np
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-19.)
np has 570 facts recorded in Dontopedia across 293 references, with 22 live disagreements.
570 facts·66 predicates·293 sources·22 in dispute
Mostly:rdf:type(256), provides(22), imported as(20)
Maturity scale
raw canonical shape-checked rule-derived certifiedFull Namein disputefullName
Rdf:typein disputerdf:type
- Library[5]all time · Beam
- Python Library[5]all time · Beam
- Library[6]all time · 45e2521d 8d30 4028 A17f 38bbb775a2d9
- Python Library[7]all time · 150d3ab0 4c59 4efc B47d 5284bb249422
- Python Library[8]all time · 90d01e05 F0d1 4a11 B8cd F7c4e756798d
- Python Library[10]all time · 489167e0 4229 4466 B79e 905c32c81235
- Python Library[11]all time · Fa73deca 3eb7 42db A3b3 D779510fbe30
- Python Library[12]all time · 94913fba 0f24 43ea 8f73 53401754259b
- Library[13]all time · 26aa4878 E021 45ea A985 14515ab2db8f
- Python Module[14]all time · C4a857a1 Dc32 4df1 930a Deafd9ad6953
Providesin disputeprovides
- Random[20]all time · Ca4e289b 7c67 4d84 A25e 6049f8b30fd0
- random function[22]all time · 3695b898 49dc 4888 8153 F8794904ea4c
- Numerical Operations[40]all time · 86eb773b F442 4031 A717 C603edeea493
- np.array[89]all time · 223f970c Afbe 47c2 91b1 85b6c4a7a90e
- Array Class[95]all time · 64cf3967 C201 4248 903c 3a8b56a0a64e
- numerical computation functionality[100]all time · 9e7b4505 0e17 45e0 B233 Db0dd53d364a
- Array[124]all time · Cc7e2701 5558 4a53 B31f 07382bf903bd
- Argsort[124]all time · Cc7e2701 5558 4a53 B31f 07382bf903bd
- np.random.rand[131]all time · 4efeeb64 8572 49af 812f E5accd46c4ad
- np.array[139]all time · 3aa97b5d 2401 4a53 A5d0 4cd1d9b8e042
Imported Asin disputeimportedAs
- Np[9]all time · 36c97130 9e0f 4219 9615 7d67d19004ec
- Np[58]all time · A9baed6e 2b15 40f1 B097 3a040af972b4
- np[62]all time · 204bc3d7 6d31 47ea 9891 3576d93b551a
- Np Alias[68]all time · Dd2d6146 E140 4698 9e58 4a7d2aa3bb8c
- Np Alias[104]all time · 01f141a1 99c2 4f2a Bef8 A90fb602c9ed
- np[123]all time · F7999e0a 925c 4a2e Afc4 B5e2483ddb0a
- np[133]all time · Bc514c72 4844 4014 9141 5a893fb1b2fe
- np[134]all time · 2b82365a Fa1b 4c40 A4d8 B4995b335ba4
- Np[147]all time · 8099970e F2d8 437f 874b E1c72a22eeb0
- Np[165]all time · 2543d3b9 8f0f 47ad B540 Af23d84524d6
Aliasin disputealias
- np[10]all time · 489167e0 4229 4466 B79e 905c32c81235
- np[17]all time · 5278119f C632 4b91 B193 F1e7bddf1e64
- Np[41]all time · 9087a46d 65a1 4efb Af6d 87d65f7c2619
- np[92]all time · 7fecae4a F2ee 4e81 B6cf Fad3aa5905d6
- np[94]all time · 8010ed4f 8d3e 42b4 B00e D7b361693632
- np[109]all time · A580d2f2 C4bb 4c45 Af1f 52789c21eaa6
- np[112]all time · 8036737b 9c5e 4cf6 8fd5 40137132613b
- np[137]all time · 92a95877 3ba8 48c1 86f2 E8a0865392f0
- np[168]all time · B5b9d4b4 F681 44eb Aa46 243df5db0e24
- np[183]all time · 4ce7908a B80a 4ae8 B9ea A2a7b9f7ae98
Provides Functionin disputeprovidesFunction
- Random Rand[37]all time · 9c3d6c77 2b58 4a3b 9618 59e705c00dfd
- Frombuffer[43]all time · Be9a8aec F79b 4994 8a8c 1dbb6dd43cd9
- Random[77]all time · E4762ba4 92ad 42cd B666 A7f736830e81
- Random[105]all time · 406dd8a8 9b3a 4822 Bc8b 168d05c875b4
- Numpy Mean[115]all time · 5bd41d22 3ca1 4003 B984 10661f0214c0
- Np Array[189]all time · Ab1747c6 6e08 4399 Aff2 920ab0033740
- Np Mean[189]all time · Ab1747c6 6e08 4399 Aff2 920ab0033740
- Np Arange[201]all time · 4f6cd2d9 Aef1 4d0e 9a37 934d0f0c4650
- Mean[211]all time · 755a2410 8559 42ef A748 3e6658f03631
- Abs[211]all time · 755a2410 8559 42ef A748 3e6658f03631
Used forin disputeusedFor
- Numerical Vector Operations[1]all time · Part 849
- Numerical Computation[11]all time · Fa73deca 3eb7 42db A3b3 D779510fbe30
- Array Definition[12]all time · 94913fba 0f24 43ea 8f73 53401754259b
- Numerical Vector Operations[53]all time · 843
- array creation[89]all time · 223f970c Afbe 47c2 91b1 85b6c4a7a90e
- numerical-computation[103]all time · 049b5e35 366c 46ac Baa9 6b55223d18c1
- Efficient Numerical Operations[157]all time · 3b48a350 103d 4a40 A8b2 616d12a69fcd
- array-processing[181]all time · 6f292328 F20a 4855 96d3 52a1dd2d8e17
- Mismatch Check[185]all time · F5a5540b 3c9d 4103 85d7 7db7b8ea25d3
