14.1. Matplotlib

import matplotlib.pyplot as plt

%matplotlib inline

x = list(range(10))
y = [ele*ele for ele in x]

x

[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

y

[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

# Method 1
# A simple plot
plt.plot(x,y)

[<matplotlib.lines.Line2D at 0x7fa7dce67fb0>]

plt.plot(x,y, 'r.')

[<matplotlib.lines.Line2D at 0x7fa7dac09ee0>]

plt.plot(x,y,          
         linewidth=2,
         linestyle='-.',
         color='red',
         marker='o',
         markersize=20,
         markeredgewidth=2,
         markeredgecolor='lawngreen',
         markerfacecolor='black',
)

[<matplotlib.lines.Line2D at 0x7fa7dac56870>]

# Shortcut for controlling the line style and color
# plt.plot(x,y, 'y--')
# https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.plot.html

plt.plot(x,y,          
         linewidth=1,
         linestyle='--',
         color='red',
         marker='o',
         markersize=15,
         markeredgewidth=3,
         markeredgecolor='blue',
         markerfacecolor='black',
         fillstyle='left')

[<matplotlib.lines.Line2D at 0x7fa7dcd06bd0>]

# Adding labels and titles
plt.plot(x,y)
plt.plot(x,x, 'r')
plt.xlabel('X Label')
plt.ylabel('Y Label')
plt.title('Title')
plt.legend(['ABCD', 'EFGH'], loc='best')

<matplotlib.legend.Legend at 0x7fa7dacbb470>

# Subplots -- have multiple plots
plt.subplot(1,2,1)
plt.plot(x,y)
plt.xlabel('X Label')
plt.ylabel('Y Label')
plt.title('Title')

plt.subplot(1,2,2)
plt.plot(x,y, '--r')
plt.xlabel('X Label')
plt.ylabel('Y Label')
plt.title('Title')

Text(0.5, 1.0, 'Title')

# Subplots -- have multiple plots
plt.subplot(2,2,1)
plt.plot(x,y)
plt.xlabel('X Label')
plt.ylabel('Y Label')
plt.title('1')

plt.subplot(2,2,2)
plt.plot(x,y, 'r')
plt.xlabel('X Label')
plt.ylabel('Y Label')
plt.title('2')

plt.subplot(2,2,3)
plt.plot(x,y)
plt.xlabel('X Label')
plt.ylabel('Y Label')
plt.title('3')

plt.subplot(2,2,4)
plt.plot(x,y, 'r')
plt.xlabel('X Label')
plt.ylabel('Y Label')
plt.title('4')

plt.tight_layout()

# Method 2 -- Object Oriented way
fig = plt.figure()

axes = fig.add_axes([0.1, 0.1, 0.8, 1])
# rect : sequence of float
# The dimensions [left, bottom, width, height] of the new axes. 
# All quantities are in fractions of figure width and height.

fig = plt.figure()

axes = fig.add_axes([0.1, 0.1, 0.5, 1])
# rect : sequence of float
# The dimensions [left, bottom, width, height] of the new axes. 
# All quantities are in fractions of figure width and height.

fig = plt.figure()

axes = fig.add_axes([0.1, 0.1, 1, 1])
# rect : sequence of float
# The dimensions [left, bottom, width, height] of the new axes. 
# All quantities are in fractions of figure width and height.

axes.plot(x,y)
axes.set_xlabel('X Label')
axes.set_ylabel('Y Label')
axes.set_title('Title')

Text(0.5, 1.0, 'Title')

fig = plt.figure()

axes1 = fig.add_axes([0, 0, 1, 1])
axes2 = fig.add_axes([0.5, 0.1, 1, 1])
# rect : sequence of float
# The dimensions [left, bottom, width, height] of the new axes. 
# All quantities are in fractions of figure width and height.

