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A short guide on features of Python 3 with examples

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Update (Jan 2020). Python 2 is now officially retired. Thanks to everyone for making this hard transition to better code happen!

Migrating to Python 3 with pleasure

A short guide on features of Python 3 for data scientists

Python became a mainstream language for machine learning and other scientific fields that heavily operate with data; it boasts various deep learning frameworks and well-established set of tools for data processing and visualization.

However, Python ecosystem co-exists in Python 2 and Python 3, and Python 2 is still used among data scientists. By the end of 2019 the scientific stack will stop supporting Python2. As for numpy, after 2018 any new feature releases will only support Python3. Update (Sep 2018): same story now with pandas, matplotlib, ipython, jupyter notebook and jupyter lab.

To make the transition less frustrating, I've collected a bunch of Python 3 features that you may find useful.

Image from Dario Bertini post (toptal)

Better paths handling with

is a default module in python3, that helps you to avoid tons of
from pathlib import Path

dataset = 'wiki_images' datasets_root = Path('/path/to/datasets/')

train_path = datasets_root / dataset / 'train' test_path = datasets_root / dataset / 'test'

for image_path in train_path.iterdir(): with as f: # note, open is a method of Path object # do something with an image

Previously it was always tempting to use string concatenation (concise, but obviously bad), now with

the code is safe, concise, and readable.


has a bunch of methods and properties, that every python novice previously had to google:
p.with_name('sibling.png') # only change the name, but keep the folder
p.with_suffix('.jpg') # only change the extension, but keep the folder and the name

should save you lots of time, please see docs and reference for more.

Type hinting is now part of the language

Example of type hinting in pycharm:

Python is not just a language for small scripts anymore, data pipelines these days include numerous steps each involving different frameworks (and sometimes very different logic).

Type hinting was introduced to help with growing complexity of programs, so machines could help with code verification. Previously different modules used custom ways to point types in docstrings (Hint: pycharm can convert old docstrings to fresh type hinting).

As a simple example, the following code may work with different types of data (that's what we like about python data stack).

def repeat_each_entry(data):
    """ Each entry in the data is doubled
    index = numpy.repeat(numpy.arange(len(data)), 2)
    return data[index]

This code e.g. works for

(incl. multidimensional ones),
and others.

This code will work for

, but in the wrong way:
repeat_each_entry(pandas.Series(data=[0, 1, 2], index=[3, 4, 5])) # returns Series with Nones inside

This was two lines of code. Imagine how unpredictable behavior of a complex system, because just one function may misbehave. Stating explicitly which types a method expects is very helpful in large systems, this will warn you if a function was passed unexpected arguments.

def repeat_each_entry(data: Union[numpy.ndarray, bcolz.carray]):

If you have a significant codebase, hinting tools like MyPy are likely to become part of your continuous integration pipeline. A webinar "Putting Type Hints to Work" by Daniel Pyrathon is good for a brief introduction.

Sidenote: unfortunately, hinting is not yet powerful enough to provide fine-grained typing for ndarrays/tensors, but maybe we'll have it once, and this will be a great feature for DS.

Type hinting → type checking in runtime

By default, function annotations do not influence how your code is working, but merely help you to point code intentions.

However, you can enforce type checking in runtime with tools like ... enforce, this can help you in debugging (there are many cases when type hinting is not working).

def foo(text: str) -> None:

foo('Hi') # ok foo(5) # fails

@enforce.runtime_validation def any2(x: List[bool]) -> bool: return any(x)

any ([False, False, True, False]) # True any2([False, False, True, False]) # True

any (['False']) # True any2(['False']) # fails

any ([False, None, "", 0]) # False any2([False, None, "", 0]) # fails

Other usages of function annotations

Update: starting from python 3.7 this behavior was deprecated, and function annotations should be used for type hinting only. Python 4 will not support other usages of annotations.

As mentioned before, annotations do not influence code execution, but rather provide some meta-information, and you can use it as you wish.

For instance, measurement units are a common pain in scientific areas,

package provides a simple decorator to control units of input quantities and convert output to required units ```python

Python 3

from astropy import units as u @u.quantity_input() def frequency(speed: u.meter / u.s, wavelength: u.nm) -> u.terahertz: return speed / wavelength

frequency(speed=300_000 * / u.s, wavelength=555 * u.nm)

output: 540.5405405405404 THz, frequency of green visible light

If you're processing tabular scientific data in python (not necessarily astronomical), you should give `astropy` a shot.

