Summer of code 2017: Python, Day 42 args and kwargs

As explained in my Summer of code 2017: Python post I decided to pick up Python

This is officially day 42.  Today I looked at args and kwargs, these notes are mostly for me but who knows, they might be helpful for someone else in the future as well


Args and kwargs? WTF is that? I had the same thought, it turns out these are magic variables  :-)

From the docs:

args

A tuple of positional arguments values. Dynamically computed from the arguments attribute.

kwargs

A dict of keyword arguments values. Dynamically computed from the arguments attribute.

Basically this is a way to pass in a unknown number of variables into a function.

Args are prefixed with an asterisk, kwargs are prefixed with 2 asterisks

The name of these  variables does not have to be *args and **kwargs, you can name it anything

For example, here is an args named bars

def test_args(foo, *bars):

And here is a kwargs also named bars

def test_kwargs(foo,**bars):

The only difference between these two is the single and double asterisk



Let's make a very simple function, that will loop through the *args and print them out. This function accepts a normal variable foo and an args variable named *bars

def test_args(foo, *bars):
    print ('first normal argument:', foo)
    for bar in bars:
        print ("another looping through all the *bars :", bar)
 
    print ('all *bars on one line', bars)

Now let's call this function like this

>>> test_args('args','Denis','likes','playing','with','Python')

Here is the output

>>> test_args('args','Denis','likes','playing','with','Python')
first normal argument: args
another looping through all the *bars : Denis
another looping through all the *bars : likes
another looping through all the *bars : playing
another looping through all the *bars : with
another looping through all the *bars : Python
all *bars on one line ('Denis', 'likes', 'playing', 'with', 'Python')
>>> 

Let's now call this function like this

>>> test_args('args','enough','Python')

Here is the output of that call

>>> test_args('args','enough','Python')
first normal argument: args
another looping through all the *bars : enough
another looping through all the *bars : Python
all *bars on one line ('enough', 'Python')
>>> 

As you can see, you can pass a variable number of values into the function by using args

Now let's take a look at kwargs, here is our function, we now named the variable **bars to denote that this is a variable of type kwargs

def test_kwargs(foo,**bars):
    print ('first normal argument:', foo)
    for bar in bars:
        print ("another looping through all the **bars :", bar)
 
    print ('all **bars on one line', bars)

Calling this function requires a change

If you try calling it like we did with args you will get an error

>>> test_kwargs('args','enough','Python')
Traceback (most recent call last):
  File "", line 1, in <module>
TypeError: test_kwargs() takes 1 positional argument but 3 were given
>>> 

What you have to do instead is use named arguments

If we change it to this it will work fine

>>> test_kwargs('kwargs',name ='Denis1', age = 200)

Here is the output

>>> test_kwargs('kwargs',name ='Denis1', age = 200)
first normal argument: kwargs
another looping through all the **bars : name
another looping through all the **bars : age
all **bars on one line {'name': 'Denis1', 'age': 200}
>>> 

Here is another example

>>> test_kwargs('kwargs',month ='July', day = 29)

Here is the output

>>> test_kwargs('kwargs',month ='July', day = 29)
first normal argument: kwargs
another looping through all the **bars : month
another looping through all the **bars : day
all **bars on one line {'month': 'July', 'day': 29}
>>> 

You might have noticed that we didn't print the value of the month or the value of the date. Let's change our function so it looks a little different, now we will print both the key and the value

def test_kwargs(foo, **bars):
    print ('first normal argument:', foo)
    if bars is not None:
        for key, value in bars.items():
            print ("%s : %s" %(key,value))
 
    print ('all *bars on one line', bars)

Now we can make the same call

test_kwargs('kwargs',name ='Denis1', age = 200)

And here is the output

>>> test_kwargs('kwargs',name ='Denis1', age = 200)
first normal argument: kwargs
name : Denis1
age : 200
all *bars on one line {'name': 'Denis1', 'age': 200}
>>> 

As you can see we now have the name as well as the value of the key printed


If you want to use args and kwargs in a function,  you need to have the args before the kwargs

def test_args(foo, **bars, *namedbars):
                               ^
SyntaxError: invalid syntax

This is an error because we have kwargs before args

def test_args(**bars, foo, *namedbars):
                            ^
SyntaxError: invalid syntax

This is also an error because we still have kwargs before args

This is how it should be, forst normal variables, then args and finally kwargs

def test_args(foo, *bars, **namedbars):

Here is an example of such a signature

def test_args(foo, *bars, **namedbars):
    print ('first normal argument:', foo)
    for bar in bars:
        print ("another looping through all the *bars :", bar)
 
    print ('all *bars on one line', bars)
    print ('all **namedbars on one line', namedbars)

