Parsing arguments and building values

Parsing arguments and building values¶
These functions are useful when creating your own extension functions and methods. Additional information and examples are available in Extending and Embedding the Python Interpreter.
The first three of these functions described, PyArg_ParseTuple()
,
PyArg_ParseTupleAndKeywords()
, and PyArg_Parse()
, all use format
strings which are used to tell the function about the expected arguments. The
format strings use the same syntax for each of these functions.
Parsing arguments¶
A format string consists of zero or more “format units.” A format unit describes one Python object; it is usually a single character or a parenthesized sequence of format units. With a few exceptions, a format unit that is not a parenthesized sequence normally corresponds to a single address argument to these functions. In the following description, the quoted form is the format unit; the entry in (round) parentheses is the Python object type that matches the format unit; and the entry in [square] brackets is the type of the C variable(s) whose address should be passed.
Strings and buffers¶
Note
On Python 3.12 and older, the macro PY_SSIZE_T_CLEAN
must be
defined before including Python.h
to use all #
variants of
formats (s#
, y#
, etc.) explained below.
This is not necessary on Python 3.13 and later.
These formats allow accessing an object as a contiguous chunk of memory. You don’t have to provide raw storage for the returned unicode or bytes area.
Unless otherwise stated, buffers are not NUL-terminated.
There are three ways strings and buffers can be converted to C:
Formats such as
y*
ands*
fill aPy_buffer
structure. This locks the underlying buffer so that the caller can subsequently use the buffer even inside aPy_BEGIN_ALLOW_THREADS
block without the risk of mutable data being resized or destroyed. As a result, you have to callPyBuffer_Release()
after you have finished processing the data (or in any early abort case).The
es
,es#
,et
andet#
formats allocate the result buffer. You have to callPyMem_Free()
after you have finished processing the data (or in any early abort case).Other formats take a
str
or a read-only bytes-like object, such asbytes
, and provide aconst char *
pointer to its buffer. In this case the buffer is “borrowed”: it is managed by the corresponding Python object, and shares the lifetime of this object. You won’t have to release any memory yourself.To ensure that the underlying buffer may be safely borrowed, the object’s
PyBufferProcs.bf_releasebuffer
field must beNULL
. This disallows common mutable objects such asbytearray
, but also some read-only objects such asmemoryview
ofbytes
.Besides this
bf_releasebuffer
requirement, there is no check to verify whether the input object is immutable (e.g. whether it would honor a request for a writable buffer, or whether another thread can mutate the data).
s
(str
) [const char *]Convert a Unicode object to a C pointer to a character string. A pointer to an existing string is stored in the character pointer variable whose address you pass. The C string is NUL-terminated. The Python string must not contain embedded null code points; if it does, a
ValueError
exception is raised. Unicode objects are converted to C strings using'utf-8'
encoding. If this conversion fails, aUnicodeError
is raised.Note
This format does not accept bytes-like objects. If you want to accept filesystem paths and convert them to C character strings, it is preferable to use the
O&
format withPyUnicode_FSConverter()
as converter.Changed in version 3.5: Previously,
TypeError
was raised when embedded null code points were encountered in the Python string.s*
(str
or bytes-like object) [Py_buffer]This format accepts Unicode objects as well as bytes-like objects. It fills a
Py_buffer
structure provided by the caller. In this case the resulting C string may contain embedded NUL bytes. Unicode objects are converted to C strings using'utf-8'
encoding.s#
(str
, read-only bytes-like object) [const char *,Py_ssize_t
]Like
s*
, except that it provides a borrowed buffer. The result is stored into two C variables, the first one a pointer to a C string, the second one its length. The string may contain embedded null bytes. Unicode objects are converted to C strings using'utf-8'
encoding.z
(str
orNone
) [const char *]Like
s
, but the Python object may also beNone
, in which case the C pointer is set toNULL
.z*
(str
, bytes-like object orNone
) [Py_buffer]Like
s*
, but the Python object may also beNone
, in which case thebuf
member of thePy_buffer
structure is set toNULL
.z#
(str
, read-only bytes-like object orNone
) [const char *,Py_ssize_t
]Like
s#
, but the Python object may also beNone
, in which case the C pointer is set toNULL
.y
(read-only bytes-like object) [const char *]This format converts a bytes-like object to a C pointer to a borrowed character string; it does not accept Unicode objects. The bytes buffer must not contain embedded null bytes; if it does, a
ValueError
exception is raised.Changed in version 3.5: Previously,
TypeError
was raised when embedded null bytes were encountered in the bytes buffer.y*
(bytes-like object) [Py_buffer]This variant on
s*
doesn’t accept Unicode objects, only bytes-like objects. This is the recommended way to accept binary data.y#
(read-only bytes-like object) [const char *,Py_ssize_t
]This variant on
s#
doesn’t accept Unicode objects, only bytes-like objects.S
(bytes
) [PyBytesObject *]Requires that the Python object is a
bytes
object, without attempting any conversion. RaisesTypeError
if the object is not a bytes object. The C variable may also be declared as PyObject*.Y
(bytearray
) [PyByteArrayObject *]Requires that the Python object is a
bytearray
object, without attempting any conversion. RaisesTypeError
if the object is not abytearray
object. The C variable may also be declared as PyObject*.U
(str
) [PyObject *]Requires that the Python object is a Unicode object, without attempting any conversion. Raises
TypeError
if the object is not a Unicode object. The C variable may also be declared as PyObject*.w*
(read-write bytes-like object) [Py_buffer]This format accepts any object which implements the read-write buffer interface. It fills a
Py_buffer
structure provided by the caller. The buffer may contain embedded null bytes. The caller has to callPyBuffer_Release()
when it is done with the buffer.es
(str
) [const char *encoding, char **buffer]This variant on
s
is used for encoding Unicode into a character buffer. It only works for encoded data without embedded NUL bytes.This format requires two arguments. The first is only used as input, and must be a const char* which points to the name of an encoding as a NUL-terminated string, or
NULL
, in which case'utf-8'
encoding is used. An exception is raised if the named encoding is not known to Python. The second argument must be a char**; the value of the pointer it references will be set to a buffer with the contents of the argument text. The text will be encoded in the encoding specified by the first argument.PyArg_ParseTuple()
will allocate a buffer of the needed size, copy the encoded data into this buffer and adjust *buffer to reference the newly allocated storage. The caller is responsible for callingPyMem_Free()
to free the allocated buffer after use.et
(str
,bytes
orbytearray
) [const char *encoding, char **buffer]Same as
es
except that byte string objects are passed through without recoding them. Instead, the implementation assumes that the byte string object uses the encoding passed in as parameter.es#
(str
) [const char *encoding, char **buffer,Py_ssize_t
*buffer_length]This variant on
s#
is used for encoding Unicode into a character buffer. Unlike thees
format, this variant allows input data which contains NUL characters.It requires three arguments. The first is only used as input, and must be a const char* which points to the name of an encoding as a NUL-terminated string, or
NULL
, in which case'utf-8'
encoding is used. An exception is raised if the named encoding is not known to Python. The second argument must be a char**; the value of the pointer it references will be set to a buffer with the contents of the argument text. The text will be encoded in the encoding specified by the first argument. The third argument must be a pointer to an integer; the referenced integer will be set to the number of bytes in the output buffer.There are two modes of operation:
If *buffer points a
NULL
pointer, the function will allocate a buffer of the needed size, copy the encoded data into this buffer and set *buffer to reference the newly allocated storage. The caller is responsible for callingPyMem_Free()
to free the allocated buffer after usage.If *buffer points to a non-
NULL
pointer (an already allocated buffer),PyArg_ParseTuple()
will use this location as the buffer and interpret the initial value of *buffer_length as the buffer size. It will then copy the encoded data into the buffer and NUL-terminate it. If the buffer is not large enough, aValueError
will be set.In both cases, *buffer_length is set to the length of the encoded data without the trailing NUL byte.
et#
(str
,bytes
orbytearray
) [const char *encoding, char **buffer,Py_ssize_t
*buffer_length]Same as
es#
except that byte string objects are passed through without recoding them. Instead, the implementation assumes that the byte string object uses the encoding passed in as parameter.
Changed in version 3.12: u
, u#
, Z
, and Z#
are removed because they used a legacy
Py_UNICODE*
representation.
Numbers¶
These formats allow representing Python numbers or single characters as C numbers.
Formats that require int
, float
or complex
can
also use the corresponding special methods __index__()
,
__float__()
or __complex__()
to convert
the Python object to the required type.
For signed integer formats, OverflowError
is raised if the value
is out of range for the C type.
For unsigned integer formats, no range checking is done — the
most significant bits are silently truncated when the receiving field is too
small to receive the value.
b
(int
) [unsigned char]Convert a nonnegative Python integer to an unsigned tiny integer, stored in a C unsigned char.
B
(int
) [unsigned char]Convert a Python integer to a tiny integer without overflow checking, stored in a C unsigned char.
h
(int
) [short int]Convert a Python integer to a C short int.
H
(int
) [unsigned short int]Convert a Python integer to a C unsigned short int, without overflow checking.
i
(int
) [int]Convert a Python integer to a plain C int.
I
(int
) [unsigned int]Convert a Python integer to a C unsigned int, without overflow checking.
l
(int
) [long int]Convert a Python integer to a C long int.
k
(int
) [unsigned long]Convert a Python integer to a C unsigned long without overflow checking.
Changed in version 3.14: Use
__index__()
if available.L
(int
) [long long]Convert a Python integer to a C long long.
K
(int
) [unsigned long long]Convert a Python integer to a C unsigned long long without overflow checking.
Changed in version 3.14: Use
__index__()
if available.n
(int
) [Py_ssize_t
]Convert a Python integer to a C
Py_ssize_t
.c
(bytes
orbytearray
of length 1) [char]Convert a Python byte, represented as a
bytes
orbytearray
object of length 1, to a C char.Changed in version 3.3: Allow
bytearray
objects.C
(str
of length 1) [int]Convert a Python character, represented as a
str
object of length 1, to a C int.f
(float
) [float]Convert a Python floating-point number to a C float.
d
(float
) [double]Convert a Python floating-point number to a C double.
D
(complex
) [Py_complex]Convert a Python complex number to a C
Py_complex
structure.
Other objects¶
O
(object) [PyObject *]Store a Python object (without any conversion) in a C object pointer. The C program thus receives the actual object that was passed. A new strong reference to the object is not created (i.e. its reference count is not increased). The pointer stored is not
NULL
.O!
(object) [typeobject, PyObject *]Store a Python object in a C object pointer. This is similar to
O
, but takes two C arguments: the first is the address of a Python type object, the second is the address of the C variable (of type PyObject*) into which the object pointer is stored. If the Python object does not have the required type,TypeError
is raised.
O&
(object) [converter, address]Convert a Python object to a C variable through a converter function. This takes two arguments: the first is a function, the second is the address of a C variable (of arbitrary type), converted to void*. The converter function in turn is called as follows:
status = converter(object, address);
where object is the Python object to be converted and address is the void* argument that was passed to the
PyArg_Parse*
function. The returned status should be1
for a successful conversion and0
if the conversion has failed. When the conversion fails, the converter function should raise an exception and leave the content of address unmodified.If the converter returns
Py_CLEANUP_SUPPORTED
, it may get called a second time if the argument parsing eventually fails, giving the converter a chance to release any memory that it had already allocated. In this second call, the object parameter will beNULL
; address will have the same value as in the original call.Examples of converters:
PyUnicode_FSConverter()
andPyUnicode_FSDecoder()
.Changed in version 3.1:
Py_CLEANUP_SUPPORTED
was added.p
(bool
) [int]Tests the value passed in for truth (a boolean predicate) and converts the result to its equivalent C true/false integer value. Sets the int to
1
if the expression was true and0
if it was false. This accepts any valid Python value. See Truth Value Testing for more information about how Python tests values for truth.Added in version 3.3.
(items)
(sequence) [matching-items]The object must be a Python sequence (except
str
,bytes
orbytearray
) whose length is the number of format units in items. The C arguments must correspond to the individual format units in items. Format units for sequences may be nested.If items contains format units which store a borrowed buffer (
s
,s#
,z
,z#
,y
, ory#
) or a borrowed reference (S
,Y
,U
,O
, orO!
), the object must be a Python tuple. The converter for theO&
format unit in items must not store a borrowed buffer or a borrowed reference.Deprecated since version 3.14: Non-tuple sequences are deprecated if items contains format units which store a borrowed buffer or a borrowed reference.
A few other characters have a meaning in a format string. These may not occur inside nested parentheses. They are:
|
Indicates that the remaining arguments in the Python argument list are optional. The C variables corresponding to optional arguments should be initialized to their default value — when an optional argument is not specified,
PyArg_ParseTuple()
does not touch the contents of the corresponding C variable(s).$
PyArg_ParseTupleAndKeywords()
only: Indicates that the remaining arguments in the Python argument list are keyword-only. Currently, all keyword-only arguments must also be optional arguments, so|
must always be specified before$
in the format string.Added in version 3.3.
:
The list of format units ends here; the string after the colon is used as the function name in error messages (the “associated value” of the exception that
PyArg_ParseTuple()
raises).;
The list of format units ends here; the string after the semicolon is used as the error message instead of the default error message.
:
and;
mutually exclude each other.
Note that any Python object references which are provided to the caller are borrowed references; do not release them (i.e. do not decrement their reference count)!
Additional arguments passed to these functions must be addresses of variables whose type is determined by the format string; these are used to store values from the input tuple. There are a few cases, as described in the list of format units above, where these parameters are used as input values; they should match what is specified for the corresponding format unit in that case.
For the conversion to succeed, the arg object must match the format
and the format must be exhausted. On success, the
PyArg_Parse*
functions return true, otherwise they return
false and raise an appropriate exception. When the
PyArg_Parse*
functions fail due to conversion failure in one
of the format units, the variables at the addresses corresponding to that
and the following format units are left untouched.
API Functions¶
-
int PyArg_ParseTuple(PyObject *args, const char *format, ...)¶
- Part of the Stable ABI.
Parse the parameters of a function that takes only positional parameters into local variables. Returns true on success; on failure, it returns false and raises the appropriate exception.
-
int PyArg_VaParse(PyObject *args, const char *format, va_list vargs)¶
- Part of the Stable ABI.
Identical to
PyArg_ParseTuple()
, except that it accepts a va_list rather than a variable number of arguments.
-
int PyArg_ParseTupleAndKeywords(PyObject *args, PyObject *kw, const char *format, char *const *keywords, ...)¶
- Part of the Stable ABI.
Parse the parameters of a function that takes both positional and keyword parameters into local variables. The keywords argument is a
NULL
-terminated array of keyword parameter names specified as null-terminated ASCII or UTF-8 encoded C strings. Empty names denote positional-only parameters. Returns true on success; on failure, it returns false and raises the appropriate exception.Note
The keywords parameter declaration is char *const* in C and const char *const* in C++. This can be overridden with the
PY_CXX_CONST
macro.Changed in version 3.6: Added support for positional-only parameters.
Changed in version 3.13: The keywords parameter has now type char *const* in C and const char *const* in C++, instead of char**. Added support for non-ASCII keyword parameter names.
-
int PyArg_VaParseTupleAndKeywords(PyObject *args, PyObject *kw, const char *format, char *const *keywords, va_list vargs)¶
- Part of the Stable ABI.
Identical to
PyArg_ParseTupleAndKeywords()
, except that it accepts a va_list rather than a variable number of arguments.
-
int PyArg_ValidateKeywordArguments(PyObject*)¶
- Part of the Stable ABI.
Ensure that the keys in the keywords argument dictionary are strings. This is only needed if
PyArg_ParseTupleAndKeywords()
is not used, since the latter already does this check.Added in version 3.2.
-
int PyArg_Parse(PyObject *args, const char *format, ...)¶
- Part of the Stable ABI.
Parse the parameter of a function that takes a single positional parameter into a local variable. Returns true on success; on failure, it returns false and raises the appropriate exception.
Example:
// Function using METH_O calling convention static PyObject* my_function(PyObject *module, PyObject *arg) { int value; if (!PyArg_Parse(arg, "i:my_function", &value)) { return NULL; } // ... use value ... }
-
int PyArg_UnpackTuple(PyObject *args, const char *name, Py_ssize_t min, Py_ssize_t max, ...)¶
- Part of the Stable ABI.
A simpler form of parameter retrieval which does not use a format string to specify the types of the arguments. Functions which use this method to retrieve their parameters should be declared as
METH_VARARGS
in function or method tables. The tuple containing the actual parameters should be passed as args; it must actually be a tuple. The length of the tuple must be at least min and no more than max; min and max may be equal. Additional arguments must be passed to the function, each of which should be a pointer to a PyObject* variable; these will be filled in with the values from args; they will contain borrowed references. The variables which correspond to optional parameters not given by args will not be filled in; these should be initialized by the caller. This function returns true on success and false if args is not a tuple or contains the wrong number of elements; an exception will be set if there was a failure.This is an example of the use of this function, taken from the sources for the
_weakref
helper module for weak references:static PyObject * weakref_ref(PyObject *self, PyObject *args) { PyObject *object; PyObject *callback = NULL; PyObject *result = NULL; if (PyArg_UnpackTuple(args, "ref", 1, 2, &object, &callback)) { result = PyWeakref_NewRef(object, callback); } return result; }
The call to
PyArg_UnpackTuple()
in this example is entirely equivalent to this call toPyArg_ParseTuple()
:PyArg_ParseTuple(args, "O|O:ref", &object, &callback)
-
PY_CXX_CONST¶
The value to be inserted, if any, before char *const* in the keywords parameter declaration of
PyArg_ParseTupleAndKeywords()
andPyArg_VaParseTupleAndKeywords()
. Default empty for C andconst
for C++ (const char *const*). To override, define it to the desired value before includingPython.h
.Added in version 3.13.
Building values¶
-
PyObject *Py_BuildValue(const char *format, ...)¶
- Return value: New reference. Part of the Stable ABI.
