Part 10 – Pandas Data Cleaning Explained | Series, DataFrames, Missing Values & Duplicates
Welcome to Lecture #10 of the Python & Data Science Masterclass.
In this lecture, we dive deeper into Pandas, covering Series, DataFrames, and one of the most important topics in Data Science: Data Cleaning.
Topics Covered
✅ Introduction to Pandas Series
✅ Introduction to Pandas DataFrames and Series
✅ Working with Named Indexes
✅ Data Cleaning Techniques
✅ Fixing Wrong Data Formats
✅ Detecting and Removing Duplicates
✅ Introduction to Outliers
✅ Using dropna()
✅ Using inplace=True
✅ Using df.isnull()
✅ Using fillna()
✅ Using Mean, Median, and Mode for Data Imputation
✅ Converting Dates with to_datetime()
✅ Using duplicated()
✅ Using drop_duplicates()
Data cleaning is one of the most critical skills for Data Scientists because real-world datasets often contain missing, incorrect, and duplicate data.
👨🏫 Instructor: Dr. Muhammad Ismail
🎓 PhD in AI | Meta AI Master Trainer | AFHEA
📚 Follow the complete Python & Data Science Masterclass playlist to learn Python, NumPy, Pandas, Data Science, Machine Learning, and AI step by step.
0:00 Introduction to Pandas & Data Exploration Tools
0:52 Creating DataFrames from Python Dictionaries
2:16 Understanding Default Indexing in Pandas
3:13 Checking Pandas Version and Environment Setup
7:14 Pandas Series vs. DataFrames: Key Differences
9:04 Customizing Labels and User-Defined Indices
12:26 Extracting Specific Data Using the .loc Method
20:06 Overview of Data Cleaning: Missing Values & Outliers
28:12 How to Drop Missing Values (NaN) with .dropna()
31:12 Filling Missing Data with Mean, Median, or Mode
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