aquisicao_clinicas/metricas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **O que precisa ser feito?**\n",
    "Seu objetivo é trazer as principais métricas relacionadas à aquisição de clínicas uma análise de dados exploratória com essa base, de forma a identificar gargalos e oportunidades para otimizar a entrada de clinicas na Real."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importando as bibliotecas Python\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from datetime import datetime, timedelta\n",
    "from scipy.stats import skew, kurtosis\n",
    "import library as lib\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'library' from 'd:\\\\repositorio\\\\datascience\\\\case\\\\aquisicao_clinicas\\\\aquisicao_clinicas\\\\library.py'>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import importlib\n",
    "importlib.reload(lib)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "lib.colormap = 'mako_r'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>CLINIC_ID</th>\n",
       "      <th>ACTIVITY_AT</th>\n",
       "      <th>ACTIVITY_TYPE</th>\n",
       "      <th>FEATURE</th>\n",
       "      <th>MODULE</th>\n",
       "      <th>IS_VALID_ACTIVITY</th>\n",
       "      <th>CLINIC_CREATED_AT</th>\n",
       "      <th>IS_CHAIN_CLINIC</th>\n",
       "      <th>FIRST_USER_HUBSPOT_SOURCE</th>\n",
       "      <th>INTEREST_CATEGORY_SIGNUP</th>\n",
       "      <th>...</th>\n",
       "      <th>IS_ACCREDITATION_APPROVED</th>\n",
       "      <th>IS_ACCREDITATION_REPROVED</th>\n",
       "      <th>CLINIC_ACCREDITATION_STATUS</th>\n",
       "      <th>STRIPE_SUBSCRIPTION_ID</th>\n",
       "      <th>SUBSCRIPTION_START_DATE</th>\n",
       "      <th>SUBSCRIPTION_END_DATE</th>\n",
       "      <th>SUBSCRIPTION_PERIOD_DAYS</th>\n",
       "      <th>SUBSCRIPTION_PERIOD</th>\n",
       "      <th>SUBSCRIPTION_STATUS</th>\n",
       "      <th>BILLING_INTERVAL_IN_MONTHS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9825</td>\n",
       "      <td>2023-08-24 18:46:47.733</td>\n",
       "      <td>patient_discarded</td>\n",
       "      <td>patient_management</td>\n",
       "      <td>patient</td>\n",
       "      <td>True</td>\n",
       "      <td>2023-08-21 09:14:34.763</td>\n",
       "      <td>False</td>\n",
       "      <td>Inbound</td>\n",
       "      <td>Apenas BNPL</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Concluído</td>\n",
       "      <td>sub_1Nj29VCpjOdJI4gTsWNO1Kzm</td>\n",
       "      <td>2023-08-25</td>\n",
       "      <td>2023-10-25</td>\n",
       "      <td>61.0</td>\n",
       "      <td>Entre 61 e 90 dias</td>\n",
       "      <td>active</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9984</td>\n",
       "      <td>2023-08-24 19:41:43.497</td>\n",
       "      <td>patient_created</td>\n",
       "      <td>patient_management</td>\n",
       "      <td>patient</td>\n",
       "      <td>False</td>\n",
       "      <td>2023-08-24 19:40:58.432</td>\n",
       "      <td>False</td>\n",
       "      <td>Inbound</td>\n",
       "      <td>BNPL e SaaS</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Concluído</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9984</td>\n",
       "      <td>2023-08-24 19:41:44.037</td>\n",
       "      <td>time_slot_created</td>\n",
       "      <td>scheduling</td>\n",
       "      <td>schedule</td>\n",
       "      <td>True</td>\n",
       "      <td>2023-08-24 19:40:58.432</td>\n",
       "      <td>False</td>\n",
       "      <td>Inbound</td>\n",
       "      <td>BNPL e SaaS</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Concluído</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9984</td>\n",
       "      <td>2023-08-24 19:51:20.451</td>\n",
       "      <td>patient_created</td>\n",
       "      <td>patient_management</td>\n",
       "      <td>patient</td>\n",
       "      <td>False</td>\n",
       "      <td>2023-08-24 19:40:58.432</td>\n",
       "      <td>False</td>\n",
       "      <td>Inbound</td>\n",
       "      <td>BNPL e SaaS</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Concluído</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9984</td>\n",
       "      <td>2023-08-24 19:51:46.663</td>\n",
       "      <td>time_slot_created</td>\n",
       "      <td>scheduling</td>\n",
       "      <td>schedule</td>\n",
       "      <td>True</td>\n",
       "      <td>2023-08-24 19:40:58.432</td>\n",
       "      <td>False</td>\n",
       "      <td>Inbound</td>\n",
       "      <td>BNPL e SaaS</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Concluído</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   CLINIC_ID             ACTIVITY_AT      ACTIVITY_TYPE             FEATURE  \\\n",
       "0       9825 2023-08-24 18:46:47.733  patient_discarded  patient_management   \n",
       "1       9984 2023-08-24 19:41:43.497    patient_created  patient_management   \n",
       "2       9984 2023-08-24 19:41:44.037  time_slot_created          scheduling   \n",
       "3       9984 2023-08-24 19:51:20.451    patient_created  patient_management   \n",
       "4       9984 2023-08-24 19:51:46.663  time_slot_created          scheduling   \n",
       "\n",
       "     MODULE  IS_VALID_ACTIVITY       CLINIC_CREATED_AT  IS_CHAIN_CLINIC  \\\n",
       "0   patient               True 2023-08-21 09:14:34.763            False   \n",
       "1   patient              False 2023-08-24 19:40:58.432            False   \n",
       "2  schedule               True 2023-08-24 19:40:58.432            False   \n",
       "3   patient              False 2023-08-24 19:40:58.432            False   \n",
       "4  schedule               True 2023-08-24 19:40:58.432            False   \n",
       "\n",
       "  FIRST_USER_HUBSPOT_SOURCE INTEREST_CATEGORY_SIGNUP  ...  \\\n",
       "0                   Inbound              Apenas BNPL  ...   \n",
       "1                   Inbound              BNPL e SaaS  ...   \n",
       "2                   Inbound              BNPL e SaaS  ...   \n",
       "3                   Inbound              BNPL e SaaS  ...   \n",
       "4                   Inbound              BNPL e SaaS  ...   \n",
       "\n",
       "   IS_ACCREDITATION_APPROVED IS_ACCREDITATION_REPROVED  \\\n",
       "0                       True                     False   \n",
       "1                       True                     False   \n",
       "2                       True                     False   \n",
       "3                       True                     False   \n",
       "4                       True                     False   \n",
       "\n",
       "  CLINIC_ACCREDITATION_STATUS        STRIPE_SUBSCRIPTION_ID  \\\n",
       "0                   Concluído  sub_1Nj29VCpjOdJI4gTsWNO1Kzm   \n",
       "1                   Concluído                           NaN   \n",
       "2                   Concluído                           NaN   \n",
       "3                   Concluído                           NaN   \n",
       "4                   Concluído                           NaN   \n",
       "\n",
       "   SUBSCRIPTION_START_DATE  SUBSCRIPTION_END_DATE  SUBSCRIPTION_PERIOD_DAYS  \\\n",
       "0               2023-08-25             2023-10-25                      61.0   \n",
       "1                      NaT                    NaT                       NaN   \n",
       "2                      NaT                    NaT                       NaN   \n",
       "3                      NaT                    NaT                       NaN   \n",
       "4                      NaT                    NaT                       NaN   \n",
       "\n",
       "   SUBSCRIPTION_PERIOD SUBSCRIPTION_STATUS BILLING_INTERVAL_IN_MONTHS  \n",
       "0   Entre 61 e 90 dias              active                        1.0  \n",
       "1                  NaN                 NaN                        NaN  \n",
       "2                  NaN                 NaN                        NaN  \n",
       "3                  NaN                 NaN                        NaN  \n",
       "4                  NaN                 NaN                        NaN  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lendo os dados de um arquivo .csv\n",
    "df_real = pd.read_csv(r'..\\dados\\aquisicao_clinicas_completo.csv',\n",
    "                       parse_dates=['ACTIVITY_AT','CLINIC_CREATED_AT','TRIAL_START_DATE','TRIAL_END_DATE','SUBSCRIPTION_START_DATE','SUBSCRIPTION_END_DATE'])\n",
    "df_real.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12421, 29)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_real.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 12421 entries, 0 to 12420\n",
      "Data columns (total 26 columns):\n",
      " #   Column                                Non-Null Count  Dtype         \n",
      "---  ------                                --------------  -----         \n",
      " 0   CLINIC_ID                             12421 non-null  int64         \n",
      " 1   ACTIVITY_AT                           12421 non-null  datetime64[ns]\n",
      " 2   ACTIVITY_TYPE                         12421 non-null  object        \n",
      " 3   FEATURE                               12421 non-null  object        \n",
      " 4   MODULE                                12421 non-null  object        \n",
      " 5   IS_VALID_ACTIVITY                     12421 non-null  bool          \n",
      " 6   CLINIC_CREATED_AT                     12421 non-null  datetime64[ns]\n",
      " 7   IS_CHAIN_CLINIC                       12421 non-null  bool          \n",
      " 8   FIRST_USER_HUBSPOT_SOURCE             12421 non-null  object        \n",
      " 9   INTEREST_CATEGORY_SIGNUP              12421 non-null  object        \n",
      " 10  TRIAL_DURATION                        12421 non-null  int64         \n",
      " 11  TRIAL_START_DATE                      12421 non-null  datetime64[ns]\n",
      " 12  TRIAL_END_DATE                        12421 non-null  datetime64[ns]\n",
      " 13  MARKETING_ATTRIBUITION_CHANNEL_GROUP  12421 non-null  object        \n",
      " 14  IS_PAID_MEDIUM                        12421 non-null  bool          \n",
      " 15  HAS_ASKED_FOR_ACCREDITATION           12421 non-null  bool          \n",
      " 16  IS_ACCREDITATION_APPROVED             12421 non-null  bool          \n",
      " 17  IS_ACCREDITATION_REPROVED             12421 non-null  bool          \n",
      " 18  CLINIC_ACCREDITATION_STATUS           12421 non-null  object        \n",
      " 19  STRIPE_SUBSCRIPTION_ID                7562 non-null   object        \n",
      " 20  SUBSCRIPTION_START_DATE               7562 non-null   datetime64[ns]\n",
      " 21  SUBSCRIPTION_END_DATE                 1209 non-null   datetime64[ns]\n",
      " 22  SUBSCRIPTION_PERIOD_DAYS              7562 non-null   float64       \n",
      " 23  SUBSCRIPTION_PERIOD                   7562 non-null   object        \n",
      " 24  SUBSCRIPTION_STATUS                   7562 non-null   object        \n",
      " 25  BILLING_INTERVAL_IN_MONTHS            7562 non-null   float64       \n",
      "dtypes: bool(6), datetime64[ns](6), float64(2), int64(2), object(10)\n",
      "memory usage: 2.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df_real.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Abaixo, estão as métricas que iremos explorar em nosso projeto"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Duração Média do Trial\n",
    "    * Indicador: Duração média do período de trial (em dias) para todas as clínicas.\n",
    "    * Indicador: Distribuição da duração do trial (quantas clínicas terminaram o trial em 7 dias, 14 dias, etc.).\n",
    "    * Por que é importante?: Ajuda a entender se o período de trial oferecido é suficiente para as clínicas explorarem todas as funcionalidades e tomarem uma decisão informada.\n",
    "    \n",
    "* Atividades por Clínica\n",
    "    * Indicador: Número médio de atividades realizadas por clínica durante o trial.\n",
    "    * Indicador: Distribuição do número de atividades por clínica (quantas clínicas realizaram mais de X atividades).\n",
    "    * Por que é importante?: Um número maior de atividades pode indicar um maior envolvimento e engajamento com o produto, aumentando a probabilidade de conversão para uma assinatura paga.  \n",
    "\n",
    "* Distribuição de Atividades\n",
    "    * Indicador: Frequência dos diferentes tipos de atividades realizadas (uso de agenda, prontuário eletrônico, gestão financeira, etc.). Quais funcionalidades são mais usadas durante o trial. \n",
    "    * Por que é importante?: Entender quais funcionalidades estão sendo mais utilizadas pode indicar quais aspectos do produto são mais valiosos para os usuários durante o trial.     \n",
    "\n",
    "* Engajamento Funcional\n",
    "    * Indicador: Análise detalhada de engajamento em funcionalidades específicas (e.g., quantas clínicas usam a agenda e com que frequência).\n",
    "    * Por que é importante?: Funcionalidades que têm uma alta taxa de uso durante o trial são boas candidatas para promoção em campanhas de marketing ou foco de melhorias. Também ajuda a identificar funcionalidades que podem precisar de maior visibilidade ou refinamento.\n",
    "\n",
    "* Taxa de Conversão Pós-Trial\n",
    "    * Indicador: Percentual de clínicas que convertem para assinaturas pagas após o trial.\n",
    "    * Por que é importante?: A taxa de conversão é o principal indicador de sucesso do período de trial. Ela revela a eficácia do trial em demonstrar o valor do produto.\n",
    "    \n",
    "* Número de atividades realizadas\n",
    "    * Indicador: Comparação de conversão entre clínicas que realizaram um número alto de atividades versus um número baixo.\n",
    "    * Por que é importante?: É fundamental para entender melhor o comportamento dos clientes, identificar padrões de sucesso e otimizar as estratégias de engajamento e conversão.\n",
    "\n",
    "* Atividades de Alto Impacto:\n",
    "    * Indicador: Identificação de atividades que têm maior correlação com a conversão (e.g., clínicas que usam a gestão financeira têm maior chance de converter).    \n",
    "    * Por que é importante?: Permite uma abordagem mais direcionada, tanto no onboarding quanto em marketing, vendas e desenvolvimento de produto. Isso aumenta a eficiência das estratégias de aquisição e retenção, além de melhorar a experiência do cliente ao destacar funcionalidades que realmente agregam valor.\n",
    "\n",
    "* Taxa de Solicitação de Credenciamento\n",
    "    * Indicador: Percentual de clínicas que solicitam credenciamento durante o trial.\n",
    "    * Por que é importante?: Credenciamento é um passo importante para o uso do Financiamento Real (BNPL). Este indicador pode revelar o interesse em uma das funcionalidades mais diferenciadas do produto.\n",
    "    \n",
    "* Conversão de clínicas credenciadas versus não credenciadas.    \n",
    "    * Indicador: Comparação da taxa de conversão entre clínicas credenciadas versus não credenciadas.\n",
    "    * Por que é importante?: Entender o impacto do credenciamento ou não, pode ofercer insights profundos sobre engajamento, validação de estratégias de marketing, retorno sobre investimento em credenciamento e melhorias no processo de onboarding. \n",
    "\n",
    "* Identificação dos Canais Mais Eficazes\n",
    "    * Indicador: Percentual de comparação de performance de conversão entre diferentes fontes de canal de marketing.\n",
    "    * Por que é importante?: Diferentes canais de marketing atraem diferentes tipos de clientes, e alguns canais podem ser mais eficazes em converter leads em clientes pagantes do que outros. Analisar a taxa de conversão por canal ajuda a identificar quais canais trazem leads qualificados que têm maior probabilidade de se tornarem clientes pagantes.\n",
    "\n",
    "* Pontos de Desistência (Churn)\n",
    "    * Indicador: Percentual de clínicas que param de usar o produto durante o trial (não realizaram a assinatura do produto).\n",
    "    * Indicador: Percentual de clínicas que param de usar o produto após o trial (realizaram a assinatura do produto, porém ela não esta mais ativa).\n",
    "    * Por que é importante?: Identificar onde as clínicas perdem interesse pode indicar problemas na usabilidade, falta de valor percebido, ou outros fatores que podem ser corrigidos. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>CLINIC_ID</th>\n",
       "      <th>ACTIVITY_AT</th>\n",
       "      <th>ACTIVITY_TYPE</th>\n",
       "      <th>FEATURE</th>\n",
       "      <th>MODULE</th>\n",
       "      <th>IS_VALID_ACTIVITY</th>\n",
       "      <th>CLINIC_CREATED_AT</th>\n",
       "      <th>IS_CHAIN_CLINIC</th>\n",
       "      <th>FIRST_USER_HUBSPOT_SOURCE</th>\n",
       "      <th>INTEREST_CATEGORY_SIGNUP</th>\n",
       "      <th>...</th>\n",
       "      <th>IS_ACCREDITATION_APPROVED</th>\n",
       "      <th>IS_ACCREDITATION_REPROVED</th>\n",
       "      <th>CLINIC_ACCREDITATION_STATUS</th>\n",
       "      <th>STRIPE_SUBSCRIPTION_ID</th>\n",
       "      <th>SUBSCRIPTION_START_DATE</th>\n",
       "      <th>SUBSCRIPTION_END_DATE</th>\n",
       "      <th>SUBSCRIPTION_PERIOD_DAYS</th>\n",
       "      <th>SUBSCRIPTION_PERIOD</th>\n",
       "      <th>SUBSCRIPTION_STATUS</th>\n",
       "      <th>BILLING_INTERVAL_IN_MONTHS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9825</td>\n",
       "      <td>2023-08-24 18:46:47.733</td>\n",
       "      <td>patient_discarded</td>\n",
       "      <td>patient_management</td>\n",
       "      <td>patient</td>\n",
       "      <td>True</td>\n",
       "      <td>2023-08-21 09:14:34.763</td>\n",
       "      <td>False</td>\n",
       "      <td>Inbound</td>\n",
       "      <td>Apenas BNPL</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Concluído</td>\n",
       "      <td>sub_1Nj29VCpjOdJI4gTsWNO1Kzm</td>\n",
       "      <td>2023-08-25</td>\n",
       "      <td>2023-10-25</td>\n",
       "      <td>61.0</td>\n",
       "      <td>Entre 61 e 90 dias</td>\n",
       "      <td>active</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9984</td>\n",
       "      <td>2023-08-24 19:41:43.497</td>\n",
       "      <td>patient_created</td>\n",
       "      <td>patient_management</td>\n",
       "      <td>patient</td>\n",
       "      <td>False</td>\n",
       "      <td>2023-08-24 19:40:58.432</td>\n",
       "      <td>False</td>\n",
       "      <td>Inbound</td>\n",
       "      <td>BNPL e SaaS</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Concluído</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9986</td>\n",
       "      <td>2023-08-24 20:32:47.128</td>\n",
       "      <td>patient_created</td>\n",
       "      <td>patient_management</td>\n",
