Byte Objects vs String:

In Python 3, the latest version of Python, byte objects are defined as ‘sequence of objects’. If we talk about Python 2 then, we can say that the byte object is similar to the Unicode of Python 2. Also in Python 2, both string and bytes are similar while using it for data science.

Below are a few dissimilarities between Byte object and String:

  1. A sequence of bytes constitute byte object, but a sequence of characters constitute a string.
  2. Byte objects are in machine-readable form whereas strings are in user-readable form.
  3. Byte objects being machine-readable are directly store on disk but on the other hand strings being in user-readable form has to be encode for storage onto the disk.

Byte Objects vs String in Python for Data Science - PST Analytics

Below are some methods for the conversion of byte object to string and vice versa:


Encoding is a format there to represent images, text, audio, etc. into bytes. There are many different forms of encoding; some of them are ASCII, UTF-8, JPEG, MP3, WAV, etc. In simple terms, when there is a conversion of a string to a byte object, it is said encoding as is clear from the figure above. Encoding is done in order to make the string machine-readable and store it into a disk.

The function there for encoding is encode(). The default technique is ‘UTF-8’.

Byte Objects vs String in Python for Data Science - PST Analytics

Decoding:- As encoding is there for converting string to byte, decoding is there for doing the exact opposite i.e., converting the byte to string. Decoding is done using decode(). If the encoding is known, then we can convert back a byte string into a character string.

Byte Objects vs String in Python for Data Science - PST Analytics

So, to learn more about it in python for data science, you can check this and this as well.

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