struct – Working with Binary Data¶

struct – Working with Binary Data - Python Module of the Week

struct – Working with Binary Data¶

Purpose:	Convert between strings and binary data.
Available In:	1.4 and later

The struct module includes functions for converting between strings of bytes and native Python data types such as numbers and strings.

Functions vs. Struct Class¶

There are a set of module-level functions for working with structured values, and there is also the Struct class (new in Python 2.5). Format specifiers are converted from their string format to a compiled representation, similar to the way regular expressions are. The conversion takes some resources, so it is typically more efficient to do it once when creating a Struct instance and call methods on the instance instead of using the module-level functions. All of the examples below use the Struct class.

Packing and Unpacking¶

Structs support packing data into strings, and unpacking data from strings using format specifiers made up of characters representing the type of the data and optional count and endian-ness indicators. For complete details, refer to the standard library documentation.

In this example, the format specifier calls for an integer or long value, a two character string, and a floating point number. The spaces between the format specifiers are included here for clarity, and are ignored when the format is compiled.

import struct
import binascii

values = (1, 'ab', 2.7)
s = struct.Struct('I 2s f')
packed_data = s.pack(*values)

print 'Original values:', values
print 'Format string  :', s.format
print 'Uses           :', s.size, 'bytes'
print 'Packed Value   :', binascii.hexlify(packed_data)

The example converts the packed value to a sequence of hex bytes for printing with binascii.hexlify(), since some of the characters are nulls.

$ python struct_pack.py

Original values: (1, 'ab', 2.7)
Format string  : I 2s f
Uses           : 12 bytes
Packed Value   : 0100000061620000cdcc2c40

If we pass the packed value to unpack(), we get basically the same values back (note the discrepancy in the floating point value).

import struct
import binascii

packed_data = binascii.unhexlify('0100000061620000cdcc2c40')

s = struct.Struct('I 2s f')
unpacked_data = s.unpack(packed_data)
print 'Unpacked Values:', unpacked_data

$ python struct_unpack.py

Unpacked Values: (1, 'ab', 2.700000047683716)

Endianness¶

By default values are encoded using the native C library notion of “endianness”. It is easy to override that choice by providing an explicit endianness directive in the format string.

import struct
import binascii

values = (1, 'ab', 2.7)
print 'Original values:', values

endianness = [
    ('@', 'native, native'),
    ('=', 'native, standard'),
    ('<', 'little-endian'),
    ('>', 'big-endian'),
    ('!', 'network'),
    ]

for code, name in endianness:
    s = struct.Struct(code + ' I 2s f')
    packed_data = s.pack(*values)
    print
    print 'Format string  :', s.format, 'for', name
    print 'Uses           :', s.size, 'bytes'
    print 'Packed Value   :', binascii.hexlify(packed_data)
    print 'Unpacked Value :', s.unpack(packed_data)

$ python struct_endianness.py

Original values: (1, 'ab', 2.7)

Format string  : @ I 2s f for native, native
Uses           : 12 bytes
Packed Value   : 0100000061620000cdcc2c40
Unpacked Value : (1, 'ab', 2.700000047683716)

Format string  : = I 2s f for native, standard
Uses           : 10 bytes
Packed Value   : 010000006162cdcc2c40
Unpacked Value : (1, 'ab', 2.700000047683716)

Format string  : < I 2s f for little-endian
Uses           : 10 bytes
Packed Value   : 010000006162cdcc2c40
Unpacked Value : (1, 'ab', 2.700000047683716)

Format string  : > I 2s f for big-endian
Uses           : 10 bytes
Packed Value   : 000000016162402ccccd
Unpacked Value : (1, 'ab', 2.700000047683716)

Format string  : ! I 2s f for network
Uses           : 10 bytes
Packed Value   : 000000016162402ccccd
Unpacked Value : (1, 'ab', 2.700000047683716)

Buffers¶

Working with binary packed data is typically reserved for highly performance sensitive situations or passing data into and out of extension modules. In such situations, you can optimize by avoiding the overhead of allocating a new buffer for each packed structure. The pack_into() and unpack_from() methods support writing to pre-allocated buffers directly.

import struct
import binascii

s = struct.Struct('I 2s f')
values = (1, 'ab', 2.7)
print 'Original:', values

print
print 'ctypes string buffer'

import ctypes
b = ctypes.create_string_buffer(s.size)
print 'Before  :', binascii.hexlify(b.raw)
s.pack_into(b, 0, *values)
print 'After   :', binascii.hexlify(b.raw)
print 'Unpacked:', s.unpack_from(b, 0)

print
print 'array'

import array
a = array.array('c', '\0' * s.size)
print 'Before  :', binascii.hexlify(a)
s.pack_into(a, 0, *values)
print 'After   :', binascii.hexlify(a)
print 'Unpacked:', s.unpack_from(a, 0)

The size attribute of the Struct tells us how big the buffer needs to be.

$ python struct_buffers.py

Original: (1, 'ab', 2.7)

ctypes string buffer
Before  : 000000000000000000000000
After   : 0100000061620000cdcc2c40
Unpacked: (1, 'ab', 2.700000047683716)

array
Before  : 000000000000000000000000
After   : 0100000061620000cdcc2c40
Unpacked: (1, 'ab', 2.700000047683716)