fig = plt.figure()

axes1 = fig.add_axes([0, 0, 1, 1])
axes2 = fig.add_axes([0.12, 0.45, 0.45, 0.45])
# rect : sequence of float
# The dimensions [left, bottom, width, height] of the new axes. 
# All quantities are in fractions of figure width and height.

axes1.plot(x,y)
axes1.set_xlabel('X Label')
axes1.set_ylabel('Y Label')
axes1.set_title('Larger Plot')

axes2.plot(x,y, 'ro')
axes2.set_xlabel('X Label')
axes2.set_ylabel('Y Label')
axes2.set_title('Smaller Plot')

axes3 = fig.add_axes([0.8, 0.2, 0.25, 0.25])
axes3.plot(x,y, 'y--')
axes3.set_xlabel('X Label')
axes3.set_ylabel('Y Label')
axes3.set_title('Smallest Plot')

Text(0.5, 1.0, 'Smallest Plot')

y

[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

fig, axes = plt.subplots(nrows=2, ncols=2)
plt.tight_layout()
axes

array([[<Axes: >, <Axes: >],
       [<Axes: >, <Axes: >]], dtype=object)

print(axes)

[[<Axes: > <Axes: >]
 [<Axes: > <Axes: >]]

type(axes)

numpy.ndarray

fig, axes = plt.subplots(nrows=2, ncols=2)


axes[0][0].plot(x,y)
plt.tight_layout()

fig, axes = plt.subplots(nrows=2, ncols=2)

line_styles = ['', '--', '-', '-.', ':']
colors = ['', 'b', 'g', 'k', 'y']
for idx, ax in enumerate(axes.reshape(-1), 1): 
    ax.plot(x,y, color=colors[idx], linestyle=line_styles[idx])
    ax.set_title(f'This is plot # {idx}.')
plt.tight_layout()

axes[0][0]

<Axes: title={'center': 'This is plot # 1.'}>

axes.reshape(-1)

array([<Axes: title={'center': 'This is plot # 1.'}>,
       <Axes: title={'center': 'This is plot # 2.'}>,
       <Axes: title={'center': 'This is plot # 3.'}>,
       <Axes: title={'center': 'This is plot # 4.'}>], dtype=object)

fig, axes = plt.subplots(nrows=2, ncols=1, figsize=(16,5))
axes[0].plot(x,y)
axes[1].plot(x,y, 'ro')
plt.tight_layout()

# figsize : (float, float), optional, default: None
# width, height in inches. If not provided, defaults to [6.4, 4.8]

# fig.savefig('figure') #default is png
# fig.savefig('figure.jpeg')
# fig.savefig('figure2', dpi=300) 
fig.savefig('figure2svg.svg', dpi=300) 

fig = plt.figure()

axes = fig.add_axes([0.1, 0.1, 1, 1])
# rect : sequence of float
# The dimensions [left, bottom, width, height] of the new axes. 
# All quantities are in fractions of figure width and height.
y3 = [ele*3 for ele in y]
axes.plot(x,y, label='x,y')
axes.plot(x,y3, label='x,y**3')
axes.set_xlabel('X Label')
axes.set_ylabel('Y Label')
axes.set_title('Title')
axes.legend(loc='lower right', fontsize=24)
# axes.legend(loc=1)
axes.legend(loc=[0.5,0.5])

# Location String 	Location Code
# 'best' 	0
# 'upper right' 	1
# 'upper left' 	2
# 'lower left' 	3
# 'lower right' 	4
# 'right' 	5
# 'center left' 	6
# 'center right' 	7
# 'lower center' 	8
# 'upper center' 	9
# 'center' 	10

<matplotlib.legend.Legend at 0x7fa7da80cd10>

fig = plt.figure()

axes = fig.add_axes([0.1, 0.1, 1, 1])
# axes.plot(x,y, 'r')
# axes.plot(x,y, color='yellow')

