rpy2/pandas: passing a pandas DataFrame with list type column into R
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I have some R code that takes as input a data.frame where one of the columns holds list instances in its cells, e.g.:
data.frame(x=c("a", "b", "c"), y=I(list(c(1,2,3),c(4,5,6),c(7,8,9))))
Which looks like
x y
1 a 1, 2, 3
2 b 4, 5, 6
3 c 7, 8, 9
I would like create a similar pandas DataFrame in Python and pass it into my R code using the tools in the rpy2 library (version 2.9.4). However, when I run the following:
from rpy2.robjects import pandas2ri
pandas2ri.activate()
df = DataFrame({
"x" : ["a", "b", "c"] ,
"y" : [[1,2,3], [4,5,6], [7,8,9] ]
})
r_df = pandas2ri.py2ri(df)
print(r_df)
I get this output:
x y.0.1L y.0.2L y.0.3L y.1.4L y.1.5L y.1.6L y.2.7L y.2.8L y.2.9L
0 a 1 2 3 4 5 6 7 8 9
1 b 1 2 3 4 5 6 7 8 9
2 c 1 2 3 4 5 6 7 8 9
Of course, the R code was using the "Inhibit" function I() in order to be able to hold the list instances in the data.frame in the first place. Is there some way to express similar intent via the rpy2 interfaces?
python r pandas rpy2
asked Nov 16 '18 at 17:41
sparc_spreadsparc_spread
6,54253244
add a comment |
I have some R code that takes as input a data.frame where one of the columns holds list instances in its cells, e.g.:
data.frame(x=c("a", "b", "c"), y=I(list(c(1,2,3),c(4,5,6),c(7,8,9))))
Which looks like
x y
1 a 1, 2, 3
2 b 4, 5, 6
3 c 7, 8, 9
I would like create a similar pandas DataFrame in Python and pass it into my R code using the tools in the rpy2 library (version 2.9.4). However, when I run the following:
from rpy2.robjects import pandas2ri
pandas2ri.activate()
df = DataFrame({
"x" : ["a", "b", "c"] ,
"y" : [[1,2,3], [4,5,6], [7,8,9] ]
})
r_df = pandas2ri.py2ri(df)
print(r_df)
I get this output:
x y.0.1L y.0.2L y.0.3L y.1.4L y.1.5L y.1.6L y.2.7L y.2.8L y.2.9L
0 a 1 2 3 4 5 6 7 8 9
1 b 1 2 3 4 5 6 7 8 9
2 c 1 2 3 4 5 6 7 8 9
Of course, the R code was using the "Inhibit" function I() in order to be able to hold the list instances in the data.frame in the first place. Is there some way to express similar intent via the rpy2 interfaces?
python r pandas rpy2
asked Nov 16 '18 at 17:41
sparc_spreadsparc_spread
6,54253244
But why are you saving a list in a data frame? What analysis needs this setup? In both pandas and R, save only atomic (not nested) values within columns. Hence why every column in a pandas DataFrame is a pandas Series and every column in a Rdata.frameis an atomic vector!
– Parfait
Nov 16 '18 at 19:14
add a comment |
I have some R code that takes as input a data.frame where one of the columns holds list instances in its cells, e.g.:
data.frame(x=c("a", "b", "c"), y=I(list(c(1,2,3),c(4,5,6),c(7,8,9))))
Which looks like
x y
1 a 1, 2, 3
2 b 4, 5, 6
3 c 7, 8, 9
I would like create a similar pandas DataFrame in Python and pass it into my R code using the tools in the rpy2 library (version 2.9.4). However, when I run the following:
from rpy2.robjects import pandas2ri
pandas2ri.activate()
df = DataFrame({
"x" : ["a", "b", "c"] ,
"y" : [[1,2,3], [4,5,6], [7,8,9] ]
})
r_df = pandas2ri.py2ri(df)
print(r_df)
I get this output:
x y.0.1L y.0.2L y.0.3L y.1.4L y.1.5L y.1.6L y.2.7L y.2.8L y.2.9L
0 a 1 2 3 4 5 6 7 8 9
1 b 1 2 3 4 5 6 7 8 9
2 c 1 2 3 4 5 6 7 8 9
Of course, the R code was using the "Inhibit" function I() in order to be able to hold the list instances in the data.frame in the first place. Is there some way to express similar intent via the rpy2 interfaces?
