Saravanan666/pypi-org huggingface dataset:
Package: easysparkcli
EasySparkEasySpark aims to make easier for users the execution of batch jobs with Apache Spark framework. It provides a user-friendly command line tool to create, setup and manage on-premise Spark clusters and ease the execution of batch jobs against those deployed clusters.Its main objective is to facilitate the whole process of running batch jobs in Spark, providing subcommands to deploy/delete the necessary infrastructure and submit the desired jobs to that infraestructure.The EasySpark project supports multiple operating systems (Windows, MacOS and Linux) and is open source licensed under the GNU General Public License v3.FeaturesEasyspark CLI receives parameters via .INI configuration files, and offers the following functionalities:Setup and manage on-premise Spark clusters.Easy batch job submission to Spark clusters.In order to facilitate the use of the mentioned features, the CLI tool has subcommands that allow validating and creating .INI configuration files with the accepted parameters.Software RequirementsIt is mandatory to have Apache Spark downloaded on the workstation and theSPARK_HOMEenvironment variable correctly configured.Additionally, depending on the requested cluster manager for the Spark cluster, it will be necessary:Standalone Spark cluster:MinikubeDockerKubernetes Spark cluster:VirtualBoxVagrantInstallationRun the following to install:pipinstalleasysparkcliInstallation from sourceClone EasySpark's repository from GitHub. Next open a terminal,cdinto that path and run:pip install -e .Quick StartUsage:easysparkcli COMMAND [ARGS]Once installed, you can check the different options of the tool through the command:easysparkcli --helpEasySpark counts with 5 subcommands:templateCreates, as a template, a .INI configuration file with all the available options for the tool.validateValidates the provided .INI configuration file, checking if it meet the requirements.clusterinitCreate and setup on-premise Spark Cluster using Kubernetes or Spark Standalone as cluster manager to be able to execute batch jobs on this infraestructure.clusterdeleteDeletes Spark clusters previously deployed with EasySpark and its associated files.submitAllows users to submit batch jobs to the Spark cluster in a more user-friendly manner
Package: easysparql
easysparqlA python wrapper to easily query knowledge graphs with SPARQLInstallvia setuptoolspython setup.pyvia pippip install easysparqlRun testspython -m unittest discoverTo publishpython setup.py sdist bdist_wheel
twine upload dist/*Functionsrun_query: run a sparql query.get_entities: get candidate entities of the given name (exact match).get_classes: get classes of a given entity.get_parents_of_class: get direct parent classes of a given class.get_subjects: Get the number of subjects of a given class.get_properties_of_subject: Get the properties of a given subject.get_numerics_from_list: Get the numbers from a list of strings and numbers (if they are more that the provided percentage).get_num: Get the number of a given number/string or None (if it was not a number).Examplefrom easysparql import easysparql

DBPEDIA_ENDPOINT = "https://dbpedia.org/sparql"

albert_uri = "http://dbpedia.org/resource/Albert_Einstein"
albert_name = "Albert Einstein"
scientist = "http://dbpedia.org/ontology/Scientist"
foaf_name = "http://xmlns.com/foaf/0.1/name"


classes = easysparql.get_classes(albert_uri, DBPEDIA_ENDPOINT)

entities = easysparql.get_entities(albert_name, DBPEDIA_ENDPOINT, "@en")

parents = easysparql.get_parents_of_class(scientist, DBPEDIA_ENDPOINT)

query = "select distinct ?Concept where {[] a ?Concept} LIMIT 100"
results = easysparql.run_query(query, DBPEDIA_ENDPOINT)

subjects = easysparql.get_subjects(class_uri=scientist, endpoint=DBPEDIA_ENDPOINT)

properties = easysparql.get_properties_of_subject(subject_uri=albert_uri, endpoint=DBPEDIA_ENDPOINT)

