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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([[1, 'A'],
[2, 'B'],
[1, 'A']],
columns = ["col1", "col2"])
print(df) col1 col2
0 1 A
1 2 B
2 1 Adf <- py$df #access df as saved in Python(py) above
print(df) col1 col2
1 1 A
2 2 B
3 1 AIn Python, we can use value_counts() to find frequency of each unique values, and use sort to keep the most frequently appeared value at the top.
df["col2"].value_counts(sort = True)A 2
B 1
Name: col2, dtype: int64We can get a percentage instead of value counts by adding the normalize = True argument.
df["col2"].value_counts(sort = True, normalize = True)\
.round(3) * 100 # round it up if you likeA 66.7
B 33.3
Name: col2, dtype: float64In R, this is easy to do with count()
library(dplyr)
df |> count(col2, sort = TRUE) col2 n
1 A 2
2 B 1To add percentage takes a bit more code, but you can have a nice table with both the counts and percentage in it together.
df |>
group_by(col2) |>
summarize(n = n()) |> # get counts
mutate(pct = n / sum(n) * 100) |> # get percentage
arrange(n |> desc()) # sort descending# A tibble: 2 × 3
col2 n pct
<chr> <int> <dbl>
1 A 2 66.7
2 B 1 33.3We could also use tabyl() in the janitor package to do it in one line.
library(janitor)
df |> tabyl(col2) col2 n percent
A 2 0.6666667
B 1 0.3333333tabyl has much advanced functionalities that would allow us to create customized frequency tables. Please see below two simple examples and much more available here
humans <- starwars |> filter(species == "Human")
# one-way table
humans |>
tabyl(eye_color) |>
adorn_totals() |>
adorn_pct_formatting(digits = 1) # format the percentage eye_color n percent
blue 12 34.3%
blue-gray 1 2.9%
brown 17 48.6%
dark 1 2.9%
hazel 2 5.7%
yellow 2 5.7%
Total 35 100.0%# two-way table
humans |>
tabyl(eye_color,gender) |>
adorn_totals(c('row','col')) |> # add total for both rows and columns
adorn_percentages("all") |> # pct among all for each cell
adorn_pct_formatting(digits = 1) |>
adorn_ns() # add counts eye_color feminine masculine Total
blue 8.6% (3) 25.7% (9) 34.3% (12)
blue-gray 0.0% (0) 2.9% (1) 2.9% (1)
brown 14.3% (5) 34.3% (12) 48.6% (17)
dark 0.0% (0) 2.9% (1) 2.9% (1)
hazel 2.9% (1) 2.9% (1) 5.7% (2)
yellow 0.0% (0) 5.7% (2) 5.7% (2)
Total 25.7% (9) 74.3% (26) 100.0% (35)In Python, we could use sidetable to create customized frequency tables. Follows please see some simple examples, and more is available here.
import sidetable
humans = r.humans # take the dataset from R
humans.stb.freq(['eye_color']) # one category eye_color count percent cumulative_count cumulative_percent
0 brown 17 48.571429 17 48.571429
1 blue 12 34.285714 29 82.857143
2 yellow 2 5.714286 31 88.571429
3 hazel 2 5.714286 33 94.285714
4 dark 1 2.857143 34 97.142857
5 blue-gray 1 2.857143 35 100.000000When it comes to two categories. I prefer tabyl output above to the sidetable version below. For me personally, it is easier to interpret when the values of the two categories are represented in the columns and rows respectively. In Python, we can use the crosstab function to do this, please refer to method #29 in this series later.
humans.stb.freq(['gender','eye_color']) # two categories gender eye_color count percent cumulative_count cumulative_percent
0 masculine brown 12 34.285714 12 34.285714
1 masculine blue 9 25.714286 21 60.000000
2 feminine brown 5 14.285714 26 74.285714
3 feminine blue 3 8.571429 29 82.857143
4 masculine yellow 2 5.714286 31 88.571429
5 masculine hazel 1 2.857143 32 91.428571
6 masculine dark 1 2.857143 33 94.285714
7 masculine blue-gray 1 2.857143 34 97.142857
8 feminine hazel 1 2.857143 35 100.000000
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] janitor_2.2.0 dplyr_1.1.2 reticulate_1.30
loaded via a namespace (and not attached):
[1] Rcpp_1.0.11 pillar_1.9.0 compiler_4.2.1 bslib_0.5.0
[5] later_1.3.1 jquerylib_0.1.4 git2r_0.32.0 workflowr_1.7.0
[9] tools_4.2.1 digest_0.6.33 timechange_0.2.0 lubridate_1.9.2
[13] lattice_0.20-45 jsonlite_1.8.7 evaluate_0.21 lifecycle_1.0.3
[17] tibble_3.2.1 png_0.1-8 pkgconfig_2.0.3 rlang_1.1.1
[21] Matrix_1.4-1 cli_3.6.1 rstudioapi_0.15.0 yaml_2.3.7
[25] xfun_0.39 fastmap_1.1.1 withr_2.5.0 stringr_1.5.0
[29] knitr_1.43 generics_0.1.3 fs_1.6.2 vctrs_0.6.3
[33] sass_0.4.7 tidyselect_1.2.0 grid_4.2.1 rprojroot_2.0.3
[37] snakecase_0.11.0 glue_1.6.2 R6_2.5.1 fansi_1.0.4
[41] rmarkdown_2.23 purrr_1.0.1 tidyr_1.3.0 magrittr_2.0.3
[45] whisker_0.4.1 promises_1.2.0.1 htmltools_0.5.5 renv_1.0.0
[49] httpuv_1.6.11 utf8_1.2.3 stringi_1.7.12 cachem_1.0.8