<Python> Matplotlib (Demo)

# import packages
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
%config InlineBackend.figure_format = 'retina'

# Data
x = range(2014, 2021)
y = [20, 25, 18, 15, 24, 30, 26]

# Graph
plt.plot(x[:4], y[:4], marker="o", label="Actual")
plt.plot(x[3:], y[3:], linestyle="--", label="Forcast")

# Aesthetic
plt.title("zzz", size=14)
plt.ylabel("yyy")
plt.xlabel("xxx")
plt.legend()
plt.show()

# Data
x = range(4)
y = (20, 25, 24, 30)
xtick = ("mocha", "latte", "espresso", "tea")
avg = sum(y)/len(y)

# Graph
plt.bar(x, y)
plt.axhline(avg, linestyle="--", alpha=0.5)

# Aesthetic
plt.title("Order by menu\nFeb 2017", size=14)
plt.xlabel("Menu")
plt.xticks(x, xtick)
plt.ylabel("Order")
plt.show()

# Data
x = range(4)
y = (20, 25, 24, 30)
xtick = ("mocha", "latte", "espresso", "tea")

# Graph
plt.barh(x, y)

# Aesthetic
plt.title("Order by menu\nFeb 2017", size=14)
plt.ylabel("Menu")
plt.xlabel("Order")
plt.yticks(x, xtick)
plt.show()

# Data
label = np.array(["China", "Russia", "Japan", "Korea"])
val = (1.8, 1, .8, .5)
explode = np.zeros(label.size)
explode[np.where(label == "Japan")] = .1
colors = ["red", "yellow", "pink", "orange"]

# Graph
plt.pie(val, labels=label, startangle=90, autopct="%1.2f%%", explode=explode, colors=colors)

# Aesthetic
plt.axis("equal")
plt.title("GDP per capita for each country", size=14)
plt.show()

# Data
labels = np.array(["Jan", "Feb", "Mar", "Apr", "Jun"])
x = np.arange(labels.size)
y1 = np.random.normal(80, 10, labels.size) #(mean, std, sample size)
y2 = np.random.normal(95, 12, labels.size)

# Graph
plt.bar(x, y1, color="deepskyblue", alpha=.2, label="We")
plt.plot(x, y2, color="orange", linestyle="--", marker="o", label="Industry")

# Aesthetic
plt.title("Total Sales", size=14)
plt.ylabel("Sales amount")
plt.xticks(x, labels)
plt.legend()
plt.show()

# Data
labels = np.array(["Jan", "Feb", "Mar", "Apr", "May", "Jun"])
x = np.arange(labels.size)
y1 = np.random.normal(800, 90, labels.size)
y2 = np.random.normal(80, 10, labels.size)

# Graph
fig, ax1 = plt.subplots()
ax1.bar(x, y1, color="purple", alpha=.2)
ax1.set_xticks(x)
ax1.set_xticklabels(labels)
ax1.tick_params("y", colors="purple")
ax1.set_ylabel("Sales", color="purple", size=12)

ax2 = ax1.twinx()
ax2.plot(x, y2, color="orange", linestyle="--", marker="o")
ax2.tick_params("y", colors="orange")
ax2.set_ylabel("Profit", color="orange", size=12)
ax2.set_ylim(ymin=0)

# Aesthetic
plt.title("Sales VS Profit\n(Jan - Jun, 2017)", size=14)
plt.show()

# Data
n = 100
x = np.arange(n)
d = np.random.normal(150, 5, n)
m, sd = np.mean(d), np.std(d)
ucl = 2 * sd
lcl = -ucl

# Graph
plt.figure(figsize=(15,10))
plt.plot(x, d, marker="o", color=".7", alpha=.7)

# Line
plt.axhline(m, linestyle="--", color="green", alpha=.5) ## Average line
plt.axhline(m+ucl, linestyle="--", color="red", alpha=.5) ## Upper control line
plt.axhline(m+lcl, linestyle="--", color="red", alpha=.5) ## Lower control line
filter = np.where((d > m + 2*sd) | (d < m - 2*sd))
plt.plot(x[filter], d[filter], marker="o", linestyle="", color="red")
plt.fill_between(x, m + ucl, m + 5*sd, color="red", alpha=.1) ## Upper control area
plt.fill_between(x, m + lcl, m - 5*sd, color="red", alpha=.1) ## Lower control area

#Aesthetic
### t = "n = {}, mean = {:.2f}, sd = {:.2f}".format(n, m, sd)
t = "n = {}, {} = {:.2f}, sd = {:.2f}".format(n, r"$\bar{x}$", m, sd)
plt.title(t, size=14)
plt.ylabel("weight (grams)")
plt.show()