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计算两个Python字典中包含的键的差异

问题:计算两个Python字典中包含的键的差异

假设我有两个Python字典- dictAdictB。我需要找出是否有任何键存在于中,dictB但没有dictA。最快的方法是什么?

我应该将字典键转换为集合然后继续吗?

有兴趣了解您的想法…


感谢您的回复。

很抱歉未能正确说明我的问题。我的情况是这样的-我有一个dictA与可能相同的dictB密钥,或者可能缺少一些密钥,dictB否则某些密钥的值可能会有所不同,必须将其设置为dictA密钥的值。

问题在于字典没有标准,并且可以具有可以作为dict的值。

dictA={'key1':a, 'key2':b, 'key3':{'key11':cc, 'key12':dd}, 'key4':{'key111':{....}}}
dictB={'key1':a, 'key2:':newb, 'key3':{'key11':cc, 'key12':newdd, 'key13':ee}.......

因此,必须将“ key2”值重置为新值,并在字典内部添加“ key13”。键值没有固定的格式。它可以是一个简单的值或dict或dict的dict。

Suppose I have two Python dictionaries – dictA and dictB. I need to find out if there are any keys which are present in dictB but not in dictA. What is the fastest way to go about it?

Should I convert the dictionary keys into a set and then go about?

Interested in knowing your thoughts…


Thanks for your responses.

Apologies for not stating my question properly. My scenario is like this – I have a dictA which can be the same as dictB or may have some keys missing as compared to dictB or else the value of some keys might be different which has to be set to that of dictA key’s value.

Problem is the dictionary has no standard and can have values which can be dict of dict.

Say

dictA={'key1':a, 'key2':b, 'key3':{'key11':cc, 'key12':dd}, 'key4':{'key111':{....}}}
dictB={'key1':a, 'key2:':newb, 'key3':{'key11':cc, 'key12':newdd, 'key13':ee}.......

So ‘key2’ value has to be reset to the new value and ‘key13’ has to be added inside the dict. The key value does not have a fixed format. It can be a simple value or a dict or a dict of dict.


回答 0

您可以在按键上使用设置操作:

diff = set(dictb.keys()) - set(dicta.keys())

这是一个查找所有可能性的类:添加了什么,删除了什么,哪些键值对相同​​以及哪些键值对已更改。

class DictDiffer(object):
    """
    Calculate the difference between two dictionaries as:
    (1) items added
    (2) items removed
    (3) keys same in both but changed values
    (4) keys same in both and unchanged values
    """
    def __init__(self, current_dict, past_dict):
        self.current_dict, self.past_dict = current_dict, past_dict
        self.set_current, self.set_past = set(current_dict.keys()), set(past_dict.keys())
        self.intersect = self.set_current.intersection(self.set_past)
    def added(self):
        return self.set_current - self.intersect 
    def removed(self):
        return self.set_past - self.intersect 
    def changed(self):
        return set(o for o in self.intersect if self.past_dict[o] != self.current_dict[o])
    def unchanged(self):
        return set(o for o in self.intersect if self.past_dict[o] == self.current_dict[o])

这是一些示例输出:

>>> a = {'a': 1, 'b': 1, 'c': 0}
>>> b = {'a': 1, 'b': 2, 'd': 0}
>>> d = DictDiffer(b, a)
>>> print "Added:", d.added()
Added: set(['d'])
>>> print "Removed:", d.removed()
Removed: set(['c'])
>>> print "Changed:", d.changed()
Changed: set(['b'])
>>> print "Unchanged:", d.unchanged()
Unchanged: set(['a'])

可以作为github存储库使用:https : //github.com/hughdbrown/dictdiffer

You can use set operations on the keys:

diff = set(dictb.keys()) - set(dicta.keys())

Here is a class to find all the possibilities: what was added, what was removed, which key-value pairs are the same, and which key-value pairs are changed.

class DictDiffer(object):
    """
    Calculate the difference between two dictionaries as:
    (1) items added
    (2) items removed
    (3) keys same in both but changed values
    (4) keys same in both and unchanged values
    """
    def __init__(self, current_dict, past_dict):
        self.current_dict, self.past_dict = current_dict, past_dict
        self.set_current, self.set_past = set(current_dict.keys()), set(past_dict.keys())
        self.intersect = self.set_current.intersection(self.set_past)
    def added(self):
        return self.set_current - self.intersect 
    def removed(self):
        return self.set_past - self.intersect 
    def changed(self):
        return set(o for o in self.intersect if self.past_dict[o] != self.current_dict[o])
    def unchanged(self):
        return set(o for o in self.intersect if self.past_dict[o] == self.current_dict[o])

Here is some sample output:

>>> a = {'a': 1, 'b': 1, 'c': 0}
>>> b = {'a': 1, 'b': 2, 'd': 0}
>>> d = DictDiffer(b, a)
>>> print "Added:", d.added()
Added: set(['d'])
>>> print "Removed:", d.removed()
Removed: set(['c'])
>>> print "Changed:", d.changed()
Changed: set(['b'])
>>> print "Unchanged:", d.unchanged()
Unchanged: set(['a'])

Available as a github repo: https://github.com/hughdbrown/dictdiffer


回答 1

如果您需要递归的区别,我已经为python编写了一个软件包:https : //github.com/seperman/deepdiff

安装

从PyPi安装:

pip install deepdiff

用法示例

输入

>>> from deepdiff import DeepDiff
>>> from pprint import pprint
>>> from __future__ import print_function # In case running on Python 2

同一对象返回空

>>> t1 = {1:1, 2:2, 3:3}
>>> t2 = t1
>>> print(DeepDiff(t1, t2))
{}

项目类型已更改

>>> t1 = {1:1, 2:2, 3:3}
>>> t2 = {1:1, 2:"2", 3:3}
>>> pprint(DeepDiff(t1, t2), indent=2)
{ 'type_changes': { 'root[2]': { 'newtype': <class 'str'>,
                                 'newvalue': '2',
                                 'oldtype': <class 'int'>,
                                 'oldvalue': 2}}}

项目的价值已更改

>>> t1 = {1:1, 2:2, 3:3}
>>> t2 = {1:1, 2:4, 3:3}
>>> pprint(DeepDiff(t1, t2), indent=2)
{'values_changed': {'root[2]': {'newvalue': 4, 'oldvalue': 2}}}

添加和/或删除项目

>>> t1 = {1:1, 2:2, 3:3, 4:4}
>>> t2 = {1:1, 2:4, 3:3, 5:5, 6:6}
>>> ddiff = DeepDiff(t1, t2)
>>> pprint (ddiff)
{'dic_item_added': ['root[5]', 'root[6]'],
 'dic_item_removed': ['root[4]'],
 'values_changed': {'root[2]': {'newvalue': 4, 'oldvalue': 2}}}

弦差异

>>> t1 = {1:1, 2:2, 3:3, 4:{"a":"hello", "b":"world"}}
>>> t2 = {1:1, 2:4, 3:3, 4:{"a":"hello", "b":"world!"}}
>>> ddiff = DeepDiff(t1, t2)
>>> pprint (ddiff, indent = 2)
{ 'values_changed': { 'root[2]': {'newvalue': 4, 'oldvalue': 2},
                      "root[4]['b']": { 'newvalue': 'world!',
                                        'oldvalue': 'world'}}}

弦差异2

>>> t1 = {1:1, 2:2, 3:3, 4:{"a":"hello", "b":"world!\nGoodbye!\n1\n2\nEnd"}}
>>> t2 = {1:1, 2:2, 3:3, 4:{"a":"hello", "b":"world\n1\n2\nEnd"}}
>>> ddiff = DeepDiff(t1, t2)
>>> pprint (ddiff, indent = 2)
{ 'values_changed': { "root[4]['b']": { 'diff': '--- \n'
                                                '+++ \n'
                                                '@@ -1,5 +1,4 @@\n'
                                                '-world!\n'
                                                '-Goodbye!\n'
                                                '+world\n'
                                                ' 1\n'
                                                ' 2\n'
                                                ' End',
                                        'newvalue': 'world\n1\n2\nEnd',
                                        'oldvalue': 'world!\n'
                                                    'Goodbye!\n'
                                                    '1\n'
                                                    '2\n'
                                                    'End'}}}

>>> 
>>> print (ddiff['values_changed']["root[4]['b']"]["diff"])
--- 
+++ 
@@ -1,5 +1,4 @@
-world!
-Goodbye!
+world
 1
 2
 End

类型变更

>>> t1 = {1:1, 2:2, 3:3, 4:{"a":"hello", "b":[1, 2, 3]}}
>>> t2 = {1:1, 2:2, 3:3, 4:{"a":"hello", "b":"world\n\n\nEnd"}}
>>> ddiff = DeepDiff(t1, t2)
>>> pprint (ddiff, indent = 2)
{ 'type_changes': { "root[4]['b']": { 'newtype': <class 'str'>,
                                      'newvalue': 'world\n\n\nEnd',
                                      'oldtype': <class 'list'>,
                                      'oldvalue': [1, 2, 3]}}}

清单差异

>>> t1 = {1:1, 2:2, 3:3, 4:{"a":"hello", "b":[1, 2, 3, 4]}}
>>> t2 = {1:1, 2:2, 3:3, 4:{"a":"hello", "b":[1, 2]}}
>>> ddiff = DeepDiff(t1, t2)
>>> pprint (ddiff, indent = 2)
{'iterable_item_removed': {"root[4]['b'][2]": 3, "root[4]['b'][3]": 4}}

清单差异2:

>>> t1 = {1:1, 2:2, 3:3, 4:{"a":"hello", "b":[1, 2, 3]}}
>>> t2 = {1:1, 2:2, 3:3, 4:{"a":"hello", "b":[1, 3, 2, 3]}}
>>> ddiff = DeepDiff(t1, t2)
>>> pprint (ddiff, indent = 2)
{ 'iterable_item_added': {"root[4]['b'][3]": 3},
  'values_changed': { "root[4]['b'][1]": {'newvalue': 3, 'oldvalue': 2},
                      "root[4]['b'][2]": {'newvalue': 2, 'oldvalue': 3}}}

列出差异忽略顺序或重复项:(具有与上述相同的字典)

>>> t1 = {1:1, 2:2, 3:3, 4:{"a":"hello", "b":[1, 2, 3]}}
>>> t2 = {1:1, 2:2, 3:3, 4:{"a":"hello", "b":[1, 3, 2, 3]}}
>>> ddiff = DeepDiff(t1, t2, ignore_order=True)
>>> print (ddiff)
{}

包含字典的列表:

>>> t1 = {1:1, 2:2, 3:3, 4:{"a":"hello", "b":[1, 2, {1:1, 2:2}]}}
>>> t2 = {1:1, 2:2, 3:3, 4:{"a":"hello", "b":[1, 2, {1:3}]}}
>>> ddiff = DeepDiff(t1, t2)
>>> pprint (ddiff, indent = 2)
{ 'dic_item_removed': ["root[4]['b'][2][2]"],
  'values_changed': {"root[4]['b'][2][1]": {'newvalue': 3, 'oldvalue': 1}}}

套装:

>>> t1 = {1, 2, 8}
>>> t2 = {1, 2, 3, 5}
>>> ddiff = DeepDiff(t1, t2)
>>> pprint (DeepDiff(t1, t2))
{'set_item_added': ['root[3]', 'root[5]'], 'set_item_removed': ['root[8]']}

命名元组:

>>> from collections import namedtuple
>>> Point = namedtuple('Point', ['x', 'y'])
>>> t1 = Point(x=11, y=22)
>>> t2 = Point(x=11, y=23)
>>> pprint (DeepDiff(t1, t2))
{'values_changed': {'root.y': {'newvalue': 23, 'oldvalue': 22}}}

自定义对象:

>>> class ClassA(object):
...     a = 1
...     def __init__(self, b):
...         self.b = b
... 
>>> t1 = ClassA(1)
>>> t2 = ClassA(2)
>>> 
>>> pprint(DeepDiff(t1, t2))
{'values_changed': {'root.b': {'newvalue': 2, 'oldvalue': 1}}}

添加对象属性:

>>> t2.c = "new attribute"
>>> pprint(DeepDiff(t1, t2))
{'attribute_added': ['root.c'],
 'values_changed': {'root.b': {'newvalue': 2, 'oldvalue': 1}}}

In case you want the difference recursively, I have written a package for python: https://github.com/seperman/deepdiff

Installation

Install from PyPi:

pip install deepdiff

Example usage

Importing

>>> from deepdiff import DeepDiff
>>> from pprint import pprint
>>> from __future__ import print_function # In case running on Python 2

Same object returns empty

>>> t1 = {1:1, 2:2, 3:3}
>>> t2 = t1
>>> print(DeepDiff(t1, t2))
{}

Type of an item has changed

>>> t1 = {1:1, 2:2, 3:3}
>>> t2 = {1:1, 2:"2", 3:3}
>>> pprint(DeepDiff(t1, t2), indent=2)
{ 'type_changes': { 'root[2]': { 'newtype': <class 'str'>,
                                 'newvalue': '2',
                                 'oldtype': <class 'int'>,
                                 'oldvalue': 2}}}

Value of an item has changed

>>> t1 = {1:1, 2:2, 3:3}
>>> t2 = {1:1, 2:4, 3:3}
>>> pprint(DeepDiff(t1, t2), indent=2)
{'values_changed': {'root[2]': {'newvalue': 4, 'oldvalue': 2}}}

Item added and/or removed

>>> t1 = {1:1, 2:2, 3:3, 4:4}
>>> t2 = {1:1, 2:4, 3:3, 5:5, 6:6}
>>> ddiff = DeepDiff(t1, t2)
>>> pprint (ddiff)
{'dic_item_added': ['root[5]', 'root[6]'],
 'dic_item_removed': ['root[4]'],
 'values_changed': {'root[2]': {'newvalue': 4, 'oldvalue': 2}}}

String difference

>>> t1 = {1:1, 2:2, 3:3, 4:{"a":"hello", "b":"world"}}
>>> t2 = {1:1, 2:4, 3:3, 4:{"a":"hello", "b":"world!"}}
>>> ddiff = DeepDiff(t1, t2)
>>> pprint (ddiff, indent = 2)
{ 'values_changed': { 'root[2]': {'newvalue': 4, 'oldvalue': 2},
                      "root[4]['b']": { 'newvalue': 'world!',
                                        'oldvalue': 'world'}}}

String difference 2

>>> t1 = {1:1, 2:2, 3:3, 4:{"a":"hello", "b":"world!\nGoodbye!\n1\n2\nEnd"}}
>>> t2 = {1:1, 2:2, 3:3, 4:{"a":"hello", "b":"world\n1\n2\nEnd"}}
>>> ddiff = DeepDiff(t1, t2)
>>> pprint (ddiff, indent = 2)
{ 'values_changed': { "root[4]['b']": { 'diff': '--- \n'
                                                '+++ \n'
                                                '@@ -1,5 +1,4 @@\n'
                                                '-world!\n'
                                                '-Goodbye!\n'
                                                '+world\n'
                                                ' 1\n'
                                                ' 2\n'
                                                ' End',
                                        'newvalue': 'world\n1\n2\nEnd',
                                        'oldvalue': 'world!\n'
                                                    'Goodbye!\n'
                                                    '1\n'
                                                    '2\n'
                                                    'End'}}}

>>> 
>>> print (ddiff['values_changed']["root[4]['b']"]["diff"])
--- 
+++ 
@@ -1,5 +1,4 @@
-world!
-Goodbye!
+world
 1
 2
 End

Type change

>>> t1 = {1:1, 2:2, 3:3, 4:{"a":"hello", "b":[1, 2, 3]}}
>>> t2 = {1:1, 2:2, 3:3, 4:{"a":"hello", "b":"world\n\n\nEnd"}}
>>> ddiff = DeepDiff(t1, t2)
>>> pprint (ddiff, indent = 2)
{ 'type_changes': { "root[4]['b']": { 'newtype': <class 'str'>,
                                      'newvalue': 'world\n\n\nEnd',
                                      'oldtype': <class 'list'>,
                                      'oldvalue': [1, 2, 3]}}}

List difference

>>> t1 = {1:1, 2:2, 3:3, 4:{"a":"hello", "b":[1, 2, 3, 4]}}
>>> t2 = {1:1, 2:2, 3:3, 4:{"a":"hello", "b":[1, 2]}}
>>> ddiff = DeepDiff(t1, t2)
>>> pprint (ddiff, indent = 2)
{'iterable_item_removed': {"root[4]['b'][2]": 3, "root[4]['b'][3]": 4}}

List difference 2:

>>> t1 = {1:1, 2:2, 3:3, 4:{"a":"hello", "b":[1, 2, 3]}}
>>> t2 = {1:1, 2:2, 3:3, 4:{"a":"hello", "b":[1, 3, 2, 3]}}
>>> ddiff = DeepDiff(t1, t2)
>>> pprint (ddiff, indent = 2)
{ 'iterable_item_added': {"root[4]['b'][3]": 3},
  'values_changed': { "root[4]['b'][1]": {'newvalue': 3, 'oldvalue': 2},
                      "root[4]['b'][2]": {'newvalue': 2, 'oldvalue': 3}}}

List difference ignoring order or duplicates: (with the same dictionaries as above)

>>> t1 = {1:1, 2:2, 3:3, 4:{"a":"hello", "b":[1, 2, 3]}}
>>> t2 = {1:1, 2:2, 3:3, 4:{"a":"hello", "b":[1, 3, 2, 3]}}
>>> ddiff = DeepDiff(t1, t2, ignore_order=True)
>>> print (ddiff)
{}

List that contains dictionary:

>>> t1 = {1:1, 2:2, 3:3, 4:{"a":"hello", "b":[1, 2, {1:1, 2:2}]}}
>>> t2 = {1:1, 2:2, 3:3, 4:{"a":"hello", "b":[1, 2, {1:3}]}}
>>> ddiff = DeepDiff(t1, t2)
>>> pprint (ddiff, indent = 2)
{ 'dic_item_removed': ["root[4]['b'][2][2]"],
  'values_changed': {"root[4]['b'][2][1]": {'newvalue': 3, 'oldvalue': 1}}}

Sets:

>>> t1 = {1, 2, 8}
>>> t2 = {1, 2, 3, 5}
>>> ddiff = DeepDiff(t1, t2)
>>> pprint (DeepDiff(t1, t2))
{'set_item_added': ['root[3]', 'root[5]'], 'set_item_removed': ['root[8]']}

Named Tuples:

>>> from collections import namedtuple
>>> Point = namedtuple('Point', ['x', 'y'])
>>> t1 = Point(x=11, y=22)
>>> t2 = Point(x=11, y=23)
>>> pprint (DeepDiff(t1, t2))
{'values_changed': {'root.y': {'newvalue': 23, 'oldvalue': 22}}}

Custom objects:

>>> class ClassA(object):
...     a = 1
...     def __init__(self, b):
...         self.b = b
... 
>>> t1 = ClassA(1)
>>> t2 = ClassA(2)
>>> 
>>> pprint(DeepDiff(t1, t2))
{'values_changed': {'root.b': {'newvalue': 2, 'oldvalue': 1}}}

Object attribute added:

>>> t2.c = "new attribute"
>>> pprint(DeepDiff(t1, t2))
{'attribute_added': ['root.c'],
 'values_changed': {'root.b': {'newvalue': 2, 'oldvalue': 1}}}

回答 2

不知道它是否“快速”,但是通常情况下,可以做到这一点

dicta = {"a":1,"b":2,"c":3,"d":4}
dictb = {"a":1,"d":2}
for key in dicta.keys():
    if not key in dictb:
        print key

not sure whether its “fast” or not, but normally, one can do this

dicta = {"a":1,"b":2,"c":3,"d":4}
dictb = {"a":1,"d":2}
for key in dicta.keys():
    if not key in dictb:
        print key

回答 3

就像Alex Martelli所写的那样,如果您只想检查B中的任何键是否不在A中,那any(True for k in dictB if k not in dictA)将是您的最佳选择。

要查找缺少的密钥:

diff = set(dictB)-set(dictA) #sets

C:\Dokumente und Einstellungen\thc>python -m timeit -s "dictA =    
dict(zip(range(1000),range
(1000))); dictB = dict(zip(range(0,2000,2),range(1000)))" "diff=set(dictB)-set(dictA)"
10000 loops, best of 3: 107 usec per loop

diff = [ k for k in dictB if k not in dictA ] #lc

C:\Dokumente und Einstellungen\thc>python -m timeit -s "dictA = 
dict(zip(range(1000),range
(1000))); dictB = dict(zip(range(0,2000,2),range(1000)))" "diff=[ k for k in dictB if
k not in dictA ]"
10000 loops, best of 3: 95.9 usec per loop

因此,这两种解决方案的速度几乎相同。

As Alex Martelli wrote, if you simply want to check if any key in B is not in A, any(True for k in dictB if k not in dictA) would be the way to go.

To find the keys that are missing:

diff = set(dictB)-set(dictA) #sets

C:\Dokumente und Einstellungen\thc>python -m timeit -s "dictA =    
dict(zip(range(1000),range
(1000))); dictB = dict(zip(range(0,2000,2),range(1000)))" "diff=set(dictB)-set(dictA)"
10000 loops, best of 3: 107 usec per loop

diff = [ k for k in dictB if k not in dictA ] #lc

C:\Dokumente und Einstellungen\thc>python -m timeit -s "dictA = 
dict(zip(range(1000),range
(1000))); dictB = dict(zip(range(0,2000,2),range(1000)))" "diff=[ k for k in dictB if
k not in dictA ]"
10000 loops, best of 3: 95.9 usec per loop

So those two solutions are pretty much the same speed.


回答 4

如果您确实要说的是真的(您只需要找出B中而不是A中“有任何键”的情况,那么ONES可能没有),最快的方法应该是:

if any(True for k in dictB if k not in dictA): ...

如果您实际上需要找出哪个键(如果有)在B中而不是在A中,而不仅仅是“ IF”,那么有这样的键,那么现有的答案就很合适了(但是我建议在以后的问题中更精确一些,如果那是确实是您的意思;-)。

If you really mean exactly what you say (that you only need to find out IF “there are any keys” in B and not in A, not WHICH ONES might those be if any), the fastest way should be:

if any(True for k in dictB if k not in dictA): ...

If you actually need to find out WHICH KEYS, if any, are in B and not in A, and not just “IF” there are such keys, then existing answers are quite appropriate (but I do suggest more precision in future questions if that’s indeed what you mean;-).


回答 5

用途set()

set(dictA.keys()).intersection(dictB.keys())

Use set():

set(dictA.keys()).intersection(dictB.keys())

回答 6

hughdbrown的最高答案是建议使用集差异,这绝对是最好的方法:

diff = set(dictb.keys()) - set(dicta.keys())

这段代码的问题在于,它仅创建两个列表就创建了两个列表,因此浪费了4N的时间和2N的空间。它也比需要的要复杂一些。

通常,这没什么大不了的,但是如果是这样的话:

diff = dictb.keys() - dicta

Python 2

在Python 2中,keys()返回键列表,而不是KeysView。因此,您必须viewkeys()直接提出要求。

diff = dictb.viewkeys() - dicta

对于双版本2.7 / 3.x代码,希望使用six或类似的代码,因此可以使用six.viewkeys(dictb)

diff = six.viewkeys(dictb) - dicta

在2.4-2.6中,没有KeysView。但是,您可以直接从迭代器中构建左集合,而不是先构建列表,至少可以将成本从4N削减到N:

diff = set(dictb) - dicta

物品

我有一个dictA可以与dictB相同,或者与dictB相比可能缺少一些键,否则某些键的值可能不同

因此,您实际上不需要比较键,而是需要比较项。ItemsViewSet当值是可哈希值(例如字符串)时,an 才是a 。如果是这样,这很容易:

diff = dictb.items() - dicta.items()

递归差异

尽管问题不是直接要求递归差异,但某些示例值是dict,并且看来预期的输出确实递归地对它们进行差异。这里已经有多个答案显示了如何执行此操作。

The top answer by hughdbrown suggests using set difference, which is definitely the best approach:

diff = set(dictb.keys()) - set(dicta.keys())

The problem with this code is that it builds two lists just to create two sets, so it’s wasting 4N time and 2N space. It’s also a bit more complicated than it needs to be.

