Saravanan666/pypi-org huggingface dataset:
Package: pandaSDMX
Source code @ Github—AuthorspandaSDMXis anApache 2.0-licensedPython package that
implementsSDMX2.1 (ISO 17369:2013), a format for exchange ofstatistical data and metadataused by national statistical agencies,
central banks, and international organisations.pandaSDMXcan be used to:explore the data available fromdata providerssuch as the World Bank, ILO, BIS, ECB, Eurostat, OECD, UNICEF and United Nations;parse data and metadata in SDMX-ML (XML) or SDMX-JSON formats—either:from local and remote files, orretrieved from pandasdmx web services, with query validation and caching;convert data and metadata intopandasobjects,
for use with the analysis, plotting, and other tools in the Python data
science ecosystem;apply theSDMX Information Modelto your own data;…and much more.Documentationstable:https://pandasdmx.readthedocs.io/latest:https://pandasdmx.readthedocs.io/en/latestorv0.9:https://pandasdmx.readthedocs.io/en/v0.9for the 0.9 release.LicenseCopyright 2014–2023,pandaSDMX developersLicensed under the Apache License, Version 2.0 (the “License”); you may not use
these files except in compliance with the License. You may obtain a copy of the
License:from the file LICENSE included with the source code, orathttp://www.apache.org/licenses/LICENSE-2.0Unless required by applicable law or agreed to in writing, software distributed
under the License is distributed on an “AS IS” BASIS, WITHOUT WARRANTIES OR
CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.pandaSDMX contains code fromSDMX1which is itself
a fork of  pandaSDMX.
Package: pandasecharts
PandasEchartsEnglish README简介基于pandas和pyecharts的可视化工具，该项目的旨在用一行代码可视化您的pandas数据。安装pip 安装$pipinstallpandasecharts源码安装$gitclonehttps://github.com/gamersover/pandasecharts
$cdpandasecharts
$pipinstall-rrequirements.txt
$pythonsetup.pyinstall使用notebook环境基本直方图带时间变化的直方图更多使用方法请访问documentationexample后续计划目前已支持图表类型PieBarBar3DLineLine3DScatterScatter3DBoxplotGeoMapFunnelCalendarWordCloud0.5版本计划支持代码提示加入datazoom修复line图形当xtype为value时需要排序的问题LicenseMIT©gamersover
Package: pandas-eda
pandas EDABig Data? Machine Learning?You work with your data -Manipulating it, merging, pivot and more.But what happened in between? Did you get a lot of nans? Maybe duplicated values?You constantly need to check your data status,But you cannot do values_counts, isna and all those stuffs every second...pandas_edais anExploratory Data Analysistool that will show you status and frequent values for each column!You will be focused on what you have on the spot.Demois the best way to understand:install:pipinstallpandas_edausage:importpandasaspdimportpandas_edafromtimeimportsleep# dummy datadf=pd._testing.makeMixedDataFrame()# or load your data with pd.read_excel('whatever.xlsx')# show original data. will pop up a web applicationdf.eda()# can use this at debug mode too!# that's it. but you can do more...# manipulation #1df.A+=10process=df.eda(title='take 2')# yes, you can open multiple EDA windows!# process.kill()# manipulation #2df.loc[df.B==1,'A']-=30df.eda()# no need at jupyter or debug mode...# ending the script will end the eda too, so delaying the exit.sleep(600)headless modeimportpandasaspdimportpandas_eda# disabling the wrapping of a long table at the print...pd.options.display.expand_frame_repr=Falsepd.options.display.max_columns=0pd.options.display.min_rows=20df=pandas_eda._testing.generate_fake_table(samples=1_000)eda=df.eda(cli_mode=True)print('\n\n*** column statistics *** ')print(eda.get_columns_statistics())print('\n\n*** frequent values *** ')print(eda.get_frequent_values())testing withpython-mpandas_eda--testnote:If you're running on remote machine, the eda will be opened on the remote...alternatives:After starting this tool I've found 2 cool alternatives:sweetvizHas a nice interactive report.mitoGreat for new table that needs also cleaning.Works only at jupyter.
