superspreader 0.2.7.1

Load data from spreadsheets easily

Superspreader 🦠

Superspreader is a little helper library that simplifies working with spreadsheets. It is built on top of openpyxl. OpenPyXL is its only dependency.

Instead of looping over rows and columns manually, the structure of a spreadsheet is described in a class:

from superspreader import fields
from superspreader.sheets import BaseSheet


class AlbumSheet(BaseSheet):
    """
    This class describes a sheet in an Excel document
    """

    sheet_name = "Albums" # The sheet is named “albums”
    header_rows = 3 # The sheet has three header rows

    # The column labels are in the second row.
    # It is *not* zero based to match the Excel row number
    label_row = 2


    # The columns
    artist = fields.CharField(source="Artist", required=True)
    album = fields.CharField(source="Album")
    release_date = fields.DateField(source="Release Date")
    average_review = fields.FloatField(source="Average Review")
    chart_position = fields.IntegerField(source="Chart Position")

Ready? Let’s load an Excel spreadsheet!

if __name__ == "__main__":
    sheet = AlbumSheet("albums.xlsx")
    # Load and parse data from the document
    sheet.load()

    print(sheet.has_errors)
    # False
    print(sheet.errors)
    # []
    print(sheet.infos)
    # []

    for row_dict in sheet:
        print(row_dict)

# {'artist': 'David Bowie', 'album': 'Toy', 'release_date': datetime.date(2022, 1, 7), 'average_review': 4.3, 'chart_position': 5}
# {'artist': 'The Wombats', 'album': 'Fix Yourself, Not The World', 'release_date': datetime.date(2022, 3, 7), 'average_review': 3.9, 'chart_position': 7}
# {'artist': 'Kokoroko', 'album': 'Could We Be More', 'release_date': datetime.date(2022, 8, 1), 'average_review': 4.7, 'chart_position': 30}

In tests/spreadsheets is a sample spreadsheet that is used for testing. Feel free to fiddle around.

There’s a lot more to say and I’ll update the documentation as I go.

Field params

Fields must have a source”parameter, that holds the column name for the spreadsheet.unique=True` may be used to indicate that a field’s value must be unique.

Adding static & dynamic data to rows

To provide additional data, use extra_data. Data from the spreadsheet take precedence over extra data.

extra_data = {
    "status": "released"
}
sheet = AlbumSheet("albums.xlsx", extra_data=extra_data)
sheet.load()
# {'artist': 'David Bowie', 'album': 'Toy', 'release_date': datetime.date(2022, 1, 7), 'average_review': 4.3, 'chart_position': 5, 'status': 'released'}

Use a callable for dynamic extra data:

extra_data = {
    "summary": lambda row: f"“{row.get('album')}” by {row.get('artist')}"
}

sheet = AlbumSheet("albums.xlsx", extra_data=extra_data)
# {'artist': 'David Bowie', 'album': 'Toy', 'release_date': datetime.date(2022, 1, 7), 'average_review': 4.3, 'chart_position': 5, 'summary': '“Toy” by David Bowie'}

Changelog

0.2.7

• Adds support for unique validation

0.2.3

• Adds support for inheriting sheets (before that, fields from base classes weren’t recognized)

0.2.2

• Adds support for callables in extra_data

0.2.1

• Adds support for providing field defaults by setting the default attribute or providing an instance-label value: fields.CharField(source="Album", default="not specified")

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The API is inspired by Django’s model API and ElasticSearch DSL.