Draft No Show Appointments Visuals Project.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports and load data\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patientid</th>\n",
       "      <th>appointmentid</th>\n",
       "      <th>gender</th>\n",
       "      <th>scheduledday</th>\n",
       "      <th>appointmentday</th>\n",
       "      <th>age</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>scholarship</th>\n",
       "      <th>hipertension</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>alcoholism</th>\n",
       "      <th>handcap</th>\n",
       "      <th>sms_received</th>\n",
       "      <th>no_show</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>29872499824296</td>\n",
       "      <td>5642903</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T18:38:08Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>62</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
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       "      <td>No</td>\n",
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       "      <td>5642503</td>\n",
       "      <td>M</td>\n",
       "      <td>2016-04-29T16:08:27Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4262962299951</td>\n",
       "      <td>5642549</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T16:19:04Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>62</td>\n",
       "      <td>MATA DA PRAIA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>No</td>\n",
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       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>8</td>\n",
       "      <td>PONTAL DE CAMBURI</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8841186448183</td>\n",
       "      <td>5642494</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-29T16:07:23Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>56</td>\n",
       "      <td>JARDIM DA PENHA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        patientid  appointmentid gender          scheduledday  \\\n",
       "0  29872499824296        5642903      F  2016-04-29T18:38:08Z   \n",
       "1 558997776694438        5642503      M  2016-04-29T16:08:27Z   \n",
       "2   4262962299951        5642549      F  2016-04-29T16:19:04Z   \n",
       "3    867951213174        5642828      F  2016-04-29T17:29:31Z   \n",
       "4   8841186448183        5642494      F  2016-04-29T16:07:23Z   \n",
       "\n",
       "         appointmentday  age      neighbourhood  scholarship  hipertension  \\\n",
       "0  2016-04-29T00:00:00Z   62    JARDIM DA PENHA            0             1   \n",
       "1  2016-04-29T00:00:00Z   56    JARDIM DA PENHA            0             0   \n",
       "2  2016-04-29T00:00:00Z   62      MATA DA PRAIA            0             0   \n",
       "3  2016-04-29T00:00:00Z    8  PONTAL DE CAMBURI            0             0   \n",
       "4  2016-04-29T00:00:00Z   56    JARDIM DA PENHA            0             1   \n",
       "\n",
       "   diabetes  alcoholism  handcap  sms_received no_show  \n",
       "0         0           0        0             0      No  \n",
       "1         0           0        0             0      No  \n",
       "2         0           0        0             0      No  \n",
       "3         0           0        0             0      No  \n",
       "4         1           0        0             0      No  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('desktop/noshowappointments-kagglev2-may-2016.csv')\n",
    "schema_df = pd.read_csv('desktop/noshowappointments-kagglev2-may-2016.csv')\n",
    "df.columns=df.columns.str.strip().str.lower().str.replace('-','_')\n",
    "pd.options.display.float_format = '{:.0f}'.format\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\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>patientid</th>\n",
       "      <th>appointmentid</th>\n",
       "      <th>gender</th>\n",
       "      <th>scheduledday</th>\n",
       "      <th>appointmentday</th>\n",
       "      <th>age</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>scholarship</th>\n",
       "      <th>hipertension</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>alcoholism</th>\n",
       "      <th>handcap</th>\n",
       "      <th>sms_received</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>no_show</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>88208</td>\n",
       "      <td>88208</td>\n",
       "      <td>88208</td>\n",
       "      <td>88208</td>\n",
       "      <td>88208</td>\n",
       "      <td>88208</td>\n",
       "      <td>88208</td>\n",
       "      <td>88208</td>\n",
       "      <td>88208</td>\n",
       "      <td>88208</td>\n",
       "      <td>88208</td>\n",
       "      <td>88208</td>\n",
       "      <td>88208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>22319</td>\n",
       "      <td>22319</td>\n",
       "      <td>22319</td>\n",
       "      <td>22319</td>\n",
       "      <td>22319</td>\n",
       "      <td>22319</td>\n",
       "      <td>22319</td>\n",
       "      <td>22319</td>\n",
       "      <td>22319</td>\n",
       "      <td>22319</td>\n",
       "      <td>22319</td>\n",
       "      <td>22319</td>\n",
       "      <td>22319</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         patientid  appointmentid  gender  scheduledday  appointmentday  \\\n",
       "no_show                                                                   \n",
       "No           88208          88208   88208         88208           88208   \n",
       "Yes          22319          22319   22319         22319           22319   \n",
       "\n",
       "           age  neighbourhood  scholarship  hipertension  diabetes  \\\n",
       "no_show                                                              \n",
       "No       88208          88208        88208         88208     88208   \n",
       "Yes      22319          22319        22319         22319     22319   \n",
       "\n",
       "         alcoholism  handcap  sms_received  \n",
       "no_show                                     \n",
       "No            88208    88208         88208  \n",
       "Yes           22319    22319         22319  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('no_show').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get counts for each rating and color\n",
    "counts1 = df.groupby(['gender']).count()['no_show']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender\n",
       "F    71840\n",
       "M    38687\n",
       "Name: no_show, dtype: int64"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [],
   "source": [
    "totals = df.groupby(['no_show']).count()['gender']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no_show\n",
       "No     88208\n",
       "Yes    22319\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "totals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "noshow = df['no_show'] == 'Yes'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         False\n",
