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Ruptura_Projetada/promoção/promoção_boti_ciclo07.ipynb
João Herculano 22cc3883c3 atualizações 04/09
2025-09-04 12:29:39 -03:00

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np \n",
"import glob\n",
"import os \n",
"from openpyxl import load_workbook\n",
"from openpyxl.styles import PatternFill, Font\n",
"from datetime import datetime"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"hoje = datetime.today().strftime('%Y-%m-%d')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"#precisa ver duplicidade de desc e vendas irece."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"df_tabela = pd.read_excel(r\"C:\\Users\\joao.herculano\\GRUPO GINSENG\\Assistência Suprimentos - 2025\\SUPRIMENTOS\\DB_PROMOÇÕES\\BOTICARIO\\C15\\TABELA DE PEDIDOS\\Pedidos Semanais Especiais - BOT - 202515 (1).xlsx\")\n",
"\n",
"df_tabela = df_tabela[df_tabela['Ação revendedor'].notna() | df_tabela['Ação consumidor'].notna()]\n",
"\n",
"df_tabela = df_tabela[df_tabela['Região'] == 'NNE'] \n",
"\n",
"df_tabela = df_tabela[(df_tabela['Canal'] != 'Ecomm')]\n",
"\n",
"#df_tabela['Canal'] = np.where((df_tabela['Canal'] == \"Loja\") | (df_tabela['Canal'] == \"Todos\") | (df_tabela['Canal'] == \"Loja | VD\"),\"TODOS\",\"VD\")\n",
"\n",
"df_tabela = df_tabela[(df_tabela['Categoria'] != \"EMBALAGENS\") | (df_tabela['Categoria'] != \"SUPORTE À VENDA\")]\n",
"\n",
"#df_tabela = df_tabela[df_tabela['Tipo de pedido'] == 'Semanal']\n",
"\n",
"df_tabela = df_tabela[~df_tabela['Descrição'].str.contains('PRM')]\n",
"\n",
"df_tabela = df_tabela[df_tabela['Tipo de produto']!= 'EDICAO LIMITADA']\n",
"\n",
"df_tabela['Ação revendedor'] = np.where(df_tabela['Ação revendedor'].isna(),df_tabela['Ação consumidor'],df_tabela['Ação revendedor'])\n",
"\n",
"df_tabela['Percentual de desconto revendedor'] = np.where(df_tabela['Percentual de desconto revendedor'].isna(),df_tabela['Percentual de desconto consumidor'],df_tabela['Percentual de desconto revendedor'])\n",
"\n",
"df_tabela['MATCH'] = 1\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\joao.herculano\\AppData\\Local\\Temp\\ipykernel_2580\\2225685327.py:39: 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",
" dfi = pd.read_sql(query, conn)\n"
]
}
],
"source": [
"import pyodbc\n",
"import configparser\n",
"\n",
"#query de vendas por ciclo\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",
")\n",
"\n",
"query = f'''\n",
"SELECT \n",
" B.PDV, \n",
" B.SKU,\n",
" bd.SKU2, \n",
" COALESCE(bd.SKU2, b.SKU) AS SKU_FINAL,\n",
" B.DESCRICAO,\n",
" SUM(CAST(b.VENDAS AS DECIMAL(18,2))) AS VENDAS_CICLO,\n",
" C.Ciclo\n",
"FROM base_vendas_bi b\n",
"INNER JOIN ciclos_data_2025 c \n",
" ON CAST(b.[DATA] AS DATE) = CONVERT(DATE, c.[Date], 103) AND C.MARCA = 'BOT'\n",
"LEFT JOIN base_depara bd on b.SKU = bd.SKU \n",
"WHERE CAST(b.[DATA] AS DATE) >= DATEADD(YEAR, -1, GETDATE())\n",
"GROUP BY\n",
" B.PDV, \n",
" B.SKU,\n",
" bd.SKU2,\n",
" B.DESCRICAO,\n",
" C.Ciclo\n",
"HAVING SUM(CAST(b.VENDAS AS DECIMAL(18,2))) > 0\n",
"'''\n",
"dfi = pd.read_sql(query, conn)\n",
"conn.close()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"dfi['SKU2'] = dfi['SKU2'].fillna(\"-\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
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"<div>\n",
"<style scoped>\n",
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" vertical-align: middle;\n",
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" vertical-align: top;\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>PDV</th>\n",
" <th>SKU</th>\n",
" <th>SKU2</th>\n",
" <th>SKU_FINAL</th>\n",
" <th>DESCRICAO</th>\n",
" <th>VENDAS_CICLO</th>\n",
" <th>Ciclo</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>20998</td>\n",
" <td>79437</td>\n",
" <td>-</td>\n",
" <td>79437</td>\n",
" <td>QDB GLIT SLT P/OLHO ROSELITTER 1g RPCK</td>\n",
" <td>1.0</td>\n",
" <td>C202413</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4560</td>\n",
" <td>51226</td>\n",
" <td>-</td>\n",
" <td>51226</td>\n",
" <td>CBEM SAB BARRA NUVEM V5 2x80g</td>\n",
" <td>30.0</td>\n",
" <td>C202413</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>21277</td>\n",
" <td>48290</td>\n",
" <td>-</td>\n",
" <td>48290</td>\n",
" <td>NSPA DES COL BDY SPLSH AMEI 200ml V2</td>\n",
" <td>3.0</td>\n",
" <td>C202414</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>23701</td>\n",
" <td>56810</td>\n",
" <td>-</td>\n",
" <td>56810</td>\n",
" <td>MATCH COND SCIENCE CRESCIMENTO V2 280ml</td>\n",
" <td>2.0</td>\n",
" <td>C202505</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5699</td>\n",
" <td>56027</td>\n",
" <td>-</td>\n",
" <td>56027</td>\n",
" <td>MEN SAB BARRA 2x90g</td>\n",
" <td>37.0</td>\n",
" <td>C202506</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PDV SKU SKU2 SKU_FINAL DESCRICAO \\\n",
"0 20998 79437 - 79437 QDB GLIT SLT P/OLHO ROSELITTER 1g RPCK \n",
"1 4560 51226 - 51226 CBEM SAB BARRA NUVEM V5 2x80g \n",
"2 21277 48290 - 48290 NSPA DES COL BDY SPLSH AMEI 200ml V2 \n",
"3 23701 56810 - 56810 MATCH COND SCIENCE CRESCIMENTO V2 280ml \n",
"4 5699 56027 - 56027 MEN SAB BARRA 2x90g \n",
"\n",
" VENDAS_CICLO Ciclo \n",
"0 1.0 C202413 \n",
"1 30.0 C202413 \n",
"2 3.0 C202414 \n",
"3 2.0 C202505 \n",
"4 37.0 C202506 "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dfi = dfi[dfi['SKU2'] == \"-\"]\n",
"\n",
"dfi.head()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PDV</th>\n",
" <th>SKU_FINAL</th>\n",
" <th>DESCRICAO</th>\n",
" <th>C202412</th>\n",
" <th>C202413</th>\n",
" <th>C202414</th>\n",
" <th>C202415</th>\n",
" <th>C202416</th>\n",
" <th>C202417</th>\n",
" <th>C202501</th>\n",
" <th>...</th>\n",
" <th>C202503</th>\n",
" <th>C202504</th>\n",
" <th>C202505</th>\n",
" <th>C202506</th>\n",
" <th>C202507</th>\n",
" <th>C202508</th>\n",
" <th>C202509</th>\n",
" <th>C202510</th>\n",
" <th>C202511</th>\n",
" <th>C202512</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>12522</td>\n",
" <td>1004</td>\n",
" <td>FLORATTA DES COL MY BLUE 75ml</td>\n",
" <td>0.0</td>\n",
" <td>15.0</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>56.0</td>\n",
" <td>6.0</td>\n",
" <td>1.0</td>\n",
" <td>...</td>\n",
" <td>1.0</td>\n",
" <td>5.0</td>\n",
" <td>12.0</td>\n",
" <td>7.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" <td>5.0</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>12522</td>\n",
" <td>1080</td>\n",
" <td>INTENSE GLOS LAB BUBBAL TUT/FRUT 5ml</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>0.0</td>\n",
" <td>10.0</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>...</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
" <td>7.0</td>\n",
" <td>17.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>12522</td>\n",
" <td>1296</td>\n",
" <td>PMPCK THE BLEND DES ANTIT AER 2x75g</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>12522</td>\n",
" <td>1302</td>\n",
" <td>PMPCK LILY DES ANTIT AER 2x75g</td>\n",
" <td>1.0</td>\n",
" <td>4.0</td>\n",
" <td>3.0</td>\n",
" <td>5.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>3.0</td>\n",
" <td>4.0</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>4.0</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>12522</td>\n",
" <td>1314</td>\n",
" <td>PMPCK ZAAD DES ANTIT AER 2x75g</td>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 21 columns</p>\n",
"</div>"
],
"text/plain": [
" PDV SKU_FINAL DESCRICAO C202412 C202413 \\\n",
"0 12522 1004 FLORATTA DES COL MY BLUE 75ml 0.0 15.0 \n",
"1 12522 1080 INTENSE GLOS LAB BUBBAL TUT/FRUT 5ml 0.0 0.0 \n",
"2 12522 1296 PMPCK THE BLEND DES ANTIT AER 2x75g 0.0 1.0 \n",
"3 12522 1302 PMPCK LILY DES ANTIT AER 2x75g 1.0 4.0 \n",
"4 12522 1314 PMPCK ZAAD DES ANTIT AER 2x75g 0.0 2.0 \n",
"\n",
" C202414 C202415 C202416 C202417 C202501 ... C202503 C202504 \\\n",
"0 2.0 3.0 56.0 6.0 1.0 ... 1.0 5.0 \n",
"1 4.0 0.0 10.0 2.0 2.0 ... 2.0 3.0 \n",
"2 3.0 3.0 2.0 0.0 0.0 ... 0.0 0.0 \n",
"3 3.0 5.0 6.0 0.0 0.0 ... 0.0 0.0 \n",
"4 3.0 1.0 1.0 0.0 0.0 ... 0.0 0.0 \n",
"\n",
" C202505 C202506 C202507 C202508 C202509 C202510 C202511 C202512 \n",
"0 12.0 7.0 2.0 0.0 1.0 2.0 5.0 2.0 \n",
"1 1.0 7.0 17.0 0.0 0.0 0.0 0.0 0.0 \n",
"2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"3 0.0 3.0 4.0 0.0 4.0 4.0 2.0 1.0 \n",
"4 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 \n",
"\n",
"[5 rows x 21 columns]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"# 1. Agrupamento\n",
"df_agrupado = dfi.groupby(['PDV', 'SKU_FINAL', 'DESCRICAO', 'Ciclo'], as_index=False)['VENDAS_CICLO'].sum()\n",
"\n",
"# 2. Pivotar o DataFrame\n",
"df_pivotado = df_agrupado.pivot_table(\n",
" index=['PDV', 'SKU_FINAL', 'DESCRICAO'],\n",
" columns='Ciclo',\n",
" values='VENDAS_CICLO',\n",
" fill_value=0 # Substitui NaN por 0\n",
")\n",
"\n",
"# 3. Resetar o índice para deixar como DataFrame normal (opcional)\n",
"df_pivotadowawa = df_pivotado.reset_index()\n",
"\n",
"# 4. (Opcional) Renomear colunas com prefixo \"Ciclo_\"\n",
"df_pivotadowawa.columns.name = None\n",
"df_pivotadowawa = df_pivotadowawa.rename(columns=lambda x: f'Ciclo_{x}' if isinstance(x, (int, str)) and str(x).isdigit() else x)\n",
"\n",
"# Resultado final\n",
"df_pivotadowawa.head()\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
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"</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_FINAL</th>\n",
" <th>DESCRICAO</th>\n",
" <th>C202412</th>\n",
" <th>C202413</th>\n",
" <th>C202414</th>\n",
" <th>C202415</th>\n",
" <th>C202416</th>\n",
" <th>C202417</th>\n",
" <th>C202501</th>\n",
" <th>...</th>\n",
" <th>C202503</th>\n",
" <th>C202504</th>\n",
" <th>C202505</th>\n",
" <th>C202506</th>\n",
" <th>C202507</th>\n",
" <th>C202508</th>\n",
" <th>C202509</th>\n",
" <th>C202510</th>\n",
" <th>C202511</th>\n",
" <th>C202512</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" </tbody>\n",
"</table>\n",
"<p>0 rows × 21 columns</p>\n",
"</div>"
],
"text/plain": [
"Empty DataFrame\n",
"Columns: [PDV, SKU_FINAL, DESCRICAO, C202412, C202413, C202414, C202415, C202416, C202417, C202501, C202502, C202503, C202504, C202505, C202506, C202507, C202508, C202509, C202510, C202511, C202512]\n",
"Index: []\n",
"\n",
"[0 rows x 21 columns]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_pivotadowawa[(df_pivotadowawa['SKU_FINAL'] == '1634') & (df_pivotadowawa['PDV'] == '20998')]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"\n",
"\n",
"df_pdv = pd.read_excel(r\"C:\\Users\\joao.herculano\\Documents\\PDV_ATT.xlsx\")\n",
"\n",
"df_pdv_origi = pd.read_excel(r\"C:\\Users\\joao.herculano\\Documents\\PDV_ATT.xlsx\")\n",
"\n",
"df_pdv = df_pdv.rename(columns={'DESCRIÇÃO':'DESCRIÇÃO PDV'})\n",
"\n",
"df_pdv = df_pdv[df_pdv['GESTÃO']!=\"Inativa\"]\n",
"\n",
"df_pdv = df_pdv.drop(columns=['REGIÃO', 'ESTADO','CIDADE','GESTÃO', 'STATUS'])\n",
"\n",
"df_pdv = df_pdv[df_pdv['CANAL']!='MTZ']\n",
"\n",
"#df_pdv['CANAL'] = np.where((df_pdv['CANAL']=='LJ')|(df_pdv['CANAL']=='HIB')|(df_pdv['CANAL']=='CD'),'TODOS','VD')\n",
"\n",
"df_pdv['MATCH'] = 1\n",
"\n",
"df_pdv = df_pdv[(df_pdv['PDV']!=910291)&(df_pdv['PDV']!=910173)]\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
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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>PDV</th>\n",
" <th>CANAL</th>\n",
" <th>DESCRIÇÃO PDV</th>\n",
" <th>PDV DESC</th>\n",
" <th>UF</th>\n",
" <th>MARCA</th>\n",
" <th>ANALISTA</th>\n",
" <th>SUPERVISOR</th>\n",
" <th>MATCH</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>21007</td>\n",
" <td>LJ</td>\n",
" <td>MOREIRA 2</td>\n",
" <td>21007-MOREIRA 2</td>\n",
" <td>AL</td>\n",
" <td>O BOTICARIO</td>\n",
" <td>LUAN</td>\n",
" <td>Maxwell Vieira</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PDV CANAL DESCRIÇÃO PDV PDV DESC UF MARCA ANALISTA \\\n",
"43 21007 LJ MOREIRA 2 21007-MOREIRA 2 AL O BOTICARIO LUAN \n",
"\n",
" SUPERVISOR MATCH \n",
"43 Maxwell Vieira 1 "
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_pdv[df_pdv['PDV']==21007]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"df_pdv = df_pdv.drop(columns=['PDV DESC'])"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\joao.herculano\\AppData\\Local\\Temp\\ipykernel_2580\\268247862.py:25: 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_draft_ = pd.read_sql(query_est_draft, 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",
"\n",
"query_est_draft = f'''\n",
"select em.PDV ,\n",
"COALESCE(em.SKU_PARA , em.SKU) as SKU,\n",
"em.DESCRICAO,\n",
"d.salescurve as curva,\n",
"d.codcategory as categoria,\n",
"d.nextcycleprojection as proj_mar\n",
",d.secondtonextcycleprojection as 'proj_mar+1',\n",
"em.[ESTOQUE ATUAL],em.[ESTOQUE EM TRANSITO],\n",
"em.[PEDIDO PENDENTE],\n",
"em.[DDV PREVISTO] ,\n",
"d.pricesellin as 'preço'\n",
"from estoque_mar em \n",
"left join draft d on d.code = COALESCE(em.SKU_PARA , em.SKU) and d.loja_id = em.PDV \n",
"where em.ORIGEM = 'BOT'\n",
"'''\n",
"df_draft_ = pd.read_sql(query_est_draft, conn)\n",
"conn.close()\n",
"\n",
"#FALTA ATUALIZAR QUERY"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"df_draft = df_draft_"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
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" <tr>\n",
" <th>337558</th>\n",
" <td>21647</td>\n",
" <td>1634</td>\n",
" <td>NSPA GEL ESF CPO MORANGO RUBY 175G</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>1634</td>\n",
" <td>NSPA GEL ESF CPO MORANGO RUBY 175G</td>\n",
" <td>None</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>8</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>358656</th>\n",
" <td>12830</td>\n",
" <td>1634</td>\n",
" <td>NSPA GEL ESF CPO MORANGO RUBY 175G</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>359288</th>\n",
" <td>20997</td>\n",
" <td>1634</td>\n",
" <td>NSPA GEL ESF CPO MORANGO RUBY 175G</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>3</td>\n",
