3. Data Transformation¶
In previous notebooks we learned how to use marks and visual encodings to represent individual data records. Here we will explore methods for transforming data, including the use of aggregates to summarize multiple records. Data transformation is an integral part of visualization: choosing the variables to show and their level of detail is just as important as choosing appropriate visual encodings. After all, it doesn’t matter how well chosen your visual encodings are if you are showing the wrong information!
As you work through this module, we recommend that you open the Altair Data Transformations documentation in another tab. It will be a useful resource if at any point you’d like more details or want to see what other transformations are available.
This notebook is part of the data visualization curriculum.
import pandas as pd import altair as alt
3.1. The Movies Dataset¶
We will be working with a table of data about motion pictures, taken from the vega-datasets collection. The data includes variables such as the film name, director, genre, release date, ratings, and gross revenues. However, be careful when working with this data: the films are from unevenly sampled years, using data combined from multiple sources. If you dig in you will find issues with missing values and even some subtle errors! Nevertheless, the data should prove interesting to explore…
Let’s retrieve the URL for the JSON data file from the vega_datasets package, and then read the data into a Pandas data frame so that we can inspect its contents.
movies_url = 'https://cdn.jsdelivr.net/npm/vega-datasets@1/data/movies.json' movies = pd.read_json(movies_url)
How many rows (records) and columns (fields) are in the movies dataset?
Now let’s peek at the first 5 rows of the table to get a sense of the fields and data types…
|0||The Land Girls||146083.0||146083.0||NaN||8000000.0||Jun 12 1998||R||NaN||Gramercy||None||None||None||None||NaN||6.1||1071.0|
|1||First Love, Last Rites||10876.0||10876.0||NaN||300000.0||Aug 07 1998||R||NaN||Strand||None||Drama||None||None||NaN||6.9||207.0|
|2||I Married a Strange Person||203134.0||203134.0||NaN||250000.0||Aug 28 1998||None||NaN||Lionsgate||None||Comedy||None||None||NaN||6.8||865.0|
|3||Let's Talk About Sex||373615.0||373615.0||NaN||300000.0||Sep 11 1998||None||NaN||Fine Line||None||Comedy||None||None||13.0||NaN||NaN|
|4||Slam||1009819.0||1087521.0||NaN||1000000.0||Oct 09 1998||R||NaN||Trimark||Original Screenplay||Drama||Contemporary Fiction||None||62.0||3.4||165.0|
We’ll start our transformation tour by binning data into discrete groups and counting records to summarize those groups. The resulting plots are known as histograms.
Let’s first look at unaggregated data: a scatter plot showing movie ratings from Rotten Tomatoes versus ratings from IMDB users. We’ll provide data to Altair by passing the movies data URL to the
Chart method. (We could also pass the Pandas data frame directly to get the same result.) We can then encode the Rotten Tomatoes and IMDB ratings fields using the
alt.Chart(movies_url).mark_circle().encode( alt.X('Rotten_Tomatoes_Rating:Q'), alt.Y('IMDB_Rating:Q') )
To summarize this data, we can bin a data field to group numeric values into discrete groups. Here we bin along the x-axis by adding
bin=True to the
x encoding channel. The result is a set of ten bins of equal step size, each corresponding to a span of ten ratings points.
alt.Chart(movies_url).mark_circle().encode( alt.X('Rotten_Tomatoes_Rating:Q', bin=True), alt.Y('IMDB_Rating:Q') )
bin=True uses default binning settings, but we can exercise more control if desired. Let’s instead set the maximum bin count (
maxbins) to 20, which has the effect of doubling the number of bins. Now each bin corresponds to a span of five ratings points.
alt.Chart(movies_url).mark_circle().encode( alt.X('Rotten_Tomatoes_Rating:Q', bin=alt.BinParams(maxbins=20)), alt.Y('IMDB_Rating:Q') )
With the data binned, let’s now summarize the distribution of Rotten Tomatoes ratings. We will drop the IMDB ratings for now and instead use the
y encoding channel to show an aggregate
count of records, so that the vertical position of each point indicates the number of movies per Rotten Tomatoes rating bin.
