fivethirtyeight.csv

plots.py

df = pd.read_csv("/Users/camerondavidson-pilon/code/election2020/fivethirtyeight.csv", sep=", ")

def integrate(x, y, mask):
    return trapz(y[mask], x[mask])/trapz(y, x)

def percent_chance(x, y, mask):
    return str(round(integrate(x,y, mask) * 100, 1)) + "%"


BIDEN_WIN = df['votes'] > 269
TRUMP_WIN =  df['votes'] < 269

## fig1

fig = plt.figure(figsize=(12, 4))
ax = fig.subplots()
plt.axvline(269, c="k", alpha=0.2)
plt.axhline(0, c="k", alpha=0.2)

plt.plot(df['votes'], df['scaled_prob'], c="#5768ac");

plt.title("Data extracted from FiveThirtyEight (Oct. 24, 2020)")
ax.set_yticklabels([])
plt.savefig("/Users/camerondavidson-pilon/code/election2020/fig1.png")



## fig2
## compare probabilities of winning


fig = plt.figure(figsize=(12, 4))
ax = fig.subplots()
plt.axvline(269, c="k", alpha=0.2)
plt.axhline(0, c="k", alpha=0.2)

plt.plot(df['votes'], df['scaled_prob'], c="#5768ac");

plt.fill_between(x=df['votes'].loc[BIDEN_WIN], y1=df['scaled_prob'].loc[BIDEN_WIN], color="#5768ac", alpha=0.2)
plt.text(350, 0.3, percent_chance(df['votes'], df['scaled_prob'], BIDEN_WIN))

plt.title("Probability of Biden win is equal to the area under the curve")
ax.set_yticklabels([])

plt.savefig("/Users/camerondavidson-pilon/code/election2020/fig2.png")

## fig3
## show that height only matters if width is large too

PEAK = (395 < df['votes']) &  (df['votes'] < 430)

fig = plt.figure(figsize=(12, 4))
ax = fig.subplots()
plt.axvline(269, c="k", alpha=0.2)
plt.axhline(0, c="k", alpha=0.2)

plt.plot(df['votes'], df['scaled_prob'], c="#5768ac");

plt.fill_between(x=df['votes'].loc[PEAK], y1=df['scaled_prob'].loc[PEAK], color="#6d904f", alpha=0.3)

plt.text(400, 0.3, percent_chance(df['votes'], df['scaled_prob'], PEAK))

plt.title("Probability of this blowout peak is " + percent_chance(df['votes'], df['scaled_prob'], PEAK))
ax.set_yticklabels([])
plt.savefig("/Users/camerondavidson-pilon/code/election2020/fig3.png")

## fig4
## you know what else is about equal to that area? 

NARROW_BIDEN_WIN = (269 < df['votes']) &  (df['votes'] < 322)

fig = plt.figure(figsize=(12, 4))
ax = fig.subplots()
plt.axvline(269, c="k", alpha=0.2)
#plt.axvline(322, c="k", alpha=0.2)
#plt.text(315, 0.05, "322")


plt.plot(df['votes'], df['scaled_prob'], c="#5768ac");

plt.fill_between(x=df['votes'].loc[PEAK], y1=df['scaled_prob'].loc[PEAK], color="#6d904f", alpha=0.3)
plt.text(400, 0.3, percent_chance(df['votes'], df['scaled_prob'], PEAK))

plt.fill_between(x=df['votes'].loc[NARROW_BIDEN_WIN], y1=df['scaled_prob'].loc[NARROW_BIDEN_WIN], color="#e5ae38", alpha=0.3)

plt.text(285, 0.3, percent_chance(df['votes'], df['scaled_prob'], NARROW_BIDEN_WIN))

plt.title("You know what else is equally as likely? \nA universe with a narrow Biden win of less than 322...")
ax.set_yticklabels([])
ax.set_xticks([100, 200,  322, 400, 500],)
plt.tight_layout()

plt.savefig("/Users/camerondavidson-pilon/code/election2020/fig4.png")


## fig5

NARROWER_BIDEN_WIN = (269 < df['votes']) &  (df['votes'] < 292)

fig = plt.figure(figsize=(12, 4))
ax = fig.subplots()
plt.axvline(269, c="k", alpha=0.2)
plt.axhline(0, c="k", alpha=0.2)

plt.plot(df['votes'], df['scaled_prob'], c="#5768ac");

plt.fill_between(x=df['votes'].loc[PEAK], y1=df['scaled_prob'].loc[PEAK], color="#6d904f", alpha=0.3)
plt.text(400, 0.3, percent_chance(df['votes'], df['scaled_prob'], PEAK))

plt.fill_between(x=df['votes'].loc[NARROWER_BIDEN_WIN], y1=df['scaled_prob'].loc[NARROWER_BIDEN_WIN], color="#5768ac", alpha=0.3)
plt.text(270, 0.3, percent_chance(df['votes'], df['scaled_prob'], NARROWER_BIDEN_WIN))

plt.fill_between(x=df['votes'].loc[TRUMP_WIN], y1=df['scaled_prob'].loc[TRUMP_WIN], color="#fc4f30", alpha=0.2)
plt.text(230, 0.1, percent_chance(df['votes'], df['scaled_prob'], TRUMP_WIN))


plt.title("...or a universe where Trump has a 62% chance of winning")
ax.set_yticklabels([])
plt.savefig("/Users/camerondavidson-pilon/code/election2020/fig5.png")