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< li > < a class = "dropdown-item" href = "../articles/AMR.html" > < span class = "fa fa-directions" > < / span > Conduct AMR Analysis< / a > < / li >
< li > < a class = "dropdown-item" href = "../reference/antibiogram.html" > < span class = "fa fa-file-prescription" > < / span > Generate Antibiogram (Trad./Syndromic/WISCA)< / a > < / li >
< li > < a class = "dropdown-item" href = "../articles/resistance_predict.html" > < span class = "fa fa-dice" > < / span > Predict Antimicrobial Resistance< / a > < / li >
< li > < a class = "dropdown-item" href = "../articles/datasets.html" > < span class = "fa fa-database" > < / span > Download Data Sets for Own Use< / a > < / li >
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< li > < a class = "dropdown-item" href = "../articles/AMR_with_tidymodels.html" > < span class = "fa fa-square-root-variable" > < / span > Use AMR for Predictive Modelling (tidymodels)< / a > < / li >
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< li > < a class = "dropdown-item" href = "../reference/AMR-options.html" > < span class = "fa fa-gear" > < / span > Set User- Or Team-specific Package Settings< / a > < / li >
< li > < a class = "dropdown-item" href = "../articles/PCA.html" > < span class = "fa fa-compress" > < / span > Conduct Principal Component Analysis for AMR< / a > < / li >
< li > < a class = "dropdown-item" href = "../articles/MDR.html" > < span class = "fa fa-skull-crossbones" > < / span > Determine Multi-Drug Resistance (MDR)< / a > < / li >
< li > < a class = "dropdown-item" href = "../articles/WHONET.html" > < span class = "fa fa-globe-americas" > < / span > Work with WHONET Data< / a > < / li >
< li > < a class = "dropdown-item" href = "../articles/EUCAST.html" > < span class = "fa fa-exchange-alt" > < / span > Apply Eucast Rules< / a > < / li >
< li > < a class = "dropdown-item" href = "../reference/mo_property.html" > < span class = "fa fa-bug" > < / span > Get Taxonomy of a Microorganism< / a > < / li >
< li > < a class = "dropdown-item" href = "../reference/ab_property.html" > < span class = "fa fa-capsules" > < / span > Get Properties of an Antibiotic Drug< / a > < / li >
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< img src = "../logo.svg" class = "logo" alt = "" > < h1 > AMR for Python< / h1 >
< small class = "dont-index" > Source: < a href = "https://github.com/msberends/AMR/blob/main/vignettes/AMR_for_Python.Rmd" class = "external-link" > < code > vignettes/AMR_for_Python.Rmd< / code > < / a > < / small >
< div class = "d-none name" > < code > AMR_for_Python.Rmd< / code > < / div >
< / div >
< div class = "section level2" >
< h2 id = "introduction" > Introduction< a class = "anchor" aria-label = "anchor" href = "#introduction" > < / a >
< / h2 >
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< p > The < code > AMR< / code > 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:
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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 the < code > AMR< / code > R
package. It uses the < code > rpy2< / code > package internally. Despite the
need to have R installed, Python users can now easily work with AMR data
directly through Python code.< / p >
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< / div >
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< 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 >
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< 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 = "cb1" > < pre class = "sourceCode bash" > < code class = "sourceCode bash" > < span id = "cb1-1" > < a href = "#cb1-1" tabindex = "-1" > < / a > < span class = "ex" > pip< / span > install AMR< / span > < / code > < / pre > < / div >
< / li >
< li >
< p > Make sure you have R installed. There is < strong > no need to
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install the < code > AMR< / code > R package< / strong > , as it will be installed
automatically.< / p >
< p > For Linux:< / p >
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< 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 = "co" > # Ubuntu / Debian< / span > < / span >
< span id = "cb2-2" > < a href = "#cb2-2" tabindex = "-1" > < / a > < span class = "fu" > sudo< / span > apt install r-base< / span >
< span id = "cb2-3" > < a href = "#cb2-3" tabindex = "-1" > < / a > < span class = "co" > # Fedora:< / span > < / span >
< span id = "cb2-4" > < a href = "#cb2-4" tabindex = "-1" > < / a > < span class = "fu" > sudo< / span > dnf install R< / span >
< span id = "cb2-5" > < a href = "#cb2-5" tabindex = "-1" > < / a > < span class = "co" > # CentOS/RHEL< / span > < / span >
< span id = "cb2-6" > < a href = "#cb2-6" tabindex = "-1" > < / a > < span class = "fu" > sudo< / span > yum install R< / span > < / code > < / pre > < / div >
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< p > For macOS (using < a href = "https://brew.sh" class = "external-link" > Homebrew< / a > ):< / p >
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< 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" > brew< / span > install r< / span > < / code > < / pre > < / div >
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< 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 >
< / 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 >
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< div class = "section level3" >
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< h3 id = "cleaning-taxonomy" > Cleaning Taxonomy< a class = "anchor" aria-label = "anchor" href = "#cleaning-taxonomy" > < / a >
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< / h3 >
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< p > Here’
drug names using the < code > AMR< / code > Python package:< / p >
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< 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 >
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< table class = "table" >
< thead > < tr class = "header" >
< th > MOs< / th >
< th > Drug< / th >
< th > MO_clean< / th >
< th > Drug_clean< / th >
< / tr > < / thead >
< tbody >
< tr class = "odd" >
< td > E. coli< / td >
< td > Cipro< / td >
< td > Escherichia coli< / td >
< td > Ciprofloxacin< / td >
< / tr >
< tr class = "even" >
< td > ESCCOL< / td >
< td > CIP< / td >
< td > Escherichia coli< / td >
< td > Ciprofloxacin< / td >
< / tr >
< tr class = "odd" >
< td > esco< / td >
< td > J01MA02< / td >
< td > Escherichia coli< / td >
< td > Ciprofloxacin< / td >
< / tr >
< tr class = "even" >
< td > Esche coli< / td >
< td > Ciproxin< / td >
< td > Escherichia coli< / td >
< td > Ciprofloxacin< / td >
< / tr >
< / tbody >
< / table >
< div class = "section level4" >
< h4 id = "explanation" > Explanation< a class = "anchor" aria-label = "anchor" href = "#explanation" > < / a >
< / h4 >
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< ul >
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< li > < p > < strong > mo_name:< / strong > This function standardises
microorganism names. Here, different variations of < em > Escherichia
coli< / em > (such as “E. coli”, “ESCCOL”, “esco”, and “Esche coli”) are
all converted into the correct, standardised form, “Escherichia
coli”.< / p > < / li >
< li > < p > < strong > ab_name< / strong > : 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”.< / p > < / li >
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< / ul >
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< / div >
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< / 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 >
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< 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 >
