[0m         \u001b[0;31m# Label the axes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1316\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_add_axis_labels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1317\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1318\u001b[0m         \u001b[0;31m# Sort out the hue variable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/python3/lib/python3.6/site-packages/seaborn/axisgrid.py\u001b[0m in \u001b[0;36m_add_axis_labels\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1533\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_add_axis_labels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1534\u001b[0m         \u001b[0;34m\"\"\"Add labels to the left and bottom Axes.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1535\u001b[0;31m         \u001b[0;32mfor\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx_vars\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1536\u001b[0m             \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_xlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1537\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0my_vars\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: index -1 is out of bounds for axis 0 with size 0"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 0x0 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Extract columns which have \"_diff\" in their name, and generate pair-plot\n",
    "columns = df.filter(regex=(\"_diff\")).columns.tolist()\n",
    "sns.pairplot(df[columns],\n",
    "             plot_kws={'alpha': 0.6, 's': 80, 'edgecolor': 'k'},\n",
    "             diag_kind='kde'\n",
    "             )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "g = sns.scatterplot(x=X.columns.tolist()[0],\n",
    "                    y=X.columns.tolist()[1],\n",
    "                    size=X.columns.tolist()[2],\n",
    "                    hue='cluster',\n",
    "                    palette=sns.color_palette('dark', n_colors=len(set(X['cluster']))),\n",
    "                    data=X)\n",
    "# TODO! get cluster value numbers, they are not shown atm\n",
    "g.legend(loc='best', bbox_to_anchor=(1.25, 1), ncol=1)"
   ]
  }
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