Saravanan666/pypi-org huggingface dataset:
Package: text-actions-girvel
This is a simple Python package I used to test packaging mechanism in Python. Also it provides a possibility to manage your UI text commands in bash-like syntax like:open last.orgordelete 1.fromtext_actionsimportui_actionactions=[]@ui_action(actions,'d','delete')defdelete_action(state,index='-1'):index=int(index)ifindex<0:index+=len(state.activities)state.activities=state.activities[:index]+state.activities[index+1:]This code will create a library of commands inside the actions variable, that you can use as argument inapply_action. First argument is always the state of application, other arguments are receivied from user's command. You probably should not worry about cast exceptions, I usually catch them in the UI code.
Package: textacy
textacy: NLP, before and after spaCytextacyis a Python library for performing a variety of natural language processing (NLP) tasks, built on the high-performance spaCy library. With the fundamentals --- tokenization, part-of-speech tagging, dependency parsing, etc. --- delegated to another library,textacyfocuses primarily on the tasks that come before and follow after.featuresAccess and extend spaCy's core functionality for working with one or many documents through convenient methods and custom extensionsLoad prepared datasets with both text content and metadata, from Congressional speeches to historical literature to Reddit commentsClean, normalize, and explore raw text before processing it with spaCyExtract structured information from processed documents, including n-grams, entities, acronyms, keyterms, and SVO triplesCompare strings and sequences using a variety of similarity metricsTokenize and vectorize documents then train, interpret, and visualize topic modelsCompute text readability and lexical diversity statistics, including Flesch-Kincaid grade level, multilingual Flesch Reading Ease, and Type-Token Ratio...and much more!linksDownload:https://pypi.org/project/textacyDocumentation:https://textacy.readthedocs.ioSource code:https://github.com/chartbeat-labs/textacyBug Tracker:https://github.com/chartbeat-labs/textacy/issuesmaintainerHowdy, y'all. 👋Burton DeWilde (burtdewilde@gmail.com)
Package: text-adventure-framework
text-adventure-frameworkWhy create this?My son wanted to program a text adventure game using python.
I didn't find any frameworks in pure python that would make that easy.
So decided to roll my own. It's rudimentary, but functional.Install:pip install text-adventure-frameworkOrpip install git+https://github.com/Caesurus/text_adventure.gitUsage:fromtext_adventureimportTextAdventureEnginee=TextAdventureEngine()e.add_room(name='starting room',description='you wake up in a starting room')e.add_room(name='room 1',description='room after leaving start')e.add_room(name='finish',description='outside world',is_end=True)e.connect_rooms(source='starting room',destination='room 1',direction='left',description='you see a door to your left')e.connect_rooms(source='room 1',destination='finish',direction='forward',description='the exit is right in front of you')e.start()
Package: text-adventures
Text AdventuresA Python library for easily making text adventure games.Inspired bytextadventure.See on PyPISee on GitHubSee on ReplitGetting StartedInstall the library usingpipor your package manager of choice:pip install text-adventuresAdd this code to import the package:importtext_adventuresSceneTo create aScene, add this code:myScene=text_adventures.Scene()Scenes take 2 arguments:title: astrthat gets printed at the top of the console.actions: anIterableofActions that will be ran in order.ActionsActions can be set to run in aScene. There are currently 4 options:TextGoToOptionsInteractionBut you can expect more soon, or if you don't want to wait, you can make your own! There is averysimpleActionclass that you can import to create your ownActions.Change LogAlphav0.0.5 (6/11/2023)Actually fixed links to the Replv0.0.4 (6/11/2023)Attempted to fix links to the Replv0.0.3 (6/11/2023)Updated the CHANGELOGv0.0.2 (6/11/2023)Updated README to include how to install the packageAdded more links to the package on PyPIv0.0.1 (6/10/2023)First release
Package: text-align