Usually, this is not a big deal, but if it is:

diff = dictb.keys() - dicta

Python 2

In Python 2, keys() returns a list of the keys, not a KeysView. So you have to ask for viewkeys() directly.

diff = dictb.viewkeys() - dicta

For dual-version 2.7/3.x code, you’re hopefully using six or something similar, so you can use six.viewkeys(dictb):

diff = six.viewkeys(dictb) - dicta

In 2.4-2.6, there is no KeysView. But you can at least cut the cost from 4N to N by building your left set directly out of an iterator, instead of building a list first:

diff = set(dictb) - dicta

Items

I have a dictA which can be the same as dictB or may have some keys missing as compared to dictB or else the value of some keys might be different

So you really don’t need to compare the keys, but the items. An ItemsView is only a Set if the values are hashable, like strings. If they are, it’s easy:

diff = dictb.items() - dicta.items()

Recursive diff

Although the question isn’t directly asking for a recursive diff, some of the example values are dicts, and it appears the expected output does recursively diff them. There are already multiple answers here showing how to do that.


回答 7

关于此参数,stackoverflow中还有另一个问题,我不得不承认有一个简单的解决方案:python 的datadiff库有助于打印两个字典之间的差异。

There is an other question in stackoverflow about this argument and i have to admit that there is a simple solution explained: the datadiff library of python helps printing the difference between two dictionaries.


回答 8

这是一种可行的方法,允许将键的值计算为False,并且在可能的情况下仍使用生成器表达式尽早退出。虽然不是特别漂亮。

any(map(lambda x: True, (k for k in b if k not in a)))

编辑:

THC4k发表了对我对另一个答案的评论的回复。这是一种更好,更漂亮的方法来执行上述操作:

any(True for k in b if k not in a)

不知道那怎么没想到…

Here’s a way that will work, allows for keys that evaluate to False, and still uses a generator expression to fall out early if possible. It’s not exceptionally pretty though.

any(map(lambda x: True, (k for k in b if k not in a)))

EDIT:

THC4k posted a reply to my comment on another answer. Here’s a better, prettier way to do the above:

any(True for k in b if k not in a)

Not sure how that never crossed my mind…


回答 9

这是一个古老的问题,要求的问题比我需要的要少,因此,此答案实际上比该问题所要求的要多。这个问题的答案帮助我解决了以下问题:

  1. (要求)记录两个词典之间的差异
  2. 将#1的差异合并到基础词典中
  3. (要求)合并两个字典之间的差异(将第2个字典视为差异字典)
  4. 尝试检测物品的移动和变化
  5. (要求)递归执行所有这些操作

所有这些与JSON相结合,提供了非常强大的配置存储支持。

解决方案(也在github上):

from collections import OrderedDict
from pprint import pprint


class izipDestinationMatching(object):
    __slots__ = ("attr", "value", "index")

    def __init__(self, attr, value, index):
        self.attr, self.value, self.index = attr, value, index

    def __repr__(self):
        return "izip_destination_matching: found match by '%s' = '%s' @ %d" % (self.attr, self.value, self.index)


def izip_destination(a, b, attrs, addMarker=True):
    """
    Returns zipped lists, but final size is equal to b with (if shorter) a padded with nulls
    Additionally also tries to find item reallocations by searching child dicts (if they are dicts) for attribute, listed in attrs)
    When addMarker == False (patching), final size will be the longer of a, b
    """
    for idx, item in enumerate(b):
        try:
            attr = next((x for x in attrs if x in item), None)  # See if the item has any of the ID attributes
            match, matchIdx = next(((orgItm, idx) for idx, orgItm in enumerate(a) if attr in orgItm and orgItm[attr] == item[attr]), (None, None)) if attr else (None, None)
            if match and matchIdx != idx and addMarker: item[izipDestinationMatching] = izipDestinationMatching(attr, item[attr], matchIdx)
        except:
            match = None
        yield (match if match else a[idx] if len(a) > idx else None), item
    if not addMarker and len(a) > len(b):
        for item in a[len(b) - len(a):]:
            yield item, item


def dictdiff(a, b, searchAttrs=[]):
    """
    returns a dictionary which represents difference from a to b
    the return dict is as short as possible:
      equal items are removed
      added / changed items are listed
      removed items are listed with value=None
    Also processes list values where the resulting list size will match that of b.
    It can also search said list items (that are dicts) for identity values to detect changed positions.
      In case such identity value is found, it is kept so that it can be re-found during the merge phase
    @param a: original dict
    @param b: new dict
    @param searchAttrs: list of strings (keys to search for in sub-dicts)
    @return: dict / list / whatever input is
    """
    if not (isinstance(a, dict) and isinstance(b, dict)):
        if isinstance(a, list) and isinstance(b, list):
            return [dictdiff(v1, v2, searchAttrs) for v1, v2 in izip_destination(a, b, searchAttrs)]
        return b
    res = OrderedDict()
    if izipDestinationMatching in b:
        keepKey = b[izipDestinationMatching].attr
        del b[izipDestinationMatching]
    else:
        keepKey = izipDestinationMatching
    for key in sorted(set(a.keys() + b.keys())):
        v1 = a.get(key, None)
        v2 = b.get(key, None)
        if keepKey == key or v1 != v2: res[key] = dictdiff(v1, v2, searchAttrs)
    if len(res) <= 1: res = dict(res)  # This is only here for pretty print (OrderedDict doesn't pprint nicely)
    return res


def dictmerge(a, b, searchAttrs=[]):
    """
    Returns a dictionary which merges differences recorded in b to base dictionary a
    Also processes list values where the resulting list size will match that of a
    It can also search said list items (that are dicts) for identity values to detect changed positions
    @param a: original dict
    @param b: diff dict to patch into a
    @param searchAttrs: list of strings (keys to search for in sub-dicts)
    @return: dict / list / whatever input is
    """
    if not (isinstance(a, dict) and isinstance(b, dict)):
        if isinstance(a, list) and isinstance(b, list):
            return [dictmerge(v1, v2, searchAttrs) for v1, v2 in izip_destination(a, b, searchAttrs, False)]
        return b
    res = OrderedDict()
    for key in sorted(set(a.keys() + b.keys())):
        v1 = a.get(key, None)
        v2 = b.get(key, None)
        #print "processing", key, v1, v2, key not in b, dictmerge(v1, v2)
        if v2 is not None: res[key] = dictmerge(v1, v2, searchAttrs)
        elif key not in b: res[key] = v1
    if len(res) <= 1: res = dict(res)  # This is only here for pretty print (OrderedDict doesn't pprint nicely)
    return res

This is an old question and asks a little bit less than what I needed so this answer actually solves more than this question asks. The answers in this question helped me solve the following:

  1. (asked) Record differences between two dictionaries
  2. Merge differences from #1 into base dictionary
  3. (asked) Merge differences between two dictionaries (treat dictionary #2 as if it were a diff dictionary)
  4. Try to detect item movements as well as changes
  5. (asked) Do all of this recursively

All this combined with JSON makes for a pretty powerful configuration storage support.

The solution (also on github):

from collections import OrderedDict
from pprint import pprint


class izipDestinationMatching(object):
    __slots__ = ("attr", "value", "index")

    def __init__(self, attr, value, index):
        self.attr, self.value, self.index = attr, value, index

    def __repr__(self):
        return "izip_destination_matching: found match by '%s' = '%s' @ %d" % (self.attr, self.value, self.index)


def izip_destination(a, b, attrs, addMarker=True):
    """
    Returns zipped lists, but final size is equal to b with (if shorter) a padded with nulls
    Additionally also tries to find item reallocations by searching child dicts (if they are dicts) for attribute, listed in attrs)
    When addMarker == False (patching), final size will be the longer of a, b
    """
    for idx, item in enumerate(b):
        try:
            attr = next((x for x in attrs if x in item), None)  # See if the item has any of the ID attributes
            match, matchIdx = next(((orgItm, idx) for idx, orgItm in enumerate(a) if attr in orgItm and orgItm[attr] == item[attr]), (None, None)) if attr else (None, None)
            if match and matchIdx != idx and addMarker: item[izipDestinationMatching] = izipDestinationMatching(attr, item[attr], matchIdx)
        except:
            match = None
        yield (match if match else a[idx] if len(a) > idx else None), item
    if not addMarker and len(a) > len(b):
        for item in a[len(b) - len(a):]:
            yield item, item


def dictdiff(a, b, searchAttrs=[]):
    """
    returns a dictionary which represents difference from a to b
    the return dict is as short as possible:
      equal items are removed
      added / changed items are listed
      removed items are listed with value=None
    Also processes list values where the resulting list size will match that of b.
    It can also search said list items (that are dicts) for identity values to detect changed positions.
      In case such identity value is found, it is kept so that it can be re-found during the merge phase
    @param a: original dict
    @param b: new dict
    @param searchAttrs: list of strings (keys to search for in sub-dicts)
    @return: dict / list / whatever input is
    """
    if not (isinstance(a, dict) and isinstance(b, dict)):
        if isinstance(a, list) and isinstance(b, list):
            return [dictdiff(v1, v2, searchAttrs) for v1, v2 in izip_destination(a, b, searchAttrs)]
        return b
    res = OrderedDict()
    if izipDestinationMatching in b:
        keepKey = b[izipDestinationMatching].attr
        del b[izipDestinationMatching]
    else:
        keepKey = izipDestinationMatching
    for key in sorted(set(a.keys() + b.keys())):
        v1 = a.get(key, None)
        v2 = b.get(key, None)
        if keepKey == key or v1 != v2: res[key] = dictdiff(v1, v2, searchAttrs)
    if len(res) <= 1: res = dict(res)  # This is only here for pretty print (OrderedDict doesn't pprint nicely)
    return res


def dictmerge(a, b, searchAttrs=[]):
    """
    Returns a dictionary which merges differences recorded in b to base dictionary a
    Also processes list values where the resulting list size will match that of a
    It can also search said list items (that are dicts) for identity values to detect changed positions
    @param a: original dict
    @param b: diff dict to patch into a
    @param searchAttrs: list of strings (keys to search for in sub-dicts)
    @return: dict / list / whatever input is
    """
    if not (isinstance(a, dict) and isinstance(b, dict)):
        if isinstance(a, list) and isinstance(b, list):
            return [dictmerge(v1, v2, searchAttrs) for v1, v2 in izip_destination(a, b, searchAttrs, False)]
        return b
    res = OrderedDict()
    for key in sorted(set(a.keys() + b.keys())):
        v1 = a.get(key, None)
        v2 = b.get(key, None)
        #print "processing", key, v1, v2, key not in b, dictmerge(v1, v2)
        if v2 is not None: res[key] = dictmerge(v1, v2, searchAttrs)
        elif key not in b: res[key] = v1
    if len(res) <= 1: res = dict(res)  # This is only here for pretty print (OrderedDict doesn't pprint nicely)
    return res

回答 10

怎么样标准(比较完整对象)

PyDev->新的PyDev模块->模块:单元测试

import unittest


class Test(unittest.TestCase):


    def testName(self):
        obj1 = {1:1, 2:2}
        obj2 = {1:1, 2:2}
        self.maxDiff = None # sometimes is usefull
        self.assertDictEqual(d1, d2)

if __name__ == "__main__":
    #import sys;sys.argv = ['', 'Test.testName']

    unittest.main()

what about standart (compare FULL Object)

PyDev->new PyDev Module->Module: unittest

import unittest


class Test(unittest.TestCase):


    def testName(self):
        obj1 = {1:1, 2:2}
        obj2 = {1:1, 2:2}
        self.maxDiff = None # sometimes is usefull
        self.assertDictEqual(d1, d2)

if __name__ == "__main__":
    #import sys;sys.argv = ['', 'Test.testName']

    unittest.main()

回答 11

如果在Python≥2.7上:

# update different values in dictB
# I would assume only dictA should be updated,
# but the question specifies otherwise

for k in dictA.viewkeys() & dictB.viewkeys():
    if dictA[k] != dictB[k]:
        dictB[k]= dictA[k]

# add missing keys to dictA

dictA.update( (k,dictB[k]) for k in dictB.viewkeys() - dictA.viewkeys() )

If on Python ≥ 2.7:

# update different values in dictB
# I would assume only dictA should be updated,
# but the question specifies otherwise

for k in dictA.viewkeys() & dictB.viewkeys():
    if dictA[k] != dictB[k]:
        dictB[k]= dictA[k]

# add missing keys to dictA

dictA.update( (k,dictB[k]) for k in dictB.viewkeys() - dictA.viewkeys() )

回答 12

这是深度比较两个字典键的解决方案:

def compareDictKeys(dict1, dict2):
  if type(dict1) != dict or type(dict2) != dict:
      return False

  keys1, keys2 = dict1.keys(), dict2.keys()
  diff = set(keys1) - set(keys2) or set(keys2) - set(keys1)

  if not diff:
      for key in keys1:
          if (type(dict1[key]) == dict or type(dict2[key]) == dict) and not compareDictKeys(dict1[key], dict2[key]):
              diff = True
              break

  return not diff

Here is a solution for deep comparing 2 dictionaries keys:

def compareDictKeys(dict1, dict2):
  if type(dict1) != dict or type(dict2) != dict:
      return False

  keys1, keys2 = dict1.keys(), dict2.keys()
  diff = set(keys1) - set(keys2) or set(keys2) - set(keys1)

  if not diff:
      for key in keys1:
          if (type(dict1[key]) == dict or type(dict2[key]) == dict) and not compareDictKeys(dict1[key], dict2[key]):
              diff = True
              break

  return not diff

回答 13

这是一个可以比较两个以上命令的解决方案:

def diff_dict(dicts, default=None):
    diff_dict = {}
    # add 'list()' around 'd.keys()' for python 3 compatibility
    for k in set(sum([d.keys() for d in dicts], [])):
        # we can just use "values = [d.get(k, default) ..." below if 
        # we don't care that d1[k]=default and d2[k]=missing will
        # be treated as equal
        if any(k not in d for d in dicts):
            diff_dict[k] = [d.get(k, default) for d in dicts]
        else:
            values = [d[k] for d in dicts]
            if any(v != values[0] for v in values):
                diff_dict[k] = values
    return diff_dict

用法示例:

import matplotlib.pyplot as plt
diff_dict([plt.rcParams, plt.rcParamsDefault, plt.matplotlib.rcParamsOrig])

here’s a solution that can compare more than two dicts:

def diff_dict(dicts, default=None):
    diff_dict = {}
    # add 'list()' around 'd.keys()' for python 3 compatibility
    for k in set(sum([d.keys() for d in dicts], [])):
        # we can just use "values = [d.get(k, default) ..." below if 
        # we don't care that d1[k]=default and d2[k]=missing will
        # be treated as equal
        if any(k not in d for d in dicts):
            diff_dict[k] = [d.get(k, default) for d in dicts]
        else:
            values = [d[k] for d in dicts]
            if any(v != values[0] for v in values):
                diff_dict[k] = values
    return diff_dict

usage example:

import matplotlib.pyplot as plt
diff_dict([plt.rcParams, plt.rcParamsDefault, plt.matplotlib.rcParamsOrig])

回答 14

我的两个字典之间的对称差异的配方:

def find_dict_diffs(dict1, dict2):
    unequal_keys = []
    unequal_keys.extend(set(dict1.keys()).symmetric_difference(set(dict2.keys())))
    for k in dict1.keys():
        if dict1.get(k, 'N\A') != dict2.get(k, 'N\A'):
            unequal_keys.append(k)
    if unequal_keys:
        print 'param', 'dict1\t', 'dict2'
        for k in set(unequal_keys):
            print str(k)+'\t'+dict1.get(k, 'N\A')+'\t '+dict2.get(k, 'N\A')
    else:
        print 'Dicts are equal'

dict1 = {1:'a', 2:'b', 3:'c', 4:'d', 5:'e'}
dict2 = {1:'b', 2:'a', 3:'c', 4:'d', 6:'f'}

find_dict_diffs(dict1, dict2)

结果是:

param   dict1   dict2
1       a       b
2       b       a
5       e       N\A
6       N\A     f

My recipe of symmetric difference between two dictionaries:

def find_dict_diffs(dict1, dict2):
    unequal_keys = []
    unequal_keys.extend(set(dict1.keys()).symmetric_difference(set(dict2.keys())))
    for k in dict1.keys():
        if dict1.get(k, 'N\A') != dict2.get(k, 'N\A'):
            unequal_keys.append(k)
    if unequal_keys:
        print 'param', 'dict1\t', 'dict2'
        for k in set(unequal_keys):
            print str(k)+'\t'+dict1.get(k, 'N\A')+'\t '+dict2.get(k, 'N\A')
    else:
        print 'Dicts are equal'

dict1 = {1:'a', 2:'b', 3:'c', 4:'d', 5:'e'}
dict2 = {1:'b', 2:'a', 3:'c', 4:'d', 6:'f'}

find_dict_diffs(dict1, dict2)

And result is:

param   dict1   dict2
1       a       b
2       b       a
5       e       N\A
6       N\A     f

回答 15

正如其他答案中提到的那样,unittest可以生成一些不错的输出来比较dict,但是在此示例中,我们不需要先构建整个测试。

废弃unittest源代码,看起来您可以通过以下方式获得公平的解决方案:

import difflib
import pprint

def diff_dicts(a, b):
    if a == b:
        return ''
    return '\n'.join(
        difflib.ndiff(pprint.pformat(a, width=30).splitlines(),
                      pprint.pformat(b, width=30).splitlines())
    )

所以

dictA = dict(zip(range(7), map(ord, 'python')))
dictB = {0: 112, 1: 'spam', 2: [1,2,3], 3: 104, 4: 111}
print diff_dicts(dictA, dictB)

结果是:

{0: 112,
-  1: 121,
-  2: 116,
+  1: 'spam',
+  2: [1, 2, 3],
   3: 104,
-  4: 111,
?        ^

+  4: 111}
?        ^

-  5: 110}

哪里:

  • ‘-‘表示第一/第二个字典中的键/值
  • “ +”表示第二个而不是第一个字典中的键/值

像在单元测试中一样,唯一的警告是由于尾随逗号/括号,最终映射可以被认为是差异。

As mentioned in other answers, unittest produces some nice output for comparing dicts, but in this example we don’t want to have to build a whole test first.

Scraping the unittest source, it looks like you can get a fair solution with just this:

import difflib
import pprint

def diff_dicts(a, b):
    if a == b:
        return ''
    return '\n'.join(
        difflib.ndiff(pprint.pformat(a, width=30).splitlines(),
                      pprint.pformat(b, width=30).splitlines())
    )

so

dictA = dict(zip(range(7), map(ord, 'python')))
dictB = {0: 112, 1: 'spam', 2: [1,2,3], 3: 104, 4: 111}
print diff_dicts(dictA, dictB)

Results in:

{0: 112,
-  1: 121,
-  2: 116,
+  1: 'spam',
+  2: [1, 2, 3],
   3: 104,
-  4: 111,
?        ^

+  4: 111}
?        ^

-  5: 110}

Where:

  • ‘-‘ indicates key/values in the first but not second dict
  • ‘+’ indicates key/values in the second but not the first dict

Like in unittest, the only caveat is that the final mapping can be thought to be a diff, due to the trailing comma/bracket.


回答 16

@Maxx有一个很好的答案,请使用unittestPython提供的工具:

import unittest


class Test(unittest.TestCase):
    def runTest(self):
        pass

    def testDict(self, d1, d2, maxDiff=None):
        self.maxDiff = maxDiff
        self.assertDictEqual(d1, d2)

然后,您可以在代码中的任何位置调用:

try:
    Test().testDict(dict1, dict2)
except Exception, e:
    print e

结果输出看起来像来自的输出diff,用不同的行+或在-每行之前添加漂亮的字典。

@Maxx has an excellent answer, use the unittest tools provided by Python:

import unittest


class Test(unittest.TestCase):
    def runTest(self):
        pass

    def testDict(self, d1, d2, maxDiff=None):
        self.maxDiff = maxDiff
        self.assertDictEqual(d1, d2)

Then, anywhere in your code you can call:

try:
    Test().testDict(dict1, dict2)
except Exception, e:
    print e

The resulting output looks like the output from diff, pretty-printing the dictionaries with + or - prepending each line that is different.


回答 17

不知道它是否仍然有用,但是我遇到了这个问题,我的情况是我只需要返回所有嵌套字典等的变化的字典。找不到合适的解决方案,但是我最终写了一个简单的函数要做到这一点。希望这可以帮助,

Not sure if it is still relevant but I came across this problem, my situation i just needed to return a dictionary of the changes for all nested dictionaries etc etc. Could not find a good solution out there but I did end up writing a simple function to do this. Hope this helps,


回答 18

如果您想使用内置解决方案与任意dict结构进行全面比较,@ Maxx的答案就是一个好的开始。

import unittest

test = unittest.TestCase()
test.assertEqual(dictA, dictB)

If you want a built-in solution for a full comparison with arbitrary dict structures, @Maxx’s answer is a good start.

import unittest

test = unittest.TestCase()
test.assertEqual(dictA, dictB)

回答 19

根据ghostdog74的回答,

dicta = {"a":1,"d":2}
dictb = {"a":5,"d":2}

for value in dicta.values():
    if not value in dictb.values():
        print value

将打印不同的dicta值

Based on ghostdog74’s answer,

dicta = {"a":1,"d":2}
dictb = {"a":5,"d":2}

for value in dicta.values():
    if not value in dictb.values():
        print value

will print differ value of dicta


回答 20

尝试此操作以找到de交集,即两个字典中的键,如果要在第二个字典中找不到键,只需使用not in

intersect = filter(lambda x, dictB=dictB.keys(): x in dictB, dictA.keys())

Try this to find de intersection, the keys that is in both dictionarie, if you want the keys not found on second dictionarie, just use the not in

intersect = filter(lambda x, dictB=dictB.keys(): x in dictB, dictA.keys())

设置Django以使用MySQL

问题:设置Django以使用MySQL

我想稍微远离PHP,学习Python。为了使用Python进行Web开发,我需要一个框架来帮助模板和其他事情。

我有一台非生产服务器,用于测试所有Web开发内容。这是一个运行MariaDB而不是常见的MySQL服务器软件包的Debian 7.1 LAMP堆栈。

昨天我安装了Django并创建了我的第一个项目firstweb。我尚未更改任何设置。

这是我的第一个大困惑。在教程中,我跟随那个家伙安装了Django,开始了他的第一个项目,重新启动了Apache,从那时起Django就开始工作了。他转到浏览器,然后毫无问题地转到Django默认页面。

但是我,我必须进入我的firstweb文件夹并运行

python manage.py runserver myip:port

而且有效。没问题。但是我想知道它是否应该像这样工作,并且这是否会引起问题?

我的第二个问题是我想对其进行设置,以便它使用我的MySQL数据库。我进入/ firstweb / firstweb下的settings.py,看到了ENGINE和NAME,但不确定在这里放什么。

然后在USER,PASSWORD和HOST区域中,这是我的数据库及其凭据吗?如果我使用本地主机,是否可以将本地主机放在HOST区域中?

I want to move away from PHP a little and learn Python. In order to do web development with Python I’ll need a framework to help with templating and other things.

I have a non-production server that I use to test all of web development stuff on. It is a Debian 7.1 LAMP stack that runs MariaDB instead of the common MySQL-server package.

Yesterday I installed Django and created my first project called firstweb. I have not changed any settings yet.

Here is my first big piece of confusion. In the tutorial I followed the guy installed Django, started his first project, restarted Apache, and Django just worked from then on. He went to his browser and went to the Django default page with no problems.

Me however, I have to cd into my firstweb folder and run

python manage.py runserver myip:port

And it works. No problem. But I’m wondering if it is supposed to work like this, and if this will cause problems down the line?

My second question is that I want to set it up so it uses my MySQL database. I go into my settings.py under /firstweb/firstweb and I see ENGINE and NAME but I’m not sure what to put here.

And then in the USER, PASSWORD, and HOST areas is this my database and its credentials? If I am using localhost can I just put localhost in the HOST area?


回答 0

MySQL支持很容易添加。在您的DATABASES字典中,您将有一个像这样的条目:

DATABASES = {
    'default': {
        'ENGINE': 'django.db.backends.mysql', 
        'NAME': 'DB_NAME',
        'USER': 'DB_USER',
        'PASSWORD': 'DB_PASSWORD',
        'HOST': 'localhost',   # Or an IP Address that your DB is hosted on
        'PORT': '3306',
    }
}

从Django 1.7开始,您还可以选择使用MySQL 选项文件。您可以这样设置DATABASES数组来完成此操作:

DATABASES = {
    'default': {
        'ENGINE': 'django.db.backends.mysql',
        'OPTIONS': {
            'read_default_file': '/path/to/my.cnf',
        },
    }
}

您还需要/path/to/my.cnf使用与上面类似的设置来创建文件

[client]
database = DB_NAME
host = localhost
user = DB_USER
password = DB_PASSWORD
default-character-set = utf8

使用Django 1.7中的这种新连接方法,重要的是要知道建立了顺序连接:

1. OPTIONS.
2. NAME, USER, PASSWORD, HOST, PORT
3. MySQL option files.

换句话说,如果在OPTIONS中设置数据库的名称,它将优先于NAME,而NAME将覆盖MySQL选项文件中的所有内容。


如果您只是在本地计算机上测试应用程序,则可以使用

python manage.py runserver

添加ip:port参数允许您自己的机器以外的其他机器访问您的开发应用程序。准备好部署应用程序后,建议您阅读djangobook上有关部署Django章节

MySQL默认字符集通常不是utf-8,因此请确保使用以下sql创建数据库:

CREATE DATABASE mydatabase CHARACTER SET utf8 COLLATE utf8_bin

如果您正在使用Oracle的MySQL的连接器ENGINE线应该是这样的:

'ENGINE': 'mysql.connector.django',

请注意,您首先需要在操作系统上安装mysql。

brew install mysql (MacOS)

此外,mysql客户端软件包已针对python 3进行了更改(MySQL-Client仅适用于python 2)

pip3 install mysqlclient

MySQL support is simple to add. In your DATABASES dictionary, you will have an entry like this:

DATABASES = {
    'default': {
        'ENGINE': 'django.db.backends.mysql', 
        'NAME': 'DB_NAME',
        'USER': 'DB_USER',
        'PASSWORD': 'DB_PASSWORD',
        'HOST': 'localhost',   # Or an IP Address that your DB is hosted on
        'PORT': '3306',
    }
}

You also have the option of utilizing MySQL option files, as of Django 1.7. You can accomplish this by setting your DATABASES array like so:

DATABASES = {
    'default': {
        'ENGINE': 'django.db.backends.mysql',
        'OPTIONS': {
            'read_default_file': '/path/to/my.cnf',
        },
    }
}

You also need to create the /path/to/my.cnf file with similar settings from above

[client]
database = DB_NAME
host = localhost
user = DB_USER
password = DB_PASSWORD
default-character-set = utf8

With this new method of connecting in Django 1.7, it is important to know the order connections are established:

1. OPTIONS.
2. NAME, USER, PASSWORD, HOST, PORT
3. MySQL option files.

In other words, if you set the name of the database in OPTIONS, this will take precedence over NAME, which would override anything in a MySQL option file.


If you are just testing your application on your local machine, you can use

python manage.py runserver

Adding the ip:port argument allows machines other than your own to access your development application. Once you are ready to deploy your application, I recommend taking a look at the chapter on Deploying Django on the djangobook

Mysql default character set is often not utf-8, therefore make sure to create your database using this sql:

CREATE DATABASE mydatabase CHARACTER SET utf8 COLLATE utf8_bin

If you are using Oracle’s MySQL connector your ENGINE line should look like this:

'ENGINE': 'mysql.connector.django',

Note that you will first need to install mysql on your OS.

brew install mysql (MacOS)

Also, the mysql client package has changed for python 3 (MySQL-Client works only for python 2)

pip3 install mysqlclient

回答 1

首先,请运行以下命令以安装python依赖项,否则python runserver命令将引发错误。

sudo apt-get install libmysqlclient-dev
sudo pip install MySQL-python

然后配置#Andy定义的settings.py文件,并在最后一次执行:

python manage.py runserver

玩得开心..!!

To the very first please run the below commands to install python dependencies otherwise python runserver command will throw error.

sudo apt-get install libmysqlclient-dev
sudo pip install MySQL-python

Then configure the settings.py file as defined by #Andy and at the last execute :

python manage.py runserver

Have fun..!!


回答 2

如果您使用的是python3.x,则运行以下命令

pip install mysqlclient

然后像这样更改setting.py

DATABASES = {
'default': {
    'ENGINE': 'django.db.backends.mysql',
    'NAME': 'DB',
     'USER': 'username',
    'PASSWORD': 'passwd',
  }
  }

If you are using python3.x then Run below command

pip install mysqlclient

Then change setting.py like

DATABASES = {
'default': {
    'ENGINE': 'django.db.backends.mysql',
    'NAME': 'DB',
     'USER': 'username',
    'PASSWORD': 'passwd',
  }
  }

回答 3

如上所述,您可以轻松地首先从https://www.apachefriends.org/download.html安装xampp, 然后按照以下说明进行操作:

  1. http://www.unixmen.com/install-xampp-stack-ubuntu-14-04/安装并运行xampp ,然后从GUI启动Apache Web Server和MySQL数据库。
  2. 您可以根据需要配置Web服务器,但默认情况下Web服务器位于http://localhost:80,数据库位于port 3306,而PhpMyadmin位于http://localhost/phpmyadmin/
  3. 从这里您可以看到您的数据库,并使用非常友好的GUI访问它们。
  4. 创建要在Django项目上使用的任何数据库。
  5. settings.py像这样编辑文件:

    DATABASES = {
    'default': {
        'ENGINE': 'django.db.backends.mysql',
        'NAME': 'DB_NAME',
        'HOST': '127.0.0.1',
        'PORT': '3306',
        'USER': 'root',
        'PASSWORD': '',
    }}
  6. 在virtualenv中安装以下软件包(如果您在virtualenv上使用django,则更可取):

    sudo apt-get安装libmysqlclient-dev

    pip安装MySQL-python

  7. 而已!!您已经以非常简单的方式为MySQL配置了Django。

  8. 现在运行您的Django项目:

    python manage.py迁移

    python manage.py运行服务器

As all said above, you can easily install xampp first from https://www.apachefriends.org/download.html Then follow the instructions as:

  1. Install and run xampp from http://www.unixmen.com/install-xampp-stack-ubuntu-14-04/, then start Apache Web Server and MySQL Database from the GUI.
  2. You can configure your web server as you want but by default web server is at http://localhost:80 and database at port 3306, and PhpMyadmin at http://localhost/phpmyadmin/
  3. From here you can see your databases and access them using very friendly GUI.
  4. Create any database which you want to use on your Django Project.
  5. Edit your settings.py file like:

    DATABASES = {
    'default': {
        'ENGINE': 'django.db.backends.mysql',
        'NAME': 'DB_NAME',
        'HOST': '127.0.0.1',
        'PORT': '3306',
        'USER': 'root',
        'PASSWORD': '',
    }}
    
  6. Install the following packages in the virtualenv (if you’re using django on virtualenv, which is more preferred):

    sudo apt-get install libmysqlclient-dev

    pip install MySQL-python

  7. That’s it!! you have configured Django with MySQL in a very easy way.

  8. Now run your Django project:

    python manage.py migrate

    python manage.py runserver


回答 4

实际上,不同的环境,python版本等等存在很多问题。您可能还需要安装python dev文件,因此要“强行安装”,我将运行所有这些文件:

sudo apt-get install python-dev python3-dev
sudo apt-get install libmysqlclient-dev
pip install MySQL-python
pip install pymysql
pip install mysqlclient

您应该接受公认的答案。如果对您很重要,可以删除不需要的软件包。

Actually, there are many issues with different environments, python versions, so on. You might also need to install python dev files, so to ‘brute-force’ the installation I would run all of these:

sudo apt-get install python-dev python3-dev
sudo apt-get install libmysqlclient-dev
pip install MySQL-python
pip install pymysql
pip install mysqlclient

You should be good to go with the accepted answer. And can remove the unnecessary packages if that’s important to you.


回答 5

运行这些命令

sudo apt-get install python-dev python3-dev
sudo apt-get install libmysqlclient-dev
pip install MySQL-python 
pip install pymysql
pip install mysqlclient

然后像这样配置settings.py

DATABASES = {
    'default': {
        'ENGINE': 'django.db.backends.mysql',
        'NAME': 'django_db',
        'HOST': '127.0.0.1',
        'PORT': '3306',
        'USER': 'root',
        'PASSWORD': '123456',
    }
}

享受mysql连接

Run these commands

sudo apt-get install python-dev python3-dev
sudo apt-get install libmysqlclient-dev
pip install MySQL-python 
pip install pymysql
pip install mysqlclient

Then configure settings.py like

DATABASES = {
    'default': {
        'ENGINE': 'django.db.backends.mysql',
        'NAME': 'django_db',
        'HOST': '127.0.0.1',
        'PORT': '3306',
        'USER': 'root',
        'PASSWORD': '123456',
    }
}

Enjoy mysql connection


回答 6

安迪的答案很有帮助,但是如果您担心要在django设置中公开数据库密码,我建议在mysql连接上遵循django官方配置:https : //docs.djangoproject.com/en/1.7/ref/databases/

在这里引用为:

# settings.py
DATABASES = {
    'default': {
        'ENGINE': 'django.db.backends.mysql',
        'OPTIONS': {
            'read_default_file': '/path/to/my.cnf',
        },
    }
}


# my.cnf
[client]
database = NAME
user = USER
password = PASSWORD
default-character-set = utf8

要在设置中替换“ HOST”:“ 127.0.0.1”,只需将其添加到my.cnf中:

# my.cnf
[client]
database = NAME
host = HOST NAME or IP
user = USER
password = PASSWORD
default-character-set = utf8

另一个有用的选项是为django设置存储引擎,您可能需要在setting.py中使用它:

'OPTIONS': {
   'init_command': 'SET storage_engine=INNODB',
}

Andy’s answer helps but if you have concern on exposing your database password in your django setting, I suggest to follow django official configuration on mysql connection: https://docs.djangoproject.com/en/1.7/ref/databases/

Quoted here as:

# settings.py
DATABASES = {
    'default': {
        'ENGINE': 'django.db.backends.mysql',
        'OPTIONS': {
            'read_default_file': '/path/to/my.cnf',
        },
    }
}


# my.cnf
[client]
database = NAME
user = USER
password = PASSWORD
default-character-set = utf8

To replace ‘HOST’: ‘127.0.0.1’ in setting, simply add it in my.cnf:

# my.cnf
[client]
database = NAME
host = HOST NAME or IP
user = USER
password = PASSWORD
default-character-set = utf8

Another OPTION that is useful, is to set your storage engine for django, you might want it in your setting.py:

'OPTIONS': {
   'init_command': 'SET storage_engine=INNODB',
}

回答 7

settings.py

DATABASES = {
'default': {
    'ENGINE': 'django.db.backends.mysql',
    'NAME': 'django',
    'USER': 'root',
    'PASSWORD': '*****',
    'HOST': '***.***.***.***',
    'PORT': '3306',
    'OPTIONS': {
        'autocommit': True,
    },
}

}

然后:

python manage.py migrate

如果成功将生成这些表:

auth_group
auth_group_permissions
auth_permission
auth_user
auth_user_groups
auth_user_user_permissions
django_admin_log
django_content_type
django_migrations
django_session

您将可以使用mysql。

这是一个展示示例,请在Django 1.11.5版上进行测试: Django-pool-showcase

settings.py

DATABASES = {
'default': {
    'ENGINE': 'django.db.backends.mysql',
    'NAME': 'django',
    'USER': 'root',
    'PASSWORD': '*****',
    'HOST': '***.***.***.***',
    'PORT': '3306',
    'OPTIONS': {
        'autocommit': True,
    },
}

}

then:

python manage.py migrate

if success will generate theses tables:

auth_group
auth_group_permissions
auth_permission
auth_user
auth_user_groups
auth_user_user_permissions
django_admin_log
django_content_type
django_migrations
django_session

and u will can use mysql.

this is a showcase example ,test on Django version 1.11.5: Django-pool-showcase


回答 8

  1. 安装 mysqlclient

sudo pip3 install mysqlclient

如果出现错误:

命令“ python setup.py egg_info”在/ tmp / pip-install-dbljg4tx / mysqlclient /中失败,错误代码为1

然后:

 1. sudo apt install libmysqlclient-dev python-mysqldb

 2. sudo pip3 install mysqlclient

  1. 修改settings.py

    DATABASES = {
        'default': {
            'ENGINE': 'django.db.backends.mysql',
            'NAME': 'website',
            'USER': 'root',
            'PASSWORD': '',
            'HOST': '127.0.0.1',
            'PORT': '3306',
            'OPTION': {'init_command':"SET sql_mode='STRICT_TRANS_TABLE',"},
        }
    }
  1. Install mysqlclient

sudo pip3 install mysqlclient

if you get error:

Command “python setup.py egg_info” failed with error code 1 in /tmp/pip-install-dbljg4tx/mysqlclient/

then:

 1. sudo apt install libmysqlclient-dev python-mysqldb

 2. sudo pip3 install mysqlclient

  1. Modify settings.py

    DATABASES = {
        'default': {
            'ENGINE': 'django.db.backends.mysql',
            'NAME': 'website',
            'USER': 'root',
            'PASSWORD': '',
            'HOST': '127.0.0.1',
            'PORT': '3306',
            'OPTION': {'init_command':"SET sql_mode='STRICT_TRANS_TABLE',"},
        }
    }
    

回答 9

请按照给定的步骤进行设置以使用MySQL数据库:

1) Install MySQL Database Connector :

    sudo apt-get install libmysqlclient-dev

2) Install the mysqlclient library :

    pip install mysqlclient

3) Install MySQL server, with the following command :

    sudo apt-get install mysql-server

4) Create the Database :

    i) Verify that the MySQL service is running:

        systemctl status mysql.service

    ii) Log in with your MySQL credentials using the following command where -u is the flag for declaring your username and -p is the flag that tells MySQL that this user requires a password :  

        mysql -u db_user -p


    iii) CREATE DATABASE db_name;

    iv) Exit MySQL server, press CTRL + D.

5) Add the MySQL Database Connection to your Application:

    i) Navigate to the settings.py file and replace the current DATABASES lines with the following:

        # Database
        # https://docs.djangoproject.com/en/2.0/ref/settings/#databases

        DATABASES = {
            'default': {
                'ENGINE': 'django.db.backends.mysql',
                'OPTIONS': {
                    'read_default_file': '/etc/mysql/my.cnf',
                },
            }
        }
        ...

    ii) Next, lets edit the config file so that it has your MySQL credentials. Use vi as sudo to edit the file and add the following information:

        sudo vi /etc/mysql/my.cnf

        database = db_name
        user = db_user
        password = db_password
        default-character-set = utf8

6) Once the file has been edited, we need to restart MySQL for the changes to take effect :

    systemctl daemon-reload

    systemctl restart mysql

7) Test MySQL Connection to Application:

    python manage.py runserver your-server-ip:8000

Follow the given steps in order to setup it up to use MySQL database:

1) Install MySQL Database Connector :

    sudo apt-get install libmysqlclient-dev

2) Install the mysqlclient library :

    pip install mysqlclient

3) Install MySQL server, with the following command :

    sudo apt-get install mysql-server

4) Create the Database :

    i) Verify that the MySQL service is running:

        systemctl status mysql.service

    ii) Log in with your MySQL credentials using the following command where -u is the flag for declaring your username and -p is the flag that tells MySQL that this user requires a password :  

        mysql -u db_user -p


    iii) CREATE DATABASE db_name;

    iv) Exit MySQL server, press CTRL + D.

5) Add the MySQL Database Connection to your Application:

    i) Navigate to the settings.py file and replace the current DATABASES lines with the following:

        # Database
        # https://docs.djangoproject.com/en/2.0/ref/settings/#databases

        DATABASES = {
            'default': {
                'ENGINE': 'django.db.backends.mysql',
                'OPTIONS': {
                    'read_default_file': '/etc/mysql/my.cnf',
                },
            }
        }
        ...

    ii) Next, let’s edit the config file so that it has your MySQL credentials. Use vi as sudo to edit the file and add the following information:

        sudo vi /etc/mysql/my.cnf

        database = db_name
        user = db_user
        password = db_password
        default-character-set = utf8

6) Once the file has been edited, we need to restart MySQL for the changes to take effect :

    systemctl daemon-reload

    systemctl restart mysql

7) Test MySQL Connection to Application:

    python manage.py runserver your-server-ip:8000

回答 10

您必须首先创建一个MySQL数据库。然后转到settings.py文件并'DATABASES'使用您的MySQL凭据编辑字典:

DATABASES = {
 'default': {
 'ENGINE': 'django.db.backends.mysql',
 'NAME': 'YOUR_DATABASE_NAME',
 'USER': 'YOUR_MYSQL_USER',
 'PASSWORD': 'YOUR_MYSQL_PASS',
 'HOST': 'localhost',   # Or an IP that your DB is hosted on
 'PORT': '3306',
 }
}

这是用于将Django设置为在virtualenv上使用MySQL的完整安装指南:

http://codex.themedelta.com/how-to-install-django-with-mysql-in-a-virtualenv-on-linux/

You must create a MySQL database first. Then go to settings.py file and edit the 'DATABASES' dictionary with your MySQL credentials:

DATABASES = {
 'default': {
 'ENGINE': 'django.db.backends.mysql',
 'NAME': 'YOUR_DATABASE_NAME',
 'USER': 'YOUR_MYSQL_USER',
 'PASSWORD': 'YOUR_MYSQL_PASS',
 'HOST': 'localhost',   # Or an IP that your DB is hosted on
 'PORT': '3306',
 }
}

Here is a complete installation guide for setting up Django to use MySQL on a virtualenv:

http://codex.themedelta.com/how-to-install-django-with-mysql-in-a-virtualenv-on-linux/


以自定义形式使用Django时间/日期小部件

问题:以自定义形式使用Django时间/日期小部件

如何使用默认管理员在自定义视图中使用的漂亮的JavaScript日期和时间小部件?

我浏览了Django表单文档,其中简要提到了django.contrib.admin.widgets,但我不知道如何使用它?

这是我希望将其应用于的模板。

<form action="." method="POST">
    <table>
        {% for f in form %}
           <tr> <td> {{ f.name }}</td> <td>{{ f }}</td> </tr>
        {% endfor %}
    </table>
    <input type="submit" name="submit" value="Add Product">
</form>

另外,我认为应该指出的是,我并未真正为这种形式编写视图,而是使用了通用视图。这是url.py中的条目:

(r'^admin/products/add/$', create_object, {'model': Product, 'post_save_redirect': ''}),

而且我对整个Django / MVC / MTV都是陌生的,所以请放轻松…

How can I use the nifty JavaScript date and time widgets that the default admin uses with my custom view?

I have looked through the Django forms documentation, and it briefly mentions django.contrib.admin.widgets, but I don’t know how to use it?

Here is my template that I want it applied on.

<form action="." method="POST">
    <table>
        {% for f in form %}
           <tr> <td> {{ f.name }}</td> <td>{{ f }}</td> </tr>
        {% endfor %}
    </table>
    <input type="submit" name="submit" value="Add Product">
</form>

Also, I think it should be noted that I haven’t really written a view up myself for this form, I am using a generic view. Here is the entry from the url.py:

(r'^admin/products/add/$', create_object, {'model': Product, 'post_save_redirect': ''}),

And I am relevantly new to the whole Django/MVC/MTV thing, so please go easy…


回答 0

随着时间的流逝,此答案的复杂性不断提高,并且需要进行许多破解,可能应该警告您不要这样做。它依赖于管理员未记录的内部实现细节,可能会在将来的Django版本中再次中断,并且比找到另一个JS日历小部件并使用它更容易实现。

就是说,如果您决心进行这项工作,这是您必须做的:

  1. 为模型定义自己的ModelForm子类(最好将其放在应用程序的forms.py中),并告诉它使用AdminDateWidget / AdminTimeWidget / AdminSplitDateTime(用模型中的正确字段名称替换“ mydate”等):

    from django import forms
    from my_app.models import Product
    from django.contrib.admin import widgets                                       
    
    class ProductForm(forms.ModelForm):
        class Meta:
            model = Product
        def __init__(self, *args, **kwargs):
            super(ProductForm, self).__init__(*args, **kwargs)
            self.fields['mydate'].widget = widgets.AdminDateWidget()
            self.fields['mytime'].widget = widgets.AdminTimeWidget()
            self.fields['mydatetime'].widget = widgets.AdminSplitDateTime()
  2. 更改您的URLconf,以将“ form_class”:ProductForm而不是“ model”:Product传递到通用的create_object视图(这当然意味着“从my_app.forms import ProductForm”而不是“从my_app.models import Product”)。

  3. 在模板的开头,包括{{form.media}},以输出指向Javascript文件的链接。

  4. hacky部分:admin日期/时间小部件假定i18n JS东西已加载,并且还需要core.js,但不会自动提供其中任何一个。因此,在{{form.media}}上方的模板中,您需要:

    <script type="text/javascript" src="/my_admin/jsi18n/"></script>
    <script type="text/javascript" src="/media/admin/js/core.js"></script>

    您可能还希望使用以下管理CSS(感谢Alex提到了这一点):

    <link rel="stylesheet" type="text/css" href="/media/admin/css/forms.css"/>
    <link rel="stylesheet" type="text/css" href="/media/admin/css/base.css"/>
    <link rel="stylesheet" type="text/css" href="/media/admin/css/global.css"/>
    <link rel="stylesheet" type="text/css" href="/media/admin/css/widgets.css"/>

这意味着Django的管理媒体(ADMIN_MEDIA_PREFIX)位于/ media / admin /-您可以更改其设置。理想情况下,您将使用上下文处理器将此值传递给模板,而不是对其进行硬编码,但这超出了此问题的范围。

这还需要将URL / my_admin / jsi18n /手动连接到django.views.i18n.javascript_catalog视图(如果未使用I18N,则为null_javascript_catalog)。您必须自己执行此操作,而不是通过admin应用程序,因此无论您是否登录到admin都可以访问它(感谢Jeremy指出了这一点)。URLconf的示例代码:

(r'^my_admin/jsi18n', 'django.views.i18n.javascript_catalog'),

最后,如果您使用的是Django 1.2或更高版本,则需要在模板中添加一些其他代码来帮助小部件找到其媒体:

{% load adminmedia %} /* At the top of the template. */

/* In the head section of the template. */
<script type="text/javascript">
window.__admin_media_prefix__ = "{% filter escapejs %}{% admin_media_prefix %}{% endfilter %}";
</script>

感谢lupefiasco的添加。

The growing complexity of this answer over time, and the many hacks required, probably ought to caution you against doing this at all. It’s relying on undocumented internal implementation details of the admin, is likely to break again in future versions of Django, and is no easier to implement than just finding another JS calendar widget and using that.

That said, here’s what you have to do if you’re determined to make this work:

  1. Define your own ModelForm subclass for your model (best to put it in forms.py in your app), and tell it to use the AdminDateWidget / AdminTimeWidget / AdminSplitDateTime (replace ‘mydate’ etc with the proper field names from your model):

    from django import forms
    from my_app.models import Product
    from django.contrib.admin import widgets                                       
    
    class ProductForm(forms.ModelForm):
        class Meta:
            model = Product
        def __init__(self, *args, **kwargs):
            super(ProductForm, self).__init__(*args, **kwargs)
            self.fields['mydate'].widget = widgets.AdminDateWidget()
            self.fields['mytime'].widget = widgets.AdminTimeWidget()
            self.fields['mydatetime'].widget = widgets.AdminSplitDateTime()
    
  2. Change your URLconf to pass ‘form_class’: ProductForm instead of ‘model’: Product to the generic create_object view (that’ll mean “from my_app.forms import ProductForm” instead of “from my_app.models import Product”, of course).

  3. In the head of your template, include {{ form.media }} to output the links to the Javascript files.

  4. And the hacky part: the admin date/time widgets presume that the i18n JS stuff has been loaded, and also require core.js, but don’t provide either one automatically. So in your template above {{ form.media }} you’ll need:

    <script type="text/javascript" src="/my_admin/jsi18n/"></script>
    <script type="text/javascript" src="/media/admin/js/core.js"></script>
    

    You may also wish to use the following admin CSS (thanks Alex for mentioning this):

    <link rel="stylesheet" type="text/css" href="/media/admin/css/forms.css"/>
    <link rel="stylesheet" type="text/css" href="/media/admin/css/base.css"/>
    <link rel="stylesheet" type="text/css" href="/media/admin/css/global.css"/>
    <link rel="stylesheet" type="text/css" href="/media/admin/css/widgets.css"/>
    

This implies that Django’s admin media (ADMIN_MEDIA_PREFIX) is at /media/admin/ – you can change that for your setup. Ideally you’d use a context processor to pass this values to your template instead of hardcoding it, but that’s beyond the scope of this question.

This also requires that the URL /my_admin/jsi18n/ be manually wired up to the django.views.i18n.javascript_catalog view (or null_javascript_catalog if you aren’t using I18N). You have to do this yourself instead of going through the admin application so it’s accessible regardless of whether you’re logged into the admin (thanks Jeremy for pointing this out). Sample code for your URLconf:

(r'^my_admin/jsi18n', 'django.views.i18n.javascript_catalog'),

Lastly, if you are using Django 1.2 or later, you need some additional code in your template to help the widgets find their media:

{% load adminmedia %} /* At the top of the template. */

/* In the head section of the template. */
<script type="text/javascript">
window.__admin_media_prefix__ = "{% filter escapejs %}{% admin_media_prefix %}{% endfilter %}";
</script>

Thanks lupefiasco for this addition.


回答 1

由于该解决方案有点漏洞,因此我认为将自己的日期/时间窗口小部件与一些JavaScript结合使用更可行。

As the solution is hackish, I think using your own date/time widget with some JavaScript is more feasible.


回答 2

是的,我最终覆盖了/ admin / jsi18n /网址。

这是我在urls.py中添加的内容。确保它在/ admin /网址上方

    (r'^admin/jsi18n', i18n_javascript),

这是我创建的i18n_javascript函数。

from django.contrib import admin
def i18n_javascript(request):
  return admin.site.i18n_javascript(request)

Yep, I ended up overriding the /admin/jsi18n/ url.

Here’s what I added in my urls.py. Make sure it’s above the /admin/ url

    (r'^admin/jsi18n', i18n_javascript),

And here is the i18n_javascript function I created.

from django.contrib import admin
def i18n_javascript(request):
  return admin.site.i18n_javascript(request)

回答 3

我发现自己经常引用这篇文章,并且发现文档定义了一种略微不友好的方法来覆盖默认小部件。

无需重写ModelForm的__init__方法

但是,您仍然需要如Carl所述适当地连接JS和CSS。

表格

from django import forms
from my_app.models import Product
from django.contrib.admin import widgets                                       


class ProductForm(forms.ModelForm):
    mydate = forms.DateField(widget=widgets.AdminDateWidget)
    mytime = forms.TimeField(widget=widgets.AdminTimeWidget)
    mydatetime = forms.SplitDateTimeField(widget=widgets.AdminSplitDateTime)

    class Meta:
        model = Product

参考字段类型以查找默认表单字段。

I find myself referencing this post a lot, and found that the documentation defines a slightly less hacky way to override default widgets.

(No need to override the ModelForm’s __init__ method)

However, you still need to wire your JS and CSS appropriately as Carl mentions.

forms.py

from django import forms
from my_app.models import Product
from django.contrib.admin import widgets                                       


class ProductForm(forms.ModelForm):
    mydate = forms.DateField(widget=widgets.AdminDateWidget)
    mytime = forms.TimeField(widget=widgets.AdminTimeWidget)
    mydatetime = forms.SplitDateTimeField(widget=widgets.AdminSplitDateTime)

    class Meta:
        model = Product

Reference Field Types to find the default form fields.


回答 4

我的1.4版头代码(有些是新增的,有些是删除的)

{% block extrahead %}

<link rel="stylesheet" type="text/css" href="{{ STATIC_URL }}admin/css/forms.css"/>
<link rel="stylesheet" type="text/css" href="{{ STATIC_URL }}admin/css/base.css"/>
<link rel="stylesheet" type="text/css" href="{{ STATIC_URL }}admin/css/global.css"/>
<link rel="stylesheet" type="text/css" href="{{ STATIC_URL }}admin/css/widgets.css"/>

<script type="text/javascript" src="/admin/jsi18n/"></script>
<script type="text/javascript" src="{{ STATIC_URL }}admin/js/core.js"></script>
<script type="text/javascript" src="{{ STATIC_URL }}admin/js/admin/RelatedObjectLookups.js"></script>
<script type="text/javascript" src="{{ STATIC_URL }}admin/js/jquery.js"></script>
<script type="text/javascript" src="{{ STATIC_URL }}admin/js/jquery.init.js"></script>
<script type="text/javascript" src="{{ STATIC_URL }}admin/js/actions.js"></script>
<script type="text/javascript" src="{{ STATIC_URL }}admin/js/calendar.js"></script>
<script type="text/javascript" src="{{ STATIC_URL }}admin/js/admin/DateTimeShortcuts.js"></script>

{% endblock %}

My head code for 1.4 version(some new and some removed)

{% block extrahead %}

<link rel="stylesheet" type="text/css" href="{{ STATIC_URL }}admin/css/forms.css"/>
<link rel="stylesheet" type="text/css" href="{{ STATIC_URL }}admin/css/base.css"/>
<link rel="stylesheet" type="text/css" href="{{ STATIC_URL }}admin/css/global.css"/>
<link rel="stylesheet" type="text/css" href="{{ STATIC_URL }}admin/css/widgets.css"/>

<script type="text/javascript" src="/admin/jsi18n/"></script>
<script type="text/javascript" src="{{ STATIC_URL }}admin/js/core.js"></script>
<script type="text/javascript" src="{{ STATIC_URL }}admin/js/admin/RelatedObjectLookups.js"></script>
<script type="text/javascript" src="{{ STATIC_URL }}admin/js/jquery.js"></script>
<script type="text/javascript" src="{{ STATIC_URL }}admin/js/jquery.init.js"></script>
<script type="text/javascript" src="{{ STATIC_URL }}admin/js/actions.js"></script>
<script type="text/javascript" src="{{ STATIC_URL }}admin/js/calendar.js"></script>
<script type="text/javascript" src="{{ STATIC_URL }}admin/js/admin/DateTimeShortcuts.js"></script>

{% endblock %}

回答 5

从Django 1.2 RC1开始,如果您使用的是Django admin日期选择器widge技巧,则必须将以下内容添加到模板中,否则您将看到通过“ / missing-admin-media-prefix”引用的日历图标url /”。

{% load adminmedia %} /* At the top of the template. */

/* In the head section of the template. */
<script type="text/javascript">
window.__admin_media_prefix__ = "{% filter escapejs %}{% admin_media_prefix %}{% endfilter %}";
</script>

Starting in Django 1.2 RC1, if you’re using the Django admin date picker widge trick, the following has to be added to your template, or you’ll see the calendar icon url being referenced through “/missing-admin-media-prefix/”.

{% load adminmedia %} /* At the top of the template. */

/* In the head section of the template. */
<script type="text/javascript">
window.__admin_media_prefix__ = "{% filter escapejs %}{% admin_media_prefix %}{% endfilter %}";
</script>

回答 6

为了补充Carl Meyer的答案,我想评论一下,您需要将该标头放在模板中的某个有效块中(标头内)。

{% block extra_head %}

<link rel="stylesheet" type="text/css" href="/media/admin/css/forms.css"/>
<link rel="stylesheet" type="text/css" href="/media/admin/css/base.css"/>
<link rel="stylesheet" type="text/css" href="/media/admin/css/global.css"/>
<link rel="stylesheet" type="text/css" href="/media/admin/css/widgets.css"/>

<script type="text/javascript" src="/admin/jsi18n/"></script>
<script type="text/javascript" src="/media/admin/js/core.js"></script>
<script type="text/javascript" src="/media/admin/js/admin/RelatedObjectLookups.js"></script>

{{ form.media }}

{% endblock %}

Complementing the answer by Carl Meyer, I would like to comment that you need to put that header in some valid block (inside the header) within your template.

{% block extra_head %}

<link rel="stylesheet" type="text/css" href="/media/admin/css/forms.css"/>
<link rel="stylesheet" type="text/css" href="/media/admin/css/base.css"/>
<link rel="stylesheet" type="text/css" href="/media/admin/css/global.css"/>
<link rel="stylesheet" type="text/css" href="/media/admin/css/widgets.css"/>

<script type="text/javascript" src="/admin/jsi18n/"></script>
<script type="text/javascript" src="/media/admin/js/core.js"></script>
<script type="text/javascript" src="/media/admin/js/admin/RelatedObjectLookups.js"></script>

{{ form.media }}

{% endblock %}

回答 7

对于Django> = 2.0

注意:对于日期时间字段使用管理小部件不是一个好主意,因为如果您使用引导程序或任何其他CSS框架,则管理样式表可能会与您的网站样式表发生冲突。如果您要在Bootstrap上构建站点,请使用我的bootstrap-datepicker小部件django-bootstrap-datepicker-plus

步骤1:新增javascript-catalog URL到您的项目(而非应用程序)urls.py文件中。

from django.views.i18n import JavaScriptCatalog

urlpatterns = [
    path('jsi18n', JavaScriptCatalog.as_view(), name='javascript-catalog'),
]

第2步:将所需的JavaScript / CSS资源添加到模板中。

<script type="text/javascript" src="{% url 'javascript-catalog' %}"></script>
<script type="text/javascript" src="{% static '/admin/js/core.js' %}"></script>
<link rel="stylesheet" type="text/css" href="{% static '/admin/css/widgets.css' %}">
<style>.calendar>table>caption{caption-side:unset}</style><!--caption fix for bootstrap4-->
{{ form.media }}        {# Form required JS and CSS #}

步骤3:在中的日期时间输入字段中使用管理小部件forms.py

from django.contrib.admin import widgets
from .models import Product

class ProductCreateForm(forms.ModelForm):
    class Meta:
        model = Product
        fields = ['name', 'publish_date', 'publish_time', 'publish_datetime']
        widgets = {
            'publish_date': widgets.AdminDateWidget,
            'publish_time': widgets.AdminTimeWidget,
            'publish_datetime': widgets.AdminSplitDateTime,
        }

For Django >= 2.0

Note: Using admin widgets for date-time fields is not a good idea as admin style-sheets can conflict with your site style-sheets in case you are using bootstrap or any other CSS frameworks. If you are building your site on bootstrap use my bootstrap-datepicker widget django-bootstrap-datepicker-plus.

Step 1: Add javascript-catalog URL to your project’s (not app’s) urls.py file.

from django.views.i18n import JavaScriptCatalog

urlpatterns = [
    path('jsi18n', JavaScriptCatalog.as_view(), name='javascript-catalog'),
]

Step 2: Add required JavaScript/CSS resources to your template.

<script type="text/javascript" src="{% url 'javascript-catalog' %}"></script>
<script type="text/javascript" src="{% static '/admin/js/core.js' %}"></script>
<link rel="stylesheet" type="text/css" href="{% static '/admin/css/widgets.css' %}">
<style>.calendar>table>caption{caption-side:unset}</style><!--caption fix for bootstrap4-->
{{ form.media }}        {# Form required JS and CSS #}

Step 3: Use admin widgets for date-time input fields in your forms.py.

from django.contrib.admin import widgets
from .models import Product

class ProductCreateForm(forms.ModelForm):
    class Meta:
        model = Product
        fields = ['name', 'publish_date', 'publish_time', 'publish_datetime']
        widgets = {
            'publish_date': widgets.AdminDateWidget,
            'publish_time': widgets.AdminTimeWidget,
            'publish_datetime': widgets.AdminSplitDateTime,
        }

回答 8

如果以上操作失败,以下内容也将作为最后的手段

class PaymentsForm(forms.ModelForm):
    class Meta:
        model = Payments

    def __init__(self, *args, **kwargs):
        super(PaymentsForm, self).__init__(*args, **kwargs)
        self.fields['date'].widget = SelectDateWidget()

和…一样

class PaymentsForm(forms.ModelForm):
    date = forms.DateField(widget=SelectDateWidget())

    class Meta:
        model = Payments

把它放在你的forms.py中 from django.forms.extras.widgets import SelectDateWidget

The below will also work as a last resort if the above failed

class PaymentsForm(forms.ModelForm):
    class Meta:
        model = Payments

    def __init__(self, *args, **kwargs):
        super(PaymentsForm, self).__init__(*args, **kwargs)
        self.fields['date'].widget = SelectDateWidget()

Same as

class PaymentsForm(forms.ModelForm):
    date = forms.DateField(widget=SelectDateWidget())

    class Meta:
        model = Payments

put this in your forms.py from django.forms.extras.widgets import SelectDateWidget


回答 9

仅向您的小部件分配一个类,然后将该类绑定到JQuery datepicker怎么样?

Django Forms.py:

class MyForm(forms.ModelForm):

  class Meta:
    model = MyModel

  def __init__(self, *args, **kwargs):
    super(MyForm, self).__init__(*args, **kwargs)
    self.fields['my_date_field'].widget.attrs['class'] = 'datepicker'

以及模板的一些JavaScript:

  $(".datepicker").datepicker();

What about just assigning a class to your widget and then binding that class to the JQuery datepicker?

Django forms.py:

class MyForm(forms.ModelForm):

  class Meta:
    model = MyModel

  def __init__(self, *args, **kwargs):
    super(MyForm, self).__init__(*args, **kwargs)
    self.fields['my_date_field'].widget.attrs['class'] = 'datepicker'

And some JavaScript for the template:

  $(".datepicker").datepicker();

回答 10

使用required = False更新了SplitDateTime的解决方案和解决方法:

表格

from django import forms

class SplitDateTimeJSField(forms.SplitDateTimeField):
    def __init__(self, *args, **kwargs):
        super(SplitDateTimeJSField, self).__init__(*args, **kwargs)
        self.widget.widgets[0].attrs = {'class': 'vDateField'}
        self.widget.widgets[1].attrs = {'class': 'vTimeField'}  


class AnyFormOrModelForm(forms.Form):
    date = forms.DateField(widget=forms.TextInput(attrs={'class':'vDateField'}))
    time = forms.TimeField(widget=forms.TextInput(attrs={'class':'vTimeField'}))
    timestamp = SplitDateTimeJSField(required=False,)

form.html

<script type="text/javascript" src="/admin/jsi18n/"></script>
<script type="text/javascript" src="/admin_media/js/core.js"></script>
<script type="text/javascript" src="/admin_media/js/calendar.js"></script>
<script type="text/javascript" src="/admin_media/js/admin/DateTimeShortcuts.js"></script>

urls.py

(r'^admin/jsi18n/', 'django.views.i18n.javascript_catalog'),

Updated solution and workaround for SplitDateTime with required=False:

forms.py

from django import forms

class SplitDateTimeJSField(forms.SplitDateTimeField):
    def __init__(self, *args, **kwargs):
        super(SplitDateTimeJSField, self).__init__(*args, **kwargs)
        self.widget.widgets[0].attrs = {'class': 'vDateField'}
        self.widget.widgets[1].attrs = {'class': 'vTimeField'}  


class AnyFormOrModelForm(forms.Form):
    date = forms.DateField(widget=forms.TextInput(attrs={'class':'vDateField'}))
    time = forms.TimeField(widget=forms.TextInput(attrs={'class':'vTimeField'}))
    timestamp = SplitDateTimeJSField(required=False,)

form.html

<script type="text/javascript" src="/admin/jsi18n/"></script>
<script type="text/javascript" src="/admin_media/js/core.js"></script>
<script type="text/javascript" src="/admin_media/js/calendar.js"></script>
<script type="text/javascript" src="/admin_media/js/admin/DateTimeShortcuts.js"></script>

urls.py

(r'^admin/jsi18n/', 'django.views.i18n.javascript_catalog'),

回答 11

我用这个很好,但是模板有两个问题:

  1. 对于模板中的每个字段,我都会两次看到日历图标。
  2. 对于TimeField,我有一个’ 输入有效日期。

models.py

from django.db import models
    name=models.CharField(max_length=100)
    create_date=models.DateField(blank=True)
    start_time=models.TimeField(blank=False)
    end_time=models.TimeField(blank=False)

表格

from django import forms
from .models import Guide
from django.contrib.admin import widgets

class GuideForm(forms.ModelForm):
    start_time = forms.DateField(widget=widgets.AdminTimeWidget)
    end_time = forms.DateField(widget=widgets.AdminTimeWidget)
    create_date = forms.DateField(widget=widgets.AdminDateWidget)
    class Meta:
        model=Guide
        fields=['name','categorie','thumb']

I use this, it’s great, but I have 2 problems with the template:

  1. I see the calendar icons twice for every filed in template.
  2. And for TimeField I have ‘Enter a valid date.

models.py

from django.db import models
    name=models.CharField(max_length=100)
    create_date=models.DateField(blank=True)
    start_time=models.TimeField(blank=False)
    end_time=models.TimeField(blank=False)

forms.py

from django import forms
from .models import Guide
from django.contrib.admin import widgets

class GuideForm(forms.ModelForm):
    start_time = forms.DateField(widget=widgets.AdminTimeWidget)
    end_time = forms.DateField(widget=widgets.AdminTimeWidget)
    create_date = forms.DateField(widget=widgets.AdminDateWidget)
    class Meta:
        model=Guide
        fields=['name','categorie','thumb']

回答 12

在Django 10中,myproject / urls.py:urlpatterns的开头

  from django.views.i18n import JavaScriptCatalog

urlpatterns = [
    url(r'^jsi18n/$', JavaScriptCatalog.as_view(), name='javascript-catalog'),
.
.
.]

在我的template.html中:

{% load staticfiles %}

    <script src="{% static "js/jquery-2.2.3.min.js" %}"></script>
    <script src="{% static "js/bootstrap.min.js" %}"></script>
    {# Loading internazionalization for js #}
    {% load i18n admin_modify %}
    <script type="text/javascript" src="{% url 'javascript-catalog' %}"></script>
    <script type="text/javascript" src="{% static "/admin/js/jquery.init.js" %}"></script>

    <link rel="stylesheet" type="text/css" href="{% static "/admin/css/base.css" %}">
    <link rel="stylesheet" type="text/css" href="{% static "/admin/css/forms.css" %}">
    <link rel="stylesheet" type="text/css" href="{% static "/admin/css/login.css" %}">
    <link rel="stylesheet" type="text/css" href="{% static "/admin/css/widgets.css" %}">



    <script type="text/javascript" src="{% static "/admin/js/core.js" %}"></script>
    <script type="text/javascript" src="{% static "/admin/js/SelectFilter2.js" %}"></script>
    <script type="text/javascript" src="{% static "/admin/js/admin/RelatedObjectLookups.js" %}"></script>
    <script type="text/javascript" src="{% static "/admin/js/actions.js" %}"></script>
    <script type="text/javascript" src="{% static "/admin/js/calendar.js" %}"></script>
    <script type="text/javascript" src="{% static "/admin/js/admin/DateTimeShortcuts.js" %}"></script>

In Django 10. myproject/urls.py: at the beginning of urlpatterns

  from django.views.i18n import JavaScriptCatalog

urlpatterns = [
    url(r'^jsi18n/$', JavaScriptCatalog.as_view(), name='javascript-catalog'),
.
.
.]

In my template.html:

{% load staticfiles %}

    <script src="{% static "js/jquery-2.2.3.min.js" %}"></script>
    <script src="{% static "js/bootstrap.min.js" %}"></script>
    {# Loading internazionalization for js #}
    {% load i18n admin_modify %}
    <script type="text/javascript" src="{% url 'javascript-catalog' %}"></script>
    <script type="text/javascript" src="{% static "/admin/js/jquery.init.js" %}"></script>

    <link rel="stylesheet" type="text/css" href="{% static "/admin/css/base.css" %}">
    <link rel="stylesheet" type="text/css" href="{% static "/admin/css/forms.css" %}">
    <link rel="stylesheet" type="text/css" href="{% static "/admin/css/login.css" %}">
    <link rel="stylesheet" type="text/css" href="{% static "/admin/css/widgets.css" %}">



    <script type="text/javascript" src="{% static "/admin/js/core.js" %}"></script>
    <script type="text/javascript" src="{% static "/admin/js/SelectFilter2.js" %}"></script>
    <script type="text/javascript" src="{% static "/admin/js/admin/RelatedObjectLookups.js" %}"></script>
    <script type="text/javascript" src="{% static "/admin/js/actions.js" %}"></script>
    <script type="text/javascript" src="{% static "/admin/js/calendar.js" %}"></script>
    <script type="text/javascript" src="{% static "/admin/js/admin/DateTimeShortcuts.js" %}"></script>

回答 13

我的Django设置:1.11引导程序:3.3.7

由于没有一个答案是完全清楚的,因此我共享我的模板代码,该代码完全没有错误。

模板的上半部分:

{% extends 'base.html' %}
{% load static %}
{% load i18n %}

{% block head %}
    <title>Add Interview</title>
{% endblock %}

{% block content %}

<script type="text/javascript" src="{% url 'javascript-catalog' %}"></script>
<script type="text/javascript" src="{% static 'admin/js/core.js' %}"></script>
<link rel="stylesheet" type="text/css" href="{% static 'admin/css/forms.css' %}"/>
<link rel="stylesheet" type="text/css" href="{% static 'admin/css/widgets.css' %}"/>
<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/css/bootstrap.min.css" >
<script type="text/javascript" src="{% static 'js/jquery.js' %}"></script>

下半区:

<script type="text/javascript" src="/admin/jsi18n/"></script>
<script type="text/javascript" src="{% static 'admin/js/vendor/jquery/jquery.min.js' %}"></script>
<script type="text/javascript" src="{% static 'admin/js/jquery.init.js' %}"></script>
<script type="text/javascript" src="{% static 'admin/js/actions.min.js' %}"></script>
{% endblock %}

My Django Setup : 1.11 Bootstrap: 3.3.7

Since none of the answers are completely clear, I am sharing my template code which presents no errors at all.

Top Half of template:

{% extends 'base.html' %}
{% load static %}
{% load i18n %}

{% block head %}
    <title>Add Interview</title>
{% endblock %}

{% block content %}

<script type="text/javascript" src="{% url 'javascript-catalog' %}"></script>
<script type="text/javascript" src="{% static 'admin/js/core.js' %}"></script>
<link rel="stylesheet" type="text/css" href="{% static 'admin/css/forms.css' %}"/>
<link rel="stylesheet" type="text/css" href="{% static 'admin/css/widgets.css' %}"/>
<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/css/bootstrap.min.css" >
<script type="text/javascript" src="{% static 'js/jquery.js' %}"></script>

Bottom Half:

<script type="text/javascript" src="/admin/jsi18n/"></script>
<script type="text/javascript" src="{% static 'admin/js/vendor/jquery/jquery.min.js' %}"></script>
<script type="text/javascript" src="{% static 'admin/js/jquery.init.js' %}"></script>
<script type="text/javascript" src="{% static 'admin/js/actions.min.js' %}"></script>
{% endblock %}

回答 14

2020年6月3日(所有答案均无效,您可以尝试使用我使用的此解决方案。仅用于TimeField)

在表单中Charfield为时间字段(在此示例中为开始结束)使用simple 。

表格

我们可以在这里使用FormModelForm

class TimeSlotForm(forms.ModelForm):
    start = forms.CharField(widget=forms.TextInput(attrs={'placeholder': 'HH:MM'}))
    end = forms.CharField(widget=forms.TextInput(attrs={'placeholder': 'HH:MM'}))

    class Meta:
        model = TimeSlots
        fields = ('start', 'end', 'provider')

将字符串输入转换为视图中的时间对象。

import datetime
def slots():
    if request.method == 'POST':
        form = create_form(request.POST)
        if form.is_valid():                
            slot = form.save(commit=False)
            start = form.cleaned_data['start']
            end = form.cleaned_data['end']
            start = datetime.datetime.strptime(start, '%H:%M').time()
            end = datetime.datetime.strptime(end, '%H:%M').time()
            slot.start = start
            slot.end = end
            slot.save()

June 3, 2020 (All answers didn’t worked, you can try this solution I used. Just for TimeField)

Use simple Charfield for time fields (start and end in this example) in forms.

forms.py

we can use Form or ModelForm here.

class TimeSlotForm(forms.ModelForm):
    start = forms.CharField(widget=forms.TextInput(attrs={'placeholder': 'HH:MM'}))
    end = forms.CharField(widget=forms.TextInput(attrs={'placeholder': 'HH:MM'}))

    class Meta:
        model = TimeSlots
        fields = ('start', 'end', 'provider')

Convert string input into time object in views.

import datetime
def slots():
    if request.method == 'POST':
        form = create_form(request.POST)
        if form.is_valid():                
            slot = form.save(commit=False)
            start = form.cleaned_data['start']
            end = form.cleaned_data['end']
            start = datetime.datetime.strptime(start, '%H:%M').time()
            end = datetime.datetime.strptime(end, '%H:%M').time()
            slot.start = start
            slot.end = end
            slot.save()

如何将python datetime转换为具有可读格式date的字符串?

问题:如何将python datetime转换为具有可读格式date的字符串?

t = e['updated_parsed']
dt = datetime.datetime(t[0],t[1],t[2],t[3],t[4],t[5],t[6]
print dt
>>>2010-01-28 08:39:49.000003

如何将其转换为字符串?:

"January 28, 2010"
t = e['updated_parsed']
dt = datetime.datetime(t[0],t[1],t[2],t[3],t[4],t[5],t[6]
print dt
>>>2010-01-28 08:39:49.000003

How do I turn that into a string?:

"January 28, 2010"

回答 0

datetime类具有strftime方法。Python文档记录了它接受的不同格式:

对于此特定示例,它将类似于:

my_datetime.strftime("%B %d, %Y")

The datetime class has a method strftime. The Python docs documents the different formats it accepts:

For this specific example, it would look something like:

my_datetime.strftime("%B %d, %Y")

回答 1

这是您可以使用python的常规格式化功能来完成的操作…

>>>from datetime import datetime
>>>"{:%B %d, %Y}".format(datetime.now())

此处使用的格式字符与strftime所使用的格式字符相同。不要错过: 格式说明符的开头。

在大多数情况下,使用format()代替strftime()可以使代码更具可读性,易于编写,并且与格式化输出的生成方式保持一致…

>>>"{} today's date is: {:%B %d, %Y}".format("Andre", datetime.now())

将以上内容与以下strftime()替代项进行比较…

>>>"{} today's date is {}".format("Andre", datetime.now().strftime("%B %d, %Y"))

此外,以下内容将无法工作…

>>>datetime.now().strftime("%s %B %d, %Y" % "Andre")
Traceback (most recent call last):
  File "<pyshell#11>", line 1, in <module>
    datetime.now().strftime("%s %B %d, %Y" % "Andre")
TypeError: not enough arguments for format string

等等…

Here is how you can accomplish the same using python’s general formatting function…

>>>from datetime import datetime
>>>"{:%B %d, %Y}".format(datetime.now())

The formatting characters used here are the same as those used by strftime. Don’t miss the leading : in the format specifier.

Using format() instead of strftime() in most cases can make the code more readable, easier to write and consistent with the way formatted output is generated…

>>>"{} today's date is: {:%B %d, %Y}".format("Andre", datetime.now())

Compare the above with the following strftime() alternative…

>>>"{} today's date is {}".format("Andre", datetime.now().strftime("%B %d, %Y"))

Moreover, the following is not going to work…

>>>datetime.now().strftime("%s %B %d, %Y" % "Andre")
Traceback (most recent call last):
  File "<pyshell#11>", line 1, in <module>
    datetime.now().strftime("%s %B %d, %Y" % "Andre")
TypeError: not enough arguments for format string

And so on…


回答 2

在Python 3.6及更高版本中使用f字符串。

from datetime import datetime

date_string = f'{datetime.now():%Y-%m-%d %H:%M:%S%z}'

Using f-strings, in Python 3.6+.

from datetime import datetime

date_string = f'{datetime.now():%Y-%m-%d %H:%M:%S%z}'

回答 3

我知道很老的问题。但是有了新的f字符串(从python 3.6开始),就有了新的选择。所以这里是为了完整性:

from datetime import datetime

dt = datetime.now()

# str.format
strg = '{:%B %d, %Y}'.format(dt)
print(strg)  # July 22, 2017

# datetime.strftime
strg = dt.strftime('%B %d, %Y')
print(strg)  # July 22, 2017

# f-strings in python >= 3.6
strg = f'{dt:%B %d, %Y}'
print(strg)  # July 22, 2017

strftime()and strptime()Behavior解释格式说明符的含义。

very old question, i know. but with the new f-strings (starting from python 3.6) there are fresh options. so here for completeness:

from datetime import datetime

dt = datetime.now()

# str.format
strg = '{:%B %d, %Y}'.format(dt)
print(strg)  # July 22, 2017

# datetime.strftime
strg = dt.strftime('%B %d, %Y')
print(strg)  # July 22, 2017

# f-strings in python >= 3.6
strg = f'{dt:%B %d, %Y}'
print(strg)  # July 22, 2017

strftime() and strptime() Behavior explains what the format specifiers mean.


回答 4

阅读官方文档中的strfrtime

Read strfrtime from the official docs.


回答 5

Python datetime对象具有method属性,该属性以可读格式打印。

>>> a = datetime.now()
>>> a.ctime()
'Mon May 21 18:35:18 2018'
>>> 

Python datetime object has a method attribute, which prints in readable format.

>>> a = datetime.now()
>>> a.ctime()
'Mon May 21 18:35:18 2018'
>>> 

回答 6

这是用于格式化的日期吗?

def format_date(day, month, year):
        # {} betekent 'plaats hier stringvoorstelling van volgend argument'
        return "{}/{}/{}".format(day, month, year)

This is for format the date?

def format_date(day, month, year):
        # {} betekent 'plaats hier stringvoorstelling van volgend argument'
        return "{}/{}/{}".format(day, month, year)

如何在不使用“ |”的情况下将两组连接在一起

问题:如何在不使用“ |”的情况下将两组连接在一起

假定ST被分配了集合。不使用join运算符|,如何找到两个集合的并集?例如,找到交叉点:

S = {1, 2, 3, 4}
T = {3, 4, 5, 6}
S_intersect_T = { i for i in S if i in T }

那么如何在不使用的情况下在一行中找到两个集合的并集|呢?

Assume that S and T are assigned sets. Without using the join operator |, how can I find the union of the two sets? This, for example, finds the intersection:

S = {1, 2, 3, 4}
T = {3, 4, 5, 6}
S_intersect_T = { i for i in S if i in T }

So how can I find the union of two sets in one line without using |?


回答 0

您可以对集合使用联合方法: set.union(other_set)

请注意,它会返回一个新集合,即不会对其自身进行修改。

You can use union method for sets: set.union(other_set)

Note that it returns a new set i.e it doesn’t modify itself.


回答 1

您可以使用or_别名:

>>> from operator import or_
>>> from functools import reduce # python3 required
>>> reduce(or_, [{1, 2, 3, 4}, {3, 4, 5, 6}])
set([1, 2, 3, 4, 5, 6])

You could use or_ alias:

>>> from operator import or_
>>> from functools import reduce # python3 required
>>> reduce(or_, [{1, 2, 3, 4}, {3, 4, 5, 6}])
set([1, 2, 3, 4, 5, 6])

回答 2

如果您可以修改原始集(在某些情况下可能需要这样做),可以使用set.update()

S.update(T)

返回值是None,但S将更新为原始S和的并集T

If you are fine with modifying the original set (which you may want to do in some cases), you can use set.update():

S.update(T)

The return value is None, but S will be updated to be the union of the original S and T.


回答 3

假设您也无法使用s.union(t),等于s | t,您可以尝试

>>> from itertools import chain
>>> set(chain(s,t))
set([1, 2, 3, 4, 5, 6])

或者,如果您想理解,

>>> {i for j in (s,t) for i in j}
set([1, 2, 3, 4, 5, 6])

Assuming you also can’t use s.union(t), which is equivalent to s | t, you could try

>>> from itertools import chain
>>> set(chain(s,t))
set([1, 2, 3, 4, 5, 6])

Or, if you want a comprehension,

>>> {i for j in (s,t) for i in j}
set([1, 2, 3, 4, 5, 6])

回答 4

如果加入表示您是工会,请尝试以下方法:

set(list(s) + list(t))

这有点骇人听闻,但我想不出更好的衬里来做。

If by join you mean union, try this:

set(list(s) + list(t))

It’s a bit of a hack, but I can’t think of a better one liner to do it.


回答 5

假设您有2个清单

 A = [1,2,3,4]
 B = [3,4,5,6]

所以你可以找到A联盟B如下

 union = set(A).union(set(B))

另外,如果要查找相交和非相交,请按照以下步骤进行操作

 intersection = set(A).intersection(set(B))
 non_intersection = union - intersection

Suppose you have 2 lists

 A = [1,2,3,4]
 B = [3,4,5,6]

so you can find A Union B as follow

 union = set(A).union(set(B))

also if you want to find intersection and non-intersection you do that as follow

 intersection = set(A).intersection(set(B))
 non_intersection = union - intersection

回答 6

您可以像这样将两个集合解压缩成一个集合:

>>> set_1 = {1, 2, 3, 4}
>>> set_2 = {3, 4, 5, 6}
>>> union = {*set_1, *set_2}
>>> union
{1, 2, 3, 4, 5, 6}

*解包集。拆包是将可迭代项(例如集合或列表)表示为它产生的每个项目的地方。这意味着上面的示例简化为{1, 2, 3, 4, 3, 4, 5, 6},然后简化为,{1, 2, 3, 4, 5, 6}因为该集合只能包含唯一项。

You can just unpack both sets into one like this:

>>> set_1 = {1, 2, 3, 4}
>>> set_2 = {3, 4, 5, 6}
>>> union = {*set_1, *set_2}
>>> union
{1, 2, 3, 4, 5, 6}

The * unpacks the set. Unpacking is where an iterable (e.g. a set or list) is represented as every item it yields. This means the above example simplifies to {1, 2, 3, 4, 3, 4, 5, 6} which then simplifies to {1, 2, 3, 4, 5, 6} because the set can only contain unique items.


回答 7

您可以做union或简单的列表理解

[A.add(_) for _ in B]

A将拥有B的所有元素

You can do union or simple list comprehension

[A.add(_) for _ in B]

A would have all the elements of B


python:将脚本工作目录更改为脚本自己的目录

问题:python:将脚本工作目录更改为脚本自己的目录

我每分钟从crontab运行python shell:

* * * * * /home/udi/foo/bar.py

/home/udi/foo有一些必要的子目录,例如/home/udi/foo/log/home/udi/foo/config,它/home/udi/foo/bar.py指的是。

问题是crontab从另一个工作目录运行脚本,因此尝试打开./log/bar.log失败。

有没有办法告诉脚本将工作目录更改为脚本自己的目录?我想找到一种适用于任何脚本位置的解决方案,而不是明确告诉脚本位置。

编辑:

os.chdir(os.path.dirname(sys.argv[0]))

是最紧凑的优雅解决方案。感谢您的回答和解释!

I run a python shell from crontab every minute:

* * * * * /home/udi/foo/bar.py

/home/udi/foo has some necessary subdirectories, like /home/udi/foo/log and /home/udi/foo/config, which /home/udi/foo/bar.py refers to.

The problem is that crontab runs the script from a different working directory, so trying to open ./log/bar.log fails.

Is there a nice way to tell the script to change the working directory to the script’s own directory? I would fancy a solution that would work for any script location, rather than explicitly telling the script where it is.

EDIT:

os.chdir(os.path.dirname(sys.argv[0]))

Was the most compact elegant solution. Thanks for your answers and explanations!


回答 0

这会将当前工作目录更改为,以便打开相对路径将起作用:

import os
os.chdir("/home/udi/foo")

但是,您询问如何将Python脚本更改为任何目录,即使您不知道编写脚本时的目录也是如此。为此,您可以使用以下os.path功能:

import os

abspath = os.path.abspath(__file__)
dname = os.path.dirname(abspath)
os.chdir(dname)

这将获取脚本的文件名,将其转换为绝对路径,然后提取该路径的目录,然后更改为该目录。

This will change your current working directory to so that opening relative paths will work:

import os
os.chdir("/home/udi/foo")

However, you asked how to change into whatever directory your Python script is located, even if you don’t know what directory that will be when you’re writing your script. To do this, you can use the os.path functions:

import os

abspath = os.path.abspath(__file__)
dname = os.path.dirname(abspath)
os.chdir(dname)

This takes the filename of your script, converts it to an absolute path, then extracts the directory of that path, then changes into that directory.


回答 1

您可以使用来获得较短的版本sys.path[0]

os.chdir(sys.path[0])

来自http://docs.python.org/library/sys.html#sys.path

在程序启动时进行初始化,该列表的第一项 path[0]是包含用于调用Python解释器的脚本的目录。

You can get a shorter version by using sys.path[0].

os.chdir(sys.path[0])

From http://docs.python.org/library/sys.html#sys.path

As initialized upon program startup, the first item of this list, path[0], is the directory containing the script that was used to invoke the Python interpreter


回答 2

不要这样

您的脚本和数据不应混入一个大目录中。把你的代码在一些已知的位置(site-packages或者/var/opt/udi什么的),从数据中分离出来。对代码使用良好的版本控制,以确保当前版本和以前的版本彼此分开,以便可以回退到以前的版本并测试将来的版本。

底线:请勿混合代码和数据。

数据是宝贵的。代码来来往往。

提供工作目录作为命令行参数值。您可以提供默认值作为环境变量。不要演绎(或猜测)

将其设为必需的参数值并执行此操作。

import sys
import os
working= os.environ.get("WORKING_DIRECTORY","/some/default")
if len(sys.argv) > 1: working = sys.argv[1]
os.chdir( working )

不要根据软件的位置“假定”目录。从长远来看,它将无法很好地工作。

Don’t do this.

Your scripts and your data should not be mashed into one big directory. Put your code in some known location (site-packages or /var/opt/udi or something) separate from your data. Use good version control on your code to be sure that you have current and previous versions separated from each other so you can fall back to previous versions and test future versions.

Bottom line: Do not mingle code and data.

Data is precious. Code comes and goes.

Provide the working directory as a command-line argument value. You can provide a default as an environment variable. Don’t deduce it (or guess at it)

Make it a required argument value and do this.

import sys
import os
working= os.environ.get("WORKING_DIRECTORY","/some/default")
if len(sys.argv) > 1: working = sys.argv[1]
os.chdir( working )

Do not “assume” a directory based on the location of your software. It will not work out well in the long run.


回答 3

将您的crontab命令更改为

* * * * * (cd /home/udi/foo/ || exit 1; ./bar.py)

(...)您的crond执行作为一个单一的命令启动一个子shell。如果|| exit 1目录不可用,则导致您的cronjob失败。

尽管从长远来看,其他解决方案对于您的特定脚本可能更优雅,但是在您无法修改要执行的程序或命令的情况下,我的示例仍然有用。

Change your crontab command to

* * * * * (cd /home/udi/foo/ || exit 1; ./bar.py)

The (...) starts a sub-shell that your crond executes as a single command. The || exit 1 causes your cronjob to fail in case that the directory is unavailable.

Though the other solutions may be more elegant in the long run for your specific scripts, my example could still be useful in cases where you can’t modify the program or command that you want to execute.


使用PyCrypto AES 256加密和解密

问题:使用PyCrypto AES 256加密和解密

我正在尝试使用PyCrypto构建两个接受两个参数的函数:消息和密钥,然后对消息进行加密/解密。

我在网络上找到了几个链接可以帮助我,但每个链接都有缺陷:

在codekoala上,此代码使用os.urandom,PyCrypto不建议这样做。

此外,不能保证我提供给函数的键具有预期的确切长度。我该怎么做才能做到这一点?

另外,有几种模式,推荐哪种?我不知道该怎么用:/

最后,IV到底是什么?我可以提供不同的IV进行加密和解密,还是返回不同的结果?

编辑:删除了代码部分,因为它不安全。

I’m trying to build two functions using PyCrypto that accept two parameters: the message and the key, and then encrypt/decrypt the message.

I found several links on the web to help me out, but each one of them has flaws:

This one at codekoala uses os.urandom, which is discouraged by PyCrypto.

Moreover, the key I give to the function is not guaranteed to have the exact length expected. What can I do to make that happen ?

Also, there are several modes, which one is recommended? I don’t know what to use :/

Finally, what exactly is the IV? Can I provide a different IV for encrypting and decrypting, or will this return in a different result?

Edit: Removed the code part since it was not secure.


回答 0

这是我的实现,并通过一些修复为我工作,并用32字节和iv到16字节增强了密钥和秘密短语的对齐方式:

import base64
import hashlib
from Crypto import Random
from Crypto.Cipher import AES

class AESCipher(object):

    def __init__(self, key): 
        self.bs = AES.block_size
        self.key = hashlib.sha256(key.encode()).digest()

    def encrypt(self, raw):
        raw = self._pad(raw)
        iv = Random.new().read(AES.block_size)
        cipher = AES.new(self.key, AES.MODE_CBC, iv)
        return base64.b64encode(iv + cipher.encrypt(raw.encode()))

    def decrypt(self, enc):
        enc = base64.b64decode(enc)
        iv = enc[:AES.block_size]
        cipher = AES.new(self.key, AES.MODE_CBC, iv)
        return self._unpad(cipher.decrypt(enc[AES.block_size:])).decode('utf-8')

    def _pad(self, s):
        return s + (self.bs - len(s) % self.bs) * chr(self.bs - len(s) % self.bs)

    @staticmethod
    def _unpad(s):
        return s[:-ord(s[len(s)-1:])]

Here is my implementation and works for me with some fixes and enhances the alignment of the key and secret phrase with 32 bytes and iv to 16 bytes:

import base64
import hashlib
from Crypto import Random
from Crypto.Cipher import AES

class AESCipher(object):

    def __init__(self, key): 
        self.bs = AES.block_size
        self.key = hashlib.sha256(key.encode()).digest()

    def encrypt(self, raw):
        raw = self._pad(raw)
        iv = Random.new().read(AES.block_size)
        cipher = AES.new(self.key, AES.MODE_CBC, iv)
        return base64.b64encode(iv + cipher.encrypt(raw.encode()))

    def decrypt(self, enc):
        enc = base64.b64decode(enc)
        iv = enc[:AES.block_size]
        cipher = AES.new(self.key, AES.MODE_CBC, iv)
        return self._unpad(cipher.decrypt(enc[AES.block_size:])).decode('utf-8')

    def _pad(self, s):
        return s + (self.bs - len(s) % self.bs) * chr(self.bs - len(s) % self.bs)

    @staticmethod
    def _unpad(s):
        return s[:-ord(s[len(s)-1:])]

回答 1

您可能需要以下两个功能:padunpad当输入的长度不是BLOCK_SIZE的倍数时,填充(执行加密时)和-取消填充(执行解密时)。

BS = 16
pad = lambda s: s + (BS - len(s) % BS) * chr(BS - len(s) % BS) 
unpad = lambda s : s[:-ord(s[len(s)-1:])]

所以您要问密钥的长度?您可以使用密钥的md5sum而不是直接使用它。

而且,根据我使用PyCrypto的经验,在输入相同的情况下,IV用于混合加密输出,因此将IV选择为随机字符串,并将其用作加密输出的一部分,然后用它来解密消息。

这是我的实现,希望它将对您有用:

import base64
from Crypto.Cipher import AES
from Crypto import Random

class AESCipher:
    def __init__( self, key ):
        self.key = key

    def encrypt( self, raw ):
        raw = pad(raw)
        iv = Random.new().read( AES.block_size )
        cipher = AES.new( self.key, AES.MODE_CBC, iv )
        return base64.b64encode( iv + cipher.encrypt( raw ) ) 

    def decrypt( self, enc ):
        enc = base64.b64decode(enc)
        iv = enc[:16]
        cipher = AES.new(self.key, AES.MODE_CBC, iv )
        return unpad(cipher.decrypt( enc[16:] ))

You may need the following two functions: pad– to pad(when doing encryption) and unpad– to unpad (when doing decryption) when the length of input is not a multiple of BLOCK_SIZE.

BS = 16
pad = lambda s: s + (BS - len(s) % BS) * chr(BS - len(s) % BS) 
unpad = lambda s : s[:-ord(s[len(s)-1:])]

So you’re asking the length of key? You can use the md5sum of the key rather than use it directly.

More, according to my little experience of using PyCrypto, the IV is used to mix up the output of a encryption when input is same, so the IV is chosen as a random string, and use it as part of the encryption output, and then use it to decrypt the message.

And here’s my implementation, hope it will be useful for you:

import base64
from Crypto.Cipher import AES
from Crypto import Random

class AESCipher:
    def __init__( self, key ):
        self.key = key

    def encrypt( self, raw ):
        raw = pad(raw)
        iv = Random.new().read( AES.block_size )
        cipher = AES.new( self.key, AES.MODE_CBC, iv )
        return base64.b64encode( iv + cipher.encrypt( raw ) ) 

    def decrypt( self, enc ):
        enc = base64.b64decode(enc)
        iv = enc[:16]
        cipher = AES.new(self.key, AES.MODE_CBC, iv )
        return unpad(cipher.decrypt( enc[16:] ))

回答 2

让我解决您有关“模式”的问题。AES256是一种分组密码。它以32字节的密钥和16字节的字符串(称为块)作为输入,并输出一个块。我们在操作模式下使用AES 进行加密。上面的解决方案建议使用CBC,这是一个示例。另一个称为CTR,使用起来更容易一些:

from Crypto.Cipher import AES
from Crypto.Util import Counter
from Crypto import Random

# AES supports multiple key sizes: 16 (AES128), 24 (AES192), or 32 (AES256).
key_bytes = 32

# Takes as input a 32-byte key and an arbitrary-length plaintext and returns a
# pair (iv, ciphtertext). "iv" stands for initialization vector.
def encrypt(key, plaintext):
    assert len(key) == key_bytes

    # Choose a random, 16-byte IV.
    iv = Random.new().read(AES.block_size)

    # Convert the IV to a Python integer.
    iv_int = int(binascii.hexlify(iv), 16) 

    # Create a new Counter object with IV = iv_int.
    ctr = Counter.new(AES.block_size * 8, initial_value=iv_int)

    # Create AES-CTR cipher.
    aes = AES.new(key, AES.MODE_CTR, counter=ctr)

    # Encrypt and return IV and ciphertext.
    ciphertext = aes.encrypt(plaintext)
    return (iv, ciphertext)

# Takes as input a 32-byte key, a 16-byte IV, and a ciphertext, and outputs the
# corresponding plaintext.
def decrypt(key, iv, ciphertext):
    assert len(key) == key_bytes

    # Initialize counter for decryption. iv should be the same as the output of
    # encrypt().
    iv_int = int(iv.encode('hex'), 16) 
    ctr = Counter.new(AES.block_size * 8, initial_value=iv_int)

    # Create AES-CTR cipher.
    aes = AES.new(key, AES.MODE_CTR, counter=ctr)

    # Decrypt and return the plaintext.
    plaintext = aes.decrypt(ciphertext)
    return plaintext

(iv, ciphertext) = encrypt(key, 'hella')
print decrypt(key, iv, ciphertext)

这通常称为AES-CTR。在将AES-CBC与PyCrypto结合使用时,我建议您谨慎使用。原因是它要求您指定填充方案,如其他给出的解决方案所示。通常,如果您对填充不太谨慎,则可以完全破坏加密的攻击

现在,必须注意,密钥必须是一个随机的32字节字符串;密码足够。通常,密钥是这样生成的:

# Nominal way to generate a fresh key. This calls the system's random number
# generator (RNG).
key1 = Random.new().read(key_bytes)

密钥也可以从密码派生

# It's also possible to derive a key from a password, but it's important that
# the password have high entropy, meaning difficult to predict.
password = "This is a rather weak password."

# For added # security, we add a "salt", which increases the entropy.
#
# In this example, we use the same RNG to produce the salt that we used to
# produce key1.
salt_bytes = 8 
salt = Random.new().read(salt_bytes)

# Stands for "Password-based key derivation function 2"
key2 = PBKDF2(password, salt, key_bytes)

上面的一些解决方案建议使用SHA256派生密钥,但这通常被认为是不良的加密做法。查阅Wikipedia,了解更多有关操作模式的信息。

Let me address your question about “modes.” AES256 is a kind of block cipher. It takes as input a 32-byte key and a 16-byte string, called the block and outputs a block. We use AES in a mode of operation in order to encrypt. The solutions above suggest using CBC, which is one example. Another is called CTR, and it’s somewhat easier to use:

from Crypto.Cipher import AES
from Crypto.Util import Counter
from Crypto import Random

# AES supports multiple key sizes: 16 (AES128), 24 (AES192), or 32 (AES256).
key_bytes = 32

# Takes as input a 32-byte key and an arbitrary-length plaintext and returns a
# pair (iv, ciphtertext). "iv" stands for initialization vector.
def encrypt(key, plaintext):
    assert len(key) == key_bytes

    # Choose a random, 16-byte IV.
    iv = Random.new().read(AES.block_size)

    # Convert the IV to a Python integer.
    iv_int = int(binascii.hexlify(iv), 16) 

    # Create a new Counter object with IV = iv_int.
    ctr = Counter.new(AES.block_size * 8, initial_value=iv_int)

    # Create AES-CTR cipher.
    aes = AES.new(key, AES.MODE_CTR, counter=ctr)

    # Encrypt and return IV and ciphertext.
    ciphertext = aes.encrypt(plaintext)
    return (iv, ciphertext)

# Takes as input a 32-byte key, a 16-byte IV, and a ciphertext, and outputs the
# corresponding plaintext.
def decrypt(key, iv, ciphertext):
    assert len(key) == key_bytes

    # Initialize counter for decryption. iv should be the same as the output of
    # encrypt().
    iv_int = int(iv.encode('hex'), 16) 
    ctr = Counter.new(AES.block_size * 8, initial_value=iv_int)

    # Create AES-CTR cipher.
    aes = AES.new(key, AES.MODE_CTR, counter=ctr)

    # Decrypt and return the plaintext.
    plaintext = aes.decrypt(ciphertext)
    return plaintext

(iv, ciphertext) = encrypt(key, 'hella')
print decrypt(key, iv, ciphertext)

This is often referred to as AES-CTR. I would advise caution in using AES-CBC with PyCrypto. The reason is that it requires you to specify the padding scheme, as exemplified by the other solutions given. In general, if you’re not very careful about the padding, there are attacks that completely break encryption!

Now, it’s important to note that the key must be a random, 32-byte string; a password does not suffice. Normally, the key is generated like so:

# Nominal way to generate a fresh key. This calls the system's random number
# generator (RNG).
key1 = Random.new().read(key_bytes)

A key may be derived from a password, too:

# It's also possible to derive a key from a password, but it's important that
# the password have high entropy, meaning difficult to predict.
password = "This is a rather weak password."

# For added # security, we add a "salt", which increases the entropy.
#
# In this example, we use the same RNG to produce the salt that we used to
# produce key1.
salt_bytes = 8 
salt = Random.new().read(salt_bytes)

# Stands for "Password-based key derivation function 2"
key2 = PBKDF2(password, salt, key_bytes)

Some solutions above suggest using SHA256 for deriving the key, but this is generally considered bad cryptographic practice. Check out wikipedia for more on modes of operation.


回答 3

对于想使用urlsafe_b64encode和urlsafe_b64decode的用户,以下是对我有用的版本(花了一些时间处理unicode问题之后)

BS = 16
key = hashlib.md5(settings.SECRET_KEY).hexdigest()[:BS]
pad = lambda s: s + (BS - len(s) % BS) * chr(BS - len(s) % BS)
unpad = lambda s : s[:-ord(s[len(s)-1:])]

class AESCipher:
    def __init__(self, key):
        self.key = key

    def encrypt(self, raw):
        raw = pad(raw)
        iv = Random.new().read(AES.block_size)
        cipher = AES.new(self.key, AES.MODE_CBC, iv)
        return base64.urlsafe_b64encode(iv + cipher.encrypt(raw)) 

    def decrypt(self, enc):
        enc = base64.urlsafe_b64decode(enc.encode('utf-8'))
        iv = enc[:BS]
        cipher = AES.new(self.key, AES.MODE_CBC, iv)
        return unpad(cipher.decrypt(enc[BS:]))

For someone who would like to use urlsafe_b64encode and urlsafe_b64decode, here are the version that’re working for me (after spending some time with the unicode issue)

BS = 16
key = hashlib.md5(settings.SECRET_KEY).hexdigest()[:BS]
pad = lambda s: s + (BS - len(s) % BS) * chr(BS - len(s) % BS)
unpad = lambda s : s[:-ord(s[len(s)-1:])]

class AESCipher:
    def __init__(self, key):
        self.key = key

    def encrypt(self, raw):
        raw = pad(raw)
        iv = Random.new().read(AES.block_size)
        cipher = AES.new(self.key, AES.MODE_CBC, iv)
        return base64.urlsafe_b64encode(iv + cipher.encrypt(raw)) 

    def decrypt(self, enc):
        enc = base64.urlsafe_b64decode(enc.encode('utf-8'))
        iv = enc[:BS]
        cipher = AES.new(self.key, AES.MODE_CBC, iv)
        return unpad(cipher.decrypt(enc[BS:]))

回答 4

您可以使用SHA-1或SHA-256之类的加密哈希函数(不是 Python的内置函数)从任意密码中获取密码短语hash。Python在其标准库中包括对两者的支持:

import hashlib

hashlib.sha1("this is my awesome password").digest() # => a 20 byte string
hashlib.sha256("another awesome password").digest() # => a 32 byte string

您可以仅使用[:16]或来截断加密哈希值,[:24]并且它将在指定长度内保持其安全性。

You can get a passphrase out of an arbitrary password by using a cryptographic hash function (NOT Python’s builtin hash) like SHA-1 or SHA-256. Python includes support for both in its standard library:

import hashlib

hashlib.sha1("this is my awesome password").digest() # => a 20 byte string
hashlib.sha256("another awesome password").digest() # => a 32 byte string

You can truncate a cryptographic hash value just by using [:16] or [:24] and it will retain its security up to the length you specify.


回答 5

感谢其他启发但对我不起作用的答案。

在花了数小时试图弄清楚它是如何工作之后,我想到了下面的实现,并带有最新的PyCryptodomex库(这是我如何在Windows上的virtualenv .. phew中成功设置它的代理)

。在实现时,请记住写下填充,编码,加密步骤(反之亦然)。您必须打包和拆包,并牢记顺序。

导入base64
导入hashlib
从Cryptodome.Cipher导入AES
从Cryptodome.Random导入get_random_bytes

__key__ = hashlib.sha256(b'16个字符的键').digest()

def加密(原始):
    BS = AES.block_size
    pad = lambda s:s +(BS-len%BS)* chr(BS-len%BS)

    原始= base64.b64encode(pad(raw).encode('utf8'))
    iv = get_random_bytes(AES.block_size)
    密码= AES.new(密钥= __密钥__,模式= AES.MODE_CFB,iv = iv)
    返回base64.b64encode(iv + cipher.encrypt(raw))

def解密(enc):
    unpad = lambda s:s [:-ord(s [-1:])]

    enc = base64.b64decode(enc)
    iv = enc [:AES.block_size]
    cipher = AES.new(__ key__,AES.MODE_CFB,iv)
    返回unpad(base64.b64decode(cipher.decrypt(enc [AES.block_size:]))。decode('utf8'))

Grateful for the other answers which inspired but didn’t work for me.

After spending hours trying to figure out how it works, I came up with the implementation below with the newest PyCryptodomex library (it is another story how I managed to set it up behind proxy, on Windows, in a virtualenv.. phew)

Working on your implementation, remember to write down padding, encoding, encrypting steps (and vice versa). You have to pack and unpack keeping in mind the order.

import base64
import hashlib
from Cryptodome.Cipher import AES
from Cryptodome.Random import get_random_bytes

__key__ = hashlib.sha256(b'16-character key').digest()

def encrypt(raw):
    BS = AES.block_size
    pad = lambda s: s + (BS - len(s) % BS) * chr(BS - len(s) % BS)

    raw = base64.b64encode(pad(raw).encode('utf8'))
    iv = get_random_bytes(AES.block_size)
    cipher = AES.new(key= __key__, mode= AES.MODE_CFB,iv= iv)
    return base64.b64encode(iv + cipher.encrypt(raw))

def decrypt(enc):
    unpad = lambda s: s[:-ord(s[-1:])]

    enc = base64.b64decode(enc)
    iv = enc[:AES.block_size]
    cipher = AES.new(__key__, AES.MODE_CFB, iv)
    return unpad(base64.b64decode(cipher.decrypt(enc[AES.block_size:])).decode('utf8'))

回答 6

为了他人的利益,这是我结合@Cyril和@Marcus的答案所获得的解密实现。假定此消息是通过HTTP请求传入的,该消息带有quoted和base64编码。

import base64
import urllib2
from Crypto.Cipher import AES


def decrypt(quotedEncodedEncrypted):
    key = 'SecretKey'

    encodedEncrypted = urllib2.unquote(quotedEncodedEncrypted)

    cipher = AES.new(key)
    decrypted = cipher.decrypt(base64.b64decode(encodedEncrypted))[:16]

    for i in range(1, len(base64.b64decode(encodedEncrypted))/16):
        cipher = AES.new(key, AES.MODE_CBC, base64.b64decode(encodedEncrypted)[(i-1)*16:i*16])
        decrypted += cipher.decrypt(base64.b64decode(encodedEncrypted)[i*16:])[:16]

    return decrypted.strip()

For the benefit of others, here is my decryption implementation which I got to by combining the answers of @Cyril and @Marcus. This assumes that this coming in via HTTP Request with the encryptedText quoted and base64 encoded.

import base64
import urllib2
from Crypto.Cipher import AES


def decrypt(quotedEncodedEncrypted):
    key = 'SecretKey'

    encodedEncrypted = urllib2.unquote(quotedEncodedEncrypted)

    cipher = AES.new(key)
    decrypted = cipher.decrypt(base64.b64decode(encodedEncrypted))[:16]

    for i in range(1, len(base64.b64decode(encodedEncrypted))/16):
        cipher = AES.new(key, AES.MODE_CBC, base64.b64decode(encodedEncrypted)[(i-1)*16:i*16])
        decrypted += cipher.decrypt(base64.b64decode(encodedEncrypted)[i*16:])[:16]

    return decrypted.strip()

回答 7

对此的另一种看法(很大程度上来自上述解决方案),但

  • 使用null进行填充
  • 不使用lambda(从不成为粉丝)
  • 用python 2.7和3.6.5测试

    #!/usr/bin/python2.7
    # you'll have to adjust for your setup, e.g., #!/usr/bin/python3
    
    
    import base64, re
    from Crypto.Cipher import AES
    from Crypto import Random
    from django.conf import settings
    
    class AESCipher:
        """
          Usage:
          aes = AESCipher( settings.SECRET_KEY[:16], 32)
          encryp_msg = aes.encrypt( 'ppppppppppppppppppppppppppppppppppppppppppppppppppppppp' )
          msg = aes.decrypt( encryp_msg )
          print("'{}'".format(msg))
        """
        def __init__(self, key, blk_sz):
            self.key = key
            self.blk_sz = blk_sz
    
        def encrypt( self, raw ):
            if raw is None or len(raw) == 0:
                raise NameError("No value given to encrypt")
            raw = raw + '\0' * (self.blk_sz - len(raw) % self.blk_sz)
            raw = raw.encode('utf-8')
            iv = Random.new().read( AES.block_size )
            cipher = AES.new( self.key.encode('utf-8'), AES.MODE_CBC, iv )
            return base64.b64encode( iv + cipher.encrypt( raw ) ).decode('utf-8')
    
        def decrypt( self, enc ):
            if enc is None or len(enc) == 0:
                raise NameError("No value given to decrypt")
            enc = base64.b64decode(enc)
            iv = enc[:16]
            cipher = AES.new(self.key.encode('utf-8'), AES.MODE_CBC, iv )
            return re.sub(b'\x00*$', b'', cipher.decrypt( enc[16:])).decode('utf-8')

Another take on this (heavily derived from solutions above) but

  • uses null for padding
  • does not use lambda (never been a fan)
  • tested with python 2.7 and 3.6.5

    #!/usr/bin/python2.7
    # you'll have to adjust for your setup, e.g., #!/usr/bin/python3
    
    
    import base64, re
    from Crypto.Cipher import AES
    from Crypto import Random
    from django.conf import settings
    
    class AESCipher:
        """
          Usage:
          aes = AESCipher( settings.SECRET_KEY[:16], 32)
          encryp_msg = aes.encrypt( 'ppppppppppppppppppppppppppppppppppppppppppppppppppppppp' )
          msg = aes.decrypt( encryp_msg )
          print("'{}'".format(msg))
        """
        def __init__(self, key, blk_sz):
            self.key = key
            self.blk_sz = blk_sz
    
        def encrypt( self, raw ):
            if raw is None or len(raw) == 0:
                raise NameError("No value given to encrypt")
            raw = raw + '\0' * (self.blk_sz - len(raw) % self.blk_sz)
            raw = raw.encode('utf-8')
            iv = Random.new().read( AES.block_size )
            cipher = AES.new( self.key.encode('utf-8'), AES.MODE_CBC, iv )
            return base64.b64encode( iv + cipher.encrypt( raw ) ).decode('utf-8')
    
        def decrypt( self, enc ):
            if enc is None or len(enc) == 0:
                raise NameError("No value given to decrypt")
            enc = base64.b64decode(enc)
            iv = enc[:16]
            cipher = AES.new(self.key.encode('utf-8'), AES.MODE_CBC, iv )
            return re.sub(b'\x00*$', b'', cipher.decrypt( enc[16:])).decode('utf-8')
    

回答 8

我都用了CryptoPyCryptodomex库,它是速度极快…

import base64
import hashlib
from Cryptodome.Cipher import AES as domeAES
from Cryptodome.Random import get_random_bytes
from Crypto import Random
from Crypto.Cipher import AES as cryptoAES

BLOCK_SIZE = AES.block_size

key = "my_secret_key".encode()
__key__ = hashlib.sha256(key).digest()
print(__key__)

def encrypt(raw):
    BS = cryptoAES.block_size
    pad = lambda s: s + (BS - len(s) % BS) * chr(BS - len(s) % BS)
    raw = base64.b64encode(pad(raw).encode('utf8'))
    iv = get_random_bytes(cryptoAES.block_size)
    cipher = cryptoAES.new(key= __key__, mode= cryptoAES.MODE_CFB,iv= iv)
    a= base64.b64encode(iv + cipher.encrypt(raw))
    IV = Random.new().read(BLOCK_SIZE)
    aes = domeAES.new(__key__, domeAES.MODE_CFB, IV)
    b = base64.b64encode(IV + aes.encrypt(a))
    return b

def decrypt(enc):
    passphrase = __key__
    encrypted = base64.b64decode(enc)
    IV = encrypted[:BLOCK_SIZE]
    aes = domeAES.new(passphrase, domeAES.MODE_CFB, IV)
    enc = aes.decrypt(encrypted[BLOCK_SIZE:])
    unpad = lambda s: s[:-ord(s[-1:])]
    enc = base64.b64decode(enc)
    iv = enc[:cryptoAES.block_size]
    cipher = cryptoAES.new(__key__, cryptoAES.MODE_CFB, iv)
    b=  unpad(base64.b64decode(cipher.decrypt(enc[cryptoAES.block_size:])).decode('utf8'))
    return b

encrypted_data =encrypt("Hi Steven!!!!!")
print(encrypted_data)
print("=======")
decrypted_data = decrypt(encrypted_data)
print(decrypted_data)

I have used both Crypto and PyCryptodomex library and it is blazing fast…

import base64
import hashlib
from Cryptodome.Cipher import AES as domeAES
from Cryptodome.Random import get_random_bytes
from Crypto import Random
from Crypto.Cipher import AES as cryptoAES

BLOCK_SIZE = AES.block_size

key = "my_secret_key".encode()
__key__ = hashlib.sha256(key).digest()
print(__key__)

def encrypt(raw):
    BS = cryptoAES.block_size
    pad = lambda s: s + (BS - len(s) % BS) * chr(BS - len(s) % BS)
    raw = base64.b64encode(pad(raw).encode('utf8'))
    iv = get_random_bytes(cryptoAES.block_size)
    cipher = cryptoAES.new(key= __key__, mode= cryptoAES.MODE_CFB,iv= iv)
    a= base64.b64encode(iv + cipher.encrypt(raw))
    IV = Random.new().read(BLOCK_SIZE)
    aes = domeAES.new(__key__, domeAES.MODE_CFB, IV)
    b = base64.b64encode(IV + aes.encrypt(a))
    return b

def decrypt(enc):
    passphrase = __key__
    encrypted = base64.b64decode(enc)
    IV = encrypted[:BLOCK_SIZE]
    aes = domeAES.new(passphrase, domeAES.MODE_CFB, IV)
    enc = aes.decrypt(encrypted[BLOCK_SIZE:])
    unpad = lambda s: s[:-ord(s[-1:])]
    enc = base64.b64decode(enc)
    iv = enc[:cryptoAES.block_size]
    cipher = cryptoAES.new(__key__, cryptoAES.MODE_CFB, iv)
    b=  unpad(base64.b64decode(cipher.decrypt(enc[cryptoAES.block_size:])).decode('utf8'))
    return b

encrypted_data =encrypt("Hi Steven!!!!!")
print(encrypted_data)
print("=======")
decrypted_data = decrypt(encrypted_data)
print(decrypted_data)

回答 9

还不晚,但是我认为这将非常有帮助。没有人提及像PKCS#7填充这样的使用方案。您可以使用它代替以前的函数进行填充(加密时)和取消填充(解密时)。i将在下面提供完整的源代码。

import base64
import hashlib
from Crypto import Random
from Crypto.Cipher import AES
import pkcs7
class Encryption:

    def __init__(self):
        pass

    def Encrypt(self, PlainText, SecurePassword):
        pw_encode = SecurePassword.encode('utf-8')
        text_encode = PlainText.encode('utf-8')

        key = hashlib.sha256(pw_encode).digest()
        iv = Random.new().read(AES.block_size)

        cipher = AES.new(key, AES.MODE_CBC, iv)
        pad_text = pkcs7.encode(text_encode)
        msg = iv + cipher.encrypt(pad_text)

        EncodeMsg = base64.b64encode(msg)
        return EncodeMsg

    def Decrypt(self, Encrypted, SecurePassword):
        decodbase64 = base64.b64decode(Encrypted.decode("utf-8"))
        pw_encode = SecurePassword.decode('utf-8')

        iv = decodbase64[:AES.block_size]
        key = hashlib.sha256(pw_encode).digest()

        cipher = AES.new(key, AES.MODE_CBC, iv)
        msg = cipher.decrypt(decodbase64[AES.block_size:])
        pad_text = pkcs7.decode(msg)

        decryptedString = pad_text.decode('utf-8')
        return decryptedString

import StringIO
import binascii


def decode(text, k=16):
    nl = len(text)
    val = int(binascii.hexlify(text[-1]), 16)
    if val > k:
        raise ValueError('Input is not padded or padding is corrupt')

    l = nl - val
    return text[:l]


def encode(text, k=16):
    l = len(text)
    output = StringIO.StringIO()
    val = k - (l % k)
    for _ in xrange(val):
        output.write('%02x' % val)
    return text + binascii.unhexlify(output.getvalue())

It’s little late but i think this will be very helpful. No one mention about use scheme like PKCS#7 padding. You can use it instead the previous functions to pad(when do encryption) and unpad(when do decryption).i will provide the full Source Code below.

import base64
import hashlib
from Crypto import Random
from Crypto.Cipher import AES
import pkcs7
class Encryption:

    def __init__(self):
        pass

    def Encrypt(self, PlainText, SecurePassword):
        pw_encode = SecurePassword.encode('utf-8')
        text_encode = PlainText.encode('utf-8')

        key = hashlib.sha256(pw_encode).digest()
        iv = Random.new().read(AES.block_size)

        cipher = AES.new(key, AES.MODE_CBC, iv)
        pad_text = pkcs7.encode(text_encode)
        msg = iv + cipher.encrypt(pad_text)

        EncodeMsg = base64.b64encode(msg)
        return EncodeMsg

    def Decrypt(self, Encrypted, SecurePassword):
        decodbase64 = base64.b64decode(Encrypted.decode("utf-8"))
        pw_encode = SecurePassword.decode('utf-8')

        iv = decodbase64[:AES.block_size]
        key = hashlib.sha256(pw_encode).digest()

        cipher = AES.new(key, AES.MODE_CBC, iv)
        msg = cipher.decrypt(decodbase64[AES.block_size:])
        pad_text = pkcs7.decode(msg)

        decryptedString = pad_text.decode('utf-8')
        return decryptedString

import StringIO
import binascii


def decode(text, k=16):
    nl = len(text)
    val = int(binascii.hexlify(text[-1]), 16)
    if val > k:
        raise ValueError('Input is not padded or padding is corrupt')

    l = nl - val
    return text[:l]


def encode(text, k=16):
    l = len(text)
    output = StringIO.StringIO()
    val = k - (l % k)
    for _ in xrange(val):
        output.write('%02x' % val)
    return text + binascii.unhexlify(output.getvalue())


回答 10

https://stackoverflow.com/a/21928790/11402877

兼容的utf-8编码

def _pad(self, s):
    s = s.encode()
    res = s + (self.bs - len(s) % self.bs) * chr(self.bs - len(s) % self.bs).encode()
    return res

https://stackoverflow.com/a/21928790/11402877

compatible utf-8 encoding

def _pad(self, s):
    s = s.encode()
    res = s + (self.bs - len(s) % self.bs) * chr(self.bs - len(s) % self.bs).encode()
    return res

回答 11

from Crypto import Random
from Crypto.Cipher import AES
import base64

BLOCK_SIZE=16
def trans(key):
     return md5.new(key).digest()

def encrypt(message, passphrase):
    passphrase = trans(passphrase)
    IV = Random.new().read(BLOCK_SIZE)
    aes = AES.new(passphrase, AES.MODE_CFB, IV)
    return base64.b64encode(IV + aes.encrypt(message))

def decrypt(encrypted, passphrase):
    passphrase = trans(passphrase)
    encrypted = base64.b64decode(encrypted)
    IV = encrypted[:BLOCK_SIZE]
    aes = AES.new(passphrase, AES.MODE_CFB, IV)
    return aes.decrypt(encrypted[BLOCK_SIZE:])
from Crypto import Random
from Crypto.Cipher import AES
import base64

BLOCK_SIZE=16
def trans(key):
     return md5.new(key).digest()

def encrypt(message, passphrase):
    passphrase = trans(passphrase)
    IV = Random.new().read(BLOCK_SIZE)
    aes = AES.new(passphrase, AES.MODE_CFB, IV)
    return base64.b64encode(IV + aes.encrypt(message))

def decrypt(encrypted, passphrase):
    passphrase = trans(passphrase)
    encrypted = base64.b64decode(encrypted)
    IV = encrypted[:BLOCK_SIZE]
    aes = AES.new(passphrase, AES.MODE_CFB, IV)
    return aes.decrypt(encrypted[BLOCK_SIZE:])

pandas loc vs. iloc vs. ix vs. at vs. iat?

