Introduction to Python

Bella Ratmelia

Welcome!

Preamble

About me:
- Librarian, Research & Data Services team, SMU Libraries.
- Bachelor of IT, MSc in Info Studies.
- Have been with SMU since the pandemic era (2021).
About this workshop:
- Live-coding format; code along with me!
- Goal of workshop: to give you enough fundamentals (at least to the point where ChatGPT can’t bluff you so easily) and confidence to explore python on your own.
- Comes with 3 quizzes that you can do at home - one quiz after each session, except for the last one.
- Don’t be afraid to ask for help! We are all here to learn

About the workshop

Live coding & lots of hands-on
Take-home quiz after session 1, 2, and 3 to reinforce the learning - Link will be put on the course website.
Only for session 4: a small group activity at the second half of the session.
- Choose an economic indicator from Federal Reserve Economic Data (FRED) e.g. Civilian Unemployment Rate.
- Apply your Python knowledge to create a “storyboard” with visualizations to derive your economic outlook.

Overview for today

Introduction to Visual Studio
Introduction to Python - objects, values, and types
Handling Lists
Handling Loops and Conditionals

Why Python?

Q: What is Python, actually?

A: Python is a general-purpose programming language used for a wide variety of applications. Website, games, CAD applications, web applications, AI, etc.

Q: Why should I learn Python?

A: It’s one of the most popular programming language and the most dominant language in AI, ML, and data analytics field. It’s a useful skill to know if you plan to go into those fields or anything adjacent.

How do you run python?

If it’s a .py file…

A regular python file.
A plain text and contains just python codes
Can be edited and run on Visual Studio Code, PyCharm, SPyder, etc or the terminal/command prompt.

If it’s a .ipynb file…

ipynb stands for Interactive Python Notebook file
Contains the notebook code, the execution results and other internal settings in a specific format
Can be edited and run on Jupyter Notebook/Lab, Google Colab, and also Visual Studio (with the Jupyter Notebook extension)

What’s .py and .ipynb??

If it’s a .py file…

If it’s a .ipynb file…

Why the different file types?

If it’s a .py file…

For “production” uses e.g., creating apps, industrial deployments
Executing the file will run all the codes contained inside the file.

If it’s a .ipynb file…

Used a lot for academic / scientific purposes; great for quick experiments or teaching / presentation.
Codes will be executed on per-block basis (we will see this in action later on)

Getting Started: VS Code Workspace and Tour

(Suggested) structure of workspace directory

In your laptop, navigate to where you usually keep your files, and create a folder called 2024-09-python-workshop. This folder will be our “workspace” where we keep our scripts and data files.
Inside your workspace folder, create the following sub-folders:
- data - we will save our raw data here. It’s best practice to keep the data here untouched.
- data-output - if we need to modify raw data, store the modified version here.
- fig-output - we will save all the graphics we created here!
Start VS Code.
Go to File > Open Folder.
Open the 2024-09-python-workshop folder we just created.

Best practice: Virtual Environment

Virtual environment helps us to ensure that any packages we install will be isolated from other environments, including the global interpreter. This helps prevent issues that can occur due to conflicting package versions - especially useful for reproducibility!

Open the Command Palette (Ctrl + Shift + P on Windows - Cmd + Shift + P on Macbook).
Type Python: Create Environment, and select the command. Choose Venv.

Select the Python interpreter that we installed in the pre-workshop activity. Give VS Code a few minutes to setup the environment.

Create new file

Open the Command Palette (Ctrl + Shift + P on Windows - Cmd + Shift + P on Macbook).
Type Python: New Python File, and select the command.
Rename the file to 01-intro.py

Some basic glossary (for your reference)

Objects: In Python, everything is an object. An object is a piece of data that can have attributes and methods associated with it.
Value: The actual data stored in a variable or object. It can be a number, string, list, or any other data type.
Variable: A value that has a name associated with it.
Assign: To give a value to a variable. This is done using the equals sign (=).

