MatPlotLib Complete Tutorial

# MatPlotLib Basics for drawing all kinds of graphs with all examples.

## Draw a line graph


%matplotlib inline
from scipy.stats import norm
import matplotlib.pyplot as plt
import numpy as np

x = np.arange(-5, 5,0.1)

plt.plot(x, norm.pdf(x))
plt.show()


## Mutiple Plots on One Graph


plt.plot(x, norm.pdf(x))
plt.plot(x, norm.pdf(x, 3.0, 1))
plt.plot(x, norm.pdf(x, 2.0,1))
plt.show()



## Save it to a File


plt.plot(x, norm.pdf(x))
plt.plot(x, norm.pdf(x, 1.0, 0.5))
plt.plot(x, norm.pdf(x, 2.0, 0.3))
plt.savefig('C:/Users/Antrixsh/Desktop/neeraj.pdf', format='pdf')


## Adjust the Axes

axes = plt.axes()
axes.set_xlim([-5, 5.0])
axes.set_ylim([0, 2.0])
axes.set_xticks([-5, -4, -3, -2, -1, 0, 1, 1.5, 2, 3, 4, 5])
axes.set_yticks([0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0,1.1,1.2,1.3,1.4])
#axes.set_xT([-5, 5.0])
#axes.set_yt([0, 1.0])
axes.grid()
plt.plot(x, norm.pdf(x))
plt.plot(x, norm.pdf(x, 1, 0.5))
plt.plot(x, norm.pdf(x, 2.0, 0.3))

plt.show()


## Add a Grid

axes = plt.axes()
axes.set_xlim([-5, 5])
axes.set_ylim([0, 1.0])
axes.set_xticks([-5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5])
axes.set_yticks([0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0])
axes.grid()
plt.plot(x, norm.pdf(x))
plt.plot(x, norm.pdf(x, 1.0, 0.5))
plt.show()



## Change Line Types and Colors


axes = plt.axes()
axes.set_xlim([-5, 5])
axes.set_ylim([0, 1.0])
axes.set_xticks([-5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5])
axes.set_yticks([0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0])
axes.grid()
plt.plot(x, norm.pdf(x), 'r*')
plt.plot(x, norm.pdf(x, 1.0, 0.5), 'c')
plt.show()


## Labeling Axes and Adding a Legend


axes = plt.axes()
axes.set_xlim([-5, 5])
axes.set_ylim([0, 1.0])
axes.set_xticks([-5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5])
axes.set_yticks([0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0])
axes.grid()
plt.xlabel('X-Axis')
plt.ylabel('Y-Axis')
plt.plot(x, norm.pdf(x), 'r')
plt.plot(x, norm.pdf(x, 1.0, 0.5), 'b')
plt.legend(['page_load_time', 'purchase_amount'], loc=10)
plt.show()

## XKCD Style :)


plt.xkcd()

fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
plt.xticks([])
plt.yticks([])
ax.set_ylim([-30, 10])

data = np.ones(100)
data[70:] -= np.arange(30)

plt.annotate(
    'Data Science',
    xy=(50, 5), arrowprops=dict(arrowstyle='->'), xytext=(15, -10))

plt.plot(data)

plt.xlabel('time')
plt.ylabel('radical')


## Pie Chart

# Remove XKCD mode:
plt.rcdefaults()

values = [12, 14, 16, 18, 22, 24, 26]
colors = ['r', 'g', 'b', 'c', 'm', 'y', 'b']
explode = [0.1, 0.0, 0.0, 0.0, 0.0,0.0,0.0]
labels = ['India', 'United States', 'Russia', 'China', 'Europe','Aus','Germany']
plt.pie(values, colors= colors, labels=labels, explode = explode)
plt.title('Radical')
plt.show()

## Bar Chart

values = [12, 55, 4, 32, 14]
colors = ['r', 'g', 'b', 'c', 'm']
plt.bar(range(0,5), values, color= colors)
#plt.show()

## Scatter Plot

from pylab import randn

X = randn(10)
Y = randn(10)
plt.scatter(X,Y)
plt.show()

## Histogram

incomes = np.random.normal(27000, 15000, 500)
plt.hist(incomes)#, 500)
plt.show()

## Box & Whisker Plot

Useful for visualizing the spread & skew of data.

The red line represents the median of the data, and the box represents the bounds of the 1st and 3rd quartiles.

So, half of the data exists within the box.

The dotted-line "whiskers" indicate the range of the data - except for outliers, which are plotted outside the whiskers. Outliers are 1.5X or more the interquartile range.

This example below creates uniformly distributed random numbers between -40 and 60, plus a few outliers above 100 and below -100:

uniformSkewed = np.random.rand(100) * 100 - 40
high_outliers = np.random.rand(10) * 50 + 100
low_outliers = np.random.rand(10) * -50 - 100
data = np.concatenate((uniformSkewed, high_outliers, low_outliers))
plt.boxplot(data)
plt.show()