Dataframe to spyder graph in python-CodePudding

The main goal is to plot a polar graph from the following dataframe sample.

df = pd.DataFrame({"Group":[1,2],
"Var1":[100,20],
"Var2":[50,40],
"Var3":[10,14],
"Var4":[10,140],
"Var5":[100,14]})

What I have so far is the following code to accomplish this issue for only one "group" or line plot.

# Create graph with values of most relevant variables
# Create a random data set
np.random.seed(1)
angles = np.linspace(0, 2 * np.pi, 5, endpoint=False)
values = np.random.random(5)

# The first value is repeated to close the chart.
angles=np.concatenate((angles, [angles[0]]))
values=np.concatenate((values, [values[0]]))
labels=['Var1', 'Var2', 'Var3', 'Var4', 'Var5']

# Representation of the spider graph
plt.polar(angles, values, 'o-', linewidth=2)
plt.fill(angles, values, alpha=0.25)
plt.thetagrids(angles * 180 / np.pi, labels)
plt.show()

Not sure how to transform df in order to plot group 1 and 2 together in same plot.

CodePudding user response：

You can just loop through data frame rows and polar plot each row:

import matplotlib.pyplot as plt
np.random.seed(1)
angles = np.linspace(0, 2 * np.pi, 5, endpoint=False)

# The first value is repeated to close the chart.
angles=np.concatenate((angles, [angles[0]]))
labels=['Var1', 'Var2', 'Var3', 'Var4', 'Var5', 'Var1']

# polar plot each row separately
for row in df.values.tolist():
    values = row[1:]   [row[1]]
    plt.polar(angles, values, 'o-', linewidth=2)
    plt.fill(angles, values, alpha=0.25)

# Representation of the spider graph
plt.thetagrids(angles * 180 / np.pi, labels)
plt.show()