1.1. Alternate Implementations

Though there is one Python implementation which is by far the most popular, there are some alternate implementations which are of particular interest to different audiences.

Known implementations include:

CPython

This is the original and most-maintained implementation of Python, written in C. New language features generally appear here first.

Jython

Python implemented in Java. This implementation can be used as a scripting language for Java applications, or can be used to create applications using the Java class libraries. It is also often used to create tests for Java libraries. More information can be found at the Jython website.

Python for .NET

This implementation actually uses the CPython implementation, but is a managed .NET application and makes .NET libraries available. It was created by Brian Lloyd. For more information, see the Python for .NET home page.

IronPython

An alternate Python for .NET. Unlike Python.NET, this is a complete Python implementation that generates IL, and compiles Python code directly to .NET assemblies. It was created by Jim Hugunin, the original creator of Jython. For more information, see the IronPython website.

PyPy

An implementation of Python written completely in Python. It supports several advanced features not found in other implementations like stackless support and a Just in Time compiler. One of the goals of the project is to encourage experimentation with the language itself by making it easier to modify the interpreter (since it is written in Python). Additional information is available on the PyPy project’s home page.

Each of these implementations varies in some way from the language as documented in this manual, or introduces specific information beyond what’s covered in the standard Python documentation. Please refer to the implementation-specific documentation to determine what else you need to know about the specific implementation you’re using.

1.2. Notation

The descriptions of lexical analysis and syntax use a modified BNF grammar notation. This uses the following style of definition:

name      ::=  lc_letter (lc_letter | "_")* lc_letter ::= "a"..."z"

The first line says that a name is an lc_letter followed by a sequence of zero or more lc_letters and underscores. An lc_letter in turn is any of the single characters 'a' through 'z'. (This rule is actually adhered to for the names defined in lexical and grammar rules in this document.)

Each rule begins with a name (which is the name defined by the rule) and ::=. A vertical bar (|) is used to separate alternatives; it is the least binding operator in this notation. A star (*) means zero or more repetitions of the preceding item; likewise, a plus (+) means one or more repetitions, and a phrase enclosed in square brackets ([ ]) means zero or one occurrences (in other words, the enclosed phrase is optional). The * and + operators bind as tightly as possible; parentheses are used for grouping. Literal strings are enclosed in quotes. White space is only meaningful to separate tokens. Rules are normally contained on a single line; rules with many alternatives may be formatted alternatively with each line after the first beginning with a vertical bar.

In lexical definitions (as the example above), two more conventions are used: Two literal characters separated by three dots mean a choice of any single character in the given (inclusive) range of ASCII characters. A phrase between angular brackets (<...>) gives an informal description of the symbol defined; e.g., this could be used to describe the notion of ‘control character’ if needed.

Even though the notation used is almost the same, there is a big difference between the meaning of lexical and syntactic definitions: a lexical definition operates on the individual characters of the input source, while a syntax definition operates on the stream of tokens generated by the lexical analysis. All uses of BNF in the next chapter (“Lexical Analysis”) are lexical definitions; uses in subsequent chapters are syntactic definitions.

2.1. Line structure

A Python program is divided into a number of logical lines.

# -*- coding:  -*-

# vim:fileencoding=

if 1900 < year < 2100 and 1 <= month <= 12 \
   and 1 <= day <= 31 and 0 <= hour < 24 \
   and 0 <= minute < 60 and 0 <= second < 60:   # Looks like a valid date
        return 1

month_names = ['Januari', 'Februari', 'Maart',      # These are the
               'April',   'Mei',      'Juni',       # Dutch names
               'Juli',    'Augustus', 'September',  # for the months
               'Oktober', 'November', 'December']   # of the year

def perm(l):
        # Compute the list of all permutations of l
    if len(l) <= 1:
                  return [l]
    r = []
    for i in range(len(l)):
             s = l[:i] + l[i+1:]
             p = perm(s)
             for x in p:
              r.append(l[i:i+1] + x)
    return r

 def perm(l):                       # error: first line indented
for i in range(len(l)):             # error: not indented
    s = l[:i] + l[i+1:]
        p = perm(l[:i] + l[i+1:])   # error: unexpected indent
        for x in p:
                r.append(l[i:i+1] + x)
            return r                # error: inconsistent dedent

2.2. Other tokens

Besides NEWLINE, INDENT and DEDENT, the following categories of tokens exist: identifiers, keywords, literals, operators, and delimiters. Whitespace characters (other than line terminators, discussed earlier) are not tokens, but serve to delimit tokens. Where ambiguity exists, a token comprises the longest possible string that forms a legal token, when read from left to right.

2.3. Identifiers and keywords

Identifiers (also referred to as names) are described by the following lexical definitions.

The syntax of identifiers in Python is based on the Unicode standard annex UAX-31, with elaboration and changes as defined below; see also PEP 3131 for further details.

Within the ASCII range (U+0001..U+007F), the valid characters for identifiers are the same as in Python 2.x: the uppercase and lowercase letters A through Z, the underscore _ and, except for the first character, the digits 0 through 9.

Python 3.0 introduces additional characters from outside the ASCII range (see PEP 3131). For these characters, the classification uses the version of the Unicode Character Database as included in the unicodedata module.

Identifiers are unlimited in length. Case is significant.

identifier   ::=  xid_start xid_continue* id_start  ::= id_continue  ::= id_start, plus characters in the categories Mn, Mc, Nd, Pc and others with the Other_ID_Continue property> xid_start  ::= id_start whose NFKC normalization is in "id_start xid_continue*"> xid_continue ::= id_continue whose NFKC normalization is in "id_continue*">

The Unicode category codes mentioned above stand for:

• Lu – uppercase letters

• Ll – lowercase letters

• Lt – titlecase letters

• Lm – modifier letters

• Lo – other letters

• Nl – letter numbers

• Mn – nonspacing marks

• Mc – spacing combining marks

• Nd – decimal numbers

• Pc – connector punctuations

• Other_ID_Start – explicit list of characters in PropList.txt to support backwards compatibility

• Other_ID_Continue – likewise

All identifiers are converted into the normal form NFKC while parsing; comparison of identifiers is based on NFKC.

A non-normative HTML file listing all valid identifier characters for Unicode 4.1 can be found at https://www.unicode.org/Public/13.0.0/ucd/DerivedCoreProperties.txt

False      await      else       import     pass
None       break      except     in         raise
True       class      finally    is         return
and        continue   for        lambda     try
as         def        from       nonlocal   while
assert     del        global     not        with
async      elif       if         or         yield

2.4. Literals

Literals are notations for constant values of some built-in types.

stringliteral   ::=  [stringprefix](shortstring | longstring) stringprefix  ::= "r" | "u" | "R" | "U" | "f" | "F" | "fr" | "Fr" | "fR" | "FR" | "rf" | "rF" | "Rf" | "RF" shortstring  ::= "'" shortstringitem* "'" | '"' shortstringitem* '"' longstring  ::= "'''" longstringitem* "'''" | '"""' longstringitem* '"""' shortstringitem ::= shortstringchar | stringescapeseq longstringitem  ::= longstringchar | stringescapeseq shortstringchar ::= longstringchar  ::= stringescapeseq ::= "\"

bytesliteral   ::=  bytesprefix(shortbytes | longbytes) bytesprefix  ::= "b" | "B" | "br" | "Br" | "bR" | "BR" | "rb" | "rB" | "Rb" | "RB" shortbytes  ::= "'" shortbytesitem* "'" | '"' shortbytesitem* '"' longbytes  ::= "'''" longbytesitem* "'''" | '"""' longbytesitem* '"""' shortbytesitem ::= shortbyteschar | bytesescapeseq longbytesitem  ::= longbyteschar | bytesescapeseq shortbyteschar ::= longbyteschar  ::= bytesescapeseq ::= "\"

re.compile("[A-Za-z_]"       # letter or underscore
           "[A-Za-z0-9_]*"   # letter, digit or underscore
          )

f_string          ::=  (literal_char | "{{" | "}}" | replacement_field)* replacement_field ::= "{" f_expression ["="] ["!" conversion] [":" format_spec] "}" f_expression  ::= (conditional_expression | "*" or_expr) ("," conditional_expression | "," "*" or_expr)* [","] | yield_expression conversion  ::= "s" | "r" | "a" format_spec  ::= (literal_char | NULL | replacement_field)* literal_char  ::=

>>> name = "Fred"
>>> f"He said his name is {name!r}."
"He said his name is 'Fred'."
>>> f"He said his name is {repr(name)}."  # repr() is equivalent to !r
"He said his name is 'Fred'."
>>> width = 10
>>> precision = 4
>>> value = decimal.Decimal("12.34567")
>>> f"result: {value:{width}.{precision}}"  # nested fields
'result:      12.35'
>>> today = datetime(year=2017, month=1, day=27)
>>> f"{today:%B %d, %Y}"  # using date format specifier
'January 27, 2017'
>>> f"{today=:%B %d, %Y}" # using date format specifier and debugging
'today=January 27, 2017'
>>> number = 1024
>>> f"{number:#0x}"  # using integer format specifier
'0x400'
>>> foo = "bar"
>>> f"{ foo = }" # preserves whitespace
" foo = 'bar'"
>>> line = "The mill's closed"
>>> f"{line = }"
'line = "The mill\'s closed"'
>>> f"{line = :20}"
"line = The mill's closed   "
>>> f"{line = !r:20}"
'line = "The mill\'s closed" '

f"abc {a["x"]} def"    # error: outer string literal ended prematurely
f"abc {a['x']} def"    # workaround: use different quoting

f"newline: {ord('\n')}"  # raises SyntaxError

>>> newline = ord('\n')
>>> f"newline: {newline}"
'newline: 10'

>>> def foo():
...     f"Not a docstring"
...
>>> foo.__doc__ is None
True

integer      ::=  decinteger | bininteger | octinteger | hexinteger decinteger  ::= nonzerodigit (["_"] digit)* | "0"+ (["_"] "0")* bininteger  ::= "0" ("b" | "B") (["_"] bindigit)+ octinteger  ::= "0" ("o" | "O") (["_"] octdigit)+ hexinteger  ::= "0" ("x" | "X") (["_"] hexdigit)+ nonzerodigit ::= "1"..."9" digit  ::= "0"..."9" bindigit  ::= "0" | "1" octdigit  ::= "0"..."7" hexdigit  ::= digit | "a"..."f" | "A"..."F"

7     2147483647                        0o177    0b100110111
3     79228162514264337593543950336     0o377    0xdeadbeef
      100_000_000_000                   0b_1110_0101

floatnumber   ::=  pointfloat | exponentfloat pointfloat  ::= [digitpart] fraction | digitpart "." exponentfloat ::= (digitpart | pointfloat) exponent digitpart  ::= digit (["_"] digit)* fraction  ::= "." digitpart exponent  ::= ("e" | "E") ["+" | "-"] digitpart

3.14    10.    .001    1e100    3.14e-10    0e0    3.14_15_93

imagnumber ::=  (floatnumber | digitpart) ("j" | "J")

3.14j   10.j    10j     .001j   1e100j   3.14e-10j   3.14_15_93j

2.5. Operators

The following tokens are operators:

+       -       *       **      /       //      %      @
<< >>      &       |       ^       ~       :=
< >       <= >=      ==      !=

2.6. Delimiters

The following tokens serve as delimiters in the grammar:

(       )       [       ]       {       }
,       :       .       ;       @       =       ->
+=      -=      *=      /=      //=     %=      @=
&=      |=      ^=      >>=     <<=     **=

The period can also occur in floating-point and imaginary literals. A sequence of three periods has a special meaning as an ellipsis literal. The second half of the list, the augmented assignment operators, serve lexically as delimiters, but also perform an operation.

The following printing ASCII characters have special meaning as part of other tokens or are otherwise significant to the lexical analyzer:

'       "       #       \

The following printing ASCII characters are not used in Python. Their occurrence outside string literals and comments is an unconditional error:

$       ?       `

Footnotes

1

https://www.unicode.org/Public/11.0.0/ucd/NameAliases.txt

2. Lexical analysis

A Python program is read by a parser. Input to the parser is a stream of tokens, generated by the lexical analyzer. This chapter describes how the lexical analyzer breaks a file into tokens.

Python reads program text as Unicode code points; the encoding of a source file can be given by an encoding declaration and defaults to UTF-8, see PEP 3120 for details. If the source file cannot be decoded, a SyntaxError is raised.

2.1. Line structure

A Python program is divided into a number of logical lines.

2.1.1. Logical lines

The end of a logical line is represented by the token NEWLINE. Statements cannot cross logical line boundaries except where NEWLINE is allowed by the syntax (e.g., between statements in compound statements). A logical line is constructed from one or more physical lines by following the explicit or implicit line joining rules.

2.1.2. Physical lines

A physical line is a sequence of characters terminated by an end-of-line sequence. In source files and strings, any of the standard platform line termination sequences can be used – the Unix form using ASCII LF (linefeed), the Windows form using the ASCII sequence CR LF (return followed by linefeed), or the old Macintosh form using the ASCII CR (return) character. All of these forms can be used equally, regardless of platform. The end of input also serves as an implicit terminator for the final physical line.

When embedding Python, source code strings should be passed to Python APIs using the standard C conventions for newline characters (the \n character, representing ASCII LF, is the line terminator).

2.1.3. Comments

A comment starts with a hash character (#) that is not part of a string literal, and ends at the end of the physical line. A comment signifies the end of the logical line unless the implicit line joining rules are invoked. Comments are ignored by the syntax.

2.1.4. Encoding declarations

If a comment in the first or second line of the Python script matches the regular expression coding[=:]\s*([-\w.]+), this comment is processed as an encoding declaration; the first group of this expression names the encoding of the source code file. The encoding declaration must appear on a line of its own. If it is the second line, the first line must also be a comment-only line. The recommended forms of an encoding expression are

# -*- coding:  -*-

which is recognized also by GNU Emacs, and

# vim:fileencoding=

which is recognized by Bram Moolenaar’s VIM.

If no encoding declaration is found, the default encoding is UTF-8. In addition, if the first bytes of the file are the UTF-8 byte-order mark (b'\xef\xbb\xbf'), the declared file encoding is UTF-8 (this is supported, among others, by Microsoft’s notepad).

If an encoding is declared, the encoding name must be recognized by Python (see Standard Encodings). The encoding is used for all lexical analysis, including string literals, comments and identifiers.

2.1.5. Explicit line joining

Two or more physical lines may be joined into logical lines using backslash characters (\), as follows: when a physical line ends in a backslash that is not part of a string literal or comment, it is joined with the following forming a single logical line, deleting the backslash and the following end-of-line character. For example:

if 1900 < year < 2100 and 1 <= month <= 12 \
   and 1 <= day <= 31 and 0 <= hour < 24 \
   and 0 <= minute < 60 and 0 <= second < 60:   # Looks like a valid date
        return 1

A line ending in a backslash cannot carry a comment. A backslash does not continue a comment. A backslash does not continue a token except for string literals (i.e., tokens other than string literals cannot be split across physical lines using a backslash). A backslash is illegal elsewhere on a line outside a string literal.

2.1.6. Implicit line joining

Expressions in parentheses, square brackets or curly braces can be split over more than one physical line without using backslashes. For example:

month_names = ['Januari', 'Februari', 'Maart',      # These are the
               'April',   'Mei',      'Juni',       # Dutch names
               'Juli',    'Augustus', 'September',  # for the months
               'Oktober', 'November', 'December']   # of the year

Implicitly continued lines can carry comments. The indentation of the continuation lines is not important. Blank continuation lines are allowed. There is no NEWLINE token between implicit continuation lines. Implicitly continued lines can also occur within triple-quoted strings (see below); in that case they cannot carry comments.

2.1.7. Blank lines

A logical line that contains only spaces, tabs, formfeeds and possibly a comment, is ignored (i.e., no NEWLINE token is generated). During interactive input of statements, handling of a blank line may differ depending on the implementation of the read-eval-print loop. In the standard interactive interpreter, an entirely blank logical line (i.e. one containing not even whitespace or a comment) terminates a multi-line statement.

2.1.8. Indentation

Leading whitespace (spaces and tabs) at the beginning of a logical line is used to compute the indentation level of the line, which in turn is used to determine the grouping of statements.

Tabs are replaced (from left to right) by one to eight spaces such that the total number of characters up to and including the replacement is a multiple of eight (this is intended to be the same rule as used by Unix). The total number of spaces preceding the first non-blank character then determines the line’s indentation. Indentation cannot be split over multiple physical lines using backslashes; the whitespace up to the first backslash determines the indentation.

Indentation is rejected as inconsistent if a source file mixes tabs and spaces in a way that makes the meaning dependent on the worth of a tab in spaces; a TabError is raised in that case.

Cross-platform compatibility note: because of the nature of text editors on non-UNIX platforms, it is unwise to use a mixture of spaces and tabs for the indentation in a single source file. It should also be noted that different platforms may explicitly limit the maximum indentation level.

A formfeed character may be present at the start of the line; it will be ignored for the indentation calculations above. Formfeed characters occurring elsewhere in the leading whitespace have an undefined effect (for instance, they may reset the space count to zero).

The indentation levels of consecutive lines are used to generate INDENT and DEDENT tokens, using a stack, as follows.

Before the first line of the file is read, a single zero is pushed on the stack; this will never be popped off again. The numbers pushed on the stack will always be strictly increasing from bottom to top. At the beginning of each logical line, the line’s indentation level is compared to the top of the stack. If it is equal, nothing happens. If it is larger, it is pushed on the stack, and one INDENT token is generated. If it is smaller, it must be one of the numbers occurring on the stack; all numbers on the stack that are larger are popped off, and for each number popped off a DEDENT token is generated. At the end of the file, a DEDENT token is generated for each number remaining on the stack that is larger than zero.

Here is an example of a correctly (though confusingly) indented piece of Python code:

def perm(l):
        # Compute the list of all permutations of l
    if len(l) <= 1:
                  return [l]
    r = []
    for i in range(len(l)):
             s = l[:i] + l[i+1:]
             p = perm(s)
             for x in p:
              r.append(l[i:i+1] + x)
    return r

The following example shows various indentation errors:

 def perm(l):                       # error: first line indented
for i in range(len(l)):             # error: not indented
    s = l[:i] + l[i+1:]
        p = perm(l[:i] + l[i+1:])   # error: unexpected indent
        for x in p:
                r.append(l[i:i+1] + x)
            return r                # error: inconsistent dedent

(Actually, the first three errors are detected by the parser; only the last error is found by the lexical analyzer — the indentation of return r does not match a level popped off the stack.)

2.1.9. Whitespace between tokens

Except at the beginning of a logical line or in string literals, the whitespace characters space, tab and formfeed can be used interchangeably to separate tokens. Whitespace is needed between two tokens only if their concatenation could otherwise be interpreted as a different token (e.g., ab is one token, but a b is two tokens).

2.2. Other tokens

Besides NEWLINE, INDENT and DEDENT, the following categories of tokens exist: identifiers, keywords, literals, operators, and delimiters. Whitespace characters (other than line terminators, discussed earlier) are not tokens, but serve to delimit tokens. Where ambiguity exists, a token comprises the longest possible string that forms a legal token, when read from left to right.

2.3. Identifiers and keywords

Identifiers (also referred to as names) are described by the following lexical definitions.

The syntax of identifiers in Python is based on the Unicode standard annex UAX-31, with elaboration and changes as defined below; see also PEP 3131 for further details.

Within the ASCII range (U+0001..U+007F), the valid characters for identifiers are the same as in Python 2.x: the uppercase and lowercase letters A through Z, the underscore _ and, except for the first character, the digits 0 through 9.

Python 3.0 introduces additional characters from outside the ASCII range (see PEP 3131). For these characters, the classification uses the version of the Unicode Character Database as included in the unicodedata module.

Identifiers are unlimited in length. Case is significant.

identifier   ::=  xid_start xid_continue* id_start  ::= id_continue  ::= id_start, plus characters in the categories Mn, Mc, Nd, Pc and others with the Other_ID_Continue property> xid_start  ::= id_start whose NFKC normalization is in "id_start xid_continue*"> xid_continue ::= id_continue whose NFKC normalization is in "id_continue*">

The Unicode category codes mentioned above stand for:

• Lu – uppercase letters

• Ll – lowercase letters

• Lt – titlecase letters

• Lm – modifier letters

• Lo – other letters

• Nl – letter numbers

• Mn – nonspacing marks

• Mc – spacing combining marks

• Nd – decimal numbers

• Pc – connector punctuations

• Other_ID_Start – explicit list of characters in PropList.txt to support backwards compatibility

• Other_ID_Continue – likewise

All identifiers are converted into the normal form NFKC while parsing; comparison of identifiers is based on NFKC.

A non-normative HTML file listing all valid identifier characters for Unicode 4.1 can be found at https://www.unicode.org/Public/13.0.0/ucd/DerivedCoreProperties.txt

2.3.1. Keywords

The following identifiers are used as reserved words, or keywords of the language, and cannot be used as ordinary identifiers. They must be spelled exactly as written here:

False      await      else       import     pass
None       break      except     in         raise
True       class      finally    is         return
and        continue   for        lambda     try
as         def        from       nonlocal   while
assert     del        global     not        with
async      elif       if         or         yield

2.3.2. Soft Keywords

New in version 3.10.

Some identifiers are only reserved under specific contexts. These are known as soft keywords. The identifiers match, case and _ can syntactically act as keywords in contexts related to the pattern matching statement, but this distinction is done at the parser level, not when tokenizing.

As soft keywords, their use with pattern matching is possible while still preserving compatibility with existing code that uses match, case and _ as identifier names.

2.3.3. Reserved classes of identifiers

Certain classes of identifiers (besides keywords) have special meanings. These classes are identified by the patterns of leading and trailing underscore characters:

_*

Not imported by from module import *.

_

In a case pattern within a match statement, _ is a soft keyword that denotes a wildcard.

Separately, the interactive interpreter makes the result of the last evaluation available in the variable _. (It is stored in the builtins module, alongside built-in functions like print.)

Elsewhere, _ is a regular identifier. It is often used to name “special” items, but it is not special to Python itself.

Note

The name _ is often used in conjunction with internationalization; refer to the documentation for the gettext module for more information on this convention.

It is also commonly used for unused variables.

__*__

System-defined names, informally known as “dunder” names. These names are defined by the interpreter and its implementation (including the standard library). Current system names are discussed in the Special method names section and elsewhere. More will likely be defined in future versions of Python. Any use of __*__ names, in any context, that does not follow explicitly documented use, is subject to breakage without warning.

__*

Class-private names. Names in this category, when used within the context of a class definition, are re-written to use a mangled form to help avoid name clashes between “private” attributes of base and derived classes. See section Identifiers (Names).

2.4. Literals

Literals are notations for constant values of some built-in types.

2.4.1. String and Bytes literals

String literals are described by the following lexical definitions:

stringliteral   ::=  [stringprefix](shortstring | longstring) stringprefix  ::= "r" | "u" | "R" | "U" | "f" | "F" | "fr" | "Fr" | "fR" | "FR" | "rf" | "rF" | "Rf" | "RF" shortstring  ::= "'" shortstringitem* "'" | '"' shortstringitem* '"' longstring  ::= "'''" longstringitem* "'''" | '"""' longstringitem* '"""' shortstringitem ::= shortstringchar | stringescapeseq longstringitem  ::= longstringchar | stringescapeseq shortstringchar ::= longstringchar  ::= stringescapeseq ::= "\"

bytesliteral   ::=  bytesprefix(shortbytes | longbytes) bytesprefix  ::= "b" | "B" | "br" | "Br" | "bR" | "BR" | "rb" | "rB" | "Rb" | "RB" shortbytes  ::= "'" shortbytesitem* "'" | '"' shortbytesitem* '"' longbytes  ::= "'''" longbytesitem* "'''" | '"""' longbytesitem* '"""' shortbytesitem ::= shortbyteschar | bytesescapeseq longbytesitem  ::= longbyteschar | bytesescapeseq shortbyteschar ::= longbyteschar  ::= bytesescapeseq ::= "\"

One syntactic restriction not indicated by these productions is that whitespace is not allowed between the stringprefix or bytesprefix and the rest of the literal. The source character set is defined by the encoding declaration; it is UTF-8 if no encoding declaration is given in the source file; see section Encoding declarations.

In plain English: Both types of literals can be enclosed in matching single quotes (') or double quotes ("). They can also be enclosed in matching groups of three single or double quotes (these are generally referred to as triple-quoted strings). The backslash (\) character is used to escape characters that otherwise have a special meaning, such as newline, backslash itself, or the quote character.

Bytes literals are always prefixed with 'b' or 'B'; they produce an instance of the bytes type instead of the str type. They may only contain ASCII characters; bytes with a numeric value of 128 or greater must be expressed with escapes.

Both string and bytes literals may optionally be prefixed with a letter 'r' or 'R'; such strings are called raw strings and treat backslashes as literal characters. As a result, in string literals, '\U' and '\u' escapes in raw strings are not treated specially. Given that Python 2.x’s raw unicode literals behave differently than Python 3.x’s the 'ur' syntax is not supported.

