excelextract 1.0.0
A simple and flexible Excel-to-CSV extraction tool using JSON configuration.
Requires Python: >=3.9


# ExcelExtract

**ExcelExtract** is a command-line tool designed to help pull data out of Excel spreadsheets (XLSX files). Either fully automatically, or extract specific cells or lists with the use of a configuration file (in JSON).

## Installation

You can install ExcelExtract using `pip`, Python's package installer. Open your terminal or command prompt and run:

```bash
pip install excelextract
```

**Note:** You need `Python 3.9` or higher installed on your system to use this tool. This documentation assumes you have `Python` and `pip` available.

## Quick Start

**Command Line Mode**

For simple configurations (each sheet contains 1 table, simple column structure), you can run ExcelExtract directly from the command line without making any config files:

```bash
excelextract surveys/*.xlsx --sheet sheet1 --output output.csv
```

If no output is specified in this mode, the output is printed to the console, and can be piped:

```bash
excelextract surveys/*.xlsx --sheet sheet1 > output.csv
```

Both examples merge (vertically stack) all xlsx files in the surveys folder, taking only `sheet1`.

The command line mode uses the automatic detection of columns and types, see the `default mode` below for more complex scenarios.

**Default Mode using a Configuration File**

ExcelExtract is designed to extract complex data from Excel files, even when it's spread across multiple files, various sheets, or doesn't adhere to a traditional header-column layout. It allows you to precisely define how to extract data from any cell, loop over ranges, and consolidate information from multiple sheets into a single CSV output.

The core of ExcelExtract's operation is a JSON configuration file, where you specify:

* **Input Sources:** Which Excel (.xlsx) files to read, using path patterns with wildcards (e.g., `*`, `**`).
* **Data Mapping:** Exact locations and structures of data within the Excel files, including dynamic row/column ranges for lists based on lookup values.
* **Extraction Automation:** Rules for automatically repeating extraction processes across different sheets, rows, or columns.
* **Output Formatting:** How to generate clean, type-detected CSV files from the extracted data.

A comprehensive guide on defining this configuration file can be found in the documentation below. Once your `config.json` is ready, simply run:

```bash
excelextract config.json
```

## How It Works: The Configuration File

The core of ExcelExtract is the JSON configuration file (e.g., `config.json`). This file tells the tool everything it needs to know. It generally contains:

1.  **Input (`input`): Which Files to Process**

      * Specifies the Excel file(s) to read using a path string.
      * Supports standard file path wildcards (glob patterns like `*`, `?`, `**`) to easily select multiple files (e.g., `"data/*.xlsx"` or `"data/**/*.xlsx"`).
      * Can also be a list of such path strings.

2.  **Output (`output`): Where to Save Results**

      * Defines the name of the output CSV file.

3.  **Lookups (`lookups`): Find Where the Data Is**

      * Lookups are instructions to find specific locations or patterns within the matched Excel files.
      * Each lookup performs a specific **`operation`** (like `looprows`, `findrow`, `loopsheets`).
      * They can find row numbers, column letters, or sheet names matching a pattern.
      * Crucially, lookups can define **Tokens** (like `%%ROW_NUMBER%%` or `%%DATA_SHEET%%`). Think of tokens as named placeholders for values that might change (like the current row number you're processing).

4.  **Columns (`columns`): Define What Data to Extract**

      * This section defines the columns that will appear in your output CSV file.
      * For each output column, you specify its name and how to get its `value` (often using Tokens defined in `lookups`).
      * You specify the data `type` (`string`, `number`, `auto`) for the output column.
      * You also control *when* a row should be created using **Triggers**.

Alternatively, for simple excel sheets (a simple row with headers), you can skip all lookups and column definitions, and use the `simpletable` option to specify the sheet you want to extract. See more information bellow.

## Simple Example: Extracting a Single Table

Let's start with a basic task. Imagine you have one Excel file named `report.xlsx` in a folder called `source_data`. Inside this file, there's a sheet named "Data". On this sheet, participant information starts at row 3. You want to extract the 'Name' from column B and 'Value' from column D for all rows where *either* the name *or* the value is present.