TextAlignLast version: 1.0.4Short description:This module allows you to align text relative to the center, right, left and both sidesIt contains:right(text, width)- this function align the "text" to right side with given "width"left(text, width)- this function align the "text" to left side with given "width"center(text, width)- this function align the "text" to center with given "width"justify(text, width)- this function align the "text" to both sides with given "width"How to use:fromtext_alignimportjustifymy_long_text="Lorem ipsum dolor sit amet, consectetur adipiscing elit. Vestibulum sagittis dolor mauris, at elementum ligula tempor eget. In quis rhoncus nunc, at aliquet orci. Fusce at dolor sit amet felis suscipit tristique. Nam a imperdiet tellus. Nulla eu vestibulum urna. Vivamus tincidunt suscipit enim, nec ultrices nisi volutpat ac. Maecenas sit amet lacinia arcu, non dictum justo. Donec sed quam vel risus faucibus euismod. Suspendisse rhoncus rhoncus felis at fermentum. Donec lorem magna, ultricies a nunc sit amet, blandit fringilla nunc. In vestibulum velit ac felis rhoncus pellentesque. Mauris at tellus enim. Aliquam eleifend tempus dapibus. Pellentesque commodo, nisi sit amet hendrerit fringilla, ante odio porta lacus, ut elementum justo nulla et dolor."aligned_text=justify(my_long_text,55)print(aligned_text)'''Output:Lorem  ipsum  dolor  sit  amet,  consectetur adipiscingelit.  Vestibulum  sagittis  dolor mauris, at elementumligula  tempor  eget.  In quis rhoncus nunc, at aliquetorci. Fusce at dolor sit amet felis suscipit tristique.Nam  a  imperdiet  tellus.  Nulla  eu  vestibulum urna.Vivamus  tincidunt  suscipit  enim,  nec  ultrices nisivolutpat ac. Maecenas sit amet lacinia arcu, non dictumjusto.  Donec  sed  quam  vel  risus  faucibus euismod.Suspendisse  rhoncus  rhoncus felis at fermentum. Doneclorem   magna,  ultricies  a  nunc  sit  amet,  blanditfringilla  nunc.  In  vestibulum velit ac felis rhoncuspellentesque.  Mauris  at tellus enim. Aliquam eleifendtempus  dapibus.  Pellentesque  commodo,  nisi sit amethendrerit   fringilla,   ante   odio  porta  lacus,  utelementum justo nulla et dolor.'''Installation:pipinstalltext-align==1.0.4
Package: textalyzer
# TextalyzerTextalyzer is a lightweight python module or library to help ease analyzing of text or words in a particular sentence and manipulating it.Textalyzer analyze repeating words, contracted words or contractions and formatting them properly.# Class Functions Involved in this module[-] ContractionReplacers <br>[-] RepeatingReplacers# Usage-Place the file in a folder where you have either read or write and read permisson. Eg. /opinionmining/lib/Textalyzer.py<br/>-Import the module into your application.<br/><hr/>from opinionmining.lib.Textalyzer import "Function To Use" <br>sample = "I can't wait for the football match" <br>object = ContractionReplacers() <br>data = object.text_replacers(sample)<br># Sample## Repeating words- Repeating words like helloooooooooooo - textalyzer help format this word by removing the o's to its normal word "hello"<br/>- Contractions like I'm - Textalyzer help format this word by converting it to it's normal word "I am"# Dependencies- wordnet- Install wordnet by installing nltk# PyPI- Textalyzer is available at python package index.## Installation Guide- Download textalyzer using pip<br>- pip install textalyzer<br>- Import textalyzer into your project by using ```from textalyzer import Textalyzer``` depending on the   function you want to use.<br>- The use ``` replacer = Textalyzer.ContractionReplacers() and repeat = Textalyzer.RepeatingReplacers() ``` <br>-    ## Or- Simple do ```import textalyzer``` <br>- Then Use ``` data = textalyzer.Textalyzer ``` <br>-   ``` replacer  = data.ContractionReplacers() ``` <br>## Alternatively- [x] You can download the tarball file from `pypi.python.org/pypi/textalyzer`- [x] Extract the tarball file- [x] Navigate into the extracted folder and install using `python setup.py install`**To Check Whether it's installed**- type  `pip freeze` to display the list of installed packages# Why This- Textalyzer is mostly used by python programmers who are more involved with Natural Language Processing , Machine Learning, Text Extraction and others.
