Ex-08-Data-Visualization-

AIM

To Perform Data Visualization on a complex dataset and save the data to a file.

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

Read the given Data

STEP 2

Clean the Data Set using Data Cleaning Process

STEP 3

Apply Feature generation and selection techniques to all the features of the data set

STEP 4

Apply data visualization techniques to identify the patterns of the data.

Developed By Gokul Nath
Ref No: 212220220013

CODE

#Reading the given dataset

import pandas as pd
df=pd.read_csv("Superstore.csv",encoding='unicode_escape')

df.head()

#Data Visualization using Seaborn

import seaborn as sns
from matplotlib import pyplot as plt

#1.Line Plot

plt.figure(figsize=(9,6))
sns.lineplot(x="Segment",y="Region",data=df,marker='o')
plt.xticks(rotation = 90)

sns.lineplot(x='Ship Mode',y='Category', hue ="Segment",data=df)

sns.lineplot(x="Category",y="Sales",data=df,marker='o')

#2.Scatterplot

sns.scatterplot(x='Category',y='Sub-Category',data=df)

sns.scatterplot(x='Category', y='Sub-Category', hue ="Segment",data=df)

plt.figure(figsize=(10,7))
sns.scatterplot(x="Region",y="Sales",data=df)
plt.xticks(rotation = 90)

#3.Boxplot

sns.boxplot(x="Sub-Category",y="Discount",data=df)

sns.boxplot( x="Profit", y="Category",data=df)

#4.Violin Plot

sns.violinplot(x="Profit",data=df)

#5.Barplot

sns.barplot(x="Sub-Category",y="Sales",data=df)
plt.xticks(rotation = 90)

sns.barplot(x="Category",y="Sales",data=df)
plt.xticks(rotation = 90)

#6.Pointplot

sns.pointplot(x=df["Quantity"],y=df["Discount"])

#7.Count plot

sns.countplot(x="Category",data=df)

sns.countplot(x="Sub-Category",data=df)

#8.Histogram

sns.histplot(data=df,x ='Ship Mode',hue='Sub-Category')

#9.KDE Plot

sns.kdeplot(x="Profit", data = df,hue='Category')

#Data Visualization Using MatPlotlib

#1.Plot

plt.plot(df['Category'], df['Sales'])
plt.show()

#2.Heatmap

df.corr()
plt.subplots(figsize=(12,7))
sns.heatmap(df.corr(),annot=True)

#3.Piechart

df1=df.groupby(by=["Ship Mode"]).sum()
labels=[]
for i in df1.index:
    labels.append(i)
colors=sns.color_palette("bright")
plt.pie(df1["Sales"],labels=labels,autopct="%0.0f%%")
plt.show()

df3=df.groupby(by=["Category"]).sum()
labels=[]
for i in df3.index:
    labels.append(i) 
plt.figure(figsize=(8,8))
colors = sns.color_palette('pastel')
plt.pie(df3["Profit"],colors = colors,labels=labels, autopct = '%0.0f%%')
plt.show()

#4.Histogram

plt.hist(df["Sub-Category"],facecolor="peru",edgecolor="blue",bins=10)
plt.show()

#5.Bargraph

plt.bar(df.index,df['Category'])
plt.show()

#6.Scatterplot

plt.scatter(df["Region"],df["Profit"], c ="blue")
plt.show() 

#7.Boxplot

plt.boxplot(x="Sales",data=df)
plt.show()

OUTPUT

Reading the given dataset

Data Visualization Using Seaborn:

1.Line Plot

2.Scatterplot

3.Boxplot

4.Violin Plot

5.Barplot

6.Pointplot

7.Count plot

8.Histogram

9.KDE Plot

Data Visualization Using Matplotlib:

1.Plot

2.Heatmap

3.Piechart

4.Histogram

5.Bargraph

6.Scatterplot

7.Boxplot

RESULT

Hence,Data Visualization is applied on the complex dataset using libraries like Seaborn and Matplotlib successfully and the data is saved to file.