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# The Python Profilers
**Source code:** [Lib/profile.py](https://github.com/python/cpython/tree/3.7/Lib/profile.py) \[https://github.com/python/cpython/tree/3.7/Lib/profile.py\] and [Lib/pstats.py](https://github.com/python/cpython/tree/3.7/Lib/pstats.py) \[https://github.com/python/cpython/tree/3.7/Lib/pstats.py\]
- - - - - -
## Introduction to the profilers
[`cProfile`](#module-cProfile "cProfile") and [`profile`](#module-profile "profile: Python source profiler.") provide *deterministic profiling* of Python programs. A *profile* is a set of statistics that describes how often and for how long various parts of the program executed. These statistics can be formatted into reports via the [`pstats`](#module-pstats "pstats: Statistics object for use with the profiler.") module.
The Python standard library provides two different implementations of the same profiling interface:
1. [`cProfile`](#module-cProfile "cProfile") is recommended for most users; it's a C extension with reasonable overhead that makes it suitable for profiling long-running programs. Based on `lsprof`, contributed by Brett Rosen and Ted Czotter.
2. [`profile`](#module-profile "profile: Python source profiler."), a pure Python module whose interface is imitated by [`cProfile`](#module-cProfile "cProfile"), but which adds significant overhead to profiled programs. If you're trying to extend the profiler in some way, the task might be easier with this module. Originally designed and written by Jim Roskind.
注解
The profiler modules are designed to provide an execution profile for a given program, not for benchmarking purposes (for that, there is [`timeit`](timeit.xhtml#module-timeit "timeit: Measure the execution time of small code snippets.") for reasonably accurate results). This particularly applies to benchmarking Python code against C code: the profilers introduce overhead for Python code, but not for C-level functions, and so the C code would seem faster than any Python one.
## Instant User's Manual
This section is provided for users that "don't want to read the manual." It provides a very brief overview, and allows a user to rapidly perform profiling on an existing application.
To profile a function that takes a single argument, you can do:
```
import cProfile
import re
cProfile.run('re.compile("foo|bar")')
```
(Use [`profile`](#module-profile "profile: Python source profiler.") instead of [`cProfile`](#module-cProfile "cProfile") if the latter is not available on your system.)
The above action would run [`re.compile()`](re.xhtml#re.compile "re.compile") and print profile results like the following:
```
197 function calls (192 primitive calls) in 0.002 seconds
Ordered by: standard name
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.000 0.000 0.001 0.001 <string>:1(<module>)
1 0.000 0.000 0.001 0.001 re.py:212(compile)
1 0.000 0.000 0.001 0.001 re.py:268(_compile)
1 0.000 0.000 0.000 0.000 sre_compile.py:172(_compile_charset)
1 0.000 0.000 0.000 0.000 sre_compile.py:201(_optimize_charset)
4 0.000 0.000 0.000 0.000 sre_compile.py:25(_identityfunction)
3/1 0.000 0.000 0.000 0.000 sre_compile.py:33(_compile)
```
The first line indicates that 197 calls were monitored. Of those calls, 192 were *primitive*, meaning that the call was not induced via recursion. The next line: `Ordered by: standard name`, indicates that the text string in the far right column was used to sort the output. The column headings include:
ncallsfor the number of calls.
tottimefor the total time spent in the given function (and excluding time made in calls to sub-functions)
percallis the quotient of `tottime` divided by `ncalls`
cumtimeis the cumulative time spent in this and all subfunctions (from invocation till exit). This figure is accurate *even* for recursive functions.
percallis the quotient of `cumtime` divided by primitive calls
filename:lineno(function)provides the respective data of each function
When there are two numbers in the first column (for example `3/1`), it means that the function recursed. The second value is the number of primitive calls and the former is the total number of calls. Note that when the function does not recurse, these two values are the same, and only the single figure is printed.
Instead of printing the output at the end of the profile run, you can save the results to a file by specifying a filename to the `run()` function:
```
import cProfile
import re
cProfile.run('re.compile("foo|bar")', 'restats')
```
The [`pstats.Stats`](#pstats.Stats "pstats.Stats") class reads profile results from a file and formats them in various ways.
The file [`cProfile`](#module-cProfile "cProfile") can also be invoked as a script to profile another script. For example:
```
python -m cProfile [-o output_file] [-s sort_order] (-m module | myscript.py)
```
`-o` writes the profile results to a file instead of to stdout
`-s` specifies one of the [`sort_stats()`](#pstats.Stats.sort_stats "pstats.Stats.sort_stats") sort values to sort the output by. This only applies when `-o` is not supplied.
`-m` specifies that a module is being profiled instead of a script.
> 3\.7 新版功能: Added the `-m` option.
The [`pstats`](#module-pstats "pstats: Statistics object for use with the profiler.") module's [`Stats`](#pstats.Stats "pstats.Stats") class has a variety of methods for manipulating and printing the data saved into a profile results file:
```
import pstats
from pstats import SortKey
p = pstats.Stats('restats')
p.strip_dirs().sort_stats(-1).print_stats()
```
The [`strip_dirs()`](#pstats.Stats.strip_dirs "pstats.Stats.strip_dirs") method removed the extraneous path from all the module names. The [`sort_stats()`](#pstats.Stats.sort_stats "pstats.Stats.sort_stats") method sorted all the entries according to the standard module/line/name string that is printed. The [`print_stats()`](#pstats.Stats.print_stats "pstats.Stats.print_stats") method printed out all the statistics. You might try the following sort calls:
```
p.sort_stats(SortKey.NAME)
p.print_stats()
```
The first call will actually sort the list by function name, and the second call will print out the statistics. The following are some interesting calls to experiment with:
```
p.sort_stats(SortKey.CUMULATIVE).print_stats(10)
```
This sorts the profile by cumulative time in a function, and then only prints the ten most significant lines. If you want to understand what algorithms are taking time, the above line is what you would use.
If you were looking to see what functions were looping a lot, and taking a lot of time, you would do:
```
p.sort_stats(SortKey.TIME).print_stats(10)
```
to sort according to time spent within each function, and then print the statistics for the top ten functions.
You might also try:
```
p.sort_stats(SortKey.FILENAME).print_stats('__init__')
```
This will sort all the statistics by file name, and then print out statistics for only the class init methods (since they are spelled with `__init__` in them). As one final example, you could try:
```
p.sort_stats(SortKey.TIME, SortKey.CUMULATIVE).print_stats(.5, 'init')
```
This line sorts statistics with a primary key of time, and a secondary key of cumulative time, and then prints out some of the statistics. To be specific, the list is first culled down to 50% (re: `.5`) of its original size, then only lines containing `init` are maintained, and that sub-sub-list is printed.
If you wondered what functions called the above functions, you could now (`p`is still sorted according to the last criteria) do:
```
p.print_callers(.5, 'init')
```
and you would get a list of callers for each of the listed functions.
If you want more functionality, you're going to have to read the manual, or guess what the following functions do:
```
p.print_callees()
p.add('restats')
```
Invoked as a script, the [`pstats`](#module-pstats "pstats: Statistics object for use with the profiler.") module is a statistics browser for reading and examining profile dumps. It has a simple line-oriented interface (implemented using [`cmd`](cmd.xhtml#module-cmd "cmd: Build line-oriented command interpreters.")) and interactive help.
