分类目录归档:知识问答

从该函数中确定函数名称(不使用回溯)

问题:从该函数中确定函数名称(不使用回溯)

在Python中,不使用traceback模块,是否有办法从该函数内部确定函数名称?

说我有一个带功能栏的模块foo。执行时foo.bar(),bar是否有办法知道bar的名称?还是更好,foo.bar名字?

#foo.py  
def bar():
    print "my name is", __myname__ # <== how do I calculate this at runtime?

In Python, without using the traceback module, is there a way to determine a function’s name from within that function?

Say I have a module foo with a function bar. When executing foo.bar(), is there a way for bar to know bar’s name? Or better yet, foo.bar‘s name?

#foo.py  
def bar():
    print "my name is", __myname__ # <== how do I calculate this at runtime?

回答 0

Python没有功能来访问函数本身或函数内部的名称。已经提出但遭到拒绝。如果您不想自己玩堆栈,则应该使用"bar"bar.__name__取决于上下文。

给定的拒绝通知是:

该PEP被拒绝。目前尚不清楚在极端情况下应该如何实现它或确切的语义,也没有足够的重要用例。回应充其量是冷淡的。

Python doesn’t have a feature to access the function or its name within the function itself. It has been proposed but rejected. If you don’t want to play with the stack yourself, you should either use "bar" or bar.__name__ depending on context.

The given rejection notice is:

This PEP is rejected. It is not clear how it should be implemented or what the precise semantics should be in edge cases, and there aren’t enough important use cases given. response has been lukewarm at best.


回答 1

import inspect

def foo():
   print(inspect.stack()[0][3])
   print(inspect.stack()[1][3]) #will give the caller of foos name, if something called foo
import inspect

def foo():
   print(inspect.stack()[0][3])
   print(inspect.stack()[1][3]) #will give the caller of foos name, if something called foo

回答 2

有几种方法可以得到相同的结果:

from __future__ import print_function
import sys
import inspect

def what_is_my_name():
    print(inspect.stack()[0][0].f_code.co_name)
    print(inspect.stack()[0][3])
    print(inspect.currentframe().f_code.co_name)
    print(sys._getframe().f_code.co_name)

请注意,inspect.stack呼叫比其他方法慢数千倍:

$ python -m timeit -s 'import inspect, sys' 'inspect.stack()[0][0].f_code.co_name'
1000 loops, best of 3: 499 usec per loop
$ python -m timeit -s 'import inspect, sys' 'inspect.stack()[0][3]'
1000 loops, best of 3: 497 usec per loop
$ python -m timeit -s 'import inspect, sys' 'inspect.currentframe().f_code.co_name'
10000000 loops, best of 3: 0.1 usec per loop
$ python -m timeit -s 'import inspect, sys' 'sys._getframe().f_code.co_name'
10000000 loops, best of 3: 0.135 usec per loop

There are a few ways to get the same result:

from __future__ import print_function
import sys
import inspect

def what_is_my_name():
    print(inspect.stack()[0][0].f_code.co_name)
    print(inspect.stack()[0][3])
    print(inspect.currentframe().f_code.co_name)
    print(sys._getframe().f_code.co_name)

Note that the inspect.stack calls are thousands of times slower than the alternatives:

$ python -m timeit -s 'import inspect, sys' 'inspect.stack()[0][0].f_code.co_name'
1000 loops, best of 3: 499 usec per loop
$ python -m timeit -s 'import inspect, sys' 'inspect.stack()[0][3]'
1000 loops, best of 3: 497 usec per loop
$ python -m timeit -s 'import inspect, sys' 'inspect.currentframe().f_code.co_name'
10000000 loops, best of 3: 0.1 usec per loop
$ python -m timeit -s 'import inspect, sys' 'sys._getframe().f_code.co_name'
10000000 loops, best of 3: 0.135 usec per loop

回答 3

您可以使用@Andreas Jung显示的方法来获得定义的名称,但这可能不是使用该函数调用的名称:

import inspect

def Foo():
   print inspect.stack()[0][3]

Foo2 = Foo

>>> Foo()
Foo

>>> Foo2()
Foo

我不能说这种区别对您是否重要。

You can get the name that it was defined with using the approach that @Andreas Jung shows, but that may not be the name that the function was called with:

import inspect

def Foo():
   print inspect.stack()[0][3]

Foo2 = Foo

>>> Foo()
Foo

>>> Foo2()
Foo

Whether that distinction is important to you or not I can’t say.


回答 4

functionNameAsString = sys._getframe().f_code.co_name

我想要一个非常类似的东西,因为我想将函数名称放在一个日志字符串中,该字符串在我的代码中占据了很多位置。可能不是执行此操作的最佳方法,但是这是一种获取当前函数名称的方法。

functionNameAsString = sys._getframe().f_code.co_name

I wanted a very similar thing because I wanted to put the function name in a log string that went in a number of places in my code. Probably not the best way to do that, but here’s a way to get the name of the current function.


回答 5

我将这个方便的实用程序放在附近:

import inspect
myself = lambda: inspect.stack()[1][3]

用法:

myself()

I keep this handy utility nearby:

import inspect
myself = lambda: inspect.stack()[1][3]

Usage:

myself()

回答 6

我想这inspect是最好的方法。例如:

import inspect
def bar():
    print("My name is", inspect.stack()[0][3])

I guess inspect is the best way to do this. For example:

import inspect
def bar():
    print("My name is", inspect.stack()[0][3])

回答 7

我找到了一个将写函数名称的包装器

from functools import wraps

def tmp_wrap(func):
    @wraps(func)
    def tmp(*args, **kwargs):
        print func.__name__
        return func(*args, **kwargs)
    return tmp

@tmp_wrap
def my_funky_name():
    print "STUB"

my_funky_name()

这将打印

my_funky_name

存根

I found a wrapper that will write the function name

from functools import wraps

def tmp_wrap(func):
    @wraps(func)
    def tmp(*args, **kwargs):
        print func.__name__
        return func(*args, **kwargs)
    return tmp

@tmp_wrap
def my_funky_name():
    print "STUB"

my_funky_name()

This will print

my_funky_name

STUB


回答 8

这实际上是从该问题的其他答案中得出的。

这是我的看法:

import sys

# for current func name, specify 0 or no argument.
# for name of caller of current func, specify 1.
# for name of caller of caller of current func, specify 2. etc.
currentFuncName = lambda n=0: sys._getframe(n + 1).f_code.co_name


def testFunction():
    print "You are in function:", currentFuncName()
    print "This function's caller was:", currentFuncName(1)    


def invokeTest():
    testFunction()


invokeTest()

# end of file

与使用inspect.stack()相比,此版本的可能优势是它应该快数千倍[请参阅Alex Melihoff的文章和有关使用sys._getframe()而不是使用inspect.stack()的时间]。

This is actually derived from the other answers to the question.

Here’s my take:

import sys

# for current func name, specify 0 or no argument.
# for name of caller of current func, specify 1.
# for name of caller of caller of current func, specify 2. etc.
currentFuncName = lambda n=0: sys._getframe(n + 1).f_code.co_name


def testFunction():
    print "You are in function:", currentFuncName()
    print "This function's caller was:", currentFuncName(1)    


def invokeTest():
    testFunction()


invokeTest()

# end of file

The likely advantage of this version over using inspect.stack() is that it should be thousands of times faster [see Alex Melihoff’s post and timings regarding using sys._getframe() versus using inspect.stack() ].


回答 9

print(inspect.stack()[0].function) 似乎也可以使用(Python 3.5)。

print(inspect.stack()[0].function) seems to work too (Python 3.5).


回答 10

这是一种面向未来的方法。

将@CamHart和@Yuval的建议与@RoshOxymoron的可接受答案结合起来,可以避免以下情况:

  • _hidden 和可能不推荐使用的方法
  • 索引到堆栈中(可以在以后的python中重新排序)

因此,我认为这对将来的python版本(在2.7.3和3.3.2中进行测试)非常有用:

from __future__ import print_function
import inspect

def bar():
    print("my name is '{}'".format(inspect.currentframe().f_code.co_name))

Here’s a future-proof approach.

Combining @CamHart’s and @Yuval’s suggestions with @RoshOxymoron’s accepted answer has the benefit of avoiding:

  • _hidden and potentially deprecated methods
  • indexing into the stack (which could be reordered in future pythons)

So I think this plays nice with future python versions (tested on 2.7.3 and 3.3.2):

from __future__ import print_function
import inspect

def bar():
    print("my name is '{}'".format(inspect.currentframe().f_code.co_name))

回答 11

import sys

def func_name():
    """
    :return: name of caller
    """
    return sys._getframe(1).f_code.co_name

class A(object):
    def __init__(self):
        pass
    def test_class_func_name(self):
        print(func_name())

def test_func_name():
    print(func_name())

测试:

a = A()
a.test_class_func_name()
test_func_name()

输出:

test_class_func_name
test_func_name
import sys

def func_name():
    """
    :return: name of caller
    """
    return sys._getframe(1).f_code.co_name

class A(object):
    def __init__(self):
        pass
    def test_class_func_name(self):
        print(func_name())

def test_func_name():
    print(func_name())

Test:

a = A()
a.test_class_func_name()
test_func_name()

Output:

test_class_func_name
test_func_name

回答 12

我不确定为什么人们会变得复杂:

import sys 
print("%s/%s" %(sys._getframe().f_code.co_filename, sys._getframe().f_code.co_name))

I am not sure why people make it complicated:

import sys 
print("%s/%s" %(sys._getframe().f_code.co_filename, sys._getframe().f_code.co_name))

回答 13

import inspect

def whoami():
    return inspect.stack()[1][3]

def whosdaddy():
    return inspect.stack()[2][3]

def foo():
    print "hello, I'm %s, daddy is %s" % (whoami(), whosdaddy())
    bar()

def bar():
    print "hello, I'm %s, daddy is %s" % (whoami(), whosdaddy())

foo()
bar()

在IDE中,代码输出

你好,我是foo,爸爸是

你好,我是酒吧,爸爸是foo

你好,我在酒吧,爸爸是

import inspect

def whoami():
    return inspect.stack()[1][3]

def whosdaddy():
    return inspect.stack()[2][3]

def foo():
    print "hello, I'm %s, daddy is %s" % (whoami(), whosdaddy())
    bar()

def bar():
    print "hello, I'm %s, daddy is %s" % (whoami(), whosdaddy())

foo()
bar()

In IDE the code outputs

hello, I’m foo, daddy is

hello, I’m bar, daddy is foo

hello, I’m bar, daddy is


回答 14

您可以使用装饰器:

def my_function(name=None):
    return name

def get_function_name(function):
    return function(name=function.__name__)

>>> get_function_name(my_function)
'my_function'

You can use a decorator:

def my_function(name=None):
    return name

def get_function_name(function):
    return function(name=function.__name__)

>>> get_function_name(my_function)
'my_function'

回答 15

我使用自己的方法在多重继承场景中安全地调用super(我把所有代码都放了进去)

def safe_super(_class, _inst):
    """safe super call"""
    try:
        return getattr(super(_class, _inst), _inst.__fname__)
    except:
        return (lambda *x,**kx: None)


def with_name(function):
    def wrap(self, *args, **kwargs):
        self.__fname__ = function.__name__
        return function(self, *args, **kwargs)
return wrap

样本用法:

class A(object):

    def __init__():
        super(A, self).__init__()

    @with_name
    def test(self):
        print 'called from A\n'
        safe_super(A, self)()

class B(object):

    def __init__():
        super(B, self).__init__()

    @with_name
    def test(self):
        print 'called from B\n'
        safe_super(B, self)()

class C(A, B):

    def __init__():
        super(C, self).__init__()

    @with_name
    def test(self):
        print 'called from C\n'
        safe_super(C, self)()

测试它:

a = C()
a.test()

输出:

called from C
called from A
called from B

在每个@with_name装饰方法中,您可以访问self .__ fname__作为当前函数名称。

I do my own approach used for calling super with safety inside multiple inheritance scenario (I put all the code)

def safe_super(_class, _inst):
    """safe super call"""
    try:
        return getattr(super(_class, _inst), _inst.__fname__)
    except:
        return (lambda *x,**kx: None)


def with_name(function):
    def wrap(self, *args, **kwargs):
        self.__fname__ = function.__name__
        return function(self, *args, **kwargs)
return wrap

sample usage:

class A(object):

    def __init__():
        super(A, self).__init__()

    @with_name
    def test(self):
        print 'called from A\n'
        safe_super(A, self)()

class B(object):

    def __init__():
        super(B, self).__init__()

    @with_name
    def test(self):
        print 'called from B\n'
        safe_super(B, self)()

class C(A, B):

    def __init__():
        super(C, self).__init__()

    @with_name
    def test(self):
        print 'called from C\n'
        safe_super(C, self)()

testing it :

a = C()
a.test()

output:

called from C
called from A
called from B

Inside each @with_name decorated method you have access to self.__fname__ as the current function name.


回答 16

我最近尝试使用以上答案从该函数的上下文访问该函数的文档字符串,但由于上述问题仅返回了名称字符串,因此它不起作用。

幸运的是,我找到了一个简单的解决方案。如果像我一样,您要引用该函数,而不是简单地获取表示名称的字符串,您可以将eval()应用于函数名称的字符串。

import sys
def foo():
    """foo docstring"""
    print(eval(sys._getframe().f_code.co_name).__doc__)

I recently tried to use the above answers to access the docstring of a function from the context of that function but as the above questions were only returning the name string it did not work.

Fortunately I found a simple solution. If like me, you want to refer to the function rather than simply get the string representing the name you can apply eval() to the string of the function name.

import sys
def foo():
    """foo docstring"""
    print(eval(sys._getframe().f_code.co_name).__doc__)

回答 17

我建议不要依赖堆栈元素。如果有人在不同的上下文(例如python解释器)中使用您的代码,则您的堆栈将更改并破坏索引([0] [3])。

我建议你这样:

class MyClass:

    def __init__(self):
        self.function_name = None

    def _Handler(self, **kwargs):
        print('Calling function {} with parameters {}'.format(self.function_name, kwargs))
        self.function_name = None

    def __getattr__(self, attr):
        self.function_name = attr
        return self._Handler


mc = MyClass()
mc.test(FirstParam='my', SecondParam='test')
mc.foobar(OtherParam='foobar')

I suggest not to rely on stack elements. If someone use your code within different contexts (python interpreter for instance) your stack will change and break your index ([0][3]).

I suggest you something like that:

class MyClass:

    def __init__(self):
        self.function_name = None

    def _Handler(self, **kwargs):
        print('Calling function {} with parameters {}'.format(self.function_name, kwargs))
        self.function_name = None

    def __getattr__(self, attr):
        self.function_name = attr
        return self._Handler


mc = MyClass()
mc.test(FirstParam='my', SecondParam='test')
mc.foobar(OtherParam='foobar')

回答 18

用装饰器很容易做到这一点。

>>> from functools import wraps

>>> def named(func):
...     @wraps(func)
...     def _(*args, **kwargs):
...         return func(func.__name__, *args, **kwargs)
...     return _
... 

>>> @named
... def my_func(name, something_else):
...     return name, something_else
... 

>>> my_func('hello, world')
('my_func', 'hello, world')

This is pretty easy to accomplish with a decorator.

>>> from functools import wraps

>>> def named(func):
...     @wraps(func)
...     def _(*args, **kwargs):
...         return func(func.__name__, *args, **kwargs)
...     return _
... 

>>> @named
... def my_func(name, something_else):
...     return name, something_else
... 

>>> my_func('hello, world')
('my_func', 'hello, world')

如何找到Python模块源的位置?

问题:如何找到Python模块源的位置?

如何了解给定Python模块的源文件的安装位置?Windows和Linux上的方法是否不同?

我正在寻找来源 datetime模块,但我也对更通用的答案感兴趣。

How do I learn where the source file for a given Python module is installed? Is the method different on Windows than on Linux?

I’m trying to look for the source of the datetime module in particular, but I’m interested in a more general answer as well.


回答 0

对于纯python模块,您可以通过查看来找到源themodule.__file__。但是,datetime模块是用C编写的,因此datetime.__file__指向.so文件(datetime.__file__在Windows中没有),因此看不到源代码。

如果您下载python源tarball并将其解压缩,则可以在模块中找到模块的代码子目录中。

例如,如果要查找python 2.6的日期时间代码,可以查看

Python-2.6/Modules/datetimemodule.c

您也可以在以下网址找到最新的Mercurial版本: https://hg.python.org/cpython/file/tip/Modules/_datetimemodule.c

For a pure python module you can find the source by looking at themodule.__file__. The datetime module, however, is written in C, and therefore datetime.__file__ points to a .so file (there is no datetime.__file__ on Windows), and therefore, you can’t see the source.

If you download a python source tarball and extract it, the modules’ code can be found in the Modules subdirectory.

For example, if you want to find the datetime code for python 2.6, you can look at

Python-2.6/Modules/datetimemodule.c

You can also find the latest Mercurial version on the web at https://hg.python.org/cpython/file/tip/Modules/_datetimemodule.c


回答 1

python -v从命令行运行应该告诉您正在导入什么以及从何处导入。这适用于Windows和Mac OSX。

C:\>python -v
# installing zipimport hook
import zipimport # builtin
# installed zipimport hook
# C:\Python24\lib\site.pyc has bad mtime
import site # from C:\Python24\lib\site.py
# wrote C:\Python24\lib\site.pyc
# C:\Python24\lib\os.pyc has bad mtime
import os # from C:\Python24\lib\os.py
# wrote C:\Python24\lib\os.pyc
import nt # builtin
# C:\Python24\lib\ntpath.pyc has bad mtime
...

我不确定那些不好的mtime在我的装置上!

Running python -v from the command line should tell you what is being imported and from where. This works for me on Windows and Mac OS X.

C:\>python -v
# installing zipimport hook
import zipimport # builtin
# installed zipimport hook
# C:\Python24\lib\site.pyc has bad mtime
import site # from C:\Python24\lib\site.py
# wrote C:\Python24\lib\site.pyc
# C:\Python24\lib\os.pyc has bad mtime
import os # from C:\Python24\lib\os.py
# wrote C:\Python24\lib\os.pyc
import nt # builtin
# C:\Python24\lib\ntpath.pyc has bad mtime
...

I’m not sure what those bad mtime’s are on my install!


回答 2

我知道这个答案要晚4年了,但是现有的答案会误导人们。

做到这一点的正确方法永远不会__file__,或者尝试逐步完成sys.path搜索并寻找自己,等等(除非您需要向后兼容2.1以上)。

这是inspect模块特别getfilegetsourcefile

除非您想学习和实现用于映射.pyc.py文件的规则(对于CPython 2.x,已记录但很痛苦,而对于其他实现或3.x则没有任何记录);处理.zip归档文件,鸡蛋和模块包;尝试不同的方法以获取.so/.pyd不支持的文件__file__ ; 弄清楚Jython / IronPython / PyPy的作用;等。在这种情况下,请继续努力。

同时,每个Python版本的2.0 或更高版本的源均可在线获得http://hg.python.org/cpython/file/X.Y/(例如2.73.3)。因此,一旦发现inspect.getfile(datetime).so或之类的.pyd文件/usr/local/lib/python2.7/lib-dynload/datetime.so,就可以在Modules目录中查找它。严格来说,无法确定哪个文件定义了哪个模块,但是几乎所有文件都不是foo.cfoomodule.c,因此不难猜测datetimemodule.c是您想要的。

I realize this answer is 4 years late, but the existing answers are misleading people.

The right way to do this is never __file__, or trying to walk through sys.path and search for yourself, etc. (unless you need to be backward compatible beyond 2.1).

It’s the inspect module—in particular, getfile or getsourcefile.

Unless you want to learn and implement the rules (which are documented, but painful, for CPython 2.x, and not documented at all for other implementations, or 3.x) for mapping .pyc to .py files; dealing with .zip archives, eggs, and module packages; trying different ways to get the path to .so/.pyd files that don’t support __file__; figuring out what Jython/IronPython/PyPy do; etc. In which case, go for it.

Meanwhile, every Python version’s source from 2.0+ is available online at http://hg.python.org/cpython/file/X.Y/ (e.g., 2.7 or 3.3). So, once you discover that inspect.getfile(datetime) is a .so or .pyd file like /usr/local/lib/python2.7/lib-dynload/datetime.so, you can look it up inside the Modules directory. Strictly speaking, there’s no way to be sure of which file defines which module, but nearly all of them are either foo.c or foomodule.c, so it shouldn’t be hard to guess that datetimemodule.c is what you want.


回答 3

sys.path列表包含将在运行时搜索模块的目录列表:

python -v
>>> import sys
>>> sys.path
['', '/usr/local/lib/python25.zip', '/usr/local/lib/python2.5', ... ]

The sys.path list contains the list of directories which will be searched for modules at runtime:

python -v
>>> import sys
>>> sys.path
['', '/usr/local/lib/python25.zip', '/usr/local/lib/python2.5', ... ]

回答 4

如果您使用pip安装模块,则仅pip show $module返回位置。

If you’re using pip to install your modules, just pip show $module the location is returned.


回答 5

从标准库尝试imp.find_module

>>> import imp
>>> imp.find_module('fontTools')
(None, 'C:\\Python27\\lib\\site-packages\\FontTools\\fontTools', ('', '', 5))
>>> imp.find_module('datetime')
(None, 'datetime', ('', '', 6))

from the standard library try imp.find_module

>>> import imp
>>> imp.find_module('fontTools')
(None, 'C:\\Python27\\lib\\site-packages\\FontTools\\fontTools', ('', '', 5))
>>> imp.find_module('datetime')
(None, 'datetime', ('', '', 6))

回答 6

datetime 是一个内置模块,因此没有(Python)源文件。

对于来自.py(或.pyc)文件的模块,可以使用mymodule.__file__,例如

> import random
> random.__file__
'C:\\Python25\\lib\\random.pyc'

datetime is a builtin module, so there is no (Python) source file.

For modules coming from .py (or .pyc) files, you can use mymodule.__file__, e.g.

> import random
> random.__file__
'C:\\Python25\\lib\\random.pyc'

回答 7

在python解释器中,您可以导入特定的模块,然后键入help(module)。这给出了详细信息,例如名称,文件,模块文档,描述等。

例如:

import os

help(os)


Help on module os:

NAME

os - OS routines for Mac, NT, or Posix depending on what system we're on.

FILE

/usr/lib/python2.6/os.py

MODULE DOCS

http://docs.python.org/library/os

DESCRIPTION

This exports:

- all functions from posix, nt, os2, or ce, e.g. unlink, stat, etc.

- os.path is one of the modules posixpath, or ntpath

- os.name is 'posix', 'nt', 'os2', 'ce' or 'riscos'

In the python interpreter you could import the particular module and then type help(module). This gives details such as Name, File, Module Docs, Description et al.

Ex:

import os

help(os)


Help on module os:

NAME

os - OS routines for Mac, NT, or Posix depending on what system we're on.

FILE

/usr/lib/python2.6/os.py

MODULE DOCS

http://docs.python.org/library/os

DESCRIPTION

This exports:

- all functions from posix, nt, os2, or ce, e.g. unlink, stat, etc.

- os.path is one of the modules posixpath, or ntpath

- os.name is 'posix', 'nt', 'os2', 'ce' or 'riscos'

et al


回答 8

这是获取模块文件名的一种方法,适用于shell别名:

echo 'import sys; t=__import__(sys.argv[1],fromlist=[\".\"]); print(t.__file__)'  | python - 

设置为别名:

alias getpmpath="echo 'import sys; t=__import__(sys.argv[1],fromlist=[\".\"]); print(t.__file__)'  | python - "

使用方法:

$ getpmpath twisted
/usr/lib64/python2.6/site-packages/twisted/__init__.pyc
$ getpmpath twisted.web
/usr/lib64/python2.6/site-packages/twisted/web/__init__.pyc

Here’s a one-liner to get the filename for a module, suitable for shell aliasing:

echo 'import sys; t=__import__(sys.argv[1],fromlist=[\".\"]); print(t.__file__)'  | python - 

Set up as an alias:

alias getpmpath="echo 'import sys; t=__import__(sys.argv[1],fromlist=[\".\"]); print(t.__file__)'  | python - "

To use:

$ getpmpath twisted
/usr/lib64/python2.6/site-packages/twisted/__init__.pyc
$ getpmpath twisted.web
/usr/lib64/python2.6/site-packages/twisted/web/__init__.pyc

回答 9

Python 3.2中的新功能,您现在可以code_info()在dis模块中使用例如:http : //docs.python.org/dev/whatsnew/3.2.html#dis

New in Python 3.2, you can now use e.g. code_info() from the dis module: http://docs.python.org/dev/whatsnew/3.2.html#dis


回答 10

出这个漂亮的“ cdp”命令以cd到包含指示的Python模块源的目录:

cdp () {
  cd "$(python -c "import os.path as _, ${1}; \
    print _.dirname(_.realpath(${1}.__file__[:-1]))"
  )"
}

Check out this nifty “cdp” command to cd to the directory containing the source for the indicated Python module:

cdp () {
  cd "$(python -c "import os.path as _, ${1}; \
    print _.dirname(_.realpath(${1}.__file__[:-1]))"
  )"
}

回答 11

在Windows上,您可以找到python模块的位置,如下所示:ie find rest_framework模块 在此处输入图片说明

On windows you can find the location of the python module as shown below:i.e find rest_framework module enter image description here


回答 12

在Ubuntu 12.04上,例如可以在以下位置找到python2的numpy软件包:

/usr/lib/python2.7/dist-packages/numpy

当然,这不是通用答案

On Ubuntu 12.04, for example numpy package for python2, can be found at:

/usr/lib/python2.7/dist-packages/numpy

Of course, this is not generic answer


回答 13

并非所有的python模块都是用python编写的。日期时间恰好是其中之一,并且(在Linux上)是datetime.so。

您必须将源代码下载到python标准库中才能使用它。

Not all python modules are written in python. Datetime happens to be one of them that is not, and (on linux) is datetime.so.

You would have to download the source code to the python standard library to get at it.


回答 14

对于那些喜欢GUI解决方案的用户:如果使用的是GUI,例如Spyder(Anaconda安装的一部分),则可以右键单击模块名称(例如“ import csv”中的“ csv”),然后选择“ go定义”-这将打开文件,但在顶部您也可以看到确切的文件位置(“ C:…. csv.py”)

For those who prefer a GUI solution: if you’re using a gui such as Spyder (part of the Anaconda installation) you can just right-click the module name (such as “csv” in “import csv”) and select “go to definition” – this will open the file, but also on the top you can see the exact file location (“C:….csv.py”)


回答 15

如果您不使用解释器,则可以运行以下代码:

import site
print (site.getsitepackages())

输出:

['C:\\Users\\<your username>\\AppData\\Local\\Programs\\Python\\Python37', 'C:\\Users\\<your username>\\AppData\\Local\\Programs\\Python\\Python37\\lib\\site-packages']

Array中的第二个元素将是您的包裹位置。在这种情况下:

C:\Users\<your username>\AppData\Local\Programs\Python\Python37\lib\site-packages

If you are not using interpreter then you can run the code below:

import site
print (site.getsitepackages())

Output:

['C:\\Users\\<your username>\\AppData\\Local\\Programs\\Python\\Python37', 'C:\\Users\\<your username>\\AppData\\Local\\Programs\\Python\\Python37\\lib\\site-packages']

The second element in Array will be your package location. In this case:

C:\Users\<your username>\AppData\Local\Programs\Python\Python37\lib\site-packages

回答 16

从终端检查是否已安装多个python版本的另一种方法。

-MBP:〜python3 -m pip显示pyperclip

位置:/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-

MBP:〜python -m pip show pyperclip

位置:/Users/umeshvuyyuru/Library/Python/2.7/lib/python/site-packages

Another way to check if you have multiple python versions installed, from the terminal.

-MBP:~python3 -m pip show pyperclip

Location: /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-

MBP:~ python -m pip show pyperclip

Location: /Users/umeshvuyyuru/Library/Python/2.7/lib/python/site-packages


回答 17

在Spyder之类的IDE中,导入模块,然后分别运行该模块。 在此处输入图片说明

In an IDE like Spyder, import the module and then run the module individually. enter image description here


如何摆脱多重循环?

问题:如何摆脱多重循环?

给出以下代码(不起作用):

while True:
    #snip: print out current state
    while True:
        ok = get_input("Is this ok? (y/n)")
        if ok.lower() == "y": break 2 #this doesn't work :(
        if ok.lower() == "n": break
    #do more processing with menus and stuff

有没有办法使这项工作?还是我要进行一次检查以打破输入循环,然后再进行另一项限制较大的检查(如果用户满意的话)在外部循环中一起分解?

Given the following code (that doesn’t work):

while True:
    #snip: print out current state
    while True:
        ok = get_input("Is this ok? (y/n)")
        if ok.lower() == "y": break 2 #this doesn't work :(
        if ok.lower() == "n": break
    #do more processing with menus and stuff

Is there a way to make this work? Or do I have do one check to break out of the input loop, then another, more limited, check in the outside loop to break out all together if the user is satisfied?


回答 0

我的第一个直觉是将嵌套循环重构为一个函数,然后使用它return来分解。

My first instinct would be to refactor the nested loop into a function and use return to break out.


回答 1

这是另一种简短的方法。缺点是您只能打破外部循环,但是有时正是您想要的。

for a in xrange(10):
    for b in xrange(20):
        if something(a, b):
            # Break the inner loop...
            break
    else:
        # Continue if the inner loop wasn't broken.
        continue
    # Inner loop was broken, break the outer.
    break

这使用了for / else构造,其解释如下:为什么python在for和while循环之后为什么使用’else’?

关键见解:似乎外部循环总是在中断。但是,如果内部循环不中断,那么外部循环也不会。

continue句话是魔术。在for-else子句中。根据定义,如果没有内部中断,则会发生这种情况。在这种情况下,可以continue巧妙地规避外部中断。

Here’s another approach that is short. The disadvantage is that you can only break the outer loop, but sometimes it’s exactly what you want.

for a in xrange(10):
    for b in xrange(20):
        if something(a, b):
            # Break the inner loop...
            break
    else:
        # Continue if the inner loop wasn't broken.
        continue
    # Inner loop was broken, break the outer.
    break

This uses the for / else construct explained at: Why does python use ‘else’ after for and while loops?

Key insight: It only seems as if the outer loop always breaks. But if the inner loop doesn’t break, the outer loop won’t either.

The continue statement is the magic here. It’s in the for-else clause. By definition that happens if there’s no inner break. In that situation continue neatly circumvents the outer break.


回答 2

PEP 3136建议标记为中断/继续。Guido 拒绝了它,因为“如此复杂的代码很少需要此功能”。PEP确实提到了一些解决方法(例如,异常技术),而Guido认为在大多数情况下,使用return进行重构将更为简单。

PEP 3136 proposes labeled break/continue. Guido rejected it because “code so complicated to require this feature is very rare”. The PEP does mention some workarounds, though (such as the exception technique), while Guido feels refactoring to use return will be simpler in most cases.


回答 3

首先,普通逻辑是有帮助的。

如果由于某种原因无法确定终止条件,则exceptions是后备计划。

class GetOutOfLoop( Exception ):
    pass

try:
    done= False
    while not done:
        isok= False
        while not (done or isok):
            ok = get_input("Is this ok? (y/n)")
            if ok in ("y", "Y") or ok in ("n", "N") : 
                done= True # probably better
                raise GetOutOfLoop
        # other stuff
except GetOutOfLoop:
    pass

对于此特定示例,可能不需要exceptions。

另一方面,在字符模式应用程序中,我们经常有“ Y”,“ N”和“ Q”选项。对于“ Q”选项,我们希望立即退出。那更exceptions。

First, ordinary logic is helpful.

If, for some reason, the terminating conditions can’t be worked out, exceptions are a fall-back plan.

class GetOutOfLoop( Exception ):
    pass

try:
    done= False
    while not done:
        isok= False
        while not (done or isok):
            ok = get_input("Is this ok? (y/n)")
            if ok in ("y", "Y") or ok in ("n", "N") : 
                done= True # probably better
                raise GetOutOfLoop
        # other stuff
except GetOutOfLoop:
    pass

For this specific example, an exception may not be necessary.

On other other hand, we often have “Y”, “N” and “Q” options in character-mode applications. For the “Q” option, we want an immediate exit. That’s more exceptional.


回答 4

我倾向于同意重构为函数通常是解决这种情况的最佳方法,但是对于确实需要打破嵌套循环的情况,这是@ S.Lott描述的异常引发方法的一个有趣变体。它使用Python的with语句使异常引发看起来更好。使用以下命令定义一个新的上下文管理器(您只需执行一次):

from contextlib import contextmanager
@contextmanager
def nested_break():
    class NestedBreakException(Exception):
        pass
    try:
        yield NestedBreakException
    except NestedBreakException:
        pass

现在,您可以按以下方式使用此上下文管理器:

with nested_break() as mylabel:
    while True:
        print "current state"
        while True:
            ok = raw_input("Is this ok? (y/n)")
            if ok == "y" or ok == "Y": raise mylabel
            if ok == "n" or ok == "N": break
        print "more processing"

优点:(1)稍微干净一些(没有显式的try-except块),并且(2)Exception每次使用都会获得一个定制的子类nested_break;无需Exception每次都声明自己的子类。

I tend to agree that refactoring into a function is usually the best approach for this sort of situation, but for when you really need to break out of nested loops, here’s an interesting variant of the exception-raising approach that @S.Lott described. It uses Python’s with statement to make the exception raising look a bit nicer. Define a new context manager (you only have to do this once) with:

from contextlib import contextmanager
@contextmanager
def nested_break():
    class NestedBreakException(Exception):
        pass
    try:
        yield NestedBreakException
    except NestedBreakException:
        pass

Now you can use this context manager as follows:

with nested_break() as mylabel:
    while True:
        print "current state"
        while True:
            ok = raw_input("Is this ok? (y/n)")
            if ok == "y" or ok == "Y": raise mylabel
            if ok == "n" or ok == "N": break
        print "more processing"

Advantages: (1) it’s slightly cleaner (no explicit try-except block), and (2) you get a custom-built Exception subclass for each use of nested_break; no need to declare your own Exception subclass each time.


回答 5

首先,您还可以考虑使获取和验证输入的过程成为一个函数。在该函数中,您可以只返回正确的值,否则返回while循环。这从根本上消除了您解决的问题,通常可以在更一般的情况下使用(突破多个循环)。如果您绝对必须在您的代码中保留此结构,并且真的不想处理簿记布尔值…

您还可以按以下方式使用goto(从此处使用April Fools模块):

#import the stuff
from goto import goto, label

while True:
    #snip: print out current state
    while True:
        ok = get_input("Is this ok? (y/n)")
        if ok == "y" or ok == "Y": goto .breakall
        if ok == "n" or ok == "N": break
    #do more processing with menus and stuff
label .breakall

我知道,我知道“您不应该使用goto”以及所有其他功能,但是在像这样的奇怪情况下它可以很好地工作。

First, you may also consider making the process of getting and validating the input a function; within that function, you can just return the value if its correct, and keep spinning in the while loop if not. This essentially obviates the problem you solved, and can usually be applied in the more general case (breaking out of multiple loops). If you absolutely must keep this structure in your code, and really don’t want to deal with bookkeeping booleans…

You may also use goto in the following way (using an April Fools module from here):

#import the stuff
from goto import goto, label

while True:
    #snip: print out current state
    while True:
        ok = get_input("Is this ok? (y/n)")
        if ok == "y" or ok == "Y": goto .breakall
        if ok == "n" or ok == "N": break
    #do more processing with menus and stuff
label .breakall

I know, I know, “thou shalt not use goto” and all that, but it works well in strange cases like this.


回答 6

引入一个新变量,将其用作“循环断路器”。首先给它分配一些东西(False,0,等等),然后在外循环内部,从它断开之前,将值更改为其他东西(True,1,…)。一旦循环退出,请在“父”循环中检查该值。让我示范一下:

breaker = False #our mighty loop exiter!
while True:
    while True:
        if conditionMet:
            #insert code here...
            breaker = True 
            break
    if breaker: # the interesting part!
        break   # <--- !

如果您有无限循环,这是唯一的出路。对于其他循环,执行速度实际上要快得多。如果您有许多嵌套循环,这也适用。您可以全部退出,也可以退出几个。无限可能!希望这对您有所帮助!

Introduce a new variable that you’ll use as a ‘loop breaker’. First assign something to it(False,0, etc.), and then, inside the outer loop, before you break from it, change the value to something else(True,1,…). Once the loop exits make the ‘parent’ loop check for that value. Let me demonstrate:

breaker = False #our mighty loop exiter!
while True:
    while True:
        if conditionMet:
            #insert code here...
            breaker = True 
            break
    if breaker: # the interesting part!
        break   # <--- !

If you have an infinite loop, this is the only way out; for other loops execution is really a lot faster. This also works if you have many nested loops. You can exit all, or just a few. Endless possibilities! Hope this helped!


回答 7

要打破多个嵌套循环而不重构为函数,请使用带有内置StopIteration异常的“模拟goto语句” :

try:
    for outer in range(100):
        for inner in range(100):
            if break_early():
                raise StopIteration

except StopIteration: pass

这个讨论关于使用goto语句的嵌套循环的突破。

To break out of multiple nested loops, without refactoring into a function, make use of a “simulated goto statement” with the built-in StopIteration exception:

try:
    for outer in range(100):
        for inner in range(100):
            if break_early():
                raise StopIteration

except StopIteration: pass

See this discussion on the use of goto statements for breaking out of nested loops.


回答 8


keeplooping=True
while keeplooping:
    #Do Stuff
    while keeplooping:
          #do some other stuff
          if finisheddoingstuff(): keeplooping=False

或类似的东西。您可以在内部循环中设置一个变量,并在内部循环退出后立即在外部循环中检查它,如果合适的话,可以中断。我有点像GOTO方法,但前提是您不介意使用愚人节的笑话模块-它不是Python语言的,但确实有道理。


keeplooping=True
while keeplooping:
    #Do Stuff
    while keeplooping:
          #do some other stuff
          if finisheddoingstuff(): keeplooping=False

or something like that. You could set a variable in the inner loop, and check it in the outer loop immediately after the inner loop exits, breaking if appropriate. I kinda like the GOTO method, provided you don’t mind using an April Fool’s joke module – its not Pythonic, but it does make sense.


回答 9

这不是最漂亮的方法,但是我认为这是最好的方法。

def loop():
    while True:
    #snip: print out current state
        while True:
            ok = get_input("Is this ok? (y/n)")
            if ok == "y" or ok == "Y": return
            if ok == "n" or ok == "N": break
        #do more processing with menus and stuff

我很确定您也可以在这里使用递归来解决问题,但是我不知道这是否对您来说是个好选择。

This isn’t the prettiest way to do it, but in my opinion, it’s the best way.

def loop():
    while True:
    #snip: print out current state
        while True:
            ok = get_input("Is this ok? (y/n)")
            if ok == "y" or ok == "Y": return
            if ok == "n" or ok == "N": break
        #do more processing with menus and stuff

I’m pretty sure you could work out something using recursion here as well, but I dunno if that’s a good option for you.


回答 10

如果两个条件都成立,为什么不继续循环呢?我认为这是一种更Python化的方式:

dejaVu = True

while dejaVu:
    while True:
        ok = raw_input("Is this ok? (y/n)")
        if ok == "y" or ok == "Y" or ok == "n" or ok == "N":
            dejaVu = False
            break

是不是

祝一切顺利。

And why not to keep looping if two conditions are true? I think this is a more pythonic way:

dejaVu = True

while dejaVu:
    while True:
        ok = raw_input("Is this ok? (y/n)")
        if ok == "y" or ok == "Y" or ok == "n" or ok == "N":
            dejaVu = False
            break

Isn’t it?

All the best.


回答 11

将循环逻辑分解为一个迭代器,该迭代器产生循环变量并在完成后返回-这是一个简单的示例,它以行/列的形式排列图像,直到我们没有图像或放置位置为止:

def it(rows, cols, images):
    i = 0
    for r in xrange(rows):
        for c in xrange(cols):
            if i >= len(images):
                return
            yield r, c, images[i]
            i += 1 

for r, c, image in it(rows=4, cols=4, images=['a.jpg', 'b.jpg', 'c.jpg']):
    ... do something with r, c, image ...

这具有拆分复杂的循环逻辑和处理的优点。

Factor your loop logic into an iterator that yields the loop variables and returns when done — here is a simple one that lays out images in rows/columns until we’re out of images or out of places to put them:

def it(rows, cols, images):
    i = 0
    for r in xrange(rows):
        for c in xrange(cols):
            if i >= len(images):
                return
            yield r, c, images[i]
            i += 1 

for r, c, image in it(rows=4, cols=4, images=['a.jpg', 'b.jpg', 'c.jpg']):
    ... do something with r, c, image ...

This has the advantage of splitting up the complicated loop logic and the processing…


回答 12

在这种情况下,正如其他人指出的那样,功能分解也是解决之道。Python 3中的代码:

def user_confirms():
    while True:
        answer = input("Is this OK? (y/n) ").strip().lower()
        if answer in "yn":
            return answer == "y"

def main():
    while True:
        # do stuff
        if user_confirms():
            break

In this case, as pointed out by others as well, functional decomposition is the way to go. Code in Python 3:

def user_confirms():
    while True:
        answer = input("Is this OK? (y/n) ").strip().lower()
        if answer in "yn":
            return answer == "y"

def main():
    while True:
        # do stuff
        if user_confirms():
            break

回答 13

Python while ... else结构中有一个隐藏的技巧,可用于模拟两次中断,而无需进行大量代码更改/添加。本质上,如果while条件为假,else则触发该块。既不exceptions,continue也不break触发该else块。有关更多信息,请参见“ Python while语句上的Else子句 ”或while(v2.7)上的Python文档的答案。

while True:
    #snip: print out current state
    ok = ""
    while ok != "y" and ok != "n":
        ok = get_input("Is this ok? (y/n)")
        if ok == "n" or ok == "N":
            break    # Breaks out of inner loop, skipping else

    else:
        break        # Breaks out of outer loop

    #do more processing with menus and stuff

唯一的缺点是您需要将双破折号条件移入while条件(或添加一个标志变量)。for循环也存在这种变化,else在循环完成后将触发该块。

There is a hidden trick in the Python while ... else structure which can be used to simulate the double break without much code changes/additions. In essence if the while condition is false, the else block is triggered. Neither exceptions, continue or break trigger the else block. For more information see answers to “Else clause on Python while statement“, or Python doc on while (v2.7).

while True:
    #snip: print out current state
    ok = ""
    while ok != "y" and ok != "n":
        ok = get_input("Is this ok? (y/n)")
        if ok == "n" or ok == "N":
            break    # Breaks out of inner loop, skipping else

    else:
        break        # Breaks out of outer loop

    #do more processing with menus and stuff

The only downside is that you need to move the double breaking condition into the while condition (or add a flag variable). Variations of this exists also for the for loop, where the else block is triggered after loop completion.


回答 14

将迭代减少到单级循环的另一种方法是通过使用python参考中也指定的生成器

for i, j in ((i, j) for i in A for j in B):
    print(i , j)
    if (some_condition):
        break

您可以将其扩展到循环的任意数量的级别

缺点是您不能再只打破一个级别。全部或全无。

另一个缺点是它不适用于while循环。我本来想在Python上发布此答案-打破所有循环中的`break`,但不幸的是,它已作为此副本的副本关闭了

Another way of reducing your iteration to a single-level loop would be via the use of generators as also specified in the python reference

for i, j in ((i, j) for i in A for j in B):
    print(i , j)
    if (some_condition):
        break

You could scale it up to any number of levels for the loop

The downside is that you can no longer break only a single level. It’s all or nothing.

Another downside is that it doesn’t work with a while loop. I originally wanted to post this answer on Python – `break` out of all loops but unfortunately that’s closed as a duplicate of this one


回答 15

我来这里的原因是我有一个外部循环和一个内部循环,如下所示:

for x in array:
  for y in dont_use_these_values:
    if x.value==y:
      array.remove(x)  # fixed, was array.pop(x) in my original answer
      continue

  do some other stuff with x

如您所见,它实际上不会转到下一个x,而是会转到下一个y。

我发现解决这个问题的方法只是遍历两次数组:

for x in array:
  for y in dont_use_these_values:
    if x.value==y:
      array.remove(x)  # fixed, was array.pop(x) in my original answer
      continue

for x in array:
  do some other stuff with x

我知道这是OP的特定情况,但我希望将其发布,以帮助某人以不同的方式思考他们的问题,同时保持简单。

My reason for coming here is that i had an outer loop and an inner loop like so:

for x in array:
  for y in dont_use_these_values:
    if x.value==y:
      array.remove(x)  # fixed, was array.pop(x) in my original answer
      continue

  do some other stuff with x

As you can see, it won’t actually go to the next x, but will go to the next y instead.

what i found to solve this simply was to run through the array twice instead:

for x in array:
  for y in dont_use_these_values:
    if x.value==y:
      array.remove(x)  # fixed, was array.pop(x) in my original answer
      continue

for x in array:
  do some other stuff with x

I know this was a specific case of OP’s question, but I am posting it in the hope that it will help someone think about their problem differently while keeping things simple.


回答 16

尝试使用无限生成器。

from itertools import repeat
inputs = (get_input("Is this ok? (y/n)") for _ in repeat(None))
response = (i.lower()=="y" for i in inputs if i.lower() in ("y", "n"))

while True:
    #snip: print out current state
    if next(response):
        break
    #do more processing with menus and stuff

Try using an infinite generator.

from itertools import repeat
inputs = (get_input("Is this ok? (y/n)") for _ in repeat(None))
response = (i.lower()=="y" for i in inputs if i.lower() in ("y", "n"))

while True:
    #snip: print out current state
    if next(response):
        break
    #do more processing with menus and stuff

回答 17

通过使用一个函数:

def myloop():
    for i in range(1,6,1):  # 1st loop
        print('i:',i)
        for j in range(1,11,2):  # 2nd loop
            print('   i, j:' ,i, j)
            for k in range(1,21,4):  # 3rd loop
                print('      i,j,k:', i,j,k)
                if i%3==0 and j%3==0 and k%3==0:
                    return  # getting out of all loops

myloop()

尝试通过注释掉来运行上面的代码return

不使用任何功能:

done = False
for i in range(1,6,1):  # 1st loop
    print('i:', i)
    for j in range(1,11,2):  # 2nd loop
        print('   i, j:' ,i, j)
        for k in range(1,21,4):  # 3rd loop
            print('      i,j,k:', i,j,k)
            if i%3==0 and j%3==0 and k%3==0:
                done = True
                break  # breaking from 3rd loop
        if done: break # breaking from 2nd loop
    if done: break     # breaking from 1st loop

现在,按原样运行上面的代码,然后尝试通过break从底部开始注释掉包含一行的每一行来运行。

By using a function:

def myloop():
    for i in range(1,6,1):  # 1st loop
        print('i:',i)
        for j in range(1,11,2):  # 2nd loop
            print('   i, j:' ,i, j)
            for k in range(1,21,4):  # 3rd loop
                print('      i,j,k:', i,j,k)
                if i%3==0 and j%3==0 and k%3==0:
                    return  # getting out of all loops

myloop()

Try running the above codes by commenting out the return as well.

