Saravanan666/pypi-org huggingface dataset:
Package: plotly-light
plotly_lightA wrapper of Plotly Python aiming for lightweight plots and ease of use.Main features:heavy_check_mark:[SMALLER PLOT SIZE]Plotly Light does not keep all raw data for a bar plot including a histogram, meaning you can keep the file size of a Jupyter Notebook file or a HTML file containing the plot very small even when drawing a histogram with a huge dataset.:heavy_check_mark:[SEPARATE DIRECTORY FOR HTML PLOTS]By default, each plot drawn by Plotly Light in a Jupyter Notebook is saved as a HTML file in a directory named<notebook-basename>.iframe_figures/, enabling us to keep the file size of a Notebook small and to easily obtain a single HTML file only of a plot. This feature can be disabled.:heavy_check_mark:[COMPREHENSIBLE FUNCTION ARGUMENTS]All positional and optional arguments of the functions in Plotly Light are explicitly written (without using*argsnor**kwargs), meaning you can easily find an argument you want by peeking the definition and docstring of a function (which is a feature typically provided by Jupyter Notebook and other editors).:x:[LIMITED FEATURES AVAILABLE]We provide only basic plotting functions and features, meaning not everything you can do with the original Plotly can be done with Plotly Light. However, Plotly Light is usually sufficient for most of the simple purposes.RequirementsPython >= 3.7 (CPythonis recommended)How to install1. Via PyPI (recommended)$pipinstallplotly-light2. Via this GitHub repository$gitclonehttps://github.com/yoshihikosuzuki/plotly_light
$cdplotly_light
$pipinstall.How to useSeeReference.
Package: plotly_modified
The NutshellUse this package to make collaborative, interactive,
publication-quality graphs from Python.Here’s how you import:import plotly.plotly as py              # for sending things to plotly
import plotly.tools as tls              # for mpl, config, etc.
from plotly.graph_objs import *         # __all__ is safely definedHere’s how you sign in:py.sign_in('PythonAPI', 'ubpiol2cve')   # get your own at https://plot.ly/Here’s how you plot data or a figure:py.plot(data_or_figure_here)Here’s what you get:an account on plotlya unique url for your data/figuresan interactive web-application to edit your figure or make new figuresa platform on which to share your data/figures with the worldYou can also convert supported matplotlib figures:py.plot_mpl(mplfig)Stop fighting with your figures; start designing them. Check out ourQuickstartto get going.AboutPlotlyis an online collaborative data analysis and graphing tool. The
Python API allows you to access all of Plotly’s functionality from Python.
Plotly figures are shared, tracked, and edited all online and the data is
always accessible from the graph.That’s it. Find out more, sign up, and start sharing by visiting us athttps://plot.ly.Install via pipAssuming you have already installed pip, you can simply enter the following
in a terminal program:$ pip install plotlyContributing!If you want to contribute to making Plotly’s Python API experience better,
head to ourGitHub repo. Instructions for installing from here,
updating the included submodules, and contributing are detailed there!Plotly, matplotlib, and mpld3The matplotlylib sub-package allows you to convert matplotlib figures to
plotly figures, with a one-liner:py.plot_mpl(fig)Checkout thePlotly and mpld3IPython notebook for more infomataion.Introduction to working with out APICheckout thePlotly and PythonIPython notebook to get a more in depth
exposition of our Python API.Plotly’sguide bookStill here? Don’t worry, we’ve got more documentation for you. Checkout ahighlycompleteset of notebooksfor walk-throughs on all the features
we offer!DetailsThe plotly package depends on requests, which will be installed by pip for you.To use the matplotlylib subpackage, you’ll also need to have matplotlib 1.3.1
properly installed on your machine.The matpotlylib package is based on the mplexporter framework for crawling
and exporting matplotlib images.Created by:Plotly,@plotlygraphs,feedback@plot.lyLicense: MIT
Package: plotlyplus
