166 lines
4.4 KiB
Plaintext
166 lines
4.4 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "2919eeaa",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>UN</th>\n",
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" <th>PDV</th>\n",
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" <th>Mês</th>\n",
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" <th>Orçamento PDV</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>BOT</td>\n",
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" <td>20969</td>\n",
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" <td>1</td>\n",
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" <td>2.684687e+05</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>BOT</td>\n",
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" <td>20991</td>\n",
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" <td>1</td>\n",
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" <td>4.163216e+05</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>BOT</td>\n",
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" <td>20996</td>\n",
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" <td>1</td>\n",
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" <td>1.378414e+06</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>BOT</td>\n",
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" <td>20997</td>\n",
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" <td>1</td>\n",
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" <td>2.254454e+06</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>BOT</td>\n",
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" <td>20998</td>\n",
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" <td>1</td>\n",
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" <td>4.559163e+06</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" UN PDV Mês Orçamento PDV\n",
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"0 BOT 20969 1 2.684687e+05\n",
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"1 BOT 20991 1 4.163216e+05\n",
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"2 BOT 20996 1 1.378414e+06\n",
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"3 BOT 20997 1 2.254454e+06\n",
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"4 BOT 20998 1 4.559163e+06"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import pandas as pd\n",
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"\n",
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"# Lê os dados\n",
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"df = pd.read_excel(r\"C:\\Users\\joao.herculano\\Downloads\\orcamento_para_pivotar.xlsx\") # Substitua pelo caminho correto\n",
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"\n",
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"# Remove colunas que não serão usadas\n",
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"df = df.drop(columns=['Total'])\n",
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"\n",
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"# Criar lista de colunas de interesse\n",
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"colunas_fixas = ['UN', 'PDV']\n",
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"colunas_mes_orcamento = df.columns[len(colunas_fixas):] # tudo que vem depois de UN e PDV\n",
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"\n",
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"# Quebrar as colunas em pares: (mês, orçamento)\n",
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"meses = list(range(1, 13))\n",
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"colunas_orcamento = colunas_mes_orcamento[1::2] # pegar apenas os valores de orçamento\n",
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"\n",
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"# Criar um DataFrame vazio\n",
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"df_resultado = pd.DataFrame()\n",
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"\n",
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"# Para cada mês, adicionar ao resultado\n",
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"for i, mes in enumerate(meses):\n",
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" nova_coluna = pd.DataFrame({\n",
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" 'UN': df['UN'],\n",
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" 'PDV': df['PDV'],\n",
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" 'Mês': mes,\n",
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" 'Orçamento PDV': df[colunas_orcamento[i]]\n",
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" })\n",
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" df_resultado = pd.concat([df_resultado, nova_coluna], ignore_index=True)\n",
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"\n",
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"\n",
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"\n",
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"df_resultado.head()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "0bfaaf90",
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"metadata": {},
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"outputs": [],
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"source": [
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"df_resultado.to_excel(r\"C:\\Users\\joao.herculano\\Downloads\\orcamento_para_pivotar2.xlsx\",index=False)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9268f5af",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.13.2"
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}
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"nbformat_minor": 5
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}
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