Pandas Fills Time Gaps For Features
A how-to post published April 5, 2026 explains step-by-step pandas techniques to construct per-user daily time series and fill missing dates for event logs. It shows reindexing with a user-date MultiIndex, zero-filling, shifting cumulative sums, and computing features like lifetime_logins, logins_yesterday, days_since_last_login and rolling logins over 7/14/21 days. These patterns prevent leakage and prepare features for next-day login prediction.
Scoring Rationale
Practical, executable pandas recipe published today with high actionability and broad applicability. Score boosted by direct code patterns and industry-wide scope; reduced for low novelty and being a single-source how-to.
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Sources
- Read OriginalFeature Engineering with Time Gapsgregreda.com