       "1         False\n",
       "2         False\n",
       "3         False\n",
       "4         False\n",
       "          ...  \n",
       "110522    False\n",
       "110523    False\n",
       "110524    False\n",
       "110525    False\n",
       "110526    False\n",
       "Name: no_show, Length: 110527, dtype: bool"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "noshow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "showup = df['no_show'] == 'No'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         True\n",
       "1         True\n",
       "2         True\n",
       "3         True\n",
       "4         True\n",
       "          ... \n",
       "110522    True\n",
       "110523    True\n",
       "110524    True\n",
       "110525    True\n",
       "110526    True\n",
       "Name: no_show, Length: 110527, dtype: bool"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "showup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "patientid         4\n",
       "appointmentid     4\n",
       "scheduledday      4\n",
       "appointmentday    4\n",
       "age               4\n",
       "neighbourhood     4\n",
       "scholarship       4\n",
       "hipertension      4\n",
       "diabetes          4\n",
       "alcoholism        4\n",
       "handcap           4\n",
       "sms_received      4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Age')"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "counts = gender\n",
    "gender.plot(kind='bar', title='No Show by Gender Age', color='b', alpha=.7)\n",
    "                 \n",
    "plt.xlabel(\"No show\" and \"Gender\", fontsize=18)\n",
    "plt.ylabel(\"Age\", fontsize=18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "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></th>\n",
       "      <th></th>\n",
       "      <th>patientid</th>\n",
       "      <th>appointmentid</th>\n",
       "      <th>scheduledday</th>\n",
       "      <th>appointmentday</th>\n",
       "      <th>age</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>scholarship</th>\n",
       "      <th>hipertension</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>alcoholism</th>\n",
       "      <th>handcap</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th>no_show</th>\n",
       "      <th>sms_received</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">F</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">No</th>\n",
       "      <th>0</th>\n",
       "      <td>39764</td>\n",
       "      <td>39764</td>\n",
       "      <td>39764</td>\n",
       "      <td>39764</td>\n",
       "      <td>39764</td>\n",
       "      <td>39764</td>\n",
       "      <td>39764</td>\n",
       "      <td>39764</td>\n",
       "      <td>39764</td>\n",
       "      <td>39764</td>\n",
       "      <td>39764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17482</td>\n",
       "      <td>17482</td>\n",
       "      <td>17482</td>\n",
       "      <td>17482</td>\n",
       "      <td>17482</td>\n",
       "      <td>17482</td>\n",
       "      <td>17482</td>\n",
       "      <td>17482</td>\n",
       "      <td>17482</td>\n",
       "      <td>17482</td>\n",
       "      <td>17482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Yes</th>\n",
       "      <th>0</th>\n",
       "      <td>7874</td>\n",
       "      <td>7874</td>\n",
       "      <td>7874</td>\n",
       "      <td>7874</td>\n",
       "      <td>7874</td>\n",
       "      <td>7874</td>\n",
       "      <td>7874</td>\n",
       "      <td>7874</td>\n",
       "      <td>7874</td>\n",
       "      <td>7874</td>\n",
       "      <td>7874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6720</td>\n",
       "      <td>6720</td>\n",
       "      <td>6720</td>\n",
       "      <td>6720</td>\n",
       "      <td>6720</td>\n",
       "      <td>6720</td>\n",
       "      <td>6720</td>\n",
       "      <td>6720</td>\n",
       "      <td>6720</td>\n",
       "      <td>6720</td>\n",
       "      <td>6720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">M</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">No</th>\n",
       "      <th>0</th>\n",
       "      <td>22746</td>\n",
       "      <td>22746</td>\n",
       "      <td>22746</td>\n",
       "      <td>22746</td>\n",
       "      <td>22746</td>\n",
       "      <td>22746</td>\n",
       "      <td>22746</td>\n",
       "      <td>22746</td>\n",
       "      <td>22746</td>\n",
       "      <td>22746</td>\n",
       "      <td>22746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8216</td>\n",
       "      <td>8216</td>\n",
       "      <td>8216</td>\n",
       "      <td>8216</td>\n",
       "      <td>8216</td>\n",
       "      <td>8216</td>\n",
       "      <td>8216</td>\n",
       "      <td>8216</td>\n",
       "      <td>8216</td>\n",
       "      <td>8216</td>\n",
       "      <td>8216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Yes</th>\n",
       "      <th>0</th>\n",
       "      <td>4661</td>\n",
       "      <td>4661</td>\n",
       "      <td>4661</td>\n",
       "      <td>4661</td>\n",
       "      <td>4661</td>\n",
       "      <td>4661</td>\n",
       "      <td>4661</td>\n",
       "      <td>4661</td>\n",
       "      <td>4661</td>\n",
       "      <td>4661</td>\n",
       "      <td>4661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3064</td>\n",
       "      <td>3064</td>\n",
       "      <td>3064</td>\n",
       "      <td>3064</td>\n",
       "      <td>3064</td>\n",
       "      <td>3064</td>\n",
       "      <td>3064</td>\n",
       "      <td>3064</td>\n",
       "      <td>3064</td>\n",
       "      <td>3064</td>\n",
       "      <td>3064</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             patientid  appointmentid  scheduledday  \\\n",
       "gender no_show sms_received                                           \n",
       "F      No      0                 39764          39764         39764   \n",
       "               1                 17482          17482         17482   \n",
       "       Yes     0                  7874           7874          7874   \n",
       "               1                  6720           6720          6720   \n",
       "M      No      0                 22746          22746         22746   \n",
       "               1                  8216           8216          8216   \n",
       "       Yes     0                  4661           4661          4661   \n",
       "               1                  3064           3064          3064   \n",
       "\n",
       "                             appointmentday    age  neighbourhood  \\\n",
       "gender no_show sms_received                                         \n",
