probablepeople | python library for parsing unstructured western names | Parser library
kandi X-RAY | probablepeople Summary
kandi X-RAY | probablepeople Summary
probablepeople is a Python library typically used in Utilities, Parser applications. probablepeople has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install probablepeople' or download it from GitHub, PyPI.
:family: a python library for parsing unstructured western names into name components.
:family: a python library for parsing unstructured western names into name components.
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Quality
Security
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Support
probablepeople has a low active ecosystem.
It has 553 star(s) with 68 fork(s). There are 29 watchers for this library.
There were 1 major release(s) in the last 12 months.
There are 57 open issues and 31 have been closed. On average issues are closed in 42 days. There are 2 open pull requests and 0 closed requests.
It has a neutral sentiment in the developer community.
The latest version of probablepeople is 0.5.5
Quality
probablepeople has no bugs reported.
Security
probablepeople has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
License
probablepeople is licensed under the MIT License. This license is Permissive.
Permissive licenses have the least restrictions, and you can use them in most projects.
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probablepeople releases are not available. You will need to build from source code and install.
Deployable package is available in PyPI.
Build file is available. You can build the component from source.
Installation instructions are not available. Examples and code snippets are available.
Top functions reviewed by kandi - BETA
kandi has reviewed probablepeople and discovered the below as its top functions. This is intended to give you an instant insight into probablepeople implemented functionality, and help decide if they suit your requirements.
- Adds the predictedPreds to the training data
- Train a model
- Parse a string into a list of tokens
- Return a dictionary of token features
- Convert a list of tokens to features
- Tokenize a string
- Return the number of digits
- Return the vowel ratio
- Split a word into n - grams
- Writes filtered names to a csv file
- Load and tagger for a given type
- Create a set of tagged data
Get all kandi verified functions for this library.
probablepeople Key Features
No Key Features are available at this moment for probablepeople.
probablepeople Examples and Code Snippets
No Code Snippets are available at this moment for probablepeople.
Community Discussions
Trending Discussions on probablepeople
QUESTION
Pandas: Run external library function to create new column efficiently
Asked 2019-Jul-06 at 04:02
def conv_name(x):
try:
#library to convert strings to name dict
return pp.tag(str(x))[0]
except:
return np.nan
dfn = df.name.to_frame()
dfn['conv'] = dfn.name.apply(lambda x: conv_name(x))
dfn['given_name'] = dfn.conv.apply(pd.Series).GivenName
dfn['sunname'] = dfn.conv.apply(pd.Series).Surname
...ANSWER
Answered 2019-Jul-06 at 04:02First, make conv_name
more efficient by simply returning two values:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install probablepeople
You can install using 'pip install probablepeople' or download it from GitHub, PyPI.
You can use probablepeople like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
You can use probablepeople like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
Support
For any new features, suggestions and bugs create an issue on GitHub.
If you have any questions check and ask questions on community page Stack Overflow .
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