{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a9aff7f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import glob\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b2dcfc46",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\joao.herculano\\AppData\\Local\\Temp\\ipykernel_7172\\2177111484.py:1: DtypeWarning: Columns (2,15,20) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  df_estoque = pd.read_csv(r\"C:\\Users\\joao.herculano\\OneDrive - GRUPO GINSENG\\Documentos\\CONSULTAS BANCO DE DADOS\\ESTOQUE ULTIMO DIA MÊS JAN-SET 20251001.csv\",sep=';')\n"
     ]
    }
   ],
   "source": [
    "df_estoque = pd.read_csv(r\"C:\\Users\\joao.herculano\\OneDrive - GRUPO GINSENG\\Documentos\\CONSULTAS BANCO DE DADOS\\ESTOQUE ULTIMO DIA MÊS JAN-SET 20251001.csv\",sep=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "a62908ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CATEGORIA\n",
       "MAQUIAGEM                    844751\n",
       "SUPORTE À VENDA              512586\n",
       "GIFTS                        471481\n",
       "CABELOS                      361316\n",
       "CUIDADOS COM A PELE          349341\n",
       "PERFUMARIA                   344606\n",
       "ACESSÓRIOS                   194688\n",
       "DESODORANTES                 189907\n",
       "SABONETE CORPO               153660\n",
       "EMBALAGENS                   152144\n",
       "CUIDADOS FACIAIS              93892\n",
       "INFANTIL                      87526\n",
       "ÓLEOS                         42178\n",
       "CUIDADOS COM A BARBA          35728\n",
       "SOLAR                         23415\n",
       "HOME CARE                     14213\n",
       "UNHAS                         12543\n",
       "CUIDADOS PETS                  5753\n",
       "MAQUIAGEM (Desativado)          288\n",
       "ALIMENTOS                        70\n",
       "DESATIVADA                       36\n",
       "DESODORANTES (Desativado)         2\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_estoque['CATEGORIA'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ce85b49f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1037441, 21)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_estoque[df_estoque['ESTOQUE ATUAL']>0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b17dd732",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_estoque = df_estoque[df_estoque['ESTOQUE ATUAL']>0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5fcb34b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_estoque = df_estoque[(df_estoque['CATEGORIA']!='SUPORTE À VENDA')&(df_estoque['CATEGORIA']!='EMBALAGENS')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bb7e9735",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'SKU', 'SKU_PARA', 'DESCRICAO', 'CATEGORIA', 'CLASSE',\n",
       "       'FASES PRODUTO', 'LANCAMENTO', 'DESATIVACAO', 'PDV', 'ESTOQUE ATUAL',\n",
       "       'ESTOQUE EM TRANSITO', 'PEDIDO PENDENTE', 'COBERTURA ALVO',\n",
       "       'ESTOQUE DE SEGURANCA', 'DDV PREVISTO', 'COBERTURA ATUAL',\n",
       "       'COBERTURA ATUAL + TRANSITO', 'COBERTURA PROJETADA', 'data_estoque',\n",
       "       'ORIGEM'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_estoque.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2556e08e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_estoque = df_estoque.drop(columns=['id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d5a5fdcc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2025-01-31\n",
       "1    2025-01-31\n",
       "2    2025-01-31\n",
       "3    2025-01-31\n",
       "4    2025-01-31\n",
       "Name: data_estoque, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_estoque['data_estoque'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "15da0447",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    1\n",
       "2    1\n",
       "3    1\n",
       "4    1\n",
       "Name: mes_data, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_estoque['mes_data'] = df_estoque['data_estoque'].str[6]\n",
    "df_estoque['mes_data'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "24094346",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_estoque = df_estoque[df_estoque['mes_data'] != '1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "feb905e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "📂 Lendo pasta: draft 2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\joao.herculano\\AppData\\Local\\Temp\\ipykernel_7172\\441796314.py:23: DtypeWarning: Columns (6,7) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  df = pd.read_csv(file_path, encoding=\"utf-8\", sep=\",\")  # ajuste o sep se precisar (; ou \\t)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "📂 Lendo pasta: draft 3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\joao.herculano\\AppData\\Local\\Temp\\ipykernel_7172\\441796314.py:23: DtypeWarning: Columns (6) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  df = pd.read_csv(file_path, encoding=\"utf-8\", sep=\",\")  # ajuste o sep se precisar (; ou \\t)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "📂 Lendo pasta: draft 4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\joao.herculano\\AppData\\Local\\Temp\\ipykernel_7172\\441796314.py:23: DtypeWarning: Columns (7) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  df = pd.read_csv(file_path, encoding=\"utf-8\", sep=\",\")  # ajuste o sep se precisar (; ou \\t)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "📂 Lendo pasta: draft 5\n",