- Efficient Array Processing[227]all time · Ea59f145 6651 454f A110 0532593f48cd
Import Statementin disputeimportStatement
- import numpy as np[32]all time · 01d47e70 2678 4424 Bb6e 17ebfb57cf51
- Import Numpy As Np[79]all time · 1230ce96 067d 46f5 8ea5 25c70af53f43
- import numpy as np[109]all time · A580d2f2 C4bb 4c45 Af1f 52789c21eaa6
- Python Module[116]all time · 49101dfd 4fc4 460c 9cd9 8e0457730c83
- import numpy as np[139]all time · 3aa97b5d 2401 4a53 A5d0 4cd1d9b8e042
- import numpy as np[166]all time · 91fac1d0 D0d5 4ffd 8ea8 C697f1dd56cc
- import numpy as np[229]all time · 015c5023 Ca31 419e 93cf 0713ac674694
- np[232]all time · 40ad9efd 31cb 4009 8b35 E5d32e632e93
- import numpy as np[233]all time · Af4125d1 0a22 4039 865e 38f47d517ba5
- import numpy as np[277]all time · 6d8d70c3 2664 497a 8f53 21477cd02036
Other facts (105)
The long tail: predicates that appear too rarely to warrant their own section. Filter or scroll to find a specific one. Each row links to its source.
105 facts
| Predicate | Value | Ref |
|---|---|---|
| Used by | Code Snippet | [37] |
| Used by | Python Code Block | [120] |
| Used by | Log Score Mismatches | [126] |
| Used by | Compute Sparse Scores | [151] |
| Used by | Vector Loader | [174] |
| Used by | Vector Processor | [174] |
| Used by | Measured Complexities | [192] |
| Used by | Enhanced Latencies | [192] |
| Used by | Evaluation Pipeline | [232] |
| Used in | Example Code | [42] |
| Used in | Calculate Accuracy | [51] |
| Used in | Scores1 Generation | [51] |
| Used in | Example Implementation | [117] |
| Used in | Dataset Creation | [163] |
| Used in | Random Vector Generation | [163] |
| Used in | Code | [255] |
| Is Used by | Test Data Generation | [78] |
| Is Used by | Design Training Stages | [201] |
| Is Used by | Evaluation Pipeline | [249] |
| Is Used by | Avg Accuracy Calculation | [252] |
| Is Used by | Avg F1 Calculation | [252] |
| Is Used by | Contextual Similarity | [289] |
| Imported in | Code Snippet | [99] |
| Imported in | Code Example | [139] |
| Imported in | Search Algorithm | [140] |
| Imported in | Code Snippet | [182] |
| Imported in | Python Code | [200] |
| Imported in | Example Implementation | [214] |
| Import Alias | Np | [23] |
| Import Alias | np | [79] |
| Import Alias | np | [175] |
| Purpose | numerical operations | [40] |
| Purpose | Array Operations | [245] |
| Purpose | Numerical Computations | [292] |
| Library for | numerical computation | [45] |
| Library for | Python | [122] |
| Library for | numerical-computation | [130] |
| Aliased As | np | [46] |
| Aliased As | np | [268] |
| Aliased As | np | [282] |
| Is Library | true | [95] |
| Is Library | Python Library | [124] |
| Is Library | Python Library | [144] |
| Is Imported As | np | [106] |
| Is Imported As | np | [152] |
| Is Imported As | np | [268] |
| Required by | Evaluate Relevance Lift | [125] |
| Required by | Hybrid Ranking | [125] |
| Required by | Precision at K | [125] |
| Abbreviation | np | [161] |
| Abbreviation | np | [211] |
| Abbreviation | np | [279] |
| Used by | Replace Oov Terms | [161] |
| Used by | tokenize-query-function | [199] |
| Used by | sparse-tuning-function | [199] |
| Imported As | np | [5] |
| Imported As | np | [199] |
| Used in | Improved Code Version | [57] |
| Used in | Python Code | [160] |
| Library Purpose | Numerical Computation | [79] |
| Library Purpose | numerical-computation | [160] |
| Library Type | numerical-computing-library | [120] |
| Library Type | numerical computing | [287] |
| Is Aliased As | np | [198] |
| Is Aliased As | np | [237] |
| Ensures | Computational Efficiency | [2] |
| References Python Library | null | [2] |
| Preferred Over | Pure Python | [2] |
| Imported in Script | true | [3] |
| Used for Computation | multiple | [4] |
| Qualified Name | numpy | [5] |
| Module | Third Party | [41] |
| Used for | Random Seed Setting | [65] |
| Is Imported | true | [69] |
| Is Used in Visible Code | false | [69] |
| Has Alias | np | [70] |
| Namespace for | Random | [77] |
| Dependency of | Code Block 1 | [89] |
| Library Name | numpy | [92] |
| Is Used by | Example Implementation | [119] |
| Is Used for | array_operations | [131] |
