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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)describe() returns standard statistics like mean, standard deviation, maximum etc. of every numeric-valued column
import pandas as pd
df = pd.read_csv('data/iris.csv')
df.describe() Sepal.Length Sepal.Width Petal.Length Petal.Width
count 150.000000 150.000000 150.000000 150.000000
mean 5.843333 3.057333 3.758000 1.199333
std 0.828066 0.435866 1.765298 0.762238
min 4.300000 2.000000 1.000000 0.100000
25% 5.100000 2.800000 1.600000 0.300000
50% 5.800000 3.000000 4.350000 1.300000
75% 6.400000 3.300000 5.100000 1.800000
max 7.900000 4.400000 6.900000 2.500000describe() can also offer some info on categorical columns: the number of unique values, the most frequent value and its frequency, if we add include = all argument
df.describe(include = 'all') Sepal.Length Sepal.Width Petal.Length Petal.Width Species
count 150.000000 150.000000 150.000000 150.000000 150
unique NaN NaN NaN NaN 3
top NaN NaN NaN NaN virginica
freq NaN NaN NaN NaN 50
mean 5.843333 3.057333 3.758000 1.199333 NaN
std 0.828066 0.435866 1.765298 0.762238 NaN
min 4.300000 2.000000 1.000000 0.100000 NaN
25% 5.100000 2.800000 1.600000 0.300000 NaN
50% 5.800000 3.000000 4.350000 1.300000 NaN
75% 6.400000 3.300000 5.100000 1.800000 NaN
max 7.900000 4.400000 6.900000 2.500000 NaNAs discussed in Method 9, summary() in base R returns the number of missings as well as some summary statistics for all numerical and factor columns.
df = py$df
df |> summary() Sepal.Length Sepal.Width Petal.Length Petal.Width
Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.100
1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.300
Median :5.800 Median :3.000 Median :4.350 Median :1.300
Mean :5.843 Mean :3.057 Mean :3.758 Mean :1.199
3rd Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100 3rd Qu.:1.800
Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.500
Species
Length:150
Class :character
Mode :character
For categorical variables, like Species in the iris dataset, summary() could give us its frequency counts if we turn it into factor type in R.
library(dplyr) #for mutate
df |>
mutate(Species = as.factor(Species)) |> # see method17 for more on mutate()
summary() Sepal.Length Sepal.Width Petal.Length Petal.Width
Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.100
1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.300
Median :5.800 Median :3.000 Median :4.350 Median :1.300
Mean :5.843 Mean :3.057 Mean :3.758 Mean :1.199
3rd Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100 3rd Qu.:1.800
Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.500
Species
setosa :50
versicolor:50
virginica :50
In python, we could use value_counts() to get frequency counts. See #method27 for more.
df['Species'].value_counts()virginica 50
versicolor 50
setosa 50
Name: Species, dtype: int64
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