Lesson 3
Variables, Values, Types, Strings, and Dates
Big question
How does Python know whether a value is a price, a label, a date, or a logical condition?
Lesson progress
Complete checkpoints as you learn
Learning objectives
- Create variables with meaningful names.
- Work with integers, floats, strings, booleans, and None.
- Format text and numeric results.
- Parse and compare dates safely.
- Prerequisites: Chapter 2.
- Key terms: variable, object, type, boolean, None, datetime.
Simple explanation
Assignment binds a name to an object. The statement rate = 0.045 does not put a value into a permanent box called rate; it creates a floating-point object and lets the name refer to it. This matters when objects are mutable, but the beginner rule is simpler: choose names that carry economic meaning, avoid spaces and punctuation, and reserve uppercase names for constants only when that convention genuinely helps. Python is dynamically typed, so the value determines the type at runtime.
Key terms
- Create variables with meaningful names
- A core idea in Chapter 3 that students apply carefully in economic analysis.
- Work with integers, floats, strings, booleans, and None
- A core idea in Chapter 3 that students apply carefully in economic analysis.
- Format text and numeric results
- A core idea in Chapter 3 that students apply carefully in economic analysis.
- Parse and compare dates safely
- A core idea in Chapter 3 that students apply carefully in economic analysis.
- Prerequisites: Chapter 2
- A core idea in Chapter 3 that students apply carefully in economic analysis.
- Key terms: variable, object, type, boolean, None, datetime
- A core idea in Chapter 3 that students apply carefully in economic analysis.
Analytical workflow
Interpret the expression in words and units before using it in a claim.
Example
Interpretation. The format specification changes presentation, not the stored value.
Prerequisites
- Complete the preceding course chapters or review their summaries as needed.
Full theory and examples
3.2
Names point to objects
Assignment binds a name to an object. The statement rate = 0.045 does not put a value into a permanent box called rate; it creates a floating-point object and lets the name refer to it. This matters when objects are mutable, but the beginner rule is simpler: choose names that carry economic meaning, avoid spaces and punctuation, and reserve uppercase names for constants only when that convention genuinely helps. Python is dynamically typed, so the value determines the type at runtime.
3.3
Types are analytical constraints
A string such as "2.5" looks like a number to a human but cannot be averaged until it is converted. A date stored as plain text does not automatically understand calendar order. Type conversion is therefore part of data validation. Floating-point numbers also have finite binary precision, so exact equality can be unreliable for some decimal calculations. Financial accounting may require Decimal; most econometric work uses floating point and numerical tolerances.
3.4
Dates carry frequency and meaning
Calendar data require explicit parsing. The same text can be interpreted differently across countries, and a monthly observation may represent an average, a period end, or a reference month. Python’s datetime and pandas date tools support arithmetic, sorting, and resampling, but the analyst must still document the observation convention. A timestamp is not merely a label: it determines ordering, lags, trading-day alignment, and forecast origin.
3.5
Python demonstrations
3.5.1
Demonstration 3.1: Types and formatted output
Verified output
Interpretation. The format specification changes presentation, not the stored value.
3.5.2
Demonstration 3.2: Parse and compare dates
Verified output
Interpretation. ISO dates avoid day-month ambiguity and support calendar arithmetic.
3.6
Visual evidence
3.7
Reference table
Why This Matters
Correct types prevent quiet errors in sorting, aggregation, filtering, and model construction.
Common Mistake
Using a numeric-looking identifier, such as a postal code, as a number. Leading zeros and categorical meaning may be lost.
Ceteris LAB Tip
Inspect type(value) when an operator behaves strangely. The error often belongs to the input type, not the formula.
R-to-Python / Source Bridge
The introductory source material used R assignments and raw numeric dates. This chapter preserves the idea of named objects while adding explicit type and calendar discipline.
| Type | Example | Use |
|---|---|---|
| int | 12 | counts and discrete quantities |
| float | 0.045 | rates and measurements |
| str | “Canada” | labels and identifiers |
| bool | True | conditions and filters |
| None | None | intentional absence |
| datetime | 2026-08-15 | calendar operations |
Visual evidence
Additional Python demonstrations
Live Python
Source demonstration 2
Source demonstration 2
Stdout
Run Python to see results here.
Status / stderr
Ready to run Python in your browser.
