Data Visualization using Matplotlib

Data visualization is one of the most important task in the field of machine learning and data science. In this article we will learn about different kind of data visualizations like static, animated and interactive using Matplotlib. Matplotlib is one of the most popular library of python for visualization task, you can get Matplotlib documentation from here.

How to install Matplotlib

It is very easy to install Matplotlib on your devices, you can just type the following command in your terminal then installing process will run.

pip install matplotlib

You can import this library by using following code.

import matplotlib.pyplot as plt

We have to use Matplotlib word many times while doing visualization so, instead to write it every time we import as plt

Line Plot

Visualized data in the form of line is one of the easiest task. For line plot you just need equal number of X and Y axis data. The data set need to be in list format, you can use NumPy array tuples etc. you can learn about different data type used in python from here. plot() method is used for plotting a line graph and the graph is only appeared when you call show method from Matplotlib. Following code shows how we plot line graph in python.

days = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15]
temperature = [36.6, 37, 37.7,39,40.1,43,43.4,45,45.6,40.1,44,45,46.8,47,47.8]
temperature1 = [39,39.4,40,40.7,41,42.5,43.5,44,44.9,44,45,45.1,46,47,46]
plt.plot(days,temperature)
plt.show()

You can give a title for the plot and label to the X and Y axis. Additionally plot() has many parameter example color, linewidth, linestyle etc,. we can use some of this parameter to make above graph meaningful and attractive.

plt.plot(days, temperature, color = "g", marker = "*", linestyle= "--", linewidth = 0.5,
        markersize = 10)
plt.title("kathmandu Temperature",fontsize=20) # define title of figure
plt.xlabel('Days',fontsize=15) #define x label
plt.ylabel('temperature', fontsize=15)# define y label
plt.show()

you can plot multiple line by follow the same pattern of single line graph. Following code shows how can you plot multiple line graph.

plt.plot(days, temperature, color = "g", marker = "o", linestyle= "--", linewidth = 0.5,
        markersize = 10, label = "chitwan")
plt.plot(days, temperature1, color = "r", marker = "*", linestyle= "--", linewidth = 0.5,
        markersize = 10,label='kathmandu')
plt.title("kathmandu Temperature",fontsize=20) # define title of figure
plt.xlabel('Days',fontsize=15) #define x label
plt.ylabel('temperature', fontsize=15)# define y label
plt.legend(loc=4) # define lable of two line
plt.show()

Histogram

Matplotlib histogram is a representation of numeric data in the form of a rectangle bar. Each bar shows some data, which belong to different categories. To plot histogram using python Matplotlib library need plt.hist() The plt. hist() method has lots of parameter. following code show how can we create histogram.

import numpy as np
import random
ml_students_age = np.random.randint(18,45, (100))
py_students_age = np.random.randint(15,40, (100))
plt.hist(ml_students_age)
plt.title("ML Students age histograms")
plt.xlabel("Students age cotegory")
plt.ylabel("No. Students age")
plt.show()

Bar Graph

Bar graph is used to visualize data along with categories. Additionally, in Matplotlib we can create a bar graph by using bar() method.Following code show how can we create a bar graph.

student=['A','B','C','D','E','F','G','H','I','J']
score_english=np.random.randint(30,100,10)
score_math=np.random.randint(40,100,10)
style.use("ggplot") # return grid
plt.bar(student,score_english)
plt.title('English Score Bye Student', fontsize=15)
plt.ylabel('Total number ')
plt.show()

We can create multiple bar graph in the similar way of single bar graph. Following code shows how can we visualized multiple bar graph.

plt.figure(figsize=(10,5))
classes_index = np.arange(len(student))
width = 0.3
plt.bar(classes_index, score_english, width , color = "b",
        label ="english score") #visible=False
plt.bar(classes_index+width, score_math, width , color = "g",
        label =" math score") 
plt.xticks(classes_index+width , student, rotation = 30)
plt.title('English Score Bye Student', fontsize=15)
plt.ylabel('Total number ')
plt.show()

Scatter Plot

Matplot has a built-in function to create scatterplots called scatter(). A scatter plot is a type of plot that shows the data as a collection of points.

N = 500
x = np.random.rand(N)
y = np.random.rand(N)
colors = (0,0,0)
plt.scatter(x, y,c=colors)
plt.title('Scatter plot')
plt.xlabel('x')
plt.ylabel('y')
plt.show()

Pie Chart

In this section , we will work on how to draw a matplotlib pie chart? To draw pie char use plt.pie() function. The matplotkib plt.pie() function help to plot pie chart of given numeric data with labels. It also support different parameters which help to make a chart more attractive.

classes = ["Python", 'R', 'Machine Learning', 'Artificial Intelligence', 
           'Data Sciece']
class1_students = [45, 15, 35, 25, 30]
plt.figure(figsize=(16,6))
explode = [0.03,0,0.1,0,0] # To slice the perticuler section
colors = ["c", 'b','r','y','g'] # Color of each section
textprops = {"fontsize":15} # Font size of text in pie chart
plt.pie(class1_students, # Values
        labels = classes, # Labels for each sections
        explode = explode, # To slice the perticuler section
        colors =colors, # Color of each section
        autopct = "%0.2f%%", # Show data in persentage for with 2 decimal point
        shadow = True, # Showing shadow of pie chart
        radius = 1.4, # Radius to increase or decrease the size of pie chart 
       startangle = 270, # Start angle of first section
        textprops =textprops
       ) 
plt.legend(loc=1)
plt.show() # To show pie chart only

Sub Plot

Sub plot is one of the most important part of visualization. Until yet we just plot a single graph, for multiple graph visualization we need a concept of subplot. With a help of subplot we can create multiple graph in a single place. following code show how can we use subplot in Matplotlib.

plt.subplot(2,2,1)
plt.pie([1])
plt.subplot(2,2,2)
plt.pie([1,2])
plt.subplot(2,2,3)
plt.pie([1,2,3])
plt.subplot(2,2,4)
plt.pie([1,2,3,4])
plt.show()

Save Figure

After creating a plot or chart using the python matplotlib library and need to save and use it further. Then the matplotlib savefig function will help you. In this Section, we are explaining, how to save a figure using matplotlib?

plt.pie([40,30,20])
plt.savefig("pie_chart.png", # file name
            dpi = 100,  # dot per inch for resolution increase value for more resolution
            quality = 99, # "1 <= value <= 100" 100 for best qulity
            facecolor = "w" # image background color
           )
plt.show()

Conclusion

OK, this is the end of the article I hope you can get a good lesson from what I deliver in this article. I ask forgiveness for any word and behave which are not to be. Thank you for your kind and attention guys. Stay tuned for the next article. if you are searching for a free python course here is a link. If you have any questions regarding this article please feel free to comment below.