Chapter 13: Hello charts¶
Python has a number of charting tools that can work hand-in-hand with pandas. Altair is a relative newbie, but it’s got good documentation and can display charts right in your Jupyter Notebook — plus it can export to lots of other formats.
Let’s take it for a spin.
Before we start, we need to make sure it is installed. Head back to your terminal and practice that pipenv install process.
$ pipenv install altair
After that completes, once again restart your notebook.
$ pipenv run jupyter notebook
Now you can head back to your notebook and add Altair to your imports. We’ll usually import it with the alias
alt so we don’t have to type out the whole thing every time we make a chart.
import altair as alt
Now rerun the entire notebook, as we learned above. You will need to do this when you halt and restart your notebook on the command line. Reminder, you can do this by pulling down the
Cell menu at the top of the notebook and selecting the
Run all option.
Let’s pick up where we last left off in the previous chapter. If we want to chart out how much the top supporters of the proposition spent, we first need to select them from the dataset. Using the grouping and sorting tricks we learned earlier, the top 10 can returned like this:
top_supporters = support.fillna("").groupby( ["contributor_firstname", "contributor_lastname"] ).amount.sum().reset_index().sort_values("amount", ascending=False).head(10)
We can then view them with a trick you may remember by now.
Now that we have
altair imported, we can pop that dataframe into a quick chart. Let’s step through the building blocks of a chart.
alt.Chart(top_supporters).mark_bar().encode( x="contributor_lastname", y="amount" )
Look at that chart!
Here’s an idea — maybe we want to do horizontal, not vertical bars. How would you rewrite this chart code to reverse those bars?
alt.Chart(top_supporters).mark_bar().encode( x="amount", y="contributor_lastname" )
What if we wanted to focus on the top five records? We can use that
head command we already know.
alt.Chart(top_supporters.head(5)).mark_bar().encode( x="amount", y="contributor_lastname" )
Okay, but what if I want to combine the first and last name? We have the data we need in two separate columns, which we can put together simply by inventing a new field on our data frame and, just like a variable, setting it equal to a combination of the other fields.
top_supporters['contributor_fullname'] = top_supporters.contributor_firstname + " " + top_supporters.contributor_lastname
Now we can use that column instead of``contributor_lastname`` in our chart.
alt.Chart(top_supporters.head(5)).mark_bar().encode( x="amount", y="contributor_fullname" )
Notice how the sort order changed when we changed the contributor column? This chart is sorted alphabetically by y-axis value, and it’s making everything look pretty sloppy and hard to parse. Let’s fix that.
We want to sort the y-axis values by their corresponding x values. We’ve been using the shorthand syntax to pass in our axis columns so far, but to add more customization to our chart we’ll have to switch to the longform way of defining the y axis.
That will look something like the way we define the chart in the first place:
alt.Y(column_name, arg="value"). There are lots of options that you might want to pass in, like ones that will sum your data on the fly or define the number range you want your axis to display. In this case, we’ll just be using the
alt.Chart(top_supporters.head(5)).mark_bar().encode( x="amount", y=alt.Y("contributor_fullname", sort="-x") )
And we can’t have a chart without context. Let’s throw in a title for good measure.
alt.Chart(top_supporters.head(5)).mark_bar().encode( x="amount", y=alt.Y("contributor_fullname", sort="-x") ).properties( title="Top Spenders in Support of Proposition 64" )
Yay, we made a chart!
Now, we have a good idea of who spent the most in support of Prop. 64. What if we wanted to see who spent money on both sides?
Add a new cell and a new dataframe,
top_contributors, summing up the top contributors in our whole
merged dataframe. We’re going to repeat a lot of the pandas functions we’ve stepped through before, all in one go this time.
top_contributors = merged.fillna("").groupby( ["contributor_firstname", "contributor_lastname","committee_position"] ).amount.sum().reset_index().sort_values("amount", ascending=False).head(10)
And once again, we’re going to want a
contributor_fullname column that combines our first and last name columns.
top_contributors["contributor_fullname"] = top_contributors["contributor_firstname"] + " " + top_contributors["contributor_lastname"]
top_contributors into a chart, just like we did before. Remember that sort function!
alt.Chart(top_contributors.head(5)).mark_bar().encode( x="amount", y=alt.Y("contributor_fullname",sort="-x"), )
What facet of the data is this chart not showing? How might we add additional context?
We have that
committee_position column in our dataframe now. Let’s try an altair option that we haven’t used yet: color. Can you guess where we should add that in?
alt.Chart(top_contributors.head(5)).mark_bar().encode( x="amount", y=alt.Y("contributor_fullname",sort="-x"), color="committee_position" )
Hey now! That wasn’t too hard, was it?
To be fair, none of these charts are ready to pop into a news story quite yet. There are lots of additional formatting and design options that you can start digging into in the Altair docs — you can even create Altair themes to specify default color schemes and fonts.
But you may not want to do all that tweaking in code, especially if you’re just working on a one-off graphic. If you wanted to hand this chart off to a graphics department, all you’d have to do is head to the top right corner of your chart.
See those three dots? Click on that, and you’ll see lots of options. Downloading the file as an SVG will let anyone with graphics software like Adobe Illustrator take this file and tweak the design.
Guess what? It’s this easy.