Saravanan666/pypi-org huggingface dataset:
Package: lightning-pose
Pose estimation models implemented inPytorch Lightning, supporting massively accelerated training onunlabeledvideos usingNVIDIA DALI.
The whole process is orchestrated byHydra.
Models can be evaluated withTensorBoard,FiftyOne, andStreamlit.Preprint:Lightning Pose: improved animal pose estimation via semi-supervised learning, Bayesian ensembling, and cloud-native open-source toolsTry our demo!Train a network on an example dataset and visualize the results in Google Colab.Getting StartedPlease see theLightning Pose documentationfor installation instructions and user guides.
Note that the Lightning Pose package provides tools for training and evaluating models onalready labeled dataand unlabeled video clips.We also offer abrowser-based applicationthat
supports the full life cycle of a pose estimation project, from data annotation to model training
to diagnostic visualizations.CommunityLightning Pose is primarily maintained byDan Biderman(Columbia University)
andMatt Whiteway(Columbia University).Lightning Pose is under active development and we welcome community contributions.
Whether you want to implement some of your own ideas or help out with ourdevelopment roadmap, please get in touch with us on Discord (see contributing guidelineshere).
Package: lightning-python
[![Build Status](https://travis-ci.org/lightning-viz/lightning-python.svg?branch=master)](https://travis-ci.org/lightning-viz/lightning-python)[![Gitter](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/lightning-viz/lightning?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)Lightning python client================Python client for the [lightning](https://github.com/mathisonian/lightning) API## installationInstall using:```pip install lightning-python```Compatible with Python 2.7 and 3.4.## usage### creating a session```pythonfrom lightning import Lightninglgn = Lightning(host="http://my-lightning-instance.herokuapp.com")lgn.create_session()lgn.create_session("provide an optional session name")```### creating a visualizationMethods are available for the default visualization types included with Lightning```pythonlgn.line([1,2,3,4,5,6,7,8,0,-2,2])lgn.scatter([1,2,3],[2,9,4])```### setting optionsVisualizations can be customized through optional parameters```pythonlgn.scatter([1,2,3],[2,9,4], label=[1,2,3], size=[5,10,20])```### using custom plotsFor custom plots not included with the default set, specify by name and provide data as a dictionary```pythonlgn.plot(data={"series": [1,2,3]}, type='line')```## examplesSee a collection of [IPython notebooks](http://nbviewer.ipython.org/github/lightning-viz/lightning-example-notebooks/tree/master/).## complete documentationAvailable [here](http://lightning-viz.github.io/lightning-python/).## running testsRequires [pytest](http://pytest.org/latest/)Clone the repo and install the library locally:```sh$ pip install -e .```The tests need to be run against a lightning server. By default they expectthis to be found at `http://localhost:3000`.To run the tests:```$ py.test```or with against a custom host url```$ py.test --host=http://mylightninghost.herokuapp.com```## helpWe maintain a [chatroom](https://gitter.im/lightning-viz/lightning) on gitter. If there's no response there: file an issue or reach out on twitter ([@mathisonian](http://twitter.com/matisonian), [@thefreemanlab](http://twitter.com/thefreemanlab))
Package: lightning-rest
# lightning-restRest server for thelightningddaemon.![screenshot](resources/snapshot_2018-02-28_093049.png)## SetupInstalling lightning-rest is easy, just execute the following in a terminal:`shell pip installlightning-rest`Run your local app!`shell python-mlightning_rest.server 8000~/.lightning/lightning-rpc`Run as docker container`shell docker run-it--rm-p8000:8000-v/path/to/lightning-rpc:/tmp/lightning-rpcsiriuslabs/lightning-rest8000/tmp/lightning-rpc`View the lightning-rest app atlocalhost:8000## License: MIT
Package: lightningrobot-cli
