Last updated: 2022-11-09
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Knit directory: Pandas-30-R/
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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 :)
# enable python in RMarkdown
library(reticulate)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 months. This should be easy to do using lag(), which is available in both R and Python.
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 3 (2 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 44Before I can apply lag(), I must make sure all the missing combinations (e.g., month 2 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 where no sales value recorded now has 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 44df = 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 3.0To do this in Python is a bit more complex. Please see steps below. I will revisit this if I find a better way, and please shoot me a message if have a better solution.
(inspired by the post here)
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 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] forcats_0.5.2 stringr_1.4.1 dplyr_1.0.10 purrr_0.3.5
[5] readr_2.1.3 tidyr_1.2.1 tibble_3.1.8 ggplot2_3.3.6
[9] tidyverse_1.3.2 reticulate_1.26
loaded via a namespace (and not attached):
[1] httr_1.4.4 sass_0.4.2 jsonlite_1.8.2
[4] modelr_0.1.9 bslib_0.4.0 assertthat_0.2.1
[7] renv_0.16.0 googlesheets4_1.0.1 cellranger_1.1.0
[10] yaml_2.3.5 pillar_1.8.1 backports_1.4.1
[13] lattice_0.20-45 glue_1.6.2 digest_0.6.29
[16] promises_1.2.0.1 rvest_1.0.3 colorspace_2.0-3
[19] htmltools_0.5.3 httpuv_1.6.6 Matrix_1.4-1
[22] pkgconfig_2.0.3 broom_1.0.1 haven_2.5.1
[25] scales_1.2.1 whisker_0.4 later_1.3.0
[28] tzdb_0.3.0 git2r_0.30.1 googledrive_2.0.0
[31] generics_0.1.3 ellipsis_0.3.2 cachem_1.0.6
[34] withr_2.5.0 cli_3.4.1 crayon_1.5.2
[37] magrittr_2.0.3 readxl_1.4.1 evaluate_0.17
[40] fs_1.5.2 fansi_1.0.3 xml2_1.3.3
[43] tools_4.2.1 hms_1.1.2 gargle_1.2.1
[46] lifecycle_1.0.3 munsell_0.5.0 reprex_2.0.2
[49] compiler_4.2.1 jquerylib_0.1.4 rlang_1.0.6
[52] grid_4.2.1 rstudioapi_0.14 rmarkdown_2.17
[55] gtable_0.3.1 DBI_1.1.3 R6_2.5.1
[58] lubridate_1.8.0 knitr_1.40 fastmap_1.1.0
[61] utf8_1.2.2 workflowr_1.7.0 rprojroot_2.0.3
[64] stringi_1.7.8 Rcpp_1.0.9 vctrs_0.4.2
[67] png_0.1-7 dbplyr_2.2.1 tidyselect_1.2.0
[70] xfun_0.33