Python

General tips on python coding

Recipes

Installing

sudo apt update
sudo apt install software-properties-common

sudo add-apt-repository ppa:deadsnakes/ppa

sudo apt install python3.8
sudo apt-get install python3.8-dev

Example module docstring

# -*- coding: utf-8 -*-
"""Example Google style docstrings.

This module demonstrates documentation as specified by the `Google Python
Style Guide`_. Docstrings may extend over multiple lines. Sections are created
with a section header and a colon followed by a block of indented text.

Example:
    Examples can be given using either the ``Example`` or ``Examples``
    sections. Sections support any reStructuredText formatting, including
    literal blocks::

        $ python example_google.py

Section breaks are created by resuming unindented text. Section breaks
are also implicitly created anytime a new section starts.

Attributes:
    module_level_variable1 (int): Module level variables may be documented in
        either the ``Attributes`` section of the module docstring, or in an
        inline docstring immediately following the variable.

        Either form is acceptable, but the two should not be mixed. Choose
        one convention to document module level variables and be consistent
        with it.

Todo:
    * For module TODOs
    * You have to also use ``sphinx.ext.todo`` extension

.. _Google Python Style Guide:
   http://google.github.io/styleguide/pyguide.html

"""

Example function Docstring

"""Connects to the next available port.

    Args:
      (int) minimum: A port value greater or equal to 1024.

    Returns:
      (int) The new minimum port.

    Raises:
      ConnectionError: If no available port is found.
"""

virtualenv

# installing
pip install virtualenv
# installing with specific python version
python3.8 -m pip install beautifulsoup4
# creating
virtualenv env_name --python=python3.6 # by default it'd be python2.7
# activate
source env_name/bin/activate
# deactivate
deactivate
# saving
pip freeze > requirements.txt

poetry

Docs

# Init project
poetry init
# create poetry.lock
poetry install

Remove directory

import shutil
shutil.rmtree(mydir)

Extract tar files

import tarfile
my_tar = tarfile.open('my_tar.tar.gz')
my_tar.extractall('./my_folder') # specify which folder to extract to
my_tar.close()

F-strings

f'{a:.2f}' # setting the number of digits for float a

Creating directory if it doesn't exist

import os
if not os.path.exists(target):
    os.makedirs(target)

Checking nans

import math
x = float('nan')
math.isnan(x)
# True

Loading config for a specific component

import os, re
from dotenv import load_dotenv, find_dotenv

def __load_env(self,begin_pattern="CONTENT_MANAGER_ENV_*"):
    """
    Env variables on system need to be stored beginning with begin_pattern
    if so, they will be stored in self.env dict
    """
    env = {}
    load_dotenv(find_dotenv())
    for var in os.environ.keys():
        if re.match(begin_pattern,var):
            env[var[len(begin_pattern)-1:]] = os.getenv(var)
    if len(env.keys()) == 0:
        return None
    else:
        return env

Confusion matrix

import numpy as np

def compute_confusion_matrix(true, pred):
  '''Computes a confusion matrix using numpy for two np.arrays
  true and pred.

  Results are identical (and similar in computation time) to: 
    "from sklearn.metrics import confusion_matrix"

  However, this function avoids the dependency on sklearn.'''

  K = len(np.unique(true)) # Number of classes 
  result = np.zeros((K, K))

  for i in range(len(true)):
    result[true[i]][pred[i]] += 1

  return result

Count elements in array

a = numpy.array([0, 3, 0, 1, 0, 1, 2, 1, 0, 0, 0, 0, 1, 3, 4])
unique, counts = numpy.unique(a, return_counts=True)
dict(zip(unique, counts))
#{0: 7, 1: 4, 2: 1, 3: 2, 4: 1}

# OR

import numpy as np
x = np.array([1,1,1,2,2,2,5,25,1,1])
y = np.bincount(x)
ii = np.nonzero(y)[0]

zip(ii, y[ii])	

Passing parameters with dictionaries

testDict = {'x': 1, 'y': 2,'z': 3}
def test(x,y,z):
    print(x,y,z)
test(**testDict)

Merge dictionaries

data = {**data1, **data2, **data3}

Store expressions in dictionaries

stdcalc = {
    'sum': lambda x, y: x + y,
    'subtract': lambda x, y: x - y
}

print(stdcalc['sum'](9,3))
print(stdcalc['subtract'](9,3))