New in version 3.3: The 'rb' prefix of raw bytes literals has been added as a synonym of 'br'.

New in version 3.3: Support for the unicode legacy literal (u'value') was reintroduced to simplify the maintenance of dual Python 2.x and 3.x codebases. See PEP 414 for more information.

A string literal with 'f' or 'F' in its prefix is a formatted string literal; see Formatted string literals. The 'f' may be combined with 'r', but not with 'b' or 'u', therefore raw formatted strings are possible, but formatted bytes literals are not.

In triple-quoted literals, unescaped newlines and quotes are allowed (and are retained), except that three unescaped quotes in a row terminate the literal. (A “quote” is the character used to open the literal, i.e. either ' or ".)

Unless an 'r' or 'R' prefix is present, escape sequences in string and bytes literals are interpreted according to rules similar to those used by Standard C. The recognized escape sequences are:

Escape Sequence	Meaning	Notes
\newline	Backslash and newline ignored
\\	Backslash (\)
\'	Single quote (')
\"	Double quote (")
\a	ASCII Bell (BEL)
\b	ASCII Backspace (BS)
\f	ASCII Formfeed (FF)
\n	ASCII Linefeed (LF)
\r	ASCII Carriage Return (CR)
\t	ASCII Horizontal Tab (TAB)
\v	ASCII Vertical Tab (VT)
\ooo	Character with octal value ooo	(1,3)
\xhh	Character with hex value hh	(2,3)

Escape sequences only recognized in string literals are:

Escape Sequence	Meaning	Notes
\N{name}	Character named name in the Unicode database	(4)
\uxxxx	Character with 16-bit hex value xxxx	(5)
\Uxxxxxxxx	Character with 32-bit hex value xxxxxxxx	(6)

Notes:

1. As in Standard C, up to three octal digits are accepted.

2. Unlike in Standard C, exactly two hex digits are required.

3. In a bytes literal, hexadecimal and octal escapes denote the byte with the given value. In a string literal, these escapes denote a Unicode character with the given value.

4. Changed in version 3.3: Support for name aliases 1 has been added.

5. Exactly four hex digits are required.

6. Any Unicode character can be encoded this way. Exactly eight hex digits are required.

Unlike Standard C, all unrecognized escape sequences are left in the string unchanged, i.e., the backslash is left in the result. (This behavior is useful when debugging: if an escape sequence is mistyped, the resulting output is more easily recognized as broken.) It is also important to note that the escape sequences only recognized in string literals fall into the category of unrecognized escapes for bytes literals.

Changed in version 3.6: Unrecognized escape sequences produce a DeprecationWarning. In a future Python version they will be a SyntaxWarning and eventually a SyntaxError.

Even in a raw literal, quotes can be escaped with a backslash, but the backslash remains in the result; for example, r"\"" is a valid string literal consisting of two characters: a backslash and a double quote; r"\" is not a valid string literal (even a raw string cannot end in an odd number of backslashes). Specifically, a raw literal cannot end in a single backslash (since the backslash would escape the following quote character). Note also that a single backslash followed by a newline is interpreted as those two characters as part of the literal, not as a line continuation.

2.4.2. String literal concatenation

Multiple adjacent string or bytes literals (delimited by whitespace), possibly using different quoting conventions, are allowed, and their meaning is the same as their concatenation. Thus, "hello" 'world' is equivalent to "helloworld". This feature can be used to reduce the number of backslashes needed, to split long strings conveniently across long lines, or even to add comments to parts of strings, for example:

re.compile("[A-Za-z_]"       # letter or underscore
           "[A-Za-z0-9_]*"   # letter, digit or underscore
          )

Note that this feature is defined at the syntactical level, but implemented at compile time. The ‘+’ operator must be used to concatenate string expressions at run time. Also note that literal concatenation can use different quoting styles for each component (even mixing raw strings and triple quoted strings), and formatted string literals may be concatenated with plain string literals.

2.4.3. Formatted string literals

New in version 3.6.

A formatted string literal or f-string is a string literal that is prefixed with 'f' or 'F'. These strings may contain replacement fields, which are expressions delimited by curly braces {}. While other string literals always have a constant value, formatted strings are really expressions evaluated at run time.

Escape sequences are decoded like in ordinary string literals (except when a literal is also marked as a raw string). After decoding, the grammar for the contents of the string is:

f_string          ::=  (literal_char | "{{" | "}}" | replacement_field)* replacement_field ::= "{" f_expression ["="] ["!" conversion] [":" format_spec] "}" f_expression  ::= (conditional_expression | "*" or_expr) ("," conditional_expression | "," "*" or_expr)* [","] | yield_expression conversion  ::= "s" | "r" | "a" format_spec  ::= (literal_char | NULL | replacement_field)* literal_char  ::=

The parts of the string outside curly braces are treated literally, except that any doubled curly braces '{{' or '}}' are replaced with the corresponding single curly brace. A single opening curly bracket '{' marks a replacement field, which starts with a Python expression. To display both the expression text and its value after evaluation, (useful in debugging), an equal sign '=' may be added after the expression. A conversion field, introduced by an exclamation point '!' may follow. A format specifier may also be appended, introduced by a colon ':'. A replacement field ends with a closing curly bracket '}'.

Expressions in formatted string literals are treated like regular Python expressions surrounded by parentheses, with a few exceptions. An empty expression is not allowed, and both lambda and assignment expressions := must be surrounded by explicit parentheses. Replacement expressions can contain line breaks (e.g. in triple-quoted strings), but they cannot contain comments. Each expression is evaluated in the context where the formatted string literal appears, in order from left to right.

Changed in version 3.7: Prior to Python 3.7, an await expression and comprehensions containing an async for clause were illegal in the expressions in formatted string literals due to a problem with the implementation.

When the equal sign '=' is provided, the output will have the expression text, the '=' and the evaluated value. Spaces after the opening brace '{', within the expression and after the '=' are all retained in the output. By default, the '=' causes the repr() of the expression to be provided, unless there is a format specified. When a format is specified it defaults to the str() of the expression unless a conversion '!r' is declared.

New in version 3.8: The equal sign '='.

If a conversion is specified, the result of evaluating the expression is converted before formatting. Conversion '!s' calls str() on the result, '!r' calls repr(), and '!a' calls ascii().

The result is then formatted using the format() protocol. The format specifier is passed to the __format__() method of the expression or conversion result. An empty string is passed when the format specifier is omitted. The formatted result is then included in the final value of the whole string.

Top-level format specifiers may include nested replacement fields. These nested fields may include their own conversion fields and format specifiers, but may not include more deeply-nested replacement fields. The format specifier mini-language is the same as that used by the str.format() method.

Formatted string literals may be concatenated, but replacement fields cannot be split across literals.

Some examples of formatted string literals:

>>>

>>> name = "Fred"
>>> f"He said his name is {name!r}."
"He said his name is 'Fred'."
>>> f"He said his name is {repr(name)}."  # repr() is equivalent to !r
"He said his name is 'Fred'."
>>> width = 10
>>> precision = 4
>>> value = decimal.Decimal("12.34567")
>>> f"result: {value:{width}.{precision}}"  # nested fields
'result:      12.35'
>>> today = datetime(year=2017, month=1, day=27)
>>> f"{today:%B %d, %Y}"  # using date format specifier
'January 27, 2017'
>>> f"{today=:%B %d, %Y}" # using date format specifier and debugging
'today=January 27, 2017'
>>> number = 1024
>>> f"{number:#0x}"  # using integer format specifier
'0x400'
>>> foo = "bar"
>>> f"{ foo = }" # preserves whitespace
" foo = 'bar'"
>>> line = "The mill's closed"
>>> f"{line = }"
'line = "The mill\'s closed"'
>>> f"{line = :20}"
"line = The mill's closed   "
>>> f"{line = !r:20}"
'line = "The mill\'s closed" '

A consequence of sharing the same syntax as regular string literals is that characters in the replacement fields must not conflict with the quoting used in the outer formatted string literal:

f"abc {a["x"]} def"    # error: outer string literal ended prematurely
f"abc {a['x']} def"    # workaround: use different quoting

Backslashes are not allowed in format expressions and will raise an error:

f"newline: {ord('\n')}"  # raises SyntaxError

To include a value in which a backslash escape is required, create a temporary variable.

>>>

>>> newline = ord('\n')
>>> f"newline: {newline}"
'newline: 10'

Formatted string literals cannot be used as docstrings, even if they do not include expressions.