Create a new value based on a format string similar to those accepted by the
PyArg_Parse*
family of functions and a sequence of values. Returns the value orNULL
in the case of an error; an exception will be raised ifNULL
is returned.Py_BuildValue()
does not always build a tuple. It builds a tuple only if its format string contains two or more format units. If the format string is empty, it returnsNone
; if it contains exactly one format unit, it returns whatever object is described by that format unit. To force it to return a tuple of size 0 or one, parenthesize the format string.When memory buffers are passed as parameters to supply data to build objects, as for the
s
ands#
formats, the required data is copied. Buffers provided by the caller are never referenced by the objects created byPy_BuildValue()
. In other words, if your code invokesmalloc()
and passes the allocated memory toPy_BuildValue()
, your code is responsible for callingfree()
for that memory oncePy_BuildValue()
returns.In the following description, the quoted form is the format unit; the entry in (round) parentheses is the Python object type that the format unit will return; and the entry in [square] brackets is the type of the C value(s) to be passed.
The characters space, tab, colon and comma are ignored in format strings (but not within format units such as
s#
). This can be used to make long format strings a tad more readable.s
(str
orNone
) [const char *]Convert a null-terminated C string to a Python
str
object using'utf-8'
encoding. If the C string pointer isNULL
,None
is used.s#
(str
orNone
) [const char *,Py_ssize_t
]Convert a C string and its length to a Python
str
object using'utf-8'
encoding. If the C string pointer isNULL
, the length is ignored andNone
is returned.y
(bytes
) [const char *]This converts a C string to a Python
bytes
object. If the C string pointer isNULL
,None
is returned.y#
(bytes
) [const char *,Py_ssize_t
]This converts a C string and its lengths to a Python object. If the C string pointer is
NULL
,None
is returned.z
(str
orNone
) [const char *]Same as
s
.z#
(str
orNone
) [const char *,Py_ssize_t
]Same as
s#
.u
(str
) [const wchar_t *]Convert a null-terminated
wchar_t
buffer of Unicode (UTF-16 or UCS-4) data to a Python Unicode object. If the Unicode buffer pointer isNULL
,None
is returned.u#
(str
) [const wchar_t *,Py_ssize_t
]Convert a Unicode (UTF-16 or UCS-4) data buffer and its length to a Python Unicode object. If the Unicode buffer pointer is
NULL
, the length is ignored andNone
is returned.U
(str
orNone
) [const char *]Same as
s
.U#
(str
orNone
) [const char *,Py_ssize_t
]Same as
s#
.i
(int
) [int]Convert a plain C int to a Python integer object.
b
(int
) [char]Convert a plain C char to a Python integer object.
h
(int
) [short int]Convert a plain C short int to a Python integer object.
l
(int
) [long int]Convert a C long int to a Python integer object.
B
(int
) [unsigned char]Convert a C unsigned char to a Python integer object.
H
(int
) [unsigned short int]Convert a C unsigned short int to a Python integer object.
I
(int
) [unsigned int]Convert a C unsigned int to a Python integer object.
k
(int
) [unsigned long]Convert a C unsigned long to a Python integer object.
L
(int
) [long long]Convert a C long long to a Python integer object.
K
(int
) [unsigned long long]Convert a C unsigned long long to a Python integer object.
n
(int
) [Py_ssize_t
]Convert a C
Py_ssize_t
to a Python integer.p
(bool
) [int]Convert a C int to a Python
bool
object.Be aware that this format requires an
int
argument. Unlike most other contexts in C, variadic arguments are not coerced to a suitable type automatically. You can convert another type (for example, a pointer or a float) to a suitableint
value using(x) ? 1 : 0
or!!x
.Added in version 3.14.
c
(bytes
of length 1) [char]Convert a C int representing a byte to a Python
bytes
object of length 1.C
(str
of length 1) [int]Convert a C int representing a character to Python
str
object of length 1.d
(float
) [double]Convert a C double to a Python floating-point number.
f
(float
) [float]Convert a C float to a Python floating-point number.
D
(complex
) [Py_complex *]Convert a C
Py_complex
structure to a Python complex number.O
(object) [PyObject *]Pass a Python object untouched but create a new strong reference to it (i.e. its reference count is incremented by one). If the object passed in is a
NULL
pointer, it is assumed that this was caused because the call producing the argument found an error and set an exception. Therefore,Py_BuildValue()
will returnNULL
but won’t raise an exception. If no exception has been raised yet,SystemError
is set.S
(object) [PyObject *]Same as
O
.N
(object) [PyObject *]Same as
O
, except it doesn’t create a new strong reference. Useful when the object is created by a call to an object constructor in the argument list.O&
(object) [converter, anything]Convert anything to a Python object through a converter function. The function is called with anything (which should be compatible with void*) as its argument and should return a “new” Python object, or
NULL
if an error occurred.(items)
(tuple
) [matching-items]Convert a sequence of C values to a Python tuple with the same number of items.
[items]
(list
) [matching-items]Convert a sequence of C values to a Python list with the same number of items.
{items}
(dict
) [matching-items]Convert a sequence of C values to a Python dictionary. Each pair of consecutive C values adds one item to the dictionary, serving as key and value, respectively.
If there is an error in the format string, the
SystemError
exception is set andNULL
returned.
-
PyObject *Py_VaBuildValue(const char *format, va_list vargs)¶
- Return value: New reference. Part of the Stable ABI.
Identical to
Py_BuildValue()
, except that it accepts a va_list rather than a variable number of arguments.


Slice Objects

Slice Objects¶
-
PyTypeObject PySlice_Type¶
- Part of the Stable ABI.
The type object for slice objects. This is the same as
slice
in the Python layer.
-
int PySlice_Check(PyObject *ob)¶
Return true if ob is a slice object; ob must not be
NULL
. This function always succeeds.
-
PyObject *PySlice_New(PyObject *start, PyObject *stop, PyObject *step)¶
- Return value: New reference. Part of the Stable ABI.
Return a new slice object with the given values. The start, stop, and step parameters are used as the values of the slice object attributes of the same names. Any of the values may be
NULL
, in which case theNone
will be used for the corresponding attribute.Return
NULL
with an exception set if the new object could not be allocated.
-
int PySlice_GetIndices(PyObject *slice, Py_ssize_t length, Py_ssize_t *start, Py_ssize_t *stop, Py_ssize_t *step)¶
- Part of the Stable ABI.
Retrieve the start, stop and step indices from the slice object slice, assuming a sequence of length length. Treats indices greater than length as errors.
Returns
0
on success and-1
on error with no exception set (unless one of the indices was notNone
and failed to be converted to an integer, in which case-1
is returned with an exception set).You probably do not want to use this function.
Changed in version 3.2: The parameter type for the slice parameter was
PySliceObject*
before.
-
int PySlice_GetIndicesEx(PyObject *slice, Py_ssize_t length, Py_ssize_t *start, Py_ssize_t *stop, Py_ssize_t *step, Py_ssize_t *slicelength)¶
- Part of the Stable ABI.
Usable replacement for
PySlice_GetIndices()
. Retrieve the start, stop, and step indices from the slice object slice assuming a sequence of length length, and store the length of the slice in slicelength. Out of bounds indices are clipped in a manner consistent with the handling of normal slices.Return
0
on success and-1
on error with an exception set.Note
This function is considered not safe for resizable sequences. Its invocation should be replaced by a combination of
PySlice_Unpack()
andPySlice_AdjustIndices()
whereif (PySlice_GetIndicesEx(slice, length, &start, &stop, &step, &slicelength) < 0) { // return error }
is replaced by
if (PySlice_Unpack(slice, &start, &stop, &step) < 0) { // return error } slicelength = PySlice_AdjustIndices(length, &start, &stop, step);
Changed in version 3.2: The parameter type for the slice parameter was
PySliceObject*
before.Changed in version 3.6.1: If
Py_LIMITED_API
is not set or set to the value between0x03050400
and0x03060000
(not including) or0x03060100
or higherPySlice_GetIndicesEx()
is implemented as a macro usingPySlice_Unpack()
andPySlice_AdjustIndices()
. Arguments start, stop and step are evaluated more than once.Deprecated since version 3.6.1: If
Py_LIMITED_API
is set to the value less than0x03050400
or between0x03060000
and0x03060100
(not including)PySlice_GetIndicesEx()
is a deprecated function.
-
int PySlice_Unpack(PyObject *slice, Py_ssize_t *start, Py_ssize_t *stop, Py_ssize_t *step)¶
- Part of the Stable ABI since version 3.7.
Extract the start, stop and step data members from a slice object as C integers. Silently reduce values larger than
PY_SSIZE_T_MAX
toPY_SSIZE_T_MAX
, silently boost the start and stop values less thanPY_SSIZE_T_MIN
toPY_SSIZE_T_MIN
, and silently boost the step values less than-PY_SSIZE_T_MAX
to-PY_SSIZE_T_MAX
.Return
-1
with an exception set on error,0
on success.Added in version 3.6.1.
-
Py_ssize_t PySlice_AdjustIndices(Py_ssize_t length, Py_ssize_t *start, Py_ssize_t *stop, Py_ssize_t step)¶
- Part of the Stable ABI since version 3.7.
Adjust start/end slice indices assuming a sequence of the specified length. Out of bounds indices are clipped in a manner consistent with the handling of normal slices.
Return the length of the slice. Always successful. Doesn’t call Python code.
Added in version 3.6.1.
Ellipsis Object¶
-
PyTypeObject PyEllipsis_Type¶
- Part of the Stable ABI.
The type of Python
Ellipsis
object. Same astypes.EllipsisType
in the Python layer.
-
PyObject *Py_Ellipsis¶
The Python
Ellipsis
object. This object has no methods. LikePy_None
, it is an immortal singleton object.Changed in version 3.12:
Py_Ellipsis
is immortal.


Regular Expression HOWTO

Regular Expression HOWTO¶
- Author:
A.M. Kuchling <amk@amk.ca>
Introduction¶
Regular expressions (called REs, or regexes, or regex patterns) are essentially
a tiny, highly specialized programming language embedded inside Python and made
available through the re
module. Using this little language, you specify
the rules for the set of possible strings that you want to match; this set might
contain English sentences, or e-mail addresses, or TeX commands, or anything you
like. You can then ask questions such as “Does this string match the pattern?”,
or “Is there a match for the pattern anywhere in this string?”. You can also
use REs to modify a string or to split it apart in various ways.
Regular expression patterns are compiled into a series of bytecodes which are then executed by a matching engine written in C. For advanced use, it may be necessary to pay careful attention to how the engine will execute a given RE, and write the RE in a certain way in order to produce bytecode that runs faster. Optimization isn’t covered in this document, because it requires that you have a good understanding of the matching engine’s internals.
The regular expression language is relatively small and restricted, so not all possible string processing tasks can be done using regular expressions. There are also tasks that can be done with regular expressions, but the expressions turn out to be very complicated. In these cases, you may be better off writing Python code to do the processing; while Python code will be slower than an elaborate regular expression, it will also probably be more understandable.
Simple Patterns¶
We’ll start by learning about the simplest possible regular expressions. Since regular expressions are used to operate on strings, we’ll begin with the most common task: matching characters.
For a detailed explanation of the computer science underlying regular expressions (deterministic and non-deterministic finite automata), you can refer to almost any textbook on writing compilers.
Matching Characters¶
Most letters and characters will simply match themselves. For example, the
regular expression test
will match the string test
exactly. (You can
enable a case-insensitive mode that would let this RE match Test
or TEST
as well; more about this later.)
There are exceptions to this rule; some characters are special metacharacters, and don’t match themselves. Instead, they signal that some out-of-the-ordinary thing should be matched, or they affect other portions of the RE by repeating them or changing their meaning. Much of this document is devoted to discussing various metacharacters and what they do.
Here’s a complete list of the metacharacters; their meanings will be discussed in the rest of this HOWTO.
. ^ $ * + ? { } [ ] \ | ( )
The first metacharacters we’ll look at are [
and ]
. They’re used for
specifying a character class, which is a set of characters that you wish to
match. Characters can be listed individually, or a range of characters can be
indicated by giving two characters and separating them by a '-'
. For
example, [abc]
will match any of the characters a
, b
, or c
; this
is the same as [a-c]
, which uses a range to express the same set of
characters. If you wanted to match only lowercase letters, your RE would be
[a-z]
.
Metacharacters (except \
) are not active inside classes. For example, [akm$]
will
match any of the characters 'a'
, 'k'
, 'm'
, or '$'
; '$'
is
usually a metacharacter, but inside a character class it’s stripped of its
special nature.
You can match the characters not listed within the class by complementing
the set. This is indicated by including a '^'
as the first character of the
class. For example, [^5]
will match any character except '5'
. If the
caret appears elsewhere in a character class, it does not have special meaning.
For example: [5^]
will match either a '5'
or a '^'
.
Perhaps the most important metacharacter is the backslash, \
. As in Python
string literals, the backslash can be followed by various characters to signal
various special sequences. It’s also used to escape all the metacharacters so
you can still match them in patterns; for example, if you need to match a [
or \
, you can precede them with a backslash to remove their special
meaning: \[
or \\
.
Some of the special sequences beginning with '\'
represent
predefined sets of characters that are often useful, such as the set
of digits, the set of letters, or the set of anything that isn’t
whitespace.
Let’s take an example: \w
matches any alphanumeric character. If
the regex pattern is expressed in bytes, this is equivalent to the
class [a-zA-Z0-9_]
. If the regex pattern is a string, \w
will
match all the characters marked as letters in the Unicode database
provided by the unicodedata
module. You can use the more
restricted definition of \w
in a string pattern by supplying the
re.ASCII
flag when compiling the regular expression.
The following list of special sequences isn’t complete. For a complete list of sequences and expanded class definitions for Unicode string patterns, see the last part of Regular Expression Syntax in the Standard Library reference. In general, the Unicode versions match any character that’s in the appropriate category in the Unicode database.
\d
Matches any decimal digit; this is equivalent to the class
[0-9]
.\D
Matches any non-digit character; this is equivalent to the class
[^0-9]
.\s
Matches any whitespace character; this is equivalent to the class
[ \t\n\r\f\v]
.\S
Matches any non-whitespace character; this is equivalent to the class
[^ \t\n\r\f\v]
.\w
Matches any alphanumeric character; this is equivalent to the class
[a-zA-Z0-9_]
.\W
Matches any non-alphanumeric character; this is equivalent to the class
[^a-zA-Z0-9_]
.
These sequences can be included inside a character class. For example,
[\s,.]
is a character class that will match any whitespace character, or
','
or '.'
.
The final metacharacter in this section is .
. It matches anything except a
newline character, and there’s an alternate mode (re.DOTALL
) where it will
match even a newline. .
is often used where you want to match “any
character”.
Repeating Things¶
Being able to match varying sets of characters is the first thing regular expressions can do that isn’t already possible with the methods available on strings. However, if that was the only additional capability of regexes, they wouldn’t be much of an advance. Another capability is that you can specify that portions of the RE must be repeated a certain number of times.
The first metacharacter for repeating things that we’ll look at is *
. *
doesn’t match the literal character '*'
; instead, it specifies that the
previous character can be matched zero or more times, instead of exactly once.
For example, ca*t
will match 'ct'
(0 'a'
characters), 'cat'
(1 'a'
),
'caaat'
(3 'a'
characters), and so forth.
Repetitions such as *
are greedy; when repeating a RE, the matching
engine will try to repeat it as many times as possible. If later portions of the
pattern don’t match, the matching engine will then back up and try again with
fewer repetitions.
A step-by-step example will make this more obvious. Let’s consider the
expression a[bcd]*b
. This matches the letter 'a'
, zero or more letters
from the class [bcd]
, and finally ends with a 'b'
. Now imagine matching
this RE against the string 'abcbd'
.
Step |
Matched |
Explanation |
|---|---|---|
1 |
|
The |
2 |
|
The engine matches |
3 |
Failure |
The engine tries to match
|
4 |
|
Back up, so that |
5 |
Failure |
Try |
6 |
|
Back up again, so that
|
6 |
|
Try |
The end of the RE has now been reached, and it has matched 'abcb'
. This
demonstrates how the matching engine goes as far as it can at first, and if no
match is found it will then progressively back up and retry the rest of the RE
again and again. It will back up until it has tried zero matches for
[bcd]*
, and if that subsequently fails, the engine will conclude that the
string doesn’t match the RE at all.
Another repeating metacharacter is +
, which matches one or more times. Pay
careful attention to the difference between *
and +
; *
matches
zero or more times, so whatever’s being repeated may not be present at all,
while +
requires at least one occurrence. To use a similar example,
ca+t
will match 'cat'
(1 'a'
), 'caaat'
(3 'a'
s), but won’t
match 'ct'
.
There are two more repeating operators or quantifiers. The question mark character, ?
,
matches either once or zero times; you can think of it as marking something as
being optional. For example, home-?brew
matches either 'homebrew'
or
'home-brew'
.
The most complicated quantifier is {m,n}
, where m and n are
decimal integers. This quantifier means there must be at least m repetitions,
and at most n. For example, a/{1,3}b
will match 'a/b'
, 'a//b'
, and
'a///b'
. It won’t match 'ab'
, which has no slashes, or 'a////b'
, which
has four.
You can omit either m or n; in that case, a reasonable value is assumed for the missing value. Omitting m is interpreted as a lower limit of 0, while omitting n results in an upper bound of infinity.
The simplest case {m}
matches the preceding item exactly m times.
For example, a/{2}b
will only match 'a//b'
.
Readers of a reductionist bent may notice that the three other quantifiers can
all be expressed using this notation. {0,}
is the same as *
, {1,}
is equivalent to +
, and {0,1}
is the same as ?
. It’s better to use
*
, +
, or ?
when you can, simply because they’re shorter and easier
to read.
Using Regular Expressions¶
Now that we’ve looked at some simple regular expressions, how do we actually use
them in Python? The re
module provides an interface to the regular
expression engine, allowing you to compile REs into objects and then perform
matches with them.
Compiling Regular Expressions¶
Regular expressions are compiled into pattern objects, which have methods for various operations such as searching for pattern matches or performing string substitutions.
>>> import re
>>> p = re.compile('ab*')
>>> p
re.compile('ab*')
re.compile()
also accepts an optional flags argument, used to enable
various special features and syntax variations. We’ll go over the available
settings later, but for now a single example will do:
>>> p = re.compile('ab*', re.IGNORECASE)
The RE is passed to re.compile()
as a string. REs are handled as strings
because regular expressions aren’t part of the core Python language, and no
special syntax was created for expressing them. (There are applications that
don’t need REs at all, so there’s no need to bloat the language specification by
including them.) Instead, the re
module is simply a C extension module
included with Python, just like the socket
or zlib
modules.
Putting REs in strings keeps the Python language simpler, but has one disadvantage which is the topic of the next section.
The Backslash Plague¶
As stated earlier, regular expressions use the backslash character ('\'
) to
indicate special forms or to allow special characters to be used without
invoking their special meaning. This conflicts with Python’s usage of the same
character for the same purpose in string literals.
Let’s say you want to write a RE that matches the string \section
, which
might be found in a LaTeX file. To figure out what to write in the program
code, start with the desired string to be matched. Next, you must escape any
backslashes and other metacharacters by preceding them with a backslash,
resulting in the string \\section
. The resulting string that must be passed
to re.compile()
must be \\section
. However, to express this as a
Python string literal, both backslashes must be escaped again.
Characters |
Stage |
|---|---|
|
Text string to be matched |
|
Escaped backslash for |
|
Escaped backslashes for a string literal |
In short, to match a literal backslash, one has to write '\\\\'
as the RE
string, because the regular expression must be \\
, and each backslash must
be expressed as \\
inside a regular Python string literal. In REs that
feature backslashes repeatedly, this leads to lots of repeated backslashes and
makes the resulting strings difficult to understand.
The solution is to use Python’s raw string notation for regular expressions;
backslashes are not handled in any special way in a string literal prefixed with
'r'
, so r"\n"
is a two-character string containing '\'
and 'n'
,
while "\n"
is a one-character string containing a newline. Regular
expressions will often be written in Python code using this raw string notation.
In addition, special escape sequences that are valid in regular expressions,
but not valid as Python string literals, now result in a
DeprecationWarning
and will eventually become a SyntaxError
,
which means the sequences will be invalid if raw string notation or escaping
the backslashes isn’t used.
Regular String |
Raw string |
|---|---|
|
|
|
|
|
|
Performing Matches¶
Once you have an object representing a compiled regular expression, what do you
do with it? Pattern objects have several methods and attributes.
Only the most significant ones will be covered here; consult the re
docs
for a complete listing.
Method/Attribute |
Purpose |
|---|---|
|
Determine if the RE matches at the beginning of the string. |
|
Scan through a string, looking for any location where this RE matches. |
|
Find all substrings where the RE matches, and returns them as a list. |
|
Find all substrings where the RE matches, and returns them as an iterator. |
match()
and search()
return None
if no match can be found. If
they’re successful, a match object instance is returned,
containing information about the match: where it starts and ends, the substring
it matched, and more.
You can learn about this by interactively experimenting with the re
module.
This HOWTO uses the standard Python interpreter for its examples. First, run the
Python interpreter, import the re
module, and compile a RE:
>>> import re
>>> p = re.compile('[a-z]+')
>>> p
re.compile('[a-z]+')
Now, you can try matching various strings against the RE [a-z]+
. An empty
string shouldn’t match at all, since +
means ‘one or more repetitions’.
match()
should return None
in this case, which will cause the
interpreter to print no output. You can explicitly print the result of
match()
to make this clear.
>>> p.match("")
>>> print(p.match(""))
None
Now, let’s try it on a string that it should match, such as tempo
. In this
case, match()
will return a match object, so you
should store the result in a variable for later use.
>>> m = p.match('tempo')
>>> m
<re.Match object; span=(0, 5), match='tempo'>
Now you can query the match object for information about the matching string. Match object instances also have several methods and attributes; the most important ones are:
Method/Attribute |
Purpose |
|---|---|
|
Return the string matched by the RE |
|
Return the starting position of the match |
|
Return the ending position of the match |
|
Return a tuple containing the (start, end) positions of the match |
Trying these methods will soon clarify their meaning:
>>> m.group()
'tempo'
>>> m.start(), m.end()
(0, 5)
>>> m.span()
(0, 5)
group()
returns the substring that was matched by the RE. start()
and end()
return the starting and ending index of the match. span()
returns both start and end indexes in a single tuple. Since the match()
method only checks if the RE matches at the start of a string, start()
will always be zero. However, the search()
method of patterns
scans through the string, so the match may not start at zero in that
case.
>>> print(p.match('::: message'))
None
>>> m = p.search('::: message'); print(m)
<re.Match object; span=(4, 11), match='message'>
>>> m.group()
'message'
>>> m.span()
(4, 11)
In actual programs, the most common style is to store the
match object in a variable, and then check if it was
None
. This usually looks like:
p = re.compile( ... )
m = p.match( 'string goes here' )
if m:
print('Match found: ', m.group())
else:
print('No match')
Two pattern methods return all of the matches for a pattern.
findall()
returns a list of matching strings:
>>> p = re.compile(r'\d+')
>>> p.findall('12 drummers drumming, 11 pipers piping, 10 lords a-leaping')
['12', '11', '10']
The r
prefix, making the literal a raw string literal, is needed in this
example because escape sequences in a normal “cooked” string literal that are
not recognized by Python, as opposed to regular expressions, now result in a
DeprecationWarning
and will eventually become a SyntaxError
. See
The Backslash Plague.
findall()
has to create the entire list before it can be returned as the
result. The finditer()
method returns a sequence of
match object instances as an iterator:
>>> iterator = p.finditer('12 drummers drumming, 11 ... 10 ...')
>>> iterator
<callable_iterator object at 0x...>
>>> for match in iterator:
... print(match.span())
...
(0, 2)
(22, 24)
(29, 31)
Module-Level Functions¶
You don’t have to create a pattern object and call its methods; the
re
module also provides top-level functions called match()
,
search()
, findall()
, sub()
, and so forth. These functions
take the same arguments as the corresponding pattern method with
the RE string added as the first argument, and still return either None
or a
match object instance.
>>> print(re.match(r'From\s+', 'Fromage amk'))
None
>>> re.match(r'From\s+', 'From amk Thu May 14 19:12:10 1998')
<re.Match object; span=(0, 5), match='From '>
Under the hood, these functions simply create a pattern object for you and call the appropriate method on it. They also store the compiled object in a cache, so future calls using the same RE won’t need to parse the pattern again and again.
Should you use these module-level functions, or should you get the pattern and call its methods yourself? If you’re accessing a regex within a loop, pre-compiling it will save a few function calls. Outside of loops, there’s not much difference thanks to the internal cache.
Compilation Flags¶
Compilation flags let you modify some aspects of how regular expressions work.
Flags are available in the re
module under two names, a long name such as
IGNORECASE
and a short, one-letter form such as I
. (If you’re
familiar with Perl’s pattern modifiers, the one-letter forms use the same
letters; the short form of re.VERBOSE
is re.X
, for example.)
Multiple flags can be specified by bitwise OR-ing them; re.I | re.M
sets
both the I
and M
flags, for example.
Here’s a table of the available flags, followed by a more detailed explanation of each one.
Flag |
Meaning |
|---|---|
Makes several escapes like |
|
Make |
|
Do case-insensitive matches. |
|
Do a locale-aware match. |
|
Multi-line matching, affecting |
|
Enable verbose REs, which can be organized more cleanly and understandably. |
- re.I
- re.IGNORECASE
Perform case-insensitive matching; character class and literal strings will match letters by ignoring case. For example,
[A-Z]
will match lowercase letters, too. Full Unicode matching also works unless theASCII
flag is used to disable non-ASCII matches. When the Unicode patterns[a-z]
or[A-Z]
are used in combination with theIGNORECASE
flag, they will match the 52 ASCII letters and 4 additional non-ASCII letters: ‘İ’ (U+0130, Latin capital letter I with dot above), ‘ı’ (U+0131, Latin small letter dotless i), ‘ſ’ (U+017F, Latin small letter long s) and ‘K’ (U+212A, Kelvin sign).Spam
will match'Spam'
,'spam'
,'spAM'
, or'ſpam'
(the latter is matched only in Unicode mode). This lowercasing doesn’t take the current locale into account; it will if you also set theLOCALE
flag.
- re.L
- re.LOCALE
Make
\w
,\W
,\b
,\B
and case-insensitive matching dependent on the current locale instead of the Unicode database.Locales are a feature of the C library intended to help in writing programs that take account of language differences. For example, if you’re processing encoded French text, you’d want to be able to write
\w+
to match words, but\w
only matches the character class[A-Za-z]
in bytes patterns; it won’t match bytes corresponding toé
orç
. If your system is configured properly and a French locale is selected, certain C functions will tell the program that the byte corresponding toé
should also be considered a letter. Setting theLOCALE
flag when compiling a regular expression will cause the resulting compiled object to use these C functions for\w
; this is slower, but also enables\w+
to match French words as you’d expect. The use of this flag is discouraged in Python 3 as the locale mechanism is very unreliable, it only handles one “culture” at a time, and it only works with 8-bit locales. Unicode matching is already enabled by default in Python 3 for Unicode (str) patterns, and it is able to handle different locales/languages.
- re.M
- re.MULTILINE
(
^
and$
haven’t been explained yet; they’ll be introduced in section More Metacharacters.)Usually
^
matches only at the beginning of the string, and$
matches only at the end of the string and immediately before the newline (if any) at the end of the string. When this flag is specified,^
matches at the beginning of the string and at the beginning of each line within the string, immediately following each newline. Similarly, the$
metacharacter matches either at the end of the string and at the end of each line (immediately preceding each newline).
- re.S
- re.DOTALL
Makes the
'.'
special character match any character at all, including a newline; without this flag,'.'
will match anything except a newline.
- re.A
- re.ASCII
Make
\w
,\W
,\b
,\B
,\s
and\S
perform ASCII-only matching instead of full Unicode matching. This is only meaningful for Unicode patterns, and is ignored for byte patterns.
- re.X
- re.VERBOSE
This flag allows you to write regular expressions that are more readable by granting you more flexibility in how you can format them. When this flag has been specified, whitespace within the RE string is ignored, except when the whitespace is in a character class or preceded by an unescaped backslash; this lets you organize and indent the RE more clearly. This flag also lets you put comments within a RE that will be ignored by the engine; comments are marked by a
'#'
that’s neither in a character class or preceded by an unescaped backslash.For example, here’s a RE that uses
re.VERBOSE
; see how much easier it is to read?charref = re.compile(r""" &[#] # Start of a numeric entity reference ( 0[0-7]+ # Octal form | [0-9]+ # Decimal form | x[0-9a-fA-F]+ # Hexadecimal form ) ; # Trailing semicolon """, re.VERBOSE)
Without the verbose setting, the RE would look like this:
charref = re.compile("&#(0[0-7]+" "|[0-9]+" "|x[0-9a-fA-F]+);")
In the above example, Python’s automatic concatenation of string literals has been used to break up the RE into smaller pieces, but it’s still more difficult to understand than the version using
re.VERBOSE
.
More Pattern Power¶
So far we’ve only covered a part of the features of regular expressions. In this section, we’ll cover some new metacharacters, and how to use groups to retrieve portions of the text that was matched.
More Metacharacters¶
There are some metacharacters that we haven’t covered yet. Most of them will be covered in this section.
Some of the remaining metacharacters to be discussed are zero-width
assertions. They don’t cause the engine to advance through the string;
instead, they consume no characters at all, and simply succeed or fail. For
example, \b
is an assertion that the current position is located at a word
boundary; the position isn’t changed by the \b
at all. This means that
zero-width assertions should never be repeated, because if they match once at a
given location, they can obviously be matched an infinite number of times.
|
Alternation, or the “or” operator. If A and B are regular expressions,
A|B
will match any string that matches either A or B.|
has very low precedence in order to make it work reasonably when you’re alternating multi-character strings.Crow|Servo
will match either'Crow'
or'Servo'
, not'Cro'
, a'w'
or an'S'
, and'ervo'
.To match a literal
'|'
, use\|
, or enclose it inside a character class, as in[|]
.^
Matches at the beginning of lines. Unless the
MULTILINE
flag has been set, this will only match at the beginning of the string. InMULTILINE
mode, this also matches immediately after each newline within the string.For example, if you wish to match the word
From
only at the beginning of a line, the RE to use is^From
.>>> print(re.search('^From', 'From Here to Eternity')) <re.Match object; span=(0, 4), match='From'> >>> print(re.search('^From', 'Reciting From Memory')) None
To match a literal
'^'
, use\^
.$
Matches at the end of a line, which is defined as either the end of the string, or any location followed by a newline character.
>>> print(re.search('}$', '{block}')) <re.Match object; span=(6, 7), match='}'> >>> print(re.search('}$', '{block} ')) None >>> print(re.search('}$', '{block}\n')) <re.Match object; span=(6, 7), match='}'>
To match a literal
'$'
, use\$
or enclose it inside a character class, as in[$]
.\A
Matches only at the start of the string. When not in
MULTILINE
mode,\A
and^
are effectively the same. InMULTILINE
mode, they’re different:\A
still matches only at the beginning of the string, but^
may match at any location inside the string that follows a newline character.\z
Matches only at the end of the string.
\Z
The same as
\z
. For compatibility with old Python versions.\b
Word boundary. This is a zero-width assertion that matches only at the beginning or end of a word. A word is defined as a sequence of alphanumeric characters, so the end of a word is indicated by whitespace or a non-alphanumeric character.
The following example matches
class
only when it’s a complete word; it won’t match when it’s contained inside another word.>>> p = re.compile(r'\bclass\b') >>> print(p.search('no class at all')) <re.Match object; span=(3, 8), match='class'> >>> print(p.search('the declassified algorithm')) None >>> print(p.search('one subclass is')) None
There are two subtleties you should remember when using this special sequence. First, this is the worst collision between Python’s string literals and regular expression sequences. In Python’s string literals,
\b
is the backspace character, ASCII value 8. If you’re not using raw strings, then Python will convert the\b
to a backspace, and your RE won’t match as you expect it to. The following example looks the same as our previous RE, but omits the'r'
in front of the RE string.>>> p = re.compile('\bclass\b') >>> print(p.search('no class at all')) None >>> print(p.search('\b' + 'class' + '\b')) <re.Match object; span=(0, 7), match='\x08class\x08'>
Second, inside a character class, where there’s no use for this assertion,
\b
represents the backspace character, for compatibility with Python’s string literals.\B
Another zero-width assertion, this is the opposite of
\b
, only matching when the current position is not at a word boundary.
Grouping¶
Frequently you need to obtain more information than just whether the RE matched
or not. Regular expressions are often used to dissect strings by writing a RE
divided into several subgroups which match different components of interest.
For example, an RFC-822 header line is divided into a header name and a value,
separated by a ':'
, like this:
From: author@example.com
User-Agent: Thunderbird 1.5.0.9 (X11/20061227)
MIME-Version: 1.0
To: editor@example.com
This can be handled by writing a regular expression which matches an entire header line, and has one group which matches the header name, and another group which matches the header’s value.
Groups are marked by the '('
, ')'
metacharacters. '('
and ')'
have much the same meaning as they do in mathematical expressions; they group
together the expressions contained inside them, and you can repeat the contents
of a group with a quantifier, such as *
, +
, ?
, or
{m,n}
. For example, (ab)*
will match zero or more repetitions of
ab
.
>>> p = re.compile('(ab)*')
>>> print(p.match('ababababab').span())
(0, 10)
Groups indicated with '('
, ')'
also capture the starting and ending
index of the text that they match; this can be retrieved by passing an argument
to group()
, start()
, end()
, and
span()
. Groups are
numbered starting with 0. Group 0 is always present; it’s the whole RE, so
match object methods all have group 0 as their default
argument. Later we’ll see how to express groups that don’t capture the span
of text that they match.
>>> p = re.compile('(a)b')
>>> m = p.match('ab')
>>> m.group()
'ab'
>>> m.group(0)
'ab'
Subgroups are numbered from left to right, from 1 upward. Groups can be nested; to determine the number, just count the opening parenthesis characters, going from left to right.
>>> p = re.compile('(a(b)c)d')
>>> m = p.match('abcd')
>>> m.group(0)
'abcd'
>>> m.group(1)
'abc'
>>> m.group(2)
'b'
group()
can be passed multiple group numbers at a time, in which case it
will return a tuple containing the corresponding values for those groups.
>>> m.group(2,1,2)
('b', 'abc', 'b')
The groups()
method returns a tuple containing the strings for all the
subgroups, from 1 up to however many there are.
>>> m.groups()
('abc', 'b')
Backreferences in a pattern allow you to specify that the contents of an earlier
capturing group must also be found at the current location in the string. For
example, \1
will succeed if the exact contents of group 1 can be found at
the current position, and fails otherwise. Remember that Python’s string
literals also use a backslash followed by numbers to allow including arbitrary
characters in a string, so be sure to use a raw string when incorporating
backreferences in a RE.
For example, the following RE detects doubled words in a string.
>>> p = re.compile(r'\b(\w+)\s+\1\b')
>>> p.search('Paris in the the spring').group()
'the the'
Backreferences like this aren’t often useful for just searching through a string — there are few text formats which repeat data in this way — but you’ll soon find out that they’re very useful when performing string substitutions.
Non-capturing and Named Groups¶
Elaborate REs may use many groups, both to capture substrings of interest, and to group and structure the RE itself. In complex REs, it becomes difficult to keep track of the group numbers. There are two features which help with this problem. Both of them use a common syntax for regular expression extensions, so we’ll look at that first.
Perl 5 is well known for its powerful additions to standard regular expressions.
For these new features the Perl developers couldn’t choose new single-keystroke metacharacters
or new special sequences beginning with \
without making Perl’s regular
expressions confusingly different from standard REs. If they chose &
as a
new metacharacter, for example, old expressions would be assuming that &
was
a regular character and wouldn’t have escaped it by writing \&
or [&]
.
The solution chosen by the Perl developers was to use (?...)
as the
extension syntax. ?
immediately after a parenthesis was a syntax error
because the ?
would have nothing to repeat, so this didn’t introduce any
compatibility problems. The characters immediately after the ?
indicate
what extension is being used, so (?=foo)
is one thing (a positive lookahead
assertion) and (?:foo)
is something else (a non-capturing group containing
the subexpression foo
).
Python supports several of Perl’s extensions and adds an extension
syntax to Perl’s extension syntax. If the first character after the
question mark is a P
, you know that it’s an extension that’s
specific to Python.
Now that we’ve looked at the general extension syntax, we can return to the features that simplify working with groups in complex REs.
Sometimes you’ll want to use a group to denote a part of a regular expression,
but aren’t interested in retrieving the group’s contents. You can make this fact
explicit by using a non-capturing group: (?:...)
, where you can replace the
...
with any other regular expression.
>>> m = re.match("([abc])+", "abc")
>>> m.groups()
('c',)
>>> m = re.match("(?:[abc])+", "abc")
>>> m.groups()
()
Except for the fact that you can’t retrieve the contents of what the group
matched, a non-capturing group behaves exactly the same as a capturing group;
you can put anything inside it, repeat it with a repetition metacharacter such
as *
, and nest it within other groups (capturing or non-capturing).
(?:...)
is particularly useful when modifying an existing pattern, since you
can add new groups without changing how all the other groups are numbered. It
should be mentioned that there’s no performance difference in searching between
capturing and non-capturing groups; neither form is any faster than the other.
A more significant feature is named groups: instead of referring to them by numbers, groups can be referenced by a name.
The syntax for a named group is one of the Python-specific extensions:
(?P<name>...)
. name is, obviously, the name of the group. Named groups
behave exactly like capturing groups, and additionally associate a name
with a group. The match object methods that deal with
capturing groups all accept either integers that refer to the group by number
or strings that contain the desired group’s name. Named groups are still
given numbers, so you can retrieve information about a group in two ways:
>>> p = re.compile(r'(?P<word>\b\w+\b)')
>>> m = p.search( '(((( Lots of punctuation )))' )
>>> m.group('word')
'Lots'
>>> m.group(1)
'Lots'
Additionally, you can retrieve named groups as a dictionary with
groupdict()
:
>>> m = re.match(r'(?P<first>\w+) (?P<last>\w+)', 'Jane Doe')
>>> m.groupdict()
{'first': 'Jane', 'last': 'Doe'}
Named groups are handy because they let you use easily remembered names, instead
of having to remember numbers. Here’s an example RE from the imaplib
module:
InternalDate = re.compile(r'INTERNALDATE "'
r'(?P<day>[ 123][0-9])-(?P<mon>[A-Z][a-z][a-z])-'
r'(?P<year>[0-9][0-9][0-9][0-9])'
r' (?P<hour>[0-9][0-9]):(?P<min>[0-9][0-9]):(?P<sec>[0-9][0-9])'
r' (?P<zonen>[-+])(?P<zoneh>[0-9][0-9])(?P<zonem>[0-9][0-9])'
r'"')
It’s obviously much easier to retrieve m.group('zonem')
, instead of having
to remember to retrieve group 9.
The syntax for backreferences in an expression such as (...)\1
refers to the
number of the group. There’s naturally a variant that uses the group name
instead of the number. This is another Python extension: (?P=name)
indicates
that the contents of the group called name should again be matched at the
current point. The regular expression for finding doubled words,
\b(\w+)\s+\1\b
can also be written as \b(?P<word>\w+)\s+(?P=word)\b
:
>>> p = re.compile(r'\b(?P<word>\w+)\s+(?P=word)\b')
>>> p.search('Paris in the the spring').group()
'the the'
Lookahead Assertions¶
Another zero-width assertion is the lookahead assertion. Lookahead assertions are available in both positive and negative form, and look like this:
(?=...)
Positive lookahead assertion. This succeeds if the contained regular expression, represented here by
...
, successfully matches at the current location, and fails otherwise. But, once the contained expression has been tried, the matching engine doesn’t advance at all; the rest of the pattern is tried right where the assertion started.(?!...)
Negative lookahead assertion. This is the opposite of the positive assertion; it succeeds if the contained expression doesn’t match at the current position in the string.
To make this concrete, let’s look at a case where a lookahead is useful.
Consider a simple pattern to match a filename and split it apart into a base
name and an extension, separated by a .
. For example, in news.rc
,
news
is the base name, and rc
is the filename’s extension.
The pattern to match this is quite simple:
.*[.].*$
Notice that the .
needs to be treated specially because it’s a
metacharacter, so it’s inside a character class to only match that
specific character. Also notice the trailing $
; this is added to
ensure that all the rest of the string must be included in the
extension. This regular expression matches foo.bar
and
autoexec.bat
and sendmail.cf
and printers.conf
.
Now, consider complicating the problem a bit; what if you want to match
filenames where the extension is not bat
? Some incorrect attempts:
.*[.][^b].*$
The first attempt above tries to exclude bat
by requiring
that the first character of the extension is not a b
. This is wrong,
because the pattern also doesn’t match foo.bar
.
.*[.]([^b]..|.[^a].|..[^t])$
The expression gets messier when you try to patch up the first solution by
requiring one of the following cases to match: the first character of the
extension isn’t b
; the second character isn’t a
; or the third character
isn’t t
. This accepts foo.bar
and rejects autoexec.bat
, but it
requires a three-letter extension and won’t accept a filename with a two-letter
extension such as sendmail.cf
. We’ll complicate the pattern again in an
effort to fix it.
.*[.]([^b].?.?|.[^a]?.?|..?[^t]?)$
In the third attempt, the second and third letters are all made optional in
order to allow matching extensions shorter than three characters, such as
sendmail.cf
.
The pattern’s getting really complicated now, which makes it hard to read and
understand. Worse, if the problem changes and you want to exclude both bat
and exe
as extensions, the pattern would get even more complicated and
confusing.
A negative lookahead cuts through all this confusion:
.*[.](?!bat$)[^.]*$
The negative lookahead means: if the expression bat
doesn’t match at this point, try the rest of the pattern; if bat$
does
match, the whole pattern will fail. The trailing $
is required to ensure
that something like sample.batch
, where the extension only starts with
bat
, will be allowed. The [^.]*
makes sure that the pattern works
when there are multiple dots in the filename.
Excluding another filename extension is now easy; simply add it as an
alternative inside the assertion. The following pattern excludes filenames that
end in either bat
or exe
:
.*[.](?!bat$|exe$)[^.]*$
Modifying Strings¶
Up to this point, we’ve simply performed searches against a static string. Regular expressions are also commonly used to modify strings in various ways, using the following pattern methods:
Method/Attribute |
Purpose |
|---|---|
|
Split the string into a list, splitting it wherever the RE matches |
|
Find all substrings where the RE matches, and replace them with a different string |
|
Does the same thing as |
Splitting Strings¶
The split()
method of a pattern splits a string apart
wherever the RE matches, returning a list of the pieces. It’s similar to the
split()
method of strings but provides much more generality in the
delimiters that you can split by; string split()
only supports splitting by
whitespace or by a fixed string. As you’d expect, there’s a module-level
re.split()
function, too.
- .split(string[, maxsplit=0])
Split string by the matches of the regular expression. If capturing parentheses are used in the RE, then their contents will also be returned as part of the resulting list. If maxsplit is nonzero, at most maxsplit splits are performed.
You can limit the number of splits made, by passing a value for maxsplit. When maxsplit is nonzero, at most maxsplit splits will be made, and the remainder of the string is returned as the final element of the list. In the following example, the delimiter is any sequence of non-alphanumeric characters.
>>> p = re.compile(r'\W+')
>>> p.split('This is a test, short and sweet, of split().')
['This', 'is', 'a', 'test', 'short', 'and', 'sweet', 'of', 'split', '']
>>> p.split('This is a test, short and sweet, of split().', 3)
['This', 'is', 'a', 'test, short and sweet, of split().']
Sometimes you’re not only interested in what the text between delimiters is, but also need to know what the delimiter was. If capturing parentheses are used in the RE, then their values are also returned as part of the list. Compare the following calls:
>>> p = re.compile(r'\W+')
>>> p2 = re.compile(r'(\W+)')
>>> p.split('This... is a test.')
['This', 'is', 'a', 'test', '']
>>> p2.split('This... is a test.')
['This', '... ', 'is', ' ', 'a', ' ', 'test', '.', '']
The module-level function re.split()
adds the RE to be used as the first
argument, but is otherwise the same.
>>> re.split(r'[\W]+', 'Words, words, words.')
['Words', 'words', 'words', '']
>>> re.split(r'([\W]+)', 'Words, words, words.')
['Words', ', ', 'words', ', ', 'words', '.', '']
>>> re.split(r'[\W]+', 'Words, words, words.', 1)
['Words', 'words, words.']
Search and Replace¶
Another common task is to find all the matches for a pattern, and replace them
with a different string. The sub()
method takes a replacement value,
which can be either a string or a function, and the string to be processed.
- .sub(replacement, string[, count=0])
Returns the string obtained by replacing the leftmost non-overlapping occurrences of the RE in string by the replacement replacement. If the pattern isn’t found, string is returned unchanged.
