<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
 Titanic Dataset
In this project, we will be working with the titanic dataset. A set of data manipulation and visualization techniques will be used.
Step1:  Jointplot comparing fare and age
Step2:  Plot the fare column as distribution
Step3:  Displaying passenger and age over a boxplot
Step4:  A simple count plot displying the number of passanger by sex
Step5:  A heatmap showing the correlations for the entire dataset
Step6:  Two histograms ploted using FacetGrid based on age and  sex
<ASSISTANT_TASK:>
Python Code:
import seaborn as sns 
import matplotlib.pyplot as plt
%matplotlib inline
sns.set_style('whitegrid')
titanic = sns.load_dataset('titanic')
titanic.head()
Explanation: Titanic Dataset
In this project, we will be working with the titanic dataset. A set of data manipulation and visualization techniques will be used.
End of explanation
sns.jointplot(x='fare',y='age',data=titanic)
Explanation: Jointplot comparing fare and age
End of explanation
sns.distplot(titanic['fare'],bins=30,kde=False,color='red')
Explanation: Plot the fare column as distribution
End of explanation
sns.boxplot(x='class',y='age',data=titanic,palette='rainbow')
sns.swarmplot(x='class',y='age',data=titanic,palette='Set2')
Explanation: Displaying passenger and age over a boxplot
End of explanation
sns.countplot(x='sex',data=titanic)
Explanation: A simple count plot displying the number of passanger by sex
End of explanation
sns.heatmap(titanic.corr(),cmap='coolwarm')
plt.title('titanic.corr()')
Explanation: A heatmap showing the correlations for the entire dataset
End of explanation
g = sns.FacetGrid(data=titanic,col='sex')
g.map(plt.hist,'age')
Explanation: Two histograms ploted using FacetGrid based on age and  sex
End of explanation
<END_TASK>