Python

ACTL3143 & ACTL5111 Deep Learning for Actuaries

Patrick Laub

Recording of this lecture

A recording covering (most of) this Python content:

Data Science & Python

Lecture Outline

• Data Science & Python

• Python Data Types

• Collections

• Control Flow

• Python Functions

• Import syntax

• Lambda functions

About Python

Free book Automate the Boring Stuff with Python

It is general purpose language

Python powers:

• Instagram
• Spotify
• Netflix
• Uber
• Reddit…

Python is on Mars.

Stack Overflow 2021 Dev. Survey

• Python is 3rd most popular language
• Python is the most wanted language
• In ‘Other frameworks and libraries’, they note that “several data science libraries for Python make strong showings”.

Popular languages.

Github’s 2021 State of the Octoverse

Top languages over the years

Python and machine learning

…[T]he entire machine learning and data science industry has been dominated by these two approaches: deep learning and gradient boosted trees… Users of gradient boosted trees tend to use Scikit-learn, XGBoost, or LightGBM. Meanwhile, most practitioners of deep learning use Keras, often in combination with its parent framework TensorFlow. The common point of these tools is they’re all Python libraries: Python is by far the most widely used language for machine learning and data science.

Python for data science

In R you can run:

pchisq(3, 10)

In Python it is

from scipy import stats
stats.chi2(10).cdf(3)

In Leganto

Google Colaboratory

An example notebook in Google Colaboratory.

http://colab.research.google.com

Python Data Types

Lecture Outline

• Data Science & Python

• Python Data Types

• Collections

• Control Flow

• Python Functions

• Import syntax

• Lambda functions

Variables and basic types

1 + 2

3

x = 1
x + 2.0

3.0

type(2.0)

float

type(1), type(x)

(int, int)

does_math_work = 1 + 1 == 2
print(does_math_work)
type(does_math_work)

True

bool

contradiction = 1 != 1
contradiction

False

Shorthand assignments

If we want to add 2 to a variable x:

x = 1
x = x + 2
x

3

x = 1
x += 2
x

3

Same for:

• x -= 2 : take 2 from the current value of x ,
• x *= 2 : double the current value of x,
• x /= 2 : halve the current value of x.

Strings

name = "Patrick"
surname = "Laub"

coffee = "This is Patrick's coffee"
quote = 'And then he said "I need a coffee!"'

name + surname

'PatrickLaub'

greeting = f"Hello {name} {surname}"
greeting

'Hello Patrick Laub'

"Patrick" in greeting

True

and & or

name = "Patrick"
surname = "Laub"
name.istitle() and surname.istitle()

True

full_name = "Dr Patrick Laub"
full_name.startswith("Dr ") or full_name.endswith(" PhD")

True

Important

The dot is used denote methods, it can’t be used inside a variable name.

i.am.an.unfortunate.R.users = True

NameError: name 'i' is not defined

help to get more details

help(name.istitle)

Help on built-in function istitle:

istitle() method of builtins.str instance
    Return True if the string is a title-cased string, False otherwise.
    
    In a title-cased string, upper- and title-case characters may only
    follow uncased characters and lowercase characters only cased ones.

f-strings

print(f"Five squared is {5*5} and five cubed is {5**3}")
print("Five squared is {5*5} and five cubed is {5**3}")

Five squared is 25 and five cubed is 125
Five squared is {5*5} and five cubed is {5**3}

Use f-strings and avoid the older alternatives:

print(f"Hello {name} {surname}")
print("Hello " + name + " " + surname)
print("Hello {} {}".format(name, surname))
print("Hello %s %s" % (name, surname))

Hello Patrick Laub
Hello Patrick Laub
Hello Patrick Laub
Hello Patrick Laub

Converting types

digit = 3
digit

3

type(digit)

int

num = float(digit)
num

3.0

type(num)

float

num_str = str(num)
num_str

'3.0'

Quiz

What is the output of:

x = 1
y = 1.0
print(f"{x == y} and {type(x) == type(y)}")

True and False

What would you add before line 3 to get “True and True”?

x = 1
y = 1.0
x = float(x)  # or y = int(y)
print(f"{x == y} and {type(x) == type(y)}")