Package: pandaserd
Create ERD diagram from pandas dataframescreate an ERD object and add tables and connections between tablessave the generated dot code to anoutput.txtfile containingcopy-paste theoutput.txtinto a graphviz rendering tool like thisonethe output image at the bottom is generated by using the above linkInstallation via pip:pip install pandaserdExample use:import pandas as pd
from pandaserd import ERD

df1 = pd.DataFrame(data=[[123, 32, '1 Ottawa Street'], [123, 80, '14 Canada Road']])
df1.columns = ['PERSON', 'AGE', 'ADDRESS']
df2 = pd.DataFrame(data=[[6342, 124123124124, '1992-01-02', 21, 'K012V4'], [3124, 154823124124, '1984-02-02',42,'L4Y6S2']])
df2.columns = ['PERSON', 'CREDIT_CARD', 'DOB', 'PERSON_AGE', 'POSTAL_CODE']

erd = ERD()
t1 = erd.add_table(df1, 'PERSON', bg_color='pink')
t2 = erd.add_table(df2, 'CREDIT_CARD', bg_color='skyblue')
erd.create_rel('PERSON', 'CREDIT_CARD', on='PERSON', right_cardinality='*')
erd.create_rel('PERSON', 'CREDIT_CARD', left_on='AGE', right_on='PERSON_AGE', left_cardinality='+', right_cardinality='+')

erd.write_to_file('output.txt')CreditsLargely inspired by this fabrepo!dot documentation can be foundhere
Package: panda-server
Core package for PanDA serverPanDA documentationhttps://panda-wms.readthedocs.io/en/latest/PanDA server installation instructionshttps://panda-wms.readthedocs.io/en/latest/installation/server.htmlRelease notesSee ChangeLog.txtPanDA server cluster details for ATLAShttps://github.com/PanDAWMS/panda-server/wiki/ATLAS-production-PanDA-servers-%5BSeptember-2023%5D
Package: pandas-etl
pandas-etlA utility for running YAML config as Pandas based ETL codeInstalling ⏬pipinstallpandas-etlUsage 📝YAML Config:my-run.yamlvariables:server:MY_SERVER_NAME.MYDOMAIN.COMdatabase:MY_DATABASEpreFlight:script:|import pandas as pdconnections:my_source:postgresql+psycopg2://${var.server}/${var.database}steps:-name:source-df# Unique step name in pipelinedescription:Read from my PostgreSQL serverfunction:pd.read_sql# Call static functionargs:# Key-word based parameter inputsql:|SELECT int_column, date_columnFROM test_datacon:${ conn.my_source }index_col:int_columnparse_dates:{"date_column":{"format":"%d/%m/%y"}}-name:Grouper for date columnfunction:pd.Grouperargs:key:date_columnfreq:W-MON-name:group-datadescription:Group data by int and date columns every weekfunction:${ steps['source-df'].output.groupby }args:by:${steps['Grouper for date column'].output}axis:columnsdropna:false-name:aggregate-datafunction:${ steps['group-data'].output.max }-name:save-datafunction:${ steps['aggregate-data'].output.to_csv }args:-./my-aggregated-data.csvRunning this YAML configpython-mpandas_etl--file"./my-run.yaml"YAML Config (short-hand format)Functions can be written in short-hand to optimize readability and minimize overall size of config file.Below is working example with Imports--importsand override Variables--var:my-run.yamlpreFlight:script:|import pandas asimports:-./etl_definition_folder/variables/postgresql_database_variables.yamlconnections:my_database:postgresql+psycopg2://${var.username}:${var.password}@${var.server}:${var.postgresql_port}/${var.database}steps:-pd.read_sql:sql:|SELECT int_column, date_columnFROM test_datacon:${ conn.my_database }index_col:int_columnparse_dates:{"date_column":{"format":"%d/%m/%y"}}-pd.Grouper:key:date_columnfreq:W-MON-${ steps['pd.read_csv'].output.groupby }:by:${steps['pd.Grouper'].output}axis:columnsdropna:false-${ steps['pd.read_csv.groupby'].output.max }:-${ steps['pd.read_csv.groupby.max'].output.to_csv }:path_or_buf:./my-aggregated-data.csvVariables:postgresql_database_variables.yamlvariables:server:MY_SERVER_NAME.MYDOMAIN.COMdatabase:MY_DATABASEpostgresql_database-secret_variables.yamlvariables:username:postgrespassword:passwordRunning this YAML config from command line:python-mpandas_etl--file"./my-run.yaml"--imports"./etl_definition_folder/variables/secrets/postgresql_database-secret_variables.yaml"--var"postgresql_port=9999"
