How do I convert CSV to JSON?

Paste, upload or drag-and-drop your CSV into the editor and the tool converts it to JSON instantly. The first row is detected as the header and each following row becomes a JSON object whose keys are the column names. You get a live preview, an interactive tree and one-click copy or download, and everything runs in your browser so your data is never uploaded.

How do I convert JSON to CSV?

Paste, upload or drag-and-drop your JSON and the tool converts it to CSV instantly with a live spreadsheet preview you can copy or download as .csv. Nested objects are flattened using path notation and arrays are handled by the strategy you choose. Like every operation, it runs locally in your browser.

What JSON output shapes can I generate from CSV?

Six shapes: Array of Objects (one object per row, the default), Array of Arrays (compact 2-D), Column / Key-Value (one key per column holding an array of values), Nested (dotted headers like user.name become nested objects), Keyed / Indexed (an object keyed by a column such as id) and Grouped (rows grouped under a chosen column). You can switch between them instantly without re-importing.

Can the converter handle large CSV files?

Yes. The RFC 4180 parser is a single-pass state machine that stays fast on multi-megabyte files, and the preview is paginated so the DOM stays small however many rows you load. Because all processing is local there is no upload step to slow things down.

How are CSV headers detected?

By default the first row is treated as the header and its cells become the JSON keys. You can toggle this off to treat every row as data, in which case keys like column_1 and column_2 are generated. The analyzer also flags empty, duplicate or invalid headers so you can fix them before converting.

What delimiters does the CSV parser support?

Comma (standard CSV), semicolon (common in European locales), tab (TSV) and pipe-delimited files. The tool auto-detects the most likely delimiter and you can override it at any time. Quoted fields, escaped quotes ("") and embedded newlines are all handled per RFC 4180.

Does the converter infer data types like numbers and booleans?

Yes. With type inference enabled, cells that look like integers, decimals, scientific notation, true/false or null become real JSON numbers, booleans and null instead of strings, so 42 becomes 42 and true becomes a boolean. You can disable inference to keep every value as a string when exact fidelity matters.

Can I create nested JSON from flat CSV?

Yes. Choose the Nested output shape and the converter splits dotted, underscore or slash header names into nested objects: columns named user.name and user.email become a single user object with name and email properties. It rebuilds hierarchy from a flat export, the inverse of flattening JSON to CSV.

How do I key JSON by a specific column?

Select the Keyed / Indexed shape and pick a key column such as id or email. The output becomes one object keyed by that column’s value, so you can look records up directly instead of scanning an array, which is ideal for lookup tables and client-side caches.

Can I group rows under a parent value?

Yes. The Grouped shape buckets rows by a chosen column, for example grouping orders by customer or rows by category, producing a parent to children structure where each key maps to an array of matching rows. It is a one-click way to build hierarchical, report-style JSON.

Can I choose which columns to include in the output?

Yes. The Field Mapping tab lists every detected column where you can include or exclude any column, rename it to a friendlier key, and reorder columns up or down. Only the columns you keep, in the order you set and with the names you choose, appear in the result.

Can I rename column headers in the JSON or CSV?

Yes. Each detected column has an editable output name in Field Mapping. The source stays untouched while the output keys or headers use your chosen labels, useful for turning "Email Address" into email or "Order #" into orderId.

How does the data profiler work?

The Analyze tab profiles every column: it detects the dominant type (integer, decimal, boolean, date, email, URL or string), counts unique and null values, computes min/max/average for numeric columns, lists the most frequent values, and assigns a per-column and overall data-quality score across completeness, consistency, validity and uniqueness.

What is the data-quality score?

It is a 0–100 grade (A–F) summarising how trustworthy your dataset is, blending completeness (how many cells are filled), consistency (how uniformly each column sticks to one type), validity (how many values match their expected format) and uniqueness (how few rows are duplicates). It tells you at a glance whether the data is clean enough to use.

Can I clean the data while converting?

Yes. The cleaning options remove empty rows and columns, drop duplicate rows, trim surrounding whitespace, collapse repeated spaces and normalise headers to snake_case, all before output is generated so the result is analysis-ready.

