1 Handling Missing Values in Python for Data Cleaning
Learn how to handle missing values in Python with practical, step-by-step examples. This tutorial covers why missing data matters, how to identify and manage it, and best practices for keeping your programs reliable. You will see how to use None for missing information, check for absent values, and clean up your data for analysis.
Explore real-world scenarios including user input, lists, and dictionaries. Discover techniques to count, fill, remove, and sort missing values, and learn how to avoid common errors. By the end, you will be able to write cleaner, safer Python code that handles incomplete data confidently.
00:00 Introduction to missing values
00:16 Why missing data matters
00:36 Using None in Python
01:00 Checking for missing values
01:29 Handling user input with missing data
02:30 Working with lists containing missing values
03:00 Counting missing entries in lists
03:31 Filling missing data with defaults
03:59 Removing missing values from lists
04:24 Finding the first non-missing value
04:53 Handling missing values in dictionaries
05:37 Using get to avoid key errors
06:07 Real-world input examples
06:39 Calculating averages while ignoring missing data
07:17 List comprehensions for cleaning data
07:45 Sorting with missing values last
08:14 Removing missing values and outliers
08:40 Mini project cleaning survey data
09:34 Counting responses in cleaned data
10:04 Using comments for clarity
10:28 Avoiding errors with missing values
10:57 Providing defaults for missing results
11:23 Challenge exercise
11:47 Lesson recap and next steps
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