Why pandas loc vs iloc returns different results (and how to fix it)

Incorrect index selections in pandas usually appear in real-world datasets from APIs or logs, where loc and iloc are used interchangeably. This leads pandas to generate different results, often silently breaking downstream logic.

Quick Answer

Pandas loc and iloc return different results due to label vs positional indexing. Fix by understanding their differences and choosing the correct method.

TL;DR

  • Loc is label-based indexing
  • Iloc is positional indexing
  • Be aware of index type before selecting

Problem Example

import pandas as pd

df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]}, index=['a', 'b', 'c'])
print(df.loc['a'])  # Label-based
print(df.iloc[0])  # Positional
# Output shows different results

Root Cause Analysis

Pandas loc and iloc behave differently due to their indexing methods. Loc is label-based, while iloc is positional. This behavior is consistent with the pandas documentation and often surprises developers unfamiliar with the differences. Related factors:

  • Index type (label vs positional)
  • Dataframe structure
  • Indexing method

How to Detect This Issue

# Check index type
print(type(df.index))

# Compare loc and iloc results
print(df.loc[0])  # Raises KeyError if index is not integer
print(df.iloc[0])  # Always returns first row

Solutions

Solution 1: Use loc for label-based indexing

print(df.loc['a'])  # Returns row with label 'a'

Solution 2: Use iloc for positional indexing

print(df.iloc[0])  # Returns first row

Solution 3: Check index type before selecting

if isinstance(df.index, pd.RangeIndex):
    print(df.iloc[0])
else:
    print(df.loc['a'])

Why validate Parameter Fails

Using the wrong indexing method will raise an error or return incorrect results. This is not a bug — it is pandas enforcing the correct usage of loc and iloc. If the index is not integer-based, use loc. If the index is integer-based, use iloc.

Production-Safe Pattern

if isinstance(df.index, pd.RangeIndex):
    print(df.iloc[0])
else:
    print(df.loc['a'])

Wrong Fixes That Make Things Worse

❌ Using try-except blocks to ignore index errors: This hides the symptom but corrupts your data

❌ Always using loc: This will fail if the index is not label-based

❌ Always using iloc: This will fail if the index is not integer-based

Common Mistakes to Avoid

  • Using loc and iloc interchangeably
  • Not checking index type before selecting
  • Ignoring the differences between label and positional indexing

Frequently Asked Questions

Q: What is the difference between loc and iloc?

Loc is label-based indexing, while iloc is positional indexing.

Q: Is this a pandas bug?

No. This behavior is consistent with the pandas documentation.

Q: How do I decide between loc and iloc?

Use loc for label-based indexing and iloc for positional indexing. Always check the index type before selecting.

→ Why pandas index alignment changes values silently → Fix pandas merge using index gives wrong result → Fix pandas drop_duplicates keeps wrong row → Fix pandas SettingWithCopyWarning false positive

Next Steps

After fixing this issue:

  1. Validate your merge with the validate parameter
  2. Add unit tests to catch similar issues
  3. Review related merge problems above