How are duplicate rows handled?

The analyzer counts duplicate rows and reflects them in the uniqueness score, and the Dedupe option removes exact duplicates in one click. You can review the duplicate count in the Analyze tab before deciding whether to strip them.

How are empty cells represented in JSON?

By default an empty cell becomes an empty string. Turn on "Empty as null" to emit null instead, or enable "Skip empty" to omit the key entirely from that object, whichever best matches your downstream schema.

Can I import a CSV or JSON from a URL?

Yes. The import bar fetches CSV or JSON directly from a public URL or API endpoint, subject to the remote server’s CORS policy, in addition to pasting, uploading or dragging files. This makes it easy to convert a hosted dataset without downloading it first.

Does it support TSV input and output?

Yes. TSV (tab-separated values) works as both an input delimiter and an output format. Data copied from a spreadsheet is tab-delimited, so you can paste straight from Excel or Google Sheets and export back to TSV when a tool prefers tabs over commas.

Can I export to formats other than JSON and CSV?

Yes. From one parsed dataset the Export tab produces JSON, CSV, TSV, Excel, SQL (CREATE TABLE plus INSERTs), an HTML table, a Markdown table, XML and YAML, all generated client-side, so the converter doubles as a multi-format transformation hub.

How do I convert CSV into a JSON API payload?

Use the Array of Objects shape (or Keyed for id-addressable data), enable type inference so numbers and booleans are real types, map columns to your API’s field names, and copy the result. The output is valid, minifiable JSON ready to POST to a REST endpoint or drop into a fixture.

Can I generate a schema from my data?

Yes. The Schema tab derives a JSON Schema (Draft-07), TypeScript interfaces and a SQL CREATE TABLE definition from your data, inferring types from the actual values. That gives a typed contract for the JSON and a ready-to-run table definition for a database.

Does it generate TypeScript interfaces?

Yes. The Schema tab emits TypeScript interfaces describing the shape of the converted records, handy for typing API responses or fixtures in a front-end or Node.js project. It infers optional fields and union types from columns with mixed or missing values.

Can I convert JSON back to CSV and vice versa?

Yes. The tool is a two-way bridge: CSV, TSV, XML and YAML convert into JSON, and JSON, XML and YAML convert back into CSV. A full round-trip preserves columns, order and types because both directions share the same in-memory table model.

What is the difference between CSV and JSON?

CSV is a flat, tabular format of untyped text understood natively by every spreadsheet and database. JSON is a hierarchical, typed format that nests objects and arrays, ideal for APIs and configuration. Converting between them means either giving flat rows structure and types, or flattening a hierarchy into a grid.

Is my data private and secure?

Completely. All parsing, type inference, transformation and export run locally in your browser using JavaScript, with nothing uploaded, logged or stored remotely. That makes the tool safe for proprietary datasets, exports containing personal data and anything subject to compliance rules.

Is the CSV ↔ JSON converter free?

Yes. It is 100% free with no sign-up, no usage limits and no watermarks. Convert, map, clean, profile and export as much data as you like in any supported format.

How are quoted fields and commas inside values handled?

Per RFC 4180. A field wrapped in double quotes can contain commas, the delimiter, line breaks and escaped quotes written as "". So a value like "Smith, John" stays one field rather than splitting into two columns.

Does it handle Unicode and special characters?

Yes. Full Unicode — accented characters, emoji and non-Latin scripts — is preserved end to end, and the JSON output is valid UTF-8. Special characters in values are escaped correctly in JSON so the result always parses.

What if a row has more or fewer columns than the header?

The validator flags ragged rows with the exact line number so you can fix them. During conversion, missing trailing values become empty cells and extra values are kept against generated keys, so you always get well-formed output.

Can I preview the data as a table before converting?

Yes. The Convert tab shows your data as an interactive spreadsheet — sortable, searchable columns with sticky headers and pagination — next to the live output, so you can verify the parse and spot issues before you copy or download.

Does the tool work offline and on mobile?

