API¶

Entry Points¶

`Canvas`([plot_width, plot_height, x_range, ...])	An abstract canvas representing the space in which to bin.
`Pipeline`(df, glyph[, agg, transform_fn, ...])	A datashading pipeline callback.

Glyphs¶

Point(x, y) A point, with center at x and y.

Reductions¶

`count`([column])	Count elements in each bin.
`sum`(column)	Sum of all elements in `column`.
`min`(column)	Minimum value of all elements in `column`.
`max`(column)	Maximum value of all elements in `column`.
`mean`(column)	Mean of all elements in `column`.
`var`(column)	Variance of all elements in `column`.
`std`(column)	Standard Deviation of all elements in `column`.
`count_cat`(column)	Count of all elements in `column`, grouped by category.
`summary`(**kwargs)	A collection of named reductions.

Transfer Functions¶

`interpolate`(agg[, low, high, how])	Convert a 2D DataArray to an image.
`colorize`(agg, color_key[, how, min_alpha])	Color a CategoricalAggregate by field.
`stack`(*imgs)	Merge a number of images together, overlapping earlier images with later ones.
`merge`(*imgs)	Merge a number of images together, averaging the channels

Definitions¶

class datashader.Canvas(plot_width=600, plot_height=600, x_range=None, y_range=None, x_axis_type='linear', y_axis_type='linear')¶

An abstract canvas representing the space in which to bin.

Parameters:

plot_width, plot_height : int, optional

Width and height of the output aggregate in pixels.

x_range, y_range : tuple, optional

A tuple representing the bounds inclusive space [min, max] along the axis.

x_axis_type, y_axis_type : str, optional

The type of the axis. Valid options are 'linear' [default], and 'log'.

class datashader.Pipeline(df, glyph, agg=<datashader.reductions.count object>, transform_fn=<function identity>, color_fn=<function interpolate>)¶

A datashading pipeline callback.

Given a declarative specification, creates a callable with the following signature:

callback(x_range, y_range, width, height)

where x_range and y_range form the bounding box on the viewport, and width and height specify the output image dimensions.

Parameters:

df : pandas.DataFrame, dask.DataFrame

glyph : Glyph

The glyph to bin by.

agg : Reduction, optional

The reduction to compute per-pixel. Default is count().

transform_fn : callable, optional

A callable that takes the computed aggregate as an argument, and returns another aggregate. This can be used to do preprocessing before passing to the color_fn function.

color_fn : callable, optional

A callable that takes the output of tranform_fn, and returns an Image object. Default is interpolate.

class datashader.glyphs.Point(x, y)¶

A point, with center at x and y.

Points map each record to a single bin.

Parameters:

x, y : str

Column names for the x and y coordinates of center of each point.

class datashader.reductions.count(column=None)¶

Count elements in each bin.

Parameters:

column : str, optional

If provided, only counts elements in column that are not NaN. Otherwise, counts every element.

class datashader.reductions.sum(column)¶

Sum of all elements in column.

Parameters:

column : str

Name of the column to aggregate over. Column data type must be numeric. NaN values in the column are skipped.

class datashader.reductions.min(column)¶

Minimum value of all elements in column.

Parameters:

column : str

Name of the column to aggregate over. Column data type must be numeric. NaN values in the column are skipped.

class datashader.reductions.max(column)¶

Maximum value of all elements in column.

Parameters:

column : str

Name of the column to aggregate over. Column data type must be numeric. NaN values in the column are skipped.

class datashader.reductions.mean(column)¶

Mean of all elements in column.

Parameters:

column : str

Name of the column to aggregate over. Column data type must be numeric. NaN values in the column are skipped.

class datashader.reductions.var(column)¶

Variance of all elements in column.

Parameters:

column : str

Name of the column to aggregate over. Column data type must be numeric. NaN values in the column are skipped.

class datashader.reductions.std(column)¶

Standard Deviation of all elements in column.

Parameters:

column : str

Name of the column to aggregate over. Column data type must be numeric. NaN values in the column are skipped.

class datashader.reductions.count_cat(column)¶

Count of all elements in column, grouped by category.

Parameters:

column : str

Name of the column to aggregate over. Column data type must be categorical. Resulting aggregate has a outer dimension axis along the categories present.

class datashader.reductions.summary(**kwargs)¶

A collection of named reductions.

Computes all aggregates simultaneously, output is stored as a xarray.Dataset.

Examples

A reduction for computing the mean of column “a”, and the sum of column “b” for each bin, all in a single pass.

>>> import datashader as ds
>>> red = ds.summary(mean_a=ds.mean('a'), sum_b=ds.sum('b'))

datashader.transfer_functions.merge(*imgs)¶: Merge a number of images together, averaging the channels

datashader.transfer_functions.stack(*imgs)¶: Merge a number of images together, overlapping earlier images with later ones.

datashader.transfer_functions.interpolate(agg, low='lightblue', high='darkblue', how='cbrt')¶

Convert a 2D DataArray to an image.

Parameters:

agg : DataArray

low : color name or tuple

The color for the low end of the scale. Can be specified either by name, hexcode, or as a tuple of (red, green, blue) values.

high : color name or tuple

The color for the high end of the scale

how : string or callable

The interpolation method to use. Valid strings are ‘cbrt’ [default], ‘log’, and ‘linear’. Callables take a 2-dimensional array of magnitudes at each pixel, and should return a numeric array of the same shape.

datashader.transfer_functions.colorize(agg, color_key, how='cbrt', min_alpha=20)¶

Color a CategoricalAggregate by field.

Parameters:

agg : DataArray

color_key : dict or iterable

A mapping of fields to colors. Can be either a dict mapping from field name to colors, or an iterable of colors in the same order as the record fields.

how : string or callable

The interpolation method to use. Valid strings are ‘cbrt’ [default], ‘log’, and ‘linear’. Callables take a 2-dimensional array of magnitudes at each pixel, and should return a numeric array of the same shape.

min_alpha : float, optional

The minimum alpha value to use for non-empty pixels, in [0, 255].