Phase 1: pandas Mastery · ~30 minutes · Python · pandas
Missing Data, For Real
A blanket dropna() on a wide real dataset doesn't clean it. It can erase all of it — every row has something missing somewhere, once there are 90 columns to check.
Hiring signal: Checks row counts after every dropna(), and treats inplace=True as broken under Copy-on-Write rather than merely old-fashioned
What you will learn
- Use .isna(), .fillna(), and .dropna() to identify and handle missing data
- Explain why blanket dropna() can silently remove far more data than intended on a wide real dataset
- Explain why inplace=True doesn't reliably modify data under pandas 3.0's Copy-on-Write
- Choose between fillna, dropna, and leaving data missing, with real reasoning
Introduction
Type: Learn Languages: Python, pandas Prerequisites: Lesson 04 (Joining and Merging) Time: ~30 minutes
Objective
Learning objectives
- Use
.isna(), .fillna(), and .dropna() to identify and handle missing data - Explain why blanket
dropna() can silently remove far more data than intended on a wide real dataset - Explain why
inplace=True doesn't reliably modify data under pandas 3.0's Copy-on-Write - Choose between fillna, dropna, and leaving data missing, with real reasoning
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