Here’s a configuration file (`config.json`) to do this:

```json
{
  "exports": [
    {
      "input": "source_data/report.xlsx",    // Path to the specific input Excel file
      "output": "extracted_data.csv",      // Name of the CSV file to create
      "lookups": [
        {
          "operation": "looprows",           // We need to loop through rows
          "token": "DATA_ROW",             // Create a placeholder named %%DATA_ROW%% for the current row number
          "start": 3,                      // Start checking from row 3
          "count": 500                     // Check up to 500 rows (a safe upper limit)
        }
      ],
      "columns": [
        {
          "name": "participant_name",        // Name of the first column in our output CSV
          "type": "string",                  // Expect text data
          "value": "Data!B%%DATA_ROW%%"      // Get value from Sheet 'Data', Column B, at the current %%DATA_ROW%%
                                             // No trigger specified, so it defaults to "nonempty"
        },
        {
          "name": "measurement_value",     // Name of the second column in our output CSV
          "type": "number",                  // Expect numeric data
          "value": "Data!D%%DATA_ROW%%"      // Get value from Sheet 'Data', Column D, at the current %%DATA_ROW%%
                                             // Also defaults to "nonempty" trigger
        }
      ]
    }
  ]
}
```

**Explanation:**

  * `input`: Specifies the exact path to the single input file.
  * `output`: The name of the CSV file that will be generated.
  * `lookups`: Defines the `%%DATA_ROW%%` token via the `looprows` operation.
  * `columns`: Defines the output columns. Both default to the `nonempty` trigger, so a row is created if either cell B or cell D (in the current `%%DATA_ROW%%`) contains data.

## Advanced Example: Handling Multiple Survey Files Recursively

Now for a more complex, realistic scenario common in research:

  * You have **multiple Excel files** spread across a folder named `data` and potentially its subfolders (e.g., `data/year1/`, `data/year2/`). Each file represents a survey response.
  * Each file has an "overview" sheet with metadata (like the survey name in cell C2).
  * Each file also has **several data sheets** named like "Survey A", "Survey B", etc.
  * On each "Survey X" sheet, there's a table listing participants starting in **column C**, but the exact starting row varies. This header might be "Participants" or "Subjects".
  * You want to extract all participant rows from all "Survey X" sheets across all found files into a **single combined CSV file**. You only want rows where a `participant_id` exists.

Here’s the configuration (`config.json`):

```json
{
  "exports": [
    {
      "input": "data/**/*.xlsx",          // Use glob pattern: find all .xlsx files in 'data' and ALL subdirectories
      "output": "participants_all.csv",   // Combined output file name
      "lookups": [
        {
          "operation": "loopsheets",        // Find sheets within each file based on a pattern
          "token": "SURVEY_SHEET",          // Create placeholder %%SURVEY_SHEET%% for the matching sheet name
          "regex": "Survey .*"              // Pattern: find sheets starting with "Survey " (Note: loopsheets still uses regex for sheet names)
        },
        {
          "operation": "findrow",           // Find the row containing the header text
          "token": "HEADER_ROW",            // Create placeholder %%HEADER_ROW%% for the row number found
          "sheet": "%%SURVEY_SHEET%%",      // Search within the sheet found by loopsheets
          "column": "C",                    // Only search in column C
          "match": ["Participants", "Subjects"], // Find row where cell C contains either "Participants" or "Subjects"
          "select": "first"                 // Take the first match if both appear (or multiple times)
        },
        {
          "operation": "looprows",          // Loop through potential participant rows
          "token": "ROW",                   // Create placeholder %%ROW%% for the current row number being checked
          "start": "%%HEADER_ROW%%",        // Start looping from the header row found above...
          "startOffset": 1,                 // ...but add 1 to start on the row *below* the header
                                            // Do not specify an end or count to loop till the end of the sheet
        }
      ],
      "columns": [
        {
          "name": "source_file",            // Output column for the original Excel filename
          "type": "string",
          "value": "%%FILE_NAME%%",         // Use the special built-in token for the filename
          "trigger": "never"                // Metadata: include this info, but don't use it to decide if a row exists
        },
        {
          "name": "survey_form_name",      // Output column for the survey name from the overview sheet
          "type": "string",
          "value": "overview!C2",           // Get value directly from cell C2 of the 'overview' sheet
          "trigger": "never"                // Metadata: also doesn't trigger row creation
        },
        {
          "name": "participant_id",         // Output column for the participant ID
          "type": "string",
          "value": "%%SURVEY_SHEET%%!B%%ROW%%", // Get value from the current survey sheet, column B, current row
          "trigger": "nonempty"             // CRITICAL: Explicitly require this field to be non-empty to create a row
        },
        {
          "name": "participant_score",      // Output column for the participant score
          "type": "number",
          "value": "%%SURVEY_SHEET%%!I%%ROW%%", // Get value from the current survey sheet, column I, current row
          "trigger": "nonempty"              // Explicitly set trigger (same as default) - row created if this OR participant_id is non-empty
        }
      ]
    }
  ]
}
```

**Explanation:**

  * `input: "data/**/*.xlsx"`: Uses `**` to find files recursively.
  * `operation`: Replaces `type` in the `lookups`.
  * `match`: Used in `findrow` to look for either "Participants" or "Subjects" without using regex syntax.
  * `trigger`: The comment on `participant_score` is corrected. Setting `trigger: "nonempty"` explicitly here has the same effect as the default, meaning a row is created if *either* `participant_id` (which also has `nonempty`) OR `participant_score` has data in the source cell for that `%%ROW%%`.

## Running the Tool

Once you have your configuration file (e.g., `config.json`) ready:

1.  Open your terminal or command prompt.
2.  Navigate to the directory where your `config.json` file is saved.
3.  Run the tool by typing:

```bash
excelextract config.json
```

This will execute the instructions in `config.json` and generate the specified CSV output file(s) in the same directory.

## Configuration Reference

This section provides details on all available options for your JSON configuration file.

Each configuration file must have a top-level `exports` key, which contains a list `[...]` of one or more "export jobs". Each job defines one process of reading from input file(s) and writing to an output file.

```json
{
  "exports": [
    {
      "input": "path/pattern/or/list/of/patterns", // e.g., "data/*.xlsx" or ["data/a*.xlsx", "data/b*.xlsx"]
      "output": "output_filename.csv",
      "lookups": [ /* List of lookup operations */ ],
      "columns": [ /* List of column definitions */ ]
    }
    // You can add more export jobs here if needed
  ]
}
```

Note: all keys/fields are case insensitive.

### Top-Level Fields per Export Job

| Field    | Type             | Required | Description                                                                                                                                                              |
|----------|------------------|----------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| `input`  | String or List   | Yes      | Specifies the input Excel file(s). <br> - If String: A path potentially containing glob wildcards (`*`, `?`, `**`). <br> - If List: A list of path strings (each supporting glob). |
| `output` | String           | Yes      | The name of the CSV file to be created with the extracted data.                                                                                                            |
| `lookups`| List             | Yes      | A list of lookup operations to find locations and define tokens. Executed in order.                                                                                        |
| `columns`| List             | Yes      | A list defining the columns for your output CSV file.                                                                                                                    |

*(Note: Glob patterns like `*` match any characters except path separators, `?` matches a single character, `**` matches directories recursively.)*

### Lookup Operations (`lookups`)

Lookups find locations or iterate over parts of your Excel files, often defining **Tokens** (placeholders like `%%NAME%%`) that you can reuse later.

**Common Fields for all Lookups:**

| Field       | Type   | Description                                                                         |
|-------------|--------|-------------------------------------------------------------------------------------|
| `operation` | String | The kind of lookup operation (e.g., `"looprows"`, `"findrow"`).                       |
| `token`     | String | The name for the placeholder (Token) this lookup defines (e.g., `"ROW_NUM"`).         |

**Lookup Operation: `loopsheets`**

Loops over sheets in the current workbook that match a regex pattern.

| Field       | Type   | Description                                                                               |
|-------------|--------|-------------------------------------------------------------------------------------------|
| `operation` | String | Must be `"loopsheets"`.                                                                   |
| `token`     | String | Name of the token that will hold the matching sheet name (e.g., `"DATA_SHEET"`).           |
| `regex`     | String | The pattern (regex) to match sheet names against (e.g., `"Survey.*"` matches Survey1, Survey2, ...). |

**Lookup Operations: `findRow`, `findColumn`**

Finds the row number(s) or column letter(s) containing specific text. Useful for locating headers or markers whose position might change or have alternative names.