You can also define your application-specific decorators to perform control / conversion of inputs and output in the same manner.

Matrix multiplication with @

Let's implement one of the simplest ML models — a linear regression with l2 regularization (a.k.a. ridge regression):

# l2-regularized linear regression: || AX - y ||^2 + alpha * ||x||^2 -> min

# Python 2
X = np.linalg.inv(, A) + alpha * np.eye(A.shape[1])).dot(
# Python 3
X = np.linalg.inv(A.T @ A + alpha * np.eye(A.shape[1])) @ (A.T @ y)
<p>The code with </p><pre>@</pre> becomes more readable and more translatable between deep learning frameworks: same code <pre>X @ W + b[None, :]</pre> for a single layer of perceptron works in <pre>numpy</pre>, <pre>cupy</pre>, <pre>pytorch</pre>, <pre>tensorflow</pre> (and other frameworks that operate with tensors).

<h2>Globbing with <pre>**</pre>

<p>Recursive folder globbing is not easy in Python 2, even though the <a href="">glob2</a> custom module exists that overcomes this. A recursive flag is supported since Python 3.5:</p>
<pre class="language-python">import glob

# Python 2
found_images = (
  + glob.glob('/path/*/*.jpg')
  + glob.glob('/path/*/*/*.jpg')
  + glob.glob('/path/*/*/*/*.jpg')
  + glob.glob('/path/*/*/*/*/*.jpg'))

# Python 3
found_images = glob.glob('/path/**/*.jpg', recursive=True)
<p>A better option is to use </p><pre>pathlib</pre> in python3 (minus one import!):

<h1>Python 3</h1>

<p>found_images = pathlib.Path('/path/').glob('*<em>/</em>.jpg')
Note: there are [minor differences]( between</pre>glob.glob<pre>,</pre>Path.glob` and bash globbing.

<h2>Print is a function now</h2>

<p>Yes, code now has these annoying parentheses, but there are some advantages:</p>

<li>simple syntax for using file descriptor:
print &gt;&gt;sys.stderr, "critical error"      # Python 2
print("critical error", file=sys.stderr)  # Python 3
<li>printing tab-aligned tables without <pre>str.join</pre>:
# Python 3
print(*array, sep='\t')
print(batch, epoch, loss, accuracy, time, sep='\t')
<p>hacky suppressing / redirection of printing output:

<h1>Python 3</h1>

<p>_print = print # store the original print function
def print(<em>args, *</em>kargs):
    pass  # do something useful, e.g. store output to some file
In jupyter it is desirable to log each output to a separate file (to track what's happening after you got disconnected), so you can override</pre>print` now.

<p>Below you can see a context manager that temporarily overrides behavior of print:
def replace_print():
    import builtins
    _print = print # saving old print function
    # or use some other function here
    builtins.print = lambda <em>args, *</em>kwargs: _print('new printing', <em>args, *</em>kwargs)
    builtins.print = _print</p>

<p>with replace_print():
    <code here will invoke other print function>

It is not a recommended approach, but a small dirty hack that is now possible.

  • print
    can participate in list comprehensions and other language constructs ```python

    Python 3

    result = process(x) if is_valid(x) else print('invalid item: ', x) ```

  • Underscores in Numeric Literal (Thousands Separator)

    PEP-515 introduced underscores in Numeric Literals. In Python3, underscores can be used to group digits visually in integral, floating-point, and complex number literals.

    # grouping decimal numbers by thousands
    one_million = 1_000_000

    grouping hexadecimal addresses by words

    addr = 0xCAFE_F00D

    grouping bits into nibbles in a binary literal

    flags = 0b_0011_1111_0100_1110

    same, for string conversions

    flags = int('0b_1111_0000', 2)

    f-strings for simple and reliable formatting

    The default formatting system provides a flexibility that is not required in data experiments. The resulting code is either too verbose or too fragile towards any changes.

