The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain code, equations, visualizations and text. The functionality is partly overlapping with R Markdown (see the tutorial), in that they both use markdown and code chunks to generate reports that integrate results of computations with the code that generated them. Jupyter Notebook comes from the Python community while R Markdown was developed by RStudio, but you could use most common programming languages in either alternative. In practice though, it's quite common that R developers use Jupyter but probably not very common that Python developers use RStudio. Some reasons to use Jupyter include:

  • Python is lacking a really good IDE for doing exploratory scientific data analysis, like RStudio or Matlab. Some people use Jupyter simply as an alternative for that.
  • The community around Jupyter notebooks is large and dynamic, and there are lots of tools for sharing, displaying or interacting with notebooks.
  • An early ambition with Jupyter notebooks (and its predecessor IPython notebooks) was to be analogous to the lab notebook used in a wet lab. It would allow the data scientist to document his or her day-to-day work and interweave results, ideas, and hypotheses with the code. From a reproducibility perspective, this is one of the main advantages.
  • Jupyter notebooks can be used, just like R Markdown, to provide a tighter connection between your data and your results by integrating results of computations with the code that generated them. They can also do this in an interactive way that makes them very appealing for sharing with others.

As always, the best way is to try it out yourself and decide what to use it for! Here are some useful resources if you want to read more:


This tutorial depends on files from the course GitHub repo. Take a look at the intro for instructions on how to set it up if you haven't done so already. Then open up a terminal and go to workshop-reproducible-research/jupyter.

If you have done the Conda tutorial you should know how to define an environment and install packages using Conda. Create an environment containing the following packages from the conda-forge channel. Don't forget to activate the environment.

  • jupyter: for running everything
  • nb_conda: for integrating Conda with Jupyter Notebook
  • matplotlib and ipywidgets and seaborn: for generating plots
  • pandas: for working with data frames and generating tables

A note on nomenclature

  • Jupyter: a project to develop open-source software, open-standards, and services for interactive computing across dozens of programming languages. Lives at
  • Jupyter Notebook: A web application that you use for creating and managing notebooks. One of the outputs of the Jupyter project.
  • Jupyter notebook: The actual .ipynb file that constitutes your notebook.

Windows users

If you are doing these exercises through a Docker container you also need the run the following:

mkdir -p -m 700 /root/.jupyter/ && \
echo "c.NotebookApp.ip = ''" >> \

Getting started

One thing that sets Jupyter Notebook apart from what you might be used to is that it's a web application, i.e. you edit and run your code from your browser. But first you have to start the Jupyter Notebook server.

$ jupyter notebook --allow-root
[I 18:02:26.722 NotebookApp] Serving notebooks from local directory: /Users/john/Documents/projects/workshop-reproducible-research/jupyter
[I 18:02:26.723 NotebookApp] 0 active kernels
[I 18:02:26.723 NotebookApp] The Jupyter Notebook is running at:
[I 18:02:26.723 NotebookApp] http://localhost:8888/?token=e03f10ccb40efc3c6154358593c410a139b76acf2cae785c
[I 18:02:26.723 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).
[C 18:02:26.724 NotebookApp]

    Copy/paste this URL into your browser when you connect for the first time,
    to login with a token:
[I 18:02:27.209 NotebookApp] Accepting one-time-token-authenticated connection from ::1

Jupyter Notebook probably opened up a web browser for you automatically, otherwise go to the address specified in the message in the terminal. Note that the server is running locally (as http://localhost:8888) so this does not require that you have an active internet connection. Also note that it says:

Serving notebooks from local directory: </some/local/path/workshop-reproducible-research/jupyter>

Everything you do in your Notebook session will be stored in this directory, so you won't lose any work if you shut down the server.