plotlyplusIntroductionplotlyplusis a Python module that simplify the calling of plotly.express function. In standard way, one needs to do the following to create a plot using plotly.express based on a dataframe, df, you need to do the followingimportpandasaspdimportplotly.expressaspxpx.scatter(df,x='namex',y='namey')The plotlyplus package add the plotly.express plotting methods to the pandas dataframe class. This will simply the call to the plotting function as:fromplotlyplus.plotlyplusimport*df.scatter(x='namex',y='namey')This is a small and almost trivial tweak, but brings a lot of convenience in daily practical work. Enjoy!Key FeaturesBetter integration of plotly.express functions to pandas dataframe: Directly integrates Plotly Express plotting functions with pandas DataFrames.Safe mode: To avoid naming conflicts,plotlyplusintroduces a 'safe mode' (plotlyplus_safe). In this mode, each plotly.express function is prefixed with `px_``, ensuring namespace integrity at the cost of slightly reduced convenience.InstallationInstallPlotlyPlususing pip with the following command:pipinstallplotlyplusUsageStandard ModeAll plotly.express fundtions become methods of pandas dataframe.fromplotlyplus.plotlyplusimport*df=pd.DataFrame(...)fig=df.bar(...)# Plotly Express function as a DataFrame methodfig.show()Safe ModeThis mode prefixes the Plotly Express functions withpx_to minimize namespace conflicts.fromplotlyplus.plotlyplus_safeimport*df=pd.DataFrame(...)fig=df.px_bar(...)# Plotly Express function as a DataFrame methodfig.show()ContributingContributions to PlotlyPlus are welcome. Please feel free to submit pull requests, report bugs, or suggest features.LicensePlotlyPlus is made available under the MIT License.
Package: plotlyshare
plotlyshareThe purpose of this package is to provide a seamless way to share interactiveplotlyplots, providing a similar experience to google docs/etc.This requires a free account on deta space:https://deta.space, which is essentially apersonalcloud computerat no cost.The idea is to preserve the interactivity and responsiveness ofplotlyplots with the ease oflink-based sharingand without all the hassle of sending an html file, or worse, a static image/document.To use this, first runpython -m plotlyshare setupand installplotlyshare on deta space.
And then whenever making a plot,...importplotly.expressaspximportplotly.graph_objectsasgoimportplotlyshare...fig=px.line(...)fig=go.Figure(...)fig.show(renderer='plotlyshare')and that's it, the package will upload your plot to your instance of plotlyshare running in deta space and assign it a randomly generated name (which can be edited from the app's page)
It will print the name that was generated and the direct link to the plot, ready to share with anyone.
Package: plotly-unbrand
# Plotly UnbrandDear Plotly-Team,I love you and your library, but sometimes, I do not openly want to show my love.Sometimes, when working with a customer or when embedding a plot into a talk, I don't want to have multiple links to your online platform directly next to my data.Luckily, you seem to be very understanding about this and made it possible for me to remove all traces of your brand (besides the trademark beautiful esthetics, of course) from my plots.I can just pass this into the config of each plotting call:```pythonfrom plotly.plotly import plotUNBRAND_CONFIG = dict(modeBarButtonsToRemove=['sendDataToCloud'], displaylogo=False, showLink=False)plot([Scatter(x=[1, 2, 3], y=[3, 1, 6])], config=UNBRAND_CONFIG)```But sometimes even this is not good enough... I want to be able to do this automatically for all plots in a project (I am so sorry Plotly-Team!).Therefore, I created this little package.Now I can do this:```pythonfrom plotly_unbrand import unbrandunbrand()from plotly.plotly import plotplot([Scatter(x=[1, 2, 3], y=[3, 1, 6])])plot([Scatter(x=[1, 2, 3], y=[3, 1, 6])])plot([Scatter(x=[1, 2, 3], y=[3, 1, 6])])```... and all my Plots have your logo and links to the cloud platform removed.## Installation```pip install plotly_unbrand```### DependenciesYou need to have plotly installed of course...## UsageSimple! Import `unbrand` and ran it **before** the first plotly import:```pythonfrom plotly_unbrand import unbrandunbrand()from plotly.plotly import plotplot([Scatter(x=[1, 2, 3], y=[3, 1, 6])])plot([Scatter(x=[1, 2, 3], y=[3, 1, 6])])plot([Scatter(x=[1, 2, 3], y=[3, 1, 6])])```This will work with `plot` and `iplot` in their online and offline variant.It should also work with `cufflings`, though I haven't tested it.### Wait there is more!You can use the tools from this package to inject any global configuration you like!```pythonfrom plotly_unbrand import decorate_all_plot_functionsdecorate_all_plot_functions(my_custom_config)from plotly.plotly import plot...```## But I don't want a separate package just for this?!Don't worry! The logic is pretty simple and self-explanatory.You can just copy the decorator from the `__init__.py` and run the decoration logic manually.
