Matplotlib Tutorial in Python

Numbers විතරක් බැලුවම pattern ගොඩක් හංගිලා තියෙනවා — plot එකක් බැලුවම එකපාරටම පේනවා, skewed distribution එකක්, outlier එකක්, කාලයත් එක්ක වෙනස්වීමක්.

Numbers alone hide patterns that a single glance at a plot reveals instantly — a skewed distribution, an outlier, a trend over time.

4 May 20264 min readSelf Guide · Stage 13#Python & Data Basics#Python Libraries#Machine learning

Lesson 11 of 64 · Part 1 · Python & Data Basics · Python Libraries · bilingual notes (English + සිංහල)

Why this matters. Numbers alone hide patterns that a single glance at a plot reveals instantly — a skewed distribution, an outlier, a trend over time.

සිංහලෙන්: Numbers විතරක් බැලුවම pattern ගොඩක් හංගිලා තියෙනවා — plot එකක් බැලුවම එකපාරටම පේනවා, skewed distribution එකක්, outlier එකක්, කාලයත් එක්ක වෙනස්වීමක්.

What you'll use this for. Before you trust any dataset enough to train a model on it, you plot it first — this is that step.

සිංහලෙන්: Model එකක් train කරන්න dataset එකක් විශ්වාස කරන්නත් කලින්, ඒක plot කරලා බලනවා — ඒ step එක තමයි මේක.

Key principles

A plot is a sanity check, not decoration. If a "price" column plots as a straight vertical line at zero, something's wrong with your data — you'd never notice that from .describe() alone.
Plot එකක් කියන්නෙ sanity check එකක්, decoration එකක් නෙවෙයි

Walkthrough

Matplotlib:

Useful for making Plots

ExamplePython

# importing matplotlib library
import matplotlib.pyplot as plt

ExamplePython

# import numpy to get data for our plots
import numpy as np

ExamplePython

x = np.linspace(0,10,100)
y = np.sin(x)
z = np.cos(x)

ExamplePython

print(x)

Output

Output

[ 0.          0.1010101   0.2020202   0.3030303   0.4040404   0.50505051
  0.60606061  0.70707071  0.80808081  0.90909091  1.01010101  1.11111111
  1.21212121  1.31313131  1.41414141  1.51515152  1.61616162  1.71717172
  1.81818182  1.91919192  2.02020202  2.12121212  2.22222222  2.32323232
  2.42424242  2.52525253  2.62626263  2.72727273  2.82828283  2.92929293
  3.03030303  3.13131313  3.23232323  3.33333333  3.43434343  3.53535354
  3.63636364  3.73737374  3.83838384  3.93939394  4.04040404  4.14141414
  4.24242424  4.34343434  4.44444444  4.54545455  4.64646465  4.74747475
  4.84848485  4.94949495  5.05050505  5.15151515  5.25252525  5.35353535
  5.45454545  5.55555556  5.65656566  5.75757576  5.85858586  5.95959596
  6.06060606  6.16161616  6.26262626  6.36363636  6.46464646  6.56565657
  6.66666667  6.76767677  6.86868687  6.96969697  7.07070707  7.17171717
  7.27272727  7.37373737  7.47474747  7.57575758  7.67676768  7.77777778
  7.87878788  7.97979798  8.08080808  8.18181818  8.28282828  8.38383838
  8.48484848  8.58585859  8.68686869  8.78787879  8.88888889  8.98989899
  9.09090909  9.19191919  9.29292929  9.39393939  9.49494949  9.5959596
  9.6969697   9.7979798   9.8989899  10.        ]

ExamplePython

print(y)

Output

Output

[ 0.          0.10083842  0.20064886  0.2984138   0.39313661  0.48385164
  0.56963411  0.64960951  0.72296256  0.78894546  0.84688556  0.8961922
  0.93636273  0.96698762  0.98775469  0.99845223  0.99897117  0.98930624
  0.96955595  0.93992165  0.90070545  0.85230712  0.79522006  0.73002623
  0.65739025  0.57805259  0.49282204  0.40256749  0.30820902  0.21070855
  0.11106004  0.01027934 -0.09060615 -0.19056796 -0.28858706 -0.38366419
 -0.47483011 -0.56115544 -0.64176014 -0.7158225  -0.7825875  -0.84137452
 -0.89158426 -0.93270486 -0.96431712 -0.98609877 -0.99782778 -0.99938456
 -0.99075324 -0.97202182 -0.94338126 -0.90512352 -0.85763861 -0.80141062
 -0.73701276 -0.66510151 -0.58640998 -0.50174037 -0.41195583 -0.31797166
 -0.22074597 -0.12126992 -0.0205576   0.0803643   0.18046693  0.27872982
  0.37415123  0.46575841  0.55261747  0.63384295  0.7086068   0.77614685
  0.83577457  0.8868821   0.92894843  0.96154471  0.98433866  0.99709789
  0.99969234  0.99209556  0.97438499  0.94674118  0.90944594  0.86287948
  0.8075165   0.74392141  0.6727425   0.59470541  0.51060568  0.42130064
  0.32770071  0.23076008  0.13146699  0.03083368 -0.07011396 -0.17034683
 -0.26884313 -0.36459873 -0.45663749 -0.54402111]

