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189
promoção/promoção_EUD_ciclo07.ipynb
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@ -39,7 +39,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\joao.herculano\\AppData\\Local\\Temp\\ipykernel_28528\\119945099.py:10: DtypeWarning: Columns (7) have mixed types. Specify dtype option on import or set low_memory=False.\n",
"C:\\Users\\joao.herculano\\AppData\\Local\\Temp\\ipykernel_42880\\119945099.py:10: DtypeWarning: Columns (7) have mixed types. Specify dtype option on import or set low_memory=False.\n",
" df_draft = pd.concat([pd.read_csv(file) for file in csv_files], ignore_index=True)\n"
]
},
@ -941,54 +941,17 @@
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Histórico de Vendas do Ciclo 202408',\n",
" 'Histórico de Vendas do Ciclo 202409',\n",
" 'Histórico de Vendas do Ciclo 202410',\n",
" 'Histórico de Vendas do Ciclo 202411',\n",
" 'Histórico de Vendas do Ciclo 202412',\n",
" 'Histórico de Vendas do Ciclo 202413',\n",
" 'Histórico de Vendas do Ciclo 202414',\n",
" 'Histórico de Vendas do Ciclo 202415',\n",
" 'Histórico de Vendas do Ciclo 202416',\n",
" 'Histórico de Vendas do Ciclo 202417',\n",
" 'Histórico de Vendas do Ciclo 202501',\n",
" 'Histórico de Vendas do Ciclo 202502',\n",
" 'Histórico de Vendas do Ciclo 202503',\n",
" 'Histórico de Vendas do Ciclo 202504',\n",
" 'Histórico de Vendas do Ciclo 202505',\n",
" 'Histórico de Vendas do Ciclo 202506',\n",
" 'Histórico de Vendas do Ciclo 202507'],\n",
" dtype='object')"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_final.columns[6:23]"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.167950276989596\n"
"-0.14227416063810053\n"
]
}
],
"source": [
"CRESCIMENTO = (df_final[df_final.columns[6]].sum() - df_final[df_final.columns[22]].sum())/df_final[df_final.columns[6]].sum() \n",
"CRESCIMENTO = (df_final[df_final.columns[17]].sum() - df_final[df_final.columns[22]].sum())/df_final[df_final.columns[17]].sum() \n",
"print(CRESCIMENTO)\n",
"\n",
"df_final['CRESCIMENTO'] = .2"
@ -996,7 +959,7 @@
},
{
"cell_type": "code",
"execution_count": 51,
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
@ -1007,7 +970,7 @@
},
{
"cell_type": "code",
"execution_count": 52,
"execution_count": 51,
"metadata": {},
"outputs": [
{
@ -1016,7 +979,7 @@
"'202410'"
]
},
"execution_count": 52,
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
@ -1027,11 +990,11 @@
},
{
"cell_type": "code",
"execution_count": 53,
"execution_count": 52,
"metadata": {},
"outputs": [],
"source": [
"vendas_todos_historicos = df_final.columns[6:23]\n",
"vendas_todos_historicos = df_final.columns[17:23]\n",
"\n",
"df_final['MEDIANA DO HISTÓRICO'] = df_final[vendas_todos_historicos].median(axis=1)\n",
"\n",
@ -1042,16 +1005,16 @@
},
{
"cell_type": "code",
"execution_count": 54,
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"np.float64(0.167950276989596)"
"np.float64(-0.14227416063810053)"
]
},
"execution_count": 54,
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
@ -1062,68 +1025,18 @@
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['PDV', 'Classe', 'SKU', 'Descrição', 'Categoria_x', 'Subcategoria',\n",
" 'C-4', 'C-3', 'C-2', 'C-1', 'Histórico de Vendas do Ciclo 202507',\n",
" 'Histórico de Vendas do Ciclo Atual', 'Dias sem venda',\n",
" 'Projeção Próximo Ciclo', 'Projeção Próximo Ciclo + 1', 'Estoque Atual',\n",
" 'Estoque em Transito', 'Pedido Pendente',\n",
" 'Compra inteligente Próximo Ciclo',\n",
" 'Compra inteligente Próximo Ciclo + 1', 'Item Desativado',\n",
" 'Data Prevista Regularização', 'Quantidade por caixa',\n",
" 'Histórico de Vendas do Ciclo 202407', 'match', 'Ciclo_x', 'Veiculo',\n",
