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25ab596b6c ... 2eadd4c18f

Author SHA1 Message Date
João Herculano
2eadd4c18f correção data no acompanhamento 2025-06-04 13:57:53 -03:00
João Herculano
a0b93ee1c5 correções nas promos 2025-06-04 13:57:32 -03:00
3 changed files with 253 additions and 717 deletions
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201
promoção/promoção_EUD_ciclo07.ipynb
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@ -39,7 +39,7 @@
"name": "stderr", "name": "stderr",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"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", "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",
" df_draft = pd.concat([pd.read_csv(file) for file in csv_files], ignore_index=True)\n" " df_draft = pd.concat([pd.read_csv(file) for file in csv_files], ignore_index=True)\n"
] ]
}, },
@ -941,17 +941,54 @@
"cell_type": "code", "cell_type": "code",
"execution_count": 49, "execution_count": 49,
"metadata": {}, "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": [ "outputs": [
{ {
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"-0.14227416063810053\n" "0.167950276989596\n"
] ]
} }
], ],
"source": [ "source": [
"CRESCIMENTO = (df_final[df_final.columns[17]].sum() - df_final[df_final.columns[22]].sum())/df_final[df_final.columns[17]].sum() \n", "CRESCIMENTO = (df_final[df_final.columns[6]].sum() - df_final[df_final.columns[22]].sum())/df_final[df_final.columns[6]].sum() \n",
"print(CRESCIMENTO)\n", "print(CRESCIMENTO)\n",
"\n", "\n",
"df_final['CRESCIMENTO'] = .2" "df_final['CRESCIMENTO'] = .2"
@ -959,7 +996,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 50, "execution_count": 51,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -970,7 +1007,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 51, "execution_count": 52,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -979,7 +1016,7 @@
"'202410'" "'202410'"
] ]
}, },
"execution_count": 51, "execution_count": 52,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
@ -990,11 +1027,11 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 52, "execution_count": 53,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"vendas_todos_historicos = df_final.columns[17:23]\n", "vendas_todos_historicos = df_final.columns[6:23]\n",
"\n", "\n",
"df_final['MEDIANA DO HISTÓRICO'] = df_final[vendas_todos_historicos].median(axis=1)\n", "df_final['MEDIANA DO HISTÓRICO'] = df_final[vendas_todos_historicos].median(axis=1)\n",
"\n", "\n",
@ -1005,16 +1042,16 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 53, "execution_count": 54,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"np.float64(-0.14227416063810053)" "np.float64(0.167950276989596)"
] ]
}, },
"execution_count": 53, "execution_count": 54,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
@ -1025,22 +1062,43 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 54, "execution_count": 88,
"metadata": {}, "metadata": {},
"outputs": [], "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", "data": {
" round(df_final['CRESCIMENTO'] * df_final['MEDIANA DO HISTÓRICO']+ df_final['MEDIANA DO HISTÓRICO'],0), \n", "text/plain": [
" round(df_final['CRESCIMENTO']*df_final[ciclo_ano_passado]+df_final[ciclo_ano_passado],0))" "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,
"cell_type": "code",
"execution_count": 55,
"metadata": {}, "metadata": {},
"outputs": [], "output_type": "execute_result"
}
],
"source": [ "source": [
"df_final = df_final.rename(columns={df_final.columns[18]: \"C-4\", df_final.columns[19]: \"C-3\",df_final.columns[20]: \"C-2\",df_final.columns[21]: \"C-1\"})" "df_final.columns"
] ]
}, },
{ {
@ -1049,7 +1107,18 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"df_final.drop(columns=df_final.columns[6:18], inplace=True)\n" "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]))"
]
},
{
"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'])"
] ]
}, },
{ {
@ -1057,6 +1126,24 @@
"execution_count": 57, "execution_count": 57,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [
"df_final = df_final.rename(columns={df_final.columns[18]: \"C-4\", df_final.columns[19]: \"C-3\",df_final.columns[20]: \"C-2\",df_final.columns[21]: \"C-1\"})"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [],
"source": [
"df_final.drop(columns=df_final.columns[6:18], inplace=True)\n"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [],
"source": [ "source": [
"# List all columns except the two\n", "# List all columns except the two\n",
"cols_to_group_by = df_final.columns.difference(['DDV PREVISTO', 'COBERTURA ATUAL'])\n", "cols_to_group_by = df_final.columns.difference(['DDV PREVISTO', 'COBERTURA ATUAL'])\n",
@ -1072,7 +1159,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 58, "execution_count": 60,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -1092,7 +1179,7 @@
"Name: Compra inteligente Próximo Ciclo, Length: 1822, dtype: int64" "Name: Compra inteligente Próximo Ciclo, Length: 1822, dtype: int64"
] ]
}, },
"execution_count": 58, "execution_count": 60,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
@ -1103,7 +1190,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 59, "execution_count": 61,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1112,7 +1199,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 60, "execution_count": 62,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1125,7 +1212,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 61, "execution_count": 63,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -1146,7 +1233,7 @@
"Name: count, Length: 195, dtype: int64" "Name: count, Length: 195, dtype: int64"
] ]
}, },
"execution_count": 61, "execution_count": 63,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
@ -1157,7 +1244,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 62, "execution_count": 64,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1166,7 +1253,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 63, "execution_count": 65,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1175,7 +1262,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 64, "execution_count": 66,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -1206,7 +1293,7 @@
" dtype='object')" " dtype='object')"
] ]
}, },
"execution_count": 64, "execution_count": 66,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
@ -1217,7 +1304,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 65, "execution_count": 67,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1226,7 +1313,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 66, "execution_count": 68,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1235,7 +1322,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 67, "execution_count": 69,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1245,7 +1332,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 68, "execution_count": 70,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1254,7 +1341,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 69, "execution_count": 71,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1266,7 +1353,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 70, "execution_count": 72,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1276,7 +1363,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 71, "execution_count": 73,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1285,7 +1372,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 72, "execution_count": 74,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1294,7 +1381,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 73, "execution_count": 75,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -1325,7 +1412,7 @@
" dtype='object')" " dtype='object')"
] ]
}, },
"execution_count": 73, "execution_count": 75,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
@ -1336,7 +1423,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 74, "execution_count": 76,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -1345,7 +1432,7 @@
"'EUD'" "'EUD'"
] ]
}, },
"execution_count": 74, "execution_count": 76,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
@ -1357,7 +1444,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 75, "execution_count": 77,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -1387,7 +1474,7 @@
" dtype='object')" " dtype='object')"
] ]
}, },
"execution_count": 75, "execution_count": 77,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
@ -1398,7 +1485,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 76, "execution_count": 78,
"metadata": {}, "metadata": {},
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"source": [ "source": [
@ -1440,7 +1527,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 77, "execution_count": 79,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1466,7 +1553,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 78, "execution_count": 80,
"metadata": {}, "metadata": {},
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"source": [ "source": [
@ -1477,7 +1564,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 79, "execution_count": 81,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -1508,7 +1595,7 @@
" dtype='object')" " dtype='object')"
] ]
}, },
"execution_count": 79, "execution_count": 81,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
@ -1519,7 +1606,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 80, "execution_count": 82,
"metadata": {}, "metadata": {},
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"source": [ "source": [
@ -1528,7 +1615,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 81, "execution_count": 83,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1537,7 +1624,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 82, "execution_count": 84,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1548,7 +1635,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 83, "execution_count": 85,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
@ -1559,7 +1646,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 84, "execution_count": 87,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [

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