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< 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 >
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< table class = "table" >
< colgroup >
< col width = "11%" >
< col width = "29%" >
< col width = "8%" >
< col width = "8%" >
< col width = "8%" >
< col width = "10%" >
< col width = "13%" >
< col width = "9%" >
< / colgroup >
< thead > < tr class = "header" >
< th > mo< / th >
< th > fullname< / th >
< th > status< / th >
< th > kingdom< / th >
< th > gbif< / th >
< th > gbif_parent< / th >
< th > gbif_renamed_to< / th >
< th > prevalence< / th >
< / tr > < / thead >
< tbody >
< tr class = "odd" >
< td > B_GRAMN< / td >
< td > (unknown Gram-negatives)< / td >
< td > unknown< / td >
< td > Bacteria< / td >
< td > None< / td >
< td > None< / td >
< td > None< / td >
< td > 2.0< / td >
< / tr >
< tr class = "even" >
< td > B_GRAMP< / td >
< td > (unknown Gram-positives)< / td >
< td > unknown< / td >
< td > Bacteria< / td >
< td > None< / td >
< td > None< / td >
< td > None< / td >
< td > 2.0< / td >
< / tr >
< tr class = "odd" >
< td > B_ANAER-NEG< / td >
< td > (unknown anaerobic Gram-negatives)< / td >
< td > unknown< / td >
< td > Bacteria< / td >
< td > None< / td >
< td > None< / td >
< td > None< / td >
< td > 2.0< / td >
< / tr >
< tr class = "even" >
< td > B_ANAER-POS< / td >
< td > (unknown anaerobic Gram-positives)< / td >
< td > unknown< / td >
< td > Bacteria< / td >
< td > None< / td >
< td > None< / td >
< td > None< / td >
< td > 2.0< / td >
< / tr >
< tr class = "odd" >
< td > B_ANAER< / td >
< td > (unknown anaerobic bacteria)< / td >
< td > unknown< / td >
< td > Bacteria< / td >
< td > None< / td >
< td > None< / td >
< td > None< / td >
< td > 2.0< / td >
< / tr >
< tr class = "even" >
< td > …< / td >
< td > …< / td >
< td > …< / td >
< td > …< / td >
< td > …< / td >
< td > …< / td >
< td > …< / td >
< td > …< / td >
< / tr >
< tr class = "odd" >
< td > B_ZYMMN_POMC< / td >
< td > Zymomonas pomaceae< / td >
< td > accepted< / td >
< td > Bacteria< / td >
< td > 10744418< / td >
< td > 3221412< / td >
< td > None< / td >
< td > 2.0< / td >
< / tr >
< tr class = "even" >
< td > B_ZYMPH< / td >
< td > Zymophilus< / td >
< td > synonym< / td >
< td > Bacteria< / td >
< td > None< / td >
< td > 9475166< / td >
< td > None< / td >
< td > 2.0< / td >
< / tr >
< tr class = "odd" >
< td > B_ZYMPH_PCVR< / td >
< td > Zymophilus paucivorans< / td >
< td > synonym< / td >
< td > Bacteria< / td >
< td > None< / td >
< td > None< / td >
< td > None< / td >
< td > 2.0< / td >
< / tr >
< tr class = "even" >
< td > B_ZYMPH_RFFN< / td >
< td > Zymophilus raffinosivorans< / td >
< td > synonym< / td >
< td > Bacteria< / td >
< td > None< / td >
< td > None< / td >
< td > None< / td >
< td > 2.0< / td >
< / tr >
< tr class = "odd" >
< td > F_ZYZYG< / td >
< td > Zyzygomyces< / td >
< td > unknown< / td >
< td > Fungi< / td >
< td > None< / td >
< td > 7581< / td >
< td > None< / td >
< td > 2.0< / td >
< / tr >
< / tbody >
< / table >
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< 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 >
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< table style = "width:100%;" class = "table" >
< colgroup >
< col width = "4%" >
< col width = "12%" >
< col width = "20%" >
< col width = "25%" >
< col width = "9%" >
< col width = "11%" >
< col width = "7%" >
< col width = "9%" >
< / colgroup >
< thead > < tr class = "header" >
< th > ab< / th >
< th > cid< / th >
< th > name< / th >
< th > group< / th >
< th > oral_ddd< / th >
< th > oral_units< / th >
< th > iv_ddd< / th >
< th > iv_units< / th >
< / tr > < / thead >
< tbody >
< tr class = "odd" >
< td > AMA< / td >
< td > 4649.0< / td >
< td > 4-aminosalicylic acid< / td >
< td > Antimycobacterials< / td >
< td > 12.00< / td >
