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numbers-parser is a Python module for parsing Apple Numbers.numbers files. It supports Numbers files generated by Numbers version 10.3, and up with the latest tested version being 14.1 (current as of June 2024).

It supports and is tested against Python versions from 3.9 onwards. It is not compatible with earlier versions of Python.


python3 -m pip install numbers-parser

A pre-requisite for this package is python-snappy which will be installed by Python automatically, but python-snappy also requires that the binary libraries for snappy compression are present.

The most straightforward way to install the binary dependencies is to use Homebrew and source Python from Homebrew rather than from macOS as described in the python-snappy github:

For Intel Macs:

brew install snappy python3
CPPFLAGS="-I/usr/local/include -L/usr/local/lib" \
python3 -m pip install python-snappy

For Apple Silicon Macs:

brew install snappy python3
CPPFLAGS="-I/opt/homebrew/include -L/opt/homebrew/lib" \
python3 -m pip install python-snappy

For Linux (your package manager may be different):

sudo apt-get -y install libsnappy-dev

On Windows, you will need to either arrange for snappy to be found for VSC++ or you can install python pre-compiled binary libraries which are only available for Windows on Arm. There appear to be no x86 pre-compiled packages for Windows.

pip install python_snappy-0.6.1-cp312-cp312-win_arm64.whl

Quick Start

Reading documents:

>>> from numbers_parser import Document
>>> doc = Document("mydoc.numbers")
>>> sheets = doc.sheets
>>> tables = sheets[0].tables
>>> rows = tables[0].rows()

Sheets and tables are iterables that can be indexed using either an integer index or using the name of the sheet/table:

>>> doc.sheets[0].name
'Sheet 1'
>>> doc.sheets["Sheet 1"].name
'Sheet 1'
>>> doc.sheets[0].tables[0].name
'Table 1'
>>> doc.sheets[0].tables["Table 1"].name
'Table 1'

Table objects have a rows method which contains a nested list with an entry for each row of the table. Each row is itself a list of the column values.

>>> data = sheets["Sheet 1"].tables["Table 1"].rows()
>>> data[0][0]
<numbers_parser.cell.EmptyCell object at 0x1022b5710>
>>> data[1][0]
<numbers_parser.cell.TextCell object at 0x101eb6790>
>>> data[1][0].value

Cell Data

Cells are objects with a common base class of Cell. All cell types have a property value which returns the contents of the cell as a python datatype. numbers-parser uses pendulum instead of python’s builtin types. Available cell types are:

Cell type value type Additional properties
NumberCell float
TextCell str
RichTextCell str See Rich text
EmptyCell None
BoolCell bool
DateCell pendulum.datetime
DurationCell pendulum.duration
ErrorCell None
MergedCell None See Merged cells

Cell references can be either zero-offset row/column integers or an Excel/Numbers A1 notation. Where cell values are not None the property formatted_value returns the cell value as a str as displayed in Numbers. Cells that have no values in a table are represented as EmptyCell and cells containing evaluation errors of any kind ErrorCell.

>>> table.cell(1,0)
<numbers_parser.cell.TextCell object at 0x1019ade50>
>>> table.cell(1,0).value
>>> table.cell("B2")
<numbers_parser.cell.NumberCell object at 0x103a99790>
>>> table.cell("B2").value
>>> table.cell("B2").formatted_value

Pandas Support

Since the return value of rows() is a list of lists, you can pass this directly to pandas. Assuming you have a Numbers table with a single header which contains the names of the pandas series you want to create you can construct a pandas dataframe using:

import pandas as pd

doc = Document("simple.numbers")
sheets = doc.sheets
tables = sheets[0].tables
data = tables[0].rows(values_only=True)
df = pd.DataFrame(data[1:], columns=data[0])

Writing Numbers Documents

Whilst support for writing numbers files has been stable since version 3.4.0, you are highly recommended not to overwrite working Numbers files and instead save data to a new file.

Cell values are written using Table.write() and numbers-parser will automatically create empty rows and columns for any cell references that are out of range of the current table.

doc = Document("write.numbers")
sheets = doc.sheets
tables = sheets[0].tables
table = tables[0]
table.write(1, 1, "This is new text")
table.write("B7", datetime(2020, 12, 25))"new-sheet.numbers")

Additional tables and worksheets can be added to a Document before saving using Document.add_sheet() and Sheet.add_table() respectively:

doc = Document()
doc.add_sheet("New Sheet", "New Table")
sheet = doc.sheets["New Sheet"]
table = sheet.tables["New Table"]
table.write(1, 1, 1000)
table.write(1, 2, 2000)
table.write(1, 3, 3000)"sheet.numbers")


numbers_parser currently only supports paragraph styles and cell styles. The following styles are supported:

  • font attributes: bold, italic, underline, strikethrough
  • font selection and size
  • text foreground color
  • horizontal and vertical alignment
  • cell background color
  • cell background images
  • cell indents (first line, left, right, and text inset)

Numbers conflates style attributes that can be stored in paragraph styles (the style menu in the text panel) with the settings that are available on the Style tab of the Text panel. Some attributes in Numbers are not applied to new cells when a style is applied.

