The Rule Everyone Misses: How to Stop Confusing loc and iloc in Pandas

2026-02-05 · Source: Towards Data Science · Field: Technology & Digital — Data Science & Analytics, Artificial Intelligence & Machine Learning · Depth: Intermediate, medium

Summary

Pandas DataFrames offer two primary methods for data extraction, `loc` and `iloc`, which often cause confusion due to their similar syntax but distinct operational logic. `loc` selects data based on explicit row and column labels, making it intuitive when datasets have meaningful, unique identifiers. In contrast, `iloc` performs selection based on integer positions, similar to Python list indexing, starting from 0. The article demonstrates these methods using a student performance dataset, covering tasks such as extracting single rows or values, retrieving multiple rows, slicing ranges, selecting specific columns, and applying boolean filtering. While `loc` is generally preferred for readability and label-based operations, `iloc` is crucial for scenarios where labels are absent, messy, or when position-based control is necessary, such as in machine learning preprocessing or when dealing with duplicate labels.

Key takeaway

For Data Scientists and Machine Learning Engineers working with Pandas, understanding the `loc` vs. `iloc` distinction is critical for efficient and error-free data manipulation. Prioritize `loc` when your DataFrame has clear, stable labels for better code readability and maintainability, especially for complex boolean filtering. Reserve `iloc` for scenarios requiring precise positional indexing, such as iterating through data chunks or when labels are dynamic or absent, ensuring your data extraction logic remains robust.

Key insights

Pandas `loc` uses labels for data selection, while `iloc` uses integer positions.

Principles

• Use `loc` for label-based selection and readability.
• Use `iloc` for position-based control or when labels are unreliable.
• Pandas `loc` slicing includes the end label; `iloc` slicing excludes it.

Method

To extract data, use `df.loc[rows, columns]` with labels or `df.iloc[rows, columns]` with integer positions. Boolean filtering is primarily done with `loc` using conditions like `df.loc[df['column'] > value]`.

In practice

• Set a meaningful column as index using `df.set_index()` for `loc`.
• Use `df.loc[:, ['col1', 'col2']]` to select specific columns by label.
• Apply `df.iloc[0:100]` for consistent chunking without overlaps.

Topics

• Pandas DataFrames
• loc and iloc
• Data Selection
• Boolean Filtering
• Data Slicing

Best for: Data Scientist, Data Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards Data Science.