>>>

>>> def foo():
...     f"Not a docstring"
...
>>> foo.__doc__ is None
True

See also PEP 498 for the proposal that added formatted string literals, and str.format(), which uses a related format string mechanism.

2.4.4. Numeric literals

There are three types of numeric literals: integers, floating point numbers, and imaginary numbers. There are no complex literals (complex numbers can be formed by adding a real number and an imaginary number).

Note that numeric literals do not include a sign; a phrase like -1 is actually an expression composed of the unary operator ‘-’ and the literal 1.

2.4.5. Integer literals

Integer literals are described by the following lexical definitions:

integer      ::=  decinteger | bininteger | octinteger | hexinteger decinteger  ::= nonzerodigit (["_"] digit)* | "0"+ (["_"] "0")* bininteger  ::= "0" ("b" | "B") (["_"] bindigit)+ octinteger  ::= "0" ("o" | "O") (["_"] octdigit)+ hexinteger  ::= "0" ("x" | "X") (["_"] hexdigit)+ nonzerodigit ::= "1"..."9" digit  ::= "0"..."9" bindigit  ::= "0" | "1" octdigit  ::= "0"..."7" hexdigit  ::= digit | "a"..."f" | "A"..."F"

There is no limit for the length of integer literals apart from what can be stored in available memory.

Underscores are ignored for determining the numeric value of the literal. They can be used to group digits for enhanced readability. One underscore can occur between digits, and after base specifiers like 0x.

Note that leading zeros in a non-zero decimal number are not allowed. This is for disambiguation with C-style octal literals, which Python used before version 3.0.

Some examples of integer literals:

7     2147483647                        0o177    0b100110111
3     79228162514264337593543950336     0o377    0xdeadbeef
      100_000_000_000                   0b_1110_0101

Changed in version 3.6: Underscores are now allowed for grouping purposes in literals.

2.4.6. Floating point literals

Floating point literals are described by the following lexical definitions:

floatnumber   ::=  pointfloat | exponentfloat pointfloat  ::= [digitpart] fraction | digitpart "." exponentfloat ::= (digitpart | pointfloat) exponent digitpart  ::= digit (["_"] digit)* fraction  ::= "." digitpart exponent  ::= ("e" | "E") ["+" | "-"] digitpart

Note that the integer and exponent parts are always interpreted using radix 10. For example, 077e010 is legal, and denotes the same number as 77e10. The allowed range of floating point literals is implementation-dependent. As in integer literals, underscores are supported for digit grouping.

Some examples of floating point literals:

3.14    10.    .001    1e100    3.14e-10    0e0    3.14_15_93

Changed in version 3.6: Underscores are now allowed for grouping purposes in literals.

2.4.7. Imaginary literals

Imaginary literals are described by the following lexical definitions:

imagnumber ::=  (floatnumber | digitpart) ("j" | "J")

An imaginary literal yields a complex number with a real part of 0.0. Complex numbers are represented as a pair of floating point numbers and have the same restrictions on their range. To create a complex number with a nonzero real part, add a floating point number to it, e.g., (3+4j). Some examples of imaginary literals:

3.14j   10.j    10j     .001j   1e100j   3.14e-10j   3.14_15_93j

2.5. Operators

The following tokens are operators:

+       -       *       **      /       //      %      @
<< >>      &       |       ^       ~       :=
< >       <= >=      ==      !=

2.6. Delimiters

The following tokens serve as delimiters in the grammar:

(       )       [       ]       {       }
,       :       .       ;       @       =       ->
+=      -=      *=      /=      //=     %=      @=
&=      |=      ^=      >>=     <<=     **=

The period can also occur in floating-point and imaginary literals. A sequence of three periods has a special meaning as an ellipsis literal. The second half of the list, the augmented assignment operators, serve lexically as delimiters, but also perform an operation.

The following printing ASCII characters have special meaning as part of other tokens or are otherwise significant to the lexical analyzer:

'       "       #       \

The following printing ASCII characters are not used in Python. Their occurrence outside string literals and comments is an unconditional error:

$       ?       `

Footnotes

1

https://www.unicode.org/Public/11.0.0/ucd/NameAliases.txt

3.1. Objects, values and types

Objects are Python’s abstraction for data. All data in a Python program is represented by objects or by relations between objects. (In a sense, and in conformance to Von Neumann’s model of a “stored program computer”, code is also represented by objects.)

Every object has an identity, a type and a value. An object’s identity never changes once it has been created; you may think of it as the object’s address in memory. The ‘is’ operator compares the identity of two objects; the id() function returns an integer representing its identity.

An object’s type determines the operations that the object supports (e.g., “does it have a length?”) and also defines the possible values for objects of that type. The type() function returns an object’s type (which is an object itself). Like its identity, an object’s type is also unchangeable. 1

The value of some objects can change. Objects whose value can change are said to be mutable; objects whose value is unchangeable once they are created are called immutable. (The value of an immutable container object that contains a reference to a mutable object can change when the latter’s value is changed; however the container is still considered immutable, because the collection of objects it contains cannot be changed. So, immutability is not strictly the same as having an unchangeable value, it is more subtle.) An object’s mutability is determined by its type; for instance, numbers, strings and tuples are immutable, while dictionaries and lists are mutable.

Objects are never explicitly destroyed; however, when they become unreachable they may be garbage-collected. An implementation is allowed to postpone garbage collection or omit it altogether — it is a matter of implementation quality how garbage collection is implemented, as long as no objects are collected that are still reachable.

Note that the use of the implementation’s tracing or debugging facilities may keep objects alive that would normally be collectable. Also note that catching an exception with a ‘try…except’ statement may keep objects alive.

Some objects contain references to “external” resources such as open files or windows. It is understood that these resources are freed when the object is garbage-collected, but since garbage collection is not guaranteed to happen, such objects also provide an explicit way to release the external resource, usually a close() method. Programs are strongly recommended to explicitly close such objects. The ‘try…finally’ statement and the ‘with’ statement provide convenient ways to do this.

Some objects contain references to other objects; these are called containers. Examples of containers are tuples, lists and dictionaries. The references are part of a container’s value. In most cases, when we talk about the value of a container, we imply the values, not the identities of the contained objects; however, when we talk about the mutability of a container, only the identities of the immediately contained objects are implied. So, if an immutable container (like a tuple) contains a reference to a mutable object, its value changes if that mutable object is changed.

Types affect almost all aspects of object behavior. Even the importance of object identity is affected in some sense: for immutable types, operations that compute new values may actually return a reference to any existing object with the same type and value, while for mutable objects this is not allowed. E.g., after a = 1; b = 1, a and b may or may not refer to the same object with the value one, depending on the implementation, but after c = []; d = [], c and d are guaranteed to refer to two different, unique, newly created empty lists. (Note that c = d = [] assigns the same object to both c and d.)

3.2. The standard type hierarchy

Below is a list of the types that are built into Python. Extension modules (written in C, Java, or other languages, depending on the implementation) can define additional types. Future versions of Python may add types to the type hierarchy (e.g., rational numbers, efficiently stored arrays of integers, etc.), although such additions will often be provided via the standard library instead.

Some of the type descriptions below contain a paragraph listing ‘special attributes.’ These are attributes that provide access to the implementation and are not intended for general use. Their definition may change in the future.

None

This type has a single value. There is a single object with this value. This object is accessed through the built-in name None. It is used to signify the absence of a value in many situations, e.g., it is returned from functions that don’t explicitly return anything. Its truth value is false.

NotImplemented

This type has a single value. There is a single object with this value. This object is accessed through the built-in name NotImplemented. Numeric methods and rich comparison methods should return this value if they do not implement the operation for the operands provided. (The interpreter will then try the reflected operation, or some other fallback, depending on the operator.) It should not be evaluated in a boolean context.

See Implementing the arithmetic operations for more details.

Changed in version 3.9: Evaluating NotImplemented in a boolean context is deprecated. While it currently evaluates as true, it will emit a DeprecationWarning. It will raise a TypeError in a future version of Python.

Ellipsis

This type has a single value. There is a single object with this value. This object is accessed through the literal ... or the built-in name Ellipsis. Its truth value is true.

numbers.Number

These are created by numeric literals and returned as results by arithmetic operators and arithmetic built-in functions. Numeric objects are immutable; once created their value never changes. Python numbers are of course strongly related to mathematical numbers, but subject to the limitations of numerical representation in computers.