Package: plotlywaterfall
plotlywaterfallDescriptionA small package adding simple waterfall plotting capabilities on top of the plotly graphing package. Waterfall graphs are useful to display e.g. financial data such ans cashflow statements in an easily digestible format.While plotly includes basic capabilities to plot waterfall graphs, it ships only limited features. Theplotlywaterfallpackage provides simple, plotly-express-style waterfall graphs with grouping, multiple stacked categories and variable colors.InstallationUse pip3:pip3 install plotlywaterfallUsageMore examples can be found in the example notebook (https://github.com/docdru/plotlywaterfall/blob/main/example.ipynb). Let's use this dataframe for a simple yet complete example:df = pd.DataFrame({
    "X": ["A", "B", "C"]*4 + ["D", "D" ], 
    "Y": [4, 1, 8, 7, 3, 2] + [i-1 for i in [4, 1, 8, 7, 3, 2]] + [8, 5],
    "category": ["one"]*3+["two"]*3 + ["one"]*3+["two"]*3 + ["two", "three"],
    "group": ["Group1"]*6 + ["Group2"]*6 + ["Group2", "Group3"]
})One can plot this data, with defined colors and automatic creating of total and subtotal, by using:from plotlywaterfall.waterfall import Waterfall

colors = {
    "Group1": {"one": "red", "two": "blue"},
    "Group2": {"one": "salmon", "two": "lightskyblue"},
    "Group3": "green"
}


c = Waterfall(df, x="X", y="Y", category="category", colors=colors, group="group", total=True, subtotals={"C": "Subtotal"})
fig = c.get_fig()
figResulting graph:DisclaimerI might maintain and improve the package. I might also not.Known open points:It is not possible to have mixed signs per X-value.I am note really happy with the interface for defining the colors.ChangelogRefer toCHANGELOG.md
Package: plotlywrapper
NewsTests are failing due to release of plotly v3.
Need to reevaluate need of library, in the meantime it still works with plotly v2.Plotlywrapper wrapsplotlyto make easy
plots easy to make. Check out thedocs!2DBrownian BridgeMotivationCompare the following code snippets which generate the same plot.PlotlyTaken fromhttps://plot.ly/python/getting-started/importplotlyfromplotly.graph_objsimportScatter,Layoutplotly.offline.plot({"data":[Scatter(x=[1,2,3,4],y=[4,1,3,7])],"layout":Layout(title="hello world")})Plotlywrapperimportplotlywrapperaspwplot=pw.line(x=[1,2,3,4],y=[4,1,3,7])plot.title('hello world')plot.show()InstallTo install the latest release:pip install plotlywrapperDemoTry out the interactive demo here,or view thenotebookstatically.JupyterLabPlotly doesn’t render in JupyterLab by default. You need to install the
JupyterLab Plotly extension:jupyter labextension install @jupyterlab/plotly-extensionDeveloped in thisrepo.TestingTo test run:make test
Package: plot-map
plot_map安装pip install -U plot-map底图加载功能地图底图加载只需要用以下代码：import plot_map
#设定显示范围
bounds = [lon1,lat1,lon2,lat2]  
plot_map.plot_map(plt,bounds,zoom = 12,style = 4)参数参数描述bounds底图的绘图边界，[lon1,lat1,lon2,lat2] (WGS84坐标系) 其中，lon1,lat1是左下角坐标，lon2,lat2是右上角坐标zoom底图的放大等级，越大越精细，加载的时间也就越久，一般单个城市大小的范围，这个参数选取12到16之间style地图底图的样式，可选1-5，或'light','dark'，style为4时为light，style为5时为darkimgsavepath瓦片地图储存路径，设置路径后，会把地图下载到本地的文件夹下，使用时也会优先搜索是否有已经下载的瓦片，默认的存放路径是C:\printlog是否显示日志绘制指北针和比例尺的功能plotscale为底图添加指北针和比例尺plot_map.plotscale(ax,bounds = bounds,textsize = 10,compasssize = 1,accuracy = 2000,rect = [0.06,0.03])参数参数描述bounds底图的绘图边界，[lon1,lat1,lon2,lat2] (WGS84坐标系) 其中，lon1,lat1是左下角坐标，lon2,lat2是右上角坐标textsize标注文字大小compasssize标注的指北针大小accuracy标注比例尺的长度unit'KM','km','M','m' 比例尺的单位style1或2，比例尺样式rect比例尺在图中的大致位置，如[0.9,0.9]则在右上角