What you're looking at is the Notebook dashboard. This is where you manage your files, notebooks, and kernels. The Files tab shows the files in your directory. If you've done the other tutorials the file names should look familiar; they are the files needed for running the RNA-seq workflow in Snakemake. The Running tab keeps track of all your processes. The third tab, Clusters, is used for parallel computing and won't be discussed further in this tutorial. The Conda tab lets us control our Conda environments. Let's take a quick look at that. You can see that I'm currently in the jupyter_exercise environment which is the name I chose when I created the environment (you may have used another name).

Let's start by creating an empty notebook by selecting the Files tab and clicking New > Notebook > Python 3. This will open up a new tab or window looking like this:


If you want to start Jupyter Notebooks on a cluster that you SSH to you have to do some port forwarding:

ssh -L8888:localhost:8888
jupyter notebook --ip --no-browser

The basics

Jupyter notebooks are made up out of cells, and you are currently standing in the first cell in your notebook. The fact that it has a green border indicates that it's in "Edit mode", so you can write stuff in it. A blue border indicates "Command mode" (see below). Cells in Jupyter notebooks can be of two types: markdown or code.

  • Markdown: These cells contain static material such as captions, text, lists, images and so on. You express this using Markdown, which is a lightweight markup language. Markdown documents can then be converted to other formats for viewing (the document you're reading now is written in Markdown and then converted to HTML). The format is discussed a little more in detail in the R Markdown tutorial. Jupyter Notebook uses a dialect of Markdown called Github Flavored Markdown, which is described here.
  • Code: These are the cells that actually do something, just as code chunks do in R Markdown. You can write code in dozens of languages and all do all kinds of clever tricks. You then run the code cell and any output the code generates, such as text or figures, will be displayed beneath the cell. We will get back to this in much more detail, but for now it's enough to understand that code cells are for executing code that is interpreted by a kernel (in this case the Python version in your Conda environment).

Before we continue, here are some shortcuts that can be useful. Note that they are only applicable when in command mode (blue frames). Most of them are also available from the menus. These shortcuts are also available from the Help menu in your notebook (there's even an option there to edit shortcuts).

Shortcut Effect
Enter enter Edit mode
Esc enter Command mode
Ctrl+Enter run the cell
Shift+Enter run the cell and select the cell below
Alt+Enter run the cell and insert a new cell below
Ctrl+S save the notebook
Tab for code completion or indentation
M/Y toggle between Markdown and Code cells
++d-d++ delete a cell
++a/b++ insert cells above/below current cell
++x/c/v++ cut/copy/paste cells
O toggle output of current cell

Writing markdown

Let's use our first cell to create a header. Change the format from Code to Markdown in the drop-down list above the cell. Double click on the cell to enter editing mode (green frame) and input "# My notebook" ("#" is used in Markdown for header 1). Run the cell with Shift-Enter. Tada!

Markdown is a simple way to structure your notebook into sections with descriptive notes, lists, links, images etc.

Below are some examples of what you can do in markdown. Paste all or parts of it into one or more cells in your notebook to see how it renders. Make sure you set the cell type to Markdown.

## Introduction
In this notebook I will try out some of the **fantastic** concepts of Jupyter

## Markdown basics
Examples of text attributes are:

* *italics*
* **bold**
* `monospace`

Sections can be separated by horizontal lines.


Blockquotes can be added, for instance to insert a Monty Python quote:


See [here]( for more information.    

Writing code

Now let's write some code! Since we chose a Python kernel, Python would be the native language to run in a cell. Enter this code in the second cell and run it:

print("Hello world!")

Note how the output is displayed below the cell. This interactive way of working is one of the things that sets Jupyter Notebook apart from RStudio and R Markdown. R Markdown is typically rendered top-to-bottom in one run, while you work in a Jupyter notebook in a different way. This has partly changed with newer versions of RStudio, but it's probably still how most people use the two tools. Another indication of this is that there is no (good) way to hide the code cells if you want to render your Jupyter notebook to a cleaner looking report (for a publication for example).

