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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({'col1': [1,5,3],
'col2': [8,4,10],
'col3': ['A','B','B']})
df col1 col2 col3
0 1 8 A
1 5 4 B
2 3 10 Bdf <- py$df
df col1 col2 col3
1 1 8 A
2 5 4 B
3 3 10 BFor example, when we want to select col2 in the above dataframe.
df['col2']0 8
1 4
2 10
Name: col2, dtype: int64Or when you want to select multiple columns together, just specify them as a list.
df[['col1','col3']] col1 col3
0 1 A
1 5 B
2 3 BTo do this in R, we can use the select() function
library(dplyr)
df |> select(col2) # select col2 col2
1 8
2 4
3 10df |> select(col1, col3) # select co1 and col3 col1 col3
1 1 A
2 5 B
3 3 BSometimes, we would want to select columns with certain characteristics
First let’s change the column names.
df.columns = ['apple','app','orange'] # change column names
df apple app orange
0 1 8 A
1 5 4 B
2 3 10 BTo select columns where names contain certain strings, we can simply add contains() function to the select call introduced above.
df = py$df #get the dataframe from python
df |> select(contains('app')) apple app
1 1 8
2 5 4
3 3 10To select columns where names contain certain strings, we can use the filter() method.
df.filter(like='app') apple app
0 1 8
1 5 4
2 3 10Special note for R users. In R, filter() is normally used to filter rows, whereas select() for columns. Here in Pandas, by default, filter() looks at columns of dataframes. But we can also set this to rows by specifying axis = 0
df.set_index(['orange'], inplace=True) # set index to be the 'orange' row
print(df.index)Index(['A', 'B', 'B'], dtype='object', name='orange')df.filter(like='B', axis=0) # filter df by index apple app
orange
B 5 4
B 3 10Here index is used for filtering. To filter rows by values of variables, we can use the query() method in Python. See details in method #18
To select columns by data types, we can use the select_dtypes method in python
df.dtypes #check data typesapple int64
app int64
dtype: objectdf.select_dtypes(include = ['int64']) apple app
orange
A 1 8
B 5 4
B 3 10df.select_dtypes(exclude = ['int64'])Empty DataFrame
Columns: []
Index: [A, B, B]To do this in R, simply add a where() in the select call introduced above.
str(df)#check data types'data.frame': 3 obs. of 3 variables:
$ apple : num 1 5 3
$ app : num 8 4 10
$ orange: chr "A" "B" "B"
- attr(*, "pandas.index")=RangeIndex(start=0, stop=3, step=1)df |> select(where(is.numeric)) apple app
1 1 8
2 5 4
3 3 10df |> select(-where(is.numeric)) orange
1 A
2 B
3 B
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 glue_1.6.2 R6_2.5.1
[37] fansi_1.0.4 rmarkdown_2.23 magrittr_2.0.3 whisker_0.4.1
[41] promises_1.2.0.1 htmltools_0.5.5 renv_1.0.0 httpuv_1.6.11
[45] utf8_1.2.3 stringi_1.7.12 cachem_1.0.8