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START_INFOCARD -->\n\nThe `@flatfile/plugin-xlsx-extractor` plugin is designed to extract structured data from Excel files. It utilizes various libraries to parse Excel files and retrieve the structured data efficiently.\n\n**Event Type:** \n`listener.on('file:created')`\n\n**Supported file types:** \n`.xls`, `.xlsx`, `.xlsm`, `.xlsb`, `.xltx`, `.xltm`\n\n<!-- END_INFOCARD -->\n\n\n> When embedding Flatfile, this plugin should be deployed in a server-side listener. [Learn more](/docs/orchestration/listeners#listener-types)\n\n\n\n## Parameters\n\n#### `raw` - `boolean` \nIn Excel, you could have formatting on a text cell (i.e. date formatting). By\ndefault, Flatfile will just take the formatted text versus the raw values. Set\nthis value to true to take the raw values and disregard how it's displayed in\nExcel.\n\n#### `rawNumbers` - `boolean`\nIn Excel, you could have rounding or formatting on a number cell to only\ndisplay say 2 decimal places. By default, Flatfile will just take the\ndisplayed decimal places versus the raw numbers. Set this value to true to\ntake the raw numbers and disregard how it's displayed in Excel.\n\n\n#### `dateNF` - `string` - (optional)\nThe `dateNF` parameter allows you to specify the date format for parsing\ndates. (i.e. `yyyy-mm-dd`)\n\n\n#### `chunkSize` - `default: \"10_000\"` - `number` - (optional)\nThe `chunkSize` parameter allows you to specify the quantity of records to in\neach chunk.\n\n\n#### `parallel` - `default: \"1\"` - `number` - (optional)\nThe `parallel` parameter allows you to specify the number of chunks to process\nin parallel.\n\n\n#### `headerDetectionOptions` - `Object` - (optional)\nThe `headerDetectionOptions` parameter allows you to specify the options for\ndetecting headers in the file. By default, the first 10 rows are scanned for\nthe row with the most non-empty cells.\n\n\n#### `skipEmptyLines` - `default: \"false\"` - `boolean` - (optional)\nThe `skipEmptyLines` parameter allows you to specify if empty lines should be\nskipped. By default, empty lines are included.\n\n#### `debug` - `default: \"false\"` - `boolean` - (optional)\nThe `debug` parameter lets you toggle on/off helpful debugging messages for\ndevelopment purposes.\n\n#### `cascadeRowValues` - `default: \"false\"` - `boolean` - (optional)\nThe `cascadeRowValues` parameter automatically cascades values down the dataset until a blank row, new value, or end of dataset. This is useful for hierarchical data where values in a column apply to multiple rows below them.\n\n#### `cascadeHeaderValues` - `default: \"false\"` - `boolean` - (optional)\nThe `cascadeHeaderValues` parameter automatically cascades values across the header rows until a blank column, new value, or end of dataset. This is useful for multi-level headers where a header value applies to multiple columns.\n\n#### `mergedCellOptions` - `Object` - (optional)\nThe `mergedCellOptions` parameter allows you to specify how merged cells should be handled during extraction. You can define different treatments for cells merged across columns, rows, or ranges.\n\n##### Merged Cell Vectors\n\n- `acrossColumns`: Applies to cells merged horizontally (across multiple columns in the same row)\n- `acrossRows`: Applies to cells merged vertically (across multiple rows in the same column)\n- `acrossRanges`: Applies to cells merged both horizontally and vertically (across multiple rows and columns)\n\n##### Treatment Options\n\nFor all vectors:\n- `applyToAll`: Applies the merged cell value to all cells in the un-merged range\n- `applyToTopLeft`: Applies the merged cell value only to the top-left cell in the un-merged range\n\nFor `acrossColumns` and `acrossRows` only:\n- `coalesce`: Keeps only the first row/column and removes other rows/columns\n- `concatenate`: Combines values from all cells using a separator (requires `separator` parameter)\n\nExample configuration:\n```js\nmergedCellOptions: {\n  acrossColumns: {\n    treatment: 'concatenate',\n    separator: ', '\n  },\n  acrossRows: {\n    treatment: 'applyToAll'\n  },\n  acrossRanges: {\n    treatment: 'applyToTopLeft'\n  }\n}\n```\n\n\n\n## API Calls\n\n- `api.files.download`\n- `api.files.get`\n- `api.files.update`\n- `api.jobs.ack`\n- `api.jobs.complete`\n- `api.jobs.create`\n- `api.jobs.fail`\n- `api.jobs.update`\n- `api.records.insert`\n- `api.workbooks.create`\n\n\n\n## Usage\n\nListen for an Excel file (all extensions supported) to be uploaded to Flatfile. The platform will then extract the file automatically. Once complete, the file will be ready for import in the Files area.