MatPlotLib Basics

Draw a line graph



In [1]:

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

x = np.arange(-3, 3, 0.01)

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

Mutiple Plots on One Graph



In [3]:

    
plt.plot(x, norm.pdf(x))
plt.plot(x, norm.pdf(x, 1.0, 0.5))
plt.show()

Save it to a File



In [4]:

    
plt.plot(x, norm.pdf(x))
plt.plot(x, norm.pdf(x, 1.0, 0.5))
plt.savefig('C:\\Users\\Frank\\MyPlot.png', format='png')

Adjust the Axes



In [5]:

    
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])
plt.plot(x, norm.pdf(x))
plt.plot(x, norm.pdf(x, 1.0, 0.5))
plt.show()

Add a Grid



In [6]:

    
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



In [3]:

    
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), 'b-')
plt.plot(x, norm.pdf(x, 1.0, 0.5), 'r:')
plt.show()

Labeling Axes and Adding a Legend



In [8]:

    
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('Greebles')
plt.ylabel('Probability')
plt.plot(x, norm.pdf(x), 'b-')
plt.plot(x, norm.pdf(x, 1.0, 0.5), 'r:')
plt.legend(['Sneetches', 'Gacks'], loc=4)
plt.show()

XKCD Style :)



In [9]:

    
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(
    'THE DAY I REALIZED\nI COULD COOK BACON\nWHENEVER I WANTED',
    xy=(70, 1), arrowprops=dict(arrowstyle='->'), xytext=(15, -10))

plt.plot(data)

plt.xlabel('time')
plt.ylabel('my overall health')









    Out[9]:





<matplotlib.text.Text at 0xa84cdd8>

Pie Chart



In [10]:

    
# Remove XKCD mode:
plt.rcdefaults()

values = [12, 55, 4, 32, 14]
colors = ['r', 'g', 'b', 'c', 'm']
explode = [0, 0, 0.2, 0, 0]
labels = ['India', 'United States', 'Russia', 'China', 'Europe']
plt.pie(values, colors= colors, labels=labels, explode = explode)
plt.title('Student Locations')
plt.show()

Bar Chart



In [11]:

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

Scatter Plot



In [14]:

    
from pylab import randn

X = randn(500)
Y = randn(500)
plt.scatter(X,Y)
plt.show()

Histogram



In [15]:

    
incomes = np.random.normal(27000, 15000, 10000)
plt.hist(incomes, 50)
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:



In [16]:

    
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()

Activity

Try creating a scatter plot representing random data on age vs. time spent watching TV. Label the axes.



In [ ]: