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Tuesday Tip #32: Fancy filtering in pandas 🎩

Published 5 months agoΒ β€’Β 3 min read

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πŸ”— Link of the week

​Visual Vocabulary (PDF)

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πŸ‘‰ Tip #32: Explore, filter, and reshape your data with pandas

​Two weeks ago, I showed you three visualizations that I created from the World Happiness Report using Datawrapper.

​Last week, I walked through the steps needed to clean the data in order to create the first visualization, namely a world map.

Today, I'll show you how I transformed the raw data into the line chart data using Python's pandas library. Here are the specific steps:

  1. Read in & clean the data
  2. Decide which years to keep
  3. Keep only those years
  4. Keep countries that have ALL of those years
  5. Change to wide format
  6. Write data to CSV file

If you want to follow along with the code, you can run it online using Google Colab.

Here’s the end result:


Step 1: Read in & clean the data

To start, I read in the dataset from a URL using the read_excel function and selected which columns to keep.

Then, I standardized the column names and rounded the happiness column to 2 digits using the round method.


Step 2: Decide which years to keep

I wanted the line chart to span multiple years so that we could examine happiness trends over time. However, not every country has happiness data for every year, so I needed to decide which years to include.

To inform this decision, I selected the year column, used the value_counts method to count the number of times each year appears, and used the sort_index method to sort the resulting Series by year.

I knew the line chart would end with 2022, so I decided to start with 2011 since that year had a lot of data and was long enough ago to create a interesting visualization.


Step 3: Keep only those years

First, I used the range and set functions to create a set of integers from 2011 through 2022, which I called years.

Then, I used the isin method to create a boolean Series marking which year values were within the years set, and I used that Series to filter the DataFrame.

As you can see, the only years left in the dataset are 2011 through 2022.


Step 4: Keep countries that have ALL of those years

I decided to limit the visualization to only include countries that had complete data for the entire 12-year span. Thus, I needed to eliminate countries that were missing any years between 2011 and 2022.

There are many ways to accomplish this (as I learned from my Stack Overflow question), but the simplest method is to group the data by country, and then for each country, check whether the set of that country's years is equal to the years set.

You can see that there are only 83 countries left in the dataset, and each country has data for all 12 years.


Step 5: Change to wide format

In order to create this particular visualization, Datawrapper needs the year as the index, the country as the columns, and the happiness as the values within the table. In other words, the data needs to be in "wide" format rather than "long" format.

To reshape the data, I used the pivot method, specifying which columns should be used as the index, the columns, and the values.


Step 6: Write data to CSV file

Finally, I wrote the contents of the DataFrame to a file using the to_csv method. I included the index in the CSV (as the first column) since it contains the year values.


Publish with Datawrapper

I uploaded the CSV into Datawrapper, customized the chart's appearance (by setting United States to red, for example), and published it online.

Here’s the end result, which you can click on and interact with:


If you enjoyed this week’s tip, please forward it to a friend! Takes only a few seconds, and it really helps me reach more people! πŸ™

Also, stay tuned for an email tomorrow about my Black Friday sale! πŸ€‘

- Kevin

P.S. Frog riding a unicycle​

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Kevin Markham

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