Function: A reusable block of code that performs a specific task. Functions can take inputs (arguments) and return outputs.
Call: To execute or run a function. This is done by using the function name followed by parentheses.
Arguments: Values passed to a function when it is called. These values are used by the function to perform its task.
Options/Parameters: Variables defined in a function’s declaration that act as placeholders for arguments. They specify what kind of data the function expects to receive.
Packages: Collections of related modules that extend Python’s capabilities. They can be installed and imported to add new functionalities within your workspace.

Section 1: Getting Started

Hands on session - Make sure to open the py file we just created.

Running your python code

Type the following in your .py file…

print("Hello World!")
5 + 5

…and then click on the run button to run the script

You should see “Hello World!” and a 10 printed at the Terminal window at the bottom

Built-in functions

Functions will typically have brackets () at the end of their name.
Python has a built-in function called print() that prints things as text.
Call the function (i.e., tell Python to run it) by using its name.

print(10 - 5)
print(10 * 2)
print("hello world")

5
20
hello world

Comments

Use comments to add a layer of documentation to your code. E.g., explain what a block of code does, etc.

Comments always start with a hashtag #

print(10 - 5)
print(10 * 2)
print("hello world")
# print("python won't print this!")

5
20
hello world

Feel free to use this method to add your own notes throughout the workshop!

Section 2: Variables and assignment

What’s a variable?

Variables are names for values. # In Python, the = symbol assigns the value on the right to the variable on the left.

Once you declare a variable, you must assign value to it.

greetings = "Hello World!"
age = 25

Variable names criteria:

cannot start with a digit
alphanumeric only (underscores are a-OK)
case sensitive
should be meaningful (e.g. quarter1_growth instead of just q1g)

On creating variables

Variables must be created before they are used.

If a variable doesn’t exist yet, or if the name has been mis-spelled, Python reports an error. e.g. if you try to simply declare a variable called name but didn’t assign any value to it, Python will not like that and reports an error!

name  # This  will give you an error.

Things we can do with variables

We can pass variables to functions, e.g. let’s pass the greetings variable to print() function. This should print out the value of greetings.

print(greetings)
print("today's greetings:", greetings)

Hello World!
today's greetings: Hello World!

We can also update the value contained inside a variable. Let’s try adding 10 to variable age.

age = age + 10
print("Age in ten years from now:", age)

Age in ten years from now: 35

You can also assign a variable value to another variable

continent = "Asia"
position = continent  
print("Position:", position)  
print("Continent:", continent)

Position: Asia
Continent: Asia

Learning Check #1

What is the final value of variable position below?

initial = "up"
position = initial
initial = "down"

What is the final value of variable country_1 below?

country_1 = "Australia"
country_2 = "New Zealand"
temp = country_1
country_1 = country_2
country_2 = temp

Section 3: Data Types and Conversion

Basic data types in Python

Integers (int): Whole numbers without a decimal point. Examples: 5, -3, 0
Floating-point numbers (float): Numbers with a decimal point. Examples: 3.14, -0.5, 2.0
Strings (str): Sequences of characters enclosed in quotes. Examples: “Hello”, ‘Python’
Booleans (bool): Logical values representing True or False. Examples: True, False
None: A special type representing the absence of a value

country = "Singapore"  # string
public_holidays = 11  # integer
avg_temp = 27.8  # float
part_of_asean = True  # Boolean

Why do I need to care about these data types?

Because a value’s data type determines what the program can do to it.

For example, the calculations below work on int data type

print(public_holidays - 5)

But the above operation won’t work on a string

print(country - "pore") \# this will give an error!

However you can use “+” and “*” on strings

print(country + " best")  # this works for string, though.
print(country * 4)  # this also works
print("=" * 10)  # this also works
print(len(country))  # this would print out the number of characters a string has

Singapore best
SingaporeSingaporeSingaporeSingapore
==========
9

Converting data types to one another

Some data types need to be converted before we can do some operations on them i.e. this will give you an error.

print("10" + 2)

Here is how you can convert the data types:

print("10" + str(2))  # convert to string
print(int(10) + 2)  # convert to int

102
12

Take note of variables assignment placement

Take note of the execution order of your variables! They only change value when something is assigned to them.

public_holiday = 11  # initial value 
new_holidays = public_holiday + 5
public_holiday = 10  # we update the variable
print("Public holiday:", public_holiday) 
print("New holidays:", new_holidays)

Public holiday: 10
New holidays: 16

Updating the value of public_holiday will not auto-update the value of new_holidays.

Learning Check #2

What is the data type of each of this variable?

a = 50  
b = "SMU Libraries"  
c = 100.5  
d = True  
e = 10 * 2.54  
f = int(5.5)  
g = 10 < 15  
h = len("The quick brown fox jumps over the lazy dog")

Section 4: Built-in Python functions

Arguments vs Parameters

There are many built-in function that we can use, one of the common one is round() which will round a float number to the nearest integer.
Some functions accept arguments, and we put them inside the brackets when we pass it to the function. It’s like telling the function “do what you’re supposed to do with this things I pass to you”.

round(3.14)

formal definition, but often used interchangeably:

arguments = the value passed to a function - in this case, 3.14
parameters = the variable passed to a function

How do I find out more about a function?

Some functions can take more than one arguments! we’ve seen this with print() earlier.
If you’re not sure what kind of arguments are accepted, etc, use the help() function. This example below should tell you more about round() once executed.

help(round)

Help on built-in function round in module builtins:

round(number, ndigits=None)
    Round a number to a given precision in decimal digits.
    
    The return value is an integer if ndigits is omitted or None.  Otherwise
    the return value has the same type as the number.  ndigits may be negative.

Section 5: Lists

More data types / structure

Other than the basic 4 types that we learned earlier, Python has more data structures where we can store multiple values in a single variable:

Lists: Ordered, mutable (i.e. can be edited) sequences of elements. Examples: [1, 2, 3], ['a', 'b', 'c']
Tuples: Ordered, immutable (i.e. cannot be edited once it is created) sequences of elements. Examples: (1, 2, 3), ('a', 'b', 'c')
Dictionaries (dict): Unordered collections of key-value pairs. Example: {'name': 'John', 'age': 30}
Sets: Unordered collections of unique elements. Example: {1, 2, 3}

We will not be covering all of the above, only list and dictionaries (for session 4)

When lists would be useful

Scenario: We want to track Singapore’s GDP year-over-year (YoY) growth from 2024 to 2018.

A list helps us store multiple years of data in one place, making it efficient.
Without a list, we’d need dozens of separate variables for each year, making it harder to analyze trends.
Using a list, we can easily manage and analyze the data for all the years together.

# growth rates from 2023 Q1 to 2024 Q2
quarterly_gdp_growth = [-2,-5.8, 2.8, 2.6, 7.3, 1.1, 3.4]

You can also initialize an empty list to fill later

future_growth_rates = []

Check number of items in a list

As a good first step, it’s good to check the number of items you have in your list!

print('GDP growth rates:', quarterly_gdp_growth)
print('Number of quarters:', len(quarterly_gdp_growth))

GDP growth rates: [-2, -5.8, 2.8, 2.6, 7.3, 1.1, 3.4]
Number of quarters: 7

Retrieve a single item from a list

Sometimes you don’t need the entire list. To retrieve a single value from a list, simply specify their index number enclosed in square brackets

Remember that index starts from 0 instead of 1.
So if you want to retrieve the fourth item (2021’s growth rate), the index will be 3.

print('GDP growth rate in 2023 Q1 (1st item):', quarterly_gdp_growth[0])
print('GDP growth rate in 2024 Q2 (6th item):', quarterly_gdp_growth[5])

GDP growth rate in 2023 Q1 (1st item): -2
GDP growth rate in 2024 Q2 (6th item): 1.1

If we put an index number that’s larger than actual number of items, python will give us an error!

