Basic Python Syntax

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Normally, each line in Python code forms a single instructional statement. Using number literals and the built-in operators + (add) and - (subtract), one can construct a simple instruction:

In [1]:

# This is a line comment.
5 + 2 
6 - 3 # This is an inline comment.

Notice there are no line terminators like as in other languages (C, for instance, which ends each instructional statement with a ;). Comments are denoted by whatever follows a # character and can occur anywhere on a line. Comments are invisible to the Python interpereter. There are no block comments in Python. This is due to one of the language’s core philosophies:

“Explicit is better than implicit”.

By forcing every comment line to start with #, the developer must make a concious choice to comment out multiple lines. Multiple instructions can be placed on the same line if separated by the ; operator as in:

In [2]:

# Now both instructions are on the same line.
5 + 2; 6 - 3

The line above will perform both instructions sequentially. Code run in an interactive shell will only print the result of the last instruction processed, but both operations are in fact being executed. We can verify this by forcing each operation to print to stdout using the built-in print function (formerly a keyword prior to Python v3), which sends it’s argument to the standard output. Function arguments are enclosed by parentheses.

In [3]:

print(5 + 2); print(6 - 3)

Like most programming languages, one can express numbers in a variety of ways using number literals. Number literals can be of three types: either an integer, a float, or a complex. Floats can be expressed in scientific notation with e between the significant figures and the powers of ten. Python supports complex numbers. Purely imaginary decimal literals are indicated with the suffix j. Ordinarily, all numbers are expressed as decimals, but one can use prefixes to denote hex (0x), binary (0b) or octal (0o) integers. Literal prefixes and suffixes cannot be mixed.

In [4]:

50       # 50 as a decimal integer
50.0     # 50 as a floating-point decimal
50.      # 50 again as a floating-point decimal
5.0e1    # 50 as a floating decimal in scientific notation
50j      # A purely imaginary 50
0x32     # 50 as a hexidecimal integer
0b110010 # 50 as a binary integer
0o62     # 50 as an octal integer

Math operations are intuitive. The basic math operators are:

Operator Operation Syntax     English Equivalent Notes
+ Addition a + b “a plus b” Overloaded for built-in types
- Subtraction a - b “a minus b”  
* Multiplication a * b “a times b”  
/ Division a / b “a divided by b” Floating-point True division: 3/5 == 0.6
// Floor Division a // b “floor of (a divided by b)” Floor division: 3/5 == 0; 5.0//2 == 2.0
% Modulus a % b “a mod b”  
** Exponentiation a**b “a to the power b”  
- Negation -a “negative a” Can be used to construct negative literals

Math operators in Python work as they do in pen-and-paper math: they preserve the most exact representation whenever possible. The can be checked using the type built-in function which returns the argument’s type. Again, the possible types are integer, float, and complex.

In [5]:


In [6]:


In [7]:

type(5 + 2)

In [8]:


In [9]:


In [10]:

type(5 + 2.0)

In [11]:

type(5 + 2.0j)

The order of operations follows the academic standard (PEMDAS: Parentheses, Exponents, Multiplication, Division, Addition, Subtraction):

In [12]:

5 * (2 + 3) - 3**2

Here, we meet parentheses again. Parentheses have a number of uses in Python. As earlier, they enclose function arguments. Here, they alter the order of operations. In general, they make logical groups of their contents. These groups always carry over new lines and are thus a way of splitting long lines:

In [13]:

(5 + 3
 - 2)

In [14]:

     + 3 -2)

Core Keywords

So far, we’ve seen the number literals, the math operators, and a few built-in functions. We have not yet seen any of the core keywords. Keywords are the heart of a programming language by which everything in the language can be written. Python has 33 core keywords.

Boolean Logic: True, False, in, and, or, not
Identities: None, is
Conditionals: if, elif, else
Flow Controls: for, while, continue, pass, break
Abstraction: class, def, return, lambda, yield
Namespaces: import, from, as, with
Excpetions: try, except, raise, finally, assert
Memory: del, global, nonlocal

Relax: you don’t need to memorize these now. We’ll learn them as we go. Less than half are used regularly and we’ll focus on only the most important ones.

Which are the important ones? Importance generally decreases from left to right and from top to bottom. Simple programs, for instance, rarely use the bottom two rows of commands. We’ll take time to discuss these keywords in detail later. I’m showing you the table now because there a few things we should learn from this list:

  • These words are reserved: they cannot be used as names for user-defined objects.
  • *True, False, and None are built-in unique *objects **. They are the only capitalized keywords.
  • All instruction keywords are lowercase. After the 3 built-in objects (True, False, and None), the remaining keywords are instructions.

