{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "849b6b4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import pyodbc\n",
    "import configparser\n",
    "\n",
    "\n",
    "config = configparser.ConfigParser()\n",
    "config.read(r\"C:\\Users\\joao.herculano\\Documents\\Enviador de email\\credenciais.ini\")\n",
    "\n",
    "conn = pyodbc.connect(\n",
    "    f\"DRIVER={{SQL Server}};\"\n",
    "    f\"SERVER={config['banco']['host']},1433;\"\n",
    "    f\"DATABASE=GINSENG;\"\n",
    "    f\"UID={config['banco']['user']};\"\n",
    "    f\"PWD={config['banco']['password']}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "62fc9678",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\joao.herculano\\AppData\\Local\\Temp\\ipykernel_16792\\1235837546.py:16: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
      "  df_vendas = pd.read_sql(query, conn)\n"
     ]
    }
   ],
   "source": [
    "query = '''\n",
    "select\n",
    "\tbvb.[DATA],\n",
    "\tbvb.pdv,\n",
    "\tbvb.SKU ,\n",
    "\tcast(replace(bvb.VENDAS,'.','') as int) as Vendas,\n",
    "\tem.ORIGEM\n",
    "from base_vendas_bi bvb \n",
    "left join (\n",
    "select *\n",
    "from\n",
    "estoque_mar\n",
    "where origem is not null) em on cast(em.SKU as int) = cast(replace(bvb.SKU,'.','') as int) and cast( em.pdv as int) = cast(bvb.PDV as int)\n",
    "WHERE EM.CATEGORIA not in ('SUPORTE À VENDA','EMBALAGENS') AND bvb.[DATA] >'2025-04-14'\n",
    "'''\n",
    "df_vendas = pd.read_sql(query, conn)\n",
    "conn.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6c7089b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_vendas['pdv'] = np.where(df_vendas['pdv'] == '23703','23708',df_vendas['pdv'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "834f4c3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_vendas['DATA'] = pd.to_datetime(df_vendas['DATA'])\n",
    "\n",
    "df_vendas['DATA_MES'] = pd.to_datetime(df_vendas['DATA'], dayfirst=True, errors='coerce').dt.to_period('M').astype(str)\n",
    "\n",
    "df_vendas['Vendas'] = df_vendas['Vendas'].astype('Int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6f94c57e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tabela = pd.read_excel(r\"C:\\Users\\joao.herculano\\Documents\\compilado_tab_pedido.xlsx\")\n",
    "\n",
    "df_tabela['extracao_desc'] = df_tabela['Descrição'].str.split(' ').str[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "532d718d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tabela['LINHA'] = np.where(df_tabela['MARCA'] == 'EUDORA',df_tabela['extracao_desc'],df_tabela['LINHA'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d8116439",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tabela = df_tabela.groupby(['Nome da Origem','SKU1', 'SKU2','Descrição', 'MARCA','CATEGORIA', 'LINHA','Tipo Preço','extracao_desc'])[['PC','PV']].max().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "117ee782",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_vendas2 = pd.merge(df_vendas,df_tabela,left_on='SKU',right_on='SKU2',how='left')\n",
    "df_vendas2 = df_vendas2.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "374e679c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_vendas2['preço_venda'] = df_vendas2['Vendas']*df_vendas2['PV']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "caecc276",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1568926, 18)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_vendas2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "619bc34d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_vendas2['PV'] = df_vendas2['PV'].astype('str').str.replace(',','')\n",
    "df_vendas2['PV'] = df_vendas2['PV'].astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9e0ef539",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(306748811.9699977)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_vendas2['preço_venda'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "42c56b2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.int64(16832)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_vendas2[(df_vendas2['SKU']==84387)& (df_vendas2['pdv']=='20998')]['Vendas'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d1ce7af2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Agrupar vendas por PDV, Marca e SKU\n",
    "vendas_por_pdv_sku = df_vendas2.groupby(['pdv', 'MARCA', 'SKU'])['preço_venda'].sum().reset_index()\n",
    "\n",
    "# Ordenar por PDV, MARCA e vendas (maior para menor)\n",
    "vendas_por_pdv_sku = vendas_por_pdv_sku.sort_values(['pdv', 'MARCA', 'preço_venda'], ascending=[True, True, False])\n",
    "\n",
    "# Calcular percentual acumulado DENTRO de cada PDV e MARCA\n",
    "vendas_por_pdv_sku['percentual_acumulado'] = vendas_por_pdv_sku.groupby(['pdv', 'MARCA'])['preço_venda'].transform(\n",
    "    lambda x: (x.cumsum() / x.sum()) * 100\n",
    ")\n",
    "\n",
    "# Criar coluna CLASSE\n",
