Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "from glob import glob\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "fold = os.getcwd()+\"/adience_fold/*.txt\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "fold_files = glob(fold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>original_image</th>\n",
       "      <th>face_id</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>dx</th>\n",
       "      <th>dy</th>\n",
       "      <th>tilt_ang</th>\n",
       "      <th>fiducial_yaw_angle</th>\n",
       "      <th>fiducial_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30601258@N03</td>\n",
       "      <td>10399646885_67c7d20df9_o.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>(25, 32)</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>414</td>\n",
       "      <td>1086</td>\n",
       "      <td>1383</td>\n",
       "      <td>-115</td>\n",
       "      <td>30</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30601258@N03</td>\n",
       "      <td>10424815813_e94629b1ec_o.jpg</td>\n",
       "      <td>2</td>\n",
       "      <td>(25, 32)</td>\n",
       "      <td>m</td>\n",
       "      <td>301</td>\n",
       "      <td>105</td>\n",
       "      <td>640</td>\n",
       "      <td>641</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30601258@N03</td>\n",
       "      <td>10437979845_5985be4b26_o.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>(25, 32)</td>\n",
       "      <td>f</td>\n",
       "      <td>2395</td>\n",
       "      <td>876</td>\n",
       "      <td>771</td>\n",
       "      <td>771</td>\n",
       "      <td>175</td>\n",
       "      <td>-30</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30601258@N03</td>\n",
       "      <td>10437979845_5985be4b26_o.jpg</td>\n",
       "      <td>3</td>\n",
       "      <td>(25, 32)</td>\n",
       "      <td>m</td>\n",
       "      <td>752</td>\n",
       "      <td>1255</td>\n",
       "      <td>484</td>\n",
       "      <td>485</td>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30601258@N03</td>\n",
       "      <td>11816644924_075c3d8d59_o.jpg</td>\n",
       "      <td>2</td>\n",
       "      <td>(25, 32)</td>\n",
       "      <td>m</td>\n",
       "      <td>175</td>\n",
       "      <td>80</td>\n",
       "      <td>769</td>\n",
       "      <td>768</td>\n",
       "      <td>-75</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        user_id                original_image  face_id       age gender     x  \\\n",
       "0  30601258@N03  10399646885_67c7d20df9_o.jpg        1  (25, 32)      f     0   \n",
       "1  30601258@N03  10424815813_e94629b1ec_o.jpg        2  (25, 32)      m   301   \n",
       "2  30601258@N03  10437979845_5985be4b26_o.jpg        1  (25, 32)      f  2395   \n",
       "3  30601258@N03  10437979845_5985be4b26_o.jpg        3  (25, 32)      m   752   \n",
       "4  30601258@N03  11816644924_075c3d8d59_o.jpg        2  (25, 32)      m   175   \n",
       "\n",
       "      y    dx    dy  tilt_ang  fiducial_yaw_angle  fiducial_score  \n",
       "0   414  1086  1383      -115                  30              17  \n",
       "1   105   640   641         0                   0              94  \n",
       "2   876   771   771       175                 -30              74  \n",
       "3  1255   484   485       180                   0              47  \n",
       "4    80   769   768       -75                   0              34  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(fold_files[0], sep='\\t')\n",
    "for file in fold_files[1:]:\n",
    "    temp = pd.read_csv(file, sep='\\t')\n",
    "    data = pd.concat([data, temp])\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19370, 12)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13082"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['original_image'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19370"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data['original_image'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['(25, 32)', '(38, 43)', '(4, 6)', '(60, 100)', '(15, 20)',\n",
       "       '(48, 53)', '(8, 12)', '(0, 2)', 'None', '(38, 48)', '35', '3',\n",
       "       '55', '58', '22', '13', '45', '36', '57', '32', '23', '(38, 42)',\n",
       "       '(8, 23)', '(27, 32)', '56', '2', '29', '34', '42', '46'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['age'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['full_path'] = data['user_id'] + '/coarse_tilt_aligned_face.' + data['face_id'].astype('str') + '.' + data['original_image']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "        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>user_id</th>\n",
       "      <th>original_image</th>\n",
       "      <th>face_id</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>dx</th>\n",
       "      <th>dy</th>\n",
       "      <th>tilt_ang</th>\n",
       "      <th>fiducial_yaw_angle</th>\n",
       "      <th>fiducial_score</th>\n",
       "      <th>full_path</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30601258@N03</td>\n",
       "      <td>10399646885_67c7d20df9_o.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>(25, 32)</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>414</td>\n",
       "      <td>1086</td>\n",
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       "      <td>-115</td>\n",
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       "      <td>17</td>\n",
       "      <td>30601258@N03/coarse_tilt_aligned_face.1.103996...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30601258@N03</td>\n",
       "      <td>10424815813_e94629b1ec_o.jpg</td>\n",
       "      <td>2</td>\n",
       "      <td>(25, 32)</td>\n",
       "      <td>m</td>\n",
       "      <td>301</td>\n",
       "      <td>105</td>\n",
       "      <td>640</td>\n",
       "      <td>641</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>94</td>\n",