quarterly_gdp_growth[10] # This would cause an IndexError

Retrieve multiple items from a list: the syntax

Slicing allows you to extract a portion of the list by specifying a range of indices. The basic syntax for slicing is:

list_name[start:end:step]

start is the index where the slice starts (inclusive)
end is the index where the slice ends (exclusive)
If start is omitted, the slice begins from the start of the list
If end is omitted, the slice goes to the end of the list
step is optional; it determines how many items to skip

Retrieve multiple items from a list: in action

Specifying the start and end:

print('First three quarters:', 
quarterly_gdp_growth[0:3])

First three quarters: [-2, -5.8, 2.8]

Omitting the end index:

print('From Q3 2023 onwards:', 
quarterly_gdp_growth[2:])

From Q3 2023 onwards: [2.8, 2.6, 7.3, 1.1, 3.4]

Omitting the start:

print('First three quarters:', 
quarterly_gdp_growth[:3])

First three quarters: [-2, -5.8, 2.8]

If we put a negative index, python list will count from the end instead of the beginning!

print('Last three quarters:', 
quarterly_gdp_growth[-3:])

Last three quarters: [7.3, 1.1, 3.4]

Stepping value in a list

You can also use a step value to skip items!

It is particularly useful if you need to reverse a list.

print('Every other quarter:', quarterly_gdp_growth[::2])
print('Quarters in reverse order:', quarterly_gdp_growth[::-1])

Every other quarter: [-2, 2.8, 7.3, 3.4]
Quarters in reverse order: [3.4, 1.1, 7.3, 2.6, 2.8, -5.8, -2]

Updating the value of items inside a list

List values can be replaced by simply assigning a new value to them! Let’s update the growth rate for 2023 Q3 (index 2) with the correct figure.

quarterly_gdp_growth[2] = -2.8
print('Updated GDP growth rates:', quarterly_gdp_growth)

Updated GDP growth rates: [-2, -5.8, -2.8, 2.6, 7.3, 1.1, 3.4]

Appending items to a list

Let’s append a new data of 3.2% growth that we get for 2024 Q3 to the list. Appending will put this new item at the end of the list.

print('GDP growth rates initially:', quarterly_gdp_growth)
quarterly_gdp_growth.append(3.2)  
print('GDP growth rates after adding 2024 Q3 prediction:', quarterly_gdp_growth)

GDP growth rates initially: [-2, -5.8, -2.8, 2.6, 7.3, 1.1, 3.4]
GDP growth rates after adding 2024 Q3 prediction: [-2, -5.8, -2.8, 2.6, 7.3, 1.1, 3.4, 3.2]

Adding data to a specific position in a list

Let’s add data to the start of the list! The data is growth for 2022 Q4, which was 9.4%.

print('GDP growth rates initially:', quarterly_gdp_growth)
1quarterly_gdp_growth.insert(0, 9.4)
print('GDP growth rates after adding 2022 Q4:', quarterly_gdp_growth)

1: The first parameter is the index position of where we want this new value to be. The second parameter is the actual value we’d like to insert.

GDP growth rates initially: [-2, -5.8, -2.8, 2.6, 7.3, 1.1, 3.4, 3.2]
GDP growth rates after adding 2022 Q4: [9.4, -2, -5.8, -2.8, 2.6, 7.3, 1.1, 3.4, 3.2]

Removing items from a list

Let’s remove the two items we added previously!

There are two ways to remove items: using del or .pop() method.

print('GDP growth rates initially:', quarterly_gdp_growth)
1del quarterly_gdp_growth[0]
2quarterly_gdp_growth.pop()
print('GDP growth rates after removing projection:', quarterly_gdp_growth)

1: with del, you need to specify the index of the items you’d like to remove
2: pop() will remove the item at the end of the list.

GDP growth rates initially: [9.4, -2, -5.8, -2.8, 2.6, 7.3, 1.1, 3.4, 3.2]
GDP growth rates after removing projection: [-2, -5.8, -2.8, 2.6, 7.3, 1.1, 3.4]

Calculations with List

Let’s say we want to calculate the average quarterly gdp growth:

total_growth = sum(quarterly_gdp_growth)
average_growth = total_growth / len(quarterly_gdp_growth)
print(f"Average quarterly GDP growth: {average_growth:.2f}%")

Average quarterly GDP growth: 0.54%

Various data types in a single list

You can include values of different types in a list, though for GDP data we’ll stick to numbers!

mixed_data = ["2024 growth", 3.0, "2023 growth", 2.7]
print(mixed_data)

['2024 growth', 3.0, '2023 growth', 2.7]

Learning Check #3

Write a code to complete the following tasks:

Write a code to print the growth rate for 2023 Q4 (remember, it’s the 5th item but index 4)

Code

print(quarterly_gdp_growth[4])

7.3

Write a code to print the growth rates for the first three quarters of 2023

Code

print(quarterly_gdp_growth[0:3])

[-2, -5.8, -2.8]

Add a projected growth rate of 3.3 and 3.6 the next two quarters at the end of the list

Code

# method 1 using insert
quarterly_gdp_growth.insert(len(quarterly_gdp_growth), 3.3)

# method 2 using append
quarterly_gdp_growth.append(3.6)

print(quarterly_gdp_growth)

[-2, -5.8, -2.8, 2.6, 7.3, 1.1, 3.4, 3.3, 3.6]

Section 6: Loops

Automate stuff with loop!

Scenario: We have Singapore’s Quarterly GDP growth rates from 2023 Q1 to 2024 Q2, and we want to print them in this format: Quarterly GDP growth: 5%

Manually printing out the items

print("Quarterly GDP growth:", quarterly_gdp_growth[0], "%") 
print("Quarterly GDP growth:", quarterly_gdp_growth[1], "%") 
print("Quarterly GDP growth:", quarterly_gdp_growth[2], "%") 
print("Quarterly GDP growth:", quarterly_gdp_growth[3], "%") 
print("Quarterly GDP growth:", quarterly_gdp_growth[4], "%") 
# and so on...
# can you imagine doing this for 100++ items?

Quarterly GDP growth: -2 %
Quarterly GDP growth: -5.8 %
Quarterly GDP growth: -2.8 %
Quarterly GDP growth: 2.6 %
Quarterly GDP growth: 7.3 %

With for loop

for growth in quarterly_gdp_growth: 
    print("Quarterly GDP growth:", growth, "%")

Quarterly GDP growth: -2 %
Quarterly GDP growth: -5.8 %
Quarterly GDP growth: -2.8 %
Quarterly GDP growth: 2.6 %
Quarterly GDP growth: 7.3 %
Quarterly GDP growth: 1.1 %
Quarterly GDP growth: 3.4 %
Quarterly GDP growth: 3.3 %
Quarterly GDP growth: 3.6 %

As you can see, we can achieve the same thing (or more) with less code to type with for loop.

Anatomy of a loop

Anatomy of loops explained using the example above:

1for growth in quarterly_gdp_growth:
2    print("Quarterly GDP growth:", growth, "%")

1: growth is the loop variable, representing the value that changes with each iteration of the loop. You can think of it as the “current item” being processed. Meanwhile, quarterly_gdp_growth is the collection that the loop iterates over. The loop statement also ends with a colon :.
2: print("Quarterly GDP growth:", growth, "%") is the body of the loop, specifying the action to take for each item in the collection. The body of the loop must be indented.

More examples: using `range()`

Below is a loop that will print out numbers from 1 to 6:

for number in range(1, 7):  # 1 to 6, representing 6 quarters
    print("Quarter", number)

Quarter 1
Quarter 2
Quarter 3
Quarter 4
Quarter 5
Quarter 6

More examples: using `enumerate()`

We can use enumerate() to loop not only the items inside a list, but also the index position of each item in the list!