       "F      No      0                      39764  39764          39764   \n",
       "               1                      17482  17482          17482   \n",
       "       Yes     0                       7874   7874           7874   \n",
       "               1                       6720   6720           6720   \n",
       "M      No      0                      22746  22746          22746   \n",
       "               1                       8216   8216           8216   \n",
       "       Yes     0                       4661   4661           4661   \n",
       "               1                       3064   3064           3064   \n",
       "\n",
       "                             scholarship  hipertension  diabetes  alcoholism  \\\n",
       "gender no_show sms_received                                                    \n",
       "F      No      0                   39764         39764     39764       39764   \n",
       "               1                   17482         17482     17482       17482   \n",
       "       Yes     0                    7874          7874      7874        7874   \n",
       "               1                    6720          6720      6720        6720   \n",
       "M      No      0                   22746         22746     22746       22746   \n",
       "               1                    8216          8216      8216        8216   \n",
       "       Yes     0                    4661          4661      4661        4661   \n",
       "               1                    3064          3064      3064        3064   \n",
       "\n",
       "                             handcap  \n",
       "gender no_show sms_received           \n",
       "F      No      0               39764  \n",
       "               1               17482  \n",
       "       Yes     0                7874  \n",
       "               1                6720  \n",
       "M      No      0               22746  \n",
       "               1                8216  \n",
       "       Yes     0                4661  \n",
       "               1                3064  "
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['gender','no_show','sms_received']).count().head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Proportion')"
      ]
     },
     "execution_count": 36,
     "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": [
    "showup = gender\n",
    "gender.plot(kind='bar', title='No Show by Gender Age', color='b', alpha=.7)\n",
    "                 \n",
    "plt.xlabel(\"No show\" and \"Gender\", fontsize=18)\n",
    "plt.ylabel(\"Age\", fontsize=18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "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>gender</th>\n",
       "      <th>no_show</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>F</td>\n",
       "      <td>No</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>F</td>\n",
       "      <td>Yes</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M</td>\n",
       "      <td>No</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M</td>\n",
       "      <td>Yes</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  gender no_show  age\n",
       "0      F      No   40\n",
       "1      F     Yes   36\n",
       "2      M      No   34\n",
       "3      M     Yes   31"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['gender', 'no_show'], as_index=False)['age'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
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       "      <th>hipertension</th>\n",
       "      <th>diabetes</th>\n",
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       "      <th>handcap</th>\n",
       "      <th>sms_received</th>\n",
       "    </tr>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>104 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           patientid  appointmentid  scholarship  hipertension  diabetes  \\\n",
       "age                                                                        \n",
       "-1   465943158731293        5775010            0             0         0   \n",
       " 0   153902715980394        5671375            0             0         0   \n",
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       "...              ...            ...          ...           ...       ...   \n",
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       " 102 488264541685994        5701660            0             0         0   \n",
       " 115 175217485140130        5684971            0             0         0   \n",
       "\n",
       "      alcoholism  handcap  sms_received  \n",
       "age                                      \n",
       "-1             0        0             0  \n",
       " 0             0        0             0  \n",
       " 1             0        0             0  \n",
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       "...          ...      ...           ...  \n",
       " 98            0        0             0  \n",
       " 99            0        0             0  \n",
       " 100           0        0             0  \n",
       " 102           0        0             0  \n",
       " 115           0        1             0  \n",
       "\n",
       "[104 rows x 8 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('age').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[df['age']<=0] = df['age'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
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       "          patientid  appointmentid  scholarship  hipertension  diabetes  \\\n",
       "age                                                                       \n",
       "1   158022368331540        5678714            0             0         0   \n",
       "2   143777271922369        5681502            0             0         0   \n",
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       "     alcoholism  handcap  sms_received  \n",
       "age                                     \n",
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       "2             0        0             0  \n",
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       "115           0        1             0  \n",
       "\n",
       "[103 rows x 8 columns]"
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     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.groupby('age').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        62\n",
       "1        56\n",
       "2        62\n",
       "3         8\n",
       "4        56\n",
       "         ..\n",
       "110522   56\n",
       "110523   51\n",
       "110524   21\n",
       "110525   38\n",
       "110526   54\n",
       "Name: age, Length: 110527, dtype: float64"
      ]
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     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df['age'].apply(np.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<p>202 rows × 1 columns</p>\n",
