Saravanan666/pypi-org huggingface dataset:
Package: csvsorter
CSV SorterFork of thecsvsortfor Python 3.
For sorting CSV files on disk that do not fit into memory. The merge sort algorithm is used to break up the original file into smaller chunks, sort these in memory, and then merge these sorted files.Example usage>>>fromcsvsorterimportcsvsort>>># sort this CSV on the 5th and 3rd columns (columns are 0 indexed)>>>csvsort('test1.csv',[4,2])>>># sort this CSV with no header on 4th column and save results to separate file>>>csvsort('test2.csv',[3],output_file='test3.csv',has_header=False)>>># sort this TSV on the first column and use a maximum of 10MB per split>>>csvsort('test3.tsv',[0],max_size=10,delimiter='\t')>>># sort this CSV on the first column, force quotes around every field (default is csv.QUOTE_MINIMAL) and use windows-1250 encoding>>>importcsv>>>csvsort('test4.csv',[0],quoting=csv.QUOTE_ALL,encoding='windows-1250')Install$pipinstallcsvsorter
Package: csv-split
CSV-SplitVertical splitting (columns)Splits a given CSV file vertically.
Accepts either a batch-size, or a split-pattern to create multiple files
from a single files. All files will contain different columns of original csv.
Order of columns will be maintained.Horizontal splitting (rows)Not supported yetUsageThe below command will take large-file.csv and split it vertically into 3 files.
First file will have 10 columns, 2nd file will have 5, and the 3rd will have 12.
If there are more columns in the original file, they will be ignored.csv-split-i~/Downloads/large-file.csv-p10,5,12Instead of split-pattern, you can also specify batch size.csv-split-i~/Downloads/large-file.csv-b7By default, all the output files will be stored in a new directory calledoutput. Generated file names will have index number appended. For example,large-file_1.csv,large-file_2.csv, ...If you want to change the output directory, you can use-ooption as shown below.csv-split-i~/Downloads/large-file.csv-b10-o~/Downloads/splitted-data/If this directory does not exist, it will be created.This will create csv files and each csv file will contain 7 columns. Number of output files will
depend on number of columns in input files.
Package: csvsqlite3
Csv reader like PEP 249 API.https://github.com/harelba/qinspiredRequirementsPython 3.xInstallation$ pip install csvsqlite3Exampleimport csvsqlite3
conn = csvsqlite3.connect('/foo/bar/baz.csv')
cur = conn.cursor()
cur.execute('select * from csv')
for r in cur.fetchall():
   print(r[0], r[1])
conn.close()
Package: csv-sqlite-converter
CSV-SQLite ConverterThe CSV-SQLite Convert allows to store data both from SQLite database in csv files and from csv files in a SQLite database.InstallationYou can install CSV-SQLite Converter from PyPi (in the future).python -m pip install csv_sqlite_converterThe converter supports Python 3.10 and higher.How to useThe converter is a command line tool. To use it, run:python -m csv_sqlite_converterAnd the following should appear:>>> Choose one of the following:
        "db" to transfer data from a database to csv files
        "csv" to transfer data from csv files to a database
        "stop" to stop
    >where:"db"allows to transfer data from a database to csv files.It needs two paths: to the database and folder where csv files can be stored. After choosing this option the following appears:>>> Copy path to database: <provide/here/path/to/database.db>
>>> Copy path to directory where folder with csv files will be stored: <provide/path/to/a/folder>
>>> Data from [name_of_table] table are now stored in [name_of_table].csv.
>>> Data from [name_of_another_table] table are now stored in [name_of_another_table].csv.
>>> ..."csv"allows to transfer data from csv files to a database.It needs two paths: to csv files with data and the database to write to. However, csv filesmust follow a very precise structure:.
+-- csv1.csv
+-- csv2.csv
+-- csv3.csvwhere:the name of the root folder matches the name of the database filenames of csv files match names of tables in the database files (ex. students.csv holds data for a table named students)After choosing this option the following appears:>>> Copy path to database: <provide/here/path/to/database.db>
>>> Copy path to directory with csv files: <provide/path/to/a/folder/with/csv/files>
>>> File [name_of_csv_file] processed successfully.
>>> File [name_of_another_csv_file] processed successfully.
>>> ..."stop"stops the execution of the package.
