Python NumPy For Your Grandma - 4.2 Math Functions

NumPy provides a variety of math functions like sum(), mean(), min(), max(), floor(), round(), exp(), log(), and countless others. When you understand how to use one of these functions, you’ll understand how to use nearly all of them. So in this section, we’re gonna dive into the sum() function, but everything we discuss will be applicable to a bunch of other math functions.

Here we have a 2d array called squee.

import numpy as np
squee = np.array(
    [[5.0, 2.0, 9.0],
     [1.0, 0.0, 2.0],
     [1.0, 7.0, 8.0]]

Let’s see how we can use the sum function to sum its elements. If we call np.sum(squee) with no other parameters, we get back 35, the sum of all elements in the array.

## 35.0

If we set the axis parameter equal to 0, we can sum across axis 0, in other words, calculating column sums.

np.sum(squee, axis = 0)
## array([ 7.,  9., 19.])

Alternatively we can set axis = 1 to calculate row sums.

np.sum(squee, axis = 1)
## array([16.,  3., 16.])

In both of these cases, numpy collapses our 2d array into a 1d array, but if you’d rather retain two dimensions, you can set keepdims = True.

np.sum(squee, axis = 0, keepdims = True)
## array([[ 7.,  9., 19.]])

Now, let’s see what happens if squee contains nan values.

squee[0, 0] = np.nan
## nan

In this case, NumPy returns nan because the sum function expects all values to be non-nan. In some cases, your data’s going to have nan values, and you’re going to want to sum them as if they were 0s. There’s a few ways you can do this.

Your first option is to use the where argument to exclude nans. So you could do

np.sum(squee, where = ~np.isnan(squee))
## 30.0

Here the where argument just needs to be a boolean array that’s either the same size as squee or can broadcast to the same size.

Your second option is to use the nan_to_num() function which takes an array and replaces nan values with some other specified value which by default is 0. So in this case, we could do

## 30.0

And then your third option, which is probably my favorite, is to use the nansum() function, which works just like sum() but it treats nans a 0s.

## 30.0

Course Curriculum

  1. Introduction
    1.1 Introduction
  2. Basic Array Stuff
    2.1 NumPy Array Motivation
    2.2 NumPy Array Basics
    2.3 Creating NumPy Arrays
    2.4 Indexing 1-D Arrays
    2.5 Indexing Multidimensional Arrays
    2.6 Basic Math On Arrays
    2.7 Challenge: High School Reunion
    2.8 Challenge: Gold Miner
    2.9 Challenge: Chic-fil-A
  3. Intermediate Array Stuff
    3.1 Broadcasting
    3.2 newaxis
    3.3 reshape()
    3.4 Boolean Indexing
    3.5 nan
    3.6 infinity
    3.7 random
    3.8 Challenge: Love Distance
    3.9 Challenge: Professor Prick
    3.10 Challenge: Psycho Parent
  4. Common Operations
    4.1 where()
    4.2 Math Functions
    4.3 all() and any()
    4.4 concatenate()
    4.5 Stacking
    4.6 Sorting
    4.7 unique()
    4.8 Challenge: Movie Ratings
    4.9 Challenge: Big Fish
    4.10 Challenge: Taco Truck
  5. Advanced Array Stuff
    5.1 Advanced Array Indexing
    5.2 View vs Copy
    5.3 Challenge: Population Verification
    5.4 Challenge: Prime Locations
    5.5 Challenge: The Game of Doors
    5.6 Challenge: Peanut Butter
  6. Final Boss
    6.1 as_strided()
    6.2 einsum()
    6.3 Challenge: One-Hot-Encoding
    6.4 Challenge: Cumulative Rainfall
    6.5 Challenge: Table Tennis
    6.6 Challenge: Where’s Waldo
    6.7 Challenge: Outer Product

Additional Content

  1. Python Pandas For Your Grandpa
  2. Neural Networks For Your Dog
  3. Introduction To Google Colab