      "✅ DataFrame final tem 447847 linhas e 51 colunas.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\joao.herculano\\AppData\\Local\\Temp\\ipykernel_7172\\441796314.py:23: DtypeWarning: Columns (7) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  df = pd.read_csv(file_path, encoding=\"utf-8\", sep=\",\")  # ajuste o sep se precisar (; ou \\t)\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import glob\n",
    "import pandas as pd\n",
    "\n",
    "# Caminho base\n",
    "folder_path1 = r'C:\\Users\\joao.herculano\\GRUPO GINSENG\\Assistência Suprimentos - 2025\\CODIGOS\\estudo estoque desativado\\desativados draft'\n",
    "\n",
    "all_dfs = []\n",
    "\n",
    "# Percorre cada subpasta\n",
    "for folder in os.listdir(folder_path1):\n",
    "    folder_full = os.path.join(folder_path1, folder)\n",
    "    \n",
    "    # Garante que é pasta\n",
    "    if os.path.isdir(folder_full):\n",
    "        print(f\"📂 Lendo pasta: {folder}\")\n",
    "\n",
    "        # Pega todos os CSVs\n",
    "        file_paths = glob.glob(os.path.join(folder_full, \"*.csv\"))\n",
    "\n",
    "        for file_path in file_paths:\n",
    "            try:\n",
    "                df = pd.read_csv(file_path, encoding=\"utf-8\", sep=\",\")  # ajuste o sep se precisar (; ou \\t)\n",
    "                df[\"source_file\"] = os.path.basename(folder)\n",
    "                all_dfs.append(df)\n",
    "            except Exception as e:\n",
    "                print(f\"❌ Erro ao ler {file_path}: {e}\")\n",
    "\n",
    "# Junta tudo em um único DF\n",
    "if all_dfs:\n",
    "    combined_df = pd.concat(all_dfs, ignore_index=True)\n",
    "    print(f\"✅ DataFrame final tem {combined_df.shape[0]} linhas e {combined_df.shape[1]} colunas.\")\n",
    "else:\n",
    "    print(\"⚠️ Nenhum CSV válido encontrado.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f51f7fc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_df2 = combined_df[['PDV', 'SKU','source_file']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "946d1515",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\joao.herculano\\AppData\\Local\\Temp\\ipykernel_7172\\3795880551.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  combined_df2['source_file'] = combined_df2['source_file'].str.replace('draft ','')\n"
     ]
    },
    {
     "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>PDV</th>\n",
       "      <th>SKU</th>\n",
       "      <th>source_file</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23701</td>\n",
       "      <td>49014</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23702</td>\n",
       "      <td>49014</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23705</td>\n",
       "      <td>49014</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23706</td>\n",
       "      <td>49014</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23703</td>\n",
       "      <td>49014</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PDV    SKU source_file\n",
       "0  23701  49014           2\n",
       "1  23702  49014           2\n",
       "2  23705  49014           2\n",
       "3  23706  49014           2\n",
       "4  23703  49014           2"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_df2['source_file'] = combined_df2['source_file'].str.replace('draft ','')\n",
    "combined_df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d7353ba8",
   "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>PDV</th>\n",
       "      <th>SKU</th>\n",
       "      <th>mes_data</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23701</td>\n",
       "      <td>49014</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23702</td>\n",
       "      <td>49014</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23705</td>\n",
       "      <td>49014</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23706</td>\n",
       "      <td>49014</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23703</td>\n",
       "      <td>49014</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PDV    SKU mes_data\n",
       "0  23701  49014        2\n",
       "1  23702  49014        2\n",
       "2  23705  49014        2\n",
       "3  23706  49014        2\n",
       "4  23703  49014        2"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined_df2 = combined_df2.rename(columns={'source_file':'mes_data'})\n",
    "combined_df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8beb5fbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "draft_06_frente = pd.read_csv(r\"C:\\Users\\joao.herculano\\GRUPO GINSENG\\Assistência Suprimentos - 2025\\CODIGOS\\estudo estoque desativado\\draft 06 em diante 20251001.csv\",sep=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "54d71498",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'loja_id', 'code', 'description', 'launch', 'deactivation',\n",
       "       'thirdtolastcyclesales', 'secondtolastcyclesales', 'lastcyclesales',\n",
       "       'currentcyclesales', 'nextcycleprojection',\n",
       "       'secondtonextcycleprojection', 'stock_actual', 'stock_intransit',\n",
       "       'purchasesuggestion', 'smartpurchase_purchasesuggestioncycle',\n",
