import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
x = [1, 2, 3, 4, 5]
y = [3, 6, 2, 7, 1]
sns.lineplot(x=x,y=y)

df = sns.load_dataset("tips")
df

sns.lineplot(x="total_bill",y="tip", data=df, hue="sex", linestyle='solid', legend="auto")

x=[1, 2, 3, 4, 5]
y1=[3, 5, 2, 6, 1]
y2=[1, 6, 4, 3, 8]
y3=[5, 2, 7, 1, 4]
sns.lineplot(x=x, y=y1)
sns.lineplot(x=x, y=y2)
sns.lineplot(x=x, y=y3)
plt.title("Multi-Line Plot")
plt.xlabel('X Label')
plt.ylabel("Y Label")

tips=sns.load_dataset('tips')
avg_total_bill = tips.groupby('day')['total_bill'].mean()
avg_tip = tips.groupby('day')['tip'].mean()
plt.figure(figsize=(8, 6))
p1 = plt.bar(avg_total_bill.index, avg_total_bill, label='Total Bill')
p2 = plt.bar(avg_tip.index, avg_tip, bottom=avg_total_bill, label='Tip')
plt.xlabel('Day of the Week')
plt.ylabel('Amount')
plt.title('Average Total Bill and Tip by Day')
plt.legend()

avg_total_bill = tips.groupby('time')['total_bill'].mean() 
avg_tip=tips.groupby('time') ['tip'].mean()
p1= plt.bar(avg_total_bill.index, avg_total_bill, label='Total Bill', width=0.4)
p2 = plt.bar(avg_tip.index,avg_tip,bottom=avg_total_bill,label='Tip', width=0.4)

years=range(2000, 2012)
apples=[0.895, 0.91, 0.919, 0.926, 0.929, 0.931, 0.934, 0.936, 0.937, 0.9375, 0.9372, 0.939] 
oranges = [0.962, 0.941, 0.930, 0.923, 0.918, 0.908, 0.907, 0.904, 0.901, 0.898, 0.9, 0.896, ]

plt.bar(years, apples)
plt.bar(years, oranges, bottom=apples)

import seaborn as sns
dt= sns.load_dataset('tips')
sns.barplot(x='day', y='total_bill', hue='sex', data=dt, palette='Set1')
plt.xlabel('Day of the Week')
plt.ylabel("Total Bill")
plt.title('Total Bill by Day and Gender')

tit=pd.read_csv("titanic_dataset.csv")
tit

plt.figure(figsize=(8,5))
sns.barplot(x='Embarked', y='Fare', data=tit, palette='rainbow') 
plt.title("Fare of Passenger by Embarked Town")

plt.figure(figsize=(8,5))
sns.barplot(x='Embarked', y='Fare', data=tit, palette='rainbow', hue='Pclass') 
plt.title("Fare of Passenger by Embarked Town, Divided by Class")

tips=sns.load_dataset('tips')
sns.scatterplot(x='total_bill', y='tip', hue='sex', data=tips)
plt.xlabel('Total Bill')
plt.ylabel("Tip Amount")
plt.title('Scatter Plot of Total Bill vs. Tip Amount')

num_var = np.random.randn(1000)
num_var=pd.Series(num_var, name = "Numerical variable")
num_var

sns.histplot(data = num_var, kde = True)

df=pd.read_csv("titanic_dataset.csv")
sns.histplot(data=df,x="Pclass", hue="Survived", kde=True)

tips=sns.load_dataset('tips')
sns.boxplot(x=tips['day'], y=tips ['total_bill'], hue=tips['sex'])

sns.boxplot(x="day", y="total_bill", hue="smoker", data=tips, linewidth=2, width=0.6, boxprops={"facecolor": "lightblue", "edgecolor": "darkblue"},
whiskerprops={"color": "black", "linestyle": "--", "linewidth": 1.5}, capprops={"color": "black", "linestyle": "--", "linewidth": 1.5})

sns.violinplot(x="day", y="total_bill", hue="smoker", data=tips, linewidth=2, width=0.6, palette="Set3", inner="quartile")
plt.xlabel("Day of the Week")
plt.ylabel("Total Bill")
plt.title("Violin Plot of Total Bill by Day and Smoker Status")

mart=pd.read_csv("titanic_dataset.csv")
mart

mart=mart[['PassengerId', 'Survived', 'Age', 'Name', 'Ticket', 'Embarked']] 
mart.head(10)

sns.kdeplot(data=mart,x='PassengerId')

sns.kdeplot(data=mart,x='Age')

sns.kdeplot(data=mart)

sns.kdeplot(data=mart,x='PassengerId',hue='Survived',multiple='stack')

sns.kdeplot(data=mart,x='PassengerId',y='Survived')

data = np.random.randint(low = 1, high = 100, size = (10,10))
hm=sns.heatmap(data=data,annot=True)

hm=sns.heatmap(data=data)