diff --git a/_includes/image_file_formats/open_czi_bioio.py b/_includes/image_file_formats/open_czi_bioio.py
index c1fbfe2b..be755d4e 100644
--- a/_includes/image_file_formats/open_czi_bioio.py
+++ b/_includes/image_file_formats/open_czi_bioio.py
@@ -4,180 +4,44 @@
 # conda create -n ImageFileFormats python=3.10
 # activate ImageFileFormat
 # pip install bioio bioio-tifffile bioio-lif bioio-czi bioio-ome-tiff bioio-ome-zarr notebook
+# Note: for only dealing with .czi just do pip install bioio bioio-czi
 
-# TODO
-# - Change the below code to only open the CZI image
-# - Implement that it opens both images that are contained in the file (see ImageJ GUI activity)
-
-# %%
-# Load .tif file with minimal metadata
-# - Observe that BioImage chooses the correct reader plugin
-# - Observe that the return object is not the image matrix
-from bioio import BioImage
-image_url = 'https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit__nuclei_PLK1_control.tif'
-bioimage = BioImage(image_url)
-print(bioimage)
-print(type(bioimage))
-
-# %%
-# Print some onject attributes
-# - Observe that the object is 5 dimensional with most dimensions being empty
-# - Observe that the dimension order is always time, channel, z, y, x, (TCZYX)
-print(bioimage.dims)
-print(bioimage.shape)
-print(f'Dimension order is: {bioimage.dims.order}')
-print(type(bioimage.dims.order))
-print(f'Size of X dimension is: {bioimage.dims.X}')
-
-# %%
-# Extract image data
-# - Observe that the returned numpy.array is still 5 dimensional
-image_data = bioimage.data
-print(type(image_data))
-print(image_data)
-print(image_data.shape)
-
-# %%
-# Extract specific part of image data
-# - Observe that numpy.array is reduced to populated dimensions only
-yx_image_data = bioimage.get_image_data('YX')
-print(type(yx_image_data))
-print(yx_image_data)
-print(yx_image_data.shape)
-
-# %%
-# Access pixel size
-import numpy as np
-print(bioimage.physical_pixel_sizes)
-print(f'An pixel has a length of {np.round(bioimage.physical_pixel_sizes.X,2)} microns in X dimension.')
-
-# %%
-# Access general metadata
-print(type(bioimage.metadata))
-print(bioimage.metadata)
-
-# %%
-# Load .tif file with extensive metadata
-image_url = "https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_16bit__collagen.md.tif"
-bioimage = BioImage(image_url)
-print(bioimage)
-print(type(bioimage))
-
-# - Observe that the image is larger than the previous
-print(bioimage.dims)
-
-# %%
-# Access image and reduce to only populated dimensions
-yx_image_data = bioimage.data.squeeze()
-print(type(yx_image_data))
-print(yx_image_data)
-print(yx_image_data.shape)
-
-# %%
-# Access pixel size
-print(bioimage.physical_pixel_sizes)
-print(f'An pixel has a length of {np.round(bioimage.physical_pixel_sizes.Y,2)} microns in Y dimension.')
-
-# Access general metadata
-# - Observe that metadata are more extensive than in the previous image
-print(type(bioimage.metadata))
-print(bioimage.metadata)
-
-# %%
-# Load .lif file
-# - Observe that BioImage chooses the correct reader plugin
-# - Observe that the return object has 4 different channels
-# - Observe that the general metadata are an abstract element
-image_url = "https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_xyc__two_images.lif"
-bioimage = BioImage(image_url)
-print(bioimage)
-print(type(bioimage))
-print(bioimage.dims)
-print(bioimage.metadata)
-print(type(bioimage.metadata))
-
-# %%
-# Access channel information
-print(bioimage.channel_names)
-
-# %%
-# Access image data for all channels
-img_4channel = bioimage.data.squeeze()
-
-# Alternative
-img_4channel = bioimage.get_image_data('CYX')
-
-# - Observe that numpy.array shape is 3 dimensional representing channel,y,x
-print(img_4channel.shape)
-
-# Access only one channel
-img_1channel = bioimage.get_image_data('YX',C=0)
-
-# Alternative
-img_1channel = img_4channel[0]
-
-# - Observe that numpy.array shape is 2 dimensional representing y,x
-print(img_1channel.shape)
-
-# %%
-# Access different images in one image file (scenes)
-# - Observe that one image file can contain several scenes
-# - Observe that they can be different in various aspects
-print(bioimage.scenes)
-print(f'Current scene: {bioimage.current_scene}')
-
-# - Observe that the image in the current scene as 4 channel and Y/X dimensions have the size of 1024
-print(bioimage.dims)
-print(bioimage.physical_pixel_sizes)
-
-# Switch to second scene
-# - Observe that the image in the other scene as only one channel and Y/X dimensions are half as large as the first scene
-# - Observe that the pixel sizes are doubled
-bioimage.set_scene(1)
-print(bioimage.dims)
-print(bioimage.physical_pixel_sizes)
 
