How to explore a CSV file
Drag a CSV or TSV file onto the drop zone, click to choose one, or paste the raw text into the box. The file is parsed instantly and you get three things: top-line counts (rows, columns, cells and overall fill rate), a per-column profile, and a scrollable preview of the data. The preview shows the first 200 rows for speed, while the profile and counts are computed across the whole file. Nothing is sent anywhere — the parse happens entirely in your browser, so even a large or confidential export is safe to open.
What the column profile tells you
The profile is a quick health check of your data, one row per column. It infers each column's type — integer, number, boolean, date or string — by testing the values, then reports the fill rate (what share of cells are non-empty, with the empty count) and the number of unique values, which instantly reveals identifiers, categorical fields and constant columns. For numeric columns it shows the minimum, maximum, mean and median; for text columns it lists the most frequent values. Together these surface the things you usually open a spreadsheet to check: missing data, unexpected types, outliers and the shape of each field.
Delimiters and headers
By default the delimiter is detected automatically, so comma, tab, semicolon and pipe files all just work; if detection guesses wrong on an unusual file, set the delimiter manually. The first row is header toggle controls whether the opening line supplies column names or is treated as data — turn it off for headerless files and the columns are named col1, col2 and so on. Parsing is handled by PapaParse, a robust RFC 4180 CSV parser, so quoted fields, embedded commas, escaped quotes and newlines inside cells are handled correctly.
Filtering the rows
The filter box does a live, case-insensitive search across every column and shows only the rows that contain your term, with a count of how many matched. It is a fast way to spot-check a value, isolate one category, or confirm whether a record exists, without loading the file into a database or spreadsheet. The filter affects the preview only; the column profile always reflects the full dataset.
Why explore CSV data locally
CSV exports are some of the most sensitive files developers handle — customer lists, transaction logs, analytics dumps, user tables. Uploading one to an online CSV viewer hands a third party the entire dataset. Running PapaParse in your browser keeps the file on your device: it is read with the local FileReader, parsed in memory, and nothing is logged or transmitted, matching the gitime.dev default that your data stays local.
- Local — the file is never uploaded.
- Profiled — types, fill rates, uniques and stats per column.
- Robust — RFC 4180 parsing via PapaParse.
- Flexible — auto or manual delimiter, header toggle.
- Searchable — live filter across all columns.
Frequently asked questions
- Is my CSV uploaded anywhere?
- No. It is read locally with FileReader and parsed by PapaParse in your browser, so nothing is uploaded.
- What does the column profile show?
- An inferred type, fill rate, unique count, and either numeric stats (min/max/mean/median) or top values per column.
- Does it handle tab-separated or other delimiters?
- Yes — auto-detected by default, or force comma, tab, semicolon or pipe, plus a header toggle.
- How large a file can it open?
- It parses in memory, so size is limited by your RAM; the preview shows 200 rows while stats use every row.