Let’s say we want to re-format our printout to 1) Quarterly GDP growth: 5%

for index, item in enumerate(quarterly_gdp_growth):
    print(f"{index + 1}) Quarterly GDP growth: {item}%")

1) Quarterly GDP growth: -2%
2) Quarterly GDP growth: -5.8%
3) Quarterly GDP growth: -2.8%
4) Quarterly GDP growth: 2.6%
5) Quarterly GDP growth: 7.3%
6) Quarterly GDP growth: 1.1%
7) Quarterly GDP growth: 3.4%
8) Quarterly GDP growth: 3.3%
9) Quarterly GDP growth: 3.6%

Section 7: Conditionals (If-Else)

Automate decisions with conditionals!

Scenario: We have Singapore’s Quarterly GDP growth rates, and we want to categorize them as “Growth”, “Stable”, or “Decline” based on their values.

Without conditionals and for loops

print(quarterly_gdp_growth[0], "% - Category: Decline")
print(quarterly_gdp_growth[1], "% - Category: Decline")
print(quarterly_gdp_growth[2], "% - Category: Decline")
print(quarterly_gdp_growth[3], "% - Category: Growth")
print(quarterly_gdp_growth[4], "% - Category: Growth")

-2 % - Category: Decline
-5.8 % - Category: Decline
-2.8 % - Category: Decline
2.6 % - Category: Growth
7.3 % - Category: Growth

With conditionals and for loops

for growth in quarterly_gdp_growth:
    if growth > 2:
        category = "Growth"
    elif -2 <= growth <= 2:
        category = "Stable"
    else:
        category = "Decline"
    
    print(f"{growth}% - Category: {category}")

-2% - Category: Stable
-5.8% - Category: Decline
-2.8% - Category: Decline
2.6% - Category: Growth
7.3% - Category: Growth
1.1% - Category: Stable
3.4% - Category: Growth
3.3% - Category: Growth
3.6% - Category: Growth

Why conditionals + for loops is great

Automated categorization based on values - no manual intervention needed!
Easily adjustable criteria (just change the numbers in the if-elif-else statements)
Works for any number of data points - again, no manual intervention needed!
Makes the code more readable and maintainable

Let’s break down the anatomy of conditionals first!

Anatomy of Conditionals

Also called if-else structure, it looks like this at the very basic level:

gdp_growth = 2.5

1if gdp_growth > 0:
2    print("The economy is growing.")
3else:
4    print("The economy is not growing.")

1: The if statement starts with the keyword ‘if’, followed by a condition, and ends with a colon ‘:’
2: The body of the if statement is indented. This line of code will be run if the condition is fulfilled
3: The else clause is optional, starts with ‘else’, and ends with a colon ‘:’
4: The body of the else clause is also indented, and only run if the condition is NOT fulfilled

The economy is growing.

Multiple conditions

Sometimes we have multiple conditions that we want to check! We can use elif (stands for else-if) to add the conditions. You can have multiple elif clauses.

gdp_growth = 0

if gdp_growth > 0:
    print("The economy is growing.")
elif gdp_growth == 0:
    print("The economy is stable.")
else:
    print("The economy is shrinking.")

The economy is stable.

Multiple conditions with Boolean operators

In some cases, we can’t split the conditions among multiple elifs. For example, we may want to check gdp_growth and inflation rate to determine the economic state.

gdp_growth = 1.5
inflation = 2.0

1if gdp_growth > 0 and inflation < 3:
    print("The economy is in a good state.")
2elif gdp_growth > 0 or inflation < 3:
    print("The economy is in a mixed state.")
else:
    print("The economy needs attention.")

1: and requires both conditions to be True
2: or requires at least one condition to be True

The economy is in a good state.

Multiple conditions - Order does matter!

Assuming variable growth has the value of 10, the conditionals below will give us the wrong result.

growth = 10
if growth > 0:
    print(f"{growth}% is a moderate growth")
elif growth > 5:  # This will never be reached for growth > 1
    print(f"{growth}% is a strong growth")
else:
    print(f"{growth}% is a negative growth")

10% is a moderate growth

This ordering would give the correct result:

if growth > 5:
    print(f"{growth}% is strong growth")
elif growth > 0:
    print(f"{growth}% is moderate growth")
else:
    print(f"{growth}% is negative growth")

10% is strong growth

Nested conditionals

For a more complicated conditionals, you can put a conditional inside another! Be careful with indentation to ensure correct nesting!

gdp_growth = 2.5
unemployment = 4.0

1if gdp_growth > 0:
2    if unemployment < 5:
        print("Strong economic performance.")
    else:
        print("Growing economy, but high unemployment.")
else:
    print("Economic challenges ahead.")

