@ -29,7 +29,7 @@
< a class = "navbar-brand me-2" href = "../index.html" > AMR (for R)< / a >
< small class = "nav-text text-muted me-auto" data-bs-toggle = "tooltip" data-bs-placement = "bottom" title = "" > 2.1.1.9095 < / small >
< small class = "nav-text text-muted me-auto" data-bs-toggle = "tooltip" data-bs-placement = "bottom" title = "" > 2.1.1.9099 < / small >
< button class = "navbar-toggler" type = "button" data-bs-toggle = "collapse" data-bs-target = "#navbar" aria-controls = "navbar" aria-expanded = "false" aria-label = "Toggle navigation" >
@ -90,53 +90,64 @@
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 < code > AMR< / code > Python package, which uses < code > rpy2 < / code >
internally. This package allows Python users to access all the functions
from the R < code > AMR< / code > package without the need to set up
< code > rpy2< / code > themselves. Since this Python package is not a tru e
‘ ’
Python), R and the AMR R package are still required to be installed.
Yet, Python users can now easily work with AMR data directly through
Python code.< / p >
< p > In this document, we explain how this works and provide simple
examples of using the < code > AMR< / code > Python package.< / p >
< div class = "section level3" >
< h3 id = "how-it-works" > How It Works< a class = "anchor" aria-label = "anchor" href = "#how-it-works" > < / a >
< / h3 >
< p > The < code > AMR< / code > Python package acts as a wrapper around the
functions in the < code > AMR< / code > R package. The package simplifies the
process of calling R functions in Python, eliminating the need to
manually manage the < code > rpy2< / code > setup, which Python uses
internally to be able to work with the R package. By just using
< code > import AMR< / code > , Python users can directly use the functions
from the < code > AMR< / code > R package as if they were native Python
functions.< / p >
< p > Internally, < code > rpy2< / code > is still being used, but all complexity
is hidden from the user. This approach keeps the Python code clean and
Pythonic, while still leveraging the full power of the R
< code > AMR< / code > package.< / p >
the < a href = "https://pypi.org/project/AMR/" class = "external-link" > < code > AMR < / code > Python
Package Index< / a > .< / p >
< p > This Python package is a wrapper round< code > AMR< / code > R
package. It uses the < code > rpy2< / code > package internally. Despite th e
need to have R installed, Python users can now easily work with AMR data
directly through Python code.< / p >
< / div >
< div class = "section level2" >
< h2 id = "install" > Install< a class = "anchor" aria-label = "anchor" href = "#install" > < / a >
< / h2 >
< ol style = "list-style-type: decimal" >
< li >
< p > First make sure you have R installed. There is < strong > no need to
install the < code > AMR< / code > R package< / strong > , as it will be installed
automatically.< / p >
< p > For Linux:< / p >
< div class = "sourceCode" id = "cb1" > < pre class = "sourceCode bash" > < code class = "sourceCode bash" > < span id = "cb1-1" > < a href = "#cb1-1" tabindex = "-1" > < / a > < span class = "co" > # Ubuntu / Debian< / span > < / span >
< span id = "cb1-2" > < a href = "#cb1-2" tabindex = "-1" > < / a > < span class = "fu" > sudo< / span > apt install r-base< / span >
< span id = "cb1-3" > < a href = "#cb1-3" tabindex = "-1" > < / a > < span class = "co" > # Fedora:< / span > < / span >
< span id = "cb1-4" > < a href = "#cb1-4" tabindex = "-1" > < / a > < span class = "fu" > sudo< / span > dnf install R< / span >
< span id = "cb1-5" > < a href = "#cb1-5" tabindex = "-1" > < / a > < span class = "co" > # CentOS/RHEL< / span > < / span >
< span id = "cb1-6" > < a href = "#cb1-6" tabindex = "-1" > < / a > < span class = "fu" > sudo< / span > yum install R< / span > < / code > < / pre > < / div >
< p > For macOS (using < a href = "https://brew.sh" class = "external-link" > Homebrew< / a > ):< / p >
< div class = "sourceCode" id = "cb2" > < pre class = "sourceCode bash" > < code class = "sourceCode bash" > < span id = "cb2-1" > < a href = "#cb2-1" tabindex = "-1" > < / a > < span class = "ex" > brew< / span > install r< / span > < / code > < / pre > < / div >
< p > For Windows, visit the < a href = "https://cran.r-project.org" class = "external-link" > CRAN
download page< / a > to download and install R.< / p >
< / li >
< li >
< p > Since the Python package is available on the official < a href = "https://pypi.org/project/AMR/" class = "external-link" > Python Package Index< / a > , you can
just run:< / p >
< div class = "sourceCode" id = "cb3" > < pre class = "sourceCode bash" > < code class = "sourceCode bash" > < span id = "cb3-1" > < a href = "#cb3-1" tabindex = "-1" > < / a > < span class = "ex" > pip< / span > install AMR< / span > < / code > < / pre > < / div >
< / li >
< / ol >
< / div >
< div class = "section level2" >
< h2 id = "examples-of-usage" > Examples of Usage< a class = "anchor" aria-label = "anchor" href = "#examples-of-usage" > < / a >
< / h2 >
< div class = "section level3" >
< h3 id = "example-of-usage" > Example of Usage < a class = "anchor" aria-label = "anchor" href = "#example-of-usage " > < / a >
< h3 id = "cleaning-taxonomy" > Cleaning Taxonomy < a class = "anchor" aria-label = "anchor" href = "#cleaning-taxonomy " > < / a >
< / h3 >
< p > Here’
drug names using the < code > AMR< / code > Python package:< / p >
< div class = "sourceCode" id = "cb1 " > < pre class = "sourceCode python" > < code class = "sourceCode python" > < span id = "cb1 -1" > < a href = "#cb1 -1" tabindex = "-1" > < / a > < span class = "im" > import< / span > pandas < span class = "im" > as< / span > pd< / span >
< span id = "cb1 -2" > < a href = "#cb1 -2" tabindex = "-1" > < / a > < span class = "im" > import< / span > AMR< / span >
< span id = "cb1 -3" > < a href = "#cb1 -3" tabindex = "-1" > < / a > < / span >
< span id = "cb1 -4" > < a href = "#cb1 -4" tabindex = "-1" > < / a > < span class = "co" > # Sample data< / span > < / span >
< span id = "cb1 -5" > < a href = "#cb1 -5" tabindex = "-1" > < / a > data < span class = "op" > =< / span > {< / span >
< span id = "cb1 -6" > < a href = "#cb1 -6" tabindex = "-1" > < / a > < span class = "st" > "MOs"< / span > : [< span class = "st" > 'E. coli'< / span > , < span class = "st" > 'ESCCOL'< / span > , < span class = "st" > 'esco'< / span > , < span class = "st" > 'Esche coli'< / span > ],< / span >
< span id = "cb1 -7" > < a href = "#cb1 -7" tabindex = "-1" > < / a > < span class = "st" > "Drug"< / span > : [< span class = "st" > 'Cipro'< / span > , < span class = "st" > 'CIP'< / span > , < span class = "st" > 'J01MA02'< / span > , < span class = "st" > 'Ciproxin'< / span > ]< / span >
< span id = "cb1 -8" > < a href = "#cb1 -8" tabindex = "-1" > < / a > }< / span >
< span id = "cb1 -9" > < a href = "#cb1 -9" tabindex = "-1" > < / a > df < span class = "op" > =< / span > pd.DataFrame(data)< / span >
< span id = "cb1 -10" > < a href = "#cb1 -10" tabindex = "-1" > < / a > < / span >
< span id = "cb1 -11" > < a href = "#cb1 -11" tabindex = "-1" > < / a > < span class = "co" > # Use AMR functions to clean microorganism and drug names< / span > < / span >
< span id = "cb1 -12" > < a href = "#cb1 -12" tabindex = "-1" > < / a > df[< span class = "st" > 'MO_clean'< / span > ] < span class = "op" > =< / span > AMR.mo_name(df[< span class = "st" > 'MOs'< / span > ])< / span >
< span id = "cb1 -13" > < a href = "#cb1 -13" tabindex = "-1" > < / a > df[< span class = "st" > 'Drug_clean'< / span > ] < span class = "op" > =< / span > AMR.ab_name(df[< span class = "st" > 'Drug'< / span > ])< / span >
< span id = "cb1 -14" > < a href = "#cb1 -14" tabindex = "-1" > < / a > < / span >
< span id = "cb1 -15" > < a href = "#cb1 -15" tabindex = "-1" > < / a > < span class = "co" > # Display the results< / span > < / span >