Package: plot-memory-usage
plot_memory_usageCreate a live plot of the CPU or GPU memory usage of your system over timeInstallingInstall:pip install plot_memory_usageUsageplot_memory_usage --help

usage: plot_memory_usage [-h] [--units {MB,GB,PERCENT}]

╭─ arguments ───────────────────────────────────────╮
│ -h, --help        show this help message and exit │
│ --units {MB,GB,PERCENT}                           │
│                   (default: MB)                   │
╰───────────────────────────────────────────────────╯
Package: plot-model
plot_modelGitHub:https://github.com/Qinbf/plot_model.gitplot_model is a API for model visualization reference totensorflow.keras.utils.plot_model.InstallationBefore installing plot_model, please install one of its engines:TensorFlow,Keras.You may also consider installing the following :pipinstallpydot
pipinstallpydot_ng
pipinstallgraphvizFinally,download and installgraphvizto plot model graphs.Then, you can install plot_model itself. There are two ways to install plot_model:Install plot_model from PyPI (recommended):Note: These installation steps assume that you are on a Linux or Mac environment.
If you are on Windows, you will need to removesudoto run the commands below.sudopipinstallplot_modelIf you are using a virtualenv, you may want to avoid using sudo:pipinstallplot_modelAlternatively: install plot_model from the GitHub source:First, clone plot_model usinggit:gitclonehttps://github.com/Qinbf/plot_model.gitThen,cdto the plot_model folder and run the install command:cdplot_model
sudopythonsetup.pyinstallGetting startedAPI is similar to tensorflow.keras.utils.plot_modelfromplot_modelimportplot_modelplot_model(model)default parameters:plot_model(model,to_file='model.png',show_shapes=True,show_layer_names=False,rankdir='TB',expand_nested=False,style=0,color=True,dpi=96)color: whether to display color. Default True.style: 0 new style. 1 original style. Default 0.ExamplesResNetInceptionResNeXt
Package: plotmygsheet
This is a very simple library that allow the user to plot the graph between the two columns of
the google spreadsheet.
To get the graph you are provided by two methods two methods :get_all_columns(url): In this method you have to provide the url of the the google sheet and it will return the list of
all the columns of your sheet.plot_graph(url,column1,column2,path=”assets”) : Now to plot the graph you are provided with plot graph method.In
this method you have to provide the url of the googlesheet and you respective column1 number i.e if it is column 1 then
you have provide only “1” as its value and the same concept will be applied with column2 value and at last if you
want to store your graph image then you must have folder names as assets, where it will store the my_plot.png image of the graph.Things to be taken Care of :Both column 1 and column 2 value should be numeric.Google sheet link should be valid.Make sure that this setting are checked :
i) Editors can change permissions and share option is checked in Share with people settings of google sheet.
ii) Also make sure that Anyone with the link option is checked as Editor.Change Log0.0.1 (10/10/2020)First Release
Package: plotmysheet
This is a very simple library that allow the user to plot the graph between the two columns of
the google spreadsheet.
