In [1]:
from functools import partial
from rpy2.ipython import html
html.html_rdataframe=partial(html.html_rdataframe, table_class="docutils")

tidyr in Python¶

In [2]:
from rpy2.robjects.lib.tidyr import DataFrame

/opt/hostedtoolcache/Python/3.10.14/x64/lib/python3.10/site-packages/rpy2/robjects/lib/dplyr.py:27: UserWarning: This was designed against dplyr versions starting with 1.0 but you have 1.1.4
  warnings.warn(
/opt/hostedtoolcache/Python/3.10.14/x64/lib/python3.10/site-packages/rpy2/robjects/lib/tidyr.py:12: UserWarning: This was designed against tidyr versions starting with 1.2. but you have 1.3.1
  warnings.warn(

(note: dplyr is implicitly used by tidyr.)

In addition to that, and because this tutorial is in a notebook, we initialize HTML rendering for R objects (pretty display of R data frames).

In [3]:
import rpy2.ipython.html
rpy2.ipython.html.init_printing()
In [4]:
from collections import OrderedDict
from rpy2.robjects.vectors import (StrVector,
                                   IntVector)
dataf = DataFrame(OrderedDict(x=StrVector(("a", "b", "b")),
                              y=IntVector((3, 4, 5)),
		              z=IntVector((6, 7, 8))))
dataf
Out[4]:
DataFrame with 3 rows and 3 columns:
x y z
0 1 a 3 6
1 2 b 4 7
2 3 b 5 8
In [5]:
dataf.spread('x', 'y')
Out[5]:
DataFrame with 3 rows and 3 columns:
z a b
0 1 6 3 NA_integer_
1 2 7 NA_integer_ 4
2 3 8 NA_integer_ 5

Reuse. Get things done. Don't reimplement.