What’s New In Python 3.10¶

SyntaxErrors¶

When parsing code that contains unclosed parentheses or brackets the interpreter now includes the location of the unclosed bracket of parentheses instead of displaying SyntaxError: unexpected EOF while parsing or pointing to some incorrect location. For instance, consider the following code (notice the unclosed ‘{‘):

expected = {9: 1, 18: 2, 19: 2, 27: 3, 28: 3, 29: 3, 36: 4, 37: 4,
            38: 4, 39: 4, 45: 5, 46: 5, 47: 5, 48: 5, 49: 5, 54: 6,
some_other_code = foo()

Previous versions of the interpreter reported confusing places as the location of the syntax error:

File "example.py", line 3
    some_other_code = foo()
                    ^
SyntaxError: invalid syntax

but in Python 3.10 a more informative error is emitted:

File "example.py", line 1
    expected = {9: 1, 18: 2, 19: 2, 27: 3, 28: 3, 29: 3, 36: 4, 37: 4,
               ^
SyntaxError: '{' was never closed

In a similar way, errors involving unclosed string literals (single and triple quoted) now point to the start of the string instead of reporting EOF/EOL.

These improvements are inspired by previous work in the PyPy interpreter.

(Contributed by Pablo Galindo in bpo-42864 and Batuhan Taskaya in bpo-40176.)

SyntaxError exceptions raised by the interpreter will now highlight the full error range of the expression that constitutes the syntax error itself, instead of just where the problem is detected. In this way, instead of displaying (before Python 3.10):

>>> foo(x, z for z in range(10), t, w)
  File "<stdin>", line 1
    foo(x, z for z in range(10), t, w)
           ^
SyntaxError: Generator expression must be parenthesized

now Python 3.10 will display the exception as:

>>> foo(x, z for z in range(10), t, w)
  File "<stdin>", line 1
    foo(x, z for z in range(10), t, w)
           ^^^^^^^^^^^^^^^^^^^^
SyntaxError: Generator expression must be parenthesized

This improvement was contributed by Pablo Galindo in bpo-43914.

A considerable amount of new specialized messages for SyntaxError exceptions have been incorporated. Some of the most notable ones are as follows:

• Missing : before blocks:

  >>> if rocket.position > event_horizon
    File "<stdin>", line 1
      if rocket.position > event_horizon
                                        ^
  SyntaxError: expected ':'
  

  (Contributed by Pablo Galindo in bpo-42997.)

• Unparenthesised tuples in comprehensions targets:

  >>> {x,y for x,y in zip('abcd', '1234')}
    File "<stdin>", line 1
      {x,y for x,y in zip('abcd', '1234')}
       ^
  SyntaxError: did you forget parentheses around the comprehension target?
  

  (Contributed by Pablo Galindo in bpo-43017.)

• Missing commas in collection literals and between expressions:

  >>> items = {
  ... x: 1,
  ... y: 2
  ... z: 3,
    File "<stdin>", line 3
      y: 2
         ^
  SyntaxError: invalid syntax. Perhaps you forgot a comma?
  

  (Contributed by Pablo Galindo in bpo-43822.)

• Multiple Exception types without parentheses:

  >>> try:
  ...     build_dyson_sphere()
  ... except NotEnoughScienceError, NotEnoughResourcesError:
    File "<stdin>", line 3
      except NotEnoughScienceError, NotEnoughResourcesError:
             ^
  SyntaxError: multiple exception types must be parenthesized
  

  (Contributed by Pablo Galindo in bpo-43149.)

• Missing : and values in dictionary literals:

  >>> values = {
  ... x: 1,
  ... y: 2,
  ... z:
  ... }
    File "<stdin>", line 4
      z:
       ^
  SyntaxError: expression expected after dictionary key and ':'
  
  >>> values = {x:1, y:2, z w:3}
    File "<stdin>", line 1
      values = {x:1, y:2, z w:3}
                          ^
  SyntaxError: ':' expected after dictionary key
  

  (Contributed by Pablo Galindo in bpo-43823.)

• try blocks without except or finally blocks:

  >>> try:
  ...     x = 2
  ... something = 3
    File "<stdin>", line 3
      something  = 3
      ^^^^^^^^^
  SyntaxError: expected 'except' or 'finally' block
  

  (Contributed by Pablo Galindo in bpo-44305.)

• Usage of = instead of == in comparisons:

  >>> if rocket.position = event_horizon:
    File "<stdin>", line 1
      if rocket.position = event_horizon:
                         ^
  SyntaxError: cannot assign to attribute here. Maybe you meant '==' instead of '='?
  

  (Contributed by Pablo Galindo in bpo-43797.)