What is a Jupyter notebook? Let's look a little at the notebook we're currently working in. Jupyter Notebook saves it every minute or so, so you will already have it available. We can be a little meta and do this from within the notebook itself. We do it by running some shell commands in the third code cell instead of Python code. This very handy functionality is possible by prepending the command with !. Try !ls to list the files in the current directory.

Aha, we have a new file called Untitled.ipynb! This is our notebook. Look at the first ten lines of the file by using !head Untitled.ipynb. Seems like it's just a plain old JSON file. Since it's a text file it's suitable for version control with for example Git. It turns out that Github and Jupyter notebooks are the best of friends, as we will see more of later. This switching between languages and whatever-works mentality is very prominent within the Jupyter notebook community.

Variables defined in cells become variables in the global namespace. You can therefore share information between cells. Try to define a function or variable in one cell and use it in the next. For example:

def print_me(str):



Your notebook should now look something like this.

The focus here is not on how to write Markdown or Python; you can make really pretty notebooks with Markdown and you can code whatever you want with Python. Rather, we will focus on the Jupyter Notebook features that allow you to do a little more than that.

Quick recap

In this section we've learned:

  • That a Jupyter notebook consists of a series of cells, and that they can be either markdown or code cells.
  • That we execute the code in a code cell with the kernel that we chose when opening the notebook.
  • We can run shell commands by prepending them with !.
  • A Jupyter notebook is simply a text file in JSON format.


Magics constitute a simple command language that significantly extends the power of Jupyter notebooks. There are two types of magics:

  • Line magics: Commands that are prepended by "%", and whose arguments only extend to the end of the line.
  • Cell magics: Commands that start with %% and then applies to the whole cell. Must be written on the first line of a cell.

Now list all available magics with %lsmagic (which itself is a magic). You add a question mark to a magic to show the help (e.g. %lsmagic?). Some of them act as shortcuts for commonly used shell commands (%ls, %cp, %cat, ..). Others are useful for debugging and optimizing your code (%timeit, %debug, %prun, ..).

A very useful magic, in particular when using shell commands a lot in your work, is %%capture. This will capture the stdout/stderr of any code cell and store them in a Python object. Run %%capture? to display the help and try to understand how it works. Try it out with either some Python code, other magics or shell commands.

Click to see an example
%%capture output
echo "Print to stdout"
echo "Print to stderr" >&2

and in another cell

print("stdout:" + output.stdout)
print("stderr:" + output.stderr)

The %%script magic is used for specifying a program (bash, perl, ruby, ..) with which to run the code (similar to a shebang). For some languages it's possible to use these shortcuts:

  • %%ruby
  • %%perl
  • %%bash
  • %%html
  • %%latex
  • %%R (here you have to first install the rpy2 extension, for example with Conda, and then load with %load_ext rpy2.ipython)

Try this out if you know any of the languages above. Otherwise you can always try to print the quadratic formula with LaTeX!

\begin{array}{*{20}c} {x = \frac{{ - b \pm \sqrt {b^2 - 4ac} }}{{2a}}} & {{\rm{when}}} & {ax^2 + bx + c = 0} \\ \end{array}

Python's favorite library for plotting, matplotlib, has its own magic as well: %matplotlib. Try out the code below, and you should hopefully get a pretty sine wave.

%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0,3*np.pi,100)
y = np.sin(x)
fig = plt.figure()
ax = fig.add_subplot(111)
line, = plt.plot(x, y, 'r-')

By default rendering is done as rasterized images which can make the quality poor. To render in scalable vector graphics format add the following line magic

%config InlineBackend.figure_format = 'svg'

Try it by adding it to the cell with the lineplot and run it again.


The %matplotlib inline and %config InlineBackend.figure_format = 'svg' line magics are only required once per notebook. You could for instance add them to the first cell where you import matplotlib for plotting.


