Last updated: 2023-10-08
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Knit directory: Pandas-30-R/
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| Rmd | d9c0296 | Mena WANG | 2022-11-10 | add method 16-17 |
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)To add a column based on simple calculation of existing columns, we
could either do the calculation directly (first chunk below) or use the
assign() method.
import pandas as pd
df = pd.DataFrame({'col1': [1,2],
'col2': [3,4]})# direct calculation
df2 = df.copy()
df2['col3'] = df['col1'] + df['col2']
df2 col1 col2 col3
0 1 3 4
1 2 4 6# use assign() method
df2 = df.assign(col3 = df['col1'] + df['col2'] )
df2 col1 col2 col3
0 1 3 4
1 2 4 6In R, mutate() function handels these tasks with
ease
library(dplyr)
df <- py$df #load the df object created in Python above
df2 <- df |>
mutate(col3 = col1 + col2)
df2 col1 col2 col3
1 1 3 4
2 2 4 6Now let’s try some slightly more complex calculations and/or create more than one columns in one go as well.
(df
.assign(col3 = lambda x: x.col1*2 + x.col2)
.assign(col4 = lambda x: (x.col1/x.col2 * 100).round(3))) col1 col2 col3 col4
0 1 3 5 33.333
1 2 4 8 50.000To accomplish a similar task in R, we will continue to use
mutate()
df |>
mutate(col3 = col1*2 + col2,
col4 = round(col1/col2 * 100, 3)) col1 col2 col3 col4
1 1 3 5 33.333
2 2 4 8 50.000df['row_mean'] = df.iloc[:,0:].mean(axis=1) # all columns
print(df) col1 col2 row_mean
0 1 3 2.0
1 2 4 3.0df['row_mean'] = df.iloc[:,0:2].mean(axis=1) # specify columns by range
print(df) col1 col2 row_mean
0 1 3 2.0
1 2 4 3.0df['row_mean'] = df.iloc[:,[0,1]].mean(axis=1) # specify columns by position
print(df) col1 col2 row_mean
0 1 3 2.0
1 2 4 3.0df |> mutate(row_mean = df |> rowMeans()) # all columns col1 col2 row_mean
1 1 3 2
2 2 4 3df |> mutate(row_mean = df |> select(1:2) |> rowMeans()) # specify columns by range col1 col2 row_mean
1 1 3 2
2 2 4 3df |> mutate(row_mean = df |> select(1,2) |> rowMeans()) # specify columns by position col1 col2 row_mean
1 1 3 2
2 2 4 3The map function is pretty helpful in the scenario
df = pd.DataFrame({'gender':['male', 'female', 'male', 'female']})
# add a column with gender encoding
df['is_male'] = df['gender'].map({'male': 1, 'female': 0})
print(df) gender is_male
0 male 1
1 female 0
2 male 1
3 female 0Of course, tihs task is easy with dplyr
df <- py$df
df |>
mutate(is_male = ifelse(gender == 'male', 1, 0)) gender is_male
1 male 1
2 female 0
3 male 1
4 female 0
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