The optional argument count is the maximum number of pattern occurrences to be replaced; count must be a non-negative integer. The default value of 0 means to replace all occurrences.
Here’s a simple example of using the sub()
method. It replaces colour
names with the word colour
:
>>> p = re.compile('(blue|white|red)')
>>> p.sub('colour', 'blue socks and red shoes')
'colour socks and colour shoes'
>>> p.sub('colour', 'blue socks and red shoes', count=1)
'colour socks and red shoes'
The subn()
method does the same work, but returns a 2-tuple containing the
new string value and the number of replacements that were performed:
>>> p = re.compile('(blue|white|red)')
>>> p.subn('colour', 'blue socks and red shoes')
('colour socks and colour shoes', 2)
>>> p.subn('colour', 'no colours at all')
('no colours at all', 0)
Empty matches are replaced only when they’re not adjacent to a previous empty match.
>>> p = re.compile('x*')
>>> p.sub('-', 'abxd')
'-a-b--d-'
If replacement is a string, any backslash escapes in it are processed. That
is, \n
is converted to a single newline character, \r
is converted to a
carriage return, and so forth. Unknown escapes such as \&
are left alone.
Backreferences, such as \6
, are replaced with the substring matched by the
corresponding group in the RE. This lets you incorporate portions of the
original text in the resulting replacement string.
This example matches the word section
followed by a string enclosed in
{
, }
, and changes section
to subsection
:
>>> p = re.compile('section{ ( [^}]* ) }', re.VERBOSE)
>>> p.sub(r'subsection{\1}','section{First} section{second}')
'subsection{First} subsection{second}'
There’s also a syntax for referring to named groups as defined by the
(?P<name>...)
syntax. \g<name>
will use the substring matched by the
group named name
, and \g<number>
uses the corresponding group number.
\g<2>
is therefore equivalent to \2
, but isn’t ambiguous in a
replacement string such as \g<2>0
. (\20
would be interpreted as a
reference to group 20, not a reference to group 2 followed by the literal
character '0'
.) The following substitutions are all equivalent, but use all
three variations of the replacement string.
>>> p = re.compile('section{ (?P<name> [^}]* ) }', re.VERBOSE)
>>> p.sub(r'subsection{\1}','section{First}')
'subsection{First}'
>>> p.sub(r'subsection{\g<1>}','section{First}')
'subsection{First}'
>>> p.sub(r'subsection{\g<name>}','section{First}')
'subsection{First}'
replacement can also be a function, which gives you even more control. If replacement is a function, the function is called for every non-overlapping occurrence of pattern. On each call, the function is passed a match object argument for the match and can use this information to compute the desired replacement string and return it.
In the following example, the replacement function translates decimals into hexadecimal:
>>> def hexrepl(match):
... "Return the hex string for a decimal number"
... value = int(match.group())
... return hex(value)
...
>>> p = re.compile(r'\d+')
>>> p.sub(hexrepl, 'Call 65490 for printing, 49152 for user code.')
'Call 0xffd2 for printing, 0xc000 for user code.'
When using the module-level re.sub()
function, the pattern is passed as
the first argument. The pattern may be provided as an object or as a string; if
you need to specify regular expression flags, you must either use a
pattern object as the first parameter, or use embedded modifiers in the
pattern string, e.g. sub("(?i)b+", "x", "bbbb BBBB")
returns 'x x'
.
Common Problems¶
Regular expressions are a powerful tool for some applications, but in some ways their behaviour isn’t intuitive and at times they don’t behave the way you may expect them to. This section will point out some of the most common pitfalls.
Use String Methods¶
Sometimes using the re
module is a mistake. If you’re matching a fixed
string, or a single character class, and you’re not using any re
features
such as the IGNORECASE
flag, then the full power of regular expressions
may not be required. Strings have several methods for performing operations with
fixed strings and they’re usually much faster, because the implementation is a
single small C loop that’s been optimized for the purpose, instead of the large,
more generalized regular expression engine.
One example might be replacing a single fixed string with another one; for
example, you might replace word
with deed
. re.sub()
seems like the
function to use for this, but consider the replace()
method. Note that
replace()
will also replace word
inside words, turning swordfish
into sdeedfish
, but the naive RE word
would have done that, too. (To
avoid performing the substitution on parts of words, the pattern would have to
be \bword\b
, in order to require that word
have a word boundary on
either side. This takes the job beyond replace()
’s abilities.)
Another common task is deleting every occurrence of a single character from a
string or replacing it with another single character. You might do this with
something like re.sub('\n', ' ', S)
, but translate()
is capable of
doing both tasks and will be faster than any regular expression operation can
be.
In short, before turning to the re
module, consider whether your problem
can be solved with a faster and simpler string method.
match() versus search()¶
The match()
function only checks if the RE matches at the beginning of the
string while search()
will scan forward through the string for a match.
It’s important to keep this distinction in mind. Remember, match()
will
only report a successful match which will start at 0; if the match wouldn’t
start at zero, match()
will not report it.
>>> print(re.match('super', 'superstition').span())
(0, 5)
>>> print(re.match('super', 'insuperable'))
None
On the other hand, search()
will scan forward through the string,
reporting the first match it finds.
>>> print(re.search('super', 'superstition').span())
(0, 5)
>>> print(re.search('super', 'insuperable').span())
(2, 7)
Sometimes you’ll be tempted to keep using re.match()
, and just add .*
to the front of your RE. Resist this temptation and use re.search()
instead. The regular expression compiler does some analysis of REs in order to
speed up the process of looking for a match. One such analysis figures out what
the first character of a match must be; for example, a pattern starting with
Crow
must match starting with a 'C'
. The analysis lets the engine
quickly scan through the string looking for the starting character, only trying
the full match if a 'C'
is found.
Adding .*
defeats this optimization, requiring scanning to the end of the
string and then backtracking to find a match for the rest of the RE. Use
re.search()
instead.
Greedy versus Non-Greedy¶
When repeating a regular expression, as in a*
, the resulting action is to
consume as much of the pattern as possible. This fact often bites you when
you’re trying to match a pair of balanced delimiters, such as the angle brackets
surrounding an HTML tag. The naive pattern for matching a single HTML tag
doesn’t work because of the greedy nature of .*
.
>>> s = '<html><head><title>Title</title>'
>>> len(s)
32
>>> print(re.match('<.*>', s).span())
(0, 32)
>>> print(re.match('<.*>', s).group())
<html><head><title>Title</title>
The RE matches the '<'
in '<html>'
, and the .*
consumes the rest of
the string. There’s still more left in the RE, though, and the >
can’t
match at the end of the string, so the regular expression engine has to
backtrack character by character until it finds a match for the >
. The
final match extends from the '<'
in '<html>'
to the '>'
in
'</title>'
, which isn’t what you want.
In this case, the solution is to use the non-greedy quantifiers *?
, +?
,
??
, or {m,n}?
, which match as little text as possible. In the above
example, the '>'
is tried immediately after the first '<'
matches, and
when it fails, the engine advances a character at a time, retrying the '>'
at every step. This produces just the right result:
>>> print(re.match('<.*?>', s).group())
<html>
(Note that parsing HTML or XML with regular expressions is painful. Quick-and-dirty patterns will handle common cases, but HTML and XML have special cases that will break the obvious regular expression; by the time you’ve written a regular expression that handles all of the possible cases, the patterns will be very complicated. Use an HTML or XML parser module for such tasks.)
Using re.VERBOSE¶
By now you’ve probably noticed that regular expressions are a very compact notation, but they’re not terribly readable. REs of moderate complexity can become lengthy collections of backslashes, parentheses, and metacharacters, making them difficult to read and understand.
For such REs, specifying the re.VERBOSE
flag when compiling the regular
expression can be helpful, because it allows you to format the regular
expression more clearly.
The re.VERBOSE
flag has several effects. Whitespace in the regular
expression that isn’t inside a character class is ignored. This means that an
expression such as dog | cat
is equivalent to the less readable dog|cat
,
but [a b]
will still match the characters 'a'
, 'b'
, or a space. In
addition, you can also put comments inside a RE; comments extend from a #
character to the next newline. When used with triple-quoted strings, this
enables REs to be formatted more neatly:
pat = re.compile(r"""
\s* # Skip leading whitespace
(?P<header>[^:]+) # Header name
\s* : # Whitespace, and a colon
(?P<value>.*?) # The header's value -- *? used to
# lose the following trailing whitespace
\s*$ # Trailing whitespace to end-of-line
""", re.VERBOSE)
This is far more readable than:
pat = re.compile(r"\s*(?P<header>[^:]+)\s*:(?P<value>.*?)\s*$")
Feedback¶
Regular expressions are a complicated topic. Did this document help you understand them? Were there parts that were unclear, or Problems you encountered that weren’t covered here? If so, please send suggestions for improvements to the author.
The most complete book on regular expressions is almost certainly Jeffrey
Friedl’s Mastering Regular Expressions, published by O’Reilly. Unfortunately,
it exclusively concentrates on Perl and Java’s flavours of regular expressions,
and doesn’t contain any Python material at all, so it won’t be useful as a
reference for programming in Python. (The first edition covered Python’s
now-removed regex
module, which won’t help you much.) Consider checking
it out from your library.