diff --git a/_includes/binarization/activities/binarization_python.md b/_includes/binarization/activities/binarization_python.md
new file mode 100644
index 0000000000000000000000000000000000000000..2f0f67b4aa64cf75ccd1f05242fee1c1a4b0e116
--- /dev/null
+++ b/_includes/binarization/activities/binarization_python.md
@@ -0,0 +1,24 @@
+``` python
+import numpy as np
+import matplotlib.pyplot as plt
+from skimage import io
+
+# load the image from file
+image_file = "/image-analysis-training-resources/image_data/xy_8bit__two_cells.tif"
+image = io.imread(image_file)
+
+# binarize the image, so that all values larger than the threshold are foreground
+threshold_value = 60
+binarized = image > threshold_value
+
+# display the original and the binarized image
+fig, ax = plt.subplots(2)
+ax[0].imshow(image)
+ax[0].set_title("Image")
+ax[1].imshow(binarized)
+ax[1].set_title("Binarized")
+
+```
+
+For associated course material in jupyter, [click here](https://nbviewer.jupyter.org/github/embl-bio-it/image-analysis-with-python/blob/carpentry/image-analysis-session/image-binarization.ipynb#Image-Binarization).
+You can also spin up an interactive [binder session](https://gke.mybinder.org/v2/gh/embl-bio-it/image-analysis-with-python/carpentry?filepath=image-analysis-session/image-binarization.ipynb).