Saravanan666/pypi-org huggingface dataset:
Package: xlsxdocument
This is a wrapper foropenpyxlwhich makes creating XLSX documents with
the purpose of exporting data less boring:from xlsxdocument import XLSXDocument

def xlsx_export(request):
    some_data = Bla.objects.all()

    xlsx = XLSXDocument()
    xlsx.table_from_queryset(Bla.objects.all())
    return xlsx.to_response('bla.xlsx')Adding in additional cells at the end is also possible:xlsx = XLSXDocument()
xlsx.table_from_queryset(
    Bla.objects.all(),
    additional=[(
        'Full URL',
        lambda instance: 'http://example.com%s' % (
            instance.get_absolute_url(),
        ),
    )],
)You can also easily add the facility to export rows to Django’s
administration interface:from django.contrib import admin
from django.utils.translation import gettext_lazy as _

from xlsxdocument import export_selected

from app import models


class AttendanceAdmin(admin.ModelAdmin):
    list_filter = ('event',)
    actions = (export_selected,)


admin.site.register(models.Event)
admin.site.register(models.Attendance, AttendanceAdmin)If you require additional columns withexport_selecteduse this
snippet instead:from xlsxdocument import create_export_selected

class AttendanceAdmin(...):
    actions = [
        create_export_selected(
            additional=[
                # ... see above
            ]
        )
    ]
Package: xlsxecute
xlsxecuteThis tool will take an Excel model (.xlsx), update any parameters as defined via command line arguments or in the parameters file and calculate all cells, resulting in an Excel spreadsheet resembling the original, but with all formula cells replaced by the calculated values.Parameters that define how to update cells in your spreadsheet can be provided in three ways:
JSON file, CSV file, or command line argumentsIf both a config file and command line arguments are provided, the command line argumentsConfig file formattingOnly one config file can be provided at a time. The config file can either beCommand line arguments:Command line arguments take the form of:-f"Sheet name.Cell1=Replacement value string"-f"Sheet name.Cell2=Replacement_value_float"Note: Quotation marks are not required if there no space in the parameter string.Example:xlsxecute -f "Variables.C2=red" -f Variables.C3=0.8 sample.xlsxJSON:{"Sheet name.Cell1":"Replacement value string","Sheet name.Cell2":Replacement_value_float}Example: params.json{"Variables.C2":"red","Variables.C3":0.8}CSV:Sheet name.Cell1,Replacement value string
Sheet name.Cell2,Replacement value floatExample: params.csvVariables.C2,red
Variables.C3,0.8NOTE: Do NOT include a header row in the CSVExecutable usage:usage: xlsxecute [-h] [--output_dir OUTPUT_DIR] [--run_dir RUN_DIR] [--param {sheet}.{cell}={new_value}] source_file [parameter_file]

positional arguments:
  source_file           Excel (xlsx) file that contains the model
  parameter_file        Path to json or csv parameter file

