We Keep Coding

What is imported from an Access database for empty boxes? [duplicate]

How do I refer to the null object in Python?

In Python, the 'null' object is the singleton None.
To check if something is None, use the is identity operator:
if foo is None:
...

None, Python's null?
There's no null in Python; instead there's None. As stated already, the most accurate way to test that something has been given None as a value is to use the is identity operator, which tests that two variables refer to the same object.
>>> foo is None
True
>>> foo = 'bar'
>>> foo is None
False
The basics
There is and can only be one None
None is the sole instance of the class NoneType and any further attempts at instantiating that class will return the same object, which makes None a singleton. Newcomers to Python often see error messages that mention NoneType and wonder what it is. It's my personal opinion that these messages could simply just mention None by name because, as we'll see shortly, None leaves little room to ambiguity. So if you see some TypeError message that mentions that NoneType can't do this or can't do that, just know that it's simply the one None that was being used in a way that it can't.
Also, None is a built-in constant. As soon as you start Python, it's available to use from everywhere, whether in module, class, or function. NoneType by contrast is not, you'd need to get a reference to it first by querying None for its class.
>>> NoneType
NameError: name 'NoneType' is not defined
>>> type(None)
NoneType
You can check None's uniqueness with Python's identity function id(). It returns the unique number assigned to an object, each object has one. If the id of two variables is the same, then they point in fact to the same object.
>>> NoneType = type(None)
>>> id(None)
10748000
>>> my_none = NoneType()
>>> id(my_none)
10748000
>>> another_none = NoneType()
>>> id(another_none)
10748000
>>> def function_that_does_nothing(): pass
>>> return_value = function_that_does_nothing()
>>> id(return_value)
10748000
None cannot be overwritten
In much older versions of Python (before 2.4) it was possible to reassign None, but not any more. Not even as a class attribute or in the confines of a function.
# In Python 2.7
>>> class SomeClass(object):
... def my_fnc(self):
... self.None = 'foo'
SyntaxError: cannot assign to None
>>> def my_fnc():
None = 'foo'
SyntaxError: cannot assign to None
# In Python 3.5
>>> class SomeClass:
... def my_fnc(self):
... self.None = 'foo'
SyntaxError: invalid syntax
>>> def my_fnc():
None = 'foo'
SyntaxError: cannot assign to keyword
It's therefore safe to assume that all None references are the same. There isn't any "custom" None.
To test for None use the is operator
When writing code you might be tempted to test for Noneness like this:
if value==None:
pass
Or to test for falsehood like this
if not value:
pass
You need to understand the implications and why it's often a good idea to be explicit.
Case 1: testing if a value is None
Why do
value is None
rather than
value==None
?
The first is equivalent to:
id(value)==id(None)
Whereas the expression value==None is in fact applied like this
value.__eq__(None)
If the value really is None then you'll get what you expected.
>>> nothing = function_that_does_nothing()
>>> nothing.__eq__(None)
True
In most common cases the outcome will be the same, but the __eq__() method opens a door that voids any guarantee of accuracy, since it can be overridden in a class to provide special behavior.
Consider this class.
>>> class Empty(object):
... def __eq__(self, other):
... return not other
So you try it on None and it works
>>> empty = Empty()
>>> empty==None
True
But then it also works on the empty string
>>> empty==''
True
And yet
>>> ''==None
False
>>> empty is None
False
Case 2: Using None as a boolean
The following two tests
if value:
# Do something
if not value:
# Do something
are in fact evaluated as
if bool(value):
# Do something
if not bool(value):
# Do something
None is a "falsey", meaning that if cast to a boolean it will return False and if applied the not operator it will return True. Note however that it's not a property unique to None. In addition to False itself, the property is shared by empty lists, tuples, sets, dicts, strings, as well as 0, and all objects from classes that implement the __bool__() magic method to return False.
>>> bool(None)
False
>>> not None
True
>>> bool([])
False
>>> not []
True
>>> class MyFalsey(object):
... def __bool__(self):
... return False
>>> f = MyFalsey()
>>> bool(f)
False
>>> not f
True
So when testing for variables in the following way, be extra aware of what you're including or excluding from the test:
def some_function(value=None):
if not value:
value = init_value()
In the above, did you mean to call init_value() when the value is set specifically to None, or did you mean that a value set to 0, or the empty string, or an empty list should also trigger the initialization? Like I said, be mindful. As it's often the case, in Python explicit is better than implicit.
None in practice
None used as a signal value
None has a special status in Python. It's a favorite baseline value because many algorithms treat it as an exceptional value. In such scenarios it can be used as a flag to signal that a condition requires some special handling (such as the setting of a default value).
