In the previous mission, we learned how to work with CSV data using NumPy. In this mission, we'll learn about datatypes in NumPy.
In particular, you'll:
- Learn what datatypes are available in NumPy.
- Learn about ndarrays' limitations of holding only one type of data.
- How to set the datatype of an ndarray.
- How to evaluate the memory consumption of an ndarray.
- Learn about the limitations of fixed-bit length number representations.
As with all Dataquest missions, this is an interactive learning experience. You'll be writing real Python code and using NumPy in our browser-based coding environment so there's no setup and no obstacles between you and learning!
- Fixed NumPy Datatypes
- Finding the Datatype
- Ndarray from Mixed Lists
- Fixed-Length Bit Representations
- Memory Consumption
- Arithmetic Overflow
- Float Limitations
- Calculating Memory Requirements
- Next Steps