Package: cs.vt
A content hash based data store with a filesystem layer, using
variable sized blocks, arbitrarily sized data and utilising some
domain knowledge to aid efficient block boundary selection.Note: the "mount" filesystem facility uses FUSE,
which may need manual OS installation.
On MacOS this means installingosxfusefor example from MacPorts.
You will also need thellfusePython module,
which is not automatically required by this package.The package provides thevtcommand to access
these facilities from the command line.This system has two main components:Stores: storage areas of variable sized data blocks
indexed by the cryptographic hashcode of their contentDirents: references to filesystem entities
containing hashcode based references to the contentThese are logically disconnected.
Dirents are not associated with particular Stores;
it is it sufficient to have access to any Store
containing the required blocks.The other common entity is the Archive,
which is just a text file containing
a timestamped log of revisions of a Dirent.
These can be mounted as a FUSE filesystem,
and thevt packcommand simply stores
a directory tree into the current Store,
and records the stored reference in an Archive file.See also the Plan 9 Venti system:
(http://library.pantek.com/general/plan9.documents/venti/venti.html,http://en.wikipedia.org/wiki/Venti)
which is also a system based on variable sized blocks.
Package: csvtables
csvtablesConverts a CSV formatted input to a readable table in text. The table is Markdown compatible.InstallationInstall package using pip:python -m pip install csvtablesUsageYou can use the package directly in the terminal or by importing it in a python script.Command line interfacecsvtables --helpLet's assume we have the following text file which is located intests/sample_data.csv(reference:https://en.wikipedia.org/wiki/List_of_galaxies#Closest_galaxies):Rank,Galaxy,Distance
1,Milky Way Galaxy,0
2,Canis Major Dwarf,0.025 Mly
3,Virgo Stellar Stream,0.030 Mly
4,Sagittarius Dwarf Elliptical Galaxy,0.081 Mly
5,Large Magellanic Cloud,0.163 Mly
6,Small Magellanic Cloud,0.197 MlyThe table is generated by running:csvtables closest_galaxies.csvand should produce the following output:| Rank | Galaxy                              | Distance  |
|------|-------------------------------------|-----------|
| 1    | Milky Way Galaxy                    | 0         |
| 2    | Canis Major Dwarf                   | 0.025 Mly |
| 3    | Virgo Stellar Stream                | 0.030 Mly |
| 4    | Sagittarius Dwarf Elliptical Galaxy | 0.081 Mly |
| 5    | Large Magellanic Cloud              | 0.163 Mly |
| 6    | Small Magellanic Cloud              | 0.197 Mly |Running as a module# example.py

from csvtables import csvtables
csv_file = open("tests/sample_data.csv", "r")
table = csvtables.CSVTable(csv_file)
print(table.generate_table())  # display the table to stdout
Package: csv-tabulator
csv-tabulatorThecsv-tabulatoris a Python package that simplifies the process of converting CSV files into styled HTML tables. The resulting HTML tables are designed to be responsive and visually appealing.InstallationYou can install the package usingpip:pipinstallcsv-tabulatorUsage>>>fromcsv_tabulatorimportconverter
>>>converter.convert('/Users/amiaynarayan/Projects/utilities/tabulator/tests/csv_file','table.html')FileReading...Done!
ConvertingtoHTML...
WritingtheHTMLfiletotable.html
Done...!
Package: csvtickerlist
csvtickerlistA simple package that allows users the ability to generate a CSV file that contains a full list of all active ticker symbols on the marketplace. This package uses a headless, Selenium Chrome browser to fetch this CSV file.The CSV file contains ticker symbols, along with some basic information (i.e. Share price, count, industry, etc.) NASDAQ hosts this information in its entirety for free and this module only requires making one page request to retrieve the CSV file.ExamplefromcsvtickerlistimportGetCSVdefGetDataFolder():returnfiledialog.askdirectory(title='Select Folder')defmain():folder=GetDataFolder()GetCSV.GetCSVFile(folder,10)#arg1 (folder) represents the output folder#arg2 (10) represents the seconds to wait before closing the web driver.#10 seconds is recommended, but increase this when facing connectivity issues.To-Do:Add functionality for a filtered output (i.e. valuation range, industry selections, etc.)