True and True

Collections

Lecture Outline

• Data Science & Python

• Python Data Types

• Collections

• Control Flow

• Python Functions

• Import syntax

• Lambda functions

Lists

desires = ["Coffee", "Cake", "Sleep"]
desires

['Coffee', 'Cake', 'Sleep']

len(desires)

3

desires[0]

'Coffee'

desires[-1]

'Sleep'

desires[2] = "Nap"
desires

['Coffee', 'Cake', 'Nap']

Slicing lists

print([0, 1, 2])
desires

[0, 1, 2]

['Coffee', 'Cake', 'Nap']

desires[0:2]

['Coffee', 'Cake']

desires[0:1]

['Coffee']

desires[:2]

['Coffee', 'Cake']

A common indexing error

desires[1.0]

TypeError: list indices must be integers or slices, not float

desires[: len(desires) / 2]

TypeError: slice indices must be integers or None or have an __index__ method

len(desires) / 2, len(desires) // 2

(1.5, 1)

desires[: len(desires) // 2]

['Coffee']

Editing lists

desires = ["Coffee", "Cake", "Sleep"]
desires.append("Gadget")
desires

['Coffee', 'Cake', 'Sleep', 'Gadget']

desires.pop()

'Gadget'

desires

['Coffee', 'Cake', 'Sleep']

desires.sort()
desires

['Cake', 'Coffee', 'Sleep']

desires[3] = "Croissant"

IndexError: list assignment index out of range

None

desires = ["Coffee", "Cake", "Sleep", "Gadget"]
sorted_list = desires.sort()
sorted_list

type(sorted_list)

NoneType

sorted_list is None

True

bool(sorted_list)

False

desires = ["Coffee", "Cake", "Sleep", "Gadget"]
sorted_list = sorted(desires)
print(desires)
sorted_list

['Coffee', 'Cake', 'Sleep', 'Gadget']

['Cake', 'Coffee', 'Gadget', 'Sleep']

Tuples (‘immutable’ lists)

weather = ("Sunny", "Cloudy", "Rainy")
print(type(weather))
print(len(weather))
print(weather[-1])

<class 'tuple'>
3
Rainy

weather.append("Snowy")

AttributeError: 'tuple' object has no attribute 'append'

weather[2] = "Snowy"

TypeError: 'tuple' object does not support item assignment

One-length tuples

using_brackets_in_math = (2 + 4) * 3
using_brackets_to_simplify = (1 + 1 == 2)

failure_of_atuple = ("Snowy")
type(failure_of_atuple)

str

happy_solo_tuple = ("Snowy",)
type(happy_solo_tuple)

tuple

cheeky_solo_list = ["Snowy"]
type(cheeky_solo_list)

list

Dictionaries

phone_book = {"Patrick": "+61 1234", "Café": "(02) 5678"}
phone_book["Patrick"]

'+61 1234'

phone_book["Café"] = "+61400 000 000"
phone_book

{'Patrick': '+61 1234', 'Café': '+61400 000 000'}

phone_book.keys()

dict_keys(['Patrick', 'Café'])

phone_book.values()

dict_values(['+61 1234', '+61400 000 000'])

factorial = {0: 1, 1: 1, 2: 2, 3: 6, 4: 24, 5: 120, 6: 720, 7: 5040}
factorial[4]

24

Quiz

animals = ["dog", "cat", "bird"]
animals.append("teddy bear")
animals.pop()
animals.pop()
animals.append("koala")
animals.append("kangaroo")
print(f"{len(animals)} and {len(animals[-2])}")

4 and 5

Control Flow

Lecture Outline

• Data Science & Python

• Python Data Types

• Collections

• Control Flow

• Python Functions

• Import syntax

• Lambda functions

if and else

age = 50

if age >= 30:
    print("Gosh you're old")

Gosh you're old

if age >= 30:
    print("Gosh you're old")
else:
    print("You're still young")

Gosh you're old

The weird part about Python…

if age >= 30:
    print("Gosh you're old")
else:
print("You're still young")

IndentationError: expected an indented block after 'else' statement on line 3 (2212277638.py, line 4)

Warning

Watch out for mixing tabs and spaces!