Line-by-line guide
- Line 1Load a Python library needed for data work or regression.
- Line 3Create or update a Python object used in the analysis.
- Line 4Create or update a Python object used in the analysis.
- Line 5Display a result so students can inspect the output.
- Line 6Display a result so students can inspect the output.
Verified source output
float Canada: 2.75%, n=120, monthly=True
226 True
float Canada: 2.75%, n=120, monthly=True
226 True
Interpretation. The format specification changes presentation, not the stored value.
Interpretation. ISO dates avoid day-month ambiguity and support calendar arithmetic.
Guided practice
- 1Re-run Demonstration 3.1 and change one input while keeping the analytical question fixed.
- 2Explain in two sentences how the output supports, or fails to support, the chapter opening question.
- 3Add one validation check that would prevent a plausible error.
Exercises
- 1Create variables for a bond price, maturity year, issuer name, and default flag.
- 2Convert the string “3.25” to a float and divide it by 100.
- 3Format 1234567.891 as currency with commas and two decimals.
- 4Calculate the days between two ISO dates.
Source and downloads
Chapter 3 of Fundamentals of Python for Financial Econometrics by Mohammad Safavi, Ph.D.. The lesson is an original Ceteris Lab web adaptation of the supplied publication package.
Live Python
Variables, Values, Types, Strings, and Dates: live Python
Variables, Values, Types, Strings, and Dates: live Python
Stdout
Run Python to see results here.
Status / stderr
Ready to run Python in your browser.
Line-by-line guide
- Line 1Create or update a Python object used in the analysis.
- Line 2Create or update a Python object used in the analysis.
- Line 3Create or update a Python object used in the analysis.
- Line 4Create or update a Python object used in the analysis.
- Line 6Display a result so students can inspect the output.
- Line 7Display a result so students can inspect the output.
Python walkthrough
- 1`country = "Canada"`: Creates or updates a named object used by later steps.
- 2`policy_rate = 0.0275`: Creates or updates a named object used by later steps.
- 3`observations = 120`: Creates or updates a named object used by later steps.
- 4`is_monthly = True`: Creates or updates a named object used by later steps.
- 5`print(type(policy_rate).__name__)`: Displays a result so it can be checked and interpreted.
- 6`print(f"{country}: {policy_rate:.2%}, n={observations}, monthly={is_monthly}")`: Displays a result so it can be checked and interpreted.
Live notebook
Run this lesson as a notebook
Open an editable notebook cell-by-cell, run Python in the browser, and download the `.ipynb` file for later.
Related dataset
Ceteris Lab teaching sample
Estimated time
25 to 40 min
Packages
pandas, numpy
Expected output
Printed Python results that can be compared with the lesson explanation.
Learning goals
- Load and inspect Ceteris Lab teaching sample.
- Run the Python cells connected to Variables, Values, Types, Strings, and Dates.
- Interpret the output using Create variables with meaningful names and Work with integers, floats, strings, booleans, and None.
Common errors
- File not found: check that wage_sample.csv is installed or use the course data folder.
- Package import error: use the browser notebook first, then download for local Jupyter if your local packages differ.
- Column name error: compare your variable names with the dataset variables listed for this notebook.
Dataset path helper
import pandas as pd
df = pd.read_csv("/data/wage_sample.csv")
df.head()Interactive activity
Chapter 3 interactive
Reproducible Python decision lab
Which step should come before trusting a successful Python run?
Immediate feedback
Choose a decision, then test how the claim changes as evidence becomes stronger or weaker.
Try it yourself
Write one plain-English sentence explaining the main idea from this lesson.
Common mistakes
Check these before you move on.
Return to the lesson assumptions, units, diagnostics, and source evidence to replace this shortcut with a defensible interpretation.
Quick quiz
Which statement best answers the Chapter 3 opening question: How does Python know whether a value is a price, a label, a date, or a logical condition?
Quick quiz
Which practice should be avoided when applying Variables, Values, Types, Strings, and Dates?
Quick quiz
What is the most defensible way to interpret the Python demonstration?
Quick quiz
Why does Chapter 3 matter in an applied econometrics workflow?
Key takeaway
Variables name objects; types determine valid operations. Formatting controls display without changing the underlying value. Dates should be parsed explicitly and documented.