\n\n```bash install\nnpm i @flatfile/plugin-xlsx-extractor\n```\n\n```js import\nimport { ExcelExtractor } from \"@flatfile/plugin-xlsx-extractor\";\n```\n\n**listener.js**\n\n```js listener.js\nlistener.use(ExcelExtractor());\n```\n\n**Additional options**  \n\n```js additional options\nlistener.use(ExcelExtractor({ \n  raw: true, \n  rawNumbers: true,\n  cascadeRowValues: true,\n  cascadeHeaderValues: true,\n  mergedCellOptions: {\n    acrossColumns: {\n      treatment: 'concatenate',\n      separator: ', '\n    },\n    acrossRows: {\n      treatment: 'applyToAll'\n    },\n    acrossRanges: {\n      treatment: 'applyToTopLeft'\n    }\n  }\n}));\n```\n\n\n### Header Detection\n\nThree detection options are provided for detecting headers in the file: `default`, `explicitHeaders`, and `specificRows`. By default, the first 10 rows are scanned for the row with the most non-empty cells. This row is then used as the header row.\n\n#### Default\n\nIt looks at the first `rowsToSearch` rows and takes the row\nwith the most non-empty cells as the header, preferring the earliest\nsuch row in the case of a tie.\n\n```js\nlistener.use(ExcelExtractor());\n// or...\nlistener.use(\n  ExcelExtractor({\n    headerDetectionOptions: {\n      algorithm: \"default\",\n      rowsToSearch: 30, // Default is 10\n    },\n  })\n);\n```\n\n#### Explicit Headers\n\nThis implementation simply returns an explicit list of headers it was provided with.\n\n```js\nlistener.use(\n  ExcelExtractor({\n    headerDetectionOptions: {\n      algorithm: \"explicitHeaders\",\n      headers: [\"fiRsT NamE\", \"LaSt nAme\", \"emAil\"],\n    },\n  })\n);\n```\n\n#### Specific Rows\n\nThis implementation looks at specific rows and combines them into a single header. For example, if you knew that the header was in the third row, you could pass it `{ rowNumbers: [2] }`.\n\n```js\nlistener.use(\n  ExcelExtractor({\n    headerDetectionOptions: {\n      algorithm: \"specificRows\",\n      rowNumbers: [2], // 0 based\n    },\n  })\n);\n```\n\n#### Data Row & Sub Header Detection\n\nThis implementation attempts to detect the first data row and select the previous\nrow as the header. If the data row cannot be detected due to all the sample\nrows being full and not castable to a number or boolean type, it also will attempt\nto detect a sub header row by checking following rows after a header is detected\nfor significant fuzzy matching. If over half of the fields in a possible sub header\nrow fuzzy match with the originally detected header row, the sub header row becomes\nthe new header.\n\n```js\nlistener.use(\n  ExcelExtractor({\n    headerDetectionOptions: {\n      algorithm: \"dataRowAndSubHeaderDetection\",\n      rowsToSearch: 30, // Default is 10\n    },\n  })\n);\n```\n\n### Full Example\n\nIn this example, the `ExcelExtractor` is initialized with optional options, and then registered as middleware with the Flatfile listener. When an Excel file is uploaded, the plugin will extract the structured data and process it using the extractor's parser.\n\n**listener.js**\n\n```js listener.js\nimport { ExcelExtractor } from \"@flatfile/plugin-xlsx-extractor\";\n\nexport default async function (listener) {\n  // Define optional options for the extractor\n  const options = {\n    raw: true,\n    rawNumbers: true,\n    cascadeRowValues: true,\n    cascadeHeaderValues: true,\n  };\n\n  // Initialize the Excel extractor\n  const excelExtractor = ExcelExtractor(options);\n\n  // Register the extractor as a middleware for the Flatfile listener\n  listener.use(excelExtractor);\n\n  // When an Excel file is uploaded, the data will be extracted and processed using the extractor's parser.\n}\n```","readmeFilename":"README.md"}