Factorial of given number

import functools
result = (lambda k: functools.reduce(int.__mul__, range(1,k+1),1))(3)
print(result)

Most frequent value on a list

test = [1,2,3,4,2,2,3,1,4,4,4]
print(max(set(test), key=test.count))

Get sizes of objects in bytes

import sys
x=1
print(sys.getsizeof(x))

Unified list without loops

import itertools
test = [[-1, -2], [30, 40], [25, 35]]
print(list(itertools.chain.from_iterable(test)))

# or (fastest)

import functools
import operator
functools.reduce(operator.iconcat, a, [])

#-> [-1, -2, 30, 40, 25, 35]

Fastest way to iterate over rows in pandas DataFrame

df_dict = df.to_dict('records')
for row in df_dict:
    temp = row['val1'] * row['val2']
    temp = temp ** 2

Progress bar for loops

from tqdm import tqdm
for i in tqdm(range(10000)):
    pass

Packing and unpacking

import pickle

with open('file.pkl', 'rb') as f:
    data = pickle.load(f)
    
with open('mypickle.pickle', 'wb') as f:
    pickle.dump(some_obj, f)

Check if file exist

import os
os.path.exists(path)

Download file

import urllib.request
urllib.request.urlretrieve(url, file_name)

Generate a set from a list of list

set(elem for elem_of_list_of_list in list_of_list for elem in elem_of_list_of_list)

Get intersection from to lists

list(set(lista).intersection(listb))

Get a sorted by value dictionary

dict(sorted(word_count.items(), key=lambda item: item[1])).keys()

Sort a list of dictionaries by value

newlist = sorted(list_to_be_sorted, key=lambda k: k['name'])

Reverse a list

test[::-1]

Transpose a matrix

test = [[1,2], [3,4], [5,6]]
[[t[j] for t in test] for j in range(len(test[0]))]

Get current time

from datetime import datetime
now = datetime.now()
current_time = now.strftime("%Y%m%d%H%M")

Useful magic methods

__add__ , __repr__

Creating a package

having setup.py like:

from setuptools import setup

setup(name='pkg_name',
      version='0.1',
      description='Pkg description',
      packages=['pkg_name'],
      zip_safe=False)

and a module at pkg_name folder, execute

cd pkg_parent_dir
python setup.py sdist bdist_wheel
pip install twine

# commands to upload to the pypi test repository
twine upload --repository-url https://test.pypi.org/legacy/ dist/*
pip install --index-url https://test.pypi.org/simple/ pkg_name

# command to upload to the pypi repository
twine upload dist/*
pip install pkg_name

Reminders

• include a README file detailing the files in your package and how to install the package.

• Comment your code - use docstrings and inline comments where appropriate.

• Refactor code when possible - if you find your functions are getting too long, then refactor your code!

• Use object-oriented programming whenever it makes sense to do so.

• You're encouraged to write unit tests! The coding exercises in this lesson contained unit tests, so you can use those tests as a model for your package.

• Use GitHub for version control, and commit your work often.

As a reminder, your package should be placed in a folder with the following folders and files:

• a folder with the name of your package that contains:

  • the Python code that makes up your package

  • a README.md file

  • an __init__.py

  • license.txt

  • setup.cfg

• setup.py file

References

• https://www.infoworld.com/article/3563878/how-to-use-python-dataclasses.html

• https://docs.python.org/3/library/dataclasses.html

• https://google.github.io/styleguide/pyguide.html

• https://github.com/PyCQA/pycodestyle

• https://docs.pytest.org/en/latest/

• https://realpython.com/documenting-python-code/

• The Python Graph Gallery – Data Visualization– with Python

• Awesome distributed deep learning

• https://www.toptal.com/flask/flask-production-recipes

• https://levelup.gitconnected.com/deploy-your-machine-learning-model-as-a-rest-api-on-aws-english-dcb1a0db3110

Frameworks

• https://sanicframework.org/en/

• https://github.com/ml-tooling/opyrator

OOP

• https://realpython.com/instance-class-and-static-methods-demystified/

• https://www.python-course.eu/python3_class_and_instance_attributes.php

• https://easyaspython.com/mixins-for-fun-and-profit-cb9962760556

• https://realpython.com/primer-on-python-decorators/

Creating a package

• https://packaging.python.org/guides/distributing-packages-using-setuptools/