Numpy 101

• Numerical Python
• array

Pandas

• Dataframe
• analagous to Excel/SQL
• Datarames are built on top of numpy
• array only contains our type and are fixed state

Array

{[1,2,3],[4,5,6]}

• ndim: 2 - dimension
• shape:(2,3) - 2 arrays with 3 each
• size: 6

Commands

import numpy as np
np.array({[0,1,2,3]})
np.array(['a','b'])

Creations

• np.ones
• np.zeroes
• np.arrange(range)
• np.linspace(computation)
• np.reshape
• np.default_rng(seed)

slicing/indexing

• myarray[0]
• myarray[-1] - last avlue
• myarray[:5] - slicing

Slicing

{
 [1,2,3],
 [4,5,6],
 [7,8,9]
}

• to get [3,6,9] - myarray[:,2]
• to get [1,2,3] - myarray[0,:]

Protips

Operations

• multiplying and named arrays:

{[1,2],[2,9]}
product = array[0]
quantity = array[1]
product * quantity

Filtering

sales_array= [1,0,1,0]
mask = sales_array != 0
sales_array[mask]

where

np.where(my_array!=0, "with stock", "no stock")

Vectorization

• Optimized C Code
• Python list versus numpy array
• 86 times faster
• always use numpy methods

Broadcasting

• auto computing trying to fit smaller arrays to big array
• not compatible shapes will throw an error, pnly one dimension needs to watch

{
 [1,2,3],
 [4,5,6],
 [7,8,9]
 }
+
{
[1]
}

• if you add the two objects below via numpy it will extend the array [1], to be 3x3 of [1]