Having seen the keywords, we can take a look at assignment.

Variables and Assignment

In programming, an object is a logical structure that encapsulates data and behaviors. Just like the word in English, an object is a ‘thing’ that can be created, manipulated, and destroyed. We can have many objects of the same type or class, but where each instance is unique.

Any object instance can be assigned to a variable name and in Python, everything is an object. Assignment is governed principally by the binary = operator.

The variable name given to an object must follow some rules:

  • It cannot be one of the core keywords. It is, however, legal to reassign the names ascribed to built-in functions (like print).
  • It cannot start with a number.
  • It cannot contain characters used in operators.
  • It is case-sensitive

The variable name should follow certain conventions (see PEP8 for reasoning):

  • It should use full descriptive words.
  • If a single character, it should be absolutely unambiguous in semantic meaning.
  • Avoid ‘l’ (lowercase letter el), ‘O’ (uppercase letter oh), or ‘I’ (uppercase letter eye) as single character variable names.
  • It should use underscores_to_separate_words.
  • Local variables should be lowercase_words.
  • Constants should be ALL_CAPITAL_WORDS.

Names like “electron_mass” and “muon_energy” are better than “em” or “me”. No one wants to come back to code a year later and try to figure out what “em” and “me” mean in their code. Take time to write clear code now: your future self will thank you.

Assignment in Python can be thought of as giving an alias to an object instance. You can give multiple aliases to the same object instance. To show this, we can use the built-in function id() which returns a unique number associated with an object in memory (in fact, it’s derived from the object’s memory address).

In [15]:

a_literal = 5  # Assign an instance of the literal '5' to the name 'a_literal'
print(a_literal, id(a_literal)) # print the value of 'a_literal' along with it's unique ID.
another_literal = a_literal     # Assign the object assigned to 'a_literal' to 'another_literal'
print(another_literal, id(another_literal)) # Look at that: both 'a_literal' and 'another_literal' have the same ID.
(5, 21008696)
(5, 21008696)

As you can see, reassigning an object doesn’t create a new instance of the object. Instead, both variables point to the same object in memory. For those who are familiar with C and C++, assignment in Python is similar to references. Those of you who haven’t learned C or C++ yet will be well prepared having learned Python first.

The fact that assignment ‘aliases’ objects when possible can lead to a couple serious ‘gotchas’ with so-called ‘mutable’ objects like lists which we will talk about later. Again, “Explicit is better than implicit”: if you want to instantiate a new object on assignment, you must do so explicitly!

Finally, most objects upon which the math operators can act (like numbers and strings) support additional do-and-reassign operators. These can be used to compactify a statement. The set of all assignment operators is tabulated below.

Operator Operation Syntax         Description
= Assignment a = b Assigns the name ‘a’ to the object ‘b’
+= Add and Reassign a += b Equivilent to a = (a + b)
-= Subtract and Reassign a -= b Equivilent to a = (a - b)
*= Multiply and Reassign a *= b Equivilent to a = (a * b)
/= Divide and Reassign a /= b Equivilent to a = (a / b)
%= Modulus and Reassign a %= b Equivilent to a = (a % b)
**= Exp and Reassign a **= b Equivilent to a = (a**b)
//= Floor Divide and Reassign a //= b Equivilent to a = (a // b)

Core Data Structures

Now that we know how to assign objects to variable names, we can talk about the core Python data structures. They are:

  • Numbers (which we’ve seen)
  • None
  • Booleans (True and False)
  • Strings
  • Lists
  • Tuples
  • Dictionaries
  • Sets

We’ve seen the numbers already. Let’s go through the rest in finer detail.

The None type

In Python, None is a special constant object that represents an explicit lack of value. It is a null value. It has type NoneType.

In [16]:


Functions with no explicit return value will return None. Default arguments or object attributes are often initialized as None when not explicitly set. All instances of None are identically equal and are literally the same instance. While None is extremely useful, it can be somewhat abstract for beginners. Don’t worry about it for now: future examples will make it clear how to make use of None. I only mention it now because it has certain implications for Booleans.


Booleans are objects that have one of two possible values: true and false, 1 and 0, off and on, etc. The Python keywords include two boolean objects (True and False) and a few boolean operators (in, and, or, not) that return either True or False.

Both True and False are objects of the type ‘bool’. Boolean types are expected as arguments to some instructions like if. Comparisons between objects return boolean types.

In [17]:


However, other types can be interpereted as booleans in certain contexts. While Python will cast a non-bool type into a bool on it’s own when necessary, we can explicitly cast non-bool types into bools using the built-in bool contructor.

In [18]:

bool(1), bool(0), bool(5), bool(-2), bool([]), bool([1, 2, 3]), bool(None)
(True, False, True, True, False, True, False)

So what’s happening here? bool(1) and bool(0) are understandably interpereted as True and False respectively. But why is bool(5) interperted as True? Because 5 is both a defined object and not explicitly interpereted as False. bool(-2) is True for the same reason. All objects that are defined and not strictly interpereted as False are considered True.

So what then is strictly considered False? Objects numerically equivalent to 0, empty iterable sequences, and None. This is a potential ‘gotcha’ when checking if an object exists: you must make sure that it is not one of the False cases. In the above example, the objects [] and [1, 2, 3] are respectively empty and non-empty ‘iterables’ called ‘lists’. More on lists in a moment.

The keyword not negates a boolean.

In [19]:

not True, not False
(False, True)

The keyword in tests for an object’s membership in an iterable sequence (More on these in a moment). Below, we check to see if 1 is a member of the ‘tuple’ (1, 2, 3). It is, so True is returned.

In [20]:

1 in (1, 2, 3)

If we check to see if 5 is a member of the same sequence, Python tells us that is is not.

In [21]:

5 in (1, 2, 3)

The binary and operator returns the logical conjunction of it’s operands. We can have Python return the truth-table:

In [22]:

True and True, True and False, False and True, False and False
(True, False, False, False)

Likewise, the binary or operator returns the logical disjunction of the operands:

In [23]:

True or True, True or False, False or True, False or False
(True, True, True, False)


Strings are containers for text. The can be constructed in several ways:

  • Single-line strings use quotation marks, either:
    • Wrapped in single quotes: ‘Like this…’
    • Wrapped in double quotes: “…or like this!”
  • Multi-line strings use quotation marks in triplicate:
    • ’'’In triple-single quotes’’’
    • or “"”in triple double quotes”””
  • They can be constructed from the string contructor str.

Why, you may ask, are there so many ways to write strings? Well, imagine if you want a quotation mark character in your string:

In [24]:

king_arthur = 'Arthur says "I am your king."'
woman_responds = "Woman: 'Well I didn't vote for you.'"
chock_full_of_quotes = '''"I've got to use a multiline string to handle all the quotes!"'''
string_from_a_number = str(2.54)
Arthur says "I am your king."
Woman: 'Well I didn't vote for you.'
"I've got to use a multiline string to handle all the quotes!"

Notice how having several ways of denoting strings saves you from having to escape characters. Single line strings must open and close on a single line or be broken over lines using the ‘' character. Multi-line strings can span several lines. They will preserve carriage returns in the string.

In [25]:

long_string = 'The quick brown fox jumped \
over the lazy dog.'
The quick brown fox jumped over the lazy dog.

In [26]:

the_squat_rack = '''allows for a free-weight workout using
a barbell without the movement restrictions imposed
by equipment such as the Smith machine.'''
allows for a free-weight workout using
a barbell without the movement restrictions imposed
by equipment such as the Smith machine.

Strings are a type of iterable container and as such you can do some neat tricks with them.

In [27]:

("barbell" in the_squat_rack), ("curls" in the_squat_rack), 
(True, False)

The in keyword can act on strings. As iterables, strings have a length that can be retrieved with the built-in function len:

In [28]:


The string the_squat_rack is 130 characters long. We’ll learn more about special iterable stuff when we discuss lists.

One important string operation is concatenation or joining two strings together. This can be done several ways. This first is just by adding them:

In [88]:

print(king_arthur + woman_responds)
Arthur says "I am your king."Woman: 'Well I didn't vote for you.'

Other ways include implicit joining, where if two string literals are one the same line, they are joined together:

In [87]:

'Some' "string is fishy here"
'Somestring is fishy here'

Strings in Python are objects (everything in Python is an object), and objects can have associated functions attached to them called methods. In the case of strings, these method functions simplify string-related operations. For those who are new to object-oriented programming, we’ll explain methods in detail later. For now, just think of methods as functions that are in some way bound to an object. To see what methods and attributes an object has associated with it, you can ‘introspect’ the object using the built-in dir function.

