diff --git a/Lançamentos/Script_lançamento_boti_v2.ipynb b/Lançamentos/Script_lançamento_boti_v2.ipynb index 5b72a9d..846b989 100644 --- a/Lançamentos/Script_lançamento_boti_v2.ipynb +++ b/Lançamentos/Script_lançamento_boti_v2.ipynb @@ -99,17 +99,17 @@ " \n", " \n", " \n", - " 2283\n", - " C202512\n", - " 2025-08-11\n", - " 2025-08-31\n", + " 2409\n", + " C202515\n", + " 2025-10-13\n", + " 2025-11-02\n", " 21\n", " BOTICARIO\n", - " 2025-08-11\n", - " 12\n", + " 2025-10-13\n", + " 15\n", " C2025\n", - " C202515\n", - " 55\n", + " C202518\n", + " 59\n", " 1\n", " \n", " \n", @@ -118,10 +118,10 @@ ], "text/plain": [ " Ciclo INICIO CICLO FIM CICLO DURAÇÃO MARCA Date \\\n", - "2283 C202512 2025-08-11 2025-08-31 21 BOTICARIO 2025-08-11 \n", + "2409 C202515 2025-10-13 2025-11-02 21 BOTICARIO 2025-10-13 \n", "\n", " NUM_CICLO ANO_CICLO CICLOMAIS2 dias_ate_inicio match \n", - "2283 12 C2025 C202515 55 1 " + "2409 15 C2025 C202518 59 1 " ] }, "execution_count": 4, @@ -152,7 +152,7 @@ { "data": { "text/plain": [ - "'C202512'" + "'C202515'" ] }, "execution_count": 6, @@ -171,7 +171,7 @@ "metadata": {}, "outputs": [], "source": [ - "df_similares = pd.read_excel(r\"C:\\Users\\joao.herculano\\GRUPO GINSENG\\Assistência Suprimentos - 2025\\SUPRIMENTOS\\BD_LANÇAMENTOS\\BOT\\BOT - C12\\LANÇAMENTOS\\arquivos para geração da sugestão\\SIMILARES\\PRODUTOS SIMILARES - BOT.xlsx\")\n", + "df_similares = pd.read_excel(r\"C:\\Users\\joao.herculano\\GRUPO GINSENG\\Assistência Suprimentos - 2025\\SUPRIMENTOS\\BD_LANÇAMENTOS\\BOT\\BOT - C15\\arquivos para geração da sugestão\\SIMILARES\\PRODUTOS SIMILARES - BOT.xlsx\")\n", "\n", "df_similares = pd.merge(left=df_similares,right=calendario[['Ciclo','INICIO CICLO','FIM CICLO','DURAÇÃO']], how= 'left', left_on = 'CICLO SIMILAR',right_on = 'Ciclo' )\n", "\n", @@ -182,13 +182,81 @@ "\n", "df_similares['MATCH'] = 1\n", "\n", - "df_similares = df_similares.drop(columns=['INICIO DO CICLO',\n", - " 'FIM DO CICLO', 'DURAÇÃO CICLO','INICIO CICLO SIMILAR','FIM CICLO SIMILAR','DURAÇÃO CICLO SIMILAR'])" + "df_similares = df_similares.drop(columns=['INICIO DO CICLO', 'FIM DO CICLO', 'DURAÇÃO CICLO','INICIO CICLO SIMILAR','FIM CICLO SIMILAR','DURAÇÃO CICLO SIMILAR'])" ] }, { "cell_type": "code", "execution_count": 8, + "id": "ad836441", + "metadata": {}, + "outputs": [], + "source": [ + "df_similares['PRODUTO SIMILAR'] = df_similares['PRODUTO SIMILAR'].astype('str')\n", + "\n", + "df_similares['PRODUTO SIMILAR'] = df_similares['PRODUTO SIMILAR'].replace(r'\\.0$', '', regex=True)\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "1fdd05a3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "PRODUTO SIMILAR\n", + "52004 1\n", + "52008 1\n", + "52005 1\n", + "72929 1\n", + "53389 1\n", + "57474 1\n", + "52012 1\n", + "58138 1\n", + "49973 1\n", + "1006 1\n", + "53342 1\n", + "47963 1\n", + "84056 1\n", + "25458 1\n", + "48136 1\n", + "51448 1\n", + "1004 1\n", + "58373 1\n", + "75792 1\n", + "55659 1\n", + "48635 1\n", + "48621 1\n", + "59306 1\n", + "49225 1\n", + "49228 1\n", + "57759 1\n", + "55766 1\n", + "87000 1\n", + "87771 1\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "\n", + "df_similares = df_similares[df_similares['PRODUTO SIMILAR']!= 'nan']\n", + "\n", + "df_similares['PRODUTO SIMILAR'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, "id": "99ea95e6", "metadata": {}, "outputs": [ @@ -202,7 +270,7 @@ " dtype='object')" ] }, - "execution_count": 8, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -213,12 +281,12 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "id": "fe922f62", "metadata": {}, "outputs": [], "source": [ - "df_tabela = pd.read_excel(r\"C:\\Users\\joao.herculano\\GRUPO GINSENG\\Assistência Suprimentos - 2025\\SUPRIMENTOS\\BD_LANÇAMENTOS\\BOT\\BOT - C12\\LANÇAMENTOS\\arquivos para geração da sugestão\\TABELA DE PEDIDO\\Pedidos Semanais Especiais - BOT - 202512.xlsx\")\n", + "df_tabela = pd.read_excel(r\"C:\\Users\\joao.herculano\\GRUPO GINSENG\\Assistência Suprimentos - 2025\\SUPRIMENTOS\\BD_LANÇAMENTOS\\BOT\\BOT - C14\\arquivos para geração da sugestão\\TABELA DE PEDIDO\\Pedidos Semanais Especiais - BOT - 202514.xlsx\")\n", "\n", "df_tabela = df_tabela[df_tabela['Região'] == 'NNE'] \n", "\n", @@ -231,19 +299,101 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "id": "e0c55962", "metadata": {}, "outputs": [], "source": [ "df_pdv = pd.read_excel(r\"C:\\Users\\joao.herculano\\Documents\\PDV_ATT.xlsx\")\n", + "df_pdv = df_pdv[~df_pdv['PDV'].isin(['910173', '910291'])]\n", "\n", - "df_pdv_origi = pd.read_excel(r\"C:\\Users\\joao.herculano\\Documents\\PDV_ATT.xlsx\")" + "df_pdv_origi = pd.read_excel(r\"C:\\Users\\joao.herculano\\Documents\\PDV_ATT.xlsx\")\n", + "df_pdv_origi = df_pdv_origi[~df_pdv_origi['PDV'].isin(['910173', '910291'])]" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 13, + "id": "f93735d1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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PDVCANALDESCRIÇÃOPDV DESCREGIÃOESTADOCIDADEUFMARCAANALISTAGESTÃOSUPERVISORSTATUS
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" + ], + "text/plain": [ + " PDV CANAL DESCRIÇÃO PDV DESC REGIÃO ESTADO CIDADE \\\n", + "82 24293 HIB COMERCIO-HIB MORRO DO CHAPEU NaN NaN NaN NaN \n", + "\n", + " UF MARCA ANALISTA GESTÃO SUPERVISOR STATUS \n", + "82 BA3 O BOTICARIO MARCYARA Alysson Arianne Sodré [Provisório] ATIVO " + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_pdv[df_pdv['PDV']==24293]" + ] + }, + { + "cell_type": "code", + "execution_count": 14, "id": "a3a045d9", "metadata": {}, "outputs": [], @@ -270,7 +420,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 15, "id": "df04a501", "metadata": {}, "outputs": [], @@ -282,25 +432,17 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 16, "id": "0da911af", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\joao.herculano\\AppData\\Local\\Temp\\ipykernel_37548\\2509858446.py:10: DtypeWarning: Columns (6,7) 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" - ] - }, { "data": { "text/plain": [ - "(130656, 47)" + "(141690, 47)" ] }, - "execution_count": 13, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -309,7 +451,7 @@ "# Caminho onde estão as subpastas com os arquivos CSV\n", "\n", "# Set the path to the folder containing CSV files\n", - "folder_path = r\"C:\\Users\\joao.herculano\\GRUPO GINSENG\\Assistência Suprimentos - 2025\\SUPRIMENTOS\\BD_LANÇAMENTOS\\BOT\\BOT - C12\\LANÇAMENTOS\\arquivos para geração da sugestão\\DRAFT\" # arquivo dos drafts\n", + "folder_path = r\"C:\\Users\\joao.herculano\\GRUPO GINSENG\\Assistência Suprimentos - 2025\\SUPRIMENTOS\\BD_LANÇAMENTOS\\BOT\\Bot - C13\\LANÇAMENTOS\\arquivos para geração da sugestão\\DRAFT\" # arquivo dos drafts\n", "\n", "# Pattern to match all CSV files\n", "csv_files = glob.glob(os.path.join(folder_path, '*.csv'))\n", @@ -324,7 +466,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 17, "id": "0c8c7493", "metadata": {}, "outputs": [], @@ -334,15 +476,14 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 18, "id": "91298cde", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "Index(['Histórico de Vendas do Ciclo 202409',\n", - " 'Histórico de Vendas do Ciclo 202410',\n", + "Index(['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", @@ -358,11 +499,12 @@ " 'Histórico de Vendas do Ciclo 202506',\n", " 'Histórico de Vendas do Ciclo 202507',\n", " 'Histórico de Vendas do Ciclo 202508',\n", + " 'Histórico de Vendas do Ciclo 202509',\n", " 'Histórico de Vendas do Ciclo Atual'],\n", " dtype='object')" ] }, - "execution_count": 15, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -373,7 +515,444 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 19, + "id": "ed859826", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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PDVClasseSKUDescriçãoSubcategoriaLançamentoDesativaçãoHistórico de Vendas do Ciclo 202410Histórico de Vendas do Ciclo 202411Histórico de Vendas do Ciclo 202412...Item DesativadoData Prevista RegularizaçãoCarteira Bloqueada Para Novos PedidosPlanogramaQuantidade por caixaPreço Sell InQuantidadeItem analisadoHistórico de Vendas do Ciclo 202409match
1224293C49014ACCORDES CREM DES HID CPO 200ml V2CUIDADOS COM O CORPONaNNaN000...Não0Não0.05417.490NãoNaN1
9024293C49016ACCORDES DES BDY SPR 100ml V6DESODORANTE FEMININONaNNaN000...Não0Não0.08516.770NãoNaN1
16824293B48141ACCORDES DES COL 80ml V5PERFUMARIA FEMININANaNNaN000...Não0Não0.04163.240NãoNaN1
24624293B51213ACCORDES DES COL HARMONIA 80ml V4PERFUMARIA FEMININANaNNaN000...NãoBaixo AtendimentoNão0.04163.240NãoNaN1
32424293E86017ARBO CREM PRE/POS BARBA 100gPRÉ-BARBEARC08NaN000...Não0Não0.08018.410NãoNaN1
..................................................................