1: “outer” conditional.
2: “inner” conditionals. Note the indent for this statement! This conditional will be run only if the outer conditions are fulfilled.

Strong economic performance.

Conditionals and For loops combi

As seen from the first example of this section, we can combine for loops with conditionals

for growth in quarterly_gdp_growth:
    if growth > 2:
        category = "Growth"
    elif -2 <= growth <= 2:
        category = "Stable"
    else:
        category = "Decline"
    
    print(f"{growth}% - Category: {category}")

-2% - Category: Stable
-5.8% - Category: Decline
-2.8% - Category: Decline
2.6% - Category: Growth
7.3% - Category: Growth
1.1% - Category: Stable
3.4% - Category: Growth
3.3% - Category: Growth
3.6% - Category: Growth

Conditionals and For loops combi: counting occurence

Other ways for loops and conditionals can be used is for when we want to count occurence. Let’s say we want to keep track how many growths and contractions have happened for the past quarters.

1growth_periods = 0
decline_periods = 0

for rate in quarterly_gdp_growth:
    if rate > 0:
2        growth_periods += 1
    elif rate < 0:
        decline_periods += 1

print("Growth periods:", growth_periods)
print("Decline periods:", decline_periods)

1: set the counters, one to keep track of growth and the other one to keep track of declines
2: increase the counters when conditions were met.

Growth periods: 6
Decline periods: 3

Learning Check #4

Write a script that identifies the highest and lowest growth quarters (5 mins)

Code

# get the value of the highest and the lowest with max and min
highest_growth = max(quarterly_gdp_growth)
lowest_growth = min(quarterly_gdp_growth)

Even more data types

We will explore these in the upcoming sessions!

DateTime
NumPy’s Arrays
Panda’s DataFrame & Time series DataFrame

End of Session 1!

We have covered: variables, data types, list, loop, and conditional

Introduction to Python

Welcome!

Preamble

About the workshop

Overview for today

Why Python?

How do you run python?

What’s .py and .ipynb??

Why the different file types?

Getting Started: VS Code Workspace and Tour

(Suggested) structure of workspace directory

Best practice: Virtual Environment

Create new file

Some basic glossary (for your reference)

Section 1: Getting Started

Running your python code

Built-in functions

Comments

Section 2: Variables and assignment

What’s a variable?

On creating variables

Things we can do with variables

Learning Check #1

Section 3: Data Types and Conversion

Refresher: Quantitative Data Types in Econs and Social Sciences

Basic data types in Python

Why do I need to care about these data types?

Converting data types to one another

Take note of variables assignment placement

Learning Check #2

Section 4: Built-in Python functions

Arguments vs Parameters

How do I find out more about a function?

Section 5: Lists

More data types / structure

When lists would be useful

Check number of items in a list

Retrieve a single item from a list

Retrieve multiple items from a list: the syntax

Retrieve multiple items from a list: in action

Stepping value in a list

Updating the value of items inside a list

Appending items to a list

Adding data to a specific position in a list

Removing items from a list

Calculations with List

Various data types in a single list

Learning Check #3

Section 6: Loops

Automate stuff with loop!

Anatomy of a loop

More examples: using range()

More examples: using enumerate()

Section 7: Conditionals (If-Else)

Automate decisions with conditionals!

Why conditionals + for loops is great

Anatomy of Conditionals

Multiple conditions

Multiple conditions with Boolean operators

Multiple conditions - Order does matter!

Nested conditionals

Conditionals and For loops combi

Conditionals and For loops combi: counting occurence

Learning Check #4

Even more data types

End of Session 1!

More examples: using `range()`

More examples: using `enumerate()`