Find and Draw Paths with OpenCV | python

find | NumPy | open | Python Methods and Functions

OpenCV has findContour () which helps in extracting contours from an image. It works best with binary images, so we must first apply thresholding, Sobel edges, etc.

Below is the code to find outlines —

import cv2

import numpy as np

 
# Let`s upload a simple image with 3 black squares

image = cv2.imread ( ` C: //Users//gfg//shapes.jpg ` )

cv2.waitKey ( 0 )

 
# О shades of gray

gray = cv2.cvtColor (image, cv2.COLOR_BGR2GRAY)

 
# Find Canny edges

edged = cv2.Canny (gray, 30 , 200 )

cv2.waitKey ( 0 )

 
# Finding outlines
# Use a copy of the image, for example edged.copy ()
# because findContours changes the image

contours, hierarchy = cv2.findContours (edge d, 

cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)

 

cv2.imshow ( `Canny Edges After Contouring` , edged)

cv2.waitKey ( 0 )

  

print ( "Number of Contours found =" + str ( len (contours)))

  
# Draw all outlines
# -1 means drawing all contours

cv2.drawContours (image, contours,  - 1 , ( 0 , 255 , 0 ), 3 )

 

cv2.imshow ( `Contours` , image)

cv2.waitKey ( 0 )

cv2.destroyAllWindows ()

Output:

We can see that there are three essential arguments in the cv2.findContours () function. The first — original image, second — contour search mode, third — a contour approximation method that outputs the image, contours, and hierarchy. & # 39;  contours & # 39; — this is a Python list of all the contours of the image. Each individual contour is a Numpy (x, y) array of coordinates of the object`s boundary points.

Path approximation method —
Above we can see that the paths are boundaries shapes with the same intensity. It stores the (x, y) coordinates of the shape`s border. But does it store all coordinates? This is set by this contour approximation method. 
If we pass in cv2.CHAIN_APPROX_NONE , all endpoints are preserved. But do we really need all the items? For example, if we have to find the outline of a straight line. We only need the two endpoints of this line. This is what cv2.CHAIN_APPROX_SIMPLE does. It removes all unnecessary points and shrinks the path, thereby saving memory.





Find and Draw Paths with OpenCV | python: StackOverflow Questions

Finding the index of an item in a list

Given a list ["foo", "bar", "baz"] and an item in the list "bar", how do I get its index (1) in Python?

Find current directory and file"s directory

In Python, what commands can I use to find:

  1. the current directory (where I was in the terminal when I ran the Python script), and
  2. where the file I am executing is?

How to find if directory exists in Python

In the os module in Python, is there a way to find if a directory exists, something like:

>>> os.direxists(os.path.join(os.getcwd()), "new_folder")) # in pseudocode
True/False

How do I find the location of my Python site-packages directory?

Question by Daryl Spitzer

How do I find the location of my site-packages directory?

Find all files in a directory with extension .txt in Python

How can I find all the files in a directory having the extension .txt in python?

Find which version of package is installed with pip

Using pip, is it possible to figure out which version of a package is currently installed?

I know about pip install XYZ --upgrade but I am wondering if there is anything like pip info XYZ. If not what would be the best way to tell what version I am currently using.

error: Unable to find vcvarsall.bat

I tried to install the Python package dulwich:

pip install dulwich

But I get a cryptic error message:

error: Unable to find vcvarsall.bat

The same happens if I try installing the package manually:

> python setup.py install
running build_ext
building "dulwich._objects" extension
error: Unable to find vcvarsall.bat

How to use glob() to find files recursively?

This is what I have:

glob(os.path.join("src","*.c"))

but I want to search the subfolders of src. Something like this would work:

glob(os.path.join("src","*.c"))
glob(os.path.join("src","*","*.c"))
glob(os.path.join("src","*","*","*.c"))
glob(os.path.join("src","*","*","*","*.c"))

But this is obviously limited and clunky.

Python: Find in list

I have come across this:

item = someSortOfSelection()
if item in myList:
    doMySpecialFunction(item)

but sometimes it does not work with all my items, as if they weren"t recognized in the list (when it"s a list of string).

Is this the most "pythonic" way of finding an item in a list: if x in l:?

How to find out the number of CPUs using python

I want to know the number of CPUs on the local machine using Python. The result should be user/real as output by time(1) when called with an optimally scaling userspace-only program.

Answer #1

How to iterate over rows in a DataFrame in Pandas?

Answer: DON"T*!

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 zipping 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.


Further Reading

* 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.

Answer #2

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.

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

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.

