Rose vs Jack, or Female vs Male

How many people in your training set survived the disaster with the Titanic? To see this, you can use the value_counts() method in combination with standard bracket notation to select a single column of a DataFrame:

# absolute numbers
train["Survived"].value_counts()

# percentages
train["Survived"].value_counts(normalize = True)

If you run these commands in the console, you'll see that 549 individuals died (62%) and 342 survived (38%). A simple way to predict heuristically could be: "majority wins". This would mean that you will predict every unseen observation to not survive.

To dive in a little deeper we can perform similar counts and percentage calculations on subsets of the Survived column. For example, maybe gender could play a role as well? You can explore this using the .value_counts() method for a two-way comparison on the number of males and females that survived, with this syntax:

train["Survived"][train["Sex"] == 'male'].value_counts()
train["Survived"][train["Sex"] == 'female'].value_counts()

To get proportions, you can again pass in the argument normalize = True to the .value_counts() method.

Instructions

Calculate and print the survival rates in absolute numbers using values_counts() method.
Calculate and print the survival rates as proportions by setting the normalize argument to True.
Repeat the same calculations but on subsets of survivals based on Sex.

Take Hint (-30xp)