why it matters

misconception: program code is mostly written, while in reality it is mainly read

a well-written code is easy to read, understand, debug, maintain, extend, etc.

clear is better than clever

source: Rob Pike’s Go Proverbs

prime check – clever vs. clear

func isPrime(n int) bool {
    if n < 2 {
        return false
    }

    for i := 2; i*i <= n; i++ {
        if n%i == 0 {
            return false
        }
    }

    return true
}

func isPrime(n int) bool {
    if n < 2 {
        return false
    }

    for i := 2; i <= n/2; i++ {
        if n%i == 0 {
            return false
        }
    }

    return true
}

source: Rob Pike’s Go Proverbs

write idiomatic code

for (i = 0; i < 10; i++) {
    console.log(i);
}

[...Array(10).keys()].forEach(i => {
    console.log(i);
});

i = 0
while i < 10:
    print(i)
    i += 1

for i in range(10):
    print(i)

for i in 0..9 do
   puts i
end

(0..9).each do |i|
    puts i
end

(0..9).each {|i| puts i}

use intention-revealing names

int d; // elapsed time in days

int elapsedTimeInDays;

multi-word names

int elapsedTimeInDays;

public class DataCollector {}

elapsed_time_in_days = 17

avoid disinformation

Do not refer to a grouping of accounts as an accountList unless it’s actually a List [3].

make meaningful distinctions

It is not sufficient to add number series or noise words, even though the compiler is satisfied. If names must be different, then they should also mean something different [3].

def calculate_distance(data: pd.DataFrame) -> pd.Series:
    # do something

def calculate_distance2(data: pd.DataFrame) -> pd.Series:
    # do something else

def calculate_eucledian_distance(data: pd.DataFrame) -> pd.Series:
    # ...

def calculate_levenshtein_distance(data: pd.DataFrame) -> pd.Series:
    # ...

make meaningful distinctions / noise words

Noise words are another meaningless distinction. Imagine that you have a Product class. If you have another called ProductInfo or ProductData, you have made the names different without making them mean anything different [3].

use pronounceable names

If you can’t pronounce it, you can’t discuss it without sounding like an idiot [3].

• Should etid be an integer?
• Should elapsed_time_in_days be an integer?

use searchable names

for i in range(10):
    print(i)

int d; // elapsed time in days

names for classes, functions

avoid encodings

with modern IDEs it is pointless to put type or role markers into names

def fnFactorial(iNum):
    if iNum == 1:
        return iNum
    else:
        return iNum * fnFactorial(iNum - 1)

interface IShapeArea // I is also a prefix
{
  void area(); 
}

interface ShapeArea 
{
  void area(); 
}

avoid mental mapping

Readers shouldn’t have to mentally translate your names into other names they already know [3].

don’t pun or use humor

• no inside jokes
• no colloquialisms or slang
• be objective and professional

Say what you mean. Mean what you say [3].

pick one word per concept

it’s confusing to have fetch , retrieve, and get as equivalent methods of different classes [3]

it also helps to search for the term

add meaningful context

Imagine that you have variables named firstName, lastName, street, houseNumber, city, state, and zipcode. Taken together it’s pretty clear that they form an address. But what if you just saw the state variable being used alone in a method? [3]

• adding a prefix?
  • e.g., addrCity, addrStreet, addrState
• as notations are discouraged, use an Address class instead to add context

functions should be as small as possible

Functions should hardly ever be 20 lines long [3]

• shorter functions are easier to understand

do one thing (single responsibility principle)

import sqlite3
import pandas as pd

con = sqlite3.connect("data.db")
data = pd.read_sql(activity_query, con)

records = []
for woy in range(36, 40):
    for dow in range(1, 8):
        records.append([woy, dow, 0])
empty = pd.DataFrame.from_records(
    records, columns=["week_of_year", "day_of_week", "count"]
)
data = (
    pd.concat([data, empty])
    .drop_duplicates(subset=["week_of_year", "day_of_week"], keep="first")
    .sort_values(["week_of_year", "day_of_week"])
    .reset_index(drop=True)
)
activity = pd.pivot(
    data, index=["week_of_year"], columns=["day_of_week"], values=["count"]
).values
res = con.execute(progress_query)
progress = res.fetchone()[0]