问题:pandas loc vs. iloc vs. ix vs. at vs. iat?

最近开始从我的安全地方(R)分支到Python,并且对中的单元格本地化/选择感到有些困惑Pandas。我已经阅读了文档,但仍在努力了解各种本地化/选择选项的实际含义。

  • 我为什么应该使用.loc.iloc超过最一般的选择.ix
  • 我的理解是.locilocat,和iat可以提供一些保证正确性是.ix不能提供的,但我也看到了在那里.ix往往是一刀切最快的解决方案。
  • 请说明使用除.ix?以外的任何东西背后的现实世界,最佳实践推理。

Recently began branching out from my safe place (R) into Python and and am a bit confused by the cell localization/selection in Pandas. I’ve read the documentation but I’m struggling to understand the practical implications of the various localization/selection options.

  • Is there a reason why I should ever use .loc or .iloc over the most general option .ix?
  • I understand that .loc, iloc, at, and iat may provide some guaranteed correctness that .ix can’t offer, but I’ve also read where .ix tends to be the fastest solution across the board.
  • Please explain the real-world, best-practices reasoning behind utilizing anything other than .ix?

回答 0

loc:仅适用于索引
iloc:适用于位置
ix:您可以从数据获取数据,而无需将其包含在索引
中:获取标量值。这是一个非常快速的定位
获取标量值。这是一个非常快的iloc

http://pyciencia.blogspot.com/2015/05/obtener-y-filtrar-datos-de-un-dataframe.html

注:由于pandas 0.20.0中,.ix索引被弃用赞成更加严格.iloc.loc索引。

loc: only work on index
iloc: work on position
ix: You can get data from dataframe without it being in the index
at: get scalar values. It’s a very fast loc
iat: Get scalar values. It’s a very fast iloc

http://pyciencia.blogspot.com/2015/05/obtener-y-filtrar-datos-de-un-dataframe.html

Note: As of pandas 0.20.0, the .ix indexer is deprecated in favour of the more strict .iloc and .loc indexers.


回答 1

已更新,pandas 0.20因为ix已弃用。这不但表明了如何使用locilocatiatset_value,但如何实现,混合位置/标签基于索引。


loc基于标签
允许您将一维数组作为索引器传递。数组可以是索引或列的切片(子集),也可以是长度与索引或列相等的布尔数组。

特别说明:当传递标量索引器时,loc可以分配以前不存在的新索引或列值。

# label based, but we can use position values
# to get the labels from the index object
df.loc[df.index[2], 'ColName'] = 3

df.loc[df.index[1:3], 'ColName'] = 3

iloc基于位置
类似于,loc除了位置而不是索引值。但是,您不能分配新的列或索引。

# position based, but we can get the position
# from the columns object via the `get_loc` method
df.iloc[2, df.columns.get_loc('ColName')] = 3

df.iloc[2, 4] = 3

df.iloc[:3, 2:4] = 3

at基于标签的
作品与loc标量索引器非常相似。 无法对数组索引器进行操作。 能够!分配新的索引和列。

优势loc是,这是速度更快。
缺点是不能将数组用于索引器。

# label based, but we can use position values
# to get the labels from the index object
df.at[df.index[2], 'ColName'] = 3

df.at['C', 'ColName'] = 3

iat基于位置的
原理相似iloc无法在数组索引器中工作。 不能!分配新的索引和列。

优势iloc是,这是速度更快。
缺点是不能将数组用于索引器。

# position based, but we can get the position
# from the columns object via the `get_loc` method
IBM.iat[2, IBM.columns.get_loc('PNL')] = 3

set_value基于标签的
作品与loc标量索引器非常相似。 无法对数组索引器进行操作。 能够!分配新的索引和列

优势超级快,因为几乎没有开销!
缺点由于pandas没有进行大量安全检查,因此开销很少。 使用风险自负。另外,这也不打算供公众使用。

# label based, but we can use position values
# to get the labels from the index object
df.set_value(df.index[2], 'ColName', 3)

set_valuetakable=True位置,并根据
原理相似iloc无法在数组索引器中工作。 不能!分配新的索引和列。

优势超级快,因为几乎没有开销!
缺点由于pandas没有进行大量安全检查,因此开销很少。 使用风险自负。另外,这也不打算供公众使用。

# position based, but we can get the position
# from the columns object via the `get_loc` method
df.set_value(2, df.columns.get_loc('ColName'), 3, takable=True)

Updated for pandas 0.20 given that ix is deprecated. This demonstrates not only how to use loc, iloc, at, iat, set_value, but how to accomplish, mixed positional/label based indexing.


loclabel based
Allows you to pass 1-D arrays as indexers. Arrays can be either slices (subsets) of the index or column, or they can be boolean arrays which are equal in length to the index or columns.

Special Note: when a scalar indexer is passed, loc can assign a new index or column value that didn’t exist before.

# label based, but we can use position values
# to get the labels from the index object
df.loc[df.index[2], 'ColName'] = 3

df.loc[df.index[1:3], 'ColName'] = 3

ilocposition based
Similar to loc except with positions rather that index values. However, you cannot assign new columns or indices.

# position based, but we can get the position
# from the columns object via the `get_loc` method
df.iloc[2, df.columns.get_loc('ColName')] = 3

df.iloc[2, 4] = 3

df.iloc[:3, 2:4] = 3

atlabel based
Works very similar to loc for scalar indexers. Cannot operate on array indexers. Can! assign new indices and columns.

Advantage over loc is that this is faster.
Disadvantage is that you can’t use arrays for indexers.

# label based, but we can use position values
# to get the labels from the index object
df.at[df.index[2], 'ColName'] = 3

df.at['C', 'ColName'] = 3

iatposition based
Works similarly to iloc. Cannot work in array indexers. Cannot! assign new indices and columns.

Advantage over iloc is that this is faster.
Disadvantage is that you can’t use arrays for indexers.

# position based, but we can get the position
# from the columns object via the `get_loc` method
IBM.iat[2, IBM.columns.get_loc('PNL')] = 3

set_valuelabel based
Works very similar to loc for scalar indexers. Cannot operate on array indexers. Can! assign new indices and columns

Advantage Super fast, because there is very little overhead!
Disadvantage There is very little overhead because pandas is not doing a bunch of safety checks. Use at your own risk. Also, this is not intended for public use.

# label based, but we can use position values
# to get the labels from the index object
df.set_value(df.index[2], 'ColName', 3)

set_value with takable=Trueposition based
Works similarly to iloc. Cannot work in array indexers. Cannot! assign new indices and columns.

Advantage Super fast, because there is very little overhead!
Disadvantage There is very little overhead because pandas is not doing a bunch of safety checks. Use at your own risk. Also, this is not intended for public use.

# position based, but we can get the position
# from the columns object via the `get_loc` method
df.set_value(2, df.columns.get_loc('ColName'), 3, takable=True)

回答 2

熊猫从DataFrame中进行选择的主要方式有两种。

  • 标签
  • 整数位置

该文档使用位置一词来指代整数位置。我不喜欢这个术语,因为我觉得它很混乱。整数位置更具描述性,正好.iloc代表该位置。此处的关键字是INTEGER-按整数位置选择时必须使用整数。

在显示摘要之前,让我们确保…

.ix已弃用且含糊不清,切勿使用

熊猫有三个主要的索引器。我们有索引运算符本身(括号[].loc,和.iloc。让我们总结一下:

  • []-主要选择列的子集,但也可以选择行。无法同时选择行和列。
  • .loc -仅按标签选择行和列的子集
  • .iloc -仅按整数位置选择行和列的子集

我几乎从未使用过,.at或者.iat因为它们没有添加任何附加功能并且只增加了一点性能。除非您有一个对时间敏感的应用程序,否则我不建议您使用它们。无论如何,我们有他们的摘要:

  • .at 仅通过标签在DataFrame中选择单个标量值
  • .iat 仅通过整数位置选择DataFrame中的单个标量值

除了按标签和整数位置进行选择外,还存在布尔选择(也称为布尔索引)


解释.loc,,.iloc布尔选择.at.iat的示例如下所示

我们将首先关注.loc和之间的差异.iloc。在讨论差异之前,必须了解DataFrame具有用于帮助标识每一列和每一行的标签,这一点很重要。让我们看一个示例DataFrame:

df = pd.DataFrame({'age':[30, 2, 12, 4, 32, 33, 69],
                   'color':['blue', 'green', 'red', 'white', 'gray', 'black', 'red'],
                   'food':['Steak', 'Lamb', 'Mango', 'Apple', 'Cheese', 'Melon', 'Beans'],
                   'height':[165, 70, 120, 80, 180, 172, 150],
                   'score':[4.6, 8.3, 9.0, 3.3, 1.8, 9.5, 2.2],
                   'state':['NY', 'TX', 'FL', 'AL', 'AK', 'TX', 'TX']
                   },
                  index=['Jane', 'Nick', 'Aaron', 'Penelope', 'Dean', 'Christina', 'Cornelia'])

所有粗体字均为标签。标签,agecolorfoodheightscorestate被用于。其他标签,JaneNickAaronPenelopeDeanChristinaCornelia用作标签的行。这些行标签统称为index


在DataFrame中选择特定行的主要方式是使用.loc.iloc索引器。这些索引器中的每一个也可以用于同时选择列,但是现在只关注行比较容易。此外,每个索引器都使用紧跟其名称的一组括号进行选择。

.loc仅通过标签选择数据

我们将首先讨论.loc仅通过索引或列标签选择数据的索引器。在示例DataFrame中,我们提供了有意义的名称作为索引值。许多DataFrame都没有任何有意义的名称,而是默认为0到n-1之间的整数,其中n是DataFrame的长度(行数)。

您可以使用三种输入中的许多不同.loc,它们是

  • 一串
  • 字符串列表
  • 使用字符串作为起始值和终止值的切片符号

用带字符串的.loc选择单行

要选择单行数据,请将索引标签放在后面的括号内.loc

df.loc['Penelope']

这将数据行作为系列返回

age           4
color     white
food      Apple
height       80
score       3.3
state        AL
Name: Penelope, dtype: object

使用.loc与字符串列表选择多行

df.loc[['Cornelia', 'Jane', 'Dean']]

这将返回一个DataFrame,其中的数据行按列表中指定的顺序进行:

使用带有切片符号的.loc选择多行

切片符号由开始值,停止值和步长值定义。按标签切片时,大熊猫在返回值中包含停止值。以下是从亚伦到迪恩(含)的片段。它的步长未明确定义,但默认为1。

df.loc['Aaron':'Dean']

可以采用与Python列表相同的方式获取复杂的切片。

.iloc仅按整数位置选择数据

现在转到.iloc。DataFrame中数据的每一行和每一列都有一个定义它的整数位置。这是输出中直观显示的标签的补充。整数位置就是从0开始从顶部/左侧开始的行数/列数。

您可以使用三种输入中的许多不同.iloc,它们是

  • 一个整数
  • 整数列表
  • 使用整数作为起始值和终止值的切片符号

用带整数的.iloc选择单行

df.iloc[4]

这将返回第5行(整数位置4)为系列

age           32
color       gray
food      Cheese
height       180
score        1.8
state         AK
Name: Dean, dtype: object

用.iloc选择带有整数列表的多行

df.iloc[[2, -2]]

这将返回第三行和倒数第二行的DataFrame:

使用带切片符号的.iloc选择多行

df.iloc[:5:3]


使用.loc和.iloc同时选择行和列

两者的一项出色功能.loc/.iloc是它们可以同时选择行和列。在上面的示例中,所有列都是从每个选择中返回的。我们可以选择输入类型与行相同的列。我们只需要用逗号分隔行和列的选择即可。

例如,我们可以选择Jane行和Dean行,它们的高度,得分和状态如下:

df.loc[['Jane', 'Dean'], 'height':]

这对行使用标签列表,对列使用切片符号

我们自然可以.iloc只使用整数来执行类似的操作。

df.iloc[[1,4], 2]
Nick      Lamb
Dean    Cheese
Name: food, dtype: object

带标签和整数位置的同时选择

.ix用来与标签和整数位置同时进行选择,这很有用,但有时会造成混淆和模棱两可,值得庆幸的是,它已弃用。如果您需要混合使用标签和整数位置进行选择,则必须同时选择标签或整数位置。

例如,如果我们要选择行Nick以及第Cornelia2列和第4列,则可以.loc通过以下方式将整数转换为标签来使用:

col_names = df.columns[[2, 4]]
df.loc[['Nick', 'Cornelia'], col_names] 

或者,可以使用get_locindex方法将索引标签转换为整数。

labels = ['Nick', 'Cornelia']
index_ints = [df.index.get_loc(label) for label in labels]
df.iloc[index_ints, [2, 4]]

布尔选择

.loc索引器还可以进行布尔选择。例如,如果我们有兴趣查找年龄在30岁以上的所有行并仅返回foodscore列,则可以执行以下操作:

df.loc[df['age'] > 30, ['food', 'score']] 

您可以使用复制它,.iloc但是不能将其传递为布尔系列。您必须将boolean Series转换为numpy数组,如下所示:

df.iloc[(df['age'] > 30).values, [2, 4]] 

选择所有行

可以.loc/.iloc仅用于列选择。您可以使用冒号选择所有行,如下所示:

df.loc[:, 'color':'score':2]


索引运算符[]可以切片也可以选择行和列,但不能同时选择。

大多数人都熟悉DataFrame索引运算符的主要目的,即选择列。字符串选择单个列作为系列,字符串列表选择多个列作为DataFrame。

df['food']

Jane          Steak
Nick           Lamb
Aaron         Mango
Penelope      Apple
Dean         Cheese
Christina     Melon
Cornelia      Beans
Name: food, dtype: object

使用列表选择多个列

df[['food', 'score']]

人们所不熟悉的是,当使用切片符号时,选择是通过行标签或整数位置进行的。这非常令人困惑,我几乎从未使用过,但是确实可以使用。

df['Penelope':'Christina'] # slice rows by label

df[2:6:2] # slice rows by integer location

.loc/.iloc选择行的明确性是高度首选的。单独的索引运算符无法同时选择行和列。

df[3:5, 'color']
TypeError: unhashable type: 'slice'

.at和选择.iat

选择与.at几乎相同,.loc但仅在DataFrame中选择一个“单元”。我们通常将此单元称为标量值。要使用.at,请将行标签和列标签都传递给它,并用逗号分隔。

df.at['Christina', 'color']
'black'

选择与.iat几乎相同,.iloc但仅选择一个标量值。您必须为行和列位置都传递一个整数

df.iat[2, 5]
'FL'

There are two primary ways that pandas makes selections from a DataFrame.

  • By Label
  • By Integer Location

The documentation uses the term position for referring to integer location. I do not like this terminology as I feel it is confusing. Integer location is more descriptive and is exactly what .iloc stands for. The key word here is INTEGER – you must use integers when selecting by integer location.

Before showing the summary let’s all make sure that …

.ix is deprecated and ambiguous and should never be used

There are three primary indexers for pandas. We have the indexing operator itself (the brackets []), .loc, and .iloc. Let’s summarize them:

  • [] – Primarily selects subsets of columns, but can select rows as well. Cannot simultaneously select rows and columns.
  • .loc – selects subsets of rows and columns by label only
  • .iloc – selects subsets of rows and columns by integer location only

I almost never use .at or .iat as they add no additional functionality and with just a small performance increase. I would discourage their use unless you have a very time-sensitive application. Regardless, we have their summary:

  • .at selects a single scalar value in the DataFrame by label only
  • .iat selects a single scalar value in the DataFrame by integer location only

In addition to selection by label and integer location, boolean selection also known as boolean indexing exists.


Examples explaining .loc, .iloc, boolean selection and .at and .iat are shown below

We will first focus on the differences between .loc and .iloc. Before we talk about the differences, it is important to understand that DataFrames have labels that help identify each column and each row. Let’s take a look at a sample DataFrame:

df = pd.DataFrame({'age':[30, 2, 12, 4, 32, 33, 69],
                   'color':['blue', 'green', 'red', 'white', 'gray', 'black', 'red'],
                   'food':['Steak', 'Lamb', 'Mango', 'Apple', 'Cheese', 'Melon', 'Beans'],
                   'height':[165, 70, 120, 80, 180, 172, 150],
                   'score':[4.6, 8.3, 9.0, 3.3, 1.8, 9.5, 2.2],
                   'state':['NY', 'TX', 'FL', 'AL', 'AK', 'TX', 'TX']
                   },
                  index=['Jane', 'Nick', 'Aaron', 'Penelope', 'Dean', 'Christina', 'Cornelia'])

All the words in bold are the labels. The labels, age, color, food, height, score and state are used for the columns. The other labels, Jane, Nick, Aaron, Penelope, Dean, Christina, Cornelia are used as labels for the rows. Collectively, these row labels are known as the index.


The primary ways to select particular rows in a DataFrame are with the .loc and .iloc indexers. Each of these indexers can also be used to simultaneously select columns but it is easier to just focus on rows for now. Also, each of the indexers use a set of brackets that immediately follow their name to make their selections.

.loc selects data only by labels

We will first talk about the .loc indexer which only selects data by the index or column labels. In our sample DataFrame, we have provided meaningful names as values for the index. Many DataFrames will not have any meaningful names and will instead, default to just the integers from 0 to n-1, where n is the length(number of rows) of the DataFrame.

There are many different inputs you can use for .loc three out of them are

  • A string
  • A list of strings
  • Slice notation using strings as the start and stop values

Selecting a single row with .loc with a string

To select a single row of data, place the index label inside of the brackets following .loc.

df.loc['Penelope']

This returns the row of data as a Series

age           4
color     white
food      Apple
height       80
score       3.3
state        AL
Name: Penelope, dtype: object

Selecting multiple rows with .loc with a list of strings

df.loc[['Cornelia', 'Jane', 'Dean']]

This returns a DataFrame with the rows in the order specified in the list:

Selecting multiple rows with .loc with slice notation

Slice notation is defined by a start, stop and step values. When slicing by label, pandas includes the stop value in the return. The following slices from Aaron to Dean, inclusive. Its step size is not explicitly defined but defaulted to 1.

df.loc['Aaron':'Dean']

Complex slices can be taken in the same manner as Python lists.

.iloc selects data only by integer location

Let’s now turn to .iloc. Every row and column of data in a DataFrame has an integer location that defines it. This is in addition to the label that is visually displayed in the output. The integer location is simply the number of rows/columns from the top/left beginning at 0.

There are many different inputs you can use for .iloc three out of them are

  • An integer
  • A list of integers
  • Slice notation using integers as the start and stop values

Selecting a single row with .iloc with an integer

df.iloc[4]

This returns the 5th row (integer location 4) as a Series

age           32
color       gray
food      Cheese
height       180
score        1.8
state         AK
Name: Dean, dtype: object

Selecting multiple rows with .iloc with a list of integers

df.iloc[[2, -2]]

This returns a DataFrame of the third and second to last rows:

Selecting multiple rows with .iloc with slice notation

df.iloc[:5:3]


Simultaneous selection of rows and columns with .loc and .iloc

One excellent ability of both .loc/.iloc is their ability to select both rows and columns simultaneously. In the examples above, all the columns were returned from each selection. We can choose columns with the same types of inputs as we do for rows. We simply need to separate the row and column selection with a comma.

For example, we can select rows Jane, and Dean with just the columns height, score and state like this:

df.loc[['Jane', 'Dean'], 'height':]

This uses a list of labels for the rows and slice notation for the columns

We can naturally do similar operations with .iloc using only integers.

df.iloc[[1,4], 2]
Nick      Lamb
Dean    Cheese
Name: food, dtype: object

Simultaneous selection with labels and integer location

.ix was used to make selections simultaneously with labels and integer location which was useful but confusing and ambiguous at times and thankfully it has been deprecated. In the event that you need to make a selection with a mix of labels and integer locations, you will have to make both your selections labels or integer locations.

For instance, if we want to select rows Nick and Cornelia along with columns 2 and 4, we could use .loc by converting the integers to labels with the following:

col_names = df.columns[[2, 4]]
df.loc[['Nick', 'Cornelia'], col_names] 

Or alternatively, convert the index labels to integers with the get_loc index method.

labels = ['Nick', 'Cornelia']
index_ints = [df.index.get_loc(label) for label in labels]
df.iloc[index_ints, [2, 4]]

Boolean Selection

The .loc indexer can also do boolean selection. For instance, if we are interested in finding all the rows where age is above 30 and return just the food and score columns we can do the following:

df.loc[df['age'] > 30, ['food', 'score']] 

You can replicate this with .iloc but you cannot pass it a boolean series. You must convert the boolean Series into a numpy array like this:

df.iloc[(df['age'] > 30).values, [2, 4]] 

Selecting all rows

It is possible to use .loc/.iloc for just column selection. You can select all the rows by using a colon like this:

df.loc[:, 'color':'score':2]


The indexing operator, [], can slice can select rows and columns too but not simultaneously.