Critiques of other answers

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.

Answer #3

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)

>>> ["F:\_python\dict.py", "F:\_python\progr.txt", "F:\_python\readl.py"]

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 Contabilità 1conti.txt
>>> _compiti18Compito Contabilità 1modula4.txt
>>> _compiti18Compito Contabilità 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. enter image description here

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()

Answer #4

I just used the following which was quite simple. First open a console then cd to where you"ve downloaded your file like some-package.whl and use

pip install some-package.whl

Note: if pip.exe is not recognized, you may find it in the "Scripts" directory from where python has been installed. If pip is not installed, this page can help: How do I install pip on Windows?

Note: for clarification
If you copy the *.whl file to your local drive (ex. C:some-dirsome-file.whl) use the following command line parameters --

pip install C:/some-dir/some-file.whl

Answer #5

Quick Answer:

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.

Answer #6

Using a for loop, how do I access the loop index, from 1 to 5 in this case?

Use enumerate to get the index with the element as you iterate:

for index, item in enumerate(items):
    print(index, item)

And note that Python"s indexes start at zero, so you would get 0 to 4 with the above. If you want the count, 1 to 5, do this:

count = 0 # in case items is empty and you need it after the loop
for count, item in enumerate(items, start=1):
    print(count, item)

Unidiomatic control flow

What you are asking for is the Pythonic equivalent of the following, which is the algorithm most programmers of lower-level languages would use:

index = 0            # Python"s indexing starts at zero
for item in items:   # Python"s for loops are a "for each" loop 
    print(index, item)
    index += 1

Or in languages that do not have a for-each loop:

index = 0
while index < len(items):
    print(index, items[index])
    index += 1

or sometimes more commonly (but unidiomatically) found in Python:

for index in range(len(items)):
    print(index, items[index])

Use the Enumerate Function

Python"s enumerate function reduces the visual clutter by hiding the accounting for the indexes, and encapsulating the iterable into another iterable (an enumerate object) that yields a two-item tuple of the index and the item that the original iterable would provide. That looks like this:

for index, item in enumerate(items, start=0):   # default is zero
    print(index, item)

This code sample is fairly well the canonical example of the difference between code that is idiomatic of Python and code that is not. Idiomatic code is sophisticated (but not complicated) Python, written in the way that it was intended to be used. Idiomatic code is expected by the designers of the language, which means that usually this code is not just more readable, but also more efficient.

Getting a count

Even if you don"t need indexes as you go, but you need a count of the iterations (sometimes desirable) you can start with 1 and the final number will be your count.

count = 0 # in case items is empty
for count, item in enumerate(items, start=1):   # default is zero
    print(item)

print("there were {0} items printed".format(count))

The count seems to be more what you intend to ask for (as opposed to index) when you said you wanted from 1 to 5.


Breaking it down - a step by step explanation

To break these examples down, say we have a list of items that we want to iterate over with an index:

items = ["a", "b", "c", "d", "e"]

Now we pass this iterable to enumerate, creating an enumerate object:

enumerate_object = enumerate(items) # the enumerate object

We can pull the first item out of this iterable that we would get in a loop with the next function:

iteration = next(enumerate_object) # first iteration from enumerate
print(iteration)

And we see we get a tuple of 0, the first index, and "a", the first item:

(0, "a")

we can use what is referred to as "sequence unpacking" to extract the elements from this two-tuple:

index, item = iteration
#   0,  "a" = (0, "a") # essentially this.

and when we inspect index, we find it refers to the first index, 0, and item refers to the first item, "a".

>>> print(index)
0
>>> print(item)
a

Conclusion

  • Python indexes start at zero
  • To get these indexes from an iterable as you iterate over it, use the enumerate function
  • Using enumerate in the idiomatic way (along with tuple unpacking) creates code that is more readable and maintainable:

So do this:

for index, item in enumerate(items, start=0):   # Python indexes start at zero
    print(index, item)

Answer #7

Getting some sort of modification date in a cross-platform way is easy - just call os.path.getmtime(path) and you"ll get the Unix timestamp of when the file at path was last modified.