    Quite typically data scientists outputs some logging information iteratively in a fixed format. It is common to have a code like:

    # Python 2
    print '{batch:3} {epoch:3} / {total_epochs:3}  accuracy: {acc_mean:0.4f}±{acc_std:0.4f} time: {avg_time:3.2f}'.format(
        batch=batch, epoch=epoch, total_epochs=total_epochs,
        acc_mean=numpy.mean(accuracies), acc_std=numpy.std(accuracies),
        avg_time=time / len(data_batch)

    Python 2 (too error-prone during fast modifications, please avoid):

    print '{:3} {:3} / {:3} accuracy: {:0.4f}±{:0.4f} time: {:3.2f}'.format( batch, epoch, total_epochs, numpy.mean(accuracies), numpy.std(accuracies), time / len(data_batch) )

    Sample output:

    120  12 / 300  accuracy: 0.8180±0.4649 time: 56.60

    f-strings aka formatted string literals were introduced in Python 3.6: ```python

    Python 3.6+

    print(f'{batch:3} {epoch:3} / {totalepochs:3} accuracy: {numpy.mean(accuracies):0.4f}±{numpy.std(accuracies):0.4f} time: {time / len(databatch):3.2f}') ```

    Explicit difference between 'true division' and 'floor division'

    For data science this is definitely a handy change

    data = pandas.read_csv('timing.csv')
    velocity = data['distance'] / data['time']

    Results in Python 2 depend on whether 'time' and 'distance' (e.g. measured in meters and seconds) are stored as integers. In Python 3, the result is correct in both cases, because the result of division is float.

    Another case is floor division, which is now an explicit operation:

    n_gifts = money // gift_price  # correct for int and float arguments

    In a nutshell:

    >>> from operator import truediv, floordiv
    >>> truediv.__doc__, floordiv.__doc__
    ('truediv(a, b) -- Same as a / b.', 'floordiv(a, b) -- Same as a // b.')
    >>> (3 / 2), (3 // 2), (3.0 // 2.0)
    (1.5, 1, 1.0)

    Note, that this applies both to built-in types and to custom types provided by data packages (e.g.


    Strict ordering

    # All these comparisons are illegal in Python 3
    3 < '3'
    2 < None
    (3, 4) < (3, None)
    (4, 5) < [4, 5]

    False in both Python 2 and Python 3

    (4, 5) == [4, 5]

    • prevents from occasional sorting of instances of different types
      sorted([2, '1', 3])  # invalid for Python 3, in Python 2 returns [2, 3, '1']
    • helps to spot some problems that arise when processing raw data

    Sidenote: proper check for None is (in both Python versions) ```python if a is not None: pass

    if a: # WRONG check for None pass ```

    Unicode for NLP

    s = '您好'

    Output: - Python 2:

    - Python 3:
    x = u'со'
    x += 'co' # ok
    x += 'со' # fail

    Python 2 fails, Python 3 works as expected (because I've used russian letters in strings).

    In Python 3

    s are unicode strings, and it is more convenient for NLP processing of non-english texts.

    There are other funny things, for instance:

    'a' < type < u'a'  # Python 2: True
    'a' < u'a'         # Python 2: False
    from collections import Counter
    • Python 2:
      Counter({'\xc3': 2, 'b': 1, 'e': 1, 'c': 1, 'k': 1, 'M': 1, 'l': 1, 's': 1, 't': 1, '\xb6': 1, '\xbc': 1})
    • Python 3:
      Counter({'M': 1, 'ö': 1, 'b': 1, 'e': 1, 'l': 1, 's': 1, 't': 1, 'ü': 1, 'c': 1, 'k': 1})

    You can handle all of this in Python 2 properly, but Python 3 is more friendly.

    Preserving order of dictionaries and **kwargs

    In CPython 3.6+ dicts behave like

    by default (and this is guaranteed in Python 3.7+). This preserves order during dict comprehensions (and other operations, e.g. during json serialization/deserialization)
    import json
    x = {str(i):i for i in range(5)}
    # Python 2
    {u'1': 1, u'0': 0, u'3': 3, u'2': 2, u'4': 4}
    # Python 3
    {'0': 0, '1': 1, '2': 2, '3': 3, '4': 4}

    Same applies to

    (in Python 3.6+), they're kept in the same order as they appear in parameters. Order is crucial when it comes to data pipelines, previously we had to write it in a cumbersome manner: ```python from torch import nn

    Python 2

    model = nn.Sequential(OrderedDict([ ('conv1', nn.Conv2d(1,20,5)), ('relu1', nn.ReLU()), ('conv2', nn.Conv2d(20,64,5)), ('relu2', nn.ReLU()) ]))

    Python 3.6+, how it can be done, not supported right now in pytorch

    model = nn.Sequential( conv1=nn.Conv2d(1,20,5), relu1=nn.ReLU(), conv2=nn.Conv2d(20,64,5), relu2=nn.ReLU()) ) ```

    Did you notice? Uniqueness of names is also checked automatically.

