Matplotlib is a python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms.
We can produce specific types of figures such as bar charts, pie charts, histograms, scatter plots, etc.
These can be output in a range of formats (SVG / PDF / PNG).
First One of jupyter/iPython Notebooks 'magic commands'.
%matplotlib inline puts matplotlib into the cell output rather than creating a popup window.
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%matplotlib inline
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# Line Plot
import numpy as np
import matplotlib.pyplot as plt
myArray = np.array([4,5,6])
plt.plot(myArray)
plt.xlabel('Some x-axis label')
plt.ylabel('Some y-axis label')
plt.title('Line Plot')
plt.show()
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# A bar chart
y = np.random.rand(5)
x = np.arange(5)
plt.xlabel('Item')
plt.ylabel('Value')
plt.bar(x,y)
plt.title('Bar chart')
plt.show()
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# A bar chart multiple inputs
N = 5
menMeans = (20, 35, 30, 35, 27)
womenMeans = (25, 32, 34, 20, 25)
ind = np.arange(N) # the x locations for the groups
width = 0.35 # the width of the bars: can also be len(x) sequence
p1 = plt.bar(ind, menMeans, width, color='r')
p2 = plt.bar(ind + width, womenMeans, width, color='y')
plt.ylabel('Scores')
plt.title('A bar chart stacked')
plt.xticks(ind + width/2., ('G1', 'G2', 'G3', 'G4', 'G5'))
plt.yticks(np.arange(0, 81, 10))
plt.legend((p1[0], p2[0]), ('Men', 'Women'))
plt.show()
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# A bar chart stacked
N = 5
menMeans = (20, 35, 30, 35, 27)
womenMeans = (25, 32, 34, 20, 25)
ind = np.arange(N) # the x locations for the groups
width = 0.35 # the width of the bars: can also be len(x) sequence
p1 = plt.bar(ind, menMeans, width, color='r')
p2 = plt.bar(ind, womenMeans, width, color='y', bottom=menMeans)
plt.ylabel('Scores')
plt.title('A bar chart stacked')
plt.xticks(ind + width/2., ('G1', 'G2', 'G3', 'G4', 'G5'))
plt.yticks(np.arange(0, 81, 10))
plt.legend((p1[0], p2[0]), ('Men', 'Women'))
plt.show()
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# Histogram
from numpy.random import normal
gaussian_numbers = normal(size=1000)
plt.hist(gaussian_numbers, bins=(-10,-1,1,10)) # Set bin bounds
plt.show()
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# Scatter Plot
N = 50
x = np.random.rand(N)
y = np.random.rand(N)
plt.scatter(x, y)
plt.xlabel('Some x-axis label')
plt.ylabel('Some y-axis label')
plt.title('Scatter Plot')
plt.show()
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# A scatter plot with colours, area and alpha blending
N = 50
x = np.random.rand(N)
y = np.random.rand(N)
colors = np.random.rand(N)
area = np.pi * (15 * np.random.rand(N))**2 # 0 to 15 point radiuses
plt.scatter(x, y, s=area, c=colors, alpha=0.5)
plt.colorbar()
plt.show()
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