## [`profile`](#module-profile "profile: Python source profiler.") and [`cProfile`](#module-cProfile "cProfile") Module Reference
Both the [`profile`](#module-profile "profile: Python source profiler.") and [`cProfile`](#module-cProfile "cProfile") modules provide the following functions:
`profile.``run`(*command*, *filename=None*, *sort=-1*)This function takes a single argument that can be passed to the [`exec()`](functions.xhtml#exec "exec")function, and an optional file name. In all cases this routine executes:
```
exec(command, __main__.__dict__, __main__.__dict__)
```
and gathers profiling statistics from the execution. If no file name is present, then this function automatically creates a [`Stats`](#pstats.Stats "pstats.Stats")instance and prints a simple profiling report. If the sort value is specified, it is passed to this [`Stats`](#pstats.Stats "pstats.Stats") instance to control how the results are sorted.
`profile.``runctx`(*command*, *globals*, *locals*, *filename=None*, *sort=-1*)This function is similar to [`run()`](#profile.run "profile.run"), with added arguments to supply the globals and locals dictionaries for the *command* string. This routine executes:
```
exec(command, globals, locals)
```
and gathers profiling statistics as in the [`run()`](#profile.run "profile.run") function above.
*class* `profile.``Profile`(*timer=None*, *timeunit=0.0*, *subcalls=True*, *builtins=True*)This class is normally only used if more precise control over profiling is needed than what the `cProfile.run()` function provides.
A custom timer can be supplied for measuring how long code takes to run via the *timer* argument. This must be a function that returns a single number representing the current time. If the number is an integer, the *timeunit*specifies a multiplier that specifies the duration of each unit of time. For example, if the timer returns times measured in thousands of seconds, the time unit would be `.001`.
Directly using the [`Profile`](#profile.Profile "profile.Profile") class allows formatting profile results without writing the profile data to a file:
```
import cProfile, pstats, io
from pstats import SortKey
pr = cProfile.Profile()
pr.enable()
# ... do something ...
pr.disable()
s = io.StringIO()
sortby = SortKey.CUMULATIVE
ps = pstats.Stats(pr, stream=s).sort_stats(sortby)
ps.print_stats()
print(s.getvalue())
```
`enable`()Start collecting profiling data.
`disable`()Stop collecting profiling data.
`create_stats`()Stop collecting profiling data and record the results internally as the current profile.
`print_stats`(*sort=-1*)Create a [`Stats`](#pstats.Stats "pstats.Stats") object based on the current profile and print the results to stdout.
`dump_stats`(*filename*)Write the results of the current profile to *filename*.
`run`(*cmd*)Profile the cmd via [`exec()`](functions.xhtml#exec "exec").
`runctx`(*cmd*, *globals*, *locals*)Profile the cmd via [`exec()`](functions.xhtml#exec "exec") with the specified global and local environment.
`runcall`(*func*, *\*args*, *\*\*kwargs*)Profile `func(*args, **kwargs)`
Note that profiling will only work if the called command/function actually returns. If the interpreter is terminated (e.g. via a [`sys.exit()`](sys.xhtml#sys.exit "sys.exit") call during the called command/function execution) no profiling results will be printed.
## The `Stats` Class
Analysis of the profiler data is done using the [`Stats`](#pstats.Stats "pstats.Stats") class.
*class* `pstats.``Stats`(*\*filenames or profile*, *stream=sys.stdout*)This class constructor creates an instance of a "statistics object" from a *filename* (or list of filenames) or from a `Profile` instance. Output will be printed to the stream specified by *stream*.
The file selected by the above constructor must have been created by the corresponding version of [`profile`](#module-profile "profile: Python source profiler.") or [`cProfile`](#module-cProfile "cProfile"). To be specific, there is *no* file compatibility guaranteed with future versions of this profiler, and there is no compatibility with files produced by other profilers, or the same profiler run on a different operating system. If several files are provided, all the statistics for identical functions will be coalesced, so that an overall view of several processes can be considered in a single report. If additional files need to be combined with data in an existing [`Stats`](#pstats.Stats "pstats.Stats") object, the [`add()`](#pstats.Stats.add "pstats.Stats.add") method can be used.
Instead of reading the profile data from a file, a `cProfile.Profile`or [`profile.Profile`](#profile.Profile "profile.Profile") object can be used as the profile data source.
[`Stats`](#pstats.Stats "pstats.Stats") objects have the following methods:
`strip_dirs`()This method for the [`Stats`](#pstats.Stats "pstats.Stats") class removes all leading path information from file names. It is very useful in reducing the size of the printout to fit within (close to) 80 columns. This method modifies the object, and the stripped information is lost. After performing a strip operation, the object is considered to have its entries in a "random" order, as it was just after object initialization and loading. If [`strip_dirs()`](#pstats.Stats.strip_dirs "pstats.Stats.strip_dirs") causes two function names to be indistinguishable (they are on the same line of the same filename, and have the same function name), then the statistics for these two entries are accumulated into a single entry.
`add`(*\*filenames*)This method of the [`Stats`](#pstats.Stats "pstats.Stats") class accumulates additional profiling information into the current profiling object. Its arguments should refer to filenames created by the corresponding version of [`profile.run()`](#profile.run "profile.run")or `cProfile.run()`. Statistics for identically named (re: file, line, name) functions are automatically accumulated into single function statistics.
`dump_stats`(*filename*)Save the data loaded into the [`Stats`](#pstats.Stats "pstats.Stats") object to a file named *filename*. The file is created if it does not exist, and is overwritten if it already exists. This is equivalent to the method of the same name on the [`profile.Profile`](#profile.Profile "profile.Profile") and `cProfile.Profile` classes.
`sort_stats`(*\*keys*)This method modifies the [`Stats`](#pstats.Stats "pstats.Stats") object by sorting it according to the supplied criteria. The argument can be either a string or a SortKey enum identifying the basis of a sort (example: `'time'`, `'name'`, `SortKey.TIME` or `SortKey.NAME`). The SortKey enums argument have advantage over the string argument in that it is more robust and less error prone.
When more than one key is provided, then additional keys are used as secondary criteria when there is equality in all keys selected before them. For example, `sort_stats(SortKey.NAME, SortKey.FILE)` will sort all the entries according to their function name, and resolve all ties (identical function names) by sorting by file name.
For the string argument, abbreviations can be used for any key names, as long as the abbreviation is unambiguous.
The following are the valid string and SortKey:
Valid String Arg
Valid enum Arg
意义
`'calls'`
SortKey.CALLS
call count
`'cumulative'`
SortKey.CUMULATIVE
cumulative time
`'cumtime'`
N/A
cumulative time
`'file'`
N/A
file name
`'filename'`
SortKey.FILENAME
file name
`'module'`
N/A
file name
`'ncalls'`
N/A
call count
`'pcalls'`
SortKey.PCALLS
primitive call count
`'line'`
SortKey.LINE
line number
`'name'`
SortKey.NAME
function name
`'nfl'`
SortKey.NFL
name/file/line
`'stdname'`
SortKey.STDNAME
standard name
`'time'`
SortKey.TIME
internal time
`'tottime'`
N/A
internal time
Note that all sorts on statistics are in descending order (placing most time consuming items first), where as name, file, and line number searches are in ascending order (alphabetical). The subtle distinction between `SortKey.NFL` and `SortKey.STDNAME` is that the standard name is a sort of the name as printed, which means that the embedded line numbers get compared in an odd way. For example, lines 3, 20, and 40 would (if the file names were the same) appear in the string order 20, 3 and 40. In contrast, `SortKey.NFL` does a numeric compare of the line numbers. In fact, `sort_stats(SortKey.NFL)` is the same as `sort_stats(SortKey.NAME, SortKey.FILENAME, SortKey.LINE)`.
For backward-compatibility reasons, the numeric arguments `-1`, `0`, `1`, and `2` are permitted. They are interpreted as `'stdname'`, `'calls'`, `'time'`, and `'cumulative'` respectively. If this old style format (numeric) is used, only one sort key (the numeric key) will be used, and additional arguments will be silently ignored.
3\.7 新版功能: Added the SortKey enum.
`reverse_order`()This method for the [`Stats`](#pstats.Stats "pstats.Stats") class reverses the ordering of the basic list within the object. Note that by default ascending vs descending order is properly selected based on the sort key of choice.
`print_stats`(*\*restrictions*)This method for the [`Stats`](#pstats.Stats "pstats.Stats") class prints out a report as described in the [`profile.run()`](#profile.run "profile.run") definition.
The order of the printing is based on the last [`sort_stats()`](#pstats.Stats.sort_stats "pstats.Stats.sort_stats") operation done on the object (subject to caveats in [`add()`](#pstats.Stats.add "pstats.Stats.add") and [`strip_dirs()`](#pstats.Stats.strip_dirs "pstats.Stats.strip_dirs")).
The arguments provided (if any) can be used to limit the list down to the significant entries. Initially, the list is taken to be the complete set of profiled functions. Each restriction is either an integer (to select a count of lines), or a decimal fraction between 0.0 and 1.0 inclusive (to select a percentage of lines), or a string that will interpreted as a regular expression (to pattern match the standard name that is printed). If several restrictions are provided, then they are applied sequentially. For example:
```
print_stats(.1, 'foo:')
```
would first limit the printing to first 10% of list, and then only print functions that were part of filename `.*foo:`. In contrast, the command:
```
print_stats('foo:', .1)
```
would limit the list to all functions having file names `.*foo:`, and then proceed to only print the first 10% of them.
`print_callers`(*\*restrictions*)This method for the [`Stats`](#pstats.Stats "pstats.Stats") class prints a list of all functions that called each function in the profiled database. The ordering is identical to that provided by [`print_stats()`](#pstats.Stats.print_stats "pstats.Stats.print_stats"), and the definition of the restricting argument is also identical. Each caller is reported on its own line. The format differs slightly depending on the profiler that produced the stats:
- With [`profile`](#module-profile "profile: Python source profiler."), a number is shown in parentheses after each caller to show how many times this specific call was made. For convenience, a second non-parenthesized number repeats the cumulative time spent in the function at the right.
- With [`cProfile`](#module-cProfile "cProfile"), each caller is preceded by three numbers: the number of times this specific call was made, and the total and cumulative times spent in the current function while it was invoked by this specific caller.
`print_callees`(*\*restrictions*)This method for the [`Stats`](#pstats.Stats "pstats.Stats") class prints a list of all function that were called by the indicated function. Aside from this reversal of direction of calls (re: called vs was called by), the arguments and ordering are identical to the [`print_callers()`](#pstats.Stats.print_callers "pstats.Stats.print_callers") method.
## What Is Deterministic Profiling?
*Deterministic profiling* is meant to reflect the fact that all *function call*, *function return*, and *exception* events are monitored, and precise timings are made for the intervals between these events (during which time the user's code is executing). In contrast, *statistical profiling* (which is not done by this module) randomly samples the effective instruction pointer, and deduces where time is being spent. The latter technique traditionally involves less overhead (as the code does not need to be instrumented), but provides only relative indications of where time is being spent.
In Python, since there is an interpreter active during execution, the presence of instrumented code is not required to do deterministic profiling. Python automatically provides a *hook* (optional callback) for each event. In addition, the interpreted nature of Python tends to add so much overhead to execution, that deterministic profiling tends to only add small processing overhead in typical applications. The result is that deterministic profiling is not that expensive, yet provides extensive run time statistics about the execution of a Python program.
Call count statistics can be used to identify bugs in code (surprising counts), and to identify possible inline-expansion points (high call counts). Internal time statistics can be used to identify "hot loops" that should be carefully optimized. Cumulative time statistics should be used to identify high level errors in the selection of algorithms. Note that the unusual handling of cumulative times in this profiler allows statistics for recursive implementations of algorithms to be directly compared to iterative implementations.
## Limitations
One limitation has to do with accuracy of timing information. There is a fundamental problem with deterministic profilers involving accuracy. The most obvious restriction is that the underlying "clock" is only ticking at a rate (typically) of about .001 seconds. Hence no measurements will be more accurate than the underlying clock. If enough measurements are taken, then the "error" will tend to average out. Unfortunately, removing this first error induces a second source of error.
The second problem is that it "takes a while" from when an event is dispatched until the profiler's call to get the time actually *gets* the state of the clock. Similarly, there is a certain lag when exiting the profiler event handler from the time that the clock's value was obtained (and then squirreled away), until the user's code is once again executing. As a result, functions that are called many times, or call many functions, will typically accumulate this error. The error that accumulates in this fashion is typically less than the accuracy of the clock (less than one clock tick), but it *can* accumulate and become very significant.
The problem is more important with [`profile`](#module-profile "profile: Python source profiler.") than with the lower-overhead [`cProfile`](#module-cProfile "cProfile"). For this reason, [`profile`](#module-profile "profile: Python source profiler.") provides a means of calibrating itself for a given platform so that this error can be probabilistically (on the average) removed. After the profiler is calibrated, it will be more accurate (in a least square sense), but it will sometimes produce negative numbers (when call counts are exceptionally low, and the gods of probability work against you :-). ) Do *not* be alarmed by negative numbers in the profile. They should *only* appear if you have calibrated your profiler, and the results are actually better than without calibration.
## Calibration
The profiler of the [`profile`](#module-profile "profile: Python source profiler.") module subtracts a constant from each event handling time to compensate for the overhead of calling the time function, and socking away the results. By default, the constant is 0. The following procedure can be used to obtain a better constant for a given platform (see [Limitations](#profile-limitations)).
```
import profile
pr = profile.Profile()
for i in range(5):
print(pr.calibrate(10000))
```
The method executes the number of Python calls given by the argument, directly and again under the profiler, measuring the time for both. It then computes the hidden overhead per profiler event, and returns that as a float. For example, on a 1.8Ghz Intel Core i5 running Mac OS X, and using Python's time.process\_time() as the timer, the magical number is about 4.04e-6.
The object of this exercise is to get a fairly consistent result. If your computer is *very* fast, or your timer function has poor resolution, you might have to pass 100000, or even 1000000, to get consistent results.
When you have a consistent answer, there are three ways you can use it:
```
import profile
# 1. Apply computed bias to all Profile instances created hereafter.
profile.Profile.bias = your_computed_bias
# 2. Apply computed bias to a specific Profile instance.
pr = profile.Profile()
pr.bias = your_computed_bias
# 3. Specify computed bias in instance constructor.
pr = profile.Profile(bias=your_computed_bias)
```
If you have a choice, you are better off choosing a smaller constant, and then your results will "less often" show up as negative in profile statistics.
## Using a custom timer
If you want to change how current time is determined (for example, to force use of wall-clock time or elapsed process time), pass the timing function you want to the `Profile` class constructor:
```
pr = profile.Profile(your_time_func)
```
The resulting profiler will then call `your_time_func`. Depending on whether you are using [`profile.Profile`](#profile.Profile "profile.Profile") or `cProfile.Profile`, `your_time_func`'s return value will be interpreted differently:
[`profile.Profile`](#profile.Profile "profile.Profile")`your_time_func` should return a single number, or a list of numbers whose sum is the current time (like what [`os.times()`](os.xhtml#os.times "os.times") returns). If the function returns a single time number, or the list of returned numbers has length 2, then you will get an especially fast version of the dispatch routine.
Be warned that you should calibrate the profiler class for the timer function that you choose (see [Calibration](#profile-calibration)). For most machines, a timer that returns a lone integer value will provide the best results in terms of low overhead during profiling. ([`os.times()`](os.xhtml#os.times "os.times") is *pretty* bad, as it returns a tuple of floating point values). If you want to substitute a better timer in the cleanest fashion, derive a class and hardwire a replacement dispatch method that best handles your timer call, along with the appropriate calibration constant.
