If you have just started or you are looking for a quick reference to useful numpy functions then you are in the right place. For advanced methods, this page might seem very basic, but hey check out, who knows you might find something interesting :)
Now, i have tried to cover some key areas, where we often struggle: looping, doing some indexing, slicing, statistics and (not to forget) linear algebra. Also, consider my methods or approaches as few of the many possibilities offered by numpy.
Let’s dive in:
create 1 a: Lets create our numpy arrays: 1-d, 2-d 3–d
create 1 b: There’s another way to create numpy arrays: np.arange and reshape()
create 1 c: random.rand, random.random, random.normal, random.uniform, random.randint
slicing : slice a 2-d array and get 1-d, 2-d and single element
Loop 1a: using for loop and np.nditer
a. variance and standard deviation
c. weighted average
d. covariance and correlation
the ones that are frequently used are dot, matmul, inner product, tensor product and an understanding of how a linear equation can be transformed into a matrix form will be useful.
a. dot and matmul
b. inner and tensor
c. Linear equation to matrix form
That’s all for this time. Hope you all like it :)
Happy learning !
NumPy, which stands for Numerical Python, is a library consisting of multidimensional array objects and a collection of…