Understanding data types
In very broad terms, data may be classified as either continuous or categorical. Continuous data is always numeric and represents some kind of measurements, such as height, wage, or salary. Continuous data can take on an infinite number of possibilities. Categorical data, on the other hand, represents discrete, finite amounts of values such as car color, type of poker hand, or brand of cereal.
pandas does not broadly classify data as either continuous or categorical. Instead, it has precise technical definitions for many distinct data types. The following describes common pandas data types:
float
– The NumPy float type, which supports missing valuesint
– The NumPy integer type, which does not support missing values'Int64'
– pandas nullable integer typeobject
– The NumPy type for storing strings (and mixed types)'category'
– pandas categorical type, which does...