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      ],
      "text/plain": [
       "             age\n",
       "age gender      \n",
       "1   F       1088\n",
       "    M       1185\n",
       "2   F        757\n",
       "    M        861\n",
       "3   F        764\n",
       "...          ...\n",
       "99  F          1\n",
       "100 F          2\n",
       "    M          2\n",
       "102 F          2\n",
       "115 F          5\n",
       "\n",
       "[202 rows x 1 columns]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = df[['age', 'gender']]\n",
    "b = a.groupby(['age', 'gender']).agg({'age' : 'count'})\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60271"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.patientid.nunique() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "103"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.age.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.gender.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-92-adf00401f9bd>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-92-adf00401f9bd>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    (make_barchart(total_female_missed_aday_per, total_male_missed_aday_per, 'pink', 'blue', 'Female', 'Male',.25, [0,50], '% missed',  'day_order, '% missed by gender on appointment day')\u001b[0m\n\u001b[1;37m                                                                                                                                                               ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "(make_barchart(total_female_missed_aday_per, total_male_missed_aday_per, 'pink', 'blue', 'Female', 'Male',.25, [0,50], '% missed',  'day_order, '% missed by gender on appointment day')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>VILA RUBIM</td>\n",
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       "    <tr>\n",
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       "      <td>VILA RUBIM</td>\n",
       "      <td>Yes</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>161 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     neighbourhood no_show  age\n",
       "0               37      37   37\n",
       "1        AEROPORTO      No   55\n",
       "2        AEROPORTO     Yes   37\n",
       "3       ANDORINHAS      No   38\n",
       "4       ANDORINHAS     Yes   36\n",
       "..             ...     ...  ...\n",
       "156     TABUAZEIRO     Yes   33\n",
       "157  UNIVERSITÁRIO      No   43\n",
       "158  UNIVERSITÁRIO     Yes   40\n",
       "159     VILA RUBIM      No   47\n",
       "160     VILA RUBIM     Yes   38\n",
       "\n",
       "[161 rows x 3 columns]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['neighbourhood', 'no_show'], as_index=False)['age'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender  neighbourhood  sms_received  no_show\n",
       "F       AEROPORTO      0             No        54\n",
       "                                     Yes       37\n",
       "        ANDORINHAS     0             No        38\n",
       "                                     Yes       38\n",
       "                       1             No        39\n",
       "                                               ..\n",
       "M       UNIVERSITÁRIO  1             Yes       34\n",
       "        VILA RUBIM     0             No        40\n",
       "                                     Yes       36\n",
       "                       1             No        41\n",
       "                                     Yes       35\n",
       "Name: age, Length: 624, dtype: float64"
      ]
     },
     "execution_count": 301,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['gender', 'neighbourhood','sms_received','no_show'], as_index=True)['age'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'Series' object has no attribute 'age'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-103-c661e5e9fd0e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mrange_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mrange_df\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'age'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mmen\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrange_df\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mage\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mage\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m&\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgender\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'M'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[0mwomen\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrange_df\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mage\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mage\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m&\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgender\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'F'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmen\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'b'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36mapply\u001b[1;34m(self, func, convert_dtype, args, **kwds)\u001b[0m\n\u001b[0;32m   3847\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3848\u001b[0m                 \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobject\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3849\u001b[1;33m                 \u001b[0mmapped\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmap_infer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconvert\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mconvert_dtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3850\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3851\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmapped\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmapped\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mSeries\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\lib.pyx\u001b[0m in \u001b[0;36mpandas._libs.lib.map_infer\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32m<ipython-input-103-c661e5e9fd0e>\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mrange_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mrange_df\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'age'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mmen\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrange_df\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mage\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mage\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m&\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgender\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'M'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[0mwomen\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrange_df\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mage\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mage\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m&\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgender\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'F'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmen\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'b'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m   5271\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5272\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5273\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5274\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5275\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'Series' object has no attribute 'age'"