14153524293E50571QDB SRUM FAC LEVANTA TUDO SKIN Q 30mlHIDRATANTES E TRATAMENTOSNaNNaN000...Não2025-08-18Não0.09931.640Não0.01
14156724293E50574QDB SRUM FAC PREENCHE TUDO SKIN 30mlHIDRATANTES E TRATAMENTOSNaNNaN000...Não0Não0.09931.640Não0.01
14159924293E9403QDB SRUM FAC TRIP COMB SKIN Q 30mlHIDRATANTES E TRATAMENTOSNaNNaN000...Não0Não0.09931.640Não0.01
14162924293E50568QDB SRUM REST FAC ACORD/GLOW SKIN Q 30mlHIDRATANTES E TRATAMENTOSNaNNaN000...Não2025-07-16Não0.09927.320Não0.01
14166124293E53912QDB TOALHA DEMAQ ALGFUNCIONAIS MAQUIAGEMNaNNaN000...Não0Não0.08417.770Não0.01
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2557 rows × 46 columns

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" + ], + "text/plain": [ + " PDV Classe SKU Descrição \\\n", + "12 24293 C 49014 ACCORDES CREM DES HID CPO 200ml V2 \n", + "90 24293 C 49016 ACCORDES DES BDY SPR 100ml V6 \n", + "168 24293 B 48141 ACCORDES DES COL 80ml V5 \n", + "246 24293 B 51213 ACCORDES DES COL HARMONIA 80ml V4 \n", + "324 24293 E 86017 ARBO CREM PRE/POS BARBA 100g \n", + "... ... ... ... ... \n", + "141535 24293 E 50571 QDB SRUM FAC LEVANTA TUDO SKIN Q 30ml \n", + "141567 24293 E 50574 QDB SRUM FAC PREENCHE TUDO SKIN 30ml \n", + "141599 24293 E 9403 QDB SRUM FAC TRIP COMB SKIN Q 30ml \n", + "141629 24293 E 50568 QDB SRUM REST FAC ACORD/GLOW SKIN Q 30ml \n", + "141661 24293 E 53912 QDB TOALHA DEMAQ ALG \n", + "\n", + " Subcategoria Lançamento Desativação \\\n", + "12 CUIDADOS COM O CORPO NaN NaN \n", + "90 DESODORANTE FEMININO NaN NaN \n", + "168 PERFUMARIA FEMININA NaN NaN \n", + "246 PERFUMARIA FEMININA NaN NaN \n", + "324 PRÉ-BARBEAR C08 NaN \n", + "... ... ... ... \n", + "141535 HIDRATANTES E TRATAMENTOS NaN NaN \n", + "141567 HIDRATANTES E TRATAMENTOS NaN NaN \n", + "141599 HIDRATANTES E TRATAMENTOS NaN NaN \n", + "141629 HIDRATANTES E TRATAMENTOS NaN NaN \n", + "141661 FUNCIONAIS MAQUIAGEM NaN NaN \n", + "\n", + " Histórico de Vendas do Ciclo 202410 \\\n", + "12 0 \n", + "90 0 \n", + "168 0 \n", + "246 0 \n", + "324 0 \n", + "... ... \n", + "141535 0 \n", + "141567 0 \n", + "141599 0 \n", + "141629 0 \n", + "141661 0 \n", + "\n", + " Histórico de Vendas do Ciclo 202411 \\\n", + "12 0 \n", + "90 0 \n", + "168 0 \n", + "246 0 \n", + "324 0 \n", + "... ... \n", + "141535 0 \n", + "141567 0 \n", + "141599 0 \n", + "141629 0 \n", + "141661 0 \n", + "\n", + " Histórico de Vendas do Ciclo 202412 ... Item Desativado \\\n", + "12 0 ... Não \n", + "90 0 ... Não \n", + "168 0 ... Não \n", + "246 0 ... Não \n", + "324 0 ... Não \n", + "... ... ... ... \n", + "141535 0 ... Não \n", + "141567 0 ... Não \n", + "141599 0 ... Não \n", + "141629 0 ... Não \n", + "141661 0 ... Não \n", + "\n", + " Data Prevista Regularização Carteira Bloqueada Para Novos Pedidos \\\n", + "12 0 Não \n", + "90 0 Não \n", + "168 0 Não \n", + "246 Baixo Atendimento Não \n", + "324 0 Não \n", + "... ... ... \n", + "141535 2025-08-18 Não \n", + "141567 0 Não \n", + "141599 0 Não \n", + "141629 2025-07-16 Não \n", + "141661 0 Não \n", + "\n", + " Planograma Quantidade por caixa Preço Sell In Quantidade \\\n", + "12 0.0 54 17.49 0 \n", + "90 0.0 85 16.77 0 \n", + "168 0.0 41 63.24 0 \n", + "246 0.0 41 63.24 0 \n", + "324 0.0 80 18.41 0 \n", + "... ... ... ... ... \n", + "141535 0.0 99 31.64 0 \n", + "141567 0.0 99 31.64 0 \n", + "141599 0.0 99 31.64 0 \n", + "141629 0.0 99 27.32 0 \n", + "141661 0.0 84 17.77 0 \n", + "\n", + " Item analisado Histórico de Vendas do Ciclo 202409 match \n", + "12 Não NaN 1 \n", + "90 Não NaN 1 \n", + "168 Não NaN 1 \n", + "246 Não NaN 1 \n", + "324 Não NaN 1 \n", + "... ... ... ... \n", + "141535 Não 0.0 1 \n", + "141567 Não 0.0 1 \n", + "141599 Não 0.0 1 \n", + "141629 Não 0.0 1 \n", + "141661 Não 0.0 1 \n", + "\n", + "[2557 rows x 46 columns]" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_draft[df_draft['PDV']==24293]" + ] + }, + { + "cell_type": "code", + "execution_count": 20, "id": "34e179cb", "metadata": {}, "outputs": [ @@ -381,7 +960,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\joao.herculano\\AppData\\Local\\Temp\\ipykernel_37548\\1463083786.py:24: 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", + "C:\\Users\\joao.herculano\\AppData\\Local\\Temp\\ipykernel_101180\\1463083786.py:24: 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", " crescimento_por_pdv = df_draft.groupby('PDV').apply(calcular_crescimento)\n" ] } @@ -418,7 +997,34 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 21, + "id": "94aa432f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 52004\n", + "1 52004\n", + "2 52004\n", + "3 52004\n", + "4 52004\n", + "Name: PRODUTO SIMILAR, dtype: Int64" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_similares['PRODUTO SIMILAR'] = df_similares['PRODUTO SIMILAR'].astype('Int64')\n", + "df_similares['PRODUTO SIMILAR'].head()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, "id": "4bc8c2b4", "metadata": {}, "outputs": [], @@ -430,43 +1036,1003 @@ }, { "cell_type": "code", - "execution_count": 18, - "id": "c1451562", + "execution_count": 23, + "id": "aae416dd", "metadata": {}, "outputs": [ { "data": { + "text/html": [ + "
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PRODUTO LANÇAMENTODESCRIÇÃO DO LANÇAMENTOPRODUTO SIMILARDESCRIÇÃO SIMILARCICLO SIMILARFOCOIAFCATEGORIAMARCA% CONSUMIDOR...Data Prevista RegularizaçãoCarteira Bloqueada Para Novos PedidosPlanogramaQuantidade por caixaPreço Sell InQuantidadeItem analisadoHistórico de Vendas do Ciclo 202409matchCRESCIMENTO
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"execution_count": 18, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df_venda_diaria = pd.read_excel(r\"C:\\Users\\joao.herculano\\GRUPO GINSENG\\Assistência Suprimentos - 2025\\SUPRIMENTOS\\BD_LANÇAMENTOS\\BOT\\BOT - C12\\LANÇAMENTOS\\arquivos para geração da sugestão\\VENDAS_DIARIAS\\FormFiltroConsultaVendaSintetica_17_06_2025_14_04_40.xls\")\n", - "df_venda_diaria.shape" + "df_final" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 24, + "id": "2c9f011d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "PRODUTO SIMILAR\n", + "52004 79\n", + "52008 79\n", + "52005 79\n", + "72929 79\n", + "53389 79\n", + "57474 79\n", + "52012 79\n", + "58138 79\n", + "49973 79\n", + "1006 79\n", + "53342 79\n", + "47963 79\n", + "84056 79\n", + "25458 79\n", + "48136 79\n", + "51448 79\n", + "1004 79\n", + "58373 79\n", + "75792 79\n", + "55659 79\n", + "48635 79\n", + "48621 79\n", + "59306 79\n", + "49225 79\n", + "49228 79\n", + "57759 79\n", + "55766 79\n", + "87000 79\n", + "87771 79\n", + "Name: count, dtype: Int64" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_similares= df_similares[df_similares['PRODUTO SIMILAR'].notna()]\n", + "\n", + "df_similares['PRODUTO SIMILAR'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "0ff66803", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "SKU\n", + "52004.0 77\n", + "52008.0 77\n", + "52005.0 77\n", + "53389.0 77\n", + "52012.0 77\n", + "58138.0 77\n", + "49973.0 77\n", + "47963.0 77\n", + "84056.0 77\n", + "25458.0 77\n", + "48136.0 77\n", + "51448.0 77\n", + "1004.0 77\n", + "75792.0 77\n", + "55659.0 77\n", + "48635.0 77\n", + "48621.0 77\n", + "59306.0 77\n", + "57759.0 77\n", + "55766.0 77\n", + "Name: count, dtype: int64" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_final['SKU'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "c1451562", + "metadata": {}, + "outputs": [], + "source": [ + "#df_venda_diaria = pd.read_excel(r\"C:\\Users\\joao.herculano\\GRUPO GINSENG\\Assistência Suprimentos - 2025\\SUPRIMENTOS\\BD_LANÇAMENTOS\\BOT\\BOT - C12\\LANÇAMENTOS\\arquivos para geração da sugestão\\VENDAS_DIARIAS\\FormFiltroConsultaVendaSintetica_17_06_2025_14_04_40.xls\")\n", + "#df_venda_diaria.