Getting file creation dates, on the other hand, is fiddly and platform-dependent, differing even between the three big OSes:

Putting this all together, cross-platform code should look something like this...

import os
import platform

def creation_date(path_to_file):
    """
    Try to get the date that a file was created, falling back to when it was
    last modified if that isn"t possible.
    See http://stackoverflow.com/a/39501288/1709587 for explanation.
    """
    if platform.system() == "Windows":
        return os.path.getctime(path_to_file)
    else:
        stat = os.stat(path_to_file)
        try:
            return stat.st_birthtime
        except AttributeError:
            # We"re probably on Linux. No easy way to get creation dates here,
            # so we"ll settle for when its content was last modified.
            return stat.st_mtime

Answer #8

I noticed that every now and then I need to Google fopen all over again, just to build a mental image of what the primary differences between the modes are. So, I thought a diagram will be faster to read next time. Maybe someone else will find that helpful too.

Answer #9

I would suggest using the duplicated method on the Pandas Index itself:

df3 = df3[~df3.index.duplicated(keep="first")]

While all the other methods work, .drop_duplicates is by far the least performant for the provided example. Furthermore, while the groupby method is only slightly less performant, I find the duplicated method to be more readable.

Using the sample data provided:

>>> %timeit df3.reset_index().drop_duplicates(subset="index", keep="first").set_index("index")
1000 loops, best of 3: 1.54 ms per loop

>>> %timeit df3.groupby(df3.index).first()
1000 loops, best of 3: 580 µs per loop

>>> %timeit df3[~df3.index.duplicated(keep="first")]
1000 loops, best of 3: 307 µs per loop

Note that you can keep the last element by changing the keep argument to "last".

It should also be noted that this method works with MultiIndex as well (using df1 as specified in Paul"s example):

>>> %timeit df1.groupby(level=df1.index.names).last()
1000 loops, best of 3: 771 µs per loop

>>> %timeit df1[~df1.index.duplicated(keep="last")]
1000 loops, best of 3: 365 µs per loop

Answer #10

Here"s a concise solution which avoids regular expressions and slow in-Python loops:

def principal_period(s):
    i = (s+s).find(s, 1, -1)
    return None if i == -1 else s[:i]

See the Community Wiki answer started by @davidism for benchmark results. In summary,

David Zhang"s solution is the clear winner, outperforming all others by at least 5x for the large example set.

(That answer"s words, not mine.)

This is based on the observation that a string is periodic if and only if it is equal to a nontrivial rotation of itself. Kudos to @AleksiTorhamo for realizing that we can then recover the principal period from the index of the first occurrence of s in (s+s)[1:-1], and for informing me of the optional start and end arguments of Python"s string.find.

Find and Draw Paths with OpenCV | python: StackOverflow Questions

How can I open multiple files using "with open" in Python?

I want to change a couple of files at one time, iff I can write to all of them. I"m wondering if I somehow can combine the multiple open calls with the with statement:

try:
  with open("a", "w") as a and open("b", "w") as b:
    do_something()
except IOError as e:
  print "Operation failed: %s" % e.strerror

If that"s not possible, what would an elegant solution to this problem look like?

open() in Python does not create a file if it doesn"t exist

What is the best way to open a file as read/write if it exists, or if it does not, then create it and open it as read/write? From what I read, file = open("myfile.dat", "rw") should do this, right?

It is not working for me (Python 2.6.2) and I"m wondering if it is a version problem, or not supposed to work like that or what.

The bottom line is, I just need a solution for the problem. I am curious about the other stuff, but all I need is a nice way to do the opening part.

The enclosing directory was writeable by user and group, not other (I"m on a Linux system... so permissions 775 in other words), and the exact error was:

IOError: no such file or directory.

Difference between modes a, a+, w, w+, and r+ in built-in open function?

In the python built-in open function, what is the exact difference between the modes w, a, w+, a+, and r+?

In particular, the documentation implies that all of these will allow writing to the file, and says that it opens the files for "appending", "writing", and "updating" specifically, but does not define what these terms mean.

Simple Digit Recognition OCR in OpenCV-Python

I am trying to implement a "Digit Recognition OCR" in OpenCV-Python (cv2). It is just for learning purposes. I would like to learn both KNearest and SVM features in OpenCV.

I have 100 samples (i.e. images) of each digit. I would like to train with them.

There is a sample letter_recog.py that comes with OpenCV sample. But I still couldn"t figure out on how to use it. I don"t understand what are the samples, responses etc. Also, it loads a txt file at first, which I didn"t understand first.

Later on searching a little bit, I could find a letter_recognition.data in cpp samples. I used it and made a code for cv2.KNearest in the model of letter_recog.py (just for testing):

import numpy as np
import cv2

fn = "letter-recognition.data"
a = np.loadtxt(fn, np.float32, delimiter=",", converters={ 0 : lambda ch : ord(ch)-ord("A") })
samples, responses = a[:,1:], a[:,0]

model = cv2.KNearest()
retval = model.train(samples,responses)
retval, results, neigh_resp, dists = model.find_nearest(samples, k = 10)
print results.ravel()

It gave me an array of size 20000, I don"t understand what it is.

