Last updated: 2023-10-08

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.

R Markdown file: up-to-date

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.

Environment: empty

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.

Seed: set.seed(20221023)

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.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: 066a00f

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 066a00f. 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:  mydatabase.db

Unstaged changes:
    Modified:   renv.lock

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/method21-filtering-a-dataframe4-select-by-position.Rmd) and HTML (docs/method21-filtering-a-dataframe4-select-by-position.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	253f970	Mena WANG	2023-09-24	Build site.
html	7139b81	Mena WANG	2023-09-18	Build site.
html	406fa10	Mena WANG	2023-09-10	Build site.
Rmd	1f7a2ea	Mena WANG	2023-09-10	assigning values by both location and label
html	e025247	Mena WANG	2023-09-03	Build site.
html	7f8aaeb	Mena WANG	2023-08-15	Build site.
html	be1850c	Mena WANG	2023-08-15	Build site.
html	f5a895a	Mena WANG	2023-08-13	Build site.
html	db3e0fe	Mena WANG	2023-07-22	Build site.
html	7aa3195	Mena WANG	2023-07-22	Build site.
html	0db3ba4	Mena WANG	2023-02-26	Build site.
html	e3221e5	Mena WANG	2022-12-13	Build site.
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.
Rmd	956d4b2	Mena WANG	2022-11-17	add method22-23
html	55def40	Mena WANG	2022-11-17	Build site.
Rmd	1589837	Mena WANG	2022-11-17	add method21

Introduction

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.

Set up

# enable python in RMarkdown
library(reticulate)

Create the dataframe in Python

import pandas as pd

df = pd.DataFrame([[6, 5,  10], 
                   [5, 8,  6], 
                   [3, 10, 4], 
                   [4, 7,  9]],
                  columns = ["Maths", "Science", "English"])

print(df)

   Maths  Science  English
0      6        5       10
1      5        8        6
2      3       10        4
3      4        7        9

Load the dataframe into R

df <- py$df #access df as saved in Python(py) above

print(df)

  Maths Science English
1     6       5      10
2     5       8       6
3     3      10       4
4     4       7       9

Method #21. Filtering a DataFrame4: Selecting by Position

Python

with .iloc[], we can select a row by position

df.iloc[0] # select the first row

Maths       6
Science     5
English    10
Name: 0, dtype: int64

df.iloc[0:2] # select the first two rows

   Maths  Science  English
0      6        5       10
1      5        8        6

df.iloc[-1] # select the last row

Maths      4
Science    7
English    9
Name: 3, dtype: int64

R

To do this in R is fairly simple as shown below, also we could slice(), slice_head() and slice_tail() in the dplyr package as fit.

library(dplyr)

df |> slice(1) # select the first row

  Maths Science English
1     6       5      10

df |> slice(1:2) # select the first two rows

  Maths Science English
1     6       5      10
2     5       8       6

df |> slice_tail(n=1) # select the last row

  Maths Science English
1     4       7       9

df |> slice_tail(prop = 0.5) # select the bottom half

  Maths Science English
1     3      10       4
2     4       7       9

df |> slice_head(prop = 0.25) # select the top 1/4

  Maths Science English
1     6       5      10

To get top or bottom perc% of rows in Python, I didn’t find a build-in method yet. But it could be done easily with some simple calculation

half_rows = int(round(0.5*len(df),0))# calculate 50% of rows

df.iloc[0:half_rows] # get the top half

   Maths  Science  English
0      6        5       10
1      5        8        6

Combining position with label identifiers?

In this note we talked about iloc which relies on position, while in the previous one we talked about loc which relies on labels. But sometimes, we might want to combine the two identifiers. Say, we want to identify the value of the ‘English’ column in the 1st row.

print('before', df.iloc[0]['English'])

before 10

df.iloc[0]['English'] = 15 # this worked here, but in some python env it doesn't ... interesting!
print('after', df.iloc[0]['English'])

after 15

Note that using .iloc[] or .loc[] alone, there is no issues at all.

`iloc` alone

print('before', df.iloc[0,2])

before 15

df.iloc[0,2] = 12
print('after', df.iloc[0,2])

after 12

`loc`. alone

Note that in this df, the row index is consistent with its location, but this is not always the case. Indexing is a powerful tool in python, pls see more here

print('before', df.loc[0,'English'])

before 12

df.loc[0,'English'] = 17
print('after', df.loc[0,'English'])

after 17

Assigning values by both location and label

Alright, go back to the question before, how do we assign value to the ‘English’ column in the 1st row? One easy way is translating the label to position (or vice versa). Pls see below

print('before', df.iloc[0]['English'])

before 17

print('location of the "English" column:', df.columns.get_loc('English'))

location of the "English" column: 2

df.iloc[0, df.columns.get_loc('English')] = 100
print('after', df.iloc[0]['English'])

after 100

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   here_1.0.1        glue_1.6.2       
[37] R6_2.5.1          fansi_1.0.4       rmarkdown_2.23    magrittr_2.0.3   
[41] whisker_0.4.1     promises_1.2.0.1  htmltools_0.5.5   renv_1.0.0       
[45] httpuv_1.6.11     utf8_1.2.3        stringi_1.7.12    cachem_1.0.8

Method #21 Filtering a DataFrame4: Selecting by Position

Python to R Translation of 30 Essential Pandas Methods

Mena WANG

2022-11-07

Introduction

Set up

Create the dataframe in Python

Load the dataframe into R

Method #21. Filtering a DataFrame4: Selecting by Position

Python

R

Combining position with label identifiers?

`iloc` alone

`loc`. alone

Assigning values by both location and label

Method #21 Filtering a DataFrame4: Selecting by Position

Python to R Translation of 30 Essential Pandas Methods

Mena WANG

2022-11-07

Introduction

Set up

Create the dataframe in Python

Load the dataframe into R

Method #21. Filtering a DataFrame4: Selecting by Position

Python

R

Combining position with label identifiers?

iloc alone

loc. alone

Assigning values by both location and label

`iloc` alone

`loc`. alone