Package: pandas-excel-limitedrows
DescriptionPandas Excel LimitedRows is a Python library used to optimize the Pandas's read_excel function, adding a max_row parameter if you need to read only the X first lines.InstallationUse the package managerpipto install foobar.pipinstallpandas-excel-limitedrowsUsageimportpandas_excel_limitedrowsaspdlr# Generate DataFrame with first 50 rowsdf=pdlr.read_excel(file,max_rows=50)# Instanciate ExcelFile with first 50 rowsexcel_file=pdlr.RowLimitedExcelFile(file,max_rows=50)Notes:If you use sheet_name as list to read more than one sheet, all dataframes will be generated with max_rows limit;All others pandas.read_excel() and pandas.ExcelFile() parameters can be used;This package uses Pandas, OpenpyXL, Pyxlsb and Xlrd dependencies and works these three engines.ContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.LicenseMIT
Package: pandas-explode
pandas_explodeauthor: Oren OvadiaOverviewExplode utility for Pandas dataframes (similar toUNNESTorexplode)Examplesimportpandasaspdimportpandas_explodepandas_explode.patch()# adds a `df.explode` method to all DataFramesdf=pd.DataFrame({'s':['a','b','c'],'values':[[1,2],[3,4,5],[]]})df#    s     values# 0  a     [1, 2]# 1  b  [3, 4, 5]# 2  c         []df.explode('values')#    s  values# 0  a       1# 0  a       2# 1  b       3# 1  b       4# 1  b       5df=pd.DataFrame({'s':['a','b','c'],'values':[{'col1':1,'col2':2},{'col1':10,'col3':20},{'col2':2}]})df#    s                    values# 0  a    {'col1': 1, 'col2': 2}# 1  b  {'col1': 10, 'col3': 20}# 2  c               {'col2': 2}df.explode('values',axis=1)#    s  col1  col2  col3# 0  a   1.0   2.0   NaN# 1  b  10.0   NaN  20.0# 2  c   NaN   2.0   NaNdf.explode('values',axis=1,record_prefix=True)#    s  values.col1  values.col2  values.col3# 0  a          1.0          2.0          NaN# 1  b         10.0          NaN         20.0# 2  c          NaN          2.0          NaNInstallation / UsageTo install use pip:$ pip install pandas_explodeOr clone the repo:$ git clone https://github.com/orenovadia/pandas_explode.git
$ python setup.py installPublishing$ ./publish.sh
Package: pandas-ext
# pandas_extPython Pandas extensions for pandas dataframes# Usage```import pandas_ext as px```## CSVBy default, pandas will natively read to s3 but won't write to s3.```px.read_csvpx.to_csv```## ExcelBy default, pandas will natively read to s3 but won't write to s3.To write to xls:```bashpip install pandas_ext[xls]```To write xlsx:```bashpip install pandas_ext[xlsx]```## GdriveBy default, pandas does not read/write to Gdrive.### For G Suite administratorsAt the organization level, one must do the necessary installation anddeployment of the [gdrive lambda service](https://github.com/richiverse/gdrive-lambda/) to get this to work.You will have to create a service account that shares your G Apps domain with the following APIs enabled:- Google Drive- Google SheetsFrom there you must download the p12 credentials file and reference it in your settings.yml when deploying the gdrive service.### For Gdrive clientsOnce that is complete, you must share the folder you are interested in reading/writing to the service account email you've received from your administrator.Locally, for client access you must set the `GDRIVE_URL` and `GDRIVE_KEY` in your projects environment variable in order to talk tothe gdrive lambda service.