 # %%
 # Load .czi file
 # file needs first to be downloaded from https://github.com/NEUBIAS/training-resources/raw/master/image_data/xyz__multiple_images.czi
 # save file in the same directory as this notebook
 # - Observe that BioImage chooses the correct reader plugin
-# - Observe that the return object has a z dimension
-bioimage = BioImage('/Users/fschneider/skimage-napari-tutorial/ExampleImages/xyz__multiple_images.czi')
+from bioio import BioImage
+bioimage = BioImage('~/skimage-napari-tutorial/ExampleImages/xyz__multiple_images.czi')
 print(bioimage)
 print(type(bioimage))
 
 # %%
-# little excersise in between
-# Access image dimensions
-print(bioimage.dims)
-
-# Access general metadata
-# - Observe that metadata are abstract
-print(bioimage.metadata)
-print(type(bioimage.metadata))
-
-# Access pixel size
-print(bioimage.physical_pixel_sizes)
-
-# Access image data for all channels
-img_3d = bioimage.data.squeeze()
-
-# Alternative
-img_3d = bioimage.get_image_data('ZYX')
-
-# - Observe that numpy.array shape is 3 dimensional representing z,y,x
-print(img_3d.shape)
-
-# Access only one channel
-img_2d = bioimage.get_image_data('YX',Z=0)
-
-# Alternative
-img_2d = img_3d[0]
-
-# - Observe that numpy.array shape is 2 dimensional representing y,x
-print(img_2d.shape)
+# Inspect number of images in object
+print(bioimage.scenes)
 
 # %%
-# little excercise:
-# paticipants should try to open one of their files with python
+# Inspect both images in the object
+for image in bioimage.scenes:
+    print(f'Image name: {image}')
+    # Select image:
+    bioimage.set_scene(image)
+    # Inspect dimension and shape of image
+    print(f'Image dimension: {bioimage.dims}')
+    print(f'Dimension order is: {bioimage.dims.order}')
+    print(f'Image shape: {bioimage.shape}')
+    # Extract image data (5D)
+    image_data = bioimage.data
+    print(f'Image type: {type(image_data)}')
+    print(f'Image array shape: {image_data.shape}')
+    # Extract specific image part
+    image_data = bioimage.get_image_data('YX')
+    print(f'Image type: {type(image_data)}')
+    print(f'Image array shape: {image_data.shape}')
+    # Read pixel size
+    print(f'Pixel size: {bioimage.physical_pixel_sizes}')
+    # Read metadata
+    print(f'Metadata type: {type(bioimage.metadata)}')
+    print(f'Metadata: {bioimage.metadata}')
+    print('\n')
diff --git a/_includes/image_file_formats/open_em_tiff_series_bioio.py b/_includes/image_file_formats/open_em_tiff_series_bioio.py
index c1fbfe2b..de318a93 100644
--- a/_includes/image_file_formats/open_em_tiff_series_bioio.py
+++ b/_includes/image_file_formats/open_em_tiff_series_bioio.py
@@ -1,183 +1,34 @@
 # %% 
-# Open a CZI image file
+# Open a TIFF series
 # minimal conda env for this module
 # conda create -n ImageFileFormats python=3.10
 # activate ImageFileFormat
 # pip install bioio bioio-tifffile bioio-lif bioio-czi bioio-ome-tiff bioio-ome-zarr notebook
+# Note: for only dealing with .tif just do pip install bioio bioio-tifffile
 
-# TODO
-# - Change the below code to only open the CZI image
-# - Implement that it opens both images that are contained in the file (see ImageJ GUI activity)
 
