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| Rmd | e187d0f | Mena WANG | 2022-11-18 | add method24 |
Provide a 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)import pandas as pd
df = pd.DataFrame([[1, 2],
[5, 8],
[3, 9]],
columns = ["col1", "col2"])
print(df) col1 col2
0 1 2
1 5 8
2 3 9df <- py$df #access df as saved in Python(py) above
df <- data.frame(
col1 = c(1,5,3),
col2 = c(2,8,9)
)
print(df) col1 col2
1 1 2
2 5 8
3 3 9In Python, we can use apply() to apply a function to a
dataframe. Let’s define a simple function first.
# create a function to be applied
def add_cols(row):
return row.col1 + row.col2Let’s apply the function to all the columns in a datafarme
df['col3'] = df.apply(add_cols, axis = 1)
df col1 col2 col3
0 1 2 3
1 5 8 13
2 3 9 12Or we could only apply a function to one particular columns in a dataframe.
def square_col(num):
return num**2
df['col3']=df.col2.apply(square_col)
df col1 col2 col3
0 1 2 4
1 5 8 64
2 3 9 81In R, we could use lapply() in dplyr package to apply a
custom function to all values in a dataframe, note that the results are
in multiple lists rather than one dataframe.
library(dplyr)
square_col = function(num){
return (num^2)
}
df2 = df |> lapply(FUN=square_col)
df2 |> str() #lapply returns result in listsList of 2
$ col1: num [1:3] 1 25 9
$ col2: num [1:3] 4 64 81df2 |> data.frame() |> str() # transform the lists into a dataframe'data.frame': 3 obs. of 2 variables:
$ col1: num 1 25 9
$ col2: num 4 64 81We can use the map_df() function in purrr()
package to do the similar task but generate a dataframe (tibble)
directly. What is a tibble? You might be wondering. well, think of it
basically as a dataframe, just even nicer. Read more about it here
if interested.
library(purrr)
df |> map_df(square_col) # A tibble: 3 × 2
col1 col2
<dbl> <dbl>
1 1 4
2 25 64
3 9 81To apply a function to one column to create a new column without
changing the existing column itself, the suitable mapping method depends
on the type of output. When the output is a numeric column, use
map_dbl(), whereas when the output is a character column,
use map_chr(). Below please see an example for
map_dbl(), and more illustrations are available here.
For other ways to create new columns in Pandas and R, please refer to
method 17.
df['col3'] = df[['col2']] |> map_dbl(square_col)
df col1 col2 col3
1 1 2 4
2 5 8 64
3 3 9 81In contrast, if we want to apply a function to change a subset of
columns in a dataframe, we could simply apply select()
function before mapping the function to the dataframe.
df |> select(col2) |> map_df(square_col)# A tibble: 3 × 1
col2
<dbl>
1 4
2 64
3 81
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] purrr_1.0.1 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