OK, so you can’t actually read the labels on the x-axis because they’re all squashed together. The last line just shows the graph. 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. 238 CE: Reign of Gordian I and Gordian II in Rome. 222 CE - 235 CE: Reign of Roman Emperor Severus Alexander. Browsing the columns, I decided to chart the different ways the emperors rose to power. But as I mentioned before, the one column I’m really interested in is the “Rise” column. Add this code to your to get the numbers and the corresponding path to power: Now, I had a list of unique paths to power, as well as the numbers that corresponded to each path. Browsing the columns, I decided to chart the different ways the emperors rose to power. Hands-on real-world examples, research, tutorials, and cutting-edge techniques delivered Monday to Thursday. 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(). You’ll notice that the first three lines of code just add the title and label the axes. This tutorial will assume knowledge of basic Python programming, but you can still follow along without it. Then the order starts again. 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). Lastly, we need to add some color. 218 CE - 222 CE: Reign of Roman Emperor Elagabalus. 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. All right, I have 16 columns of data. Now we can use pandas to read the CSV file: Yes, it’s that easy! I initially thought that I wanted to make a histogram, but as I learned from Python Graph Gallery, a histogram ONLY takes numerical data and just shows the distribution of it. 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! 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. 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! You can view the CSV file of data about Roman emperors here. We can read CSV data easily with the Python library pandas. More emperors without a dynastic label, although it includes the year of the 6 emperors, 238. Sure, you could be born as the son of the emperor, but how often did “seizing power” actually work? 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) (Read more about colors in matplotlib here.). The code for making a graph with Python is so simple, it feels like it can’t be real. 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!. If you view the raw CSV, you’ll see all the data squished together, each column separated by only a comma. 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 … All the heavy-lifting takes place by calling plt.bar() in the fourth line. But it is! 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! 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. With one categorical variable and one numerical variable (to be calculated), what I wanted to make was a bar chart. The emperors from Augustus to Commodus can be organised into one large family tree. Furthermore, I could call keys() on value_counts() to find out which numbers corresponded to which method of becoming emperor. It’s all prettified in a nice data table that hides what CSV actually means: comma-separated values. Our visualization should be eye-catching! But actually, CSV is a great file type. Make learning your daily ritual. 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. 395: PARTITION - EASTERN EMPIRE: Dynasty of Theodosius: 395-408: Arcadius: 408-450: Theodosius II: 450-457: Marcian (m. Pulcheria, gnddghtr Theod I) 457-474 The Roman emperors were the rulers of the Roman Empire dating from the granting of the title of Augustus to Gaius Julius Caesar Octavianus by the Roman Senate in 27 BC, after major roles played by the populist dictator and military leader Julius Caesar.Augustus maintained a facade of Republican rule, rejecting monarchical titles but calling himself princeps senatus (first man … 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. Yikes! When you call plt.bar(), you can also specify the colors you want through the color parameter. I needed some way to calculate the number of birthrights, the number of appointments by the army, and so on.

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