pd
From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-19.)
pd has 285 facts recorded in Dontopedia across 130 references, with 20 live disagreements.
285 facts·63 predicates·130 sources·20 in dispute
Mostly:rdf:type(116), provides(13), used by(9)
Maturity scale
raw canonical shape-checked rule-derived certifiedFull NamefullName
- pandas[73]all time · Af41abe5 82b4 4b21 A9cb Afafa726d066
Rdf:typein disputerdf:type
- Library[1]all time · Fcff22b3 B7dd 466c B061 0a08176e2dd2
- Programming Library[2]all time · 924a6db5 B2b0 42d4 9e5c Bd5a7a159a3a
- Python Library[3]all time · E3b7ad28 C610 499f B527 47a2d7f6872f
- Python Library[5]all time · 69d53d99 9e74 491d A1aa Ba8c5b9b0e4c
- Library[6]all time · 831feb09 B7cb 4304 A2c2 8c9ed2cd23a0
- Python Library[8]all time · B6878ca0 9a69 4de7 9700 1830da12fcc1
- Library[9]all time · 15c12db4 C4d3 4659 8ce6 1da2d5b7b4fb
- Library[10]all time · 405aac9d 5ddc 42e0 9010 231fd6ae90bb
- Python Library[11]all time · 02853550 4955 4b56 87b4 5d2837b10de2
- Python Library[12]all time · A3a5d835 1848 42bd 98e5 0660dbb98a7f
Providesin disputeprovides
- DataFrame[19]all time · 92f9d4b6 659a 439c Ae2a 0330d3d8ab30
- simple_interface[24]all time · Dd064674 37b1 4f57 Ad58 28af115a4278
- intuitive_interface[24]all time · Dd064674 37b1 4f57 Ad58 28af115a4278
- Ease of Complex Operations[24]all time · Dd064674 37b1 4f57 Ad58 28af115a4278
- Data Frame Class[42]all time · 0847c3fb 2167 45e0 Baa8 Dc4abfbfbe22
- Dataframe Structure[43]all time · E06228ca 08d1 403f Af94 242c605c308e
- get_dummies function[51]all time · 47820af8 74e9 40cc B155 2fbe76a9689e
- Data Handling Functions[69]all time · 8fa5829f 15f2 482b 85e0 F9cec79dbd29
- vectorized-operations[83]all time · 6754c089 A9ba 4d68 A4bf 7f175c66d000
- DataFrame[86]all time · D20f04e6 Ac24 40a3 Ba7d A928d5401600
Other facts (106)
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.
106 facts
| Predicate | Value | Ref |
|---|---|---|
| Used by | Code | [32] |
| Used by | Flask Application | [33] |
| Used by | Python Script | [43] |
| Used by | Vectorize Data Function | [51] |
| Used by | Analyze Corpus | [54] |
| Used by | Analytics System | [63] |
| Used by | Pandas Dataframe Construction | [98] |
| Used by | Load Dictionary | [104] |
| Used by | Cut Correction Errors | [104] |
| Supports | data_manipulation | [24] |
| Supports | data_analysis | [24] |
| Supports | filtering | [24] |
| Supports | grouping | [24] |
| Supports | aggregation | [24] |
| Supports | team_dynamics_visualization | [24] |
| Supports | role_clarity_visualization | [24] |
| Used for | Data Storage | [35] |
| Used for | Data Manipulation | [35] |
| Used for | load and analyze the log data | [66] |
| Used for | Data Loading | [75] |
| Used for | CSV reading | [104] |
| Used for | DataFrame iteration | [104] |
| Used for | Data Manipulation and Feature Creation | [130] |
| Has Attribute | ease_of_use | [24] |
| Has Attribute | efficiency | [24] |
| Has Attribute | Practicality | [24] |
| Has Attribute | Effectiveness | [24] |
| Alias | pd | [44] |
| Alias | pd | [67] |
| Alias | pd | [84] |
| Alias | Pd | [123] |
| Imported in | Code Block | [2] |
| Imported in | Larger Dataset Example | [42] |
