Dataset.plot.streamplot(x, y, ax, u, v, **kwargs)[source]

Quiver plot with Dataset variables.

  • ds (Dataset)

  • x, y (str) – Variable names for x, y axis.

  • u, v (str, optional) – Variable names for quiver or streamplot plots only

  • hue (str, optional) – Variable by which to color scattered points or arrows

  • hue_style (str, optional) – Can be either ‘discrete’ (legend) or ‘continuous’ (color bar).

  • markersize (str, optional) – scatter only. Variable by which to vary size of scattered points.

  • size_norm (optional) – Either None or ‘Norm’ instance to normalize the ‘markersize’ variable.

  • scale (scalar, optional) – Quiver only. Number of data units per arrow length unit. Use this to control the length of the arrows: larger values lead to smaller arrows

  • add_guide (bool, optional) –

    Add a guide that depends on hue_style
    • for “discrete”, build a legend. This is the default for non-numeric hue variables.

    • for “continuous”, build a colorbar

  • row (str, optional) – If passed, make row faceted plots on this dimension name

  • col (str, optional) – If passed, make column faceted plots on this dimension name

  • col_wrap (int, optional) – Use together with col to wrap faceted plots

  • ax (matplotlib axes object, optional) – If None, uses the current axis. Not applicable when using facets.

  • subplot_kws (dict, optional) – Dictionary of keyword arguments for matplotlib subplots. Only applies to FacetGrid plotting.

  • aspect (scalar, optional) – Aspect ratio of plot, so that aspect * size gives the width in inches. Only used if a size is provided.

  • size (scalar, optional) – If provided, create a new figure for the plot with the given size. Height (in inches) of each plot. See also: aspect.

  • norm (matplotlib.colors.Normalize instance, optional) – If the norm has vmin or vmax specified, the corresponding kwarg must be None.

  • vmin, vmax (float, optional) – Values to anchor the colormap, otherwise they are inferred from the data and other keyword arguments. When a diverging dataset is inferred, setting one of these values will fix the other by symmetry around center. Setting both values prevents use of a diverging colormap. If discrete levels are provided as an explicit list, both of these values are ignored.

  • cmap (str or colormap, optional) – The mapping from data values to color space. Either a matplotlib colormap name or object. If not provided, this will be either viridis (if the function infers a sequential dataset) or RdBu_r (if the function infers a diverging dataset). When Seaborn is installed, cmap may also be a seaborn color palette. If cmap is seaborn color palette, levels must also be specified.

  • colors (color-like or list of color-like, optional) – A single color or a list of colors. The levels argument is required.

  • center (float, optional) – The value at which to center the colormap. Passing this value implies use of a diverging colormap. Setting it to False prevents use of a diverging colormap.

  • robust (bool, optional) – If True and vmin or vmax are absent, the colormap range is computed with 2nd and 98th percentiles instead of the extreme values.

  • extend ({"neither", "both", "min", "max"}, optional) – How to draw arrows extending the colorbar beyond its limits. If not provided, extend is inferred from vmin, vmax and the data limits.

  • levels (int or list-like object, optional) – Split the colormap (cmap) into discrete color intervals. If an integer is provided, “nice” levels are chosen based on the data range: this can imply that the final number of levels is not exactly the expected one. Setting vmin and/or vmax with levels=N is equivalent to setting levels=np.linspace(vmin, vmax, N).

  • **kwargs (optional) – Additional keyword arguments to matplotlib