# axes.plot(x,y, 'k')
# axes.plot(x,y, color='black')

# # https://www.color-hex.com/
axes.plot(x,y, color='#F75D5D', linewidth=10)

[<matplotlib.lines.Line2D at 0x7fa7da44e4e0>]

fig = plt.figure()

axes = fig.add_axes([0.1, 0.1, 1, 1])
# axes.plot(x,y, 'r', linewidth=4)



axes.plot(x,y, 'o--b', lw=4, alpha=0.1, ms=20)

[<matplotlib.lines.Line2D at 0x7fa7da4b3d70>]

fig = plt.figure()

axes = fig.add_axes([0.1, 0.1, 1, 1])
axes.plot(x,y, 'r-.', lw=5, linestyle=':')
# Linestyle 	Description
# '-' or 'solid' 	solid line
# '--' or 'dashed' 	dashed line
# '-.' or 'dashdot' 	dash-dotted line
# ':' or 'dotted' 	dotted line
# 'None' or ' ' or '' 	draw nothing

/tmp/ipykernel_7422/75132765.py:4: UserWarning: linestyle is redundantly defined by the 'linestyle' keyword argument and the fmt string "r-." (-> linestyle='-.'). The keyword argument will take precedence.
  axes.plot(x,y, 'r-.', lw=5, linestyle=':')

[<matplotlib.lines.Line2D at 0x7fa7da505220>]

fig = plt.figure()

axes = fig.add_axes([0.1, 0.1, 1, 1])
# axes.plot(x,y, marker='o')
axes.plot(x,y, ' b', marker='^', markersize=10)
#https://matplotlib.org/3.1.1/api/markers_api.html

[<matplotlib.lines.Line2D at 0x7fa7da074920>]

fig = plt.figure()

axes = fig.add_axes([0.1, 0.1, 1, 1])
# axes.plot(x,y, 'b', marker='o', markersize=10, markerfacecolor='red')
axes.plot(x,y, 'b', marker='o', markersize=30, markerfacecolor='yellow', markeredgewidth=3, markeredgecolor='k')
#https://matplotlib.org/3.1.1/api/markers_api.html

[<matplotlib.lines.Line2D at 0x7fa7da0d5be0>]

fig = plt.figure()

axes = fig.add_axes([0, 0, 1, 1])
axes.plot(x,y)
axes.set_xlim([0,5])
axes.set_ylim([0,30])

(0.0, 30.0)

# https://github.com/rougier/matplotlib-tutorial
# https://medium.com/@kapil.mathur1987/matplotlib-an-introduction-to-its-object-oriented-interface-a318b1530aed

import numpy as np

t = 2*np.pi/3
plt.plot([t,t],[0,np.cos(t)], color ='blue', linewidth=1.5, linestyle="--")
plt.scatter([t,],[np.cos(t),], 50, color ='blue')

plt.annotate(r'$\sin(\frac{2\pi}{3})=\frac{\sqrt{3}}{2}$',
             xy=(t, np.sin(t)), xycoords='data',
             xytext=(+10, +30), textcoords='offset points', fontsize=16,
             arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=.2"))

plt.plot([t,t],[0,np.sin(t)], color ='red', linewidth=1.5, linestyle="--")
plt.scatter([t,],[np.sin(t),], 50, color ='red')

plt.annotate(r'$\cos(\frac{2\pi}{3})=-\frac{1}{2}$',
             xy=(t, np.cos(t)), xycoords='data',
             xytext=(-90, -50), textcoords='offset points', fontsize=16,
             arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=.2"))