     ]
    }
   ],
   "source": [
    "'showup'\n",
    "range_df = pd.DataFrame()\n",
    "range_df['age'] = range(100)\n",
    "men = range_df.age.apply(lambda x: len(df[(df.age == x) & (df.gender == 'M')]))\n",
    "women = range_df.age.apply(lambda x: len(df[(df.age == x) & (df.gender == 'F')]))\n",
    "plt.plot(range(100),men, 'b')\n",
    "plt.plot(range(100),women, color = 'r')\n",
    "plt.legend(['M','F'])\n",
    "plt.xlabel('Age')\n",
    "plt.title('Women visit the doctor more often')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [
    {
     "ename": "DataError",
     "evalue": "No numeric types to aggregate",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mDataError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-291-07939ee08565>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'neighbourhood'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'sms_received'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mas_index\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'gender'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\groupby\\groupby.py\u001b[0m in \u001b[0;36mmean\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1230\u001b[0m         \u001b[0mnv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalidate_groupby_func\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"mean\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m\"numeric_only\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1231\u001b[0m         return self._cython_agg_general(\n\u001b[1;32m-> 1232\u001b[1;33m             \u001b[1;34m\"mean\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malt\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mSeries\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1233\u001b[0m         )\n\u001b[0;32m   1234\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\groupby\\generic.py\u001b[0m in \u001b[0;36m_cython_agg_general\u001b[1;34m(self, how, alt, numeric_only, min_count)\u001b[0m\n\u001b[0;32m   1002\u001b[0m     ) -> DataFrame:\n\u001b[0;32m   1003\u001b[0m         agg_blocks, agg_items = self._cython_agg_blocks(\n\u001b[1;32m-> 1004\u001b[1;33m             \u001b[0mhow\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malt\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0malt\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnumeric_only\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnumeric_only\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmin_count\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmin_count\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1005\u001b[0m         )\n\u001b[0;32m   1006\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_wrap_agged_blocks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0magg_blocks\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mitems\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0magg_items\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\groupby\\generic.py\u001b[0m in \u001b[0;36m_cython_agg_blocks\u001b[1;34m(self, how, alt, numeric_only, min_count)\u001b[0m\n\u001b[0;32m   1091\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1092\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0magg_blocks\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1093\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mDataError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"No numeric types to aggregate\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1094\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1095\u001b[0m         \u001b[1;31m# reset the locs in the blocks to correspond to our\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mDataError\u001b[0m: No numeric types to aggregate"
     ]
    }
   ],
   "source": [
    "df.groupby(['neighbourhood', 'sms_received'], as_index=False)['gender'].mean()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     88208\n",
       "Yes    22319\n",
       "Name: no_show, dtype: int64"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['no_show'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F    71840\n",
       "M    38687\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "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>gender</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>no_show</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>57246</td>\n",
       "      <td>30962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yes</th>\n",
       "      <td>14594</td>\n",
       "      <td>7725</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "gender       F      M\n",
       "no_show              \n",
       "No       57246  30962\n",
       "Yes      14594   7725"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['gender', 'no_show'] )['no_show'].count().unstack('gender')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use(\"fivethirtyeight\")\n",
    "              \n",
    "slices = [57246,30962,14594,7725] \n",
    "labels = ['Female Showup', 'Male Showup', 'Female Noshow', 'Male Noshow']\n",
    "explode = [1,0,0,0]\n",
    "\n",
    "plt.pie(slices, labels=labels,startangle=45,autopct='%1.1f%%',wedgeprops={'edgecolor':'black'})\n",
    "              \n",
    "plt.title(\"My Awesome Pie Chart\")\n",
    "plt.tight_layout()\n",
    "plt.show()            \n",
    "             "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ages = df['age']\n",
    "gender = df.groupby(['gender', 'age'] )['no_show'].count().unstack('gender')\n",
    "py_salaries = df['no_show']\n",
    "js_salaries = df['sms_received']\n",
    "\n",
    "plt.plot(gender, color='#444444',\n",
    "         linestyle='--', label='All Ages')\n",
    "\n",
    "#plt.plot(ages, js_salaries, label='SMS')\n",
    "\n",
    "\n",
    "#overall_median = 57287\n",
    "\n",
    "\n",
    "#plt.fill_between(ages, py_salaries, overall_median,\n",
    "                 #where=(py_salaries > overal_median),\n",
    "                 #Interpolate=True,color='red', alpha=0.25)\n",
    "\n",
    "#plt.fill_between(ages, py_salaries, overall_median,\n",
    "                 #where=(py_salaries <= overal_median),\n",
    "                 #Interpolate=True, alpha=0.25)\n",
