Python Tools for Management Research

0b: Python Basics I

Jason T. Kiley

Python I

As a start, we’ll learn to read, run, modify, and reason about small pieces of Python.

This segment is about basic data types and built-in data structures. There’s no need to memorize syntax; you’ll find that you retain the stuff you use often and can look up the rest.

Python I

Object types

Strings, integers, floats, booleans, None, and dates.

Data structures

Lists and dictionaries.

Learning habit

Run small examples, edit them, and observe the changes (if any).

Python objects

Everything in Python is an object.

Objects have attributes and methods that allow us to work with them.

name = "Jason"
name.lower()

'jason'

Assignment

Variables in Python can be thought of as names we give to objects.

project = "earnings-call-study"
year = 2026

print(project)
print(year + 1)

earnings-call-study
2027

Names point to objects, but they are distinct. As we’ll see, an object can have no name, one name, or many names. In general, names let us refer to objects without recreating them.

What you already know

You have likely worked with data type issues in existing stats software.

• Stata often requires a format change for dates in datasets to be recognizable as dates.
• You may have used destring to change a number represented as text into a numeric datatype.
• Python is explicit about these distinctions, which seems pedantic until it becomes useful.

Integers

Same as math: counting numbers, zero, and negatives.

Integer literals are numbers without decimals.

a = 2
b = 3

print(f"Addition:       {a + b}")
print(f"Subtraction:    {a - b}")
print(f"Multiplication: {a * b}")
print(f"Exponentiation: {b ** a}")

Addition:       5
Subtraction:    -1
Multiplication: 6
Exponentiation: 9

Convert with int().

Floating point numbers

Any number with a decimal.

Stored as an approximation, but it is rarely an issue for our use.

price = 19.99
growth = 1.23456e02

print(price)
print(growth)
print(float("6.02"))

19.99
123.456
6.02

Scientific notation works, too. Convert with float().

Ints and floats

Ints can be added, subtracted, multiplied, and exponentiated.

Anything with a float, or int division, returns a float.

print(type(3 + 2))
print(type(3 / 2))
print(type(3 // 2))

<class 'int'>
<class 'float'>
<class 'int'>

Strings

One Zero or more characters.

That includes special characters like newlines.

greeting = "Hello!"
name = "Jason"

print(f"{greeting} My name is {name}.")

Hello! My name is Jason.

Convert with str().

Other common types

Boolean

True or False, often returned by comparisons.

5 > 3

True

None

A deliberate null value.

Example: result = None

Datetime

Dates, times, and differences between them.

Example: datetime.datetime.now()

Package types

Packages add many more object types. We can also make our own, though that is mostly beyond our scope.

Lists

Lists are data structures that contain a sequence of objects.

They can contain many types of objects, though usually we use one main type at a time.

tickers = ["msft", "aapl", "nvda"]

print(tickers[0])
print(tickers[:2])

msft
['msft', 'aapl']

Dictionaries

Dictionaries are data structures with key-value pairs.

Keys must be unique and immutable.

firm = {
    "ticker": "msft",
    "founding_year": 1975,
}

print(firm["ticker"])
print(firm)

msft
{'ticker': 'msft', 'founding_year': 1975}

We often use dictionaries for lookups or as a lightweight representation of a row of data.

Hands-on

Open notebooks/0b_python1.ipynb.

Summary

• Python basic data types should be generally familiar.
• Data structures like lists and dictionaries are an upgrade in functionality from most stats software.
• The notebook is our practice surface: run code, change code, observe output, repeat.