Package: textana4sc
textana4sc中文文本分析库，可对文本进行词频统计、情绪分析、话题分析等github地址https://github.com/hidadeng/cntextpypi地址https://pypi.org/project/cntext/功能模块含word_cloud文本统计,可读性等get_keyword获取文本关键词get_entity获取文本实体get_emotion获取文本情绪get_cosemantic获取词语共现语义图get_topic获取话题visualization可视化，如词云图安装pip install textanalyze4sc一、读取数据fromtexttoolimportanalyzedf_data=analyze.load_data(thepathofyourdata)二、提取关键词df_data_key=analyze.get_keyword(df_data)三、提取实体df_data_entity=analyze.get_entity(df_data)四、情感分析这里提供两种粒度的情感分析。1，这里分为三种“积极”，“负面”，“中立”analyze.get_emotion('我很开心，你是这么认为的吗')结果'pos'2，这里进行更为细粒度的区分，分为“好”，“乐”，“哀”，“怒”，“惧”，“恶”，“惊” 七类情绪。analyze.get_emotion_sp('我很开心，你是这么认为的吗')结果{'words': 10,
 'sentences': 1,
 '好': 0,
 '乐': 1,
 '哀': 0,
 '怒': 0,
 '惧': 0,
 '恶': 0,
 '惊': 0}五、词语共现图本文使用筛选出现频率出现前50的实体，并作出共现图analyze.get_cosemantic(df_data,top50_all)六、抽取三元组text = "他叫汤姆去拿外衣。"
get_graph(text)结果[['他', '叫', '汤姆'], ['汤姆', '拿', '外衣']]七、生成摘要本文应用抽取式摘要技术，可以设置sent_num参数控制输出摘要局数。text='2013年，信号与信息处理专业硕士毕业的张超凡进入国铁南宁局南宁电务段工作。那一年，广西同时开通多条高铁线路，高铁营业里程从0公里跃升至1000多公里。10年间，伴随着中国铁路高速发展，张超凡收获颇多。'
get_summary(text,sent_num=1)结果'2013年，信号与信息处理专业硕士毕业的张超凡进入国铁南宁局南宁电务段工作。'八、可视化本文提供各类可视化工具，柱状图，趋势图，词云图等。
Package: text-analysis-helpers
IntroductionText-analysis-helpers is a collection of classes and functions for text analysis.InstallationA Python 3 interpreter is required. It is recommended to install the package in
a virtual environment in order to avoid corrupting the system's Python interpreter
packages.Install the package using pip.pipinstalltext-analysis-helpers

python-mnltk.downloader"punkt"python-mnltk.downloader"averaged_perceptron_tagger"python-mnltk.downloader"maxent_ne_chunker"python-mnltk.downloader"words"python-mnltk.downloader"stopwords"UsageYou can use the HtmlAnalyser object to analyse the contents of a url.fromtext_analysis_helpers.htmlimportHtmlAnalyseranalyser=HtmlAnalyser()analysis_result=analyser.analyse_url("https://www.bbc.com/sport/formula1/64983451")analysis_result.save("analysis_result.json")You can see the scripts in theexamplesfolder for some usage examples.There is also an cli utility that can be used to analyse a url. For example to
analyse a url and save the analysis result to a json encoded file execute the
following command in the terminal.text-analysis-helpers-clianalyse-url--outputanalysis_result.jsonhttps://www.bbc.com/sport/formula1/64983451
Package: textanalytics
text_analyticsBasic computational linguistics and natural language processing in Python.pip install textanalytics

pip install git+https://github.com/jonathandunn/text_analytics.gitThis package provides code to support introductory courses in computational linguistics or natural language processing. These courses are available free on edX:Introduction to Text Analytics and Natural Language Processing with PythonVisualizing Text Analytics and Natural Language Processing with PythonUsagefrom text_analytics import TextAnalytics

ai = TextAnalytics()Getting featuresstyle, vocab_size = ai.get_features(df, features="style")style= Function word n-gramssentiment= Positive and negative wordscontent= Top content words with TD-IDF weighting, PMI for finding phrases, no stop wordsconstructions= A bag-of-constructions syntactic representationUsing a classifierai.shallow_classification(df, label, features="style", cv=False, classifier='svm')

ai.mlp(df, label, features="style", validation_set=False, test_size=0.10)Unsupervised methodsTopic Modelsai.train_lda(df, n_topics, min_count)
    
topic_df = ai.use_lda(df, labels="Author")Vector Semanticsai.train_word2vec(file, min_count, workers)Document and Word Clusterscluster_df = ai.cluster(x, y=None, k)*Nearest document searchesy_sample, y_closest = ai.linguistic_distance(x, y, sample=1, n=3)Corpus DescriptionsPMI-based Phrasesai.fit_phrases(df)Delta P-based Phrasesassociation_df = ai.get_association(df, min_count = 1, save_phraser = True)Basic word frequenciesvocab = ai._get_vocab_list(df, min_count, return_freq = True)Corpus Comparisonssimilarity = ai.get_corpus_similarity(df1, df2)
Package: text-analytics
Note: This package is no longer maintainedWe recommend using the official package for Azure:https://aka.ms/azsdk/python/textanalyticsAzure Cognitive Service Text Analytics clientThe Text Analytics API is a suite of text analytics web services built with best-in-class Microsoft machine learning algorithms.The API can be used to analyze unstructured text for tasks such as sentiment analysis, key phrase extraction and language detection.No training data is needed to use this API; just bring your text data.This API uses advanced natural language processing techniques to deliver best in class predictions.Further documentation can be found inhttps://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/overviewThis package is a client generated to make usage of the text analytics API easier from any Python application.