`cProfile.Profile``your_time_func` should return a single number. If it returns integers, you can also invoke the class constructor with a second argument specifying the real duration of one unit of time. For example, if `your_integer_time_func` returns times measured in thousands of seconds, you would construct the `Profile` instance as follows:
```
pr = cProfile.Profile(your_integer_time_func, 0.001)
```
As the `cProfile.Profile` class cannot be calibrated, custom timer functions should be used with care and should be as fast as possible. For the best results with a custom timer, it might be necessary to hard-code it in the C source of the internal `_lsprof` module.
Python 3.3 adds several new functions in [`time`](time.xhtml#module-time "time: Time access and conversions.") that can be used to make precise measurements of process or wall-clock time. For example, see [`time.perf_counter()`](time.xhtml#time.perf_counter "time.perf_counter").
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- Python 3.6.4 中的重要变化
- Python 3.6.5 中的重要变化
- Python 3.6.7 中的重要变化
- Python 3.5 有什么新变化
- 摘要 - 发布重点
- 新的特性
- 其他语言特性修改
- 新增模块
- 改进的模块
- Other module-level changes
- 性能优化
- Build and C API Changes
- 弃用
- 移除
- Porting to Python 3.5
- Notable changes in Python 3.5.4
- What's New In Python 3.4
- 摘要 - 发布重点
- 新的特性
- 新增模块
- 改进的模块
- CPython Implementation Changes
- 弃用
- 移除
- Porting to Python 3.4
- Changed in 3.4.3
- What's New In Python 3.3
- 摘要 - 发布重点
- PEP 405: Virtual Environments
- PEP 420: Implicit Namespace Packages
- PEP 3118: New memoryview implementation and buffer protocol documentation
- PEP 393: Flexible String Representation
- PEP 397: Python Launcher for Windows
- PEP 3151: Reworking the OS and IO exception hierarchy
- PEP 380: Syntax for Delegating to a Subgenerator
- PEP 409: Suppressing exception context
- PEP 414: Explicit Unicode literals
- PEP 3155: Qualified name for classes and functions
- PEP 412: Key-Sharing Dictionary
- PEP 362: Function Signature Object
- PEP 421: Adding sys.implementation
- Using importlib as the Implementation of Import
- 其他语言特性修改
- A Finer-Grained Import Lock
- Builtin functions and types
- 新增模块
- 改进的模块
- 性能优化
- Build and C API Changes
- 弃用
- Porting to Python 3.3
- What's New In Python 3.2
- PEP 384: Defining a Stable ABI
- PEP 389: Argparse Command Line Parsing Module
- PEP 391: Dictionary Based Configuration for Logging
- PEP 3148: The concurrent.futures module
- PEP 3147: PYC Repository Directories
- PEP 3149: ABI Version Tagged .so Files
- PEP 3333: Python Web Server Gateway Interface v1.0.1
- 其他语言特性修改
- New, Improved, and Deprecated Modules
- 多线程
- 性能优化
- Unicode
- Codecs
- 文档
- IDLE
- Code Repository
- Build and C API Changes
- Porting to Python 3.2
- What's New In Python 3.1
- PEP 372: Ordered Dictionaries
- PEP 378: Format Specifier for Thousands Separator
- 其他语言特性修改
- New, Improved, and Deprecated Modules
- 性能优化
- IDLE
- Build and C API Changes
- Porting to Python 3.1
- What's New In Python 3.0
- Common Stumbling Blocks
- Overview Of Syntax Changes
- Changes Already Present In Python 2.6
- Library Changes
- PEP 3101: A New Approach To String Formatting
- Changes To Exceptions
- Miscellaneous Other Changes
- Build and C API Changes
- 性能
- Porting To Python 3.0
- What's New in Python 2.7
- The Future for Python 2.x
- Changes to the Handling of Deprecation Warnings
- Python 3.1 Features
- PEP 372: Adding an Ordered Dictionary to collections
- PEP 378: Format Specifier for Thousands Separator
- PEP 389: The argparse Module for Parsing Command Lines
- PEP 391: Dictionary-Based Configuration For Logging
- PEP 3106: Dictionary Views
- PEP 3137: The memoryview Object
- 其他语言特性修改
- New and Improved Modules
- Build and C API Changes
- Other Changes and Fixes
- Porting to Python 2.7
- New Features Added to Python 2.7 Maintenance Releases
- Acknowledgements
- Python 2.6 有什么新变化
- Python 3.0
- Changes to the Development Process
- PEP 343: The 'with' statement
- PEP 366: Explicit Relative Imports From a Main Module
- PEP 370: Per-user site-packages Directory
- PEP 371: The multiprocessing Package
- PEP 3101: Advanced String Formatting
- PEP 3105: print As a Function
- PEP 3110: Exception-Handling Changes
- PEP 3112: Byte Literals
- PEP 3116: New I/O Library
- PEP 3118: Revised Buffer Protocol
- PEP 3119: Abstract Base Classes
- PEP 3127: Integer Literal Support and Syntax
- PEP 3129: Class Decorators
- PEP 3141: A Type Hierarchy for Numbers
- 其他语言特性修改
- New and Improved Modules
- Deprecations and Removals
- Build and C API Changes
- Porting to Python 2.6
- Acknowledgements
- What's New in Python 2.5
- PEP 308: Conditional Expressions
- PEP 309: Partial Function Application
- PEP 314: Metadata for Python Software Packages v1.1
- PEP 328: Absolute and Relative Imports
- PEP 338: Executing Modules as Scripts
- PEP 341: Unified try/except/finally
- PEP 342: New Generator Features
- PEP 343: The 'with' statement
- PEP 352: Exceptions as New-Style Classes
- PEP 353: Using ssize_t as the index type
- PEP 357: The 'index' method
- 其他语言特性修改
- New, Improved, and Removed Modules
- Build and C API Changes
- Porting to Python 2.5
- Acknowledgements
- What's New in Python 2.4
- PEP 218: Built-In Set Objects
- PEP 237: Unifying Long Integers and Integers
- PEP 289: Generator Expressions
- PEP 292: Simpler String Substitutions
- PEP 318: Decorators for Functions and Methods
- PEP 322: Reverse Iteration
- PEP 324: New subprocess Module
- PEP 327: Decimal Data Type
- PEP 328: Multi-line Imports
- PEP 331: Locale-Independent Float/String Conversions
- 其他语言特性修改
- New, Improved, and Deprecated Modules
- Build and C API Changes
- Porting to Python 2.4
- Acknowledgements
- What's New in Python 2.3
- PEP 218: A Standard Set Datatype
- PEP 255: Simple Generators
- PEP 263: Source Code Encodings
- PEP 273: Importing Modules from ZIP Archives
- PEP 277: Unicode file name support for Windows NT
- PEP 278: Universal Newline Support
- PEP 279: enumerate()
- PEP 282: The logging Package
- PEP 285: A Boolean Type
- PEP 293: Codec Error Handling Callbacks
- PEP 301: Package Index and Metadata for Distutils
- PEP 302: New Import Hooks
- PEP 305: Comma-separated Files
- PEP 307: Pickle Enhancements
- Extended Slices
- 其他语言特性修改
- New, Improved, and Deprecated Modules
- Pymalloc: A Specialized Object Allocator
- Build and C API Changes
- Other Changes and Fixes
- Porting to Python 2.3
- Acknowledgements
- What's New in Python 2.2
- 概述
- PEPs 252 and 253: Type and Class Changes
- PEP 234: Iterators
- PEP 255: Simple Generators
- PEP 237: Unifying Long Integers and Integers
- PEP 238: Changing the Division Operator
- Unicode Changes
- PEP 227: Nested Scopes
- New and Improved Modules
- Interpreter Changes and Fixes
- Other Changes and Fixes
- Acknowledgements
- What's New in Python 2.1
- 概述
- PEP 227: Nested Scopes
- PEP 236: future Directives
- PEP 207: Rich Comparisons
- PEP 230: Warning Framework
- PEP 229: New Build System
- PEP 205: Weak References
- PEP 232: Function Attributes
- PEP 235: Importing Modules on Case-Insensitive Platforms
- PEP 217: Interactive Display Hook
- PEP 208: New Coercion Model
- PEP 241: Metadata in Python Packages
- New and Improved Modules
- Other Changes and Fixes
- Acknowledgements
- What's New in Python 2.0
- 概述
- What About Python 1.6?
