As an enthusiast of both ancient history and Python programming, when I stumbled upon this data set about Roman emperors, I knew what I had to do… use it to make a data visualization in Python!. 222 CE - 235 CE: Reign of Roman Emperor Severus Alexander. Add this code to produce a bar chart of emperor’s causes of death in Python: Literally amazing (except for some squished text on the x-axis — we’ll deal with that soon). Fortunately, you can rotate the labels on the x-axis with this line of code: Be sure to add it in before calling plt.show(). To add more features to your bar charts, or for inspiration to create a new one, check out the bar charts at Python Graph Gallery! But it is! OK, a few words about the code: It’s always good to see what’s being stored inside your variables. 218 CE - 222 CE: Reign of Roman Emperor Elagabalus. Then the order starts again. Hands-on real-world examples, research, tutorials, and cutting-edge techniques delivered Monday to Thursday. 27 BC – AD 192. 395: PARTITION - EASTERN EMPIRE: Dynasty of Theodosius: 395-408: Arcadius: 408-450: Theodosius II: 450-457: Marcian (m. Pulcheria, gnddghtr Theod I) 457-474

If you can see something in your console that looks vaguely like a data table with index numbers, you’re probably on the right track! The last line just shows the graph. Browsing the columns, I decided to chart the different ways the emperors rose to power. Using the shorthand pd for pandas and plt for matplotlib.pyplot is fairly standard, and it also saves us a lot of trouble when it comes to typing out long library names. This is a family tree of the Roman Emperors, showing only the relationships between the emperors. If I called value_counts() on the column df["rise"], it would give me a list of numbers: exactly how many emperors gained power by birthright, by appointment of the senate, and so on. Our visualization should be eye-catching! Take a look, plt.title("Roman Emperors' Paths to Power"), https://raw.githubusercontent.com/zonination/emperors/master/emperors.csv, Tiny Machine Learning: The Next AI Revolution, 4 Reasons Why You Shouldn’t Be a Data Scientist, A Learning Path To Becoming a Data Scientist, Why did I call the variable I’m storing the CSV in. Sure, you could be born as the son of the emperor, but how often did “seizing power” actually work? All right, I have 16 columns of data. Feel free to code along with me to learn how to read a CSV file and make a bar chart in Python! Let’s get started by importing pandas (so we can read this CSV) and matplotlib, another library that will allow us to produce some publication-quality data visualizations. You can view the CSV file of data about Roman emperors here. Yikes! For example, if I write: That says I want the first column to be magenta (m), the second to be cyan (c), then blue (b), black (k), yellow (y), red (r), and green (g). But as I mentioned before, the one column I’m really interested in is the “Rise” column.

238 CE: Reign of Gordian I and Gordian II in Rome. Of course, it’s always a good idea to print the value of your variables, just to make sure they hold what you think they hold. Size: 24 x 36 inches on sturdy cardstock Description: Starts with Julius Caesar and includes: Family trees of every emperor from Augustus to the fall of Rome in the West and to Justinian the Great in the East Timeline of the Roman history Explanation of titles used by Roman Emperors Maps of the Roman …

I, on the other hand, had categorical data: different paths to power, like “Birthright” and “Seized Power.” I also needed to calculate a second, numerical variable: how many different emperors rose to power in that way. 235 - 238 Maximinus 238 Gordian I and II 238 Balbinus and Pupienus 238 - 244 Gordian III 244 - 249 Philip the Arab 249 - 251 Decius 251 - 253 Gallus 253 - 260 Valerian 254 - 268 Gallienus 268 - 270 Claudius Gothicus 270 - 275 Aurelian 275 - 276 Tacitus 276 - 282 Probus 282 - 285 Carus Carinus … As an enthusiast of both ancient history and Python programming, when I stumbled upon this data set about Roman emperors, I knew what I had to do… use it to make a data visualization in Python! 1st century ce Augustus (31 bce –14 ce) Tiberius (14–37 ce) Caligula (37–41 ce) Claudius (41–54 ce) Nero (54–68 ce) Galba (68–69 ce) Otho (January–April 69 ce) Aulus Vitellius (July–December 69 ce) Vespasian (69–79 ce) Titus (79–81 ce) Domitian (81–96 ce) Nerva (96–98 ce) And here we can see that the “Birthright” bar seems way higher than the rest of them… so that’s probably a more reliable way of becoming emperor than “seizing power”! Browsing the columns, I decided to chart the different ways the emperors rose to power. To see the data visualization, I’ll be coding in the Spyder IDE, which you can download as part of the Anaconda distribution. I needed some way to calculate the number of birthrights, the number of appointments by the army, and so on. You’ll notice that the first three lines of code just add the title and label the axes. With df["rise"], I could access the whole column of paths to power, a big long list that went like “birthright, birthright, appointment by senate, appointment by army,” etc. (Read more about colors in matplotlib here.). We can see the top and bottom of the DataFrame df by running the following code: You can run programs in Spyder by pressing what looks like a green “play” button. 217 CE - 218 CE: Reign of Roman Emperors Macrinus with his son Diadumenian. If you view the raw CSV, you’ll see all the data squished together, each column separated by only a comma.

We can read CSV data easily with the Python library pandas. It’s all prettified in a nice data table that hides what CSV actually means: comma-separated values. Furthermore, I could call keys() on value_counts() to find out which numbers corresponded to which method of becoming emperor. OK, so you can’t actually read the labels on the x-axis because they’re all squashed together. Now we can use pandas to read the CSV file: Yes, it’s that easy!

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