" 'Tipo de Promoção', 'Estratégia de Promoção', 'Tipo_mecanica',\n",
" 'Promo Período Limitado?', 'EAM', 'Categoria_y', 'Cód. Combo',\n",
" 'Código do Item', 'Descrição do Item', 'Chamada Promocional',\n",
" 'Valor do Guia', 'Preço Promocionado', '% de Desconto', 'RE compra por',\n",
" 'RE Vende por', 'RE lucra (R$)', 'MATCH', 'UF', 'DESCRIÇÃO PDV',\n",
" 'ANALISTA', 'INICIO CICLO', 'FIM CICLO', 'DURAÇÃO', 'dias_ate_inicio',\n",
" 'SKU_FINAL', 'DDV PREVISTO', 'COBERTURA ATUAL', 'SKU1_x', 'SKU2_x',\n",
" 'Tipo Preço', 'PC_x', 'PV_x', 'SKU1_y', 'SKU2_y', 'PC_y', 'PV_y',\n",
" 'PRECO DE COMPRA', 'PRECO DE VENDA', 'MARCA', 'PDV_SKU', 'UFPRODUTO',\n",
" 'PROJEÇÃO DO CICLO PROMOCIONADO', 'PICO DE VENDAS 2024',\n",
" 'Pico Vendas Ultimos 6 ciclos', 'CRESCIMENTO', '202410',\n",
" 'MEDIANA DO HISTÓRICO', 'MEDIA DO HISTÓRICO', 'PV GINSENG'],\n",
" dtype='object')"
]
},
"execution_count": 88,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_final.columns"
]
},
{
"cell_type": "code",
"execution_count": 56,
"execution_count": 54,
"metadata": {},
"outputs": [],
"source": [
"df_final['PV GINSENG'] = np.where(df_final['CRESCIMENTO'] * df_final[ciclo_ano_passado] + df_final[ciclo_ano_passado] < df_final['MEDIANA DO HISTÓRICO'],\n",
" np.ceil(df_final['CRESCIMENTO'] * df_final['MEDIANA DO HISTÓRICO']+ df_final['MEDIANA DO HISTÓRICO']), \n",
" np.ceil(df_final['CRESCIMENTO']*df_final[ciclo_ano_passado]+df_final[ciclo_ano_passado]))"
" round(df_final['CRESCIMENTO'] * df_final['MEDIANA DO HISTÓRICO']+ df_final['MEDIANA DO HISTÓRICO'],0), \n",
" round(df_final['CRESCIMENTO']*df_final[ciclo_ano_passado]+df_final[ciclo_ano_passado],0))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df_final['PV GINSENG'] = np.where(df_final['PV GINSENG']<1, df_final['Projeção Próximo Ciclo + 1'],df_final['PV GINSENG'])"
]
},
{
"cell_type": "code",
"execution_count": 57,
"execution_count": 55,
"metadata": {},
"outputs": [],
"source": [
@ -1132,7 +1045,7 @@
},
{
"cell_type": "code",
"execution_count": 58,
"execution_count": 56,
"metadata": {},
"outputs": [],
"source": [
@ -1141,7 +1054,7 @@
},
{
"cell_type": "code",
"execution_count": 59,
"execution_count": 57,
"metadata": {},
"outputs": [],
"source": [
@ -1159,7 +1072,7 @@
},
{
"cell_type": "code",
"execution_count": 60,
"execution_count": 58,
"metadata": {},
"outputs": [
{
@ -1179,7 +1092,7 @@
"Name: Compra inteligente Próximo Ciclo, Length: 1822, dtype: int64"
]
},
"execution_count": 60,
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
@ -1190,7 +1103,7 @@
},
{
"cell_type": "code",
"execution_count": 61,
"execution_count": 59,
"metadata": {},
"outputs": [],
"source": [
@ -1199,7 +1112,7 @@
},
{
"cell_type": "code",
"execution_count": 62,
"execution_count": 60,
"metadata": {},
"outputs": [],
"source": [
@ -1212,7 +1125,7 @@
},
{
"cell_type": "code",
"execution_count": 63,
"execution_count": 61,
"metadata": {},
"outputs": [
{
@ -1233,7 +1146,7 @@
"Name: count, Length: 195, dtype: int64"
]
},
"execution_count": 63,
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
@ -1244,7 +1157,7 @@
},
{
"cell_type": "code",
"execution_count": 64,
"execution_count": 62,
"metadata": {},
"outputs": [],
"source": [
@ -1253,7 +1166,7 @@
},
{
"cell_type": "code",
"execution_count": 65,
"execution_count": 63,
"metadata": {},
"outputs": [],
"source": [
@ -1262,7 +1175,7 @@
},
{
"cell_type": "code",
"execution_count": 66,
"execution_count": 64,
"metadata": {},
"outputs": [
{
@ -1293,7 +1206,7 @@
" dtype='object')"
]
},
"execution_count": 66,
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
@ -1304,7 +1217,7 @@
},
{
"cell_type": "code",
"execution_count": 67,
"execution_count": 65,
"metadata": {},
"outputs": [],
