To Perform Data Visualization on a complex dataset and save the data to a file.
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.
- 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: SANJAY G
Reg No: 212222230131
import pandas as pd
df=pd.read_csv("/content/Superstore.csv",encoding='unicode_escape')
df.head()
import seaborn as sns
from matplotlib import pyplot as plt
- LINE PLOT:
plt.figure(figsize=(9,6))
sns.lineplot(x="Segment",y="Region",data=df,marker='o')
plt.xticks(rotation = 90)
sns.lineplot(x="Category",y="Sales",data=df,marker='o')
- 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)
- BOXPLOT:
sns.boxplot(x="Sub-Category",y="Discount",data=df)
sns.boxplot( x="Profit", y="Category",data=df)
- VIOLIN PLOT:
sns.violinplot(x="Profit",data=df)
- 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)
- POINTPLOT
sns.pointplot(x=df["Quantity"],y=df["Discount"])
- COUNT PLOT
sns.countplot(x="Category",data=df)
sns.countplot(x="Sub-Category",data=df)
- HISTOGRAM
sns.histplot(data=df,x ='Ship Mode',hue='Sub-Category')
- KDE PLOT
sns.kdeplot(x="Profit", data = df,hue='Category')
- PLOT
plt.plot(df['Category'], df['Sales'])
plt.show()
- HEATMAP:
df.corr()
plt.subplots(figsize=(12,7))
sns.heatmap(df.corr(),annot=True)
- 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()
- HISTOGRAM:
plt.hist(df["Sub-Category"],facecolor="peru",edgecolor="blue",bins=10)
plt.show()
- BARGRAPH:
plt.bar(df.index,df['Category'])
plt.show()
- SCATTERPLOT:
plt.scatter(df["Region"],df["Profit"], c ="blue")
plt.show() 
- BOXPLOT:
plt.boxplot(x="Sales",data=df)
plt.show()
Hence, Data Visualization is applied on the complex dataset using libraries like Seaborn and Matplotlib successfully and the data is saved to file.
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