Last updated: 2022-12-13
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 1d27b07. 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/
Untracked files:
Untracked: data/df_method30.csv
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/method19-filtering-a-dataframe2-getting-columns.Rmd) and HTML (docs/method19-filtering-a-dataframe2-getting-columns.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 |
|---|---|---|---|---|
| Rmd | 1d27b07 | Mena WANG | 2022-12-13 | update method19 with bonus: get cols by conditions |
| html | 2522fc8 | Mena WANG | 2022-12-10 | Build site. |
| html | b06fbb1 | Mena WANG | 2022-12-02 | Build site. |
| 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. |
| html | d77b114 | Mena WANG | 2022-11-17 | Build site. |
| html | 55def40 | Mena WANG | 2022-11-17 | Build site. |
| html | ca76451 | Mena WANG | 2022-11-13 | Build site. |
| html | efd27b1 | Mena WANG | 2022-11-12 | Build site. |
| Rmd | 521e4ee | Mena WANG | 2022-11-12 | add method19 |
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 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] 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