a =  ["1.2","2","4","3",3,6,"a","b", "ccc", "1jasdf"]
nums = easysparql.get_numerics_from_list(a, 0.5)
Package: easy-spider-tool
easy spider tool在实际工作中，沉淀的一些简易、好用的爬虫工具，减少重复代码与文件冗余，希望一样能为使用者带来益处。如果您也想贡献好的代码片段，请将代码以及描述，通过邮箱（xinkonghan@gmail.com）发送给我。代码格式是遵循自我主观，如存在不足敬请指出！安装pipinstalleasy_spider_tool主要功能时间相关before_day昨天日期（可用于时间递减）after_day明天日期（可用于时间递增）between_day两个日期之间current_date当前时间timestamp当前时间戳（支持精确到毫秒）date_parse任意格式时间解析(支持时区转换，指定保留日期/时间（可设置默认值）部分)json相关format_json漂亮美观的格式化输出jsonpath任意多个json路径解析（支持设置默认值，选取首个匹配值）hash摘要相关md5字符经md5编码正则匹配相关regex_match条件匹配（支持多个不相关条件匹配,支持设置默认值，选取首个匹配值）for_to_regx_match多个不相关条件匹配（兼容老版本保留）数据清洗/转换相关cookie_to_diccookie转换为字典（Dict）格式clear_value清除列表（List）或字典（Dict）中的指定值（递归清除所有嵌套字典和列表中的指定值）合法性验证相关verify_ip_addressIP地址合法性验证verify_domain_name域名合法性验证verify_port端口合法性验证verify_urlURL合法性验证通知相关暂无简单使用fromeasy_spider_toolimportformat_json,jsonpathdata={"code":200,"data":[{"id":1,"username":"admin","level":"boss"},{"id":2,"username":"user","level":"staff"}]}boss_name=jsonpath(data,'$.data[?(@.level=="boss")].username',first=True)all_user_info=jsonpath(data,'$.data[*].username')print(boss_name)print(format_json(all_user_info))链接Github：https://github.com/hanxinkong/easy-spider-tool在线文档：https://easy-spider-tool.xink.top/注明该工具借鉴作者【xingcweb】,根据主观新增部分功能
Package: easy-spider-tool-document
simple-spider-tool-documenteasy-spider-tool 可选xpath/jsonpath聚合解析扩展包安装pipinstalleasy-spider-tool[document]主要功能data_extractor表达式数据解析（支持jsonpath,xpath）xpathxpath语法解析数据（支持首选项，设置默认值）简单使用fromeasy_spider_tool_documentimportdata_extractordata='<p>这是一个easy_spider_tool的document扩展的示例</p>'print(data_extractor(data,['//p//text()'],first=True,default=''))# 这是一个easy_spider_tool的document扩展的示例data={"code":200,"data":[{"id":1,"username":"admin","level":"boss"},{"id":2,"username":"user","level":"staff"}]}print(data_extractor(data,['$.data[*].username'],first=False,default=''))# ['admin', 'user']链接Github：https://github.com/hanxinkong/easy-spider-tool-document在线文档：https://easy-spider-tool-document.xink.top/注明
Package: easy-splunk
easy_slpunk>A simple and complete package to abstract main operations with Splunk API (send data / run searches).Installpip install easy_splunkUpgradepip install easy_splunk -UUsagefromeasy_splunkimportSplunkhost="EVENT_HOST"source="EVENT_SOURCE"try:spk_hec=Splunk(protocol="https",url="10.0.0.2",port="8088",timeout=60,hec_key="e51e9c62-5f25-46cf-9a4e-218638cdab77")spk_syslog=Splunk(protocol="syslog",url="10.0.0.2",port="5514")except:raise#Send a dict data as JSON to Splunk APIdata_hec={}data_hec["Key_1"]="Valor_1"data_hec["Key_2"]="Valor_2"data_hec["Key_3"]="Valor_3"spk_hec.send_data(event_host=host,event_source=source,event_data=data_hec)spk_hec.send_data(event_source=source,event_data=data_hec)#Send a basic syslog message to Splunkdata_syslog="Syslog message sent by easy_splunk"spk_syslog.send_data(event_data=data_syslog)#Run a specific search and get the result as a list of JSONssearch='index=raw_syslog | head 1'search_output=spk_hec.run_search(username='admin',password='admin',search=search)print(search_output)OUTPUTS SEND_DATA()OUTPUT RUN_SEARCH()[
    {
        'preview': False, 
        'offset': 0, 
        'result': 
        {
            '_bkt': 'raw_syslog~0~1C4DDDBB-BFC8-49A2-A2FC-6418F3E80CAD', 
            '_cd': '0:56', 
            '_indextime': '1561619057', 
            '_raw': 'Syslog message sent by easy_splunk', 
            '_serial': '0', 
            '_si': ['localhost', 'raw_syslog'], 
            '_sourcetype': 'syslog', 
            '_time': '2019-06-27 15:04:17.000 CST', 
            'host': '10.0.0.2', 
            'index': 'raw_syslog', 
            'linecount': '1', 
            'source': 'udp:5514', 
            'sourcetype': 'syslog', 
            'splunk_server': 'localhost'
        }
    }
]
Package: easy-spotify
# Spotify-Wrapper
Easy-Spotify is a python Wrapper for the Spotify Web API. It was conceived with ease of use in mind. It allows you to retrieve data from Spotify such as track or artist information, images, related artists, top tracks, and more.## Documentation
Install the package as usual.~~~bash
pip install easy-spotify
~~~To be able to access Spotify Web API you need to provide easy-spotify with your <code>client_id</code> and <code>client_secret</code> which you can obtain [here](https://developer.spotify.com/dashboard/).~~~python
from easy_spotify import Spotifyspotify = Spotify(MY_CLIENT_ID, MY_CLIENT_SECRET)
~~~##### Get Artist or Track ID
Search for an artist or track in Spotify’s catalogue and get back the top results.
~~~python
my_artist_id = spotify.get_artist_id(“Adele”, only_id=False)my_track_id = spotify.get_track_id(“Starboy”)
multiple_tracks_id = spotify.get_multiple_track_id([“Starboy”], [“Hello”], [“Cake By The Ocean”])
~~~##### Artist related methods
~~~python
artist_info_name = spotify.get_artist_info_from_name(“Adele”)
artist_info_id = spotify.get_artist_info_from_id(“ARTIST_ID”)my_albums_name = spotify.get_albums_from_name(“Adele”)
my_albums_id = spotify.get_albums_from_id(“ARTIST_ID”)top_tracks_name = spotify.get_artist_top_tracks_from_name(“Adele”, just_id_and_name=False)
top_tracks_id = spotify.get_artist_top_tracks_from_id(“ARTIST_ID”, just_id_and_name=True)related_artists = spotify.get_related_artists(“ARTIST_ID”)