< td > g< / td >
< td > NaN< / td >
< td > None< / td >
< / tr >
< tr class = "even" >
< td > ACM< / td >
< td > 6450012.0< / td >
< td > Acetylmidecamycin< / td >
< td > Macrolides/lincosamides< / td >
< td > NaN< / td >
< td > None< / td >
< td > NaN< / td >
< td > None< / td >
< / tr >
< tr class = "odd" >
< td > ASP< / td >
< td > 49787020.0< / td >
< td > Acetylspiramycin< / td >
< td > Macrolides/lincosamides< / td >
< td > NaN< / td >
< td > None< / td >
< td > NaN< / td >
< td > None< / td >
< / tr >
< tr class = "even" >
< td > ALS< / td >
< td > 8954.0< / td >
< td > Aldesulfone sodium< / td >
< td > Other antibacterials< / td >
< td > 0.33< / td >
< td > g< / td >
< td > NaN< / td >
< td > None< / td >
< / tr >
< tr class = "odd" >
< td > AMK< / td >
< td > 37768.0< / td >
< td > Amikacin< / td >
< td > Aminoglycosides< / td >
< td > NaN< / td >
< td > None< / td >
< td > 1.0< / td >
< td > g< / td >
< / tr >
< tr class = "even" >
< td > …< / td >
< td > …< / td >
< td > …< / td >
< td > …< / td >
< td > …< / td >
< td > …< / td >
< td > …< / td >
< td > …< / td >
< / tr >
< tr class = "odd" >
< td > VIR< / td >
< td > 11979535.0< / td >
< td > Virginiamycine< / td >
< td > Other antibacterials< / td >
< td > NaN< / td >
< td > None< / td >
< td > NaN< / td >
< td > None< / td >
< / tr >
< tr class = "even" >
< td > VOR< / td >
< td > 71616.0< / td >
< td > Voriconazole< / td >
< td > Antifungals/antimycotics< / td >
< td > 0.40< / td >
< td > g< / td >
< td > 0.4< / td >
< td > g< / td >
< / tr >
< tr class = "odd" >
< td > XBR< / td >
< td > 72144.0< / td >
< td > Xibornol< / td >
< td > Other antibacterials< / td >
< td > NaN< / td >
< td > None< / td >
< td > NaN< / td >
< td > None< / td >
< / tr >
< tr class = "even" >
< td > ZID< / td >
< td > 77846445.0< / td >
< td > Zidebactam< / td >
< td > Other antibacterials< / td >
< td > NaN< / td >
< td > None< / td >
< td > NaN< / td >
< td > None< / td >
< / tr >
< tr class = "odd" >
< td > ZFD< / td >
< td > NaN< / td >
< td > Zoliflodacin< / td >
< td > None< / td >
< td > NaN< / td >
< td > None< / td >
< td > NaN< / td >
< td > None< / td >
< / tr >
< / tbody >
< / table >
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< / div >
< div class = "section level3" >
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< h3 id = "calculating-amr" > Calculating AMR< a class = "anchor" aria-label = "anchor" href = "#calculating-amr" > < / a >
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< / h3 >
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< 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 >
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< pre > < code > [0.59555556]< / code > < / pre >
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< / div >
< div class = "section level3" >
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< h3 id = "generating-antibiograms" > Generating Antibiograms< a class = "anchor" aria-label = "anchor" href = "#generating-antibiograms" > < / a >
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< / 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 >
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< 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 >
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< table class = "table" >
< colgroup >
< col width = "22%" >
< col width = "22%" >
< col width = "22%" >
< col width = "33%" >
< / colgroup >
< thead > < tr class = "header" >
< th > Pathogen< / th >
< th > Amoxicillin< / th >
< th > Ciprofloxacin< / th >
< th > Piperacillin/tazobactam< / th >
< / tr > < / thead >
< tbody >
< tr class = "odd" >
< td > CoNS< / td >
< td > 7% (10/142)< / td >
< td > 73% (183/252)< / td >
< td > 30% (10/33)< / td >
< / tr >
< tr class = "even" >
< td > E. coli< / td >
< td > 50% (196/392)< / td >
< td > 88% (399/456)< / td >
< td > 94% (393/416)< / td >
