Controls & Information
Understanding loc and iloc
loc uses labels for indexing (row and column names).
iloc uses integer positions for indexing (0-based indices).
Selection Information
Current Mode:
loc (Label-based)
Selected Cells:
None
Pandas Code:
df
# Click on cells in the grid to generate code
# Example for loc: df.loc['B', 'Y']
# Example for iloc: df.iloc[1, 2]
How to Use
1. Select loc or iloc mode
2. Click on row and column headers to select cells
3. View the generated Pandas code
4. Compare the differences between loc and iloc
Interactive DataFrame Grid
Grid Information
This grid represents a DataFrame with:
Row Labels: A, B, C, D, E
Column Labels: W, X, Y, Z, Q
Integer Indices: 0, 1, 2, 3, 4 (for both rows and columns)
DataFrame Visualization - Click on cells to select them
loc vs iloc Comparison
| Aspect | loc | iloc |
|---|---|---|
| Indexing Type | Label-based | Integer position-based |
| Syntax | df.loc[row_label, col_label] | df.iloc[row_idx, col_idx] |
| Inclusive/Exclusive | End label is inclusive | End index is exclusive |
| Slicing Example | df.loc['A':'C'] → A, B, C | df.iloc[0:3] → 0, 1, 2 |
| Use Case | When you know row/column labels | When you know positions |