Last updated: 2023-09-24

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Introduction

This repo was initially set up only for 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.

But I will add some R to Python translation of loved R functions as bonus :)

Set up

# enable python in RMarkdown
library(reticulate)

Bonus #1. complete()

Set up

Imagine a scenario, we have some products and we hope to track their sale over months. For each product, we hope to have its sale this month compare to the previous month. This can be done easily using lag() in R or shift() in Python, IF our dataframe is complete.

However, our issue here is we don’t have values for all the months. For example, product 1 only has sale values for month 1 and 2 (3 and 4 missing), and product 2 only has values for month 2 and 4, with 1 and 3 skipped … (please see the hypothetical dataframe below)

library(tidyverse) #arrange in dplyr, complete in tidyr

set.seed(123)
df <- data.frame(
  product_id = c(1:4,1:2),
  month = c(1:4,2,4),
  sale = runif(6, min=1, max=50) |> round()
)

df |> 
  arrange(product_id, month)
  product_id month sale
1          1     1   15
2          1     2   47
3          2     2   40
4          2     4    3
5          3     3   21
6          4     4   44

complete() in R

Before I can apply lag(), I must make sure all the missing combinations (e.g., month 3 for product 1) are added to the dataframe with the sales value equals 0. This is super easy to do in R with complete(). As you can see now, each product now has sales values from month 1 to 4, and months with no sales value recorded now have sale value as 0.

df |> 
  complete(product_id, month, fill=list(sale=0))
# A tibble: 16 × 3
   product_id month  sale
        <int> <dbl> <dbl>
 1          1     1    15
 2          1     2    47
 3          1     3     0
 4          1     4     0
 5          2     1     0
 6          2     2    40
 7          2     3     0
 8          2     4     3
 9          3     1     0
10          3     2     0
11          3     3    21
12          3     4     0
13          4     1     0
14          4     2     0
15          4     3     0
16          4     4    44

To do this in Python

df = r.df # get the df from R

df
   product_id  month  sale
0           1    1.0  15.0
1           2    2.0  40.0
2           3    3.0  21.0
3           4    4.0  44.0
4           1    2.0  47.0
5           2    4.0   3.0

To do this in Python is a bit more complex. Please see steps below, inspired by this post on stackoverflow.

I will revisit this from time to time for a better way. Any comments and suggestions will be highly appreciated! You can find me on Twitter, LinkedIn, and GitHub.

import pandas as pd
import itertools

df.set_index(['product_id', 'month'])\
    .reindex(pd.MultiIndex\
    .from_tuples(itertools.product(df.product_id.unique(), df.month.unique())))\
    .reset_index()\
    .rename(columns={'level_0':'product_id', 'level_1':'month'})\
    .fillna(0)
    product_id  month  sale
0            1    1.0  15.0
1            1    2.0  47.0
2            1    3.0   0.0
3            1    4.0   0.0
4            2    1.0   0.0
5            2    2.0  40.0
6            2    3.0   0.0
7            2    4.0   3.0
8            3    1.0   0.0
9            3    2.0   0.0
10           3    3.0  21.0
11           3    4.0   0.0
12           4    1.0   0.0
13           4    2.0   0.0
14           4    3.0   0.0
15           4    4.0  44.0

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] lubridate_1.9.2 forcats_1.0.0   stringr_1.5.0   dplyr_1.1.2    
 [5] purrr_1.0.1     readr_2.1.4     tidyr_1.3.0     tibble_3.2.1   
 [9] ggplot2_3.4.2   tidyverse_2.0.0 reticulate_1.30

loaded via a namespace (and not attached):
 [1] tidyselect_1.2.0  xfun_0.39         bslib_0.5.0       lattice_0.20-45  
 [5] colorspace_2.1-0  vctrs_0.6.3       generics_0.1.3    htmltools_0.5.5  
 [9] yaml_2.3.7        utf8_1.2.3        rlang_1.1.1       jquerylib_0.1.4  
[13] later_1.3.1       pillar_1.9.0      glue_1.6.2        withr_2.5.0      
[17] lifecycle_1.0.3   munsell_0.5.0     gtable_0.3.3      workflowr_1.7.0  
[21] evaluate_0.21     knitr_1.43        tzdb_0.4.0        fastmap_1.1.1    
[25] httpuv_1.6.11     fansi_1.0.4       Rcpp_1.0.11       renv_1.0.0       
[29] promises_1.2.0.1  scales_1.2.1      cachem_1.0.8      jsonlite_1.8.7   
[33] fs_1.6.2          hms_1.1.3         png_0.1-8         digest_0.6.33    
[37] stringi_1.7.12    rprojroot_2.0.3   grid_4.2.1        here_1.0.1       
[41] cli_3.6.1         tools_4.2.1       magrittr_2.0.3    sass_0.4.7       
[45] whisker_0.4.1     pkgconfig_2.0.3   Matrix_1.4-1      timechange_0.2.0 
[49] rmarkdown_2.23    rstudioapi_0.15.0 R6_2.5.1          git2r_0.32.0     
[53] compiler_4.2.1