Python vs Stata vs R: Syntax Comparison Quick Reference

Quickly Build Python Thinking — Understand Python Through Familiar Stata/R

Core Concept Comparison

1. DataFrame Concept

The core of all three languages is the two-dimensional data table:

Concept	Stata	R	Python (Pandas)
Data Frame	Dataset (only one in memory)	data.frame	DataFrame (multiple allowed)
Variable (Column)	Variable	Column	Column
Observation (Row)	Observation	Row	Row

Key Difference:

Stata: Can only work with one dataset at a time
R/Python: Can handle multiple data frames simultaneously

Common Operations Comparison

Operation 1: Read CSV File

stata

* Stata
import delimited "data.csv", clear

# R
df <- read.csv("data.csv")

python

# Python
import pandas as pd
df = pd.read_csv("data.csv")

Operation 2: View First Few Rows

stata

* Stata
list in 1/5
browse in 1/5

# R
head(df)

python

# Python
df.head()

Operation 3: Create New Variable

Example: Create log of income

stata

* Stata
gen log_income = log(income)

# R
df$log_income <- log(df$income)

python

# Python
df['log_income'] = np.log(df['income'])

Operation 4: Conditional Filtering

Example: Filter observations where age > 30

stata

* Stata
keep if age > 30

# R
df_filtered <- df[df$age > 30, ]
# Or using dplyr
df_filtered <- df %>% filter(age > 30)

python

# Python
df_filtered = df[df['age'] > 30]

Operation 5: Group Aggregation

Example: Calculate average income by country

stata

* Stata
collapse (mean) avg_income=income, by(country)

# R (base R)
aggregate(income ~ country, data=df, FUN=mean)

# R (dplyr)
df %>%
  group_by(country) %>%
  summarise(avg_income = mean(income))

python

# Python
df.groupby('country')['income'].mean()

# Or more detailed syntax
df.groupby('country').agg({'income': 'mean'})

Operation 6: Descriptive Statistics

stata

* Stata
summarize income age education

# R
summary(df[c("income", "age", "education")])

python

# Python
df[['income', 'age', 'education']].describe()

Operation 7: Regression Analysis

Example: OLS Regression

stata

* Stata
regress income education age i.gender

# R
model <- lm(income ~ education + age + factor(gender), data=df)
summary(model)

python

# Python (statsmodels, closest to Stata)
import statsmodels.formula.api as smf
model = smf.ols('income ~ education + age + C(gender)', data=df).fit()
print(model.summary())

# Or using sklearn (more concise, but different output)
from sklearn.linear_model import LinearRegression
X = df[['education', 'age']]
y = df['income']
model = LinearRegression().fit(X, y)

Operation 8: Merge Data

Example: Merge two datasets by country

stata

* Stata
merge 1:1 country using "gdp_data.dta"

# R
merged <- merge(df1, df2, by="country", all=TRUE)

# R (dplyr)
merged <- df1 %>% full_join(df2, by="country")

python

# Python
merged = pd.merge(df1, df2, on='country', how='outer')

Key Syntax Differences

1. Indexing Methods

Operation	Stata	R	Python
Select Column	`income`	`df$income`	`df['income']`
Select Multiple Columns	`keep income age`	`df[c("income", "age")]`	`df[['income', 'age']]`
Select Rows	`keep if age > 30`	`df[df$age > 30, ]`	`df[df['age'] > 30]`

2. Assignment Operations

Operation	Stata	R	Python
Create New Variable	`gen x = 1`	`df$x <- 1`	`df['x'] = 1`
Replace Variable	`replace x = 2 if age > 30`	`df$x[df$age > 30] <- 2`	`df.loc[df['age'] > 30, 'x'] = 2`

3. Missing Values

Operation	Stata	R	Python
Missing Value Symbol	`.`	`NA`	`NaN` or `None`
Drop Missing	`drop if missing(income)`	`df <- na.omit(df)`	`df.dropna()`
Fill Missing	`replace income = 0 if missing(income)`	`df$income[is.na(df$income)] <- 0`	`df['income'].fillna(0)`

Mental Shift: From Stata/R to Python

Stata User Transition

Stata Thinking: Work with one dataset at a time

stata

use "data1.dta", clear
gen new_var = old_var * 2
save "data1_new.dta", replace

Python Thinking: Work with multiple data frames simultaneously

python

df1 = pd.read_csv("data1.csv")
df1['new_var'] = df1['old_var'] * 2
df1.to_csv("data1_new.csv")

# Can have df2, df3, ... in memory at the same time
df2 = pd.read_csv("data2.csv")

R User Transition

R Thinking: Functional programming

df %>%
  filter(age > 30) %>%
  mutate(log_income = log(income)) %>%
  group_by(country) %>%
  summarise(mean_income = mean(income))

Python Thinking: Object method chaining (similar to R pipes)

python

(df
 .query('age > 30')
 .assign(log_income=lambda x: np.log(x['income']))
 .groupby('country')
 .agg({'income': 'mean'})
)

Practical Example: Replicating Classic Stata Analysis

Stata Code

stata

* 1. Load data
use "survey_data.dta", clear

* 2. Data cleaning
drop if missing(income)
keep if age >= 18 & age <= 65

* 3. Create new variables
gen log_income = log(income)
gen age_squared = age^2

* 4. Descriptive statistics
tabstat income education age, by(gender) stat(mean sd)

* 5. Regression analysis
regress log_income education age age_squared i.gender

Python Equivalent Code

python

import pandas as pd
import numpy as np
import statsmodels.formula.api as smf

# 1. Load data
df = pd.read_stata("survey_data.dta")

# 2. Data cleaning
df = df.dropna(subset=['income'])
df = df[(df['age'] >= 18) & (df['age'] <= 65)]

# 3. Create new variables
df['log_income'] = np.log(df['income'])
df['age_squared'] = df['age'] ** 2

# 4. Descriptive statistics
df.groupby('gender')[['income', 'education', 'age']].agg(['mean', 'std'])

# 5. Regression analysis
model = smf.ols('log_income ~ education + age + age_squared + C(gender)', data=df).fit()
print(model.summary())

Output Comparison: Python output is nearly identical to Stata!

Python's Unique Advantages

1. Handle Multiple Datasets Simultaneously

python

# Load data from multiple countries at once
df_china = pd.read_csv("china_data.csv")
df_usa = pd.read_csv("usa_data.csv")
df_india = pd.read_csv("india_data.csv")

# Combine them
df_all = pd.concat([df_china, df_usa, df_india])

2. Loop Processing (More Flexible Than Stata)

python

# Log transform multiple variables
for var in ['income', 'gdp', 'population']:
    df[f'log_{var}'] = np.log(df[var])

3. Direct API Calls

python

# Difficult in Stata/R: get data directly from API
import requests
response = requests.get("https://api.worldbank.org/v2/country/CHN/indicator/NY.GDP.PCAP.CD?format=json")
data = response.json()

Advanced Operations Comparison

Operation 9: Panel Data Regression (Fixed Effects)

Example: Analyze impact of education on wages (controlling for individual fixed effects)

stata

* Stata - Very concise!
xtset individual_id year
xtreg wage education experience, fe

# R (plm package)
library(plm)
model <- plm(wage ~ education + experience,
             data = panel_data,
             index = c("individual_id", "year"),
             model = "within")
summary(model)

python

# Python (linearmodels)
from linearmodels.panel import PanelOLS

# Set multi-index
panel_data = df.set_index(['individual_id', 'year'])

# Fixed effects regression
model = PanelOLS.from_formula(
    'wage ~ education + experience + EntityEffects',
    data=panel_data
)
result = model.fit(cov_type='clustered', cluster_entity=True)
print(result.summary)

Note: Stata is most concise for panel data, but Python's linearmodels is equally powerful.

Operation 10: Handle Categorical Variables (Factor Encoding)

stata

* Stata - Auto-handled
regress wage i.education_level i.industry

# R - Auto-handled
model <- lm(wage ~ factor(education_level) + factor(industry),
            data = df)

python

# Python - Need explicit specification
import statsmodels.formula.api as smf

model = smf.ols('wage ~ C(education_level) + C(industry)',
                data=df).fit()
# Or manually encode with pd.get_dummies()
df_encoded = pd.get_dummies(df,
                             columns=['education_level', 'industry'],
                             drop_first=True)

Operation 11: Time Series Operations

Example: Calculate lags and growth rates

stata

* Stata
tsset date
gen gdp_lag1 = L.gdp
gen gdp_growth = (gdp - L.gdp) / L.gdp

# R (dplyr)
df <- df %>%
  arrange(date) %>%
  mutate(gdp_lag1 = lag(gdp),
         gdp_growth = (gdp - lag(gdp)) / lag(gdp))

python

# Python (pandas)
df = df.sort_values('date')
df['gdp_lag1'] = df['gdp'].shift(1)
df['gdp_growth'] = df['gdp'].pct_change()

Operation 12: String Processing

Example: Extract last name, convert case

stata

* Stata
gen last_name = word(name, -1)
gen name_upper = upper(name)

# R (stringr)
library(stringr)
df$last_name <- word(df$name, -1)
df$name_upper <- str_to_upper(df$name)

python

# Python (pandas + str methods)
df['last_name'] = df['name'].str.split().str[-1]
df['name_upper'] = df['name'].str.upper()

# Or use regex
df['last_name'] = df['name'].str.extract(r'(\w+)$')

Complex Analysis Workflow Comparison

Case: Complete Empirical Research Workflow

Research Question: Analyze impact of minimum wage policy on employment (DID design)

Stata Implementation

stata

* 1. Load data
use "employment_data.dta", clear

* 2. Data cleaning
drop if missing(employment, wage, treatment)
keep if year >= 2010 & year <= 2020

* 3. Generate interaction terms
gen post = (year >= 2015)
gen treated = (state == "CA")
gen did = post * treated

* 4. Descriptive statistics
tabstat employment, by(treated post) stat(mean sd n)

* 5. DID regression
regress employment did treated post controls, cluster(state)

* 6. Parallel trends test
forvalues y = 2010/2020 {
    gen year_`y' = (year == `y')
    gen treat_year_`y' = treated * year_`y'
}
regress employment treat_year_*, cluster(state)

Python Implementation

python

import pandas as pd
import numpy as np
import statsmodels.formula.api as smf
import matplotlib.pyplot as plt

# 1. Load data
df = pd.read_stata("employment_data.dta")

# 2. Data cleaning
df = df.dropna(subset=['employment', 'wage', 'treatment'])
df = df[(df['year'] >= 2010) & (df['year'] <= 2020)]

# 3. Generate interaction terms
df['post'] = (df['year'] >= 2015).astype(int)
df['treated'] = (df['state'] == "CA").astype(int)
df['did'] = df['post'] * df['treated']

# 4. Descriptive statistics
summary = df.groupby(['treated', 'post'])['employment'].agg([
    ('mean', 'mean'),
    ('std', 'std'),
    ('n', 'count')
])
print(summary)

# 5. DID regression (clustered standard errors)
model = smf.ols('employment ~ did + treated + post + controls',
                data=df).fit(cov_type='cluster',
                             cov_kwds={'groups': df['state']})
print(model.summary())

# 6. Parallel trends test
df['year_str'] = df['year'].astype(str)
model_parallel = smf.ols(
    'employment ~ C(year_str):treated + C(year_str) + controls',
    data=df
).fit(cov_type='cluster', cov_kwds={'groups': df['state']})

# Extract coefficients and visualize
coefs = model_parallel.params.filter(regex='year_str.*treated')
ci = model_parallel.conf_int().loc[coefs.index]

plt.figure(figsize=(10, 6))
plt.errorbar(range(len(coefs)), coefs,
             yerr=[(coefs - ci[0]), (ci[1] - coefs)],
             fmt='o-')
plt.axhline(y=0, color='red', linestyle='--')
plt.axvline(x=5, color='gray', linestyle='--', label='Treatment Year')
plt.title('Parallel Trends Test')
plt.xlabel('Year')
plt.ylabel('Treatment Effect')
plt.legend()
plt.show()

Comparison:

Stata: More concise code (~20 lines)
Python: Slightly longer code (~40 lines), but stronger visualization and flexibility

Data Visualization Comparison

Case: Plot Coefficient Plot of Regression Results

Stata

stata

regress wage education experience female urban
coefplot, drop(_cons) xline(0)

R (ggplot2)

library(ggplot2)
library(broom)

model <- lm(wage ~ education + experience + female + urban,
            data = df)
coef_df <- tidy(model, conf.int = TRUE)

ggplot(coef_df, aes(x = estimate, y = term)) +
  geom_point() +
  geom_errorbarh(aes(xmin = conf.low, xmax = conf.high)) +
  geom_vline(xintercept = 0, linetype = "dashed") +
  theme_minimal() +
  labs(title = "Regression Coefficients",
       x = "Estimate", y = "Variable")

Python (matplotlib + seaborn)

python

import matplotlib.pyplot as plt
import seaborn as sns

model = smf.ols('wage ~ education + experience + female + urban',
                data=df).fit()

# Extract coefficients and confidence intervals
coefs = model.params.drop('Intercept')
ci = model.conf_int().drop('Intercept')

fig, ax = plt.subplots(figsize=(8, 6))
y_pos = range(len(coefs))

ax.errorbar(coefs, y_pos,
            xerr=[(coefs - ci[0]), (ci[1] - coefs)],
            fmt='o', capsize=5)
ax.axvline(x=0, color='red', linestyle='--', alpha=0.5)
ax.set_yticks(y_pos)
ax.set_yticklabels(coefs.index)
ax.set_xlabel('Coefficient Estimate')
ax.set_title('Regression Coefficients with 95% CI')
ax.grid(axis='x', alpha=0.3)
plt.tight_layout()
plt.show()

Performance Comparison

Big Data Processing Speed (1 million rows)

Operation	Stata	R (dplyr)	Python (pandas)
Read CSV	~5 sec	~3 sec	~2 sec
Group Aggregation	~2 sec	~1 sec	~0.8 sec
Merge Data	~4 sec	~2 sec	~1.5 sec
Regression Analysis	~0.5 sec	~0.8 sec	~0.6 sec

Note: Python + pandas is usually fastest for big data, but Stata is optimized for regression analysis.

Ecosystem Comparison

Stata's Advantages

Most comprehensive econometric methods (IV, DID, RDD, PSM)
Most concise panel data handling
Regression output format closest to academic requirements
Concentrated community (StataList, SSC)

R's Advantages

Richest statistical methods (Bayesian, survival analysis, factor analysis)
Most elegant data visualization (ggplot2)
Open source and free
Huge CRAN package ecosystem (20,000+ packages)

Python's Advantages

Strongest machine learning ecosystem (sklearn, PyTorch, TensorFlow)
Only choice for deep learning and LLMs
Strongest general programming capabilities
Most comprehensive data engineering tools (scraping, APIs, databases)
Largest job market demand

Learning Recommendations

If You're a Stata User

Focus on learning Pandas (80% of Stata functionality can be replicated)
Use statsmodels (output format closest to Stata)
Remember: df['var'] ≈ Stata variable name
Learning Path:
- Week 1: Pandas basics (equivalent to Stata data operations)
- Week 2: statsmodels regression (equivalent to Stata regress)
- Week 3: linearmodels panel data (equivalent to Stata xtreg)
- Week 4: scikit-learn machine learning (Stata cannot do)

If You're an R User

Learn Pandas (similar to dplyr + data.table)
Use plotnine (Python version of ggplot2)
Remember: Python uses . not %>%
Learning Path:
- Week 1: Python basic syntax (R users fastest: 3 days)
- Week 2: Pandas (similar to tidyverse)
- Week 3: Matplotlib/Seaborn (not as good as ggplot2, but sufficient)
- Week 4: sklearn + PyTorch (R's weakness)

Three-Language Mixed Use Strategy

Best Practice: Choose tool based on task

Data Cleaning → Python (pandas)
   ↓
Descriptive Statistics → Stata/R (personal preference)
   ↓
Traditional Econometrics → Stata (panel data, IV)
   ↓
Machine Learning → Python (sklearn)
   ↓
Text Analysis → Python (transformers)
   ↓
Visualization → R (ggplot2) or Python (seaborn)

Next Steps

In the next section, we will write Your First Python Program and experience Python's simplicity and power.

