In this lecture, we'll go over the different collections of objects that Python natively supports. In previous lectures we dealt only with single strings, ints, and floats; now we'll be able to handle arbitrarily large numbers of them. By the end of the lecture, you should be able to
Lists are probably the most basic data structure in Python; they contain ordered elements and can be of abitrary length. Other languages may refer to this basic structure as an "array", and indeed there are similarities. But for our purposes and anytime you're coding in Python, we'll use the term "list."
When I say "data structures," I mean any type that is more sophisticated than the int
s and float
s we've been working with up until now. I purposefully omitted str
s, because they are in fact a data structure unto themselves: they build on the single character, and are effective a "list of characters." In much the same way, we'll see in this lecture how to define a "list of int
s", a "list of float
s", and even a "list of str
s"!
Anything in Python that holds an arbitrary number of simpler objects is known as a collection. Today we'll discuss four different kinds of collections: lists, sets, tuples, and dictionaries.
Lists in Python have a few core properties:
In [1]:
x = list()
print(x)
Here I've defined an empty list, called x
. Like our previous variables, this has both a name (x
) and a type (list
). However, it doesn't have any actual value beyond that; it's just an empty list. Imagine a filing cabinet with nothing in it.
So how do we add things? Lists, as it turns out, have a few methods we can invoke (methods are pieces of functionality that we'll cover more when we get to functions). Here's a useful one:
In [2]:
x.append(1)
The append()
method takes whatever argument I supply to the function, and inserts it into the next available position in the list. Which, in this case, is the very first position (since the list was previously empty).
What does our list look like now?
In [3]:
print(x)
It's tough to tell that there's really anything going on, but those square brackets [
and ]
are the key: those denote a list, and anything inside those brackets is an element of the list.
Let's look at another list, a bit more interesting this time.
In [4]:
y = list()
y.append(1)
y.append(2)
y.append(3)
print(y)
In this example, I've created a new list y
, initially empty, and added three integer values. Notice the ordering of the elements when I print the list at the end: from left-to-right, you'll see the elements in the order that they were added.
You can put any elements you want into a list. If you wanted, you could add strings, floats, and even other lists!
In [5]:
y.append("this is perfectly legal")
print(y)
y.append(4.2)
print(y)
y.append(list()) # Inception BWAAAAAAAAAA
print(y)
So I have these lists and I've stored some things in them. I can print them out and see what I've stored...but so far they seem pretty unwieldy. How do I remove things? If someone asks me for whatever was added 3$^{rd}$, how do I give that to them without giving them the whole list?
The answers to these questions involve indexing. Indexing is what happens when you refer to an existing element in a list. For example, in our hybrid list y
with lots of random stuff in it, what's the first element?
In [6]:
first_element = y[0]
print(first_element)
print(y)
Python and its spiritual progenitors C and C++ are known as zero-indexed languages. This means when you're dealing with lists or arrays, the index of the first element is always 0.
This stands in contrast with languages such as Julia and Matlab, where the index of the first element of a list or array is, indeed, 1. Preference for one or the other tends to covary with whatever you were first taught, though in scientific circles it's generally preferred that languages be 0-indexed$^{[\text{citation needed}]}$.
This little caveat is usually the main culprit of errors for new programmers. Give yourself some time to get used to Python's 0-indexed lists. You'll see what I mean when we get to loops.
In addition to elements 0 and 1, we can also directly index elements at the end of the list.
In [7]:
print(y[-1])
print(y)
You can think of this indexing strategy as "wrapping around" the list to the end of it. Similarly, you can also negate other numbers to access the second-to-last element, third-to-last element...
In [8]:
print(y[-2])
print(y[-3])
print(y)
Another very useful method is len
, which tells you how many elements are in your list.
In [9]:
num = len(y)
print(num)
Because lists in Python are 0-indexed, what would the positive integer index of the last element in a list be?
In [10]:
int_elements = y[0:3] # Slicing!
print(y)
print(int_elements)
In [11]:
int_elements = y[0:3] # Slicing!
print(y)
print(int_elements)
That y[0:3]
notation is the slicing. The first number, 0, indicates the first index of values we want to keep. The colon :
indicates slicing, and the second number, 3, indicates the last index of values.
You could even say this out loud: "With list y
, slice starting at index 0 to index 3." The colon is the "to".
When you slice an array, the first (starting) index is inclusive; the second (ending) index, however, is exclusive. In mathematical notation, it would look something like this:
$[ starting : ending )$
Therefore, the end index is one after the last index you want to keep.
In [12]:
z = [42, 502.4, "some string", 0]
print(z)
If you understood lists, sets and tuples are easy-peasy. They're both exactly the same as lists...except:
Tuples:
Sets:
Other than these two rules, pretty much anything you can do with lists can also be done with tuples and sets.
Whereas we used square brackets to create a list, we use regular parentheses to create a tuple!
In [13]:
x = [3, 64.2, "some list"]
print(type(x))
y = (3, 64.2, "some tuple")
print(type(y))
With lists, if you wanted to change the item at index 2, you could go right ahead. But with tuples, you'll get an error.
