for ax in axes:
ax.plot(x, y, 'r')
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_title('title')

fig.tight_layout()

ax.plot(x, x**2, label="y = x**2")
ax.plot(x, x**3, label="y = x**3")
ax.legend(loc=2); # upper left corner
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_title('title');

axes[1].plot(x, x**2, x, np.exp(x))
axes[1].set_yscale("log")
axes[1].set_title("Logarithmic scale (y)");

axes[0].scatter(xx, xx + 0.25*np.random.randn(len(xx)))
axes[0].set_title("scatter")

axes[1].step(n, n**2, lw=2)
axes[1].set_title("step")

axes[2].bar(n, n**2, align="center", width=0.5, alpha=0.5)
axes[2].set_title("bar")

axes[3].fill_between(x, x**2, x**3, color="green", alpha=0.5);
axes[3].set_title("fill_between");

# Matplot lib picks different colors for different plot.

for val in data1.T: #loop over each and every value in data1.T
plt.plot(data1[:,0], val) #data1[:,0] is the first row in data1.T

# data in scipy.txt looks like this:
# 0 0 6
# 1 1 5
# 2 4 4
# 4 16 3
# 5 25 2
# 6 36 1

[[ 0. 1. 2. 4. 5. 6.]
[ 0. 1. 4. 16. 25. 36.]
[ 6. 5. 4. 3. 2. 1.]]

#plt.show()

q =np.random.standard_normal((50,2))
q += np.array((1,1)) #center the distribution at (-1,1)

plt.scatter(p[:,0], p[:,1], color ='.25')
plt.scatter(q[:,0], q[:,1], color = '.75')
Out[22]:
<matplotlib.collections.PathCollection at 0x71dab90>

In [9]:
vals = np.random.random_integers(99, size =50)
color_set = ['.00', '.25', '.50','.75']
color_lists = [color_set[(len(color_set)* val) // 100] for val in vals]
c = plt.bar(np.arange(50), vals, color = color_lists)

In [34]:
# how to color scatter plots
#Colormaps are defined in the matplotib.cm module. This module provides
#functions to create and use colormaps. It also provides an exhaustive choice of predefined color maps.
import matplotlib.cm as cm
N = 256
angle = np.linspace(0, 8 * 2 * np.pi, N)
radius = np.linspace(.5, 1., N)
X = radius * np.cos(angle)
Y = radius * np.sin(angle)
plt.scatter(X,Y, c=angle, cmap = cm.hsv)
Out[34]:
<matplotlib.collections.PathCollection at 0x714d9f0>

def pq(I, mu, sigma):
a = 1. / (sigma * np.sqrt(2. * np.pi))
b = -1. / (2. * sigma ** 2)
return a * np.exp(b * (I - mu) ** 2)

I =np.linspace(-6,6, 1024)

plt.plot(I, pq(I, 0., 1.), color = 'k', linestyle ='solid')
plt.plot(I, pq(I, 0., .5), color = 'k', linestyle ='dashed')
plt.plot(I, pq(I, 0., .25), color = 'k', linestyle ='dashdot')
Out[4]:
[<matplotlib.lines.Line2D at 0x562ffb0>]

X = np.linspace(-6, 6, 1024)
for i in range(64):
samples = np.random.standard_normal(50)
mu,sigma = np.mean(samples), np.std(samples)
plt.plot(X, gf(X, mu, sigma), color = '.75', linewidth = .5)

plt.plot(X, gf(X, 0., 1.), color ='.00', linewidth = 3.)
Out[20]:
[<matplotlib.lines.Line2D at 0x59fbab0>]

# some other hatch attributes are :
#/
#\
#|
#-
#+
#x
#o
#O
#.
#*
Out[27]:
<Container object of 15 artists>

Yb = np.sinc(X) +1

plt.plot(X, Ya, marker ='o', color ='.75')
plt.plot(X, Yb, marker ='^', color='.00', markevery= 32)# this one marks every 32 nd element
Out[14]:
[<matplotlib.lines.Line2D at 0x7063150>]

B = np.random.standard_normal((50,2))
B += np.array((1, 1))

plt.scatter(A[:,0], A[:,1], color ='k', s =25.0)
plt.scatter(B[:,0], B[:,1], color ='g', s = 100.0) # size of the marker is specified using 's' attribute
Out[31]:
<matplotlib.collections.PathCollection at 0x7d015f0>

