Python's 2 different string representations

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Series: Strings
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Trey Hunner
3 min. read Watch as video Python 3.8—3.12
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Let's talk about the two different string representations that all Python objects have.

str vs repr in Python

We have a datetime.date object here that represents the Python 2 end of life date:

>>> from datetime import date
>>> eol = date(2020, 1, 1)

If we type eol from a Python shell, we're gonna see something that looks kind of like the code that I originally typed in to get that object:

>>> eol
datetime.date(2020, 1, 1)

If we print out this datetime.date object, we instead see something that looks more like a human-readable date:

>>> print(eol)
2020-01-01

When we called print, it actually called the built-in str function to get this string representation:

>>> str(eol)
'2020-01-01'

This is the human-readable string representation for this object.

Likewise, typing just eol at the Python REPL also resulted in a built-in function being called, the built-in repr function function:

>>> repr(eol)
'datetime.date(2020, 1, 1)'

This is the programmer-readable string representation for this object.

The human-readable string representation is the thing that an end-user of our program might want to see, which is the reason that it's what we get when we print something out.

The programmer-readable string representation is what another Python programmer might want to see, which is the reason that we see it when we're playing around with objects at the Python REPL.

repr: the programmer-readable string representation

If we look at help on the built-in repr function, we will see that many objects, including most of the built-ins, use a particular convention for the programmer-readable string representation:

>>> help(repr)
Help on built-in function repr in module builtins:

repr(obj, /)
    Return the canonical string representation of the object.

    For many object types, including most builtins, eval(repr(obj)) == obj.

>>>

This convention says that the string that you get back, should actually be the Python code which if you were to execute it, would give you an object that would be equivalent to the object you started with.

So what that really means is, the result of typing just eol at the REPL should represent something that if we would execute that code, it would give us back basically the same date object:

>>> eol
datetime.date(2020, 1, 1)

Most objects just have one string representation

Now, we should note that most Python objects actually only have one string representation.

The one-string representation they have is the programmer-readable one.

So if we take a list, a dictionary, an integer, a floating-point number, or most objects in Python, and we convert them to a string, we see something that looks like Python code:

>>> numbers = [2, 1, 3, 4, 7]
>>> str(numbers)
'[2, 1, 3, 4, 7]'

If we call the built-in repr function on them, we'll see the same thing:

>>> repr(numbers)
'[2, 1, 3, 4, 7]'
>>> print(numbers)
[2, 1, 3, 4, 7]
>>> numbers
[2, 1, 3, 4, 7]

Most objects in Python do not have a human-readable string representation. They're meant to be used by other Python programmers, they're not necessarily meant to be printed out.

Summary

So in Python, we have two different string representations. str is the human-readable string representation, if that exists.

If it doesn't exist, the built-in str function, will call the built-in repr function, which gives you the programmer-readable string representation, which is meant for other Python programmers.

If you're converting something to a string, the str function is a pretty great way to do it. But if you specifically want a string representation of an object that's meant for another Python programmer, you might want to use the built-in repr function.

To customize these two different string representations on your Python objects, see Customizing the string representation of your Python objects.

Series: Strings

Regardless of what you're doing in Python, you almost certainly use strings all the time. A string is usually the default tool we reach for when we don't have a more specific way to represent our data.

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