    "\n",
    "plt.legend()\n",
    "\n",
    "plt.title('Median Salary by Age')\n",
    "plt.xlabel('Ages')\n",
    "plt.ylabel('Median Salary (USD)')\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(['gender', 'no_show'] )['no_show'].count().plot.bar(figsize=(8, 6));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(['gender', 'sms_received'] )['sms_received'].count().plot.bar(figsize=(8, 6));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'method' object is not subscriptable",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-214-ee815226ece2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# get counts for each rating and color\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mgen_now_show\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'no_show'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'age'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'gender'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: 'method' object is not subscriptable"
     ]
    }
   ],
   "source": [
    "# get counts for each rating and color\n",
    "gen_now_show = df.groupby(['no_show', 'age']).count['gender']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "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></th>\n",
       "      <th>patientid</th>\n",
       "      <th>appointmentid</th>\n",
       "      <th>scheduledday</th>\n",
       "      <th>appointmentday</th>\n",
       "      <th>age</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>scholarship</th>\n",
       "      <th>hipertension</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>alcoholism</th>\n",
       "      <th>handcap</th>\n",
       "      <th>sms_received</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>no_show</th>\n",
       "      <th>gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">No</th>\n",
       "      <th>F</th>\n",
       "      <td>57246</td>\n",
       "      <td>57246</td>\n",
       "      <td>57246</td>\n",
       "      <td>57246</td>\n",
       "      <td>57246</td>\n",
       "      <td>57246</td>\n",
       "      <td>57246</td>\n",
       "      <td>57246</td>\n",
       "      <td>57246</td>\n",
       "      <td>57246</td>\n",
       "      <td>57246</td>\n",
       "      <td>57246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>30962</td>\n",
       "      <td>30962</td>\n",
       "      <td>30962</td>\n",
       "      <td>30962</td>\n",
       "      <td>30962</td>\n",
       "      <td>30962</td>\n",
       "      <td>30962</td>\n",
       "      <td>30962</td>\n",
       "      <td>30962</td>\n",
       "      <td>30962</td>\n",
       "      <td>30962</td>\n",
       "      <td>30962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Yes</th>\n",
       "      <th>F</th>\n",
       "      <td>14594</td>\n",
       "      <td>14594</td>\n",
       "      <td>14594</td>\n",
       "      <td>14594</td>\n",
       "      <td>14594</td>\n",
       "      <td>14594</td>\n",
       "      <td>14594</td>\n",
       "      <td>14594</td>\n",
       "      <td>14594</td>\n",
       "      <td>14594</td>\n",
       "      <td>14594</td>\n",
       "      <td>14594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>7725</td>\n",
       "      <td>7725</td>\n",
       "      <td>7725</td>\n",
       "      <td>7725</td>\n",
       "      <td>7725</td>\n",
       "      <td>7725</td>\n",
       "      <td>7725</td>\n",
       "      <td>7725</td>\n",
       "      <td>7725</td>\n",
       "      <td>7725</td>\n",
       "      <td>7725</td>\n",
       "      <td>7725</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                patientid  appointmentid  scheduledday  appointmentday    age  \\\n",
       "no_show gender                                                                  \n",
       "No      F           57246          57246         57246           57246  57246   \n",
       "        M           30962          30962         30962           30962  30962   \n",
       "Yes     F           14594          14594         14594           14594  14594   \n",
       "        M            7725           7725          7725            7725   7725   \n",
       "\n",
       "                neighbourhood  scholarship  hipertension  diabetes  \\\n",
       "no_show gender                                                       \n",
       "No      F               57246        57246         57246     57246   \n",
       "        M               30962        30962         30962     30962   \n",
       "Yes     F               14594        14594         14594     14594   \n",
       "        M                7725         7725          7725      7725   \n",
       "\n",
       "                alcoholism  handcap  sms_received  \n",
       "no_show gender                                     \n",
       "No      F            57246    57246         57246  \n",
       "        M            30962    30962         30962  \n",
       "Yes     F            14594    14594         14594  \n",
       "        M             7725     7725          7725  "
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gen_now_show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 4 artists>"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "data = [[30, 25, 50, 20],\n",
    "[40, 23, 51, 17],\n",
    "[35, 22, 45, 19]]\n",
    "X = np.arange(4)\n",
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "ax.bar(X + 0.00, data[0], color = 'b', width = 0.25)\n",
    "ax.bar(X + 0.25, data[1], color = 'g', width = 0.25)\n",
    "ax.bar(X + 0.50, data[2], color = 'r', width = 0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "langs = ['C', 'C++', 'Java', 'Python', 'PHP']\n",
    "students = [23,17,35,29,12]\n",
    "ax.bar(langs,students)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>patientid</th>\n",
       "      <th>appointmentid</th>\n",
       "      <th>gender</th>\n",
       "      <th>scheduledday</th>\n",
       "      <th>appointmentday</th>\n",
       "      <th>age</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>scholarship</th>\n",
       "      <th>hipertension</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>alcoholism</th>\n",
       "      <th>handcap</th>\n",
       "      <th>sms_received</th>\n",
       "      <th>no_show</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>733688164476661</td>\n",
       "      <td>5630279</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-27T15:05:12Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>23</td>\n",
       "      <td>GOIABEIRAS</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3449833394123</td>\n",
       "      <td>5630575</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-27T15:39:58Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>39</td>\n",
       "      <td>GOIABEIRAS</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>7542951368435</td>\n",