- New Development Process
- Unicode
- 列表推导式
- Augmented Assignment
- 字符串的方法
- Garbage Collection of Cycles
- Other Core Changes
- Porting to 2.0
- Extending/Embedding Changes
- Distutils: Making Modules Easy to Install
- XML Modules
- Module changes
- New modules
- IDLE Improvements
- Deleted and Deprecated Modules
- Acknowledgements
- 更新日志
- Python 下一版
- Python 3.7.3 最终版
- Python 3.7.3 发布候选版 1
- Python 3.7.2 最终版
- Python 3.7.2 发布候选版 1
- Python 3.7.1 最终版
- Python 3.7.1 RC 2版本
- Python 3.7.1 发布候选版 1
- Python 3.7.0 正式版
- Python 3.7.0 release candidate 1
- Python 3.7.0 beta 5
- Python 3.7.0 beta 4
- Python 3.7.0 beta 3
- Python 3.7.0 beta 2
- Python 3.7.0 beta 1
- Python 3.7.0 alpha 4
- Python 3.7.0 alpha 3
- Python 3.7.0 alpha 2
- Python 3.7.0 alpha 1
- Python 3.6.6 final
- Python 3.6.6 RC 1
- Python 3.6.5 final
- Python 3.6.5 release candidate 1
- Python 3.6.4 final
- Python 3.6.4 release candidate 1
- Python 3.6.3 final
- Python 3.6.3 release candidate 1
- Python 3.6.2 final
- Python 3.6.2 release candidate 2
- Python 3.6.2 release candidate 1
- Python 3.6.1 final
- Python 3.6.1 release candidate 1
- Python 3.6.0 final
- Python 3.6.0 release candidate 2
- Python 3.6.0 release candidate 1
- Python 3.6.0 beta 4
- Python 3.6.0 beta 3
- Python 3.6.0 beta 2
- Python 3.6.0 beta 1
- Python 3.6.0 alpha 4
- Python 3.6.0 alpha 3
- Python 3.6.0 alpha 2
- Python 3.6.0 alpha 1
- Python 3.5.5 final
- Python 3.5.5 release candidate 1
- Python 3.5.4 final
- Python 3.5.4 release candidate 1
- Python 3.5.3 final
- Python 3.5.3 release candidate 1
- Python 3.5.2 final
- Python 3.5.2 release candidate 1
- Python 3.5.1 final
- Python 3.5.1 release candidate 1
- Python 3.5.0 final
- Python 3.5.0 release candidate 4
- Python 3.5.0 release candidate 3
- Python 3.5.0 release candidate 2
- Python 3.5.0 release candidate 1
- Python 3.5.0 beta 4
- Python 3.5.0 beta 3
- Python 3.5.0 beta 2
- Python 3.5.0 beta 1
- Python 3.5.0 alpha 4
- Python 3.5.0 alpha 3
- Python 3.5.0 alpha 2
- Python 3.5.0 alpha 1
- Python 教程
- 课前甜点
- 使用 Python 解释器
- 调用解释器
- 解释器的运行环境
- Python 的非正式介绍
- Python 作为计算器使用
- 走向编程的第一步
- 其他流程控制工具
- if 语句
- for 语句
- range() 函数
- break 和 continue 语句，以及循环中的 else 子句
- pass 语句
- 定义函数
- 函数定义的更多形式
- 小插曲：编码风格
- 数据结构
- 列表的更多特性
- del 语句
- 元组和序列
- 集合
- 字典
- 循环的技巧
- 深入条件控制
- 序列和其它类型的比较
- 模块
- 有关模块的更多信息
- 标准模块
- dir() 函数
- 包
- 输入输出
- 更漂亮的输出格式
- 读写文件
- 错误和异常
- 语法错误
- 异常
- 处理异常
- 抛出异常
- 用户自定义异常
- 定义清理操作
- 预定义的清理操作
- 类
- 名称和对象
- Python 作用域和命名空间
- 初探类
- 补充说明
- 继承
- 私有变量
- 杂项说明
- 迭代器
- 生成器
- 生成器表达式
- 标准库简介
- 操作系统接口
- 文件通配符
- 命令行参数
- 错误输出重定向和程序终止
- 字符串模式匹配
- 数学
- 互联网访问
- 日期和时间
- 数据压缩
- 性能测量
- 质量控制
- 自带电池
- 标准库简介 —— 第二部分
- 格式化输出
- 模板
- 使用二进制数据记录格式
- 多线程
- 日志
- 弱引用
- 用于操作列表的工具
- 十进制浮点运算
- 虚拟环境和包
- 概述
- 创建虚拟环境
- 使用pip管理包
- 接下来？
- 交互式编辑和编辑历史
- Tab 补全和编辑历史
- 默认交互式解释器的替代品
- 浮点算术：争议和限制
- 表示性错误
- 附录
- 交互模式
- 安装和使用 Python
- 命令行与环境
- 命令行
- 环境变量
- 在Unix平台中使用Python
- 获取最新版本的Python
- 构建Python
- 与Python相关的路径和文件
- 杂项
- 编辑器和集成开发环境
- 在Windows上使用 Python
- 完整安装程序
- Microsoft Store包
- nuget.org 安装包
- 可嵌入的包
- 替代捆绑包
- 配置Python
- 适用于Windows的Python启动器
- 查找模块
- 附加模块
- 在Windows上编译Python
- 其他平台
- 在苹果系统上使用 Python
- 获取和安装 MacPython
- IDE
- 安装额外的 Python 包
- Mac 上的图形界面编程
- 在 Mac 上分发 Python 应用程序
- 其他资源
- Python 语言参考
- 概述
- 其他实现
- 标注
- 词法分析
- 行结构
- 其他形符
- 标识符和关键字
- 字面值
- 运算符
- 分隔符
- 数据模型
- 对象、值与类型
- 标准类型层级结构
- 特殊方法名称
- 协程
- 执行模型
- 程序的结构
- 命名与绑定
- 异常
- 导入系统
- importlib
- 包
- 搜索
- 加载
- 基于路径的查找器
- 替换标准导入系统
- Package Relative Imports
- 有关 main 的特殊事项
- 开放问题项
- 参考文献
- 表达式
- 算术转换
- 原子
- 原型
- await 表达式
- 幂运算符
- 一元算术和位运算
- 二元算术运算符
- 移位运算
- 二元位运算
- 比较运算
- 布尔运算
- 条件表达式
- lambda 表达式
- 表达式列表
- 求值顺序
- 运算符优先级
- 简单语句
- 表达式语句
- 赋值语句
- assert 语句
- pass 语句
- del 语句
- return 语句
- yield 语句
- raise 语句
- break 语句
- continue 语句
- import 语句
- global 语句
- nonlocal 语句
- 复合语句
- if 语句
- while 语句
- for 语句
- try 语句
- with 语句
- 函数定义
- 类定义
- 协程
- 最高层级组件
- 完整的 Python 程序
- 文件输入
- 交互式输入
- 表达式输入
- 完整的语法规范
- Python 标准库
- 概述
- 可用性注释
- 内置函数
- 内置常量
- 由 site 模块添加的常量
- 内置类型
- 逻辑值检测
- 布尔运算 — and, or, not
- 比较
- 数字类型 — int, float, complex
- 迭代器类型
- 序列类型 — list, tuple, range
- 文本序列类型 — str
- 二进制序列类型 — bytes, bytearray, memoryview
- 集合类型 — set, frozenset
- 映射类型 — dict
- 上下文管理器类型
- 其他内置类型
- 特殊属性
- 内置异常
- 基类
- 具体异常
- 警告
- 异常层次结构
- 文本处理服务
- string — 常见的字符串操作
- re — 正则表达式操作
- 模块 difflib 是一个计算差异的助手
- textwrap — Text wrapping and filling
- unicodedata — Unicode 数据库
- stringprep — Internet String Preparation
- readline — GNU readline interface
- rlcompleter — GNU readline的完成函数
- 二进制数据服务
- struct — Interpret bytes as packed binary data
- codecs — Codec registry and base classes
- 数据类型
- datetime — 基础日期/时间数据类型
- calendar — General calendar-related functions
- collections — 容器数据类型
- collections.abc — 容器的抽象基类
- heapq — 堆队列算法
- bisect — Array bisection algorithm
- array — Efficient arrays of numeric values
- weakref — 弱引用
- types — Dynamic type creation and names for built-in types
- copy — 浅层 (shallow) 和深层 (deep) 复制操作
- pprint — 数据美化输出
- reprlib — Alternate repr() implementation
- enum — Support for enumerations
- 数字和数学模块