 # %%
-# Load .tif file with minimal metadata
-# - Observe that BioImage chooses the correct reader plugin
-# - Observe that the return object is not the image matrix
-from bioio import BioImage
-image_url = 'https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit__nuclei_PLK1_control.tif'
-bioimage = BioImage(image_url)
-print(bioimage)
-print(type(bioimage))
-
-# %%
-# Print some onject attributes
-# - Observe that the object is 5 dimensional with most dimensions being empty
-# - Observe that the dimension order is always time, channel, z, y, x, (TCZYX)
-print(bioimage.dims)
-print(bioimage.shape)
-print(f'Dimension order is: {bioimage.dims.order}')
-print(type(bioimage.dims.order))
-print(f'Size of X dimension is: {bioimage.dims.X}')
+# create list of image file names and order them
+from pathlib import Path
+path_to_files = Path('/Users/fschneider/Training/training-resources/image_data/xyz_8bit__em_volume_tiff_series')
+tiff_files = [file for file in path_to_files.glob("*.tif")]
+# order files
+tiff_files.sort()
+print(tiff_files)
 
 # %%
-# Extract image data
-# - Observe that the returned numpy.array is still 5 dimensional
-image_data = bioimage.data
-print(type(image_data))
-print(image_data)
-print(image_data.shape)
+# Open each image with BioIO
+from bioio import BioImage
+bioimages = [BioImage(file) for file in tiff_files]
 
-# %%
-# Extract specific part of image data
-# - Observe that numpy.array is reduced to populated dimensions only
-yx_image_data = bioimage.get_image_data('YX')
-print(type(yx_image_data))
-print(yx_image_data)
-print(yx_image_data.shape)
+# Print pixel sizes
+for bioimage in bioimages:
+    print(bioimage.physical_pixel_sizes)
 