It is python 2.7 and 3.x compatible.InstallationYou can install this package from pypi using the following command:pip install text-analyticsAlternatively you can get the bleeding edge version from github by performing the following steps:git clone https://github.com/wmeints/text-analyticspython setup.py installDevelopmentFor information about development, please refer to the Wiki.
Package: textanalyze4sc
textana4sc中文文本分析库，可对文本进行词频统计、情绪分析、话题分析等github地址https://github.com/hidadeng/cntextpypi地址https://pypi.org/project/cntext/功能模块含word_cloud文本统计,可读性等get_keyword获取文本关键词get_entity获取文本实体get_emotion获取文本情绪get_cosemantic获取词语共现语义图get_topic获取话题visualization可视化，如词云图安装pip install textanalyze4sc一、读取数据fromtexttoolimportanalyzedf_data=analyze.load_data(thepathofyourdata)二、提取关键词df_data_key=analyze.get_keyword(df_data)三、提取实体df_data_entity=analyze.get_entity(df_data)四、情感分析这里提供两种粒度的情感分析。1，这里分为三种“积极”，“负面”，“中立”analyze.get_emotion('我很开心，你是这么认为的吗')结果'pos'2，这里进行更为细粒度的区分，分为“好”，“乐”，“哀”，“怒”，“惧”，“恶”，“惊” 七类情绪。analyze.get_emotion_sp('我很开心，你是这么认为的吗')结果{'words': 10,
 'sentences': 1,
 '好': 0,
 '乐': 1,
 '哀': 0,
 '怒': 0,
 '惧': 0,
 '恶': 0,
 '惊': 0}五、词语共现图本文使用筛选出现频率出现前50的实体，并作出共现图analyze.get_cosemantic(df_data,top50_all)六、抽取三元组text = "他叫汤姆去拿外衣。"
get_graph(text)结果[['他', '叫', '汤姆'], ['汤姆', '拿', '外衣']]七、生成摘要本文应用抽取式摘要技术，可以设置sent_num参数控制输出摘要局数。text='2013年，信号与信息处理专业硕士毕业的张超凡进入国铁南宁局南宁电务段工作。那一年，广西同时开通多条高铁线路，高铁营业里程从0公里跃升至1000多公里。10年间，伴随着中国铁路高速发展，张超凡收获颇多。'
get_summary(text,sent_num=1)结果'2013年，信号与信息处理专业硕士毕业的张超凡进入国铁南宁局南宁电务段工作。'八、可视化本文提供各类可视化工具，柱状图，趋势图，词云图等。
Package: text-animator
text-animatorA simple text animator.Ths is a subclass of theAnimationabstract class and uses theAnimatorclass to display the animated text to the terminal; both classes are defined in theascii-animatorpackage.Installationpipinstalltext_animatorUsageexample1Animate some text and display characters from left to right (default).Codefromtext_animatorimportTextAnimationtext="""This is the textthat we want to animatelet's see how wellthis works ..."""TextAnimation(text)()example2Animate some text and display characters from right to left.Codefromtext_animatorimportTextAnimation,Effecttext="""This is the textthat we want to animatelet's see how wellthis works ..."""TextAnimation(text,effect=Effect.RIGHT_TO_LEFT)()example3Animate some text and display characters at random.Codefromtext_animatorimportTextAnimation,Effecttext="""This is the textthat we want to animatelet's see how wellthis works ..."""TextAnimation(text,effect=Effect.RANDOM)()example4Animate some text and display characters from left to right then surround text with a border. A border can be customized with top|bottom|left|right margins as well as top|bottom|left|right padding, default for all margins and padding is 1.  