Yes. Because everything runs client-side, conversion keeps working without a connection once the page has loaded, and the interface is fully responsive: the editors stack and the preview table scrolls horizontally on small screens.

How do I use the JSON in Python, JavaScript or a database?

Copy the Array of Objects output and use json.loads() in Python (then pandas.json_normalize for a DataFrame), JSON.parse() in JavaScript, or import the JSON straight into MongoDB and document stores. For relational databases, switch to the SQL export for CREATE TABLE and INSERT statements.

Can I use this for ETL and data pipelines?

Absolutely. Parsing, type inference, field mapping, cleaning, profiling, schema generation and multi-format export make the tool a lightweight, browser-based ETL step: extract a file, transform it (clean, map, reshape) and load it as JSON, SQL or another format into the next system.

How are nested objects handled when converting JSON to CSV?

Nested objects are flattened into columns using path notation, so {"user": {"name": "Ada"}} becomes the column user.name. You can switch between dot, bracket or underscore notation and cap the depth so very deep structures are stringified rather than producing endless columns.

Can I convert an API response to CSV?

Yes, it is one of the most common uses. Paste a REST or GraphQL response or fetch it from a URL, and the converter flattens the nested structure into a clean table for Excel, Sheets or a database. Because processing is local, responses containing tokens or internal data stay private.

Can I export to Excel?

Yes. The Export tab offers Excel (.xls) alongside CSV, TSV, SQL, HTML, Markdown, XML, YAML and JSON. The Excel export opens directly in Microsoft Excel, LibreOffice Calc and Google Sheets with the columns and header laid out.

How do I flatten JSON into a table?

Flattening is automatic. The engine walks the JSON tree and turns every nested key into a column using path notation. You control the path style (dot/bracket/underscore), the maximum depth and how arrays are treated, and the Structure tab lets you explore the JSON as a tree before converting.

Does it handle deeply nested JSON?

Yes. The flattener is recursive and handles arbitrarily deep structures. For extreme nesting you can set a maximum depth so deeper values are serialized as JSON strings, and a safety cap prevents pathological inputs (many large nested arrays under Expand) from exploding the row count.

Does it work with an array of objects?

Yes, an array of objects is the ideal input. Each object becomes a row and the union of all keys becomes the header, so objects with slightly different shapes are merged into one consistent table with empty cells where a key is missing.

What if my objects have different keys?

The converter collects the union of all keys across every object in first-seen order. Rows lacking a particular key get an empty cell for that column, so heterogeneous data still produces a valid, rectangular CSV.

Can I keep the nested hierarchy instead of flattening?

Tabular outputs (CSV, Excel, SQL) require flattening because those formats are flat. To preserve hierarchy, export to XML, YAML or JSON instead, or use the Index or Stringify array modes to retain nested structure within cells.

How do I convert JSON to SQL?

Select SQL in the Export tab. The tool generates a CREATE TABLE statement with column types inferred from your data (INTEGER, DECIMAL, BOOLEAN, VARCHAR) followed by INSERT statements for every row, ready to run in PostgreSQL, MySQL or SQLite.

Can I generate a Markdown or HTML table?

Yes. The Export tab produces a GitHub-flavored Markdown table or a clean HTML table, both built from the same flattened data, so you can drop a dataset straight into documentation, a README or a web page.

Does it support JSON Lines (NDJSON)?

Yes. NDJSON / JSON Lines, where each line is its own JSON object, is supported for both reading and writing. It is the streaming-friendly variant: each object becomes a row, and you can process huge logs line by line without loading the whole file at once.

Will boolean and number types be preserved in CSV?

CSV itself is untyped — every cell is text — so numbers and booleans are written as their literal text (42, true). When converting back to JSON, or when profiling, the tool re-infers these types automatically.

How do I import the CSV into a spreadsheet?

Download the CSV or Excel export and open it in Excel, Google Sheets or Numbers, which recognise the format automatically. For locale-specific spreadsheets, choose the semicolon delimiter so columns split correctly.

What is a CSV parser and how does this one work?