 # %%
-# Access pixel size
+# Concatenate in 3D volume
+em_volume = [bioimage.data.squeeze() for bioimage in bioimages]
+# make numpy array
 import numpy as np
-print(bioimage.physical_pixel_sizes)
-print(f'An pixel has a length of {np.round(bioimage.physical_pixel_sizes.X,2)} microns in X dimension.')
-
-# %%
-# Access general metadata
-print(type(bioimage.metadata))
-print(bioimage.metadata)
-
-# %%
-# Load .tif file with extensive metadata
-image_url = "https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_16bit__collagen.md.tif"
-bioimage = BioImage(image_url)
-print(bioimage)
-print(type(bioimage))
-
-# - Observe that the image is larger than the previous
-print(bioimage.dims)
-
-# %%
-# Access image and reduce to only populated dimensions
-yx_image_data = bioimage.data.squeeze()
-print(type(yx_image_data))
-print(yx_image_data)
-print(yx_image_data.shape)
-
-# %%
-# Access pixel size
-print(bioimage.physical_pixel_sizes)
-print(f'An pixel has a length of {np.round(bioimage.physical_pixel_sizes.Y,2)} microns in Y dimension.')
-
-# Access general metadata
-# - Observe that metadata are more extensive than in the previous image
-print(type(bioimage.metadata))
-print(bioimage.metadata)
-
-# %%
-# Load .lif file
-# - Observe that BioImage chooses the correct reader plugin
-# - Observe that the return object has 4 different channels
-# - Observe that the general metadata are an abstract element
-image_url = "https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_xyc__two_images.lif"
-bioimage = BioImage(image_url)
-print(bioimage)
-print(type(bioimage))
-print(bioimage.dims)
-print(bioimage.metadata)
-print(type(bioimage.metadata))
-
-# %%
-# Access channel information
-print(bioimage.channel_names)
-
-# %%
-# Access image data for all channels
-img_4channel = bioimage.data.squeeze()
-
-# Alternative
-img_4channel = bioimage.get_image_data('CYX')
-
-# - Observe that numpy.array shape is 3 dimensional representing channel,y,x
-print(img_4channel.shape)
-
-# Access only one channel
-img_1channel = bioimage.get_image_data('YX',C=0)
-
-# Alternative
-img_1channel = img_4channel[0]
-
-# - Observe that numpy.array shape is 2 dimensional representing y,x
-print(img_1channel.shape)
-
-# %%
-# Access different images in one image file (scenes)
-# - Observe that one image file can contain several scenes
-# - Observe that they can be different in various aspects
-print(bioimage.scenes)
-print(f'Current scene: {bioimage.current_scene}')
-
-# - Observe that the image in the current scene as 4 channel and Y/X dimensions have the size of 1024
-print(bioimage.dims)
-print(bioimage.physical_pixel_sizes)
-
-# Switch to second scene
-# - Observe that the image in the other scene as only one channel and Y/X dimensions are half as large as the first scene
-# - Observe that the pixel sizes are doubled
-bioimage.set_scene(1)
-print(bioimage.dims)
-print(bioimage.physical_pixel_sizes)
-
-# %%
-# Load .czi file
-# file needs first to be downloaded from https://github.com/NEUBIAS/training-resources/raw/master/image_data/xyz__multiple_images.czi
-# save file in the same directory as this notebook
-# - Observe that BioImage chooses the correct reader plugin
-# - Observe that the return object has a z dimension
-bioimage = BioImage('/Users/fschneider/skimage-napari-tutorial/ExampleImages/xyz__multiple_images.czi')
-print(bioimage)
-print(type(bioimage))
-
-# %%
-# little excersise in between
-# Access image dimensions
-print(bioimage.dims)
-
-# Access general metadata
-# - Observe that metadata are abstract
-print(bioimage.metadata)
-print(type(bioimage.metadata))
-
-# Access pixel size
-print(bioimage.physical_pixel_sizes)
-
-# Access image data for all channels
-img_3d = bioimage.data.squeeze()
-
-# Alternative
-img_3d = bioimage.get_image_data('ZYX')
-
-# - Observe that numpy.array shape is 3 dimensional representing z,y,x
-print(img_3d.shape)
-
-# Access only one channel
-img_2d = bioimage.get_image_data('YX',Z=0)
-
-# Alternative
-img_2d = img_3d[0]
-
-# - Observe that numpy.array shape is 2 dimensional representing y,x
-print(img_2d.shape)
-
-# %%
-# little excercise:
-# paticipants should try to open one of their files with python
+em_volume = np.stack(em_volume,axis=0)
+print(em_volume.shape)
diff --git a/_includes/image_file_formats/open_lif_bioio.py b/_includes/image_file_formats/open_lif_bioio.py
new file mode 100644
index 00000000..afc674a5
--- /dev/null
+++ b/_includes/image_file_formats/open_lif_bioio.py
@@ -0,0 +1,46 @@
+# %% 
+# Open a CZI image file
+# minimal conda env for this module
+# conda create -n ImageFileFormats python=3.10
+# activate ImageFileFormat
+# pip install bioio bioio-tifffile bioio-lif bioio-czi bioio-ome-tiff bioio-ome-zarr notebook
+# Note: for only dealing with .lif just do pip install bioio bioio-lif
+
+
+# %%
+# Load .lif file
+# - Observe that BioImage chooses the correct reader plugin
+from bioio import BioImage
+image_url = "https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_xyc__two_images.lif"
+bioimage = BioImage(image_url)
+print(bioimage)
+print(type(bioimage))
+
+# %%
+# Inspect number of images in object
+print(bioimage.scenes)
+
+# %%
+# Inspect both images in the object
+for image in bioimage.scenes:
+    print(f'Image name: {image}')
+    # Select image:
+    bioimage.set_scene(image)
+    # Inspect dimension and shape of image
+    print(f'Image dimension: {bioimage.dims}')
+    print(f'Dimension order is: {bioimage.dims.order}')
+    print(f'Image shape: {bioimage.shape}')
