C# to python

FROM C# TO PYTHON
10 THINGS I LEARNED ALONG THE WAY
Tess Ferrandez

TESS
SOFTWARE
ENGINEER
&
DATA
SCIENTIST
at MICROSOFT

NOTEBOOKS ARE
FOR EXPLORATION
1

REUSE CODE
SOURCE CONTROL
DEBUG
TEST
CI/CD PIPELINE

IF IT’S GOING INTO PROD,
IT’S GOING IN A .PY FILE

# read the data
df = pd.read_csv('../../data/houses.csv')
# print the first five records
print(df.head())
# plot the price
df.price.plot(kind='hist', bins=100)
plt.show()
IMPERATIVE

def clean_region(region: str) -> str:…
def clean_broker(broker_name: str) -> str:…
def clean_data(input_file: str, output_file: str):…
if __name__ == '__main__':
clean_data('data/interim/houses.csv',
'data/processed/houses.csv')
PROCEDURAL

FUNCTIONAL
def square(x: int) -> int:
return x * x
numbers = [1, 2, 3, 4, 5]
num_sum = reduce(lambda x, y: x + y, numbers, 0)
squares = map(square, numbers)

OBJECT ORIENTED
class StringOps:
def __init__(self, characters):
self.characters = characters
def stringify(self):
self.string = ''.join(self.characters)
sample_str = StringOps(['p', 'y', 't', 'h', 'o', 'n'])
sample_str.stringify()
print(sample_str.string)

YOU CAN MIX AND MATCH
PARADIGMS AS YOU PLEASE,
BUT KEEP YOUR CODE AND
SOCKS DRY

USE A COOKIE CUTTER
PROJECT STRUCTURE
3

A BIG PILE O’ FILES
clean_dataset.py
clean_dataset2.py
clean-2019-02-01.py
clean-tf-1.py
super-final-version-of-this-
cleaning-script.py

MAKEFILE
SETUP
DOCS
NOTEBOOKS / REPORTS
REQUIREMENTS.TXT
TESTS SEPARATELY

USE A COOKIE CUTTER
PROJECT STRUCTURE
OTHER PEOPLE
WILL THANK YOU

USE A COOKIE CUTTER
PROJECT STRUCTURE
OTHER PEOPLE
I WILL THANK YOU
(PERSONALLY!)

WRITING READABLE
& MAINTAINABLE
CODE
4

import random, sys
import os
def myfunc():
rando = random.random()
return random.randint(0,100)
def multiply (a, b):
return a * b
print(multiply(myfunc(), myfunc()))

PEP8.ORG
PYTHON ENHANCEMENT PROPOSAL

import random
def myfunc():
return a * b
UNUSED IMPORTS

import random
def myfunc():
return a * b
SEPARATING LINES

import random
def myfunc():
return random.randint(0, 100)
def multiply(a, b):
return a * b
WHITE SPACES

import random
def myfunc():
def multiply(a, b):
return a * b
UNUSED VARIABLES

import random
def random_number():
def multiply(a, b):
return a * b
print(multiply(random_number(), random_number()))
WEIRD FUNCTION NAMES

import random
def random_number():
def multiply(a, b):
return a * b
print(multiply(random_number(), random_number()))

# See https://pre-commit.com for more information
# See https://pre-commit.com/hooks.html for more hooks
repos:
- repo: https://github.com/ambv/black
rev: stable
hooks:
- id: black
language_version: python3.7
- repo: https://github.com/pre-commit/pre-commit-hooks
rev: v2.0.0
hooks:
- id: flake8

def add(a, b):
return a + b
result = add('hello', 'world')
result = add(2, 3)
def add(a: int, b: int) -> int:
return a + b
result = add('hello', 'world')
result = add(2, 3)

A SWEET
DEV ENVIRONMENT
SETUP
5

PIP, CONDA AND
VIRTUAL
ENVIRONMENTS
6

pip install pandas
conda install pandas

conda create –name myenv python=3.6
conda activate myenv
# Install all the things
# Work on the application
conda deactivate myenv

