Saravanan666/pypi-org huggingface dataset:
Package: csv-diff
csv-diffTool for viewing the difference between two CSV, TSV or JSON files. SeeGenerating a commit log for San Francisco’s official list of trees(and thesf-tree-history repo commit log) for background information on this project.Installationpip install csv-diffUsageConsider two CSV files:one.csvid,name,age
1,Cleo,4
2,Pancakes,2two.csvid,name,age
1,Cleo,5
3,Bailey,1csv-diffcan show a human-readable summary of differences between the files:$ csv-diff one.csv two.csv --key=id
1 row changed, 1 row added, 1 row removed

1 row changed

  Row 1
    age: "4" => "5"

1 row added

  id: 3
  name: Bailey
  age: 1

1 row removed

  id: 2
  name: Pancakes
  age: 2The--key=idoption means that theidcolumn should be treated as the unique key, to identify which records have changed.The tool will automatically detect if your files are comma- or tab-separated. You can over-ride this automatic detection and force the tool to use a specific format using--format=tsvor--format=csv.You can also feed it JSON files, provided they are a JSON array of objects where each object has the same keys. Use--format=jsonif your input files are JSON.Use--show-unchangedto include full details of the unchanged values for rows with at least one change in the diff output:% csv-diff one.csv two.csv --key=id --show-unchanged
1 row changed

  id: 1
    age: "4" => "5"

    Unchanged:
      name: "Cleo"You can use the--jsonoption to get a machine-readable difference:$ csv-diff one.csv two.csv --key=id --json
{
    "added": [
        {
            "id": "3",
            "name": "Bailey",
            "age": "1"
        }
    ],
    "removed": [
        {
            "id": "2",
            "name": "Pancakes",
            "age": "2"
        }
    ],
    "changed": [
        {
            "key": "1",
            "changes": {
                "age": [
                    "4",
                    "5"
                ]
            }
        }
    ],
    "columns_added": [],
    "columns_removed": []
}As a Python libraryYou can also import the Python library into your own code like so:from csv_diff import load_csv, compare
diff = compare(
    load_csv(open("one.csv"), key="id"),
    load_csv(open("two.csv"), key="id")
)diffwill now contain the same data structure as the output in the--jsonexample above.If the columns in the CSV have changed, those added or removed columns will be ignored when calculating changes made to specific rows.
Package: csv-diff-yhua
csv-diffTool for viewing the difference between two CSV files. SeeGenerating a commit log for San Francisco’s official list of trees(and thesf-tree-history repo commit log) for background information on this project.Consider two CSV files:one.csvid,name,age
1,Cleo,4
2,Pancakes,2two.csvid,name,age
1,Cleo,5
3,Bailey,1csv-diffcan show a human-readable summary of differences between the files:$ csv-diff one.csv two.csv --key=id
1 row changed, 1 row added, 1 row removed

1 row changed

  Row 1
    age: "4" => "5"

1 row added

  id: 3
  name: Bailey
  age: 1

1 row removed

  id: 2
  name: Pancakes
  age: 2The--key=idoption means that theidcolumn should be treated as the unique key, to identify which records have changed.You can also run it using the--jsonoption to get a machine-readable difference:$ csv-diff one.csv two.csv --key=id --json
{
    "added": [
        {
            "id": "3",
            "name": "Bailey",
            "age": "1"
        }
    ],
    "removed": [
        {
            "id": "2",
            "name": "Pancakes",
            "age": "2"
        }
    ],
    "changed": [
        {
            "key": "1",
            "changes": {
                "age": [
                    "4",
                    "5"
                ]
            }
        }
    ],
    "columns_added": [],
    "columns_removed": []
}You can also import the Python library into your own code like so:from csv_diff import load_csv, compare
diff = compare(
    load_csv(open("one.csv"), key="id"),
    load_csv(open("two.csv"), key="id")
)diffwill now contain the same data structure as the output in the--jsonexample above.If the columns in the CSV have changed, those added or removed olumns will be ignored when calculating changes made to specific rows.
Package: csvdivider
csvdividercsvdivider is a command-line tool for dividing CSV into separated CSVsExamplecsvdividexxx.csv1000#morecsvdivide-h
usage:csvdivide[-h][-iINPUT_FILE][-o[OUTPUT_PATH]][--head|--no-head]lines

