empresa6.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "IMPORTANDO TABELA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "      <td>22967215</td>\n",
       "      <td>VIVIAN CRISTINA FARIAS DA SILVA 00903464403</td>\n",
       "      <td>2135</td>\n",
       "      <td>50</td>\n",
       "      <td>5000,00</td>\n",
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       "      <th>1</th>\n",
       "      <td>22967216</td>\n",
       "      <td>PALOMA GOMES DE OLIVEIRA 43155887842</td>\n",
       "      <td>2135</td>\n",
       "      <td>50</td>\n",
       "      <td>1,00</td>\n",
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       "    <tr>\n",
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       "      <td>22967217</td>\n",
       "      <td>TEREZA DIAS COSTA DOS SANTOS 04631932880</td>\n",
       "      <td>2135</td>\n",
       "      <td>50</td>\n",
       "      <td>26400,00</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>FNAS PARTICIPACOES LTDA.</td>\n",
       "      <td>2062</td>\n",
       "      <td>49</td>\n",
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       "      <td>27499604</td>\n",
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       "      <th>4494857</th>\n",
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       "      <td>SILVANA BATISTA MOREIRA LOPES 94094780904</td>\n",
       "      <td>2135</td>\n",
       "      <td>50</td>\n",
       "      <td>1,00</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>PEDRO OSCAR PIAUHY MULLER 25578880525</td>\n",
       "      <td>2135</td>\n",
       "      <td>50</td>\n",
       "      <td>1000,00</td>\n",
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       "<p>4494859 rows × 7 columns</p>\n",
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       "         22967214                             NILSON DA SILVA SANTOS  2135  \\\n",
       "0        22967215        VIVIAN CRISTINA FARIAS DA SILVA 00903464403  2135   \n",
       "1        22967216               PALOMA GOMES DE OLIVEIRA 43155887842  2135   \n",
       "2        22967217           TEREZA DIAS COSTA DOS SANTOS 04631932880  2135   \n",
       "3        22967218                  LUIS CARLOS CASTANEDA 26988367845  2135   \n",
       "4        22967219                           FNAS PARTICIPACOES LTDA.  2062   \n",
       "...           ...                                                ...   ...   \n",
       "4494854  27499602              JULIANA GUIMARAES PEREIRA 64430154104  2135   \n",
       "4494855  27499603                  27.499.603 RAIONE XAVIER DE PAIVA  2135   \n",
       "4494856  27499604  ALEXANDRE KARIM BUENO DE OLIVEIRA RODRIGUES SA...  2135   \n",
       "4494857  27499605          SILVANA BATISTA MOREIRA LOPES 94094780904  2135   \n",
       "4494858  27499606              PEDRO OSCAR PIAUHY MULLER 25578880525  2135   \n",
       "\n",
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       "...      ..       ...  ...        ...  \n",
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       "4494857  50      1,00  1.0        NaN  \n",
       "4494858  50   1000,00  1.0        NaN  \n",
       "\n",
       "[4494859 rows x 7 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('empresa6.CSV', encoding='LATIN-1', sep=';')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ELIMINAR O HEAD (CABAÇALHO)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <td>27499604</td>\n",
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       "      <td>2135</td>\n",
       "      <td>50</td>\n",
       "      <td>1,00</td>\n",
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       "      <th>4494858</th>\n",
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       "      <td>2135</td>\n",
       "      <td>50</td>\n",
       "      <td>1,00</td>\n",
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       "      <td>2135</td>\n",
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       "      <td>1000,00</td>\n",
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       "                0                                                  1     2  \\\n",
       "0        22967214                             NILSON DA SILVA SANTOS  2135   \n",
       "1        22967215        VIVIAN CRISTINA FARIAS DA SILVA 00903464403  2135   \n",
       "2        22967216               PALOMA GOMES DE OLIVEIRA 43155887842  2135   \n",
       "3        22967217           TEREZA DIAS COSTA DOS SANTOS 04631932880  2135   \n",