       "      <td>patient</td>\n",
       "      <td>False</td>\n",
       "      <td>2023-08-24 20:31:43.062</td>\n",
       "      <td>False</td>\n",
       "      <td>Inbound</td>\n",
       "      <td>BNPL e SaaS</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Concluído</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9988</td>\n",
       "      <td>2023-08-24 22:06:04.848</td>\n",
       "      <td>patient_created</td>\n",
       "      <td>patient_management</td>\n",
       "      <td>patient</td>\n",
       "      <td>False</td>\n",
       "      <td>2023-08-24 22:04:10.022</td>\n",
       "      <td>False</td>\n",
       "      <td>Inbound</td>\n",
       "      <td>Apenas SaaS</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Não aderiu ao plano</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9690</td>\n",
       "      <td>2023-08-24 22:07:59.840</td>\n",
       "      <td>patient_created</td>\n",
       "      <td>patient_management</td>\n",
       "      <td>patient</td>\n",
       "      <td>False</td>\n",
       "      <td>2023-08-17 04:33:00.786</td>\n",
       "      <td>False</td>\n",
       "      <td>Inbound</td>\n",
       "      <td>Apenas BNPL</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Não aderiu ao plano</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "      <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>12393</th>\n",
       "      <td>9011</td>\n",
       "      <td>2023-10-09 18:16:08.793</td>\n",
       "      <td>pre_analysis</td>\n",
       "      <td>bnpl</td>\n",
       "      <td>finance</td>\n",
       "      <td>False</td>\n",
       "      <td>2023-08-01 13:01:05.019</td>\n",
       "      <td>False</td>\n",
       "      <td>Inbound</td>\n",
       "      <td>Apenas BNPL</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Concluído</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12395</th>\n",
       "      <td>11180</td>\n",
       "      <td>2023-10-10 17:42:02.047</td>\n",
       "      <td>pre_analysis</td>\n",
       "      <td>bnpl</td>\n",
       "      <td>finance</td>\n",
       "      <td>False</td>\n",
       "      <td>2023-09-27 14:01:04.040</td>\n",
       "      <td>False</td>\n",
       "      <td>Inbound</td>\n",
       "      <td>Apenas BNPL</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Concluído</td>\n",
       "      <td>sub_1NzmetCpjOdJI4gTROeKTccm</td>\n",
       "      <td>2023-10-10</td>\n",
       "      <td>NaT</td>\n",
       "      <td>999.0</td>\n",
       "      <td>Período em aberto</td>\n",
       "      <td>active</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12397</th>\n",
       "      <td>11146</td>\n",
       "      <td>2023-10-16 19:56:48.050</td>\n",
       "      <td>pre_analysis</td>\n",
       "      <td>bnpl</td>\n",
       "      <td>finance</td>\n",
       "      <td>False</td>\n",
       "      <td>2023-09-26 19:21:15.702</td>\n",
       "      <td>False</td>\n",
       "      <td>Inbound</td>\n",
       "      <td>Apenas BNPL</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Concluído</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12399</th>\n",
       "      <td>10314</td>\n",
       "      <td>2023-10-17 21:30:57.007</td>\n",
       "      <td>patient_created</td>\n",
       "      <td>patient_management</td>\n",
       "      <td>patient</td>\n",
       "      <td>False</td>\n",
       "      <td>2023-09-05 01:05:51.799</td>\n",
       "      <td>False</td>\n",
       "      <td>Inbound</td>\n",
       "      <td>Apenas BNPL</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Concluído</td>\n",
       "      <td>sub_1O2F0FCpjOdJI4gT1kqeULiV</td>\n",
       "      <td>2023-10-17</td>\n",
       "      <td>NaT</td>\n",
       "      <td>999.0</td>\n",
       "      <td>Período em aberto</td>\n",
       "      <td>active</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12401</th>\n",
       "      <td>11031</td>\n",
       "      <td>2023-10-18 11:24:06.971</td>\n",
       "      <td>patient_created</td>\n",
       "      <td>patient_management</td>\n",
       "      <td>patient</td>\n",
       "      <td>False</td>\n",
       "      <td>2023-09-23 20:03:38.288</td>\n",
       "      <td>False</td>\n",
       "      <td>Inbound</td>\n",
       "      <td>Apenas BNPL</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>Reprovado</td>\n",
       "      <td>sub_1O2gIxCpjOdJI4gTm01tQrxp</td>\n",
       "      <td>2023-10-18</td>\n",
       "      <td>2023-10-19</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Até 30 dias</td>\n",
       "      <td>canceled</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>983 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       CLINIC_ID             ACTIVITY_AT      ACTIVITY_TYPE  \\\n",
       "0           9825 2023-08-24 18:46:47.733  patient_discarded   \n",
       "1           9984 2023-08-24 19:41:43.497    patient_created   \n",
       "5           9986 2023-08-24 20:32:47.128    patient_created   \n",
       "7           9988 2023-08-24 22:06:04.848    patient_created   \n",
       "9           9690 2023-08-24 22:07:59.840    patient_created   \n",
       "...          ...                     ...                ...   \n",
       "12393       9011 2023-10-09 18:16:08.793       pre_analysis   \n",
       "12395      11180 2023-10-10 17:42:02.047       pre_analysis   \n",
       "12397      11146 2023-10-16 19:56:48.050       pre_analysis   \n",
       "12399      10314 2023-10-17 21:30:57.007    patient_created   \n",
       "12401      11031 2023-10-18 11:24:06.971    patient_created   \n",
       "\n",
       "                  FEATURE   MODULE  IS_VALID_ACTIVITY       CLINIC_CREATED_AT  \\\n",
       "0      patient_management  patient               True 2023-08-21 09:14:34.763   \n",
       "1      patient_management  patient              False 2023-08-24 19:40:58.432   \n",
       "5      patient_management  patient              False 2023-08-24 20:31:43.062   \n",
       "7      patient_management  patient              False 2023-08-24 22:04:10.022   \n",
       "9      patient_management  patient              False 2023-08-17 04:33:00.786   \n",
       "...                   ...      ...                ...                     ...   \n",
       "12393                bnpl  finance              False 2023-08-01 13:01:05.019   \n",
       "12395                bnpl  finance              False 2023-09-27 14:01:04.040   \n",
       "12397                bnpl  finance              False 2023-09-26 19:21:15.702   \n",
       "12399  patient_management  patient              False 2023-09-05 01:05:51.799   \n",
       "12401  patient_management  patient              False 2023-09-23 20:03:38.288   \n",
       "\n",
       "       IS_CHAIN_CLINIC FIRST_USER_HUBSPOT_SOURCE INTEREST_CATEGORY_SIGNUP  \\\n",
       "0                False                   Inbound              Apenas BNPL   \n",
       "1                False                   Inbound              BNPL e SaaS   \n",
       "5                False                   Inbound              BNPL e SaaS   \n",
       "7                False                   Inbound              Apenas SaaS   \n",
       "9                False                   Inbound              Apenas BNPL   \n",
       "...                ...                       ...                      ...   \n",
       "12393            False                   Inbound              Apenas BNPL   \n",
       "12395            False                   Inbound              Apenas BNPL   \n",
       "12397            False                   Inbound              Apenas BNPL   \n",
       "12399            False                   Inbound              Apenas BNPL   \n",
       "12401            False                   Inbound              Apenas BNPL   \n",
       "\n",
       "       ...  IS_ACCREDITATION_APPROVED IS_ACCREDITATION_REPROVED  \\\n",
       "0      ...                       True                     False   \n",
       "1      ...                       True                     False   \n",
       "5      ...                       True                     False   \n",
       "7      ...                      False                     False   \n",
       "9      ...                      False                     False   \n",
       "...    ...                        ...                       ...   \n",
       "12393  ...                       True                     False   \n",
       "12395  ...                       True                     False   \n",
       "12397  ...                       True                     False   \n",
       "12399  ...                       True                     False   \n",
       "12401  ...                      False                      True   \n",
       "\n",
       "      CLINIC_ACCREDITATION_STATUS        STRIPE_SUBSCRIPTION_ID  \\\n",
       "0                       Concluído  sub_1Nj29VCpjOdJI4gTsWNO1Kzm   \n",
       "1                       Concluído                           NaN   \n",
       "5                       Concluído                           NaN   \n",
       "7             Não aderiu ao plano                           NaN   \n",
       "9             Não aderiu ao plano                           NaN   \n",
       "...                           ...                           ...   \n",
       "12393                   Concluído                           NaN   \n",
       "12395                   Concluído  sub_1NzmetCpjOdJI4gTROeKTccm   \n",
       "12397                   Concluído                           NaN   \n",
       "12399                   Concluído  sub_1O2F0FCpjOdJI4gT1kqeULiV   \n",
       "12401                   Reprovado  sub_1O2gIxCpjOdJI4gTm01tQrxp   \n",
       "\n",
       "       SUBSCRIPTION_START_DATE  SUBSCRIPTION_END_DATE  \\\n",
       "0                   2023-08-25             2023-10-25   \n",
       "1                          NaT                    NaT   \n",
       "5                          NaT                    NaT   \n",
       "7                          NaT                    NaT   \n",
       "9                          NaT                    NaT   \n",
       "...                        ...                    ...   \n",
       "12393                      NaT                    NaT   \n",
       "12395               2023-10-10                    NaT   \n",
       "12397                      NaT                    NaT   \n",
       "12399               2023-10-17                    NaT   \n",
       "12401               2023-10-18             2023-10-19   \n",
       "\n",
       "       SUBSCRIPTION_PERIOD_DAYS  SUBSCRIPTION_PERIOD SUBSCRIPTION_STATUS  \\\n",
       "0                          61.0   Entre 61 e 90 dias              active   \n",
       "1                           NaN                  NaN                 NaN   \n",
       "5                           NaN                  NaN                 NaN   \n",
       "7                           NaN                  NaN                 NaN   \n",
       "9                           NaN                  NaN                 NaN   \n",
       "...                         ...                  ...                 ...   \n",
       "12393                       NaN                  NaN                 NaN   \n",
       "12395                     999.0    Período em aberto              active   \n",
       "12397                       NaN                  NaN                 NaN   \n",
       "12399                     999.0    Período em aberto              active   \n",
       "12401                       1.0          Até 30 dias            canceled   \n",
       "\n",
       "      BILLING_INTERVAL_IN_MONTHS  \n",
       "0                            1.0  \n",
       "1                            NaN  \n",
       "5                            NaN  \n",
       "7                            NaN  \n",
       "9                            NaN  \n",
       "...                          ...  \n",
       "12393                        NaN  \n",
       "12395                        1.0  \n",
       "12397                        NaN  \n",
       "12399                        1.0  \n",
       "12401                        1.0  \n",
       "\n",
       "[983 rows x 26 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cria um dataframe contendo registros unicos por clinica\n",
    "clinicas_unicas_periodo_trial = df_real.drop_duplicates(subset=['CLINIC_ID'])\n",
    "clinicas_unicas_periodo_trial"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* #### Métricas de Duração do Trial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.611393692777213"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Duração média do período de trial para todas as clínicas.\n",
    "media_duracao_trial = clinicas_unicas_periodo_trial['TRIAL_DURATION'].mean()\n",
    "media_duracao_trial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TRIAL_DURATION</th>\n",
       "      <td>983.0</td>\n",
       "      <td>8.611394</td>\n",
       "      <td>17.544345</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>365.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                count      mean        std  min  25%  50%  75%    max\n",
       "TRIAL_DURATION  983.0  8.611394  17.544345  0.0  7.0  7.0  7.0  365.0"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Distribuição da duração do trial.\n",
    "clinicas_unicas_periodo_trial['TRIAL_DURATION'].describe().to_frame().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* #### Métricas de Interação Durante o Trial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.573598130841122"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Número médio de atividades realizadas por clínica durante o trial.\n",
    "media_atividade_por_clinica = df_real.loc[df_real['IS_VALID_ACTIVITY']].groupby(by='CLINIC_ID').size().mean()\n",
    "media_atividade_por_clinica\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8195"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_real.loc[df_real['IS_VALID_ACTIVITY']].groupby(by='CLINIC_ID').size().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>856.0</td>\n",
       "      <td>9.573598</td>\n",
       "      <td>28.864624</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>599.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   count      mean        std  min  25%  50%  75%    max\n",
       "0  856.0  9.573598  28.864624  1.0  1.0  3.0  8.0  599.0"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Distribuição do número de atividades por clínica (quantas clínicas realizaram mais de X atividades).\n",
    "df_real.loc[df_real['IS_VALID_ACTIVITY']].groupby(by='CLINIC_ID').size().describe().to_frame().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>frequência de atividades</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLINIC_ID</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10449</th>\n",
       "      <td>599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10138</th>\n",
       "      <td>264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11275</th>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10915</th>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9825</th>\n",
       "      <td>173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9959</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9960</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10640</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10639</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11302</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>856 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           frequência de atividades\n",
       "CLINIC_ID                          \n",
       "10449                           599\n",
       "10138                           264\n",
       "11275                           193\n",
       "10915                           175\n",
       "9825                            173\n",
       "...                             ...\n",
       "9959                              1\n",
       "9960                              1\n",
       "10640                             1\n",
       "10639                             1\n",
       "11302                             1\n",
       "\n",
       "[856 rows x 1 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Distribuição do número de atividades por clínica (quantas clínicas realizaram mais de X atividades).\n",
    "distribuicao_atividades_por_clinica = df_real.loc[df_real['IS_VALID_ACTIVITY']].groupby(by='CLINIC_ID').size().sort_values(ascending=False).to_frame(name='frequência de atividades')\n",
    "distribuicao_atividades_por_clinica"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  Distribuição do número de atividades por clínica (quantas clínicas realizaram mais de X atividades).\n",
    "lib.plot_distribuicao(dataframe=distribuicao_atividades_por_clinica, coluna='frequência de atividades')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_208b6_row0_col2, #T_208b6_row1_col2, #T_208b6_row2_col2, #T_208b6_row3_col2 {\n",
       "  background-color: #def5e5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_208b6_row4_col2, #T_208b6_row5_col2, #T_208b6_row6_col2 {\n",
       "  background-color: #dcf4e3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_208b6_row7_col2, #T_208b6_row8_col2 {\n",
       "  background-color: #daf3e1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_208b6_row9_col2 {\n",
       "  background-color: #d4f1dc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_208b6_row10_col2 {\n",
       "  background-color: #d0efd9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_208b6_row11_col2 {\n",
       "  background-color: #ccedd6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_208b6_row12_col2 {\n",
       "  background-color: #caedd4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_208b6_row13_col2 {\n",
       "  background-color: #c8ecd2;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_208b6_row14_col2 {\n",
       "  background-color: #c4eacf;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_208b6_row15_col2 {\n",
       "  background-color: #bbe7c8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_208b6_row16_col2, #T_208b6_row17_col2 {\n",
       "  background-color: #b9e6c7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_208b6_row18_col2 {\n",
       "  background-color: #a4e0bb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_208b6_row19_col2 {\n",
       "  background-color: #99ddb6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_208b6_row20_col2 {\n",
       "  background-color: #5bcdad;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_208b6_row21_col2 {\n",
       "  background-color: #37659e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_208b6_row22_col2 {\n",