To get the graph you are provided by two methods two methods :get_all_columns(url): In this method you have to provide the url of the the google sheet and it will return the list of
all the columns of your sheet.plot_graph(url,column1,column2,path=”assets”) : Now to plot the graph you are provided with plot graph method.In
this method you have to provide the url of the googlesheet and you respective column1 number i.e if it is column 1 then
you have provide only “1” as its value and the same concept will be applied with column2 value and at last if you
want to store your graph image then you must have folder names as assets, where it will store the my_plot.png image of the graph.Things to be taken Care of :Both column 1 and column 2 value should be numeric.Google sheet link should be valid.Change Log0.0.1 (10/10/2020)First Release
Package: plotneat
plotneatThis package is meant help with simple neat plotting in Python 3.CapabilitiesNone yetInstallationDevelopment versionsIf you want to use the development version, then just keep on using the one in therepository on Gitlabby checking out the right branches:To get the latest stable version, pull the master branch from Gitlab:pip install git+https://gitlab.com/Pierre_VF/plotneat.git@masterTo get the latest developments (can be unstable), pull the Development branch from Gitlab:pip install git+https://gitlab.com/Pierre_VF/plotneat.git@Development
Package: plotnine
plotnineplotnine is an implementation of agrammar of graphicsin Python
based onggplot2.
The grammar allows you to compose plots by explicitly mapping variables in a
dataframe to the visual objects that make up the plot.Plotting with agrammar of graphicsis powerful. Custom (and otherwise
complex) plots are easy to think about and build incrementaly, while the
simple plots remain simple to create.To learn more about how to use plotnine, check out thedocumentation. Since plotnine
has an API similar to ggplot2, where it lacks in coverage theggplot2 documentationmay be helpful.Examplefromplotnineimport*fromplotnine.dataimportmtcarsBuilding a complex plot piece by piece.Scatter plot(ggplot(mtcars,aes("wt","mpg"))+geom_point())Scatter plot colored according some variable(ggplot(mtcars,aes("wt","mpg",color="factor(gear)"))+geom_point())Scatter plot colored according some variable and
smoothed with a linear model with confidence intervals.(ggplot(mtcars,aes("wt","mpg",color="factor(gear)"))+geom_point()+stat_smooth(method="lm"))Scatter plot colored according some variable,
smoothed with a linear model with confidence intervals and
plotted on separate panels.(ggplot(mtcars,aes("wt","mpg",color="factor(gear)"))+geom_point()+stat_smooth(method="lm")+facet_wrap("gear"))Adjust the themesI) Make it playful(ggplot(mtcars,aes("wt","mpg",color="factor(gear)"))+geom_point()+stat_smooth(method="lm")+facet_wrap("gear")+theme_xkcd())II) Or professional(ggplot(mtcars,aes("wt","mpg",color="factor(gear)"))+geom_point()+stat_smooth(method="lm")+facet_wrap("gear")+theme_tufte())InstallationOfficial release#Usingpip$pipinstallplotnine# 1. should be sufficient for most$pipinstall'plotnine[extra]'# 2. includes extra/optional packages$pipinstall'plotnine[test]'# 3. testing$pipinstall'plotnine[doc]'# 4. generating docs$pipinstall'plotnine[dev]'# 5. development (making releases)$pipinstall'plotnine[all]'# 6. everyting#Orusingconda$condainstall-cconda-forgeplotnineDevelopment version$pipinstallgit+https://github.com/has2k1/plotnine.gitContributingOur documentation could use some examples, but we are looking for something
a little bit special. We have two criteria:Simple looking plots that otherwise require a trick or two.Plots that are part of a data analytic narrative. That is, they provide
some form of clarity showing off thegeom,stat, ... at their
differential best.If you come up with something that meets those criteria, we would love to
see it. Seeplotnine-examples.If you discover a bug checkout theissuesif it has not been reported, yet please file an issue.And if you can fix a bug, your contribution is welcome.TestingPlotnine has tests that generate images which are compared to baseline images known
to be correct. To generate images that are consistent across all systems you have
to install matplotlib from source. You can do that withpipusing the command.$pipinstallmatplotlib--no-binarymatplotlibOtherwise there may be small differences in the text rendering that throw off the
image comparisons.