Margins define the space outside the border, and padding define the space between the border and text.Codefromtext_animatorimportTextAnimation,Effect,Bordertext="""This is the textthat we want to animatelet's see how wellthis works ..."""TextAnimation(text,border=Border(lm=0,tm=0,bm=0,tp=0,bp=0))()DevelopmentClone the repository and ensure the latest version of Docker is installed on your development server.Build the Docker image:dockerimagebuild\-t\text-animator:latest.Run the Docker container:dockercontainerrun\--rm\-it\-v$PWD:/code\text-animator:latest\bashExecute the build:pyb-X
Package: texta-parsers
texta-parsersA Python package for file parsing.The main class in the package isDocParser. The package also supports sophisticated parsing of emails which is implemented in classEmailParser. If you only need to parse emails then you can specify it with parameterparse_only_extensions. It is possible to useEmailParserindependently as well but then attachments will not be parsed.RequirementsMost of the file types are parsed withtika. Other tools that are required:ToolFile Typepst-utils.pstdigidoc-tool.ddoc .bdoc .asics .asicerar-nonfree.rarlxmlXML HTMLInstallation of required packages on Ubuntu/Debian:sudo apt-get install pst-utils rar python3-lxml cmake build-essential -ysudo sh install-digidoc.shRequires our custom version of Apache TIKA with relevant Tesseract language packs installed:sudo docker run -p 9998:9998 docker.texta.ee/texta/texta-parsers-python/tikaserver:latestInstallationBase install (without MLP & Face Analyzer):pip install texta-parsersInstall with MLP:pip install texta-parsers[mlp]Install with whole bundle:pip install texta-parsers[mlp]Testingpython -m  pytest -rx -v testsDescriptionDocParserA file parser. Input can either be in bytes or a path to the file as a string. Seeuser guidemore information. DocParser also includes EmailParser.EmailParserFor parsing email messages and mailboxes. Supported file formats are Outlook Data File (.pst), mbox (.mbox) and EML (.eml). Can be used separately from DocParser but then attachments are not parsed.
User guide can be foundhereand documentationhere.
Package: textapp
textappA small facility for making a terminal applicationRequirementsUse the package managerpipto install textapppip install textappFunctionalityRuns in standard terminals.Uses single key entry for menu choices.Can be used to either:Read menus from a JSON file orBe passed JSON as parameter.Will recognize default choices that only require "Enter."Menu choices may be de-activated according to circumstances.Utilizes termcolor package (if installed) in order to color the menu.Menu theme is set depending on environment variables.