       "4        22967218                  LUIS CARLOS CASTANEDA 26988367845  2135   \n",
       "...           ...                                                ...   ...   \n",
       "4494855  27499602              JULIANA GUIMARAES PEREIRA 64430154104  2135   \n",
       "4494856  27499603                  27.499.603 RAIONE XAVIER DE PAIVA  2135   \n",
       "4494857  27499604  ALEXANDRE KARIM BUENO DE OLIVEIRA RODRIGUES SA...  2135   \n",
       "4494858  27499605          SILVANA BATISTA MOREIRA LOPES 94094780904  2135   \n",
       "4494859  27499606              PEDRO OSCAR PIAUHY MULLER 25578880525  2135   \n",
       "\n",
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       "3        50  26400,00  1.0  NaN  \n",
       "4        50      1,00  1.0  NaN  \n",
       "...      ..       ...  ...  ...  \n",
       "4494855  50      1,00  1.0  NaN  \n",
       "4494856  50  25000,00  1.0  NaN  \n",
       "4494857  50      1,00  1.0  NaN  \n",
       "4494858  50      1,00  1.0  NaN  \n",
       "4494859  50   1000,00  1.0  NaN  \n",
       "\n",
       "[4494860 rows x 7 columns]"
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('empresa6.CSV', encoding='LATIN-1', sep=';', header=None)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "INSERIR O HAD (CABEÇALHO)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>CNPJ</th>\n",
       "      <th>INDEFICADOR MATRIZ/FILIAL</th>\n",
       "      <th>RAZÃO SOCIAL</th>\n",
       "      <th>NOME FANTASIA</th>\n",
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       "      <td>27.499.603 RAIONE XAVIER DE PAIVA</td>\n",
       "      <td>2135</td>\n",
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       "      <td>ALEXANDRE KARIM BUENO DE OLIVEIRA RODRIGUES SA...</td>\n",
       "      <td>2135</td>\n",
       "      <td>50</td>\n",
       "      <td>1,00</td>\n",
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       "      <th>4494858</th>\n",
       "      <td>27499605</td>\n",
       "      <td>SILVANA BATISTA MOREIRA LOPES 94094780904</td>\n",
       "      <td>2135</td>\n",
       "      <td>50</td>\n",
       "      <td>1,00</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>27499606</td>\n",
       "      <td>PEDRO OSCAR PIAUHY MULLER 25578880525</td>\n",
       "      <td>2135</td>\n",
       "      <td>50</td>\n",
       "      <td>1000,00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4494860 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             CNPJ                          INDEFICADOR MATRIZ/FILIAL  \\\n",
       "0        22967214                             NILSON DA SILVA SANTOS   \n",
       "1        22967215        VIVIAN CRISTINA FARIAS DA SILVA 00903464403   \n",
       "2        22967216               PALOMA GOMES DE OLIVEIRA 43155887842   \n",
       "3        22967217           TEREZA DIAS COSTA DOS SANTOS 04631932880   \n",
       "4        22967218                  LUIS CARLOS CASTANEDA 26988367845   \n",
       "...           ...                                                ...   \n",
       "4494855  27499602              JULIANA GUIMARAES PEREIRA 64430154104   \n",
       "4494856  27499603                  27.499.603 RAIONE XAVIER DE PAIVA   \n",
       "4494857  27499604  ALEXANDRE KARIM BUENO DE OLIVEIRA RODRIGUES SA...   \n",
       "4494858  27499605          SILVANA BATISTA MOREIRA LOPES 94094780904   \n",
       "4494859  27499606              PEDRO OSCAR PIAUHY MULLER 25578880525   \n",
       "\n",
       "         RAZÃO SOCIAL  NOME FANTASIA SITUAÇÃO CADASTRAL  \\\n",
       "0                2135             50           24000,00   \n",
       "1                2135             50            5000,00   \n",
       "2                2135             50               1,00   \n",
       "3                2135             50           26400,00   \n",
       "4                2135             50               1,00   \n",
       "...               ...            ...                ...   \n",
       "4494855          2135             50               1,00   \n",
       "4494856          2135             50           25000,00   \n",
       "4494857          2135             50               1,00   \n",
       "4494858          2135             50               1,00   \n",
       "4494859          2135             50            1000,00   \n",
       "\n",
       "         DATA SITUAÇÃO CADASTRAL CO-PAÍS  \n",
       "0                            1.0     NaN  \n",
       "1                            1.0     NaN  \n",