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" <td>0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>73 rows × 12 columns</p>\n",
"</div>"
],
"text/plain": [
" PDV SKU DESCRICAO curva categoria \\\n",
"128590 24293 1634 NSPA GEL ESF CPO MORANGO RUBY 175G None None \n",
"128840 21278 1634 NSPA GEL ESF CPO MORANGO RUBY 175G None None \n",
"128956 12826 1634 NSPA GEL ESF CPO MORANGO RUBY 175G None None \n",
"129361 4560 1634 NSPA GEL ESF CPO MORANGO RUBY 175G None None \n",
"129451 5699 1634 NSPA GEL ESF CPO MORANGO RUBY 175G None None \n",
"... ... ... ... ... ... \n",
"337558 21647 1634 NSPA GEL ESF CPO MORANGO RUBY 175G None None \n",
"345348 20009 1634 NSPA GEL ESF CPO MORANGO RUBY 175G None None \n",
"349112 20968 1634 NSPA GEL ESF CPO MORANGO RUBY 175G None None \n",
"358656 12830 1634 NSPA GEL ESF CPO MORANGO RUBY 175G None None \n",
"359288 20997 1634 NSPA GEL ESF CPO MORANGO RUBY 175G None None \n",
"\n",
" proj_mar proj_mar+1 ESTOQUE ATUAL ESTOQUE EM TRANSITO \\\n",
"128590 NaN NaN 3 0 \n",
"128840 NaN NaN 2 0 \n",
"128956 NaN NaN 3 0 \n",
"129361 NaN NaN 5 0 \n",
"129451 NaN NaN 4 0 \n",
"... ... ... ... ... \n",
"337558 NaN NaN 4 0 \n",
"345348 NaN NaN 10 0 \n",
"349112 NaN NaN 8 0 \n",
"358656 NaN NaN 5 0 \n",
"359288 NaN NaN 3 0 \n",
"\n",
" PEDIDO PENDENTE DDV PREVISTO preço \n",
"128590 0 0 NaN \n",
"128840 0 0 NaN \n",
"128956 0 0 NaN \n",
"129361 0 0 NaN \n",
"129451 0 0,05 NaN \n",
"... ... ... ... \n",
"337558 0 0 NaN \n",
"345348 0 0 NaN \n",
"349112 0 0 NaN \n",
"358656 0 0 NaN \n",
"359288 0 0 NaN \n",
"\n",
"[73 rows x 12 columns]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_draft[df_draft['SKU']=='1634']"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(350945, 12)"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_draft = df_draft[~df_draft['DESCRICAO'].str.contains('OUI', na=False)]\n",
"\n",
"df_draft.shape"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\joao.herculano\\AppData\\Local\\Temp\\ipykernel_2580\\1765304903.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",
" df_draft['ESTOQUE ATUAL'] = df_draft['ESTOQUE ATUAL'].astype(float)\n",
"C:\\Users\\joao.herculano\\AppData\\Local\\Temp\\ipykernel_2580\\1765304903.py:3: 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",
" df_draft['ESTOQUE EM TRANSITO'] = df_draft['ESTOQUE EM TRANSITO'].astype(float)\n",
"C:\\Users\\joao.herculano\\AppData\\Local\\Temp\\ipykernel_2580\\1765304903.py:5: 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",
" df_draft['PEDIDO PENDENTE'] = df_draft['PEDIDO PENDENTE'].astype(float)\n"
]
},
{
"data": {
"text/plain": [
"PDV object\n",
"SKU object\n",
"DESCRICAO object\n",
"curva object\n",
"categoria object\n",
"proj_mar float64\n",
"proj_mar+1 float64\n",
"ESTOQUE ATUAL float64\n",
"ESTOQUE EM TRANSITO float64\n",
"PEDIDO PENDENTE float64\n",
"DDV PREVISTO object\n",
"preço float64\n",
"dtype: object"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_draft['ESTOQUE ATUAL'] = df_draft['ESTOQUE ATUAL'].astype(float)\n",
"\n",
"df_draft['ESTOQUE EM TRANSITO'] = df_draft['ESTOQUE EM TRANSITO'].astype(float)\n",
"\n",
"df_draft['PEDIDO PENDENTE'] = df_draft['PEDIDO PENDENTE'].astype(float)\n",
"\n",
"df_draft.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\joao.herculano\\AppData\\Local\\Temp\\ipykernel_2580\\1952797845.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",
" df_draft['DDV PREVISTO'] = df_draft['DDV PREVISTO'].str.replace(',','.')\n",
"C:\\Users\\joao.herculano\\AppData\\Local\\Temp\\ipykernel_2580\\1952797845.py:3: 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",
" df_draft['DDV PREVISTO'] = df_draft['DDV PREVISTO'].astype(float)\n",
"C:\\Users\\joao.herculano\\AppData\\Local\\Temp\\ipykernel_2580\\1952797845.py:5: 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",
" df_draft['DDV PREVISTO'] = df_draft['DDV PREVISTO'].fillna(0)\n"
]
}
],
"source": [
"df_draft['DDV PREVISTO'] = df_draft['DDV PREVISTO'].str.replace(',','.')\n",
"\n",
"df_draft['DDV PREVISTO'] = df_draft['DDV PREVISTO'].astype(float)\n",
"\n",
"df_draft['DDV PREVISTO'] = df_draft['DDV PREVISTO'].fillna(0)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"df_draft = df_draft.groupby(['PDV','SKU','curva','categoria'])[['proj_mar','proj_mar+1','ESTOQUE ATUAL','ESTOQUE EM TRANSITO','PEDIDO PENDENTE','DDV PREVISTO','preço']].max().reset_index()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"df_tabela = df_tabela.drop_duplicates()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"df_bi_preco = pd.read_excel(r\"C:\\Users\\joao.herculano\\GRUPO GINSENG\\Assistência Suprimentos - 2025\\SUPRIMENTOS\\DB_PROMOÇÕES\\BOTICARIO\\C13\\preço BI\\TABELA DE PREÇOS (2).xlsx\")\n",
"\n",
"df_bi_preco = df_bi_preco.drop(columns=['Tipo Preço','CATEGORIA','LINHA','MARCA'])\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"df_pivotadowawa['SKU_FINAL'] = df_pivotadowawa['SKU_FINAL'].astype('str')"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 1004\n",
"1 1080\n",
"2 1296\n",
"3 1302\n",
"4 1314\n",
"Name: SKU_FINAL, dtype: object"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_pivotadowawa['SKU_FINAL'] = df_pivotadowawa['SKU_FINAL'].astype(str)\n",
"\n",
"df_pivotadowawa['SKU_FINAL'].head()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"text/plain": [
"Empty DataFrame\n",
"Columns: [PDV, SKU, curva, categoria, proj_mar, proj_mar+1, ESTOQUE ATUAL, ESTOQUE EM TRANSITO, PEDIDO PENDENTE, DDV PREVISTO, preço]\n",
"Index: []"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_draft[df_draft['SKU']=='1634']"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"df_draft = pd.merge(df_draft,df_pivotadowawa,left_on=['PDV','SKU'],right_on=['PDV','SKU_FINAL'],how='inner')"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PDV</th>\n",
" <th>SKU</th>\n",
" <th>curva</th>\n",
" <th>categoria</th>\n",
" <th>proj_mar</th>\n",
" <th>proj_mar+1</th>\n",
" <th>ESTOQUE ATUAL</th>\n",
" <th>ESTOQUE EM TRANSITO</th>\n",
" <th>PEDIDO PENDENTE</th>\n",
" <th>DDV PREVISTO</th>\n",
" <th>...</th>\n",
" <th>C202503</th>\n",
" <th>C202504</th>\n",
" <th>C202505</th>\n",
" <th>C202506</th>\n",
" <th>C202507</th>\n",
" <th>C202508</th>\n",
" <th>C202509</th>\n",
" <th>C202510</th>\n",
" <th>C202511</th>\n",
" <th>C202512</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" </tbody>\n",
"</table>\n",
"<p>0 rows × 31 columns</p>\n",
"</div>"
],
"text/plain": [
"Empty DataFrame\n",
"Columns: [PDV, SKU, curva, categoria, proj_mar, proj_mar+1, ESTOQUE ATUAL, ESTOQUE EM TRANSITO, PEDIDO PENDENTE, DDV PREVISTO, preço, SKU_FINAL, DESCRICAO, C202412, C202413, C202414, C202415, C202416, C202417, C202501, C202502, C202503, C202504, C202505, C202506, C202507, C202508, C202509, C202510, C202511, C202512]\n",
"Index: []\n",
"\n",
"[0 rows x 31 columns]"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_draft[df_draft['SKU']=='1634']"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(34265, 31)"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_final = pd.merge(left=df_tabela,right=df_pdv,on='MATCH',how='left')\n",
"\n",
"df_final = df_final.drop_duplicates()\n",
"\n",
"df_final.shape"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"PDV\n",
"12522 445\n",
"12817 445\n",
"12818 445\n",
"12820 445\n",
"12823 445\n",
" ... \n",
"24257 445\n",
"24268 445\n",
"24269 445\n",
"24293 445\n",
"23813 445\n",
"Name: count, Length: 77, dtype: int64"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_final['PDV'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"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>curva</th>\n",
" <th>categoria</th>\n",
" <th>proj_mar</th>\n",
" <th>proj_mar+1</th>\n",
" <th>ESTOQUE ATUAL</th>\n",
" <th>ESTOQUE EM TRANSITO</th>\n",
" <th>PEDIDO PENDENTE</th>\n",
" <th>DDV PREVISTO</th>\n",
" <th>...</th>\n",
" <th>C202503</th>\n",
" <th>C202504</th>\n",
" <th>C202505</th>\n",
" <th>C202506</th>\n",
" <th>C202507</th>\n",
" <th>C202508</th>\n",
" <th>C202509</th>\n",
" <th>C202510</th>\n",
" <th>C202511</th>\n",
" <th>C202512</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>74681</th>\n",
" <td>3546</td>\n",
" <td>52627</td>\n",
" <td>B</td>\n",
" <td>CUIDADOS FACIAIS</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>0.09</td>\n",
" <td>...</td>\n",
" <td>5.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>74682</th>\n",
" <td>3546</td>\n",
" <td>52627</td>\n",
" <td>B</td>\n",
" <td>CUIDADOS FACIAIS</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>0.09</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>3.0</td>\n",
" <td>0.0</td>\n",
" <td>3.0</td>\n",
" <td>4.0</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2 rows × 31 columns</p>\n",
"</div>"
],
"text/plain": [
" PDV SKU curva categoria proj_mar proj_mar+1 \\\n",
"74681 3546 52627 B CUIDADOS FACIAIS 2.0 2.0 \n",
"74682 3546 52627 B CUIDADOS FACIAIS 2.0 2.0 \n",
"\n",
" ESTOQUE ATUAL ESTOQUE EM TRANSITO PEDIDO PENDENTE DDV PREVISTO ... \\\n",
"74681 0.0 2.0 0.0 0.09 ... \n",
"74682 0.0 2.0 0.0 0.09 ... \n",
"\n",
" C202503 C202504 C202505 C202506 C202507 C202508 C202509 C202510 \\\n",
"74681 5.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"74682 0.0 0.0 4.0 3.0 0.0 3.0 4.0 1.0 \n",
"\n",
" C202511 C202512 \n",
"74681 0.0 0.0 \n",
"74682 3.0 3.0 \n",
"\n",
"[2 rows x 31 columns]"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_draft[(df_draft['SKU'] == '52627') & (df_draft['PDV'] == '3546') ].head()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['PDV', 'SKU', 'curva', 'categoria', 'proj_mar', 'proj_mar+1',\n",
" 'ESTOQUE ATUAL', 'ESTOQUE EM TRANSITO', 'PEDIDO PENDENTE',\n",
" 'DDV PREVISTO', 'preço', 'SKU_FINAL', 'C202412', 'C202413', 'C202414',\n",
" 'C202415', 'C202416', 'C202417', 'C202501', 'C202502', 'C202503',\n",
" 'C202504', 'C202505', 'C202506', 'C202507', 'C202508', 'C202509',\n",
" 'C202510', 'C202511', 'C202512'],\n",
" dtype='object')"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_draft = df_draft.drop(columns='DESCRICAO')\n",
"\n",
"df_draft.columns"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"PDV\n",
"20998 1209\n",
"23711 1190\n",
"20997 1182\n",
"20993 1172\n",
"20996 1161\n",
" ... \n",
"24269 490\n",
"24253 472\n",
"24268 452\n",
"24254 439\n",
"24258 336\n",
"Name: count, Length: 75, dtype: int64"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"numericos = df_draft.columns[-18:]\n",
"\n",
"chaves = [col for col in df_draft.columns if col not in numericos]\n",
"\n",
"# 2. Fazer o groupby\n",
"df_draft_agg = (\n",
" df_draft\n",
" .groupby(chaves, as_index=False)[numericos]\n",
" .max()\n",
")\n",
"\n",
"df_draft_agg['PDV'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 32,
"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>curva</th>\n",
" <th>categoria</th>\n",
" <th>proj_mar</th>\n",
" <th>proj_mar+1</th>\n",
" <th>ESTOQUE ATUAL</th>\n",
" <th>ESTOQUE EM TRANSITO</th>\n",
" <th>PEDIDO PENDENTE</th>\n",
" <th>DDV PREVISTO</th>\n",
" <th>...</th>\n",
" <th>C202503</th>\n",
" <th>C202504</th>\n",
" <th>C202505</th>\n",
" <th>C202506</th>\n",
" <th>C202507</th>\n",
" <th>C202508</th>\n",
" <th>C202509</th>\n",
" <th>C202510</th>\n",
" <th>C202511</th>\n",
" <th>C202512</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" </tbody>\n",
"</table>\n",
"<p>0 rows × 30 columns</p>\n",
"</div>"
],
"text/plain": [
"Empty DataFrame\n",
"Columns: [PDV, SKU, curva, categoria, proj_mar, proj_mar+1, ESTOQUE ATUAL, ESTOQUE EM TRANSITO, PEDIDO PENDENTE, DDV PREVISTO, preço, SKU_FINAL, C202412, C202413, C202414, C202415, C202416, C202417, C202501, C202502, C202503, C202504, C202505, C202506, C202507, C202508, C202509, C202510, C202511, C202512]\n",
"Index: []\n",
"\n",
"[0 rows x 30 columns]"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_draft[df_draft['SKU']==1634]"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"df_draft1 = df_draft_agg"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['PDV', 'SKU', 'curva', 'categoria', 'proj_mar', 'proj_mar+1',\n",
" 'ESTOQUE ATUAL', 'ESTOQUE EM TRANSITO', 'PEDIDO PENDENTE',\n",
" 'DDV PREVISTO', 'preço', 'SKU_FINAL', 'C202412', 'C202413', 'C202414',\n",
" 'C202415', 'C202416', 'C202417', 'C202501', 'C202502', 'C202503',\n",
" 'C202504', 'C202505', 'C202506', 'C202507', 'C202508', 'C202509',\n",
" 'C202510', 'C202511', 'C202512'],\n",
" dtype='object')"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_draft1.columns"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"df_draft1.to_clipboard()"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(34265, 60)"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_final['PDV'] = df_final['PDV'].astype('str')\n",
"df_final['Código'] = df_final['Código'].astype('str')\n",
"\n",
"\n",
"df_final = pd.merge(left=df_final,right=df_draft1,left_on=['PDV','Código'],right_on=['PDV','SKU'],how='left')\n",
"df_final.shape"
]
},
{
"cell_type": "code",
"execution_count": 37,
"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>Ciclo</th>\n",
" <th>Região</th>\n",
" <th>Canal</th>\n",
" <th>Código</th>\n",
" <th>Descrição</th>\n",
" <th>IAF</th>\n",
" <th>Tipo de pedido</th>\n",
" <th>Foco</th>\n",
" <th>Unidade de negócio</th>\n",
" <th>Marca</th>\n",
" <th>...</th>\n",
" <th>C202503</th>\n",
" <th>C202504</th>\n",
" <th>C202505</th>\n",
" <th>C202506</th>\n",
" <th>C202507</th>\n",
" <th>C202508</th>\n",
" <th>C202509</th>\n",
" <th>C202510</th>\n",
" <th>C202511</th>\n",
" <th>C202512</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>12936</th>\n",
" <td>202515</td>\n",
" <td>NNE</td>\n",
" <td>Loja</td>\n",
" <td>1634</td>\n",
" <td>NSPA GEL ESF CPO MORANGO RUBY 175G</td>\n",
" <td>Não</td>\n",
" <td>Especial</td>\n",
" <td>Não</td>\n",
" <td>BOT</td>\n",
" <td>NATIVA SPA</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>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12937</th>\n",
