# K-th non-repeating character in Python using list comprehension and OrderedDict

Python Methods and Functions | repeat | sin

Examples:

` Input: str = pythonengineering, k = 3 Output: r First non-repeating character is f, second is o and third is r. Input: str = pythonengineering, k = 2 Output: o Input: str = pythonengineering, k = 4 Output: Less than k non-repeating characters in input. `

This problem has an existing solution, please refer to the List Comp Representation and

` # Function for finding the kth non-repeating character `
` # in a line `

` from ` ` collections ` ` import ` ` OrderedDict `

` def ` ` kthRepeating (` ` input ` `, k): `

< code class = "comments"> # OrderedDict returns dictionary data

` # structure with input characters `

` # string as keys in the same order as they are `

` # were inserted and 0 as default `

` dict ` ` = ` ` OrderedDict.fromkeys (` ` input ` `, ` ` 0 ` ` ) `

` # now go to the input line to calculate `

` ` ` # frequency of each character `

< p> ` for ` ` ch ` ` in ` ` input ` `: `

` dict ` ` [ch] ` ` + ` ` = ` ` 1 `

` # now retrieve a list of all keys whose value `

` ` ` # 1 from the order dictionary `

` nonRepeatDict ` ` = ` ` [key ` ` for ` ` (key, value ) ` ` in ` ` `

`  dict . iteritems () if value = = 1 ] `

`   # now returns the (k-1) th character from the list above if len (nonRepeatDict) & lt; k: return 'Less than k non - repeating characters in input . '   else : return nonRepeatDict [k - 1 ]    # Driver function if __ name__ = = "__ main __" :  input = "pythonengineering" k = 3 print kthRepeating ( input , k) `

` `

Output:

` r `

This article courtesy of Shashank Mishra (Gullu) . If you are as Python.Engineering and would like to contribute, you can also write an article using contribute.python.engineering or by posting an article contribute @ python.engineering. See my article appearing on the Python.Engineering homepage and help other geeks.

## Create list of single item repeated N times

I want to create a series of lists, all of varying lengths. Each list will contain the same element `e`, repeated `n` times (where `n` = length of the list).

How do I create the lists, without using a list comprehension `[e for number in xrange(n)]` for each list?

## What is the best way to repeatedly execute a function every x seconds?

### Question by DavidM

I want to repeatedly execute a function in Python every 60 seconds forever (just like an NSTimer in Objective C). This code will run as a daemon and is effectively like calling the python script every minute using a cron, but without requiring that to be set up by the user.

In this question about a cron implemented in Python, the solution appears to effectively just sleep() for x seconds. I don"t need such advanced functionality so perhaps something like this would work

``````while True:
# Code executed here
time.sleep(60)
``````

Are there any foreseeable problems with this code?

## How can I tell if a string repeats itself in Python?

I"m looking for a way to test whether or not a given string repeats itself for the entire string or not.

Examples:

``````[
"0045662100456621004566210045662100456621",             # "00456621"
"0072992700729927007299270072992700729927",             # "00729927"
"001443001443001443001443001443001443001443",           # "001443"
"037037037037037037037037037037037037037037037",        # "037"
"047619047619047619047619047619047619047619",           # "047619"
"002457002457002457002457002457002457002457",           # "002457"
"001221001221001221001221001221001221001221",           # "001221"
"001230012300123001230012300123001230012300123",        # "00123"
"0013947001394700139470013947001394700139470013947",    # "0013947"
"001001001001001001001001001001001001001001001001001",  # "001"
"001406469760900140646976090014064697609",              # "0014064697609"
]
``````

are strings which repeat themselves, and

``````[
"004608294930875576036866359447",
"00469483568075117370892018779342723",
"004739336492890995260663507109",
"001508295625942684766214177978883861236802413273",
"007518796992481203",
"0071942446043165467625899280575539568345323741",
"0434782608695652173913",
"0344827586206896551724137931",
"002481389578163771712158808933",
"002932551319648093841642228739",
"0035587188612099644128113879",
"003484320557491289198606271777",
"00115074798619102416570771",
]
``````

are examples of ones that do not.

The repeating sections of the strings I"m given can be quite long, and the strings themselves can be 500 or more characters, so looping through each character trying to build a pattern then checking the pattern vs the rest of the string seems awful slow. Multiply that by potentially hundreds of strings and I can"t see any intuitive solution.

I"ve looked into regexes a bit and they seem good for when you know what you"re looking for, or at least the length of the pattern you"re looking for. Unfortunately, I know neither.

How can I tell if a string is repeating itself and if it is, what the shortest repeating subsequence is?

## Repeat string to certain length

What is an efficient way to repeat a string to a certain length? Eg: `repeat("abc", 7) -> "abcabca"`

Here is my current code:

``````def repeat(string, length):
cur, old = 1, string
while len(string) < length:
string += old[cur-1]
cur = (cur+1)%len(old)
return string
``````

Is there a better (more pythonic) way to do this? Maybe using list comprehension?

## tqdm in Jupyter Notebook prints new progress bars repeatedly

I am using `tqdm` to print progress in a script I"m running in a Jupyter notebook. I am printing all messages to the console via `tqdm.write()`. However, this still gives me a skewed output like so:

That is, each time a new line has to be printed, a new progress bar is printed on the next line. This does not happen when I run the script via terminal. How can I solve this?

## How to repeat last command in python interpreter shell?

How do I repeat the last command? The usual keys: Up, Ctrl+Up, Alt-p don"t work. They produce nonsensical characters.

``````(ve)[[email protected] ve]\$ python
Python 2.6.6 (r266:84292, Nov 15 2010, 21:48:32)
[GCC 4.4.4 20100630 (Red Hat 4.4.4-10)] on linux2
>>> print "hello world"
hello world
>>> ^[[A
File "<stdin>", line 1
^
SyntaxError: invalid syntax
>>> ^[[1;5A
File "<stdin>", line 1
[1;5A
^
SyntaxError: invalid syntax
>>> ^[p
File "<stdin>", line 1
p
^
SyntaxError: invalid syntax
>>>
``````

## Given a string of a million numbers, return all repeating 3 digit numbers

I had an interview with a hedge fund company in New York a few months ago and unfortunately, I did not get the internship offer as a data/software engineer. (They also asked the solution to be in Python.)

I pretty much screwed up on the first interview problem...

Question: Given a string of a million numbers (Pi for example), write a function/program that returns all repeating 3 digit numbers and number of repetition greater than 1

For example: if the string was: `123412345123456` then the function/program would return:

``````123 - 3 times
234 - 3 times
345 - 2 times
``````

They did not give me the solution after I failed the interview, but they did tell me that the time complexity for the solution was constant of 1000 since all the possible outcomes are between:

000 --> 999

Now that I"m thinking about it, I don"t think it"s possible to come up with a constant time algorithm. Is it?

## Python threading.timer - repeat function every "n" seconds

I want to fire off a function every 0.5 seconds and be able to start and stop and reset the timer. I"m not too knowledgeable of how Python threads work and am having difficulties with the python timer.

However, I keep getting `RuntimeError: threads can only be started once` when I execute `threading.timer.start()` twice. Is there a work around for this? I tried applying `threading.timer.cancel()` before each start.

Pseudo code:

``````t=threading.timer(0.5,function)
while True:
t.cancel()
t.start()
``````

## How to assign to repeated field?

I am using protocol buffers in python and I have a `Person` message

``````repeated uint64 id
``````

but when I try to assign a value to it like:

``````person.id = [1, 32, 43432]
``````

I get an error: `Assigment not allowed for repeated field "id" in protocol message object` How to assign a value to a repeated field ?

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
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.onlamp.com/pub/a/python/2003/04/17/metaclasses.html",
"http://www.python.org/doc/",
"http://www.python.org/getit/",
"http://www.python.org/community/",
"https://wiki.python.org/moin/",
]

# Make the Pool of workers

# 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?

## TLDR:

The special attribute `__slots__` allows you to explicitly state which instance attributes you expect your object instances to have, with the expected results:

1. faster attribute access.
2. space savings in memory.

The space savings is from

1. Storing value references in slots instead of `__dict__`.
2. Denying `__dict__` and `__weakref__` creation if parent classes deny them and you declare `__slots__`.

### Quick Caveats

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.

### Requirements:

• 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.

## Why use `__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.

## Why use `__slots__`: Memory Savings

Another purpose of `__slots__` is to reduce the space in memory that each object instance takes up.

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‚Ä†   16         56 + 112‚Ä† | ‚Ä†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
``````

## Demonstration of `__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!
``````

### Add `"__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.

### Set to empty tuple when subclassing a namedtuple:

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.

## Biggest Caveat: Multiple inheritance

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"
``````

## Other cases to avoid slots:

• Avoid them when you want to perform `__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.)
• Avoid them if you want to subclass variable length builtins like long, tuple, or str, and you want to add attributes to them.
• Avoid them if you insist on providing default values via class attributes for instance variables.

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.

### Do not "only use `__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 pickling

When 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.

## Critique of the (until Oct 2, 2016) accepted answer

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.

# Memory usage evidence

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.

# The short answer, or TL;DR

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:

1. `eval` accepts only a single expression, `exec` can take a code block that has Python statements: loops, `try: except:`, `class` and function/method `def`initions 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)
``````
2. `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 `compile`d 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
``````

# The longer answer, a.k.a the gory details

## `exec` and `eval`

The `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 `exec`uted 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).

## `compile`

The `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"))
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"))
``````
• `"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 `compile`d into bytecode; that is, with Python 2, `print` is a statement, and cannot be `eval`led 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`.

## Syntax differences of `exec` between Python 2 and Python 3

One 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 to `exec expr in globals`, while the form `exec(expr, globals, locals)` is equivalent to `exec expr in globals, locals`. The tuple form of `exec` provides compatibility with Python 3, where `exec` is 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
>>> 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!).

## What is the difference between the list methods append and extend?

• `append` adds its argument as a single element to the end of a list. The length of the list itself will increase by one.
• `extend` iterates over its argument adding each element to the list, extending the list. The length of the list will increase by however many elements were in the iterable argument.

## `append`

The `list.append` method appends an object to the end of the list.

``````my_list.append(object)
``````

Whatever the object is, whether a number, a string, another list, or something else, it gets added onto the end of `my_list` as a single entry on the list.

``````>>> my_list
["foo", "bar"]
>>> my_list.append("baz")
>>> my_list
["foo", "bar", "baz"]
``````

So keep in mind that a list is an object. If you append another list onto a list, the first list will be a single object at the end of the list (which may not be what you want):

``````>>> another_list = [1, 2, 3]
>>> my_list.append(another_list)
>>> my_list
["foo", "bar", "baz", [1, 2, 3]]
#^^^^^^^^^--- single item at the end of the list.
``````

## `extend`

The `list.extend` method extends a list by appending elements from an iterable:

``````my_list.extend(iterable)
``````

So with extend, each element of the iterable gets appended onto the list. For example:

``````>>> my_list
["foo", "bar"]
>>> another_list = [1, 2, 3]
>>> my_list.extend(another_list)
>>> my_list
["foo", "bar", 1, 2, 3]
``````

Keep in mind that a string is an iterable, so if you extend a list with a string, you"ll append each character as you iterate over the string (which may not be what you want):

``````>>> my_list.extend("baz")
>>> my_list
["foo", "bar", 1, 2, 3, "b", "a", "z"]
``````

## Operator Overload, `__add__` (`+`) and `__iadd__` (`+=`)

Both `+` and `+=` operators are defined for `list`. They are semantically similar to extend.

`my_list + another_list` creates a third list in memory, so you can return the result of it, but it requires that the second iterable be a list.

`my_list += another_list` modifies the list in-place (it is the in-place operator, and lists are mutable objects, as we"ve seen) so it does not create a new list. It also works like extend, in that the second iterable can be any kind of iterable.

Don"t get confused - `my_list = my_list + another_list` is not equivalent to `+=` - it gives you a brand new list assigned to my_list.

## Time Complexity

Append has (amortized) constant time complexity, O(1).

Extend has time complexity, O(k).

Iterating through the multiple calls to `append` adds to the complexity, making it equivalent to that of extend, and since extend"s iteration is implemented in C, it will always be faster if you intend to append successive items from an iterable onto a list.

Regarding "amortized" - from the list object implementation source:

``````    /* This over-allocates proportional to the list size, making room
* for additional growth.  The over-allocation is mild, but is
* enough to give linear-time amortized behavior over a long
* sequence of appends() in the presence of a poorly-performing
* system realloc().
``````

This means that we get the benefits of a larger than needed memory reallocation up front, but we may pay for it on the next marginal reallocation with an even larger one. Total time for all appends is linear at O(n), and that time allocated per append, becomes O(1).

## Performance

You may wonder what is more performant, since append can be used to achieve the same outcome as extend. The following functions do the same thing:

``````def append(alist, iterable):
for item in iterable:
alist.append(item)

def extend(alist, iterable):
alist.extend(iterable)
``````

So let"s time them:

``````import timeit

>>> min(timeit.repeat(lambda: append([], "abcdefghijklmnopqrstuvwxyz")))
2.867846965789795
>>> min(timeit.repeat(lambda: extend([], "abcdefghijklmnopqrstuvwxyz")))
0.8060121536254883
``````

### Addressing a comment on timings

A commenter said:

Perfect answer, I just miss the timing of comparing adding only one element

Do the semantically correct thing. If you want to append all elements in an iterable, use `extend`. If you"re just adding one element, use `append`.

Ok, so let"s create an experiment to see how this works out in time:

``````def append_one(a_list, element):
a_list.append(element)

def extend_one(a_list, element):
"""creating a new list is semantically the most direct
way to create an iterable to give to extend"""
a_list.extend([element])

import timeit
``````

And we see that going out of our way to create an iterable just to use extend is a (minor) waste of time:

``````>>> min(timeit.repeat(lambda: append_one([], 0)))
0.2082819009956438
>>> min(timeit.repeat(lambda: extend_one([], 0)))
0.2397019260097295
``````

We learn from this that there"s nothing gained from using `extend` when we have only one element to append.

Also, these timings are not that important. I am just showing them to make the point that, in Python, doing the semantically correct thing is doing things the Right Way‚Ñ¢.

It"s conceivable that you might test timings on two comparable operations and get an ambiguous or inverse result. Just focus on doing the semantically correct thing.

## Conclusion

We see that `extend` is semantically clearer, and that it can run much faster than `append`, when you intend to append each element in an iterable to a list.

If you only have a single element (not in an iterable) to add to the list, use `append`.

(Note: this answer is based on a short blog post about `einsum` I wrote a while ago.)

## What does `einsum` do?

Imagine that we have two multi-dimensional arrays, `A` and `B`. Now let"s suppose we want to...

• multiply `A` with `B` in a particular way to create new array of products; and then maybe
• sum this new array along particular axes; and then maybe
• transpose the axes of the new array in a particular order.

There"s a good chance that `einsum` will help us do this faster and more memory-efficiently than combinations of the NumPy functions like `multiply`, `sum` and `transpose` will allow.

## How does `einsum` work?

Here"s a simple (but not completely trivial) example. Take the following two arrays:

``````A = np.array([0, 1, 2])

B = np.array([[ 0,  1,  2,  3],
[ 4,  5,  6,  7],
[ 8,  9, 10, 11]])
``````

We will multiply `A` and `B` element-wise and then sum along the rows of the new array. In "normal" NumPy we"d write:

``````>>> (A[:, np.newaxis] * B).sum(axis=1)
array([ 0, 22, 76])
``````

So here, the indexing operation on `A` lines up the first axes of the two arrays so that the multiplication can be broadcast. The rows of the array of products are then summed to return the answer.

Now if we wanted to use `einsum` instead, we could write:

``````>>> np.einsum("i,ij->i", A, B)
array([ 0, 22, 76])
``````

The signature string `"i,ij->i"` is the key here and needs a little bit of explaining. You can think of it in two halves. On the left-hand side (left of the `->`) we"ve labelled the two input arrays. To the right of `->`, we"ve labelled the array we want to end up with.

Here is what happens next:

• `A` has one axis; we"ve labelled it `i`. And `B` has two axes; we"ve labelled axis 0 as `i` and axis 1 as `j`.

• By repeating the label `i` in both input arrays, we are telling `einsum` that these two axes should be multiplied together. In other words, we"re multiplying array `A` with each column of array `B`, just like `A[:, np.newaxis] * B` does.

• Notice that `j` does not appear as a label in our desired output; we"ve just used `i` (we want to end up with a 1D array). By omitting the label, we"re telling `einsum` to sum along this axis. In other words, we"re summing the rows of the products, just like `.sum(axis=1)` does.

That"s basically all you need to know to use `einsum`. It helps to play about a little; if we leave both labels in the output, `"i,ij->ij"`, we get back a 2D array of products (same as `A[:, np.newaxis] * B`). If we say no output labels, `"i,ij->`, we get back a single number (same as doing `(A[:, np.newaxis] * B).sum()`).

The great thing about `einsum` however, is that it does not build a temporary array of products first; it just sums the products as it goes. This can lead to big savings in memory use.

## A slightly bigger example

To explain the dot product, here are two new arrays:

``````A = array([[1, 1, 1],
[2, 2, 2],
[5, 5, 5]])

B = array([[0, 1, 0],
[1, 1, 0],
[1, 1, 1]])
``````

We will compute the dot product using `np.einsum("ij,jk->ik", A, B)`. Here"s a picture showing the labelling of the `A` and `B` and the output array that we get from the function:

You can see that label `j` is repeated - this means we"re multiplying the rows of `A` with the columns of `B`. Furthermore, the label `j` is not included in the output - we"re summing these products. Labels `i` and `k` are kept for the output, so we get back a 2D array.

It might be even clearer to compare this result with the array where the label `j` is not summed. Below, on the left you can see the 3D array that results from writing `np.einsum("ij,jk->ijk", A, B)` (i.e. we"ve kept label `j`):

Summing axis `j` gives the expected dot product, shown on the right.

## Some exercises

To get more of a feel for `einsum`, it can be useful to implement familiar NumPy array operations using the subscript notation. Anything that involves combinations of multiplying and summing axes can be written using `einsum`.

Let A and B be two 1D arrays with the same length. For example, `A = np.arange(10)` and `B = np.arange(5, 15)`.

• The sum of `A` can be written:

``````np.einsum("i->", A)
``````
• Element-wise multiplication, `A * B`, can be written:

``````np.einsum("i,i->i", A, B)
``````
• The inner product or dot product, `np.inner(A, B)` or `np.dot(A, B)`, can be written:

``````np.einsum("i,i->", A, B) # or just use "i,i"
``````
• The outer product, `np.outer(A, B)`, can be written:

``````np.einsum("i,j->ij", A, B)
``````

For 2D arrays, `C` and `D`, provided that the axes are compatible lengths (both the same length or one of them of has length 1), here are a few examples:

• The trace of `C` (sum of main diagonal), `np.trace(C)`, can be written:

``````np.einsum("ii", C)
``````
• Element-wise multiplication of `C` and the transpose of `D`, `C * D.T`, can be written:

``````np.einsum("ij,ji->ij", C, D)
``````
• Multiplying each element of `C` by the array `D` (to make a 4D array), `C[:, :, None, None] * D`, can be written:

``````np.einsum("ij,kl->ijkl", C, D)
``````

`root` is the old (pre-conda 4.4) name for the main environment; after conda 4.4, it was renamed to be `base`. source

## What 95% of people actually want

In most cases what you want to do when you say that you want to update Anaconda is to execute the command:

``````conda update --all
``````

(But this should be preceeded by `conda update -n base conda` so you have the latest `conda` version installed)

This will update all packages in the current environment to the latest version -- with the small print being that it may use an older version of some packages in order to satisfy dependency constraints (often this won"t be necessary and when it is necessary the package plan solver will do its best to minimize the impact).

This needs to be executed from the command line, and the best way to get there is from Anaconda Navigator, then the "Environments" tab, then click on the triangle beside the `base` environment, selecting "Open Terminal":

This operation will only update the one selected environment (in this case, the `base` environment). If you have other environments you"d like to update you can repeat the process above, but first click on the environment. When it is selected there is a triangular marker on the right (see image above, step 3). Or from the command line you can provide the environment name (`-n envname`) or path (`-p /path/to/env`), for example to update your `dspyr` environment from the screenshot above:

``````conda update -n dspyr --all
``````

## Update individual packages

If you are only interested in updating an individual package then simply click on the blue arrow or blue version number in Navigator, e.g. for `astroid` or `astropy` in the screenshot above, and this will tag those packages for an upgrade. When you are done you need to click the "Apply" button:

Or from the command line:

``````conda update astroid astropy
``````

## Updating just the packages in the standard Anaconda Distribution

If you don"t care about package versions and just want "the latest set of all packages in the standard Anaconda Distribution, so long as they work together", then you should take a look at this gist.

## Why updating the Anaconda package is almost always a bad idea

In most cases updating the Anaconda package in the package list will have a surprising result: you may actually downgrade many packages (in fact, this is likely if it indicates the version as `custom`). The gist above provides details.

## Leverage conda environments

Your `base` environment is probably not a good place to try and manage an exact set of packages: it is going to be a dynamic working space with new packages installed and packages randomly updated. If you need an exact set of packages then create a conda environment to hold them. Thanks to the conda package cache and the way file linking is used doing this is typically i) fast and ii) consumes very little additional disk space. E.g.

``````conda create -n myspecialenv -c bioconda -c conda-forge python=3.5 pandas beautifulsoup seaborn nltk
``````

The conda documentation has more details and examples.

## pip, PyPI, and setuptools?

None of this is going to help with updating packages that have been installed from PyPI via `pip` or any packages installed using `python setup.py install`. `conda list` will give you some hints about the pip-based Python packages you have in an environment, but it won"t do anything special to update them.

## Commercial use of Anaconda or Anaconda Enterprise

It is pretty much exactly the same story, with the exception that you may not be able to update the `base` environment if it was installed by someone else (say to `/opt/anaconda/latest`). If you"re not able to update the environments you are using you should be able to clone and then update:

``````conda create -n myenv --clone base
conda update -n myenv --all
``````

First consider if you really need to iterate over rows in a DataFrame. See this answer for alternatives.

If you still need to iterate over rows, you can use methods below. Note some important caveats which are not mentioned in any of the other answers.

• DataFrame.iterrows()

``````for index, row in df.iterrows():
print(row["c1"], row["c2"])
``````
• DataFrame.itertuples()

``````for row in df.itertuples(index=True, name="Pandas"):
print(row.c1, row.c2)
``````

`itertuples()` is supposed to be faster than `iterrows()`

But be aware, according to the docs (pandas 0.24.2 at the moment):

• iterrows: `dtype` might not match from row to row

Because iterrows returns a Series for each row, it does not preserve dtypes across the rows (dtypes are preserved across columns for DataFrames). To preserve dtypes while iterating over the rows, it is better to use itertuples() which returns namedtuples of the values and which is generally much faster than iterrows()

• iterrows: Do not modify rows

You should never modify something you are iterating over. This is not guaranteed to work in all cases. Depending on the data types, the iterator returns a copy and not a view, and writing to it will have no effect.

``````new_df = df.apply(lambda x: x * 2)
``````
• itertuples:

The column names will be renamed to positional names if they are invalid Python identifiers, repeated, or start with an underscore. With a large number of columns (>255), regular tuples are returned.

See pandas docs on iteration for more details.

Client and Resource are two different abstractions within the boto3 SDK for making AWS service requests. You would typically choose to use either the Client abstraction or the Resource abstraction. I"ve outlined the differences between Client and Resource below to help readers decide which to use.

Session is largely orthogonal to the concepts of Client and Resource (but is used by both).

Here"s some more detailed information on what Client, Resource, and Session are all about.

Client:

• this is the original boto3 API abstraction
• provides low-level AWS service access
• all AWS service operations are supported by clients
• exposes botocore client to the developer
• typically maps 1:1 with the AWS service API
• snake-cased method names (e.g. ListBuckets API => list_buckets method)
• generated from AWS service description

``````import boto3

client = boto3.client("s3")
response = client.list_objects_v2(Bucket="mybucket")
for content in response["Contents"]:
obj_dict = client.get_object(Bucket="mybucket", Key=content["Key"])
print(content["Key"], obj_dict["LastModified"])
``````

Note: this client-level code is limited to listing at most 1000 objects. You would have to use a paginator, or implement your own loop, calling list_objects_v2() repeatedly with a continuation marker if there were more than 1000 objects.

Resource:

• this is the newer boto3 API abstraction
• provides high-level, object-oriented API
• does not provide 100% API coverage of AWS services
• uses identifiers and attributes
• has actions (operations on resources)
• exposes subresources and collections of AWS resources
• generated from resource description

Here"s the equivalent example using resource-level access to an S3 bucket"s objects (all):

``````import boto3

s3 = boto3.resource("s3")
bucket = s3.Bucket("mybucket")
for obj in bucket.objects.all():
print(obj.key, obj.last_modified)
``````

Note: in this case you do not have to make a second API call to get the objects; they"re available to you as a collection on the bucket. These collections of subresources are lazily-loaded.

You can see that the `Resource` version of the code is much simpler, more compact, and has more capability (it does pagination for you). The `Client` version of the code would actually be more complicated than shown above if you wanted to include pagination.

Session:

• stores configuration information (primarily credentials and selected region)
• allows you to create service clients and resources
• boto3 creates a default session for you when needed

## Best way to check if a list is empty

For example, if passed the following:

``````a = []
``````

How do I check to see if a is empty?

Place the list in a boolean context (for example, with an `if` or `while` statement). It will test `False` if it is empty, and `True` otherwise. For example:

``````if not a:                           # do this!
print("a is an empty list")
``````

## PEP 8

PEP 8, the official Python style guide for Python code in Python"s standard library, asserts:

For sequences, (strings, lists, tuples), use the fact that empty sequences are false.

``````Yes: if not seq:
if seq:

No: if len(seq):
if not len(seq):
``````

We should expect that standard library code should be as performant and correct as possible. But why is that the case, and why do we need this guidance?

## Explanation

I frequently see code like this from experienced programmers new to Python:

``````if len(a) == 0:                     # Don"t do this!
print("a is an empty list")
``````

And users of lazy languages may be tempted to do this:

``````if a == []:                         # Don"t do this!
print("a is an empty list")
``````

These are correct in their respective other languages. And this is even semantically correct in Python.

But we consider it un-Pythonic because Python supports these semantics directly in the list object"s interface via boolean coercion.

From the docs (and note specifically the inclusion of the empty list, `[]`):

By default, an object is considered true unless its class defines either a `__bool__()` method that returns `False` or a `__len__()` method that returns zero, when called with the object. Here are most of the built-in objects considered false:

• constants defined to be false: `None` and `False`.
• zero of any numeric type: `0`, `0.0`, `0j`, `Decimal(0)`, `Fraction(0, 1)`
• empty sequences and collections: `""`, `()`, `[]`, `{}`, `set()`, `range(0)`

And the datamodel documentation:

`object.__bool__(self)`

Called to implement truth value testing and the built-in operation `bool()`; should return `False` or `True`. When this method is not defined, `__len__()` is called, if it is defined, and the object is considered true if its result is nonzero. If a class defines neither `__len__()` nor `__bool__()`, all its instances are considered true.

and

`object.__len__(self)`

Called to implement the built-in function `len()`. Should return the length of the object, an integer >= 0. Also, an object that doesn‚Äôt define a `__bool__()` method and whose `__len__()` method returns zero is considered to be false in a Boolean context.

``````if len(a) == 0:                     # Don"t do this!
print("a is an empty list")
``````

or this:

``````if a == []:                     # Don"t do this!
print("a is an empty list")
``````

Do this:

``````if not a:
print("a is an empty list")
``````

## Doing what"s Pythonic usually pays off in performance:

Does it pay off? (Note that less time to perform an equivalent operation is better:)

``````>>> import timeit
>>> min(timeit.repeat(lambda: len([]) == 0, repeat=100))
0.13775854044661884
>>> min(timeit.repeat(lambda: [] == [], repeat=100))
0.0984637276455409
>>> min(timeit.repeat(lambda: not [], repeat=100))
0.07878462291455435
``````

For scale, here"s the cost of calling the function and constructing and returning an empty list, which you might subtract from the costs of the emptiness checks used above:

``````>>> min(timeit.repeat(lambda: [], repeat=100))
0.07074015751817342
``````

We see that either checking for length with the builtin function `len` compared to `0` or checking against an empty list is much less performant than using the builtin syntax of the language as documented.

Why?

For the `len(a) == 0` check:

First Python has to check the globals to see if `len` is shadowed.

Then it must call the function, load `0`, and do the equality comparison in Python (instead of with C):

``````>>> import dis
>>> dis.dis(lambda: len([]) == 0)
2 BUILD_LIST               0
4 CALL_FUNCTION            1
8 COMPARE_OP               2 (==)
10 RETURN_VALUE
``````

And for the `[] == []` it has to build an unnecessary list and then, again, do the comparison operation in Python"s virtual machine (as opposed to C)

``````>>> dis.dis(lambda: [] == [])
1           0 BUILD_LIST               0
2 BUILD_LIST               0
4 COMPARE_OP               2 (==)
6 RETURN_VALUE
``````

The "Pythonic" way is a much simpler and faster check since the length of the list is cached in the object instance header:

``````>>> dis.dis(lambda: not [])
1           0 BUILD_LIST               0
2 UNARY_NOT
4 RETURN_VALUE
``````

## Evidence from the C source and documentation

`PyVarObject`

This is an extension of `PyObject` that adds the `ob_size` field. This is only used for objects that have some notion of length. This type does not often appear in the Python/C API. It corresponds to the fields defined by the expansion of the `PyObject_VAR_HEAD` macro.

From the c source in Include/listobject.h:

``````typedef struct {
/* Vector of pointers to list elements.  list[0] is ob_item[0], etc. */
PyObject **ob_item;

/* ob_item contains space for "allocated" elements.  The number
* currently in use is ob_size.
* Invariants:
*     0 <= ob_size <= allocated
*     len(list) == ob_size
``````

I would point out that this is also true for the non-empty case though its pretty ugly as with `l=[]` then `%timeit len(l) != 0` 90.6 ns ¬± 8.3 ns, `%timeit l != []` 55.6 ns ¬± 3.09, `%timeit not not l` 38.5 ns ¬± 0.372. But there is no way anyone is going to enjoy `not not l` despite triple the speed. It looks ridiculous. But the speed wins out
I suppose the problem is testing with timeit since just `if l:` is sufficient but surprisingly `%timeit bool(l)` yields 101 ns ¬± 2.64 ns. Interesting there is no way to coerce to bool without this penalty. `%timeit l` is useless since no conversion would occur.

IPython magic, `%timeit`, is not entirely useless here:

``````In [1]: l = []

In [2]: %timeit l
20 ns ¬± 0.155 ns per loop (mean ¬± std. dev. of 7 runs, 100000000 loops each)

In [3]: %timeit not l
24.4 ns ¬± 1.58 ns per loop (mean ¬± std. dev. of 7 runs, 10000000 loops each)

In [4]: %timeit not not l
30.1 ns ¬± 2.16 ns per loop (mean ¬± std. dev. of 7 runs, 10000000 loops each)
``````

We can see there"s a bit of linear cost for each additional `not` here. We want to see the costs, ceteris paribus, that is, all else equal - where all else is minimized as far as possible:

``````In [5]: %timeit if l: pass
22.6 ns ¬± 0.963 ns per loop (mean ¬± std. dev. of 7 runs, 10000000 loops each)

In [6]: %timeit if not l: pass
24.4 ns ¬± 0.796 ns per loop (mean ¬± std. dev. of 7 runs, 10000000 loops each)

In [7]: %timeit if not not l: pass
23.4 ns ¬± 0.793 ns per loop (mean ¬± std. dev. of 7 runs, 10000000 loops each)
``````

Now let"s look at the case for an unempty list:

``````In [8]: l = [1]

In [9]: %timeit if l: pass
23.7 ns ¬± 1.06 ns per loop (mean ¬± std. dev. of 7 runs, 10000000 loops each)

In [10]: %timeit if not l: pass
23.6 ns ¬± 1.64 ns per loop (mean ¬± std. dev. of 7 runs, 10000000 loops each)

In [11]: %timeit if not not l: pass
26.3 ns ¬± 1 ns per loop (mean ¬± std. dev. of 7 runs, 10000000 loops each)
``````

What we can see here is that it makes little difference whether you pass in an actual `bool` to the condition check or the list itself, and if anything, giving the list, as is, is faster.

Python is written in C; it uses its logic at the C level. Anything you write in Python will be slower. And it will likely be orders of magnitude slower unless you"re using the mechanisms built into Python directly.

I know `object` columns `type` makes the data hard to convert with a `pandas` function. When I received the data like this, the first thing that came to mind was to "flatten" or unnest the columns .

I am using `pandas` and `python` functions for this type of question. If you are worried about the speed of the above solutions, check user3483203"s answer, since it"s using `numpy` and most of the time `numpy` is faster . I recommend `Cpython` and `numba` if speed matters.

Method 0 [pandas >= 0.25]
Starting from pandas 0.25, if you only need to explode one column, you can use the `pandas.DataFrame.explode` function:

``````df.explode("B")

A  B
0  1  1
1  1  2
0  2  1
1  2  2
``````

Given a dataframe with an empty `list` or a `NaN` in the column. An empty list will not cause an issue, but a `NaN` will need to be filled with a `list`

``````df = pd.DataFrame({"A": [1, 2, 3, 4],"B": [[1, 2], [1, 2], [], np.nan]})
df.B = df.B.fillna({i: [] for i in df.index})  # replace NaN with []
df.explode("B")

A    B
0  1    1
0  1    2
1  2    1
1  2    2
2  3  NaN
3  4  NaN
``````

Method 1
`apply + pd.Series` (easy to understand but in terms of performance not recommended . )

``````df.set_index("A").B.apply(pd.Series).stack().reset_index(level=0).rename(columns={0:"B"})
Out[463]:
A  B
0  1  1
1  1  2
0  2  1
1  2  2
``````

Method 2
Using `repeat` with `DataFrame` constructor , re-create your dataframe (good at performance, not good at multiple columns )

``````df=pd.DataFrame({"A":df.A.repeat(df.B.str.len()),"B":np.concatenate(df.B.values)})
df
Out[465]:
A  B
0  1  1
0  1  2
1  2  1
1  2  2
``````

Method 2.1
for example besides A we have A.1 .....A.n. If we still use the method(Method 2) above it is hard for us to re-create the columns one by one .

Solution : `join` or `merge` with the `index` after "unnest" the single columns

``````s=pd.DataFrame({"B":np.concatenate(df.B.values)},index=df.index.repeat(df.B.str.len()))
s.join(df.drop("B",1),how="left")
Out[477]:
B  A
0  1  1
0  2  1
1  1  2
1  2  2
``````

If you need the column order exactly the same as before, add `reindex` at the end.

``````s.join(df.drop("B",1),how="left").reindex(columns=df.columns)
``````

Method 3
recreate the `list`

``````pd.DataFrame([[x] + [z] for x, y in df.values for z in y],columns=df.columns)
Out[488]:
A  B
0  1  1
1  1  2
2  2  1
3  2  2
``````

If more than two columns, use

``````s=pd.DataFrame([[x] + [z] for x, y in zip(df.index,df.B) for z in y])
s.merge(df,left_on=0,right_index=True)
Out[491]:
0  1  A       B
0  0  1  1  [1, 2]
1  0  2  1  [1, 2]
2  1  1  2  [1, 2]
3  1  2  2  [1, 2]
``````

Method 4
using `reindex` or `loc`

``````df.reindex(df.index.repeat(df.B.str.len())).assign(B=np.concatenate(df.B.values))
Out[554]:
A  B
0  1  1
0  1  2
1  2  1
1  2  2

#df.loc[df.index.repeat(df.B.str.len())].assign(B=np.concatenate(df.B.values))
``````

Method 5
when the list only contains unique values:

``````df=pd.DataFrame({"A":[1,2],"B":[[1,2],[3,4]]})
from collections import ChainMap
d = dict(ChainMap(*map(dict.fromkeys, df["B"], df["A"])))
pd.DataFrame(list(d.items()),columns=df.columns[::-1])
Out[574]:
B  A
0  1  1
1  2  1
2  3  2
3  4  2
``````

Method 6
using `numpy` for high performance:

``````newvalues=np.dstack((np.repeat(df.A.values,list(map(len,df.B.values))),np.concatenate(df.B.values)))
pd.DataFrame(data=newvalues[0],columns=df.columns)
A  B
0  1  1
1  1  2
2  2  1
3  2  2
``````

Method 7
using base function `itertools` `cycle` and `chain`: Pure python solution just for fun

``````from itertools import cycle,chain
l=df.values.tolist()
l1=[list(zip([x[0]], cycle(x[1])) if len([x[0]]) > len(x[1]) else list(zip(cycle([x[0]]), x[1]))) for x in l]
pd.DataFrame(list(chain.from_iterable(l1)),columns=df.columns)
A  B
0  1  1
1  1  2
2  2  1
3  2  2
``````

Generalizing to multiple columns

``````df=pd.DataFrame({"A":[1,2],"B":[[1,2],[3,4]],"C":[[1,2],[3,4]]})
df
Out[592]:
A       B       C
0  1  [1, 2]  [1, 2]
1  2  [3, 4]  [3, 4]
``````

Self-def function:

``````def unnesting(df, explode):
idx = df.index.repeat(df[explode[0]].str.len())
df1 = pd.concat([
pd.DataFrame({x: np.concatenate(df[x].values)}) for x in explode], axis=1)
df1.index = idx

return df1.join(df.drop(explode, 1), how="left")

unnesting(df,["B","C"])
Out[609]:
B  C  A
0  1  1  1
0  2  2  1
1  3  3  2
1  4  4  2
``````

### Column-wise Unnesting

All above method is talking about the vertical unnesting and explode , If you do need expend the list horizontal, Check with `pd.DataFrame` constructor

``````df.join(pd.DataFrame(df.B.tolist(),index=df.index).add_prefix("B_"))
Out[33]:
A       B       C  B_0  B_1
0  1  [1, 2]  [1, 2]    1    2
1  2  [3, 4]  [3, 4]    3    4
``````

Updated function

``````def unnesting(df, explode, axis):
if axis==1:
idx = df.index.repeat(df[explode[0]].str.len())
df1 = pd.concat([
pd.DataFrame({x: np.concatenate(df[x].values)}) for x in explode], axis=1)
df1.index = idx

return df1.join(df.drop(explode, 1), how="left")
else :
df1 = pd.concat([
pd.DataFrame(df[x].tolist(), index=df.index).add_prefix(x) for x in explode], axis=1)
return df1.join(df.drop(explode, 1), how="left")
``````

Test Output

``````unnesting(df, ["B","C"], axis=0)
Out[36]:
B0  B1  C0  C1  A
0   1   2   1   2  1
1   3   4   3   4  2
``````

Update 2021-02-17 with original explode function

``````def unnesting(df, explode, axis):
if axis==1:
df1 = pd.concat([df[x].explode() for x in explode], axis=1)
return df1.join(df.drop(explode, 1), how="left")
else :
df1 = pd.concat([
pd.DataFrame(df[x].tolist(), index=df.index).add_prefix(x) for x in explode], axis=1)
return df1.join(df.drop(explode, 1), how="left")
``````

## How do I merge two dictionaries in a single expression (taking union of dictionaries)?

### Question by Carl Meyer

I have two Python dictionaries, and I want to write a single expression that returns these two dictionaries, merged (i.e. taking the union). The `update()` method would be what I need, if it returned its result instead of modifying a dictionary in-place.

``````>>> x = {"a": 1, "b": 2}
>>> y = {"b": 10, "c": 11}
>>> z = x.update(y)
>>> print(z)
None
>>> x
{"a": 1, "b": 10, "c": 11}
``````

How can I get that final merged dictionary in `z`, not `x`?

(To be extra-clear, the last-one-wins conflict-handling of `dict.update()` is what I"m looking for as well.)

## Accessing the index in "for" loops?

### Question by Joan Venge

How do I access the index in a `for` loop like the following?

``````ints = [8, 23, 45, 12, 78]
for i in ints:
print("item #{} = {}".format(???, i))
``````

I want to get this output:

``````item #1 = 8
item #2 = 23
item #3 = 45
item #4 = 12
item #5 = 78
``````

When I loop through it using a `for` loop, how do I access the loop index, from 1 to 5 in this case?

## Iterating over dictionaries using "for" loops

I am a bit puzzled by the following code:

``````d = {"x": 1, "y": 2, "z": 3}
for key in d:
print (key, "corresponds to", d[key])
``````

What I don"t understand is the `key` portion. How does Python recognize that it needs only to read the key from the dictionary? Is `key` a special word in Python? Or is it simply a variable?

## Using global variables in a function

How can I create or use a global variable in a function?

If I create a global variable in one function, how can I use that global variable in another function? Do I need to store the global variable in a local variable of the function which needs its access?

## Manually raising (throwing) an exception in Python

How can I raise an exception in Python so that it can later be caught via an `except` block?

## Calling a function of a module by using its name (a string)

What is the best way to go about calling a function given a string with the function"s name in a Python program. For example, let"s say that I have a module `foo`, and I have a string whose content is `"bar"`. What is the best way to call `foo.bar()`?

I need to get the return value of the function, which is why I don"t just use `eval`. I figured out how to do it by using `eval` to define a temp function that returns the result of that function call, but I"m hoping that there is a more elegant way to do this.

## What is the meaning of single and double underscore before an object name?

Can someone please explain the exact meaning of having single and double leading underscores before an object"s name in Python, and the difference between both?

Also, does that meaning stay the same regardless of whether the object in question is a variable, a function, a method, etc.?

## Save plot to image file instead of displaying it using Matplotlib

I am writing a quick-and-dirty script to generate plots on the fly. I am using the code below (from Matplotlib documentation) as a starting point:

``````from pylab import figure, axes, pie, title, show

# Make a square figure and axes
figure(1, figsize=(6, 6))
ax = axes([0.1, 0.1, 0.8, 0.8])

labels = "Frogs", "Hogs", "Dogs", "Logs"
fracs = [15, 30, 45, 10]

explode = (0, 0.05, 0, 0)
title("Raining Hogs and Dogs", bbox={"facecolor": "0.8", "pad": 5})

show()  # Actually, don"t show, just save to foo.png
``````

I don"t want to display the plot on a GUI, instead, I want to save the plot to a file (say foo.png), so that, for example, it can be used in batch scripts. How do I do that?

## What are the differences between type() and isinstance()?

What are the differences between these two code fragments?

Using `type()`:

``````import types

if type(a) is types.DictType:
do_something()
if type(b) in types.StringTypes:
do_something_else()
``````

Using `isinstance()`:

``````if isinstance(a, dict):
do_something()
if isinstance(b, str) or isinstance(b, unicode):
do_something_else()
``````

## How can I install packages using pip according to the requirements.txt file from a local directory?

Here is the problem:

I have a requirements.txt file that looks like:

``````BeautifulSoup==3.2.0
Django==1.3
Fabric==1.2.0
Jinja2==2.5.5
PyYAML==3.09
Pygments==1.4
SQLAlchemy==0.7.1
South==0.7.3
amqplib==0.6.1
anyjson==0.3
...
``````

I have a local archive directory containing all the packages + others.

I have created a new virtualenv with

``````bin/virtualenv testing
``````

Upon activating it, I tried to install the packages according to requirements.txt from the local archive directory.

``````source bin/activate
pip install -r /path/to/requirements.txt -f file:///path/to/archive/
``````

I got some output that seems to indicate that the installation is fine:

``````Downloading/unpacking Fabric==1.2.0 (from -r ../testing/requirements.txt (line 3))
Running setup.py egg_info for package Fabric
warning: no previously-included files matching "*" found under directory "docs/_build"
warning: no files found matching "fabfile.py"
Running setup.py egg_info for package South
....
``````

But a later check revealed none of the package is installed properly. I cannot import the package, and none is found in the site-packages directory of my virtualenv. So what went wrong?

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.

The advantage of the `range` type over a regular `list` or `tuple` is 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 the `start`, `stop` and `step` values, 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‚Äôs all executed in optimised C code and Python stores integer values in 30-bit chunks, you‚Äôd run out of memory before you saw any performance impact due to the size of the integers involved here.

# Recommendation for beginners:

This is my personal recommendation for beginners: start by learning `virtualenv` and `pip`, tools which work with both Python 2 and 3 and in a variety of situations, and pick up other tools once you start needing them.

# PyPI packages not in the standard library:

• `virtualenv` is a very popular tool that creates isolated Python environments for Python libraries. If you"re not familiar with this tool, I highly recommend learning it, as it is a very useful tool, and I"ll be making comparisons to it for the rest of this answer.

It works by installing a bunch of files in a directory (eg: `env/`), and then modifying the `PATH` environment variable to prefix it with a custom `bin` directory (eg: `env/bin/`). An exact copy of the `python` or `python3` binary is placed in this directory, but Python is programmed to look for libraries relative to its path first, in the environment directory. It"s not part of Python"s standard library, but is officially blessed by the PyPA (Python Packaging Authority). Once activated, you can install packages in the virtual environment using `pip`.

• `pyenv` is used to isolate Python versions. For example, you may want to test your code against Python 2.7, 3.6, 3.7 and 3.8, so you"ll need a way to switch between them. Once activated, it prefixes the `PATH` environment variable with `~/.pyenv/shims`, where there are special files matching the Python commands (`python`, `pip`). These are not copies of the Python-shipped commands; they are special scripts that decide on the fly which version of Python to run based on the `PYENV_VERSION` environment variable, or the `.python-version` file, or the `~/.pyenv/version` file. `pyenv` also makes the process of downloading and installing multiple Python versions easier, using the command `pyenv install`.

• `pyenv-virtualenv` is a plugin for `pyenv` by the same author as `pyenv`, to allow you to use `pyenv` and `virtualenv` at the same time conveniently. However, if you"re using Python 3.3 or later, `pyenv-virtualenv` will try to run `python -m venv` if it is available, instead of `virtualenv`. You can use `virtualenv` and `pyenv` together without `pyenv-virtualenv`, if you don"t want the convenience features.

• `virtualenvwrapper` is a set of extensions to `virtualenv` (see docs). It gives you commands like `mkvirtualenv`, `lssitepackages`, and especially `workon` for switching between different `virtualenv` directories. This tool is especially useful if you want multiple `virtualenv` directories.

• `pyenv-virtualenvwrapper` is a plugin for `pyenv` by the same author as `pyenv`, to conveniently integrate `virtualenvwrapper` into `pyenv`.

• `pipenv` aims to combine `Pipfile`, `pip` and `virtualenv` into one command on the command-line. The `virtualenv` directory typically gets placed in `~/.local/share/virtualenvs/XXX`, with `XXX` being a hash of the path of the project directory. This is different from `virtualenv`, where the directory is typically in the current working directory. `pipenv` is meant to be used when developing Python applications (as opposed to libraries). There are alternatives to `pipenv`, such as `poetry`, which I won"t list here since this question is only about the packages that are similarly named.

# Standard library:

• `pyvenv` (not to be confused with `pyenv` in the previous section) is a script shipped with Python 3 but deprecated in Python 3.6 as it had problems (not to mention the confusing name). In Python 3.6+, the exact equivalent is `python3 -m venv`.

• `venv` is a package shipped with Python 3, which you can run using `python3 -m venv` (although for some reason some distros separate it out into a separate distro package, such as `python3-venv` on Ubuntu/Debian). It serves the same purpose as `virtualenv`, but only has a subset of its features (see a comparison here). `virtualenv` continues to be more popular than `venv`, especially since the former supports both Python 2 and 3.

You have four main options for converting types in pandas:

1. `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()`.)

2. `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).

3. `infer_objects()` - a utility method to convert object columns holding Python objects to a pandas type if possible.

4. `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.

# 1. `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.

## Basic usage

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.

## Error handling

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.

## Downcasting

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
``````

# 2. `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.

## Basic usage

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.

## Be careful

`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.

# 3. `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.

# 4. `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
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.onlamp.com/pub/a/python/2003/04/17/metaclasses.html",
"http://www.python.org/doc/",
"http://www.python.org/getit/",
"http://www.python.org/community/",
"https://wiki.python.org/moin/",
]

# Make the Pool of workers

# 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.)

## How to iterate over rows in a DataFrame in Pandas?

Iteration in Pandas is an anti-pattern and is something you should only do when you have exhausted every other option. You should not use any function with "`iter`" in its name for more than a few thousand rows or you will have to get used to a lot of waiting.

Do you want to print a DataFrame? Use `DataFrame.to_string()`.

Do you want to compute something? In that case, search for methods in this order (list modified from here):

1. Vectorization
2. Cython routines
3. List Comprehensions (vanilla `for` loop)
4. `DataFrame.apply()`: i) ¬†Reductions that can be performed in Cython, ii) Iteration in Python space
5. `DataFrame.itertuples()` and `iteritems()`
6. `DataFrame.iterrows()`

`iterrows` and `itertuples` (both receiving many votes in answers to this question) should be used in very rare circumstances, such as generating row objects/nametuples for sequential processing, which is really the only thing these functions are useful for.

Appeal to Authority

The documentation page on iteration has a huge red warning box that says:

Iterating through pandas objects is generally slow. In many cases, iterating manually over the rows is not needed [...].

* It"s actually a little more complicated than "don"t". `df.iterrows()` is the correct answer to this question, but "vectorize your ops" is the better one. I will concede that there are circumstances where iteration cannot be avoided (for example, some operations where the result depends on the value computed for the previous row). However, it takes some familiarity with the library to know when. If you"re not sure whether you need an iterative solution, you probably don"t. PS: To know more about my rationale for writing this answer, skip to the very bottom.

## Faster than Looping: Vectorization, Cython

A good number of basic operations and computations are "vectorised" by pandas (either through NumPy, or through Cythonized functions). This includes arithmetic, comparisons, (most) reductions, reshaping (such as pivoting), joins, and groupby operations. Look through the documentation on Essential Basic Functionality to find a suitable vectorised method for your problem.

If none exists, feel free to write your own using custom Cython extensions.

## Next Best Thing: List Comprehensions*

List comprehensions should be your next port of call if 1) there is no vectorized solution available, 2) performance is important, but not important enough to go through the hassle of cythonizing your code, and 3) you"re trying to perform elementwise transformation on your code. There is a good amount of evidence to suggest that list comprehensions are sufficiently fast (and even sometimes faster) for many common Pandas tasks.

The formula is simple,

``````# Iterating over one column - `f` is some function that processes your data
result = [f(x) for x in df["col"]]
# Iterating over two columns, use `zip`
result = [f(x, y) for x, y in zip(df["col1"], df["col2"])]
# Iterating over multiple columns - same data type
result = [f(row[0], ..., row[n]) for row in df[["col1", ...,"coln"]].to_numpy()]
# Iterating over multiple columns - differing data type
result = [f(row[0], ..., row[n]) for row in zip(df["col1"], ..., df["coln"])]
``````

If you can encapsulate your business logic into a function, you can use a list comprehension that calls it. You can make arbitrarily complex things work through the simplicity and speed of raw Python code.

Caveats

List comprehensions assume that your data is easy to work with - what that means is your data types are consistent and you don"t have NaNs, but this cannot always be guaranteed.

1. The first one is more obvious, but when dealing with NaNs, prefer in-built pandas methods if they exist (because they have much better corner-case handling logic), or ensure your business logic includes appropriate NaN handling logic.
2. When dealing with mixed data types you should iterate over `zip(df["A"], df["B"], ...)` instead of `df[["A", "B"]].to_numpy()` as the latter implicitly upcasts data to the most common type. As an example if A is numeric and B is string, `to_numpy()` will cast the entire array to string, which may not be what you want. Fortunately `zip`ping your columns together is the most straightforward workaround to this.

*Your mileage may vary for the reasons outlined in the Caveats section above.

## An Obvious Example

Let"s demonstrate the difference with a simple example of adding two pandas columns `A + B`. This is a vectorizable operaton, so it will be easy to contrast the performance of the methods discussed above.

Benchmarking code, for your reference. The line at the bottom measures a function written in numpandas, a style of Pandas that mixes heavily with NumPy to squeeze out maximum performance. Writing numpandas code should be avoided unless you know what you"re doing. Stick to the API where you can (i.e., prefer `vec` over `vec_numpy`).

I should mention, however, that it isn"t always this cut and dry. Sometimes the answer to "what is the best method for an operation" is "it depends on your data". My advice is to test out different approaches on your data before settling on one.

* Pandas string methods are "vectorized" in the sense that they are specified on the series but operate on each element. The underlying mechanisms are still iterative, because string operations are inherently hard to vectorize.

## Why I Wrote this Answer

A common trend I notice from new users is to ask questions of the form "How can I iterate over my df to do X?". Showing code that calls `iterrows()` while doing something inside a `for` loop. Here is why. A new user to the library who has not been introduced to the concept of vectorization will likely envision the code that solves their problem as iterating over their data to do something. Not knowing how to iterate over a DataFrame, the first thing they do is Google it and end up here, at this question. They then see the accepted answer telling them how to, and they close their eyes and run this code without ever first questioning if iteration is not the right thing to do.

The aim of this answer is to help new users understand that iteration is not necessarily the solution to every problem, and that better, faster and more idiomatic solutions could exist, and that it is worth investing time in exploring them. I"m not trying to start a war of iteration vs. vectorization, but I want new users to be informed when developing solutions to their problems with this library.

# In Python, what is the purpose of `__slots__` and what are the cases one should avoid this?

## TLDR:

The special attribute `__slots__` allows you to explicitly state which instance attributes you expect your object instances to have, with the expected results:

1. faster attribute access.
2. space savings in memory.

The space savings is from

1. Storing value references in slots instead of `__dict__`.
2. Denying `__dict__` and `__weakref__` creation if parent classes deny them and you declare `__slots__`.

### Quick Caveats

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.

### Requirements:

• 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.

## Why use `__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.

## Why use `__slots__`: Memory Savings

Another purpose of `__slots__` is to reduce the space in memory that each object instance takes up.

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‚Ä†   16         56 + 112‚Ä† | ‚Ä†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
``````

## Demonstration of `__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!
``````

### Add `"__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.

### Set to empty tuple when subclassing a namedtuple:

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.

## Biggest Caveat: Multiple inheritance

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"
``````

## Other cases to avoid slots:

• Avoid them when you want to perform `__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.)
• Avoid them if you want to subclass variable length builtins like long, tuple, or str, and you want to add attributes to them.
• Avoid them if you insist on providing default values via class attributes for instance variables.

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.

### Do not "only use `__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 pickling

When 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.

## Critique of the (until Oct 2, 2016) accepted answer

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.

# Memory usage evidence

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.

# `os.listdir()` - list in the current directory

With listdir in os module you get the files and the folders in the current dir

`````` import os
arr = os.listdir()
print(arr)

>>> ["\$RECYCLE.BIN", "work.txt", "3ebooks.txt", "documents"]
``````

## Looking in a directory

``````arr = os.listdir("c:\files")
``````

# `glob` from glob

with glob you can specify a type of file to list like this

``````import glob

txtfiles = []
for file in glob.glob("*.txt"):
txtfiles.append(file)
``````

## `glob` in a list comprehension

``````mylist = [f for f in glob.glob("*.txt")]
``````

## get the full path of only files in the current directory

``````import os
from os import listdir
from os.path import isfile, join

cwd = os.getcwd()
onlyfiles = [os.path.join(cwd, f) for f in os.listdir(cwd) if
os.path.isfile(os.path.join(cwd, f))]
print(onlyfiles)

["G:\getfilesname\getfilesname.py", "G:\getfilesname\example.txt"]
``````

## Getting the full path name with `os.path.abspath`

You get the full path in return

`````` import os
files_path = [os.path.abspath(x) for x in os.listdir()]
print(files_path)

["F:\documentiapplications.txt", "F:\documenticollections.txt"]
``````

## Walk: going through sub directories

os.walk returns the root, the directories list and the files list, that is why I unpacked them in r, d, f in the for loop; it, then, looks for other files and directories in the subfolders of the root and so on until there are no subfolders.

``````import os

# Getting the current work directory (cwd)
thisdir = os.getcwd()

# r=root, d=directories, f = files
for r, d, f in os.walk(thisdir):
for file in f:
if file.endswith(".docx"):
print(os.path.join(r, file))
``````

### `os.listdir()`: get files in the current directory (Python 2)

In Python 2, if you want the list of the files in the current directory, you have to give the argument as "." or os.getcwd() in the os.listdir method.

`````` import os
arr = os.listdir(".")
print(arr)

>>> ["\$RECYCLE.BIN", "work.txt", "3ebooks.txt", "documents"]
``````

### To go up in the directory tree

``````# Method 1
x = os.listdir("..")

# Method 2
x= os.listdir("/")
``````

### Get files: `os.listdir()` in a particular directory (Python 2 and 3)

`````` import os
arr = os.listdir("F:\python")
print(arr)

>>> ["\$RECYCLE.BIN", "work.txt", "3ebooks.txt", "documents"]
``````

### Get files of a particular subdirectory with `os.listdir()`

``````import os

x = os.listdir("./content")
``````

### `os.walk(".")` - current directory

`````` import os
arr = next(os.walk("."))[2]
print(arr)

>>> ["5bs_Turismo1.pdf", "5bs_Turismo1.pptx", "esperienza.txt"]
``````

### `next(os.walk("."))` and `os.path.join("dir", "file")`

`````` import os
arr = []
for d,r,f in next(os.walk("F:\_python")):
for file in f:
arr.append(os.path.join(r,file))

for f in arr:
print(files)

>>> F:\_python\dict_class.py
>>> F:\_python\programmi.txt
``````

### `next(os.walk("F:\")` - get the full path - list comprehension

`````` [os.path.join(r,file) for r,d,f in next(os.walk("F:\_python")) for file in f]

>>> ["F:\_python\dict_class.py", "F:\_python\programmi.txt"]
``````

### `os.walk` - get full path - all files in sub dirs**

``````x = [os.path.join(r,file) for r,d,f in os.walk("F:\_python") for file in f]
print(x)

``````

### `os.listdir()` - get only txt files

`````` arr_txt = [x for x in os.listdir() if x.endswith(".txt")]
print(arr_txt)

>>> ["work.txt", "3ebooks.txt"]
``````

## Using `glob` to get the full path of the files

If I should need the absolute path of the files:

``````from path import path
from glob import glob
x = [path(f).abspath() for f in glob("F:\*.txt")]
for f in x:
print(f)

>>> F:acquistionline.txt
>>> F:acquisti_2018.txt
>>> F:ootstrap_jquery_ecc.txt
``````

## Using `os.path.isfile` to avoid directories in the list

``````import os.path
listOfFiles = [f for f in os.listdir() if os.path.isfile(f)]
print(listOfFiles)

>>> ["a simple game.py", "data.txt", "decorator.py"]
``````

## Using `pathlib` from Python 3.4

``````import pathlib

flist = []
for p in pathlib.Path(".").iterdir():
if p.is_file():
print(p)
flist.append(p)

>>> error.PNG
>>> exemaker.bat
>>> guiprova.mp3
>>> setup.py
>>> speak_gui2.py
>>> thumb.PNG
``````

With `list comprehension`:

``````flist = [p for p in pathlib.Path(".").iterdir() if p.is_file()]
``````

Alternatively, use `pathlib.Path()` instead of `pathlib.Path(".")`

## Use glob method in pathlib.Path()

``````import pathlib

py = pathlib.Path().glob("*.py")
for file in py:
print(file)

>>> stack_overflow_list.py
>>> stack_overflow_list_tkinter.py
``````

## Get all and only files with os.walk

``````import os
x = [i[2] for i in os.walk(".")]
y=[]
for t in x:
for f in t:
y.append(f)
print(y)

>>> ["append_to_list.py", "data.txt", "data1.txt", "data2.txt", "data_180617", "os_walk.py", "READ2.py", "read_data.py", "somma_defaltdic.py", "substitute_words.py", "sum_data.py", "data.txt", "data1.txt", "data_180617"]
``````

## Get only files with next and walk in a directory

`````` import os
x = next(os.walk("F://python"))[2]
print(x)

>>> ["calculator.bat","calculator.py"]
``````

## Get only directories with next and walk in a directory

`````` import os
next(os.walk("F://python"))[1] # for the current dir use (".")

>>> ["python3","others"]
``````

## Get all the subdir names with `walk`

``````for r,d,f in os.walk("F:\_python"):
for dirs in d:
print(dirs)

>>> .vscode
>>> pyexcel
>>> pyschool.py
>>> subtitles
>>> _metaprogramming
>>> .ipynb_checkpoints
``````

## `os.scandir()` from Python 3.5 and greater

``````import os
x = [f.name for f in os.scandir() if f.is_file()]
print(x)

>>> ["calculator.bat","calculator.py"]

# Another example with scandir (a little variation from docs.python.org)
# This one is more efficient than os.listdir.
# In this case, it shows the files only in the current directory
# where the script is executed.

import os
with os.scandir() as i:
for entry in i:
if entry.is_file():
print(entry.name)

>>> ebookmaker.py
>>> error.PNG
>>> exemaker.bat
>>> guiprova.mp3
>>> setup.py
>>> speakgui4.py
>>> speak_gui2.py
>>> speak_gui3.py
>>> thumb.PNG
``````

# Examples:

## Ex. 1: How many files are there in the subdirectories?

In this example, we look for the number of files that are included in all the directory and its subdirectories.

``````import os

def count(dir, counter=0):
"returns number of files in dir and subdirs"
for pack in os.walk(dir):
for f in pack[2]:
counter += 1
return dir + " : " + str(counter) + "files"

print(count("F:\python"))

>>> "F:\python" : 12057 files"
``````

## Ex.2: How to copy all files from a directory to another?

A script to make order in your computer finding all files of a type (default: pptx) and copying them in a new folder.

``````import os
import shutil
from path import path

destination = "F:\file_copied"
# os.makedirs(destination)

def copyfile(dir, filetype="pptx", counter=0):
"Searches for pptx (or other - pptx is the default) files and copies them"
for pack in os.walk(dir):
for f in pack[2]:
if f.endswith(filetype):
fullpath = pack[0] + "\" + f
print(fullpath)
shutil.copy(fullpath, destination)
counter += 1
if counter > 0:
print("-" * 30)
print("	==> Found in: `" + dir + "` : " + str(counter) + " files
")

for dir in os.listdir():
"searches for folders that starts with `_`"
if dir[0] == "_":
# copyfile(dir, filetype="pdf")
copyfile(dir, filetype="txt")

>>> _compiti18Compito Contabilit√† 1conti.txt
>>> _compiti18Compito Contabilit√† 1modula4.txt
>>> _compiti18Compito Contabilit√† 1moduloa4.txt
>>> ------------------------
>>> ==> Found in: `_compiti18` : 3 files
``````

## Ex. 3: How to get all the files in a txt file

In case you want to create a txt file with all the file names:

``````import os
mylist = ""
with open("filelist.txt", "w", encoding="utf-8") as file:
for eachfile in os.listdir():
mylist += eachfile + "
"
file.write(mylist)
``````

## Example: txt with all the files of an hard drive

``````"""
We are going to save a txt file with all the files in your directory.
We will use the function walk()
"""

import os

# see all the methods of os
# print(*dir(os), sep=", ")
listafile = []
percorso = []
with open("lista_file.txt", "w", encoding="utf-8") as testo:
for root, dirs, files in os.walk("D:\"):
for file in files:
listafile.append(file)
percorso.append(root + "\" + file)
testo.write(file + "
")
listafile.sort()
print("N. of files", len(listafile))
with open("lista_file_ordinata.txt", "w", encoding="utf-8") as testo_ordinato:
for file in listafile:
testo_ordinato.write(file + "
")

with open("percorso.txt", "w", encoding="utf-8") as file_percorso:
for file in percorso:
file_percorso.write(file + "
")

os.system("lista_file.txt")
os.system("lista_file_ordinata.txt")
os.system("percorso.txt")
``````

## All the file of C: in one text file

This is a shorter version of the previous code. Change the folder where to start finding the files if you need to start from another position. This code generate a 50 mb on text file on my computer with something less then 500.000 lines with files with the complete path.

``````import os

with open("file.txt", "w", encoding="utf-8") as filewrite:
for r, d, f in os.walk("C:\"):
for file in f:
filewrite.write(f"{r + file}
")
``````

## How to write a file with all paths in a folder of a type

With this function you can create a txt file that will have the name of a type of file that you look for (ex. pngfile.txt) with all the full path of all the files of that type. It can be useful sometimes, I think.

``````import os

def searchfiles(extension=".ttf", folder="H:\"):
"Create a txt file with all the file of a type"
with open(extension[1:] + "file.txt", "w", encoding="utf-8") as filewrite:
for r, d, f in os.walk(folder):
for file in f:
if file.endswith(extension):
filewrite.write(f"{r + file}
")

# looking for png file (fonts) in the hard disk H:
searchfiles(".png", "H:\")

>>> H:4bs_18Dolphins5.png
>>> H:4bs_18Dolphins6.png
>>> H:4bs_18Dolphins7.png
>>> H:5_18marketing htmlassetsimageslogo2.png
>>> H:7z001.png
>>> H:7z002.png
``````

## (New) Find all files and open them with tkinter GUI

I just wanted to add in this 2019 a little app to search for all files in a dir and be able to open them by doubleclicking on the name of the file in the list.

``````import tkinter as tk
import os

def searchfiles(extension=".txt", folder="H:\"):
"insert all files in the listbox"
for r, d, f in os.walk(folder):
for file in f:
if file.endswith(extension):
lb.insert(0, r + "\" + file)

def open_file():
os.startfile(lb.get(lb.curselection()[0]))

root = tk.Tk()
root.geometry("400x400")
bt = tk.Button(root, text="Search", command=lambda:searchfiles(".png", "H:\"))
bt.pack()
lb = tk.Listbox(root)
lb.pack(fill="both", expand=1)
lb.bind("<Double-Button>", lambda x: open_file())
root.mainloop()
``````

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

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.

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:

• "No! Please! Don"t! I can"t live without you!" (which is said `PROTECT` or `RESTRICT` in Django/SQL)
• "All right, if I"m not yours, then I"m nobody"s" (which is said `SET_NULL`)
• "Good bye world, I can"t live without article_B" and commit suicide (this is the `CASCADE` behavior).
• "It"s OK, I"ve got spare lover, and I"ll reference article_C from now" (`SET_DEFAULT`, or even `SET(...)`).
• "I can"t face reality, and I"ll keep calling your name even if that"s the only thing left to me!" (`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_NOTHING` can be useful: If you want to skip Django"s implementation and implement the constraint yourself at the database-level.

## Label vs. Location

The main distinction between the two methods is:

• `loc` gets rows (and/or columns) with particular labels.

• `iloc` gets rows (and/or columns) at integer locations.

To demonstrate, consider a series `s` of characters with a non-monotonic integer index:

``````>>> s = pd.Series(list("abcdef"), index=[49, 48, 47, 0, 1, 2])
49    a
48    b
47    c
0     d
1     e
2     f

>>> s.loc[0]    # value at index label 0
"d"

>>> s.iloc[0]   # value at index location 0
"a"

>>> s.loc[0:1]  # rows at index labels between 0 and 1 (inclusive)
0    d
1    e

>>> s.iloc[0:1] # rows at index location between 0 and 1 (exclusive)
49    a
``````

Here are some of the differences/similarities between `s.loc` and `s.iloc` when passed various objects:

<object> description `s.loc[<object>]` `s.iloc[<object>]`
`0` single item Value at index label `0` (the string `"d"`) Value at index location 0 (the string `"a"`)
`0:1` slice Two rows (labels `0` and `1`) One row (first row at location 0)
`1:47` slice with out-of-bounds end Zero rows (empty Series) Five rows (location 1 onwards)
`1:47:-1` slice with negative step three rows (labels `1` back to `47`) Zero rows (empty Series)
`[2, 0]` integer list Two rows with given labels Two rows with given locations
`s > "e"` Bool series (indicating which values have the property) One row (containing `"f"`) `NotImplementedError`
`(s>"e").values` Bool array One row (containing `"f"`) Same as `loc`
`999` int object not in index `KeyError` `IndexError` (out of bounds)
`-1` int object not in index `KeyError` Returns last value in `s`
`lambda x: x.index[3]` callable applied to series (here returning 3rd item in index) `s.loc[s.index[3]]` `s.iloc[s.index[3]]`

`loc`"s label-querying capabilities extend well-beyond integer indexes and it"s worth highlighting a couple of additional examples.

Here"s a Series where the index contains string objects:

``````>>> s2 = pd.Series(s.index, index=s.values)
>>> s2
a    49
b    48
c    47
d     0
e     1
f     2
``````

Since `loc` is label-based, it can fetch the first value in the Series using `s2.loc["a"]`. It can also slice with non-integer objects:

``````>>> s2.loc["c":"e"]  # all rows lying between "c" and "e" (inclusive)
c    47
d     0
e     1
``````

For DateTime indexes, we don"t need to pass the exact date/time to fetch by label. For example:

``````>>> s3 = pd.Series(list("abcde"), pd.date_range("now", periods=5, freq="M"))
>>> s3
2021-01-31 16:41:31.879768    a
2021-02-28 16:41:31.879768    b
2021-03-31 16:41:31.879768    c
2021-04-30 16:41:31.879768    d
2021-05-31 16:41:31.879768    e
``````

Then to fetch the row(s) for March/April 2021 we only need:

``````>>> s3.loc["2021-03":"2021-04"]
2021-03-31 17:04:30.742316    c
2021-04-30 17:04:30.742316    d
``````

## Rows and Columns

`loc` and `iloc` work the same way with DataFrames as they do with Series. It"s useful to note that both methods can address columns and rows together.

When given a tuple, the first element is used to index the rows and, if it exists, the second element is used to index the columns.

Consider the DataFrame defined below:

``````>>> import numpy as np
>>> df = pd.DataFrame(np.arange(25).reshape(5, 5),
index=list("abcde"),
columns=["x","y","z", 8, 9])
>>> df
x   y   z   8   9
a   0   1   2   3   4
b   5   6   7   8   9
c  10  11  12  13  14
d  15  16  17  18  19
e  20  21  22  23  24
``````

Then for example:

``````>>> df.loc["c": , :"z"]  # rows "c" and onwards AND columns up to "z"
x   y   z
c  10  11  12
d  15  16  17
e  20  21  22

>>> df.iloc[:, 3]        # all rows, but only the column at index location 3
a     3
b     8
c    13
d    18
e    23
``````

Sometimes we want to mix label and positional indexing methods for the rows and columns, somehow combining the capabilities of `loc` and `iloc`.

For example, consider the following DataFrame. How best to slice the rows up to and including "c" and take the first four columns?

``````>>> import numpy as np
>>> df = pd.DataFrame(np.arange(25).reshape(5, 5),
index=list("abcde"),
columns=["x","y","z", 8, 9])
>>> df
x   y   z   8   9
a   0   1   2   3   4
b   5   6   7   8   9
c  10  11  12  13  14
d  15  16  17  18  19
e  20  21  22  23  24
``````

We can achieve this result using `iloc` and the help of another method:

``````>>> df.iloc[:df.index.get_loc("c") + 1, :4]
x   y   z   8
a   0   1   2   3
b   5   6   7   8
c  10  11  12  13
``````

`get_loc()` is an index method meaning "get the position of the label in this index". Note that since slicing with `iloc` is exclusive of its endpoint, we must add 1 to this value if we want row "c" as well.

The simplest way to get row counts per group is by calling `.size()`, which returns a `Series`:

``````df.groupby(["col1","col2"]).size()
``````

Usually you want this result as a `DataFrame` (instead of a `Series`) so you can do:

``````df.groupby(["col1", "col2"]).size().reset_index(name="counts")
``````

If you want to find out how to calculate the row counts and other statistics for each group continue reading below.

## Detailed example:

Consider the following example dataframe:

``````In [2]: df
Out[2]:
col1 col2  col3  col4  col5  col6
0    A    B  0.20 -0.61 -0.49  1.49
1    A    B -1.53 -1.01 -0.39  1.82
2    A    B -0.44  0.27  0.72  0.11
3    A    B  0.28 -1.32  0.38  0.18
4    C    D  0.12  0.59  0.81  0.66
5    C    D -0.13 -1.65 -1.64  0.50
6    C    D -1.42 -0.11 -0.18 -0.44
7    E    F -0.00  1.42 -0.26  1.17
8    E    F  0.91 -0.47  1.35 -0.34
9    G    H  1.48 -0.63 -1.14  0.17
``````

First let"s use `.size()` to get the row counts:

``````In [3]: df.groupby(["col1", "col2"]).size()
Out[3]:
col1  col2
A     B       4
C     D       3
E     F       2
G     H       1
dtype: int64
``````

Then let"s use `.size().reset_index(name="counts")` to get the row counts:

``````In [4]: df.groupby(["col1", "col2"]).size().reset_index(name="counts")
Out[4]:
col1 col2  counts
0    A    B       4
1    C    D       3
2    E    F       2
3    G    H       1
``````

### Including results for more statistics

When you want to calculate statistics on grouped data, it usually looks like this:

``````In [5]: (df
...: .groupby(["col1", "col2"])
...: .agg({
...:     "col3": ["mean", "count"],
...:     "col4": ["median", "min", "count"]
...: }))
Out[5]:
col4                  col3
median   min count      mean count
col1 col2
A    B    -0.810 -1.32     4 -0.372500     4
C    D    -0.110 -1.65     3 -0.476667     3
E    F     0.475 -0.47     2  0.455000     2
G    H    -0.630 -0.63     1  1.480000     1
``````

The result above is a little annoying to deal with because of the nested column labels, and also because row counts are on a per column basis.

To gain more control over the output I usually split the statistics into individual aggregations that I then combine using `join`. It looks like this:

``````In [6]: gb = df.groupby(["col1", "col2"])
...: counts = gb.size().to_frame(name="counts")
...: (counts
...:  .join(gb.agg({"col3": "mean"}).rename(columns={"col3": "col3_mean"}))
...:  .join(gb.agg({"col4": "median"}).rename(columns={"col4": "col4_median"}))
...:  .join(gb.agg({"col4": "min"}).rename(columns={"col4": "col4_min"}))
...:  .reset_index()
...: )
...:
Out[6]:
col1 col2  counts  col3_mean  col4_median  col4_min
0    A    B       4  -0.372500       -0.810     -1.32
1    C    D       3  -0.476667       -0.110     -1.65
2    E    F       2   0.455000        0.475     -0.47
3    G    H       1   1.480000       -0.630     -0.63
``````

### Footnotes

The code used to generate the test data is shown below:

``````In [1]: import numpy as np
...: import pandas as pd
...:
...: keys = np.array([
...:         ["A", "B"],
...:         ["A", "B"],
...:         ["A", "B"],
...:         ["A", "B"],
...:         ["C", "D"],
...:         ["C", "D"],
...:         ["C", "D"],
...:         ["E", "F"],
...:         ["E", "F"],
...:         ["G", "H"]
...:         ])
...:
...: df = pd.DataFrame(
...:     np.hstack([keys,np.random.randn(10,4).round(2)]),
...:     columns = ["col1", "col2", "col3", "col4", "col5", "col6"]
...: )
...:
...: df[["col3", "col4", "col5", "col6"]] =
...:     df[["col3", "col4", "col5", "col6"]].astype(float)
...:
``````

Disclaimer:

If some of the columns that you are aggregating have null values, then you really want to be looking at the group row counts as an independent aggregation for each column. Otherwise you may be misled as to how many records are actually being used to calculate things like the mean because pandas will drop `NaN` entries in the mean calculation without telling you about it.