Listening on software engineering radio podcast Diomidis Spinellis mentions how debugging is so much important in software development but still, we don’t teach much in-depth this skill in our universities. I and believe any other programmer will agree that debugging tools are the key arsenal in fixing bugs and even understanding the system.
Either you use modern tools or just by basic print/printf statements that don’t matter. Students should learn these key skills and professors should emphasize on educating and not only in universities even in industry set-up when a new developer joins in there should be good exposure to debugging so that they dissect code base and become productive fast.
Found this interesting podcast focusing on the Indian IT landscape, start-up, journey and entrepreneurship in general. The bio says ” A podcast about the ones who chose to take the road not taken often. It’s about the crazy and the curious. Those that dared to stand out, and stand-alone. It’s about their journey through hope and disillusionment, failures and pitfalls, joy and success, pain and bliss. It’s a candid exploration of experiences and ideas that have driven some of the shining stars, told as is. “
Perl Date::Manip is one of the modules which I use a lot. It’s a wonderful lib and has very clean API with great documentation. Below is a quick look at Date::Manip print format options which sometimes is very handy. For detailed interpretation and other options encourage to go through Date::Manip on CPAN
my $present_date_hash = Date::Manip::Date->new("today");
my $present_date = $present_date_hash->printf("%Y-%m-%d %H:%M:%S");
Earthbound maneuvering starts today for Chandrayan-2 (Moon mission) sent by Indian Space Agency (ISRO) for its onward journey towards the moon. ISRO will be using many gravity assist orbits around Earth to give it a boost to the Moon rather than using fuel.
The theme of the book is to find out what is truly essential? Focus on essential and eliminate everything else. Greg McKeown defines Essentialism as “Less but better”.
The key takeaway is that only once you give yourself the permission to stop trying to do it all, to stop saying yes to everyone, can you make your highest contribution towards the things that really matter. How often we say “yes” to everything, there is so much going on in our lives that nothing ever gets our true focus and attention. How can we give our best in any area when we’re being pulled in so many different directions? We can’t. We need to identify what is truly important, learn to say no to things that don’t fit into the “essential” category and simplify our lives. This book is an essential read for anyone who feels overcommitted, overloaded, or overworked.
Very good read. Highly recommended.
Note: My friend Rishi has done a detailed review of this book. Check this out here:
This is a quick share on git. Scenario: I want to push my local changes to git but am having a few commits which I don’t want to push now. In other words, I just want to push changes till a certain commit. Solution: $git push <remotename><commit SHA>:<remote_branch_name>
To elaborate First, fetch the SHA of the commit you want to push $ git log
Copy the SHA and use the command below (Make sure that you replace the SHA of your commit with the given in the example).
The big O notation is used to analyze runtime time complexity. big O notation provides an abstract measurement by which we can judge the performance of algorithms without using mathematical proofs. Some of the most common big O notations are:
O(1) : constant: the operation doesn’t depend on the size of its input, e.g. adding a node to the tail of a linked list where we always maintain a pointer to the tail node.
O(n): linear: the run time complexity is proportionate to the size of n.
O(log n): logarithmic: normally associated with algorithms that break the problem into similar chunks per each invocation, e.g. searching a binary search tree.
O(n log n): just n log n: usually associated with an algorithm that breaks the problem into smaller chunks per each invocation, and then takes the results of these smaller chunks and stitches them back together, e.g, quicksort.
O(n2): quadratic: e.g. bubble sort.
O(n3):cubic: very rare
O(2n):exponential: incredibly rare.
Brief explanation: Cubic and exponential algorithms should only ever be used for very small problems (if ever!); avoid them if feasibly possible. If you encounter them then this is really a signal for you to review the design of your algorithm always look for algorithm optimization particularly loops and recursive calls.
The biggest asset that big O notation gives us is that it allows us to essentially discard things like hardware means if you have two sorting algorithms, one with a quadric run time and the other with a logarithmic run time then logarithmic algorithm will always be faster than the quadratic one when the data set becomes suitably large. This applies even if the former is ran on a machine that is far faster than the latter, Why?
Because big O notation isolates a key factor in algorithm analysis: growth. An algorithm with quadratic run time grows faster than one with logarithmic run time.
Note: The above notes are for quick reference. Understanding algorithmic performance is a complex but interesting field. I would recommend picking a good book to understand the nitty-gritty of big O and other notations.
While browsing my Evernote I found a scrapbook which I have made while learning Python some years back. Thought to share if this helps someone. I am pasting directly (no editing so there might be some spell and grammar mistake).
Python 2 division, just use integer part (3/2=1) whereas Python 3 uses real division 3/2 = 1.5
Strings in Python are immutable means you can’t change the in-place value of a char. Once string is created you can’t change/replace its elements
s= “Hello World” s[::-1] this will reverse string s “dlroW olleH” double colon is used to tell the range and also how many elements can be skipped
if you want to use Python 3 functions in Python 2 then use ‘from __future__ import print_function‘ and similarly other functions
List are mutable but tuples are not mutable (does not support item assignment) aka immutable, fewer methods in tuples then why to use instead of a list? The key is immutability. in a program if you want sequence/Val does not to get changed then tuple is a solution e.g.; storing calendar dates which know will not change during your programs.
Set is a collection of un-ordered unique items it looks like a dictionary (in notation) but only keys which are unique. It can help in removing repeated items means you can use set to cast list.
List comprehensive are an excellent way to write clean and efficient code – they are actually de-constructed for loop flatted out in a list
Lambda expressions can be used to shorten function this is really useful when used with map(), reduce() and filter() functions
First class functions: Treat functions like any other object, we can pass functions, we can return functions, we can assign functions to a variable
Closure: Closure takes advantage of first-class functions and returns inner functions and variables local to them.
Decorators: It is a function which takes another function as an argument and returns as a function without changing the source code of the original function. Decorator allows easily to add functionality inside our wrapper without modifying original function.
Note: These are notes for quick reference. If you are serious in learning Python I encourage you to take a book or a tutorial.