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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

filter in R vs Python

One thing that might be a bit confusing for R users learning Python and vice versa is probably the use of filter() in these two languages. In R, filter is used to keep rows by certain condition, very much like query in python, as shown in the examples above. In contrast, filter is used to either select rows by indices or columns by names in Python. Below please see its four arguments, like is my favorite.

item – Takes list of axis labels that you wanted to filter.
like – Takes axis string label that you wanted to filter
regex – regular expression
axis: 0 or ‘index’, 1 or ‘columns’. When not specified it uses columns

Python

# first, let's create a dataframe with more complex names 
technologies= {
    'Courses':["Spark","PySpark","Spark","Java","PySpark","PHP"],
    'Fee' :[22000,25000,23000,24000,26000,27000],
    'Duration':['30days','50days','30days','60days','35days','30days']
          }
df = pd.DataFrame(technologies)
print(df)

   Courses    Fee Duration
0    Spark  22000   30days
1  PySpark  25000   50days
2    Spark  23000   30days
3     Java  24000   60days
4  PySpark  26000   35days
5      PHP  27000   30days

df.filter(items = ['Courses', 'Duration'])

   Courses Duration
0    Spark   30days
1  PySpark   50days
2    Spark   30days
3     Java   60days
4  PySpark   35days
5      PHP   30days

I think like argument is very useful when we have group of columns sharing meaningful names. For example, in a large covid datset, we can use vaccin to filter all columns relate to vaccination or vaccine.

df.filter(like = 'tion')

  Duration
0   30days
1   50days
2   30days
3   60days
4   35days
5   30days

We could also use filter to select rows by index, for example

df.filter(items=[1,3], axis =0)

   Courses    Fee Duration
1  PySpark  25000   50days
3     Java  24000   60days

We can use query to do this as well, but it is not restricted to indices.

df.query("index in [1,3]")

   Courses    Fee Duration
1  PySpark  25000   50days
3     Java  24000   60days

df.query("Courses in ['Spark','PySpark']")

   Courses    Fee Duration
0    Spark  22000   30days
1  PySpark  25000   50days
2    Spark  23000   30days
4  PySpark  26000   35days

filter() in Python and select() in R are also discussed in Method #19 on selecting columns.

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

Method #18. Filtering a DataFrame1: Boolean Filtering

Python to R Translation of 30 Essential Pandas Methods

Mena WANG

2022-11-11

Introduction

Set up

Create a dataframe in Python

Load the dataframe into R

Method #18. Filtering a DataFrame1: Boolean Filtering

Filter with a value

Python

R

Filter with a list

Python

R

Multiple conditions

Python

R

filter in R vs Python

Python