Introductory Python for Humanists + Data Visualization

Last Updated: May 1, 2020

This tutorial will introduce you to the Python programming language
and how it can be used to make scatter plots, bar graphs, and pie charts, like these:

1) Start a new Trinket project

1.1) You can either use the embedded trinket on the right side of this page or work in a new tab at trinket.io/python3.

1.2) You will write your code under "main.py" in the editing tab, and your result will show up under the play tab.

2) Test out the trinket

2.1) Write the following in the trinket editing tab under "main.py": 
print("hello world")
2.2) Run your code by pressing the play button at the top. In the result box, you should get hello world.

2.3) Go back to the editing tab and delete your code.

2.4) Try doing some math: 
print(2+2)

3) Create variables

3.1) Create a string variable: 
greeting = "hello"
print(greeting)
3.2) Concatenate strings: 
greeting = "hello, "
animal = "puppies!"
mySentence = greeting+animal
print(mySentence)
3.3) Create a float variable: 
myNumber = 3.14159
print(myNumber)
print(myNumber + 10)
3.4) Create an array: 
myArray = [1,2,3,4,5]
print(myArray)

4) Write an If block

4.1) Test if two numbers are equal: 
myFirstNum = 5
mySecondNum = 10
if (myFirstNum == mySecondNum):
  print("My numbers are equal.")
else:
  print("My numbers are not equal.")
4.2) Try changing the values of myFirstNum and mySecondNum to make sure the test works.

5) Write a For loop

myArray = [2,4,5,9,14]
for i in myArray:
  print(i+1)

6) Now let's start a scatter plot

6.1) Import two useful python libraries: 
import numpy as np
from matplotlib import pyplot as plt
6.2) Add some data: 
import numpy as np
from matplotlib import pyplot as plt


data_x = [2,4,5,9,14]
data_y = [3,3,6,8,10]
6.3) Plot the data points: 
import numpy as np
from matplotlib import pyplot as plt

data_x = [2,4,5,9,14]
data_y = [3,3,6,8,10]

plt.plot(data_x,data_y, 'ro')
plt.show()
The output should look like this:


6.4) 'ro' means your data points will be represented by red circles. Try changing it to 'bx' or 'g^'.

6.5) Add a line of best fit: 
import numpy as np
from matplotlib import pyplot as plt

data_x = [2,4,5,9,14]
data_y = [3,3,6,8,10]

plt.plot(data_x,data_y, 'ro')
fit = np.polyfit(data_x,data_y,1)
fit_fn = np.poly1d(fit)
plt.plot(data_x, fit_fn(data_x), 'y-')
plt.show()
The output should look like this:


6.6) 'y-' produces a solid yellow line. Try changing it to 'r--' or 'b.'

7) Add some lables to your plot

7.1) Add a title: 
import numpy as np
from matplotlib import pyplot as plt

data_x = [2,4,5,9,14]
data_y = [3,3,6,8,10]

plt.plot(data_x,data_y, 'ro')
fit = np.polyfit(data_x,data_y,1)
fit_fn = np.poly1d(fit)
plt.plot(data_x, fit_fn(data_x), 'y-')

plt.title('My Awesome Data Viz')

plt.show()
7.2) Add lables to the X and Y axes: 
import numpy as np
from matplotlib import pyplot as plt

data_x = [2,4,5,9,14]
data_y = [3,3,6,8,10]

plt.plot(data_x,data_y, 'ro')
fit = np.polyfit(data_x,data_y,1)
fit_fn = np.poly1d(fit)
plt.plot(data_x, fit_fn(data_x), 'y-')

plt.title('My Awesome Data Viz')
plt.xlabel('My Awesome X-Axis')
plt.ylabel('My Awesome Y-Axis')

plt.show()
Your plot should now look like this:

8) Make a Bar Chart

8.1) Delete the lines creating your scatter plot: 
import numpy as np
from matplotlib import pyplot as plt

data_x = [2,4,5,9,14]
data_y = [3,3,6,8,10]

plt.title('My Awesome Data Viz')
plt.xlabel('My Awesome X-Axis')
plt.ylabel('My Awesome Y-Axis')

plt.show()
8.2) Display your data as a bar chart: 
import numpy as np
from matplotlib import pyplot as plt

data_x = [2,4,5,9,14]
data_y = [3,3,6,8,10]

x_pos = np.arange(len(data_x))
plt.bar(x_pos,data_y)
plt.xticks(x_pos, data_x)

plt.title('My Awesome Data Viz')
plt.xlabel('My Awesome X-Axis')
plt.ylabel('My Awesome Y-Axis')

plt.show()
Your result should look like this:


8.3) Add a second set of Y-values: 
import numpy as np
from matplotlib import pyplot as plt

data_x = [2,4,5,9,14]
data_y = [3,3,6,8,10]
data_y2 = [1,2,3,5,5]

x_pos = np.arange(len(data_x))
plt.bar(x_pos,data_y)
plt.bar(x_pos,data_y2)

plt.xticks(x_pos, data_x)

plt.title('My Awesome Data Viz')
plt.xlabel('My Awesome X-Axis')
plt.ylabel('My Awesome Y-Axis')

plt.show()
Your result should look like this:


8.4) Display the two sets of Y-values side-by-side and change the colors: 
import numpy as np
from matplotlib import pyplot as plt

data_x = [2,4,5,9,14]
data_y = [3,3,6,8,10]
data_y2 = [1,2,3,5,5]

x_pos = np.arange(len(data_x))
plt.bar(x_pos-.2,data_y, width=.4, color="purple")
plt.bar(x_pos+.2,data_y2, width=.4, color="orange")

plt.xticks(x_pos, data_x)

plt.title('My Awesome Data Viz')
plt.xlabel('My Awesome X-Axis')
plt.ylabel('My Awesome Y-Axis')

plt.show()
Your result should look like this:


9) Make a Pie Chart

9.1) Delete the data, the lines creating a bar graph, and the x and y axis lables: 
import numpy as np
from matplotlib import pyplot as plt

plt.title('My Awesome Data Viz')

plt.show()
9.2) Add lables and percents as arrays: 
import numpy as np
from matplotlib import pyplot as plt

lables = [A,B,C,D]
percents = [33.3,22.2,16.7,5.6,22.2]

plt.title('My Awesome Data Viz')

plt.show()
9.3) Make a pie chart: 
import numpy as np
from matplotlib import pyplot as plt

names = ['A','B','C','D','E']
percents = [33.3,22.2,16.7,5.6,22.2]

plt.pie(percents, labels=names)
plt.axis('equal')

plt.title('My Awesome Data Viz')

plt.show()
The output should look like this:


9.4) Write the percents on the pie chart: 
import numpy as np
from matplotlib import pyplot as plt

names = ['A','B','C','D','E']
percents = [33.3,22.2,16.7,5.6,22.2]

plt.pie(percents, labels=names, autopct='%1.1f%%')
plt.axis('equal')

plt.title('My Awesome Data Viz')

plt.show()
9.5) Highlight the second slice: 
import numpy as np
from matplotlib import pyplot as plt

names = ['A','B','C','D','E']
percents = [33.3,22.2,16.7,5.6,22.2]
separate = (0,0.1,0,0,0)

plt.pie(percents, labels=names, autopct='%1.1f%%', explode=separate)
plt.axis('equal')

plt.title('My Awesome Data Viz')

plt.show()
Your result should look like this:


You learned how to make three kinds of plots using Python!

To learn more about Python data visualization, check out matplotlib.org and Mode Blog.