DividingCSVintoseparatedCSVs.

positionalarguments:linesInsertacolumnoflinenumbersatthefrontoftheoutput.

optionalarguments:-h,--helpshowthishelpmessageandexit-iINPUT_FILE,--input_fileINPUT_FILETheCSVfiletooperateon.-o[OUTPUT_PATH],--output_path[OUTPUT_PATH]outputfolder--head,--no-headwithorwithoutcolumnnames,defaultvalue:TrueInstalling threado and Supported Versionsthreado is available on PyPI:$python-mpipinstallcsvdivider
Package: csv-download
csv_downloadScript to specify the column with URL in csv and download and save in bulkusagecsv_downloadreads csv and downloads the URLs of the specified columns in sequence.
You can see the available options with-h$csv_download-h

usage:csv_download[-h][-oOUT_PATH][-pPREFIX][-tTARGET_URL_COLUM][-nNAME_COLUM][--sleepSLEEP][--header-skip]filepath

positionalarguments:filepath

optionalarguments:-h,--helpshowthishelpmessageandexit-oOUT_PATH,--outOUT_PATH-pPREFIX,--prefixPREFIX-tTARGET_URL_COLUM,--target-url-colTARGET_URL_COLUM-nNAME_COLUM,--name-colNAME_COLUM--sleepSLEEP--header-skipexample$csv_download./example/example.csv-o./image-pexample--header-skipAuthor[Godan] (https://github.com/godan)Translated with Google Translate
Package: csvEmail
csvEmail.pyA simple script for sending mass emails based on a CSV file containing
email addresses plus per-recipient info and a template file containing
slots to be filled in from columns in the CSV file.I've solved this problem too many times so I'm putting this script up
where others can use it and where I can find and install it easily.Runpython3 csvEmail.py --helpfor usage info.Changelogv0.1: Created based on the script I had been kicking around for a few
semesters.
Package: csv-embeddings-creator
csv_embeddings_creator v1.0.4 by Bowen Chiu這東西可以把 google sheet 或者任意 .csv 變成知識庫，未來就能拿 .pt 檔案群來做句子相似度比對了。csv_embeddings_creator是一個 Python 套件，用於從 CSV 文件中創建句子嵌入。它使用 Hugging Face Transformers 的paraphrase-multilingual-MiniLM-L12-v2模型為文本生成嵌入。該套件將文本轉換為 txt 格式並將嵌入保存為 PyTorch 張量文件。安裝在安裝csv_embeddings_creator之前，請確保您的系統已安裝 Python 3.6 或更高版本。要安裝csv_embeddings_creator，請運行以下命令：pipinstallcsv-embeddings-creator使用方法CLI 示範用法您可以通過以下 CLI 呼叫方法使用csv_embeddings_creator：python3-mcsv_embeddings_creator--input-folder'input_csv_files'--output-txt-folder'output_txt_files'--embeddings-folder'output_embeddings'[--force]其中：--input-folder：包含 CSV 文件的輸入文件夾。--output-txt-folder：輸出 txt 文件的文件夾。--embeddings-folder：輸出嵌入文件的文件夾。--force（可選）：強制重新創建嵌入，即使它們已經存在。Python API 示範用法要在 Python 腳本中使用csv_embeddings_creator，請按照以下步驟操作：導入csv_embeddings_creator：importcsv_embeddings_creator使用create_embeddings函數創建嵌入：input_folder='input_csv_files'output_txt_folder='output_txt_files'output_embeddings_folder='output_embeddings'force=Falsecsv_embeddings_creator.create_embeddings(input_folder,output_txt_folder,output_embeddings_folder,force)其中：input_folder：包含 CSV 文件的輸入文件夾。output_txt_folder：輸出 txt 文件的文件夾。output_embeddings_folder：輸出嵌入文件的文件夾。force（可選）：強制重新創建嵌入，即使它們已經存在。常見問題如果您在使用csv_embeddings_creator時遇到問題，請查看以下常見問題解答。如果您仍無法解決問題，請在 GitHub 存儲庫中創建一個問題。Q: 我可以使用其他 Transformer 模型生成嵌入嗎？A: 是的，您可以在csv_embeddings_creator.py文件中修改SentenceTransformer('paraphrase-multilingual-MiniLM-L12-v2')以使用其他 Hugging Face Transformer 模型。確保您已安裝所需的模型及其依賴項。Q: 句子嵌入的尺寸是多少？A: 使用paraphrase-multilingual-MiniLM-L12-v2模型生成的句子嵌入的尺寸為 384。不同的 Transformer 模型可能具有不同的嵌入尺寸。貢獻我們歡迎您為csv_embeddings_creator做出貢獻！如果您有任何建議、改進或修復錯誤，請在 GitHub 存儲庫中創建一個 Pull 請求。
Package: csverve
csverveCsverve, pronounced like “swerve” with a “v”, is a package for manipulating tabular data.Free software: MIT licenseDocumentation:https://csverve.readthedocs.io.FeaturesTake in a regular gzipped CSV file and convert it tocsverveformatMerge gzipped CSZ filesConcatenate gzipped CSV files (handles large datasets)Rewrite a gzipped CSV file (delete headers etc.)Annotate - add a column based on provided dictionaryWrite pandas DataFrame tocsverveCSVRead acsverveCSVRequirementsEvery gzipped CSV file must be accompanied by a meta YAML file. The meta yaml file must have the exact name as the
gzipped CSV file, with the addition of a.yamlending.csv.gz.yaml must contain:column namesdtypes for each columnseparatorheader (bool) to specify if file has header or notExample:columns:-dtype:intname:prediction_id-dtype:strname:chromosome_1-dtype:strname:strand_1header:truesep:"\t"CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2020-12-16)First release on PyPI.
