Four Advanced Data Visualization Techniques in Python: Heat Map, 2D Density Plot, Spider Plot, and Tree Diagram

Data visualization is a crucial step in data science and machine learning projects, helping both exploratory analysis and final presentation for non‑technical audiences.

Heat Map – A matrix‑style visual where each cell’s value is encoded by color, useful for revealing relationships among multiple features.

# Importing libs
import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# Create a random dataset
data = pd.DataFrame(np.random.random((10,6)), columns=["Iron Man","Captain America","Black Widow","Thor","Hulk", "Hawkeye"])
print(data)

# Plot the heatmap
heatmap_plot = sns.heatmap(data, center=0, cmap='gist_ncar')

plt.show()

2D Density Plot – Extends the 1‑D density plot to two variables, showing joint probability distribution with color shading.

# Importing libs
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.stats import skewnorm

# Create the data
speed = skewnorm.rvs(4, size=50)
size = skewnorm.rvs(4, size=50)

# Create and show the 2D Density plot
ax = sns.kdeplot(speed, size, cmap="Reds", shade=False, bw=.15, cbar=True)
ax.set(xlabel='speed', ylabel='size')
plt.show()

Spider Plot – Also known as a radar chart, it visualizes multiple variables for a single entity, making it easy to compare attributes such as the stats of Avengers characters.

# Import libs
import pandas as pd
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt

# Get the data
df = pd.read_csv("avengers_data.csv")
print(df)

"""
   #             Name  Attack  Defense  Speed  Range  Health
0  1         Iron Man      83       80     75    70      70
1  2  Captain America      60       62     63    80      80
2  3             Thor      80       82     83   100     100
3  3             Hulk      80      100     67    44      92
4  4      Black Widow      52       43     60    50      65
5  5          Hawkeye      58       64     58    80      65
"""

# Get the data for Iron Man
labels = np.array(["Attack","Defense","Speed","Range","Health"])
stats = df.loc[0, labels].values

# Prepare angles for radar chart
angles = np.linspace(0, 2*np.pi, len(labels), endpoint=False)
stats = np.concatenate((stats, [stats[0]]))
angles = np.concatenate((angles, [angles[0]]))

# Plot radar chart
fig = plt.figure()
ax = fig.add_subplot(111, polar=True)
ax.plot(angles, stats, 'o-', linewidth=2)
ax.fill(angles, stats, alpha=0.25)
ax.set_thetagrids(angles * 180/np.pi, labels)
ax.set_title([df.loc[0, "Name"]])
ax.grid(True)

plt.show()

Tree Diagram – A hierarchical clustering visualization built with SciPy’s Ward linkage, useful for showing similarity relationships, illustrated here with a subset of a Pokémon dataset.

# Import libs
import pandas as pd
import matplotlib.pyplot as plt
from scipy.cluster import hierarchy
import numpy as np

# Read in the dataset
# Drop any fields that are strings
# Only get the first 40 because this dataset is big
df = pd.read_csv('Pokemon.csv')
df = df.set_index('Name')
del df.index.name
df = df.drop(["Type 1","Type 2","Legendary"], axis=1)
df = df.head(n=40)

# Calculate the distance between each sample
Z = hierarchy.linkage(df, 'ward')

# Orientation our tree
hierarchy.dendrogram(Z, orientation="left", labels=df.index)
plt.show()

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