```px.read_gdrivepx.to_gdrive```## ParquetBy default, pandas ~does not read/write to Parquet~. This has been added in pandas version 24 and my methods will eventually update to use them but still allow writing to s3.```px.read_parquetpx.to_parquet```## Spectrumto_spectrum is unique to pandas_ext.```px.to_spectrum```## Salesforcesalesforce methods are unique to pandas_ext.```px.read_sfdcpx.sfdc_metadatapx.patch_sfdcpx.async_patch_sfdc```## SQL service```px.read_sqlpx.list_backends## XMLPandas doesn't natively support writing to XML format.```px.to_xml```
Package: pandas-extensions
pandas_extensionsauthor: Ben de Veraview at:https://test.pypi.org/project/pandas-extensions/0.1/classesSuperDataFrameExtension of the pandas.DataFrame class that adds additional methods.- SuperDataFrame.null_report

    - Displays number of null values in each column of a pandas.DataFrame

    - Params
        self : pandas.DataFrame
        verbose : boolean

- SuperDataFrame.train_test_val_split

    - Provides a train/test/val split of a pandas.DataFrame

    - Params 
        self : pandas.DataFrame
        train_size : float 
        test_size : float
        verbose : booleanfunctionscould_mergeNaive check for whether DataFrames can merge- Params
    df1 : pandas.DataFrame
    df2 : pandas.DataFrame
Package: pandas-extras
pandas-extrasExtension package for the popular Pandas libraryInstallationThe package can be installed through pip:$pipinstallpandas-extrasUsageThe package contains helper function that are best called with thepipeoperator ofDataFrame.frompandas_extrasimportexpand_listsdf=DataFrame(...)df.pipe(expand_lists,*args,**kwargs)LicenseThis project is licensed under the BSD-3-Clause license - see theLICENSE.
Package: pandas-finance
High level API for access to and analysis of financial data.InstallationInstall via pip$pipinstallpandas-financeUsagefrompandas_financeimportEquityaapl=Equity('AAPL')aapl.annual_dividendaapl.dividend_yieldaapl.priceaapl.optionsaapl.hist_vol(30)aapl.rolling_hist_vol(30)Data is automatically cached for 1 hr using requests_cache.See thepandas-finance documentationfor more details.
Package: pandas-fpl
Pandas wrapper for Fantasy Premier League APITheFPLPandasclass in this package uses the excellentFPLlibrary to retrieve data from theFantasy Premier League API. It maps the results to the pandas data frames so that the data can be analysed interactively, e.g. in a Jupyter notebook.FPLlibrary is an asynchronous wrapper for theFantasy Premier League APIBut the Jupyter notebook work better with synchronous code, the methods exposed by theFPLPandasclass are synchronous.InstallationUsing pipYou can install using the pip package manager by runningpip install pandas-fplFrom sourceDownload the source code by cloning the repository or by pressingDownload ZIPon this page.
Install by navigating to the proper directory and runningpython setup.py installUsageFor usage guidance and testing the package interactively, hit theUsage Jupyter Notebook.DocumentationFor the code documentation, please visit theDocumentation Github Pages.ContributingFork the repository on GitHub.Run the tests withpython -m pytest tests/to confirm they all pass on your system.
If the tests fail, then try and find out why this is happening. If you aren't
able to do this yourself, then don't hesitate to either create an issue on
GitHub, contact me on Discord or send an email topy@177arc.net.Either create your feature and then write tests for it, or do this the other
way around.Run all tests again with withpython -m pytest tests/to confirm that everything
still passes, including your newly added test(s).Create a pull request for the main repository'smasterbranch.
