Open and use a Jupyter notebook

Natalie Nelson, University of Florida

Initial Publication Date: July 27, 2016

Introduction

All data analysis will be performed using a cloud-based Jupyter Notebook that was created for this assignment. The Jupyter Notebook is accessed through GitHub from the command line.

Conceptual Outcomes

Students will be:

Aware of cloud-based platforms used by programmers to share data analysis tools
Able to analyze time series data using simple descriptive statistics

Practical Outcomes

Students will be:

Familiar with using the command line and accessing GitHub repositories
Able to analyze data using Jupyter notebooks

Time Required

2-3 hours

Computing/Data Inputs

Computing/Data Outputs

Hardware/Software Required

Internet browser

Instructions

Open the Terminal window and navigate to the folder where you'd like the directory from GitHub to be copied to. You can do this with:

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Provenance: Natalie Nelson, University of Florida
Reuse: This item is offered under a Creative Commons Attribution-NonCommercial-ShareAlike license http://creativecommons.org/licenses/by-nc-sa/3.0/ You may reuse this item for non-commercial purposes as long as you provide attribution and offer any derivative works under a similar license.
Once you've changed directories, enter:

×

Provenance: Natalie Nelson, University of Florida
Reuse: This item is offered under a Creative Commons Attribution-NonCommercial-ShareAlike license http://creativecommons.org/licenses/by-nc-sa/3.0/ You may reuse this item for non-commercial purposes as long as you provide attribution and offer any derivative works under a similar license.
Copy your Data folder into the LSJR_Jupyter_project folder. This folder should be structured as follows:

×

Provenance: Natalie Nelson, University of Florida
Reuse: This item is offered under a Creative Commons Attribution-NonCommercial-ShareAlike license http://creativecommons.org/licenses/by-nc-sa/3.0/ You may reuse this item for non-commercial purposes as long as you provide attribution and offer any derivative works under a similar license.
Return to your Terminal window and move to your LSJR_Jupyter_project folder by entering:

×

Provenance: Natalie Nelson, University of Florida
Reuse: This item is offered under a Creative Commons Attribution-NonCommercial-ShareAlike license http://creativecommons.org/licenses/by-nc-sa/3.0/ You may reuse this item for non-commercial purposes as long as you provide attribution and offer any derivative works under a similar license.
Open Jupyter by entering:

×

Provenance: Natalie Nelson, University of Florida
Reuse: This item is offered under a Creative Commons Attribution-NonCommercial-ShareAlike license http://creativecommons.org/licenses/by-nc-sa/3.0/ You may reuse this item for non-commercial purposes as long as you provide attribution and offer any derivative works under a similar license.
Wait for Jupyter to open in your browser. This might take a minute or two. Once Jupyter opens in your browser, verify that you are in the LSJR_Jupyter_project directory.
Click on "Analyzing DO data across a fresh-estuarine gradient of the LSJR using Python.ipynb" to open the Jupyter notebook.
You need to start by clearing all output. At the top of the page, go to Cell > All Output > Clear
You're ready to use the Jupyter notebook! To execute the code, click in a code block and enter Shift+Enter. Or, if you'd like to run the code all at once, you can go to Cell > Run All.