Comparison with R / R libraries¶
Since pandas
aims to provide a lot of the data manipulation and analysis
functionality that people use R for, this page
was started to provide a more detailed look at the R language and its many third
party libraries as they relate to pandas
. In comparisons with R and CRAN
libraries, we care about the following things:
- Functionality / flexibility: what can/cannot be done with each tool
- Performance: how fast are operations. Hard numbers/benchmarks are preferable
- Ease-of-use: Is one tool easier/harder to use (you may have to be the judge of this, given side-by-side code comparisons)
This page is also here to offer a bit of a translation guide for users of these R packages.
For transfer of DataFrame
objects from pandas
to R, one option is to
use HDF5 files, see External Compatibility for an
example.
Quick Reference¶
We’ll start off with a quick reference guide pairing some common R operations using dplyr with pandas equivalents.
Querying, Filtering, Sampling¶
R | pandas |
---|---|
dim(df) |
df.shape |
head(df) |
df.head() |
slice(df, 1:10) |
df.iloc[:9] |
filter(df, col1 == 1, col2 == 1) |
df.query('col1 == 1 & col2 == 1') |
df[df$col1 == 1 & df$col2 == 1,] |
df[(df.col1 == 1) & (df.col2 == 1)] |
select(df, col1, col2) |
df[['col1', 'col2']] |
select(df, col1:col3) |
df.loc[:, 'col1':'col3'] |
select(df, -(col1:col3)) |
df.drop(cols_to_drop, axis=1) but see [1] |
distinct(select(df, col1)) |
df[['col1']].drop_duplicates() |
distinct(select(df, col1, col2)) |
df[['col1', 'col2']].drop_duplicates() |
sample_n(df, 10) |
df.sample(n=10) |
sample_frac(df, 0.01) |
df.sample(frac=0.01) |
[1] | R’s shorthand for a subrange of columns
(select(df, col1:col3) ) can be approached
cleanly in pandas, if you have the list of columns,
for example df[cols[1:3]] or
df.drop(cols[1:3]) , but doing this by column
name is a bit messy. |
Sorting¶
R | pandas |
---|---|
arrange(df, col1, col2) |
df.sort_values(['col1', 'col2']) |
arrange(df, desc(col1)) |
df.sort_values('col1', ascending=False) |
Transforming¶
R | pandas |
---|---|
select(df, col_one = col1) |
df.rename(columns={'col1': 'col_one'})['col_one'] |
rename(df, col_one = col1) |
df.rename(columns={'col1': 'col_one'}) |
mutate(df, c=a-b) |
df.assign(c=df.a-df.b) |
Grouping and Summarizing¶
R | pandas |
---|---|
summary(df) |
df.describe() |
gdf <- group_by(df, col1) |
gdf = df.groupby('col1') |
summarise(gdf, avg=mean(col1, na.rm=TRUE)) |
df.groupby('col1').agg({'col1': 'mean'}) |
summarise(gdf, total=sum(col1)) |
df.groupby('col1').sum() |
Base R¶
Slicing with R’s c
¶
R makes it easy to access data.frame
columns by name
df <- data.frame(a=rnorm(5), b=rnorm(5), c=rnorm(5), d=rnorm(5), e=rnorm(5))
df[, c("a", "c", "e")]
or by integer location
df <- data.frame(matrix(rnorm(1000), ncol=100))
df[, c(1:10, 25:30, 40, 50:100)]
Selecting multiple columns by name in pandas
is straightforward
In [1]: df = pd.DataFrame(np.random.randn(10, 3), columns=list('abc'))
In [2]: df[['a', 'c']]
Out[2]:
a c
0 -1.039575 -0.424972
1 0.567020 -1.087401
2 -0.673690 -1.478427
3 0.524988 0.577046
4 -1.715002 -0.370647
5 -1.157892 0.844885
6 1.075770 1.643563
7 -1.469388 -0.674600
8 -1.776904 -1.294524
9 0.413738 -0.472035
In [3]: df.loc[:, ['a', 'c']]