Package: pandasfruit
pandasfruitSummaryTakes in a pandas dataframe and returns a string containing markdown format where the width of the markdown text is more narrow and is suitable for printing. The more narrow table can fit on a printed page, such as a PDF generated from a Jupyter notebook.Takes in an optional width parameter (number of characters) and will make the resulting markdown this wide. Else will use a default width of 115.Also will use a default dictionary of words/abbreviations to shorten column names using the abbreviations instead of the original words. Alternatively, a  user-defined dictionary may be passed in. Pass in empty dictionary to do no replacements.ProcessingTo achieve the more-narrow markdown version of the df, first column names are shortened using the default abbreviations dictionary (or the optional user-provided dictionary). Keywords in column names are replaced with abbreviations. Note that column names may contain words separated by either underscores or spaces.If, after wrapping column names, the width of the markdown is still greater than the desired markdown width, the columns are iteratively wrapped to increasingly shorter widths until the desired width of the markdown is achieved.Installationpip install pandasfruitUsageimportpandasaspdfrompandasfruitimportpandasfruitx=pd.DataFrame({'c foobar person number clean':['00000001','00000002','00000003','00000004','00000005','00000006','00000007','00000008'],'c foobar first name clean':['Eduard','Andriel','Faris','Shaye','Rodney','Arledge','Cory','Madison'],'c foobar last name clean':['Davis','Bell','Russell','Fisher','Wilson','Campbell','Collins','Thomas'],'c _foobar_home_street_address_1_clean':['1234 Maple Street REALLY REALLY REALLY SUPER REAL LONG ''STREET MORE MORE MORE WORDS SOME MORE','5678 Oakwood Avenue','910 Willow Lane','1122 Elm Drive','1314 Cedar Boulevard','1516 Pine Court','1718 Birch Road','1920 Juniper Place REALLY REALLY REALLY SUPER DUPER LONG ''APARTMENT NAME EVEN MORE WORDS HERE OK SOME MORE'],'c foobar home street address 2 clean':['Apartment 200','Suite 6 MORE MORE MORE AND EVEN MORE WORDS AND ADDING SOME ''MORE WORDS AND HERE ARE EVEN SOME MORE AND EVEN MORE WORDS ''ARE ADDED HERE OK','Apt. 2','Apartment 4021','2nd Floor','Unit C','Apt. 3','Downstairs']})print(x.to_markdown(tablefmt='grid'))x_for_display=pandasfruit.df_to_fitted_markdown(x)print(x_for_display)x_for_display=pandasfruit.df_to_fitted_markdown(x,width=80,abbreviations={'foo_bar':'fb','baz ham':'baz h'})
Package: pandas-gbq
pandas-gbqis a package providing an interface to the Google BigQuery API from pandas.Library DocumentationProduct DocumentationInstallationInstall latest release version via conda$condainstallpandas-gbq--channelconda-forgeInstall latest release version via pip$pipinstallpandas-gbqInstall latest development version$pipinstallgit+https://github.com/googleapis/python-bigquery-pandas.gitUsagePerform a queryimportpandas_gbqresult_dataframe=pandas_gbq.read_gbq("SELECT column FROM dataset.table WHERE value = 'something'")Upload a dataframeimportpandas_gbqpandas_gbq.to_gbq(dataframe,"dataset.table")More samplesSee thepandas-gbq documentationfor more details.
