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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)We can use the df.fillna() method to replace missing
values with a specific value. Let’s start by creating a dataframe with
missing values.
import pandas as pd
import numpy as np
df = pd.DataFrame({'col1': [1,2],
'col2': [3,np.nan],
'col3': ['A',np.nan]})
print(df) col1 col2 col3
0 1 3.0 A
1 2 NaN NaNTo replace the missing value with 0, just do
.fillna(0).
# replace all NA values with 0, inplace = True means df itself will be modefied
df1 = df.copy()
df1.fillna(0, inplace = True)
print(df1) col1 col2 col3
0 1 3.0 A
1 2 0.0 0Or if you only wnat to replace missing values in one particular column, simply select it first
df2 = df.copy()
df2['col2'].fillna(0, inplace = True)
print(df2) col1 col2 col3
0 1 3.0 A
1 2 0.0 NaNAnother cool thing you could do with fillna() is you can
specify different na replacement values for different columns.
df3 = df.copy()
df3.fillna({
'col2': -999,
'col3': 'Missing'
}, inplace = True)
print(df3) col1 col2 col3
0 1 3.0 A
1 2 -999.0 MissingOne more thing to note about NA replacement is that missing in the
raw data many times come in different messy forms. Like the example
above, it could be any special code (e.g., -999, ‘Missing’, ‘Unknown’).
Here a more versatile function replace could be our best
friend.
df4 = df3.copy()
df4.replace({
None: 0,
'Missing': 0,
-999: 0
}) col1 col2 col3
0 1 3.0 A
1 2 0.0 0We can use either replace() in base R to replace all
missings in a dataframe with a specific value, or
replace_na in tidyr to offer tailored replacement for each
specific column.
df = data.frame(col1 = c(1,2),
col2 = c(3, NA),
col3 = c('A',NA))
print(df) col1 col2 col3
1 1 3 A
2 2 NA <NA># use replace() in base R to replace all missings to 0
replace(df, is.na(df), 0) col1 col2 col3
1 1 3 A
2 2 0 0library(tidyr)
# or use replace_na() in tidyverse to offer tailored replacement for each column.
df |> replace_na(list(col2=0,col3="Unknown")) col1 col2 col3
1 1 3 A
2 2 0 Unknown
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] tidyr_1.3.0 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] dplyr_1.1.2 withr_2.5.0 stringr_1.5.0 knitr_1.43
[29] generics_0.1.3 fs_1.6.2 vctrs_0.6.3 sass_0.4.7
[33] tidyselect_1.2.0 grid_4.2.1 rprojroot_2.0.3 here_1.0.1
[37] glue_1.6.2 R6_2.5.1 fansi_1.0.4 rmarkdown_2.23
[41] purrr_1.0.1 magrittr_2.0.3 whisker_0.4.1 promises_1.2.0.1
[45] htmltools_0.5.5 renv_1.0.0 httpuv_1.6.11 utf8_1.2.3
[49] stringi_1.7.12 cachem_1.0.8