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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.read_csv("./data/df_method30.csv")
print(df) Name Subject Marks
0 John Maths 6
1 Mark Maths 5
2 Peter Maths 3
3 John Science 5
4 Mark Science 8
5 Peter Science 10
6 John English 10
7 Mark English 6
8 Peter English 4df <- py$df #access df as saved in Python(py) above
print(df) Name Subject Marks
1 John Maths 6
2 Mark Maths 5
3 Peter Maths 3
4 John Science 5
5 Mark Science 8
6 Peter Science 10
7 John English 10
8 Mark English 6
9 Peter English 4In Python, pivot_table() can convert the column entries to column headers.
df_wider = pd.pivot_table(df,
index = ["Name"],
columns=["Subject"],
values='Marks',
fill_value=0)
print(df_wider) Subject English Maths Science
Name
John 10 6 5
Mark 6 5 8
Peter 4 3 10In R, this can be done with the pivot_wider() function.
library(tidyr)
df_wider = df |>
pivot_wider(id_cols=Name,
names_from=Subject,
values_from = Marks,
values_fill = 0)
print(df_wider)# A tibble: 3 × 4
Name Maths Science English
<chr> <dbl> <dbl> <dbl>
1 John 6 5 10
2 Mark 5 8 6
3 Peter 3 10 4Above we used the pivot_wider() function to turn a table from long to wide. Naturally, we can reverse the process and turn a table from wide to long. And the function to do this is, you guessed it, pivot_longer() :P
df_wider |>
pivot_longer(cols = c(Maths, Science, English),
names_to = "Subject",
values_to = "Marks")# A tibble: 9 × 3
Name Subject Marks
<chr> <chr> <dbl>
1 John Maths 6
2 John Science 5
3 John English 10
4 Mark Maths 5
5 Mark Science 8
6 Mark English 6
7 Peter Maths 3
8 Peter Science 10
9 Peter English 4Similar task can be done by the melt() method in Pandas.
df_wider.index # remember that "Name" is set as index aboveIndex(['John', 'Mark', 'Peter'], dtype='object', name='Name')df_wider.melt(ignore_index = False, # use index as the id field
value_vars=["Maths", "Science", "English"],
value_name = "Marks") Subject Marks
Name
John Maths 6
Mark Maths 5
Peter Maths 3
John Science 5
Mark Science 8
Peter Science 10
John English 10
Mark English 6
Peter English 4For dataframe where the index is not the id field we want to use, it is simple as well.
df_wider.reset_index(level=["Name"], inplace=True) # reset name to be a column
df_wider.melt(id_vars = "Name", # set id field manually
value_vars=["Maths", "Science", "English"],
value_name = "Marks") Name Subject Marks
0 John Maths 6
1 Mark Maths 5
2 Peter Maths 3
3 John Science 5
4 Mark Science 8
5 Peter Science 10
6 John English 10
7 Mark English 6
8 Peter English 4
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] tidyr_1.2.1 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 withr_2.5.0 dplyr_1.0.10 stringr_1.4.1
[29] knitr_1.40 generics_0.1.3 fs_1.5.2 vctrs_0.4.2
[33] sass_0.4.2 tidyselect_1.2.0 grid_4.2.1 rprojroot_2.0.3
[37] glue_1.6.2 R6_2.5.1 fansi_1.0.3 rmarkdown_2.17
[41] purrr_0.3.5 magrittr_2.0.3 whisker_0.4 ellipsis_0.3.2
[45] promises_1.2.0.1 htmltools_0.5.3 assertthat_0.2.1 renv_0.16.0
[49] httpuv_1.6.6 utf8_1.2.2 stringi_1.7.8 cachem_1.0.6