       "  background-color: #40498e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_208b6\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_208b6_level0_col0\" class=\"col_heading level0 col0\" >ACTIVITY_TYPE</th>\n",
       "      <th id=\"T_208b6_level0_col1\" class=\"col_heading level0 col1\" >frequencia</th>\n",
       "      <th id=\"T_208b6_level0_col2\" class=\"col_heading level0 col2\" >porcentagem</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_208b6_level0_row0\" class=\"row_heading level0 row0\" >22</th>\n",
       "      <td id=\"T_208b6_row0_col0\" class=\"data row0 col0\" >payment_book_created</td>\n",
       "      <td id=\"T_208b6_row0_col1\" class=\"data row0 col1\" >3</td>\n",
       "      <td id=\"T_208b6_row0_col2\" class=\"data row0 col2\" >0.036608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_208b6_level0_row1\" class=\"row_heading level0 row1\" >21</th>\n",
       "      <td id=\"T_208b6_row1_col0\" class=\"data row1 col0\" >contract</td>\n",
       "      <td id=\"T_208b6_row1_col1\" class=\"data row1 col1\" >7</td>\n",
       "      <td id=\"T_208b6_row1_col2\" class=\"data row1 col2\" >0.085418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_208b6_level0_row2\" class=\"row_heading level0 row2\" >20</th>\n",
       "      <td id=\"T_208b6_row2_col0\" class=\"data row2 col0\" >anamnesis_template_discarded</td>\n",
       "      <td id=\"T_208b6_row2_col1\" class=\"data row2 col1\" >9</td>\n",
       "      <td id=\"T_208b6_row2_col2\" class=\"data row2 col2\" >0.109823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_208b6_level0_row3\" class=\"row_heading level0 row3\" >19</th>\n",
       "      <td id=\"T_208b6_row3_col0\" class=\"data row3 col0\" >observation_discarded</td>\n",
       "      <td id=\"T_208b6_row3_col1\" class=\"data row3 col1\" >10</td>\n",
       "      <td id=\"T_208b6_row3_col2\" class=\"data row3 col2\" >0.122026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_208b6_level0_row4\" class=\"row_heading level0 row4\" >18</th>\n",
       "      <td id=\"T_208b6_row4_col0\" class=\"data row4 col0\" >complete_credit_check</td>\n",
       "      <td id=\"T_208b6_row4_col1\" class=\"data row4 col1\" >19</td>\n",
       "      <td id=\"T_208b6_row4_col2\" class=\"data row4 col2\" >0.231849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_208b6_level0_row5\" class=\"row_heading level0 row5\" >17</th>\n",
       "      <td id=\"T_208b6_row5_col0\" class=\"data row5 col0\" >time_slot_attendance_update</td>\n",
       "      <td id=\"T_208b6_row5_col1\" class=\"data row5 col1\" >22</td>\n",
       "      <td id=\"T_208b6_row5_col2\" class=\"data row5 col2\" >0.268456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_208b6_level0_row6\" class=\"row_heading level0 row6\" >16</th>\n",
       "      <td id=\"T_208b6_row6_col0\" class=\"data row6 col0\" >anamnesis_template_created</td>\n",
       "      <td id=\"T_208b6_row6_col1\" class=\"data row6 col1\" >28</td>\n",
       "      <td id=\"T_208b6_row6_col2\" class=\"data row6 col2\" >0.341672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_208b6_level0_row7\" class=\"row_heading level0 row7\" >15</th>\n",
       "      <td id=\"T_208b6_row7_col0\" class=\"data row7 col0\" >prescription_created</td>\n",
       "      <td id=\"T_208b6_row7_col1\" class=\"data row7 col1\" >35</td>\n",
       "      <td id=\"T_208b6_row7_col2\" class=\"data row7 col2\" >0.427090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_208b6_level0_row8\" class=\"row_heading level0 row8\" >14</th>\n",
       "      <td id=\"T_208b6_row8_col0\" class=\"data row8 col0\" >basic_credit_check</td>\n",
       "      <td id=\"T_208b6_row8_col1\" class=\"data row8 col1\" >38</td>\n",
       "      <td id=\"T_208b6_row8_col2\" class=\"data row8 col2\" >0.463697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_208b6_level0_row9\" class=\"row_heading level0 row9\" >13</th>\n",
       "      <td id=\"T_208b6_row9_col0\" class=\"data row9 col0\" >document_created</td>\n",
       "      <td id=\"T_208b6_row9_col1\" class=\"data row9 col1\" >77</td>\n",
       "      <td id=\"T_208b6_row9_col2\" class=\"data row9 col2\" >0.939597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_208b6_level0_row10\" class=\"row_heading level0 row10\" >12</th>\n",
       "      <td id=\"T_208b6_row10_col0\" class=\"data row10 col0\" >folder_created</td>\n",
       "      <td id=\"T_208b6_row10_col1\" class=\"data row10 col1\" >120</td>\n",
       "      <td id=\"T_208b6_row10_col2\" class=\"data row10 col2\" >1.464308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_208b6_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_208b6_row11_col0\" class=\"data row11 col0\" >patient_discarded</td>\n",
       "      <td id=\"T_208b6_row11_col1\" class=\"data row11 col1\" >144</td>\n",
       "      <td id=\"T_208b6_row11_col2\" class=\"data row11 col2\" >1.757169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_208b6_level0_row12\" class=\"row_heading level0 row12\" >10</th>\n",
       "      <td id=\"T_208b6_row12_col0\" class=\"data row12 col0\" >budget_discarded</td>\n",
       "      <td id=\"T_208b6_row12_col1\" class=\"data row12 col1\" >160</td>\n",
       "      <td id=\"T_208b6_row12_col2\" class=\"data row12 col2\" >1.952410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_208b6_level0_row13\" class=\"row_heading level0 row13\" >9</th>\n",
       "      <td id=\"T_208b6_row13_col0\" class=\"data row13 col0\" >observation_added</td>\n",
       "      <td id=\"T_208b6_row13_col1\" class=\"data row13 col1\" >173</td>\n",
       "      <td id=\"T_208b6_row13_col2\" class=\"data row13 col2\" >2.111043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_208b6_level0_row14\" class=\"row_heading level0 row14\" >8</th>\n",
       "      <td id=\"T_208b6_row14_col0\" class=\"data row14 col0\" >new_user_created</td>\n",
       "      <td id=\"T_208b6_row14_col1\" class=\"data row14 col1\" >207</td>\n",
       "      <td id=\"T_208b6_row14_col2\" class=\"data row14 col2\" >2.525930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_208b6_level0_row15\" class=\"row_heading level0 row15\" >7</th>\n",
       "      <td id=\"T_208b6_row15_col0\" class=\"data row15 col0\" >procedure_discarded</td>\n",
       "      <td id=\"T_208b6_row15_col1\" class=\"data row15 col1\" >257</td>\n",
       "      <td id=\"T_208b6_row15_col2\" class=\"data row15 col2\" >3.136059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_208b6_level0_row16\" class=\"row_heading level0 row16\" >6</th>\n",
       "      <td id=\"T_208b6_row16_col0\" class=\"data row16 col0\" >time_slot_discarded</td>\n",
       "      <td id=\"T_208b6_row16_col1\" class=\"data row16 col1\" >271</td>\n",
       "      <td id=\"T_208b6_row16_col2\" class=\"data row16 col2\" >3.306894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_208b6_level0_row17\" class=\"row_heading level0 row17\" >5</th>\n",
       "      <td id=\"T_208b6_row17_col0\" class=\"data row17 col0\" >expense_created</td>\n",
       "      <td id=\"T_208b6_row17_col1\" class=\"data row17 col1\" >271</td>\n",
       "      <td id=\"T_208b6_row17_col2\" class=\"data row17 col2\" >3.306894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_208b6_level0_row18\" class=\"row_heading level0 row18\" >4</th>\n",
       "      <td id=\"T_208b6_row18_col0\" class=\"data row18 col0\" >anamnesis_created</td>\n",
       "      <td id=\"T_208b6_row18_col1\" class=\"data row18 col1\" >407</td>\n",
       "      <td id=\"T_208b6_row18_col2\" class=\"data row18 col2\" >4.966443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_208b6_level0_row19\" class=\"row_heading level0 row19\" >3</th>\n",
       "      <td id=\"T_208b6_row19_col0\" class=\"data row19 col0\" >revenue_created</td>\n",
       "      <td id=\"T_208b6_row19_col1\" class=\"data row19 col1\" >466</td>\n",
       "      <td id=\"T_208b6_row19_col2\" class=\"data row19 col2\" >5.686394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_208b6_level0_row20\" class=\"row_heading level0 row20\" >2</th>\n",
       "      <td id=\"T_208b6_row20_col0\" class=\"data row20 col0\" >budget_created</td>\n",
       "      <td id=\"T_208b6_row20_col1\" class=\"data row20 col1\" >789</td>\n",
       "      <td id=\"T_208b6_row20_col2\" class=\"data row20 col2\" >9.627822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_208b6_level0_row21\" class=\"row_heading level0 row21\" >1</th>\n",
       "      <td id=\"T_208b6_row21_col0\" class=\"data row21 col0\" >time_slot_created</td>\n",
       "      <td id=\"T_208b6_row21_col1\" class=\"data row21 col1\" >2166</td>\n",
       "      <td id=\"T_208b6_row21_col2\" class=\"data row21 col2\" >26.430750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_208b6_level0_row22\" class=\"row_heading level0 row22\" >0</th>\n",
       "      <td id=\"T_208b6_row22_col0\" class=\"data row22 col0\" >procedure_created</td>\n",
       "      <td id=\"T_208b6_row22_col1\" class=\"data row22 col1\" >2516</td>\n",
       "      <td id=\"T_208b6_row22_col2\" class=\"data row22 col2\" >30.701647</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2b8bfe13d10>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Frequência dos diferentes tipos de atividades realizadas (uso de agenda, prontuário eletrônico, gestão financeira, etc.).\n",
    "lib.frequencia_cat(dataframe=df_real.loc[df_real['IS_VALID_ACTIVITY']], coluna='ACTIVITY_TYPE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lib.plot_frequencia_cat(df_real.loc[df_real['IS_VALID_ACTIVITY']], coluna='ACTIVITY_TYPE', orient='h')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_9f51c_row0_col2, #T_9f51c_row1_col2 {\n",
       "  background-color: #def5e5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_9f51c_row2_col2 {\n",
       "  background-color: #d8f2e0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_9f51c_row3_col2 {\n",
       "  background-color: #c6ebd1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_9f51c_row4_col2 {\n",
       "  background-color: #c2e9cd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_9f51c_row5_col2 {\n",
       "  background-color: #b7e6c5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_9f51c_row6_col2 {\n",
       "  background-color: #a4e0bb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_9f51c_row7_col2 {\n",
       "  background-color: #70d4ad;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_9f51c_row8_col2 {\n",
       "  background-color: #52c7ad;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_9f51c_row9_col2 {\n",
       "  background-color: #385f9c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_9f51c_row10_col2 {\n",
       "  background-color: #40498e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_9f51c\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_9f51c_level0_col0\" class=\"col_heading level0 col0\" >FEATURE</th>\n",
       "      <th id=\"T_9f51c_level0_col1\" class=\"col_heading level0 col1\" >frequencia</th>\n",
       "      <th id=\"T_9f51c_level0_col2\" class=\"col_heading level0 col2\" >porcentagem</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_9f51c_level0_row0\" class=\"row_heading level0 row0\" >10</th>\n",
       "      <td id=\"T_9f51c_row0_col0\" class=\"data row0 col0\" >payment_book</td>\n",
       "      <td id=\"T_9f51c_row0_col1\" class=\"data row0 col1\" >3</td>\n",
       "      <td id=\"T_9f51c_row0_col2\" class=\"data row0 col2\" >0.036608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9f51c_level0_row1\" class=\"row_heading level0 row1\" >9</th>\n",
       "      <td id=\"T_9f51c_row1_col0\" class=\"data row1 col0\" >bnpl</td>\n",
       "      <td id=\"T_9f51c_row1_col1\" class=\"data row1 col1\" >7</td>\n",
       "      <td id=\"T_9f51c_row1_col2\" class=\"data row1 col2\" >0.085418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9f51c_level0_row2\" class=\"row_heading level0 row2\" >8</th>\n",
       "      <td id=\"T_9f51c_row2_col0\" class=\"data row2 col0\" >credit_check</td>\n",
       "      <td id=\"T_9f51c_row2_col1\" class=\"data row2 col1\" >57</td>\n",
       "      <td id=\"T_9f51c_row2_col2\" class=\"data row2 col2\" >0.695546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9f51c_level0_row3\" class=\"row_heading level0 row3\" >7</th>\n",
       "      <td id=\"T_9f51c_row3_col0\" class=\"data row3 col0\" >user_management</td>\n",
       "      <td id=\"T_9f51c_row3_col1\" class=\"data row3 col1\" >207</td>\n",
       "      <td id=\"T_9f51c_row3_col2\" class=\"data row3 col2\" >2.525930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9f51c_level0_row4\" class=\"row_heading level0 row4\" >6</th>\n",
       "      <td id=\"T_9f51c_row4_col0\" class=\"data row4 col0\" >documents_and_files</td>\n",
       "      <td id=\"T_9f51c_row4_col1\" class=\"data row4 col1\" >232</td>\n",
       "      <td id=\"T_9f51c_row4_col2\" class=\"data row4 col2\" >2.830995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9f51c_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_9f51c_row5_col0\" class=\"data row5 col0\" >patient_management</td>\n",
       "      <td id=\"T_9f51c_row5_col1\" class=\"data row5 col1\" >327</td>\n",
       "      <td id=\"T_9f51c_row5_col2\" class=\"data row5 col2\" >3.990238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9f51c_level0_row6\" class=\"row_heading level0 row6\" >4</th>\n",
       "      <td id=\"T_9f51c_row6_col0\" class=\"data row6 col0\" >anamnese</td>\n",
       "      <td id=\"T_9f51c_row6_col1\" class=\"data row6 col1\" >444</td>\n",
       "      <td id=\"T_9f51c_row6_col2\" class=\"data row6 col2\" >5.417938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9f51c_level0_row7\" class=\"row_heading level0 row7\" >3</th>\n",
       "      <td id=\"T_9f51c_row7_col0\" class=\"data row7 col0\" >revenue_management</td>\n",
       "      <td id=\"T_9f51c_row7_col1\" class=\"data row7 col1\" >737</td>\n",
       "      <td id=\"T_9f51c_row7_col2\" class=\"data row7 col2\" >8.993289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9f51c_level0_row8\" class=\"row_heading level0 row8\" >2</th>\n",
       "      <td id=\"T_9f51c_row8_col0\" class=\"data row8 col0\" >budget_management</td>\n",
       "      <td id=\"T_9f51c_row8_col1\" class=\"data row8 col1\" >949</td>\n",
       "      <td id=\"T_9f51c_row8_col2\" class=\"data row8 col2\" >11.580232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9f51c_level0_row9\" class=\"row_heading level0 row9\" >1</th>\n",
       "      <td id=\"T_9f51c_row9_col0\" class=\"data row9 col0\" >scheduling</td>\n",
       "      <td id=\"T_9f51c_row9_col1\" class=\"data row9 col1\" >2459</td>\n",
       "      <td id=\"T_9f51c_row9_col2\" class=\"data row9 col2\" >30.006101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9f51c_level0_row10\" class=\"row_heading level0 row10\" >0</th>\n",
       "      <td id=\"T_9f51c_row10_col0\" class=\"data row10 col0\" >medical_record</td>\n",
       "      <td id=\"T_9f51c_row10_col1\" class=\"data row10 col1\" >2773</td>\n",
       "      <td id=\"T_9f51c_row10_col2\" class=\"data row10 col2\" >33.837706</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2b8becdbda0>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Quais funcionalidades são mais usadas durante o trial.   \n",
    "lib.frequencia_cat(dataframe=df_real.loc[df_real['IS_VALID_ACTIVITY']], coluna='FEATURE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lib.plot_frequencia_cat(df_real.loc[df_real['IS_VALID_ACTIVITY']], coluna='FEATURE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_b5bd2_row0_col2 {\n",
       "  background-color: #def5e5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b5bd2_row1_col2 {\n",
       "  background-color: #dcf4e3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b5bd2_row2_col2 {\n",
       "  background-color: #d2f0db;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b5bd2_row3_col2 {\n",
       "  background-color: #a6e1bc;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b5bd2_row4_col2 {\n",
       "  background-color: #a4e0bb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b5bd2_row5_col2 {\n",
       "  background-color: #9edfb8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b5bd2_row6_col2 {\n",
       "  background-color: #68d1ad;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b5bd2_row7_col2 {\n",
       "  background-color: #47bfad;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_b5bd2_row8_col2 {\n",
       "  background-color: #44bcad;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_b5bd2_row9_col2 {\n",
       "  background-color: #36a2ab;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_b5bd2_row10_col2 {\n",
       "  background-color: #40498e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_b5bd2\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_b5bd2_level0_col0\" class=\"col_heading level0 col0\" >FEATURE</th>\n",
       "      <th id=\"T_b5bd2_level0_col1\" class=\"col_heading level0 col1\" >frequencia</th>\n",
       "      <th id=\"T_b5bd2_level0_col2\" class=\"col_heading level0 col2\" >porcentagem</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_b5bd2_level0_row0\" class=\"row_heading level0 row0\" >10</th>\n",
       "      <td id=\"T_b5bd2_row0_col0\" class=\"data row0 col0\" >payment_book</td>\n",
       "      <td id=\"T_b5bd2_row0_col1\" class=\"data row0 col1\" >2</td>\n",
       "      <td id=\"T_b5bd2_row0_col2\" class=\"data row0 col2\" >0.086994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5bd2_level0_row1\" class=\"row_heading level0 row1\" >9</th>\n",
       "      <td id=\"T_b5bd2_row1_col0\" class=\"data row1 col0\" >bnpl</td>\n",
       "      <td id=\"T_b5bd2_row1_col1\" class=\"data row1 col1\" >7</td>\n",
       "      <td id=\"T_b5bd2_row1_col2\" class=\"data row1 col2\" >0.304480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5bd2_level0_row2\" class=\"row_heading level0 row2\" >8</th>\n",
       "      <td id=\"T_b5bd2_row2_col0\" class=\"data row2 col0\" >credit_check</td>\n",
       "      <td id=\"T_b5bd2_row2_col1\" class=\"data row2 col1\" >30</td>\n",
       "      <td id=\"T_b5bd2_row2_col2\" class=\"data row2 col2\" >1.304915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5bd2_level0_row3\" class=\"row_heading level0 row3\" >7</th>\n",
       "      <td id=\"T_b5bd2_row3_col0\" class=\"data row3 col0\" >documents_and_files</td>\n",
       "      <td id=\"T_b5bd2_row3_col1\" class=\"data row3 col1\" >115</td>\n",
       "      <td id=\"T_b5bd2_row3_col2\" class=\"data row3 col2\" >5.002175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5bd2_level0_row4\" class=\"row_heading level0 row4\" >6</th>\n",
       "      <td id=\"T_b5bd2_row4_col0\" class=\"data row4 col0\" >user_management</td>\n",
