Last updated: 2022-12-02
Checks: 7 0
Knit directory: Pandas-30-R/
This reproducible R Markdown analysis was created with workflowr (version 1.7.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.
Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.
The command set.seed(20221023) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.
Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.
Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.
The results in this page were generated with repository version 5e3738a. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.
Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:
Ignored files:
Ignored: .Rhistory
Ignored: .Rproj.user/
Ignored: dev/
Ignored: renv/
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the repository in which changes were made to the R Markdown (analysis/method24-apply-a-function-to-a-dataframe.Rmd) and HTML (docs/method24-apply-a-function-to-a-dataframe.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | a20b54f | Mena WANG | 2022-11-29 | Build site. |
| html | 5d4a24a | Mena WANG | 2022-11-29 | Build site. |
| html | 114c4b5 | Mena WANG | 2022-11-25 | Build site. |
| html | 043f5fa | Mena WANG | 2022-11-18 | Build site. |
| Rmd | e187d0f | Mena WANG | 2022-11-18 | add method24 |
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([[1, 2],
[5, 8],
[3, 9]],
columns = ["col1", "col2"])
print(df) col1 col2
0 1 2
1 5 8
2 3 9df <- py$df #access df as saved in Python(py) above
df <- data.frame(
col1 = c(1,5,3),
col2 = c(2,8,9)
)
print(df) col1 col2
1 1 2
2 5 8
3 3 9In Python, we can use apply() to apply a function to a dataframe. Let’s define a simple function first.
# create a function to be applied
def add_cols(row):
return row.col1 + row.col2Let’s apply the function to all the columns in a datafarme
df['col3'] = df.apply(add_cols, axis = 1)
df col1 col2 col3
0 1 2 3
1 5 8 13
2 3 9 12Or we could only apply a function to one particular columns in a dataframe.
def square_col(num):
return num**2
df['col3']=df.col2.apply(square_col)
df col1 col2 col3
0 1 2 4
1 5 8 64
2 3 9 81In R, we could use lapply() in dplyr package to apply a custom function to all values in a dataframe, note that the results are in multiple lists rather than one dataframe.
library(dplyr)
square_col = function(num){
return (num^2)
}
df2 = df |> lapply(FUN=square_col)
df2 |> str() #lapply returns result in listsList of 2
$ col1: num [1:3] 1 25 9
$ col2: num [1:3] 4 64 81df2 |> data.frame() |> str() # transform the lists into a dataframe'data.frame': 3 obs. of 2 variables:
$ col1: num 1 25 9
$ col2: num 4 64 81We can use the map_df() function in purrr() package to do the similar task but generate a dataframe (tibble) directly. What is a tibble? You might be wondering. well, think of it basically as a dataframe, just even nicer. Read more about it here if interested.
library(purrr)
df |> map_df(square_col) # A tibble: 3 × 2
col1 col2
<dbl> <dbl>
1 1 4
2 25 64
3 9 81To apply a function to one column to create a new column without changing the existing column itself, the suitable mapping method depends on the type of output. When the output is a numeric column, use map_dbl(), whereas when the output is a character column, use map_chr(). Below please see an example for map_dbl(), and more illustrations are available here. For other ways to create new columns in Pandas and R, please refer to method 17.
df['col3'] = df[['col2']] |> map_dbl(square_col)
df col1 col2 col3
1 1 2 4
2 5 8 64
3 3 9 81In contrast, if we want to apply a function to change a subset of columns in a dataframe, we could simply apply select() function before mapping the function to the dataframe.
df |> select(col2) |> map_df(square_col)# A tibble: 3 × 1
col2
<dbl>
1 4
2 64
3 81
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] purrr_0.3.5 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 withr_2.5.0 stringr_1.4.1 knitr_1.40
[29] generics_0.1.3 fs_1.5.2 vctrs_0.4.2 sass_0.4.2
[33] tidyselect_1.2.0 grid_4.2.1 rprojroot_2.0.3 glue_1.6.2
[37] R6_2.5.1 fansi_1.0.3 rmarkdown_2.17 magrittr_2.0.3
[41] whisker_0.4 promises_1.2.0.1 htmltools_0.5.3 assertthat_0.2.1
[45] renv_0.16.0 httpuv_1.6.6 utf8_1.2.2 stringi_1.7.8
[49] cachem_1.0.6