Last updated: 2022-11-08
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Knit directory: Pandas-30-R/
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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)We can use the pd.merge() method to join two dataframes.
import pandas as pd
df1 = pd.DataFrame({'col1': [1,3,5],
'col2': [2,4,6],
'col3': ['A','A','B']})
df2 = pd.DataFrame({'col3': ['A','B','C'],
'col4': ['x','y','z']})
pd.merge(df1, df2, on = 'col3', how = 'right') # the default behavior is an 'inner' join col1 col2 col3 col4
0 1.0 2.0 A x
1 3.0 4.0 A x
2 5.0 6.0 B y
3 NaN NaN C zIn another article by Avi, he also discussed the advantage of join() over merge() method in terms of run-time performance. Therefore, it would be a good idea to look at how join() works as well.
# set the columns to be joined on as index first
df1.set_index('col3', inplace = True)
df2.set_index('col3', inplace = True)
df = df1.join(df2, how = 'right') # the default behavior is a 'left' join
print(df) col1 col2 col4
col3
A 1.0 2.0 x
A 3.0 4.0 x
B 5.0 6.0 y
C NaN NaN zdf1.reset_index(inplace = True)
df2.reset_index(inplace = True)In tidyverse, R has separate functions for inner_join(), left_join(), etc. The columns to be joined on can be specified within the function call.
library(tidyverse)
df1 <- py$df1
df2 <- py$df2
df <- right_join(df1, df2, by = c('col3' = 'col3'))
#since we are joining on a column that has the same name in both dataframes the by= argument can be dropped
df <- right_join(df1, df2)
df col3 col1 col2 col4
1 A 1 2 x
2 A 3 4 x
3 B 5 6 y
4 C NA NA z
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