" <td>202515</td>\n",
" <td>NNE</td>\n",
" <td>Loja</td>\n",
" <td>1634</td>\n",
" <td>NSPA GEL ESF CPO MORANGO RUBY 175G</td>\n",
" <td>Não</td>\n",
" <td>Especial</td>\n",
" <td>Não</td>\n",
" <td>BOT</td>\n",
" <td>NATIVA SPA</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>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12938</th>\n",
" <td>202515</td>\n",
" <td>NNE</td>\n",
" <td>Loja</td>\n",
" <td>1634</td>\n",
" <td>NSPA GEL ESF CPO MORANGO RUBY 175G</td>\n",
" <td>Não</td>\n",
" <td>Especial</td>\n",
" <td>Não</td>\n",
" <td>BOT</td>\n",
" <td>NATIVA SPA</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>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12939</th>\n",
" <td>202515</td>\n",
" <td>NNE</td>\n",
" <td>Loja</td>\n",
" <td>1634</td>\n",
" <td>NSPA GEL ESF CPO MORANGO RUBY 175G</td>\n",
" <td>Não</td>\n",
" <td>Especial</td>\n",
" <td>Não</td>\n",
" <td>BOT</td>\n",
" <td>NATIVA SPA</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>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12940</th>\n",
" <td>202515</td>\n",
" <td>NNE</td>\n",
" <td>Loja</td>\n",
" <td>1634</td>\n",
" <td>NSPA GEL ESF CPO MORANGO RUBY 175G</td>\n",
" <td>Não</td>\n",
" <td>Especial</td>\n",
" <td>Não</td>\n",
" <td>BOT</td>\n",
" <td>NATIVA SPA</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>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 60 columns</p>\n",
"</div>"
],
"text/plain": [
" Ciclo Região Canal Código Descrição IAF \\\n",
"12936 202515 NNE Loja 1634 NSPA GEL ESF CPO MORANGO RUBY 175G Não \n",
"12937 202515 NNE Loja 1634 NSPA GEL ESF CPO MORANGO RUBY 175G Não \n",
"12938 202515 NNE Loja 1634 NSPA GEL ESF CPO MORANGO RUBY 175G Não \n",
"12939 202515 NNE Loja 1634 NSPA GEL ESF CPO MORANGO RUBY 175G Não \n",
"12940 202515 NNE Loja 1634 NSPA GEL ESF CPO MORANGO RUBY 175G Não \n",
"\n",
" Tipo de pedido Foco Unidade de negócio Marca ... C202503 C202504 \\\n",
"12936 Especial Não BOT NATIVA SPA ... NaN NaN \n",
"12937 Especial Não BOT NATIVA SPA ... NaN NaN \n",
"12938 Especial Não BOT NATIVA SPA ... NaN NaN \n",
"12939 Especial Não BOT NATIVA SPA ... NaN NaN \n",
"12940 Especial Não BOT NATIVA SPA ... NaN NaN \n",
"\n",
" C202505 C202506 C202507 C202508 C202509 C202510 C202511 C202512 \n",
"12936 NaN NaN NaN NaN NaN NaN NaN NaN \n",
"12937 NaN NaN NaN NaN NaN NaN NaN NaN \n",
"12938 NaN NaN NaN NaN NaN NaN NaN NaN \n",
"12939 NaN NaN NaN NaN NaN NaN NaN NaN \n",
"12940 NaN NaN NaN NaN NaN NaN NaN NaN \n",
"\n",
"[5 rows x 60 columns]"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_final[df_final['Código']=='1634'].head()"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"PDV\n",
"12522 445\n",
"12817 445\n",
"12818 445\n",
"12820 445\n",
"12823 445\n",
" ... \n",
"24257 445\n",
"24268 445\n",
"24269 445\n",
"24293 445\n",
"23813 445\n",
"Name: count, Length: 77, dtype: int64"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_final['PDV'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [],
"source": [
"df_final = df_final.drop(columns=['Sortimento P', 'Sortimento M',\n",
" 'Sortimento G','MARCA'])"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"calendario = pd.read_excel(r\"C:\\Users\\joao.herculano\\GRUPO GINSENG\\Assistência Suprimentos - 2025\\SUPRIMENTOS\\BD_LANÇAMENTOS\\BASE DE DADOS LANÇAMENTO\\BOT\\CICLO 9\\CALENDARIO_CICLO\\Ciclo_Expandido_com_Datas.xlsx\")\n"
]
},
{
"cell_type": "code",
"execution_count": 41,
"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>Ciclo</th>\n",
" <th>INICIO CICLO</th>\n",
" <th>FIM CICLO</th>\n",
" <th>DURAÇÃO</th>\n",
" <th>Date</th>\n",
" <th>NUM_CICLO</th>\n",
" <th>ANO_CICLO</th>\n",
" <th>CICLOMAIS2</th>\n",
" <th>dias_ate_inicio</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2367</th>\n",
" <td>C202514</td>\n",
" <td>2025-09-22</td>\n",
" <td>2025-10-12</td>\n",
" <td>21</td>\n",
" <td>2025-09-22</td>\n",
" <td>14</td>\n",
" <td>C2025</td>\n",
" <td>C202516</td>\n",
" <td>25</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Ciclo INICIO CICLO FIM CICLO DURAÇÃO Date NUM_CICLO \\\n",
"2367 C202514 2025-09-22 2025-10-12 21 2025-09-22 14 \n",
"\n",
" ANO_CICLO CICLOMAIS2 dias_ate_inicio \n",
"2367 C2025 C202516 25 "
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"calendario['Date'] = pd.to_datetime(calendario['Date'])\n",
"\n",
"# Get today (normalized to midnight)\n",
"today = pd.Timestamp(\"today\").normalize()\n",
"\n",
"calendario['NUM_CICLO'] = calendario['Ciclo'].str[-2:].astype(int)\n",
"calendario['ANO_CICLO'] = calendario['Ciclo'].str[0:5]\n",
"\n",
"\n",
"calendario = calendario[calendario['MARCA'] == \"BOTICARIO\"]\n",
"\n",
"calendario = calendario.drop(columns='MARCA')\n",
"\n",
"calendario['CICLOMAIS2'] = calendario['ANO_CICLO'].astype(str) + (calendario['NUM_CICLO'].astype(int) + 2).astype(str).str.zfill(2) # >>>>>>>>> MUDAR PRA CICLO CORRETO \n",
"\n",
"ciclo_mais2 = calendario[calendario['Date'].dt.normalize() == today]['CICLOMAIS2'].iloc[0]\n",
"\n",
"\n",
"# Filter rows where date matches today\n",
"filtered_calendario = calendario[calendario['Ciclo'] == ciclo_mais2][:1]\n",
"\n",
"\n",
"filtered_calendario['dias_ate_inicio'] = filtered_calendario['INICIO CICLO'].iloc[0] - today\n",
"\n",
"filtered_calendario['dias_ate_inicio'] = filtered_calendario['dias_ate_inicio'].dt.days.astype(int)\n",
"\n",
"filtered_calendario\n"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
"filtered_calendario['MATCH'] = 1"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"df_pdv['UF'] = np.where(df_pdv['UF'] == 'VDC','BA',df_pdv['UF'])"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"df_draft['PDV'] = df_draft['PDV'].astype(str)"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(34265, 61)"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_final = pd.merge(left=df_final, right=filtered_calendario[['Ciclo','INICIO CICLO','FIM CICLO','DURAÇÃO','MATCH','dias_ate_inicio']], on='MATCH',how='left')\n",
"df_final.shape"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [],
"source": [
"df_final['Código'] = df_final['Código'].astype('Int64') \n",
"df_final['PDV'] = df_final['PDV'].astype('Int64') "
]
},
{
"cell_type": "code",
"execution_count": 47,
"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>SKU1</th>\n",
" <th>SKU2</th>\n",
" <th>Descrição</th>\n",
" <th>UF</th>\n",
" <th>PC</th>\n",
" <th>PV</th>\n",
" <th>chave sku2</th>\n",
" <th>chave sku1</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>84918</td>\n",
" <td>84918</td>\n",
" <td>ESTJ BOTI CALENDARIO ADVENTO NAT/24</td>\n",
" <td>AL</td>\n",
" <td>393.70</td>\n",
" <td>1300.0</td>\n",
" <td>AL84918</td>\n",
" <td>AL84918</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>84918</td>\n",
" <td>84918</td>\n",
" <td>ESTJ BOTI CALENDARIO ADVENTO NAT/24</td>\n",
" <td>BA</td>\n",
" <td>436.73</td>\n",
" <td>1300.0</td>\n",
" <td>BA84918</td>\n",
" <td>BA84918</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>84918</td>\n",
" <td>84918</td>\n",
" <td>ESTJ BOTI CALENDARIO ADVENTO NAT/24</td>\n",
" <td>SE</td>\n",
" <td>379.94</td>\n",
" <td>1300.0</td>\n",
" <td>SE84918</td>\n",
" <td>SE84918</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>54230</td>\n",
" <td>54230</td>\n",
" <td>MALBEC DES COL 20 ANOS 100ml</td>\n",
" <td>AL</td>\n",
" <td>138.54</td>\n",
" <td>499.9</td>\n",
" <td>AL54230</td>\n",
" <td>AL54230</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>54230</td>\n",
" <td>54230</td>\n",
" <td>MALBEC DES COL 20 ANOS 100ml</td>\n",
" <td>BA</td>\n",
" <td>148.66</td>\n",
" <td>499.9</td>\n",
" <td>BA54230</td>\n",
" <td>BA54230</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" SKU1 SKU2 Descrição UF PC PV \\\n",
"0 84918 84918 ESTJ BOTI CALENDARIO ADVENTO NAT/24 AL 393.70 1300.0 \n",
"1 84918 84918 ESTJ BOTI CALENDARIO ADVENTO NAT/24 BA 436.73 1300.0 \n",
"2 84918 84918 ESTJ BOTI CALENDARIO ADVENTO NAT/24 SE 379.94 1300.0 \n",
"3 54230 54230 MALBEC DES COL 20 ANOS 100ml AL 138.54 499.9 \n",
"4 54230 54230 MALBEC DES COL 20 ANOS 100ml BA 148.66 499.9 \n",
"\n",
" chave sku2 chave sku1 \n",
"0 AL84918 AL84918 \n",
"1 BA84918 BA84918 \n",
"2 SE84918 SE84918 \n",
"3 AL54230 AL54230 \n",
"4 BA54230 BA54230 "
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_bi_preco['SKU2'] = df_bi_preco['SKU2'].astype('Int64')\n",
"df_bi_preco.head()"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(34265, 68)"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"\n",
"df_final = pd.merge(left=df_final,right=df_bi_preco,right_on=['UF','SKU2'],left_on=['UF','Código'],how='left')\n",
"df_final.shape "
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [],
"source": [
"df_bi_preco['SKU1'] = df_bi_preco['SKU1'].fillna(0)"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(34265, 72)\n"
]
}
],
"source": [
"# Conversão segura para Int64 (nullable)\n",
"df_bi_preco['SKU1'] = pd.to_numeric(df_bi_preco['SKU1'], errors='coerce').astype('Int64')\n",
"\n",
"# Realiza o merge\n",
"df_final = pd.merge(\n",
" left=df_final,\n",
" right=df_bi_preco[['SKU1', 'SKU2', 'UF', 'PC', 'PV']],\n",
" right_on=['UF', 'SKU1'],\n",
" left_on=['UF', 'Código'],\n",
" how='left'\n",
")\n",
"\n",
"# Verifica o resultado\n",
"print(df_final.shape)\n"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [],
"source": [
"df_final['PRECO DE COMPRA'] = np.where(~df_final['PC_x'].isna(),df_final['PC_x'],df_final['PC_y'])\n",
"\n",
"df_final['PRECO DE VENDA'] = np.where(~df_final['PV_x'].isna(),df_final['PV_x'],df_final['PV_y'])\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"CANAL\n",
"TODOS 27145\n",
"VD 7120\n",
"Name: count, dtype: int64"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_final['CANAL'] = np.where((df_final['CANAL'] == 'LJ') | (df_final['CANAL'] == 'HIB'), \"TODOS\" , np.where((df_final['CANAL'] == 'CD') | (df_final['CANAL'] == 'VD'), \"VD\", df_final['CANAL']))\n",
"\n",
"df_final['CANAL'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [],
"source": [
"df_final = df_final.drop_duplicates()"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [],
"source": [
"df_final = df_final.drop(columns=['SKU1_x','SKU1_y','SKU2_x','SKU2_y','PC_x', 'PV_x','PC_y', 'PV_y','Subcategoria',\n",
"'Quantidade por caixa'\n",
"])"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [],
"source": [
"df_final['UFPRODUTO'] = df_final['UF'].astype(str) + df_final['Código'].astype(str)\n"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [],
"source": [
"df_final = df_final.rename(columns={'Compra inteligente Próximo Ciclo + 1':'Compra inteligente Próximo Ciclo','Arquivo_Origem': 'MARCA'})"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"np.int64(0)"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_final = df_final[~df_final['Marca'].isna()]\n",
"df_final['Marca'].isna().sum()"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Ciclo_x', 'Região', 'Canal', 'Código', 'Descrição_x', 'IAF',\n",
" 'Tipo de pedido', 'Foco', 'Unidade de negócio', 'Marca', 'Categoria',\n",
" 'Tipo de promoção', 'Catálogo', 'Tipo de produto', 'Ação consumidor',\n",
" 'Percentual de desconto consumidor', 'Ação revendedor',\n",
" 'Percentual de desconto revendedor', 'MATCH', 'PDV', 'CANAL',\n",
" 'DESCRIÇÃO PDV', 'UF', 'ANALISTA', 'SUPERVISOR', 'SKU', 'curva',\n",
" 'categoria', 'proj_mar', 'proj_mar+1', 'ESTOQUE ATUAL',\n",
" 'ESTOQUE EM TRANSITO', 'PEDIDO PENDENTE', 'DDV PREVISTO', 'preço',\n",
" 'SKU_FINAL', 'C202412', 'C202413', 'C202414', 'C202415', 'C202416',\n",
" 'C202417', 'C202501', 'C202502', 'C202503', 'C202504', 'C202505',\n",
" 'C202506', 'C202507', 'C202508', 'C202509', 'C202510', 'C202511',\n",
" 'C202512', 'Ciclo_y', 'INICIO CICLO', 'FIM CICLO', 'DURAÇÃO',\n",
" 'dias_ate_inicio', 'Descrição_y', 'chave sku2', 'chave sku1',\n",
" 'PRECO DE COMPRA', 'PRECO DE VENDA', 'UFPRODUTO'],\n",
" dtype='object')"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_final.columns"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [],
"source": [
"df_vdc = pd.read_csv(r\"C:\\Users\\joao.herculano\\GRUPO GINSENG\\Assistência Suprimentos - 2025\\SUPRIMENTOS\\DB_PROMOÇÕES\\BOTICARIO\\C10\\VENDA VITORIA 2024\\VENDA VITORIA.csv\")\n",
"\n",
"df_vdc['PRODUTO'] = df_vdc['PRODUTO'].astype('Int64')\n",
"\n",
"df_final['Código'] = df_final['Código'].astype('Int64')\n",
"\n",
"df_final =pd.merge(left=df_final,right=df_vdc,left_on= ['PDV','Código'],right_on= ['PDV GINSENG','PRODUTO'],how='left' )\n",
"\n",
"df_final[df_final.columns[-1]] = df_final[df_final.columns[-1]].fillna(0)"
]
},
{
"cell_type": "code",
"execution_count": 60,
"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 GINSENG</th>\n",
" <th>PRODUTO</th>\n",
" <th>202401</th>\n",
" <th>202402</th>\n",
" <th>202403</th>\n",
" <th>202404</th>\n",
" <th>202405</th>\n",
" <th>202406</th>\n",
" <th>202407</th>\n",
" <th>202408</th>\n",
" <th>202409</th>\n",
" <th>202410</th>\n",
" <th>202411</th>\n",
" <th>202412</th>\n",
" <th>202413</th>\n",
" <th>202414</th>\n",
" <th>202415</th>\n",
" <th>202416</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1235</th>\n",
" <td>23701</td>\n",
" <td>74043</td>\n",
" <td>12</td>\n",
" <td>24</td>\n",
" <td>12</td>\n",
" <td>16</td>\n",
" <td>8</td>\n",
" <td>14</td>\n",
" <td>6</td>\n",
" <td>14</td>\n",
" <td>12</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" <td>10</td>\n",
" <td>4</td>\n",
" <td>42</td>\n",
" <td>0</td>\n",
" <td>14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3033</th>\n",
" <td>23702</td>\n",
" <td>74043</td>\n",
" <td>2</td>\n",
" <td>22</td>\n",
" <td>8</td>\n",
" <td>22</td>\n",
" <td>10</td>\n",
" <td>14</td>\n",
" <td>14</td>\n",
" <td>10</td>\n",
" <td>12</td>\n",
" <td>8</td>\n",
" <td>22</td>\n",
" <td>6</td>\n",
" <td>14</td>\n",
" <td>36</td>\n",
" <td>10</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5171</th>\n",
" <td>23703</td>\n",
" <td>74043</td>\n",