+    # Extract image data (5D)
+    image_data = bioimage.data
+    print(f'Image type: {type(image_data)}')
+    print(f'Image array shape: {image_data.shape}')
+    # Extract specific image part
+    image_data = bioimage.get_image_data('YX')
+    print(f'Image type: {type(image_data)}')
+    print(f'Image array shape: {image_data.shape}')
+    # Read pixel size
+    print(f'Pixel size: {bioimage.physical_pixel_sizes}')
+    # Read metadata
+    print(f'Metadata type: {type(bioimage.metadata)}')
+    print(f'Metadata: {bioimage.metadata}')
+    print('\n')
diff --git a/_includes/image_file_formats/open_tif_bioio.py b/_includes/image_file_formats/open_tif_bioio.py
new file mode 100644
index 00000000..817fb10b
--- /dev/null
+++ b/_includes/image_file_formats/open_tif_bioio.py
@@ -0,0 +1,52 @@
+# %% 
+# Open a tif image file
+# minimal conda env for this module
+# conda create -n ImageFileFormats python=3.10
+# activate ImageFileFormat
+# pip install bioio bioio-tifffile bioio-lif bioio-czi bioio-ome-tiff bioio-ome-zarr notebook
+# Note: for only dealing with .tif just do pip install bioio bioio-tifffile
+
+
+# %%
+# Load .tif file
+# - Observe that BioImage chooses the correct reader plugin
+from bioio import BioImage
+image_url = 'https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit__nuclei_PLK1_control.tif'
+bioimage = BioImage(image_url)
+print(bioimage)
+print(type(bioimage))
+
+# %%
+# Inspect dimension and shape of image
+print(f'Image dimension: {bioimage.dims}')
+print(f'Dimension order is: {bioimage.dims.order}')
+print(f'Image shape: {bioimage.shape}')
+
+# %%
+# Extract image data (5D)
+image_data = bioimage.data
+print(f'Image type: {type(image_data)}')
+print(f'Image array shape: {image_data.shape}')
+# Extract specific image part
+image_data = bioimage.get_image_data('YX')
+print(f'Image type: {type(image_data)}')
+print(f'Image array shape: {image_data.shape}')
+
+# %%
+# Read pixel size
+print(f'Pixel size: {bioimage.physical_pixel_sizes}')
+# Read metadata
+print(f'Metadata type: {type(bioimage.metadata)}')
+print(f'Metadata: {bioimage.metadata}')
+
+# %%
+# Load .tif file with extensive metadata
+image_url = "https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_16bit__collagen.md.tif"
+bioimage = BioImage(image_url)
+
+# %%
+# Read pixel size
+print(f'Pixel size: {bioimage.physical_pixel_sizes}')
+# Read metadata
+print(f'Metadata type: {type(bioimage.metadata)}')
+print(f'Metadata: {bioimage.metadata}')
\ No newline at end of file
diff --git a/_includes/script_for_loop/script_advanced_for_loop.py b/_includes/script_for_loop/script_advanced_for_loop.py
new file mode 100644
index 00000000..a339ec69
--- /dev/null
+++ b/_includes/script_for_loop/script_advanced_for_loop.py
@@ -0,0 +1,98 @@
+# %%
+# Anything iterable can be iterated over with a for loop
+# Observe that:
+# - list content don't matter
+# - that the iterator will always be overwritten by the next item
+for i in ['a','b','c',1,2,3,[1,2,3],1.5,2.7]:
+    print(f'"i" has the value "{i}" and type "{type(i)}"')
+
+# Iterate over two or more list (with the same length)
+# e.g. iterate over a list of coordinates
+import numpy as np
+image = np.random.randint(0,255,100).reshape(10,10)
+x_coordinates = range(10)
+y_coordinates = range(10)
+for y,x in zip(y_coordinates,x_coordinates):
+    print(f'At position x: {x} and y: {y} the intensity value is: {image[y,x]}.')
+
+# %%
+# list comprehension
+squares = []
+for i in range(10):
+    squares.append(i**2)
+print(squares)
+# use list comprehension
+squares = [i**2 for i in range(10)]
+print(squares)
+
+# usefull for creating an filepath iterable
+from pathlib import Path
+[file for file in Path().cwd().iterdir()]
+
+# %%
+# for loop with if
+for x in range(10):
+    if x != 0:
+        print(f'X: {x}, 1/X: {1/x}')
+
+# for loop with if and else
+for x in range(10):
+    if x == 0:
+        print(f'X: {x}, 1/X: Division by {x} not defined.')
+    else:
+        print(f'X: {x}, 1/X: {1/x}')
+
+# %%
+# for loop with continue and break
+# Usage:
+# - skip files/data for processing
+# - debugging
+# continue
+for x in range(10):
+    if x==0:
+        continue
+    print(f'X: {x}, 1/X: {1/x}')
+
+# break
+for x in range(10):
+    if x==3:
+        break
+    print(x)
+
+# %%
+# Nested loops
+for x in range(1,4):
+    for y in range(1,4):
+        print(f'X: {x}, Y: {y}, X*Y: {x*y}')
+
+# Nested lists
+for sublist in [[1,2,3],[4,5,6],[7,8,9]]:
+    for i in sublist:
+        print(i)
+
+# np.array
+# Observe that you iterate only over the first dimension when using simple loop
+array = np.random.randint(0,10,100).reshape(10,10)
+print(array.shape)
+for i in array:
+    print(i)
+
+for row in array:
+    for i in row:
+        print(i)
+
+# %%
+# Looping over pandas DataFrame
+# Observe that
+# - looping with iterrows always gives an index
+# - the second variable will always be a pandas Series object
+import pandas as pd
+df = pd.DataFrame({
+    'a':[1,2,3],
+    'b':[4,5,6]})
+print(df)
+for i,row in df.iterrows():
+    print(type(row))
+    value_a = row['a']
+    value_b = row['b']
+    print(f'Row index {i}\nColumn value "a": {value_a} and Column value "b": {value_b}')
\ No newline at end of file
diff --git a/_includes/script_for_loop/script_for_loop_python.py b/_includes/script_for_loop/script_for_loop_python.py
index 2c8920cf..39cf436b 100644
--- a/_includes/script_for_loop/script_for_loop_python.py
+++ b/_includes/script_for_loop/script_for_loop_python.py
@@ -42,14 +42,6 @@
 for i in range(10):
     print(i)
 
