Python | Introduction to Matplotlib Library Tutorial

1.Python Matplotlib: Bar Graph

To compare data between various groups, a bar graph uses bars. When you want to calculate the changes over a period of time, it is well suited. Horizontally or vertically, it can be interpreted. The crucial thing to note too is that the higher the bar, the greater the value.
Now, using python matplotlib, let us implement it functionally.

import matplotlib.pyplot as plt
   
Country = ['USA','India','Germany','UK','France']
GDP_Per_Capita = [48000,38000,50000,45000,44000]

plt.bar(Country, GDP_Per_Capita)
plt.title('Country Vs GDP Per Capita')
plt.xlabel('Country')
plt.ylabel('GDP Per Capita')
plt.show()

Output:

2. Python Matplotlib: Histogram

A histogram is used to represent a distribution, while a bar chart is used to compare various entities. When you have arrays or a long list, histograms are useful. Now, using python matplotlib, let us implement it functionally.

import matplotlib.pyplot as plt
population_age= [22,55,62,45,21,22,34,42,42,4,2,102,95,85,55,110,
                                 120,70,65,55,111,115,80,75,65,54,44,43,42,48]
bins=[0,10,20,30,40,50,60,70,80,90,100]
plt.hist(population_age,bins,histtype='bar',rwidth=0.8)
plt.xlabel('age groups')
plt.ylabel('Number of people')
plt.title('Histogram')
plt.show()
plt.show()

Output:

3. Python Matplotlib: Scatter plot

In order to compare variables, we typically need scatter plots, for instance, how much one variable is influenced by another variable to construct a relationship out of it. The data is shown as a set of points, each of which has the value of a single variable that determines the position on the horizontal axis, and the position on the vertical axis is determined by the value of another variable.

import matplotlib.pyplot as plt

x = [1,2,3,4,5,6,7,8]
y = [4,1,3,6,1,3,5,2]

plt.scatter(x,y)

plt.title('scatter plot')
plt.xlabel('x')
plt.ylabel('y')


plt.show()

Output:

4. Python Matplotlib: Area plot

Such plots are very similar to the line plot. They are also called as stack plots. For two or more similar classes that make up one whole category, these plots can be used to track changes over time.

import matplotlib.pyplot as plt

days = [1,2,3,4,5,6,7,8]
sleeping= [7,8,6,11,7,5,13,4]
eating=[2,3,4,3,2,2,4,5]
working=[7,8,7,2,2,5,6,8]
playing=[8,5,7,8,13,9,7,10]

plt.plot([],[],color='m', label='Sleeping',linewidth=5)
plt.plot([],[],color='c', label='Eating',linewidth=5)
plt.plot([],[],color='r', label='Programming',linewidth=5)
plt.plot([],[],color='k', label='Playing',linewidth=5)

plt.stackplot(days,sleeping,eating,working,playing,colors=['m','c','r','k'])

plt.title('Stack plot')
plt.xlabel('x')
plt.ylabel('y')
plt.legend()
plt.show()

Output:

5. Python Matplotlib: Pie Chart

A pie chart refers to a circular graph that is divided into parts, i.e. pie slices. Basically, it is used to display the percentage or relative data where a segment is represented by each slice of pie.

import matplotlib.pyplot as plt

days = [1,2,3,4,5]
sleeping= [7,8,6,11,7]
eating=[2,3,4,3,2]
working=[7,8,7,2,2]
playing=[8,5,7,8,13]
slices=[7,2,2,13]
activities=['Sleeping', 'Eating' , 'Playing',  'Programming']
cols=['c', 'm', 'r', 'b']


plt.pie(slices,labels=activities,colors=cols,startangle=90,
              shadow=True,explode=(0,0.1,0,0),autopct='%1.1f%%')

plt.title('Pie plot')
plt.show()

Output: