Variables and Data Types

From Stata/R to Python — Understanding the Essence of Variables

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What are Variables?

In all programming languages, variables are "containers" for storing data.

Comparative Understanding

Concept	Stata	R	Python
Create variable	gen age = 25	age <- 25 or age = 25	age = 25
Variable type	Auto-inferred	Auto-inferred	Auto-inferred
View variable	display age	print(age)	print(age)

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Basic Variable Usage in Python

1. Creating Variables (Assignment)

# Create variables
age = 25
name = "Alice"
income = 50000.5
is_student = True

# Print variables
print(age)        # Output: 25
print(name)       # Output: Alice
print(income)     # Output: 50000.5
print(is_student) # Output: True

Python Rules:

• Variable names can contain letters, numbers, underscores
• Variable names must start with a letter or underscore
• Variable names cannot contain spaces or special characters (except underscores)
• Variable names cannot be Python reserved words (like if, for, class)

Good Variable Names:

student_age = 25
avg_income = 50000
is_employed = True
gdp_growth_rate = 0.03

Bad Variable Names:

a = 25              # Too short, unclear meaning
StudentAge = 25     # Python convention uses lowercase + underscores
student age = 25    # Syntax error: contains space
2020_data = 100     # Syntax error: starts with number

2. Variable Reassignment

# Initial value
income = 50000
print(income)  # 50000

# Modify value
income = 60000
print(income)  # 60000

# Modify based on old value
income = income + 5000
print(income)  # 65000

# Shorthand syntax
income += 5000  # Equivalent to income = income + 5000
print(income)  # 70000

Stata Comparison:

* Stata
gen income = 50000
replace income = 60000
replace income = income + 5000

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Basic Python Data Types

1. Numeric Types

(1) Integers (int)

age = 25
population = 1400000000
year = 2024

print(type(age))  # <class 'int'>

(2) Floats (float)

gpa = 3.85
income = 50000.5
interest_rate = 0.05

print(type(gpa))  # <class 'float'>

(3) Numeric Operations

# Basic operations
x = 10
y = 3

print(x + y)   # 13  (addition)
print(x - y)   # 7   (subtraction)
print(x * y)   # 30  (multiplication)
print(x / y)   # 3.333... (division, result is float)
print(x // y)  # 3   (floor division)
print(x % y)   # 1   (modulus)
print(x ** y)  # 1000 (exponentiation)

Stata/R Comparison:

* Stata
gen x = 10
gen y = 3
gen sum = x + y
gen division = x / y
gen power = x^y

# R
x <- 10
y <- 3
sum <- x + y
division <- x / y
power <- x^y

2. Strings (String)

# Create strings (single or double quotes work)
name = "Alice"
major = 'Economics'
country = "China"

# String concatenation
full_name = "Alice" + " " + "Smith"
print(full_name)  # Alice Smith

# String repetition
laugh = "ha" * 3
print(laugh)  # hahaha

# String length
print(len("Hello"))  # 5

# String methods
text = "hello world"
print(text.upper())       # HELLO WORLD
print(text.capitalize())  # Hello world
print(text.replace("world", "Python"))  # hello Python

Practical Example (Social Science Context):

# Create variable label
variable_label = "respondent_age_in_years"
cleaned_label = variable_label.replace("_", " ").title()
print(cleaned_label)  # Respondent Age In Years

# Extract country code
country_code = "USA_2020_data"
code = country_code[:3]  # USA (slicing)
year = country_code[4:8]  # 2020
print(f"Country: {code}, Year: {year}")

3. Booleans (Boolean)

# Booleans have only two values: True and False
is_student = True
has_degree = False
is_employed = True

print(type(is_student))  # <class 'bool'>

# Boolean operations
print(True and False)  # False (and)
print(True or False)   # True  (or)
print(not True)        # False (not)

# Comparison operations (return boolean)
age = 25
print(age > 18)        # True
print(age == 25)       # True (note: two equal signs)
print(age != 30)       # True (not equal)

Social Science Example:

age = 25
income = 50000

# Check survey eligibility
is_eligible = (age >= 18) and (age <= 65) and (income > 0)
print(f"Eligible for survey: {is_eligible}")  # Eligible for survey: True

# Check if high income
is_high_income = income > 100000
print(f"High income: {is_high_income}")  # High income: False

4. None (Null Value)

# None represents "no value" (similar to Stata's . or R's NA)
missing_data = None
print(missing_data)  # None
print(type(missing_data))  # <class 'NoneType'>

# Check if None
if missing_data is None:
    print("Data is missing")

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Type Conversion

# String to number
age_str = "25"
age_int = int(age_str)
print(age_int + 5)  # 30

income_str = "50000.5"
income_float = float(income_str)
print(income_float)  # 50000.5

