# The Type Parameter The `type` parameter lets you change the output format of your export endpoint on the fly. Append it to your URL: ``` https://api.csvgetter.com/abc123?type=json_records ``` If you don't specify a `type`, the endpoint uses its default format (CSV or JSON, depending on how you configured it). *** ### Format Comparison | `type` Value | Format | MIME Type | Best For | | ----------------- | --------------- | ----------------- | -------------------------------------------------- | | *(none/default)* | CSV | `text/csv` | Spreadsheets, data import, universal compatibility | | `json_records` | JSON (Records) | `text/json` | APIs, JavaScript apps, most JSON use cases | | `json_split` | JSON (Split) | `text/json` | Data science tools, columnar processing | | `json_index` | JSON (Index) | `text/json` | Row-keyed lookups | | `json_columns` | JSON (Columns) | `text/json` | Column-oriented analysis | | `json_values` | JSON (Values) | `text/json` | Lightweight data transfer (no headers in body) | | `json_table` | JSON (Table) | `text/json` | Schema-aware consumers, typed data | | `xml` | XML | `application/xml` | Enterprise integrations, SOAP services | | `html_table` | HTML Table | `text/html` | Embedding in web pages, simple viewing | | `dynamic_table` | Dynamic Table | `text/html` | Interactive viewing with search and pagination | | `excel_web_query` | Excel Web Query | `text/html` | Excel "Data from Web" feature | *** ### Sample Output for Each Format Using this sample data: | name | email | age | | ----- | ------------------- | --- | | Alice | | 30 | | Bob | | 25 | #### CSV (Default) ``` name,email,age Alice,alice@example.com,30 Bob,bob@example.com,25 ``` **When to use:** Universal format. Works with Excel, Google Sheets, pandas, any tool that reads CSV. *** #### `json_records` ```json [ {"name": "Alice", "email": "alice@example.com", "age": 30}, {"name": "Bob", "email": "bob@example.com", "age": 25} ] ``` **When to use:** The most common JSON format. Each row is an object. Ideal for JavaScript `fetch()`, REST APIs, and most programming use cases. *** #### `json_split` ```json { "columns": ["name", "email", "age"], "index": [1, 2], "data": [ ["Alice", "alice@example.com", 30], ["Bob", "bob@example.com", 25] ] } ``` **When to use:** Separates column names from data. Useful when you need to process headers independently, or for data science tools like pandas (`pd.read_json(url, orient='split')`). *** #### `json_index` ```json { "1": {"name": "Alice", "email": "alice@example.com", "age": 30}, "2": {"name": "Bob", "email": "bob@example.com", "age": 25} } ``` **When to use:** When you need to look up rows by their index. Each row is keyed by its row number. *** #### `json_columns` ```json { "name": {"1": "Alice", "2": "Bob"}, "email": {"1": "alice@example.com", "2": "bob@example.com"}, "age": {"1": 30, "2": 25} } ``` **When to use:** When you want to access all values for a specific column. Good for column-by-column processing. *** #### `json_values` ```json [ ["Alice", "alice@example.com", 30], ["Bob", "bob@example.com", 25] ] ``` **When to use:** Smallest JSON payload — no column names, no indices. Use when you already know the column order and want minimal data transfer. *** #### `json_table` ```json { "schema": { "fields": [ {"name": "index", "type": "integer"}, {"name": "name", "type": "string"}, {"name": "email", "type": "string"}, {"name": "age", "type": "integer"} ], "primaryKey": ["index"], "pandas_version": "1.4.0" }, "data": [ {"index": 1, "name": "Alice", "email": "alice@example.com", "age": 30}, {"index": 2, "name": "Bob", "email": "bob@example.com", "age": 25} ] } ``` **When to use:** Includes a schema with data types. Good for consumers that need to know column types (integer vs string) without guessing. CSV Getter automatically detects numeric columns. *** #### `xml` ```xml Alice alice@example.com 30 Bob bob@example.com 25 ``` **When to use:** Enterprise integrations, SOAP services, or any system that requires XML input. > **Note:** Column names with special characters (spaces, punctuation) are automatically converted to underscores in XML output. *** #### `html_table` Renders a static HTML page with the data in a `` element. **When to use:** Quick data viewing in a browser, embedding in web pages via iframe, or simple reporting. *** #### `dynamic_table` Renders an interactive HTML page with: * Searchable columns * Sortable headers * Pagination **When to use:** Sharing data with non-technical users who want to browse and search interactively. *** #### `excel_web_query` Renders an HTML table optimized for Excel's "Data from Web" import feature. **When to use:** Specifically for pulling data into Excel using **Data > From Web**. The HTML is structured so Excel recognizes the table correctly. *** ### Using `nest_json` with JSON Formats The `nest_json` parameter wraps any JSON output in a named top-level key: ``` ?type=json_records&nest_json=results ``` **Without `nest_json`:** ```json [ {"name": "Alice", "age": 30} ] ``` **With `nest_json=results`:** ```json { "results": [ {"name": "Alice", "age": 30} ] } ``` This works with all `json_*` formats and is useful when your consuming application expects a specific JSON structure. *** ### Choosing the Right Format | Use Case | Recommended Format | | ------------------------------ | ---------------------------- | | Import into Excel | CSV or `excel_web_query` | | Import into Google Sheets | CSV (via `IMPORTDATA`) | | JavaScript / web app | `json_records` | | Python / pandas | CSV or `json_split` | | Enterprise / legacy system | `xml` | | Share with non-technical users | `dynamic_table` | | Embed in a web page | `html_table` | | Minimal payload size | `json_values` | | Need schema/type info | `json_table` | | API that expects nested JSON | `json_records` + `nest_json` | --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.csvgetter.com/platform-features/the-type-parameter.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.