Introduction
Transitioning between R and Python is a common challenge for data scientists and programmers. Both languages offer powerful tools for data analysis, yet they differ in syntax, idioms, and underlying paradigms. This guide provides a side-by-side reference for translating common R code into Python. We cover general operations, dataframe manipulations, object types, and other key differences. Additionally, we include detailed comparisons of equivalent libraries and real-world scenarios to illustrate how these translations work in practical projects.
1. General Syntax and Operations
Below is a summary table that presents common R expressions alongside their Python equivalents:
| R Code | Python Code |
|---|---|
new_function <- function(a, b=5) { return(a+b) } |
def new_function(a, b=5): return a+b |
for (val in c(1,3,5)) { print(val) } |
for val in [1,3,5]: print(val) |
a <- c(1,3,5,7) |
a = [1,3,5,7] |
a <- c(3:9) |
a = list(range(3,9)) |
class(a) |
type(a) |
a <- 5 |
a = 5 |
a^2 |
a**2 |
a%%5 |
a % 5 |
a & b |
a and b |
a | b |
a or b |
rev(a) |
a[::-1] |
a %*% b |
a @ b |
paste("one", "two", "three", sep="") |
'one' + 'two' + 'three' |
substr("hello", 1, 4) |
'hello'[:4] |
strsplit('foo,bar,baz', ',') |
'foo,bar,baz'.split(',') |
paste(c('foo', 'bar', 'baz'), collapse=',') |
','.join(['foo', 'bar', 'baz']) |
gsub("(^[\\n\\t ]+|[\\n\\t ]+$)", "", " foo ") |
' foo '.strip() |
sprintf("%10s", "lorem") |
'lorem'.rjust(10) |
paste("value: ", toString("8")) |
'value: ' + str(8) |
toupper("foo") |
'foo'.upper() |
nchar("hello") |
len("hello") |
substr("hello", 1, 1) |
"hello"[0] |
a = rbind(c(1,2,3), c('a','b','c')) |
list(zip([1,2,3], ['a','b','c'])) |
d = list(n=10, avg=3.7, sd=0.4) |
{'n': 10, 'avg': 3.7, 'sd': 0.4} |
quit() |
exit() |
2. Dataframe Operations
Below is a table comparing common dataframe operations in R and Python:
| R Code | Python Code |
|---|---|
head(df) |
df.head() |
tail(df) |
df.tail() |
nrow(df) |
df.shape[0] or len(df) |
ncol(df) |
df.shape[1] or len(df.columns) |
df$col_name |
df['col_name'] or df.col_name |
summary(df) |
df.describe() |
df %>% arrange(c1, desc(c2)) |
df.sort_values(by=['c1','c2'], ascending=[True, False]) |
df %>% rename(new_col = old_col) |
df.rename(columns={'old_col': 'new_col'}) |
df$smoker <- mapvalues(df$smoker, from=c('yes','no'), to=c(0,1)) |
df['smoker'] = df['smoker'].map({'yes': 0, 'no': 1}) |
df$c1 <- as.character(df$c1) |
df['c1'] = df['c1'].astype(str) |
unique(df$c1) |
df['col_name'].unique() |
length(unique(df$c1)) |
len(df['col_name'].unique()) |
max(df$c1, na.rm=TRUE) |
df['col_name'].max() |
df$c1[is.na(df$c1)] <- 0 |
df['col_name'] = df['col_name'].fillna(0) |
df <- data.frame(col_a=c('a','b','c'), col_b=c(1,2,3)) |
df = pd.DataFrame({'col_a': ['a','b','c'], 'col_b': [1,2,3]}) |
df <- read.csv("input.csv", header=TRUE, na.strings=c("","NA"), sep=",") |
df = pd.read_csv("input.csv") |
write.csv(df, "output.csv", row.names=FALSE) |
df.to_csv("output.csv", index=False) |
df[c(4:6)] |
df.iloc[:, 3:6] |
mutate(df, c=a-b) |
df.assign(c=df['a']-df['b']) |
distinct(select(df, col1)) |
df[['col1']].drop_duplicates() |
3. Object Types
A quick reference for object types in R and Python:
| R Object | Python Object |
|---|---|
| character | string (str) |
| integer | integer (int) |
| logical | boolean (bool) |
| numeric | float |
| complex | complex |
| Single-element vector | Scalar |
| Multi-element vector | List |
| List (mixed types) | Tuple |
| Named list | Dictionary (dict) |
| Matrix/Array | numpy ndarray |
NULL, TRUE, FALSE |
None, True, False |
| Inf | inf |
| NaN | nan |
4. Other Key Differences
Assignment Operators:
R uses<-while Python uses=.Indexing:
R indices start at 1; Python indices start at 0.Error Handling:
R usestryCatch(), Python usestry...except.Piping:
R uses%>%for chaining operations; Python relies on method chaining or additional libraries.Naming Conventions:
R often uses dots in variable names (e.g.,var.name), whereas Python uses underscores (e.g.,var_name).
