Getting started

This getting started guide walks you through opening and using Anaconda Fusion for the first time after it has been installed.

After completing this guide, you will be able to:

  • Open a demo data file and run a clustering algorithm in Excel.
  • Execute an interactive plot of the clustering results in the Anaconda Fusion pane, and
  • Create a Jupyter Notebook to export functions to Excel.

NOTE: If you have not yet installed and started Anaconda Fusion, follow the Install instructions.

Next, open the clustering notebook, following the instructions below for your operating system.

Opening the notebook on Windows

  • Open the Anaconda Fusion examples folder: Click on the Anaconda Fusion Example Spreadsheet icon from the start menu.
  • Open the demo ‘clustering.xlsx’ spreadsheet in Excel 2016.
  • Activate the Anaconda Fusion Add-In: In Excel, click Insert > Add-ins > My Add-ins > Shared Folder > Anaconda Fusion > OK.
  • A new right pane appears. Click Notebooks to see the list of example notebooks. Click on the one named clustering.ipynb.

Opening the notebook on macOS

  • Open the Anaconda Fusion examples folder: Open a terminal window, type the command fusion-examples and press return. A new finder will open with the examples.

  • Open the demo ‘clustering.xlsx’ spreadsheet in Excel 2016.

  • Activate the Anaconda Fusion Add-In: In Excel, click Insert (Ribbon Menu) > My Add-ins (click on the small arrow on the right side of the button) > Anaconda Fusion (see picture bellow).

    ../../_images/fusion_menu_activation_mac.png
  • A new right pane appears. Click Notebooks to see the list of example notebooks. Click on the one named clustering.ipynb.

Execute a clustering algorithm in Excel

To see how to execute code from other data scientists directly from Excel, you will run a clustering demonstration. When you complete this section, you will have executed Python code to run a machine learning algorithm on your data and visualize its output.

Select some data in the spreadsheet to make it visible to Anaconda Fusion

Under the table NOISY_CIRCLES in your demo spreadsheet, find the columns x and y. Highlight to select at least 100 rows of those two columns (without selecting the x and y column headers). In the Anaconda Fusion pane, click on Data > Current Selection.

../../_images/fusion_add_data.png

A small form pops up. In the name field type the name for the dataset you selected, noisy_circles_small. Click Confirm to save and click the “clustering.ipynb” tab at the bottom of the pane.

../../_images/fusion_confirm_add_data.png

Run the clustering algorithm

In the Anaconda Fusion pane, from the drop-down menu select clustering. You will see three additional drop-down input menus for selecting the parameters of the algorithm. Set them as follows:

  • Select Data Select your dataset noisy_small_circles.
  • Select Algorithm Select MiniBatchKMeans.
  • n_clusters Leave the selection as ---.
../../_images/fusion_clustering_params.png

Now click the Run button to produce a plot of the clustering results in the Anaconda Fusion pane.

../../_images/fusion_clustering_plot.png

You just executed Python code from within Excel to run a machine learning algorithm on your data and visualize its output!

Run the clustering algorithm from the formula bar with `clustering(data, [algorithm], [n_clusters])`

Note that parameters in brackets are optional and the default value will be used if not specified.

Example: Select an empty cell and write =clustering(B3:C1502, “MiniBatchKMeans”, 5). Hit enter to execute and produce the same results shown previously.

Create a notebook to export functions to Excel

If you already use Python for data analysis and want to make your code available for your coworkers using Excel, this section is for you. It demonstrates writing Python code that others can open and use within Excel. This script will calculate and display the sum of the even numbers in the Excel list.

  • Open Anaconda Fusion. If the Anaconda Fusion server is not running, open it from the Start menu. Anaconda Fusion will show a black window with white text displaying the Fusion server log.
  • Open Jupyter Notebook in your browser. Open your browser and enter the address of the Jupyter kernel that was installed with Anaconda Fusion. By default, the address is https://localhost:9888.

TIP: If you did not use the default address, you may find the address by opening the Anaconda Fusion window and using the localhost:... string you find there as the address. You should see a screen like the following:

../../_images/fusion_jupyter_opening.png
  • Open a new Python 3 notebook. In your Jupyter Notebooks browser window, in the top tab named Files, click on the folder named notebooks, click the New button, and select Python [default] to create a new Jupyter Notebook with a Python 3 kernel.
  • Create the Python script. In the new notebook’s single cell, copy and paste or type this code:
from anacondafusion.fusion import fusion

@fusion.register()
def add_evens(data):
  total = 0
  for row in data:
      for item in row:
          if item % 2 == 0:
              total = total+item
  return total

TIP: This function add_evens is exposed to Excel with the @fusion.register decorator.

Your notebook should now look like this:

../../_images/fusion_jupyter_function.png
  • Save the new notebook. To save the Jupyter notebook, click on File > Save and Checkpoint.

NOTE: At the top left next to the Jupyter symbol is the notebook name followed by “Last Checkpoint: ...”. By default the notebook name is “Untitled”. Now that you’ve saved it, the add_evens function can be used.

The Jupyter Notebooks interface in your browser is the best way to create, edit, and delete the notebooks used with Anaconda Fusion.

  • Open a new blank Excel worksheet and Anaconda Fusion. Open Excel, then open the Anaconda Fusion pane by clicking Insert > Add-Ins > My Add-Ins > Shared Folder > Anaconda Fusion. Click Notebooks and select the new notebook you just saved named Untitled.ipynb. Note the drop-down menu with the add_evens function from the Jupyter notebook. Make sure add_evens is selected in this drop-down.
  • Enter your data in the worksheet. On the blank Excel worksheet, in the cells B2:E2 enter 1, 2, 3, and 4. Highlight to select cells B2:E2. In Anaconda Fusion we will name this dataset mydata.
  • Define mydata. In the Anaconda Fusion pane, click Data and Current Selection, enter “mydata” in the “Name” field, and click Confirm to define mydata as the dataset you selected in Excel. This creates an object accessible to Jupyter that points to your Excel dataset.
  • Calculate result. In Excel, click on cell C4. This cell will contain the result.
  • Run script. We can now call the add_evens function on mydata. In Anaconda Fusion’s Inputs drop-down menu, select mydata. Click Run. In Excel, in the blank cell C4 that you selected, you should see the sum 6.

More practice

At this point you may want to go back to your browser and look at the first example to see how we wrote the clustering.ipynb notebook.

Other Output options

In the Output section Anaconda Fusion displays a link “Options”. This shows the “Select Default Output” drop-down menu which sets the default way for Fusion to output data to Excel: “Selection” or “Cell/Range”.

The Output section also shows the link “Export” which can export the most recent result from Fusion to Excel. The “Select Export Destination” drop-down menu also offers a choice of “Selection” or “Cell/Range”.