Matplotlib is the default library to create charts for data analysis with Python. It works pretty well, and I get the satisfaction of learning how to use the code set I watched all my friends in college swear at for multiple years. Neat!
from matplotlib import pyplot as plt
# sample data
days = [0, 1, 2, 3, 4, 5, 6]
money_spent = [10, 12, 12, 10, 14, 22, 24]
plt.plot(days, money_spent)
plt.show()
These are pretty basic examples, and don't show off the strength of being able to generate a chart with a couple hundred points of data very quickly. This library can chew what Excel chokes on -- nothing is worse than sending over a good chart and finding out the CFO with a 4GB Surface Pro has no idea what chart you're talking about.
Charting with Style
Matplotlib can get much fancier. Below are charts demonstrating some bells and whistles, and a more complete list can be found in the docs, linked here
from matplotlib import pyplot as plt
x = list(range(1,6))
y1 = [3.5, 3.5, 7, 7, 10.5]
y2 = [5.5, 6.5, 12, 13, 18.5]
legend_labels = ['Rogue Damage', 'Cleric Healing']
plt.plot(x, y1, color='red', linestyle='--', marker='o')
plt.plot(x, y2, color='blue', linestyle=':', marker='s')
plt.title('Two Lines on One Graph')
plt.xlabel('Level')
plt.ylabel('Damage')
plt.legend(legend_labels, loc=4)
plt.show()
from matplotlib import pyplot as plt
x = range(7)
straight_line = [0, 1, 2, 3, 4, 5, 6]
parabola = [0, 1, 4, 9, 16, 25, 36]
cubic = [0, 1, 8, 27, 64, 125, 216]
plt.subplot(2, 1, 1)
plt.plot(x, straight_line)
plt.subplot(2, 2, 3) # The positioning is as if the table was created and the index, even if all of that index isn't used.
plt.plot(x, parabola)
plt.subplot(2, 2, 4)
plt.plot(x, cubic)
plt.subplots_adjust(wspace=0.35, bottom=0.2)
plt.show()
