from functools import partial
from rpy2.ipython import html
html.html_rdataframe=partial(html.html_rdataframe, table_class="docutils")

R and pandas data frames

R data.frame and :class:pandas.DataFrame objects share a lot of conceptual similarities, and :mod:pandas chose to use the class name DataFrame after R objects.

In a nutshell, both are sequences of vectors (or arrays) of consistent length or size for the first dimension (the “number of rows”). if coming from the database world, an other way to look at them is column-oriented data tables, or data table API.

rpy2 is providing an interface between Python and R, and a convenience conversion layer between :class:rpy2.robjects.vectors.DataFrame and :class:pandas.DataFrame objects, implemented in :mod:rpy2.robjects.pandas2ri.

import pandas as pd
import rpy2.robjects as ro
from rpy2.robjects.packages import importr
from rpy2.robjects import pandas2ri

From pandas to R

Pandas data frame:

pd_df = pd.DataFrame({'int_values': [1,2,3],
                      'str_values': ['abc', 'def', 'ghi']})

int_values str_values
0 1 abc
1 2 def
2 3 ghi

R data frame converted from a pandas data frame:

with (ro.default_converter + pandas2ri.converter).context():
  r_from_pd_df = ro.conversion.get_conversion().py2rpy(pd_df)

R/rpy2 DataFrame (3 x 2)
int_values str_values
... ...

The conversion is automatically happening when calling R functions. For example, when calling the R function base::summary:

base = importr('base')

with (ro.default_converter + pandas2ri.converter).context():
  df_summary = base.summary(pd_df)
  int_values   str_values
Min.   :1.0   Length:3
1st Qu.:1.5   Class :character
Median :2.0   Mode  :character
Mean   :2.0
3rd Qu.:2.5
Max.   :3.0

Note that a ContextManager is used to limit the scope of the conversion. Without it, rpy2 will not know how to convert a pandas data frame:

  df_summary = base.summary(pd_df)
except NotImplementedError as nie:
Conversion 'py2rpy' not defined for objects of type '<class 'pandas.core.frame.DataFrame'>'

From R to pandas

Starting from an R data frame this time:

r_df = ro.DataFrame({'int_values': ro.IntVector([1,2,3]),
                     'str_values': ro.StrVector(['abc', 'def', 'ghi'])})

R/rpy2 DataFrame (3 x 2)
int_values str_values
... ...

It can be converted to a pandas data frame using the same converter:

with (ro.default_converter + pandas2ri.converter).context():
  pd_from_r_df = ro.conversion.get_conversion().rpy2py(r_df)

int_values str_values
1 1 abc
2 2 def
3 3 ghi

Date and time objects

pd_df = pd.DataFrame({
    'Timestamp': pd.date_range('2017-01-01 00:00:00', periods=10, freq='s')

0 2017-01-01 00:00:00
1 2017-01-01 00:00:01
2 2017-01-01 00:00:02
3 2017-01-01 00:00:03
4 2017-01-01 00:00:04
5 2017-01-01 00:00:05
6 2017-01-01 00:00:06
7 2017-01-01 00:00:07
8 2017-01-01 00:00:08
9 2017-01-01 00:00:09
with (ro.default_converter + pandas2ri.converter).context():
  r_from_pd_df = ro.conversion.py2rpy(pd_df)

R/rpy2 DataFrame (10 x 1)

The timezone used for conversion is the system’s default timezone unless rpy2.robjects.vectors.default_timezone is specified… or unless the time zone is specified in the original time object:

pd_tz_df = pd.DataFrame({
    'Timestamp': pd.date_range('2017-01-01 00:00:00', periods=10, freq='s',

with (ro.default_converter + pandas2ri.converter).context():
  r_from_pd_tz_df = ro.conversion.py2rpy(pd_tz_df)

R/rpy2 DataFrame (10 x 1)