-# %%
-# Anything iterable can be iterated over with a for loop
-# Observe that:
-# - list content don't matter
-# - that the iterator will always be overwritten by the next item
-for i in ['a','b','c',1,2,3,[1,2,3],1.5,2.7]:
-    print(f'"i" has the value "{i}" and type "{type(i)}"')
-
 # %%
 # Use a for loop for the example in the beginning
 # First pack images into a list for looping over
@@ -70,95 +62,6 @@
 for i in range(number_of_images):
     print(f'Image {i} has an avg intensity of {image_list[i].mean()}.')
 
-# Iterate over two or more list (with the same length)
-for i,image in zip(range(number_of_images),image_list):
-    print(f'Image {i} has an avg intensity of {image.mean()}.')
-
 # Iterate with additional index
 for i,image in enumerate(image_list):
     print(f'Image {i} has an avg intensity of {image.mean()}.')
-
-# %%
-# For loops but advanced
-
-# %%
-# list comprehension
-squares = []
-for i in range(10):
-    squares.append(i**2)
-print(squares)
-# use list comprehension
-squares = [i**2 for i in range(10)]
-print(squares)
-
-# usefull for creating an filepath iterable
-from pathlib import Path
-[file for file in Path().cwd().iterdir()]
-
-# %%
-# for loop with if
-for x in range(10):
-    if x != 0:
-        print(f'X: {x}, 1/X: {1/x}')
-
-# for loop with if and else
-for x in range(10):
-    if x == 0:
-        print(f'X: {x}, 1/X: Division by {x} not defined.')
-    else:
-        print(f'X: {x}, 1/X: {1/x}')
-
-# %%
-# for loop with continue and break
-# Usage:
-# - skip files/data for processing
-# - debugging
-# continue
-for x in range(10):
-    if x==0:
-        continue
-    print(f'X: {x}, 1/X: {1/x}')
-
-# break
-for x in range(10):
-    if x==3:
-        break
-    print(x)
-
-# %%
-# Nested loops
-for x in range(1,4):
-    for y in range(1,4):
-        print(f'X: {x}, Y: {y}, X*Y: {x*y}')
-
-# Nested lists
-for sublist in [[1,2,3],[4,5,6],[7,8,9]]:
-    for i in sublist:
-        print(i)
-
-# np.array
-# Observe that you iterate only over the first dimension when using simple loop
-array = np.random.randint(0,10,100).reshape(10,10)
-print(array.shape)
-for i in array:
-    print(i)
-
-for row in array:
-    for i in row:
-        print(i)
-
-# %%
-# Looping over pandas DataFrame
-# Observe that
-# - looping with iterrows always gives an index
-# - the second variable will always be a pandas Series object
-import pandas as pd
-df = pd.DataFrame({
-    'a':[1,2,3],
-    'b':[4,5,6]})
-print(df)
-for i,row in df.iterrows():
-    print(type(row))
-    value_a = row['a']
-    value_b = row['b']
-    print(f'Row index {i}\nColumn value "a": {value_a} and Column value "b": {value_b}')
\ No newline at end of file
diff --git a/_modules/script_for_loop.md b/_modules/script_for_loop.md
index 735a9143..f9bad817 100644
--- a/_modules/script_for_loop.md
+++ b/_modules/script_for_loop.md
@@ -37,6 +37,7 @@ activities:
  - ["ImageJ Macro, example no loop", "script_for_loop/activities/script_for_loop_measure_distances_noloop.ijm"]
  - ["ImageJ Macro, example with loop", "script_for_loop/activities/script_for_loop_measure_distances_withloop.ijm"]
  - ["Python, for loop", "script_for_loop/script_for_loop_python.py"]
+ - ["Python, advanced for loop", "script_for_loop/script_advanced_for_loop.py"]
     
 exercise_preface: |
  ### Multiple erosion