Why numpy random seed doesn’t ensure reproducibility (and how to fix it)

Reproducibility issues in numpy random seed usually appear in machine learning pipelines, where numpy is used alongside pandas DataFrames, leading to inconsistent results due to hidden state changes. This stems from numpy’s random number generator being affected by external factors.

Quick Answer

Numpy random seed doesn’t ensure reproducibility because the global random state can be modified externally. Fix by setting the seed immediately before use and controlling external randomness.

TL;DR

  • Numpy random seed can be altered by external code
  • Set seed right before use for reproducibility
  • Control external randomness for consistent results

Problem Example

import numpy as np
import pandas as pd

np.random.seed(0)
print(np.random.randint(0,10,5))
# External code modifies the global random state
np.random.randint(0,10,5)
print(np.random.randint(0,10,5))
# Results are different despite the same seed

Root Cause Analysis

The global random state in numpy can be modified by other parts of the code, leading to different results even with the same seed. This behavior is due to numpy’s design and the way it handles randomness. Related factors:

  • External code modifying the global random state
  • Lack of control over randomness sources
  • Insufficient seed setting

How to Detect This Issue

# Check if the global random state has been modified
import numpy as np
state = np.random.get_state()
print(state)

Solutions

Solution 1: Set the seed immediately before use

import numpy as np
np.random.seed(0)
result = np.random.randint(0,10,5)

Solution 2: Use a local random state

import numpy as np
with np.random.RandomState(0):
    result = np.random.randint(0,10,5)

Solution 3: Control external randomness

import numpy as np
import pandas as pd
# Ensure pandas doesn't alter the numpy random state
pd.set_option('mode.use_inf_as_na', True)
np.random.seed(0)
result = np.random.randint(0,10,5)

Why validate Parameter Fails

Using numpy’s random seed alone will not guarantee reproducibility if external code modifies the global random state. To ensure reproducibility, set the seed immediately before use and control external randomness. If you’re using numpy with pandas, ensure pandas doesn’t alter the numpy random state.

Production-Safe Pattern

import numpy as np
with np.random.RandomState(0):
    result = np.random.randint(0,10,5)
assert np.all(result == np.array([5, 0, 3, 3, 7])), 'Random result is not reproducible'

Wrong Fixes That Make Things Worse

❌ Setting the seed only once at the beginning of the program: This doesn’t account for external modifications to the global random state

❌ Using the same seed for different random number generators: This can lead to correlated results

❌ Ignoring external sources of randomness: Failing to control external randomness can lead to irreproducibility

Common Mistakes to Avoid

  • Not setting the seed immediately before use
  • Not controlling external randomness sources
  • Assuming numpy’s global random state remains constant

Frequently Asked Questions

Q: Why doesn’t numpy’s random seed ensure reproducibility?

Because the global random state can be modified by external code, leading to different results despite the same seed.

Q: Is this a numpy bug?

No, it’s due to numpy’s design and the way it handles randomness. The behavior is consistent with the documentation.

Q: How do I ensure reproducibility with numpy’s random seed?

Set the seed right before using the random number generator and control external sources of randomness.

→ Fix numpy broadcasting shape mismatch in array ops → Fix numpy broadcasting shape mismatch → Fix numpy random choice probabilities sum not one → Fix numpy concatenate memory allocation issue

Next Steps

After improving RNG reproducibility:

  • Migrate code to the numpy.random.Generator API (create a Generator per-test or per-task) and update tests to use a fixed PCG64 seed.
  • Add unit tests that seed the local generator immediately before sampling to guarantee deterministic outputs in CI.
  • Document RNG ownership in your codebase (who is allowed to modify global state) and add a linter/check to enforce local generators in critical code paths.