different signs(marker types) to draw points and lines in plot function in python

Here is a list of option you need to provide for plot function to draw what you want:

example: plt.plot(range(10), linestyle='--', marker='o', color='b')

================    ===============================
character           description
================    ===============================
``'-'``             solid line style
``'--'``            dashed line style
``'-.'``            dash-dot line style
``':'``             dotted line style
``'.'``             point marker
``','``             pixel marker
``'o'``             circle marker
``'v'``             triangle_down marker
``'^'``             triangle_up marker
``'<'``             triangle_left marker
``'>'``             triangle_right marker
``'1'``             tri_down marker
``'2'``             tri_up marker
``'3'``             tri_left marker
``'4'``             tri_right marker
``'s'``             square marker
``'p'``             pentagon marker
``'*'``             star marker
``'h'``             hexagon1 marker
``'H'``             hexagon2 marker
``'+'``             plus marker
``'x'``             x marker
``'D'``             diamond marker
``'d'``             thin_diamond marker
``'|'``             vline marker
``'_'``             hline marker
================    ===============================

Plot CDF in Python

perhaps the most easy way of plotting the cumilative distribution function in python:


import numpy as np
import statsmodels.api as sm # recommended import according to the docs
import matplotlib.pyplot as plt

sample = np.random.uniform(0, 1, 50)
sample=[1,2,2,3,2,3,3,3,3,3,3,4,4,4,4,4,60,3,3,3,10]
ecdf = sm.distributions.ECDF(sample)

dsum = sum(sample);
normalized_data = sample/dsum;

x = np.linspace(min(sample), max(sample),10000)
print x
y = ecdf(x)
plt.step(x, y)

plt.show()


Here is the plot:

Fitting data with SciPy using curve_fit

Suppose that you have a data set consisting of Throughput vs Latency data for your experiment. We’ll start by importing the needed libraries and defining a fitting function. in this example we define a quadratic function. you may define your own function:

from scipy.optimize import curve_fit
def fitFunc(x, a, b, c):
    return a*(x**2)+b*x + c

now we create some points:

y=[25.6, 31.21, 36.82, 42.43, 44.67, 46.91, 48.04, 49.15, 51.2] 
x=[26.0, 41.0, 50.0, 62.5, 69.0, 74.0, 75.050000000000182, 109.0, 98.5]

The scipy.optimize module contains a least squares curve fit routine that requires as input a user-defined fitting function (in our case fitFunc ), the x-axis data (in our case, t) and the y-axis data (in our case, noisy). The curve_fit routine returns an array of fit parameters, and a matrix of covariance data.

tt = np.linspace(min(x),max(x),100)

x=numpy.asarray(x)

y=numpy.asarray(y)

fitParams, fitCovariances = curve_fit(fitFunc, x, y)

print fitParams

print fitCovariances

plt.ylabel('Throughput (%)', fontsize = 16)

plt.xlabel('Latency' , fontsize = 16)

'''plot the real data'''

plt.plot(t,noisy,'b+',markersize=19)   

'''plot the best curving fit'''

plt.plot(tt, fitFunc(tt, fitParams[0], fitParams[1], fitParams[2]))

output:
[-0.00420386  0.88363413  3.96364659]
[[  4.78028309e-07  -6.52166801e-05   1.92964390e-03]
 [ -6.52166801e-05   9.24484129e-03  -2.86615224e-01]
 [  1.92964390e-03  -2.86615224e-01   9.57342339e+00]]

result:

How to plot a function using matplotlib

In this example I show how to evaluate a function using numpy and how to plot the result:

import pylab
import numpy

x = numpy.linspace(-15,15,100) # 100 linearly spaced numbers
y = numpy.sin(x)/x # computing the values of sin(x)/x

# compose plot
pylab.plot(x,y) # sin(x)/x
pylab.plot(x,y,'co') # same function with cyan dots
pylab.plot(x,2*y,x,3*y) # 2*sin(x)/x and 3*sin(x)/x
pylab.show() # show the plot

Here is the plot:

Give it a try :)

Subplot in python using matplotlib

Here is an example of how to create subplot in python:

import matplotlib.pyplot as plt
import numpy as np

fig=plt.figure()
data=np.arange(900).reshape((30,30))
for i in range(1,5):
    ax=fig.add_subplot(2,2,i)        
    ax.imshow(data)

plt.suptitle('Main title')
plt.show() 

Note that: the first two element in subplot(2,2,i) specify the number of subplot and the number of row and column. in this example it the plot has 2 rows and 2 columns. and i is the position of the subplot.
Also the title of the figure is defined by suptitle function .



Here is the result:

color the boxplot in python matplotlib

This is how you could color your plot in python using matplotlib library.. Basically, we should pass patch_artist=True to boxplot:

import matplotlib.pyplot as plt
import numpy as np

data = [np.random.normal(0, std, 1000) for std in range(1, 6)]
plt.boxplot(data, notch=True, patch_artist=True)

plt.show()

If you'd like to control the color, do something similar to this:

import matplotlib.pyplot as plt
import numpy as np

data = [np.random.normal(0, std, 1000) for std in range(1, 6)]

box = plt.boxplot(data, notch=True, patch_artist=True)

colors = ['cyan', 'lightblue', 'lightgreen', 'tan', 'pink']
for patch, color in zip(box['boxes'], colors):
    patch.set_facecolor(color)

plt.show()

Rotate axis text in python matplotlib

Here is na example of how to rotate the xaxis using step function in Pyploy library:

import matplotlib.pyplot as plt

x = range(len(time))
plt.xticks(x,  time)
locs, labels = plt.xticks()
plt.setp(labels, rotation=90)
plt.plot(x, delay)