| Namespace | Python | [139] |
| Version | unknown | [139] |
| Inverse of | Python | [139] |
| Import | np | [153] |
| Imported | true | [156] |
| Imported in | Code Example | [159] |
| Called on | Embeddings | [166] |
| Imported for | np.nan | [168] |
| Is Part of | Numpy Package | [169] |
| Imported by | Calculate Term Frequencies Function | [195] |
| Imported With Alias | Np Alias | [201] |
| Full Name | Numerical Python | [215] |
| Has Import Alias | np | [218] |
| Used With | Pandas | [220] |
| Written in | Python | [251] |
| Used for Array Creation | true | [255] |
| Import As | np | [257] |
| Import Alias | np | [275] |
| Suggested for | Numerical Data | [278] |
Timeline
Timeline axis is valid_time — when each source says the fact was true in the world, not when Dontopedia learned about it. Retracted rows are kept for provenance; coloured stripes indicate the context kind.
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numpy
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typebeam/d484fb83-3798-4b15-8e73-8c01c48cbe47
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false
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ex:Python_Library
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typebeam/cdd51d1c-232b-4579-bc7b-6fee02a86cab
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typebeam/b85c734a-9098-42cd-ab77-73fd28699205
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numpy
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numpy
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ex:ExternalLibrary
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labelbeam/0cb60209-6aed-4aab-9fcf-4a2b2c8059a3
NumPy library
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typebeam/53ec8134-9816-445b-82ba-001949a77ddd
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labelbeam/9e7b4505-0e17-45e0-b233-db0dd53d364a
NumPy
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typebeam/c1884d4f-6cc0-42a1-9d04-1b18cb1f2a49
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numpy
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typebeam/b90feaf0-1adf-45f8-bfbc-be1d12a23cb9
ex:Library
References (293)
293 references
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See also
- Numerical Vector Operations
- Computational Efficiency
- Pure Python
- Library
- Python Library
- Np
- Numerical Computation
- Array Definition
- Python Module
- Programming Library
- Library
- Random
- Numerical Computing Library
- Numerical Computing Library
- Code Snippet
- Random Rand
- Numerical Operations
- Third Party
- Software Library
- Example Code
- Frombuffer
- Calculate Accuracy
- Scores1 Generation
- Scientific Computing Library
- Improved Code Version
- Random Seed Setting
- Python Package
- Np Alias
- Scientific Computing Library
- Test Data Generation
- Import Numpy As Np
- Python Library
- Code Block 1
- Python Library
- Array Class
- Module
- External Library
- Python Library
- Numpy Mean
- Python Module
- Example Implementation
- Python Code Block
- Array
- Argsort
- Evaluate Relevance Lift
- Hybrid Ranking
- Precision at K
- Log Score Mismatches
- Code Example
- Python
- Search Algorithm
- Compute Sparse Scores
- Efficient Numerical Operations
- Python Code
- Replace Oov Terms
- Dataset Creation
- Random Vector Generation
- Numerical Computing Library
- Embeddings
- Numpy Package
- Vector Loader
- Vector Processor
- Mismatch Check
- Np Array
- Np Mean
- Measured Complexities
- Enhanced Latencies
- Calculate Term Frequencies Function
- Arange Function
- Zeros Like Function
- Np Arange
- Design Training Stages
- Mean Function
- Percentile Function
- Module
- Mean
- Abs
- Numpy.load
- Pandas
- Efficient Array Processing
- Evaluation Pipeline
- Vectorized Operations
- Random Functions
- Import
- Array Operations
- Np Mean
- Evaluation Pipeline
- Python
- Avg Accuracy Calculation
- Avg F1 Calculation
- Code
- Numpy Random Rand
- Numpy Mean
- Numerical Data
- Memory Efficient Arrays
- Contextual Similarity
- Efficient Array Operations
- Pandas Library
- Numerical Computations and Feature Transformations
- Numerical Computations
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