django-spreadsheets 1.2.0

Import from and export to XLSX files with data validation.

django-spreadsheet

Validate your XLSX spreadsheet file before importing it. Export your data to a formatted XLSX spreadsheet with data validation set up.

Requirements

• Python 3.8+
• Django 2.0+

Installation

• run pip install django_spreadsheets
• add django_spreadsheets.apps.SpreadsheetAppConfig to your INSTALLED_APPS
• add path("admin/spreadsheets/", include("django_spreadsheets.urls", namespace="spreadsheets")) to your root urls.py
• run python manage.py migrate django_spreadsheets

Usage

Start to create a file spreadsheets.py in your app. Inside this file, describe your spreadsheets configurations such as the following example:

from django_spreadsheets.base_config import SpreadsheetConfig, SheetConfig
from django_spreadsheets.registry import register
from libs.bananas.models import Banana


class BananaSheetConfig(SheetConfig):
    """Describes each columns of this sheet."""

    ID = {
        "name": "Identifier",
        "required": True,
    }
    COLOR = {
        "name": "Color",
        "validation": {
            "type": "list",
            "formula1": ["yellow", "green", "brown"],
        },
        "required": True,
    }
    SIZE = {
      "name": "Size",
      "validation": {
          "type": "whole",
          "operator": "greaterThan",
          "formula1": 0,
          "error": "Please enter a positive number",
      }
    }

    def get_data_to_export(self):
        return Banana.objects.values_list("id", "color", "size")


@register
class BananaSpreadsheetConfig(SpreadsheetConfig):
    sheets_config = {
        "Banana": BananaSheetConfig()
        # the key will be used as the name of the sheet, the value is a class describing the sheet's configuration.
    }

    class Meta:
        verbose_name = "Bananas"

    def update_database(self, sheets):
        for sheet_name, rows in sheets.items():
            for row in rows:
                Banana.objects.get_or_create(
                  id=row["ID"],
                  color=row["COLOR"],
                  size=row["SIZE"],
                )

Then, you can go to //localhost:8000/admin/spreadsheets/export/ to export a XSLX file and //localhost:8000/admin/spreadsheets/import/ to import into your database.

SpreadsheetConfig class

Inherit this class to describe your spreadsheets' configuration. Define a sheets_config dict attribute containing the name of your sheet as a key, and a SheetConfig class as a value.

Validations

Several validations are run before importing a spreadsheet:

Config version validation

Validate that the configuration version stored in the file is the same as the SpreadsheetConfig.version used.

Sheets validation

Validate that all sheets in this SpreadsheetConfig class used, exist in imported file. Validate that all sheets in imported file, exist in this SpreadsheetConfig class used.

Columns headers validation

Validate that the imported file contains the same columns headers as described in the SheetConfig used.

Required data validation

Validate that there is a value in the imported file for each required, required_unless, required_if columns.

Uniqueness validation [/!\ not implemented]

Validate that multiple cells in a row are unique together in the imported file.

Types validation

Validate that each column in the imported file contains data of correct type (date, positive integer, ...).

Data match validation

Validate that we find an object in the database matching the value in the imported file. If not, we ask the user to chose from a list of possibles values.

Import data

If you want to import data in y our database from this spreadsheet, you need to define a update_database(self, sheets) method. This method is called after all data validations have been completed, and receive a python dict with an item for each sheet. The key is the name of the sheet, and the value is a python list of rows. Each row is a list containing a value for each column.

def update_database(self, sheets):
    for sheet_name, rows in sheets.items():
        for row in rows:
            for value in row:
                print(value)

SheetConfig class

Inherit this class to describe your sheets' configuration. Each attribute describes a column using a dictionary such as the following:

<column_identifier> = {
  "name": str (required),  # The name of the column used in the header row
  "required": bool,  # Whether a value in the column is required when importing
  "required_unless": str,  # Whether a value in this column is required if the pointed column is empty
  "required_if": str,  # Whether a value in this column is required if the pointed column is filled
  "comment": str,  # A comment that appears in the header cell
  "admin_only": bool,  # Whether this column should be exported only when the current user is an administrator
  "validation": {  # A data validation applied to each cell of this column
    "name": str,  # A name used as header in the hidden config sheet
    "type": str (required),  # <url|date|list|range|whole...> See openpyxl documentation to get all available validation types
    ...  # Same args as openpyxl's DataValidation objects
  },
  "header_style": str,  # The name of a style that already exists in the SpreadsheetConfig to which this sheet belongs
  "style": str,  # The name of a style that already exists in the SpreadsheetConfig to which this sheet belongs
  "number_format": str # A value among FORMAT_* constants from openpyxl.styles.numbers or others values supported by XLSX spec
},

Note on validation

To create a boolean validation, you may use a list validation type with django_spreadsheets.constants.YES_NO_CHOICES as formula1 value. To ensure a boolean value is displayed in your exported file, add "number_format": "BOOLEAN" in your column configuration.

Note on number format

When the value in your exported file is not displayed correctly or not consistent across rows, you may define the number_format key in your column configuration.

Openxpyxl's format constants are not exhaustive. You may use other supported strings (eg: "BOOLEAN").

Export data

If you want to export data to this sheet, you need to define a get_data_to_export(self) method. This method must return a python list containing an item by row. Each item in the list is a list containing a value for each column.

def get_data_to_export(self):
  return [
    [1, "yellow", 20],
    [1, "green", 22],
  ]

Settings

SPREADSHEET_CONFIG_SHEET_TITLE

Default: _("Configuration")

The name of the (hidden) config sheet in the generated file. This special sheet is used to store data needed for validation (eg. lists of choices).

SPREADSHEET_MAX_VALIDATION_ROWS

Default: 700

When exporting data, the generated spreadsheet will have at least SPREADSHEET_MAX_VALIDATION_ROWS rows with data validation set up.

SPREADSHEET_MAX_STYLED_ROWS

Default: 700

When exporting data, the generated spreadsheet will have at least SPREADSHEET_MAX_STYLED_ROWS rows styled.

SPREADSHEET_COMMENT_AUTHOR

Default: "Django Spreadsheet"

The name of the author used when adding comments on cells.

SPREADSHEET_EXPORT_FILE_NAME

Default: "export {config_name} {date}.xlsx"

The name of the exported file when downloaded. Token config_name and date will be replaced respectively by the name of the configuration that is currently exported and the current date.

SPREADSHEET_TEMPLATE_FILE_NAME

Default: "{config_name} template file.xlsx"

The name of the template file when downloaded. Token config_name will be replaced by the name of the configuration that is currently exported.