A CSV parser reads delimited text and splits it into fields and records while respecting quoting rules. This one is a single-pass state machine that follows RFC 4180, so it correctly handles quoted commas, escaped quotes and newlines inside fields rather than naively splitting on every comma.

What is a JSON parser and is this one strict?

A JSON parser reads JSON text into in-memory values. This tool uses strict, RFC 8259-compliant parsing, so trailing commas, single quotes and unquoted keys are rejected. NDJSON is supported by parsing each line as its own document.

Is there a CSV validator built in?

Yes. The validator checks for ragged rows, empty or duplicate headers, inconsistent field counts and encoding artifacts like a stray BOM, reporting each issue with a line number. It runs automatically as you import so problems surface before conversion.

CSV to JSON Converter Free Online – Convert JSON to CSV Instantly

Everything in One Data Converter

A complete, private, in-browser suite to convert, validate, clean, transform, flatten and map data between CSV, JSON, TSV, XML, YAML and Excel — with six JSON output shapes, nested-JSON flattening, an A–F data-quality score and batch processing.

Two-Way Conversion

Convert CSV to JSON and JSON back to CSV in one tool. The same engine bridges tabular and hierarchical data, so a round-trip preserves your columns, types and structure.

Six JSON Shapes

Output Array of Objects, Array of Arrays, Column / Key-Value, Nested, Keyed / Indexed or Grouped JSON. Switch shapes instantly without re-importing your data.

Nested & Flatten Engine

Build nested JSON from dotted CSV headers, or flatten deep JSON into columns using dot, bracket or underscore paths with configurable depth and four array strategies.

Auto Delimiter Detection

The parser sniffs comma, semicolon, tab and pipe delimiters automatically and you can override it. Quoted fields, escaped quotes and embedded newlines follow RFC 4180.

Validation & Quality Score

Flag ragged rows, duplicate headers and invalid values, then read an A–F data-quality grade across completeness, consistency, validity and uniqueness.

One-Click Data Cleaning

Trim whitespace, remove empty rows and columns, drop duplicate rows and normalize headers to snake_case so the output is analysis-ready before you export.

Column Profiling

Detect each column’s dominant type, count unique and null values, compute min/max/average for numbers and list the most frequent values to understand your data fast.

Multi-Format Export

From one parsed dataset, export JSON, CSV, TSV, XML, YAML, Excel, SQL inserts, an HTML table or a Markdown table — a complete data-transformation hub.

100% In-Browser Privacy

All parsing, transformation and export run locally with JavaScript. Your data is never uploaded, logged or stored, so it is safe for proprietary and regulated datasets.

Batch & ZIP Processing

Convert many files at once and download the results as a single ZIP. Settings apply across the batch so a whole folder of exports becomes consistent JSON or CSV.

Transformation Studio

A real ETL transform stage: include, exclude, rename and reorder fields, filter rows with query conditions, sort on multiple columns, and merge or split columns — all applied non-destructively with one-click reset.

Formula / Computed Columns

Add columns from a safe formula engine — full names, arithmetic, date maths and conditionals — with functions like CONCAT, IF, ROUND, DATEDIFF and AGE. No code runs in the page, so even untrusted files are safe.

Schema & Code Generation

Detect a typed schema from your data and generate JSON Schema (Draft-07), a SQL CREATE TABLE, and TypeScript, Python, Go, Java, C# or GraphQL models — inferring optional and nullable fields automatically.

JSON Tree Explorer

Inspect structured data as an interactive tree with expand and collapse, type indicators, key and value search, and one-click copy of any node’s JSON path — alongside a searchable, sortable spreadsheet grid.

What is CSV?

CSV (Comma-Separated Values) is a plain-text format that stores tabular data as rows of fields separated by a delimiter — usually a comma. The first line is typically a header row naming each column, and every line below it is a record. Because it is just text, CSV opens in any spreadsheet, database or text editor without special software.

