Last Updated : 26 Jul, 2020
Plotly library of Python can be very useful for data visualization and understanding the data simply and easily.
Function that creates annotated heatmaps and adds annotations to each cell of the heatmap.
Syntax:plotly.figure_factory.create_annotated_heatmap(z, x=None, y=None, annotation_text=None, colorscale='Plasma', font_colors=None, showscale=False, reversescale=False, **kwargs)
Parameter:
z ((list[list]|ndarray)) – it describe the z matrix to create heatmap.
x ((list)) – it describe the x axis labels.
y ((list)) – it describe the y axis labels.annotation_text ((list[list]|ndarray)) – Text strings for annotations. Should have the same dimensions as the z matrix. If no text is added, the values of the z matrix are annotated. Default = z matrix values.
colorscale ((list|str)) – it describe heatmap colorscale.
font_colors ((list)) – it describes the list of two color strings: [min_text_color, max_text_color]
showscale ((bool)) – it display colorscale with Default as False
reversescale ((bool)) – it reverse colorscale with Default as False
Example 1: Simple annotated heatmap with default configuration
Python3
import plotly.figure_factory as ff
z = [[0.300000, 0.00000, 0.65, 0.300000],
[1, 0.100005, 0.45, 0.4300],
[0.300000, 0.00000, 0.65, 0.300000],
[1, 0.100005, 0.45, 0.00000]]
fig = ff.create_annotated_heatmap(z)
fig.show()
Output:
Example 2: Defined colorscale
Python3
import plotly.figure_factory as ff
z = [[.1, .3, .5, .7],
[1, .8, .6, .4],
[.6, .4, .2, .0],
[.9, .7, .5, .3]]
fig = ff.create_annotated_heatmap(z, colorscale='Viridis')
fig.show()
Output:
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