An example of aging

age = 16

if age < 18:
    friday_evening_schedule = "School things"
if age < 30:
    friday_evening_schedule = "Party 🥳🍾"
if age >= 30:
    friday_evening_schedule = "Work"

print(friday_evening_schedule)

Party 🥳🍾

Using elif

age = 16

if age < 18:
    friday_evening_schedule = "School things"
elif age < 30:
    friday_evening_schedule = "Party 🥳🍾"
else:
    friday_evening_schedule = "Work"

print(friday_evening_schedule)

School things

for Loops

desires = ["coffee", "cake", "sleep"]
for desire in desires:
    print(f"Patrick really wants a {desire}.")

Patrick really wants a coffee.
Patrick really wants a cake.
Patrick really wants a sleep.

for i in range(3):
    print(i)

0
1
2

for i in range(3, 6):
    print(i)

3
4
5

range(5)

range(0, 5)

type(range(5))

range

list(range(5))

[0, 1, 2, 3, 4]

Advanced for loops

for i, desire in enumerate(desires):
    print(f"Patrick wants a {desire}, it is priority #{i+1}.")

Patrick wants a coffee, it is priority #1.
Patrick wants a cake, it is priority #2.
Patrick wants a sleep, it is priority #3.

desires = ["coffee", "cake", "nap"]
times = ["in the morning", "at lunch", "during a boring lecture"]

for desire, time in zip(desires, times):
    print(f"Patrick enjoys a {desire} {time}.")

Patrick enjoys a coffee in the morning.
Patrick enjoys a cake at lunch.
Patrick enjoys a nap during a boring lecture.

List comprehensions

[x**2 for x in range(10)]

[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

[x**2 for x in range(10) if x % 2 == 0]

[0, 4, 16, 36, 64]

They can get more complicated:

[x * y for x in range(4) for y in range(4)]

[0, 0, 0, 0, 0, 1, 2, 3, 0, 2, 4, 6, 0, 3, 6, 9]

[[x * y for x in range(4)] for y in range(4)]

[[0, 0, 0, 0], [0, 1, 2, 3], [0, 2, 4, 6], [0, 3, 6, 9]]

but I’d recommend just using for loops at that point.

While Loops

Say that we want to simulate (X \,\mid\, X \ge 100) where X \sim \mathrm{Pareto}(1). Assuming we have simulate_pareto, a function to generate \mathrm{Pareto}(1) variables:

samples = []
while len(samples) < 5:
    x = simulate_pareto()
    if x >= 100:
        samples.append(x)

samples

[125.28600493316272,
 186.04974709289712,
 154.45723763510398,
 101.08310878885993,
 2852.8305399214996]

Breaking out of a loop

while True:
    user_input = input(">> What would you like to do? ")

    if user_input == "order cake":
        print("Here's your cake! 🎂")

    elif user_input == "order coffee":
        print("Here's your coffee! ☕️")

    elif user_input == "quit":
        break

>> What would you like to do? order cake
Here's your cake! 🎂
>> What would you like to do? order coffee
Here's your coffee! ☕️
>> What would you like to do? order cake
Here's your cake! 🎂
>> What would you like to do? quit

Quiz

What does this print out?

if 1 / 3 + 1 / 3 + 1 / 3 == 1:
    if 2**3 == 6:
        print("Math really works!")
    else:
        print("Math sometimes works..")
else:
    print("Math doesn't work")

Math sometimes works..

What does this print out?

count = 0
for i in range(1, 10):
    count += i
    if i > 3:
        break
print(count)

10

Debugging the quiz code

count = 0
for i in range(1, 10):
    count += i
    print(f"After i={i} count={count}")
    if i > 3:
        break

After i=1 count=1
After i=2 count=3
After i=3 count=6
After i=4 count=10

Python Functions

Lecture Outline

• Data Science & Python

• Python Data Types

• Collections

• Control Flow

• Python Functions

• Import syntax

• Lambda functions

Making a function

def add_one(x):
    return x + 1


def greet_a_student(name):
    print(f"Hi {name}, welcome to the AI class!")

add_one(10)

11

greet_a_student("Josephine")

Hi Josephine, welcome to the AI class!

greet_a_student("Joseph")

Hi Joseph, welcome to the AI class!

Here, name is a parameter and the value supplied is an argument.