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "64bde00b", + "metadata": {}, + "outputs": [], + "source": [ + "df_venda_diaria = pd.read_csv(\n", + " r\"C:\\Users\\joao.herculano\\GRUPO GINSENG\\Assistência Suprimentos - 2025\\SUPRIMENTOS\\BD_LANÇAMENTOS\\BOT\\BOT - C15\\arquivos para geração da sugestão\\VENDAS_DIARIAS\\vendas 20250815.csv\",\n", + " sep=';',\n", + " dtype={'SKU_2': str},decimal=',')" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "6ab155b5", + "metadata": {}, + "outputs": [], + "source": [ + "df_venda_diaria['VENDAS_CICLO'] = df_venda_diaria['VENDAS_CICLO'].str.replace(r'\\.', '', regex=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "1d25e5df", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " PDV SKU DESCRICAO Vendas_Total \\\n", + "0 12522 51.944 COFFEE DES COL MAN SEDUC TCH PRM 10ml 11 \n", + "1 20998 77.557 QDB BASE LIQ SUPERMAT COR 06 Q 30ml RPCK 1 \n", + "2 20858 74.103 GLAMOUR DES COL SEC BL 75ml V4 5 \n", + "3 5699 47.950 ZAAD EDP 95ml V6 12 \n", + "4 20993 50.297 ESTJ DEM LILY ESSENCE 4x4ml 2 \n", + "\n", + " Ciclo \n", + "0 C202307 \n", + "1 C202405 \n", + "2 C202409 \n", + "3 C202406 \n", + "4 C202416 " + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_venda_diaria['Vendas_Total'] = df_venda_diaria['Vendas_Total'].str.replace(',', '.', regex=False)\n", + "\n", + "df_venda_diaria['Vendas_Total'] = df_venda_diaria['Vendas_Total'].astype('float')\n", + "\n", + "df_venda_diaria['Vendas_Total'] = round(df_venda_diaria['Vendas_Total'],0)\n", + "\n", + "df_venda_diaria['Vendas_Total'] = df_venda_diaria['Vendas_Total'].astype(str)\n", + "\n", + "df_venda_diaria['Vendas_Total'] = (\n", + " df_venda_diaria['Vendas_Total']\n", + " .str.replace('.0', '', regex=False)\n", + " .str.replace('.', '', regex=False)\n", + " .astype('Int64')\n", + ")\n", + "\n", + "\n", + "df_venda_diaria.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "6ddec856", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " PDV SKU DESCRICAO Vendas_Total \\\n", + "0 12522 51.944 COFFEE DES COL MAN SEDUC TCH PRM 10ml 11 \n", + "1 20998 77.557 QDB BASE LIQ SUPERMAT COR 06 Q 30ml RPCK 1 \n", + "2 20858 74.103 GLAMOUR DES COL SEC BL 75ml V4 5 \n", + "3 5699 47.950 ZAAD EDP 95ml V6 12 \n", + "4 20993 50.297 ESTJ DEM LILY ESSENCE 4x4ml 2 \n", + "\n", + " Ciclo \n", + "0 C202307 \n", + "1 C202405 \n", + "2 C202409 \n", + "3 C202406 \n", + "4 C202416 " + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_venda_diaria2 = df_venda_diaria.pivot_table(\n", + " index=[\"PDV\", \"SKU\", \"DESCRICAO\"],\n", + " columns=\"Ciclo\",\n", + " values=\"Vendas_Total\",\n", + " fill_value=0\n", + ").reset_index()\n", + "\n", + "# Se quiser, reorganize as colunas colocando os ciclos ao final\n", + "df_venda_diaria.columns.name = None\n", + "\n", + "df_venda_diaria.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, "id": "882e68aa", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "Index(['Unidade de Negócio', 'Ano', 'Mês', 'Dia', 'Código do Produto',\n", - " 'Descrição do Produto', 'Quantidade', 'Valor Bruto', 'Valor Desconto',\n", - " 'Valor Líquido', 'Valor Vale Troca', 'Líquido - Troca', 'Estoque Atual',\n", - " 'Estoque Mínimo'],\n", - " dtype='object')" + "Index(['PDV', 'SKU', 'DESCRICAO', 'Vendas_Total', 'Ciclo'], dtype='object')" ] }, - "execution_count": 19, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -477,71 +2043,8 @@ }, { "cell_type": "code", - "execution_count": 20, - "id": "c7ddaf20", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(17025, 16)" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_venda_diaria['PDV'] = df_venda_diaria['Unidade de Negócio'].str.split(\"-\").str[0].str.strip()\n", - "\n", - "df_venda_diaria['Dia'] = pd.to_datetime(df_venda_diaria['Dia'], format='%d/%m/%Y')\n", - "\n", - "df_venda_diaria = pd.merge(left=df_venda_diaria,right=calendario[['Ciclo','Date']],left_on='Dia',right_on='Date',how='inner')\n", - "\n", - "df_venda_diaria = df_venda_diaria.drop(columns='Date')\n", - "\n", - "df_venda_diaria.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "7119556a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(17025, 17)" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 'Dia' já está em formato datetime, então renomeamos para 'Data' diretamente\n", - "# ou apenas usamos 'Dia' como referência de data\n", - "\n", - "# Ordena o DataFrame para garantir que a cumulativa funcione corretamente\n", - "df_venda_diaria = df_venda_diaria.sort_values(by=['Unidade de Negócio', 'Código do Produto', 'Dia'])\n", - "\n", - "# Calcula a quantidade acumulada até o dia para cada grupo\n", - "df_venda_diaria['Quantidade Acumulada'] = (\n", - " df_venda_diaria\n", - " .groupby(['Unidade de Negócio', 'Código do Produto'])['Quantidade']\n", - " .cumsum()\n", - ") # acumulado por grupo até a data da linha\n", - "\n", - "df_venda_diaria.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "c707a1b6", + "execution_count": 34, + "id": "0c288d75", "metadata": {}, "outputs": [ { @@ -566,46 +2069,52 @@ " \n", " \n", " PDV\n", - " Código do Produto\n", + " SKU\n", + " DESCRICAO\n", + " Vendas_Total\n", " Ciclo\n", - " Quantidade Acumulada\n", " \n", " \n", " \n", " \n", " 0\n", " 12522\n", - " 4522\n", - " C202312\n", - " 1\n", + " 51.944\n", + " COFFEE DES COL MAN SEDUC TCH PRM 10ml\n", + " 11\n", + " C202307\n", " \n", " \n", " 1\n", - " 12522\n", - " 4522\n", - " C202314\n", - " 2\n", + " 20998\n", + " 77.557\n", + " QDB BASE LIQ SUPERMAT COR 06 Q 30ml RPCK\n", + " 1\n", + " C202405\n", " \n", " \n", " 2\n", - " 12522\n", - " 4522\n", - " C202406\n", - " 3\n", + " 20858\n", + " 74.103\n", + " GLAMOUR DES COL SEC BL 75ml V4\n", + " 5\n", + " C202409\n", " \n", " \n", " 3\n", - " 12522\n", - " 4522\n", - " C202407\n", - " 4\n", + " 5699\n", + " 47.950\n", + " ZAAD EDP 95ml V6\n", + " 12\n", + " C202406\n", " \n", " \n", " 4\n", - " 12522\n", - " 4522\n", - " C202410\n", - " 5\n", + " 20993\n", + " 50.297\n", + " ESTJ DEM LILY ESSENCE 4x4ml\n", + " 2\n", + " C202416\n", " \n", " \n", " ...\n", @@ -613,175 +2122,178 @@ " ...\n", " ...\n", " ...\n", + " ...\n", " \n", " \n", - " 5800\n", - " 5699\n", - " 81054\n", - " C202503\n", - " 167\n", + " 1663315\n", + " 20996\n", + " 83.039\n", + " QUASAR DES COL RUSH 100ml\n", + " 1\n", + " C202415\n", " \n", " \n", - " 5801\n", - " 5699\n", - " 81054\n", - " C202504\n", - " 168\n", + " 1663316\n", + " 20970\n", + " 52.025\n", + " BOTI BABY SAB LIQ GLIC V2 400ml\n", + " 6\n", + " C202511\n", " \n", " \n", - " 5802\n", - " 5699\n", - " 81054\n", - " C202505\n", - " 169\n", + " 1663317\n", + " 20996\n", + " 93.069\n", + " LA PIEL D/COL SPR CP PER AMBR DOUR 200ml\n", + " 10\n", + " C202414\n", " \n", " \n", - " 5803\n", - " 5699\n", - " 81054\n", - " C202506\n", - " 170\n", + " 1663318\n", + " 13427\n", + " 51.279\n", + " MAKE B BAS LIQ C/ GLYC TX 140 30g\n", + " 1\n", + " C202309\n", " \n", " \n", - " 5804\n", - " 5699\n", - " 85293\n", - " C202506\n", - " 5\n", + " 1663319\n", + " 21278\n", + " 52.014\n", + " CJ FLAC OUI EDP ICONIQUE INTENSE 3x1ml\n", + " 2\n", + " C202511\n", " \n", " \n", "\n", - "