SELECT
    CAST(
        strftime('%W', timestamp) 
        AS INTEGER
    ) AS week_of_year,
    CAST(
        strftime('%u', timestamp)
        AS INTEGER
    ) AS day_of_week,
    count(*) AS count
FROM activity
WHERE
    user_id = 42 AND
    week_of_year > 35 AND
    week_of_year < 40
GROUP BY
    week_of_year,
    day_of_week;

SELECT
    lesson / 50.0 AS progress
FROM activity
WHERE
    user_id = 42 AND
    result = 'success'
ORDER BY lesson DESC
LIMIT 1;

debug tables

the inverse scope law of function names

function arguments

def build_empty_dataframe(start, end, cols):
    records = []
    for woy in range(start, end + 1):
        for dow in range(1, 8):
            records.append([woy, dow, 0])
    return pd.DataFrame.from_records(
        records, columns=cols
    )

def query_progress(as_percentage: bool):
    res = con.execute(progress_query)
    progress = res.fetchone()[0]

    if as_percentage:
        return progress * 100
    else:
        return progress

function as interface

DataFrame.to_csv(
    path_or_buf=None, *,
    sep=',',
    na_rep='',
    float_format=None,
    columns=None,
    header=True,
    index=True,
    index_label=None,
    mode='w',
    encoding=None,
    compression='infer',
    quoting=None,
    quotechar='"',
    lineterminator=None,
    chunksize=None,
    date_format=None,
    doublequote=True,
    escapechar=None,
    decimal='.',
    errors='strict',
    storage_options=None
)

no side effects

side effect example

class Something:
    foo = 0
    
    def increase(self, by):
        self.foo += by
    
    def decrease(self, by):
        self.foo -= by
    
something = Something()
print(something.foo)  # 0
something.increase(2)
print(something.foo)  # 2

smth = {"foo": 0}

def increase(what, by):
    return what + by

def decrease(what, by):
    return what - by

print(smth["foo"])  # 0
increase(smth["foo"], 2)  # 2
print(smth["foo"])  # 0
smth["foo"] = increase(smth["foo"], 2)
print(smth["foo"])  # 2

prefer exceptions to returning error codes

• in unix-like systems processes still return 0 if the execution was successful
• but returning error codes in functions are discouraged
• FileNotFoundException is better than ERRCODE_26375
  • meaningful name
  • no mental mapping
  • exception handling syntactically more readable

separating comments

# connect to the database
con = sqlite3.connect("data.db")
# query activity data
data = pd.read_sql(activity_query, con)
# create empty dataframe
records = []
for woy in range(36, 40):
    for dow in range(1, 8):
        records.append([woy, dow, 0])
empty = pd.DataFrame.from_records(records, columns=["week_of_year", "day_of_week", "count"])
# combine empty and sparse dataframe
data = (
    pd.concat([data, empty])
    .drop_duplicates(subset=["week_of_year", "day_of_week"], keep="first")
    .sort_values(["week_of_year", "day_of_week"])
    .reset_index(drop=True)
)
# pivot dataframe
activity = pd.pivot(
    data, index=["week_of_year"], columns=["day_of_week"], values=["count"]
).values

separated functions

def create_empty_dataframe(start_week, end_week):
    records = []
    for woy in range(start_week, end_week+1):
        for dow in range(1, 8):
            records.append([woy, dow, 0])
    return pd.DataFrame.from_records(
        records, columns=["week_of_year", "day_of_week", "count"]
    )

def fill_empty_with_activities(empty, activities):
    return (
        pd.concat([activities, empty])
        .drop_duplicates(subset=["week_of_year", "day_of_week"], keep="first")
        .sort_values(["week_of_year", "day_of_week"])
        .reset_index(drop=True)
    )

def pivot_dataframe(data):
    return pd.pivot(
        data, index=["week_of_year"], columns=["day_of_week"], values=["count"]
    ).values