       "2                            1.0     NaN  \n",
       "3                            1.0     NaN  \n",
       "4                            1.0     NaN  \n",
       "...                          ...     ...  \n",
       "4494855                      1.0     NaN  \n",
       "4494856                      1.0     NaN  \n",
       "4494857                      1.0     NaN  \n",
       "4494858                      1.0     NaN  \n",
       "4494859                      1.0     NaN  \n",
       "\n",
       "[4494860 rows x 7 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns = ['CNPJ', 'INDEFICADOR MATRIZ/FILIAL', 'RAZÃO SOCIAL', 'NOME FANTASIA', 'SITUAÇÃO CADASTRAL',\n",
    "                    'DATA SITUAÇÃO CADASTRAL', 'CO-PAÍS']\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ELIMINAR A ÚLTIMA LINHA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
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       "      <td>22967217</td>\n",
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       "      <td>27499602</td>\n",
       "      <td>JULIANA GUIMARAES PEREIRA 64430154104</td>\n",
       "      <td>2135</td>\n",
       "      <td>50</td>\n",
       "      <td>1,00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4494856</th>\n",
       "      <td>27499603</td>\n",
       "      <td>27.499.603 RAIONE XAVIER DE PAIVA</td>\n",
       "      <td>2135</td>\n",
       "      <td>50</td>\n",
       "      <td>25000,00</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
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       "      <td>27499604</td>\n",
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       "      <td>2135</td>\n",
       "      <td>50</td>\n",
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       "      <td>50</td>\n",
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      "text/plain": [
       "             CNPJ                          INDEFICADOR MATRIZ/FILIAL  \\\n",
       "0        22967214                             NILSON DA SILVA SANTOS   \n",
       "1        22967215        VIVIAN CRISTINA FARIAS DA SILVA 00903464403   \n",
       "2        22967216               PALOMA GOMES DE OLIVEIRA 43155887842   \n",
       "3        22967217           TEREZA DIAS COSTA DOS SANTOS 04631932880   \n",
       "4        22967218                  LUIS CARLOS CASTANEDA 26988367845   \n",
       "...           ...                                                ...   \n",
       "4494854  27499601                               SILVANIO JORGE PINTO   \n",
       "4494855  27499602              JULIANA GUIMARAES PEREIRA 64430154104   \n",
       "4494856  27499603                  27.499.603 RAIONE XAVIER DE PAIVA   \n",
       "4494857  27499604  ALEXANDRE KARIM BUENO DE OLIVEIRA RODRIGUES SA...   \n",
       "4494858  27499605          SILVANA BATISTA MOREIRA LOPES 94094780904   \n",
       "\n",
       "         RAZÃO SOCIAL  NOME FANTASIA SITUAÇÃO CADASTRAL  \\\n",
       "0                2135             50           24000,00   \n",
       "1                2135             50            5000,00   \n",
       "2                2135             50               1,00   \n",
       "3                2135             50           26400,00   \n",
       "4                2135             50               1,00   \n",
       "...               ...            ...                ...   \n",
       "4494854          2135             50           60000,00   \n",
       "4494855          2135             50               1,00   \n",
       "4494856          2135             50           25000,00   \n",
       "4494857          2135             50               1,00   \n",
       "4494858          2135             50               1,00   \n",
       "\n",
       "         DATA SITUAÇÃO CADASTRAL CO-PAÍS  \n",
       "0                            1.0     NaN  \n",
       "1                            1.0     NaN  \n",
       "2                            1.0     NaN  \n",
       "3                            1.0     NaN  \n",
       "4                            1.0     NaN  \n",
       "...                          ...     ...  \n",
       "4494854                      1.0     NaN  \n",
       "4494855                      1.0     NaN  \n",
       "4494856                      1.0     NaN  \n",
       "4494857                      1.0     NaN  \n",
       "4494858                      1.0     NaN  \n",
       "\n",