Despite the name, CSV files routinely use other delimiters such as semicolons (common in European locales where the comma is a decimal mark), tabs (TSV) or pipes. The de-facto standard, RFC 4180, defines how fields containing commas, quotes or newlines are wrapped in double quotes and how embedded quotes are escaped by doubling them.

Flat: Every record has the same columns — there is no nesting or hierarchy.
Untyped: Cells are text; numbers and booleans are just their printed form.
Universal: Read natively by Excel, Google Sheets, pandas, SQL importers and more.

What is JSON?

JSON (JavaScript Object Notation) is a lightweight, text-based format for structured data. It represents values as objects (key/value maps), arrays (ordered lists), strings, numbers, booleans and null, and those can nest to any depth. This makes JSON the native language of REST APIs, configuration files and document databases.

Unlike CSV, JSON is typed and hierarchical. A single record can hold nested objects and arrays, so a customer can contain an address object and a list of orders without flattening anything. That expressiveness is why application code consumes JSON directly while still relying on CSV for bulk import and spreadsheet workflows.

Hierarchical: Objects and arrays nest freely to model real relationships.
Typed: Distinguishes strings, numbers, booleans and null.
API-native: The default payload format for web services and JavaScript.

CSV vs JSON

CSV and JSON solve different problems. CSV is compact, streamable and perfect for flat tables you open in a spreadsheet or bulk-load into a database. JSON is structured and typed, ideal for APIs and code that needs nested relationships and exact value types.

Most data work needs both: you receive an API response in JSON and need a CSV for analysts, or you export a sheet as CSV and need JSON to feed an application. Converting between them is therefore an everyday task, and the right shape and flattening strategy is what makes the result usable rather than just valid.

Why Convert Between CSV and JSON

You convert CSV to JSON when flat exports need to flow into application code, REST APIs, MongoDB or front-end fixtures — giving rows real types and, optionally, nested structure. You convert JSON to CSV when nested API or database data must reach analysts, spreadsheets or a relational table that only understands rows and columns.

A good converter does more than reshape brackets. It infers types, maps and renames fields, cleans messy values, validates structure and reports data quality, so the output is not just the other format but a cleaner, more trustworthy dataset ready for the next system.

Nested JSON & Flattening

Flattening turns a nested JSON tree into flat columns. The engine walks each object and joins the path to every leaf into a column name — {"user": {"city": "London"}} becomes the column user.city. You choose the path style: dot (user.city), bracket (user[city]) or underscore (user_city), and cap the depth so very deep structures are stringified instead of exploding into hundreds of columns.

Arrays need a strategy because they have no natural column. Expand emits one row per element (denormalizing arrays of objects), Index creates numbered columns like tags.0 and tags.1, Join concatenates primitives into one cell with a separator, and Stringify keeps the raw JSON array in a single cell. The reverse direction rebuilds hierarchy from dotted CSV headers using the Nested output shape.

Dot / bracket / underscore: Pick the path notation that matches your downstream tools.
Depth cap: Stringify anything deeper than a chosen level to control width.
Array modes: Stringify, join, index or expand — the right one per use case.

Data Cleaning & Validation

Real-world files are messy: stray whitespace, blank rows, duplicate records, inconsistent headers and ragged lines that have too few or too many fields. The validator surfaces these with exact line numbers, and the cleaning pipeline fixes the common ones in one click — trimming whitespace, removing empty rows and columns, deduplicating and normalizing headers.

Validation is paired with profiling. Each column gets a detected type, null and unique counts and a sample of frequent values, and the dataset earns an A–F quality score across completeness, consistency, validity and uniqueness — a quick read on whether the data is clean enough to ship.

Trim & collapse: Strip surrounding whitespace and collapse repeated spaces.
Dedupe: Remove exact duplicate rows and reflect it in the uniqueness score.
Normalize headers: Convert headers to consistent snake_case keys.

Schema Mapping

Schema mapping is how you reconcile the shape of your source with the shape your target expects. In Field Mapping you include or exclude columns, rename them to friendly keys (turning "Email Address" into email), reorder them and decide which CSV header becomes which JSON property — without touching the original file.