~~~##### Track related methods
~~~python
track_features = spotify.get_track_audio_features(“TRACK_ID”)
multiple_track_features = spotify.get_multiple_tracks_audio_features([“TRACK_ID”, “TRACK_ID”, “TRACK_ID”])track_info = spotify.get_track_info(“TRACK_ID”)
multiple_track_info = spotify.get_multiple_tracks_info([“TRACK_ID”, “TRACK_ID”, “TRACK_ID”])
~~~
Package: easysql
EasySQLOverviewEasySQL is a dependency-free, pythonic module to make your SQL projects easy and fastTo install EasySQL, simply usepip install easysql.
To view examples, look inexamples.py, or continue reading for a more extensive description.ConditionalsConditionals can be imported directly fromeasysql.fromeasysqlimportSQLConditionalConditionals are initiated with a string representation of a condition.fromeasysqlimportSQLConditionalassqccondition=sqc("age > 17")To join conditionals, use&, |, ~forAND, OR, NOT, respectively. When compared, a new conditional is returned.fromeasysqlimportSQLConditionalassqccondition=sqc("age > 17")condition2=sqc("age < 100")condition3=condition&condition2# ANDcondition4=condition|condition2# ORcondition5=~condition# NOTQueriesQueries are inititated with the query type of the Query, the args for the type, and inititated from a DBType or with a DBType as a param.fromeasysqlimportDBType,Querydb=DBType("<db name>")Q_param=Query("<type>",<args>,db=db)#  With DBType as a parameterQ_method=db.query("<type>",<args>)#  From DBType methodQueries currently have the following types:WIPselect         -- SQL SELECT method.           Params: column_names, table_name, constraints=None
delete         -- SQL DELETE method.           Params: table_name, constraints=None
update         -- SQL UPDATE method.           Params: 
insert         -- SQL INSERT method.           Params:
table          -- SQL CREATE TABLE method.     Params:
selectdistinct -- SQL SELECT DISTINCT method.  Params:
Package: easySQL-fiachia
Name:easySQLAuthor:fiachiaFunction:use easy function to execute pyMySQL moduleDefect:only easy SQL can be useduse:database connect:db=easySQL(databaseName, user, passwd, host) or easySQL(database)table creat:table = tableSQL(tableName, engine, charset)table.addAttribute(aName, aType, aLong, default, isNotNull, isPrimaryKey, isAuto, isUnique, isUnsigned)sql = table.creatLanguage()db.commit(sql)data find:data = db.find(tableName, key1=value1, key2=value2…)data add:db.add(tableName, key1=value1, key2=value2…) or db.add(tableName, data)data delete:db.delete(tableName, key1=value1, key2=value2…) or db.add(tableName, data)data update:db.update(data, key1=value3, key2=value4…)other:db.commit(sql) or db.getData(sql)data add dataList:add all data in dataList. if input data is not list, it will be list in this function. this function need all input list type have the same length.db.addList(tableName, key1=list1, key2=list2…)data add(data add data rather than data add to table)data.add(data1)creat table by DIC(json.loads()):db.creat(tableName, childDeep=0, **kwargs)2.0.4:fix the columns in mysql maybe in confusion2.0.5:you can get data by a dict type2.0.6:fix a bug that dict use the same addr2.0.7:Modify a small amount of code and logic to improve efficiencydb.commit(sql) change to db.execute(sql)you can use db.addSQL(sql(s)) db.showSQL(index=None) db.deleteSQL(index=None) to change sqlList and then db.commit() to run sqlList2.0.8:fix a wrong that NoneType in data
Package: easysqulite-321onoff
Easy SquliteA easy version of squlite for begginersDocumentation= Requred ArgumentsKey!! Don't type the "*" !!e.g. connect(databasename) *
format : command(args)Required Perameters:connect(databasename) *Commands:maketable(tablename,columns) *
Creats a table in the database
TABLENAME is the name of the table and COLUMS is the type eg: number INTEGERinserttable(tablename,columns,values) *
Used to insert data in the databasedeleteinput(tablename,columns,values) *
Delete stuff from tablesdeletetable(tablename) *
Delete Entire Tablescheckiftableexists(tablename) *
Check if a spesific table exists
TIP: This command returns True/Falsecheckifvalueexists(tablename,columns,values)
Checks if a value exists in a table
TIP: This command returns true/falsegettable(tablename)
Returns the value(s) and columns of a entire tablegetcolumn(tablename,columns)
Returns the value(s) of a columncleardatabase(databasename)
Deletes a entire databasegetdatabase(databasename)
Get all the stuff in a database
Package: easysrv
Easy State Reversible VampnetThis is an package providign State Reversible Vampnet from the J. Chem. Phys. 150, 214114 (2019). This is a deep dimensionality reduction used for time series to extract low dimensional slow process description. Usually it is used as an pre-processing tool to build Markov State Model to reduce number of dimensions in the system.InstallationAt the moment installation is available through github and pypigithub:git clone https://github.com/DanielWicz/easysrv
cd easysrv
pip install .PyPipip install easysrvUsageTo use it you need two thingsYour data as an list of numpy arrays. Where each element of the list is an numpy array of shape (time_series_length, number_of_dims).Tensorflow 2 Sequence Modelimport tensorflow as tf