       "      <td>5620163</td>\n",
       "      <td>M</td>\n",
       "      <td>2016-04-26T08:44:12Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>29</td>\n",
       "      <td>NOVA PALESTINA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>14794966191172</td>\n",
       "      <td>5633460</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-28T09:28:57Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>40</td>\n",
       "      <td>CONQUISTA</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>622257462899397</td>\n",
       "      <td>5626083</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-04-27T07:51:14Z</td>\n",
       "      <td>2016-04-29T00:00:00Z</td>\n",
       "      <td>30</td>\n",
       "      <td>NOVA PALESTINA</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110484</th>\n",
       "      <td>513364984676544</td>\n",
       "      <td>5772155</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-06-03T14:43:56Z</td>\n",
       "      <td>2016-06-07T00:00:00Z</td>\n",
       "      <td>45</td>\n",
       "      <td>BARRO VERMELHO</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
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       "    <tr>\n",
       "      <th>110492</th>\n",
       "      <td>645634214296344</td>\n",
       "      <td>5786741</td>\n",
       "      <td>M</td>\n",
       "      <td>2016-06-08T08:50:19Z</td>\n",
       "      <td>2016-06-08T00:00:00Z</td>\n",
       "      <td>33</td>\n",
       "      <td>MARIA ORTIZ</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110496</th>\n",
       "      <td>85442954737999</td>\n",
       "      <td>5779046</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-06-06T17:35:38Z</td>\n",
       "      <td>2016-06-08T00:00:00Z</td>\n",
       "      <td>37</td>\n",
       "      <td>MARIA ORTIZ</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110515</th>\n",
       "      <td>645634214296344</td>\n",
       "      <td>5778621</td>\n",
       "      <td>M</td>\n",
       "      <td>2016-06-06T15:58:05Z</td>\n",
       "      <td>2016-06-08T00:00:00Z</td>\n",
       "      <td>33</td>\n",
       "      <td>MARIA ORTIZ</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110516</th>\n",
       "      <td>69237724436761</td>\n",
       "      <td>5780205</td>\n",
       "      <td>F</td>\n",
       "      <td>2016-06-07T07:45:16Z</td>\n",
       "      <td>2016-06-08T00:00:00Z</td>\n",
       "      <td>37</td>\n",
       "      <td>MARIA ORTIZ</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
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       "  </tbody>\n",
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       "<p>22319 rows × 14 columns</p>\n",
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      "text/plain": [
       "             patientid  appointmentid gender          scheduledday  \\\n",
       "6      733688164476661        5630279      F  2016-04-27T15:05:12Z   \n",
       "7        3449833394123        5630575      F  2016-04-27T15:39:58Z   \n",
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       "20     622257462899397        5626083      F  2016-04-27T07:51:14Z   \n",
       "...                ...            ...    ...                   ...   \n",
       "110484 513364984676544        5772155      F  2016-06-03T14:43:56Z   \n",
       "110492 645634214296344        5786741      M  2016-06-08T08:50:19Z   \n",
       "110496  85442954737999        5779046      F  2016-06-06T17:35:38Z   \n",
       "110515 645634214296344        5778621      M  2016-06-06T15:58:05Z   \n",
       "110516  69237724436761        5780205      F  2016-06-07T07:45:16Z   \n",
       "\n",
       "              appointmentday  age   neighbourhood  scholarship  hipertension  \\\n",
       "6       2016-04-29T00:00:00Z   23      GOIABEIRAS            0             0   \n",
       "7       2016-04-29T00:00:00Z   39      GOIABEIRAS            0             0   \n",
       "11      2016-04-29T00:00:00Z   29  NOVA PALESTINA            0             0   \n",
       "17      2016-04-29T00:00:00Z   40       CONQUISTA            1             0   \n",
       "20      2016-04-29T00:00:00Z   30  NOVA PALESTINA            0             0   \n",
       "...                      ...  ...             ...          ...           ...   \n",
       "110484  2016-06-07T00:00:00Z   45  BARRO VERMELHO            0             0   \n",
       "110492  2016-06-08T00:00:00Z   33     MARIA ORTIZ            0             1   \n",
       "110496  2016-06-08T00:00:00Z   37     MARIA ORTIZ            0             1   \n",
       "110515  2016-06-08T00:00:00Z   33     MARIA ORTIZ            0             1   \n",
       "110516  2016-06-08T00:00:00Z   37     MARIA ORTIZ            0             0   \n",
       "\n",
       "        diabetes  alcoholism  handcap  sms_received no_show  \n",
       "6              0           0        0             0     Yes  \n",
       "7              0           0        0             0     Yes  \n",
       "11             0           0        0             1     Yes  \n",
       "17             0           0        0             0     Yes  \n",
       "20             0           0        0             0     Yes  \n",
       "...          ...         ...      ...           ...     ...  \n",
       "110484         0           0        0             0     Yes  \n",
       "110492         0           0        0             0     Yes  \n",
       "110496         0           0        0             0     Yes  \n",
       "110515         0           0        0             0     Yes  \n",
       "110516         0           0        0             0     Yes  \n",
       "\n",
       "[22319 rows x 14 columns]"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['no_show'] == 'Yes']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
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       "      <td>2016-06-07T00:00:00Z</td>\n",
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       "</table>\n",
       "<p>110527 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             patientid  appointmentid gender          scheduledday  \\\n",
       "0       29872499824296        5642903      F  2016-04-29T18:38:08Z   \n",
       "1      558997776694438        5642503      M  2016-04-29T16:08:27Z   \n",
       "2        4262962299951        5642549      F  2016-04-29T16:19:04Z   \n",
       "3         867951213174        5642828      F  2016-04-29T17:29:31Z   \n",
       "4        8841186448183        5642494      F  2016-04-29T16:07:23Z   \n",
       "...                ...            ...    ...                   ...   \n",
       "110522   2572134369293        5651768      F  2016-05-03T09:15:35Z   \n",
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       "110524  15576631729893        5630692      F  2016-04-27T16:03:52Z   \n",