In [29]:


There are a lot of methods in a string object. To access an object’s method, you write object_name.method_name. The methods starting with double underscores like __add__ are special. Methods named this way are ones that aren’t normally called directly, even though you can. For example, __add__ is the method called when you use the + operator:

In [89]:

print(king_arthur.__add__(woman_responds)) # We don't normally call __add__ directly, since...
print(king_arthur + woman_responds)        #  ... __add__ is the internal name for the + operator.
Arthur says "I am your king."Woman: 'Well I didn't vote for you.'
Arthur says "I am your king."Woman: 'Well I didn't vote for you.'

We can find out more information about the string object’s methods using the built-in help function. Let’s look at the help for the __add__, format and upper methods:

In [92]:

help(long_string.format), help(long_string.upper)
Help on built-in function format:

    S.format(*args, **kwargs) -> string
    Return a formatted version of S, using substitutions from args and kwargs.
    The substitutions are identified by braces ('{' and '}').

Help on built-in function upper:

    S.upper() -> string
    Return a copy of the string S converted to uppercase.

(None, None)

Looking at the above output, we see that help prints a string attached to the method that explains what it does and how it does it. Notice also that the return value of the help function is a an instance of None meaning that all the help function does is print the method’s documentation called a “docstring”. All objects CAN have docstrings and it is good practice to include them in your programming. They’re easy to make and greatly improve the readability of your code. We’ll show how you can write them for your functions when we get there.

Notice that the docstrings show indicate that the method syntax included parentheses. What happens if we ommit the parentheses when trying to use a method?

In [31]:

<function upper>

That didn’t do anthing because a method is a kind of function. As hinted at earlier, to call a function requires parentheses around it’s arguments. If the parentheses are omitted like above, Python thinks you want to manipulate the method which is itself a type of object. Python tells us that the function upper is, well, a function called upper. No surprises here. So to call the function, we must wrap it’s arguments with parentheses. The docstring for string.upper tells us the method takes no arguments, so to call it we write:

In [32]:


So now you can find out what methods an object has and what they do with their help docstrings. Some smart shells like IPython provide easy access to an object’s methods and docstrings with tab completion. As an aside, format is a really useful string method, but it’s kind of advanced to use. We’ll come to it again later.


Lists are among the most useful of Pythons built-in data structures. We’ve seen some lists already. We can make a list by calling the list constructor (a built-in function) or by enclosing and ordered set of comma-separated objects in square braces:

In [33]:

example_list = []                       # An empty list
another_example = list()                # Another empty list 
constructed_list = list('Some string')  # The list constructor takes an interable as it's argument.
some_numbers = list(range(10))          # A list of the numbers 0-9.
mixed_content_list = [1, 2.0, 'three']  # Notice that the contents needn't be the same type.

# We can even do wild things like
#   a list inside a list inside a list inside a list!
listception = [1, 2, [3, 4, 5, [6, 7, 8, [9, 10]]]] 
  #  Notice that the dimensions of the sublists needn't
  #  be the same! This is much like a tree graph.

Let’s see what we’ve done. Lists are iterables, so we can check a list’s length:

In [34]:


We can print the contents of a list:

In [35]:

[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

We can use iterable-aware keywords on a list:

In [36]:

2 in mixed_content_list
'two' in mixed_content_list
'three' in mixed_content_list

And we can access specific elements of a list. The following is true for all iterable objects (including strings). We do this by way of square braces containing the desired element’s index appended to an iterable object. In Python, iterable indexes start from 0!

In [37]:

# New school iterable awesomeness.

This syntax is actually a means of using one those ‘hidden methods’ I told you to ignore earlier. In this case, the [] syntax calls an object’s __getitem__ method, if it has one:

In [38]:


We can get more information about __getitem__ using help:

In [39]:

Help on built-in function __getitem__:

    x.__getitem__(y) <==> x[y]

The help docstring tells us that the two methods are equivalent. Another thing we can do with iterables is ‘slicing’. Slicing uses similar syntax to getitem, but a different argument: 2 indexes separated by ‘:’ in the special notation:

In [40]:

 some_numbers[0:5],  # Slice from index [0,5).
 some_numbers[:5],   # Another slice from index [0,5).
 some_numbers[3:5],  # A slice from index [3, 5).
 some_numbers[3:10], # A slice from index [3, 10)
 some_numbers[3:])   # A slice from index [3, infinity).
([0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
 [0, 1, 2, 3, 4],
 [0, 1, 2, 3, 4],
 [3, 4],
 [3, 4, 5, 6, 7, 8, 9],
 [3, 4, 5, 6, 7, 8, 9])

Python can even interperet negative indexes as counting from the end of the list.