    "vendas_por_pdv_sku['CLASSE'] = vendas_por_pdv_sku['percentual_acumulado'].apply(\n",
    "    lambda x: 'A' if x <= 80 else ('B' if x <= 95 else 'C')\n",
    ")\n",
    "\n",
    "\n",
    "# Visualizar resultado\n",
    "vendas_por_pdv2 =vendas_por_pdv_sku.reset_index().sort_values(['pdv','percentual_acumulado'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "de01395e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\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>index</th>\n",
       "      <th>PDV</th>\n",
       "      <th>MARCA</th>\n",
       "      <th>SKU</th>\n",
       "      <th>preço_venda</th>\n",
       "      <th>percentual_acumulado</th>\n",
       "      <th>CLASSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>972</td>\n",
       "      <td>12522</td>\n",
       "      <td>BOTICARIO</td>\n",
       "      <td>84387</td>\n",
       "      <td>31384.3</td>\n",
       "      <td>2.360871</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>787</td>\n",
       "      <td>12522</td>\n",
       "      <td>BOTICARIO</td>\n",
       "      <td>75792</td>\n",
       "      <td>23384.4</td>\n",
       "      <td>4.119953</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>12522</td>\n",
       "      <td>BOTICARIO</td>\n",
       "      <td>74043</td>\n",
       "      <td>22028.4</td>\n",
       "      <td>5.77703</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>315</td>\n",
       "      <td>12522</td>\n",
       "      <td>BOTICARIO</td>\n",
       "      <td>50677</td>\n",
       "      <td>20946.9</td>\n",
       "      <td>7.352751</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>816</td>\n",
       "      <td>12522</td>\n",
       "      <td>BOTICARIO</td>\n",
       "      <td>77524</td>\n",
       "      <td>19423.3</td>\n",
       "      <td>8.813861</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107809</th>\n",
       "      <td>107809</td>\n",
       "      <td>5699</td>\n",
       "      <td>BOTICARIO</td>\n",
       "      <td>87421</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>107810</th>\n",
       "      <td>107810</td>\n",
       "      <td>5699</td>\n",
       "      <td>BOTICARIO</td>\n",
       "      <td>87422</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107811</th>\n",
       "      <td>107811</td>\n",
       "      <td>5699</td>\n",
       "      <td>BOTICARIO</td>\n",
       "      <td>87423</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107812</th>\n",
       "      <td>107812</td>\n",
       "      <td>5699</td>\n",
       "      <td>BOTICARIO</td>\n",
       "      <td>87424</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107813</th>\n",
       "      <td>107813</td>\n",
       "      <td>5699</td>\n",
       "      <td>EUDORA</td>\n",
       "      <td>53095</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>107814 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         index    PDV      MARCA    SKU  preço_venda  percentual_acumulado  \\\n",
       "0          972  12522  BOTICARIO  84387      31384.3              2.360871   \n",
       "1          787  12522  BOTICARIO  75792      23384.4              4.119953   \n",
       "2          747  12522  BOTICARIO  74043      22028.4               5.77703   \n",
       "3          315  12522  BOTICARIO  50677      20946.9              7.352751   \n",
       "4          816  12522  BOTICARIO  77524      19423.3              8.813861   \n",
       "...        ...    ...        ...    ...          ...                   ...   \n",
       "107809  107809   5699  BOTICARIO  87421          0.0                 100.0   \n",
       "107810  107810   5699  BOTICARIO  87422          0.0                 100.0   \n",
       "107811  107811   5699  BOTICARIO  87423          0.0                 100.0   \n",
       "107812  107812   5699  BOTICARIO  87424          0.0                 100.0   \n",
       "107813  107813   5699     EUDORA  53095          0.0                   NaN   \n",
       "\n",
       "       CLASSE  \n",
       "0           A  \n",
       "1           A  \n",
       "2           A  \n",
       "3           A  \n",
       "4           A  \n",
       "...       ...  \n",
       "107809      C  \n",
       "107810      C  \n",
       "107811      C  \n",
       "107812      C  \n",
       "107813      C  \n",
       "\n",
       "[107814 rows x 7 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vendas_por_pdv2 = vendas_por_pdv2.rename(columns={'pdv':'PDV'})\n",
    "\n",
    "vendas_por_pdv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f3ae19e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\joao.herculano\\AppData\\Local\\Temp\\ipykernel_16792\\145927781.py:15: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
      "  df_estoque = pd.read_sql(query, conn)\n"
     ]
    }
   ],
   "source": [
    "conn = pyodbc.connect(\n",
    "    f\"DRIVER={{SQL Server}};\"\n",
    "    f\"SERVER={config['banco']['host']},1433;\"\n",