Questions:

1) What is letter_recognition.data file? How to build that file from my own data set?

2) What does results.reval() denote?

3) How we can write a simple digit recognition tool using letter_recognition.data file (either KNearest or SVM)?

Does reading an entire file leave the file handle open?

If you read an entire file with content = open("Path/to/file", "r").read() is the file handle left open until the script exits? Is there a more concise method to read a whole file?

Store output of subprocess.Popen call in a string

I"m trying to make a system call in Python and store the output to a string that I can manipulate in the Python program.

#!/usr/bin/python
import subprocess
p2 = subprocess.Popen("ntpq -p")

I"ve tried a few things including some of the suggestions here:

Retrieving the output of subprocess.call()

but without any luck.

"Unicode Error "unicodeescape" codec can"t decode bytes... Cannot open text files in Python 3

I am using Python 3.1 on a Windows 7 machine. Russian is the default system language, and utf-8 is the default encoding.

Looking at the answer to a previous question, I have attempting using the "codecs" module to give me a little luck. Here"s a few examples:

>>> g = codecs.open("C:UsersEricDesktopeeline.txt", "r", encoding="utf-8")
SyntaxError: (unicode error) "unicodeescape" codec can"t decode bytes in position 2-4: truncated UXXXXXXXX escape (<pyshell#39>, line 1)
>>> g = codecs.open("C:UsersEricDesktopSite.txt", "r", encoding="utf-8")
SyntaxError: (unicode error) "unicodeescape" codec can"t decode bytes in position 2-4: truncated UXXXXXXXX escape (<pyshell#40>, line 1)
>>> g = codecs.open("C:Python31Notes.txt", "r", encoding="utf-8")
SyntaxError: (unicode error) "unicodeescape" codec can"t decode bytes in position 11-12: malformed N character escape (<pyshell#41>, line 1)
>>> g = codecs.open("C:UsersEricDesktopSite.txt", "r", encoding="utf-8")
SyntaxError: (unicode error) "unicodeescape" codec can"t decode bytes in position 2-4: truncated UXXXXXXXX escape (<pyshell#44>, line 1)

My last idea was, I thought it might have been the fact that Windows "translates" a few folders, such as the "users" folder, into Russian (though typing "users" is still the correct path), so I tried it in the Python31 folder. Still, no luck. Any ideas?

Python subprocess/Popen with a modified environment

I believe that running an external command with a slightly modified environment is a very common case. That"s how I tend to do it:

import subprocess, os
my_env = os.environ
my_env["PATH"] = "/usr/sbin:/sbin:" + my_env["PATH"]
subprocess.Popen(my_command, env=my_env)

I"ve got a gut feeling that there"s a better way; does it look alright?

Cannot find module cv2 when using OpenCV

I have installed OpenCV on the Occidentalis operating system (a variant of Raspbian) on a Raspberry Pi, using jayrambhia"s script found here. It installed version 2.4.5.

When I try import cv2 in a Python program, I get the following message:

[email protected]~$ python cam.py
Traceback (most recent call last)
File "cam.py", line 1, in <module>
    import cv2
ImportError: No module named cv2

The file cv2.so is stored in /usr/local/lib/python2.7/site-packages/...

There are also folders in /usr/local/lib called python3.2 and python2.6, which could be a problem but I"m not sure.

Is this a path error perhaps? Any help is appreciated, I am new to Linux.

How to crop an image in OpenCV using Python

How can I crop images, like I"ve done before in PIL, using OpenCV.

Working example on PIL

im = Image.open("0.png").convert("L")
im = im.crop((1, 1, 98, 33))
im.save("_0.png")

But how I can do it on OpenCV?

This is what I tried:

im = cv.imread("0.png", cv.CV_LOAD_IMAGE_GRAYSCALE)
(thresh, im_bw) = cv.threshold(im, 128, 255, cv.THRESH_OTSU)
im = cv.getRectSubPix(im_bw, (98, 33), (1, 1))
cv.imshow("Img", im)
cv.waitKey(0)

But it doesn"t work.

I think I incorrectly used getRectSubPix. If this is the case, please explain how I can correctly use this function.

Answer #1

Since this question was asked in 2010, there has been real simplification in how to do simple multithreading with Python with map and pool.

