Python Data Cleaning Techniques
Data cleaning is often the most time-consuming part of data analysis. This article explores practical techniques for cleaning data using Python and Pandas.
Common Data Quality Issues
- Missing values
- Duplicate records
- Inconsistent formatting
- Outliers
- Incorrect data types
Handling Missing Values
Detection
df.isnull().sum()
df.info()
Treatment Options
- Remove missing values
- Imputation (mean, median, mode)
- Forward/backward fill
- Interpolation
Removing Duplicates
df.drop_duplicates(subset=['column_name'], keep='first')
Data Type Conversion
df['date_column'] = pd.to_datetime(df['date_column'])
df['numeric_column'] = pd.to_numeric(df['numeric_column'], errors='coerce')
Conclusion
Clean data is the foundation of reliable analysis. Invest time in proper data cleaning to ensure accurate insights.
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