import seaborn as sns
import matplotlib.pyplot as plt
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 Lbel')

import seaborn as sns
import matplotlib.pyplot as plt
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)
plt.xlabel('Time of Day')
plt.ylabel('Amount')
plt.title('Average Total Bill and Tip by Time of Day')
plt.legend()

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')
# Bar plot with hue parameter
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")

import seaborn as sns
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)