Basic Charts in Python

Basic Charts in Python

• Line Chart
• Bar Chart
• Pie Chart

Matplotlib is a 2D plotting library which can be used to generate publication quality figures.

Matplotlib Line chart

A line chart can be created using the Matplotlib plot() function. While we can just plot a line, we are not limited to that. We can explicitly define the grid, the x and y axis scale and labels, title and display options.

from pylab import *

t = arange(0.0, 2.0, 0.01)

s = sin(2.5*pi*t)

plot(t, s)

xlabel('time (s)')

ylabel('voltage (mV)')

title('Sine Wave')

grid(True)

show()

Statements xlabel() sets the x-axis text, ylabel() sets the y-axis text, title() sets the chart title and grid(True) simply turns on the grid.

If you want to save the plot to the disk, call the statement:

savefig("line_chart.png")

Plot a custom Line Chart

If you want to plot using an array (list), you can execute this script:

from pylab import *

t = arange(0.0, 20.0, 1)

s = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20]

plot(t, s)

xlabel('Item (s)')

ylabel('Value')

title('Python Line Chart: Plotting numbers')

grid(True)

show()

If you want to plot multiple lines in one chart, simply call the plot() function multiple times. 

In case you want to plot them in different views in the same window you can use this:

import matplotlib.pyplot as plt

from pylab import *

t = arange(0.0, 20.0, 1)

s = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20]

s2 = [4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23]

plt.subplot(2, 1, 1)

plt.plot(t, s)

plt.ylabel('Value')

plt.title('First chart')

plt.grid(True)

plt.subplot(2, 1, 2)

plt.plot(t, s2)

plt.xlabel('Item (s)')

plt.ylabel('Value')

plt.title('Second chart')

plt.grid(True)

plt.show()

The plt.subplot() statement is key here. The subplot() command specifies numrows, numcols and fignum.

Styling the plot

If you want thick lines or set the color, use:

plot(t, s, color="red", linewidth=2.5, linestyle="-")

Matplotlib Bar chart

import matplotlib.pyplot as plt; plt.rcdefaults()

import numpy as np

import matplotlib.pyplot as plt

objects = ('Python', 'C++', 'Java', 'Perl', 'Scala', 'Lisp')

y_pos = np.arange(len(objects))

performance = [10,8,6,4,2,1]

plt.bar(y_pos, performance, align='center', alpha=0.5)

plt.xticks(y_pos, objects)

plt.ylabel('Usage')

plt.title('Programming language usage')

plt.show()

Matplotlib charts can be horizontal, to create a horizontal bar chart:

plt.yticks(y_pos, objects)

plt.xlabel('Usage')

You can compare two data series using this Matplotlib code:

import numpy as np

import matplotlib.pyplot as plt

# data to plot

n_groups = 4

means_frank = (90, 55, 40, 65)

means_guido = (85, 62, 54, 20)

# create plot

fig, ax = plt.subplots()

index = np.arange(n_groups)

bar_width = 0.35

opacity = 0.8

rects1 = plt.bar(index, means_frank, bar_width,

                 alpha=opacity,

                 color='b',

                 label='Frank')

rects2 = plt.bar(index + bar_width, means_guido, bar_width,

                 alpha=opacity,

                 color='g',

                 label='Guido')

plt.xlabel('Person')

plt.ylabel('Scores')

plt.title('Scores by person')

plt.xticks(index + bar_width, ('A', 'B', 'C', 'D'))

plt.legend()

plt.tight_layout()

plt.show()

Matplotlib supports pie charts using the pie() function.

import matplotlib.pyplot as plt

# Data to plot

labels = 'Python', 'C++', 'Ruby', 'Java'

sizes = [215, 130, 245, 210]

colors = ['gold', 'yellowgreen', 'lightcoral', 'lightskyblue']

explode = (0.1, 0, 0, 0)  # explode 1st slice

# Plot

plt.pie(sizes, explode=explode, labels=labels, colors=colors,

        autopct='%1.1f%%', shadow=True, startangle=140)

plt.axis('equal')

plt.show()

To add a legend use the plt.legend() function:

import matplotlib.pyplot as plt

labels = ['Cookies', 'Jellybean', 'Milkshake', 'Cheesecake']

sizes = [38.4, 40.6, 20.7, 10.3]

colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral']

patches, texts = plt.pie(sizes, colors=colors, shadow=True, startangle=90)

plt.legend(patches, labels, loc="best")

plt.axis('equal')

plt.tight_layout()

plt.show()