- numbers — 数字的抽象基类
- math — 数学函数
- cmath — Mathematical functions for complex numbers
- decimal — 十进制定点和浮点运算
- fractions — 分数
- random — 生成伪随机数
- statistics — Mathematical statistics functions
- 函数式编程模块
- itertools — 为高效循环而创建迭代器的函数
- functools — 高阶函数和可调用对象上的操作
- operator — 标准运算符替代函数
- 文件和目录访问
- pathlib — 面向对象的文件系统路径
- os.path — 常见路径操作
- fileinput — Iterate over lines from multiple input streams
- stat — Interpreting stat() results
- filecmp — File and Directory Comparisons
- tempfile — Generate temporary files and directories
- glob — Unix style pathname pattern expansion
- fnmatch — Unix filename pattern matching
- linecache — Random access to text lines
- shutil — High-level file operations
- macpath — Mac OS 9 路径操作函数
- 数据持久化
- pickle —— Python 对象序列化
- copyreg — Register pickle support functions
- shelve — Python object persistence
- marshal — Internal Python object serialization
- dbm — Interfaces to Unix “databases”
- sqlite3 — SQLite 数据库 DB-API 2.0 接口模块
- 数据压缩和存档
- zlib — 与 gzip 兼容的压缩
- gzip — 对 gzip 格式的支持
- bz2 — 对 bzip2 压缩算法的支持
- lzma — 用 LZMA 算法压缩
- zipfile — 在 ZIP 归档中工作
- tarfile — Read and write tar archive files
- 文件格式
- csv — CSV 文件读写
- configparser — Configuration file parser
- netrc — netrc file processing
- xdrlib — Encode and decode XDR data
- plistlib — Generate and parse Mac OS X .plist files
- 加密服务
- hashlib — 安全哈希与消息摘要
- hmac — 基于密钥的消息验证
- secrets — Generate secure random numbers for managing secrets
- 通用操作系统服务
- os — 操作系统接口模块
- io — 处理流的核心工具
- time — 时间的访问和转换
- argparse — 命令行选项、参数和子命令解析器
- getopt — C-style parser for command line options
- 模块 logging — Python 的日志记录工具
- logging.config — 日志记录配置
- logging.handlers — Logging handlers
- getpass — 便携式密码输入工具
- curses — 终端字符单元显示的处理
- curses.textpad — Text input widget for curses programs
- curses.ascii — Utilities for ASCII characters
- curses.panel — A panel stack extension for curses
- platform — Access to underlying platform's identifying data
- errno — Standard errno system symbols
- ctypes — Python 的外部函数库
- 并发执行
- threading — 基于线程的并行
- multiprocessing — 基于进程的并行
- concurrent 包
- concurrent.futures — 启动并行任务
- subprocess — 子进程管理
- sched — 事件调度器
- queue — 一个同步的队列类
- _thread — 底层多线程 API
- _dummy_thread — _thread 的替代模块
- dummy_threading — 可直接替代 threading 模块。
- contextvars — Context Variables
- Context Variables
- Manual Context Management
- asyncio support
- 网络和进程间通信
- asyncio — 异步 I/O
- socket — 底层网络接口
- ssl — TLS/SSL wrapper for socket objects
- select — Waiting for I/O completion
- selectors — 高级 I/O 复用库
- asyncore — 异步socket处理器
- asynchat — 异步 socket 指令/响应 处理器
- signal — Set handlers for asynchronous events
- mmap — Memory-mapped file support
- 互联网数据处理
- email — 电子邮件与 MIME 处理包
- json — JSON 编码和解码器
- mailcap — Mailcap file handling
- mailbox — Manipulate mailboxes in various formats
- mimetypes — Map filenames to MIME types
- base64 — Base16, Base32, Base64, Base85 数据编码
- binhex — 对binhex4文件进行编码和解码
- binascii — 二进制和 ASCII 码互转
- quopri — Encode and decode MIME quoted-printable data
- uu — Encode and decode uuencode files
- 结构化标记处理工具
- html — 超文本标记语言支持
- html.parser — 简单的 HTML 和 XHTML 解析器
- html.entities — HTML 一般实体的定义
- XML处理模块
- xml.etree.ElementTree — The ElementTree XML API
- xml.dom — The Document Object Model API
- xml.dom.minidom — Minimal DOM implementation
- xml.dom.pulldom — Support for building partial DOM trees
- xml.sax — Support for SAX2 parsers
- xml.sax.handler — Base classes for SAX handlers
- xml.sax.saxutils — SAX Utilities
- xml.sax.xmlreader — Interface for XML parsers
- xml.parsers.expat — Fast XML parsing using Expat
- 互联网协议和支持
- webbrowser — 方便的Web浏览器控制器
- cgi — Common Gateway Interface support
- cgitb — Traceback manager for CGI scripts
- wsgiref — WSGI Utilities and Reference Implementation
- urllib — URL 处理模块
- urllib.request — 用于打开 URL 的可扩展库
- urllib.response — Response classes used by urllib
- urllib.parse — Parse URLs into components
- urllib.error — Exception classes raised by urllib.request
- urllib.robotparser — Parser for robots.txt
- http — HTTP 模块
- http.client — HTTP协议客户端
- ftplib — FTP protocol client
- poplib — POP3 protocol client
- imaplib — IMAP4 protocol client
- nntplib — NNTP protocol client
- smtplib —SMTP协议客户端
- smtpd — SMTP Server
- telnetlib — Telnet client
- uuid — UUID objects according to RFC 4122
- socketserver — A framework for network servers
- http.server — HTTP 服务器
- http.cookies — HTTP state management
- http.cookiejar — Cookie handling for HTTP clients
- xmlrpc — XMLRPC 服务端与客户端模块
- xmlrpc.client — XML-RPC client access
- xmlrpc.server — Basic XML-RPC servers
- ipaddress — IPv4/IPv6 manipulation library
- 多媒体服务
- audioop — Manipulate raw audio data
- aifc — Read and write AIFF and AIFC files
- sunau — 读写 Sun AU 文件
- wave — 读写WAV格式文件
- chunk — Read IFF chunked data
- colorsys — Conversions between color systems
- imghdr — 推测图像类型
- sndhdr — 推测声音文件的类型
- ossaudiodev — Access to OSS-compatible audio devices
- 国际化
- gettext — 多语种国际化服务
- locale — 国际化服务
- 程序框架
- turtle — 海龟绘图
- cmd — 支持面向行的命令解释器