5805 rows × 4 columns

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1663320 rows × 5 columns

\n", "" ], "text/plain": [ - " PDV Código do Produto Ciclo Quantidade Acumulada\n", - "0 12522 4522 C202312 1\n", - "1 12522 4522 C202314 2\n", - "2 12522 4522 C202406 3\n", - "3 12522 4522 C202407 4\n", - "4 12522 4522 C202410 5\n", - "... ... ... ... ...\n", - "5800 5699 81054 C202503 167\n", - "5801 5699 81054 C202504 168\n", - "5802 5699 81054 C202505 169\n", - "5803 5699 81054 C202506 170\n", - "5804 5699 85293 C202506 5\n", + " PDV SKU DESCRICAO \\\n", + "0 12522 51.944 COFFEE DES COL MAN SEDUC TCH PRM 10ml \n", + "1 20998 77.557 QDB BASE LIQ SUPERMAT COR 06 Q 30ml RPCK \n", + "2 20858 74.103 GLAMOUR DES COL SEC BL 75ml V4 \n", + "3 5699 47.950 ZAAD EDP 95ml V6 \n", + "4 20993 50.297 ESTJ DEM LILY ESSENCE 4x4ml \n", + "... ... ... ... \n", + "1663315 20996 83.039 QUASAR DES COL RUSH 100ml \n", + "1663316 20970 52.025 BOTI BABY SAB LIQ GLIC V2 400ml \n", + "1663317 20996 93.069 LA PIEL D/COL SPR CP PER AMBR DOUR 200ml \n", + "1663318 13427 51.279 MAKE B BAS LIQ C/ GLYC TX 140 30g \n", + "1663319 21278 52.014 CJ FLAC OUI EDP ICONIQUE INTENSE 3x1ml \n", "\n", - "[5805 rows x 4 columns]" + " Vendas_Total Ciclo \n", + "0 11 C202307 \n", + "1 1 C202405 \n", + "2 5 C202409 \n", + "3 12 C202406 \n", + "4 2 C202416 \n", + "... ... ... \n", + "1663315 1 C202415 \n", + "1663316 6 C202511 \n", + "1663317 10 C202414 \n", + "1663318 1 C202309 \n", + "1663319 2 C202511 \n", + "\n", + "[1663320 rows x 5 columns]" ] }, - "execution_count": 22, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df_venda_diaria = df_venda_diaria.drop_duplicates()\n", - "\n", - "df_venda_agrupado = df_venda_diaria.fillna(0).groupby(['PDV', 'Código do Produto','Ciclo'])['Quantidade Acumulada'].max().reset_index()\n", - "df_venda_agrupado" + "df_venda_diaria" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 35, + "id": "c7ddaf20", + "metadata": {}, + "outputs": [], + "source": [ + "#df_venda_diaria['PDV'] = df_venda_diaria['Unidade de Negócio'].str.split(\"-\").str[0].str.strip()\n", + "\n", + "#df_venda_diaria['Dia'] = pd.to_datetime(df_venda_diaria['Dia'], format='%d/%m/%Y')\n", + "\n", + "#df_venda_diaria = pd.merge(left=df_venda_diaria,right=calendario[['Ciclo','Date']],left_on='Dia',right_on='Date',how='inner')\n", + "\n", + "#df_venda_diaria = df_venda_diaria.drop(columns='Date')\n", + "\n", + "#df_venda_diaria.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "7119556a", + "metadata": {}, + "outputs": [], + "source": [ + "# 'Dia' já está em formato datetime, então renomeamos para 'Data' diretamente\n", + "# ou apenas usamos 'Dia' como referência de data\n", + "\n", + "# Ordena o DataFrame para garantir que a cumulativa funcione corretamente\n", + "#df_venda_diaria = df_venda_diaria.sort_values(by=['Unidade de Negócio', 'Código do Produto', 'Dia'])\n", + "\n", + "# Calcula a quantidade acumulada até o dia para cada grupo\n", + "#df_venda_diaria['Quantidade Acumulada'] = (df_venda_diaria.groupby(['Unidade de Negócio', 'Código do Produto'])['Quantidade'].cumsum()) # acumulado por grupo até a data da linha\n", + "\n", + "#df_venda_diaria.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "c707a1b6", + "metadata": {}, + "outputs": [], + "source": [ + "#df_venda_diaria = df_venda_diaria.drop_duplicates()\n", + "\n", + "#df_venda_agrupado = df_venda_diaria.fillna(0).groupby(['PDV', 'Código do Produto','Ciclo'])['Quantidade Acumulada'].max().reset_index()\n", + "#df_venda_agrupado" + ] + }, + { + "cell_type": "code", + "execution_count": 38, "id": "083ff829", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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PRODUTO LANÇAMENTODESCRIÇÃO DO LANÇAMENTOPRODUTO SIMILARDESCRIÇÃO SIMILARCICLO SIMILARFOCOIAFCATEGORIAMARCA% CONSUMIDOR...Item analisadoHistórico de Vendas do Ciclo 202409match_xCRESCIMENTOCicloINICIO CICLOFIM CICLODURAÇÃOmatch_ydias_ate_inicio
059823NATIVA SPA ORQUÍDEA LUMIÈRE CREME DESODORANTE ...52004NSPA LOC RAD DES HID CPO AMEI DOUR 400mlC202315SimSimCUIDADOS COM A PELENATIVA SPA31,07 | 39,52...NãoNaN1.0-0.0154C2025152025-10-132025-11-0221159
159823NATIVA SPA ORQUÍDEA LUMIÈRE CREME DESODORANTE ...52004NSPA LOC RAD DES HID CPO AMEI DOUR 400mlC202315SimSimCUIDADOS COM A PELENATIVA SPA31,07 | 39,52...NãoNaN1.0-0.0341C2025152025-10-132025-11-0221159
259823NATIVA SPA ORQUÍDEA LUMIÈRE CREME DESODORANTE ...52004NSPA LOC RAD DES HID CPO AMEI DOUR 400mlC202315SimSimCUIDADOS COM A PELENATIVA SPA31,07 | 39,52...NãoNaN1.0-0.0138C2025152025-10-132025-11-0221159
359823NATIVA SPA ORQUÍDEA LUMIÈRE CREME DESODORANTE ...52004NSPA LOC RAD DES HID CPO AMEI DOUR 400mlC202315SimSimCUIDADOS COM A PELENATIVA SPA31,07 | 39,52...NãoNaN1.0-0.0595C2025152025-10-132025-11-0221159
459823NATIVA SPA ORQUÍDEA LUMIÈRE CREME DESODORANTE ...52004NSPA LOC RAD DES HID CPO AMEI DOUR 400mlC202315SimSimCUIDADOS COM A PELENATIVA SPA31,07 | 39,52...NãoNaN1.0-0.0171C2025152025-10-132025-11-0221159
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PRODUTO LANÇAMENTODESCRIÇÃO DO LANÇAMENTOPRODUTO SIMILARDESCRIÇÃO SIMILARCICLO SIMILARFOCOIAFCATEGORIAMARCA% CONSUMIDOR...DURAÇÃO_xmatch_ydias_ate_inicioCiclo_yINICIO CICLO_yFIM CICLO_yDURAÇÃO_ySKU_yCicloVendas Ciclo Lançamento
059823NATIVA SPA ORQUÍDEA LUMIÈRE CREME DESODORANTE ...52004NSPA LOC RAD DES HID CPO AMEI DOUR 400mlC202315SimSimCUIDADOS COM A PELENATIVA SPA31,07 | 39,52...21159C2023152023-10-162023-10-2914.052004C20231522
159823NATIVA SPA ORQUÍDEA LUMIÈRE CREME DESODORANTE ...52004NSPA LOC RAD DES HID CPO AMEI DOUR 400mlC202315SimSimCUIDADOS COM A PELENATIVA SPA31,07 | 39,52...21159C2023152023-10-162023-10-2914.052004C20231536
259823NATIVA SPA ORQUÍDEA LUMIÈRE CREME DESODORANTE ...52004NSPA LOC RAD DES HID CPO AMEI DOUR 400mlC202315SimSimCUIDADOS COM A PELENATIVA SPA31,07 | 39,52...21159C2023152023-10-162023-10-2914.052004C2023159
359823NATIVA SPA ORQUÍDEA LUMIÈRE CREME DESODORANTE ...52004NSPA LOC RAD DES HID CPO AMEI DOUR 400mlC202315SimSimCUIDADOS COM A PELENATIVA SPA31,07 | 39,52...21159C2023152023-10-162023-10-2914.052004C2023155
459823NATIVA SPA ORQUÍDEA LUMIÈRE CREME DESODORANTE ...52004NSPA LOC RAD DES HID CPO AMEI DOUR 400mlC202315SimSimCUIDADOS COM A PELENATIVA SPA31,07 | 39,52...21159C2023152023-10-162023-10-2914.052004C20231528
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PRODUTO LANÇAMENTODESCRIÇÃO DO LANÇAMENTOPRODUTO SIMILARDESCRIÇÃO SIMILARCICLO SIMILARFOCOIAFCATEGORIAMARCA% CONSUMIDOR...DURAÇÃO_xmatch_ydias_ate_inicioCiclo_yINICIO CICLO_yFIM CICLO_yDURAÇÃO_ySKU_yCicloVendas Ciclo Lançamento
059823NATIVA SPA ORQUÍDEA LUMIÈRE CREME DESODORANTE ...52004NSPA LOC RAD DES HID CPO AMEI DOUR 400mlC202315SimSimCUIDADOS COM A PELENATIVA SPA31,07 | 39,52...21159C2023152023-10-162023-10-2914.052004C20231522
159823NATIVA SPA ORQUÍDEA LUMIÈRE CREME DESODORANTE ...52004NSPA LOC RAD DES HID CPO AMEI DOUR 400mlC202315SimSimCUIDADOS COM A PELENATIVA SPA31,07 | 39,52...21159C2023152023-10-162023-10-2914.052004C20231536
259823NATIVA SPA ORQUÍDEA LUMIÈRE CREME DESODORANTE ...52004NSPA LOC RAD DES HID CPO AMEI DOUR 400mlC202315SimSimCUIDADOS COM A PELENATIVA SPA31,07 | 39,52...21159C2023152023-10-162023-10-2914.052004C2023159
359823NATIVA SPA ORQUÍDEA LUMIÈRE CREME DESODORANTE ...52004NSPA LOC RAD DES HID CPO AMEI DOUR 400mlC202315SimSimCUIDADOS COM A PELENATIVA SPA31,07 | 39,52...21159C2023152023-10-162023-10-2914.052004C2023155