For contracts and downstream typing, the tool can derive a JSON Schema (Draft-07), TypeScript interfaces and a SQL CREATE TABLE definition from the actual values, inferring optional fields and union types where columns are mixed or sparse. That gives both a validation schema and a ready-to-run table from the same dataset.

Large Files & Performance

The parser is a single-pass state machine, so it stays fast on multi-megabyte files, and the preview is paginated and virtualized so the DOM stays small no matter how many rows you load. Because everything is local there is no upload step to wait on, which is often the slowest part of cloud converters.

For very large or streaming workloads, NDJSON / JSON Lines lets each record be processed one line at a time, and array Expand has a safety cap to stop pathological inputs from exploding the row count. When files are genuinely huge, splitting them first keeps the browser responsive while still avoiding any server round-trip.

Security & Privacy

Every operation — parsing, type inference, flattening, transformation, profiling and export — runs entirely in your browser using JavaScript. No data is uploaded, logged or stored remotely, so there is no server that could leak or retain it.

That client-side model makes the tool safe for proprietary datasets, API responses containing tokens, exports with personal data and anything subject to GDPR, HIPAA or internal compliance rules. You can even disconnect from the network after the page loads and conversion still works.

How to Convert CSV to JSON

Upload CSV

Paste, drag-and-drop or upload your CSV (or TSV) file, or fetch it from a public URL. Everything is read locally in your browser — nothing is uploaded to a server. The first row is detected as the header and the delimiter is auto-detected.

Configure Conversion

Choose one of six JSON shapes (Array of Objects, Array of Arrays, Column / Key-Value, Nested, Keyed or Grouped), toggle type inference, set how empty cells are emitted, and map, rename, include or reorder the columns you want in the output.

Validate Data

Open the Analyze tab to profile every column, flag ragged rows and duplicate or invalid headers, and read the A–F data-quality score. Apply one-click cleaning — trim whitespace, drop empty rows and columns, dedupe and normalize headers — before you export.

Download JSON

Copy the live JSON output or download it as a .json file. You can also export the same dataset to TSV, XML, YAML, Excel, SQL, an HTML table or a Markdown table — all generated client-side from the parsed data.

How to Convert JSON to CSV

Upload JSON

Paste, drag-and-drop or upload your JSON, or fetch an API response from a URL. NDJSON / JSON Lines and single objects are accepted alongside arrays. The structure is parsed in-browser and shown as an interactive tree.

Map Fields

Nested objects are flattened to columns using dot, bracket or underscore path notation, and you pick how arrays are handled (stringify, join, index or expand). In Field Mapping you can include, exclude, rename and reorder every detected column.

Preview Output

Review the flattened data in the live spreadsheet preview — sortable, searchable, paginated — to confirm columns split correctly. Apply cleaning options and choose your delimiter (comma, semicolon, tab or pipe) before exporting.

Download CSV

Copy or download the result as a .csv file. The same flattened dataset can also be exported to TSV, Excel, SQL inserts, XML, YAML, HTML or Markdown without re-importing.

CSV vs JSON

Aspect	CSV	JSON
Readability	Easy to scan as a grid in any spreadsheet; hard to read once values contain commas or newlines.	Self-describing with named keys and nesting; verbose but clear in a text editor.
File size	Compact — keys appear once in the header, so it is the smaller format for flat tabular data.	Larger because every record repeats its keys, though minification and NDJSON reduce overhead.
Performance	Extremely fast to parse line by line and stream; ideal for bulk import and large flat datasets.	Fast to parse with native parsers, but whole-document loads cost memory for very large files.
API compatibility	Rarely used as an API payload; better for downloads, imports and reporting.	The default request and response format for REST and GraphQL services.
Data structure support	Flat only — one rectangular table with no nesting or types.	Nested objects and arrays with real string, number, boolean and null types.
Tooling	Opens in Excel, Google Sheets, pandas and every database importer out of the box.	First-class in programming languages, document stores and configuration systems.
Streaming	Naturally line-oriented, so it streams row by row with minimal memory.	Standard JSON loads whole; NDJSON / JSON Lines is the streaming-friendly variant.

