Visual Python Manual
  • Visual Python Manual
  • GETTING STARTED
    • Welcome to Visual Python
    • How to install
    • Installing FAQ
    • Official homepage
    • Link to Github
  • Data Analysis
    • 1. Import
    • 2. File
    • 3. Data Info
    • 4. Frame
      • 4-1. Frame - Edit
      • 4-2. Frame - Transform
      • 4-3. Frame - Sort
      • 4-4. Frame - Encoding
      • 4-5. Frame - Data Cleaning
    • 5. Subset
    • 6. Groupby
    • 7. Bind
    • 8. Reshape
  • Visualization
    • 1. Chart Style
    • 2. Pandas Plot
    • 3. Matplotlib
    • 4. Seaborn
    • 5. Plotly
    • 6. WordCloud
  • Statistics
    • 1. Prob. Distribution
    • 2. Descriptive Statistics
    • 3. Normality Test
    • 4. Equal Var. Test
    • 5. Correlation Analysis
    • 6. Reliability Analysis
    • 7. Chi-square Test
    • 8. Student's T-test
    • 9. ANOVA
    • 10. Factor Analysis
    • 11. Regression
    • 12. Logistic Regression
  • Machine Learning
    • 1. Data Sets
    • 2. Data Split
    • 3. Data Prep
    • 4. AutoML
    • 5. Regressor
    • 6. Classifier
    • 7. Clustering
    • 8. Dimension
    • 9. GridSearch
    • 10. Fit/Predict
    • 11. Model Info
    • 12. Evaluation
    • 13. Pipeline
    • 14. Save / Load
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  1. Data Analysis

5. Subset

Extract the data that meet specific conditions from Data Frame.

Previous4-5. Frame - Data CleaningNext6. Groupby

Last updated 8 months ago

  1. Click the Subset in the Data Analysis category.

  1. Data Frame: Select the data frame you want to use for your work.

  2. Make a copy: Check if you don't want the operation to affect the original data frame.

  3. Method: Select a method to specify which rows or columns the action will be applied to.

  4. Allocate to: Specify the name of the variable where the results of your work will be stored.

  5. Row Subset: Select the rows you want to work with.

    1. Slicing: Extract rows by specifying a range.

    2. Condition: Specify the range of rows based on a condition.

    3. Indexing: Manually select the desired rows.

  6. Column Subset: Select the columns you want to work with.

    1. Indexing: Manually extract the desired columns.

    2. Slicing: Specify the range of columns to extract.

  7. Code View: Preview the code that will be output.

  8. Data View: Preview the output that will be printed.

  9. Run: Print and run the code.