You can capture the output of some magics directly like this:

my_dir = %pwd

Widgets and plotting

Since we're typically running our notebooks in a web browser, they are quite well suited for also including more interactive elements. A typical use case could be that you want to communicate some results to a collaborator or to a wider audience, and that you would like them to be able to affect how the results are displayed. It could, for example, be to select which gene to plot for, or to see how some parameter value affects a clustering. Jupyter notebooks has great support for this in the form of widgets.

Widgets are eventful Python objects that have a representation in the browser, often as a control like a slider, textbox, etc. These are implemented in the ipywidgets package.

The easiest way to get started with using widgets are via the interact and interactive functions. These functions autogenerate widgets from functions that you define, and then call those functions when you manipulate the widgets. Too abstract? Let's put it into practice!

Let's try to add sliders that allow us to change the frequency, amplitude and phase of the sine curve we plotted previously.

# Import the interact and interactive functions from ipywidgets
from ipywidgets import interact, interactive
# Also import numpy (for calculating the sine curve) 
# and pyplot from matplotlib for plotting
import numpy as np
import matplotlib.pyplot as plt

# Define the function for plotting the sine curve
def sine_curve(A, f, p):
    # Set up the plot
    plt.figure(1, figsize=(4,4))
    # Create a range of 100 evenly spaced numbers between 0 and 100
    x = np.linspace(0,10,100)
    # Calculate the y values using the supplied parameters
    y = A*np.sin(x*f+p)
    # Plot the x and y values ('r-' specifies color and line style)
    plt.plot(x, y, 'r-')

# Here we supply the sine_curve function to interactive, 
# and set some limits on the input parameters
interactive_plot = interactive(sine_curve, 
            A=(1, 5, 1), 
            f=(0, 5, 1), 
            p=(1, 5, 0.5))

# Display the widgets and the plot

The code above defines a function called sine_curve which takes three arguments:

  • A = the amplitude of the curve
  • f = the frequency of the curve
  • p = the phase of the curve

The function creates a plot area, generates x-values and calculates y-values using the np.sin function and the supplied parameters. Finally, the x and y values are plotted.

Below the function definition we use interactive with the sine_curve function as the first parameter. This means that the widgets will be tied to the sine_curve function. As you can see we also supply the A, f and p keyword arguments. Importantly, all parameters defined in the sine_curve function must be given in the interactive call and a widget is created for each one.

Depending on the type of the passed argument different types of widgets will be created by interactive. For instance:

  • int or float arguments will generate a slider
  • bool arguments (True/False) will generate checkbox widgets
  • list arguments will generate a dropdown
  • str arguments will generate a text-box

By supplying the arguments in the form of tuples we can adjust the properties of the sliders. f=(1, 5, 1) creates a widget with minimum value of 1, maximum value of 5 and a step size of 1. Try adjusting these numbers in the interactive call to see how the sliders change (you have to re-execute the cell).

The final line of the cell (interactive_plot) is where the actual widgets and plot are displayed. This code can be put in a separate cell, so that you can define functions and widgets in one part of your notebook, and reuse them somewhere else.

This is how it should look if everything works. You can now set the frequency amplitude and phase of the sine curve by moving the sliders.

There are lots of widgets, e.g.:

  • Dropdown menus
  • Toggle buttons
  • Range sliders
  • File uploader

... and much, much more. Here is a list of all available widgets together with documentation and examples. Some of these widgets cannot be autogenerated by interactive, but fear not! Instead of relying on autogeneration we can define the widget and supply it directly to interactive.

To see this in practice, change out the A argument to a pre-defined IntSlider widget. First define the slider:

from ipywidgets import widgets
A = widgets.IntSlider(value=2, min=1, max=5, step=1)

Then replace the call to interactive so that it looks like this:

interactive_plot = interactive(sine_curve, A=A, f=5, p=5)

Extra challenge

If you can't get enough of widgets you might want to try this out: see if you can figure out how to add a widget that lets you pick the color for the sine curve line. Search for the appropriate widget in the Widget list. You'll need to update the sine_curve function and pass the new widget as an argument in the call to interactive. If you need help, click below.