Package: csv-to-custom-json
csv-to-custom-json-pythonPyPi pagePyPiStatsHow to installinstallation docspython3-mpipinstallcsv-to-custom-jsonTests && coverageCoverage is 100%# installpython-mpipinstallcoverageunittest# only testpython-munittest# coveragecoveragerun-munittest&&coveragereport-mHow to use csv-to-custom-jsonClassic usageJust import the function and use it !fromcsv_to_custom_jsonimportparseFileresult=parseFile("myfile.csv")How to use the schemaCreate a schema variable and put it as second parameter !Exemple with a simplecsv:num1,num2,num3
1,2,3
4,5,6
7,8,9fromcsv_to_custom_jsonimportparseFiledefcallback(value):returnNoneschema={"num1":"string","num2":callback,"num3":"int"}result=parseFile("myfile.csv",schema)Caption :ad you can see the schema can contains function, or string with the typethe values with type will be parsedattribute of the object are the word in the first line of the csvMore complexe schemaIt's the same as a simple schema :fromcsv_to_custom_jsonimportparseFileschema={"obj1":{"obj2":{"num4":"string"}},"num2":"","num3":""}result=parseFile("myfile.csv",schema)If you want to check some real case, check out the foldertestin theGitHub repositoryIf you want to see and use options check that documentation:How-to-optionsSee alsoTricksHow-to-optionsCHANGELOG.mdcsv-to-custom-json in JavaScriptLicenseLicensed under the MIT License -LICENSE
Package: csvtodat
This is a Python Library to Read the CSV file and convert it into .dat fileChange Log0.0.1 (2022-11-07)Added the functionality to perform opetions of reading the csv and converting it to dat0.1.1 (2022-11-08)[MINOR] Handled the columns error if no columns list is defined0.1.2 (2022-11-08)[PATCH] Handled the columns error if no columns list is defined0.2.0 (2022-11-14)Added new functionality to convert dat to csv
Package: csv-to-db-converter-saatwikmehta
CSV_TO_DB_CONVERTERIt takes a CSV file as input and converts
and save the data into a default set DB (fileupload).
The table name will consist of the name of the file user is uploading.
After a successful upload user can perform CRUD operations on the file data using a localhost server.Installationpip install csvtodb-converter-saatwikmehtaLicense© 2022 Saatwik MehtaThis repository is licensed under the MIT license. See LICENSE for details.
Package: csvtodeta
A Lightweight tool to upload CSV files data into Deta Base.CLI operationsNeededSecret Id of Deta BasePath to the csv fileDatabase name in DetaUse$csvtodeta--idID_OF_BASE--pathpath/to/csv.csv--dbDETABASE_NAMEReadingdatafromDETABASE_NAMEUploaded..Uploaded..Uploaded......succesfullyuploadedndatatoDETABASE_NAMEBaseExampleData of deta.csv file (sample) :name,age
fredy,34
god,34345345
mannu,34$csvtodeta--id45345dhsgh3rjdf2ur34hhwf--pathsrc/deta.csv--dbdetabasenameReadingdatafromsrc/deta.csvUploaded{'name':'fredy','age':'34'}Uploaded{'name':'god','age':'34345345'}Uploaded{'name':'maanu','age':'34'}succesfullyuploaded3datatodetabasenameBaseResult
Package: csv-to-dictionary
I created this project after I discovered that one of my friends had abandoned python after being unable to find a simple and intuitive csv to dictionary converter.  I created this package in the hopes that I would not lose another friend from the pythonverse.Authors:Ethan DayleyVersion:1.0.2Dedication:To Jacob, may he return to the ways of python.Feel free to contribute to this project hosted at theUpstream URL.
Package: csv-to-dot
Csv > DotConvert a 2 columns CSV file to a .dot, who can be converted withGraphvizto .jpg, .ps, ...How to use itpythonentry_points_csv_to_dot/csv_to_dot.py-h
>usage:csv_to_dot[-h][--csv-file[CSV_FILE]][--csv-delim[CSV_DELIM]][--dot-file[DOT_FILE]][--nodes-shape[NODES_SHAPE]][--nodes-style[NODES_STYLE]][--nodes-bg-color[NODES_BG_COLOR]][--nodes-font-color[NODES_FONT_COLOR]][--version]Available node shapes :https://graphviz.org/doc/info/shapes.htmlNode styles :https://graphviz.org/doc/info/shapes.html#styles-for-nodesNode font/background colors :https://graphviz.org/docs/attr-types/color/Support versionPython :>=3.9
Package: csvtoexcel
csvtoexcelcsvtoexcel is a tool that converts a .csv file to .xlsx (Excel) and vice versa.By defaultcsvtoexcelcreates a file with the same filename, but the file
extension changed from ".csv" to ".xlsx".