The code below comes from an article/blog post that you should definitely check out (no affiliation) - Parallelism in one line: A Better Model for Day to Day Threading Tasks. I"ll summarize below - it ends up being just a few lines of code:

from multiprocessing.dummy import Pool as ThreadPool
pool = ThreadPool(4)
results = pool.map(my_function, my_array)

Which is the multithreaded version of:

results = []
for item in my_array:
    results.append(my_function(item))

Description

Map is a cool little function, and the key to easily injecting parallelism into your Python code. For those unfamiliar, map is something lifted from functional languages like Lisp. It is a function which maps another function over a sequence.

Map handles the iteration over the sequence for us, applies the function, and stores all of the results in a handy list at the end.

Enter image description here


Implementation

Parallel versions of the map function are provided by two libraries:multiprocessing, and also its little known, but equally fantastic step child:multiprocessing.dummy.

multiprocessing.dummy is exactly the same as multiprocessing module, but uses threads instead (an important distinction - use multiple processes for CPU-intensive tasks; threads for (and during) I/O):

multiprocessing.dummy replicates the API of multiprocessing, but is no more than a wrapper around the threading module.

import urllib2
from multiprocessing.dummy import Pool as ThreadPool

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

# Make the Pool of workers
pool = ThreadPool(4)

# Open the URLs in their own threads
# and return the results
results = pool.map(urllib2.urlopen, urls)

# Close the pool and wait for the work to finish
pool.close()
pool.join()

And the timing results:

Single thread:   14.4 seconds
       4 Pool:   3.1 seconds
       8 Pool:   1.4 seconds
      13 Pool:   1.3 seconds

Passing multiple arguments (works like this only in Python 3.3 and later):

To pass multiple arrays:

results = pool.starmap(function, zip(list_a, list_b))

Or to pass a constant and an array:

results = pool.starmap(function, zip(itertools.repeat(constant), list_a))

If you are using an earlier version of Python, you can pass multiple arguments via this workaround).

(Thanks to user136036 for the helpful comment.)

Answer #2

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)

>>> ["F:\_python\dict.py", "F:\_python\progr.txt", "F:\_python\readl.py"]

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 Contabilità 1conti.txt
>>> _compiti18Compito Contabilità 1modula4.txt
>>> _compiti18Compito Contabilità 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. enter image description here

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()

Answer #3

I just used the following which was quite simple. First open a console then cd to where you"ve downloaded your file like some-package.whl and use

pip install some-package.whl

Note: if pip.exe is not recognized, you may find it in the "Scripts" directory from where python has been installed. If pip is not installed, this page can help: How do I install pip on Windows?

Note: for clarification
If you copy the *.whl file to your local drive (ex. C:some-dirsome-file.whl) use the following command line parameters --

pip install C:/some-dir/some-file.whl

Answer #4

This is the behaviour to adopt when the referenced object is deleted. It is not specific to Django; this is an SQL standard. Although Django has its own implementation on top of SQL. (1)

There are seven possible actions to take when such event occurs:

  • CASCADE: When the referenced object is deleted, also delete the objects that have references to it (when you remove a blog post for instance, you might want to delete comments as well). SQL equivalent: CASCADE.
  • PROTECT: Forbid the deletion of the referenced object. To delete it you will have to delete all objects that reference it manually. SQL equivalent: RESTRICT.
  • RESTRICT: (introduced in Django 3.1) Similar behavior as PROTECT that matches SQL"s RESTRICT more accurately. (See django documentation example)
  • SET_NULL: Set the reference to NULL (requires the field to be nullable). For instance, when you delete a User, you might want to keep the comments he posted on blog posts, but say it was posted by an anonymous (or deleted) user. SQL equivalent: SET NULL.
  • SET_DEFAULT: Set the default value. SQL equivalent: SET DEFAULT.
  • SET(...): Set a given value. This one is not part of the SQL standard and is entirely handled by Django.
  • DO_NOTHING: Probably a very bad idea since this would create integrity issues in your database (referencing an object that actually doesn"t exist). SQL equivalent: NO ACTION. (2)

Source: Django documentation

See also the documentation of PostgreSQL for instance.

In most cases, CASCADE is the expected behaviour, but for every ForeignKey, you should always ask yourself what is the expected behaviour in this situation. PROTECT and SET_NULL are often useful. Setting CASCADE where it should not, can potentially delete all of your database in cascade, by simply deleting a single user.


Additional note to clarify cascade direction

It"s funny to notice that the direction of the CASCADE action is not clear to many people. Actually, it"s funny to notice that only the CASCADE action is not clear. I understand the cascade behavior might be confusing, however you must think that it is the same direction as any other action. Thus, if you feel that CASCADE direction is not clear to you, it actually means that on_delete behavior is not clear to you.