       "      <td>30601258@N03/coarse_tilt_aligned_face.2.104248...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30601258@N03</td>\n",
       "      <td>10437979845_5985be4b26_o.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>(25, 32)</td>\n",
       "      <td>f</td>\n",
       "      <td>2395</td>\n",
       "      <td>876</td>\n",
       "      <td>771</td>\n",
       "      <td>771</td>\n",
       "      <td>175</td>\n",
       "      <td>-30</td>\n",
       "      <td>74</td>\n",
       "      <td>30601258@N03/coarse_tilt_aligned_face.1.104379...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30601258@N03</td>\n",
       "      <td>10437979845_5985be4b26_o.jpg</td>\n",
       "      <td>3</td>\n",
       "      <td>(25, 32)</td>\n",
       "      <td>m</td>\n",
       "      <td>752</td>\n",
       "      <td>1255</td>\n",
       "      <td>484</td>\n",
       "      <td>485</td>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "      <td>30601258@N03/coarse_tilt_aligned_face.3.104379...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30601258@N03</td>\n",
       "      <td>11816644924_075c3d8d59_o.jpg</td>\n",
       "      <td>2</td>\n",
       "      <td>(25, 32)</td>\n",
       "      <td>m</td>\n",
       "      <td>175</td>\n",
       "      <td>80</td>\n",
       "      <td>769</td>\n",
       "      <td>768</td>\n",
       "      <td>-75</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>30601258@N03/coarse_tilt_aligned_face.2.118166...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        user_id                original_image  face_id       age gender     x  \\\n",
       "0  30601258@N03  10399646885_67c7d20df9_o.jpg        1  (25, 32)      f     0   \n",
       "1  30601258@N03  10424815813_e94629b1ec_o.jpg        2  (25, 32)      m   301   \n",
       "2  30601258@N03  10437979845_5985be4b26_o.jpg        1  (25, 32)      f  2395   \n",
       "3  30601258@N03  10437979845_5985be4b26_o.jpg        3  (25, 32)      m   752   \n",
       "4  30601258@N03  11816644924_075c3d8d59_o.jpg        2  (25, 32)      m   175   \n",
       "\n",
       "      y    dx    dy  tilt_ang  fiducial_yaw_angle  fiducial_score  \\\n",
       "0   414  1086  1383      -115                  30              17   \n",
       "1   105   640   641         0                   0              94   \n",
       "2   876   771   771       175                 -30              74   \n",
       "3  1255   484   485       180                   0              47   \n",
       "4    80   769   768       -75                   0              34   \n",
       "\n",
       "                                           full_path  \n",
       "0  30601258@N03/coarse_tilt_aligned_face.1.103996...  \n",
       "1  30601258@N03/coarse_tilt_aligned_face.2.104248...  \n",
       "2  30601258@N03/coarse_tilt_aligned_face.1.104379...  \n",
       "3  30601258@N03/coarse_tilt_aligned_face.3.104379...  \n",
       "4  30601258@N03/coarse_tilt_aligned_face.2.118166...  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['f', 'm', nan, 'u'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['gender'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['35953373@N04/coarse_tilt_aligned_face.905.9494119869_174fb904e7_o.jpg'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[(data['age'] == '(8, 23)') , ['full_path']].values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f62d45a99e8>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.imread('adience/' + '35953373@N04/coarse_tilt_aligned_face.905.9494119869_174fb904e7_o.jpg')\n",
    "plt.imshow(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['(25, 32)', '(38, 43)', '(4, 6)', '(60, 100)', '(15, 20)',\n",
       "       '(48, 53)', '(8, 12)', '(0, 2)', 'None', '(38, 48)', '35', '3',\n",
       "       '55', '58', '22', '13', '45', '36', '57', '32', '23', '(38, 42)',\n",
       "       '(8, 23)', '(27, 32)', '56', '2', '29', '34', '42', '46'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['age'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "def rnd(low, high):\n",
    "    return np.random.randint(low, high + 1)\n",
    "def makeAge(age):\n",
    "    if age == '(0, 2)':\n",
    "        return rnd(0, 2)\n",
    "    elif age == '(4, 6)':\n",
    "        return rnd(4, 6)\n",
    "    elif age in ['(8, 12)', '(8, 23)']:\n",
    "        return rnd(8, 12)\n",
    "    elif age == '(15, 20)':\n",
    "        return rnd(15, 20)\n",
    "    elif age in ['(25, 32)', '(27, 32)']:\n",
    "        return rnd(25, 32)\n",
    "    elif age in ['(38, 43)', '(38, 48)', '(38, 42)']:\n",
    "        return rnd(38, 43)\n",
    "    elif age == '(48, 53)':\n",
    "        return rnd(48, 53)\n",
    "    elif age == '(60, 100)':\n",
    "        return rnd(60, 100)\n",
    "    elif age == 'None':\n",
    "        return np.nan\n",
    "    else :\n",
    "        return int(age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['umur'] = data['age'].map(makeAge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>tilt_ang</th>\n",
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       "      <th>umur</th>\n",
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       "      <td>(25, 32)</td>\n",
       "      <td>m</td>\n",
       "      <td>301</td>\n",
       "      <td>105</td>\n",
       "      <td>640</td>\n",
       "      <td>641</td>\n",
       "      <td>0</td>\n",
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       "      <td>94</td>\n",
       "      <td>30601258@N03/coarse_tilt_aligned_face.2.104248...</td>\n",
       "      <td>30.0</td>\n",
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       "      <th>2</th>\n",
       "      <td>30601258@N03</td>\n",
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       "      <td>1</td>\n",
       "      <td>(25, 32)</td>\n",
       "      <td>f</td>\n",
       "      <td>2395</td>\n",