" <td>10</td>\n",
" <td>18</td>\n",
" <td>6</td>\n",
" <td>18</td>\n",
" <td>6</td>\n",
" <td>2</td>\n",
" <td>20</td>\n",
" <td>0</td>\n",
" <td>10</td>\n",
" <td>2</td>\n",
" <td>22</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>26</td>\n",
" <td>2</td>\n",
" <td>16</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PDV GINSENG PRODUTO 202401 202402 202403 202404 202405 202406 \\\n",
"1235 23701 74043 12 24 12 16 8 14 \n",
"3033 23702 74043 2 22 8 22 10 14 \n",
"5171 23703 74043 10 18 6 18 6 2 \n",
"\n",
" 202407 202408 202409 202410 202411 202412 202413 202414 202415 \\\n",
"1235 6 14 12 0 8 10 4 42 0 \n",
"3033 14 10 12 8 22 6 14 36 10 \n",
"5171 20 0 10 2 22 4 2 26 2 \n",
"\n",
" 202416 \n",
"1235 14 \n",
"3033 0 \n",
"5171 16 "
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_vdc[df_vdc['PRODUTO']== 74043].head()"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [],
"source": [
"df_final = df_final.fillna(0)\n"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Ciclo_x', 'Região', 'Canal', 'Código', 'Descrição_x', 'IAF',\n",
" 'Tipo de pedido', 'Foco', 'Unidade de negócio', 'Marca', 'Categoria',\n",
" 'Tipo de promoção', 'Catálogo', 'Tipo de produto', 'Ação consumidor',\n",
" 'Percentual de desconto consumidor', 'Ação revendedor',\n",
" 'Percentual de desconto revendedor', 'MATCH', 'PDV', 'CANAL',\n",
" 'DESCRIÇÃO PDV', 'UF', 'ANALISTA', 'SUPERVISOR', 'SKU', 'curva',\n",
" 'categoria', 'proj_mar', 'proj_mar+1', 'ESTOQUE ATUAL',\n",
" 'ESTOQUE EM TRANSITO', 'PEDIDO PENDENTE', 'DDV PREVISTO', 'preço',\n",
" 'SKU_FINAL', 'C202412', 'C202413', 'C202414', 'C202415', 'C202416',\n",
" 'C202417', 'C202501', 'C202502', 'C202503', 'C202504', 'C202505',\n",
" 'C202506', 'C202507', 'C202508', 'C202509', 'C202510', 'C202511',\n",
" 'C202512', 'Ciclo_y', 'INICIO CICLO', 'FIM CICLO', 'DURAÇÃO',\n",
" 'dias_ate_inicio', 'Descrição_y', 'chave sku2', 'chave sku1',\n",
" 'PRECO DE COMPRA', 'PRECO DE VENDA', 'UFPRODUTO', 'PDV GINSENG',\n",
" 'PRODUTO', '202401', '202402', '202403', '202404', '202405', '202406',\n",
" '202407', '202408', '202409', '202410', '202411', '202412', '202413',\n",
" '202414', '202415', '202416'],\n",
" dtype='object')"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_final.columns"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['202401', '202402', '202403', '202404', '202405', '202406', '202407',\n",
" '202408', '202409', '202410', '202411', '202412', '202413', '202414',\n",
" '202415', '202416'],\n",
" dtype='object')"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_final.columns[-16:]"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [],
"source": [
"df_final.to_clipboard()"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [],
"source": [
"import re\n",
"\n",
"# identificar colunas de ciclo puro (ex: '202407')\n",
"colunas_ciclo_puro = [col for col in df_final.columns[-16:]]\n",
"\n",
"for col in colunas_ciclo_puro:\n",
" col_hist = f'C{col}'\n",
" if col_hist in df_final.columns:\n",
" # mantém o maior valor entre a coluna histórica e a pura\n",
" df_final[col_hist] = df_final[[col_hist, col]].max(axis=1)\n",
"\n",
"# remove as colunas puras\n",
"if colunas_ciclo_puro:\n",
" df_final.drop(columns=colunas_ciclo_puro, inplace=True)\n"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Ciclo_x', 'Região', 'Canal', 'Código', 'Descrição_x', 'IAF',\n",
" 'Tipo de pedido', 'Foco', 'Unidade de negócio', 'Marca', 'Categoria',\n",
" 'Tipo de promoção', 'Catálogo', 'Tipo de produto', 'Ação consumidor',\n",
" 'Percentual de desconto consumidor', 'Ação revendedor',\n",
" 'Percentual de desconto revendedor', 'MATCH', 'PDV', 'CANAL',\n",
" 'DESCRIÇÃO PDV', 'UF', 'ANALISTA', 'SUPERVISOR', 'SKU', 'curva',\n",
" 'categoria', 'proj_mar', 'proj_mar+1', 'ESTOQUE ATUAL',\n",
" 'ESTOQUE EM TRANSITO', 'PEDIDO PENDENTE', 'DDV PREVISTO', 'preço',\n",
" 'SKU_FINAL', 'C202412', 'C202413', 'C202414', 'C202415', 'C202416',\n",
" 'C202417', 'C202501', 'C202502', 'C202503', 'C202504', 'C202505',\n",
" 'C202506', 'C202507', 'C202508', 'C202509', 'C202510', 'C202511',\n",
" 'C202512', 'Ciclo_y', 'INICIO CICLO', 'FIM CICLO', 'DURAÇÃO',\n",
" 'dias_ate_inicio', 'Descrição_y', 'chave sku2', 'chave sku1',\n",
" 'PRECO DE COMPRA', 'PRECO DE VENDA', 'UFPRODUTO', 'PDV GINSENG',\n",
" 'PRODUTO'],\n",
" dtype='object')"
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_final.columns"
]
},
{
"cell_type": "code",
"execution_count": 67,
"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>DATA_VENDA</th>\n",
" <th>PDV</th>\n",
" <th>pdvs cp</th>\n",
" <th>DESC PDV</th>\n",
" <th>Código</th>\n",
" <th>Descrição</th>\n",
" <th>Quantidade</th>\n",
" <th>Faturamento</th>\n",
" <th>Ciclo</th>\n",
" <th>Date</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2025-04-01</td>\n",
" <td>12993</td>\n",
" <td>24253</td>\n",
" <td>PERFUMARIA E COSMETICOS QUEIROZ DOURADO LTDA</td>\n",
" <td>1004</td>\n",
" <td>FLORATTA DES COL MY BLUE 75ml</td>\n",
" <td>1</td>\n",
" <td>98,99</td>\n",
" <td>C202505</td>\n",
" <td>2025-04-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2025-04-01</td>\n",
" <td>12993</td>\n",
" <td>24253</td>\n",
" <td>PERFUMARIA E COSMETICOS QUEIROZ DOURADO LTDA</td>\n",
" <td>4539</td>\n",
" <td>BOTIK CR FAC FIRMADOR AC/HIAL 40g V2</td>\n",
" <td>1</td>\n",
" <td>129,05</td>\n",
" <td>C202505</td>\n",
" <td>2025-04-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2025-04-01</td>\n",
" <td>12993</td>\n",
" <td>24253</td>\n",
" <td>PERFUMARIA E COSMETICOS QUEIROZ DOURADO LTDA</td>\n",
" <td>29046</td>\n",
" <td>BOTICOLL CONNEX DES BDY SPR 100ml V6 PCK</td>\n",
" <td>1</td>\n",
" <td>38,9</td>\n",
" <td>C202505</td>\n",
" <td>2025-04-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2025-04-01</td>\n",
" <td>12993</td>\n",
" <td>24253</td>\n",
" <td>PERFUMARIA E COSMETICOS QUEIROZ DOURADO LTDA</td>\n",
" <td>47154</td>\n",
" <td>CBEM DES ROLL S/ALUM 55ml</td>\n",
" <td>1</td>\n",
" <td>21,98</td>\n",
" <td>C202505</td>\n",
" <td>2025-04-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2025-04-01</td>\n",
" <td>12993</td>\n",
" <td>24253</td>\n",
" <td>PERFUMARIA E COSMETICOS QUEIROZ DOURADO LTDA</td>\n",
" <td>47411</td>\n",
" <td>ARBO NECESS LONA</td>\n",
" <td>1</td>\n",
" <td>57,26</td>\n",
" <td>C202505</td>\n",
" <td>2025-04-01</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" DATA_VENDA PDV pdvs cp DESC PDV \\\n",
"0 2025-04-01 12993 24253 PERFUMARIA E COSMETICOS QUEIROZ DOURADO LTDA \n",
"1 2025-04-01 12993 24253 PERFUMARIA E COSMETICOS QUEIROZ DOURADO LTDA \n",
"2 2025-04-01 12993 24253 PERFUMARIA E COSMETICOS QUEIROZ DOURADO LTDA \n",
"3 2025-04-01 12993 24253 PERFUMARIA E COSMETICOS QUEIROZ DOURADO LTDA \n",
"4 2025-04-01 12993 24253 PERFUMARIA E COSMETICOS QUEIROZ DOURADO LTDA \n",
"\n",
" Código Descrição Quantidade Faturamento \\\n",
"0 1004 FLORATTA DES COL MY BLUE 75ml 1 98,99 \n",
"1 4539 BOTIK CR FAC FIRMADOR AC/HIAL 40g V2 1 129,05 \n",
"2 29046 BOTICOLL CONNEX DES BDY SPR 100ml V6 PCK 1 38,9 \n",
"3 47154 CBEM DES ROLL S/ALUM 55ml 1 21,98 \n",
"4 47411 ARBO NECESS LONA 1 57,26 \n",
"\n",
" Ciclo Date \n",
"0 C202505 2025-04-01 \n",
"1 C202505 2025-04-01 \n",
"2 C202505 2025-04-01 \n",
"3 C202505 2025-04-01 \n",
"4 C202505 2025-04-01 "
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_irece = pd.read_csv(r\"C:\\Users\\joao.herculano\\GRUPO GINSENG\\Assistência Suprimentos - 2025\\DADOS DOURADO\\VENDA\\COMPILADO_VENDAS.csv\", sep=';')\n",
"\n",
"df_irece['DATA_VENDA'] = pd.to_datetime(df_irece['DATA_VENDA'], dayfirst=True)\n",
"\n",
"df_irece = pd.merge(df_irece,calendario[['Ciclo','Date']],how='left', left_on='DATA_VENDA',right_on='Date')\n",
"\n",
"df_irece.head()"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [],
"source": [
"df_irece_agrupado = df_irece.groupby(['PDV','Código','Ciclo'])['Quantidade'].sum().reset_index()\n",
"\n",
"df_pivotado = df_irece_agrupado.pivot(index=['PDV', 'Código'], columns='Ciclo', values='Quantidade').reset_index()\n",
"\n",
"df_pivotado = df_pivotado.fillna(0)"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['C202413', 'C202414', 'C202415', 'C202416', 'C202417', 'C202501',\n",
" 'C202502', 'C202503', 'C202504', 'C202505', 'C202506', 'C202507',\n",
" 'C202508', 'C202509', 'C202510', 'C202511', 'C202512', 'Ciclo_y'],\n",
" dtype='object')"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_final.columns[37:55]"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Ciclo_x', 'Região', 'Canal', 'Código', 'Descrição_x', 'IAF',\n",
" 'Tipo de pedido', 'Foco', 'Unidade de negócio', 'Marca', 'Categoria',\n",
" 'Tipo de promoção', 'Catálogo', 'Tipo de produto', 'Ação consumidor',\n",
" 'Percentual de desconto consumidor', 'Ação revendedor',\n",
" 'Percentual de desconto revendedor', 'MATCH', 'PDV', 'CANAL',\n",
" 'DESCRIÇÃO PDV', 'UF', 'ANALISTA', 'SUPERVISOR', 'SKU', 'curva',\n",
" 'categoria', 'proj_mar', 'proj_mar+1', 'ESTOQUE ATUAL',\n",
" 'ESTOQUE EM TRANSITO', 'PEDIDO PENDENTE', 'DDV PREVISTO', 'preço',\n",
" 'SKU_FINAL', '202412', '202413', '202414', '202415', '202416', '202417',\n",
" '202501', '202502', '202503', '202504', '202505', '202506', '202507',\n",
" '202508', '202509', '202510', '202511', '202512', 'Ciclo_y',\n",
" 'INICIO CICLO', 'FIM CICLO', 'DURAÇÃO', 'dias_ate_inicio',\n",
" 'Descrição_y', 'chave sku2', 'chave sku1', 'PRECO DE COMPRA',\n",
" 'PRECO DE VENDA', 'UFPRODUTO', 'PDV GINSENG', 'PRODUTO'],\n",
" dtype='object')"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"for col_c in df_final.columns[37:55]:\n",
" sufixo = col_c[1:] # Remove o 'C' do início, ex: 'C202407' -> '202407'\n",
" df_final.columns = df_final.columns.str.replace(\"C20\", \"20\", regex=True)\n",
"\n",
"\n",
"df_final.columns"
]
},
{
"cell_type": "code",
"execution_count": 71,
"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>Ciclo_x</th>\n",
" <th>Região</th>\n",
" <th>Canal</th>\n",
" <th>Código</th>\n",
" <th>Descrição_x</th>\n",
" <th>IAF</th>\n",
" <th>Tipo de pedido</th>\n",
" <th>Foco</th>\n",
" <th>Unidade de negócio</th>\n",
" <th>Marca</th>\n",
" <th>...</th>\n",
" <th>C202415</th>\n",
" <th>C202416</th>\n",
" <th>C202417</th>\n",
" <th>C202501</th>\n",
" <th>C202502</th>\n",
" <th>C202503</th>\n",
" <th>C202504</th>\n",
" <th>C202505</th>\n",
" <th>C202506</th>\n",
" <th>C202507</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>202515</td>\n",
" <td>NNE</td>\n",
" <td>Loja</td>\n",
" <td>50052</td>\n",
" <td>ARBO LOC DES HID CPO 75ml</td>\n",
" <td>Não</td>\n",
" <td>Semanal</td>\n",
" <td>Não</td>\n",
" <td>BOT</td>\n",
" <td>ARBO</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>202515</td>\n",
" <td>NNE</td>\n",
" <td>Loja</td>\n",
" <td>50052</td>\n",
" <td>ARBO LOC DES HID CPO 75ml</td>\n",
" <td>Não</td>\n",
" <td>Semanal</td>\n",
" <td>Não</td>\n",
" <td>BOT</td>\n",
" <td>ARBO</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>202515</td>\n",
" <td>NNE</td>\n",
" <td>Loja</td>\n",
" <td>50052</td>\n",
" <td>ARBO LOC DES HID CPO 75ml</td>\n",
" <td>Não</td>\n",
" <td>Semanal</td>\n",
" <td>Não</td>\n",
" <td>BOT</td>\n",
" <td>ARBO</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>202515</td>\n",
" <td>NNE</td>\n",
" <td>Loja</td>\n",
" <td>50052</td>\n",
" <td>ARBO LOC DES HID CPO 75ml</td>\n",
" <td>Não</td>\n",
" <td>Semanal</td>\n",
" <td>Não</td>\n",
" <td>BOT</td>\n",
" <td>ARBO</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>202515</td>\n",
" <td>NNE</td>\n",
" <td>Loja</td>\n",
" <td>50052</td>\n",
" <td>ARBO LOC DES HID CPO 75ml</td>\n",
" <td>Não</td>\n",
" <td>Semanal</td>\n",
" <td>Não</td>\n",
" <td>BOT</td>\n",
" <td>ARBO</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 91 columns</p>\n",
"</div>"
],
"text/plain": [
" Ciclo_x Região Canal Código Descrição_x IAF \\\n",
"0 202515 NNE Loja 50052 ARBO LOC DES HID CPO 75ml Não \n",
"1 202515 NNE Loja 50052 ARBO LOC DES HID CPO 75ml Não \n",
"2 202515 NNE Loja 50052 ARBO LOC DES HID CPO 75ml Não \n",
"3 202515 NNE Loja 50052 ARBO LOC DES HID CPO 75ml Não \n",
"4 202515 NNE Loja 50052 ARBO LOC DES HID CPO 75ml Não \n",
"\n",
" Tipo de pedido Foco Unidade de negócio Marca ... C202415 C202416 C202417 \\\n",
"0 Semanal Não BOT ARBO ... 0.0 0.0 0.0 \n",
"1 Semanal Não BOT ARBO ... 0.0 0.0 0.0 \n",
"2 Semanal Não BOT ARBO ... 0.0 0.0 0.0 \n",
"3 Semanal Não BOT ARBO ... 0.0 0.0 0.0 \n",
"4 Semanal Não BOT ARBO ... 0.0 0.0 0.0 \n",
"\n",
" C202501 C202502 C202503 C202504 C202505 C202506 C202507 \n",
"0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"\n",
"[5 rows x 91 columns]"
]
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_final = pd.merge(df_final,df_pivotado,how='left',on=['PDV','Código'])\n",
"\n",
"df_final = df_final.fillna(0)\n",
"\n",
"df_final.head()"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Ciclo_x', 'Região', 'Canal', 'Código', 'Descrição_x', 'IAF',\n",
" 'Tipo de pedido', 'Foco', 'Unidade de negócio', 'Marca', 'Categoria',\n",
" 'Tipo de promoção', 'Catálogo', 'Tipo de produto', 'Ação consumidor',\n",
" 'Percentual de desconto consumidor', 'Ação revendedor',\n",
" 'Percentual de desconto revendedor', 'MATCH', 'PDV', 'CANAL',\n",
" 'DESCRIÇÃO PDV', 'UF', 'ANALISTA', 'SUPERVISOR', 'SKU', 'curva',\n",
" 'categoria', 'proj_mar', 'proj_mar+1', 'ESTOQUE ATUAL',\n",
" 'ESTOQUE EM TRANSITO', 'PEDIDO PENDENTE', 'DDV PREVISTO', 'preço',\n",
" 'SKU_FINAL', '202412', '202413', '202414', '202415', '202416', '202417',\n",
" '202501', '202502', '202503', '202504', '202505', '202506', '202507',\n",
" '202508', '202509', '202510', '202511', '202512', 'Ciclo_y',\n",
" 'INICIO CICLO', 'FIM CICLO', 'DURAÇÃO', 'dias_ate_inicio',\n",
" 'Descrição_y', 'chave sku2', 'chave sku1', 'PRECO DE COMPRA',\n",
" 'PRECO DE VENDA', 'UFPRODUTO', 'PDV GINSENG', 'PRODUTO', 'C202401',\n",
" 'C202402', 'C202403', 'C202404', 'C202405', 'C202406', 'C202407',\n",