       "110525  92134931435557        5630323      F  2016-04-27T15:09:23Z   \n",
       "110526 377511518121127        5629448      F  2016-04-27T13:30:56Z   \n",
       "\n",
       "              appointmentday  age      neighbourhood  scholarship  \\\n",
       "0       2016-04-29T00:00:00Z   62    JARDIM DA PENHA            0   \n",
       "1       2016-04-29T00:00:00Z   56    JARDIM DA PENHA            0   \n",
       "2       2016-04-29T00:00:00Z   62      MATA DA PRAIA            0   \n",
       "3       2016-04-29T00:00:00Z    8  PONTAL DE CAMBURI            0   \n",
       "4       2016-04-29T00:00:00Z   56    JARDIM DA PENHA            0   \n",
       "...                      ...  ...                ...          ...   \n",
       "110522  2016-06-07T00:00:00Z   56        MARIA ORTIZ            0   \n",
       "110523  2016-06-07T00:00:00Z   51        MARIA ORTIZ            0   \n",
       "110524  2016-06-07T00:00:00Z   21        MARIA ORTIZ            0   \n",
       "110525  2016-06-07T00:00:00Z   38        MARIA ORTIZ            0   \n",
       "110526  2016-06-07T00:00:00Z   54        MARIA ORTIZ            0   \n",
       "\n",
       "        hipertension  diabetes  alcoholism  handcap  sms_received no_show  \n",
       "0                  1         0           0        0             0      No  \n",
       "1                  0         0           0        0             0      No  \n",
       "2                  0         0           0        0             0      No  \n",
       "3                  0         0           0        0             0      No  \n",
       "4                  1         1           0        0             0      No  \n",
       "...              ...       ...         ...      ...           ...     ...  \n",
       "110522             0         0           0        0             1      No  \n",
       "110523             0         0           0        0             1      No  \n",
       "110524             0         0           0        0             1      No  \n",
       "110525             0         0           0        0             1      No  \n",
       "110526             0         0           0        0             1      No  \n",
       "\n",
       "[110527 rows x 14 columns]"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['gender'] != 'Yes']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = np.arange(0.0, 2.0, 0.01 )\n",
    "s = 1 + np.sin(2*np.pi*t)\n",
    "plt.plot(t, s)\n",
    "\n",
    "plt.xlabel('time (s)')\n",
    "plt.ylabel('voltage (mV)')\n",
    "plt.title('About as simple as it gets, folks')\n",
    "plt.grid(True)\n",
    "plt.savefig(\"test.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "ax.axis('equal')\n",
    "langs = ['C', 'C++', 'Java', 'Python', 'PHP']\n",
    "students = [23,17,35,29,12]\n",
    "ax.pie(students, labels = langs,autopct='%1.2f%%')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "        \n",
    "slices = [57246,30962,14594,7725] \n",
    "labels = ['Female Showup', 'Male Showup', 'Female Noshow', 'Male Noshow']\n",
    "explode = [1,0,0,0]\n",
    "\n",
    "plt.pie(slices, labels=labels,startangle=90,autopct='%1.1f%%',wedgeprops={'edgecolor':'black'})\n",
    "              \n",
    "plt.title(\"My Awesome Pie Chart\")\n",
    "plt.tight_layout()\n",
    "plt.show()       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "EOL while scanning string literal (<ipython-input-293-f0d3070ab56f>, line 2)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-293-f0d3070ab56f>\"\u001b[1;36m, line \u001b[1;32m2\u001b[0m\n\u001b[1;33m    sns.barplot(x = 'gender', y = 'SMS_count', hue = 'sms_received', data = test_df, autopct='%1.1f%%,)\u001b[0m\n\u001b[1;37m                                                                                                       ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m EOL while scanning string literal\n"
     ]
    }
   ],
   "source": [
    "test_df = df.groupby(['gender', 'sms_received'])['sms_received'].count().reset_index(name = 'SMS_count')\n",
    "sns.barplot(x = 'gender', y = 'SMS_count', hue = 'sms_received', data = test_df, autopct='%1.1f%%,)\n",
    "plt.xlabel('Gender')\n",
    "plt.ylabel('SMS Count')\n",
    "plt.title('Gender Count by SMS Recieved')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(['gender', 'sms_received'] )['sms_received'].count().plot.bar(figsize=(8, 6));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Gender Count by Showup Noshow')"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_df = df.groupby(['gender', 'no_show'])['no_show'].count().reset_index(name = 'Noshow_count')\n",
    "sns.barplot(x = 'gender', y = 'Noshow_count', hue = 'no_show', data = test_df)\n",
    "plt.xlabel('Gender')\n",
    "plt.ylabel(' Count')\n",
    "plt.title('Gender Count by Showup Noshow')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'AGE Count by Showup Noshow')"
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_df = df.groupby(['gender', 'no_show'])['no_show'].count().reset_index(name = 'Noshow_count')\n",
    "sns.barplot(x = 'gender', y = 'Noshow_count', hue = 'no_show', data = test_df)\n",
    "plt.xlabel('AGE')\n",
    "plt.ylabel(' Count')\n",
    "plt.title('AGE Count by Showup Noshow')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'AGE Count by Showup Noshow')"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_df = df.groupby(['neighbourhood', 'no_show'])['no_show'].count().reset_index(name = 'Noshow_count')\n",
    "sns.barplot(x = 'neighbourhood', y = 'Noshow_count', hue = 'no_show', data = test_df)\n",
    "plt.xlabel('AGE')\n",
    "plt.ylabel(' Count')\n",
    "plt.title('AGE Count by Showup Noshow')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "ax = sns.countplot(x=df.gender, hue=df.no_show, data=df)\n",
    "ax.set_title(\"Show/NoShow for Females and Males\")\n",
    "x_ticks_labels=['Female', 'Male']\n",
    "ax.set_xticklabels(x_ticks_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender  neighbourhood  sms_received  no_show\n",
       "F       AEROPORTO      0             No        54\n",
       "                                     Yes       37\n",
       "        ANDORINHAS     0             No        38\n",
       "                                     Yes       38\n",
       "                       1             No        39\n",
       "                                               ..\n",
       "M       UNIVERSITÁRIO  1             Yes       34\n",
       "        VILA RUBIM     0             No        40\n",
       "                                     Yes       36\n",
       "                       1             No        41\n",
       "                                     Yes       35\n",
       "Name: age, Length: 624, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['gender', 'neighbourhood','sms_received','no_show'], as_index=True)['age'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}