| Imported in | code-example | [58] |
| Used in | Step 1 | [20] |
| Used in | Example Implementation | [73] |
| Used in | Dataframe Conversion | [102] |
| Enables | quick_start | [24] |
| Enables | visualization | [24] |
| Enables | Quick Start | [24] |
| Has Benefit | Ease of Use | [24] |
| Has Benefit | Efficiency | [24] |
| Has Benefit | Flexibility | [24] |
| Supports Operation | Filtering | [24] |
| Supports Operation | Grouping | [24] |
| Supports Operation | Aggregation | [24] |
| Import Statement | import pandas as pd | [65] |
| Import Statement | import pandas as pd | [78] |
| Import Statement | Pd Alias | [117] |
| Is Imported As | Pd | [4] |
| Is Imported As | Pd | [69] |
| Provides Capability | powerful data manipulation capabilities | [22] |
| Provides Capability | data-manipulation | [22] |
| Integrates With | Matplotlib | [24] |
| Integrates With | Seaborn | [24] |
| Has Limitation | Scalability Limitation | [24] |
| Has Limitation | Dataset Size Limitation | [24] |
| Is Used for | Data Manipulation | [25] |
| Is Used for | Data Loading | [82] |
| Library Name | pandas | [51] |
| Library Name | pandas | [59] |
| Imported by | Code Example | [110] |
| Imported by | Spa Cy Code Section | [115] |
| Purpose | Data Manipulation | [129] |
| Purpose | Data Analysis | [129] |
| Imported As | Pd | [1] |
| Imported From | Python | [3] |
| Is Used in | document processing code | [7] |
| Recommended for | data management | [22] |
| Performance Characteristic | can handle large datasets efficiently | [22] |
| Replaces | lists-and-dictionaries | [22] |
| Handles | moderate_sized_datasets | [24] |
| Is Suited for | in_memory_processing | [24] |
| Has Feature | flexibility | [24] |
| Manipulates | Dataframes | [24] |
| Has Capability | Visualization | [24] |
| Uses Processing Model | In Memory Processing | [24] |
| Satisfies | Current Needs | [24] |
| Has Performance Characteristic | Efficiency for Moderate Datasets | [24] |
| Member of | Data Manipulation Libraries | [24] |
| Has Interface | Simple and Intuitive Interface | [24] |
| Is Well Suited for | In Memory Handling | [24] |
| Is a | Library | [25] |
| Is Used in | Example Implementation | [25] |
| Imported | true | [38] |
| Imported As | Pd Alias | [38] |
| Exports | Data Frame | [41] |
| Has Alias | Pd | [41] |
| Is Aliased As | Pd | [49] |
| Is Data Analysis Library | true | [51] |
| Is Library for | Data Processing | [57] |
| Is Imported in | Query Rewriting Code | [57] |
| Is Unused in | Rewrite Query Function | [57] |
| Installed Via | pip | [66] |
| Is Part of | Pandas Package | [68] |
| Is Memory Efficient Library for | Large Scale Data Processing | [71] |
| Designed for | Large Scale Data Processing | [71] |
| Provides Class | Pandas.data Frame | [86] |
| Full Name | Python Data Analysis Library | [88] |
| Used With | Numpy | [89] |
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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—
typebeam/a085a169-aa15-4448-83bc-ecb888dadb5c
ex:PythonLibrary
—
importedInbeam/a085a169-aa15-4448-83bc-ecb888dadb5c
code-example
—
libraryNamebeam/495977be-9a3c-4555-9004-9809144cb44a
pandas
—
typebeam/7cba2fe8-30b3-466d-923c-296e18c5333e
