numpy
for python devs, data engineers
Why this scored 6
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
Outlook
low confidence · estimate, not a guarantee7-day
~6
range 0–18
14-day
~6
range 0–20
30-day
~6
range 0–25
Signal history
7-day window (free)projected trajectory (estimate, not a guarantee)
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.
- 129
The last mile of a long road: faster NumPy in the browser
Optimized NumPy runs in browsers, enabling faster scientific computing on the client side. · for data scientists
lobstersSteady 2 · numpydata36d ago - 225
Teaching NumPy’s ufuncs new tricks
C extensions for NumPy ufuncs enable faster numerical operations for Python users. · for scientific python developers
lobstersCooling 2 · numpydata47d ago - 324
Tracing np.add, all the way down
Shows how to trace NumPy's np.add down to native calls, aiding performance debugging. · for data engineers, scientific programmers
lobstersEmergingdata54d ago - 420
Scaling NumPy on Free-Threaded Python
Improving numpy performance · for python devs
lobstersEmerging 3 · pythondevtools87d ago - 518
Making np.searchsorted up to 25× Faster in NumPy 2.5
Boosts np.searchsorted speed up to 25× in NumPy 2.5, aiding large-scale array processing. · for data scientists, scientific python developers
lobstersSteadydata15d ago - 618
Python performance: native loops, NumPy and the vectorize trap
Compares native loops, NumPy and vectorize trap for Python performance. · for python devs, data engineers
devtoSteadydevtools9d ago - 715
How can I make numpy arrays the same shape before doing conditional computations?
Properly shaping numpy arrays ensures correct broadcasting for conditional calculations. · for python data scientists
stackoverflowSteadydata51d ago - 815
Create a new column in pandas conditionally based on two other columns that may have overlapping values
Shows how to add a conditional column in pandas using overlapping values from two columns. · for python data scientists, pandas users
stackoverflowSteadydata67d ago - 914
How to compute a gradient column-wise in a pandas DataFrame using the index as an input?
Demonstrates column-wise gradient computation in a pandas DataFrame using numpy. · for data scientists
stackoverflowSteadydata36d ago - 109
Trying to Change A Pixel in Python
Changing pixels in Python · for python devs
stackoverflowSteady 3 · pythondevtools87d ago - 117
Full string not added to dictionary in loop
Dictionary issue · for python devs
stackoverflowSteady 2 · pythondata103d ago - 126
Check if matrix contains only specific values in numpy
Numpy matrix check · for data scientists
stackoverflowSteadydata97d ago - 134
How can I boxplot values that wrap around using matplotlib?
Python plot issue · for data scientists
stackoverflowSteadydata104d ago - 144
Why do C- and Fortran-ordered NumPy arrays show the same L1 cache-miss percentage when formatting a row?
NumPy array cache performance · for data scientists
stackoverflowSteadydata97d ago