    "    f\"DATABASE=GINSENG;\"\n",
    "    f\"UID={config['banco']['user']};\"\n",
    "    f\"PWD={config['banco']['password']}\")\n",
    "\n",
    "query = '''\n",
    "select\n",
    "em.PDV,\n",
    "em.SKU,\n",
    "em.CLASSE as CLASSE_MAR\n",
    "from estoque_mar em\n",
    "'''\n",
    "df_estoque = pd.read_sql(query, conn)\n",
    "conn.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a7b971ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_estoque['SKU'] = df_estoque['SKU'].astype('Int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d02a8433",
   "metadata": {},
   "outputs": [],
   "source": [
    "vendas_por_pdv2 = vendas_por_pdv2.rename(columns={'pdv':'PDV'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3aee39ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
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       "      <th>PDV</th>\n",
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       "      <td>31384.3</td>\n",
       "      <td>2.360871</td>\n",
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       "      <th>2</th>\n",
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       "      <td>12522</td>\n",
       "      <td>BOTICARIO</td>\n",
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       "      <td>A</td>\n",
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       "      <td>BOTICARIO</td>\n",
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       "      <td>A</td>\n",
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       "    <tr>\n",
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       "      <td>12522</td>\n",
       "      <td>BOTICARIO</td>\n",
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       "      <td>8.813861</td>\n",
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       "      <th>...</th>\n",
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       "      <td>100.0</td>\n",
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       "      <th>107810</th>\n",
       "      <td>107810</td>\n",
       "      <td>5699</td>\n",
       "      <td>BOTICARIO</td>\n",
       "      <td>87422</td>\n",
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       "      <td>C</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107812</th>\n",
       "      <td>107812</td>\n",
       "      <td>5699</td>\n",
       "      <td>BOTICARIO</td>\n",
       "      <td>87424</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>C</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107813</th>\n",
       "      <td>107813</td>\n",
       "      <td>5699</td>\n",
       "      <td>EUDORA</td>\n",
       "      <td>53095</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>107814 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         index    PDV      MARCA    SKU  preço_venda  percentual_acumulado  \\\n",
       "0          972  12522  BOTICARIO  84387      31384.3              2.360871   \n",
       "1          787  12522  BOTICARIO  75792      23384.4              4.119953   \n",
       "2          747  12522  BOTICARIO  74043      22028.4               5.77703   \n",
       "3          315  12522  BOTICARIO  50677      20946.9              7.352751   \n",
       "4          816  12522  BOTICARIO  77524      19423.3              8.813861   \n",
       "...        ...    ...        ...    ...          ...                   ...   \n",
       "107809  107809   5699  BOTICARIO  87421          0.0                 100.0   \n",
       "107810  107810   5699  BOTICARIO  87422          0.0                 100.0   \n",
       "107811  107811   5699  BOTICARIO  87423          0.0                 100.0   \n",
       "107812  107812   5699  BOTICARIO  87424          0.0                 100.0   \n",
       "107813  107813   5699     EUDORA  53095          0.0                   NaN   \n",
       "\n",
       "       CLASSE CLASSE_MAR  \n",
       "0           A          A  \n",
       "1           A          E  \n",
       "2           A          A  \n",
       "3           A          A  \n",
       "4           A          A  \n",
       "...       ...        ...  \n",
       "107809      C          E  \n",
       "107810      C          E  \n",
       "107811      C          E  \n",
       "107812      C          E  \n",
       "107813      C          E  \n",
       "\n",
       "[107814 rows x 8 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vendas_por_pdv3 = pd.merge(vendas_por_pdv2,df_estoque,on=['PDV','SKU'],how='left')\n",
    "\n",
    "vendas_por_pdv3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "44067ef7",
   "metadata": {},
   "outputs": [],
   "source": [
    "vendas_por_pdv3.to_excel(r'C:\\Users\\joao.herculano\\GRUPO GINSENG\\Assistência Suprimentos - 2025\\CODIGOS\\Estudo Curva\\avaliação_curva.xlsx',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26a70844",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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