In your database, a foreign key is basically represented by an integer field which value is the primary key of the foreign object. Let"s say you have an entry comment_A, which has a foreign key to an entry article_B. If you delete the entry comment_A, everything is fine. article_B used to live without comment_A and don"t bother if it"s deleted. However, if you delete article_B, then comment_A panics! It never lived without article_B and needs it, and it"s part of its attributes (article=article_B, but what is article_B???). This is where on_delete steps in, to determine how to resolve this integrity error, either by saying:

  • "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.

Answer #5

Running brew reinstall [email protected] didn"t work for my existing Python 2.7 virtual environments. Inside them there were still ERROR:root:code for hash sha1 was not found errors.

I encountered this problem after I ran brew upgrade openssl. And here"s the fix:

$ ls /usr/local/Cellar/openssl

...which shows

1.0.2t

According to the existing version, run:

$ brew switch openssl 1.0.2t

...which shows

Cleaning /usr/local/Cellar/openssl/1.0.2t
Opt link created for /usr/local/Cellar/openssl/1.0.2t

After that, run the following command in a Python 2.7 virtualenv:

(my-venv) $ python -c "import hashlib;m=hashlib.md5();print(m.hexdigest())"

...which shows

d41d8cd98f00b204e9800998ecf8427e

No more errors.

Answer #6

You opened the file in binary mode:

with open(fname, "rb") as f:

This means that all data read from the file is returned as bytes objects, not str. You cannot then use a string in a containment test:

if "some-pattern" in tmp: continue

You"d have to use a bytes object to test against tmp instead:

if b"some-pattern" in tmp: continue

or open the file as a textfile instead by replacing the "rb" mode with "r".

Answer #7

⚡️ TL;DR — One line solution.

All you have to do is:

sudo easy_install pip

2019: ⚠️easy_install has been deprecated. Check Method #2 below for preferred installation!

Details:

⚡️ OK, I read the solutions given above, but here"s an EASY solution to install pip.

MacOS comes with Python installed. But to make sure that you have Python installed open the terminal and run the following command.

python --version

If this command returns a version number that means Python exists. Which also means that you already have access to easy_install considering you are using macOS/OSX.

ℹ️ Now, all you have to do is run the following command.

sudo easy_install pip

After that, pip will be installed and you"ll be able to use it for installing other packages.

Let me know if you have any problems installing pip this way.

Cheers!

P.S. I ended up blogging a post about it. QuickTip: How Do I Install pip on macOS or OS X?


✅ UPDATE (Jan 2019): METHOD #2: Two line solution —

easy_install has been deprecated. Please use get-pip.py instead.

First of all download the get-pip file

curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py

Now run this file to install pip

python get-pip.py

That should do it.

Another gif you said? Here ya go!

Answer #8

I noticed that every now and then I need to Google fopen all over again, just to build a mental image of what the primary differences between the modes are. So, I thought a diagram will be faster to read next time. Maybe someone else will find that helpful too.

Answer #9

It helps to install a python package foo on your machine (can also be in virtualenv) so that you can import the package foo from other projects and also from [I]Python prompts.

It does the similar job of pip, easy_install etc.,


Using setup.py

Let"s start with some definitions:

Package - A folder/directory that contains __init__.py file.
Module - A valid python file with .py extension.
Distribution - How one package relates to other packages and modules.

Let"s say you want to install a package named foo. Then you do,

$ git clone https://github.com/user/foo  
$ cd foo
$ python setup.py install

Instead, if you don"t want to actually install it but still would like to use it. Then do,

$ python setup.py develop  

This command will create symlinks to the source directory within site-packages instead of copying things. Because of this, it is quite fast (particularly for large packages).


Creating setup.py

If you have your package tree like,

foo
├── foo
│   ├── data_struct.py
│   ├── __init__.py
│   └── internals.py
├── README
├── requirements.txt
└── setup.py

Then, you do the following in your setup.py script so that it can be installed on some machine:

from setuptools import setup

setup(
   name="foo",
   version="1.0",
   description="A useful module",
   author="Man Foo",
   author_email="[email protected]",
   packages=["foo"],  #same as name
   install_requires=["wheel", "bar", "greek"], #external packages as dependencies
)

Instead, if your package tree is more complex like the one below:

foo
├── foo
│   ├── data_struct.py
│   ├── __init__.py
│   └── internals.py
├── README
├── requirements.txt
├── scripts
│   ├── cool
│   └── skype
└── setup.py