optional arguments:
  -h, --help            show this help message and exit
  --output_dir OUTPUT_DIR
                        Optional output location. (Default: output)
  --run_dir RUN_DIR     Optional directory to store intermediate files. (Default: runs)
  --param {sheet}.{cell}={new_value}, -p {sheet}.{cell}={new_value}
Package: xlsx-evaluate
Calculate XLSX formulasxlsx_evaluate- python library to convert excel functions in python code without the need for Excel itself within the scope of supported features.This library is forkxlcalculator. Use this library.SummaryCurrently supportsSupported FunctionsAdding/Registering Excel FunctionsExcel number precisionTestInstallation# pippipinstallxlsx-evaluate# poetrypoetryaddxlsx-evaluateExampleinput_dict={'B4':0.95,'B2':1000,"B19":0.001,'B20':4,'B22':1,'B23':2,'B24':3,'B25':'=B2*B4','B26':5,'B27':6,'B28':'=B19 * B20 * B22','C22':'=SUM(B22:B28)',"D1":"abc","D2":"bca","D3":"=CONCATENATE(D1, D2)",}fromxlsx_evaluateimportModelCompilerfromxlsx_evaluateimportEvaluatorcompiler=ModelCompiler()my_model=compiler.read_and_parse_dict(input_dict)evaluator=Evaluator(my_model)forformulainmy_model.formulae:print(f'Formula{formula}evaluates to{evaluator.evaluate(formula)}')# cells need a sheet and Sheet1 is default.evaluator.set_cell_value('Sheet1!B22',100)print('Formula B28 now evaluates to',evaluator.evaluate('Sheet1!B28'))print('Formula C22 now evaluates to',evaluator.evaluate('Sheet1!C22'))print('Formula D3 now evaluates to',evaluator.evaluate("Sheet1!D3"))TODODo not treat ranges as a granular AST node it instead as an operation ":" of
two cell references to create the range. That will make implementing
features likeA1:OFFSET(...)easy to implement.Support for alternative range evaluation: by ref (pointer), by expr (lazy
eval) and current eval mode.Pointers would allow easy implementations of functions like OFFSET().Lazy evals will allow efficient implementation of IF() since execution
of true and false expressions can be delayed until it is decided which
expression is needed.Implement array functions. It is really not that hard once a proper
RangeData class has been implemented on which one can easily act with scalar
functions.Improve testingRefactor model and evaluator to use pass-by-object-reference for values of
cells which then get "used"/referenced by ranges, defined names and formulasHandle multi-file addressesImprove integration with pyopenxl for reading and writing filesexample of
problem space
Package: xlsx-from-json
xlsx_from_jsonCreates xlsx from json viaopenpyxl.UsageCreate .json file with following structure:{"rows":[{"cells":[{"value":"Sample text",// merge 5x2 cell range"width":5,"height":2,// openpyxl style: https://openpyxl.readthedocs.io/en/2.5/styles.html"style":{"font":{"name":"Times New Roman","size":12},"border":{"bottom":{// openpyxl border styles: // https://openpyxl.readthedocs.io/en/stable/_modules/openpyxl/styles/borders.html"border_style":"medium","color":"FFFFFFFF"}}}}],// move row by 2x1"skip_columns":2,"skip_rows":1,// change row height"row_height":10}],"start_column":1,"start_row":1,// change column widths"column_widths":[{// column_number or column_letter"column_number":1,"width":10}],// set number format (e.g. 1.234 -> 1.23)"number_format":"0.00",// apply style to all cells"default_style":{"font":{"bold":true}}}Create openpyxl workbook viaxlsx_from_jsonfunction:importjsonfromxlsx_from_jsonimportxlsx_from_jsonwithopen("data.json",encoding="utf-8")asf:json_data=json.load(f)wb=xlsx_from_json(json_data)Created workbook will have values:sheet=wb.activeassertsheet.cell(row=2,cell=3).value=="Sample text"The same true for the styles (cell ctyle + default style):sheet=wb.activeassertsheet.cell(row=2,cell=3).font.name=="Times New Roman"assertsheet.cell(row=2,cell=3).font.bold==TrueNow you can use workbook according to openpyxlguide.
Package: xlsxgen
xlsxgenpip install で xlsxgenのモジュールを落として、pythonコードを以下の通り記述する。スピード重視の場合はメソッド(process_bytes)をコメント解除するメモリ容量を抑えたい場合はメソッド(process_bytes_zlib)と、zlipの圧縮処理の箇所をコメント解除する使い方importpandasaspdimportzipfileimportxlsxgenimportresourceimporttimeimportioimportzlib# 開始start_time=time.perf_counter()file_name='tenpo_shohin_pattern3.xlsx'excel_sheet='sheet1'withopen(file_name,'rb')asfile_obj:withzipfile.ZipFile(file_obj,'r')aszip_ref:# エクセルファイルを展開してオブジェクトを取得s_list=[f'xl/worksheets/{excel_sheet}.xml']sheet_list=[iforiinzip_ref.namelist()ifiins_list]solve_bytes='xl/sharedStrings.xml'bytes_obj=zip_ref.read([iforiinzip_ref.namelist()ifiinsheet_list][0])bytes_solve_obj=zip_ref.read(solve_bytes)generator=xlsxgen.DataGenerator()# スピード重視のケース(メモリ容量は大きく消費する)generator.process_bytes(10000,bytes_obj,bytes_solve_obj)# メモリ容量を抑えたいケース(スピードは少し遅い)# bytes_obj = zlib.compress(bytes_obj)# bytes_solve_obj = zlib.compress(bytes_solve_obj)# generator.process_bytes_zlib(10000, bytes_obj, bytes_solve_obj)whileTrue:csv_data=generator.generate_data_chunk()ifcsv_data=="finish":print(csv_data)breakifcsv_data:print(csv_data)#df = pd.read_csv(io.StringIO(csv_data), header=None)#print(df)memory_info=resource.getrusage(resource.RUSAGE_SELF).ru_maxrssprint(f"Max RSS:{memory_info/1024/1024}MB")end_time=time.perf_counter()# 経過時間を出力(秒)elapsed_time=end_time-start_timeprint(elapsed_time)
Package: xlsxgrep
Owerviewxlsxgrepis a CLI tool to search text in XLSX, XLS, CSV, TSV and ODS files. It works similarly to Unix/GNU Linuxgrep.FeaturesGrep compatible: xlsxgrep tries to be compatible with Unix/Linux grep, where it makes sense.
Some of grep options are supported (such as-r,-ior-c).Search many XLSX, XLS, CSV, TSV and ODS files at once, even recursively in directories.Regular expressions: Python regex.Supported file types: csv, ods, tsv, xls, xlsxUsage:usage: xlsxgrep [-h] [-V] [-P] [-F] [-i] [-w] [-c] [-r] [-H] [-N] [-l] [-L] [-S SEPARATOR] [-Z]
                pattern path [path ...]