< span id = "cb1 -16" > < a href = "#cb1 -16" tabindex = "-1" > < / a > < span class = "bu" > print< / span > (df)< / span > < / code > < / pre > < / div >
< div class = "sourceCode" id = "cb4 " > < pre class = "sourceCode python" > < code class = "sourceCode python" > < span id = "cb4 -1" > < a href = "#cb4 -1" tabindex = "-1" > < / a > < span class = "im" > import< / span > pandas < span class = "im" > as< / span > pd< / span >
< span id = "cb4 -2" > < a href = "#cb4 -2" tabindex = "-1" > < / a > < span class = "im" > import< / span > AMR< / span >
< span id = "cb4 -3" > < a href = "#cb4 -3" tabindex = "-1" > < / a > < / span >
< span id = "cb4 -4" > < a href = "#cb4 -4" tabindex = "-1" > < / a > < span class = "co" > # Sample data< / span > < / span >
< span id = "cb4 -5" > < a href = "#cb4 -5" tabindex = "-1" > < / a > data < span class = "op" > =< / span > {< / span >
< span id = "cb4 -6" > < a href = "#cb4 -6" tabindex = "-1" > < / a > < span class = "st" > "MOs"< / span > : [< span class = "st" > 'E. coli'< / span > , < span class = "st" > 'ESCCOL'< / span > , < span class = "st" > 'esco'< / span > , < span class = "st" > 'Esche coli'< / span > ],< / span >
< span id = "cb4 -7" > < a href = "#cb4 -7" tabindex = "-1" > < / a > < span class = "st" > "Drug"< / span > : [< span class = "st" > 'Cipro'< / span > , < span class = "st" > 'CIP'< / span > , < span class = "st" > 'J01MA02'< / span > , < span class = "st" > 'Ciproxin'< / span > ]< / span >
< span id = "cb4 -8" > < a href = "#cb4 -8" tabindex = "-1" > < / a > }< / span >
< span id = "cb4 -9" > < a href = "#cb4 -9" tabindex = "-1" > < / a > df < span class = "op" > =< / span > pd.DataFrame(data)< / span >
< span id = "cb4 -10" > < a href = "#cb4 -10" tabindex = "-1" > < / a > < / span >
< span id = "cb4 -11" > < a href = "#cb4 -11" tabindex = "-1" > < / a > < span class = "co" > # Use AMR functions to clean microorganism and drug names< / span > < / span >
< span id = "cb4 -12" > < a href = "#cb4 -12" tabindex = "-1" > < / a > df[< span class = "st" > 'MO_clean'< / span > ] < span class = "op" > =< / span > AMR.mo_name(df[< span class = "st" > 'MOs'< / span > ])< / span >
< span id = "cb4 -13" > < a href = "#cb4 -13" tabindex = "-1" > < / a > df[< span class = "st" > 'Drug_clean'< / span > ] < span class = "op" > =< / span > AMR.ab_name(df[< span class = "st" > 'Drug'< / span > ])< / span >
< span id = "cb4 -14" > < a href = "#cb4 -14" tabindex = "-1" > < / a > < / span >
< span id = "cb4 -15" > < a href = "#cb4 -15" tabindex = "-1" > < / a > < span class = "co" > # Display the results< / span > < / span >
< span id = "cb4 -16" > < a href = "#cb4 -16" tabindex = "-1" > < / a > < span class = "bu" > print< / span > (df)< / span > < / code > < / pre > < / div >
< table class = "table" >
< thead > < tr class = "header" >
< th > MOs< / th >
@ -186,15 +197,16 @@ antimicrobial names. The different representations of ciprofloxacin
the standard name, “Ciprofloxacin”.< / p > < / li >
< / ul >
< / div >
< div class = "section level4" >
< h4 id = "taxonomic-data-sets-now-in-python" > Taxonomic Data Sets Now in Python!< a class = "anchor" aria-label = "anchor" href = "#taxonomic-data-sets-now-in-python" > < / a >
< / h4 >
< / div >
< div class = "section level3" >
< h3 id = "taxonomic-data-sets-now-in-python" > Taxonomic Data Sets Now in Python!< a class = "anchor" aria-label = "anchor" href = "#taxonomic-data-sets-now-in-python" > < / a >
< / h3 >
< p > As a Python user, you might like that the most important data sets of
the < code > AMR< / code > R package, < code > microorganisms< / code > ,
< code > antibiotics< / code > , < code > clinical_breakpoints< / code > , and
< code > example_isolates< / code > , are now available as regular Python data
frames:< / p >
