CSV Pivot Table Generator
指导
CSV Pivot Table Generator
Turn flat CSV data into an interactive pivot table in seconds. Drag column headers into the Rows, Columns, Values, and Filters zones and watch the cross-tabulation recompute live — no spreadsheet, no formulas, no upload to a server. Perfect for slicing exported reports, analytics dumps, or any tabular dataset right in your browser.
如何使用
- Paste your CSV data into the input area, or upload a .csv file.
- Confirm the delimiter (auto-detected) and the header-row toggle.
- Drag fields from Available Fields into the 行, 列, 价值观， 或者 筛选器 zone.
- For each Value field, pick an aggregation: Sum, Count, Average, Min, Max, or Distinct Count.
- Optionally apply filters by clicking the value-picker on a Filter chip and choosing which values to include.
- Toggle grand-total rows and columns, pick a value format (Number, Currency, Percent) and decimal precision.
- Copy or download the result as CSV, or copy as a Markdown table.
特征
- Drag-and-drop pivot builder – Compose Rows, Columns, Values, and Filters by dragging chips between zones.
- Six aggregations per value – Sum, Count, Average, Min, Max, and Distinct Count, configurable independently for each value field.
- Multiple value fields – Stack several measures side by side in the same pivot.
- Per-field filters – Click a Filter chip to include or exclude any value from the source data.
- Grand totals – Optional total row and total column with matching aggregation logic.
- Value formatting – Number, currency, or percent display with configurable decimal places.
- Auto delimiter detection – Works with comma, tab, semicolon, or pipe-separated files.
- RFC 4180 CSV parser – Handles quoted fields, escaped quotes, and embedded line breaks correctly.
- One-click export – Copy or download the pivot as CSV, or copy as a Markdown table for docs and tickets.
- 100% 客户端 – Your data never leaves the browser; nothing is uploaded.
常问问题
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What is a pivot table?
A pivot table is a data-summarisation technique that takes a long, flat table (one row per record) and reshapes it into a compact cross-tabulation. You pick one or more fields to group by on the row axis, optionally cross-tab by another field on the column axis, and apply an aggregation (sum, count, average, etc.) to a measure. The result reveals patterns and totals that are hard to see in the raw data.
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What is the difference between Rows, Columns, Values, and Filters?
Rows define the groups that appear down the left side of the result. Columns cross-tabulate the data into separate columns by another field. Values are the numeric measures that get aggregated for each row/column intersection. Filters narrow the source rows before any aggregation runs, so excluded values do not contribute to totals.
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Which aggregation should I use?
Sum is right for additive measures like revenue or units. Average works for rates and per-unit metrics, but be careful: averaging averages can be misleading. Count tallies non-empty rows. Distinct Count counts unique values, useful for things like unique customers per region. Min and Max highlight outliers and ranges.
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What is RFC 4180 and why does it matter?
RFC 4180 is the informal standard for the CSV format. It defines how fields are separated, how line breaks work, and crucially how to quote fields that contain commas, quotes, or newlines. A strict RFC 4180 parser is required to correctly read CSVs exported from spreadsheets, databases, or analytics tools without mangling records that contain punctuation inside quoted text.
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Why aggregate client-side instead of in a database?
Client-side aggregation is instant, private, and free. For datasets up to a few hundred thousand rows, modern browsers can compute pivot summaries in milliseconds without any round-trip to a server. The data never leaves the machine, which matters for confidential exports, sensitive logs, or simply when there is no backend available.