Calling the function above gives us the following output

>>> test_args('args','Denis','likes','playing','with','Python')
first normal argument: args
another looping through all the *bars : Denis
another looping through all the *bars : likes
another looping through all the *bars : playing
another looping through all the *bars : with
another looping through all the *bars : Python
all *bars on one line ('Denis', 'likes', 'playing', 'with', 'Python')
all **namedbars on one line {}

As you can see, there is nothing printed for kwargs, this is because we did not pass anything in.
Let's make a change and add a value for the kwargs

Here is the output from that call

>>> test_args('args','enough','Python', namedbars='Denis')
first normal argument: args
another looping through all the *bars : enough
another looping through all the *bars : Python
all *bars on one line ('enough', 'Python')
all **namedbars on one line {'namedbars': 'Denis'}
>>> 

There you have it.. a rather simple blog post that that explains the difference between args and kwargs

Summer of code 2017: Python, Day 37 Pandas, Spyder, IPython, Numpy and more

As explained in my Summer of code 2017: Python post I decided to pick up Python

This is officially day 37. You might be thinking I have been slacking off since I have not posted anything. This is actually not true, it is true that I have not posted anything but I have been quite busy with Python. I watched a couple of sections of the Python – Beyond the Basics course by Austin Bingham and Robert Smallshire on Pluralsight.

I learned about packages, subpackages, lambdas, local functions, decorators and more. I will probably need to watch it again since this course is more intense than the previous course. Expect some posts about these things I learned this week

I also listened to a bunch of podcasts on Python the last couple of days

Talk Python To Me podcast

Here are the episodes I listened to

#121 2017-07-19 Microservices in Python Miguel Grinberg

#120 2017-07-12 Python in Finance Yves Hilpisch

#119 2017-07-06 Python in Engineering Allen Downey

#101 2017-03-03 Adding a full featured Python environment to Visual Studio Code Don Jayamanne

#100 2017-02-22 Python past, present, and future with Guido van Rossum Guido van Rossum

PODCAST.__INIT_ podcast

Here are the episodes I listened to

Moving to MongoDB with Michael Kennedy – Episode 119

Zulip Chat with Tim Abbott – Episode 118

Pandas with Jeff Reback – Episode 98

PyTables with Francesc Alted – Episode 97

I also messed around with different projects in Visual Studio.

I created a bunch of web projects in Django as well as in Flask to understand  what the differences were. I then looked at some documentation about these web frameworks to see which one looks easier to use and setup

Finally I also messed around with Spyder, iPython, pandas and numpy while following some examples from the Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython book

Since the book is a bit dated I ran into some problems with the pandas.io.data module. But a helpful error message informed me that the pandas.io.data module got replaced by pandas-datareader package. So i then installed the pandas-datareader package and the code worked fine

Summer of code 2017: Python, Day 32 unit testing in Python with unittest

As explained in my Summer of code 2017: Python post I decided to pick up Python

This is officially day 32. today I decided to take a look at unit testing. I decided to look at the unittest unit testing framework that ships with Python

The unittest unit testing framework was originally inspired by JUnit and has a similar flavor as major unit testing frameworks in other languages. It supports test automation, sharing of setup and shutdown code for tests, aggregation of tests into collections, and independence of the tests from the reporting framework.

So let's see what it all looks like. First we are going to create a simple method and save it in a file named utils.py

This method will return what was passed in if it was none or a string, it will convert to utf-8 if bytes were passed in, for everything else a type error will be returned

def ToString(data):
    if isinstance(data, str):
        return data
    elif isinstance(data, bytes):
        return data.decode('utf-8')
    elif data is None:
         return data
    else:
        raise TypeError('Must supply string or bytes,'
                        ' found: %r' % data)

To unit test this method, we are creating our test class and we will save this in a file named UnitTestSample.py

Here is what it looks like

from unittest import TestCase, main
from utils import ToString
 
 
class UtilsTestCase(TestCase):
    def test_ToString_bytes(self):
        self.assertEqual('hello', ToString(b'hello'))
 
    def test_ToString_str(self):
        self.assertEqual('hello', ToString('hello'))
 
    def test_ToString_bad(self):
        self.assertRaises(TypeError, ToString, object())
 
    def test_ToString_none(self):
        self.assertIsNone(  ToString(None))
 
    def test_ToString_not_none(self):
        self.assertIsNotNone(  ToString('some val'))
 
if __name__ == '__main__':
   main()

We need to import unittest as well as our ToString method

As you can see, we have a couple of AssertEqual calls,  AssertEqual tests that first and second are equal. If the values do not compare equal, the test will fail.