Package: csv-export-gsheets
Import/Export CSV files to Google SpreadsheetsSimple CSV import/export wrapper forgspreadpackage.InstallationPIPYou can install the latest stable package running this command:$ pip install csv_export_gsheetsAlso you can install the development version running this command:$ pip install git+http://github.com/dlancer/csv_export_gsheets.git@devUsageBefore you start you should:Create Google Service Account key (use JSON format):https://gspread.readthedocs.io/en/latest/oauth2.htmlCreate new spreadsheet in the Google Spreadsheets.Share this spreadsheet with email from created service account file.From command line:$ csv2gsheets --helpFrom python code:fromcsv_export_gsheets.gsheetimportimport_csv# src - path to source CSV file or StringIO object# url - destination sheet url# cell - destination sheet cell (can include tab name: 'MyTab!A1')# credentials - path to service account credentials or dict# config - path to config file or dictimport_csv(source=src,url=url,cell=cell,credentials=credentials,config=config)Please note: destination sheet will be cleared before import.TODOexport from google spreadsheet to CSV
Package: csvfaker
Generate CSV files with fake data, usingFaker.UsageGenerate a CSV file with 100 namescsvfaker –rows 100 name > names.csvGenerate 100 rows of first names, last names and job titlescsvfaker -r 100 first_name last_name jobGenerate 100 rows of first names and last names in Spanishcsvfaker -r100 –locale=es first_name last_nameGenerate 100 cities of Spaincsvfaker -r100 –locale=es_ES cityGenerate 100 Russian namescsvfaker -r100 -l ru nameGenerate 100 rows of latitudes and longitudes. Do not output headers rowcsvfaker -r 100 –no-headers latitude longitudeGenerate 100 rows of names and addresses. Replace generated newline characters with a comma and spacecsvfaker -r 100 name address –replace-newline=’, ‘Show a list of all available fakescsvfaker –list-fakesInstallationpip install csvfakerAuthorCopyright 2016 Pere OrgaLicensed under the Apache License, Version 2.0 (the “License”);
you may not use this file except in compliance with the License.
You may obtain a copy of the License athttp://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.
Package: csvfile
csvfileA simple way of working with csv files.You can use:data=csvfile.load("my-data.csv")data[0].field="new value"data.sync()Instead of:data=[]withopen("my-data.csv")asf:reader=csv.DictReader(f)fieldnames=reader.fieldnamesforrowinreader:data.append(row)data[0]["field"]="new value"withopen("my-data.csv, "w") as f:writer=csv.DictWriter(f,fieldnames=fieldnames)writer.writeheader()writer.writerows(data)Also you can declaratively define types in a table's header:>>>print(open("/tmp/csvfile_test.csv").read())language,created:ipython,1991js,1995rust,2010>>>pprint(csvfile.load("/tmp/csvfile_test.csv"))[{'created':1991,'language':'python'},{'created':1995,'language':'js'},{'created':2010,'language':'rust'}]Notice, thatcreatedis automatically converted into integer as we typed it in
the header ascreated:i.Built-in typesTypeAliasCommentstrsdefault, if no type specifiedboolbreadercan understand next pairs asTrue / Falsecase-insensitevely:true / false,t / f,1 / 0,y / n,yes / no,on / off, but thewriterwill always stringify them intotrue / falseintifloatfdecimalndatedISO 8601, YYYY-MM-DDdatetimetISO 8601, YYYY-MM-DDTHH:MM:SS.ffffffjsonjEmpty cells will becomeNonefor any type except ofstr.
As in case ofstrthere's no way to distinguish it from an empty string.APIThe api consists in a single functionloadwith the signature:table=load(filename:Union[Path,str],# the file should exist and contain headers*csvargs:Any,# `*args` for the standard `csv.reader`limit:Optional[int]=None,# maximum number of rows to read**csvkwargs:Any,# `**fmtparams` for the standard `csv.reader`)The returned object has alistinterface with an additionalsyncmethod to
save changes back into the file.Running testspip install -r requirements-dev.txt
pytest
Package: csv-file
csv_file libraryAuthor R.A.VidonchikovThis library provides the ability to read a csv file,
find the maximum and minimum elements in the matrix,
plot the values of the matrix elements, and also transpose the matrix to another csv file.def __init__(self, road, new_road):
    self.road = road
    self.results = []
    self.line = 0
    self.column = 0
    self.new_road = new_road

