Data manipulation is a critical skill in data analysis. Here are essential techniques to master:

🔍 1. Data Cleaning

  • Remove duplicates: Use drop_duplicates() in pandas.
  • Handle missing values: Replace NaN with interpolation or forward fill.
  • Correct inconsistencies: Standardize formats (e.g., dates, currencies).
    Data Cleaning

🔄 2. Data Transformation

  • Normalize data using Min-Max scaling: 
    (X - min) / (max - min)
  • Encode categorical variables with one-hot encoding.
  • Apply logarithmic transformations for skewed distributions.
    Data Transformation

📊 3. Data Aggregation

  • Group data by categories and calculate summary statistics: 
    SELECT category, AVG(value) FROM table GROUP BY category;
  • Use pivot tables to reshape datasets.
  • Merge datasets using JOIN operations.
    Data Aggregation

🧠 4. Advanced Techniques

  • Feature engineering: Create new variables from existing data.
  • Resampling: Upsample or downsample datasets for balance.
  • Dimensionality reduction: Apply PCA or t-SNE.
    Dimensionality Reduction

For deeper insights, explore our Data Processing Fundamentals guide. 🚀

Data Processing