python r pandas rpy2
asked Nov 16 '18 at 17:41
sparc_spreadsparc_spread
6,54253244
I have some R code that takes as input a data.frame where one of the columns holds list instances in its cells, e.g.:
data.frame(x=c("a", "b", "c"), y=I(list(c(1,2,3),c(4,5,6),c(7,8,9))))
Which looks like
x y
1 a 1, 2, 3
2 b 4, 5, 6
3 c 7, 8, 9
I would like create a similar pandas DataFrame in Python and pass it into my R code using the tools in the rpy2 library (version 2.9.4). However, when I run the following:
from rpy2.robjects import pandas2ri
pandas2ri.activate()
df = DataFrame({
"x" : ["a", "b", "c"] ,
"y" : [[1,2,3], [4,5,6], [7,8,9] ]
})
r_df = pandas2ri.py2ri(df)
print(r_df)
I get this output:
x y.0.1L y.0.2L y.0.3L y.1.4L y.1.5L y.1.6L y.2.7L y.2.8L y.2.9L
0 a 1 2 3 4 5 6 7 8 9
1 b 1 2 3 4 5 6 7 8 9
2 c 1 2 3 4 5 6 7 8 9
Of course, the R code was using the "Inhibit" function I() in order to be able to hold the list instances in the data.frame in the first place. Is there some way to express similar intent via the rpy2 interfaces?
python r pandas rpy2
python r pandas rpy2
asked Nov 16 '18 at 17:41
sparc_spreadsparc_spread
6,54253244
asked Nov 16 '18 at 17:41
sparc_spreadsparc_spread
6,54253244
edited Nov 16 '18 at 19:07
sparc_spread
asked Nov 16 '18 at 17:41
sparc_spreadsparc_spread
6,54253244
asked Nov 16 '18 at 17:41
sparc_spreadsparc_spread
6,54253244
asked Nov 16 '18 at 17:41
sparc_spreadsparc_spread
6,54253244
6,54253244
But why are you saving a list in a data frame? What analysis needs this setup? In both pandas and R, save only atomic (not nested) values within columns. Hence why every column in a pandas DataFrame is a pandas Series and every column in a Rdata.frameis an atomic vector!
– Parfait
Nov 16 '18 at 19:14
add a comment |
But why are you saving a list in a data frame? What analysis needs this setup? In both pandas and R, save only atomic (not nested) values within columns. Hence why every column in a pandas DataFrame is a pandas Series and every column in a Rdata.frameis an atomic vector!
– Parfait
Nov 16 '18 at 19:14
But why are you saving a list in a data frame? What analysis needs this setup? In both pandas and R, save only atomic (not nested) values within columns. Hence why every column in a pandas DataFrame is a pandas Series and every column in a R
data.frame is an atomic vector!– Parfait
Nov 16 '18 at 19:14
But why are you saving a list in a data frame? What analysis needs this setup? In both pandas and R, save only atomic (not nested) values within columns. Hence why every column in a pandas DataFrame is a pandas Series and every column in a R
data.frame is an atomic vector!– Parfait
Nov 16 '18 at 19:14
add a comment |
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But why are you saving a list in a data frame? What analysis needs this setup? In both pandas and R, save only atomic (not nested) values within columns. Hence why every column in a pandas DataFrame is a pandas Series and every column in a R
data.frameis an atomic vector!– Parfait
Nov 16 '18 at 19:14