# importing data # DON'T MODIFY THIS CELL

from pandasql import sqldf import pandas as pd pysqldf = lambda q: sqldf(q, globals()) titanic = pd.read_csv('https://raw.githubusercontent.com/mwaskom/seaborn-data/master/titanic.csv') titanic.head()

was the largest ship afloat at the time it was made. Unfortunately, the ship sank in its maiden voyage. In the following cells, you will see data about the passengers in Titanic. You will use SQL and Python techniques to parsel process data about those passengers. 1 # importing data # DON'T MODIFY THIS CELL from pandasql import sqldf import pandas as pd pysqldf = lambda q: sqldf(q, globals()) titanic = pd. read_csv('https://raw.githubusercontent.com/mwaskom/seaborn-data/master/ti titanic.head() survived pclass sex age sibsp parch fare embarked class who adult_male deck 0 0 3 male 22.0 1 0 7.2500 S Third man True NaN 1 1 1 female 38.0 1 0 71.2833 First woman False 2 1 3 female 26.0 0 0 7.9250 S Third woman False NaN 3 1 1 female 35.0 1 0 53.1000 S First woman False C 4 0 3 male 35.0 0 0 8.0500 S Third man True NaN Unless otherwise mentioned, you are free to use either Python or SQL or both as you see fit. # Write your name after the '#' in the next line. # Q1 [5 points]: Show only the first 20 samples of the dataset? Q2 [10 points]: Order the dataset by fare in descending order. Show only the first 15 rows. # guideline: must use SQL, along with Python Q3 [5 points]: How many passengers' records can be found in this dataset? adult_male deck embark_town alive alone True NaN Southampton no False False False NaN Cherbourg yes False Southampton yes True Southampton yes False False True NaN Southampton no True