#loading the dataset
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
df=pd.read_csv("Superstore.csv")
df

#removing unnecessary data variables
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=(12,10))
plt.title("Data with outliers")
df.boxplot()
plt.show()

plt.figure(figsize=(12,10))
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()

#data visualization
#line plots
import seaborn as sns
sns.lineplot(x="Sub-Category",y="Sales",data=df,marker='o')
plt.title("Sub Categories vs Sales")
plt.xticks(rotation = 90)
plt.show()

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

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

sns.lineplot(x="Category",y="Discount",data=df,marker='o')
plt.title("Categories vs Discount")
plt.show()

sns.lineplot(x="Sub-Category",y="Quantity",data=df,marker='o')
plt.xticks(rotation = 90)
plt.title("Sub Categories vs Quantity")
plt.show()

#bar plots
sns.barplot(x="Sub-Category",y="Sales",data=df)
plt.title("Sub Categories vs Sales")
plt.xticks(rotation = 90)
plt.show()

sns.barplot(x="Category",y="Profit",data=df)
plt.title("Categories vs Profit")
plt.show()

sns.barplot(x="Sub-Category",y="Quantity",data=df)
plt.title("Sub Categories vs Quantity")
plt.xticks(rotation = 90)
plt.show()

sns.barplot(x="Category",y="Discount",data=df)
plt.title("Categories vs Discount")
plt.show()

plt.figure(figsize=(12,7))
sns.barplot(x="State",y="Sales",data=df)
plt.title("States vs Sales")
plt.xticks(rotation = 90)
plt.show()

plt.figure(figsize=(25,8))
sns.barplot(x="State",y="Sales",hue="Region",data=df)
plt.title("State vs Sales based on Region")
plt.xticks(rotation = 90)
plt.show()

## Histogram
sns.histplot(data = df,x = 'Region',hue='Ship Mode')
sns.histplot(data = df,x = 'Category',hue='Quantity')
sns.histplot(data = df,x = 'Sub-Category',hue='Category')
plt.xticks(rotation = 90)
plt.show()
sns.histplot(data = df,x = 'Quantity',hue='Segment')
plt.hist(data = df,x = 'Profit')
plt.show()

## count plot
plt.figure(figsize=(10,7))
sns.countplot(x ='Segment', data = df,hue = 'Sub-Category')
sns.countplot(x ='Region', data = df,hue = 'Segment')
sns.countplot(x ='Category', data = df,hue='Discount')
sns.countplot(x ='Ship Mode', data = df,hue = 'Quantity')

## Barplot 
sns.boxplot(x="Sub-Category",y="Discount",data=df)
plt.xticks(rotation = 90)
plt.show()
sns.boxplot( x="Profit", y="Category",data=df)
plt.xticks(rotation = 90)
plt.show()
plt.figure(figsize=(10,7))
sns.boxplot(x="Sub-Category",y="Sales",data=df)
plt.xticks(rotation = 90)
plt.show()
sns.boxplot(x="Category",y="Profit",data=df)
sns.boxplot(x="Region",y="Sales",data=df)
plt.figure(figsize=(10,7))
sns.boxplot(x="Sub-Category",y="Quantity",data=df)
plt.xticks(rotation = 90)
plt.show()
sns.boxplot(x="Category",y="Discount",data=df)
plt.figure(figsize=(15,7))
sns.boxplot(x="State",y="Sales",data=df)
plt.xticks(rotation = 90)
plt.show()

## KDE plot
sns.kdeplot(x="Profit", data = df,hue='Category')
sns.kdeplot(x="Sales", data = df,hue='Region')
sns.kdeplot(x="Quantity", data = df,hue='Segment')
sns.kdeplot(x="Discount", data = df,hue='Segment')

## violin plot
sns.violinplot(x="Profit",data=df)
sns.violinplot(x="Discount",y="Ship Mode",data=df)
sns.violinplot(x="Quantity",y="Ship Mode",data=df)

## point plot
sns.pointplot(x=df["Quantity"],y=df["Discount"])
sns.pointplot(x=df["Quantity"],y=df["Category"])
sns.pointplot(x=df["Sales"],y=df["Sub-Category"])

#Pie Chart
df.groupby(['Category']).sum().plot(kind='pie', y='Discount',figsize=(6,10),pctdistance=1.7,labeldistance=1.2)
df.groupby(['Sub-Category']).sum().plot(kind='pie', y='Sales',figsize=(10,10),pctdistance=1.7,labeldistance=1.2)
df.groupby(['Region']).sum().plot(kind='pie', y='Profit',figsize=(6,9),pctdistance=1.7,labeldistance=1.2)
df.groupby(['Ship Mode']).sum().plot(kind='pie', y='Quantity',figsize=(8,11),pctdistance=1.7,labeldistance=1.2)

df1=df.groupby(by=["Category"]).sum()
labels=[]
for i in df1.index:
    labels.append(i)  
plt.figure(figsize=(8,8))
colors = sns.color_palette('pastel')
plt.pie(df1["Profit"],colors = colors,labels=labels, autopct = '%0.0f%%')
plt.show()

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()

#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()