Python Tutorial: Working with data types

Key Takeaways

now that we've reviewed some pandas basics we need to start thinking about other steps we have to take in order to prepare data for modeling one of these steps is to think about the types that are present in your data set because you'll likely have to transform some of these columns to other types later on let's take a deeper look at types as well as how to convert column types in your data set recall that you can check the types of a data frame by using the D types attribute like this pandas data types are similar to native Python types but there are a couple of things to be aware of the most common types you'll be working with are the object in 64 and float 64 types the object type is what pandas uses to refer to a column that consists of string values or is of mixed types in 64 is equivalent to the Python integer type the 64 simply refers to the allocation of memory allotted for storing the values and float 64 is equivalent to the float type another type you might see as you work with data in pandas is the date/time 64 type or the time delta type this is because you can store dates as date/time types and pandas dataframes and even used 8 times as a special kind of index all you need to be familiar with as we work through this course are the object in 64 and float types though before any pre-processing can begin you have to understand what types you're dealing with in your data set sometimes you'll start working with a data set that has an incorrect column type maybe a numerical column was written out into a CSV as a string and when you try to work with that column numerical operations won't work let's take a look at how to adjust the type of a column if the type of pandas has inferred upon reading in the file is incorrect here we have a simple data set with a couple of columns if you run dfd types you'll see that the type for column c is object however if we simply look at this data frame you can see that these are float values numbers with decimal points we want to pre-process and model this data we're going to have to adjust the column type changing the type of a column is very straightforward Panda already has a method for converting the type of a column to a new type you can change the type using the as type method and passing in the type you want to convert it to make sure you're only assigning it to the column you want converted it's also good to be assured as you can that the column type you want to convert is representative of the whole column remember that the object type can represent a column that includes both string and numeric types now it's your turn to do some type conversion