# use smooth activation, to have smooth slow process assigment
# use non-smooth activation (relu, lerelu etc.) to have non-smooth slow process assigment
model = tf.keras.models.Sequential([
    tf.keras.layers.Dense(100, kernel_regularizer=tf.keras.regularizers.L2(0.0001), kernel_initializer='lecun_normal'),
    tf.keras.layers.Activation("swish"),
    tf.keras.layers.BatchNormalization(),
    tf.keras.layers.Dense(100, kernel_regularizer=tf.keras.regularizers.L2(0.0001), kernel_initializer='lecun_normal'),
    tf.keras.layers.Activation("swish"),
    tf.keras.layers.BatchNormalization(),
    tf.keras.layers.Dense(2, kernel_regularizer=tf.keras.regularizers.L2(0.0001)),
    tf.keras.layers.GaussianNoise(1)])
# assume that list with data are described as a variable features
# pass single datapoint to set shapes of the matrices
model(features[0])

# initialize optimizer
optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)

# initialize SRV, epochs - number of training iterations, split - train:validation split
srv = SRV(model=model, optimizer=optimizer, epochs=20, lagtime=1, split=0.05)

# fit SRV model
history = srv.fit(features)

# depict training process
plt.plot(history['Training loss'])
plt.title("Training loss")
plt.xlabel("Epochs")
plt.ylabel("Loss")

plt.plot(history['Val Training loss'])
plt.title("Validation Training loss")
plt.xlabel("Epochs")
plt.ylabel("Loss")

plt.plot(history['VAMP2 valid score'])
plt.title("Validation VAMP2")
plt.xlabel("Epochs")
plt.ylabel("VAMP2 score")

modes = ["IC{}".format(i+1) for i in range(len(history['eigenvalues']))]
plt.bar(modes, history['eigenvalues'])
plt.title("Slow processses eigenvalues")

