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Introduction

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.

Set up

# enable python in RMarkdown
library(reticulate)

Create a dataframe in Python

import pandas as pd

df = pd.DataFrame({'col1': [1,5,3],
                   'col2': [8,4,10],
                   'col3': ['A','B','B']})
df
   col1  col2 col3
0     1     8    A
1     5     4    B
2     3    10    B

Load the dataframe into R

df <- py$df #load the df object created in Python above

df
  col1 col2 col3
1    1    8    A
2    5    4    B
3    3   10    B

Method #18. Filtering a DataFrame1: Boolean Filtering

Filter with a value

A row can be selected when the condition specified is evaluated to be True for it. For example

Python

df[df['col2']>5]
   col1  col2 col3
0     1     8    A
2     3    10    B
# or to improve readability, we could do it in two steps
col2_larger_than_5 = df['col2'] > 5
df[col2_larger_than_5]
   col1  col2 col3
0     1     8    A
2     3    10    B

R

library(dplyr)

df |> 
  filter(col2 > 5)
  col1 col2 col3
1    1    8    A
2    3   10    B

Filter with a list

A row can be selected when its value belongs to a list specified. For example

Python

filter_list = ["B","C"]

df[df['col3'].isin(filter_list)]
   col1  col2 col3
1     5     4    B
2     3    10    B

Alternatively, this can be done with query() in Python. @ in the query indicating that we are using a variable in the environment.

df.query('col3 in @filter_list')
   col1  col2 col3
1     5     4    B
2     3    10    B

R

filter_list = c('B','C')

df |>  
  filter(col3 %in% filter_list)
  col1 col2 col3
1    5    4    B
2    3   10    B

Multiple conditions

Now let’s try filter the dataframe with both conditions

Python

df[(df['col2']>5) & (df['col3'].isin(filter_list))]
   col1  col2 col3
2     3    10    B

For me, the syntax is probably more readable with query()

df.query('(col2 > 5) and (col3 in @filter_list)')
   col1  col2 col3
2     3    10    B

R

The syntax for dplyr is pretty similar to that with query() above.

df |> 
  filter(col2 > 5 &
         col3 %in% filter_list)
  col1 col2 col3
1    3   10    B

sessionInfo()
R version 4.2.1 (2022-06-23 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19043)

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_1.0.10    reticulate_1.26

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.9       pillar_1.8.1     compiler_4.2.1   bslib_0.4.0     
 [5] later_1.3.0      jquerylib_0.1.4  git2r_0.30.1     workflowr_1.7.0 
 [9] tools_4.2.1      digest_0.6.29    lattice_0.20-45  jsonlite_1.8.2  
[13] evaluate_0.17    lifecycle_1.0.3  tibble_3.1.8     png_0.1-7       
[17] pkgconfig_2.0.3  rlang_1.0.6      Matrix_1.4-1     DBI_1.1.3       
[21] cli_3.4.1        rstudioapi_0.14  yaml_2.3.5       xfun_0.33       
[25] fastmap_1.1.0    stringr_1.4.1    knitr_1.40       generics_0.1.3  
[29] fs_1.5.2         vctrs_0.4.2      sass_0.4.2       tidyselect_1.2.0
[33] grid_4.2.1       rprojroot_2.0.3  glue_1.6.2       R6_2.5.1        
[37] fansi_1.0.3      rmarkdown_2.17   magrittr_2.0.3   whisker_0.4     
[41] promises_1.2.0.1 htmltools_0.5.3  assertthat_0.2.1 renv_0.16.0     
[45] httpuv_1.6.6     utf8_1.2.2       stringi_1.7.8    cachem_1.0.6