You can assign None to the keyword arguments of a function and then explicitly test for it.
def my_function(value, param=None):
if param is None:
# Do something outrageous!
You can return it as the default when trying to get to an object's attribute and then explicitly test for it before doing something special.
value = getattr(some_obj, 'some_attribute', None)
if value is None:
# do something spectacular!
By default a dictionary's get() method returns None when trying to access a non-existing key:
>>> some_dict = {}
>>> value = some_dict.get('foo')
>>> value is None
True
If you were to try to access it by using the subscript notation a KeyError would be raised
>>> value = some_dict['foo']
KeyError: 'foo'
Likewise if you attempt to pop a non-existing item
>>> value = some_dict.pop('foo')
KeyError: 'foo'
which you can suppress with a default value that is usually set to None
value = some_dict.pop('foo', None)
if value is None:
# Booom!
None used as both a flag and valid value
The above described uses of None apply when it is not considered a valid value, but more like a signal to do something special. There are situations however where it sometimes matters to know where None came from because even though it's used as a signal it could also be part of the data.
When you query an object for its attribute with getattr(some_obj, 'attribute_name', None) getting back None doesn't tell you if the attribute you were trying to access was set to None or if it was altogether absent from the object. The same situation when accessing a key from a dictionary, like some_dict.get('some_key'), you don't know if some_dict['some_key'] is missing or if it's just set to None. If you need that information, the usual way to handle this is to directly attempt accessing the attribute or key from within a try/except construct:
try:
# Equivalent to getattr() without specifying a default
# value = getattr(some_obj, 'some_attribute')
value = some_obj.some_attribute
# Now you handle `None` the data here
if value is None:
# Do something here because the attribute was set to None
except AttributeError:
# We're now handling the exceptional situation from here.
# We could assign None as a default value if required.
value = None
# In addition, since we now know that some_obj doesn't have the
# attribute 'some_attribute' we could do something about that.
log_something(some_obj)
Similarly with dict:
try:
value = some_dict['some_key']
if value is None:
# Do something here because 'some_key' is set to None
except KeyError:
# Set a default
value = None
# And do something because 'some_key' was missing
# from the dict.
log_something(some_dict)
The above two examples show how to handle object and dictionary cases. What about functions? The same thing, but we use the double asterisks keyword argument to that end:
def my_function(**kwargs):
try:
value = kwargs['some_key']
if value is None:
# Do something because 'some_key' is explicitly
# set to None
except KeyError:
# We assign the default
value = None
# And since it's not coming from the caller.
log_something('did not receive "some_key"')
None used only as a valid value
If you find that your code is littered with the above try/except pattern simply to differentiate between None flags and None data, then just use another test value. There's a pattern where a value that falls outside the set of valid values is inserted as part of the data in a data structure and is used to control and test special conditions (e.g. boundaries, state, etc.). Such a value is called a sentinel and it can be used the way None is used as a signal. It's trivial to create a sentinel in Python.
undefined = object()
The undefined object above is unique and doesn't do much of anything that might be of interest to a program, it's thus an excellent replacement for None as a flag. Some caveats apply, more about that after the code.
With function
def my_function(value, param1=undefined, param2=undefined):
if param1 is undefined:
# We know nothing was passed to it, not even None
log_something('param1 was missing')
param1 = None
if param2 is undefined:
# We got nothing here either
log_something('param2 was missing')
param2 = None
With dict
value = some_dict.get('some_key', undefined)
if value is None:
log_something("'some_key' was set to None")
if value is undefined:
# We know that the dict didn't have 'some_key'
log_something("'some_key' was not set at all")
value = None
With an object
value = getattr(obj, 'some_attribute', undefined)
if value is None:
log_something("'obj.some_attribute' was set to None")
if value is undefined:
# We know that there's no obj.some_attribute
log_something("no 'some_attribute' set on obj")
value = None
As I mentioned earlier, custom sentinels come with some caveats. First, they're not keywords like None, so Python doesn't protect them. You can overwrite your undefined above at any time, anywhere in the module it's defined, so be careful how you expose and use them. Next, the instance returned by object() is not a singleton. If you make that call 10 times you get 10 different objects. Finally, usage of a sentinel is highly idiosyncratic. A sentinel is specific to the library it's used in and as such its scope should generally be limited to the library's internals. It shouldn't "leak" out. External code should only become aware of it, if their purpose is to extend or supplement the library's API.

It's not called null as in other languages, but None. There is always only one instance of this object, so you can check for equivalence with x is None (identity comparison) instead of x == None, if you want.

In Python, to represent the absence of a value, you can use the None value (types.NoneType.None) for objects and "" (or len() == 0) for strings. Therefore:
if yourObject is None: # if yourObject == None:
...
if yourString == "": # if yourString.len() == 0:
...
Regarding the difference between "==" and "is", testing for object identity using "==" should be sufficient. However, since the operation "is" is defined as the object identity operation, it is probably more correct to use it, rather than "==". Not sure if there is even a speed difference.
Anyway, you can have a look at:
Python Built-in Constants doc page.
Python Truth Value Testing doc page.

The above answers only will result True for None, but there is such a thing as float('nan'). You could use pandas isnull:
>>> import pandas as pd
>>> pd.isnull(None)
True
>>> pd.isnull(float('nan'))
True
>>> pd.isnull('abc')
False
>>>
Or without pandas:
>>> a = float('nan')
>>> (a != a) or (a == None)
True
>>> a = None
>>> (a != a) or (a == None)
True
>>>
The reason this works is because float('nan') != float('nan'):
>>> float('nan') == float('nan')
False
>>> float('nan') != float('nan')
True
>>>

Use f string for getting this solved.
year=None
year_val= 'null' if year is None else str(year)
print(f'{year_val}')
null

Simple function to tackle "empty" element in Python:
Code:
def is_empty(element) -> bool:
"""
Function to check if input `element` is empty.
Other than some special exclusions and inclusions,
this function returns boolean result of Falsy check.
"""
if (isinstance(element, int) or isinstance(element, float)) and element == 0:
# Exclude 0 and 0.0 from the Falsy set.
return False
elif isinstance(element, str) and len(element.strip()) == 0:
# Include string with one or more empty space(s) into Falsy set.
return True
elif isinstance(element, bool):
# Exclude False from the Falsy set.
return False
else:
# Falsy check.
return False if element else True
Test:
print("Is empty?\n")
print('"" -> ', is_empty(""))
print('" " -> ', is_empty(" "))
print('"A" -> ', is_empty("A"))
print('"a" -> ', is_empty("a"))
print('"0" -> ', is_empty("0"))
print("0 -> ", is_empty(0))
print("0.0 -> ", is_empty(0.0))
print("[] -> ", is_empty([]))
print("{} -> ", is_empty({}))
print("() -> ", is_empty(()))
print("[1, 2] -> ", is_empty([1, 2]))
print("(3, 5) -> ", is_empty((3, 5)))
print('{"a": 1} -> ', is_empty({"a": 1}))
print("None -> ", is_empty(None))
print("True -> ", is_empty(True))
print("False -> ", is_empty(False))
print("NaN -> ", is_empty(float("nan")))
print("range(0) -> ", is_empty(range(0)))
Output:
Is empty?
"" -> True
" " -> True
"A" -> False
"a" -> False
"0" -> False
0 -> False
0.0 -> False
[] -> True
{} -> True
() -> True
[1, 2] -> False
(3, 5) -> False
{"a": 1} -> False
None -> True
True -> False
False -> False
NaN -> False
range(0) -> True

Per Truth value testing, 'None' directly tests as FALSE, so the simplest expression will suffice:
if not foo:

Null is a special object type like:
>>>type(None)
<class 'NoneType'>
You can check if an object is in class 'NoneType':
>>>variable = None
>>>variable is None
True
More information is available at Python Docs

Is it better to use 'elif' or consecutive 'if' statements alongside return statements?

This question is specifically regarding coding convention. I know that using if or elif in this case will produce the same results. Just wondering which is the "proper" way to construct this function:
With consecutive if:
def can_take(self, selectedCourse):
if selectedCourse.hasPassed():
return False
if selectedCourse.getPrereqs() != 'none':
for prereq in selectedCourse.getPrereqs():
if not self.courses[prereq].hasPassed():
return False
return True
With elif:
def can_take(self, selectedCourse):
if selectedCourse.hasPassed():
return False
elif selectedCourse.getPrereqs() != 'none':
for prereq in selectedCourse.getPrereqs():
if not self.courses[prereq].hasPassed():
return False
return True

If I had to choose between the two, I would probably use two if statements, but that's just a matter of personal preference.
If I had a third choice, I wouldn't have any return statements with Boolean literals. I would write a single return statement that uses and and or.
return (not selected.hasPassed()
and (selected.getPrereqs() == 'none'
or all(x.hasPassed()
for x in selected.getPrereqs()))
This is close to how you would describe this in English: you can take the class if you have not passed it, and if the class either has no prerequisites or if you have passed all the prerequisites.
As John Kugelman points out, if getPrereqs returned an empty list instead of 'none', you could further reduce this to
return (not selected.hasPassed()
or all(x.hasPassed()
for x in selected.getPrereqs())

I love the early return pattern:
Get invalid cases out of the way first, either simply exiting or raising exceptions as appropriate, put a blank line in there, then add the "real" body of the method. I find it easier to read.
Returning early keeps the nesting level down, which is great way to reduce cognitive load. I would take it one step further and flip the second if statement around so it too returns early:
def can_take(self, selectedCourse):
if selectedCourse.hasPassed():
return False
if selectedCourse.getPrereqs() == 'none':
return True
for prereq in selectedCourse.getPrereqs():
if not self.courses[prereq].hasPassed():
return False
return True
That said, some other improvements I would make:
Avoid stringly typed variables. Switch that 'none' to None.
But then, when a method returns a list don't return None when there are no results. Return an empty list. Then the caller can blindly iterate over the list without checking if it's None or empty.
def can_take(self, selectedCourse):
if selectedCourse.hasPassed():
return False
for prereq in selectedCourse.getPrereqs():
if not self.courses[prereq].hasPassed():
return False
return True
If you're comfortable with generator expressions you could even convert the loop into an all(...) call, removing the need for the final return True.
def can_take(self, selectedCourse):
if selectedCourse.hasPassed():
return False
return all(self.courses[prereq].hasPassed()
for prereq in selectedCourse.getPrereqs())
I like this because it's a more direct encoding of the question: "Has the student passed all of the prereqs?"

I think I prefer the first version. Why? When you have an if...elif...elif... thing with returns in each branch, there are two "competing" control structures: the if statement and the returns. Obviously, the returns will "win", so we might as well remove the elif stuff. Either that, or have just one return statement, which returns a value computed by a preceding if...elif...elif...else structure.

We use elif but please understand it depends on your problem statement.
Note: Please do not create a big ladder out of it as then it becomes difficult to maintain and debug the code.

What is the difference between None and boolean (True, False) in python's default argument values?

In function definitions, one can define a boolean default argument's values as argument=None or argument=False.
An example from pandas concat:
def concat(
objs,
axis=0,
join="outer",
join_axes=None,
ignore_index=False,
keys=None,
levels=None,
names=None,
verify_integrity=False,
sort=None,
copy=True,
):
While both usages can be found, why would one be using one over the other?
Is there any PEP on this?

True and False are specific bool values. Use default False when you have a bool field and you want the default to be False.Don't use False as a value for a non-bool field.
None is used as a generic placeholder when the value will be set later. It can be typed as Optional[T], which is equivalent to Union[T, None].
You might be thinking of None and False as similar because they're both "falsy" (bool(x) returns False), but the same is true of several other values, [] () {} 0 0.0, etc and we don't use them like None either.
In your example, True/False are used where the field takes a boolean value. None is used where the field takes an Optional[List]. (The exception is sort: Optional[bool], which is being used temporarily as an ad-hoc compatibility tool for a deprecated behavior.)

True and False are boolean values, so in this context None can make sense if you have a boolean variable that can be in an 'unknown' state. Strictly spoken, such a variable would not be boolean, but in reality, this can be quite handy.
For example:
# In the beginning, we simply don't know if it's true or not
is_cat_alive = None
# But later we will determine the status
is_cat_alive = check_cat_in(box)
Note that if you are using a boolean like this, then if is_cat_alive will be false when is_cat_alive is either False or None which makes sense but might not be what you want to know. So to explicitly check for a dead cat, you would have to use if is_cat_alive == False.

None is similar to a null value if you know anything about those. Someone might say it's equal to 0, but None means that it is nothing. Say age = None for example. age is a variable, but it is equal to nothing. False is an actual value. True and False can be used as an indicator such as
def name_check(name)
if name == "Brittany":
return True
else:
return False
Calling that function with "Brittany" as the name parameter would return True which could be used in other conditions as well. Hope this helped! Good luck!

Look at the following code:
my_string = ''
my_other_string = None
my_final_string = 'I am a string'
If you were using these within python in something like an if statement they would actually equal the following:
my_string = False
my_other_string = None
my_final_string = True
Hope that helps

how does python operator overloading works

I can understand that some langurage allows user to do some operator overloading. I know this in C++ area first. But c++ also has some restrictions on operator overloading and I think that's reasonable.
but when I come to python pandams library. I'm start to confused.
Take a look at my code at nbviewer.jupyter.org
complaints['Complaint Type'] == "Noise - Street/Sidewalk"
doesn't return a True or False.
This is crazy to me. Does anyone can help me to understand this?
in Python, can we overloading operator == so that it doesn't return a boolean?
If it is true for question 1, how can I wrote a simple code to demo this?
Some relevant results copied from the link:
>>> complaints['Complaint Type'] == "Noise - Street/Sidewalk"
0 True
1 False
2 False
3 False
4 False
...
111063 False
111064 False
111065 False
111066 True
111067 False
111068 False
Name: Complaint Type, Length: 111069, dtype: bool

You can overload operators if you create your own classes and add a __eq__ method to them.
class MyClass(object):
def __eq__(self, other):
# compare self with other, return whatever you need
This will be invoked whenever you compare your type with self == other. It is considered very normal to return a boolean from this function in python, so you might want to have a think about returning anything else if you want your code to make sense to other developers.
See the docs for python 2 on this here

Boolean identity == True vs is True

It is standard convention to use if foo is None rather than if foo == None to test if a value is specifically None.
If you want to determine whether a value is exactly True (not just a true-like value), is there any reason to use if foo == True rather than if foo is True? Does this vary between implementations such as CPython (2.x and 3.x), Jython, PyPy, etc.?
Example: say True is used as a singleton value that you want to differentiate from the value 'bar', or any other true-like value:
if foo is True: # vs foo == True
...
elif foo == 'bar':
...
Is there a case where using if foo is True would yield different results from if foo == True?
NOTE: I am aware of Python booleans - if x:, vs if x == True, vs if x is True. However, it only addresses whether if foo, if foo == True, or if foo is True should generally be used to determine whether foo has a true-like value.
UPDATE: According to PEP 285 § Specification:
The values False and True will be singletons, like None.

If you want to determine whether a value is exactly True (not just a true-like value), is there any reason to use if foo == True rather than if foo is True?
If you want to make sure that foo really is a boolean and of value True, use the is operator.
Otherwise, if the type of foo implements its own __eq__() that returns a true-ish value when comparing to True, you might end up with an unexpected result.
As a rule of thumb, you should always use is with the built-in constants True, False and None.
Does this vary between implementations such as CPython (2.x and 3.x), Jython, PyPy, etc.?
In theory, is will be faster than == since the latter must honor types' custom __eq__ implementations, while is can directly compare object identities (e.g., memory addresses).
I don't know the source code of the various Python implementations by heart, but I assume that most of them can optimize that by using some internal flags for the existence of magic methods, so I suspect that you won't notice the speed difference in practice.

