How to Clean and Remove Duplicate Data in Python Using Pandas
Learn how to clean and prepare real-world data using pandas in Python. This tutorial covers essential techniques for finding and removing duplicate rows, handling missing values, and fixing inconsistent entries in your data frame. Using the Titanic dataset, you will gain hands-on experience with practical data cleaning steps to ensure your analysis is accurate and reliable.
Follow along as we demonstrate how to identify duplicates, standardize text columns, fill or drop missing values, and build a complete data cleaning pipeline. By the end of this lesson, you will be able to confidently clean your own datasets and save your results for future projects.
00:00 Introduction to duplicates and inconsistencies
00:27 Loading and previewing the Titanic dataset
01:08 Identifying data issues in the sample
01:48 Understanding duplicates and their impact
02:13 Finding duplicate rows with pandas
02:34 Removing duplicate rows for clean data
03:48 Removing duplicates by specific columns
04:35 Spotting and fixing inconsistent data
05:01 Checking unique values in columns
05:47 Standardizing text entries
06:25 Cleaning and filling missing codes
07:16 Validating allowed codes in columns
08:02 Formatting names for consistency
08:43 Finding missing values in columns
09:41 Filling missing numeric data with the median
10:29 Dropping rows with missing critical fields
11:11 Mini challenge: cleaning a sample dataset
12:03 Building a complete cleaning pipeline
13:28 Saving the cleaned dataset to CSV
14:07 Recap and next steps
#Pandas #DataCleaning #DataScience
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