csv-data-visualizer

To visualize a CSV file with interactive charts, you parse the data and generate Plotly-based outputs like histograms, scatter plots, and pie charts that can be exported as interactive HTML or static images.

Yes, you can automatically profile CSV data to check for missing values and duplicates. The profiling feature summarizes numeric statistics, categorical frequencies, and data quality issues before generating visualizations.

You build a multi-plot dashboard from CSV data by auto-generating or customizing multiple Plotly charts into a single interactive HTML file, which is ideal for exploratory analysis and reporting.

Chart types available for CSV exploratory data analysis include histograms, box plots, violin plots, scatter plots, line charts, bar charts, pie charts, and correlation heatmaps.

Yes, this approach requires Plotly for generating correlation heatmaps from a CSV. The visualization process specifically depends on Plotly, pandas, and numpy to parse data and render interactive outputs.

The best way to inspect trends and compare categories in a CSV export is using line charts for trend inspection and bar or pie charts for category comparison, producing shareable HTML dashboards.