" 'C202408', 'C202409', 'C202410', 'C202411', 'C202412', 'C202413',\n",
" 'C202414', 'C202415', 'C202416', 'C202417', 'C202501', 'C202502',\n",
" 'C202503', 'C202504', 'C202505', 'C202506', 'C202507'],\n",
" dtype='object')"
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_final.columns"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [],
"source": [
"\n",
"\n",
"# 1. Identificar colunas no formato 'C2024xx', 'C2025xx', etc.\n",
"colunas_ciclo_c = [col for col in df_pivotado.columns if re.fullmatch(r'C20\\d{4}', col)]\n",
"\n",
"for col_c in colunas_ciclo_c:\n",
" sufixo = col_c[1:] # Remove o 'C' do início, ex: 'C202407' -> '202407'\n",
" col_hist = col_c\n",
" \n",
" if col_hist in df_final.columns:\n",
" # Pega o maior valor entre a coluna de histórico e a de ciclo com \"C\"\n",
" df_final[col_hist] = df_final[[col_hist, col_c]].max(axis=1)\n",
"\n",
"# (Opcional) Remover as colunas 'C20xxxx' após o merge\n",
"df_final.drop(columns=colunas_ciclo_c, inplace=True)\n"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Ciclo_x', 'Região', 'Canal', 'Código', 'Descrição_x', 'IAF',\n",
" 'Tipo de pedido', 'Foco', 'Unidade de negócio', 'Marca', 'Categoria',\n",
" 'Tipo de promoção', 'Catálogo', 'Tipo de produto', 'Ação consumidor',\n",
" 'Percentual de desconto consumidor', 'Ação revendedor',\n",
" 'Percentual de desconto revendedor', 'MATCH', 'PDV', 'CANAL',\n",
" 'DESCRIÇÃO PDV', 'UF', 'ANALISTA', 'SUPERVISOR', 'SKU', 'curva',\n",
" 'categoria', 'proj_mar', 'proj_mar+1', 'ESTOQUE ATUAL',\n",
" 'ESTOQUE EM TRANSITO', 'PEDIDO PENDENTE', 'DDV PREVISTO', 'preço',\n",
" 'SKU_FINAL', '202412', '202413', '202414', '202415', '202416', '202417',\n",
" '202501', '202502', '202503', '202504', '202505', '202506', '202507',\n",
" '202508', '202509', '202510', '202511', '202512', 'Ciclo_y',\n",
" 'INICIO CICLO', 'FIM CICLO', 'DURAÇÃO', 'dias_ate_inicio',\n",
" 'Descrição_y', 'chave sku2', 'chave sku1', 'PRECO DE COMPRA',\n",
" 'PRECO DE VENDA', 'UFPRODUTO', 'PDV GINSENG', 'PRODUTO'],\n",
" dtype='object')"
]
},
"execution_count": 74,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_final.columns"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"202401 C202401\n",
"202402 C202402\n",
"202403 C202403\n",
"202404 C202404\n",
"202405 C202405\n",
"202406 C202406\n",
"202407 C202407\n",
"202408 C202408\n",
"202409 C202409\n",
"202410 C202410\n",
"202411 C202411\n",
"202412 C202412\n",
"202413 C202413\n",
"202414 C202414\n",
"202415 C202415\n",
"202416 C202416\n",
"202417 C202417\n",
"202501 C202501\n",
"202502 C202502\n",
"202503 C202503\n",
"202504 C202504\n",
"202505 C202505\n",
"202506 C202506\n",
"202507 C202507\n"
]
}
],
"source": [
"colunas_ciclo_c = [col for col in df_pivotado.columns if re.fullmatch(r'C20\\d{4}', col)]\n",
"\n",
"for col_c in colunas_ciclo_c:\n",
" sufixo = col_c[1:] # Remove o 'C' do início, ex: 'C202407' -> '202407'\n",
" col_hist = col_c\n",
" print(sufixo,col_c)\n"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {},
"outputs": [],
"source": [
"df_final = df_final.drop(columns=['Ciclo_y'])"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Ciclo_x', 'Região', 'Canal', 'Código', 'Descrição_x', 'IAF',\n",
" 'Tipo de pedido', 'Foco', 'Unidade de negócio', 'Marca', 'Categoria',\n",
" 'Tipo de promoção', 'Catálogo', 'Tipo de produto', 'Ação consumidor',\n",
" 'Percentual de desconto consumidor', 'Ação revendedor',\n",
" 'Percentual de desconto revendedor', 'MATCH', 'PDV', 'CANAL',\n",
" 'DESCRIÇÃO PDV', 'UF', 'ANALISTA', 'SUPERVISOR', 'SKU', 'curva',\n",
" 'categoria', 'proj_mar', 'proj_mar+1', 'ESTOQUE ATUAL',\n",
" 'ESTOQUE EM TRANSITO', 'PEDIDO PENDENTE', 'DDV PREVISTO', 'preço',\n",
" 'SKU_FINAL', '202412', '202413', '202414', '202415', '202416', '202417',\n",
" '202501', '202502', '202503', '202504', '202505', '202506', '202507',\n",
" '202508', '202509', '202510', '202511', '202512', 'INICIO CICLO',\n",
" 'FIM CICLO', 'DURAÇÃO', 'dias_ate_inicio', 'Descrição_y', 'chave sku2',\n",
" 'chave sku1', 'PRECO DE COMPRA', 'PRECO DE VENDA', 'UFPRODUTO',\n",
" 'PDV GINSENG', 'PRODUTO'],\n",
" dtype='object')"
]
},
"execution_count": 77,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_final.columns"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(34265, 66)"
]
},
"execution_count": 78,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_final = df_final.drop_duplicates()\n",
"df_final.shape"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [],
"source": [
"df_final['chave'] = df_final['PDV'].astype('str') + df_final['Código'].astype('str')"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {},
"outputs": [],
"source": [
"df_final = df_final.fillna(0)\n"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['202412', '202413', '202414', '202415', '202416', '202417', '202501',\n",
" '202502', '202503', '202504', '202505', '202506', '202507', '202508',\n",
" '202509', '202510', '202511', '202512'],\n",
" dtype='object')"
]
},
"execution_count": 81,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_final.columns[36:54]"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Ciclo_x', 'Região', 'Canal', 'Código', 'Descrição_x', 'IAF',\n",
" 'Tipo de pedido', 'Foco', 'Unidade de negócio', 'Marca', 'Categoria',\n",
" 'Tipo de promoção', 'Catálogo', 'Tipo de produto', 'Ação consumidor',\n",
" 'Percentual de desconto consumidor', 'Ação revendedor',\n",
" 'Percentual de desconto revendedor', 'MATCH', 'PDV', 'CANAL',\n",
" 'DESCRIÇÃO PDV', 'UF', 'ANALISTA', 'SUPERVISOR', 'SKU', 'curva',\n",
" 'categoria', 'proj_mar', 'proj_mar+1', 'ESTOQUE ATUAL',\n",
" 'ESTOQUE EM TRANSITO', 'PEDIDO PENDENTE', 'DDV PREVISTO', 'preço',\n",
" 'SKU_FINAL', '202412', '202413', '202414', '202415', '202416', '202417',\n",
" '202501', '202502', '202503', '202504', '202505', '202506', '202507',\n",
" '202508', '202509', '202510', '202511', '202512', 'INICIO CICLO',\n",
" 'FIM CICLO', 'DURAÇÃO', 'dias_ate_inicio', 'Descrição_y', 'chave sku2',\n",
" 'chave sku1', 'PRECO DE COMPRA', 'PRECO DE VENDA', 'UFPRODUTO',\n",
" 'PDV GINSENG', 'PRODUTO', 'chave'],\n",
" dtype='object')"
]
},
"execution_count": 82,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_final.columns"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {},
"outputs": [],
"source": [
"# Define list of target columns\n",
"sales_2024_cols = df_final.columns[36:54]\n",
"# Create a new column with the row-wise max\n",
"df_final['PICO DE VENDAS 2024'] = df_final[sales_2024_cols].max(axis=1)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {},
"outputs": [],
"source": [
"vendas_6_meses = df_final.columns[48:54]\n",
"\n",
"df_final['Pico Vendas Ultimos 6 ciclos'] = df_final[vendas_6_meses].max(axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['202413', '202414', '202415', '202416', '202417', '202501', '202502'], dtype='object')"
]
},
"execution_count": 85,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_final.columns[37:44]"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Ciclo_x', 'Região', 'Canal', 'Código', 'Descrição_x', 'IAF',\n",
" 'Tipo de pedido', 'Foco', 'Unidade de negócio', 'Marca', 'Categoria',\n",
" 'Tipo de promoção', 'Catálogo', 'Tipo de produto', 'Ação consumidor',\n",
" 'Percentual de desconto consumidor', 'Ação revendedor',\n",
" 'Percentual de desconto revendedor', 'MATCH', 'PDV', 'CANAL',\n",
" 'DESCRIÇÃO PDV', 'UF', 'ANALISTA', 'SUPERVISOR', 'SKU', 'curva',\n",
" 'categoria', 'proj_mar', 'proj_mar+1', 'ESTOQUE ATUAL',\n",
" 'ESTOQUE EM TRANSITO', 'PEDIDO PENDENTE', 'DDV PREVISTO', 'preço',\n",
" 'SKU_FINAL', '202412', '202413', '202414', '202415', '202416', '202417',\n",
" '202501', '202502', '202503', '202504', '202505', '202506', '202507',\n",
" '202508', '202509', '202510', '202511', '202512', 'INICIO CICLO',\n",
" 'FIM CICLO', 'DURAÇÃO', 'dias_ate_inicio', 'Descrição_y', 'chave sku2',\n",
" 'chave sku1', 'PRECO DE COMPRA', 'PRECO DE VENDA', 'UFPRODUTO',\n",
" 'PDV GINSENG', 'PRODUTO', 'chave', 'PICO DE VENDAS 2024',\n",
" 'Pico Vendas Ultimos 6 ciclos'],\n",
" dtype='object')"
]
},
"execution_count": 86,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_final.columns"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Ciclo_x', 'Região', 'Canal', 'Código', 'Descrição_x', 'IAF',\n",
" 'Tipo de pedido', 'Foco', 'Unidade de negócio', 'Marca', 'Categoria',\n",
" 'Tipo de promoção', 'Catálogo', 'Tipo de produto', 'Ação consumidor',\n",
" 'Percentual de desconto consumidor', 'Ação revendedor',\n",
" 'Percentual de desconto revendedor', 'MATCH', 'PDV', 'CANAL',\n",
" 'DESCRIÇÃO PDV', 'UF', 'ANALISTA', 'SUPERVISOR', 'SKU', 'curva',\n",
" 'categoria', 'proj_mar', 'proj_mar+1', 'ESTOQUE ATUAL',\n",
" 'ESTOQUE EM TRANSITO', 'PEDIDO PENDENTE', 'DDV PREVISTO', 'preço',\n",
" 'SKU_FINAL', '202412', '202413', '202414', '202415', '202416', '202417',\n",
" '202501', '202502', '202503', '202504', '202505', '202506', '202507',\n",
" '202508', '202509', '202510', '202511', '202512', 'INICIO CICLO',\n",
" 'FIM CICLO', 'DURAÇÃO', 'dias_ate_inicio', 'Descrição_y', 'chave sku2',\n",
" 'chave sku1', 'PRECO DE COMPRA', 'PRECO DE VENDA', 'UFPRODUTO',\n",
" 'PDV GINSENG', 'PRODUTO', 'chave', 'PICO DE VENDAS 2024',\n",
" 'Pico Vendas Ultimos 6 ciclos'],\n",
" dtype='object')"
]
},
"execution_count": 87,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_final.columns"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['202412', '202413', '202414', '202415', '202416', '202417', '202501',\n",
" '202502', '202503', '202504', '202505', '202506', '202507', '202508',\n",
" '202509', '202510', '202511', '202512'],\n",
" dtype='object')"
]
},
"execution_count": 88,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_final.columns[36:54]"
]
},
{
"cell_type": "code",
"execution_count": 89,
"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>Ciclo_x</th>\n",
" <th>Região</th>\n",
" <th>Canal</th>\n",
" <th>Código</th>\n",
" <th>Descrição_x</th>\n",
" <th>IAF</th>\n",
" <th>Tipo de pedido</th>\n",
" <th>Foco</th>\n",
" <th>Unidade de negócio</th>\n",
" <th>Marca</th>\n",
" <th>...</th>\n",
" <th>chave sku2</th>\n",
" <th>chave sku1</th>\n",
" <th>PRECO DE COMPRA</th>\n",
" <th>PRECO DE VENDA</th>\n",
" <th>UFPRODUTO</th>\n",
" <th>PDV GINSENG</th>\n",
" <th>PRODUTO</th>\n",
" <th>chave</th>\n",
" <th>PICO DE VENDAS 2024</th>\n",
" <th>Pico Vendas Ultimos 6 ciclos</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>202515</td>\n",
" <td>NNE</td>\n",
" <td>Loja</td>\n",
" <td>50052</td>\n",
" <td>ARBO LOC DES HID CPO 75ml</td>\n",
" <td>Não</td>\n",
" <td>Semanal</td>\n",
" <td>Não</td>\n",
" <td>BOT</td>\n",
" <td>ARBO</td>\n",
" <td>...</td>\n",
" <td>AL50052</td>\n",
" <td>AL50052</td>\n",
" <td>11.72</td>\n",
" <td>37.9</td>\n",
" <td>AL50052</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>1252250052</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>202515</td>\n",
" <td>NNE</td>\n",
" <td>Loja</td>\n",
" <td>50052</td>\n",
" <td>ARBO LOC DES HID CPO 75ml</td>\n",
" <td>Não</td>\n",
" <td>Semanal</td>\n",
" <td>Não</td>\n",
" <td>BOT</td>\n",
" <td>ARBO</td>\n",
" <td>...</td>\n",
" <td>AL50052</td>\n",
" <td>AL50052</td>\n",
" <td>11.72</td>\n",
" <td>37.9</td>\n",
" <td>AL50052</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>1281750052</td>\n",
" <td>5.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>202515</td>\n",
" <td>NNE</td>\n",
" <td>Loja</td>\n",
" <td>50052</td>\n",
" <td>ARBO LOC DES HID CPO 75ml</td>\n",
" <td>Não</td>\n",
" <td>Semanal</td>\n",
" <td>Não</td>\n",
" <td>BOT</td>\n",
" <td>ARBO</td>\n",
" <td>...</td>\n",
" <td>AL50052</td>\n",
" <td>AL50052</td>\n",
" <td>11.72</td>\n",
" <td>37.9</td>\n",
" <td>AL50052</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>1281850052</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>202515</td>\n",
" <td>NNE</td>\n",
" <td>Loja</td>\n",
" <td>50052</td>\n",
" <td>ARBO LOC DES HID CPO 75ml</td>\n",
" <td>Não</td>\n",
" <td>Semanal</td>\n",
" <td>Não</td>\n",
" <td>BOT</td>\n",
" <td>ARBO</td>\n",
" <td>...</td>\n",
" <td>AL50052</td>\n",
" <td>AL50052</td>\n",
" <td>11.72</td>\n",
" <td>37.9</td>\n",
" <td>AL50052</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>1282050052</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>202515</td>\n",
" <td>NNE</td>\n",
" <td>Loja</td>\n",
" <td>50052</td>\n",
" <td>ARBO LOC DES HID CPO 75ml</td>\n",
" <td>Não</td>\n",
" <td>Semanal</td>\n",
" <td>Não</td>\n",
" <td>BOT</td>\n",
" <td>ARBO</td>\n",
" <td>...</td>\n",
" <td>AL50052</td>\n",
" <td>AL50052</td>\n",
" <td>11.72</td>\n",
" <td>37.9</td>\n",
" <td>AL50052</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>1282350052</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 69 columns</p>\n",
"</div>"
],
"text/plain": [
" Ciclo_x Região Canal Código Descrição_x IAF \\\n",
"0 202515 NNE Loja 50052 ARBO LOC DES HID CPO 75ml Não \n",
"1 202515 NNE Loja 50052 ARBO LOC DES HID CPO 75ml Não \n",
"2 202515 NNE Loja 50052 ARBO LOC DES HID CPO 75ml Não \n",
"3 202515 NNE Loja 50052 ARBO LOC DES HID CPO 75ml Não \n",
"4 202515 NNE Loja 50052 ARBO LOC DES HID CPO 75ml Não \n",
"\n",
" Tipo de pedido Foco Unidade de negócio Marca ... chave sku2 chave sku1 \\\n",
"0 Semanal Não BOT ARBO ... AL50052 AL50052 \n",
"1 Semanal Não BOT ARBO ... AL50052 AL50052 \n",
"2 Semanal Não BOT ARBO ... AL50052 AL50052 \n",
"3 Semanal Não BOT ARBO ... AL50052 AL50052 \n",
"4 Semanal Não BOT ARBO ... AL50052 AL50052 \n",
"\n",
" PRECO DE COMPRA PRECO DE VENDA UFPRODUTO PDV GINSENG PRODUTO chave \\\n",
"0 11.72 37.9 AL50052 0.0 0 1252250052 \n",
"1 11.72 37.9 AL50052 0.0 0 1281750052 \n",
"2 11.72 37.9 AL50052 0.0 0 1281850052 \n",
"3 11.72 37.9 AL50052 0.0 0 1282050052 \n",
"4 11.72 37.9 AL50052 0.0 0 1282350052 \n",
"\n",
" PICO DE VENDAS 2024 Pico Vendas Ultimos 6 ciclos \n",
"0 2.0 0.0 \n",
"1 5.0 0.0 \n",
"2 1.0 1.0 \n",
"3 1.0 0.0 \n",
"4 1.0 0.0 \n",