positional arguments:
  pattern               use PATTERN as the pattern to search for.
  path                  file or folder location

optional arguments:
  -h, --help            show this help message and exit
  -V, --version         display version information and exit.
  -P, --python-regex    PATTERN is a Python regular expression. This is the default.
  -F, --fixed-strings   interpret PATTERN as fixed strings, not regular expressions.
  -i, --ignore-case     ignore case distinctions.
  -w, --word-regexp     force PATTERN to match only whole words.
  -c, --count           print only a count of matches per file.
  -r, --recursive       search directories recursively.
  -H, --with-filename   print the file name for each match.
  -N, --with-sheetname  print the sheet name for each match.
  -l, --files-with-match
                        print only names of FILEs with match pattern.
  -L, --files-without-match
                        print only names of FILEs with no match pattern.
  -S SEPARATOR, --separator SEPARATOR
                        define custom list separator for output, the default is TAB
  -Z, --null            output a zero byte (the ASCII NUL character) instead of the usual newline.Examples:xlsxgrep-i"foo"foobar.xlsxxlsxgrep-c-H"(?i)foo|bar"/folderInstallationpipinstallxlsxgrep
Package: xlsxhelper
xlsxhelperExcel file manipulation toolset.NoticeThe library is based on library openpyxl, so only xlsx (new excel format) files are supported.Help Functionsget_workbookget_worksheetget_merged_range_stringparse_colsparse_rowsget_cellsset_cells_dataload_data_from_workbookget_merged_rangescopy_cellsmerge_rangesTest Passed With Pythons2.73.43.53.63.73.83.93.103.11Releasesv0.3.2 2023/09/10Fix unit test problem.v0.3.1 2022/01/10Fix license file missing problem.Add set_cells_data function.v0.3.0 2020/07/19Add workbook and worksheet create support.v0.2.1 2019/08/31Fix load_data_from_workbook get raw function statement problem.Fix worksheet.merged_cell_ranges deprecating problem, use worksheet.merged_cells.ranges instead.
Package: xlsxlite
XLSXLiteThis is a lightweight XLSX writer with emphasis on minimizing memory usage. It's also really fast.fromxlsxlite.writerimportXLSXBookbook=XLSXBook()sheet1=book.add_sheet("People")sheet1.append_row("Name","Email","Age")sheet1.append_row("Jim","jim@acme.com",45)book.finalize(to_file="simple.xlsx")BenchmarksThebenchmarking testwrites
rows with 10 cells of random string data to a single sheet workbook. The table below gives the times in seconds (lower is better)
to write a spreadsheet with the given number of rows, and includesxlxswriterandopenpyxlfor comparison.Implementation100,000 rows1,000,000 rowsopenpyxl43.5469.1openpyxl + lxml21.1226.3xlsxwriter17.2186.2xlsxlite1.919.2LimitationsThis library is for projects which need to generate large spreadsheets, quickly, for the purposes of data exchange, and
so it intentionally only supports a tiny subset of SpreadsheetML specification:No styling or themesOnly strings, numbers, booleans and dates are supported cell typesIf you need to do anything fancier then take a look atxlxswriterandopenpyxl.DevelopmentTo run all tests:py.test xlsxlite -s
Package: xlsxmetadata
# xlsxmetadataReally lightweight lib for peeking into xlsx column/row size before you try to open the file with something else### setup```pip install xlsxmetadata```### reading from file...```pythonfrom xlsxmetadata.metadata import get_dimensions, get_sheet_namesmy_big_file = '/path/to/my/real_big_file.xlsx'sheet_names = get_sheet_names(my_big_file)print(sheet_names)>>> {'test_sheet': 1}dimensions = get_dimensions('/path/to/my/real_big_workbook.xlsx', 'test_sheet')print(dimensions['end_column'])>>> 16834print(dimensions['end_row'])>>> 1200000```### reading from flask form...```pythonfrom io import BytesIOfrom xlsxmetadata.metadata import get_dimensions, get_sheet_namesmy_big_file = request.files.get('my_big_file')sheet_names = get_sheet_names(BytesIO(my_big_file.read()))print(sheet_names)>>> {'test_sheet': 1}# you will probably have to reset the read-headmy_big_file.seek(0)dimensions = get_dimensions(BytesIO(my_big_file.read()), 'test_sheet')print(dimensions['end_column'])>>> 16834print(dimensions['end_row'])>>> 1200000```This information is stored as metadata in the first few bytes of `.xlsx` files. For some reason no other libraries (xlrd, openpyxl) seem to give the users access to this data directly.
Package: xlsxopera
