Summary
Summary
In this chapter, you learned how to work with NumPy arrays. NumPy is a Python package used for numerical calculations and arrays. An array is a data structure which only contains objects that share the same data type. Arrays are faster than lists in large-scale numerical calculations.
You learned how to create arrays in a variety of ways:
- Create an array from a Python list with
np.array()
- Create an array of regularly spaced numbers with
np.arange()
,np.linspace()
, andnp.logspace
- Create an array of random numbers with
np.random.ranint()
,np.random.rand()
, andnp.random.randn()
- Create two 2D arrays from two 1D arrays with
np.meshgrid()
andnp.mgrid()
You learned how to index and slice arrays. Slicing NumPy arrays share the same syntax used to slice Python lists and strings.
At the end of the chapter, you learned how to run mathematical operations on arrays. NumPy's mathematical functions operate on arrays like Python's math functions operate on integers and floats. NumPy has additional functions like np.dot()
and np.cross()
that cannot be applied to scalars. NumPy's np.linalg.solve()
function can be used to solve systems of linear equations.
Key Terms and Concepts
NumPy
array
scalar
computationally expensive
slice
index
data type
homogenous
homogenous data type
element-wise
system of linear equations
attribute
scientific computing
Unicode
iterable
logarithmically spaced numbers
normal distribution
meshgrid
matrix multiplication methods
dot product
cross product