In [41]:

([0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
 [7, 8, 9],
 [0, 1, 2, 3, 4, 5, 6],
 [5, 6, 7],
 [3, 4, 5, 6])

Be aware of this in case you use a malformed out-of-range index. Python lets you do a lot of things that would be considered “dangerous” in other languages (like C/C++). The philosophy behind this is summed up as:

We are all consenting adults here.

Basically, the language authors trust that you’ll be careful in exchange for having the freedom to do what you want.

Speaking of doing things that we want, We can insert, append, remove, pop, and sort lists (and most other iterables) using their methods:

In [42]:

method_examples = []               # Make an empty list.
method_examples.append('appended') # Append something to it.
method_examples.append(4)          # Append something else.
method_examples.insert(1, 'insertion') # Insert something at index 1
popped_item = method_examples.pop()    # Pop removes the last element from the list and returns it
method_examples.append(4)  # Append some new stuff to the list.
method_examples.append(2)  # Rinse and repeat.
method_examples.sort()     # Sort in place.

# Remove doesn't take an index, 
#   instead it removes the first occurance of 4.
['appended', 4]
['appended', 'insertion', 4]
['appended', 'insertion']
['appended', 'insertion', 4, 2]
[2, 4, 'appended', 'insertion']
[2, 'appended', 'insertion']

That sort is interesting. Python tries to be smart about the default case but, when push comes to shove, you can override the sort method with your own specific comparator. The messy list of operations above requires us to print the list out after each operation because these methods all return None and modify the list ‘in-place’. This means that the list is ‘mutated’ without making a new object. When an object supports this, it is called a ‘mutable object’ as opposed to an ‘immutable object’ which cannot be changed without altering it’s location in memory. lists, dictionaries, sets and most objects are mutable; numbers, strings, and tuples are immutable. This has important consequences for lists as we can see below:

In [43]:

first_list = [1, 2, 3, 4]   # Let's make a new list. A new object is created.
second_list = first_list    # Let's assign another name to that list.
second_list[2] = 1          # Let's mutate the second list by reassigning an item in it.

# What are the contents of 'first_list' and 'second_list' now?
print(first_list, second_list)
([1, 2, 1, 4], [1, 2, 1, 4])

Here we see that the assignment operation second_list = first_list does not copy the first list into a new object. Instead both names first_list and second_list refer to the same object! Because lists are mutable, if you mutate one, you’ll mutate the other because the two variables are just names for the same object. If we wish to make a copy, we must do so explicitly:

In [44]:

first_list = [1, 2, 3, 4]       # We create a new object.
second_list = list(first_list)  # We use the list ctor to make a new list from the first one.
second_list[2] = 1              # Let's mutate the second list by changing an item.

# What are the contents of 'first_list' and 'second_list' now?
print(first_list, second_list)
([1, 2, 3, 4], [1, 2, 1, 4])

This is extremely important to be aware of, especially in large programs and especially in data analysis.


Tuples are another sort of iterable container. They are created by separating objects with commas:

In [45]:

my_tuple = 'a', 'tuple', 'is formed'
('a', 'tuple', 'is formed')

You may have seen this done in previous examples to list multiple outputs on a single line. Looking at the output, we see Python has put parentheses around the output tuple. This is entirely superfluous: the parentheses only there to make it clear where the tuple starts and stops. Notice then that the arguments to a function are a tuple. The arguments to a list contructor like [1,2,3] are a tuple. There are other ways to construct a tuple, like using the tuple ctor, which will make a tuple out of an iterable:

In [46]:

another_tuple = tuple(range(5))
(0, 1, 2, 3, 4)

Our first example with tuples makes a single object (the tuple) out of three. We can go the other way making three objects from one using tuples:

In [47]:

one, two, three = my_tuple
is formed

Any iterable can be cast over assignment to variables, as long as the number of variables created is equal to the length of the iterable. Let’s see it again:

In [48]:

one, two, three = 'wut'

We’ll make use of this extensively when we get to loops. As with other iterables, we can access tuple elements by the get-item syntax:

In [49]:

'is formed'

Tuples are immutable so unlike lists, we can’t change them once their made:

In [50]:

my_tuple[2] = 'is modified'
TypeError                                 Traceback (most recent call last)

<ipython-input-50-fa5b9e48f84e> in <module>()
----> 1 my_tuple[2] = 'is modified'

TypeError: 'tuple' object does not support item assignment

Here we see the first example of a Python stack trace. When we issue a command that gives rise to an error, the error is pointed out at runtime and a stack trace is issued to help us debug the problem. Here, the error is a TypeError. The error occurs on line 1 at my_tuple[2] = 'is modified' and the error tells us that tuples don’t support item assignment. Fancy words for “tuples are immutable types”. Strings are immutable iterables as well so while we can get a string’s items, we can’t modify them:

In [51]:

long_string[4] = 'Q'
The quick brown fox jumped over the lazy dog.

