List of articles

Chapter one Use matplotlib Draw line chart
Chapter two Use matplotlib Draw a bar graph
The third chapter Use matplotlib Draw histogram
Chapter four Use matplotlib Draw a scatter plot
The fifth chapter Use matplotlib Draw the pie chart
Chapter six Use matplotlib Draw a heat map
Chapter vii. Use matplotlib Draw a stacked bar chart
Chapter viii. Use matplotlib Draw multiple diagrams in one canvas

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Preface

Last chapter We talked about the drawing of pie chart , In this chapter, we will talk about the drawing of thermal diagram .

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One 、 What is a heat map ？

A heat map is a statistical chart that displays data by coloring color blocks . When drawing , You need to specify the rules for color mapping . for example , Larger values are represented by darker colors , Smaller values are represented by lighter colors ; Larger values are represented by warmer colors , Indicated by the lower value of the color , wait .

Divide... From data structure , Thermodynamic diagrams are generally divided into two types . First of all , Tabular thermodynamic diagram , Also known as color block diagram . It needs to 2 Category fields and 1 A numeric field , The classification field determines x、y Axis , Divide the chart into regular rectangular blocks . The numeric field determines the color of the rectangular block . second , Non tabular thermodynamic diagram , Or a smooth heat map , It needs to 3 A numeric field , Can be drawn in parallel coordinate system （2 The two numeric fields are determined separately x、y Axis ,1 A numeric field determines the color ）.

The heat map is suitable for viewing the overall situation 、 Abnormal values were found 、 Show the difference between multiple variables , And whether there is any correlation between them .

Two 、 Drawing of thermal diagram

Let's talk about the drawing of thermal diagram through examples , The sample code is as follows ：

import matplotlib.pyplot as plt
import numpy as np

harvest = np.array([[0.8, 2.4, 2.5, 3.9, 0.0, 4.0, 0.0],
                    [2.4, 0.0, 4.0, 1.0, 2.7, 0.0, 0.0],
                    [1.1, 2.4, 0.8, 4.3, 1.9, 4.4, 0.0],
                    [0.6, 0.0, 0.3, 0.0, 3.1, 0.0, 0.0],
                    [0.7, 1.7, 0.6, 2.6, 2.2, 6.2, 0.0],
                    [1.3, 1.2, 0.0, 0.0, 0.0, 3.2, 5.1],
                    [0.1, 2.0, 0.0, 1.4, 0.0, 1.9, 6.3]])

plt.imshow(harvest)
plt.tight_layout()
plt.show()

In the code above , Pass a two-dimensional array into imshow In this method, a thermal diagram can be drawn , The color of each color block represents the size of the data . The graphics obtained after code execution are shown in the figure below ：

It's just a color block , There is no indication that x Axis and y Meaning of axis , Let's add x Axis and y The label of the shaft , And add a title . The sample code is as follows ：

import matplotlib.pyplot as plt
import numpy as np

vegetables = ["cucumber", "tomato", "lettuce", "asparagus",
              "potato", "wheat", "barley"]
farmers = ["Farmer Joe", "Upland Bros.", "Smith Gardening",
           "Agrifun", "Organiculture", "BioGoods Ltd.", "Cornylee Corp."]

harvest = np.array([[0.8, 2.4, 2.5, 3.9, 0.0, 4.0, 0.0],
                    [2.4, 0.0, 4.0, 1.0, 2.7, 0.0, 0.0],
                    [1.1, 2.4, 0.8, 4.3, 1.9, 4.4, 0.0],
                    [0.6, 0.0, 0.3, 0.0, 3.1, 0.0, 0.0],
                    [0.7, 1.7, 0.6, 2.6, 2.2, 6.2, 0.0],
                    [1.3, 1.2, 0.0, 0.0, 0.0, 3.2, 5.1],
                    [0.1, 2.0, 0.0, 1.4, 0.0, 1.9, 6.3]])

plt.xticks(np.arange(len(farmers)), labels=farmers, 
                     rotation=45, rotation_mode="anchor", ha="right")
plt.yticks(np.arange(len(vegetables)), labels=vegetables)    

plt.title("Harvest of local farmers (in tons/year)")

plt.imshow(harvest)
plt.tight_layout()
plt.show()

In the above code ,x The axis represents the name of the farmer ,y The axis represents the name of the vegetable , The data in the two-dimensional array represents the yield of a certain kind of vegetables planted by a farmer . The graphics obtained after code execution are shown in the figure below ：

