Question

Using the titanic dataset from seaborn with modifications shown below, write the Pandas code to show the results for the following questions. The code should be as elegant as possible. Do not use any loops. No plots are required. \\ * a) How many males and females are there on the ship? How many children are there on the ship based on the who attribute? Among the children, how many of them are male and female? * b) Based on the gender (sex), what is the average age of the survivors in each group? * c) Using the age range (0, 18] as Child and (18,80] as Adult derived from the age attribute, show the number of survivors based on gender (sex), age range, and traveling class (class). [2]: titanic = sns.load_dataset('titanic') titanic = titanic[['sex', 'age', 'class', 'who', 'survived']] titanic [2]: sex age class who survived 0 male 22.0 Third man 0 1 female 38.0 First woman 1 2 female 26.0 Third woman 1 3 female 35.0 First woman 1 4 male 35.0 Third man 0 ... ... ... ... ... 886 male 27.0 Second man 0 887 female 19.0 First woman 1 888 female NaN Third woman 0 889 male 26.0 First man 1 890 male 32.0 Third man 0 891 rows \times 5 columns

          Using the titanic dataset from seaborn with modifications shown below, write the Pandas code to show the results for the following questions. The code should
be as elegant as possible. Do not use any loops. No plots are required.
\\
* a) How many males and females are there on the ship? How many children are there on the ship based on the who attribute? Among the children, how
many of them are male and female?
* b) Based on the gender (sex), what is the average age of the survivors in each group?
* c) Using the age range (0, 18] as Child and (18,80] as Adult derived from the age attribute, show the number of survivors based on gender (sex), age range,
and traveling class (class).
[2]: titanic = sns.load_dataset('titanic')
titanic = titanic[['sex', 'age', 'class', 'who', 'survived']]
titanic
[2]:
sex age class who survived
0 male 22.0 Third man 0
1 female 38.0 First woman 1
2 female 26.0 Third woman 1
3 female 35.0 First woman 1
4 male 35.0 Third man 0
... ... ... ... ...
886 male 27.0 Second man 0
887 female 19.0 First woman 1
888 female NaN Third woman 0
889 male 26.0 First man 1
890 male 32.0 Third man 0
891 rows \times 5 columns

Using the titanic dataset from seaborn with modifications shown below, write the Pandas code to show the results for the following questions. The code should
be as elegant as possible. Do not use any loops. No plots are required.

a) How many males and females are there on the ship? How many children are there on the ship based on the who attribute? Among the children, how
many of them are male and female?
* b) Based on the gender (sex), what is the average age of the survivors in each group?
* c) Using the age range (0, 18] as Child and (18,80] as Adult derived from the age attribute, show the number of survivors based on gender (sex), age range,
and traveling class (class).
[2]: titanic = sns.loaddataset('titanic')
titanic = titanic[['sex', 'age', 'class', 'who', 'survived']]
titanic
[2]:
sex age class who survived
0 male 22.0 Third man 0
1 female 38.0 First woman 1
2 female 26.0 Third woman 1
3 female 35.0 First woman 1
4 male 35.0 Third man 0
... ... ... ... ...
886 male 27.0 Second man 0
887 female 19.0 First woman 1
888 female NaN Third woman 0
889 male 26.0 First man 1
890 male 32.0 Third man 0
891 rows ×5 columns

Added by Gabrielle C.

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