       "[4494859 rows x 7 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(df.tail(1).index, inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "EXPORTAR TABELA:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>JULIANA GUIMARAES PEREIRA 64430154104</td>\n",
       "      <td>2135</td>\n",
       "      <td>50</td>\n",
       "      <td>1,00</td>\n",
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       "    <tr>\n",
       "      <th>4494856</th>\n",
       "      <td>27499603</td>\n",
       "      <td>27.499.603 RAIONE XAVIER DE PAIVA</td>\n",
       "      <td>2135</td>\n",
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       "      <td>27499604</td>\n",
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       "      <td>2135</td>\n",
       "      <td>50</td>\n",
       "      <td>1,00</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>50</td>\n",
       "      <td>1,00</td>\n",
       "      <td>1.0</td>\n",
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       "<p>4494859 rows × 7 columns</p>\n",
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      ],
      "text/plain": [
       "             CNPJ                          INDEFICADOR MATRIZ/FILIAL  \\\n",
       "0        22967214                             NILSON DA SILVA SANTOS   \n",
       "1        22967215        VIVIAN CRISTINA FARIAS DA SILVA 00903464403   \n",
       "2        22967216               PALOMA GOMES DE OLIVEIRA 43155887842   \n",
       "3        22967217           TEREZA DIAS COSTA DOS SANTOS 04631932880   \n",
       "4        22967218                  LUIS CARLOS CASTANEDA 26988367845   \n",
       "...           ...                                                ...   \n",
       "4494854  27499601                               SILVANIO JORGE PINTO   \n",
       "4494855  27499602              JULIANA GUIMARAES PEREIRA 64430154104   \n",
       "4494856  27499603                  27.499.603 RAIONE XAVIER DE PAIVA   \n",
       "4494857  27499604  ALEXANDRE KARIM BUENO DE OLIVEIRA RODRIGUES SA...   \n",
       "4494858  27499605          SILVANA BATISTA MOREIRA LOPES 94094780904   \n",
       "\n",
       "         RAZÃO SOCIAL  NOME FANTASIA SITUAÇÃO CADASTRAL  \\\n",
       "0                2135             50           24000,00   \n",
       "1                2135             50            5000,00   \n",
       "2                2135             50               1,00   \n",
       "3                2135             50           26400,00   \n",
       "4                2135             50               1,00   \n",
       "...               ...            ...                ...   \n",
       "4494854          2135             50           60000,00   \n",
       "4494855          2135             50               1,00   \n",
       "4494856          2135             50           25000,00   \n",
       "4494857          2135             50               1,00   \n",
       "4494858          2135             50               1,00   \n",
       "\n",
       "         DATA SITUAÇÃO CADASTRAL CO-PAÍS  \n",
       "0                            1.0     NaN  \n",
       "1                            1.0     NaN  \n",
       "2                            1.0     NaN  \n",
       "3                            1.0     NaN  \n",
       "4                            1.0     NaN  \n",
       "...                          ...     ...  \n",
       "4494854                      1.0     NaN  \n",
       "4494855                      1.0     NaN  \n",
       "4494856                      1.0     NaN  \n",
       "4494857                      1.0     NaN  \n",
       "4494858                      1.0     NaN  \n",
       "\n",
       "[4494859 rows x 7 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.to_csv('n_empresa6.csv', index=False)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "This sheet is too large! Your sheet size is: 4494859, 7 Max sheet size is: 1048576, 16384",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[12], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_excel\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mn_empresa6.xlsx\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m      2\u001b[0m df\n",