    Iterable unpacking

    # handy when amount of additional stored info may vary between experiments, but the same code can be used in all cases
    model_paramteres, optimizer_parameters, *other_params = load(checkpoint_name)

    picking two last values from a sequence

    *prev, next_to_last, last = values_history

    This also works with any iterables, so if you have a function that yields e.g. qualities,

    below is a simple way to take only last two values from a list

    *prev, next_to_last, last = iter_train(args)

    Default pickle engine provides better compression for arrays

    Pickling is a mechanism to pass data between threads / processes, in particular used inside

    # Python 2
    import cPickle as pickle
    import numpy
    print len(pickle.dumps(numpy.random.normal(size=[1000, 1000])))
    # result: 23691675

    Python 3

    import pickle import numpy len(pickle.dumps(numpy.random.normal(size=[1000, 1000])))

    result: 8000162

    Three times less space. And it is much faster. Actually similar compression (but not speed) is achievable with

    parameter, but developers typically ignore this option (or simply are not aware of it).

    Note: pickle is not safe (and not quite transferrable), so never unpickle data received from an untrusted or unauthenticated source.

    Safer comprehensions

    labels = 
    predictions = [model.predict(data) for data, labels in dataset]

    labels are overwritten in Python 2

    labels are not affected by comprehension in Python 3

    Super, simply super()

    Python 2

    was a frequent source of mistakes in code.
    # Python 2
    class MySubClass(MySuperClass):
        def __init__(self, name, **options):
            super(MySubClass, self).__init__(name='subclass', **options)

    Python 3

    class MySubClass(MySuperClass): def init(self, name, **options): super().init(name='subclass', **options)

    More on

    and method resolution order on stackoverflow.

    Better IDE suggestions with variable annotations

    The most enjoyable thing about programming in languages like Java, C# and alike is that IDE can make very good suggestions, because type of each identifier is known before executing a program.

    In python this is hard to achieve, but annotations will help you - write your expectations in a clear form - and get good suggestions from IDE

    This is an example of PyCharm suggestions with variable annotations. This works even in situations when functions you use are not annotated (e.g. due to backward compatibility).

    Multiple unpacking

    Here is how you merge two dicts now: ```python x = dict(a=1, b=2) y = dict(b=3, d=4)

    Python 3.5+

    z = {*x, *y}

    z = {'a': 1, 'b': 3, 'd': 4}, note that value for
    is taken from the latter dict.

    See [this thread at StackOverflow]( for a comparison with Python 2.

    The same approach also works for lists, tuples, and sets (a, b, c are any iterables):

    ```python [*a, *b, *c] # list, concatenating (*a, *b, *c) # tuple, concatenating {*a, *b, *c} # set, union

    Functions also support multiple unpacking for

    : ```python

    Python 3.5+

    dosomething({defaultsettings, **custom_settings})

    Also possible, this code also checks there is no intersection between keys of dictionaries

    dosomething(**firstargs, **second_args) ```

    Future-proof APIs with keyword-only arguments

    Let's consider this snippet

    model = sklearn.svm.SVC(2, 'poly', 2, 4, 0.5)
    Obviously, an author of this code didn't get the Python style of coding yet (most probably, just jumped from cpp or rust). Unfortunately, this is not just question of taste, because changing the order of arguments (adding/deleting) in
    will break this code. In particular,
    does some reordering/renaming from time to time of numerous algorithm parameters to provide consistent API. Each such refactoring may drive to broken code.

