I want to create a heat map to visualize some production data, but without geolocation. I am finishing some experiments in a greenhouse, divided in different sectors. The idea is to make a heat map to watch in which areas we are harvesting more fruits, or less, in each season.

Which tool, software or language do you think is better for this purpose? I was looking for information, but the main tools are for geolocation.

  • $\begingroup$ It should be easy enough to create a heat map using Python's matplotlib library using the "go.heatmap" function. I have also created heat maps using Matlab and imagine that its simple to do in languages like R too. $\endgroup$
    – Derek
    Commented Aug 12, 2017 at 14:04

2 Answers 2


I believe that the simplest is to use Seaborn. Check out the example below:

import numpy as np
import seaborn as sns 


uniform_data = np.random.rand(10, 12)
ax = sns.heatmap(uniform_data)

And the output looks as follows:

enter image description here

This is taken from the seaborn documentation


I think you can use Seaborn for this purpose.

import seaborn as sn
import matplotlib.pyplot as plt
%matplotlib inline


enter image description here


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