Lists
In Python, a list is a way to store multiple values together. In this episode, we will learn how to store multiple values in a list as well as how to work with lists.
Python lists
Unlike NumPy arrays, lists are built into the language so we do not have to load a library to use them. We create a list by putting values inside square brackets and separating the values with commas:
odds = [1, 3, 5, 7] print('odds are:', odds)
odds are: [1, 3, 5, 7]
We can access elements of a list using indices -- numbered positions of elements in the list. These positions are numbered starting at 0, so the first element has an index of 0.
print('first element:', odds[0]) print('last element:', odds[3]) print('"-1" element:', odds[-1])
first element: 1 last element: 7 "-1" element: 7
Yes, we can use negative numbers as indices in Python. When we do so, the index -1 gives us the
last element in the list, -2 the second to last, and so on.
Because of this, odds[3] and odds[-1] point to the same element here.
There is one important difference between lists and strings: we can change the values in a list, but we cannot change individual characters in a string. For example:
names = ['Curie', 'Darwing', 'Turing'] # typo in Darwin's name print('names is originally:', names) names[1] = 'Darwin' # correct the name print('final value of names:', names)
names is originally: ['Curie', 'Darwing', 'Turing'] final value of names: ['Curie', 'Darwin', 'Turing']
works, but:
name = 'Darwin' name[0] = 'd'
--------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-8-220df48aeb2ein <module>() 1 name = 'Darwin' ----2 name[0] = 'd' TypeError: 'str' object does not support item assignment
does not.
Ch-Ch-Ch-Ch-Changes
Data which can be modified in place is called [mutable]({{ page.root }}/reference.html#mutable), while data which cannot be modified is called [immutable]({{ page.root }}/reference.html#immutable). Strings and numbers are immutable. This does not mean that variables with string or number values are constants, but when we want to change the value of a string or number variable, we can only replace the old value with a completely new value.
Lists and arrays, on the other hand, are mutable: we can modify them after they have been created. We can change individual elements, append new elements, or reorder the whole list. For some operations, like sorting, we can choose whether to use a function that modifies the data in-place or a function that returns a modified copy and leaves the original unchanged.
Be careful when modifying data in-place. If two variables refer to the same list, and you modify the list value, it will change for both variables!
salsa = ['peppers', 'onions', 'cilantro', 'tomatoes'] my_salsa = salsa # <-- my_salsa and salsa point to the *same* list data in memory salsa[0] = 'hot peppers' print('Ingredients in my salsa:', my_salsa)
Ingredients in my salsa: ['hot peppers', 'onions', 'cilantro', 'tomatoes']
If you want variables with mutable values to be independent, you must make a copy of the value when you assign it.
salsa = ['peppers', 'onions', 'cilantro', 'tomatoes'] my_salsa = list(salsa) # <-- makes a *copy* of the list salsa[0] = 'hot peppers' print('Ingredients in my salsa:', my_salsa)
Ingredients in my salsa: ['peppers', 'onions', 'cilantro', 'tomatoes']
Because of pitfalls like this, code which modifies data in place can be more difficult to understand. However, it is often far more efficient to modify a large data structure in place than to create a modified copy for every small change. You should consider both of these aspects when writing your code.
Nested Lists
Since a list can contain any Python variables, it can even contain other lists.
For example, we could represent the products in the shelves of a small grocery shop:
x = [['pepper', 'zucchini', 'onion'], ['cabbage', 'lettuce', 'garlic'], ['apple', 'pear', 'banana']]
Here is a visual example of how indexing a list of lists x works:
[![x is represented as a pepper shaker containing several packets of pepper. [x[0]] is represented as a pepper shaker containing a single packet of pepper. x[0] is represented as a single packet of pepper. x[0][0] is represented as single grain of pepper. Adapted from @hadleywickham.](fig/indexing_lists_python.png)](https://twitter.com/hadleywickham/status/643381054758363136)
Using the previously declared list x, these would be the results of the
index operations shown in the image:
print([x[0]])
[['pepper', 'zucchini', 'onion']]
print(x[0])
['pepper', 'zucchini', 'onion']
print(x[0][0])
'pepper'
Thanks to Hadley Wickham for the image above.