separated functions - usage

con = sqlite3.connect("data.db")

activities = pd.read_sql(activity_query, con)

empty = create_empty_dataframe(36, 39)

data = fill_empty_with_activities(emty, activities)

activities_matrix = pivot_dataframe(data)

only the comments remained, which can be read as a prose

more bad comments

journal comment

# 2024-10-17 -- Add idiomatic coding examples 
# 2024-10-18 -- Add meaningful names section 

the version tracker keeps better journal

noise comments

# creates an empty dataframe
def create_empty_dataframe(start_week, end_week):
    # ...

don’t write something that is already in the code

closing brace comments

for (i = 0; i < 10; i++) {
    console.log(i);
} // for

modern editors can find (end display) the block endings

Apollo 11 - Colossus 2A

P21VSAVE    DLOAD           # SAVE CURRENT BASE VECTOR
            TAT
        STOVL   P21TIME     # ..TIME
            RATT1
        STOVL   P21BASER    # ..POS B-29 OR B-27
            VATT1
        STORE   P21BASEV    # ..VEL B-7  OR B-5
        ABVAL   SL*
            0,2
        STOVL   P21VEL      # /VEL/ FOR N73 DSP
            RATT
        UNIT    DOT
            VATT        # U(R).(V)
        DDV ASIN        # U(R).U(V)
            P21VEL
        STORE   P21GAM      # SIN-1 U(R).U(V), -90 TO +90
        SXA,2   SET
            P21ORIG     # 0 = EARTH  2 = MOON
            P21FLAG

source, GitHub repository, more about the Apollo Guidance Computer: [7]

good comments

legal comments

some open source licences should be included to the beginning of the files

informative comments

import re

timestamp = "2024-10-22 09:30:42"
# matches for timestamps in the format of: YYYY-MM-DD HH:MM:SS
re.match(r"\d{4}-\d{2}-\d{2} \d{2}:\d{2}:\d{2}", timestamp)

# TODO: this allows invalid month, day, hour, minute and second values
re.match(r"\d{4}-\d{2}-\d{2} \d{2}:\d{2}:\d{2}", timestamp)

documentation

def fizzbuzz(i: int) -> str:
    """Fizzbuzz is a game for children to teach them about division.
    It is also a common coding practice.
    
    Parameters
    ----------
    i : int
        Input number tested against division by 3, 5 and 15.
    
    Returns
    -------
    str
        `Fizz` if input divisible by 3, `Buzz` if divisible by 5 and `FizzBuzz` if both.
    """
    result = ""
    if i % 15 == 0:
        result += "FizzBuzz"
    elif i % 3 == 0:
        result += "Fizz"
    elif i % 5 == 0:
        result += "Buzz"
    else:
        result = str(i)
    return result

doctest

def fizzbuzz(i: int) -> str:
    """
    >>> fizzbuzz(3)
    'Fizz'
    >>> fizzbuzz(5)
    'Buzz'
    >>> fizzbuzz(12)
    'Fizz'
    >>> fizzbuzz(15)
    'FizzBuzz'
    >>> fizzbuzz(17)
    '17'
    """
    result = ""
    if i % 15 == 0:
        result += "FizzBuzz"
    elif i % 3 == 0:
        result += "Fizz"
    elif i % 5 == 0:
        result += "Buzz"
    else:
        result = str(i)
    return result

python -m doctest -v fizzbuzz_doctest.py
Trying:
    fizzbuzz(3)
Expecting:
    'Fizz'
ok
Trying:
    fizzbuzz(5)
Expecting:
    'Buzz'
ok
Trying:
    fizzbuzz(15)
Expecting:
    'FizzBuzz'
ok
Trying:
    fizzbuzz(17)
Expecting:
    '17'
ok
4 passed.
Test passed.

clean code / meaningful names [3]

names for classes, functions

clean code / functions [3]

clean code / comments [3]

avoid

• journal comments
• noise comments
• writing something that is already in the code
• closing brace comments
• separating comments

however, comments can be used if they help to understand the code

• informative comments, that explain what is happening
  • math, physics, domain-specific things
• (API) documentation with examples