In [14]:
x[2] = "a different string"
print(x)
#y[2] = "does this work?"
Like list
, there is a method for building an empty tuple
. And like lists, you have (almost) all of the other methods at your disposal, such as slicing and len
:
In [15]:
z = tuple()
print(y[0:2])
print(len(y))
In [16]:
x = list()
x.append(1)
x.append(2)
x.append(2) # Add the same thing twice.
s = set()
s.add(1)
s.add(2)
s.add(2) # Add the same thing twice...again.
In [17]:
print(x)
print(s)
There are certain situations where this can be very useful. It should be noted that sets can actually be built from lists, so you can build a list and then turn it into a set:
In [18]:
x = [1, 2, 3, 3]
s = set(x) # Take the list x as the starting point.
print(s)
Sets also don't index the same way lists and tuples do:
In [19]:
# Literally causes an error:
#s[0]
If you want to add elements to a set, you can use the add
method.
If you want to remove elements from a set, you can use the discard
or remove
methods.
But you can't index or slice a set.
A few important points on dictionaries before we get into examples:
Dictionaries are created using the dict()
method, or using curly braces:
In [20]:
d = dict()
# Or...
d = {}
New elements can be added to the dictionary in much the same way as lists:
In [21]:
d["some_key"] = 14.3
d["shannon_quinn"] = ["some", "personal", "information"]
print(d)
Since dictionaries do not maintain any kind of ordering of elements, using integers as indices won't give us anything useful. However, dictionaries do have a keys()
method that gives us a list of all the keys in the dictionary:
and a values()
method for (you guessed it) the values in the dictionary:
In [22]:
print(d.keys())
print(d.values())
To further induce Inception-style headaches, dictionaries also have a items()
method that returns a list of tuples where each tuple is a key-value pair in the dictionary!
(it's basically the entire dictionary, but this method is useful for looping)
In [23]:
print(d.items())
Now, back to why sets--or any data structure, really--are useful for testing if we've seen something before.
In [24]:
s = set([1, 3, 6, 2, 5, 8, 8, 3, 2, 3, 10])
print(10 in s) # Basically asking: is 10 in our set?
print(11 in s)
In [25]:
l = [1,2,3]
In [26]:
1 in l
Out[26]:
In [27]:
345 in l
Out[27]:
In [28]:
"1" not in l
Out[28]:
In [29]:
"good" in "goodness" # Yep, strings are considered "collections"!
Out[29]:
Looping, like lists, is a critical component in programming and data science. When we're training models on data, we'll need to loop over each data point, examining it in turn and adjusting our model accordingly regardless of how many data points there are. This kind of repetitive task is ideal for looping.
The structure of loops is pretty simple:
In [30]:
letters = ['a','b','c','d','e','f','g']
for i in letters: #for every item in this collection...
print(i) #...execute this block of code with i set to the object
There are two main parts to the loop: the header and the body.
i
).
In [31]:
i = 0
while i < 3: #as long as this condition is true...
print(i) #...execute this block of code
i += 1
Word of warning with while
loops: Don't forget to update the condition variable!
In [32]:
range(3)
Out[32]:
In [33]:
range(1,10)
Out[33]:
In [34]:
range(1,10,3)
Out[34]:
What's the value of val
?
In [35]:
val = 0
for i in range(100):
val += i
In [36]:
print(val)
In [37]:
i = 0
while True:
i += 1
if i < 6:
continue
print(i)
if i > 4:
break
Also known as "if
statements". These conditionally execute a block of code.
In an if..elif..else
statement, only one block of code will be executed
In [38]:
if False:
print("1")
elif True:
print("2")
else:
print("3")
In [39]:
1 < 3
Out[39]:
In [40]:
"hello" != "hi"
Out[40]:
In [41]:
[1,2,3] == [1,2,3]
Out[41]:
In [42]:
x = 3
y = 4
x >= y
Out[42]:
What is the value of val
?
In [43]:
val = 0
if val >= 0:
val += 1
elif val < 1:
val += 2
elif True:
val += 3
else:
val += 5
val += 7
In [44]:
print(val)
Logical operators are used to join multiple "sub-conditions" together, each evaluating to True
or False
, into one large condition that evaluates to one final True
or False
for the whole thing.
and
, or
, and not
and
: All sub-conditions joined with and
must be True
for the full condition to be True
.or
: Only one sub-condition joined with or
needs to be True
for the full condition to be True
.not
: Flips the condition from True
to False
or from False
to True
.
In [45]:
if x > 3 and y == 4:
print("a")
elif x > 3 or y == 4:
print("b")
elif not (x > 3 or y == 4):
print("c")
What is the value of val
?
In [46]:
x = 3
y = 4
z = 5
val = (x != 3) or ((not y < 0) and z < 10) or ((not y == 0) and z < 0)
In [47]:
print(val)
In [48]:
if True:
pass
else:
print("I will never print")