• Usage of * in f-strings:

  >>> f"Black holes {*all_black_holes} and revelations"
    File "<stdin>", line 1
      (*all_black_holes)
       ^
  SyntaxError: f-string: cannot use starred expression here
  

  (Contributed by Pablo Galindo in bpo-41064.)

IndentationErrors¶

Many IndentationError exceptions now have more context regarding what kind of block was expecting an indentation, including the location of the statement:

>>> def foo():
...    if lel:
...    x = 2
  File "<stdin>", line 3
    x = 2
    ^
IndentationError: expected an indented block after 'if' statement in line 2

AttributeErrors¶

When printing AttributeError, PyErr_Display() will offer suggestions of similar attribute names in the object that the exception was raised from:

>>> collections.namedtoplo
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
AttributeError: module 'collections' has no attribute 'namedtoplo'. Did you mean: namedtuple?

(Contributed by Pablo Galindo in bpo-38530.)

NameErrors¶

When printing NameError raised by the interpreter, PyErr_Display() will offer suggestions of similar variable names in the function that the exception was raised from:

>>> schwarzschild_black_hole = None
>>> schwarschild_black_hole
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
NameError: name 'schwarschild_black_hole' is not defined. Did you mean: schwarzschild_black_hole?

(Contributed by Pablo Galindo in bpo-38530.)

Syntax and operations¶

The generic syntax of pattern matching is:

match subject:
    case <pattern_1>:
        <action_1>
    case <pattern_2>:
        <action_2>
    case <pattern_3>:
        <action_3>
    case _:
        <action_wildcard>

A match statement takes an expression and compares its value to successive patterns given as one or more case blocks. Specifically, pattern matching operates by:

1. using data with type and shape (the subject)

2. evaluating the subject in the match statement

3. comparing the subject with each pattern in a case statement from top to bottom until a match is confirmed.

4. executing the action associated with the pattern of the confirmed match

5. If an exact match is not confirmed, the last case, a wildcard _, if provided, will be used as the matching case. If an exact match is not confirmed and a wildcard case does not exist, the entire match block is a no-op.

Declarative approach¶

Readers may be aware of pattern matching through the simple example of matching a subject (data object) to a literal (pattern) with the switch statement found in C, Java or JavaScript (and many other languages). Often the switch statement is used for comparison of an object/expression with case statements containing literals.

More powerful examples of pattern matching can be found in languages such as Scala and Elixir. With structural pattern matching, the approach is “declarative” and explicitly states the conditions (the patterns) for data to match.

While an “imperative” series of instructions using nested “if” statements could be used to accomplish something similar to structural pattern matching, it is less clear than the “declarative” approach. Instead the “declarative” approach states the conditions to meet for a match and is more readable through its explicit patterns. While structural pattern matching can be used in its simplest form comparing a variable to a literal in a case statement, its true value for Python lies in its handling of the subject’s type and shape.

Simple pattern: match to a literal¶

Let’s look at this example as pattern matching in its simplest form: a value, the subject, being matched to several literals, the patterns. In the example below, status is the subject of the match statement. The patterns are each of the case statements, where literals represent request status codes. The associated action to the case is executed after a match:

def http_error(status):
    match status:
        case 400:
            return "Bad request"
        case 404:
            return "Not found"
        case 418:
            return "I'm a teapot"
        case _:
            return "Something's wrong with the internet"

If the above function is passed a status of 418, “I’m a teapot” is returned. If the above function is passed a status of 500, the case statement with _ will match as a wildcard, and “Something’s wrong with the internet” is returned. Note the last block: the variable name, _, acts as a wildcard and insures the subject will always match. The use of _ is optional.

You can combine several literals in a single pattern using | (“or”):

case 401 | 403 | 404:
    return "Not allowed"

def http_error(status):
    match status:
        case 400:
            return "Bad request"
        case 404:
            return "Not found"
        case 418:
            return "I'm a teapot"

Patterns with a literal and variable¶

Patterns can look like unpacking assignments, and a pattern may be used to bind variables. In this example, a data point can be unpacked to its x-coordinate and y-coordinate:

# point is an (x, y) tuple
match point:
    case (0, 0):
        print("Origin")
    case (0, y):
        print(f"Y={y}")
    case (x, 0):
        print(f"X={x}")
    case (x, y):
        print(f"X={x}, Y={y}")
    case _:
        raise ValueError("Not a point")

The first pattern has two literals, (0, 0), and may be thought of as an extension of the literal pattern shown above. The next two patterns combine a literal and a variable, and the variable binds a value from the subject (point). The fourth pattern captures two values, which makes it conceptually similar to the unpacking assignment (x, y) = point.

