Last updated: 2022-11-18
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Knit directory: Pandas-30-R/
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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.
# enable python in RMarkdown
library(reticulate)import pandas as pd
df = pd.DataFrame([[6, 5, 10],
[5, 8, 6],
[3, 10, 4],
[4, 7, 9]],
columns = ["Maths", "Science", "English"])
print(df) Maths Science English
0 6 5 10
1 5 8 6
2 3 10 4
3 4 7 9df <- py$df #access df as saved in Python(py) above
print(df) Maths Science English
1 6 5 10
2 5 8 6
3 3 10 4
4 4 7 9with .iloc[], we can select a row by position
df.iloc[0] # select the first rowMaths 6
Science 5
English 10
Name: 0, dtype: int64df.iloc[0:2] # select the first two rows Maths Science English
0 6 5 10
1 5 8 6df.iloc[-1] # select the last rowMaths 4
Science 7
English 9
Name: 3, dtype: int64To do this in R is fairly simple as shown below, also we could slice(), slice_head() and slice_tail() in the dplyr package as fit.
library(dplyr)
df |> slice(1) # select the first row Maths Science English
1 6 5 10df |> slice(1:2) # select the first two rows Maths Science English
1 6 5 10
2 5 8 6df |> slice_tail(n=1) # select the last row Maths Science English
1 4 7 9df |> slice_tail(prop = 0.5) # select the bottom half Maths Science English
1 3 10 4
2 4 7 9df |> slice_head(prop = 0.25) # select the top 1/4 Maths Science English
1 6 5 10To get top or bottom perc% of rows in Python, I didn’t find a build-in method yet. But it could be done easily with some simple calculation
half_rows = int(round(0.5*len(df),0))# calculate 50% of rows
df.iloc[0:half_rows] # get the top half Maths Science English
0 6 5 10
1 5 8 6
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