Matplotlib Python | (Data Science ss : 9)

Matplotlib Python

Matplotlib is a plotting library for the Python programming language  & its numerical mathematical extension Numpy. It provides an object-oriented API for embedding plots into applications using general-purpose GUI-Tool kits.

I will cover all the basic concepts of the Matplotlib with an overview of each graph’s. Before directly diving into this please check out my blog regarding to Numpy & Pandas where I had covered all the basics which will help you to understand the further concept easily.

To use matplotlib you must install the matplotlib library using pip command.

To import matplotlib we have to just type :

 import matplotlib.pyplot as plt

Now open you Jupyter Notebook & Lets start to code :

Code 1 :   Basic

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

%matplotlib inline

x=[1,2,3]

y=[5,6,7]

plt.plot(x,y)

plt.show

output :

Code 2 : Label , Legends & Title

Label : Name which we give to the x & y axis.

                               For label = label="_val_"

                               For X-axis = xlabel()

                               For Y-axis = ylabel()

Legends : Legends is basically the index where we describe which design serve what purpose.

Title : Which describes our graph

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

x=[11,12,13]

y=[9,16,7]

x1=[10,12,13]

x2=[15,6,17]

plt.plot(x,y,label="First_Line")

plt.plot(x1,y1,label="Second_Line")

plt.xlabel("X-Axis")

plt.ylabel("Y-Axis")

plt.title("Graph 2 for practise \nCode2")

plt.legend()

plt.show()

output :

Code 3 : Bar Chat v/s Histogram

(i)Bar Chat    : Compare Things

(ii)Histogram : Show Distribution of Values of single Parameter

(i)Bar Charts Example :

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

x=[2,4,6,8,10]

y=[6,7,8,9,10]

x1=[3,5,4,3,4]

y1=[8,6,7,8,5]

plt.bar(x,y,label='Bar_1',color='red')

plt.bar(x,y,label='Bar_2',color='blue')

plt.xlabel('X')

plt.ylabel('Y')

plt.title('Bar Chart Example')

plt.legend()

plt.show()

output :

(ii)Histogram Charts Example :

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

marks=[12,43,4,6,56,23,37,9,70,65,55,82,91,100]

range=[0,10,20,30,40,50,60,70,80,90,100]

plt.hist(marks,range,histtype='bar',rwidth=0.8,label='Scores of Students')

plt.xlabel('X-Axis')

plt.ylabel('Y-Axis')

plt.title('Histogram Chart Example (Bar type)')

plt.legend()

plt.show() 

output :

Code 4 : Scatter Plot Graph

A Scatter Plot is a type of plot or mathematical diagram using Cartesian coordinates to display values for typically 2 variables for a set of data.

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

x=[1,2,8,9,5]

y=[6,7,3,4,10]

plt.scatter(x,y,label='val',color='black',marker='*',s=200)

plt.xlabel('X-Axis')

plt.ylabel('Y-Axis')

plt.title('Scatter Plot Graph')

plt.legend()

plt.show() 

output :

Code 5 : Stack Plot

A stack plot is a plot that shows the whole data set with easy visualization of how each part makes up the whole. Each constituent of the stack plot is stacked on top of each other.

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

Total = [80,90,100,60,50]

english          = [30,40,50,20,10]

maths            = [10,20,20,30,30]

history          = [40,30,30,10,10]

plt.plot([],[],color='blue',label='english',linewidth=5)

plt.plot([],[],color='green',label='maths',linewidth=5)

plt.plot([],[],color='yellow',label='history',linewidth=5)

plt.stackplot(Total,english,maths,history,colors=['blue', 'green', 'yellow'])

plt.xlabel('X-Axis')

plt.ylabel('Y-Axis')

plt.title('Stack Graph')

plt.legend()

plt.show() 

output :

Code 6 :Pie Charts Example

A Pie Chart is the circular statistical graphic, which is divided into slices to illustrate numerical proportion.

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

Total = [80,90,100,60,50]

english          = [30,40,50,20,10]

maths            = [10,20,20,30,30]

history          = [40,30,30,10,10]

slices=[7,2,13]

x=['english','maths','history']

cols=['blue', 'green', 'yellow']

plt.pie(slices, labels=x,startangle=90,shadow=True,explode=(0,0,0.05),autopct=''%1.1f%%'')

plt.xlabel('X-Axis')

plt.ylabel('Y-Axis')

plt.title('Pie')

plt.show() 

output :

Code : Box Plot

In descriptive statistics, a boxplot is a method for graphically depicting groups of numerical data through their quartiles. Box plots may also have lines extending from the boxes indicating variability outside the upper & lower quartiles, hence the terms box-and-whisker plot diagram.

Theoretical Knowledge :

From Q1 to Q3 the number of values comes between them is consider its height.

eg :

                               

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

%matplotlib inline

data=pd.read_csv(r"D:\MY_ML\home.csv")

print(data)

tarea=data.Area

tprice=data.Price

x=list([tarea,tprice])

plt.boxplot(x,showmeans=True)

plt.grid(True)

plt.show()

output :