< div class = "sourceCode" id = "cb2 " > < pre class = "sourceCode python" > < code class = "sourceCode python" > < span id = "cb2 -1" > < a href = "#cb2 -1" tabindex = "-1" > < / a > AMR.microorganisms< / span > < / code > < / pre > < / div >
< div class = "sourceCode" id = "cb5 " > < pre class = "sourceCode python" > < code class = "sourceCode python" > < span id = "cb5 -1" > < a href = "#cb5 -1" tabindex = "-1" > < / a > AMR.microorganisms< / span > < / code > < / pre > < / div >
< table class = "table" >
< colgroup >
< col width = "11%" >
@ -329,7 +341,7 @@ frames:</p>
< / tr >
< / tbody >
< / table >
< div class = "sourceCode" id = "cb3 " > < pre class = "sourceCode python" > < code class = "sourceCode python" > < span id = "cb3 -1" > < a href = "#cb3 -1" tabindex = "-1" > < / a > AMR.antibiotics< / span > < / code > < / pre > < / div >
< div class = "sourceCode" id = "cb6 " > < pre class = "sourceCode python" > < code class = "sourceCode python" > < span id = "cb6 -1" > < a href = "#cb6 -1" tabindex = "-1" > < / a > AMR.antibiotics< / span > < / code > < / pre > < / div >
< table style = "width:100%;" class = "table" >
< colgroup >
< col width = "4%" >
@ -465,64 +477,26 @@ frames:</p>
< / tbody >
< / table >
< / div >
< / div >
< / div >
< div class = "section level2" >
< h2 id = "installation" > Installation< a class = "anchor" aria-label = "anchor" href = "#installation" > < / a >
< / h2 >
< p > To be able to use the < code > AMR< / code > Python package, it is required
to install both R and the < code > AMR< / code > R package.< / p >
< div class = "section level4" >
< h4 id = "preparation-install-r-and-amr-r-package" > Preparation: Install R and < code > AMR< / code > R package< a class = "anchor" aria-label = "anchor" href = "#preparation-install-r-and-amr-r-package" > < / a >
< / h4 >
< p > For Linux and macOS, this is just:< / p >
< div class = "sourceCode" id = "cb4" > < pre class = "sourceCode bash" > < code class = "sourceCode bash" > < span id = "cb4-1" > < a href = "#cb4-1" tabindex = "-1" > < / a > < span class = "co" > # Ubuntu / Debian< / span > < / span >
< span id = "cb4-2" > < a href = "#cb4-2" tabindex = "-1" > < / a > < span class = "fu" > sudo< / span > apt install r-base < span class = "kw" > & & < / span > < span class = "ex" > Rscript< / span > < span class = "at" > -e< / span > < span class = "st" > 'install.packages("AMR")'< / span > < / span >
< span id = "cb4-3" > < a href = "#cb4-3" tabindex = "-1" > < / a > < span class = "co" > # Fedora:< / span > < / span >
< span id = "cb4-4" > < a href = "#cb4-4" tabindex = "-1" > < / a > < span class = "fu" > sudo< / span > dnf install R < span class = "kw" > & & < / span > < span class = "ex" > Rscript< / span > < span class = "at" > -e< / span > < span class = "st" > 'install.packages("AMR")'< / span > < / span >
< span id = "cb4-5" > < a href = "#cb4-5" tabindex = "-1" > < / a > < span class = "co" > # CentOS/RHEL< / span > < / span >
< span id = "cb4-6" > < a href = "#cb4-6" tabindex = "-1" > < / a > < span class = "fu" > sudo< / span > yum install R < span class = "kw" > & & < / span > < span class = "ex" > Rscript< / span > < span class = "at" > -e< / span > < span class = "st" > 'install.packages("AMR")'< / span > < / span >
< span id = "cb4-7" > < a href = "#cb4-7" tabindex = "-1" > < / a > < span class = "co" > # Arch Linux< / span > < / span >
< span id = "cb4-8" > < a href = "#cb4-8" tabindex = "-1" > < / a > < span class = "fu" > sudo< / span > pacman < span class = "at" > -S< / span > r < span class = "kw" > & & < / span > < span class = "ex" > Rscript< / span > < span class = "at" > -e< / span > < span class = "st" > 'install.packages("AMR")'< / span > < / span >
< span id = "cb4-9" > < a href = "#cb4-9" tabindex = "-1" > < / a > < span class = "co" > # macOS< / span > < / span >
< span id = "cb4-10" > < a href = "#cb4-10" tabindex = "-1" > < / a > < span class = "ex" > brew< / span > install r < span class = "kw" > & & < / span > < span class = "ex" > Rscript< / span > < span class = "at" > -e< / span > < span class = "st" > 'install.packages("AMR")'< / span > < / span > < / code > < / pre > < / div >