"\n",
"[5 rows x 69 columns]"
]
},
"execution_count": 89,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_final.head()"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\joao.herculano\\AppData\\Local\\Temp\\ipykernel_2580\\3082173574.py:27: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
" .apply(calcular_crescimento) # retorna índice com PDV\n"
]
}
],
"source": [
"# Define as colunas mensais\n",
"colunas_mensais = df_final.columns[36:54]\n",
"\n",
"# Função de cálculo por grupo\n",
"def calcular_crescimento(grupo):\n",
" soma_mensal = grupo[colunas_mensais].sum()\n",
" variacao_mensal = soma_mensal.pct_change().dropna()\n",
" variacao_mensal = variacao_mensal[np.isfinite(variacao_mensal)]\n",
"\n",
" if len(variacao_mensal) == 0:\n",
" return pd.Series({'CRESCIMENTO': np.nan})\n",
"\n",
" media = variacao_mensal.mean()\n",
" desvio = variacao_mensal.std()\n",
"\n",
" limite_sup = media + 2 * desvio\n",
" limite_inf = media - 2 * desvio\n",
"\n",
" variacoes_filtradas = variacao_mensal[variacao_mensal.between(limite_inf, limite_sup)]\n",
" crescimento = round(variacoes_filtradas.mean(), 4)\n",
" return pd.Series({'CRESCIMENTO': crescimento})\n",
"\n",
"# Aplica a função por PDV\n",
"crescimento_por_pdv = (\n",
" df_final\n",
" .groupby('PDV')\n",
" .apply(calcular_crescimento) # retorna índice com PDV\n",
" .reset_index(level=0) # reseta só o nível PDV\n",
")\n",
"\n",
"# Merge do resultado de volta no dataframe original\n",
"df_final = df_final.merge(crescimento_por_pdv, on='PDV', how='left')\n"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"np.float64(0.0857)"
]
},
"execution_count": 91,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Suponha que os meses estão nas colunas 10 a 26 (17 colunas = 17 meses)\n",
"colunas_mensais = df_final.columns[36:54]\n",
"\n",
"# Passo 1: Soma todas as linhas (itens) por mês → resultado: total por mês\n",
"soma_mensal = df_final[colunas_mensais].sum()\n",
"\n",
"# Passo 2: Calcula a variação percentual de um mês para o outro\n",
"variacao_mensal = soma_mensal.pct_change()\n",
"variacao_mensal = variacao_mensal.dropna()\n",
"\n",
"variacao_mensal = variacao_mensal[np.isfinite(variacao_mensal)]\n",
"\n",
"# Passo 3: Calcula a média da variação (ignorando o primeiro NaN)\n",
"media_variacao = variacao_mensal[1:].mean()\n",
"\n",
"# Calcula média e desvio padrão\n",
"media = variacao_mensal.mean()\n",
"desvio = variacao_mensal.std()\n",
"\n",
"# Define limite (ex: 2 desvios padrão)\n",
"limite_superior = media + 2 * desvio\n",
"limite_inferior = media - 2 * desvio\n",
"\n",
"# Filtra dados dentro do limite\n",
"filtro = variacao_mensal.between(limite_inferior, limite_superior)\n",
"df_filtrado = variacao_mensal[filtro]\n",
"CRESCIMENTO = round(df_filtrado.mean(),4)\n",
"\n",
"df_final['CRESCIMENTO_GERAL'] = 0.2\n",
"\n",
"CRESCIMENTO\n"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['202412', '202413', '202414', '202415', '202416', '202417', '202501',\n",
" '202502', '202503', '202504', '202505', '202506', '202507', '202508',\n",
" '202509', '202510', '202511', '202512'],\n",
" dtype='object')"
]
},
"execution_count": 92,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_final.columns[36:54]"
]
},
{
"cell_type": "code",
"execution_count": 93,
"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>CANAL</th>\n",
" <th>UF</th>\n",
" <th>Código</th>\n",
" <th>med_por_canal</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>TODOS</td>\n",
" <td>AL</td>\n",
" <td>1428</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>TODOS</td>\n",
" <td>AL</td>\n",
" <td>1634</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>TODOS</td>\n",
" <td>AL</td>\n",
" <td>1640</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>TODOS</td>\n",
" <td>AL</td>\n",
" <td>1650</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>TODOS</td>\n",
" <td>AL</td>\n",
" <td>1806</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4445</th>\n",
" <td>VD</td>\n",
" <td>VDC</td>\n",
" <td>88471</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4446</th>\n",
" <td>VD</td>\n",
" <td>VDC</td>\n",
" <td>88563</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4447</th>\n",
" <td>VD</td>\n",
" <td>VDC</td>\n",
" <td>88730</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4448</th>\n",
" <td>VD</td>\n",
" <td>VDC</td>\n",
" <td>89421</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4449</th>\n",
" <td>VD</td>\n",
" <td>VDC</td>\n",
" <td>89495</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>4450 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" CANAL UF Código med_por_canal\n",
"0 TODOS AL 1428 0.0\n",
"1 TODOS AL 1634 0.0\n",
"2 TODOS AL 1640 0.0\n",
"3 TODOS AL 1650 0.0\n",
"4 TODOS AL 1806 0.0\n",
"... ... ... ... ...\n",
"4445 VD VDC 88471 0.0\n",
"4446 VD VDC 88563 0.0\n",
"4447 VD VDC 88730 0.0\n",
"4448 VD VDC 89421 0.0\n",
"4449 VD VDC 89495 0.0\n",
"\n",
"[4450 rows x 4 columns]"
]
},
"execution_count": 93,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vendas_todos_historicos = df_final.columns[36:54]\n",
"\n",
"df_final['MEDIANA DO HISTÓRICO'] = df_final[vendas_todos_historicos].median(axis=1)\n",
"\n",
"df_final['MEDIA DO HISTÓRICO'] = df_final[vendas_todos_historicos].mean(axis=1)\n",
"\n",
"medi = df_final.groupby(['CANAL','UF','Código'])['MEDIANA DO HISTÓRICO'].max().reset_index()\n",
"medi = medi.rename(columns={'MEDIANA DO HISTÓRICO':'med_por_canal'})\n",
"\n",
"df_final = pd.merge(left=df_final, right=medi,on=['CANAL','UF','Código'],how='left')\n",
"\n",
"medi"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'202415'"
]
},
"execution_count": 94,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_final.columns[39]"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {},
"outputs": [],
"source": [
"mesmo_ciclo_ano_passado = df_final.columns[39]\n",
"ciclo_ano_passado = df_final.columns[39]\n",
"df_final[ciclo_ano_passado] = df_final[mesmo_ciclo_ano_passado]"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\"df_final['CRESCIMENTO_GERAL'] = 0.2\\ndf_final['CRESCIMENTO'] = 0.2\""
]
},
"execution_count": 96,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'''df_final['CRESCIMENTO_GERAL'] = 0.2\n",
"df_final['CRESCIMENTO'] = 0.2'''"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(34265, 77)"
]
},
"execution_count": 97,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_final['CRESCIMENTO_FINAL'] = df_final['CRESCIMENTO_GERAL'] + df_final['CRESCIMENTO'] #crescimento do pdv\n",
"\n",
"df_final['CRESCIMENTO_FINAL'] = np.where(df_final['CRESCIMENTO_GERAL'] + df_final['CRESCIMENTO']>0.8,0.8,df_final['CRESCIMENTO_GERAL'] + df_final['CRESCIMENTO'])\n",
"\n",
"df_final['CRESCIMENTO_FINAL'] = np.where(df_final['CRESCIMENTO_GERAL'] + df_final['CRESCIMENTO']<0,0,df_final['CRESCIMENTO_GERAL'] + df_final['CRESCIMENTO'])\n",
"\n",
"df_final['MEDIANA DO HISTÓRICO2'] = np.where(df_final['MEDIANA DO HISTÓRICO']==0,df_final['MEDIA DO HISTÓRICO'] ,df_final['MEDIANA DO HISTÓRICO'])\n",
"\n",
"# Primeiro cálculo intermediário\n",
"df_final['PV GINSENG'] = np.where(df_final['CRESCIMENTO_FINAL'] * df_final[ciclo_ano_passado] + df_final[ciclo_ano_passado] <1,\n",
" round(df_final['CRESCIMENTO_FINAL'] * df_final['MEDIANA DO HISTÓRICO2']+ df_final['MEDIANA DO HISTÓRICO2'],0), \n",
" round(df_final['CRESCIMENTO_FINAL']*df_final[ciclo_ano_passado]+df_final[ciclo_ano_passado],0))\n",
"\n",
"\n",
"df_final.shape"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"PDV\n",
"12522 445\n",
"12817 445\n",
"12818 445\n",
"12820 445\n",
"12823 445\n",
" ... \n",
"24257 445\n",
"24268 445\n",
"24269 445\n",
"24293 445\n",
"23813 445\n",
"Name: count, Length: 77, dtype: Int64"
]
},
"execution_count": 98,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_final['PDV'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" 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>Ciclo_x</th>\n",
" <th>Região</th>\n",
" <th>Canal</th>\n",
" <th>Código</th>\n",
" <th>Descrição_x</th>\n",
" <th>IAF</th>\n",
" <th>Tipo de pedido</th>\n",
" <th>Foco</th>\n",
" <th>Unidade de negócio</th>\n",
" <th>Marca</th>\n",
" <th>...</th>\n",
" <th>PICO DE VENDAS 2024</th>\n",
" <th>Pico Vendas Ultimos 6 ciclos</th>\n",
" <th>CRESCIMENTO</th>\n",
" <th>CRESCIMENTO_GERAL</th>\n",
" <th>MEDIANA DO HISTÓRICO</th>\n",
" <th>MEDIA DO HISTÓRICO</th>\n",
" <th>med_por_canal</th>\n",
" <th>CRESCIMENTO_FINAL</th>\n",
" <th>MEDIANA DO HISTÓRICO2</th>\n",
" <th>PV GINSENG</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>202515</td>\n",
" <td>NNE</td>\n",
" <td>Loja</td>\n",
" <td>50052</td>\n",
" <td>ARBO LOC DES HID CPO 75ml</td>\n",
" <td>Não</td>\n",
" <td>Semanal</td>\n",
" <td>Não</td>\n",
" <td>BOT</td>\n",
" <td>ARBO</td>\n",
" <td>...</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0762</td>\n",
" <td>0.2</td>\n",
" <td>0.0</td>\n",
" <td>0.111111</td>\n",
" <td>0.0</td>\n",
" <td>0.2762</td>\n",
" <td>0.111111</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>202515</td>\n",
" <td>NNE</td>\n",
" <td>Loja</td>\n",
" <td>50052</td>\n",
" <td>ARBO LOC DES HID CPO 75ml</td>\n",
" <td>Não</td>\n",
" <td>Semanal</td>\n",
" <td>Não</td>\n",
" <td>BOT</td>\n",
" <td>ARBO</td>\n",
" <td>...</td>\n",
" <td>5.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0867</td>\n",
" <td>0.2</td>\n",
" <td>0.0</td>\n",
" <td>0.555556</td>\n",
" <td>0.0</td>\n",
" <td>0.2867</td>\n",
" <td>0.555556</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>202515</td>\n",
" <td>NNE</td>\n",
" <td>Loja</td>\n",
" <td>50052</td>\n",
" <td>ARBO LOC DES HID CPO 75ml</td>\n",
" <td>Não</td>\n",
" <td>Semanal</td>\n",
" <td>Não</td>\n",
" <td>BOT</td>\n",
" <td>ARBO</td>\n",
" <td>...</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.1065</td>\n",
" <td>0.2</td>\n",
" <td>0.0</td>\n",
" <td>0.055556</td>\n",
" <td>0.0</td>\n",
" <td>0.3065</td>\n",
" <td>0.055556</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>202515</td>\n",
" <td>NNE</td>\n",
" <td>Loja</td>\n",
" <td>50052</td>\n",
" <td>ARBO LOC DES HID CPO 75ml</td>\n",
" <td>Não</td>\n",
" <td>Semanal</td>\n",
" <td>Não</td>\n",
" <td>BOT</td>\n",
" <td>ARBO</td>\n",
" <td>...</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0882</td>\n",
" <td>0.2</td>\n",
" <td>0.0</td>\n",
" <td>0.055556</td>\n",
" <td>0.0</td>\n",
" <td>0.2882</td>\n",
" <td>0.055556</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>202515</td>\n",
" <td>NNE</td>\n",
" <td>Loja</td>\n",
" <td>50052</td>\n",
" <td>ARBO LOC DES HID CPO 75ml</td>\n",
" <td>Não</td>\n",
" <td>Semanal</td>\n",
" <td>Não</td>\n",
" <td>BOT</td>\n",
" <td>ARBO</td>\n",
" <td>...</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0745</td>\n",
" <td>0.2</td>\n",
" <td>0.0</td>\n",
" <td>0.111111</td>\n",
" <td>0.0</td>\n",
" <td>0.2745</td>\n",
" <td>0.111111</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 77 columns</p>\n",
"</div>"
],
"text/plain": [
" Ciclo_x Região Canal Código Descrição_x IAF \\\n",
"0 202515 NNE Loja 50052 ARBO LOC DES HID CPO 75ml Não \n",
"1 202515 NNE Loja 50052 ARBO LOC DES HID CPO 75ml Não \n",
"2 202515 NNE Loja 50052 ARBO LOC DES HID CPO 75ml Não \n",
"3 202515 NNE Loja 50052 ARBO LOC DES HID CPO 75ml Não \n",
"4 202515 NNE Loja 50052 ARBO LOC DES HID CPO 75ml Não \n",
"\n",
" Tipo de pedido Foco Unidade de negócio Marca ... PICO DE VENDAS 2024 \\\n",
"0 Semanal Não BOT ARBO ... 2.0 \n",
"1 Semanal Não BOT ARBO ... 5.0 \n",
"2 Semanal Não BOT ARBO ... 1.0 \n",
"3 Semanal Não BOT ARBO ... 1.0 \n",
"4 Semanal Não BOT ARBO ... 1.0 \n",
"\n",
" Pico Vendas Ultimos 6 ciclos CRESCIMENTO CRESCIMENTO_GERAL \\\n",
"0 0.0 0.0762 0.2 \n",
"1 0.0 0.0867 0.2 \n",
"2 1.0 0.1065 0.2 \n",
"3 0.0 0.0882 0.2 \n",
"4 0.0 0.0745 0.2 \n",
"\n",
" MEDIANA DO HISTÓRICO MEDIA DO HISTÓRICO med_por_canal CRESCIMENTO_FINAL \\\n",
"0 0.0 0.111111 0.0 0.2762 \n",
"1 0.0 0.555556 0.0 0.2867 \n",
"2 0.0 0.055556 0.0 0.3065 \n",
"3 0.0 0.055556 0.0 0.2882 \n",
"4 0.0 0.111111 0.0 0.2745 \n",
"\n",
" MEDIANA DO HISTÓRICO2 PV GINSENG \n",
"0 0.111111 0.0 \n",
"1 0.555556 1.0 \n",
"2 0.055556 0.0 \n",
"3 0.055556 0.0 \n",
"4 0.111111 0.0 \n",
"\n",
"[5 rows x 77 columns]"
]
},
"execution_count": 99,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_final.head()"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {},
"outputs": [],
"source": [
"df_final = df_final.drop(columns=['MEDIANA DO HISTÓRICO2'] )"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['202414', '202415', '202416', '202417', '202501', '202502', '202503'], dtype='object')"
]
},
"execution_count": 101,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_final.columns[38:45]"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['202508', '202509', '202510', '202511', '202512'], dtype='object')"
]
},
"execution_count": 102,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_final.columns[49:54]"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {},
"outputs": [],
"source": [
"df_final = df_final.rename(columns={df_final.columns[49]: \"C-4\", df_final.columns[50]: \"C-3\",df_final.columns[51]: \"C-2\",df_final.columns[52]: \"C-1\",df_final.columns[53]: \"Atual\"})"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['202416', '202417', '202501', '202502', '202503', '202504', '202505',\n",
" '202506', '202507'],\n",
" dtype='object')"
]
},
"execution_count": 104,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_final.columns[40:49]"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {},
"outputs": [],
"source": [
"df_final.drop(columns=df_final.columns[40:49], inplace=True)\n",
"df_final.drop(columns=df_final.columns[37:39], inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {},
"outputs": [],
"source": [
"df_final['COBERTURA ATUAL'] = df_final['ESTOQUE ATUAL'].astype(float) / df_final['DDV PREVISTO'].astype(float)"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {},
