Interpreting Bitstrings

Bitstrings don’t know or care how they were created; they are just collections of bits. This means that you are quite free to interpret them in any way that makes sense.

Several Python properties are used to create interpretations for the bitstring. These properties call private functions which will calculate and return the appropriate interpretation. These don’t change the bitstring in any way and it remains just a collection of bits. If you use the property again then the calculation will be repeated.

Note that these properties can potentially be very expensive in terms of both computation and memory requirements. For example if you have initialised a bitstring from a 10 GB file object and ask for its binary string representation then that string will be around 80 GB in size!

For the properties described below we will use these:

>>> a = BitArray('0x123')
>>> b = BitArray('0b111')


The most fundamental interpretation is perhaps as a binary string (a ‘bitstring’). The bin property returns a string of the binary representation of the bitstring. All bitstrings can use this property and it is used to test equality between bitstrings.

>>> a.bin
>>> b.bin

Note that the initial zeros are significant; for bitstrings the zeros are just as important as the ones!


For whole-byte bitstrings the most natural interpretation is often as hexadecimal, with each byte represented by two hex digits.

If the bitstring does not have a length that is a multiple of four bits then an InterpretError exception will be raised. This is done in preference to truncating or padding the value, which could hide errors in user code.

>>> a.hex
>>> b.hex
ValueError: Cannot convert to hex unambiguously - not multiple of 4 bits.


For an octal interpretation use the oct property.

If the bitstring does not have a length that is a multiple of three then an InterpretError exception will be raised.

>>> a.oct
>>> b.oct
>>> (b + '0b0').oct
ValueError: Cannot convert to octal unambiguously - not multiple of 3 bits.

uint / uintbe / uintle / uintne

To interpret the bitstring as a binary (base-2) bit-wise big-endian unsigned integer (i.e. a non-negative integer) use the uint property.

>>> a.uint
>>> b.uint

For byte-wise big-endian, little-endian and native-endian interpretations use uintbe, uintle and uintne respectively. These will raise a ValueError if the bitstring is not a whole number of bytes long.

>>> s = BitArray('0x000001')
>>> s.uint     # bit-wise big-endian
>>> s.uintbe   # byte-wise big-endian
>>> s.uintle   # byte-wise little-endian
>>> s.uintne   # byte-wise native-endian (will be 1 on a big-endian platform!)

int / intbe / intle / intne

For a two’s complement interpretation as a base-2 signed integer use the int property. If the first bit of the bitstring is zero then the int and uint interpretations will be equal, otherwise the int will represent a negative number.


For byte-wise big, little and native endian signed integer interpretations use intbe, intle and intne respectively. These work in the same manner as their unsigned counterparts described above.

float / floatbe / floatle / floatne

For a floating point interpretation use the float property. This uses your machine’s underlying floating point representation and will only work if the bitstring is 32 or 64 bits long.

Different endiannesses are provided via floatle and floatne. Note that as floating point interpretations are only valid on whole-byte bitstrings there is no difference between the bit-wise big-endian float and the byte-wise big-endian floatbe.

Note also that standard floating point numbers in Python are stored in 64 bits, so use this size if you wish to avoid rounding errors.


A common need is to retrieve the raw bytes from a bitstring for further processing or for writing to a file. For this use the bytes interpretation, which returns a bytes object (which is equivalent to an ordinary str in Python 2.6/2.7).

If the length of the bitstring isn’t a multiple of eight then a ValueError will be raised. This is because there isn’t an unequivocal representation as bytes. You may prefer to use the method tobytes as this will be pad with between one and seven zero bits up to a byte boundary if neccessary.

>>> open('somefile', 'wb').write(a.tobytes())
>>> open('anotherfile', 'wb').write(('0x0'+a).bytes)
>>> a1 = BitArray(filename='somefile')
>>> a1.hex
>>> a2 = BitArray(filename='anotherfile')
>>> a2.hex

Note that the tobytes method automatically padded with four zero bits at the end, whereas for the other example we explicitly padded at the start to byte align before using the bytes property.


The ue property interprets the bitstring as a single unsigned exponential-Golomb code and returns an integer. If the bitstring is not exactly one code then an InterpretError is raised instead. If you instead wish to read the next bits in the stream and interpret them as a code use the read function with a ue format string. See Exponential-Golomb Codes for a short explanation of this type of integer representation.

>>> s = BitArray(ue=12)
>>> s.bin
>>> s.append(BitArray(ue=3))
>>> print(s.readlist('2*ue'))
[12, 3]


The se property does much the same as ue and the provisos there all apply. The obvious difference is that it interprets the bitstring as a signed exponential-Golomb rather than unsigned - see Exponential-Golomb Codes for more information.

>>> s = BitArray('0x164b')
InterpretError: BitArray, is not a single exponential-Golomb code.
>>> while s.pos < s.length:
...     print('se'))

uie / sie

A slightly different type, interleaved exponential-Golomb codes are also supported. The principles are the same as with ue and se - see Exponential-Golomb Codes for detail of the differences.