# Python exploring and cleaning data for Analysis

I'm noticing right away actually that the cabin variable contains some not a number entries so those are missing so already we see that this data set is going to contain some missing values we're probably gonna have to figure out how to deal with so after gaining some sense of the types of Records we have let's run another summary function described to get an idea of the distribution of these numerical columns so we'll scroll down here and run Titanic train dot describe so this is pulling up summary statistics on each of the numeric columns so you can see here for instance passenger ID well this just seems to be a number going from 1 to 891 so essentially each row is just given a unique passenger ID so that's not particularly interesting for us the columns survived is actually the target for prediction for this competition so this variable actually tells us whether a given passenger survived the Titanic disaster or not so that is actually one that's very important for us to look at so let's see what that is we can see the average of survived is 0.38 that means that approximately 38% of these passengers survived which means most of them actually did not survive now notice that when we ran describe it only kept the numeric columns because it can't run summary statistics on categorical columns so we'll look at those separately here let's just make a list of the columns that are categorical so to do that.

We're going to make a list of the categorical columns with this construction here.