pyxlmapper

This library can help reading and mapping hierarchical .xlsx spreadsheets like this one:

into data such as:

[
  {
    "category": {
      "group_a": {
        "a": "...",
        "b": "..."
      },
      "group_b": {
        "c": "...",
        "d": "...",
      }
    },
    "lone_field": "...",
  },
  ...
]

Or this:

[
  {
    "category.group_a.a": "...",
    "category.group_a.b": "...",
    "category.group_b.c": "...",
    "category.group_b.d": "...",
    "lone_field": "...",
  },
  ...
]

Installation

WARNING: PyPI has no package yet. Name clame is pending: https://github.com/pypi/support/issues/5471

pip install pyxlmapper

Usage

You can write your own mapper or use code generation to infer mapper automatically

Writing and using mapper

pyxlmapper uses DSL based on python classes. First, you need to define a mapper class that inherits SpreadsheetMapper. And then define all headers. Class nesting is resambling headers nesting in the spreadsheet. You do not need to specify offsets manually, it will be calculated automatically, considering that header are nested from top to bottom and column order is from left to right without gaps (by default, but could be modified). For the example from above, the mapper would look like this:

import json
import openpyxl
from pyxlmapper import SpreadsheetMapper


class Mapper(SpreadsheetMapper):
    class Category:
        class GroupA:
            class A:
                pass

            class B:
                pass

        class GroupB:
            class C:
                pass

            class D:
                pass

    class LoneField:
        pass



# Open spreadhseet:
wb = openpyxl.open('data.xlsx', data_only=True)
ws = wb['sheet name']  # or ws.active for the first one

# instantiate mapper:
mapper = Mapper()

data = []

for row in mapper.map_rows(ws, start_at=3):
    print(row)
    # OR
    data.append(row)

# Save a file
with open('output.json', 'w') as fd:
    fd.write(json.dump(data, fd))

Syntax

Class name

Class names are used to automaticaly derive input_name and output_name if not provided. input_name would be derived as such: SomeFieldName into Some Field Name. output_name on the other hand would be derived as some_field_name.

output_name (str)

If specified, used as an override for the name of the field in the output JSON file.

Example:

class Mapper(SpreadsheetMapper):
    class SomeField:
        output_name = "other_name"

input_name (str)

If specified, used as an override for the column name in the xlsx spreadsheet.

Example:

class Mapper(SpreadsheetMapper):
    class SomeField:
        input_name = "You can use ::any:: symbols"

offset tuple[int, int]

Specifies an offset, relative to default position. Useful when some column or row is skipped

Format: offset = (v_offset: int, h_offset: int)

class Mapper(SpreadsheetMapper):
    class SomeField:
        offset = (0, 1)  # To skip a column