EXNO-5-DS-DATA VISUALIZATION USING MATPLOT LIBRARY

Aim:

To Perform Data Visualization using matplot python library for the given datas.

EXPLANATION:

Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data.

Algorithm:

STEP 1:Include the necessary Library.

STEP 2:Read the given Data.

STEP 3:Apply data visualization techniques to identify the patterns of the data.

STEP 4:Apply the various data visualization tools wherever necessary.

STEP 5:Include Necessary parameters in each functions.

Coding and Output:

TO CAPTURE A TREND

1. Line Chart

import matplotlib.pyplot as plt
import numpy as np
x=[0,1,2,3,4,5]
y=[0,1,4,9,16,25]
plt.plot(x,y)
plt.show()

Output

2.Multi-Line Chart

x1=[1,2,3]
y1=[2,4,1]
plt.plot(x1,y1,label="line 1")
x2=[1,2,3]
y2=[4,1,3]
plt.plot(x2,y2,label="line 2")
plt.xlabel('x-axis')
plt.ylabel('y-axis')
plt.title('Multi-Line Chart')
plt.legend()
plt.show()

Output

3.Area Chart

x=[1,2,3,4,5]
y1=[10,12,14,16,18]
y2=[5,7,9,11,13]
y3=[2,4,6,8,10]
plt.fill_between(x,y1,color='blue')
plt.fill_between(x,y2,color='green')
plt.plot(x,y1,color='red')
plt.plot(x,y2,color='black')
plt.legend(['y1','y2'])
plt.show()

Output

4.Stacked Area Chart

plt.stackplot(x,y1,y2,y3,labels=['Line 1','Line 2','Line 3'])
plt.legend(loc='upper left')
plt.title('Stacked Line Chart')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.show()

Output

5.Spline Chart

from scipy.interpolate import make_interp_spline
x=np.array([1,2,3,4,5,6,7,8,9,10])
y=np.array([2,4,5,7,8,8,9,10,11,12])
spl=make_interp_spline(x,y)
x1=np.linspace(x.min(),x.max(),100)
y1=spl(x1)
plt.plot(x,y,'*',label='data')
plt.plot(x1,y1,'-',label="spline")
plt.legend()
plt.show()

Output

TO VISUALIZE RELATIONSHIPS

1.Bar Chart

val=[5,6,3,7,2]
names=["A","B","C","D","E"]
plt.bar(names,val,color="blue")
plt.show()

Output

2.Scatter Plot

x=[0,1,2,3,4,5]
y=[0,1,4,9,16,25]
plt.scatter(x,y,s=30,color="red")
plt.show()

Output

3.Bubble Chart

x = [1, 2, 3, 4, 5]
y = [10, 15, 20, 25, 30]
sizes = [100, 200, 300, 400, 500]
plt.scatter(x, y, s=sizes, alpha=0.5)
plt.xlabel('x_values')
plt.ylabel('y_values')
plt.title('Bubble Chart')
plt.show()

Output

TO CAPTURE DISTRIBUTIONS

1.Histogram

ages=[2,5,70,40,30,45,50,45,43,40,44,60,7,13,57,18,90,77,32,21,20,40]
range=(0,100)
bins=10
plt.hist(ages,bins,range,color='purple',histtype='bar',rwidth=0.8)
plt.xlabel('age')
plt.ylabel('No. Of People')
plt.title('Histogram')
plt.show()

Output

2.Box Plot

np.random.seed(0)
data=np.random.normal(loc=0,scale=1,size=100)
data
fig,ax=plt.subplots()
ax.boxplot(data)
ax.set_xlabel('Data')
ax.set_ylabel('Values')
ax.set_title('Box Plot')

Output

3.Violin Plot

data = [np.random.normal(loc=0, scale=1, size=100),
        np.random.normal(loc=2, scale=1, size=100),
        np.random.normal(loc=1, scale=2, size=100)]
plt.violinplot(data)
plt.xlabel('Groups')
plt.ylabel('Values')
plt.title('Violin Plot')
plt.xticks([1, 2, 3], ['Group 1', 'Group 2', 'Group 3'])
plt.show()

Output

4.Density Chart

data = np.random.normal(0, 1, 1000)
plt.hist(data, bins=30, density=True, alpha=0.5)
plt.title('Density Plot Example')
plt.xlabel('Values')
plt.ylabel('Density')
from scipy.stats import gaussian_kde
kde = gaussian_kde(data)
x_vals = np.linspace(min(data), max(data), 1000)
plt.plot(x_vals, kde(x_vals), 'r')
plt.show()

Output

5.Pie Chart

act=['eat','sleep','work','play']
slices=[3,7,8,6]
color=['r','y','g','b']
plt.pie(slices,labels=act,colors=color,startangle=90,shadow=True,explode=(0.1,0.1,0.1,0.1),radius=1.2,
autopct='%1.1f%%')
plt.legend()
plt.show()

Output

Result:

Thus, the data visualization techniques using matplotlib has been executed successfully.