Most people are familiar with the primary purpose of the DataFrame indexing operator, which is to select columns. A string selects a single column as a Series and a list of strings selects multiple columns as a DataFrame.

df['food']

Jane          Steak
Nick           Lamb
Aaron         Mango
Penelope      Apple
Dean         Cheese
Christina     Melon
Cornelia      Beans
Name: food, dtype: object

Using a list selects multiple columns

df[['food', 'score']]

What people are less familiar with, is that, when slice notation is used, then selection happens by row labels or by integer location. This is very confusing and something that I almost never use but it does work.

df['Penelope':'Christina'] # slice rows by label

df[2:6:2] # slice rows by integer location

The explicitness of .loc/.iloc for selecting rows is highly preferred. The indexing operator alone is unable to select rows and columns simultaneously.

df[3:5, 'color']
TypeError: unhashable type: 'slice'

Selection by .at and .iat

Selection with .at is nearly identical to .loc but it only selects a single ‘cell’ in your DataFrame. We usually refer to this cell as a scalar value. To use .at, pass it both a row and column label separated by a comma.

df.at['Christina', 'color']
'black'

Selection with .iat is nearly identical to .iloc but it only selects a single scalar value. You must pass it an integer for both the row and column locations

df.iat[2, 5]
'FL'

回答 3

df = pd.DataFrame({'A':['a', 'b', 'c'], 'B':[54, 67, 89]}, index=[100, 200, 300])

df

                        A   B
                100     a   54
                200     b   67
                300     c   89
In [19]:    
df.loc[100]

Out[19]:
A     a
B    54
Name: 100, dtype: object

In [20]:    
df.iloc[0]

Out[20]:
A     a
B    54
Name: 100, dtype: object

In [24]:    
df2 = df.set_index([df.index,'A'])
df2

Out[24]:
        B
    A   
100 a   54
200 b   67
300 c   89

In [25]:    
df2.ix[100, 'a']

Out[25]:    
B    54
Name: (100, a), dtype: int64
df = pd.DataFrame({'A':['a', 'b', 'c'], 'B':[54, 67, 89]}, index=[100, 200, 300])

df

                        A   B
                100     a   54
                200     b   67
                300     c   89
In [19]:    
df.loc[100]

Out[19]:
A     a
B    54
Name: 100, dtype: object

In [20]:    
df.iloc[0]

Out[20]:
A     a
B    54
Name: 100, dtype: object

In [24]:    
df2 = df.set_index([df.index,'A'])
df2

Out[24]:
        B
    A   
100 a   54
200 b   67
300 c   89

In [25]:    
df2.ix[100, 'a']

Out[25]:    
B    54
Name: (100, a), dtype: int64

回答 4

让我们从这个小df开始:

import pandas as pd
import time as tm
import numpy as np
n=10
a=np.arange(0,n**2)
df=pd.DataFrame(a.reshape(n,n))

我们会这样

df
Out[25]: 
        0   1   2   3   4   5   6   7   8   9
    0   0   1   2   3   4   5   6   7   8   9
    1  10  11  12  13  14  15  16  17  18  19
    2  20  21  22  23  24  25  26  27  28  29
    3  30  31  32  33  34  35  36  37  38  39
    4  40  41  42  43  44  45  46  47  48  49
    5  50  51  52  53  54  55  56  57  58  59
    6  60  61  62  63  64  65  66  67  68  69
    7  70  71  72  73  74  75  76  77  78  79
    8  80  81  82  83  84  85  86  87  88  89
    9  90  91  92  93  94  95  96  97  98  99

有了这个我们有:

df.iloc[3,3]
Out[33]: 33

df.iat[3,3]
Out[34]: 33

df.iloc[:3,:3]
Out[35]: 
    0   1   2   3
0   0   1   2   3
1  10  11  12  13
2  20  21  22  23
3  30  31  32  33



df.iat[:3,:3]
Traceback (most recent call last):
   ... omissis ...
ValueError: At based indexing on an integer index can only have integer indexers

因此,我们不能将.iat用于子集,而只能在其中使用.iloc。

但是,让我们尝试从较大的df中进行选择,并检查速度…

# -*- coding: utf-8 -*-
"""
Created on Wed Feb  7 09:58:39 2018

@author: Fabio Pomi
"""

import pandas as pd
import time as tm
import numpy as np
n=1000
a=np.arange(0,n**2)
df=pd.DataFrame(a.reshape(n,n))
t1=tm.time()
for j in df.index:
    for i in df.columns:
        a=df.iloc[j,i]
t2=tm.time()
for j in df.index:
    for i in df.columns:
        a=df.iat[j,i]
t3=tm.time()
loc=t2-t1
at=t3-t2
prc = loc/at *100
print('\nloc:%f at:%f prc:%f' %(loc,at,prc))

loc:10.485600 at:7.395423 prc:141.784987

因此,使用.loc我们可以管理子集,并且仅使用单个标量即可使用.loc,但是.at比.loc更快

:-)

Let’s start with this small df:

import pandas as pd
import time as tm
import numpy as np
n=10
a=np.arange(0,n**2)
df=pd.DataFrame(a.reshape(n,n))

We’ll so have

df
Out[25]: 
        0   1   2   3   4   5   6   7   8   9
    0   0   1   2   3   4   5   6   7   8   9
    1  10  11  12  13  14  15  16  17  18  19
    2  20  21  22  23  24  25  26  27  28  29
    3  30  31  32  33  34  35  36  37  38  39
    4  40  41  42  43  44  45  46  47  48  49
    5  50  51  52  53  54  55  56  57  58  59
    6  60  61  62  63  64  65  66  67  68  69
    7  70  71  72  73  74  75  76  77  78  79
    8  80  81  82  83  84  85  86  87  88  89
    9  90  91  92  93  94  95  96  97  98  99

With this we have:

df.iloc[3,3]
Out[33]: 33

df.iat[3,3]
Out[34]: 33

df.iloc[:3,:3]
Out[35]: 
    0   1   2   3
0   0   1   2   3
1  10  11  12  13
2  20  21  22  23
3  30  31  32  33



df.iat[:3,:3]
Traceback (most recent call last):
   ... omissis ...
ValueError: At based indexing on an integer index can only have integer indexers

Thus we cannot use .iat for subset, where we must use .iloc only.

But let’s try both to select from a larger df and let’s check the speed …

# -*- coding: utf-8 -*-
"""
Created on Wed Feb  7 09:58:39 2018

@author: Fabio Pomi
"""

import pandas as pd
import time as tm
import numpy as np
n=1000
a=np.arange(0,n**2)
df=pd.DataFrame(a.reshape(n,n))
t1=tm.time()
for j in df.index:
    for i in df.columns:
        a=df.iloc[j,i]
t2=tm.time()
for j in df.index:
    for i in df.columns:
        a=df.iat[j,i]
t3=tm.time()
loc=t2-t1
at=t3-t2
prc = loc/at *100
print('\nloc:%f at:%f prc:%f' %(loc,at,prc))

loc:10.485600 at:7.395423 prc:141.784987

So with .loc we can manage subsets and with .at only a single scalar, but .at is faster than .loc

:-)


如何使用python执行curl命令

问题:如何使用python执行curl命令

我想在python中执行curl命令。

通常,我只需要在终端中输入命令并按回车键即可。但是,我不知道它如何在python中工作。

该命令显示如下:

curl -d @request.json --header "Content-Type: application/json" https://www.googleapis.com/qpxExpress/v1/trips/search?key=mykeyhere

有一个request.json文件要发送以获得响应。

我搜索了很多,感到困惑。尽管我无法完全理解,但我还是尝试编写了一段代码。没用

import pycurl
import StringIO

response = StringIO.StringIO()
c = pycurl.Curl()
c.setopt(c.URL, 'https://www.googleapis.com/qpxExpress/v1/trips/search?key=mykeyhere')
c.setopt(c.WRITEFUNCTION, response.write)
c.setopt(c.HTTPHEADER, ['Content-Type: application/json','Accept-Charset: UTF-8'])
c.setopt(c.POSTFIELDS, '@request.json')
c.perform()
c.close()
print response.getvalue()
response.close()

错误信息为“解析错误”。有人可以告诉我如何解决吗?或如何正确获取服务器的响应?

I want to execute a curl command in python.

Usually, I just need enter the command in terminal and press return key. However, I don’t know how it works in python.

The command shows below:

curl -d @request.json --header "Content-Type: application/json" https://www.googleapis.com/qpxExpress/v1/trips/search?key=mykeyhere

There is a request.json file to be sent to get response.

I searched a lot and got confused. I tried to write a piece of code, although I could not fully understand. It didn’t work.

import pycurl
import StringIO

response = StringIO.StringIO()
c = pycurl.Curl()
c.setopt(c.URL, 'https://www.googleapis.com/qpxExpress/v1/trips/search?key=mykeyhere')
c.setopt(c.WRITEFUNCTION, response.write)
c.setopt(c.HTTPHEADER, ['Content-Type: application/json','Accept-Charset: UTF-8'])
c.setopt(c.POSTFIELDS, '@request.json')
c.perform()
c.close()
print response.getvalue()
response.close()

The error message is ‘Parse Error’.Can anyone tell me how to fix it? or how to get response from the sever correctly?


回答 0

为了简单起见,也许您应该考虑使用Requests库。

带有json响应内容的示例如下所示:

import requests
r = requests.get('https://github.com/timeline.json')
r.json()

如果您需要更多信息,请在“ 快速入门”部分中找到许多可行的示例。

编辑:

对于您特定的curl翻译:

import requests
url = 'https://www.googleapis.com/qpxExpress/v1/trips/search?key=mykeyhere'
payload = open("request.json")
headers = {'content-type': 'application/json', 'Accept-Charset': 'UTF-8'}
r = requests.post(url, data=payload, headers=headers)

For sake of simplicity, maybe you should consider using the Requests library.

An example with json response content would be something like:

import requests
r = requests.get('https://github.com/timeline.json')
r.json()

If you look for further information, in the Quickstart section, they have lots of working examples.

EDIT:

For your specific curl translation:

import requests
url = 'https://www.googleapis.com/qpxExpress/v1/trips/search?key=mykeyhere'
payload = open("request.json")
headers = {'content-type': 'application/json', 'Accept-Charset': 'UTF-8'}
r = requests.post(url, data=payload, headers=headers)

回答 1

只要使用这个网站。它将任何curl命令转换为Python,Node.js,PHP,R或Go。

例:

curl -X POST -H 'Content-type: application/json' --data '{"text":"Hello, World!"}' https://hooks.slack.com/services/asdfasdfasdf

在Python中成为这个

import requests

headers = {
    'Content-type': 'application/json',
}

data = '{"text":"Hello, World!"}'

response = requests.post('https://hooks.slack.com/services/asdfasdfasdf', headers=headers, data=data)

Just use this website. It’ll convert any curl command into Python, Node.js, PHP, R, or Go.

Example:

curl -X POST -H 'Content-type: application/json' --data '{"text":"Hello, World!"}' https://hooks.slack.com/services/asdfasdfasdf

Becomes this in Python,

import requests

headers = {
    'Content-type': 'application/json',
}

data = '{"text":"Hello, World!"}'

response = requests.post('https://hooks.slack.com/services/asdfasdfasdf', headers=headers, data=data)

回答 2

import requests
url = "https://www.googleapis.com/qpxExpress/v1/trips/search?key=mykeyhere"
data = requests.get(url).json

也许?

如果您要发送文件

files = {'request_file': open('request.json', 'rb')}
r = requests.post(url, files=files)
print r.text, print r.json

啊,谢谢@LukasGraf现在,我更好地了解了他的原始代码在做什么

import requests,json
url = "https://www.googleapis.com/qpxExpress/v1/trips/search?key=mykeyhere"
my_json_data = json.load(open("request.json"))
req = requests.post(url,data=my_json_data)
print req.text
print 
print req.json # maybe? 
import requests
url = "https://www.googleapis.com/qpxExpress/v1/trips/search?key=mykeyhere"
data = requests.get(url).json

maybe?

if you are trying to send a file

files = {'request_file': open('request.json', 'rb')}
r = requests.post(url, files=files)
print r.text, print r.json

ahh thanks @LukasGraf now i better understand what his original code is doing

import requests,json
url = "https://www.googleapis.com/qpxExpress/v1/trips/search?key=mykeyhere"
my_json_data = json.load(open("request.json"))
req = requests.post(url,data=my_json_data)
print req.text
print 
print req.json # maybe? 

回答 3

curl -d @request.json --header "Content-Type: application/json" https://www.googleapis.com/qpxExpress/v1/trips/search?key=mykeyhere

它的python实现就像

import requests

headers = {
    'Content-Type': 'application/json',
}

params = (
    ('key', 'mykeyhere'),
)

data = open('request.json')
response = requests.post('https://www.googleapis.com/qpxExpress/v1/trips/search', headers=headers, params=params, data=data)

#NB. Original query string below. It seems impossible to parse and
#reproduce query strings 100% accurately so the one below is given
#in case the reproduced version is not "correct".
# response = requests.post('https://www.googleapis.com/qpxExpress/v1/trips/search?key=mykeyhere', headers=headers, data=data)

检查此链接,它将有助于将cURl命令转换为python,php和nodejs

curl -d @request.json --header "Content-Type: application/json" https://www.googleapis.com/qpxExpress/v1/trips/search?key=mykeyhere

its python implementation be like

import requests

headers = {
    'Content-Type': 'application/json',
}

params = (
    ('key', 'mykeyhere'),
)

data = open('request.json')
response = requests.post('https://www.googleapis.com/qpxExpress/v1/trips/search', headers=headers, params=params, data=data)

#NB. Original query string below. It seems impossible to parse and
#reproduce query strings 100% accurately so the one below is given
#in case the reproduced version is not "correct".
# response = requests.post('https://www.googleapis.com/qpxExpress/v1/trips/search?key=mykeyhere', headers=headers, data=data)

check this link, it will help convert cURl command to python,php and nodejs


回答 4

我的答案是WRT python 2.6.2。

import commands

status, output = commands.getstatusoutput("curl -H \"Content-Type:application/json\" -k -u (few other parameters required) -X GET https://example.org -s")

print output

对于未提供必需的参数,我深表歉意,因为这是机密信息。

My answer is WRT python 2.6.2.

import commands

status, output = commands.getstatusoutput("curl -H \"Content-Type:application/json\" -k -u (few other parameters required) -X GET https://example.org -s")

print output

I apologize for not providing the required parameters ‘coz it’s confidential.


回答 5

背景知识:我一直在寻找这个问题,因为我不得不做一些事情来检索内容,但是我所能得到的只是一个旧版本的python,它没有足够的SSL支持。如果您使用的是较旧的MacBook,那么您就会知道我在说什么。无论如何,都curl可以从Shell正常运行(我怀疑它已链接了现代SSL支持),因此有时您想要在不使用requests或的情况下执行此操作urllib2

您可以使用该subprocess模块执行curl并获取检索到的内容:

import subprocess

// 'response' contains a []byte with the retrieved content.
// use '-s' to keep curl quiet while it does its job, but
// it's useful to omit that while you're still writing code
// so you know if curl is working
response = subprocess.check_output(['curl', '-s', baseURL % page_num])

Python 3的subprocess模块还包含.run()许多有用的选项。我将其留给实际上正在运行python 3的人提供该答案。

Some background: I went looking for exactly this question because I had to do something to retrieve content, but all I had available was an old version of python with inadequate SSL support. If you’re on an older MacBook, you know what I’m talking about. In any case, curl runs fine from a shell (I suspect it has modern SSL support linked in) so sometimes you want to do this without using requests or urllib2.

You can use the subprocess module to execute curl and get at the retrieved content:

import subprocess

// 'response' contains a []byte with the retrieved content.
// use '-s' to keep curl quiet while it does its job, but
// it's useful to omit that while you're still writing code
// so you know if curl is working
response = subprocess.check_output(['curl', '-s', baseURL % page_num])

Python 3’s subprocess module also contains .run() with a number of useful options. I’ll leave it to someone who is actually running python 3 to provide that answer.


回答 6

这可以通过下面提到的伪代码方法来实现

Import os导入请求Data = os.execute(curl URL)R = Data.json()

This could be achieve with the below mentioned psuedo code approach

Import os import requests Data = os.execute(curl URL) R= Data.json()


如何将Seaborn图保存到文件中

问题:如何将Seaborn图保存到文件中

我尝试了以下代码(test_seaborn.py):

import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
matplotlib.style.use('ggplot')
import seaborn as sns
sns.set()
df = sns.load_dataset('iris')
sns_plot = sns.pairplot(df, hue='species', size=2.5)
fig = sns_plot.get_figure()
fig.savefig("output.png")
#sns.plt.show()

但是我得到这个错误:

  Traceback (most recent call last):
  File "test_searborn.py", line 11, in <module>
    fig = sns_plot.get_figure()
AttributeError: 'PairGrid' object has no attribute 'get_figure'

我希望决赛output.png将存在,看起来像这样:

我该如何解决该问题?

I tried the following code (test_seaborn.py):

import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
matplotlib.style.use('ggplot')
import seaborn as sns
sns.set()
df = sns.load_dataset('iris')
sns_plot = sns.pairplot(df, hue='species', size=2.5)
fig = sns_plot.get_figure()
fig.savefig("output.png")
#sns.plt.show()

But I get this error:

  Traceback (most recent call last):
  File "test_searborn.py", line 11, in <module>
    fig = sns_plot.get_figure()
AttributeError: 'PairGrid' object has no attribute 'get_figure'

I expect the final output.png will exist and look like this:

How can I resolve the problem?


回答 0

删除get_figure并使用sns_plot.savefig('output.png')

df = sns.load_dataset('iris')
sns_plot = sns.pairplot(df, hue='species', size=2.5)
sns_plot.savefig("output.png")

Remove the get_figure and just use sns_plot.savefig('output.png')

df = sns.load_dataset('iris')
sns_plot = sns.pairplot(df, hue='species', size=2.5)
sns_plot.savefig("output.png")

回答 1

建议的解决方案与Seaborn 0.8.1不兼容

由于Seaborn界面已更改,因此出现以下错误:

AttributeError: 'AxesSubplot' object has no attribute 'fig'
When trying to access the figure

AttributeError: 'AxesSubplot' object has no attribute 'savefig'
when trying to use the savefig directly as a function

以下调用允许您访问该图(与Seaborn 0.8.1兼容):

swarm_plot = sns.swarmplot(...)
fig = swarm_plot.get_figure()
fig.savefig(...) 

如先前在此答案中所见。

更新: 我最近使用了seaborn的PairGrid对象生成了一个类似于本示例中的图。在这种情况下,由于GridPlot不是像sns.swarmplot这样的绘图对象,因此它没有get_figure()函数。可以通过以下方式直接访问matplotlib图

fig = myGridPlotObject.fig

就像之前在该主题的其他文章中建议的那样。

The suggested solutions are incompatible with Seaborn 0.8.1

giving the following errors because the Seaborn interface has changed:

AttributeError: 'AxesSubplot' object has no attribute 'fig'
When trying to access the figure

AttributeError: 'AxesSubplot' object has no attribute 'savefig'
when trying to use the savefig directly as a function

The following calls allow you to access the figure (Seaborn 0.8.1 compatible):

swarm_plot = sns.swarmplot(...)
fig = swarm_plot.get_figure()
fig.savefig(...) 

as seen previously in this answer.

UPDATE: I have recently used PairGrid object from seaborn to generate a plot similar to the one in this example. In this case, since GridPlot is not a plot object like, for example, sns.swarmplot, it has no get_figure() function. It is possible to directly access the matplotlib figure by

fig = myGridPlotObject.fig

Like previously suggested in other posts in this thread.


回答 2

上述某些解决方案对我不起作用。.fig尝试该属性时未找到该属性,因此无法.savefig()直接使用。但是,起作用的是:

sns_plot.figure.savefig("output.png")

我是Python新用户,所以我不知道这是否是由于更新引起的。我想提一下,以防其他人遇到和我一样的问题。

Some of the above solutions did not work for me. The .fig attribute was not found when I tried that and I was unable to use .savefig() directly. However, what did work was:

sns_plot.figure.savefig("output.png")

I am a newer Python user, so I do not know if this is due to an update. I wanted to mention it in case anybody else runs into the same issues as I did.


回答 3

您应该只能够直接使用savefig方法sns_plot

sns_plot.savefig("output.png")

为了使您的代码更加清晰,如果您确实要访问sns_plot驻留在其中的matplotlib图形,则可以直接通过

fig = sns_plot.fig

在这种情况下get_figure,您的代码将假定没有方法。

You should just be able to use the savefig method of sns_plot directly.

sns_plot.savefig("output.png")

For clarity with your code if you did want to access the matplotlib figure that sns_plot resides in then you can get it directly with

fig = sns_plot.fig

In this case there is no get_figure method as your code assumes.


回答 4

我使用distplotget_figure成功保存了图片。

sns_hist = sns.distplot(df_train['SalePrice'])
fig = sns_hist.get_figure()
fig.savefig('hist.png')

I use distplot and get_figure to save picture successfully.

sns_hist = sns.distplot(df_train['SalePrice'])
fig = sns_hist.get_figure()
fig.savefig('hist.png')

回答 5

2019年搜索者的台词更少:

import matplotlib.pyplot as plt
import seaborn as sns

df = sns.load_dataset('iris')
sns_plot = sns.pairplot(df, hue='species', height=2.5)
plt.savefig('output.png')

更新说明:size已更改为height

Fewer lines for 2019 searchers:

import matplotlib.pyplot as plt
import seaborn as sns

df = sns.load_dataset('iris')
sns_plot = sns.pairplot(df, hue='species', height=2.5)
plt.savefig('output.png')

UPDATE NOTE: size was changed to height.


回答 6

这对我有用

import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline

sns.factorplot(x='holiday',data=data,kind='count',size=5,aspect=1)
plt.savefig('holiday-vs-count.png')

This works for me

import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline

sns.factorplot(x='holiday',data=data,kind='count',size=5,aspect=1)
plt.savefig('holiday-vs-count.png')

回答 7

也可以只创建一个matplotlib figure对象,然后使用plt.savefig(...)

from matplotlib import pyplot as plt
import seaborn as sns
import pandas as pd

df = sns.load_dataset('iris')
plt.figure() # Push new figure on stack
sns_plot = sns.pairplot(df, hue='species', size=2.5)
plt.savefig('output.png') # Save that figure

Its also possible to just create a matplotlib figure object and then use plt.savefig(...):

from matplotlib import pyplot as plt
import seaborn as sns
import pandas as pd

df = sns.load_dataset('iris')
plt.figure() # Push new figure on stack
sns_plot = sns.pairplot(df, hue='species', size=2.5)
plt.savefig('output.png') # Save that figure

回答 8

sns.figure.savefig("output.png")在seaborn 0.8.1中使用会出错。

而是使用:

import seaborn as sns

df = sns.load_dataset('iris')
sns_plot = sns.pairplot(df, hue='species', size=2.5)
sns_plot.savefig("output.png")

You would get an error for using sns.figure.savefig("output.png") in seaborn 0.8.1.

Instead use:

import seaborn as sns

df = sns.load_dataset('iris')
sns_plot = sns.pairplot(df, hue='species', size=2.5)
sns_plot.savefig("output.png")

回答 9

仅供参考,下面的命令在seaborn 0.8.1中起作用,因此我想最初的答案仍然有效。

sns_plot = sns.pairplot(data, hue='species', size=3)
sns_plot.savefig("output.png")

Just FYI, the below command worked in seaborn 0.8.1 so I guess the initial answer is still valid.

sns_plot = sns.pairplot(data, hue='species', size=3)
sns_plot.savefig("output.png")