The-o/--outputoption can be used choose a different filename for the
output.Similarlyexceltocsvconverts a .xlsx file to .csv.Usage> csvtoexcel <file.csv>
<Creates the file file.xlsx>

> csvtoexcel <file.csv> -o <other_file.xlsx>
<Creates the file other_file.xlsx>

> exceltocsv <file.csv>
<Creates the file file.xlsx>

> exceltocsv <file.csv> -o <other_file.xlsx>
<Creates the file other_file.xlsx>InstallThe recommended way to installcsvtoexcelis viapipx:pipx csvtoexcel
Package: csvtogitissue
CSV To Git Hub Issue CreatorA simple package to help convert CSV file entries to GITHub issues via API v3 (Private and Public repositories).
This package helps developers to maintain private repositories who want to keep their code hidden but issues section open to the public.A platform to collect results can be set up for example via google forms to collect responses and convert these responses to issues which include Assignee, Labels, Title, Description and Date raised.How to use:from csvtogitissue import csvtogitissueFILE_ID = 'file ID from google drive'
FILE_NAME = 'Name after downloaded'
file1 = csvtogitissue.download_responses_gform(FILE_ID,FILE_NAME)
> Returns 1 for success 0 for fail

FILE_LINK = 'File link from the web'
FILE_NAME = 'Name after downloaded'
file2 = csvtogitissue.download_responses_CSV(FILE_LINK,FILE_NAME)
> Returns 1 for success 0 for fail


FILE_NAME = 'Name after downloaded'
TOKEN = 'GIT hub API token'
REPO_NAME = 'name of repo on git hub (private or public)'
REPO_OWNER = 'Repo owner used as as assignee'
(Column numbers)
TITLE = 'Column number of title'
DESCRIPTION = 'Column number of title'
LABELS = 'Column number of label'
CREATED_DT = 'Column number of label'
EMAIL = 'Column number of email if column does not exist set it to str(NULL)'
file3 = csvtogitissue.create_issues(FILE_NAME,TOKEN,REPO_NAME,REPO_OWNER,TITLE,DESCRIPTION,LABELS,EMAIL,CREATED_DT)
> Returns 1 for success 0 for failAlternatively you may want to use a config.ini file:config.ini

[DEFAULT]
TOKEN = xxxxxxxxxxxxxxxxxxxx
REPO_OWNER = Your name here
REPO_NAME = Repo name here
FILE_ID = File ID here
FILE_NAME = File name here
CREATED_DT = 0
DESCRIPTION = 3
TITLE = 2
LABELS = 1
EMAIL = 4

In your python code

TOKEN = config['DEFAULT']['TOKEN']
REPO_OWNER = config['DEFAULT']['REPO_OWNER']
REPO_NAME = config['DEFAULT']['REPO_NAME']
FILE_ID = config['DEFAULT']['FILE_ID']
FILE_NAME = config['DEFAULT']['FILE_NAME']
DESCRIPTION = config['DEFAULT']['DESCRIPTION']
TITLE = config['DEFAULT']['TITLE']
LABELS = config['DEFAULT']['LABELS']
EMAIL = config['DEFAULT']['EMAIL']
CREATED_DT = config['DEFAULT']['CREATED_DT']Reminder:Remember to make sure that the labels defined in the CSV are available in the repository to be tagged.Bugs can be reported athttps://goo.gl/forms/Vxusa4xJQH4WEqHG3or athttps://github.com/sanjeven/CSV-to-GIThub-issue-creator
Package: csv-to-info-chunks
CSV 轉換為資訊區塊🐔動機: 
為了讓每個 CSV row 單獨成為語境探索的資訊區塊，需將 CSV row 轉換成 JSON 資訊區塊。
切割完畢之後，下一個階段可以針對 content 欄位計算來增添 embeddings 詞向量欄位。
 
💣地雷: 
這個套件的輸入格式是 google-sheet-downloader 的輸出檔案群CSV and JSON。簡介該工具可以將 CSV 文件中的數據轉換成 JSON 形式的資訊區塊。您可以選擇強制重新生成所有 JSON 文件，或者僅生成不存在的 JSON 文件。安裝python3 -m pip install csv-to-info-chunks套件用法from csv_to_info_chunks import csv_to_info_chunks
用法的定義：