闪电机器人-脚手架简介帮助使用闪电机器人框架搭建你的机器人。用法使用下面的用法时，请先执行下面的命令安装脚手架。pipinstalllightningrobot-cli1. 创建打开终端，执行下面的命令创建一个标准的闪电机器人框架。lightningrobotcreate2.安装打开终端，执行下面的命令安装一个适配器/插件。lightningrobotinstalladapter/pluginname3.启动打开终端，进入装有闪电机器人的目录，执行下面的命令启动机器人。lightningrobotrun
Package: lightning-rod
lightning-rodFunction library for the Bolt scripting language.IntroductionMinecraft version:1.20.1This library features an API for the minecraft commands, usually improved by adding dynamic support or even adding new abstract functions.fromlightning_rod:apiimportBossbar,set_level,random# example of OOP bossbars ^^bb=Bossbar()bb.name='gas prices'bb.color='red'bb.show()# example of dynamic level setting at runtime ^^set_level(random(5,20))Installationpipinstalllightning_rodGetting startedThe library is designed to be used within anyboltscript (either a.mcfunctionorboltfile) inside aboltenabled project.require:-bolt-lightning_rodpipeline:-mechaOnce you've requiredboltandlightning_rod, you are able to import the package'sapimodule directly inside your bolt script.Most of the imports come from thelightning_rod:apimodule as shown below.fromlightning_rod:apiimportrandom,sqrtNow you're free to use the API!DocumentationComing soon...License -MIT
Package: lightning-sdk
Lightning SDKSoftware Development Kit (SDK) for Lightning AIInstallationThe package can be installed usingpip install lightning-sdkUsageTo use the SDK, you need to export the environment variablesLIGHTNING_USER_IDandLIGHTNING_API_KEYwith the values found in your account settings -> Keys -> Programmatic Login.If you want to use it from within a Studio, these variables are already available for you.Examplefromlightning_sdkimportMachine,Studio# or s =  Studio("my-studio", "my-teamspace", org="my-org", create_ok=True)# or (inside a Studio) s = Studio()  # will infer name, teamspace and owner of the current studio automatically.#    can also just pass some arguments: s = Studio("my-new-studio", create_ok=True)s=Studio("my-studio","my-teamspace",user="my-username",create_ok=True)print("starting Studio...")s.start()# prints Machine.CPU-4print(s.machine)# or start directly on this machine with s.start(Machine.A10G)print("switching Studio machine...")s.switch_machine(Machine.A10G)# prints Machine.A10Gprint(s.machine)# prints Status.Runningprint(s.status)print(s.run("nvidia-smi"))# install plugins for jobs and multi-machine trainings.install_plugin("jobs")s.install_plugin("multi-machine-training")# run the resulting plugins to start 1 job and 1 multi-machine trainings.installed_plugins["jobs"].run("python my_dummy_file",name="my_first_job",cloud_compute=Machine.A10G)s.installed_plugins["multi-machine-training"].run("python my_dummy_file",name="my_first_mmt",cloud_compute=Machine.T4,num_instances=42)print("Stopping Studio")s.stop()# duplicates Studio, this will also duplicate the environment and all files in the Studioduplicate=s.duplicate()# delete original Studio, duplicated Studio is it's own entity and not related to original anymores.delete()# stop and delete duplicated Studioduplicate.stop()duplicate.delete()
Package: lightning-server
LightningA socket-based lightly python server frameworkChoose your language: English(current)简体中文InstallInstall and update with pip:$ pip install -U lightning-serverCreate an Example# save this as example.pyimportlightningserver=lightning.Server()@server.bind('/')defhello(request):returnf'Hello World!\nThe request is received from{request.addr}'server.run()$pythonexample.py
Serverrunningon('0.0.0.0',80).PressCtrl+Ctoquit.AdvantageFewer dependencies, only depends on built-in modulesHigh extensibility, you can control the server from http-layer to socket-layer by inheriting classesEasily developing, just drop your functions into Interface class to implement featuresAbout AuthorI am a middle student from China. If I am at school, the developing progress may bevery slow. Sorry :(You can subscribe me onbilibili. Welcome!
