Data at Sea: Series Operations on the Titanic Dataset

Use a vectorized operation to compute the mean age of all passengers in the df.

Enter the exact value returned.

Create a boolean series named is_survivor using the Survived column , indicating whether each passenger survived.

Your result should look something like this :

df['Age'].fillna(df['Age'].mean(), inplace=True)

Calculate the mean age from the Age column and store in a variable named mean_age.

Create a series named age_difference to store age difference from the mean_age.

Your result would look something like this :

Calculate the minimum and maximum values from the Fare column and store them in variables named fare_min and fare_max.

Next, create a series named normalized_fare to store the normalized fare values, calculated using the formula:

(fare - min_fare ) / ( max_fare - min_fare ) .

Your result would look something like this:

Create a series named family_size by calculating the total family size for each passenger.

This is done by summing the values from the Siblings/Spouses Aboard and Parents/Children Aboard columns, and adding 1 (to include the passenger themselves).

Your result would look something like this:

Calculate the fare per family member by dividing the Fare column by the family_size.

Store the result in a series named fare_per_family_member.

Your result would look something like this:

Create a series fare_weight by dividing the Fare values by the maximum fare value.

Then, calculate the weighted age by multiplying the Age column by the fare_weight and store the result in a series named weighted_age.

Your result would look something like this:

Sort the Fare column in ascending order and store it in a series named sorted_fares.

Then, calculate the cumulative fare percentage by taking the cumulative sum of the sorted_fares and dividing it by the total fare sum.

Multiply the result by 100 to express it as a percentage.

Store the final result in a series named cumulative_fare_percentage.

Your result would look something like this:

Calculate the first (Q1) and third (Q3) quartiles of the Fare column.

Then, compute the interquartile range (IQR) as Q3 - Q1.

Identify any outliers where the fare is less than Q1 - 1.5 * IQR or greater than Q3 + 1.5 * IQR.

Store the result as a boolean series named is_fare_outlier.

Your result would look something like this:

Sort the dataset by its index and then calculate the rolling average of the Fare over a window of 10 rows.

The minimum number of periods required for calculation is 1.

Store the result in a series named rolling_average_fare.

Your result would look something like this: