numpy.core.defchararray.rsplit (arr, sep = None, maxsplit = None) — another function to do string operations in numpy. Returns a list of words in a string, using sep as the separator string for each element in arr. The rsplit () method splits each element of the string array into a list starting from the right, while the split () method splits each element of the string array starting from the left.
Parameters:
arr: array_like of str or unicode.Input array.
sep: [str or unicode, optional] specifies the separator to use when splitting the string.
maxsplit: [int, optional] how many maximum splits to do.Returns: [ndarray] Output Array containing of list objects.
Code # 1:
| |
Output:
Input array: [`geeks for geeks`] Output splitted array: [ [`geeks`,` for`, `geeks`]]
Code # 2:
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|
Output :
Input array: [`Num-py`` Py-th-on` `Pan -das`] Output splitted array: [[`Num`,` py`] [`Py`,` th`, `on`] [` Pan`, `das`]]
Code # 3:
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Exit:
Input array: [`Num-py`` Py-th-on` `Pan-das`] Output splitted array: [[` Num`, `py`] [` Py-th`, `on`] [` Pan`, `das`]]
I have a list of arbitrary length, and I need to split it up into equal size chunks and operate on it. There are some obvious ways to do this, like keeping a counter and two lists, and when the second list fills up, add it to the first list and empty the second list for the next round of data, but this is potentially extremely expensive.
I was wondering if anyone had a good solution to this for lists of any length, e.g. using generators.
I was looking for something useful in itertools but I couldn"t find anything obviously useful. Might"ve missed it, though.
Related question: What is the most “pythonic” way to iterate over a list in chunks?
I think what I want to do is a fairly common task but I"ve found no reference on the web. I have text with punctuation, and I want a list of the words.
"Hey, you - what are you doing here!?"
should be
["hey", "you", "what", "are", "you", "doing", "here"]
But Python"s str.split() only works with one argument, so I have all words with the punctuation after I split with whitespace. Any ideas?
I found some answers online, but I have no experience with regular expressions, which I believe is what is needed here.
I have a string that needs to be split by either a ";" or ", " That is, it has to be either a semicolon or a comma followed by a space. Individual commas without trailing spaces should be left untouched
Example string:
"b-staged divinylsiloxane-bis-benzocyclobutene [124221-30-3], mesitylene [000108-67-8]; polymerized 1,2-dihydro-2,2,4- trimethyl quinoline [026780-96-1]"
should be split into a list containing the following:
("b-staged divinylsiloxane-bis-benzocyclobutene [124221-30-3]" , "mesitylene [000108-67-8]", "polymerized 1,2-dihydro-2,2,4- trimethyl quinoline [026780-96-1]")
I want my Python function to split a sentence (input) and store each word in a list. My current code splits the sentence, but does not store the words as a list. How do I do that?
def split_line(text):
# split the text
words = text.split()
# for each word in the line:
for word in words:
# print the word
print(words)
I"m looking for the Python equivalent of
String str = "many fancy word
hello hi";
String whiteSpaceRegex = "\s";
String[] words = str.split(whiteSpaceRegex);
["many", "fancy", "word", "hello", "hi"]
I"ve tried to look around the web for answers to splitting a string into a list of characters but I can"t seem to find a simple method.
str.split(//) does not seem to work like Ruby does. Is there a simple way of doing this without looping?
Is it possible to split a string every nth character?
For example, suppose I have a string containing the following:
"1234567890"
How can I get it to look like this:
["12","34","56","78","90"]
I have some python code that splits on comma, but doesn"t strip the whitespace:
>>> string = "blah, lots , of , spaces, here "
>>> mylist = string.split(",")
>>> print mylist
["blah", " lots ", " of ", " spaces", " here "]
I would rather end up with whitespace removed like this:
["blah", "lots", "of", "spaces", "here"]
I am aware that I could loop through the list and strip() each item but, as this is Python, I"m guessing there"s a quicker, easier and more elegant way of doing it.
What would be the best way to split a string on the first occurrence of a delimiter?
For example:
"123mango abcd mango kiwi peach"
splitting on the first mango to get:
"abcd mango kiwi peach"
What"s the best way, both aesthetically and from a performance perspective, to split a list of items into multiple lists based on a conditional? The equivalent of:
good = [x for x in mylist if x in goodvals]
bad = [x for x in mylist if x not in goodvals]
is there a more elegant way to do this?
Update: here"s the actual use case, to better explain what I"m trying to do:
# files looks like: [ ("file1.jpg", 33L, ".jpg"), ("file2.avi", 999L, ".avi"), ... ]
IMAGE_TYPES = (".jpg",".jpeg",".gif",".bmp",".png")
images = [f for f in files if f[2].lower() in IMAGE_TYPES]
anims = [f for f in files if f[2].lower() not in IMAGE_TYPES]
In Python, what is the purpose of
__slots__and what are the cases one should avoid this?
The special attribute __slots__ allows you to explicitly state which instance attributes you expect your object instances to have, with the expected results:
The space savings is from
__dict__.__dict__ and __weakref__ creation if parent classes deny them and you declare __slots__.Small caveat, you should only declare a particular slot one time in an inheritance tree. For example:
class Base:
__slots__ = "foo", "bar"
class Right(Base):
__slots__ = "baz",
class Wrong(Base):
__slots__ = "foo", "bar", "baz" # redundant foo and bar
Python doesn"t object when you get this wrong (it probably should), problems might not otherwise manifest, but your objects will take up more space than they otherwise should. Python 3.8:
>>> from sys import getsizeof
>>> getsizeof(Right()), getsizeof(Wrong())
(56, 72)
This is because the Base"s slot descriptor has a slot separate from the Wrong"s. This shouldn"t usually come up, but it could:
>>> w = Wrong()
>>> w.foo = "foo"
>>> Base.foo.__get__(w)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: foo
>>> Wrong.foo.__get__(w)
"foo"
The biggest caveat is for multiple inheritance - multiple "parent classes with nonempty slots" cannot be combined.
To accommodate this restriction, follow best practices: Factor out all but one or all parents" abstraction which their concrete class respectively and your new concrete class collectively will inherit from - giving the abstraction(s) empty slots (just like abstract base classes in the standard library).
See section on multiple inheritance below for an example.
To have attributes named in __slots__ to actually be stored in slots instead of a __dict__, a class must inherit from object (automatic in Python 3, but must be explicit in Python 2).
To prevent the creation of a __dict__, you must inherit from object and all classes in the inheritance must declare __slots__ and none of them can have a "__dict__" entry.
There are a lot of details if you wish to keep reading.
__slots__: Faster attribute access.The creator of Python, Guido van Rossum, states that he actually created __slots__ for faster attribute access.
It is trivial to demonstrate measurably significant faster access:
import timeit
class Foo(object): __slots__ = "foo",
class Bar(object): pass
slotted = Foo()
not_slotted = Bar()
def get_set_delete_fn(obj):
def get_set_delete():
obj.foo = "foo"
obj.foo
del obj.foo
return get_set_delete
and
>>> min(timeit.repeat(get_set_delete_fn(slotted)))
0.2846834529991611
>>> min(timeit.repeat(get_set_delete_fn(not_slotted)))
0.3664822799983085
The slotted access is almost 30% faster in Python 3.5 on Ubuntu.
>>> 0.3664822799983085 / 0.2846834529991611
1.2873325658284342
In Python 2 on Windows I have measured it about 15% faster.
__slots__: Memory SavingsAnother purpose of __slots__ is to reduce the space in memory that each object instance takes up.
My own contribution to the documentation clearly states the reasons behind this:
The space saved over using
__dict__can be significant.
SQLAlchemy attributes a lot of memory savings to __slots__.
To verify this, using the Anaconda distribution of Python 2.7 on Ubuntu Linux, with guppy.hpy (aka heapy) and sys.getsizeof, the size of a class instance without __slots__ declared, and nothing else, is 64 bytes. That does not include the __dict__. Thank you Python for lazy evaluation again, the __dict__ is apparently not called into existence until it is referenced, but classes without data are usually useless. When called into existence, the __dict__ attribute is a minimum of 280 bytes additionally.
In contrast, a class instance with __slots__ declared to be () (no data) is only 16 bytes, and 56 total bytes with one item in slots, 64 with two.
For 64 bit Python, I illustrate the memory consumption in bytes in Python 2.7 and 3.6, for __slots__ and __dict__ (no slots defined) for each point where the dict grows in 3.6 (except for 0, 1, and 2 attributes):
Python 2.7 Python 3.6
attrs __slots__ __dict__* __slots__ __dict__* | *(no slots defined)
none 16 56 + 272† 16 56 + 112† | †if __dict__ referenced
one 48 56 + 272 48 56 + 112
two 56 56 + 272 56 56 + 112
six 88 56 + 1040 88 56 + 152
11 128 56 + 1040 128 56 + 240
22 216 56 + 3344 216 56 + 408
43 384 56 + 3344 384 56 + 752
So, in spite of smaller dicts in Python 3, we see how nicely __slots__ scale for instances to save us memory, and that is a major reason you would want to use __slots__.
Just for completeness of my notes, note that there is a one-time cost per slot in the class"s namespace of 64 bytes in Python 2, and 72 bytes in Python 3, because slots use data descriptors like properties, called "members".
>>> Foo.foo
<member "foo" of "Foo" objects>
>>> type(Foo.foo)
<class "member_descriptor">
>>> getsizeof(Foo.foo)
72
__slots__:To deny the creation of a __dict__, you must subclass object. Everything subclasses object in Python 3, but in Python 2 you had to be explicit:
class Base(object):
__slots__ = ()
now:
>>> b = Base()
>>> b.a = "a"
Traceback (most recent call last):
File "<pyshell#38>", line 1, in <module>
b.a = "a"
AttributeError: "Base" object has no attribute "a"
Or subclass another class that defines __slots__
class Child(Base):
__slots__ = ("a",)
and now:
c = Child()
c.a = "a"
but:
>>> c.b = "b"
Traceback (most recent call last):
File "<pyshell#42>", line 1, in <module>
c.b = "b"
AttributeError: "Child" object has no attribute "b"
To allow __dict__ creation while subclassing slotted objects, just add "__dict__" to the __slots__ (note that slots are ordered, and you shouldn"t repeat slots that are already in parent classes):
class SlottedWithDict(Child):
__slots__ = ("__dict__", "b")
swd = SlottedWithDict()
swd.a = "a"
swd.b = "b"
swd.c = "c"
and
>>> swd.__dict__
{"c": "c"}
Or you don"t even need to declare __slots__ in your subclass, and you will still use slots from the parents, but not restrict the creation of a __dict__:
class NoSlots(Child): pass
ns = NoSlots()
ns.a = "a"
ns.b = "b"
And:
>>> ns.__dict__
{"b": "b"}
However, __slots__ may cause problems for multiple inheritance:
class BaseA(object):
__slots__ = ("a",)
class BaseB(object):
__slots__ = ("b",)
Because creating a child class from parents with both non-empty slots fails:
>>> class Child(BaseA, BaseB): __slots__ = ()
Traceback (most recent call last):
File "<pyshell#68>", line 1, in <module>
class Child(BaseA, BaseB): __slots__ = ()
TypeError: Error when calling the metaclass bases
multiple bases have instance lay-out conflict
If you run into this problem, You could just remove __slots__ from the parents, or if you have control of the parents, give them empty slots, or refactor to abstractions:
from abc import ABC
class AbstractA(ABC):
__slots__ = ()
class BaseA(AbstractA):
__slots__ = ("a",)
class AbstractB(ABC):
__slots__ = ()
class BaseB(AbstractB):
__slots__ = ("b",)
class Child(AbstractA, AbstractB):
__slots__ = ("a", "b")
c = Child() # no problem!
"__dict__" to __slots__ to get dynamic assignment:class Foo(object):
__slots__ = "bar", "baz", "__dict__"
and now:
>>> foo = Foo()
>>> foo.boink = "boink"
So with "__dict__" in slots we lose some of the size benefits with the upside of having dynamic assignment and still having slots for the names we do expect.
When you inherit from an object that isn"t slotted, you get the same sort of semantics when you use __slots__ - names that are in __slots__ point to slotted values, while any other values are put in the instance"s __dict__.
Avoiding __slots__ because you want to be able to add attributes on the fly is actually not a good reason - just add "__dict__" to your __slots__ if this is required.
You can similarly add __weakref__ to __slots__ explicitly if you need that feature.
The namedtuple builtin make immutable instances that are very lightweight (essentially, the size of tuples) but to get the benefits, you need to do it yourself if you subclass them:
from collections import namedtuple
class MyNT(namedtuple("MyNT", "bar baz")):
"""MyNT is an immutable and lightweight object"""
__slots__ = ()
usage:
>>> nt = MyNT("bar", "baz")
>>> nt.bar
"bar"
>>> nt.baz
"baz"
And trying to assign an unexpected attribute raises an AttributeError because we have prevented the creation of __dict__:
>>> nt.quux = "quux"
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: "MyNT" object has no attribute "quux"
You can allow __dict__ creation by leaving off __slots__ = (), but you can"t use non-empty __slots__ with subtypes of tuple.
Even when non-empty slots are the same for multiple parents, they cannot be used together:
class Foo(object):
__slots__ = "foo", "bar"
class Bar(object):
__slots__ = "foo", "bar" # alas, would work if empty, i.e. ()
>>> class Baz(Foo, Bar): pass
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: Error when calling the metaclass bases
multiple bases have instance lay-out conflict
Using an empty __slots__ in the parent seems to provide the most flexibility, allowing the child to choose to prevent or allow (by adding "__dict__" to get dynamic assignment, see section above) the creation of a __dict__:
class Foo(object): __slots__ = ()
class Bar(object): __slots__ = ()
class Baz(Foo, Bar): __slots__ = ("foo", "bar")
b = Baz()
b.foo, b.bar = "foo", "bar"
You don"t have to have slots - so if you add them, and remove them later, it shouldn"t cause any problems.
Going out on a limb here: If you"re composing mixins or using abstract base classes, which aren"t intended to be instantiated, an empty __slots__ in those parents seems to be the best way to go in terms of flexibility for subclassers.
To demonstrate, first, let"s create a class with code we"d like to use under multiple inheritance
class AbstractBase:
__slots__ = ()
def __init__(self, a, b):
self.a = a
self.b = b
def __repr__(self):
return f"{type(self).__name__}({repr(self.a)}, {repr(self.b)})"
We could use the above directly by inheriting and declaring the expected slots:
class Foo(AbstractBase):
__slots__ = "a", "b"
But we don"t care about that, that"s trivial single inheritance, we need another class we might also inherit from, maybe with a noisy attribute:
class AbstractBaseC:
__slots__ = ()
@property
def c(self):
print("getting c!")
return self._c
@c.setter
def c(self, arg):
print("setting c!")
self._c = arg
Now if both bases had nonempty slots, we couldn"t do the below. (In fact, if we wanted, we could have given AbstractBase nonempty slots a and b, and left them out of the below declaration - leaving them in would be wrong):
class Concretion(AbstractBase, AbstractBaseC):
__slots__ = "a b _c".split()
And now we have functionality from both via multiple inheritance, and can still deny __dict__ and __weakref__ instantiation:
>>> c = Concretion("a", "b")
>>> c.c = c
setting c!
>>> c.c
getting c!
Concretion("a", "b")
>>> c.d = "d"
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: "Concretion" object has no attribute "d"
__class__ assignment with another class that doesn"t have them (and you can"t add them) unless the slot layouts are identical. (I am very interested in learning who is doing this and why.)You may be able to tease out further caveats from the rest of the __slots__ documentation (the 3.7 dev docs are the most current), which I have made significant recent contributions to.
The current top answers cite outdated information and are quite hand-wavy and miss the mark in some important ways.
__slots__ when instantiating lots of objects"I quote:
"You would want to use
__slots__if you are going to instantiate a lot (hundreds, thousands) of objects of the same class."
Abstract Base Classes, for example, from the collections module, are not instantiated, yet __slots__ are declared for them.
Why?
If a user wishes to deny __dict__ or __weakref__ creation, those things must not be available in the parent classes.
__slots__ contributes to reusability when creating interfaces or mixins.
It is true that many Python users aren"t writing for reusability, but when you are, having the option to deny unnecessary space usage is valuable.
__slots__ doesn"t break picklingWhen pickling a slotted object, you may find it complains with a misleading TypeError:
>>> pickle.loads(pickle.dumps(f))
TypeError: a class that defines __slots__ without defining __getstate__ cannot be pickled
This is actually incorrect. This message comes from the oldest protocol, which is the default. You can select the latest protocol with the -1 argument. In Python 2.7 this would be 2 (which was introduced in 2.3), and in 3.6 it is 4.
>>> pickle.loads(pickle.dumps(f, -1))
<__main__.Foo object at 0x1129C770>
in Python 2.7:
>>> pickle.loads(pickle.dumps(f, 2))
<__main__.Foo object at 0x1129C770>
in Python 3.6
>>> pickle.loads(pickle.dumps(f, 4))
<__main__.Foo object at 0x1129C770>
So I would keep this in mind, as it is a solved problem.
The first paragraph is half short explanation, half predictive. Here"s the only part that actually answers the question
The proper use of
__slots__is to save space in objects. Instead of having a dynamic dict that allows adding attributes to objects at anytime, there is a static structure which does not allow additions after creation. This saves the overhead of one dict for every object that uses slots
The second half is wishful thinking, and off the mark:
While this is sometimes a useful optimization, it would be completely unnecessary if the Python interpreter was dynamic enough so that it would only require the dict when there actually were additions to the object.
Python actually does something similar to this, only creating the __dict__ when it is accessed, but creating lots of objects with no data is fairly ridiculous.
The second paragraph oversimplifies and misses actual reasons to avoid __slots__. The below is not a real reason to avoid slots (for actual reasons, see the rest of my answer above.):
They change the behavior of the objects that have slots in a way that can be abused by control freaks and static typing weenies.
It then goes on to discuss other ways of accomplishing that perverse goal with Python, not discussing anything to do with __slots__.
The third paragraph is more wishful thinking. Together it is mostly off-the-mark content that the answerer didn"t even author and contributes to ammunition for critics of the site.
Create some normal objects and slotted objects:
>>> class Foo(object): pass
>>> class Bar(object): __slots__ = ()
Instantiate a million of them:
>>> foos = [Foo() for f in xrange(1000000)]
>>> bars = [Bar() for b in xrange(1000000)]
Inspect with guppy.hpy().heap():
>>> guppy.hpy().heap()
Partition of a set of 2028259 objects. Total size = 99763360 bytes.
Index Count % Size % Cumulative % Kind (class / dict of class)
0 1000000 49 64000000 64 64000000 64 __main__.Foo
1 169 0 16281480 16 80281480 80 list
2 1000000 49 16000000 16 96281480 97 __main__.Bar
3 12284 1 987472 1 97268952 97 str
...
Access the regular objects and their __dict__ and inspect again:
>>> for f in foos:
... f.__dict__
>>> guppy.hpy().heap()
Partition of a set of 3028258 objects. Total size = 379763480 bytes.
Index Count % Size % Cumulative % Kind (class / dict of class)
0 1000000 33 280000000 74 280000000 74 dict of __main__.Foo
1 1000000 33 64000000 17 344000000 91 __main__.Foo
2 169 0 16281480 4 360281480 95 list
3 1000000 33 16000000 4 376281480 99 __main__.Bar
4 12284 0 987472 0 377268952 99 str
...
This is consistent with the history of Python, from Unifying types and classes in Python 2.2
If you subclass a built-in type, extra space is automatically added to the instances to accomodate
__dict__and__weakrefs__. (The__dict__is not initialized until you use it though, so you shouldn"t worry about the space occupied by an empty dictionary for each instance you create.) If you don"t need this extra space, you can add the phrase "__slots__ = []" to your class.
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.
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
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
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.
For the simple case of:
The simplest solution is:
df[["A", "B"]] = df["AB"].str.split(" ", 1, expand=True)
You must use expand=True if your strings have a non-uniform number of splits and you want None to replace the missing values.
Notice how, in either case, the .tolist() method is not necessary. Neither is zip().
Andy Hayden"s solution is most excellent in demonstrating the power of the str.extract() method.
But for a simple split over a known separator (like, splitting by dashes, or splitting by whitespace), the .str.split() method is enough1. It operates on a column (Series) of strings, and returns a column (Series) of lists:
>>> import pandas as pd
>>> df = pd.DataFrame({"AB": ["A1-B1", "A2-B2"]})
>>> df
AB
0 A1-B1
1 A2-B2
>>> df["AB_split"] = df["AB"].str.split("-")
>>> df
AB AB_split
0 A1-B1 [A1, B1]
1 A2-B2 [A2, B2]
1: If you"re unsure what the first two parameters of .str.split() do,
I recommend the docs for the plain Python version of the method.
But how do you go from:
to:
Well, we need to take a closer look at the .str attribute of a column.
It"s a magical object that is used to collect methods that treat each element in a column as a string, and then apply the respective method in each element as efficient as possible:
>>> upper_lower_df = pd.DataFrame({"U": ["A", "B", "C"]})
>>> upper_lower_df
U
0 A
1 B
2 C
>>> upper_lower_df["L"] = upper_lower_df["U"].str.lower()
>>> upper_lower_df
U L
0 A a
1 B b
2 C c
But it also has an "indexing" interface for getting each element of a string by its index:
>>> df["AB"].str[0]
0 A
1 A
Name: AB, dtype: object
>>> df["AB"].str[1]
0 1
1 2
Name: AB, dtype: object
Of course, this indexing interface of .str doesn"t really care if each element it"s indexing is actually a string, as long as it can be indexed, so:
>>> df["AB"].str.split("-", 1).str[0]
0 A1
1 A2
Name: AB, dtype: object
>>> df["AB"].str.split("-", 1).str[1]
0 B1
1 B2
Name: AB, dtype: object
Then, it"s a simple matter of taking advantage of the Python tuple unpacking of iterables to do
>>> df["A"], df["B"] = df["AB"].str.split("-", 1).str
>>> df
AB AB_split A B
0 A1-B1 [A1, B1] A1 B1
1 A2-B2 [A2, B2] A2 B2
Of course, getting a DataFrame out of splitting a column of strings is so useful that the .str.split() method can do it for you with the expand=True parameter:
>>> df["AB"].str.split("-", 1, expand=True)
0 1
0 A1 B1
1 A2 B2
So, another way of accomplishing what we wanted is to do:
>>> df = df[["AB"]]
>>> df
AB
0 A1-B1
1 A2-B2
>>> df.join(df["AB"].str.split("-", 1, expand=True).rename(columns={0:"A", 1:"B"}))
AB A B
0 A1-B1 A1 B1
1 A2-B2 A2 B2
The expand=True version, although longer, has a distinct advantage over the tuple unpacking method. Tuple unpacking doesn"t deal well with splits of different lengths:
>>> df = pd.DataFrame({"AB": ["A1-B1", "A2-B2", "A3-B3-C3"]})
>>> df
AB
0 A1-B1
1 A2-B2
2 A3-B3-C3
>>> df["A"], df["B"], df["C"] = df["AB"].str.split("-")
Traceback (most recent call last):
[...]
ValueError: Length of values does not match length of index
>>>
But expand=True handles it nicely by placing None in the columns for which there aren"t enough "splits":
>>> df.join(
... df["AB"].str.split("-", expand=True).rename(
... columns={0:"A", 1:"B", 2:"C"}
... )
... )
AB A B C
0 A1-B1 A1 B1 None
1 A2-B2 A2 B2 None
2 A3-B3-C3 A3 B3 C3
There are several ways to select rows from a Pandas dataframe:
df[df["col"] == value] )df.iloc[...])df.xs(...))df.query(...) APIBelow I show you examples of each, with advice when to use certain techniques. Assume our criterion is column "A" == "foo"
(Note on performance: For each base type, we can keep things simple by using the Pandas API or we can venture outside the API, usually into NumPy, and speed things up.)
Setup
The first thing we"ll need is to identify a condition that will act as our criterion for selecting rows. We"ll start with the OP"s case column_name == some_value, and include some other common use cases.
Borrowing from @unutbu:
import pandas as pd, numpy as np
df = pd.DataFrame({"A": "foo bar foo bar foo bar foo foo".split(),
"B": "one one two three two two one three".split(),
"C": np.arange(8), "D": np.arange(8) * 2})
... Boolean indexing requires finding the true value of each row"s "A" column being equal to "foo", then using those truth values to identify which rows to keep. Typically, we"d name this series, an array of truth values, mask. We"ll do so here as well.
mask = df["A"] == "foo"
We can then use this mask to slice or index the data frame
df[mask]
A B C D
0 foo one 0 0
2 foo two 2 4
4 foo two 4 8
6 foo one 6 12
7 foo three 7 14
This is one of the simplest ways to accomplish this task and if performance or intuitiveness isn"t an issue, this should be your chosen method. However, if performance is a concern, then you might want to consider an alternative way of creating the mask.
Positional indexing (df.iloc[...]) has its use cases, but this isn"t one of them. In order to identify where to slice, we first need to perform the same boolean analysis we did above. This leaves us performing one extra step to accomplish the same task.
mask = df["A"] == "foo"
pos = np.flatnonzero(mask)
df.iloc[pos]
A B C D
0 foo one 0 0
2 foo two 2 4
4 foo two 4 8
6 foo one 6 12
7 foo three 7 14
Label indexing can be very handy, but in this case, we are again doing more work for no benefit
df.set_index("A", append=True, drop=False).xs("foo", level=1)
A B C D
0 foo one 0 0
2 foo two 2 4
4 foo two 4 8
6 foo one 6 12
7 foo three 7 14
df.query() APIpd.DataFrame.query is a very elegant/intuitive way to perform this task, but is often slower. However, if you pay attention to the timings below, for large data, the query is very efficient. More so than the standard approach and of similar magnitude as my best suggestion.
df.query("A == "foo"")
A B C D
0 foo one 0 0
2 foo two 2 4
4 foo two 4 8
6 foo one 6 12
7 foo three 7 14
My preference is to use the Boolean mask
Actual improvements can be made by modifying how we create our Boolean mask.
mask alternative 1
Use the underlying NumPy array and forgo the overhead of creating another pd.Series
mask = df["A"].values == "foo"
I"ll show more complete time tests at the end, but just take a look at the performance gains we get using the sample data frame. First, we look at the difference in creating the mask
%timeit mask = df["A"].values == "foo"
%timeit mask = df["A"] == "foo"
5.84 µs ± 195 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
166 µs ± 4.45 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)
Evaluating the mask with the NumPy array is ~ 30 times faster. This is partly due to NumPy evaluation often being faster. It is also partly due to the lack of overhead necessary to build an index and a corresponding pd.Series object.
Next, we"ll look at the timing for slicing with one mask versus the other.
mask = df["A"].values == "foo"
%timeit df[mask]
mask = df["A"] == "foo"
%timeit df[mask]
219 µs ± 12.3 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
239 µs ± 7.03 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
The performance gains aren"t as pronounced. We"ll see if this holds up over more robust testing.
mask alternative 2
We could have reconstructed the data frame as well. There is a big caveat when reconstructing a dataframe—you must take care of the dtypes when doing so!
Instead of df[mask] we will do this
pd.DataFrame(df.values[mask], df.index[mask], df.columns).astype(df.dtypes)
If the data frame is of mixed type, which our example is, then when we get df.values the resulting array is of dtype object and consequently, all columns of the new data frame will be of dtype object. Thus requiring the astype(df.dtypes) and killing any potential performance gains.
%timeit df[m]
%timeit pd.DataFrame(df.values[mask], df.index[mask], df.columns).astype(df.dtypes)
216 µs ± 10.4 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
1.43 ms ± 39.6 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
However, if the data frame is not of mixed type, this is a very useful way to do it.
Given
np.random.seed([3,1415])
d1 = pd.DataFrame(np.random.randint(10, size=(10, 5)), columns=list("ABCDE"))
d1
A B C D E
0 0 2 7 3 8
1 7 0 6 8 6
2 0 2 0 4 9
3 7 3 2 4 3
4 3 6 7 7 4
5 5 3 7 5 9
6 8 7 6 4 7
7 6 2 6 6 5
8 2 8 7 5 8
9 4 7 6 1 5
%%timeit
mask = d1["A"].values == 7
d1[mask]
179 µs ± 8.73 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)
Versus
%%timeit
mask = d1["A"].values == 7
pd.DataFrame(d1.values[mask], d1.index[mask], d1.columns)
87 µs ± 5.12 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)
We cut the time in half.
mask alternative 3
@unutbu also shows us how to use pd.Series.isin to account for each element of df["A"] being in a set of values. This evaluates to the same thing if our set of values is a set of one value, namely "foo". But it also generalizes to include larger sets of values if needed. Turns out, this is still pretty fast even though it is a more general solution. The only real loss is in intuitiveness for those not familiar with the concept.
mask = df["A"].isin(["foo"])
df[mask]
A B C D
0 foo one 0 0
2 foo two 2 4
4 foo two 4 8
6 foo one 6 12
7 foo three 7 14
However, as before, we can utilize NumPy to improve performance while sacrificing virtually nothing. We"ll use np.in1d
mask = np.in1d(df["A"].values, ["foo"])
df[mask]
A B C D
0 foo one 0 0
2 foo two 2 4
4 foo two 4 8
6 foo one 6 12
7 foo three 7 14
Timing
I"ll include other concepts mentioned in other posts as well for reference.
Code Below
Each column in this table represents a different length data frame over which we test each function. Each column shows relative time taken, with the fastest function given a base index of 1.0.
res.div(res.min())
10 30 100 300 1000 3000 10000 30000
mask_standard 2.156872 1.850663 2.034149 2.166312 2.164541 3.090372 2.981326 3.131151
mask_standard_loc 1.879035 1.782366 1.988823 2.338112 2.361391 3.036131 2.998112 2.990103
mask_with_values 1.010166 1.000000 1.005113 1.026363 1.028698 1.293741 1.007824 1.016919
mask_with_values_loc 1.196843 1.300228 1.000000 1.000000 1.038989 1.219233 1.037020 1.000000
query 4.997304 4.765554 5.934096 4.500559 2.997924 2.397013 1.680447 1.398190
xs_label 4.124597 4.272363 5.596152 4.295331 4.676591 5.710680 6.032809 8.950255
mask_with_isin 1.674055 1.679935 1.847972 1.724183 1.345111 1.405231 1.253554 1.264760
mask_with_in1d 1.000000 1.083807 1.220493 1.101929 1.000000 1.000000 1.000000 1.144175
You"ll notice that the fastest times seem to be shared between mask_with_values and mask_with_in1d.
res.T.plot(loglog=True)
Functions
def mask_standard(df):
mask = df["A"] == "foo"
return df[mask]
def mask_standard_loc(df):
mask = df["A"] == "foo"
return df.loc[mask]
def mask_with_values(df):
mask = df["A"].values == "foo"
return df[mask]
def mask_with_values_loc(df):
mask = df["A"].values == "foo"
return df.loc[mask]
def query(df):
return df.query("A == "foo"")
def xs_label(df):
return df.set_index("A", append=True, drop=False).xs("foo", level=-1)
def mask_with_isin(df):
mask = df["A"].isin(["foo"])
return df[mask]
def mask_with_in1d(df):
mask = np.in1d(df["A"].values, ["foo"])
return df[mask]
Testing
res = pd.DataFrame(
index=[
"mask_standard", "mask_standard_loc", "mask_with_values", "mask_with_values_loc",
"query", "xs_label", "mask_with_isin", "mask_with_in1d"
],
columns=[10, 30, 100, 300, 1000, 3000, 10000, 30000],
dtype=float
)
for j in res.columns:
d = pd.concat([df] * j, ignore_index=True)
for i in res.index:a
stmt = "{}(d)".format(i)
setp = "from __main__ import d, {}".format(i)
res.at[i, j] = timeit(stmt, setp, number=50)
Special Timing
Looking at the special case when we have a single non-object dtype for the entire data frame.
Code Below
spec.div(spec.min())
10 30 100 300 1000 3000 10000 30000
mask_with_values 1.009030 1.000000 1.194276 1.000000 1.236892 1.095343 1.000000 1.000000
mask_with_in1d 1.104638 1.094524 1.156930 1.072094 1.000000 1.000000 1.040043 1.027100
reconstruct 1.000000 1.142838 1.000000 1.355440 1.650270 2.222181 2.294913 3.406735
Turns out, reconstruction isn"t worth it past a few hundred rows.
spec.T.plot(loglog=True)
Functions
np.random.seed([3,1415])
d1 = pd.DataFrame(np.random.randint(10, size=(10, 5)), columns=list("ABCDE"))
def mask_with_values(df):
mask = df["A"].values == "foo"
return df[mask]
def mask_with_in1d(df):
mask = np.in1d(df["A"].values, ["foo"])
return df[mask]
def reconstruct(df):
v = df.values
mask = np.in1d(df["A"].values, ["foo"])
return pd.DataFrame(v[mask], df.index[mask], df.columns)
spec = pd.DataFrame(
index=["mask_with_values", "mask_with_in1d", "reconstruct"],
columns=[10, 30, 100, 300, 1000, 3000, 10000, 30000],
dtype=float
)
Testing
for j in spec.columns:
d = pd.concat([df] * j, ignore_index=True)
for i in spec.index:
stmt = "{}(d)".format(i)
setp = "from __main__ import d, {}".format(i)
spec.at[i, j] = timeit(stmt, setp, number=50)
It"s much easier if you use Response.raw and shutil.copyfileobj():
import requests
import shutil
def download_file(url):
local_filename = url.split("/")[-1]
with requests.get(url, stream=True) as r:
with open(local_filename, "wb") as f:
shutil.copyfileobj(r.raw, f)
return local_filename
This streams the file to disk without using excessive memory, and the code is simple.
Explain all in Python?
I keep seeing the variable
__all__set in different__init__.pyfiles.What does this do?
__all__ do?It declares the semantically "public" names from a module. If there is a name in __all__, users are expected to use it, and they can have the expectation that it will not change.
It also will have programmatic effects:
import *__all__ in a module, e.g. module.py:
__all__ = ["foo", "Bar"]
means that when you import * from the module, only those names in the __all__ are imported:
from module import * # imports foo and Bar
Documentation and code autocompletion tools may (in fact, should) also inspect the __all__ to determine what names to show as available from a module.
__init__.py makes a directory a Python packageFrom the docs:
The
__init__.pyfiles are required to make Python treat the directories as containing packages; this is done to prevent directories with a common name, such as string, from unintentionally hiding valid modules that occur later on the module search path.
In the simplest case,
__init__.pycan just be an empty file, but it can also execute initialization code for the package or set the__all__variable.
So the __init__.py can declare the __all__ for a package.
A package is typically made up of modules that may import one another, but that are necessarily tied together with an __init__.py file. That file is what makes the directory an actual Python package. For example, say you have the following files in a package:
package
├── __init__.py
├── module_1.py
└── module_2.py
Let"s create these files with Python so you can follow along - you could paste the following into a Python 3 shell:
from pathlib import Path
package = Path("package")
package.mkdir()
(package / "__init__.py").write_text("""
from .module_1 import *
from .module_2 import *
""")
package_module_1 = package / "module_1.py"
package_module_1.write_text("""
__all__ = ["foo"]
imp_detail1 = imp_detail2 = imp_detail3 = None
def foo(): pass
""")
package_module_2 = package / "module_2.py"
package_module_2.write_text("""
__all__ = ["Bar"]
imp_detail1 = imp_detail2 = imp_detail3 = None
class Bar: pass
""")
And now you have presented a complete api that someone else can use when they import your package, like so:
import package
package.foo()
package.Bar()
And the package won"t have all the other implementation details you used when creating your modules cluttering up the package namespace.
__all__ in __init__.pyAfter more work, maybe you"ve decided that the modules are too big (like many thousands of lines?) and need to be split up. So you do the following:
package
├── __init__.py
├── module_1
│   ├── foo_implementation.py
│   └── __init__.py
└── module_2
├── Bar_implementation.py
└── __init__.py
First make the subpackage directories with the same names as the modules:
subpackage_1 = package / "module_1"
subpackage_1.mkdir()
subpackage_2 = package / "module_2"
subpackage_2.mkdir()
Move the implementations:
package_module_1.rename(subpackage_1 / "foo_implementation.py")
package_module_2.rename(subpackage_2 / "Bar_implementation.py")
create __init__.pys for the subpackages that declare the __all__ for each:
(subpackage_1 / "__init__.py").write_text("""
from .foo_implementation import *
__all__ = ["foo"]
""")
(subpackage_2 / "__init__.py").write_text("""
from .Bar_implementation import *
__all__ = ["Bar"]
""")
And now you still have the api provisioned at the package level:
>>> import package
>>> package.foo()
>>> package.Bar()
<package.module_2.Bar_implementation.Bar object at 0x7f0c2349d210>
And you can easily add things to your API that you can manage at the subpackage level instead of the subpackage"s module level. If you want to add a new name to the API, you simply update the __init__.py, e.g. in module_2:
from .Bar_implementation import *
from .Baz_implementation import *
__all__ = ["Bar", "Baz"]
And if you"re not ready to publish Baz in the top level API, in your top level __init__.py you could have:
from .module_1 import * # also constrained by __all__"s
from .module_2 import * # in the __init__.py"s
__all__ = ["foo", "Bar"] # further constraining the names advertised
and if your users are aware of the availability of Baz, they can use it:
import package
package.Baz()
but if they don"t know about it, other tools (like pydoc) won"t inform them.
You can later change that when Baz is ready for prime time:
from .module_1 import *
from .module_2 import *
__all__ = ["foo", "Bar", "Baz"]
_ versus __all__:By default, Python will export all names that do not start with an _. You certainly could rely on this mechanism. Some packages in the Python standard library, in fact, do rely on this, but to do so, they alias their imports, for example, in ctypes/__init__.py:
import os as _os, sys as _sys
Using the _ convention can be more elegant because it removes the redundancy of naming the names again. But it adds the redundancy for imports (if you have a lot of them) and it is easy to forget to do this consistently - and the last thing you want is to have to indefinitely support something you intended to only be an implementation detail, just because you forgot to prefix an _ when naming a function.
I personally write an __all__ early in my development lifecycle for modules so that others who might use my code know what they should use and not use.
Most packages in the standard library also use __all__.
__all__ makes senseIt makes sense to stick to the _ prefix convention in lieu of __all__ when:
export decoratorThe downside of using __all__ is that you have to write the names of functions and classes being exported twice - and the information is kept separate from the definitions. We could use a decorator to solve this problem.
I got the idea for such an export decorator from David Beazley"s talk on packaging. This implementation seems to work well in CPython"s traditional importer. If you have a special import hook or system, I do not guarantee it, but if you adopt it, it is fairly trivial to back out - you"ll just need to manually add the names back into the __all__
So in, for example, a utility library, you would define the decorator:
import sys
def export(fn):
mod = sys.modules[fn.__module__]
if hasattr(mod, "__all__"):
mod.__all__.append(fn.__name__)
else:
mod.__all__ = [fn.__name__]
return fn
and then, where you would define an __all__, you do this:
$ cat > main.py
from lib import export
__all__ = [] # optional - we create a list if __all__ is not there.
@export
def foo(): pass
@export
def bar():
"bar"
def main():
print("main")
if __name__ == "__main__":
main()
And this works fine whether run as main or imported by another function.
$ cat > run.py
import main
main.main()
$ python run.py
main
And API provisioning with import * will work too:
$ cat > run.py
from main import *
foo()
bar()
main() # expected to error here, not exported
$ python run.py
Traceback (most recent call last):
File "run.py", line 4, in <module>
main() # expected to error here, not exported
NameError: name "main" is not defined
A comment in the Python source code for float objects acknowledges that:
This is especially true when comparing a float to an integer, because, unlike floats, integers in Python can be arbitrarily large and are always exact. Trying to cast the integer to a float might lose precision and make the comparison inaccurate. Trying to cast the float to an integer is not going to work either because any fractional part will be lost.
To get around this problem, Python performs a series of checks, returning the result if one of the checks succeeds. It compares the signs of the two values, then whether the integer is "too big" to be a float, then compares the exponent of the float to the length of the integer. If all of these checks fail, it is necessary to construct two new Python objects to compare in order to obtain the result.
When comparing a float v to an integer/long w, the worst case is that:
v and w have the same sign (both positive or both negative),w has few enough bits that it can be held in the size_t type (typically 32 or 64 bits),w has at least 49 bits,v is the same as the number of bits in w.And this is exactly what we have for the values in the question:
>>> import math
>>> math.frexp(562949953420000.7) # gives the float"s (significand, exponent) pair
(0.9999999999976706, 49)
>>> (562949953421000).bit_length()
49
We see that 49 is both the exponent of the float and the number of bits in the integer. Both numbers are positive and so the four criteria above are met.
Choosing one of the values to be larger (or smaller) can change the number of bits of the integer, or the value of the exponent, and so Python is able to determine the result of the comparison without performing the expensive final check.
This is specific to the CPython implementation of the language.
The float_richcompare function handles the comparison between two values v and w.
Below is a step-by-step description of the checks that the function performs. The comments in the Python source are actually very helpful when trying to understand what the function does, so I"ve left them in where relevant. I"ve also summarised these checks in a list at the foot of the answer.
The main idea is to map the Python objects v and w to two appropriate C doubles, i and j, which can then be easily compared to give the correct result. Both Python 2 and Python 3 use the same ideas to do this (the former just handles int and long types separately).
The first thing to do is check that v is definitely a Python float and map it to a C double i. Next the function looks at whether w is also a float and maps it to a C double j. This is the best case scenario for the function as all the other checks can be skipped. The function also checks to see whether v is inf or nan:
static PyObject*
float_richcompare(PyObject *v, PyObject *w, int op)
{
double i, j;
int r = 0;
assert(PyFloat_Check(v));
i = PyFloat_AS_DOUBLE(v);
if (PyFloat_Check(w))
j = PyFloat_AS_DOUBLE(w);
else if (!Py_IS_FINITE(i)) {
if (PyLong_Check(w))
j = 0.0;
else
goto Unimplemented;
}
Now we know that if w failed these checks, it is not a Python float. Now the function checks if it"s a Python integer. If this is the case, the easiest test is to extract the sign of v and the sign of w (return 0 if zero, -1 if negative, 1 if positive). If the signs are different, this is all the information needed to return the result of the comparison:
else if (PyLong_Check(w)) {
int vsign = i == 0.0 ? 0 : i < 0.0 ? -1 : 1;
int wsign = _PyLong_Sign(w);
size_t nbits;
int exponent;
if (vsign != wsign) {
/* Magnitudes are irrelevant -- the signs alone
* determine the outcome.
*/
i = (double)vsign;
j = (double)wsign;
goto Compare;
}
}
If this check failed, then v and w have the same sign.
The next check counts the number of bits in the integer w. If it has too many bits then it can"t possibly be held as a float and so must be larger in magnitude than the float v:
nbits = _PyLong_NumBits(w);
if (nbits == (size_t)-1 && PyErr_Occurred()) {
/* This long is so large that size_t isn"t big enough
* to hold the # of bits. Replace with little doubles
* that give the same outcome -- w is so large that
* its magnitude must exceed the magnitude of any
* finite float.
*/
PyErr_Clear();
i = (double)vsign;
assert(wsign != 0);
j = wsign * 2.0;
goto Compare;
}
On the other hand, if the integer w has 48 or fewer bits, it can safely turned in a C double j and compared:
if (nbits <= 48) {
j = PyLong_AsDouble(w);
/* It"s impossible that <= 48 bits overflowed. */
assert(j != -1.0 || ! PyErr_Occurred());
goto Compare;
}
From this point onwards, we know that w has 49 or more bits. It will be convenient to treat w as a positive integer, so change the sign and the comparison operator as necessary:
if (nbits <= 48) {
/* "Multiply both sides" by -1; this also swaps the
* comparator.
*/
i = -i;
op = _Py_SwappedOp[op];
}
Now the function looks at the exponent of the float. Recall that a float can be written (ignoring sign) as significand * 2exponent and that the significand represents a number between 0.5 and 1:
(void) frexp(i, &exponent);
if (exponent < 0 || (size_t)exponent < nbits) {
i = 1.0;
j = 2.0;
goto Compare;
}
This checks two things. If the exponent is less than 0 then the float is smaller than 1 (and so smaller in magnitude than any integer). Or, if the exponent is less than the number of bits in w then we have that v < |w| since significand * 2exponent is less than 2nbits.
Failing these two checks, the function looks to see whether the exponent is greater than the number of bit in w. This shows that significand * 2exponent is greater than 2nbits and so v > |w|:
if ((size_t)exponent > nbits) {
i = 2.0;
j = 1.0;
goto Compare;
}
If this check did not succeed we know that the exponent of the float v is the same as the number of bits in the integer w.
The only way that the two values can be compared now is to construct two new Python integers from v and w. The idea is to discard the fractional part of v, double the integer part, and then add one. w is also doubled and these two new Python objects can be compared to give the correct return value. Using an example with small values, 4.65 < 4 would be determined by the comparison (2*4)+1 == 9 < 8 == (2*4) (returning false).
{
double fracpart;
double intpart;
PyObject *result = NULL;
PyObject *one = NULL;
PyObject *vv = NULL;
PyObject *ww = w;
// snip
fracpart = modf(i, &intpart); // split i (the double that v mapped to)
vv = PyLong_FromDouble(intpart);
// snip
if (fracpart != 0.0) {
/* Shift left, and or a 1 bit into vv
* to represent the lost fraction.
*/
PyObject *temp;
one = PyLong_FromLong(1);
temp = PyNumber_Lshift(ww, one); // left-shift doubles an integer
ww = temp;
temp = PyNumber_Lshift(vv, one);
vv = temp;
temp = PyNumber_Or(vv, one); // a doubled integer is even, so this adds 1
vv = temp;
}
// snip
}
}
For brevity I"ve left out the additional error-checking and garbage-tracking Python has to do when it creates these new objects. Needless to say, this adds additional overhead and explains why the values highlighted in the question are significantly slower to compare than others.
Here is a summary of the checks that are performed by the comparison function.
Let v be a float and cast it as a C double. Now, if w is also a float:
Check whether w is nan or inf. If so, handle this special case separately depending on the type of w.
If not, compare v and w directly by their representations as C doubles.
If w is an integer:
Extract the signs of v and w. If they are different then we know v and w are different and which is the greater value.
(The signs are the same.) Check whether w has too many bits to be a float (more than size_t). If so, w has greater magnitude than v.
Check if w has 48 or fewer bits. If so, it can be safely cast to a C double without losing its precision and compared with v.
(w has more than 48 bits. We will now treat w as a positive integer having changed the compare op as appropriate.)
Consider the exponent of the float v. If the exponent is negative, then v is less than 1 and therefore less than any positive integer. Else, if the exponent is less than the number of bits in w then it must be less than w.
If the exponent of v is greater than the number of bits in w then v is greater than w.
(The exponent is the same as the number of bits in w.)
The final check. Split v into its integer and fractional parts. Double the integer part and add 1 to compensate for the fractional part. Now double the integer w. Compare these two new integers instead to get the result.
To somewhat expand on the earlier answers here, there are a number of details which are commonly overlooked.
subprocess.run() over subprocess.check_call() and friends over subprocess.call() over subprocess.Popen() over os.system() over os.popen()text=True, aka universal_newlines=True.shell=True or shell=False and how it changes quoting and the availability of shell conveniences.sh and BashThese topics are covered in some more detail below.
subprocess.run() or subprocess.check_call()The subprocess.Popen() function is a low-level workhorse but it is tricky to use correctly and you end up copy/pasting multiple lines of code ... which conveniently already exist in the standard library as a set of higher-level wrapper functions for various purposes, which are presented in more detail in the following.
Here"s a paragraph from the documentation:
The recommended approach to invoking subprocesses is to use the
run()function for all use cases it can handle. For more advanced use cases, the underlyingPopeninterface can be used directly.
Unfortunately, the availability of these wrapper functions differs between Python versions.
subprocess.run() was officially introduced in Python 3.5. It is meant to replace all of the following.subprocess.check_output() was introduced in Python 2.7 / 3.1. It is basically equivalent to subprocess.run(..., check=True, stdout=subprocess.PIPE).stdoutsubprocess.check_call() was introduced in Python 2.5. It is basically equivalent to subprocess.run(..., check=True)subprocess.call() was introduced in Python 2.4 in the original subprocess module (PEP-324). It is basically equivalent to subprocess.run(...).returncodesubprocess.Popen()The refactored and extended subprocess.run() is more logical and more versatile than the older legacy functions it replaces. It returns a CompletedProcess object which has various methods which allow you to retrieve the exit status, the standard output, and a few other results and status indicators from the finished subprocess.
subprocess.run() is the way to go if you simply need a program to run and return control to Python. For more involved scenarios (background processes, perhaps with interactive I/O with the Python parent program) you still need to use subprocess.Popen() and take care of all the plumbing yourself. This requires a fairly intricate understanding of all the moving parts and should not be undertaken lightly. The simpler Popen object represents the (possibly still-running) process which needs to be managed from your code for the remainder of the lifetime of the subprocess.
It should perhaps be emphasized that just subprocess.Popen() merely creates a process. If you leave it at that, you have a subprocess running concurrently alongside with Python, so a "background" process. If it doesn"t need to do input or output or otherwise coordinate with you, it can do useful work in parallel with your Python program.
os.system() and os.popen()Since time eternal (well, since Python 2.5) the os module documentation has contained the recommendation to prefer subprocess over os.system():
The
subprocessmodule provides more powerful facilities for spawning new processes and retrieving their results; using that module is preferable to using this function.
The problems with system() are that it"s obviously system-dependent and doesn"t offer ways to interact with the subprocess. It simply runs, with standard output and standard error outside of Python"s reach. The only information Python receives back is the exit status of the command (zero means success, though the meaning of non-zero values is also somewhat system-dependent).
PEP-324 (which was already mentioned above) contains a more detailed rationale for why os.system is problematic and how subprocess attempts to solve those issues.
os.popen() used to be even more strongly discouraged:
Deprecated since version 2.6: This function is obsolete. Use the
subprocessmodule.
However, since sometime in Python 3, it has been reimplemented to simply use subprocess, and redirects to the subprocess.Popen() documentation for details.
check=TrueYou"ll also notice that subprocess.call() has many of the same limitations as os.system(). In regular use, you should generally check whether the process finished successfully, which subprocess.check_call() and subprocess.check_output() do (where the latter also returns the standard output of the finished subprocess). Similarly, you should usually use check=True with subprocess.run() unless you specifically need to allow the subprocess to return an error status.
In practice, with check=True or subprocess.check_*, Python will throw a CalledProcessError exception if the subprocess returns a nonzero exit status.
A common error with subprocess.run() is to omit check=True and be surprised when downstream code fails if the subprocess failed.
On the other hand, a common problem with check_call() and check_output() was that users who blindly used these functions were surprised when the exception was raised e.g. when grep did not find a match. (You should probably replace grep with native Python code anyway, as outlined below.)
All things counted, you need to understand how shell commands return an exit code, and under what conditions they will return a non-zero (error) exit code, and make a conscious decision how exactly it should be handled.
text=True aka universal_newlines=TrueSince Python 3, strings internal to Python are Unicode strings. But there is no guarantee that a subprocess generates Unicode output, or strings at all.
(If the differences are not immediately obvious, Ned Batchelder"s Pragmatic Unicode is recommended, if not outright obligatory, reading. There is a 36-minute video presentation behind the link if you prefer, though reading the page yourself will probably take significantly less time.)
Deep down, Python has to fetch a bytes buffer and interpret it somehow. If it contains a blob of binary data, it shouldn"t be decoded into a Unicode string, because that"s error-prone and bug-inducing behavior - precisely the sort of pesky behavior which riddled many Python 2 scripts, before there was a way to properly distinguish between encoded text and binary data.
With text=True, you tell Python that you, in fact, expect back textual data in the system"s default encoding, and that it should be decoded into a Python (Unicode) string to the best of Python"s ability (usually UTF-8 on any moderately up to date system, except perhaps Windows?)
If that"s not what you request back, Python will just give you bytes strings in the stdout and stderr strings. Maybe at some later point you do know that they were text strings after all, and you know their encoding. Then, you can decode them.
normal = subprocess.run([external, arg],
stdout=subprocess.PIPE, stderr=subprocess.PIPE,
check=True,
text=True)
print(normal.stdout)
convoluted = subprocess.run([external, arg],
stdout=subprocess.PIPE, stderr=subprocess.PIPE,
check=True)
# You have to know (or guess) the encoding
print(convoluted.stdout.decode("utf-8"))
Python 3.7 introduced the shorter and more descriptive and understandable alias text for the keyword argument which was previously somewhat misleadingly called universal_newlines.
shell=True vs shell=FalseWith shell=True you pass a single string to your shell, and the shell takes it from there.
With shell=False you pass a list of arguments to the OS, bypassing the shell.
When you don"t have a shell, you save a process and get rid of a fairly substantial amount of hidden complexity, which may or may not harbor bugs or even security problems.
On the other hand, when you don"t have a shell, you don"t have redirection, wildcard expansion, job control, and a large number of other shell features.
A common mistake is to use shell=True and then still pass Python a list of tokens, or vice versa. This happens to work in some cases, but is really ill-defined and could break in interesting ways.
# XXX AVOID THIS BUG
buggy = subprocess.run("dig +short stackoverflow.com")
# XXX AVOID THIS BUG TOO
broken = subprocess.run(["dig", "+short", "stackoverflow.com"],
shell=True)
# XXX DEFINITELY AVOID THIS
pathological = subprocess.run(["dig +short stackoverflow.com"],
shell=True)
correct = subprocess.run(["dig", "+short", "stackoverflow.com"],
# Probably don"t forget these, too
check=True, text=True)
# XXX Probably better avoid shell=True
# but this is nominally correct
fixed_but_fugly = subprocess.run("dig +short stackoverflow.com",
shell=True,
# Probably don"t forget these, too
check=True, text=True)
The common retort "but it works for me" is not a useful rebuttal unless you understand exactly under what circumstances it could stop working.
Very often, the features of the shell can be replaced with native Python code. Simple Awk or sed scripts should probably simply be translated to Python instead.
To partially illustrate this, here is a typical but slightly silly example which involves many shell features.
cmd = """while read -r x;
do ping -c 3 "$x" | grep "round-trip min/avg/max"
done <hosts.txt"""
# Trivial but horrible
results = subprocess.run(
cmd, shell=True, universal_newlines=True, check=True)
print(results.stdout)
# Reimplement with shell=False
with open("hosts.txt") as hosts:
for host in hosts:
host = host.rstrip("
") # drop newline
ping = subprocess.run(
["ping", "-c", "3", host],
text=True,
stdout=subprocess.PIPE,
check=True)
for line in ping.stdout.split("
"):
if "round-trip min/avg/max" in line:
print("{}: {}".format(host, line))
Some things to note here:
shell=False you don"t need the quoting that the shell requires around strings. Putting quotes anyway is probably an error.The refactored code also illustrates just how much the shell really does for you with a very terse syntax -- for better or for worse. Python says explicit is better than implicit but the Python code is rather verbose and arguably looks more complex than this really is. On the other hand, it offers a number of points where you can grab control in the middle of something else, as trivially exemplified by the enhancement that we can easily include the host name along with the shell command output. (This is by no means challenging to do in the shell, either, but at the expense of yet another diversion and perhaps another process.)
For completeness, here are brief explanations of some of these shell features, and some notes on how they can perhaps be replaced with native Python facilities.
glob.glob() or very often with simple Python string comparisons like for file in os.listdir("."): if not file.endswith(".png"): continue. Bash has various other expansion facilities like .{png,jpg} brace expansion and {1..100} as well as tilde expansion (~ expands to your home directory, and more generally ~account to the home directory of another user)$SHELL or $my_exported_var can sometimes simply be replaced with Python variables. Exported shell variables are available as e.g. os.environ["SHELL"] (the meaning of export is to make the variable available to subprocesses -- a variable which is not available to subprocesses will obviously not be available to Python running as a subprocess of the shell, or vice versa. The env= keyword argument to subprocess methods allows you to define the environment of the subprocess as a dictionary, so that"s one way to make a Python variable visible to a subprocess). With shell=False you will need to understand how to remove any quotes; for example, cd "$HOME" is equivalent to os.chdir(os.environ["HOME"]) without quotes around the directory name. (Very often cd is not useful or necessary anyway, and many beginners omit the double quotes around the variable and get away with it until one day ...)grep "foo" <inputfile >outputfile opens outputfile for writing and inputfile for reading, and passes its contents as standard input to grep, whose standard output then lands in outputfile. This is not generally hard to replace with native Python code.echo foo | nl runs two subprocesses, where the standard output of echo is the standard input of nl (on the OS level, in Unix-like systems, this is a single file handle). If you cannot replace one or both ends of the pipeline with native Python code, perhaps think about using a shell after all, especially if the pipeline has more than two or three processes (though look at the pipes module in the Python standard library or a number of more modern and versatile third-party competitors).ls -l / is equivalent to "ls" "-l" "/" but the quoting around literals is completely optional. Unquoted strings which contain shell metacharacters undergo parameter expansion, whitespace tokenization and wildcard expansion; double quotes prevent whitespace tokenization and wildcard expansion but allow parameter expansions (variable substitution, command substitution, and backslash processing). This is simple in theory but can get bewildering, especially when there are several layers of interpretation (a remote shell command, for example).sh and Bashsubprocess runs your shell commands with /bin/sh unless you specifically request otherwise (except of course on Windows, where it uses the value of the COMSPEC variable). This means that various Bash-only features like arrays, [[ etc are not available.
If you need to use Bash-only syntax, you can
pass in the path to the shell as executable="/bin/bash" (where of course if your Bash is installed somewhere else, you need to adjust the path).
subprocess.run("""
# This for loop syntax is Bash only
for((i=1;i<=$#;i++)); do
# Arrays are Bash-only
array[i]+=123
done""",
shell=True, check=True,
executable="/bin/bash")
subprocess is separate from its parent, and cannot change itA somewhat common mistake is doing something like
subprocess.run("cd /tmp", shell=True)
subprocess.run("pwd", shell=True) # Oops, doesn"t print /tmp
The same thing will happen if the first subprocess tries to set an environment variable, which of course will have disappeared when you run another subprocess, etc.
A child process runs completely separate from Python, and when it finishes, Python has no idea what it did (apart from the vague indicators that it can infer from the exit status and output from the child process). A child generally cannot change the parent"s environment; it cannot set a variable, change the working directory, or, in so many words, communicate with its parent without cooperation from the parent.
The immediate fix in this particular case is to run both commands in a single subprocess;
subprocess.run("cd /tmp; pwd", shell=True)
though obviously this particular use case isn"t very useful; instead, use the cwd keyword argument, or simply os.chdir() before running the subprocess. Similarly, for setting a variable, you can manipulate the environment of the current process (and thus also its children) via
os.environ["foo"] = "bar"
or pass an environment setting to a child process with
subprocess.run("echo "$foo"", shell=True, env={"foo": "bar"})
(not to mention the obvious refactoring subprocess.run(["echo", "bar"]); but echo is a poor example of something to run in a subprocess in the first place, of course).
This is slightly dubious advice; there are certainly situations where it does make sense or is even an absolute requirement to run the Python interpreter as a subprocess from a Python script. But very frequently, the correct approach is simply to import the other Python module into your calling script and call its functions directly.
If the other Python script is under your control, and it isn"t a module, consider turning it into one. (This answer is too long already so I will not delve into details here.)
If you need parallelism, you can run Python functions in subprocesses with the multiprocessing module. There is also threading which runs multiple tasks in a single process (which is more lightweight and gives you more control, but also more constrained in that threads within a process are tightly coupled, and bound to a single GIL.)
urllib has been split up in Python 3.
The urllib.urlencode() function is now urllib.parse.urlencode(),
the urllib.urlopen() function is now urllib.request.urlopen().
This is an update and modification to Saullo"s answer, that uses the full list of the current scipy.stats distributions and returns the distribution with the least SSE between the distribution"s histogram and the data"s histogram.
Using the El Niño dataset from statsmodels, the distributions are fit and error is determined. The distribution with the least error is returned.
%matplotlib inline
import warnings
import numpy as np
import pandas as pd
import scipy.stats as st
import statsmodels.api as sm
from scipy.stats._continuous_distns import _distn_names
import matplotlib
import matplotlib.pyplot as plt
matplotlib.rcParams["figure.figsize"] = (16.0, 12.0)
matplotlib.style.use("ggplot")
# Create models from data
def best_fit_distribution(data, bins=200, ax=None):
"""Model data by finding best fit distribution to data"""
# Get histogram of original data
y, x = np.histogram(data, bins=bins, density=True)
x = (x + np.roll(x, -1))[:-1] / 2.0
# Best holders
best_distributions = []
# Estimate distribution parameters from data
for ii, distribution in enumerate([d for d in _distn_names if not d in ["levy_stable", "studentized_range"]]):
print("{:>3} / {:<3}: {}".format( ii+1, len(_distn_names), distribution ))
distribution = getattr(st, distribution)
# Try to fit the distribution
try:
# Ignore warnings from data that can"t be fit
with warnings.catch_warnings():
warnings.filterwarnings("ignore")
# fit dist to data
params = distribution.fit(data)
# Separate parts of parameters
arg = params[:-2]
loc = params[-2]
scale = params[-1]
# Calculate fitted PDF and error with fit in distribution
pdf = distribution.pdf(x, loc=loc, scale=scale, *arg)
sse = np.sum(np.power(y - pdf, 2.0))
# if axis pass in add to plot
try:
if ax:
pd.Series(pdf, x).plot(ax=ax)
end
except Exception:
pass
# identify if this distribution is better
best_distributions.append((distribution, params, sse))
except Exception:
pass
return sorted(best_distributions, key=lambda x:x[2])
def make_pdf(dist, params, size=10000):
"""Generate distributions"s Probability Distribution Function """
# Separate parts of parameters
arg = params[:-2]
loc = params[-2]
scale = params[-1]
# Get sane start and end points of distribution
start = dist.ppf(0.01, *arg, loc=loc, scale=scale) if arg else dist.ppf(0.01, loc=loc, scale=scale)
end = dist.ppf(0.99, *arg, loc=loc, scale=scale) if arg else dist.ppf(0.99, loc=loc, scale=scale)
# Build PDF and turn into pandas Series
x = np.linspace(start, end, size)
y = dist.pdf(x, loc=loc, scale=scale, *arg)
pdf = pd.Series(y, x)
return pdf
# Load data from statsmodels datasets
data = pd.Series(sm.datasets.elnino.load_pandas().data.set_index("YEAR").values.ravel())
# Plot for comparison
plt.figure(figsize=(12,8))
ax = data.plot(kind="hist", bins=50, density=True, alpha=0.5, color=list(matplotlib.rcParams["axes.prop_cycle"])[1]["color"])
# Save plot limits
dataYLim = ax.get_ylim()
# Find best fit distribution
best_distibutions = best_fit_distribution(data, 200, ax)
best_dist = best_distibutions[0]
# Update plots
ax.set_ylim(dataYLim)
ax.set_title(u"El Niño sea temp.
All Fitted Distributions")
ax.set_xlabel(u"Temp (°C)")
ax.set_ylabel("Frequency")
# Make PDF with best params
pdf = make_pdf(best_dist[0], best_dist[1])
# Display
plt.figure(figsize=(12,8))
ax = pdf.plot(lw=2, label="PDF", legend=True)
data.plot(kind="hist", bins=50, density=True, alpha=0.5, label="Data", legend=True, ax=ax)
param_names = (best_dist[0].shapes + ", loc, scale").split(", ") if best_dist[0].shapes else ["loc", "scale"]
param_str = ", ".join(["{}={:0.2f}".format(k,v) for k,v in zip(param_names, best_dist[1])])
dist_str = "{}({})".format(best_dist[0].name, param_str)
ax.set_title(u"El Niño sea temp. with best fit distribution
" + dist_str)
ax.set_xlabel(u"Temp. (°C)")
ax.set_ylabel("Frequency")
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