Generators in Python

Python Review Free Hints — #PySeries#Episode 17

#GENERATORS IN PYTHON: What is it?

Generators in Python allows us to write a function that can send back a value and then later resume to pick up where it left off.

01#PyEx — Python — Function— Fibonacci:

Let’s start with a fibonacci function:

Output (Entering 10):

How many terms? 10 Fibonacci sequence: 
0 
1 
1 
2 
3 
5 
8 
13 
21 
34

YET A SIMPLE FUNCTION FOR FIBONACCI ALGORITHM:

02#PyEx — Python —Function — Fubonacci Simplified:

Let’s simplify the code for this lesson, consider this piece of code (Zero out:)

def gen_fibo(n):

  a = 1

  b = 1

  output = []

  for i in range(n):

      output.append(a)

      a,b = b, a + b

return output

The output:

gen_fibo(10)

[1, 1, 2, 3, 5, 8, 13, 21, 34, 55]

These two codes above are not generators.

Let’s get one!

Now using yield keyword in the second fibo function to turn it more memory efficient :-)

03#PyEx — Python — Function — Fibonacci w/ yield:

def gen_fibo(n):

  a = 1

  b = 1

  for i in range(n - 1):

      yield a

      a, b = a, a + b

The output (a Generator):

g = gen_fibo(10)
g

<generator object gen_fibo at 0x7f0cecb33ed0>

As you can see we don’t need to store the information in the list anymore; the two items highlighted in the previous code denounce this. And, more importantly, the insert of the yield keyword instead of the append method… This is the secret of Python generators: working in streaming rather than to store the whole list in memory!

So, it is now a Generator!

Do you see the difference between between those last two codes? No? Fine! So keep reading …

This last function is a Generator. What is a Generator after all?

A Generators it is much more efficient in relation to the use of memory; we are not loading the data completely, in memory, as in the code with append();

We only work in streams (one number at a time), that is, we save only the data that immediately preceding it (we know the formula to generate the next one ;) and thus we are saving memory; the object is much more simpler and efficient:)

Do you want another interactable function to bring the concept home?

Let’s see another generator:

04#PyEx — Python — Function — Simple Generator:

def simple_gen():

  for x in range (4):

      yield x

The output:

g = simple_gen()
g

<generator object simple_gen at 0x7f0cecb334d0>

See that the Generator object can be readily interacted with; it is built to be :)

The output:

next(g)
0
next(g)
1
next(g)
2
next(g)
3

Fine, Now Are Strings Interactables?

05#PyEx — Python — String —Are interactable?

Now, let’s see if a string object is interactable…

s = ‘hello’
for letter in s:
  print(letter)

h
e
l
l
o

The output:

next(s)
---------------------------------------------------------------TypeError                           Traceback (most recent call last)
<ipython-input-262-46f008985a16> in <module>()
      1 # String is not interactable:/
----> 2 next(s)
TypeError: 'str' object is not an iterator

So, Strings are not interactable:/

For transforming Strings into an interactable object use iter() function:

s_iter = iter(s)
next(s_iter)

The output:

next(s_iter)
'h'
next(s_iter)
'e'

(...)

06#PyEx — Python — String — Final Code

So, here is the final generator code for our fibonacci function:

The output:

g = prog_fibo_2(10)
g

<generator object gen_fibo_ef at 0x7f0cecb34350>

next(g)
1
next(g)
1
next(g)
2
(...)

And note that it is this very function, next(), that the for loop use internally to bring all the data sequentially and efficiently, and that is cool, right?

for item in enumerate(word):
  print(word)

Profiling and Timing Code

This note contains an excerpt from the Python Data Science Handbook by Jake VanderPlas;

The content is available on GitHub.

The text is released under the CC-BY-NC-ND license, and code is released under the MIT license. If you find this content useful, please consider supporting the work by buying the book!

Now run pip in your Colab Notebook:

07#PyEx — Python — String —Profiling & Timing

pip install memory_profiler

Requirement already satisfied: memory_profiler in /usr/local/lib/python3.7/dist-packages (0.58.0) Requirement already satisfied: psutil in /usr/local/lib/python3.7/dist-packages (from memory_profiler) (5.4.8)

And this profiler:

%load_ext memory_profiler

The memory_profiler extension is already loaded. To reload it, use:   %reload_ext memory_profiler

Running again the original & final code:

def gen_fibo(n):

 a = 1

 b = 1

 output = []

 for i in range(n):

    output.append(a)

    a,b = b, a + b

    return output

Timing it:

%timeit gen_fibo(10)

1000000 loops, best of 5: 1.31 µs per loop

The other one:

def prog_fibo_2(n):
 a = 1
 b = 1
 for i in range(n):
    yield a
    a, b = b, a + b

Timing it:

%timeit prog_fibo_2(10)

1000000 loops, best of 5: 236 ns per loop

As you can see, the code using the python generator is running in the nanoseconds(10**-9), instead of microseconds(10**-6)

A bit faster, right?

Generators in Python works!

Check it out! Get this under your belt in your next Python code! :b

print(“That’s it! thank you for reading this episode about the wonders of Python:) “)

That's it! thank you for reading this episode about the wonders of Python:)

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Until next time!

I wish you excellent day!

Be safe!

Cheers!

Colab Notebook link:)

Google Drive link:)

Google Colab Notebooks Official:

Credits & References

Jose Portilla — Python for Data Science and Machine Learning Bootcamp — Learn how to use NumPy, Pandas, Seaborn , Matplotlib , Plotly , Scikit-Learn , Machine Learning, Tensorflow , and more!

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