459823NATIVA SPA ORQUÍDEA LUMIÈRE CREME DESODORANTE ...52004NSPA LOC RAD DES HID CPO AMEI DOUR 400mlC202315SimSimCUIDADOS COM A PELENATIVA SPA31,07 | 39,52...21159C2023152023-10-162023-10-2914.052004C20231528
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159823NATIVA SPA ORQUÍDEA LUMIÈRE CREME DESODORANTE ...52004NSPA LOC RAD DES HID CPO AMEI DOUR 400mlC202315SimSimCUIDADOS COM A PELENATIVA SPA31,07 | 39,52...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
259823NATIVA SPA ORQUÍDEA LUMIÈRE CREME DESODORANTE ...52004NSPA LOC RAD DES HID CPO AMEI DOUR 400mlC202315SimSimCUIDADOS COM A PELENATIVA SPA31,07 | 39,52...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
359823NATIVA SPA ORQUÍDEA LUMIÈRE CREME DESODORANTE ...52004NSPA LOC RAD DES HID CPO AMEI DOUR 400mlC202315SimSimCUIDADOS COM A PELENATIVA SPA31,07 | 39,52...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
459823NATIVA SPA ORQUÍDEA LUMIÈRE CREME DESODORANTE ...52004NSPA LOC RAD DES HID CPO AMEI DOUR 400mlC202315SimSimCUIDADOS COM A PELENATIVA SPA31,07 | 39,52...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", - " variacao_mensal = soma_mensal.pct_change()\n" + "C:\\Users\\joao.herculano\\AppData\\Local\\Temp\\ipykernel_101180\\260461722.py:1: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", + " df_final = df_final.fillna(0)\n" ] - }, + } + ], + "source": [ + "df_final = df_final.fillna(0)" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "0a1bb832", + "metadata": {}, + "outputs": [ { "data": { "text/plain": [ - "np.float64(0.1817)" + "np.float64(0.2267)" ] }, - "execution_count": 40, + "execution_count": 70, "metadata": {}, "output_type": "execute_result" } @@ -1080,6 +4667,8 @@ "df_filtrado = variacao_mensal[filtro]\n", "CRESCIMENTO = round(df_filtrado.mean(),4)\n", "\n", + "CRESCIMENTO = 0.2 if CRESCIMENTO < 0.2 else CRESCIMENTO\n", + "\n", "df_final['CRESCIMENTO_GERAL'] = CRESCIMENTO\n", "\n", "CRESCIMENTO\n" @@ -1087,7 +4676,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 71, "id": "a9647c32", "metadata": {}, "outputs": [], @@ -1100,24 +4689,24 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 72, "id": "b107e519", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "Index(['Histórico de Vendas do Ciclo 202502',\n", - " 'Histórico de Vendas do Ciclo 202503',\n", - " 'Histórico de Vendas do Ciclo 202504',\n", + "Index(['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", - " 'Histórico de Vendas do Ciclo 202508'],\n", + " 'Histórico de Vendas do Ciclo 202508',\n", + " 'Histórico de Vendas do Ciclo 202509',\n", + " 'Histórico de Vendas do Ciclo Atual'],\n", " dtype='object')" ] }, - "execution_count": 42, + "execution_count": 72, "metadata": {}, "output_type": "execute_result" } @@ -1128,7 +4717,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 73, "id": "8290853c", "metadata": {}, "outputs": [], @@ -1146,28 +4735,28 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 74, "id": "d8b30560", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0 1.0\n", - "1 3.5\n", - "2 0.5\n", + "0 2.0\n", + "1 2.0\n", + "2 0.0\n", "3 0.0\n", "4 1.0\n", " ... \n", - "1059 0.0\n", - "1060 0.0\n", - "1061 0.0\n", - "1062 0.0\n", - "1063 0.0\n", - "Length: 1064, dtype: object" + "2286 0.0\n", + "2287 0.0\n", + "2288 0.0\n", + "2289 0.0\n", + "2290 0.0\n", + "Length: 2291, dtype: float64" ] }, - "execution_count": 44, + "execution_count": 74, "metadata": {}, "output_type": "execute_result" } @@ -1178,7 +4767,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 75, "id": "07f043f2", "metadata": {}, "outputs": [ @@ -1204,6 +4793,7 @@ " \n", " \n", " CANAL\n", + " UF\n", " med_por_canal\n", " \n", " \n", @@ -1211,86 +4801,529 @@ " \n", " 0\n", " CD\n", - " 20.0\n", + " AL\n", + " 618.0\n", " \n", " \n", " 1\n", " HIB\n", - " 2.0\n", + " AL\n", + " 50.0\n", " \n", " \n", " 2\n", - " LJ\n", - " 3.5\n", + " HIB\n", + " BA\n", + " 96.0\n", " \n", " \n", " 3\n", + " HIB\n", + " BA3\n", + " 0.0\n", + " \n", + " \n", + " 4\n", + " HIB\n", + " SE\n", + " 42.0\n", + " \n", + " \n", + " 5\n", + " LJ\n", + " AL\n", + " 87.0\n", + " \n", + " \n", + " 6\n", + " LJ\n", + " BA\n", + " 25.0\n", + " \n", + " \n", + " 7\n", + " LJ\n", + " BA3\n", + " 19.0\n", + " \n", + " \n", + " 8\n", + " LJ\n", + " SE\n", + " 20.0\n", + " \n", + " \n", + " 9\n", " VD\n", - " 10.0\n", + " AL\n", + " 202.0\n", + " \n", + " \n", + " 10\n", + " VD\n", + " BA\n", + " 205.0\n", + " \n", + " \n", + " 11\n", + " VD\n", + " BA3\n", + " 47.0\n", + " \n", + " \n", + " 12\n", + " VD\n", + " SE\n", + " 164.0\n", " \n", " \n", "\n", "" ], "text/plain": [ - " CANAL med_por_canal\n", - "0 CD 20.0\n", - "1 HIB 2.0\n", - "2 LJ 3.5\n", - "3 VD 10.0" + " CANAL UF med_por_canal\n", + "0 CD AL 618.0\n", + "1 HIB AL 50.0\n", + "2 HIB BA 96.0\n", + "3 HIB BA3 0.0\n", + "4 HIB SE 42.0\n", + "5 LJ AL 87.0\n", + "6 LJ BA 25.0\n", + "7 LJ BA3 19.0\n", + "8 LJ SE 20.0\n", + "9 VD AL 202.0\n", + "10 VD BA 205.0\n", + "11 VD BA3 47.0\n", + "12 VD SE 164.0" ] }, - "execution_count": 45, + "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "medi = df_final.groupby(['CANAL'])['MEDIANA DO HISTÓRICO'].max().reset_index()\n", + "medi = df_final.groupby(['CANAL','UF'])['MEDIANA DO HISTÓRICO'].max().reset_index()\n", "medi = medi.rename(columns={'MEDIANA DO HISTÓRICO':'med_por_canal'})\n", "medi" ] }, { "cell_type": "code", - 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" + ], + "text/plain": [ + " PDV SKU DESCRICAO \\\n", + "0 12522 51944 COFFEE DES COL MAN SEDUC TCH PRM 10ml \n", + "1 20998 77557 QDB BASE LIQ SUPERMAT COR 06 Q 30ml RPCK \n", + "2 20858 74103 GLAMOUR DES COL SEC BL 75ml V4 \n", + "3 5699 47950 ZAAD EDP 95ml V6 \n", + "4 20993 50297 ESTJ DEM LILY ESSENCE 4x4ml \n", + "\n", + " Vendas Ciclo Lançamento Ciclo \n", + "0 11 C202307 \n", + "1 1 C202405 \n", + "2 5 C202409 \n", + "3 12 C202406 \n", + "4 2 C202416 " + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_venda_diaria.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "id": "ef979be5", "metadata": {}, "outputs": [], "source": [ - "df_vdc = pd.read_csv(r\"C:\\Users\\joao.herculano\\GRUPO GINSENG\\Assistência Suprimentos - 2025\\SUPRIMENTOS\\BD_LANÇAMENTOS\\BOT\\BOT - C11\\arquivos para geração da sugestão\\VENDAS VDC\\vendas_vdc22.02.csv\")\n", + "df_dourado = pd.read_excel(r\"C:\\Users\\joao.herculano\\GRUPO GINSENG\\Assistência Suprimentos - 2025\\DADOS DOURADO\\VENDA\\compilado de vendas.xlsx\")\n", "\n", + "df_dourado['Data de venda'] = pd.to_datetime(df_dourado['Data de venda'], dayfirst=True)\n", "\n", + "df_dourado['de para pdv'] = df_dourado['de para pdv'].astype(str)\n", "\n", - "df_vdc['DATA VENDA'] = pd.to_datetime(df_vdc['DATA VENDA'])\n", + "df_dourado['de para pdv'] = df_dourado['de para pdv'].str.replace(r'\\.0$', '', regex=True)\n", "\n", "# 'Dia' já está em formato datetime, então renomeamos para 'Data' diretamente\n", "# ou apenas usamos 'Dia' como referência de data\n", "\n", "# Ordena o DataFrame para garantir que a cumulativa funcione corretamente\n", - "df_venda_diaria = df_venda_diaria.sort_values(by=['Unidade de Negócio', 'Código do Produto', 'Dia'])\n", + "df_venda_diaria = df_venda_diaria.sort_values(by=['PDV', 'SKU', 'Ciclo'])\n", + "\n", + "# Calcula a quantidade acumulada até o dia para cada grupo\n", + "df_dourado['Quantidade Acumulada dourado'] = (\n", + " df_dourado\n", + " .groupby(['de para pdv', 'sku'])['Quantidade