TypeError                                 Traceback (most recent call last)

<ipython-input-51-d1bdc3471de6> in <module>()
      1 print(long_string)
      2 print(long_string[4])
----> 3 long_string[4] = 'Q'

TypeError: 'str' object does not support item assignment


Dictionaries are a type iterable container called a map. They use (key : value) pairs to identify elements. They are unordered, which is to say they their elements cannot be accessed by an index. Instead, elements are accessed by their ‘key’. To create a dictionary, we can use the dict ctor or enclose a tuple of (key : value) pairs in curly-braces. Keys are separated from their value by a colon. Keys must be unique in the dictionary and must be immutable objects (strings, number literals, tuples, etc.).

In [52]:

empty_dict = {}
what_colliders_collide = dict(lhcb='hadrons',
mad_dict = {1:'first element',
            'third key':[1, 2, 3],
            (4, 4):'value mapped by a tuple'}

In [53]:

{'kekb': 'leptons', 'lhcb': 'hadrons', 'tevatron': None}

In [54]:

'first element'

In [55]:


In [56]:

mad_dict['third key']
[1, 2, 3]

In [57]:

'value mapped by a tuple'

To see list of keys in a dictionary, one can use the keys method:

In [58]:

[1, (4, 4), 'twos', 'third key']

Notice that the keys are in a different order than how we constructed the dictionary. In a dictionary order does not matter. The usual sequence keywords apply to dictionary keys, but not values:

In [59]:

1 in mad_dict, (4, 4) in mad_dict, 2222 in mad_dict
(True, True, False)


Python sets are the last principle data structure we’ll look at. Sets are unordered lists of unique elements: Unlike other data structures we’ve seen, elements cannot be repeated in a set. To ensure this, all the objects in a list must be hashable. Sets lend themselves well to operations like unions, intersections, differences, and symmetric difference. Sets are iterable, so are subject to iterable-aware keywords like in and len(). Sets are created by enclosing comma-separated objects in curly braces or via the set ctor, which like other data structure ctors we’ve seen takes an iterable as it’s argument. Note that the difference between set construction and dictionary construction is the lack of the colon denoted (key : value) pairs used in dictionaries.

In [60]:

first_set = {1, 2, 3}
second_set = set((-1, 0, 1, 2))
third_set = {1, 2}

Set methods overload many of the math operators to do what you would expect in a math class. For instance, set intersection can be mediated by the & operator:

In [61]:

first_set & second_set, first_set.intersection(second_set)
({1, 2}, {1, 2})

Set union can be mediated by the | operator.

In [62]:

first_set | second_set, first_set.union(second_set)
({-1, 0, 1, 2, 3}, {-1, 0, 1, 2, 3})

Set difference is mediated by the - operator.

In [63]:

first_set - second_set, first_set.difference(second_set)
({3}, {3})

Symmetric difference is mediated by the ^ operator.

In [64]:

first_set ^ second_set, first_set.symmetric_difference(second_set)
({-1, 0, 3}, {-1, 0, 3})

And testing whether one set is a subset or superset of another can be done with the comparison operators, which will talk about next.

In [65]:

first_set.issubset(third_set), first_set.issuperset(third_set)
(False, True)

In [66]:

first_set <= third_set, first_set >= third_set
(False, True)


So far we have discussed the basic syntax for writing instructions in Python. We have learned about basic numbers and math operations in Python. We have seen how to assign variables and saw some of the caveats to assignment concerning mutable objects. We are aware of the None object and have seen it appear as output. We have discussed the most salient features of the core data structures: Booleans, Strings, Lists, Tuples, Dictionaries, and Sets. And we’ve discussed some of the core philosophies in Python such as “Explicit is better than implicit” and “We’re all consenting adults.”

Next we will learn about the comparison operators, conditionals/branching, and loops.