Creating and Using Python-Based Taxonomy Validation Scripts

Introduction

Managing taxonomies is crucial for structuring data in e-commerce, healthcare, legal, and enterprise searchapplications. However, taxonomies can suffer from inconsistencies, duplicate terms, and improper hierarchies. This blog post explores how to use Python to validate taxonomy structures, ensuring data quality and consistency across large datasets.

Why Validate Taxonomies?

Taxonomies organize data into structured categories, but poor management can lead to:

Duplicate categories (e.g., “Mobile Phones” vs. “Smartphones”).
Misclassified items (e.g., “Tomato” under “Dairy”).
Inconsistent hierarchy depth (e.g., categories with widely varying subcategory depths).

Using Python, we can create scripts to:

Check for duplicate terms.
Validate parent-child relationships.
Ensure hierarchical depth consistency.

Setting Up the Environment

We’ll use pandas and networkx to analyze the taxonomy.

pip install pandas networkx

Loading the Taxonomy Data

Let’s assume we have a CSV file taxonomy.csv with three columns: ID, Term, and Parent_ID.

ID,Term,Parent_ID
1,Fruits,
2,Apples,1
3,Bananas,1
4,Smartphones,5
5,Mobile Phones,
6,Tablets,5
7,Laptops,
8,Tomato,3

Load the taxonomy into a pandas DataFrame:

import pandas as pd

def load_taxonomy(file_path):
    df = pd.read_csv(file_path)
    return df

taxonomy = load_taxonomy("taxonomy.csv")
print(taxonomy.head())

Detecting Duplicate Terms

Duplicate category names can cause confusion in classification.

def check_duplicates(df):
    duplicates = df[df.duplicated(subset=["Term"], keep=False)]
    return duplicates

duplicates = check_duplicates(taxonomy)
if not duplicates.empty:
    print("Duplicate terms found:")
    print(duplicates)
else:
    print("No duplicate terms found.")

Checking Hierarchy Consistency

Ensuring that each category has a valid parent is essential.

def validate_parents(df):
    invalid_parents = df[~df["Parent_ID"].isin(df["ID"]) & df["Parent_ID"].notna()]
    return invalid_parents

invalid_parents = validate_parents(taxonomy)
if not invalid_parents.empty:
    print("Invalid parent-child relationships found:")
    print(invalid_parents)
else:
    print("All parent-child relationships are valid.")

Visualizing the Taxonomy as a Graph

Using networkx, we can visualize the taxonomy structure.

import networkx as nx
import matplotlib.pyplot as plt

def visualize_taxonomy(df):
    G = nx.DiGraph()
    for _, row in df.iterrows():
        if pd.notna(row["Parent_ID"]):
            G.add_edge(row["Parent_ID"], row["ID"], label=row["Term"])
    
    plt.figure(figsize=(8, 6))
    pos = nx.spring_layout(G)
    nx.draw(G, pos, with_labels=True, node_size=3000, node_color='lightblue')
    plt.show()

visualize_taxonomy(taxonomy)

Conclusion

Python-based taxonomy validation helps ensure data consistency, hierarchy correctness, and improved search experiences. By detecting duplicates, hierarchy issues, and visualizing taxonomies, businesses can maintain clean, structured classification systems.

Would you like a deeper dive into automating taxonomy corrections? Let me know in the comments!