The string representations of the numeric classes, computed by __repr__() and __str__(), have the following properties:

• They are valid numeric literals which, when passed to their class constructor, produce an object having the value of the original numeric.

• The representation is in base 10, when possible.

• Leading zeros, possibly excepting a single zero before a decimal point, are not shown.

• Trailing zeros, possibly excepting a single zero after a decimal point, are not shown.

• A sign is shown only when the number is negative.

Python distinguishes between integers, floating point numbers, and complex numbers:

numbers.Integral

These represent elements from the mathematical set of integers (positive and negative).

There are two types of integers:

Integers (int)

These represent numbers in an unlimited range, subject to available (virtual) memory only. For the purpose of shift and mask operations, a binary representation is assumed, and negative numbers are represented in a variant of 2’s complement which gives the illusion of an infinite string of sign bits extending to the left.

Booleans (bool)

These represent the truth values False and True. The two objects representing the values False and True are the only Boolean objects. The Boolean type is a subtype of the integer type, and Boolean values behave like the values 0 and 1, respectively, in almost all contexts, the exception being that when converted to a string, the strings "False" or "True" are returned, respectively.

The rules for integer representation are intended to give the most meaningful interpretation of shift and mask operations involving negative integers.

numbers.Real (float)

These represent machine-level double precision floating point numbers. You are at the mercy of the underlying machine architecture (and C or Java implementation) for the accepted range and handling of overflow. Python does not support single-precision floating point numbers; the savings in processor and memory usage that are usually the reason for using these are dwarfed by the overhead of using objects in Python, so there is no reason to complicate the language with two kinds of floating point numbers.

numbers.Complex (complex)

These represent complex numbers as a pair of machine-level double precision floating point numbers. The same caveats apply as for floating point numbers. The real and imaginary parts of a complex number z can be retrieved through the read-only attributes z.real and z.imag.

Sequences

These represent finite ordered sets indexed by non-negative numbers. The built-in function len() returns the number of items of a sequence. When the length of a sequence is n, the index set contains the numbers 0, 1, …, n-1. Item i of sequence a is selected by a[i].

Sequences also support slicing: a[i:j] selects all items with index k such that i <= k < j. When used as an expression, a slice is a sequence of the same type. This implies that the index set is renumbered so that it starts at 0.

Some sequences also support “extended slicing” with a third “step” parameter: a[i:j:k] selects all items of a with index x where x = i + n*k, n >= 0 and i <= x < j.

Sequences are distinguished according to their mutability:

Immutable sequences

An object of an immutable sequence type cannot change once it is created. (If the object contains references to other objects, these other objects may be mutable and may be changed; however, the collection of objects directly referenced by an immutable object cannot change.)

The following types are immutable sequences:

Strings

A string is a sequence of values that represent Unicode code points. All the code points in the range U+0000 - U+10FFFF can be represented in a string. Python doesn’t have a char type; instead, every code point in the string is represented as a string object with length 1. The built-in function ord() converts a code point from its string form to an integer in the range 0 - 10FFFF; chr() converts an integer in the range 0 - 10FFFF to the corresponding length 1 string object. str.encode() can be used to convert a str to bytes using the given text encoding, and bytes.decode() can be used to achieve the opposite.

Tuples

The items of a tuple are arbitrary Python objects. Tuples of two or more items are formed by comma-separated lists of expressions. A tuple of one item (a ‘singleton’) can be formed by affixing a comma to an expression (an expression by itself does not create a tuple, since parentheses must be usable for grouping of expressions). An empty tuple can be formed by an empty pair of parentheses.

Bytes

A bytes object is an immutable array. The items are 8-bit bytes, represented by integers in the range 0 <= x < 256. Bytes literals (like b'abc') and the built-in bytes() constructor can be used to create bytes objects. Also, bytes objects can be decoded to strings via the decode() method.

Mutable sequences

Mutable sequences can be changed after they are created. The subscription and slicing notations can be used as the target of assignment and del (delete) statements.

There are currently two intrinsic mutable sequence types:

Lists

The items of a list are arbitrary Python objects. Lists are formed by placing a comma-separated list of expressions in square brackets. (Note that there are no special cases needed to form lists of length 0 or 1.)

Byte Arrays

A bytearray object is a mutable array. They are created by the built-in bytearray() constructor. Aside from being mutable (and hence unhashable), byte arrays otherwise provide the same interface and functionality as immutable bytes objects.

The extension module array provides an additional example of a mutable sequence type, as does the collections module.

Set types

These represent unordered, finite sets of unique, immutable objects. As such, they cannot be indexed by any subscript. However, they can be iterated over, and the built-in function len() returns the number of items in a set. Common uses for sets are fast membership testing, removing duplicates from a sequence, and computing mathematical operations such as intersection, union, difference, and symmetric difference.

For set elements, the same immutability rules apply as for dictionary keys. Note that numeric types obey the normal rules for numeric comparison: if two numbers compare equal (e.g., 1 and 1.0), only one of them can be contained in a set.

There are currently two intrinsic set types:

Sets

These represent a mutable set. They are created by the built-in set() constructor and can be modified afterwards by several methods, such as add().

Frozen sets

These represent an immutable set. They are created by the built-in frozenset() constructor. As a frozenset is immutable and hashable, it can be used again as an element of another set, or as a dictionary key.

Mappings

These represent finite sets of objects indexed by arbitrary index sets. The subscript notation a[k] selects the item indexed by k from the mapping a; this can be used in expressions and as the target of assignments or del statements. The built-in function len() returns the number of items in a mapping.

There is currently a single intrinsic mapping type:

Dictionaries

These represent finite sets of objects indexed by nearly arbitrary values. The only types of values not acceptable as keys are values containing lists or dictionaries or other mutable types that are compared by value rather than by object identity, the reason being that the efficient implementation of dictionaries requires a key’s hash value to remain constant. Numeric types used for keys obey the normal rules for numeric comparison: if two numbers compare equal (e.g., 1 and 1.0) then they can be used interchangeably to index the same dictionary entry.

Dictionaries preserve insertion order, meaning that keys will be produced in the same order they were added sequentially over the dictionary. Replacing an existing key does not change the order, however removing a key and re-inserting it will add it to the end instead of keeping its old place.

Dictionaries are mutable; they can be created by the {...} notation (see section Dictionary displays).

The extension modules dbm.ndbm and dbm.gnu provide additional examples of mapping types, as does the collections module.

Changed in version 3.7: Dictionaries did not preserve insertion order in versions of Python before 3.6. In CPython 3.6, insertion order was preserved, but it was considered an implementation detail at that time rather than a language guarantee.

Callable types

These are the types to which the function call operation (see section Calls) can be applied:

User-defined functions

A user-defined function object is created by a function definition (see section Function definitions). It should be called with an argument list containing the same number of items as the function’s formal parameter list.

Special attributes:

Attribute	Meaning
__doc__	The function’s documentation string, or None if unavailable; not inherited by subclasses.	Writable
__name__	The function’s name.	Writable
__qualname__	The function’s qualified name. New in version 3.3.	Writable
__module__	The name of the module the function was defined in, or None if unavailable.	Writable
__defaults__	A tuple containing default argument values for those arguments that have defaults, or None if no arguments have a default value.	Writable
__code__	The code object representing the compiled function body.	Writable
__globals__	A reference to the dictionary that holds the function’s global variables — the global namespace of the module in which the function was defined.	Read-only
__dict__	The namespace supporting arbitrary function attributes.	Writable
__closure__	None or a tuple of cells that contain bindings for the function’s free variables. See below for information on the cell_contents attribute.	Read-only
__annotations__	A dict containing annotations of parameters. The keys of the dict are the parameter names, and 'return' for the return annotation, if provided. For more information on working with this attribute, see Annotations Best Practices.	Writable
__kwdefaults__	A dict containing defaults for keyword-only parameters.	Writable

Most of the attributes labelled “Writable” check the type of the assigned value.

Function objects also support getting and setting arbitrary attributes, which can be used, for example, to attach metadata to functions. Regular attribute dot-notation is used to get and set such attributes. Note that the current implementation only supports function attributes on user-defined functions. Function attributes on built-in functions may be supported in the future.

A cell object has the attribute cell_contents. This can be used to get the value of the cell, as well as set the value.

Additional information about a function’s definition can be retrieved from its code object; see the description of internal types below. The cell type can be accessed in the types module.

Instance methods

An instance method object combines a class, a class instance and any callable object (normally a user-defined function).

