polars
for data engineers, python developers
Why this scored 0
every term, weightedIts fastest-moving item, measured against the pace of its own source
Whether that velocity is itself speeding up, as a per-hour rate
How many independent communities its own items come from
Decays to zero over 14 days, counted from when we first saw it
Subtracted once something is big and old — sized by its biggest item, aged from when we first saw it
Weights are hand-tuned, not learned — we're calibrating them against realized trends as history accumulates. On an entity's first sighting there's no previous reading to compare against, so acceleration starts from a neutral prior rather than a measurement, and velocity falls back to engagement over its whole lifetime until a second reading exists. Full methodology
Signal history
7-day window (free)The evidence
The live items this entity's score aggregates — every community independently talking about it right now. This is the corroboration, shown, not claimed.
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Reading from Postgres DB using Polars and filter on list of strings
Demonstrates efficient Postgres reads and string list filtering using Polars in Python. · for data engineers, python developers
stackoverflowSteady 4 · postgresdata32d ago - 216
Python Polars Cheatsheet (based on our O'Reilly book)
Polars cheatsheet speeds Python data-frame work, improving performance engineering. · for data engineers, python developers
hackernewsSteadydata35d ago - 316
Pre-Release of Polars 2.0
Polars 2.0 pre-release brings speed and API upgrades for data-frame workloads. · for data engineers, python developers
hackernewsSteadydata20d ago - 415
Chaining programmatic expression functions failing in with_columns
Polars' with_columns chaining fails, indicating potential API stability issues. · for python developers, data engineers
stackoverflowSteadydevtools30d ago - 54
Using Polars in a Pandas world
Polars vs Pandas compared · for data scientists
lobstersSteadydata−6 saturated47d ago