< p > For Windows, visit the < a href = "https://cran.r-project.org" class = "external-link" > CRAN
download page< / a > in install R, then afterwards install the ‘ ’
package manually.< / p >
< / div >
< div class = "section level4" >
< h4 id = "install-amr-python-package" > Install < code > AMR< / code > Python Package< a class = "anchor" aria-label = "anchor" href = "#install-amr-python-package" > < / a >
< / h4 >
< p > Since the Python package is available on the official < a href = "https://pypi.org/project/AMR/" class = "external-link" > Python Package Index< / a > , you can
just run:< / p >
< div class = "sourceCode" id = "cb5" > < pre class = "sourceCode bash" > < code class = "sourceCode bash" > < span id = "cb5-1" > < a href = "#cb5-1" tabindex = "-1" > < / a > < span class = "ex" > pip< / span > install AMR< / span > < / code > < / pre > < / div >
< / div >
< / div >
< div class = "section level2" >
< h2 id = "working-with-amr-in-python" > Working with < code > AMR< / code > in Python< a class = "anchor" aria-label = "anchor" href = "#working-with-amr-in-python" > < / a >
< / h2 >
< p > Now that we have everything set up, let’
examples of using the < code > AMR< / code > package within Python.< / p >
< div class = "section level3" >
< h3 id = "example-1- calculating-amr" > Example 1: Calculating AMR< a class = "anchor" aria-label = "anchor" href = "#example-1- calculating-amr" > < / a >
< h3 id = "calculating-amr" > Calculating AMR< a class = "anchor" aria-label = "anchor" href = "#calculating-amr" > < / a >
< / h3 >
< div class = "sourceCode" id = "cb6 " > < pre class = "sourceCode python" > < code class = "sourceCode python" > < span id = "cb6 -1" > < a href = "#cb6 -1" tabindex = "-1" > < / a > < span class = "im" > import< / span > AMR< / span >
< span id = "cb6 -2" > < a href = "#cb6 -2" tabindex = "-1" > < / a > < span class = "im" > import< / span > pandas < span class = "im" > as< / span > pd< / span >
< span id = "cb6 -3" > < a href = "#cb6 -3" tabindex = "-1" > < / a > < / span >
< span id = "cb6 -4" > < a href = "#cb6 -4" tabindex = "-1" > < / a > df < span class = "op" > =< / span > AMR.example_isolates< / span >
< span id = "cb6 -5" > < a href = "#cb6 -5" tabindex = "-1" > < / a > result < span class = "op" > =< / span > AMR.resistance(df[< span class = "st" > "AMX"< / span > ])< / span >
< span id = "cb6 -6" > < a href = "#cb6 -6" tabindex = "-1" > < / a > < span class = "bu" > print< / span > (result)< / span > < / code > < / pre > < / div >
< div class = "sourceCode" id = "cb7 " > < pre class = "sourceCode python" > < code class = "sourceCode python" > < span id = "cb7 -1" > < a href = "#cb7 -1" tabindex = "-1" > < / a > < span class = "im" > import< / span > AMR< / span >
< span id = "cb7 -2" > < a href = "#cb7 -2" tabindex = "-1" > < / a > < span class = "im" > import< / span > pandas < span class = "im" > as< / span > pd< / span >
< span id = "cb7 -3" > < a href = "#cb7 -3" tabindex = "-1" > < / a > < / span >
< span id = "cb7 -4" > < a href = "#cb7 -4" tabindex = "-1" > < / a > df < span class = "op" > =< / span > AMR.example_isolates< / span >
< span id = "cb7 -5" > < a href = "#cb7 -5" tabindex = "-1" > < / a > result < span class = "op" > =< / span > AMR.resistance(df[< span class = "st" > "AMX"< / span > ])< / span >
< span id = "cb7 -6" > < a href = "#cb7 -6" tabindex = "-1" > < / a > < span class = "bu" > print< / span > (result)< / span > < / code > < / pre > < / div >
< pre > < code > [0.59555556]< / code > < / pre >
< / div >
< div class = "section level3" >
< h3 id = "example-2- generating-antibiograms" > Example 2: Generating Antibiograms< a class = "anchor" aria-label = "anchor" href = "#example-2- generating-antibiograms" > < / a >
