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AMR for Python

Source: vignettes/AMR_for_Python.Rmd
AMR_for_Python.Rmd

Introduction

The AMR package for R is a powerful tool for antimicrobial resistance (AMR) analysis. It provides extensive features for handling microbial and antimicrobial data. However, for those who work primarily in Python, we now have a more intuitive option available: the AMR Python package.

This Python package is a wrapper around the AMR R package. It uses the rpy2 package internally. Despite the need to have R installed, Python users can now easily work with AMR data directly through Python code.

Prerequisites

This package was only tested with a virtual environment (venv). You can set up such an environment by running:

# linux and macOS:
python -m venv /path/to/new/virtual/environment

# Windows:
python -m venv C:\path\to\new\virtual\environment

Then you can activate the environment, after which the venv is ready to work with.

Install AMR

  1. Since the Python package is available on the official Python Package Index, you can just run:

    pip install AMR

  2. Make sure you have R installed. There is no need to install the AMR R package, as it will be installed automatically.

    For Linux:

    # Ubuntu / Debian
sudo apt install r-base
# Fedora:
sudo dnf install R
# CentOS/RHEL
sudo yum install R

    For macOS (using Homebrew):

    brew install r

    For Windows, visit the CRAN download page to download and install R.

Examples of Usage

Cleaning Taxonomy

Here’s an example that demonstrates how to clean microorganism and drug names using the AMR Python package:

import pandas as pd
import AMR

# Sample data
data = {
    "MOs": ['E. coli', 'ESCCOL', 'esco', 'Esche coli'],
    "Drug": ['Cipro', 'CIP', 'J01MA02', 'Ciproxin']
}
df = pd.DataFrame(data)

# Use AMR functions to clean microorganism and drug names
df['MO_clean'] = AMR.mo_name(df['MOs'])
df['Drug_clean'] = AMR.ab_name(df['Drug'])

# Display the results
print(df)
MOs Drug MO_clean Drug_clean
E. coli Cipro Escherichia coli Ciprofloxacin
ESCCOL CIP Escherichia coli Ciprofloxacin
esco J01MA02 Escherichia coli Ciprofloxacin
Esche coli Ciproxin Escherichia coli Ciprofloxacin

Explanation

  • mo_name: This function standardises microorganism names. Here, different variations of Escherichia coli (such as “E. coli”, “ESCCOL”, “esco”, and “Esche coli”) are all converted into the correct, standardised form, “Escherichia coli”.

  • ab_name: Similarly, this function standardises antimicrobial names. The different representations of ciprofloxacin (e.g., “Cipro”, “CIP”, “J01MA02”, and “Ciproxin”) are all converted to the standard name, “Ciprofloxacin”.

Calculating AMR 

import AMR
import pandas as pd

df = AMR.example_isolates
result = AMR.resistance(df["AMX"])
print(result)
[0.59555556]

Generating Antibiograms

One of the core functions of the AMR package is generating an antibiogram, a table that summarises the antimicrobial susceptibility of bacterial isolates. Here’s how you can generate an antibiogram from Python:

result2a = AMR.antibiogram(df[["mo", "AMX", "CIP", "TZP"]])
print(result2a)
Pathogen Amoxicillin Ciprofloxacin Piperacillin/tazobactam
CoNS 7% (10/142) 73% (183/252) 30% (10/33)
E. coli 50% (196/392) 88% (399/456) 94% (393/416)
K. pneumoniae 0% (0/58) 96% (53/55) 89% (47/53)
P. aeruginosa 0% (0/30) 100% (30/30) None
P. mirabilis None 94% (34/36) None
S. aureus 6% (8/131) 90% (171/191) None
S. epidermidis 1% (1/91) 64% (87/136) None
S. hominis None 80% (56/70) None
S. pneumoniae 100% (112/112) None 100% (112/112) 
result2b = AMR.antibiogram(df[["mo", "AMX", "CIP", "TZP"]], mo_transform = "gramstain")
print(result2b)
Pathogen Amoxicillin Ciprofloxacin Piperacillin/tazobactam
Gram-negative 36% (226/631) 91% (621/684) 88% (565/641)
Gram-positive 43% (305/703) 77% (560/724) 86% (296/345)

In this example, we generate an antibiogram by selecting various antibiotics.

Taxonomic Data Sets Now in Python!