Patterns and classes¶

If you are using classes to structure your data, you can use as a pattern the class name followed by an argument list resembling a constructor. This pattern has the ability to capture instance attributes into variables:

class Point:
    def __init__(self, x, y):
        self.x = x
        self.y = y

def location(point):
    match point:
        case Point(x=0, y=0):
            print("Origin is the point's location.")
        case Point(x=0, y=y):
            print(f"Y={y} and the point is on the y-axis.")
        case Point(x=x, y=0):
            print(f"X={x} and the point is on the x-axis.")
        case Point():
            print("The point is located somewhere else on the plane.")
        case _:
            print("Not a point")

Point(1, var)
Point(1, y=var)
Point(x=1, y=var)
Point(y=var, x=1)

Nested patterns¶

Patterns can be arbitrarily nested. For example, if our data is a short list of points, it could be matched like this:

match points:
    case []:
        print("No points in the list.")
    case [Point(0, 0)]:
        print("The origin is the only point in the list.")
    case [Point(x, y)]:
        print(f"A single point {x}, {y} is in the list.")
    case [Point(0, y1), Point(0, y2)]:
        print(f"Two points on the Y axis at {y1}, {y2} are in the list.")
    case _:
        print("Something else is found in the list.")

Complex patterns and the wildcard¶

To this point, the examples have used _ alone in the last case statement. A wildcard can be used in more complex patterns, such as ('error', code, _). For example:

match test_variable:
    case ('warning', code, 40):
        print("A warning has been received.")
    case ('error', code, _):
        print(f"An error {code} occurred.")

In the above case, test_variable will match for (‘error’, code, 100) and (‘error’, code, 800).

Guard¶

We can add an if clause to a pattern, known as a “guard”. If the guard is false, match goes on to try the next case block. Note that value capture happens before the guard is evaluated:

match point:
    case Point(x, y) if x == y:
        print(f"The point is located on the diagonal Y=X at {x}.")
    case Point(x, y):
        print(f"Point is not on the diagonal.")

Other Key Features¶

Several other key features:

• Like unpacking assignments, tuple and list patterns have exactly the same meaning and actually match arbitrary sequences. Technically, the subject must be a sequence. Therefore, an important exception is that patterns don’t match iterators. Also, to prevent a common mistake, sequence patterns don’t match strings.

• Sequence patterns support wildcards: [x, y, *rest] and (x, y, *rest) work similar to wildcards in unpacking assignments. The name after * may also be _, so (x, y, *_) matches a sequence of at least two items without binding the remaining items.

• Mapping patterns: {"bandwidth": b, "latency": l} captures the "bandwidth" and "latency" values from a dict. Unlike sequence patterns, extra keys are ignored. A wildcard **rest is also supported. (But **_ would be redundant, so is not allowed.)

• Subpatterns may be captured using the as keyword:

  case (Point(x1, y1), Point(x2, y2) as p2): ...
  

  This binds x1, y1, x2, y2 like you would expect without the as clause, and p2 to the entire second item of the subject.

• Most literals are compared by equality. However, the singletons True, False and None are compared by identity.

• Named constants may be used in patterns. These named constants must be dotted names to prevent the constant from being interpreted as a capture variable:

  from enum import Enum
  class Color(Enum):
      RED = 0
      GREEN = 1
      BLUE = 2
  
  color = Color.GREEN
  match color:
      case Color.RED:
          print("I see red!")
      case Color.GREEN:
          print("Grass is green")
      case Color.BLUE:
          print("I'm feeling the blues :(")
  

For the full specification see PEP 634. Motivation and rationale are in PEP 635, and a longer tutorial is in PEP 636.

__slots__¶

Added slots parameter in dataclasses.dataclass() decorator. (Contributed by Yurii Karabas in bpo-42269)

Keyword-only fields¶

dataclasses now supports fields that are keyword-only in the generated __init__ method. There are a number of ways of specifying keyword-only fields.

You can say that every field is keyword-only:

from dataclasses import dataclass

@dataclass(kw_only=True)
class Birthday:
    name: str
    birthday: datetime.date

Both name and birthday are keyword-only parameters to the generated __init__ method.

You can specify keyword-only on a per-field basis:

from dataclasses import dataclass, field

@dataclass
class Birthday:
    name: str
    birthday: datetime.date = field(kw_only=True)

Here only birthday is keyword-only. If you set kw_only on individual fields, be aware that there are rules about re-ordering fields due to keyword-only fields needing to follow non-keyword-only fields. See the full dataclasses documentation for details.

You can also specify that all fields following a KW_ONLY marker are keyword-only. This will probably be the most common usage:

from dataclasses import dataclass, KW_ONLY

@dataclass
class Point:
    x: float
    y: float
    _: KW_ONLY
    z: float = 0.0
    t: float = 0.0

Here, z and t are keyword-only parameters, while x and y are not. (Contributed by Eric V. Smith in bpo-43532.)