       "      <td id=\"T_b5bd2_row4_col1\" class=\"data row4 col1\" >117</td>\n",
       "      <td id=\"T_b5bd2_row4_col2\" class=\"data row4 col2\" >5.089169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5bd2_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_b5bd2_row5_col0\" class=\"data row5 col0\" >patient_management</td>\n",
       "      <td id=\"T_b5bd2_row5_col1\" class=\"data row5 col1\" >126</td>\n",
       "      <td id=\"T_b5bd2_row5_col2\" class=\"data row5 col2\" >5.480644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5bd2_level0_row6\" class=\"row_heading level0 row6\" >4</th>\n",
       "      <td id=\"T_b5bd2_row6_col0\" class=\"data row6 col0\" >revenue_management</td>\n",
       "      <td id=\"T_b5bd2_row6_col1\" class=\"data row6 col1\" >208</td>\n",
       "      <td id=\"T_b5bd2_row6_col2\" class=\"data row6 col2\" >9.047412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5bd2_level0_row7\" class=\"row_heading level0 row7\" >3</th>\n",
       "      <td id=\"T_b5bd2_row7_col0\" class=\"data row7 col0\" >anamnese</td>\n",
       "      <td id=\"T_b5bd2_row7_col1\" class=\"data row7 col1\" >283</td>\n",
       "      <td id=\"T_b5bd2_row7_col2\" class=\"data row7 col2\" >12.309700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5bd2_level0_row8\" class=\"row_heading level0 row8\" >2</th>\n",
       "      <td id=\"T_b5bd2_row8_col0\" class=\"data row8 col0\" >budget_management</td>\n",
       "      <td id=\"T_b5bd2_row8_col1\" class=\"data row8 col1\" >294</td>\n",
       "      <td id=\"T_b5bd2_row8_col2\" class=\"data row8 col2\" >12.788169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5bd2_level0_row9\" class=\"row_heading level0 row9\" >1</th>\n",
       "      <td id=\"T_b5bd2_row9_col0\" class=\"data row9 col0\" >medical_record</td>\n",
       "      <td id=\"T_b5bd2_row9_col1\" class=\"data row9 col1\" >393</td>\n",
       "      <td id=\"T_b5bd2_row9_col2\" class=\"data row9 col2\" >17.094389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b5bd2_level0_row10\" class=\"row_heading level0 row10\" >0</th>\n",
       "      <td id=\"T_b5bd2_row10_col0\" class=\"data row10 col0\" >scheduling</td>\n",
       "      <td id=\"T_b5bd2_row10_col1\" class=\"data row10 col1\" >724</td>\n",
       "      <td id=\"T_b5bd2_row10_col2\" class=\"data row10 col2\" >31.491953</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2b8bf61f440>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Percentual de clínicas que utilizam funcionalidades específicas (como prontuário eletrônico, agenda, gestão financeira).\n",
    "lib.frequencia_cat(dataframe=df_real.loc[(df_real['IS_VALID_ACTIVITY'])].drop_duplicates(subset=['FEATURE','CLINIC_ID']), coluna='FEATURE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lib.plot_frequencia_cat(dataframe=df_real.loc[(df_real['IS_VALID_ACTIVITY'])].drop_duplicates(subset=['FEATURE','CLINIC_ID']), coluna='FEATURE')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* #### Métricas de Conversão no Trial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27.46693794506612"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Percentual de clínicas que convertem para assinaturas pagas após o trial. \n",
    "# A conversão requer um registro de assinatura onde STRIPE_SUBSCRIPTION_ID seja diferente de NaN, não importando o status.\n",
    "clinicas_convertidas = df_real.loc[~df_real['STRIPE_SUBSCRIPTION_ID'].isna()].drop_duplicates(subset=['CLINIC_ID'])['CLINIC_ID']\n",
    "num_clinicas_convertidas = clinicas_convertidas.size\n",
    "\n",
    "percentual_clinicas_convertidas = (num_clinicas_convertidas / (clinicas_unicas_periodo_trial.shape[0])) * 100\n",
    "percentual_clinicas_convertidas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CLINIC_ID</th>\n",
       "      <th>ACTIVITY_AT</th>\n",
       "      <th>ACTIVITY_TYPE</th>\n",
       "      <th>FEATURE</th>\n",
       "      <th>MODULE</th>\n",
       "      <th>IS_VALID_ACTIVITY</th>\n",
       "      <th>CLINIC_CREATED_AT</th>\n",
       "      <th>IS_CHAIN_CLINIC</th>\n",
       "      <th>FIRST_USER_HUBSPOT_SOURCE</th>\n",
       "      <th>INTEREST_CATEGORY_SIGNUP</th>\n",
       "      <th>...</th>\n",
       "      <th>IS_ACCREDITATION_REPROVED</th>\n",
       "      <th>CLINIC_ACCREDITATION_STATUS</th>\n",
       "      <th>STRIPE_SUBSCRIPTION_ID</th>\n",
       "      <th>SUBSCRIPTION_START_DATE</th>\n",
       "      <th>SUBSCRIPTION_END_DATE</th>\n",
       "      <th>SUBSCRIPTION_PERIOD_DAYS</th>\n",
       "      <th>SUBSCRIPTION_PERIOD</th>\n",
       "      <th>SUBSCRIPTION_STATUS</th>\n",
       "      <th>BILLING_INTERVAL_IN_MONTHS</th>\n",
       "      <th>CLINICA_CONVERTIDA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9825</td>\n",
       "      <td>2023-08-24 18:46:47.733</td>\n",
       "      <td>patient_discarded</td>\n",
       "      <td>patient_management</td>\n",
       "      <td>patient</td>\n",
       "      <td>True</td>\n",
       "      <td>2023-08-21 09:14:34.763</td>\n",
       "      <td>False</td>\n",
       "      <td>Inbound</td>\n",
       "      <td>Apenas BNPL</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>Concluído</td>\n",
       "      <td>sub_1Nj29VCpjOdJI4gTsWNO1Kzm</td>\n",
       "      <td>2023-08-25</td>\n",
       "      <td>2023-10-25</td>\n",
       "      <td>61.0</td>\n",
       "      <td>Entre 61 e 90 dias</td>\n",
       "      <td>active</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9984</td>\n",
       "      <td>2023-08-24 19:41:43.497</td>\n",
       "      <td>patient_created</td>\n",
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       "      <td>patient</td>\n",
       "      <td>False</td>\n",
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       "      <td>Inbound</td>\n",
       "      <td>BNPL e SaaS</td>\n",
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       "      <td>True</td>\n",
       "      <td>2023-08-24 19:40:58.432</td>\n",
       "      <td>False</td>\n",
       "      <td>Inbound</td>\n",
       "      <td>BNPL e SaaS</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    </tr>\n",
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       "      <th>3</th>\n",
       "      <td>9984</td>\n",
       "      <td>2023-08-24 19:51:20.451</td>\n",
       "      <td>patient_created</td>\n",
       "      <td>patient_management</td>\n",
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       "      <td>False</td>\n",
       "      <td>2023-08-24 19:40:58.432</td>\n",
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       "      <td>2023-08-24 19:51:46.663</td>\n",
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       "      <td>True</td>\n",
       "      <td>2023-08-24 19:40:58.432</td>\n",
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       "      <th>12416</th>\n",
       "      <td>11234</td>\n",
       "      <td>2023-10-19 14:29:27.826</td>\n",
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       "      <td>True</td>\n",
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       "      <th>12417</th>\n",
       "      <td>11234</td>\n",
       "      <td>2023-10-19 14:37:20.492</td>\n",
       "      <td>pre_analysis</td>\n",
       "      <td>bnpl</td>\n",
       "      <td>finance</td>\n",
       "      <td>False</td>\n",
       "      <td>2023-09-28 16:27:41.579</td>\n",
       "      <td>False</td>\n",
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       "      <td>BNPL e SaaS</td>\n",
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       "      <td>pre_analysis</td>\n",
       "      <td>bnpl</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>12419</th>\n",
       "      <td>10254</td>\n",
       "      <td>2023-10-19 15:09:50.063</td>\n",
       "      <td>pre_analysis</td>\n",
       "      <td>bnpl</td>\n",
       "      <td>finance</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>pre_analysis</td>\n",
       "      <td>bnpl</td>\n",
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       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12421 rows × 27 columns</p>\n",
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      ],
      "text/plain": [
       "       CLINIC_ID             ACTIVITY_AT      ACTIVITY_TYPE  \\\n",
       "0           9825 2023-08-24 18:46:47.733  patient_discarded   \n",
       "1           9984 2023-08-24 19:41:43.497    patient_created   \n",
       "2           9984 2023-08-24 19:41:44.037  time_slot_created   \n",
       "3           9984 2023-08-24 19:51:20.451    patient_created   \n",
       "4           9984 2023-08-24 19:51:46.663  time_slot_created   \n",
       "...          ...                     ...                ...   \n",
       "12416      11234 2023-10-19 14:29:27.826    revenue_created   \n",
       "12417      11234 2023-10-19 14:37:20.492       pre_analysis   \n",
       "12418      11234 2023-10-19 14:53:48.231       pre_analysis   \n",
       "12419      10254 2023-10-19 15:09:50.063       pre_analysis   \n",
       "12420      10254 2023-10-19 15:11:47.165       pre_analysis   \n",
       "\n",
       "                  FEATURE    MODULE  IS_VALID_ACTIVITY  \\\n",
       "0      patient_management   patient               True   \n",
       "1      patient_management   patient              False   \n",
       "2              scheduling  schedule               True   \n",
       "3      patient_management   patient              False   \n",
       "4              scheduling  schedule               True   \n",
       "...                   ...       ...                ...   \n",
       "12416  revenue_management   finance               True   \n",
       "12417                bnpl   finance              False   \n",
       "12418                bnpl   finance              False   \n",
       "12419                bnpl   finance              False   \n",
       "12420                bnpl   finance              False   \n",
       "\n",
       "            CLINIC_CREATED_AT  IS_CHAIN_CLINIC FIRST_USER_HUBSPOT_SOURCE  \\\n",
       "0     2023-08-21 09:14:34.763            False                   Inbound   \n",
       "1     2023-08-24 19:40:58.432            False                   Inbound   \n",
       "2     2023-08-24 19:40:58.432            False                   Inbound   \n",
       "3     2023-08-24 19:40:58.432            False                   Inbound   \n",
       "4     2023-08-24 19:40:58.432            False                   Inbound   \n",
       "...                       ...              ...                       ...   \n",
       "12416 2023-09-28 16:27:41.579            False                   Inbound   \n",
       "12417 2023-09-28 16:27:41.579            False                   Inbound   \n",
       "12418 2023-09-28 16:27:41.579            False                   Inbound   \n",
       "12419 2023-09-03 07:55:29.365            False                   Inbound   \n",
       "12420 2023-09-03 07:55:29.365            False                   Inbound   \n",
       "\n",
       "      INTEREST_CATEGORY_SIGNUP  ...  IS_ACCREDITATION_REPROVED  \\\n",
       "0                  Apenas BNPL  ...                      False   \n",
       "1                  BNPL e SaaS  ...                      False   \n",
       "2                  BNPL e SaaS  ...                      False   \n",
       "3                  BNPL e SaaS  ...                      False   \n",
       "4                  BNPL e SaaS  ...                      False   \n",
       "...                        ...  ...                        ...   \n",
       "12416              BNPL e SaaS  ...                      False   \n",
       "12417              BNPL e SaaS  ...                      False   \n",
       "12418              BNPL e SaaS  ...                      False   \n",
       "12419              BNPL e SaaS  ...                      False   \n",
       "12420              BNPL e SaaS  ...                      False   \n",
       "\n",
       "      CLINIC_ACCREDITATION_STATUS        STRIPE_SUBSCRIPTION_ID  \\\n",
       "0                       Concluído  sub_1Nj29VCpjOdJI4gTsWNO1Kzm   \n",
       "1                       Concluído                           NaN   \n",
       "2                       Concluído                           NaN   \n",
       "3                       Concluído                           NaN   \n",
       "4                       Concluído                           NaN   \n",
       "...                           ...                           ...   \n",
       "12416                   Concluído                           NaN   \n",
       "12417                   Concluído                           NaN   \n",
       "12418                   Concluído                           NaN   \n",
       "12419                   Concluído                           NaN   \n",
       "12420                   Concluído                           NaN   \n",
       "\n",
       "      SUBSCRIPTION_START_DATE  SUBSCRIPTION_END_DATE  \\\n",
       "0                  2023-08-25             2023-10-25   \n",
       "1                         NaT                    NaT   \n",
       "2                         NaT                    NaT   \n",
       "3                         NaT                    NaT   \n",
       "4                         NaT                    NaT   \n",
       "...                       ...                    ...   \n",
       "12416                     NaT                    NaT   \n",
       "12417                     NaT                    NaT   \n",
       "12418                     NaT                    NaT   \n",
       "12419                     NaT                    NaT   \n",
       "12420                     NaT                    NaT   \n",
       "\n",
       "       SUBSCRIPTION_PERIOD_DAYS  SUBSCRIPTION_PERIOD  SUBSCRIPTION_STATUS  \\\n",
       "0                          61.0   Entre 61 e 90 dias               active   \n",
       "1                           NaN                  NaN                  NaN   \n",
       "2                           NaN                  NaN                  NaN   \n",
       "3                           NaN                  NaN                  NaN   \n",
       "4                           NaN                  NaN                  NaN   \n",
       "...                         ...                  ...                  ...   \n",
       "12416                       NaN                  NaN                  NaN   \n",
       "12417                       NaN                  NaN                  NaN   \n",
       "12418                       NaN                  NaN                  NaN   \n",
       "12419                       NaN                  NaN                  NaN   \n",
       "12420                       NaN                  NaN                  NaN   \n",
       "\n",
       "      BILLING_INTERVAL_IN_MONTHS CLINICA_CONVERTIDA  \n",
       "0                            1.0               True  \n",
       "1                            NaN              False  \n",
       "2                            NaN              False  \n",
       "3                            NaN              False  \n",
       "4                            NaN              False  \n",
       "...                          ...                ...  \n",
       "12416                        NaN              False  \n",
       "12417                        NaN              False  \n",
       "12418                        NaN              False  \n",
       "12419                        NaN              False  \n",
       "12420                        NaN              False  \n",
       "\n",
       "[12421 rows x 27 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Clinica foi convertida? Fez a assinatura ou não.\n",
    "df_real['CLINICA_CONVERTIDA'] =  np.where(df_real['CLINIC_ID'].isin(clinicas_convertidas), True, False)\n",
    "df_real"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2b8c8270fe0>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Percentual de clínicas que convertem para assinaturas pagas após o trial. \n",
    "# A conversão requer um registro de assinatura onde STRIPE_SUBSCRIPTION_ID seja diferente de NaN, não importando o status.\n",
    "lib.frequencia_cat(dataframe=df_real.drop_duplicates(subset=['CLINIC_ID']), coluna='CLINICA_CONVERTIDA')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lib.plot_frequencia_cat(dataframe=df_real.drop_duplicates(subset=['CLINIC_ID']), coluna='CLINICA_CONVERTIDA')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    270.000000\n",
       "mean       9.311111\n",
       "std       10.432981\n",
       "min        0.000000\n",
       "25%        2.000000\n",
       "50%        7.000000\n",
       "75%       12.000000\n",
       "max       60.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tempo médio desde o início do trial até a conversão para assinatura paga.\n",
    "tempo_medio_conversao = (df_real.loc[df_real['CLINICA_CONVERTIDA']].drop_duplicates(subset=['CLINIC_ID'])['SUBSCRIPTION_START_DATE'] - \n",
    "                         df_real.loc[df_real['CLINICA_CONVERTIDA']].drop_duplicates(subset=['CLINIC_ID'])['TRIAL_START_DATE']).dt.days.describe()\n",
    "tempo_medio_conversao"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.311111111111112"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tempo médio desde o início do trial até a conversão para assinatura paga.\n",
    "tempo_medio_conversao = (df_real.loc[df_real['CLINICA_CONVERTIDA']].drop_duplicates(subset=['CLINIC_ID'])['SUBSCRIPTION_START_DATE'] - \n",
    "                         df_real.loc[df_real['CLINICA_CONVERTIDA']].drop_duplicates(subset=['CLINIC_ID'])['TRIAL_START_DATE']).dt.days.mean()\n",
    "tempo_medio_conversao"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "def faixa_nro_atividades(clinica_id):\n",
    "\n",
    "    nro_atividades = df_real.loc[(df_real['IS_VALID_ACTIVITY']) & (df_real['CLINIC_ID'] == clinica_id)]['CLINIC_ID'].count()\n",
    "\n",
    "    if (nro_atividades > 0) & (nro_atividades <= 10): return 'Até 10 atividades'\n",
    "    elif (nro_atividades > 10) & (nro_atividades <= 20): return 'Entre 11 e 20 atividades'\n",
    "    elif (nro_atividades > 20) & (nro_atividades <= 50): return 'Entre 21 e 50 atividades'\n",
    "    elif (nro_atividades > 50): return 'Acima de 50 atividades'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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>CLINIC_ID</th>\n",
       "      <th>ACTIVITY_AT</th>\n",
       "      <th>ACTIVITY_TYPE</th>\n",
       "      <th>FEATURE</th>\n",
       "      <th>MODULE</th>\n",
       "      <th>IS_VALID_ACTIVITY</th>\n",
       "      <th>CLINIC_CREATED_AT</th>\n",
       "      <th>IS_CHAIN_CLINIC</th>\n",