def reader_csv_file(self):
    """Reads a CSV file and print it as a list of rows."""
    with open(self.road, newline='', encoding='utf-8') as file:
        inp = csv.reader(file, delimiter=';', quotechar=',', quoting=csv.QUOTE_NONE)
        for row in inp:
            self.line += 1
            self.column = 0
            for number in row:
                self.column += 1
                number = int(float(number))
                self.results.append(number)

def finder_max(self):
    maximum = np.amax(self.results)
    return maximum

def finder_min(self):
    minimum = np.amin(self.results)
    return minimum

def graph(self):
    x_list = list(range(0, len(self.results)))
    y1_list = list(self.results)
    fig, ax = plt.subplots(figsize=(10, 5))
    ax.set_title('Демонстрация заполения матрицы')
    ax.set_ylabel('Числа матрицы')
    plt.plot(x_list, y1_list)
    plt.ylabel("", fontsize=14, fontweight="bold")
    plt.show()

def transpose(self):
    matrix = np.asarray(self.results).reshape(-1, self.column)
    matrix = matrix.transpose()
    with open(self.new_road, "w", newline='') as f:
        for row in matrix:
            print(' '.join(map(repr, row)), file=f)
Package: csvfilter
A simple wrapper around Python’s CSV module to provide a command-line tool for
filtering columns from a CSV file.  This is useful as standard tools like awk
can’t easily handle the quoting and escaping used in CSV files.Basically, it’s a bit likecutbut for CSVs.InstallFrom PyPi:pip install csvfilterUsePluck fields 1, 3 and 5 fromin.csv:csvfilter -f 1,3,5 in.csv > out.csvPluck all fields apart from column 2 from STDIN:cat in.csv | csvfilter -f 2 -i > out.csvConvert pipe-separated file to comma-separated (by default, output is
comma-separated):csvfilter -d"|" in.psv > out.csvSkip that pesky header row:cat in.csv | csvfilter --skip=1As you can see, CSV data can be supplied through STDIN or by runningcsvfilterdirectly on a
file.Help is in the usual place:$ csvfilter --help