Package: plotnine3d
plotnine3d3D geoms forplotnine(grammar of graphics in Python).Status: experimental. Please leave feedback; pull requests welcome.ExamplesPlease refer to thenotebook with examplesfor more details on data preparation.Surface(ggplot_3d(mt_bruno_long)+geom_polygon_3d(size=0.01)+aes(x='x',y='y',z='height')+theme_minimal())Scatter(ggplot_3d(mtcars)+aes(x='hp',y='disp',z='mpg',shape='transmission',fill='transmission')+theme_minimal()+scale_shape_manual(values={'automatic':'o','manual':'^'})+geom_point_3d(stroke=0.25,size=3,color='black')+scale_fill_manual(values={'automatic':'orange','manual':'blue'}))Voxels(ggplot_3d(voxels_long)+aes(x='x',y='y',z='z',fill='object')+geom_voxel_3d(size=0.01)+theme_minimal()+ylim(0,8)+xlim(0,8)+scale_fill_manual(values={'link':'red','cube1':'blue','cube2':'green'}))Line(ggplot_3d(data)+aes(x='x',y='y',z='z',color='z')+geom_line_3d(size=2)+theme_minimal())InstallationInstallation from PyPI:pip install plotnine3d
Package: plotnine-examples
This repository contains examples in the form of jupyter notebooks
that are also part of theplotninedocumentation.Rebase Notice: When the repository gets large and the
will be reduced by deleting the history.ContributingClone repository or pull in latest changesgit clone https://github.com/has2k1/plotnine-examplesOrcd plotnine-examples
git pullCreate a notebook or edit an existing notebook. The name of the
notebook should match the object. e.g if examples are forgeom_pointthe name should begeom_point.ipynb.Add your github username and/or name tocontributors.txt(sorted according to the username) and commit.Push / Force pushgit pushOr if it warns of a non forward mergegit push -fSubmit Pull Request
Package: plotnine-prism
plotnine-prismPrism themes forplotnine, inspired byggprism.Installationpip install -U plotnine-prismDocumentationhttps://pwwang.github.io/plotnine-prismUsageSeethis notebookfor the following example, and alsoGetting startedfor a quick overview ofplotnine_prismfeatures.fromplotnineimport*fromplotnine_prismimport*fromdatar.allimportf,as_categorical,mutatefromdatar.datasetsimportToothGrowthToothGrowth>>=mutate(dose=as_categorical(f.dose))base=(ggplot(ToothGrowth,aes(x="dose",y="len"))+geom_violin(aes(colour="dose",fill="dose"),trim=False)+geom_boxplot(aes(fill="dose"),width=0.2,colour="black"))p1=base+ylim(-5,40)p2=(base+scale_y_continuous(limits=[-5,40],guide=guide_prism_offset_minor())+scale_color_prism('floral')+scale_fill_prism('floral')+theme_prism())# See examples/nb_helpers.py for plot_grid function# plot_grid(p1, p2)More examplesThe Dose Response Curve was recreated. Seethis vignettefor the source code and step-by-step instructions.
Package: plotnineseqsuite
plotnineSeqSuite: a Python package for visualizing sequence data using ggplot2Cite this workCao, T., Li, Q., Huang, Y. et al. plotnineSeqSuite: a Python package for visualizing sequence data using ggplot2 style. BMC Genomics 24, 585 (2023).https://doi.org/10.1186/s12864-023-09677-8Installationpip install plotnineseqsuiteGetting StartedSeeplotnineSeqSuite DocumentationGalleryDevelopment environmentPyCharm 2023.2 (Community Edition) and Spyder version: 5.4.3  (conda)DependenciesPython version: 3.11.4 64-bitplotnine: 0.12.3Similar projectsR packageggmsaandggseqlogoDerivative projectsPyggseqlogo: Python version of ggseqlogo.