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Subsetting by Row and Column Number in Pandas – .iloc[] Best Practices 2026

When you need to select data by position (row number and column number) rather than by labels, Pandas provides the powerful .iloc[] indexer. In 2026, understanding .iloc[] is essential for tasks like taking the first N rows, selecting specific column ranges, or creating training/test splits.

TL;DR — .iloc[] Syntax

df.iloc[row_index] – Select by row position
df.iloc[:, col_index] – Select by column position
df.iloc[start:end, start:end] – Slice both rows and columns
Negative numbers count from the end

1. Basic Row and Column Subsetting with .iloc[]

import pandas as pd

df = pd.read_csv("sales_data.csv")

# First 10 rows
first_10 = df.iloc[:10]

# Last 5 rows
last_5 = df.iloc[-5:]

# Row 0 to 19, columns 2 to 5 (exclusive end)
subset = df.iloc[0:20, 2:6]

print(subset.head())

2. Selecting Specific Rows and Columns by Position

# Select rows 5, 10, 15, 20
selected_rows = df.iloc[[5, 10, 15, 20]]

# Select first 5 columns for first 100 rows
first_100_first_5_cols = df.iloc[:100, :5]

# Select last 3 columns for last 50 rows
last_50_last_3 = df.iloc[-50:, -3:]

3. Real-World Examples

# Create training and test sets by position
train = df.iloc[:8000]      # First 80% as training
test  = df.iloc[8000:]      # Remaining as test

# Get only numeric columns (assuming they are in positions 3 to 7)
numeric_features = df.iloc[:, 3:8]

# Get the most recent 100 records and only key columns
recent = df.iloc[-100:, [0, 3, 5, 7]]   # order_date, amount, region, customer_id

4. Best Practices in 2026

Use .iloc[] when you need to select by position (row/column number)
Use .loc[] when selecting by labels (column names or index values)
Prefer df[["col1", "col2"]] for selecting specific named columns when possible
Be careful with negative indexing — -1 means the last row/column
After heavy slicing, consider resetting the index with .reset_index(drop=True)

Conclusion

Subsetting by row and column number using .iloc[] is essential when you need positional access. In 2026, use .iloc[] for tasks like taking the first/last N rows, creating train/test splits, or selecting columns by position. For most day-to-day work, prefer label-based selection with .loc[] or direct column selection, but .iloc[] remains the go-to tool whenever position matters.

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