xlsxoperaWorking on xlsx (Microsoft Excel) files. Convert to list, dict, get headers, cell values, positions and more.Developed by voidayan (c) 2022InstallInstall package from pip:pip3 install xlsxoperaNote: Required packages is openpyxlHow to workCreate an objectfromxlsxoperaimportNotebook# Load file to operate and sheet namenotebook=Notebook("file-to-path.xlsx","sheet_name")# Or load file to operate without sheet name. Default will be active spreadsheet.notebook=Notebook("file-to-path.xlsx")Get rows in list of lists:# Get all rows (from 1 to last)rows_in_spreadsheet=notebook.list_rows()# Get rows (for e.g, from 9 to 13)rows_in_spreadsheet=notebook.list_rows(start=9,end=13)
Package: xlsx-ptable
xlsx_ptable python包介绍xlsx_ptable Python Package是一个基于用户log文件，并根据配置文件生成excel表格的python库。它可以帮助你生成优雅的Excel表格。安装使用以下命令安装My Python Package：python-mpipinstallxlsx_ptable用法以下是如何使用My Python Package的一些基本示例：假设用户user_log.txt文件为:%tab1: (X1@1), (X2@a), (Y@sample y1), (Z@sample z1)
.............
%tab1: (X1@2), (X2@), (Y@sample y2), (Z@sample z2)
.............
%tab1: (X1@3), (Z@sample z3)
%tab1: (X1@4), (X2@c), (Y@sample y3), (Z@sample z4)
%tab1: (X1@4), (X2@d), (Y@sample y4),
%tab1: (X1@5), (X2@e), (Y@sample y5), (Z@sample z5)
.............
%tab1: (X1@5), (X2@f), (Y@sample y6), (Z@sample z6)用户配置文件user.cfg(YAML格式)为：excel_path:'sample1.xlsx'# 输出的excel表格路径sheets:tag:[sheet1]#excel表格sheet名称tables:# 表1-sheet_tag:sheet1# 这个表属于sheet1name:"%tab1"# 该表的records记录在path/to/table1.log中以%tab1为关键字的行中head-0:[X,X,Y,Z]#一级表头(不支持中文)head-1:[X1,X2,Y,Z]#二级表头(不支持中文), 长度必须和一级表头相等head-key:[X1]# 列方向上以X1排序且合并alias:# 表头的别名，支持中文转译X:表头1Y:表头2Z:表头3X-1:选择1X-2:选择2record_file:'user_log.txt'# log记录，每行最多一个record记录，格式为 ...%tab1...(X1@2.0)...(X2@3)...(Y@content1)...(Z@content2)使用方法如下：fromptableimportgen_excel_tablegen_excel_table("user.cfg")输出excel表格为:表头1表头2表头3X1X21asample y1sample z12sample y2sample z23sample z34csample y3sample z4dsample y45esample y5sample z5fsample y6sample z6注意事项log的格式要求表格中每个记录只能在log中一行必须携带表格tag标记(比如例子中%tab1)每个记录由(key@value)表示一个cell的记录，其中key为表头head-1中的关键字对于head-key关键字，记录中必须包含head-key的关键字，比如例子中head-key为[X1]，那么记录必须有(X1@value)的单元值配置文件要求符合YAML配置文件规范保留字符（例如%，！#，：，!等）应用引号引起来。确保缩进一致，使用空格而不是制表符。列表项应以短划线和空格开始。支持多sheet多表格, 由于每个table会动态计算列的宽度并调整sheet的列宽度，所以一般一个sheet中存放一个table;table中所在的sheet_tag，必须在sheets的tag中；table支持2级header表示，其中head-1中包括记录log中所允许的keys；table会动态在head进行行方向、列方向合并；alias为header的别名，主要解决log中都是英文记录，而表格header需要中文解释，如果记录的key太长，也可以通过alias别名替代(比如log中使用$0代表"dclxxxyyyzzz")head-key为关键key，所有表格记录会基于该key，在列方向上排序以及合并;支持单个log存放多个table 记录;Sample更多sample，请访问xlsx_ptable。贡献欢迎任何形式的贡献！请阅读我们的贡献指南了解如何开始。许可根据MIT许可证发布。问题和建议如果你有任何问题或建议，请通过邮箱gu.peng@intelllif.com联系或者
请在此提出Issue。
Package: xlsxq
xlsxqxlsxq is a lightweight and flexible command-line .xlsx processor.UsageThis is a beta version and specifications are subject to change.# Show help message and exit.xlsxq-h# List worksheets.xlsxqsheetlist--infileinfile.xlsx--outputjson# Show range values.xlsxqrangeshow--infileinfile.xlsx--sheet'Sheet1'--range'A1:B3'--outputjson# Show range values in tab-delimited format.xlsxqrangeshow--infileinfile.xlsx--sheet'Sheet1'--range'A1:B3'--outputtsvRequirementsPython 3.8+Installationpipinstall-UxlsxqTODOWrite docs
Package: xlsxr
xlsx-readerPython3 library optimised for reading very large Excel XLSX files, including those with hundreds of columns as well as rows.Simple examplefrom xlsxr import Workbook