Package: lightning-template
Lightning Template📘Documentation|🛠️Installation|🤔Reporting IssuesLightning Template is a generic project template lib based onpytorch lightningforproject-template. See thedocumentationfor more details.Installationpipinstalllightning-templateSeeinstallation docsfor details.ContributionSeecontribution docsfor details.Release versionsWe usesetuptools_scmfor versioning. For the versions available, see thetagson this repository.LicenseThis project is licensed under the MIT License.Star History
Package: lightning-time
Lightning TimeTODO: add description hereLearn more about how Lightning Time works & play with it hands-on:hereInstallationpip3installlightning-timefromLightningTime.LightningimportLightning,TimestringUsageColorsfromLightningTime.LightningimportLightning,Timestring# Create a Lightning objectlt=Lightning(Timestring("a~b~c|d"))By default, theLightningcolors are set to the following:bolt: (dynamic value), 161, 0
zap: 50, (dynamic value), 214
spark: 246, 133, (dynamic value)You canchangethese colors by passing in color arguments to theLightningobject:lt=Lightning(Timestring("a~b~c|d"),bolt_color=(g,b),zap_color=(r,b),spark_color=(r,g))r, g and b are integers between 0 and 255, inclusive.For example, to change the color of abolt, use the following:lt=Lightning(Timestring("a~b~c|d"),bolt_color=(155,35))You can set the static colors of aLightningobject with theset_static_colorsmethod:lt.set_static_colors((g,b),(r,b),(r,g))Conversionsfromdatetimeimportdatetime#convert from datetime to Lightning timeLightning.to_lightning(datetime.now())#e~3~3|8#convert from Lightning time to a time stLightning.from_lightning(Lightning.to_lightning(datetime.now()),withseconds=True)# 21:18:06Color StringfromdatetimeimportdatetimeLightning.to_lightning(datetime.now()).color_strings()# ('#e3a100', '#3238d6', '#f68582')Print without chargesfromdatetimeimportdatetimeLightning.to_lightning(datetime.now()).strip_charges()# e~3~aAccessing valueslt=Lightning(Timestring("a~b~c|d"))lt.timestring.bolts# alt.timestring.zaps# blt.timestring.sparks# clt.timestring.charges# dContributingThis project is still under development, feel free to contribute by opening a pull request.
Package: lightning-toolbox
LightningToolbox: A PyTorch Lightning FacilitatorInstallation•Docs•LicenseWelcome to lightning-toolbox, a python package that offers a set of automation tools built on top of PyTorch Lightning. As a deep learning researcher, I found that PyTorch Lightning offloads a significant portion of redundant work. However, I still found myself spending a considerable amount of time on writing boilerplate code for datamodules, training/validation steps, logging, and specially for not so complicated costum training loops. This is why I created lightning-toolbox - to make it easier to focus on writing experiment-specific code rather than dealing with tedious setup tasks.By passing your PyTorch model onto a genericlightning.LightningModule(lightning_toolbox.TrainingModule), lightning-toolbox automatically populates the objective function, optimizer step, and more. In addition, lightning-toolbox's genericlightning.LightningDataModule(lightning_toolbox.DataModule) can turn any PyTorch dataset, into a experiment-ready lightning data module, completing the cycle for writing lightning deep learning experiments.Most of the functionality provided in this package is based ondypy, which enables lazy evaluation of variables and runtime code injections. Although lightning-toolbox is currently in its early stages and mainly serves as a facilitator for my personal research projects, I believe it can be helpful for many others who deal with similar deep learning experiments. Therefore, I decided to open-source this project and continue to add on to it as I move further in my research.As a disclaimer this package does not intend to solve field-specific problems and provides more generic facilitator codes that the officiallightning-bolts, that you can easily mold into your desired deep learning experiment.Installationpipinstalllightning-toolboxLightning toolbox is tested onlightning==1.9.0, although there's no version restriction setup for this package, things might break down if the community decides to roll backward incompatible changes to the core Pytorch Lightning API (as they usually do).LicenseThis project is licensed under the terms of the Apache 2.0 license. SeeLICENSEfor more details.