Package: pandas-gbq-magic
Big-Query-MagicThe package has been created for Mac OSRequirementsIt is assumed that you have basic packages like pandas installed
Install Auth2client and pandas_gbq
Ensure that you can query data using pandas gbqThis packacge installs a magic in your startup directory which makes querying in jupyter environment more user friendly and easier to use.Post intallation simply type %%BQSQL and write your query
example%%BQSQLSELECT*FROMtableLIMIT10;There are other features also present which makes it even more user friendly which can be explained with help of the argument this magic can take-job [Y or None] If Y is passed as job argument this will run just the query but will not pull the out put, this is useful when one is creating a large temp table during the workflow and does not want to download and load it in python, the existing pandas_gbq does not offer that-save_to [Table Name] Pass a variable in which the output needs to be storeNote Do not pass both the arguments at the same time-para [Y or None] If Y is passed the query will look if parameters or python variables are used in the query. Yes this is one of the best features of this magic you can use python variables directly in your queryexamplex=['user1','user2']``````python%%BQSQL-paraY-jobYSELECT*FROMtableWHEREuser_idIN@xCurrently string int and list of these two can be passed as a parameter. Note parameter needs to be preceded by@in case one was planning to use @ just as character in that case please avoid using parameterInstallationRunfrompandas_gbq_magicimportmagicmagic.install('You Bigquery Project name')Restart Jupyter notebook
Package: pandas-gdc-agent
pandas_gdc_agentThis project explores the Hasura Graphql Data Connectors (GDC) using pandas
Dataframe as backend.To quickly get started, please go through a sample app inexample.py.GDCAgentLink to pip package:https://pypi.org/project/pandas-gdc-agent/TheGDCAgentconstructor takes a list of tuples, where the first element
is the table name and the second element is theDataFramewhich corresponds
to the table name. To setup aGDCAgent, run the following:# setting up GDC Agentagent=GDCAgent([("table1",df1),("table2",df2)])Please note that the dataframe must have the primary key as index.After setting up theGDCAgent, start the GDCAgent server by running the
following:# starting the GDC Agentagent.run_agent()When you run theGDCAgent, it starts a flask server and exposes the required
endpoints explainedhere.Run exampleTheexample.pyuses the chinook database. To run the example, first create a
virtual environment:python3-mvenvgdc_envNow, activate the virtual environment and install the requirementssourcegdc_env/bin/activate
pipinstall-rrequirements.txtNext, start the GDC agent:pythonexample.pyNow start a Hasura Graphql Engine:curlhttps://raw.githubusercontent.com/hasura/graphql-engine/stable/install-manifests/docker-compose/docker-compose.yaml-odocker-compose.yml

docker-composeupNow apply the following metadata:POST /v1/metadata{"type":"replace_metadata","args":{"metadata":{"version":3,"backend_configs":{"dataconnector":{"reference":{"uri":"http://localhost:5000/"}}},"sources":[{"name":"chinook","kind":"reference","tables":[{"table":"Album","object_relationships":[{"name":"Artist","using":{"manual_configuration":{"remote_table":"Artist","column_mapping":{"ArtistId":"ArtistId"}}}}]},{"table":"Artist","array_relationships":[{"name":"Album","using":{"manual_configuration":{"remote_table":"Album","column_mapping":{"ArtistId":"ArtistId"}}}}]}],"configuration":{"tables":["Artist","Album"]}}]}}}
Package: pandas-genomics
Pandas ExtensionDtypes and ExtensionArray for working with genomics dataQuickstartVariantobjects holds information about a particular variant:frompandas_genomics.scalarsimportVariantvariant=Variant('12',112161652,id='rs12462',ref='A',alt=['C','T'])print(variant)rs12462[chr=12;pos=112161652;ref=A;alt=C,T]Each variant should have a unique ID, and a random ID is generated if one is not specified.Genotypeobjects are associated with a particularVariant:gt=variant.make_genotype("A","C")print(gt)A/CTheGenotypeArraystores genotypes with an associated variant and has useful methods and properties:frompandas_genomics.scalarsimportVariantfrompandas_genomics.arraysimportGenotypeArrayvariant=Variant('12',112161652,id='rs12462',ref='A',alt=['C'])gt_array=GenotypeArray([variant.make_genotype_from_str(s)forsin["C/C","A/C","A/A"]])print(gt_array)<GenotypeArray>
[Genotype(variant=rs12462[chr=12;pos=112161652;ref=A;alt=C], allele1=1, allele2=1),
Genotype(variant=rs12462[chr=12;pos=112161652;ref=A;alt=C], allele1=0, allele2=1),
Genotype(variant=rs12462[chr=12;pos=112161652;ref=A;alt=C], allele1=0, allele2=0)]
Length: 3, dtype: genotype[12; 112161652; rs12462; A; C]print(gt_array.astype(str))['C/C' 'A/C' 'A/A']print(gt_array.encode_dominant())<IntegerArray>
    [1.0, 1.0, 0.0]
    Length: 3, dtype: floatThere are alsogenomicsaccessors for Series and DataFrameimportpandasaspdprint(pd.Series(gt_array).genomics.encode_codominant())0    Hom
    1    Het
    2    Ref
    Name: rs12462_C, dtype: category
    Categories (3, object): ['Ref' < 'Het' < 'Hom']