Click to see how to add a color picker
# Import the interact and interactive functions from ipywidgets
from ipywidgets import interact, interactive
# Also import numpy (for calculating the sine curve) 
# and pyplot from matplotlib for plotting
import numpy as np
from ipywidgets import widgets ## <- import widgets
import matplotlib.pyplot as plt

# Define the function for plotting the sine curve
def sine_curve(A, f, p, color): ## <- add parameter here
    # Set up the plot
    plt.figure(1, figsize=(4,4))
    # Create a range of 100 evenly spaced numbers between 0 and 100
    x = np.linspace(0,10,100)
    # Calculate the y values using the supplied parameters
    y = A*np.sin(x*f+p)
    # Plot the x and y values 
    plt.plot(x, y, color=color) ## <- Use color from widget here

# Here we supply the sine_curve function to interactive, 
# and set some limits on the input parameters
# Define the colorpicker widget
colorpicker = widgets.ColorPicker(description='color',value="red")
interactive_plot = interactive(sine_curve, 
            A=(1, 5, 1), 
            f=(0, 5, 1), 
            p=(1, 5, 0.5),
            color=colorpicker) ## <- Supply the colorpicker to the function

# Display the widgets and the plot

Other interactive plots

Jupyter widgets, like we used here, is the most vanilla way of getting interactive graphs in Jupyter notebooks. Some other alternatives are:

  • Plotly is actually an API to a web service that renders your graph and returns it for display in your Jupyter notebook. Generates very visually appealing graphs, but from a reproducibility perspective it's maybe not a good idea to be so reliant on a third party.
  • Bokeh is another popular tool for interactive graphs. Most plotting packages for Python are built on top of matplotlib, but Bokeh has its own library. This can give a steeper learning curve if you're used to the standard packages.
  • mpld3 tries to integrate matplotlib with Javascript and the D3js package. It doesn't scale well for very large datasets, but it's easy to use and works quite seamlessly.

Quick recap

In the two previous sections we've learned:

  • How magics can be used to extend the power of Jupyter notebooks, and the difference between line magics and cell magics.
  • How to switch between different languages by using magics.
  • How to use widgets and the mpld3 library for interactive plotting.

Using the command line

Converting notebooks

Notebooks can be converted to various output formats such as HTML, PDF, LaTeX etc. directly from the File -> Download as menu.

Conversion can also be performed on the command line using the jupyter nbconvert command. nbconvert is installed together with the jupyter Conda package and is executed on the command line by running jupyter nbconvert.

The syntax for converting a Jupyter notebook is:

jupyter nbconvert --to <FORMAT> notebook.ipynb

Here <FORMAT> can be any of asciidoc, custom, html, latex, markdown, notebook, pdf, python, rst, script, slides. Converting to some output formats (e.g. PDF) may require you to install separate software such as Pandoc or a TeX environment.

Try converting the Untitled.ipynb notebook that you have been working on so far to HTML using jupyter nbconvert.

Executing notebooks

nbconvert can also be used to run a Jupyter notebook from the commandline. By running:

jupyter nbconvert --execute --to <FORMAT> notebook.ipynb 

nbconvert executes the cells in a notebook, captures the output and saves the results in a new file. Try running it on the Untitled.ipynb notebook.

You can also specify a different output file with --output <filename>.

So in order to execute your Untitled.ipynb notebook and save it to a file named report.html you could run:

jupyter nbconvert --to html --output report.html --execute Untitled.ipynb

Jupyter and the case study

As you might remember from the intro, we are attempting to understand how lytic bacteriophages can be used as a future therapy for the multiresistant bacteria MRSA (methicillin-resistant Staphylococcus aureus). We have already seen how to define the project environment in the Conda tutorial and how to set up the workflow in the Snakemake tutorial. Here we explore the results from a the snakemake workflow in a Jupyter notebook as an example of how you can document your day-to-day work as a dry lab scientist.