| Field       | Type              | Description                                                                                                                                                                                                    |
|-------------|-------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| `operation` | String            | Must be `"findRow"` or `"findColumn"`.                                                                                                                                                                         |
| `token`     | String            | Name of the token that will hold the row number(s) or column letter(s) found (e.g., `"HEADER_ROW"`). If multiple values are selected (via `select`), the token might hold a list.                               |
| `sheet`     | String            | Name of the sheet to search within. Can use tokens (e.g., `"%%DATA_SHEET%%"`). Required.                                                                                                                       |
| `match`     | String or List    | Required. Specifies the text to find in cells. <br> - If String: Matches cells containing this exact (case-sensitive) string. <br> - If List: Matches cells containing exactly (case-sensitive) any string in the list. |
| `column`    | String            | **For `findRow` only:** Optional. Restrict the search to this specific column (e.g., `"A"`).                                                                                                                  |
| `row`       | Number            | **For `findColumn` only:** Optional. Restrict the search to this specific row number (e.g., `1`).                                                                                                               |
| `select`    | String/Num/List | Optional (default: `"first"`). Controls which match(es) to use if multiple cells match the criteria. <br> - `"first"`: Use the first match found. <br> - `"last"`: Use the last match found. <br> - *Future:* Integer or List support. |
| `unique` | Bool | Optional (default: `false`). Regardless which match is selected (e.g., `"first"`), if this is set and more than 1 match is found, the export exists with an error |

**Lookup Operation: `findcell`**

Searches an entire sheet for a cell containing specific text and defines tokens for both its row number and column letter. This is useful when you need to locate a specific anchor point in a sheet and then potentially use its row and column context for further extractions.

| Field       | Type   | Description                                                                                                                                                                                                     |
|-------------|--------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| `operation`   | String           | Must be `"findcell"` |
| `match`     | String or List | The exact (case-sensitive) text to find within a cell on the specified sheet.                                                                                                                                                                                           |
| `sheet`     | String | Name of the sheet to search within. Can use tokens (e.g., `"%%DATA_SHEET%%"`).                                                                                                                                                                                           |
| `rowtoken`  | String | Name of the token that will hold the row number of the found cell (e.g., `"HEADER_ROW"`).                                                                                                                                                                                          |
| `columntoken` | String | Name of the token that will hold the column letter of the found cell (e.g., `"DATA_COLUMN"`).                                                                                                                                                                                      |
| `unique` | Bool | Optional (default: `false`). If this is set and more than 1 match is found, the export exists with an error |

This operation is the exception on the rule, which does not have the `token` field, but generates 2 tokens: `rowtoken` and `columntoken`.

**Lookup Operations: `loopRows`, `loopColumns`**

Iterates through a range of row numbers or column letters, assigning the current value to a token for each step.

| Field         | Type             | Description                                                                                                                          |
|---------------|------------------|--------------------------------------------------------------------------------------------------------------------------------------|
| `operation`   | String           | Must be `"loopRows"` or `"loopColumns"`.                                                                                               |
| `token`       | String           | Name of the token that will hold the current row number or column letter (e.g., `"ROW"`, `"COL"`).                                       |
| `start`       | String or Number | Starting row number or column letter. Can be a literal (`1`, `"B"`) or a token (`"%%HEADER_ROW%%"`). Required.                          |
| `end`         | Number | Optional. The row number or column letter to stop at (inclusive). Cannot be used together with `count`. If missing or set to None, it will loop to the end of the sheet |
| `count`       | Number           | Optional. The maximum number of steps to take. Useful if you don't know the exact end but want to set a limit. Cannot be used with `end`. |
| `startOffset` | Number           | Optional (default 0). Adds this offset to the `start` value. E.g., `start: "%%HEADER_ROW%%", startOffset: 1` starts one row *below* the header. |
| `endOffset`   | Number           | Optional (default 0). Adds this offset to the `end` value.                                                                           |
| `stride`      | Number           | Optional (default 1). Step size for the loop. E.g., `stride: 2` processes every second row/column.                                   |
| `hint`        | String           | Automatic end detection can be slow if there is a sheet which contains a lot of rows/columns while trying to iterate a smaller sheet. `Hint` specifies which sheet you are trying to iterate over. |

### Column Definitions (`columns`)

Defines the structure of your output CSV file. Each object in the `columns` list corresponds to one column in the CSV.

