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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([[6, 5, 10],
[5, 8, 6],
[3, 10, 4]],
columns = ["Maths", "Science", "English"],
index = ["John", "Mark", "Peter"])
print(df) Maths Science English
John 6 5 10
Mark 5 8 6
Peter 3 10 4We notice that the index has been passed on to the dataframe in R.
df <- py$df #access df as saved in Python(py) above
print(df) Maths Science English
John 6 5 10
Mark 5 8 6
Peter 3 10 4with .loc[], we can select by labels as follows
df.loc['John'] # select one specific rowMaths 6
Science 5
English 10
Name: John, dtype: int64df.loc['John','Maths'] # select one specific row and column6df.loc[['John','Mark'],['Maths','Science']] # select a list of rows and columns Maths Science
John 6 5
Mark 5 8To do this in base R is fairly simple as shown below
df['John',] # select one specific row Maths Science English
John 6 5 10df['John','Maths'] # select one specific row and column[1] 6df[c('John','Mark'),c('Maths','Science')] # select a list of rows and columns Maths Science
John 6 5
Mark 5 8Imagine a dataframe like below, we have scores of multiple students and would like to identify all students who score 80 or above in all three courses.
data = {
'subject': ['math', 'science', 'english'],
'John': [80, 95, 78],
'Alex': [90, 67, 84],
'Angela': [87, 86, 90],
'Bella': [78, 91, 80],
'Trice': [87, 75, 91]
}
df = pd.DataFrame(data)
df.set_index('subject', inplace = True)
df John Alex Angela Bella Trice
subject
math 80 90 87 78 87
science 95 67 86 91 75
english 78 84 90 80 91In python, loc can be used to identify students who score higher than 80 in each course.
math_80 = df.loc[:, df.loc['math']>80].columns
science_80 = df.loc[:, df.loc['science']>80].columns
english_80 = df.loc[:, df.loc['english']>80].columns
print(f'{math_80}\n{science_80}\n{english_80}')Index(['Alex', 'Angela', 'Trice'], dtype='object')
Index(['John', 'Angela', 'Bella'], dtype='object')
Index(['Alex', 'Angela', 'Trice'], dtype='object')We can turn the lists into set then use set.intersection to find common elements in it.
all_rounder = list(set.intersection(*map(set, [math_80, science_80, english_80])))
all_rounder['Angela']To do this in r, select_if which is a variation/extension of select introduced in method #19 can be used to identify students who score above 80 in each course.
library(dplyr)
df = py$df
math_80 <- df |> select_if(df['math',] > 80) |> colnames()
science_80 <- df |> select_if(df['science',] > 80) |> colnames()
english_80 <- df |> select_if(df['english',] > 80) |> colnames()Then use intersect to identify common elements.
all_rounder = Reduce(intersect, list(math_80, science_80, english_80))
all_rounder[1] "Angela"
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 glue_1.6.2 R6_2.5.1
[37] fansi_1.0.4 rmarkdown_2.23 magrittr_2.0.3 whisker_0.4.1
[41] promises_1.2.0.1 htmltools_0.5.5 renv_1.0.0 httpuv_1.6.11
[45] utf8_1.2.3 stringi_1.7.12 cachem_1.0.8