diff --git a/compiler/src/knitr/__test__/knitr.test.ts b/compiler/src/knitr/__test__/knitr.test.ts
index 396edb1f..157dae33 100644
--- a/compiler/src/knitr/__test__/knitr.test.ts
+++ b/compiler/src/knitr/__test__/knitr.test.ts
@@ -347,4 +347,20 @@ describe('knitr', () => {
 
     expect(ignoreWhitespace(html)).toBe(result);
   }, 120000);
+
+  it('should remove python warnings from the top of knitr output', async () => {
+    const { md } = await testProcessor(`
+      WARNING - All triples will be processed in the same batch (batches_count=1).
+      When processing large graphs it is recommended to batch the input knowledge
+      graph instead.
+
+      ## Introduction
+
+      In the first two week of this course we have focused on the use of networks
+      to analyse social systems, using a mixture of global statistics, node
+      statistics, and ERGM modelling.
+    `);
+
+    expect(md.trim().startsWith('WARNING')).toBe(false);
+  });
 });
diff --git a/compiler/src/knitr/knitr.ts b/compiler/src/knitr/knitr.ts
index 631a0c9e..90b329ba 100644
--- a/compiler/src/knitr/knitr.ts
+++ b/compiler/src/knitr/knitr.ts
@@ -153,6 +153,7 @@ function reportErrors(response: string, file: VFile) {
 async function formatResponse(response: string) {
   let md = response;
   md = removeCustomPythonBinNotice(md);
+  md = removePythonWarningMessage(md);
   md = addCodeBlockClasses(md);
   md = addErrorCodeBlock(md);
   md = removeHashSigns(md);
@@ -165,6 +166,10 @@ function removeCustomPythonBinNotice(md: string) {
   return md.replace(/^\$python\s\[1\]\s"\S+"/, '');
 }
 
+function removePythonWarningMessage(md: string) {
+  return md.replace(/^WARNING - .+?[\r\n]+/m, '');
+}
+
 function addCodeBlockClasses(md: string) {
   return md
     .split('\n')