Default arguments

Assuming we have simulate_standard_normal, a function to generate \mathrm{Normal}(0, 1) variables:

def simulate_normal(mean=0, std=1):
    return mean + std * simulate_standard_normal()

simulate_normal()  # same as 'simulate_normal(0, 1)'

0.47143516373249306

simulate_normal(1_000)  # same as 'simulate_normal(1_000, 1)'

998.8090243052935

Note

We’ll cover random numbers next week (using numpy).

Use explicit parameter name

simulate_normal(mean=1_000)  # same as 'simulate_normal(1_000, 1)'

1001.4327069684261

simulate_normal(std=1_000)  # same as 'simulate_normal(0, 1_000)'

-312.6518960917129

simulate_normal(10, std=0.001)  # same as 'simulate_normal(10, 0.001)'

9.999279411266635

simulate_normal(std=10, 1_000)

SyntaxError: positional argument follows keyword argument (1723181129.py, line 1)

Why would we need that?

E.g. to fit a Keras model, we use the .fit method:

model.fit(x=None, y=None, batch_size=None, epochs=1, verbose='auto',
        callbacks=None, validation_split=0.0, validation_data=None,
        shuffle=True, class_weight=None, sample_weight=None,
        initial_epoch=0, steps_per_epoch=None, validation_steps=None,
        validation_batch_size=None, validation_freq=1,
        max_queue_size=10, workers=1, use_multiprocessing=False)

Say we want all the defaults except changing use_multiprocessing=True:

model.fit(None, None, None, 1, 'auto', None, 0.0, None, True, None,
        None, 0, None, None, None, 1, 10, 1, True)

but it is much nicer to just have:

model.fit(use_multiprocessing=True)

Quiz

What does the following print out?

def get_half_of_list(numbers, first=True):
    if first:
        return numbers[: len(numbers) // 2]
    else:
        return numbers[len(numbers) // 2 :]

nums = [1, 2, 3, 4, 5, 6]
chunk = get_half_of_list(nums, False)
second_chunk = get_half_of_list(chunk)
print(second_chunk)

[4]

f"nums ~> {nums[:len(nums)//2]} and {nums[len(nums)//2:]}"

'nums ~> [1, 2, 3] and [4, 5, 6]'

f"chunk ~> {chunk[:len(chunk)//2]} and {chunk[len(chunk)//2:]}"

'chunk ~> [4] and [5, 6]'

Multiple return values

def limits(numbers):
    return min(numbers), max(numbers)

limits([1, 2, 3, 4, 5])

(1, 5)

type(limits([1, 2, 3, 4, 5]))

tuple

min_num, max_num = limits([1, 2, 3, 4, 5])
print(f"The numbers are between {min_num} and {max_num}.")

The numbers are between 1 and 5.

_, max_num = limits([1, 2, 3, 4, 5])
print(f"The maximum is {max_num}.")

The maximum is 5.

print(f"The maximum is {limits([1, 2, 3, 4, 5])[1]}.")

The maximum is 5.

Tuple unpacking

lims = limits([1, 2, 3, 4, 5])
smallest_num = lims[0]
largest_num = lims[1]
print(f"The numbers are between {smallest_num} and {largest_num}.")

The numbers are between 1 and 5.

smallest_num, largest_num = limits([1, 2, 3, 4, 5])
print(f"The numbers are between {smallest_num} and {largest_num}.")

The numbers are between 1 and 5.

This doesn’t just work for functions with multiple return values:

RESOLUTION = (1920, 1080)
WIDTH, HEIGHT = RESOLUTION
print(f"The resolution is {WIDTH} wide and {HEIGHT} tall.")

The resolution is 1920 wide and 1080 tall.