As a Python user, you might like that the most important data sets of the AMR R package, microorganisms, antimicrobials, clinical_breakpoints, and example_isolates, are now available as regular Python data frames:

AMR.microorganisms
mo fullname status kingdom gbif gbif_parent gbif_renamed_to prevalence
B_GRAMN (unknown Gram-negatives) unknown Bacteria None None None 2.0
B_GRAMP (unknown Gram-positives) unknown Bacteria None None None 2.0
B_ANAER-NEG (unknown anaerobic Gram-negatives) unknown Bacteria None None None 2.0
B_ANAER-POS (unknown anaerobic Gram-positives) unknown Bacteria None None None 2.0
B_ANAER (unknown anaerobic bacteria) unknown Bacteria None None None 2.0
… … … … … … … …
B_ZYMMN_POMC Zymomonas pomaceae accepted Bacteria 10744418 3221412 None 2.0
B_ZYMPH Zymophilus synonym Bacteria None 9475166 None 2.0
B_ZYMPH_PCVR Zymophilus paucivorans synonym Bacteria None None None 2.0
B_ZYMPH_RFFN Zymophilus raffinosivorans synonym Bacteria None None None 2.0
F_ZYZYG Zyzygomyces unknown Fungi None 7581 None 2.0 
AMR.antimicrobials
ab cid name group oral_ddd oral_units iv_ddd iv_units
AMA 4649.0 4-aminosalicylic acid Antimycobacterials 12.00 g NaN None
ACM 6450012.0 Acetylmidecamycin Macrolides/lincosamides NaN None NaN None
ASP 49787020.0 Acetylspiramycin Macrolides/lincosamides NaN None NaN None
ALS 8954.0 Aldesulfone sodium Other antibacterials 0.33 g NaN None
AMK 37768.0 Amikacin Aminoglycosides NaN None 1.0 g
… … … … … … … …
VIR 11979535.0 Virginiamycine Other antibacterials NaN None NaN None
VOR 71616.0 Voriconazole Antifungals/antimycotics 0.40 g 0.4 g
XBR 72144.0 Xibornol Other antibacterials NaN None NaN None
ZID 77846445.0 Zidebactam Other antibacterials NaN None NaN None
ZFD NaN Zoliflodacin None NaN None NaN None

Installation Channels

Stable Release (CRAN)

The default AMR Python package uses the latest stable version of the AMR R package, published on CRAN. After running pip install AMR, import it as usual:

import AMR

AMR.example_isolates

Development Version (GitHub)

To use the latest development version of the AMR R package (sourced directly from GitHub), import the beta sub-package and alias it as AMR:

import AMR.beta as AMR

AMR.example_isolates

Aliasing with as AMR keeps all downstream code identical to the stable import. Switching between the stable release and the development version requires changing only the import line — nothing else in your script needs to change.

SIR Classification with as_sir()

Using enforce_method

The as_sir() function in R uses S3 method dispatch to select the correct calculation method based on the input class: <mic> for MIC values and <disk> for disk diffusion values. Because Python objects do not carry R class attributes through the rpy2 bridge, this automatic dispatch may not resolve correctly.

To explicitly specify the input type, use the enforce_method argument:

# Treat the column as MIC values — maps to R's as.sir.mic()
AMR.as_sir(df["MIC_col"], mo="E. coli", ab="AMX", guideline="EUCAST", enforce_method="mic")

# Treat the column as disk diffusion values — maps to R's as.sir.disk()
AMR.as_sir(df["disk_col"], mo="E. coli", ab="AMX", guideline="EUCAST", enforce_method="disk")

Without enforce_method, R falls back to class-based dispatch on the raw Python input, which may fail or return unexpected results. Always supply enforce_method when calling as_sir() from Python.

Conclusion

With the AMR Python package, Python users can now effortlessly call R functions from the AMR R package. This eliminates the need for complex rpy2 configurations and provides a clean, easy-to-use interface for antimicrobial resistance analysis. The examples provided above demonstrate how this can be applied to typical workflows, such as standardising microorganism and antimicrobial names or calculating resistance.

By just running import AMR, users can seamlessly integrate the robust features of the R AMR package into Python workflows.

Whether you’re cleaning data or analysing resistance patterns, the AMR Python package makes it easy to work with AMR data in Python.