Tuples are objects that can store a specific number of other objects in order. They are immutable, so we can't add, remove, or replace objects on the fly. This may seem like a massive restriction, but the truth is, if you need to modify a tuple, you're using the wrong data type (usually a list would be more suitable). The primary benefit of tuples' immutability is that we can use them as keys in dictionaries, and in other locations where an object requires a hash value.
Tuples are used to store data; behavior cannot be stored in a tuple. If we require behavior to manipulate a tuple, we have to pass the tuple into a function (or method on another object) that performs the action.
Tuples should generally store values that are somehow different from each other. For example, we would not put three stock symbols in a tuple, but we might create a tuple of stock symbol, current price, high, and low for the day. The primary purpose of a tuple is to aggregate different pieces of data together into one container. Thus, a tuple can be the easiest tool to replace the "object with no data" idiom.
We can create a tuple by separating the values with a comma. Usually, tuples are wrapped in parentheses to make them easy to read and to separate them from other parts of an expression, but this is not always mandatory. The following two assignments are identical (they record a stock, the current price, the high, and the low for a rather profitable company):
>>> stock = "FB", 75.00, 75.03, 74.90 >>> stock2 = ("FB", 75.00, 75.03, 74.90)
If we're grouping a tuple inside of some other object, such as a function call, list comprehension, or generator, the parentheses are required. Otherwise, it would be impossible for the interpreter to know whether it is a tuple or the next function parameter. For example, the following function accepts a tuple and a date, and returns a tuple of the date and the middle value between the stock's high and low value:
import datetime
def middle(stock, date):
symbol, current, high, low = stock
return (((high + low) / 2), date)
mid_value, date = middle(("FB", 75.00, 75.03, 74.90),
datetime.date(2014, 10, 31))
The tuple is created directly inside the function call by separating the values with commas and enclosing the entire tuple in parenthesis. This tuple is then followed by a comma to separate it from the second argument.
This example also illustrates tuple unpacking. The first line inside the function unpacks the stock parameter into four different variables. The tuple has to be exactly the same length as the number of variables, or it will raise an exception. We can also see an example of tuple unpacking on the last line, where the tuple returned inside the function is unpacked into two values, mid_value and date. Granted, this is a strange thing to do, since we supplied the date to the function in the first place, but it gave us a chance to see unpacking at work.
Unpacking is a very useful feature in Python. We can group variables together to make storing and passing them around simpler, but the moment we need to access all of them, we can unpack them into separate variables. Of course, sometimes we only need access to one of the variables in the tuple. We can use the same syntax that we use for other sequence types (lists and strings, for example) to access an individual value:
>>> stock = "FB", 75.00, 75.03, 74.90 >>> high = stock[2] >>> high 75.03
We can even use slice notation to extract larger pieces of tuples:
>>> stock[1:3] (75.00, 75.03)
These examples, while illustrating how flexible tuples can be, also demonstrate one of their major disadvantages: readability. How does someone reading this code know what is in the second position of a specific tuple? They can guess, from the name of the variable we assigned it to, that it is high of some sort, but if we had just accessed the tuple value in a calculation without assigning it, there would be no such indication. They would have to paw through the code to find where the tuple was declared before they could discover what it does.
Accessing tuple members directly is fine in some circumstances, but don't make a habit of it. Such so-called "magic numbers" (numbers that seem to come out of thin air with no apparent meaning within the code) are the source of many coding errors and lead to hours of frustrated debugging. Try to use tuples only when you know that all the values are going to be useful at once and it's normally going to be unpacked when it is accessed. If you have to access a member directly or using a slice and the purpose of that value is not immediately obvious, at least include a comment explaining where it came from.
So, what do we do when we want to group values together, but know we're frequently going to need to access them individually? Well, we could use an empty object, as discussed in the previous section (but that is rarely useful unless we anticipate adding behavior later), or we could use a dictionary (most useful if we don't know exactly how many or which specific data will be stored), as we'll cover in the next section.
If, however, we do not need to add behavior to the object, and we know in advance what attributes we need to store, we can use a named tuple. Named tuples are tuples with attitude. They are a great way to group read-only data together.
Constructing a named tuple takes a bit more work than a normal tuple. First, we have to import namedtuple, as it is not in the namespace by default. Then, we describe the named tuple by giving it a name and outlining its attributes. This returns a class-like object that we can instantiate with the required values as many times as we want:
from collections import namedtuple
Stock = namedtuple("Stock", "symbol current high low")
stock = Stock("FB", 75.00, high=75.03, low=74.90)The namedtuple constructor accepts two arguments. The first is an identifier for the named tuple. The second is a string of space-separated attributes that the named tuple can have. The first attribute should be listed, followed by a space (or comma if you prefer), then the second attribute, then another space, and so on. The result is an object that can be called just like a normal class to instantiate other objects. The constructor must have exactly the right number of arguments that can be passed in as arguments or keyword arguments. As with normal objects, we can create as many instances of this "class" as we like, with different values for each.
The resulting namedtuple can then be packed, unpacked, and otherwise treated like a normal tuple, but we can also access individual attributes on it as if it were an object:
>>> stock.high 75.03 >>> symbol, current, high, low = stock >>> current 75.00
Named tuples are perfect for many "data only" representations, but they are not ideal for all situations. Like tuples and strings, named tuples are immutable, so we cannot modify an attribute once it has been set. For example, the current value of my company's stock has gone down since we started this discussion, but we can't set the new value:
>>> stock.current = 74.98 Traceback (most recent call last): File "<stdin>", line 1, in <module> AttributeError: can't set attribute
If we need to be able to change stored data, a dictionary may be what we need instead.