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       "      <td>771</td>\n",
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       "      <td>(25, 32)</td>\n",
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       "      <td>752</td>\n",
       "      <td>1255</td>\n",
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       "      <td>485</td>\n",
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       "      <td>47</td>\n",
       "      <td>30601258@N03/coarse_tilt_aligned_face.3.104379...</td>\n",
       "      <td>28.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30601258@N03</td>\n",
       "      <td>11816644924_075c3d8d59_o.jpg</td>\n",
       "      <td>2</td>\n",
       "      <td>(25, 32)</td>\n",
       "      <td>m</td>\n",
       "      <td>175</td>\n",
       "      <td>80</td>\n",
       "      <td>769</td>\n",
       "      <td>768</td>\n",
       "      <td>-75</td>\n",
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       "      <td>34</td>\n",
       "      <td>30601258@N03/coarse_tilt_aligned_face.2.118166...</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        user_id                original_image  face_id       age gender     x  \\\n",
       "0  30601258@N03  10399646885_67c7d20df9_o.jpg        1  (25, 32)      f     0   \n",
       "1  30601258@N03  10424815813_e94629b1ec_o.jpg        2  (25, 32)      m   301   \n",
       "2  30601258@N03  10437979845_5985be4b26_o.jpg        1  (25, 32)      f  2395   \n",
       "3  30601258@N03  10437979845_5985be4b26_o.jpg        3  (25, 32)      m   752   \n",
       "4  30601258@N03  11816644924_075c3d8d59_o.jpg        2  (25, 32)      m   175   \n",
       "\n",
       "      y    dx    dy  tilt_ang  fiducial_yaw_angle  fiducial_score  \\\n",
       "0   414  1086  1383      -115                  30              17   \n",
       "1   105   640   641         0                   0              94   \n",
       "2   876   771   771       175                 -30              74   \n",
       "3  1255   484   485       180                   0              47   \n",
       "4    80   769   768       -75                   0              34   \n",
       "\n",
       "                                           full_path  umur  \n",
       "0  30601258@N03/coarse_tilt_aligned_face.1.103996...  25.0  \n",
       "1  30601258@N03/coarse_tilt_aligned_face.2.104248...  30.0  \n",
       "2  30601258@N03/coarse_tilt_aligned_face.1.104379...  32.0  \n",
       "3  30601258@N03/coarse_tilt_aligned_face.3.104379...  28.0  \n",
       "4  30601258@N03/coarse_tilt_aligned_face.2.118166...  25.0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['db_name'] = 'adience'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "bersih = data.loc[(data['umur'].isnull() == False) & \n",
    "                  ((data['gender'] == 'f') | (data['gender'] == 'm')), ['db_name', 'full_path', 'umur', 'gender']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>gender</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>adience</td>\n",
       "      <td>30601258@N03/coarse_tilt_aligned_face.1.103996...</td>\n",
       "      <td>25.0</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>adience</td>\n",
       "      <td>30601258@N03/coarse_tilt_aligned_face.2.104248...</td>\n",
       "      <td>30.0</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>adience</td>\n",
       "      <td>30601258@N03/coarse_tilt_aligned_face.1.104379...</td>\n",
       "      <td>32.0</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>adience</td>\n",
       "      <td>30601258@N03/coarse_tilt_aligned_face.3.104379...</td>\n",
       "      <td>28.0</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>adience</td>\n",
       "      <td>30601258@N03/coarse_tilt_aligned_face.2.118166...</td>\n",
       "      <td>25.0</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   db_name                                          full_path  umur gender\n",
       "0  adience  30601258@N03/coarse_tilt_aligned_face.1.103996...  25.0      f\n",
       "1  adience  30601258@N03/coarse_tilt_aligned_face.2.104248...  30.0      m\n",
       "2  adience  30601258@N03/coarse_tilt_aligned_face.1.104379...  32.0      f\n",
       "3  adience  30601258@N03/coarse_tilt_aligned_face.3.104379...  28.0      m\n",
       "4  adience  30601258@N03/coarse_tilt_aligned_face.2.118166...  25.0      m"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bersih.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17452, 4)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bersih.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>adience</td>\n",
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       "      <td>30.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>adience</td>\n",
       "      <td>30601258@N03/coarse_tilt_aligned_face.1.104379...</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>adience</td>\n",
       "      <td>30601258@N03/coarse_tilt_aligned_face.3.104379...</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>adience</td>\n",
       "      <td>30601258@N03/coarse_tilt_aligned_face.2.118166...</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   db_name                                          full_path  umur  gender\n",
       "0  adience  30601258@N03/coarse_tilt_aligned_face.1.103996...  25.0       0\n",
       "1  adience  30601258@N03/coarse_tilt_aligned_face.2.104248...  30.0       1\n",
       "2  adience  30601258@N03/coarse_tilt_aligned_face.1.104379...  32.0       0\n",
       "3  adience  30601258@N03/coarse_tilt_aligned_face.3.104379...  28.0       1\n",
       "4  adience  30601258@N03/coarse_tilt_aligned_face.2.118166...  25.0       1"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gender = {'m' : 1, 'f' : 0}\n",
    "bersih['gender'] = bersih['gender'].map(gender)\n",
    "bersih.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = bersih.loc[bersih['umur'] <= 20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