- shlex — Simple lexical analysis
- Tk图形用户界面(GUI)
- tkinter — Tcl/Tk的Python接口
- tkinter.ttk — Tk themed widgets
- tkinter.tix — Extension widgets for Tk
- tkinter.scrolledtext — 滚动文字控件
- IDLE
- 其他图形用户界面（GUI）包
- 开发工具
- typing — 类型标注支持
- pydoc — Documentation generator and online help system
- doctest — Test interactive Python examples
- unittest — 单元测试框架
- unittest.mock — mock object library
- unittest.mock 上手指南
- 2to3 - 自动将 Python 2 代码转为 Python 3 代码
- test — Regression tests package for Python
- test.support — Utilities for the Python test suite
- test.support.script_helper — Utilities for the Python execution tests
- 调试和分析
- bdb — Debugger framework
- faulthandler — Dump the Python traceback
- pdb — The Python Debugger
- The Python Profilers
- timeit — 测量小代码片段的执行时间
- trace — Trace or track Python statement execution
- tracemalloc — Trace memory allocations
- 软件打包和分发
- distutils — 构建和安装 Python 模块
- ensurepip — Bootstrapping the pip installer
- venv — 创建虚拟环境
- zipapp — Manage executable Python zip archives
- Python运行时服务
- sys — 系统相关的参数和函数
- sysconfig — Provide access to Python's configuration information
- builtins — 内建对象
- main — 顶层脚本环境
- warnings — Warning control
- dataclasses — 数据类
- contextlib — Utilities for with-statement contexts
- abc — 抽象基类
- atexit — 退出处理器
- traceback — Print or retrieve a stack traceback
- future — Future 语句定义
- gc — 垃圾回收器接口
- inspect — 检查对象
- site — Site-specific configuration hook
- 自定义 Python 解释器
- code — Interpreter base classes
- codeop — Compile Python code
- 导入模块
- zipimport — Import modules from Zip archives
- pkgutil — Package extension utility
- modulefinder — 查找脚本使用的模块
- runpy — Locating and executing Python modules
- importlib — The implementation of import
- Python 语言服务
- parser — Access Python parse trees
- ast — 抽象语法树
- symtable — Access to the compiler's symbol tables
- symbol — 与 Python 解析树一起使用的常量
- token — 与Python解析树一起使用的常量
- keyword — 检验Python关键字
- tokenize — Tokenizer for Python source
- tabnanny — 模糊缩进检测
- pyclbr — Python class browser support
- py_compile — Compile Python source files
- compileall — Byte-compile Python libraries
- dis — Python 字节码反汇编器
- pickletools — Tools for pickle developers
- 杂项服务
- formatter — Generic output formatting
- Windows系统相关模块
- msilib — Read and write Microsoft Installer files
- msvcrt — Useful routines from the MS VC++ runtime
- winreg — Windows 注册表访问
- winsound — Sound-playing interface for Windows
- Unix 专有服务
- posix — The most common POSIX system calls
- pwd — 用户密码数据库
- spwd — The shadow password database
- grp — The group database
- crypt — Function to check Unix passwords
- termios — POSIX style tty control
- tty — 终端控制功能
- pty — Pseudo-terminal utilities
- fcntl — The fcntl and ioctl system calls
- pipes — Interface to shell pipelines
- resource — Resource usage information
- nis — Interface to Sun's NIS (Yellow Pages)
- Unix syslog 库例程
- 被取代的模块
- optparse — Parser for command line options
- imp — Access the import internals
- 未创建文档的模块
- 平台特定模块
- 扩展和嵌入 Python 解释器
- 推荐的第三方工具
- 不使用第三方工具创建扩展
- 使用 C 或 C++ 扩展 Python
- 自定义扩展类型：教程
- 定义扩展类型：已分类主题
- 构建C/C++扩展
- 在Windows平台编译C和C++扩展
- 在更大的应用程序中嵌入 CPython 运行时
- Embedding Python in Another Application
- Python/C API 参考手册
- 概述
- 代码标准
- 包含文件
- 有用的宏
- 对象、类型和引用计数
- 异常
- 嵌入Python
- 调试构建
- 稳定的应用程序二进制接口
- The Very High Level Layer
- Reference Counting
- 异常处理
- Printing and clearing
- 抛出异常
- Issuing warnings
- Querying the error indicator
- Signal Handling
- Exception Classes
- Exception Objects
- Unicode Exception Objects
- Recursion Control
- 标准异常
- 标准警告类别
- 工具
- 操作系统实用程序
- 系统功能
- 过程控制
- 导入模块
- Data marshalling support
- 语句解释及变量编译
- 字符串转换与格式化
- 反射
- 编解码器注册与支持功能
- 抽象对象层
- Object Protocol
- 数字协议
- Sequence Protocol
- Mapping Protocol
- 迭代器协议
- 缓冲协议
- Old Buffer Protocol
- 具体的对象层
- 基本对象
- 数值对象
- 序列对象
- 容器对象
- 函数对象
- 其他对象
- Initialization, Finalization, and Threads
- 在Python初始化之前
- 全局配置变量
- Initializing and finalizing the interpreter
- Process-wide parameters
- Thread State and the Global Interpreter Lock
- Sub-interpreter support
- Asynchronous Notifications
- Profiling and Tracing
- Advanced Debugger Support
- Thread Local Storage Support
- 内存管理
- 概述
- 原始内存接口
- Memory Interface
- 对象分配器
- 默认内存分配器
- Customize Memory Allocators
- The pymalloc allocator
- tracemalloc C API
- 示例
- 对象实现支持
- 在堆中分配对象
- Common Object Structures
- Type 对象
- Number Object Structures
- Mapping Object Structures
- Sequence Object Structures
- Buffer Object Structures
- Async Object Structures
- 使对象类型支持循环垃圾回收
- API 和 ABI 版本管理
- 分发 Python 模块
- 关键术语
- 开源许可与协作
- 安装工具
- 阅读指南
- 我该如何...？
- ...为我的项目选择一个名字？
- ...创建和分发二进制扩展？
- 安装 Python 模块
- 关键术语
- 基本使用
- 我应如何 ...？
- ... 在 Python 3.4 之前的 Python 版本中安装 pip ？
- ... 只为当前用户安装软件包？
- ... 安装科学计算类 Python 软件包？
- ... 使用并行安装的多个 Python 版本？
- 常见的安装问题
- 在 Linux 的系统 Python 版本上安装
- 未安装 pip
- 安装二进制编译扩展
- Python 常用指引
- 将 Python 2 代码迁移到 Python 3
- 简要说明
- 详情
- 将扩展模块移植到 Python 3
- 条件编译
- 对象API的更改
- 模块初始化和状态
- CObject 替换为 Capsule
- 其他选项
- Curses Programming with Python
- What is curses?
