I put together this code for my economics classes and I am posting it in the hopes that others will not have to duplicate my efforts. I am a beginner in Python. For other beginners: This does not tell you how to install Python, but it is surprisingly easy. Install Anaconda, which includes the needed packages and code editor. To call up the editor, open a command-line window in Windows (the black DOS window) and type 'spyder' (no quotes), and it will come up in a minute or so (what seems like a long time). Then open your Python program file in the main window, something like myfile.py, and click the green arrow at the top of the window to run it and see the output in the console window.

This program is to solve for the equilibrium of a supply and demand diagram and to also add in an externality of 3 per unit and show how total surplus rises when regulation is imposed. For simple supply and demand, just omit the Psocial curve. You may also wish to turn on the axis ticks and you can use this as a pattern to see how to draw vertical and horizontal lines for whatever areas of consumer and producer surplus interest you.

#September 9, 2017 #This is a supply and demand curve diagram with the axes ticks turned off. #I will post it on the web in case others might find it useful. #http://moneymarketsandmisperceptions.blogspot.com/2016/09/building-supply-and-demand-graphs-in.html #http://www.scipy-lectures.org/intro/matplotlib/matplotlib.html import matplotlib.pyplot as plt # for plot diagrams import numpy as np #for constructing vectors. from sympy import * #For solving out for the equilibrium price, etc. ,symbolically. #We need to establish that Q is just a symbol, with no number value. Q = Symbol('Q') a1 = 2; a2=1; externality =3; Ps = a1+a2*Q; Psocial = a1 + externality + a2*Q; a3 = 12; a4 = 1; Pd= a3- a4*Q; #Now we find the equilibrium quantity. The blank print is to skip a line for easier reading. Qeq= solve( Ps-Pd, Q) ; print(Qeq); print(" "); #But a solution to the solve command is a list, not a float number, so we convert. Qeq = float(Qeq[0]) Peq= a1+a2*Qeq; print("Peq = ", Peq, "Qeq= ", Qeq);print(" "); #We also want the height of the S, S+3, and D curves at the equilibrium quantity. PsQeq = a1 + a2*Qeq; PsocialQeq= a1 + 3 + a2*Qeq; PdQeq= a3- a4*Qeq; print(("PsQeq= ", PsQeq , "PsocialQeq= ",PsocialQeq, "PdQeq = ", PdQeq));print(" "); #Now we find the optimal quantity. Qstar= solve( Psocial-Pd, Q) ; print(Qstar);print(" "); #But a solution to the solve command is a list, not a float number, so we convert. Qstar = float(Qstar[0]) Pstar = a1+3+ a2*Qstar; print("Pstar = ", Pstar, "Qstar = ", Qstar);print(" "); #We also want the height of the S, S+3, and D curves at the value-maximizing quantity. PsQstar = a1 + a2*Qstar; PsocialQstar= a1 + 3 + a2*Qstar; PdQstar= a3- a4*Qstar; print(("PsQstar = ", PsQstar , "PsocialQstar= ",PsocialQstar, "PdQstar = ", PdQstar)); print(" "); ################################################################## #Now we start drawing the supply and demand curves. plt.rcParams['lines.linewidth'] = 5 #This sets the default width of plot lines. plt.rcParams['lines.color'] = 'black' #This is for straight lines. plt.rcParams['axes.color_cycle']='black' #This is for plot lines, oddly enough. #It is hard to find the names to set markerface color, for example. #We will have a figure and axes, and we name them. #Also, we say the figure will be big in size, so we can judge points better. fig, ax= plt.subplots(figsize=(20, 10)) #We want to get rid of the box around the diagram this way: ax.spines['top'].set_visible(False) ax.spines['right'].set_visible(False) #In putting on labels, we want a horizontal label on the y-axis as well as the x-axis. plt.xlabel("Quantity", fontsize=28) plt.ylabel("Price ", fontsize = 28, rotation = 0) #Here are the actual things to plot. #Q is now a set of 200 points between 0 and 20 at which to sample the function plotted. Q = np.linspace(0, 20, 200) # We need to redefine our supply and demand equations (but the parameters are still OK) Ps = a1+a2*Q Psocial = a1 + externality + a2*Q; Pd= a3- a4*Q; #Let's turn the x and y ticks off, since this is a qualitative diagram. plt.xticks([]); plt.yticks([]); #We can turn on axis ticks for drawing help. #plt.xticks(np.arange(0, max(Q)+1, 1.0)) #plt.yticks(np.arange(0, max(Ps)+1, 1.0)) # Set x, y limits for the points covered by the diagram. plt.xlim(0, 6) plt.ylim(0, 14) #plt.plot is the object that has the actual plot. We will ahve three plots on one figure. #I made the demadn curve look a little different. plt.plot(Q, Ps ) plt.plot(Q, Pd, marker = 'o' ) plt.plot(Q, Psocial ) #Here is how to connect two points [x1,x2] and [y1,y2] with a line: plt.plot([Qstar, Qstar],[PsQstar,PsocialQstar], linewidth=1, color='black') plt.plot([Qeq, Qeq],[PsQeq,PsocialQeq], linewidth=1) #Put dots (o) at [x1,x2] and [y1,y2]. Make sure they aren't clipped off at the axes. plt.plot([0, 0, Qstar, Qstar, Qeq, Qeq],[PsQeq,PdQstar, PdQstar,0,PsQeq,0], 'o', markerfacecolor='black', markersize=12, clip_on=False,) # This next was supposed to fill in between the curves gray, but it leaves off a little bit.So I gave up. #ax.fill_between(Q, Ps, Psocial, where= Q<=Qstar, facecolor='gray', interpolate=True) # Add some text to point (2,3) using Latex to get a subscript. plt.text(2,3 ,"$Supply, Q_s$", fontsize = 24) plt.text(2, 11,"$Demand, Q_d$", fontsize = 24) plt.text(4.8, 11,"$Supply + Externality$ of 3", fontsize = 20) plt.text(1,4,"A", fontsize = 32) plt.text(3.8, 7,"B", fontsize = 32) plt.text(4.2, 8,"C", fontsize = 32) #Turn the next line on to help with guessing where text should be. #ax.minorticks_on(); ax.grid(which='both'); #The annotate method inserts an arrow and a phrase. #It points the arrow at xy and puts the text xytext distance away. plt.annotate(r' $(Q_{unregulated}, P_{unregulated})$', xy=(Qeq,PsQeq), xycoords='data', xytext=(+10, +50), textcoords='offset points', fontsize=16, arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=.2")) plt.annotate(r' $Q_{unregulated}$', xy=(Qeq,0), xycoords='data', xytext=(+10, +50), textcoords='offset points', fontsize=16, arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=.2")) plt.annotate(r' $Q_{regulated}$', xy=(Qstar,0), xycoords='data', xytext=(+10, +50), textcoords='offset points', fontsize=16, arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=.2")) plt.annotate(r' $P_{unregulated}$', xy=(0, PsQeq), xycoords='data', xytext=(+10, +50), textcoords='offset points', fontsize=16, arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=.2")) plt.annotate(r' $P_{regulated}$', xy=(0,PdQstar), xycoords='data', xytext=(+10, +50), textcoords='offset points', fontsize=16, arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=.2")) #The next command decides how much white space to put around the plot when it is saved. plt.subplots_adjust(left=0.18, right=0.9, top=0.9,bottom= 0.15) plt.savefig("ans02-04.png") #Needs to be before plt.show() plt.show() plt.close("all")