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.

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.

Exercise

Rose vs Jack, or Female vs Male

Instructions