< h3 id = "generating-antibiograms" > Generating Antibiograms< a class = "anchor" aria-label = "anchor" href = "#generating-antibiograms" > < / a >
< / h3 >
< p > One of the core functions of the < code > AMR< / code > package is
generating an antibiogram, a table that summarises the antimicrobial
susceptibility of bacterial isolates. Here’
antibiogram from Python:< / p >
< div class = "sourceCode" id = "cb8 " > < pre class = "sourceCode python" > < code class = "sourceCode python" > < span id = "cb8 -1" > < a href = "#cb8 -1" tabindex = "-1" > < / a > result2a < span class = "op" > =< / span > AMR.antibiogram(df[[< span class = "st" > "mo"< / span > , < span class = "st" > "AMX"< / span > , < span class = "st" > "CIP"< / span > , < span class = "st" > "TZP"< / span > ]])< / span >
< span id = "cb8 -2" > < a href = "#cb8 -2" tabindex = "-1" > < / a > < span class = "bu" > print< / span > (result2a)< / span > < / code > < / pre > < / div >
< div class = "sourceCode" id = "cb9 " > < pre class = "sourceCode python" > < code class = "sourceCode python" > < span id = "cb9 -1" > < a href = "#cb9 -1" tabindex = "-1" > < / a > result2a < span class = "op" > =< / span > AMR.antibiogram(df[[< span class = "st" > "mo"< / span > , < span class = "st" > "AMX"< / span > , < span class = "st" > "CIP"< / span > , < span class = "st" > "TZP"< / span > ]])< / span >
< span id = "cb9 -2" > < a href = "#cb9 -2" tabindex = "-1" > < / a > < span class = "bu" > print< / span > (result2a)< / span > < / code > < / pre > < / div >
< table class = "table" >
< colgroup >
< col width = "22%" >
@ -593,8 +567,8 @@ antibiogram from Python:</p>
< / tr >
< / tbody >
< / table >
< div class = "sourceCode" id = "cb9 " > < pre class = "sourceCode python" > < code class = "sourceCode python" > < span id = "cb9 -1" > < a href = "#cb9 -1" tabindex = "-1" > < / a > result2b < span class = "op" > =< / span > AMR.antibiogram(df[[< span class = "st" > "mo"< / span > , < span class = "st" > "AMX"< / span > , < span class = "st" > "CIP"< / span > , < span class = "st" > "TZP"< / span > ]], mo_transform < span class = "op" > =< / span > < span class = "st" > "gramstain"< / span > )< / span >
< span id = "cb9 -2" > < a href = "#cb9 -2" tabindex = "-1" > < / a > < span class = "bu" > print< / span > (result2b)< / span > < / code > < / pre > < / div >
< div class = "sourceCode" id = "cb10 " > < pre class = "sourceCode python" > < code class = "sourceCode python" > < span id = "cb10 -1" > < a href = "#cb10 -1" tabindex = "-1" > < / a > result2b < span class = "op" > =< / span > AMR.antibiogram(df[[< span class = "st" > "mo"< / span > , < span class = "st" > "AMX"< / span > , < span class = "st" > "CIP"< / span > , < span class = "st" > "TZP"< / span > ]], mo_transform < span class = "op" > =< / span > < span class = "st" > "gramstain"< / span > )< / span >
< span id = "cb10 -2" > < a href = "#cb10 -2" tabindex = "-1" > < / a > < span class = "bu" > print< / span > (result2b)< / span > < / code > < / pre > < / div >
< table class = "table" >
< colgroup >
< col width = "20%" >
@ -637,11 +611,12 @@ 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.< / p >
< p > By using < code > import AMR< / code > , you can seamlessly integrate the
robust features of the R < code > AMR< / code > package into your Python
workflows. Whether you’
patterns, the < code > AMR< / code > Python package makes it easy to work wi th
AMR data in Python.< / p >
< p > By j ust runn ing < code > import AMR< / code > , users can seamlessly
integrate the robust features of the R < code > AMR< / code > package into
Python workflows.< / p >
< p > Whether you’ e
< code > AMR< / code > Python package makes it easy to work with AMR data in
Python.< / p >
< / div >
< / main > < aside class = "col-md-3" > < nav id = "toc" aria-label = "Table of contents" > < h2 > On this page< / h2 >
< / nav > < / aside >
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