Without using any function:

done = False
for i in range(1,6,1):  # 1st loop
    print('i:', i)
    for j in range(1,11,2):  # 2nd loop
        print('   i, j:' ,i, j)
        for k in range(1,21,4):  # 3rd loop
            print('      i,j,k:', i,j,k)
            if i%3==0 and j%3==0 and k%3==0:
                done = True
                break  # breaking from 3rd loop
        if done: break # breaking from 2nd loop
    if done: break     # breaking from 1st loop

Now, run the above codes as is first and then try running by commenting out each line containing break one at a time from the bottom.


回答 18

将多个循环转换为单个可中断循环的一种简单方法是使用 numpy.ndindex

for i in range(n):
  for j in range(n):
    val = x[i, j]
    break # still inside the outer loop!

for i, j in np.ndindex(n, n):
  val = x[i, j]
  break # you left the only loop there was!

您确实必须索引到您的对象中,而不是能够显式地遍历值,但是至少在简单的情况下,它似乎比大多数建议的答案简单约2至20倍。

An easy way to turn multiple loops into a single, breakable loop is to use numpy.ndindex

for i in range(n):
  for j in range(n):
    val = x[i, j]
    break # still inside the outer loop!

for i, j in np.ndindex(n, n):
  val = x[i, j]
  break # you left the only loop there was!

You do have to index into your objects, as opposed to being able to iterate through the values explicitly, but at least in simple cases it seems to be approximately 2-20 times simpler than most of the answers suggested.


回答 19

# this version uses a level counter to choose how far to break out

break_levels = 0
while True:
    # snip: print out current state
    while True:
        ok = get_input("Is this ok? (y/n)")
        if ok == "y" or ok == "Y":
            break_levels = 1        # how far nested, excluding this break
            break
        if ok == "n" or ok == "N":
            break                   # normal break
    if break_levels:
        break_levels -= 1
        break                       # pop another level
if break_levels:
    break_levels -= 1
    break

# ...and so on
# this version uses a level counter to choose how far to break out

break_levels = 0
while True:
    # snip: print out current state
    while True:
        ok = get_input("Is this ok? (y/n)")
        if ok == "y" or ok == "Y":
            break_levels = 1        # how far nested, excluding this break
            break
        if ok == "n" or ok == "N":
            break                   # normal break
    if break_levels:
        break_levels -= 1
        break                       # pop another level
if break_levels:
    break_levels -= 1
    break

# ...and so on

回答 20

如果不喜欢将其重构为函数,可能会出现如下所示的小技巧

添加了1个break_level变量来控制while循环条件

break_level = 0
# while break_level < 3: # if we have another level of nested loop here
while break_level < 2:
    #snip: print out current state
    while break_level < 1:
        ok = get_input("Is this ok? (y/n)")
        if ok == "y" or ok == "Y": break_level = 2 # break 2 level
        if ok == "n" or ok == "N": break_level = 1 # break 1 level

probably little trick like below will do if not prefer to refactorial into function

added 1 break_level variable to control the while loop condition

break_level = 0
# while break_level < 3: # if we have another level of nested loop here
while break_level < 2:
    #snip: print out current state
    while break_level < 1:
        ok = get_input("Is this ok? (y/n)")
        if ok == "y" or ok == "Y": break_level = 2 # break 2 level
        if ok == "n" or ok == "N": break_level = 1 # break 1 level

回答 21

您可以定义一个变量(例如break_statement),然后在发生两次中断条件时将其更改为另一个值,并在if语句中使用它来从第二个循环中断。

while True:
    break_statement=0
    while True:
        ok = raw_input("Is this ok? (y/n)")
        if ok == "n" or ok == "N": 
            break
        if ok == "y" or ok == "Y": 
            break_statement=1
            break
    if break_statement==1:
        break

You can define a variable( for example break_statement ), then change it to a different value when two-break condition occurs and use it in if statement to break from second loop also.

while True:
    break_statement=0
    while True:
        ok = raw_input("Is this ok? (y/n)")
        if ok == "n" or ok == "N": 
            break
        if ok == "y" or ok == "Y": 
            break_statement=1
            break
    if break_statement==1:
        break

回答 22

我想提醒您,Python中的函数可以在代码中间创建,并且可以透明地访问周围的变量以进行读取以及使用with nonlocalglobal声明进行写入。

因此,您可以将函数用作“易碎控件结构”,从而定义要返回的位置:

def is_prime(number):

    foo = bar = number

    def return_here():
        nonlocal foo, bar
        init_bar = bar
        while foo > 0:
            bar = init_bar
            while bar >= foo:
                if foo*bar == number:
                    return
                bar -= 1
            foo -= 1

    return_here()

    if foo == 1:
        print(number, 'is prime')
    else:
        print(number, '=', bar, '*', foo)

>>> is_prime(67)
67 is prime
>>> is_prime(117)
117 = 13 * 9
>>> is_prime(16)
16 = 4 * 4

I’d like to remind you that functions in Python can be created right in the middle of the code and can access the surrounding variables transparently for reading and with nonlocal or global declaration for writing.

So you can use a function as a “breakable control structure”, defining a place you want to return to:

def is_prime(number):

    foo = bar = number

    def return_here():
        nonlocal foo, bar
        init_bar = bar
        while foo > 0:
            bar = init_bar
            while bar >= foo:
                if foo*bar == number:
                    return
                bar -= 1
            foo -= 1

    return_here()

    if foo == 1:
        print(number, 'is prime')
    else:
        print(number, '=', bar, '*', foo)

>>> is_prime(67)
67 is prime
>>> is_prime(117)
117 = 13 * 9
>>> is_prime(16)
16 = 4 * 4

回答 23

两种解决方案

举一个例子:这两个矩阵相等/相同吗?
matrix1和matrix2的大小相同,分别是n,2个二维矩阵。

第一个解决方案没有一个功能

same_matrices = True
inner_loop_broken_once = False
n = len(matrix1)

for i in range(n):
    for j in range(n):

        if matrix1[i][j] != matrix2[i][j]:
            same_matrices = False
            inner_loop_broken_once = True
            break

    if inner_loop_broken_once:
        break

第二个解决方案带有功能
这是我的案例的最终解决方案

def are_two_matrices_the_same (matrix1, matrix2):
    n = len(matrix1)
    for i in range(n):
        for j in range(n):
            if matrix1[i][j] != matrix2[i][j]:
                return False
    return True

祝你今天愉快!

Solutions in 2 Ways

With an example: Are these two matrices equal/same?
matrix1 and matrix2 are same size, n, 2 dimentional matrices.

First Solution, without a function

same_matrices = True
inner_loop_broken_once = False
n = len(matrix1)

for i in range(n):
    for j in range(n):

        if matrix1[i][j] != matrix2[i][j]:
            same_matrices = False
            inner_loop_broken_once = True
            break

    if inner_loop_broken_once:
        break

Second Solution, with a function
This is the final solution for my case

def are_two_matrices_the_same (matrix1, matrix2):
    n = len(matrix1)
    for i in range(n):
        for j in range(n):
            if matrix1[i][j] != matrix2[i][j]:
                return False
    return True

Have a nice day!


回答 24

# this version breaks up to a certain label

break_label = None
while True:
    # snip: print out current state
    while True:
        ok = get_input("Is this ok? (y/n)")
        if ok == "y" or ok == "Y":
            break_label = "outer"   # specify label to break to
            break
        if ok == "n" or ok == "N":
            break
    if break_label:
        if break_label != "inner":
            break                   # propagate up
        break_label = None          # we have arrived!
if break_label:
    if break_label != "outer":
        break                       # propagate up
    break_label = None              # we have arrived!

#do more processing with menus and stuff
# this version breaks up to a certain label

break_label = None
while True:
    # snip: print out current state
    while True:
        ok = get_input("Is this ok? (y/n)")
        if ok == "y" or ok == "Y":
            break_label = "outer"   # specify label to break to
            break
        if ok == "n" or ok == "N":
            break
    if break_label:
        if break_label != "inner":
            break                   # propagate up
        break_label = None          # we have arrived!
if break_label:
    if break_label != "outer":
        break                       # propagate up
    break_label = None              # we have arrived!

#do more processing with menus and stuff

回答 25

希望这会有所帮助:

x = True
y = True
while x == True:
    while y == True:
         ok = get_input("Is this ok? (y/n)") 
         if ok == "y" or ok == "Y":
             x,y = False,False #breaks from both loops
         if ok == "n" or ok == "N": 
             break #breaks from just one

Hopefully this helps:

x = True
y = True
while x == True:
    while y == True:
         ok = get_input("Is this ok? (y/n)") 
         if ok == "y" or ok == "Y":
             x,y = False,False #breaks from both loops
         if ok == "n" or ok == "N": 
             break #breaks from just one

回答 26

这是一个似乎可行的实现:

break_ = False
for i in range(10):
    if break_:
        break
    for j in range(10):
        if j == 3:
            break_ = True
            break
        else:
            print(i, j)

唯一的缺点是您必须break_在循环之前定义。

Here’s an implementation that seems to work:

break_ = False
for i in range(10):
    if break_:
        break
    for j in range(10):
        if j == 3:
            break_ = True
            break
        else:
            print(i, j)

The only draw back is that you have to define break_ before the loops.


回答 27

从语言层面上无法做到这一点。有些语言可以跳转,而其他语言则需要带参数,而python则不需要。

最好的选择是:

  1. 设置一个由外循环检查的标志,或设置外循环条件。

  2. 将循环放入函数中,然后使用return一次退出所有循环。

  3. 重新制定您的逻辑。

自1987年以来担任程序员的Vivek Nagarajan


使用功能

def doMywork(data):
    for i in data:
       for e in i:
         return 

使用标志

is_break = False
for i in data:
   if is_break:
      break # outer loop break
   for e in i:
      is_break = True
      break # inner loop break

There is no way to do this from a language level. Some languages have a goto others have a break that takes an argument, python does not.

The best options are:

  1. Set a flag which is checked by the outer loop, or set the outer loops condition.

  2. Put the loop in a function and use return to break out of all the loops at once.

  3. Reformulate your logic.

Credit goes to Vivek Nagarajan, Programmer since 1987


Using Function

def doMywork(data):
    for i in data:
       for e in i:
         return 

Using flag

is_break = False
for i in data:
   if is_break:
      break # outer loop break
   for e in i:
      is_break = True
      break # inner loop break

回答 28

与之前的相似,但更紧凑。(布尔只是数字)

breaker = False #our mighty loop exiter!
while True:
    while True:
        ok = get_input("Is this ok? (y/n)")
        breaker+= (ok.lower() == "y")
        break

    if breaker: # the interesting part!
        break   # <--- !

Similar like the one before, but more compact. (Booleans are just numbers)

breaker = False #our mighty loop exiter!
while True:
    while True:
        ok = get_input("Is this ok? (y/n)")
        breaker+= (ok.lower() == "y")
        break

    if breaker: # the interesting part!
        break   # <--- !

回答 29

由于此问题已成为进入特定循环的标准问题,因此我想用给出示例Exception

尽管在多重循环结构中没有名为“循环中断”的标签,但是我们可以利用用户定义的异常来进入我们选择的特定循环。考虑下面的示例,在该示例中,我们将在base-6编号系统中打印最多4位数字的所有数字:

class BreakLoop(Exception):
    def __init__(self, counter):
        Exception.__init__(self, 'Exception 1')
        self.counter = counter

for counter1 in range(6):   # Make it 1000
    try:
        thousand = counter1 * 1000
        for counter2 in range(6):  # Make it 100
            try:
                hundred = counter2 * 100
                for counter3 in range(6): # Make it 10
                    try:
                        ten = counter3 * 10
                        for counter4 in range(6):
                            try:
                                unit = counter4
                                value = thousand + hundred + ten + unit
                                if unit == 4 :
                                    raise BreakLoop(4) # Don't break from loop
                                if ten == 30: 
                                    raise BreakLoop(3) # Break into loop 3
                                if hundred == 500:
                                    raise BreakLoop(2) # Break into loop 2
                                if thousand == 2000:
                                    raise BreakLoop(1) # Break into loop 1

                                print('{:04d}'.format(value))
                            except BreakLoop as bl:
                                if bl.counter != 4:
                                    raise bl
                    except BreakLoop as bl:
                        if bl.counter != 3:
                            raise bl
            except BreakLoop as bl:
                if bl.counter != 2:
                    raise bl
    except BreakLoop as bl:
        pass

当我们打印输出时,我们将永远不会得到单位位置为4的任何值。在那种情况下,我们不会像BreakLoop(4)引发和陷入同一循环那样从任何循环中中断。同样,只要十位有3,我们就会使用进入第三循环BreakLoop(3)。每当百位有5时,我们就使用进入第二个循环BreakLoop(2),每千位有2时,我们使用进入第一个循环BreakLoop(1)

简而言之,在内部循环中引发Exception(内置的或用户定义的),然后将其捕获到要从中恢复控制的循环中。如果要中断所有循环,请在所有循环之外捕获Exception。(我没有在示例中显示这种情况)。

Since this question has become a standard question for breaking into a particular loop, I would like to give my answer with example using Exception.

Although there exists no label named breaking of loop in multipally looped construct, we can make use of User-defined Exceptions to break into a particular loop of our choice. Consider the following example where let us print all numbers upto 4 digits in base-6 numbering system:

class BreakLoop(Exception):
    def __init__(self, counter):
        Exception.__init__(self, 'Exception 1')
        self.counter = counter

for counter1 in range(6):   # Make it 1000
    try:
        thousand = counter1 * 1000
        for counter2 in range(6):  # Make it 100
            try:
                hundred = counter2 * 100
                for counter3 in range(6): # Make it 10
                    try:
                        ten = counter3 * 10
                        for counter4 in range(6):
                            try:
                                unit = counter4
                                value = thousand + hundred + ten + unit
                                if unit == 4 :
                                    raise BreakLoop(4) # Don't break from loop
                                if ten == 30: 
                                    raise BreakLoop(3) # Break into loop 3
                                if hundred == 500:
                                    raise BreakLoop(2) # Break into loop 2
                                if thousand == 2000:
                                    raise BreakLoop(1) # Break into loop 1

                                print('{:04d}'.format(value))
                            except BreakLoop as bl:
                                if bl.counter != 4:
                                    raise bl
                    except BreakLoop as bl:
                        if bl.counter != 3:
                            raise bl
            except BreakLoop as bl:
                if bl.counter != 2:
                    raise bl
    except BreakLoop as bl:
        pass

When we print the output, we will never get any value whose unit place is with 4. In that case, we don’t break from any loop as BreakLoop(4) is raised and caught in same loop. Similarly, whenever ten place is having 3, we break into third loop using BreakLoop(3). Whenever hundred place is having 5, we break into second loop using BreakLoop(2) and whenver the thousand place is having 2, we break into first loop using BreakLoop(1).

In short, raise your Exception (in-built or user defined) in the inner loops, and catch it in the loop from where you want to resume your control to. If you want to break from all loops, catch the Exception outside all the loops. (I have not shown this case in example).


有什么pythonic方式可以合并两个字典(为同时出现在两个字典中的键添加值)?

问题:有什么pythonic方式可以合并两个字典(为同时出现在两个字典中的键添加值)?

例如,我有两个字典:

Dict A: {'a': 1, 'b': 2, 'c': 3}
Dict B: {'b': 3, 'c': 4, 'd': 5}

我需要一种“结合”两个字典的pythonic方式,使得结果是:

{'a': 1, 'b': 5, 'c': 7, 'd': 5}

也就是说:如果一个键同时出现在两个字典中,则将其值相加;如果仅出现在一个字典中,则保留其值。

For example I have two dicts:

Dict A: {'a': 1, 'b': 2, 'c': 3}
Dict B: {'b': 3, 'c': 4, 'd': 5}

I need a pythonic way of ‘combining’ two dicts such that the result is:

{'a': 1, 'b': 5, 'c': 7, 'd': 5}

That is to say: if a key appears in both dicts, add their values, if it appears in only one dict, keep its value.


回答 0

用途collections.Counter

>>> from collections import Counter
>>> A = Counter({'a':1, 'b':2, 'c':3})
>>> B = Counter({'b':3, 'c':4, 'd':5})
>>> A + B
Counter({'c': 7, 'b': 5, 'd': 5, 'a': 1})

计数器基本上是的子类dict,因此您仍然可以使用该类型对它们执行其他所有操作,例如遍历其键和值。

Use collections.Counter:

>>> from collections import Counter
>>> A = Counter({'a':1, 'b':2, 'c':3})
>>> B = Counter({'b':3, 'c':4, 'd':5})
>>> A + B
Counter({'c': 7, 'b': 5, 'd': 5, 'a': 1})

Counters are basically a subclass of dict, so you can still do everything else with them you’d normally do with that type, such as iterate over their keys and values.


回答 1

一个更通用的解决方案,也适用于非数字值:

a = {'a': 'foo', 'b':'bar', 'c': 'baz'}
b = {'a': 'spam', 'c':'ham', 'x': 'blah'}

r = dict(a.items() + b.items() +
    [(k, a[k] + b[k]) for k in set(b) & set(a)])

或更通用的:

def combine_dicts(a, b, op=operator.add):
    return dict(a.items() + b.items() +
        [(k, op(a[k], b[k])) for k in set(b) & set(a)])

例如:

>>> a = {'a': 2, 'b':3, 'c':4}
>>> b = {'a': 5, 'c':6, 'x':7}

>>> import operator
>>> print combine_dicts(a, b, operator.mul)
{'a': 10, 'x': 7, 'c': 24, 'b': 3}

A more generic solution, which works for non-numeric values as well:

a = {'a': 'foo', 'b':'bar', 'c': 'baz'}
b = {'a': 'spam', 'c':'ham', 'x': 'blah'}

r = dict(a.items() + b.items() +
    [(k, a[k] + b[k]) for k in set(b) & set(a)])

or even more generic:

def combine_dicts(a, b, op=operator.add):
    return dict(a.items() + b.items() +
        [(k, op(a[k], b[k])) for k in set(b) & set(a)])

For example:

>>> a = {'a': 2, 'b':3, 'c':4}
>>> b = {'a': 5, 'c':6, 'x':7}

>>> import operator
>>> print combine_dicts(a, b, operator.mul)
{'a': 10, 'x': 7, 'c': 24, 'b': 3}

回答 2

>>> A = {'a':1, 'b':2, 'c':3}
>>> B = {'b':3, 'c':4, 'd':5}
>>> c = {x: A.get(x, 0) + B.get(x, 0) for x in set(A).union(B)}
>>> print(c)

{'a': 1, 'c': 7, 'b': 5, 'd': 5}
>>> A = {'a':1, 'b':2, 'c':3}
>>> B = {'b':3, 'c':4, 'd':5}
>>> c = {x: A.get(x, 0) + B.get(x, 0) for x in set(A).union(B)}
>>> print(c)

{'a': 1, 'c': 7, 'b': 5, 'd': 5}

回答 3

介绍: 有(可能)最好的解决方案。但是您必须了解它并记住它,有时您必须希望您的Python版本不是太旧或任何问题。

然后是最“ hacky”的解决方案。它们虽然长短,但有时难以理解,阅读和记忆。

但是,还有另一种方法可以尝试重新发明轮子。-为什么要重新发明轮子?-通常,因为这是一种非常好的学习方法(有时只是因为现有工具无法完全实现您想要的和/或您想要的方式),而如果您不知道或不了解,则是最简单的方法忘记了解决问题的理想工具。

因此,我建议Countercollections模块(至少部分地)重塑类的方向:

class MyDict(dict):
    def __add__(self, oth):
        r = self.copy()

        try:
            for key, val in oth.items():
                if key in r:
                    r[key] += val  # You can custom it here
                else:
                    r[key] = val
        except AttributeError:  # In case oth isn't a dict
            return NotImplemented  # The convention when a case isn't handled

        return r

a = MyDict({'a':1, 'b':2, 'c':3})
b = MyDict({'b':3, 'c':4, 'd':5})

print(a+b)  # Output {'a':1, 'b': 5, 'c': 7, 'd': 5}

可能会有其他方式来实现它,并且已经有工具可以做到这一点,但是可视化事物的基本工作原理总是很高兴的。

Intro: There are the (probably) best solutions. But you have to know it and remember it and sometimes you have to hope that your Python version isn’t too old or whatever the issue could be.

Then there are the most ‘hacky’ solutions. They are great and short but sometimes are hard to understand, to read and to remember.

There is, though, an alternative which is to to try to reinvent the wheel. – Why reinventing the wheel? – Generally because it’s a really good way to learn (and sometimes just because the already-existing tool doesn’t do exactly what you would like and/or the way you would like it) and the easiest way if you don’t know or don’t remember the perfect tool for your problem.

So, I propose to reinvent the wheel of the Counter class from the collections module (partially at least):

class MyDict(dict):
    def __add__(self, oth):
        r = self.copy()

        try:
            for key, val in oth.items():
                if key in r:
                    r[key] += val  # You can custom it here
                else:
                    r[key] = val
        except AttributeError:  # In case oth isn't a dict
            return NotImplemented  # The convention when a case isn't handled

        return r

a = MyDict({'a':1, 'b':2, 'c':3})
b = MyDict({'b':3, 'c':4, 'd':5})

print(a+b)  # Output {'a':1, 'b': 5, 'c': 7, 'd': 5}

There would probably others way to implement that and there are already tools to do that but it’s always nice to visualize how things would basically works.


回答 4

myDict = {}
for k in itertools.chain(A.keys(), B.keys()):
    myDict[k] = A.get(k, 0)+B.get(k, 0)
myDict = {}
for k in itertools.chain(A.keys(), B.keys()):
    myDict[k] = A.get(k, 0)+B.get(k, 0)

回答 5

一个没有额外的进口!

他们是一个称为EAFP的pythonic 标准(要求宽恕比许可容易)。下面的代码基于该python标准

# The A and B dictionaries
A = {'a': 1, 'b': 2, 'c': 3}
B = {'b': 3, 'c': 4, 'd': 5}

# The final dictionary. Will contain the final outputs.
newdict = {}

# Make sure every key of A and B get into the final dictionary 'newdict'.
newdict.update(A)
newdict.update(B)

# Iterate through each key of A.
for i in A.keys():

    # If same key exist on B, its values from A and B will add together and
    # get included in the final dictionary 'newdict'.
    try:
        addition = A[i] + B[i]
        newdict[i] = addition

    # If current key does not exist in dictionary B, it will give a KeyError,
    # catch it and continue looping.
    except KeyError:
        continue

编辑:感谢jerzyk的改进建议。

The one with no extra imports!

Their is a pythonic standard called EAFP(Easier to Ask for Forgiveness than Permission). Below code is based on that python standard.

# The A and B dictionaries
A = {'a': 1, 'b': 2, 'c': 3}
B = {'b': 3, 'c': 4, 'd': 5}

# The final dictionary. Will contain the final outputs.
newdict = {}

# Make sure every key of A and B get into the final dictionary 'newdict'.
newdict.update(A)
newdict.update(B)

# Iterate through each key of A.
for i in A.keys():

    # If same key exist on B, its values from A and B will add together and
    # get included in the final dictionary 'newdict'.
    try:
        addition = A[i] + B[i]
        newdict[i] = addition

    # If current key does not exist in dictionary B, it will give a KeyError,
    # catch it and continue looping.
    except KeyError:
        continue

EDIT: thanks to jerzyk for his improvement suggestions.


回答 6

Counter()在这种情况下,将s 绝对相加是最有效的方法,但前提是它会导致正值。这是一个示例,如您所见,在字典中c取反c的值后没有结果B

In [1]: from collections import Counter

In [2]: A = Counter({'a':1, 'b':2, 'c':3})

In [3]: B = Counter({'b':3, 'c':-4, 'd':5})

In [4]: A + B
Out[4]: Counter({'d': 5, 'b': 5, 'a': 1})

这是因为Counters最初主要用于与正整数一起表示运行计数(负计数是没有意义的)。但是为了帮助解决这些用例,python记录了最小范围和类型限制,如下所示:

  • Counter类本身是一个字典子类,对其键和值没有限制。这些值应为代表计数的数字,但您可以在值字段中存储任何内容。
  • most_common()方法仅要求值是可排序的。
  • 对于诸如的就地操作c[key] += 1,值类型仅需要支持加法和减法。因此,分数,浮点数和小数将起作用,并且支持负值。对于update()和也是如此subtract()其允许输入和输出的负序和零值。
  • 多重集方法仅设计用于具有正值的用例。输入可以为负或零,但仅创建具有正值的输出。没有类型限制,但是值类型需要支持加,减和比较。
  • elements()方法需要整数计数。它忽略零和负计数。

因此,为解决计数器加总后的问题,可以使用Counter.update以获得所需的输出。它的工作原理类似,dict.update()但增加了计数而不是取代它们。

In [24]: A.update(B)

In [25]: A
Out[25]: Counter({'d': 5, 'b': 5, 'a': 1, 'c': -1})

Definitely summing the Counter()s is the most pythonic way to go in such cases but only if it results in a positive value. Here is an example and as you can see there is no c in result after negating the c‘s value in B dictionary.

In [1]: from collections import Counter

In [2]: A = Counter({'a':1, 'b':2, 'c':3})

In [3]: B = Counter({'b':3, 'c':-4, 'd':5})

In [4]: A + B
Out[4]: Counter({'d': 5, 'b': 5, 'a': 1})

That’s because Counters were primarily designed to work with positive integers to represent running counts (negative count is meaningless). But to help with those use cases,python documents the minimum range and type restrictions as follows:

  • The Counter class itself is a dictionary subclass with no restrictions on its keys and values. The values are intended to be numbers representing counts, but you could store anything in the value field.
  • The most_common() method requires only that the values be orderable.
  • For in-place operations such as c[key] += 1, the value type need only support addition and subtraction. So fractions, floats, and decimals would work and negative values are supported. The same is also true for update() and subtract() which allow negative and zero values for both inputs and outputs.
  • The multiset methods are designed only for use cases with positive values. The inputs may be negative or zero, but only outputs with positive values are created. There are no type restrictions, but the value type needs to support addition, subtraction, and comparison.
  • The elements() method requires integer counts. It ignores zero and negative counts.

So for getting around that problem after summing your Counter you can use Counter.update in order to get the desire output. It works like dict.update() but adds counts instead of replacing them.

In [24]: A.update(B)

In [25]: A
Out[25]: Counter({'d': 5, 'b': 5, 'a': 1, 'c': -1})

回答 7

import itertools
import collections

dictA = {'a':1, 'b':2, 'c':3}
dictB = {'b':3, 'c':4, 'd':5}

new_dict = collections.defaultdict(int)
# use dict.items() instead of dict.iteritems() for Python3
for k, v in itertools.chain(dictA.iteritems(), dictB.iteritems()):
    new_dict[k] += v

print dict(new_dict)

# OUTPUT
{'a': 1, 'c': 7, 'b': 5, 'd': 5}

要么

您也可以使用@Martijn上面提到的Counter。

import itertools
import collections

dictA = {'a':1, 'b':2, 'c':3}
dictB = {'b':3, 'c':4, 'd':5}

new_dict = collections.defaultdict(int)
# use dict.items() instead of dict.iteritems() for Python3
for k, v in itertools.chain(dictA.iteritems(), dictB.iteritems()):
    new_dict[k] += v

print dict(new_dict)

# OUTPUT
{'a': 1, 'c': 7, 'b': 5, 'd': 5}

OR

Alternative you can use Counter as @Martijn has mentioned above.


回答 8

有关更通用和可扩展的方法,请检查mergedict。它用singledispatch并可以根据其类型合并值。

例:

from mergedict import MergeDict

class SumDict(MergeDict):
    @MergeDict.dispatch(int)
    def merge_int(this, other):
        return this + other

d2 = SumDict({'a': 1, 'b': 'one'})
d2.merge({'a':2, 'b': 'two'})

assert d2 == {'a': 3, 'b': 'two'}

For a more generic and extensible way check mergedict. It uses singledispatch and can merge values based on its types.

Example:

from mergedict import MergeDict

class SumDict(MergeDict):
    @MergeDict.dispatch(int)
    def merge_int(this, other):
        return this + other

d2 = SumDict({'a': 1, 'b': 'one'})
d2.merge({'a':2, 'b': 'two'})

assert d2 == {'a': 3, 'b': 'two'}

回答 9

从python 3.5开始:合并和求和

感谢@tokeinizer_fsj在评论中告诉我,我并没有完全理解问题的含义(我认为添加意味着仅添加最终在两个字典中有所不同的键,相反,我的意思是公用键值应该加起来)。因此,我在合并之前添加了该循环,以便第二个字典包含公用键的总和。最后典将是其值将在新字典中持续存在的字典,这是两者合并的结果,所以我认为问题已解决。该解决方案从python 3.5及以下版本开始有效。

a = {
    "a": 1,
    "b": 2,
    "c": 3
}

b = {
    "a": 2,
    "b": 3,
    "d": 5
}

# Python 3.5

for key in b:
    if key in a:
        b[key] = b[key] + a[key]

c = {**a, **b}
print(c)

>>> c
{'a': 3, 'b': 5, 'c': 3, 'd': 5}

可重用代码

a = {'a': 1, 'b': 2, 'c': 3}
b = {'b': 3, 'c': 4, 'd': 5}


def mergsum(a, b):
    for k in b:
        if k in a:
            b[k] = b[k] + a[k]
    c = {**a, **b}
    return c


print(mergsum(a, b))

From python 3.5: merging and summing

Thanks to @tokeinizer_fsj that told me in a comment that I didn’t get completely the meaning of the question (I thought that add meant just adding keys that eventually where different in the two dictinaries and, instead, i meant that the common key values should be summed). So I added that loop before the merging, so that the second dictionary contains the sum of the common keys. The last dictionary will be the one whose values will last in the new dictionary that is the result of the merging of the two, so I thing the problem is solved. The solution is valid from python 3.5 and following versions.

a = {
    "a": 1,
    "b": 2,
    "c": 3
}

b = {
    "a": 2,
    "b": 3,
    "d": 5
}

# Python 3.5

for key in b:
    if key in a:
        b[key] = b[key] + a[key]

c = {**a, **b}
print(c)

>>> c
{'a': 3, 'b': 5, 'c': 3, 'd': 5}

Reusable code

a = {'a': 1, 'b': 2, 'c': 3}
b = {'b': 3, 'c': 4, 'd': 5}


def mergsum(a, b):
    for k in b:
        if k in a:
            b[k] = b[k] + a[k]
    c = {**a, **b}
    return c


print(mergsum(a, b))

回答 10

此外,请注意a.update( b ),速度是2倍a + b

from collections import Counter
a = Counter({'menu': 20, 'good': 15, 'happy': 10, 'bar': 5})
b = Counter({'menu': 1, 'good': 1, 'bar': 3})

%timeit a + b;
## 100000 loops, best of 3: 8.62 µs per loop
## The slowest run took 4.04 times longer than the fastest. This could mean that an intermediate result is being cached.

%timeit a.update(b)
## 100000 loops, best of 3: 4.51 µs per loop

Additionally, please note a.update( b ) is 2x faster than a + b

from collections import Counter
a = Counter({'menu': 20, 'good': 15, 'happy': 10, 'bar': 5})
b = Counter({'menu': 1, 'good': 1, 'bar': 3})

%timeit a + b;
## 100000 loops, best of 3: 8.62 µs per loop
## The slowest run took 4.04 times longer than the fastest. This could mean that an intermediate result is being cached.

%timeit a.update(b)
## 100000 loops, best of 3: 4.51 µs per loop

回答 11

def merge_with(f, xs, ys):
    xs = a_copy_of(xs) # dict(xs), maybe generalizable?
    for (y, v) in ys.iteritems():
        xs[y] = v if y not in xs else f(xs[x], v)

merge_with((lambda x, y: x + y), A, B)

您可以轻松地对此进行概括:

def merge_dicts(f, *dicts):
    result = {}
    for d in dicts:
        for (k, v) in d.iteritems():
            result[k] = v if k not in result else f(result[k], v)

然后它可以采用任意数量的字典。

def merge_with(f, xs, ys):
    xs = a_copy_of(xs) # dict(xs), maybe generalizable?
    for (y, v) in ys.iteritems():
        xs[y] = v if y not in xs else f(xs[x], v)

merge_with((lambda x, y: x + y), A, B)

You could easily generalize this:

def merge_dicts(f, *dicts):
    result = {}
    for d in dicts:
        for (k, v) in d.iteritems():
            result[k] = v if k not in result else f(result[k], v)

Then it can take any number of dicts.


回答 12

这是合并两个+=可应用于值的字典的简单解决方案,它只需要对字典进行一次迭代

a = {'a':1, 'b':2, 'c':3}

dicts = [{'b':3, 'c':4, 'd':5},
         {'c':9, 'a':9, 'd':9}]

def merge_dicts(merged,mergedfrom):
    for k,v in mergedfrom.items():
        if k in merged:
            merged[k] += v
        else:
            merged[k] = v
    return merged

for dct in dicts:
    a = merge_dicts(a,dct)
print (a)
#{'c': 16, 'b': 5, 'd': 14, 'a': 10}

This is a simple solution for merging two dictionaries where += can be applied to the values, it has to iterate over a dictionary only once

a = {'a':1, 'b':2, 'c':3}

dicts = [{'b':3, 'c':4, 'd':5},
         {'c':9, 'a':9, 'd':9}]

def merge_dicts(merged,mergedfrom):
    for k,v in mergedfrom.items():
        if k in merged:
            merged[k] += v
        else:
            merged[k] = v
    return merged

for dct in dicts:
    a = merge_dicts(a,dct)
print (a)
#{'c': 16, 'b': 5, 'd': 14, 'a': 10}

回答 13

此解决方案易于使用,它用作普通词典,但是您可以使用sum函数。

class SumDict(dict):
    def __add__(self, y):
        return {x: self.get(x, 0) + y.get(x, 0) for x in set(self).union(y)}

A = SumDict({'a': 1, 'c': 2})
B = SumDict({'b': 3, 'c': 4})  # Also works: B = {'b': 3, 'c': 4}
print(A + B)  # OUTPUT {'a': 1, 'b': 3, 'c': 6}

This solution is easy to use, it is used as a normal dictionary, but you can use the sum function.

class SumDict(dict):
    def __add__(self, y):
        return {x: self.get(x, 0) + y.get(x, 0) for x in set(self).union(y)}

A = SumDict({'a': 1, 'c': 2})
B = SumDict({'b': 3, 'c': 4})  # Also works: B = {'b': 3, 'c': 4}
print(A + B)  # OUTPUT {'a': 1, 'b': 3, 'c': 6}

回答 14

关于什么:

def dict_merge_and_sum( d1, d2 ):
    ret = d1
    ret.update({ k:v + d2[k] for k,v in d1.items() if k in d2 })
    ret.update({ k:v for k,v in d2.items() if k not in d1 })
    return ret

A = {'a': 1, 'b': 2, 'c': 3}
B = {'b': 3, 'c': 4, 'd': 5}

print( dict_merge_and_sum( A, B ) )

输出:

{'d': 5, 'a': 1, 'c': 7, 'b': 5}

What about:

def dict_merge_and_sum( d1, d2 ):
    ret = d1
    ret.update({ k:v + d2[k] for k,v in d1.items() if k in d2 })
    ret.update({ k:v for k,v in d2.items() if k not in d1 })
    return ret

A = {'a': 1, 'b': 2, 'c': 3}
B = {'b': 3, 'c': 4, 'd': 5}

print( dict_merge_and_sum( A, B ) )

Output:

{'d': 5, 'a': 1, 'c': 7, 'b': 5}

回答 15

上述解决方案非常适合Counters 较少的情况。如果您有很多清单,那么这样会更好:

from collections import Counter

A = Counter({'a':1, 'b':2, 'c':3})
B = Counter({'b':3, 'c':4, 'd':5}) 
C = Counter({'a': 5, 'e':3})
list_of_counts = [A, B, C]

total = sum(list_of_counts, Counter())

print(total)
# Counter({'c': 7, 'a': 6, 'b': 5, 'd': 5, 'e': 3})

上面的解决方案本质上Counter是通过以下方式将s 相加:

total = Counter()
for count in list_of_counts:
    total += count
print(total)
# Counter({'c': 7, 'a': 6, 'b': 5, 'd': 5, 'e': 3})

这做同样的事情,但我认为它始终有助于了解其在下面的有效工作。

The above solutions are great for the scenario where you have a small number of Counters. If you have a big list of them though, something like this is much nicer:

from collections import Counter

A = Counter({'a':1, 'b':2, 'c':3})
B = Counter({'b':3, 'c':4, 'd':5}) 
C = Counter({'a': 5, 'e':3})
list_of_counts = [A, B, C]

total = sum(list_of_counts, Counter())

print(total)
# Counter({'c': 7, 'a': 6, 'b': 5, 'd': 5, 'e': 3})

The above solution is essentially summing the Counters by:

total = Counter()
for count in list_of_counts:
    total += count
print(total)
# Counter({'c': 7, 'a': 6, 'b': 5, 'd': 5, 'e': 3})

This does the same thing but I think it always helps to see what it is effectively doing underneath.


回答 16

在没有任何其他模块或库的情况下,在一行中合并三个字典a,b,c

如果我们有三个决定

a = {"a":9}
b = {"b":7}
c = {'b': 2, 'd': 90}

用一行合并所有内容,并使用返回一个dict对象

c = dict(a.items() + b.items() + c.items())

归来

{'a': 9, 'b': 2, 'd': 90}

Merging three dicts a,b,c in a single line without any other modules or libs

If we have the three dicts

a = {"a":9}
b = {"b":7}
c = {'b': 2, 'd': 90}

Merge all with a single line and return a dict object using

c = dict(a.items() + b.items() + c.items())

Returning

{'a': 9, 'b': 2, 'd': 90}

更改matplotlib中x或y轴上的“刻度频率”?

问题:更改matplotlib中x或y轴上的“刻度频率”?

我正在尝试修复python如何绘制我的数据。

x = [0,5,9,10,15]

y = [0,1,2,3,4]

然后我会做:

matplotlib.pyplot.plot(x,y)
matplotlib.pyplot.show()

并且x轴的刻度线以5的间隔绘制。是否有办法使其显示1的间隔?

I am trying to fix how python plots my data.

Say

x = [0,5,9,10,15]

and

y = [0,1,2,3,4]

Then I would do:

matplotlib.pyplot.plot(x,y)
matplotlib.pyplot.show()

and the x axis’ ticks are plotted in intervals of 5. Is there a way to make it show intervals of 1?


回答 0

您可以使用以下命令显式设置要在标记上打勾的位置plt.xticks

plt.xticks(np.arange(min(x), max(x)+1, 1.0))

例如,

import numpy as np
import matplotlib.pyplot as plt

x = [0,5,9,10,15]
y = [0,1,2,3,4]
plt.plot(x,y)
plt.xticks(np.arange(min(x), max(x)+1, 1.0))
plt.show()

(以防万一,np.arange使用它而不是Python的range函数min(x)max(x)它们是浮点数而不是整数。)


plt.plot(或ax.plot)功能将自动设置默认xy限制。如果您希望保留这些限制,而只是更改刻度线的步长,则可以使用ax.get_xlim()Matplotlib设置哪些限制。

start, end = ax.get_xlim()
ax.xaxis.set_ticks(np.arange(start, end, stepsize))

默认的滴答格式器应将滴答值四舍五入为有意义的有效数字位数。但是,如果希望对格式有更多控制,则可以定义自己的格式器。例如,

ax.xaxis.set_major_formatter(ticker.FormatStrFormatter('%0.1f'))

这是一个可运行的示例:

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker

x = [0,5,9,10,15]
y = [0,1,2,3,4]
fig, ax = plt.subplots()
ax.plot(x,y)
start, end = ax.get_xlim()
ax.xaxis.set_ticks(np.arange(start, end, 0.712123))
ax.xaxis.set_major_formatter(ticker.FormatStrFormatter('%0.1f'))
plt.show()

You could explicitly set where you want to tick marks with plt.xticks:

plt.xticks(np.arange(min(x), max(x)+1, 1.0))

For example,

import numpy as np
import matplotlib.pyplot as plt

x = [0,5,9,10,15]
y = [0,1,2,3,4]
plt.plot(x,y)
plt.xticks(np.arange(min(x), max(x)+1, 1.0))
plt.show()

(np.arange was used rather than Python’s range function just in case min(x) and max(x) are floats instead of ints.)


The plt.plot (or ax.plot) function will automatically set default x and y limits. If you wish to keep those limits, and just change the stepsize of the tick marks, then you could use ax.get_xlim() to discover what limits Matplotlib has already set.

start, end = ax.get_xlim()
ax.xaxis.set_ticks(np.arange(start, end, stepsize))

The default tick formatter should do a decent job rounding the tick values to a sensible number of significant digits. However, if you wish to have more control over the format, you can define your own formatter. For example,

ax.xaxis.set_major_formatter(ticker.FormatStrFormatter('%0.1f'))

Here’s a runnable example:

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker

x = [0,5,9,10,15]
y = [0,1,2,3,4]
fig, ax = plt.subplots()
ax.plot(x,y)
start, end = ax.get_xlim()
ax.xaxis.set_ticks(np.arange(start, end, 0.712123))
ax.xaxis.set_major_formatter(ticker.FormatStrFormatter('%0.1f'))
plt.show()

回答 1

另一种方法是设置轴定位器:

import matplotlib.ticker as plticker

loc = plticker.MultipleLocator(base=1.0) # this locator puts ticks at regular intervals
ax.xaxis.set_major_locator(loc)

根据您的需要,有几种不同类型的定位器。

这是一个完整的示例:

import matplotlib.pyplot as plt
import matplotlib.ticker as plticker

x = [0,5,9,10,15]
y = [0,1,2,3,4]
fig, ax = plt.subplots()
ax.plot(x,y)
loc = plticker.MultipleLocator(base=1.0) # this locator puts ticks at regular intervals
ax.xaxis.set_major_locator(loc)
plt.show()

Another approach is to set the axis locator:

import matplotlib.ticker as plticker

loc = plticker.MultipleLocator(base=1.0) # this locator puts ticks at regular intervals
ax.xaxis.set_major_locator(loc)

There are several different types of locator depending upon your needs.

Here is a full example:

import matplotlib.pyplot as plt
import matplotlib.ticker as plticker

x = [0,5,9,10,15]
y = [0,1,2,3,4]
fig, ax = plt.subplots()
ax.plot(x,y)
loc = plticker.MultipleLocator(base=1.0) # this locator puts ticks at regular intervals
ax.xaxis.set_major_locator(loc)
plt.show()

回答 2

我喜欢这个解决方案(来自Matplotlib绘图食谱):

import matplotlib.pyplot as plt
import matplotlib.ticker as ticker

x = [0,5,9,10,15]
y = [0,1,2,3,4]

tick_spacing = 1

fig, ax = plt.subplots(1,1)
ax.plot(x,y)
ax.xaxis.set_major_locator(ticker.MultipleLocator(tick_spacing))
plt.show()

该解决方案可以通过给给出的数字来明确控制刻度线间隔ticker.MultipleLocater(),允许自动确定极限,并且以后易于阅读。

I like this solution (from the Matplotlib Plotting Cookbook):

import matplotlib.pyplot as plt
import matplotlib.ticker as ticker

x = [0,5,9,10,15]
y = [0,1,2,3,4]

tick_spacing = 1

fig, ax = plt.subplots(1,1)
ax.plot(x,y)
ax.xaxis.set_major_locator(ticker.MultipleLocator(tick_spacing))
plt.show()

This solution give you explicit control of the tick spacing via the number given to ticker.MultipleLocater(), allows automatic limit determination, and is easy to read later.


回答 3

如果有人对通用单线感兴趣,只需获取当前的报价,并通过对其他报价进行采样就可以使用它来设置新的报价。

ax.set_xticks(ax.get_xticks()[::2])

In case anyone is interested in a general one-liner, simply get the current ticks and use it to set the new ticks by sampling every other tick.

ax.set_xticks(ax.get_xticks()[::2])

回答 4

这有点棘手,但是到目前为止,我发现这样做是最干净/最容易理解的示例。这是从SO的答案中获得的:

隐藏matplotlib颜色栏中的每个第n个刻度标签的最干净方法?

for label in ax.get_xticklabels()[::2]:
    label.set_visible(False)

然后,您可以遍历标签,根据所需的密度将其设置为可见或不可见。

编辑:请注意,有时matplotlib会设置标签== '',因此看起来标签似乎不存在,而实际上却并不显示任何内容。为确保您遍历实际可见标签,可以尝试:

visible_labels = [lab for lab in ax.get_xticklabels() if lab.get_visible() is True and lab.get_text() != '']
plt.setp(visible_labels[::2], visible=False)

This is a bit hacky, but by far the cleanest/easiest to understand example that I’ve found to do this. It’s from an answer on SO here:

Cleanest way to hide every nth tick label in matplotlib colorbar?

for label in ax.get_xticklabels()[::2]:
    label.set_visible(False)

Then you can loop over the labels setting them to visible or not depending on the density you want.

edit: note that sometimes matplotlib sets labels == '', so it might look like a label is not present, when in fact it is and just isn’t displaying anything. To make sure you’re looping through actual visible labels, you could try:

visible_labels = [lab for lab in ax.get_xticklabels() if lab.get_visible() is True and lab.get_text() != '']
plt.setp(visible_labels[::2], visible=False)

回答 5

这是一个古老的话题,但是我时不时地碰到这个问题,并做了这个功能。这很方便:

import matplotlib.pyplot as pp
import numpy as np

def resadjust(ax, xres=None, yres=None):
    """
    Send in an axis and I fix the resolution as desired.
    """

    if xres:
        start, stop = ax.get_xlim()
        ticks = np.arange(start, stop + xres, xres)
        ax.set_xticks(ticks)
    if yres:
        start, stop = ax.get_ylim()
        ticks = np.arange(start, stop + yres, yres)
        ax.set_yticks(ticks)

像这样控制刻度线的一个警告是,添加一行之后,不再享受最大比例的交互式自动魔术更新。然后做

gca().set_ylim(top=new_top) # for example

并再次运行resadjust函数。

This is an old topic, but I stumble over this every now and then and made this function. It’s very convenient:

import matplotlib.pyplot as pp
import numpy as np

def resadjust(ax, xres=None, yres=None):
    """
    Send in an axis and I fix the resolution as desired.
    """

    if xres:
        start, stop = ax.get_xlim()
        ticks = np.arange(start, stop + xres, xres)
        ax.set_xticks(ticks)
    if yres:
        start, stop = ax.get_ylim()
        ticks = np.arange(start, stop + yres, yres)
        ax.set_yticks(ticks)

One caveat of controlling the ticks like this is that one does no longer enjoy the interactive automagic updating of max scale after an added line. Then do

gca().set_ylim(top=new_top) # for example

and run the resadjust function again.


回答 6

我开发了一个优雅的解决方案。考虑我们有X轴,还有X中每个点的标签列表。

例:
import matplotlib.pyplot as plt

x = [0,1,2,3,4,5]
y = [10,20,15,18,7,19]
xlabels = ['jan','feb','mar','apr','may','jun']
假设我只想显示“ feb”和“ jun”的刻度标签
xlabelsnew = []
for i in xlabels:
    if i not in ['feb','jun']:
        i = ' '
        xlabelsnew.append(i)
    else:
        xlabelsnew.append(i)
好,现在我们有一个虚假的标签列表。首先,我们绘制了原始版本。
plt.plot(x,y)
plt.xticks(range(0,len(x)),xlabels,rotation=45)
plt.show()
现在,修改版本。
plt.plot(x,y)
plt.xticks(range(0,len(x)),xlabelsnew,rotation=45)
plt.show()

I developed an inelegant solution. Consider that we have the X axis and also a list of labels for each point in X.

Example:
import matplotlib.pyplot as plt

x = [0,1,2,3,4,5]
y = [10,20,15,18,7,19]
xlabels = ['jan','feb','mar','apr','may','jun']
Let’s say that I want to show ticks labels only for ‘feb’ and ‘jun’
xlabelsnew = []
for i in xlabels:
    if i not in ['feb','jun']:
        i = ' '
        xlabelsnew.append(i)
    else:
        xlabelsnew.append(i)
Good, now we have a fake list of labels. First, we plotted the original version.
plt.plot(x,y)
plt.xticks(range(0,len(x)),xlabels,rotation=45)
plt.show()
Now, the modified version.
plt.plot(x,y)
plt.xticks(range(0,len(x)),xlabelsnew,rotation=45)
plt.show()

回答 7

如果您只想设置间距最小的简单衬板:

plt.gca().xaxis.set_major_locator(plt.MultipleLocator(1))

对于较小的滴答声也很容易工作:

plt.gca().xaxis.set_minor_locator(plt.MultipleLocator(1))

有点满口,但是很紧凑

if you just want to set the spacing a simple one liner with minimal boilerplate:

plt.gca().xaxis.set_major_locator(plt.MultipleLocator(1))

also works easily for minor ticks:

plt.gca().xaxis.set_minor_locator(plt.MultipleLocator(1))

a bit of a mouthfull, but pretty compact


回答 8

xmarks=[i for i in range(1,length+1,1)]

plt.xticks(xmarks)

这对我有用

如果您想在[1,5](包括1和5)之间打勾,请替换

length = 5
xmarks=[i for i in range(1,length+1,1)]

plt.xticks(xmarks)

This worked for me

if you want ticks between [1,5] (1 and 5 inclusive) then replace

length = 5

回答 9

纯Python实现

以下是所需功能的纯python实现,该功能可处理带有正,负或混合值的任何数字系列(int或float),并允许用户指定所需的步长:

import math

def computeTicks (x, step = 5):
    """
    Computes domain with given step encompassing series x
    @ params
    x    - Required - A list-like object of integers or floats
    step - Optional - Tick frequency
    """
    xMax, xMin = math.ceil(max(x)), math.floor(min(x))
    dMax, dMin = xMax + abs((xMax % step) - step) + (step if (xMax % step != 0) else 0), xMin - abs((xMin % step))
    return range(dMin, dMax, step)

样本输出

# Negative to Positive
series = [-2, 18, 24, 29, 43]
print(list(computeTicks(series)))

[-5, 0, 5, 10, 15, 20, 25, 30, 35, 40, 45]

# Negative to 0
series = [-30, -14, -10, -9, -3, 0]
print(list(computeTicks(series)))

[-30, -25, -20, -15, -10, -5, 0]

# 0 to Positive
series = [19, 23, 24, 27]
print(list(computeTicks(series)))

[15, 20, 25, 30]

# Floats
series = [1.8, 12.0, 21.2]
print(list(computeTicks(series)))

[0, 5, 10, 15, 20, 25]

# Step – 100
series = [118.3, 293.2, 768.1]
print(list(computeTicks(series, step = 100)))

[100, 200, 300, 400, 500, 600, 700, 800]

样品用量

import matplotlib.pyplot as plt

x = [0,5,9,10,15]
y = [0,1,2,3,4]
plt.plot(x,y)
plt.xticks(computeTicks(x))
plt.show()

样本使用情况图

请注意,x轴具有均等以5间隔的整数值,而y轴具有不同的间隔(matplotlib默认行为,因为未指定刻度)。

Pure Python Implementation

Below’s a pure python implementation of the desired functionality that handles any numeric series (int or float) with positive, negative, or mixed values and allows for the user to specify the desired step size:

import math

def computeTicks (x, step = 5):
    """
    Computes domain with given step encompassing series x
    @ params
    x    - Required - A list-like object of integers or floats
    step - Optional - Tick frequency
    """
    xMax, xMin = math.ceil(max(x)), math.floor(min(x))
    dMax, dMin = xMax + abs((xMax % step) - step) + (step if (xMax % step != 0) else 0), xMin - abs((xMin % step))
    return range(dMin, dMax, step)

Sample Output

# Negative to Positive
series = [-2, 18, 24, 29, 43]
print(list(computeTicks(series)))

[-5, 0, 5, 10, 15, 20, 25, 30, 35, 40, 45]

# Negative to 0
series = [-30, -14, -10, -9, -3, 0]
print(list(computeTicks(series)))

[-30, -25, -20, -15, -10, -5, 0]

# 0 to Positive
series = [19, 23, 24, 27]
print(list(computeTicks(series)))

[15, 20, 25, 30]

# Floats
series = [1.8, 12.0, 21.2]
print(list(computeTicks(series)))

[0, 5, 10, 15, 20, 25]

# Step – 100
series = [118.3, 293.2, 768.1]
print(list(computeTicks(series, step = 100)))

[100, 200, 300, 400, 500, 600, 700, 800]

Sample Usage

import matplotlib.pyplot as plt

x = [0,5,9,10,15]
y = [0,1,2,3,4]
plt.plot(x,y)
plt.xticks(computeTicks(x))
plt.show()

Plot of sample usage

Notice the x-axis has integer values all evenly spaced by 5, whereas the y-axis has a different interval (the matplotlib default behavior, because the ticks weren’t specified).


使用索引为pandas DataFrame中的特定单元格设置值

问题:使用索引为pandas DataFrame中的特定单元格设置值

我创建了一个Pandas DataFrame

df = DataFrame(index=['A','B','C'], columns=['x','y'])

并得到这个

    y
NaN NaN
B NaN NaN
Na


然后,我想为特定的单元格赋值,例如行“ C”和列“ x”。我期望得到这样的结果:

    y
NaN NaN
B NaN NaN
C 10 NaN

使用此代码:

df.xs('C')['x'] = 10

但内容df没有改变。再次仅NaN在DataFrame中。

有什么建议么?

I’ve created a Pandas DataFrame

df = DataFrame(index=['A','B','C'], columns=['x','y'])

and got this

    x    y
A  NaN  NaN
B  NaN  NaN
C  NaN  NaN


Then I want to assign value to particular cell, for example for row ‘C’ and column ‘x’. I’ve expected to get such result:

    x    y
A  NaN  NaN
B  NaN  NaN
C  10  NaN

with this code:

df.xs('C')['x'] = 10

but contents of df haven’t changed. It’s again only NaNs in DataFrame.

Any suggestions?


回答 0

RukTech的答案df.set_value('C', 'x', 10)远比我在下面建议的选项要快得多。但是,已将其淘汰

展望未来,推荐的方法是.iat/.at


为什么df.xs('C')['x']=10不起作用:

df.xs('C')默认情况下,返回带有数据副本的新数据框,因此

df.xs('C')['x']=10

仅修改此新数据框。

df['x']返回df数据框的视图,因此

df['x']['C'] = 10

修改df自己。

警告:有时很难预测操作是否返回副本或视图。因此,文档建议避免使用“链接索引”进行赋值


所以推荐的替代方法是

df.at['C', 'x'] = 10

修改df


In [18]: %timeit df.set_value('C', 'x', 10)
100000 loops, best of 3: 2.9 µs per loop

In [20]: %timeit df['x']['C'] = 10
100000 loops, best of 3: 6.31 µs per loop

In [81]: %timeit df.at['C', 'x'] = 10
100000 loops, best of 3: 9.2 µs per loop

RukTech’s answer, df.set_value('C', 'x', 10), is far and away faster than the options I’ve suggested below. However, it has been slated for deprecation.

Going forward, the recommended method is .iat/.at.


Why df.xs('C')['x']=10 does not work:

df.xs('C') by default, returns a new dataframe with a copy of the data, so

df.xs('C')['x']=10

modifies this new dataframe only.

df['x'] returns a view of the df dataframe, so

df['x']['C'] = 10

modifies df itself.

Warning: It is sometimes difficult to predict if an operation returns a copy or a view. For this reason the docs recommend avoiding assignments with “chained indexing”.


So the recommended alternative is

df.at['C', 'x'] = 10

which does modify df.


In [18]: %timeit df.set_value('C', 'x', 10)
100000 loops, best of 3: 2.9 µs per loop

In [20]: %timeit df['x']['C'] = 10
100000 loops, best of 3: 6.31 µs per loop

In [81]: %timeit df.at['C', 'x'] = 10
100000 loops, best of 3: 9.2 µs per loop

回答 1

更新:该.set_value方法将不推荐使用.iat/.at是很好的替代品,不幸的是熊猫提供的文件很少


最快的方法是使用set_value。该方法比.ix方法快100倍。例如:

df.set_value('C', 'x', 10)

Update: The .set_value method is going to be deprecated. .iat/.at are good replacements, unfortunately pandas provides little documentation


The fastest way to do this is using set_value. This method is ~100 times faster than .ix method. For example:

df.set_value('C', 'x', 10)


回答 2

您还可以使用条件查询,.loc如下所示:

df.loc[df[<some_column_name>] == <condition>, [<another_column_name>]] = <value_to_add>

<some_column_name您要在其中检查<condition>变量的列在哪里,您要添加到的列在哪里<another_column_name>(可以是新列,也可以是已经存在的列)。<value_to_add>是要添加到该列/行的值。

该示例不能完全解决当前的问题,但对于希望根据条件添加特定值的人来说可能很有用。

You can also use a conditional lookup using .loc as seen here:

df.loc[df[<some_column_name>] == <condition>, [<another_column_name>]] = <value_to_add>

where <some_column_name is the column you want to check the <condition> variable against and <another_column_name> is the column you want to add to (can be a new column or one that already exists). <value_to_add> is the value you want to add to that column/row.

This example doesn’t work precisely with the question at hand, but it might be useful for someone wants to add a specific value based on a condition.


回答 3

推荐的方法(根据维护者)是:

df.ix['x','C']=10

使用“链接索引”(df['x']['C'])可能会导致问题。

看到:

The recommended way (according to the maintainers) to set a value is:

df.ix['x','C']=10

Using ‘chained indexing’ (df['x']['C']) may lead to problems.

See:


回答 4

尝试使用 df.loc[row_index,col_indexer] = value

Try using df.loc[row_index,col_indexer] = value


回答 5

这是唯一对我有用的东西!

df.loc['C', 'x'] = 10

.loc 在此处了解更多信息。

This is the only thing that worked for me!

df.loc['C', 'x'] = 10

Learn more about .loc here.


回答 6

.iat/.at是很好的解决方案。假设您有一个简单的data_frame:

   A   B   C
0  1   8   4 
1  3   9   6
2  22 33  52

如果我们要修改单元格的值,则[0,"A"]可以使用以下解决方案之一:

  1. df.iat[0,0] = 2
  2. df.at[0,'A'] = 2

这是一个完整的示例,说明如何iat用于获取和设置cell的值:

def prepossessing(df):
  for index in range(0,len(df)): 
      df.iat[index,0] = df.iat[index,0] * 2
  return df

y_train之前:

    0
0   54
1   15
2   15
3   8
4   31
5   63
6   11

调用预设函数后的y_train iat进行更改,以将每个单元格的值乘以2:

     0
0   108
1   30
2   30
3   16
4   62
5   126
6   22

.iat/.at is the good solution. Supposing you have this simple data_frame:

   A   B   C
0  1   8   4 
1  3   9   6
2  22 33  52

if we want to modify the value of the cell [0,"A"] u can use one of those solution :

  1. df.iat[0,0] = 2
  2. df.at[0,'A'] = 2

And here is a complete example how to use iat to get and set a value of cell :

def prepossessing(df):
  for index in range(0,len(df)): 
      df.iat[index,0] = df.iat[index,0] * 2
  return df

y_train before :

    0
0   54
1   15
2   15
3   8
4   31
5   63
6   11

y_train after calling prepossessing function that iat to change to multiply the value of each cell by 2:

     0
0   108
1   30
2   30
3   16
4   62
5   126
6   22

回答 7

要设置值,请使用:

df.at[0, 'clm1'] = 0
  • 推荐的最快的设置变量的方法。
  • set_valueix已弃用。
  • 没有警告,与ilocloc

To set values, use:

df.at[0, 'clm1'] = 0
  • The fastest recommended method for setting variables.
  • set_value, ix have been deprecated.
  • No warning, unlike iloc and loc

回答 8

您可以使用.iloc

df.iloc[[2], [0]] = 10

you can use .iloc.

df.iloc[[2], [0]] = 10

回答 9

在我的示例中,我只是在选定的单元格中对其进行了更改

    for index, row in result.iterrows():
        if np.isnan(row['weight']):
            result.at[index, 'weight'] = 0.0

“结果”是带有“权重”列的dataField

In my example i just change it in selected cell

    for index, row in result.iterrows():
        if np.isnan(row['weight']):
            result.at[index, 'weight'] = 0.0

‘result’ is a dataField with column ‘weight’


回答 10

set_value() 不推荐使用。

从版本0.23.4开始,Pandas“ 宣布了未来 ”。

>>> df
                   Cars  Prices (U$)
0               Audi TT        120.0
1 Lamborghini Aventador        245.0
2      Chevrolet Malibu        190.0
>>> df.set_value(2, 'Prices (U$)', 240.0)
__main__:1: FutureWarning: set_value is deprecated and will be removed in a future release.
Please use .at[] or .iat[] accessors instead

                   Cars  Prices (U$)
0               Audi TT        120.0
1 Lamborghini Aventador        245.0
2      Chevrolet Malibu        240.0

考虑到此建议,下面是如何使用它们的演示:

  • 按行/列整数位置

>>> df.iat[1, 1] = 260.0
>>> df
                   Cars  Prices (U$)
0               Audi TT        120.0
1 Lamborghini Aventador        260.0
2      Chevrolet Malibu        240.0
  • 按行/列标签

>>> df.at[2, "Cars"] = "Chevrolet Corvette"
>>> df
                  Cars  Prices (U$)
0               Audi TT        120.0
1 Lamborghini Aventador        260.0
2    Chevrolet Corvette        240.0

参考文献:

set_value() is deprecated.

Starting from the release 0.23.4, Pandas “announces the future“…

>>> df
                   Cars  Prices (U$)
0               Audi TT        120.0
1 Lamborghini Aventador        245.0
2      Chevrolet Malibu        190.0
>>> df.set_value(2, 'Prices (U$)', 240.0)
__main__:1: FutureWarning: set_value is deprecated and will be removed in a future release.
Please use .at[] or .iat[] accessors instead

                   Cars  Prices (U$)
0               Audi TT        120.0
1 Lamborghini Aventador        245.0
2      Chevrolet Malibu        240.0

Considering this advice, here’s a demonstration of how to use them:

  • by row/column integer positions

>>> df.iat[1, 1] = 260.0
>>> df
                   Cars  Prices (U$)
0               Audi TT        120.0
1 Lamborghini Aventador        260.0
2      Chevrolet Malibu        240.0
  • by row/column labels

>>> df.at[2, "Cars"] = "Chevrolet Corvette"
>>> df
                  Cars  Prices (U$)
0               Audi TT        120.0
1 Lamborghini Aventador        260.0
2    Chevrolet Corvette        240.0

References:


回答 11

这是所有用户针对整数和字符串索引的数据帧提供的有效解决方案的摘要。

df.iloc,df.loc和df.at适用于两种类型的数据帧,df.iloc仅适用于行/列整数索引,df.loc和df.at支持使用列名和/或整数索引设置值。

如果指定的索引不存在,则df.loc和df.at都将新插入的行/列追加到现有数据帧中,但是df.iloc将引发“ IndexError:位置索引器越界”。在Python 2.7和3.7中测试的一个工作示例如下:

import numpy as np, pandas as pd

df1 = pd.DataFrame(index=np.arange(3), columns=['x','y','z'])
df1['x'] = ['A','B','C']
df1.at[2,'y'] = 400

# rows/columns specified does not exist, appends new rows/columns to existing data frame
df1.at['D','w'] = 9000
df1.loc['E','q'] = 499

# using df[<some_column_name>] == <condition> to retrieve target rows
df1.at[df1['x']=='B', 'y'] = 10000
df1.loc[df1['x']=='B', ['z','w']] = 10000

# using a list of index to setup values
df1.iloc[[1,2,4], 2] = 9999
df1.loc[[0,'D','E'],'w'] = 7500
df1.at[[0,2,"D"],'x'] = 10
df1.at[:, ['y', 'w']] = 8000

df1
>>> df1
     x     y     z     w      q
0   10  8000   NaN  8000    NaN
1    B  8000  9999  8000    NaN
2   10  8000  9999  8000    NaN
D   10  8000   NaN  8000    NaN
E  NaN  8000  9999  8000  499.0

Here is a summary of the valid solutions provided by all users, for data frames indexed by integer and string.

df.iloc, df.loc and df.at work for both type of data frames, df.iloc only works with row/column integer indices, df.loc and df.at supports for setting values using column names and / or integer indices.

When the specified index does not exist, both df.loc and df.at would append the newly inserted rows/columns to the existing data frame, but df.iloc would raise “IndexError: positional indexers are out-of-bounds”. A working example tested in Python 2.7 and 3.7 is as follows:

import numpy as np, pandas as pd

df1 = pd.DataFrame(index=np.arange(3), columns=['x','y','z'])
df1['x'] = ['A','B','C']
df1.at[2,'y'] = 400

# rows/columns specified does not exist, appends new rows/columns to existing data frame
df1.at['D','w'] = 9000
df1.loc['E','q'] = 499

# using df[<some_column_name>] == <condition> to retrieve target rows
df1.at[df1['x']=='B', 'y'] = 10000
df1.loc[df1['x']=='B', ['z','w']] = 10000

# using a list of index to setup values
df1.iloc[[1,2,4], 2] = 9999
df1.loc[[0,'D','E'],'w'] = 7500
df1.at[[0,2,"D"],'x'] = 10
df1.at[:, ['y', 'w']] = 8000

df1
>>> df1
     x     y     z     w      q
0   10  8000   NaN  8000    NaN
1    B  8000  9999  8000    NaN
2   10  8000  9999  8000    NaN
D   10  8000   NaN  8000    NaN
E  NaN  8000  9999  8000  499.0

回答 12

我进行了测试,输出df.set_value速度稍快一些,但是官方方法df.at似乎是最快且不建议使用的方法。

import numpy as np
import pandas as pd

df = pd.DataFrame(np.random.rand(100, 100))

%timeit df.iat[50,50]=50 # ✓
%timeit df.at[50,50]=50 #  ✔
%timeit df.set_value(50,50,50) # will deprecate
%timeit df.iloc[50,50]=50
%timeit df.loc[50,50]=50

7.06 µs ± 118 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
5.52 µs ± 64.2 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
3.68 µs ± 80.8 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
98.7 µs ± 1.07 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)
109 µs ± 1.42 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)

请注意,这是为单个单元格设置值。对于向量lociloc由于它们已向量化,因此应该是更好的选择。

I tested and the output is df.set_value is little faster, but the official method df.at looks like the fastest non deprecated way to do it.

import numpy as np
import pandas as pd

df = pd.DataFrame(np.random.rand(100, 100))

%timeit df.iat[50,50]=50 # ✓
%timeit df.at[50,50]=50 #  ✔
%timeit df.set_value(50,50,50) # will deprecate
%timeit df.iloc[50,50]=50
%timeit df.loc[50,50]=50

7.06 µs ± 118 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
5.52 µs ± 64.2 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
3.68 µs ± 80.8 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
98.7 µs ± 1.07 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)
109 µs ± 1.42 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)

Note this is setting the value for a single cell. For the vectors loc and iloc should be better options since they are vectorized.


回答 13

将索引与条件一起使用的一种方法是,首先获取满足您条件的所有行的索引,然后简单地以多种方式使用这些行索引

conditional_index = df.loc[ df['col name'] <condition> ].index

示例条件如下

==5, >10 , =="Any string", >= DateTime

然后,您可以通过多种方式使用这些行索引,例如

  1. 将一列的值替换为conditional_index
df.loc[conditional_index , [col name]]= <new value>
  1. 将多列的值替换为conditional_index
df.loc[conditional_index, [col1,col2]]= <new value>
  1. 保存conditional_index的一个好处是,您可以将一列的值分配给具有相同行索引的另一列
df.loc[conditional_index, [col1,col2]]= df.loc[conditional_index,'col name']

这都是可能的,因为.index返回一个索引数组,.loc可以将其与直接寻址一起使用,从而避免了一次又一次的遍历。

One way to use index with condition is first get the index of all the rows that satisfy your condition and then simply use those row indexes in a multiple of ways

conditional_index = df.loc[ df['col name'] <condition> ].index

Example condition is like

==5, >10 , =="Any string", >= DateTime

Then you can use these row indexes in variety of ways like

  1. Replace value of one column for conditional_index
df.loc[conditional_index , [col name]]= <new value>
  1. Replace value of multiple column for conditional_index
df.loc[conditional_index, [col1,col2]]= <new value>
  1. One benefit with saving the conditional_index is that you can assign value of one column to another column with same row index
df.loc[conditional_index, [col1,col2]]= df.loc[conditional_index,'col name']

This is all possible because .index returns a array of index which .loc can use with direct addressing so it avoids traversals again and again.


回答 14

df.loc['c','x']=10 这将更改第c行和 第x列的值。

df.loc['c','x']=10 This will change the value of cth row and xth column.


回答 15

除上述答案外,这是一个基准测试,比较了将数据行添加到现有数据框的不同方法。它表明对于大型数据框(至少对于这些测试条件),使用at或set-value是最有效的方法。

  • 为每一行创建新的数据框并…
    • …附加(13.0 s)
    • …将其串联(13.1 s)
  • 首先将所有新行存储在另一个容器中,一次转换为新数据框,然后追加…
    • container =清单清单(2.0 s)
    • container =列表字典(1.9 s)
  • 预分配整个数据框,遍历新行和所有列,并使用
    • …在(0.6 s)
    • … set_value(0.4秒)

对于测试,使用了一个包含100,000行和1,000列以及随机numpy值的现有数据框。向该数据框添加了100个新行。

代码见下:

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Nov 21 16:38:46 2018

@author: gebbissimo
"""

import pandas as pd
import numpy as np
import time

NUM_ROWS = 100000
NUM_COLS = 1000
data = np.random.rand(NUM_ROWS,NUM_COLS)
df = pd.DataFrame(data)

NUM_ROWS_NEW = 100
data_tot = np.random.rand(NUM_ROWS + NUM_ROWS_NEW,NUM_COLS)
df_tot = pd.DataFrame(data_tot)

DATA_NEW = np.random.rand(1,NUM_COLS)


#%% FUNCTIONS

# create and append
def create_and_append(df):
    for i in range(NUM_ROWS_NEW):
        df_new = pd.DataFrame(DATA_NEW)
        df = df.append(df_new)
    return df

# create and concatenate
def create_and_concat(df):
    for i in range(NUM_ROWS_NEW):
        df_new = pd.DataFrame(DATA_NEW)
        df = pd.concat((df, df_new))
    return df


# store as dict and 
def store_as_list(df):
    lst = [[] for i in range(NUM_ROWS_NEW)]
    for i in range(NUM_ROWS_NEW):
        for j in range(NUM_COLS):
            lst[i].append(DATA_NEW[0,j])
    df_new = pd.DataFrame(lst)
    df_tot = df.append(df_new)
    return df_tot

# store as dict and 
def store_as_dict(df):
    dct = {}
    for j in range(NUM_COLS):
        dct[j] = []
        for i in range(NUM_ROWS_NEW):
            dct[j].append(DATA_NEW[0,j])
    df_new = pd.DataFrame(dct)
    df_tot = df.append(df_new)
    return df_tot




# preallocate and fill using .at
def fill_using_at(df):
    for i in range(NUM_ROWS_NEW):
        for j in range(NUM_COLS):
            #print("i,j={},{}".format(i,j))
            df.at[NUM_ROWS+i,j] = DATA_NEW[0,j]
    return df


# preallocate and fill using .at
def fill_using_set(df):
    for i in range(NUM_ROWS_NEW):
        for j in range(NUM_COLS):
            #print("i,j={},{}".format(i,j))
            df.set_value(NUM_ROWS+i,j,DATA_NEW[0,j])
    return df


#%% TESTS
t0 = time.time()    
create_and_append(df)
t1 = time.time()
print('Needed {} seconds'.format(t1-t0))

t0 = time.time()    
create_and_concat(df)
t1 = time.time()
print('Needed {} seconds'.format(t1-t0))

t0 = time.time()    
store_as_list(df)
t1 = time.time()
print('Needed {} seconds'.format(t1-t0))

t0 = time.time()    
store_as_dict(df)
t1 = time.time()
print('Needed {} seconds'.format(t1-t0))

t0 = time.time()    
fill_using_at(df_tot)
t1 = time.time()
print('Needed {} seconds'.format(t1-t0))

t0 = time.time()    
fill_using_set(df_tot)
t1 = time.time()
print('Needed {} seconds'.format(t1-t0))

In addition to the answers above, here is a benchmark comparing different ways to add rows of data to an already existing dataframe. It shows that using at or set-value is the most efficient way for large dataframes (at least for these test conditions).

  • Create new dataframe for each row and…
    • … append it (13.0 s)
    • … concatenate it (13.1 s)
  • Store all new rows in another container first, convert to new dataframe once and append…
    • container = lists of lists (2.0 s)
    • container = dictionary of lists (1.9 s)
  • Preallocate whole dataframe, iterate over new rows and all columns and fill using
    • … at (0.6 s)
    • … set_value (0.4 s)

For the test, an existing dataframe comprising 100,000 rows and 1,000 columns and random numpy values was used. To this dataframe, 100 new rows were added.

Code see below:

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Nov 21 16:38:46 2018

@author: gebbissimo
"""

import pandas as pd
import numpy as np
import time

NUM_ROWS = 100000
NUM_COLS = 1000
data = np.random.rand(NUM_ROWS,NUM_COLS)
df = pd.DataFrame(data)

NUM_ROWS_NEW = 100
data_tot = np.random.rand(NUM_ROWS + NUM_ROWS_NEW,NUM_COLS)
df_tot = pd.DataFrame(data_tot)

DATA_NEW = np.random.rand(1,NUM_COLS)


#%% FUNCTIONS

# create and append
def create_and_append(df):
    for i in range(NUM_ROWS_NEW):
        df_new = pd.DataFrame(DATA_NEW)
        df = df.append(df_new)
    return df

# create and concatenate
def create_and_concat(df):
    for i in range(NUM_ROWS_NEW):
        df_new = pd.DataFrame(DATA_NEW)
        df = pd.concat((df, df_new))
    return df


# store as dict and 
def store_as_list(df):
    lst = [[] for i in range(NUM_ROWS_NEW)]
    for i in range(NUM_ROWS_NEW):
        for j in range(NUM_COLS):
            lst[i].append(DATA_NEW[0,j])
    df_new = pd.DataFrame(lst)
    df_tot = df.append(df_new)
    return df_tot

# store as dict and 
def store_as_dict(df):
    dct = {}
    for j in range(NUM_COLS):
        dct[j] = []
        for i in range(NUM_ROWS_NEW):
            dct[j].append(DATA_NEW[0,j])
    df_new = pd.DataFrame(dct)
    df_tot = df.append(df_new)
    return df_tot




# preallocate and fill using .at
def fill_using_at(df):
    for i in range(NUM_ROWS_NEW):
        for j in range(NUM_COLS):
            #print("i,j={},{}".format(i,j))
            df.at[NUM_ROWS+i,j] = DATA_NEW[0,j]
    return df


# preallocate and fill using .at
def fill_using_set(df):
    for i in range(NUM_ROWS_NEW):
        for j in range(NUM_COLS):
            #print("i,j={},{}".format(i,j))
            df.set_value(NUM_ROWS+i,j,DATA_NEW[0,j])
    return df


#%% TESTS
t0 = time.time()    
create_and_append(df)
t1 = time.time()
print('Needed {} seconds'.format(t1-t0))

t0 = time.time()    
create_and_concat(df)
t1 = time.time()
print('Needed {} seconds'.format(t1-t0))

t0 = time.time()    
store_as_list(df)
t1 = time.time()
print('Needed {} seconds'.format(t1-t0))

t0 = time.time()    
store_as_dict(df)
t1 = time.time()
print('Needed {} seconds'.format(t1-t0))

t0 = time.time()    
fill_using_at(df_tot)
t1 = time.time()
print('Needed {} seconds'.format(t1-t0))

t0 = time.time()    
fill_using_set(df_tot)
t1 = time.time()
print('Needed {} seconds'.format(t1-t0))

回答 16

如果您不想更改整个行的值,而只更改某些列的值:

x = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
x.iloc[1] = dict(A=10, B=-10)

If you want to change values not for whole row, but only for some columns:

x = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
x.iloc[1] = dict(A=10, B=-10)

回答 17

从0.21.1版开始,您还可以使用.at方法。与.loc此处提到的相比-Pandas .at与.loc有一些区别,但是在单值替换上它更快

From version 0.21.1 you can also use .at method. There are some differences compared to .loc as mentioned here – pandas .at versus .loc, but it’s faster on single value replacement


回答 18

如此,您的问题是将[‘x’,C]的NaN转换为值10

答案是..

df['x'].loc['C':]=10
df

替代代码是

df.loc['C':'x']=10
df

Soo, your question to convert NaN at [‘x’,C] to value 10

the answer is..

df['x'].loc['C':]=10
df

alternative code is

df.loc['C':'x']=10
df

回答 19

我也在寻找这个主题,并且提出了一种方法来遍历DataFrame并使用第二个DataFrame中的查找值对其进行更新。这是我的代码。

src_df = pd.read_sql_query(src_sql,src_connection)
for index1, row1 in src_df.iterrows():
    for index, row in vertical_df.iterrows():
        src_df.set_value(index=index1,col=u'etl_load_key',value=etl_load_key)
        if (row1[u'src_id'] == row['SRC_ID']) is True:
            src_df.set_value(index=index1,col=u'vertical',value=row['VERTICAL'])

I too was searching for this topic and I put together a way to iterate through a DataFrame and update it with lookup values from a second DataFrame. Here is my code.

src_df = pd.read_sql_query(src_sql,src_connection)
for index1, row1 in src_df.iterrows():
    for index, row in vertical_df.iterrows():
        src_df.set_value(index=index1,col=u'etl_load_key',value=etl_load_key)
        if (row1[u'src_id'] == row['SRC_ID']) is True:
            src_df.set_value(index=index1,col=u'vertical',value=row['VERTICAL'])

安装mysqldb python接口时找不到mysql_config

问题:安装mysqldb python接口时找不到mysql_config

我正在尝试使Python脚本在通过ssh连接到的Linux服务器上运行。该脚本使用mysqldb。我有我需要的所有其他组件,但是当我尝试通过setuptools安装mySQLdb时,如下所示:

python setup.py install

我得到以下与mysql_config命令有关的错误报告。

sh: mysql_config: command not found
Traceback (most recent call last):
  File "setup.py", line 15, in <module>
    metadata, options = get_config()
  File "/usr/lib/python2.5/MySQL-python-1.2.3/setup_posix.py", line 43, in get_config
    libs = mysql_config("libs_r")
  File "/usr/lib/python2.5/MySQL-python-1.2.3/setup_posix.py", line 24, in mysql_config
    raise EnvironmentError("%s not found" % (mysql_config.path,))
EnvironmentError: mysql_config not found

还有其他人遇到此错误吗?如果是,您如何解决该错误/我该怎么做才能成功安装mysqldb?

I am trying to get a Python script to run on the linux server I’m connected to via ssh. The script uses mysqldb. I have all the other components I need, but when I try to install mySQLdb via setuptools like so:,

python setup.py install

I get the following error report related to the mysql_config command.

sh: mysql_config: command not found
Traceback (most recent call last):
  File "setup.py", line 15, in <module>
    metadata, options = get_config()
  File "/usr/lib/python2.5/MySQL-python-1.2.3/setup_posix.py", line 43, in get_config
    libs = mysql_config("libs_r")
  File "/usr/lib/python2.5/MySQL-python-1.2.3/setup_posix.py", line 24, in mysql_config
    raise EnvironmentError("%s not found" % (mysql_config.path,))
EnvironmentError: mysql_config not found

Has anyone else encountered this error and if so how did you resolve it/what can I do to successfully install mysqldb?


回答 0

mySQLdb是mysql的python接口,但不是mysql本身。显然,mySQLdb需要命令“ mysql_config”,因此您需要先安装该命令。

您可以通过从shell运行“ mysql”来确认自己是否安装了mysql吗?这应该给您一个响应,而不是“ mysql:not found”。

您使用的是哪个Linux发行版?Mysql已为大多数Linux发行版预先打包。例如,对于debian / ubuntu,安装mysql就像

sudo apt-get install mysql-server

mysql-config位于不同的软件包中,可以从安装(同样,假设debian / ubuntu):

sudo apt-get install libmysqlclient-dev

如果您使用的是mariadb,请替换为mysql,然后运行

sudo apt-get install libmariadbclient-dev

参考:https : //github.com/JudgeGirl/Judge-sender/issues/4#issuecomment-186542797

mySQLdb is a python interface for mysql, but it is not mysql itself. And apparently mySQLdb needs the command ‘mysql_config’, so you need to install that first.

Can you confirm that you did or did not install mysql itself, by running “mysql” from the shell? That should give you a response other than “mysql: command not found”.

Which linux distribution are you using? Mysql is pre-packaged for most linux distributions. For example, for debian / ubuntu, installing mysql is as easy as

sudo apt-get install mysql-server

mysql-config is in a different package, which can be installed from (again, assuming debian / ubuntu):

sudo apt-get install libmysqlclient-dev

if you are using mariadb, the drop in replacement for mysql, then run

sudo apt-get install libmariadbclient-dev

Reference: https://github.com/JudgeGirl/Judge-sender/issues/4#issuecomment-186542797


回答 1

我正在python-mysql使用以下命令在Ubuntu 12.04上安装

pip install mysql-python

首先,我有同样的问题:

Not Found "mysql_config"

这对我有用

$ sudo apt-get install libmysqlclient-dev

然后我有这个问题:

...
_mysql.c:29:20: error fatal: Python.h: No existe el archivo o el directorio

compilación terminada.

error: command 'gcc' failed with exit status 1

然后我尝试了

apt-get install python-dev

然后我很高兴:)

pip install mysql-python
    Installing collected packages: mysql-python
      Running setup.py install for mysql-python
        building '_mysql' extension
        gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O2 -Wall -Wstrict-prototypes -fPIC -Dversion_info=(1,2,4,'beta',4) -D__version__=1.2.4b4 -I/usr/include/mysql -I/usr/include/python2.7 -c _mysql.c -o build/temp.linux-x86_64-2.7/_mysql.o -DBIG_JOINS=1 -fno-strict-aliasing -g
        In file included from _mysql.c:44:0:
        /usr/include/mysql/my_config.h:422:0: aviso: se redefinió "HAVE_WCSCOLL" [activado por defecto]
        /usr/include/python2.7/pyconfig.h:890:0: nota: esta es la ubicación de la definición previa
        gcc -pthread -shared -Wl,-O1 -Wl,-Bsymbolic-functions -Wl,-Bsymbolic-functions -Wl,-z,relro build/temp.linux-x86_64-2.7/_mysql.o -L/usr/lib/x86_64-linux-gnu -lmysqlclient_r -lpthread -lz -lm -lrt -ldl -o build/lib.linux-x86_64-2.7/_mysql.so

Successfully installed mysql-python
Cleaning up...

I was installing python-mysql on Ubuntu 12.04 using

pip install mysql-python

First I had the same problem:

Not Found "mysql_config"

This worked for me

$ sudo apt-get install libmysqlclient-dev

Then I had this problem:

...
_mysql.c:29:20: error fatal: Python.h: No existe el archivo o el directorio

compilación terminada.

error: command 'gcc' failed with exit status 1

Then I tried with

apt-get install python-dev

And then I was happy :)

pip install mysql-python
    Installing collected packages: mysql-python
      Running setup.py install for mysql-python
        building '_mysql' extension
        gcc -pthread -fno-strict-aliasing -DNDEBUG -g -fwrapv -O2 -Wall -Wstrict-prototypes -fPIC -Dversion_info=(1,2,4,'beta',4) -D__version__=1.2.4b4 -I/usr/include/mysql -I/usr/include/python2.7 -c _mysql.c -o build/temp.linux-x86_64-2.7/_mysql.o -DBIG_JOINS=1 -fno-strict-aliasing -g
        In file included from _mysql.c:44:0:
        /usr/include/mysql/my_config.h:422:0: aviso: se redefinió "HAVE_WCSCOLL" [activado por defecto]
        /usr/include/python2.7/pyconfig.h:890:0: nota: esta es la ubicación de la definición previa
        gcc -pthread -shared -Wl,-O1 -Wl,-Bsymbolic-functions -Wl,-Bsymbolic-functions -Wl,-z,relro build/temp.linux-x86_64-2.7/_mysql.o -L/usr/lib/x86_64-linux-gnu -lmysqlclient_r -lpthread -lz -lm -lrt -ldl -o build/lib.linux-x86_64-2.7/_mysql.so

Successfully installed mysql-python
Cleaning up...

回答 2

(特定于Mac OS X)

我做了很多尝试,但是这些命令最终对我有用。

  1. 安装 mysql
    brew install mysql
  2. brew unlink mysql
  3. brew install mysql-connector-c
  4. 将mysql bin文件夹添加到PATH
    export PATH=/usr/local/Cellar/mysql/8.0.11/bin:$PATH
  5. mkdir /usr/local/Cellar/lib/
  6. 创建一个符号链接
    sudo ln -s /usr/local/Cellar/mysql/8.0.11/lib/libmysqlclient.21.dylib /usr/local/Cellar/lib/libmysqlclient.21.dylib
  7. brew reinstall openssl来源
  8. 最后,安装mysql-client
    LIBRARY_PATH=$LIBRARY_PATH:/usr/local/opt/openssl/lib/ pip install mysqlclient

更新:如果此方法不起作用,@ vinyll建议brew link mysql在步骤8之前运行。

(Specific to Mac OS X)

I have tried a lot of things, but these set of commands finally worked for me.

  1. Install mysql
    brew install mysql
    
  2. brew unlink mysql
  3. brew install mysql-connector-c
  4. Add the mysql bin folder to PATH
    export PATH=/usr/local/Cellar/mysql/8.0.11/bin:$PATH
    
  5. mkdir /usr/local/Cellar/lib/
  6. Create a symlink
    sudo ln -s /usr/local/Cellar/mysql/8.0.11/lib/libmysqlclient.21.dylib /usr/local/Cellar/lib/libmysqlclient.21.dylib
    
  7. brew reinstall openssl (source)
  8. Finally, install mysql-client
    LIBRARY_PATH=$LIBRARY_PATH:/usr/local/opt/openssl/lib/ pip install mysqlclient
    

Update: In case this doesn’t work, @vinyll suggests to run brew link mysql before step 8.


回答 3

在红帽上,我不得不做

sudo yum install mysql-devel gcc gcc-devel python-devel
sudo easy_install mysql-python

然后它起作用了。

On Red Hat I had to do

sudo yum install mysql-devel gcc gcc-devel python-devel
sudo easy_install mysql-python

Then it worked.


回答 4

以下内容适用于Ubuntu 12.04 LTS:

apt-get install libmysqlclient-dev python-dev

尽管一切正常,但我仍然继续执行以下操作:

export PATH=$PATH:/usr/local/mysql/bin/

The below worked for me on Ubuntu 12.04 LTS:

apt-get install libmysqlclient-dev python-dev

All though it worked, i still went ahead to do the below:

export PATH=$PATH:/usr/local/mysql/bin/

回答 5

尝试安装时出现相同的错误mysql-python

这就是我修复它的方式。

sudo PATH=/usr/local/mysql/bin/:$PATH pip install mysql-python

问题是安装程序无法在默认路径中找到mysql_config。现在它可以..并且有效..

 15 warnings generated.
    clang -bundle -undefined dynamic_lookup -Wl,-F. build/temp.macosx-10.8-intel-2.7/_mysql.o -L/usr/local/mysql/lib -lmysqlclient_r -lz -lm -lmygcc -o build/lib.macosx-10.8-intel-2.7/_mysql.so -arch x86_64

Successfully installed mysql-python
Cleaning up...

希望这可以帮助。

谢谢。

I got the same error while trying to install mysql-python.

This is how I fixed it.

sudo PATH=/usr/local/mysql/bin/:$PATH pip install mysql-python

The problem was that the installer could not find the mysql_config in the default path. Now it can ..and it worked..

 15 warnings generated.
    clang -bundle -undefined dynamic_lookup -Wl,-F. build/temp.macosx-10.8-intel-2.7/_mysql.o -L/usr/local/mysql/lib -lmysqlclient_r -lz -lm -lmygcc -o build/lib.macosx-10.8-intel-2.7/_mysql.so -arch x86_64

Successfully installed mysql-python
Cleaning up...

Hope this helps.

Thanks.


回答 6

我通过以下步骤解决了此问题:

sudo apt-get install libmysqlclient-dev
sudo apt-get install python-dev
sudo python setup.py install

I fixed this problem with the following steps:

sudo apt-get install libmysqlclient-dev
sudo apt-get install python-dev
sudo python setup.py install

回答 7

命令(也是mysql)mPATH可能丢失。

export PATH=$PATH:/usr/local/mysql/bin/

The commands (mysql too) mPATH might be missing.

export PATH=$PATH:/usr/local/mysql/bin/


回答 8

步骤1:-同时安装Python3和Python3-dev

sudo apt-get install python3 python3-dev

步骤2:- 安装Python和Mysql连接器

sudo apt-get install libmysqlclient-dev

步骤3:- 安装python mysql客户端

sudo apt-get install mysqlclient

这将解决您的问题

Step1:-Install Python3 & Python3-dev Both

sudo apt-get install python3 python3-dev

Step2:- Install Python & Mysql Connector

sudo apt-get install libmysqlclient-dev

step3:- Install python mysql client

sudo apt-get install mysqlclient

This will Solve your Problem


回答 9

如果您使用的是macOS,并且已经通过brew install安装了mysql@5.7,请执行以下操作:

  1. brew install mysql-connector-c
  2. brew unlink mysql@5.7
  3. brew link --overwrite --dry-run mysql@5.7 首先,查看哪些符号链接被覆盖
  4. brew link --overwrite --force mysql@5.7 用mysql@5.7实际覆盖与mysql相关的符号链接
  5. pip install mysqlclient

If you’re on macOS and already installed mysql@5.7 via brew install:

  1. brew install mysql-connector-c
  2. brew unlink mysql@5.7
  3. brew link --overwrite --dry-run mysql@5.7 first, to see what symlinks are getting overwritten
  4. brew link --overwrite --force mysql@5.7 to actually overwrite mysql-related symlinks with mysql@5.7
  5. pip install mysqlclient

回答 10

我通过安装libmysqlclient来修复它:

sudo apt-get install libmysqlclient16-dev

I fixed it by installing libmysqlclient:

sudo apt-get install libmysqlclient16-dev

回答 11

MySQL-python软件包正在使用该mysql_config命令来了解mysql主机上的配置。您的主机没有该mysql_config命令。

来自dev.mysql.com的MySQL开发库程序包(MySQL-devel-xxx)提供了此命令以及MySQL-python程序包所需的库。MySQL-devel可在下载-社区服务器区域中找到这些软件包。MySQL开发库软件包名称MySQL-devel以MySQL版本和linux平台(例如MySQL-devel-5.5.24-1.linux2.6.x86_64.rpm)为基础,并有所不同。

注意,您不需要安装mysql服务器。

The MySQL-python package is using the mysql_config command to learn about the mysql configuration on your host. Your host does not have the mysql_config command.

The MySQL development libraries package (MySQL-devel-xxx) from dev.mysql.com provides this command and the libraries needed by the MySQL-python package. The MySQL-devel packages are found in the download – community server area. The MySQL development library package names start with MySQL-devel and vary based MySQL version and linux platform (e.g. MySQL-devel-5.5.24-1.linux2.6.x86_64.rpm.)

Note that you do not need to install mysql server.


回答 12

不建议使用libmysqlclient-dev软件包,因此请使用以下命令对其进行修复。

软件包libmysqlclient-dev不可用,但是由另一个软件包引用。这可能意味着该软件包已丢失,已被废弃或只能从其他来源获得

sudo apt-get install default-libmysqlclient-dev

The package libmysqlclient-dev is deprecated, so use the below command to fix it.

Package libmysqlclient-dev is not available, but is referred to by another package. This may mean that the package is missing, has been obsoleted, or is only available from another source

sudo apt-get install default-libmysqlclient-dev

回答 13

在centos 7中,这对我有用:

yum install mariadb-devel
pip install mysqlclient

In centos 7 this works for me :

yum install mariadb-devel
pip install mysqlclient

回答 14

在我的Fedora 23机器上,我必须运行以下命令:

sudo dnf install mysql-devel

On my Fedora 23 machine I had to run the following:

sudo dnf install mysql-devel

回答 15

对于Alpine Linux:

$ apk add mariadb-dev mariadb-client mariadb-libs

MariaDB是MySQL的直接替代品,并成为Alpine 3.2的新标准。参见https://bugs.alpinelinux.org/issues/4264

For Alpine Linux:

$ apk add mariadb-dev mariadb-client mariadb-libs

MariaDB is a drop-in replacement for MySQL and became the new standard as of Alpine 3.2. See https://bugs.alpinelinux.org/issues/4264


回答 16

我认为,以下几行可以在终端上执行

 sudo ln -s /usr/local/zend/mysql/bin/mysql_config /usr/sbin/

该mysql_config目录用于MacOSx上的zend服务器。您可以像下面的几行一样在linux上做

sudo ln -s /usr/local/mysql/bin/mysql_config /usr/sbin/

这是默认的Linux mysql目录。

I think, following lines can be executed on terminal

 sudo ln -s /usr/local/zend/mysql/bin/mysql_config /usr/sbin/

This mysql_config directory is for zend server on MacOSx. You can do it for linux like following lines

sudo ln -s /usr/local/mysql/bin/mysql_config /usr/sbin/

This is default linux mysql directory.


回答 17

我遇到了这个问题,并通过将符号链接添加到来解决mysql_config

我已经用自制软件安装了mysql,并在输出中看到了这一点。

Error: The `brew link` step did not complete successfully

根据您的购买方式,mysql它会出现在不同的地方。以我为例,/usr/local/Cellar/mysql
一旦您知道它在哪里,就应该可以建立一个指向python寻找位置的符号链接。 /usr/local/mysql

这对我有用。

ln -s /usr/local/Cellar/mysql/<< VERSION >>/bin/mysql_config   /usr/local/mysql/bin/mysql_config

I had this issues and solved if by adding a symlink to mysql_config.