Never use is True in combination with numpy (and derivatives such as pandas):
In[1]: import numpy as np
In[2]: a = np.array([1, 2]).any()
In[4]: a is True
Out[4]: False
In[5]: a == True
Out[5]: True
This was unexpected to me as:
In[3]: a
Out[3]: True
I guess the explanation is given by:
In[6]: type(a)
Out[6]: numpy.bool_

is there any reason to use if foo == True rather than if foo is True?"
>>> d = True
>>> d is True
True
>>> d = 1
>>> d is True
False
>>> d == True
True
>>> d = 2
>>> d == True
False
Note that bool is a subclass of int, and that True has the integer value 1. To answer your question, if you want to check that some variable "is exactly True", you have to use the identity operator is. But that's really not pythonic... May I ask what's your real use case - IOW : why do you want to make a difference between True, 1 or any 'truth' value ?

edit: regarding:
Is there a case where using if foo is True would yield different results from if foo == True?
there is a case, and it's this:
In [24]: 1 is True
Out[24]: False
In [25]: 1 == True
Out[25]: True
additionally, if you're looking to use a singleton as a sentinel value, you can just create an object:
sentinel_time = object()
def f(snth):
if snth is sentinel_time:
print 'got em!'
f(sentinel_time)
you don't want to use if var == True:, you really want if var:.
imagine you have a list. you don't care if a list is "True" or not, you just want to know whether or not it's empty. so...
l = ['snth']
if l:
print l
check out this post for what evaluates to False: Evaluation of boolean expressions in Python

Using foo is True instead of foo == True (or just foo) if is most of the time not what you want.
I have seen foo is True used for checking that the parameter foo really was a boolean.
It contradicts python's duck-typing philosophy (you should in general not check for types. A function acting differently with True than with other truthy values is counter-intuitive for a programmer who assumes duck-typing)
Even if you want to check for types, it is better to do it explicity like :
def myFunction(foo):
if not isinstance(foo, bool):
raise ValueError("foo should be a boolean")
>>> myFunction(1)
Exception: ValueError "foo should be a boolean"
For several reasons:
Bool is the only type where the is operator will be equivalent to isinstance(a, bool) and a. The reason for that is the fact that True and False are singletons. In other words, this works because of a poorly known feature of python (especially when some tutorials teach you that True and False are just aliases for 1 and 0).
If you use isinstance and the programmer was not aware that your function did not accept truthy-values, or if they are using numpy and forgot to cast their numpy-boolean to a python-boolean, they will know what they did wrong, and will be able to debug.
Compare with
def myFunction(foo):
if foo is True:
doSomething()
else:
doSomethingElse()
In this case, myFunction(1) not only does not raise an exception, but probably does the opposite of what it was expected to do. This makes for a hard to find bug in case someone was using a numpy boolean for example.
When should you use is True then ?
EDIT: this is bad practice, starting from 3.9, python raises a warning when you try to use is to compare with a literal. See # JayDadhania's comment below. In conclusion is should not be used to compare to literals, only to check the equality of memory address.
Just don't use it. If you need to check for type, use isinstance.
Old paragraph:
Basically, use it only as a shorthand for isinstance(foo, bool) and foo
The only case I see is when you explicitely want to check if a value is true, and you will also check if the value is another truthy value later on. Examples include:
if foo is True:
doSomething()
elif foo is False:
doSomethingElse()
elif foo is 1: #EDIT: raises a warning, use == 1 instead
doYetSomethingElse()
else:
doSomethingElseEntirely()

Here's a test that allows you to see the difference between the 3 forms of testing for True:
for test in ([], [1], 0, 1, 2):
print repr(test), 'T' if test else 'F', 'T' if test == True else 'F', 'T' if test is True else 'F'
[] F F F
[1] T F F
0 F F F
1 T T F
2 T F F
As you can see there are cases where all of them deliver different results.

Most of the time, you should not care about a detail like this. Either you already know that foo is a boolean (and you can thus use if foo), or you know that foo is something else (in which case there's no need to test). If you don't know the types of your variables, you may want to refactor your code.
But if you really need to be sure it is exactly True and nothing else, use is. Using == will give you 1 == True.