Package: lightning-utilities
Lightning UtilitiesThis repository covers the following use-cases:Reusable GitHub workflowsShared GitHub actionsCLIpython -m lightning_utilities.cli --helpGeneral Python utilities inlightning_utilities.core1. Reusable workflowsUsage:name:Check schemaon:[push]jobs:check-schema:uses:Lightning-AI/utilities/.github/workflows/check-schema.yml@v0.5.0with:azure-dir:""# skip Azure checkcheck-code:uses:Lightning-AI/utilities/.github/workflows/check-code.yml@mainwith:actions-ref:main# normally you shall use the same version as the workflowSee usage of other workflows in.github/workflows/ci-use-checks.yaml.2. Reusable composite actionsSee available composite actions.github/actions/.Usage:name:Do something with cacheon:[push]jobs:pytest:runs-on:ubuntu-20.04steps:-uses:actions/checkout@v3-uses:actions/setup-python@v4with:python-version:3.9-uses:Lightning-AI/utilities/.github/actions/cachewith:python-version:3.9requires:oldest# or latest3. CLIlightning_utilities.cliThe package provides common CLI commands.InstallationFrom source:pipinstallhttps://github.com/Lightning-AI/utilities/archive/refs/heads/main.zipFrom pypi:pipinstalllightning_utilities[cli]Usage:python-mlightning_utilities.cli[group][command]Example for setting min versions$catrequirements/test.txtcoverage>=5.0codecov>=2.1pytest>=6.0pytest-covpytest-timeout$python-mlightning_utilities.clirequirementsset-oldest$catrequirements/test.txtcoverage==5.0codecov==2.1pytest==6.0pytest-covpytest-timeout4. General Python utilitieslightning_utilities.coreInstallationFrom pypi:pipinstalllightning_utilitiesUsage:Example for optional imports:fromlightning_utilities.core.importsimportmodule_availableifmodule_available("some_package.something"):fromsome_packageimportsomething
Package: lightNLP
lightNLP, 很基础的自然语言处理框架简介本项目基于Pytorch和torchtext，旨在提供一个基础的自然语言处理相关任务实现的深度学习框架。有关详细说明和教程请参阅项目文档：lightnlp-cookbook声明本项目从定位上来说只是一次收集和尝试，目的并不作为企业级和生产级使用，目标群体主要是对自然语言处理各任务实践感兴趣的其他方向计算机开发人员以及初学者，更主要的是自娱自乐。若有真正场景需求的用户或开发人员们可以参考Industry 中文NLP商业服务寻求商业服务，当然本渣也乐意提供有偿服务。对Pytorch和自然语言处理有一定了解的童鞋们，如果想追求快速开发且自由定制nlp应用，可以考虑复旦大学nlp实验室开源的fastNLP，框架功能丰富，且简洁易用。本项目并未像其他一些框架一样为各任务提供了一些训练数据和训练好的模型从而可以直接下载使用。本项目的诸多模型许多都是参考Github上原有实现，然后在基础之上二次加工而成，在这里要向相关作者致以诚挚的谢意！未对各任务模型的各种参数进行精细微调，仅仅只是能跑通的程度。本项目仅在以下本人两种开发环境下能跑通，其他环境出现的各种问题本人概不负责。Windows 10， Python 3.6，Pytorch 1.3Manjaro， Python 3.7，Pytorch 1.3安装pipinstalllightNLP建议使用国内源来安装，如使用以下命令：pipinstall-ihttps://pypi.douban.com/simple/lightNLP安装依赖由于有些库如pytorch、torchtext并不在pypi源中或者里面只有比较老旧的版本，我们需要单独安装一些库。安装pytorch请使用最新版本的Pytorch！具体安装参见pytorch官网来根据平台、安装方式、Python版本、CUDA版本来选择适合自己的版本。安装torchtext使用以下命令安装最新版本torchtext：pipinstallhttps://github.com/pytorch/text/archive/master.zip示例命名实体识别（ner）1.训练数据BIO训练数据示例如下：清 B_Time
明 I_Time
是 O
人 B_Person
们 I_Person
祭 O
扫 O
先 B_Person
人 I_Person
， O
怀 O
念 O
追 O
思 O
的 O
日 B_Time
子 I_Time
。 O

正 O
如 O
宋 B_Time
代 I_Time
诗 B_Person
人 I_Person2.使用示例1.训练from lightnlp.sl import NER

# 创建NER对象
ner_model = NER()

train_path = '/home/lightsmile/NLP/corpus/ner/train.sample.txt'
dev_path = '/home/lightsmile/NLP/corpus/ner/test.sample.txt'
vec_path = '/home/lightsmile/NLP/embedding/char/token_vec_300.bin'

# 只需指定训练数据路径和TensorBoard日志文件路径，预训练字向量可选，开发集路径可选，模型保存路径可选（模型保存路径默认为`xx_saves`，其中xx为模型简称，如ner）。
ner_model.train(train_path, vectors_path=vec_path, dev_path=dev_path, save_path='./ner_saves', log_dir='E:/Test/tensorboard/')2.测试# 加载模型，默认当前目录下的`ner_saves`目录
ner_model.load('./ner_saves')
# 对train_path下的测试集进行读取测试
ner_model.test(train_path)3.预测from pprint import pprint

