Getting Started with pfDataViewer — A Quick TutorialpfDataViewer is a lightweight, specialized tool for inspecting, visualizing, and exporting tabular and time-series data. Whether you’re a data analyst quickly exploring a CSV, a developer debugging structured logs, or a scientist plotting measurement results, pfDataViewer aims to make the process fast, intuitive, and reproducible. This tutorial walks you through installation, basic workflows, advanced features, tips for performance, and how to integrate pfDataViewer into your data pipeline.
What pfDataViewer does (at a glance)
pfDataViewer helps you:
- Quickly open and browse tabular files (CSV, TSV, Excel).
- Filter, sort, and query data without writing code.
- Create interactive plots (line, scatter, bar, histogram) for quick exploration.
- Export filtered subsets and visualizations in common formats.
- Connect to basic time-series data sources and visualize temporal trends.
System requirements
pfDataViewer runs on Windows, macOS, and Linux. For smooth performance:
- CPU: Dual-core 2.0 GHz or higher.
- RAM: 4 GB minimum, 8 GB recommended for larger datasets.
- Disk: 200 MB free for application files plus storage for datasets.
- Optional GPU acceleration for plotting when available.
Installing pfDataViewer
-
macOS (Homebrew):
brew install pfdataviewer -
Linux (Debian/Ubuntu .deb):
sudo dpkg -i pfdataviewer_1.2.0_amd64.deb sudo apt-get install -f -
Windows: Download the installer from the official site and run the .exe.
After installation, launch the app from your OS launcher or run:
pfdataviewer Opening your first file
- From the File menu choose Open, or drag-and-drop a CSV/TSV/Excel file into the window.
- pfDataViewer will auto-detect delimiters and encodings. If detection fails, use the import dialog to set:
- Delimiter (comma, tab, semicolon)
- Header row present (yes/no)
- Character encoding (UTF-8, ISO-8859-1, etc.)
- The data loads into a spreadsheet-like grid for immediate browsing.
Basic navigation and data exploration
- Grid view: Click column headers to sort ascending/descending. Use the search box to find values across the dataset.
- Row selection: Click a row to see a detailed record pane with metadata and options to copy/export.
- Column statistics: Hover over a column header or open the column menu to see basic stats (count, mean, median, min, max, unique values).
- Filtering: Use the filter bar under headers to apply quick filters (equals, contains, range). Combine multiple filters across columns.
Creating visualizations
pfDataViewer provides an interactive plotting pane with common chart types.
- Select one or more columns (use Ctrl/Cmd-click for multiple).
- Click the Plot button and choose a chart:
- Line: for time-series data (x-axis typically a datetime or numeric index)
- Scatter: for relationships between two numeric columns
- Bar: for categorical counts or aggregated numeric values
- Histogram: for distribution of a single numeric column
- Customize the plot: axis labels, log scale, aggregation function (sum, mean, count), colors, and point size.
- Zoom and pan directly in the plot. Use the export menu to save as PNG, SVG, or CSV of sampled points.
Example: plotting temperature over time
- Ensure your datetime column is parsed as a datetime type (use the column type menu if needed).
- Select datetime as X and temperature as Y, choose Line, then apply a 1-hour rolling average if data is noisy.
Transformations and derived columns
- Column types: Change a column’s type to numeric, datetime, or categorical via the column menu.
- Calculated columns: Create new columns using simple expressions (arithmetic, conditional logic, built-in functions like round(), diff(), moving_avg()).
- Example expression to convert Celsius to Fahrenheit:
F = (C * 9/5) + 32 - Group & aggregate: Group by categorical columns and compute aggregates (sum, mean, median) on numeric columns. Results can be visualized or exported.
Working with large datasets
- Lazy loading: pfDataViewer streams data instead of loading everything into memory when possible.
- Sampling: Use the sample mode to visualize a representative subset quickly.
- Indexing: Create an index on frequently filtered columns to speed repeated queries.
- Export in chunks: When saving large filtered datasets, use the chunked export option to avoid memory spikes.
Time-series features
- Time parsing: Automatic recognition for ISO, common US/European formats, and epoch timestamps. Manual parsing options available.
- Resampling: Resample data to common intervals (minute, hour, day) with aggregation functions.
- Rolling windows: Built-in rolling mean/median/std functions for smoothing and volatility analysis.
- Anomaly detection: Simple z-score and IQR-based outlier flags to highlight unusually large deviations.
Integration and automation
- Command-line interface: Automate common tasks (convert, filter, export, generate plots) using the CLI. Example: convert CSV to parquet and generate a PNG plot
pfdataviewer convert --input data.csv --output data.parquet pfdataviewer plot --input data.parquet --x time --y temperature --type line --output temp.png - Scripting API: Python bindings let you load data, run transformations, and export results programmatically.
- Plugins: Extend with custom parsers or plot types via a plugin system (JS/TS or Python).
Shortcuts and productivity tips
- Ctrl/Cmd+F — global search
- Ctrl/Cmd+L — filter pane toggle
- Space — preview selected row
- Double-click column header — quick type conversion
- Use keyboard-driven selection (Shift+arrows) for faster row operations
Troubleshooting common issues
- File won’t open: Check encoding and delimiter settings in the import dialog.
- Slow UI with big files: Enable sampling or increase memory limit in preferences.
- Date column not recognized: Manually set column type to datetime and specify format.
- Missing plots: Ensure selected columns are numeric (for scatter/histogram) or datetime (for line).
Example workflow: from raw CSV to report-ready plot
- Open data.csv and set header row.
- Convert timestamp column to datetime and set timezone if needed.
- Filter rows for the last 90 days.
- Create a 24-hour rolling average on the target metric.
- Group by day and compute daily mean.
- Plot daily mean as a line chart, adjust labels and export PNG at 300 DPI.
- Export the filtered dataset to CSV for sharing.
Where to learn more
- Built-in help: Help → Tutorials inside pfDataViewer.
- Documentation: API reference and plugin guides available on the official site.
- Community: Forums and issue tracker for feature requests and bug reports.
pfDataViewer is designed to speed up exploratory analysis without forcing you into code. Once you know these basics—opening files, filtering, plotting, and exporting—you’ll be able to turn raw data into insight quickly.
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