Q&A 1 What are common data types in Python and R?
1.1 Explanation
Before you clean, visualize, or model data, it’s important to understand what types of values you’re working with — numeric, text, logical, or otherwise.
These data types affect how values are stored, displayed, and processed in both Python and R — and they play a major role in how functions behave.
1.2 Common Data Types in Python and R
| Concept | Python (pandas / base) |
R (base) |
Notes |
|---|---|---|---|
| Integer | int |
integer |
Use astype(int) or as.integer() |
| Decimal Number | float |
numeric, double |
numeric is typically double in R |
| Text / String | str, object (pandas) |
character |
Use astype(str) or as.character() |
| Logical / Boolean | bool |
logical |
True/False in Python, TRUE/FALSE in R |
| Date / Time | datetime64[ns] |
Date, POSIXct |
Use pd.to_datetime() or as.Date() |
| Category | category |
factor |
Ideal for grouping and modeling |
| Missing Values | NaN |
NA |
Use pd.isna() or is.na() |
| Complex Numbers | complex |
complex |
Rare in typical data work |
| List | list |
list |
Flexible containers |
| Dictionary | dict |
named list, list() |
R lists can mimic dictionaries |
| Tuple | tuple |
c(), list() |
No exact match — use vectors or lists |
✅ Knowing the common data types — and how to interpret them — lays the foundation for all future data work.