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Avoiding Overflow in Polars: What Every Python Developer Who Use Polars Should Know

Summary

When working with Polars, a lightning-fast DataFrame library built in Rust and designed for performance, many Python developers are surprised to encounter something they rarely see in standard Python: Overflow.

This post explains why overflow happens in Polars, how it differs from standard Python behaviour, and what you can do to prevent it.

📦 What Is Polars?

Polars is a next-generation DataFrame library designed for speed and scalability. Unlike pandas, which is written in Python and C, Polars is written in Rust — a systems programming language known for memory safety and performance.

Polars stands out because:

  • It uses columnar memory layout (Apache Arrow format), making analytics much faster.
  • It supports lazy evaluation, allowing optimization of full query plans before execution.
  • It has native multithreading.
  • It offers strict typing with fixed-width types like Int32, Int64, and Float32.

While Polars integrates seamlessly with Python, its foundation in Rust means that it behaves differently in some low-level ways — such as integer overflow.

🤯 A Surprising Example

The other day I encountered an issue, where I expected all numbers in the column that I was trying to sum to be positive. Yet, I got a negative total just like the example below: 

import polars as pl

df = pl.DataFrame({
    "small_ints": [2_000_000_000, 2_000_000_000]
}, schema={"small_ints": pl.Int32})

# Aggregation with overflow!
result = df.select(
    pl.col("small_ints").min().alias("min_num"),
    pl.col("small_ints").max().alias("max_num"),
    pl.col("small_ints").sum().alias("sum_sum"))

print(result)

Output:

shape: (1, 3)
| min_num    | max_num    | sum_sum    |
| ---        | ---        | ---        |
| i32        | i32        | i32        |
|------------|------------|------------|
| 2000000000 | 2000000000 | -294967296 |
And surprise, although min is positive, the total is a negative number. You may ask why is this, and the answer is overflow.

🧠 The Problem: Summing int32 Can Overflow

In Python, integers are arbitrary-precision — they can grow as large as needed (limited only by memory). But Polars is built in Rust, where types like int32 and int64 are fixed-width integers. That means they have a hard limit:

  • int32: from -2,147,483,648 to 2,147,483,647
  • int64: from -9,223,372,036,854,775,808 to 9,223,372,036,854,775,807

If you sum a column of int32 values in Polars and the result exceeds this limit, you'll get a silent overflow, not an error. This can cause unexpected results and subtle bugs.

➗ How Integer Overflow Works (and Why You Get a Negative Number)

Integer overflow occurs when a calculation produces a number outside the range a fixed-width integer can represent. For example, in a 32-bit signed integer:

  • The maximum value is 2,147,483,647 (0x7FFFFFFF)
  • If you add 1 to that, it wraps around to the minimum value: -2,147,483,648 (0x80000000)

Why? Because fixed-width integers use two’s complement binary representation. When you go past the limit, the number wraps around — just like a clock going from 12 to 1, but in binary space.

info

In our earlier example, summing 2_000_000_000 + 2_000_000_000 = 4_000_000_000, which is too big for int32. The bits overflow and result in a large negative number, -294,967,296.

So if your sum “should” be positive but turns negative, it’s likely an overflow.

🧪 Why This Happens

Polars doesn’t automatically promote types during aggregations. So, when summing int32, the accumulator stays int32, even if the actual result needs more space.

This design is intentional — it keeps performance tight and predictable. But it shifts responsibility to the developer to ensure the types are large enough.

✅ How to Avoid It

1. Use Wider Integer Types

If you're summing numbers that might exceed the int32 limit, it's safer to store them as int64.

df = pl.DataFrame({
    "big_ints": [2_000_000_000, 2_000_000_000]
}, schema={"big_ints": pl.Int64})

result = df.select(pl.col("big_ints").sum())
print(result)  # Safe!

2. Cast Before Aggregating

If your data is already loaded as int32, you can cast it to int64 before performing the aggregation:

df.select(pl.col("small_ints").cast(pl.Int64).sum())

3. Check Data Types Proactively

You can inspect the column types before any computation using:

print(df.schema)

or

print(df.dtypes)

This helps avoid surprises before running large operations.

🔍 Key Takeaways

  • Python’s integers don’t overflow, but Polars (like NumPy and Rust) uses fixed-width types.
  • Aggregations like .sum() can silently overflow when the result exceeds the type’s range.
  • Always use int64 if your integers may accumulate into large numbers.
  • Consider explicit type casting or schema definitions when working with numerical data in Polars.

🏁 Final Thoughts

Silent overflow is a classic systems programming issue — and Polars brings some of those constraints into the high-performance Python data world. While the performance gains are massive, it’s important to understand how types work under the hood.

Next time you see a weirdly negative sum, don’t panic — just check your data types.

Stay safe, and may your integers never overflow!


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