We will create a report similar to the one in the R Markdown tutorial and generate and visualize read coverage across samples for the S. aureus genome.

Install a new Conda environment

For the purposes of this part of the tutorial we will install a new Conda environment and run a slightly slimmed down version of the MRSA Snakemake workflow to generate some output to work with.

In the jupyter/ directory you'll find a Snakefile containing the workflow as well as a Conda environment.yml file which contains all packages required for both the execution of the workflow as well as the downstream analyses we will perform in the Jupyter notebook.

Install a new Conda environment using the environment.yml file and then activate it. You can choose the name of the environment yourself. Here's an example using the name jupyter-snakemake:

conda env create -f environment.yml -n jupyter-snakemake
# Activate the environment 
conda activate jupyter-snakemake


If you are doing these exercises through a Docker container you should instead update the current conda base environment by running conda env update -f environment.yml -n base.

Open the MRSA notebook

In the jupyter/ directory you will also see a notebook called mrsa_notebook .ipynb. With the newly created conda environment active, open this notebook directly by running:

jupyter notebook mrsa_notebook.ipynb


Using what you've learned about markdown in notebooks, add headers and descriptive text to subdivide sections as you add them. This will help you train how to structure and keep note of your work with a notebook.

You will see that the notebook contains only two cells: one with some import statements and one with two function definitions. We'll come back to those later. Now, run the cells and add a new empty cell to the notebook. Typically the Snakemake workflow would be executed from a terminal but let's try to actually run the workflow directly from within the Jupyter notebook.

In the current directory you'll find the necessary Snakefile and config.yml to run the workflow. In an empty cell in your notebook, add code to run the workflow. Then run the cell.

Click to see how to run the workflow from a cell

Once the workflow is finished we can start to explore the results.

Plot QC status

First let's take a look at the FastQC summary for the samples. Add the following code to a cell then run the cell. This will extract and concatenate summary files for all samples using FastQC output in the intermediate/ directory.

import glob
import os
import zipfile

with open('summary.txt', 'w') as fhout:
    # Find all zip files from fastqc
    for f in glob.glob('intermediate/*'):
        # Extract the archive name
        arc_name = os.path.splitext(os.path.basename(f))[0]
        # Open up the 'summary.txt' in the zip archive
        # and output the contents to 'summary.txt'
        with zipfile.ZipFile(f) as myzip:
            with'{arc_name}/summary.txt'.format(arc_name=arc_name), 'r') as fhin:

Read the summary results into a data frame using the pandas package:

# Read the concatenated summary.txt
qc = pd.read_csv("summary.txt", sep="\t", header=None,
    names=["Status","Statistic","Sample"], index_col=0)
# Rename strings in the Sample column
qc["Sample"] = [x.rstrip(".fastq.gz") for x in qc["Sample"]]
# Map the status strings to numeric values for plotting
qc.rename(index={"PASS": 1, "WARN": 0, "FAIL": -1}, inplace=True)
# Convert from long to wide format
qc = pd.pivot_table(qc.reset_index(), columns="Sample", index="Statistic")

Take a look at the qc DataFrame by adding the variable to an empty cell and running the cell.

Now let's plot the heatmap using the heatmap function from the seaborn package.

# Plot the heatmap
ax = sns.heatmap(qc["Status"], cmap=["Red","Yellow","Green"], linewidth=.5,
ax.set_ylim(11,0); # Only necessary in cases where matplotlib cuts the y-axis

To save the plot to a file add the following to the cell:

plt.savefig("qc_heatmap.png", dpi=300, bbox_inches="tight")

Genome coverage

In the workflow reads were aligned to the S. aureus reference genome using bowtie2. Let's take a look at genome coverage for the samples. To do this we will first generate coverage files with bedtools.

