Extracting Nested Tags from CSV with Pandas and Regex – A Step‑by‑Step Guide

1. Introduction

The author encountered a Pandas‑related problem in a Python community group and decided to share a solution for extracting tag information stored as stringified dictionaries within a CSV column.

2. Implementation

The initial attempt using eval proved insufficient, so a regular‑expression based extraction was applied. The following code reads the CSV, applies re.findall to pull the value of the name field, and stores the list of tags back into a new column.

import pandas as pd
import re

df = pd.read_csv('test.csv')
df['tblTags'] = df['tblTags'].map(lambda x: re.findall("'name': '(.*?)'", x))
print(df)

The output shows each row with a list of extracted tag names. To place each tag on its own row, the explode method is used:

df = df.explode('tblTags')

Community members suggested additional techniques, such as merging all lists before extracting, exploding first then rebuilding a DataFrame, or converting the string to valid JSON by replacing single quotes with double quotes and loading it with json.loads. These alternatives illustrate the flexibility of Pandas for handling semi‑structured data.

3. Conclusion

The article summarizes a practical Pandas workflow for parsing embedded tag data, demonstrates the use of regular expressions and the explode function, and acknowledges several community contributions that provide further ways to achieve the same goal.