Ex-08-Data-Visualization-

AIM

To Perform Data Visualization on the given 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.

Data Pre-Processing

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

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

df.head()

df.info()

df.drop('Row ID',axis=1,inplace=True)
df.drop('Order ID',axis=1,inplace=True)
df.drop('Customer ID',axis=1,inplace=True)
df.drop('Customer Name',axis=1,inplace=True)
df.drop('Country',axis=1,inplace=True)
df.drop('Postal Code',axis=1,inplace=True)
df.drop('Product ID',axis=1,inplace=True)
df.drop('Product Name',axis=1,inplace=True)
df.drop('Order Date',axis=1,inplace=True)
df.drop('Ship Date',axis=1,inplace=True)
print("Updated dataset")
df

df.isnull().sum()

#detecting and removing outliers in current numeric data
plt.figure(figsize=(8,8))
plt.title("Data with outliers")
df.boxplot()
plt.show()

plt.figure(figsize=(8,8))
cols = ['Sales','Quantity','Discount','Profit']
Q1 = df[cols].quantile(0.25)
Q3 = df[cols].quantile(0.75)
IQR = Q3 - Q1
df = df[~((df[cols] < (Q1 - 1.5 * IQR)) |(df[cols] > (Q3 + 1.5 * IQR))).any(axis=1)]
plt.title("Dataset after removing outliers")
df.boxplot()
plt.show()

Which Segment has Highest sales?

sns.lineplot(x="Segment",y="Sales",data=df,marker='o')
plt.title("Segment vs Sales")
plt.xticks(rotation = 90)
plt.show()

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

Which City has Highest profit?

df.shape
df1 = df[(df.Profit >= 60)]
df1.shape

plt.figure(figsize=(30,8))
states=df1.loc[:,["City","Profit"]]
states=states.groupby(by=["City"]).sum().sort_values(by="Profit")
sns.barplot(x=states.index,y="Profit",data=states)
plt.xticks(rotation = 90)
plt.xlabel=("City")
plt.ylabel=("Profit")
plt.show()

Which ship mode is profitable?

sns.barplot(x="Ship Mode",y="Profit",data=df)
plt.show()

sns.lineplot(x="Ship Mode",y="Profit",data=df)
plt.show()

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

sns.pointplot(x=df["Profit"],y=df["Ship Mode"])

Sales of the product based on region.

states=df.loc[:,["Region","Sales"]]
states=states.groupby(by=["Region"]).sum().sort_values(by="Sales")
sns.barplot(x=states.index,y="Sales",data=states)
plt.xticks(rotation = 90)
plt.xlabel=("Region")
plt.ylabel=("Sales")
plt.show()

df.groupby(['Region']).sum().plot(kind='pie', y='Sales',figsize=(6,9),pctdistance=1.7,labeldistance=1.2)

Find the relation between sales and profit.

df["Sales"].corr(df["Profit"])

df_corr = df.copy()
df_corr = df_corr[["Sales","Profit"]]
df_corr.corr()

sns.pairplot(df_corr, kind="scatter")
plt.show()

Heatmap

df4=df.copy()

#encoding
from sklearn.preprocessing import LabelEncoder,OrdinalEncoder,OneHotEncoder
le=LabelEncoder()
ohe=OneHotEncoder
oe=OrdinalEncoder()

df4["Ship Mode"]=oe.fit_transform(df[["Ship Mode"]])
df4["Segment"]=oe.fit_transform(df[["Segment"]])
df4["City"]=le.fit_transform(df[["City"]])
df4["State"]=le.fit_transform(df[["State"]])
df4['Region'] = oe.fit_transform(df[['Region']])
df4["Category"]=oe.fit_transform(df[["Category"]])
df4["Sub-Category"]=le.fit_transform(df[["Sub-Category"]])

#scaling
from sklearn.preprocessing import RobustScaler
sc=RobustScaler()
df5=pd.DataFrame(sc.fit_transform(df4),columns=['Ship Mode', 'Segment', 'City', 'State','Region',
                                               'Category','Sub-Category','Sales','Quantity','Discount','Profit'])

#Heatmap
plt.subplots(figsize=(12,7))
sns.heatmap(df5.corr(),cmap="PuBu",annot=True)
plt.show()

Find the relation between sales and profit based on the following category.

Segment

grouped_data = df.groupby('Segment')[['Sales', 'Profit']].mean()
# Create a bar chart of the grouped data
fig, ax = plt.subplots()
ax.bar(grouped_data.index, grouped_data['Sales'], label='Sales')
ax.bar(grouped_data.index, grouped_data['Profit'], bottom=grouped_data['Sales'], label='Profit')
ax.set_xlabel('Segment')
ax.set_ylabel('Value')
ax.legend()
plt.show()

City

grouped_data = df.groupby('City')[['Sales', 'Profit']].mean()
# Create a bar chart of the grouped data
fig, ax = plt.subplots()
ax.bar(grouped_data.index, grouped_data['Sales'], label='Sales')
ax.bar(grouped_data.index, grouped_data['Profit'], bottom=grouped_data['Sales'], label='Profit')
ax.set_xlabel('City')
ax.set_ylabel('Value')
ax.legend()
plt.show()

States

grouped_data = df.groupby('State')[['Sales', 'Profit']].mean()
# Create a bar chart of the grouped data
fig, ax = plt.subplots()
ax.bar(grouped_data.index, grouped_data['Sales'], label='Sales')
ax.bar(grouped_data.index, grouped_data['Profit'], bottom=grouped_data['Sales'], label='Profit')
ax.set_xlabel('State')
ax.set_ylabel('Value')
ax.legend()
plt.show()

Segment and Ship Mode

grouped_data = df.groupby(['Segment', 'Ship Mode'])[['Sales', 'Profit']].mean()
pivot_data = grouped_data.reset_index().pivot(index='Segment', columns='Ship Mode', values=['Sales', 'Profit'])
# Create a bar chart of the grouped data
fig, ax = plt.subplots()
pivot_data.plot(kind='bar', ax=ax)
ax.set_xlabel('Segment')
ax.set_ylabel('Value')
plt.legend(title='Ship Mode')
plt.show()

Segment, Ship mode and Region

grouped_data = df.groupby(['Segment', 'Ship Mode','Region'])[['Sales', 'Profit']].mean()
pivot_data = grouped_data.reset_index().pivot(index=['Segment', 'Ship Mode'], columns='Region', values=['Sales', 'Profit'])
sns.set_style("whitegrid")
sns.set_palette("Set1")
pivot_data.plot(kind='bar', stacked=True, figsize=(10, 5))
plt.xlabel('Segment - Ship Mode')
plt.ylabel('Value')
plt.legend(title='Region')
plt.show()