Pandas Isn’t Going Anywhere: Why It’s Still My Go-To for Data Wrangling

2026-05-17 · Source: Towards Data Science · Field: Technology & Digital — Data Science & Analytics, Software Development & Engineering, Artificial Intelligence & Machine Learning · Depth: Intermediate, medium

Summary

This article demonstrates Pandas' capabilities for data cleaning and processing using a dataset of stock keeping units (SKUs) and their search API responses. It addresses common challenges like parsing malformed string representations of lists of dictionaries, specifically removing extraneous text and converting strings to proper Python list objects using `ast.literal_eval`. The author illustrates how to extract specific data, such as "my_id" values, into new columns using lambda functions and list comprehensions. Furthermore, the article covers transforming data structures, including using the `explode` function to convert list-like entries into multiple rows and employing `groupby().apply(list)` to aggregate rows back into lists. It also shows two methods for expanding list columns into multiple new columns, highlighting a vectorized approach using `pd.DataFrame(column.tolist())` for improved performance over `apply(pd.Series)`.

Key takeaway

For Data Scientists and Data Analysts working with datasets not exceeding billions of rows, Pandas offers robust and efficient solutions for complex data cleaning and transformation. You should prioritize vectorized operations and leverage built-in functions like `explode` and `groupby` to maintain performance, ensuring your data preparation workflows are optimized and scalable for typical business needs.

Key insights

Pandas remains highly capable for data cleaning and processing tasks, especially for datasets under billions of rows.

Principles

• Prefer vectorized operations over loops.
• Use `ast.literal_eval` for safe string-to-list conversion.

Method

Clean malformed string data using regex, convert to list of dicts with `ast.literal_eval`, extract specific keys, then reshape data using `explode` or `groupby` and `pd.DataFrame().tolist()`.

In practice

• Clean messy string data with `str.replace(r"\.\.\..*", "", regex=True)`.
• Expand list columns into rows using `df.explode("column_name")`.
• Convert list columns to new columns with `pd.DataFrame(df["column"].tolist())`.

Topics

• Pandas
• Data Wrangling
• Structured String Parsing
• Vectorized Operations
• Pandas Explode Function

Best for: Data Scientist, Data Analyst, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards Data Science.