Automatic Data-Type Detection

Type	Example	How it is handled
String	`"Ada Lovelace"`	Default for any value that is not recognised as another type; always quoted in JSON.
Integer	`128000`	Whole numbers are emitted as real JSON numbers when type inference is on.
Float	`135500.50`	Decimals and scientific notation become JSON numbers; locale decimal commas are normalised.
Boolean	`true`	true/false, and optionally yes/no or 1/0, are detected and emitted as JSON booleans.
Date	`2026-06-23`	ISO 8601 and common date patterns are recognised for profiling; kept as strings in JSON unless requested.
Email	`ada@example.com`	Validated against an email pattern so the validity score can flag malformed addresses.
URL	`https://example.com`	Detected as a URL for profiling and validation; stored as a string value.
Currency	`$1,299.00`	Currency symbols and thousands separators are recognised and can be normalised to a numeric value.
Phone	`+1-202-555-0147`	Phone-like values are profiled so inconsistent formatting can be detected and cleaned.
UUID	`3f2504e0-4f89-41d3-9a0c-0305e82c3301`	Recognised as a unique identifier, useful as a key column for Keyed / Indexed output.

Use Cases

Developers

Turn CSV exports into JSON fixtures and API payloads with real types, or flatten API responses into CSV for quick inspection — all without leaving the browser.

API Engineers

Convert REST and GraphQL responses to CSV for review, generate JSON request bodies from spreadsheets, and map fields to match an API contract before sending.

Data Analysts

Flatten nested JSON into clean, spreadsheet-ready tables, clean and dedupe messy CSVs, and profile columns to understand quality before analysis.

Data Scientists

Prepare datasets for pandas and notebooks — infer types, normalize headers, expand arrays of records into rows and export tidy CSV or NDJSON.

BI Teams

Standardize exports from many sources into consistent JSON or CSV, then push them into dashboards, warehouses and reporting pipelines.

Ecommerce

Transform product, order and inventory feeds between supplier CSVs and platform JSON, mapping columns and units to the target catalogue schema.

CRM Exports

Convert contact and lead exports into JSON for migration or APIs, deduplicate records and rename fields to match the destination system.

Database Migration

Generate SQL CREATE TABLE and INSERT statements from CSV or JSON, or produce JSON documents for a NoSQL store during a platform move.

ETL Pipelines

Use the tool as a lightweight extract-transform-load step: clean, reshape and re-type data, then output the format the next stage expects.

Marketing

Reshape campaign, ad and analytics exports into the format a platform or import wizard requires, mapping and renaming columns on the way.

Private by Design

Your data never leaves your device. Parsing, validation, cleaning, conversion and export all run in your browser — there is no server upload, so even sensitive datasets stay private.

No Upload, Ever

Files are read and converted entirely in your browser. They never travel to a server, so there is no transfer risk and nothing to delete afterwards.

Works Offline

Once the page has loaded, conversion keeps working with no network connection — ideal for air-gapped machines and sensitive environments.

Nothing Stored

No data is logged, cached on a server or retained. When you close the tab, your dataset is gone from everywhere but your own device.

Compliance-Friendly

Because data stays local, the tool fits GDPR, HIPAA and internal data-handling policies for proprietary, personal and regulated datasets.

How It Works Under the Hood

RFC 4180 Parser

A single-pass state machine tokenizes CSV per RFC 4180, correctly handling quoted fields, escaped quotes (""), embedded commas and newlines, and mixed delimiters with auto-detection.

Flattening Engine

A recursive walker joins object paths into column names using dot, bracket or underscore notation, applies a depth cap, and resolves arrays via stringify, join, index or expand strategies.

Type Inference

Each value is tested against ordered patterns — integer, float, boolean, null, date, email, URL — so JSON gets real types while ambiguous or mixed columns safely fall back to strings.

Quality Scoring

A scorer blends completeness, consistency, validity and uniqueness per column into a weighted A–F grade, giving an at-a-glance measure of how trustworthy the dataset is.