Short-circuiting

def is_positive(x):
    print("Called is_positive")
    return x > 0

def is_negative(x):
    print("Called is_negative")
    return x < 0

x = 10

x_is_positive = is_positive(x)
x_is_positive

Called is_positive

True

x_is_negative = is_negative(x)
x_is_negative

Called is_negative

False

x_not_zero = is_positive(x) or is_negative(x)
x_not_zero

Called is_positive

True

Import syntax

Lecture Outline

• Data Science & Python

• Python Data Types

• Collections

• Control Flow

• Python Functions

• Import syntax

• Lambda functions

Python standard library

import os
import time

time.sleep(0.1)

os.getlogin()

'plaub'

os.getcwd()

'/home/plaub/Dropbox/Lecturing/ACTL3143/DeepLearningForActuaries/Artificial-Intelligence'

Import a few functions

from os import getcwd, getlogin
from time import sleep

sleep(0.1)

getlogin()

'plaub'

getcwd()

'/home/plaub/Dropbox/Lecturing/ACTL3143/DeepLearningForActuaries/Artificial-Intelligence'

Timing using pure Python

from time import time

start_time = time()

counting = 0
for i in range(1_000_000):
    counting += 1

end_time = time()

elapsed = end_time - start_time
print(f"Elapsed time: {elapsed} secs")

Elapsed time: 0.05615687370300293 secs

Data science packages

Common data science packages

Importing using as

import pandas

pandas.DataFrame(
    {
        "x": [1, 2, 3],
        "y": [4, 5, 6],
    }
)

	x	y
0	1	4
1	2	5
2	3	6

import pandas as pd

pd.DataFrame(
    {
        "x": [1, 2, 3],
        "y": [4, 5, 6],
    }
)

	x	y
0	1	4
1	2	5
2	3	6

Importing from a subdirectory

Want keras.models.Sequential().

import keras

model = keras.models.Sequential()

Alternatives using from:

from keras import models

model = models.Sequential()

from keras.models import Sequential

model = Sequential()

Lambda functions

Lecture Outline

• Data Science & Python

• Python Data Types

• Collections

• Control Flow

• Python Functions

• Import syntax

• Lambda functions

Anonymous ‘lambda’ functions

Example: how to sort strings by their second letter?

names = ["Josephine", "Patrick", "Bert"]

If you try help(sorted) you’ll find the key parameter.

for name in names:
    print(f"The length of '{name}' is {len(name)}.")

The length of 'Josephine' is 9.
The length of 'Patrick' is 7.
The length of 'Bert' is 4.

sorted(names, key=len)

['Bert', 'Patrick', 'Josephine']

Anonymous ‘lambda’ functions

Example: how to sort strings by their second letter?

names = ["Josephine", "Patrick", "Bert"]

If you try help(sorted) you’ll find the key parameter.

def second_letter(name):
    return name[1]

for name in names:
    print(f"The second letter of '{name}' is '{second_letter(name)}'.")

The second letter of 'Josephine' is 'o'.
The second letter of 'Patrick' is 'a'.
The second letter of 'Bert' is 'e'.

sorted(names, key=second_letter)

['Patrick', 'Bert', 'Josephine']

Anonymous ‘lambda’ functions

Example: how to sort strings by their second letter?

names = ["Josephine", "Patrick", "Bert"]

If you try help(sorted) you’ll find the key parameter.

sorted(names, key=lambda name: name[1])

['Patrick', 'Bert', 'Josephine']

Caution

Don’t use lambda as a variable name! You commonly see lambd or lambda_ or λ.

with keyword

Example, opening a file:

Most basic way is:

f = open("haiku1.txt", "r")
print(f.read())
f.close()

Chaos reigns within.
Reflect, repent, and reboot.
Order shall return.

Instead, use:

with open("haiku2.txt", "r") as f:
    print(f.read())

The Web site you seek
Cannot be located, but
Countless more exist.

Package Versions

from watermark import watermark
print(watermark(python=True, packages="keras,matplotlib,numpy,pandas,seaborn,scipy,torch,tensorflow,tf_keras"))

Python implementation: CPython
Python version       : 3.11.9
IPython version      : 8.24.0

keras     : 3.3.3
matplotlib: 3.9.0
numpy     : 1.26.4
pandas    : 2.2.2
seaborn   : 0.13.2
scipy     : 1.11.0
torch     : 2.3.1
tensorflow: 2.16.1
tf_keras  : 2.16.0

Links

If you came from C (i.e. are a joint computer science student), and were super interested in Python’s internals, maybe you’d be interested in this How variables work in Python video.

Glossary

• default arguments
• dictionaries
• f-strings
• function definitions
• Google Colaboratory
• help
• list

• pip install ...
• range
• slicing
• tuple
• type
• whitespace indentation
• zero-indexing