ex:PythonLibrary
—
typebeam/38d92a29-4823-4db1-821e-66cd13355b01
ex:PythonLibrary
—
labelbeam/38d92a29-4823-4db1-821e-66cd13355b01
pandas
—
typebeam/c0a643d3-be7b-4c8f-b794-2d7d40828ff1
ex:DataProcessingLibrary
—
usedBybeam/ed46774e-605a-4c5e-af74-736da6cd3a7a
ex:analytics-system
—
typebeam/030958ff-4542-4c75-87d6-fc94dc83547f
ex:DataAnalysisLibrary
—
typebeam/4a088a5e-6a12-45b6-b031-285c95cb8181
ex:Library
—
importStatementbeam/4a088a5e-6a12-45b6-b031-285c95cb8181
import pandas as pd
—
usedForbeam/2a60160e-1c6e-44ba-85fc-7743ec2eb648
load and analyze the log data
—
typebeam/2a60160e-1c6e-44ba-85fc-7743ec2eb648
ex:Library
—
installedViabeam/2a60160e-1c6e-44ba-85fc-7743ec2eb648
pip
—
aliasbeam/b5b9d4b4-f681-44eb-aa46-243df5db0e24
pd
—
typebeam/cbdde171-e744-47c2-9a16-4733fcbf7b3b
ex:Library
—
labelbeam/cbdde171-e744-47c2-9a16-4733fcbf7b3b
pandas
—
isPartOfbeam/cbdde171-e744-47c2-9a16-4733fcbf7b3b
ex:pandas-package
—
isImportedAsbeam/8fa5829f-15f2-482b-85e0-f9cec79dbd29
ex:pd
—
providesbeam/8fa5829f-15f2-482b-85e0-f9cec79dbd29
ex:data_handling_functions
—
typebeam/73e89087-b607-4f8e-8f21-44e5e8aeccf8
ex:PythonLibrary
—
isMemoryEfficientLibraryForbeam/cfc419c2-9958-4d26-bdd9-d7ecab6a366a
ex:large-scale-data-processing
—
typebeam/cfc419c2-9958-4d26-bdd9-d7ecab6a366a
ex:Library
—
designedForbeam/cfc419c2-9958-4d26-bdd9-d7ecab6a366a
ex:large-scale-data-processing
—
typebeam/bf1ce843-2325-435a-a001-56a2f7c1b679
ex:DataProcessingLibrary
—
labelbeam/bf1ce843-2325-435a-a001-56a2f7c1b679
Pandas
—
typebeam/af41abe5-82b4-4b21-a9cb-afafa726d066
ex:Python-Library
—
fullNamebeam/af41abe5-82b4-4b21-a9cb-afafa726d066
pandas
—
usedInbeam/af41abe5-82b4-4b21-a9cb-afafa726d066
ex:example-implementation
—
typebeam/cfe02f37-07f9-4c90-a560-7a82f99b5d25
ex:DataProcessingLibrary
—
typebeam/cd20f999-1387-4a3e-9486-0da4fc043940
ex:DataProcessingLibrary
—
labelbeam/cd20f999-1387-4a3e-9486-0da4fc043940
Pandas library
—
usedForbeam/cd20f999-1387-4a3e-9486-0da4fc043940
ex:data-loading
—
typebeam/0daa7c15-b2c7-44ef-a5e9-390bf6864c0a
ex:DataAnalysisLibrary
—
typebeam/684b0c2c-1042-46ec-af7a-469a189d44aa
ex:PythonLibrary
—
typebeam/0e70d7ad-2e63-4603-8495-9b5dca2aa774
ex:PythonLibrary
—
importStatementbeam/0e70d7ad-2e63-4603-8495-9b5dca2aa774
import pandas as pd
—
typebeam/94855c3b-a31f-4886-9071-82d1097226a5
ex:PythonLibrary
—
labelbeam/94855c3b-a31f-4886-9071-82d1097226a5
pandas
—
typebeam/75f776d1-ab4d-401c-9c1b-0e4947b7c4ec
ex:python-library
—
typebeam/7d9f9a7f-e5af-457f-9c5d-e4afaa92c958
ex:DataLibrary
—
labelbeam/7d9f9a7f-e5af-457f-9c5d-e4afaa92c958
pandas
—
typebeam/46068d53-96d3-4709-a18e-0c4041019936
ex:PythonLibrary
—
isUsedForbeam/46068d53-96d3-4709-a18e-0c4041019936
ex:data-loading
—
typebeam/6754c089-a9ba-4d68-a4bf-7f175c66d000
ex:Library
—
providesbeam/6754c089-a9ba-4d68-a4bf-7f175c66d000
vectorized-operations
—
typebeam/8a8ba0bd-963d-48a2-bf75-5996f4b183b0
ex:Library
—
aliasbeam/8a8ba0bd-963d-48a2-bf75-5996f4b183b0
pd
—
typebeam/8cf0486b-7a52-401d-a035-133c1cdeb419
ex:Library
References (130)
130 references