      "File \u001b[1;32me:\\TRABALHO\\pandas\\venv\\Lib\\site-packages\\pandas\\util\\_decorators.py:333\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    327\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m num_allow_args:\n\u001b[0;32m    328\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[0;32m    329\u001b[0m         msg\u001b[38;5;241m.\u001b[39mformat(arguments\u001b[38;5;241m=\u001b[39m_format_argument_list(allow_args)),\n\u001b[0;32m    330\u001b[0m         \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[0;32m    331\u001b[0m         stacklevel\u001b[38;5;241m=\u001b[39mfind_stack_level(),\n\u001b[0;32m    332\u001b[0m     )\n\u001b[1;32m--> 333\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32me:\\TRABALHO\\pandas\\venv\\Lib\\site-packages\\pandas\\core\\generic.py:2417\u001b[0m, in \u001b[0;36mNDFrame.to_excel\u001b[1;34m(self, excel_writer, sheet_name, na_rep, float_format, columns, header, index, index_label, startrow, startcol, engine, merge_cells, inf_rep, freeze_panes, storage_options, engine_kwargs)\u001b[0m\n\u001b[0;32m   2404\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mio\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mformats\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mexcel\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ExcelFormatter\n\u001b[0;32m   2406\u001b[0m formatter \u001b[38;5;241m=\u001b[39m ExcelFormatter(\n\u001b[0;32m   2407\u001b[0m     df,\n\u001b[0;32m   2408\u001b[0m     na_rep\u001b[38;5;241m=\u001b[39mna_rep,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   2415\u001b[0m     inf_rep\u001b[38;5;241m=\u001b[39minf_rep,\n\u001b[0;32m   2416\u001b[0m )\n\u001b[1;32m-> 2417\u001b[0m \u001b[43mformatter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwrite\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   2418\u001b[0m \u001b[43m    \u001b[49m\u001b[43mexcel_writer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2419\u001b[0m \u001b[43m    \u001b[49m\u001b[43msheet_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msheet_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2420\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstartrow\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstartrow\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2421\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstartcol\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstartcol\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2422\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfreeze_panes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfreeze_panes\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2423\u001b[0m \u001b[43m    \u001b[49m\u001b[43mengine\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2424\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstorage_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2425\u001b[0m \u001b[43m    \u001b[49m\u001b[43mengine_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mengine_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2426\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32me:\\TRABALHO\\pandas\\venv\\Lib\\site-packages\\pandas\\io\\formats\\excel.py:931\u001b[0m, in \u001b[0;36mExcelFormatter.write\u001b[1;34m(self, writer, sheet_name, startrow, startcol, freeze_panes, engine, storage_options, engine_kwargs)\u001b[0m\n\u001b[0;32m    929\u001b[0m num_rows, num_cols \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdf\u001b[38;5;241m.\u001b[39mshape\n\u001b[0;32m    930\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m num_rows \u001b[38;5;241m>\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmax_rows \u001b[38;5;129;01mor\u001b[39;00m num_cols \u001b[38;5;241m>\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmax_cols:\n\u001b[1;32m--> 931\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m    932\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThis sheet is too large! Your sheet size is: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnum_rows\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnum_cols\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    933\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMax sheet size is: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmax_rows\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmax_cols\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    934\u001b[0m     )\n\u001b[0;32m    936\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m engine_kwargs \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    937\u001b[0m     engine_kwargs \u001b[38;5;241m=\u001b[39m {}\n",
      "\u001b[1;31mValueError\u001b[0m: This sheet is too large! Your sheet size is: 4494859, 7 Max sheet size is: 1048576, 16384"
     ]
    }
   ],
   "source": [
    "df.to_excel('n_empresa6.xlsx', index=False)\n",
    "df"
   ]
  }
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