CSV прегледач

View any CSV file online — instantly

This CSV viewer opens comma-separated files right in your browser. Drag a file onto the drop zone, click Upload, or paste raw CSV or TSV text, and it renders as a clean, sortable, searchable table with a sticky header and row numbers — no Excel, no Google Sheets and no sign-up. As a free online CSV viewer it works the same on Windows, Mac, Linux, Android and iPhone.

Because the parser follows the real CSV rules, it correctly reads quoted fields that contain commas, quotes and even embedded line breaks — exactly the cases that break a naive split-on-comma. Load the built-in sample to see it in action in one click.

Built for large CSV files

Most browser-based viewers slow to a crawl after a few thousand rows. This one is a true large CSV viewer: it virtualizes the table, drawing only the rows visible on screen, so a file with 100,000+ rows and several megabytes scrolls smoothly instead of freezing the tab or crashing a spreadsheet app.

Sorting, filtering and column statistics still run across the entire dataset, not just the visible window, and live counts tell you exactly how many rows and columns loaded — for example 128,442 rows · 12 columns — so you always know the size of what you're working with.

Fix the 'everything in one column' problem

If a CSV ever opened in Excel with every value crammed into a single column, the culprit is the delimiter. Files exported in many European locales use semicolons, and data dumps often use tabs or pipes. This viewer samples your file and auto-detects the delimiter — comma, semicolon, tab or pipe — then splits the columns correctly.

If the guess is ever wrong you can force the right delimiter from the menu, and toggle whether the first row should be treated as a header or as data. That makes it easy to view a CSV with semicolons or tabs without re-saving the file first.

How this compares to Excel, Google Sheets and other CSV tools

Microsoft Excel silently corrupts CSV data: it strips leading zeros from ZIP codes, turns product codes like '1-2' into dates, and refuses to open files beyond 1,048,576 rows. Google Sheets caps files at 10 million cells and uploads your data to Google's servers. csvjson.com and convertcsv.com are server-side — your file is uploaded and processed remotely.

This viewer is different: everything runs locally in your browser. Your file never leaves your device. There's no row limit beyond your device's RAM, no data transformation, and no account to create. It reads the raw values as they appear in the file, preserving leading zeros, long numeric strings and every other edge case Excel mangles.

A CSV viewer and editor

It isn't read-only. Switch on Edit mode to change any cell in place, rename headers, and add or delete rows. When you're done, click Download CSV to save the rebuilt file straight to your device — nothing is sent to a server. That makes it a quick CSV editor for fixing a typo, removing a bad record, or trimming a column you don't need before passing the data on to another tool or sharing it with a colleague.

Unlike csvjson.com, which only reads and converts files, or convertcsv.com, which only exports, this tool lets you modify the data and then export it in whichever format you need. The edited result is rebuilt entirely in the browser from the current table state, so what you see is exactly what you download.

Sort, search, hide columns and inspect

Click any column header to sort it — numbers sort numerically, text alphabetically, and a second click reverses the order. Type in the filter box to show only rows containing your search term, filtered in real time as you type. Open the Columns menu to hide fields you don't need, which also removes them from exports. The sticky header and frozen row-number column keep you oriented while scrolling a very wide or very long file.

The built-in column stats panel checks each column and reports its inferred type, the count of empty cells, the number of unique values, and the minimum and maximum of any numeric column. That's a fast way to spot data-quality problems — blank cells you didn't expect, values that should be unique but aren't, or a numeric column that came through as text — before you move the data into a database or hand it off.

Convert and export — CSV, TSV, JSON or Markdown

When you're done, export the current table directly from your browser in four formats. Download CSV saves the file with your edits and the current sort/filter applied. Copy as TSV puts tab-separated text on the clipboard, which pastes cleanly into Excel or Google Sheets without triggering Excel's date-mangling. Copy as JSON turns each row into an object keyed by the header — useful for feeding into an API or a code project. Copy as Markdown produces a pipe-delimited table you can drop straight into a GitHub README or a documentation page.

Every export reflects the table as it currently appears: if you filtered to 500 rows from a 50,000-row file, only those 500 rows export. If you hid three columns, they stay hidden in the export. You always get exactly the slice of data you can see on screen, nothing more.

Private by design — works on any device

Every step runs locally with JavaScript: your file is parsed, edited and exported entirely on your device. Nothing is uploaded to a server. That makes this CSV file viewer safe even for exports containing customer records, financial data, health information or other confidential content that you wouldn't want passing through a third-party server. Many popular online CSV tools — including convertcsv.com and csvjson.com — are server-side and do receive your file.

Once the page has loaded, the tool keeps working offline — useful on a plane, in a location with poor signal, or on a machine with outbound traffic restrictions. It's fully functional on iPhone, Android, Mac, Windows and Linux with no install, no app download, no sign-up and nothing to pay.

The CSV format in depth: RFC 4180, quoting rules and encoding pitfalls

RFC 4180, published in 2005, is the closest thing CSV has to a formal specification — it is not an official internet standard but is by far the most-cited reference for how CSV files should be structured. The document codifies what most tools already assumed: each record occupies one line, fields are separated by a delimiter character (the comma by default), and the first row may optionally serve as a header naming the columns. In practice the comma is only one of many delimiters in use. Tab-separated values (TSV) are common in bioinformatics and database exports; pipe-delimited files appear in legacy financial and ERP systems; semicolons are the default in European locales where the comma is already the decimal separator — Germany, France, the Netherlands and others — which is why a CSV from a French accounting system often looks broken when opened on an English-language machine.