Special read-only attributes: __self__ is the class instance object, __func__ is the function object; __doc__ is the method’s documentation (same as __func__.__doc__); __name__ is the method name (same as __func__.__name__); __module__ is the name of the module the method was defined in, or None if unavailable.

Methods also support accessing (but not setting) the arbitrary function attributes on the underlying function object.

User-defined method objects may be created when getting an attribute of a class (perhaps via an instance of that class), if that attribute is a user-defined function object or a class method object.

When an instance method object is created by retrieving a user-defined function object from a class via one of its instances, its __self__ attribute is the instance, and the method object is said to be bound. The new method’s __func__ attribute is the original function object.

When an instance method object is created by retrieving a class method object from a class or instance, its __self__ attribute is the class itself, and its __func__ attribute is the function object underlying the class method.

When an instance method object is called, the underlying function (__func__) is called, inserting the class instance (__self__) in front of the argument list. For instance, when C is a class which contains a definition for a function f(), and x is an instance of C, calling x.f(1) is equivalent to calling C.f(x, 1).

When an instance method object is derived from a class method object, the “class instance” stored in __self__ will actually be the class itself, so that calling either x.f(1) or C.f(1) is equivalent to calling f(C,1) where f is the underlying function.

Note that the transformation from function object to instance method object happens each time the attribute is retrieved from the instance. In some cases, a fruitful optimization is to assign the attribute to a local variable and call that local variable. Also notice that this transformation only happens for user-defined functions; other callable objects (and all non-callable objects) are retrieved without transformation. It is also important to note that user-defined functions which are attributes of a class instance are not converted to bound methods; this only happens when the function is an attribute of the class.

Generator functions

A function or method which uses the yield statement (see section The yield statement) is called a generator function. Such a function, when called, always returns an iterator object which can be used to execute the body of the function: calling the iterator’s iterator.__next__() method will cause the function to execute until it provides a value using the yield statement. When the function executes a return statement or falls off the end, a StopIteration exception is raised and the iterator will have reached the end of the set of values to be returned.

Coroutine functions

A function or method which is defined using async def is called a coroutine function. Such a function, when called, returns a coroutine object. It may contain await expressions, as well as async with and async for statements. See also the Coroutine Objects section.

Asynchronous generator functions

A function or method which is defined using async def and which uses the yield statement is called a asynchronous generator function. Such a function, when called, returns an asynchronous iterator object which can be used in an async for statement to execute the body of the function.

Calling the asynchronous iterator’s aiterator.__anext__ method will return an awaitable which when awaited will execute until it provides a value using the yield expression. When the function executes an empty return statement or falls off the end, a StopAsyncIteration exception is raised and the asynchronous iterator will have reached the end of the set of values to be yielded.

Built-in functions

A built-in function object is a wrapper around a C function. Examples of built-in functions are len() and math.sin() (math is a standard built-in module). The number and type of the arguments are determined by the C function. Special read-only attributes: __doc__ is the function’s documentation string, or None if unavailable; __name__ is the function’s name; __self__ is set to None (but see the next item); __module__ is the name of the module the function was defined in or None if unavailable.

Built-in methods

This is really a different disguise of a built-in function, this time containing an object passed to the C function as an implicit extra argument. An example of a built-in method is alist.append(), assuming alist is a list object. In this case, the special read-only attribute __self__ is set to the object denoted by alist.

Classes

Classes are callable. These objects normally act as factories for new instances of themselves, but variations are possible for class types that override __new__(). The arguments of the call are passed to __new__() and, in the typical case, to __init__() to initialize the new instance.

Class Instances

Instances of arbitrary classes can be made callable by defining a __call__() method in their class.

Modules

Modules are a basic organizational unit of Python code, and are created by the import system as invoked either by the import statement, or by calling functions such as importlib.import_module() and built-in __import__(). A module object has a namespace implemented by a dictionary object (this is the dictionary referenced by the __globals__ attribute of functions defined in the module). Attribute references are translated to lookups in this dictionary, e.g., m.x is equivalent to m.__dict__["x"]. A module object does not contain the code object used to initialize the module (since it isn’t needed once the initialization is done).

Attribute assignment updates the module’s namespace dictionary, e.g., m.x = 1 is equivalent to m.__dict__["x"] = 1.

Predefined (writable) attributes:

__name__

The module’s name.

__doc__

The module’s documentation string, or None if unavailable.

__file__

The pathname of the file from which the module was loaded, if it was loaded from a file. The __file__ attribute may be missing for certain types of modules, such as C modules that are statically linked into the interpreter. For extension modules loaded dynamically from a shared library, it’s the pathname of the shared library file.

__annotations__

A dictionary containing variable annotations collected during module body execution. For best practices on working with __annotations__, please see Annotations Best Practices.

Special read-only attribute: __dict__ is the module’s namespace as a dictionary object.

CPython implementation detail: Because of the way CPython clears module dictionaries, the module dictionary will be cleared when the module falls out of scope even if the dictionary still has live references. To avoid this, copy the dictionary or keep the module around while using its dictionary directly.

Custom classes

Custom class types are typically created by class definitions (see section Class definitions). A class has a namespace implemented by a dictionary object. Class attribute references are translated to lookups in this dictionary, e.g., C.x is translated to C.__dict__["x"] (although there are a number of hooks which allow for other means of locating attributes). When the attribute name is not found there, the attribute search continues in the base classes. This search of the base classes uses the C3 method resolution order which behaves correctly even in the presence of ‘diamond’ inheritance structures where there are multiple inheritance paths leading back to a common ancestor. Additional details on the C3 MRO used by Python can be found in the documentation accompanying the 2.3 release at https://www.python.org/download/releases/2.3/mro/.

When a class attribute reference (for class C, say) would yield a class method object, it is transformed into an instance method object whose __self__ attribute is C. When it would yield a static method object, it is transformed into the object wrapped by the static method object. See section Implementing Descriptors for another way in which attributes retrieved from a class may differ from those actually contained in its __dict__.

Class attribute assignments update the class’s dictionary, never the dictionary of a base class.

A class object can be called (see above) to yield a class instance (see below).

Special attributes:

__name__

The class name.

__module__

The name of the module in which the class was defined.

__dict__

The dictionary containing the class’s namespace.

__bases__

A tuple containing the base classes, in the order of their occurrence in the base class list.

__doc__

The class’s documentation string, or None if undefined.

__annotations__

A dictionary containing variable annotations collected during class body execution. For best practices on working with __annotations__, please see Annotations Best Practices.

Class instances

A class instance is created by calling a class object (see above). A class instance has a namespace implemented as a dictionary which is the first place in which attribute references are searched. When an attribute is not found there, and the instance’s class has an attribute by that name, the search continues with the class attributes. If a class attribute is found that is a user-defined function object, it is transformed into an instance method object whose __self__ attribute is the instance. Static method and class method objects are also transformed; see above under “Classes”. See section Implementing Descriptors for another way in which attributes of a class retrieved via its instances may differ from the objects actually stored in the class’s __dict__. If no class attribute is found, and the object’s class has a __getattr__() method, that is called to satisfy the lookup.

Attribute assignments and deletions update the instance’s dictionary, never a class’s dictionary. If the class has a __setattr__() or __delattr__() method, this is called instead of updating the instance dictionary directly.

Class instances can pretend to be numbers, sequences, or mappings if they have methods with certain special names. See section Special method names.

Special attributes: __dict__ is the attribute dictionary; __class__ is the instance’s class.

I/O objects (also known as file objects)

A file object represents an open file. Various shortcuts are available to create file objects: the open() built-in function, and also os.popen(), os.fdopen(), and the makefile() method of socket objects (and perhaps by other functions or methods provided by extension modules).

The objects sys.stdin, sys.stdout and sys.stderr are initialized to file objects corresponding to the interpreter’s standard input, output and error streams; they are all open in text mode and therefore follow the interface defined by the io.TextIOBase abstract class.

Internal types

A few types used internally by the interpreter are exposed to the user. Their definitions may change with future versions of the interpreter, but they are mentioned here for completeness.

Code objects

Code objects represent byte-compiled executable Python code, or bytecode. The difference between a code object and a function object is that the function object contains an explicit reference to the function’s globals (the module in which it was defined), while a code object contains no context; also the default argument values are stored in the function object, not in the code object (because they represent values calculated at run-time). Unlike function objects, code objects are immutable and contain no references (directly or indirectly) to mutable objects.