def csv_to_info_chunks(input_folder, output_folder, force=False)CLI使用方法首先，打開命令提示符或終端機，並將工作路徑切換至包含csv_to_info_chunks.py的文件夹。然後，執行以下命令：python csv_to_info_chunks.py --input-folder <CSV 文件夾路徑> --output-folder <JSON 文件夾路徑> [--force]其中：<CSV 文件夾路徑>是存放 CSV 文件的文件夾路徑。<JSON 文件夾路徑>是要將生成的 JSON 文件存放的文件夾路徑。如果要強制重新生成所有 JSON 文件，請在命令中添加--force選項。該工具將會轉換指定文件夾中的所有 CSV 文件，並將生成的 JSON 文件存放在指定文件夾中。輸入的 CSV 文件格式doc-id_14GQkXftKXoaIJ_TKxtXzae1J7p10XznGgF_lg7cDG8g_sha_4e09fa0d.csv
提問,回答
知識工作者的自治和責任,"知識工作需要自治和責任..."輸入的 JSON 文件格式doc-id_14GQkXftKXoaIJ_TKxtXzae1J7p10XznGgF_lg7cDG8g_sha_4e09fa0d.json
{
    "sheet_id": "1H0NTLvF0oyURCw_YWBQJ8CkcL7pij5D_8zO_1gXMc5k",
    "sheet_name": "健康百科問答知識庫",
    "worksheet_name": "工作表1",
    "download_time": "2023-06-04T12:50:06.217484Z",
    "download_filepath": "./data/users/bohachu_gmail_com/google_sheet_downloader/2023-06-04/doc-id_1H0NTLvF0oyURCw_YWBQJ8CkcL7pij5D_8zO_1gXMc5k_sha_0f813133.csv"
}輸出的 JSON 文件格式{
    "id": "9b70686d-948f-4091-8564-cb4727695482_7",
    "time": "2023-06-04T15:59:20.666483Z",
    "content": "組織惰性 所有的組織都需要一套可以因應現實的紀律。\n\n所有的組織都要知道，天底下沒有什麼計畫或活動可以不需要調整或是重新設計，就能長期持續運作。所有的活動最終都會落伍。忽視這個事實的組織，尤以政府機關最甚。事實上，當今政府部門之所以問題重重，最主要的癥結在於，所有事物都是沿襲過去，無法適時擺脫昨日的牽絆。這方面，醫院和大學只比政府好一點。\n\n企業人士念舊的程度不下於政府官僚。如果某個產品或是計畫失敗，企業人士很可能會照樣加倍投資。不過幸好，他們無法完全照自己的意思這麼做。企業受制於一套客觀的規範，那就是市場。企業有一套客觀的外部衡量標準，那就是利潤。企業早晚得淘汰失敗或不具生產力的產品或計畫。對政府機構、醫院、軍隊等其他組織而言，經濟只是一項邊界條件。",
    "embeddings": [],
    "type": "google_sheet",
    "sheet_id": "14GQkXftKXoaIJ_TKxtXzae1J7p10XznGgF_lg7cDG8g",
    "sheet_name": "經營百科問答知識庫",
    "worksheet_name": "工作表1",
    "download_time": "2023-06-04T12:50:08.519968Z",
    "download_filepath": "./data/users/bohachu_gmail_com/google_sheet_downloader/2023-06-04/doc-id_14GQkXftKXoaIJ_TKxtXzae1J7p10XznGgF_lg7cDG8g_sha_4e09fa0d.csv",
    "row": 7
}注意事項輸入的 CSV 文件應該使用 UTF-8 字元編碼。生成的 JSON 文件將使用相同的前綴名，但擴展名將替換為.json。例如，example.csv將生成名為example.json的文件。若要查看轉換過程中的具體信息，您可以在終端或命令提示符窗口查看。疑難排解如果遇到任何問題，請確保您使用的是 Python 3 及相關庫的最新版本。確保輸入文件夾中的 CSV 文件使用了正確的字元編碼（推薦使用 UTF-8）。祝使用順利！
Package: csvtojsonlib
Python Library to convert CSV file into JSONChange Log1.0.0 (2022-11-14)Planning the library creation1.1.0 (2022-11-16)Added the functionality to convert csv file into json1.1.1 (2022-11-16)[FIX] Source code files were not included in the final distribution1.1.2 (2022-11-17)Testing for Production1.1.3 (2022-11-17)Testing for Production (file name changed)
Package: csv-to-list-of-dict
convert_csv_to_jsonTheconvert_csv_to_jsonis a simple package that helps toy convert a CSV file into a list of dictionaries.MIT LicenseCopyright (c) 2023 Micheal DurodolaPermission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Package: csv_tools
#CSVToolsTools for working with delimited files.1. DialectDetectorWhen passed delimited file path will sniff file for dialect and header information using specified detection settings for DialectDetector instance.1. sniff_for_dialect:Sniff delimited file and return csv.Dialect.2. sniff_for_headers:Sniff delimited file and return first row if the file has headers else list of numbered columns as headers in the form of 'Column' + column #.3. sniff_for_encoding:Sniff file to try and determine and return encoding else returns latin-1.2. SchemaEngineEngine for data-typing delimited file into field_names, their data type, and max length (if applicable).1. build_schema_string:Process delimited file and return schema string for file passed.3. integrity_checkTake filepath and report on the number of columns detected per line, extra quotechars, etc. Helps to detect problems in source files which may cause issues when creating schema files and indicator for whether file will need preprocessor.