| Field       | Type    | Required | Description                                                                                                                                                                                             |
|-------------|---------|----------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| `name`      | String  | Yes      | The header name for this column in the output CSV file.                                                                                                                                                 |
| `type`      | String  | No      | Data type for the output column. Use `"string"` for text, `"number"` for numeric values (integers or decimals). Affects how data is read and potentially formatted. Default is `"auto"`. See all available types below.                                      |
| `value`     | String  | Yes      | How to get the value for this column. Can be: <br> - A **literal string**: `value: "Constant Value"` <br> - A **cell reference**: `value: "SheetName!A1"` or using tokens: `value: "%%SHEET%%!B%%ROW%%"` <br> - An **Excel style formula** (e.g., `=sum(sheet!B2:B20)`)|
| `trigger`   | String  | No       | Controls if this column can trigger the creation of a new row in the CSV. Options: `"nonempty"` (default), `"never"`, `"nonzero"`. See Trigger System below.                                           |
| `rowOffset` | Number  | No       | Optional (default 0). Adds an offset to the row number part of a cell reference in `value`. Useful for getting data from adjacent rows (e.g., `value: "Data!A%%ROW%%", rowOffset: 1` gets data from row below). |
| `colOffset` | Number  | No       | Optional (default 0). Adds an offset to the column part of a cell reference in `value`. Useful for getting data from adjacent columns.                                                                  |

**Important `value` Syntax:**

  * If `value` starts with a equals sign (`=`), it's parsed as an **Excel style formula**. The Python [Formulas](https://pypi.org/project/formulas/) Package is used to parse and calculate these values.
  * If not a formula, and `value` contains an exclamation mark (`!`), it's treated as a **cell reference** (`SheetName!CellAddress`). Tokens like `%%ROW%%` or `%%SHEET%%` will be substituted before reading the cell.
  * Otherwise, `value` is treated as a **literal string** that will be put directly into the CSV cell, this can include a token.

**Examples:**

Assuming a ROW (e.g., "10"), COLUMN (e.g., "B"), and SHEET (e.g., "overview") token are used to generate a row in the csv file, then these columns will be evaluated as:

```json
"value": "Row = %%ROW%% and column = %%COLUMN%%" --> Literal string: "row = 10 and column = B"
"value": "=sum(%%SHEET%%!%%COLUMN%%2:%%COLUMN%%20)" --> Evaluate formula: "=sum(overview!B2:B20)"
"value": "overview!C%%ROW%%" --> Extract value from "C20" in sheet "overview"
```

### Trigger System (`trigger`)

The `trigger` property on a column definition controls *if* and *when* a new row is added to your output CSV file.

  * If the `trigger` key is **not specified** for a column, it **defaults to `"nonempty"`**.
  * For each potential data point identified by your `lookups` (e.g., for each row number in a `looprows`), the tool checks the `trigger` conditions of your columns.
  * If **at least one** column definition has its trigger condition met for the current context (e.g., current `%%ROW%%`), a new row is created in the CSV file.
  * Once a row is created, the tool calculates the `value` for **all** defined columns for that specific context and writes them to the CSV. If a referenced cell for a column is empty, an empty value will be written in the CSV for that column in the created row.

**Available Trigger Values:**

  * `"nonempty"` (Default):
      * The trigger condition is met if the cell referenced in the `value` field is **not empty**.
      * Use this (or rely on the default) for key data columns that indicate a valid record exists.
  * `"never"`:
      * This column **never** triggers the creation of a new row, even if its referenced cell has data.
      * Use this for metadata columns (like `source_file` or fixed survey details) that you want to include *alongside* the main data but shouldn't determine if a row exists on their own. If all data columns are empty but a metadata column with `trigger: "never"` references a non-empty cell, no row will be created.
  * `"nonzero"`:
      * The trigger condition is met only if the cell referenced in the `value` field contains a **numeric value** that is **not equal to zero**. Blank cells or cells with text do not meet this condition.
      * Useful if you only want to include rows where a specific measurement or count is actually greater than zero.

### Types and Type Detection

`ExcelExtract` is designed to intelligently handle various data types commonly found in Excel files. It can automatically detect the most appropriate type for each column or allow you to specify types for precise control.