# transform relevant features onto slow processes
# remove_modes allows for removing not interesting slow processes from slowest to the fastest
model_out = svr.transform(features, remove_modes=[])ReferencesChen, Wei, Hythem Sidky, and Andrew L. Ferguson. "Nonlinear discovery of slow molecular modes using state-free reversible VAMPnets." The Journal of chemical physics 150.21 (2019): 214114.
Package: easyssl
easysslFunctionsModuleFunctionUse Casesslto simplify opensslSimple en/de-cryption implement to build your own app.cato simplify x.509Maintain your own CA and sign crt on demand easily.Encryptionimportjsonimportdatetimeimportsocketfromeasysslimportsslhostname=socket.gethostname()time_stamp=datetime.datetime.now().strftime("%Y%m%d%H%M%S")k=ssl.Key()k.load_pubkey()plain_text=hostname+'__-__'+time_stampplain_text=json.dumps({'k1':'v1','k2':'v2','k3':'v3',})k.encrypt(plain_text=plain_text)Decryptionimportjsonimportdatetimeimportsocketfromeasysslimportsslk=ssl.Key()k.load_prikey()encrypted_text='F/IicdulrsDfrTsrR7ainjM23+kV8/7Li7pbLb/Zn6VGJD4Y9Nz18f4rb7WCW7di1TIzr+qTpY8nAI4DbphCC3FfGrIYgoBY0pCuzOng79R/Rrpgm4VIHbiGdssP7W1ihsksgd4ttcG9KR7GfJeeGaOlK5w1vl/9inveuUB8hQA4bhmrpEK/+HmYxXPtVadW3ZqRNf8wyGJwED63d1U7N7iO6Q5Rph2t1hmVKKRZ9uTeeAno1oo2a2N6xQ1IiwUkqsjggYkiLbCjm28/lony/2QtqvSdEzOmHzlTFeamcZqN5zxrkh2ozq07kJo6xibpN0EMSSgBZUaTLG2WtGbvoA=='k.decrypt(encrypted_text)
Package: easystac
A Python package for simple STAC queriesGitHub:https://github.com/cloudsen12/easystacDocumentation:https://easystac.readthedocs.io/PyPI:https://pypi.org/project/easystac/Conda-forge:https://anaconda.org/conda-forge/easystacOverviewSpatioTemporal Asset Catalogs (STAC)provide a standardized format that describes
geospatial information. Multiple platforms are using this standard to provide clients several datasets.
Platforms such asPlanetary Computer,Radiant ML HubandGoogle Earth Engineuse this standard,
however, only Google Earth Engine provides a fully easy API that is transparent for clients.easystacis a Python package that provides users of STAC objects as well as clients from Planetary Computer and Radiant ML Hub
with an easy API that is transparent for them, implementing Google Earth Engine-like methods
and classes to query, explore and convert STAC assets toxarrayobjects.Some of theeasystacfeatures are listed here:Simple authentication for Planetary Computer and Radiant ML Hub.Access to STAC collections from Planetary Computer and Radiant ML Hub.Earth Engine-like classes such as ImageCollection, including filtering methods.Compatibility with xarray.Check the simple usage ofeasystachere:importeasystacasesfromgeojsonimportPolygongeom=Polygon([[[-122.1553,38.7578],[-121.8321,39.7444],[-123.0002,39.7503],[-123.0002,38.7609],[-122.1553,38.7578]]])HLSS30=(es.ImageCollection("HLSS30.v2.0").fromSTAC("https://cmr.earthdata.nasa.gov/stac/LPCLOUD/").filterBounds(geom).filterDate("2021-01-01","2022-01-01").getInfo(epsg=4326,resolution=0.0001,assets=["B02","B03","B04"]))In the case of specialized STAC objects, we have created special modules for Planetary Computer:importeasystac.planetaryaspcfromgeojsonimportPointpc.Authenticate()pc.Initialize()geom=Point([-76.1,4.3])S2=(pc.ImageCollection("sentinel-2-l2a").filterBounds(geom).filterDate("2020-01-01","2021-01-01").getInfo(resolution=10))This principle applies also for Radiant ML Hub.importeasystac.radiantasrdrd.Authenticate()rd.Initialize()S1floods=(rd.ImageCollection("sen12floods_s1_source").filterDate("2019-01-01","2019-01-05").getInfo(epsg=4326,resolution=0.0001))InstallationInstall the latest version from PyPI:pip install easystacUpgradeeasystacby running:pip install -U easystacInstall the latest version from conda-forge:conda install -c conda-forge easystacInstall the latest dev version from GitHub by running:pip install git+https://github.com/cloudsen12/easystacLicenseThe project is licensed under the MIT license.
Package: easy-staff-required