Text(-90, -50, '$\\cos(\\frac{2\\pi}{3})=-\\frac{1}{2}$')

import matplotlib.pyplot as plt 
import pandas as pd 
import numpy as np
data = pd.read_csv("https://raw.githubusercontent.com/fivethirtyeight/data/master/airline-safety/airline-safety.csv")
# Get figure object and an array of axes objects
fig, arr_ax = plt.subplots(2, 2)
display(data)

	airline	avail_seat_km_per_week	incidents_85_99	fatal_accidents_85_99	fatalities_85_99	incidents_00_14	fatal_accidents_00_14	fatalities_00_14
0	Aer Lingus	320906734	2	0	0	0	0	0
1	Aeroflot*	1197672318	76	14	128	6	1	88
2	Aerolineas Argentinas	385803648	6	0	0	1	0	0
3	Aeromexico*	596871813	3	1	64	5	0	0
4	Air Canada	1865253802	2	0	0	2	0	0
5	Air France	3004002661	14	4	79	6	2	337
6	Air India*	869253552	2	1	329	4	1	158
7	Air New Zealand*	710174817	3	0	0	5	1	7
8	Alaska Airlines*	965346773	5	0	0	5	1	88
9	Alitalia	698012498	7	2	50	4	0	0
10	All Nippon Airways	1841234177	3	1	1	7	0	0
11	American*	5228357340	21	5	101	17	3	416
12	Austrian Airlines	358239823	1	0	0	1	0	0
13	Avianca	396922563	5	3	323	0	0	0
14	British Airways*	3179760952	4	0	0	6	0	0
15	Cathay Pacific*	2582459303	0	0	0	2	0	0
16	China Airlines	813216487	12	6	535	2	1	225
17	Condor	417982610	2	1	16	0	0	0
18	COPA	550491507	3	1	47	0	0	0
19	Delta / Northwest*	6525658894	24	12	407	24	2	51
20	Egyptair	557699891	8	3	282	4	1	14
21	El Al	335448023	1	1	4	1	0	0
22	Ethiopian Airlines	488560643	25	5	167	5	2	92
23	Finnair	506464950	1	0	0	0	0	0
24	Garuda Indonesia	613356665	10	3	260	4	2	22
25	Gulf Air	301379762	1	0	0	3	1	143
26	Hawaiian Airlines	493877795	0	0	0	1	0	0
27	Iberia	1173203126	4	1	148	5	0	0
28	Japan Airlines	1574217531	3	1	520	0	0	0
29	Kenya Airways	277414794	2	0	0	2	2	283
30	KLM*	1874561773	7	1	3	1	0	0
31	Korean Air	1734522605	12	5	425	1	0	0
32	LAN Airlines	1001965891	3	2	21	0	0	0
33	Lufthansa*	3426529504	6	1	2	3	0	0
34	Malaysia Airlines	1039171244	3	1	34	3	2	537
35	Pakistan International	348563137	8	3	234	10	2	46
36	Philippine Airlines	413007158	7	4	74	2	1	1
37	Qantas*	1917428984	1	0	0	5	0	0
38	Royal Air Maroc	295705339	5	3	51	3	0	0
39	SAS*	682971852	5	0	0	6	1	110
40	Saudi Arabian	859673901	7	2	313	11	0	0
41	Singapore Airlines	2376857805	2	2	6	2	1	83
42	South African	651502442	2	1	159	1	0	0
43	Southwest Airlines	3276525770	1	0	0	8	0	0
44	Sri Lankan / AirLanka	325582976	2	1	14	4	0	0
45	SWISS*	792601299	2	1	229	3	0	0
46	TACA	259373346	3	1	3	1	1	3
47	TAM	1509195646	8	3	98	7	2	188
48	TAP - Air Portugal	619130754	0	0	0	0	0	0
49	Thai Airways	1702802250	8	4	308	2	1	1
50	Turkish Airlines	1946098294	8	3	64	8	2	84
51	United / Continental*	7139291291	19	8	319	14	2	109
52	US Airways / America West*	2455687887	16	7	224	11	2	23
53	Vietnam Airlines	625084918	7	3	171	1	0	0
54	Virgin Atlantic	1005248585	1	0	0	0	0	0
55	Xiamen Airlines	430462962	9	1	82	2	0	0

import matplotlib.pyplot as plt 
import pandas as pd 
import numpy as np
data = pd.read_csv("https://raw.githubusercontent.com/fivethirtyeight/data/master/airline-safety/airline-safety.csv")
# Get figure object and an array of axes objects
fig, arr_ax = plt.subplots(2, 2)