5. Library Comparisons
For those transitioning from R to Python, it’s important to know which libraries in Python provide similar functionalities to your favorite R packages. Here’s a quick comparison:
- Data Manipulation:
- R: dplyr
- Python: pandas (with alternatives like polars, tidypolars, datar, siuba, and pyjanitor)
- R: dplyr
- Data Visualization:
- R: ggplot2
- Python: lets-plot, plotnine, matplotlib, Seaborn
- R: ggplot2
- Statistical Modeling:
- R: tidymodels, caret
- Python: scikit-learn, statsmodels
- R: tidymodels, caret
- Reporting:
- R: knitr, r markdown
- Python: Quarto, Jupyter Notebooks
- R: knitr, r markdown
- Web Scraping:
- R: rvest
- Python: BeautifulSoup
- R: rvest
- Testing:
- R: testthat
- Python: pytest
- R: testthat
6. Real-World Scenarios
Case Study: Data Analysis Pipeline
Imagine you have a dataset for customer reviews. In R, you might use the tidyverse to clean and visualize the data, while in Python you’d use pandas and matplotlib/Seaborn. Consider the following scenario:
- R Workflow:
- Import data using readr.
- Clean and transform the data using dplyr.
- Visualize review trends using ggplot2.
- Build a predictive model using tidymodels.
- Import data using readr.
- Python Workflow:
- Import data with pandas.
- Clean and transform using pandas (or dplyr-like libraries such as siuba).
- Visualize trends with matplotlib/Seaborn or lets-plot.
- Build a predictive model using scikit-learn.
- Import data with pandas.
In both cases, the steps are similar. This scenario demonstrates how core data science tasks can be performed in either language, highlighting the ease of switching contexts while leveraging familiar methods.
Practical Example
For example, if you are analyzing customer sentiment, you might:
- R: Use dplyr to filter positive reviews and ggplot2 to create a bar chart of sentiment scores.
- Python: Use pandas to filter the data and Seaborn to create a similar bar chart.
These examples help illustrate that the key differences often lie in syntax rather than in the underlying concepts.
Conclusion
This guide serves as a comprehensive reference for translating code between R and Python. By covering general syntax, dataframe operations, object types, library comparisons, and real-world scenarios, you gain a holistic view of the differences and similarities between these two powerful languages. Whether you’re transitioning from R to Python or working in a multi-language environment, this guide will help you navigate the journey with confidence.
Further Reading
- Python for R Users: Transitioning to Python for Data Science
- Data Manipulation in Python vs. R: dplyr vs. pandas
- R Syntax vs. Python Syntax: A Comparative Guide for Beginners
Happy coding, and enjoy bridging the gap between R and Python!
References
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Citation
@online{kassambara2024,
author = {Kassambara, Alboukadel},
title = {R Vs. {Python:} {A} {Comprehensive} {Code} {Translation}
{Guide}},
date = {2024-02-13},
url = {https://www.datanovia.com/learn/programming/transition/r-vs-python-code-translations.html},
langid = {en}
}