< / tr >
< tr class = "odd" >
< td > K. pneumoniae< / td >
< td > 0% (0/58)< / td >
< td > 96% (53/55)< / td >
< td > 89% (47/53)< / td >
< / tr >
< tr class = "even" >
< td > P. aeruginosa< / td >
< td > 0% (0/30)< / td >
< td > 100% (30/30)< / td >
< td > None< / td >
< / tr >
< tr class = "odd" >
< td > P. mirabilis< / td >
< td > None< / td >
< td > 94% (34/36)< / td >
< td > None< / td >
< / tr >
< tr class = "even" >
< td > S. aureus< / td >
< td > 6% (8/131)< / td >
< td > 90% (171/191)< / td >
< td > None< / td >
< / tr >
< tr class = "odd" >
< td > S. epidermidis< / td >
< td > 1% (1/91)< / td >
< td > 64% (87/136)< / td >
< td > None< / td >
< / tr >
< tr class = "even" >
< td > S. hominis< / td >
< td > None< / td >
< td > 80% (56/70)< / td >
< td > None< / td >
< / tr >
< tr class = "odd" >
< td > S. pneumoniae< / td >
< td > 100% (112/112)< / td >
< td > None< / td >
< td > 100% (112/112)< / td >
< / tr >
< / tbody >
< / table >
2024-10-17 12:03:11 +02:00
< 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 >
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< table class = "table" >
< colgroup >
< col width = "20%" >
< col width = "22%" >
< col width = "23%" >
< col width = "33%" >
< / colgroup >
< thead > < tr class = "header" >
< th > Pathogen< / th >
< th > Amoxicillin< / th >
< th > Ciprofloxacin< / th >
< th > Piperacillin/tazobactam< / th >
< / tr > < / thead >
< tbody >
< tr class = "odd" >
< td > Gram-negative< / td >
< td > 36% (226/631)< / td >
< td > 91% (621/684)< / td >
< td > 88% (565/641)< / td >
< / tr >
< tr class = "even" >
< td > Gram-positive< / td >
< td > 43% (305/703)< / td >
< td > 77% (560/724)< / td >
< td > 86% (296/345)< / td >
< / tr >
< / tbody >
< / table >
< p > In this example, we generate an antibiogram by selecting various
antibiotics.< / p >
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< / div >
< / div >
< div class = "section level2" >
< h2 id = "conclusion" > Conclusion< a class = "anchor" aria-label = "anchor" href = "#conclusion" > < / a >
< / h2 >
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< p > With the < code > AMR< / code > Python package, Python users can now
effortlessly call R functions from the < code > AMR< / code > R package. This
eliminates the need for complex < code > rpy2< / code > 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.< / p >
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< p > By just running < 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’
< code > AMR< / code > Python package makes it easy to work with AMR data in
Python.< / p >
2024-09-30 22:12:21 +02:00
< / div >
< / main > < aside class = "col-md-3" > < nav id = "toc" aria-label = "Table of contents" > < h2 > On this page< / h2 >
< / nav > < / aside >
< / div >
< footer > < div class = "pkgdown-footer-left" >
< p > < code > AMR< / code > (for R). Free and open-source, licenced under the < a target = "_blank" href = "https://github.com/msberends/AMR/blob/main/LICENSE" class = "external-link" > GNU General Public License version 2.0 (GPL-2)< / a > .< br > Developed at the < a target = "_blank" href = "https://www.rug.nl" class = "external-link" > University of Groningen< / a > and < a target = "_blank" href = "https://www.umcg.nl" class = "external-link" > University Medical Center Groningen< / a > in The Netherlands.< / p >
< / div >
< div class = "pkgdown-footer-right" >
< p > < a target = "_blank" href = "https://www.rug.nl" class = "external-link" > < img src = "https://github.com/msberends/AMR/raw/main/pkgdown/assets/logo_rug.svg" style = "max-width: 150px;" > < / a > < a target = "_blank" href = "https://www.umcg.nl" class = "external-link" > < img src = "https://github.com/msberends/AMR/raw/main/pkgdown/assets/logo_umcg.svg" style = "max-width: 150px;" > < / a > < / p >
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