# Plotting with matplotlib

Gregor von Laszewski (laszewski@gmail.com)

A brief overview of plotting with matplotlib along with examples is provided. First, matplotlib must be installed, which can be accomplished with pip install as follows:

$pip install matplotlib  We will start by plotting a simple line graph using built in NumPy functions for sine and cosine. This first step is to import the proper libraries shown next. import numpy as np import matplotlib.pyplot as plt  Next, we will define the values for the x-axis, we do this with the linspace option in numpy. The first two parameters are the starting and ending points, these must be scalars. The third parameter is optional and defines the number of samples to be generated between the starting and ending points, this value must be an integer. Additional parameters for the linspace utility can be found here: x = np.linspace(-np.pi, np.pi, 16)  Now we will use the sine and cosine functions in order to generate y values, for this we will use the values of x for the argument of both our sine and cosine functions i.e.$cos(x)\$.

cos = np.cos(x)
sin = np.sin(x)


You can display the values of the three parameters we have defined by typing them in a python shell.

x
array([-3.14159265, -2.72271363, -2.30383461, -1.88495559, -1.46607657,
-1.04719755, -0.62831853, -0.20943951, 0.20943951, 0.62831853,
1.04719755, 1.46607657, 1.88495559, 2.30383461, 2.72271363,
3.14159265])


Having defined x and y values we can generate a line plot and since we imported matplotlib.pyplot as plt we simply use plt.plot.

plt.plot(x,cos)


We can display the plot using plt.show() which will pop up a figure displaying the plot defined.

plt.show()


Additionally, we can add the sine line to outline graph by entering the following.

plt.plot(x,sin)


Invoking plt.show() now will show a figure with both sine and cosine lines displayed. Now that we have a figure generated it would be useful to label the x and y-axis and provide a title. This is done by the following three commands:

plt.xlabel("X - label (units)")
plt.ylabel("Y - label (units)")
plt.title("A clever Title for your Figure")


Along with axis labels and a title another useful figure feature may be a legend. In order to create a legend you must first designate a label for the line, this label will be what shows up in the legend. The label is defined in the initial plt.plot(x,y) instance, next is an example.

plt.plot(x,cos, label="cosine")


Then in order to display the legend, the following command is issued:

plt.legend(loc='upper right')


The location is specified by using upper or lower and left or right. Naturally, all these commands can be combined and put in a file with the .py extension and run from the command line.

import numpy as np
import matplotlib.pyplot as plt

x = np.linspace(-np.pi, np.pi, 16)
cos = np.cos(x)
sin = np.sin(x)
plt.plot(x,cos, label="cosine")
plt.plot(x,sin, label="sine")

plt.xlabel("X - label (units)")
plt.ylabel("Y - label (units)")
plt.title("A clever Title for your Figure")

plt.legend(loc='upper right')

plt.show()


An example of a bar chart is preceded next using data from [T:fast-cars]{reference-type=“ref” reference=“T:fast-cars”}.

import matplotlib.pyplot as plt

x = [' Toyota Prius',
' Bugatti Veyron',
' Honda Civic ',
horse_power = [120, 288, 1200, 158, 695]

x_pos = [i for i, _ in enumerate(x)]

plt.bar(x_pos, horse_power, color='green')
plt.xlabel("Car Model")
plt.ylabel("Horse Power (Hp)")
plt.title("Horse Power for Selected Cars")

plt.xticks(x_pos, x)

plt.show()


You can customize plots further by using plt.style.use(), in python 3. If you provide the following command inside a python command shell you will see a list of available styles.

print(plt.style.available)


An example of using a predefined style is shown next.

plt.style.use('seaborn')


Up to this point, we have only showcased how to display figures through python output, however web browsers are a popular way to display figures. One example is Bokeh, the following lines can be entered in a python shell and the figure is outputted to a browser.

from bokeh.io import show
from bokeh.plotting import figure

x_values = [1, 2, 3, 4, 5]
y_values = [6, 7, 2, 3, 6]

p = figure()
p.circle(x=x_values, y=y_values)
show(p)