I had installed mysql with homebrew and saw this in the output.

Error: The `brew link` step did not complete successfully

Depending on how you got mysql it will be in different places. In my case /usr/local/Cellar/mysql
Once you know where it is you should be able to ma a symbolic link to where python is looking for it. /usr/local/mysql

This worked for me.

ln -s /usr/local/Cellar/mysql/<< VERSION >>/bin/mysql_config   /usr/local/mysql/bin/mysql_config

回答 18

我有同样的问题。我通过按照本教程在Ubuntu 16.04上使用python3-dev安装Python的方式解决了问题:

sudo apt-get update
sudo apt-get -y upgrade
sudo apt-get install -y python3-pip
sudo apt-get install build-essential libssl-dev libffi-dev python3-dev

现在您可以设置虚拟环境:

sudo apt-get install -y python3-venv
pyvenv my_env
source my_env/bin/activate

I had the same problem. I solved it by following this tutorial to install Python with python3-dev on Ubuntu 16.04:

sudo apt-get update
sudo apt-get -y upgrade
sudo apt-get install -y python3-pip
sudo apt-get install build-essential libssl-dev libffi-dev python3-dev

And now you can set up your virtual environment:

sudo apt-get install -y python3-venv
pyvenv my_env
source my_env/bin/activate

回答 19

您需要安装python-dev软件包:

sudo apt-get install python-dev

You need to install the python-dev package:

sudo apt-get install python-dev

回答 20

须藤apt-get install python-mysqldb

Python 2.5?听起来您正在使用非常旧的Ubuntu Server版本(Hardy 8.04?)-请确认服务器使用的Linux版本。

在ubuntu软件包数据库上搜索python-mysql

一些其他信息:

从mysql-python的自述文件-

红帽Linux ………….

MySQL-python已预包装在Red Hat Linux 7.x和更高版本中。这包括Fedora Core和Red Hat Enterprise Linux。您还可以如上所述构建自己的RPM软件包。

Debian GNU / Linux …………….

打包为python-mysqldb_ ::

# apt-get install python-mysqldb

或使用Synaptic。

.. _ python-mysqldbhttp : //packages.debian.org/python-mysqldb

Ubuntu ……

与Debian相同。

脚注:如果您确实使用的服务器发行版早于Ubuntu 10.04,则您不受官方支持,因此应尽快升级。

sudo apt-get install python-mysqldb

Python 2.5? Sounds like you are using a very old version of Ubuntu Server (Hardy 8.04?) – please confirm which Linux version the server uses.

python-mysql search on ubuntu package database

Some additional info:

From the README of mysql-python –

Red Hat Linux ………….

MySQL-python is pre-packaged in Red Hat Linux 7.x and newer. This includes Fedora Core and Red Hat Enterprise Linux. You can also build your own RPM packages as described above.

Debian GNU/Linux …………….

Packaged as python-mysqldb_::

# apt-get install python-mysqldb

Or use Synaptic.

.. _python-mysqldb: http://packages.debian.org/python-mysqldb

Ubuntu ……

Same as with Debian.

Footnote: If you really are using a server distribution older than Ubuntu 10.04 then you are out of official support, and should upgrade sooner rather than later.


回答 21

该方法仅适用于那些知道已安装Mysql但仍找不到mysql_config的用户。如果python安装无法在系统路径中找到mysql_config,则会发生这种情况,如果通过.dmg Mac Package完成安装或在某个自定义路径中进行安装,则通常会发生这种情况。MySqlDB最简单且记录在案的方法是更改site.cfg。找到可能位于/ usr / local / mysql / bin /中的mysql_config,然后像下面那样更改变量mysql_config,然后再次运行安装。不要忘记通过删除“#”取消注释

在行下更改

“ #mysql_config = / usr / local / bin / mysql_config”

“ mysql_config = / usr / local / mysql / bin / mysql_config”

取决于系统中的路径。

顺便说一句,我在更改site.cfg之后使用python安装

sudo /System/Library/Frameworks/Python.framework/Versions/2.7/bin/python setup.py安装

This method is only for those who know that Mysql is installed but still mysql_config can’t be find. This happens if python install can’t find mysql_config in your system path, which mostly happens if you have done the installation via .dmg Mac Package or installed at some custom path. The easiest and documented way by MySqlDB is to change the site.cfg. Find the mysql_config which is probably in /usr/local/mysql/bin/ and change the variable namely mysql_config just like below and run the installation again. Don’t forget to un-comment it by removing “#”

Change below line

“#mysql_config = /usr/local/bin/mysql_config”

to

“mysql_config = /usr/local/mysql/bin/mysql_config”

depending upon the path in your system.

By the way I used python install after changing the site.cfg

sudo /System/Library/Frameworks/Python.framework/Versions/2.7/bin/python setup.py install


回答 22

到目前为止,所有解决方案(Linux)都需要具有sudoroot用户权限才能安装。如果您没有root权限且没有root权限,则这是一个解决方案sudo。(否sudo apt install ...):

  1. 从此镜像下载libmysqlclient-dev的.deb文件
  2. 导航到下载的文件并运行。dpkg -x libmysqlclient-dev_<version tag>.deb .这将提取一个名为的文件夹usr
  3. 符号链接./usr/bin/mysql_config到您的上找到的某个位置$PATH

    ln -s `pwd` /usr/bin/mysql_config FOLDER_IN_YOUR_PATH

  4. 现在应该可以找到 mysql_config

在Ubuntu 18.04上测试。

So far, all solutions (Linux) require sudo or root rights to install . Here is a solution if you do not have root rights and without sudo. (no sudo apt install ...):

  1. Download the .deb file of the libmysqlclient-dev, e.g. from this mirror
  2. Navigate to the downloaded file and run dpkg -x libmysqlclient-dev_<version tag>.deb . This will extract a folder called usr.
  3. Symlink ./usr/bin/mysql_config to somewhere that is found on your $PATH:

    ln -s `pwd` /usr/bin/mysql_config FOLDER_IN_YOUR_PATH

  4. It should now be able to find mysql_config

Tested on Ubuntu 18.04.


回答 23

对于macOS Mojave,需要附加配置,编译器才能找到openssl,您可能需要设置:

export LDFLAGS="-L/usr/local/opt/openssl/lib"
export CPPFLAGS="-I/usr/local/opt/openssl/include"

For macOS Mojave , additional configuration was required, for compilers to find openssl you may need to set:

export LDFLAGS="-L/usr/local/opt/openssl/lib"
export CPPFLAGS="-I/usr/local/opt/openssl/include"

回答 24

我认为在2020年解决此问题的最便捷方法是使用另一个python软件包。我们不需要安装任何其他二进制软件。

尝试这个

pip install mysql-connector-python

然后

import mysql.connector

mydb = mysql.connector.connect(
          host="",
          user="",
          passwd="",
          database=""
          )      
cursor = mydb.cursor( buffered=True)
cursor.execute('show tables;')
cursor.execute('insert into test values (null, "a",10)')
mydb.commit()
mydb.disconnect()

I think the most convenient way to solve this problem in 2020 is using another python package. We don’t need install any other binary software.

Try this

pip install mysql-connector-python

and then

import mysql.connector

mydb = mysql.connector.connect(
          host="",
          user="",
          passwd="",
          database=""
          )      
cursor = mydb.cursor( buffered=True)
cursor.execute('show tables;')
cursor.execute('insert into test values (null, "a",10)')
mydb.commit()
mydb.disconnect()

回答 25

我遇到了同样的问题,只是将* mysql_config *所在的路径添加到环境变量PATH中,它对我有用。

I encountered the same problem, just added the path where *mysql_config* resided to the environment variable PATH and it worked for me.


回答 26

sudo apt-get build-dep python-mysqldb 将安装所有依赖项以从PIP / easy_install构建软件包

sudo apt-get build-dep python-mysqldb will install all the dependencies to build the package from PIP/easy_install


回答 27

由于实际错误是

gcc ... -I/usr/include/python2.7 ...

_mysql.c:29:20: error: Python.h: No such file or directory

并且如果您无法安装python-dev或python-devel软件包,则可以从http://hg.python.org/下载包含所需版本的python源的存档,并将标头文件放置在正确的文件夹中,以包含

As actual error is

gcc ... -I/usr/include/python2.7 ...

_mysql.c:29:20: error: Python.h: No such file or directory

and If you can’t install python-dev or python-devel packages, you may download archive with needed version of python sources from http://hg.python.org/ and place headers files in proper folder for include


回答 28

只需输入:

$ sudo apt-get install python-dev
$ venv/bin/pip install MySQL-python

这样可以解决这个问题。

Just type:

$ sudo apt-get install python-dev
$ venv/bin/pip install MySQL-python

This will solve this problems.


回答 29

在CentOS 7中,应执行以下操作:

#step1:install mysql 
https://dev.mysql.com/doc/mysql-yum-repo-quick-guide/en/

#step2:
sudo yum install mysql-devel

In CentOS 7 , the following things should be done:

#step1:install mysql 
https://dev.mysql.com/doc/mysql-yum-repo-quick-guide/en/

#step2:
sudo yum install mysql-devel

Python是否有序集?

问题:Python是否有序集?

Python有一个有序的字典。有序套呢?

Python has an ordered dictionary. What about an ordered set?


回答 0

为此,有一个有序的设置(可能的新链接)配方,可从Python 2文档中引用。无需修改即可在Py2.6或更高版本以及3.0或更高版本上运行。该接口几乎与普通集合完全相同,不同之处在于初始化应使用列表进行。

OrderedSet([1, 2, 3])

这是一个MutableSet,因此for的签名.union与set 的签名不匹配,但是由于它包含__or__类似的内容,因此可以轻松添加:

@staticmethod
def union(*sets):
    union = OrderedSet()
    union.union(*sets)
    return union

def union(self, *sets):
    for set in sets:
        self |= set

There is an ordered set (possible new link) recipe for this which is referred to from the Python 2 Documentation. This runs on Py2.6 or later and 3.0 or later without any modifications. The interface is almost exactly the same as a normal set, except that initialisation should be done with a list.

OrderedSet([1, 2, 3])

This is a MutableSet, so the signature for .union doesn’t match that of set, but since it includes __or__ something similar can easily be added:

@staticmethod
def union(*sets):
    union = OrderedSet()
    union.union(*sets)
    return union

def union(self, *sets):
    for set in sets:
        self |= set

回答 1

有序集在功能上是有序字典的特例。

字典的键是唯一的。因此,如果人们不理会有序字典中的值(例如,通过分配它们None),那么实质上就是一个有序集合。

对于Python 3.1的存在collections.OrderedDict。以下是OrderedSet的示例实现。(请注意,只有很少的方法需要定义或重写:collections.OrderedDictcollections.MutableSet。做繁重)

import collections

class OrderedSet(collections.OrderedDict, collections.MutableSet):

    def update(self, *args, **kwargs):
        if kwargs:
            raise TypeError("update() takes no keyword arguments")

        for s in args:
            for e in s:
                 self.add(e)

    def add(self, elem):
        self[elem] = None

    def discard(self, elem):
        self.pop(elem, None)

    def __le__(self, other):
        return all(e in other for e in self)

    def __lt__(self, other):
        return self <= other and self != other

    def __ge__(self, other):
        return all(e in self for e in other)

    def __gt__(self, other):
        return self >= other and self != other

    def __repr__(self):
        return 'OrderedSet([%s])' % (', '.join(map(repr, self.keys())))

    def __str__(self):
        return '{%s}' % (', '.join(map(repr, self.keys())))

    difference = __sub__ 
    difference_update = __isub__
    intersection = __and__
    intersection_update = __iand__
    issubset = __le__
    issuperset = __ge__
    symmetric_difference = __xor__
    symmetric_difference_update = __ixor__
    union = __or__

An ordered set is functionally a special case of an ordered dictionary.

The keys of a dictionary are unique. Thus, if one disregards the values in an ordered dictionary (e.g. by assigning them None), then one has essentially an ordered set.

As of Python 3.1 there is collections.OrderedDict. The following is an example implementation of an OrderedSet. (Note that only few methods need to be defined or overridden: collections.OrderedDict and collections.MutableSet do the heavy lifting.)

import collections

class OrderedSet(collections.OrderedDict, collections.MutableSet):

    def update(self, *args, **kwargs):
        if kwargs:
            raise TypeError("update() takes no keyword arguments")

        for s in args:
            for e in s:
                 self.add(e)

    def add(self, elem):
        self[elem] = None

    def discard(self, elem):
        self.pop(elem, None)

    def __le__(self, other):
        return all(e in other for e in self)

    def __lt__(self, other):
        return self <= other and self != other

    def __ge__(self, other):
        return all(e in self for e in other)

    def __gt__(self, other):
        return self >= other and self != other

    def __repr__(self):
        return 'OrderedSet([%s])' % (', '.join(map(repr, self.keys())))

    def __str__(self):
        return '{%s}' % (', '.join(map(repr, self.keys())))

    difference = __sub__ 
    difference_update = __isub__
    intersection = __and__
    intersection_update = __iand__
    issubset = __le__
    issuperset = __ge__
    symmetric_difference = __xor__
    symmetric_difference_update = __ixor__
    union = __or__

回答 2

答案是否定的,但是您可以collections.OrderedDict在Python标准库中仅使用键(和None)作为同一目的。

更新:从Python 3.7(和CPython 3.6)开始,标准dict可以保证保持顺序,并且比更具性能OrderedDict。(但是,为了向后兼容,尤其是为了可读性,您可能希望继续使用OrderedDict。)

这是一个示例,该示例说明如何dict在保留订单的同时用作有序集来过滤出重复项,从而模拟有序集。使用dictclass方法fromkeys()创建字典,然后简单地要求它keys()

>>> keywords = ['foo', 'bar', 'bar', 'foo', 'baz', 'foo']

>>> list(dict.fromkeys(keywords))
['foo', 'bar', 'baz']

The answer is no, but you can use collections.OrderedDict from the Python standard library with just keys (and values as None) for the same purpose.

Update: As of Python 3.7 (and CPython 3.6), standard dict is guaranteed to preserve order and is more performant than OrderedDict. (For backward compatibility and especially readability, however, you may wish to continue using OrderedDict.)

Here’s an example of how to use dict as an ordered set to filter out duplicate items while preserving order, thereby emulating an ordered set. Use the dict class method fromkeys() to create a dict, then simply ask for the keys() back.

>>> keywords = ['foo', 'bar', 'bar', 'foo', 'baz', 'foo']

>>> list(dict.fromkeys(keywords))
['foo', 'bar', 'baz']

回答 3

我可以做得比OrderedSet更好:bollton具有2/3兼容的纯Python IndexedSet类型,该类型不仅是有序集合,而且还支持索引编制(与列表一样)。

简单地pip install boltons(或复制setutils.py到您的代码库中),导入IndexedSet和:

>>> from boltons.setutils import IndexedSet
>>> x = IndexedSet(list(range(4)) + list(range(8)))
>>> x
IndexedSet([0, 1, 2, 3, 4, 5, 6, 7])
>>> x - set(range(2))
IndexedSet([2, 3, 4, 5, 6, 7])
>>> x[-1]
7
>>> fcr = IndexedSet('freecreditreport.com')
>>> ''.join(fcr[:fcr.index('.')])
'frecditpo'

一切都是独一无二的,并保持秩序。全面披露:我写了IndexedSet,但是这也意味着如果有任何问题,您可以给我个麻烦。:)

I can do you one better than an OrderedSet: boltons has a pure-Python, 2/3-compatible IndexedSet type that is not only an ordered set, but also supports indexing (as with lists).

Simply pip install boltons (or copy setutils.py into your codebase), import the IndexedSet and:

>>> from boltons.setutils import IndexedSet
>>> x = IndexedSet(list(range(4)) + list(range(8)))
>>> x
IndexedSet([0, 1, 2, 3, 4, 5, 6, 7])
>>> x - set(range(2))
IndexedSet([2, 3, 4, 5, 6, 7])
>>> x[-1]
7
>>> fcr = IndexedSet('freecreditreport.com')
>>> ''.join(fcr[:fcr.index('.')])
'frecditpo'

Everything is unique and retained in order. Full disclosure: I wrote the IndexedSet, but that also means you can bug me if there are any issues. :)


回答 4

在PyPI上的实现

尽管其他人指出,Python中还没有内置的插入顺序保留集实现(但是),但我感到这个问题缺少一个答案,该答案指出了在PyPI上可以找到的内容。

有软件包:

其中一些实现是基于Raymond Hettinger提交给ActiveState配方的,在其他答案中也提到了该配方

一些差异

  • 有序集(1.1版)
    • 优势:O(1)用于按索引查找(例如my_set[5]
  • oset(0.1.3版)
    • 优势:O(1)为 remove(item)
    • 缺点:显然是O(n)用于按索引查找

这两个实现都为add(item)__contains__(item)item in my_set)具有O(1 )。

Implementations on PyPI

While others have pointed out that there is no built-in implementation of an insertion-order preserving set in Python (yet), I am feeling that this question is missing an answer which states what there is to be found on PyPI.

There are the packages:

Some of these implementations are based on the recipe posted by Raymond Hettinger to ActiveState which is also mentioned in other answers here.

Some differences

  • ordered-set (version 1.1)
    • advantage: O(1) for lookups by index (e.g. my_set[5])
  • oset (version 0.1.3)
    • advantage: O(1) for remove(item)
    • disadvantage: apparently O(n) for lookups by index

Both implementations have O(1) for add(item) and __contains__(item) (item in my_set).


回答 5

如果您使用排序集来维护排序顺序,请考虑使用PyPI中的排序集实现。该sortedcontainers模块提供了一个SortedSet的只是这个目的。一些好处:纯Python,快速C实现,100%单元测试覆盖率,数小时的压力测试。

通过pip从PyPI安装很容易:

pip install sortedcontainers

请注意,如果不能pip install,则只需从开源存储库中下拉sortedlist.py和sortedset.py文件。

安装完成后,您可以:

from sortedcontainers import SortedSet
help(SortedSet)

sortedcontainers模块还与几种替代实现保持性能比较

对于询问Python的bag数据类型的评论,还有一种SortedList数据类型,可用于有效地实现bag。

If you’re using the ordered set to maintain a sorted order, consider using a sorted set implementation from PyPI. The sortedcontainers module provides a SortedSet for just this purpose. Some benefits: pure-Python, fast-as-C implementations, 100% unit test coverage, hours of stress testing.

Installing from PyPI is easy with pip:

pip install sortedcontainers

Note that if you can’t pip install, simply pull down the sortedlist.py and sortedset.py files from the open-source repository.

Once installed you can simply:

from sortedcontainers import SortedSet
help(SortedSet)

The sortedcontainers module also maintains a performance comparison with several alternative implementations.

For the comment that asked about Python’s bag data type, there’s alternatively a SortedList data type which can be used to efficiently implement a bag.


回答 6

如果您已经在代码中使用了pandas,则其Index对象的行为就很像有序集,如本文中所示。

文章中的示例:

indA = pd.Index([1, 3, 5, 7, 9])
indB = pd.Index([2, 3, 5, 7, 11])

indA & indB  # intersection
indA | indB  # union
indA - indB  # difference
indA ^ indB  # symmetric difference

In case you’re already using pandas in your code, its Index object behaves pretty like an ordered set, as shown in this article.

Examples from the article:

indA = pd.Index([1, 3, 5, 7, 9])
indB = pd.Index([2, 3, 5, 7, 11])

indA & indB  # intersection
indA | indB  # union
indA - indB  # difference
indA ^ indB  # symmetric difference

回答 7

太迟了一点,但我已经写了一类setlist作为一部分collections-extended完全实现双方SequenceSet

>>> from collections_extended import setlist
>>> sl = setlist('abracadabra')
>>> sl
setlist(('a', 'b', 'r', 'c', 'd'))
>>> sl[3]
'c'
>>> sl[-1]
'd'
>>> 'r' in sl  # testing for inclusion is fast
True
>>> sl.index('d')  # so is finding the index of an element
4
>>> sl.insert(1, 'd')  # inserting an element already in raises a ValueError
ValueError
>>> sl.index('d')
4

GitHub:https : //github.com/mlenzen/collections-extended

文档:http : //collections-extended.lenzm.net/en/latest/

PyPI:https://pypi.python.org/pypi/collections-extended

A little late to the game, but I’ve written a class setlist as part of collections-extended that fully implements both Sequence and Set

>>> from collections_extended import setlist
>>> sl = setlist('abracadabra')
>>> sl
setlist(('a', 'b', 'r', 'c', 'd'))
>>> sl[3]
'c'
>>> sl[-1]
'd'
>>> 'r' in sl  # testing for inclusion is fast
True
>>> sl.index('d')  # so is finding the index of an element
4
>>> sl.insert(1, 'd')  # inserting an element already in raises a ValueError
ValueError
>>> sl.index('d')
4

GitHub: https://github.com/mlenzen/collections-extended

Documentation: http://collections-extended.lenzm.net/en/latest/

PyPI: https://pypi.python.org/pypi/collections-extended


回答 8

OrderedSet官方图书馆没有。我为所有数据结构制作了详尽的备忘单,以供您参考。

DataStructure = {
    'Collections': {
        'Map': [
            ('dict', 'OrderDict', 'defaultdict'),
            ('chainmap', 'types.MappingProxyType')
        ],
        'Set': [('set', 'frozenset'), {'multiset': 'collection.Counter'}]
    },
    'Sequence': {
        'Basic': ['list', 'tuple', 'iterator']
    },
    'Algorithm': {
        'Priority': ['heapq', 'queue.PriorityQueue'],
        'Queue': ['queue.Queue', 'multiprocessing.Queue'],
        'Stack': ['collection.deque', 'queue.LifeQueue']
        },
    'text_sequence': ['str', 'byte', 'bytearray']
}

There’s no OrderedSet in official library. I make an exhaustive cheatsheet of all the data structure for your reference.

DataStructure = {
    'Collections': {
        'Map': [
            ('dict', 'OrderDict', 'defaultdict'),
            ('chainmap', 'types.MappingProxyType')
        ],
        'Set': [('set', 'frozenset'), {'multiset': 'collection.Counter'}]
    },
    'Sequence': {
        'Basic': ['list', 'tuple', 'iterator']
    },
    'Algorithm': {
        'Priority': ['heapq', 'queue.PriorityQueue'],
        'Queue': ['queue.Queue', 'multiprocessing.Queue'],
        'Stack': ['collection.deque', 'queue.LifeQueue']
        },
    'text_sequence': ['str', 'byte', 'bytearray']
}

回答 9

所述ParallelRegression包提供了SETLIST()有序集类,它是多个方法完成比基于ActiveState的配方的选项。它支持可用于列表的所有方法以及大多数(如果不是全部)可用于集合的方法。

The ParallelRegression package provides a setList( ) ordered set class that is more method-complete than the options based on the ActiveState recipe. It supports all methods available for lists and most if not all methods available for sets.


