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Python to R translation of 30 essential Pandas methods introduced by Avi Chawla in The Only 30 Methods You Should Master To Become A Pandas Pro published on TowardsDataScience.
# enable python in RMarkdown
library(reticulate)We can use the groupby method in Pandas to group a
dataframe and then perform aggregations with agg(). We
could put both methods in one line, or wrap the chain of methods in
brackets and show them in separate lines. The latter can enhance
readability when we have multiple methods chained together.
import pandas as pd
df = pd.DataFrame([[1, 2, "A"],
[5, 8, "B"],
[3, 10, "B"]],
columns = ["col1", "col2", "col3"])
# put both methods in one line
df.groupby('col3').agg({'col1':sum, 'col2':max}) # col1 col2
col3
A 1 2
B 8 10# alternatively, show each method in separate lines
(df
.groupby("col3")
.agg({"col1":sum, "col2":max}) # get sum for col1 and max for col2
) col1 col2
col3
A 1 2
B 8 10Above we specify different aggregates for each column, but the code can be simplified if same aggregates are needed for all columns.
(df
.groupby("col3")
.agg(['min','max','median']) # get these three aggregates for all
) col1 col2
min max median min max median
col3
A 1 1 1 2 2 2
B 3 5 4 8 10 9In tidyverse, similarly we use group_by()
to do the grouping, then use summarize() for the
aggregation.
library(dplyr)
df <- py$df
df |>
group_by(col3) |>
summarize(col1_sum = sum(col1),
col2_max = max(col2))# A tibble: 2 × 3
col3 col1_sum col2_max
<chr> <dbl> <dbl>
1 A 1 2
2 B 8 10What if we want to do a slightly more complex aggregation which is
not available as a default function/method? Let’s say we want to add a
column to represent percentage within each group. For example, below we
have the sale of three types of fruits in two months. We would like to
add a column pct_month to represent the sale of each fruit
within each month.
df <- data.frame(
month = c(rep('Jan',3), rep('Feb',3)),
fruit = c('Apple', 'Kiwi','banana', 'Apple', 'Kiwi','banana'),
sale = c(20,30, 30,30,20,15)
)
df month fruit sale
1 Jan Apple 20
2 Jan Kiwi 30
3 Jan banana 30
4 Feb Apple 30
5 Feb Kiwi 20
6 Feb banana 15Notice that here we need one value for each row, rather than
one value for each group. Therefore, instead of using
summarize() as we did above, this time
mutate() function is our friend. We can also easily add a
round() function to round the percentage.
df |>
group_by(month) |>
mutate(pct_month = sale/sum(sale) |> round(3) * 100)# A tibble: 6 × 4
# Groups: month [2]
month fruit sale pct_month
<chr> <chr> <dbl> <dbl>
1 Jan Apple 20 25
2 Jan Kiwi 30 37.5
3 Jan banana 30 37.5
4 Feb Apple 30 46.2
5 Feb Kiwi 20 30.8
6 Feb banana 15 23.1Now let’s see how to do this in Python. We can create a function
first, then call it with the transform() method.
df = r.df
def pct_total(s):
return s/sum(s)
df['pct_month'] = (df
.groupby('month')['sale']
.transform(pct_total).round(3) * 100
)
df month fruit sale pct_month
0 Jan Apple 20.0 25.0
1 Jan Kiwi 30.0 37.5
2 Jan banana 30.0 37.5
3 Feb Apple 30.0 46.2
4 Feb Kiwi 20.0 30.8
5 Feb banana 15.0 23.1So here we passed a custom function into transform(). In
some scenarios, it is helpful to pass a built-in function in it too. Say
I have a dataframe where id is the primary key, and multiple ids can
relate to one group_id. Now I would like to add a new column that count
the number of unique ids in each group
df = pd.DataFrame({
'id': [1, 2, 3, 4, 5, 6],
'group_id': [1, 1, 2, 3, 3, 3]
})
df2 = df.copy()
df2['id_count'] = (df2
.groupby('group_id')
.transform('count')
)
print(df2) id group_id id_count
0 1 1 2
1 2 1 2
2 3 2 1
3 4 3 3
4 5 3 3
5 6 3 3Similar task is easy with R as well. The code is super similar, note
one interesting difference between the two though. in Pandas, we pass
the aggregation function count() as a string into
transform(), while with dplyr in R, we directly use functions like
n() within mutate().
df = py$df
df |>
group_by(group_id) |>
mutate(id_count = n())# A tibble: 6 × 3
# Groups: group_id [3]
id group_id id_count
<dbl> <dbl> <int>
1 1 1 2
2 2 1 2
3 3 2 1
4 4 3 3
5 5 3 3
6 6 3 3
sessionInfo()R version 4.2.1 (2022-06-23 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19045)
Matrix products: default
locale:
[1] LC_COLLATE=English_Australia.utf8 LC_CTYPE=English_Australia.utf8
[3] LC_MONETARY=English_Australia.utf8 LC_NUMERIC=C
[5] LC_TIME=English_Australia.utf8
attached base packages:
[1] stats graphics grDevices datasets utils methods base
other attached packages:
[1] dplyr_1.1.2 reticulate_1.30
loaded via a namespace (and not attached):
[1] Rcpp_1.0.11 pillar_1.9.0 compiler_4.2.1 bslib_0.5.0
[5] later_1.3.1 jquerylib_0.1.4 git2r_0.32.0 workflowr_1.7.0
[9] tools_4.2.1 digest_0.6.33 lattice_0.20-45 jsonlite_1.8.7
[13] evaluate_0.21 lifecycle_1.0.3 tibble_3.2.1 png_0.1-8
[17] pkgconfig_2.0.3 rlang_1.1.1 Matrix_1.4-1 cli_3.6.1
[21] rstudioapi_0.15.0 yaml_2.3.7 xfun_0.39 fastmap_1.1.1
[25] withr_2.5.0 stringr_1.5.0 knitr_1.43 generics_0.1.3
[29] fs_1.6.2 vctrs_0.6.3 sass_0.4.7 tidyselect_1.2.0
[33] grid_4.2.1 rprojroot_2.0.3 here_1.0.1 glue_1.6.2
[37] R6_2.5.1 fansi_1.0.4 rmarkdown_2.23 magrittr_2.0.3
[41] whisker_0.4.1 promises_1.2.0.1 htmltools_0.5.5 renv_1.0.0
[45] httpuv_1.6.11 utf8_1.2.3 stringi_1.7.12 cachem_1.0.8