       "       'smartpurchase_nextcyclepurchasesuggestion', 'pendingorder',\n",
       "       'salescurve', 'promotions_description', 'promotions_discountpercent',\n",
       "       'pricesellin', 'businessunit', 'codcategory',\n",
       "       'criticalitem_dtprovidedregularization', 'criticalitem_blockedwallet',\n",
       "       'criticalitem_iscritical', 'codsubcategory', 'isproductdeactivated',\n",
       "       'brandgroupcode', 'dayswithoutsales', 'coveragedays', 'hascoverage',\n",
       "       'TRIAL949', 'data'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "draft_06_frente.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1e9492e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "draft_06_frente = draft_06_frente[['loja_id', 'code','data']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "81d03059",
   "metadata": {},
   "outputs": [],
   "source": [
    "draft_06_frente['mes_data'] = draft_06_frente['data'].str[6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1ec3e38b",
   "metadata": {},
   "outputs": [],
   "source": [
    "draft_06_frente = draft_06_frente.rename(columns={'loja_id':'PDV','code':'SKU'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "37f327fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "draft_06_frente = draft_06_frente.drop(columns='data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a72422ab",
   "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",
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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>PDV</th>\n",
       "      <th>SKU</th>\n",
       "      <th>mes_data</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12824</td>\n",
       "      <td>59035</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12824</td>\n",
       "      <td>58968</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12824</td>\n",
       "      <td>59031</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21647</td>\n",
       "      <td>76664</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21647</td>\n",
       "      <td>53933</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PDV    SKU mes_data\n",
       "0  12824  59035        6\n",
       "1  12824  58968        6\n",
       "2  12824  59031        6\n",
       "3  21647  76664        6\n",
       "4  21647  53933        6"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "draft_06_frente.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "15571e09",
   "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>PDV</th>\n",
       "      <th>SKU</th>\n",
       "      <th>mes_data</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23701</td>\n",
       "      <td>49014</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23702</td>\n",
       "      <td>49014</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23705</td>\n",
       "      <td>49014</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23706</td>\n",
       "      <td>49014</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23703</td>\n",
       "      <td>49014</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PDV    SKU mes_data\n",
       "0  23701  49014        2\n",
       "1  23702  49014        2\n",
       "2  23705  49014        2\n",
       "3  23706  49014        2\n",
       "4  23703  49014        2"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drafts = [combined_df2,draft_06_frente]\n",
    "df_draft = pd.concat(drafts)\n",
    "df_draft.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1098e037",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mes_data\n",
       "9    150950\n",
       "7    147723\n",
       "8    145559\n",
       "6    130690\n",
       "5    114464\n",
       "3    111840\n",
       "2    111546\n",
       "4    109997\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_draft['mes_data'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "aaeedaec",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_draft = df_draft.rename(columns={'SKU':'SKU_DRAFT'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0ee0b85e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_draft['mes_data'] = df_draft['mes_data'].astype('Int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "71d320e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PDV          int64\n",
       "SKU_DRAFT    int64\n",
       "mes_data     Int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_draft.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b20879c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "137565     2\n",
       "138944     2\n",
       "138945     2\n",
       "138946     2\n",
       "138948     2\n",
       "          ..\n",
       "3890084    8\n",
       "3890085    8\n",
       "3890086    8\n",
       "3890090    8\n",
       "3890091    8\n",
       "Name: mes_data, Length: 852738, dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_estoque['mes_data']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "889e57d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_estoque['mes_data'] = df_estoque['mes_data'].astype('Int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0040c987",
   "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>PDV</th>\n",
       "      <th>SKU_DRAFT</th>\n",
