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 datadays = [0,1,2,3,4,5,6]money_spent = [10,12,12,10,14,22,24]plt.plot(days, money_spent)plt.show()
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time = [0,1,2,3,4]revenue = [200,400,650,800,850]costs = [150,500,550,550,560]plt.plot(time, revenue)plt.plot(time, costs)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 pltx =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 pltx =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()
