Graphing Data with Python – Matplotlib

When dealing with data analysis problems, it is essential to have the possibility of observing them in some graphical form, therefore in this series of articles I will try to illustrate how to draw graphs in a simple way using python and some libraries created for the purpose.

 

For  the following examples I will use Jupyter  notebook, so if you don’t know what it is  I invite you to take a look at this article. A possible  alternative could be  google colab.

For  the following examples I will use Jupyter  notebook, so if you don’t know what it is  I invite you to take a look at this article. A possible  alternative could be    google colab .

Well, if you’ve already installed Jupyter, start creating a new notebook, otherwise I suggest you take a step back and install the software to follow the example step by step.

Creating the Jupyter notebook project

Open a shell, create a new directory and run the command jupyter notebook

mkdir MATPLOTLIB
cd MATPLOTLIB/
jupyter notebook

a window of your browser will open

at this point select the Python 3 interpreter to initialize the environment

The first library we are going to study is:

Matplotlib

Matplotlib is a library with which you can draw 2d and 3d plots. To use it, the first thing we have to do is import it,  so we will take advantage of pyplot which offers a programming interface similar to MATLAB.

import matplotlib.pyplot as plt

we also import a very powerful math library numpy

import numpy as np

we choose a function to graph

def retta(x):
   return 4*x+1

we define the x range  from 0 to 100

x=np.arange(100)

the straight line is defined by:

y=retta(x)

let’s draw the graph

plt.plot(x,y)

 

here I report the example in jupyter.

 

let’s try to do the same example but with a slightly more complex graph

import matplotlib.pyplot as plt

import numpy as np

def f(x):
   return 20 + 105*np.cos(x)*np.sin(x+30) + 144*np.exp(0.5/x+1)

x=np.arange(-100,100)

y=f(x)

plt.plot(x,y)

let’s try to explore the possibilities offered by pyplot.

To change the color of the graphs we can use the instruction:

plt.plot(x,y, color="<color_RGB>")

for example

plt.plot(x, y, color='3412F3')

to change the range of the axes I can use the instructions:

plt.xlim(left_x,right_x) or plt.ylim(bottom_y,top_y)

for example

plt.xlim(-10,76)
plt.ylim(-100,600)
plt.plot(x, y)

if instead we wanted, for example, to draw a graph with a dashed or dotted line, we could use the parameter  linestyle=”dashed” o linestyle=”dotted” for example:

plt.plot(x,y,linestyle="dashed")

If we wanted to use markers we could use  the parameter marker=’o’

plt.plot(x,y, marker='o')

to add information to the axes or a legend we can use the following instructions:

plt.plot(x,y,label="y=f(x)")
plt.title("esempio di grafico")
plt.xlabel("andamento delle x")
plt.ylabel("andamento della f(x)")
plt.legend()

To draw multiple graphs we can follow the following approach:

• we define the functions to graph,
• we define the range of the x’s
• we “plot” the graphs by repeating the plot command for the number of functions to be represented.

import matplotlib.pyplot as plt
import numpy as np

def f(x):
   return 20 + 105*np.cos(x)*np.sin(x+30) + 144*np.exp(0.5/x+1)

def retta(x): 
   return 4*x+1

x=np.arange(-100,100)
plt.plot(x,f(x))
plt.plot(x,retta(x))

Obviously it is possible to use the various parameters in a single instruction, for example to represent a purple function, with circular markers and dashed line style we can write:

plt.plot(x, y, color='#8712F3', marker='o', linestyle='dashed', linewidth=2, markersize=12)

Plot in a window and not in Jupyter notebook

Graphing data with Python – Matplotlib : Second Part>>