       "      <th>FIRST_USER_HUBSPOT_SOURCE</th>\n",
       "      <th>INTEREST_CATEGORY_SIGNUP</th>\n",
       "      <th>...</th>\n",
       "      <th>CLINIC_ACCREDITATION_STATUS</th>\n",
       "      <th>STRIPE_SUBSCRIPTION_ID</th>\n",
       "      <th>SUBSCRIPTION_START_DATE</th>\n",
       "      <th>SUBSCRIPTION_END_DATE</th>\n",
       "      <th>SUBSCRIPTION_PERIOD_DAYS</th>\n",
       "      <th>SUBSCRIPTION_PERIOD</th>\n",
       "      <th>SUBSCRIPTION_STATUS</th>\n",
       "      <th>BILLING_INTERVAL_IN_MONTHS</th>\n",
       "      <th>CLINICA_CONVERTIDA</th>\n",
       "      <th>FAIXA_NRO_ATIVIDADES</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9825</td>\n",
       "      <td>2023-08-24 18:46:47.733</td>\n",
       "      <td>patient_discarded</td>\n",
       "      <td>patient_management</td>\n",
       "      <td>patient</td>\n",
       "      <td>True</td>\n",
       "      <td>2023-08-21 09:14:34.763</td>\n",
       "      <td>False</td>\n",
       "      <td>Inbound</td>\n",
       "      <td>Apenas BNPL</td>\n",
       "      <td>...</td>\n",
       "      <td>Concluído</td>\n",
       "      <td>sub_1Nj29VCpjOdJI4gTsWNO1Kzm</td>\n",
       "      <td>2023-08-25</td>\n",
       "      <td>2023-10-25</td>\n",
       "      <td>61.0</td>\n",
       "      <td>Entre 61 e 90 dias</td>\n",
       "      <td>active</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>Acima de 50 atividades</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9984</td>\n",
       "      <td>2023-08-24 19:41:43.497</td>\n",
       "      <td>patient_created</td>\n",
       "      <td>patient_management</td>\n",
       "      <td>patient</td>\n",
       "      <td>False</td>\n",
       "      <td>2023-08-24 19:40:58.432</td>\n",
       "      <td>False</td>\n",
       "      <td>Inbound</td>\n",
       "      <td>BNPL e SaaS</td>\n",
       "      <td>...</td>\n",
       "      <td>Concluído</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>Entre 11 e 20 atividades</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9984</td>\n",
       "      <td>2023-08-24 19:41:44.037</td>\n",
       "      <td>time_slot_created</td>\n",
       "      <td>scheduling</td>\n",
       "      <td>schedule</td>\n",
       "      <td>True</td>\n",
       "      <td>2023-08-24 19:40:58.432</td>\n",
       "      <td>False</td>\n",
       "      <td>Inbound</td>\n",
       "      <td>BNPL e SaaS</td>\n",
       "      <td>...</td>\n",
       "      <td>Concluído</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>Entre 11 e 20 atividades</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9984</td>\n",
       "      <td>2023-08-24 19:51:20.451</td>\n",
       "      <td>patient_created</td>\n",
       "      <td>patient_management</td>\n",
       "      <td>patient</td>\n",
       "      <td>False</td>\n",
       "      <td>2023-08-24 19:40:58.432</td>\n",
       "      <td>False</td>\n",
       "      <td>Inbound</td>\n",
       "      <td>BNPL e SaaS</td>\n",
       "      <td>...</td>\n",
       "      <td>Concluído</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>Entre 11 e 20 atividades</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9984</td>\n",
       "      <td>2023-08-24 19:51:46.663</td>\n",
       "      <td>time_slot_created</td>\n",
       "      <td>scheduling</td>\n",
       "      <td>schedule</td>\n",
       "      <td>True</td>\n",
       "      <td>2023-08-24 19:40:58.432</td>\n",
       "      <td>False</td>\n",
       "      <td>Inbound</td>\n",
       "      <td>BNPL e SaaS</td>\n",
       "      <td>...</td>\n",
       "      <td>Concluído</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>Entre 11 e 20 atividades</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",
       "      <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>12416</th>\n",
       "      <td>11234</td>\n",
       "      <td>2023-10-19 14:29:27.826</td>\n",
       "      <td>revenue_created</td>\n",
       "      <td>revenue_management</td>\n",
       "      <td>finance</td>\n",
       "      <td>True</td>\n",
       "      <td>2023-09-28 16:27:41.579</td>\n",
       "      <td>False</td>\n",
       "      <td>Inbound</td>\n",
       "      <td>BNPL e SaaS</td>\n",
       "      <td>...</td>\n",
       "      <td>Concluído</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>Até 10 atividades</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12417</th>\n",
       "      <td>11234</td>\n",
       "      <td>2023-10-19 14:37:20.492</td>\n",
       "      <td>pre_analysis</td>\n",
       "      <td>bnpl</td>\n",
       "      <td>finance</td>\n",
       "      <td>False</td>\n",
       "      <td>2023-09-28 16:27:41.579</td>\n",
       "      <td>False</td>\n",
       "      <td>Inbound</td>\n",
       "      <td>BNPL e SaaS</td>\n",
       "      <td>...</td>\n",
       "      <td>Concluído</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>Até 10 atividades</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12418</th>\n",
       "      <td>11234</td>\n",
       "      <td>2023-10-19 14:53:48.231</td>\n",
       "      <td>pre_analysis</td>\n",
       "      <td>bnpl</td>\n",
       "      <td>finance</td>\n",
       "      <td>False</td>\n",
       "      <td>2023-09-28 16:27:41.579</td>\n",
       "      <td>False</td>\n",
       "      <td>Inbound</td>\n",
       "      <td>BNPL e SaaS</td>\n",
       "      <td>...</td>\n",
       "      <td>Concluído</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>Até 10 atividades</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12419</th>\n",
       "      <td>10254</td>\n",
       "      <td>2023-10-19 15:09:50.063</td>\n",
       "      <td>pre_analysis</td>\n",
       "      <td>bnpl</td>\n",
       "      <td>finance</td>\n",
       "      <td>False</td>\n",
       "      <td>2023-09-03 07:55:29.365</td>\n",
       "      <td>False</td>\n",
       "      <td>Inbound</td>\n",
       "      <td>BNPL e SaaS</td>\n",
       "      <td>...</td>\n",
       "      <td>Concluído</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>Até 10 atividades</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12420</th>\n",
       "      <td>10254</td>\n",
       "      <td>2023-10-19 15:11:47.165</td>\n",
       "      <td>pre_analysis</td>\n",
       "      <td>bnpl</td>\n",
       "      <td>finance</td>\n",
       "      <td>False</td>\n",
       "      <td>2023-09-03 07:55:29.365</td>\n",
       "      <td>False</td>\n",
       "      <td>Inbound</td>\n",
       "      <td>BNPL e SaaS</td>\n",
       "      <td>...</td>\n",
       "      <td>Concluído</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>Até 10 atividades</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12421 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       CLINIC_ID             ACTIVITY_AT      ACTIVITY_TYPE  \\\n",
       "0           9825 2023-08-24 18:46:47.733  patient_discarded   \n",
       "1           9984 2023-08-24 19:41:43.497    patient_created   \n",
       "2           9984 2023-08-24 19:41:44.037  time_slot_created   \n",
       "3           9984 2023-08-24 19:51:20.451    patient_created   \n",
       "4           9984 2023-08-24 19:51:46.663  time_slot_created   \n",
       "...          ...                     ...                ...   \n",
       "12416      11234 2023-10-19 14:29:27.826    revenue_created   \n",
       "12417      11234 2023-10-19 14:37:20.492       pre_analysis   \n",
       "12418      11234 2023-10-19 14:53:48.231       pre_analysis   \n",
       "12419      10254 2023-10-19 15:09:50.063       pre_analysis   \n",
       "12420      10254 2023-10-19 15:11:47.165       pre_analysis   \n",
       "\n",
       "                  FEATURE    MODULE  IS_VALID_ACTIVITY  \\\n",
       "0      patient_management   patient               True   \n",
       "1      patient_management   patient              False   \n",
       "2              scheduling  schedule               True   \n",
       "3      patient_management   patient              False   \n",
       "4              scheduling  schedule               True   \n",
       "...                   ...       ...                ...   \n",
       "12416  revenue_management   finance               True   \n",
       "12417                bnpl   finance              False   \n",
       "12418                bnpl   finance              False   \n",
       "12419                bnpl   finance              False   \n",
       "12420                bnpl   finance              False   \n",
       "\n",
       "            CLINIC_CREATED_AT  IS_CHAIN_CLINIC FIRST_USER_HUBSPOT_SOURCE  \\\n",
       "0     2023-08-21 09:14:34.763            False                   Inbound   \n",
       "1     2023-08-24 19:40:58.432            False                   Inbound   \n",
       "2     2023-08-24 19:40:58.432            False                   Inbound   \n",
       "3     2023-08-24 19:40:58.432            False                   Inbound   \n",
       "4     2023-08-24 19:40:58.432            False                   Inbound   \n",
       "...                       ...              ...                       ...   \n",
       "12416 2023-09-28 16:27:41.579            False                   Inbound   \n",
       "12417 2023-09-28 16:27:41.579            False                   Inbound   \n",
       "12418 2023-09-28 16:27:41.579            False                   Inbound   \n",
       "12419 2023-09-03 07:55:29.365            False                   Inbound   \n",
       "12420 2023-09-03 07:55:29.365            False                   Inbound   \n",
       "\n",
       "      INTEREST_CATEGORY_SIGNUP  ...  CLINIC_ACCREDITATION_STATUS  \\\n",
       "0                  Apenas BNPL  ...                    Concluído   \n",
       "1                  BNPL e SaaS  ...                    Concluído   \n",
       "2                  BNPL e SaaS  ...                    Concluído   \n",
       "3                  BNPL e SaaS  ...                    Concluído   \n",
       "4                  BNPL e SaaS  ...                    Concluído   \n",
       "...                        ...  ...                          ...   \n",
       "12416              BNPL e SaaS  ...                    Concluído   \n",
       "12417              BNPL e SaaS  ...                    Concluído   \n",
       "12418              BNPL e SaaS  ...                    Concluído   \n",
       "12419              BNPL e SaaS  ...                    Concluído   \n",
       "12420              BNPL e SaaS  ...                    Concluído   \n",
       "\n",
       "             STRIPE_SUBSCRIPTION_ID SUBSCRIPTION_START_DATE  \\\n",
       "0      sub_1Nj29VCpjOdJI4gTsWNO1Kzm              2023-08-25   \n",
       "1                               NaN                     NaT   \n",
       "2                               NaN                     NaT   \n",
       "3                               NaN                     NaT   \n",
       "4                               NaN                     NaT   \n",
       "...                             ...                     ...   \n",
       "12416                           NaN                     NaT   \n",
       "12417                           NaN                     NaT   \n",
       "12418                           NaN                     NaT   \n",
       "12419                           NaN                     NaT   \n",
       "12420                           NaN                     NaT   \n",
       "\n",
       "      SUBSCRIPTION_END_DATE  SUBSCRIPTION_PERIOD_DAYS  SUBSCRIPTION_PERIOD  \\\n",
       "0                2023-10-25                      61.0   Entre 61 e 90 dias   \n",
       "1                       NaT                       NaN                  NaN   \n",
       "2                       NaT                       NaN                  NaN   \n",
       "3                       NaT                       NaN                  NaN   \n",
       "4                       NaT                       NaN                  NaN   \n",
       "...                     ...                       ...                  ...   \n",
       "12416                   NaT                       NaN                  NaN   \n",
       "12417                   NaT                       NaN                  NaN   \n",
       "12418                   NaT                       NaN                  NaN   \n",
       "12419                   NaT                       NaN                  NaN   \n",
       "12420                   NaT                       NaN                  NaN   \n",
       "\n",
       "       SUBSCRIPTION_STATUS  BILLING_INTERVAL_IN_MONTHS CLINICA_CONVERTIDA  \\\n",
       "0                   active                         1.0               True   \n",
       "1                      NaN                         NaN              False   \n",
       "2                      NaN                         NaN              False   \n",
       "3                      NaN                         NaN              False   \n",
       "4                      NaN                         NaN              False   \n",
       "...                    ...                         ...                ...   \n",
       "12416                  NaN                         NaN              False   \n",
       "12417                  NaN                         NaN              False   \n",
       "12418                  NaN                         NaN              False   \n",
       "12419                  NaN                         NaN              False   \n",
       "12420                  NaN                         NaN              False   \n",
       "\n",
       "           FAIXA_NRO_ATIVIDADES  \n",
       "0        Acima de 50 atividades  \n",
       "1      Entre 11 e 20 atividades  \n",
       "2      Entre 11 e 20 atividades  \n",
       "3      Entre 11 e 20 atividades  \n",
       "4      Entre 11 e 20 atividades  \n",
       "...                         ...  \n",
       "12416         Até 10 atividades  \n",
       "12417         Até 10 atividades  \n",
       "12418         Até 10 atividades  \n",
       "12419         Até 10 atividades  \n",
       "12420         Até 10 atividades  \n",
       "\n",
       "[12421 rows x 28 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_real['FAIXA_NRO_ATIVIDADES'] = df_real['CLINIC_ID'].apply(faixa_nro_atividades)\n",
    "df_real"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "#T_cf992_row4_col3 {\n",
       "  background-color: #3498a9;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_cf992_row5_col3 {\n",
       "  background-color: #44bcad;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_cf992_row6_col3 {\n",
       "  background-color: #3fb5ad;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_cf992_row7_col3 {\n",
       "  background-color: #359fab;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_cf992\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_cf992_level0_col0\" class=\"col_heading level0 col0\" >FAIXA_NRO_ATIVIDADES</th>\n",
       "      <th id=\"T_cf992_level0_col1\" class=\"col_heading level0 col1\" >CLINICA_CONVERTIDA</th>\n",
       "      <th id=\"T_cf992_level0_col2\" class=\"col_heading level0 col2\" >frequencia</th>\n",
       "      <th id=\"T_cf992_level0_col3\" class=\"col_heading level0 col3\" >porcentagem</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_cf992_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
       "      <td id=\"T_cf992_row0_col0\" class=\"data row0 col0\" >Acima de 50 atividades</td>\n",
       "      <td id=\"T_cf992_row0_col1\" class=\"data row0 col1\" >False</td>\n",
       "      <td id=\"T_cf992_row0_col2\" class=\"data row0 col2\" >398</td>\n",
       "      <td id=\"T_cf992_row0_col3\" class=\"data row0 col3\" >12.291538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cf992_level0_row1\" class=\"row_heading level0 row1\" >0</th>\n",
       "      <td id=\"T_cf992_row1_col0\" class=\"data row1 col0\" >Acima de 50 atividades</td>\n",
       "      <td id=\"T_cf992_row1_col1\" class=\"data row1 col1\" >True</td>\n",
       "      <td id=\"T_cf992_row1_col2\" class=\"data row1 col2\" >2840</td>\n",
       "      <td id=\"T_cf992_row1_col3\" class=\"data row1 col3\" >87.708462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cf992_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_cf992_row2_col0\" class=\"data row2 col0\" >Até 10 atividades</td>\n",
       "      <td id=\"T_cf992_row2_col1\" class=\"data row2 col1\" >False</td>\n",
       "      <td id=\"T_cf992_row2_col2\" class=\"data row2 col2\" >1724</td>\n",
       "      <td id=\"T_cf992_row2_col3\" class=\"data row2 col3\" >78.865508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cf992_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_cf992_row3_col0\" class=\"data row3 col0\" >Até 10 atividades</td>\n",
       "      <td id=\"T_cf992_row3_col1\" class=\"data row3 col1\" >True</td>\n",
       "      <td id=\"T_cf992_row3_col2\" class=\"data row3 col2\" >462</td>\n",
       "      <td id=\"T_cf992_row3_col3\" class=\"data row3 col3\" >21.134492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cf992_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_cf992_row4_col0\" class=\"data row4 col0\" >Entre 11 e 20 atividades</td>\n",
       "      <td id=\"T_cf992_row4_col1\" class=\"data row4 col1\" >False</td>\n",
       "      <td id=\"T_cf992_row4_col2\" class=\"data row4 col2\" >650</td>\n",
       "      <td id=\"T_cf992_row4_col3\" class=\"data row4 col3\" >57.067603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cf992_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_cf992_row5_col0\" class=\"data row5 col0\" >Entre 11 e 20 atividades</td>\n",
       "      <td id=\"T_cf992_row5_col1\" class=\"data row5 col1\" >True</td>\n",
       "      <td id=\"T_cf992_row5_col2\" class=\"data row5 col2\" >489</td>\n",
       "      <td id=\"T_cf992_row5_col3\" class=\"data row5 col3\" >42.932397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cf992_level0_row6\" class=\"row_heading level0 row6\" >7</th>\n",
       "      <td id=\"T_cf992_row6_col0\" class=\"data row6 col0\" >Entre 21 e 50 atividades</td>\n",
       "      <td id=\"T_cf992_row6_col1\" class=\"data row6 col1\" >False</td>\n",
       "      <td id=\"T_cf992_row6_col2\" class=\"data row6 col2\" >745</td>\n",
       "      <td id=\"T_cf992_row6_col3\" class=\"data row6 col3\" >45.649510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cf992_level0_row7\" class=\"row_heading level0 row7\" >6</th>\n",
       "      <td id=\"T_cf992_row7_col0\" class=\"data row7 col0\" >Entre 21 e 50 atividades</td>\n",
       "      <td id=\"T_cf992_row7_col1\" class=\"data row7 col1\" >True</td>\n",
