VT Mai Hương
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![Data Cleaning in Pandas - 80% of Real-World Work.
Logic: Messy data leads to wrong insights. Clean data leads to accurate analysis and powerful ML models.
Section 1: MISSING DATA
- Detect: http://df.isnull() , http://df.notnull()
- Drop: http://df.dropna() , http://df.dropna(subset=['Age','City'])
- Fill: http://df.fillna(0) , http://df.fillna(df['Age'].mean()) , http://df.ffill() , http://df.bfill()
Tip: Always analyze missing data pattern before filling or dropping. Never drop too much data blindly.
Section 2: DUPLICATES
- Detect: df.duplicated() , df.duplicated(subset=['ID'])
- Remove: df.drop_duplicates() , df.drop_duplicates(subset=['ID'], keep='first')
Key Point: Use subset to define duplicates based on specific columns.
Section 3: DATA TYPE FIXING
- Check: df.dtypes
- Fix: df['Age'] = df['Age'].astype(int) , df['Salary'].astype(float) , pd.to_datetime(df['Join_Date'])
Key Point: Correct data types save memory and avoid errors.
Section 4: STRING CLEANING
- Methods: http://str.lower() , http://str.upper() , http://str.strip() , http://str.title() , http://str.replace()
- Used to standardize text before grouping or joining.
REAL-WORLD CLEANING WORKFLOW
1. Understand Data: head(), info(), describe()
2. Handle Missing Values: isnull(), dropna(), fillna()
3. Remove Duplicates: duplicated(), drop_duplicates()
4. Fix Data Types: astype(), to_datetime(), to_numeric()
5. Clean Strings: http://str.lower(), http://str.strip(), replace()
6. Validate Data: Check again with info(), describe()
PRO TIP: 80% of real-world data projects = Cleaning. 20% = Analysis / Modeling
Golden Rule: Garbage In = Garbage Out. Clean Data = Accurate Models
Remember: Clean data is the foundation of EVERY successful data project.
Tagline: CLEAN DATA TODAY, POWERFUL INSIGHTS TOMORROW. #python #datascientist #techskills #python #pandas](/video/cover/7666035740421278997.webp)