Heterogeneous Lists
Lists in Python can contain elements of different types. Example:
sample_ages = [10, 12.5, 'Unknown']
There are many ways to change the contents of lists besides assigning new values to individual elements:
odds.append(11) print('odds after adding a value:', odds)
odds after adding a value: [1, 3, 5, 7, 11]
removed_element = odds.pop(0) print('odds after removing the first element:', odds) print('removed_element:', removed_element)
odds after removing the first element: [3, 5, 7, 11] removed_element: 1
odds.reverse() print('odds after reversing:', odds)
odds after reversing: [11, 7, 5, 3]
While modifying in place, it is useful to remember that Python treats lists in a slightly counter-intuitive way.
As we saw earlier, when we modified the salsa list item in-place, if we make a list, (attempt to)
copy it and then modify this list, we can cause all sorts of trouble. This also applies to modifying
the list using the above functions:
odds = [1, 3, 5, 7] primes = odds primes.append(2) print('primes:', primes) print('odds:', odds)
primes: [1, 3, 5, 7, 2] odds: [1, 3, 5, 7, 2]
This is because Python stores a list in memory, and then can use multiple names to refer to the
same list. If all we want to do is copy a (simple) list, we can again use the list function, so we
do not modify a list we did not mean to:
odds = [1, 3, 5, 7] primes = list(odds) primes.append(2) print('primes:', primes) print('odds:', odds)
primes: [1, 3, 5, 7, 2] odds: [1, 3, 5, 7]
Subsets of lists and strings can be accessed by specifying ranges of values in brackets, similar to how we accessed ranges of positions in a NumPy array. This is commonly referred to as "slicing" the list/string.
binomial_name = 'Drosophila melanogaster' group = binomial_name[0:10] print('group:', group) species = binomial_name[11:23] print('species:', species) chromosomes = ['X', 'Y', '2', '3', '4'] autosomes = chromosomes[2:5] print('autosomes:', autosomes) last = chromosomes[-1] print('last:', last)
group: Drosophila species: melanogaster autosomes: ['2', '3', '4'] last: 4
You can also select non-consecutive elements from a list by slicing with a step size, for example
numbers = [1, 2, 3, 4, 5, 6, 7, 8] odd_numbers = numbers[0:6:2] print('odd numbers:', odd_numbers)
odd numbers: [1, 3, 5]
In the above example, numbers[0:2:7] tells python to slice the list
numbers from element 0 (1) to element 6 (7), exluded with a
step of elements.
Slicing From the End
Use slicing to access only the last four characters of a string or entries of a list.
string_for_slicing = 'Observation date: 02-Feb-2013' list_for_slicing = [['fluorine', 'F'], ['chlorine', 'Cl'], ['bromine', 'Br'], ['iodine', 'I'], ['astatine', 'At']]
'2013' [['chlorine', 'Cl'], ['bromine', 'Br'], ['iodine', 'I'], ['astatine', 'At']]
Would your solution work regardless of whether you knew beforehand the length of the string or list (e.g. if you wanted to apply the solution to a set of lists of different lengths)? If not, try to change your approach to make it more robust.
Hint: Remember that indices can be negative as well as positive
If you want to take a slice from the beginning of a sequence, you can omit the first index in the range:
date = 'Monday 4 January 2016' day = date[0:6] print('Using 0 to begin range:', day) day = date[:6] print('Omitting beginning index:', day)
Using 0 to begin range: Monday Omitting beginning index: Monday
And similarly, you can omit the ending index in the range to take a slice to the very end of the sequence:
months = ['jan', 'feb', 'mar', 'apr', 'may', 'jun', 'jul', 'aug', 'sep', 'oct', 'nov', 'dec'] sond = months[8:12] print('With known last position:', sond) sond = months[8:len(months)] print('Using len() to get last entry:', sond) sond = months[8:] print('Omitting ending index:', sond)
With known last position: ['sep', 'oct', 'nov', 'dec'] Using len() to get last entry: ['sep', 'oct', 'nov', 'dec'] Omitting ending index: ['sep', 'oct', 'nov', 'dec']
Overloading
+ usually means addition, but when used on strings or lists, it means "concatenate".
Given that, what do you think the multiplication operator * does on lists?
In particular, what will be the output of the following code?
counts = [2, 4, 6, 8, 10] repeats = counts * 2 print(repeats)
[2, 4, 6, 8, 10, 2, 4, 6, 8, 10][4, 8, 12, 16, 20][[2, 4, 6, 8, 10],[2, 4, 6, 8, 10]][2, 4, 6, 8, 10, 4, 8, 12, 16, 20]
The technical term for this is operator overloading:
a single operator, like + or *,
can do different things depending on what it's applied to.