Install & Compatibility
Where this runs
tested against v? · pip install
Install × environment matrix
Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux
muslpy 3.10–3.95 runs
build_error
Code
Verified usage
Verified import paths — ran on the pinned version, not inferred.
pypots
✓ import pypots
The main package; no common wrong pattern.
Impute missing values in 3D time series data using SAITS.
import pypots
from pypots.utils.random import set_random_seed
import numpy as np
# Generate random data with missing values
X = np.random.randn(100, 10, 5)
X[X < -1] = np.nan # introduce missing values
# Impute using a simple model
from pypots.imputation import SAITS
model = SAITS(
n_steps=10,
n_features=5,
n_layers=2,
d_model=32,
d_ffn=64,
n_heads=4,
d_k=16,
d_v=16,
epochs=10,
batch_size=32,
loss_fn='mse',
optimizer='adam',
lr=1e-3,
verbose=True
)
model.fit(X)
imputed_X = model.impute(X)
print(imputed_X.shape)
Errors
Common errors & fixes
ValueError: Expected data to be 3D, got 2D
Input data is 2D instead of 3D (n_samples, n_timesteps, n_features).
fixReshape data to 3D: data = data.reshape(n_samples, -1, n_features) or add a dimension.
ModuleNotFoundError: No module named 'pypots.data'
Trying to import from the old 'pypots.data' subpackage which was removed in v1.0.
fixUse 'from pypots.utils.random import set_random_seed' or other correct imports from actual subpackages.
AttributeError: 'NoneType' object has no attribute 'endswith'
Known bug in TimeLLM model when loading pretrained LLM, fixed in v1.2.
fixUpgrade to pypots>=1.2.
Upgrade
Version history
1.5latest on PyPI · released May 5, 2026
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Dependencies
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