ctx:claims/beam/fcff22b3-b7dd-466c-b061-0a08176e2dd2ctx:claims/beam/924a6db5-b2b0-42d4-9e5c-bd5a7a159a3actx:claims/beam/e3b7ad28-c610-499f-b527-47a2d7f6872fctx:claims/beam/c017aa14-d297-41b4-88ff-66825370d070ctx:claims/beam/69d53d99-9e74-491d-a1aa-ba8c5b9b0e4cctx:claims/beam/831feb09-b7cb-4304-a2c2-8c9ed2cd23a0ctx:claims/beam/d731f3b8-94e3-4321-ad3d-a9d9881d4504ctx:claims/beam/b6878ca0-9a69-4de7-9700-1830da12fcc1ctx:claims/beam/15c12db4-c4d3-4659-8ce6-1da2d5b7b4fbctx:claims/beam/405aac9d-5ddc-42e0-9010-231fd6ae90bbctx:claims/beam/02853550-4955-4b56-87b4-5d2837b10de2ctx:claims/beam/a3a5d835-1848-42bd-98e5-0660dbb98a7fctx:claims/beam/660cd3af-647f-43b9-8a94-21d80b2e4b1fctx:claims/beam/0e56e8f7-6bb5-47d4-bd16-a0b896835d01ctx:claims/beam/4c0b780e-77bc-43f6-89c0-9fc02ba7ab53ctx:claims/beam/da04535a-2bc8-4334-9bca-f9b43cd01117ctx:claims/beam/4e2e0c84-748e-486e-aa7b-8ca3d8be204actx:claims/beam/9bbaf7ec-d1f0-4843-9bbf-e2b297fec107ctx:claims/beam/92f9d4b6-659a-439c-ae2a-0330d3d8ab30ctx:claims/beam/be6814ba-aa07-4fc4-b58d-d8d7b642906fctx:claims/beam/24137226-1b44-4eda-ac8a-c89b480883e0ctx:claims/beam/abc06278-4d34-4aaa-a9f7-c35d156b37d6ctx:claims/beam/50d13900-1748-4e86-8895-a464c13b54e4ctx:claims/beam/dd064674-37b1-4f57-ad58-28af115a4278ctx:claims/beam/e39061c2-5736-4349-8e36-a6ca658aad94ctx:claims/beam/07784e66-59a7-437c-8fd9-abcd5135d305ctx:claims/beam/958de32b-049e-43bb-bff6-e31d2ca4988bctx:claims/beam/d1ef4531-121c-41be-8f23-7ac884bf2416ctx:claims/beam/b7b11d30-7113-4b2c-bd0d-7ff9648aaa5actx:claims/beam/d2a4c12e-7db6-4472-9ac5-a358de5c91cactx:claims/beam/845ef0dd-c655-43a6-9b85-4b9a8fb2942actx:claims/beam/b319ca50-b146-4eaa-8e05-83887534100ectx:claims/beam/320d3af8-439e-425a-92c5-57b8d18095d4ctx:claims/beam/109b3bb3-4794-4653-ae3a-fefa0c5daeaactx:claims/beam/84602440-6d9a-41c8-a1e1-b5a3786c575bctx:claims/beam/bf039b7a-fbcb-40c4-a6b0-5082c155a07actx:claims/beam/3ec0a0cc-d43f-4ce3-97d3-35cfa9087750ctx:claims/beam/c532c691-90fc-4914-ba4e-9bcfc218979ectx:claims/beam/4b947c23-8d2a-4883-9f74-9a7056c248e0ctx:claims/beam/0123a18b-fee4-4314-a023-bd1bd05bc5e9ctx:claims/beam/d9c72668-b906-482c-b262-cc3a3a3c706dctx:claims/beam/0847c3fb-2167-45e0-baa8-dc4abfbfbe22ctx:claims/beam/e06228ca-08d1-403f-af94-242c605c308ectx:claims/beam/3b6a0db6-5dd7-4045-ac38-4822bbb3fa4cctx:claims/beam/f971d9d3-7050-4d32-844b-58db9f4972d7ctx:claims/beam/b00c301c-c592-4cd6-ad07-b1de426fb5c4ctx:claims/beam/336f50f5-6e67-42bf-b2f1-406aa219718ectx:claims/beam/8875379a-0096-4edc-9bd8-85818abb8b5actx:claims/beam/f2754305-6955-44bf-83aa-e6a05c8d10a7ctx:claims/beam/880a7477-37b5-426d-bb73-9791216942eectx:claims/beam/47820af8-74e9-40cc-b155-2fbe76a9689ectx:claims/beam/19298204-c17d-4ff3-9158-f6e8c9bd1fa6ctx:claims/beam/e917b099-e934-4dd8-801b-fa9559f4e5b0ctx:claims/beam/fac7b295-c13f-4a70-a0ab-5144053a3215ctx:claims/beam/5234c864-c1e1-4f57-ae6b-a148088ab40bctx:claims/beam/acff0dc1-a514-4332-be73-3d1241e3f63fctx:claims/beam/12312cab-c28d-4376-a351-2e8169a3598fctx:claims/beam/a085a169-aa15-4448-83bc-ecb888dadb5cctx:claims/beam/495977be-9a3c-4555-9004-9809144cb44actx:claims/beam/7cba2fe8-30b3-466d-923c-296e18c5333ectx:claims/beam/38d92a29-4823-4db1-821e-66cd13355b01ctx:claims/beam/c0a643d3-be7b-4c8f-b794-2d7d40828ff1ctx:claims/beam/ed46774e-605a-4c5e-af74-736da6cd3a7actx:claims/beam/030958ff-4542-4c75-87d6-fc94dc83547fctx:claims/beam/4a088a