# Number to string
age = 25
age_str = str(age)
print("Age: " + age_str)  # Age: 25

# Other types to boolean
print(bool(0))      # False
print(bool(1))      # True
print(bool(""))     # False (empty string)
print(bool("Hi"))   # True
print(bool(None))   # False

Common Error:

# Error example
age = "25"
result = age + 5  # TypeError: can only concatenate str to str

# Correct approach
age = int("25")
result = age + 5  # 30

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Practical Example: Social Science Data Processing

Example 1: Calculate BMI

# Respondent information
name = "Alice"
height_cm = 170  # centimeters
weight_kg = 65   # kilograms

# Calculate BMI
height_m = height_cm / 100
bmi = weight_kg / (height_m ** 2)

print(f"{name}'s BMI: {bmi:.2f}")  # Alice's BMI: 22.49

# Determine weight status
if bmi < 18.5:
    status = "Underweight"
elif bmi < 25:
    status = "Normal"
elif bmi < 30:
    status = "Overweight"
else:
    status = "Obese"

print(f"Weight status: {status}")  # Weight status: Normal

Example 2: Income Grouping

# Respondent income
respondent_id = 1001
annual_income = 75000  # annual income (USD)

# Income quartiles
if annual_income < 30000:
    income_quartile = "Q1 (Low income)"
elif annual_income < 60000:
    income_quartile = "Q2 (Lower-middle income)"
elif annual_income < 100000:
    income_quartile = "Q3 (Upper-middle income)"
else:
    income_quartile = "Q4 (High income)"

print(f"Respondent {respondent_id}: {income_quartile}")
# Output: Respondent 1001: Q3 (Upper-middle income)

Example 3: Education Years Encoding

# Education level (text)
education_level = "Bachelor's Degree"

# Convert to education years (numeric)
education_mapping = {
    "High School": 12,
    "Associate Degree": 14,
    "Bachelor's Degree": 16,
    "Master's Degree": 18,
    "Doctoral Degree": 22
}

years_of_education = education_mapping.get(education_level, 0)
print(f"Years of education: {years_of_education} years")  # Years of education: 16 years

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Stata vs R vs Python Data Type Comparison

Python Type	Stata Type	R Type	Example
int	numeric (integer)	integer	age = 25
float	numeric (decimal)	numeric	gpa = 3.85
str	string (str#)	character	name = "Alice"
bool	numeric (0/1)	logical	is_student = True
None	. (missing)	NA	missing = None

Note:

• Stata doesn't have a true boolean type (uses 0/1 instead)
• Python's None ≈ R's NA ≈ Stata's .

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Formatted Output (f-string)

Python 3.6+ supports f-strings, which are very convenient:

name = "Alice"
age = 25
gpa = 3.856

# Basic usage
print(f"Name: {name}, Age: {age}")
# Output: Name: Alice, Age: 25

# Format numbers
print(f"GPA: {gpa:.2f}")  # 2 decimal places
# Output: GPA: 3.86

# Format large numbers
income = 1234567.89
print(f"Income: ${income:,.2f}")
# Output: Income: $1,234,567.89

# Percentages
growth_rate = 0.0523
print(f"Growth rate: {growth_rate:.2%}")
# Output: Growth rate: 5.23%

Practical Template:

# Generate survey report
respondent_id = 1001
age = 35
gender = "Male"
income = 85000
education = "Master's"

report = f"""
Respondent Report
====================
ID: {respondent_id}
Age: {age} years
Gender: {gender}
Income: ${income:,}
Education: {education}
====================
"""
print(report)

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Common Errors

Error 1: Variable Name Typo

age = 25
print(aeg)  # NameError: name 'aeg' is not defined

Error 2: Mixing Strings and Numbers

result = "25" + 5  # TypeError
result = int("25") + 5  # 30

Error 3: Confusing = and ==

age = 25       # Assignment
if age = 25:   # SyntaxError
if age == 25:  # Comparison

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Practice Exercises

Exercise 1: Basic Variable Operations

Create the following variables and perform calculations:

# 1. Create variables
base_salary = 50000
bonus = 8000
tax_rate = 0.25

# 2. Calculate after-tax income
# Hint: after_tax_income = (base_salary + bonus) * (1 - tax_rate)

# 3. Format and output
# Output format: "After-tax income: $X,XXX.XX"

Exercise 2: Type Conversion

# Given strings
age_str = "35"
income_str = "75000.50"

# 1. Convert to numbers
# 2. Check if high income (> 100000)
# 3. Check if middle-aged (30-50 years)

Exercise 3: Formatted Output

# Given data
country = "China"
gdp_per_capita = 12000.45
population = 1400000000
growth_rate = 0.062

# Output format:
# Country: China
# GDP per capita: $12,000.45
# Population: 1,400,000,000
# Growth rate: 6.20%

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Next Steps

In the next section, we'll learn about operators, including arithmetic, comparison, and logical operators, laying the foundation for conditional statements and loops.

Keep going!