Problem: Integer Overflow (When Dtype Silently Breaks Your Math)

You're doing some arithmetic, and suddenly your numbers wrap around to negative values. This is integer overflow—and NumPy doesn't warn you about it by default. It just silently wraps around, leaving you with garbage results.

The problem

Here's a classic example. You're summing some large integers:

import numpy as np

# The problem: int32 overflow

arr = np.array([2000000000, 2000000000], dtype=np.int32)
result = arr.sum()
print(result) # ❌ Overflow! Wrapped around

# Why? int32 max value is 2,147,483,647

# 2,000,000,000 + 2,000,000,000 = 4,000,000,000

# But that's too big for int32, so it wraps

-294967296

The sum should be 4,000,000,000, but you got -294,967,296 instead. That's because int32 can only represent values from -2,147,483,648 to 2,147,483,647. When you exceed the maximum, it wraps around to negative numbers.

Why dtype matters

NumPy preserves dtype during operations. If you start with int8, arithmetic stays int8 even if results overflow:

# Be careful with operations that preserve dtype
arr_small = np.array([100, 200], dtype=np.int8)
multiplied = arr_small * 10   # Still int8!
print(multiplied)             # ❌ Overflow again!

# int8 range: -128 to 127

# 100 * 10 = 1000 → wraps to -56

# 200 * 10 = 2000 → wraps to -48

[-56 -48]

The fix: use larger dtypes

The solution is to use a dtype that's large enough for your values:

# Option 1: Use int64 from the start
arr_fixed = np.array([2000000000, 2000000000], dtype=np.int64)
result_fixed = arr_fixed.sum()
print(result_fixed)           # ✅ Correct!

# Option 2: Let NumPy infer (defaults to int64 on 64-bit systems)

arr_auto = np.array([2000000000, 2000000000])
print(arr_auto.dtype) # int64 (on most systems)

# Option 3: Force dtype promotion before operations

arr_small = np.array([100, 200], dtype=np.int8)
multiplied_safe = arr_small.astype(np.int16) * 10
print(multiplied_safe) # ✅ Correct!

4000000000
int64
[1000 2000]

When this bites you

This is especially sneaky when:

• Loading data from files (CSV, HDF5) that specify small dtypes to save space
• Doing multiplication or exponentiation (values grow fast)
• Working with image data (often stored as uint8, range 0-255)
• Summing many values (even if each is small, the sum can overflow)

How to avoid it

Always check your dtype, especially when:

# Check dtype before operations
arr = np.array([100, 200])
print(arr.dtype)              # Check what you have

# Check max/min values for your dtype

print(np.iinfo(np.int32).max) # 2147483647
print(np.iinfo(np.int8).max) # 127

# If you're not sure, use int64 or float64

arr_safe = np.array([100, 200], dtype=np.int64)

int64
2147483647
127

The key insight

NumPy preserves dtype during operations. If you start with int8, arithmetic stays int8 even if results overflow. Always check your dtype, especially when working with large numbers or doing multiplication. Use .astype() to promote to a larger dtype before operations that might overflow.

The rule: if you're doing arithmetic that could produce values larger than your dtype can represent, promote the dtype first. It's better to use more memory than to get wrong results.