- Starting and ending a curses application
- Windows and Pads
- Displaying Text
- User Input
- For More Information
- 实现描述器
- 摘要
- 定义和简介
- 描述器协议
- 发起调用描述符
- 描述符示例
- Properties
- 函数和方法
- Static Methods and Class Methods
- 函数式编程指引
- 概述
- 迭代器
- 生成器表达式和列表推导式
- 生成器
- 内置函数
- itertools 模块
- The functools module
- Small functions and the lambda expression
- Revision History and Acknowledgements
- 引用文献
- 日志 HOWTO
- 日志基础教程
- 进阶日志教程
- 日志级别
- 有用的处理程序
- 记录日志中引发的异常
- 使用任意对象作为消息
- 优化
- 日志操作手册
- 在多个模块中使用日志
- 在多线程中使用日志
- 使用多个日志处理器和多种格式化
- 在多个地方记录日志
- 日志服务器配置示例
- 处理日志处理器的阻塞
- Sending and receiving logging events across a network
- Adding contextual information to your logging output
- Logging to a single file from multiple processes
- Using file rotation
- Use of alternative formatting styles
- Customizing LogRecord
- Subclassing QueueHandler - a ZeroMQ example
- Subclassing QueueListener - a ZeroMQ example
- An example dictionary-based configuration
- Using a rotator and namer to customize log rotation processing
- A more elaborate multiprocessing example
- Inserting a BOM into messages sent to a SysLogHandler
- Implementing structured logging
- Customizing handlers with dictConfig()
- Using particular formatting styles throughout your application
- Configuring filters with dictConfig()
- Customized exception formatting
- Speaking logging messages
- Buffering logging messages and outputting them conditionally
- Formatting times using UTC (GMT) via configuration
- Using a context manager for selective logging
- 正则表达式HOWTO
- 概述
- 简单模式
- 使用正则表达式
- 更多模式能力
- 修改字符串
- 常见问题
- 反馈
- 套接字编程指南
- 套接字
- 创建套接字
- 使用一个套接字
- 断开连接
- 非阻塞的套接字
- 排序指南
- 基本排序
- 关键函数
- Operator 模块函数
- 升序和降序
- 排序稳定性和排序复杂度
- 使用装饰-排序-去装饰的旧方法
- 使用 cmp 参数的旧方法
- 其它
- Unicode 指南
- Unicode 概述
- Python's Unicode Support
- Reading and Writing Unicode Data
- Acknowledgements
- 如何使用urllib包获取网络资源
- 概述
- Fetching URLs
- 处理异常
- info and geturl
- Openers and Handlers
- Basic Authentication
- Proxies
- Sockets and Layers
- 脚注
- Argparse 教程
- 概念
- 基础
- 位置参数介绍
- Introducing Optional arguments
- Combining Positional and Optional arguments
- Getting a little more advanced
- Conclusion
- ipaddress模块介绍
- 创建 Address/Network/Interface 对象
- 审查 Address/Network/Interface 对象
- Network 作为 Address 列表
- 比较
- 将IP地址与其他模块一起使用
- 实例创建失败时获取更多详细信息
- Argument Clinic How-To
- The Goals Of Argument Clinic
- Basic Concepts And Usage
- Converting Your First Function
- Advanced Topics
- 使用 DTrace 和 SystemTap 检测CPython
- Enabling the static markers
- Static DTrace probes
- Static SystemTap markers
- Available static markers
- SystemTap Tapsets
- 示例
- Python 常见问题
- Python常见问题
- 一般信息
- 现实世界中的 Python
- 编程常见问题
- 一般问题
- 核心语言
- 数字和字符串
- 性能
- 序列（元组/列表）
- 对象
- 模块
- 设计和历史常见问题
- 为什么Python使用缩进来分组语句？
- 为什么简单的算术运算得到奇怪的结果？
- 为什么浮点计算不准确？
- 为什么Python字符串是不可变的？
- 为什么必须在方法定义和调用中显式使用“self”？
- 为什么不能在表达式中赋值？
- 为什么Python对某些功能（例如list.index()）使用方法来实现，而其他功能（例如len(List)）使用函数实现？
- 为什么 join()是一个字符串方法而不是列表或元组方法？
- 异常有多快？
- 为什么Python中没有switch或case语句？
- 难道不能在解释器中模拟线程，而非得依赖特定于操作系统的线程实现吗？
- 为什么lambda表达式不能包含语句？
- 可以将Python编译为机器代码，C或其他语言吗？
- Python如何管理内存？
- 为什么CPython不使用更传统的垃圾回收方案？
- CPython退出时为什么不释放所有内存？
- 为什么有单独的元组和列表数据类型？
- 列表是如何在CPython中实现的？
- 字典是如何在CPython中实现的？
- 为什么字典key必须是不可变的？
- 为什么 list.sort() 没有返回排序列表？
- 如何在Python中指定和实施接口规范？
- 为什么没有goto？
- 为什么原始字符串（r-strings）不能以反斜杠结尾？
- 为什么Python没有属性赋值的“with”语句？
- 为什么 if/while/def/class语句需要冒号？
- 为什么Python在列表和元组的末尾允许使用逗号？
- 代码库和插件 FAQ
- 通用的代码库问题
- 通用任务
- 线程相关
- 输入输出
- 网络 / Internet 编程
- 数据库
- 数学和数字
- 扩展/嵌入常见问题
- 可以使用C语言中创建自己的函数吗？
- 可以使用C++语言中创建自己的函数吗？
- C很难写，有没有其他选择？
- 如何从C执行任意Python语句？
- 如何从C中评估任意Python表达式？
- 如何从Python对象中提取C的值？
- 如何使用Py_BuildValue()创建任意长度的元组？
- 如何从C调用对象的方法？
- 如何捕获PyErr_Print()（或打印到stdout / stderr的任何内容）的输出？
- 如何从C访问用Python编写的模块？
- 如何从Python接口到C ++对象？
- 我使用Setup文件添加了一个模块，为什么make失败了？
- 如何调试扩展？
- 我想在Linux系统上编译一个Python模块，但是缺少一些文件。为什么?
- 如何区分“输入不完整”和“输入无效”？
- 如何找到未定义的g++符号__builtin_new或__pure_virtual？
- 能否创建一个对象类，其中部分方法在C中实现，而其他方法在Python中实现（例如通过继承）？
- Python在Windows上的常见问题
- 我怎样在Windows下运行一个Python程序？
- 我怎么让 Python 脚本可执行？
- 为什么有时候 Python 程序会启动缓慢？
- 我怎样使用Python脚本制作可执行文件？
- *.pyd 文件和DLL文件相同吗？
- 我怎样将Python嵌入一个Windows程序？
- 如何让编辑器不要在我的 Python 源代码中插入 tab ？
- 如何在不阻塞的情况下检查按键？
- 图形用户界面（GUI）常见问题
- 图形界面常见问题
- Python 是否有平台无关的图形界面工具包？
- 有哪些Python的GUI工具是某个平台专用的？
- 有关Tkinter的问题
- “为什么我的电脑上安装了 Python ？”
- 什么是Python？
- 为什么我的电脑上安装了 Python ？
- 我能删除 Python 吗？
- 术语对照表
- 文档说明
- Python 文档贡献者
- 解决 Bug
- 文档错误
- 使用 Python 的错误追踪系统
- 开始为 Python 贡献您的知识
- 版权
- 历史和许可证
- 软件历史
- 访问Python或以其他方式使用Python的条款和条件
- Python 3.7.3 的 PSF 许可协议
- Python 2.0 的 BeOpen.com 许可协议
- Python 1.6.1 的 CNRI 许可协议
- Python 0.9.0 至 1.2 的 CWI 许可协议
- 集成软件的许可和认可
- Mersenne Twister
- 套接字
- Asynchronous socket services
- Cookie management
- Execution tracing
- UUencode and UUdecode functions
- XML Remote Procedure Calls
- test_epoll
- Select kqueue
- SipHash24
- strtod and dtoa
- OpenSSL
- expat
- libffi
- zlib
- cfuhash
- libmpdec