KEEP YOUR MACHINE CLEAN
AND YOUR PANDAS
SEPARATED

nums = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

squares = []
i = 0
while i < len(nums):
squares.append(nums[i] * nums[i])
i += 1

squares = []
for i in range(len(nums)):
squares.append(nums[i] * nums[i])

squares = []
for num in nums:
squares.append(num * num)

squares = [num * num for num in nums]

squares = [num * num for num in nums if num % 2 == 0]

fruits = ['apple', 'mango', 'banana', 'cherry’]
fruit_lens = {fruit: len(fruit) for fruit in fruits}
{'apple': 5, 'mango': 5, 'banana': 6, 'cherry': 6}

SUM ALL NUMBERS
BETWEEN 10 AND 1000

a = 10
b = 1000
total_sum = 0
while b >= a:
total_sum += a
a += 1

total_sum = sum(range(10, 1001))

fruits = ['apples', 'oranges', 'bananas', 'grapes']
found = False
size = len(fruits)
for i in range(size):
if fruits[i] == 'cherries':
found = True

>>> import this
The Zen of Python, by Tim Peters
Beautiful is better than ugly.
Explicit is better than implicit.
Simple is better than complex.
Complex is better than complicated.
Flat is better than nested.
Sparse is better than dense.
Readability counts.
Special cases aren't special enough to break the rules.
Although practicality beats purity.
Errors should never pass silently.
Unless explicitly silenced.
In the face of ambiguity, refuse the temptation to guess.
There should be one-- and preferably only one --obvious way to do it.
Although that way may not be obvious at first unless you're Dutch.
Now is better than never.
Although never is often better than *right* now.
If the implementation is hard to explain, it's a bad idea.
If the implementation is easy to explain, it may be a good idea.
Namespaces are one honking great idea -- let's do more of those!

def main():
parser = argparse.ArgumentParser()
parser.add_argument('input_file', default='in.txt’, type=str, help=‘…')
parser.add_argument('ouput_file', default='out.txt’, type=str, help=‘…')
parser.add_argument(‘--debug', required=True, type=bool, help=‘…')
args = parser.parse_args()
# do some work
print(args.debug)
if __name__ == '__main__':
main()

python myscript.py --help
Usage: myscript.py [OPTIONS] [INPUT_FILE]
[OUTPUT_FILE]
Options:
--debug BOOLEAN [required]
--help Show this message and exit.

@click.command()
@click.argument('input_file', default='in.txt', type=click.Path(), help=‘…')
@click.argument('output_file', default='out.txt', type=click.Path(), help=‘…')
@click.option('--debug', required=True, type=click.BOOL, help=‘…')
def main(input_file, output_file, debug):
print(input_file)
print(output_file)
print(debug)
if __name__ == '__main__':
main()

CLICK MAKES ARGUMENT
PARSING READABLE AND
TESTABLE

def test_add_positive():
assert add(1, 2) == 3

@pytest.mark.parametrize('val1, val2, expected_result',
[
# small values
(1, 2, 3),
# negative values
(-2, -1, 3)
])
def test_add(val1, val2, expected_result):
actual_result = add(val1, val2)
assert actual_result == expected_result

@pytest.mark.longrunning
def test_integration_between_two_systems():
# this might take a while

def remove_file(filename):
if os.path.isfile(filename):
os.remove(filename)

@mock.patch('src.utils.file_utils.os.path')
@mock.patch('src.utils.file_utils.os')
def test_remove_file_not_removed_if…(mock_os, mock_path):
mock_path.isfile.return_value = False
remove_file('anyfile.txt')
assert mock_os.remove.called == False

A TEST FOLDER IN THE ROOT
IS PRETTY NICE

THERE IS A PACKAGE
FOR THAT
10

model_path = '../resnet50_coco_best_v2.1.0.h5'
model = models.load_model(model_path, backbone_name='resnet50’)
image_path = '../data/images/basket_image.jpg'
image = read_image_bgr(image_path)
image = preprocess_image(image)
image, scale = resize_image(image)
# process image
boxes, scores, labels = model.predict_on_batch(np.expand_dims(image, axis=0))
from keras_retinanet import models
from keras_retinanet.utils.image import read_image_bgr, preprocess_image,
resize_image
OBJECT DETECTION

THIS IS THE REASON WHY WE
DO ML IN PYTHON

C# to python

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