Electronic Laboratory Notebooks

Jupyter Notebook is an open-source web application to record, create and share computational documents. It is an interactive computing platform and utilizes Python. Jupyter Notebook can be used to compute and analyze mathematics and physical sciences data. It offers a simple, streamlined, document centric experience.

• Jupyter notebook allows writing text using Latex syntax, which is particularly useful to record equations in physics or math. 
• The notebook allows more control over writing procedures, i.e. users can write procedures step-by-step using cells
• Computational code can be stored and retrieved more easily. Moreover, the stored data and simple codes in Python enable graph plotting and data analysis. 
• Users can perform data mining, image manipulation, dynamic data visualization, numerical simulation, statistical modeling, machine learning, regressions and much more with Jupyter notebooks as well as storing/ sharing data in one place. 
• One can execute code in different cells, and view the results of all cells. A user does not need to compile the whole file while making incremental changes in the code.

Two common ways to install Jupyter notebooks

1) Using Anaconda:

Anaconda is an open-source software that includes Python and Jupyter notebooks, and many other scientific computing utilities. To install Anaconda, go to Anaconda and download the latest Python version on your computer. 

Once finished installing, open the Anaconda navigator.

Click the 'launch' button on the Jupyter Notebooks section. It would open another window in your browser such as:

2) PIP

Pip is the package installer for Python. 

Install the Jupyter Notebook with the following commands:

pip install notebook

To run the notebook:

jupyter notebook

More information on this installation method can be found here:

Install Jupyter

Getting Started with pip

Python packaging user guide

For finding more information on installation, the following links can be helpful:

 How-to-install-jupyter-notebooks

• Importing libraries or packages
  Here is one example where we import relevant packages for our purposes

• Reading a CSV File
  

• Plotting Data
  • Here is an example showing a way to graph recorded experimental data from a CSV file

Using math functions, or plotting more than one graph. 
Learn more about Plotting here: 

Making-plots-Jupyter-notebooks
Draw-graphs-Jupyter-notebooks

• Word analysis
  Jupyter can be utilized for many data science and analysis requirements. Word scraping is a common utility. For instance, here is one example of word scraping of the Wikipedia page on 'Quantum computing'. It can quickly show us the most frequent words used in the text. 
  
  
  
  Learn more about Word Scraping here: WordCloud