       "      <th>mes_data</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23702</td>\n",
       "      <td>49014</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PDV  SKU_DRAFT  mes_data\n",
       "1  23702      49014         2"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_draft[(df_draft['PDV']==23702)&(df_draft['SKU_DRAFT']==49014)&(df_draft['mes_data']==2)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d8547d86",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_draft['SKU_DRAFT'] = df_draft['SKU_DRAFT'].astype(str).str.replace('.0', '', regex=False)\n",
    "\n",
    "df_draft['SKU_DRAFT'] = df_draft['SKU_DRAFT'] .astype('Int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "3b02dd27",
   "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>SKU</th>\n",
       "      <th>SKU_PARA</th>\n",
       "      <th>DESCRICAO</th>\n",
       "      <th>CATEGORIA</th>\n",
       "      <th>CLASSE</th>\n",
       "      <th>FASES PRODUTO</th>\n",
       "      <th>LANCAMENTO</th>\n",
       "      <th>DESATIVACAO</th>\n",
       "      <th>PDV</th>\n",
       "      <th>ESTOQUE ATUAL</th>\n",
       "      <th>...</th>\n",
       "      <th>PEDIDO PENDENTE</th>\n",
       "      <th>COBERTURA ALVO</th>\n",
       "      <th>ESTOQUE DE SEGURANCA</th>\n",
       "      <th>DDV PREVISTO</th>\n",
       "      <th>COBERTURA ATUAL</th>\n",
       "      <th>COBERTURA ATUAL + TRANSITO</th>\n",
       "      <th>COBERTURA PROJETADA</th>\n",
       "      <th>data_estoque</th>\n",
       "      <th>ORIGEM</th>\n",
       "      <th>mes_data</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>408003</th>\n",
       "      <td>49014</td>\n",
       "      <td>-</td>\n",
       "      <td>ACCORDES CREM DES HID CPO 200ml V2</td>\n",
       "      <td>CUIDADOS COM A PELE</td>\n",
       "      <td>B</td>\n",
       "      <td>Maduro</td>\n",
       "      <td>202208.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23702</td>\n",
       "      <td>16.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.07</td>\n",
       "      <td>228.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>2025-02-26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          SKU SKU_PARA                           DESCRICAO  \\\n",
       "408003  49014        -  ACCORDES CREM DES HID CPO 200ml V2   \n",
       "\n",
       "                  CATEGORIA CLASSE FASES PRODUTO  LANCAMENTO  DESATIVACAO  \\\n",
       "408003  CUIDADOS COM A PELE      B        Maduro    202208.0          NaN   \n",
       "\n",
       "          PDV  ESTOQUE ATUAL  ...  PEDIDO PENDENTE  COBERTURA ALVO  \\\n",
       "408003  23702           16.0  ...              0.0            24.0   \n",
       "\n",
       "        ESTOQUE DE SEGURANCA  DDV PREVISTO COBERTURA ATUAL  \\\n",
       "408003                   2.0          0.07           228.0   \n",
       "\n",
       "        COBERTURA ATUAL + TRANSITO  COBERTURA PROJETADA  data_estoque ORIGEM  \\\n",
       "408003                       228.0                228.0    2025-02-26    NaN   \n",
       "\n",
       "       mes_data  \n",
       "408003        2  \n",
       "\n",
       "[1 rows x 21 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_estoque[(df_estoque['PDV']==23702)&(df_estoque['SKU']==49014)&(df_estoque['mes_data']==2)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c6441324",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final = pd.merge(df_estoque,df_draft,left_on=['PDV','SKU','mes_data'], right_on=['PDV','SKU_DRAFT','mes_data'],how='left')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "76fe1b4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SKU</th>\n",
       "      <th>SKU_PARA</th>\n",
       "      <th>DESCRICAO</th>\n",
       "      <th>CATEGORIA</th>\n",
       "      <th>CLASSE</th>\n",
       "      <th>FASES PRODUTO</th>\n",
       "      <th>LANCAMENTO</th>\n",
       "      <th>DESATIVACAO</th>\n",
       "      <th>PDV</th>\n",
       "      <th>ESTOQUE ATUAL</th>\n",
       "      <th>...</th>\n",
       "      <th>COBERTURA ALVO</th>\n",
       "      <th>ESTOQUE DE SEGURANCA</th>\n",
       "      <th>DDV PREVISTO</th>\n",
       "      <th>COBERTURA ATUAL</th>\n",
       "      <th>COBERTURA ATUAL + TRANSITO</th>\n",
       "      <th>COBERTURA PROJETADA</th>\n",
       "      <th>data_estoque</th>\n",
       "      <th>ORIGEM</th>\n",
       "      <th>mes_data</th>\n",
       "      <th>SKU_DRAFT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>88146</td>\n",
       "      <td>-</td>\n",
       "      <td>MAKE B BAS LIQ ONE 220Q 30g</td>\n",
       "      <td>MAQUIAGEM</td>\n",
       "      <td>E</td>\n",
       "      <td>Maduro</td>\n",
       "      <td>202514.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21277</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2025-02-26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>87201</td>\n",
       "      <td>-</td>\n",
       "      <td>CBEM SAB BARR DUO FEIR MELAN/M VER 2x80g</td>\n",
       "      <td>SABONETE CORPO</td>\n",
       "      <td>E</td>\n",
       "      <td>Descontinuado</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12818</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2025-02-26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>87201</td>\n",
       "      <td>-</td>\n",