Vendida']\n", + " .cumsum()\n", + ") # acumulado por grupo até a data da linha\n", + "\n", + "df_dourado = pd.merge(left=df_dourado,right=calendario[['Date','Ciclo']],left_on='Data de venda',right_on='Date',how='inner')\n", + "\n", + "\n", + "\n", + "df_dourado_agrupado = df_dourado.groupby(['de para pdv','sku','Ciclo'])['Quantidade Acumulada dourado'].max().reset_index()\n", + "\n", + "df_dourado_agrupado = df_dourado_agrupado.rename(columns={'Ciclo':'Ciclo dourado'})\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "id": "41eb7ebf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 12522\n", + "1 12817\n", + "2 12818\n", + "3 12820\n", + "4 12823\n", + "Name: PDV, dtype: object" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_final['PDV'] = df_final['PDV'].astype(str)\n", + "df_final['PDV'].head()" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "id": "6dbe67bb", + "metadata": {}, + "outputs": [], + "source": [ + "df_final = pd.merge(left=df_final, right = df_dourado_agrupado, right_on=['Ciclo dourado','sku','de para pdv'],left_on=['CICLO SIMILAR','PRODUTO SIMILAR','PDV'],how='left')\n", + "\n", + "df_final['Quantidade Acumulada dourado'] = df_final['Quantidade Acumulada dourado'].fillna(0)\n", + "\n", + "\n", + "df_final['Vendas Ciclo Lançamento'] = np.where(df_final['Quantidade Acumulada dourado'] > 0, df_final['Quantidade Acumulada dourado'], df_final['Vendas Ciclo Lançamento'])\n", + "\n", + "df_final = df_final.drop(columns='Quantidade Acumulada dourado')\n", + "\n", + "\n", + "df_final = df_final.drop(columns='Ciclo dourado')" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "id": "7986b8fa", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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CP VIVA BEMProtheusPDVDEPARA.PracticoCanalEndereçoCNPJCNPJ Locação
0355970101.023713.0MATRIZ - LJRua Zeferino Correia, nº 17, Centro, CEP 45.00...14.378.160/0001-8314.378.160/0001-83
1729270103.023712.0LOJAAl. Rio Branco, nº 373, Centro, Cândido Sales ...14.378.160/0006-9814.378.160/0006-98
2870870102.023711.0VDAl. Lima Guerra, nº 04, Centro, Vitória da Con...14.378.160/0005-0714.378.160/0001-83
38931NaN23710.0NaNAvenida Rosa Cruz, nº 80, Hiper B Preço Loja B...14.378.160/0008-5014.378.160/0008-50
41119170108.023709.0LOJAAv. Juracy Magalhães, nº 3340, Shopping Conqui...14.378.160/0009-3014.378.160/0001-83
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" + ], + "text/plain": [ + " CP VIVA BEM Protheus PDVDEPARA.Practico Canal \\\n", + "0 3559 70101.0 23713.0 MATRIZ - LJ \n", + "1 7292 70103.0 23712.0 LOJA \n", + "2 8708 70102.0 23711.0 VD \n", + "3 8931 NaN 23710.0 NaN \n", + "4 11191 70108.0 23709.0 LOJA \n", + "\n", + " Endereço CNPJ \\\n", + "0 Rua Zeferino Correia, nº 17, Centro, CEP 45.00... 14.378.160/0001-83 \n", + "1 Al. Rio Branco, nº 373, Centro, Cândido Sales ... 14.378.160/0006-98 \n", + "2 Al. Lima Guerra, nº 04, Centro, Vitória da Con... 14.378.160/0005-07 \n", + "3 Avenida Rosa Cruz, nº 80, Hiper B Preço Loja B... 14.378.160/0008-50 \n", + "4 Av. Juracy Magalhães, nº 3340, Shopping Conqui... 14.378.160/0009-30 \n", + "\n", + " CNPJ Locação \n", + "0 14.378.160/0001-83 \n", + "1 14.378.160/0006-98 \n", + "2 14.378.160/0001-83 \n", + "3 14.378.160/0008-50 \n", + "4 14.378.160/0001-83 " + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_depara_vdc = pd.read_excel(r\"C:\\Users\\joao.herculano\\GRUPO GINSENG\\Assistência Suprimentos - 2025\\SUPRIMENTOS\\Vitoria da Conquista\\HISTORICO VENDAS VITORIA DA CONQUISTA\\Filiais - Vitoria da Conquista 4.xlsx\")\n", + "\n", + "df_depara_vdc.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "id": "09efe938", + "metadata": {}, + "outputs": [], + "source": [ + "df_vdc = pd.read_parquet(r\"C:\\Users\\joao.herculano\\GRUPO GINSENG\\Assistência Suprimentos - 2025\\SUPRIMENTOS\\Vitoria da Conquista\\HISTORICO VENDAS VITORIA DA CONQUISTA\\VENDAS VITORIA DA CONQUISTA\\concatenado.parquet\")\n", + "\n", + "df_vdc['PDV'] = df_vdc['Quebra'].str.split(\" -\").str[0]\n", + "\n", + "df_vdc['PDV'] = df_vdc['PDV'].astype('int64')\n", + "\n", + "df_vdc = df_vdc.rename(columns={'Quebra2':'DATA VENDA'})\n", + "\n", + "df_vdc['DATA VENDA'] = pd.to_datetime(df_vdc['DATA VENDA'], format='%d/%m/%Y')" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "id": "330154b2", + "metadata": {}, + "outputs": [], + "source": [ + "df_vdc = pd.merge(df_vdc,df_depara_vdc[['CP VIVA BEM','PDVDEPARA.Practico']],left_on='PDV',right_on='CP VIVA BEM',how='inner')\n", + "\n", + "df_vdc['Quantidade'] = df_vdc['Quantidade'].str.replace(r',000$','',regex=True)\n", + "\n", + "df_vdc['Quantidade'] = df_vdc['Quantidade'].astype('Int64') " + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "id": "09cc2f82", + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "\n", + "\n", + "\n", + "# 'Dia' já está em formato datetime, então renomeamos para 'Data' diretamente\n", + "# ou apenas usamos 'Dia' como referência de data\n", + "\n", + "# Ordena o DataFrame para garantir que a cumulativa funcione corretamente\n", + "#df_venda_diaria = df_venda_diaria.sort_values(by=['PDV', 'SKU', 'Ciclo'])\n", "\n", "# Calcula a quantidade acumulada até o dia para cada grupo\n", "df_vdc['Quantidade Acumulada vdc'] = (\n", " df_vdc\n", - " .groupby(['PDVDEPARA.Practico', 'Código'])['Soma de Quantidade']\n", + " .groupby(['PDVDEPARA.Practico', 'Código'])['Quantidade']\n", " .cumsum()\n", ") # acumulado por grupo até a data da linha" ] }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 86, "id": "5a827c08", "metadata": {}, "outputs": [ @@ -1325,52 +5358,52 @@ " \n", " 0\n", " 23701\n", - " 48617\n", - " C202212\n", - " 6\n", + " 1004\n", + " C202203\n", + " 139.0\n", " \n", " \n", " 1\n", " 23701\n", - " 48617\n", - " C202213\n", - " 8\n", + " 1004\n", + " C202204\n", + " 161.0\n", " \n", " \n", " 2\n", " 23701\n", - " 48617\n", - " C202214\n", - " 10\n", + " 1004\n", + " C202205\n", + " 162.0\n", " \n", " \n", " 3\n", " 23701\n", - " 48617\n", - " C202215\n", - " 12\n", + " 1004\n", + " C202206\n", + " 9.0\n", " \n", " \n", " 4\n", " 23701\n", - " 48617\n", - " C202216\n", - " 22\n", + " 1004\n", + " C202207\n", + " 15.0\n", " \n", " \n", "\n", "" ], "text/plain": [ - " PDVDEPARA.Practico Código Ciclo vdc Quantidade Acumulada vdc\n", - "0 23701 48617 C202212 6\n", - "1 23701 48617 C202213 8\n", - "2 23701 48617 C202214 10\n", - "3 23701 48617 C202215 12\n", - "4 23701 48617 C202216 22" + " PDVDEPARA.Practico Código Ciclo vdc Quantidade Acumulada vdc\n", + "0 23701 1004 C202203 139.0\n", + "1 23701 1004 C202204 161.0\n", + "2 23701 1004 C202205 162.0\n", + "3 23701 1004 C202206 9.0\n", + "4 23701 1004 C202207 15.0" ] }, - "execution_count": 48, + "execution_count": 86, "metadata": {}, "output_type": "execute_result" } @@ -1383,19 +5416,103 @@ "df_vdc_agrupado = df_vdc_agrupado.rename(columns={'Ciclo':'Ciclo vdc'})\n", "\n", "\n", - "df_vdc_agrupado.head()" + "df_vdc_agrupado['PDVDEPARA.Practico'] = df_vdc_agrupado['PDVDEPARA.Practico'].astype(str).str.replace(r'\\.0$', '', regex=True)\n", + "df_vdc_agrupado.head()\n", + "\n" ] }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 87, + "id": "4933b6d2", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\joao.herculano\\AppData\\Local\\Temp\\ipykernel_101180\\3455958411.py:3: FutureWarning: DataFrame.replace without 'value' and with non-dict-like 'to_replace' is deprecated and will raise in a future version. Explicitly specify the new values instead.