Package: csvtoparquet
# Convert CSV object files to Apache Parquet with IBM Cloud Object StorageThis tool was developed to help users on IBM Cloud convert their CSV objects in IBM Cloud Object Storage (COS) to Apache Parquet objects. It’s developed using Python 3.6.6 and will work with Python 3 versions up to 3.6.6.### Installation
To install the tool, runpipwith:` pip install csvtoparquet `After the tool’s installed, you must have an IBM Cloud API Key and IBM COS service to make the command line tool work. It requires that you insert your IBM Cloud API Key and a IBM COS service. You can find the API Key from your IBM Cloud management panel:Manage > Security > Platform API Keys. If you don’t have IBM COS as a service, you can find it in the cloudCatalogunder Object Storage, which has a lite tier (free).If you already have the COS service, you’ll need the name of the bucket where your CSV objects are located. Right now, the tool doesn’t support multiple buckets, so you can’t convert objects from one bucket and store them in another. Nonetheless, you can rename your converted objects to use prefixes such as:[object name] -mycsvfile.csv[renamed object stored as parquet] -new/prefix/mycsvfile.parquetThe file extension.parquetwill be automatically added to your new object name.### UsageRuncsvtoparqueton the command line using the following required flags:` csvtoparquet-a<IBM_CLOUD_API_KEY>-e<IBM_CLOUD_COS_ENDPOINT>-b<IBM_COS_BUCKET> `-aor–apikey- IBM Cloud API Key-eor–endpoint- COS bucket endpoint-bor–bucket- COS bucket name where the CSV objects are storedAfter using the flag you can append the following flags to the command:-lor–list- Lists all the objects in the bucket-cor–csv- Lists all CSV objects in the bucket-cnor–csv-names- Lists only the names of CSV objects in the bucket-for–file- Name of the CSV object you want to convert - used with-n-nor–name- Name of thenewobject - can include prefixes - used with-f#### Converting objects##### Convert one objectInput:` csvtoparquet-a<IBM_CLOUD_API_KEY>-e<IBM_CLOUD_COS_ENDPOINT>-b<IBM_COS_BUCKET> \-fcsvfile.csv-ncsvfile `Output:` Now Converting: csvfile.csv-->csvfile.parquet `##### Convert more than one objectInput:` csvtoparquet-a<IBM_CLOUD_API_KEY>-e<IBM_CLOUD_COS_ENDPOINT>-b<IBM_COS_BUCKET> \-fcsvfile.csv anothercsvfile.csv-ncsvfile new/csvfile `Output:` Now Converting: csvfile.csv-->csvfile.parquet Now Converting: anothercsvfile.csv-->new/csvfile.parquet `
Package: csvtoparquetlib
# Serverless using CSVtoParquet for IBM CloudThis repository uses a version of the Python library csvtoparquet that’s been modified from a command-line tool to a library you can place into your application and run.TheDockerfilegives an example of how it’s put together and the folderserverlesscontains the code for the serverless function that will list the CSV files in your IBM Cloud Object Storage bucket.More docs to come …