I would like to express gratitude for all the experience and support that TaskUs has given me. Being part of TaskUs’ success and seeing it grow from a startup to a publicly traded company is something I feel very proud of.
It’s been a pleasure working with a great team in the Business Insights and Data Science department. I am blessed to have worked with talented people, in particular, Scott Gamester, Rachel Perez, and Darcy Delamore. I have built a lasting friendship with colleagues that I will continue to cherish. I am grateful for my teammates, William Li, Dahlia Curtin, Sabrina Castillo, Antonio Morena, Tim Reyna, Sanjana Putchala, Priyanka Manchanda, and countless others.
I am sad to leave. At the same time, I am excited that everything I have learned during my time with TaskUs will help shape the rest of my career. Being with TaskUs afforded me the opportunity to apply data science to bring about actionable insights.
Lastly, I would like to offer my sincerest gratitude to Shauna Zamarippa and Tom Flynn for taking a chance on me.
I was writing about my journey from slacker to data scientist and I was reminded of just how fortunate I am because I had a lot of help along the way.
I am blessed to be working in the field of data science.
I am blessed to be employed a ridiculously good company.
I am blessed to still have a job amidst the COVID-19 crisis.
And most importantly, I truly am very fortunate to have family and friends– both professional and personal– that help me get to where I am now.
Today, I created a Kiva Team “Data Scientists for Good” with hopes of encouraging other data scientists, data analysts, and data engineers to give back. Click here if you’re interested in joining the team.
In a previous article, I wrote a quick start guide to visualize a Pandas dataframe using networkx and matplotlib. While it was fun to learn and explore more about network graphs in Python, I got to thinking about how to present the results to others who don’t have Python or Jupyter Notebook installed in their machines. At TaskUs, we use Power BI for most of our reporting so I began to search for a custom Power BI visualization that can take the data and transform it into a meaningful network graph.
Enter Network Navigator.
Network Navigator is a custom visual in Power BI that is created by Microsoft. It allows you to “explore node-link data by panning over and zooming into a force-directed node layout (which can be precomputed or animated live).”¹ In this post, we’ll walk through the steps needed to create a network graph using the custom visual.
First, let’s get our data. You can download the sample dataset here. Then, we could load the data into Power BI Desktop as shown below:
Select Text/CSV and click on “Connect”.
Select the file in the Windows Explorer folder and click open:
Click on “Transform Data”.
Click on “Use first Row as Headers”.
Click on “Close & Apply”.
Next, find the three dots at the end of the “Visualizations” panel.
And select “Get more visuals”.
Point your mouse cursor inside the search text box and type in “network” and hit the “Enter” key and click on the “Add” button.
Wait a few moments and you’ll see the notification below. Click on the “OK” button to close the notification.
You’ll see a new icon appear at the bottom of the “Visualizations” panel as shown below.
Click on the new icon and you will see something similar to the picture below.
With the new visual placeholder selected, click on “Reporter” and “Assignee” in the “Fields” panel and it will automatically assign the columns as the Source and Target Node.
Let’s add labels by clicking on the paintbrush icon.
Click on “Layout” to expand the section and scroll down until you see the “Labels” section.
Click on the toggle switch under “Labels” to turn it on
That’s it! With a few simple clicks of the mouse, we’re able to create a network graph from a csv file.
I hope you enjoy today’s post on one of Power BI’s coolest visuals. Network graph analysis is a big topic but I hope this gentle introduction will encourage you to explore more and expand your repertoire.
In the next article, I’ll share my journey from slacker to data scientist and I hope it’ll inspire others instead of being dissuaded by haters.
The Pandas function below takes a list of dataframes and concatenates them into. This basic flavor of concat()joins the dataframes vertically. In other words, the rows of one dataframe gets added on to the previous one.
df = pd.concat([df1,df2,df3])
Or if you want, you can store the list of dataframes into a variable first and then call the concat function. Like so:
# we must import pandas first
# put it in the beginning of your file
import pandas as pd
frames = [df1, df2, df3, df4, df5]
df = pd.concat(frames)
On the other hand, if I want to join the dataframes horizontally, then I can use merge().
For example, in the code below, we are merging df1 with df2 using ‘column_name’ as the common column. This is the column from which to base the merge. If there are any other identical columns that exist between the two dataframes, the suffixes are then appended to the each of the column names accordingly.
This particular flavor of merge() joins the dataframes horizontally. In the words, the columns of the dataframes gets added together to make one big mamma jamma of a dataframe;