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Slicing Columns in Pandas – Best Practices for Selecting Columns 2026

Selecting and slicing columns efficiently is a fundamental skill in Pandas data manipulation. In 2026, knowing the different ways to slice columns helps you write cleaner, faster, and more readable code.

TL;DR — Recommended Column Selection Methods

df[["col1", "col2"]] – Best for selecting specific columns by name
df.loc[:, "col1":"col3"] – Label-based slicing (inclusive)
df.iloc[:, 0:5] – Position-based slicing
df.filter() – Flexible pattern-based selection

1. Basic Column Selection

import pandas as pd

df = pd.read_csv("sales_data.csv", parse_dates=["order_date"])

# Most common and recommended way
sales_info = df[["order_date", "customer_id", "amount", "region"]]

# Select multiple columns by name
key_metrics = df[["amount", "quantity", "profit"]]

2. Label-based Column Slicing with .loc[]

# Slice columns by label (inclusive)
financial_cols = df.loc[:, "order_date":"profit"]

# Select from "customer_id" to "region" inclusive
customer_slice = df.loc[:, "customer_id":"region"]

3. Position-based Slicing with .iloc[]

# First 5 columns
first_five = df.iloc[:, :5]

# Columns from position 2 to 7 (exclusive end)
middle_cols = df.iloc[:, 2:8]

# Last 3 columns
last_three = df.iloc[:, -3:]

4. Advanced Column Selection with filter()

# Select columns containing a pattern
amount_cols = df.filter(like="amount")

# Select columns starting with "cust"
customer_cols = df.filter(regex="^cust")

# Select columns ending with "_id"
id_cols = df.filter(regex="_id$")

5. Best Practices in 2026

Use df[["col1", "col2"]] when you know exact column names — it's the most readable
Use .loc[:, "start":"end"] for label-based range slicing
Use .iloc[:, start:end] when working by position
Use .filter() with like or regex for pattern-based selection
Combine with .reindex() or column lists for consistent column order

Conclusion

Slicing columns effectively is essential for clean data manipulation. In 2026, the best practice is to use explicit column selection with df[["col1", "col2"]] for readability, .loc[] for label ranges, .iloc[] for positional slicing, and .filter() for pattern matching. Choosing the right method for each situation will make your code more maintainable and efficient.

Next steps:

Review your current code and replace any loose column selection with explicit, readable slicing techniques