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       "      <th>full_path</th>\n",
       "      <th>umur</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>742</th>\n",
       "      <td>adience</td>\n",
       "      <td>20254529@N04/coarse_tilt_aligned_face.14.94032...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>743</th>\n",
       "      <td>adience</td>\n",
       "      <td>20254529@N04/coarse_tilt_aligned_face.14.94060...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>745</th>\n",
       "      <td>adience</td>\n",
       "      <td>20254529@N04/coarse_tilt_aligned_face.14.94046...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>748</th>\n",
       "      <td>adience</td>\n",
       "      <td>20254529@N04/coarse_tilt_aligned_face.17.94029...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>749</th>\n",
       "      <td>adience</td>\n",
       "      <td>20254529@N04/coarse_tilt_aligned_face.14.99315...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     db_name                                          full_path  umur  gender\n",
       "742  adience  20254529@N04/coarse_tilt_aligned_face.14.94032...   4.0       0\n",
       "743  adience  20254529@N04/coarse_tilt_aligned_face.14.94060...   6.0       0\n",
       "745  adience  20254529@N04/coarse_tilt_aligned_face.14.94046...   4.0       0\n",
       "748  adience  20254529@N04/coarse_tilt_aligned_face.17.94029...   5.0       0\n",
       "749  adience  20254529@N04/coarse_tilt_aligned_face.14.99315...   6.0       0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "a.to_csv('adience_u20.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dandy/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f62c5d3a908>"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.distplot(bersih['umur'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "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>umur</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>17452.000000</td>\n",
       "      <td>17452.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>25.489170</td>\n",
       "      <td>0.465276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>19.116065</td>\n",
       "      <td>0.498807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>26.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>35.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               umur        gender\n",
       "count  17452.000000  17452.000000\n",
       "mean      25.489170      0.465276\n",
       "std       19.116065      0.498807\n",
       "min        0.000000      0.000000\n",
       "25%        9.000000      0.000000\n",
       "50%       26.000000      0.000000\n",
       "75%       35.000000      1.000000\n",
       "max      100.000000      1.000000"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bersih.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>db_name</th>\n",
       "      <th>full_path</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>imdb</td>\n",
       "      <td>01/nm0000001_rm946909184_1899-5-10_1968.jpg</td>\n",
       "      <td>69.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>imdb</td>\n",
       "      <td>01/nm0000001_rm980463616_1899-5-10_1968.jpg</td>\n",
       "      <td>69.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>imdb</td>\n",
       "      <td>02/nm0000002_rm1346607872_1924-9-16_2004.jpg</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>imdb</td>\n",
       "      <td>02/nm0000002_rm1363385088_1924-9-16_2004.jpg</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>imdb</td>\n",
       "      <td>02/nm0000002_rm221957120_1924-9-16_1974.jpg</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  db_name                                     full_path   age  gender\n",
       "0    imdb   01/nm0000001_rm946909184_1899-5-10_1968.jpg  69.0     1.0\n",
       "1    imdb   01/nm0000001_rm980463616_1899-5-10_1968.jpg  69.0     1.0\n",
       "2    imdb  02/nm0000002_rm1346607872_1924-9-16_2004.jpg  80.0     0.0\n",
       "3    imdb  02/nm0000002_rm1363385088_1924-9-16_2004.jpg  80.0     0.0\n",
       "4    imdb   02/nm0000002_rm221957120_1924-9-16_1974.jpg  50.0     0.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imdb = pd.read_csv('imdb_cleaned.csv')\n",
    "wiki = pd.read_csv('wiki_cleaned.csv')\n",
    "adience = pd.read_csv('adience_u20.csv')\n",
    "data = pd.concat([imdb, wiki, adience], axis=0)\n",
    "data2 = pd.concat([imdb, wiki], axis=0)\n",
    "del imdb, wiki, adience\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(226003, 4)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dandy/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f579311be10>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TSoergvR4/usNy9l+soUtBxrcDseYkGcJwbzPgfp2orxCSXai26FM2+cuL2BJdiL//Nwh+oeG3Q7HmJAW+pPUmDl3sL6DpTlJRPvC9/vCw9vevQ/hipJMfvVWJQ9treIrVy52MSpjQlv4/sWbWaGqHKzv4MKF4d1cNNrSnCSW5iTyo5eO0W3rJRgzIUsI5j0aOvpp7h4I+/6DsdaXZNHZN8TbJ5rdDsWYkGUJwbzHgfp2AC7MS3E5kplVlBFPXJSX147afFjGTMQSgnmPg/WBEUYrIqyG4PN6WLc4ndctIRgzIUsI5j0O1HdQlBFPYhgsinO+rlqaRWVzD9XNPW6HYkxIsoRg3uPAqXYuXBhZzUUjPrQ0MFvua8eslmDMeCLva6CZsvbeQWpaetl4eaHbocyKrcebSYuP4uFt1XhHLb9x29rIvF5jzpfVEMxZh0buUI6gIaejiQhLspM4fqaLIb/f7XCMCTmWEMxZB5wO5Ui6B2GspTmJDAz5qW6xfgRjxrImI3PW5t11JMf6ePFgo9uhzJrFWYl4BCoau1icGf5Tcxgzk6yGYM6qbumhMD2yp4mOjfKSlxrHyTPdbodiTMixhGAAaOjoo7VnkMKMBLdDmXXFmYnUtvYyMGT9CMaMZgnBALCzqhWARRFeQwBYnJXAsKr1IxgzhiUEAwQSgs8j5KbGuh3KrFuUHo9H4ERTl9uhGBNSLCEYIJAQ8tPi8Hki/79EjPUjGDOuyP/rN5PqGxzmQH07hemR338wwvoRjHk/SwiGfXXtDA4ri+bRQvTWj2DM+wWVEETkBhE5IiIVInLXOPtjROQxZ/82ESlytl8vIjtFZJ/z85pRx6x2tleIyI9FRs0lYOZUeWWgQ7lgHnQoj7B+BGPeb9KEICJe4F7gRqAU+JyIlI4pdgfQqqolwA+Be5ztTcAnVHUVcDvw0KhjfgpsApY4jxumcR1mGnZWtbI4MyEiZzidyEg/wgnrRzDmrGBqCGuAClU9oaoDwKPAhjFlNgAPOM+fBK4VEVHVXapa72w/AMQ6tYlcIFlVt6qqAg8Cn5r21Zjzpqq8U93KZYvS3A5lzi3NSaKmpYdT7b1uh2JMSAgmIeQBNaNe1zrbxi2jqkNAO5AxpsxngV2q2u+Ur53knACIyCYRKReR8jNnbNrimVbZ3ENL9wCr52FCuKQgFQWe3lU/aVlj5oNgEsJ4bft6PmVE5EICzUh/eR7nDGxUvU9Vy1S1LCsrK4hwzfkYuSFtPiaEjMQYFqXH89t3aglUVI2Z34JJCLVAwajX+cDYr1Rny4iID0gBWpzX+cBTwBdV9fio8vmTnNPMgZ1VrSTF+ijJmp8TvV1amMaxxi7213W4HYoxrgumF3EHsEREioE6YCNw25gymwl0Gm8FbgZeVlUVkVTgGeA7qvrmSGFVPSUinSKyDtgGfBH4t2lfjTlv71S1cllhGh7P/BzktSovhT/sred/PXeIT1y08D37bOEcM99MWkNw+gTuBLYAh4DHVfWAiNwtIp90it0PZIhIBfAtYGRo6p1ACfBdEdntPLKdff8F+DlQARwHnpupizLBae8d5GhjJ2XzsLloRFy0l+W5yeypaWPYb81GZn4Lapyhqj4LPDtm2/dGPe8DbhnnuO8D35/gnOXAyvMJ1sysXdWtqM7P/oPRLitIZX9dO3tr27i0cH7/W5j5ze5UnsfeqWrFI3BxQarbobhq6YIkFqbE8sdDDQwO21QWZv6yhDCP7axuZUVuMgnz6Ia08XhEuHFVLm09g2w93ux2OMa4xhLCPDU07Gd3ddu8by4acUFWIstyknj1aCPd/UNuh2OMKywhzFNHGjrpHhi2hDDKDSsX0D/o55UjkbumtDHnYglhntpX2w4E7tY1ATnJsaxelMa2ky209Qy4HY4xc84Swjx1tKGLuCgvBWnzZ4bTYFyzPDAq+uXDVksw848lhHnqWGMnJdmJ8/aGtImkxkezpjidd6pbOdlkM6Ga+cUSwjx1rKGLJdnzc7qKyVy9NAuvR/jXF4+6HYoxc2p+jzecp+7/00lOd/TRPTDMw9uq3Q4n5CTFRvHBCzLZvKeev7t+GYXzaCU5M79ZDWEeauzsAyAnKcblSELXusUZCPB4ec2kZY2JFJYQ5qHGjn4AspNjXY4kdKXERXH1smye2FnDkN29bOYJSwjzUGNnH1FeITU+yu1QQtqtlxfQ0NHPq0dsYSYzP1hCmIcaOvvJTorFIzbC6FyuWZ5NZmIMj+6wZiMzP1hCmIcaO/rItv6DST1RXktpbjIvH27g3189bh3wJuJZQphn2nsH6egbsv6DIJUVpeFX2F3T5nYoxsw6SwjzTEVjJ2AjjIKVmRjDwtRYDp6yJTZN5LOEMM8cbegCbITR+VixIJmalh66bBZUE+EsIcwzRxs6bYTReVqRm4wCh62WYCKcJYR5pqKxi6ykGBthdB5yU2JJiYvi0OlOt0MxZlZZQphnqlt6yEiw/oPzISKsyE2iorGT3oFht8MxZtZYQphHhob91LX2kp4Q7XYoYWdFbjKDw8qbFU1uh2LMrAkqIYjIDSJyREQqROSucfbHiMhjzv5tIlLkbM8QkVdEpEtEfjLmmFedc+52HtkzcUFmYqfa+xjyqyWEKSjOTCDG5+HFQw1uh2LMrJl0tlMR8QL3AtcDtcAOEdmsqgdHFbsDaFXVEhHZCNwD3Ar0Ad8FVjqPsT6vquXTvAYTpJqWHgBLCFPg83hYmpPEi4ca8fvV1pEwESmYGsIaoEJVT6jqAPAosGFMmQ3AA87zJ4FrRURUtVtV3yCQGIzLalqdhBBvCWEqVuQm09TVz+5au0nNRKZgEkIeMHoyl1pn27hlVHUIaAcygjj3L53mou+K2LCX2Vbd0oPPIyTH2ZDTqViWk4TXI/zxoDUbmcgUTEIY74Nap1BmrM+r6irgSufxhXF/ucgmESkXkfIzZ2zWyemobuklLy0OrzV3TElctJe1xem8aAnBRKhgEkItUDDqdT5QP1EZEfEBKUDLuU6qqnXOz07gYQJNU+OVu09Vy1S1LCsrK4hwzUSqW3ooTLfVv6bjuhU5HGvsotLWWzYRKJiEsANYIiLFIhINbAQ2jymzGbjdeX4z8LKqTlhDEBGfiGQ6z6OAjwP7zzd4c35qWnoosIQwLdeX5gDYaCMTkSYdZaSqQyJyJ7AF8AK/UNUDInI3UK6qm4H7gYdEpIJAzWDjyPEiUgkkA9Ei8ingI0AVsMVJBl7gReA/ZvTKzHt09g3S0j1gNYRp+tOxJhYkx/LrbdXER7/753Pb2kIXozJmZkyaEABU9Vng2THbvjfqeR9wywTHFk1w2tXBhWhmQk1LLwCF6fG09Qy6HE14W56bxGtHztDTP0R8TFB/QsaEBbtTeZ6odu5BsBrC9JU6k90dsMnuTISxhDBPjNyUZn0I05eXGkdmYrQtmmMijiWEeaK6pYeUuChS7B6EaRMRLilI5WRTN209A26HY8yMsYQwT1S39FCQHud2GBHjkoI0APbUtrsciTEzxxLCPFFj9yDMqPSEaArT49lV3co5RlgbE1YsIcwDfr9S29pr/Qcz7JKCVBo7+zndYVN1mchgCWEeON3Rx8Cw32oIM+yivBQ8Au9UtbodijEzwhLCPFDZHJhmoSgjweVIIkt8jI+L8lPZXtlCU1e/2+EYM22WEOaByqbAkNNFGVZDmGnXLMtmaFj56avH3Q7FmGmzhDAPVDV3E+3zsDDFRhnNtMykGC4tTOM/367idLv1JZjwZglhHjjZ1E1heryt8jVLrlmezbBfufeVCrdDMWZabCKWeaCqucf6D2ZRekI0lxWm8fC2ahJjfO8ZzWWT3plwYjWECOf3K5XN3RRZ/8Gs+khpDklxPh7eXk13/5Db4RgzJZYQIlxDZx/9Q36KMq2GMJviY3x8fs0iuvuHeGxHDX67Wc2EIUsIEe5kkw05nSt5aXF88uKFVJzp4vn9p90Ox5jzZgkhwlU1B4acFmVak9FcKCtK5wOLM3ijoom3jje5HY4x58USQoSrbLIhp3PtpotyKc1N5pm9p3h+/ym3wzEmaJYQIpwNOZ17HhH+rKyA/LQ4/v6JvTR22v0JJjxYQohwNuTUHdE+DzevLqBvaJh7njvidjjGBMUSQgSzIafuykqK4Y71i/nNO7XstAnwTBiwhBDBbMip+3KSY0iO9fHXj7zDf75dxcPbqt0OyZgJBZUQROQGETkiIhUictc4+2NE5DFn/zYRKXK2Z4jIKyLSJSI/GXPMahHZ5xzzYxGxRu4ZZkNO3Rfj83Ljylzq2/rYV2erq5nQNmlCEBEvcC9wI1AKfE5ESscUuwNoVdUS4IfAPc72PuC7wLfHOfVPgU3AEudxw1QuwEzMhpyGhlX5KWQkRPNmRZOtrmZCWjA1hDVAhaqeUNUB4FFgw5gyG4AHnOdPAteKiKhqt6q+QSAxnCUiuUCyqm7VwF/Ig8CnpnMh5v2ON3YR7fOQa0NOXeUR4YMlmdS29lLd0uN2OMZMKJjJ7fKAmlGva4G1E5VR1SERaQcygInuzMlzzjP6nHnBBGyC99LhRrKTYnhsR83khc2suqwwlT8ePM2bFXazmgldwdQQxmvbH1vvDabMlMqLyCYRKReR8jNnzpzjlGY0v1+pb+tlYarVDkJBjM/LmqJ0DtR3UGO1BBOigkkItUDBqNf5QP1EZUTEB6QALZOcM3+ScwKgqvepapmqlmVlZQURrgGoaumhf8hPniWEkLFucQYi8Ku3Kt0OxZhxBZMQdgBLRKRYRKKBjcDmMWU2A7c7z28GXtZz9J6p6imgU0TWOaOLvgj87ryjNxMaGdFiCSF0pMZHc1F+Ko9sr6a1e8DtcIx5n0kTgqoOAXcCW4BDwOOqekBE7haRTzrF7gcyRKQC+BZwdmiqiFQC/wL8hYjUjhqh9F+AnwMVwHHguZm5JAOwv64dr0fITo5xOxQzylVLs+gZGLZagglJQa2YpqrPAs+O2fa9Uc/7gFsmOLZogu3lwMpgAzXnZ39dOwuSY/F57N7DUJKTHMtHSnP41VuVfPVDi0mMsUULTeiwT4sIpKrsr2u35qIQ9bUPl9DeO8jD26rcDsWY97CvJxGouqWHjr4hG2EUog7Wd1CSlci/vVRBlMdDTJQXsPWXjfushhCBrEM59F1XmkNX/xAvHGxwOxRjzrKEEIH21bUT5RVyrEM5ZBWmx7N2cQZvn2imurnb7XCMASwhRKQDdR0sW5CEz2tvbyj7aGkOyXFR/HZXHUN+v9vhGGMJIdIMDfvZU9vGqrwUt0Mxk4iJ8rLh4oU0dvazeXc9fr9NfGfcZQkhwrxT3UZn3xBXLrG7usPB8txkrl6WRXlVK/+4+YDNhmpcZQkhwrx0uAGfR7hySabboZggXb8ihytLMnno7Sq+/cRem+vIuMaGnUaYlw81snZxOkmxUW6HYoIkItywcgEr81P4j9dP8NSuWq5bkcPVy7JZU5zGBVmJ2PpRZi5YQoggNS09HGvsYuMaG88ebkSE/3bDcr74gUU8tLWKJ3bWnh2SellhKv/8mYtYtiDJ5ShNpLOEEEFePtwIwDXLs12OxEzFyHrL+Wnx/M21S2juGuBoYydvHW/mph//ia9/uIS/uW6J1RbMrLGEEEFePtzI4swEijNtDeVwJyJkJsWQmRTDxfmpPLPvFD966RjllS18bFUun1+3yO0QTQSyTuUI0d0/xNbjzVY7iEAJMT5uWZ3PBy/I4M3jzbx4qNHtkEyEsoQQIV4/eoaBYb8lhAglIty0KpeyRWm8cqSRX7xx0u2QTASyJqMI8Vh5DQuSY1m7OMPtUMwsERE+dWkevYPD/M9nDlKQHs/1pTluh2UiiCWECFDf1strR85w9bJsHttR43Y4ZhZ5RLhldQE/f+MEX/v1TjZdeQF5ae9OYmgzpprpsCajCPB4eSAJlC1KczkSMxeifR6+sG4RCdE+frW1ksbOPrdDMhHCEkKYG/YrT5TXUpKdSFpCtNvhmDmSFBvFl64oRoD73zhJc1e/2yGZCGAJIcz96dgZ6tp6KStKdzsUM8eykmK4Y30xw37l52+cpL6t1+2QTJizhBDmHtleTXpCNCty7S7W+SgnOZY71hejqvxc0oZTAAARiklEQVT7a8d5cGulTZBnpswSQhiraenhjwcbuPXyAnweeyvnq9yUOO68ZgmLsxL43u8O8NUHy2nosH4Fc/6C+hQRkRtE5IiIVIjIXePsjxGRx5z920SkaNS+7zjbj4jIR0dtrxSRfSKyW0TKZ+Ji5psHt1biEeGLH7C7Vue7xBgfX/xAER9blcurR85w1f9+hb9/Yg8Pb6s+OyWGMZOZdNipiHiBe4HrgVpgh4hsVtWDo4rdAbSqaomIbATuAW4VkVJgI3AhsBB4UUSWquqwc9yHVbVpBq9n3ujqH+LRHTV8bFUuuSm2drIJDEldX5LJ8pwkfrOrlid21tLcPcC1drOiCVIwNYQ1QIWqnlDVAeBRYMOYMhuAB5znTwLXSmAGrg3Ao6rar6ongQrnfGaafrOzls6+Ib68vtjtUEyIyUyK4SvrF7O6MI2XDzfy+722GpsJTjAJIQ8YfbdTrbNt3DKqOgS0AxmTHKvACyKyU0Q2nX/o85ffr/zyzZNcVpjKJQWpbodjQpDXI3zmsjzWl2Ty9okW/un3thqbmVwwdyqPN9fu2P9ZE5U517FXqGq9iGQDfxSRw6r6+vt+eSBZbAIoLLS7MAGe2XeKyuYe/v6jy90OxYQwEeHGlQsAeGBrFflp8Xz1Q4tdjsqEsmBqCLVAwajX+UD9RGVExAekAC3nOlZVR342Ak8xQVOSqt6nqmWqWpaVZesED/uVH710jKU5iWf/2I2ZyMhqbDetyuUHzx7iD3vH/uka865gEsIOYImIFItINIFO4s1jymwGbnee3wy8rIH66WZgozMKqRhYAmwXkQQRSQIQkQTgI8D+6V9O5PvD3noqGrv45rVL8XhsoRQzOY8Ia4rTWZQRzzcf2c0/bT5go4/MuCZNCE6fwJ3AFuAQ8LiqHhCRu0Xkk06x+4EMEakAvgXc5Rx7AHgcOAg8D3zdGWGUA7whInuA7cAzqvr