Usage: csvfilter [options] [inputfile]

Source: https://github.com/codeinthehole/csvfilter/

Options:
-h, --help            show this help message and exit
-f FIELDS, --fields=FIELDS
                        Specify which fields to pluck
-s SKIP, --skip=SKIP  Number of rows to skip
-d DELIMITER, --delimiter=DELIMITER
                        Delimiter of incoming CSV data
-q QUOTECHAR, --quotechar=QUOTECHAR
                        Quotechar of incoming CSV data

-i, --inverse         Invert the filter - ie drop the selected fields
--out-delimiter=OUT_DELIMITER
                        Delimiter to use for output
--out-quotechar=OUT_QUOTECHAR
                        Quote character to use for outputReport issuesUse theGithub issue trackeror, better still…ContributeAfter cloning, install the testing requirements:pip install -r requirements.txtInstall the package in ‘develop’ mode to bring the executable onto your path:./setup.py developRun the tests with:nosetestsand, if it helps, use the fixture files to test your amendments:cat fixtures/au.csv | csvfilter -f 3,1,2 -s 1
csvfilter fixutres/au.csv -f 1,2 -iHave fun.
Package: csvformatmail
csvformatmail: A tool to format mails from csv and to send themInstallingFrom pypi:pip3 install csvformatmailOr developer version:git clone <this repo>
cd csvformatmail
pip3 install -e .Enable completion (for bash or other shells using bash-completion)mkdir -p ~/.local/share/bash-completion/completions
register-python-argcomplete csvformatmail > ~/.local/share/bash-completion/completions/csvformatmailPython moduleAll methods and functions are accessible in the python module.ExampleWrite a mail template:From: My name <my-mail-address@example.org>
To: {mail}
Subject: Result of the last test

Dear {firstname} {lastname.capitalize()},

Your results of the last test are:

 - Part A: {a:.1f}
 - Part B: {b:.1f}

Therefore you {"pass" if a+b>50 else "fail"} the test.

-- 
signatureUse a csv file with (at least) the columnsfirstname,lastname,mail,aandb:firstname,lastname,mail,a,b,c
Jacques,MARTIN,jacques.martin@example.org,12.54441,14,1111.221
…And the command if you have a local smtp:csvformatmail template.txt -t a:float -t b:float listing.csvOr if you use a distant smtp:csvformatmail -h smtp.example.org -l mylogin template.txt -t a:float -t b:float listing.csvComplex exampleThe template file can contain python definition, and all the column is
accessible by the namescols(default name, can be changed with arg--colsname). See the following template file:# python preamble begin
import numpy as np
def quartiles(list_values):
    q1, q2, q3 = np.percentile(list_values, (25,50,75))
    return f"{q1:.1f}, {q2:.1f}, {q3:.1f}"
# python preamble end

From: My name <my-mail-address@example.org>
To: {mail}
Subject: Result of the last test

Dear {firstname} {lastname.capitalize()},

Your results of the last test are:

 - Part A: {a:.1f}
 - Part B: {b:.1f}

For all the class, the mean of part A is {np.mean(cols['a']):.1}, and the
quartiles are {quartiles(cols['a'])}.

Therefore you {"pass" if a+b>50 else "fail"} the test.

