time-aware-imputer
Time-aware missing data imputation for irregular time series
Decision gist · record as of 2026-08-14
Yes, if you work with irregular time-series data and need intelligent gap-filling. The library is actively maintained, has no known vulnerabilities, and integrates cleanly with pandas and scikit-learn. However, it is early-stage (Alpha, 2 stars, 167 days old), so expect the API or behavior to evolve; use it for production work only if you can tolerate potential breaking changes or have capacity to adapt.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires a 'timestamp' column (or explicitly named time column) in your DataFrame; the imputer expects pandas DataFrames with datetime index or explicit timestamp column.
- Low friction install with five standard scientific dependencies (numpy, pandas, scipy, scikit-learn, matplotlib).
- Active maintenance as of 2026-02-28 with recent commits; early-stage project (Alpha status, 2 stars) so community validation is limited.
License · maintenance · safety
MIT (permissive) — MIT license permits commercial and private use with minimal restrictions; you must include the license text in distributions but face no copyleft obligations.
last release 2026-02-28 (167 days) · last repo commit 2026-02-28 · 2 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 345,368 downloads/mo, #7,364 on PyPI
Alternatives
Verify before relying
pip install time-aware-imputer
import pandas as pd
import numpy as np
from time_aware_imputer import SplineImputer, GapAnalyzer
df = pd.DataFrame({
'timestamp': pd.date_range('2024-01-01', periods=100, freq='h'),
'temperature': np.random.randn(100)
})
df.loc[10:15, 'temperature'] = np.nan
imputer = SplineImputer(method='cubic')
df_imputed = imputer.fit_transform(df)- Whether the library handles multivariate imputation (correlations between columns) or treats each column independently.
- Performance characteristics on large datasets (memory usage, computational cost for different interpolation methods).
- How the library behaves with very sparse data or extremely irregular sampling intervals.
What it is and what it does
Time-aware-imputer is a Python library that fills missing values in time-series data by understanding that gaps of different durations require different treatment. Unlike generic imputation methods that ignore temporal structure, this library uses spline interpolation (linear, cubic, quadratic, PCHIP, or Akima) to reconstruct missing values while preserving temporal trends. It wraps scipy's interpolation functions with a scikit-learn compatible API, so it integrates into standard ML pipelines.
The package provides two main tools: SplineImputer for filling missing values with your choice of interpolation method, and GapAnalyzer for diagnosing missing data patterns—counting gaps, measuring their duration, and visualizing them. It's designed for IoT sensor networks, industrial monitoring, and other scenarios where time-series data arrives irregularly and you need to understand and fill the gaps before analysis or modeling.
Use it for
- Impute sensor readings from IoT devices that report at irregular intervals or have communication failures.
- Fill gaps in financial time-series data (stock prices, exchange rates) while preserving trend direction.
- Analyze and visualize missing data patterns in multi-sensor industrial equipment before preprocessing for ML.
- Prepare time-series data for scikit-learn pipelines by handling missing values with time-aware methods.
- Reconstruct monotonic or smooth trends in environmental monitoring data (temperature, humidity, pressure).
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you work with irregular time-series data and need intelligent gap-filling.
The library is actively maintained, has no known vulnerabilities, and integrates cleanly with pandas and scikit-learn. However, it is early-stage (Alpha, 2 stars, 167 days old), so expect the API or behavior to evolve; use it for production work only if you can tolerate potential breaking changes or have capacity to adapt.
Install
time-aware-imputer on PyPI
Before you install
Low friction install with five standard scientific dependencies (numpy, pandas, scipy, scikit-learn, matplotlib). Active maintenance as of 2026-02-28 with recent commits; early-stage project (Alpha status, 2 stars) so community validation is limited.
Requires a 'timestamp' column (or explicitly named time column) in your DataFrame; the imputer expects pandas DataFrames with datetime index or explicit timestamp column.
License in practice
MIT license permits commercial and private use with minimal restrictions; you must include the license text in distributions but face no copyleft obligations.
Quickstart
pip install time-aware-imputer
import pandas as pd
import numpy as np
from time_aware_imputer import SplineImputer, GapAnalyzer
df = pd.DataFrame({
'timestamp': pd.date_range('2024-01-01', periods=100, freq='h'),
'temperature': np.random.randn(100)
})
df.loc[10:15, 'temperature'] = np.nan
imputer = SplineImputer(method='cubic')
df_imputed = imputer.fit_transform(df)Verify before relying
- Whether the library handles multivariate imputation (correlations between columns) or treats each column independently.
- Performance characteristics on large datasets (memory usage, computational cost for different interpolation methods).
- How the library behaves with very sparse data or extremely irregular sampling intervals.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.8 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 5 packagesnumpypandasscipyscikit-learnmatplotlib |
| Maintenance | Actively maintained 167 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 345,368 / month, #7,364 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 3 - AlphaIntended Audience :: DevelopersIntended Audience :: Science/ResearchLicense :: OSI Approved :: MIT LicenseProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Topic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Scientific/Engineering :: Information Analysis |
Evidence: time_aware_imputer-1.0.0-py3-none-any.whl
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