To keep the API simple, numbers-parser packs all styling into a single Style object. When a document is saved, the attributes not stored in a paragraph style are applied to each cell that includes it.

Styles are read from cells using the property and you can add new styles with Document.add_style.

red_text = doc.add_style(
    name="Red Text",
    font_name="Lucida Grande",
    font_color=RGB(230, 25, 25),
    alignment=Alignment("right", "top"),
table.write("B2", "Red", style=red_text)
table.set_cell_style("C2", red_text)

Cell Data Formatting

Numbers has two different cell formatting types: data formats and custom formats.

Data formats are presented in Numbers in the Cell tab of the Format pane and are applied to individual cells. Like Numbers, numbers-parsers caches formatting information that is identical across multiple cells. You do not need to take any action for this to happen; this is handled internally by the package. Changing a data format for cell has no impact on any other cells.

Cell formats are changed using Table.set_cell_formatting():

   date_time_format="EEEE, d MMMM yyyy"

Custom formats are shared across a Document and can be applied to multiple cells in multiple tables. Editing a custom format changes the appearance of data in all cells that share that format. You must first add a custom format to the document using Document.add_custom_format() before assigning it to cells using Table.set_cell_formatting():

long_date = doc.add_custom_format(
   name="Long Date",
   date_time_format="EEEE, d MMMM yyyy"
table.set_cell_formatting("C1", "custom", format=long_date)

A limited number of currencies are formatted using symbolic notation rather than an ISO code. These are defined in numbers_parser.currencies and match the ones chosen by Numbers. For example, US dollars are referred to as US$ whereas Euros and British Pounds are referred to using their symbols of and £ respectively.


numbers-parser supports reading and writing cell borders, though the interface for each differs. Individual cells can have each of their four borders tested, but when drawing new borders, these are set for the table to allow for drawing borders across multiple cells. Setting the border of merged cells is not possible unless the edge of the cells is at the end of the merged region.

Borders are represented using the Border class that can be initialized with line width, color and line style. The current state of a cell border is read using the Cell.border property and Table.set_cell_border() sets the border for a cell edge or a range of cells.


For more examples and details of all available classes and methods, see the full API docs.

Command-line scripts

When installed from PyPI, a number of command-line scripts are installed:

  • cat-numbers: converts Numbers documents into CSV
  • csv2numbers: converts CSV files to Numbers documents
  • unpack-numbers: converts Numbers documents into JSON files for debug purposes


This script dumps Numbers spreadsheets into Excel-compatible CSV format, iterating through all the spreadsheets passed on the command-line.

usage: cat-numbers [-h] [-T | -S | -b] [-V] [--formulas] [--formatting]
                  [-s SHEET] [-t TABLE] [--debug]
                  [document ...]

Export data from Apple Numbers spreadsheet tables

positional arguments:
  document              Document(s) to export

  -h, --help            show this help message and exit
  -T, --list-tables     List the names of tables and exit
  -S, --list-sheets     List the names of sheets and exit
  -b, --brief           Don't prefix data rows with name of sheet/table
                        (default: false)
  -V, --version
  --formulas            Dump formulas instead of formula results
  --formatting          Dump formatted cells (durations) as they appear
                        in Numbers
  -s SHEET, --sheet SHEET
                        Names of sheet(s) to include in export
  -t TABLE, --table TABLE
                        Names of table(s) to include in export
  --debug               Enable debug logging

Note: --formatting will return different capitalization for 12-hour times due to differences between Numbers’ representation of these dates and datetime.strftime. Numbers in English locales displays 12-hour times with ‘am’ and ‘pm’, but datetime.strftime on macOS at least cannot return lower-case versions of AM/PM.