workbook = Workbook(filename="myworkbook.xlsx", convert_values=True)

for sheet in workbook.sheets:
    print("Sheet ", sheet.name)
    for row in sheet.rows:
        print(row)ConversionsBy default, everything is a string, and all dates and datetimes will appear in ISO 8601 format (YYYY-mm-dd or YYYY-mm-ddTHH:MM:SS). If you supply the optionconvert_valuesto the Worksheet constructor, the library will convert numbers to ints or floats, and dates to datetime.datetime or datetime.date objects. There is no attempt to handle standalone times.Empty cells appear as the empty string ''.xlsxr.workbook.Workbook classConstructorThe xlsxr.Workbook class constructor takes the following keyword arguments:ArgumentDescriptionfilenamePath to an Excel file on the local filesystem.streamA file-like object (byte stream)urlThe URL of a remote Excel fileconvert_valuesIf True, convert numbers and dates from strings to Python values (default is False)fill_mergedIf True, repeat values to fill merged areas (default is False)You may specify only one offilename,stream,orurl.PropertiesPropertyDescriptionsheetsA list of xlsxr.sheet.Sheet objectsstylesA list of xlsxr.style.Style objectsxlsxr.sheet.Sheet classPropertiesProperty | Description
workbook | The parent Workbook objet
name | The name of the sheet
sheet_id | The internal identifier of the sheet
state | The state of the sheet (normally 'visible' or 'hidden')
relation_id | ??
cols | A list of metadata for each column.
rows | A list of the data rows in the sheet (parsed on demand).
merges | A list of merges in the sheet (parsed on demand).Each row is a list of scalar values. The will all be strings or None unless you specified theconvert_valuesoption for the Workbook.Merges appear as strings defining ranges, e.g. "A1:C3".ColumnsColumns are represented as dict objects with the following properties:KeyDescriptioncollapsedTrue if the column is collapsed.hiddenTrue if the column is hiddenmin??max??styleA key into thestylesproperty of the workbook.xlsxr.style.Style classPropertiesPropertyDescriptionnumber_formats??cell_style_formats??cell_formats??cell_styles??LicenseThis is free and unencumbered software released into the public domain. See UNLICENSE.md for details.
Package: xlsx-split
xlsx-splitCreate new file for every row in excel sheet, with support of header and footer replication.Installpip install xlsx-splitInstalled Commandsxlsx-splitCommand HelpsE:\xlsx-split>xlsx-split --help
    Usage: xlsx-split [OPTIONS] WORKBOOK