Then, your setup.py in this case would be like:

from setuptools import setup

setup(
   name="foo",
   version="1.0",
   description="A useful module",
   author="Man Foo",
   author_email="[email protected]",
   packages=["foo"],  #same as name
   install_requires=["wheel", "bar", "greek"], #external packages as dependencies
   scripts=[
            "scripts/cool",
            "scripts/skype",
           ]
)

Add more stuff to (setup.py) & make it decent:

from setuptools import setup

with open("README", "r") as f:
    long_description = f.read()

setup(
   name="foo",
   version="1.0",
   description="A useful module",
   license="MIT",
   long_description=long_description,
   author="Man Foo",
   author_email="[email protected]",
   url="http://www.foopackage.com/",
   packages=["foo"],  #same as name
   install_requires=["wheel", "bar", "greek"], #external packages as dependencies
   scripts=[
            "scripts/cool",
            "scripts/skype",
           ]
)

The long_description is used in pypi.org as the README description of your package.


And finally, you"re now ready to upload your package to PyPi.org so that others can install your package using pip install yourpackage.

At this point there are two options.

  • publish in the temporary test.pypi.org server to make oneself familiarize with the procedure, and then publish it on the permanent pypi.org server for the public to use your package.
  • publish straight away on the permanent pypi.org server, if you are already familiar with the procedure and have your user credentials (e.g., username, password, package name)

Once your package name is registered in pypi.org, nobody can claim or use it. Python packaging suggests the twine package for uploading purposes (of your package to PyPi). Thus,

(1) the first step is to locally build the distributions using:

# prereq: wheel (pip install wheel)  
$ python setup.py sdist bdist_wheel   

(2) then using twine for uploading either to test.pypi.org or pypi.org:

$ twine upload --repository testpypi dist/*  
username: ***  
password: ***  

It will take few minutes for the package to appear on test.pypi.org. Once you"re satisfied with it, you can then upload your package to the real & permanent index of pypi.org simply with:

$ twine upload dist/*  

Optionally, you can also sign the files in your package with a GPG by:

$ twine upload dist/* --sign 

Bonus Reading:

Answer #10

tl;dr / quick fix

  • Don"t decode/encode willy nilly
  • Don"t assume your strings are UTF-8 encoded
  • Try to convert strings to Unicode strings as soon as possible in your code
  • Fix your locale: How to solve UnicodeDecodeError in Python 3.6?
  • Don"t be tempted to use quick reload hacks

Unicode Zen in Python 2.x - The Long Version

Without seeing the source it"s difficult to know the root cause, so I"ll have to speak generally.

UnicodeDecodeError: "ascii" codec can"t decode byte generally happens when you try to convert a Python 2.x str that contains non-ASCII to a Unicode string without specifying the encoding of the original string.

In brief, Unicode strings are an entirely separate type of Python string that does not contain any encoding. They only hold Unicode point codes and therefore can hold any Unicode point from across the entire spectrum. Strings contain encoded text, beit UTF-8, UTF-16, ISO-8895-1, GBK, Big5 etc. Strings are decoded to Unicode and Unicodes are encoded to strings. Files and text data are always transferred in encoded strings.

The Markdown module authors probably use unicode() (where the exception is thrown) as a quality gate to the rest of the code - it will convert ASCII or re-wrap existing Unicodes strings to a new Unicode string. The Markdown authors can"t know the encoding of the incoming string so will rely on you to decode strings to Unicode strings before passing to Markdown.

Unicode strings can be declared in your code using the u prefix to strings. E.g.

>>> my_u = u"my ünicôdé strįng"
>>> type(my_u)
<type "unicode">

Unicode strings may also come from file, databases and network modules. When this happens, you don"t need to worry about the encoding.

Gotchas

Conversion from str to Unicode can happen even when you don"t explicitly call unicode().

