# 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` |