Format Pipeline

Parsed data lands in one normalized in-memory table; serializers then render JSON, CSV, TSV, XML, YAML, Excel, SQL, HTML and Markdown from that single source so all outputs stay consistent.

Round-Trip Engine

Because parsing and serialization share the same table model, CSV→JSON→CSV (and the reverse) preserves columns, order and types, making the tool a reliable two-way bridge.

Pro Tips

Pick the right JSON shape

Use Array of Objects for APIs and most code, Keyed / Indexed for fast id lookups, Grouped for report-style hierarchies and Column / Key-Value when a downstream tool wants column arrays.

Match the delimiter to your locale

If columns refuse to split in Excel, switch to the semicolon delimiter — many European locales use the comma as a decimal separator, which breaks comma-delimited parsing.

Choose array mode deliberately

Expand is best for arrays of objects you want as rows; use Join for tags or labels, Index when position matters, and Stringify to keep the raw array intact in one cell.

Cap flattening depth

For deeply nested JSON, set a maximum depth so very deep branches are stringified rather than producing hundreds of sparse columns that are hard to read.

Clean before you export

Run trim, dedupe and remove-empty before generating output. Fixing data once at the source beats patching it in every downstream system later.

Generate a schema for contracts

Derive JSON Schema and TypeScript interfaces from a representative sample so consumers can validate payloads and get autocomplete instead of guessing the shape.

Data Conversion API

Automate data transformation at scale with a REST API: CSV ↔ JSON, validation, schema generation, flattening and format conversion, with batch processing, async jobs, webhooks, API keys, team accounts and usage analytics.

Free

Unlimited in-browser conversions

All six JSON output shapes
CSV, TSV, JSON, XML, YAML & Excel input
Validation, profiling & quality score
Data cleaning & field mapping
100% client-side — no sign-up

Pro

$9/mo

50,000 API calls / month

REST API for CSV ↔ JSON automation
NDJSON / JSON Lines streaming
Batch & ZIP processing
Schema, TypeScript & SQL generation
Priority email support

Business

$29/mo

500,000 API calls / month

Everything in Pro
Webhook & pipeline integrations
Saved transformation presets
Team workspaces & shared mappings
Higher rate limits & SLA

Enterprise

Custom

Unlimited / dedicated capacity

Self-hosted & on-prem deployment
SSO, audit logs & access controls
Custom connectors & formats
Dedicated support & onboarding
Security review & DPA

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CSV ↔ JSON Converter

Everything in One Data Converter

Two-Way Conversion

Six JSON Shapes

Nested & Flatten Engine

Auto Delimiter Detection

Validation & Quality Score

One-Click Data Cleaning

Column Profiling

Multi-Format Export

100% In-Browser Privacy

Batch & ZIP Processing

Transformation Studio

Formula / Computed Columns

Schema & Code Generation

JSON Tree Explorer

What is CSV?

What is JSON?

CSV vs JSON

Why Convert Between CSV and JSON

Nested JSON & Flattening

Data Cleaning & Validation

Schema Mapping

Large Files & Performance

Security & Privacy

How to Convert CSV to JSON

Upload CSV

Configure Conversion

Validate Data

Download JSON

How to Convert JSON to CSV

Upload JSON

Map Fields

Preview Output

Download CSV

CSV vs JSON

Automatic Data-Type Detection

Use Cases

Developers

API Engineers

Data Analysts

Data Scientists

BI Teams

Ecommerce

CRM Exports

Database Migration

ETL Pipelines

Marketing

Private by Design

No Upload, Ever

Works Offline

Nothing Stored

Compliance-Friendly

How It Works Under the Hood

RFC 4180 Parser

Flattening Engine

Type Inference

Quality Scoring

Format Pipeline

Round-Trip Engine

Pro Tips

Pick the right JSON shape

Match the delimiter to your locale

Choose array mode deliberately

Cap flattening depth

Clean before you export

Generate a schema for contracts

Data Conversion API

Free

Pro

Business

Enterprise

Related Tools

Frequently Asked Questions

Troubleshooting