The following scenarios cause UnicodeDecodeError exceptions:

# Explicit conversion without encoding
unicode("€")

# New style format string into Unicode string
# Python will try to convert value string to Unicode first
u"The currency is: {}".format("€")

# Old style format string into Unicode string
# Python will try to convert value string to Unicode first
u"The currency is: %s" % "€"

# Append string to Unicode
# Python will try to convert string to Unicode first
u"The currency is: " + "€"         

Examples

In the following diagram, you can see how the word café has been encoded in either "UTF-8" or "Cp1252" encoding depending on the terminal type. In both examples, caf is just regular ascii. In UTF-8, é is encoded using two bytes. In "Cp1252", é is 0xE9 (which is also happens to be the Unicode point value (it"s no coincidence)). The correct decode() is invoked and conversion to a Python Unicode is successfull: Diagram of a string being converted to a Python Unicode string

In this diagram, decode() is called with ascii (which is the same as calling unicode() without an encoding given). As ASCII can"t contain bytes greater than 0x7F, this will throw a UnicodeDecodeError exception:

Diagram of a string being converted to a Python Unicode string with the wrong encoding

The Unicode Sandwich

It"s good practice to form a Unicode sandwich in your code, where you decode all incoming data to Unicode strings, work with Unicodes, then encode to strs on the way out. This saves you from worrying about the encoding of strings in the middle of your code.

Input / Decode

Source code

If you need to bake non-ASCII into your source code, just create Unicode strings by prefixing the string with a u. E.g.

u"Zürich"

To allow Python to decode your source code, you will need to add an encoding header to match the actual encoding of your file. For example, if your file was encoded as "UTF-8", you would use:

# encoding: utf-8

This is only necessary when you have non-ASCII in your source code.

Files

Usually non-ASCII data is received from a file. The io module provides a TextWrapper that decodes your file on the fly, using a given encoding. You must use the correct encoding for the file - it can"t be easily guessed. For example, for a UTF-8 file:

import io
with io.open("my_utf8_file.txt", "r", encoding="utf-8") as my_file:
     my_unicode_string = my_file.read() 

my_unicode_string would then be suitable for passing to Markdown. If a UnicodeDecodeError from the read() line, then you"ve probably used the wrong encoding value.

CSV Files

The Python 2.7 CSV module does not support non-ASCII characters üò©. Help is at hand, however, with https://pypi.python.org/pypi/backports.csv.

Use it like above but pass the opened file to it:

from backports import csv
import io
with io.open("my_utf8_file.txt", "r", encoding="utf-8") as my_file:
    for row in csv.reader(my_file):
        yield row

Databases

Most Python database drivers can return data in Unicode, but usually require a little configuration. Always use Unicode strings for SQL queries.

MySQL

In the connection string add:

charset="utf8",
use_unicode=True

E.g.

>>> db = MySQLdb.connect(host="localhost", user="root", passwd="passwd", db="sandbox", use_unicode=True, charset="utf8")
PostgreSQL

Add:

psycopg2.extensions.register_type(psycopg2.extensions.UNICODE)
psycopg2.extensions.register_type(psycopg2.extensions.UNICODEARRAY)

HTTP

Web pages can be encoded in just about any encoding. The Content-type header should contain a charset field to hint at the encoding. The content can then be decoded manually against this value. Alternatively, Python-Requests returns Unicodes in response.text.

Manually

If you must decode strings manually, you can simply do my_string.decode(encoding), where encoding is the appropriate encoding. Python 2.x supported codecs are given here: Standard Encodings. Again, if you get UnicodeDecodeError then you"ve probably got the wrong encoding.

The meat of the sandwich

Work with Unicodes as you would normal strs.

Output

stdout / printing

print writes through the stdout stream. Python tries to configure an encoder on stdout so that Unicodes are encoded to the console"s encoding. For example, if a Linux shell"s locale is en_GB.UTF-8, the output will be encoded to UTF-8. On Windows, you will be limited to an 8bit code page.

An incorrectly configured console, such as corrupt locale, can lead to unexpected print errors. PYTHONIOENCODING environment variable can force the encoding for stdout.

Files

Just like input, io.open can be used to transparently convert Unicodes to encoded byte strings.

Database

The same configuration for reading will allow Unicodes to be written directly.

Python 3

Python 3 is no more Unicode capable than Python 2.x is, however it is slightly less confused on the topic. E.g the regular str is now a Unicode string and the old str is now bytes.

The default encoding is UTF-8, so if you .decode() a byte string without giving an encoding, Python 3 uses UTF-8 encoding. This probably fixes 50% of people"s Unicode problems.

Further, open() operates in text mode by default, so returns decoded str (Unicode ones). The encoding is derived from your locale, which tends to be UTF-8 on Un*x systems or an 8-bit code page, such as windows-1251, on Windows boxes.

Why you shouldn"t use sys.setdefaultencoding("utf8")

It"s a nasty hack (there"s a reason you have to use reload) that will only mask problems and hinder your migration to Python 3.x. Understand the problem, fix the root cause and enjoy Unicode zen. See Why should we NOT use sys.setdefaultencoding("utf-8") in a py script? for further details

Get Solution for free from DataCamp guru