count aggregate counts the number of total records in each bin regardless of the field values, we do not need to include a field name in the
alt.Chart(movies_url).mark_circle().encode( alt.X('Rotten_Tomatoes_Rating:Q', bin=alt.BinParams(maxbins=20)), alt.Y('count()') )
To arrive at a standard histogram, let’s change the mark type from
alt.Chart(movies_url).mark_bar().encode( alt.X('Rotten_Tomatoes_Rating:Q', bin=alt.BinParams(maxbins=20)), alt.Y('count()') )
We can now examine the distribution of ratings more clearly: we can see fewer movies on the negative end, and a bit more movies on the high end, but a generally uniform distribution overall. Rotten Tomatoes ratings are determined by taking “thumbs up” and “thumbs down” judgments from film critics and calculating the percentage of positive reviews. It appears this approach does a good job of utilizing the full range of rating values.
Similarly, we can create a histogram for IMDB ratings by changing the field in the
x encoding channel:
alt.Chart(movies_url).mark_bar().encode( alt.X('IMDB_Rating:Q', bin=alt.BinParams(maxbins=20)), alt.Y('count()') )
In contrast to the more uniform distribution we saw before, IMDB ratings exhibit a bell-shaped (though negatively skewed) distribution. IMDB ratings are formed by averaging scores (ranging from 1 to 10) provided by the site’s users. We can see that this form of measurement leads to a different shape than the Rotten Tomatoes ratings. We can also see that the mode of the distribution is between 6.5 and 7: people generally enjoy watching movies, potentially explaining the positive bias!
Now let’s turn back to our scatter plot of Rotten Tomatoes and IMDB ratings. Here’s what happens if we bin both axes of our original plot.
alt.Chart(movies_url).mark_circle().encode( alt.X('Rotten_Tomatoes_Rating:Q', bin=alt.BinParams(maxbins=20)), alt.Y('IMDB_Rating:Q', bin=alt.BinParams(maxbins=20)), )
Detail is lost due to overplotting, with many points drawn directly on top of each other.
To form a two-dimensional histogram we can add a
count aggregate as before. As both the
y encoding channels are already taken, we must use a different encoding channel to convey the counts. Here is the result of using circular area by adding a size encoding channel.
alt.Chart(movies_url).mark_circle().encode( alt.X('Rotten_Tomatoes_Rating:Q', bin=alt.BinParams(maxbins=20)), alt.Y('IMDB_Rating:Q', bin=alt.BinParams(maxbins=20)), alt.Size('count()') )
Alternatively, we can encode counts using the
color channel and change the mark type to
bar. The result is a two-dimensional histogram in the form of a heatmap.
alt.Chart(movies_url).mark_bar().encode( alt.X('Rotten_Tomatoes_Rating:Q', bin=alt.BinParams(maxbins=20)), alt.Y('IMDB_Rating:Q', bin=alt.BinParams(maxbins=20)), alt.Color('count()') )
Compare the size and color-based 2D histograms above. Which encoding do you think should be preferred? Why? In which plot can you more precisely compare the magnitude of individual values? In which plot can you more accurately see the overall density of ratings?
Counts are just one type of aggregate. We might also calculate summaries using measures such as the
max. The Altair documentation includes the full set of available aggregation functions.
Let’s look at some examples!
3.3.1. Averages and Sorting¶
Do different genres of films receive consistently different ratings from critics? As a first step towards answering this question, we might examine the average (a.k.a. the arithmetic mean) rating for each genre of movie.
Let’s visualize genre along the
y axis and plot
average Rotten Tomatoes ratings along the
alt.Chart(movies_url).mark_bar().encode( alt.X('average(Rotten_Tomatoes_Rating):Q'), alt.Y('Major_Genre:N') )
There does appear to be some interesting variation, but looking at the data as an alphabetical list is not very helpful for ranking critical reactions to the genres.
For a tidier picture, let’s sort the genres in descending order of average rating. To do so, we will add a
sort parameter to the
y encoding channel, stating that we wish to sort by the average (
op, the aggregate operation) Rotten Tomatoes rating (the
field) in descending
alt.Chart(movies_url).mark_bar().encode( alt.X('average(Rotten_Tomatoes_Rating):Q'), alt.Y('Major_Genre:N', sort=alt.EncodingSortField( op='average', field='Rotten_Tomatoes_Rating', order='descending') ) )
The sorted plot suggests that critics think highly of documentaries, musicals, westerns, and dramas, but look down upon romantic comedies and horror films… and who doesn’t love
3.3.2. Medians and the Inter-Quartile Range¶
While averages are a common way to summarize data, they can sometimes mislead. For example, very large or very small values (outliers) might skew the average. To be safe, we can compare the genres according to the median ratings as well.