8zF5a5Bn2Kz9+6RjLFyRZ7cCclyivhy+uKyI7OYb/3FZFVXO32yGZEBTUbKeq+izw7Jht3xv1vA+4ZYJjfwD8YMy2E8DF5xvsfPfkzhqOn+nmp5+/zGoH5rzFRXv50hXF3Pf6cR7YWskX1hW5HZIJMTb9dZjYVd3K9353gKKMeJq7B6y6b6YkMcbHl68o5pdvVvKLN06yfEESn12d73ZYJkTYfAdhoK6tl68+uJOc5FhuW7sIjy2ybqYhNT6av7rqAooy4/m7J/bwj7/bT2ffoNthmRBgNYQQNTTs51hjF8/sPcVv3qmlf2iYRzetZfvJVrdDMxEgLtrLX3ywmGf3n+LBrVU8tauOj63KZWVeCn++zqZCma8sIYQIv195+2Qzj++oYWd1K3Wtvfg1cCPHBVmJfPLihZYMzIzyeoRPXLSQS/JTeXp3HY/uqCH9YAODw35uKSsgMcY+HuYbCae7F8vKyrS8PPLmwXuzoonvPr2fE03dJMX6+NDSLLr7hshIjGFpTiJJsVFuh2gi3LBfOXiqgzcrmqhu6SEpxsctZQV8eX0R+WnxbodnpklEdqpq2WTl7CuAi7r7h/jBs4d4eFs1xZkJ/PDWi7lxZS6xUV7rNDZzyusRVuWlsCovhdKFyfzyzZM8uLWSX2+r4mtXl/CXVy0mNsrrdphmllkNYRom+tAOZqHzpq5+/uKX2zlQ38EVF2Ry3Yocon3Wx29CR1vPAM/tP82+unYyEqL59y+s5nJbmS8sBVtDsE+gGdDeO0h7b/CjNGpaerj5p29R0djFL26/nI+tyrVkYEJOanw0n1tTyJevKEaBW3+2lf+95TADQ363QzOzxJqMpmn7yRZ+v6cevypLc5L44AUZ5yxf29rDn/1sKz0Dw/z6K+tYvSjNmodMSCvJTuSvP1zCH/ad4t5XjvObnXV86tI8CtMDfQvB1IhNeLCEMEWDw36e3lXH9soWlmQnkpcWx87KVn75ViWFGfF86Yr3r1Pws9eOc9/rJ+geGOKrVy7myOlOjpzudCF6Y85PTJSXz16WT2luMpv31POz145zeXE619jiOxHFEsIU/eCZQ2yvbOGqpVlcX5qDR4Rrlmfz6PYa/un3B/Er3DFq8ZqGjj5+9VYlHX2D3HFFsa1yZsLSitxkFmcm8MKhBradaGZXdSst3QPcsb6YnORYt8Mz02SdylPw23dq+dbje7jiggxuumjhe/YN+5W3jjfx3P7TXF+aw21rC6lr7eWe5w7TOzjMF9YtYklOkkuRGzNzmrv6eflwI7tr2hCBlXkprC3OYFFGvN3cFmKC7VS2hDDGZCOH9tS08Wc/28olBal8/KKFeMeZZO6Wsnx+/NIxHtleTVPXAADrSzJZW5xORmLM7AVvjAuau/p5+0Qz5VWt9A/5SYmLYuOaAj51SR4rcpPdDs9gCWHKRieE9t5BWroHSIjx8plL87nvT8d5ZHsN2Ukx/P6v1/PCgYZxzzGSPAaG/Lx8uAGPCNeX5vDI9ppxyxsTCQaG/Bw81cGemjaOn+liyK8sy0ni4xflcuOqXEqyE90Ocd6yhDBF//l2FS8cOM2e2vb3DSX1eYTb1hbyjWuXkJkYY6ODjJlAV/8Q++va2VPTRlVLDwA5yTF8bk0hH1uVy5LsRMQmaZwzdqfyFPj9ylO76thZ1cqKBUlcuSSTzMQYegaGWZGbxFVLs1icZd9yjJlMYoyPdYszWLc4g/beQQ7Ut7O/rp0fvXiMf33xGKlxUZRkJ3JBdiIXZCWyydZ6DgmWEByqyj88vZ+dVa1cszyb61bkvGe/jbU2ZmpS4qL44AWZfPCCTDr6Bjl0qoOKxi7217dTXhWYsPHpXXVcXpTGZYvSWL0ojbzUOKtBuMASguP+N07yyPZqrlqaxbU2ttqYWZEcG8Xa4gzWFmcw7Ffq23o51tjFiaYuHtlewwNbq5xyPq5als1HSnP48PJsm3l1jlgfAnCgvp1P3/sWVy3L4uqlWfbNxBgXDPuVho4+qlp6qGrupr6tl6auAaK9Hq4oyeCjFy7gutIcMm2k3nmzTuUg9Q4M84mfvEFH7yDP/82HeH7/6Rk9vzFmavyqVDX3cLC+nYOnOmjtGUSAy4vT+UhpDmVF6ZTmJts8YEGwTuUg+P3KPzy9j4rGLh66Yw3pCdFuh2SMcXhEKM5MoDgzgY+tyuVUex8H6js4eKqd7z9zCAiM/MtKiiErKYbrVuRQkp1ISXYiRRkJliimIKiEICI3AD8CvMDPVfX/HrM/BngQWA00A7eqaqWz7zvAHcAw8A1V3RLMOWebqnL3Hw7y23fq+NvrlnLlkqy5/PXGmPMgIixMjWNhahzXl+bQ3jtITUsPNS09NHT2Ud3Sw7/88ejZ8h6BtPhoLi1MC4xmykqgID2e/LQ4FiTH4vNashjPpAlBRLzAvcD1QC2wQ0Q2q+rBUcXuAFpVtURENgL3ALeKSCmwEbgQWAi8KCJLnWMmO+es6R8a5v+8cJRfvVXJV9YX841rS+bi1xpjZkhKXBQpeSmszEs5u21gyE9TVz+Nnf2c6eyjsbOfvbVtvHK4keFRTeMegYWpcRSkBRJEXlocafHRJMf5AueNiyI51vkZFzWvFgYKpoawBqhQ1RMAIvIosAEY/eG9AfgfzvMngZ9IoGd2A/CoqvYDJ0WkwjkfQZxzxqgqLd0D1LX18tbxZn7xxkkaO/u5bW0h/3DTCutENiYCRPs8Z2sRow37lbaeAVp7BmntGaC1Z4C2nkHq23o5UN9OZ98Q5+pJTYj2kpkUQ2ZiDJmJ0WQkxpAY4yM2ykt8tJe4KOfhPI/yeYjyCFE+Dz6PEOX1EOX14PMKPo/gEcHrCTzOPhfB63V+nt3HnH82BZMQ8oDRcy7UAmsnKqOqQyLSDmQ4298ec2ye83yyc86Y63/4OhWNXWdfX1GSwf/5s4tZX5JpycCYCOf1CBmJMRPOIzbsV3oHh+kbGKZ38N1H3+AwvQPDdPcP0dk/RGv3ADUtPXT1DzE47GdwePYH5IwkB68Iu753/azXVoJJCON9Yo79l5iozETbx2vAG/dfV0Q2AZucl10icmSCOINWBTw88e5MoGm6vyPE2TWGv0i/PrBrfI+470/5dwT9bxhMQqgFCka9zgfqJyhTKyI+IAVomeTYyc4JgKreB9wXRJwzQkTKgxmeFc7sGsNfpF8f2DW6IZiu9h3AEhEpFpFoAp3Em8eU2Qzc7jy/GXhZAzc4bAY2ikiMiBQDS4DtQZ7TGGPMHJq0huD0CdwJbCEwRPQXqnpARO4GylV1M3A/8JDTadxC4AMep9zjBDqLh4Cvq+owwHjnnPnLM8YYE6ywulN5LojIJqeZKmLZNYa/SL8+sGt0gyUEY4wxQHB9CMYYY+YBSwijiMgNInJERCpE5C6345kuESkQkVdE5JCIHBCRbzrb00XkjyJyzPmZ5nas0yUiXhHZJSJ/cF4Xi8g25xofcwYvhC0RSRWRJ0XksPN+fiDS3kcR+Vvn/+l+EXlERGLD/X0UkV+ISKOI7B+1bdz3TQJ+7Hz+7BWRy+Y6XksIjlFTdNwIlAKfc6beCGdDwN+p6gpgHfB155ruAl5S1SXAS87rcPdN4NCo1/cAP3SusZXA9Crh7EfA86q6HLiYwLVGzPsoInnAN4AyVV1JYLDJyDQ44fw+/gq4Ycy2id63GwmMxFxC4N6rn85RjGdZQnjX2Sk6VHUAGJlOI2yp6ilVfcd53kngQySPwHU94BR7APiUOxHODBHJB24Cfu68FuAaAtOoQJhfo4gkAx8iMJoPVR1Q1TYi7H0kMOoxzrmXKR44RZi/j6r6OoGRl6NN9L5tAB7UgLeBVBHJnZtIAywhvGu8KTryJigbdkSkCLgU2AbkqOopCCQNINyXiPtX4L8Cfud1BtCmqkPO63B/LxcDZ4BfOs1iPxeRBCLofVTVOuD/BaoJJIJ2YCeR9T6OmOh9c/0zyBLCu4KZoiMsiUgi8Bvgb1S1w+14ZpKIfBxoVNWdozePUzSc30sfcBnwU1W9FOgmjJuHxuO0o28AignMjJxAoAllrHB+Hyfj+v9bSwjvCmaKjrAjIlEEksGvVfW3zuaGkaqo87PRrfhmwBXAJ0WkkkAz3zUEagypTtMDhP97WQvUquo25/WTBBJEJL2P1wEnVfWMqg4CvwU+SGS9jyMmet9c/wyyhPCuiJtOw2lLvx84pKr/MmrX6KlGbgd+N9exzRRV/Y6q5qtqEYH37GVV/TzwCoFpVCD8r/E0UCMiy5xN1xK4+z9i3kcCTUXrRCTe+X87co0R8z6OMtH7thn4ojPaaB3QPtK0NFfsxrRRRORjBL5djkyn8QOXQ5oWEVkP/AnYx7vt6/8XgX6Ex4FCAn+It6jq2I6vsCMiVwPfVtWPi8hiAjWGdGAX8OfOuhxhSUQuIdBpHg2cAL5E4AtdxLyPIvJPwK0ERsftAr5CoA09bN9HEXkEuJrArKYNwD8CTzPO++Ykwp8QGJXUA3xJVWd2EfnJ4rWEYIwxBqzJyBhjjMMSgjHGGMASgjHGGIclBGOMMYAlBGOMMQ5LCMYYYwBLCMYYYxyWEIwJgog8LSI7nfn6Nznb7hCRoyLyqoj8h4j8xNmeJSK/EZEdzuMKd6M3Jjh2Y5oxQRCRdOdu0jgC05x8FHiTwJxCncDLwB5VvVNEHgb+P1V9Q0QKgS3OmhTGhDTf5EWMMcA3ROTTzvMC4AvAayNTRYjIE8BSZ/91QGlgJgIAkkUkyVmTwpiQZQnBmEk4cyRdB3xAVXtE5FXgCDDRt36PU7Z3biI0ZmZYH4Ixk0sBWp1ksJzAcqTxwFUikuZMz/zZUeVfAO4ceeFMTGdMyLOEYMzkngd8IrIX+J/A20Ad8L8IzBz7IoGpmtud8t8AypyF0g8CfzX3IRtz/qxT2ZgpEpFEVe1yaghPEZgy/Sm34zJmqqyGYMzU/Q8R2Q3sB04SmOfemLBlNQRjjDGA1RCMMcY4LCEYY4wBLCEYY4xxWEIwxhgDWEIwxhjjsIRgjDEGgP8f/7ZNUjf7Nr0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data['age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dandy/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5785afcbe0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data2['age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dandy/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f57845b9128>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xhJDAxvoPNpTnRDmSc+cSIT/NR02r1RCMiRRLCAlsZ00bGX4PF8TIhLSJ8tJ91JyyGoIxkWIJIYHtOtbO+vIc3K7Y6j8Yk5fmo6FjgMGR0WiHYkxcsISQoE52D3LsVB8bymOv/2BMfpoPVTjeZs1GxkSCJYQENbY72voY7D8Yk5/uA7B+BGMiJKyEICIbRaRKRKpF5P5JXveJyPPO67tEpMw5fr2I7BWR/c73T4475zXnmvucrwWRuikzvWPOcM1lC9KiHMn5y0sbSwjWj2BMJEy7H4KIuIFHgeuBBmCPiFSo6qFxxe4FOlR1mYjcATwE3A6cAj6rqo0ispbQHsrF4867S1UrI3Qv5hzUtvWRn+4j1Re7W2J4PS6KMv0ctRqCMRERTg1hPVCtqjWqOgw8B2yaUGYT8LTzeCvwKRERVX1XVRud4wcBv4j4IhG4mZnjbf2UzuO9k8O1JD/VagjGREg4fx4WA/XjnjcAG6Yqo6oBEekCcgnVEMZ8