Shape / inspect · All operations

Chart the data

Plot the data for exploration.

Status

In arcform

A feature, not a step

It lives in Brightfield's view. crates/brightfield-shell/src/chart_kinds.rs in brightfield at 0a8362e

Shape of the result
a table describing the input rather than holding its rows
Level
not a step

Variants

Write a custom chart

Draw a chart from code the user writes, such as a JavaScript view or a chart-library option object.

Draw a chart of a chosen kind

Draw a bar, line, pie, box, density, heatmap, area, sunburst or other chart of a table to explore it.

Plot geometries or images

Draw the geometries, map tiles or image pixels held in rows on a canvas.

Draw a word cloud

Show how often words occur in text, sized by frequency.

What other tools call it

Meridian's classification; an independent blind rating agreed on 86.2% of 312 rows (exact class, version 2).

Alteryx Designer (5)
  • Heat Plotread 2026-09-27

    Plot the joint distribution of two numeric or ordered fields as a colour map

  • Plot of Meansread 2026-09-27

    Plot the mean of a response field for each level of a categorical field

  • Scatterplotread 2026-09-27

    Plot two fields with optional marginal boxplots, regression line and smooth curve

  • TS Plotread 2026-09-27

    Draw time series, seasonal, decomposition and autocorrelation plots

  • Word Cloudread 2026-09-27

    Visualise word frequency in text as a word cloud

KNIME Analytics Platform (41)
OpenRefine (1)
  • Scatterplot facetread 2026-09-27

    Plot every pair of numeric columns, linear or log, and filter rows by a rectangle drawn on one plot

Polars (1)
  • DataFrame.plotread 2026-09-27

    Chart the frame through Altair, to which Polars hands the plotting: line, point and other marks

VisiData (12)
  • addcol-histogramread 2026-09-23

    Add column drawing a histogram bar for current column's value in each row

  • addcol-sparklineread 2026-09-23

    Add column drawing a sparkline across the numeric columns of each row

  • plot-columnread 2026-09-23

    Plot current numeric column against key columns as a terminal scatter graph

  • plot-column-extread 2026-09-23

    Plot current numeric column against key columns with matplotlib/seaborn

  • plot-extread 2026-09-23

    Replot current graph with matplotlib/seaborn

  • plot-numericsread 2026-09-23

    Plot all visible numeric columns against key columns

  • plot-numerics-extread 2026-09-23

    Plot all visible numeric columns with matplotlib/seaborn

  • plot-rowread 2026-09-23

    Plot the geometry, tile or block in current row on a map canvas

  • plot-rowsread 2026-09-23

    Plot the geometries, tiles or blocks of all rows on a map canvas

  • plot-selectedread 2026-09-23

    Plot selected map tiles

  • plot-sheetread 2026-09-23

    Plot a PNG pixel sheet as an image

  • plot-source-selectedread 2026-09-23

    From Columns Sheet, plot selected source columns against key columns

Yggdrasil Decision Forests (1)
  • plot_treeread 2026-09-27

    Render one tree as an interactive HTML plot

Background

Exploratory data analysis (EDA) analyses data sets to summarise their main characteristics, often using statistical graphics and other visualisation methods.[1] Most EDA techniques are graphical in nature, with a few quantitative techniques.[2] Graphical tools are the shortest path to gaining insight into a data set for aims such as testing assumptions, identifying relationships and detecting outliers.[3]

When to use it

The objectives of EDA include enabling unexpected discoveries in the data, suggesting hypotheses about the causes of observed phenomena, and assessing the assumptions on which statistical inference will be based.[1] There are arguments for visualising data during EDA before modelling, in order to avoid misleading conclusions.[1] Chart a table before fitting anything to it, and before trusting a summary of it. (Meridian's view)

John Tukey held that confusing exploratory and confirmatory analysis and employing them on the same set of data can lead to systematic bias.[1] Treat a pattern found by charting as a hypothesis to test on fresh data, not as a finding about the data that suggested it. (Meridian's view)

The method

Exploratory data analysis has been promoted by John Tukey since 1970 to encourage statisticians to explore the data and possibly formulate hypotheses that could lead to new data collection and experiments.[1] The seminal work in EDA is Exploratory Data Analysis, published by John Tukey in 1977.[2][1] The graphical techniques of EDA are often quite simple: plotting the raw data, as in histograms, plotting simple statistics, as in box plots, and placing several plots on one page to help pattern recognition.[2] Typical graphical techniques used in EDA include the box plot, the histogram, the scatter plot, parallel coordinates, the heat map and the bar chart.[1]

Compared with the alternatives

EDA is not identical to statistical graphics, although the two terms are used almost interchangeably.[2] EDA postpones the usual assumptions about what kind of model the data follow, allowing the data itself to reveal its underlying structure and model.[2] EDA contrasts with traditional hypothesis testing, in which a model is supposed to be selected before the data is seen and tested.[1]

Quantitative techniques are statistical procedures that yield numeric or tabular output, such as hypothesis testing, analysis of variance and least squares regression.[3] A summary statistic answers a question already asked; a chart is how an analyst finds the question. (Meridian's view)

Sources

  1. [1]Exploratory data analysis - Wikipediasecondary source
  2. [2]1.1.1. What is EDA?secondary source
  3. [3]1.1.5. The Role of Graphicssecondary source