## #Tree #Traversals

Below is the Python code for traversing trees in various recursive modes like In order, Preorder, Post Order and their reverse orders…

The code is provided in python, but can be easily translated to Java/JS/PHP etc

## #HackerRank: Computing the Correlation

### Problem

You are given the scores of N students in three different subjects – MathematicsPhysics and Chemistry; all of which have been graded on a scale of 0 to 100. Your task is to compute the Pearson product-moment correlation coefficient between the scores of different pairs of subjects (Mathematics and Physics, Physics and Chemistry, Mathematics and Chemistry) based on this data. This data is based on the records of the CBSE K-12 Examination – a national school leaving examination in India, for the year 2013.

Pearson product-moment correlation coefficient

This is a measure of linear correlation described well on this Wikipedia page. The formula, in brief, is given by:

where x and y denote the two vectors between which the correlation is to be measured.

Input Format

The first row contains an integer N.
This is followed by N rows containing three tab-space (‘\t’) separated integers, M P C corresponding to a candidate’s scores in Mathematics, Physics and Chemistry respectively.
Each row corresponds to the scores attained by a unique candidate in these three subjects.

Input Constraints

1 <= N <= 5 x 105
0 <= M, P, C <= 100

Output Format

The output should contain three lines, with correlation coefficients computed
and rounded off correct to exactly 2 decimal places.
The first line should contain the correlation coefficient between Mathematics and Physics scores.
The second line should contain the correlation coefficient between Physics and Chemistry scores.
The third line should contain the correlation coefficient between Chemistry and Mathematics scores.

So, your output should look like this (these values are only for explanatory purposes):

``````0.12
0.13
0.95
``````

Test Cases

There is one sample test case with scores obtained in Mathematics, Physics and Chemistry by 20 students. The hidden test case contains the scores obtained by all the candidates who appeared for the examination and took all three tests (Mathematics, Physics and Chemistry).
Think: How can you efficiently compute the correlation coefficients within the given time constraints, while handling the scores of nearly 400k students?

Sample Input

``````20
73  72  76
48  67  76
95  92  95
95  95  96
33  59  79
47  58  74
98  95  97
91  94  97
95  84  90
93  83  90
70  70  78
85  79  91
33  67  76
47  73  90
95  87  95
84  86  95
43  63  75
95  92  100
54  80  87
72  76  90
``````

Sample Output

``````0.89
0.92
0.81
``````

There is no special library support available for this challenge.

## Launch HTML code in browser from Python

Lets say you have generated some html content for a web page dynamically and have it in memory variable in python.

In order to view and test this content, you would need a Python program that prints out the HTML code, which then would have to be copied and pasted to a HTML file, then from there, you can test it in a browser.

In Python, there is a way to launch such HTML code in a web browser so that you don’t have to go through the copy and paste method every time

Source

## Sort a list of tuples by Nth item in Python

Suppose you have a list of tuples that looks something like this:

``[('abc', 121),('abc', 231),('abc', 148), ('abc',221)]``

And you want to sort this list in ascending order by the integer value inside the tuples.

We can achieve this using the `key` keyword with `sorted()`.

``sorted([('abc', 121),('abc', 231),('abc', 148), ('abc',221)], key=lambda x: x[1])``

`key` should be a function that identifies how to retrieve the comparable element from your data structure. For example, the second element of the tuple, so we access `[1]`.

Source: StackOverflow.com

## Hacker Scripts..Based on a true story

An worker who had left the company had some scripts to automate his life..his coworker has posted this gem to a russian bash.org.

Based on a true story:

xxx: OK, so, our build engineer has left for another company. The dude was literally living inside the terminal. You know, that type of a guy who loves Vim, creates diagrams in Dot and writes wiki-posts in Markdown… If something – anything – requires more than 90 seconds of his time, he writes a script to automate that.

xxx: So we’re sitting here, looking through his, uhm, “legacy”

xxx: You’re gonna love this

xxx: `smack-my-bitch-up.sh` – sends a text message “late at work” to his wife (apparently). Automatically picks reasons from an array of strings, randomly. Runs inside a cron-job. The job fires if there are active SSH-sessions on the server after 9pm with his login.

xxx: `kumar-asshole.sh` – scans the inbox for emails from “Kumar” (a DBA at our clients). Looks for keywords like “help”, “trouble”, “sorry” etc. If keywords are found – the script SSHes into the clients server and rolls back the staging database to the latest backup. Then sends a reply “no worries mate, be careful next time”.

xxx: `hangover.sh` – another cron-job that is set to specific dates. Sends automated emails like “not feeling well/gonna work from home” etc. Adds a random “reason” from another predefined array of strings. Fires if there are no interactive sessions on the server at 8:45am.

xxx: (and the oscar goes to) `fucking-coffee.sh` – this one waits exactly 17 seconds (!), then opens an SSH session to our coffee-machine (we had no frikin idea the coffee machine is on the network, runs linux and has SSHD up and running) and sends some weird gibberish to it. Looks binary. Turns out this thing starts brewing a mid-sized half-caf latte and waits another 24 (!) seconds before pouring it into a cup. The timing is exactly how long it takes to walk to the machine from the dudes desk.

xxx: holy sh*t I’m keeping those

Source

Github scripts