       "      <td>CBEM SAB BARR DUO FEIR MELAN/M VER 2x80g</td>\n",
       "      <td>SABONETE CORPO</td>\n",
       "      <td>E</td>\n",
       "      <td>Descontinuado</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12824</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2025-02-26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>87201</td>\n",
       "      <td>-</td>\n",
       "      <td>CBEM SAB BARR DUO FEIR MELAN/M VER 2x80g</td>\n",
       "      <td>SABONETE CORPO</td>\n",
       "      <td>E</td>\n",
       "      <td>Descontinuado</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21068</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2025-02-26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>87201</td>\n",
       "      <td>-</td>\n",
       "      <td>CBEM SAB BARR DUO FEIR MELAN/M VER 2x80g</td>\n",
       "      <td>SABONETE CORPO</td>\n",
       "      <td>E</td>\n",
       "      <td>Descontinuado</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21381</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2025-02-26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      SKU SKU_PARA                                 DESCRICAO       CATEGORIA  \\\n",
       "0   88146        -               MAKE B BAS LIQ ONE 220Q 30g       MAQUIAGEM   \n",
       "87  87201        -  CBEM SAB BARR DUO FEIR MELAN/M VER 2x80g  SABONETE CORPO   \n",
       "88  87201        -  CBEM SAB BARR DUO FEIR MELAN/M VER 2x80g  SABONETE CORPO   \n",
       "89  87201        -  CBEM SAB BARR DUO FEIR MELAN/M VER 2x80g  SABONETE CORPO   \n",
       "90  87201        -  CBEM SAB BARR DUO FEIR MELAN/M VER 2x80g  SABONETE CORPO   \n",
       "\n",
       "   CLASSE  FASES PRODUTO  LANCAMENTO  DESATIVACAO    PDV  ESTOQUE ATUAL  ...  \\\n",
       "0       E         Maduro    202514.0          NaN  21277            1.0  ...   \n",
       "87      E  Descontinuado         NaN          NaN  12818            2.0  ...   \n",
       "88      E  Descontinuado         NaN          NaN  12824            2.0  ...   \n",
       "89      E  Descontinuado         NaN          NaN  21068            1.0  ...   \n",
       "90      E  Descontinuado         NaN          NaN  21381            2.0  ...   \n",
       "\n",
       "    COBERTURA ALVO  ESTOQUE DE SEGURANCA  DDV PREVISTO  COBERTURA ATUAL  \\\n",
       "0              NaN                   NaN           NaN              NaN   \n",
       "87             NaN                   NaN           NaN              NaN   \n",
       "88             NaN                   NaN           NaN              NaN   \n",
       "89             NaN                   NaN           NaN              NaN   \n",
       "90             NaN                   NaN           NaN              NaN   \n",
       "\n",
       "   COBERTURA ATUAL + TRANSITO  COBERTURA PROJETADA  data_estoque  ORIGEM  \\\n",
       "0                         NaN                  NaN    2025-02-26     NaN   \n",
       "87                        NaN                  NaN    2025-02-26     NaN   \n",
       "88                        NaN                  NaN    2025-02-26     NaN   \n",
       "89                        NaN                  NaN    2025-02-26     NaN   \n",
       "90                        NaN                  NaN    2025-02-26     NaN   \n",
       "\n",
       "   mes_data SKU_DRAFT  \n",
       "0         2      <NA>  \n",
       "87        2      <NA>  \n",
       "88        2      <NA>  \n",
       "89        2      <NA>  \n",
       "90        2      <NA>  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final_desativado = df_final[df_final['SKU_DRAFT'].isna()]\n",
    "\n",
    "df_final_desativado.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a00988e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(228121, 22)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final_desativado.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "56723a8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final_desativado.head(10000).to_clipboard()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "5c77ceda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mes_data\n",
       "9    150950\n",
       "7    147723\n",
       "8    145559\n",
       "6    130690\n",
       "5    114464\n",
       "3    111840\n",
       "2    111546\n",
       "4    109997\n",
       "Name: count, dtype: Int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_draft['mes_data'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "12b953c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mes_data\n",
       "9    124582\n",
       "8    120773\n",
       "7    110117\n",
       "6    108662\n",
       "4     99104\n",
       "5     98746\n",
       "3     97120\n",
       "2     93634\n",
       "Name: count, dtype: Int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final['mes_data'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "f1639562",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final.to_excel(r'C:\\Users\\joao.herculano\\Documents\\df_final.xlsx',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "6ec55259",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final.to_clipboard()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d04017a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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