The Type Parameter

Create custom functionality with 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).

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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

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Sample Output for Each Format

Using this sample data:

name	email	age
Alice	[email protected]	30
Bob	[email protected]	25

CSV (Default)

name,email,age
Alice,[email protected],30
Bob,[email protected],25

When to use: Universal format. Works with Excel, Google Sheets, pandas, any tool that reads CSV.

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json_records

[
  {"name": "Alice", "email": "[email protected]", "age": 30},
  {"name": "Bob", "email": "[email protected]", "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.

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json_split

{
  "columns": ["name", "email", "age"],
  "index": [1, 2],
  "data": [
    ["Alice", "[email protected]", 30],
    ["Bob", "[email protected]", 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')).

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json_index

{
  "1": {"name": "Alice", "email": "[email protected]", "age": 30},
  "2": {"name": "Bob", "email": "[email protected]", "age": 25}
}

When to use: When you need to look up rows by their index. Each row is keyed by its row number.

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json_columns

{
  "name": {"1": "Alice", "2": "Bob"},
  "email": {"1": "[email protected]", "2": "[email protected]"},
  "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

[
  ["Alice", "[email protected]", 30],
  ["Bob", "[email protected]", 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

{
  "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": "[email protected]", "age": 30},
    {"index": 2, "name": "Bob", "email": "[email protected]", "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 version='1.0' encoding='utf-8'?>
<data>
  <row>
    <name>Alice</name>
    <email>[email protected]</email>
    <age>30</age>
  </row>
  <row>
    <name>Bob</name>
    <email>[email protected]</email>
    <age>25</age>
  </row>
</data>

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.

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html_table

Renders a static HTML page with the data in a <table> element.

When to use: Quick data viewing in a browser, embedding in web pages via iframe, or simple reporting.

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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.

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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.

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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:

[
  {"name": "Alice", "age": 30}
]

With nest_json=results:

{
  "results": [
    {"name": "Alice", "age": 30}
  ]
}

This works with all json_* formats and is useful when your consuming application expects a specific JSON structure.

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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