       "      <td id=\"T_cf992_row7_col2\" class=\"data row7 col2\" >887</td>\n",
       "      <td id=\"T_cf992_row7_col3\" class=\"data row7 col3\" >54.350490</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2b8c70ecc50>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Comparação de conversão entre clínicas que realizaram um número alto de atividades válidas versus um número baixo.\n",
    "lib.grpby_2cat(dataframe=df_real.loc[df_real['IS_VALID_ACTIVITY']], coluna1='FAIXA_NRO_ATIVIDADES', coluna2='CLINICA_CONVERTIDA')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lib.plot_relacao_entre_2cat(df_real.loc[df_real['IS_VALID_ACTIVITY']], coluna1='FAIXA_NRO_ATIVIDADES', coluna2='CLINICA_CONVERTIDA')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(53, 28)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Clinicas que tiveram somente atividades invalidas, porem foram convertidas.\n",
    "tabela = pd.crosstab(df_real['CLINIC_ID'], df_real['IS_VALID_ACTIVITY']).reset_index()\n",
    "somente_atividades_invalidas = tabela.loc[tabela[True] == 0]['CLINIC_ID']\n",
    "df_real.loc[(df_real['CLINIC_ID'].isin(somente_atividades_invalidas)) & (df_real['CLINICA_CONVERTIDA'])].drop_duplicates(subset=['CLINIC_ID']).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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>CLINIC_ID</th>\n",
       "      <th>ACTIVITY_AT</th>\n",
       "      <th>ACTIVITY_TYPE</th>\n",
       "      <th>FEATURE</th>\n",
       "      <th>MODULE</th>\n",
       "      <th>IS_VALID_ACTIVITY</th>\n",
       "      <th>CLINIC_CREATED_AT</th>\n",
       "      <th>IS_CHAIN_CLINIC</th>\n",
       "      <th>FIRST_USER_HUBSPOT_SOURCE</th>\n",
       "      <th>INTEREST_CATEGORY_SIGNUP</th>\n",
       "      <th>...</th>\n",
       "      <th>CLINIC_ACCREDITATION_STATUS</th>\n",
       "      <th>STRIPE_SUBSCRIPTION_ID</th>\n",
       "      <th>SUBSCRIPTION_START_DATE</th>\n",
       "      <th>SUBSCRIPTION_END_DATE</th>\n",
       "      <th>SUBSCRIPTION_PERIOD_DAYS</th>\n",
       "      <th>SUBSCRIPTION_PERIOD</th>\n",
       "      <th>SUBSCRIPTION_STATUS</th>\n",
       "      <th>BILLING_INTERVAL_IN_MONTHS</th>\n",
       "      <th>CLINICA_CONVERTIDA</th>\n",
       "      <th>FAIXA_NRO_ATIVIDADES</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>9933</td>\n",
       "      <td>2023-08-25 10:04:00.409</td>\n",
       "      <td>pre_analysis</td>\n",
       "      <td>bnpl</td>\n",
       "      <td>finance</td>\n",
       "      <td>False</td>\n",
       "      <td>2023-08-23 12:07:17.826</td>\n",
       "      <td>False</td>\n",
       "      <td>Inbound</td>\n",
       "      <td>Apenas BNPL</td>\n",
       "      <td>...</td>\n",
       "      <td>Concluído</td>\n",
       "      <td>sub_1Nkq5JCpjOdJI4gTFQQbh2Oh</td>\n",
       "      <td>2023-08-30</td>\n",
       "      <td>NaT</td>\n",
       "      <td>999.0</td>\n",
       "      <td>Período em aberto</td>\n",
       "      <td>active</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>9940</td>\n",
       "      <td>2023-08-25 11:50:11.634</td>\n",
       "      <td>pre_analysis</td>\n",
       "      <td>bnpl</td>\n",
       "      <td>finance</td>\n",
       "      <td>False</td>\n",
       "      <td>2023-08-23 15:04:20.665</td>\n",
       "      <td>False</td>\n",
       "      <td>Inbound</td>\n",
       "      <td>Apenas BNPL</td>\n",
       "      <td>...</td>\n",
       "      <td>Concluído</td>\n",
       "      <td>sub_1NqcOJCpjOdJI4gTxoRWE20N</td>\n",
       "      <td>2023-09-15</td>\n",
       "      <td>NaT</td>\n",
       "      <td>999.0</td>\n",
       "      <td>Período em aberto</td>\n",
       "      <td>active</td>\n",
       "      <td>6.0</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>9948</td>\n",
       "      <td>2023-08-25 17:07:50.377</td>\n",
       "      <td>pre_analysis</td>\n",
       "      <td>bnpl</td>\n",
       "      <td>finance</td>\n",
       "      <td>False</td>\n",
       "      <td>2023-08-23 17:09:54.743</td>\n",
       "      <td>False</td>\n",
       "      <td>Inbound</td>\n",
       "      <td>Apenas BNPL</td>\n",
       "      <td>...</td>\n",
       "      <td>Concluído</td>\n",
       "      <td>sub_1Nv3NHCpjOdJI4gT8Fp8v9oG</td>\n",
       "      <td>2023-09-27</td>\n",
       "      <td>NaT</td>\n",
       "      <td>999.0</td>\n",
       "      <td>Período em aberto</td>\n",
       "      <td>active</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523</th>\n",
       "      <td>9935</td>\n",
       "      <td>2023-08-28 14:53:52.307</td>\n",
       "      <td>pre_analysis</td>\n",
       "      <td>bnpl</td>\n",
       "      <td>finance</td>\n",
       "      <td>False</td>\n",
       "      <td>2023-08-23 13:16:41.598</td>\n",
       "      <td>False</td>\n",
       "      <td>Inbound</td>\n",
       "      <td>Apenas BNPL</td>\n",
       "      <td>...</td>\n",
       "      <td>Concluído</td>\n",
       "      <td>sub_1NlYTSCpjOdJI4gTiZRbOo5T</td>\n",
       "      <td>2023-09-01</td>\n",
       "      <td>2023-11-01</td>\n",
       "      <td>61.0</td>\n",
       "      <td>Entre 61 e 90 dias</td>\n",
       "      <td>active</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>615</th>\n",
       "      <td>9766</td>\n",
       "      <td>2023-08-28 19:37:45.760</td>\n",
       "      <td>pre_analysis</td>\n",
       "      <td>bnpl</td>\n",
       "      <td>finance</td>\n",
       "      <td>False</td>\n",
       "      <td>2023-08-18 19:02:19.079</td>\n",
       "      <td>False</td>\n",
       "      <td>Inbound</td>\n",
       "      <td>Apenas BNPL</td>\n",
       "      <td>...</td>\n",
       "      <td>Concluído</td>\n",
       "      <td>sub_1Npt5NCpjOdJI4gTA3YVFnlF</td>\n",
       "      <td>2023-09-13</td>\n",
       "      <td>NaT</td>\n",
       "      <td>999.0</td>\n",
       "      <td>Período em aberto</td>\n",
       "      <td>active</td>\n",
       "      <td>6.0</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     CLINIC_ID             ACTIVITY_AT ACTIVITY_TYPE FEATURE   MODULE  \\\n",
       "59        9933 2023-08-25 10:04:00.409  pre_analysis    bnpl  finance   \n",
       "94        9940 2023-08-25 11:50:11.634  pre_analysis    bnpl  finance   \n",
       "213       9948 2023-08-25 17:07:50.377  pre_analysis    bnpl  finance   \n",
       "523       9935 2023-08-28 14:53:52.307  pre_analysis    bnpl  finance   \n",
       "615       9766 2023-08-28 19:37:45.760  pre_analysis    bnpl  finance   \n",
       "\n",
       "     IS_VALID_ACTIVITY       CLINIC_CREATED_AT  IS_CHAIN_CLINIC  \\\n",
       "59               False 2023-08-23 12:07:17.826            False   \n",
       "94               False 2023-08-23 15:04:20.665            False   \n",
       "213              False 2023-08-23 17:09:54.743            False   \n",
       "523              False 2023-08-23 13:16:41.598            False   \n",
       "615              False 2023-08-18 19:02:19.079            False   \n",
       "\n",
       "    FIRST_USER_HUBSPOT_SOURCE INTEREST_CATEGORY_SIGNUP  ...  \\\n",
       "59                    Inbound              Apenas BNPL  ...   \n",
       "94                    Inbound              Apenas BNPL  ...   \n",
       "213                   Inbound              Apenas BNPL  ...   \n",
       "523                   Inbound              Apenas BNPL  ...   \n",
       "615                   Inbound              Apenas BNPL  ...   \n",
       "\n",
       "     CLINIC_ACCREDITATION_STATUS        STRIPE_SUBSCRIPTION_ID  \\\n",
       "59                     Concluído  sub_1Nkq5JCpjOdJI4gTFQQbh2Oh   \n",
       "94                     Concluído  sub_1NqcOJCpjOdJI4gTxoRWE20N   \n",
       "213                    Concluído  sub_1Nv3NHCpjOdJI4gT8Fp8v9oG   \n",
       "523                    Concluído  sub_1NlYTSCpjOdJI4gTiZRbOo5T   \n",
       "615                    Concluído  sub_1Npt5NCpjOdJI4gTA3YVFnlF   \n",
       "\n",
       "    SUBSCRIPTION_START_DATE SUBSCRIPTION_END_DATE  SUBSCRIPTION_PERIOD_DAYS  \\\n",
       "59               2023-08-30                   NaT                     999.0   \n",
       "94               2023-09-15                   NaT                     999.0   \n",
       "213              2023-09-27                   NaT                     999.0   \n",
       "523              2023-09-01            2023-11-01                      61.0   \n",
       "615              2023-09-13                   NaT                     999.0   \n",
       "\n",
       "     SUBSCRIPTION_PERIOD  SUBSCRIPTION_STATUS  BILLING_INTERVAL_IN_MONTHS  \\\n",
       "59     Período em aberto               active                         1.0   \n",
       "94     Período em aberto               active                         6.0   \n",
       "213    Período em aberto               active                         1.0   \n",
       "523   Entre 61 e 90 dias               active                         1.0   \n",
       "615    Período em aberto               active                         6.0   \n",
       "\n",
       "    CLINICA_CONVERTIDA FAIXA_NRO_ATIVIDADES  \n",
       "59                True                 None  \n",
       "94                True                 None  \n",
       "213               True                 None  \n",
       "523               True                 None  \n",
       "615               True                 None  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Clinicas que tiveram somente atividades invalidas, porem foram convertidas.\n",
    "df_real.loc[(df_real['CLINIC_ID'].isin(somente_atividades_invalidas)) & (df_real['CLINICA_CONVERTIDA'])].drop_duplicates(subset=['CLINIC_ID']).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table id=\"T_7a35e\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_7a35e_level0_col0\" class=\"col_heading level0 col0\" >FEATURE</th>\n",
       "      <th id=\"T_7a35e_level0_col1\" class=\"col_heading level0 col1\" >CLINICA_CONVERTIDA</th>\n",
       "      <th id=\"T_7a35e_level0_col2\" class=\"col_heading level0 col2\" >frequencia</th>\n",
       "      <th id=\"T_7a35e_level0_col3\" class=\"col_heading level0 col3\" >porcentagem</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_7a35e_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_7a35e_row0_col0\" class=\"data row0 col0\" >anamnese</td>\n",
       "      <td id=\"T_7a35e_row0_col1\" class=\"data row0 col1\" >False</td>\n",
       "      <td id=\"T_7a35e_row0_col2\" class=\"data row0 col2\" >196</td>\n",
       "      <td id=\"T_7a35e_row0_col3\" class=\"data row0 col3\" >69.257951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7a35e_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_7a35e_row1_col0\" class=\"data row1 col0\" >anamnese</td>\n",
       "      <td id=\"T_7a35e_row1_col1\" class=\"data row1 col1\" >True</td>\n",
       "      <td id=\"T_7a35e_row1_col2\" class=\"data row1 col2\" >87</td>\n",
       "      <td id=\"T_7a35e_row1_col3\" class=\"data row1 col3\" >30.742049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7a35e_level0_row2\" class=\"row_heading level0 row2\" >3</th>\n",
       "      <td id=\"T_7a35e_row2_col0\" class=\"data row2 col0\" >bnpl</td>\n",
       "      <td id=\"T_7a35e_row2_col1\" class=\"data row2 col1\" >False</td>\n",
       "      <td id=\"T_7a35e_row2_col2\" class=\"data row2 col2\" >1</td>\n",
       "      <td id=\"T_7a35e_row2_col3\" class=\"data row2 col3\" >14.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7a35e_level0_row3\" class=\"row_heading level0 row3\" >2</th>\n",
       "      <td id=\"T_7a35e_row3_col0\" class=\"data row3 col0\" >bnpl</td>\n",
       "      <td id=\"T_7a35e_row3_col1\" class=\"data row3 col1\" >True</td>\n",
       "      <td id=\"T_7a35e_row3_col2\" class=\"data row3 col2\" >6</td>\n",
       "      <td id=\"T_7a35e_row3_col3\" class=\"data row3 col3\" >85.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7a35e_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_7a35e_row4_col0\" class=\"data row4 col0\" >budget_management</td>\n",
       "      <td id=\"T_7a35e_row4_col1\" class=\"data row4 col1\" >False</td>\n",
       "      <td id=\"T_7a35e_row4_col2\" class=\"data row4 col2\" >203</td>\n",
       "      <td id=\"T_7a35e_row4_col3\" class=\"data row4 col3\" >69.047619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7a35e_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_7a35e_row5_col0\" class=\"data row5 col0\" >budget_management</td>\n",
       "      <td id=\"T_7a35e_row5_col1\" class=\"data row5 col1\" >True</td>\n",
       "      <td id=\"T_7a35e_row5_col2\" class=\"data row5 col2\" >91</td>\n",
       "      <td id=\"T_7a35e_row5_col3\" class=\"data row5 col3\" >30.952381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7a35e_level0_row6\" class=\"row_heading level0 row6\" >7</th>\n",
       "      <td id=\"T_7a35e_row6_col0\" class=\"data row6 col0\" >credit_check</td>\n",
       "      <td id=\"T_7a35e_row6_col1\" class=\"data row6 col1\" >False</td>\n",
       "      <td id=\"T_7a35e_row6_col2\" class=\"data row6 col2\" >12</td>\n",
       "      <td id=\"T_7a35e_row6_col3\" class=\"data row6 col3\" >40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7a35e_level0_row7\" class=\"row_heading level0 row7\" >6</th>\n",
       "      <td id=\"T_7a35e_row7_col0\" class=\"data row7 col0\" >credit_check</td>\n",
       "      <td id=\"T_7a35e_row7_col1\" class=\"data row7 col1\" >True</td>\n",
       "      <td id=\"T_7a35e_row7_col2\" class=\"data row7 col2\" >18</td>\n",
       "      <td id=\"T_7a35e_row7_col3\" class=\"data row7 col3\" >60.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7a35e_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_7a35e_row8_col0\" class=\"data row8 col0\" >documents_and_files</td>\n",
       "      <td id=\"T_7a35e_row8_col1\" class=\"data row8 col1\" >False</td>\n",
       "      <td id=\"T_7a35e_row8_col2\" class=\"data row8 col2\" >82</td>\n",
       "      <td id=\"T_7a35e_row8_col3\" class=\"data row8 col3\" >71.304348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7a35e_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_7a35e_row9_col0\" class=\"data row9 col0\" >documents_and_files</td>\n",
       "      <td id=\"T_7a35e_row9_col1\" class=\"data row9 col1\" >True</td>\n",
       "      <td id=\"T_7a35e_row9_col2\" class=\"data row9 col2\" >33</td>\n",
       "      <td id=\"T_7a35e_row9_col3\" class=\"data row9 col3\" >28.695652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7a35e_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_7a35e_row10_col0\" class=\"data row10 col0\" >medical_record</td>\n",
       "      <td id=\"T_7a35e_row10_col1\" class=\"data row10 col1\" >False</td>\n",
       "      <td id=\"T_7a35e_row10_col2\" class=\"data row10 col2\" >276</td>\n",
       "      <td id=\"T_7a35e_row10_col3\" class=\"data row10 col3\" >70.229008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7a35e_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_7a35e_row11_col0\" class=\"data row11 col0\" >medical_record</td>\n",
       "      <td id=\"T_7a35e_row11_col1\" class=\"data row11 col1\" >True</td>\n",
       "      <td id=\"T_7a35e_row11_col2\" class=\"data row11 col2\" >117</td>\n",
       "      <td id=\"T_7a35e_row11_col3\" class=\"data row11 col3\" >29.770992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7a35e_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_7a35e_row12_col0\" class=\"data row12 col0\" >patient_management</td>\n",
       "      <td id=\"T_7a35e_row12_col1\" class=\"data row12 col1\" >False</td>\n",
       "      <td id=\"T_7a35e_row12_col2\" class=\"data row12 col2\" >73</td>\n",
       "      <td id=\"T_7a35e_row12_col3\" class=\"data row12 col3\" >57.936508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7a35e_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_7a35e_row13_col0\" class=\"data row13 col0\" >patient_management</td>\n",
       "      <td id=\"T_7a35e_row13_col1\" class=\"data row13 col1\" >True</td>\n",
       "      <td id=\"T_7a35e_row13_col2\" class=\"data row13 col2\" >53</td>\n",
       "      <td id=\"T_7a35e_row13_col3\" class=\"data row13 col3\" >42.063492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7a35e_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_7a35e_row14_col0\" class=\"data row14 col0\" >payment_book</td>\n",
       "      <td id=\"T_7a35e_row14_col1\" class=\"data row14 col1\" >True</td>\n",
       "      <td id=\"T_7a35e_row14_col2\" class=\"data row14 col2\" >2</td>\n",
       "      <td id=\"T_7a35e_row14_col3\" class=\"data row14 col3\" >100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7a35e_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_7a35e_row15_col0\" class=\"data row15 col0\" >revenue_management</td>\n",
       "      <td id=\"T_7a35e_row15_col1\" class=\"data row15 col1\" >False</td>\n",
       "      <td id=\"T_7a35e_row15_col2\" class=\"data row15 col2\" >150</td>\n",
       "      <td id=\"T_7a35e_row15_col3\" class=\"data row15 col3\" >72.115385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7a35e_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_7a35e_row16_col0\" class=\"data row16 col0\" >revenue_management</td>\n",
       "      <td id=\"T_7a35e_row16_col1\" class=\"data row16 col1\" >True</td>\n",
       "      <td id=\"T_7a35e_row16_col2\" class=\"data row16 col2\" >58</td>\n",
       "      <td id=\"T_7a35e_row16_col3\" class=\"data row16 col3\" >27.884615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7a35e_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_7a35e_row17_col0\" class=\"data row17 col0\" >scheduling</td>\n",
       "      <td id=\"T_7a35e_row17_col1\" class=\"data row17 col1\" >False</td>\n",
       "      <td id=\"T_7a35e_row17_col2\" class=\"data row17 col2\" >545</td>\n",
       "      <td id=\"T_7a35e_row17_col3\" class=\"data row17 col3\" >75.276243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7a35e_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "      <td id=\"T_7a35e_row18_col0\" class=\"data row18 col0\" >scheduling</td>\n",
       "      <td id=\"T_7a35e_row18_col1\" class=\"data row18 col1\" >True</td>\n",
       "      <td id=\"T_7a35e_row18_col2\" class=\"data row18 col2\" >179</td>\n",
       "      <td id=\"T_7a35e_row18_col3\" class=\"data row18 col3\" >24.723757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7a35e_level0_row19\" class=\"row_heading level0 row19\" >20</th>\n",
       "      <td id=\"T_7a35e_row19_col0\" class=\"data row19 col0\" >user_management</td>\n",
       "      <td id=\"T_7a35e_row19_col1\" class=\"data row19 col1\" >False</td>\n",
       "      <td id=\"T_7a35e_row19_col2\" class=\"data row19 col2\" >54</td>\n",