pprint(ner_model.predict('另一个很酷的事情是，通过框架我们可以停止并在稍后恢复训练。'))预测结果：[{'end': 15, 'entity': '我们', 'start': 14, 'type': 'Person'}]4.查看训练效果命令行中执行以下命令，其中E:\Test\tensorBoard修改为模型训练时日志存储路径，port端口指定可选：tensorboard--logdir=E:\Test\tensorBoard--port=2019可以看到类似如下效果：5.部署服务ner_model.deploy(host="localhost",port=2020,debug=False)其中所有参数都可选，host参数默认为localhost，port端口将由程序自动向系统申请空闲端口，默认不开启debug模式。可以使用Postman或者编写程序进行调用测试，如下图：todo业务增加项目简要说明文档提供各任务Demo训练数据工程重构项目结构，将相同冗余的地方合并起来，保持项目结构清晰增加TensorBoard可视化功能，主要包含如loss和score的scalar和以及各model的graph（目前Pytorch中的SummaryWriter的add_graph功能有一些bug，所以暂时添加不了了。）。增加简易的基于flask的模型部署功能（当前仅用于模型的训练效果测试）现在模型保存的路径和名字默认一致，会冲突，接下来每个模型都有自己的name。增加断点重训功能。增加earlyStopping。功能增加CBOW词向量相关模型以及训练预测代码增加skip_gram相关模型以及训练预测代码增加情感分析相关模型以及训练预测代码增加文本蕴含相关模型以及训练预测代码增加文本生成相关模型以及训练预测代码增加语言模型相关模型以及训练预测代码增加依存分析相关模型以及训练预测代码增加关系抽取相关模型以及训练预测代码增加中文分词相关模型以及训练预测代码增加词性标注相关模型以及训练预测代码增加事件抽取相关模型以及训练预测代码增加自动摘要相关模型以及训练预测代码增加机器翻译相关模型以及训练预测代码增加句子相似度相关模型以及训练预测代码增加序列到序列相关模型以及训练预测代码增加聊天机器人相关模型以及训练预测代码增加命名实体识别相关模型以及预测训练代码增加一些任务的transformer实现（基于Pytorch官方实现的Transformer）增加Elmo相关模型以及训练预测代码增加GloVe相关模型以及训练预测代码增加GPT相关模型以及训练预测代码增加Bert相关模型以及训练预测代码增加属性抽取相关模型以及训练预测代码增加指代消解相关模型以及训练预测代码增加词义消歧相关模型以及训练预测代码增加阅读理解相关模型以及训练预测代码增加关键词抽取相关模型以及训练预测代码增加成分句法分析相关模型以及预测训练代码打赏如果该项目对您有所帮助，欢迎打赏~
Package: lightnovelapi
LightNovelApiClient sided API to light novel hosters.Please send some love to the supported hosters by reading the material on their sites. They need the ad revenue.Dropped in favour oflightnovel-crawler.Still working on it occasionally.Currently supported hosters:www.wuxiaworld.comPlanned hosters to be supported (found through ducking andfullnovels.com):www.webnovel.comtoc.qidianunderground.org(completes webnovel)gravitytales.comwww.volarenovels.comcreativenovels.comwww.wuxiaworld.co(imposters)wuxiaworld.site(imposters)wuxiaworld.online(imposters)www.wuxiaworld.in(imposters)wuxiaworld.world(imposters)kiss-novel.comboxnovel.netwuxia.todaywww.aihristdreamtranslations.comzenithnovels.comwww.radianttranslations.comlightnovelstranslations.combcat00.wpcomstaging.comwww.asianhobbyist.comjingletranslations.wordpress.comwww.thenovelst.comtranzgeek.wordpress.comrosyfantasy.netwhitemoonxblacksun.wordpress.comtranslations.gocreateme.comomgtranslations.comdmtranslationscn.comliberspark.comxaiomoge.comdhh-workshop.blogspot.combubbleteatranslations.wordpress.comvanillamusenovels.wordpress.comlmsnovel.wordpress.comzhanlantranslations.commoonruneworks.com
Package: lightonml
LightOnML libraryLightOnML is a high level machine learning-oriented API that allows to perform random projections on
LightOn’s optical processing units (OPUs). LightOn’s OPUs are available throughLightOn’s Cloud service.FeaturesRun large-scale non-linear and linear random projections using LightOn’s Aurora OPUsSimulate these projections on any machine without access to an OPUEncode input data in a binary form using various encoders, for OPU inputInstallationlightonmldoesn't require access to an OPU for some functionalities, but for performing
computations on an OPU you'll need one. Otherwise, a simulated OPU can be used.To install, usepip:pip install lightonmlOptional dependencies are :torch, required for the encoder classes, and the PyTorchOPUMap.scikit-learn, required for using the correspondingOPUMapto work.Documentation, examples and helpMain documentation can be found at theAPI docs website.Check the examples directory in the repo, if you don't have access to an OPU you can run the code locally with a simulated OPUFor getting help on the LightOn Cloud service check theCommunity websiteFor help on the library itself, you can use issues on this repository.Access to Optical Processing UnitsTo request access to LightOn Cloud and try our photonic co-processor, please visit:https://cloud.lighton.ai/For researchers, we also have a LightOn Cloud for Research program, please visithttps://cloud.lighton.ai/lighton-research/for more information.
