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devtoolsentity · one source so far

numpy

for python devs, data engineers

Steadydevto
Signal score
6
as of 6d ago
Live items
1
Trajectory
→ Steady
7-day est. ~6

Why this scored 6

every term, weighted
Velocity+0.0 / 40

Its fastest-moving item, measured against the pace of its own source

Acceleration+11.3 / 25

Whether that velocity is itself speeding up, as a per-hour rate

Cross-source spread+0.0 / 25

How many independent communities its own items come from

Recency+0.0 / 10

Decays to zero over 14 days, counted from when we first saw it

Saturation penalty−5.7 / 30

Subtracted once something is big and old — sized by its biggest item, aged from when we first saw it

Composite5.6

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 guarantee

7-day

~6

range 0–18

14-day

~6

range 0–20

30-day

~6

range 0–25

Signal history

7-day window (free)
56

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.

  1. 1
    29

    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
  2. 2
    25

    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
  3. 3
    24

    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
  4. 4
    20

    Scaling NumPy on Free-Threaded Python

    Improving numpy performance · for python devs

    lobstersEmerging 3 · pythondevtools87d ago
  5. 5
    18

    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
  6. 6
    18

    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
  7. 7
    15

    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
  8. 8
    15

    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
  9. 9
    14

    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
  10. 10
    9

    Trying to Change A Pixel in Python

    Changing pixels in Python · for python devs

    stackoverflowSteady 3 · pythondevtools87d ago
  11. 11
    7

    Full string not added to dictionary in loop

    Dictionary issue · for python devs

    stackoverflowSteady 2 · pythondata103d ago
  12. 12
    6

    Check if matrix contains only specific values in numpy

    Numpy matrix check · for data scientists

    stackoverflowSteadydata97d ago
  13. 13
    4

    How can I boxplot values that wrap around using matplotlib?

    Python plot issue · for data scientists

    stackoverflowSteadydata104d ago
  14. 14
    4

    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