Notebook1_intro_to_Jupyter_notebooks.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["# Notebook 1: Introduction to Jupyter Notebooks\n","\n","Here, we'll be using a Jupyter notebook, which is an interactive programming notebook you can edit and run in a browser. Since we're running it on Google Drive, this is called Google Colab. This notebook is written in python and demonstrates some basic functionality.\n","\n","## 1. Running code\n","\n","The code below can be run and edited right here in the browser. To see, hover your mouse over the code and click the arrow that appears in the upper left."],"metadata":{"id":"1menKzW2u3Kj"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"CZqHzzdEubEN"},"outputs":[],"source":["import numpy as np\n","number_array = np.arange(1,11)\n","print(number_array)"]},{"cell_type":"code","source":["print('The sum of all numbers from',number_array[0],'to',number_array[-1],'is',sum(number_array))"],"metadata":{"id":"G_M-2qd1QhXM"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Once complete, a check mark will appear beside the code cell, along with the amount of time it took to run. If a cell takes some time to run, an indicator will circle around the button and a green arrow will appear beside the line currently being run. Any output will be displayed below the code.\n","\n","Code is seperated into discrete \"cells\", but any variable you define will be available in future code cells, just like normal python. So feel free to split up code into multiple cells.\n","\n","## 2. Installing python packages\n","\n","If you want to use a python library that isn't pre-installed, you can install it with pip. To demonstrate, run the code below to install \"lipd\", which is a library to work with proxy data stored in the lipd format (https://pypi.org/project/LiPD/). You may get a warning that the notebook was authored by me instead of Google. Click \"run anyway\"."],"metadata":{"id":"javwR2uJu9pl"}},{"cell_type":"code","source":["!pip install lipd"],"metadata":{"id":"C3SCQsQfwp6G"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## 3. Setting things up for this workshop\n","\n","The code and data for the Paleo DA Workshop is stored on my (Michael Erb's) Google Drive. You can run notebooks, edit notebooks, and access data, but to save an edited notebook you need to create your own copy of the file. So the first time you open a notebook, go ahead and save a copy (Step 1 below). The other two steps only need to be done right now.\n","\n","**Step 1. Create a copy of this file:** In the toolbar at the top of this page, go to \"File\" > \"Save a copy in Drive\". A new file will be saved in the \"Colab Notebooks\" folder in your Google Drive and automatically open. To make a more memorable filename, change the initial part of the filename (\"Copy of \") to your name. In this new file, you can edit code and save all of your changes.\n","\n","**Step 2. Accessing data from my Drive:** To access data that I've already downloaded, do the following:\n","- Go to the main \"da_workshop\" folder in Google Drive.\n","- Click the downward arrow to the right of the \"da_workshop\" name.\n","- Click Organize > Add Shortcut.\n","- Click All locations > My Drive > Add.\n","\n","This will create a shortcut of the \"da_workshop\" folder in your Google Drive, allowing you to access data from that folder when you execute code.\n","\n","**Step 3. Create data and figure folders:** Finally, create two folders to save data and figures that you create. In your Google Drive, open the \"Colab Notebooks\" folder and create two empty folders: \"data\" and \"figures\". You'll be saving things in these folders in later notebooks.\n","\n","Each time you open a new notebook from my Drive, do step 1. That will make a copy you can edit and save. (You don't need to repeat steps 2 and 3.)\n","\n","Once you've finished up this setup, continue with the rest of the notebook."],"metadata":{"id":"ii4hooj63QMQ"}},{"cell_type":"markdown","source":["## 4. Editing code\n","\n","To edit code, click on a code cell and edit it. To demonstrate, run the code cell below to plot a linear function. Then uncomment the commented line and run it again to plot an exponential function."],"metadata":{"id":"JOBKX3kIwyM9"}},{"cell_type":"code","source":["import matplotlib.pyplot as plt\n","\n","x_values = np.arange(10)\n","y_values = x_values * 2\n","#y_values = np.square(x_values)\n","plt.plot(x_values,y_values)"],"metadata":{"id":"yWF3gWwbxpst"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## 5. Adding a new code cell\n","\n","To add a new code box, hover your mouse between other cells and click the \"+ Code\" button that appears. This will allow you to add pieces of code in a new code cell.\n","\n","Create a new code cell below and copy the following code into it.\n","\n","`for i in range(10): print('The square of',i,'is',i*i)`\n","\n","Then run the code cell. You can use the tools on the right side of the cell to move or delete the cell."],"metadata":{"id":"1QMduRFQyXEd"}},{"cell_type":"markdown","source":["## 6. Adding code snippets\n","\n","When doing an analysis, you may find it useful to start with a pre-made \"code snippet\". To try this, go to \"Insert\" > \"Code snippets\" at the top of the screen, then insert one of the snippets starting with \"Visualization\" and run the newly created code cell.\n","\n","Code snippets provide some specific functionality, but are limited and will need to be customised to fit your purposes. You may find it easier to code from scratch or copy code from previous analyses of yours. Whichever approach works for you, go for it!"],"metadata":{"id":"OFvnLzyfROeA"}},{"cell_type":"markdown","source":["## 7. Final notes\n","\n","A few things to note:\n","- Google Colab does have some limitations on RAM and Disk space, so memory-intensive analyses may fail. You can see your RAM and Disk usage in the upper right part of the page.\n","- If you want to convert a Google Colab notebook into normal python code (for use in Linux after the workshop), just go to File > Download > Download .py. You will also need to download any datasets you're using.\n","- If you want to learn more about Google Colab, check out this page: https://colab.research.google.com/. (Click \"Cancel\" when prompted to make a new notebook.)\n","- You can also use R in Google Colab notebooks. We'll only be using Python in this workshop, but if you want to use R in Google Colab in the future, refer to this page: https://towardsdatascience.com/how-to-use-r-in-google-colab-b6e02d736497."],"metadata":{"id":"JMeUhlHZ3olt"}}]}