"source": [
@ -1313,7 +1226,7 @@
},
{
"cell_type": "code",
"execution_count": 68,
"execution_count": 66,
"metadata": {},
"outputs": [],
"source": [
@ -1322,7 +1235,7 @@
},
{
"cell_type": "code",
"execution_count": 69,
"execution_count": 67,
"metadata": {},
"outputs": [],
"source": [
@ -1332,7 +1245,7 @@
},
{
"cell_type": "code",
"execution_count": 70,
"execution_count": 68,
"metadata": {},
"outputs": [],
"source": [
@ -1341,7 +1254,7 @@
},
{
"cell_type": "code",
"execution_count": 71,
"execution_count": 69,
"metadata": {},
"outputs": [],
"source": [
@ -1353,7 +1266,7 @@
},
{
"cell_type": "code",
"execution_count": 72,
"execution_count": 70,
"metadata": {},
"outputs": [],
"source": [
@ -1363,7 +1276,7 @@
},
{
"cell_type": "code",
"execution_count": 73,
"execution_count": 71,
"metadata": {},
"outputs": [],
"source": [
@ -1372,7 +1285,7 @@
},
{
"cell_type": "code",
"execution_count": 74,
"execution_count": 72,
"metadata": {},
"outputs": [],
"source": [
@ -1381,7 +1294,7 @@
},
{
"cell_type": "code",
"execution_count": 75,
"execution_count": 73,
"metadata": {},
"outputs": [
{
@ -1412,7 +1325,7 @@
" dtype='object')"
]
},
"execution_count": 75,
"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
}
@ -1423,7 +1336,7 @@
},
{
"cell_type": "code",
"execution_count": 76,
"execution_count": 74,
"metadata": {},
"outputs": [
{
@ -1432,7 +1345,7 @@
"'EUD'"
]
},
"execution_count": 76,
"execution_count": 74,
"metadata": {},
"output_type": "execute_result"
}
@ -1444,7 +1357,7 @@
},
{
"cell_type": "code",
"execution_count": 77,
"execution_count": 75,
"metadata": {},
"outputs": [
{
@ -1474,7 +1387,7 @@
" dtype='object')"
]
},
"execution_count": 77,
"execution_count": 75,
"metadata": {},
"output_type": "execute_result"
}
@ -1485,7 +1398,7 @@
},
{
"cell_type": "code",
"execution_count": 78,
"execution_count": 76,
"metadata": {},
"outputs": [],
"source": [
@ -1527,7 +1440,7 @@
},
{
"cell_type": "code",
"execution_count": 79,
"execution_count": 77,
"metadata": {},
"outputs": [],
"source": [
@ -1553,7 +1466,7 @@
},
{
"cell_type": "code",
"execution_count": 80,
"execution_count": 78,
"metadata": {},
"outputs": [],
"source": [
@ -1564,7 +1477,7 @@
},
{
"cell_type": "code",
"execution_count": 81,
"execution_count": 79,
"metadata": {},
"outputs": [
{
@ -1595,7 +1508,7 @@
" dtype='object')"
]
},
"execution_count": 81,
"execution_count": 79,
"metadata": {},
"output_type": "execute_result"
}
@ -1606,7 +1519,7 @@
},
{
"cell_type": "code",
"execution_count": 82,
"execution_count": 80,
"metadata": {},
"outputs": [],
"source": [
@ -1615,7 +1528,7 @@
},
{
"cell_type": "code",
"execution_count": 83,
"execution_count": 81,
"metadata": {},
"outputs": [],
"source": [
@ -1624,7 +1537,7 @@
},
{
"cell_type": "code",
"execution_count": 84,
"execution_count": 82,
"metadata": {},
"outputs": [],
"source": [
@ -1635,7 +1548,7 @@
},
{
"cell_type": "code",
"execution_count": 85,
"execution_count": 83,
"metadata": {},
"outputs": [],
"source": [
@ -1646,7 +1559,7 @@
},
{
"cell_type": "code",
"execution_count": 87,
"execution_count": 84,
"metadata": {},
"outputs": [],
"source": [

760
promoção/promoção_boti_ciclo07.ipynb
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3
relatório_improdutivo/relatório_improdutivo.py
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@ -126,7 +126,6 @@ plt.close()
# Obtém a hora atual
agora = datetime.now().time()
hoje = datetime.today().strftime("%d/%m/%Y")
# Define os intervalos de tempo
manhã_inicio = time(5, 0)
manhã_fim = time(12, 0)
@ -143,7 +142,7 @@ elif tarde_inicio <= agora <= tarde_fim:
else:
boa = "Boa noite!"
df2['DATA'] = hoje
df2['DATA'] = agora
df3 = df2.groupby('DATA', as_index=False)['valor_estoque_parado'].sum()