Reshaping, Duplicates & Strings in Python DataFrames — Essential Data Cleaning - Dr. Colleen Spiegel
If you work with real engineering or scientific data, messy DataFrames are a daily reality. In this video I cover three essential data cleaning and reshaping techniques that every scientist and engineer working with Python needs to know.
WHAT YOU WILL LEARN:
🔄 Reshaping Data with Stack and Unstack
Transform your DataFrame from wide to long format and back again using Pandas stack() and unstack(). Understand when and why to reshape — including how to turn column names into index values and index values into column names. Essential for preparing data for analysis, visualization, and machine learning pipelines.
🔁 Finding and Removing Duplicates
Identify duplicate rows in your DataFrame using duplicated() and remove them cleanly with drop_duplicates(). A critical step in any data cleaning workflow — duplicate records in sensor data, experimental results, or lab measurements can silently skew your analysis.
🔤 String Manipulation in DataFrames
Work with string data in Python using split() and join() — two of the most commonly used string methods for cleaning and reformatting text columns in engineering datasets. Learn how to split a string on a delimiter and rejoin elements cleanly.
These three techniques together form the foundation of data cleaning and reshaping for any scientific or engineering dataset — whether you are working with sensor readings, experimental results, manufacturing data, or research output.
🐍 Python for Scientists and Engineers — practical data analysis for real-world technical problems.
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