-- 
signatureMiscConsider use functionfillandindentoftextwrapmodule. For this you
need use-b "import textwrap"or add a python preamble in you template file.e.g.in a template to rewrap to 72 chars:{textwrap.fill(somevalue, width=72)}Or, for rewrap and indent by>:{textwrap.indent(textwrap.fill(somevalue, width=70), "> ")}
Package: csv-from-mbox
Python script to extract emails from an .mbox file.Python script for extracting emails contained in the 'from' field of your mailbox from an .mbox file. Install the package usingpip install, run the script, paste the input directory, paste the output directory -> done.All the emails that contain one of the following are filtered out: noreply, no-reply, do-not-reply, do_not_reply.How to use:To installcsv-from-mboxusingpip install:pip install csv-from-mboxTo execute the script enter:csv-from-mboxYou'll be prompted to specify theinput path, for example to use an .mbox file in your current directory named mail.mbox, you would paste:./mail.mboxYou'll be prompted to specify theoutput path, for example to output the .csv file into your current directory and name it emails.csv, you would paste:./emails.csvThank you and have fun!
Package: csvgen
csvgenA command line application that randomly generates values, including mocked address data as CSV
(comma separated Values) output.OverviewThis is a quick overview for people not wanting to read the manual file.In general, this application is a utility for producing data for testing which does not require a database. The data is
output as CSV values and represents a varying set of use-cases.COMMON USE CASESEntering data in a UI that requires a field which captures product information as CSV values, indicators or numbers.ETL applications where CSV data is needed to test ETL application processes.Database applications, where CSV data is needed to test automation of inserts and database constraints.Spreadsheet applications to test automated spreadsheet imports and external queries common in spreadsheets.Any use-case where you don’t have access to a database but need data-sets to test your code.Any import-export automation process which requires output files in CSV format for testing.Possible ExamplesA website where users are required to enter defective product identifiers in CSV format for return processing.A database application that bulk processes delivery addresses.A ETL application responsible for scrubbing data and removing duplicate items or addresses.Testing of applications that require minimum and maximum field lengths.About DataThis application contains compiled data as the basis for generating output data. See the data readme file for how and
where that data was collected. The raw source data was scrubbed and altered to make the data usable in this utility
application. All data was obtained from freely available public sources. All data and licensing is subject to the terms
of the data suppliers covered in the data “read me” file.Note, that unless otherwise specified in the “read me” file all data is now and was previously under public domain.LicenseThis application is published under the MIT standard license.CommentsThis application is a utility for developers wanting to test data aspects of their software. This is NOT an enterprise
application and should NOT be used in a production environment.  This is a work in progress and may contain bugs or
other oddities including but-not-limited to verbose code that really should be refactored at some point.
It was designed for my use-cases only and may not fit your use-cases so feel free to fork this project, provide feedback,
offer changes etc…
Package: csv-ical
CSV/iCal ConverterA simple script to convert data in CSV format to iCal format and vice
versa.Installationpipinstallcsv-icalUsageSee the example files.Developmentpipinstall-rrequirements-test.txt
ruffcheck.
mypy.--strict--ignore-missing-imports
coveragerun-munittest
coveragereport-m
Package: cs.video
Introductioncs.video provides a Video object to refere to externally hosted FLV files. It uses FlowPlayer to show the video in the Plone site.It also provides to additional views for Folder and Large Folders to show current folder’s videos and also a video archive to show videos on folders inside current folder.We are using this athttps://dantzan.eus/bideoakIn its template the video is shown using FlowPlayer, which is included and distributed with the product.A macro called player is defined in the video_view template to be able to reuse the player in other templates such as portletsCode repository:https://github.com/codesyntax/cs.videoChangelog3.1 (2020-07-13)Set Panoramic as default value
[erral]3.0.4 (2017-12-12)this should use an IntegerWidget [Mikel Larreategi <mlarreategi@codesyntax.com>][Mikel Larreategi <mlarreategi@codesyntax.com>][ci skip] [Mikel Larreategi <mlarreategi@codesyntax.com>]3.0.3 (2017-11-24)readme [Mikel Larreategi <mlarreategi@codesyntax.com>][Mikel Larreategi <mlarreategi@codesyntax.com>][ci skip] [Mikel Larreategi <mlarreategi@codesyntax.com>]3.0.2 (2017-11-24)translations [Mikel Larreategi <mlarreategi@codesyntax.com>]remove fatsyndication/basesyndication traces [Mikel Larreategi <mlarreategi@codesyntax.com>]indentation [Mikel Larreategi <mlarreategi@codesyntax.com>]add new field [Mikel Larreategi <mlarreategi@codesyntax.com>][Mikel Larreategi <mlarreategi@codesyntax.com>][ci skip] [Mikel Larreategi <mlarreategi@codesyntax.com>]3.0.1 (2017-11-24)Plone 4.3 compatibility
[erral]2.0 (2012/10/17)Plone 4 version
[erral]1.x (LONG TIME AGO)Old versions…
[erral]