"outputs": [],
"source": [
"df_final['COBERTURA ATUAL'] = df_final['COBERTURA ATUAL'].replace([np.inf, -np.inf], 0)\n",
"\n",
"df_final['COBERTURA ATUAL'] = round(df_final['COBERTURA ATUAL'],0)"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"COBERTURA ATUAL\n",
"0.0 6787\n",
"100.0 699\n",
"50.0 447\n",
"40.0 375\n",
"20.0 342\n",
" ... \n",
"245.0 1\n",
"778.0 1\n",
"811.0 1\n",
"1967.0 1\n",
"644.0 1\n",
"Name: count, Length: 641, dtype: int64"
]
},
"execution_count": 108,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_final['COBERTURA ATUAL'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {},
"outputs": [],
"source": [
"df_final[\"DDV PREVISTO\"] = df_final[\"DDV PREVISTO\"].astype(float)\n",
"\n",
"df_final[\"DDV PREVISTO\"] = df_final[\"DDV PREVISTO\"].fillna(0)"
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"PDV\n",
"12522 445\n",
"12817 445\n",
"12818 445\n",
"12820 445\n",
"12823 445\n",
" ... \n",
"24257 445\n",
"24268 445\n",
"24269 445\n",
"24293 445\n",
"23813 445\n",
"Name: count, Length: 77, dtype: Int64"
]
},
"execution_count": 110,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_final['PDV'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {},
"outputs": [],
"source": [
"r'''colunas_chave = [c for c in df_final.columns if c not in [\"DDV PREVISTO\", \"COBERTURA ATUAL\"]]\n",
"\n",
"\n",
"# encontrar índice da linha com maior DDV e depois maior COBERTURA\n",
"idx = (\n",
"df_final\n",
".sort_values([\"DDV PREVISTO\", \"COBERTURA ATUAL\"], ascending=[False, False])\n",
".groupby(colunas_chave, as_index=False)\n",
".head(1)\n",
")\n",
"\n",
"\n",
"# reordenar colunas como no original\n",
"df_final_dedup = idx[df_final.columns.tolist()].reset_index(drop=True)'''\n",
"\n",
"df_final_dedup = df_final"
]
},
{
"cell_type": "code",
"execution_count": 112,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Ciclo_x', 'Região', 'Canal', 'Código', 'Descrição_x', 'IAF',\n",
" 'Tipo de pedido', 'Foco', 'Unidade de negócio', 'Marca', 'Categoria',\n",
" 'Tipo de promoção', 'Catálogo', 'Tipo de produto', 'Ação consumidor',\n",
" 'Percentual de desconto consumidor', 'Ação revendedor',\n",
" 'Percentual de desconto revendedor', 'MATCH', 'PDV', 'CANAL',\n",
" 'DESCRIÇÃO PDV', 'UF', 'ANALISTA', 'SUPERVISOR', 'SKU', 'curva',\n",
" 'categoria', 'proj_mar', 'proj_mar+1', 'ESTOQUE ATUAL',\n",
" 'ESTOQUE EM TRANSITO', 'PEDIDO PENDENTE', 'DDV PREVISTO', 'preço',\n",
" 'SKU_FINAL', '202412', '202415', 'C-4', 'C-3', 'C-2', 'C-1', 'Atual',\n",
" 'INICIO CICLO', 'FIM CICLO', 'DURAÇÃO', 'dias_ate_inicio',\n",
" 'Descrição_y', 'chave sku2', 'chave sku1', 'PRECO DE COMPRA',\n",
" 'PRECO DE VENDA', 'UFPRODUTO', 'PDV GINSENG', 'PRODUTO', 'chave',\n",
" 'PICO DE VENDAS 2024', 'Pico Vendas Ultimos 6 ciclos', 'CRESCIMENTO',\n",
" 'CRESCIMENTO_GERAL', 'MEDIANA DO HISTÓRICO', 'MEDIA DO HISTÓRICO',\n",
" 'med_por_canal', 'CRESCIMENTO_FINAL', 'PV GINSENG', 'COBERTURA ATUAL'],\n",
" dtype='object')"
]
},
"execution_count": 112,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_final.columns"
]
},
{
"cell_type": "code",
"execution_count": 113,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"('Ação consumidor',\n",
" 'Percentual de desconto consumidor',\n",
" 'Ação revendedor',\n",
" 'Percentual de desconto revendedor',\n",
" '202408')"
]
},
"execution_count": 113,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Columns to bring up front\n",
"priority_cols = [\n",
" 'SKU',\t'Marca',\t'INICIO CICLO',\n",
" 'FIM CICLO',\t'DURAÇÃO',\t'PRECO DE COMPRA',\t'PRECO DE VENDA',\n",
" 'UFPRODUTO',\t'Item Desativado',\t'Data Prevista Regularização',\n",
" 'ANALISTA',\t'UF',\t'CANAL',\t'PDV',\t'DESCRIÇÃO PDV',\t'Classe',\n",
" 'Descrição',\t'Categoria',\n",
"\t'ESTOQUE ATUAL', 'ESTOQUE EM TRANSITO',\t'COBERTURA ATUAL',\n",
" 'Pedido Pendente',\t'PICO DE VENDAS 2024','Pico Vendas Ultimos 6 ciclos',\n",
" 'C-4',\t'C-3',\t'C-2',\t'C-1',\t'Histórico de Vendas do Ciclo Atual',\n",
" 'Dias sem venda'\n",
"]\n",
"\n",
"# All remaining columns\n",
"other_cols = [col for col in df_final_dedup.columns if col not in priority_cols]\n",
"\n",
"# Reorder\n",
"'Ação consumidor', 'Percentual de desconto consumidor', 'Ação revendedor', 'Percentual de desconto revendedor', '202408'\n"
]
},
{
"cell_type": "code",
"execution_count": 114,
"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>Ciclo_x</th>\n",
" <th>Região</th>\n",
" <th>Canal</th>\n",
" <th>Código</th>\n",
" <th>Descrição_x</th>\n",
" <th>IAF</th>\n",
" <th>Tipo de pedido</th>\n",
" <th>Foco</th>\n",
" <th>Unidade de negócio</th>\n",
" <th>Marca</th>\n",
" <th>...</th>\n",
" <th>PICO DE VENDAS 2024</th>\n",
" <th>Pico Vendas Ultimos 6 ciclos</th>\n",
" <th>CRESCIMENTO</th>\n",
" <th>CRESCIMENTO_GERAL</th>\n",
" <th>MEDIANA DO HISTÓRICO</th>\n",
" <th>MEDIA DO HISTÓRICO</th>\n",
" <th>med_por_canal</th>\n",
" <th>CRESCIMENTO_FINAL</th>\n",
" <th>PV GINSENG</th>\n",
" <th>COBERTURA ATUAL</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>202515</td>\n",
" <td>NNE</td>\n",
" <td>Loja</td>\n",
" <td>50052</td>\n",
" <td>ARBO LOC DES HID CPO 75ml</td>\n",
" <td>Não</td>\n",
" <td>Semanal</td>\n",
" <td>Não</td>\n",
" <td>BOT</td>\n",
" <td>ARBO</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>0.2</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>116</th>\n",
" <td>202515</td>\n",
" <td>NNE</td>\n",
" <td>Todos</td>\n",
" <td>57640</td>\n",
" <td>ARBO NECESSAIRE PES</td>\n",
" <td>Não</td>\n",
" <td>Semanal</td>\n",
" <td>Não</td>\n",
" <td>BOT</td>\n",
" <td>ARBO</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>0.2</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>193</th>\n",
" <td>202515</td>\n",
" <td>NNE</td>\n",
" <td>Todos</td>\n",
" <td>51803</td>\n",
" <td>BOTI BABY COND V2 400ml</td>\n",
" <td>Não</td>\n",
" <td>Semanal</td>\n",
" <td>Não</td>\n",
" <td>BOT</td>\n",
" <td>BOTI BABY</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>0.2</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>3.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>270</th>\n",
" <td>202515</td>\n",
" <td>NNE</td>\n",
" <td>Todos</td>\n",
" <td>52023</td>\n",
" <td>BOTI BABY LOC HID CPO 400ml</td>\n",
" <td>Não</td>\n",
" <td>Semanal</td>\n",
" <td>Não</td>\n",
" <td>BOT</td>\n",
" <td>BOTI BABY</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>0.2</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>3.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>347</th>\n",
" <td>202515</td>\n",
" <td>NNE</td>\n",
" <td>Todos</td>\n",
" <td>52025</td>\n",
" <td>BOTI BABY SAB LIQ GLIC V2 400ml</td>\n",
" <td>Não</td>\n",
" <td>Semanal</td>\n",
" <td>Não</td>\n",
" <td>BOT</td>\n",
" <td>BOTI BABY</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>0.2</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>8.0</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>33919</th>\n",
" <td>202515</td>\n",
" <td>NNE</td>\n",
" <td>VD</td>\n",
" <td>36826</td>\n",
" <td>REF MAKE B PO COMP FAC HYAL 70 8g</td>\n",
" <td>Não</td>\n",
" <td>Semanal</td>\n",
" <td>Não</td>\n",
" <td>BOT</td>\n",
" <td>MAKE B.</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>0.2</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33996</th>\n",
" <td>202515</td>\n",
" <td>NNE</td>\n",
" <td>VD</td>\n",
" <td>46568</td>\n",
" <td>SOPHIE BOLSA NYLON</td>\n",
" <td>Não</td>\n",
" <td>Semanal</td>\n",
" <td>Não</td>\n",
" <td>BOT</td>\n",
" <td>SOPHIE</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>0.2</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34073</th>\n",
" <td>202515</td>\n",
" <td>NNE</td>\n",
" <td>VD</td>\n",
" <td>47949</td>\n",
" <td>THE BLEND EDP 100ml V2</td>\n",
" <td>Não</td>\n",
" <td>Semanal</td>\n",
" <td>Não</td>\n",
" <td>BOT</td>\n",
" <td>THE BLEND</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>0.2</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>17.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34150</th>\n",
" <td>202515</td>\n",
" <td>NNE</td>\n",
" <td>VD</td>\n",
" <td>49038</td>\n",
" <td>ZAAD BALM POS BARB 110g V4</td>\n",
" <td>Não</td>\n",
" <td>Semanal</td>\n",
" <td>Não</td>\n",
" <td>BOT</td>\n",
" <td>ZAAD</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>0.2</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34227</th>\n",
" <td>202515</td>\n",
" <td>NNE</td>\n",
" <td>VD</td>\n",
" <td>48368</td>\n",
" <td>ZAAD ESPUM P/ BARB 200ml V4</td>\n",
" <td>Não</td>\n",
" <td>Semanal</td>\n",
" <td>Não</td>\n",
" <td>BOT</td>\n",
" <td>ZAAD</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>0.2</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>445 rows × 66 columns</p>\n",
"</div>"
],
"text/plain": [
" Ciclo_x Região Canal Código Descrição_x IAF \\\n",
"39 202515 NNE Loja 50052 ARBO LOC DES HID CPO 75ml Não \n",
"116 202515 NNE Todos 57640 ARBO NECESSAIRE PES Não \n",
"193 202515 NNE Todos 51803 BOTI BABY COND V2 400ml Não \n",
"270 202515 NNE Todos 52023 BOTI BABY LOC HID CPO 400ml Não \n",
"347 202515 NNE Todos 52025 BOTI BABY SAB LIQ GLIC V2 400ml Não \n",
"... ... ... ... ... ... ... \n",
"33919 202515 NNE VD 36826 REF MAKE B PO COMP FAC HYAL 70 8g Não \n",
"33996 202515 NNE VD 46568 SOPHIE BOLSA NYLON Não \n",
"34073 202515 NNE VD 47949 THE BLEND EDP 100ml V2 Não \n",
"34150 202515 NNE VD 49038 ZAAD BALM POS BARB 110g V4 Não \n",
"34227 202515 NNE VD 48368 ZAAD ESPUM P/ BARB 200ml V4 Não \n",
"\n",
" Tipo de pedido Foco Unidade de negócio Marca ... \\\n",
"39 Semanal Não BOT ARBO ... \n",
"116 Semanal Não BOT ARBO ... \n",
"193 Semanal Não BOT BOTI BABY ... \n",
"270 Semanal Não BOT BOTI BABY ... \n",
"347 Semanal Não BOT BOTI BABY ... \n",
"... ... ... ... ... ... \n",
"33919 Semanal Não BOT MAKE B. ... \n",
"33996 Semanal Não BOT SOPHIE ... \n",
"34073 Semanal Não BOT THE BLEND ... \n",
"34150 Semanal Não BOT ZAAD ... \n",
"34227 Semanal Não BOT ZAAD ... \n",
"\n",
" PICO DE VENDAS 2024 Pico Vendas Ultimos 6 ciclos CRESCIMENTO \\\n",
"39 0.0 0.0 NaN \n",
"116 0.0 0.0 NaN \n",
"193 0.0 0.0 NaN \n",
"270 0.0 0.0 NaN \n",
"347 0.0 0.0 NaN \n",
"... ... ... ... \n",
"33919 0.0 0.0 NaN \n",
"33996 0.0 0.0 NaN \n",
"34073 0.0 0.0 NaN \n",
"34150 0.0 0.0 NaN \n",
"34227 0.0 0.0 NaN \n",
"\n",
" CRESCIMENTO_GERAL MEDIANA DO HISTÓRICO MEDIA DO HISTÓRICO med_por_canal \\\n",
"39 0.2 0.0 0.0 0.0 \n",
"116 0.2 0.0 0.0 0.0 \n",
"193 0.2 0.0 0.0 3.0 \n",
"270 0.2 0.0 0.0 3.0 \n",
"347 0.2 0.0 0.0 8.0 \n",
"... ... ... ... ... \n",
"33919 0.2 0.0 0.0 0.0 \n",
"33996 0.2 0.0 0.0 1.0 \n",
"34073 0.2 0.0 0.0 17.0 \n",
"34150 0.2 0.0 0.0 0.0 \n",
"34227 0.2 0.0 0.0 1.0 \n",
"\n",
" CRESCIMENTO_FINAL PV GINSENG COBERTURA ATUAL \n",
"39 NaN NaN NaN \n",
"116 NaN NaN NaN \n",
"193 NaN NaN NaN \n",
"270 NaN NaN NaN \n",
"347 NaN NaN NaN \n",
"... ... ... ... \n",
"33919 NaN NaN NaN \n",
"33996 NaN NaN NaN \n",
"34073 NaN NaN NaN \n",
"34150 NaN NaN NaN \n",
"34227 NaN NaN NaN \n",
"\n",
"[445 rows x 66 columns]"
]
},
"execution_count": 114,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_final_dedup[df_final_dedup['PDV'] == 21007]"
]
},
{
"cell_type": "code",
"execution_count": 115,
"metadata": {},
"outputs": [],
"source": [
"df_final_dedup.drop(columns=['dias_ate_inicio','SKU_FINAL',\n",
" 'CRESCIMENTO'],inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 116,
"metadata": {},
"outputs": [],
"source": [
"df_final_dedup['INICIO CICLO'] = pd.to_datetime(df_final_dedup['INICIO CICLO'], dayfirst=True).dt.strftime('%d/%m/%Y')\n",
"\n",
"df_final_dedup['FIM CICLO'] = pd.to_datetime(df_final_dedup['FIM CICLO'], dayfirst=True).dt.strftime('%d/%m/%Y')"
]
},
{
"cell_type": "code",
"execution_count": 117,
"metadata": {},
"outputs": [],
"source": [
"df_final_dedup['Percentual de desconto revendedor'] = np.where((df_final_dedup['Percentual de desconto revendedor'].isna()) & (~df_final_dedup['Percentual de desconto consumidor'].isna()),df_final_dedup['Percentual de desconto consumidor'],df_final_dedup['Percentual de desconto revendedor'])"
]
},
{
"cell_type": "code",
"execution_count": 118,
"metadata": {},
"outputs": [],
"source": [
"df_pdv_origi['PDV'] = df_pdv_origi['PDV'].astype('Int64')\n",
"df_final_dedup['PDV'] = df_final_dedup['PDV'].astype('Int64')\n",
"\n",
"\n",
"df_final_dedup = pd.merge(left=df_final_dedup,right=df_pdv_origi[['PDV','CANAL','UF']],how='left',on='PDV')"
]
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"np.int64(0)"
]
},
"execution_count": 119,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_final_dedup['Código'].isna().sum()"
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {},
"outputs": [],
"source": [
"df_irece_agrupado = df_irece.groupby(['PDV','Código','Ciclo'])['Quantidade'].sum().reset_index()"
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {},
"outputs": [],
"source": [
"df_final_dedup = df_final_dedup.rename(columns={'CANAL_x':'CANAL','UF_X':'UF','Marca':'LINHA'})\n"
]
},
{
"cell_type": "code",
"execution_count": 122,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"PDV\n",
"12522 445\n",
"12817 445\n",
"12818 445\n",
"12820 445\n",
"12823 445\n",
" ... \n",
"24257 445\n",
"24268 445\n",
"24269 445\n",
"24293 445\n",
"23813 445\n",
"Name: count, Length: 77, dtype: Int64"
]
},
"execution_count": 122,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_final_dedup['PDV'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 123,
"metadata": {},
"outputs": [],
"source": [
"df_final_dedup = df_final_dedup.rename(columns={'UF_x':'UF','DESCRICAO_y':'DESCRICAO'})"
]
},
{
"cell_type": "code",
"execution_count": 124,
"metadata": {},
"outputs": [],
"source": [
"df_final_dedup = df_final_dedup.drop(columns={'CANAL_y', 'UF_y'})\n"
]
},
{
"cell_type": "code",
"execution_count": 125,
"metadata": {},
"outputs": [],
"source": [
