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Introduction

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.

Set up

# enable python in RMarkdown
library(reticulate)

Method #10. Printing Descriptive Info about the DataFrame (Method 2)

Python

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     2.500000

describe() 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  versicolor
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         NaN

R

As 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()

versicolor    50
setosa        50
virginica     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 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] 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     lattice_0.20-45   jsonlite_1.8.7   
[13] evaluate_0.21     lifecycle_1.0.3   tibble_3.2.1      png_0.1-8        
[17] pkgconfig_2.0.3   rlang_1.1.1       Matrix_1.4-1      cli_3.6.1        
[21] rstudioapi_0.15.0 yaml_2.3.7        xfun_0.39         fastmap_1.1.1    
[25] withr_2.5.0       stringr_1.5.0     knitr_1.43        generics_0.1.3   
[29] fs_1.6.2          vctrs_0.6.3       sass_0.4.7        tidyselect_1.2.0 
[33] grid_4.2.1        rprojroot_2.0.3   here_1.0.1        glue_1.6.2       
[37] R6_2.5.1          fansi_1.0.4       rmarkdown_2.23    magrittr_2.0.3   
[41] whisker_0.4.1     promises_1.2.0.1  htmltools_0.5.5   renv_1.0.0       
[45] httpuv_1.6.11     utf8_1.2.3        stringi_1.7.12    cachem_1.0.8

Method #10. Printing Descriptive Info about the DataFrame (Method 2)

Python to R Translation of 30 Essential Pandas Methods

Mena WANG

2022-11-03

Introduction

Set up

Method #10. Printing Descriptive Info about the DataFrame (Method 2)

Python

R