Package: lightonmuse
lightonmusePython bindings for the Muse API: production-ready intelligence primitives powered by state-of-the-art language models. By LightOn.Create. Process. Understand. Learn.Uplift your product with the natural language generation & understanding capabilities of Muse. State-of-the-art large language models in French, English, Italian, and Spanish—with more to come—are just an API call away. Our models can help you build conversational AI, copywriting tools, text classifiers, semantic search, and more.🛣️  Accessing the Muse API public betaThe Muse API is currently in public beta. Learn more about Muse and sign up atmuse.lighton.ai.Installation and documentationYou can install this package from PyPi with:pipinstalllightonmuseTo install from source:gitclonehttps://github.com/lightonai/lightonmuse.gitcdlightonmuse
pipinstall./Once the package is installed, make sure to define an environment variableMUSE_API_KEYto your API key, e.g. by adding the following line to your.bashrcexport MUSE_API_KEY="<your api key>"Guides and documentation can be found at theAPI docs website.QuickstartUsinglightonmuseis pretty simple, the interface matches the endpoints offered by the Muse APICreatefromlightonmuseimportCreatecreator=Create("lyra-en")print(creator("Wow, the Muse API is really amazing"))SelectfromlightonmuseimportSelectselector=Select("orion-fr-v2")print(selector("Quel nom est correct?",candidates=["pain au chocolat","chocolatine"]))CalibratedSelectfromlightonmuseimportCalibratedSelectselector=CalibratedSelect("orion-fr-v2")selector.fit(content_free_inputs='Voici une critique : ""\n',candidates=["positive","négative"],conjunction="Cette critique est")critique='Voici une critique : "Ce film est super pour s\'endormir"'print(selector(critique,candidates=["positive","négative"],conjunction="Cette critique est"))AnalysefromlightonmuseimportAnalyseanalyser=Analyse("orion-fr-v2")print(analyser("Avec\"Analyse\"on peut toujours trouver les parties plus surprenantes d'une phrase."))EmbedfromlightonmuseimportEmbedembedder=Embed("lyra-en")print(embedder("This sentence will be transformed in a nice matrix of numbers."))ComparefromlightonmuseimportComparecomparer=Compare("lyra-en")print(comparer("This is the reference.",candidates=["This is close to the reference","While this is most definitely not"]))TokenizefromlightonmuseimportTokenizetokenizer=Tokenize("lyra-en")print(tokenizer("Let's discover how many tokens is this text"))Access to LightOn MUSEAccess the public beta of LightOn MUSE and try our intelligence primitives atmuse.lighton.ai
Package: light-pandas
Light PandasBriefPandasis a great tools for data analysis.But the execution speed is very slow. So I try to provide a light library.
The properties as below:Light Pandasonly provide part of functions which provided by pandas.The code can compatible pandas. That's mean the code should execute OK if replaced light pandas by pandas.Light Pandasexecution speed is faster than pandas.The cell type only support str.TBDInstallpip install light-pandasExampleCreate an empty DataFrame which contained two columns.import lightpandas as pd
df = lpd.DataFrame(columns=('item1', 'item2'))Filter in the rows which the column "item4" is littler than "t4" (string compare).df.loc[df['item1'] < 't4']
Package: lightpath
Python module for control of LCLS beamlinesBy abstracting individual devices into larger collections of paths, operators
can quickly guide beam to experimental end stations. Instead of dealing with
the individual interfaces for each device, devices are summarized in states.
This allows operators to quickly view and manipulate large sections of the
beamline when the goal is to simply handle beam delivery.CondaInstall the most recent tagged build:conda install lightpath -c pcds-tag  -c conda-forgeInstall the most recent development build:conda install lightpath -c pcds-dev -c conda-forge