# Tag Archives: Python

# How to setup a Data Science workflow with Kaggle Python Docker Image on Laptop

# How can you use eight 8s to get the number 1000?

# What is the equivalent of JavaScript Map, Filter, and Reduce Functions in Python?

# NumPy Exercise – Argsort and Fancy Indexing

This post summarises my solution to this NumPy Fancy Indexing Exercise (Challenge 3) – originated from scipy-lectures.org

# The Problem

Generate a `10 x 3`

array of random numbers (in range `[0,1]`

). For each row, pick the number closest to `0.5`

.

- Use
`abs`

and`argsort`

to find the column j closest for each row. - Use fancy indexing to extract the numbers. (Hint:
`a[i,j]`

– the array`i`

must contain the row numbers corresponding to stuff in`j`

.)

# The Solution

First version code to illustrate how things work:

Now we know how things work, let’s compact the solution (optional).

# Major Learning Summary

- Fancy Indexing –
`a[rows, cols]`

or`a[[1, 2, 3, 4], [2, 1, 0, 1]]`

- Sorting –
`sort`

,`argsort`

,`argmin`

/`argmax`

.

# Python NumPy Advance Element Selection Trick – Slice and Jump

# TL;DR

This visual example will show you how to a neatly select elements in a NumPy Matrix (2 dimensional array) in a pretty entertaining way (I promise).

(Caution: this is a NumPy array specific example with the aim of illustrating the a use case of “double colons” `::`

for jumping of elements in multiple axes. This example does not cover native Python data structures like `List`

).

# One concrete example to rule them all…

Say we have a NumPy matrix that looks like this:

In [1]: import numpy as np In [2]: X = np.arange(100).reshape(10,10) In [3]: X Out[3]: array([[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [10, 11, 12, 13, 14, 15, 16, 17, 18, 19], [20, 21, 22, 23, 24, 25, 26, 27, 28, 29], [30, 31, 32, 33, 34, 35, 36, 37, 38, 39], [40, 41, 42, 43, 44, 45, 46, 47, 48, 49], [50, 51, 52, 53, 54, 55, 56, 57, 58, 59], [60, 61, 62, 63, 64, 65, 66, 67, 68, 69], [70, 71, 72, 73, 74, 75, 76, 77, 78, 79], [80, 81, 82, 83, 84, 85, 86, 87, 88, 89], [90, 91, 92, 93, 94, 95, 96, 97, 98, 99]])

Say for some reason, your boss wants you to select the following elements:

“But How???”… Read on! (We can do this in a 2-step approach)

# Step 1 – Obtain subset

Specify the “start index” and “end index” in both row-wise and column-wise directions.

In code:

In [5]: X2 = X[2:9,3:8] In [6]: X2 Out[6]: array([[23, 24, 25, 26, 27], [33, 34, 35, 36, 37], [43, 44, 45, 46, 47], [53, 54, 55, 56, 57], [63, 64, 65, 66, 67], [73, 74, 75, 76, 77], [83, 84, 85, 86, 87]])

Notice now we’ve just obtained our subset, with the use of simple start and end indexing technique. Next up, how to do that “jumping”… (read on!)

# Step 2 – Select elements (with the “jump step” argument)

We can now specify the “jump steps” in both row-wise and column-wise directions (to select elements in a “jumping” way) like this:

In code (note the double colons):

In [7]: X3 = X2[::3, ::2] In [8]: X3 Out[8]: array([[23, 25, 27], [53, 55, 57], [83, 85, 87]])

We have just selected all the elements as required! :)

# Consolidate Step 1 (start and end) and Step 2 (“jumping”)

Now we know the concept, we can easily combine step 1 and step 2 into one consolidated step – for compactness:

In [9]: X4 = X[2:9,3:8][::3,::2] In [10]: X4 Out[10]: array([[23, 25, 27], [53, 55, 57], [83, 85, 87]])

Done!

(I’ve also posted this trick in this Stackoverflow forum)

# Python Generator and Yield – Examples

# Concatenate elements within an array / list – JavaScript vs Python

# Function Programming Example – square elements in an array (JavaScript) or list (Python)

# Python Decorator 3-minute Tutorial

A very simple Youtube tutorial explaining how Python Decorator work. It’s sort of similar to closure (I guess?).