We also have assertIsNone and assertIsNotNone, these test that something is none or is not none

Finally we use assertRaises. AssertRaises tests that an exception is raised when callable is called with any positional or keyword arguments that are also passed to assertRaises(). The test passes if exception is raised, is an error if another exception is raised, or fails if no exception is raised.

Running the code above will give us this output, all 5 tests have passed

Running C:\Python\Projects\UnitTestSample\UnitTestSample.py
.....
----------------------------------------------------------------------
Ran 5 tests in 0.000s

To print out the name of each test, we need to change main and pass in verbosity level 2

In the code main will now look like this

main(verbosity=2)

Running the same test class again will give us also the test methods that were called, here is the output

The interactive window has not yet started.
Running C:\Python\Projects\UnitTestSample\UnitTestSample.py
test_ToString_bad (__main__.UtilsTestCase) ... ok
test_ToString_bytes (__main__.UtilsTestCase) ... ok
test_ToString_none (__main__.UtilsTestCase) ... ok
test_ToString_not_none (__main__.UtilsTestCase) ... ok
test_ToString_str (__main__.UtilsTestCase) ... ok

----------------------------------------------------------------------
Ran 5 tests in 0.016s

OK
The interactive Python process has exited.
>>> 

That is all for this post, if you want to know more about unit testing with Python, I suggest you start here: https://docs.python.org/3/library/unittest.html#module-unittest

You might also want to look at Nose2, you can find that here: http://nose2.readthedocs.io/en/latest/

nose2 is the next generation of nicer testing for Python, based on the plugins branch of unittest2. nose2 aims to improve on nose by:

• providing a better plugin api
• being easier for users to configure
• simplifying internal interfaces and processes
• supporting Python 2 and 3 from the same codebase, without translation
• encouraging greater community involvement in its development

Summer of code 2017: Python, Day 29 Else after For and While loops

As explained in my Summer of code 2017: Python post I decided to pick up Python

This is officially day 29. today I decided to take a look at how an Else block works with For and While loops in Python. This is not how you would expect it to work if you are coming to Python from another language.


Take a look at the following code

for i in range(5):
    print(i)
else:
    print('else')

What do you think will happen? Will the else part be printed?

Let's run it

    >>> for i in range(5):
...     print(i)
... else:
...     print('else')
... 
0
1
2
3
4
else
>>> 

So after the for loop finished, the else block gets executed. To me that was very surprising, I had not expected this.


What will happen if we put a break in the loop, now the code looks like this

for i in range(5):
    print(i)
    if i == 2:
        break
else:
    print('else')

What do you think happens now? Let's executed it

>>> for i in range(5):
...     print(i)
...     if i == 2:
...         break
... else:
...     print('else')
... 
0
1
2
>>> 

Interestingly the else block did not get executed, this is because the else will only execute if the loop completed

From the docs(I have made the relevant part bold)

The for statement is used to iterate over the elements of a sequence (such as a string, tuple or list) or other iterable object:
for_stmt ::= "for" target_list "in" expression_list ":" suite ["else" ":" suite]
The expression list is evaluated once; it should yield an iterable object. An iterator is created for the result of the expression_list. The suite is then executed once for each item provided by the iterator, in the order of ascending indices. Each item in turn is assigned to the target list using the standard rules for assignments, and then the suite is executed.

When the items are exhausted (which is immediately when the sequence is empty), the suite in the else clause, if present, is executed, and the loop terminates.
A break statement executed in the first suite terminates the loop without executing the else clause's suite. A continue statement executed in the first suite skips the rest of the suite and continues with the next item, or with the else clause if there was no next item.

Let's look at what happens when the sequence is empty now

>>> for i in []:
...     print(i)
... else:
...     print('else')
... 
else
>>> 

As you can see the else block get executed

If you have a while loop that is initially false, the else block will also get executed

>>> while False:
...     print('it is false')
... else:
...     print('else')
... 
else
>>> 

So why does Python have this? One way is to know that you have executed the loop all the way to the end of the loop without having run a break statement.

Take a look at this silly example

>>> x = 10
>>> for i in range(5):
...     if i == x:
...         print('x found')
...         break
... else:
...     print('x not found')
... 
x not found
>>> 

As you can see the else block is executed and we can see that x was not found. Now normally you would store the state in a variable or you would have a helper function that would return false instead.

So that is all for today. Next up is unit testing in Python