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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)import pandas as pd
data = {'id': [1,2,3,4,5],
'name': ['Jason', 'Jackie', 'Joe', 'Joshua', 'James']}
df = pd.DataFrame(data)
df.dtypesid int64
name object
dtype: objectThe id field is of integer type. Use astype() method to change it to string/character type as follows.
df2 = df.copy()
# change the id field to string/character type
df2['id'] = df2['id'].astype(str)
df2.dtypesid object
name object
dtype: objectSometimes we might have multiple columns to be transformed, this can be done super easily with astype().
# Sample DataFrame
data = {
'col1': [1, 2, 3],
'col2': [4, 5, 6],
'col3': [7, 8, 9]
}
df3 = pd.DataFrame(data)
# Convert specific columns from numeric to string
columns_to_convert = ['col1', 'col2', 'col3']
df3[columns_to_convert] = df3[columns_to_convert].astype(str)
# Verify the data types
print(df3.dtypes)col1 object
col2 object
col3 object
dtype: objectTo change strings to numeric type, we could use astype(int), but this will throw an error when encountering non-numeric characters. Therefore, I prefer using pd.to_numeric() instead, which offers the errors parameter to determine how we want to handle non-numeric characters.
# Sample DataFrame
data = {
'col1': ['1', 'Unknown', '3'],
'col2': ['4', 'N.A.', '6'],
'col3': ['7', '8', 'Missing']
}
df4 = pd.DataFrame(data)
# Using 'coerce': Non-numeric values will be converted to NaN
df4['col1'] = df4['col1'].apply(pd.to_numeric, errors='coerce')
# Using 'ignore': Non-numeric values will be left unchanged
df4['col2'] = df4['col2'].apply(pd.to_numeric, errors = 'ignore')
print(df4) col1 col2 col3
0 1.0 4 7
1 NaN N.A. 8
2 3.0 6 Missingprint(df4.dtypes)col1 float64
col2 object
col3 object
dtype: object# Using 'raise': This will raise an error due to the presence of non-numeric values
try:
df4['col3'] = pd.to_numeric(df4['col3'], errors='raise')
except ValueError as e:
print("Error:", e)Error: Unable to parse string "Missing" at position 2# get the dataframe from python
df = py$df
library(dplyr)
df |> glimpse()Rows: 5
Columns: 2
$ id <dbl> 1, 2, 3, 4, 5
$ name <chr> "Jason", "Jackie", "Joe", "Joshua", "James"Use the as.character() function to change the id field into string/character type, and use as.numeric() function to change it back to numeric. (id: what did I do? :p)
# change the id field to string/character type
df = df |> mutate(id = as.character(id))
df |> glimpse()Rows: 5
Columns: 2
$ id <chr> "1", "2", "3", "4", "5"
$ name <chr> "Jason", "Jackie", "Joe", "Joshua", "James"# change the id field back to numeric type
df = df |> mutate(id = as.numeric(id))
df |> glimpse()Rows: 5
Columns: 2
$ id <dbl> 1, 2, 3, 4, 5
$ name <chr> "Jason", "Jackie", "Joe", "Joshua", "James"
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