回答 10

正如其他答案所提到的,对于python 3.7+,该字典按定义排序。除了子类化之外,OrderedDict我们还可以子类化abc.collections.MutableSettyping.MutableSet使用dict的键存储值。

class OrderedSet(typing.MutableSet[T]):
    """A set that preserves insertion order by internally using a dict."""

    def __init__(self, iterable: t.Iterator[T]):
        self._d = dict.fromkeys(iterable)

    def add(self, x: T) -> None:
        self._d[x] = None

    def discard(self, x: T) -> None:
        self._d.pop(x)

    def __contains__(self, x: object) -> bool:
        return self._d.__contains__(x)

    def __len__(self) -> int:
        return self._d.__len__()

    def __iter__(self) -> t.Iterator[T]:
        return self._d.__iter__()

然后:

x = OrderedSet([1, 2, -1, "bar"])
x.add(0)
assert list(x) == [1, 2, -1, "bar", 0]

我将此代码放在一个小的库中,所以任何人都可以pip install

As other answers mention, as for python 3.7+, the dict is ordered by definition. Instead of subclassing OrderedDict we can subclass abc.collections.MutableSet or typing.MutableSet using the dict’s keys to store our values.

class OrderedSet(typing.MutableSet[T]):
    """A set that preserves insertion order by internally using a dict."""

    def __init__(self, iterable: t.Iterator[T]):
        self._d = dict.fromkeys(iterable)

    def add(self, x: T) -> None:
        self._d[x] = None

    def discard(self, x: T) -> None:
        self._d.pop(x)

    def __contains__(self, x: object) -> bool:
        return self._d.__contains__(x)

    def __len__(self) -> int:
        return self._d.__len__()

    def __iter__(self) -> t.Iterator[T]:
        return self._d.__iter__()

Then just:

x = OrderedSet([1, 2, -1, "bar"])
x.add(0)
assert list(x) == [1, 2, -1, "bar", 0]

I put this code in a small library, so anyone can just pip install it.


回答 11

对于许多目的,只需调用sorted就足够了。例如

>>> s = set([0, 1, 2, 99, 4, 40, 3, 20, 24, 100, 60])
>>> sorted(s)
[0, 1, 2, 3, 4, 20, 24, 40, 60, 99, 100]

如果要重复使用此功能,则调用sorted函数会产生开销,因此,只要您完成更改集合的操作,就可能希望保存结果列表。如果您需要维护唯一元素并进行排序,那么我建议您使用具有任意值(例如无)的集合中的OrderedDict。

For many purposes simply calling sorted will suffice. For example

>>> s = set([0, 1, 2, 99, 4, 40, 3, 20, 24, 100, 60])
>>> sorted(s)
[0, 1, 2, 3, 4, 20, 24, 40, 60, 99, 100]

If you are going to use this repeatedly, there will be overhead incurred by calling the sorted function so you might want to save the resulting list, as long as you’re done changing the set. If you need to maintain unique elements and sorted, I agree with the suggestion of using OrderedDict from collections with an arbitrary value such as None.


回答 12

因此,我还有一个小清单,很明显可以引入非唯一值。

我搜索了某种唯一列表的存在,但是后来意识到在添加元素之前测试元素的存在就可以了。

if(not new_element in my_list):
    my_list.append(new_element)

我不知道这种简单方法是否有警告,但可以解决我的问题。

So i also had a small list where i clearly had the possibility of introducing non-unique values.

I searched for the existence of a unique list of some sort, but then realized that testing the existence of the element before adding it works just fine.

if(not new_element in my_list):
    my_list.append(new_element)

I don’t know if there are caveats to this simple approach, but it solves my problem.


->在Python函数定义中是什么意思?

问题:->在Python函数定义中是什么意思?

我最近在查看Python 3.3语法规范时发现了一些有趣的东西:

funcdef: 'def' NAME parameters ['->' test] ':' suite

Python 2中没有可选的“箭头”块,我在Python 3中找不到有关其含义的任何信息。事实证明这是正确的Python,并且已被解释器接受:

def f(x) -> 123:
    return x

我认为这可能是某种前提语法,但是:

  • 我无法x在此处进行测试,因为它仍未定义,
  • 无论我在箭头后面加什么(例如2 < 1),它都不会影响功能行为。

习惯此语法的任何人都可以解释吗?

I’ve recently noticed something interesting when looking at Python 3.3 grammar specification:

funcdef: 'def' NAME parameters ['->' test] ':' suite

The optional ‘arrow’ block was absent in Python 2 and I couldn’t find any information regarding its meaning in Python 3. It turns out this is correct Python and it’s accepted by the interpreter:

def f(x) -> 123:
    return x

I thought that this might be some kind of a precondition syntax, but:

  • I cannot test x here, at it is still undefined,
  • No matter what I put after the arrow (e.g. 2 < 1), it doesn’t affect the function behaviour.

Could anyone accustomed with this syntax explain it?


回答 0

这是一个功能注释

更详细地讲,Python 2.x具有文档字符串,可用于将元数据字符串附加到各种类型的对象。这非常方便,因此Python 3通过允许您将元数据附加到描述其参数和返回值的函数来扩展了该功能。

没有预想的用例,但是PEP建议了几个。一种非常方便的方法是允许您使用期望的类型注释参数。这样就很容易编写一个装饰器来验证注释或将参数强制为正确的类型。另一个是允许特定于参数的文档,而不是将其编码为文档字符串。

It’s a function annotation.

In more detail, Python 2.x has docstrings, which allow you to attach a metadata string to various types of object. This is amazingly handy, so Python 3 extends the feature by allowing you to attach metadata to functions describing their parameters and return values.

There’s no preconceived use case, but the PEP suggests several. One very handy one is to allow you to annotate parameters with their expected types; it would then be easy to write a decorator that verifies the annotations or coerces the arguments to the right type. Another is to allow parameter-specific documentation instead of encoding it into the docstring.


回答 1

这些是PEP 3107中涵盖的功能注释。具体来说,->标记是返回函数注释。

例子:

>>> def kinetic_energy(m:'in KG', v:'in M/S')->'Joules': 
...    return 1/2*m*v**2
... 
>>> kinetic_energy.__annotations__
{'return': 'Joules', 'v': 'in M/S', 'm': 'in KG'}

注释是字典,因此您可以执行以下操作:

>>> '{:,} {}'.format(kinetic_energy(20,3000),
      kinetic_energy.__annotations__['return'])
'90,000,000.0 Joules'

您还可以拥有一个python数据结构,而不仅仅是一个字符串:

>>> rd={'type':float,'units':'Joules','docstring':'Given mass and velocity returns kinetic energy in Joules'}
>>> def f()->rd:
...    pass
>>> f.__annotations__['return']['type']
<class 'float'>
>>> f.__annotations__['return']['units']
'Joules'
>>> f.__annotations__['return']['docstring']
'Given mass and velocity returns kinetic energy in Joules'

或者,您可以使用函数属性来验证调用的值:

def validate(func, locals):
    for var, test in func.__annotations__.items():
        value = locals[var]
        try: 
            pr=test.__name__+': '+test.__docstring__
        except AttributeError:
            pr=test.__name__   
        msg = '{}=={}; Test: {}'.format(var, value, pr)
        assert test(value), msg

def between(lo, hi):
    def _between(x):
            return lo <= x <= hi
    _between.__docstring__='must be between {} and {}'.format(lo,hi)       
    return _between

def f(x: between(3,10), y:lambda _y: isinstance(_y,int)):
    validate(f, locals())
    print(x,y)

版画

>>> f(2,2) 
AssertionError: x==2; Test: _between: must be between 3 and 10
>>> f(3,2.1)
AssertionError: y==2.1; Test: <lambda>

These are function annotations covered in PEP 3107. Specifically, the -> marks the return function annotation.

Examples:

>>> def kinetic_energy(m:'in KG', v:'in M/S')->'Joules': 
...    return 1/2*m*v**2
... 
>>> kinetic_energy.__annotations__
{'return': 'Joules', 'v': 'in M/S', 'm': 'in KG'}

Annotations are dictionaries, so you can do this:

>>> '{:,} {}'.format(kinetic_energy(20,3000),
      kinetic_energy.__annotations__['return'])
'90,000,000.0 Joules'

You can also have a python data structure rather than just a string:

>>> rd={'type':float,'units':'Joules','docstring':'Given mass and velocity returns kinetic energy in Joules'}
>>> def f()->rd:
...    pass
>>> f.__annotations__['return']['type']
<class 'float'>
>>> f.__annotations__['return']['units']
'Joules'
>>> f.__annotations__['return']['docstring']
'Given mass and velocity returns kinetic energy in Joules'

Or, you can use function attributes to validate called values:

def validate(func, locals):
    for var, test in func.__annotations__.items():
        value = locals[var]
        try: 
            pr=test.__name__+': '+test.__docstring__
        except AttributeError:
            pr=test.__name__   
        msg = '{}=={}; Test: {}'.format(var, value, pr)
        assert test(value), msg

def between(lo, hi):
    def _between(x):
            return lo <= x <= hi
    _between.__docstring__='must be between {} and {}'.format(lo,hi)       
    return _between

def f(x: between(3,10), y:lambda _y: isinstance(_y,int)):
    validate(f, locals())
    print(x,y)

Prints

>>> f(2,2) 
AssertionError: x==2; Test: _between: must be between 3 and 10
>>> f(3,2.1)
AssertionError: y==2.1; Test: <lambda>

回答 2

如其他答案所述,该->符号用作功能注释的一部分。>= 3.5但是,在最新版本的Python中,它具有定义的含义。

PEP 3107-功能注释描述了规范,定义了语法更改,func.__annotations__它们存储在其中的存在以及用例的事实仍然是开放的。

但是在Python中3.5PEP 484-类型提示对此具有唯一含义:->用于指示函数返回的类型。看起来这将在将来的版本中强制执行,如注释的现有用法如何

最快的可能方案将在3.6中引入对非类型提示注释的静默弃用,在3.7中引入完全弃用,并将类型提示声明为Python 3.8中唯一允许使用的注释。

(强调我的)

3.6据我所知,实际上尚未真正实现,因此可能会与将来的版本发生冲突。

据此,您提供了示例:

def f(x) -> 123:
    return x

将来会被禁止(并且在当前版本中会令人困惑),因此需要将其更改为:

def f(x) -> int:
    return x

为了有效地描述该函数f返回一个类型的对象int

Python本身不以任何方式使用这些注释,它几乎填充并忽略了它们。与他们合作的取决于第三方图书馆。

As other answers have stated, the -> symbol is used as part of function annotations. In more recent versions of Python >= 3.5, though, it has a defined meaning.

PEP 3107 — Function Annotations described the specification, defining the grammar changes, the existence of func.__annotations__ in which they are stored and, the fact that it’s use case is still open.

In Python 3.5 though, PEP 484 — Type Hints attaches a single meaning to this: -> is used to indicate the type that the function returns. It also seems like this will be enforced in future versions as described in What about existing uses of annotations:

The fastest conceivable scheme would introduce silent deprecation of non-type-hint annotations in 3.6, full deprecation in 3.7, and declare type hints as the only allowed use of annotations in Python 3.8.

(Emphasis mine)

This hasn’t been actually implemented as of 3.6 as far as I can tell so it might get bumped to future versions.

According to this, the example you’ve supplied:

def f(x) -> 123:
    return x

will be forbidden in the future (and in current versions will be confusing), it would need to be changed to:

def f(x) -> int:
    return x

for it to effectively describe that function f returns an object of type int.

The annotations are not used in any way by Python itself, it pretty much populates and ignores them. It’s up to 3rd party libraries to work with them.


回答 3

在下面的代码中:

def f(x) -> int:
    return int(x)

-> int刚刚告诉f()返回一个整数(但并不强制函数返回一个整数)。它称为返回注释,可以通过进行访问f.__annotations__['return']

Python还支持参数注释:

def f(x: float) -> int:
    return int(x)

: float告诉阅读该程序(和某些第三方库/程序,例如pylint)的人员x应为float。它以形式访问f.__annotations__['x'],其本身没有任何意义。请参阅文档以获取更多信息:

https://docs.python.org/3/reference/compound_stmts.html#function-definitions https://www.python.org/dev/peps/pep-3107/

In the following code:

def f(x) -> int:
    return int(x)

the -> int just tells that f() returns an integer (but it doesn’t force the function to return an integer). It is called a return annotation, and can be accessed as f.__annotations__['return'].

Python also supports parameter annotations:

def f(x: float) -> int:
    return int(x)

: float tells people who read the program (and some third-party libraries/programs, e. g. pylint) that x should be a float. It is accessed as f.__annotations__['x'], and doesn’t have any meaning by itself. See the documentation for more information:

https://docs.python.org/3/reference/compound_stmts.html#function-definitions https://www.python.org/dev/peps/pep-3107/


回答 4

这意味着函数返回的结果类型,但可以是None

它在面向Python 3.x的现代库中很普遍。

例如,在很多地方,库pandas-profiling中都有代码,例如:

def get_description(self) -> dict:

def get_rejected_variables(self, threshold: float = 0.9) -> list:

def to_file(self, output_file: Path or str, silent: bool = True) -> None:
"""Write the report to a file.

This means the type of result the function returns, but it can be None.

It is widespread in modern libraries oriented on Python 3.x.

For example, it there is in code of library pandas-profiling in many places for example:

def get_description(self) -> dict:

def get_rejected_variables(self, threshold: float = 0.9) -> list:

def to_file(self, output_file: Path or str, silent: bool = True) -> None:
"""Write the report to a file.

回答 5

def function(arg)->123:

它只是一个返回类型,在这种情况下,整数与您写入的数字无关紧要。

Java一样:

public int function(int args){...}

但是对于Python(Jim Fasarakis Hilliard怎么说) ,返回类型只是一个提示,因此建议返回,但是无论如何都允许返回其他类型,例如字符串。

def function(arg)->123:

It’s simply a return type, integer in this case doesn’t matter which number you write.

like Java :

public int function(int args){...}

But for Python (how Jim Fasarakis Hilliard said) the return type it’s just an hint, so it’s suggest the return but allow anyway to return other type like a string..


回答 6

def f(x) -> 123:
    return x

我的总结:

  1. 简单->介绍它是为了使开发人员可以选择指定函数的返回类型。请参阅Python增强建议3107

  2. 这表明随着Python的广泛采用,事情将来会如何发展-这是强类型化的迹象-这是我个人的观察。

  3. 您也可以为参数指定类型。指定函数和参数的返回类型将有助于减少逻辑错误并改进代码增强功能。

  4. 您可以将表达式作为返回类型(对于函数和参数级别),并且可以通过注释对象的’return’属性访问表达式的结果。对于lambda内联函数的表达式/返回值,注释将为空。

def f(x) -> 123:
    return x

My summary:

  1. Simply -> is introduced to get developers to optionally specify the return type of the function. See Python Enhancement Proposal 3107

  2. This is an indication of how things may develop in future as Python is adopted extensively – an indication towards strong typing – this is my personal observation.

  3. You can specify types for arguments as well. Specifying return type of the functions and arguments will help in reducing logical errors and improving code enhancements.

  4. You can have expressions as return type (for both at function and parameter level) and the result of the expressions can be accessed via annotations object’s ‘return’ attribute. annotations will be empty for the expression/return value for lambda inline functions.


使用Python在Selenium WebDriver中获取WebElement的HTML源

问题:使用Python在Selenium WebDriver中获取WebElement的HTML源

我正在使用Python绑定来运行Selenium WebDriver:

from selenium import webdriver
wd = webdriver.Firefox()

我知道我可以像这样抓取网络元素:

elem = wd.find_element_by_css_selector('#my-id')

我知道我可以通过…

wd.page_source

但是无论如何,有没有获得“元素来源”?

elem.source   # <-- returns the HTML as a string

Python的Selenium Webdriver文档基本上不存在,我在代码中看不到任何能够启用该功能的东西。

对访问元素(及其子元素)的HTML的最佳方法有何想法?

I’m using the Python bindings to run Selenium WebDriver:

from selenium import webdriver
wd = webdriver.Firefox()

I know I can grab a webelement like so:

elem = wd.find_element_by_css_selector('#my-id')

And I know I can get the full page source with…

wd.page_source

But is there anyway to get the “element source”?

elem.source   # <-- returns the HTML as a string

The selenium webdriver docs for Python are basically non-existent and I don’t see anything in the code that seems to enable that functionality.

Any thoughts on the best way to access the HTML of an element (and its children)?


回答 0

您可以读取innerHTML属性以获取元素内容outerHTML来源或包含当前元素的来源。

Python:

element.get_attribute('innerHTML')

Java:

elem.getAttribute("innerHTML");

C#:

element.GetAttribute("innerHTML");

红宝石:

element.attribute("innerHTML")

JS:

element.getAttribute('innerHTML');

PHP:

$element->getAttribute('innerHTML');

经过测试并与ChromeDriver

You can read innerHTML attribute to get source of the content of the element or outerHTML for source with the current element.

Python:

element.get_attribute('innerHTML')

Java:

elem.getAttribute("innerHTML");

C#:

element.GetAttribute("innerHTML");

Ruby:

element.attribute("innerHTML")

JS:

element.getAttribute('innerHTML');

PHP:

$element->getAttribute('innerHTML');

Tested and works with the ChromeDriver.


回答 1

获取a的html源代码实际上并没有直接的方法webelement。您将不得不使用JS。我不太确定python绑定,但是您可以在Java中轻松地做到这一点。我确信一定有一些类似于JavascriptExecutorPython中的类。

 WebElement element = driver.findElement(By.id("foo"));
 String contents = (String)((JavascriptExecutor)driver).executeScript("return arguments[0].innerHTML;", element); 

There is not really a straight-forward way of getting the html source code of a webelement. You will have to use JS. I am not too sure about python bindings but you can easily do like this in Java. I am sure there must be something similar to JavascriptExecutor class in Python.

 WebElement element = driver.findElement(By.id("foo"));
 String contents = (String)((JavascriptExecutor)driver).executeScript("return arguments[0].innerHTML;", element); 

回答 2

当然,我们可以在下面的Selenium Python中使用此脚本获取所有HTML源代码:

elem = driver.find_element_by_xpath("//*")
source_code = elem.get_attribute("outerHTML")

如果要保存到文件:

with open('c:/html_source_code.html', 'w') as f:
    f.write(source_code.encode('utf-8'))

我建议保存到文件,因为源代码非常长。

Sure we can get all HTML source code with this script below in Selenium Python:

elem = driver.find_element_by_xpath("//*")
source_code = elem.get_attribute("outerHTML")

If you you want to save it to file:

with open('c:/html_source_code.html', 'w') as f:
    f.write(source_code.encode('utf-8'))

I suggest saving to a file because source code is very very long.


回答 3

在Ruby中,使用selenium-webdriver(2.32.1),存在一种page_source包含整个页面源的方法。

In Ruby, using selenium-webdriver (2.32.1), there is a page_source method that contains the entire page source.


回答 4

实际上,使用属性方法更容易,更直接。

将Ruby与Selenium和PageObject宝石一起使用,以获取与某个元素关联的类,该行将为element.attribute(Class)

如果您想将其他属性绑定到元素,则适用相同的概念。例如,如果我想要一个元素的String element.attribute(String)

Using the attribute method is, in fact, easier and more straight forward.

Using Ruby with the Selenium and PageObject gems, to get the class associated with a certain element, the line would be element.attribute(Class).

The same concept applies if you wanted to get other attributes tied to the element. For example, if I wanted the String of an element, element.attribute(String).


回答 5

看起来已经过时了,但无论如何还是要放在这里。在您的情况下,正确的做法是:

elem = wd.find_element_by_css_selector('#my-id')
html = wd.execute_script("return arguments[0].innerHTML;", elem)

要么

html = elem.get_attribute('innerHTML')

两者都为我工作(selenium-server-standalone-2.35.0)

Looks outdated, but let it be here anyway. The correct way to do it in your case:

elem = wd.find_element_by_css_selector('#my-id')
html = wd.execute_script("return arguments[0].innerHTML;", elem)

or

html = elem.get_attribute('innerHTML')

Both are working for me (selenium-server-standalone-2.35.0)


回答 6

Java与Selenium 2.53.0

driver.getPageSource();

Java with Selenium 2.53.0

driver.getPageSource();

回答 7

希望对您有所帮助:http : //selenium.googlecode.com/svn/trunk/docs/api/java/org/openqa/selenium/WebElement.html

这里介绍Java方法:

java.lang.String    getText() 

但不幸的是,它在Python中不可用。因此,您可以将方法名称从Java转换为Python,并使用当前方法尝试另一种逻辑,而无需获取整个页面的源代码…

例如

 my_id = elem[0].get_attribute('my-id')

I hope this could help: http://selenium.googlecode.com/svn/trunk/docs/api/java/org/openqa/selenium/WebElement.html

Here is described Java method:

java.lang.String    getText() 

But unfortunately it’s not available in Python. So you can translate the method names to Python from Java and try another logic using present methods without getting the whole page source…

E.g.

 my_id = elem[0].get_attribute('my-id')

回答 8

这对我来说是无缝的。

element.get_attribute('innerHTML')

This works seamlessly for me.

element.get_attribute('innerHTML')

回答 9

InnerHTML将返回所选元素内的元素,而outerHTML将连同所选元素一起返回HTML内

示例:-现在假设您的Element如下

<tr id="myRow"><td>A</td><td>B</td></tr>

innerHTML元素输出

<td>A</td><td>B</td>

outsideHTML元素输出

<tr id="myRow"><td>A</td><td>B</td></tr>

现场示例:-

http://www.java2s.com/Tutorials/JavascriptDemo/f/find_out_the_difference_between_innerhtml_and_outerhtml_in_javascript_example.htm

在下面,您将找到根据不同绑定要求的语法。根据需要将更innerHTML改为outerHTML

Python:

element.get_attribute('innerHTML')

Java:

elem.getAttribute("innerHTML");

如果您想使用整页HTML,请使用以下代码:-

driver.getPageSource();

InnerHTML will return element inside the selected element and outerHTML will return inside HTML along with the element you have selected

Example :- Now suppose your Element is as below

<tr id="myRow"><td>A</td><td>B</td></tr>

innerHTML element Output

<td>A</td><td>B</td>

outerHTML element Output

<tr id="myRow"><td>A</td><td>B</td></tr>

Live Example :-

http://www.java2s.com/Tutorials/JavascriptDemo/f/find_out_the_difference_between_innerhtml_and_outerhtml_in_javascript_example.htm

Below you will find the syntax which require as per different binding. Change the innerHTML to outerHTML as per required.

Python:

element.get_attribute('innerHTML')

Java:

elem.getAttribute("innerHTML");

If you want whole page HTML use below code :-

driver.getPageSource();

回答 10

WebElement element = driver.findElement(By.id("foo"));
String contents = (String)((JavascriptExecutor)driver).executeScript("return      arguments[0].innerHTML;", element); 

该代码也确实可以从源代码中获取JavaScript!

WebElement element = driver.findElement(By.id("foo"));
String contents = (String)((JavascriptExecutor)driver).executeScript("return      arguments[0].innerHTML;", element); 

This code really works to get JavaScript from source as well!


回答 11

在PHPUnit硒测试中,它是这样的:

$text = $this->byCssSelector('.some-class-nmae')->attribute('innerHTML');

And in PHPUnit selenium test it’s like this:

$text = $this->byCssSelector('.some-class-nmae')->attribute('innerHTML');

回答 12

如果您对Python中的远程控制解决方案感兴趣,请按照以下方法获取innerHTML:

innerHTML = sel.get_eval("window.document.getElementById('prodid').innerHTML")

If you are interested in a solution for Remote Control in Python, here is how to get innerHTML:

innerHTML = sel.get_eval("window.document.getElementById('prodid').innerHTML")

回答 13

我更喜欢获取呈现的HTML的方法如下:

driver.get("http://www.google.com")
body_html = driver.find_element_by_xpath("/html/body")
print body_html.text

但是,上述方法会删除所有标签(也是嵌套标签),并且仅返回文本内容。如果您也有兴趣获取HTML标记,请使用以下方法。

print body_html.getAttribute("innerHTML")

The method to get the rendered HTML I prefer is following:

driver.get("http://www.google.com")
body_html = driver.find_element_by_xpath("/html/body")
print body_html.text

However the above method removes all the tags( yes the nested tags as well ) and returns only text content. If you interested in getting the HTML markup as well, then use the method below.

print body_html.getAttribute("innerHTML")