    In Python 3, library authors may demand explicitly named parameters by using

    class SVC(BaseSVC):
        def __init__(self, *, C=1.0, kernel='rbf', degree=3, gamma='auto', coef0=0.0, ... )
    - users have to specify names of parameters
    sklearn.svm.SVC(C=2, kernel='poly', degree=2, gamma=4, coef0=0.5)
    now - this mechanism provides a great combination of reliability and flexibility of APIs

    Data classes

    Python 3.7 introduces data classes, a good replacement for

    in most cases. ```python @dataclass class Person: name: str age: int

    @dataclass class Coder(Person): preferred_language: str = 'Python 3' ```

    decorator takes the job of implementing routine methods for you (initialization, representation, comparison, and hashing when applicable). Let's name some features: - data classes can be both mutable and immutable - default values for fields are supported - inheritance - data classes are still old good classes: you can define new methods and override existing - post-init processing (e.g. to verify consistency)

    Geir Arne Hjelle gives a good overview of dataclasses in his post.

    Customizing access to module attributes

    In Python you can control attribute access and hinting with

    for any object. Since python 3.7 you can do it for modules too.

    A natural example is implementing a

    submodule of tensor libraries, which is typically a shortcut to skip initialization and passing of RandomState objects. Here's implementation for numpy:

    import numpy _randomstate = numpy.random.RandomState()

    def getattr(name): return getattr(_randomstate, name)

    def dir(): return dir(_randomstate)

    def seed(seed): _randomstate = numpy.random.RandomState(seed=seed) ```

    One can also mix this way functionalities of different objects/submodules. Compare with tricks in pytorch and cupy.

    Additionally, now one can - use it for lazy loading of submodules. For example,

    import tensorflow
    takes ~150MB of RAM is imports all submodules (and dependencies). - use this for deprecations in API - introduce runtime routing between submodules

    Built-in breakpoint()

    Just write

    in the code to invoke debugger. ```python

    Python 3.7+, not all IDEs support this at the moment

    foo() breakpoint() bar() ```

    For remote debugging you may want to try combining breakpoint() with


    Minor: constants in

    # Python 3
    math.inf # Infinite float
    math.nan # not a number

    max_quality = -math.inf # no more magic initial values!

    for model in trained_models: max_quality = max(max_quality, compute_quality(model, data))

    Minor: single integer type

    Python 2 provides two basic integer types, which are

    (64-bit signed integer) and
    for long arithmetics (quite confusing after C++).

    Python 3 has a single type

    , which incorporates long arithmetics.

    Here is how you check that value is integer:

    isinstance(x, numbers.Integral) # Python 2, the canonical way
    isinstance(x, (long, int))      # Python 2
    isinstance(x, int)              # Python 3, easier to remember

    Update: first check also works for other integral types, such as

    , but others don't. So they're not equivalent.

    Other stuff

    • Enum
      s are theoretically useful, but
      • string-typing is already widely adopted in the python data stack
      • Enum
        s don't seem to interplay with numpy and categorical from pandas
    • coroutines also sound very promising for data pipelining (see slides by David Beazley), but I don't see their adoption in the wild.
    • Python 3 has stable ABI
    • Python 3 supports unicode identifies (so
      ω = Δφ / Δt
      is ok), but you'd better use good old ASCII names
    • some libraries e.g. jupyterhub (jupyter in cloud), django and fresh ipython only support Python 3, so features that sound useless for you are useful for libraries you'll probably want to use once.

    Problems for code migration specific for data science (and how to resolve those)

    • support for nested arguments was dropped
      map(lambda x, (y, z): x, z, dict.items())

    However, it is still perfectly working with different comprehensions:

      {x:z for x, (y, z) in d.items()}
    In general, comprehensions are also better 'translatable' between Python 2 and 3.
    • map()
      , etc. return iterators, not lists. Main problems with iterators are:
      • no trivial slicing
      • can't be iterated twice

    Almost all of the problems are resolved by converting result to list.

    Main problems for teaching machine learning and data science with python

    Course authors should spend time in the first lectures to explain what is an iterator, why it can't be sliced / concatenated / multiplied / iterated twice like a string (and how to deal with it).

    I think most course authors would be happy to avoid these details, but now it is hardly possible.


    Python 2 and Python 3 have co-existed for almost 10 years, but we should move to Python 3.

    Research and production code should become a bit shorter, more readable, and significantly safer after moving to Python 3-only codebase.

    Right now most libraries support both Python versions. And I can't wait for the bright moment when packages drop support for Python 2 and enjoy new language features.

    Following migrations are promised to be smoother: "we will never do this kind of backwards-incompatible change again"



    This text was published by Alex Rogozhnikov and contributors under CC BY-SA 3.0 License (excluding images).

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