Add the following to a new cell:

for f in $(ls intermediate/*.sorted.bam);
    bedtools genomecov -ibam $f -d | gzip -c > $f.cov.gz
This will run bedtools genomecov on all bam-files in the intermediate/ directory and generate coverage files.

Next, read coverage files and generate a table of genome positions and aligned reads in each sample. For this we make use of the read_cov_files function defined in the beginning of the notebook.

files = glob.glob("intermediate/*.sorted.bam.cov.gz")
coverage_table = read_cov_tables(files)
Take a look at the coverage_table DataFrame. Because this is a relatively large table you can use:
to only view the first 5 rows. With

you will see 5 randomly sampled rows.

Next let's calculate reads aligned to the genome using a sliding window. For this we'll use the sliding_window function defined at the start of the notebook. You can try different sizes of the sliding window, in the example below we're using 10 kbp.

coverage_window = sliding_window(coverage_table, window=10000)

Now we'll plot the read coverage for all samples:

# Set the figure size
fig = plt.figure(figsize=(6,4))
# Set colors
colors = sns.color_palette("Dark2", n_colors=3)
# Set legend handles
handles = []
# Iterate samples and plot coverage
for i, sample in enumerate(coverage_window.columns):
    ax = sns.lineplot(x=coverage_window.index, y=coverage_window[sample],
        linewidth=.75, color=colors[i])
    # Update legend handles
    handles.append(mpatches.Patch(color=colors[i], label=sample))
# Set y and x labels
ax.set_ylabel("Reads aligned");
ax.set_xlabel("Genome position");
# Plot legend

Not too bad, but it's a bit difficult to see individual samples in one plot.

Let's also make three subplots and plot each sample separately. First we define the subplots grid using plt.subplots, then each sample is plotted in a separate subplot using the ax= keyword argument in sns.lineplot:

# Define the subplots
fig, axes = plt.subplots(ncols=1, nrows=3, sharey=True, sharex=True,
# Iterate samples and plot in separate subplot
for i, sample in enumerate(coverage_window.columns):
    ax = sns.lineplot(x=coverage_window.index, y=coverage_window[sample],
        ax=axes[i], linewidth=.75)
    ax.set_ylabel("Reads aligned");
# Adjust space between subplots

We can also visualize how the coverage correlates between the samples using the scatterplot function in seaborn:


Let's see if you can figure out how to visualize coverage correlation as above for all sample combinations in a subplot figure. Try to combine what we used in the previous two cells. Then take a look at the answer below.

Click to see how to plot correlations in subplots
fig, axes = plt.subplots(ncols=3, nrows=1, figsize=(12,3), sharex=False,
ax1 = sns.scatterplot(x=coverage_window["SRR935090"],
    y=coverage_window["SRR935091"], ax=axes[0])
ax2 = sns.scatterplot(x=coverage_window["SRR935090"],
    y=coverage_window["SRR935092"], ax=axes[1])
ax3 = sns.scatterplot(x=coverage_window["SRR935092"],
    y=coverage_window["SRR935091"], ax=axes[2])


Seaborn actually has a function that essentially let's us generate the plot above with one function call. Take a look at the pairplot function by running ?sns.pairplot in a new cell. Can you figure out how to use it with our data?

Integrating the notebook into the workflow

So now we have a Jupyter notebook that uses output from a Snakemake workflow and produces some summary results and plots. Wouldn't it be nice if this was actually part of the workflow itself? We've already seen how we can execute notebooks from the commandline using nbconvert. If you've done the Snakemake tutorial you should have an understanding of how to add/modify rules in the Snakefile that's available in the current jupyter/ folder.

Make sure you save the mrsa_notebook.ipynb notebook. Then open the Snakefile in a text editor and try to add a rule calledgenerate_report that uses the mrsa_notebook.ipynb you've been working on and produces a report file called report.html in the results/ directory. Hint: because the notebook uses output from the current Snakemake workflow the input to generate_report should come from rules that are run towards the end of the workflow.