       "      <td id=\"T_7a35e_row19_col3\" class=\"data row19 col3\" >46.153846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7a35e_level0_row20\" class=\"row_heading level0 row20\" >19</th>\n",
       "      <td id=\"T_7a35e_row20_col0\" class=\"data row20 col0\" >user_management</td>\n",
       "      <td id=\"T_7a35e_row20_col1\" class=\"data row20 col1\" >True</td>\n",
       "      <td id=\"T_7a35e_row20_col2\" class=\"data row20 col2\" >63</td>\n",
       "      <td id=\"T_7a35e_row20_col3\" class=\"data row20 col3\" >53.846154</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2b8c70ea330>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identificação de atividades que têm maior correlação com a conversão (e.g., clínicas que usam a gestão financeira têm maior chance de converter).\n",
    "lib.grpby_2cat(dataframe=df_real.loc[(df_real['IS_VALID_ACTIVITY'])].drop_duplicates(subset=['FEATURE','CLINIC_ID']), coluna1='FEATURE', coluna2='CLINICA_CONVERTIDA')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  background-color: #4dc4ad;\n",
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       "  background-color: #3aaead;\n",
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       "#T_7c61e_row6_col3 {\n",
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       "  color: #f1f1f1;\n",
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       "#T_7c61e_row8_col3 {\n",
       "  background-color: #348fa7;\n",
       "  color: #f1f1f1;\n",
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       "#T_7c61e_row9_col3 {\n",
       "  background-color: #96ddb5;\n",
       "  color: #000000;\n",
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       "#T_7c61e_row10_col3 {\n",
       "  background-color: #7cd6af;\n",
       "  color: #000000;\n",
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       "#T_7c61e_row11_col3 {\n",
       "  background-color: #3499aa;\n",
       "  color: #f1f1f1;\n",
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       "#T_7c61e_row12_col3 {\n",
       "  background-color: #82d8b0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_7c61e_row13_col3 {\n",
       "  background-color: #3496a9;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_7c61e_row14_col3 {\n",
       "  background-color: #40498e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_7c61e_row15_col3, #T_7c61e_row18_col3 {\n",
       "  background-color: #39acac;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_7c61e_row16_col3, #T_7c61e_row17_col3 {\n",
       "  background-color: #50c6ad;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_7c61e_row19_col3 {\n",
       "  background-color: #65d0ad;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_7c61e_row20_col3 {\n",
       "  background-color: #36a2ab;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_7c61e\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_7c61e_level0_col0\" class=\"col_heading level0 col0\" >FEATURE</th>\n",
       "      <th id=\"T_7c61e_level0_col1\" class=\"col_heading level0 col1\" >CLINICA_CONVERTIDA</th>\n",
       "      <th id=\"T_7c61e_level0_col2\" class=\"col_heading level0 col2\" >frequencia</th>\n",
       "      <th id=\"T_7c61e_level0_col3\" class=\"col_heading level0 col3\" >porcentagem</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_7c61e_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_7c61e_row0_col0\" class=\"data row0 col0\" >anamnese</td>\n",
       "      <td id=\"T_7c61e_row0_col1\" class=\"data row0 col1\" >False</td>\n",
       "      <td id=\"T_7c61e_row0_col2\" class=\"data row0 col2\" >255</td>\n",
       "      <td id=\"T_7c61e_row0_col3\" class=\"data row0 col3\" >57.432432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7c61e_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_7c61e_row1_col0\" class=\"data row1 col0\" >anamnese</td>\n",
       "      <td id=\"T_7c61e_row1_col1\" class=\"data row1 col1\" >True</td>\n",
       "      <td id=\"T_7c61e_row1_col2\" class=\"data row1 col2\" >189</td>\n",
       "      <td id=\"T_7c61e_row1_col3\" class=\"data row1 col3\" >42.567568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7c61e_level0_row2\" class=\"row_heading level0 row2\" >3</th>\n",
       "      <td id=\"T_7c61e_row2_col0\" class=\"data row2 col0\" >bnpl</td>\n",
       "      <td id=\"T_7c61e_row2_col1\" class=\"data row2 col1\" >False</td>\n",
       "      <td id=\"T_7c61e_row2_col2\" class=\"data row2 col2\" >1</td>\n",
       "      <td id=\"T_7c61e_row2_col3\" class=\"data row2 col3\" >14.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7c61e_level0_row3\" class=\"row_heading level0 row3\" >2</th>\n",
       "      <td id=\"T_7c61e_row3_col0\" class=\"data row3 col0\" >bnpl</td>\n",
       "      <td id=\"T_7c61e_row3_col1\" class=\"data row3 col1\" >True</td>\n",
       "      <td id=\"T_7c61e_row3_col2\" class=\"data row3 col2\" >6</td>\n",
       "      <td id=\"T_7c61e_row3_col3\" class=\"data row3 col3\" >85.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7c61e_level0_row4\" class=\"row_heading level0 row4\" >5</th>\n",
       "      <td id=\"T_7c61e_row4_col0\" class=\"data row4 col0\" >budget_management</td>\n",
       "      <td id=\"T_7c61e_row4_col1\" class=\"data row4 col1\" >False</td>\n",
       "      <td id=\"T_7c61e_row4_col2\" class=\"data row4 col2\" >427</td>\n",
       "      <td id=\"T_7c61e_row4_col3\" class=\"data row4 col3\" >44.994731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7c61e_level0_row5\" class=\"row_heading level0 row5\" >4</th>\n",
       "      <td id=\"T_7c61e_row5_col0\" class=\"data row5 col0\" >budget_management</td>\n",
       "      <td id=\"T_7c61e_row5_col1\" class=\"data row5 col1\" >True</td>\n",
       "      <td id=\"T_7c61e_row5_col2\" class=\"data row5 col2\" >522</td>\n",
       "      <td id=\"T_7c61e_row5_col3\" class=\"data row5 col3\" >55.005269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7c61e_level0_row6\" class=\"row_heading level0 row6\" >7</th>\n",
       "      <td id=\"T_7c61e_row6_col0\" class=\"data row6 col0\" >credit_check</td>\n",
       "      <td id=\"T_7c61e_row6_col1\" class=\"data row6 col1\" >False</td>\n",
       "      <td id=\"T_7c61e_row6_col2\" class=\"data row6 col2\" >15</td>\n",
       "      <td id=\"T_7c61e_row6_col3\" class=\"data row6 col3\" >26.315789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7c61e_level0_row7\" class=\"row_heading level0 row7\" >6</th>\n",
       "      <td id=\"T_7c61e_row7_col0\" class=\"data row7 col0\" >credit_check</td>\n",
       "      <td id=\"T_7c61e_row7_col1\" class=\"data row7 col1\" >True</td>\n",
       "      <td id=\"T_7c61e_row7_col2\" class=\"data row7 col2\" >42</td>\n",
       "      <td id=\"T_7c61e_row7_col3\" class=\"data row7 col3\" >73.684211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7c61e_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_7c61e_row8_col0\" class=\"data row8 col0\" >documents_and_files</td>\n",
       "      <td id=\"T_7c61e_row8_col1\" class=\"data row8 col1\" >False</td>\n",
       "      <td id=\"T_7c61e_row8_col2\" class=\"data row8 col2\" >161</td>\n",
       "      <td id=\"T_7c61e_row8_col3\" class=\"data row8 col3\" >69.396552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7c61e_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_7c61e_row9_col0\" class=\"data row9 col0\" >documents_and_files</td>\n",
       "      <td id=\"T_7c61e_row9_col1\" class=\"data row9 col1\" >True</td>\n",
       "      <td id=\"T_7c61e_row9_col2\" class=\"data row9 col2\" >71</td>\n",
       "      <td id=\"T_7c61e_row9_col3\" class=\"data row9 col3\" >30.603448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7c61e_level0_row10\" class=\"row_heading level0 row10\" >11</th>\n",
       "      <td id=\"T_7c61e_row10_col0\" class=\"data row10 col0\" >medical_record</td>\n",
       "      <td id=\"T_7c61e_row10_col1\" class=\"data row10 col1\" >False</td>\n",
       "      <td id=\"T_7c61e_row10_col2\" class=\"data row10 col2\" >971</td>\n",
       "      <td id=\"T_7c61e_row10_col3\" class=\"data row10 col3\" >35.016228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7c61e_level0_row11\" class=\"row_heading level0 row11\" >10</th>\n",
       "      <td id=\"T_7c61e_row11_col0\" class=\"data row11 col0\" >medical_record</td>\n",
       "      <td id=\"T_7c61e_row11_col1\" class=\"data row11 col1\" >True</td>\n",
       "      <td id=\"T_7c61e_row11_col2\" class=\"data row11 col2\" >1802</td>\n",
       "      <td id=\"T_7c61e_row11_col3\" class=\"data row11 col3\" >64.983772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7c61e_level0_row12\" class=\"row_heading level0 row12\" >13</th>\n",
       "      <td id=\"T_7c61e_row12_col0\" class=\"data row12 col0\" >patient_management</td>\n",
       "      <td id=\"T_7c61e_row12_col1\" class=\"data row12 col1\" >False</td>\n",
       "      <td id=\"T_7c61e_row12_col2\" class=\"data row12 col2\" >111</td>\n",
       "      <td id=\"T_7c61e_row12_col3\" class=\"data row12 col3\" >33.944954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7c61e_level0_row13\" class=\"row_heading level0 row13\" >12</th>\n",
       "      <td id=\"T_7c61e_row13_col0\" class=\"data row13 col0\" >patient_management</td>\n",
       "      <td id=\"T_7c61e_row13_col1\" class=\"data row13 col1\" >True</td>\n",
       "      <td id=\"T_7c61e_row13_col2\" class=\"data row13 col2\" >216</td>\n",
       "      <td id=\"T_7c61e_row13_col3\" class=\"data row13 col3\" >66.055046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7c61e_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_7c61e_row14_col0\" class=\"data row14 col0\" >payment_book</td>\n",
       "      <td id=\"T_7c61e_row14_col1\" class=\"data row14 col1\" >True</td>\n",
       "      <td id=\"T_7c61e_row14_col2\" class=\"data row14 col2\" >3</td>\n",
       "      <td id=\"T_7c61e_row14_col3\" class=\"data row14 col3\" >100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7c61e_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_7c61e_row15_col0\" class=\"data row15 col0\" >revenue_management</td>\n",
       "      <td id=\"T_7c61e_row15_col1\" class=\"data row15 col1\" >False</td>\n",
       "      <td id=\"T_7c61e_row15_col2\" class=\"data row15 col2\" >413</td>\n",
       "      <td id=\"T_7c61e_row15_col3\" class=\"data row15 col3\" >56.037992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7c61e_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_7c61e_row16_col0\" class=\"data row16 col0\" >revenue_management</td>\n",
       "      <td id=\"T_7c61e_row16_col1\" class=\"data row16 col1\" >True</td>\n",
       "      <td id=\"T_7c61e_row16_col2\" class=\"data row16 col2\" >324</td>\n",
       "      <td id=\"T_7c61e_row16_col3\" class=\"data row16 col3\" >43.962008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7c61e_level0_row17\" class=\"row_heading level0 row17\" >18</th>\n",
       "      <td id=\"T_7c61e_row17_col0\" class=\"data row17 col0\" >scheduling</td>\n",
       "      <td id=\"T_7c61e_row17_col1\" class=\"data row17 col1\" >False</td>\n",
       "      <td id=\"T_7c61e_row17_col2\" class=\"data row17 col2\" >1082</td>\n",
       "      <td id=\"T_7c61e_row17_col3\" class=\"data row17 col3\" >44.001627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7c61e_level0_row18\" class=\"row_heading level0 row18\" >17</th>\n",
       "      <td id=\"T_7c61e_row18_col0\" class=\"data row18 col0\" >scheduling</td>\n",
       "      <td id=\"T_7c61e_row18_col1\" class=\"data row18 col1\" >True</td>\n",
       "      <td id=\"T_7c61e_row18_col2\" class=\"data row18 col2\" >1377</td>\n",
       "      <td id=\"T_7c61e_row18_col3\" class=\"data row18 col3\" >55.998373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7c61e_level0_row19\" class=\"row_heading level0 row19\" >20</th>\n",
       "      <td id=\"T_7c61e_row19_col0\" class=\"data row19 col0\" >user_management</td>\n",
       "      <td id=\"T_7c61e_row19_col1\" class=\"data row19 col1\" >False</td>\n",
       "      <td id=\"T_7c61e_row19_col2\" class=\"data row19 col2\" >81</td>\n",
       "      <td id=\"T_7c61e_row19_col3\" class=\"data row19 col3\" >39.130435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7c61e_level0_row20\" class=\"row_heading level0 row20\" >19</th>\n",
       "      <td id=\"T_7c61e_row20_col0\" class=\"data row20 col0\" >user_management</td>\n",
       "      <td id=\"T_7c61e_row20_col1\" class=\"data row20 col1\" >True</td>\n",
       "      <td id=\"T_7c61e_row20_col2\" class=\"data row20 col2\" >126</td>\n",
       "      <td id=\"T_7c61e_row20_col3\" class=\"data row20 col3\" >60.869565</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2b8bfe50890>"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lib.grpby_2cat(dataframe=df_real.loc[(df_real['IS_VALID_ACTIVITY'])], coluna1='FEATURE', coluna2='CLINICA_CONVERTIDA')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lib.plot_relacao_entre_2cat(dataframe=df_real.loc[(df_real['IS_VALID_ACTIVITY'])].drop_duplicates(subset=['FEATURE','CLINIC_ID']), coluna1='FEATURE', coluna2='CLINICA_CONVERTIDA', orient='h')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21.557603686635943"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Média da quantidade de atividades válidas de clínicas convertidas\n",
    "atividades_validas_clinicas_convertidas = df_real.loc[(df_real['IS_VALID_ACTIVITY']) & (df_real['CLINICA_CONVERTIDA'])].groupby(by='CLINIC_ID')['IS_VALID_ACTIVITY'].count()\n",
    "atividades_validas_clinicas_convertidas.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.503912363067292"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Média da quantidade de atividades válidas de clínicas não convertidas\n",
    "atividades_validas_clinicas_nao_convertidas = df_real.loc[(df_real['IS_VALID_ACTIVITY']) & (~df_real['CLINICA_CONVERTIDA'])].groupby(by='CLINIC_ID')['IS_VALID_ACTIVITY'].count()\n",
    "atividades_validas_clinicas_nao_convertidas.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Criando a coluna TRIAL_DURATION_INTERVAL para categorizar o TRIAL_DURATION em (Até 7 dias) ou (Acima de 7 dias).\n",
    "df_real['TRIAL_DURATION_INTERVAL'] = pd.cut(df_real['TRIAL_DURATION'], bins=[0,7,14,365], labels=['Até 7 dias', 'Entre 8 e 14 dias', 'Acima de 14 dias'], include_lowest=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_d944e_row0_col3 {\n",
       "  background-color: #40498e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d944e_row1_col3 {\n",
       "  background-color: #def5e5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d944e_row2_col3 {\n",
       "  background-color: #3498a9;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d944e_row3_col3 {\n",
       "  background-color: #44bcad;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d944e_row4_col3 {\n",
       "  background-color: #37669e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d944e_row5_col3 {\n",
       "  background-color: #a9e1bd;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_d944e\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_d944e_level0_col0\" class=\"col_heading level0 col0\" >TRIAL_DURATION_INTERVAL</th>\n",
       "      <th id=\"T_d944e_level0_col1\" class=\"col_heading level0 col1\" >CLINICA_CONVERTIDA</th>\n",
       "      <th id=\"T_d944e_level0_col2\" class=\"col_heading level0 col2\" >frequencia</th>\n",
       "      <th id=\"T_d944e_level0_col3\" class=\"col_heading level0 col3\" >porcentagem</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_d944e_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_d944e_row0_col0\" class=\"data row0 col0\" >Até 7 dias</td>\n",
       "      <td id=\"T_d944e_row0_col1\" class=\"data row0 col1\" >False</td>\n",
       "      <td id=\"T_d944e_row0_col2\" class=\"data row0 col2\" >653</td>\n",
       "      <td id=\"T_d944e_row0_col3\" class=\"data row0 col3\" >73.785311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d944e_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_d944e_row1_col0\" class=\"data row1 col0\" >Até 7 dias</td>\n",
       "      <td id=\"T_d944e_row1_col1\" class=\"data row1 col1\" >True</td>\n",
       "      <td id=\"T_d944e_row1_col2\" class=\"data row1 col2\" >232</td>\n",
       "      <td id=\"T_d944e_row1_col3\" class=\"data row1 col3\" >26.214689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d944e_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_d944e_row2_col0\" class=\"data row2 col0\" >Entre 8 e 14 dias</td>\n",
       "      <td id=\"T_d944e_row2_col1\" class=\"data row2 col1\" >False</td>\n",
       "      <td id=\"T_d944e_row2_col2\" class=\"data row2 col2\" >24</td>\n",
       "      <td id=\"T_d944e_row2_col3\" class=\"data row2 col3\" >54.545455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d944e_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_d944e_row3_col0\" class=\"data row3 col0\" >Entre 8 e 14 dias</td>\n",
       "      <td id=\"T_d944e_row3_col1\" class=\"data row3 col1\" >True</td>\n",
       "      <td id=\"T_d944e_row3_col2\" class=\"data row3 col2\" >20</td>\n",
       "      <td id=\"T_d944e_row3_col3\" class=\"data row3 col3\" >45.454545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d944e_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_d944e_row4_col0\" class=\"data row4 col0\" >Acima de 14 dias</td>\n",
       "      <td id=\"T_d944e_row4_col1\" class=\"data row4 col1\" >False</td>\n",
       "      <td id=\"T_d944e_row4_col2\" class=\"data row4 col2\" >36</td>\n",
       "      <td id=\"T_d944e_row4_col3\" class=\"data row4 col3\" >66.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d944e_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_d944e_row5_col0\" class=\"data row5 col0\" >Acima de 14 dias</td>\n",
       "      <td id=\"T_d944e_row5_col1\" class=\"data row5 col1\" >True</td>\n",
       "      <td id=\"T_d944e_row5_col2\" class=\"data row5 col2\" >18</td>\n",
       "      <td id=\"T_d944e_row5_col3\" class=\"data row5 col3\" >33.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2b8c70ef050>"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identificação do intervalo de período de Trial que têm maior correlação com a conversão.\n",
    "lib.grpby_2cat(dataframe=df_real.drop_duplicates(subset=['CLINIC_ID']), coluna1='TRIAL_DURATION_INTERVAL', coluna2='CLINICA_CONVERTIDA')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lib.plot_relacao_entre_2cat(dataframe=df_real.drop_duplicates(subset=['CLINIC_ID']), coluna1='TRIAL_DURATION_INTERVAL', coluna2='CLINICA_CONVERTIDA')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_de734_row0_col3 {\n",
       "  background-color: #348ea7;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_de734_row1_col3 {\n",
       "  background-color: #4fc5ad;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_de734_row2_col3 {\n",