Above picture , We added x Axis and y The meaning of the axis and the title of the whole figure , But now we don't know the size of the corresponding values of different color patches , Now let's add . The sample code is as follows ：

import matplotlib.pyplot as plt
import numpy as np

vegetables = ["cucumber", "tomato", "lettuce", "asparagus",
              "potato", "wheat", "barley"]
farmers = ["Farmer Joe", "Upland Bros.", "Smith Gardening",
           "Agrifun", "Organiculture", "BioGoods Ltd.", "Cornylee Corp."]

harvest = np.array([[0.8, 2.4, 2.5, 3.9, 0.0, 4.0, 0.0],
                    [2.4, 0.0, 4.0, 1.0, 2.7, 0.0, 0.0],
                    [1.1, 2.4, 0.8, 4.3, 1.9, 4.4, 0.0],
                    [0.6, 0.0, 0.3, 0.0, 3.1, 0.0, 0.0],
                    [0.7, 1.7, 0.6, 2.6, 2.2, 6.2, 0.0],
                    [1.3, 1.2, 0.0, 0.0, 0.0, 3.2, 5.1],
                    [0.1, 2.0, 0.0, 1.4, 0.0, 1.9, 6.3]])

plt.xticks(np.arange(len(farmers)), labels=farmers, 
                     rotation=45, rotation_mode="anchor", ha="right")
plt.yticks(np.arange(len(vegetables)), labels=vegetables)    
plt.title("Harvest of local farmers (in tons/year)")

plt.imshow(harvest)
plt.colorbar()
plt.tight_layout()
plt.show()

In the above code , We call colorbar Function to add the corresponding rules of values and colors . The graphics obtained after code execution are shown in the figure below ：

In the figure above, we add the corresponding rules of color and value . Next, we add the value represented by each color block . for example ：

import matplotlib.pyplot as plt
import numpy as np

vegetables = ["cucumber", "tomato", "lettuce", "asparagus",
              "potato", "wheat", "barley"]
farmers = ["Farmer Joe", "Upland Bros.", "Smith Gardening",
           "Agrifun", "Organiculture", "BioGoods Ltd.", "Cornylee Corp."]

harvest = np.array([[0.8, 2.4, 2.5, 3.9, 0.0, 4.0, 0.0],
                    [2.4, 0.0, 4.0, 1.0, 2.7, 0.0, 0.0],
                    [1.1, 2.4, 0.8, 4.3, 1.9, 4.4, 0.0],
                    [0.6, 0.0, 0.3, 0.0, 3.1, 0.0, 0.0],
                    [0.7, 1.7, 0.6, 2.6, 2.2, 6.2, 0.0],
                    [1.3, 1.2, 0.0, 0.0, 0.0, 3.2, 5.1],
                    [0.1, 2.0, 0.0, 1.4, 0.0, 1.9, 6.3]])

plt.xticks(np.arange(len(farmers)), labels=farmers, 
                     rotation=45, rotation_mode="anchor", ha="right")
plt.yticks(np.arange(len(vegetables)), labels=vegetables)    
plt.title("Harvest of local farmers (in tons/year)")

for i in range(len(vegetables)):
    for j in range(len(farmers)):
        text = plt.text(j, i, harvest[i, j], ha="center", va="center", color="w")

plt.imshow(harvest)
plt.colorbar()
plt.tight_layout()
plt.show()

In the above code , We add the corresponding value to each color block through a cycle . The graphics obtained after code execution are shown in the figure below ：

3、 ... and 、 Application scenarios

1. Applicable scenario

1. The advantage of a heat map is “ High space utilization ”, It can accommodate relatively large data . Thermal maps not only help to find the relationship between data 、 Find the extreme value , It is also commonly used to describe the overall appearance of data , Facilitate comparison between data sets （ For example, the results of each athlete over the years are condensed into a heat map , Then compare ）.
2. If you set a row or column as a time variable , The thermodynamic diagram can also be used to show the change of data over time . for example , Use the heat map to reflect the temperature change of a city in a year , The cold and warm trend of the climate , Be clear at a glance .

2. Not applicable to the scene

Although the heat map can accommodate more data , But on the contrary , It is difficult to convert the color blocks into accurate numbers . therefore , When you need to know the value clearly , Additional annotations may be required .

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summary

In this chapter, we describe the drawing of thermal map and the applicable and inapplicable scenarios of thermal map .

Last chapter Use matplotlib Draw the pie chart