Introduction to Taxonomy Hierarchy Representation

In the field of taxonomy and classification, a well-structured hierarchy is crucial for efficient data management and analysis. This post aims to provide an overview of how to represent a taxonomy hierarchy from a DataFrame as text with indentation, using Python.

Understanding the Problem

The provided Stack Overflow question revolves around printing a taxonomy hierarchy in indented form. The goal is to display the relationships between terms in the hierarchy. A recursive function seems to be used, but there’s an issue with indenting out, specifically decrementing prefix_level.

Background and Context

Taxonomy hierarchies can be represented using various data structures such as directed acyclic graphs (DAGs) or simple adjacency lists. In this case, we’ll focus on representing the hierarchy from a DataFrame.

Data Representation

The provided example uses a pandas DataFrame to store taxonomy terms with their parent-child relationships. The subject and object columns contain the term names, while the indices represent the unique identifiers for each term.

Recursive Functionality

A recursive function is employed to traverse the taxonomy hierarchy. However, the implementation seems incomplete, leading to issues with indenting out correctly.

Solution Overview

The solution involves using recursion and clever manipulation of string formatting to achieve indented output.

We’ll explore two approaches:

1. Approach 1: Using a list comprehension-based approach with string formatting.
2. Approach 2: Employing a generator-based implementation for more flexibility.

Approach 1: List Comprehension-Based Solution

The first solution involves using a list comprehension to create the desired output format and then joining it into a single string with indentation.

Code:

data = {'subject': {986: 'ENVO:01000025', 989: 'ENVO:01000028', 990: 'ENVO:01000029', 991: 'ENVO:01000030', 1011: 'ENVO:01000050', 1014: 'ENVO:01000053', 1015: 'ENVO:01000054', 1096: 'ENVO:01000127', 1242: 'ENVO:01000252', 1243: 'ENVO:01000253'}, 
        'object': {986: 'ENVO:01000024', 989: 'ENVO:01000024', 990: 'ENVO:01000024', 991: 'ENVO:01000024', 1011: 'ENVO:01000029', 1014: 'ENVO:01000030', 1015: 'ENVO:01000030', 1096: 'ENVO:01000024', 1242: 'ENVO:00000873', 1243: 'ENVO:00000873'}}

vals = [[data['subject'][i], data['object'][i]] for i in data['subject']]

def nest(n, c=0):
    return ((c*"   ") + n) + ('' if not ([nest(a, c+1) for a, b in vals if b == n]) 
               else '\n' + ('\n'.join([nest(a, c+1) for a, b in vals if b == n])) 

roots = {b for _, b in vals if all(j != b for j, _ in vals)}

print('\n'.join(nest(b) for b in roots))

Output:

ENVO:01000024
   ENVO:01000025
   ENVO:01000028
   ENVO:01000029
      ENVO:01000050
   ENVO:01000030
      ENVO:01000053
      ENVO:01000054
   ENVO:01000127
ENVO:00000873
   ENVO:01000252
   ENVO:01000253

Approach 2: Generator-Based Solution

The second approach employs a generator to achieve the same result with improved flexibility.

Code:

data = {'subject': {986: 'ENVO:01000025', 989: 'ENVO:01000028', 990: 'ENVO:01000029', 991: 'ENVO:01000030', 1011: 'ENVO:01000050', 1014: 'ENVO:01000053', 1015: 'ENVO:01000054', 1096: 'ENVO:01000127', 1242: 'ENVO:01000252', 1243: 'ENVO:01000253'}, 
        'object': {986: 'ENVO:01000024', 989: 'ENVO:01000024', 990: 'ENVO:01000024', 991: 'ENVO:01000024', 1011: 'ENVO:01000029', 1014: 'ENVO:01000030', 1015: 'ENVO:01000030', 1096: 'ENVO:01000024', 1242: 'ENVO:00000873', 1243: 'ENVO:00000873'}}

vals = [[data['subject'][i], data['object'][i]] for i in data['subject']]

def nest(n, c=0):
    yield (c*"   ") + n
    for a, b in vals:
        if b == n:
            yield from nest(a, c+1)

roots = {b for _, b in vals if all(j != b for j, _ in vals)}

print('\n'.join(i for b in roots for i in nest(b)))

Output:

ENVO:01000024
   ENVO:01000025
   ENVO:01000028
   ENVO:01000029
      ENVO:01000050
   ENVO:01000030
      ENVO:01000053
      ENVO:01000054
   ENVO:01000127
ENVO:00000873
   ENVO:01000252
   ENVO:01000253

Conclusion

In conclusion, representing a taxonomy hierarchy from a DataFrame as text with indentation can be achieved using recursion. The proposed approaches showcase two ways to accomplish this task efficiently.

By employing list comprehension and clever string formatting, the first approach provides a straightforward solution for those familiar with pandas DataFrames. For a more flexible implementation, the generator-based approach offers a promising alternative.

Feel free to experiment with different variations to optimize performance or accommodate specific requirements.

Last modified on 2023-08-17