Pandas Practice Problems and Solutions
  1. Data Structures:
  2. Data Cleaning:
    • Handling missing data (NaN)
    • Removing duplicates
    • Data type conversions
    • Renaming columns
    • Data normalization and standardization
  3. Data Exploration:
    • Basic statistics (mean, median, etc.)
    • Grouping and aggregation
    • Pivot tables
    • Value counts and frequencies
    • Sorting and ranking data
  4. Data Filtering and Selection:
    • Conditional selection
    • Boolean indexing
    • Querying DataFrames
    • Slicing and subsetting data
  5. Data Transformation:
    • Merging and joining DataFrames
    • Reshaping data (pivoting, melting, stacking)
    • Combining and splitting columns
    • Applying functions to data
  6. Time Series Data:
    • Working with dates and times
    • Resampling and time-based operations
    • Time series plotting
  7. Visualization:
    • Plotting with Matplotlib and Seaborn
    • Creating bar charts, line charts, and scatter plots
    • Customizing plots and adding labels
  8. File I/O:
    • Reading and writing data from/to various file formats (CSV, Excel, SQL, etc.)
  9. Performance Optimization:
    • Vectorized operations
    • Using the apply function efficiently
    • Working with large datasets
  10. Advanced Topics:
    • Hierarchical indexing
    • Handling multi-level DataFrames
    • Categorical data
    • Cross-tabulation and contingency tables
    • Time series analysis and forecasting
  11. Real-World Projects:
    • Working on real datasets and solving data analysis problems
    • Building data pipelines
    • Data cleaning and preprocessing for machine learning
  12. Pandas Best Practices:
    • Writing efficient and readable code
    • Using the appropriate methods and functions
    • Avoiding common pitfalls and mistakes

Leave a Reply

Your email address will not be published. Required fields are marked *