    Options:
    -h, --header TEXT            Header row number list.
    -f, --footer TEXT            Footer row number list.
    -c, --cols TEXT              Column number list.
    -r, --rows TEXT              Content row number list.
    -t, --test TEXT              Conditions that a good row must matchs. Format
                                like COL_LETTER:REGEX, e.g. A:\d+ means the
                                value of A column must be an Integer.
    -s, --sheet TEXT             Sheet name. Default to Current Active Sheet
    -w, --workspace TEXT         Where new files saved. Default to
                                "{FILENAME_ROOT}"
    -p, --filename-pattern TEXT  Default to
                                "{FILENAME_ROOT}-{ROW}.{FILENAME_EXT}"
    --help                       Show this message and exit.Noticeheader/footer/rows obeys ROWS-RULE.cols obeys COLS-RULE.test obeys TEST-RULE。filename-pattern obeys FILENAME-RULE。ROWS-RULE1         == [1]1,2,3     == [1,2,3]1-3       == [1,2,3]1-3,5     == [1,2,3,5]3-        == [3,4,5,6....] # From the third row to the sheet.max_rowsCOLS-RULEA         == [1]A,B,C     == [1,2,3]A-C       == [1,2,3]A-C,E     == [1,2,3,5]C-        == [3,4,5,6....] # From column C to the sheet.max_colsTEST-RULECan provide multiple tests.The final result is the result of LOGIC AND of all tests result.Test format: Column:TestRegex, e.g. A:\d+ means the Column A must contain digits.FILENAME-RULEfilename-pattern is a python string format template.Available variablesFILEPATH          Source file's full path, e.g. c:\a\b.xlsxDIRNAME           Source file's direct folder path, e.g. c:\aFILENAME          Source file's filename, e.g. b.xlsxFILENAME_ROOT     Source file's filename without ext, e.g. bFILENAME_EXT      Source file's file ext, e.g. .xlsxROW               Index of current row，如1,2,3...A..Z,AA...        The value of the cell [ROW, COLUMN]. A or Z or ZZ is the column.Releases0.1.2Fix parse_rows & parse_cols problem.Document changes.0.1.1Document changes.0.1.0First release
Package: xlsx-streaming
xlsx_streaminglibrary lets you stream data from your database to an xlsx document. The xlsx document can hence be streamed over HTTP without the need to store it in memory on the server. Database queries are made by batch, making it safe to export even very large tables.The full documentation can be found onreadthedocs.Bug reports, patches and suggestions welcome! Just open anissueor send apull request.Running the testsAs simple as:python setup.py test
Package: xlsxToCsv
excelToCSVThexlsxToCsvpackage contains the ability to convert .xlsx file to a .csv file that is more consumable by many other packages/programs/scripts.The source code for this package is available athttps://github.com/willmichel81/excelToCSV.The importable name of the package is excelToCSVImport into your code>>> from excelToCSV import excelToCSVTest to make sure package is connecting>>> pip install xlsxToCsv>>> python>>> from excelToCSV import excelToCSV>>> test = excelToCSV(C:/complete/path/to/file/example.txt)>>> print(test)If you have succesfully installed xlsxToCsv then you should get the following results from the above code"('C:/complete/path/to/file/', '.txt')"Short SummeryTakes user specified range data (ex. A1:A16) data from sheet(s)(ex. "MAIN") in side of .xlsx and then converts data to json dict and/or csv.
Package: xlsx_to_handontable
# xlsx_to_handontablexlsx to handontableconfiguration+sample# INSTALLpip install xlsx_to_handontable# USAGEfrom xlsx_to_handontable import xlsx_to_configs_samples
from yaml_dump import yaml_dump
p = ‘0.xlsx’
configs, samples = xlsx_to_configs_samples(p)
print(yaml_dump(configs))
print(‘-’ * 20)
print(yaml_dump(samples))# History## 0.1.0 (2017-01-13)
- First release on PyPI.