       "  background-color: #385f9c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_de734_row3_col3 {\n",
       "  background-color: #b9e6c7;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_de734_row4_col3 {\n",
       "  background-color: #3c5598;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_de734_row5_col3 {\n",
       "  background-color: #caedd4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_de734_row6_col3 {\n",
       "  background-color: #40498e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_de734_row7_col3 {\n",
       "  background-color: #def5e5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_de734_row8_col3 {\n",
       "  background-color: #3fb5ad;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_de734_row9_col3 {\n",
       "  background-color: #359fab;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_de734\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_de734_level0_col0\" class=\"col_heading level0 col0\" >MARKETING_ATTRIBUITION_CHANNEL_GROUP</th>\n",
       "      <th id=\"T_de734_level0_col1\" class=\"col_heading level0 col1\" >CLINICA_CONVERTIDA</th>\n",
       "      <th id=\"T_de734_level0_col2\" class=\"col_heading level0 col2\" >frequencia</th>\n",
       "      <th id=\"T_de734_level0_col3\" class=\"col_heading level0 col3\" >porcentagem</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_de734_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_de734_row0_col0\" class=\"data row0 col0\" >Organic</td>\n",
       "      <td id=\"T_de734_row0_col1\" class=\"data row0 col1\" >False</td>\n",
       "      <td id=\"T_de734_row0_col2\" class=\"data row0 col2\" >39</td>\n",
       "      <td id=\"T_de734_row0_col3\" class=\"data row0 col3\" >58.208955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_de734_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_de734_row1_col0\" class=\"data row1 col0\" >Organic</td>\n",
       "      <td id=\"T_de734_row1_col1\" class=\"data row1 col1\" >True</td>\n",
       "      <td id=\"T_de734_row1_col2\" class=\"data row1 col2\" >28</td>\n",
       "      <td id=\"T_de734_row1_col3\" class=\"data row1 col3\" >41.791045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_de734_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_de734_row2_col0\" class=\"data row2 col0\" >Other</td>\n",
       "      <td id=\"T_de734_row2_col1\" class=\"data row2 col1\" >False</td>\n",
       "      <td id=\"T_de734_row2_col2\" class=\"data row2 col2\" >161</td>\n",
       "      <td id=\"T_de734_row2_col3\" class=\"data row2 col3\" >71.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_de734_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_de734_row3_col0\" class=\"data row3 col0\" >Other</td>\n",
       "      <td id=\"T_de734_row3_col1\" class=\"data row3 col1\" >True</td>\n",
       "      <td id=\"T_de734_row3_col2\" class=\"data row3 col2\" >63</td>\n",
       "      <td id=\"T_de734_row3_col3\" class=\"data row3 col3\" >28.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_de734_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_de734_row4_col0\" class=\"data row4 col0\" >Paid - google</td>\n",
       "      <td id=\"T_de734_row4_col1\" class=\"data row4 col1\" >False</td>\n",
       "      <td id=\"T_de734_row4_col2\" class=\"data row4 col2\" >271</td>\n",
       "      <td id=\"T_de734_row4_col3\" class=\"data row4 col3\" >74.655647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_de734_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_de734_row5_col0\" class=\"data row5 col0\" >Paid - google</td>\n",
       "      <td id=\"T_de734_row5_col1\" class=\"data row5 col1\" >True</td>\n",
       "      <td id=\"T_de734_row5_col2\" class=\"data row5 col2\" >92</td>\n",
       "      <td id=\"T_de734_row5_col3\" class=\"data row5 col3\" >25.344353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_de734_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_de734_row6_col0\" class=\"data row6 col0\" >Paid - meta</td>\n",
       "      <td id=\"T_de734_row6_col1\" class=\"data row6 col1\" >False</td>\n",
       "      <td id=\"T_de734_row6_col2\" class=\"data row6 col2\" >220</td>\n",
       "      <td id=\"T_de734_row6_col3\" class=\"data row6 col3\" >78.014184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_de734_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_de734_row7_col0\" class=\"data row7 col0\" >Paid - meta</td>\n",
       "      <td id=\"T_de734_row7_col1\" class=\"data row7 col1\" >True</td>\n",
       "      <td id=\"T_de734_row7_col2\" class=\"data row7 col2\" >62</td>\n",
       "      <td id=\"T_de734_row7_col3\" class=\"data row7 col3\" >21.985816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_de734_level0_row8\" class=\"row_heading level0 row8\" >9</th>\n",
       "      <td id=\"T_de734_row8_col0\" class=\"data row8 col0\" >Site ou Indicação Interna</td>\n",
       "      <td id=\"T_de734_row8_col1\" class=\"data row8 col1\" >False</td>\n",
       "      <td id=\"T_de734_row8_col2\" class=\"data row8 col2\" >22</td>\n",
       "      <td id=\"T_de734_row8_col3\" class=\"data row8 col3\" >46.808511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_de734_level0_row9\" class=\"row_heading level0 row9\" >8</th>\n",
       "      <td id=\"T_de734_row9_col0\" class=\"data row9 col0\" >Site ou Indicação Interna</td>\n",
       "      <td id=\"T_de734_row9_col1\" class=\"data row9 col1\" >True</td>\n",
       "      <td id=\"T_de734_row9_col2\" class=\"data row9 col2\" >25</td>\n",
       "      <td id=\"T_de734_row9_col3\" class=\"data row9 col3\" >53.191489</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2b8c871e000>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Percentual de comparação de performance de conversão entre diferentes fontes de canal de marketing.\n",
    "lib.grpby_2cat(dataframe=df_real.drop_duplicates(subset=['CLINIC_ID']), coluna1='MARKETING_ATTRIBUITION_CHANNEL_GROUP', coluna2='CLINICA_CONVERTIDA')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lib.plot_relacao_entre_2cat(dataframe=df_real.drop_duplicates(subset=['CLINIC_ID']), coluna1='MARKETING_ATTRIBUITION_CHANNEL_GROUP', coluna2='CLINICA_CONVERTIDA')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MARKETING_ATTRIBUITION_CHANNEL_GROUP</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Organic</th>\n",
       "      <td>28</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>63</td>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paid - google</th>\n",
       "      <td>92</td>\n",
       "      <td>271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paid - meta</th>\n",
       "      <td>62</td>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Site ou Indicação Interna</th>\n",
       "      <td>25</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       0    1\n",
       "MARKETING_ATTRIBUITION_CHANNEL_GROUP         \n",
       "Organic                               28   39\n",
       "Other                                 63  161\n",
       "Paid - google                         92  271\n",
       "Paid - meta                           62  220\n",
       "Site ou Indicação Interna             25   22"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conversion_counts = clinicas_unicas_periodo_trial.groupby('MARKETING_ATTRIBUITION_CHANNEL_GROUP')['CLINIC_ID'].apply(\n",
    "    lambda x: pd.Series([x.isin(clinicas_convertidas).sum(), (~x.isin(clinicas_convertidas)).sum()])\n",
    ").unstack()\n",
    "conversion_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MARKETING_ATTRIBUITION_CHANNEL_GROUP\n",
       "Organic                      41.791045\n",
       "Other                        28.125000\n",
       "Paid - google                25.344353\n",
       "Paid - meta                  21.985816\n",
       "Site ou Indicação Interna    53.191489\n",
       "Name: CLINIC_ID, dtype: float64"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conversion_rate_by_marketing_channel = clinicas_unicas_periodo_trial.groupby('MARKETING_ATTRIBUITION_CHANNEL_GROUP')['CLINIC_ID'].apply(\n",
    "    lambda x: x.isin(clinicas_convertidas).mean() * 100\n",
    ")\n",
    "conversion_rate_by_marketing_channel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* #### Métricas de Credenciamento no Trial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "frequência    349.000000\n",
       "percentual     35.503561\n",
       "Name: CLINIC_ID, dtype: float64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Percentual de clínicas que solicitaram credenciamento durante o trial.\n",
    "clinicas_unicas_periodo_trial.loc[clinicas_unicas_periodo_trial['HAS_ASKED_FOR_ACCREDITATION']]['CLINIC_ID'].agg({'frequência':'count'\n",
    "                                                                                                                          ,'percentual': lambda x: (x.count() / clinicas_unicas_periodo_trial.shape[0]) * 100})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_39188_row0_col3 {\n",
       "  background-color: #40498e;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_39188_row1_col3 {\n",
       "  background-color: #def5e5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_39188_row2_col3 {\n",
       "  background-color: #6dd3ad;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_39188_row3_col3 {\n",
       "  background-color: #347fa4;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_39188\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_39188_level0_col0\" class=\"col_heading level0 col0\" >IS_ACCREDITATION_APPROVED</th>\n",
       "      <th id=\"T_39188_level0_col1\" class=\"col_heading level0 col1\" >CLINICA_CONVERTIDA</th>\n",
       "      <th id=\"T_39188_level0_col2\" class=\"col_heading level0 col2\" >frequencia</th>\n",
       "      <th id=\"T_39188_level0_col3\" class=\"col_heading level0 col3\" >porcentagem</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_39188_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_39188_row0_col0\" class=\"data row0 col0\" >False</td>\n",
       "      <td id=\"T_39188_row0_col1\" class=\"data row0 col1\" >False</td>\n",
       "      <td id=\"T_39188_row0_col2\" class=\"data row0 col2\" >621</td>\n",
       "      <td id=\"T_39188_row0_col3\" class=\"data row0 col3\" >88.587732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_39188_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_39188_row1_col0\" class=\"data row1 col0\" >False</td>\n",
       "      <td id=\"T_39188_row1_col1\" class=\"data row1 col1\" >True</td>\n",
       "      <td id=\"T_39188_row1_col2\" class=\"data row1 col2\" >80</td>\n",
       "      <td id=\"T_39188_row1_col3\" class=\"data row1 col3\" >11.412268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_39188_level0_row2\" class=\"row_heading level0 row2\" >3</th>\n",
       "      <td id=\"T_39188_row2_col0\" class=\"data row2 col0\" >True</td>\n",
       "      <td id=\"T_39188_row2_col1\" class=\"data row2 col1\" >False</td>\n",
       "      <td id=\"T_39188_row2_col2\" class=\"data row2 col2\" >92</td>\n",
       "      <td id=\"T_39188_row2_col3\" class=\"data row2 col3\" >32.624113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_39188_level0_row3\" class=\"row_heading level0 row3\" >2</th>\n",
       "      <td id=\"T_39188_row3_col0\" class=\"data row3 col0\" >True</td>\n",
       "      <td id=\"T_39188_row3_col1\" class=\"data row3 col1\" >True</td>\n",
       "      <td id=\"T_39188_row3_col2\" class=\"data row3 col2\" >190</td>\n",
       "      <td id=\"T_39188_row3_col3\" class=\"data row3 col3\" >67.375887</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2b8c5770e30>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Conversão de clínicas credenciadas versus não credenciadas.  \n",
    "lib.grpby_2cat(dataframe=df_real.drop_duplicates(subset=['CLINIC_ID']), coluna1='IS_ACCREDITATION_APPROVED', coluna2='CLINICA_CONVERTIDA')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lib.plot_relacao_entre_2cat(dataframe=df_real.drop_duplicates(subset=['CLINIC_ID']), coluna1='IS_ACCREDITATION_APPROVED', coluna2='CLINICA_CONVERTIDA', orient='h')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">    * De um total de 282 clínicas credenciadas, 190 (67.38%) foram convertidas e 92 (32.62%) não foram convertidas.\n",
    ">    * De um total de 701 clínicas não credenciadas, 80 (11.41%) foram convertidas e 621 (88.59%) não foram convertidas."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* #### Métricas de Desempenho do Trial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "De um total de 983 clínicas cadastradas no período Trial, 713 (72.53%) pararam de usar o produto durante o trial.\n"
     ]
    }
   ],
   "source": [
    "# Percentual de clínicas que param de usar o produto durante o trial (não realizaram a assinatura do produto - não foram convertidas).  \n",
    "num_churn_clinicas_durante_trial = df_real.loc[~df_real['CLINICA_CONVERTIDA']]['CLINIC_ID'].nunique()\n",
    "perc_churn_clinicas_durante_trial = (num_churn_clinicas_durante_trial / clinicas_unicas_periodo_trial.shape[0]) * 100\n",
    "print(f'De um total de {clinicas_unicas_periodo_trial.shape[0]} clínicas cadastradas no período Trial, {num_churn_clinicas_durante_trial} ({perc_churn_clinicas_durante_trial:.2f}%) pararam de usar o produto durante o trial.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "De um total de 983 clínicas cadastradas no período Trial, 29 (2.95%) pararam de usar o produto após o trial.\n"
     ]
    }
   ],
   "source": [
    "# Percentual de clínicas que param de usar o produto após o trial (realizaram a assinatura do produto, porem não esta mais ativa).  \n",
    "num_churn_clinicas_apos_trial = df_real.loc[(df_real['CLINICA_CONVERTIDA']) & (df_real['SUBSCRIPTION_STATUS'] != 'active')]['CLINIC_ID'].nunique()\n",
    "perc_churn_clinicas_apos_trial = (num_churn_clinicas_apos_trial / clinicas_unicas_periodo_trial.shape[0]) * 100\n",
    "print(f'De um total de {clinicas_unicas_periodo_trial.shape[0]} clínicas cadastradas no período Trial, {num_churn_clinicas_apos_trial} ({perc_churn_clinicas_apos_trial:.2f}%) pararam de usar o produto após o trial.')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Exportando os dados para um arquivo .csv\n",
    "df_real.to_csv(r'..\\dados\\aquisicao_clinicas_completo.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Vamos analisar os resultados das métricas:\n",
    "\n",
    "* Distribuição da duração do período de trial para todas as clínicas.\n",
    "    * A média de duração do período de trial foi de aproximadamente 8.61 dias.\n",
    "    * 75% das clínicas tiveram seu período de trial de até 7 dias.\n",
    "\n",
    "* Distribuição do número de atividades válidas por clínica durante o trial.\n",
    "    * A média de atividades realizadas por clínica durante o trial foi de 9.573.\n",
    "    * Aproximadamente 75% (642) das clínicas realizaram até 8 atividades durante o período trial.\n",
    "    * O maior número de atividades registradas foi de 599.\n",
    "\n",
    "* Quais funcionalidades são mais usadas durante o trial.\n",
    "    * medical_record - frequência:  2773 - 33.83%\n",
    "    * scheduling - frequência: 2460 - 30.00%\n",
    "    * budget_management - frequência: 949 - 11.58%\n",
    "\n",
    "* Percentual de clínicas que utilizam funcionalidades específicas.\n",
    "    * payment_book : 0.086%\n",
    "    * bnpl : 0.304%\n",
    "    * credit_check : 1.304%\n",
    "    * documents_and_files : 5.002%\n",
    "    * user_management : 5.089%\n",
    "    * patient_management : 5.480%\n",
    "    * revenue_management : 9.047%\n",
    "    * anamnese : 12.309%\n",
    "    * budget_management : 12.788%\n",
    "    * medical_record : 17.094%\n",
    "    * scheduling : 31.491%\n",
    "\n",
    "* Percentual de clínicas que convertem para assinaturas pagas após o trial. \n",
    "    *   Aproximadamente 27.46% das clínicas cadastradas.\n",
    "\n",
    "* Tempo médio desde o início do trial até a conversão para assinatura paga.\n",
    "    * A média de tempo foi de aproximadamente 9.31 dias.\n",
    "    * Aproximadamente 75% das clínicas converteram em no máximo 12 dias.\n",
    "    * O número máximo de dias para conversão foi de 60 dias.\n",
    "     \n",
    "* Comparação de conversão entre clínicas que realizaram um número alto de atividades válidas versus um número baixo.\n",
    "    * A maior proporção de clínicas convertidas (88%) foram aquelas que realizaram acima de 50 atividades válidas.\n",
    "    * A menor proporção de clínicas convertidas (22%) foram aquelas que realizaram até 10 atividades válidas.\n",
    "\n",
    "\n",
    "* Identificação de atividades específicas que têm maior correlação com a conversão para assinaturas pagas.    \n",
    "    * user_management: 53.846%\n",
    "    * bnpl : 85.714%\n",
    "    * credit_check : 60.00%\n",
    "\n",
    "* Média da quantidade de atividades válidas de clínicas convertidas.\n",
    "    * A média foi de aproximadamente 21.55 atividades.\n",
    "\n",
    "* Média da quantidade de atividades válidas de clínicas não convertidas.\n",
    "    * A média foi de aproximadamente 5.50 atividades.\n",
    "\n",
    "* Identificação do intervalo de período de Trial que têm maior correlação com a conversão.\n",
    "    * A maior proporção de clínicas convertidas (45.45%), tiveram seu período de Trial entre 8 e 14 dias.\n",
    "\n",
    "* Percentual de comparação de performance de conversão entre diferentes fontes de canal de marketing.\n",
    "    * Paid - google\t: 25.344%\n",
    "    * Other\t: 28.125%\n",
    "    * Paid - meta :\t21.985%\n",
    "    * Organic :\t41.791%\n",
    "    * Site ou Indicação Interna\t: 53.191%\n",
    "\n",
    "* Percentual de clínicas que solicitaram credenciamento durante o trial.\n",
    "    * Aproximadamente 35.50% das clínicas cadastradas.\n",
    "\n",
    "* Conversão de clínicas credenciadas versus não credenciadas.\n",
    "    * De um total de 282 clínicas credenciadas, 190 (67.38%) foram convertidas e 92 (32.62%) não foram convertidas.\n",
    "    * De um total de 701 clínicas não credenciadas, 80 (11.41%) foram convertidas e 621 (88.59%) não foram convertidas.\n",
    "\n",
    "* Percentual de clínicas que param de usar o produto durante o trial (não realizaram a assinatura do produto). \n",
    "    * De um total de 983 clínicas cadastradas no período Trial, 713 (72.53%) pararam de usar o produto durante o trial.\n",
    "\n",
    "* Percentual de clínicas que param de usar o produto após o trial (realizaram a assinatura do produto, porem não esta mais ativa).      \n",
    "    * De um total de 983 clínicas cadastradas no período Trial, 29 (2.95%) pararam de usar o produto após o trial."
   ]
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