plotly.express.density_heatmap

plotly.express.density_heatmap(data_frame=None, x=None, y=None, z=None, facet_row=None, facet_col=None, facet_col_wrap=0, hover_name=None, hover_data=None, animation_frame=None, animation_group=None, category_orders={}, labels={}, color_continuous_scale=None, range_color=None, color_continuous_midpoint=None, marginal_x=None, marginal_y=None, opacity=None, log_x=False, log_y=False, range_x=None, range_y=None, histfunc=None, histnorm=None, nbinsx=None, nbinsy=None, title=None, template=None, width=None, height=None)

In a density heatmap, rows of data_frame are grouped together into colored rectangular tiles to visualize the 2D distribution of an aggregate function histfunc (e.g. the count or sum) of the value z.

Parameters
  • data_frame (DataFrame or array-like or dict) – This argument needs to be passed for column names (and not keyword names) to be used. Array-like and dict are tranformed internally to a pandas DataFrame.

  • x (str or int or Series or array-like) – Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like are used to position marks along the x axis in cartesian coordinates. For horizontal histograms, these values are used as inputs to histfunc.

  • y (str or int or Series or array-like) – Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like are used to position marks along the y axis in cartesian coordinates. For vertical histograms, these values are used as inputs to histfunc.

  • z (str or int or Series or array-like) – Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like are used to position marks along the z axis in cartesian coordinates. For density_heatmap and density_contour these values are used as the inputs to histfunc.

  • facet_row (str or int or Series or array-like) – Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like are used to assign marks to facetted subplots in the vertical direction.

  • facet_col (str or int or Series or array-like) – Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like are used to assign marks to facetted subplots in the horizontal direction.

  • facet_col_wrap (int) – Maximum number of facet columns. Wraps the column variable at this width, so that the column facets span multiple rows. Ignored if 0, and forced to 0 if facet_row or a marginal is set.

  • hover_name (str or int or Series or array-like) – Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like appear in bold in the hover tooltip.

  • hover_data (list of str or int, or Series or array-like) – Either names of columns in data_frame, or pandas Series, or array_like objects Values from these columns appear as extra data in the hover tooltip.

  • animation_frame (str or int or Series or array-like) – Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like are used to assign marks to animation frames.

  • animation_group (str or int or Series or array-like) – Either a name of a column in data_frame, or a pandas Series or array_like object. Values from this column or array_like are used to provide object-constancy across animation frames: rows with matching `animation_group`s will be treated as if they describe the same object in each frame.

  • category_orders (dict with str keys and list of str values (default {})) – By default, in Python 3.6+, the order of categorical values in axes, legends and facets depends on the order in which these values are first encountered in data_frame (and no order is guaranteed by default in Python below 3.6). This parameter is used to force a specific ordering of values per column. The keys of this dict should correspond to column names, and the values should be lists of strings corresponding to the specific display order desired.

  • labels (dict with str keys and str values (default {})) – By default, column names are used in the figure for axis titles, legend entries and hovers. This parameter allows this to be overridden. The keys of this dict should correspond to column names, and the values should correspond to the desired label to be displayed.

  • color_continuous_scale (list of str) – Strings should define valid CSS-colors This list is used to build a continuous color scale when the column denoted by color contains numeric data. Various useful color scales are available in the plotly_express.colors submodules, specifically plotly_express.colors.sequential, plotly_express.colors.diverging and plotly_express.colors.cyclical.

  • range_color (list of two numbers) – If provided, overrides auto-scaling on the continuous color scale.

  • color_continuous_midpoint (number (default None)) – If set, computes the bounds of the continuous color scale to have the desired midpoint. Setting this value is recommended when using plotly_express.colors.diverging color scales as the inputs to color_continuous_scale.

  • marginal_x (str) – One of 'rug', 'box', 'violin', or 'histogram'. If set, a horizontal subplot is drawn above the main plot, visualizing the x-distribution.

  • marginal_y (str) – One of 'rug', 'box', 'violin', or 'histogram'. If set, a vertical subplot is drawn to the right of the main plot, visualizing the y-distribution.

  • opacity (float) – Value between 0 and 1. Sets the opacity for markers.

  • log_x (boolean (default False)) – If True, the x-axis is log-scaled in cartesian coordinates.

  • log_y (boolean (default False)) – If True, the y-axis is log-scaled in cartesian coordinates.

  • range_x (list of two numbers) – If provided, overrides auto-scaling on the x-axis in cartesian coordinates.

  • range_y (list of two numbers) – If provided, overrides auto-scaling on the y-axis in cartesian coordinates.

  • histfunc (str (default 'count')) – One of 'count', 'sum', 'avg', 'min', or 'max'.Function used to aggregate values for summarization (note: can be normalized with histnorm). The arguments to this function for histogram are the values of y if orientation is 'v', otherwise the arguements are the values of x. The arguments to this function for density_heatmap and density_contour are the values of z.

  • histnorm (str (default None)) – One of 'percent', 'probability', 'density', or 'probability density' If None, the output of histfunc is used as is. If 'probability', the output of histfunc for a given bin is divided by the sum of the output of histfunc for all bins. If 'percent', the output of histfunc for a given bin is divided by the sum of the output of histfunc for all bins and multiplied by 100. If 'density', the output of histfunc for a given bin is divided by the size of the bin. If 'probability density', the output of histfunc for a given bin is normalized such that it corresponds to the probability that a random event whose distribution is described by the output of histfunc will fall into that bin.

  • nbinsx (int) – Positive integer. Sets the number of bins along the x axis.

  • nbinsy (int) – Positive integer. Sets the number of bins along the y axis.

  • title (str) – The figure title.

  • template (or dict or plotly.graph_objects.layout.Template instance) – The figure template name or definition.

  • width (int (default None)) – The figure width in pixels.

  • height (int (default 600)) – The figure height in pixels.

Returns

Return type

A Figure object.