The median is a point that splits the data evenly, such that half of the values are less than the median and the other half are greater. The median is less sensitive to outliers and so is referred to as a robust statistic. For example, arbitrarily increasing the largest rating value will not cause the median to change.
Let’s update our plot to use a
median aggregate and sort by those values:
alt.Chart(movies_url).mark_bar().encode( alt.X('median(Rotten_Tomatoes_Rating):Q'), alt.Y('Major_Genre:N', sort=alt.EncodingSortField( op='median', field='Rotten_Tomatoes_Rating', order='descending') ) )
We can see that some of the genres with similar averages have swapped places (films of unknown genre, or
null, are now rated highest!), but the overall groups have stayed stable. Horror films continue to get little love from professional film critics.
It’s a good idea to stay skeptical when viewing aggregate statistics. So far we’ve only looked at point estimates. We have not examined how ratings vary within a genre.
Let’s visualize the variation among the ratings to add some nuance to our rankings. Here we will encode the inter-quartile range (IQR) for each genre. The IQR is the range in which the middle half of data values reside. A quartile contains 25% of the data values. The inter-quartile range consists of the two middle quartiles, and so contains the middle 50%.
To visualize ranges, we can use the
x2 encoding channels to indicate the starting and ending points. We use the aggregate functions
q1 (the lower quartile boundary) and
q3 (the upper quartile boundary) to provide the inter-quartile range. (In case you are wondering, q2 would be the median.)
alt.Chart(movies_url).mark_bar().encode( alt.X('q1(Rotten_Tomatoes_Rating):Q'), alt.X2('q3(Rotten_Tomatoes_Rating):Q'), alt.Y('Major_Genre:N', sort=alt.EncodingSortField( op='median', field='Rotten_Tomatoes_Rating', order='descending') ) )
3.3.3. Time Units¶
Now let’s ask a completely different question: do box office returns vary by season?
To get an initial answer, let’s plot the median U.S. gross revenue by month.
To make this chart, use the
timeUnit transform to map release dates to the
month of the year. The result is similar to binning, but using meaningful time intervals. Other valid time units include
date (numeric day in month),
day (day of the week), and
hours, as well as compound units such as
hoursminutes. See the Altair documentation for a complete list of time units.
alt.Chart(movies_url).mark_area().encode( alt.X('month(Release_Date):T'), alt.Y('median(US_Gross):Q') )
Looking at the resulting plot, median movie sales in the U.S. appear to spike around the summer blockbuster season and the end of year holiday period. Of course, people around the world (not just the U.S.) go out to the movies. Does a similar pattern arise for worldwide gross revenue?
alt.Chart(movies_url).mark_area().encode( alt.X('month(Release_Date):T'), alt.Y('median(Worldwide_Gross):Q') )
3.4. Advanced Data Transformation¶
The examples above all use transformations (bin, timeUnit, aggregate, sort) that are defined relative to an encoding channel. However, at times you may want to apply a chain of multiple transformations prior to visualization, or use transformations that don’t integrate into encoding definitions. For such cases, Altair and Vega-Lite support data transformations defined separately from encodings. These transformations are applied to the data before any encodings are considered.
We could also perform transformations using Pandas directly, and then visualize the result. However, using the built-in transforms allows our visualizations to be published more easily in other contexts; for example, exporting the Vega-Lite JSON to use in a stand-alone web interface. Let’s look at the built-in transforms supported by Altair, such as
Think back to our comparison of U.S. gross and worldwide gross. Doesn’t worldwide revenue include the U.S.? (Indeed it does.) How might we get a better sense of trends outside the U.S.?
calculate transform we can derive new fields. Here we want to subtract U.S. gross from worldwide gross. The
datum. prefix accesses a field value on the input record.
alt.Chart(movies).mark_area().transform_calculate( NonUS_Gross='datum.Worldwide_Gross - datum.US_Gross' ).encode( alt.X('month(Release_Date):T'), alt.Y('median(NonUS_Gross):Q') )