Package: textarbeit
Titel: textarbeitVersion: 0.8.0Zweck: Routinen, um reine Texte (*.txt) zu bearbeitenPythonversion: Python3Autor: Batt Bucheremail: batt.bucher.basel@bluewin.ch---------------------------------------------------------Das Modul 'textarbeit' bietet Routinen, um reine Texte (*.txt) zu bearbeiten.ACHTUNG: Es werden nur reine Textdateien verarbeitet(Ich arbeite mit Linux!)Folgende Funktionen stellt es zur Verfügung:help(textarbeit) - gibt eine eine kurze Hilfe zu jeder Funktion ausText2Liste(einDatei, ausDatei) - erstellt eine Liste aller WoerterText2ListeRein(einDatei, ausDatei): - erstellt eine Liste ohne SonderzeichenListeOhneLeerzeilen(einDatei, ausDatei): entfernt alle LeerzeilenListeRein(einDatei, ausDatei) - entfernt SonderzeichenListeSortiert(einDatei, ausDatei): - gibt eine sortierte Liste ausListeOhneDuplikate(einDatei, ausDatei) - entfernt alle DuplikateListeAnzahlWort(einDatei, ausDatei): - gibt eine Liste geordnet nach Anzahl ausListeWortAnzahl(einDatei, ausDatei): gibt eine alphabetische Liste mit Anzahl ausListeMinus100(einDatei, ausDatei): löscht die 100 häufigsten deutschen Wörter (aus der Wikipedia)ZahlPunktLoeschen(einDatei, ausDatei) - 123. Hallo wird HalloMinus3Lz(einDatei, ausDatei) - macht aus 3 Leerzeichen 1Minus2Lz(einDatei, ausDatei) - macht aus 2 Leerzeichen 1MinusLzLinks(einDatei, ausDatei) - entfernt Leerzeichen linksMinusLzRechts(einDatei, ausDatei) - entfernt Leerzeichen rechtsAnwendungsbeispiel:import textarbeittextarbeit.Minus3Lz("/home/beat/texte.txt","/home/beat/texte2.txt")textarbeit.Text2Liste("/home/beat/texte.txt","/home/beat/texte2.lst")
Package: textarium
textarium: easy-to-use Python package for text analysis.What is it?textariumis a Python package that provides flexible text analysis functions designed to
make text analysis intuitive and easy. It aims to be the high-level tool for
preparing text-data and complex analysis or NLP modeling.InstallationBinary installer for the latest released version are available at thePython
Package Index (PyPI).# Type this in your command-linepipinstalltextariumGetting startedfromtextariumimportTextimportnltknltk.download('wordnet')s="This a text example. You can preprocess and analyze it with this package."text=Text(s,lang='en')text.prepare()print(text.prepared_text)fromtextariumimportCorpusimportnltknltk.download('wordnet')txts=["Hello! My name is Mr.Parker.","I have a website https://parker.com.","It has about 5000 visitors per day.","I track it with a simple html-block like this:","<div>Google.Analytics</div>",]c=Corpus(txts,lang='en')c.info()c=c.filter(condition=lambdax:len(x.split()>5),attribute="raw_text")c.info()c.prepare()print(c)DocumentationThe official documentation is hosted on Github.io:https://6b656b.github.io/textariumLicenseMIT
Package: texta-tagger
TEXTA TaggerInstallationUsing built packagepip install texta-taggerUsing Git (for development)pip install git+https://git.texta.ee/texta/texta-tagger.gitUsagePredicting Using Zipped ModelPredicting without MLP & lemmatization>>> from texta_tagger.tagger import Tagger
>>>
>>> t = Tagger()
>>> t.load_zip('test_data/tagger.zip')
True
>>> print('Tagger:', t)
Tagger: Eesti
>>> t.tag_text('eesti keel ja eesti meel')
{'prediction': True, 'probability': 0.9999999322365133}Predicting with MLP & lemmatizationPredicting with lemmatization requires either a running MLP server or MLP Python package installed. In following example MLP from package is used:>>> from texta_tagger.tagger import Tagger
>>> from texta_mlp.mlp import MLP
>>>
>>> mlp = MLP()
>>>
>>> t = Tagger(mlp=mlp)
>>> t.load_zip('test_data/tagger.zip')
True
>>> print('Tagger:', t)
Tagger: Eesti
>>> t.tag_text('eesti keel ja eesti meel')
{'prediction': True, 'probability': 0.9999999322365133}In following example MLP server version is used:>>> from texta_tagger.tagger import Tagger
>>> from texta_tagger.mlp_analyzer import get_mlp_analyzer
>>>
>>> mlp = get_mlp_analyzer(mlp_host="http://my-mlp-server:5000")
>>>
>>> t = Tagger(mlp=mlp)
>>> t.load_zip('test_data/tagger.zip')
True
>>> print('Tagger:', t)
Tagger: Eesti
>>> t.tag_text('eesti keel ja eesti meel')
{'prediction': True, 'probability': 0.9999999322365133}TrainingTODOEnvironment VariablesTEXTA_TAGGER_MLP_URL - MLP host used for lemmatization (e.g.http://mlp-dev.texta.ee:5000)TEXTA_TAGGER_MLP_MAJOR_VERSION - MLP major version (2/3).