Ready?

Python vs Stata vs R: Syntax Comparison Quick Reference ​

Core Concept Comparison ​

1. DataFrame Concept ​

Common Operations Comparison ​

Operation 1: Read CSV File ​

Operation 2: View First Few Rows ​

Operation 3: Create New Variable ​

Operation 4: Conditional Filtering ​

Operation 5: Group Aggregation ​

Operation 6: Descriptive Statistics ​

Operation 7: Regression Analysis ​

Operation 8: Merge Data ​

Key Syntax Differences ​

1. Indexing Methods ​

2. Assignment Operations ​

3. Missing Values ​

Mental Shift: From Stata/R to Python ​

Stata User Transition ​

R User Transition ​

Practical Example: Replicating Classic Stata Analysis ​

Stata Code ​

Python Equivalent Code ​

Python's Unique Advantages ​

1. Handle Multiple Datasets Simultaneously ​

2. Loop Processing (More Flexible Than Stata) ​

3. Direct API Calls ​

Advanced Operations Comparison ​

Operation 9: Panel Data Regression (Fixed Effects) ​

Operation 10: Handle Categorical Variables (Factor Encoding) ​

Operation 11: Time Series Operations ​

Operation 12: String Processing ​

Complex Analysis Workflow Comparison ​

Case: Complete Empirical Research Workflow ​

Stata Implementation ​

Python Implementation ​

Data Visualization Comparison ​

Case: Plot Coefficient Plot of Regression Results ​

Stata ​

R (ggplot2) ​

Python (matplotlib + seaborn) ​

Performance Comparison ​

Big Data Processing Speed (1 million rows) ​

Ecosystem Comparison ​

Stata's Advantages ​

R's Advantages ​

Python's Advantages ​

Learning Recommendations ​

If You're a Stata User ​

If You're an R User ​

Three-Language Mixed Use Strategy ​

Next Steps ​

Python vs Stata vs R: Syntax Comparison Quick Reference

Core Concept Comparison

1. DataFrame Concept

Common Operations Comparison

Operation 1: Read CSV File

Operation 2: View First Few Rows

Operation 3: Create New Variable

Operation 4: Conditional Filtering

Operation 5: Group Aggregation

Operation 6: Descriptive Statistics

Operation 7: Regression Analysis

Operation 8: Merge Data

Key Syntax Differences

1. Indexing Methods

2. Assignment Operations

3. Missing Values

Mental Shift: From Stata/R to Python

Stata User Transition

R User Transition

Practical Example: Replicating Classic Stata Analysis

Stata Code

Python Equivalent Code

Python's Unique Advantages

1. Handle Multiple Datasets Simultaneously

2. Loop Processing (More Flexible Than Stata)

3. Direct API Calls

Advanced Operations Comparison

Operation 9: Panel Data Regression (Fixed Effects)

Operation 10: Handle Categorical Variables (Factor Encoding)

Operation 11: Time Series Operations

Operation 12: String Processing

Complex Analysis Workflow Comparison

Case: Complete Empirical Research Workflow

Stata Implementation

Python Implementation

Data Visualization Comparison

Case: Plot Coefficient Plot of Regression Results

Stata

R (ggplot2)

Python (matplotlib + seaborn)

Performance Comparison

Big Data Processing Speed (1 million rows)

Ecosystem Comparison

Stata's Advantages

R's Advantages

Python's Advantages

Learning Recommendations

If You're a Stata User

If You're an R User

Three-Language Mixed Use Strategy

Next Steps