Easy Staff RequiredA simple Django decorator to restrict view access to staff users.InstallationInstall the package using pip:pipinstalleasy-staff-requiredUsageAfter installing the package, you can use the@staff_requireddecorator in your Django views to restrict access to staff users. Here's an example:fromdjango.shortcutsimportrenderfromeasy_staff_requiredimportstaff_required@staff_requireddefmy_view(request):returnrender(request,"my_template.html")The @staff_required decorator checks if the user is authenticated and has the is_staff attribute set to True. If the user is not a staff member, a PermissionDenied exception is raised.You can also use the @staff_required decorator in combination with Django's built-in @login_required decorator:fromdjango.contrib.auth.decoratorsimportlogin_requiredfromeasy_staff_requiredimportstaff_required@login_required@staff_requireddefmy_view(request):returnrender(request,"my_template.html")
Package: easystate
Easy State-Machine for pythonfrom easystate import *
import time
SPEED = 1class Starting(State):def eval(self):print ‘Starting’
time.sleep(SPEED)def on_start(self,e):print “Hello”def on_finish(self,e):self.transition(“Listening”)class Listening(State):def eval(self):print ‘Listening’
time.sleep(SPEED)
self.raiseEvent(“connect”)def on_start(self,e):print “Preparing for listening”def on_connect(self,e):self.transition(‘Negotiation’)class Negotiation(State):def eval(self):print ‘checking validity’
time.sleep(SPEED)
print ‘authenticating’
time.sleep(SPEED)
print ‘registering’
self.transition(“Connected”)def on_start(self,e):print ‘Negotiating’class Connected(State):def eval(self):print ‘Connected’def on_finish(self,e):self.transition(“Listening”)m = StateMachine(initialState=”Starting”)
m.appendState(Starting())
m.appendState(Listening())
m.appendState(Negotiation())
m.appendState(Connected())
m.start()
Package: easysteam
easysteamCurrently in development stages, this tool can get Steam marketplace games, software, soundtracks, etc. relatively quickly.UsageCurrently the tool only has two functions; Pull, Pull_All.First create an instance of the class:store = Market()Then you either pull using the steam category link:store.Pull('https://store.steampowered.com/genre/Free%20to%20Play/', 'NewReleasesRows')Or using a name for the category:store.Pull('Free to Play, 'NewReleasesRows')The second input as you can see has 'NewReleasesRows' as the row input. You can set it to eitherNewReleasesRowsorTopSellersRowsto choose between:New and Trending (for the category)Top Selling (for the category)
Package: easystockdata
EasyStocksDataA python module that gives stock price data and stock symbols.InstallationInstalleasystocksdatausingpip:$ pip install easystockdataDescription of functionsget_prices :Input:symbol: Symbol of stock (string)intv: Interval of time (string, optional, default=1d)rng: Range of time (string, optional, default=1wk)intvandrngboth takes values from1m,2m,5m,15m,30m,60m,90m,1h,1d,5d,1wk,1moand3mo.ohlc: Specify High, Low, Open and Close prices (string, optional, default='high')stock_exc: specify stock exchange to get data from (string, optional, default='nse')Output:
list: A list of lists containing the timestamp and corresponding price for each data point.get_nse_symbols :Input: NoneOutput: A list of dictionaries containing the stock names and symbols of nse.get_nasdaq_symbols :Input: NoneOutput: A list of dictionaries containing the stock names and symbols of nasdaq.find_nse_stock :Input:search_term: search term to search for stock symbol in nse (string)Output: A list of dictionaries containing the stock names and symbols of nse.find_nasdaq_stock :Input:search_term: search term to search for stock symbol in nse (string)Output: A list of dictionaries containing the stock names and symbols of nasdaq.
Package: easystockplot
easystockplot - An easy Python plot package for stocks!Overvieweasystockplotis a Python package that allows users to easily plot the daily stock price and return of a stock over the past year by default. You can also specify the period (by specifyingend_dateandnumber of days) of the stock you want to plot.InstallationTo download easystockplot, either fork this github repo or simply use Pypi via pip.pipinstalleasystockplotUsageImport the packagefromeasystockplotimportStockPlotPlot the daily stock price and 30-day moving average over the past yearApple_plot=StockPlot('AAPL')Apple_plot.plot_stock()Plott the daily stock return over the past yearApple_plot.plot_stock_return()