**Supported Data Types:**

The package can work with the following data types:

* **`string`**: Textual data. This is the most general type and can represent any cell content.
* **`integer`**: Whole numbers (e.g., `10`, `-5`, `0`).
* **`float`** or **`number`**: Numbers with decimal points (e.g., `3.14`, `-0.001`).
* **`boolean`**: True or false values. Common string representations like "TRUE", "FALSE", "T", "F", "1", "0" are recognized during conversion if the column is determined or specified as boolean.
* **`date`**: Dates without time components (e.g., `2023-10-26`). `ExcelExtract` can parse various common date string formats.
* **`datetime`**: Dates with time components (e.g., `2023-10-26 14:30:00`). Various common datetime string formats are supported.
* **`time`**: Time values without date components (e.g., `14:30:00` or `2:30 PM`).
* **`timedelta`**: Represents a duration or difference between two dates or times (e.g., "3 days, 4:00:00" or as a representation of Excel's numeric time values if directly present as `datetime.timedelta` objects). *Note: Automatic detection of timedelta from varied string formats is limited; specifying the type might be necessary for complex duration strings.*

**Type Handling Modes:**

1.  **Automatic Detection (`"auto"`)**:
    This is the default behavior for columns where no specific type is provided by the user. `ExcelExtract` will analyze the data in each column and infer the most suitable and specific data type that can represent all values in that column. For example, if a column contains `1`, `2`, and `3`, it will be detected as `integer`. If it contains `1`, `2.5`, and `3`, it will be detected as `float`. If it contains `2023-01-01` and `2023-01-02 10:00:00`, it will be detected as `datetime`.

2.  **Specified Type**:
    You can explicitly define the desired data type for each column (e.g., by providing a schema like `"type": "number"`).
    * When a type is specified for a column, `ExcelExtract` will attempt to **force-parse** every cell value in that column into the specified type.
    * If a cell's value cannot be successfully parsed or converted to the specified type, its value will be set to `None` (representing an empty or unconvertible cell) in the extracted data. This ensures data consistency for the column according to your definition. For instance, if a column is specified as `integer` and a cell contains "hello", that cell's value will become `None`.

This system provides a balance between convenience through automatic detection and precision through user-defined type enforcement.

### Simplified Table Extraction (`simpletable`)

For scenarios where your data is organized as a straightforward rectangular table within one or more sheets, with headers clearly defined in a single row, the `simpletable` mode provides a convenient way to automatically define columns based on these headers and extract the data rows below.

When a `simpletable` key is present in an export job, it instructs ExcelExtract to operate in a simplified mode for extracting data from a specific table range within a sheet. It automatically generates the necessary internal structure (equivalent to a row loop and multiple `columns` definitions) based on your simple specification.

This mode is particularly powerful when combined with the `loopsheets` operation, allowing you to easily extract data from identically or similarly structured tables across multiple sheets into a single output file. The generated column definitions from the `simpletable` are added to any columns explicitly defined in the `columns` list for the same export job.

#### `simpletable` Fields

The `simpletable` key should contain an object with the following fields:

| Field              | Type             | Required | Default         | Description                                                                                                                                                                                                                                                                                          |
|--------------------|------------------|----------|-----------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| `sheet`            | String           | Yes      | None            | The name of the sheet containing the simple table. This **must** be a literal sheet name (e.g., `"Data"`) or a token (e.g., `"%%SHEET_NAME%%"`) obtained from a preceding `loopsheets` operation.                                                                                                    |
| `header_row`       | Number or String | No       | 1               | The row number containing the column headers for the table. ExcelExtract will read cells in this row to determine output column names. Can be a literal number or a token (e.g., `"%%HEADER_LOC%%"`).                                                                                                   |
| `startrow`        | Number or String | No       | `headerrow` + 1| The row number where the actual data begins (immediately below the header). Can be a literal number or a token (e.g., `"%%DATA_START%%"`).                                                                                                                                                           |
| `endrow`          | Number or String | No       | None            | The row number where the data extraction should stop (inclusive). Mutually exclusive with `count`. Can be a literal number or a token.                                                                                                                                                                   |
| `count`            | Number           | No       | None             | The maximum number of data rows to extract starting from `start_row`. Mutually exclusive with `endrow`. |
| `startcolumn`     | String           | No       | "A"             | The letter of the first column to include in the table extraction. Can be a literal column letter or a token (e.g., `"%%START_COL%%"`).                                                                                                                                                                 |
| `end_column`       | String           | No       | Auto-detected   | The letter of the last column to include in the table extraction. If omitted, ExcelExtract will detect the last non-empty cell in the `headerrow` starting from `startcolumn` and use that column as the end. Can be a literal column letter or a token.                                           |
| `sourcefilecolumn` | String           | No       | None            | If provided, a column with this header name will be added to the beginning of the output CSV. Its value for each row will be the filename of the source Excel file being processed. This field does NOT support tokens for its value (it's the literal header name).                                      |
| `sheetnamecolumn` | String           | No       | None            | If provided, a column with this header name will be added to the beginning of the output CSV. Its value for each row will be the sheet name being processed. |