The quoting rule is the part of RFC 4180 that trips up the most naive parsers. Any field that contains the delimiter, a double-quote character, or an embedded newline must be enclosed in double quotes: "New York, NY" is a valid single field even though it contains a comma. Embedded double quotes inside a quoted field must themselves be doubled: the text She said "hello" is encoded as "She said ""hello""". A parser that simply splits on commas, or that treats every double quote as the start of a new token, will corrupt data as soon as it encounters either of these cases. This viewer implements the full quoting spec, which is why it correctly displays quoted fields that span multiple lines.

Line endings and encoding are a separate source of problems. RFC 4180 specifies CRLF (\r\n) as the record terminator, but Unix and Linux tools write LF (\n) only, and Excel on classic Mac OS used bare CR (\r) — a format still found in files exported from older systems. A more subtle issue is the BOM (Byte Order Mark): when Excel saves a CSV as UTF-8 it prepends a three-byte sequence (\xEF\xBB\xBF) to signal the encoding. Tools that do not strip the BOM see three garbage characters prepended to the very first column name — often displayed as id instead of id. This viewer detects and discards the BOM automatically, so column names appear clean regardless of which version of Excel or operating system generated the file.

CSV in data science and data engineering: pandas, type inference and Excel interoperability

Despite its limitations, CSV remains the dominant data interchange format in data science and data engineering. Its chief advantage is universality: a CSV file is human-readable in any text editor, writable from any language without a library, and accepted as input by every tool from R and Python to SQL databases, BI platforms and cloud storage services. When a data pipeline needs to hand data from one system to another — a database exporting to a Python script, a Spark job writing results for an analyst — CSV is usually the lowest-friction option. The format's simplicity is also its limitation: it carries no schema, no types, no nullability constraints and no knowledge of encoding beyond whatever the writer chose.

Python's pandas library is the most widely used tool for reading CSV files in data science. Its read_csv() function accepts dozens of parameters that control parsing behavior. The sep parameter sets the delimiter (default ','; pass sep='\t' for TSV or sep=None with engine='python' for auto-detection). The dtype parameter is critical: pandas infers column types by scanning values, which means a column of numeric customer IDs like 007412 is silently read as the integer 7412, discarding the leading zero. Explicitly passing dtype={'customer_id': str} preserves the raw string. Similarly, na_values controls which strings are converted to NaN — by default pandas treats empty strings, NA, NaN, null and several others as missing, which can incorrectly classify legitimate values in domain-specific exports. The encoding parameter defaults to the operating system's locale setting, which means the same script can silently misread accented characters on Windows versus Linux; always specifying encoding='utf-8' (or 'utf-8-sig' to strip the BOM) is best practice.

The Excel CSV interoperability problem is one of the most persistent pain points in data work. When you double-click a CSV on Windows, Excel opens it using the system's regional list separator — a comma in English locales, a semicolon in German, French and dozens of others. A file that displays perfectly in one region becomes a single-column mess in another. Beyond delimiters, Excel applies its own type inference without asking: it converts 1-2 to January 2, 3/4 to March 4, and gene names like SEPT2 to September 2, 2000 — a problem severe enough that the genetics community has proposed renaming affected genes. Numeric strings with leading zeros lose them permanently once Excel touches the file. The correct workaround when Excel interoperability matters is to use the "From Text/CSV" import wizard rather than double-clicking, which lets you specify the delimiter, set column types explicitly, and prevent automatic date conversion. Alternatively, share the file in XLSX format rather than CSV to preserve types across the handoff.

Large CSV files: streaming parsers, row limits and tools built for scale

The single biggest practical limitation of CSV is what happens when files get large. Microsoft Excel imposes a hard limit of 1,048,576 rows — exactly 2²⁰, a ceiling baked in since Excel 2007. Files that exceed this limit are silently truncated when opened: Excel shows no error, simply stops at row 1,048,576, and the user has no indication that 30% of their data was discarded. Wide files hit a separate limit of 16,384 columns. For files below these limits, Excel still struggles with CSVs over ~50 MB, which can take minutes to open and consume several gigabytes of RAM. Google Sheets caps individual files at 10 million cells — a limit that a 200-column file can hit after only 50,000 rows. For anything beyond these bounds, data professionals move to purpose-built tools.

Streaming CSV parsing is the architectural pattern that makes large-file handling practical. A naive parser loads the entire file into memory before processing a single row; a streaming parser processes one record at a time and discards it once it is no longer needed. Python's built-in csv module is streaming by default — a for row in csv.reader(f) loop reads one line at a time regardless of file size. Pandas offers chunksize parameter in read_csv() to iterate over the file in batches of N rows, which allows aggregating a multi-gigabyte file without ever holding it all in memory. In JavaScript, Papa Parse exposes a step callback for the same pattern, processing each row as it is parsed from a stream rather than building the entire array first. This is how this viewer handles files with hundreds of thousands of rows without stalling the browser tab.

DuckDB has become the tool of choice for ad-hoc analysis of large CSV files without a database setup. It is an in-process analytical SQL engine — no server, no configuration — that can query a CSV file directly: SELECT region, SUM(revenue) FROM 'sales_2024.csv' GROUP BY region. DuckDB auto-detects the delimiter, header row and column types, and can process multi-gigabyte files efficiently using vectorized execution and out-of-core algorithms that spill to disk when data exceeds available RAM. For browser-based viewers, the equivalent technique is virtual (windowed) rendering: instead of inserting all rows into the DOM — which for a million-row file would create tens of millions of DOM nodes and consume hundreds of megabytes of browser memory — the viewer tracks the scroll position and renders only the rows currently visible on screen, typically a few dozen at a time. The full dataset lives in a JavaScript array in memory; only the visible slice is materialized as HTML. This reduces DOM node count from millions to hundreds, keeping the page responsive regardless of file size.