A heatmap uses three dimensions to represent information. The first two are the vertical and horizontal axis, and the last one is the value (and color) of the entry point. It takes the shape of a table, it can be used effectively to find relations and trends between variables (f.e. the heat of a point given its coordinates, the correlation between the two axis).
The Method To Use
The method is s.plt.heatmap()
It must contain the following input variables:
Copy order : int
x : str
y : str
values : str
data : Union [ List [ Dict ], pd . DataFrame , str ] Accepts the following input variables as optional:
Copy color_range : Optional [ Tuple [ int , int ]]
x_axis_name : Optional [ str ]
y_axis_name : Optional [ str ]
title : Optional [ str ]
padding : Optional [ List [ int ]]
rows_size : Optional [ int ]
cols_size : Optional [ int ]
calculate_color_range : Optional [ bool ]
continuous : Optional [ bool ]
option_modifications : Optional [ Dict ]
variant : Optional [ str ] Examples
1. Default Configuration
Using this sample of data:
Copy data = [
{
"xAxis" : "Lunes" ,
"yAxis" : "12 a.m" ,
"value" : 9
},
{
"xAxis" : "Lunes" ,
"yAxis" : "6 p.m" ,
"value" : 10
},
{
"xAxis" : "Lunes" ,
"yAxis" : "12 p.m" ,
"value" : 9
},
{
"xAxis" : "Lunes" ,
"yAxis" : "6 a.m" ,
"value" : 10
},
{
"xAxis" : "Martes" ,
"yAxis" : "12 a.m" ,
"value" : 9
},
{
"xAxis" : "Martes" ,
"yAxis" : "6 p.m" ,
"value" : 9
},
{
"xAxis" : "Martes" ,
"yAxis" : "12 p.m" ,
"value" : 8
},
{
"xAxis" : "Martes" ,
"yAxis" : "6 a.m" ,
"value" : 0
},
{
"xAxis" : "Miercoles" ,
"yAxis" : "12 a.m" ,
"value" : 2
},
{
"xAxis" : "Miercoles" ,
"yAxis" : "6 p.m" ,
"value" : 7
},
{
"xAxis" : "Miercoles" ,
"yAxis" : "12 p.m" ,
"value" : 0
},
{
"xAxis" : "Miercoles" ,
"yAxis" : "6 a.m" ,
"value" : 2
},
{
"xAxis" : "Jueves" ,
"yAxis" : "12 a.m" ,
"value" : 4
},
{
"xAxis" : "Jueves" ,
"yAxis" : "6 p.m" ,
"value" : 0
},
{
"xAxis" : "Jueves" ,
"yAxis" : "12 p.m" ,
"value" : 1
},
{
"xAxis" : "Jueves" ,
"yAxis" : "6 a.m" ,
"value" : 6
}
] And this code to call the heatmap function:
Copy s . plt . heatmap (
data = data,
x = 'xAxis' , y = 'yAxis' ,
values = 'value' , order = 0
) 2. Customization And Context
It is possible to personalize the title of the chart, name for each axis, the legend, size and space occupied. One example can be obtained, using the same data as before, by running the following code:
Copy s . plt . heatmap (
data = data, title = "Heatmap" ,
x = 'xAxis' , y = 'yAxis' , values = 'value' ,
order = 0 , rows_size = 3 , cols_size = 10 ,
padding = "0,0,0,1" , x_axis_name = "Day" , y_axis_name = "Hour"
) Variants
By setting the parameter variant to the following values the appearance of the chart can be changed:
Interesting Usages
As mentioned before this type of plot is very useful and versatile, here we present some examples of possible usages that might be of your interest.
Cohort analysis:
With the heatmap function we can create the typical cohort analysis plot, the following images show how we can visualize churn rates by cohorts.
Perlin noise visualization:
In this example is very clear how it can be useful in heat-like visualizations.
