4 Powerful Techniques to Split and Extract Data from Template Strings in Python

Preface

Hello, I am a Python enthusiast. Recently a fan from a data‑analysis group asked a question about splitting template strings.

Problem Description

The data provided looks like the following:

At first glance the task seems simple, and many participants offered solutions.

Approaches and Implementation

Method 1

Use list slicing twice, first by commas then by parentheses, with conditional checks; regular expressions can also be applied.

Method 2

Use Excel’s text‑to‑columns feature to split by commas, then replace brackets [ and ] with nothing.

Method 3

Apply slice operations in Python; if some entries lack commas, first replace right parentheses with commas before splitting.

Method 4

Use lambda x: eval(x) to convert strings to lists and apply apply for faster processing.

df['新增一列']=df.数据1.apply(lambda x:x.replace('[','').replace(']',''))
df.新增一列=df.新增一列.str.split(',',expand=True)[0]

Note: Original strings cannot be directly converted to lists, otherwise they will be split incorrectly.

Summary

The four methods demonstrate various ways to split and extract values from template strings, with the apply ‑ lambda approach generally offering the best performance and flexibility.