"df_final_dedup['Estoque Total'] = df_final_dedup['ESTOQUE ATUAL'].astype(float) + df_final_dedup['ESTOQUE EM TRANSITO'].astype(float) + df_final_dedup['PEDIDO PENDENTE'].astype(float)"
]
},
{
"cell_type": "code",
"execution_count": 126,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'202415'"
]
},
"execution_count": 126,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ciclo_ano_passado"
]
},
{
"cell_type": "code",
"execution_count": 127,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Ciclo_x', 'Região', 'Canal', 'Código', 'Descrição_x', 'IAF',\n",
" 'Tipo de pedido', 'Foco', 'Unidade de negócio', 'LINHA', 'Categoria',\n",
" 'Tipo de promoção', 'Catálogo', 'Tipo de produto', 'Ação consumidor',\n",
" 'Percentual de desconto consumidor', 'Ação revendedor',\n",
" 'Percentual de desconto revendedor', 'MATCH', 'PDV', 'CANAL',\n",
" 'DESCRIÇÃO PDV', 'UF', 'ANALISTA', 'SUPERVISOR', 'SKU', 'curva',\n",
" 'categoria', 'proj_mar', 'proj_mar+1', 'ESTOQUE ATUAL',\n",
" 'ESTOQUE EM TRANSITO', 'PEDIDO PENDENTE', 'DDV PREVISTO', 'preço',\n",
" '202412', '202415', 'C-4', 'C-3', 'C-2', 'C-1', 'Atual', 'INICIO CICLO',\n",
" 'FIM CICLO', 'DURAÇÃO', 'Descrição_y', 'chave sku2', 'chave sku1',\n",
" 'PRECO DE COMPRA', 'PRECO DE VENDA', 'UFPRODUTO', 'PDV GINSENG',\n",
" 'PRODUTO', 'chave', 'PICO DE VENDAS 2024',\n",
" 'Pico Vendas Ultimos 6 ciclos', 'CRESCIMENTO_GERAL',\n",
" 'MEDIANA DO HISTÓRICO', 'MEDIA DO HISTÓRICO', 'med_por_canal',\n",
" 'CRESCIMENTO_FINAL', 'PV GINSENG', 'COBERTURA ATUAL', 'Estoque Total'],\n",
" dtype='object')"
]
},
"execution_count": 127,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_final_dedup.columns"
]
},
{
"cell_type": "code",
"execution_count": 128,
"metadata": {},
"outputs": [],
"source": [
"df_final_dedup = df_final_dedup.drop(columns='Descrição_y')\n",
"\n",
"df_final_dedup = df_final_dedup.rename(columns={'Descrição_x':'Descrição'})"
]
},
{
"cell_type": "code",
"execution_count": 129,
"metadata": {},
"outputs": [],
"source": [
"df_final_dedup = df_final_dedup[['DURAÇÃO','CANAL','ANALISTA','SUPERVISOR','UF','chave','PDV','DESCRIÇÃO PDV','Código','Descrição','LINHA','Categoria','curva','Percentual de desconto consumidor','Ação consumidor',\n",
" 'Percentual de desconto revendedor','Ação revendedor','C-4', 'C-3', 'C-2', 'C-1','Atual',ciclo_ano_passado ,'ESTOQUE ATUAL','ESTOQUE EM TRANSITO','PEDIDO PENDENTE','Estoque Total',\n",
" 'DDV PREVISTO','COBERTURA ATUAL','proj_mar', 'proj_mar+1','MEDIA DO HISTÓRICO','PICO DE VENDAS 2024','Pico Vendas Ultimos 6 ciclos','PRECO DE COMPRA',\n",
" 'PRECO DE VENDA','PV GINSENG']]"
]
},
{
"cell_type": "code",
"execution_count": 130,
"metadata": {},
"outputs": [],
"source": [
"df_final_dedup['Percentual de desconto revendedor'] = np.where(df_final_dedup['Percentual de desconto revendedor'] == '0,00', df_final_dedup['Percentual de desconto consumidor'],df_final_dedup['Percentual de desconto revendedor'])"
]
},
{
"cell_type": "code",
"execution_count": 131,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'202415'"
]
},
"execution_count": 131,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ciclo_ano_passado"
]
},
{
"cell_type": "code",
"execution_count": 132,
"metadata": {},
"outputs": [],
"source": [
"df_final_dedup = df_final_dedup.drop_duplicates()"
]
},
{
"cell_type": "code",
"execution_count": 133,
"metadata": {},
"outputs": [],
"source": [
"df_final_dedup['Valor Sugestão'] = \"\"\n",
"\n",
"df_final_dedup['Sugestão Abastecimento'] = \"\""
]
},
{
"cell_type": "code",
"execution_count": 134,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"DDV PREVISTO float64\n",
"COBERTURA ATUAL float64\n",
"dtype: object"
]
},
"execution_count": 134,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_final_dedup[['DDV PREVISTO',\n",
" 'COBERTURA ATUAL',]].dtypes"
]
},
{
"cell_type": "code",
"execution_count": 135,
"metadata": {},
"outputs": [],
"source": [
"df_final_dedup[ 'DDV PREVISTO'] = df_final_dedup['DDV PREVISTO'].fillna(0)\n",
"\n",
"df_final_dedup = df_final_dedup.drop_duplicates()"
]
},
{
"cell_type": "code",
"execution_count": 136,
"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>DURAÇÃO</th>\n",
" <th>CANAL</th>\n",
" <th>ANALISTA</th>\n",
" <th>SUPERVISOR</th>\n",
" <th>UF</th>\n",
" <th>chave</th>\n",
" <th>PDV</th>\n",
" <th>DESCRIÇÃO PDV</th>\n",
" <th>Código</th>\n",
" <th>Descrição</th>\n",
" <th>...</th>\n",
" <th>proj_mar</th>\n",
" <th>proj_mar+1</th>\n",
" <th>MEDIA DO HISTÓRICO</th>\n",
" <th>PICO DE VENDAS 2024</th>\n",
" <th>Pico Vendas Ultimos 6 ciclos</th>\n",
" <th>PRECO DE COMPRA</th>\n",
" <th>PRECO DE VENDA</th>\n",
" <th>PV GINSENG</th>\n",
" <th>Valor Sugestão</th>\n",
" <th>Sugestão Abastecimento</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>32171</th>\n",
" <td>21</td>\n",
" <td>VD</td>\n",
" <td>JEFFERSON</td>\n",
" <td>Juliana Vasconcelos</td>\n",
" <td>VDC</td>\n",
" <td>2371184387</td>\n",
" <td>23711</td>\n",
" <td>ER VITORIA DA CONQUISTA</td>\n",
" <td>84387</td>\n",
" <td>MALBEC DES COL V6 100ml</td>\n",
" <td>...</td>\n",
" <td>1505.0</td>\n",
" <td>242.0</td>\n",
" <td>612.388889</td>\n",
" <td>2734.0</td>\n",
" <td>857.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>12.0</td>\n",
" <td></td>\n",
" <td></td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1 rows × 39 columns</p>\n",
"</div>"
],
"text/plain": [
" DURAÇÃO CANAL ANALISTA SUPERVISOR UF chave PDV \\\n",
"32171 21 VD JEFFERSON Juliana Vasconcelos VDC 2371184387 23711 \n",
"\n",
" DESCRIÇÃO PDV Código Descrição ... proj_mar \\\n",
"32171 ER VITORIA DA CONQUISTA 84387 MALBEC DES COL V6 100ml ... 1505.0 \n",
"\n",
" proj_mar+1 MEDIA DO HISTÓRICO PICO DE VENDAS 2024 \\\n",
"32171 242.0 612.388889 2734.0 \n",
"\n",
" Pico Vendas Ultimos 6 ciclos PRECO DE COMPRA PRECO DE VENDA PV GINSENG \\\n",
"32171 857.0 0.0 0.0 12.0 \n",
"\n",
" Valor Sugestão Sugestão Abastecimento \n",
"32171 \n",
"\n",
"[1 rows x 39 columns]"
]
},
"execution_count": 136,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_final_dedup[df_final_dedup['chave'] == '2371184387'].head()"
]
},
{
"cell_type": "code",
"execution_count": 137,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"32171 88.7\n",
"Name: DDV PREVISTO, dtype: float64"
]
},
"execution_count": 137,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_final_dedup[df_final_dedup['chave'] == '2371184387']['DDV PREVISTO'].head()\n"
]
},
{
"cell_type": "code",
"execution_count": 138,
"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>DURAÇÃO</th>\n",
" <th>CANAL</th>\n",
" <th>ANALISTA</th>\n",
" <th>SUPERVISOR</th>\n",
" <th>UF</th>\n",
" <th>chave</th>\n",
" <th>PDV</th>\n",
" <th>DESCRIÇÃO PDV</th>\n",
" <th>Código</th>\n",
" <th>Descrição</th>\n",
" <th>...</th>\n",
" <th>proj_mar</th>\n",
" <th>proj_mar+1</th>\n",
" <th>MEDIA DO HISTÓRICO</th>\n",
" <th>PICO DE VENDAS 2024</th>\n",
" <th>Pico Vendas Ultimos 6 ciclos</th>\n",
" <th>PRECO DE COMPRA</th>\n",
" <th>PRECO DE VENDA</th>\n",
" <th>PV GINSENG</th>\n",
" <th>Valor Sugestão</th>\n",
" <th>Sugestão Abastecimento</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>21</td>\n",
" <td>TODOS</td>\n",
" <td>LUAN</td>\n",
" <td>Efigênia Herculano</td>\n",
" <td>AL</td>\n",
" <td>1252250052</td>\n",
" <td>12522</td>\n",
" <td>MACEIO SHOP EXP</td>\n",
" <td>50052</td>\n",
" <td>ARBO LOC DES HID CPO 75ml</td>\n",
" <td>...</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.111111</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>11.72</td>\n",
" <td>37.9</td>\n",
" <td>0.0</td>\n",
" <td></td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>21</td>\n",
" <td>TODOS</td>\n",
" <td>JEFFERSON</td>\n",
" <td>Maxwell Vieira</td>\n",
" <td>AL</td>\n",
" <td>1281750052</td>\n",
" <td>12817</td>\n",
" <td>SHOPPING PATIO</td>\n",
" <td>50052</td>\n",
" <td>ARBO LOC DES HID CPO 75ml</td>\n",
" <td>...</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.555556</td>\n",
" <td>5.0</td>\n",
" <td>0.0</td>\n",
" <td>11.72</td>\n",
" <td>37.9</td>\n",
" <td>1.0</td>\n",
" <td></td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>21</td>\n",
" <td>TODOS</td>\n",
" <td>PEDRO</td>\n",
" <td>Maxwell Vieira</td>\n",
" <td>AL</td>\n",
" <td>1281850052</td>\n",
" <td>12818</td>\n",
" <td>GB SERRARIA</td>\n",
" <td>50052</td>\n",
" <td>ARBO LOC DES HID CPO 75ml</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.055556</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>11.72</td>\n",
" <td>37.9</td>\n",
" <td>0.0</td>\n",
" <td></td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>21</td>\n",
" <td>TODOS</td>\n",
" <td>THAYLLAN</td>\n",
" <td>Maxwell Vieira</td>\n",
" <td>AL</td>\n",
" <td>1282050052</td>\n",
" <td>12820</td>\n",
" <td>ATACADÃO</td>\n",
" <td>50052</td>\n",
" <td>ARBO LOC DES HID CPO 75ml</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.055556</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>11.72</td>\n",
" <td>37.9</td>\n",
" <td>0.0</td>\n",
" <td></td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>21</td>\n",
" <td>TODOS</td>\n",
" <td>MARCYARA</td>\n",
" <td>Efigênia Herculano</td>\n",
" <td>AL</td>\n",
" <td>1282350052</td>\n",
" <td>12823</td>\n",
" <td>PONTA VERDE</td>\n",
" <td>50052</td>\n",
" <td>ARBO LOC DES HID CPO 75ml</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.111111</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>11.72</td>\n",
" <td>37.9</td>\n",
" <td>0.0</td>\n",
" <td></td>\n",
" <td></td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 39 columns</p>\n",
"</div>"
],
"text/plain": [
" DURAÇÃO CANAL ANALISTA SUPERVISOR UF chave PDV \\\n",
"0 21 TODOS LUAN Efigênia Herculano AL 1252250052 12522 \n",
"1 21 TODOS JEFFERSON Maxwell Vieira AL 1281750052 12817 \n",
"2 21 TODOS PEDRO Maxwell Vieira AL 1281850052 12818 \n",
"3 21 TODOS THAYLLAN Maxwell Vieira AL 1282050052 12820 \n",
"4 21 TODOS MARCYARA Efigênia Herculano AL 1282350052 12823 \n",
"\n",
" DESCRIÇÃO PDV Código Descrição ... proj_mar \\\n",
"0 MACEIO SHOP EXP 50052 ARBO LOC DES HID CPO 75ml ... 1.0 \n",
"1 SHOPPING PATIO 50052 ARBO LOC DES HID CPO 75ml ... 1.0 \n",
"2 GB SERRARIA 50052 ARBO LOC DES HID CPO 75ml ... 0.0 \n",
"3 ATACADÃO 50052 ARBO LOC DES HID CPO 75ml ... 0.0 \n",
"4 PONTA VERDE 50052 ARBO LOC DES HID CPO 75ml ... 0.0 \n",
"\n",
" proj_mar+1 MEDIA DO HISTÓRICO PICO DE VENDAS 2024 \\\n",
"0 0.0 0.111111 2.0 \n",
"1 0.0 0.555556 5.0 \n",
"2 0.0 0.055556 1.0 \n",
"3 0.0 0.055556 1.0 \n",
"4 0.0 0.111111 1.0 \n",
"\n",
" Pico Vendas Ultimos 6 ciclos PRECO DE COMPRA PRECO DE VENDA PV GINSENG \\\n",
"0 0.0 11.72 37.9 0.0 \n",
"1 0.0 11.72 37.9 1.0 \n",
"2 1.0 11.72 37.9 0.0 \n",
"3 0.0 11.72 37.9 0.0 \n",
"4 0.0 11.72 37.9 0.0 \n",
"\n",
" Valor Sugestão Sugestão Abastecimento \n",
"0 \n",
"1 \n",
"2 \n",
"3 \n",
"4 \n",
"\n",
"[5 rows x 39 columns]"
]
},
"execution_count": 138,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_final_dedup.head()"
]
},
{
"cell_type": "code",
"execution_count": 139,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['DURAÇÃO', 'CANAL', 'ANALISTA', 'SUPERVISOR', 'UF', 'chave', 'PDV',\n",
" 'DESCRIÇÃO PDV', 'Código', 'Descrição', 'LINHA', 'Categoria', 'curva',\n",
" 'Percentual de desconto consumidor', 'Ação consumidor',\n",
" 'Percentual de desconto revendedor', 'Ação revendedor', 'C-4', 'C-3',\n",
" 'C-2', 'C-1', 'Atual', '202415', 'ESTOQUE ATUAL', 'ESTOQUE EM TRANSITO',\n",
" 'PEDIDO PENDENTE', 'Estoque Total', 'DDV PREVISTO', 'COBERTURA ATUAL',\n",
" 'proj_mar', 'proj_mar+1', 'MEDIA DO HISTÓRICO', 'PICO DE VENDAS 2024',\n",
" 'Pico Vendas Ultimos 6 ciclos', 'PRECO DE COMPRA', 'PRECO DE VENDA',\n",
" 'PV GINSENG', 'Valor Sugestão', 'Sugestão Abastecimento',\n",
" 'CANAL_CERTO'],\n",
" dtype='object')"
]
},
"execution_count": 139,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_pdv_origi['PDV'] = df_pdv_origi['PDV'].astype('Int64')\n",
"\n",
"\n",
"\n",
"df_pdv_origi = df_pdv_origi.rename(columns={'CANAL':'CANAL_CERTO'})\n",
"\n",
"df_final_dedup= pd.merge(df_final_dedup,df_pdv_origi[['PDV','CANAL_CERTO']],on='PDV',how='inner')\n",
"\n",
"df_final_dedup['CANAL']= df_final_dedup['CANAL_CERTO']\n",
"\n",
"\n",
"df_final_dedup.columns"
]
},
{
"cell_type": "code",
"execution_count": 140,
"metadata": {},
"outputs": [],
"source": [
"# Export to Excel\n",
"output_file = f'C:\\\\Users\\\\joao.herculano\\\\Documents\\\\promoção_BOTI_{ciclo_mais2}.{hoje}.xlsx'\n",
"with pd.ExcelWriter(output_file, engine='openpyxl') as writer:\n",
" df_final_dedup.to_excel(writer, index=False)\n",
"\n",
"# Apply styles\n",
"wb = load_workbook(output_file)\n",
"ws = wb['Sheet1']\n",
"\n",
"# Style header\n",
"header_fill = PatternFill(start_color='ADD8E6', end_color='ADD8E6', fill_type='solid') # Light Blue\n",
"header_font = Font(color='FFFFFF', bold=True) # White & Bold\n",
"\n",
"for cell in ws[1]:\n",
" cell.fill = header_fill\n",
" cell.font = header_font\n",
"\n",
"# Style rows: gray/white alternating\n",
"gray_fill = PatternFill(start_color='DDDDDD', end_color='DDDDDD', fill_type='solid') # Light gray\n",
"\n",
"for i, row in enumerate(ws.iter_rows(min_row=2, max_row=ws.max_row), start=2):\n",
" if i % 2 == 0:\n",
" for cell in row:\n",
" cell.fill = gray_fill\n",
"\n",
"# Save styled workbook\n",
"wb.save(output_file)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"coisas q saem\n",
"\n",
"Lançamento\n",
"Subcategoria\n",
"Projeção Próximo Ciclo \n",
"Promoção Próximo Ciclo\n",
"Compra inteligente semanal/Sugestão de compra\n",
"Compra inteligente Próximo Ciclo\n",
"Planograma\n",
"Carteira Bloqueada Para Novos Pedidos\n",
"Quantidade por caixa\n",
"Preço Sell In\n",
"Quantidade\n",
"Item analisado\n",
"Tipo Preço\n",
"\n",
">>>>>>>>>>>>>NAO ESTÁ PEGANDO O MERGE COM O DF_ESTOQUE\n",
"\n",
"\n",
"CRIAR PROJEÇÃO DE VENDA DO CICLO ATUAL\n",
"Compra inteligente Próximo Ciclo + 1 >>>>>>> RENAME PRA NOROMAL"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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