ExamplePython

print(z)

Output

Output

[ 1.          0.99490282  0.97966323  0.95443659  0.91948007  0.87515004
  0.8218984   0.76026803  0.69088721  0.61446323  0.53177518  0.44366602
  0.35103397  0.25482335  0.15601496  0.0556161  -0.04534973 -0.14585325
 -0.24486989 -0.34139023 -0.43443032 -0.52304166 -0.60632092 -0.68341913
 -0.75355031 -0.81599952 -0.87013012 -0.91539031 -0.95131866 -0.97754893
 -0.9938137  -0.99994717 -0.9958868  -0.981674   -0.95745366 -0.92347268
 -0.88007748 -0.82771044 -0.76690542 -0.69828229 -0.6225406  -0.54045251
 -0.45285485 -0.36064061 -0.26474988 -0.16616018 -0.06587659  0.03507857
  0.13567613  0.23489055  0.33171042  0.4251487   0.51425287  0.59811455
  0.67587883  0.74675295  0.8100144   0.86501827  0.91120382  0.94810022
  0.97533134  0.99261957  0.99978867  0.99676556  0.98358105  0.96036956
  0.9273677   0.88491192  0.83343502  0.77346177  0.70560358  0.63055219
  0.54907273  0.46199582  0.37020915  0.27464844  0.17628785  0.07613012
 -0.0248037  -0.12548467 -0.2248864  -0.32199555 -0.41582217 -0.50540974
 -0.58984498 -0.66826712 -0.7398767  -0.8039437  -0.859815   -0.90692104
 -0.94478159 -0.97301068 -0.99132055 -0.99952453 -0.99753899 -0.98538417
 -0.96318398 -0.93116473 -0.88965286 -0.83907153]

Plotting the data

ExamplePython

# sine wave
plt.plot(x,y)
plt.show()

Matplotlib Tutorial in Python — figure 1

ExamplePython

# cosine wave
plt.plot(x,z)
plt.show()

Matplotlib Tutorial in Python — figure 2

ExamplePython

# adding title, x-axis & y-axis labels
plt.plot(x,y)
plt.xlabel('angle')
plt.ylabel('sine value')
plt.title('sine wave')
plt.show()

Matplotlib Tutorial in Python — figure 3

ExamplePython

# parabola
x = np.linspace(-10,10,20)
y = x**2
plt.plot(x,y)
plt.show()

Matplotlib Tutorial in Python — figure 4

ExamplePython

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

Matplotlib Tutorial in Python — figure 5

ExamplePython

plt.plot(x, y, 'g.')
plt.show()

Matplotlib Tutorial in Python — figure 6

ExamplePython

plt.plot(x, y, 'rx')
plt.show()

Matplotlib Tutorial in Python — figure 7

ExamplePython

x = np.linspace(-5,5,50)
plt.plot(x, np.sin(x), 'g-')
plt.plot(x, np.cos(x), 'r--')
plt.show()

Matplotlib Tutorial in Python — figure 8

Bar Plot

ExamplePython

fig = plt.figure()
ax = fig.add_axes([0,0,1,1])
languages = ['English','French','Spanish','Latin','German']
people = [100, 50, 150, 40, 70]
ax.bar(languages, people)
plt.xlabel('LANGUAGES')
plt.ylabel('NUMBER OF PEOPLE')
plt.show()

Matplotlib Tutorial in Python — figure 9

Pie Chart

ExamplePython

fig1 = plt.figure()
ax = fig1.add_axes([0,0,1,1])
languages = ['English','French','Spanish','Latin','German']
people = [100, 50, 150, 40, 70]
ax.pie(people, labels=languages, autopct='%1.1f%%')
plt.show()

Matplotlib Tutorial in Python — figure 10

Scatter Plot

ExamplePython

x = np.linspace(0,10,30)
y = np.sin(x)
z = np.cos(x)
fig2 = plt.figure()
ax = fig2.add_axes([0,0,1,1])
ax.scatter(x,y,color='g')
ax.scatter(x,z,color='b')
plt.show()

Matplotlib Tutorial in Python — figure 11

3D Scatter Plot

ExamplePython

fig3 = plt.figure()
ax = plt.axes(projection='3d')
z = 20 * np.random.random(100)
x = np.sin(z)
y = np.cos(z)
ax.scatter(x,y,z,c=z,cmap='Blues')
plt.show()