This script converts Excel-compatible CSV files into Numbers documents. Output files can optionally be provided, but is none are provided, the output is created by replacing the input’s files suffix with .numbers. For example:

csv2numbers file1.csv file2.csv -o file1.numbers file2.numbers

Columns of data can have a number of transformations applied to them. The primary use- case intended for csv2numbers is converting banking exports to well-formatted spreadsheets.

usage: csv2numbers [-h] [-V] [--whitespace] [--reverse] [--no-header]
                   [--day-first] [--date COLUMNS] [--rename COLUMNS-MAP]
                   [--transform COLUMNS-MAP] [--delete COLUMNS]
                   [-o [FILENAME ...]]
                   [csvfile ...]

positional arguments:
  csvfile               CSV file to convert

  -h, --help            show this help message and exit
  -V, --version
  --whitespace          strip whitespace from beginning and end of strings
                        and collapse other whitespace into single space
                        (default: false)
  --reverse             reverse the order of the data rows (default:
  --no-header           CSV file has no header row (default: false)
  --day-first           dates are represented day first in the CSV file
                        (default: false)
  --date COLUMNS        comma-separated list of column names/indexes to
                        parse as dates
  --rename COLUMNS-MAP  comma-separated list of column names/indexes to
                        renamed as 'OLD:NEW'
  --transform COLUMNS-MAP
                        comma-separated list of column names/indexes to
                        transform as 'NEW:FUNC=OLD'
  --delete COLUMNS      comma-separated list of column names/indexes to
  -o [FILENAME ...], --output [FILENAME ...]
                        output filename (default: use source file with

The following options affecting the output of the entire file. The default for each is always false.

  • --whitespace: strip whitespace from beginning and end of strings and collapse other whitespace into single space
  • --reverse: reverse the order of the data rows
  • --no-header: CSV file has no header row
  • ``--day-first`: dates are represented day first in the CSV file

csv2numbers can also perform column manipulation. Columns can be identified using their name if the CSV file has a header or using a column index. Columns are zero-indexed and names and indices can be used together on the same command-line. When multiple columns are required, you can specify them using comma-separated values. The format for these arguments, like for the CSV file itself, the Excel dialect.

Deleting columns

Delete columns using --delete. The names or indices of the columns to delete are specified as comma-separated values:

csv2numbers file1.csv --delete=Account,3

Renaming columns

Rename columns using --rename. The current column name and new column name are separated by a : and each renaming is specified as comma-separated values:

csv2numbers file1.csv --rename=2:Account,"Paid In":Amount

Date columns

The --date option identifies a comma-separated list of columns that should be parsed as dates. Use --day-first where the day and month is ambiguous anf the day comes first rather than the month.

Transforming columns

Columns can be merged and new columns created using simple functions. The –transform option takes a comma-seperated list of transformations of the form NEW:FUNC=OLD. Supported functions are:

Function Arguments Description
MERGE dest=MERGE:source The dest column is writen with values from one or more columns
indicated by source. For multiple columns, which are separated
by ;, the first empty value is chosen.
NEG dest=NEG:source The dest column contains absolute values of any column that is
negative. This is useful for isolating debits from account
POS dest=NEG:source The dest column contains values of any column that is
positive. This is useful for isolating credits from account
LOOKUP dest=LOOKUP:source;filename A lookup map is read from filename which must be an Apple
Numbers file containing a single table of two columns. The table
is used to match agsinst source, searching the first column
for matches and writing the corresponding value from the second
column to dest. Values are chosen based on the longest
matching substring.


csv2numbers --transform="Paid In"=POS:Amount,Withdrawn=NEG:Amount file1.csv
csv2numbers --transform='Category=LOOKUP:Transaction;mapping.numbers' file1.csv


Current known limitations of numbers-parser which may be implemented in the future are:

  • Table styles that allow new tables to adopt a style across the whole table are not suppported
  • Creating cells of type BulletedTextCell is not supported
  • New tables are inserted with a fixed offset below the last table in a worksheet which does not take into account title or caption size
  • Captions can be created and edited as of numbers-parser version 4.12, but cannot be styled. New captions adopt the first caption style available in the current document
  • Formulas cannot be written to a document
  • Pivot tables are unsupported and saving a document with a pivot table issues a UnsupportedWarning (see issue 73 for details).

The following limitations are expected to always remain:

  • New sheets insert tables with formats copied from the first table in the previous sheet rather than default table formats
  • Due to a limitation in Python’s ZipFile, Python versions older than 3.11 do not support image filenames with UTF-8 characters Cell.add_style.bg_image() returns None for such files and issues a RuntimeWarning (see issue 69 for details).
  • Password-encrypted documents cannot be opened. You must first re-save without a password to read (see issue 88 for details).


All code in this repository is licensed under the MIT License.


Python module for parsing Apple Numbers .numbers files