\n", + " df_vdc_agrupado = df_vdc_agrupado.replace()\n" + ] + } + ], + "source": [ + "df_vdc_agrupado = df_vdc_agrupado.astype(str)\n", + "\n", + "df_vdc_agrupado = df_vdc_agrupado.replace()" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "id": "8d5f2c33", + "metadata": {}, + "outputs": [], + "source": [ + "#df_vdc.to_excel(r\"C:\\Users\\joao.herculano\\Documents\\df_vdc.xlsx\",index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "id": "63fea2ea", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 23701\n", + "1 23701\n", + "2 23701\n", + "3 23701\n", + "4 23701\n", + "Name: PDVDEPARA.Practico, dtype: object" + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_vdc_agrupado['PDVDEPARA.Practico'] = df_vdc_agrupado['PDVDEPARA.Practico'].astype(str)\n", + "\n", + "df_vdc_agrupado['PDVDEPARA.Practico'].head()" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "id": "84b5917c", + "metadata": {}, + "outputs": [], + "source": [ + "df_final['PRODUTO SIMILAR'] = df_final['PRODUTO SIMILAR'].astype('Int64')" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "id": "ca911bd1", + "metadata": {}, + "outputs": [], + "source": [ + "df_vdc_agrupado['Código'] = df_vdc_agrupado['Código'].astype('Int64')" + ] + }, + { + "cell_type": "code", + "execution_count": 92, "id": "8ec14143", "metadata": {}, "outputs": [], "source": [ "df_final = pd.merge(left=df_final, right = df_vdc_agrupado, right_on=['Ciclo vdc','Código','PDVDEPARA.Practico'],left_on=['CICLO SIMILAR','PRODUTO SIMILAR','PDV'],how='left')\n", "\n", - "df_final['Quantidade Acumulada vdc'] = df_final['Quantidade Acumulada vdc'].fillna(0)\n", + "df_final['Quantidade Acumulada vdc'] = pd.to_numeric(\n", + " df_final['Quantidade Acumulada vdc'],\n", + " errors='coerce' # valores inválidos viram NaN\n", + ").fillna(0).astype('Int64')\n", "\n", "\n", "df_final['Vendas Ciclo Lançamento'] = np.where(df_final['Quantidade Acumulada vdc'] > 0, df_final['Quantidade Acumulada vdc'], df_final['Vendas Ciclo Lançamento'])\n", @@ -1409,27 +5526,17 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 93, "id": "1a625e69", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\joao.herculano\\AppData\\Local\\Temp\\ipykernel_37548\\2817892533.py:10: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", - " df_final['MEDIANA DO HISTÓRICO'] = df_final['MEDIANA DO HISTÓRICO'].fillna(0)\n", - "C:\\Users\\joao.herculano\\AppData\\Local\\Temp\\ipykernel_37548\\2817892533.py:19: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n", - " df_final['MEDIANA DO HISTÓRICO'] = df_final['MEDIANA DO HISTÓRICO'].fillna(0)\n" - ] - }, { "data": { "text/plain": [ - "(1022, 108)" + "(2291, 108)" ] }, - "execution_count": 50, + "execution_count": 93, "metadata": {}, "output_type": "execute_result" } @@ -1450,7 +5557,7 @@ "# Corrige mediana do histórico onde for zero\n", "df_final['MEDIANA DO HISTÓRICO'] = np.where(\n", " df_final['MEDIANA DO HISTÓRICO'] == 0,\n", - " df_final['med_por_canal'],\n", + " 0,\n", " df_final['MEDIANA DO HISTÓRICO']\n", ")\n", "\n", @@ -1469,7 +5576,7 @@ ")\n", "\n", "# Se PV GINSENG for NA, substitui pela média por canal\n", - "df_final['PV GINSENG'] = df_final['PV GINSENG'].fillna(df_final['med_por_canal'].round(0))\n", + "df_final['PV GINSENG'] = df_final['PV GINSENG'].fillna(0)\n", "\n", "df_final['PV GINSENG'] = df_final['PV GINSENG'].round(0).astype(int)\n", "\n", @@ -1478,7 +5585,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 94, "id": "cfdae71d", "metadata": {}, "outputs": [], @@ -1488,61 +5595,76 @@ }, { "cell_type": "code", - "execution_count": 52, - "id": "7b058db6", + "execution_count": 95, + "id": "ad10c069", "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "Index(['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", - " dtype='object')" - ] - }, - "execution_count": 52, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "Index(['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", + " dtype='object')\n" + ] } ], "source": [ - "df_final.columns[30:43]" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "id": "ad10c069", - "metadata": {}, - "outputs": [], - "source": [ + "print(df_final.columns[30:43])\n", + "\n", "df_final.drop(columns=df_final.columns[30:43],inplace=True)" ] }, { "cell_type": "code", - "execution_count": 54, - "id": "15b7149f", + "execution_count": 96, + "id": "3b7f3a82", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['Histórico de Vendas do Ciclo 202410',\n", + " 'Histórico de Vendas do Ciclo 202507',\n", + " 'Histórico de Vendas do Ciclo 202508',\n", + " 'Histórico de Vendas do Ciclo 202509',\n", + " 'Histórico de Vendas do Ciclo Atual'],\n", + " dtype='object')" + ] + }, + "execution_count": 96, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "df_final = df_final.rename(columns={df_final.columns[30]: \"C-4\", df_final.columns[31]: \"C-3\",df_final.columns[32]: \"C-2\",df_final.columns[33]: \"C-1\",df_final.columns[34]:'VENDAS CICLO ATUAL'})\n" + "df_final.columns[29:34]" ] }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 97, + "id": "15b7149f", + "metadata": {}, + "outputs": [], + "source": [ + "df_final = df_final.rename(columns={df_final.columns[30]: \"C-4\", df_final.columns[31]: \"C-3\",df_final.columns[32]: \"C-2\",df_final.columns[29]: \"C-1\",df_final.columns[33]:'VENDAS CICLO ATUAL'})\n" + ] + }, + { + "cell_type": "code", + "execution_count": 98, "id": "9333bc77", "metadata": {}, "outputs": [ @@ -1552,11 +5674,11 @@ "Index(['PRODUTO LANÇAMENTO', 'DESCRIÇÃO DO LANÇAMENTO', 'PRODUTO SIMILAR',\n", " 'DESCRIÇÃO SIMILAR', 'CICLO SIMILAR', 'FOCO', 'IAF', 'CATEGORIA',\n", " 'MARCA', '% CONSUMIDOR', 'MECANICA CONSUMIDOR', '% REVENDEDOR',\n", - " 'MECANICA REVENDEDOR', 'TIPO DE PRODUTO', 'MATCH', 'PDV',\n", - " 'PDV COMO TEXTO', 'CANAL', 'DESCRIÇÃO PDV', 'PDV DESC', 'UF',\n", - " 'ANALISTA', 'SUPERVISOR', 'STATUS', 'CLASSE', 'SKU_X', 'DESCRIÇÃO',\n", - " 'SUBCATEGORIA', 'LANÇAMENTO', 'DESATIVAÇÃO', 'C-4', 'C-3', 'C-2', 'C-1',\n", - " 'VENDAS CICLO ATUAL', 'DIAS SEM VENDA', 'PROJEÇÃO PRÓXIMO CICLO',\n", + " 'MECANICA REVENDEDOR', 'TIPO DE PRODUTO', 'MATCH', 'PDV', 'CANAL',\n", + " 'DESCRIÇÃO PDV', 'PDV DESC', 'UF', 'ANALISTA', 'SUPERVISOR', 'STATUS',\n", + " 'CLASSE', 'SKU_X', 'DESCRIÇÃO', 'SUBCATEGORIA', 'LANÇAMENTO',\n", + " 'DESATIVAÇÃO', 'C-1', 'C-4', 'C-3', 'C-2', 'VENDAS CICLO ATUAL',\n", + " 'DIAS SEM VENDA', 'PROJEÇÃO PRÓXIMO CICLO',\n", " 'PROJEÇÃO PRÓXIMO CICLO + 1', 'PROMOÇÃO PRÓXIMO CICLO',\n", " 'PROMOÇÃO PRÓXIMO CICLO + 1', 'ESTOQUE ATUAL', 'ESTOQUE EM TRANSITO',\n", " 'PEDIDO PENDENTE', 'COMPRA INTELIGENTE SEMANAL/SUGESTÃO DE COMPRA',\n", @@ -1567,17 +5689,16 @@ " 'ITEM ANALISADO', 'MATCH_X', 'CRESCIMENTO', 'CICLO', 'INICIO CICLO',\n", " 'FIM CICLO', 'DURAÇÃO', 'MATCH_Y', 'DIAS_ATE_INICIO',\n", " 'INICIO CICLO SIMILAR', 'FIM CICLO SIMILAR', 'DURAÇÃO CICLO SIMILAR',\n", - " 'CÓDIGO DO PRODUTO', 'CICLO', 'VENDAS CICLO LANÇAMENTO', 'SKU_Y',\n", - " '202408', '202409', '202410', '202411', '202412', '202413', '202414',\n", - " '202415', '202416', '202417', '202501', '202502', '202503', '202504',\n", - " '202505', '202506', '202507', 'CRESCIMENTO_GERAL',\n", - " 'PICO VENDAS SIMILAR ULTIMOS 6 CICLOS', 'MEDIANA DO HISTÓRICO',\n", - " 'MED_POR_CANAL', 'PDVDEPARA.PRACTICO', 'CÓDIGO', 'CRESCIMENTO_FINAL',\n", - " 'PV GINSENG'],\n", + " 'SKU_Y', 'CICLO', 'VENDAS CICLO LANÇAMENTO', 