Special read-only attributes: co_name gives the function name; co_argcount is the total number of positional arguments (including positional-only arguments and arguments with default values); co_posonlyargcount is the number of positional-only arguments (including arguments with default values); co_kwonlyargcount is the number of keyword-only arguments (including arguments with default values); co_nlocals is the number of local variables used by the function (including arguments); co_varnames is a tuple containing the names of the local variables (starting with the argument names); co_cellvars is a tuple containing the names of local variables that are referenced by nested functions; co_freevars is a tuple containing the names of free variables; co_code is a string representing the sequence of bytecode instructions; co_consts is a tuple containing the literals used by the bytecode; co_names is a tuple containing the names used by the bytecode; co_filename is the filename from which the code was compiled; co_firstlineno is the first line number of the function; co_lnotab is a string encoding the mapping from bytecode offsets to line numbers (for details see the source code of the interpreter); co_stacksize is the required stack size; co_flags is an integer encoding a number of flags for the interpreter.

The following flag bits are defined for co_flags: bit 0x04 is set if the function uses the *arguments syntax to accept an arbitrary number of positional arguments; bit 0x08 is set if the function uses the **keywords syntax to accept arbitrary keyword arguments; bit 0x20 is set if the function is a generator.

Future feature declarations (from __future__ import division) also use bits in co_flags to indicate whether a code object was compiled with a particular feature enabled: bit 0x2000 is set if the function was compiled with future division enabled; bits 0x10 and 0x1000 were used in earlier versions of Python.

Other bits in co_flags are reserved for internal use.

If a code object represents a function, the first item in co_consts is the documentation string of the function, or None if undefined.

Frame objects

Frame objects represent execution frames. They may occur in traceback objects (see below), and are also passed to registered trace functions.

Special read-only attributes: f_back is to the previous stack frame (towards the caller), or None if this is the bottom stack frame; f_code is the code object being executed in this frame; f_locals is the dictionary used to look up local variables; f_globals is used for global variables; f_builtins is used for built-in (intrinsic) names; f_lasti gives the precise instruction (this is an index into the bytecode string of the code object).

Accessing f_code raises an auditing event object.__getattr__ with arguments obj and "f_code".

Special writable attributes: f_trace, if not None, is a function called for various events during code execution (this is used by the debugger). Normally an event is triggered for each new source line – this can be disabled by setting f_trace_lines to False.

Implementations may allow per-opcode events to be requested by setting f_trace_opcodes to True. Note that this may lead to undefined interpreter behaviour if exceptions raised by the trace function escape to the function being traced.

f_lineno is the current line number of the frame — writing to this from within a trace function jumps to the given line (only for the bottom-most frame). A debugger can implement a Jump command (aka Set Next Statement) by writing to f_lineno.

Frame objects support one method:

frame.clear()

This method clears all references to local variables held by the frame. Also, if the frame belonged to a generator, the generator is finalized. This helps break reference cycles involving frame objects (for example when catching an exception and storing its traceback for later use).

RuntimeError is raised if the frame is currently executing.

New in version 3.4.

Traceback objects

Traceback objects represent a stack trace of an exception. A traceback object is implicitly created when an exception occurs, and may also be explicitly created by calling types.TracebackType.

For implicitly created tracebacks, when the search for an exception handler unwinds the execution stack, at each unwound level a traceback object is inserted in front of the current traceback. When an exception handler is entered, the stack trace is made available to the program. (See section The try statement.) It is accessible as the third item of the tuple returned by sys.exc_info(), and as the __traceback__ attribute of the caught exception.

When the program contains no suitable handler, the stack trace is written (nicely formatted) to the standard error stream; if the interpreter is interactive, it is also made available to the user as sys.last_traceback.

For explicitly created tracebacks, it is up to the creator of the traceback to determine how the tb_next attributes should be linked to form a full stack trace.

Special read-only attributes: tb_frame points to the execution frame of the current level; tb_lineno gives the line number where the exception occurred; tb_lasti indicates the precise instruction. The line number and last instruction in the traceback may differ from the line number of its frame object if the exception occurred in a try statement with no matching except clause or with a finally clause.

Accessing tb_frame raises an auditing event object.__getattr__ with arguments obj and "tb_frame".

Special writable attribute: tb_next is the next level in the stack trace (towards the frame where the exception occurred), or None if there is no next level.

Changed in version 3.7: Traceback objects can now be explicitly instantiated from Python code, and the tb_next attribute of existing instances can be updated.

Slice objects

Slice objects are used to represent slices for __getitem__() methods. They are also created by the built-in slice() function.

Special read-only attributes: start is the lower bound; stop is the upper bound; step is the step value; each is None if omitted. These attributes can have any type.

Slice objects support one method:

slice.indices(self, length)

This method takes a single integer argument length and computes information about the slice that the slice object would describe if applied to a sequence of length items. It returns a tuple of three integers; respectively these are the start and stop indices and the step or stride length of the slice. Missing or out-of-bounds indices are handled in a manner consistent with regular slices.

Static method objects

Static method objects provide a way of defeating the transformation of function objects to method objects described above. A static method object is a wrapper around any other object, usually a user-defined method object. When a static method object is retrieved from a class or a class instance, the object actually returned is the wrapped object, which is not subject to any further transformation. Static method objects are also callable. Static method objects are created by the built-in staticmethod() constructor.

Class method objects

A class method object, like a static method object, is a wrapper around another object that alters the way in which that object is retrieved from classes and class instances. The behaviour of class method objects upon such retrieval is described above, under “User-defined methods”. Class method objects are created by the built-in classmethod() constructor.

3.3. Special method names

A class can implement certain operations that are invoked by special syntax (such as arithmetic operations or subscripting and slicing) by defining methods with special names. This is Python’s approach to operator overloading, allowing classes to define their own behavior with respect to language operators. For instance, if a class defines a method named __getitem__(), and x is an instance of this class, then x[i] is roughly equivalent to type(x).__getitem__(x, i). Except where mentioned, attempts to execute an operation raise an exception when no appropriate method is defined (typically AttributeError or TypeError).

Setting a special method to None indicates that the corresponding operation is not available. For example, if a class sets __iter__() to None, the class is not iterable, so calling iter() on its instances will raise a TypeError (without falling back to __getitem__()). 2

When implementing a class that emulates any built-in type, it is important that the emulation only be implemented to the degree that it makes sense for the object being modelled. For example, some sequences may work well with retrieval of individual elements, but extracting a slice may not make sense. (One example of this is the NodeList interface in the W3C’s Document Object Model.)

import sys
from types import ModuleType

class VerboseModule(ModuleType):
    def __repr__(self):
        return f'Verbose {self.__name__}'

    def __setattr__(self, attr, value):
        print(f'Setting {attr}...')
        super().__setattr__(attr, value)

sys.modules[__name__].__class__ = VerboseModule

class Meta(type):
    pass

class MyClass(metaclass=Meta):
    pass

class MySubclass(MyClass):
    pass

from inspect import isclass

def subscribe(obj, x):
    """Return the result of the expression `obj[x]`"""

    class_of_obj = type(obj)

    # If the class of obj defines __getitem__,
    # call class_of_obj.__getitem__(obj, x)
    if hasattr(class_of_obj, '__getitem__'):
        return class_of_obj.__getitem__(obj, x)

    # Else, if obj is a class and defines __class_getitem__,
    # call obj.__class_getitem__(x)
    elif isclass(obj) and hasattr(obj, '__class_getitem__'):
        return obj.__class_getitem__(x)

    # Else, raise an exception
    else:
        raise TypeError(
            f"'{class_of_obj.__name__}' object is not subscriptable"
        )

>>> # list has class "type" as its metaclass, like most classes:
>>> type(list)
<class 'type'>
>>> type(dict) == type(list) == type(tuple) == type(str) == type(bytes)
True
>>> # "list[int]" calls "list.__class_getitem__(int)"
>>> list[int]
list[int]
>>> # list.__class_getitem__ returns a GenericAlias object:
>>> type(list[int])
<class 'types.GenericAlias'>

>>> from enum import Enum
>>> class Menu(Enum):
...     """A breakfast menu"""
...     SPAM = 'spam'
...     BACON = 'bacon'
...
>>> # Enum classes have a custom metaclass:
>>> type(Menu)
<class 'enum.EnumMeta'>
>>> # EnumMeta defines __getitem__,
>>> # so __class_getitem__ is not called,
>>> # and the result is not a GenericAlias object:
>>> Menu['SPAM']
<Menu.SPAM: 'spam'>
>>> type(Menu['SPAM'])
<enum 'Menu'>

a[1:2] = b

a[slice(1, 2, None)] = b