# Vector Recap#

Numpy is the workhorse of data science in Python. Numpy is not only orders of magnitude faster than vanilla Python, but it uses memory much more efficiently,

Vectors are collections of data

*of the same type*.Simple vectors can be easily created by passing a list to the

`np.array()`

function, or by using the`np.arange()`

function the same way you would use`range()`

.You can easily do math between any vector and a scalar/vector of length 1. The operation will just be repeated for each entry in the longer vector.

You can also easily do math between a vector and another vector of the same length. Entries in the two vectors will just be matched up pair-wise.

If data of different types are passed to the

`np.array()`

function,`numpy`

will type promote them to the lowest type that can store all the input types.

## Next Steps#

Now that we’re familiar with vectors, let’s do some exercises!