# arr_ax[x][x] or arr_ax[x,x]

# Histogram - fatal_accidents_85_99
arr_ax[0,0].hist(data['fatal_accidents_85_99'])
arr_ax[0,0].set_title('fatal_accidents_85_99')

# Histogram - fatal_accidents_00_14
arr_ax[0,1].hist(data['fatal_accidents_00_14'])
arr_ax[0,1].set_title('fatal_accidents_00_14')

# Scatter - fatal_accidents_85_99 vs fatalities_85_99
arr_ax[1,0].scatter(data['fatal_accidents_85_99'], data['fatalities_85_99'])
arr_ax[1,0].set_xlabel('fatal_accidents_85_99')
arr_ax[1,0].set_ylabel('fatalities_85_99')

# scatter - avail_seat_km_per_week vs fatalities_00_14
arr_ax[1,1].scatter(data['avail_seat_km_per_week'], data['fatalities_00_14'])
arr_ax[1,1].set_xlabel('avail_seat_km_per_week')
arr_ax[1,1].set_ylabel('fatalities_00_14')
plt.tight_layout()
plt.show()

# https://www.machinelearningplus.com/plots/top-50-matplotlib-visualizations-the-master-plots-python/
# https://levelup.gitconnected.com/an-introduction-of-python-matplotlib-with-40-basic-examples-5174383a6889

import pandas as pd
import numpy as np
# Prepare Data
df = pd.read_csv("https://github.com/selva86/datasets/raw/master/mtcars.csv")
x = df.loc[:, ['mpg']]
df['mpg_z'] = (x - x.mean())/x.std()
df['colors'] = ['red' if x < 0 else 'darkgreen' for x in df['mpg_z']]
df.sort_values('mpg_z', inplace=True)
df.reset_index(inplace=True)

# Draw plot
plt.figure(figsize=(14,16), dpi= 80)
plt.scatter(df.mpg_z, df.index, s=450, alpha=.6, color=df.colors)
for x, y, tex in zip(df.mpg_z, df.index, df.mpg_z):
    t = plt.text(x, y, round(tex, 1), horizontalalignment='center', 
                 verticalalignment='center', fontdict={'color':'white'})

# Decorations
# Lighten borders
plt.gca().spines["top"].set_alpha(.3)
plt.gca().spines["bottom"].set_alpha(.3)
plt.gca().spines["right"].set_alpha(.3)
plt.gca().spines["left"].set_alpha(.3)

plt.yticks(df.index, df.cars)
plt.title('Diverging Dotplot of Car Mileage', fontdict={'size':20})
plt.xlabel('$Mileage$')
plt.grid(linestyle='--', alpha=0.5)
plt.xlim(-2.5, 2.5)
plt.show()

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.widgets import Button

freqs = np.arange(2, 20, 3)

fig, ax = plt.subplots()
fig.subplots_adjust(bottom=0.2)
t = np.arange(0.0, 1.0, 0.001)
s = np.sin(2*np.pi*freqs[0]*t)
l, = ax.plot(t, s, lw=2)


class Index:
    ind = 0

    def next(self, event):
        self.ind += 1
        i = self.ind % len(freqs)
        ydata = np.sin(2*np.pi*freqs[i]*t)
        l.set_ydata(ydata)
        plt.draw()

    def prev(self, event):
        self.ind -= 1
        i = self.ind % len(freqs)
        ydata = np.sin(2*np.pi*freqs[i]*t)
        l.set_ydata(ydata)
        plt.draw()

callback = Index()
axprev = fig.add_axes([0.7, 0.05, 0.1, 0.075])
axnext = fig.add_axes([0.81, 0.05, 0.1, 0.075])
bnext = Button(axnext, 'Next')
bnext.on_clicked(callback.next)
bprev = Button(axprev, 'Previous')
bprev.on_clicked(callback.prev)

plt.show()