Try to add the rule on your own first. If you get stuck, take a look at the example below.

Click to see an example on how to implement generate_report
rule generate_report:
Generate a report from a Jupyter notebook with nbconvert
    jupyter \
        nbconvert \
        --to html \
        --execute mrsa_notebook.ipynb \
        --output {output}

To get snakemake to run the new rule as part of the rest of the workflow (i.e. when only running snakemake) add results/report.html to the input of the all rule. Now that the notebook is integrated into the workflow you can remove the cell where we executed snakemake (e.g. using !snakemake).

Finally, try to re-run the updated workflow either by deleting the data/, intermediate/ and results/ directories and executing snakemake again, or by running snakemake --forceall.

Sharing your work

The files you're working with come from a GitHub repo. Both GitHub and Bitbucket can render Jupyter notebooks as well as other types of Markdown documents. Now go to our GitHub repo at and navigate to jupyter/mrsa_notebook.ipynb.

As you can imagine, having this very effortless way of sharing results can greatly increase the visibility of your work. You work as normal on your project, and push regularly to the repository as you would anyways, and the output is automatically available for anyone to see. Or for a select few if you're not ready to share your findings with the world quite yet.

Say your notebook isn't on Github/Bitbucket. All hope isn't lost there. provides a neat functionality called nbviewer, where you can past an URL to any notebook and they will render it for you. Go to and try this out with our notebook.

Shared interactive notebooks

So far we've only shared static representations of notebooks. A strong trend at the moment is to run your notebooks in the cloud, so that the person you want to share with could actually execute and modify your code. This is a great way of increasing visibility and letting collaborators or readers get more hands-on with your data and analyses. From a reproducibility perspective, there are both advantages and drawbacks. On the plus side is that running your work remotely forces you to be strict when it comes to defining the environment it uses (probably in the form of a Conda environment or Docker image). On the negative side is that you become reliant on a third-party service that might change input formats, go out of business, or change payment model.

Here we will try out a service called Binder, which lets you run and share Jupyter Notebooks in Git repositories for free. There are a number of example repositories that are setup to be used with Binder. Navigate to to see one such example. As you can see the repository contains a LICENSE file, a README, an environment file and a notebook. To use a repository with Binder the environment file should contain all the packages needed to run notebooks in the repo. So let's try to run the index.ipynb file using Binder:

Just go to and paste the link to the GitHub repo. Note the link that you can use to share your notebook. Then press "launch".

What will happen now it that:

  • Binder detects the environment.yml file in the root of the repo. Binder then builds a Docker image based on the file. This might take a minute or two. You can follow the progress in the build log.
  • Binder then launches the Jupyter Notebook server in the Docker container..
  • ..and opens a browser tab with it for you.

Once the process is finished you will be presented with a Jupyter server overview of the contents in the repository. Click on the index.ipynb notebook to open it. Tada! You are now able to interact with (and modify) someone else's notebook online.

Applied to your own projects you now have a way to run analyses in the cloud and in an environment that you define yourself. All that's needed for someone to replicate your analyses is that you share a link with them. Note that notebooks on Binder are read-only; its purpose is for trying out and showing existing notebooks rather than making new ones.

Binder configuration files

By default Binder looks for configuration files such as environment.yml in the root of the repository being built. But you may also put such files outside the root by making a binder/ folder in the root and placing the file there.

A note on transparency

Resources like Github/Bitbucket and Jupyter Notebooks have changed the way we do scientific research by encouraging visibility, social interaction and transparency. It was not long ago that the analysis scripts and workflows in a lab were well-guarded secrets that we only most reluctantly shared with others. Assuming that it was even possible. In most cases, the only postdoc who knew how to get it to work had left for a new position in industry, or no one could remember the password to the file server. If you're a PhD student, we encourage you to embrace this new development wholeheartedly, for it will make your research better and make you into a better scientist. And you will have more fun.