EXhXVccvPvNDERkFfgp8VydZqUxENgObAUpKSsII14Sjrq2fK5flRTuMGRsNKkeae/jxztqP7AX95Q32u2LMuQqnhjDZmMSJH9xnLSMiawg1I31l3Ot3qeqFwNXO1+9P9sNV9XFVXaeq6/Lz88MI10xncGSU5u5BSnNjv4aQl+ZjKBCkdygQ7VCMiXnh1BAagMXjni8CGqco0yAiHiATaAcQkUXAi8Ddqnp07ARVPeF87xGRLYSapp45z/sw5+Cx10Jvw4mOAbbsqotyNDOT73Qst/YOke5PinI0xsS2cGoIe4DlIlIuIl7gDqBiQpkK4B7n8a3AdlVVEckCfgF8S1XfGissIh4RyXMeJwE3AwdmdismXG29wwDkpHqjHMnMjQ09bbVlsI2ZsWkTgqoGgPsIjRA6DLygqgdF5EERucUp9iSQKyLVwNeAsaGp9wHLgG9PGF7qA14WkfeBfcAJ4AeRvDEztfa+0IdnblrsJ4SM5CS8HhcnuwejHYoxMS+sMYequg3YNuHYA+MeDwK3TXLed4HvTnHZy8IP00RSW98w/iQXKd7YHXI6xiXCwsxkTnTYdprGzJTNVE5A7X3D5KbGz+jf4iw/TV2DjAZtO01jZsISQgJq6xuOi/6DMcXZKQSCSkuPNRsZMxOWEBLMyGiQzv7huOg/GLMoKxnAmo2MmSFLCAnmRMcAQYXcOKoh5KR58XlcnOi0hGDMTFhCSDC17f0A5MRRH4JLhIVZyZYQjJkhSwgJptbZOyCeaggAxVnJNFvHsjEzYgkhwdS29ZPkFtL9sT/kdLzi7GQCQbX5CMbMgCWEBFPb1k9OqvcjC8HFg+KxjmVrNjLmvFlCSDAnOgfITomv5iIILcPhT3LZSCNjZsASQoJp6hogMzn+FoGzjmVjZs4SQgIZGB6ls38kLhMCQHleKo2dA3T2D0c7FGNikiWEBNLYFfrrOV4TwscWZ6PAO3Wd0Q7FmJhkCSGBNHWGRuBkpsRnQshJ9VKel8o7dR1MsvmeMWYalhASyFgNISs5/jqVx1xWkk173zCVtR3RDsWYmGMJIYGM1RAy4mwOwnhrijPwul1srWyIdijGxBxLCAmkqWuAvDQfHnf8vu0+j5u1xZn8Yn8T/cO2z7Ix5yKsTwYR2SgiVSJSLSL3T/K6T0Sed17fJSJlzvHrRWSviOx3vn9y3DmXOcerReT7Em8zpeahxq5BFmb5ox3GrFtXmk3vUIC/3nbE+hKMOQfTth2IiBt4FLgeaAD2iEiFqh4aV+xeoENVl4nIHcBDwO3AKeCzqtooImsJbcNZ7JzzGLAZ2EloN7aNwEuRuS0zmabOAZbkp0Y7jFlXlpfKVcvyeHZnLe19w1y5LA+AL28oiXJkxsxv4dQQ1gPVqlqjqsPAc8CmCWU2AU87j7cCnxIRUdV3VbXROX4Q8Du1iSIgQ1Xf1tCfcM8An5vx3ZizauoapCgzOdphzImNawtZXZTBtv1NHGnqjnY4xsSEcBJCMVA/7nkDv/0r/4wyqhoAuoDcCWW+CLyrqkNO+fG9fpNd00RQ9+AIvUOBhGgygtDM5S+tW0xRpp+fvnuCgeHRaIdkzLwXTkKYrG1/YsPsWcuIyBpCzUhfOYdrjp27WUQqRaSytbU1jHDNZMZGGCVKDQHA63HxhUsX0T8U4OVDzdEOx5h5L5yE0AAsHvd8EdA4VRkR8QCZQLvzfBHwInC3qh4dV37RNNcEQFUfV9V1qrouPz8/jHDNZMbmICRKDWHMwqxkrliay55j7eyrtxnMxpxNOAlhD7BcRMpFxAvcAVRMKFMB3OM8vhXYrqoqIlnAL4BvqepbY4VVtQnoEZHLndFFdwM/m+G9mLNIxBrCmE9fUEC638NfvLifoG2gY8yUpk0ITp/AfYRGCB0GXlDVgyLyoIjc4hR7EsgVkWrga8DY0NT7gGXAt0Vkn/O1wHntj4EngGrgKDbCaFY1dQ3gEliQHj9bZ4bLn+Tm0xcUcLCxm/carJZgzFTCmrKqqtsIDQ0df+yBcY8HgdsmOe+7wHenuGYlsPZcgjXnr7FzkIIMf1xPSjub1Qsz+Nd9J9hxpIWPlWRHOxxj5qXE/HRIQE1dAxRlJlb/wXgpXg+XlmSzo8oGJhgzFUsICaKpa5CirMTrPxjvulUL2H+iixbbd9mYSVlCSACqSmPnAAsTuIYAcN3KUPfVa1ZLMGZSlhASQEf/CEOBYEKOMBrvgqJ0CjP87KhqiXYoxsxL8bsOsjmtvr0fgEXZiZ0QfrK7nsU5yWw/0sIzbx/H4wr9PWRrHBkTYjWEBFDrJITS3Phf2G46KwsyGAoEqW3rj3Yoxsw7lhASQF1bHwCLcxK7hgCwdEEqbhE+aO6JdijGzDuWEBJAbVs/+ek+UrzWQujzuCnJTaG6tTfaoRgz71hCSAC17f2U5qREO4x5Y/mCNJq6BukZHIl2KMbMK5YQEkBdWz8luZYQxixbkAbAUaslGPMRlhDi3ODIKM3dg5TmWIfymIVZyaR43VS3WEIwZjxLCHGu/vQII6shjHGJsDQ/jQ9bem3PZWPGsYQQ5+qchGBNRh+1fEEaPYMBWnqGoh2KMfOGJYQ4Nzbe3jqVP2qsH+FDazYy5jRLCHGurr2fNJ+HnFRvtEOZV7JSvOSl+ahusfkIxoyxhBDnatv6KMlJIbQxnRlvVWE6R1v6ONVrzUbGQJgJQUQ2ikiViFSLyP2TvO4Tkeed13eJSJlzPFdEdohIr4g8MuGc15xrTtxJzURQbXu/dShPYV1pNqOqbN3bEO1QjJkXpk0IIuIGHgVuAlYDd4rI6gnF7gU6VHUZ8DDwkHN8EPg28I0pLn+Xql7ifNkSlBE2GlQa2gesQ3kKCzL8lOelsmVXne21bAzhrXa6HqhW1RoAEXkO2AQcGldmE/BXzuOtwCMiIqraB7wpIssiF7IJV3P3IMOjQUqsQ3lK68tzeH5PPf/j54dYXpD+kddsFVSTaMJpMioG6sc9b3COTVpGVQNAF5AbxrV/6DQXfVuskTviap1F7WxS2tTWFGWQ6nWz61h7tEMxJurCSQiTfVBPrF+HU2aiu1T1QuBq5+v3J/3hIptFpFJEKltbbaerc1HXZpPSpuNxu7isNIcjzd10DdjaRiaxhZMQGoDF454vAhqnKiMiHiATOOufXKp6wvneA2wh1DQ1WbnHVXWdqq7Lz88PI1wz5lhbH0luoSjBt86czvryHFRhZ01btEMxJqrCSQh7gOUiUi4iXuAOoGJCmQrgHufxrcB2PcuaACLiEZE853EScDNw4FyDN2d38EQ3KwvT8bhtdPHZ5KR6WbMwg13H2hgaGY12OMZEzbSfFE6fwH3Ay8Bh4AVVPSgiD4rILU6xJ4FcEakGvgacHpoqIseBvwX+QEQanBFKPuBlEXkf2AecAH4Qudsyqsr+E11cWJwZ7VBiwtXL8xkcCVJZ2xHtUIyJmrB2TFHVbcC2CcceGPd4ELhtinPLprjsZeGFaM5HffsAXQMjrLWEEJbFOSmU5qbw1tFTXL4kF7fLxjiYxGNbaMWp/Se6ADjRMcCWXXVRjiY2XL0sn/+3q5aDjV1ctCgr2uEYM+escTlO7T/RhVuEwgzrUA7XqqJ0clO9NgTVJCxLCHHqwIkuCjJ91qF8DlwiXLw4i+On+ui27TVNArJPizg01qFcnJUc7VBizoXFmSihhGpMorGEEIfGOpQXWkI4ZwUZfgoyfKf7YIxJJJYQ4tDYh5nVEM7PhcVZ1Lb109Q1EO1QjJlTlhDi0P4TXSS5rUP5fF3kDNXdtr85ypEYM7csIcShAye6bIbyDOSl+yjK9PPz9yeu0GJMfLNPjDgTGA3yXkOnzVCeoQuLM3m3rpPGTms2MonDEkKcebe+k57BAFcts4UAZ2LNwlBCfeWgNRuZxGEJIc7sONKC2yVctTwv2qHEtPx0H8sXpPFLSwgmgVhCiDOvVbVyWWk2mclJ0Q4l5t24ppDdx9pp7xuOdijGzAlLCHGkuWuQQ03dXLdyQbRDiQsb1xYSVPjVoZPRDsWYOWEJIY68/kELANetsv6DSFizMIPirGRetmYjkyAsIcSRHUdaKczws3LCZvHm/Pxkdz1luSm89kErP3zzmK0aa+KeJYQ4MRwI8mb1Ka5blY+IreUfKasXZjIaVKpO9kQ7FGNmnSWEOFFZ207vUIBrrf8gokpzU8jwe3inznZSM/EvrIQgIhtFpEpEqkXk/kle94nI887ru0SkzDmeKyI7RKRXRB6ZcM5lIrLfOef7Yn/WzsjrVa0kuYUrl9lw00hyibCuLIcPT/bSYaONTJybNiGIiBt4FLgJWA3c6eyLPN69QIeqLgMeBh5yjg8C3wa+McmlHwM2A8udr43ncwMmZEdVC+vLc0jz2SZ4kbauNBsI1cKMiWfh1BDWA9WqWqOqw8BzwKYJZTYBTzuPtwKfEhFR1T5VfZNQYjhNRIqADFV9W1UVeAb43ExuJJE1dPTzwcleG246S7JSvKwoSKeytoPAaDDa4Rgza8JJCMVA/bjnDc6xScuoagDoAnKnuWbDNNcEQEQ2i0iliFS2traGEW7iea0q9O9y7UobbjpbPl6WQ89ggO1HWqIdijGzJpyEMFnbvp5HmfMqr6qPq+o6VV2Xn28feJN5raqFRdnJLM1Pi3YocWtlYTrpfg9bdtvQUxO/wmlwbgAWj3u+CJi4LvBYmQYR8QCZwNkaXBuc65ztmiYMgyOjvP5BK5eWZPOT3fXTn2DOi9slrC/P4dXDLVQ197Cy0OZ6mPgTTg1hD7BcRMpFxAvcAVRMKFMB3OM8vhXY7vQNTEpVm4AeEbncGV10N/Czc47esOd4OyOjah9Qc+CKJbmkeN089lp1tEMxZlZMW0NQ1YCI3Ae8DLiBp1T1oIg8CFSqagXwJPCsiFQTqhncMXa+iBwHMgCviHwOuEFVDwF/DPwISAZecr7MOdpxpBWPS1iSZ81Fsy3F6+HSkmwq3mtk2YJ0clK9p1/78oaSKEZmTGSENUZRVbcB2yYce2Dc40HgtinOLZvieCWwNtxAzeReq2phSX4qXo/NMZwLVy3L4+2aNt74sJXPXTLpOAhjYpZ9isSw46f6qDnVxwpbu2jOZCQncWlJNntrO+geHIl2OMZElCWEGPZaVWgIpC1mN7euWZ5HMKi8VX0q2qEYE1GWEGLYjqpWyvNSyU3zRTuUhJKb5uPCRZnsOtbOwPBotMMxJmIsIcSogeFRdta02WS0KPmdFfkMB4K8XWO1BBM/LCHEqJ01bQwFgrZcRZQUZSazqjCd3xxtYzhgy1mY+GAJIUbtqGohOcnN+vKcaIeSsK5dkU//8Ci7j9uidyY+WEKIQarK9iMtfGJpLv4kd7TDSVgluakszU/l9aoWemzEkYkDlhBi0Acne2noGODaVdZcFG03rimkb3iUf3q9JtqhGDNjlhBi0M/fb8QlsHFNYbRDSXiLslO4aFEmT7xZQ3PX4PQnGDOPWUKIMarKv73XyBVLc8lPt+Gm88ENqwsJBuHhf/8g2qEYMyOWEGLMwcZujrf1c/NFC6MdinHkpHq5+4pSXthbz0v7m6IdjjHnzfZbjDH/9l4jHpdYc9E8szgnhcXZKfznn7zLH9ZfUeOOAAAQ00lEQVR1UpaXCtiidya2WA0hhqgqP3+/iauX55E9bqVNE31Jbhd3X15KVkoSz+6s5b36ToZGbBaziS1WQ4gh79R1cqJzgK9dvyLaoZhJpPg8/MEnynnyzRqer6zH4xKer6ynf3iUoCq/e1ERd19RRnFWcrRDNWZSlhBiyHf+7SAel9A1MMKWXbaV43yUk+rl6zespK6tn4ONXaT4PKR63fQMBvjBGzU88etj/Mm1S/na9SsI7Q1lzPxhCSFG7D7WzvsNXXxy1QKbjDbPuUQoy0ulLC/1I30IDR39/J9XPuAftldT1dzDtROWHbH+BhNtYSUEEdkI/D2hHdOeUNX/PeF1H/AMcBnQBtyuqsed174F3AuMAn+qqi87x48DPc7xgKqui8D9xKXRoPKXFQfJTE7imuW2mF0smViTu6w0m5rWXl45dJJkr5sN5blRisyYM02bEETEDTwKXA80AHtEpMLZBnPMvUCHqi4TkTuAh4DbRWQ1oe001wALgV+JyApVHettu05VbbnIafxkdx2Hm7q5c32J7YwW41wi3HrZYgZHglTsayQr2Wv7YZt5I5xPl/VAtarWqOow8BywaUKZTcDTzuOtwKck1EC6CXhOVYdU9RhQ7VzPhGFgeJTvvVzFg/92iMuX5LB2YUa0QzIR4HYJd64voTDTz/OVdbT2DEU7JGOA8BJCMVA/7nmDc2zSMqoaALqA3GnOVeAVEdkrIpvPPfT41D04wkv7m7jrBzu5/K9f5ZEd1axemMEnVxVYJ2Qc8Xpc/N7lpbhFeHbncdtox8wL4fQhTPYppGGWOdu5V6pqo4gsAP5dRI6o6htn/PBQstgMUFISv51uqsq2/c38ZcUBTvUO43EJpbkpfGndYsqdSU4mvmSneLlrQylPvnmMH/y6hs9cVEhRpg1JNdETTg2hAVg87vkioHGqMiLiATKB9rOdq6pj31uAF5miKUlVH1fVdaq6Lj8/PjtUB0dG+eqWd/jqlncoykzmuc2X88BnV3PvVUssGcS5srxU7v5EKR39w3z+0d9w4ERXtEMyCSycGsIeYLmIlAMnCHUSf3lCmQrgHuBt4FZgu6qqiFQAW0Tkbwl1Ki8HdotIKuBS1R7n8Q3AgxG5ozk01VyAcxk+GAwqX//n99i2v5lv3riSr1yzBI/bRU1rX6TCNPPc8gXpbL5mCf9c2cDN//AmVy/P4/aPL+ai4iyKs5Nxu6yp0MyNaROCqgZE5D7gZULDTp9S1YMi8iBQqaoVwJPAsyJSTahmcIdz7kEReQE4BASAr6rqqIgUAC86beIeYIuq/nIW7m9OqOp5t+9/75UqfvF+E/fftIo/+p2lEY7MxIqizGT+8Kpydh9rZ8/xdn79YWjwndslFGb4yUn1UpTp5/IluVy9PI9lC9KsT8lEnKhO7A6Yv9atW6eVlZXRDuO0Lbvq6B0K8NSbx8hI9vCldYtJ8XrCriE8v6eO//bT/dy5voT/9fm1H/kPbjORE1dQlfr2flp7hjjVO0xeupf2vmGOneqjtq0fgA3lOfz5xlVcVpod5WhNLBCRveHM9bKZyjMwODLKj946RlvfEK29Q/zf147yextKwzr3zQ9P8RcvHmD5gjRWF2Xwk931059kEoJLhNLcVEpzz+w/6ugb5lBTN69/0MoXH/sNFxRlcMPqAgoy/IDNdjYzY7OcztPgyCjPvH2ck91D3LWhlP909RJGRoP80xtHaewcOOu5H5zs4Y//316WLUjjzvUl1kZswpad6uXKZXl8/YYVXL+6gJrWXr7/6oe8UFnPkaZueocC0Q7RxDBLCOfpR785zvG2fm5dt4gVBemU5KTwlWuWElTlgZ8dYKqmuBOdA/zBU7vxe908+Qcft3WJzHnxedxct3IB37xhJVcty+NgYxfP7Kzlku+8wt1P7abivUYGbfltc46syeg8dA+O8I+vH2VFQRoXL8o6fTwn1cunLyjgpQPN/PJAMzddWHT6tS276ugZHOEHv66hdyjAf7xqCa9XtUYjfBNHUnwebrqwiE+vLqCuvZ8PT/byfn0nb3zQSpov1K+1bEGaNSWZsFgN4Tw89eYxOvtHuP6CM3ct+8TSPFYXZfCXFQfpGhg5fbx7cIQf/eY4XQMj3HNFGQttTXwTQUluF0vz09i4tpBv3LiSP7yynBSvmx++dYxXD59kNBg7g0dM9FhCOEcdfcM88etjbFxTSHH2mR/qbpfw11+4kFO9Q9zw8Ov8c2U9/1xZz9/96gNae0L9DZN1FhoTKS4Rli1I40+uXcbFi7N49UgL/+mZSroHR6Y/2SQ0SwjnQFX53itV9A0H+NoNU+9advHiLF74yhUUZvj55tb3+ebW9ynI8POnn1zOigJb2dLMDa/HxW2XLeKWixfyxgetfP7Rtzh2yiY8mqlZH8I5eGR7NT/eVce9V5WzoiCdyuMdk5Ybm0Nw27rFrF6YydDIKJeWZuOyiURmjokIly/JZUG6jy2767jh4de5buUCrlqWh8ftsr4F8xGWEMbZsquO0aCyo6qF1p4h0nweUn0ePrlqASc6+3l0x1G+cGkxf/GZC8K6nkuEC4szZzlqY6a3JD+N+65bxi/2N/HKoZPsre3gymV5fPbiItL9SdEOz8wTlhDGGQqM8tzueqpO9pCdksTAyCiDI0F+dfgkADetLeRvvngRLps3YGJQlrO66ocne3jl0Ekq3mvk1cMnuXFNITeuLeSa5fkke20YdCKzhODoHhzhiV8fo7FzgE2XLDy9tWFgNMgNawrpHRphSV6aJQMT85YXpLO8IJ2Gjn52HWvnpQPN/Mu7J/B6XFxYnMm3bgotiWFrJSUeSwiOv/nlERo7B/i9y0u5oOi3O5N53C4KM/2AP3rBGTMLFmWnsCg7hc9dohw71cd7DZ3sb+ji1n98m4+VZPFfPr2Ca5bnWWJIIJYQgHfrOvjxrjquWJr7kWRgTCJwu0LDVJctSOPmi4pIcrt47LWj3PPUbi5alMk9V5TxuxcV2az6BJDwq50GRoPc8shbtPcN85VrluCzX3pjCASD7K3t4DfVbbT2DuFPcnH5klwuXpTF4pwUspKTyEpJIivFS1ZKEpnJSSS5bRT7fGWrnYbph28d51BTN4/ddSkd/TZxxxgAj8vFhvJc1pflUHOqj331nRxu6ub1qtYz9s8d4/O4SPN5yExJIivZy1XLcinKSibN58HjEjxul/NdyE/3UZSZTIbfY01S80hCJ4R36jr4m5ePcP3qAjauLbQlqI2ZQERYmp/G0vw0AIYDQXqHAvQPBxgYHqV/ZDT0fThA//AovUMBOvtHONray776DqZbMcPrdpGZnMSqonSKMv0UZiazMNNPUVboe2Gm34bFzqGwEoKIbAT+ntCOaU+o6v+e8LoPeAa4DGgDblfV485r3wLuBUaBP1XVl8O55mxr7xvmvh+/Q0GGn+/derH9lWJMGLweFzkeLzmp3mnLjgaVnsERhgNBghra+Ceoysio0jsUoKt/mK6BEToHRugZDPDByVZaeoaY2IrtT3KRl+ajIN3PggwfX/r4YpbmpZGVmkSa12Mj/yJo2oQgIm7gUeB6oAHYIyIVqnpoXLF7gQ5VXSYidwAPAbeLyGpC22muIbSn8q9EZGzNh+muOWtae4b4s+fe5VTfMP/yx58gM8X+AjEm0twuIStl+sQx3mhQ6R4codtJFF39I3QODNPSM0TVyR721nXw0oHm0+VdAun+JDKSPWQmJ5HhD/Vn5Kf7KMjwk+734HYJSS4XHneo2SrJab5K9bopyPRTmOEn1ZfQjSWnhfOvsB6oVtUaABF5DthEaJ/kMZuAv3IebwUekdCf3JuA51R1CDjm7Lm83ik33TUjRlXp7B+hoWOAXx0+yQ9+XcNwIMhff+FC1tpMYmPmDbdLyE7xkp3iZbK9B/uHApzsGaK9b4iB4VEGRkYZGAky6DRdnegY4MOWXnoHAwycw34Q6X4PhRl+CjL8LEj3kZ/hY0G6n/x0H35PKJm4RHC7BPfY98m+RHCNK+M6fYwzjrldv72mS5gXrRThJIRiYHzjegOwYaoyqhoQkS4g1zm+c8K5xc7j6a4ZMTf+3Rt8cLL39PPPXFjIN29cRXmerTpqTCxJ8Xko93nC+r87MhpkKBAkGFRGVcd9h1FVhkZG6R4coWsgQPfACF0DI9S29XHgRBc9Q4E5XzLcJZxOEuNzgxB68u4D18/60N9wEsJkaWviv9RUZaY6Ptn4tEn/9UVkM7DZedorIlVTxBm2x5yvSeQBp2Z6/RiQCPdp9xgf7B4dyd+d0c8Ia7P3cBJCA7B43PNFQOMUZRpExANkAu3TnDvdNQFQ1ceBx8OIc8ZEpDKcsbqxLhHu0+4xPtg9zq1wZpLsAZaLSLmIeAl1EldMKFMB3OM8vhXYrqEZbxXAHSLiE5FyYDmwO8xrGmOMmUPT1hCcPoH7gJcJDRF9SlUPisiDQKWqVgBPAs86ncbthD7gccq9QKizOAB8VVVHASa7ZuRvzxhjTLhiaumK2SYim50mqriWCPdp9xgf7B7nliUEY4wxgO2pbIwxxmEJwSEiG0WkSkSqReT+aMcTCSKyWER2iMhhETkoIn/mHM8RkX8XkQ+d79nRjnWmRMQtIu+KyM+d5+Uissu5x+edwQsxS0SyRGSriBxx3s8r4u19FJH/6vyeHhCRn4iIP9bfRxF5SkRaROTAuGOTvm8S8n3nM+h9Ebl0ruO1hMBHlue4CVgN3OksuxHrAsDXVfUC4HLgq8593Q+8qqrLgVed57Huz4DD454/BDzs3GMHoeVVYtnfA79U1VXAxYTuNW7eRxEpBv4UWKeqawkNNhlbBieW38cfARsnHJvqfbuJ0EjM5YTmXk0xXWr2WEIIOb08h6oOA2NLacQ0VW1S1Xecxz2EPkSKCd3b006xp4HPRSfCyBCRRcDvAk84zwX4JKFlVCDG71FEMoBrCI3mQ1WHVbWTOHsfCY16THbmMqUATcT4+6iqbxAaeTneVO/bJuAZDdkJZIlI0dxEGmIJIWSy5TmKpygbk0SkDPgYsAsoUNUmCCUNYEH0IouIvwP+HAg6z3OBTlUNOM9j/f1cArQCP3SaxZ4QkVTi6H1U1RPA94A6QomgC9hLfL2PY6Z636L+OWQJISSc5TliloikAT8F/ouqdkc7nkgSkZuBFlXdO/7wJEVj+f30AJcCj6nqx4A+Yrh5aDJOO/omoJzQysiphJpQJorl93E6Uf+9tYQQEs7yHDFJRJIIJYMfq+q/OIdPjlVFne8t0YovAq4EbhGR44Sa+j5JqMaQ5TQ9QOy/nw1Ag6rucp5vJZQg4ul9/DRwTFVbVXUE+BfgE8TX+zhmqvct6p9DlhBC4nIpDact/UngsKr+7biXxi81cg/ws7mOLVJU9VuqukhVywi9b9tV9S5gB6FlVCD277EZqBeRlc6hTxGa/R837yOhpqLLRSTF+b0du8e4eR/Hmep9qwDudkYbXQ50jTUtzRWbmOYQkc8Q+stybCmN/xnlkGZMRK4Cfg3s57ft6/+dUD/CC0AJof+It6nqxI6vmCMi1wLfUNWbRWQJoRpDDvAu8HvOvhwxSUQuIdRp7gVqgP9A6A+6uHkfReQ7wO2ERse9C/xHQm3oMfs+ishPgGsJrWh6EvhL4F+Z5H1zEuEjhEYl9QP/QVUr5zReSwjGGGPAmoyMMcY4LCEYY4wBLCEYY4xxWEIwxhgDWEIwxhjjsIRgjDEGsIRgjDHGYQnBGGMMAP8fHNYXud0KA30AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data.loc[data['gender'] == 0, ['age']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dandy/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f578469c128>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
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    "sns.distplot(data.loc[data['gender'] == 1, ['age']])"
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   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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