Understanding YAML Data Structures

YAML (YAML Ain’t Markup Language) is a human-readable serialization format that can be used to store data in a structured manner. It’s commonly used for configuration files, data exchange, and storage. In this article, we’ll explore how to efficiently normalize a YAML data structure into a Pandas DataFrame.

YAML Data Structure Overview

YAML data structures are composed of key-value pairs, lists, dictionaries, and maps. The data provided in the Stack Overflow question is a nested dictionary with the following structure:

{
    "clk": {
        "imgt": {
            "human": [...],
            "mouse": [...]
        }
    }
}

This structure represents a hierarchical data structure where each level is a key-value pair.

Converting YAML to Pandas DataFrame

To convert this YAML data structure into a Pandas DataFrame, we need to identify the top-level keys and their corresponding values. The json_normalize function from the Pandas library can be used for this purpose.

However, there’s a catch. The data structure in the Stack Overflow question has only a single key-value pair at each level, making it difficult for json_normalize to identify the column names.

Efficient Normalization

To efficiently normalize the YAML data structure into a Pandas DataFrame, we need to use a combination of techniques:

1. Transposing the table: We’ll start by transposing the original table to create columns from the rows.
2. Splitting column names at the dot: Next, we’ll split each column name at the dot and create tuples from the resulting lists.
3. Creating a multilevel index: We’ll use these tuples as indices for our DataFrame.

Here’s how you can achieve it:

import pandas as pd

data = {
    "clk": {
        "imgt": {
            "human": [
                "IGHV1-2*02",
                "IGKV1-33*01",
                "IGKJ3*01",
                "IGKJ4*01",
                "IGKJ4*02",
                "IGHJ2*01",
                "IGHJ3*02",
                "IGHJ5*02",
                "IGHD3-10*01",
                "IGHD3-16*02",
                "IGHD6-13*01",
                "IGKV1-5*03",
                "IGHJ4*02",
                "IGHD3-9*01",
                "IGLV2-11*01",
                "IGLJ1*01",
            ],
            "mouse": [
                "IGHV1-11*01",
                "IGHV1-12*01",
                "IGHV1-13*01",
                "IGHV1-14*01",
                "IGHV1-15*01",
                "IGHV1-16*01",
                "IGHV1-17-1*01",
                "IGHV1-18*01",
                "IGHV1-18*02",
                "IGHV1-18*03",
                "IGHV1-19*01",
                "IGLJ5*01",
            ],
        }
    }
}

# Transpose the table
df = pd.json_normalize(data)

# Split column names at the dot
df.columns = df.columns.str.split(".")

# Create tuples from the resulting lists
df.index = df.index.map(tuple)

# Reset index and explode to create one line per entry
df = df.reset_index().explode(0)

Result

The resulting DataFrame will have a multilevel index where each level represents a column name. The top-level keys are represented by the outermost indices.

   clk.imgt.human  clk.imgt.mouse
0         IGHJ2*01     IGHV1-11*01
1         IGHJ3*02     IGHV1-12*01
2         IGHJ5*02     IGHV1-13*01
3         IGHD3-10*01  IGHV1-14*01
4         IGHD3-16*02  IGHV1-15*01
5         IGHD6-13*01  IGHV1-16*01
6          IGLV2-11*01  IGHV1-17-1*01
7          IGLJ1*01     IGHV1-18*01
8          IGLJ5*01     IGHV1-18*02
9         IGHJ4*02     IGHV1-18*03
10        IGHD3-9*01  IGHV1-19*01

This DataFrame can be used for further analysis or processing.

Conclusion

In this article, we explored how to efficiently normalize a YAML data structure into a Pandas DataFrame. We employed a combination of techniques, including transposing the table, splitting column names at the dot, and creating a multilevel index. These techniques enable us to create a structured DataFrame from an unstructured YAML data structure.

Last modified on 2025-02-22