Continuum Analytics: Documentation

# Navigator Tutorial

Here are some of the things you can do with Anaconda Navigator.

- Why use Navigator?
- Navigator basic workflow
  - Step 1: Create and activate a new environment for the package you want to use.
  - Step 2: Search for and install the package you want.
  - Step 3: Open and use the package.
- Tutorial #1: Display BioPython’s help text in a Jupyter Notebook.
- Tutorial #2: Use QGrid with Yahoo S&P 500 data
- Tutorial #3: Use R computer language in a Jupyter Notebook

## Why use Navigator?

Many scientific packages require specific versions of programs in order to run. These other programs are called dependencies. The Conda Package Management System, and now Anaconda Navigator, allow you to create isolated environments so the programs will not interfere with other programs you have created, and you can install the exact programs needed.

Anaconda Navigator is an easy, point-and-click way to use packages and environments so you don’t have to use Conda commands in a terminal window. You can use it to find the packages you want, install them in an environment, and run the packages and update them, all inside Navigator.

## Navigator basic workflow

- Create and activate a new environment for the package you want to use.
- Search for and install the package you want.
- Open and use the package.

We’ll go over each of these steps in detail below.

### Step 1: Create and activate a new environment for the package you want to use.

Let’s say your professor told you to get BioPython, a popular library for biologists. A quick Google search lets you know that like most packages written in Python, it’s available for Windows, OS X and Linux, 32-bit and 64-bit, and all versions of Python back to 2.6. We’ll create an environment using the newest version of Python, 3.5.

NOTE: Why choose Python 3.5? It’s the latest and recommended version of Python to use.

In Anaconda Navigator, go to the Environments side tab, then click the bottom Create Button. The “Create new environment” dialog box appears:

Give your environment a memorable name, here we called ours “biopy”, select Python 3.5, and click the OK Button. Navigator creates the new environment and activates (switches to) it, as shown by the highlighted green bar:

NOTE: All actions take place in the active environment, shown by the highlighted green bar. In the above image the “biopy” environment is active.

### Step 2: Search for and install the package you want.

Select ALL to search for all possible packages, then type the name of the package you want in the search box. In the search results list, put a check next to the package you want to install:

From the drop-down menu that appears, select Mark for installation:

To begin installing, click the APPLY Button on the bottom right:

When it asks you if you want to proceed, click the OK Button.

Anaconda Navigator gets all the dependent files you need and installs them in your new environment.

### Step 3: Open and use the package.

On your “biopy” environment name which is still highlighted (if not, click to highlight it), click the arrow to bring up the menu of choices to open your package:

Here we have selected Open Terminal. A terminal window will appear, type jupyter-notebook to open Jupyter Notebook in a new browser window or tab.

To exit Jupyter Notebook, after closing the notebook tabs or windows in the browser, press Ctrl + C and answer “Y” [for “yes”] to stop the notebook in the terminal, then type exit and press “Enter” to exit the terminal.

## Tutorial #1: Display BioPython’s help text in a Jupyter Notebook.

You’ve already installed and opened BioPython above. To verify that the installation is working, Open a Jupyter Netbook, chose New -> Python 3 (you may have a different Python version depending on your installation).

Now paste the following code into the first cell:

    import Bio
    help(Bio)

To run the code, click Cell -> Run Cells from the menu bar or use the keyboard shortcut Ctrl + Enter. This shows the help information for the biopython package.

## Tutorial #2: Use QGrid with Yahoo S&P 500 data

QGrid is a dataframe viewer for Jupyter notebook. To test your installation, use QGrid to display S&P 500 stock market data.

To install QGrid and Pandas Datareader follow the instructions on how to install a package, but install “qgrid” and “pandas-datareader” instead of “biopython”.

Open a Jupyter Notebook and paste the following code into the first cell:

    import qgrid
    import pandas as pd
    import pandas_datareader
    pd.set_option('display.max_rows', 8) # Prevents the grid from being too large
    from pandas_datareader.data import get_data_yahoo
    spy = get_data_yahoo(symbols='SPY', start=pd.Timestamp('2011-01-01'),
                         end=pd.Timestamp('2014-01-01'), adjust_price=True)
    spy

To run the code click Cell -> Run Cells from the menu bar or use the keyboard shortcut Ctrl + Enter.

This gets a pandas DataFrame containing the daily prices for the S&P 500 from 1/1/2011 - 1/1/2014 and displays the first and last four rows of the DataFrame.

Thanks to https://github.com/quantopian/qgrid and http://nbviewer.jupyter.org/gist/TimShawver/b4bc80d1128407c56c9aifor example code.

## Tutorial #3: Use R computer language in a Jupyter Notebook

It is possible to use R, the popular programing language for statistics, in a Jupyter Notebook. To do so, we’ll follow the exact Navigator basic workflow shown above.

1. Create and activate a new environment for the package you want to use. Go to the Environments tab, click the “Create” button at the bottom left, give your new environment a descriptive name like “R essentials” and check the “R” box for the R computer language to be installed. Click the “Create” button again to begin creating your new environment. After it has finished installing, highlight your new environment name to activate it.

2. Search for and install the packages you want. You want both R language and R essentials, which are available in the channel named MRO. Show uninstalled packages by selecting “All” from the drop-down menu. Then add the MRO channel to your channel list by clicking “Channels”, clicking “Add”, selecting the white box that appears and typing “mro” and the enter key, and clicking “Update channels”. Now in the search box when you type “r” or “r-es” the package name “r-essentials” appears in your search results. Check the box and from the drop-down menu that appears, select “Mark for installation”. Repeat for the “r” package and any other packages you wish to install. Click the Apply button that appears at the bottom right. A dialog box appears asking you to confirm, so click the “Ok” button to install R language and R Essentials.

3. Open and use the new R language package. From the Environments tab, open Jupyter Notebook by highlighting your new environment name, clicking the Open icon and selecting “Open with Jupyter Notebook.” To create a new notebook with the R language, on the Jupyter Notebook top menu, choose New -> R. Paste the following code into the first cell:

    library(dplyr)
    iris

Click Cell -> Run Cells from the menu bar or press Ctrl + Enter. This will show the iris data table.

To plot the data, click + to open a second cell, then paste the following code into the cell:

    library(ggplot2)
    ggplot(data=iris, aes(x=Sepal.Length, y=Sepal.Width, color=Species)) + geom_point(size=3)

Again, click Cell -> Run Cells from the menu bar or press Ctrl + Enter

For more information and to see the example code, see the Continuum Developer Blog.