chatgpt enables Python-pythonpandas radar chart code

Foreword

Radar charts are a very commonly used chart type in data visualization. The pandas library in Python provides a quick and easy way to draw a radar chart, so in this article, we will focus on how to use the pandas library to draw a radar chart and provide some useful code examples.

What is a radar chart?

Radar charts are also known as spider charts, polar charts, or star charts. It is a type of chart based on the polar coordinate system and is often used to compare the values of different data points along multiple dimensions.

How pandas draws a radar chart

A very handy feature of the Pandas library is its built-in plotting functionality. For radar charts, using the DataFrame.plot() function is very convenient and fast.

To draw a radar chart, we need to prepare a DataFrame first, where the column names represent the names of each dimension, and each row corresponds to the value of a specific data point in each dimension. Code example:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# create DataFrame
df = pd.DataFrame({<!-- -->
        'Gender':['Male', 'Female', 'Male', 'Female'],
        'Chinese':[88, 76, 84, 93],
        'Math':[95, 85, 72, 88],
        'English':[92, 88, 96, 90],
        'Physics':[64, 76, 69, 85],
        'Chemistry': [79, 80, 70, 92]
})

# set row index to gender column
df.set_index('gender', inplace=True)

# print DataFrame
print(df)

Output result:

 Chinese Mathematics English Physics Chemistry
gender                        
Male 88 95 92 64 79
Female 76 85 88 76 80
Male 84 72 96 69 70
Female 93 88 90 85 92

With this DataFrame, we can plot the radar chart using the .plot(kind=’area’) function. Code example:

# draw radar chart
ax = df.plot(kind='area', legend=False, ylim=[0,100])

# add axis labels
ax.set_xlabel('subject')
ax.set_ylabel('grade')

# Loop to add custom text
for i in range(df. shape[0]):
    values = df.iloc[i].tolist()
    ax.plot(np.linspace(0, 2*np.pi, len(df.columns) + 1)[:-1], values, label=df.index[i])
    ax.fill(np.linspace(0, 2*np.pi, len(df.columns) + 1)[:-1], values, alpha=0.3)

# add legend
ax. legend()

# show the graph
plt. show()

After running the code, we can get a beautiful radar chart:

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This chart shows the results of four students in five subjects: Chinese, Mathematics, English, Physics and Chemistry. Each dimension is spread out in the form of concentric circles, and each concentric circle has a corresponding score range value. Different fill colors of data points represent different classmates.

Code explanation

Now, let’s break down the code above.

Step 1: Create a DataFrame

df = pd.DataFrame({<!-- -->
        'Gender':['Male', 'Female', 'Male', 'Female'],
        'Chinese':[88, 76, 84, 93],
        'Math':[95, 85, 72, 88],
        'English':[92, 88, 96, 90],
        'Physics':[64, 76, 69, 85],
        'Chemistry': [79, 80, 70, 92]
})

We create a DataFrame using the dictionary form, the column names are the names of the subjects, and the row names are the corresponding student genders.

Step 2: Set row index

df.set_index('gender', inplace=True)

To make the data easier to understand, we set the student’s gender column as the row index.

Step 3: Draw a radar chart

ax = df.plot(kind='area', legend=False, ylim=[0,100])

This line of code draws and initializes a radar chart. We set the kind parameter to ‘area’ to draw an area radar chart, and set the ylim parameter to [0,100] to ensure that the y-axis range of the radar chart is always 0 to 100.

Step 4: Loop to add customized data points

for i in range(df. shape[0]):
    values = df.iloc[i].tolist()
    ax.plot(np.linspace(0, 2*np.pi, len(df.columns) + 1)[:-1], values, label=df.index[i])
    ax.fill(np.linspace(0, 2*np.pi, len(df.columns) + 1)[:-1], values, alpha=0.3)

We use a for loop to plot each data point one by one. Specifically, we first create a values list that contains the current student’s marks for each subject. Then, we use the np.linspace function to generate a list of angles, and then use the .plot() method to plot the values and angle parameters on the chart. Finally, we use the .fill() method to fill the current data point with color.

Step 5: Add axis labels and legend

ax.set_xlabel('subject')
ax.set_ylabel('grade')
ax. legend()

We add axis labels and legend elements to make the chart easier to interpret.

Conclusion

In this article, we learned how to draw beautiful radar charts using the pandas library. We first prepare a DataFrame, where the column names represent different dimensions, and each row corresponds to the value of a specific data point in each dimension. We then use the .plot() function to plot the radar chart with a custom set of colors for each data point. Finally, we added axis labels and legend elements to make the chart more beautiful and readable.

Radar charts are a useful tool if you’re looking for a quick and easy way to explore interactive visualizations of multidimensional data. Especially when designing data dashboards or reports, radar charts can make data more vivid and convincing.

The last last

This article is generated by chatgpt, and the article has not been modified on the basis of chatgpt. The above is just the tip of the iceberg of chatgpt capabilities. As a general Aigc large model, it just shows its original strength.

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