I =np.linspace(-6,6, 1024)

plt.plot(I, pq(I, 0., 1.), color = 'k', linestyle ='solid')
plt.plot(I, pq(I, 0., .5), color = 'k', linestyle ='dashed')
plt.plot(I, pq(I, 0., .25), color = 'k', linestyle ='dashdot')

# I have created a dictinary of styles
design = {
'facecolor' : 'y', # color used for the text box
'edgecolor' : 'g',
'boxstyle' : 'round'
}
plt.text(-4, 1.5, 'Matplot Lib', bbox = design)
plt.plot(X, Y, c='k')
plt.show()

#This sets the style of the box, which can either be 'round' or 'square'
#'pad': If 'boxstyle' is set to 'square', it defines the amount of padding between the text and the box's sides

The vertical alignment options are as follows:
'center': This is relative to the center of the textbox
'top': This is relative to the upper side of the textbox
'bottom': This is relative to the lower side of the textbox
'baseline': This is relative to the text's baseline

Horizontal alignment options are as follows:

align ='bottom' align ='baseline'
------------------------align = center--------------------------------------
align= 'top

In [41]:
cd C:\Users\tk\Desktop

C:\Users\tk\Desktop
In [44]:
from IPython.display import Image
Image(filename='text alignment.png')
#The horizontal alignment options are as follows:
#'center': This is relative to the center of the textbox
#'left': This is relative to the left side of the textbox
#'right': This is relative to the right-hand side of the textbox
Out[44]:

plt.annotate('Big Data',
ha ='center', va ='bottom',
xytext =(-1.5, 3.0), xy =(0.75, -2.7),
arrowprops ={'facecolor': 'green', 'shrink':0.05, 'edgecolor': 'black'}) #arrow properties
plt.plot(X, Y)
Out[76]:
[<matplotlib.lines.Line2D at 0x9d1def0>]

from IPython.display import Image
Image(filename='arrows.png')
Out[74]:

'shadow': This can be either True or False, and it renders the legend with a shadow effect.

'fancybox': This can be either True or False and renders the legend with a rounded box.

'title': This renders the legend with the title passed as a parameter.

'ncol': This forces the passed value to be the number of columns for the legend

In [101]:
x =np.linspace(0, 6,1024)
y1 =np.sin(x)
y2 =np.cos(x)
plt.xlabel('Sin Wave')
plt.ylabel('Cos Wave')
plt.plot(x, y1, c='b', lw =3.0, label ='Sin(x)') # labels are specified
plt.plot(x, y2, c ='r', lw =3.0, ls ='--', label ='Cos(x)')
plt.legend(loc ='best', shadow = True, fancybox = False, title ='Waves', ncol =1) # displays the labels
plt.grid(True, lw = 2, ls ='--', c='.75') # adds grid lines to the figure
plt.show()

dis = patches.Circle((0,0), radius = 1.0, color ='.75' )
plt.gca().add_patch(dis) # used to render the image.

dis = patches.Rectangle((2.5, -.5), 2.0, 1.0, color ='.75') #patches.rectangle((x & y coordinates), length, breadth)
plt.gca().add_patch(dis)

dis = patches.Ellipse((0, -2.0), 2.0, 1.0, angle =45, color ='.00')
plt.gca().add_patch(dis)

dis = patches.FancyBboxPatch((2.5, -2.5), 2.0, 1.0, boxstyle ='roundtooth', color ='g')
plt.gca().add_patch(dis)

plt.grid(True)
plt.axis('scaled') # displays the images within the prescribed axis
plt.show()

#FancyBox: This is like a rectangle but takes an additional boxstyle parameter
#(either 'larrow', 'rarrow', 'round', 'round4', 'roundtooth', 'sawtooth', or 'square')

#The matplotlib.patches.Polygon()constructor takes a list of coordinates as the inputs, that is, the vertices of the polygon

In [59]:
name_list = ('Omar', 'Serguey', 'Max', 'Zhou', '

Abidin')
value_list = np.random.randint(0, 99, size =

len(name_list))
pos_list = np.arange(len(name_list))
ax = plt.axes()
ax.xaxis.set_major_locator(ticker.FixedLocator

((pos_list)))
ax.xaxis.set_major_formatter(ticker.FixedFormatter

((name_list)))
plt.bar(pos_list, value_list, color = '.75',align =

'center')
plt.show()

#The logarithm base is 10 by default, but it can be changed with the optional parameters basex and basey.