5e-6a12-45b6-b031-285c95cb8181ctx:claims/beam/2a60160e-1c6e-44ba-85fc-7743ec2eb648ctx:claims/beam/b5b9d4b4-f681-44eb-aa46-243df5db0e24ctx:claims/beam/cbdde171-e744-47c2-9a16-4733fcbf7b3bctx:claims/beam/8fa5829f-15f2-482b-85e0-f9cec79dbd29ctx:claims/beam/73e89087-b607-4f8e-8f21-44e5e8aeccf8ctx:claims/beam/cfc419c2-9958-4d26-bdd9-d7ecab6a366actx:claims/beam/bf1ce843-2325-435a-a001-56a2f7c1b679ctx:claims/beam/af41abe5-82b4-4b21-a9cb-afafa726d066ctx:claims/beam/cfe02f37-07f9-4c90-a560-7a82f99b5d25ctx:claims/beam/cd20f999-1387-4a3e-9486-0da4fc043940ctx:claims/beam/0daa7c15-b2c7-44ef-a5e9-390bf6864c0actx:claims/beam/684b0c2c-1042-46ec-af7a-469a189d44aactx:claims/beam/0e70d7ad-2e63-4603-8495-9b5dca2aa774ctx:claims/beam/94855c3b-a31f-4886-9071-82d1097226a5ctx:claims/beam/75f776d1-ab4d-401c-9c1b-0e4947b7c4ecctx:claims/beam/7d9f9a7f-e5af-457f-9c5d-e4afaa92c958ctx:claims/beam/46068d53-96d3-4709-a18e-0c4041019936ctx:claims/beam/6754c089-a9ba-4d68-a4bf-7f175c66d000ctx:claims/beam/8a8ba0bd-963d-48a2-bf75-5996f4b183b0ctx:claims/beam/8cf0486b-7a52-401d-a035-133c1cdeb419ctx:claims/beam/d20f04e6-ac24-40a3-ba7d-a928d5401600ctx:claims/beam/ca82f6df-035e-4bb4-92d9-e1c0a1e83da2ctx:claims/beam/99534192-4073-4a92-bd14-2edff1bacfa4ctx:claims/beam/c4e701bb-4e00-4f70-9342-4c8b5db03a6fctx:claims/beam/42448813-8021-446b-a5c3-56e15a8d68d9ctx:claims/beam/527fefe1-46d5-4d54-9aa0-7be33730650cctx:claims/beam/8fa6e3db-4d56-496e-901c-9b168ca60d74ctx:claims/beam/95b9663d-3d72-47e6-8cf0-569608927cacctx:claims/beam/1c4871a0-44bd-488f-a027-7e91230cbb93ctx:claims/beam/3ebb20de-f707-4c6f-96f0-960bd77ef508ctx:claims/beam/53b6e60a-57f4-4a01-b2a5-ba77515229e4ctx:clai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See also
- Library
- Pd
- Code Block
- Programming Library
- Python Library
- Python
- Library
- Step 1
- Data Management Tool
- Data Manipulation Library
- Dataframes
- Matplotlib
- Seaborn
- Ease of Use
- Efficiency
- Flexibility
- Quick Start
- Visualization
- Filtering
- Grouping
- Aggregation
- Scalability Limitation
- In Memory Processing
- Practicality
- Current Needs
- Effectiveness
- Ease of Complex Operations
- Efficiency for Moderate Datasets
- Data Manipulation Libraries
- Dataset Size Limitation
- Simple and Intuitive Interface
- In Memory Handling
- Data Manipulation
- Example Implementation
- Data Processing Library
- Software Library
- Software Package
- Code
- Flask Application
- Data Storage
- Pd Alias
- Data Frame
- Larger Dataset Example
- Data Frame Class
- Python Script
- Dataframe Structure
- Vectorize Data Function
- Software Library
- Analyze Corpus
- Data Processing
- Query Rewriting Code
- Rewrite Query Function
- Analytics System
- Data Analysis Library
- Pandas Package
- Data Handling Functions
- Large Scale Data Processing
- Python Library
- Data Loading
- Python Library
- Data Library
- Pandas.data Frame
- Numpy
- Read Csv
- Pandas Dataframe Construction
- Dataframe Conversion
- Load Dictionary
- Cut Correction Errors
- Code Example
- Spa Cy Code Section
- Read Csv
- Data Processing Library
- Vectorized Operations
- Data Manipulation and Feature Creation
- Data Analysis
Keep researching
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