'SKU', '202408', '202409',\n", + " '202410', '202411', '202412', '202413', '202414', '202415', '202416',\n", + " '202417', '202501', '202502', '202503', '202504', '202505', '202506',\n", + " '202507', 'CRESCIMENTO_GERAL', 'PICO VENDAS SIMILAR ULTIMOS 6 CICLOS',\n", + " 'MEDIANA DO HISTÓRICO', 'DE PARA PDV', 'SKU', 'PDVDEPARA.PRACTICO',\n", + " 'CÓDIGO', 'CRESCIMENTO_FINAL', 'PV GINSENG'],\n", " dtype='object')" ] }, - "execution_count": 55, + "execution_count": 98, "metadata": {}, "output_type": "execute_result" } @@ -1592,17 +5713,27 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 99, + "id": "dceebd16", + "metadata": {}, + "outputs": [], + "source": [ + "df_final = df_final.rename(columns={'MARCA_X':'MARCA'})" + ] + }, + { + "cell_type": "code", + "execution_count": 100, "id": "62ce5c62", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(1022, 94)" + "(2291, 94)" ] }, - "execution_count": 56, + "execution_count": 100, "metadata": {}, "output_type": "execute_result" } @@ -1613,7 +5744,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 101, "id": "cecca116", "metadata": {}, "outputs": [], @@ -1623,14 +5754,16 @@ }, { "cell_type": "code", - "execution_count": 58, - "id": "733e6e45", + "execution_count": 102, + "id": "8ad318b0", "metadata": {}, "outputs": [], "source": [ - "df_final = df_final[[\n", + "df_final = df_final.loc[:, ~df_final.columns.duplicated()]\n", + "\n", + "colunas_desejadas = [\n", " 'SUPERVISOR',\n", - " 'ANALISTA', \n", + " 'ANALISTA',\n", " 'CANAL',\n", " 'UF',\n", " 'PDV',\n", @@ -1639,10 +5772,10 @@ " 'DESCRIÇÃO DO LANÇAMENTO',\n", " 'MARCA',\n", " 'CATEGORIA',\n", - " 'MECANICA CONSUMIDOR',\n", " '% CONSUMIDOR',\n", - " 'MECANICA REVENDEDOR',\n", + " 'MECANICA CONSUMIDOR',\n", " '% REVENDEDOR',\n", + " 'MECANICA REVENDEDOR',\n", " 'TIPO DE PRODUTO',\n", " 'IAF',\n", " 'FOCO',\n", @@ -1657,56 +5790,33 @@ " 'VENDAS CICLO ATUAL',\n", " 'PICO VENDAS SIMILAR ULTIMOS 6 CICLOS',\n", " 'PV GINSENG'\n", - "]]" + "]\n", + "\n", + "df_final = df_final.reindex(columns=colunas_desejadas)\n", + "\n", + "colunas_filtradas = [col for col in colunas_desejadas if col in df_final.columns]\n", + "df_final = df_final[colunas_filtradas]\n" ] }, { "cell_type": "code", - "execution_count": 59, - "id": "2e3a04cd", - "metadata": {}, - "outputs": [], - "source": [ - "df_final = df_final[[\n", - " 'SUPERVISOR',\n", - " 'ANALISTA', \n", - " 'CANAL',\n", - " 'UF',\n", - " 'PDV',\n", - " 'DESCRIÇÃO PDV',\n", - " 'PRODUTO LANÇAMENTO',\n", - " 'DESCRIÇÃO DO LANÇAMENTO',\n", - " 'MARCA',\n", - " 'CATEGORIA',\n", - " 'MECANICA CONSUMIDOR',\n", - " '% CONSUMIDOR',\n", - " 'MECANICA REVENDEDOR',\n", - " '% REVENDEDOR',\n", - " 'TIPO DE PRODUTO',\n", - " 'IAF',\n", - " 'FOCO',\n", - " 'PRODUTO SIMILAR',\n", - " 'DESCRIÇÃO SIMILAR',\n", - " 'CICLO SIMILAR',\n", - " 'VENDAS CICLO LANÇAMENTO',\n", - " 'C-4',\n", - " 'C-3',\n", - " 'C-2',\n", - " 'C-1',\n", - " 'VENDAS CICLO ATUAL',\n", - " 'PICO VENDAS SIMILAR ULTIMOS 6 CICLOS',\n", - " 'PV GINSENG'\n", - "]]" - ] - }, - { - "cell_type": "code", - "execution_count": 60, + "execution_count": 103, "id": "25cbff26", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "\"df_final = df_final.reindex(columns=[\\n 'SUPERVISOR',\\n 'ANALISTA',\\n 'CANAL',\\n 'UF',\\n 'PDV',\\n 'DESCRIÇÃO PDV',\\n 'PRODUTO LANÇAMENTO',\\n 'DESCRIÇÃO DO LANÇAMENTO',\\n 'MARCA',\\n 'CATEGORIA',\\n 'MECANICA CONSUMIDOR',\\n '% CONSUMIDOR',\\n 'MECANICA REVENDEDOR',\\n '% REVENDEDOR',\\n 'TIPO DE PRODUTO',\\n 'IAF',\\n 'FOCO',\\n 'PRODUTO SIMILAR',\\n 'DESCRIÇÃO SIMILAR',\\n 'CICLO SIMILAR',\\n 'VENDAS CICLO LANÇAMENTO',\\n 'C-4',\\n 'C-3',\\n 'C-2',\\n 'C-1',\\n 'VENDAS CICLO ATUAL',\\n 'PICO VENDAS SIMILAR ULTIMOS 6 CICLOS',\\n 'PV GINSENG'])\\n\"" + ] + }, + "execution_count": 103, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "df_final = df_final.reindex(columns=[\n", + "'''df_final = df_final.reindex(columns=[\n", " 'SUPERVISOR',\n", " 'ANALISTA',\n", " 'CANAL',\n", @@ -1734,12 +5844,13 @@ " 'C-1',\n", " 'VENDAS CICLO ATUAL',\n", " 'PICO VENDAS SIMILAR ULTIMOS 6 CICLOS',\n", - " 'PV GINSENG'])\n" + " 'PV GINSENG'])\n", + "'''" ] }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 104, "id": "a3e80cb4", "metadata": {}, "outputs": [], @@ -1751,7 +5862,289 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 105, + "id": "cc9c6ee6", + "metadata": {}, + "outputs": [], + "source": [ + "df_pdv_origi['PDV'] = df_pdv_origi['PDV'].astype(str)\n", + "\n", + "df_pdv_origi['PDV'].head()\n", + "\n", + "df_pdv_origi = df_pdv_origi.rename(columns={'UF':'UF_CERTO'})" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "id": "df9f0130", + "metadata": {}, + "outputs": [], + "source": [ + "df_final= pd.merge(df_final,df_pdv_origi[['UF_CERTO','PDV']],on='PDV',how='inner')" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "id": "7778d82f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['SUPERVISOR', 'ANALISTA', 'CANAL', 'UF', 'PDV', 'DESCRIÇÃO PDV',\n", + " 'PRODUTO LANÇAMENTO', 'DESCRIÇÃO DO LANÇAMENTO', 'MARCA', 'CATEGORIA',\n", + " '% CONSUMIDOR', 'MECANICA CONSUMIDOR', '% REVENDEDOR',\n", + " 'MECANICA REVENDEDOR', 'TIPO DE PRODUTO', 'IAF', 'FOCO',\n", + " 'PRODUTO SIMILAR', 'DESCRIÇÃO SIMILAR', 'CICLO SIMILAR',\n", + " 'VENDAS CICLO LANÇAMENTO', 'C-4', 'C-3', 'C-2', 'C-1',\n", + " 'VENDAS CICLO ATUAL', 'PICO VENDAS SIMILAR ULTIMOS 6 CICLOS',\n", + " 'PV GINSENG', 'SUGESTÃO METASELLIN', 'SUGESTÃO ABASTECIMENTO',\n", + " 'SUGESTÃO COMERCIAL', 'UF_CERTO'],\n", + " dtype='object')" + ] + }, + "execution_count": 107, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_final.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "id": "f8cd886a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " SUPERVISOR ANALISTA CANAL UF PDV DESCRIÇÃO PDV \\\n", + "0 Efigênia Herculano LUAN LJ AL 12522 MACEIO SHOP EXP \n", + "1 Maxwell Vieira JEFFERSON LJ AL 12817 SHOPPING PATIO \n", + "2 Maxwell Vieira PEDRO LJ AL 12818 GB SERRARIA \n", + "3 Maxwell Vieira THAYLLAN LJ AL 12820 ATACADÃO \n", + "4 Efigênia Herculano MARCYARA LJ AL 12823 PONTA VERDE \n", + "\n", + " PRODUTO LANÇAMENTO DESCRIÇÃO DO LANÇAMENTO \\\n", + "0 59823 NATIVA SPA ORQUÍDEA LUMIÈRE CREME DESODORANTE ... \n", + "1 59823 NATIVA SPA ORQUÍDEA LUMIÈRE CREME DESODORANTE ... \n", + "2 59823 NATIVA SPA ORQUÍDEA LUMIÈRE CREME DESODORANTE ... \n", + "3 59823 NATIVA SPA ORQUÍDEA LUMIÈRE CREME DESODORANTE ... \n", + "4 59823 NATIVA SPA ORQUÍDEA LUMIÈRE CREME DESODORANTE ... \n", + "\n", + " MARCA CATEGORIA ... C-4 C-3 C-2 C-1 \\\n", + "0 NATIVA SPA CUIDADOS COM A PELE ... 2.0 3.0 0.0 2.0 \n", + "1 NATIVA SPA CUIDADOS COM A PELE ... 3.0 0.0 0.0 3.0 \n", + "2 NATIVA SPA CUIDADOS COM A PELE ... 4.0 2.0 0.0 1.0 \n", + "3 NATIVA SPA CUIDADOS COM A PELE ... 0.0 2.0 0.0 1.0 \n", + "4 NATIVA SPA CUIDADOS COM A PELE ... 4.0 0.0 1.0 5.0 \n", + "\n", + " VENDAS CICLO ATUAL PICO VENDAS SIMILAR ULTIMOS 6 CICLOS PV GINSENG \\\n", + "0 0.0 3.0 27 \n", + "1 0.0 5.0 43 \n", + "2 4.0 4.0 11 \n", + "3 2.0 3.0 6 \n", + "4 5.0 7.0 34 \n", + "\n", + " SUGESTÃO METASELLIN SUGESTÃO ABASTECIMENTO SUGESTÃO COMERCIAL \n", + "0 \n", + "1 \n", + "2 \n", + "3 \n", + "4 \n", + "\n", + "[5 rows x 31 columns]" + ] + }, + "execution_count": 108, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_final['UF']= df_final['UF_CERTO']\n", + "\n", + "df_final = df_final.drop(columns=['UF_CERTO'])\n", + "\n", + "df_final.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 110, "id": "2df3e2e9", "metadata": {}, "outputs": [],