#### How `simpletable` Generates Columns (Technical Explanation)

When `simpletable` is used, ExcelExtract performs the following steps internally for each input file matched by the top-level `input` pattern:

1.  It resolves any tokens in the `sheet`, `headerrow`, `startrow`, `endrow`, `count`, `startcolumn`, and `endcolumn` fields based on any preceding lookups.
2.  It identifies the `headerrow` within the specified `sheet`.
3.  It reads the cells in the `headerrow` starting from the resolved `startcolumn` to the resolved `endcolumn` (or the auto-detected `endcolumn`).
4.  For each non-empty cell found in this header range, it uses the cell's string value as the `name` for an output CSV column.
5.  It generates an internal column definition for each detected header, setting its `value` to reference the corresponding cell in the table data rows. The `type` for these auto-generated data columns defaults to `"auto"`. The `trigger` defaults to `"nonempty"`.
6.  If `sourcefilecolumn` is specified, it adds an additional column definition *before* the detected data columns. This column uses the specified name as its header, has `"type": "string"`, its value is the internal `%%FILE_NAME%%` token, and its `trigger` is set to `"never"`.
7.  These auto-generated column definitions are combined with any columns explicitly listed under the `columns` key in the export job.
8.  The tool then implicitly sets up a row loop based on the resolved `startrow`, `endrow`, and `count` parameters and proceeds to extract data using the combined list of column definitions for each row in that loop.

Essentially, the `simpletable` mode is a shortcut for defining a common set of `lookups` (`looprows` from `startrow`/`count`/`endrow`) and `columns` (one for each header cell in the specified range, plus an optional filename column). If your data structure requires more complex logic than this (e.g., non-rectangular tables, headers spanning multiple rows, data scattered in non-contiguous cells, different trigger logic per column, calculations), you will need to use the full `lookups` and `columns` configuration.

#### Interaction with Lookups and Edge Cases

The `simpletable` configuration streamlines the process of extracting rectangular data. Understanding its interaction with other lookup types is key to effective use:

* **Compatibility with `loopsheets`:** This is a recommended use case. Include a `loopsheets` lookup to iterate over sheets, and use the sheet token (e.g., `%%SHEET_NAME%%`) in the `simpletable`'s `sheet` field. ExcelExtract will apply the `simpletable` logic to each sheet found by `loopsheets`, combining the results.
* **Handling Varying Columns Across Sheets (with `loopsheets`):** When using the `simpletable` configuration in conjunction with a `loopsheets` lookup, ExcelExtract identifies *all unique header names* present in the specified `headerrow` (`startcolumn` to `endcolumn`) across **all sheets** matched by the `loopsheets` pattern. The output CSV will include a column for every single unique header name encountered across all sheets. For rows extracted from a specific sheet, if that sheet's header row did not contain a header corresponding to a column in the final, combined list of headers, the value in the output CSV for that cell will be empty.
* **Incompatibility with `looprows` and `loopcolumns`:** You **must not** include `looprows` or `loopcolumns` lookups in the same export job as a `simpletable` configuration. The `simpletable` mode generates its own internal row iteration (from `startrow` for `count`/to `endrow`). Including another explicit row or column loop will create conflicting iteration logic and lead to incorrect or massively duplicated output.
* **Compatibility with `find*` Lookups:** Non-looping lookups such as `findrow`, `findcolumn`, and `findcell` can be used effectively before the `simpletable` configuration. Use these lookups to dynamically find locations (like a header row or table corner) and define tokens (e.g., `%%HEADER_ROW_TOKEN%%`, `%%FIRST_COL_TOKEN%%`). You can then use these tokens in the `simpletable`'