Data manipulation: R - Python conversion guide

Main concepts

File management

Table 1: File management commands
Category	R	Python
Paths	`setwd(path)`	`os.chdir(path)`
	`getwd()`	`os.getcwd()`
	`file.path(path_1, ..., path_n)`	`os.path.join(path_1, ..., path_n)`
Files	`list.files(path, include.dirs=True)`	`os.listdir(path)`
	`file_test('-f', path)`	`os.path.isfile(path)`
	`file_test('-d', path)`	`os.path.isdir(path)`
	`read.csv(path_to_csv_file)`	`pd.read_csv(path_to_csv_file)`
	`write.csv(df, path_to_csv_file)`	`df.to_csv(path_to_csv_file)`

Chaining

The concept of chaining refers to the feature of applying operations successively. In R the symbol used to denote chaining of operations is %>%, while in python the symbol is a dot, ..

For example the following succession of operations in R

df %>%
  operation_1(parameters_1) %>%
  operation_2(parameters_2) %>%
  ...
  operation_n(parameters_n)

is equivalent to the python code block below

(df.
  .operation_1(parameters_1)
  .operation_2(parameters_2)
  ...
  .operation_n(parameters_n))

Exploring the data

Table 2: Commands to exploring the data
Category	R	Python
Look at Data	`df %>% select(col_list)`	`df[col_list]`
	`df %>% select(-col_list)`	`df.drop(col_list, axis=1)`
	`df %>% head(n)`	`df.head(n)`
	`df %>% tail(n)`	`df.tail(n)`
	`df %>% summary()`	`df.describe()`
Data Types	`df %>% str()`	`df.dtypes` or `df.info`
	`df %>% NROW()` and `df %>% NCOL()`	`df.shape`

Data types

Table 3: Brief list of common data types
R	Python	Description
`character`	`object`	String-related data
`factor`	`object`	String-related data that can be ordered or classified
`numeric`	`float64`	Numerical data (with decimals)
`int`	`int64`	Integer numerical data
`POSIXct`	`datetime64`	Timestamps

Data preprocessing

Filtering

Table 4: Commands to preprocessing data
Category	R	Python
Overview	`df %>% filter(col operation val_or_col)`	`df[df['col'] operation val_or_col]`
Operation	`==` and `!=`	`==` and `!=`
	`<`, `>`, `<=`, `>=`	`<`, `>`, `<=`, `>=`
	`&`, `⏐`	`&`, `⏐`
	`is.na()`	`pd.isnull()`
	`%in% (val_1, ..., val_n)`	`.isin([val_1, ..., val_n])`
	`%like% 'val'`	`.str.contains('val')`

Changing columns

Table 5: Commands to modifying columns of data
Category	R	Python
New column on top of an old one	`df %>% mutate(new_col = operation(other_cols))`	`df.assign(new_col=lambda x: operation(x))`
Unite columns	`df %>% unite(merged_col, old_col_list)`	`df['new_col'] = df[old_col_list].agg('-'.join, axis=1)`

Mathematical operations

Table 6: Some mathematical operations to transform data
Operation	R	Python
$\sqrt{x}$	`sqrt(x)`	`np.sqrt(x)`
$\sin(x)$	`sin(x)`	`np.sin(x)`
$\cos(x)$	`cos(x)`	`np.cos(x)`
$\tan(x)$	`tan(x)`	`np.tan(x)`

Datetime conversion

Table 7: Command to transform a string representation of date/time to `datetime`
Action	R	Python
Converts string to datetime	`as.POSITct(col, format)`	`pd.to_datetime(col, format)`

In the above table, format is a string describing the structure of the field. For example:

'$Y': complete year, 2025.
'$y': abbreviated year, 25.
'%B': full name of the month, December.
'%b': abbreviated name of the month, Dec.
'%m': number of the month, 12.
'%d': day the month, 29.
'%j': day of the year, 363.

There are many other format options.

Operation	R	Python
\(\sqrt{x}\)	`sqrt(x)`	`np.sqrt(x)`
\(\sin(x)\)	`sin(x)`	`np.sin(x)`
\(\cos(x)\)	`cos(x)`	`np.cos(x)`
\(\tan(x)\)	`tan(x)`	`np.tan(x)`