Purpose: to delete collection records / documents in the database. Suppose the collection name is my_collection.
Method used: delete_one () or delete_many ()
result = my_collection.delete_many ({"name": "Mr. Geek"}) print (result.deleted_count)
result = my_collection.delete_many ({}) Method 2: delete all documents using collection.remove ()
result = my_collection.remove ()
The best way to remove is to remove the collection so that the data indices are also removed, then create a new collection in that insert data.
db.my_collection.drop ()
First we insert the document into the collection, then we drop the documents as requested.
|
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OUTPUT (comment line denoted by #) Connected successfully !!! Data inserted with record ids #Data INSERT {'_id': ObjectId (' 5a02227c37b8552becf5ed2b'), 'name':' Mr.GfG', 'eid': 45,' location': 'noida'} {' _id': ObjectId ('5a0c734937b8551c1cd03349'),' name': 'Mr.Shaurya',' eid': 14, 'location':' delhi'} {'_id': ObjectId (' 5a0c734937b8551c1cd0334a'), 'name':' Mr.Coder ',' eid': 14, 'location':' gurugram'} # Mr.Coder is deleted {'_id': ObjectId (' 5a02227c37b8552becf5ed2b'), 'name':' Mr.GfG', 'eid': 45, 'location':' noida'} {'_id': ObjectId (' 5a0c734937b8551c1cd03349'), 'name':' Mr.Shaurya', 'eid': 14,' location': 'delhi'} I would like to know how to put a time delay in a Python script.
How do I delete a file or folder in Python?
>>> a=[1,2,3]
>>> a.remove(2)
>>> a
[1, 3]
>>> a=[1,2,3]
>>> del a[1]
>>> a
[1, 3]
>>> a= [1,2,3]
>>> a.pop(1)
2
>>> a
[1, 3]
>>>
Is there any difference between the above three methods to remove an element from a list?
I want to remove a value from a list if it exists in the list (which it may not).
a = [1, 2, 3, 4]
b = a.index(6)
del a[b]
print(a)
The above case (in which it does not exist) shows the following error:
Traceback (most recent call last):
File "D:zjm_codea.py", line 6, in <module>
b = a.index(6)
ValueError: list.index(x): x not in list
So I have to do this:
a = [1, 2, 3, 4]
try:
b = a.index(6)
del a[b]
except:
pass
print(a)
But is there not a simpler way to do this?
I am getting an "access is denied" error when I attempt to delete a folder that is not empty. I used the following command in my attempt: os.remove("/folder_name").
What is the most effective way of removing/deleting a folder/directory that is not empty?
I think what I want to do is a fairly common task but I"ve found no reference on the web. I have text with punctuation, and I want a list of the words.
"Hey, you - what are you doing here!?"
should be
["hey", "you", "what", "are", "you", "doing", "here"]
But Python"s str.split() only works with one argument, so I have all words with the punctuation after I split with whitespace. Any ideas?
I found some answers online, but I have no experience with regular expressions, which I believe is what is needed here.
I have a string that needs to be split by either a ";" or ", " That is, it has to be either a semicolon or a comma followed by a space. Individual commas without trailing spaces should be left untouched
Example string:
"b-staged divinylsiloxane-bis-benzocyclobutene [124221-30-3], mesitylene [000108-67-8]; polymerized 1,2-dihydro-2,2,4- trimethyl quinoline [026780-96-1]"
should be split into a list containing the following:
("b-staged divinylsiloxane-bis-benzocyclobutene [124221-30-3]" , "mesitylene [000108-67-8]", "polymerized 1,2-dihydro-2,2,4- trimethyl quinoline [026780-96-1]")
After you train a model in Tensorflow:
How can I delete the contents of a local folder in Python?
The current project is for Windows, but I would like to see *nix also.
I created an environment with the following command: virtualenv venv --distribute
I cannot remove it with the following command: rmvirtualenv venv -
This is part of virtualenvwrapper as mentioned in answer below for virtualenvwrapper
I do an lson my current directory and I still see venv
The only way I can remove it seems to be: sudo rm -rf venv
Note that the environment is not active. I"m running Ubuntu 11.10. Any ideas? I"ve tried rebooting my system to no avail.
The Python 3 range() object doesn"t produce numbers immediately; it is a smart sequence object that produces numbers on demand. All it contains is your start, stop and step values, then as you iterate over the object the next integer is calculated each iteration.
The object also implements the object.__contains__ hook, and calculates if your number is part of its range. Calculating is a (near) constant time operation *. There is never a need to scan through all possible integers in the range.
From the range() object documentation:
The advantage of the
rangetype over a regularlistortupleis that a range object will always take the same (small) amount of memory, no matter the size of the range it represents (as it only stores thestart,stopandstepvalues, calculating individual items and subranges as needed).
So at a minimum, your range() object would do:
class my_range:
def __init__(self, start, stop=None, step=1, /):
if stop is None:
start, stop = 0, start
self.start, self.stop, self.step = start, stop, step
if step < 0:
lo, hi, step = stop, start, -step
else:
lo, hi = start, stop
self.length = 0 if lo > hi else ((hi - lo - 1) // step) + 1
def __iter__(self):
current = self.start
if self.step < 0:
while current > self.stop:
yield current
current += self.step
else:
while current < self.stop:
yield current
current += self.step
def __len__(self):
return self.length
def __getitem__(self, i):
if i < 0:
i += self.length
if 0 <= i < self.length:
return self.start + i * self.step
raise IndexError("my_range object index out of range")
def __contains__(self, num):
if self.step < 0:
if not (self.stop < num <= self.start):
return False
else:
if not (self.start <= num < self.stop):
return False
return (num - self.start) % self.step == 0
This is still missing several things that a real range() supports (such as the .index() or .count() methods, hashing, equality testing, or slicing), but should give you an idea.
I also simplified the __contains__ implementation to only focus on integer tests; if you give a real range() object a non-integer value (including subclasses of int), a slow scan is initiated to see if there is a match, just as if you use a containment test against a list of all the contained values. This was done to continue to support other numeric types that just happen to support equality testing with integers but are not expected to support integer arithmetic as well. See the original Python issue that implemented the containment test.
* Near constant time because Python integers are unbounded and so math operations also grow in time as N grows, making this a O(log N) operation. Since it’s all executed in optimised C code and Python stores integer values in 30-bit chunks, you’d run out of memory before you saw any performance impact due to the size of the integers involved here.
You have four main options for converting types in pandas:
to_numeric() - provides functionality to safely convert non-numeric types (e.g. strings) to a suitable numeric type. (See also to_datetime() and to_timedelta().)
astype() - convert (almost) any type to (almost) any other type (even if it"s not necessarily sensible to do so). Also allows you to convert to categorial types (very useful).
infer_objects() - a utility method to convert object columns holding Python objects to a pandas type if possible.
convert_dtypes() - convert DataFrame columns to the "best possible" dtype that supports pd.NA (pandas" object to indicate a missing value).
Read on for more detailed explanations and usage of each of these methods.
to_numeric()The best way to convert one or more columns of a DataFrame to numeric values is to use pandas.to_numeric().
This function will try to change non-numeric objects (such as strings) into integers or floating point numbers as appropriate.
The input to to_numeric() is a Series or a single column of a DataFrame.
>>> s = pd.Series(["8", 6, "7.5", 3, "0.9"]) # mixed string and numeric values
>>> s
0 8
1 6
2 7.5
3 3
4 0.9
dtype: object
>>> pd.to_numeric(s) # convert everything to float values
0 8.0
1 6.0
2 7.5
3 3.0
4 0.9
dtype: float64
As you can see, a new Series is returned. Remember to assign this output to a variable or column name to continue using it:
# convert Series
my_series = pd.to_numeric(my_series)
# convert column "a" of a DataFrame
df["a"] = pd.to_numeric(df["a"])
You can also use it to convert multiple columns of a DataFrame via the apply() method:
# convert all columns of DataFrame
df = df.apply(pd.to_numeric) # convert all columns of DataFrame
# convert just columns "a" and "b"
df[["a", "b"]] = df[["a", "b"]].apply(pd.to_numeric)
As long as your values can all be converted, that"s probably all you need.
But what if some values can"t be converted to a numeric type?
to_numeric() also takes an errors keyword argument that allows you to force non-numeric values to be NaN, or simply ignore columns containing these values.
Here"s an example using a Series of strings s which has the object dtype:
>>> s = pd.Series(["1", "2", "4.7", "pandas", "10"])
>>> s
0 1
1 2
2 4.7
3 pandas
4 10
dtype: object
The default behaviour is to raise if it can"t convert a value. In this case, it can"t cope with the string "pandas":
>>> pd.to_numeric(s) # or pd.to_numeric(s, errors="raise")
ValueError: Unable to parse string
Rather than fail, we might want "pandas" to be considered a missing/bad numeric value. We can coerce invalid values to NaN as follows using the errors keyword argument:
>>> pd.to_numeric(s, errors="coerce")
0 1.0
1 2.0
2 4.7
3 NaN
4 10.0
dtype: float64
The third option for errors is just to ignore the operation if an invalid value is encountered:
>>> pd.to_numeric(s, errors="ignore")
# the original Series is returned untouched
This last option is particularly useful when you want to convert your entire DataFrame, but don"t not know which of our columns can be converted reliably to a numeric type. In that case just write:
df.apply(pd.to_numeric, errors="ignore")
The function will be applied to each column of the DataFrame. Columns that can be converted to a numeric type will be converted, while columns that cannot (e.g. they contain non-digit strings or dates) will be left alone.
By default, conversion with to_numeric() will give you either a int64 or float64 dtype (or whatever integer width is native to your platform).
That"s usually what you want, but what if you wanted to save some memory and use a more compact dtype, like float32, or int8?
to_numeric() gives you the option to downcast to either "integer", "signed", "unsigned", "float". Here"s an example for a simple series s of integer type:
>>> s = pd.Series([1, 2, -7])
>>> s
0 1
1 2
2 -7
dtype: int64
Downcasting to "integer" uses the smallest possible integer that can hold the values:
>>> pd.to_numeric(s, downcast="integer")
0 1
1 2
2 -7
dtype: int8
Downcasting to "float" similarly picks a smaller than normal floating type:
>>> pd.to_numeric(s, downcast="float")
0 1.0
1 2.0
2 -7.0
dtype: float32
astype()The astype() method enables you to be explicit about the dtype you want your DataFrame or Series to have. It"s very versatile in that you can try and go from one type to the any other.
Just pick a type: you can use a NumPy dtype (e.g. np.int16), some Python types (e.g. bool), or pandas-specific types (like the categorical dtype).
Call the method on the object you want to convert and astype() will try and convert it for you:
# convert all DataFrame columns to the int64 dtype
df = df.astype(int)
# convert column "a" to int64 dtype and "b" to complex type
df = df.astype({"a": int, "b": complex})
# convert Series to float16 type
s = s.astype(np.float16)
# convert Series to Python strings
s = s.astype(str)
# convert Series to categorical type - see docs for more details
s = s.astype("category")
Notice I said "try" - if astype() does not know how to convert a value in the Series or DataFrame, it will raise an error. For example if you have a NaN or inf value you"ll get an error trying to convert it to an integer.
As of pandas 0.20.0, this error can be suppressed by passing errors="ignore". Your original object will be return untouched.
astype() is powerful, but it will sometimes convert values "incorrectly". For example:
>>> s = pd.Series([1, 2, -7])
>>> s
0 1
1 2
2 -7
dtype: int64
These are small integers, so how about converting to an unsigned 8-bit type to save memory?
>>> s.astype(np.uint8)
0 1
1 2
2 249
dtype: uint8
The conversion worked, but the -7 was wrapped round to become 249 (i.e. 28 - 7)!
Trying to downcast using pd.to_numeric(s, downcast="unsigned") instead could help prevent this error.
infer_objects()Version 0.21.0 of pandas introduced the method infer_objects() for converting columns of a DataFrame that have an object datatype to a more specific type (soft conversions).
For example, here"s a DataFrame with two columns of object type. One holds actual integers and the other holds strings representing integers:
>>> df = pd.DataFrame({"a": [7, 1, 5], "b": ["3","2","1"]}, dtype="object")
>>> df.dtypes
a object
b object
dtype: object
Using infer_objects(), you can change the type of column "a" to int64:
>>> df = df.infer_objects()
>>> df.dtypes
a int64
b object
dtype: object
Column "b" has been left alone since its values were strings, not integers. If you wanted to try and force the conversion of both columns to an integer type, you could use df.astype(int) instead.
convert_dtypes()Version 1.0 and above includes a method convert_dtypes() to convert Series and DataFrame columns to the best possible dtype that supports the pd.NA missing value.
Here "best possible" means the type most suited to hold the values. For example, this a pandas integer type if all of the values are integers (or missing values): an object column of Python integer objects is converted to Int64, a column of NumPy int32 values will become the pandas dtype Int32.
With our object DataFrame df, we get the following result:
>>> df.convert_dtypes().dtypes
a Int64
b string
dtype: object
Since column "a" held integer values, it was converted to the Int64 type (which is capable of holding missing values, unlike int64).
Column "b" contained string objects, so was changed to pandas" string dtype.
By default, this method will infer the type from object values in each column. We can change this by passing infer_objects=False:
>>> df.convert_dtypes(infer_objects=False).dtypes
a object
b string
dtype: object
Now column "a" remained an object column: pandas knows it can be described as an "integer" column (internally it ran infer_dtype) but didn"t infer exactly what dtype of integer it should have so did not convert it. Column "b" was again converted to "string" dtype as it was recognised as holding "string" values.
Since this question was asked in 2010, there has been real simplification in how to do simple multithreading with Python with map and pool.
The code below comes from an article/blog post that you should definitely check out (no affiliation) - Parallelism in one line: A Better Model for Day to Day Threading Tasks. I"ll summarize below - it ends up being just a few lines of code:
from multiprocessing.dummy import Pool as ThreadPool
pool = ThreadPool(4)
results = pool.map(my_function, my_array)
Which is the multithreaded version of:
results = []
for item in my_array:
results.append(my_function(item))
Description
Map is a cool little function, and the key to easily injecting parallelism into your Python code. For those unfamiliar, map is something lifted from functional languages like Lisp. It is a function which maps another function over a sequence.
Map handles the iteration over the sequence for us, applies the function, and stores all of the results in a handy list at the end.
Implementation
Parallel versions of the map function are provided by two libraries:multiprocessing, and also its little known, but equally fantastic step child:multiprocessing.dummy.
multiprocessing.dummy is exactly the same as multiprocessing module, but uses threads instead (an important distinction - use multiple processes for CPU-intensive tasks; threads for (and during) I/O):
multiprocessing.dummy replicates the API of multiprocessing, but is no more than a wrapper around the threading module.
import urllib2
from multiprocessing.dummy import Pool as ThreadPool
urls = [
"http://www.python.org",
"http://www.python.org/about/",
"http://www.onlamp.com/pub/a/python/2003/04/17/metaclasses.html",
"http://www.python.org/doc/",
"http://www.python.org/download/",
"http://www.python.org/getit/",
"http://www.python.org/community/",
"https://wiki.python.org/moin/",
]
# Make the Pool of workers
pool = ThreadPool(4)
# Open the URLs in their own threads
# and return the results
results = pool.map(urllib2.urlopen, urls)
# Close the pool and wait for the work to finish
pool.close()
pool.join()
And the timing results:
Single thread: 14.4 seconds
4 Pool: 3.1 seconds
8 Pool: 1.4 seconds
13 Pool: 1.3 seconds
Passing multiple arguments (works like this only in Python 3.3 and later):
To pass multiple arrays:
results = pool.starmap(function, zip(list_a, list_b))
Or to pass a constant and an array:
results = pool.starmap(function, zip(itertools.repeat(constant), list_a))
If you are using an earlier version of Python, you can pass multiple arguments via this workaround).
(Thanks to user136036 for the helpful comment.)
In Python, what is the purpose of
__slots__and what are the cases one should avoid this?
The special attribute __slots__ allows you to explicitly state which instance attributes you expect your object instances to have, with the expected results:
The space savings is from
__dict__.__dict__ and __weakref__ creation if parent classes deny them and you declare __slots__.Small caveat, you should only declare a particular slot one time in an inheritance tree. For example:
class Base:
__slots__ = "foo", "bar"
class Right(Base):
__slots__ = "baz",
class Wrong(Base):
__slots__ = "foo", "bar", "baz" # redundant foo and bar
Python doesn"t object when you get this wrong (it probably should), problems might not otherwise manifest, but your objects will take up more space than they otherwise should. Python 3.8:
>>> from sys import getsizeof
>>> getsizeof(Right()), getsizeof(Wrong())
(56, 72)
This is because the Base"s slot descriptor has a slot separate from the Wrong"s. This shouldn"t usually come up, but it could:
>>> w = Wrong()
>>> w.foo = "foo"
>>> Base.foo.__get__(w)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: foo
>>> Wrong.foo.__get__(w)
"foo"
The biggest caveat is for multiple inheritance - multiple "parent classes with nonempty slots" cannot be combined.
To accommodate this restriction, follow best practices: Factor out all but one or all parents" abstraction which their concrete class respectively and your new concrete class collectively will inherit from - giving the abstraction(s) empty slots (just like abstract base classes in the standard library).
See section on multiple inheritance below for an example.
To have attributes named in __slots__ to actually be stored in slots instead of a __dict__, a class must inherit from object (automatic in Python 3, but must be explicit in Python 2).
To prevent the creation of a __dict__, you must inherit from object and all classes in the inheritance must declare __slots__ and none of them can have a "__dict__" entry.
There are a lot of details if you wish to keep reading.
__slots__: Faster attribute access.The creator of Python, Guido van Rossum, states that he actually created __slots__ for faster attribute access.
It is trivial to demonstrate measurably significant faster access:
import timeit
class Foo(object): __slots__ = "foo",
class Bar(object): pass
slotted = Foo()
not_slotted = Bar()
def get_set_delete_fn(obj):
def get_set_delete():
obj.foo = "foo"
obj.foo
del obj.foo
return get_set_delete
and
>>> min(timeit.repeat(get_set_delete_fn(slotted)))
0.2846834529991611
>>> min(timeit.repeat(get_set_delete_fn(not_slotted)))
0.3664822799983085
The slotted access is almost 30% faster in Python 3.5 on Ubuntu.
>>> 0.3664822799983085 / 0.2846834529991611
1.2873325658284342
In Python 2 on Windows I have measured it about 15% faster.
__slots__: Memory SavingsAnother purpose of __slots__ is to reduce the space in memory that each object instance takes up.
My own contribution to the documentation clearly states the reasons behind this:
The space saved over using
__dict__can be significant.
SQLAlchemy attributes a lot of memory savings to __slots__.
To verify this, using the Anaconda distribution of Python 2.7 on Ubuntu Linux, with guppy.hpy (aka heapy) and sys.getsizeof, the size of a class instance without __slots__ declared, and nothing else, is 64 bytes. That does not include the __dict__. Thank you Python for lazy evaluation again, the __dict__ is apparently not called into existence until it is referenced, but classes without data are usually useless. When called into existence, the __dict__ attribute is a minimum of 280 bytes additionally.
In contrast, a class instance with __slots__ declared to be () (no data) is only 16 bytes, and 56 total bytes with one item in slots, 64 with two.
For 64 bit Python, I illustrate the memory consumption in bytes in Python 2.7 and 3.6, for __slots__ and __dict__ (no slots defined) for each point where the dict grows in 3.6 (except for 0, 1, and 2 attributes):
Python 2.7 Python 3.6
attrs __slots__ __dict__* __slots__ __dict__* | *(no slots defined)
none 16 56 + 272† 16 56 + 112† | †if __dict__ referenced
one 48 56 + 272 48 56 + 112
two 56 56 + 272 56 56 + 112
six 88 56 + 1040 88 56 + 152
11 128 56 + 1040 128 56 + 240
22 216 56 + 3344 216 56 + 408
43 384 56 + 3344 384 56 + 752
So, in spite of smaller dicts in Python 3, we see how nicely __slots__ scale for instances to save us memory, and that is a major reason you would want to use __slots__.
Just for completeness of my notes, note that there is a one-time cost per slot in the class"s namespace of 64 bytes in Python 2, and 72 bytes in Python 3, because slots use data descriptors like properties, called "members".
>>> Foo.foo
<member "foo" of "Foo" objects>
>>> type(Foo.foo)
<class "member_descriptor">
>>> getsizeof(Foo.foo)
72
__slots__:To deny the creation of a __dict__, you must subclass object. Everything subclasses object in Python 3, but in Python 2 you had to be explicit:
class Base(object):
__slots__ = ()
now:
>>> b = Base()
>>> b.a = "a"
Traceback (most recent call last):
File "<pyshell#38>", line 1, in <module>
b.a = "a"
AttributeError: "Base" object has no attribute "a"
Or subclass another class that defines __slots__
class Child(Base):
__slots__ = ("a",)
and now:
c = Child()
c.a = "a"
but:
>>> c.b = "b"
Traceback (most recent call last):
File "<pyshell#42>", line 1, in <module>
c.b = "b"
AttributeError: "Child" object has no attribute "b"
To allow __dict__ creation while subclassing slotted objects, just add "__dict__" to the __slots__ (note that slots are ordered, and you shouldn"t repeat slots that are already in parent classes):
class SlottedWithDict(Child):
__slots__ = ("__dict__", "b")
swd = SlottedWithDict()
swd.a = "a"
swd.b = "b"
swd.c = "c"
and
>>> swd.__dict__
{"c": "c"}
Or you don"t even need to declare __slots__ in your subclass, and you will still use slots from the parents, but not restrict the creation of a __dict__:
class NoSlots(Child): pass
ns = NoSlots()
ns.a = "a"
ns.b = "b"
And:
>>> ns.__dict__
{"b": "b"}
However, __slots__ may cause problems for multiple inheritance:
class BaseA(object):
__slots__ = ("a",)
class BaseB(object):
__slots__ = ("b",)
Because creating a child class from parents with both non-empty slots fails:
>>> class Child(BaseA, BaseB): __slots__ = ()
Traceback (most recent call last):
File "<pyshell#68>", line 1, in <module>
class Child(BaseA, BaseB): __slots__ = ()
TypeError: Error when calling the metaclass bases
multiple bases have instance lay-out conflict
If you run into this problem, You could just remove __slots__ from the parents, or if you have control of the parents, give them empty slots, or refactor to abstractions:
from abc import ABC
class AbstractA(ABC):
__slots__ = ()
class BaseA(AbstractA):
__slots__ = ("a",)
class AbstractB(ABC):
__slots__ = ()
class BaseB(AbstractB):
__slots__ = ("b",)
class Child(AbstractA, AbstractB):
__slots__ = ("a", "b")
c = Child() # no problem!
"__dict__" to __slots__ to get dynamic assignment:class Foo(object):
__slots__ = "bar", "baz", "__dict__"
and now:
>>> foo = Foo()
>>> foo.boink = "boink"
So with "__dict__" in slots we lose some of the size benefits with the upside of having dynamic assignment and still having slots for the names we do expect.
When you inherit from an object that isn"t slotted, you get the same sort of semantics when you use __slots__ - names that are in __slots__ point to slotted values, while any other values are put in the instance"s __dict__.
Avoiding __slots__ because you want to be able to add attributes on the fly is actually not a good reason - just add "__dict__" to your __slots__ if this is required.
You can similarly add __weakref__ to __slots__ explicitly if you need that feature.
The namedtuple builtin make immutable instances that are very lightweight (essentially, the size of tuples) but to get the benefits, you need to do it yourself if you subclass them:
from collections import namedtuple
class MyNT(namedtuple("MyNT", "bar baz")):
"""MyNT is an immutable and lightweight object"""
__slots__ = ()
usage:
>>> nt = MyNT("bar", "baz")
>>> nt.bar
"bar"
>>> nt.baz
"baz"
And trying to assign an unexpected attribute raises an AttributeError because we have prevented the creation of __dict__:
>>> nt.quux = "quux"
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: "MyNT" object has no attribute "quux"
You can allow __dict__ creation by leaving off __slots__ = (), but you can"t use non-empty __slots__ with subtypes of tuple.
Even when non-empty slots are the same for multiple parents, they cannot be used together:
class Foo(object):
__slots__ = "foo", "bar"
class Bar(object):
__slots__ = "foo", "bar" # alas, would work if empty, i.e. ()
>>> class Baz(Foo, Bar): pass
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: Error when calling the metaclass bases
multiple bases have instance lay-out conflict
Using an empty __slots__ in the parent seems to provide the most flexibility, allowing the child to choose to prevent or allow (by adding "__dict__" to get dynamic assignment, see section above) the creation of a __dict__:
class Foo(object): __slots__ = ()
class Bar(object): __slots__ = ()
class Baz(Foo, Bar): __slots__ = ("foo", "bar")
b = Baz()
b.foo, b.bar = "foo", "bar"
You don"t have to have slots - so if you add them, and remove them later, it shouldn"t cause any problems.
Going out on a limb here: If you"re composing mixins or using abstract base classes, which aren"t intended to be instantiated, an empty __slots__ in those parents seems to be the best way to go in terms of flexibility for subclassers.
To demonstrate, first, let"s create a class with code we"d like to use under multiple inheritance
class AbstractBase:
__slots__ = ()
def __init__(self, a, b):
self.a = a
self.b = b
def __repr__(self):
return f"{type(self).__name__}({repr(self.a)}, {repr(self.b)})"
We could use the above directly by inheriting and declaring the expected slots:
class Foo(AbstractBase):
__slots__ = "a", "b"
But we don"t care about that, that"s trivial single inheritance, we need another class we might also inherit from, maybe with a noisy attribute:
class AbstractBaseC:
__slots__ = ()
@property
def c(self):
print("getting c!")
return self._c
@c.setter
def c(self, arg):
print("setting c!")
self._c = arg
Now if both bases had nonempty slots, we couldn"t do the below. (In fact, if we wanted, we could have given AbstractBase nonempty slots a and b, and left them out of the below declaration - leaving them in would be wrong):
class Concretion(AbstractBase, AbstractBaseC):
__slots__ = "a b _c".split()
And now we have functionality from both via multiple inheritance, and can still deny __dict__ and __weakref__ instantiation:
>>> c = Concretion("a", "b")
>>> c.c = c
setting c!
>>> c.c
getting c!
Concretion("a", "b")
>>> c.d = "d"
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: "Concretion" object has no attribute "d"
__class__ assignment with another class that doesn"t have them (and you can"t add them) unless the slot layouts are identical. (I am very interested in learning who is doing this and why.)You may be able to tease out further caveats from the rest of the __slots__ documentation (the 3.7 dev docs are the most current), which I have made significant recent contributions to.
The current top answers cite outdated information and are quite hand-wavy and miss the mark in some important ways.
__slots__ when instantiating lots of objects"I quote:
"You would want to use
__slots__if you are going to instantiate a lot (hundreds, thousands) of objects of the same class."
Abstract Base Classes, for example, from the collections module, are not instantiated, yet __slots__ are declared for them.
Why?
If a user wishes to deny __dict__ or __weakref__ creation, those things must not be available in the parent classes.
__slots__ contributes to reusability when creating interfaces or mixins.
It is true that many Python users aren"t writing for reusability, but when you are, having the option to deny unnecessary space usage is valuable.
__slots__ doesn"t break picklingWhen pickling a slotted object, you may find it complains with a misleading TypeError:
>>> pickle.loads(pickle.dumps(f))
TypeError: a class that defines __slots__ without defining __getstate__ cannot be pickled
This is actually incorrect. This message comes from the oldest protocol, which is the default. You can select the latest protocol with the -1 argument. In Python 2.7 this would be 2 (which was introduced in 2.3), and in 3.6 it is 4.
>>> pickle.loads(pickle.dumps(f, -1))
<__main__.Foo object at 0x1129C770>
in Python 2.7:
>>> pickle.loads(pickle.dumps(f, 2))
<__main__.Foo object at 0x1129C770>
in Python 3.6
>>> pickle.loads(pickle.dumps(f, 4))
<__main__.Foo object at 0x1129C770>
So I would keep this in mind, as it is a solved problem.
The first paragraph is half short explanation, half predictive. Here"s the only part that actually answers the question
The proper use of
__slots__is to save space in objects. Instead of having a dynamic dict that allows adding attributes to objects at anytime, there is a static structure which does not allow additions after creation. This saves the overhead of one dict for every object that uses slots
The second half is wishful thinking, and off the mark:
While this is sometimes a useful optimization, it would be completely unnecessary if the Python interpreter was dynamic enough so that it would only require the dict when there actually were additions to the object.
Python actually does something similar to this, only creating the __dict__ when it is accessed, but creating lots of objects with no data is fairly ridiculous.
The second paragraph oversimplifies and misses actual reasons to avoid __slots__. The below is not a real reason to avoid slots (for actual reasons, see the rest of my answer above.):
They change the behavior of the objects that have slots in a way that can be abused by control freaks and static typing weenies.
It then goes on to discuss other ways of accomplishing that perverse goal with Python, not discussing anything to do with __slots__.
The third paragraph is more wishful thinking. Together it is mostly off-the-mark content that the answerer didn"t even author and contributes to ammunition for critics of the site.
Create some normal objects and slotted objects:
>>> class Foo(object): pass
>>> class Bar(object): __slots__ = ()
Instantiate a million of them:
>>> foos = [Foo() for f in xrange(1000000)]
>>> bars = [Bar() for b in xrange(1000000)]
Inspect with guppy.hpy().heap():
>>> guppy.hpy().heap()
Partition of a set of 2028259 objects. Total size = 99763360 bytes.
Index Count % Size % Cumulative % Kind (class / dict of class)
0 1000000 49 64000000 64 64000000 64 __main__.Foo
1 169 0 16281480 16 80281480 80 list
2 1000000 49 16000000 16 96281480 97 __main__.Bar
3 12284 1 987472 1 97268952 97 str
...
Access the regular objects and their __dict__ and inspect again:
>>> for f in foos:
... f.__dict__
>>> guppy.hpy().heap()
Partition of a set of 3028258 objects. Total size = 379763480 bytes.
Index Count % Size % Cumulative % Kind (class / dict of class)
0 1000000 33 280000000 74 280000000 74 dict of __main__.Foo
1 1000000 33 64000000 17 344000000 91 __main__.Foo
2 169 0 16281480 4 360281480 95 list
3 1000000 33 16000000 4 376281480 99 __main__.Bar
4 12284 0 987472 0 377268952 99 str
...
This is consistent with the history of Python, from Unifying types and classes in Python 2.2
If you subclass a built-in type, extra space is automatically added to the instances to accomodate
__dict__and__weakrefs__. (The__dict__is not initialized until you use it though, so you shouldn"t worry about the space occupied by an empty dictionary for each instance you create.) If you don"t need this extra space, you can add the phrase "__slots__ = []" to your class.
This is the behaviour to adopt when the referenced object is deleted. It is not specific to Django; this is an SQL standard. Although Django has its own implementation on top of SQL. (1)
There are seven possible actions to take when such event occurs:
CASCADE: When the referenced object is deleted, also delete the objects that have references to it (when you remove a blog post for instance, you might want to delete comments as well). SQL equivalent: CASCADE.PROTECT: Forbid the deletion of the referenced object. To delete it you will have to delete all objects that reference it manually. SQL equivalent: RESTRICT.RESTRICT: (introduced in Django 3.1) Similar behavior as PROTECT that matches SQL"s RESTRICT more accurately. (See django documentation example)SET_NULL: Set the reference to NULL (requires the field to be nullable). For instance, when you delete a User, you might want to keep the comments he posted on blog posts, but say it was posted by an anonymous (or deleted) user. SQL equivalent: SET NULL.SET_DEFAULT: Set the default value. SQL equivalent: SET DEFAULT.SET(...): Set a given value. This one is not part of the SQL standard and is entirely handled by Django.DO_NOTHING: Probably a very bad idea since this would create integrity issues in your database (referencing an object that actually doesn"t exist). SQL equivalent: NO ACTION. (2)Source: Django documentation
See also the documentation of PostgreSQL for instance.
In most cases, CASCADE is the expected behaviour, but for every ForeignKey, you should always ask yourself what is the expected behaviour in this situation. PROTECT and SET_NULL are often useful. Setting CASCADE where it should not, can potentially delete all of your database in cascade, by simply deleting a single user.
Additional note to clarify cascade direction
It"s funny to notice that the direction of the CASCADE action is not clear to many people. Actually, it"s funny to notice that only the CASCADE action is not clear. I understand the cascade behavior might be confusing, however you must think that it is the same direction as any other action. Thus, if you feel that CASCADE direction is not clear to you, it actually means that on_delete behavior is not clear to you.
In your database, a foreign key is basically represented by an integer field which value is the primary key of the foreign object. Let"s say you have an entry comment_A, which has a foreign key to an entry article_B. If you delete the entry comment_A, everything is fine. article_B used to live without comment_A and don"t bother if it"s deleted. However, if you delete article_B, then comment_A panics! It never lived without article_B and needs it, and it"s part of its attributes (article=article_B, but what is article_B???). This is where on_delete steps in, to determine how to resolve this integrity error, either by saying:
PROTECT or RESTRICT in Django/SQL)SET_NULL)CASCADE behavior).SET_DEFAULT, or even SET(...)).DO_NOTHING)I hope it makes cascade direction clearer. :)
Footnotes
(1) Django has its own implementation on top of SQL. And, as mentioned by @JoeMjr2 in the comments below, Django will not create the SQL constraints. If you want the constraints to be ensured by your database (for instance, if your database is used by another application, or if you hang in the database console from time to time), you might want to set the related constraints manually yourself. There is an open ticket to add support for database-level on delete constrains in Django.
(2) Actually, there is one case where
DO_NOTHINGcan be useful: If you want to skip Django"s implementation and implement the constraint yourself at the database-level.
TL;DR: If you are using Python 3.10 or later, it just works. As of today (2019), in 3.7+ you must turn this feature on using a future statement (from __future__ import annotations). In Python 3.6 or below, use a string.
I guess you got this exception:
NameError: name "Position" is not defined
This is because Position must be defined before you can use it in an annotation unless you are using Python 3.10 or later.
from __future__ import annotationsPython 3.7 introduces PEP 563: postponed evaluation of annotations. A module that uses the future statement from __future__ import annotations will store annotations as strings automatically:
from __future__ import annotations
class Position:
def __add__(self, other: Position) -> Position:
...
This is scheduled to become the default in Python 3.10. Since Python still is a dynamically typed language so no type checking is done at runtime, typing annotations should have no performance impact, right? Wrong! Before python 3.7 the typing module used to be one of the slowest python modules in core so if you import typing you will see up to 7 times increase in performance when you upgrade to 3.7.
According to PEP 484, you should use a string instead of the class itself:
class Position:
...
def __add__(self, other: "Position") -> "Position":
...
If you use the Django framework this may be familiar as Django models also use strings for forward references (foreign key definitions where the foreign model is self or is not declared yet). This should work with Pycharm and other tools.
The relevant parts of PEP 484 and PEP 563, to spare you the trip:
Forward references
When a type hint contains names that have not been defined yet, that definition may be expressed as a string literal, to be resolved later.
A situation where this occurs commonly is the definition of a container class, where the class being defined occurs in the signature of some of the methods. For example, the following code (the start of a simple binary tree implementation) does not work:
class Tree: def __init__(self, left: Tree, right: Tree): self.left = left self.right = rightTo address this, we write:
class Tree: def __init__(self, left: "Tree", right: "Tree"): self.left = left self.right = rightThe string literal should contain a valid Python expression (i.e., compile(lit, "", "eval") should be a valid code object) and it should evaluate without errors once the module has been fully loaded. The local and global namespace in which it is evaluated should be the same namespaces in which default arguments to the same function would be evaluated.
and PEP 563:
Implementation
In Python 3.10, function and variable annotations will no longer be evaluated at definition time. Instead, a string form will be preserved in the respective 
__annotations__ dictionary. Static type checkers will see no difference in behavior, whereas tools using annotations at runtime will have to perform postponed evaluation....
Enabling the future behavior in Python 3.7
The functionality described above can be enabled starting from Python 3.7 using the following special import:
from __future__ import annotations
PositionBefore the class definition, place a dummy definition:
class Position(object):
pass
class Position(object):
...
This will get rid of the NameError and may even look OK:
>>> Position.__add__.__annotations__
{"other": __main__.Position, "return": __main__.Position}
But is it?
>>> for k, v in Position.__add__.__annotations__.items():
... print(k, "is Position:", v is Position)
return is Position: False
other is Position: False
You may want to try some Python meta programming magic and write a decorator to monkey-patch the class definition in order to add annotations:
class Position:
...
def __add__(self, other):
return self.__class__(self.x + other.x, self.y + other.y)
The decorator should be responsible for the equivalent of this:
Position.__add__.__annotations__["return"] = Position
Position.__add__.__annotations__["other"] = Position
At least it seems right:
>>> for k, v in Position.__add__.__annotations__.items():
... print(k, "is Position:", v is Position)
return is Position: True
other is Position: True
Probably too much trouble.
Is there any reason for a class declaration to inherit from
object?
In Python 3, apart from compatibility between Python 2 and 3, no reason. In Python 2, many reasons.
In Python 2.x (from 2.2 onwards) there"s two styles of classes depending on the presence or absence of object as a base-class:
"classic" style classes: they don"t have object as a base class:
>>> class ClassicSpam: # no base class
... pass
>>> ClassicSpam.__bases__
()
"new" style classes: they have, directly or indirectly (e.g inherit from a built-in type), object as a base class:
>>> class NewSpam(object): # directly inherit from object
... pass
>>> NewSpam.__bases__
(<type "object">,)
>>> class IntSpam(int): # indirectly inherit from object...
... pass
>>> IntSpam.__bases__
(<type "int">,)
>>> IntSpam.__bases__[0].__bases__ # ... because int inherits from object
(<type "object">,)
Without a doubt, when writing a class you"ll always want to go for new-style classes. The perks of doing so are numerous, to list some of them:
Support for descriptors. Specifically, the following constructs are made possible with descriptors:
classmethod: A method that receives the class as an implicit argument instead of the instance.staticmethod: A method that does not receive the implicit argument self as a first argument.property: Create functions for managing the getting, setting and deleting of an attribute. __slots__: Saves memory consumptions of a class and also results in faster attribute access. Of course, it does impose limitations.The __new__ static method: lets you customize how new class instances are created.
Method resolution order (MRO): in what order the base classes of a class will be searched when trying to resolve which method to call.
Related to MRO, super calls. Also see, super() considered super.
If you don"t inherit from object, forget these. A more exhaustive description of the previous bullet points along with other perks of "new" style classes can be found here.
One of the downsides of new-style classes is that the class itself is more memory demanding. Unless you"re creating many class objects, though, I doubt this would be an issue and it"s a negative sinking in a sea of positives.
In Python 3, things are simplified. Only new-style classes exist (referred to plainly as classes) so, the only difference in adding object is requiring you to type in 8 more characters. This:
class ClassicSpam:
pass
is completely equivalent (apart from their name :-) to this:
class NewSpam(object):
pass
and to this:
class Spam():
pass
All have object in their __bases__.
>>> [object in cls.__bases__ for cls in {Spam, NewSpam, ClassicSpam}]
[True, True, True]
In Python 2: always inherit from object explicitly. Get the perks.
In Python 3: inherit from object if you are writing code that tries to be Python agnostic, that is, it needs to work both in Python 2 and in Python 3. Otherwise don"t, it really makes no difference since Python inserts it for you behind the scenes.
It must relate to the renaming and deprecation of cross_validation sub-module to model_selection. Try substituting cross_validation to model_selection
There are many ways to convert an instance to a dictionary, with varying degrees of corner case handling and closeness to the desired result.
instance.__dict__instance.__dict__
which returns
{"_foreign_key_cache": <OtherModel: OtherModel object>,
"_state": <django.db.models.base.ModelState at 0x7ff0993f6908>,
"auto_now_add": datetime.datetime(2018, 12, 20, 21, 34, 29, 494827, tzinfo=<UTC>),
"foreign_key_id": 2,
"id": 1,
"normal_value": 1,
"readonly_value": 2}
This is by far the simplest, but is missing many_to_many, foreign_key is misnamed, and it has two unwanted extra things in it.
model_to_dictfrom django.forms.models import model_to_dict
model_to_dict(instance)
which returns
{"foreign_key": 2,
"id": 1,
"many_to_many": [<OtherModel: OtherModel object>],
"normal_value": 1}
This is the only one with many_to_many, but is missing the uneditable fields.
model_to_dict(..., fields=...)from django.forms.models import model_to_dict
model_to_dict(instance, fields=[field.name for field in instance._meta.fields])
which returns
{"foreign_key": 2, "id": 1, "normal_value": 1}
This is strictly worse than the standard model_to_dict invocation.
query_set.values()SomeModel.objects.filter(id=instance.id).values()[0]
which returns
{"auto_now_add": datetime.datetime(2018, 12, 20, 21, 34, 29, 494827, tzinfo=<UTC>),
"foreign_key_id": 2,
"id": 1,
"normal_value": 1,
"readonly_value": 2}
This is the same output as instance.__dict__ but without the extra fields.
foreign_key_id is still wrong and many_to_many is still missing.
The code for django"s model_to_dict had most of the answer. It explicitly removed non-editable fields, so removing that check and getting the ids of foreign keys for many to many fields results in the following code which behaves as desired:
from itertools import chain
def to_dict(instance):
opts = instance._meta
data = {}
for f in chain(opts.concrete_fields, opts.private_fields):
data[f.name] = f.value_from_object(instance)
for f in opts.many_to_many:
data[f.name] = [i.id for i in f.value_from_object(instance)]
return data
While this is the most complicated option, calling to_dict(instance) gives us exactly the desired result:
{"auto_now_add": datetime.datetime(2018, 12, 20, 21, 34, 29, 494827, tzinfo=<UTC>),
"foreign_key": 2,
"id": 1,
"many_to_many": [2],
"normal_value": 1,
"readonly_value": 2}
Django Rest Framework"s ModelSerialzer allows you to build a serializer automatically from a model.
from rest_framework import serializers
class SomeModelSerializer(serializers.ModelSerializer):
class Meta:
model = SomeModel
fields = "__all__"
SomeModelSerializer(instance).data
returns
{"auto_now_add": "2018-12-20T21:34:29.494827Z",
"foreign_key": 2,
"id": 1,
"many_to_many": [2],
"normal_value": 1,
"readonly_value": 2}
This is almost as good as the custom function, but auto_now_add is a string instead of a datetime object.
If you want a django model that has a better python command-line display, have your models child-class the following:
from django.db import models
from itertools import chain
class PrintableModel(models.Model):
def __repr__(self):
return str(self.to_dict())
def to_dict(instance):
opts = instance._meta
data = {}
for f in chain(opts.concrete_fields, opts.private_fields):
data[f.name] = f.value_from_object(instance)
for f in opts.many_to_many:
data[f.name] = [i.id for i in f.value_from_object(instance)]
return data
class Meta:
abstract = True
So, for example, if we define our models as such:
class OtherModel(PrintableModel): pass
class SomeModel(PrintableModel):
normal_value = models.IntegerField()
readonly_value = models.IntegerField(editable=False)
auto_now_add = models.DateTimeField(auto_now_add=True)
foreign_key = models.ForeignKey(OtherModel, related_name="ref1")
many_to_many = models.ManyToManyField(OtherModel, related_name="ref2")
Calling SomeModel.objects.first() now gives output like this:
{"auto_now_add": datetime.datetime(2018, 12, 20, 21, 34, 29, 494827, tzinfo=<UTC>),
"foreign_key": 2,
"id": 1,
"many_to_many": [2],
"normal_value": 1,
"readonly_value": 2}
Basically, eval is used to evaluate a single dynamically generated Python expression, and exec is used to execute dynamically generated Python code only for its side effects.
eval and exec have these two differences:
eval accepts only a single expression, exec can take a code block that has Python statements: loops, try: except:, class and function/method definitions and so on.
An expression in Python is whatever you can have as the value in a variable assignment:
a_variable = (anything you can put within these parentheses is an expression)
eval returns the value of the given expression, whereas exec ignores the return value from its code, and always returns None (in Python 2 it is a statement and cannot be used as an expression, so it really does not return anything).
In versions 1.0 - 2.7, exec was a statement, because CPython needed to produce a different kind of code object for functions that used exec for its side effects inside the function.
In Python 3, exec is a function; its use has no effect on the compiled bytecode of the function where it is used.
Thus basically:
>>> a = 5
>>> eval("37 + a") # it is an expression
42
>>> exec("37 + a") # it is an expression statement; value is ignored (None is returned)
>>> exec("a = 47") # modify a global variable as a side effect
>>> a
47
>>> eval("a = 47") # you cannot evaluate a statement
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<string>", line 1
a = 47
^
SyntaxError: invalid syntax
The compile in "exec" mode compiles any number of statements into a bytecode that implicitly always returns None, whereas in "eval" mode it compiles a single expression into bytecode that returns the value of that expression.
>>> eval(compile("42", "<string>", "exec")) # code returns None
>>> eval(compile("42", "<string>", "eval")) # code returns 42
42
>>> exec(compile("42", "<string>", "eval")) # code returns 42,
>>> # but ignored by exec
In the "eval" mode (and thus with the eval function if a string is passed in), the compile raises an exception if the source code contains statements or anything else beyond a single expression:
>>> compile("for i in range(3): print(i)", "<string>", "eval")
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<string>", line 1
for i in range(3): print(i)
^
SyntaxError: invalid syntax
Actually the statement "eval accepts only a single expression" applies only when a string (which contains Python source code) is passed to eval. Then it is internally compiled to bytecode using compile(source, "<string>", "eval") This is where the difference really comes from.
If a code object (which contains Python bytecode) is passed to exec or eval, they behave identically, excepting for the fact that exec ignores the return value, still returning None always. So it is possible use eval to execute something that has statements, if you just compiled it into bytecode before instead of passing it as a string:
>>> eval(compile("if 1: print("Hello")", "<string>", "exec"))
Hello
>>>
works without problems, even though the compiled code contains statements. It still returns None, because that is the return value of the code object returned from compile.
In the "eval" mode (and thus with the eval function if a string is passed in), the compile raises an exception if the source code contains statements or anything else beyond a single expression:
>>> compile("for i in range(3): print(i)", "<string>". "eval")
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<string>", line 1
for i in range(3): print(i)
^
SyntaxError: invalid syntax
exec and evalThe exec function (which was a statement in Python 2) is used for executing a dynamically created statement or program:
>>> program = """
for i in range(3):
print("Python is cool")
"""
>>> exec(program)
Python is cool
Python is cool
Python is cool
>>>
The eval function does the same for a single expression, and returns the value of the expression:
>>> a = 2
>>> my_calculation = "42 * a"
>>> result = eval(my_calculation)
>>> result
84
exec and eval both accept the program/expression to be run either as a str, unicode or bytes object containing source code, or as a code object which contains Python bytecode.
If a str/unicode/bytes containing source code was passed to exec, it behaves equivalently to:
exec(compile(source, "<string>", "exec"))
and eval similarly behaves equivalent to:
eval(compile(source, "<string>", "eval"))
Since all expressions can be used as statements in Python (these are called the Expr nodes in the Python abstract grammar; the opposite is not true), you can always use exec if you do not need the return value. That is to say, you can use either eval("my_func(42)") or exec("my_func(42)"), the difference being that eval returns the value returned by my_func, and exec discards it:
>>> def my_func(arg):
... print("Called with %d" % arg)
... return arg * 2
...
>>> exec("my_func(42)")
Called with 42
>>> eval("my_func(42)")
Called with 42
84
>>>
Of the 2, only exec accepts source code that contains statements, like def, for, while, import, or class, the assignment statement (a.k.a a = 42), or entire programs:
>>> exec("for i in range(3): print(i)")
0
1
2
>>> eval("for i in range(3): print(i)")
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<string>", line 1
for i in range(3): print(i)
^
SyntaxError: invalid syntax
Both exec and eval accept 2 additional positional arguments - globals and locals - which are the global and local variable scopes that the code sees. These default to the globals() and locals() within the scope that called exec or eval, but any dictionary can be used for globals and any mapping for locals (including dict of course). These can be used not only to restrict/modify the variables that the code sees, but are often also used for capturing the variables that the executed code creates:
>>> g = dict()
>>> l = dict()
>>> exec("global a; a, b = 123, 42", g, l)
>>> g["a"]
123
>>> l
{"b": 42}
(If you display the value of the entire g, it would be much longer, because exec and eval add the built-ins module as __builtins__ to the globals automatically if it is missing).
In Python 2, the official syntax for the exec statement is actually exec code in globals, locals, as in
>>> exec "global a; a, b = 123, 42" in g, l
However the alternate syntax exec(code, globals, locals) has always been accepted too (see below).
compileThe compile(source, filename, mode, flags=0, dont_inherit=False, optimize=-1) built-in can be used to speed up repeated invocations of the same code with exec or eval by compiling the source into a code object beforehand. The mode parameter controls the kind of code fragment the compile function accepts and the kind of bytecode it produces. The choices are "eval", "exec" and "single":
"eval" mode expects a single expression, and will produce bytecode that when run will return the value of that expression:
>>> dis.dis(compile("a + b", "<string>", "eval"))
1 0 LOAD_NAME 0 (a)
3 LOAD_NAME 1 (b)
6 BINARY_ADD
7 RETURN_VALUE
"exec" accepts any kinds of python constructs from single expressions to whole modules of code, and executes them as if they were module top-level statements. The code object returns None:
>>> dis.dis(compile("a + b", "<string>", "exec"))
1 0 LOAD_NAME 0 (a)
3 LOAD_NAME 1 (b)
6 BINARY_ADD
7 POP_TOP <- discard result
8 LOAD_CONST 0 (None) <- load None on stack
11 RETURN_VALUE <- return top of stack
"single" is a limited form of "exec" which accepts a source code containing a single statement (or multiple statements separated by ;) if the last statement is an expression statement, the resulting bytecode also prints the repr of the value of that expression to the standard output(!).
An if-elif-else chain, a loop with else, and try with its except, else and finally blocks is considered a single statement.
A source fragment containing 2 top-level statements is an error for the "single", except in Python 2 there is a bug that sometimes allows multiple toplevel statements in the code; only the first is compiled; the rest are ignored:
In Python 2.7.8:
>>> exec(compile("a = 5
a = 6", "<string>", "single"))
>>> a
5
And in Python 3.4.2:
>>> exec(compile("a = 5
a = 6", "<string>", "single"))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<string>", line 1
a = 5
^
SyntaxError: multiple statements found while compiling a single statement
This is very useful for making interactive Python shells. However, the value of the expression is not returned, even if you eval the resulting code.
Thus greatest distinction of exec and eval actually comes from the compile function and its modes.
In addition to compiling source code to bytecode, compile supports compiling abstract syntax trees (parse trees of Python code) into code objects; and source code into abstract syntax trees (the ast.parse is written in Python and just calls compile(source, filename, mode, PyCF_ONLY_AST)); these are used for example for modifying source code on the fly, and also for dynamic code creation, as it is often easier to handle the code as a tree of nodes instead of lines of text in complex cases.
While eval only allows you to evaluate a string that contains a single expression, you can eval a whole statement, or even a whole module that has been compiled into bytecode; that is, with Python 2, print is a statement, and cannot be evalled directly:
>>> eval("for i in range(3): print("Python is cool")")
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<string>", line 1
for i in range(3): print("Python is cool")
^
SyntaxError: invalid syntax
compile it with "exec" mode into a code object and you can eval it; the eval function will return None.
>>> code = compile("for i in range(3): print("Python is cool")",
"foo.py", "exec")
>>> eval(code)
Python is cool
Python is cool
Python is cool
If one looks into eval and exec source code in CPython 3, this is very evident; they both call PyEval_EvalCode with same arguments, the only difference being that exec explicitly returns None.
exec between Python 2 and Python 3One of the major differences in Python 2 is that exec is a statement and eval is a built-in function (both are built-in functions in Python 3).
It is a well-known fact that the official syntax of exec in Python 2 is exec code [in globals[, locals]].
Unlike majority of the Python 2-to-3 porting guides seem to suggest, the exec statement in CPython 2 can be also used with syntax that looks exactly like the exec function invocation in Python 3. The reason is that Python 0.9.9 had the exec(code, globals, locals) built-in function! And that built-in function was replaced with exec statement somewhere before Python 1.0 release.
Since it was desirable to not break backwards compatibility with Python 0.9.9, Guido van Rossum added a compatibility hack in 1993: if the code was a tuple of length 2 or 3, and globals and locals were not passed into the exec statement otherwise, the code would be interpreted as if the 2nd and 3rd element of the tuple were the globals and locals respectively. The compatibility hack was not mentioned even in Python 1.4 documentation (the earliest available version online); and thus was not known to many writers of the porting guides and tools, until it was documented again in November 2012:
The first expression may also be a tuple of length 2 or 3. In this case, the optional parts must be omitted. The form
exec(expr, globals)is equivalent toexec expr in globals, while the formexec(expr, globals, locals)is equivalent toexec expr in globals, locals. The tuple form ofexecprovides compatibility with Python 3, whereexecis a function rather than a statement.
Yes, in CPython 2.7 that it is handily referred to as being a forward-compatibility option (why confuse people over that there is a backward compatibility option at all), when it actually had been there for backward-compatibility for two decades.
Thus while exec is a statement in Python 1 and Python 2, and a built-in function in Python 3 and Python 0.9.9,
>>> exec("print(a)", globals(), {"a": 42})
42
has had identical behaviour in possibly every widely released Python version ever; and works in Jython 2.5.2, PyPy 2.3.1 (Python 2.7.6) and IronPython 2.6.1 too (kudos to them following the undocumented behaviour of CPython closely).
What you cannot do in Pythons 1.0 - 2.7 with its compatibility hack, is to store the return value of exec into a variable:
Python 2.7.11+ (default, Apr 17 2016, 14:00:29)
[GCC 5.3.1 20160413] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> a = exec("print(42)")
File "<stdin>", line 1
a = exec("print(42)")
^
SyntaxError: invalid syntax
(which wouldn"t be useful in Python 3 either, as exec always returns None), or pass a reference to exec:
>>> call_later(exec, "print(42)", delay=1000)
File "<stdin>", line 1
call_later(exec, "print(42)", delay=1000)
^
SyntaxError: invalid syntax
Which a pattern that someone might actually have used, though unlikely;
Or use it in a list comprehension:
>>> [exec(i) for i in ["print(42)", "print(foo)"]
File "<stdin>", line 1
[exec(i) for i in ["print(42)", "print(foo)"]
^
SyntaxError: invalid syntax
which is abuse of list comprehensions (use a for loop instead!).
How do I delete a file or folder in Python?
I want to remove a value from a list if it exists in the list (which it may not).
a = [1, 2, 3, 4]
b = a.index(6)
del a[b]
print(a)
The above case (in which it does not exist) shows the following error:
Traceback (most recent call last):
File "D:zjm_codea.py", line 6, in <module>
b = a.index(6)
ValueError: list.index(x): x not in list
So I have to do this:
a = [1, 2, 3, 4]
try:
b = a.index(6)
del a[b]
except:
pass
print(a)
But is there not a simpler way to do this?
I am getting an "access is denied" error when I attempt to delete a folder that is not empty. I used the following command in my attempt: os.remove("/folder_name").
What is the most effective way of removing/deleting a folder/directory that is not empty?
How can I delete the contents of a local folder in Python?
The current project is for Windows, but I would like to see *nix also.
I created an environment with the following command: virtualenv venv --distribute
I cannot remove it with the following command: rmvirtualenv venv -
This is part of virtualenvwrapper as mentioned in answer below for virtualenvwrapper
I do an lson my current directory and I still see venv
The only way I can remove it seems to be: sudo rm -rf venv
Note that the environment is not active. I"m running Ubuntu 11.10. Any ideas? I"ve tried rebooting my system to no avail.
I want to delete the file filename if it exists. Is it proper to say
if os.path.exists(filename):
os.remove(filename)
Is there a better way? A one-line way?
I"m quite familiar with Django, but I recently noticed there exists an on_delete=models.CASCADE option with the models. I have searched for the documentation for the same, but I couldn"t find anything more than:
Changed in Django 1.9:
on_deletecan now be used as the second positional argument (previously it was typically only passed as a keyword argument). It will be a required argument in Django 2.0.
from django.db import models
class Car(models.Model):
manufacturer = models.ForeignKey(
"Manufacturer",
on_delete=models.CASCADE,
)
# ...
class Manufacturer(models.Model):
# ...
pass
What does on_delete do? (I guess the actions to be done if the model is deleted.)
What does models.CASCADE do? (any hints in documentation)
What other options are available (if my guess is correct)?
Where does the documentation for this reside?
There is a string, for example. EXAMPLE.
How can I remove the middle character, i.e., M from it? I don"t need the code. I want to know:
I have a pandas DataFrame and I want to delete rows from it where the length of the string in a particular column is greater than 2.
I expect to be able to do this (per this answer):
df[(len(df["column name"]) < 2)]
but I just get the error:
KeyError: u"no item named False"
What am I doing wrong?
(Note: I know I can use df.dropna() to get rid of rows that contain any NaN, but I didn"t see how to remove rows based on a conditional expression.)
I want to delete a particular record. Such as
delete from table_name where id = 1;
How can I do this in a django model?
In Python, what is the purpose of
__slots__and what are the cases one should avoid this?
The special attribute __slots__ allows you to explicitly state which instance attributes you expect your object instances to have, with the expected results:
The space savings is from
__dict__.__dict__ and __weakref__ creation if parent classes deny them and you declare __slots__.Small caveat, you should only declare a particular slot one time in an inheritance tree. For example:
class Base:
__slots__ = "foo", "bar"
class Right(Base):
__slots__ = "baz",
class Wrong(Base):
__slots__ = "foo", "bar", "baz" # redundant foo and bar
Python doesn"t object when you get this wrong (it probably should), problems might not otherwise manifest, but your objects will take up more space than they otherwise should. Python 3.8:
>>> from sys import getsizeof
>>> getsizeof(Right()), getsizeof(Wrong())
(56, 72)
This is because the Base"s slot descriptor has a slot separate from the Wrong"s. This shouldn"t usually come up, but it could:
>>> w = Wrong()
>>> w.foo = "foo"
>>> Base.foo.__get__(w)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: foo
>>> Wrong.foo.__get__(w)
"foo"
The biggest caveat is for multiple inheritance - multiple "parent classes with nonempty slots" cannot be combined.
To accommodate this restriction, follow best practices: Factor out all but one or all parents" abstraction which their concrete class respectively and your new concrete class collectively will inherit from - giving the abstraction(s) empty slots (just like abstract base classes in the standard library).
See section on multiple inheritance below for an example.
To have attributes named in __slots__ to actually be stored in slots instead of a __dict__, a class must inherit from object (automatic in Python 3, but must be explicit in Python 2).
To prevent the creation of a __dict__, you must inherit from object and all classes in the inheritance must declare __slots__ and none of them can have a "__dict__" entry.
There are a lot of details if you wish to keep reading.
__slots__: Faster attribute access.The creator of Python, Guido van Rossum, states that he actually created __slots__ for faster attribute access.
It is trivial to demonstrate measurably significant faster access:
import timeit
class Foo(object): __slots__ = "foo",
class Bar(object): pass
slotted = Foo()
not_slotted = Bar()
def get_set_delete_fn(obj):
def get_set_delete():
obj.foo = "foo"
obj.foo
del obj.foo
return get_set_delete
and
>>> min(timeit.repeat(get_set_delete_fn(slotted)))
0.2846834529991611
>>> min(timeit.repeat(get_set_delete_fn(not_slotted)))
0.3664822799983085
The slotted access is almost 30% faster in Python 3.5 on Ubuntu.
>>> 0.3664822799983085 / 0.2846834529991611
1.2873325658284342
In Python 2 on Windows I have measured it about 15% faster.
__slots__: Memory SavingsAnother purpose of __slots__ is to reduce the space in memory that each object instance takes up.
My own contribution to the documentation clearly states the reasons behind this:
The space saved over using
__dict__can be significant.
SQLAlchemy attributes a lot of memory savings to __slots__.
To verify this, using the Anaconda distribution of Python 2.7 on Ubuntu Linux, with guppy.hpy (aka heapy) and sys.getsizeof, the size of a class instance without __slots__ declared, and nothing else, is 64 bytes. That does not include the __dict__. Thank you Python for lazy evaluation again, the __dict__ is apparently not called into existence until it is referenced, but classes without data are usually useless. When called into existence, the __dict__ attribute is a minimum of 280 bytes additionally.
In contrast, a class instance with __slots__ declared to be () (no data) is only 16 bytes, and 56 total bytes with one item in slots, 64 with two.
For 64 bit Python, I illustrate the memory consumption in bytes in Python 2.7 and 3.6, for __slots__ and __dict__ (no slots defined) for each point where the dict grows in 3.6 (except for 0, 1, and 2 attributes):
Python 2.7 Python 3.6
attrs __slots__ __dict__* __slots__ __dict__* | *(no slots defined)
none 16 56 + 272† 16 56 + 112† | †if __dict__ referenced
one 48 56 + 272 48 56 + 112
two 56 56 + 272 56 56 + 112
six 88 56 + 1040 88 56 + 152
11 128 56 + 1040 128 56 + 240
22 216 56 + 3344 216 56 + 408
43 384 56 + 3344 384 56 + 752
So, in spite of smaller dicts in Python 3, we see how nicely __slots__ scale for instances to save us memory, and that is a major reason you would want to use __slots__.
Just for completeness of my notes, note that there is a one-time cost per slot in the class"s namespace of 64 bytes in Python 2, and 72 bytes in Python 3, because slots use data descriptors like properties, called "members".
>>> Foo.foo
<member "foo" of "Foo" objects>
>>> type(Foo.foo)
<class "member_descriptor">
>>> getsizeof(Foo.foo)
72
__slots__:To deny the creation of a __dict__, you must subclass object. Everything subclasses object in Python 3, but in Python 2 you had to be explicit:
class Base(object):
__slots__ = ()
now:
>>> b = Base()
>>> b.a = "a"
Traceback (most recent call last):
File "<pyshell#38>", line 1, in <module>
b.a = "a"
AttributeError: "Base" object has no attribute "a"
Or subclass another class that defines __slots__
class Child(Base):
__slots__ = ("a",)
and now:
c = Child()
c.a = "a"
but:
>>> c.b = "b"
Traceback (most recent call last):
File "<pyshell#42>", line 1, in <module>
c.b = "b"
AttributeError: "Child" object has no attribute "b"
To allow __dict__ creation while subclassing slotted objects, just add "__dict__" to the __slots__ (note that slots are ordered, and you shouldn"t repeat slots that are already in parent classes):
class SlottedWithDict(Child):
__slots__ = ("__dict__", "b")
swd = SlottedWithDict()
swd.a = "a"
swd.b = "b"
swd.c = "c"
and
>>> swd.__dict__
{"c": "c"}
Or you don"t even need to declare __slots__ in your subclass, and you will still use slots from the parents, but not restrict the creation of a __dict__:
class NoSlots(Child): pass
ns = NoSlots()
ns.a = "a"
ns.b = "b"
And:
>>> ns.__dict__
{"b": "b"}
However, __slots__ may cause problems for multiple inheritance:
class BaseA(object):
__slots__ = ("a",)
class BaseB(object):
__slots__ = ("b",)
Because creating a child class from parents with both non-empty slots fails:
>>> class Child(BaseA, BaseB): __slots__ = ()
Traceback (most recent call last):
File "<pyshell#68>", line 1, in <module>
class Child(BaseA, BaseB): __slots__ = ()
TypeError: Error when calling the metaclass bases
multiple bases have instance lay-out conflict
If you run into this problem, You could just remove __slots__ from the parents, or if you have control of the parents, give them empty slots, or refactor to abstractions:
from abc import ABC
class AbstractA(ABC):
__slots__ = ()
class BaseA(AbstractA):
__slots__ = ("a",)
class AbstractB(ABC):
__slots__ = ()
class BaseB(AbstractB):
__slots__ = ("b",)
class Child(AbstractA, AbstractB):
__slots__ = ("a", "b")
c = Child() # no problem!
"__dict__" to __slots__ to get dynamic assignment:class Foo(object):
__slots__ = "bar", "baz", "__dict__"
and now:
>>> foo = Foo()
>>> foo.boink = "boink"
So with "__dict__" in slots we lose some of the size benefits with the upside of having dynamic assignment and still having slots for the names we do expect.
When you inherit from an object that isn"t slotted, you get the same sort of semantics when you use __slots__ - names that are in __slots__ point to slotted values, while any other values are put in the instance"s __dict__.
Avoiding __slots__ because you want to be able to add attributes on the fly is actually not a good reason - just add "__dict__" to your __slots__ if this is required.
You can similarly add __weakref__ to __slots__ explicitly if you need that feature.
The namedtuple builtin make immutable instances that are very lightweight (essentially, the size of tuples) but to get the benefits, you need to do it yourself if you subclass them:
from collections import namedtuple
class MyNT(namedtuple("MyNT", "bar baz")):
"""MyNT is an immutable and lightweight object"""
__slots__ = ()
usage:
>>> nt = MyNT("bar", "baz")
>>> nt.bar
"bar"
>>> nt.baz
"baz"
And trying to assign an unexpected attribute raises an AttributeError because we have prevented the creation of __dict__:
>>> nt.quux = "quux"
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: "MyNT" object has no attribute "quux"
You can allow __dict__ creation by leaving off __slots__ = (), but you can"t use non-empty __slots__ with subtypes of tuple.
Even when non-empty slots are the same for multiple parents, they cannot be used together:
class Foo(object):
__slots__ = "foo", "bar"
class Bar(object):
__slots__ = "foo", "bar" # alas, would work if empty, i.e. ()
>>> class Baz(Foo, Bar): pass
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: Error when calling the metaclass bases
multiple bases have instance lay-out conflict
Using an empty __slots__ in the parent seems to provide the most flexibility, allowing the child to choose to prevent or allow (by adding "__dict__" to get dynamic assignment, see section above) the creation of a __dict__:
class Foo(object): __slots__ = ()
class Bar(object): __slots__ = ()
class Baz(Foo, Bar): __slots__ = ("foo", "bar")
b = Baz()
b.foo, b.bar = "foo", "bar"
You don"t have to have slots - so if you add them, and remove them later, it shouldn"t cause any problems.
Going out on a limb here: If you"re composing mixins or using abstract base classes, which aren"t intended to be instantiated, an empty __slots__ in those parents seems to be the best way to go in terms of flexibility for subclassers.
To demonstrate, first, let"s create a class with code we"d like to use under multiple inheritance
class AbstractBase:
__slots__ = ()
def __init__(self, a, b):
self.a = a
self.b = b
def __repr__(self):
return f"{type(self).__name__}({repr(self.a)}, {repr(self.b)})"
We could use the above directly by inheriting and declaring the expected slots:
class Foo(AbstractBase):
__slots__ = "a", "b"
But we don"t care about that, that"s trivial single inheritance, we need another class we might also inherit from, maybe with a noisy attribute:
class AbstractBaseC:
__slots__ = ()
@property
def c(self):
print("getting c!")
return self._c
@c.setter
def c(self, arg):
print("setting c!")
self._c = arg
Now if both bases had nonempty slots, we couldn"t do the below. (In fact, if we wanted, we could have given AbstractBase nonempty slots a and b, and left them out of the below declaration - leaving them in would be wrong):
class Concretion(AbstractBase, AbstractBaseC):
__slots__ = "a b _c".split()
And now we have functionality from both via multiple inheritance, and can still deny __dict__ and __weakref__ instantiation:
>>> c = Concretion("a", "b")
>>> c.c = c
setting c!
>>> c.c
getting c!
Concretion("a", "b")
>>> c.d = "d"
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: "Concretion" object has no attribute "d"
__class__ assignment with another class that doesn"t have them (and you can"t add them) unless the slot layouts are identical. (I am very interested in learning who is doing this and why.)You may be able to tease out further caveats from the rest of the __slots__ documentation (the 3.7 dev docs are the most current), which I have made significant recent contributions to.
The current top answers cite outdated information and are quite hand-wavy and miss the mark in some important ways.
__slots__ when instantiating lots of objects"I quote:
"You would want to use
__slots__if you are going to instantiate a lot (hundreds, thousands) of objects of the same class."
Abstract Base Classes, for example, from the collections module, are not instantiated, yet __slots__ are declared for them.
Why?
If a user wishes to deny __dict__ or __weakref__ creation, those things must not be available in the parent classes.
__slots__ contributes to reusability when creating interfaces or mixins.
It is true that many Python users aren"t writing for reusability, but when you are, having the option to deny unnecessary space usage is valuable.
__slots__ doesn"t break picklingWhen pickling a slotted object, you may find it complains with a misleading TypeError:
>>> pickle.loads(pickle.dumps(f))
TypeError: a class that defines __slots__ without defining __getstate__ cannot be pickled
This is actually incorrect. This message comes from the oldest protocol, which is the default. You can select the latest protocol with the -1 argument. In Python 2.7 this would be 2 (which was introduced in 2.3), and in 3.6 it is 4.
>>> pickle.loads(pickle.dumps(f, -1))
<__main__.Foo object at 0x1129C770>
in Python 2.7:
>>> pickle.loads(pickle.dumps(f, 2))
<__main__.Foo object at 0x1129C770>
in Python 3.6
>>> pickle.loads(pickle.dumps(f, 4))
<__main__.Foo object at 0x1129C770>
So I would keep this in mind, as it is a solved problem.
The first paragraph is half short explanation, half predictive. Here"s the only part that actually answers the question
The proper use of
__slots__is to save space in objects. Instead of having a dynamic dict that allows adding attributes to objects at anytime, there is a static structure which does not allow additions after creation. This saves the overhead of one dict for every object that uses slots
The second half is wishful thinking, and off the mark:
While this is sometimes a useful optimization, it would be completely unnecessary if the Python interpreter was dynamic enough so that it would only require the dict when there actually were additions to the object.
Python actually does something similar to this, only creating the __dict__ when it is accessed, but creating lots of objects with no data is fairly ridiculous.
The second paragraph oversimplifies and misses actual reasons to avoid __slots__. The below is not a real reason to avoid slots (for actual reasons, see the rest of my answer above.):
They change the behavior of the objects that have slots in a way that can be abused by control freaks and static typing weenies.
It then goes on to discuss other ways of accomplishing that perverse goal with Python, not discussing anything to do with __slots__.
The third paragraph is more wishful thinking. Together it is mostly off-the-mark content that the answerer didn"t even author and contributes to ammunition for critics of the site.
Create some normal objects and slotted objects:
>>> class Foo(object): pass
>>> class Bar(object): __slots__ = ()
Instantiate a million of them:
>>> foos = [Foo() for f in xrange(1000000)]
>>> bars = [Bar() for b in xrange(1000000)]
Inspect with guppy.hpy().heap():
>>> guppy.hpy().heap()
Partition of a set of 2028259 objects. Total size = 99763360 bytes.
Index Count % Size % Cumulative % Kind (class / dict of class)
0 1000000 49 64000000 64 64000000 64 __main__.Foo
1 169 0 16281480 16 80281480 80 list
2 1000000 49 16000000 16 96281480 97 __main__.Bar
3 12284 1 987472 1 97268952 97 str
...
Access the regular objects and their __dict__ and inspect again:
>>> for f in foos:
... f.__dict__
>>> guppy.hpy().heap()
Partition of a set of 3028258 objects. Total size = 379763480 bytes.
Index Count % Size % Cumulative % Kind (class / dict of class)
0 1000000 33 280000000 74 280000000 74 dict of __main__.Foo
1 1000000 33 64000000 17 344000000 91 __main__.Foo
2 169 0 16281480 4 360281480 95 list
3 1000000 33 16000000 4 376281480 99 __main__.Bar
4 12284 0 987472 0 377268952 99 str
...
This is consistent with the history of Python, from Unifying types and classes in Python 2.2
If you subclass a built-in type, extra space is automatically added to the instances to accomodate
__dict__and__weakrefs__. (The__dict__is not initialized until you use it though, so you shouldn"t worry about the space occupied by an empty dictionary for each instance you create.) If you don"t need this extra space, you can add the phrase "__slots__ = []" to your class.
This is the behaviour to adopt when the referenced object is deleted. It is not specific to Django; this is an SQL standard. Although Django has its own implementation on top of SQL. (1)
There are seven possible actions to take when such event occurs:
CASCADE: When the referenced object is deleted, also delete the objects that have references to it (when you remove a blog post for instance, you might want to delete comments as well). SQL equivalent: CASCADE.PROTECT: Forbid the deletion of the referenced object. To delete it you will have to delete all objects that reference it manually. SQL equivalent: RESTRICT.RESTRICT: (introduced in Django 3.1) Similar behavior as PROTECT that matches SQL"s RESTRICT more accurately. (See django documentation example)SET_NULL: Set the reference to NULL (requires the field to be nullable). For instance, when you delete a User, you might want to keep the comments he posted on blog posts, but say it was posted by an anonymous (or deleted) user. SQL equivalent: SET NULL.SET_DEFAULT: Set the default value. SQL equivalent: SET DEFAULT.SET(...): Set a given value. This one is not part of the SQL standard and is entirely handled by Django.DO_NOTHING: Probably a very bad idea since this would create integrity issues in your database (referencing an object that actually doesn"t exist). SQL equivalent: NO ACTION. (2)Source: Django documentation
See also the documentation of PostgreSQL for instance.
In most cases, CASCADE is the expected behaviour, but for every ForeignKey, you should always ask yourself what is the expected behaviour in this situation. PROTECT and SET_NULL are often useful. Setting CASCADE where it should not, can potentially delete all of your database in cascade, by simply deleting a single user.
Additional note to clarify cascade direction
It"s funny to notice that the direction of the CASCADE action is not clear to many people. Actually, it"s funny to notice that only the CASCADE action is not clear. I understand the cascade behavior might be confusing, however you must think that it is the same direction as any other action. Thus, if you feel that CASCADE direction is not clear to you, it actually means that on_delete behavior is not clear to you.
In your database, a foreign key is basically represented by an integer field which value is the primary key of the foreign object. Let"s say you have an entry comment_A, which has a foreign key to an entry article_B. If you delete the entry comment_A, everything is fine. article_B used to live without comment_A and don"t bother if it"s deleted. However, if you delete article_B, then comment_A panics! It never lived without article_B and needs it, and it"s part of its attributes (article=article_B, but what is article_B???). This is where on_delete steps in, to determine how to resolve this integrity error, either by saying:
PROTECT or RESTRICT in Django/SQL)SET_NULL)CASCADE behavior).SET_DEFAULT, or even SET(...)).DO_NOTHING)I hope it makes cascade direction clearer. :)
Footnotes
(1) Django has its own implementation on top of SQL. And, as mentioned by @JoeMjr2 in the comments below, Django will not create the SQL constraints. If you want the constraints to be ensured by your database (for instance, if your database is used by another application, or if you hang in the database console from time to time), you might want to set the related constraints manually yourself. There is an open ticket to add support for database-level on delete constrains in Django.
(2) Actually, there is one case where
DO_NOTHINGcan be useful: If you want to skip Django"s implementation and implement the constraint yourself at the database-level.
import os
os.remove("/tmp/<file_name>.txt")
Or
import os
os.unlink("/tmp/<file_name>.txt")
Or
pathlib Library for Python version >= 3.4
file_to_rem = pathlib.Path("/tmp/<file_name>.txt")
file_to_rem.unlink()
Unlink method used to remove the file or the symbolik link.
If missing_ok is false (the default), FileNotFoundError is raised if the path does not exist.
If missing_ok is true, FileNotFoundError exceptions will be ignored (same behavior as the POSIX rm -f command).
Changed in version 3.8: The missing_ok parameter was added.
os.path.isfile("/path/to/file")exception handling.EXAMPLE for os.path.isfile
#!/usr/bin/python
import os
myfile="/tmp/foo.txt"
## If file exists, delete it ##
if os.path.isfile(myfile):
os.remove(myfile)
else: ## Show an error ##
print("Error: %s file not found" % myfile)
#!/usr/bin/python
import os
## Get input ##
myfile= raw_input("Enter file name to delete: ")
## Try to delete the file ##
try:
os.remove(myfile)
except OSError as e: ## if failed, report it back to the user ##
print ("Error: %s - %s." % (e.filename, e.strerror))
Enter file name to delete : demo.txt Error: demo.txt - No such file or directory. Enter file name to delete : rrr.txt Error: rrr.txt - Operation not permitted. Enter file name to delete : foo.txt
shutil.rmtree()
Example for shutil.rmtree()
#!/usr/bin/python
import os
import sys
import shutil
# Get directory name
mydir= raw_input("Enter directory name: ")
## Try to remove tree; if failed show an error using try...except on screen
try:
shutil.rmtree(mydir)
except OSError as e:
print ("Error: %s - %s." % (e.filename, e.strerror))
What"s the pythonic way to use getters and setters?
The "Pythonic" way is not to use "getters" and "setters", but to use plain attributes, like the question demonstrates, and del for deleting (but the names are changed to protect the innocent... builtins):
value = "something"
obj.attribute = value
value = obj.attribute
del obj.attribute
If later, you want to modify the setting and getting, you can do so without having to alter user code, by using the property decorator:
class Obj:
"""property demo"""
#
@property # first decorate the getter method
def attribute(self): # This getter method name is *the* name
return self._attribute
#
@attribute.setter # the property decorates with `.setter` now
def attribute(self, value): # name, e.g. "attribute", is the same
self._attribute = value # the "value" name isn"t special
#
@attribute.deleter # decorate with `.deleter`
def attribute(self): # again, the method name is the same
del self._attribute
(Each decorator usage copies and updates the prior property object, so note that you should use the same name for each set, get, and delete function/method.
After defining the above, the original setting, getting, and deleting code is the same:
obj = Obj()
obj.attribute = value
the_value = obj.attribute
del obj.attribute
You should avoid this:
def set_property(property,value): def get_property(property):
Firstly, the above doesn"t work, because you don"t provide an argument for the instance that the property would be set to (usually self), which would be:
class Obj:
def set_property(self, property, value): # don"t do this
...
def get_property(self, property): # don"t do this either
...
Secondly, this duplicates the purpose of two special methods, __setattr__ and __getattr__.
Thirdly, we also have the setattr and getattr builtin functions.
setattr(object, "property_name", value)
getattr(object, "property_name", default_value) # default is optional
The @property decorator is for creating getters and setters.
For example, we could modify the setting behavior to place restrictions the value being set:
class Protective(object):
@property
def protected_value(self):
return self._protected_value
@protected_value.setter
def protected_value(self, value):
if acceptable(value): # e.g. type or range check
self._protected_value = value
In general, we want to avoid using property and just use direct attributes.
This is what is expected by users of Python. Following the rule of least-surprise, you should try to give your users what they expect unless you have a very compelling reason to the contrary.
For example, say we needed our object"s protected attribute to be an integer between 0 and 100 inclusive, and prevent its deletion, with appropriate messages to inform the user of its proper usage:
class Protective(object):
"""protected property demo"""
#
def __init__(self, start_protected_value=0):
self.protected_value = start_protected_value
#
@property
def protected_value(self):
return self._protected_value
#
@protected_value.setter
def protected_value(self, value):
if value != int(value):
raise TypeError("protected_value must be an integer")
if 0 <= value <= 100:
self._protected_value = int(value)
else:
raise ValueError("protected_value must be " +
"between 0 and 100 inclusive")
#
@protected_value.deleter
def protected_value(self):
raise AttributeError("do not delete, protected_value can be set to 0")
(Note that __init__ refers to self.protected_value but the property methods refer to self._protected_value. This is so that __init__ uses the property through the public API, ensuring it is "protected".)
And usage:
>>> p1 = Protective(3)
>>> p1.protected_value
3
>>> p1 = Protective(5.0)
>>> p1.protected_value
5
>>> p2 = Protective(-5)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<stdin>", line 3, in __init__
File "<stdin>", line 15, in protected_value
ValueError: protectected_value must be between 0 and 100 inclusive
>>> p1.protected_value = 7.3
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<stdin>", line 17, in protected_value
TypeError: protected_value must be an integer
>>> p1.protected_value = 101
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<stdin>", line 15, in protected_value
ValueError: protectected_value must be between 0 and 100 inclusive
>>> del p1.protected_value
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "<stdin>", line 18, in protected_value
AttributeError: do not delete, protected_value can be set to 0
Yes they do. .setter and .deleter make copies of the original property. This allows subclasses to properly modify behavior without altering the behavior in the parent.
class Obj:
"""property demo"""
#
@property
def get_only(self):
return self._attribute
#
@get_only.setter
def get_or_set(self, value):
self._attribute = value
#
@get_or_set.deleter
def get_set_or_delete(self):
del self._attribute
Now for this to work, you have to use the respective names:
obj = Obj()
# obj.get_only = "value" # would error
obj.get_or_set = "value"
obj.get_set_or_delete = "new value"
the_value = obj.get_only
del obj.get_set_or_delete
# del obj.get_or_set # would error
I"m not sure where this would be useful, but the use-case is if you want a get, set, and/or delete-only property. Probably best to stick to semantically same property having the same name.
Start with simple attributes.
If you later need functionality around the setting, getting, and deleting, you can add it with the property decorator.
Avoid functions named set_... and get_... - that"s what properties are for.
I would just avoid the use of virtualenv after Python3.3+ and instead use the standard shipped library venv. To create a new virtual environment you would type:
$ python3 -m venv <MYVENV>
virtualenv tries to copy the Python binary into the virtual environment"s bin directory. However it does not update library file links embedded into that binary, so if you build Python from source into a non-system directory with relative path names, the Python binary breaks. Since this is how you make a copy distributable Python, it is a big flaw. BTW to inspect embedded library file links on OS X, use otool. For example from within your virtual environment, type:
$ otool -L bin/python
python:
@executable_path/../Python (compatibility version 3.4.0, current version 3.4.0)
/usr/lib/libSystem.B.dylib (compatibility version 1.0.0, current version 1238.0.0)
Consequently I would avoid virtualenvwrapper and pipenv. pyvenv is deprecated. pyenv seems to be used often where virtualenv is used but I would stay away from it also since I think venv also does what pyenv is built for.
venv creates virtual environments in the shell that are fresh and sandboxed, with user-installable libraries, and it"s multi-python safe.
Fresh: because virtual environments only start with the standard libraries that ship with python, you have to install any other libraries all over again with pip install while the virtual environment is active.
Sandboxed: because none of these new library installs are visible outside the virtual environment, so you can delete the whole environment and start again without worrying about impacting your base python install.
User-installable libraries: because the virtual environment"s target folder is created without sudo in some directory you already own, so you won"t need sudo permissions to install libraries into it.
multi-python safe: because when virtual environments activate, the shell only sees the python version (3.4, 3.5 etc.) that was used to build that virtual environment.
pyenv is similar to venv in that it lets you manage multiple python environments. However with pyenv you can"t conveniently rollback library installs to some start state and you will likely need admin privileges at some point to update libraries. So I think it is also best to use venv.
In the last couple of years I have found many problems in build systems (emacs packages, python standalone application builders, installers...) that ultimately come down to issues with virtualenv. I think python will be a better platform when we eliminate this additional option and only use venv.
EDIT: Tweet of the BDFL,
I use venv (in the stdlib) and a bunch of shell aliases to quickly switch.— Guido van Rossum (@gvanrossum) October 22, 2020To remove the configs:
conda install anaconda-clean
anaconda-clean --yes
Once the configs are removed you can delete the anaconda install folder, which is usually under your home dir:
rm -rf ~/anaconda3
Also, the anaconda-clean --yes command creates a backup in your home directory of the format ~/.anaconda_backup/<timestamp>. Make sure to delete that one also.
EDIT (v5.2.0): Now if you want to clean all, you will also have to delete the two last lines added to your .bash_profile. They look like:
# added by Anaconda3 5.2.0 installer
export PATH="/Users/ody/anaconda3/bin:$PATH"
How do I convert a list of dictionaries to a pandas DataFrame?
The other answers are correct, but not much has been explained in terms of advantages and limitations of these methods. The aim of this post will be to show examples of these methods under different situations, discuss when to use (and when not to use), and suggest alternatives.
DataFrame(), DataFrame.from_records(), and .from_dict()Depending on the structure and format of your data, there are situations where either all three methods work, or some work better than others, or some don"t work at all.
Consider a very contrived example.
np.random.seed(0)
data = pd.DataFrame(
np.random.choice(10, (3, 4)), columns=list("ABCD")).to_dict("r")
print(data)
[{"A": 5, "B": 0, "C": 3, "D": 3},
{"A": 7, "B": 9, "C": 3, "D": 5},
{"A": 2, "B": 4, "C": 7, "D": 6}]
This list consists of "records" with every keys present. This is the simplest case you could encounter.
# The following methods all produce the same output.
pd.DataFrame(data)
pd.DataFrame.from_dict(data)
pd.DataFrame.from_records(data)
A B C D
0 5 0 3 3
1 7 9 3 5
2 2 4 7 6
orient="index"/"columns"Before continuing, it is important to make the distinction between the different types of dictionary orientations, and support with pandas. There are two primary types: "columns", and "index".
orient="columns"
Dictionaries with the "columns" orientation will have their keys correspond to columns in the equivalent DataFrame.
For example, data above is in the "columns" orient.
data_c = [
{"A": 5, "B": 0, "C": 3, "D": 3},
{"A": 7, "B": 9, "C": 3, "D": 5},
{"A": 2, "B": 4, "C": 7, "D": 6}]
pd.DataFrame.from_dict(data_c, orient="columns")
A B C D
0 5 0 3 3
1 7 9 3 5
2 2 4 7 6
Note: If you are using pd.DataFrame.from_records, the orientation is assumed to be "columns" (you cannot specify otherwise), and the dictionaries will be loaded accordingly.
orient="index"
With this orient, keys are assumed to correspond to index values. This kind of data is best suited for pd.DataFrame.from_dict.
data_i ={
0: {"A": 5, "B": 0, "C": 3, "D": 3},
1: {"A": 7, "B": 9, "C": 3, "D": 5},
2: {"A": 2, "B": 4, "C": 7, "D": 6}}
pd.DataFrame.from_dict(data_i, orient="index")
A B C D
0 5 0 3 3
1 7 9 3 5
2 2 4 7 6
This case is not considered in the OP, but is still useful to know.
If you need a custom index on the resultant DataFrame, you can set it using the index=... argument.
pd.DataFrame(data, index=["a", "b", "c"])
# pd.DataFrame.from_records(data, index=["a", "b", "c"])
A B C D
a 5 0 3 3
b 7 9 3 5
c 2 4 7 6
This is not supported by pd.DataFrame.from_dict.
All methods work out-of-the-box when handling dictionaries with missing keys/column values. For example,
data2 = [
{"A": 5, "C": 3, "D": 3},
{"A": 7, "B": 9, "F": 5},
{"B": 4, "C": 7, "E": 6}]
# The methods below all produce the same output.
pd.DataFrame(data2)
pd.DataFrame.from_dict(data2)
pd.DataFrame.from_records(data2)
A B C D E F
0 5.0 NaN 3.0 3.0 NaN NaN
1 7.0 9.0 NaN NaN NaN 5.0
2 NaN 4.0 7.0 NaN 6.0 NaN
"What if I don"t want to read in every single column"? You can easily specify this using the columns=... parameter.
For example, from the example dictionary of data2 above, if you wanted to read only columns "A", "D", and "F", you can do so by passing a list:
pd.DataFrame(data2, columns=["A", "D", "F"])
# pd.DataFrame.from_records(data2, columns=["A", "D", "F"])
A D F
0 5.0 3.0 NaN
1 7.0 NaN 5.0
2 NaN NaN NaN
This is not supported by pd.DataFrame.from_dict with the default orient "columns".
pd.DataFrame.from_dict(data2, orient="columns", columns=["A", "B"])
ValueError: cannot use columns parameter with orient="columns"
Not supported by any of these methods directly. You will have to iterate over your data and perform a reverse delete in-place as you iterate. For example, to extract only the 0th and 2nd rows from data2 above, you can use:
rows_to_select = {0, 2}
for i in reversed(range(len(data2))):
if i not in rows_to_select:
del data2[i]
pd.DataFrame(data2)
# pd.DataFrame.from_dict(data2)
# pd.DataFrame.from_records(data2)
A B C D E
0 5.0 NaN 3 3.0 NaN
1 NaN 4.0 7 NaN 6.0
json_normalize for Nested DataA strong, robust alternative to the methods outlined above is the json_normalize function which works with lists of dictionaries (records), and in addition can also handle nested dictionaries.
pd.json_normalize(data)
A B C D
0 5 0 3 3
1 7 9 3 5
2 2 4 7 6
pd.json_normalize(data2)
A B C D E
0 5.0 NaN 3 3.0 NaN
1 NaN 4.0 7 NaN 6.0
Again, keep in mind that the data passed to json_normalize needs to be in the list-of-dictionaries (records) format.
As mentioned, json_normalize can also handle nested dictionaries. Here"s an example taken from the documentation.
data_nested = [
{"counties": [{"name": "Dade", "population": 12345},
{"name": "Broward", "population": 40000},
{"name": "Palm Beach", "population": 60000}],
"info": {"governor": "Rick Scott"},
"shortname": "FL",
"state": "Florida"},
{"counties": [{"name": "Summit", "population": 1234},
{"name": "Cuyahoga", "population": 1337}],
"info": {"governor": "John Kasich"},
"shortname": "OH",
"state": "Ohio"}
]
pd.json_normalize(data_nested,
record_path="counties",
meta=["state", "shortname", ["info", "governor"]])
name population state shortname info.governor
0 Dade 12345 Florida FL Rick Scott
1 Broward 40000 Florida FL Rick Scott
2 Palm Beach 60000 Florida FL Rick Scott
3 Summit 1234 Ohio OH John Kasich
4 Cuyahoga 1337 Ohio OH John Kasich
For more information on the meta and record_path arguments, check out the documentation.
Here"s a table of all the methods discussed above, along with supported features/functionality.
* Use orient="columns" and then transpose to get the same effect as orient="index".
Use the Windows search bar to find "Manage app execution aliases". There should be two aliases for python, unselect them, this will allow the usual python aliases "python" and "python3". See image below.
I think we have this problem when installing python because in a new Windows installation the aliases are in the ON position as in image below. When turned on, Windows puts a empty or fake file named python.exe and python3.exe in the directory named %USERPROFILE%AppDataLocalMicrosoftWindowsApps. This is the alias.
Then Microsoft put that directory at the top of the list in the "Path" environment variables.
When you enter "python" in cmd, it searches the directories listed in your "Path" environment variables page from top to bottom. So if you installed Python after a new Windows 10 install then get redirected to the Windows Store it"s because there are two python.exe"s, the alias in the App Execution Alias page, and the real one wherever you installed Python but cmd finds the App execution alias python.exe first because that directory is at the top of the Path.
I think the easiest solution is to just check the python.exe and python3.exe to OFF as I suggested before, which deletes the fake exe"s. Based on this Microsoft Devblog https://devblogs.microsoft.com/python/python-in-the-windows-10-may-2019-update/ they stated they created this system partially for new python users specifically kids learning python in school that had trouble installing it. Creating this alias was to helps kids just starting python install it and focus on learning to code. I think Windows probably deletes those aliases if you install Python from the Windows App Store. What we are noticing is that they do not get deleted if you manually install from another source.
(Also the empty/fake python.exe is not really empty it says 0 KB in the screenshot but entering "start ms-windows-store:" in cmd opens the Windows App Store so it probably just has a line with that and a way to direct it to Python page.)
Finally, as Chipjust suggested you can create a new alias for python using something like DOSKEY as explained in this article for example: https://winaero.com/how-to-set-aliases-for-the-command-prompt-in-windows/
Pandas will recognise a value as null if it is a np.nan object, which will print as NaN in the DataFrame. Your missing values are probably empty strings, which Pandas doesn"t recognise as null. To fix this, you can convert the empty stings (or whatever is in your empty cells) to np.nan objects using replace(), and then call dropna()on your DataFrame to delete rows with null tenants.
To demonstrate, we create a DataFrame with some random values and some empty strings in a Tenants column:
>>> import pandas as pd
>>> import numpy as np
>>>
>>> df = pd.DataFrame(np.random.randn(10, 2), columns=list("AB"))
>>> df["Tenant"] = np.random.choice(["Babar", "Rataxes", ""], 10)
>>> print df
A B Tenant
0 -0.588412 -1.179306 Babar
1 -0.008562 0.725239
2 0.282146 0.421721 Rataxes
3 0.627611 -0.661126 Babar
4 0.805304 -0.834214
5 -0.514568 1.890647 Babar
6 -1.188436 0.294792 Rataxes
7 1.471766 -0.267807 Babar
8 -1.730745 1.358165 Rataxes
9 0.066946 0.375640
Now we replace any empty strings in the Tenants column with np.nan objects, like so:
>>> df["Tenant"].replace("", np.nan, inplace=True)
>>> print df
A B Tenant
0 -0.588412 -1.179306 Babar
1 -0.008562 0.725239 NaN
2 0.282146 0.421721 Rataxes
3 0.627611 -0.661126 Babar
4 0.805304 -0.834214 NaN
5 -0.514568 1.890647 Babar
6 -1.188436 0.294792 Rataxes
7 1.471766 -0.267807 Babar
8 -1.730745 1.358165 Rataxes
9 0.066946 0.375640 NaN
Now we can drop the null values:
>>> df.dropna(subset=["Tenant"], inplace=True)
>>> print df
A B Tenant
0 -0.588412 -1.179306 Babar
2 0.282146 0.421721 Rataxes
3 0.627611 -0.661126 Babar
5 -0.514568 1.890647 Babar
6 -1.188436 0.294792 Rataxes
7 1.471766 -0.267807 Babar
8 -1.730745 1.358165 Rataxes
__pycache__ is a folder containing Python 3 bytecode compiled and ready to be executed.
I don"t recommend routinely laboriously deleting these files or suppressing creation during development as it wastes your time. Just have a recursive command ready (see below) to clean up when needed as bytecode can become stale in edge cases (see comments).
Python programmers usually ignore bytecode. Indeed __pycache__ and *.pyc are common lines to see in .gitignore files. Bytecode is not meant for distribution and can be disassembled using dis module.
If you are using OS X you can easily hide all of these folders in your project by running following command from the root folder of your project.
find . -name "__pycache__" -exec chflags hidden {} ;
Replace __pycache__ with *.pyc for Python 2.
This sets a flag on all those directories (.pyc files) telling Finder/Textmate 2 to exclude them from listings. Importantly the bytecode is there, it"s just hidden.
Rerun the command if you create new modules and wish to hide new bytecode or if you delete the hidden bytecode files.
On Windows the equivalent command might be (not tested, batch script welcome):
dir * /s/b | findstr __pycache__ | attrib +h +s +r
Which is same as going through the project hiding folders using right-click > hide...
Running unit tests is one scenario (more in comments) where deleting the *.pyc files and __pycache__ folders is indeed useful. I use the following lines in my ~/.bash_profile and just run cl to clean up when needed.
alias cpy="find . -name "__pycache__" -delete"
alias cpc="find . -name "*.pyc" -delete"
...
alias cl="cpy && cpc && ..."
and more lately
# pip install pyclean
pyclean .